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LinuxDistribution.__init__
(self, include_lsb=True, os_release_file='', distro_release_file='', include_uname=True)
The initialization method of this class gathers information from the available data sources, and stores that in private instance attributes. Subsequent access to the information items uses these private instance attributes, so that the data sources are read only once. Parameters: * ``include_lsb`` (bool): Controls whether the `lsb_release command output`_ is included as a data source. If the lsb_release command is not available in the program execution path, the data source for the lsb_release command will be empty. * ``os_release_file`` (string): The path name of the `os-release file`_ that is to be used as a data source. An empty string (the default) will cause the default path name to be used (see `os-release file`_ for details). If the specified or defaulted os-release file does not exist, the data source for the os-release file will be empty. * ``distro_release_file`` (string): The path name of the `distro release file`_ that is to be used as a data source. An empty string (the default) will cause a default search algorithm to be used (see `distro release file`_ for details). If the specified distro release file does not exist, or if no default distro release file can be found, the data source for the distro release file will be empty. * ``include_uname`` (bool): Controls whether uname command output is included as a data source. If the uname command is not available in the program execution path the data source for the uname command will be empty. Public instance attributes: * ``os_release_file`` (string): The path name of the `os-release file`_ that is actually used as a data source. The empty string if no distro release file is used as a data source. * ``distro_release_file`` (string): The path name of the `distro release file`_ that is actually used as a data source. The empty string if no distro release file is used as a data source. * ``include_lsb`` (bool): The result of the ``include_lsb`` parameter. This controls whether the lsb information will be loaded. * ``include_uname`` (bool): The result of the ``include_uname`` parameter. This controls whether the uname information will be loaded. Raises: * :py:exc:`IOError`: Some I/O issue with an os-release file or distro release file. * :py:exc:`subprocess.CalledProcessError`: The lsb_release command had some issue (other than not being available in the program execution path). * :py:exc:`UnicodeError`: A data source has unexpected characters or uses an unexpected encoding.
The initialization method of this class gathers information from the available data sources, and stores that in private instance attributes. Subsequent access to the information items uses these private instance attributes, so that the data sources are read only once.
def __init__(self, include_lsb=True, os_release_file='', distro_release_file='', include_uname=True): """ The initialization method of this class gathers information from the available data sources, and stores that in private instance attributes. Subsequent access to the information items uses these private instance attributes, so that the data sources are read only once. Parameters: * ``include_lsb`` (bool): Controls whether the `lsb_release command output`_ is included as a data source. If the lsb_release command is not available in the program execution path, the data source for the lsb_release command will be empty. * ``os_release_file`` (string): The path name of the `os-release file`_ that is to be used as a data source. An empty string (the default) will cause the default path name to be used (see `os-release file`_ for details). If the specified or defaulted os-release file does not exist, the data source for the os-release file will be empty. * ``distro_release_file`` (string): The path name of the `distro release file`_ that is to be used as a data source. An empty string (the default) will cause a default search algorithm to be used (see `distro release file`_ for details). If the specified distro release file does not exist, or if no default distro release file can be found, the data source for the distro release file will be empty. * ``include_uname`` (bool): Controls whether uname command output is included as a data source. If the uname command is not available in the program execution path the data source for the uname command will be empty. Public instance attributes: * ``os_release_file`` (string): The path name of the `os-release file`_ that is actually used as a data source. The empty string if no distro release file is used as a data source. * ``distro_release_file`` (string): The path name of the `distro release file`_ that is actually used as a data source. The empty string if no distro release file is used as a data source. * ``include_lsb`` (bool): The result of the ``include_lsb`` parameter. This controls whether the lsb information will be loaded. * ``include_uname`` (bool): The result of the ``include_uname`` parameter. This controls whether the uname information will be loaded. Raises: * :py:exc:`IOError`: Some I/O issue with an os-release file or distro release file. * :py:exc:`subprocess.CalledProcessError`: The lsb_release command had some issue (other than not being available in the program execution path). * :py:exc:`UnicodeError`: A data source has unexpected characters or uses an unexpected encoding. """ self.os_release_file = os_release_file or \ os.path.join(_UNIXCONFDIR, _OS_RELEASE_BASENAME) self.distro_release_file = distro_release_file or '' # updated later self.include_lsb = include_lsb self.include_uname = include_uname
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[ 577, 4 ]
[ 653, 42 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.__repr__
(self)
Return repr of all info
Return repr of all info
def __repr__(self): """Return repr of all info """ return \ "LinuxDistribution(" \ "os_release_file={self.os_release_file!r}, " \ "distro_release_file={self.distro_release_file!r}, " \ "include_lsb={self.include_lsb!r}, " \ "include_uname={self.include_uname!r}, " \ "_os_release_info={self._os_release_info!r}, " \ "_lsb_release_info={self._lsb_release_info!r}, " \ "_distro_release_info={self._distro_release_info!r}, " \ "_uname_info={self._uname_info!r})".format( self=self)
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[ 655, 4 ]
[ 668, 26 ]
python
en
['en', 'no', 'en']
True
LinuxDistribution.linux_distribution
(self, full_distribution_name=True)
Return information about the OS distribution that is compatible with Python's :func:`platform.linux_distribution`, supporting a subset of its parameters. For details, see :func:`distro.linux_distribution`.
Return information about the OS distribution that is compatible with Python's :func:`platform.linux_distribution`, supporting a subset of its parameters.
def linux_distribution(self, full_distribution_name=True): """ Return information about the OS distribution that is compatible with Python's :func:`platform.linux_distribution`, supporting a subset of its parameters. For details, see :func:`distro.linux_distribution`. """ return ( self.name() if full_distribution_name else self.id(), self.version(), self.codename() )
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[ 670, 4 ]
[ 682, 9 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.id
(self)
Return the distro ID of the OS distribution, as a string. For details, see :func:`distro.id`.
Return the distro ID of the OS distribution, as a string.
def id(self): """Return the distro ID of the OS distribution, as a string. For details, see :func:`distro.id`. """ def normalize(distro_id, table): distro_id = distro_id.lower().replace(' ', '_') return table.get(distro_id, distro_id) distro_id = self.os_release_attr('id') if distro_id: return normalize(distro_id, NORMALIZED_OS_ID) distro_id = self.lsb_release_attr('distributor_id') if distro_id: return normalize(distro_id, NORMALIZED_LSB_ID) distro_id = self.distro_release_attr('id') if distro_id: return normalize(distro_id, NORMALIZED_DISTRO_ID) distro_id = self.uname_attr('id') if distro_id: return normalize(distro_id, NORMALIZED_DISTRO_ID) return ''
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[ 684, 4 ]
[ 709, 17 ]
python
en
['en', 'en', 'en']
True
LinuxDistribution.name
(self, pretty=False)
Return the name of the OS distribution, as a string. For details, see :func:`distro.name`.
Return the name of the OS distribution, as a string.
def name(self, pretty=False): """ Return the name of the OS distribution, as a string. For details, see :func:`distro.name`. """ name = self.os_release_attr('name') \ or self.lsb_release_attr('distributor_id') \ or self.distro_release_attr('name') \ or self.uname_attr('name') if pretty: name = self.os_release_attr('pretty_name') \ or self.lsb_release_attr('description') if not name: name = self.distro_release_attr('name') \ or self.uname_attr('name') version = self.version(pretty=True) if version: name = name + ' ' + version return name or ''
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[ 711, 4 ]
[ 730, 25 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.version
(self, pretty=False, best=False)
Return the version of the OS distribution, as a string. For details, see :func:`distro.version`.
Return the version of the OS distribution, as a string.
def version(self, pretty=False, best=False): """ Return the version of the OS distribution, as a string. For details, see :func:`distro.version`. """ versions = [ self.os_release_attr('version_id'), self.lsb_release_attr('release'), self.distro_release_attr('version_id'), self._parse_distro_release_content( self.os_release_attr('pretty_name')).get('version_id', ''), self._parse_distro_release_content( self.lsb_release_attr('description')).get('version_id', ''), self.uname_attr('release') ] version = '' if best: # This algorithm uses the last version in priority order that has # the best precision. If the versions are not in conflict, that # does not matter; otherwise, using the last one instead of the # first one might be considered a surprise. for v in versions: if v.count(".") > version.count(".") or version == '': version = v else: for v in versions: if v != '': version = v break if pretty and version and self.codename(): version = '{0} ({1})'.format(version, self.codename()) return version
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[ 732, 4 ]
[ 764, 22 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.version_parts
(self, best=False)
Return the version of the OS distribution, as a tuple of version numbers. For details, see :func:`distro.version_parts`.
Return the version of the OS distribution, as a tuple of version numbers.
def version_parts(self, best=False): """ Return the version of the OS distribution, as a tuple of version numbers. For details, see :func:`distro.version_parts`. """ version_str = self.version(best=best) if version_str: version_regex = re.compile(r'(\d+)\.?(\d+)?\.?(\d+)?') matches = version_regex.match(version_str) if matches: major, minor, build_number = matches.groups() return major, minor or '', build_number or '' return '', '', ''
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[ 766, 4 ]
[ 780, 25 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.major_version
(self, best=False)
Return the major version number of the current distribution. For details, see :func:`distro.major_version`.
Return the major version number of the current distribution.
def major_version(self, best=False): """ Return the major version number of the current distribution. For details, see :func:`distro.major_version`. """ return self.version_parts(best)[0]
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[ 782, 4 ]
[ 788, 42 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.minor_version
(self, best=False)
Return the minor version number of the current distribution. For details, see :func:`distro.minor_version`.
Return the minor version number of the current distribution.
def minor_version(self, best=False): """ Return the minor version number of the current distribution. For details, see :func:`distro.minor_version`. """ return self.version_parts(best)[1]
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[ 790, 4 ]
[ 796, 42 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.build_number
(self, best=False)
Return the build number of the current distribution. For details, see :func:`distro.build_number`.
Return the build number of the current distribution.
def build_number(self, best=False): """ Return the build number of the current distribution. For details, see :func:`distro.build_number`. """ return self.version_parts(best)[2]
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[ 798, 4 ]
[ 804, 42 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.like
(self)
Return the IDs of distributions that are like the OS distribution. For details, see :func:`distro.like`.
Return the IDs of distributions that are like the OS distribution.
def like(self): """ Return the IDs of distributions that are like the OS distribution. For details, see :func:`distro.like`. """ return self.os_release_attr('id_like') or ''
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[ 806, 4 ]
[ 812, 52 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.codename
(self)
Return the codename of the OS distribution. For details, see :func:`distro.codename`.
Return the codename of the OS distribution.
def codename(self): """ Return the codename of the OS distribution. For details, see :func:`distro.codename`. """ try: # Handle os_release specially since distros might purposefully set # this to empty string to have no codename return self._os_release_info['codename'] except KeyError: return self.lsb_release_attr('codename') \ or self.distro_release_attr('codename') \ or ''
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[ 814, 4 ]
[ 827, 21 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.info
(self, pretty=False, best=False)
Return certain machine-readable information about the OS distribution. For details, see :func:`distro.info`.
Return certain machine-readable information about the OS distribution.
def info(self, pretty=False, best=False): """ Return certain machine-readable information about the OS distribution. For details, see :func:`distro.info`. """ return dict( id=self.id(), version=self.version(pretty, best), version_parts=dict( major=self.major_version(best), minor=self.minor_version(best), build_number=self.build_number(best) ), like=self.like(), codename=self.codename(), )
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python
en
['en', 'error', 'th']
False
LinuxDistribution.os_release_info
(self)
Return a dictionary containing key-value pairs for the information items from the os-release file data source of the OS distribution. For details, see :func:`distro.os_release_info`.
Return a dictionary containing key-value pairs for the information items from the os-release file data source of the OS distribution.
def os_release_info(self): """ Return a dictionary containing key-value pairs for the information items from the os-release file data source of the OS distribution. For details, see :func:`distro.os_release_info`. """ return self._os_release_info
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[ 855, 36 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.lsb_release_info
(self)
Return a dictionary containing key-value pairs for the information items from the lsb_release command data source of the OS distribution. For details, see :func:`distro.lsb_release_info`.
Return a dictionary containing key-value pairs for the information items from the lsb_release command data source of the OS distribution.
def lsb_release_info(self): """ Return a dictionary containing key-value pairs for the information items from the lsb_release command data source of the OS distribution. For details, see :func:`distro.lsb_release_info`. """ return self._lsb_release_info
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[ 865, 37 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.distro_release_info
(self)
Return a dictionary containing key-value pairs for the information items from the distro release file data source of the OS distribution. For details, see :func:`distro.distro_release_info`.
Return a dictionary containing key-value pairs for the information items from the distro release file data source of the OS distribution.
def distro_release_info(self): """ Return a dictionary containing key-value pairs for the information items from the distro release file data source of the OS distribution. For details, see :func:`distro.distro_release_info`. """ return self._distro_release_info
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python
en
['en', 'error', 'th']
False
LinuxDistribution.uname_info
(self)
Return a dictionary containing key-value pairs for the information items from the uname command data source of the OS distribution. For details, see :func:`distro.uname_info`.
Return a dictionary containing key-value pairs for the information items from the uname command data source of the OS distribution.
def uname_info(self): """ Return a dictionary containing key-value pairs for the information items from the uname command data source of the OS distribution. For details, see :func:`distro.uname_info`. """ return self._uname_info
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[ 877, 4 ]
[ 884, 31 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.os_release_attr
(self, attribute)
Return a single named information item from the os-release file data source of the OS distribution. For details, see :func:`distro.os_release_attr`.
Return a single named information item from the os-release file data source of the OS distribution.
def os_release_attr(self, attribute): """ Return a single named information item from the os-release file data source of the OS distribution. For details, see :func:`distro.os_release_attr`. """ return self._os_release_info.get(attribute, '')
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[ 893, 55 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.lsb_release_attr
(self, attribute)
Return a single named information item from the lsb_release command output data source of the OS distribution. For details, see :func:`distro.lsb_release_attr`.
Return a single named information item from the lsb_release command output data source of the OS distribution.
def lsb_release_attr(self, attribute): """ Return a single named information item from the lsb_release command output data source of the OS distribution. For details, see :func:`distro.lsb_release_attr`. """ return self._lsb_release_info.get(attribute, '')
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[ 902, 56 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.distro_release_attr
(self, attribute)
Return a single named information item from the distro release file data source of the OS distribution. For details, see :func:`distro.distro_release_attr`.
Return a single named information item from the distro release file data source of the OS distribution.
def distro_release_attr(self, attribute): """ Return a single named information item from the distro release file data source of the OS distribution. For details, see :func:`distro.distro_release_attr`. """ return self._distro_release_info.get(attribute, '')
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[ 904, 4 ]
[ 911, 59 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution.uname_attr
(self, attribute)
Return a single named information item from the uname command output data source of the OS distribution. For details, see :func:`distro.uname_release_attr`.
Return a single named information item from the uname command output data source of the OS distribution.
def uname_attr(self, attribute): """ Return a single named information item from the uname command output data source of the OS distribution. For details, see :func:`distro.uname_release_attr`. """ return self._uname_info.get(attribute, '')
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[ 913, 4 ]
[ 920, 50 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution._os_release_info
(self)
Get the information items from the specified os-release file. Returns: A dictionary containing all information items.
Get the information items from the specified os-release file.
def _os_release_info(self): """ Get the information items from the specified os-release file. Returns: A dictionary containing all information items. """ if os.path.isfile(self.os_release_file): with open(self.os_release_file) as release_file: return self._parse_os_release_content(release_file) return {}
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[ 923, 4 ]
[ 933, 17 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution._parse_os_release_content
(lines)
Parse the lines of an os-release file. Parameters: * lines: Iterable through the lines in the os-release file. Each line must be a unicode string or a UTF-8 encoded byte string. Returns: A dictionary containing all information items.
Parse the lines of an os-release file.
def _parse_os_release_content(lines): """ Parse the lines of an os-release file. Parameters: * lines: Iterable through the lines in the os-release file. Each line must be a unicode string or a UTF-8 encoded byte string. Returns: A dictionary containing all information items. """ props = {} lexer = shlex.shlex(lines, posix=True) lexer.whitespace_split = True # The shlex module defines its `wordchars` variable using literals, # making it dependent on the encoding of the Python source file. # In Python 2.6 and 2.7, the shlex source file is encoded in # 'iso-8859-1', and the `wordchars` variable is defined as a byte # string. This causes a UnicodeDecodeError to be raised when the # parsed content is a unicode object. The following fix resolves that # (... but it should be fixed in shlex...): if sys.version_info[0] == 2 and isinstance(lexer.wordchars, bytes): lexer.wordchars = lexer.wordchars.decode('iso-8859-1') tokens = list(lexer) for token in tokens: # At this point, all shell-like parsing has been done (i.e. # comments processed, quotes and backslash escape sequences # processed, multi-line values assembled, trailing newlines # stripped, etc.), so the tokens are now either: # * variable assignments: var=value # * commands or their arguments (not allowed in os-release) if '=' in token: k, v = token.split('=', 1) props[k.lower()] = v else: # Ignore any tokens that are not variable assignments pass if 'version_codename' in props: # os-release added a version_codename field. Use that in # preference to anything else Note that some distros purposefully # do not have code names. They should be setting # version_codename="" props['codename'] = props['version_codename'] elif 'ubuntu_codename' in props: # Same as above but a non-standard field name used on older Ubuntus props['codename'] = props['ubuntu_codename'] elif 'version' in props: # If there is no version_codename, parse it from the version codename = re.search(r'(\(\D+\))|,(\s+)?\D+', props['version']) if codename: codename = codename.group() codename = codename.strip('()') codename = codename.strip(',') codename = codename.strip() # codename appears within paranthese. props['codename'] = codename return props
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[ 936, 4 ]
[ 998, 20 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution._lsb_release_info
(self)
Get the information items from the lsb_release command output. Returns: A dictionary containing all information items.
Get the information items from the lsb_release command output.
def _lsb_release_info(self): """ Get the information items from the lsb_release command output. Returns: A dictionary containing all information items. """ if not self.include_lsb: return {} with open(os.devnull, 'w') as devnull: try: cmd = ('lsb_release', '-a') stdout = subprocess.check_output(cmd, stderr=devnull) except OSError: # Command not found return {} content = self._to_str(stdout).splitlines() return self._parse_lsb_release_content(content)
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[ 1001, 4 ]
[ 1017, 55 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution._parse_lsb_release_content
(lines)
Parse the output of the lsb_release command. Parameters: * lines: Iterable through the lines of the lsb_release output. Each line must be a unicode string or a UTF-8 encoded byte string. Returns: A dictionary containing all information items.
Parse the output of the lsb_release command.
def _parse_lsb_release_content(lines): """ Parse the output of the lsb_release command. Parameters: * lines: Iterable through the lines of the lsb_release output. Each line must be a unicode string or a UTF-8 encoded byte string. Returns: A dictionary containing all information items. """ props = {} for line in lines: kv = line.strip('\n').split(':', 1) if len(kv) != 2: # Ignore lines without colon. continue k, v = kv props.update({k.replace(' ', '_').lower(): v.strip()}) return props
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[ 1020, 4 ]
[ 1041, 20 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution._distro_release_info
(self)
Get the information items from the specified distro release file. Returns: A dictionary containing all information items.
Get the information items from the specified distro release file.
def _distro_release_info(self): """ Get the information items from the specified distro release file. Returns: A dictionary containing all information items. """ if self.distro_release_file: # If it was specified, we use it and parse what we can, even if # its file name or content does not match the expected pattern. distro_info = self._parse_distro_release_file( self.distro_release_file) basename = os.path.basename(self.distro_release_file) # The file name pattern for user-specified distro release files # is somewhat more tolerant (compared to when searching for the # file), because we want to use what was specified as best as # possible. match = _DISTRO_RELEASE_BASENAME_PATTERN.match(basename) if 'name' in distro_info \ and 'cloudlinux' in distro_info['name'].lower(): distro_info['id'] = 'cloudlinux' elif match: distro_info['id'] = match.group(1) return distro_info else: try: basenames = os.listdir(_UNIXCONFDIR) # We sort for repeatability in cases where there are multiple # distro specific files; e.g. CentOS, Oracle, Enterprise all # containing `redhat-release` on top of their own. basenames.sort() except OSError: # This may occur when /etc is not readable but we can't be # sure about the *-release files. Check common entries of # /etc for information. If they turn out to not be there the # error is handled in `_parse_distro_release_file()`. basenames = ['SuSE-release', 'arch-release', 'base-release', 'centos-release', 'fedora-release', 'gentoo-release', 'mageia-release', 'mandrake-release', 'mandriva-release', 'mandrivalinux-release', 'manjaro-release', 'oracle-release', 'redhat-release', 'sl-release', 'slackware-version'] for basename in basenames: if basename in _DISTRO_RELEASE_IGNORE_BASENAMES: continue match = _DISTRO_RELEASE_BASENAME_PATTERN.match(basename) if match: filepath = os.path.join(_UNIXCONFDIR, basename) distro_info = self._parse_distro_release_file(filepath) if 'name' in distro_info: # The name is always present if the pattern matches self.distro_release_file = filepath distro_info['id'] = match.group(1) if 'cloudlinux' in distro_info['name'].lower(): distro_info['id'] = 'cloudlinux' return distro_info return {}
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[ 1086, 4 ]
[ 1151, 21 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution._parse_distro_release_file
(self, filepath)
Parse a distro release file. Parameters: * filepath: Path name of the distro release file. Returns: A dictionary containing all information items.
Parse a distro release file.
def _parse_distro_release_file(self, filepath): """ Parse a distro release file. Parameters: * filepath: Path name of the distro release file. Returns: A dictionary containing all information items. """ try: with open(filepath) as fp: # Only parse the first line. For instance, on SLES there # are multiple lines. We don't want them... return self._parse_distro_release_content(fp.readline()) except (OSError, IOError): # Ignore not being able to read a specific, seemingly version # related file. # See https://github.com/nir0s/distro/issues/162 return {}
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[ 1153, 4 ]
[ 1173, 21 ]
python
en
['en', 'error', 'th']
False
LinuxDistribution._parse_distro_release_content
(line)
Parse a line from a distro release file. Parameters: * line: Line from the distro release file. Must be a unicode string or a UTF-8 encoded byte string. Returns: A dictionary containing all information items.
Parse a line from a distro release file.
def _parse_distro_release_content(line): """ Parse a line from a distro release file. Parameters: * line: Line from the distro release file. Must be a unicode string or a UTF-8 encoded byte string. Returns: A dictionary containing all information items. """ matches = _DISTRO_RELEASE_CONTENT_REVERSED_PATTERN.match( line.strip()[::-1]) distro_info = {} if matches: # regexp ensures non-None distro_info['name'] = matches.group(3)[::-1] if matches.group(2): distro_info['version_id'] = matches.group(2)[::-1] if matches.group(1): distro_info['codename'] = matches.group(1)[::-1] elif line: distro_info['name'] = line.strip() return distro_info
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[ 1176, 4 ]
[ 1199, 26 ]
python
en
['en', 'error', 'th']
False
expand_longitude_slice_by_degrees
(min_lon, max_lon, include_prime_meridian, degrees)
Returns a tuple of expanded min and max longitude, whether the expanded area includes the prime meridian, and the final span of the expanded angle in degrees.
Returns a tuple of expanded min and max longitude, whether the expanded area includes the prime meridian, and the final span of the expanded angle in degrees.
def expand_longitude_slice_by_degrees(min_lon, max_lon, include_prime_meridian, degrees) \ -> (int, int, bool, int): """ Returns a tuple of expanded min and max longitude, whether the expanded area includes the prime meridian, and the final span of the expanded angle in degrees. """ if min_lon > max_lon: # Swap min and max to simplify math min_lon, max_lon = max_lon, min_lon original_span = calculate_longitude_angle_in_degrees(min_lon, max_lon, include_prime_meridian) # Handle trivial case where expanded angle is the whole globe if original_span + (degrees * 2) >= 360: return -180, 180, True, 360 expanded_min_lon = normalize_longitude_arithmetic(min_lon - degrees) expanded_max_lon = normalize_longitude_arithmetic(max_lon + degrees) if expanded_min_lon > expanded_max_lon: # Swap min and max to simplify math expanded_min_lon, expanded_max_lon = expanded_max_lon, expanded_min_lon if not include_prime_meridian: # Does this expanded area now include the prime meridian? if 0 < min_lon <= degrees: include_prime_meridian = True elif max_lon < 0 and (degrees + max_lon >= 0): include_prime_meridian = True expanded_span = calculate_longitude_angle_in_degrees(expanded_min_lon, expanded_max_lon, include_prime_meridian) # Round to 0.001 for sake of this check original_span = round(original_span, 3) if not (expanded_span >= original_span): raise ValueError('Expanded span is smaller than original span. {} < {}'.format(expanded_span, original_span)) expected_span = original_span + (degrees * 2) # Round to 0.001 for sake of this check expected_span = round(expected_span, 3) if expected_span > 360: expected_span = 360 error_amount = abs(expected_span - expanded_span) if error_amount >= 0.002 and expanded_span < 360: raise ValueError( 'Expanded span is smaller than expected. Original {}, expanded to {}, expected {} (expansion angle {})' .format(original_span, expanded_span, expected_span, degrees)) return expanded_min_lon, expanded_max_lon, include_prime_meridian, expanded_span
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[ 56, 0 ]
[ 108, 84 ]
python
en
['en', 'error', 'th']
False
loadImageSeries
(filelist=None)
create a list of :py:class:`~PIL.Image.Image` objects for use in a montage
create a list of :py:class:`~PIL.Image.Image` objects for use in a montage
def loadImageSeries(filelist=None): """create a list of :py:class:`~PIL.Image.Image` objects for use in a montage""" if filelist is None or len(filelist) < 1: return imglist = [] for img in filelist: if not os.path.exists(img): print(f"unable to find {img}") continue try: with Image.open(img) as im: im = im.convert2byte() except Exception: if not isSpiderImage(img): print(img + " is not a Spider image file") continue im.info["filename"] = img imglist.append(im) return imglist
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[ 207, 0 ]
[ 226, 18 ]
python
en
['en', 'en', 'en']
True
PostGISIntrospection.get_geometry_type
(self, table_name, description)
The geometry type OID used by PostGIS does not indicate the particular type of field that a geometry column is (e.g., whether it's a PointField or a PolygonField). Thus, this routine queries the PostGIS metadata tables to determine the geometry type.
The geometry type OID used by PostGIS does not indicate the particular type of field that a geometry column is (e.g., whether it's a PointField or a PolygonField). Thus, this routine queries the PostGIS metadata tables to determine the geometry type.
def get_geometry_type(self, table_name, description): """ The geometry type OID used by PostGIS does not indicate the particular type of field that a geometry column is (e.g., whether it's a PointField or a PolygonField). Thus, this routine queries the PostGIS metadata tables to determine the geometry type. """ with self.connection.cursor() as cursor: cursor.execute(""" SELECT t.coord_dimension, t.srid, t.type FROM ( SELECT * FROM geometry_columns UNION ALL SELECT * FROM geography_columns ) AS t WHERE t.f_table_name = %s AND t.f_geometry_column = %s """, (table_name, description.name)) row = cursor.fetchone() if not row: raise Exception('Could not find a geometry or geography column for "%s"."%s"' % (table_name, description.name)) dim, srid, field_type = row # OGRGeomType does not require GDAL and makes it easy to convert # from OGC geom type name to Django field. field_type = OGRGeomType(field_type).django # Getting any GeometryField keyword arguments that are not the default. field_params = {} if self.postgis_oid_lookup.get(description.type_code) == 'geography': field_params['geography'] = True if srid != 4326: field_params['srid'] = srid if dim != 2: field_params['dim'] = dim return field_type, field_params
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[ 28, 4 ]
[ 59, 39 ]
python
en
['en', 'error', 'th']
False
with_metaclass
(meta, *bases)
Create a base class with a metaclass.
Create a base class with a metaclass.
def with_metaclass(meta, *bases): """Create a base class with a metaclass.""" # This requires a bit of explanation: the basic idea is to make a # dummy metaclass for one level of class instantiation that replaces # itself with the actual metaclass. class metaclass(type): def __new__(cls, name, this_bases, d): return meta(name, bases, d) return type.__new__(metaclass, 'temporary_class', (), {})
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[ 55, 0 ]
[ 63, 61 ]
python
en
['en', 'en', 'en']
True
ETagRequestMixin.cache_control
(self)
A :class:`~werkzeug.datastructures.RequestCacheControl` object for the incoming cache control headers.
A :class:`~werkzeug.datastructures.RequestCacheControl` object for the incoming cache control headers.
def cache_control(self): """A :class:`~werkzeug.datastructures.RequestCacheControl` object for the incoming cache control headers. """ cache_control = self.environ.get("HTTP_CACHE_CONTROL") return parse_cache_control_header(cache_control, None, RequestCacheControl)
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[ 29, 4 ]
[ 34, 83 ]
python
de
['de', 'en', 'de']
True
ETagRequestMixin.if_match
(self)
An object containing all the etags in the `If-Match` header. :rtype: :class:`~werkzeug.datastructures.ETags`
An object containing all the etags in the `If-Match` header.
def if_match(self): """An object containing all the etags in the `If-Match` header. :rtype: :class:`~werkzeug.datastructures.ETags` """ return parse_etags(self.environ.get("HTTP_IF_MATCH"))
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[ 37, 4 ]
[ 42, 61 ]
python
en
['en', 'en', 'en']
True
ETagRequestMixin.if_none_match
(self)
An object containing all the etags in the `If-None-Match` header. :rtype: :class:`~werkzeug.datastructures.ETags`
An object containing all the etags in the `If-None-Match` header.
def if_none_match(self): """An object containing all the etags in the `If-None-Match` header. :rtype: :class:`~werkzeug.datastructures.ETags` """ return parse_etags(self.environ.get("HTTP_IF_NONE_MATCH"))
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[ 45, 4 ]
[ 50, 66 ]
python
en
['en', 'en', 'en']
True
ETagRequestMixin.if_modified_since
(self)
The parsed `If-Modified-Since` header as datetime object.
The parsed `If-Modified-Since` header as datetime object.
def if_modified_since(self): """The parsed `If-Modified-Since` header as datetime object.""" return parse_date(self.environ.get("HTTP_IF_MODIFIED_SINCE"))
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[ 53, 4 ]
[ 55, 69 ]
python
en
['en', 'en', 'en']
True
ETagRequestMixin.if_unmodified_since
(self)
The parsed `If-Unmodified-Since` header as datetime object.
The parsed `If-Unmodified-Since` header as datetime object.
def if_unmodified_since(self): """The parsed `If-Unmodified-Since` header as datetime object.""" return parse_date(self.environ.get("HTTP_IF_UNMODIFIED_SINCE"))
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[ 58, 4 ]
[ 60, 71 ]
python
en
['en', 'en', 'en']
True
ETagRequestMixin.if_range
(self)
The parsed `If-Range` header. .. versionadded:: 0.7 :rtype: :class:`~werkzeug.datastructures.IfRange`
The parsed `If-Range` header.
def if_range(self): """The parsed `If-Range` header. .. versionadded:: 0.7 :rtype: :class:`~werkzeug.datastructures.IfRange` """ return parse_if_range_header(self.environ.get("HTTP_IF_RANGE"))
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[ 63, 4 ]
[ 70, 71 ]
python
en
['en', 'af', 'en']
True
ETagRequestMixin.range
(self)
The parsed `Range` header. .. versionadded:: 0.7 :rtype: :class:`~werkzeug.datastructures.Range`
The parsed `Range` header.
def range(self): """The parsed `Range` header. .. versionadded:: 0.7 :rtype: :class:`~werkzeug.datastructures.Range` """ return parse_range_header(self.environ.get("HTTP_RANGE"))
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[ 73, 4 ]
[ 80, 65 ]
python
en
['en', 'af', 'en']
True
ETagResponseMixin.cache_control
(self)
The Cache-Control general-header field is used to specify directives that MUST be obeyed by all caching mechanisms along the request/response chain.
The Cache-Control general-header field is used to specify directives that MUST be obeyed by all caching mechanisms along the request/response chain.
def cache_control(self): """The Cache-Control general-header field is used to specify directives that MUST be obeyed by all caching mechanisms along the request/response chain. """ def on_update(cache_control): if not cache_control and "cache-control" in self.headers: del self.headers["cache-control"] elif cache_control: self.headers["Cache-Control"] = cache_control.to_header() return parse_cache_control_header( self.headers.get("cache-control"), on_update, ResponseCacheControl )
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[ 95, 4 ]
[ 109, 9 ]
python
en
['en', 'en', 'en']
True
ETagResponseMixin._wrap_response
(self, start, length)
Wrap existing Response in case of Range Request context.
Wrap existing Response in case of Range Request context.
def _wrap_response(self, start, length): """Wrap existing Response in case of Range Request context.""" if self.status_code == 206: self.response = _RangeWrapper(self.response, start, length)
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[ 111, 4 ]
[ 114, 71 ]
python
en
['en', 'en', 'en']
True
ETagResponseMixin._is_range_request_processable
(self, environ)
Return ``True`` if `Range` header is present and if underlying resource is considered unchanged when compared with `If-Range` header.
Return ``True`` if `Range` header is present and if underlying resource is considered unchanged when compared with `If-Range` header.
def _is_range_request_processable(self, environ): """Return ``True`` if `Range` header is present and if underlying resource is considered unchanged when compared with `If-Range` header. """ return ( "HTTP_IF_RANGE" not in environ or not is_resource_modified( environ, self.headers.get("etag"), None, self.headers.get("last-modified"), ignore_if_range=False, ) ) and "HTTP_RANGE" in environ
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[ 116, 4 ]
[ 129, 37 ]
python
en
['en', 'en', 'en']
True
ETagResponseMixin._process_range_request
(self, environ, complete_length=None, accept_ranges=None)
Handle Range Request related headers (RFC7233). If `Accept-Ranges` header is valid, and Range Request is processable, we set the headers as described by the RFC, and wrap the underlying response in a RangeWrapper. Returns ``True`` if Range Request can be fulfilled, ``False`` otherwise. :raises: :class:`~werkzeug.exceptions.RequestedRangeNotSatisfiable` if `Range` header could not be parsed or satisfied.
Handle Range Request related headers (RFC7233). If `Accept-Ranges` header is valid, and Range Request is processable, we set the headers as described by the RFC, and wrap the underlying response in a RangeWrapper.
def _process_range_request(self, environ, complete_length=None, accept_ranges=None): """Handle Range Request related headers (RFC7233). If `Accept-Ranges` header is valid, and Range Request is processable, we set the headers as described by the RFC, and wrap the underlying response in a RangeWrapper. Returns ``True`` if Range Request can be fulfilled, ``False`` otherwise. :raises: :class:`~werkzeug.exceptions.RequestedRangeNotSatisfiable` if `Range` header could not be parsed or satisfied. """ from ..exceptions import RequestedRangeNotSatisfiable if accept_ranges is None: return False self.headers["Accept-Ranges"] = accept_ranges if not self._is_range_request_processable(environ) or complete_length is None: return False parsed_range = parse_range_header(environ.get("HTTP_RANGE")) if parsed_range is None: raise RequestedRangeNotSatisfiable(complete_length) range_tuple = parsed_range.range_for_length(complete_length) content_range_header = parsed_range.to_content_range_header(complete_length) if range_tuple is None or content_range_header is None: raise RequestedRangeNotSatisfiable(complete_length) content_length = range_tuple[1] - range_tuple[0] # Be sure not to send 206 response # if requested range is the full content. if content_length != complete_length: self.headers["Content-Length"] = content_length self.content_range = content_range_header self.status_code = 206 self._wrap_response(range_tuple[0], content_length) return True return False
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[ 131, 4 ]
[ 165, 20 ]
python
en
['en', 'en', 'en']
True
ETagResponseMixin.make_conditional
( self, request_or_environ, accept_ranges=False, complete_length=None )
Make the response conditional to the request. This method works best if an etag was defined for the response already. The `add_etag` method can be used to do that. If called without etag just the date header is set. This does nothing if the request method in the request or environ is anything but GET or HEAD. For optimal performance when handling range requests, it's recommended that your response data object implements `seekable`, `seek` and `tell` methods as described by :py:class:`io.IOBase`. Objects returned by :meth:`~werkzeug.wsgi.wrap_file` automatically implement those methods. It does not remove the body of the response because that's something the :meth:`__call__` function does for us automatically. Returns self so that you can do ``return resp.make_conditional(req)`` but modifies the object in-place. :param request_or_environ: a request object or WSGI environment to be used to make the response conditional against. :param accept_ranges: This parameter dictates the value of `Accept-Ranges` header. If ``False`` (default), the header is not set. If ``True``, it will be set to ``"bytes"``. If ``None``, it will be set to ``"none"``. If it's a string, it will use this value. :param complete_length: Will be used only in valid Range Requests. It will set `Content-Range` complete length value and compute `Content-Length` real value. This parameter is mandatory for successful Range Requests completion. :raises: :class:`~werkzeug.exceptions.RequestedRangeNotSatisfiable` if `Range` header could not be parsed or satisfied.
Make the response conditional to the request. This method works best if an etag was defined for the response already. The `add_etag` method can be used to do that. If called without etag just the date header is set.
def make_conditional( self, request_or_environ, accept_ranges=False, complete_length=None ): """Make the response conditional to the request. This method works best if an etag was defined for the response already. The `add_etag` method can be used to do that. If called without etag just the date header is set. This does nothing if the request method in the request or environ is anything but GET or HEAD. For optimal performance when handling range requests, it's recommended that your response data object implements `seekable`, `seek` and `tell` methods as described by :py:class:`io.IOBase`. Objects returned by :meth:`~werkzeug.wsgi.wrap_file` automatically implement those methods. It does not remove the body of the response because that's something the :meth:`__call__` function does for us automatically. Returns self so that you can do ``return resp.make_conditional(req)`` but modifies the object in-place. :param request_or_environ: a request object or WSGI environment to be used to make the response conditional against. :param accept_ranges: This parameter dictates the value of `Accept-Ranges` header. If ``False`` (default), the header is not set. If ``True``, it will be set to ``"bytes"``. If ``None``, it will be set to ``"none"``. If it's a string, it will use this value. :param complete_length: Will be used only in valid Range Requests. It will set `Content-Range` complete length value and compute `Content-Length` real value. This parameter is mandatory for successful Range Requests completion. :raises: :class:`~werkzeug.exceptions.RequestedRangeNotSatisfiable` if `Range` header could not be parsed or satisfied. """ environ = _get_environ(request_or_environ) if environ["REQUEST_METHOD"] in ("GET", "HEAD"): # if the date is not in the headers, add it now. We however # will not override an already existing header. Unfortunately # this header will be overriden by many WSGI servers including # wsgiref. if "date" not in self.headers: self.headers["Date"] = http_date() accept_ranges = _clean_accept_ranges(accept_ranges) is206 = self._process_range_request(environ, complete_length, accept_ranges) if not is206 and not is_resource_modified( environ, self.headers.get("etag"), None, self.headers.get("last-modified"), ): if parse_etags(environ.get("HTTP_IF_MATCH")): self.status_code = 412 else: self.status_code = 304 if ( self.automatically_set_content_length and "content-length" not in self.headers ): length = self.calculate_content_length() if length is not None: self.headers["Content-Length"] = length return self
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[ 167, 4 ]
[ 233, 19 ]
python
en
['en', 'en', 'en']
True
ETagResponseMixin.add_etag
(self, overwrite=False, weak=False)
Add an etag for the current response if there is none yet.
Add an etag for the current response if there is none yet.
def add_etag(self, overwrite=False, weak=False): """Add an etag for the current response if there is none yet.""" if overwrite or "etag" not in self.headers: self.set_etag(generate_etag(self.get_data()), weak)
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[ 235, 4 ]
[ 238, 63 ]
python
en
['en', 'en', 'en']
True
ETagResponseMixin.set_etag
(self, etag, weak=False)
Set the etag, and override the old one if there was one.
Set the etag, and override the old one if there was one.
def set_etag(self, etag, weak=False): """Set the etag, and override the old one if there was one.""" self.headers["ETag"] = quote_etag(etag, weak)
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[ 240, 4 ]
[ 242, 53 ]
python
en
['en', 'en', 'en']
True
ETagResponseMixin.get_etag
(self)
Return a tuple in the form ``(etag, is_weak)``. If there is no ETag the return value is ``(None, None)``.
Return a tuple in the form ``(etag, is_weak)``. If there is no ETag the return value is ``(None, None)``.
def get_etag(self): """Return a tuple in the form ``(etag, is_weak)``. If there is no ETag the return value is ``(None, None)``. """ return unquote_etag(self.headers.get("ETag"))
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[ 244, 4 ]
[ 248, 53 ]
python
en
['en', 'en', 'en']
True
ETagResponseMixin.freeze
(self, no_etag=False)
Call this method if you want to make your response object ready for pickeling. This buffers the generator if there is one. This also sets the etag unless `no_etag` is set to `True`.
Call this method if you want to make your response object ready for pickeling. This buffers the generator if there is one. This also sets the etag unless `no_etag` is set to `True`.
def freeze(self, no_etag=False): """Call this method if you want to make your response object ready for pickeling. This buffers the generator if there is one. This also sets the etag unless `no_etag` is set to `True`. """ if not no_etag: self.add_etag() super(ETagResponseMixin, self).freeze()
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[ 250, 4 ]
[ 257, 47 ]
python
en
['en', 'en', 'en']
True
extract_from_ast
(node, gettext_functions=GETTEXT_FUNCTIONS, babel_style=True)
Extract localizable strings from the given template node. Per default this function returns matches in babel style that means non string parameters as well as keyword arguments are returned as `None`. This allows Babel to figure out what you really meant if you are using gettext functions that allow keyword arguments for placeholder expansion. If you don't want that behavior set the `babel_style` parameter to `False` which causes only strings to be returned and parameters are always stored in tuples. As a consequence invalid gettext calls (calls without a single string parameter or string parameters after non-string parameters) are skipped. This example explains the behavior: >>> from jinja2 import Environment >>> env = Environment() >>> node = env.parse('{{ (_("foo"), _(), ngettext("foo", "bar", 42)) }}') >>> list(extract_from_ast(node)) [(1, '_', 'foo'), (1, '_', ()), (1, 'ngettext', ('foo', 'bar', None))] >>> list(extract_from_ast(node, babel_style=False)) [(1, '_', ('foo',)), (1, 'ngettext', ('foo', 'bar'))] For every string found this function yields a ``(lineno, function, message)`` tuple, where: * ``lineno`` is the number of the line on which the string was found, * ``function`` is the name of the ``gettext`` function used (if the string was extracted from embedded Python code), and * ``message`` is the string itself (a ``unicode`` object, or a tuple of ``unicode`` objects for functions with multiple string arguments). This extraction function operates on the AST and is because of that unable to extract any comments. For comment support you have to use the babel extraction interface or extract comments yourself.
Extract localizable strings from the given template node. Per default this function returns matches in babel style that means non string parameters as well as keyword arguments are returned as `None`. This allows Babel to figure out what you really meant if you are using gettext functions that allow keyword arguments for placeholder expansion. If you don't want that behavior set the `babel_style` parameter to `False` which causes only strings to be returned and parameters are always stored in tuples. As a consequence invalid gettext calls (calls without a single string parameter or string parameters after non-string parameters) are skipped.
def extract_from_ast(node, gettext_functions=GETTEXT_FUNCTIONS, babel_style=True): """Extract localizable strings from the given template node. Per default this function returns matches in babel style that means non string parameters as well as keyword arguments are returned as `None`. This allows Babel to figure out what you really meant if you are using gettext functions that allow keyword arguments for placeholder expansion. If you don't want that behavior set the `babel_style` parameter to `False` which causes only strings to be returned and parameters are always stored in tuples. As a consequence invalid gettext calls (calls without a single string parameter or string parameters after non-string parameters) are skipped. This example explains the behavior: >>> from jinja2 import Environment >>> env = Environment() >>> node = env.parse('{{ (_("foo"), _(), ngettext("foo", "bar", 42)) }}') >>> list(extract_from_ast(node)) [(1, '_', 'foo'), (1, '_', ()), (1, 'ngettext', ('foo', 'bar', None))] >>> list(extract_from_ast(node, babel_style=False)) [(1, '_', ('foo',)), (1, 'ngettext', ('foo', 'bar'))] For every string found this function yields a ``(lineno, function, message)`` tuple, where: * ``lineno`` is the number of the line on which the string was found, * ``function`` is the name of the ``gettext`` function used (if the string was extracted from embedded Python code), and * ``message`` is the string itself (a ``unicode`` object, or a tuple of ``unicode`` objects for functions with multiple string arguments). This extraction function operates on the AST and is because of that unable to extract any comments. For comment support you have to use the babel extraction interface or extract comments yourself. """ for node in node.find_all(nodes.Call): if not isinstance(node.node, nodes.Name) or \ node.node.name not in gettext_functions: continue strings = [] for arg in node.args: if isinstance(arg, nodes.Const) and \ isinstance(arg.value, string_types): strings.append(arg.value) else: strings.append(None) for arg in node.kwargs: strings.append(None) if node.dyn_args is not None: strings.append(None) if node.dyn_kwargs is not None: strings.append(None) if not babel_style: strings = tuple(x for x in strings if x is not None) if not strings: continue else: if len(strings) == 1: strings = strings[0] else: strings = tuple(strings) yield node.lineno, node.node.name, strings
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[ 436, 0 ]
[ 501, 50 ]
python
en
['en', 'en', 'en']
True
babel_extract
(fileobj, keywords, comment_tags, options)
Babel extraction method for Jinja templates. .. versionchanged:: 2.3 Basic support for translation comments was added. If `comment_tags` is now set to a list of keywords for extraction, the extractor will try to find the best preceeding comment that begins with one of the keywords. For best results, make sure to not have more than one gettext call in one line of code and the matching comment in the same line or the line before. .. versionchanged:: 2.5.1 The `newstyle_gettext` flag can be set to `True` to enable newstyle gettext calls. .. versionchanged:: 2.7 A `silent` option can now be provided. If set to `False` template syntax errors are propagated instead of being ignored. :param fileobj: the file-like object the messages should be extracted from :param keywords: a list of keywords (i.e. function names) that should be recognized as translation functions :param comment_tags: a list of translator tags to search for and include in the results. :param options: a dictionary of additional options (optional) :return: an iterator over ``(lineno, funcname, message, comments)`` tuples. (comments will be empty currently)
Babel extraction method for Jinja templates.
def babel_extract(fileobj, keywords, comment_tags, options): """Babel extraction method for Jinja templates. .. versionchanged:: 2.3 Basic support for translation comments was added. If `comment_tags` is now set to a list of keywords for extraction, the extractor will try to find the best preceeding comment that begins with one of the keywords. For best results, make sure to not have more than one gettext call in one line of code and the matching comment in the same line or the line before. .. versionchanged:: 2.5.1 The `newstyle_gettext` flag can be set to `True` to enable newstyle gettext calls. .. versionchanged:: 2.7 A `silent` option can now be provided. If set to `False` template syntax errors are propagated instead of being ignored. :param fileobj: the file-like object the messages should be extracted from :param keywords: a list of keywords (i.e. function names) that should be recognized as translation functions :param comment_tags: a list of translator tags to search for and include in the results. :param options: a dictionary of additional options (optional) :return: an iterator over ``(lineno, funcname, message, comments)`` tuples. (comments will be empty currently) """ extensions = set() for extension in options.get('extensions', '').split(','): extension = extension.strip() if not extension: continue extensions.add(import_string(extension)) if InternationalizationExtension not in extensions: extensions.add(InternationalizationExtension) def getbool(options, key, default=False): return options.get(key, str(default)).lower() in \ ('1', 'on', 'yes', 'true') silent = getbool(options, 'silent', True) environment = Environment( options.get('block_start_string', BLOCK_START_STRING), options.get('block_end_string', BLOCK_END_STRING), options.get('variable_start_string', VARIABLE_START_STRING), options.get('variable_end_string', VARIABLE_END_STRING), options.get('comment_start_string', COMMENT_START_STRING), options.get('comment_end_string', COMMENT_END_STRING), options.get('line_statement_prefix') or LINE_STATEMENT_PREFIX, options.get('line_comment_prefix') or LINE_COMMENT_PREFIX, getbool(options, 'trim_blocks', TRIM_BLOCKS), getbool(options, 'lstrip_blocks', LSTRIP_BLOCKS), NEWLINE_SEQUENCE, getbool(options, 'keep_trailing_newline', KEEP_TRAILING_NEWLINE), frozenset(extensions), cache_size=0, auto_reload=False ) if getbool(options, 'trimmed'): environment.policies['ext.i18n.trimmed'] = True if getbool(options, 'newstyle_gettext'): environment.newstyle_gettext = True source = fileobj.read().decode(options.get('encoding', 'utf-8')) try: node = environment.parse(source) tokens = list(environment.lex(environment.preprocess(source))) except TemplateSyntaxError as e: if not silent: raise # skip templates with syntax errors return finder = _CommentFinder(tokens, comment_tags) for lineno, func, message in extract_from_ast(node, keywords): yield lineno, func, message, finder.find_comments(lineno)
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[ 541, 0 ]
[ 618, 65 ]
python
en
['nb', 'en', 'en']
True
Extension.bind
(self, environment)
Create a copy of this extension bound to another environment.
Create a copy of this extension bound to another environment.
def bind(self, environment): """Create a copy of this extension bound to another environment.""" rv = object.__new__(self.__class__) rv.__dict__.update(self.__dict__) rv.environment = environment return rv
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[ 74, 4 ]
[ 79, 17 ]
python
en
['en', 'en', 'en']
True
Extension.preprocess
(self, source, name, filename=None)
This method is called before the actual lexing and can be used to preprocess the source. The `filename` is optional. The return value must be the preprocessed source.
This method is called before the actual lexing and can be used to preprocess the source. The `filename` is optional. The return value must be the preprocessed source.
def preprocess(self, source, name, filename=None): """This method is called before the actual lexing and can be used to preprocess the source. The `filename` is optional. The return value must be the preprocessed source. """ return source
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[ 81, 4 ]
[ 86, 21 ]
python
en
['en', 'en', 'en']
True
Extension.filter_stream
(self, stream)
It's passed a :class:`~jinja2.lexer.TokenStream` that can be used to filter tokens returned. This method has to return an iterable of :class:`~jinja2.lexer.Token`\\s, but it doesn't have to return a :class:`~jinja2.lexer.TokenStream`. In the `ext` folder of the Jinja2 source distribution there is a file called `inlinegettext.py` which implements a filter that utilizes this method.
It's passed a :class:`~jinja2.lexer.TokenStream` that can be used to filter tokens returned. This method has to return an iterable of :class:`~jinja2.lexer.Token`\\s, but it doesn't have to return a :class:`~jinja2.lexer.TokenStream`.
def filter_stream(self, stream): """It's passed a :class:`~jinja2.lexer.TokenStream` that can be used to filter tokens returned. This method has to return an iterable of :class:`~jinja2.lexer.Token`\\s, but it doesn't have to return a :class:`~jinja2.lexer.TokenStream`. In the `ext` folder of the Jinja2 source distribution there is a file called `inlinegettext.py` which implements a filter that utilizes this method. """ return stream
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[ 88, 4 ]
[ 98, 21 ]
python
en
['en', 'en', 'en']
True
Extension.parse
(self, parser)
If any of the :attr:`tags` matched this method is called with the parser as first argument. The token the parser stream is pointing at is the name token that matched. This method has to return one or a list of multiple nodes.
If any of the :attr:`tags` matched this method is called with the parser as first argument. The token the parser stream is pointing at is the name token that matched. This method has to return one or a list of multiple nodes.
def parse(self, parser): """If any of the :attr:`tags` matched this method is called with the parser as first argument. The token the parser stream is pointing at is the name token that matched. This method has to return one or a list of multiple nodes. """ raise NotImplementedError()
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[ 100, 4 ]
[ 106, 35 ]
python
en
['en', 'en', 'en']
True
Extension.attr
(self, name, lineno=None)
Return an attribute node for the current extension. This is useful to pass constants on extensions to generated template code. :: self.attr('_my_attribute', lineno=lineno)
Return an attribute node for the current extension. This is useful to pass constants on extensions to generated template code.
def attr(self, name, lineno=None): """Return an attribute node for the current extension. This is useful to pass constants on extensions to generated template code. :: self.attr('_my_attribute', lineno=lineno) """ return nodes.ExtensionAttribute(self.identifier, name, lineno=lineno)
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[ 108, 4 ]
[ 116, 77 ]
python
en
['en', 'en', 'en']
True
Extension.call_method
(self, name, args=None, kwargs=None, dyn_args=None, dyn_kwargs=None, lineno=None)
Call a method of the extension. This is a shortcut for :meth:`attr` + :class:`jinja2.nodes.Call`.
Call a method of the extension. This is a shortcut for :meth:`attr` + :class:`jinja2.nodes.Call`.
def call_method(self, name, args=None, kwargs=None, dyn_args=None, dyn_kwargs=None, lineno=None): """Call a method of the extension. This is a shortcut for :meth:`attr` + :class:`jinja2.nodes.Call`. """ if args is None: args = [] if kwargs is None: kwargs = [] return nodes.Call(self.attr(name, lineno=lineno), args, kwargs, dyn_args, dyn_kwargs, lineno=lineno)
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[ 118, 4 ]
[ 128, 62 ]
python
en
['en', 'en', 'en']
True
InternationalizationExtension.parse
(self, parser)
Parse a translatable tag.
Parse a translatable tag.
def parse(self, parser): """Parse a translatable tag.""" lineno = next(parser.stream).lineno num_called_num = False # find all the variables referenced. Additionally a variable can be # defined in the body of the trans block too, but this is checked at # a later state. plural_expr = None plural_expr_assignment = None variables = {} trimmed = None while parser.stream.current.type != 'block_end': if variables: parser.stream.expect('comma') # skip colon for python compatibility if parser.stream.skip_if('colon'): break name = parser.stream.expect('name') if name.value in variables: parser.fail('translatable variable %r defined twice.' % name.value, name.lineno, exc=TemplateAssertionError) # expressions if parser.stream.current.type == 'assign': next(parser.stream) variables[name.value] = var = parser.parse_expression() elif trimmed is None and name.value in ('trimmed', 'notrimmed'): trimmed = name.value == 'trimmed' continue else: variables[name.value] = var = nodes.Name(name.value, 'load') if plural_expr is None: if isinstance(var, nodes.Call): plural_expr = nodes.Name('_trans', 'load') variables[name.value] = plural_expr plural_expr_assignment = nodes.Assign( nodes.Name('_trans', 'store'), var) else: plural_expr = var num_called_num = name.value == 'num' parser.stream.expect('block_end') plural = None have_plural = False referenced = set() # now parse until endtrans or pluralize singular_names, singular = self._parse_block(parser, True) if singular_names: referenced.update(singular_names) if plural_expr is None: plural_expr = nodes.Name(singular_names[0], 'load') num_called_num = singular_names[0] == 'num' # if we have a pluralize block, we parse that too if parser.stream.current.test('name:pluralize'): have_plural = True next(parser.stream) if parser.stream.current.type != 'block_end': name = parser.stream.expect('name') if name.value not in variables: parser.fail('unknown variable %r for pluralization' % name.value, name.lineno, exc=TemplateAssertionError) plural_expr = variables[name.value] num_called_num = name.value == 'num' parser.stream.expect('block_end') plural_names, plural = self._parse_block(parser, False) next(parser.stream) referenced.update(plural_names) else: next(parser.stream) # register free names as simple name expressions for var in referenced: if var not in variables: variables[var] = nodes.Name(var, 'load') if not have_plural: plural_expr = None elif plural_expr is None: parser.fail('pluralize without variables', lineno) if trimmed is None: trimmed = self.environment.policies['ext.i18n.trimmed'] if trimmed: singular = self._trim_whitespace(singular) if plural: plural = self._trim_whitespace(plural) node = self._make_node(singular, plural, variables, plural_expr, bool(referenced), num_called_num and have_plural) node.set_lineno(lineno) if plural_expr_assignment is not None: return [plural_expr_assignment, node] else: return node
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[ 216, 4 ]
[ 319, 23 ]
python
en
['en', 'en', 'it']
True
InternationalizationExtension._parse_block
(self, parser, allow_pluralize)
Parse until the next block tag with a given name.
Parse until the next block tag with a given name.
def _parse_block(self, parser, allow_pluralize): """Parse until the next block tag with a given name.""" referenced = [] buf = [] while 1: if parser.stream.current.type == 'data': buf.append(parser.stream.current.value.replace('%', '%%')) next(parser.stream) elif parser.stream.current.type == 'variable_begin': next(parser.stream) name = parser.stream.expect('name').value referenced.append(name) buf.append('%%(%s)s' % name) parser.stream.expect('variable_end') elif parser.stream.current.type == 'block_begin': next(parser.stream) if parser.stream.current.test('name:endtrans'): break elif parser.stream.current.test('name:pluralize'): if allow_pluralize: break parser.fail('a translatable section can have only one ' 'pluralize section') parser.fail('control structures in translatable sections are ' 'not allowed') elif parser.stream.eos: parser.fail('unclosed translation block') else: assert False, 'internal parser error' return referenced, concat(buf)
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[ 324, 4 ]
[ 354, 38 ]
python
en
['en', 'en', 'en']
True
InternationalizationExtension._make_node
(self, singular, plural, variables, plural_expr, vars_referenced, num_called_num)
Generates a useful node from the data provided.
Generates a useful node from the data provided.
def _make_node(self, singular, plural, variables, plural_expr, vars_referenced, num_called_num): """Generates a useful node from the data provided.""" # no variables referenced? no need to escape for old style # gettext invocations only if there are vars. if not vars_referenced and not self.environment.newstyle_gettext: singular = singular.replace('%%', '%') if plural: plural = plural.replace('%%', '%') # singular only: if plural_expr is None: gettext = nodes.Name('gettext', 'load') node = nodes.Call(gettext, [nodes.Const(singular)], [], None, None) # singular and plural else: ngettext = nodes.Name('ngettext', 'load') node = nodes.Call(ngettext, [ nodes.Const(singular), nodes.Const(plural), plural_expr ], [], None, None) # in case newstyle gettext is used, the method is powerful # enough to handle the variable expansion and autoescape # handling itself if self.environment.newstyle_gettext: for key, value in iteritems(variables): # the function adds that later anyways in case num was # called num, so just skip it. if num_called_num and key == 'num': continue node.kwargs.append(nodes.Keyword(key, value)) # otherwise do that here else: # mark the return value as safe if we are in an # environment with autoescaping turned on node = nodes.MarkSafeIfAutoescape(node) if variables: node = nodes.Mod(node, nodes.Dict([ nodes.Pair(nodes.Const(key), value) for key, value in variables.items() ])) return nodes.Output([node])
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[ 356, 4 ]
[ 402, 35 ]
python
en
['en', 'en', 'en']
True
hello_monkey
()
Respond and greet the caller by name.
Respond and greet the caller by name.
def hello_monkey(): """Respond and greet the caller by name.""" # Try adding your own number to this list! callers = { "+14158675308": "Curious George", "+12349013030": "Boots", "+12348134522": "Virgil", } from_number = request.values.get('From', None) message = callers[from_number] if from_number in callers else "Monkey" resp = MessagingResponse() resp.message("{}, thanks for the message!".format(message)) return str(resp)
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[ 8, 0 ]
[ 22, 20 ]
python
en
['en', 'en', 'en']
True
bdist_wininst.reinitialize_command
(self, command, reinit_subcommands=0)
Supplement reinitialize_command to work around http://bugs.python.org/issue20819
Supplement reinitialize_command to work around http://bugs.python.org/issue20819
def reinitialize_command(self, command, reinit_subcommands=0): """ Supplement reinitialize_command to work around http://bugs.python.org/issue20819 """ cmd = self.distribution.reinitialize_command( command, reinit_subcommands) if command in ('install', 'install_lib'): cmd.install_lib = None return cmd
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[ 4, 4 ]
[ 13, 18 ]
python
en
['en', 'error', 'th']
False
find_best_app
(module)
Given a module instance this tries to find the best possible application in the module or raises an exception.
Given a module instance this tries to find the best possible application in the module or raises an exception.
def find_best_app(module): """Given a module instance this tries to find the best possible application in the module or raises an exception. """ from . import Flask # Search for the most common names first. for attr_name in 'app', 'application': app = getattr(module, attr_name, None) if app is not None and isinstance(app, Flask): return app # Otherwise find the only object that is a Flask instance. matches = [v for k, v in iteritems(module.__dict__) if isinstance(v, Flask)] if len(matches) == 1: return matches[0] raise NoAppException('Failed to find application in module "%s". Are ' 'you sure it contains a Flask application? Maybe ' 'you wrapped it in a WSGI middleware or you are ' 'using a factory function.' % module.__name__)
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[ 26, 0 ]
[ 47, 71 ]
python
en
['en', 'en', 'en']
True
prepare_exec_for_file
(filename)
Given a filename this will try to calculate the python path, add it to the search path and return the actual module name that is expected.
Given a filename this will try to calculate the python path, add it to the search path and return the actual module name that is expected.
def prepare_exec_for_file(filename): """Given a filename this will try to calculate the python path, add it to the search path and return the actual module name that is expected. """ module = [] # Chop off file extensions or package markers if os.path.split(filename)[1] == '__init__.py': filename = os.path.dirname(filename) elif filename.endswith('.py'): filename = filename[:-3] else: raise NoAppException('The file provided (%s) does exist but is not a ' 'valid Python file. This means that it cannot ' 'be used as application. Please change the ' 'extension to .py' % filename) filename = os.path.realpath(filename) dirpath = filename while 1: dirpath, extra = os.path.split(dirpath) module.append(extra) if not os.path.isfile(os.path.join(dirpath, '__init__.py')): break sys.path.insert(0, dirpath) return '.'.join(module[::-1])
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[ 50, 0 ]
[ 76, 33 ]
python
en
['en', 'en', 'en']
True
locate_app
(app_id)
Attempts to locate the application.
Attempts to locate the application.
def locate_app(app_id): """Attempts to locate the application.""" __traceback_hide__ = True if ':' in app_id: module, app_obj = app_id.split(':', 1) else: module = app_id app_obj = None try: __import__(module) except ImportError: # Reraise the ImportError if it occurred within the imported module. # Determine this by checking whether the trace has a depth > 1. if sys.exc_info()[-1].tb_next: raise else: raise NoAppException('The file/path provided (%s) does not appear' ' to exist. Please verify the path is ' 'correct. If app is not on PYTHONPATH, ' 'ensure the extension is .py' % module) mod = sys.modules[module] if app_obj is None: app = find_best_app(mod) else: app = getattr(mod, app_obj, None) if app is None: raise RuntimeError('Failed to find application in module "%s"' % module) return app
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[ 79, 0 ]
[ 110, 14 ]
python
en
['en', 'en', 'en']
True
with_appcontext
(f)
Wraps a callback so that it's guaranteed to be executed with the script's application context. If callbacks are registered directly to the ``app.cli`` object then they are wrapped with this function by default unless it's disabled.
Wraps a callback so that it's guaranteed to be executed with the script's application context. If callbacks are registered directly to the ``app.cli`` object then they are wrapped with this function by default unless it's disabled.
def with_appcontext(f): """Wraps a callback so that it's guaranteed to be executed with the script's application context. If callbacks are registered directly to the ``app.cli`` object then they are wrapped with this function by default unless it's disabled. """ @click.pass_context def decorator(__ctx, *args, **kwargs): with __ctx.ensure_object(ScriptInfo).load_app().app_context(): return __ctx.invoke(f, *args, **kwargs) return update_wrapper(decorator, f)
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[ 247, 0 ]
[ 257, 39 ]
python
en
['en', 'en', 'en']
True
run_command
(info, host, port, reload, debugger, eager_loading, with_threads)
Runs a local development server for the Flask application. This local server is recommended for development purposes only but it can also be used for simple intranet deployments. By default it will not support any sort of concurrency at all to simplify debugging. This can be changed with the --with-threads option which will enable basic multithreading. The reloader and debugger are by default enabled if the debug flag of Flask is enabled and disabled otherwise.
Runs a local development server for the Flask application.
def run_command(info, host, port, reload, debugger, eager_loading, with_threads): """Runs a local development server for the Flask application. This local server is recommended for development purposes only but it can also be used for simple intranet deployments. By default it will not support any sort of concurrency at all to simplify debugging. This can be changed with the --with-threads option which will enable basic multithreading. The reloader and debugger are by default enabled if the debug flag of Flask is enabled and disabled otherwise. """ from werkzeug.serving import run_simple debug = get_debug_flag() if reload is None: reload = bool(debug) if debugger is None: debugger = bool(debug) if eager_loading is None: eager_loading = not reload app = DispatchingApp(info.load_app, use_eager_loading=eager_loading) # Extra startup messages. This depends a bit on Werkzeug internals to # not double execute when the reloader kicks in. if os.environ.get('WERKZEUG_RUN_MAIN') != 'true': # If we have an import path we can print it out now which can help # people understand what's being served. If we do not have an # import path because the app was loaded through a callback then # we won't print anything. if info.app_import_path is not None: print(' * Serving Flask app "%s"' % info.app_import_path) if debug is not None: print(' * Forcing debug mode %s' % (debug and 'on' or 'off')) run_simple(host, port, app, use_reloader=reload, use_debugger=debugger, threaded=with_threads)
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[ 399, 0 ]
[ 437, 60 ]
python
en
['en', 'en', 'en']
True
shell_command
()
Runs an interactive Python shell in the context of a given Flask application. The application will populate the default namespace of this shell according to it's configuration. This is useful for executing small snippets of management code without having to manually configuring the application.
Runs an interactive Python shell in the context of a given Flask application. The application will populate the default namespace of this shell according to it's configuration.
def shell_command(): """Runs an interactive Python shell in the context of a given Flask application. The application will populate the default namespace of this shell according to it's configuration. This is useful for executing small snippets of management code without having to manually configuring the application. """ import code from flask.globals import _app_ctx_stack app = _app_ctx_stack.top.app banner = 'Python %s on %s\nApp: %s%s\nInstance: %s' % ( sys.version, sys.platform, app.import_name, app.debug and ' [debug]' or '', app.instance_path, ) ctx = {} # Support the regular Python interpreter startup script if someone # is using it. startup = os.environ.get('PYTHONSTARTUP') if startup and os.path.isfile(startup): with open(startup, 'r') as f: eval(compile(f.read(), startup, 'exec'), ctx) ctx.update(app.make_shell_context()) code.interact(banner=banner, local=ctx)
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[ 442, 0 ]
[ 471, 43 ]
python
en
['en', 'en', 'en']
True
ScriptInfo.load_app
(self)
Loads the Flask app (if not yet loaded) and returns it. Calling this multiple times will just result in the already loaded app to be returned.
Loads the Flask app (if not yet loaded) and returns it. Calling this multiple times will just result in the already loaded app to be returned.
def load_app(self): """Loads the Flask app (if not yet loaded) and returns it. Calling this multiple times will just result in the already loaded app to be returned. """ __traceback_hide__ = True if self._loaded_app is not None: return self._loaded_app if self.create_app is not None: rv = self.create_app(self) else: if not self.app_import_path: raise NoAppException( 'Could not locate Flask application. You did not provide ' 'the FLASK_APP environment variable.\n\nFor more ' 'information see ' 'http://flask.pocoo.org/docs/latest/quickstart/') rv = locate_app(self.app_import_path) debug = get_debug_flag() if debug is not None: rv.debug = debug self._loaded_app = rv return rv
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[ 219, 4 ]
[ 241, 17 ]
python
en
['en', 'en', 'en']
True
AppGroup.command
(self, *args, **kwargs)
This works exactly like the method of the same name on a regular :class:`click.Group` but it wraps callbacks in :func:`with_appcontext` unless it's disabled by passing ``with_appcontext=False``.
This works exactly like the method of the same name on a regular :class:`click.Group` but it wraps callbacks in :func:`with_appcontext` unless it's disabled by passing ``with_appcontext=False``.
def command(self, *args, **kwargs): """This works exactly like the method of the same name on a regular :class:`click.Group` but it wraps callbacks in :func:`with_appcontext` unless it's disabled by passing ``with_appcontext=False``. """ wrap_for_ctx = kwargs.pop('with_appcontext', True) def decorator(f): if wrap_for_ctx: f = with_appcontext(f) return click.Group.command(self, *args, **kwargs)(f) return decorator
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[ 268, 4 ]
[ 278, 24 ]
python
en
['en', 'en', 'en']
True
AppGroup.group
(self, *args, **kwargs)
This works exactly like the method of the same name on a regular :class:`click.Group` but it defaults the group class to :class:`AppGroup`.
This works exactly like the method of the same name on a regular :class:`click.Group` but it defaults the group class to :class:`AppGroup`.
def group(self, *args, **kwargs): """This works exactly like the method of the same name on a regular :class:`click.Group` but it defaults the group class to :class:`AppGroup`. """ kwargs.setdefault('cls', AppGroup) return click.Group.group(self, *args, **kwargs)
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[ 280, 4 ]
[ 286, 55 ]
python
en
['en', 'en', 'en']
True
make_breseq_folder
(folder: Path)
Mocks a breseq run for a single sample. .folder |---- output/ |----|---- index.html |---- data/ |----|---- output.vcf
Mocks a breseq run for a single sample. .folder |---- output/ |----|---- index.html |---- data/ |----|---- output.vcf
def make_breseq_folder(folder: Path) -> Dict[str, Path]: """ Mocks a breseq run for a single sample. .folder |---- output/ |----|---- index.html |---- data/ |----|---- output.vcf """ folder = checkdir(folder) folder_output = checkdir(folder / "output") folder_data = checkdir(folder / "data") folder_evidence = checkdir(folder_output / "evidence") filename_index = make_file(folder_output, 'index.html') filename_vcf = make_file(folder_data, 'output.vcf') filename_gd = make_file(folder_evidence, "annotated.gd") filename_summary = make_file(folder_data, "summary.json") result = { 'index': filename_index, 'gd': filename_gd, 'summary': filename_summary, 'vcf': filename_vcf } return result
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[ 31, 0 ]
[ 57, 14 ]
python
en
['en', 'error', 'th']
False
structure1
(tmp_path)
.parent |---- CeN0L1G5/ |----|---- data/ |----|---- output/ |---- CeN0L1G3/ |----|---- data/ |----|---- output/ |---- CeN0L1G29/ |----|---- data/ |----|---- output/
.parent |---- CeN0L1G5/ |----|---- data/ |----|---- output/ |---- CeN0L1G3/ |----|---- data/ |----|---- output/ |---- CeN0L1G29/ |----|---- data/ |----|---- output/
def structure1(tmp_path) -> BreseqFolder: """ .parent |---- CeN0L1G5/ |----|---- data/ |----|---- output/ |---- CeN0L1G3/ |----|---- data/ |----|---- output/ |---- CeN0L1G29/ |----|---- data/ |----|---- output/ """ parent_folder = checkdir(tmp_path / "cefepime") filenames_sample_1 = make_breseq_folder(parent_folder / "CeN0L1G5") filenames_sample_2 = make_breseq_folder(parent_folder / "CeN0L1G3") filenames_sample_3 = make_breseq_folder(parent_folder / "CeN0L1G29") samples = [ filenames_sample_1, filenames_sample_2, filenames_sample_3 ] result = BreseqFolder( parent = parent_folder, samples = samples ) return result
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[ 61, 0 ]
[ 92, 14 ]
python
en
['en', 'error', 'th']
False
structure2
(tmp_path)
.parent |---- sample1/ |----|---- breseq |---- sample2/ |----|---- breseq |---- sample3/ |----|---- breseq
.parent |---- sample1/ |----|---- breseq |---- sample2/ |----|---- breseq |---- sample3/ |----|---- breseq
def structure2(tmp_path) -> BreseqFolder: """ .parent |---- sample1/ |----|---- breseq |---- sample2/ |----|---- breseq |---- sample3/ |----|---- breseq """ parent_folder = checkdir(tmp_path / "cefepime") folder_1 = checkdir(parent_folder / "CeN0L1G5") folder_2 = checkdir(parent_folder / "CeN0L1G3") folder_3 = checkdir(parent_folder / "CeN0L1G29") filenames_sample_1 = make_breseq_folder(folder_1 / "breseq") filenames_sample_2 = make_breseq_folder(folder_2 / "breseq") filenames_sample_3 = make_breseq_folder(folder_3 / "breseq") result = BreseqFolder( parent = parent_folder, samples = [ filenames_sample_1, filenames_sample_2, filenames_sample_3 ] ) return result
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[ 96, 0 ]
[ 125, 14 ]
python
en
['en', 'error', 'th']
False
upload_docs._build_multipart
(cls, data)
Build up the MIME payload for the POST data
Build up the MIME payload for the POST data
def _build_multipart(cls, data): """ Build up the MIME payload for the POST data """ boundary = b'--------------GHSKFJDLGDS7543FJKLFHRE75642756743254' sep_boundary = b'\n--' + boundary end_boundary = sep_boundary + b'--' end_items = end_boundary, b"\n", builder = functools.partial( cls._build_part, sep_boundary=sep_boundary, ) part_groups = map(builder, data.items()) parts = itertools.chain.from_iterable(part_groups) body_items = itertools.chain(parts, end_items) content_type = 'multipart/form-data; boundary=%s' % boundary.decode('ascii') return b''.join(body_items), content_type
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[ 125, 4 ]
[ 141, 49 ]
python
en
['en', 'error', 'th']
False
do_block
(parser, token)
Define a block that can be overridden by child templates.
Define a block that can be overridden by child templates.
def do_block(parser, token): """ Define a block that can be overridden by child templates. """ # token.split_contents() isn't useful here because this tag doesn't accept variable as arguments bits = token.contents.split() if len(bits) != 2: raise TemplateSyntaxError("'%s' tag takes only one argument" % bits[0]) block_name = bits[1] # Keep track of the names of BlockNodes found in this template, so we can # check for duplication. try: if block_name in parser.__loaded_blocks: raise TemplateSyntaxError("'%s' tag with name '%s' appears more than once" % (bits[0], block_name)) parser.__loaded_blocks.append(block_name) except AttributeError: # parser.__loaded_blocks isn't a list yet parser.__loaded_blocks = [block_name] nodelist = parser.parse(('endblock',)) # This check is kept for backwards-compatibility. See #3100. endblock = parser.next_token() acceptable_endblocks = ('endblock', 'endblock %s' % block_name) if endblock.contents not in acceptable_endblocks: parser.invalid_block_tag(endblock, 'endblock', acceptable_endblocks) return BlockNode(block_name, nodelist)
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[ 198, 0 ]
[ 223, 42 ]
python
en
['en', 'error', 'th']
False
construct_relative_path
(current_template_name, relative_name)
Convert a relative path (starting with './' or '../') to the full template name based on the current_template_name.
Convert a relative path (starting with './' or '../') to the full template name based on the current_template_name.
def construct_relative_path(current_template_name, relative_name): """ Convert a relative path (starting with './' or '../') to the full template name based on the current_template_name. """ has_quotes = ( (relative_name.startswith('"') and relative_name.endswith('"')) or (relative_name.startswith("'") and relative_name.endswith("'")) ) new_name = relative_name.strip('\'"') if not new_name.startswith(('./', '../')): # relative_name is a variable or a literal that doesn't contain a # relative path. return relative_name new_name = posixpath.normpath( posixpath.join( posixpath.dirname(current_template_name.lstrip('/')), new_name, ) ) if new_name.startswith('../'): raise TemplateSyntaxError( "The relative path '%s' points outside the file hierarchy that " "template '%s' is in." % (relative_name, current_template_name) ) if current_template_name.lstrip('/') == new_name: raise TemplateSyntaxError( "The relative path '%s' was translated to template name '%s', the " "same template in which the tag appears." % (relative_name, current_template_name) ) return f'"{new_name}"' if has_quotes else new_name
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[ 226, 0 ]
[ 258, 54 ]
python
en
['en', 'error', 'th']
False
do_extends
(parser, token)
Signal that this template extends a parent template. This tag may be used in two ways: ``{% extends "base" %}`` (with quotes) uses the literal value "base" as the name of the parent template to extend, or ``{% extends variable %}`` uses the value of ``variable`` as either the name of the parent template to extend (if it evaluates to a string) or as the parent template itself (if it evaluates to a Template object).
Signal that this template extends a parent template.
def do_extends(parser, token): """ Signal that this template extends a parent template. This tag may be used in two ways: ``{% extends "base" %}`` (with quotes) uses the literal value "base" as the name of the parent template to extend, or ``{% extends variable %}`` uses the value of ``variable`` as either the name of the parent template to extend (if it evaluates to a string) or as the parent template itself (if it evaluates to a Template object). """ bits = token.split_contents() if len(bits) != 2: raise TemplateSyntaxError("'%s' takes one argument" % bits[0]) bits[1] = construct_relative_path(parser.origin.template_name, bits[1]) parent_name = parser.compile_filter(bits[1]) nodelist = parser.parse() if nodelist.get_nodes_by_type(ExtendsNode): raise TemplateSyntaxError("'%s' cannot appear more than once in the same template" % bits[0]) return ExtendsNode(nodelist, parent_name)
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[ 262, 0 ]
[ 280, 45 ]
python
en
['en', 'error', 'th']
False
do_include
(parser, token)
Load a template and render it with the current context. You can pass additional context using keyword arguments. Example:: {% include "foo/some_include" %} {% include "foo/some_include" with bar="BAZZ!" baz="BING!" %} Use the ``only`` argument to exclude the current context when rendering the included template:: {% include "foo/some_include" only %} {% include "foo/some_include" with bar="1" only %}
Load a template and render it with the current context. You can pass additional context using keyword arguments.
def do_include(parser, token): """ Load a template and render it with the current context. You can pass additional context using keyword arguments. Example:: {% include "foo/some_include" %} {% include "foo/some_include" with bar="BAZZ!" baz="BING!" %} Use the ``only`` argument to exclude the current context when rendering the included template:: {% include "foo/some_include" only %} {% include "foo/some_include" with bar="1" only %} """ bits = token.split_contents() if len(bits) < 2: raise TemplateSyntaxError( "%r tag takes at least one argument: the name of the template to " "be included." % bits[0] ) options = {} remaining_bits = bits[2:] while remaining_bits: option = remaining_bits.pop(0) if option in options: raise TemplateSyntaxError('The %r option was specified more ' 'than once.' % option) if option == 'with': value = token_kwargs(remaining_bits, parser, support_legacy=False) if not value: raise TemplateSyntaxError('"with" in %r tag needs at least ' 'one keyword argument.' % bits[0]) elif option == 'only': value = True else: raise TemplateSyntaxError('Unknown argument for %r tag: %r.' % (bits[0], option)) options[option] = value isolated_context = options.get('only', False) namemap = options.get('with', {}) bits[1] = construct_relative_path(parser.origin.template_name, bits[1]) return IncludeNode(parser.compile_filter(bits[1]), extra_context=namemap, isolated_context=isolated_context)
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[ 284, 0 ]
[ 328, 57 ]
python
en
['en', 'error', 'th']
False
ExtendsNode.find_template
(self, template_name, context)
This is a wrapper around engine.find_template(). A history is kept in the render_context attribute between successive extends calls and passed as the skip argument. This enables extends to work recursively without extending the same template twice.
This is a wrapper around engine.find_template(). A history is kept in the render_context attribute between successive extends calls and passed as the skip argument. This enables extends to work recursively without extending the same template twice.
def find_template(self, template_name, context): """ This is a wrapper around engine.find_template(). A history is kept in the render_context attribute between successive extends calls and passed as the skip argument. This enables extends to work recursively without extending the same template twice. """ history = context.render_context.setdefault( self.context_key, [self.origin], ) template, origin = context.template.engine.find_template( template_name, skip=history, ) history.append(origin) return template
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[ 92, 4 ]
[ 106, 23 ]
python
en
['en', 'error', 'th']
False
IncludeNode.render
(self, context)
Render the specified template and context. Cache the template object in render_context to avoid reparsing and loading when used in a for loop.
Render the specified template and context. Cache the template object in render_context to avoid reparsing and loading when used in a for loop.
def render(self, context): """ Render the specified template and context. Cache the template object in render_context to avoid reparsing and loading when used in a for loop. """ template = self.template.resolve(context) # Does this quack like a Template? if not callable(getattr(template, 'render', None)): # If not, try the cache and select_template(). template_name = template or () if isinstance(template_name, str): template_name = (construct_relative_path( self.origin.template_name, template_name, ),) else: template_name = tuple(template_name) cache = context.render_context.dicts[0].setdefault(self, {}) template = cache.get(template_name) if template is None: template = context.template.engine.select_template(template_name) cache[template_name] = template # Use the base.Template of a backends.django.Template. elif hasattr(template, 'template'): template = template.template values = { name: var.resolve(context) for name, var in self.extra_context.items() } if self.isolated_context: return template.render(context.new(values)) with context.push(**values): return template.render(context)
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[ 161, 4 ]
[ 194, 43 ]
python
en
['en', 'error', 'th']
False
save_agent
(output_dir, batch_id, model, optimizer, scheduler, keep_last=3)
Store agent to disk.
Store agent to disk.
def save_agent(output_dir, batch_id, model, optimizer, scheduler, keep_last=3): """Store agent to disk.""" fpath = os.path.join(output_dir, 'ckpt.%08d' % (batch_id)) logging.info('Saving: %s', fpath) torch.save( dict(model=model.state_dict(), optim=optimizer.state_dict(), done_batches=batch_id, scheduler=scheduler and scheduler.state_dict()), fpath) # Delete the older ones, keep the last few all_ckpts = sorted(glob.glob(os.path.join(output_dir, 'ckpt.*'))) to_del = all_ckpts[:-keep_last] for fpath in to_del: os.remove(fpath)
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[ 47, 0 ]
[ 60, 24 ]
python
en
['en', 'en', 'en']
True
convert_to_video_vis
(vid, is_solved=None)
Generate a video visualization to go into tensorboard. Args: vid np.ndarray(BxTxHxW): Video in standard PHYRE style is_solved (int): Whether this video solves the task or not. Returns: vid_vis (BxTxHxWx3)
Generate a video visualization to go into tensorboard. Args: vid np.ndarray(BxTxHxW): Video in standard PHYRE style is_solved (int): Whether this video solves the task or not. Returns: vid_vis (BxTxHxWx3)
def convert_to_video_vis(vid, is_solved=None): """ Generate a video visualization to go into tensorboard. Args: vid np.ndarray(BxTxHxW): Video in standard PHYRE style is_solved (int): Whether this video solves the task or not. Returns: vid_vis (BxTxHxWx3) """ return np.stack([ # The is_solved argument adds a little bar to the top for visualization # green if solved, red if not. np.stack([ phyre.vis.observations_to_uint8_rgb(frame, is_solved=is_solved) for frame in clip ]) for clip in vid ])
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[ 63, 0 ]
[ 79, 6 ]
python
en
['en', 'error', 'th']
False
get_num_workers
(num_workers, frames_per_clip)
Finetunes the nworkers if batch size/frames per clip too large, since otherwise jobs crash.
Finetunes the nworkers if batch size/frames per clip too large, since otherwise jobs crash.
def get_num_workers(num_workers, frames_per_clip): """Finetunes the nworkers if batch size/frames per clip too large, since otherwise jobs crash.""" del frames_per_clip return num_workers
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[ 86, 22 ]
python
en
['en', 'en', 'en']
True
gen_vis_vid_preds
(orig_vid, model, n_fwd_times=None, run_decode=True, n_hist_frames=3)
Generate a visualization of some training videos, along with model rollout (actual autoregressive rollout, so need to test again). Args: orig_vid: (B, T, Nobj, H, W) video batch model: the pytorch model for forward prediction Returns: RGB frames (B, T, 3, H, W) as torch tensor, in the standard format that can be used with tensorboard.
Generate a visualization of some training videos, along with model rollout (actual autoregressive rollout, so need to test again). Args: orig_vid: (B, T, Nobj, H, W) video batch model: the pytorch model for forward prediction Returns: RGB frames (B, T, 3, H, W) as torch tensor, in the standard format that can be used with tensorboard.
def gen_vis_vid_preds(orig_vid, model, n_fwd_times=None, run_decode=True, n_hist_frames=3): """ Generate a visualization of some training videos, along with model rollout (actual autoregressive rollout, so need to test again). Args: orig_vid: (B, T, Nobj, H, W) video batch model: the pytorch model for forward prediction Returns: RGB frames (B, T, 3, H, W) as torch tensor, in the standard format that can be used with tensorboard. """ # Generate the predictions if n_fwd_times is None: n_fwd_times = orig_vid.shape[1] - n_hist_frames # As many we've GT for # For vis, at least 1 frame would be needed for following code n_fwd_times = max(n_fwd_times, 1) vid = orig_vid[:, :n_hist_frames, ...] # crop out the first part for pred with torch.no_grad(): model.eval() all_preds, _ = model.forward(vid, None, n_hist_frames=n_hist_frames, n_fwd_times=n_fwd_times, compute_losses=False, need_intermediate=True, run_decode=run_decode, nslices=1) stacked, _, _, _ = ImgTrainer.vis_stacked_pred_gt( nets.combine_obj_pixels(orig_vid, 2).cpu().numpy(), nets.combine_obj_pixels(vid, 2), all_preds['pixels'] if run_decode else None) # For some reason need to flip the image in space and time for corr vis stacked_rgb = np.array( convert_to_video_vis(stacked).transpose((0, 1, 4, 2, 3))) return torch.as_tensor(stacked_rgb)
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[ 89, 0 ]
[ 128, 39 ]
python
en
['en', 'error', 'th']
False
phyre_batchvidresize
(t, shape)
Args: t: Input video tensor batch, Long dtype, BxTxHxW shape: Output shape required, (H', W')
Args: t: Input video tensor batch, Long dtype, BxTxHxW shape: Output shape required, (H', W')
def phyre_batchvidresize(t, shape): """ Args: t: Input video tensor batch, Long dtype, BxTxHxW shape: Output shape required, (H', W') """ return nn.functional.interpolate(t.to(torch.float), size=list(shape), mode='nearest').to(torch.long)
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python
en
['en', 'error', 'th']
False
overlay_pred_scores
(vid, scores, ch=1)
Args: vid (B, 1, H, W): PHYRE style video (torch.Tensor) scores (B,) Scores for each batch element to be overlayed in text on the frame ch: Which channel to overlay on. Returns: vid (B, H, W) with the score overlayed
Args: vid (B, 1, H, W): PHYRE style video (torch.Tensor) scores (B,) Scores for each batch element to be overlayed in text on the frame ch: Which channel to overlay on. Returns: vid (B, H, W) with the score overlayed
def overlay_pred_scores(vid, scores, ch=1): """ Args: vid (B, 1, H, W): PHYRE style video (torch.Tensor) scores (B,) Scores for each batch element to be overlayed in text on the frame ch: Which channel to overlay on. Returns: vid (B, H, W) with the score overlayed """ overlays = [] for batch_id in range(vid.shape[0]): img = Image.new('1', vid.shape[1:][::-1], 0) draw = ImageDraw.Draw(img) draw.text((10, 10), '{:04f}'.format(scores[batch_id]), (1, )) overlays.append(np.array(img)[::-1, :]) overlay = torch.LongTensor(np.stack(overlays)).to(vid.device) vid = vid * (overlay == 0) + (overlay > 0) * ch return vid
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[ 148, 0 ]
[ 166, 14 ]
python
en
['en', 'error', 'th']
False
compute_pixel_accuracy
(gt, pred)
Args: gt torch.Tensor(B, T, H, W) pred torch.Tensor(B, T, H, W) Returns: acc torch.Tensor(B, phyre.NUM_COLORS)
Args: gt torch.Tensor(B, T, H, W) pred torch.Tensor(B, T, H, W) Returns: acc torch.Tensor(B, phyre.NUM_COLORS)
def compute_pixel_accuracy(gt, pred): """ Args: gt torch.Tensor(B, T, H, W) pred torch.Tensor(B, T, H, W) Returns: acc torch.Tensor(B, phyre.NUM_COLORS) """ match = (gt == pred) res = torch.zeros(( gt.shape[0], phyre.NUM_COLORS, )) for col in range(phyre.NUM_COLORS): relevant = (gt == col) res[:, col] = torch.sum(match * relevant, dim=(1, 2, 3)) / torch.sum( relevant, dim=(1, 2, 3)).float() return res
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[ 169, 0 ]
[ 186, 14 ]
python
en
['en', 'error', 'th']
False
store_frames
(frames, task_ids, outdir, subdir, actions)
Args: frames: (B, T, H, W) outdir (path where to store all frames) actions: (B, 3)
Args: frames: (B, T, H, W) outdir (path where to store all frames) actions: (B, 3)
def store_frames(frames, task_ids, outdir, subdir, actions): """ Args: frames: (B, T, H, W) outdir (path where to store all frames) actions: (B, 3) """ assert frames.shape[0] == len(task_ids) assert frames.shape[0] == actions.shape[0] for i, task_id in enumerate(task_ids): action = actions[i] action_str = '{:.5f}_{:.5f}_{:.5f}'.format(action[0], action[1], action[2]) template, _ = task_id.split(':') this_outdir = os.path.join(outdir, 'eval_vis', template, task_id + '_' + action_str, subdir) os.makedirs(this_outdir, exist_ok=True) all_rendered = [] for time_step in range(frames[i].shape[0]): rendered = phyre.vis.observations_to_uint8_rgb( frames[i][time_step]) # Storing individually was super slow! # Image.fromarray(rendered).save( # os.path.join(this_outdir, '%d.png' % time_step)) all_rendered.append(rendered) imageio.mimwrite(os.path.join(this_outdir, 'combined.gif'), all_rendered, fps=2)
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[ 189, 0 ]
[ 216, 31 ]
python
en
['en', 'error', 'th']
False
ImgTrainer.load_agent_from_folder
(cls, model: NeuralModel, agent_folder: str, strict: bool = True)
This loader is used in the offline_agents code, to load at test time.
This loader is used in the offline_agents code, to load at test time.
def load_agent_from_folder(cls, model: NeuralModel, agent_folder: str, strict: bool = True) -> NeuralModel: """ This loader is used in the offline_agents code, to load at test time. """ last_checkpoint = get_latest_checkpoint(agent_folder) assert last_checkpoint is not None, agent_folder logging.info('Loading a model from: %s', last_checkpoint) last_checkpoint = torch.load(last_checkpoint) missing_keys, unexp_keys = model.load_state_dict( last_checkpoint['model'], strict=strict) logging.warning('Could not init: %s', missing_keys) logging.warning('Unused keys in ckpt: %s', unexp_keys) model.to(nets.DEVICE) return model
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[ 221, 4 ]
[ 237, 20 ]
python
en
['en', 'error', 'th']
False
ImgTrainer.gen_model
(cls, cfg)
Generate the random init model.
Generate the random init model.
def gen_model(cls, cfg): """Generate the random init model.""" model = nets.Fwd(agent_cfg=cfg.agent) assert cfg.num_gpus <= torch.cuda.device_count() model = torch.nn.DataParallel(model, device_ids=list(range(cfg.num_gpus))) return model
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[ 240, 4 ]
[ 246, 20 ]
python
en
['en', 'ms', 'en']
True
ImgTrainer.train
(cls, model, dataset, output_dir, summary_writer, full_eval_from_model, cfg)
Main train function.
Main train function.
def train(cls, model, dataset, output_dir, summary_writer, full_eval_from_model, cfg): """Main train function.""" updates = cfg.train.num_iter report_every = cfg.train.report_every save_checkpoints_every = cfg.train.save_checkpoints_every full_eval_every = cfg.train.full_eval_every train_batch_size = cfg.train.batch_size max_frames_fwd = cfg.train.frames_per_clip n_hist_frames = cfg.train.n_hist_frames # Frames used to predict the future loss_cfg = cfg.train.loss opt_params = cfg.opt # action_tier_name = cfg.tier n_fwd_times = cfg.train.n_fwd_times n_fwd_times_incur_loss = cfg.train.n_fwd_times_incur_loss run_decode = cfg.train.run_decode train_modules_subset = cfg.train.modules_to_train # nslices (slice out the input for training) num_slices = cfg.train.num_slices if max_frames_fwd is not None and (max_frames_fwd <= n_hist_frames): logging.warning( 'Cant train prediction model, max_frames_fwd too low') assert loss_cfg.wt_pix == 0 or run_decode is True, ( 'If the loss is non zero, the decoder should be running') # logging.info('Creating eval subset from train') # eval_train = create_balanced_eval_set(cache, dataset.task_ids, # XE_EVAL_SIZE, action_tier_name) # if dev_tasks_ids is not None: # logging.info('Creating eval subset from dev') # eval_dev = create_balanced_eval_set(cache, dev_tasks_ids, XE_EVAL_SIZE, # action_tier_name) # else: # eval_dev = None device = nets.DEVICE model.train() model.to(device) logging.info("%s", model) params_to_train = [] if train_modules_subset is not None: mod_names = train_modules_subset.split('%') logging.warning( 'Training only a few modules, listed below. NOTE: ' 'BNs/dropout will still be in train mode. Explicitly ' 'set those to eval mode if thats not desired.') for mod_name in mod_names: mod = getattr(model.module, mod_name) logging.warning('Training %s: %s', mod_name, mod) params_to_train.extend(mod.parameters()) else: params_to_train = model.parameters() optimizer = hydra.utils.instantiate(opt_params, params_to_train) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=updates) logging.info('Starting actual training for %d updates', updates) last_checkpoint = get_latest_checkpoint(output_dir) batch_start = 0 # By default, starting from iteration 0, unles loading mdl if last_checkpoint is not None: logging.info('Going to load from %s', last_checkpoint) last_checkpoint = torch.load(last_checkpoint) model.load_state_dict(last_checkpoint['model']) optimizer.load_state_dict(last_checkpoint['optim']) # Subtracting 1 since we store batch_id + 1 when calling save_agent batch_start = last_checkpoint['done_batches'] - 1 if scheduler is not None: scheduler.load_state_dict(last_checkpoint['scheduler']) def run_full_eval(batch_id): logging.info('Running full eval') results = {} # To store to a json eval_stats = full_eval_from_model(model) metric = eval_stats.compute_all_metrics() results['metrics'] = metric results[ 'metrics_rollout'] = eval_stats.compute_all_metrics_over_rollout( ) results[ 'metrics_per_task'] = eval_stats.compute_all_metrics_per_task( ) max_test_attempts_per_task = (cfg.max_test_attempts_per_task or phyre.MAX_TEST_ATTEMPTS) results['parsed_args'] = dict( # cfg=cfg, # Not json serializable, anyway will be stored in dir main_kwargs=dict( eval_setup_name=cfg.eval_setup_name, fold_id=cfg.fold_id, use_test_split=cfg.use_test_split, agent_type=cfg.agent.type, max_test_attempts_per_task=max_test_attempts_per_task, output_dir=output_dir)) results['target_metric'] = ( results['metrics']['independent_solved_by_aucs'] [max_test_attempts_per_task]) results['target_metric_over_time'] = [ el['independent_solved_by_aucs'][max_test_attempts_per_task] for el in results['metrics_rollout'] ] logging.info('Iter %d: %s; Over rollout: %s', (batch_id + 1), results['target_metric'], results['target_metric_over_time']) score = metric['independent_solved_by_aucs'][-1] summary_writer.add_scalar('FullEval/AUCCESS', score, batch_id + 1) for solved_by_iter in metric['global_solved_by']: summary_writer.add_scalar( 'FullEval/solved_by_{}'.format(solved_by_iter), metric['global_solved_by'][solved_by_iter], batch_id + 1) logging.info('Full eval perf @ %d: %s', batch_id + 1, score) for i, metric in enumerate(results['metrics_rollout']): summary_writer.add_scalar( 'FullEvalRollout/AUCCESS/{}'.format(i + 1), metric['independent_solved_by_aucs'][-1], batch_id + 1) summary_writer.add_scalar( 'FullEvalRollout/solved_by_100/{}'.format(i), metric['global_solved_by'][100], batch_id + 1) respath = os.path.join( output_dir, 'results_intermediate/{:08d}.json'.format(batch_id + 1)) os.makedirs(os.path.dirname(respath), exist_ok=True) with open(respath, 'w') as fout: json.dump(results, fout) logging.info('Report every %d; full eval every %d', report_every, full_eval_every) if save_checkpoints_every > full_eval_every: save_checkpoints_every -= save_checkpoints_every % full_eval_every losses_report = {} last_time = time.time() assert train_batch_size > 1 and train_batch_size % 2 == 0, ( 'Needs to get 2 elements at least to balance out') for batch_data_id, batch_data in enumerate( torch.utils.data.DataLoader( dataset, num_workers=get_num_workers( cfg.train.data_loader.num_workers, dataset.frames_per_clip), pin_memory=False, # Asking for half the batch size since the dataloader is designed # to give 2 elements per batch (for class balancing) batch_size=train_batch_size // 2)): # When the training restarts, it resets to the start of the data loader batch_id = batch_data_id + batch_start if (batch_id + 1) >= updates: save_agent(output_dir, batch_id + 1, model, optimizer, scheduler) break model.train() batch_is_solved = batch_data['is_solved'] batch_is_solved = batch_is_solved.to(device, non_blocking=True) batch_is_solved = batch_is_solved.reshape((-1, )) batch_vid_obs = batch_data['vid_obs'] batch_vid_obs = batch_vid_obs.reshape( [-1] + list(batch_vid_obs.shape[2:])) batch_vid_obs = batch_vid_obs.to(device) # Run the forward image model on the video _, batch_losses = model.forward( batch_vid_obs, batch_is_solved, n_hist_frames=n_hist_frames, n_fwd_times=n_fwd_times, n_fwd_times_incur_loss=n_fwd_times_incur_loss, run_decode=run_decode, compute_losses=True, need_intermediate=loss_cfg.on_intermediate, autoenc_loss_ratio=loss_cfg.autoenc_loss_ratio, nslices=num_slices) optimizer.zero_grad() total_loss = 0 # Mean over each loss type from each replica for loss_type in batch_losses: loss_wt = getattr(loss_cfg, 'wt_' + loss_type) if loss_wt <= 0: continue loss_val = loss_wt * torch.mean(batch_losses[loss_type], dim=0) if loss_type not in losses_report: losses_report[loss_type] = [] losses_report[loss_type].append(loss_val.item()) total_loss += loss_val total_loss.backward() optimizer.step() if (save_checkpoints_every > 0 and (batch_id + 1) % save_checkpoints_every == 0): save_agent(output_dir, batch_id + 1, model, optimizer, scheduler) # Removing intermediate eval since it doesnt seem very useful, using the # full eval for now. # if (batch_id + 1) % eval_every == 0: # print_eval_stats(batch_id) if (batch_id + 1) % report_every == 0: speed = report_every / (time.time() - last_time) last_time = time.time() loss_stats = { typ: np.mean(losses_report[typ][-report_every:]) for typ in losses_report if len(losses_report[typ]) > 0 } logging.info( 'Iter: %s, examples: %d, mean loss: %s, speed: %.1f batch/sec,' ' lr: %f', batch_id + 1, (batch_id + 1) * train_batch_size, loss_stats, speed, get_lr(optimizer)) for typ in loss_stats: summary_writer.add_scalar('Loss/{}'.format(typ), loss_stats[typ], batch_id + 1) summary_writer.add_scalar('Loss/Total', sum(loss_stats.values()), batch_id + 1) summary_writer.add_scalar('LR', get_lr(optimizer), batch_id + 1) summary_writer.add_scalar('Speed', speed, batch_id + 1) # Add a histogram of the batch task IDs, to make sure it picks a # variety of task batch_templates = np.array( dataset.task_ids)[batch_data['task_indices'].reshape( (-1, ))].tolist() batch_templates = np.array( [int(el.split(':')[0]) for el in batch_templates]) gpu_mem_max = max([ torch.cuda.max_memory_allocated(device=i) for i in range(torch.cuda.device_count()) ]) summary_writer.add_scalar('GPU/Mem/Max', gpu_mem_max, batch_id + 1) summary_writer.add_histogram('Templates', batch_templates, global_step=(batch_id + 1), bins=25) # Visualize a couple train videos, and actual rollouts if pix is # being trained # Just visualizing the first 256 videos in case the batch size is # larger; somehow the visualizations get corrupted (grey bg) for # more. Also no point filling up the memory. # Storing less frequently than the rest of the logs (takes lot of space) if n_fwd_times > 0 and (batch_id + 1) % (report_every * 10) == 0: summary_writer.add_video( 'InputAndRollout/train', gen_vis_vid_preds(batch_vid_obs[:256], model, n_fwd_times=None, run_decode=run_decode, n_hist_frames=n_hist_frames), (batch_id + 1)) if (batch_id + 1) % full_eval_every == 0: run_full_eval(batch_id) if scheduler is not None: scheduler.step() return model.cpu()
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"batch_id", "=", "batch_data_id", "+", "batch_start", "if", "(", "batch_id", "+", "1", ")", ">=", "updates", ":", "save_agent", "(", "output_dir", ",", "batch_id", "+", "1", ",", "model", ",", "optimizer", ",", "scheduler", ")", "break", "model", ".", "train", "(", ")", "batch_is_solved", "=", "batch_data", "[", "'is_solved'", "]", "batch_is_solved", "=", "batch_is_solved", ".", "to", "(", "device", ",", "non_blocking", "=", "True", ")", "batch_is_solved", "=", "batch_is_solved", ".", "reshape", "(", "(", "-", "1", ",", ")", ")", "batch_vid_obs", "=", "batch_data", "[", "'vid_obs'", "]", "batch_vid_obs", "=", "batch_vid_obs", ".", "reshape", "(", "[", "-", "1", "]", "+", "list", "(", "batch_vid_obs", ".", "shape", "[", "2", ":", "]", ")", ")", "batch_vid_obs", "=", "batch_vid_obs", ".", "to", "(", "device", ")", "# Run the forward image model on the video", "_", ",", "batch_losses", "=", "model", ".", "forward", "(", "batch_vid_obs", ",", "batch_is_solved", ",", "n_hist_frames", "=", "n_hist_frames", ",", "n_fwd_times", "=", "n_fwd_times", ",", "n_fwd_times_incur_loss", "=", "n_fwd_times_incur_loss", ",", "run_decode", "=", "run_decode", ",", "compute_losses", "=", "True", ",", "need_intermediate", "=", "loss_cfg", ".", "on_intermediate", ",", "autoenc_loss_ratio", "=", "loss_cfg", ".", "autoenc_loss_ratio", ",", "nslices", "=", "num_slices", ")", "optimizer", ".", "zero_grad", "(", ")", "total_loss", "=", "0", "# Mean over each loss type from each replica", "for", "loss_type", "in", "batch_losses", ":", "loss_wt", "=", "getattr", "(", "loss_cfg", ",", "'wt_'", "+", "loss_type", ")", "if", "loss_wt", "<=", "0", ":", "continue", "loss_val", "=", "loss_wt", "*", "torch", ".", "mean", "(", "batch_losses", "[", "loss_type", "]", ",", "dim", "=", "0", ")", "if", "loss_type", "not", "in", "losses_report", ":", "losses_report", "[", "loss_type", "]", "=", "[", "]", "losses_report", "[", "loss_type", "]", ".", "append", "(", "loss_val", ".", "item", "(", ")", ")", "total_loss", "+=", "loss_val", "total_loss", ".", "backward", "(", ")", "optimizer", ".", "step", "(", ")", "if", "(", "save_checkpoints_every", ">", "0", "and", "(", "batch_id", "+", "1", ")", "%", "save_checkpoints_every", "==", "0", ")", ":", "save_agent", "(", "output_dir", ",", "batch_id", "+", "1", ",", "model", ",", "optimizer", ",", "scheduler", ")", "# Removing intermediate eval since it doesnt seem very useful, using the", "# full eval for now.", "# if (batch_id + 1) % eval_every == 0:", "# print_eval_stats(batch_id)", "if", "(", "batch_id", "+", "1", ")", "%", "report_every", "==", "0", ":", "speed", "=", "report_every", "/", "(", "time", ".", "time", "(", ")", "-", "last_time", ")", "last_time", "=", "time", ".", "time", "(", ")", "loss_stats", "=", "{", "typ", ":", "np", ".", "mean", "(", "losses_report", "[", "typ", "]", "[", "-", "report_every", ":", "]", ")", "for", "typ", "in", "losses_report", "if", "len", "(", "losses_report", "[", "typ", "]", ")", ">", "0", "}", "logging", ".", "info", "(", "'Iter: %s, examples: %d, mean loss: %s, speed: %.1f batch/sec,'", "' lr: %f'", ",", "batch_id", "+", "1", ",", "(", "batch_id", "+", "1", ")", "*", "train_batch_size", ",", "loss_stats", ",", "speed", ",", "get_lr", "(", "optimizer", ")", ")", "for", "typ", "in", "loss_stats", ":", "summary_writer", ".", "add_scalar", "(", "'Loss/{}'", ".", "format", "(", "typ", ")", ",", "loss_stats", "[", "typ", "]", ",", "batch_id", "+", "1", ")", "summary_writer", ".", "add_scalar", "(", "'Loss/Total'", ",", "sum", "(", "loss_stats", ".", "values", "(", ")", ")", ",", "batch_id", "+", "1", ")", "summary_writer", ".", "add_scalar", "(", "'LR'", ",", "get_lr", "(", "optimizer", ")", ",", "batch_id", "+", "1", ")", "summary_writer", ".", "add_scalar", "(", "'Speed'", ",", "speed", ",", "batch_id", "+", "1", ")", "# Add a histogram of the batch task IDs, to make sure it picks a", "# variety of task", "batch_templates", "=", "np", ".", "array", "(", "dataset", ".", "task_ids", ")", "[", "batch_data", "[", "'task_indices'", "]", ".", "reshape", "(", "(", "-", "1", ",", ")", ")", "]", ".", "tolist", "(", ")", "batch_templates", "=", "np", ".", "array", "(", "[", "int", "(", "el", ".", "split", "(", "':'", ")", "[", "0", "]", ")", "for", "el", "in", "batch_templates", "]", ")", "gpu_mem_max", "=", "max", "(", "[", "torch", ".", "cuda", ".", "max_memory_allocated", "(", "device", "=", "i", ")", "for", "i", "in", "range", "(", "torch", ".", "cuda", ".", "device_count", "(", ")", ")", "]", ")", "summary_writer", ".", "add_scalar", "(", "'GPU/Mem/Max'", ",", "gpu_mem_max", ",", "batch_id", "+", "1", ")", "summary_writer", ".", "add_histogram", "(", "'Templates'", ",", "batch_templates", ",", "global_step", "=", "(", "batch_id", "+", "1", ")", ",", "bins", "=", "25", ")", "# Visualize a couple train videos, and actual rollouts if pix is", "# being trained", "# Just visualizing the first 256 videos in case the batch size is", "# larger; somehow the visualizations get corrupted (grey bg) for", "# more. Also no point filling up the memory.", "# Storing less frequently than the rest of the logs (takes lot of space)", "if", "n_fwd_times", ">", "0", "and", "(", "batch_id", "+", "1", ")", "%", "(", "report_every", "*", "10", ")", "==", "0", ":", "summary_writer", ".", "add_video", "(", "'InputAndRollout/train'", ",", "gen_vis_vid_preds", "(", "batch_vid_obs", "[", ":", "256", "]", ",", "model", ",", "n_fwd_times", "=", "None", ",", "run_decode", "=", "run_decode", ",", "n_hist_frames", "=", "n_hist_frames", ")", ",", "(", "batch_id", "+", "1", ")", ")", "if", "(", "batch_id", "+", "1", ")", "%", "full_eval_every", "==", "0", ":", "run_full_eval", "(", "batch_id", ")", "if", "scheduler", "is", "not", "None", ":", "scheduler", ".", "step", "(", ")", "return", "model", ".", "cpu", "(", ")" ]
[ 249, 4 ]
[ 501, 26 ]
python
en
['en', 'ja', 'en']
True
ImgTrainer.eval_actions
(cls, model, dataset, nactionsXtasks, batch_size, cfg)
Evaluate likelihood of actions solving the task.
Evaluate likelihood of actions solving the task.
def eval_actions(cls, model, dataset, nactionsXtasks, batch_size, cfg): """Evaluate likelihood of actions solving the task.""" init_frames_to_sim = cfg.eval.init_frames_to_sim # Run it for these many n_hist_frames = cfg.eval.n_hist_frames n_fwd_times = cfg.eval.n_fwd_times store_vis = cfg.eval.store_vis train_run_decode = cfg.train.run_decode assert init_frames_to_sim >= n_hist_frames, 'Need those many to start pred' def pad_tensor(tensor: torch.Tensor, sz: int): """ Pad the tensor's bottom (along batch dim), using the last element, sz times. """ bottom_tensor_rep = [tensor[-1:, ...]] * sz return torch.cat([tensor] + bottom_tensor_rep, dim=0) def unpad_tensor(tensor: torch.Tensor, sz: int): return tensor[:tensor.shape[0] - sz, ...] # Clear the directory of older vis if any out_dir = 'vis/' if store_vis: logging.warning('Removing older vis from %s/%s', os.getcwd(), out_dir) subprocess.call(f'rm {out_dir}/*.gif', shell=True) subprocess.call(f'rm -r {out_dir}/eval_vis', shell=True) if os.path.exists('temp_storage'): final_scores = torch.load(os.path.join('temp_storage', 'final_scores.pt')) final_actions = torch.load(os.path.join('temp_storage', 'final_actions.pt')) final_task_indices = torch.load(os.path.join('temp_storage', 'final_task_indices.pt')) final_pixel_accs = torch.load(os.path.join('temp_storage', 'final_pixel_accs.pt')) else: scores = [] actions = [] task_indices = [] pixel_accs = [] # If generating output, how accurately did we do with torch.no_grad(): model.eval() for batch_id, batch_data in enumerate( tqdm(torch.utils.data.DataLoader( dataset, num_workers=get_num_workers( cfg.eval.data_loader.num_workers, dataset.frames_per_clip), pin_memory=False, batch_size=batch_size, drop_last=False), desc='All tasks X actions batches', total=nactionsXtasks // batch_size)): batch_task_indices = batch_data['task_indices'] print(batch_id) batch_vid_obs = batch_data[ f'{cfg.agent.input_space}_obs'].squeeze(1) batch_vid_obs_orig = batch_data[ f'{cfg.agent.input_space}_obs_orig'].squeeze(1) batch_actions = batch_data['actions'] # Since the code might be run with DataParallel, need to make sure # the batch size is divisible by the ngpus, so stick to the # requested batch size by padding actions. uniq_batch_size = batch_actions.shape[0] pad_len = max(batch_size - uniq_batch_size, 0) batch_vid_obs = pad_tensor(batch_vid_obs, pad_len) # Setting run_decode always true when true in training.. # (earlier only when visualizing) # Sometimes evaluation might depend on the decoded frame, so might # as well... other_kwargs = { 'need_intermediate': True, 'run_decode': train_run_decode, 'nslices': 1 } all_preds, batch_losses = model.forward( batch_vid_obs, None, n_hist_frames=n_hist_frames, n_fwd_times=n_fwd_times, compute_losses=False, **other_kwargs) # Unpad parts of all_preds that will be used further # Since the model is trained with BCELoss, normalize using sigmoid # On 2020/02/11, I changed it to return only one prediction for # any n_fwd_times (max-pool all to give 1 prediction), hence this # list will only contain a single element. To stay consistent with # prior code that expects a prediction at each time step, simply # repeating that prediction n_fwd_times. batch_scores = nn.Sigmoid()(unpad_tensor( all_preds['is_solved'], pad_len)) batch_vid_obs = unpad_tensor(batch_vid_obs, pad_len) if store_vis: # Sum the vid obs over the channels, in case it was split into # components if cfg.agent.input_space == 'obj': # update to videos, for storing vis batch_vid_obs = batch_data['vid_obs'].squeeze(1) batch_vid_obs_orig = batch_data[ 'vid_obs_orig'].squeeze(1) batch_vid_obs = pad_tensor(batch_vid_obs, pad_len) batch_vid_obs = unpad_tensor(batch_vid_obs, pad_len) task_ids = batch_data['task_ids'] _, pixel_acc, gt_frames, pred_frames = cls.vis_stacked_pred_gt( torch.sum(batch_vid_obs_orig, axis=-3).cpu().numpy(), torch.sum(batch_vid_obs, dim=-3), [ unpad_tensor(el.cpu(), pad_len) for el in all_preds['pixels'] ]) ''' [batch_scores] * len(all_preds['pixels']), # Could take any batch_task_indices, all are same '{}/{:04d}_{:04d}.gif'.format(out_dir, batch_task_indices[0], batch_id)) ''' # Also store pure frames individually, will be used for rollout # accuracy evaluation store_frames(gt_frames, task_ids, out_dir, 'gt', batch_actions) store_frames(pred_frames, task_ids, out_dir, 'predictions', batch_actions) else: pixel_acc = torch.zeros( (batch_scores.shape[0], phyre.NUM_COLORS)) assert len(batch_scores) == len(batch_actions), ( batch_actions.shape, batch_scores.shape) # IMP: Don't convert to cpu() numpy() here.. it makes the function # much slower. Convert in one go at the end when returning scores.append(deepcopy(batch_scores)) pixel_accs.append(deepcopy(pixel_acc)) actions.append(deepcopy(batch_actions)) task_indices.append(deepcopy(batch_task_indices)) # There is only 1 element in scores, but unsqueezing so that # it's compatible with following code that expects a score prediction # over time. Here it will give 1 prediction, the final one. final_scores = torch.cat(scores, dim=0).unsqueeze(0).cpu().numpy() final_actions = torch.cat(actions, dim=0).cpu().numpy() final_task_indices = torch.cat(task_indices, dim=0).cpu().numpy() final_pixel_accs = torch.cat(pixel_accs, dim=0).cpu().numpy() if not os.path.exists('temp_storage'): os.makedirs('temp_storage') torch.save(final_scores, os.path.join('temp_storage', 'final_scores.pt')) torch.save(final_actions, os.path.join('temp_storage', 'final_actions.pt')) torch.save(final_task_indices, os.path.join('temp_storage', 'final_task_indices.pt')) torch.save(final_pixel_accs, os.path.join('temp_storage', 'final_pixel_accs.pt')) if nactionsXtasks != len(final_actions): logging.warning('Only evaluated %d actions instead of full %d', len(final_actions), nactionsXtasks) assert (nactionsXtasks - len(actions)) <= batch_size, ( 'Shouldnt miss more') return final_scores, final_actions, final_task_indices, final_pixel_accs
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Convert in one go at the end when returning", "scores", ".", "append", "(", "deepcopy", "(", "batch_scores", ")", ")", "pixel_accs", ".", "append", "(", "deepcopy", "(", "pixel_acc", ")", ")", "actions", ".", "append", "(", "deepcopy", "(", "batch_actions", ")", ")", "task_indices", ".", "append", "(", "deepcopy", "(", "batch_task_indices", ")", ")", "# There is only 1 element in scores, but unsqueezing so that", "# it's compatible with following code that expects a score prediction", "# over time. Here it will give 1 prediction, the final one.", "final_scores", "=", "torch", ".", "cat", "(", "scores", ",", "dim", "=", "0", ")", ".", "unsqueeze", "(", "0", ")", ".", "cpu", "(", ")", ".", "numpy", "(", ")", "final_actions", "=", "torch", ".", "cat", "(", "actions", ",", "dim", "=", "0", ")", ".", "cpu", "(", ")", ".", "numpy", "(", ")", "final_task_indices", "=", "torch", ".", "cat", "(", "task_indices", ",", "dim", "=", "0", ")", ".", "cpu", "(", ")", ".", "numpy", "(", ")", "final_pixel_accs", "=", "torch", ".", "cat", "(", "pixel_accs", ",", "dim", "=", "0", ")", ".", "cpu", "(", ")", ".", "numpy", "(", ")", "if", "not", "os", ".", "path", ".", "exists", "(", "'temp_storage'", ")", ":", "os", ".", "makedirs", "(", "'temp_storage'", ")", "torch", ".", "save", "(", "final_scores", ",", "os", ".", "path", ".", "join", "(", "'temp_storage'", ",", "'final_scores.pt'", ")", ")", "torch", ".", "save", "(", "final_actions", ",", "os", ".", "path", ".", "join", "(", "'temp_storage'", ",", "'final_actions.pt'", ")", ")", "torch", ".", "save", "(", "final_task_indices", ",", "os", ".", "path", ".", "join", "(", "'temp_storage'", ",", "'final_task_indices.pt'", ")", ")", "torch", ".", "save", "(", "final_pixel_accs", ",", "os", ".", "path", ".", "join", "(", "'temp_storage'", ",", "'final_pixel_accs.pt'", ")", ")", "if", "nactionsXtasks", "!=", "len", "(", "final_actions", ")", ":", "logging", ".", "warning", "(", "'Only evaluated %d actions instead of full %d'", ",", "len", "(", "final_actions", ")", ",", "nactionsXtasks", ")", "assert", "(", "nactionsXtasks", "-", "len", "(", "actions", ")", ")", "<=", "batch_size", ",", "(", "'Shouldnt miss more'", ")", "return", "final_scores", ",", "final_actions", ",", "final_task_indices", ",", "final_pixel_accs" ]
[ 504, 4 ]
[ 666, 80 ]
python
en
['en', 'en', 'en']
True
ImgTrainer.vis_stacked_pred_gt
(cls, orig_vid_full, orig_vid, pred_vid_qnt, pred_solved=None, store_path=None)
Args: orig_vid_full: list of videos [T'x256x256] for each batch element in orig_vid, for even the frames that are going to be predicted orig_vid (BxTx256x256) pred_vid_qnt [(BxHxW)] (or None, if not available) (unprocessed; i.e. argmaxed from 1-hot if need be, done already) pred_solved: [(B,)] list of is_solved scores from the model. Or can be None store_path (str): Path to store the video. None if not store. Returns: (B, T, H, W) Combined output (B, phyre.NUM_COLORS) pixel accuracy of the generated video
Args: orig_vid_full: list of videos [T'x256x256] for each batch element in orig_vid, for even the frames that are going to be predicted orig_vid (BxTx256x256) pred_vid_qnt [(BxHxW)] (or None, if not available) (unprocessed; i.e. argmaxed from 1-hot if need be, done already) pred_solved: [(B,)] list of is_solved scores from the model. Or can be None store_path (str): Path to store the video. None if not store. Returns: (B, T, H, W) Combined output (B, phyre.NUM_COLORS) pixel accuracy of the generated video
def vis_stacked_pred_gt(cls, orig_vid_full, orig_vid, pred_vid_qnt, pred_solved=None, store_path=None): """ Args: orig_vid_full: list of videos [T'x256x256] for each batch element in orig_vid, for even the frames that are going to be predicted orig_vid (BxTx256x256) pred_vid_qnt [(BxHxW)] (or None, if not available) (unprocessed; i.e. argmaxed from 1-hot if need be, done already) pred_solved: [(B,)] list of is_solved scores from the model. Or can be None store_path (str): Path to store the video. None if not store. Returns: (B, T, H, W) Combined output (B, phyre.NUM_COLORS) pixel accuracy of the generated video """ if pred_vid_qnt is None: return (orig_vid.cpu().numpy(), np.zeros( (orig_vid.shape[0], phyre.NUM_COLORS)), None, None) assert len(orig_vid_full) == orig_vid.shape[0] # Prepare full GT predictions to go below each clip orig_vid_full_padded = [] all_t = min(orig_vid_full[0].shape[0], len(pred_vid_qnt)) for vid in orig_vid_full: if vid.shape[0] >= all_t: orig_vid_full_padded.append(vid.astype(np.long)) else: # Pad the videos with white frames if that frame is not returned raise NotImplementedError('This should not happen') gt_video = phyre_batchvidresize( torch.stack([torch.as_tensor(el) for el in orig_vid_full_padded]), pred_vid_qnt[0].shape[1:]).cpu() gt_video = gt_video.numpy() # Convert the gt clip to same size as predictions, add temporal dim orig_vid = phyre_batchvidresize(orig_vid, pred_vid_qnt[0].shape[1:]) frames_quantized = torch.cat([orig_vid] + [el.unsqueeze(1) for el in pred_vid_qnt], dim=1).cpu().numpy() # Pad the video with empty frames to match the size of predicted videos padder = np.tile( np.zeros_like(gt_video[:, -1:]), (1, abs(frames_quantized.shape[1] - gt_video.shape[1]), 1, 1)) gt_video_padded = gt_video frames_quantized_padded = frames_quantized if gt_video.shape[1] > frames_quantized.shape[1]: frames_quantized_padded = np.concatenate( [frames_quantized, padder], axis=1) else: gt_video_padded = np.concatenate([gt_video, padder], axis=1) # Compute the accuracy between the generated frames, and the GT frames # Only do for generated frames (so taking the last few ones) # If few GT frames are given (eg, when just training classifier), it will # be comparing to empty frames and get low score but that is okay, we don't # care about the pixel numbers at that point anyway # Update April 2 2020: This is using frames with numbers overlayed etc... so # deprecating this eval. pix_acc = compute_pixel_accuracy( torch.as_tensor(gt_video_padded[:, -len(pred_vid_qnt):, ...]), torch.as_tensor( frames_quantized_padded[:, -len(pred_vid_qnt):, ...])) # Stack them on the height axis final_vid = np.concatenate([gt_video_padded, frames_quantized_padded], axis=-2) if store_path: os.makedirs(os.path.dirname(store_path), exist_ok=True) phyre.vis.save_observation_series_to_gif( [frames_quantized_padded, gt_video_padded], store_path, # Piggy-backing on the solved_state markers to show which parts are # GT and which parts are being predicted solved_states=([True] * orig_vid.shape[1] + [False] * final_vid.shape[1]), solved_wrt_step=True, fps=2) return final_vid, pix_acc, gt_video, frames_quantized
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[ 669, 4 ]
[ 749, 61 ]
python
en
['en', 'error', 'th']
False
retry
(*dargs: t.Any, **dkw: t.Any)
Wrap a function with a new `Retrying` object. :param dargs: positional arguments passed to Retrying object :param dkw: keyword arguments passed to the Retrying object
Wrap a function with a new `Retrying` object.
def retry(*dargs: t.Any, **dkw: t.Any) -> t.Union[WrappedFn, t.Callable[[WrappedFn], WrappedFn]]: # noqa """Wrap a function with a new `Retrying` object. :param dargs: positional arguments passed to Retrying object :param dkw: keyword arguments passed to the Retrying object """ # support both @retry and @retry() as valid syntax if len(dargs) == 1 and callable(dargs[0]): return retry()(dargs[0]) else: def wrap(f: WrappedFn) -> WrappedFn: if isinstance(f, retry_base): warnings.warn( f"Got retry_base instance ({f.__class__.__name__}) as callable argument, " f"this will probably hang indefinitely (did you mean retry={f.__class__.__name__}(...)?)" ) if iscoroutinefunction(f): r: "BaseRetrying" = AsyncRetrying(*dargs, **dkw) elif tornado and hasattr(tornado.gen, "is_coroutine_function") and tornado.gen.is_coroutine_function(f): r = TornadoRetrying(*dargs, **dkw) else: r = Retrying(*dargs, **dkw) return r.wraps(f) return wrap
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[ 106, 0 ]
[ 132, 19 ]
python
en
['en', 'en', 'en']
True
BaseRetrying.copy
( self, sleep: t.Union[t.Callable[[t.Union[int, float]], None], object] = _unset, stop: t.Union["stop_base", object] = _unset, wait: t.Union["wait_base", object] = _unset, retry: t.Union[retry_base, object] = _unset, before: t.Union[t.Callable[["RetryCallState"], None], object] = _unset, after: t.Union[t.Callable[["RetryCallState"], None], object] = _unset, before_sleep: t.Union[t.Optional[t.Callable[["RetryCallState"], None]], object] = _unset, reraise: t.Union[bool, object] = _unset, retry_error_cls: t.Union[t.Type[RetryError], object] = _unset, retry_error_callback: t.Union[t.Optional[t.Callable[["RetryCallState"], t.Any]], object] = _unset, )
Copy this object with some parameters changed if needed.
Copy this object with some parameters changed if needed.
def copy( self, sleep: t.Union[t.Callable[[t.Union[int, float]], None], object] = _unset, stop: t.Union["stop_base", object] = _unset, wait: t.Union["wait_base", object] = _unset, retry: t.Union[retry_base, object] = _unset, before: t.Union[t.Callable[["RetryCallState"], None], object] = _unset, after: t.Union[t.Callable[["RetryCallState"], None], object] = _unset, before_sleep: t.Union[t.Optional[t.Callable[["RetryCallState"], None]], object] = _unset, reraise: t.Union[bool, object] = _unset, retry_error_cls: t.Union[t.Type[RetryError], object] = _unset, retry_error_callback: t.Union[t.Optional[t.Callable[["RetryCallState"], t.Any]], object] = _unset, ) -> "BaseRetrying": """Copy this object with some parameters changed if needed.""" return self.__class__( sleep=_first_set(sleep, self.sleep), stop=_first_set(stop, self.stop), wait=_first_set(wait, self.wait), retry=_first_set(retry, self.retry), before=_first_set(before, self.before), after=_first_set(after, self.after), before_sleep=_first_set(before_sleep, self.before_sleep), reraise=_first_set(reraise, self.reraise), retry_error_cls=_first_set(retry_error_cls, self.retry_error_cls), retry_error_callback=_first_set(retry_error_callback, self.retry_error_callback), )
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[ 251, 4 ]
[ 276, 9 ]
python
en
['en', 'en', 'en']
True
BaseRetrying.statistics
(self)
Return a dictionary of runtime statistics. This dictionary will be empty when the controller has never been ran. When it is running or has ran previously it should have (but may not) have useful and/or informational keys and values when running is underway and/or completed. .. warning:: The keys in this dictionary **should** be some what stable (not changing), but there existence **may** change between major releases as new statistics are gathered or removed so before accessing keys ensure that they actually exist and handle when they do not. .. note:: The values in this dictionary are local to the thread running call (so if multiple threads share the same retrying object - either directly or indirectly) they will each have there own view of statistics they have collected (in the future we may provide a way to aggregate the various statistics from each thread).
Return a dictionary of runtime statistics.
def statistics(self) -> t.Dict[str, t.Any]: """Return a dictionary of runtime statistics. This dictionary will be empty when the controller has never been ran. When it is running or has ran previously it should have (but may not) have useful and/or informational keys and values when running is underway and/or completed. .. warning:: The keys in this dictionary **should** be some what stable (not changing), but there existence **may** change between major releases as new statistics are gathered or removed so before accessing keys ensure that they actually exist and handle when they do not. .. note:: The values in this dictionary are local to the thread running call (so if multiple threads share the same retrying object - either directly or indirectly) they will each have there own view of statistics they have collected (in the future we may provide a way to aggregate the various statistics from each thread). """ try: return self._local.statistics except AttributeError: self._local.statistics = {} return self._local.statistics
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[ 290, 4 ]
[ 315, 41 ]
python
en
['en', 'en', 'en']
True
BaseRetrying.wraps
(self, f: WrappedFn)
Wrap a function for retrying. :param f: A function to wraps for retrying.
Wrap a function for retrying.
def wraps(self, f: WrappedFn) -> WrappedFn: """Wrap a function for retrying. :param f: A function to wraps for retrying. """ @functools.wraps(f) def wrapped_f(*args: t.Any, **kw: t.Any) -> t.Any: return self(f, *args, **kw) def retry_with(*args: t.Any, **kwargs: t.Any) -> WrappedFn: return self.copy(*args, **kwargs).wraps(f) wrapped_f.retry = self wrapped_f.retry_with = retry_with return wrapped_f
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python
en
['en', 'en', 'en']
True
Future.failed
(self)
Return whether a exception is being held in this future.
Return whether a exception is being held in this future.
def failed(self) -> bool: """Return whether a exception is being held in this future.""" return self.exception() is not None
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python
en
['en', 'en', 'en']
True
Future.construct
(cls, attempt_number: int, value: t.Any, has_exception: bool)
Construct a new Future object.
Construct a new Future object.
def construct(cls, attempt_number: int, value: t.Any, has_exception: bool) -> "Future": """Construct a new Future object.""" fut = cls(attempt_number) if has_exception: fut.set_exception(value) else: fut.set_result(value) return fut
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python
en
['en', 'en', 'en']
True
SharedDataMiddleware.is_allowed
(self, filename)
Subclasses can override this method to disallow the access to certain files. However by providing `disallow` in the constructor this method is overwritten.
Subclasses can override this method to disallow the access to certain files. However by providing `disallow` in the constructor this method is overwritten.
def is_allowed(self, filename): """Subclasses can override this method to disallow the access to certain files. However by providing `disallow` in the constructor this method is overwritten. """ return True
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python
en
['en', 'en', 'en']
True