partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | KaggleCompetitionDownloader.download_file | Downloads competition file to output_dir. | tensorflow_datasets/core/download/kaggle.py | def download_file(self, fname, output_dir):
"""Downloads competition file to output_dir."""
if fname not in self.competition_files: # pylint: disable=unsupported-membership-test
raise ValueError("%s is not one of the competition's "
"files: %s" % (fname, self.competition_files))
... | def download_file(self, fname, output_dir):
"""Downloads competition file to output_dir."""
if fname not in self.competition_files: # pylint: disable=unsupported-membership-test
raise ValueError("%s is not one of the competition's "
"files: %s" % (fname, self.competition_files))
... | [
"Downloads",
"competition",
"file",
"to",
"output_dir",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/kaggle.py#L118-L135 | [
"def",
"download_file",
"(",
"self",
",",
"fname",
",",
"output_dir",
")",
":",
"if",
"fname",
"not",
"in",
"self",
".",
"competition_files",
":",
"# pylint: disable=unsupported-membership-test",
"raise",
"ValueError",
"(",
"\"%s is not one of the competition's \"",
"\"... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | TFFlowers._generate_examples | Generate flower images and labels given the image directory path.
Args:
images_dir_path: path to the directory where the images are stored.
Yields:
The image path and its corresponding label. | tensorflow_datasets/image/flowers.py | def _generate_examples(self, images_dir_path):
"""Generate flower images and labels given the image directory path.
Args:
images_dir_path: path to the directory where the images are stored.
Yields:
The image path and its corresponding label.
"""
parent_dir = tf.io.gfile.listdir(images_... | def _generate_examples(self, images_dir_path):
"""Generate flower images and labels given the image directory path.
Args:
images_dir_path: path to the directory where the images are stored.
Yields:
The image path and its corresponding label.
"""
parent_dir = tf.io.gfile.listdir(images_... | [
"Generate",
"flower",
"images",
"and",
"labels",
"given",
"the",
"image",
"directory",
"path",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/flowers.py#L71-L93 | [
"def",
"_generate_examples",
"(",
"self",
",",
"images_dir_path",
")",
":",
"parent_dir",
"=",
"tf",
".",
"io",
".",
"gfile",
".",
"listdir",
"(",
"images_dir_path",
")",
"[",
"0",
"]",
"walk_dir",
"=",
"os",
".",
"path",
".",
"join",
"(",
"images_dir_pa... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _checksum_paths | Returns dict {'dataset_name': 'path/to/checksums/file'}. | tensorflow_datasets/core/download/checksums.py | def _checksum_paths():
"""Returns dict {'dataset_name': 'path/to/checksums/file'}."""
dataset2path = {}
for dir_path in _CHECKSUM_DIRS:
for fname in _list_dir(dir_path):
if not fname.endswith(_CHECKSUM_SUFFIX):
continue
fpath = os.path.join(dir_path, fname)
dataset_name = fname[:-len... | def _checksum_paths():
"""Returns dict {'dataset_name': 'path/to/checksums/file'}."""
dataset2path = {}
for dir_path in _CHECKSUM_DIRS:
for fname in _list_dir(dir_path):
if not fname.endswith(_CHECKSUM_SUFFIX):
continue
fpath = os.path.join(dir_path, fname)
dataset_name = fname[:-len... | [
"Returns",
"dict",
"{",
"dataset_name",
":",
"path",
"/",
"to",
"/",
"checksums",
"/",
"file",
"}",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/checksums.py#L46-L56 | [
"def",
"_checksum_paths",
"(",
")",
":",
"dataset2path",
"=",
"{",
"}",
"for",
"dir_path",
"in",
"_CHECKSUM_DIRS",
":",
"for",
"fname",
"in",
"_list_dir",
"(",
"dir_path",
")",
":",
"if",
"not",
"fname",
".",
"endswith",
"(",
"_CHECKSUM_SUFFIX",
")",
":",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _get_path | Returns path to where checksums are stored for a given dataset. | tensorflow_datasets/core/download/checksums.py | def _get_path(dataset_name):
"""Returns path to where checksums are stored for a given dataset."""
path = _checksum_paths().get(dataset_name, None)
if path:
return path
msg = ('No checksums file could be find for dataset %s. Please create one in '
'one of: %s') % (dataset_name, ', '.join(_CHECKSUM_... | def _get_path(dataset_name):
"""Returns path to where checksums are stored for a given dataset."""
path = _checksum_paths().get(dataset_name, None)
if path:
return path
msg = ('No checksums file could be find for dataset %s. Please create one in '
'one of: %s') % (dataset_name, ', '.join(_CHECKSUM_... | [
"Returns",
"path",
"to",
"where",
"checksums",
"are",
"stored",
"for",
"a",
"given",
"dataset",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/checksums.py#L59-L66 | [
"def",
"_get_path",
"(",
"dataset_name",
")",
":",
"path",
"=",
"_checksum_paths",
"(",
")",
".",
"get",
"(",
"dataset_name",
",",
"None",
")",
"if",
"path",
":",
"return",
"path",
"msg",
"=",
"(",
"'No checksums file could be find for dataset %s. Please create on... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _get_sizes_checksums | Returns {URL: (size, checksum)}s stored within file. | tensorflow_datasets/core/download/checksums.py | def _get_sizes_checksums(checksums_path):
"""Returns {URL: (size, checksum)}s stored within file."""
checksums = {}
for line in _read_file(checksums_path).split('\n'):
if not line:
continue
# URL might have spaces inside, but size and checksum will not.
url, size, checksum = line.rsplit(' ', 2)
... | def _get_sizes_checksums(checksums_path):
"""Returns {URL: (size, checksum)}s stored within file."""
checksums = {}
for line in _read_file(checksums_path).split('\n'):
if not line:
continue
# URL might have spaces inside, but size and checksum will not.
url, size, checksum = line.rsplit(' ', 2)
... | [
"Returns",
"{",
"URL",
":",
"(",
"size",
"checksum",
")",
"}",
"s",
"stored",
"within",
"file",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/checksums.py#L75-L84 | [
"def",
"_get_sizes_checksums",
"(",
"checksums_path",
")",
":",
"checksums",
"=",
"{",
"}",
"for",
"line",
"in",
"_read_file",
"(",
"checksums_path",
")",
".",
"split",
"(",
"'\\n'",
")",
":",
"if",
"not",
"line",
":",
"continue",
"# URL might have spaces insi... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | get_all_sizes_checksums | Returns dict associating URL to (size, sha256). | tensorflow_datasets/core/download/checksums.py | def get_all_sizes_checksums():
"""Returns dict associating URL to (size, sha256)."""
sizes_checksums = {}
for path in _checksum_paths().values():
data = _get_sizes_checksums(path)
for url, size_checksum in data.items():
if (url in sizes_checksums and
sizes_checksums[url] != size_checksum):... | def get_all_sizes_checksums():
"""Returns dict associating URL to (size, sha256)."""
sizes_checksums = {}
for path in _checksum_paths().values():
data = _get_sizes_checksums(path)
for url, size_checksum in data.items():
if (url in sizes_checksums and
sizes_checksums[url] != size_checksum):... | [
"Returns",
"dict",
"associating",
"URL",
"to",
"(",
"size",
"sha256",
")",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/checksums.py#L88-L99 | [
"def",
"get_all_sizes_checksums",
"(",
")",
":",
"sizes_checksums",
"=",
"{",
"}",
"for",
"path",
"in",
"_checksum_paths",
"(",
")",
".",
"values",
"(",
")",
":",
"data",
"=",
"_get_sizes_checksums",
"(",
"path",
")",
"for",
"url",
",",
"size_checksum",
"i... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | store_checksums | Store given checksums and sizes for specific dataset.
Content of file is never disgarded, only updated. This is to ensure that if
process is killed right after first download finishes, checksums registered
during previous runs aren't lost.
It is the responsibility of the caller not to call function multiple t... | tensorflow_datasets/core/download/checksums.py | def store_checksums(dataset_name, sizes_checksums):
"""Store given checksums and sizes for specific dataset.
Content of file is never disgarded, only updated. This is to ensure that if
process is killed right after first download finishes, checksums registered
during previous runs aren't lost.
It is the res... | def store_checksums(dataset_name, sizes_checksums):
"""Store given checksums and sizes for specific dataset.
Content of file is never disgarded, only updated. This is to ensure that if
process is killed right after first download finishes, checksums registered
during previous runs aren't lost.
It is the res... | [
"Store",
"given",
"checksums",
"and",
"sizes",
"for",
"specific",
"dataset",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/checksums.py#L102-L127 | [
"def",
"store_checksums",
"(",
"dataset_name",
",",
"sizes_checksums",
")",
":",
"path",
"=",
"_get_path",
"(",
"dataset_name",
")",
"original_data",
"=",
"_get_sizes_checksums",
"(",
"path",
")",
"new_data",
"=",
"original_data",
".",
"copy",
"(",
")",
"new_dat... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _guess_extract_method | Guess extraction method, given file name (or path). | tensorflow_datasets/core/download/resource.py | def _guess_extract_method(fname):
"""Guess extraction method, given file name (or path)."""
for method, extensions in _EXTRACTION_METHOD_TO_EXTS:
for ext in extensions:
if fname.endswith(ext):
return method
return ExtractMethod.NO_EXTRACT | def _guess_extract_method(fname):
"""Guess extraction method, given file name (or path)."""
for method, extensions in _EXTRACTION_METHOD_TO_EXTS:
for ext in extensions:
if fname.endswith(ext):
return method
return ExtractMethod.NO_EXTRACT | [
"Guess",
"extraction",
"method",
"given",
"file",
"name",
"(",
"or",
"path",
")",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/resource.py#L93-L99 | [
"def",
"_guess_extract_method",
"(",
"fname",
")",
":",
"for",
"method",
",",
"extensions",
"in",
"_EXTRACTION_METHOD_TO_EXTS",
":",
"for",
"ext",
"in",
"extensions",
":",
"if",
"fname",
".",
"endswith",
"(",
"ext",
")",
":",
"return",
"method",
"return",
"E... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _sanitize_url | Sanitize and shorten url to fit in max_length.
Function is stable: same input MUST ALWAYS give same result, accros changes
in code as well. Different URLs might give same result.
As much as possible, the extension should be kept.
Heuristics are applied to only keep useful info from url.
1- Drop generic [su... | tensorflow_datasets/core/download/resource.py | def _sanitize_url(url, max_length):
"""Sanitize and shorten url to fit in max_length.
Function is stable: same input MUST ALWAYS give same result, accros changes
in code as well. Different URLs might give same result.
As much as possible, the extension should be kept.
Heuristics are applied to only keep use... | def _sanitize_url(url, max_length):
"""Sanitize and shorten url to fit in max_length.
Function is stable: same input MUST ALWAYS give same result, accros changes
in code as well. Different URLs might give same result.
As much as possible, the extension should be kept.
Heuristics are applied to only keep use... | [
"Sanitize",
"and",
"shorten",
"url",
"to",
"fit",
"in",
"max_length",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/resource.py#L102-L166 | [
"def",
"_sanitize_url",
"(",
"url",
",",
"max_length",
")",
":",
"url",
"=",
"urllib",
".",
"parse",
".",
"urlparse",
"(",
"url",
")",
"netloc",
"=",
"url",
".",
"netloc",
"for",
"prefix",
"in",
"_NETLOC_COMMON_PREFIXES",
":",
"if",
"netloc",
".",
"start... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | get_dl_fname | Returns name of file for (url, checksum).
The max length of linux and windows filenames is 255 chars.
Windows however expects short paths (260 chars), so we limit the file name
to an arbitrary 90 chars.
Naming pattern: '${url}${checksum}'.
- url: url sanitized and shortened to 46 chars.
- checksum: base... | tensorflow_datasets/core/download/resource.py | def get_dl_fname(url, checksum):
"""Returns name of file for (url, checksum).
The max length of linux and windows filenames is 255 chars.
Windows however expects short paths (260 chars), so we limit the file name
to an arbitrary 90 chars.
Naming pattern: '${url}${checksum}'.
- url: url sanitized and shor... | def get_dl_fname(url, checksum):
"""Returns name of file for (url, checksum).
The max length of linux and windows filenames is 255 chars.
Windows however expects short paths (260 chars), so we limit the file name
to an arbitrary 90 chars.
Naming pattern: '${url}${checksum}'.
- url: url sanitized and shor... | [
"Returns",
"name",
"of",
"file",
"for",
"(",
"url",
"checksum",
")",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/resource.py#L169-L190 | [
"def",
"get_dl_fname",
"(",
"url",
",",
"checksum",
")",
":",
"checksum",
"=",
"base64",
".",
"urlsafe_b64encode",
"(",
"_decode_hex",
"(",
"checksum",
")",
")",
"checksum",
"=",
"tf",
".",
"compat",
".",
"as_text",
"(",
"checksum",
")",
"[",
":",
"-",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | get_dl_dirname | Returns name of temp dir for given url. | tensorflow_datasets/core/download/resource.py | def get_dl_dirname(url):
"""Returns name of temp dir for given url."""
checksum = hashlib.sha256(tf.compat.as_bytes(url)).hexdigest()
return get_dl_fname(url, checksum) | def get_dl_dirname(url):
"""Returns name of temp dir for given url."""
checksum = hashlib.sha256(tf.compat.as_bytes(url)).hexdigest()
return get_dl_fname(url, checksum) | [
"Returns",
"name",
"of",
"temp",
"dir",
"for",
"given",
"url",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/resource.py#L193-L196 | [
"def",
"get_dl_dirname",
"(",
"url",
")",
":",
"checksum",
"=",
"hashlib",
".",
"sha256",
"(",
"tf",
".",
"compat",
".",
"as_bytes",
"(",
"url",
")",
")",
".",
"hexdigest",
"(",
")",
"return",
"get_dl_fname",
"(",
"url",
",",
"checksum",
")"
] | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _read_info | Returns info dict or None. | tensorflow_datasets/core/download/resource.py | def _read_info(info_path):
"""Returns info dict or None."""
if not tf.io.gfile.exists(info_path):
return None
with tf.io.gfile.GFile(info_path) as info_f:
return json.load(info_f) | def _read_info(info_path):
"""Returns info dict or None."""
if not tf.io.gfile.exists(info_path):
return None
with tf.io.gfile.GFile(info_path) as info_f:
return json.load(info_f) | [
"Returns",
"info",
"dict",
"or",
"None",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/resource.py#L204-L209 | [
"def",
"_read_info",
"(",
"info_path",
")",
":",
"if",
"not",
"tf",
".",
"io",
".",
"gfile",
".",
"exists",
"(",
"info_path",
")",
":",
"return",
"None",
"with",
"tf",
".",
"io",
".",
"gfile",
".",
"GFile",
"(",
"info_path",
")",
"as",
"info_f",
":... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | write_info_file | Write the INFO file next to local file.
Although the method is synchronized, there is still a risk two processes
running at the same time overlap here. Risk accepted, since potentially lost
data (`dataset_name`) is only for human consumption.
Args:
resource: resource for which to write the INFO file.
... | tensorflow_datasets/core/download/resource.py | def write_info_file(resource, path, dataset_name, original_fname):
"""Write the INFO file next to local file.
Although the method is synchronized, there is still a risk two processes
running at the same time overlap here. Risk accepted, since potentially lost
data (`dataset_name`) is only for human consumption... | def write_info_file(resource, path, dataset_name, original_fname):
"""Write the INFO file next to local file.
Although the method is synchronized, there is still a risk two processes
running at the same time overlap here. Risk accepted, since potentially lost
data (`dataset_name`) is only for human consumption... | [
"Write",
"the",
"INFO",
"file",
"next",
"to",
"local",
"file",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/resource.py#L214-L240 | [
"def",
"write_info_file",
"(",
"resource",
",",
"path",
",",
"dataset_name",
",",
"original_fname",
")",
":",
"info_path",
"=",
"_get_info_path",
"(",
"path",
")",
"info",
"=",
"_read_info",
"(",
"info_path",
")",
"or",
"{",
"}",
"urls",
"=",
"set",
"(",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | get_extract_method | Returns `ExtractMethod` to use on resource at path. Cannot be None. | tensorflow_datasets/core/download/resource.py | def get_extract_method(path):
"""Returns `ExtractMethod` to use on resource at path. Cannot be None."""
info_path = _get_info_path(path)
info = _read_info(info_path)
fname = info.get('original_fname', path) if info else path
return _guess_extract_method(fname) | def get_extract_method(path):
"""Returns `ExtractMethod` to use on resource at path. Cannot be None."""
info_path = _get_info_path(path)
info = _read_info(info_path)
fname = info.get('original_fname', path) if info else path
return _guess_extract_method(fname) | [
"Returns",
"ExtractMethod",
"to",
"use",
"on",
"resource",
"at",
"path",
".",
"Cannot",
"be",
"None",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/resource.py#L243-L248 | [
"def",
"get_extract_method",
"(",
"path",
")",
":",
"info_path",
"=",
"_get_info_path",
"(",
"path",
")",
"info",
"=",
"_read_info",
"(",
"info_path",
")",
"fname",
"=",
"info",
".",
"get",
"(",
"'original_fname'",
",",
"path",
")",
"if",
"info",
"else",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | Resource.exists_locally | Returns whether the resource exists locally, at `resource.path`. | tensorflow_datasets/core/download/resource.py | def exists_locally(cls, path):
"""Returns whether the resource exists locally, at `resource.path`."""
# If INFO file doesn't exist, consider resource does NOT exist, as it would
# prevent guessing the `extract_method`.
return (tf.io.gfile.exists(path) and
tf.io.gfile.exists(_get_info_path(pa... | def exists_locally(cls, path):
"""Returns whether the resource exists locally, at `resource.path`."""
# If INFO file doesn't exist, consider resource does NOT exist, as it would
# prevent guessing the `extract_method`.
return (tf.io.gfile.exists(path) and
tf.io.gfile.exists(_get_info_path(pa... | [
"Returns",
"whether",
"the",
"resource",
"exists",
"locally",
"at",
"resource",
".",
"path",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/resource.py#L273-L278 | [
"def",
"exists_locally",
"(",
"cls",
",",
"path",
")",
":",
"# If INFO file doesn't exist, consider resource does NOT exist, as it would",
"# prevent guessing the `extract_method`.",
"return",
"(",
"tf",
".",
"io",
".",
"gfile",
".",
"exists",
"(",
"path",
")",
"and",
"... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | Coco2014._split_generators | Returns SplitGenerators. | tensorflow_datasets/image/coco.py | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
root_url = "http://images.cocodataset.org/"
urls = {
# Train/validation set
"train_images": "zips/train2014.zip",
"val_images": "zips/val2014.zip",
"trainval_annotations": "annotations/annotations_trainva... | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
root_url = "http://images.cocodataset.org/"
urls = {
# Train/validation set
"train_images": "zips/train2014.zip",
"val_images": "zips/val2014.zip",
"trainval_annotations": "annotations/annotations_trainva... | [
"Returns",
"SplitGenerators",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/coco.py#L94-L149 | [
"def",
"_split_generators",
"(",
"self",
",",
"dl_manager",
")",
":",
"root_url",
"=",
"\"http://images.cocodataset.org/\"",
"urls",
"=",
"{",
"# Train/validation set",
"\"train_images\"",
":",
"\"zips/train2014.zip\"",
",",
"\"val_images\"",
":",
"\"zips/val2014.zip\"",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | Coco2014._generate_examples | Generate examples as dicts.
Args:
image_dir: `str`, directory containing the images
annotation_dir: `str`, directory containing
split_type: `str`, <split_name><year> (ex: train2014)
has_annotation: `bool`, when False (for the testing set), the annotations
are not recorded
Yield... | tensorflow_datasets/image/coco.py | def _generate_examples(
self, image_dir, annotation_dir, split_type, has_annotation=True):
"""Generate examples as dicts.
Args:
image_dir: `str`, directory containing the images
annotation_dir: `str`, directory containing
split_type: `str`, <split_name><year> (ex: train2014)
has_a... | def _generate_examples(
self, image_dir, annotation_dir, split_type, has_annotation=True):
"""Generate examples as dicts.
Args:
image_dir: `str`, directory containing the images
annotation_dir: `str`, directory containing
split_type: `str`, <split_name><year> (ex: train2014)
has_a... | [
"Generate",
"examples",
"as",
"dicts",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/coco.py#L151-L252 | [
"def",
"_generate_examples",
"(",
"self",
",",
"image_dir",
",",
"annotation_dir",
",",
"split_type",
",",
"has_annotation",
"=",
"True",
")",
":",
"if",
"has_annotation",
":",
"instance_filename",
"=",
"\"instances_{}.json\"",
"else",
":",
"instance_filename",
"=",... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | Text.str2ints | Conversion string => encoded list[int]. | tensorflow_datasets/core/features/text_feature.py | def str2ints(self, str_value):
"""Conversion string => encoded list[int]."""
if not self._encoder:
raise ValueError(
"Text.str2ints is not available because encoder hasn't been defined.")
return self._encoder.encode(str_value) | def str2ints(self, str_value):
"""Conversion string => encoded list[int]."""
if not self._encoder:
raise ValueError(
"Text.str2ints is not available because encoder hasn't been defined.")
return self._encoder.encode(str_value) | [
"Conversion",
"string",
"=",
">",
"encoded",
"list",
"[",
"int",
"]",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/features/text_feature.py#L83-L88 | [
"def",
"str2ints",
"(",
"self",
",",
"str_value",
")",
":",
"if",
"not",
"self",
".",
"_encoder",
":",
"raise",
"ValueError",
"(",
"\"Text.str2ints is not available because encoder hasn't been defined.\"",
")",
"return",
"self",
".",
"_encoder",
".",
"encode",
"(",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | Text.ints2str | Conversion list[int] => decoded string. | tensorflow_datasets/core/features/text_feature.py | def ints2str(self, int_values):
"""Conversion list[int] => decoded string."""
if not self._encoder:
raise ValueError(
"Text.ints2str is not available because encoder hasn't been defined.")
return self._encoder.decode(int_values) | def ints2str(self, int_values):
"""Conversion list[int] => decoded string."""
if not self._encoder:
raise ValueError(
"Text.ints2str is not available because encoder hasn't been defined.")
return self._encoder.decode(int_values) | [
"Conversion",
"list",
"[",
"int",
"]",
"=",
">",
"decoded",
"string",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/features/text_feature.py#L90-L95 | [
"def",
"ints2str",
"(",
"self",
",",
"int_values",
")",
":",
"if",
"not",
"self",
".",
"_encoder",
":",
"raise",
"ValueError",
"(",
"\"Text.ints2str is not available because encoder hasn't been defined.\"",
")",
"return",
"self",
".",
"_encoder",
".",
"decode",
"(",... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | Text.maybe_build_from_corpus | Call SubwordTextEncoder.build_from_corpus is encoder_cls is such. | tensorflow_datasets/core/features/text_feature.py | def maybe_build_from_corpus(self, corpus_generator, **kwargs):
"""Call SubwordTextEncoder.build_from_corpus is encoder_cls is such."""
if self._encoder_cls is not text_lib.SubwordTextEncoder:
return
if self.encoder:
return
vocab_size = self._encoder_config.vocab_size
self.encoder = text... | def maybe_build_from_corpus(self, corpus_generator, **kwargs):
"""Call SubwordTextEncoder.build_from_corpus is encoder_cls is such."""
if self._encoder_cls is not text_lib.SubwordTextEncoder:
return
if self.encoder:
return
vocab_size = self._encoder_config.vocab_size
self.encoder = text... | [
"Call",
"SubwordTextEncoder",
".",
"build_from_corpus",
"is",
"encoder_cls",
"is",
"such",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/features/text_feature.py#L137-L148 | [
"def",
"maybe_build_from_corpus",
"(",
"self",
",",
"corpus_generator",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"self",
".",
"_encoder_cls",
"is",
"not",
"text_lib",
".",
"SubwordTextEncoder",
":",
"return",
"if",
"self",
".",
"encoder",
":",
"return",
"voc... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | sharded_filenames | Sharded filenames given prefix and number of shards. | tensorflow_datasets/core/naming.py | def sharded_filenames(filename_prefix, num_shards):
"""Sharded filenames given prefix and number of shards."""
shard_suffix = "%05d-of-%05d"
return [
"%s-%s" % (filename_prefix, shard_suffix % (i, num_shards))
for i in range(num_shards)
] | def sharded_filenames(filename_prefix, num_shards):
"""Sharded filenames given prefix and number of shards."""
shard_suffix = "%05d-of-%05d"
return [
"%s-%s" % (filename_prefix, shard_suffix % (i, num_shards))
for i in range(num_shards)
] | [
"Sharded",
"filenames",
"given",
"prefix",
"and",
"number",
"of",
"shards",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/naming.py#L52-L58 | [
"def",
"sharded_filenames",
"(",
"filename_prefix",
",",
"num_shards",
")",
":",
"shard_suffix",
"=",
"\"%05d-of-%05d\"",
"return",
"[",
"\"%s-%s\"",
"%",
"(",
"filename_prefix",
",",
"shard_suffix",
"%",
"(",
"i",
",",
"num_shards",
")",
")",
"for",
"i",
"in"... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _walk_omniglot_dir | Walk an Omniglot directory and yield examples. | tensorflow_datasets/image/omniglot.py | def _walk_omniglot_dir(directory):
"""Walk an Omniglot directory and yield examples."""
directory = os.path.join(directory, tf.io.gfile.listdir(directory)[0])
alphabets = sorted(tf.io.gfile.listdir(directory))
for alphabet in alphabets:
alphabet_dir = os.path.join(directory, alphabet)
characters = sorte... | def _walk_omniglot_dir(directory):
"""Walk an Omniglot directory and yield examples."""
directory = os.path.join(directory, tf.io.gfile.listdir(directory)[0])
alphabets = sorted(tf.io.gfile.listdir(directory))
for alphabet in alphabets:
alphabet_dir = os.path.join(directory, alphabet)
characters = sorte... | [
"Walk",
"an",
"Omniglot",
"directory",
"and",
"yield",
"examples",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/omniglot.py#L128-L143 | [
"def",
"_walk_omniglot_dir",
"(",
"directory",
")",
":",
"directory",
"=",
"os",
".",
"path",
".",
"join",
"(",
"directory",
",",
"tf",
".",
"io",
".",
"gfile",
".",
"listdir",
"(",
"directory",
")",
"[",
"0",
"]",
")",
"alphabets",
"=",
"sorted",
"(... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _get_names | Get alphabet and label names, union across all dirs. | tensorflow_datasets/image/omniglot.py | def _get_names(dirs):
"""Get alphabet and label names, union across all dirs."""
alphabets = set()
label_names = {}
for d in dirs:
for example in _walk_omniglot_dir(d):
alphabet, alphabet_char_id, label, _ = example
alphabets.add(alphabet)
label_name = "%s_%d" % (alphabet, alphabet_char_id... | def _get_names(dirs):
"""Get alphabet and label names, union across all dirs."""
alphabets = set()
label_names = {}
for d in dirs:
for example in _walk_omniglot_dir(d):
alphabet, alphabet_char_id, label, _ = example
alphabets.add(alphabet)
label_name = "%s_%d" % (alphabet, alphabet_char_id... | [
"Get",
"alphabet",
"and",
"label",
"names",
"union",
"across",
"all",
"dirs",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/omniglot.py#L146-L160 | [
"def",
"_get_names",
"(",
"dirs",
")",
":",
"alphabets",
"=",
"set",
"(",
")",
"label_names",
"=",
"{",
"}",
"for",
"d",
"in",
"dirs",
":",
"for",
"example",
"in",
"_walk_omniglot_dir",
"(",
"d",
")",
":",
"alphabet",
",",
"alphabet_char_id",
",",
"lab... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | size_str | Returns a human readable size string.
If size_in_bytes is None, then returns "?? GiB".
For example `size_str(1.5 * tfds.units.GiB) == "1.50 GiB"`.
Args:
size_in_bytes: `int` or `None`, the size, in bytes, that we want to
format as a human-readable size string. | tensorflow_datasets/core/units.py | def size_str(size_in_bytes):
"""Returns a human readable size string.
If size_in_bytes is None, then returns "?? GiB".
For example `size_str(1.5 * tfds.units.GiB) == "1.50 GiB"`.
Args:
size_in_bytes: `int` or `None`, the size, in bytes, that we want to
format as a human-readable size string.
"""
... | def size_str(size_in_bytes):
"""Returns a human readable size string.
If size_in_bytes is None, then returns "?? GiB".
For example `size_str(1.5 * tfds.units.GiB) == "1.50 GiB"`.
Args:
size_in_bytes: `int` or `None`, the size, in bytes, that we want to
format as a human-readable size string.
"""
... | [
"Returns",
"a",
"human",
"readable",
"size",
"string",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/units.py#L34-L53 | [
"def",
"size_str",
"(",
"size_in_bytes",
")",
":",
"if",
"not",
"size_in_bytes",
":",
"return",
"\"?? GiB\"",
"size_in_bytes",
"=",
"float",
"(",
"size_in_bytes",
")",
"for",
"(",
"name",
",",
"size_bytes",
")",
"in",
"_NAME_LIST",
":",
"value",
"=",
"size_i... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _Downloader.tqdm | Add a progression bar for the current download. | tensorflow_datasets/core/download/downloader.py | def tqdm(self):
"""Add a progression bar for the current download."""
async_tqdm = utils.async_tqdm
with async_tqdm(total=0, desc='Dl Completed...', unit=' url') as pbar_url:
with async_tqdm(total=0, desc='Dl Size...', unit=' MiB') as pbar_dl_size:
self._pbar_url = pbar_url
self._pbar_... | def tqdm(self):
"""Add a progression bar for the current download."""
async_tqdm = utils.async_tqdm
with async_tqdm(total=0, desc='Dl Completed...', unit=' url') as pbar_url:
with async_tqdm(total=0, desc='Dl Size...', unit=' MiB') as pbar_dl_size:
self._pbar_url = pbar_url
self._pbar_... | [
"Add",
"a",
"progression",
"bar",
"for",
"the",
"current",
"download",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/downloader.py#L84-L91 | [
"def",
"tqdm",
"(",
"self",
")",
":",
"async_tqdm",
"=",
"utils",
".",
"async_tqdm",
"with",
"async_tqdm",
"(",
"total",
"=",
"0",
",",
"desc",
"=",
"'Dl Completed...'",
",",
"unit",
"=",
"' url'",
")",
"as",
"pbar_url",
":",
"with",
"async_tqdm",
"(",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _Downloader.download | Download url to given path.
Returns Promise -> sha256 of downloaded file.
Args:
url: address of resource to download.
destination_path: `str`, path to directory where to download the resource.
Returns:
Promise obj -> (`str`, int): (downloaded object checksum, size in bytes). | tensorflow_datasets/core/download/downloader.py | def download(self, url, destination_path):
"""Download url to given path.
Returns Promise -> sha256 of downloaded file.
Args:
url: address of resource to download.
destination_path: `str`, path to directory where to download the resource.
Returns:
Promise obj -> (`str`, int): (downl... | def download(self, url, destination_path):
"""Download url to given path.
Returns Promise -> sha256 of downloaded file.
Args:
url: address of resource to download.
destination_path: `str`, path to directory where to download the resource.
Returns:
Promise obj -> (`str`, int): (downl... | [
"Download",
"url",
"to",
"given",
"path",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/downloader.py#L93-L107 | [
"def",
"download",
"(",
"self",
",",
"url",
",",
"destination_path",
")",
":",
"self",
".",
"_pbar_url",
".",
"update_total",
"(",
"1",
")",
"future",
"=",
"self",
".",
"_executor",
".",
"submit",
"(",
"self",
".",
"_sync_download",
",",
"url",
",",
"d... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _Downloader._sync_kaggle_download | Download with Kaggle API. | tensorflow_datasets/core/download/downloader.py | def _sync_kaggle_download(self, kaggle_url, destination_path):
"""Download with Kaggle API."""
kaggle_file = kaggle.KaggleFile.from_url(kaggle_url)
downloader = self.kaggle_downloader(kaggle_file.competition)
filepath = downloader.download_file(kaggle_file.filename, destination_path)
dl_size = tf.i... | def _sync_kaggle_download(self, kaggle_url, destination_path):
"""Download with Kaggle API."""
kaggle_file = kaggle.KaggleFile.from_url(kaggle_url)
downloader = self.kaggle_downloader(kaggle_file.competition)
filepath = downloader.download_file(kaggle_file.filename, destination_path)
dl_size = tf.i... | [
"Download",
"with",
"Kaggle",
"API",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/downloader.py#L109-L123 | [
"def",
"_sync_kaggle_download",
"(",
"self",
",",
"kaggle_url",
",",
"destination_path",
")",
":",
"kaggle_file",
"=",
"kaggle",
".",
"KaggleFile",
".",
"from_url",
"(",
"kaggle_url",
")",
"downloader",
"=",
"self",
".",
"kaggle_downloader",
"(",
"kaggle_file",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _Downloader._get_drive_url | Returns url, possibly with confirmation token. | tensorflow_datasets/core/download/downloader.py | def _get_drive_url(self, url, session):
"""Returns url, possibly with confirmation token."""
response = session.get(url, stream=True)
if response.status_code != 200:
raise DownloadError(
'Failed to get url %s. HTTP code: %d.' % (url, response.status_code))
for k, v in response.cookies.it... | def _get_drive_url(self, url, session):
"""Returns url, possibly with confirmation token."""
response = session.get(url, stream=True)
if response.status_code != 200:
raise DownloadError(
'Failed to get url %s. HTTP code: %d.' % (url, response.status_code))
for k, v in response.cookies.it... | [
"Returns",
"url",
"possibly",
"with",
"confirmation",
"token",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/downloader.py#L125-L135 | [
"def",
"_get_drive_url",
"(",
"self",
",",
"url",
",",
"session",
")",
":",
"response",
"=",
"session",
".",
"get",
"(",
"url",
",",
"stream",
"=",
"True",
")",
"if",
"response",
".",
"status_code",
"!=",
"200",
":",
"raise",
"DownloadError",
"(",
"'Fa... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _Downloader._sync_download | Synchronous version of `download` method. | tensorflow_datasets/core/download/downloader.py | def _sync_download(self, url, destination_path):
"""Synchronous version of `download` method."""
proxies = {
'http': os.environ.get('TFDS_HTTP_PROXY', None),
'https': os.environ.get('TFDS_HTTPS_PROXY', None),
'ftp': os.environ.get('TFDS_FTP_PROXY', None)
}
if kaggle.KaggleFile.is... | def _sync_download(self, url, destination_path):
"""Synchronous version of `download` method."""
proxies = {
'http': os.environ.get('TFDS_HTTP_PROXY', None),
'https': os.environ.get('TFDS_HTTPS_PROXY', None),
'ftp': os.environ.get('TFDS_FTP_PROXY', None)
}
if kaggle.KaggleFile.is... | [
"Synchronous",
"version",
"of",
"download",
"method",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/downloader.py#L144-L208 | [
"def",
"_sync_download",
"(",
"self",
",",
"url",
",",
"destination_path",
")",
":",
"proxies",
"=",
"{",
"'http'",
":",
"os",
".",
"environ",
".",
"get",
"(",
"'TFDS_HTTP_PROXY'",
",",
"None",
")",
",",
"'https'",
":",
"os",
".",
"environ",
".",
"get"... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _resize_image_if_necessary | Resize an image to have (roughly) the given number of target pixels.
Args:
image_fobj: File object containing the original image.
target_pixels: If given, number of pixels that the image must have.
Returns:
A file object. | tensorflow_datasets/image/diabetic_retinopathy_detection.py | def _resize_image_if_necessary(image_fobj, target_pixels=None):
"""Resize an image to have (roughly) the given number of target pixels.
Args:
image_fobj: File object containing the original image.
target_pixels: If given, number of pixels that the image must have.
Returns:
A file object.
"""
if ... | def _resize_image_if_necessary(image_fobj, target_pixels=None):
"""Resize an image to have (roughly) the given number of target pixels.
Args:
image_fobj: File object containing the original image.
target_pixels: If given, number of pixels that the image must have.
Returns:
A file object.
"""
if ... | [
"Resize",
"an",
"image",
"to",
"have",
"(",
"roughly",
")",
"the",
"given",
"number",
"of",
"target",
"pixels",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/diabetic_retinopathy_detection.py#L181-L206 | [
"def",
"_resize_image_if_necessary",
"(",
"image_fobj",
",",
"target_pixels",
"=",
"None",
")",
":",
"if",
"target_pixels",
"is",
"None",
":",
"return",
"image_fobj",
"cv2",
"=",
"tfds",
".",
"core",
".",
"lazy_imports",
".",
"cv2",
"# Decode image using OpenCV2."... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | DiabeticRetinopathyDetection._generate_examples | Yields Example instances from given CSV.
Args:
images_dir_path: path to dir in which images are stored.
csv_path: optional, path to csv file with two columns: name of image and
label. If not provided, just scan image directory, don't set labels.
csv_usage: optional, subset of examples fro... | tensorflow_datasets/image/diabetic_retinopathy_detection.py | def _generate_examples(self, images_dir_path, csv_path=None, csv_usage=None):
"""Yields Example instances from given CSV.
Args:
images_dir_path: path to dir in which images are stored.
csv_path: optional, path to csv file with two columns: name of image and
label. If not provided, just scan... | def _generate_examples(self, images_dir_path, csv_path=None, csv_usage=None):
"""Yields Example instances from given CSV.
Args:
images_dir_path: path to dir in which images are stored.
csv_path: optional, path to csv file with two columns: name of image and
label. If not provided, just scan... | [
"Yields",
"Example",
"instances",
"from",
"given",
"CSV",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/diabetic_retinopathy_detection.py#L150-L178 | [
"def",
"_generate_examples",
"(",
"self",
",",
"images_dir_path",
",",
"csv_path",
"=",
"None",
",",
"csv_usage",
"=",
"None",
")",
":",
"if",
"csv_path",
":",
"with",
"tf",
".",
"io",
".",
"gfile",
".",
"GFile",
"(",
"csv_path",
")",
"as",
"csv_f",
":... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | FileAdapterBuilder._slice_split_info_to_instruction_dicts | Return the list of files and reading mask of the files to read. | tensorflow_datasets/core/dataset_builder.py | def _slice_split_info_to_instruction_dicts(self, list_sliced_split_info):
"""Return the list of files and reading mask of the files to read."""
instruction_dicts = []
for sliced_split_info in list_sliced_split_info:
mask = splits_lib.slice_to_percent_mask(sliced_split_info.slice_value)
# Comput... | def _slice_split_info_to_instruction_dicts(self, list_sliced_split_info):
"""Return the list of files and reading mask of the files to read."""
instruction_dicts = []
for sliced_split_info in list_sliced_split_info:
mask = splits_lib.slice_to_percent_mask(sliced_split_info.slice_value)
# Comput... | [
"Return",
"the",
"list",
"of",
"files",
"and",
"reading",
"mask",
"of",
"the",
"files",
"to",
"read",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/dataset_builder.py#L707-L739 | [
"def",
"_slice_split_info_to_instruction_dicts",
"(",
"self",
",",
"list_sliced_split_info",
")",
":",
"instruction_dicts",
"=",
"[",
"]",
"for",
"sliced_split_info",
"in",
"list_sliced_split_info",
":",
"mask",
"=",
"splits_lib",
".",
"slice_to_percent_mask",
"(",
"sli... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | FileAdapterBuilder._build_split_filenames | Construct the split filenames associated with the split info.
The filenames correspond to the pre-processed datasets files present in
the root directory of the dataset.
Args:
split_info_list: (list[SplitInfo]) List of split from which generate the
filenames
Returns:
filenames: (li... | tensorflow_datasets/core/dataset_builder.py | def _build_split_filenames(self, split_info_list):
"""Construct the split filenames associated with the split info.
The filenames correspond to the pre-processed datasets files present in
the root directory of the dataset.
Args:
split_info_list: (list[SplitInfo]) List of split from which generat... | def _build_split_filenames(self, split_info_list):
"""Construct the split filenames associated with the split info.
The filenames correspond to the pre-processed datasets files present in
the root directory of the dataset.
Args:
split_info_list: (list[SplitInfo]) List of split from which generat... | [
"Construct",
"the",
"split",
"filenames",
"associated",
"with",
"the",
"split",
"info",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/dataset_builder.py#L741-L765 | [
"def",
"_build_split_filenames",
"(",
"self",
",",
"split_info_list",
")",
":",
"filenames",
"=",
"[",
"]",
"for",
"split_info",
"in",
"split_info_list",
":",
"filenames",
".",
"extend",
"(",
"naming",
".",
"filepaths_for_dataset_split",
"(",
"dataset_name",
"=",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | MovingMnist._generate_examples | Generate MovingMnist sequences.
Args:
data_path (str): Path to the data file
Yields:
20 x 64 x 64 x 1 uint8 numpy arrays | tensorflow_datasets/video/moving_mnist.py | def _generate_examples(self, data_path):
"""Generate MovingMnist sequences.
Args:
data_path (str): Path to the data file
Yields:
20 x 64 x 64 x 1 uint8 numpy arrays
"""
with tf.io.gfile.GFile(data_path, "rb") as fp:
images = np.load(fp)
images = np.transpose(images, (1, 0, 2,... | def _generate_examples(self, data_path):
"""Generate MovingMnist sequences.
Args:
data_path (str): Path to the data file
Yields:
20 x 64 x 64 x 1 uint8 numpy arrays
"""
with tf.io.gfile.GFile(data_path, "rb") as fp:
images = np.load(fp)
images = np.transpose(images, (1, 0, 2,... | [
"Generate",
"MovingMnist",
"sequences",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/video/moving_mnist.py#L85-L99 | [
"def",
"_generate_examples",
"(",
"self",
",",
"data_path",
")",
":",
"with",
"tf",
".",
"io",
".",
"gfile",
".",
"GFile",
"(",
"data_path",
",",
"\"rb\"",
")",
"as",
"fp",
":",
"images",
"=",
"np",
".",
"load",
"(",
"fp",
")",
"images",
"=",
"np",... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | StarcraftVideo._parse_single_video | Parses single video from the input tfrecords.
Args:
example_proto: tfExample proto with a single video.
Returns:
dict with all frames, positions and actions. | tensorflow_datasets/video/starcraft.py | def _parse_single_video(self, example_proto):
"""Parses single video from the input tfrecords.
Args:
example_proto: tfExample proto with a single video.
Returns:
dict with all frames, positions and actions.
"""
context_features = {
"game_duration_loops": tf.io.FixedLenFeature([... | def _parse_single_video(self, example_proto):
"""Parses single video from the input tfrecords.
Args:
example_proto: tfExample proto with a single video.
Returns:
dict with all frames, positions and actions.
"""
context_features = {
"game_duration_loops": tf.io.FixedLenFeature([... | [
"Parses",
"single",
"video",
"from",
"the",
"input",
"tfrecords",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/video/starcraft.py#L181-L208 | [
"def",
"_parse_single_video",
"(",
"self",
",",
"example_proto",
")",
":",
"context_features",
"=",
"{",
"\"game_duration_loops\"",
":",
"tf",
".",
"io",
".",
"FixedLenFeature",
"(",
"[",
"1",
"]",
",",
"tf",
".",
"int64",
")",
",",
"\"game_duration_seconds\""... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | Dsprites._generate_examples | Generates examples for the dSprites data set.
Args:
filepath: path to the dSprites hdf5 file.
Yields:
Dictionaries with images, latent classes, and latent values. | tensorflow_datasets/image/dsprites.py | def _generate_examples(self, filepath):
"""Generates examples for the dSprites data set.
Args:
filepath: path to the dSprites hdf5 file.
Yields:
Dictionaries with images, latent classes, and latent values.
"""
# Simultaneously iterating through the different data sets in the hdf5
#... | def _generate_examples(self, filepath):
"""Generates examples for the dSprites data set.
Args:
filepath: path to the dSprites hdf5 file.
Yields:
Dictionaries with images, latent classes, and latent values.
"""
# Simultaneously iterating through the different data sets in the hdf5
#... | [
"Generates",
"examples",
"for",
"the",
"dSprites",
"data",
"set",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/dsprites.py#L117-L143 | [
"def",
"_generate_examples",
"(",
"self",
",",
"filepath",
")",
":",
"# Simultaneously iterating through the different data sets in the hdf5",
"# file is >100x slower and the data set is small (26.7MB). Hence, we first",
"# load everything into memory before yielding the samples.",
"image_arra... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | OxfordIIITPet._split_generators | Returns splits. | tensorflow_datasets/image/oxford_iiit_pet.py | def _split_generators(self, dl_manager):
"""Returns splits."""
# Download images and annotations that come in separate archives.
# Note, that the extension of archives is .tar.gz even though the actual
# archives format is uncompressed tar.
dl_paths = dl_manager.download_and_extract({
"image... | def _split_generators(self, dl_manager):
"""Returns splits."""
# Download images and annotations that come in separate archives.
# Note, that the extension of archives is .tar.gz even though the actual
# archives format is uncompressed tar.
dl_paths = dl_manager.download_and_extract({
"image... | [
"Returns",
"splits",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/oxford_iiit_pet.py#L65-L102 | [
"def",
"_split_generators",
"(",
"self",
",",
"dl_manager",
")",
":",
"# Download images and annotations that come in separate archives.",
"# Note, that the extension of archives is .tar.gz even though the actual",
"# archives format is uncompressed tar.",
"dl_paths",
"=",
"dl_manager",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _load_objects | Returns objects listed within given CSV files. | tensorflow_datasets/image/open_images.py | def _load_objects(csv_paths, csv_positions, prefix):
"""Returns objects listed within given CSV files."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_paths, csv_positions, prefix)
objects = collections.defaultdict(list)
for i, labels_path in enumerate(csv_paths):
with... | def _load_objects(csv_paths, csv_positions, prefix):
"""Returns objects listed within given CSV files."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_paths, csv_positions, prefix)
objects = collections.defaultdict(list)
for i, labels_path in enumerate(csv_paths):
with... | [
"Returns",
"objects",
"listed",
"within",
"given",
"CSV",
"files",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/open_images.py#L322-L341 | [
"def",
"_load_objects",
"(",
"csv_paths",
",",
"csv_positions",
",",
"prefix",
")",
":",
"logging",
".",
"info",
"(",
"'Loading CSVs %s from positions %s with prefix %s'",
",",
"csv_paths",
",",
"csv_positions",
",",
"prefix",
")",
"objects",
"=",
"collections",
"."... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _load_bboxes | Returns bounded boxes listed within given CSV file. | tensorflow_datasets/image/open_images.py | def _load_bboxes(csv_path, csv_positions, prefix):
"""Returns bounded boxes listed within given CSV file."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_path, csv_positions, prefix)
boxes = collections.defaultdict(list)
with tf.io.gfile.GFile(csv_path) as csv_f:
if cs... | def _load_bboxes(csv_path, csv_positions, prefix):
"""Returns bounded boxes listed within given CSV file."""
logging.info('Loading CSVs %s from positions %s with prefix %s',
csv_path, csv_positions, prefix)
boxes = collections.defaultdict(list)
with tf.io.gfile.GFile(csv_path) as csv_f:
if cs... | [
"Returns",
"bounded",
"boxes",
"listed",
"within",
"given",
"CSV",
"file",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/open_images.py#L344-L369 | [
"def",
"_load_bboxes",
"(",
"csv_path",
",",
"csv_positions",
",",
"prefix",
")",
":",
"logging",
".",
"info",
"(",
"'Loading CSVs %s from positions %s with prefix %s'",
",",
"csv_path",
",",
"csv_positions",
",",
"prefix",
")",
"boxes",
"=",
"collections",
".",
"... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | OpenImagesV4._split_generators | Returns SplitGenerators. | tensorflow_datasets/image/open_images.py | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
paths = dl_manager.download_and_extract(_URLS)
# Load labels from CSVs:
def load(names):
csv_positions = [0] * len(names)
return functools.partial(_load_objects, [paths[name] for name in names],
... | def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
paths = dl_manager.download_and_extract(_URLS)
# Load labels from CSVs:
def load(names):
csv_positions = [0] * len(names)
return functools.partial(_load_objects, [paths[name] for name in names],
... | [
"Returns",
"SplitGenerators",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/open_images.py#L221-L262 | [
"def",
"_split_generators",
"(",
"self",
",",
"dl_manager",
")",
":",
"paths",
"=",
"dl_manager",
".",
"download_and_extract",
"(",
"_URLS",
")",
"# Load labels from CSVs:",
"def",
"load",
"(",
"names",
")",
":",
"csv_positions",
"=",
"[",
"0",
"]",
"*",
"le... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | OpenImagesV4._generate_examples | Yields examples. | tensorflow_datasets/image/open_images.py | def _generate_examples(self, archive_paths, objects_getter, bboxes_getter,
prefixes=None):
"""Yields examples."""
trainable_classes = set(
self.info.features['objects_trainable']['label'].names)
for i, archive_path in enumerate(archive_paths):
prefix = prefixes[i] if p... | def _generate_examples(self, archive_paths, objects_getter, bboxes_getter,
prefixes=None):
"""Yields examples."""
trainable_classes = set(
self.info.features['objects_trainable']['label'].names)
for i, archive_path in enumerate(archive_paths):
prefix = prefixes[i] if p... | [
"Yields",
"examples",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/image/open_images.py#L264-L291 | [
"def",
"_generate_examples",
"(",
"self",
",",
"archive_paths",
",",
"objects_getter",
",",
"bboxes_getter",
",",
"prefixes",
"=",
"None",
")",
":",
"trainable_classes",
"=",
"set",
"(",
"self",
".",
"info",
".",
"features",
"[",
"'objects_trainable'",
"]",
"[... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | IMDBReviews._generate_examples | Generate IMDB examples. | tensorflow_datasets/text/imdb.py | def _generate_examples(self, archive, directory):
"""Generate IMDB examples."""
reg = re.compile(os.path.join("^%s" % directory, "(?P<label>neg|pos)", ""))
for path, imdb_f in archive:
res = reg.match(path)
if not res:
continue
text = imdb_f.read().strip()
yield {
"... | def _generate_examples(self, archive, directory):
"""Generate IMDB examples."""
reg = re.compile(os.path.join("^%s" % directory, "(?P<label>neg|pos)", ""))
for path, imdb_f in archive:
res = reg.match(path)
if not res:
continue
text = imdb_f.read().strip()
yield {
"... | [
"Generate",
"IMDB",
"examples",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/text/imdb.py#L146-L157 | [
"def",
"_generate_examples",
"(",
"self",
",",
"archive",
",",
"directory",
")",
":",
"reg",
"=",
"re",
".",
"compile",
"(",
"os",
".",
"path",
".",
"join",
"(",
"\"^%s\"",
"%",
"directory",
",",
"\"(?P<label>neg|pos)\"",
",",
"\"\"",
")",
")",
"for",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _get_url_hashes | Get hashes of urls in file. | tensorflow_datasets/text/cnn_dailymail.py | def _get_url_hashes(path):
"""Get hashes of urls in file."""
urls = _read_text_file(path)
def url_hash(u):
h = hashlib.sha1()
try:
u = u.encode('utf-8')
except UnicodeDecodeError:
logging.error('Cannot hash url: %s', u)
h.update(u)
return h.hexdigest()
return {url_hash(u): True f... | def _get_url_hashes(path):
"""Get hashes of urls in file."""
urls = _read_text_file(path)
def url_hash(u):
h = hashlib.sha1()
try:
u = u.encode('utf-8')
except UnicodeDecodeError:
logging.error('Cannot hash url: %s', u)
h.update(u)
return h.hexdigest()
return {url_hash(u): True f... | [
"Get",
"hashes",
"of",
"urls",
"in",
"file",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/text/cnn_dailymail.py#L97-L108 | [
"def",
"_get_url_hashes",
"(",
"path",
")",
":",
"urls",
"=",
"_read_text_file",
"(",
"path",
")",
"def",
"url_hash",
"(",
"u",
")",
":",
"h",
"=",
"hashlib",
".",
"sha1",
"(",
")",
"try",
":",
"u",
"=",
"u",
".",
"encode",
"(",
"'utf-8'",
")",
"... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _find_files | Find files corresponding to urls. | tensorflow_datasets/text/cnn_dailymail.py | def _find_files(dl_paths, publisher, url_dict):
"""Find files corresponding to urls."""
if publisher == 'cnn':
top_dir = os.path.join(dl_paths['cnn_stories'], 'cnn', 'stories')
elif publisher == 'dm':
top_dir = os.path.join(dl_paths['dm_stories'], 'dailymail', 'stories')
else:
logging.fatal('Unsuppo... | def _find_files(dl_paths, publisher, url_dict):
"""Find files corresponding to urls."""
if publisher == 'cnn':
top_dir = os.path.join(dl_paths['cnn_stories'], 'cnn', 'stories')
elif publisher == 'dm':
top_dir = os.path.join(dl_paths['dm_stories'], 'dailymail', 'stories')
else:
logging.fatal('Unsuppo... | [
"Find",
"files",
"corresponding",
"to",
"urls",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/text/cnn_dailymail.py#L111-L126 | [
"def",
"_find_files",
"(",
"dl_paths",
",",
"publisher",
",",
"url_dict",
")",
":",
"if",
"publisher",
"==",
"'cnn'",
":",
"top_dir",
"=",
"os",
".",
"path",
".",
"join",
"(",
"dl_paths",
"[",
"'cnn_stories'",
"]",
",",
"'cnn'",
",",
"'stories'",
")",
... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _subset_filenames | Get filenames for a particular split. | tensorflow_datasets/text/cnn_dailymail.py | def _subset_filenames(dl_paths, split):
"""Get filenames for a particular split."""
assert isinstance(dl_paths, dict), dl_paths
# Get filenames for a split.
if split == tfds.Split.TRAIN:
urls = _get_url_hashes(dl_paths['train_urls'])
elif split == tfds.Split.VALIDATION:
urls = _get_url_hashes(dl_paths... | def _subset_filenames(dl_paths, split):
"""Get filenames for a particular split."""
assert isinstance(dl_paths, dict), dl_paths
# Get filenames for a split.
if split == tfds.Split.TRAIN:
urls = _get_url_hashes(dl_paths['train_urls'])
elif split == tfds.Split.VALIDATION:
urls = _get_url_hashes(dl_paths... | [
"Get",
"filenames",
"for",
"a",
"particular",
"split",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/text/cnn_dailymail.py#L129-L143 | [
"def",
"_subset_filenames",
"(",
"dl_paths",
",",
"split",
")",
":",
"assert",
"isinstance",
"(",
"dl_paths",
",",
"dict",
")",
",",
"dl_paths",
"# Get filenames for a split.",
"if",
"split",
"==",
"tfds",
".",
"Split",
".",
"TRAIN",
":",
"urls",
"=",
"_get_... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | _get_art_abs | Get abstract (highlights) and article from a story file path. | tensorflow_datasets/text/cnn_dailymail.py | def _get_art_abs(story_file):
"""Get abstract (highlights) and article from a story file path."""
# Based on https://github.com/abisee/cnn-dailymail/blob/master/
# make_datafiles.py
lines = _read_text_file(story_file)
# Lowercase everything
lines = [line.lower() for line in lines]
# Put periods on ... | def _get_art_abs(story_file):
"""Get abstract (highlights) and article from a story file path."""
# Based on https://github.com/abisee/cnn-dailymail/blob/master/
# make_datafiles.py
lines = _read_text_file(story_file)
# Lowercase everything
lines = [line.lower() for line in lines]
# Put periods on ... | [
"Get",
"abstract",
"(",
"highlights",
")",
"and",
"article",
"from",
"a",
"story",
"file",
"path",
"."
] | tensorflow/datasets | python | https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/text/cnn_dailymail.py#L163-L207 | [
"def",
"_get_art_abs",
"(",
"story_file",
")",
":",
"# Based on https://github.com/abisee/cnn-dailymail/blob/master/",
"# make_datafiles.py",
"lines",
"=",
"_read_text_file",
"(",
"story_file",
")",
"# Lowercase everything",
"lines",
"=",
"[",
"line",
".",
"lower",
"(",... | 46ceb0cf7b4690f38ecbbc689e4d659a903d08dc |
train | exporter | Export the results. | plugins/exporter.py | def exporter(directory, method, datasets):
"""Export the results."""
if method.lower() == 'json':
# Convert json_dict to a JSON styled string
json_string = json.dumps(datasets, indent=4)
savefile = open('{}/exported.json'.format(directory), 'w+')
savefile.write(json_string)
... | def exporter(directory, method, datasets):
"""Export the results."""
if method.lower() == 'json':
# Convert json_dict to a JSON styled string
json_string = json.dumps(datasets, indent=4)
savefile = open('{}/exported.json'.format(directory), 'w+')
savefile.write(json_string)
... | [
"Export",
"the",
"results",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/plugins/exporter.py#L6-L24 | [
"def",
"exporter",
"(",
"directory",
",",
"method",
",",
"datasets",
")",
":",
"if",
"method",
".",
"lower",
"(",
")",
"==",
"'json'",
":",
"# Convert json_dict to a JSON styled string",
"json_string",
"=",
"json",
".",
"dumps",
"(",
"datasets",
",",
"indent",... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | time_machine | Query archive.org. | plugins/wayback.py | def time_machine(host, mode):
"""Query archive.org."""
now = datetime.datetime.now()
to = str(now.year) + str(now.day) + str(now.month)
if now.month > 6:
fro = str(now.year) + str(now.day) + str(now.month - 6)
else:
fro = str(now.year - 1) + str(now.day) + str(now.month + 6)
url = "htt... | def time_machine(host, mode):
"""Query archive.org."""
now = datetime.datetime.now()
to = str(now.year) + str(now.day) + str(now.month)
if now.month > 6:
fro = str(now.year) + str(now.day) + str(now.month - 6)
else:
fro = str(now.year - 1) + str(now.day) + str(now.month + 6)
url = "htt... | [
"Query",
"archive",
".",
"org",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/plugins/wayback.py#L8-L22 | [
"def",
"time_machine",
"(",
"host",
",",
"mode",
")",
":",
"now",
"=",
"datetime",
".",
"datetime",
".",
"now",
"(",
")",
"to",
"=",
"str",
"(",
"now",
".",
"year",
")",
"+",
"str",
"(",
"now",
".",
"day",
")",
"+",
"str",
"(",
"now",
".",
"m... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | zap | Extract links from robots.txt and sitemap.xml. | core/zap.py | def zap(input_url, archive, domain, host, internal, robots, proxies):
"""Extract links from robots.txt and sitemap.xml."""
if archive:
print('%s Fetching URLs from archive.org' % run)
if False:
archived_urls = time_machine(domain, 'domain')
else:
archived_urls = t... | def zap(input_url, archive, domain, host, internal, robots, proxies):
"""Extract links from robots.txt and sitemap.xml."""
if archive:
print('%s Fetching URLs from archive.org' % run)
if False:
archived_urls = time_machine(domain, 'domain')
else:
archived_urls = t... | [
"Extract",
"links",
"from",
"robots",
".",
"txt",
"and",
"sitemap",
".",
"xml",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/zap.py#L10-L57 | [
"def",
"zap",
"(",
"input_url",
",",
"archive",
",",
"domain",
",",
"host",
",",
"internal",
",",
"robots",
",",
"proxies",
")",
":",
"if",
"archive",
":",
"print",
"(",
"'%s Fetching URLs from archive.org'",
"%",
"run",
")",
"if",
"False",
":",
"archived_... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | requester | Handle the requests and return the response body. | core/requester.py | def requester(
url,
main_url=None,
delay=0,
cook=None,
headers=None,
timeout=10,
host=None,
proxies=[None],
user_agents=[None],
failed=None,
processed=None
):
"""Handle the requests and return the response body."""
cook ... | def requester(
url,
main_url=None,
delay=0,
cook=None,
headers=None,
timeout=10,
host=None,
proxies=[None],
user_agents=[None],
failed=None,
processed=None
):
"""Handle the requests and return the response body."""
cook ... | [
"Handle",
"the",
"requests",
"and",
"return",
"the",
"response",
"body",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/requester.py#L11-L72 | [
"def",
"requester",
"(",
"url",
",",
"main_url",
"=",
"None",
",",
"delay",
"=",
"0",
",",
"cook",
"=",
"None",
",",
"headers",
"=",
"None",
",",
"timeout",
"=",
"10",
",",
"host",
"=",
"None",
",",
"proxies",
"=",
"[",
"None",
"]",
",",
"user_ag... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | intel_extractor | Extract intel from the response body. | photon.py | def intel_extractor(url, response):
"""Extract intel from the response body."""
for rintel in rintels:
res = re.sub(r'<(script).*?</\1>(?s)', '', response)
res = re.sub(r'<[^<]+?>', '', res)
matches = rintel[0].findall(res)
if matches:
for match in matches:
... | def intel_extractor(url, response):
"""Extract intel from the response body."""
for rintel in rintels:
res = re.sub(r'<(script).*?</\1>(?s)', '', response)
res = re.sub(r'<[^<]+?>', '', res)
matches = rintel[0].findall(res)
if matches:
for match in matches:
... | [
"Extract",
"intel",
"from",
"the",
"response",
"body",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/photon.py#L208-L217 | [
"def",
"intel_extractor",
"(",
"url",
",",
"response",
")",
":",
"for",
"rintel",
"in",
"rintels",
":",
"res",
"=",
"re",
".",
"sub",
"(",
"r'<(script).*?</\\1>(?s)'",
",",
"''",
",",
"response",
")",
"res",
"=",
"re",
".",
"sub",
"(",
"r'<[^<]+?>'",
"... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | js_extractor | Extract js files from the response body | photon.py | def js_extractor(response):
"""Extract js files from the response body"""
# Extract .js files
matches = rscript.findall(response)
for match in matches:
match = match[2].replace('\'', '').replace('"', '')
verb('JS file', match)
bad_scripts.add(match) | def js_extractor(response):
"""Extract js files from the response body"""
# Extract .js files
matches = rscript.findall(response)
for match in matches:
match = match[2].replace('\'', '').replace('"', '')
verb('JS file', match)
bad_scripts.add(match) | [
"Extract",
"js",
"files",
"from",
"the",
"response",
"body"
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/photon.py#L220-L227 | [
"def",
"js_extractor",
"(",
"response",
")",
":",
"# Extract .js files\r",
"matches",
"=",
"rscript",
".",
"findall",
"(",
"response",
")",
"for",
"match",
"in",
"matches",
":",
"match",
"=",
"match",
"[",
"2",
"]",
".",
"replace",
"(",
"'\\''",
",",
"''... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | extractor | Extract details from the response body. | photon.py | def extractor(url):
"""Extract details from the response body."""
response = requester(url, main_url, delay, cook, headers, timeout, host, proxies, user_agents, failed, processed)
if clone:
mirror(url, response)
matches = rhref.findall(response)
for link in matches:
# Remove e... | def extractor(url):
"""Extract details from the response body."""
response = requester(url, main_url, delay, cook, headers, timeout, host, proxies, user_agents, failed, processed)
if clone:
mirror(url, response)
matches = rhref.findall(response)
for link in matches:
# Remove e... | [
"Extract",
"details",
"from",
"the",
"response",
"body",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/photon.py#L239-L287 | [
"def",
"extractor",
"(",
"url",
")",
":",
"response",
"=",
"requester",
"(",
"url",
",",
"main_url",
",",
"delay",
",",
"cook",
",",
"headers",
",",
"timeout",
",",
"host",
",",
"proxies",
",",
"user_agents",
",",
"failed",
",",
"processed",
")",
"if",... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | jscanner | Extract endpoints from JavaScript code. | photon.py | def jscanner(url):
"""Extract endpoints from JavaScript code."""
response = requester(url, main_url, delay, cook, headers, timeout, host, proxies, user_agents, failed, processed)
# Extract URLs/endpoints
matches = rendpoint.findall(response)
# Iterate over the matches, match is a tuple
for... | def jscanner(url):
"""Extract endpoints from JavaScript code."""
response = requester(url, main_url, delay, cook, headers, timeout, host, proxies, user_agents, failed, processed)
# Extract URLs/endpoints
matches = rendpoint.findall(response)
# Iterate over the matches, match is a tuple
for... | [
"Extract",
"endpoints",
"from",
"JavaScript",
"code",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/photon.py#L290-L302 | [
"def",
"jscanner",
"(",
"url",
")",
":",
"response",
"=",
"requester",
"(",
"url",
",",
"main_url",
",",
"delay",
",",
"cook",
",",
"headers",
",",
"timeout",
",",
"host",
",",
"proxies",
",",
"user_agents",
",",
"failed",
",",
"processed",
")",
"# Ext... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | updater | Update the current installation.
git clones the latest version and merges it with the current directory. | core/updater.py | def updater():
"""Update the current installation.
git clones the latest version and merges it with the current directory.
"""
print('%s Checking for updates' % run)
# Changes must be separated by ;
changes = '''major bug fixes;removed ninja mode;dropped python < 3.2 support;fixed unicode outpu... | def updater():
"""Update the current installation.
git clones the latest version and merges it with the current directory.
"""
print('%s Checking for updates' % run)
# Changes must be separated by ;
changes = '''major bug fixes;removed ninja mode;dropped python < 3.2 support;fixed unicode outpu... | [
"Update",
"the",
"current",
"installation",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/updater.py#L8-L40 | [
"def",
"updater",
"(",
")",
":",
"print",
"(",
"'%s Checking for updates'",
"%",
"run",
")",
"# Changes must be separated by ;",
"changes",
"=",
"'''major bug fixes;removed ninja mode;dropped python < 3.2 support;fixed unicode output;proxy support;more intels'''",
"latest_commit",
"=... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | find_subdomains | Find subdomains according to the TLD. | plugins/find_subdomains.py | def find_subdomains(domain):
"""Find subdomains according to the TLD."""
result = set()
response = get('https://findsubdomains.com/subdomains-of/' + domain).text
matches = findall(r'(?s)<div class="domains js-domain-name">(.*?)</div>', response)
for match in matches:
result.add(match.replace... | def find_subdomains(domain):
"""Find subdomains according to the TLD."""
result = set()
response = get('https://findsubdomains.com/subdomains-of/' + domain).text
matches = findall(r'(?s)<div class="domains js-domain-name">(.*?)</div>', response)
for match in matches:
result.add(match.replace... | [
"Find",
"subdomains",
"according",
"to",
"the",
"TLD",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/plugins/find_subdomains.py#L7-L14 | [
"def",
"find_subdomains",
"(",
"domain",
")",
":",
"result",
"=",
"set",
"(",
")",
"response",
"=",
"get",
"(",
"'https://findsubdomains.com/subdomains-of/'",
"+",
"domain",
")",
".",
"text",
"matches",
"=",
"findall",
"(",
"r'(?s)<div class=\"domains js-domain-name... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | flash | Process the URLs and uses a threadpool to execute a function. | core/flash.py | def flash(function, links, thread_count):
"""Process the URLs and uses a threadpool to execute a function."""
# Convert links (set) to list
links = list(links)
threadpool = concurrent.futures.ThreadPoolExecutor(
max_workers=thread_count)
futures = (threadpool.submit(function, link) for l... | def flash(function, links, thread_count):
"""Process the URLs and uses a threadpool to execute a function."""
# Convert links (set) to list
links = list(links)
threadpool = concurrent.futures.ThreadPoolExecutor(
max_workers=thread_count)
futures = (threadpool.submit(function, link) for l... | [
"Process",
"the",
"URLs",
"and",
"uses",
"a",
"threadpool",
"to",
"execute",
"a",
"function",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/flash.py#L6-L17 | [
"def",
"flash",
"(",
"function",
",",
"links",
",",
"thread_count",
")",
":",
"# Convert links (set) to list",
"links",
"=",
"list",
"(",
"links",
")",
"threadpool",
"=",
"concurrent",
".",
"futures",
".",
"ThreadPoolExecutor",
"(",
"max_workers",
"=",
"thread_c... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | regxy | Extract a string based on regex pattern supplied by user. | core/utils.py | def regxy(pattern, response, supress_regex, custom):
"""Extract a string based on regex pattern supplied by user."""
try:
matches = re.findall(r'%s' % pattern, response)
for match in matches:
verb('Custom regex', match)
custom.add(match)
except:
supress_regex ... | def regxy(pattern, response, supress_regex, custom):
"""Extract a string based on regex pattern supplied by user."""
try:
matches = re.findall(r'%s' % pattern, response)
for match in matches:
verb('Custom regex', match)
custom.add(match)
except:
supress_regex ... | [
"Extract",
"a",
"string",
"based",
"on",
"regex",
"pattern",
"supplied",
"by",
"user",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/utils.py#L15-L23 | [
"def",
"regxy",
"(",
"pattern",
",",
"response",
",",
"supress_regex",
",",
"custom",
")",
":",
"try",
":",
"matches",
"=",
"re",
".",
"findall",
"(",
"r'%s'",
"%",
"pattern",
",",
"response",
")",
"for",
"match",
"in",
"matches",
":",
"verb",
"(",
"... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | is_link | Determine whether or not a link should be crawled
A url should not be crawled if it
- Is a file
- Has already been crawled
Args:
url: str Url to be processed
processed: list[str] List of urls that have already been crawled
Returns:
bool If `url` should be crawled | core/utils.py | def is_link(url, processed, files):
"""
Determine whether or not a link should be crawled
A url should not be crawled if it
- Is a file
- Has already been crawled
Args:
url: str Url to be processed
processed: list[str] List of urls that have already been crawled
Ret... | def is_link(url, processed, files):
"""
Determine whether or not a link should be crawled
A url should not be crawled if it
- Is a file
- Has already been crawled
Args:
url: str Url to be processed
processed: list[str] List of urls that have already been crawled
Ret... | [
"Determine",
"whether",
"or",
"not",
"a",
"link",
"should",
"be",
"crawled",
"A",
"url",
"should",
"not",
"be",
"crawled",
"if",
"it",
"-",
"Is",
"a",
"file",
"-",
"Has",
"already",
"been",
"crawled"
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/utils.py#L26-L46 | [
"def",
"is_link",
"(",
"url",
",",
"processed",
",",
"files",
")",
":",
"if",
"url",
"not",
"in",
"processed",
":",
"is_file",
"=",
"url",
".",
"endswith",
"(",
"BAD_TYPES",
")",
"if",
"is_file",
":",
"files",
".",
"add",
"(",
"url",
")",
"return",
... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | remove_regex | Parse a list for non-matches to a regex.
Args:
urls: iterable of urls
regex: string regex to be parsed for
Returns:
list of strings not matching regex | core/utils.py | def remove_regex(urls, regex):
"""
Parse a list for non-matches to a regex.
Args:
urls: iterable of urls
regex: string regex to be parsed for
Returns:
list of strings not matching regex
"""
if not regex:
return urls
# To avoid iterating over the characters... | def remove_regex(urls, regex):
"""
Parse a list for non-matches to a regex.
Args:
urls: iterable of urls
regex: string regex to be parsed for
Returns:
list of strings not matching regex
"""
if not regex:
return urls
# To avoid iterating over the characters... | [
"Parse",
"a",
"list",
"for",
"non",
"-",
"matches",
"to",
"a",
"regex",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/utils.py#L49-L73 | [
"def",
"remove_regex",
"(",
"urls",
",",
"regex",
")",
":",
"if",
"not",
"regex",
":",
"return",
"urls",
"# To avoid iterating over the characters of a string",
"if",
"not",
"isinstance",
"(",
"urls",
",",
"(",
"list",
",",
"set",
",",
"tuple",
")",
")",
":"... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | writer | Write the results. | core/utils.py | def writer(datasets, dataset_names, output_dir):
"""Write the results."""
for dataset, dataset_name in zip(datasets, dataset_names):
if dataset:
filepath = output_dir + '/' + dataset_name + '.txt'
with open(filepath, 'w+') as out_file:
joined = '\n'.join(dataset)
... | def writer(datasets, dataset_names, output_dir):
"""Write the results."""
for dataset, dataset_name in zip(datasets, dataset_names):
if dataset:
filepath = output_dir + '/' + dataset_name + '.txt'
with open(filepath, 'w+') as out_file:
joined = '\n'.join(dataset)
... | [
"Write",
"the",
"results",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/utils.py#L76-L84 | [
"def",
"writer",
"(",
"datasets",
",",
"dataset_names",
",",
"output_dir",
")",
":",
"for",
"dataset",
",",
"dataset_name",
"in",
"zip",
"(",
"datasets",
",",
"dataset_names",
")",
":",
"if",
"dataset",
":",
"filepath",
"=",
"output_dir",
"+",
"'/'",
"+",
... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | timer | Return the passed time. | core/utils.py | def timer(diff, processed):
"""Return the passed time."""
# Changes seconds into minutes and seconds
minutes, seconds = divmod(diff, 60)
try:
# Finds average time taken by requests
time_per_request = diff / float(len(processed))
except ZeroDivisionError:
time_per_request = 0
... | def timer(diff, processed):
"""Return the passed time."""
# Changes seconds into minutes and seconds
minutes, seconds = divmod(diff, 60)
try:
# Finds average time taken by requests
time_per_request = diff / float(len(processed))
except ZeroDivisionError:
time_per_request = 0
... | [
"Return",
"the",
"passed",
"time",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/utils.py#L87-L96 | [
"def",
"timer",
"(",
"diff",
",",
"processed",
")",
":",
"# Changes seconds into minutes and seconds",
"minutes",
",",
"seconds",
"=",
"divmod",
"(",
"diff",
",",
"60",
")",
"try",
":",
"# Finds average time taken by requests",
"time_per_request",
"=",
"diff",
"/",
... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | entropy | Calculate the entropy of a string. | core/utils.py | def entropy(string):
"""Calculate the entropy of a string."""
entropy = 0
for number in range(256):
result = float(string.encode('utf-8').count(
chr(number))) / len(string.encode('utf-8'))
if result != 0:
entropy = entropy - result * math.log(result, 2)
return ent... | def entropy(string):
"""Calculate the entropy of a string."""
entropy = 0
for number in range(256):
result = float(string.encode('utf-8').count(
chr(number))) / len(string.encode('utf-8'))
if result != 0:
entropy = entropy - result * math.log(result, 2)
return ent... | [
"Calculate",
"the",
"entropy",
"of",
"a",
"string",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/utils.py#L99-L107 | [
"def",
"entropy",
"(",
"string",
")",
":",
"entropy",
"=",
"0",
"for",
"number",
"in",
"range",
"(",
"256",
")",
":",
"result",
"=",
"float",
"(",
"string",
".",
"encode",
"(",
"'utf-8'",
")",
".",
"count",
"(",
"chr",
"(",
"number",
")",
")",
")... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | extract_headers | This function extracts valid headers from interactive input. | core/utils.py | def extract_headers(headers):
"""This function extracts valid headers from interactive input."""
sorted_headers = {}
matches = re.findall(r'(.*):\s(.*)', headers)
for match in matches:
header = match[0]
value = match[1]
try:
if value[-1] == ',':
value ... | def extract_headers(headers):
"""This function extracts valid headers from interactive input."""
sorted_headers = {}
matches = re.findall(r'(.*):\s(.*)', headers)
for match in matches:
header = match[0]
value = match[1]
try:
if value[-1] == ',':
value ... | [
"This",
"function",
"extracts",
"valid",
"headers",
"from",
"interactive",
"input",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/utils.py#L122-L135 | [
"def",
"extract_headers",
"(",
"headers",
")",
":",
"sorted_headers",
"=",
"{",
"}",
"matches",
"=",
"re",
".",
"findall",
"(",
"r'(.*):\\s(.*)'",
",",
"headers",
")",
"for",
"match",
"in",
"matches",
":",
"header",
"=",
"match",
"[",
"0",
"]",
"value",
... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | top_level | Extract the top level domain from an URL. | core/utils.py | def top_level(url, fix_protocol=True):
"""Extract the top level domain from an URL."""
ext = tld.get_tld(url, fix_protocol=fix_protocol)
toplevel = '.'.join(urlparse(url).netloc.split('.')[-2:]).split(
ext)[0] + ext
return toplevel | def top_level(url, fix_protocol=True):
"""Extract the top level domain from an URL."""
ext = tld.get_tld(url, fix_protocol=fix_protocol)
toplevel = '.'.join(urlparse(url).netloc.split('.')[-2:]).split(
ext)[0] + ext
return toplevel | [
"Extract",
"the",
"top",
"level",
"domain",
"from",
"an",
"URL",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/utils.py#L138-L143 | [
"def",
"top_level",
"(",
"url",
",",
"fix_protocol",
"=",
"True",
")",
":",
"ext",
"=",
"tld",
".",
"get_tld",
"(",
"url",
",",
"fix_protocol",
"=",
"fix_protocol",
")",
"toplevel",
"=",
"'.'",
".",
"join",
"(",
"urlparse",
"(",
"url",
")",
".",
"net... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | proxy_type | Match IP:PORT or DOMAIN:PORT in a losse manner | core/utils.py | def proxy_type(v):
""" Match IP:PORT or DOMAIN:PORT in a losse manner """
proxies = []
if re.match(r"((http|socks5):\/\/.)?(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}):(\d{1,5})", v):
proxies.append({"http": v,
"https": v})
return proxies
elif re.match(r"((http|socks5):\/... | def proxy_type(v):
""" Match IP:PORT or DOMAIN:PORT in a losse manner """
proxies = []
if re.match(r"((http|socks5):\/\/.)?(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}):(\d{1,5})", v):
proxies.append({"http": v,
"https": v})
return proxies
elif re.match(r"((http|socks5):\/... | [
"Match",
"IP",
":",
"PORT",
"or",
"DOMAIN",
":",
"PORT",
"in",
"a",
"losse",
"manner"
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/utils.py#L162-L177 | [
"def",
"proxy_type",
"(",
"v",
")",
":",
"proxies",
"=",
"[",
"]",
"if",
"re",
".",
"match",
"(",
"r\"((http|socks5):\\/\\/.)?(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}):(\\d{1,5})\"",
",",
"v",
")",
":",
"proxies",
".",
"append",
"(",
"{",
"\"http\"",
":",
"v",... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | dnsdumpster | Query dnsdumpster.com. | plugins/dnsdumpster.py | def dnsdumpster(domain, output_dir):
"""Query dnsdumpster.com."""
response = requests.Session().get('https://dnsdumpster.com/').text
csrf_token = re.search(
r"name='csrfmiddlewaretoken' value='(.*?)'", response).group(1)
cookies = {'csrftoken': csrf_token}
headers = {'Referer': 'https://dns... | def dnsdumpster(domain, output_dir):
"""Query dnsdumpster.com."""
response = requests.Session().get('https://dnsdumpster.com/').text
csrf_token = re.search(
r"name='csrfmiddlewaretoken' value='(.*?)'", response).group(1)
cookies = {'csrftoken': csrf_token}
headers = {'Referer': 'https://dns... | [
"Query",
"dnsdumpster",
".",
"com",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/plugins/dnsdumpster.py#L7-L22 | [
"def",
"dnsdumpster",
"(",
"domain",
",",
"output_dir",
")",
":",
"response",
"=",
"requests",
".",
"Session",
"(",
")",
".",
"get",
"(",
"'https://dnsdumpster.com/'",
")",
".",
"text",
"csrf_token",
"=",
"re",
".",
"search",
"(",
"r\"name='csrfmiddlewaretoken... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | prompt | Present the user a prompt. | core/prompt.py | def prompt(default=None):
"""Present the user a prompt."""
editor = 'nano'
with tempfile.NamedTemporaryFile(mode='r+') as tmpfile:
if default:
tmpfile.write(default)
tmpfile.flush()
child_pid = os.fork()
is_child = child_pid == 0
if is_child:
... | def prompt(default=None):
"""Present the user a prompt."""
editor = 'nano'
with tempfile.NamedTemporaryFile(mode='r+') as tmpfile:
if default:
tmpfile.write(default)
tmpfile.flush()
child_pid = os.fork()
is_child = child_pid == 0
if is_child:
... | [
"Present",
"the",
"user",
"a",
"prompt",
"."
] | s0md3v/Photon | python | https://github.com/s0md3v/Photon/blob/6a29f2c9782ea9b3dc090db1774a259033600e39/core/prompt.py#L6-L22 | [
"def",
"prompt",
"(",
"default",
"=",
"None",
")",
":",
"editor",
"=",
"'nano'",
"with",
"tempfile",
".",
"NamedTemporaryFile",
"(",
"mode",
"=",
"'r+'",
")",
"as",
"tmpfile",
":",
"if",
"default",
":",
"tmpfile",
".",
"write",
"(",
"default",
")",
"tm... | 6a29f2c9782ea9b3dc090db1774a259033600e39 |
train | QA_RealTrade.start_market | start the market thread and register backtest broker thread
QAMarket 继承QATrader， QATrader 中有 trade_engine属性 ， trade_engine类型是QA_Engine从 QA_Thread继承 | QUANTAXIS/QAApplication/QATradeRealtime.py | def start_market(self):
"""
start the market thread and register backtest broker thread
QAMarket 继承QATrader， QATrader 中有 trade_engine属性 ， trade_engine类型是QA_Engine从 QA_Thread继承
"""
# 启动 trade_engine 线程
self.market.start()
# 注册 backtest_broker ，并且启动和它关联线程QAThread 存... | def start_market(self):
"""
start the market thread and register backtest broker thread
QAMarket 继承QATrader， QATrader 中有 trade_engine属性 ， trade_engine类型是QA_Engine从 QA_Thread继承
"""
# 启动 trade_engine 线程
self.market.start()
# 注册 backtest_broker ，并且启动和它关联线程QAThread 存... | [
"start",
"the",
"market",
"thread",
"and",
"register",
"backtest",
"broker",
"thread",
"QAMarket",
"继承QATrader，",
"QATrader",
"中有",
"trade_engine属性",
"，",
"trade_engine类型是QA_Engine从",
"QA_Thread继承"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAApplication/QATradeRealtime.py#L72-L82 | [
"def",
"start_market",
"(",
"self",
")",
":",
"# 启动 trade_engine 线程",
"self",
".",
"market",
".",
"start",
"(",
")",
"# 注册 backtest_broker ，并且启动和它关联线程QAThread 存放在 kernels 词典中， { 'broker_name': QAThread }",
"#self.market.register(self.broker_name, self.broker)",
"self",
".",
"mark... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_RealTrade.run | generator driven data flow | QUANTAXIS/QAApplication/QATradeRealtime.py | def run(self):
"""generator driven data flow
"""
# 如果出现了日期的改变 才会进行结算的事件
_date = None
while QA_util_if_tradetime(self.now):
for data in self.ingest_data: # 对于在ingest_data中的数据
# <class 'QUANTAXIS.QAData.QADataStruct.QA_DataStruct_Stock_day'>
... | def run(self):
"""generator driven data flow
"""
# 如果出现了日期的改变 才会进行结算的事件
_date = None
while QA_util_if_tradetime(self.now):
for data in self.ingest_data: # 对于在ingest_data中的数据
# <class 'QUANTAXIS.QAData.QADataStruct.QA_DataStruct_Stock_day'>
... | [
"generator",
"driven",
"data",
"flow"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAApplication/QATradeRealtime.py#L84-L117 | [
"def",
"run",
"(",
"self",
")",
":",
"# 如果出现了日期的改变 才会进行结算的事件",
"_date",
"=",
"None",
"while",
"QA_util_if_tradetime",
"(",
"self",
".",
"now",
")",
":",
"for",
"data",
"in",
"self",
".",
"ingest_data",
":",
"# 对于在ingest_data中的数据",
"# <class 'QUANTAXIS.QAData.QADat... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.message | the standard message which can be transfer | QUANTAXIS/QAARP/QAAccount.py | def message(self):
'the standard message which can be transfer'
return {
'source':
'account',
'frequence':
self.frequence,
'account_cookie':
self.account_cookie,
'portfolio_cookie':
self.portfolio_cookie,
... | def message(self):
'the standard message which can be transfer'
return {
'source':
'account',
'frequence':
self.frequence,
'account_cookie':
self.account_cookie,
'portfolio_cookie':
self.portfolio_cookie,
... | [
"the",
"standard",
"message",
"which",
"can",
"be",
"transfer"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L429-L489 | [
"def",
"message",
"(",
"self",
")",
":",
"return",
"{",
"'source'",
":",
"'account'",
",",
"'frequence'",
":",
"self",
".",
"frequence",
",",
"'account_cookie'",
":",
"self",
".",
"account_cookie",
",",
"'portfolio_cookie'",
":",
"self",
".",
"portfolio_cookie... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.init_hold_with_account | 带account_cookie的初始化持仓
Returns:
[type] -- [description] | QUANTAXIS/QAARP/QAAccount.py | def init_hold_with_account(self):
"""带account_cookie的初始化持仓
Returns:
[type] -- [description]
"""
return self.init_hold.reset_index().assign(
account_cookie=self.account_cookie
).set_index(['code',
'account_cookie']) | def init_hold_with_account(self):
"""带account_cookie的初始化持仓
Returns:
[type] -- [description]
"""
return self.init_hold.reset_index().assign(
account_cookie=self.account_cookie
).set_index(['code',
'account_cookie']) | [
"带account_cookie的初始化持仓"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L508-L518 | [
"def",
"init_hold_with_account",
"(",
"self",
")",
":",
"return",
"self",
".",
"init_hold",
".",
"reset_index",
"(",
")",
".",
"assign",
"(",
"account_cookie",
"=",
"self",
".",
"account_cookie",
")",
".",
"set_index",
"(",
"[",
"'code'",
",",
"'account_cook... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.start_date | 账户的起始交易日期(只在回测中使用)
Raises:
RuntimeWarning -- [description]
Returns:
[type] -- [description] | QUANTAXIS/QAARP/QAAccount.py | def start_date(self):
"""账户的起始交易日期(只在回测中使用)
Raises:
RuntimeWarning -- [description]
Returns:
[type] -- [description]
"""
if self.start_==None:
if len(self.time_index_max) > 0:
return str(min(self.time_index_max))[0:10]
... | def start_date(self):
"""账户的起始交易日期(只在回测中使用)
Raises:
RuntimeWarning -- [description]
Returns:
[type] -- [description]
"""
if self.start_==None:
if len(self.time_index_max) > 0:
return str(min(self.time_index_max))[0:10]
... | [
"账户的起始交易日期",
"(",
"只在回测中使用",
")"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L558-L577 | [
"def",
"start_date",
"(",
"self",
")",
":",
"if",
"self",
".",
"start_",
"==",
"None",
":",
"if",
"len",
"(",
"self",
".",
"time_index_max",
")",
">",
"0",
":",
"return",
"str",
"(",
"min",
"(",
"self",
".",
"time_index_max",
")",
")",
"[",
"0",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.end_date | 账户的交易结束日期(只在回测中使用)
Raises:
RuntimeWarning -- [description]
Returns:
[type] -- [description] | QUANTAXIS/QAARP/QAAccount.py | def end_date(self):
"""账户的交易结束日期(只在回测中使用)
Raises:
RuntimeWarning -- [description]
Returns:
[type] -- [description]
"""
if self.start_==None:
if len(self.time_index_max) > 0:
return str(max(self.time_index_max))[0:10]
... | def end_date(self):
"""账户的交易结束日期(只在回测中使用)
Raises:
RuntimeWarning -- [description]
Returns:
[type] -- [description]
"""
if self.start_==None:
if len(self.time_index_max) > 0:
return str(max(self.time_index_max))[0:10]
... | [
"账户的交易结束日期",
"(",
"只在回测中使用",
")"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L580-L599 | [
"def",
"end_date",
"(",
"self",
")",
":",
"if",
"self",
".",
"start_",
"==",
"None",
":",
"if",
"len",
"(",
"self",
".",
"time_index_max",
")",
">",
"0",
":",
"return",
"str",
"(",
"max",
"(",
"self",
".",
"time_index_max",
")",
")",
"[",
"0",
":... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.history_table_min | 区间交易历史的table | QUANTAXIS/QAARP/QAAccount.py | def history_table_min(self):
'区间交易历史的table'
if len(self.history_min) > 0:
lens = len(self.history_min[0])
else:
lens = len(self._history_headers)
return pd.DataFrame(
data=self.history_min,
columns=self._history_headers[:lens]
).so... | def history_table_min(self):
'区间交易历史的table'
if len(self.history_min) > 0:
lens = len(self.history_min[0])
else:
lens = len(self._history_headers)
return pd.DataFrame(
data=self.history_min,
columns=self._history_headers[:lens]
).so... | [
"区间交易历史的table"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L639-L649 | [
"def",
"history_table_min",
"(",
"self",
")",
":",
"if",
"len",
"(",
"self",
".",
"history_min",
")",
">",
"0",
":",
"lens",
"=",
"len",
"(",
"self",
".",
"history_min",
"[",
"0",
"]",
")",
"else",
":",
"lens",
"=",
"len",
"(",
"self",
".",
"_his... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.history_table | 交易历史的table | QUANTAXIS/QAARP/QAAccount.py | def history_table(self):
'交易历史的table'
if len(self.history) > 0:
lens = len(self.history[0])
else:
lens = len(self._history_headers)
return pd.DataFrame(
data=self.history,
columns=self._history_headers[:lens]
).sort_index() | def history_table(self):
'交易历史的table'
if len(self.history) > 0:
lens = len(self.history[0])
else:
lens = len(self._history_headers)
return pd.DataFrame(
data=self.history,
columns=self._history_headers[:lens]
).sort_index() | [
"交易历史的table"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L670-L680 | [
"def",
"history_table",
"(",
"self",
")",
":",
"if",
"len",
"(",
"self",
".",
"history",
")",
">",
"0",
":",
"lens",
"=",
"len",
"(",
"self",
".",
"history",
"[",
"0",
"]",
")",
"else",
":",
"lens",
"=",
"len",
"(",
"self",
".",
"_history_headers... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.cash_table | 现金的table | QUANTAXIS/QAARP/QAAccount.py | def cash_table(self):
'现金的table'
_cash = pd.DataFrame(
data=[self.cash[1::],
self.time_index_max],
index=['cash',
'datetime']
).T
_cash = _cash.assign(
date=_cash.datetime.apply(lambda x: pd.to_datetime(str(x)[0:10]... | def cash_table(self):
'现金的table'
_cash = pd.DataFrame(
data=[self.cash[1::],
self.time_index_max],
index=['cash',
'datetime']
).T
_cash = _cash.assign(
date=_cash.datetime.apply(lambda x: pd.to_datetime(str(x)[0:10]... | [
"现金的table"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L690-L727 | [
"def",
"cash_table",
"(",
"self",
")",
":",
"_cash",
"=",
"pd",
".",
"DataFrame",
"(",
"data",
"=",
"[",
"self",
".",
"cash",
"[",
"1",
":",
":",
"]",
",",
"self",
".",
"time_index_max",
"]",
",",
"index",
"=",
"[",
"'cash'",
",",
"'datetime'",
"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.hold | 真实持仓 | QUANTAXIS/QAARP/QAAccount.py | def hold(self):
"""真实持仓
"""
return pd.concat(
[self.init_hold,
self.hold_available]
).groupby('code').sum().replace(0,
np.nan).dropna().sort_index() | def hold(self):
"""真实持仓
"""
return pd.concat(
[self.init_hold,
self.hold_available]
).groupby('code').sum().replace(0,
np.nan).dropna().sort_index() | [
"真实持仓"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L730-L737 | [
"def",
"hold",
"(",
"self",
")",
":",
"return",
"pd",
".",
"concat",
"(",
"[",
"self",
".",
"init_hold",
",",
"self",
".",
"hold_available",
"]",
")",
".",
"groupby",
"(",
"'code'",
")",
".",
"sum",
"(",
")",
".",
"replace",
"(",
"0",
",",
"np",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.hold_available | 可用持仓 | QUANTAXIS/QAARP/QAAccount.py | def hold_available(self):
"""可用持仓
"""
return self.history_table.groupby('code').amount.sum().replace(
0,
np.nan
).dropna().sort_index() | def hold_available(self):
"""可用持仓
"""
return self.history_table.groupby('code').amount.sum().replace(
0,
np.nan
).dropna().sort_index() | [
"可用持仓"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L741-L747 | [
"def",
"hold_available",
"(",
"self",
")",
":",
"return",
"self",
".",
"history_table",
".",
"groupby",
"(",
"'code'",
")",
".",
"amount",
".",
"sum",
"(",
")",
".",
"replace",
"(",
"0",
",",
"np",
".",
"nan",
")",
".",
"dropna",
"(",
")",
".",
"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.trade | 每次交易的pivot表
Returns:
pd.DataFrame
此处的pivot_table一定要用np.sum | QUANTAXIS/QAARP/QAAccount.py | def trade(self):
"""每次交易的pivot表
Returns:
pd.DataFrame
此处的pivot_table一定要用np.sum
"""
return self.history_table.pivot_table(
index=['datetime',
'account_cookie'],
columns='code',
values='amount',
a... | def trade(self):
"""每次交易的pivot表
Returns:
pd.DataFrame
此处的pivot_table一定要用np.sum
"""
return self.history_table.pivot_table(
index=['datetime',
'account_cookie'],
columns='code',
values='amount',
a... | [
"每次交易的pivot表"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L755-L770 | [
"def",
"trade",
"(",
"self",
")",
":",
"return",
"self",
".",
"history_table",
".",
"pivot_table",
"(",
"index",
"=",
"[",
"'datetime'",
",",
"'account_cookie'",
"]",
",",
"columns",
"=",
"'code'",
",",
"values",
"=",
"'amount'",
",",
"aggfunc",
"=",
"np... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.daily_cash | 每日交易结算时的现金表 | QUANTAXIS/QAARP/QAAccount.py | def daily_cash(self):
'每日交易结算时的现金表'
res = self.cash_table.drop_duplicates(subset='date', keep='last')
le=pd.DataFrame(pd.Series(data=None, index=pd.to_datetime(self.trade_range_max).set_names('date'), name='predrop'))
ri=res.set_index('date')
res_=pd.merge(le,ri,how='left',left_i... | def daily_cash(self):
'每日交易结算时的现金表'
res = self.cash_table.drop_duplicates(subset='date', keep='last')
le=pd.DataFrame(pd.Series(data=None, index=pd.to_datetime(self.trade_range_max).set_names('date'), name='predrop'))
ri=res.set_index('date')
res_=pd.merge(le,ri,how='left',left_i... | [
"每日交易结算时的现金表"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L773-L781 | [
"def",
"daily_cash",
"(",
"self",
")",
":",
"res",
"=",
"self",
".",
"cash_table",
".",
"drop_duplicates",
"(",
"subset",
"=",
"'date'",
",",
"keep",
"=",
"'last'",
")",
"le",
"=",
"pd",
".",
"DataFrame",
"(",
"pd",
".",
"Series",
"(",
"data",
"=",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.daily_hold | 每日交易结算时的持仓表 | QUANTAXIS/QAARP/QAAccount.py | def daily_hold(self):
'每日交易结算时的持仓表'
data = self.trade.cumsum()
if len(data) < 1:
return None
else:
# print(data.index.levels[0])
data = data.assign(account_cookie=self.account_cookie).assign(
date=pd.to_datetime(data.index.levels[0]).da... | def daily_hold(self):
'每日交易结算时的持仓表'
data = self.trade.cumsum()
if len(data) < 1:
return None
else:
# print(data.index.levels[0])
data = data.assign(account_cookie=self.account_cookie).assign(
date=pd.to_datetime(data.index.levels[0]).da... | [
"每日交易结算时的持仓表"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L784-L804 | [
"def",
"daily_hold",
"(",
"self",
")",
":",
"data",
"=",
"self",
".",
"trade",
".",
"cumsum",
"(",
")",
"if",
"len",
"(",
"data",
")",
"<",
"1",
":",
"return",
"None",
"else",
":",
"# print(data.index.levels[0])",
"data",
"=",
"data",
".",
"assign",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.daily_frozen | 每日交易结算时的持仓表 | QUANTAXIS/QAARP/QAAccount.py | def daily_frozen(self):
'每日交易结算时的持仓表'
res_=self.history_table.assign(date=pd.to_datetime(self.history_table.datetime)).set_index('date').resample('D').frozen.last().fillna(method='pad')
res_=res_[res_.index.isin(self.trade_range)]
return res_ | def daily_frozen(self):
'每日交易结算时的持仓表'
res_=self.history_table.assign(date=pd.to_datetime(self.history_table.datetime)).set_index('date').resample('D').frozen.last().fillna(method='pad')
res_=res_[res_.index.isin(self.trade_range)]
return res_ | [
"每日交易结算时的持仓表"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L807-L811 | [
"def",
"daily_frozen",
"(",
"self",
")",
":",
"res_",
"=",
"self",
".",
"history_table",
".",
"assign",
"(",
"date",
"=",
"pd",
".",
"to_datetime",
"(",
"self",
".",
"history_table",
".",
"datetime",
")",
")",
".",
"set_index",
"(",
"'date'",
")",
".",... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.hold_table | 到某一个时刻的持仓 如果给的是日期,则返回当日开盘前的持仓 | QUANTAXIS/QAARP/QAAccount.py | def hold_table(self, datetime=None):
"到某一个时刻的持仓 如果给的是日期,则返回当日开盘前的持仓"
if datetime is None:
hold_available = self.history_table.set_index(
'datetime'
).sort_index().groupby('code').amount.sum().sort_index()
else:
hold_available = self.history_tab... | def hold_table(self, datetime=None):
"到某一个时刻的持仓 如果给的是日期,则返回当日开盘前的持仓"
if datetime is None:
hold_available = self.history_table.set_index(
'datetime'
).sort_index().groupby('code').amount.sum().sort_index()
else:
hold_available = self.history_tab... | [
"到某一个时刻的持仓",
"如果给的是日期",
"则返回当日开盘前的持仓"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L822-L836 | [
"def",
"hold_table",
"(",
"self",
",",
"datetime",
"=",
"None",
")",
":",
"if",
"datetime",
"is",
"None",
":",
"hold_available",
"=",
"self",
".",
"history_table",
".",
"set_index",
"(",
"'datetime'",
")",
".",
"sort_index",
"(",
")",
".",
"groupby",
"("... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.current_hold_price | 计算目前持仓的成本 用于模拟盘和实盘查询
Returns:
[type] -- [description] | QUANTAXIS/QAARP/QAAccount.py | def current_hold_price(self):
"""计算目前持仓的成本 用于模拟盘和实盘查询
Returns:
[type] -- [description]
"""
def weights(x):
n=len(x)
res=1
while res>0 or res<0:
res=sum(x[:n]['amount'])
n=n-1
... | def current_hold_price(self):
"""计算目前持仓的成本 用于模拟盘和实盘查询
Returns:
[type] -- [description]
"""
def weights(x):
n=len(x)
res=1
while res>0 or res<0:
res=sum(x[:n]['amount'])
n=n-1
... | [
"计算目前持仓的成本",
"用于模拟盘和实盘查询"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L838-L865 | [
"def",
"current_hold_price",
"(",
"self",
")",
":",
"def",
"weights",
"(",
"x",
")",
":",
"n",
"=",
"len",
"(",
"x",
")",
"res",
"=",
"1",
"while",
"res",
">",
"0",
"or",
"res",
"<",
"0",
":",
"res",
"=",
"sum",
"(",
"x",
"[",
":",
"n",
"]"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.hold_price | 计算持仓成本 如果给的是日期,则返回当日开盘前的持仓
Keyword Arguments:
datetime {[type]} -- [description] (default: {None})
Returns:
[type] -- [description] | QUANTAXIS/QAARP/QAAccount.py | def hold_price(self, datetime=None):
"""计算持仓成本 如果给的是日期,则返回当日开盘前的持仓
Keyword Arguments:
datetime {[type]} -- [description] (default: {None})
Returns:
[type] -- [description]
"""
def weights(x):
if sum(x['amount']) != 0:
return... | def hold_price(self, datetime=None):
"""计算持仓成本 如果给的是日期,则返回当日开盘前的持仓
Keyword Arguments:
datetime {[type]} -- [description] (default: {None})
Returns:
[type] -- [description]
"""
def weights(x):
if sum(x['amount']) != 0:
return... | [
"计算持仓成本",
"如果给的是日期",
"则返回当日开盘前的持仓"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L867-L897 | [
"def",
"hold_price",
"(",
"self",
",",
"datetime",
"=",
"None",
")",
":",
"def",
"weights",
"(",
"x",
")",
":",
"if",
"sum",
"(",
"x",
"[",
"'amount'",
"]",
")",
"!=",
"0",
":",
"return",
"np",
".",
"average",
"(",
"x",
"[",
"'price'",
"]",
","... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.hold_time | 持仓时间
Keyword Arguments:
datetime {[type]} -- [description] (default: {None}) | QUANTAXIS/QAARP/QAAccount.py | def hold_time(self, datetime=None):
"""持仓时间
Keyword Arguments:
datetime {[type]} -- [description] (default: {None})
"""
def weights(x):
if sum(x['amount']) != 0:
return pd.Timestamp(self.datetime
) - pd.to_datet... | def hold_time(self, datetime=None):
"""持仓时间
Keyword Arguments:
datetime {[type]} -- [description] (default: {None})
"""
def weights(x):
if sum(x['amount']) != 0:
return pd.Timestamp(self.datetime
) - pd.to_datet... | [
"持仓时间"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L900-L924 | [
"def",
"hold_time",
"(",
"self",
",",
"datetime",
"=",
"None",
")",
":",
"def",
"weights",
"(",
"x",
")",
":",
"if",
"sum",
"(",
"x",
"[",
"'amount'",
"]",
")",
"!=",
"0",
":",
"return",
"pd",
".",
"Timestamp",
"(",
"self",
".",
"datetime",
")",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.reset_assets | reset_history/cash/ | QUANTAXIS/QAARP/QAAccount.py | def reset_assets(self, init_cash=None):
'reset_history/cash/'
self.sell_available = copy.deepcopy(self.init_hold)
self.history = []
self.init_cash = init_cash
self.cash = [self.init_cash]
self.cash_available = self.cash[-1] | def reset_assets(self, init_cash=None):
'reset_history/cash/'
self.sell_available = copy.deepcopy(self.init_hold)
self.history = []
self.init_cash = init_cash
self.cash = [self.init_cash]
self.cash_available = self.cash[-1] | [
"reset_history",
"/",
"cash",
"/"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L926-L932 | [
"def",
"reset_assets",
"(",
"self",
",",
"init_cash",
"=",
"None",
")",
":",
"self",
".",
"sell_available",
"=",
"copy",
".",
"deepcopy",
"(",
"self",
".",
"init_hold",
")",
"self",
".",
"history",
"=",
"[",
"]",
"self",
".",
"init_cash",
"=",
"init_ca... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.receive_simpledeal | 快速撮合成交接口
此接口是一个直接可以成交的接口, 所以务必确保给出的信息是可以成交的
此接口涉及的是
1. 股票/期货的成交
2. 历史记录的增加
3. 现金/持仓/冻结资金的处理
Arguments:
code {[type]} -- [description]
trade_price {[type]} -- [description]
trade_amount {[type]} -- [description]
trade_tow... | QUANTAXIS/QAARP/QAAccount.py | def receive_simpledeal(
self,
code,
trade_price,
trade_amount,
trade_towards,
trade_time,
message=None,
order_id=None,
trade_id=None,
realorder_id=None
):
"""快速撮合成交接口
此接口是一个直接可以成... | def receive_simpledeal(
self,
code,
trade_price,
trade_amount,
trade_towards,
trade_time,
message=None,
order_id=None,
trade_id=None,
realorder_id=None
):
"""快速撮合成交接口
此接口是一个直接可以成... | [
"快速撮合成交接口"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L934-L1182 | [
"def",
"receive_simpledeal",
"(",
"self",
",",
"code",
",",
"trade_price",
",",
"trade_amount",
",",
"trade_towards",
",",
"trade_time",
",",
"message",
"=",
"None",
",",
"order_id",
"=",
"None",
",",
"trade_id",
"=",
"None",
",",
"realorder_id",
"=",
"None"... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.receive_deal | 更新deal
Arguments:
code {str} -- [description]
trade_id {str} -- [description]
order_id {str} -- [description]
realorder_id {str} -- [description]
trade_price {float} -- [description]
trade_amount {int} -- [description]
trade_to... | QUANTAXIS/QAARP/QAAccount.py | def receive_deal(
self,
code: str,
trade_id: str,
order_id: str,
realorder_id: str,
trade_price: float,
trade_amount: int,
trade_towards: int,
trade_time: str,
message=None
):
"""更新deal
... | def receive_deal(
self,
code: str,
trade_id: str,
order_id: str,
realorder_id: str,
trade_price: float,
trade_amount: int,
trade_towards: int,
trade_time: str,
message=None
):
"""更新deal
... | [
"更新deal"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1194-L1249 | [
"def",
"receive_deal",
"(",
"self",
",",
"code",
":",
"str",
",",
"trade_id",
":",
"str",
",",
"order_id",
":",
"str",
",",
"realorder_id",
":",
"str",
",",
"trade_price",
":",
"float",
",",
"trade_amount",
":",
"int",
",",
"trade_towards",
":",
"int",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.send_order | ATTENTION CHANGELOG 1.0.28
修改了Account的send_order方法, 区分按数量下单和按金额下单两种方式
- AMOUNT_MODEL.BY_PRICE ==> AMOUNT_MODEL.BY_MONEY # 按金额下单
- AMOUNT_MODEL.BY_AMOUNT # 按数量下单
在按金额下单的时候,应给予 money参数
在按数量下单的时候,应给予 amount参数
python code:
Account=QA.QA_Account()
Order_bym... | QUANTAXIS/QAARP/QAAccount.py | def send_order(
self,
code=None,
amount=None,
time=None,
towards=None,
price=None,
money=None,
order_model=None,
amount_model=None,
*args,
**kwargs
):
"""
ATTENTION CHA... | def send_order(
self,
code=None,
amount=None,
time=None,
towards=None,
price=None,
money=None,
order_model=None,
amount_model=None,
*args,
**kwargs
):
"""
ATTENTION CHA... | [
"ATTENTION",
"CHANGELOG",
"1",
".",
"0",
".",
"28",
"修改了Account的send_order方法",
"区分按数量下单和按金额下单两种方式"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1251-L1477 | [
"def",
"send_order",
"(",
"self",
",",
"code",
"=",
"None",
",",
"amount",
"=",
"None",
",",
"time",
"=",
"None",
",",
"towards",
"=",
"None",
",",
"price",
"=",
"None",
",",
"money",
"=",
"None",
",",
"order_model",
"=",
"None",
",",
"amount_model",... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.close_positions_order | 平仓单
Raises:
RuntimeError -- if ACCOUNT.RUNNING_ENVIRONMENT is NOT TZERO
Returns:
list -- list with order | QUANTAXIS/QAARP/QAAccount.py | def close_positions_order(self):
"""平仓单
Raises:
RuntimeError -- if ACCOUNT.RUNNING_ENVIRONMENT is NOT TZERO
Returns:
list -- list with order
"""
order_list = []
time = '{} 15:00:00'.format(self.date)
if self.running_environment == RUNNIN... | def close_positions_order(self):
"""平仓单
Raises:
RuntimeError -- if ACCOUNT.RUNNING_ENVIRONMENT is NOT TZERO
Returns:
list -- list with order
"""
order_list = []
time = '{} 15:00:00'.format(self.date)
if self.running_environment == RUNNIN... | [
"平仓单"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1493-L1538 | [
"def",
"close_positions_order",
"(",
"self",
")",
":",
"order_list",
"=",
"[",
"]",
"time",
"=",
"'{} 15:00:00'",
".",
"format",
"(",
"self",
".",
"date",
")",
"if",
"self",
".",
"running_environment",
"==",
"RUNNING_ENVIRONMENT",
".",
"TZERO",
":",
"for",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.settle | 股票/期货的日结算
股票的结算: 结转股票可卖额度
T0的结算: 结转T0的额度
期货的结算: 结转静态资金
@2019-02-25 yutiansut
hold 在下面要进行大变化:
从 只计算数量 ==> 数量+成本+买入价 (携带更多信息)
基于history去计算hold ==> last_settle+ today_pos_change | QUANTAXIS/QAARP/QAAccount.py | def settle(self, settle_data = None):
"""
股票/期货的日结算
股票的结算: 结转股票可卖额度
T0的结算: 结转T0的额度
期货的结算: 结转静态资金
@2019-02-25 yutiansut
hold 在下面要进行大变化:
从 只计算数量 ==> 数量+成本+买入价 (携带更多信息)
基于history去计算hold ==> last_settle+ today_pos_change
"""
#pr... | def settle(self, settle_data = None):
"""
股票/期货的日结算
股票的结算: 结转股票可卖额度
T0的结算: 结转T0的额度
期货的结算: 结转静态资金
@2019-02-25 yutiansut
hold 在下面要进行大变化:
从 只计算数量 ==> 数量+成本+买入价 (携带更多信息)
基于history去计算hold ==> last_settle+ today_pos_change
"""
#pr... | [
"股票",
"/",
"期货的日结算"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1540-L1600 | [
"def",
"settle",
"(",
"self",
",",
"settle_data",
"=",
"None",
")",
":",
"#print('FROM QUANTAXIS QA_ACCOUNT: account settle')",
"if",
"self",
".",
"running_environment",
"==",
"RUNNING_ENVIRONMENT",
".",
"TZERO",
"and",
"self",
".",
"hold_available",
".",
"sum",
"("... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.on_bar | 策略事件
:param event:
:return: | QUANTAXIS/QAARP/QAAccount.py | def on_bar(self, event):
'''
策略事件
:param event:
:return:
'''
'while updating the market data'
print(
"on_bar account {} ".format(self.account_cookie),
event.market_data.data
)
print(event.send_order)
try:
... | def on_bar(self, event):
'''
策略事件
:param event:
:return:
'''
'while updating the market data'
print(
"on_bar account {} ".format(self.account_cookie),
event.market_data.data
)
print(event.send_order)
try:
... | [
"策略事件",
":",
"param",
"event",
":",
":",
"return",
":"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1602-L1649 | [
"def",
"on_bar",
"(",
"self",
",",
"event",
")",
":",
"'while updating the market data'",
"print",
"(",
"\"on_bar account {} \"",
".",
"format",
"(",
"self",
".",
"account_cookie",
")",
",",
"event",
".",
"market_data",
".",
"data",
")",
"print",
"(",
"event",... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.from_message | resume the account from standard message
这个是从数据库恢复账户时需要的 | QUANTAXIS/QAARP/QAAccount.py | def from_message(self, message):
"""resume the account from standard message
这个是从数据库恢复账户时需要的"""
self.account_cookie = message.get('account_cookie', None)
self.portfolio_cookie = message.get('portfolio_cookie', None)
self.user_cookie = message.get('user_cookie', None)
self... | def from_message(self, message):
"""resume the account from standard message
这个是从数据库恢复账户时需要的"""
self.account_cookie = message.get('account_cookie', None)
self.portfolio_cookie = message.get('portfolio_cookie', None)
self.user_cookie = message.get('user_cookie', None)
self... | [
"resume",
"the",
"account",
"from",
"standard",
"message",
"这个是从数据库恢复账户时需要的"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1661-L1697 | [
"def",
"from_message",
"(",
"self",
",",
"message",
")",
":",
"self",
".",
"account_cookie",
"=",
"message",
".",
"get",
"(",
"'account_cookie'",
",",
"None",
")",
"self",
".",
"portfolio_cookie",
"=",
"message",
".",
"get",
"(",
"'portfolio_cookie'",
",",
... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.from_otgdict | [summary]
balance = static_balance + float_profit
"currency": "", # "CNY" (币种)
"pre_balance": float("nan"), # 9912934.78 (昨日账户权益)
"static_balance": float("nan"), # (静态权益)
"balance": float("nan"), # 9963216.55 (账户权益)
"available": float("nan"), # ... | QUANTAXIS/QAARP/QAAccount.py | def from_otgdict(self, message):
"""[summary]
balance = static_balance + float_profit
"currency": "", # "CNY" (币种)
"pre_balance": float("nan"), # 9912934.78 (昨日账户权益)
"static_balance": float("nan"), # (静态权益)
"balance": float("nan"), # 9963216.55 (账户权益... | def from_otgdict(self, message):
"""[summary]
balance = static_balance + float_profit
"currency": "", # "CNY" (币种)
"pre_balance": float("nan"), # 9912934.78 (昨日账户权益)
"static_balance": float("nan"), # (静态权益)
"balance": float("nan"), # 9963216.55 (账户权益... | [
"[",
"summary",
"]",
"balance",
"=",
"static_balance",
"+",
"float_profit"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1699-L1748 | [
"def",
"from_otgdict",
"(",
"self",
",",
"message",
")",
":",
"self",
".",
"allow_margin",
"=",
"True",
"self",
".",
"allow_sellopen",
"=",
"True",
"self",
".",
"allow_t0",
"=",
"True",
"self",
".",
"account_cookie",
"=",
"message",
"[",
"'accounts'",
"]",... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.table | 打印出account的内容 | QUANTAXIS/QAARP/QAAccount.py | def table(self):
"""
打印出account的内容
"""
return pd.DataFrame([
self.message,
]).set_index(
'account_cookie',
drop=False
).T | def table(self):
"""
打印出account的内容
"""
return pd.DataFrame([
self.message,
]).set_index(
'account_cookie',
drop=False
).T | [
"打印出account的内容"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1751-L1760 | [
"def",
"table",
"(",
"self",
")",
":",
"return",
"pd",
".",
"DataFrame",
"(",
"[",
"self",
".",
"message",
",",
"]",
")",
".",
"set_index",
"(",
"'account_cookie'",
",",
"drop",
"=",
"False",
")",
".",
"T"
] | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.run | 这个方法是被 QA_ThreadEngine 处理队列时候调用的， QA_Task 中 do 方法调用 run （在其它线程中）
'QA_WORKER method 重载'
:param event: 事件类型 QA_Event
:return: | QUANTAXIS/QAARP/QAAccount.py | def run(self, event):
'''
这个方法是被 QA_ThreadEngine 处理队列时候调用的， QA_Task 中 do 方法调用 run （在其它线程中）
'QA_WORKER method 重载'
:param event: 事件类型 QA_Event
:return:
'''
'QA_WORKER method'
if event.event_type is ACCOUNT_EVENT.SETTLE:
print('account_settle')
... | def run(self, event):
'''
这个方法是被 QA_ThreadEngine 处理队列时候调用的， QA_Task 中 do 方法调用 run （在其它线程中）
'QA_WORKER method 重载'
:param event: 事件类型 QA_Event
:return:
'''
'QA_WORKER method'
if event.event_type is ACCOUNT_EVENT.SETTLE:
print('account_settle')
... | [
"这个方法是被",
"QA_ThreadEngine",
"处理队列时候调用的，",
"QA_Task",
"中",
"do",
"方法调用",
"run",
"（在其它线程中）",
"QA_WORKER",
"method",
"重载",
":",
"param",
"event",
":",
"事件类型",
"QA_Event",
":",
"return",
":"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1762-L1812 | [
"def",
"run",
"(",
"self",
",",
"event",
")",
":",
"'QA_WORKER method'",
"if",
"event",
".",
"event_type",
"is",
"ACCOUNT_EVENT",
".",
"SETTLE",
":",
"print",
"(",
"'account_settle'",
")",
"self",
".",
"settle",
"(",
")",
"# elif event.event_type is ACCOUNT_EVEN... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.sync_account | 同步账户
Arguments:
sync_message {[type]} -- [description] | QUANTAXIS/QAARP/QAAccount.py | def sync_account(self, sync_message):
"""同步账户
Arguments:
sync_message {[type]} -- [description]
"""
self.init_hold = sync_message['hold_available']
self.init_cash = sync_message['cash_available']
self.sell_available = copy.deepcopy(self.init_hold)
s... | def sync_account(self, sync_message):
"""同步账户
Arguments:
sync_message {[type]} -- [description]
"""
self.init_hold = sync_message['hold_available']
self.init_cash = sync_message['cash_available']
self.sell_available = copy.deepcopy(self.init_hold)
s... | [
"同步账户"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1835-L1848 | [
"def",
"sync_account",
"(",
"self",
",",
"sync_message",
")",
":",
"self",
".",
"init_hold",
"=",
"sync_message",
"[",
"'hold_available'",
"]",
"self",
".",
"init_cash",
"=",
"sync_message",
"[",
"'cash_available'",
"]",
"self",
".",
"sell_available",
"=",
"co... | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
train | QA_Account.change_cash | 外部操作|高危| | QUANTAXIS/QAARP/QAAccount.py | def change_cash(self, money):
"""
外部操作|高危|
"""
res = self.cash[-1] + money
if res >= 0:
# 高危操作
self.cash[-1] = res | def change_cash(self, money):
"""
外部操作|高危|
"""
res = self.cash[-1] + money
if res >= 0:
# 高危操作
self.cash[-1] = res | [
"外部操作|高危|"
] | QUANTAXIS/QUANTAXIS | python | https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAARP/QAAccount.py#L1850-L1857 | [
"def",
"change_cash",
"(",
"self",
",",
"money",
")",
":",
"res",
"=",
"self",
".",
"cash",
"[",
"-",
"1",
"]",
"+",
"money",
"if",
"res",
">=",
"0",
":",
"# 高危操作",
"self",
".",
"cash",
"[",
"-",
"1",
"]",
"=",
"res"
] | bb1fe424e4108b62a1f712b81a05cf829297a5c0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.