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Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.format_epilog_section
def format_epilog_section(self, section, text): """Format a section for the epilog by inserting a format""" try: func = self._epilog_formatters[self.epilog_formatter] except KeyError: if not callable(self.epilog_formatter): raise func = self.epilog_formatter return func(section, text)
python
def format_epilog_section(self, section, text): """Format a section for the epilog by inserting a format""" try: func = self._epilog_formatters[self.epilog_formatter] except KeyError: if not callable(self.epilog_formatter): raise func = self.epilog_formatter return func(section, text)
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Format a section for the epilog by inserting a format
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L578-L586
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.extract_as_epilog
def extract_as_epilog(self, text, sections=None, overwrite=False, append=True): """Extract epilog sections from the a docstring Parameters ---------- text The docstring to use sections: list of str The headers of the sections to extract. If None, the :attr:`epilog_sections` attribute is used overwrite: bool If True, overwrite the existing epilog append: bool If True, append to the existing epilog""" if sections is None: sections = self.epilog_sections if ((not self.epilog or overwrite or append) and sections): epilog_parts = [] for sec in sections: text = docstrings._get_section(text, sec).strip() if text: epilog_parts.append( self.format_epilog_section(sec, text)) if epilog_parts: epilog = '\n\n'.join(epilog_parts) if overwrite or not self.epilog: self.epilog = epilog else: self.epilog += '\n\n' + epilog
python
def extract_as_epilog(self, text, sections=None, overwrite=False, append=True): """Extract epilog sections from the a docstring Parameters ---------- text The docstring to use sections: list of str The headers of the sections to extract. If None, the :attr:`epilog_sections` attribute is used overwrite: bool If True, overwrite the existing epilog append: bool If True, append to the existing epilog""" if sections is None: sections = self.epilog_sections if ((not self.epilog or overwrite or append) and sections): epilog_parts = [] for sec in sections: text = docstrings._get_section(text, sec).strip() if text: epilog_parts.append( self.format_epilog_section(sec, text)) if epilog_parts: epilog = '\n\n'.join(epilog_parts) if overwrite or not self.epilog: self.epilog = epilog else: self.epilog += '\n\n' + epilog
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Extract epilog sections from the a docstring Parameters ---------- text The docstring to use sections: list of str The headers of the sections to extract. If None, the :attr:`epilog_sections` attribute is used overwrite: bool If True, overwrite the existing epilog append: bool If True, append to the existing epilog
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L588-L617
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.grouparg
def grouparg(self, arg, my_arg=None, parent_cmds=[]): """ Grouper function for chaining subcommands Parameters ---------- arg: str The current command line argument that is parsed my_arg: str The name of this subparser. If None, this parser is the main parser and has no parent parser parent_cmds: list of str The available commands of the parent parsers Returns ------- str or None The grouping key for the given `arg` or None if the key does not correspond to this parser or this parser is the main parser and does not have seen a subparser yet Notes ----- Quite complicated, there is no real need to deal with this function """ if self._subparsers_action is None: return None commands = self._subparsers_action.choices currentarg = self.__currentarg # the default return value is the current argument we are in or the # name of the subparser itself ret = currentarg or my_arg if currentarg is not None: # if we are already in a sub command, we use the sub parser sp_key = commands[currentarg].grouparg(arg, currentarg, chain( commands, parent_cmds)) if sp_key is None and arg in commands: # if the subparser did not recognize the command, we use the # command the corresponds to this parser or (of this parser # is the parent parser) the current subparser self.__currentarg = currentarg = arg ret = my_arg or currentarg elif sp_key not in commands and arg in parent_cmds: # otherwise, if the subparser recognizes the commmand but it is # not in the known command of this parser, it must be another # command of the subparser and this parser can ignore it ret = None else: # otherwise the command belongs to this subparser (if this one # is not the subparser) or the current subparser ret = my_arg or currentarg elif arg in commands: # if the argument is a valid subparser, we return this one self.__currentarg = arg ret = arg elif arg in parent_cmds: # if the argument is not a valid subparser but in one of our # parents, we return None to signalize that we cannot categorize # it ret = None return ret
python
def grouparg(self, arg, my_arg=None, parent_cmds=[]): """ Grouper function for chaining subcommands Parameters ---------- arg: str The current command line argument that is parsed my_arg: str The name of this subparser. If None, this parser is the main parser and has no parent parser parent_cmds: list of str The available commands of the parent parsers Returns ------- str or None The grouping key for the given `arg` or None if the key does not correspond to this parser or this parser is the main parser and does not have seen a subparser yet Notes ----- Quite complicated, there is no real need to deal with this function """ if self._subparsers_action is None: return None commands = self._subparsers_action.choices currentarg = self.__currentarg # the default return value is the current argument we are in or the # name of the subparser itself ret = currentarg or my_arg if currentarg is not None: # if we are already in a sub command, we use the sub parser sp_key = commands[currentarg].grouparg(arg, currentarg, chain( commands, parent_cmds)) if sp_key is None and arg in commands: # if the subparser did not recognize the command, we use the # command the corresponds to this parser or (of this parser # is the parent parser) the current subparser self.__currentarg = currentarg = arg ret = my_arg or currentarg elif sp_key not in commands and arg in parent_cmds: # otherwise, if the subparser recognizes the commmand but it is # not in the known command of this parser, it must be another # command of the subparser and this parser can ignore it ret = None else: # otherwise the command belongs to this subparser (if this one # is not the subparser) or the current subparser ret = my_arg or currentarg elif arg in commands: # if the argument is a valid subparser, we return this one self.__currentarg = arg ret = arg elif arg in parent_cmds: # if the argument is not a valid subparser but in one of our # parents, we return None to signalize that we cannot categorize # it ret = None return ret
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Grouper function for chaining subcommands Parameters ---------- arg: str The current command line argument that is parsed my_arg: str The name of this subparser. If None, this parser is the main parser and has no parent parser parent_cmds: list of str The available commands of the parent parsers Returns ------- str or None The grouping key for the given `arg` or None if the key does not correspond to this parser or this parser is the main parser and does not have seen a subparser yet Notes ----- Quite complicated, there is no real need to deal with this function
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L619-L679
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.__parse_main
def __parse_main(self, args): """Parse the main arguments only. This is a work around for python 2.7 because argparse does not allow to parse arguments without subparsers """ if six.PY2: self._subparsers_action.add_parser("__dummy") return super(FuncArgParser, self).parse_known_args( list(args) + ['__dummy']) return super(FuncArgParser, self).parse_known_args(args)
python
def __parse_main(self, args): """Parse the main arguments only. This is a work around for python 2.7 because argparse does not allow to parse arguments without subparsers """ if six.PY2: self._subparsers_action.add_parser("__dummy") return super(FuncArgParser, self).parse_known_args( list(args) + ['__dummy']) return super(FuncArgParser, self).parse_known_args(args)
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Parse the main arguments only. This is a work around for python 2.7 because argparse does not allow to parse arguments without subparsers
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L709-L717
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.update_short
def update_short(self, **kwargs): """ Update the short optional arguments (those with one leading '-') This method updates the short argument name for the specified function arguments as stored in :attr:`unfinished_arguments` Parameters ---------- ``**kwargs`` Keywords must be keys in the :attr:`unfinished_arguments` dictionary (i.e. keywords of the root functions), values the short argument names Examples -------- Setting:: >>> parser.update_short(something='s', something_else='se') is basically the same as:: >>> parser.update_arg('something', short='s') >>> parser.update_arg('something_else', short='se') which in turn is basically comparable to:: >>> parser.add_argument('-s', '--something', ...) >>> parser.add_argument('-se', '--something_else', ...) See Also -------- update_shortf, update_long""" for key, val in six.iteritems(kwargs): self.update_arg(key, short=val)
python
def update_short(self, **kwargs): """ Update the short optional arguments (those with one leading '-') This method updates the short argument name for the specified function arguments as stored in :attr:`unfinished_arguments` Parameters ---------- ``**kwargs`` Keywords must be keys in the :attr:`unfinished_arguments` dictionary (i.e. keywords of the root functions), values the short argument names Examples -------- Setting:: >>> parser.update_short(something='s', something_else='se') is basically the same as:: >>> parser.update_arg('something', short='s') >>> parser.update_arg('something_else', short='se') which in turn is basically comparable to:: >>> parser.add_argument('-s', '--something', ...) >>> parser.add_argument('-se', '--something_else', ...) See Also -------- update_shortf, update_long""" for key, val in six.iteritems(kwargs): self.update_arg(key, short=val)
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Update the short optional arguments (those with one leading '-') This method updates the short argument name for the specified function arguments as stored in :attr:`unfinished_arguments` Parameters ---------- ``**kwargs`` Keywords must be keys in the :attr:`unfinished_arguments` dictionary (i.e. keywords of the root functions), values the short argument names Examples -------- Setting:: >>> parser.update_short(something='s', something_else='se') is basically the same as:: >>> parser.update_arg('something', short='s') >>> parser.update_arg('something_else', short='se') which in turn is basically comparable to:: >>> parser.add_argument('-s', '--something', ...) >>> parser.add_argument('-se', '--something_else', ...) See Also -------- update_shortf, update_long
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L721-L755
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.update_long
def update_long(self, **kwargs): """ Update the long optional arguments (those with two leading '-') This method updates the short argument name for the specified function arguments as stored in :attr:`unfinished_arguments` Parameters ---------- ``**kwargs`` Keywords must be keys in the :attr:`unfinished_arguments` dictionary (i.e. keywords of the root functions), values the long argument names Examples -------- Setting:: >>> parser.update_long(something='s', something_else='se') is basically the same as:: >>> parser.update_arg('something', long='s') >>> parser.update_arg('something_else', long='se') which in turn is basically comparable to:: >>> parser.add_argument('--s', dest='something', ...) >>> parser.add_argument('--se', dest='something_else', ...) See Also -------- update_short, update_longf""" for key, val in six.iteritems(kwargs): self.update_arg(key, long=val)
python
def update_long(self, **kwargs): """ Update the long optional arguments (those with two leading '-') This method updates the short argument name for the specified function arguments as stored in :attr:`unfinished_arguments` Parameters ---------- ``**kwargs`` Keywords must be keys in the :attr:`unfinished_arguments` dictionary (i.e. keywords of the root functions), values the long argument names Examples -------- Setting:: >>> parser.update_long(something='s', something_else='se') is basically the same as:: >>> parser.update_arg('something', long='s') >>> parser.update_arg('something_else', long='se') which in turn is basically comparable to:: >>> parser.add_argument('--s', dest='something', ...) >>> parser.add_argument('--se', dest='something_else', ...) See Also -------- update_short, update_longf""" for key, val in six.iteritems(kwargs): self.update_arg(key, long=val)
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Update the long optional arguments (those with two leading '-') This method updates the short argument name for the specified function arguments as stored in :attr:`unfinished_arguments` Parameters ---------- ``**kwargs`` Keywords must be keys in the :attr:`unfinished_arguments` dictionary (i.e. keywords of the root functions), values the long argument names Examples -------- Setting:: >>> parser.update_long(something='s', something_else='se') is basically the same as:: >>> parser.update_arg('something', long='s') >>> parser.update_arg('something_else', long='se') which in turn is basically comparable to:: >>> parser.add_argument('--s', dest='something', ...) >>> parser.add_argument('--se', dest='something_else', ...) See Also -------- update_short, update_longf
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L792-L826
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.parse2func
def parse2func(self, args=None, func=None): """Parse the command line arguments to the setup function This method parses the given command line arguments to the function used in the :meth:`setup_args` method to setup up this parser Parameters ---------- args: list The list of command line arguments func: function An alternative function to use. If None, the last function or the one specified through the `setup_as` parameter in the :meth:`setup_args` is used. Returns ------- object What ever is returned by the called function Note ---- This method does not cover subparsers!""" kws = vars(self.parse_args(args)) if func is None: if self._setup_as: func = kws.pop(self._setup_as) else: func = self._used_functions[-1] return func(**kws)
python
def parse2func(self, args=None, func=None): """Parse the command line arguments to the setup function This method parses the given command line arguments to the function used in the :meth:`setup_args` method to setup up this parser Parameters ---------- args: list The list of command line arguments func: function An alternative function to use. If None, the last function or the one specified through the `setup_as` parameter in the :meth:`setup_args` is used. Returns ------- object What ever is returned by the called function Note ---- This method does not cover subparsers!""" kws = vars(self.parse_args(args)) if func is None: if self._setup_as: func = kws.pop(self._setup_as) else: func = self._used_functions[-1] return func(**kws)
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Parse the command line arguments to the setup function This method parses the given command line arguments to the function used in the :meth:`setup_args` method to setup up this parser Parameters ---------- args: list The list of command line arguments func: function An alternative function to use. If None, the last function or the one specified through the `setup_as` parameter in the :meth:`setup_args` is used. Returns ------- object What ever is returned by the called function Note ---- This method does not cover subparsers!
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L861-L890
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.parse_known2func
def parse_known2func(self, args=None, func=None): """Parse the command line arguments to the setup function This method parses the given command line arguments to the function used in the :meth:`setup_args` method to setup up this parser Parameters ---------- args: list The list of command line arguments func: function or str An alternative function to use. If None, the last function or the one specified through the `setup_as` parameter in the :meth:`setup_args` is used. Returns ------- object What ever is returned by the called function list The remaining command line arguments that could not be interpreted Note ---- This method does not cover subparsers!""" ns, remainder = self.parse_known_args(args) kws = vars(ns) if func is None: if self._setup_as: func = kws.pop(self._setup_as) else: func = self._used_functions[-1] return func(**kws), remainder
python
def parse_known2func(self, args=None, func=None): """Parse the command line arguments to the setup function This method parses the given command line arguments to the function used in the :meth:`setup_args` method to setup up this parser Parameters ---------- args: list The list of command line arguments func: function or str An alternative function to use. If None, the last function or the one specified through the `setup_as` parameter in the :meth:`setup_args` is used. Returns ------- object What ever is returned by the called function list The remaining command line arguments that could not be interpreted Note ---- This method does not cover subparsers!""" ns, remainder = self.parse_known_args(args) kws = vars(ns) if func is None: if self._setup_as: func = kws.pop(self._setup_as) else: func = self._used_functions[-1] return func(**kws), remainder
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Parse the command line arguments to the setup function This method parses the given command line arguments to the function used in the :meth:`setup_args` method to setup up this parser Parameters ---------- args: list The list of command line arguments func: function or str An alternative function to use. If None, the last function or the one specified through the `setup_as` parameter in the :meth:`setup_args` is used. Returns ------- object What ever is returned by the called function list The remaining command line arguments that could not be interpreted Note ---- This method does not cover subparsers!
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L892-L924
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.parse_chained
def parse_chained(self, args=None): """ Parse the argument directly to the function used for setup This function parses the command line arguments to the function that has been used for the :meth:`setup_args`. Parameters ---------- args: list The arguments parsed to the :meth:`parse_args` function Returns ------- argparse.Namespace The namespace with mapping from command name to the function return See also -------- parse_known_chained """ kws = vars(self.parse_args(args)) return self._parse2subparser_funcs(kws)
python
def parse_chained(self, args=None): """ Parse the argument directly to the function used for setup This function parses the command line arguments to the function that has been used for the :meth:`setup_args`. Parameters ---------- args: list The arguments parsed to the :meth:`parse_args` function Returns ------- argparse.Namespace The namespace with mapping from command name to the function return See also -------- parse_known_chained """ kws = vars(self.parse_args(args)) return self._parse2subparser_funcs(kws)
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Parse the argument directly to the function used for setup This function parses the command line arguments to the function that has been used for the :meth:`setup_args`. Parameters ---------- args: list The arguments parsed to the :meth:`parse_args` function Returns ------- argparse.Namespace The namespace with mapping from command name to the function return See also -------- parse_known_chained
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L926-L950
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.parse_known_chained
def parse_known_chained(self, args=None): """ Parse the argument directly to the function used for setup This function parses the command line arguments to the function that has been used for the :meth:`setup_args` method. Parameters ---------- args: list The arguments parsed to the :meth:`parse_args` function Returns ------- argparse.Namespace The namespace with mapping from command name to the function return list The remaining arguments that could not be interpreted See also -------- parse_known """ ns, remainder = self.parse_known_args(args) kws = vars(ns) return self._parse2subparser_funcs(kws), remainder
python
def parse_known_chained(self, args=None): """ Parse the argument directly to the function used for setup This function parses the command line arguments to the function that has been used for the :meth:`setup_args` method. Parameters ---------- args: list The arguments parsed to the :meth:`parse_args` function Returns ------- argparse.Namespace The namespace with mapping from command name to the function return list The remaining arguments that could not be interpreted See also -------- parse_known """ ns, remainder = self.parse_known_args(args) kws = vars(ns) return self._parse2subparser_funcs(kws), remainder
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Parse the argument directly to the function used for setup This function parses the command line arguments to the function that has been used for the :meth:`setup_args` method. Parameters ---------- args: list The arguments parsed to the :meth:`parse_args` function Returns ------- argparse.Namespace The namespace with mapping from command name to the function return list The remaining arguments that could not be interpreted See also -------- parse_known
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L952-L979
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser._parse2subparser_funcs
def _parse2subparser_funcs(self, kws): """ Recursive function to parse arguments to chained parsers """ choices = getattr(self._subparsers_action, 'choices', {}) replaced = {key.replace('-', '_'): key for key in choices} sp_commands = set(replaced).intersection(kws) if not sp_commands: if self._setup_as is not None: func = kws.pop(self._setup_as) else: try: func = self._used_functions[-1] except IndexError: return None return func(**{ key: kws[key] for key in set(kws).difference(choices)}) else: ret = {} for key in sp_commands: ret[key.replace('-', '_')] = \ choices[replaced[key]]._parse2subparser_funcs( vars(kws[key])) return Namespace(**ret)
python
def _parse2subparser_funcs(self, kws): """ Recursive function to parse arguments to chained parsers """ choices = getattr(self._subparsers_action, 'choices', {}) replaced = {key.replace('-', '_'): key for key in choices} sp_commands = set(replaced).intersection(kws) if not sp_commands: if self._setup_as is not None: func = kws.pop(self._setup_as) else: try: func = self._used_functions[-1] except IndexError: return None return func(**{ key: kws[key] for key in set(kws).difference(choices)}) else: ret = {} for key in sp_commands: ret[key.replace('-', '_')] = \ choices[replaced[key]]._parse2subparser_funcs( vars(kws[key])) return Namespace(**ret)
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Recursive function to parse arguments to chained parsers
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L981-L1004
Chilipp/funcargparse
funcargparse/__init__.py
FuncArgParser.get_subparser
def get_subparser(self, name): """ Convenience method to get a certain subparser Parameters ---------- name: str The name of the subparser Returns ------- FuncArgParser The subparsers corresponding to `name` """ if self._subparsers_action is None: raise ValueError("%s has no subparsers defined!" % self) return self._subparsers_action.choices[name]
python
def get_subparser(self, name): """ Convenience method to get a certain subparser Parameters ---------- name: str The name of the subparser Returns ------- FuncArgParser The subparsers corresponding to `name` """ if self._subparsers_action is None: raise ValueError("%s has no subparsers defined!" % self) return self._subparsers_action.choices[name]
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Convenience method to get a certain subparser Parameters ---------- name: str The name of the subparser Returns ------- FuncArgParser The subparsers corresponding to `name`
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train
https://github.com/Chilipp/funcargparse/blob/398ce8e7fa5aa35c465215446bda151cf1ecf7ad/funcargparse/__init__.py#L1006-L1022
coded-by-hand/mass
mass/parse.py
parse_file
def parse_file(src): """ find file in config and output to dest dir """ #clear the stack between parses if config.dest_dir == None: dest = src.dir else: dest = config.dest_dir output = get_output(src) output_file = dest + '/' + src.basename + '.min.js' f = open(output_file,'w') f.write(jsmin.jsmin(output)) f.close() print "Wrote combined and minified file to: %s" % (output_file)
python
def parse_file(src): """ find file in config and output to dest dir """ #clear the stack between parses if config.dest_dir == None: dest = src.dir else: dest = config.dest_dir output = get_output(src) output_file = dest + '/' + src.basename + '.min.js' f = open(output_file,'w') f.write(jsmin.jsmin(output)) f.close() print "Wrote combined and minified file to: %s" % (output_file)
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find file in config and output to dest dir
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train
https://github.com/coded-by-hand/mass/blob/59005479efed3cd8598a8f0c66791a4482071899/mass/parse.py#L12-L26
coded-by-hand/mass
mass/parse.py
get_output
def get_output(src): """ parse lines looking for commands """ output = '' lines = open(src.path, 'rU').readlines() for line in lines: m = re.match(config.import_regex,line) if m: include_path = os.path.abspath(src.dir + '/' + m.group('script')); if include_path not in config.sources: script = Script(include_path) script.parents.append(src) config.sources[script.path] = script include_file = config.sources[include_path] #require statements dont include if the file has already been included if include_file not in config.stack or m.group('command') == 'import': config.stack.append(include_file) output += get_output(include_file) else: output += line return output
python
def get_output(src): """ parse lines looking for commands """ output = '' lines = open(src.path, 'rU').readlines() for line in lines: m = re.match(config.import_regex,line) if m: include_path = os.path.abspath(src.dir + '/' + m.group('script')); if include_path not in config.sources: script = Script(include_path) script.parents.append(src) config.sources[script.path] = script include_file = config.sources[include_path] #require statements dont include if the file has already been included if include_file not in config.stack or m.group('command') == 'import': config.stack.append(include_file) output += get_output(include_file) else: output += line return output
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parse lines looking for commands
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train
https://github.com/coded-by-hand/mass/blob/59005479efed3cd8598a8f0c66791a4482071899/mass/parse.py#L28-L49
duniter/duniter-python-api
examples/save_revoke_document.py
get_signed_raw_revocation_document
def get_signed_raw_revocation_document(identity: Identity, salt: str, password: str) -> str: """ Generate account revocation document for given identity :param identity: Self Certification of the identity :param salt: Salt :param password: Password :rtype: str """ revocation = Revocation(PROTOCOL_VERSION, identity.currency, identity, "") key = SigningKey.from_credentials(salt, password) revocation.sign([key]) return revocation.signed_raw()
python
def get_signed_raw_revocation_document(identity: Identity, salt: str, password: str) -> str: """ Generate account revocation document for given identity :param identity: Self Certification of the identity :param salt: Salt :param password: Password :rtype: str """ revocation = Revocation(PROTOCOL_VERSION, identity.currency, identity, "") key = SigningKey.from_credentials(salt, password) revocation.sign([key]) return revocation.signed_raw()
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Generate account revocation document for given identity :param identity: Self Certification of the identity :param salt: Salt :param password: Password :rtype: str
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train
https://github.com/duniter/duniter-python-api/blob/3a1e5d61a2f72f5afaf29d010c6cf4dff3648165/examples/save_revoke_document.py#L77-L91
duniter/duniter-python-api
examples/save_revoke_document.py
main
async def main(): """ Main code """ # Create Client from endpoint string in Duniter format client = Client(BMAS_ENDPOINT) # Get the node summary infos to test the connection response = await client(bma.node.summary) print(response) # prompt hidden user entry salt = getpass.getpass("Enter your passphrase (salt): ") # prompt hidden user entry password = getpass.getpass("Enter your password: ") # prompt public key pubkey = input("Enter your public key: ") # init signer instance signer = SigningKey.from_credentials(salt, password) # check public key if signer.pubkey != pubkey: print("Bad credentials!") exit(0) # capture current block to get currency name current_block = await client(bma.blockchain.current) # create our Identity document to sign the revoke document identity_document = await get_identity_document(client, current_block['currency'], pubkey) # get the revoke document revocation_signed_raw_document = get_signed_raw_revocation_document(identity_document, salt, password) # save revoke document in a file fp = open(REVOCATION_DOCUMENT_FILE_PATH, 'w') fp.write(revocation_signed_raw_document) fp.close() # document saved print("Revocation document saved in %s" % REVOCATION_DOCUMENT_FILE_PATH) # Close client aiohttp session await client.close()
python
async def main(): """ Main code """ # Create Client from endpoint string in Duniter format client = Client(BMAS_ENDPOINT) # Get the node summary infos to test the connection response = await client(bma.node.summary) print(response) # prompt hidden user entry salt = getpass.getpass("Enter your passphrase (salt): ") # prompt hidden user entry password = getpass.getpass("Enter your password: ") # prompt public key pubkey = input("Enter your public key: ") # init signer instance signer = SigningKey.from_credentials(salt, password) # check public key if signer.pubkey != pubkey: print("Bad credentials!") exit(0) # capture current block to get currency name current_block = await client(bma.blockchain.current) # create our Identity document to sign the revoke document identity_document = await get_identity_document(client, current_block['currency'], pubkey) # get the revoke document revocation_signed_raw_document = get_signed_raw_revocation_document(identity_document, salt, password) # save revoke document in a file fp = open(REVOCATION_DOCUMENT_FILE_PATH, 'w') fp.write(revocation_signed_raw_document) fp.close() # document saved print("Revocation document saved in %s" % REVOCATION_DOCUMENT_FILE_PATH) # Close client aiohttp session await client.close()
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Main code
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train
https://github.com/duniter/duniter-python-api/blob/3a1e5d61a2f72f5afaf29d010c6cf4dff3648165/examples/save_revoke_document.py#L94-L140
azraq27/neural
neural/alignment.py
align_epi
def align_epi(anatomy,epis,suffix='_al',base=3,skull_strip=True): '''[[currently in progress]]: a simple replacement for the ``align_epi_anat.py`` script, because I've found it to be unreliable, in my usage''' for epi in epis: nl.tshift(epi,suffix='_tshift') nl.affine_align(nl.suffix(epi,'_tshift'),'%s[%d]'%(epis[0],base),skull_strip=False,epi=True,cost='crM',resample='wsinc5',grid_size=nl.dset_info(epi).voxel_size[0],suffix='_al') ss = [anatomy] if skull_strip else False nl.affine_align(anatomy,'%s[%d]'%(epis[0],base),skull_strip=ss,cost='lpa',grid_size=1,opts=['-interp','cubic'],suffix='_al-to-EPI')
python
def align_epi(anatomy,epis,suffix='_al',base=3,skull_strip=True): '''[[currently in progress]]: a simple replacement for the ``align_epi_anat.py`` script, because I've found it to be unreliable, in my usage''' for epi in epis: nl.tshift(epi,suffix='_tshift') nl.affine_align(nl.suffix(epi,'_tshift'),'%s[%d]'%(epis[0],base),skull_strip=False,epi=True,cost='crM',resample='wsinc5',grid_size=nl.dset_info(epi).voxel_size[0],suffix='_al') ss = [anatomy] if skull_strip else False nl.affine_align(anatomy,'%s[%d]'%(epis[0],base),skull_strip=ss,cost='lpa',grid_size=1,opts=['-interp','cubic'],suffix='_al-to-EPI')
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[[currently in progress]]: a simple replacement for the ``align_epi_anat.py`` script, because I've found it to be unreliable, in my usage
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L7-L13
azraq27/neural
neural/alignment.py
motion_from_params
def motion_from_params(param_file,motion_file,individual=True,rms=True): '''calculate a motion regressor from the params file given by 3dAllineate Basically just calculates the rms change in the translation and rotation components. Returns the 6 motion vector (if ``individual`` is ``True``) and the RMS difference (if ``rms`` is ``True``).''' with open(param_file) as inf: translate_rotate = np.array([[float(y) for y in x.strip().split()[:6]] for x in inf.readlines() if x[0]!='#']) motion = np.array([]) if individual: motion = np.vstack((np.zeros(translate_rotate.shape[1]),np.diff(translate_rotate,axis=0))) if rms: translate = [sqrt(sum([x**2 for x in y[:3]])) for y in translate_rotate] rotate = [sqrt(sum([x**2 for x in y[3:]])) for y in translate_rotate] translate_rotate = np.array(map(add,translate,rotate)) translate_rotate_diff = np.hstack(([0],np.diff(translate_rotate,axis=0))) if motion.shape==(0,): motion = rms_motion else: motion = np.column_stack((motion,translate_rotate_diff)) with open(motion_file,'w') as outf: outf.write('\n'.join(['\t'.join([str(y) for y in x]) for x in motion]))
python
def motion_from_params(param_file,motion_file,individual=True,rms=True): '''calculate a motion regressor from the params file given by 3dAllineate Basically just calculates the rms change in the translation and rotation components. Returns the 6 motion vector (if ``individual`` is ``True``) and the RMS difference (if ``rms`` is ``True``).''' with open(param_file) as inf: translate_rotate = np.array([[float(y) for y in x.strip().split()[:6]] for x in inf.readlines() if x[0]!='#']) motion = np.array([]) if individual: motion = np.vstack((np.zeros(translate_rotate.shape[1]),np.diff(translate_rotate,axis=0))) if rms: translate = [sqrt(sum([x**2 for x in y[:3]])) for y in translate_rotate] rotate = [sqrt(sum([x**2 for x in y[3:]])) for y in translate_rotate] translate_rotate = np.array(map(add,translate,rotate)) translate_rotate_diff = np.hstack(([0],np.diff(translate_rotate,axis=0))) if motion.shape==(0,): motion = rms_motion else: motion = np.column_stack((motion,translate_rotate_diff)) with open(motion_file,'w') as outf: outf.write('\n'.join(['\t'.join([str(y) for y in x]) for x in motion]))
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L15-L34
azraq27/neural
neural/alignment.py
volreg
def volreg(dset,suffix='_volreg',base=3,tshift=3,dfile_suffix='_volreg.1D'): '''simple interface to 3dvolreg :suffix: suffix to add to ``dset`` for volreg'ed file :base: either a number or ``dset[#]`` of the base image to register to :tshift: if a number, then tshift ignoring that many images, if ``None`` then don't tshift :dfile_suffix: suffix to add to ``dset`` to save the motion parameters to ''' cmd = ['3dvolreg','-prefix',nl.suffix(dset,suffix),'-base',base,'-dfile',nl.prefix(dset)+dfile_suffix] if tshift: cmd += ['-tshift',tshift] cmd += [dset] nl.run(cmd,products=nl.suffix(dset,suffix))
python
def volreg(dset,suffix='_volreg',base=3,tshift=3,dfile_suffix='_volreg.1D'): '''simple interface to 3dvolreg :suffix: suffix to add to ``dset`` for volreg'ed file :base: either a number or ``dset[#]`` of the base image to register to :tshift: if a number, then tshift ignoring that many images, if ``None`` then don't tshift :dfile_suffix: suffix to add to ``dset`` to save the motion parameters to ''' cmd = ['3dvolreg','-prefix',nl.suffix(dset,suffix),'-base',base,'-dfile',nl.prefix(dset)+dfile_suffix] if tshift: cmd += ['-tshift',tshift] cmd += [dset] nl.run(cmd,products=nl.suffix(dset,suffix))
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L36-L49
azraq27/neural
neural/alignment.py
affine_align
def affine_align(dset_from,dset_to,skull_strip=True,mask=None,suffix='_aff',prefix=None,cost=None,epi=False,resample='wsinc5',grid_size=None,opts=[]): ''' interface to 3dAllineate to align anatomies and EPIs ''' dset_ss = lambda dset: os.path.split(nl.suffix(dset,'_ns'))[1] def dset_source(dset): if skull_strip==True or skull_strip==dset: return dset_ss(dset) else: return dset dset_affine = prefix if dset_affine==None: dset_affine = os.path.split(nl.suffix(dset_from,suffix))[1] dset_affine_mat_1D = nl.prefix(dset_affine) + '_matrix.1D' dset_affine_par_1D = nl.prefix(dset_affine) + '_params.1D' if os.path.exists(dset_affine): # final product already exists return for dset in [dset_from,dset_to]: if skull_strip==True or skull_strip==dset: nl.skull_strip(dset,'_ns') mask_use = mask if mask: # the mask was probably made in the space of the original dset_to anatomy, # which has now been cropped from the skull stripping. So the lesion mask # needs to be resampled to match the corresponding mask if skull_strip==True or skull_strip==dset_to: nl.run(['3dresample','-master',dset_u(dset_ss(dset)),'-inset',mask,'-prefix',nl.suffix(mask,'_resam')],products=nl.suffix(mask,'_resam')) mask_use = nl.suffix(mask,'_resam') all_cmd = [ '3dAllineate', '-prefix', dset_affine, '-base', dset_source(dset_to), '-source', dset_source(dset_from), '-source_automask', '-1Dmatrix_save', dset_affine_mat_1D, '-1Dparam_save',dset_affine_par_1D, '-autoweight', '-final',resample, '-cmass' ] + opts if grid_size: all_cmd += ['-newgrid',grid_size] if cost: all_cmd += ['-cost',cost] if epi: all_cmd += ['-EPI'] if mask: all_cmd += ['-emask', mask_use] nl.run(all_cmd,products=dset_affine)
python
def affine_align(dset_from,dset_to,skull_strip=True,mask=None,suffix='_aff',prefix=None,cost=None,epi=False,resample='wsinc5',grid_size=None,opts=[]): ''' interface to 3dAllineate to align anatomies and EPIs ''' dset_ss = lambda dset: os.path.split(nl.suffix(dset,'_ns'))[1] def dset_source(dset): if skull_strip==True or skull_strip==dset: return dset_ss(dset) else: return dset dset_affine = prefix if dset_affine==None: dset_affine = os.path.split(nl.suffix(dset_from,suffix))[1] dset_affine_mat_1D = nl.prefix(dset_affine) + '_matrix.1D' dset_affine_par_1D = nl.prefix(dset_affine) + '_params.1D' if os.path.exists(dset_affine): # final product already exists return for dset in [dset_from,dset_to]: if skull_strip==True or skull_strip==dset: nl.skull_strip(dset,'_ns') mask_use = mask if mask: # the mask was probably made in the space of the original dset_to anatomy, # which has now been cropped from the skull stripping. So the lesion mask # needs to be resampled to match the corresponding mask if skull_strip==True or skull_strip==dset_to: nl.run(['3dresample','-master',dset_u(dset_ss(dset)),'-inset',mask,'-prefix',nl.suffix(mask,'_resam')],products=nl.suffix(mask,'_resam')) mask_use = nl.suffix(mask,'_resam') all_cmd = [ '3dAllineate', '-prefix', dset_affine, '-base', dset_source(dset_to), '-source', dset_source(dset_from), '-source_automask', '-1Dmatrix_save', dset_affine_mat_1D, '-1Dparam_save',dset_affine_par_1D, '-autoweight', '-final',resample, '-cmass' ] + opts if grid_size: all_cmd += ['-newgrid',grid_size] if cost: all_cmd += ['-cost',cost] if epi: all_cmd += ['-EPI'] if mask: all_cmd += ['-emask', mask_use] nl.run(all_cmd,products=dset_affine)
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L51-L105
azraq27/neural
neural/alignment.py
affine_apply
def affine_apply(dset_from,affine_1D,master,affine_suffix='_aff',interp='NN',inverse=False,prefix=None): '''apply the 1D file from a previously aligned dataset Applies the matrix in ``affine_1D`` to ``dset_from`` and makes the final grid look like the dataset ``master`` using the interpolation method ``interp``. If ``inverse`` is True, will apply the inverse of ``affine_1D`` instead''' affine_1D_use = affine_1D if inverse: with tempfile.NamedTemporaryFile(delete=False) as temp: temp.write(subprocess.check_output(['cat_matvec',affine_1D,'-I'])) affine_1D_use = temp.name if prefix==None: prefix = nl.suffix(dset_from,affine_suffix) nl.run(['3dAllineate','-1Dmatrix_apply',affine_1D_use,'-input',dset_from,'-prefix',prefix,'-master',master,'-final',interp],products=prefix)
python
def affine_apply(dset_from,affine_1D,master,affine_suffix='_aff',interp='NN',inverse=False,prefix=None): '''apply the 1D file from a previously aligned dataset Applies the matrix in ``affine_1D`` to ``dset_from`` and makes the final grid look like the dataset ``master`` using the interpolation method ``interp``. If ``inverse`` is True, will apply the inverse of ``affine_1D`` instead''' affine_1D_use = affine_1D if inverse: with tempfile.NamedTemporaryFile(delete=False) as temp: temp.write(subprocess.check_output(['cat_matvec',affine_1D,'-I'])) affine_1D_use = temp.name if prefix==None: prefix = nl.suffix(dset_from,affine_suffix) nl.run(['3dAllineate','-1Dmatrix_apply',affine_1D_use,'-input',dset_from,'-prefix',prefix,'-master',master,'-final',interp],products=prefix)
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L107-L118
azraq27/neural
neural/alignment.py
convert_coord
def convert_coord(coord_from,matrix_file,base_to_aligned=True): '''Takes an XYZ array (in DICOM coordinates) and uses the matrix file produced by 3dAllineate to transform it. By default, the 3dAllineate matrix transforms from base to aligned space; to get the inverse transform set ``base_to_aligned`` to ``False``''' with open(matrix_file) as f: try: values = [float(y) for y in ' '.join([x for x in f.readlines() if x.strip()[0]!='#']).strip().split()] except: nl.notify('Error reading values from matrix file %s' % matrix_file, level=nl.level.error) return False if len(values)!=12: nl.notify('Error: found %d values in matrix file %s (expecting 12)' % (len(values),matrix_file), level=nl.level.error) return False matrix = np.vstack((np.array(values).reshape((3,-1)),[0,0,0,1])) if not base_to_aligned: matrix = np.linalg.inv(matrix) return np.dot(matrix,list(coord_from) + [1])[:3]
python
def convert_coord(coord_from,matrix_file,base_to_aligned=True): '''Takes an XYZ array (in DICOM coordinates) and uses the matrix file produced by 3dAllineate to transform it. By default, the 3dAllineate matrix transforms from base to aligned space; to get the inverse transform set ``base_to_aligned`` to ``False``''' with open(matrix_file) as f: try: values = [float(y) for y in ' '.join([x for x in f.readlines() if x.strip()[0]!='#']).strip().split()] except: nl.notify('Error reading values from matrix file %s' % matrix_file, level=nl.level.error) return False if len(values)!=12: nl.notify('Error: found %d values in matrix file %s (expecting 12)' % (len(values),matrix_file), level=nl.level.error) return False matrix = np.vstack((np.array(values).reshape((3,-1)),[0,0,0,1])) if not base_to_aligned: matrix = np.linalg.inv(matrix) return np.dot(matrix,list(coord_from) + [1])[:3]
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L120-L135
azraq27/neural
neural/alignment.py
qwarp_align
def qwarp_align(dset_from,dset_to,skull_strip=True,mask=None,affine_suffix='_aff',suffix='_qwarp',prefix=None): '''aligns ``dset_from`` to ``dset_to`` using 3dQwarp Will run ``3dSkullStrip`` (unless ``skull_strip`` is ``False``), ``3dUnifize``, ``3dAllineate``, and then ``3dQwarp``. This method will add suffixes to the input dataset for the intermediate files (e.g., ``_ss``, ``_u``). If those files already exist, it will assume they were intelligently named, and use them as is :skull_strip: If True/False, turns skull-stripping of both datasets on/off. If a string matching ``dset_from`` or ``dset_to``, will only skull-strip the given dataset :mask: Applies the given mask to the alignment. Because of the nature of the alignment algorithms, the mask is **always** applied to the ``dset_to``. If this isn't what you want, you need to reverse the transform and re-apply it (e.g., using :meth:`qwarp_invert` and :meth:`qwarp_apply`). If the ``dset_to`` dataset is skull-stripped, the mask will also be resampled to match the ``dset_to`` grid. :affine_suffix: Suffix applied to ``dset_from`` to name the new dataset, as well as the ``.1D`` file. :suffix: Suffix applied to the final ``dset_from`` dataset. An additional file with the additional suffix ``_WARP`` will be created containing the parameters (e.g., with the default ``_qwarp`` suffix, the parameters will be in a file with the suffix ``_qwarp_WARP``) :prefix: Alternatively to ``suffix``, explicitly give the full output filename The output affine dataset and 1D, as well as the output of qwarp are named by adding the given suffixes (``affine_suffix`` and ``qwarp_suffix``) to the ``dset_from`` file If ``skull_strip`` is a string instead of ``True``/``False``, it will only skull strip the given dataset instead of both of them # TODO: currently does not work with +tlrc datasets because the filenames get mangled ''' dset_ss = lambda dset: os.path.split(nl.suffix(dset,'_ns'))[1] dset_u = lambda dset: os.path.split(nl.suffix(dset,'_u'))[1] def dset_source(dset): if skull_strip==True or skull_strip==dset: return dset_ss(dset) else: return dset dset_affine = os.path.split(nl.suffix(dset_from,affine_suffix))[1] dset_affine_1D = nl.prefix(dset_affine) + '.1D' dset_qwarp = prefix if dset_qwarp==None: dset_qwarp = os.path.split(nl.suffix(dset_from,suffix))[1] if os.path.exists(dset_qwarp): # final product already exists return affine_align(dset_from,dset_to,skull_strip,mask,affine_suffix) for dset in [dset_from,dset_to]: nl.run([ '3dUnifize', '-prefix', dset_u(dset_source(dset)), '-input', dset_source(dset) ],products=[dset_u(dset_source(dset))]) mask_use = mask if mask: # the mask was probably made in the space of the original dset_to anatomy, # which has now been cropped from the skull stripping. So the lesion mask # needs to be resampled to match the corresponding mask if skull_strip==True or skull_strip==dset_to: nl.run(['3dresample','-master',dset_u(dset_ss(dset)),'-inset',mask,'-prefix',nl.suffix(mask,'_resam')],products=nl.suffix(mask,'_resam')) mask_use = nl.suffix(mask,'_resam') warp_cmd = [ '3dQwarp', '-prefix', dset_qwarp, '-duplo', '-useweight', '-blur', '0', '3', '-iwarp', '-base', dset_u(dset_source(dset_to)), '-source', dset_affine ] if mask: warp_cmd += ['-emask', mask_use] nl.run(warp_cmd,products=dset_qwarp)
python
def qwarp_align(dset_from,dset_to,skull_strip=True,mask=None,affine_suffix='_aff',suffix='_qwarp',prefix=None): '''aligns ``dset_from`` to ``dset_to`` using 3dQwarp Will run ``3dSkullStrip`` (unless ``skull_strip`` is ``False``), ``3dUnifize``, ``3dAllineate``, and then ``3dQwarp``. This method will add suffixes to the input dataset for the intermediate files (e.g., ``_ss``, ``_u``). If those files already exist, it will assume they were intelligently named, and use them as is :skull_strip: If True/False, turns skull-stripping of both datasets on/off. If a string matching ``dset_from`` or ``dset_to``, will only skull-strip the given dataset :mask: Applies the given mask to the alignment. Because of the nature of the alignment algorithms, the mask is **always** applied to the ``dset_to``. If this isn't what you want, you need to reverse the transform and re-apply it (e.g., using :meth:`qwarp_invert` and :meth:`qwarp_apply`). If the ``dset_to`` dataset is skull-stripped, the mask will also be resampled to match the ``dset_to`` grid. :affine_suffix: Suffix applied to ``dset_from`` to name the new dataset, as well as the ``.1D`` file. :suffix: Suffix applied to the final ``dset_from`` dataset. An additional file with the additional suffix ``_WARP`` will be created containing the parameters (e.g., with the default ``_qwarp`` suffix, the parameters will be in a file with the suffix ``_qwarp_WARP``) :prefix: Alternatively to ``suffix``, explicitly give the full output filename The output affine dataset and 1D, as well as the output of qwarp are named by adding the given suffixes (``affine_suffix`` and ``qwarp_suffix``) to the ``dset_from`` file If ``skull_strip`` is a string instead of ``True``/``False``, it will only skull strip the given dataset instead of both of them # TODO: currently does not work with +tlrc datasets because the filenames get mangled ''' dset_ss = lambda dset: os.path.split(nl.suffix(dset,'_ns'))[1] dset_u = lambda dset: os.path.split(nl.suffix(dset,'_u'))[1] def dset_source(dset): if skull_strip==True or skull_strip==dset: return dset_ss(dset) else: return dset dset_affine = os.path.split(nl.suffix(dset_from,affine_suffix))[1] dset_affine_1D = nl.prefix(dset_affine) + '.1D' dset_qwarp = prefix if dset_qwarp==None: dset_qwarp = os.path.split(nl.suffix(dset_from,suffix))[1] if os.path.exists(dset_qwarp): # final product already exists return affine_align(dset_from,dset_to,skull_strip,mask,affine_suffix) for dset in [dset_from,dset_to]: nl.run([ '3dUnifize', '-prefix', dset_u(dset_source(dset)), '-input', dset_source(dset) ],products=[dset_u(dset_source(dset))]) mask_use = mask if mask: # the mask was probably made in the space of the original dset_to anatomy, # which has now been cropped from the skull stripping. So the lesion mask # needs to be resampled to match the corresponding mask if skull_strip==True or skull_strip==dset_to: nl.run(['3dresample','-master',dset_u(dset_ss(dset)),'-inset',mask,'-prefix',nl.suffix(mask,'_resam')],products=nl.suffix(mask,'_resam')) mask_use = nl.suffix(mask,'_resam') warp_cmd = [ '3dQwarp', '-prefix', dset_qwarp, '-duplo', '-useweight', '-blur', '0', '3', '-iwarp', '-base', dset_u(dset_source(dset_to)), '-source', dset_affine ] if mask: warp_cmd += ['-emask', mask_use] nl.run(warp_cmd,products=dset_qwarp)
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aligns ``dset_from`` to ``dset_to`` using 3dQwarp Will run ``3dSkullStrip`` (unless ``skull_strip`` is ``False``), ``3dUnifize``, ``3dAllineate``, and then ``3dQwarp``. This method will add suffixes to the input dataset for the intermediate files (e.g., ``_ss``, ``_u``). If those files already exist, it will assume they were intelligently named, and use them as is :skull_strip: If True/False, turns skull-stripping of both datasets on/off. If a string matching ``dset_from`` or ``dset_to``, will only skull-strip the given dataset :mask: Applies the given mask to the alignment. Because of the nature of the alignment algorithms, the mask is **always** applied to the ``dset_to``. If this isn't what you want, you need to reverse the transform and re-apply it (e.g., using :meth:`qwarp_invert` and :meth:`qwarp_apply`). If the ``dset_to`` dataset is skull-stripped, the mask will also be resampled to match the ``dset_to`` grid. :affine_suffix: Suffix applied to ``dset_from`` to name the new dataset, as well as the ``.1D`` file. :suffix: Suffix applied to the final ``dset_from`` dataset. An additional file with the additional suffix ``_WARP`` will be created containing the parameters (e.g., with the default ``_qwarp`` suffix, the parameters will be in a file with the suffix ``_qwarp_WARP``) :prefix: Alternatively to ``suffix``, explicitly give the full output filename The output affine dataset and 1D, as well as the output of qwarp are named by adding the given suffixes (``affine_suffix`` and ``qwarp_suffix``) to the ``dset_from`` file If ``skull_strip`` is a string instead of ``True``/``False``, it will only skull strip the given dataset instead of both of them # TODO: currently does not work with +tlrc datasets because the filenames get mangled
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L138-L220
azraq27/neural
neural/alignment.py
qwarp_apply
def qwarp_apply(dset_from,dset_warp,affine=None,warp_suffix='_warp',master='WARP',interp=None,prefix=None): '''applies the transform from a previous qwarp Uses the warp parameters from the dataset listed in ``dset_warp`` (usually the dataset name ends in ``_WARP``) to the dataset ``dset_from``. If a ``.1D`` file is given in the ``affine`` parameter, it will be applied simultaneously with the qwarp. If the parameter ``interp`` is given, will use as interpolation method, otherwise it will just use the default (currently wsinc5) Output dataset with have the ``warp_suffix`` suffix added to its name ''' out_dset = prefix if out_dset==None: out_dset = os.path.split(nl.suffix(dset_from,warp_suffix))[1] dset_from_info = nl.dset_info(dset_from) dset_warp_info = nl.dset_info(dset_warp) if(dset_from_info.orient!=dset_warp_info.orient): # If the datasets are different orientations, the transform won't be applied correctly nl.run(['3dresample','-orient',dset_warp_info.orient,'-prefix',nl.suffix(dset_from,'_reorient'),'-inset',dset_from],products=nl.suffix(dset_from,'_reorient')) dset_from = nl.suffix(dset_from,'_reorient') warp_opt = str(dset_warp) if affine: warp_opt += ' ' + affine cmd = [ '3dNwarpApply', '-nwarp', warp_opt] cmd += [ '-source', dset_from, '-master',master, '-prefix', out_dset ] if interp: cmd += ['-interp',interp] nl.run(cmd,products=out_dset)
python
def qwarp_apply(dset_from,dset_warp,affine=None,warp_suffix='_warp',master='WARP',interp=None,prefix=None): '''applies the transform from a previous qwarp Uses the warp parameters from the dataset listed in ``dset_warp`` (usually the dataset name ends in ``_WARP``) to the dataset ``dset_from``. If a ``.1D`` file is given in the ``affine`` parameter, it will be applied simultaneously with the qwarp. If the parameter ``interp`` is given, will use as interpolation method, otherwise it will just use the default (currently wsinc5) Output dataset with have the ``warp_suffix`` suffix added to its name ''' out_dset = prefix if out_dset==None: out_dset = os.path.split(nl.suffix(dset_from,warp_suffix))[1] dset_from_info = nl.dset_info(dset_from) dset_warp_info = nl.dset_info(dset_warp) if(dset_from_info.orient!=dset_warp_info.orient): # If the datasets are different orientations, the transform won't be applied correctly nl.run(['3dresample','-orient',dset_warp_info.orient,'-prefix',nl.suffix(dset_from,'_reorient'),'-inset',dset_from],products=nl.suffix(dset_from,'_reorient')) dset_from = nl.suffix(dset_from,'_reorient') warp_opt = str(dset_warp) if affine: warp_opt += ' ' + affine cmd = [ '3dNwarpApply', '-nwarp', warp_opt] cmd += [ '-source', dset_from, '-master',master, '-prefix', out_dset ] if interp: cmd += ['-interp',interp] nl.run(cmd,products=out_dset)
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applies the transform from a previous qwarp Uses the warp parameters from the dataset listed in ``dset_warp`` (usually the dataset name ends in ``_WARP``) to the dataset ``dset_from``. If a ``.1D`` file is given in the ``affine`` parameter, it will be applied simultaneously with the qwarp. If the parameter ``interp`` is given, will use as interpolation method, otherwise it will just use the default (currently wsinc5) Output dataset with have the ``warp_suffix`` suffix added to its name
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L222-L260
azraq27/neural
neural/alignment.py
qwarp_invert
def qwarp_invert(warp_param_dset,output_dset,affine_1Dfile=None): '''inverts a qwarp (defined in ``warp_param_dset``) (and concatenates affine matrix ``affine_1Dfile`` if given) outputs the inverted warp + affine to ``output_dset``''' cmd = ['3dNwarpCat','-prefix',output_dset] if affine_1Dfile: cmd += ['-warp1','INV(%s)' % affine_1Dfile, '-warp2','INV(%s)' % warp_param_dset] else: cmd += ['-warp1','INV(%s)' % warp_param_dset] nl.run(cmd,products=output_dset)
python
def qwarp_invert(warp_param_dset,output_dset,affine_1Dfile=None): '''inverts a qwarp (defined in ``warp_param_dset``) (and concatenates affine matrix ``affine_1Dfile`` if given) outputs the inverted warp + affine to ``output_dset``''' cmd = ['3dNwarpCat','-prefix',output_dset] if affine_1Dfile: cmd += ['-warp1','INV(%s)' % affine_1Dfile, '-warp2','INV(%s)' % warp_param_dset] else: cmd += ['-warp1','INV(%s)' % warp_param_dset] nl.run(cmd,products=output_dset)
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inverts a qwarp (defined in ``warp_param_dset``) (and concatenates affine matrix ``affine_1Dfile`` if given) outputs the inverted warp + affine to ``output_dset``
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L262-L273
azraq27/neural
neural/alignment.py
qwarp_epi
def qwarp_epi(dset,align_subbrick=5,suffix='_qwal',prefix=None): '''aligns an EPI time-series using 3dQwarp Very expensive and not efficient at all, but it can produce pretty impressive alignment for EPI time-series with significant distortions due to motion''' info = nl.dset_info(dset) if info==None: nl.notify('Error reading dataset "%s"' % (dset),level=nl.level.error) return False if prefix==None: prefix = nl.suffix(dset,suffix) dset_sub = lambda x: '_tmp_qwarp_epi-%s_%d.nii.gz' % (nl.prefix(dset),x) try: align_dset = nl.suffix(dset_sub(align_subbrick),'_warp') nl.calc('%s[%d]' % (dset,align_subbrick),expr='a',prefix=align_dset,datum='float') for i in xrange(info.reps): if i != align_subbrick: nl.calc('%s[%d]' % (dset,i),expr='a',prefix=dset_sub(i),datum='float') nl.run([ '3dQwarp', '-nowarp', '-workhard', '-superhard', '-minpatch', '9', '-blur', '0', '-pear', '-nopenalty', '-base', align_dset, '-source', dset_sub(i), '-prefix', nl.suffix(dset_sub(i),'_warp') ],quiet=True) cmd = ['3dTcat','-prefix',prefix] if info.TR: cmd += ['-tr',info.TR] if info.slice_timing: cmd += ['-tpattern',info.slice_timing] cmd += [nl.suffix(dset_sub(i),'_warp') for i in xrange(info.reps)] nl.run(cmd,quiet=True) except Exception as e: raise e finally: for i in xrange(info.reps): for suffix in ['','warp']: try: os.remove(nl.suffix(dset_sub(i),suffix)) except: pass
python
def qwarp_epi(dset,align_subbrick=5,suffix='_qwal',prefix=None): '''aligns an EPI time-series using 3dQwarp Very expensive and not efficient at all, but it can produce pretty impressive alignment for EPI time-series with significant distortions due to motion''' info = nl.dset_info(dset) if info==None: nl.notify('Error reading dataset "%s"' % (dset),level=nl.level.error) return False if prefix==None: prefix = nl.suffix(dset,suffix) dset_sub = lambda x: '_tmp_qwarp_epi-%s_%d.nii.gz' % (nl.prefix(dset),x) try: align_dset = nl.suffix(dset_sub(align_subbrick),'_warp') nl.calc('%s[%d]' % (dset,align_subbrick),expr='a',prefix=align_dset,datum='float') for i in xrange(info.reps): if i != align_subbrick: nl.calc('%s[%d]' % (dset,i),expr='a',prefix=dset_sub(i),datum='float') nl.run([ '3dQwarp', '-nowarp', '-workhard', '-superhard', '-minpatch', '9', '-blur', '0', '-pear', '-nopenalty', '-base', align_dset, '-source', dset_sub(i), '-prefix', nl.suffix(dset_sub(i),'_warp') ],quiet=True) cmd = ['3dTcat','-prefix',prefix] if info.TR: cmd += ['-tr',info.TR] if info.slice_timing: cmd += ['-tpattern',info.slice_timing] cmd += [nl.suffix(dset_sub(i),'_warp') for i in xrange(info.reps)] nl.run(cmd,quiet=True) except Exception as e: raise e finally: for i in xrange(info.reps): for suffix in ['','warp']: try: os.remove(nl.suffix(dset_sub(i),suffix)) except: pass
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L276-L317
azraq27/neural
neural/alignment.py
align_epi_anat
def align_epi_anat(anatomy,epi_dsets,skull_strip_anat=True): ''' aligns epis to anatomy using ``align_epi_anat.py`` script :epi_dsets: can be either a string or list of strings of the epi child datasets :skull_strip_anat: if ``True``, ``anatomy`` will be skull-stripped using the default method The default output suffix is "_al" ''' if isinstance(epi_dsets,basestring): epi_dsets = [epi_dsets] if len(epi_dsets)==0: nl.notify('Warning: no epi alignment datasets given for anatomy %s!' % anatomy,level=nl.level.warning) return if all(os.path.exists(nl.suffix(x,'_al')) for x in epi_dsets): return anatomy_use = anatomy if skull_strip_anat: nl.skull_strip(anatomy,'_ns') anatomy_use = nl.suffix(anatomy,'_ns') inputs = [anatomy_use] + epi_dsets dset_products = lambda dset: [nl.suffix(dset,'_al'), nl.prefix(dset)+'_al_mat.aff12.1D', nl.prefix(dset)+'_tsh_vr_motion.1D'] products = nl.flatten([dset_products(dset) for dset in epi_dsets]) with nl.run_in_tmp(inputs,products): if nl.is_nifti(anatomy_use): anatomy_use = nl.afni_copy(anatomy_use) epi_dsets_use = [] for dset in epi_dsets: if nl.is_nifti(dset): epi_dsets_use.append(nl.afni_copy(dset)) else: epi_dsets_use.append(dset) cmd = ["align_epi_anat.py", "-epi2anat", "-anat_has_skull", "no", "-epi_strip", "3dAutomask","-anat", anatomy_use, "-epi_base", "5", "-epi", epi_dsets_use[0]] if len(epi_dsets_use)>1: cmd += ['-child_epi'] + epi_dsets_use[1:] out = nl.run(cmd) for dset in epi_dsets: if nl.is_nifti(dset): dset_nifti = nl.nifti_copy(nl.prefix(dset)+'_al+orig') if dset_nifti and os.path.exists(dset_nifti) and dset_nifti.endswith('.nii') and dset.endswith('.gz'): nl.run(['gzip',dset_nifti])
python
def align_epi_anat(anatomy,epi_dsets,skull_strip_anat=True): ''' aligns epis to anatomy using ``align_epi_anat.py`` script :epi_dsets: can be either a string or list of strings of the epi child datasets :skull_strip_anat: if ``True``, ``anatomy`` will be skull-stripped using the default method The default output suffix is "_al" ''' if isinstance(epi_dsets,basestring): epi_dsets = [epi_dsets] if len(epi_dsets)==0: nl.notify('Warning: no epi alignment datasets given for anatomy %s!' % anatomy,level=nl.level.warning) return if all(os.path.exists(nl.suffix(x,'_al')) for x in epi_dsets): return anatomy_use = anatomy if skull_strip_anat: nl.skull_strip(anatomy,'_ns') anatomy_use = nl.suffix(anatomy,'_ns') inputs = [anatomy_use] + epi_dsets dset_products = lambda dset: [nl.suffix(dset,'_al'), nl.prefix(dset)+'_al_mat.aff12.1D', nl.prefix(dset)+'_tsh_vr_motion.1D'] products = nl.flatten([dset_products(dset) for dset in epi_dsets]) with nl.run_in_tmp(inputs,products): if nl.is_nifti(anatomy_use): anatomy_use = nl.afni_copy(anatomy_use) epi_dsets_use = [] for dset in epi_dsets: if nl.is_nifti(dset): epi_dsets_use.append(nl.afni_copy(dset)) else: epi_dsets_use.append(dset) cmd = ["align_epi_anat.py", "-epi2anat", "-anat_has_skull", "no", "-epi_strip", "3dAutomask","-anat", anatomy_use, "-epi_base", "5", "-epi", epi_dsets_use[0]] if len(epi_dsets_use)>1: cmd += ['-child_epi'] + epi_dsets_use[1:] out = nl.run(cmd) for dset in epi_dsets: if nl.is_nifti(dset): dset_nifti = nl.nifti_copy(nl.prefix(dset)+'_al+orig') if dset_nifti and os.path.exists(dset_nifti) and dset_nifti.endswith('.nii') and dset.endswith('.gz'): nl.run(['gzip',dset_nifti])
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train
https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L319-L365
azraq27/neural
neural/alignment.py
skullstrip_template
def skullstrip_template(dset,template,prefix=None,suffix=None,dilate=0): '''Takes the raw anatomy ``dset``, aligns it to a template brain, and applies a templated skullstrip. Should produce fairly reliable skullstrips as long as there is a decent amount of normal brain and the overall shape of the brain is normal-ish''' if suffix==None: suffix = '_sstemplate' if prefix==None: prefix = nl.suffix(dset,suffix) if not os.path.exists(prefix): with nl.notify('Running template-based skull-strip on %s' % dset): dset = os.path.abspath(dset) template = os.path.abspath(template) tmp_dir = tempfile.mkdtemp() cwd = os.getcwd() with nl.run_in(tmp_dir): nl.affine_align(template,dset,skull_strip=None,cost='mi',opts=['-nmatch','100%']) nl.run(['3dQwarp','-minpatch','20','-penfac','10','-noweight','-source',nl.suffix(template,'_aff'),'-base',dset,'-prefix',nl.suffix(template,'_qwarp')],products=nl.suffix(template,'_qwarp')) info = nl.dset_info(nl.suffix(template,'_qwarp')) max_value = info.subbricks[0]['max'] nl.calc([dset,nl.suffix(template,'_qwarp')],'a*step(b-%f*0.05)'%max_value,prefix) shutil.move(prefix,cwd) shutil.rmtree(tmp_dir)
python
def skullstrip_template(dset,template,prefix=None,suffix=None,dilate=0): '''Takes the raw anatomy ``dset``, aligns it to a template brain, and applies a templated skullstrip. Should produce fairly reliable skullstrips as long as there is a decent amount of normal brain and the overall shape of the brain is normal-ish''' if suffix==None: suffix = '_sstemplate' if prefix==None: prefix = nl.suffix(dset,suffix) if not os.path.exists(prefix): with nl.notify('Running template-based skull-strip on %s' % dset): dset = os.path.abspath(dset) template = os.path.abspath(template) tmp_dir = tempfile.mkdtemp() cwd = os.getcwd() with nl.run_in(tmp_dir): nl.affine_align(template,dset,skull_strip=None,cost='mi',opts=['-nmatch','100%']) nl.run(['3dQwarp','-minpatch','20','-penfac','10','-noweight','-source',nl.suffix(template,'_aff'),'-base',dset,'-prefix',nl.suffix(template,'_qwarp')],products=nl.suffix(template,'_qwarp')) info = nl.dset_info(nl.suffix(template,'_qwarp')) max_value = info.subbricks[0]['max'] nl.calc([dset,nl.suffix(template,'_qwarp')],'a*step(b-%f*0.05)'%max_value,prefix) shutil.move(prefix,cwd) shutil.rmtree(tmp_dir)
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https://github.com/azraq27/neural/blob/fe91bfeecbf73ad99708cf5dca66cb61fcd529f5/neural/alignment.py#L367-L387
Kraymer/high
high/__init__.py
printmp
def printmp(msg): """Print temporarily, until next print overrides it. """ filler = (80 - len(msg)) * ' ' print(msg + filler, end='\r') sys.stdout.flush()
python
def printmp(msg): """Print temporarily, until next print overrides it. """ filler = (80 - len(msg)) * ' ' print(msg + filler, end='\r') sys.stdout.flush()
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train
https://github.com/Kraymer/high/blob/11bb86733875ec708264ffb92bf5ef09a9d2f08c/high/__init__.py#L29-L34
Kraymer/high
high/__init__.py
contacts
def contacts(github, logins): """Extract public contact info from users. """ printmp('Fetching contacts') users = [github.user(login).as_dict() for login in logins] mails = set() blogs = set() for user in users: contact = user.get('name', 'login') if user['email']: contact += ' <%s>' % user['email'] mails.add(contact) elif user['blog']: contact += ' <%s>' % user['blog'] blogs.add(contact) else: continue return mails, blogs
python
def contacts(github, logins): """Extract public contact info from users. """ printmp('Fetching contacts') users = [github.user(login).as_dict() for login in logins] mails = set() blogs = set() for user in users: contact = user.get('name', 'login') if user['email']: contact += ' <%s>' % user['email'] mails.add(contact) elif user['blog']: contact += ' <%s>' % user['blog'] blogs.add(contact) else: continue return mails, blogs
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train
https://github.com/Kraymer/high/blob/11bb86733875ec708264ffb92bf5ef09a9d2f08c/high/__init__.py#L37-L54
Kraymer/high
high/__init__.py
extract_mail
def extract_mail(issues): """Extract mails that sometimes leak from issue comments. """ contacts = set() for idx, issue in enumerate(issues): printmp('Fetching issue #%s' % idx) for comment in issue.comments(): comm = comment.as_dict() emails = list(email[0] for email in re.findall(MAIL_REGEX, comm['body']) if not email[0].startswith('//') and not email[0].endswith('github.com') and '@' in email[0]) contacts |= set(emails) return contacts
python
def extract_mail(issues): """Extract mails that sometimes leak from issue comments. """ contacts = set() for idx, issue in enumerate(issues): printmp('Fetching issue #%s' % idx) for comment in issue.comments(): comm = comment.as_dict() emails = list(email[0] for email in re.findall(MAIL_REGEX, comm['body']) if not email[0].startswith('//') and not email[0].endswith('github.com') and '@' in email[0]) contacts |= set(emails) return contacts
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train
https://github.com/Kraymer/high/blob/11bb86733875ec708264ffb92bf5ef09a9d2f08c/high/__init__.py#L57-L69
Kraymer/high
high/__init__.py
fetch_logins
def fetch_logins(roles, repo): """Fetch logins for users with given roles. """ users = set() if 'stargazer' in roles: printmp('Fetching stargazers') users |= set(repo.stargazers()) if 'collaborator' in roles: printmp('Fetching collaborators') users |= set(repo.collaborators()) if 'issue' in roles: printmp('Fetching issues creators') users |= set([i.user for i in repo.issues(state='all')]) return users
python
def fetch_logins(roles, repo): """Fetch logins for users with given roles. """ users = set() if 'stargazer' in roles: printmp('Fetching stargazers') users |= set(repo.stargazers()) if 'collaborator' in roles: printmp('Fetching collaborators') users |= set(repo.collaborators()) if 'issue' in roles: printmp('Fetching issues creators') users |= set([i.user for i in repo.issues(state='all')]) return users
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train
https://github.com/Kraymer/high/blob/11bb86733875ec708264ffb92bf5ef09a9d2f08c/high/__init__.py#L72-L85
Kraymer/high
high/__init__.py
high_cli
def high_cli(repo_name, login, with_blog, as_list, role): """Extract mails from stargazers, collaborators and people involved with issues of given repository. """ passw = getpass.getpass() github = gh_login(login, passw) repo = github.repository(login, repo_name) role = [ROLES[k] for k in role] users = fetch_logins(role, repo) mails, blogs = contacts(github, users) if 'issue' in role: mails |= extract_mail(repo.issues(state='all')) # Print results sep = ', ' if as_list else '\n' print(sep.join(mails)) if with_blog: print(sep.join(blogs))
python
def high_cli(repo_name, login, with_blog, as_list, role): """Extract mails from stargazers, collaborators and people involved with issues of given repository. """ passw = getpass.getpass() github = gh_login(login, passw) repo = github.repository(login, repo_name) role = [ROLES[k] for k in role] users = fetch_logins(role, repo) mails, blogs = contacts(github, users) if 'issue' in role: mails |= extract_mail(repo.issues(state='all')) # Print results sep = ', ' if as_list else '\n' print(sep.join(mails)) if with_blog: print(sep.join(blogs))
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Extract mails from stargazers, collaborators and people involved with issues of given repository.
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https://github.com/Kraymer/high/blob/11bb86733875ec708264ffb92bf5ef09a9d2f08c/high/__init__.py#L100-L118
cohorte/cohorte-herald
python/herald/transports/xmpp/bot.py
HeraldBot.__callback
def __callback(self, data): """ Safely calls back a method :param data: Associated stanza """ method = self.__cb_message if method is not None: try: method(data) except Exception as ex: _logger.exception("Error calling method: %s", ex)
python
def __callback(self, data): """ Safely calls back a method :param data: Associated stanza """ method = self.__cb_message if method is not None: try: method(data) except Exception as ex: _logger.exception("Error calling method: %s", ex)
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https://github.com/cohorte/cohorte-herald/blob/bb3445d0031c8b3abad71e6219cc559b49faa3ee/python/herald/transports/xmpp/bot.py#L81-L92
cohorte/cohorte-herald
python/herald/transports/xmpp/bot.py
HeraldBot.__on_message
def __on_message(self, msg): """ XMPP message received """ msgtype = msg['type'] msgfrom = msg['from'] if msgtype == 'groupchat': # MUC Room chat if self._nick == msgfrom.resource: # Loopback message return elif msgtype not in ('normal', 'chat'): # Ignore non-chat messages return # Callback self.__callback(msg)
python
def __on_message(self, msg): """ XMPP message received """ msgtype = msg['type'] msgfrom = msg['from'] if msgtype == 'groupchat': # MUC Room chat if self._nick == msgfrom.resource: # Loopback message return elif msgtype not in ('normal', 'chat'): # Ignore non-chat messages return # Callback self.__callback(msg)
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XMPP message received
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https://github.com/cohorte/cohorte-herald/blob/bb3445d0031c8b3abad71e6219cc559b49faa3ee/python/herald/transports/xmpp/bot.py#L94-L110
etcher-be/epab
epab/cmd/_freeze.py
freeze
def freeze(ctx, version: str, clean: bool): """ Freeze current package into a single file """ if clean: _clean_spec() ctx.invoke(epab.cmd.compile_qt_resources) _freeze(version)
python
def freeze(ctx, version: str, clean: bool): """ Freeze current package into a single file """ if clean: _clean_spec() ctx.invoke(epab.cmd.compile_qt_resources) _freeze(version)
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https://github.com/etcher-be/epab/blob/024cde74d058281aa66e6e4b7b71dccbe803b1c1/epab/cmd/_freeze.py#L120-L127
scivision/gridaurora
gridaurora/ztanh.py
setupz
def setupz(Np: int, zmin: float, gridmin: float, gridmax: float) -> np.ndarray: """ np: number of grid points zmin: minimum STEP SIZE at minimum grid altitude [km] gridmin: minimum altitude of grid [km] gridmax: maximum altitude of grid [km] """ dz = _ztanh(Np, gridmin, gridmax) return np.insert(np.cumsum(dz)+zmin, 0, zmin)[:-1]
python
def setupz(Np: int, zmin: float, gridmin: float, gridmax: float) -> np.ndarray: """ np: number of grid points zmin: minimum STEP SIZE at minimum grid altitude [km] gridmin: minimum altitude of grid [km] gridmax: maximum altitude of grid [km] """ dz = _ztanh(Np, gridmin, gridmax) return np.insert(np.cumsum(dz)+zmin, 0, zmin)[:-1]
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train
https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/ztanh.py#L9-L19
scivision/gridaurora
gridaurora/ztanh.py
_ztanh
def _ztanh(Np: int, gridmin: float, gridmax: float) -> np.ndarray: """ typically call via setupz instead """ x0 = np.linspace(0, 3.14, Np) # arbitrarily picking 3.14 as where tanh gets to 99% of asymptote return np.tanh(x0)*gridmax+gridmin
python
def _ztanh(Np: int, gridmin: float, gridmax: float) -> np.ndarray: """ typically call via setupz instead """ x0 = np.linspace(0, 3.14, Np) # arbitrarily picking 3.14 as where tanh gets to 99% of asymptote return np.tanh(x0)*gridmax+gridmin
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train
https://github.com/scivision/gridaurora/blob/c3957b93c2201afff62bd104e0acead52c0d9e90/gridaurora/ztanh.py#L22-L27
clinicedc/edc-notification
edc_notification/site_notifications.py
SiteNotifications.get
def get(self, name): """Returns a Notification by name. """ if not self.loaded: raise RegistryNotLoaded(self) if not self._registry.get(name): raise NotificationNotRegistered( f"Notification not registered. Got '{name}'." ) return self._registry.get(name)
python
def get(self, name): """Returns a Notification by name. """ if not self.loaded: raise RegistryNotLoaded(self) if not self._registry.get(name): raise NotificationNotRegistered( f"Notification not registered. Got '{name}'." ) return self._registry.get(name)
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Returns a Notification by name.
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train
https://github.com/clinicedc/edc-notification/blob/79e43a44261e37566c63a8780d80b0d8ece89cc9/edc_notification/site_notifications.py#L47-L56
clinicedc/edc-notification
edc_notification/site_notifications.py
SiteNotifications.register
def register(self, notification_cls=None): """Registers a Notification class unique by name. """ self.loaded = True display_names = [n.display_name for n in self.registry.values()] if ( notification_cls.name not in self.registry and notification_cls.display_name not in display_names ): self.registry.update({notification_cls.name: notification_cls}) models = getattr(notification_cls, "models", []) if not models and getattr(notification_cls, "model", None): models = [getattr(notification_cls, "model")] for model in models: try: if notification_cls.name not in [ n.name for n in self.models[model] ]: self.models[model].append(notification_cls) except KeyError: self.models.update({model: [notification_cls]}) else: raise AlreadyRegistered( f"Notification {notification_cls.name}: " f"{notification_cls.display_name} is already registered." )
python
def register(self, notification_cls=None): """Registers a Notification class unique by name. """ self.loaded = True display_names = [n.display_name for n in self.registry.values()] if ( notification_cls.name not in self.registry and notification_cls.display_name not in display_names ): self.registry.update({notification_cls.name: notification_cls}) models = getattr(notification_cls, "models", []) if not models and getattr(notification_cls, "model", None): models = [getattr(notification_cls, "model")] for model in models: try: if notification_cls.name not in [ n.name for n in self.models[model] ]: self.models[model].append(notification_cls) except KeyError: self.models.update({model: [notification_cls]}) else: raise AlreadyRegistered( f"Notification {notification_cls.name}: " f"{notification_cls.display_name} is already registered." )
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train
https://github.com/clinicedc/edc-notification/blob/79e43a44261e37566c63a8780d80b0d8ece89cc9/edc_notification/site_notifications.py#L58-L84
clinicedc/edc-notification
edc_notification/site_notifications.py
SiteNotifications.notify
def notify(self, instance=None, **kwargs): """A wrapper to call notification.notify for each notification class associated with the given model instance. Returns a dictionary of {notification.name: model, ...} including only notifications sent. """ notified = {} for notification_cls in self.registry.values(): notification = notification_cls() if notification.notify(instance=instance, **kwargs): notified.update({notification_cls.name: instance._meta.label_lower}) return notified
python
def notify(self, instance=None, **kwargs): """A wrapper to call notification.notify for each notification class associated with the given model instance. Returns a dictionary of {notification.name: model, ...} including only notifications sent. """ notified = {} for notification_cls in self.registry.values(): notification = notification_cls() if notification.notify(instance=instance, **kwargs): notified.update({notification_cls.name: instance._meta.label_lower}) return notified
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A wrapper to call notification.notify for each notification class associated with the given model instance. Returns a dictionary of {notification.name: model, ...} including only notifications sent.
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train
https://github.com/clinicedc/edc-notification/blob/79e43a44261e37566c63a8780d80b0d8ece89cc9/edc_notification/site_notifications.py#L86-L98
clinicedc/edc-notification
edc_notification/site_notifications.py
SiteNotifications.update_notification_list
def update_notification_list(self, apps=None, schema_editor=None, verbose=False): """Updates the notification model to ensure all registered notifications classes are listed. Typically called from a post_migrate signal. Also, in tests you can register a notification and the Notification class (not model) will automatically call this method if the named notification does not exist. See notification.notify() """ Notification = (apps or django_apps).get_model("edc_notification.notification") # flag all notifications as disabled and re-enable as required Notification.objects.all().update(enabled=False) if site_notifications.loaded: if verbose: sys.stdout.write( style.MIGRATE_HEADING("Populating Notification model:\n") ) self.delete_unregistered_notifications(apps=apps) for name, notification_cls in site_notifications.registry.items(): if verbose: sys.stdout.write( f" * Adding '{name}': '{notification_cls().display_name}'\n" ) try: obj = Notification.objects.get(name=name) except ObjectDoesNotExist: Notification.objects.create( name=name, display_name=notification_cls().display_name, enabled=True, ) else: obj.display_name = notification_cls().display_name obj.enabled = True obj.save()
python
def update_notification_list(self, apps=None, schema_editor=None, verbose=False): """Updates the notification model to ensure all registered notifications classes are listed. Typically called from a post_migrate signal. Also, in tests you can register a notification and the Notification class (not model) will automatically call this method if the named notification does not exist. See notification.notify() """ Notification = (apps or django_apps).get_model("edc_notification.notification") # flag all notifications as disabled and re-enable as required Notification.objects.all().update(enabled=False) if site_notifications.loaded: if verbose: sys.stdout.write( style.MIGRATE_HEADING("Populating Notification model:\n") ) self.delete_unregistered_notifications(apps=apps) for name, notification_cls in site_notifications.registry.items(): if verbose: sys.stdout.write( f" * Adding '{name}': '{notification_cls().display_name}'\n" ) try: obj = Notification.objects.get(name=name) except ObjectDoesNotExist: Notification.objects.create( name=name, display_name=notification_cls().display_name, enabled=True, ) else: obj.display_name = notification_cls().display_name obj.enabled = True obj.save()
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train
https://github.com/clinicedc/edc-notification/blob/79e43a44261e37566c63a8780d80b0d8ece89cc9/edc_notification/site_notifications.py#L100-L136
clinicedc/edc-notification
edc_notification/site_notifications.py
SiteNotifications.delete_unregistered_notifications
def delete_unregistered_notifications(self, apps=None): """Delete orphaned notification model instances. """ Notification = (apps or django_apps).get_model("edc_notification.notification") return Notification.objects.exclude( name__in=[n.name for n in site_notifications.registry.values()] ).delete()
python
def delete_unregistered_notifications(self, apps=None): """Delete orphaned notification model instances. """ Notification = (apps or django_apps).get_model("edc_notification.notification") return Notification.objects.exclude( name__in=[n.name for n in site_notifications.registry.values()] ).delete()
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Delete orphaned notification model instances.
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train
https://github.com/clinicedc/edc-notification/blob/79e43a44261e37566c63a8780d80b0d8ece89cc9/edc_notification/site_notifications.py#L138-L144
clinicedc/edc-notification
edc_notification/site_notifications.py
SiteNotifications.create_mailing_lists
def create_mailing_lists(self, verbose=True): """Creates the mailing list for each registered notification. """ responses = {} if ( settings.EMAIL_ENABLED and self.loaded and settings.EMAIL_BACKEND != "django.core.mail.backends.locmem.EmailBackend" ): sys.stdout.write(style.MIGRATE_HEADING(f"Creating mailing lists:\n")) for name, notification_cls in self.registry.items(): message = None notification = notification_cls() manager = MailingListManager( address=notification.email_to, name=notification.name, display_name=notification.display_name, ) try: response = manager.create() except ConnectionError as e: sys.stdout.write( style.ERROR( f" * Failed to create mailing list {name}. " f"Got {e}\n" ) ) else: if verbose: try: message = response.json().get("message") except JSONDecodeError: message = response.text sys.stdout.write( f" * Creating mailing list {name}. " f'Got {response.status_code}: "{message}"\n' ) responses.update({name: response}) return responses
python
def create_mailing_lists(self, verbose=True): """Creates the mailing list for each registered notification. """ responses = {} if ( settings.EMAIL_ENABLED and self.loaded and settings.EMAIL_BACKEND != "django.core.mail.backends.locmem.EmailBackend" ): sys.stdout.write(style.MIGRATE_HEADING(f"Creating mailing lists:\n")) for name, notification_cls in self.registry.items(): message = None notification = notification_cls() manager = MailingListManager( address=notification.email_to, name=notification.name, display_name=notification.display_name, ) try: response = manager.create() except ConnectionError as e: sys.stdout.write( style.ERROR( f" * Failed to create mailing list {name}. " f"Got {e}\n" ) ) else: if verbose: try: message = response.json().get("message") except JSONDecodeError: message = response.text sys.stdout.write( f" * Creating mailing list {name}. " f'Got {response.status_code}: "{message}"\n' ) responses.update({name: response}) return responses
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Creates the mailing list for each registered notification.
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train
https://github.com/clinicedc/edc-notification/blob/79e43a44261e37566c63a8780d80b0d8ece89cc9/edc_notification/site_notifications.py#L146-L184
clinicedc/edc-notification
edc_notification/site_notifications.py
SiteNotifications.autodiscover
def autodiscover(self, module_name=None, verbose=False): """Autodiscovers classes in the notifications.py file of any INSTALLED_APP. """ module_name = module_name or "notifications" verbose = True if verbose is None else verbose sys.stdout.write(f" * checking for {module_name} ...\n") for app in django_apps.app_configs: try: mod = import_module(app) try: before_import_registry = copy.copy(site_notifications._registry) import_module(f"{app}.{module_name}") if verbose: sys.stdout.write( f" * registered notifications from application '{app}'\n" ) except Exception as e: if f"No module named '{app}.{module_name}'" not in str(e): site_notifications._registry = before_import_registry if module_has_submodule(mod, module_name): raise except ModuleNotFoundError: pass
python
def autodiscover(self, module_name=None, verbose=False): """Autodiscovers classes in the notifications.py file of any INSTALLED_APP. """ module_name = module_name or "notifications" verbose = True if verbose is None else verbose sys.stdout.write(f" * checking for {module_name} ...\n") for app in django_apps.app_configs: try: mod = import_module(app) try: before_import_registry = copy.copy(site_notifications._registry) import_module(f"{app}.{module_name}") if verbose: sys.stdout.write( f" * registered notifications from application '{app}'\n" ) except Exception as e: if f"No module named '{app}.{module_name}'" not in str(e): site_notifications._registry = before_import_registry if module_has_submodule(mod, module_name): raise except ModuleNotFoundError: pass
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Autodiscovers classes in the notifications.py file of any INSTALLED_APP.
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train
https://github.com/clinicedc/edc-notification/blob/79e43a44261e37566c63a8780d80b0d8ece89cc9/edc_notification/site_notifications.py#L186-L209
six8/corona-cipr
src/cipr/commands/cfg.py
CiprCfg.add_package
def add_package(self, package): """ Add a package to this project """ self._data.setdefault('packages', {}) self._data['packages'][package.name] = package.source for package in package.deploy_packages: self.add_package(package) self._save()
python
def add_package(self, package): """ Add a package to this project """ self._data.setdefault('packages', {}) self._data['packages'][package.name] = package.source for package in package.deploy_packages: self.add_package(package) self._save()
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Add a package to this project
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train
https://github.com/six8/corona-cipr/blob/a2f45761080c874afa39bf95fd5c4467c8eae272/src/cipr/commands/cfg.py#L73-L84
Gr1N/copypaste
copypaste/unix.py
copy
def copy(string, cmd=copy_cmd, stdin=PIPE): """Copy given string into system clipboard. """ Popen(cmd, stdin=stdin).communicate(string.encode('utf-8'))
python
def copy(string, cmd=copy_cmd, stdin=PIPE): """Copy given string into system clipboard. """ Popen(cmd, stdin=stdin).communicate(string.encode('utf-8'))
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Copy given string into system clipboard.
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train
https://github.com/Gr1N/copypaste/blob/7d515d1d31db5ae686378b60e4c8717d7f6834de/copypaste/unix.py#L24-L27
Gr1N/copypaste
copypaste/unix.py
paste
def paste(cmd=paste_cmd, stdout=PIPE): """Returns system clipboard contents. """ return Popen(cmd, stdout=stdout).communicate()[0].decode('utf-8')
python
def paste(cmd=paste_cmd, stdout=PIPE): """Returns system clipboard contents. """ return Popen(cmd, stdout=stdout).communicate()[0].decode('utf-8')
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Returns system clipboard contents.
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train
https://github.com/Gr1N/copypaste/blob/7d515d1d31db5ae686378b60e4c8717d7f6834de/copypaste/unix.py#L30-L33
victorfsf/djurls
djurls/decorators.py
umap
def umap(path, name=None, include=None, namespace=None, priority=None): """ Maps a given URL path, name and namespace to a view. Arguments: - path: the URL regex, e.g.: '^teste/(?P<pk>[0-9])/$'. Optional arguments: - name: the URL name, which Django uses to identify the URL; - include: A custom URL list, previously set on the module's urls.py; - namespace: the URL's namespace; - priority: the URL's priority; """ def url_wrapper(view): # gets the module name module = _find_urls_module(view) # gets the view function (checking if it's a class-based view) fn = view.as_view() if hasattr(view, 'as_view') else view if namespace and include: raise TypeError( 'You can\'t use \'namespace\' and \'include\'' ' at the same time!' ) if namespace: # imports the urlpatterns object base = import_string('{}.urls.urlpatterns'.format(module)) # searchs for the namespace urlpatterns_list = [ x for x in base if getattr(x, 'namespace', None) == namespace ] # if the list length is different than 1, # then the namespace is either duplicated or doesn't exist if len(urlpatterns_list) != 1: raise ValueError( 'Namespace \'{}\' not in list.'.format(namespace) ) # if the namespace was found, get its object urlpatterns = urlpatterns_list.pop(0).url_patterns else: # imports the urlpatterns object urlpatterns = import_string('{}.urls.{}'.format( module, include or 'urlpatterns' )) # appends the url with its given name call = ( urlpatterns.append if priority is None else partial(urlpatterns.insert, priority) ) call(url(path, fn, name=name)) return view return url_wrapper
python
def umap(path, name=None, include=None, namespace=None, priority=None): """ Maps a given URL path, name and namespace to a view. Arguments: - path: the URL regex, e.g.: '^teste/(?P<pk>[0-9])/$'. Optional arguments: - name: the URL name, which Django uses to identify the URL; - include: A custom URL list, previously set on the module's urls.py; - namespace: the URL's namespace; - priority: the URL's priority; """ def url_wrapper(view): # gets the module name module = _find_urls_module(view) # gets the view function (checking if it's a class-based view) fn = view.as_view() if hasattr(view, 'as_view') else view if namespace and include: raise TypeError( 'You can\'t use \'namespace\' and \'include\'' ' at the same time!' ) if namespace: # imports the urlpatterns object base = import_string('{}.urls.urlpatterns'.format(module)) # searchs for the namespace urlpatterns_list = [ x for x in base if getattr(x, 'namespace', None) == namespace ] # if the list length is different than 1, # then the namespace is either duplicated or doesn't exist if len(urlpatterns_list) != 1: raise ValueError( 'Namespace \'{}\' not in list.'.format(namespace) ) # if the namespace was found, get its object urlpatterns = urlpatterns_list.pop(0).url_patterns else: # imports the urlpatterns object urlpatterns = import_string('{}.urls.{}'.format( module, include or 'urlpatterns' )) # appends the url with its given name call = ( urlpatterns.append if priority is None else partial(urlpatterns.insert, priority) ) call(url(path, fn, name=name)) return view return url_wrapper
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train
https://github.com/victorfsf/djurls/blob/c90a46e5e17c12dd6920100419a89e428baf4711/djurls/decorators.py#L8-L62
pulseenergy/vacation
vacation/rc.py
touch
def touch(): """ Create a .vacationrc file if none exists. """ if not os.path.isfile(get_rc_path()): open(get_rc_path(), 'a').close() print('Created file: {}'.format(get_rc_path()))
python
def touch(): """ Create a .vacationrc file if none exists. """ if not os.path.isfile(get_rc_path()): open(get_rc_path(), 'a').close() print('Created file: {}'.format(get_rc_path()))
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Create a .vacationrc file if none exists.
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train
https://github.com/pulseenergy/vacation/blob/23c6122590852a5e55d84d366143469af6602839/vacation/rc.py#L9-L13
pulseenergy/vacation
vacation/rc.py
write
def write(entries): """ Write an entire rc file. """ try: with open(get_rc_path(), 'w') as rc: rc.writelines(entries) except IOError: print('Error writing your ~/.vacationrc file!')
python
def write(entries): """ Write an entire rc file. """ try: with open(get_rc_path(), 'w') as rc: rc.writelines(entries) except IOError: print('Error writing your ~/.vacationrc file!')
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Write an entire rc file.
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train
https://github.com/pulseenergy/vacation/blob/23c6122590852a5e55d84d366143469af6602839/vacation/rc.py#L26-L32
pulseenergy/vacation
vacation/rc.py
append
def append(entry): """ Append either a list of strings or a string to our file. """ if not entry: return try: with open(get_rc_path(), 'a') as f: if isinstance(entry, list): f.writelines(entry) else: f.write(entry + '\n') except IOError: print('Error writing your ~/.vacationrc file!')
python
def append(entry): """ Append either a list of strings or a string to our file. """ if not entry: return try: with open(get_rc_path(), 'a') as f: if isinstance(entry, list): f.writelines(entry) else: f.write(entry + '\n') except IOError: print('Error writing your ~/.vacationrc file!')
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Append either a list of strings or a string to our file.
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train
https://github.com/pulseenergy/vacation/blob/23c6122590852a5e55d84d366143469af6602839/vacation/rc.py#L44-L55
pulseenergy/vacation
vacation/rc.py
delete
def delete(bad_entry): """ Removes an entry from rc file. """ entries = read() kept_entries = [x for x in entries if x.rstrip() != bad_entry] write(kept_entries)
python
def delete(bad_entry): """ Removes an entry from rc file. """ entries = read() kept_entries = [x for x in entries if x.rstrip() != bad_entry] write(kept_entries)
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Removes an entry from rc file.
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train
https://github.com/pulseenergy/vacation/blob/23c6122590852a5e55d84d366143469af6602839/vacation/rc.py#L58-L62
ulif/dinsort
dinsort.py
normalize
def normalize(text, variant=VARIANT1, case_sensitive=False): """Create a normalized version of `text`. With `variant` set to ``VARIANT1`` (default), german umlauts are transformed to plain chars: ``ä`` -> ``a``, ``ö`` -> ``o``, ...:: >>> print(normalize("mäßig")) massig With `variant` set to ``VARIANT2``, german umlauts are transformed ``ä`` -> ``ae``, etc.:: >>> print(normalize("mäßig", variant=VARIANT2)) maessig All words are turned to lower-case.:: >>> print(normalize("Maße")) masse except if `case_sensitive` is set to `True`:: >>> print(normalize("Maße", case_sensitive=True)) Masse Other chars with diacritics will be returned with the diacritics stripped off:: >>> print(normalize("Česká")) ceska """ text = text.replace("ß", "ss") if not case_sensitive: text = text.lower() if variant == VARIANT2: for char, repl in ( ('ä', 'ae'), ('ö', 'oe'), ('ü', 'ue'), ('Ä', 'AE'), ('Ö', 'OE'), ('Ü', 'UE')): text = text.replace(char, repl) text = unicodedata.normalize("NFKD", text).encode("ASCII", "ignore") return text.decode()
python
def normalize(text, variant=VARIANT1, case_sensitive=False): """Create a normalized version of `text`. With `variant` set to ``VARIANT1`` (default), german umlauts are transformed to plain chars: ``ä`` -> ``a``, ``ö`` -> ``o``, ...:: >>> print(normalize("mäßig")) massig With `variant` set to ``VARIANT2``, german umlauts are transformed ``ä`` -> ``ae``, etc.:: >>> print(normalize("mäßig", variant=VARIANT2)) maessig All words are turned to lower-case.:: >>> print(normalize("Maße")) masse except if `case_sensitive` is set to `True`:: >>> print(normalize("Maße", case_sensitive=True)) Masse Other chars with diacritics will be returned with the diacritics stripped off:: >>> print(normalize("Česká")) ceska """ text = text.replace("ß", "ss") if not case_sensitive: text = text.lower() if variant == VARIANT2: for char, repl in ( ('ä', 'ae'), ('ö', 'oe'), ('ü', 'ue'), ('Ä', 'AE'), ('Ö', 'OE'), ('Ü', 'UE')): text = text.replace(char, repl) text = unicodedata.normalize("NFKD", text).encode("ASCII", "ignore") return text.decode()
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train
https://github.com/ulif/dinsort/blob/06c95323dfbe5e536a72a7a58789e61b38fcebc4/dinsort.py#L31-L73
ulif/dinsort
dinsort.py
sort_func
def sort_func(variant=VARIANT1, case_sensitive=False): """A function generator that can be used for sorting. All keywords are passed to `normalize()` and generate keywords that can be passed to `sorted()`:: >>> key = sort_func() >>> print(sorted(["fur", "far"], key=key)) [u'far', u'fur'] Please note, that `sort_func` returns a function. """ return lambda x: normalize( x, variant=variant, case_sensitive=case_sensitive)
python
def sort_func(variant=VARIANT1, case_sensitive=False): """A function generator that can be used for sorting. All keywords are passed to `normalize()` and generate keywords that can be passed to `sorted()`:: >>> key = sort_func() >>> print(sorted(["fur", "far"], key=key)) [u'far', u'fur'] Please note, that `sort_func` returns a function. """ return lambda x: normalize( x, variant=variant, case_sensitive=case_sensitive)
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train
https://github.com/ulif/dinsort/blob/06c95323dfbe5e536a72a7a58789e61b38fcebc4/dinsort.py#L76-L89
onjin/ntv
ntv/web.py
fetcher
def fetcher(date=datetime.today(), url_pattern=URL_PATTERN): """ Fetch json data from n.pl Args: date (date) - default today url_patter (string) - default URL_PATTERN Returns: dict - data from api """ api_url = url_pattern % date.strftime('%Y-%m-%d') headers = {'Referer': 'http://n.pl/program-tv'} raw_result = requests.get(api_url, headers=headers).json() return raw_result
python
def fetcher(date=datetime.today(), url_pattern=URL_PATTERN): """ Fetch json data from n.pl Args: date (date) - default today url_patter (string) - default URL_PATTERN Returns: dict - data from api """ api_url = url_pattern % date.strftime('%Y-%m-%d') headers = {'Referer': 'http://n.pl/program-tv'} raw_result = requests.get(api_url, headers=headers).json() return raw_result
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train
https://github.com/onjin/ntv/blob/9baa9cfdff9eca0ebd12b6c456951345a269039f/ntv/web.py#L13-L28
onjin/ntv
ntv/web.py
result_to_dict
def result_to_dict(raw_result): """ Parse raw result from fetcher into readable dictionary Args: raw_result (dict) - raw data from `fetcher` Returns: dict - readable dictionary """ result = {} for channel_index, channel in enumerate(raw_result): channel_id, channel_name = channel[0], channel[1] channel_result = { 'id': channel_id, 'name': channel_name, 'movies': [] } for movie in channel[2]: channel_result['movies'].append({ 'title': movie[1], 'start_time': datetime.fromtimestamp(movie[2]), 'end_time': datetime.fromtimestamp(movie[2] + movie[3]), 'inf': True if movie[3] else False, }) result[channel_id] = channel_result return result
python
def result_to_dict(raw_result): """ Parse raw result from fetcher into readable dictionary Args: raw_result (dict) - raw data from `fetcher` Returns: dict - readable dictionary """ result = {} for channel_index, channel in enumerate(raw_result): channel_id, channel_name = channel[0], channel[1] channel_result = { 'id': channel_id, 'name': channel_name, 'movies': [] } for movie in channel[2]: channel_result['movies'].append({ 'title': movie[1], 'start_time': datetime.fromtimestamp(movie[2]), 'end_time': datetime.fromtimestamp(movie[2] + movie[3]), 'inf': True if movie[3] else False, }) result[channel_id] = channel_result return result
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train
https://github.com/onjin/ntv/blob/9baa9cfdff9eca0ebd12b6c456951345a269039f/ntv/web.py#L31-L60
mozilla/socorrolib
socorrolib/lib/threadlib.py
TaskManager.waitForCompletion
def waitForCompletion (self): """Wait for all threads to complete their work The worker threads are told to quit when they receive a task that is a tuple of (None, None). This routine puts as many of those tuples in the task queue as there are threads. As soon as a thread receives one of these tuples, it dies. """ for x in range(self.numberOfThreads): self.taskQueue.put((None, None)) for t in self.threadList: # print "attempting to join %s" % t.getName() t.join()
python
def waitForCompletion (self): """Wait for all threads to complete their work The worker threads are told to quit when they receive a task that is a tuple of (None, None). This routine puts as many of those tuples in the task queue as there are threads. As soon as a thread receives one of these tuples, it dies. """ for x in range(self.numberOfThreads): self.taskQueue.put((None, None)) for t in self.threadList: # print "attempting to join %s" % t.getName() t.join()
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train
https://github.com/mozilla/socorrolib/blob/4ec08c6a4ee2c8a69150268afdd324f5f22b90c8/socorrolib/lib/threadlib.py#L84-L96
mozilla/socorrolib
socorrolib/lib/threadlib.py
TaskManagerThread.run
def run(self): """The main routine for a thread's work. The thread pulls tasks from the manager's task queue and executes them until it encounters a task with a function that is None. """ try: while True: aFunction, arguments = self.manager.taskQueue.get() if aFunction is None: break aFunction(arguments) except KeyboardInterrupt: import thread print >>sys.stderr, "%s caught KeyboardInterrupt" % threading.currentThread().getName() thread.interrupt_main() except Exception, x: print >>sys.stderr, "Something BAD happened in %s:" % threading.currentThread().getName() traceback.print_exc(file=sys.stderr) print >>sys.stderr, x
python
def run(self): """The main routine for a thread's work. The thread pulls tasks from the manager's task queue and executes them until it encounters a task with a function that is None. """ try: while True: aFunction, arguments = self.manager.taskQueue.get() if aFunction is None: break aFunction(arguments) except KeyboardInterrupt: import thread print >>sys.stderr, "%s caught KeyboardInterrupt" % threading.currentThread().getName() thread.interrupt_main() except Exception, x: print >>sys.stderr, "Something BAD happened in %s:" % threading.currentThread().getName() traceback.print_exc(file=sys.stderr) print >>sys.stderr, x
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train
https://github.com/mozilla/socorrolib/blob/4ec08c6a4ee2c8a69150268afdd324f5f22b90c8/socorrolib/lib/threadlib.py#L117-L136
henzk/ape
ape/container_mode/tasks.py
info
def info(): """ List information about this productive environment :return: """ print() print('root directory :', tasks.conf.APE_ROOT) print() print('active container :', os.environ.get('CONTAINER_NAME', '')) print() print('active product :', os.environ.get('PRODUCT_NAME', '')) print() print('ape feature selection :', tasks.FEATURE_SELECTION) print() print('containers and products:') print('-' * 30) print() for container_name in tasks.get_containers(): print(container_name) for product_name in tasks.get_products(container_name): print(' ' + product_name) print()
python
def info(): """ List information about this productive environment :return: """ print() print('root directory :', tasks.conf.APE_ROOT) print() print('active container :', os.environ.get('CONTAINER_NAME', '')) print() print('active product :', os.environ.get('PRODUCT_NAME', '')) print() print('ape feature selection :', tasks.FEATURE_SELECTION) print() print('containers and products:') print('-' * 30) print() for container_name in tasks.get_containers(): print(container_name) for product_name in tasks.get_products(container_name): print(' ' + product_name) print()
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train
https://github.com/henzk/ape/blob/a1b7ea5e5b25c42beffeaaa5c32d94ad82634819/ape/container_mode/tasks.py#L54-L75
henzk/ape
ape/container_mode/tasks.py
cd
def cd(doi): """ cd to directory of interest(doi) a doi can be: herbert - the container named "herbert" sdox:dev - product "website" located in container "herbert" :param doi: :return: """ parts = doi.split(':') if len(parts) == 2: container_name, product_name = parts[0], parts[1] elif len(parts) == 1 and os.environ.get('CONTAINER_NAME'): # interpret poi as product name if already zapped into a product in order # to enable simply switching products by doing ape zap prod. product_name = parts[0] container_name = os.environ.get('CONTAINER_NAME') else: print('unable to parse context - format: <container_name>:<product_name>') sys.exit(1) if container_name not in tasks.get_containers(): print('No such container') else: if product_name: if product_name not in tasks.get_products(container_name): print('No such product') else: print(tasks.conf.SOURCE_HEADER) print('cd ' + tasks.get_product_dir(container_name, product_name)) else: print(tasks.conf.SOURCE_HEADER) print('cd ' + tasks.get_container_dir(container_name))
python
def cd(doi): """ cd to directory of interest(doi) a doi can be: herbert - the container named "herbert" sdox:dev - product "website" located in container "herbert" :param doi: :return: """ parts = doi.split(':') if len(parts) == 2: container_name, product_name = parts[0], parts[1] elif len(parts) == 1 and os.environ.get('CONTAINER_NAME'): # interpret poi as product name if already zapped into a product in order # to enable simply switching products by doing ape zap prod. product_name = parts[0] container_name = os.environ.get('CONTAINER_NAME') else: print('unable to parse context - format: <container_name>:<product_name>') sys.exit(1) if container_name not in tasks.get_containers(): print('No such container') else: if product_name: if product_name not in tasks.get_products(container_name): print('No such product') else: print(tasks.conf.SOURCE_HEADER) print('cd ' + tasks.get_product_dir(container_name, product_name)) else: print(tasks.conf.SOURCE_HEADER) print('cd ' + tasks.get_container_dir(container_name))
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train
https://github.com/henzk/ape/blob/a1b7ea5e5b25c42beffeaaa5c32d94ad82634819/ape/container_mode/tasks.py#L79-L115
henzk/ape
ape/container_mode/tasks.py
switch
def switch(poi): """ Zaps into a specific product specified by switch context to the product of interest(poi) A poi is: sdox:dev - for product "dev" located in container "sdox" If poi does not contain a ":" it is interpreted as product name implying that a product within this container is already active. So if this task is called with ape zap prod (and the corresponding container is already zapped in), than only the product is switched. After the context has been switched to sdox:dev additional commands may be available that are relevant to sdox:dev :param poi: product of interest, string: <container_name>:<product_name> or <product_name>. """ parts = poi.split(':') if len(parts) == 2: container_name, product_name = parts elif len(parts) == 1 and os.environ.get('CONTAINER_NAME'): # interpret poi as product name if already zapped into a product in order # to enable simply switching products by doing ape zap prod. container_name = os.environ.get('CONTAINER_NAME') product_name = parts[0] else: print('unable to find poi: ', poi) sys.exit(1) if container_name not in tasks.get_containers(): raise ContainerNotFound('No such container %s' % container_name) elif product_name not in tasks.get_products(container_name): raise ProductNotFound('No such product %s' % product_name) else: print(SWITCH_TEMPLATE.format( source_header=tasks.conf.SOURCE_HEADER, container_name=container_name, product_name=product_name ))
python
def switch(poi): """ Zaps into a specific product specified by switch context to the product of interest(poi) A poi is: sdox:dev - for product "dev" located in container "sdox" If poi does not contain a ":" it is interpreted as product name implying that a product within this container is already active. So if this task is called with ape zap prod (and the corresponding container is already zapped in), than only the product is switched. After the context has been switched to sdox:dev additional commands may be available that are relevant to sdox:dev :param poi: product of interest, string: <container_name>:<product_name> or <product_name>. """ parts = poi.split(':') if len(parts) == 2: container_name, product_name = parts elif len(parts) == 1 and os.environ.get('CONTAINER_NAME'): # interpret poi as product name if already zapped into a product in order # to enable simply switching products by doing ape zap prod. container_name = os.environ.get('CONTAINER_NAME') product_name = parts[0] else: print('unable to find poi: ', poi) sys.exit(1) if container_name not in tasks.get_containers(): raise ContainerNotFound('No such container %s' % container_name) elif product_name not in tasks.get_products(container_name): raise ProductNotFound('No such product %s' % product_name) else: print(SWITCH_TEMPLATE.format( source_header=tasks.conf.SOURCE_HEADER, container_name=container_name, product_name=product_name ))
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train
https://github.com/henzk/ape/blob/a1b7ea5e5b25c42beffeaaa5c32d94ad82634819/ape/container_mode/tasks.py#L127-L163
henzk/ape
ape/container_mode/tasks.py
install_container
def install_container(container_name): """ Installs the container specified by container_name :param container_name: string, name of the container """ container_dir = os.path.join(os.environ['APE_ROOT_DIR'], container_name) if os.path.exists(container_dir): os.environ['CONTAINER_DIR'] = container_dir else: raise ContainerNotFound('ERROR: container directory not found: %s' % container_dir) install_script = os.path.join(container_dir, 'install.py') if os.path.exists(install_script): print('... running install.py for %s' % container_name) subprocess.check_call(['python', install_script]) else: raise ContainerError('ERROR: this container does not provide an install.py!')
python
def install_container(container_name): """ Installs the container specified by container_name :param container_name: string, name of the container """ container_dir = os.path.join(os.environ['APE_ROOT_DIR'], container_name) if os.path.exists(container_dir): os.environ['CONTAINER_DIR'] = container_dir else: raise ContainerNotFound('ERROR: container directory not found: %s' % container_dir) install_script = os.path.join(container_dir, 'install.py') if os.path.exists(install_script): print('... running install.py for %s' % container_name) subprocess.check_call(['python', install_script]) else: raise ContainerError('ERROR: this container does not provide an install.py!')
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train
https://github.com/henzk/ape/blob/a1b7ea5e5b25c42beffeaaa5c32d94ad82634819/ape/container_mode/tasks.py#L184-L201
henzk/ape
ape/container_mode/tasks.py
get_poi_tuple
def get_poi_tuple(poi=None): """ Takes the poi or None and returns the container_dir and the product name either of the passed poi (<container_name>: <product_name>) or from os.environ- :param poi: optional; <container_name>: <product_name> :return: tuple of the container directory and the product name """ if poi: parts = poi.split(':') if len(parts) == 2: container_name, product_name = parts if container_name not in tasks.get_containers(): print('No such container') sys.exit(1) elif product_name not in tasks.get_products(container_name): print('No such product') sys.exit(1) else: container_dir = tasks.get_container_dir(container_name) else: print('Please check your arguments: --poi <container>:<product>') sys.exit(1) else: container_dir = os.environ.get('CONTAINER_DIR') product_name = os.environ.get('PRODUCT_NAME') return container_dir, product_name
python
def get_poi_tuple(poi=None): """ Takes the poi or None and returns the container_dir and the product name either of the passed poi (<container_name>: <product_name>) or from os.environ- :param poi: optional; <container_name>: <product_name> :return: tuple of the container directory and the product name """ if poi: parts = poi.split(':') if len(parts) == 2: container_name, product_name = parts if container_name not in tasks.get_containers(): print('No such container') sys.exit(1) elif product_name not in tasks.get_products(container_name): print('No such product') sys.exit(1) else: container_dir = tasks.get_container_dir(container_name) else: print('Please check your arguments: --poi <container>:<product>') sys.exit(1) else: container_dir = os.environ.get('CONTAINER_DIR') product_name = os.environ.get('PRODUCT_NAME') return container_dir, product_name
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train
https://github.com/henzk/ape/blob/a1b7ea5e5b25c42beffeaaa5c32d94ad82634819/ape/container_mode/tasks.py#L211-L237
henzk/ape
ape/container_mode/tasks.py
validate_product_equation
def validate_product_equation(poi=None): """ Validates the product equation. * Validates the feature order * Validates the product spec (mandatory functional features) :param poi: optional product of interest """ from . import utils from . import validators container_dir, product_name = tasks.get_poi_tuple(poi=poi) feature_list = utils.get_features_from_equation(container_dir, product_name) ordering_constraints = utils.get_feature_order_constraints(container_dir) spec_path = utils.get_feature_ide_paths(container_dir, product_name).product_spec_path print('*** Starting product.equation validation') # -------------------------------------------------------- # Validate the feature order print('\tChecking feature order') feature_order_validator = validators.FeatureOrderValidator(feature_list, ordering_constraints) feature_order_validator.check_order() if feature_order_validator.has_errors(): print('\t\txxx ERROR in your product.equation feature order xxx') for error in feature_order_validator.get_violations(): print('\t\t\t', error[1]) else: print('\t\tOK') # -------------------------------------------------------- # Validate the functional product specification print('\tChecking functional product spec') if not os.path.exists(spec_path): print( '\t\tSkipped - No product spec exists.\n' '\t\tYou may create a product spec if you want to ensure that\n' '\t\trequired functional features are represented in the product equation\n' '\t\t=> Create spec file featuremodel/productline/<container>/product_spec.json' ) return spec_validator = validators.ProductSpecValidator(spec_path, product_name, feature_list) if not spec_validator.is_valid(): if spec_validator.get_errors_mandatory(): print('\t\tERROR: The following feature are missing', spec_validator.get_errors_mandatory()) if spec_validator.get_errors_never(): print('\t\tERROR: The following feature are not allowed', spec_validator.get_errors_never()) else: print('\t\tOK') if feature_order_validator.has_errors() or spec_validator.has_errors(): sys.exit(1)
python
def validate_product_equation(poi=None): """ Validates the product equation. * Validates the feature order * Validates the product spec (mandatory functional features) :param poi: optional product of interest """ from . import utils from . import validators container_dir, product_name = tasks.get_poi_tuple(poi=poi) feature_list = utils.get_features_from_equation(container_dir, product_name) ordering_constraints = utils.get_feature_order_constraints(container_dir) spec_path = utils.get_feature_ide_paths(container_dir, product_name).product_spec_path print('*** Starting product.equation validation') # -------------------------------------------------------- # Validate the feature order print('\tChecking feature order') feature_order_validator = validators.FeatureOrderValidator(feature_list, ordering_constraints) feature_order_validator.check_order() if feature_order_validator.has_errors(): print('\t\txxx ERROR in your product.equation feature order xxx') for error in feature_order_validator.get_violations(): print('\t\t\t', error[1]) else: print('\t\tOK') # -------------------------------------------------------- # Validate the functional product specification print('\tChecking functional product spec') if not os.path.exists(spec_path): print( '\t\tSkipped - No product spec exists.\n' '\t\tYou may create a product spec if you want to ensure that\n' '\t\trequired functional features are represented in the product equation\n' '\t\t=> Create spec file featuremodel/productline/<container>/product_spec.json' ) return spec_validator = validators.ProductSpecValidator(spec_path, product_name, feature_list) if not spec_validator.is_valid(): if spec_validator.get_errors_mandatory(): print('\t\tERROR: The following feature are missing', spec_validator.get_errors_mandatory()) if spec_validator.get_errors_never(): print('\t\tERROR: The following feature are not allowed', spec_validator.get_errors_never()) else: print('\t\tOK') if feature_order_validator.has_errors() or spec_validator.has_errors(): sys.exit(1)
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train
https://github.com/henzk/ape/blob/a1b7ea5e5b25c42beffeaaa5c32d94ad82634819/ape/container_mode/tasks.py#L241-L296
henzk/ape
ape/container_mode/tasks.py
get_ordered_feature_list
def get_ordered_feature_list(info_object, feature_list): """ Orders the passed feature list by the given, json-formatted feature dependency file using feaquencer's topsort algorithm. :param feature_list: :param info_object: :return: """ feature_dependencies = json.load(open(info_object.feature_order_json)) feature_selection = [feature for feature in [feature.strip().replace('\n', '') for feature in feature_list] if len(feature) > 0 and not feature.startswith('_') and not feature.startswith('#')] return [feature + '\n' for feature in feaquencer.get_total_order(feature_selection, feature_dependencies)]
python
def get_ordered_feature_list(info_object, feature_list): """ Orders the passed feature list by the given, json-formatted feature dependency file using feaquencer's topsort algorithm. :param feature_list: :param info_object: :return: """ feature_dependencies = json.load(open(info_object.feature_order_json)) feature_selection = [feature for feature in [feature.strip().replace('\n', '') for feature in feature_list] if len(feature) > 0 and not feature.startswith('_') and not feature.startswith('#')] return [feature + '\n' for feature in feaquencer.get_total_order(feature_selection, feature_dependencies)]
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train
https://github.com/henzk/ape/blob/a1b7ea5e5b25c42beffeaaa5c32d94ad82634819/ape/container_mode/tasks.py#L300-L311
henzk/ape
ape/container_mode/tasks.py
config_to_equation
def config_to_equation(poi=None): """ Generates a product.equation file for the given product name. It generates it from the <product_name>.config file in the products folder. For that you need to have your project imported to featureIDE and set the correct settings. """ from . import utils container_dir, product_name = tasks.get_poi_tuple(poi=poi) info_object = utils.get_feature_ide_paths(container_dir, product_name) feature_list = list() try: print('*** Processing ', info_object.config_file_path) with open(info_object.config_file_path, 'r') as config_file: config_file = config_file.readlines() for line in config_file: # in FeatureIDE we cant use '.' for the paths to sub-features so we used '__' # e.g. django_productline__features__development if len(line.split('__')) <= 2: line = line else: line = line.replace('__', '.') if line.startswith('abstract_'): # we skipp abstract features; this is a special case as featureIDE does not work with abstract # sub trees / leafs. line = '' feature_list.append(line) except IOError: print('{} does not exist. Make sure your config file exists.'.format(info_object.config_file_path)) feature_list = tasks.get_ordered_feature_list(info_object, feature_list) try: with open(info_object.equation_file_path, 'w') as eq_file: eq_file.writelines(feature_list) print('*** Successfully generated product.equation') except IOError: print('product.equation file not found. Please make sure you have a valid product.equation in your chosen product') # finally performing the validation of the product equation tasks.validate_product_equation()
python
def config_to_equation(poi=None): """ Generates a product.equation file for the given product name. It generates it from the <product_name>.config file in the products folder. For that you need to have your project imported to featureIDE and set the correct settings. """ from . import utils container_dir, product_name = tasks.get_poi_tuple(poi=poi) info_object = utils.get_feature_ide_paths(container_dir, product_name) feature_list = list() try: print('*** Processing ', info_object.config_file_path) with open(info_object.config_file_path, 'r') as config_file: config_file = config_file.readlines() for line in config_file: # in FeatureIDE we cant use '.' for the paths to sub-features so we used '__' # e.g. django_productline__features__development if len(line.split('__')) <= 2: line = line else: line = line.replace('__', '.') if line.startswith('abstract_'): # we skipp abstract features; this is a special case as featureIDE does not work with abstract # sub trees / leafs. line = '' feature_list.append(line) except IOError: print('{} does not exist. Make sure your config file exists.'.format(info_object.config_file_path)) feature_list = tasks.get_ordered_feature_list(info_object, feature_list) try: with open(info_object.equation_file_path, 'w') as eq_file: eq_file.writelines(feature_list) print('*** Successfully generated product.equation') except IOError: print('product.equation file not found. Please make sure you have a valid product.equation in your chosen product') # finally performing the validation of the product equation tasks.validate_product_equation()
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duniter/duniter-python-api
duniterpy/grammars/output.py
SIG.token
def token(cls: Type[SIGType], pubkey: str) -> SIGType: """ Return SIG instance from pubkey :param pubkey: Public key of the signature issuer :return: """ sig = cls() sig.pubkey = pubkey return sig
python
def token(cls: Type[SIGType], pubkey: str) -> SIGType: """ Return SIG instance from pubkey :param pubkey: Public key of the signature issuer :return: """ sig = cls() sig.pubkey = pubkey return sig
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train
https://github.com/duniter/duniter-python-api/blob/3a1e5d61a2f72f5afaf29d010c6cf4dff3648165/duniterpy/grammars/output.py#L52-L61
duniter/duniter-python-api
duniterpy/grammars/output.py
CSV.token
def token(cls: Type[CSVType], time: int) -> CSVType: """ Return CSV instance from time :param time: Timestamp :return: """ csv = cls() csv.time = str(time) return csv
python
def token(cls: Type[CSVType], time: int) -> CSVType: """ Return CSV instance from time :param time: Timestamp :return: """ csv = cls() csv.time = str(time) return csv
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train
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duniter/duniter-python-api
duniterpy/grammars/output.py
CSV.compose
def compose(self, parser: Any, grammar: Any = None, attr_of: str = None): """ Return the CSV(time) expression as string format :param parser: Parser instance :param grammar: Grammar :param attr_of: Attribute of... """ return "CSV({0})".format(self.time)
python
def compose(self, parser: Any, grammar: Any = None, attr_of: str = None): """ Return the CSV(time) expression as string format :param parser: Parser instance :param grammar: Grammar :param attr_of: Attribute of... """ return "CSV({0})".format(self.time)
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train
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duniter/duniter-python-api
duniterpy/grammars/output.py
CLTV.token
def token(cls: Type[CLTVType], timestamp: int) -> CLTVType: """ Return CLTV instance from timestamp :param timestamp: Timestamp :return: """ cltv = cls() cltv.timestamp = str(timestamp) return cltv
python
def token(cls: Type[CLTVType], timestamp: int) -> CLTVType: """ Return CLTV instance from timestamp :param timestamp: Timestamp :return: """ cltv = cls() cltv.timestamp = str(timestamp) return cltv
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train
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duniter/duniter-python-api
duniterpy/grammars/output.py
XHX.token
def token(cls: Type[XHXType], sha_hash: str) -> XHXType: """ Return XHX instance from sha_hash :param sha_hash: SHA256 hash :return: """ xhx = cls() xhx.sha_hash = sha_hash return xhx
python
def token(cls: Type[XHXType], sha_hash: str) -> XHXType: """ Return XHX instance from sha_hash :param sha_hash: SHA256 hash :return: """ xhx = cls() xhx.sha_hash = sha_hash return xhx
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train
https://github.com/duniter/duniter-python-api/blob/3a1e5d61a2f72f5afaf29d010c6cf4dff3648165/duniterpy/grammars/output.py#L188-L197
duniter/duniter-python-api
duniterpy/grammars/output.py
Operator.token
def token(cls: Type[OperatorType], keyword: str) -> OperatorType: """ Return Operator instance from keyword :param keyword: Operator keyword in expression :return: """ op = cls(keyword) return op
python
def token(cls: Type[OperatorType], keyword: str) -> OperatorType: """ Return Operator instance from keyword :param keyword: Operator keyword in expression :return: """ op = cls(keyword) return op
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train
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duniter/duniter-python-api
duniterpy/grammars/output.py
Condition.token
def token(cls: Type[ConditionType], left: Any, op: Optional[Any] = None, right: Optional[Any] = None) -> ConditionType: """ Return Condition instance from arguments and Operator :param left: Left argument :param op: Operator :param right: Right argument :return: """ condition = cls() condition.left = left if op: condition.op = op if right: condition.right = right return condition
python
def token(cls: Type[ConditionType], left: Any, op: Optional[Any] = None, right: Optional[Any] = None) -> ConditionType: """ Return Condition instance from arguments and Operator :param left: Left argument :param op: Operator :param right: Right argument :return: """ condition = cls() condition.left = left if op: condition.op = op if right: condition.right = right return condition
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train
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duniter/duniter-python-api
duniterpy/grammars/output.py
Condition.compose
def compose(self, parser: Any, grammar: Any = None, attr_of: str = None) -> str: """ Return the Condition as string format :param parser: Parser instance :param grammar: Grammar :param attr_of: Attribute of... """ if type(self.left) is Condition: left = "({0})".format(parser.compose(self.left, grammar=grammar, attr_of=attr_of)) else: left = parser.compose(self.left, grammar=grammar, attr_of=attr_of) if getattr(self, 'op', None): if type(self.right) is Condition: right = "({0})".format(parser.compose(self.right, grammar=grammar, attr_of=attr_of)) else: right = parser.compose(self.right, grammar=grammar, attr_of=attr_of) op = parser.compose(self.op, grammar=grammar, attr_of=attr_of) result = "{0} {1} {2}".format(left, op, right) else: result = left return result
python
def compose(self, parser: Any, grammar: Any = None, attr_of: str = None) -> str: """ Return the Condition as string format :param parser: Parser instance :param grammar: Grammar :param attr_of: Attribute of... """ if type(self.left) is Condition: left = "({0})".format(parser.compose(self.left, grammar=grammar, attr_of=attr_of)) else: left = parser.compose(self.left, grammar=grammar, attr_of=attr_of) if getattr(self, 'op', None): if type(self.right) is Condition: right = "({0})".format(parser.compose(self.right, grammar=grammar, attr_of=attr_of)) else: right = parser.compose(self.right, grammar=grammar, attr_of=attr_of) op = parser.compose(self.op, grammar=grammar, attr_of=attr_of) result = "{0} {1} {2}".format(left, op, right) else: result = left return result
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funkybob/knights-templater
knights/parser.py
Parser.load_library
def load_library(self, path): ''' Load a template library into the state. ''' module = import_module(path) self.tags.update(module.register.tags) self.helpers.update(module.register.helpers)
python
def load_library(self, path): ''' Load a template library into the state. ''' module = import_module(path) self.tags.update(module.register.tags) self.helpers.update(module.register.helpers)
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train
https://github.com/funkybob/knights-templater/blob/b15cdbaae7d824d02f7f03ca04599ae94bb759dd/knights/parser.py#L38-L44
lokhman/pydbal
pydbal/connection.py
Connection.get_default_logger
def get_default_logger(): """Returns default driver logger. :return: logger instance :rtype: logging.Logger """ handler = logging.StreamHandler() handler.setLevel(logging.DEBUG) handler.setFormatter(logging.Formatter( "[%(levelname)1.1s %(asctime)s %(name)s] %(message)s", "%y%m%d %H:%M:%S")) logger_name = "pydbal" if Connection._instance_count > 1: logger_name += ":" + str(Connection._instance_count) logger = logging.getLogger(logger_name) logger.setLevel(logging.DEBUG) logger.addHandler(handler) return logger
python
def get_default_logger(): """Returns default driver logger. :return: logger instance :rtype: logging.Logger """ handler = logging.StreamHandler() handler.setLevel(logging.DEBUG) handler.setFormatter(logging.Formatter( "[%(levelname)1.1s %(asctime)s %(name)s] %(message)s", "%y%m%d %H:%M:%S")) logger_name = "pydbal" if Connection._instance_count > 1: logger_name += ":" + str(Connection._instance_count) logger = logging.getLogger(logger_name) logger.setLevel(logging.DEBUG) logger.addHandler(handler) return logger
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train
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lokhman/pydbal
pydbal/connection.py
Connection.ensure_connected
def ensure_connected(self): """Ensures database connection is still open.""" if not self.is_connected(): if not self._auto_connect: raise DBALConnectionError.connection_closed() self.connect()
python
def ensure_connected(self): """Ensures database connection is still open.""" if not self.is_connected(): if not self._auto_connect: raise DBALConnectionError.connection_closed() self.connect()
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lokhman/pydbal
pydbal/connection.py
Connection.query
def query(self, sql, *args, **kwargs): """Executes an SQL SELECT query, returning a result set as a Statement object. :param sql: query to execute :param args: parameters iterable :param kwargs: parameters iterable :return: result set as a Statement object :rtype: pydbal.statement.Statement """ self.ensure_connected() stmt = Statement(self) stmt.execute(sql, *args, **kwargs) return stmt
python
def query(self, sql, *args, **kwargs): """Executes an SQL SELECT query, returning a result set as a Statement object. :param sql: query to execute :param args: parameters iterable :param kwargs: parameters iterable :return: result set as a Statement object :rtype: pydbal.statement.Statement """ self.ensure_connected() stmt = Statement(self) stmt.execute(sql, *args, **kwargs) return stmt
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lokhman/pydbal
pydbal/connection.py
Connection.execute
def execute(self, sql, *args, **kwargs): """Executes an SQL INSERT/UPDATE/DELETE query with the given parameters and returns the number of affected rows. :param sql: statement to execute :param args: parameters iterable :param kwargs: parameters iterable :return: number of affected rows :rtype: int """ self.ensure_connected() return Statement(self).execute(sql, *args, **kwargs)
python
def execute(self, sql, *args, **kwargs): """Executes an SQL INSERT/UPDATE/DELETE query with the given parameters and returns the number of affected rows. :param sql: statement to execute :param args: parameters iterable :param kwargs: parameters iterable :return: number of affected rows :rtype: int """ self.ensure_connected() return Statement(self).execute(sql, *args, **kwargs)
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lokhman/pydbal
pydbal/connection.py
Connection.begin_transaction
def begin_transaction(self): """Starts a transaction by suspending auto-commit mode.""" self.ensure_connected() self._transaction_nesting_level += 1 if self._transaction_nesting_level == 1: self._driver.begin_transaction() elif self._nest_transactions_with_savepoints: self.create_savepoint(self._get_nested_transaction_savepoint_name())
python
def begin_transaction(self): """Starts a transaction by suspending auto-commit mode.""" self.ensure_connected() self._transaction_nesting_level += 1 if self._transaction_nesting_level == 1: self._driver.begin_transaction() elif self._nest_transactions_with_savepoints: self.create_savepoint(self._get_nested_transaction_savepoint_name())
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lokhman/pydbal
pydbal/connection.py
Connection.commit
def commit(self): """Commits the current transaction.""" if self._transaction_nesting_level == 0: raise DBALConnectionError.no_active_transaction() if self._is_rollback_only: raise DBALConnectionError.commit_failed_rollback_only() self.ensure_connected() if self._transaction_nesting_level == 1: self._driver.commit() elif self._nest_transactions_with_savepoints: self.release_savepoint(self._get_nested_transaction_savepoint_name()) self._transaction_nesting_level -= 1 if not self._auto_commit and self._transaction_nesting_level == 0: self.begin_transaction()
python
def commit(self): """Commits the current transaction.""" if self._transaction_nesting_level == 0: raise DBALConnectionError.no_active_transaction() if self._is_rollback_only: raise DBALConnectionError.commit_failed_rollback_only() self.ensure_connected() if self._transaction_nesting_level == 1: self._driver.commit() elif self._nest_transactions_with_savepoints: self.release_savepoint(self._get_nested_transaction_savepoint_name()) self._transaction_nesting_level -= 1 if not self._auto_commit and self._transaction_nesting_level == 0: self.begin_transaction()
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L329-L345
lokhman/pydbal
pydbal/connection.py
Connection.commit_all
def commit_all(self): """Commits all current nesting transactions.""" while self._transaction_nesting_level != 0: if not self._auto_commit and self._transaction_nesting_level == 1: return self.commit() self.commit()
python
def commit_all(self): """Commits all current nesting transactions.""" while self._transaction_nesting_level != 0: if not self._auto_commit and self._transaction_nesting_level == 1: return self.commit() self.commit()
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Commits all current nesting transactions.
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L347-L352
lokhman/pydbal
pydbal/connection.py
Connection.rollback
def rollback(self): """Cancels any database changes done during the current transaction.""" if self._transaction_nesting_level == 0: raise DBALConnectionError.no_active_transaction() self.ensure_connected() if self._transaction_nesting_level == 1: self._transaction_nesting_level = 0 self._driver.rollback() self._is_rollback_only = False if not self._auto_commit: self.begin_transaction() elif self._nest_transactions_with_savepoints: self.rollback_savepoint(self._get_nested_transaction_savepoint_name()) self._transaction_nesting_level -= 1 else: self._is_rollback_only = True self._transaction_nesting_level -= 1
python
def rollback(self): """Cancels any database changes done during the current transaction.""" if self._transaction_nesting_level == 0: raise DBALConnectionError.no_active_transaction() self.ensure_connected() if self._transaction_nesting_level == 1: self._transaction_nesting_level = 0 self._driver.rollback() self._is_rollback_only = False if not self._auto_commit: self.begin_transaction() elif self._nest_transactions_with_savepoints: self.rollback_savepoint(self._get_nested_transaction_savepoint_name()) self._transaction_nesting_level -= 1 else: self._is_rollback_only = True self._transaction_nesting_level -= 1
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Cancels any database changes done during the current transaction.
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L354-L371
lokhman/pydbal
pydbal/connection.py
Connection.transaction
def transaction(self, callback): """Executes a function in a transaction. The function gets passed this Connection instance as an (optional) parameter. If an exception occurs during execution of the function or transaction commit, the transaction is rolled back and the exception re-thrown. :param callback: the function to execute in a transaction :return: the value returned by the `callback` :raise: Exception """ self.begin_transaction() try: result = callback(self) self.commit() return result except: self.rollback() raise
python
def transaction(self, callback): """Executes a function in a transaction. The function gets passed this Connection instance as an (optional) parameter. If an exception occurs during execution of the function or transaction commit, the transaction is rolled back and the exception re-thrown. :param callback: the function to execute in a transaction :return: the value returned by the `callback` :raise: Exception """ self.begin_transaction() try: result = callback(self) self.commit() return result except: self.rollback() raise
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Executes a function in a transaction. The function gets passed this Connection instance as an (optional) parameter. If an exception occurs during execution of the function or transaction commit, the transaction is rolled back and the exception re-thrown. :param callback: the function to execute in a transaction :return: the value returned by the `callback` :raise: Exception
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L373-L392
lokhman/pydbal
pydbal/connection.py
Connection.set_auto_commit
def set_auto_commit(self, auto_commit): """Sets auto-commit mode for this connection. If a connection is in auto-commit mode, then all its SQL statements will be executed and committed as individual transactions. Otherwise, its SQL statements are grouped into transactions that are terminated by a call to either the method commit or the method rollback. By default, new connections are in auto-commit mode. NOTE: If this method is called during a transaction and the auto-commit mode is changed, the transaction is committed. If this method is called and the auto-commit mode is not changed, the call is a no-op. :param auto_commit: `True` to enable auto-commit mode; `False` to disable it """ auto_commit = bool(auto_commit) if auto_commit == self._auto_commit: return self._auto_commit = auto_commit if self.is_connected() and self._transaction_nesting_level != 0: self.commit_all()
python
def set_auto_commit(self, auto_commit): """Sets auto-commit mode for this connection. If a connection is in auto-commit mode, then all its SQL statements will be executed and committed as individual transactions. Otherwise, its SQL statements are grouped into transactions that are terminated by a call to either the method commit or the method rollback. By default, new connections are in auto-commit mode. NOTE: If this method is called during a transaction and the auto-commit mode is changed, the transaction is committed. If this method is called and the auto-commit mode is not changed, the call is a no-op. :param auto_commit: `True` to enable auto-commit mode; `False` to disable it """ auto_commit = bool(auto_commit) if auto_commit == self._auto_commit: return self._auto_commit = auto_commit if self.is_connected() and self._transaction_nesting_level != 0: self.commit_all()
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Sets auto-commit mode for this connection. If a connection is in auto-commit mode, then all its SQL statements will be executed and committed as individual transactions. Otherwise, its SQL statements are grouped into transactions that are terminated by a call to either the method commit or the method rollback. By default, new connections are in auto-commit mode. NOTE: If this method is called during a transaction and the auto-commit mode is changed, the transaction is committed. If this method is called and the auto-commit mode is not changed, the call is a no-op. :param auto_commit: `True` to enable auto-commit mode; `False` to disable it
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L402-L421
lokhman/pydbal
pydbal/connection.py
Connection.set_transaction_isolation
def set_transaction_isolation(self, level): """Sets the transaction isolation level. :param level: the level to set """ self.ensure_connected() self._transaction_isolation_level = level self._platform.set_transaction_isolation(level)
python
def set_transaction_isolation(self, level): """Sets the transaction isolation level. :param level: the level to set """ self.ensure_connected() self._transaction_isolation_level = level self._platform.set_transaction_isolation(level)
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Sets the transaction isolation level. :param level: the level to set
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L447-L454
lokhman/pydbal
pydbal/connection.py
Connection.get_transaction_isolation
def get_transaction_isolation(self): """Returns the currently active transaction isolation level. :return: the current transaction isolation level :rtype: int """ if self._transaction_isolation_level is None: self._transaction_isolation_level = self._platform.get_default_transaction_isolation_level() return self._transaction_isolation_level
python
def get_transaction_isolation(self): """Returns the currently active transaction isolation level. :return: the current transaction isolation level :rtype: int """ if self._transaction_isolation_level is None: self._transaction_isolation_level = self._platform.get_default_transaction_isolation_level() return self._transaction_isolation_level
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Returns the currently active transaction isolation level. :return: the current transaction isolation level :rtype: int
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L456-L464
lokhman/pydbal
pydbal/connection.py
Connection.set_nest_transactions_with_savepoints
def set_nest_transactions_with_savepoints(self, nest_transactions_with_savepoints): """Sets if nested transactions should use savepoints. :param nest_transactions_with_savepoints: `True` or `False` """ if self._transaction_nesting_level > 0: raise DBALConnectionError.may_not_alter_nested_transaction_with_savepoints_in_transaction() if not self._platform.is_savepoints_supported(): raise DBALConnectionError.savepoints_not_supported() self._nest_transactions_with_savepoints = bool(nest_transactions_with_savepoints)
python
def set_nest_transactions_with_savepoints(self, nest_transactions_with_savepoints): """Sets if nested transactions should use savepoints. :param nest_transactions_with_savepoints: `True` or `False` """ if self._transaction_nesting_level > 0: raise DBALConnectionError.may_not_alter_nested_transaction_with_savepoints_in_transaction() if not self._platform.is_savepoints_supported(): raise DBALConnectionError.savepoints_not_supported() self._nest_transactions_with_savepoints = bool(nest_transactions_with_savepoints)
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Sets if nested transactions should use savepoints. :param nest_transactions_with_savepoints: `True` or `False`
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L466-L475
lokhman/pydbal
pydbal/connection.py
Connection.create_savepoint
def create_savepoint(self, savepoint): """Creates a new savepoint. :param savepoint: the name of the savepoint to create :raise: pydbal.exception.DBALConnectionError """ if not self._platform.is_savepoints_supported(): raise DBALConnectionError.savepoints_not_supported() self.ensure_connected() self._platform.create_savepoint(savepoint)
python
def create_savepoint(self, savepoint): """Creates a new savepoint. :param savepoint: the name of the savepoint to create :raise: pydbal.exception.DBALConnectionError """ if not self._platform.is_savepoints_supported(): raise DBALConnectionError.savepoints_not_supported() self.ensure_connected() self._platform.create_savepoint(savepoint)
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L493-L502
lokhman/pydbal
pydbal/connection.py
Connection.release_savepoint
def release_savepoint(self, savepoint): """Releases the given savepoint. :param savepoint: the name of the savepoint to release :raise: pydbal.exception.DBALConnectionError """ if not self._platform.is_savepoints_supported(): raise DBALConnectionError.savepoints_not_supported() if self._platform.is_release_savepoints_supported(): self.ensure_connected() self._platform.release_savepoint(savepoint)
python
def release_savepoint(self, savepoint): """Releases the given savepoint. :param savepoint: the name of the savepoint to release :raise: pydbal.exception.DBALConnectionError """ if not self._platform.is_savepoints_supported(): raise DBALConnectionError.savepoints_not_supported() if self._platform.is_release_savepoints_supported(): self.ensure_connected() self._platform.release_savepoint(savepoint)
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L504-L514
lokhman/pydbal
pydbal/connection.py
Connection.rollback_savepoint
def rollback_savepoint(self, savepoint): """Rolls back to the given savepoint. :param savepoint: the name of the savepoint to rollback to :raise: pydbal.exception.DBALConnectionError """ if not self._platform.is_savepoints_supported(): raise DBALConnectionError.savepoints_not_supported() self.ensure_connected() self._platform.rollback_savepoint(savepoint)
python
def rollback_savepoint(self, savepoint): """Rolls back to the given savepoint. :param savepoint: the name of the savepoint to rollback to :raise: pydbal.exception.DBALConnectionError """ if not self._platform.is_savepoints_supported(): raise DBALConnectionError.savepoints_not_supported() self.ensure_connected() self._platform.rollback_savepoint(savepoint)
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L516-L525
lokhman/pydbal
pydbal/connection.py
Connection.insert
def insert(self, table, values): """Inserts a table row with specified data. :param table: the expression of the table to insert data into, quoted or unquoted :param values: a dictionary containing column-value pairs :return: last inserted ID """ assert isinstance(values, dict) sb = self.sql_builder().insert(table) for column, value in values.iteritems(): values[column] = sb.create_positional_parameter(value) return sb.values(values).execute()
python
def insert(self, table, values): """Inserts a table row with specified data. :param table: the expression of the table to insert data into, quoted or unquoted :param values: a dictionary containing column-value pairs :return: last inserted ID """ assert isinstance(values, dict) sb = self.sql_builder().insert(table) for column, value in values.iteritems(): values[column] = sb.create_positional_parameter(value) return sb.values(values).execute()
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L527-L539
lokhman/pydbal
pydbal/connection.py
Connection.update
def update(self, table, values, identifier): """Updates a table row with specified data by given identifier. :param table: the expression of the table to update quoted or unquoted :param values: a dictionary containing column-value pairs :param identifier: the update criteria; a dictionary containing column-value pairs :return: the number of affected rows :rtype: int """ assert isinstance(values, dict) assert isinstance(identifier, dict) sb = self.sql_builder().update(table) for column, value in values.iteritems(): sb.set(column, sb.create_positional_parameter(value)) for column, value in identifier.iteritems(): func = self._expr.in_ if isinstance(value, (list, tuple)) else self._expr.eq sb.and_where(func(column, sb.create_positional_parameter(value))) return sb.execute()
python
def update(self, table, values, identifier): """Updates a table row with specified data by given identifier. :param table: the expression of the table to update quoted or unquoted :param values: a dictionary containing column-value pairs :param identifier: the update criteria; a dictionary containing column-value pairs :return: the number of affected rows :rtype: int """ assert isinstance(values, dict) assert isinstance(identifier, dict) sb = self.sql_builder().update(table) for column, value in values.iteritems(): sb.set(column, sb.create_positional_parameter(value)) for column, value in identifier.iteritems(): func = self._expr.in_ if isinstance(value, (list, tuple)) else self._expr.eq sb.and_where(func(column, sb.create_positional_parameter(value))) return sb.execute()
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L541-L559
lokhman/pydbal
pydbal/connection.py
Connection.delete
def delete(self, table, identifier): """Deletes a table row by given identifier. :param table: the expression of the table to update quoted or unquoted :param identifier: the delete criteria; a dictionary containing column-value pairs :return: the number of affected rows :rtype: int """ assert isinstance(identifier, dict) sb = self.sql_builder().delete(table) for column, value in identifier.iteritems(): func = self._expr.in_ if isinstance(value, (list, tuple)) else self._expr.eq sb.and_where(func(column, sb.create_positional_parameter(value))) return sb.execute()
python
def delete(self, table, identifier): """Deletes a table row by given identifier. :param table: the expression of the table to update quoted or unquoted :param identifier: the delete criteria; a dictionary containing column-value pairs :return: the number of affected rows :rtype: int """ assert isinstance(identifier, dict) sb = self.sql_builder().delete(table) for column, value in identifier.iteritems(): func = self._expr.in_ if isinstance(value, (list, tuple)) else self._expr.eq sb.and_where(func(column, sb.create_positional_parameter(value))) return sb.execute()
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/connection.py#L561-L575
lokhman/pydbal
pydbal/drivers/mysql.py
MySQLDriver._execute
def _execute(self, sql, params): """Execute statement with reconnecting by connection closed error codes. 2006 (CR_SERVER_GONE_ERROR): MySQL server has gone away 2013 (CR_SERVER_LOST): Lost connection to MySQL server during query 2055 (CR_SERVER_LOST_EXTENDED): Lost connection to MySQL server at '%s', system error: %d """ try: return self._execute_unsafe(sql, params) except MySQLdb.OperationalError as ex: if ex.args[0] in (2006, 2013, 2055): self._log("Connection with server is lost. Trying to reconnect.") self.connect() return self._execute_unsafe(sql, params) raise
python
def _execute(self, sql, params): """Execute statement with reconnecting by connection closed error codes. 2006 (CR_SERVER_GONE_ERROR): MySQL server has gone away 2013 (CR_SERVER_LOST): Lost connection to MySQL server during query 2055 (CR_SERVER_LOST_EXTENDED): Lost connection to MySQL server at '%s', system error: %d """ try: return self._execute_unsafe(sql, params) except MySQLdb.OperationalError as ex: if ex.args[0] in (2006, 2013, 2055): self._log("Connection with server is lost. Trying to reconnect.") self.connect() return self._execute_unsafe(sql, params) raise
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train
https://github.com/lokhman/pydbal/blob/53f396a2a18826e9fff178cd2c0636c1656cbaea/pydbal/drivers/mysql.py#L98-L112
simodalla/pygmount
pygmount/utils/mount.py
MountSmbSharesOld.requirements
def requirements(self): """ Verifica che tutti i pacchetti apt necessari al "funzionamento" della classe siano installati. Se cosi' non fosse li installa. """ cache = apt.cache.Cache() for pkg in self.pkgs_required: try: pkg = cache[pkg] if not pkg.is_installed: try: pkg.mark_install() cache.commit() except LockFailedException as lfe: logging.error( 'Errore "{}" probabilmente l\'utente {} non ha i ' 'diritti di amministratore'.format(lfe, self.username)) raise lfe except Exception as e: logging.error('Errore non classificato "{}"'.format(e)) raise e except KeyError: logging.error('Il pacchetto "{}" non e\' presente in questa' ' distribuzione'.format(pkg))
python
def requirements(self): """ Verifica che tutti i pacchetti apt necessari al "funzionamento" della classe siano installati. Se cosi' non fosse li installa. """ cache = apt.cache.Cache() for pkg in self.pkgs_required: try: pkg = cache[pkg] if not pkg.is_installed: try: pkg.mark_install() cache.commit() except LockFailedException as lfe: logging.error( 'Errore "{}" probabilmente l\'utente {} non ha i ' 'diritti di amministratore'.format(lfe, self.username)) raise lfe except Exception as e: logging.error('Errore non classificato "{}"'.format(e)) raise e except KeyError: logging.error('Il pacchetto "{}" non e\' presente in questa' ' distribuzione'.format(pkg))
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train
https://github.com/simodalla/pygmount/blob/8027cfa2ed5fa8e9207d72b6013ecec7fcf2e5f5/pygmount/utils/mount.py#L57-L81
simodalla/pygmount
pygmount/utils/mount.py
MountSmbSharesOld.set_shares
def set_shares(self): """ Setta la variabile membro 'self.samba_shares' il quale e' una lista di dizionari con i dati da passare ai comandi di "umount" e "mount". I vari dizionari sono popolati o da un file ~/.pygmount.rc e da un file passato dall'utente. """ if self.filename is None: self.filenamename = os.path.expanduser( '~%s/%s' % (self.host_username, FILE_RC)) if not os.path.exists(self.filename): error_msg = (u"Impossibile trovare il file di configurazione " u"'%s'.\nLe unità di rete non saranno collegate." % ( FILE_RC.lstrip('.'))) if not self.shell_mode: ErrorMessage(error_msg) logging.error(error_msg) sys.exit(5) if self.verbose: logging.warning("File RC utilizzato: %s", self.filename) self.samba_shares = read_config(self.filename)
python
def set_shares(self): """ Setta la variabile membro 'self.samba_shares' il quale e' una lista di dizionari con i dati da passare ai comandi di "umount" e "mount". I vari dizionari sono popolati o da un file ~/.pygmount.rc e da un file passato dall'utente. """ if self.filename is None: self.filenamename = os.path.expanduser( '~%s/%s' % (self.host_username, FILE_RC)) if not os.path.exists(self.filename): error_msg = (u"Impossibile trovare il file di configurazione " u"'%s'.\nLe unità di rete non saranno collegate." % ( FILE_RC.lstrip('.'))) if not self.shell_mode: ErrorMessage(error_msg) logging.error(error_msg) sys.exit(5) if self.verbose: logging.warning("File RC utilizzato: %s", self.filename) self.samba_shares = read_config(self.filename)
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Setta la variabile membro 'self.samba_shares' il quale e' una lista di dizionari con i dati da passare ai comandi di "umount" e "mount". I vari dizionari sono popolati o da un file ~/.pygmount.rc e da un file passato dall'utente.
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train
https://github.com/simodalla/pygmount/blob/8027cfa2ed5fa8e9207d72b6013ecec7fcf2e5f5/pygmount/utils/mount.py#L83-L103
simodalla/pygmount
pygmount/utils/mount.py
MountSmbSharesOld.run
def run(self): """ Esegue il montaggio delle varie condivisioni chiedendo all'utente username e password di dominio. """ logging.info('start run with "{}" at {}'.format( self.username, datetime.datetime.now())) progress = Progress(text="Controllo requisiti software...", pulsate=True, auto_close=True) progress(1) try: self.requirements() except LockFailedException as lfe: ErrorMessage('Errore "{}" probabilmente l\'utente {} non ha i' ' diritti di amministratore'.format(lfe, self.username)) sys.exit(20) except Exception as e: ErrorMessage("Si e' verificato un errore generico: {}".format(e)) sys.exit(21) progress(100) self.set_shares() # richiesta username del dominio insert_msg = "Inserisci l'utente del Dominio/Posta Elettronica" default_username = (self.host_username if self.host_username else os.environ['USER']) self.domain_username = GetText(text=insert_msg, entry_text=self.username) if self.domain_username is None or len(self.domain_username) == 0: error_msg = "Inserimento di un username di dominio vuoto" ErrorMessage(self.msg_error % error_msg) sys.exit(2) # richiesta della password di dominio insert_msg = u"Inserisci la password del Dominio/Posta Elettronica" self.domain_password = GetText(text=insert_msg, entry_text='password', password=True) if self.domain_password is None or len(self.domain_password) == 0: error_msg = u"Inserimento di una password di dominio vuota" ErrorMessage(self.msg_error % error_msg) sys.exit(3) progress_msg = u"Collegamento unità di rete in corso..." progress = Progress(text=progress_msg, pulsate=True, auto_close=True) progress(1) # ciclo per montare tutte le condivisioni result = [] for share in self.samba_shares: # print("#######") # print(share) if 'mountpoint' not in share.keys(): # creazione stringa che rappresente il mount-point locale mountpoint = os.path.expanduser( '~%s/%s/%s' % (self.host_username, share['hostname'], share['share'])) share.update({'mountpoint': mountpoint}) elif not share['mountpoint'].startswith('/'): mountpoint = os.path.expanduser( '~%s/%s' % (self.host_username, share['mountpoint'])) share.update({'mountpoint': mountpoint}) share.update({ 'host_username': self.host_username, 'domain_username': share.get( 'username', self.domain_username), 'domain_password': share.get( 'password', self.domain_password)}) # controllo che il mount-point locale esista altrimenti non # viene creato if not os.path.exists(share['mountpoint']): if self.verbose: logging.warning('Mountpoint "%s" not exist.' % share['mountpoint']) if not self.dry_run: os.makedirs(share['mountpoint']) # smonto la condivisione prima di rimontarla umont_cmd = self.cmd_umount % share if self.verbose: logging.warning("Umount command: %s" % umont_cmd) if not self.dry_run: umount_p = subprocess.Popen(umont_cmd, shell=True) returncode = umount_p.wait() time.sleep(2) mount_cmd = self.cmd_mount % share if self.verbose: placeholder = ",password=" logging.warning("Mount command: %s%s" % (mount_cmd.split( placeholder)[0], placeholder + "******\"")) # print(mount_cmd) # print("#######") if not self.dry_run: # montaggio della condivisione p_mnt = subprocess.Popen(mount_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) returncode = p_mnt.wait() result.append({'share': share['share'], 'returncode': returncode, 'stdout': p_mnt.stdout.read(), 'stderr': p_mnt.stderr.read()}) progress(100) if self.verbose: logging.warning("Risultati: %s" % result)
python
def run(self): """ Esegue il montaggio delle varie condivisioni chiedendo all'utente username e password di dominio. """ logging.info('start run with "{}" at {}'.format( self.username, datetime.datetime.now())) progress = Progress(text="Controllo requisiti software...", pulsate=True, auto_close=True) progress(1) try: self.requirements() except LockFailedException as lfe: ErrorMessage('Errore "{}" probabilmente l\'utente {} non ha i' ' diritti di amministratore'.format(lfe, self.username)) sys.exit(20) except Exception as e: ErrorMessage("Si e' verificato un errore generico: {}".format(e)) sys.exit(21) progress(100) self.set_shares() # richiesta username del dominio insert_msg = "Inserisci l'utente del Dominio/Posta Elettronica" default_username = (self.host_username if self.host_username else os.environ['USER']) self.domain_username = GetText(text=insert_msg, entry_text=self.username) if self.domain_username is None or len(self.domain_username) == 0: error_msg = "Inserimento di un username di dominio vuoto" ErrorMessage(self.msg_error % error_msg) sys.exit(2) # richiesta della password di dominio insert_msg = u"Inserisci la password del Dominio/Posta Elettronica" self.domain_password = GetText(text=insert_msg, entry_text='password', password=True) if self.domain_password is None or len(self.domain_password) == 0: error_msg = u"Inserimento di una password di dominio vuota" ErrorMessage(self.msg_error % error_msg) sys.exit(3) progress_msg = u"Collegamento unità di rete in corso..." progress = Progress(text=progress_msg, pulsate=True, auto_close=True) progress(1) # ciclo per montare tutte le condivisioni result = [] for share in self.samba_shares: # print("#######") # print(share) if 'mountpoint' not in share.keys(): # creazione stringa che rappresente il mount-point locale mountpoint = os.path.expanduser( '~%s/%s/%s' % (self.host_username, share['hostname'], share['share'])) share.update({'mountpoint': mountpoint}) elif not share['mountpoint'].startswith('/'): mountpoint = os.path.expanduser( '~%s/%s' % (self.host_username, share['mountpoint'])) share.update({'mountpoint': mountpoint}) share.update({ 'host_username': self.host_username, 'domain_username': share.get( 'username', self.domain_username), 'domain_password': share.get( 'password', self.domain_password)}) # controllo che il mount-point locale esista altrimenti non # viene creato if not os.path.exists(share['mountpoint']): if self.verbose: logging.warning('Mountpoint "%s" not exist.' % share['mountpoint']) if not self.dry_run: os.makedirs(share['mountpoint']) # smonto la condivisione prima di rimontarla umont_cmd = self.cmd_umount % share if self.verbose: logging.warning("Umount command: %s" % umont_cmd) if not self.dry_run: umount_p = subprocess.Popen(umont_cmd, shell=True) returncode = umount_p.wait() time.sleep(2) mount_cmd = self.cmd_mount % share if self.verbose: placeholder = ",password=" logging.warning("Mount command: %s%s" % (mount_cmd.split( placeholder)[0], placeholder + "******\"")) # print(mount_cmd) # print("#######") if not self.dry_run: # montaggio della condivisione p_mnt = subprocess.Popen(mount_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) returncode = p_mnt.wait() result.append({'share': share['share'], 'returncode': returncode, 'stdout': p_mnt.stdout.read(), 'stderr': p_mnt.stderr.read()}) progress(100) if self.verbose: logging.warning("Risultati: %s" % result)
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Esegue il montaggio delle varie condivisioni chiedendo all'utente username e password di dominio.
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train
https://github.com/simodalla/pygmount/blob/8027cfa2ed5fa8e9207d72b6013ecec7fcf2e5f5/pygmount/utils/mount.py#L105-L219
MacHu-GWU/pymongo_mate-project
pymongo_mate/mongomock_mate.py
_dump
def _dump(db): """ Dump :class:`mongomock.database.Database` to dict data. """ db_data = {"name": db.name, "_collections": dict()} for col_name, collection in iteritems(db._collections): if col_name != "system.indexes": col_data = { "_documents": collection._documents, "_uniques": collection._uniques, } db_data["_collections"][col_name] = col_data return db_data
python
def _dump(db): """ Dump :class:`mongomock.database.Database` to dict data. """ db_data = {"name": db.name, "_collections": dict()} for col_name, collection in iteritems(db._collections): if col_name != "system.indexes": col_data = { "_documents": collection._documents, "_uniques": collection._uniques, } db_data["_collections"][col_name] = col_data return db_data
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Dump :class:`mongomock.database.Database` to dict data.
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