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))
command = [
"kaggle",
"competitions",
"download",
"--file",
fname,
"--path",
output_dir,
"-c",
self._competition_name,
]
_run_kaggle_command(command, self._competition_name)
return os.path.join(output_dir, fname) | 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))
command = [
"kaggle",
"competitions",
"download",
"--file",
fname,
"--path",
output_dir,
"-c",
self._competition_name,
]
_run_kaggle_command(command, self._competition_name)
return os.path.join(output_dir, fname) | [
"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_dir_path)[0]
walk_dir = os.path.join(images_dir_path, parent_dir)
dirs = tf.io.gfile.listdir(walk_dir)
for d in dirs:
if tf.io.gfile.isdir(os.path.join(walk_dir, d)):
for full_path, _, fname in tf.io.gfile.walk(os.path.join(walk_dir, d)):
for image_file in fname:
if image_file.endswith(".jpg"):
image_path = os.path.join(full_path, image_file)
yield {
"image": image_path,
"label": d.lower(),
} | 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_dir_path)[0]
walk_dir = os.path.join(images_dir_path, parent_dir)
dirs = tf.io.gfile.listdir(walk_dir)
for d in dirs:
if tf.io.gfile.isdir(os.path.join(walk_dir, d)):
for full_path, _, fname in tf.io.gfile.walk(os.path.join(walk_dir, d)):
for image_file in fname:
if image_file.endswith(".jpg"):
image_path = os.path.join(full_path, image_file)
yield {
"image": image_path,
"label": d.lower(),
} | [
"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(_CHECKSUM_SUFFIX)]
dataset2path[dataset_name] = fpath
return dataset2path | 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(_CHECKSUM_SUFFIX)]
dataset2path[dataset_name] = fpath
return dataset2path | [
"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_DIRS))
raise AssertionError(msg) | 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_DIRS))
raise AssertionError(msg) | [
"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)
checksums[url] = (int(size), checksum)
return checksums | 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)
checksums[url] = (int(size), checksum)
return checksums | [
"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):
raise AssertionError(
'URL %s is registered with 2+ distinct size/checksum tuples.' % url)
sizes_checksums.update(data)
return sizes_checksums | 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):
raise AssertionError(
'URL %s is registered with 2+ distinct size/checksum tuples.' % url)
sizes_checksums.update(data)
return sizes_checksums | [
"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 times in
parallel for a given dataset.
Only original file content is updated. This means the entire set of new sizes
and checksums must be given at every call.
Args:
dataset_name: string.
sizes_checksums: dict, {url: (size_in_bytes, checksum)}. | 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 responsibility of the caller not to call function multiple times in
parallel for a given dataset.
Only original file content is updated. This means the entire set of new sizes
and checksums must be given at every call.
Args:
dataset_name: string.
sizes_checksums: dict, {url: (size_in_bytes, checksum)}.
"""
path = _get_path(dataset_name)
original_data = _get_sizes_checksums(path)
new_data = original_data.copy()
new_data.update(sizes_checksums)
if original_data == new_data:
return
with tf.io.gfile.GFile(path, 'w') as f:
for url, (size, checksum) in sorted(new_data.items()):
f.write('%s %s %s\n' % (url, size, checksum)) | 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 responsibility of the caller not to call function multiple times in
parallel for a given dataset.
Only original file content is updated. This means the entire set of new sizes
and checksums must be given at every call.
Args:
dataset_name: string.
sizes_checksums: dict, {url: (size_in_bytes, checksum)}.
"""
path = _get_path(dataset_name)
original_data = _get_sizes_checksums(path)
new_data = original_data.copy()
new_data.update(sizes_checksums)
if original_data == new_data:
return
with tf.io.gfile.GFile(path, 'w') as f:
for url, (size, checksum) in sorted(new_data.items()):
f.write('%s %s %s\n' % (url, size, checksum)) | [
"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 [sub]domains.
'www.cs.toronto.edu/...' -> 'cs.toronto.edu/...'
'storage.googleapis.com/foo/...' -> 'foo/...'
'drive.google.com/bar/...' -> 'bar/...'
'github.com/baz/...' -> 'baz/...'
2- Remove leading '0's from url components:
'foo/train-00004-of-00010.tfrecords' -> 'foo/train-4-of-10.tfrecords'
3- Truncate each component of url until total size fits or each component is
left with 4 chars (or total size is <= limit):
'MoveUnitToBorder_64x64_png/train-4-of-10.tfrecords'
(here truncate components to 4 chars per component max)
-> 'Move_64x6_png/trai-4-of-10.tfrecords'
4- Truncate result, keeping prefix: 'abc_def_ghi_jkl' -> 'abc_def'
Args:
url: string, url to sanitize and shorten.
max_length: int, max length of result.
Returns:
(string, string): sanitized and shorted url, file extension. | 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 useful info from url.
1- Drop generic [sub]domains.
'www.cs.toronto.edu/...' -> 'cs.toronto.edu/...'
'storage.googleapis.com/foo/...' -> 'foo/...'
'drive.google.com/bar/...' -> 'bar/...'
'github.com/baz/...' -> 'baz/...'
2- Remove leading '0's from url components:
'foo/train-00004-of-00010.tfrecords' -> 'foo/train-4-of-10.tfrecords'
3- Truncate each component of url until total size fits or each component is
left with 4 chars (or total size is <= limit):
'MoveUnitToBorder_64x64_png/train-4-of-10.tfrecords'
(here truncate components to 4 chars per component max)
-> 'Move_64x6_png/trai-4-of-10.tfrecords'
4- Truncate result, keeping prefix: 'abc_def_ghi_jkl' -> 'abc_def'
Args:
url: string, url to sanitize and shorten.
max_length: int, max length of result.
Returns:
(string, string): sanitized and shorted url, file extension.
"""
url = urllib.parse.urlparse(url)
netloc = url.netloc
for prefix in _NETLOC_COMMON_PREFIXES:
if netloc.startswith(prefix):
netloc = netloc[len(prefix):]
for suffix in _NETLOC_COMMON_SUFFIXES:
if netloc.endswith(suffix):
netloc = netloc[:-len(suffix)]
url = '%s%s%s%s' % (netloc, url.path, url.params, url.query)
# Get the extension:
for ext in _KNOWN_EXTENSIONS:
if url.endswith(ext):
extension = ext
url = url[:-len(extension)]
break
else:
url, extension = os.path.splitext(url)
max_length -= len(extension)
# Replace non authorized chars (including '/') by '_':
url = re.sub(r'[^a-zA-Z0-9\.\-_]+', '_', url)
# Remove parts with no info:
for common_part in _URL_COMMON_PARTS:
url = url.replace(common_part, '_')
url = url.strip('_')
# Remove leading zeros in groups of numbers:
url = re.sub('(?<![0-9])0+(?=[0-9])', '', url)
# Decrease max size of URL components:
c_size = max(len(c) for c in re.split(r'[\.\-_]', url))
while c_size > 4 and len(url) > max_length:
c_size -= 1
url = re.sub(r'[^\.\-_]{4,}', lambda match: match.group(0)[:c_size], url)
return url[:max_length], extension | 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 useful info from url.
1- Drop generic [sub]domains.
'www.cs.toronto.edu/...' -> 'cs.toronto.edu/...'
'storage.googleapis.com/foo/...' -> 'foo/...'
'drive.google.com/bar/...' -> 'bar/...'
'github.com/baz/...' -> 'baz/...'
2- Remove leading '0's from url components:
'foo/train-00004-of-00010.tfrecords' -> 'foo/train-4-of-10.tfrecords'
3- Truncate each component of url until total size fits or each component is
left with 4 chars (or total size is <= limit):
'MoveUnitToBorder_64x64_png/train-4-of-10.tfrecords'
(here truncate components to 4 chars per component max)
-> 'Move_64x6_png/trai-4-of-10.tfrecords'
4- Truncate result, keeping prefix: 'abc_def_ghi_jkl' -> 'abc_def'
Args:
url: string, url to sanitize and shorten.
max_length: int, max length of result.
Returns:
(string, string): sanitized and shorted url, file extension.
"""
url = urllib.parse.urlparse(url)
netloc = url.netloc
for prefix in _NETLOC_COMMON_PREFIXES:
if netloc.startswith(prefix):
netloc = netloc[len(prefix):]
for suffix in _NETLOC_COMMON_SUFFIXES:
if netloc.endswith(suffix):
netloc = netloc[:-len(suffix)]
url = '%s%s%s%s' % (netloc, url.path, url.params, url.query)
# Get the extension:
for ext in _KNOWN_EXTENSIONS:
if url.endswith(ext):
extension = ext
url = url[:-len(extension)]
break
else:
url, extension = os.path.splitext(url)
max_length -= len(extension)
# Replace non authorized chars (including '/') by '_':
url = re.sub(r'[^a-zA-Z0-9\.\-_]+', '_', url)
# Remove parts with no info:
for common_part in _URL_COMMON_PARTS:
url = url.replace(common_part, '_')
url = url.strip('_')
# Remove leading zeros in groups of numbers:
url = re.sub('(?<![0-9])0+(?=[0-9])', '', url)
# Decrease max size of URL components:
c_size = max(len(c) for c in re.split(r'[\.\-_]', url))
while c_size > 4 and len(url) > max_length:
c_size -= 1
url = re.sub(r'[^\.\-_]{4,}', lambda match: match.group(0)[:c_size], url)
return url[:max_length], extension | [
"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: base64url encoded sha256: 44 chars (removing trailing '=').
Args:
url: `str`, url of the file.
checksum: `str` (hex), the sha256 hexdigest of file or url.
Returns:
string of 90 chars max. | 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 shortened to 46 chars.
- checksum: base64url encoded sha256: 44 chars (removing trailing '=').
Args:
url: `str`, url of the file.
checksum: `str` (hex), the sha256 hexdigest of file or url.
Returns:
string of 90 chars max.
"""
checksum = base64.urlsafe_b64encode(_decode_hex(checksum))
checksum = tf.compat.as_text(checksum)[:-1]
name, extension = _sanitize_url(url, max_length=46)
return '%s%s%s' % (name, checksum, extension) | 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 shortened to 46 chars.
- checksum: base64url encoded sha256: 44 chars (removing trailing '=').
Args:
url: `str`, url of the file.
checksum: `str` (hex), the sha256 hexdigest of file or url.
Returns:
string of 90 chars max.
"""
checksum = base64.urlsafe_b64encode(_decode_hex(checksum))
checksum = tf.compat.as_text(checksum)[:-1]
name, extension = _sanitize_url(url, max_length=46)
return '%s%s%s' % (name, checksum, extension) | [
"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.
path: path of downloaded file.
dataset_name: data used to dl the file.
original_fname: name of file as downloaded. | 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.
Args:
resource: resource for which to write the INFO file.
path: path of downloaded file.
dataset_name: data used to dl the file.
original_fname: name of file as downloaded.
"""
info_path = _get_info_path(path)
info = _read_info(info_path) or {}
urls = set(info.get('urls', []) + [resource.url])
dataset_names = info.get('dataset_names', [])
if dataset_name:
dataset_names.append(dataset_name)
if 'original_fname' in info and info['original_fname'] != original_fname:
raise AssertionError(
'`original_fname` "%s" stored in %s does NOT match "%s".' % (
info['original_fname'], info_path, original_fname))
info = dict(urls=list(urls), dataset_names=list(set(dataset_names)),
original_fname=original_fname)
with py_utils.atomic_write(info_path, 'w') as info_f:
json.dump(info, info_f, sort_keys=True) | 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.
Args:
resource: resource for which to write the INFO file.
path: path of downloaded file.
dataset_name: data used to dl the file.
original_fname: name of file as downloaded.
"""
info_path = _get_info_path(path)
info = _read_info(info_path) or {}
urls = set(info.get('urls', []) + [resource.url])
dataset_names = info.get('dataset_names', [])
if dataset_name:
dataset_names.append(dataset_name)
if 'original_fname' in info and info['original_fname'] != original_fname:
raise AssertionError(
'`original_fname` "%s" stored in %s does NOT match "%s".' % (
info['original_fname'], info_path, original_fname))
info = dict(urls=list(urls), dataset_names=list(set(dataset_names)),
original_fname=original_fname)
with py_utils.atomic_write(info_path, 'w') as info_f:
json.dump(info, info_f, sort_keys=True) | [
"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(path))) | 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(path))) | [
"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_trainval2014.zip",
# Testing set (no annotations) (2014)
"test_images": "zips/test2014.zip",
"test_annotations": "annotations/image_info_test2014.zip",
# Testing set (no annotations) (2015)
"test2015_images": "zips/test2015.zip",
"test2015_annotations": "annotations/image_info_test2015.zip",
}
extracted_paths = dl_manager.download_and_extract({
key: root_url + url for key, url in urls.items()
})
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=10,
gen_kwargs=dict(
image_dir=extracted_paths["train_images"],
annotation_dir=extracted_paths["trainval_annotations"],
split_type="train2014",
)),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
num_shards=10,
gen_kwargs=dict(
image_dir=extracted_paths["val_images"],
annotation_dir=extracted_paths["trainval_annotations"],
split_type="val2014",
)),
# Warning: Testing split only contains the images without any annotation
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=10,
gen_kwargs=dict(
image_dir=extracted_paths["test_images"],
annotation_dir=extracted_paths["test_annotations"],
split_type="test2014",
has_annotation=False,
)),
tfds.core.SplitGenerator(
name="test2015",
num_shards=10,
gen_kwargs=dict(
image_dir=extracted_paths["test2015_images"],
annotation_dir=extracted_paths["test2015_annotations"],
split_type="test2015",
has_annotation=False,
)),
] | 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_trainval2014.zip",
# Testing set (no annotations) (2014)
"test_images": "zips/test2014.zip",
"test_annotations": "annotations/image_info_test2014.zip",
# Testing set (no annotations) (2015)
"test2015_images": "zips/test2015.zip",
"test2015_annotations": "annotations/image_info_test2015.zip",
}
extracted_paths = dl_manager.download_and_extract({
key: root_url + url for key, url in urls.items()
})
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=10,
gen_kwargs=dict(
image_dir=extracted_paths["train_images"],
annotation_dir=extracted_paths["trainval_annotations"],
split_type="train2014",
)),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
num_shards=10,
gen_kwargs=dict(
image_dir=extracted_paths["val_images"],
annotation_dir=extracted_paths["trainval_annotations"],
split_type="val2014",
)),
# Warning: Testing split only contains the images without any annotation
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=10,
gen_kwargs=dict(
image_dir=extracted_paths["test_images"],
annotation_dir=extracted_paths["test_annotations"],
split_type="test2014",
has_annotation=False,
)),
tfds.core.SplitGenerator(
name="test2015",
num_shards=10,
gen_kwargs=dict(
image_dir=extracted_paths["test2015_images"],
annotation_dir=extracted_paths["test2015_annotations"],
split_type="test2015",
has_annotation=False,
)),
] | [
"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
Yields:
Generator yielding the next samples | 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_annotation: `bool`, when False (for the testing set), the annotations
are not recorded
Yields:
Generator yielding the next samples
"""
if has_annotation:
instance_filename = "instances_{}.json"
else:
instance_filename = "image_info_{}.json"
# Load the label names and images
instance_path = os.path.join(
annotation_dir,
"annotations",
instance_filename.format(split_type),
)
coco_annotation = CocoAnnotation(instance_path)
# Each category is a dict:
# {
# 'id': 51, # From 1-91, some entry missing
# 'name': 'bowl',
# 'supercategory': 'kitchen',
# }
categories = coco_annotation.categories
# Each image is a dict:
# {
# 'id': 262145,
# 'file_name': 'COCO_train2014_000000262145.jpg'
# 'flickr_url': 'http://farm8.staticflickr.com/7187/xyz.jpg',
# 'coco_url': 'http://images.cocodataset.org/train2014/xyz.jpg',
# 'license': 2,
# 'date_captured': '2013-11-20 02:07:55',
# 'height': 427,
# 'width': 640,
# }
images = coco_annotation.images
# TODO(b/121375022): ClassLabel names should also contains 'id' and
# and 'supercategory' (in addition to 'name')
# Warning: As Coco only use 80 out of the 91 labels, the c['id'] and
# dataset names ids won't match.
self.info.features["objects"]["label"].names = [
c["name"] for c in categories
]
# TODO(b/121375022): Conversion should be done by ClassLabel
categories_id2name = {c["id"]: c["name"] for c in categories}
# Iterate over all images
annotation_skipped = 0
for image_info in sorted(images, key=lambda x: x["id"]):
if has_annotation:
# Each instance annotation is a dict:
# {
# 'iscrowd': 0,
# 'bbox': [116.95, 305.86, 285.3, 266.03],
# 'image_id': 480023,
# 'segmentation': [[312.29, 562.89, 402.25, ...]],
# 'category_id': 58,
# 'area': 54652.9556,
# 'id': 86,
# }
instances = coco_annotation.get_annotations(img_id=image_info["id"])
else:
instances = [] # No annotations
if not instances:
annotation_skipped += 1
def build_bbox(x, y, width, height):
# pylint: disable=cell-var-from-loop
# build_bbox is only used within the loop so it is ok to use image_info
return tfds.features.BBox(
ymin=y / image_info["height"],
xmin=x / image_info["width"],
ymax=(y + height) / image_info["height"],
xmax=(x + width) / image_info["width"],
)
# pylint: enable=cell-var-from-loop
yield {
"image": os.path.join(image_dir, split_type, image_info["file_name"]),
"image/filename": image_info["file_name"],
"objects": [{
"bbox": build_bbox(*instance_info["bbox"]),
"label": categories_id2name[instance_info["category_id"]],
"is_crowd": bool(instance_info["iscrowd"]),
} for instance_info in instances],
}
logging.info(
"%d/%d images do not contains any annotations",
annotation_skipped,
len(images),
) | 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_annotation: `bool`, when False (for the testing set), the annotations
are not recorded
Yields:
Generator yielding the next samples
"""
if has_annotation:
instance_filename = "instances_{}.json"
else:
instance_filename = "image_info_{}.json"
# Load the label names and images
instance_path = os.path.join(
annotation_dir,
"annotations",
instance_filename.format(split_type),
)
coco_annotation = CocoAnnotation(instance_path)
# Each category is a dict:
# {
# 'id': 51, # From 1-91, some entry missing
# 'name': 'bowl',
# 'supercategory': 'kitchen',
# }
categories = coco_annotation.categories
# Each image is a dict:
# {
# 'id': 262145,
# 'file_name': 'COCO_train2014_000000262145.jpg'
# 'flickr_url': 'http://farm8.staticflickr.com/7187/xyz.jpg',
# 'coco_url': 'http://images.cocodataset.org/train2014/xyz.jpg',
# 'license': 2,
# 'date_captured': '2013-11-20 02:07:55',
# 'height': 427,
# 'width': 640,
# }
images = coco_annotation.images
# TODO(b/121375022): ClassLabel names should also contains 'id' and
# and 'supercategory' (in addition to 'name')
# Warning: As Coco only use 80 out of the 91 labels, the c['id'] and
# dataset names ids won't match.
self.info.features["objects"]["label"].names = [
c["name"] for c in categories
]
# TODO(b/121375022): Conversion should be done by ClassLabel
categories_id2name = {c["id"]: c["name"] for c in categories}
# Iterate over all images
annotation_skipped = 0
for image_info in sorted(images, key=lambda x: x["id"]):
if has_annotation:
# Each instance annotation is a dict:
# {
# 'iscrowd': 0,
# 'bbox': [116.95, 305.86, 285.3, 266.03],
# 'image_id': 480023,
# 'segmentation': [[312.29, 562.89, 402.25, ...]],
# 'category_id': 58,
# 'area': 54652.9556,
# 'id': 86,
# }
instances = coco_annotation.get_annotations(img_id=image_info["id"])
else:
instances = [] # No annotations
if not instances:
annotation_skipped += 1
def build_bbox(x, y, width, height):
# pylint: disable=cell-var-from-loop
# build_bbox is only used within the loop so it is ok to use image_info
return tfds.features.BBox(
ymin=y / image_info["height"],
xmin=x / image_info["width"],
ymax=(y + height) / image_info["height"],
xmax=(x + width) / image_info["width"],
)
# pylint: enable=cell-var-from-loop
yield {
"image": os.path.join(image_dir, split_type, image_info["file_name"]),
"image/filename": image_info["file_name"],
"objects": [{
"bbox": build_bbox(*instance_info["bbox"]),
"label": categories_id2name[instance_info["category_id"]],
"is_crowd": bool(instance_info["iscrowd"]),
} for instance_info in instances],
}
logging.info(
"%d/%d images do not contains any annotations",
annotation_skipped,
len(images),
) | [
"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_lib.SubwordTextEncoder.build_from_corpus(
corpus_generator=corpus_generator,
target_vocab_size=vocab_size,
**kwargs) | 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_lib.SubwordTextEncoder.build_from_corpus(
corpus_generator=corpus_generator,
target_vocab_size=vocab_size,
**kwargs) | [
"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 = sorted(tf.io.gfile.listdir(alphabet_dir))
for character in characters:
character_id = int(character[len("character"):]) - 1
character_dir = os.path.join(alphabet_dir, character)
images = tf.io.gfile.listdir(character_dir)
for image in images:
label, _ = image.split("_")
label = int(label) - 1
image_path = os.path.join(character_dir, image)
yield alphabet, character_id, label, image_path | 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 = sorted(tf.io.gfile.listdir(alphabet_dir))
for character in characters:
character_id = int(character[len("character"):]) - 1
character_dir = os.path.join(alphabet_dir, character)
images = tf.io.gfile.listdir(character_dir)
for image in images:
label, _ = image.split("_")
label = int(label) - 1
image_path = os.path.join(character_dir, image)
yield alphabet, character_id, label, image_path | [
"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)
if label in label_names:
assert label_names[label] == label_name
else:
label_names[label] = label_name
label_names = [label_names[k] for k in sorted(label_names)]
return alphabets, label_names | 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)
if label in label_names:
assert label_names[label] == label_name
else:
label_names[label] = label_name
label_names = [label_names[k] for k in sorted(label_names)]
return alphabets, label_names | [
"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.
"""
if not size_in_bytes:
return "?? GiB"
size_in_bytes = float(size_in_bytes)
for (name, size_bytes) in _NAME_LIST:
value = size_in_bytes / size_bytes
if value >= 1.0:
return "{:.2f} {}".format(value, name)
return "{} {}".format(int(size_in_bytes), "bytes") | 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.
"""
if not size_in_bytes:
return "?? GiB"
size_in_bytes = float(size_in_bytes)
for (name, size_bytes) in _NAME_LIST:
value = size_in_bytes / size_bytes
if value >= 1.0:
return "{:.2f} {}".format(value, name)
return "{} {}".format(int(size_in_bytes), "bytes") | [
"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_dl_size = pbar_dl_size
yield | 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_dl_size = pbar_dl_size
yield | [
"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): (downloaded object checksum, size in bytes).
"""
self._pbar_url.update_total(1)
future = self._executor.submit(self._sync_download, url, destination_path)
return promise.Promise.resolve(future) | 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): (downloaded object checksum, size in bytes).
"""
self._pbar_url.update_total(1)
future = self._executor.submit(self._sync_download, url, destination_path)
return promise.Promise.resolve(future) | [
"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.io.gfile.stat(filepath).length
checksum = self._checksumer()
with tf.io.gfile.GFile(filepath, 'rb') as f:
while True:
block = f.read(io.DEFAULT_BUFFER_SIZE)
if not block:
break
checksum.update(block)
return checksum.hexdigest(), dl_size | 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.io.gfile.stat(filepath).length
checksum = self._checksumer()
with tf.io.gfile.GFile(filepath, 'rb') as f:
while True:
block = f.read(io.DEFAULT_BUFFER_SIZE)
if not block:
break
checksum.update(block)
return checksum.hexdigest(), dl_size | [
"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.items():
if k.startswith('download_warning'):
return url + '&confirm=' + v # v is the confirm token
# No token found, let's try with original URL:
return url | 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.items():
if k.startswith('download_warning'):
return url + '&confirm=' + v # v is the confirm token
# No token found, let's try with original URL:
return url | [
"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_kaggle_url(url):
if proxies['http']:
os.environ['KAGGLE_PROXY'] = proxies['http']
return self._sync_kaggle_download(url, destination_path)
try:
# If url is on a filesystem that gfile understands, use copy. Otherwise,
# use requests.
if not url.startswith('http'):
return self._sync_file_copy(url, destination_path)
except tf.errors.UnimplementedError:
pass
session = requests.Session()
session.proxies = proxies
if _DRIVE_URL.match(url):
url = self._get_drive_url(url, session)
use_urllib = url.startswith('ftp')
if use_urllib:
if proxies['ftp']:
proxy = urllib.request.ProxyHandler({'ftp': proxies['ftp']})
opener = urllib.request.build_opener(proxy)
urllib.request.install_opener(opener) # pylint: disable=too-many-function-args
request = urllib.request.Request(url)
response = urllib.request.urlopen(request)
else:
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))
fname = _get_filename(response)
path = os.path.join(destination_path, fname)
size = 0
size_mb = 0
unit_mb = units.MiB
self._pbar_dl_size.update_total(
int(response.headers.get('Content-length', 0)) // unit_mb)
with tf.io.gfile.GFile(path, 'wb') as file_:
checksum = self._checksumer()
if use_urllib:
iterator = iter(lambda: response.read(io.DEFAULT_BUFFER_SIZE), b'')
else:
iterator = response.iter_content(chunk_size=io.DEFAULT_BUFFER_SIZE)
for block in iterator:
size += len(block)
# Update the progress bar
size_mb += len(block)
if size_mb > unit_mb:
self._pbar_dl_size.update(size_mb // unit_mb)
size_mb %= unit_mb
checksum.update(block)
file_.write(block)
self._pbar_url.update(1)
return checksum.hexdigest(), size | 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_kaggle_url(url):
if proxies['http']:
os.environ['KAGGLE_PROXY'] = proxies['http']
return self._sync_kaggle_download(url, destination_path)
try:
# If url is on a filesystem that gfile understands, use copy. Otherwise,
# use requests.
if not url.startswith('http'):
return self._sync_file_copy(url, destination_path)
except tf.errors.UnimplementedError:
pass
session = requests.Session()
session.proxies = proxies
if _DRIVE_URL.match(url):
url = self._get_drive_url(url, session)
use_urllib = url.startswith('ftp')
if use_urllib:
if proxies['ftp']:
proxy = urllib.request.ProxyHandler({'ftp': proxies['ftp']})
opener = urllib.request.build_opener(proxy)
urllib.request.install_opener(opener) # pylint: disable=too-many-function-args
request = urllib.request.Request(url)
response = urllib.request.urlopen(request)
else:
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))
fname = _get_filename(response)
path = os.path.join(destination_path, fname)
size = 0
size_mb = 0
unit_mb = units.MiB
self._pbar_dl_size.update_total(
int(response.headers.get('Content-length', 0)) // unit_mb)
with tf.io.gfile.GFile(path, 'wb') as file_:
checksum = self._checksumer()
if use_urllib:
iterator = iter(lambda: response.read(io.DEFAULT_BUFFER_SIZE), b'')
else:
iterator = response.iter_content(chunk_size=io.DEFAULT_BUFFER_SIZE)
for block in iterator:
size += len(block)
# Update the progress bar
size_mb += len(block)
if size_mb > unit_mb:
self._pbar_dl_size.update(size_mb // unit_mb)
size_mb %= unit_mb
checksum.update(block)
file_.write(block)
self._pbar_url.update(1)
return checksum.hexdigest(), size | [
"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 target_pixels is None:
return image_fobj
cv2 = tfds.core.lazy_imports.cv2
# Decode image using OpenCV2.
image = cv2.imdecode(
np.fromstring(image_fobj.read(), dtype=np.uint8), flags=3)
# Get image height and width.
height, width, _ = image.shape
actual_pixels = height * width
if actual_pixels > target_pixels:
factor = np.sqrt(target_pixels / actual_pixels)
image = cv2.resize(image, dsize=None, fx=factor, fy=factor)
# Encode the image with quality=72 and store it in a BytesIO object.
_, buff = cv2.imencode(".jpg", image, [int(cv2.IMWRITE_JPEG_QUALITY), 72])
return io.BytesIO(buff.tostring()) | 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 target_pixels is None:
return image_fobj
cv2 = tfds.core.lazy_imports.cv2
# Decode image using OpenCV2.
image = cv2.imdecode(
np.fromstring(image_fobj.read(), dtype=np.uint8), flags=3)
# Get image height and width.
height, width, _ = image.shape
actual_pixels = height * width
if actual_pixels > target_pixels:
factor = np.sqrt(target_pixels / actual_pixels)
image = cv2.resize(image, dsize=None, fx=factor, fy=factor)
# Encode the image with quality=72 and store it in a BytesIO object.
_, buff = cv2.imencode(".jpg", image, [int(cv2.IMWRITE_JPEG_QUALITY), 72])
return io.BytesIO(buff.tostring()) | [
"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 from the csv file to use based on
the "Usage" column from the csv. | 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 image directory, don't set labels.
csv_usage: optional, subset of examples from the csv file to use based on
the "Usage" column from the csv.
"""
if csv_path:
with tf.io.gfile.GFile(csv_path) as csv_f:
reader = csv.DictReader(csv_f)
data = [(row["image"], int(row["level"]))
for row in reader
if csv_usage is None or row["Usage"] == csv_usage]
else:
data = [(fname[:-5], -1)
for fname in tf.io.gfile.listdir(images_dir_path)
if fname.endswith(".jpeg")]
for name, label in data:
yield {
"name": name,
"image": _resize_image_if_necessary(
tf.io.gfile.GFile("%s/%s.jpeg" % (images_dir_path, name),
mode="rb"),
target_pixels=self.builder_config.target_pixels),
"label": label,
} | 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 image directory, don't set labels.
csv_usage: optional, subset of examples from the csv file to use based on
the "Usage" column from the csv.
"""
if csv_path:
with tf.io.gfile.GFile(csv_path) as csv_f:
reader = csv.DictReader(csv_f)
data = [(row["image"], int(row["level"]))
for row in reader
if csv_usage is None or row["Usage"] == csv_usage]
else:
data = [(fname[:-5], -1)
for fname in tf.io.gfile.listdir(images_dir_path)
if fname.endswith(".jpeg")]
for name, label in data:
yield {
"name": name,
"image": _resize_image_if_necessary(
tf.io.gfile.GFile("%s/%s.jpeg" % (images_dir_path, name),
mode="rb"),
target_pixels=self.builder_config.target_pixels),
"label": label,
} | [
"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)
# Compute filenames from the given split
filepaths = list(sorted(self._build_split_filenames(
split_info_list=[sliced_split_info.split_info],
)))
# Compute the offsets
if sliced_split_info.split_info.num_examples:
shard_id2num_examples = splits_lib.get_shard_id2num_examples(
sliced_split_info.split_info.num_shards,
sliced_split_info.split_info.num_examples,
)
mask_offsets = splits_lib.compute_mask_offsets(shard_id2num_examples)
else:
logging.warning(
"Statistics not present in the dataset. TFDS is not able to load "
"the total number of examples, so using the subsplit API may not "
"provide precise subsplits."
)
mask_offsets = [0] * len(filepaths)
for filepath, mask_offset in zip(filepaths, mask_offsets):
instruction_dicts.append({
"filepath": filepath,
"mask": mask,
"mask_offset": mask_offset,
})
return instruction_dicts | 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)
# Compute filenames from the given split
filepaths = list(sorted(self._build_split_filenames(
split_info_list=[sliced_split_info.split_info],
)))
# Compute the offsets
if sliced_split_info.split_info.num_examples:
shard_id2num_examples = splits_lib.get_shard_id2num_examples(
sliced_split_info.split_info.num_shards,
sliced_split_info.split_info.num_examples,
)
mask_offsets = splits_lib.compute_mask_offsets(shard_id2num_examples)
else:
logging.warning(
"Statistics not present in the dataset. TFDS is not able to load "
"the total number of examples, so using the subsplit API may not "
"provide precise subsplits."
)
mask_offsets = [0] * len(filepaths)
for filepath, mask_offset in zip(filepaths, mask_offsets):
instruction_dicts.append({
"filepath": filepath,
"mask": mask,
"mask_offset": mask_offset,
})
return instruction_dicts | [
"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: (list[str]) The list of filenames path corresponding to the
split info object | 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 generate the
filenames
Returns:
filenames: (list[str]) The list of filenames path corresponding to the
split info object
"""
filenames = []
for split_info in split_info_list:
filenames.extend(naming.filepaths_for_dataset_split(
dataset_name=self.name,
split=split_info.name,
num_shards=split_info.num_shards,
data_dir=self._data_dir,
filetype_suffix=self._file_format_adapter.filetype_suffix,
))
return filenames | 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 generate the
filenames
Returns:
filenames: (list[str]) The list of filenames path corresponding to the
split info object
"""
filenames = []
for split_info in split_info_list:
filenames.extend(naming.filepaths_for_dataset_split(
dataset_name=self.name,
split=split_info.name,
num_shards=split_info.num_shards,
data_dir=self._data_dir,
filetype_suffix=self._file_format_adapter.filetype_suffix,
))
return filenames | [
"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, 3))
images = np.expand_dims(images, axis=-1)
for sequence in images:
yield dict(image_sequence=sequence) | 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, 3))
images = np.expand_dims(images, axis=-1)
for sequence in images:
yield dict(image_sequence=sequence) | [
"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([1], tf.int64),
"game_duration_seconds": tf.io.FixedLenFeature([1], tf.float32),
"n_steps": tf.io.FixedLenFeature([1], tf.int64),
"screen_size": tf.io.FixedLenFeature([2], tf.int64),
}
sequence_features = {
"rgb_screen": tf.io.FixedLenSequenceFeature([], tf.string),
}
_, seq_feat = tf.io.parse_single_sequence_example(
example_proto,
context_features=context_features,
sequence_features=sequence_features)
video_frames = tf.map_fn(
tf.image.decode_png, seq_feat["rgb_screen"], dtype=tf.uint8)
return video_frames | 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([1], tf.int64),
"game_duration_seconds": tf.io.FixedLenFeature([1], tf.float32),
"n_steps": tf.io.FixedLenFeature([1], tf.int64),
"screen_size": tf.io.FixedLenFeature([2], tf.int64),
}
sequence_features = {
"rgb_screen": tf.io.FixedLenSequenceFeature([], tf.string),
}
_, seq_feat = tf.io.parse_single_sequence_example(
example_proto,
context_features=context_features,
sequence_features=sequence_features)
video_frames = tf.map_fn(
tf.image.decode_png, seq_feat["rgb_screen"], dtype=tf.uint8)
return video_frames | [
"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
# file is >100x slower and the data set is small (26.7MB). Hence, we first
# load everything into memory before yielding the samples.
image_array, class_array, values_array = _load_data(filepath)
for image, classes, values in moves.zip(image_array, class_array,
values_array):
yield dict(
image=np.expand_dims(image, -1),
label_shape=classes[1],
label_scale=classes[2],
label_orientation=classes[3],
label_x_position=classes[4],
label_y_position=classes[5],
value_shape=values[1],
value_scale=values[2],
value_orientation=values[3],
value_x_position=values[4],
value_y_position=values[5]) | 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
# file is >100x slower and the data set is small (26.7MB). Hence, we first
# load everything into memory before yielding the samples.
image_array, class_array, values_array = _load_data(filepath)
for image, classes, values in moves.zip(image_array, class_array,
values_array):
yield dict(
image=np.expand_dims(image, -1),
label_shape=classes[1],
label_scale=classes[2],
label_orientation=classes[3],
label_x_position=classes[4],
label_y_position=classes[5],
value_shape=values[1],
value_scale=values[2],
value_orientation=values[3],
value_x_position=values[4],
value_y_position=values[5]) | [
"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({
"images": tfds.download.Resource(
url=os.path.join(_BASE_URL, "images.tar.gz"),
extract_method=tfds.download.ExtractMethod.TAR),
"annotations": tfds.download.Resource(
url=os.path.join(_BASE_URL, "annotations.tar.gz"),
extract_method=tfds.download.ExtractMethod.TAR)
})
images_path_dir = os.path.join(dl_paths["images"], "images")
annotations_path_dir = os.path.join(dl_paths["annotations"], "annotations")
# Setup train and test splits
train_split = tfds.core.SplitGenerator(
name="train",
num_shards=_NUM_SHARDS,
gen_kwargs={
"images_dir_path": images_path_dir,
"images_list_file": os.path.join(annotations_path_dir,
"trainval.txt"),
},
)
test_split = tfds.core.SplitGenerator(
name="test",
num_shards=_NUM_SHARDS,
gen_kwargs={
"images_dir_path": images_path_dir,
"images_list_file": os.path.join(annotations_path_dir,
"test.txt")
},
)
return [train_split, test_split] | 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({
"images": tfds.download.Resource(
url=os.path.join(_BASE_URL, "images.tar.gz"),
extract_method=tfds.download.ExtractMethod.TAR),
"annotations": tfds.download.Resource(
url=os.path.join(_BASE_URL, "annotations.tar.gz"),
extract_method=tfds.download.ExtractMethod.TAR)
})
images_path_dir = os.path.join(dl_paths["images"], "images")
annotations_path_dir = os.path.join(dl_paths["annotations"], "annotations")
# Setup train and test splits
train_split = tfds.core.SplitGenerator(
name="train",
num_shards=_NUM_SHARDS,
gen_kwargs={
"images_dir_path": images_path_dir,
"images_list_file": os.path.join(annotations_path_dir,
"trainval.txt"),
},
)
test_split = tfds.core.SplitGenerator(
name="test",
num_shards=_NUM_SHARDS,
gen_kwargs={
"images_dir_path": images_path_dir,
"images_list_file": os.path.join(annotations_path_dir,
"test.txt")
},
)
return [train_split, test_split] | [
"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 tf.io.gfile.GFile(labels_path) as csv_f:
if csv_positions[i] > 0:
csv_f.seek(csv_positions[i])
else:
csv_f.readline() # Drop headers
reader = csv.reader(csv_f)
for image_id, source, label, confidence in reader:
if prefix and image_id[0] != prefix:
break
csv_positions[i] = csv_f.tell()
image_id = int(image_id, 16)
current_obj = _Object(label, int(float(confidence) * 10), source)
objects[image_id].append(current_obj)
return dict(objects) | 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 tf.io.gfile.GFile(labels_path) as csv_f:
if csv_positions[i] > 0:
csv_f.seek(csv_positions[i])
else:
csv_f.readline() # Drop headers
reader = csv.reader(csv_f)
for image_id, source, label, confidence in reader:
if prefix and image_id[0] != prefix:
break
csv_positions[i] = csv_f.tell()
image_id = int(image_id, 16)
current_obj = _Object(label, int(float(confidence) * 10), source)
objects[image_id].append(current_obj)
return dict(objects) | [
"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 csv_positions[0] > 0:
csv_f.seek(csv_positions[0])
else:
csv_f.readline() # Drop headers
reader = csv.reader(csv_f)
for (image_id, source, label, confidence, xmin, xmax, ymin, ymax,
is_occluded, is_truncated, is_group_of, is_depiction, is_inside,
) in reader:
if prefix and image_id[0] != prefix:
break
csv_positions[0] = csv_f.tell()
image_id = int(image_id, 16)
del confidence # always 1 in bounding boxes.
current_row = _Bbox(
label, source, tfds.features.BBox(
float(ymin), float(xmin), float(ymax), float(xmax)),
int(is_occluded), int(is_truncated),
int(is_group_of), int(is_depiction), int(is_inside))
boxes[image_id].append(current_row)
return dict(boxes) | 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 csv_positions[0] > 0:
csv_f.seek(csv_positions[0])
else:
csv_f.readline() # Drop headers
reader = csv.reader(csv_f)
for (image_id, source, label, confidence, xmin, xmax, ymin, ymax,
is_occluded, is_truncated, is_group_of, is_depiction, is_inside,
) in reader:
if prefix and image_id[0] != prefix:
break
csv_positions[0] = csv_f.tell()
image_id = int(image_id, 16)
del confidence # always 1 in bounding boxes.
current_row = _Bbox(
label, source, tfds.features.BBox(
float(ymin), float(xmin), float(ymax), float(xmax)),
int(is_occluded), int(is_truncated),
int(is_group_of), int(is_depiction), int(is_inside))
boxes[image_id].append(current_row)
return dict(boxes) | [
"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],
csv_positions)
train_objects = load(['train_human_labels', 'train_machine_labels'])
test_objects = load(['test_human_labels', 'test_machine_labels'])
validation_objects = load(['validation_human_labels',
'validation_machine_labels'])
def load_boxes(name):
csv_positions = [0]
return functools.partial(_load_bboxes, paths[name], csv_positions)
train_bbox = load_boxes('train-annotations-bbox')
test_bbox = load_boxes('test-annotations-bbox')
validation_bbox = load_boxes('validation-annotations-bbox')
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=512,
gen_kwargs=dict(archive_paths=paths['train_images'],
objects_getter=train_objects,
bboxes_getter=train_bbox,
prefixes='0123456789abcdef'),
),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=36,
gen_kwargs=dict(archive_paths=[paths['test_images']],
objects_getter=test_objects,
bboxes_getter=test_bbox),
),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
num_shards=12,
gen_kwargs=dict(archive_paths=[paths['validation_images']],
objects_getter=validation_objects,
bboxes_getter=validation_bbox),
),
] | 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],
csv_positions)
train_objects = load(['train_human_labels', 'train_machine_labels'])
test_objects = load(['test_human_labels', 'test_machine_labels'])
validation_objects = load(['validation_human_labels',
'validation_machine_labels'])
def load_boxes(name):
csv_positions = [0]
return functools.partial(_load_bboxes, paths[name], csv_positions)
train_bbox = load_boxes('train-annotations-bbox')
test_bbox = load_boxes('test-annotations-bbox')
validation_bbox = load_boxes('validation-annotations-bbox')
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
num_shards=512,
gen_kwargs=dict(archive_paths=paths['train_images'],
objects_getter=train_objects,
bboxes_getter=train_bbox,
prefixes='0123456789abcdef'),
),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
num_shards=36,
gen_kwargs=dict(archive_paths=[paths['test_images']],
objects_getter=test_objects,
bboxes_getter=test_bbox),
),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
num_shards=12,
gen_kwargs=dict(archive_paths=[paths['validation_images']],
objects_getter=validation_objects,
bboxes_getter=validation_bbox),
),
] | [
"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 prefixes else None
objects = objects_getter(prefix)
bboxes = bboxes_getter(prefix)
logging.info('Opening archive %s ...', archive_path)
archive = tfds.download.iter_archive(
archive_path, tfds.download.ExtractMethod.TAR_STREAM)
for fpath, fobj in archive:
fname = os.path.basename(fpath)
image_id = int(os.path.splitext(fname)[0], 16)
image_objects = [obj._asdict() for obj in objects.get(image_id, [])]
image_bboxes = [bbox._asdict() for bbox in bboxes.get(image_id, [])]
image_objects_trainable = [
obj for obj in image_objects if obj['label'] in trainable_classes
]
yield {
'image': _resize_image_if_necessary(
fobj, target_pixels=self.builder_config.target_pixels),
'image/filename': fname,
'objects': image_objects,
'objects_trainable': image_objects_trainable,
'bobjects': image_bboxes,
} | 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 prefixes else None
objects = objects_getter(prefix)
bboxes = bboxes_getter(prefix)
logging.info('Opening archive %s ...', archive_path)
archive = tfds.download.iter_archive(
archive_path, tfds.download.ExtractMethod.TAR_STREAM)
for fpath, fobj in archive:
fname = os.path.basename(fpath)
image_id = int(os.path.splitext(fname)[0], 16)
image_objects = [obj._asdict() for obj in objects.get(image_id, [])]
image_bboxes = [bbox._asdict() for bbox in bboxes.get(image_id, [])]
image_objects_trainable = [
obj for obj in image_objects if obj['label'] in trainable_classes
]
yield {
'image': _resize_image_if_necessary(
fobj, target_pixels=self.builder_config.target_pixels),
'image/filename': fname,
'objects': image_objects,
'objects_trainable': image_objects_trainable,
'bobjects': image_bboxes,
} | [
"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 {
"text": text,
"label": res.groupdict()["label"],
} | 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 {
"text": text,
"label": res.groupdict()["label"],
} | [
"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 for u in urls} | 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 for u in urls} | [
"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('Unsupported publisher: %s', publisher)
files = tf.io.gfile.listdir(top_dir)
ret_files = []
for p in files:
basename = os.path.basename(p)
if basename[0:basename.find('.story')] in url_dict:
ret_files.append(os.path.join(top_dir, p))
return ret_files | 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('Unsupported publisher: %s', publisher)
files = tf.io.gfile.listdir(top_dir)
ret_files = []
for p in files:
basename = os.path.basename(p)
if basename[0:basename.find('.story')] in url_dict:
ret_files.append(os.path.join(top_dir, p))
return ret_files | [
"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['val_urls'])
elif split == tfds.Split.TEST:
urls = _get_url_hashes(dl_paths['test_urls'])
else:
logging.fatal('Unsupported split: %s', split)
cnn = _find_files(dl_paths, 'cnn', urls)
dm = _find_files(dl_paths, 'dm', urls)
return cnn + dm | 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['val_urls'])
elif split == tfds.Split.TEST:
urls = _get_url_hashes(dl_paths['test_urls'])
else:
logging.fatal('Unsupported split: %s', split)
cnn = _find_files(dl_paths, 'cnn', urls)
dm = _find_files(dl_paths, 'dm', urls)
return cnn + dm | [
"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 the ends of lines that are missing them
# (this is a problem in the dataset because many image captions don't end in
# periods; consequently they end up in the body of the article as run-on
# sentences)
def fix_missing_period(line):
"""Adds a period to a line that is missing a period."""
if '@highlight' in line: return line
if not line: return line
if line[-1] in END_TOKENS: return line
return line + ' .'
lines = [fix_missing_period(line) for line in lines]
# Separate out article and abstract sentences
article_lines = []
highlights = []
next_is_highlight = False
for line in lines:
if not line:
continue # empty line
elif line.startswith('@highlight'):
next_is_highlight = True
elif next_is_highlight:
highlights.append(line)
else:
article_lines.append(line)
# Make article into a single string
article = ' '.join(article_lines)
# Make abstract into a single string, putting <s> and </s> tags around
# the sentences.
abstract = ' '.join(['%s %s %s' % (SENTENCE_START, sent,
SENTENCE_END) for sent in highlights])
return article, abstract | 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 the ends of lines that are missing them
# (this is a problem in the dataset because many image captions don't end in
# periods; consequently they end up in the body of the article as run-on
# sentences)
def fix_missing_period(line):
"""Adds a period to a line that is missing a period."""
if '@highlight' in line: return line
if not line: return line
if line[-1] in END_TOKENS: return line
return line + ' .'
lines = [fix_missing_period(line) for line in lines]
# Separate out article and abstract sentences
article_lines = []
highlights = []
next_is_highlight = False
for line in lines:
if not line:
continue # empty line
elif line.startswith('@highlight'):
next_is_highlight = True
elif next_is_highlight:
highlights.append(line)
else:
article_lines.append(line)
# Make article into a single string
article = ' '.join(article_lines)
# Make abstract into a single string, putting <s> and </s> tags around
# the sentences.
abstract = ' '.join(['%s %s %s' % (SENTENCE_START, sent,
SENTENCE_END) for sent in highlights])
return article, abstract | [
"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)
savefile.close()
if method.lower() == 'csv':
with open('{}/exported.csv'.format(directory), 'w+') as csvfile:
csv_writer = csv.writer(
csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
for key, values in datasets.items():
if values is None:
csv_writer.writerow([key])
else:
csv_writer.writerow([key] + values)
csvfile.close() | 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)
savefile.close()
if method.lower() == 'csv':
with open('{}/exported.csv'.format(directory), 'w+') as csvfile:
csv_writer = csv.writer(
csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
for key, values in datasets.items():
if values is None:
csv_writer.writerow([key])
else:
csv_writer.writerow([key] + values)
csvfile.close() | [
"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 = "http://web.archive.org/cdx/search?url=%s&matchType=%s&collapse=urlkey&fl=original&filter=mimetype:text/html&filter=statuscode:200&output=json&from=%s&to=%s" % (host, mode, fro, to)
response = get(url).text
parsed = json.loads(response)[1:]
urls = []
for item in parsed:
urls.append(item[0])
return urls | 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 = "http://web.archive.org/cdx/search?url=%s&matchType=%s&collapse=urlkey&fl=original&filter=mimetype:text/html&filter=statuscode:200&output=json&from=%s&to=%s" % (host, mode, fro, to)
response = get(url).text
parsed = json.loads(response)[1:]
urls = []
for item in parsed:
urls.append(item[0])
return urls | [
"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 = time_machine(host, 'host')
print('%s Retrieved %i URLs from archive.org' % (
good, len(archived_urls) - 1))
for url in archived_urls:
verb('Internal page', url)
internal.add(url)
# Makes request to robots.txt
response = requests.get(input_url + '/robots.txt',
proxies=random.choice(proxies)).text
# Making sure robots.txt isn't some fancy 404 page
if '<body' not in response:
# If you know it, you know it
matches = re.findall(r'Allow: (.*)|Disallow: (.*)', response)
if matches:
# Iterating over the matches, match is a tuple here
for match in matches:
# One item in match will always be empty so will combine both
# items
match = ''.join(match)
# If the URL doesn't use a wildcard
if '*' not in match:
url = input_url + match
# Add the URL to internal list for crawling
internal.add(url)
# Add the URL to robots list
robots.add(url)
print('%s URLs retrieved from robots.txt: %s' % (good, len(robots)))
# Makes request to sitemap.xml
response = requests.get(input_url + '/sitemap.xml',
proxies=random.choice(proxies)).text
# Making sure robots.txt isn't some fancy 404 page
if '<body' not in response:
matches = xml_parser(response)
if matches: # if there are any matches
print('%s URLs retrieved from sitemap.xml: %s' % (
good, len(matches)))
for match in matches:
verb('Internal page', match)
# Cleaning up the URL and adding it to the internal list for
# crawling
internal.add(match) | 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 = time_machine(host, 'host')
print('%s Retrieved %i URLs from archive.org' % (
good, len(archived_urls) - 1))
for url in archived_urls:
verb('Internal page', url)
internal.add(url)
# Makes request to robots.txt
response = requests.get(input_url + '/robots.txt',
proxies=random.choice(proxies)).text
# Making sure robots.txt isn't some fancy 404 page
if '<body' not in response:
# If you know it, you know it
matches = re.findall(r'Allow: (.*)|Disallow: (.*)', response)
if matches:
# Iterating over the matches, match is a tuple here
for match in matches:
# One item in match will always be empty so will combine both
# items
match = ''.join(match)
# If the URL doesn't use a wildcard
if '*' not in match:
url = input_url + match
# Add the URL to internal list for crawling
internal.add(url)
# Add the URL to robots list
robots.add(url)
print('%s URLs retrieved from robots.txt: %s' % (good, len(robots)))
# Makes request to sitemap.xml
response = requests.get(input_url + '/sitemap.xml',
proxies=random.choice(proxies)).text
# Making sure robots.txt isn't some fancy 404 page
if '<body' not in response:
matches = xml_parser(response)
if matches: # if there are any matches
print('%s URLs retrieved from sitemap.xml: %s' % (
good, len(matches)))
for match in matches:
verb('Internal page', match)
# Cleaning up the URL and adding it to the internal list for
# crawling
internal.add(match) | [
"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 = cook or set()
headers = headers or set()
user_agents = user_agents or ['Photon']
failed = failed or set()
processed = processed or set()
# Mark the URL as crawled
processed.add(url)
# Pause/sleep the program for specified time
time.sleep(delay)
def make_request(url):
"""Default request"""
final_headers = headers or {
'Host': host,
# Selecting a random user-agent
'User-Agent': random.choice(user_agents),
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Language': 'en-US,en;q=0.5',
'Accept-Encoding': 'gzip',
'DNT': '1',
'Connection': 'close',
}
try:
response = SESSION.get(
url,
cookies=cook,
headers=final_headers,
verify=False,
timeout=timeout,
stream=True,
proxies=random.choice(proxies)
)
except TooManyRedirects:
return 'dummy'
if 'text/html' in response.headers['content-type'] or \
'text/plain' in response.headers['content-type']:
if response.status_code != '404':
return response.text
else:
response.close()
failed.add(url)
return 'dummy'
else:
response.close()
return 'dummy'
return make_request(url) | 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 = cook or set()
headers = headers or set()
user_agents = user_agents or ['Photon']
failed = failed or set()
processed = processed or set()
# Mark the URL as crawled
processed.add(url)
# Pause/sleep the program for specified time
time.sleep(delay)
def make_request(url):
"""Default request"""
final_headers = headers or {
'Host': host,
# Selecting a random user-agent
'User-Agent': random.choice(user_agents),
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Language': 'en-US,en;q=0.5',
'Accept-Encoding': 'gzip',
'DNT': '1',
'Connection': 'close',
}
try:
response = SESSION.get(
url,
cookies=cook,
headers=final_headers,
verify=False,
timeout=timeout,
stream=True,
proxies=random.choice(proxies)
)
except TooManyRedirects:
return 'dummy'
if 'text/html' in response.headers['content-type'] or \
'text/plain' in response.headers['content-type']:
if response.status_code != '404':
return response.text
else:
response.close()
failed.add(url)
return 'dummy'
else:
response.close()
return 'dummy'
return make_request(url) | [
"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:
verb('Intel', match)
bad_intel.add((match, rintel[1], url)) | 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:
verb('Intel', match)
bad_intel.add((match, rintel[1], url)) | [
"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 everything after a "#" to deal with in-page anchors
link = link[1].replace('\'', '').replace('"', '').split('#')[0]
# Checks if the URLs should be crawled
if is_link(link, processed, files):
if link[:4] == 'http':
if link.startswith(main_url):
verb('Internal page', link)
internal.add(link)
else:
verb('External page', link)
external.add(link)
elif link[:2] == '//':
if link.split('/')[2].startswith(host):
verb('Internal page', link)
internal.add(schema + '://' + link)
else:
verb('External page', link)
external.add(link)
elif link[:1] == '/':
verb('Internal page', link)
internal.add(remove_file(url) + link)
else:
verb('Internal page', link)
usable_url = remove_file(url)
if usable_url.endswith('/'):
internal.add(usable_url + link)
elif link.startswith('/'):
internal.add(usable_url + link)
else:
internal.add(usable_url + '/' + link)
if not only_urls:
intel_extractor(url, response)
js_extractor(response)
if args.regex and not supress_regex:
regxy(args.regex, response, supress_regex, custom)
if api:
matches = rentropy.findall(response)
for match in matches:
if entropy(match) >= 4:
verb('Key', match)
keys.add(url + ': ' + match) | 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 everything after a "#" to deal with in-page anchors
link = link[1].replace('\'', '').replace('"', '').split('#')[0]
# Checks if the URLs should be crawled
if is_link(link, processed, files):
if link[:4] == 'http':
if link.startswith(main_url):
verb('Internal page', link)
internal.add(link)
else:
verb('External page', link)
external.add(link)
elif link[:2] == '//':
if link.split('/')[2].startswith(host):
verb('Internal page', link)
internal.add(schema + '://' + link)
else:
verb('External page', link)
external.add(link)
elif link[:1] == '/':
verb('Internal page', link)
internal.add(remove_file(url) + link)
else:
verb('Internal page', link)
usable_url = remove_file(url)
if usable_url.endswith('/'):
internal.add(usable_url + link)
elif link.startswith('/'):
internal.add(usable_url + link)
else:
internal.add(usable_url + '/' + link)
if not only_urls:
intel_extractor(url, response)
js_extractor(response)
if args.regex and not supress_regex:
regxy(args.regex, response, supress_regex, custom)
if api:
matches = rentropy.findall(response)
for match in matches:
if entropy(match) >= 4:
verb('Key', match)
keys.add(url + ': ' + match) | [
"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 match in matches:
# Combining the items because one of them is always empty
match = match[0] + match[1]
# Making sure it's not some JavaScript code
if not re.search(r'[}{><"\']', match) and not match == '/':
verb('JS endpoint', match)
endpoints.add(match) | 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 match in matches:
# Combining the items because one of them is always empty
match = match[0] + match[1]
# Making sure it's not some JavaScript code
if not re.search(r'[}{><"\']', match) and not match == '/':
verb('JS endpoint', match)
endpoints.add(match) | [
"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 output;proxy support;more intels'''
latest_commit = requester('https://raw.githubusercontent.com/s0md3v/Photon/master/core/updater.py', host='raw.githubusercontent.com')
# Just a hack to see if a new version is available
if changes not in latest_commit:
changelog = re.search(r"changes = '''(.*?)'''", latest_commit)
# Splitting the changes to form a list
changelog = changelog.group(1).split(';')
print('%s A new version of Photon is available.' % good)
print('%s Changes:' % info)
for change in changelog: # print changes
print('%s>%s %s' % (green, end, change))
current_path = os.getcwd().split('/') # if you know it, you know it
folder = current_path[-1] # current directory name
path = '/'.join(current_path) # current directory path
choice = input('%s Would you like to update? [Y/n] ' % que).lower()
if choice != 'n':
print('%s Updating Photon' % run)
os.system('git clone --quiet https://github.com/s0md3v/Photon %s'
% (folder))
os.system('cp -r %s/%s/* %s && rm -r %s/%s/ 2>/dev/null'
% (path, folder, path, path, folder))
print('%s Update successful!' % good)
else:
print('%s Photon is up to date!' % good) | 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 output;proxy support;more intels'''
latest_commit = requester('https://raw.githubusercontent.com/s0md3v/Photon/master/core/updater.py', host='raw.githubusercontent.com')
# Just a hack to see if a new version is available
if changes not in latest_commit:
changelog = re.search(r"changes = '''(.*?)'''", latest_commit)
# Splitting the changes to form a list
changelog = changelog.group(1).split(';')
print('%s A new version of Photon is available.' % good)
print('%s Changes:' % info)
for change in changelog: # print changes
print('%s>%s %s' % (green, end, change))
current_path = os.getcwd().split('/') # if you know it, you know it
folder = current_path[-1] # current directory name
path = '/'.join(current_path) # current directory path
choice = input('%s Would you like to update? [Y/n] ' % que).lower()
if choice != 'n':
print('%s Updating Photon' % run)
os.system('git clone --quiet https://github.com/s0md3v/Photon %s'
% (folder))
os.system('cp -r %s/%s/* %s && rm -r %s/%s/ 2>/dev/null'
% (path, folder, path, path, folder))
print('%s Update successful!' % good)
else:
print('%s Photon is up to date!' % good) | [
"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(' ', '').replace('\n', ''))
return list(result) | 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(' ', '').replace('\n', ''))
return list(result) | [
"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 link in links)
for i, _ in enumerate(concurrent.futures.as_completed(futures)):
if i + 1 == len(links) or (i + 1) % thread_count == 0:
print('%s Progress: %i/%i' % (info, i + 1, len(links)),
end='\r')
print('') | 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 link in links)
for i, _ in enumerate(concurrent.futures.as_completed(futures)):
if i + 1 == len(links) or (i + 1) % thread_count == 0:
print('%s Progress: %i/%i' % (info, i + 1, len(links)),
end='\r')
print('') | [
"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 = True | 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 = True | [
"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
Returns:
bool If `url` should be crawled
"""
if url not in processed:
is_file = url.endswith(BAD_TYPES)
if is_file:
files.add(url)
return False
return True
return False | 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
Returns:
bool If `url` should be crawled
"""
if url not in processed:
is_file = url.endswith(BAD_TYPES)
if is_file:
files.add(url)
return False
return True
return False | [
"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 of a string
if not isinstance(urls, (list, set, tuple)):
urls = [urls]
try:
non_matching_urls = [url for url in urls if not re.search(regex, url)]
except TypeError:
return []
return non_matching_urls | 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 of a string
if not isinstance(urls, (list, set, tuple)):
urls = [urls]
try:
non_matching_urls = [url for url in urls if not re.search(regex, url)]
except TypeError:
return []
return non_matching_urls | [
"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)
out_file.write(str(joined.encode('utf-8').decode('utf-8')))
out_file.write('\n') | 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)
out_file.write(str(joined.encode('utf-8').decode('utf-8')))
out_file.write('\n') | [
"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
return minutes, seconds, time_per_request | 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 minutes, seconds, time_per_request | [
"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 entropy | 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 entropy | [
"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 = value[:-1]
sorted_headers[header] = value
except IndexError:
pass
return sorted_headers | 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 = value[:-1]
sorted_headers[header] = value
except IndexError:
pass
return sorted_headers | [
"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):\/\/.)?[-a-zA-Z0-9@:%._\+~#=]{2,256}\.[a-z]{2,6}:(\d{1,5})", v):
proxies.append({"http": v,
"https": v})
return proxies
elif is_proxy_list(v, proxies):
return proxies
else:
raise argparse.ArgumentTypeError(
"Proxy should follow IP:PORT or DOMAIN:PORT format") | 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):\/\/.)?[-a-zA-Z0-9@:%._\+~#=]{2,256}\.[a-z]{2,6}:(\d{1,5})", v):
proxies.append({"http": v,
"https": v})
return proxies
elif is_proxy_list(v, proxies):
return proxies
else:
raise argparse.ArgumentTypeError(
"Proxy should follow IP:PORT or DOMAIN:PORT format") | [
"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://dnsdumpster.com/'}
data = {'csrfmiddlewaretoken': csrf_token, 'targetip': domain}
response = requests.Session().post(
'https://dnsdumpster.com/', cookies=cookies, data=data, headers=headers)
image = requests.get('https://dnsdumpster.com/static/map/%s.png' % domain)
if image.status_code == 200:
with open('%s/%s.png' % (output_dir, domain), 'wb') as f:
f.write(image.content) | 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://dnsdumpster.com/'}
data = {'csrfmiddlewaretoken': csrf_token, 'targetip': domain}
response = requests.Session().post(
'https://dnsdumpster.com/', cookies=cookies, data=data, headers=headers)
image = requests.get('https://dnsdumpster.com/static/map/%s.png' % domain)
if image.status_code == 200:
with open('%s/%s.png' % (output_dir, domain), 'wb') as f:
f.write(image.content) | [
"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:
os.execvp(editor, [editor, tmpfile.name])
else:
os.waitpid(child_pid, 0)
tmpfile.seek(0)
return tmpfile.read().strip() | 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:
os.execvp(editor, [editor, tmpfile.name])
else:
os.waitpid(child_pid, 0)
tmpfile.seek(0)
return tmpfile.read().strip() | [
"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 存放在 kernels 词典中， { 'broker_name': QAThread }
#self.market.register(self.broker_name, self.broker)
self.market.connect(self.broker_name) | 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 存放在 kernels 词典中， { 'broker_name': QAThread }
#self.market.register(self.broker_name, self.broker)
self.market.connect(self.broker_name) | [
"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'>
date = data.date[0]
if self.market_type is MARKET_TYPE.STOCK_CN: # 如果是股票市场
if _date != date: # 如果新的date
# 前一天的交易日已经过去
# 往 broker 和 account 发送 settle 事件
try:
self.market.trade_engine.join()
# time.sleep(2)
self.market._settle(self.broker_name)
except Exception as e:
raise e
# 基金 指数 期货
elif self.market_type in [MARKET_TYPE.FUND_CN, MARKET_TYPE.INDEX_CN, MARKET_TYPE.FUTURE_CN]:
self.market._settle(self.broker_name)
# print(data)
self.broker.run(
QA_Event(event_type=ENGINE_EVENT.UPCOMING_DATA, market_data=data))
# 生成 UPCOMING_DATA 事件放到 队列中去执行
self.market.upcoming_data(self.broker_name, data)
self.market.trade_engine.join()
_date = date | 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'>
date = data.date[0]
if self.market_type is MARKET_TYPE.STOCK_CN: # 如果是股票市场
if _date != date: # 如果新的date
# 前一天的交易日已经过去
# 往 broker 和 account 发送 settle 事件
try:
self.market.trade_engine.join()
# time.sleep(2)
self.market._settle(self.broker_name)
except Exception as e:
raise e
# 基金 指数 期货
elif self.market_type in [MARKET_TYPE.FUND_CN, MARKET_TYPE.INDEX_CN, MARKET_TYPE.FUTURE_CN]:
self.market._settle(self.broker_name)
# print(data)
self.broker.run(
QA_Event(event_type=ENGINE_EVENT.UPCOMING_DATA, market_data=data))
# 生成 UPCOMING_DATA 事件放到 队列中去执行
self.market.upcoming_data(self.broker_name, data)
self.market.trade_engine.join()
_date = date | [
"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,
'user_cookie':
self.user_cookie,
'broker':
self.broker,
'market_type':
self.market_type,
'strategy_name':
self.strategy_name,
'current_time':
str(self._currenttime),
'allow_sellopen':
self.allow_sellopen,
'allow_margin':
self.allow_margin,
'allow_t0':
self.allow_t0,
'margin_level':
self.margin_level,
'init_assets':
self.init_assets,
'init_cash':
self.init_cash,
'init_hold':
self.init_hold.to_dict(),
'commission_coeff':
self.commission_coeff,
'tax_coeff':
self.tax_coeff,
'cash':
self.cash,
'history':
self.history,
'trade_index':
self.time_index_max,
'running_time':
str(datetime.datetime.now())
if self.running_time is None else str(self.running_time),
'quantaxis_version':
self.quantaxis_version,
'running_environment':
self.running_environment,
'start_date':
self.start_date,
'end_date':
self.end_date,
'frozen':
self.frozen,
'finished_id':
self.finishedOrderid
} | 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,
'user_cookie':
self.user_cookie,
'broker':
self.broker,
'market_type':
self.market_type,
'strategy_name':
self.strategy_name,
'current_time':
str(self._currenttime),
'allow_sellopen':
self.allow_sellopen,
'allow_margin':
self.allow_margin,
'allow_t0':
self.allow_t0,
'margin_level':
self.margin_level,
'init_assets':
self.init_assets,
'init_cash':
self.init_cash,
'init_hold':
self.init_hold.to_dict(),
'commission_coeff':
self.commission_coeff,
'tax_coeff':
self.tax_coeff,
'cash':
self.cash,
'history':
self.history,
'trade_index':
self.time_index_max,
'running_time':
str(datetime.datetime.now())
if self.running_time is None else str(self.running_time),
'quantaxis_version':
self.quantaxis_version,
'running_environment':
self.running_environment,
'start_date':
self.start_date,
'end_date':
self.end_date,
'frozen':
self.frozen,
'finished_id':
self.finishedOrderid
} | [
"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]
else:
print(
RuntimeWarning(
'QAACCOUNT: THIS ACCOUNT DOESNOT HAVE ANY TRADE'
)
)
else:
return self.start_ | 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]
else:
print(
RuntimeWarning(
'QAACCOUNT: THIS ACCOUNT DOESNOT HAVE ANY TRADE'
)
)
else:
return self.start_ | [
"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]
else:
print(
RuntimeWarning(
'QAACCOUNT: THIS ACCOUNT DOESNOT HAVE ANY TRADE'
)
)
else:
return self.end_ | 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]
else:
print(
RuntimeWarning(
'QAACCOUNT: THIS ACCOUNT DOESNOT HAVE ANY TRADE'
)
)
else:
return self.end_ | [
"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]
).sort_index() | 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]
).sort_index() | [
"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]))
).assign(account_cookie=self.account_cookie) # .sort_values('datetime')
return _cash.set_index(['datetime', 'account_cookie'], drop=False)
"""
实验性质
@2018-06-09
# 对于账户持仓的分解
1. 真实持仓hold:
正常模式/TZero模式:
hold = 历史持仓(init_hold)+ 初始化账户后发生的所有交易导致的持仓(hold_available)
动态持仓(初始化账户后的持仓)hold_available:
self.history 计算而得
2. 账户的可卖额度(sell_available)
正常模式:
sell_available
结算前: init_hold+ 买卖交易(卖-)
结算后: init_hold+ 买卖交易(买+ 卖-)
TZero模式:
sell_available
结算前: init_hold - 买卖交易占用的额度(abs(买+ 卖-))
结算过程 是为了补平(等于让hold={})
结算后: init_hold
""" | 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]))
).assign(account_cookie=self.account_cookie) # .sort_values('datetime')
return _cash.set_index(['datetime', 'account_cookie'], drop=False)
"""
实验性质
@2018-06-09
# 对于账户持仓的分解
1. 真实持仓hold:
正常模式/TZero模式:
hold = 历史持仓(init_hold)+ 初始化账户后发生的所有交易导致的持仓(hold_available)
动态持仓(初始化账户后的持仓)hold_available:
self.history 计算而得
2. 账户的可卖额度(sell_available)
正常模式:
sell_available
结算前: init_hold+ 买卖交易(卖-)
结算后: init_hold+ 买卖交易(买+ 卖-)
TZero模式:
sell_available
结算前: init_hold - 买卖交易占用的额度(abs(买+ 卖-))
结算过程 是为了补平(等于让hold={})
结算后: init_hold
""" | [
"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',
aggfunc=np.sum
).fillna(0).sort_index() | 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',
aggfunc=np.sum
).fillna(0).sort_index() | [
"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_index=True,right_index=True)
res_=res_.ffill().fillna(self.init_cash).drop(['predrop','datetime','account_cookie'], axis=1).reset_index().set_index(['date'],drop=False).sort_index()
res_=res_[res_.index.isin(self.trade_range)]
return res_ | 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_index=True,right_index=True)
res_=res_.ffill().fillna(self.init_cash).drop(['predrop','datetime','account_cookie'], axis=1).reset_index().set_index(['date'],drop=False).sort_index()
res_=res_[res_.index.isin(self.trade_range)]
return res_ | [
"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]).date
)
data.date = pd.to_datetime(data.date)
data = data.set_index(['date', 'account_cookie'])
res = data[~data.index.duplicated(keep='last')].sort_index()
# 这里会导致股票停牌时的持仓也被计算 但是计算market_value的时候就没了
le=pd.DataFrame(pd.Series(data=None, index=pd.to_datetime(self.trade_range_max).set_names('date'), name='predrop'))
ri=res.reset_index().set_index('date')
res_=pd.merge(le,ri,how='left',left_index=True,right_index=True)
res_=res_.ffill().fillna(0).drop(['predrop','account_cookie'], axis=1).reset_index().set_index(['date']).sort_index()
res_=res_[res_.index.isin(self.trade_range)]
return res_ | 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]).date
)
data.date = pd.to_datetime(data.date)
data = data.set_index(['date', 'account_cookie'])
res = data[~data.index.duplicated(keep='last')].sort_index()
# 这里会导致股票停牌时的持仓也被计算 但是计算market_value的时候就没了
le=pd.DataFrame(pd.Series(data=None, index=pd.to_datetime(self.trade_range_max).set_names('date'), name='predrop'))
ri=res.reset_index().set_index('date')
res_=pd.merge(le,ri,how='left',left_index=True,right_index=True)
res_=res_.ffill().fillna(0).drop(['predrop','account_cookie'], axis=1).reset_index().set_index(['date']).sort_index()
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#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_table.set_index(
'datetime'
).sort_index().loc[:datetime].groupby('code'
).amount.sum().sort_index()
return pd.concat([self.init_hold,
hold_available]).groupby('code').sum().sort_index(
).apply(lambda x: x if x > 0 else None).dropna() | 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_table.set_index(
'datetime'
).sort_index().loc[:datetime].groupby('code'
).amount.sum().sort_index()
return pd.concat([self.init_hold,
hold_available]).groupby('code').sum().sort_index(
).apply(lambda x: x if x > 0 else None).dropna() | [
"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
x=x[n+1:]
if sum(x['amount']) != 0:
return np.average(
x['price'],
weights=x['amount'],
returned=True
)
else:
return np.nan
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().groupby('code').apply(weights).dropna() | 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
x=x[n+1:]
if sum(x['amount']) != 0:
return np.average(
x['price'],
weights=x['amount'],
returned=True
)
else:
return np.nan
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().groupby('code').apply(weights).dropna() | [
"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 np.average(
x['price'],
weights=x['amount'],
returned=True
)
else:
return np.nan
if datetime is None:
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().groupby('code').apply(weights).dropna()
else:
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().loc[:datetime].groupby('code').apply(weights
).dropna() | 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 np.average(
x['price'],
weights=x['amount'],
returned=True
)
else:
return np.nan
if datetime is None:
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().groupby('code').apply(weights).dropna()
else:
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().loc[:datetime].groupby('code').apply(weights
).dropna() | [
"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_datetime(x.datetime.max())
else:
return np.nan
if datetime is None:
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().groupby('code').apply(weights).dropna()
else:
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().loc[:datetime].groupby('code').apply(weights
).dropna() | 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_datetime(x.datetime.max())
else:
return np.nan
if datetime is None:
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().groupby('code').apply(weights).dropna()
else:
return self.history_table.set_index(
'datetime',
drop=False
).sort_index().loc[:datetime].groupby('code').apply(weights
).dropna() | [
"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_towards {[type]} -- [description]
trade_time {[type]} -- [description]
Keyword Arguments:
message {[type]} -- [description] (default: {None})
2018/11/7 @yutiansut
修复一个bug: 在直接使用该快速撮合接口的时候, 期货卖出会扣减保证金, 买回来的时候应该反算利润
如 3800卖空 3700买回平仓 应为100利润
@2018-12-31 保证金账户ok
@2019/1/3 一些重要的意思
frozen = self.market_preset.get_frozen(code) # 保证金率
unit = self.market_preset.get_unit(code) # 合约乘数
raw_trade_money = trade_price*trade_amount*market_towards # 总市值
value = raw_trade_money * unit # 合约总价值
trade_money = value * frozen # 交易保证金 | 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_towards {[type]} -- [description]
trade_time {[type]} -- [description]
Keyword Arguments:
message {[type]} -- [description] (default: {None})
2018/11/7 @yutiansut
修复一个bug: 在直接使用该快速撮合接口的时候, 期货卖出会扣减保证金, 买回来的时候应该反算利润
如 3800卖空 3700买回平仓 应为100利润
@2018-12-31 保证金账户ok
@2019/1/3 一些重要的意思
frozen = self.market_preset.get_frozen(code) # 保证金率
unit = self.market_preset.get_unit(code) # 合约乘数
raw_trade_money = trade_price*trade_amount*market_towards # 总市值
value = raw_trade_money * unit # 合约总价值
trade_money = value * frozen # 交易保证金
"""
self.datetime = trade_time
if realorder_id in self.finishedOrderid:
pass
else:
self.finishedOrderid.append(realorder_id)
market_towards = 1 if trade_towards > 0 else -1
# value 合约价值 unit 合约乘数
if self.allow_margin:
frozen = self.market_preset.get_frozen(code) # 保证金率
unit = self.market_preset.get_unit(code) # 合约乘数
raw_trade_money = trade_price * trade_amount * market_towards # 总市值
value = raw_trade_money * unit # 合约总价值
trade_money = value * frozen # 交易保证金
else:
trade_money = trade_price * trade_amount * market_towards
raw_trade_money = trade_money
value = trade_money
unit = 1
frozen = 1
# 计算费用
# trade_price
if self.market_type == MARKET_TYPE.FUTURE_CN:
# 期货不收税
# 双边手续费 也没有最小手续费限制
commission_fee_preset = self.market_preset.get_code(code)
if trade_towards in [ORDER_DIRECTION.BUY_OPEN,
ORDER_DIRECTION.BUY_CLOSE,
ORDER_DIRECTION.SELL_CLOSE,
ORDER_DIRECTION.SELL_OPEN]:
commission_fee = commission_fee_preset['commission_coeff_pervol'] * trade_amount + \
commission_fee_preset['commission_coeff_peramount'] * \
abs(value)
elif trade_towards in [ORDER_DIRECTION.BUY_CLOSETODAY,
ORDER_DIRECTION.SELL_CLOSETODAY]:
commission_fee = commission_fee_preset['commission_coeff_today_pervol'] * trade_amount + \
commission_fee_preset['commission_coeff_today_peramount'] * \
abs(value)
tax_fee = 0 # 买入不收印花税
elif self.market_type == MARKET_TYPE.STOCK_CN:
commission_fee = self.commission_coeff * \
abs(trade_money)
commission_fee = 5 if commission_fee < 5 else commission_fee
if int(trade_towards) > 0:
tax_fee = 0 # 买入不收印花税
else:
tax_fee = self.tax_coeff * abs(trade_money)
# 结算交易
if self.cash[-1] > trade_money + commission_fee + tax_fee:
self.time_index_max.append(trade_time)
# TODO: 目前还不支持期货的锁仓
if self.allow_sellopen:
if trade_towards in [ORDER_DIRECTION.BUY_OPEN,
ORDER_DIRECTION.SELL_OPEN]:
# 开仓单占用现金 计算avg
# 初始化
if code in self.frozen.keys():
if trade_towards in self.frozen[code].keys():
pass
else:
self.frozen[code][str(trade_towards)] = {
'money': 0,
'amount': 0,
'avg_price': 0
}
else:
self.frozen[code] = {
str(ORDER_DIRECTION.BUY_OPEN): {
'money': 0,
'amount': 0,
'avg_price': 0
},
str(ORDER_DIRECTION.SELL_OPEN): {
'money': 0,
'amount': 0,
'avg_price': 0
}
}
"""[summary]
# frozen的计算
# money 冻结的资金
# amount 冻结的数量
2018-12-31
"""
self.frozen[code][str(trade_towards)]['money'] = (
(
self.frozen[code][str(trade_towards)]['money'] *
self.frozen[code][str(trade_towards)]['amount']
) + abs(trade_money)
) / (
self.frozen[code][str(trade_towards)]['amount'] +
trade_amount
)
self.frozen[code][str(trade_towards)]['avg_price'] = (
(
self.frozen[code][str(trade_towards)]['avg_price'] *
self.frozen[code][str(trade_towards)]['amount']
) + abs(raw_trade_money)
) / (
self.frozen[code][str(trade_towards)]['amount'] +
trade_amount
)
self.frozen[code][str(trade_towards)]['amount'] += trade_amount
self.cash.append(
self.cash[-1] - abs(trade_money) - commission_fee -
tax_fee
)
elif trade_towards in [ORDER_DIRECTION.BUY_CLOSE, ORDER_DIRECTION.BUY_CLOSETODAY,
ORDER_DIRECTION.SELL_CLOSE, ORDER_DIRECTION.SELL_CLOSETODAY]:
# 平仓单释放现金
# if trade_towards == ORDER_DIRECTION.BUY_CLOSE:
# 卖空开仓 平仓买入
# self.cash
if trade_towards in [ORDER_DIRECTION.BUY_CLOSE, ORDER_DIRECTION.BUY_CLOSETODAY]: # 买入平仓 之前是空开
# self.frozen[code][ORDER_DIRECTION.SELL_OPEN]['money'] -= trade_money
self.frozen[code][str(ORDER_DIRECTION.SELL_OPEN)
]['amount'] -= trade_amount
frozen_part = self.frozen[code][
str(ORDER_DIRECTION.SELL_OPEN)]['money'] * trade_amount
# 账户的现金+ 冻结的的释放 + 买卖价差* 杠杆
self.cash.append(
self.cash[-1] + frozen_part +
(frozen_part - trade_money) / frozen -
commission_fee - tax_fee
)
if self.frozen[code][str(ORDER_DIRECTION.SELL_OPEN)
]['amount'] == 0:
self.frozen[code][str(ORDER_DIRECTION.SELL_OPEN)
]['money'] = 0
self.frozen[code][str(ORDER_DIRECTION.SELL_OPEN)
]['avg_price'] = 0
elif trade_towards in [ORDER_DIRECTION.SELL_CLOSE, ORDER_DIRECTION.SELL_CLOSETODAY]: # 卖出平仓 之前是多开
# self.frozen[code][ORDER_DIRECTION.BUY_OPEN]['money'] -= trade_money
self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['amount'] -= trade_amount
frozen_part = self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['money'] * trade_amount
self.cash.append(
self.cash[-1] + frozen_part +
(abs(trade_money) - frozen_part) / frozen -
commission_fee - tax_fee
)
if self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['amount'] == 0:
self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['money'] = 0
self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['avg_price'] = 0
else: # 不允许卖空开仓的==> 股票
self.cash.append(
self.cash[-1] - trade_money - tax_fee - commission_fee
)
if self.allow_t0 or trade_towards == ORDER_DIRECTION.SELL:
self.sell_available[code] = self.sell_available.get(
code,
0
) + trade_amount * market_towards
self.buy_available = self.sell_available
self.cash_available = self.cash[-1]
frozen_money = abs(trade_money) if trade_towards in [
ORDER_DIRECTION.BUY_OPEN,
ORDER_DIRECTION.SELL_OPEN
] else 0
self.history.append(
[
str(trade_time),
code,
trade_price,
market_towards * trade_amount,
self.cash[-1],
order_id,
realorder_id,
trade_id,
self.account_cookie,
commission_fee,
tax_fee,
message,
frozen_money,
trade_towards
]
)
else:
print('ALERT MONEY NOT ENOUGH!!!')
print(self.cash[-1])
self.cash_available = self.cash[-1] | 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_towards {[type]} -- [description]
trade_time {[type]} -- [description]
Keyword Arguments:
message {[type]} -- [description] (default: {None})
2018/11/7 @yutiansut
修复一个bug: 在直接使用该快速撮合接口的时候, 期货卖出会扣减保证金, 买回来的时候应该反算利润
如 3800卖空 3700买回平仓 应为100利润
@2018-12-31 保证金账户ok
@2019/1/3 一些重要的意思
frozen = self.market_preset.get_frozen(code) # 保证金率
unit = self.market_preset.get_unit(code) # 合约乘数
raw_trade_money = trade_price*trade_amount*market_towards # 总市值
value = raw_trade_money * unit # 合约总价值
trade_money = value * frozen # 交易保证金
"""
self.datetime = trade_time
if realorder_id in self.finishedOrderid:
pass
else:
self.finishedOrderid.append(realorder_id)
market_towards = 1 if trade_towards > 0 else -1
# value 合约价值 unit 合约乘数
if self.allow_margin:
frozen = self.market_preset.get_frozen(code) # 保证金率
unit = self.market_preset.get_unit(code) # 合约乘数
raw_trade_money = trade_price * trade_amount * market_towards # 总市值
value = raw_trade_money * unit # 合约总价值
trade_money = value * frozen # 交易保证金
else:
trade_money = trade_price * trade_amount * market_towards
raw_trade_money = trade_money
value = trade_money
unit = 1
frozen = 1
# 计算费用
# trade_price
if self.market_type == MARKET_TYPE.FUTURE_CN:
# 期货不收税
# 双边手续费 也没有最小手续费限制
commission_fee_preset = self.market_preset.get_code(code)
if trade_towards in [ORDER_DIRECTION.BUY_OPEN,
ORDER_DIRECTION.BUY_CLOSE,
ORDER_DIRECTION.SELL_CLOSE,
ORDER_DIRECTION.SELL_OPEN]:
commission_fee = commission_fee_preset['commission_coeff_pervol'] * trade_amount + \
commission_fee_preset['commission_coeff_peramount'] * \
abs(value)
elif trade_towards in [ORDER_DIRECTION.BUY_CLOSETODAY,
ORDER_DIRECTION.SELL_CLOSETODAY]:
commission_fee = commission_fee_preset['commission_coeff_today_pervol'] * trade_amount + \
commission_fee_preset['commission_coeff_today_peramount'] * \
abs(value)
tax_fee = 0 # 买入不收印花税
elif self.market_type == MARKET_TYPE.STOCK_CN:
commission_fee = self.commission_coeff * \
abs(trade_money)
commission_fee = 5 if commission_fee < 5 else commission_fee
if int(trade_towards) > 0:
tax_fee = 0 # 买入不收印花税
else:
tax_fee = self.tax_coeff * abs(trade_money)
# 结算交易
if self.cash[-1] > trade_money + commission_fee + tax_fee:
self.time_index_max.append(trade_time)
# TODO: 目前还不支持期货的锁仓
if self.allow_sellopen:
if trade_towards in [ORDER_DIRECTION.BUY_OPEN,
ORDER_DIRECTION.SELL_OPEN]:
# 开仓单占用现金 计算avg
# 初始化
if code in self.frozen.keys():
if trade_towards in self.frozen[code].keys():
pass
else:
self.frozen[code][str(trade_towards)] = {
'money': 0,
'amount': 0,
'avg_price': 0
}
else:
self.frozen[code] = {
str(ORDER_DIRECTION.BUY_OPEN): {
'money': 0,
'amount': 0,
'avg_price': 0
},
str(ORDER_DIRECTION.SELL_OPEN): {
'money': 0,
'amount': 0,
'avg_price': 0
}
}
"""[summary]
# frozen的计算
# money 冻结的资金
# amount 冻结的数量
2018-12-31
"""
self.frozen[code][str(trade_towards)]['money'] = (
(
self.frozen[code][str(trade_towards)]['money'] *
self.frozen[code][str(trade_towards)]['amount']
) + abs(trade_money)
) / (
self.frozen[code][str(trade_towards)]['amount'] +
trade_amount
)
self.frozen[code][str(trade_towards)]['avg_price'] = (
(
self.frozen[code][str(trade_towards)]['avg_price'] *
self.frozen[code][str(trade_towards)]['amount']
) + abs(raw_trade_money)
) / (
self.frozen[code][str(trade_towards)]['amount'] +
trade_amount
)
self.frozen[code][str(trade_towards)]['amount'] += trade_amount
self.cash.append(
self.cash[-1] - abs(trade_money) - commission_fee -
tax_fee
)
elif trade_towards in [ORDER_DIRECTION.BUY_CLOSE, ORDER_DIRECTION.BUY_CLOSETODAY,
ORDER_DIRECTION.SELL_CLOSE, ORDER_DIRECTION.SELL_CLOSETODAY]:
# 平仓单释放现金
# if trade_towards == ORDER_DIRECTION.BUY_CLOSE:
# 卖空开仓 平仓买入
# self.cash
if trade_towards in [ORDER_DIRECTION.BUY_CLOSE, ORDER_DIRECTION.BUY_CLOSETODAY]: # 买入平仓 之前是空开
# self.frozen[code][ORDER_DIRECTION.SELL_OPEN]['money'] -= trade_money
self.frozen[code][str(ORDER_DIRECTION.SELL_OPEN)
]['amount'] -= trade_amount
frozen_part = self.frozen[code][
str(ORDER_DIRECTION.SELL_OPEN)]['money'] * trade_amount
# 账户的现金+ 冻结的的释放 + 买卖价差* 杠杆
self.cash.append(
self.cash[-1] + frozen_part +
(frozen_part - trade_money) / frozen -
commission_fee - tax_fee
)
if self.frozen[code][str(ORDER_DIRECTION.SELL_OPEN)
]['amount'] == 0:
self.frozen[code][str(ORDER_DIRECTION.SELL_OPEN)
]['money'] = 0
self.frozen[code][str(ORDER_DIRECTION.SELL_OPEN)
]['avg_price'] = 0
elif trade_towards in [ORDER_DIRECTION.SELL_CLOSE, ORDER_DIRECTION.SELL_CLOSETODAY]: # 卖出平仓 之前是多开
# self.frozen[code][ORDER_DIRECTION.BUY_OPEN]['money'] -= trade_money
self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['amount'] -= trade_amount
frozen_part = self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['money'] * trade_amount
self.cash.append(
self.cash[-1] + frozen_part +
(abs(trade_money) - frozen_part) / frozen -
commission_fee - tax_fee
)
if self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['amount'] == 0:
self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['money'] = 0
self.frozen[code][str(ORDER_DIRECTION.BUY_OPEN)
]['avg_price'] = 0
else: # 不允许卖空开仓的==> 股票
self.cash.append(
self.cash[-1] - trade_money - tax_fee - commission_fee
)
if self.allow_t0 or trade_towards == ORDER_DIRECTION.SELL:
self.sell_available[code] = self.sell_available.get(
code,
0
) + trade_amount * market_towards
self.buy_available = self.sell_available
self.cash_available = self.cash[-1]
frozen_money = abs(trade_money) if trade_towards in [
ORDER_DIRECTION.BUY_OPEN,
ORDER_DIRECTION.SELL_OPEN
] else 0
self.history.append(
[
str(trade_time),
code,
trade_price,
market_towards * trade_amount,
self.cash[-1],
order_id,
realorder_id,
trade_id,
self.account_cookie,
commission_fee,
tax_fee,
message,
frozen_money,
trade_towards
]
)
else:
print('ALERT MONEY NOT ENOUGH!!!')
print(self.cash[-1])
self.cash_available = self.cash[-1] | [
"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_towards {int} -- [description]
trade_time {str} -- [description]
Returns:
[type] -- [description] | 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
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_towards {int} -- [description]
trade_time {str} -- [description]
Returns:
[type] -- [description]
"""
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!receive deal')
trade_time = str(trade_time)
code = str(code)
trade_price = float(trade_price)
trade_towards = int(trade_towards)
realorder_id = str(realorder_id)
trade_id = str(trade_id)
trade_amount = int(trade_amount)
order_id = str(order_id)
market_towards = 1 if trade_towards > 0 else -1
"""2019/01/03 直接使用快速撮合接口了
2333 这两个接口现在也没啥区别了....
太绝望了
"""
self.receive_simpledeal(
code,
trade_price,
trade_amount,
trade_towards,
trade_time,
message=message,
order_id=order_id,
trade_id=trade_id,
realorder_id=realorder_id
) | 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
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_towards {int} -- [description]
trade_time {str} -- [description]
Returns:
[type] -- [description]
"""
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!receive deal')
trade_time = str(trade_time)
code = str(code)
trade_price = float(trade_price)
trade_towards = int(trade_towards)
realorder_id = str(realorder_id)
trade_id = str(trade_id)
trade_amount = int(trade_amount)
order_id = str(order_id)
market_towards = 1 if trade_towards > 0 else -1
"""2019/01/03 直接使用快速撮合接口了
2333 这两个接口现在也没啥区别了....
太绝望了
"""
self.receive_simpledeal(
code,
trade_price,
trade_amount,
trade_towards,
trade_time,
message=message,
order_id=order_id,
trade_id=trade_id,
realorder_id=realorder_id
) | [
"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_bymoney=Account.send_order(code='000001',
price=11,
money=0.3*Account.cash_available,
time='2018-05-09',
towards=QA.ORDER_DIRECTION.BUY,
order_model=QA.ORDER_MODEL.MARKET,
amount_model=QA.AMOUNT_MODEL.BY_MONEY
)
Order_byamount=Account.send_order(code='000001',
price=11,
amount=100,
time='2018-05-09',
towards=QA.ORDER_DIRECTION.BUY,
order_model=QA.ORDER_MODEL.MARKET,
amount_model=QA.AMOUNT_MODEL.BY_AMOUNT
)
:param code: 证券代码
:param amount: 买卖 数量多数股
:param time: Timestamp 对象 下单时间
:param towards: int , towards>0 买入 towards<0 卖出
:param price: 买入，卖出 标的证券的价格
:param money: 买卖 价格
:param order_model: 类型 QA.ORDER_MODE
:param amount_model:类型 QA.AMOUNT_MODEL
:return: QA_Order | False
@2018/12/23
send_order 是QA的标准返回, 如需对接其他接口, 只需要对于QA_Order做适配即可
@2018/12/27
在判断账户为期货账户(及 允许双向交易)
@2018/12/30 保证金账户的修改
1. 保证金账户冻结的金额
2. 保证金账户的结算
3. 保证金账户的判断 | 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 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_bymoney=Account.send_order(code='000001',
price=11,
money=0.3*Account.cash_available,
time='2018-05-09',
towards=QA.ORDER_DIRECTION.BUY,
order_model=QA.ORDER_MODEL.MARKET,
amount_model=QA.AMOUNT_MODEL.BY_MONEY
)
Order_byamount=Account.send_order(code='000001',
price=11,
amount=100,
time='2018-05-09',
towards=QA.ORDER_DIRECTION.BUY,
order_model=QA.ORDER_MODEL.MARKET,
amount_model=QA.AMOUNT_MODEL.BY_AMOUNT
)
:param code: 证券代码
:param amount: 买卖 数量多数股
:param time: Timestamp 对象 下单时间
:param towards: int , towards>0 买入 towards<0 卖出
:param price: 买入，卖出 标的证券的价格
:param money: 买卖 价格
:param order_model: 类型 QA.ORDER_MODE
:param amount_model:类型 QA.AMOUNT_MODEL
:return: QA_Order | False
@2018/12/23
send_order 是QA的标准返回, 如需对接其他接口, 只需要对于QA_Order做适配即可
@2018/12/27
在判断账户为期货账户(及 允许双向交易)
@2018/12/30 保证金账户的修改
1. 保证金账户冻结的金额
2. 保证金账户的结算
3. 保证金账户的判断
"""
wrong_reason = None
assert code is not None and time is not None and towards is not None and order_model is not None and amount_model is not None
# 🛠todo 移到Utils类中， 时间转换
# date 字符串 2011-10-11 长度10
date = str(time)[0:10] if len(str(time)) == 19 else str(time)
# time 字符串 20011-10-11 09:02:00 长度 19
time = str(time) if len(str(time)) == 19 else '{} 09:31:00'.format(
str(time)[0:10]
)
# 🛠todo 移到Utils类中， amount_to_money 成交量转金额
# BY_MONEY :: amount --钱 如10000元 因此 by_money里面 需要指定价格,来计算实际的股票数
# by_amount :: amount --股数 如10000股
if self.allow_margin:
amount = amount if amount_model is AMOUNT_MODEL.BY_AMOUNT else int(
money / (
self.market_preset.get_unit(code) *
self.market_preset.get_frozen(code) * price *
(1 + self.commission_coeff)
) / 100
) * 100
else:
amount = amount if amount_model is AMOUNT_MODEL.BY_AMOUNT else int(
money / (price * (1 + self.commission_coeff)) / 100
) * 100
# 🛠todo 移到Utils类中， money_to_amount 金额转成交量
if self.allow_margin:
money = amount * price * self.market_preset.get_unit(code)*self.market_preset.get_frozen(code) * \
(1+self.commission_coeff) if amount_model is AMOUNT_MODEL.BY_AMOUNT else money
else:
money = amount * price * \
(1+self.commission_coeff) if amount_model is AMOUNT_MODEL.BY_AMOUNT else money
# flag 判断买卖 数量和价格以及买卖方向是否正确
flag = False
assert (int(towards) != 0)
if int(towards) in [1, 2, 3]:
# 是买入的情况(包括买入.买开.买平)
if self.cash_available >= money:
if self.market_type == MARKET_TYPE.STOCK_CN: # 如果是股票 买入的时候有100股的最小限制
amount = int(amount / 100) * 100
self.cash_available -= money
flag = True
if self.running_environment == RUNNING_ENVIRONMENT.TZERO:
if abs(self.buy_available.get(code, 0)) >= amount:
flag = True
self.cash_available -= money
self.buy_available[code] -= amount
else:
flag = False
wrong_reason = 'T0交易买入超出限额'
if self.market_type == MARKET_TYPE.FUTURE_CN:
# 如果有负持仓-- 允许卖空的时候
if towards == 3: # 多平
_hold = self.sell_available.get(code, 0)
# 假设有负持仓:
# amount为下单数量 如 账户原先-3手 现在平1手
#left_amount = amount+_hold if _hold < 0 else amount
_money = abs(
float(amount * price * (1 + self.commission_coeff))
)
print(_hold)
if self.cash_available >= _money:
if _hold < 0:
self.cash_available -= _money
flag = True
else:
wrong_reason = '空单仓位不足'
else:
wrong_reason = '平多剩余资金不够'
if towards == 2:
self.cash_available -= money
flag = True
else:
wrong_reason = 'QAACCOUNT: 可用资金不足 cash_available {} code {} time {} amount {} towards {}'.format(
self.cash_available,
code,
time,
amount,
towards
)
elif int(towards) in [-1, -2, -3]:
# 是卖出的情况(包括卖出，卖出开仓allow_sellopen如果允许. 卖出平仓)
# print(self.sell_available[code])
_hold = self.sell_available.get(code, 0) # _hold 是你的持仓
# 如果你的hold> amount>0
# 持仓数量>卖出数量
if _hold >= amount:
self.sell_available[code] -= amount
# towards = ORDER_DIRECTION.SELL
flag = True
# 如果持仓数量<卖出数量
else:
# 如果是允许卖空开仓 实际计算时 先减去持仓(正持仓) 再计算 负持仓 就按原先的占用金额计算
if self.allow_sellopen and towards == -2:
if self.cash_available >= money: # 卖空的市值小于现金（有担保的卖空）， 不允许裸卖空
# self.cash_available -= money
flag = True
else:
print('sellavailable', _hold)
print('amount', amount)
print('aqureMoney', money)
print('cash', self.cash_available)
wrong_reason = "卖空资金不足/不允许裸卖空"
else:
wrong_reason = "卖出仓位不足"
if flag and (amount > 0):
_order = QA_Order(
user_cookie=self.user_cookie,
strategy=self.strategy_name,
frequence=self.frequence,
account_cookie=self.account_cookie,
code=code,
market_type=self.market_type,
date=date,
datetime=time,
sending_time=time,
callback=self.receive_deal,
amount=amount,
price=price,
order_model=order_model,
towards=towards,
money=money,
broker=self.broker,
amount_model=amount_model,
commission_coeff=self.commission_coeff,
tax_coeff=self.tax_coeff,
*args,
**kwargs
) # init
# 历史委托order状态存储， 保存到 QA_Order 对象中的队列中
self.datetime = time
self.orders.insert_order(_order)
return _order
else:
print(
'ERROR : CODE {} TIME {} AMOUNT {} TOWARDS {}'.format(
code,
time,
amount,
towards
)
)
print(wrong_reason)
return False | 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 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_bymoney=Account.send_order(code='000001',
price=11,
money=0.3*Account.cash_available,
time='2018-05-09',
towards=QA.ORDER_DIRECTION.BUY,
order_model=QA.ORDER_MODEL.MARKET,
amount_model=QA.AMOUNT_MODEL.BY_MONEY
)
Order_byamount=Account.send_order(code='000001',
price=11,
amount=100,
time='2018-05-09',
towards=QA.ORDER_DIRECTION.BUY,
order_model=QA.ORDER_MODEL.MARKET,
amount_model=QA.AMOUNT_MODEL.BY_AMOUNT
)
:param code: 证券代码
:param amount: 买卖 数量多数股
:param time: Timestamp 对象 下单时间
:param towards: int , towards>0 买入 towards<0 卖出
:param price: 买入，卖出 标的证券的价格
:param money: 买卖 价格
:param order_model: 类型 QA.ORDER_MODE
:param amount_model:类型 QA.AMOUNT_MODEL
:return: QA_Order | False
@2018/12/23
send_order 是QA的标准返回, 如需对接其他接口, 只需要对于QA_Order做适配即可
@2018/12/27
在判断账户为期货账户(及 允许双向交易)
@2018/12/30 保证金账户的修改
1. 保证金账户冻结的金额
2. 保证金账户的结算
3. 保证金账户的判断
"""
wrong_reason = None
assert code is not None and time is not None and towards is not None and order_model is not None and amount_model is not None
# 🛠todo 移到Utils类中， 时间转换
# date 字符串 2011-10-11 长度10
date = str(time)[0:10] if len(str(time)) == 19 else str(time)
# time 字符串 20011-10-11 09:02:00 长度 19
time = str(time) if len(str(time)) == 19 else '{} 09:31:00'.format(
str(time)[0:10]
)
# 🛠todo 移到Utils类中， amount_to_money 成交量转金额
# BY_MONEY :: amount --钱 如10000元 因此 by_money里面 需要指定价格,来计算实际的股票数
# by_amount :: amount --股数 如10000股
if self.allow_margin:
amount = amount if amount_model is AMOUNT_MODEL.BY_AMOUNT else int(
money / (
self.market_preset.get_unit(code) *
self.market_preset.get_frozen(code) * price *
(1 + self.commission_coeff)
) / 100
) * 100
else:
amount = amount if amount_model is AMOUNT_MODEL.BY_AMOUNT else int(
money / (price * (1 + self.commission_coeff)) / 100
) * 100
# 🛠todo 移到Utils类中， money_to_amount 金额转成交量
if self.allow_margin:
money = amount * price * self.market_preset.get_unit(code)*self.market_preset.get_frozen(code) * \
(1+self.commission_coeff) if amount_model is AMOUNT_MODEL.BY_AMOUNT else money
else:
money = amount * price * \
(1+self.commission_coeff) if amount_model is AMOUNT_MODEL.BY_AMOUNT else money
# flag 判断买卖 数量和价格以及买卖方向是否正确
flag = False
assert (int(towards) != 0)
if int(towards) in [1, 2, 3]:
# 是买入的情况(包括买入.买开.买平)
if self.cash_available >= money:
if self.market_type == MARKET_TYPE.STOCK_CN: # 如果是股票 买入的时候有100股的最小限制
amount = int(amount / 100) * 100
self.cash_available -= money
flag = True
if self.running_environment == RUNNING_ENVIRONMENT.TZERO:
if abs(self.buy_available.get(code, 0)) >= amount:
flag = True
self.cash_available -= money
self.buy_available[code] -= amount
else:
flag = False
wrong_reason = 'T0交易买入超出限额'
if self.market_type == MARKET_TYPE.FUTURE_CN:
# 如果有负持仓-- 允许卖空的时候
if towards == 3: # 多平
_hold = self.sell_available.get(code, 0)
# 假设有负持仓:
# amount为下单数量 如 账户原先-3手 现在平1手
#left_amount = amount+_hold if _hold < 0 else amount
_money = abs(
float(amount * price * (1 + self.commission_coeff))
)
print(_hold)
if self.cash_available >= _money:
if _hold < 0:
self.cash_available -= _money
flag = True
else:
wrong_reason = '空单仓位不足'
else:
wrong_reason = '平多剩余资金不够'
if towards == 2:
self.cash_available -= money
flag = True
else:
wrong_reason = 'QAACCOUNT: 可用资金不足 cash_available {} code {} time {} amount {} towards {}'.format(
self.cash_available,
code,
time,
amount,
towards
)
elif int(towards) in [-1, -2, -3]:
# 是卖出的情况(包括卖出，卖出开仓allow_sellopen如果允许. 卖出平仓)
# print(self.sell_available[code])
_hold = self.sell_available.get(code, 0) # _hold 是你的持仓
# 如果你的hold> amount>0
# 持仓数量>卖出数量
if _hold >= amount:
self.sell_available[code] -= amount
# towards = ORDER_DIRECTION.SELL
flag = True
# 如果持仓数量<卖出数量
else:
# 如果是允许卖空开仓 实际计算时 先减去持仓(正持仓) 再计算 负持仓 就按原先的占用金额计算
if self.allow_sellopen and towards == -2:
if self.cash_available >= money: # 卖空的市值小于现金（有担保的卖空）， 不允许裸卖空
# self.cash_available -= money
flag = True
else:
print('sellavailable', _hold)
print('amount', amount)
print('aqureMoney', money)
print('cash', self.cash_available)
wrong_reason = "卖空资金不足/不允许裸卖空"
else:
wrong_reason = "卖出仓位不足"
if flag and (amount > 0):
_order = QA_Order(
user_cookie=self.user_cookie,
strategy=self.strategy_name,
frequence=self.frequence,
account_cookie=self.account_cookie,
code=code,
market_type=self.market_type,
date=date,
datetime=time,
sending_time=time,
callback=self.receive_deal,
amount=amount,
price=price,
order_model=order_model,
towards=towards,
money=money,
broker=self.broker,
amount_model=amount_model,
commission_coeff=self.commission_coeff,
tax_coeff=self.tax_coeff,
*args,
**kwargs
) # init
# 历史委托order状态存储， 保存到 QA_Order 对象中的队列中
self.datetime = time
self.orders.insert_order(_order)
return _order
else:
print(
'ERROR : CODE {} TIME {} AMOUNT {} TOWARDS {}'.format(
code,
time,
amount,
towards
)
)
print(wrong_reason)
return False | [
"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 == RUNNING_ENVIRONMENT.TZERO:
for code, amount in self.hold_available.iteritems():
order = False
if amount < 0:
# 先卖出的单子 买平
order = self.send_order(
code=code,
price=0,
amount=abs(amount),
time=time,
towards=ORDER_DIRECTION.BUY,
order_model=ORDER_MODEL.CLOSE,
amount_model=AMOUNT_MODEL.BY_AMOUNT,
)
elif amount > 0:
# 先买入的单子, 卖平
order = self.send_order(
code=code,
price=0,
amount=abs(amount),
time=time,
towards=ORDER_DIRECTION.SELL,
order_model=ORDER_MODEL.CLOSE,
amount_model=AMOUNT_MODEL.BY_AMOUNT
)
if order:
order_list.append(order)
return order_list
else:
raise RuntimeError(
'QAACCOUNT with {} environments cannot use this methods'.format(
self.running_environment
)
) | 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 == RUNNING_ENVIRONMENT.TZERO:
for code, amount in self.hold_available.iteritems():
order = False
if amount < 0:
# 先卖出的单子 买平
order = self.send_order(
code=code,
price=0,
amount=abs(amount),
time=time,
towards=ORDER_DIRECTION.BUY,
order_model=ORDER_MODEL.CLOSE,
amount_model=AMOUNT_MODEL.BY_AMOUNT,
)
elif amount > 0:
# 先买入的单子, 卖平
order = self.send_order(
code=code,
price=0,
amount=abs(amount),
time=time,
towards=ORDER_DIRECTION.SELL,
order_model=ORDER_MODEL.CLOSE,
amount_model=AMOUNT_MODEL.BY_AMOUNT
)
if order:
order_list.append(order)
return order_list
else:
raise RuntimeError(
'QAACCOUNT with {} environments cannot use this methods'.format(
self.running_environment
)
) | [
"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
"""
#print('FROM QUANTAXIS QA_ACCOUNT: account settle')
if self.running_environment == RUNNING_ENVIRONMENT.TZERO and self.hold_available.sum(
) != 0:
raise RuntimeError(
'QAACCOUNT: 该T0账户未当日仓位,请平仓 {}'.format(
self.hold_available.to_dict()
)
)
if self.market_type == MARKET_TYPE.FUTURE_CN:
# 增加逐日盯市制度
self.static_balance['frozen'].append(
sum(
[
rx['money'] * rx['amount']
for var in self.frozen.values()
for rx in var.values()
]
)
)
self.static_balance['cash'].append(self.cash[-1])
self.static_balance['hold'].append(self.hold.to_dict())
self.static_balance['date'].append(self.date)
"""静态权益的结算
只关心开仓价/ 不做盯市制度
动态权益的结算需要关心
"""
self.static_balance['static_assets'].append(
self.static_balance['cash'][-1] +
self.static_balance['frozen'][-1]
)
self.sell_available = self.hold
self.buy_available = self.hold
self.cash_available = self.cash[-1]
self.datetime = '{} 09:30:00'.format(
QA_util_get_next_day(self.date)
) if self.date is not None else None | 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
"""
#print('FROM QUANTAXIS QA_ACCOUNT: account settle')
if self.running_environment == RUNNING_ENVIRONMENT.TZERO and self.hold_available.sum(
) != 0:
raise RuntimeError(
'QAACCOUNT: 该T0账户未当日仓位,请平仓 {}'.format(
self.hold_available.to_dict()
)
)
if self.market_type == MARKET_TYPE.FUTURE_CN:
# 增加逐日盯市制度
self.static_balance['frozen'].append(
sum(
[
rx['money'] * rx['amount']
for var in self.frozen.values()
for rx in var.values()
]
)
)
self.static_balance['cash'].append(self.cash[-1])
self.static_balance['hold'].append(self.hold.to_dict())
self.static_balance['date'].append(self.date)
"""静态权益的结算
只关心开仓价/ 不做盯市制度
动态权益的结算需要关心
"""
self.static_balance['static_assets'].append(
self.static_balance['cash'][-1] +
self.static_balance['frozen'][-1]
)
self.sell_available = self.hold
self.buy_available = self.hold
self.cash_available = self.cash[-1]
self.datetime = '{} 09:30:00'.format(
QA_util_get_next_day(self.date)
) if self.date is not None else None | [
"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:
for code in event.market_data.code:
if self.sell_available.get(code, 0) > 0:
print('可以卖出 {}'.format(self._currenttime))
event.send_order(
account_cookie=self.account_cookie,
amount=self.sell_available[code],
amount_model=AMOUNT_MODEL.BY_AMOUNT,
time=self.current_time,
code=code,
price=0,
order_model=ORDER_MODEL.MARKET,
towards=ORDER_DIRECTION.SELL,
market_type=self.market_type,
frequence=self.frequence,
broker_name=self.broker
)
else:
print('{} 无仓位, 买入{}'.format(self._currenttime, code))
event.send_order(
account_cookie=self.account_cookie,
amount=100,
amount_model=AMOUNT_MODEL.BY_AMOUNT,
time=self.current_time,
code=code,
price=0,
order_model=ORDER_MODEL.MARKET,
towards=ORDER_DIRECTION.BUY,
market_type=self.market_type,
frequence=self.frequence,
broker_name=self.broker
)
except Exception as e:
print(e) | 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:
for code in event.market_data.code:
if self.sell_available.get(code, 0) > 0:
print('可以卖出 {}'.format(self._currenttime))
event.send_order(
account_cookie=self.account_cookie,
amount=self.sell_available[code],
amount_model=AMOUNT_MODEL.BY_AMOUNT,
time=self.current_time,
code=code,
price=0,
order_model=ORDER_MODEL.MARKET,
towards=ORDER_DIRECTION.SELL,
market_type=self.market_type,
frequence=self.frequence,
broker_name=self.broker
)
else:
print('{} 无仓位, 买入{}'.format(self._currenttime, code))
event.send_order(
account_cookie=self.account_cookie,
amount=100,
amount_model=AMOUNT_MODEL.BY_AMOUNT,
time=self.current_time,
code=code,
price=0,
order_model=ORDER_MODEL.MARKET,
towards=ORDER_DIRECTION.BUY,
market_type=self.market_type,
frequence=self.frequence,
broker_name=self.broker
)
except Exception as e:
print(e) | [
"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.broker = message.get('broker', None)
self.market_type = message.get('market_type', None)
self.strategy_name = message.get('strategy_name', None)
self._currenttime = message.get('current_time', None)
self.allow_sellopen = message.get('allow_sellopen', False)
self.allow_margin = message.get('allow_margin', False)
self.allow_t0 = message.get('allow_t0', False)
self.margin_level = message.get('margin_level', False)
self.frequence = message.get('frequence', FREQUENCE.FIFTEEN_MIN) #默认15min
self.init_cash = message.get(
'init_cash',
message.get('init_assets',
1000000)
) # 兼容修改
self.init_hold = pd.Series(message.get('init_hold', {}), name='amount')
self.init_hold.index.name = 'code'
self.commission_coeff = message.get('commission_coeff', 0.00015)
self.tax_coeff = message.get('tax_coeff', 0.0015)
self.history = message['history']
self.cash = message['cash']
self.time_index_max = message['trade_index']
self.running_time = message.get('running_time', None)
self.quantaxis_version = message.get('quantaxis_version', None)
self.running_environment = message.get(
'running_environment',
RUNNING_ENVIRONMENT.BACKETEST
)
self.frozen = message.get('frozen', {})
self.finishedOrderid = message.get('finished_id', [])
self.settle()
return 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.broker = message.get('broker', None)
self.market_type = message.get('market_type', None)
self.strategy_name = message.get('strategy_name', None)
self._currenttime = message.get('current_time', None)
self.allow_sellopen = message.get('allow_sellopen', False)
self.allow_margin = message.get('allow_margin', False)
self.allow_t0 = message.get('allow_t0', False)
self.margin_level = message.get('margin_level', False)
self.frequence = message.get('frequence', FREQUENCE.FIFTEEN_MIN) #默认15min
self.init_cash = message.get(
'init_cash',
message.get('init_assets',
1000000)
) # 兼容修改
self.init_hold = pd.Series(message.get('init_hold', {}), name='amount')
self.init_hold.index.name = 'code'
self.commission_coeff = message.get('commission_coeff', 0.00015)
self.tax_coeff = message.get('tax_coeff', 0.0015)
self.history = message['history']
self.cash = message['cash']
self.time_index_max = message['trade_index']
self.running_time = message.get('running_time', None)
self.quantaxis_version = message.get('quantaxis_version', None)
self.running_environment = message.get(
'running_environment',
RUNNING_ENVIRONMENT.BACKETEST
)
self.frozen = message.get('frozen', {})
self.finishedOrderid = message.get('finished_id', [])
self.settle()
return 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"), # 9480176.15 (可用资金)
"float_profit": float("nan"), # 8910.0 (浮动盈亏)
"position_profit": float("nan"), # 1120.0(持仓盈亏)
"close_profit": float("nan"), # -11120.0 (本交易日内平仓盈亏)
"frozen_margin": float("nan"), # 0.0(冻结保证金)
"margin": float("nan"), # 11232.23 (保证金占用)
"frozen_commission": float("nan"), # 0.0 (冻结手续费)
"commission": float("nan"), # 123.0 (本交易日内交纳的手续费)
"frozen_premium": float("nan"), # 0.0 (冻结权利金)
"premium": float("nan"), # 0.0 (本交易日内交纳的权利金)
"deposit": float("nan"), # 1234.0 (本交易日内的入金金额)
"withdraw": float("nan"), # 890.0 (本交易日内的出金金额)
"risk_ratio": float("nan"), # 0.048482375 (风险度) | 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)
"available": float("nan"), # 9480176.15 (可用资金)
"float_profit": float("nan"), # 8910.0 (浮动盈亏)
"position_profit": float("nan"), # 1120.0(持仓盈亏)
"close_profit": float("nan"), # -11120.0 (本交易日内平仓盈亏)
"frozen_margin": float("nan"), # 0.0(冻结保证金)
"margin": float("nan"), # 11232.23 (保证金占用)
"frozen_commission": float("nan"), # 0.0 (冻结手续费)
"commission": float("nan"), # 123.0 (本交易日内交纳的手续费)
"frozen_premium": float("nan"), # 0.0 (冻结权利金)
"premium": float("nan"), # 0.0 (本交易日内交纳的权利金)
"deposit": float("nan"), # 1234.0 (本交易日内的入金金额)
"withdraw": float("nan"), # 890.0 (本交易日内的出金金额)
"risk_ratio": float("nan"), # 0.048482375 (风险度)
"""
self.allow_margin = True
self.allow_sellopen = True
self.allow_t0 = True
self.account_cookie = message['accounts']['user_id']
# 可用资金
self.cash_available = message['accounts']['available']
self.balance = message['accounts']['balance']
# 都是在结算的时候计算的
# 昨日权益/静态权益 ==> 这两个是一样的
self.static_balance = message['accounts']['static_balance']
self.pre_balance = message['accounts']['pre_balance']
# 平仓盈亏
self.close_profit = message['accounts']['close_profit']
# 持仓盈亏
self.position_profit = message['accounts']['position_profit']
# 动态权益
self.float_profit = message['accounts']['float_profit']
# 占用保证金
self.margin = message['accounts']['margin']
self.commission = message['accounts']['commission'] | 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)
"available": float("nan"), # 9480176.15 (可用资金)
"float_profit": float("nan"), # 8910.0 (浮动盈亏)
"position_profit": float("nan"), # 1120.0(持仓盈亏)
"close_profit": float("nan"), # -11120.0 (本交易日内平仓盈亏)
"frozen_margin": float("nan"), # 0.0(冻结保证金)
"margin": float("nan"), # 11232.23 (保证金占用)
"frozen_commission": float("nan"), # 0.0 (冻结手续费)
"commission": float("nan"), # 123.0 (本交易日内交纳的手续费)
"frozen_premium": float("nan"), # 0.0 (冻结权利金)
"premium": float("nan"), # 0.0 (本交易日内交纳的权利金)
"deposit": float("nan"), # 1234.0 (本交易日内的入金金额)
"withdraw": float("nan"), # 890.0 (本交易日内的出金金额)
"risk_ratio": float("nan"), # 0.048482375 (风险度)
"""
self.allow_margin = True
self.allow_sellopen = True
self.allow_t0 = True
self.account_cookie = message['accounts']['user_id']
# 可用资金
self.cash_available = message['accounts']['available']
self.balance = message['accounts']['balance']
# 都是在结算的时候计算的
# 昨日权益/静态权益 ==> 这两个是一样的
self.static_balance = message['accounts']['static_balance']
self.pre_balance = message['accounts']['pre_balance']
# 平仓盈亏
self.close_profit = message['accounts']['close_profit']
# 持仓盈亏
self.position_profit = message['accounts']['position_profit']
# 动态权益
self.float_profit = message['accounts']['float_profit']
# 占用保证金
self.margin = message['accounts']['margin']
self.commission = message['accounts']['commission'] | [
"[",
"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')
self.settle()
# elif event.event_type is ACCOUNT_EVENT.UPDATE:
# self.receive_deal(event.message)
elif event.event_type is ACCOUNT_EVENT.MAKE_ORDER:
"""generate order
if callback callback the order
if not return back the order
"""
data = self.send_order(
code=event.code,
amount=event.amount,
time=event.time,
amount_model=event.amount_model,
towards=event.towards,
price=event.price,
order_model=event.order_model
)
if event.callback:
event.callback(data)
else:
return data
elif event.event_type is ENGINE_EVENT.UPCOMING_DATA:
"""update the market_data
1. update the inside market_data struct
2. tell the on_bar methods
# 这样有点慢
"""
self._currenttime = event.market_data.datetime[0]
if self._market_data is None:
self._market_data = event.market_data
else:
self._market_data = self._market_data + event.market_data
self.on_bar(event)
if event.callback:
event.callback(event) | 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')
self.settle()
# elif event.event_type is ACCOUNT_EVENT.UPDATE:
# self.receive_deal(event.message)
elif event.event_type is ACCOUNT_EVENT.MAKE_ORDER:
"""generate order
if callback callback the order
if not return back the order
"""
data = self.send_order(
code=event.code,
amount=event.amount,
time=event.time,
amount_model=event.amount_model,
towards=event.towards,
price=event.price,
order_model=event.order_model
)
if event.callback:
event.callback(data)
else:
return data
elif event.event_type is ENGINE_EVENT.UPCOMING_DATA:
"""update the market_data
1. update the inside market_data struct
2. tell the on_bar methods
# 这样有点慢
"""
self._currenttime = event.market_data.datetime[0]
if self._market_data is None:
self._market_data = event.market_data
else:
self._market_data = self._market_data + event.market_data
self.on_bar(event)
if event.callback:
event.callback(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",
":"
] | 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)
self.history = []
self.cash = [self.init_cash]
self.cash_available = self.cash[-1] | 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)
self.history = []
self.cash = [self.init_cash]
self.cash_available = self.cash[-1] | [
"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.