Datasets:

espejelomar

/

code_search_net_python_10000_examples

like 8

bxlab/bx-python

lib/bx/seq/seq.py

SeqFile.get

def get(self, start, length): """ Fetch subsequence starting at position `start` with length `length`. This method is picky about parameters, the requested interval must have non-negative length and fit entirely inside the NIB sequence, the returned string will contain exactly 'length' characters, or an AssertionError will be generated. """ # Check parameters assert length >= 0, "Length must be non-negative (got %d)" % length assert start >= 0,"Start must be greater than 0 (got %d)" % start assert start + length <= self.length, \ "Interval beyond end of sequence (%s..%s > %s)" % ( start, start + length, self.length ) # Fetch sequence and reverse complement if necesary if not self.revcomp: return self.raw_fetch( start, length ) if self.revcomp == "-3'": return self.reverse_complement(self.raw_fetch(start,length)) assert self.revcomp == "-5'", "unrecognized reverse complement scheme" start = self.length - (start+length) return self.reverse_complement(self.raw_fetch(start,length))

python

def get(self, start, length): """ Fetch subsequence starting at position `start` with length `length`. This method is picky about parameters, the requested interval must have non-negative length and fit entirely inside the NIB sequence, the returned string will contain exactly 'length' characters, or an AssertionError will be generated. """ # Check parameters assert length >= 0, "Length must be non-negative (got %d)" % length assert start >= 0,"Start must be greater than 0 (got %d)" % start assert start + length <= self.length, \ "Interval beyond end of sequence (%s..%s > %s)" % ( start, start + length, self.length ) # Fetch sequence and reverse complement if necesary if not self.revcomp: return self.raw_fetch( start, length ) if self.revcomp == "-3'": return self.reverse_complement(self.raw_fetch(start,length)) assert self.revcomp == "-5'", "unrecognized reverse complement scheme" start = self.length - (start+length) return self.reverse_complement(self.raw_fetch(start,length))

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Fetch subsequence starting at position `start` with length `length`. This method is picky about parameters, the requested interval must have non-negative length and fit entirely inside the NIB sequence, the returned string will contain exactly 'length' characters, or an AssertionError will be generated.

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train

https://github.com/bxlab/bx-python/blob/09cb725284803df90a468d910f2274628d8647de/lib/bx/seq/seq.py#L74-L94

sosy-lab/benchexec

benchexec/check_cgroups.py

check_cgroup_availability_in_thread

def check_cgroup_availability_in_thread(options): """ Run check_cgroup_availability() in a separate thread to detect the following problem: If "cgexec --sticky" is used to tell cgrulesengd to not interfere with our child processes, the sticky flag unfortunately works only for processes spawned by the main thread, not those spawned by other threads (and this will happen if "benchexec -N" is used). """ thread = _CheckCgroupsThread(options) thread.start() thread.join() if thread.error: raise thread.error

python

def check_cgroup_availability_in_thread(options): """ Run check_cgroup_availability() in a separate thread to detect the following problem: If "cgexec --sticky" is used to tell cgrulesengd to not interfere with our child processes, the sticky flag unfortunately works only for processes spawned by the main thread, not those spawned by other threads (and this will happen if "benchexec -N" is used). """ thread = _CheckCgroupsThread(options) thread.start() thread.join() if thread.error: raise thread.error

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Run check_cgroup_availability() in a separate thread to detect the following problem: If "cgexec --sticky" is used to tell cgrulesengd to not interfere with our child processes, the sticky flag unfortunately works only for processes spawned by the main thread, not those spawned by other threads (and this will happen if "benchexec -N" is used).

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train

https://github.com/sosy-lab/benchexec/blob/44428f67f41384c03aea13e7e25f884764653617/benchexec/check_cgroups.py#L83-L95

f3at/feat

src/feat/extern/log/log.py

Loggable.warning

def warning(self, *args): """Log a warning. Used for non-fatal problems.""" if _canShortcutLogging(self.logCategory, WARN): return warningObject(self.logObjectName(), self.logCategory, *self.logFunction(*args))

python

def warning(self, *args): """Log a warning. Used for non-fatal problems.""" if _canShortcutLogging(self.logCategory, WARN): return warningObject(self.logObjectName(), self.logCategory, *self.logFunction(*args))

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Log a warning. Used for non-fatal problems.

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train

https://github.com/f3at/feat/blob/15da93fc9d6ec8154f52a9172824e25821195ef8/src/feat/extern/log/log.py#L805-L810

tensorflow/datasets

tensorflow_datasets/core/download/kaggle.py

KaggleCompetitionDownloader.download_file

def download_file(self, fname, output_dir): """Downloads competition file to output_dir.""" if fname not in self.competition_files: # pylint: disable=unsupported-membership-test raise ValueError("%s is not one of the competition's " "files: %s" % (fname, self.competition_files)) command = [ "kaggle", "competitions", "download", "--file", fname, "--path", output_dir, "-c", self._competition_name, ] _run_kaggle_command(command, self._competition_name) return os.path.join(output_dir, fname)

python

def download_file(self, fname, output_dir): """Downloads competition file to output_dir.""" if fname not in self.competition_files: # pylint: disable=unsupported-membership-test raise ValueError("%s is not one of the competition's " "files: %s" % (fname, self.competition_files)) command = [ "kaggle", "competitions", "download", "--file", fname, "--path", output_dir, "-c", self._competition_name, ] _run_kaggle_command(command, self._competition_name) return os.path.join(output_dir, fname)

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Downloads competition file to output_dir.

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train

https://github.com/tensorflow/datasets/blob/46ceb0cf7b4690f38ecbbc689e4d659a903d08dc/tensorflow_datasets/core/download/kaggle.py#L118-L135

pandas-dev/pandas

pandas/core/computation/eval.py

eval

def eval(expr, parser='pandas', engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False): """Evaluate a Python expression as a string using various backends. The following arithmetic operations are supported: ``+``, ``-``, ``*``, ``/``, ``**``, ``%``, ``//`` (python engine only) along with the following boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not). Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`, :keyword:`or`, and :keyword:`not` with the same semantics as the corresponding bitwise operators. :class:`~pandas.Series` and :class:`~pandas.DataFrame` objects are supported and behave as they would with plain ol' Python evaluation. Parameters ---------- expr : str or unicode The expression to evaluate. This string cannot contain any Python `statements <https://docs.python.org/3/reference/simple_stmts.html#simple-statements>`__, only Python `expressions <https://docs.python.org/3/reference/simple_stmts.html#expression-statements>`__. parser : string, default 'pandas', {'pandas', 'python'} The parser to use to construct the syntax tree from the expression. The default of ``'pandas'`` parses code slightly different than standard Python. Alternatively, you can parse an expression using the ``'python'`` parser to retain strict Python semantics. See the :ref:`enhancing performance <enhancingperf.eval>` documentation for more details. engine : string or None, default 'numexpr', {'python', 'numexpr'} The engine used to evaluate the expression. Supported engines are - None : tries to use ``numexpr``, falls back to ``python`` - ``'numexpr'``: This default engine evaluates pandas objects using numexpr for large speed ups in complex expressions with large frames. - ``'python'``: Performs operations as if you had ``eval``'d in top level python. This engine is generally not that useful. More backends may be available in the future. truediv : bool, optional Whether to use true division, like in Python >= 3 local_dict : dict or None, optional A dictionary of local variables, taken from locals() by default. global_dict : dict or None, optional A dictionary of global variables, taken from globals() by default. resolvers : list of dict-like or None, optional A list of objects implementing the ``__getitem__`` special method that you can use to inject an additional collection of namespaces to use for variable lookup. For example, this is used in the :meth:`~DataFrame.query` method to inject the ``DataFrame.index`` and ``DataFrame.columns`` variables that refer to their respective :class:`~pandas.DataFrame` instance attributes. level : int, optional The number of prior stack frames to traverse and add to the current scope. Most users will **not** need to change this parameter. target : object, optional, default None This is the target object for assignment. It is used when there is variable assignment in the expression. If so, then `target` must support item assignment with string keys, and if a copy is being returned, it must also support `.copy()`. inplace : bool, default False If `target` is provided, and the expression mutates `target`, whether to modify `target` inplace. Otherwise, return a copy of `target` with the mutation. Returns ------- ndarray, numeric scalar, DataFrame, Series Raises ------ ValueError There are many instances where such an error can be raised: - `target=None`, but the expression is multiline. - The expression is multiline, but not all them have item assignment. An example of such an arrangement is this: a = b + 1 a + 2 Here, there are expressions on different lines, making it multiline, but the last line has no variable assigned to the output of `a + 2`. - `inplace=True`, but the expression is missing item assignment. - Item assignment is provided, but the `target` does not support string item assignment. - Item assignment is provided and `inplace=False`, but the `target` does not support the `.copy()` method See Also -------- DataFrame.query DataFrame.eval Notes ----- The ``dtype`` of any objects involved in an arithmetic ``%`` operation are recursively cast to ``float64``. See the :ref:`enhancing performance <enhancingperf.eval>` documentation for more details. """ from pandas.core.computation.expr import Expr inplace = validate_bool_kwarg(inplace, "inplace") if isinstance(expr, str): _check_expression(expr) exprs = [e.strip() for e in expr.splitlines() if e.strip() != ''] else: exprs = [expr] multi_line = len(exprs) > 1 if multi_line and target is None: raise ValueError("multi-line expressions are only valid in the " "context of data, use DataFrame.eval") ret = None first_expr = True target_modified = False for expr in exprs: expr = _convert_expression(expr) engine = _check_engine(engine) _check_parser(parser) _check_resolvers(resolvers) _check_for_locals(expr, level, parser) # get our (possibly passed-in) scope env = _ensure_scope(level + 1, global_dict=global_dict, local_dict=local_dict, resolvers=resolvers, target=target) parsed_expr = Expr(expr, engine=engine, parser=parser, env=env, truediv=truediv) # construct the engine and evaluate the parsed expression eng = _engines[engine] eng_inst = eng(parsed_expr) ret = eng_inst.evaluate() if parsed_expr.assigner is None: if multi_line: raise ValueError("Multi-line expressions are only valid" " if all expressions contain an assignment") elif inplace: raise ValueError("Cannot operate inplace " "if there is no assignment") # assign if needed assigner = parsed_expr.assigner if env.target is not None and assigner is not None: target_modified = True # if returning a copy, copy only on the first assignment if not inplace and first_expr: try: target = env.target.copy() except AttributeError: raise ValueError("Cannot return a copy of the target") else: target = env.target # TypeError is most commonly raised (e.g. int, list), but you # get IndexError if you try to do this assignment on np.ndarray. # we will ignore numpy warnings here; e.g. if trying # to use a non-numeric indexer try: with warnings.catch_warnings(record=True): # TODO: Filter the warnings we actually care about here. target[assigner] = ret except (TypeError, IndexError): raise ValueError("Cannot assign expression output to target") if not resolvers: resolvers = ({assigner: ret},) else: # existing resolver needs updated to handle # case of mutating existing column in copy for resolver in resolvers: if assigner in resolver: resolver[assigner] = ret break else: resolvers += ({assigner: ret},) ret = None first_expr = False # We want to exclude `inplace=None` as being False. if inplace is False: return target if target_modified else ret

python

def eval(expr, parser='pandas', engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False): """Evaluate a Python expression as a string using various backends. The following arithmetic operations are supported: ``+``, ``-``, ``*``, ``/``, ``**``, ``%``, ``//`` (python engine only) along with the following boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not). Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`, :keyword:`or`, and :keyword:`not` with the same semantics as the corresponding bitwise operators. :class:`~pandas.Series` and :class:`~pandas.DataFrame` objects are supported and behave as they would with plain ol' Python evaluation. Parameters ---------- expr : str or unicode The expression to evaluate. This string cannot contain any Python `statements <https://docs.python.org/3/reference/simple_stmts.html#simple-statements>`__, only Python `expressions <https://docs.python.org/3/reference/simple_stmts.html#expression-statements>`__. parser : string, default 'pandas', {'pandas', 'python'} The parser to use to construct the syntax tree from the expression. The default of ``'pandas'`` parses code slightly different than standard Python. Alternatively, you can parse an expression using the ``'python'`` parser to retain strict Python semantics. See the :ref:`enhancing performance <enhancingperf.eval>` documentation for more details. engine : string or None, default 'numexpr', {'python', 'numexpr'} The engine used to evaluate the expression. Supported engines are - None : tries to use ``numexpr``, falls back to ``python`` - ``'numexpr'``: This default engine evaluates pandas objects using numexpr for large speed ups in complex expressions with large frames. - ``'python'``: Performs operations as if you had ``eval``'d in top level python. This engine is generally not that useful. More backends may be available in the future. truediv : bool, optional Whether to use true division, like in Python >= 3 local_dict : dict or None, optional A dictionary of local variables, taken from locals() by default. global_dict : dict or None, optional A dictionary of global variables, taken from globals() by default. resolvers : list of dict-like or None, optional A list of objects implementing the ``__getitem__`` special method that you can use to inject an additional collection of namespaces to use for variable lookup. For example, this is used in the :meth:`~DataFrame.query` method to inject the ``DataFrame.index`` and ``DataFrame.columns`` variables that refer to their respective :class:`~pandas.DataFrame` instance attributes. level : int, optional The number of prior stack frames to traverse and add to the current scope. Most users will **not** need to change this parameter. target : object, optional, default None This is the target object for assignment. It is used when there is variable assignment in the expression. If so, then `target` must support item assignment with string keys, and if a copy is being returned, it must also support `.copy()`. inplace : bool, default False If `target` is provided, and the expression mutates `target`, whether to modify `target` inplace. Otherwise, return a copy of `target` with the mutation. Returns ------- ndarray, numeric scalar, DataFrame, Series Raises ------ ValueError There are many instances where such an error can be raised: - `target=None`, but the expression is multiline. - The expression is multiline, but not all them have item assignment. An example of such an arrangement is this: a = b + 1 a + 2 Here, there are expressions on different lines, making it multiline, but the last line has no variable assigned to the output of `a + 2`. - `inplace=True`, but the expression is missing item assignment. - Item assignment is provided, but the `target` does not support string item assignment. - Item assignment is provided and `inplace=False`, but the `target` does not support the `.copy()` method See Also -------- DataFrame.query DataFrame.eval Notes ----- The ``dtype`` of any objects involved in an arithmetic ``%`` operation are recursively cast to ``float64``. See the :ref:`enhancing performance <enhancingperf.eval>` documentation for more details. """ from pandas.core.computation.expr import Expr inplace = validate_bool_kwarg(inplace, "inplace") if isinstance(expr, str): _check_expression(expr) exprs = [e.strip() for e in expr.splitlines() if e.strip() != ''] else: exprs = [expr] multi_line = len(exprs) > 1 if multi_line and target is None: raise ValueError("multi-line expressions are only valid in the " "context of data, use DataFrame.eval") ret = None first_expr = True target_modified = False for expr in exprs: expr = _convert_expression(expr) engine = _check_engine(engine) _check_parser(parser) _check_resolvers(resolvers) _check_for_locals(expr, level, parser) # get our (possibly passed-in) scope env = _ensure_scope(level + 1, global_dict=global_dict, local_dict=local_dict, resolvers=resolvers, target=target) parsed_expr = Expr(expr, engine=engine, parser=parser, env=env, truediv=truediv) # construct the engine and evaluate the parsed expression eng = _engines[engine] eng_inst = eng(parsed_expr) ret = eng_inst.evaluate() if parsed_expr.assigner is None: if multi_line: raise ValueError("Multi-line expressions are only valid" " if all expressions contain an assignment") elif inplace: raise ValueError("Cannot operate inplace " "if there is no assignment") # assign if needed assigner = parsed_expr.assigner if env.target is not None and assigner is not None: target_modified = True # if returning a copy, copy only on the first assignment if not inplace and first_expr: try: target = env.target.copy() except AttributeError: raise ValueError("Cannot return a copy of the target") else: target = env.target # TypeError is most commonly raised (e.g. int, list), but you # get IndexError if you try to do this assignment on np.ndarray. # we will ignore numpy warnings here; e.g. if trying # to use a non-numeric indexer try: with warnings.catch_warnings(record=True): # TODO: Filter the warnings we actually care about here. target[assigner] = ret except (TypeError, IndexError): raise ValueError("Cannot assign expression output to target") if not resolvers: resolvers = ({assigner: ret},) else: # existing resolver needs updated to handle # case of mutating existing column in copy for resolver in resolvers: if assigner in resolver: resolver[assigner] = ret break else: resolvers += ({assigner: ret},) ret = None first_expr = False # We want to exclude `inplace=None` as being False. if inplace is False: return target if target_modified else ret

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Evaluate a Python expression as a string using various backends. The following arithmetic operations are supported: ``+``, ``-``, ``*``, ``/``, ``**``, ``%``, ``//`` (python engine only) along with the following boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not). Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`, :keyword:`or`, and :keyword:`not` with the same semantics as the corresponding bitwise operators. :class:`~pandas.Series` and :class:`~pandas.DataFrame` objects are supported and behave as they would with plain ol' Python evaluation. Parameters ---------- expr : str or unicode The expression to evaluate. This string cannot contain any Python `statements <https://docs.python.org/3/reference/simple_stmts.html#simple-statements>`__, only Python `expressions <https://docs.python.org/3/reference/simple_stmts.html#expression-statements>`__. parser : string, default 'pandas', {'pandas', 'python'} The parser to use to construct the syntax tree from the expression. The default of ``'pandas'`` parses code slightly different than standard Python. Alternatively, you can parse an expression using the ``'python'`` parser to retain strict Python semantics. See the :ref:`enhancing performance <enhancingperf.eval>` documentation for more details. engine : string or None, default 'numexpr', {'python', 'numexpr'} The engine used to evaluate the expression. Supported engines are - None : tries to use ``numexpr``, falls back to ``python`` - ``'numexpr'``: This default engine evaluates pandas objects using numexpr for large speed ups in complex expressions with large frames. - ``'python'``: Performs operations as if you had ``eval``'d in top level python. This engine is generally not that useful. More backends may be available in the future. truediv : bool, optional Whether to use true division, like in Python >= 3 local_dict : dict or None, optional A dictionary of local variables, taken from locals() by default. global_dict : dict or None, optional A dictionary of global variables, taken from globals() by default. resolvers : list of dict-like or None, optional A list of objects implementing the ``__getitem__`` special method that you can use to inject an additional collection of namespaces to use for variable lookup. For example, this is used in the :meth:`~DataFrame.query` method to inject the ``DataFrame.index`` and ``DataFrame.columns`` variables that refer to their respective :class:`~pandas.DataFrame` instance attributes. level : int, optional The number of prior stack frames to traverse and add to the current scope. Most users will **not** need to change this parameter. target : object, optional, default None This is the target object for assignment. It is used when there is variable assignment in the expression. If so, then `target` must support item assignment with string keys, and if a copy is being returned, it must also support `.copy()`. inplace : bool, default False If `target` is provided, and the expression mutates `target`, whether to modify `target` inplace. Otherwise, return a copy of `target` with the mutation. Returns ------- ndarray, numeric scalar, DataFrame, Series Raises ------ ValueError There are many instances where such an error can be raised: - `target=None`, but the expression is multiline. - The expression is multiline, but not all them have item assignment. An example of such an arrangement is this: a = b + 1 a + 2 Here, there are expressions on different lines, making it multiline, but the last line has no variable assigned to the output of `a + 2`. - `inplace=True`, but the expression is missing item assignment. - Item assignment is provided, but the `target` does not support string item assignment. - Item assignment is provided and `inplace=False`, but the `target` does not support the `.copy()` method See Also -------- DataFrame.query DataFrame.eval Notes ----- The ``dtype`` of any objects involved in an arithmetic ``%`` operation are recursively cast to ``float64``. See the :ref:`enhancing performance <enhancingperf.eval>` documentation for more details.

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train

https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/computation/eval.py#L155-L350

tobami/littlechef

littlechef/runner.py

plugin

def plugin(name): """Executes the selected plugin Plugins are expected to be found in the kitchen's 'plugins' directory """ env.host_string = lib.get_env_host_string() plug = lib.import_plugin(name) lib.print_header("Executing plugin '{0}' on " "{1}".format(name, env.host_string)) node = lib.get_node(env.host_string) if node == {'run_list': []}: node['name'] = env.host_string plug.execute(node) print("Finished executing plugin")

python

def plugin(name): """Executes the selected plugin Plugins are expected to be found in the kitchen's 'plugins' directory """ env.host_string = lib.get_env_host_string() plug = lib.import_plugin(name) lib.print_header("Executing plugin '{0}' on " "{1}".format(name, env.host_string)) node = lib.get_node(env.host_string) if node == {'run_list': []}: node['name'] = env.host_string plug.execute(node) print("Finished executing plugin")

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train

https://github.com/tobami/littlechef/blob/aab8c94081b38100a69cc100bc4278ae7419c58e/littlechef/runner.py#L269-L282

pavelsof/ipatok

ipatok/tokens.py

normalise

def normalise(string): """ Convert each character of the string to the normal form in which it was defined in the IPA spec. This would be normal form D, except for the voiceless palatar fricative (ç) which should be in normal form C. Helper for tokenise_word(string, ..). """ string = unicodedata.normalize('NFD', string) for char_c in ipa.get_precomposed_chars(): char_d = unicodedata.normalize('NFD', char_c) if char_d in string: string = string.replace(char_d, char_c) return string

python

def normalise(string): """ Convert each character of the string to the normal form in which it was defined in the IPA spec. This would be normal form D, except for the voiceless palatar fricative (ç) which should be in normal form C. Helper for tokenise_word(string, ..). """ string = unicodedata.normalize('NFD', string) for char_c in ipa.get_precomposed_chars(): char_d = unicodedata.normalize('NFD', char_c) if char_d in string: string = string.replace(char_d, char_c) return string

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Convert each character of the string to the normal form in which it was defined in the IPA spec. This would be normal form D, except for the voiceless palatar fricative (ç) which should be in normal form C. Helper for tokenise_word(string, ..).

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train

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Duke-GCB/DukeDSClient

ddsc/ddsclient.py

ShareCommand.run

def run(self, args): """ Gives user permission based on auth_role arg and sends email to that user. :param args Namespace arguments parsed from the command line """ email = args.email # email of person to send email to username = args.username # username of person to send email to, will be None if email is specified force_send = args.resend # is this a resend so we should force sending auth_role = args.auth_role # authorization role(project permissions) to give to the user msg_file = args.msg_file # message file who's contents will be sent with the share message = read_argument_file_contents(msg_file) print("Sharing project.") to_user = self.remote_store.lookup_or_register_user_by_email_or_username(email, username) try: project = self.fetch_project(args, must_exist=True, include_children=False) dest_email = self.service.share(project, to_user, force_send, auth_role, message) print("Share email message sent to " + dest_email) except D4S2Error as ex: if ex.warning: print(ex.message) else: raise

python

def run(self, args): """ Gives user permission based on auth_role arg and sends email to that user. :param args Namespace arguments parsed from the command line """ email = args.email # email of person to send email to username = args.username # username of person to send email to, will be None if email is specified force_send = args.resend # is this a resend so we should force sending auth_role = args.auth_role # authorization role(project permissions) to give to the user msg_file = args.msg_file # message file who's contents will be sent with the share message = read_argument_file_contents(msg_file) print("Sharing project.") to_user = self.remote_store.lookup_or_register_user_by_email_or_username(email, username) try: project = self.fetch_project(args, must_exist=True, include_children=False) dest_email = self.service.share(project, to_user, force_send, auth_role, message) print("Share email message sent to " + dest_email) except D4S2Error as ex: if ex.warning: print(ex.message) else: raise

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train

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SuryaSankar/flask-sqlalchemy-booster

flask_sqlalchemy_booster/model_booster/queryable_mixin.py

QueryableMixin.find_or_create

def find_or_create(cls, **kwargs): """Checks if an instance already exists by filtering with the kwargs. If yes, returns that instance. If not, creates a new instance with kwargs and returns it Args: **kwargs: The keyword arguments which are used for filtering and initialization. keys(list, optional): A special keyword argument. If passed, only the set of keys mentioned here will be used for filtering. Useful when we want to 'find' based on a subset of the keys and create with all the keys Examples: >>> customer = Customer.find_or_create( ... name="vicky", email="[email protected]", country="India") >>> customer.id 45 >>> customer1 = Customer.find_or_create( ... name="vicky", email="[email protected]", country="India") >>> customer1==customer True >>> customer2 = Customer.find_or_create( ... name="vicky", email="[email protected]", country="Russia") >>> customer2==customer False >>> customer3 = Customer.find_or_create( ... name="vicky", email="[email protected]", country="Russia", ... keys=['name', 'email']) >>> customer3==customer True """ keys = kwargs.pop('keys') if 'keys' in kwargs else [] return cls.first(**subdict(kwargs, keys)) or cls.create(**kwargs)

python

def find_or_create(cls, **kwargs): """Checks if an instance already exists by filtering with the kwargs. If yes, returns that instance. If not, creates a new instance with kwargs and returns it Args: **kwargs: The keyword arguments which are used for filtering and initialization. keys(list, optional): A special keyword argument. If passed, only the set of keys mentioned here will be used for filtering. Useful when we want to 'find' based on a subset of the keys and create with all the keys Examples: >>> customer = Customer.find_or_create( ... name="vicky", email="[email protected]", country="India") >>> customer.id 45 >>> customer1 = Customer.find_or_create( ... name="vicky", email="[email protected]", country="India") >>> customer1==customer True >>> customer2 = Customer.find_or_create( ... name="vicky", email="[email protected]", country="Russia") >>> customer2==customer False >>> customer3 = Customer.find_or_create( ... name="vicky", email="[email protected]", country="Russia", ... keys=['name', 'email']) >>> customer3==customer True """ keys = kwargs.pop('keys') if 'keys' in kwargs else [] return cls.first(**subdict(kwargs, keys)) or cls.create(**kwargs)

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train

https://github.com/SuryaSankar/flask-sqlalchemy-booster/blob/444048d167ab7718f758e943665ef32d101423a5/flask_sqlalchemy_booster/model_booster/queryable_mixin.py#L580-L615

CI-WATER/gsshapy

gsshapy/lib/db_tools.py

init_sqlite_db

def init_sqlite_db(path, initTime=False): """ Initialize SQLite Database Args: path(str): Path to database (Ex. '/home/username/my_sqlite.db'). initTime(Optional[bool]): If True, it will print the amount of time to generate database. Example:: from gsshapy.lib.db_tools import init_sqlite_db, create_session sqlite_db_path = '/home/username/my_sqlite.db' init_postgresql_db(path=sqlite_db_path) sqlalchemy_url = init_sqlite_db(path=sqlite_db_path) db_work_sessionmaker = get_sessionmaker(sqlalchemy_url) db_work_session = db_work_sessionmaker() ##DO WORK db_work_session.close() """ sqlite_base_url = 'sqlite:///' sqlalchemy_url = sqlite_base_url + path init_time = init_db(sqlalchemy_url) if initTime: print('TIME: {0} seconds'.format(init_time)) return sqlalchemy_url

python

def init_sqlite_db(path, initTime=False): """ Initialize SQLite Database Args: path(str): Path to database (Ex. '/home/username/my_sqlite.db'). initTime(Optional[bool]): If True, it will print the amount of time to generate database. Example:: from gsshapy.lib.db_tools import init_sqlite_db, create_session sqlite_db_path = '/home/username/my_sqlite.db' init_postgresql_db(path=sqlite_db_path) sqlalchemy_url = init_sqlite_db(path=sqlite_db_path) db_work_sessionmaker = get_sessionmaker(sqlalchemy_url) db_work_session = db_work_sessionmaker() ##DO WORK db_work_session.close() """ sqlite_base_url = 'sqlite:///' sqlalchemy_url = sqlite_base_url + path init_time = init_db(sqlalchemy_url) if initTime: print('TIME: {0} seconds'.format(init_time)) return sqlalchemy_url

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train

https://github.com/CI-WATER/gsshapy/blob/00fd4af0fd65f1614d75a52fe950a04fb0867f4c/gsshapy/lib/db_tools.py#L82-L117

saltstack/salt

salt/modules/ps.py

_get_proc_status

def _get_proc_status(proc): ''' Returns the status of a Process instance. It's backward compatible with < 2.0 versions of psutil. ''' try: return salt.utils.data.decode(proc.status() if PSUTIL2 else proc.status) except (psutil.NoSuchProcess, psutil.AccessDenied): return None

python

def _get_proc_status(proc): ''' Returns the status of a Process instance. It's backward compatible with < 2.0 versions of psutil. ''' try: return salt.utils.data.decode(proc.status() if PSUTIL2 else proc.status) except (psutil.NoSuchProcess, psutil.AccessDenied): return None

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https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/ps.py#L86-L95

bwhite/hadoopy

hadoopy/thirdparty/pyinstaller/PyInstaller/utils/winmanifest.py

Manifest.todom

def todom(self): """ Return the manifest as DOM tree """ doc = Document() docE = doc.cE(self.manifestType) if self.manifestType == "assemblyBinding": cfg = doc.cE("configuration") win = doc.cE("windows") win.aChild(docE) cfg.aChild(win) doc.aChild(cfg) else: doc.aChild(docE) if self.manifestType != "dependentAssembly": docE.setA("xmlns", "urn:schemas-microsoft-com:asm.v1") if self.manifestType != "assemblyBinding": docE.setA("manifestVersion", ".".join([str(i) for i in self.manifestVersion])) if self.noInheritable: docE.aChild(doc.cE("noInheritable")) if self.noInherit: docE.aChild(doc.cE("noInherit")) aId = doc.cE("assemblyIdentity") if self.type: aId.setAttribute("type", self.type) if self.name: aId.setAttribute("name", self.name) if self.language: aId.setAttribute("language", self.language) if self.processorArchitecture: aId.setAttribute("processorArchitecture", self.processorArchitecture) if self.version: aId.setAttribute("version", ".".join([str(i) for i in self.version])) if self.publicKeyToken: aId.setAttribute("publicKeyToken", self.publicKeyToken) if aId.hasAttributes(): docE.aChild(aId) else: aId.unlink() if self.applyPublisherPolicy != None: ppE = doc.cE("publisherPolicy") if self.applyPublisherPolicy: ppE.setA("apply", "yes") else: ppE.setA("apply", "no") docE.aChild(ppE) if self.description: descE = doc.cE("description") descE.aChild(doc.cT(self.description)) docE.aChild(descE) if self.requestedExecutionLevel in ("asInvoker", "highestAvailable", "requireAdministrator"): tE = doc.cE("trustInfo") tE.setA("xmlns", "urn:schemas-microsoft-com:asm.v3") sE = doc.cE("security") rpE = doc.cE("requestedPrivileges") relE = doc.cE("requestedExecutionLevel") relE.setA("level", self.requestedExecutionLevel) if self.uiAccess: relE.setA("uiAccess", "true") else: relE.setA("uiAccess", "false") rpE.aChild(relE) sE.aChild(rpE) tE.aChild(sE) docE.aChild(tE) if self.dependentAssemblies: for assembly in self.dependentAssemblies: if self.manifestType != "assemblyBinding": dE = doc.cE("dependency") if assembly.optional: dE.setAttribute("optional", "yes") daE = doc.cE("dependentAssembly") adom = assembly.todom() for child in adom.documentElement.childNodes: daE.aChild(child.cloneNode(False)) adom.unlink() if self.manifestType != "assemblyBinding": dE.aChild(daE) docE.aChild(dE) else: docE.aChild(daE) if self.bindingRedirects: for bindingRedirect in self.bindingRedirects: brE = doc.cE("bindingRedirect") brE.setAttribute("oldVersion", "-".join([".".join([str(i) for i in part]) for part in bindingRedirect[0]])) brE.setAttribute("newVersion", ".".join([str(i) for i in bindingRedirect[1]])) docE.aChild(brE) if self.files: for file_ in self.files: fE = doc.cE("file") for attr in ("name", "hashalg", "hash"): val = getattr(file_, attr) if val: fE.setA(attr, val) docE.aChild(fE) return doc

python

def todom(self): """ Return the manifest as DOM tree """ doc = Document() docE = doc.cE(self.manifestType) if self.manifestType == "assemblyBinding": cfg = doc.cE("configuration") win = doc.cE("windows") win.aChild(docE) cfg.aChild(win) doc.aChild(cfg) else: doc.aChild(docE) if self.manifestType != "dependentAssembly": docE.setA("xmlns", "urn:schemas-microsoft-com:asm.v1") if self.manifestType != "assemblyBinding": docE.setA("manifestVersion", ".".join([str(i) for i in self.manifestVersion])) if self.noInheritable: docE.aChild(doc.cE("noInheritable")) if self.noInherit: docE.aChild(doc.cE("noInherit")) aId = doc.cE("assemblyIdentity") if self.type: aId.setAttribute("type", self.type) if self.name: aId.setAttribute("name", self.name) if self.language: aId.setAttribute("language", self.language) if self.processorArchitecture: aId.setAttribute("processorArchitecture", self.processorArchitecture) if self.version: aId.setAttribute("version", ".".join([str(i) for i in self.version])) if self.publicKeyToken: aId.setAttribute("publicKeyToken", self.publicKeyToken) if aId.hasAttributes(): docE.aChild(aId) else: aId.unlink() if self.applyPublisherPolicy != None: ppE = doc.cE("publisherPolicy") if self.applyPublisherPolicy: ppE.setA("apply", "yes") else: ppE.setA("apply", "no") docE.aChild(ppE) if self.description: descE = doc.cE("description") descE.aChild(doc.cT(self.description)) docE.aChild(descE) if self.requestedExecutionLevel in ("asInvoker", "highestAvailable", "requireAdministrator"): tE = doc.cE("trustInfo") tE.setA("xmlns", "urn:schemas-microsoft-com:asm.v3") sE = doc.cE("security") rpE = doc.cE("requestedPrivileges") relE = doc.cE("requestedExecutionLevel") relE.setA("level", self.requestedExecutionLevel) if self.uiAccess: relE.setA("uiAccess", "true") else: relE.setA("uiAccess", "false") rpE.aChild(relE) sE.aChild(rpE) tE.aChild(sE) docE.aChild(tE) if self.dependentAssemblies: for assembly in self.dependentAssemblies: if self.manifestType != "assemblyBinding": dE = doc.cE("dependency") if assembly.optional: dE.setAttribute("optional", "yes") daE = doc.cE("dependentAssembly") adom = assembly.todom() for child in adom.documentElement.childNodes: daE.aChild(child.cloneNode(False)) adom.unlink() if self.manifestType != "assemblyBinding": dE.aChild(daE) docE.aChild(dE) else: docE.aChild(daE) if self.bindingRedirects: for bindingRedirect in self.bindingRedirects: brE = doc.cE("bindingRedirect") brE.setAttribute("oldVersion", "-".join([".".join([str(i) for i in part]) for part in bindingRedirect[0]])) brE.setAttribute("newVersion", ".".join([str(i) for i in bindingRedirect[1]])) docE.aChild(brE) if self.files: for file_ in self.files: fE = doc.cE("file") for attr in ("name", "hashalg", "hash"): val = getattr(file_, attr) if val: fE.setA(attr, val) docE.aChild(fE) return doc

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train

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RiotGames/cloud-inquisitor

backend/cloud_inquisitor/plugins/types/resources.py

BaseResource.get_owner_emails

def get_owner_emails(self, partial_owner_match=True): """Return a list of email addresses associated with the instance, based on tags Returns: List of email addresses if any, else None """ for tag in self.tags: if tag.key.lower() == 'owner': rgx = re.compile(RGX_EMAIL_VALIDATION_PATTERN, re.I) if partial_owner_match: match = rgx.findall(tag.value) if match: return [NotificationContact('email', email) for email in match] else: match = rgx.match(tag.value) if match: return [NotificationContact('email', email) for email in match.groups()] return None

python

def get_owner_emails(self, partial_owner_match=True): """Return a list of email addresses associated with the instance, based on tags Returns: List of email addresses if any, else None """ for tag in self.tags: if tag.key.lower() == 'owner': rgx = re.compile(RGX_EMAIL_VALIDATION_PATTERN, re.I) if partial_owner_match: match = rgx.findall(tag.value) if match: return [NotificationContact('email', email) for email in match] else: match = rgx.match(tag.value) if match: return [NotificationContact('email', email) for email in match.groups()] return None

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train

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fermiPy/fermipy

fermipy/jobs/job_archive.py

JobArchive.remove_jobs

def remove_jobs(self, mask): """Mark all jobs that match a mask as 'removed' """ jobnames = self.table[mask]['jobname'] jobkey = self.table[mask]['jobkey'] self.table[mask]['status'] = JobStatus.removed for jobname, jobkey in zip(jobnames, jobkey): fullkey = JobDetails.make_fullkey(jobname, jobkey) self._cache.pop(fullkey).status = JobStatus.removed self.write_table_file()

python

def remove_jobs(self, mask): """Mark all jobs that match a mask as 'removed' """ jobnames = self.table[mask]['jobname'] jobkey = self.table[mask]['jobkey'] self.table[mask]['status'] = JobStatus.removed for jobname, jobkey in zip(jobnames, jobkey): fullkey = JobDetails.make_fullkey(jobname, jobkey) self._cache.pop(fullkey).status = JobStatus.removed self.write_table_file()

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train

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deepmind/pysc2

pysc2/lib/actions.py

select_army

def select_army(action, action_space, select_add): """Select the entire army.""" del action_space action.action_ui.select_army.selection_add = select_add

python

def select_army(action, action_space, select_add): """Select the entire army.""" del action_space action.action_ui.select_army.selection_add = select_add

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Select the entire army.

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train

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gem/oq-engine

openquake/baselib/node.py

pprint

def pprint(self, stream=None, indent=1, width=80, depth=None): """ Pretty print the underlying literal Python object """ pp.pprint(to_literal(self), stream, indent, width, depth)

python

def pprint(self, stream=None, indent=1, width=80, depth=None): """ Pretty print the underlying literal Python object """ pp.pprint(to_literal(self), stream, indent, width, depth)

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Pretty print the underlying literal Python object

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train

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fabioz/PyDev.Debugger

pydevd_attach_to_process/winappdbg/textio.py

Color.default

def default(cls): "Make the current foreground color the default." wAttributes = cls._get_text_attributes() wAttributes &= ~win32.FOREGROUND_MASK wAttributes |= win32.FOREGROUND_GREY wAttributes &= ~win32.FOREGROUND_INTENSITY cls._set_text_attributes(wAttributes)

python

def default(cls): "Make the current foreground color the default." wAttributes = cls._get_text_attributes() wAttributes &= ~win32.FOREGROUND_MASK wAttributes |= win32.FOREGROUND_GREY wAttributes &= ~win32.FOREGROUND_INTENSITY cls._set_text_attributes(wAttributes)

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Make the current foreground color the default.

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train

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jsfenfen/990-xml-reader

irs_reader/xmlrunner.py

XMLRunner.run_sked

def run_sked(self, object_id, sked, verbose=False): """ sked is the proper name of the schedule: IRS990, IRS990EZ, IRS990PF, IRS990ScheduleA, etc. """ self.whole_filing_data = [] self.filing_keyerr_data = [] this_filing = Filing(object_id) this_filing.process(verbose=verbose) this_version = this_filing.get_version() if this_version in ALLOWED_VERSIONSTRINGS or ( self.csv_format and this_version in CSV_ALLOWED_VERSIONSTRINGS ): this_version = this_filing.get_version() ein = this_filing.get_ein() sked_dict = this_filing.get_schedule(sked) self._run_schedule(sked, object_id, sked_dict, ein) this_filing.set_result(self.whole_filing_data) this_filing.set_keyerrors(self.filing_keyerr_data) return this_filing else: print("Filing version %s isn't supported for this operation" % this_version ) return this_filing

python

def run_sked(self, object_id, sked, verbose=False): """ sked is the proper name of the schedule: IRS990, IRS990EZ, IRS990PF, IRS990ScheduleA, etc. """ self.whole_filing_data = [] self.filing_keyerr_data = [] this_filing = Filing(object_id) this_filing.process(verbose=verbose) this_version = this_filing.get_version() if this_version in ALLOWED_VERSIONSTRINGS or ( self.csv_format and this_version in CSV_ALLOWED_VERSIONSTRINGS ): this_version = this_filing.get_version() ein = this_filing.get_ein() sked_dict = this_filing.get_schedule(sked) self._run_schedule(sked, object_id, sked_dict, ein) this_filing.set_result(self.whole_filing_data) this_filing.set_keyerrors(self.filing_keyerr_data) return this_filing else: print("Filing version %s isn't supported for this operation" % this_version ) return this_filing

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YeoLab/anchor

anchor/infotheory.py

cross_phenotype_jsd

def cross_phenotype_jsd(data, groupby, bins, n_iter=100): """Jensen-Shannon divergence of features across phenotypes Parameters ---------- data : pandas.DataFrame A (n_samples, n_features) Dataframe groupby : mappable A samples to phenotypes mapping n_iter : int Number of bootstrap resampling iterations to perform for the within-group comparisons n_bins : int Number of bins to binify the singles data on Returns ------- jsd_df : pandas.DataFrame A (n_features, n_phenotypes^2) dataframe of the JSD between each feature between and within phenotypes """ grouped = data.groupby(groupby) jsds = [] seen = set([]) for phenotype1, df1 in grouped: for phenotype2, df2 in grouped: pair = tuple(sorted([phenotype1, phenotype2])) if pair in seen: continue seen.add(pair) if phenotype1 == phenotype2: seriess = [] bs = cross_validation.Bootstrap(df1.shape[0], n_iter=n_iter, train_size=0.5) for i, (ind1, ind2) in enumerate(bs): df1_subset = df1.iloc[ind1, :] df2_subset = df2.iloc[ind2, :] seriess.append( binify_and_jsd(df1_subset, df2_subset, None, bins)) series = pd.concat(seriess, axis=1, names=None).mean(axis=1) series.name = pair jsds.append(series) else: series = binify_and_jsd(df1, df2, pair, bins) jsds.append(series) return pd.concat(jsds, axis=1)

python

def cross_phenotype_jsd(data, groupby, bins, n_iter=100): """Jensen-Shannon divergence of features across phenotypes Parameters ---------- data : pandas.DataFrame A (n_samples, n_features) Dataframe groupby : mappable A samples to phenotypes mapping n_iter : int Number of bootstrap resampling iterations to perform for the within-group comparisons n_bins : int Number of bins to binify the singles data on Returns ------- jsd_df : pandas.DataFrame A (n_features, n_phenotypes^2) dataframe of the JSD between each feature between and within phenotypes """ grouped = data.groupby(groupby) jsds = [] seen = set([]) for phenotype1, df1 in grouped: for phenotype2, df2 in grouped: pair = tuple(sorted([phenotype1, phenotype2])) if pair in seen: continue seen.add(pair) if phenotype1 == phenotype2: seriess = [] bs = cross_validation.Bootstrap(df1.shape[0], n_iter=n_iter, train_size=0.5) for i, (ind1, ind2) in enumerate(bs): df1_subset = df1.iloc[ind1, :] df2_subset = df2.iloc[ind2, :] seriess.append( binify_and_jsd(df1_subset, df2_subset, None, bins)) series = pd.concat(seriess, axis=1, names=None).mean(axis=1) series.name = pair jsds.append(series) else: series = binify_and_jsd(df1, df2, pair, bins) jsds.append(series) return pd.concat(jsds, axis=1)

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train

https://github.com/YeoLab/anchor/blob/1f9c9d6d30235b1e77b945e6ef01db5a0e55d53a/anchor/infotheory.py#L218-L266

bsolomon1124/pyfinance

pyfinance/options.py

OpStrat.summary

def summary(self, St=None): """Tabular summary of strategy composition, broken out by option. Returns ------- pd.DataFrame Columns: kind, position, strike, price, St, payoff, profit. """ St = self.St if St is None else St if self.options: payoffs = [op.payoff(St=St) for op in self.options] profits = [op.profit(St=St) for op in self.options] strikes = [op.K for op in self.options] prices = [op.price for op in self.options] exprs = [St] * len(self.options) kinds = [op.kind for op in self.options] poss = [op.pos for op in self.options] res = OrderedDict( [ ("kind", kinds), ("position", poss), ("strike", strikes), ("price", prices), ("St", exprs), ("payoff", payoffs), ("profit", profits), ] ) return DataFrame(res) else: return None

python

def summary(self, St=None): """Tabular summary of strategy composition, broken out by option. Returns ------- pd.DataFrame Columns: kind, position, strike, price, St, payoff, profit. """ St = self.St if St is None else St if self.options: payoffs = [op.payoff(St=St) for op in self.options] profits = [op.profit(St=St) for op in self.options] strikes = [op.K for op in self.options] prices = [op.price for op in self.options] exprs = [St] * len(self.options) kinds = [op.kind for op in self.options] poss = [op.pos for op in self.options] res = OrderedDict( [ ("kind", kinds), ("position", poss), ("strike", strikes), ("price", prices), ("St", exprs), ("payoff", payoffs), ("profit", profits), ] ) return DataFrame(res) else: return None

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Tabular summary of strategy composition, broken out by option. Returns ------- pd.DataFrame Columns: kind, position, strike, price, St, payoff, profit.

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train

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kervi/kervi-core

kervi/values/value_list.py

ValueList.add

def add(self, value_id, name, value_class): """ Factory function that creates a value. :param value_id: id of the value, used to reference the value within this list.BaseException :param value_class: The class of the value that should be created with this function. """ item = value_class( name, value_id=self.controller.component_id + "." + value_id, is_input=self.is_input, index=self.count, spine = self.controller.spine ) #if self._inject and self.controller: # setattr(self.controller, value_id, item) #setattr(self, value_id, item) self.count += 1 self._items[value_id] = item if self.is_input and self.controller: item.add_observer(self.controller) return item

python

def add(self, value_id, name, value_class): """ Factory function that creates a value. :param value_id: id of the value, used to reference the value within this list.BaseException :param value_class: The class of the value that should be created with this function. """ item = value_class( name, value_id=self.controller.component_id + "." + value_id, is_input=self.is_input, index=self.count, spine = self.controller.spine ) #if self._inject and self.controller: # setattr(self.controller, value_id, item) #setattr(self, value_id, item) self.count += 1 self._items[value_id] = item if self.is_input and self.controller: item.add_observer(self.controller) return item

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train

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skyfielders/python-skyfield

skyfield/earthlib.py

terra

def terra(latitude, longitude, elevation, gast): """Compute the position and velocity of a terrestrial observer. `latitude` - Latitude in radians. `longitude` - Longitude in radians. `elevation` - Elevation above sea level in au. `gast` - Hours of Greenwich Apparent Sidereal Time (can be an array). The return value is a tuple of two 3-vectors `(pos, vel)` in the dynamical reference system (the true equator and equinox of date) whose components are measured in au with respect to the center of the Earth. """ zero = zeros_like(gast) sinphi = sin(latitude) cosphi = cos(latitude) c = 1.0 / sqrt(cosphi * cosphi + sinphi * sinphi * one_minus_flattening_squared) s = one_minus_flattening_squared * c ach = earth_radius_au * c + elevation ash = earth_radius_au * s + elevation # Compute local sidereal time factors at the observer's longitude. stlocl = 15.0 * DEG2RAD * gast + longitude sinst = sin(stlocl) cosst = cos(stlocl) # Compute position vector components in kilometers. ac = ach * cosphi acsst = ac * sinst accst = ac * cosst pos = array((accst, acsst, zero + ash * sinphi)) # Compute velocity vector components in kilometers/sec. vel = ANGVEL * DAY_S * array((-acsst, accst, zero)) return pos, vel

python

def terra(latitude, longitude, elevation, gast): """Compute the position and velocity of a terrestrial observer. `latitude` - Latitude in radians. `longitude` - Longitude in radians. `elevation` - Elevation above sea level in au. `gast` - Hours of Greenwich Apparent Sidereal Time (can be an array). The return value is a tuple of two 3-vectors `(pos, vel)` in the dynamical reference system (the true equator and equinox of date) whose components are measured in au with respect to the center of the Earth. """ zero = zeros_like(gast) sinphi = sin(latitude) cosphi = cos(latitude) c = 1.0 / sqrt(cosphi * cosphi + sinphi * sinphi * one_minus_flattening_squared) s = one_minus_flattening_squared * c ach = earth_radius_au * c + elevation ash = earth_radius_au * s + elevation # Compute local sidereal time factors at the observer's longitude. stlocl = 15.0 * DEG2RAD * gast + longitude sinst = sin(stlocl) cosst = cos(stlocl) # Compute position vector components in kilometers. ac = ach * cosphi acsst = ac * sinst accst = ac * cosst pos = array((accst, acsst, zero + ash * sinphi)) # Compute velocity vector components in kilometers/sec. vel = ANGVEL * DAY_S * array((-acsst, accst, zero)) return pos, vel

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train

https://github.com/skyfielders/python-skyfield/blob/51d9e042e06457f6b1f2415296d50a38cb3a300f/skyfield/earthlib.py#L15-L55

kapadia/usgs

usgs/payloads.py

download

def download(dataset, node, entityids, products, api_key=None): """ The use of this request will be to obtain valid data download URLs. :param dataset: :param entityIds: list :param products: list :param node: :param api_key: API key is required. """ payload = { "datasetName": dataset, "node": node, "apiKey": api_key, "entityIds": entityids, "products": products } return json.dumps(payload)

python

def download(dataset, node, entityids, products, api_key=None): """ The use of this request will be to obtain valid data download URLs. :param dataset: :param entityIds: list :param products: list :param node: :param api_key: API key is required. """ payload = { "datasetName": dataset, "node": node, "apiKey": api_key, "entityIds": entityids, "products": products } return json.dumps(payload)

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train

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google/prettytensor

prettytensor/tutorial/data_utils.py

maybe_download

def maybe_download(url, filename): """Download the data from Yann's website, unless it's already here.""" if not os.path.exists(WORK_DIRECTORY): os.mkdir(WORK_DIRECTORY) filepath = os.path.join(WORK_DIRECTORY, filename) if not os.path.exists(filepath): filepath, _ = request.urlretrieve(url + filename, filepath) statinfo = os.stat(filepath) print('Successfully downloaded', filename, statinfo.st_size, 'bytes.') return filepath

python

def maybe_download(url, filename): """Download the data from Yann's website, unless it's already here.""" if not os.path.exists(WORK_DIRECTORY): os.mkdir(WORK_DIRECTORY) filepath = os.path.join(WORK_DIRECTORY, filename) if not os.path.exists(filepath): filepath, _ = request.urlretrieve(url + filename, filepath) statinfo = os.stat(filepath) print('Successfully downloaded', filename, statinfo.st_size, 'bytes.') return filepath

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train

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PMEAL/OpenPNM

openpnm/algorithms/FickianDiffusion.py

FickianDiffusion.calc_effective_diffusivity

def calc_effective_diffusivity(self, inlets=None, outlets=None, domain_area=None, domain_length=None): r""" This calculates the effective diffusivity in this linear transport algorithm. Parameters ---------- inlets : array_like The pores where the inlet composition boundary conditions were applied. If not given an attempt is made to infer them from the algorithm. outlets : array_like The pores where the outlet composition boundary conditions were applied. If not given an attempt is made to infer them from the algorithm. domain_area : scalar, optional The area of the inlet (and outlet) boundary faces. If not given then an attempt is made to estimate it, but it is usually underestimated. domain_length : scalar, optional The length of the domain between the inlet and outlet boundary faces. If not given then an attempt is made to estimate it, but it is usually underestimated. Notes ----- The area and length of the domain are found using the bounding box around the inlet and outlet pores which do not necessarily lie on the edge of the domain, resulting in underestimation of sizes. """ return self._calc_eff_prop(inlets=inlets, outlets=outlets, domain_area=domain_area, domain_length=domain_length)

python

def calc_effective_diffusivity(self, inlets=None, outlets=None, domain_area=None, domain_length=None): r""" This calculates the effective diffusivity in this linear transport algorithm. Parameters ---------- inlets : array_like The pores where the inlet composition boundary conditions were applied. If not given an attempt is made to infer them from the algorithm. outlets : array_like The pores where the outlet composition boundary conditions were applied. If not given an attempt is made to infer them from the algorithm. domain_area : scalar, optional The area of the inlet (and outlet) boundary faces. If not given then an attempt is made to estimate it, but it is usually underestimated. domain_length : scalar, optional The length of the domain between the inlet and outlet boundary faces. If not given then an attempt is made to estimate it, but it is usually underestimated. Notes ----- The area and length of the domain are found using the bounding box around the inlet and outlet pores which do not necessarily lie on the edge of the domain, resulting in underestimation of sizes. """ return self._calc_eff_prop(inlets=inlets, outlets=outlets, domain_area=domain_area, domain_length=domain_length)

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train

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deepmind/pysc2

pysc2/lib/features.py

Features.available_actions

def available_actions(self, obs): """Return the list of available action ids.""" available_actions = set() hide_specific_actions = self._agent_interface_format.hide_specific_actions for i, func in six.iteritems(actions.FUNCTIONS_AVAILABLE): if func.avail_fn(obs): available_actions.add(i) for a in obs.abilities: if a.ability_id not in actions.ABILITY_IDS: logging.warning("Unknown ability %s seen as available.", a.ability_id) continue for func in actions.ABILITY_IDS[a.ability_id]: if func.function_type in actions.POINT_REQUIRED_FUNCS[a.requires_point]: if func.general_id == 0 or not hide_specific_actions: available_actions.add(func.id) if func.general_id != 0: # Always offer generic actions. for general_func in actions.ABILITY_IDS[func.general_id]: if general_func.function_type is func.function_type: # Only the right type. Don't want to expose the general action # to minimap if only the screen version is available. available_actions.add(general_func.id) break return list(available_actions)

python

def available_actions(self, obs): """Return the list of available action ids.""" available_actions = set() hide_specific_actions = self._agent_interface_format.hide_specific_actions for i, func in six.iteritems(actions.FUNCTIONS_AVAILABLE): if func.avail_fn(obs): available_actions.add(i) for a in obs.abilities: if a.ability_id not in actions.ABILITY_IDS: logging.warning("Unknown ability %s seen as available.", a.ability_id) continue for func in actions.ABILITY_IDS[a.ability_id]: if func.function_type in actions.POINT_REQUIRED_FUNCS[a.requires_point]: if func.general_id == 0 or not hide_specific_actions: available_actions.add(func.id) if func.general_id != 0: # Always offer generic actions. for general_func in actions.ABILITY_IDS[func.general_id]: if general_func.function_type is func.function_type: # Only the right type. Don't want to expose the general action # to minimap if only the screen version is available. available_actions.add(general_func.id) break return list(available_actions)

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ionelmc/python-cogen

cogen/core/pubsub.py

PublishSubscribeQueue.compact

def compact(self): """Compacts the queue: removes all the messages from the queue that have been fetched by all the subscribed coroutines. Returns the number of messages that have been removed.""" if self.subscribers: level = min(self.subscribers.itervalues()) if level: del self.messages[:level] return level else: level = len(self.messages) del self.messages[:] return level

python

def compact(self): """Compacts the queue: removes all the messages from the queue that have been fetched by all the subscribed coroutines. Returns the number of messages that have been removed.""" if self.subscribers: level = min(self.subscribers.itervalues()) if level: del self.messages[:level] return level else: level = len(self.messages) del self.messages[:] return level

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train

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watchforstock/evohome-client

evohomeclient2/__init__.py

EvohomeClient._headers

def _headers(self): """Ensure the Authorization Header has a valid Access Token.""" if not self.access_token or not self.access_token_expires: self._basic_login() elif datetime.now() > self.access_token_expires - timedelta(seconds=30): self._basic_login() return {'Accept': HEADER_ACCEPT, 'Authorization': 'bearer ' + self.access_token}

python

def _headers(self): """Ensure the Authorization Header has a valid Access Token.""" if not self.access_token or not self.access_token_expires: self._basic_login() elif datetime.now() > self.access_token_expires - timedelta(seconds=30): self._basic_login() return {'Accept': HEADER_ACCEPT, 'Authorization': 'bearer ' + self.access_token}

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Ensure the Authorization Header has a valid Access Token.

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spotify/luigi

luigi/contrib/hadoop.py

JobTask.run_mapper

def run_mapper(self, stdin=sys.stdin, stdout=sys.stdout): """ Run the mapper on the hadoop node. """ self.init_hadoop() self.init_mapper() outputs = self._map_input((line[:-1] for line in stdin)) if self.reducer == NotImplemented: self.writer(outputs, stdout) else: self.internal_writer(outputs, stdout)

python

def run_mapper(self, stdin=sys.stdin, stdout=sys.stdout): """ Run the mapper on the hadoop node. """ self.init_hadoop() self.init_mapper() outputs = self._map_input((line[:-1] for line in stdin)) if self.reducer == NotImplemented: self.writer(outputs, stdout) else: self.internal_writer(outputs, stdout)

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saltstack/salt

salt/utils/openstack/nova.py

SaltNova._get_version_from_url

def _get_version_from_url(self, url): ''' Exctract API version from provided URL ''' regex = re.compile(r"^https?:\/\/.*\/(v[0-9])(\.[0-9])?(\/)?$") try: ver = regex.match(url) if ver.group(1): retver = ver.group(1) if ver.group(2): retver = retver + ver.group(2) return retver except AttributeError: return ''

python

def _get_version_from_url(self, url): ''' Exctract API version from provided URL ''' regex = re.compile(r"^https?:\/\/.*\/(v[0-9])(\.[0-9])?(\/)?$") try: ver = regex.match(url) if ver.group(1): retver = ver.group(1) if ver.group(2): retver = retver + ver.group(2) return retver except AttributeError: return ''

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lra/mackup

mackup/config.py

Config._parse_apps_to_ignore

def _parse_apps_to_ignore(self): """ Parse the applications to ignore in the config. Returns: set """ # We ignore nothing by default apps_to_ignore = set() # Is the "[applications_to_ignore]" in the cfg file ? section_title = 'applications_to_ignore' if self._parser.has_section(section_title): apps_to_ignore = set(self._parser.options(section_title)) return apps_to_ignore

python

def _parse_apps_to_ignore(self): """ Parse the applications to ignore in the config. Returns: set """ # We ignore nothing by default apps_to_ignore = set() # Is the "[applications_to_ignore]" in the cfg file ? section_title = 'applications_to_ignore' if self._parser.has_section(section_title): apps_to_ignore = set(self._parser.options(section_title)) return apps_to_ignore

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pdkit/pdkit

pdkit/gait_processor.py

GaitProcessor.walk_direction_preheel

def walk_direction_preheel(self, data_frame): """ Estimate local walk (not cardinal) direction with pre-heel strike phase. Inspired by Nirupam Roy's B.E. thesis: "WalkCompass: Finding Walking Direction Leveraging Smartphone's Inertial Sensors" :param data_frame: The data frame. It should have x, y, and z columns. :type data_frame: pandas.DataFrame :return: Unit vector of local walk (not cardinal) direction. :rtype: numpy.ndarray """ # Sum of absolute values across accelerometer axes: data = data_frame.x.abs() + data_frame.y.abs() + data_frame.z.abs() # Find maximum peaks of smoothed data: dummy, ipeaks_smooth = self.heel_strikes(data) data = data.values # Compute number of samples between peaks using the real part of the FFT: interpeak = compute_interpeak(data, self.sampling_frequency) decel = np.int(np.round(self.stride_fraction * interpeak)) # Find maximum peaks close to maximum peaks of smoothed data: ipeaks = [] for ipeak_smooth in ipeaks_smooth: ipeak = np.argmax(data[ipeak_smooth - decel:ipeak_smooth + decel]) ipeak += ipeak_smooth - decel ipeaks.append(ipeak) # Compute the average vector for each deceleration phase: vectors = [] for ipeak in ipeaks: decel_vectors = np.asarray([[data_frame.x[i], data_frame.y[i], data_frame.z[i]] for i in range(ipeak - decel, ipeak)]) vectors.append(np.mean(decel_vectors, axis=0)) # Compute the average deceleration vector and take the opposite direction: direction = -1 * np.mean(vectors, axis=0) # Return the unit vector in this direction: direction /= np.sqrt(direction.dot(direction)) return direction

python

def walk_direction_preheel(self, data_frame): """ Estimate local walk (not cardinal) direction with pre-heel strike phase. Inspired by Nirupam Roy's B.E. thesis: "WalkCompass: Finding Walking Direction Leveraging Smartphone's Inertial Sensors" :param data_frame: The data frame. It should have x, y, and z columns. :type data_frame: pandas.DataFrame :return: Unit vector of local walk (not cardinal) direction. :rtype: numpy.ndarray """ # Sum of absolute values across accelerometer axes: data = data_frame.x.abs() + data_frame.y.abs() + data_frame.z.abs() # Find maximum peaks of smoothed data: dummy, ipeaks_smooth = self.heel_strikes(data) data = data.values # Compute number of samples between peaks using the real part of the FFT: interpeak = compute_interpeak(data, self.sampling_frequency) decel = np.int(np.round(self.stride_fraction * interpeak)) # Find maximum peaks close to maximum peaks of smoothed data: ipeaks = [] for ipeak_smooth in ipeaks_smooth: ipeak = np.argmax(data[ipeak_smooth - decel:ipeak_smooth + decel]) ipeak += ipeak_smooth - decel ipeaks.append(ipeak) # Compute the average vector for each deceleration phase: vectors = [] for ipeak in ipeaks: decel_vectors = np.asarray([[data_frame.x[i], data_frame.y[i], data_frame.z[i]] for i in range(ipeak - decel, ipeak)]) vectors.append(np.mean(decel_vectors, axis=0)) # Compute the average deceleration vector and take the opposite direction: direction = -1 * np.mean(vectors, axis=0) # Return the unit vector in this direction: direction /= np.sqrt(direction.dot(direction)) return direction

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tensorflow/tensor2tensor

tensor2tensor/data_generators/imagenet.py

_normalize

def _normalize(image): """Normalize the image to zero mean and unit variance.""" offset = tf.constant(MEAN_RGB, shape=[1, 1, 3]) image -= offset scale = tf.constant(STDDEV_RGB, shape=[1, 1, 3]) image /= scale return image

python

def _normalize(image): """Normalize the image to zero mean and unit variance.""" offset = tf.constant(MEAN_RGB, shape=[1, 1, 3]) image -= offset scale = tf.constant(STDDEV_RGB, shape=[1, 1, 3]) image /= scale return image

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Normalize the image to zero mean and unit variance.

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oisinmulvihill/stomper

lib/stomper/stomp_10.py

Frame.setCmd

def setCmd(self, cmd): """Check the cmd is valid, FrameError will be raised if its not.""" cmd = cmd.upper() if cmd not in VALID_COMMANDS: raise FrameError("The cmd '%s' is not valid! It must be one of '%s' (STOMP v%s)." % ( cmd, VALID_COMMANDS, STOMP_VERSION) ) else: self._cmd = cmd

python

def setCmd(self, cmd): """Check the cmd is valid, FrameError will be raised if its not.""" cmd = cmd.upper() if cmd not in VALID_COMMANDS: raise FrameError("The cmd '%s' is not valid! It must be one of '%s' (STOMP v%s)." % ( cmd, VALID_COMMANDS, STOMP_VERSION) ) else: self._cmd = cmd

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Check the cmd is valid, FrameError will be raised if its not.

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palankai/pyrs-schema

pyrs/schema/types.py

Object.to_raw

def to_raw(self, value, context=None): """Convert the value to a JSON compatible value""" if value is None: return None res = {} value = value.copy() errors = [] for field in list(set(value) & set(self._fields)): schema = self._fields.get(field) name = schema.get_attr('name', field) try: res[name] = \ schema.to_raw(value.pop(field), context=context) except exceptions.ValidationErrors as ex: self._update_errors_by_exception(errors, ex, name) self._raise_exception_when_errors(errors, value) res.update(value) return res

python

def to_raw(self, value, context=None): """Convert the value to a JSON compatible value""" if value is None: return None res = {} value = value.copy() errors = [] for field in list(set(value) & set(self._fields)): schema = self._fields.get(field) name = schema.get_attr('name', field) try: res[name] = \ schema.to_raw(value.pop(field), context=context) except exceptions.ValidationErrors as ex: self._update_errors_by_exception(errors, ex, name) self._raise_exception_when_errors(errors, value) res.update(value) return res

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johntruckenbrodt/spatialist

spatialist/explorer.py

RasterViewer.__reset_crosshair

def __reset_crosshair(self): """ redraw the cross-hair on the horizontal slice plot Parameters ---------- x: int the x image coordinate y: int the y image coordinate Returns ------- """ self.lhor.set_ydata(self.y_coord) self.lver.set_xdata(self.x_coord)

python

def __reset_crosshair(self): """ redraw the cross-hair on the horizontal slice plot Parameters ---------- x: int the x image coordinate y: int the y image coordinate Returns ------- """ self.lhor.set_ydata(self.y_coord) self.lver.set_xdata(self.x_coord)

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train

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mathiasertl/django-ca

ca/django_ca/management/base.py

BaseCommand.add_format

def add_format(self, parser, default=Encoding.PEM, help_text=None, opts=None): """Add the --format option.""" if opts is None: opts = ['-f', '--format'] if help_text is None: help_text = 'The format to use ("ASN1" is an alias for "DER", default: %(default)s).' help_text = help_text % {'default': default.name} parser.add_argument(*opts, metavar='{PEM,ASN1,DER}', default=default, action=FormatAction, help=help_text)

python

def add_format(self, parser, default=Encoding.PEM, help_text=None, opts=None): """Add the --format option.""" if opts is None: opts = ['-f', '--format'] if help_text is None: help_text = 'The format to use ("ASN1" is an alias for "DER", default: %(default)s).' help_text = help_text % {'default': default.name} parser.add_argument(*opts, metavar='{PEM,ASN1,DER}', default=default, action=FormatAction, help=help_text)

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hydpy-dev/hydpy

hydpy/models/lland/lland_model.py

calc_schm_wats_v1

def calc_schm_wats_v1(self): """Calculate the actual amount of water melting within the snow cover. Required control parameters: |NHRU| |Lnk| Required flux sequences: |SBes| |WGTF| Calculated flux sequence: |Schm| Updated state sequence: |WATS| Basic equations: :math:`\\frac{dWATS}{dt} = SBes - Schm` :math:`Schm = \\Bigl \\lbrace { {WGTF \\ | \\ WATS > 0} \\atop {0 \\ | \\ WATS = 0} }` Examples: Initialize two water (|FLUSS| and |SEE|) and four arable land (|ACKER|) HRUs. Assume the same values for the initial amount of frozen water (|WATS|) and the frozen part of stand precipitation (|SBes|), but different values for potential snowmelt (|WGTF|): >>> from hydpy.models.lland import * >>> parameterstep('1d') >>> nhru(6) >>> lnk(FLUSS, SEE, ACKER, ACKER, ACKER, ACKER) >>> states.wats = 2.0 >>> fluxes.sbes = 1.0 >>> fluxes.wgtf = 1.0, 1.0, 0.0, 1.0, 3.0, 5.0 >>> model.calc_schm_wats_v1() >>> states.wats wats(0.0, 0.0, 3.0, 2.0, 0.0, 0.0) >>> fluxes.schm schm(0.0, 0.0, 0.0, 1.0, 3.0, 3.0) For the water areas, both the frozen amount of water and actual melt are set to zero. For all other land use classes, actual melt is either limited by potential melt or the available frozen water, which is the sum of initial frozen water and the frozen part of stand precipitation. """ con = self.parameters.control.fastaccess flu = self.sequences.fluxes.fastaccess sta = self.sequences.states.fastaccess for k in range(con.nhru): if con.lnk[k] in (WASSER, FLUSS, SEE): sta.wats[k] = 0. flu.schm[k] = 0. else: sta.wats[k] += flu.sbes[k] flu.schm[k] = min(flu.wgtf[k], sta.wats[k]) sta.wats[k] -= flu.schm[k]

python

def calc_schm_wats_v1(self): """Calculate the actual amount of water melting within the snow cover. Required control parameters: |NHRU| |Lnk| Required flux sequences: |SBes| |WGTF| Calculated flux sequence: |Schm| Updated state sequence: |WATS| Basic equations: :math:`\\frac{dWATS}{dt} = SBes - Schm` :math:`Schm = \\Bigl \\lbrace { {WGTF \\ | \\ WATS > 0} \\atop {0 \\ | \\ WATS = 0} }` Examples: Initialize two water (|FLUSS| and |SEE|) and four arable land (|ACKER|) HRUs. Assume the same values for the initial amount of frozen water (|WATS|) and the frozen part of stand precipitation (|SBes|), but different values for potential snowmelt (|WGTF|): >>> from hydpy.models.lland import * >>> parameterstep('1d') >>> nhru(6) >>> lnk(FLUSS, SEE, ACKER, ACKER, ACKER, ACKER) >>> states.wats = 2.0 >>> fluxes.sbes = 1.0 >>> fluxes.wgtf = 1.0, 1.0, 0.0, 1.0, 3.0, 5.0 >>> model.calc_schm_wats_v1() >>> states.wats wats(0.0, 0.0, 3.0, 2.0, 0.0, 0.0) >>> fluxes.schm schm(0.0, 0.0, 0.0, 1.0, 3.0, 3.0) For the water areas, both the frozen amount of water and actual melt are set to zero. For all other land use classes, actual melt is either limited by potential melt or the available frozen water, which is the sum of initial frozen water and the frozen part of stand precipitation. """ con = self.parameters.control.fastaccess flu = self.sequences.fluxes.fastaccess sta = self.sequences.states.fastaccess for k in range(con.nhru): if con.lnk[k] in (WASSER, FLUSS, SEE): sta.wats[k] = 0. flu.schm[k] = 0. else: sta.wats[k] += flu.sbes[k] flu.schm[k] = min(flu.wgtf[k], sta.wats[k]) sta.wats[k] -= flu.schm[k]

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Calculate the actual amount of water melting within the snow cover. Required control parameters: |NHRU| |Lnk| Required flux sequences: |SBes| |WGTF| Calculated flux sequence: |Schm| Updated state sequence: |WATS| Basic equations: :math:`\\frac{dWATS}{dt} = SBes - Schm` :math:`Schm = \\Bigl \\lbrace { {WGTF \\ | \\ WATS > 0} \\atop {0 \\ | \\ WATS = 0} }` Examples: Initialize two water (|FLUSS| and |SEE|) and four arable land (|ACKER|) HRUs. Assume the same values for the initial amount of frozen water (|WATS|) and the frozen part of stand precipitation (|SBes|), but different values for potential snowmelt (|WGTF|): >>> from hydpy.models.lland import * >>> parameterstep('1d') >>> nhru(6) >>> lnk(FLUSS, SEE, ACKER, ACKER, ACKER, ACKER) >>> states.wats = 2.0 >>> fluxes.sbes = 1.0 >>> fluxes.wgtf = 1.0, 1.0, 0.0, 1.0, 3.0, 5.0 >>> model.calc_schm_wats_v1() >>> states.wats wats(0.0, 0.0, 3.0, 2.0, 0.0, 0.0) >>> fluxes.schm schm(0.0, 0.0, 0.0, 1.0, 3.0, 3.0) For the water areas, both the frozen amount of water and actual melt are set to zero. For all other land use classes, actual melt is either limited by potential melt or the available frozen water, which is the sum of initial frozen water and the frozen part of stand precipitation.

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train

https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/lland/lland_model.py#L604-L666

mikekatz04/BOWIE

snr_calculator_folder/gwsnrcalc/genconutils/genprocess.py

GenProcess.set_parameters

def set_parameters(self): """Setup all the parameters for the binaries to be evaluated. Grid values and store necessary parameters for input into the SNR function. """ # declare 1D arrays of both paramters if self.xscale != 'lin': self.xvals = np.logspace(np.log10(float(self.x_low)), np.log10(float(self.x_high)), self.num_x) else: self.xvals = np.linspace(float(self.x_low), float(self.x_high), self.num_x) if self.yscale != 'lin': self.yvals = np.logspace(np.log10(float(self.y_low)), np.log10(float(self.y_high)), self.num_y) else: self.yvals = np.linspace(float(self.y_low), float(self.y_high), self.num_y) self.xvals, self.yvals = np.meshgrid(self.xvals, self.yvals) self.xvals, self.yvals = self.xvals.ravel(), self.yvals.ravel() for which in ['x', 'y']: setattr(self, getattr(self, which + 'val_name'), getattr(self, which + 'vals')) self.ecc = 'eccentricity' in self.__dict__ if self.ecc: if 'observation_time' not in self.__dict__: if 'start_time' not in self.__dict__: raise ValueError('If no observation time is provided, the time before' + 'merger must be the inital starting condition.') self.observation_time = self.start_time # small number so it is not zero else: if 'spin' in self.__dict__: self.spin_1 = self.spin self.spin_2 = self.spin for key in ['redshift', 'luminosity_distance', 'comoving_distance']: if key in self.__dict__: self.dist_type = key self.z_or_dist = getattr(self, key) if self.ecc: for key in ['start_frequency', 'start_time', 'start_separation']: if key in self.__dict__: self.initial_cond_type = key.split('_')[-1] self.initial_point = getattr(self, key) # add m1 and m2 self.m1 = (self.total_mass / (1. + self.mass_ratio)) self.m2 = (self.total_mass * self.mass_ratio / (1. + self.mass_ratio)) return

python

def set_parameters(self): """Setup all the parameters for the binaries to be evaluated. Grid values and store necessary parameters for input into the SNR function. """ # declare 1D arrays of both paramters if self.xscale != 'lin': self.xvals = np.logspace(np.log10(float(self.x_low)), np.log10(float(self.x_high)), self.num_x) else: self.xvals = np.linspace(float(self.x_low), float(self.x_high), self.num_x) if self.yscale != 'lin': self.yvals = np.logspace(np.log10(float(self.y_low)), np.log10(float(self.y_high)), self.num_y) else: self.yvals = np.linspace(float(self.y_low), float(self.y_high), self.num_y) self.xvals, self.yvals = np.meshgrid(self.xvals, self.yvals) self.xvals, self.yvals = self.xvals.ravel(), self.yvals.ravel() for which in ['x', 'y']: setattr(self, getattr(self, which + 'val_name'), getattr(self, which + 'vals')) self.ecc = 'eccentricity' in self.__dict__ if self.ecc: if 'observation_time' not in self.__dict__: if 'start_time' not in self.__dict__: raise ValueError('If no observation time is provided, the time before' + 'merger must be the inital starting condition.') self.observation_time = self.start_time # small number so it is not zero else: if 'spin' in self.__dict__: self.spin_1 = self.spin self.spin_2 = self.spin for key in ['redshift', 'luminosity_distance', 'comoving_distance']: if key in self.__dict__: self.dist_type = key self.z_or_dist = getattr(self, key) if self.ecc: for key in ['start_frequency', 'start_time', 'start_separation']: if key in self.__dict__: self.initial_cond_type = key.split('_')[-1] self.initial_point = getattr(self, key) # add m1 and m2 self.m1 = (self.total_mass / (1. + self.mass_ratio)) self.m2 = (self.total_mass * self.mass_ratio / (1. + self.mass_ratio)) return

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bwhite/hadoopy

hadoopy/_hdfs.py

cp

def cp(hdfs_src, hdfs_dst): """Copy a file :param hdfs_src: Source (str) :param hdfs_dst: Destination (str) :raises: IOError: If unsuccessful """ cmd = "hadoop fs -cp %s %s" % (hdfs_src, hdfs_dst) rcode, stdout, stderr = _checked_hadoop_fs_command(cmd)

python

def cp(hdfs_src, hdfs_dst): """Copy a file :param hdfs_src: Source (str) :param hdfs_dst: Destination (str) :raises: IOError: If unsuccessful """ cmd = "hadoop fs -cp %s %s" % (hdfs_src, hdfs_dst) rcode, stdout, stderr = _checked_hadoop_fs_command(cmd)

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Copy a file :param hdfs_src: Source (str) :param hdfs_dst: Destination (str) :raises: IOError: If unsuccessful

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osrg/ryu

ryu/lib/stplib.py

Port.transmit_ack_bpdu

def transmit_ack_bpdu(self): """ Send Topology Change Ack BPDU. """ ack_flags = 0b10000001 bpdu_data = self._generate_config_bpdu(ack_flags) self.ofctl.send_packet_out(self.ofport.port_no, bpdu_data)

python

def transmit_ack_bpdu(self): """ Send Topology Change Ack BPDU. """ ack_flags = 0b10000001 bpdu_data = self._generate_config_bpdu(ack_flags) self.ofctl.send_packet_out(self.ofport.port_no, bpdu_data)

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Send Topology Change Ack BPDU.

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markchil/gptools

gptools/kernel/warping.py

WarpedKernel.free_params

def free_params(self, value): """Set the free parameters. Note that this bypasses enforce_bounds. """ value = scipy.asarray(value, dtype=float) self.K_up_to_date = False self.k.free_params = value[:self.k.num_free_params] self.w.free_params = value[self.k.num_free_params:self.k.num_free_params + self.w.num_free_params]

python

def free_params(self, value): """Set the free parameters. Note that this bypasses enforce_bounds. """ value = scipy.asarray(value, dtype=float) self.K_up_to_date = False self.k.free_params = value[:self.k.num_free_params] self.w.free_params = value[self.k.num_free_params:self.k.num_free_params + self.w.num_free_params]

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Set the free parameters. Note that this bypasses enforce_bounds.

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train

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lra/mackup

mackup/application.py

ApplicationProfile.backup

def backup(self): """ Backup the application config files. Algorithm: if exists home/file if home/file is a real file if exists mackup/file are you sure ? if sure rm mackup/file mv home/file mackup/file link mackup/file home/file else mv home/file mackup/file link mackup/file home/file """ # For each file used by the application for filename in self.files: (home_filepath, mackup_filepath) = self.getFilepaths(filename) # If the file exists and is not already a link pointing to Mackup if ((os.path.isfile(home_filepath) or os.path.isdir(home_filepath)) and not (os.path.islink(home_filepath) and (os.path.isfile(mackup_filepath) or os.path.isdir(mackup_filepath)) and os.path.samefile(home_filepath, mackup_filepath))): if self.verbose: print("Backing up\n {}\n to\n {} ..." .format(home_filepath, mackup_filepath)) else: print("Backing up {} ...".format(filename)) if self.dry_run: continue # Check if we already have a backup if os.path.exists(mackup_filepath): # Name it right if os.path.isfile(mackup_filepath): file_type = 'file' elif os.path.isdir(mackup_filepath): file_type = 'folder' elif os.path.islink(mackup_filepath): file_type = 'link' else: raise ValueError("Unsupported file: {}" .format(mackup_filepath)) # Ask the user if he really want to replace it if utils.confirm("A {} named {} already exists in the" " backup.\nAre you sure that you want to" " replace it ?" .format(file_type, mackup_filepath)): # Delete the file in Mackup utils.delete(mackup_filepath) # Copy the file utils.copy(home_filepath, mackup_filepath) # Delete the file in the home utils.delete(home_filepath) # Link the backuped file to its original place utils.link(mackup_filepath, home_filepath) else: # Copy the file utils.copy(home_filepath, mackup_filepath) # Delete the file in the home utils.delete(home_filepath) # Link the backuped file to its original place utils.link(mackup_filepath, home_filepath) elif self.verbose: if os.path.exists(home_filepath): print("Doing nothing\n {}\n " "is already backed up to\n {}" .format(home_filepath, mackup_filepath)) elif os.path.islink(home_filepath): print("Doing nothing\n {}\n " "is a broken link, you might want to fix it." .format(home_filepath)) else: print("Doing nothing\n {}\n does not exist" .format(home_filepath))

python

def backup(self): """ Backup the application config files. Algorithm: if exists home/file if home/file is a real file if exists mackup/file are you sure ? if sure rm mackup/file mv home/file mackup/file link mackup/file home/file else mv home/file mackup/file link mackup/file home/file """ # For each file used by the application for filename in self.files: (home_filepath, mackup_filepath) = self.getFilepaths(filename) # If the file exists and is not already a link pointing to Mackup if ((os.path.isfile(home_filepath) or os.path.isdir(home_filepath)) and not (os.path.islink(home_filepath) and (os.path.isfile(mackup_filepath) or os.path.isdir(mackup_filepath)) and os.path.samefile(home_filepath, mackup_filepath))): if self.verbose: print("Backing up\n {}\n to\n {} ..." .format(home_filepath, mackup_filepath)) else: print("Backing up {} ...".format(filename)) if self.dry_run: continue # Check if we already have a backup if os.path.exists(mackup_filepath): # Name it right if os.path.isfile(mackup_filepath): file_type = 'file' elif os.path.isdir(mackup_filepath): file_type = 'folder' elif os.path.islink(mackup_filepath): file_type = 'link' else: raise ValueError("Unsupported file: {}" .format(mackup_filepath)) # Ask the user if he really want to replace it if utils.confirm("A {} named {} already exists in the" " backup.\nAre you sure that you want to" " replace it ?" .format(file_type, mackup_filepath)): # Delete the file in Mackup utils.delete(mackup_filepath) # Copy the file utils.copy(home_filepath, mackup_filepath) # Delete the file in the home utils.delete(home_filepath) # Link the backuped file to its original place utils.link(mackup_filepath, home_filepath) else: # Copy the file utils.copy(home_filepath, mackup_filepath) # Delete the file in the home utils.delete(home_filepath) # Link the backuped file to its original place utils.link(mackup_filepath, home_filepath) elif self.verbose: if os.path.exists(home_filepath): print("Doing nothing\n {}\n " "is already backed up to\n {}" .format(home_filepath, mackup_filepath)) elif os.path.islink(home_filepath): print("Doing nothing\n {}\n " "is a broken link, you might want to fix it." .format(home_filepath)) else: print("Doing nothing\n {}\n does not exist" .format(home_filepath))

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Backup the application config files. Algorithm: if exists home/file if home/file is a real file if exists mackup/file are you sure ? if sure rm mackup/file mv home/file mackup/file link mackup/file home/file else mv home/file mackup/file link mackup/file home/file

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train

https://github.com/lra/mackup/blob/ed0b5626b033f232868900bfd5108df448873725/mackup/application.py#L46-L130

mozilla/DeepSpeech

bin/benchmark_nc.py

exec_command

def exec_command(command, cwd=None): r''' Helper to exec locally (subprocess) or remotely (paramiko) ''' rc = None stdout = stderr = None if ssh_conn is None: ld_library_path = {'LD_LIBRARY_PATH': '.:%s' % os.environ.get('LD_LIBRARY_PATH', '')} p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=ld_library_path, cwd=cwd) stdout, stderr = p.communicate() rc = p.returncode else: # environment= requires paramiko >= 2.1 (fails with 2.0.2) final_command = command if cwd is None else 'cd %s && %s %s' % (cwd, 'LD_LIBRARY_PATH=.:$LD_LIBRARY_PATH', command) ssh_stdin, ssh_stdout, ssh_stderr = ssh_conn.exec_command(final_command) stdout = ''.join(ssh_stdout.readlines()) stderr = ''.join(ssh_stderr.readlines()) rc = ssh_stdout.channel.recv_exit_status() return rc, stdout, stderr

python

def exec_command(command, cwd=None): r''' Helper to exec locally (subprocess) or remotely (paramiko) ''' rc = None stdout = stderr = None if ssh_conn is None: ld_library_path = {'LD_LIBRARY_PATH': '.:%s' % os.environ.get('LD_LIBRARY_PATH', '')} p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=ld_library_path, cwd=cwd) stdout, stderr = p.communicate() rc = p.returncode else: # environment= requires paramiko >= 2.1 (fails with 2.0.2) final_command = command if cwd is None else 'cd %s && %s %s' % (cwd, 'LD_LIBRARY_PATH=.:$LD_LIBRARY_PATH', command) ssh_stdin, ssh_stdout, ssh_stderr = ssh_conn.exec_command(final_command) stdout = ''.join(ssh_stdout.readlines()) stderr = ''.join(ssh_stderr.readlines()) rc = ssh_stdout.channel.recv_exit_status() return rc, stdout, stderr

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r''' Helper to exec locally (subprocess) or remotely (paramiko)

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train

https://github.com/mozilla/DeepSpeech/blob/f64aa73e7fbe9dde40d4fcf23b42ab304747d152/bin/benchmark_nc.py#L41-L61

doakey3/DashTable

dashtable/simple2data/simple2data.py

simple2data

def simple2data(text): """ Convert a simple table to data (the kind used by DashTable) Parameters ---------- text : str A valid simple rst table Returns ------- table : list of lists of str spans : list of lists of lists of int A span is a [row, column] pair that defines a group of merged cells in the table. In a simple rst table, spans can only be colspans. use_headers : bool Whether or not this table uses headers headers_row : int The row where headers are located Notes ----- This function requires docutils_. .. _docutils: http://docutils.sourceforge.net/ Example ------- >>> html_text = ''' ... ====== ===== ====== ... Inputs Output ... ------------- ------ ... A B A or B ... ====== ===== ====== ... False False False ... True False True ... False True True ... True True True ... ====== ===== ====== ... ''' >>> from dashtable import simple2data >>> table, spans, use_headers, headers_row = simple2data(html_text) >>> from pprint import pprint >>> pprint(table) [['Inputs', 'Output', ''], ['A', 'B', 'A or B'], ['False', 'False', 'False'], ['True, 'False', 'True'], ['False', 'True', 'True'], ['True', 'True', 'True']] >>> print(spans) [[[0, 0], [0, 1]]] >>> print(use_headers) True >>> print(headers_row) 1 """ try: import docutils.statemachine import docutils.parsers.rst.tableparser except ImportError: print("ERROR: You must install the docutils library to use simple2data") return lines = text.split('\n') lines = truncate_empty_lines(lines) leading_space = lines[0].replace(lines[0].lstrip(), '') for i in range(len(lines)): lines[i] = lines[i][len(leading_space)::] parser = docutils.parsers.rst.tableparser.SimpleTableParser() block = docutils.statemachine.StringList(list(lines)) simple_data = list(parser.parse(block)) column_widths = simple_data.pop(0) column_count = len(column_widths) headers_row = 0 if len(simple_data[0]) > 0: use_headers = True headers_row = len(simple_data[0]) - 1 headers = simple_data[0][0] row_count = len(simple_data[1]) + len(simple_data[0]) while len(simple_data[0]) > 0: simple_data[1].insert(0, simple_data[0][-1]) simple_data[0].pop(-1) simple_data.pop(0) else: use_headers = False simple_data.pop(0) row_count = len(simple_data[0]) simple_data = simple_data[0] table = make_empty_table(row_count, column_count) spans = [] for row in range(len(simple_data)): for column in range(len(simple_data[row])): try: text = '\n'.join(simple_data[row][column][3]).rstrip() table[row][column] = text extra_rows = simple_data[row][column][0] extra_columns = simple_data[row][column][1] span = make_span(row, column, extra_rows, extra_columns) span = sorted(span) span = list(span for span,_ in itertools.groupby(span)) if not len(span) == 1: spans.append(span) except TypeError: pass spans = sorted(spans) return table, spans, use_headers, headers_row

python

def simple2data(text): """ Convert a simple table to data (the kind used by DashTable) Parameters ---------- text : str A valid simple rst table Returns ------- table : list of lists of str spans : list of lists of lists of int A span is a [row, column] pair that defines a group of merged cells in the table. In a simple rst table, spans can only be colspans. use_headers : bool Whether or not this table uses headers headers_row : int The row where headers are located Notes ----- This function requires docutils_. .. _docutils: http://docutils.sourceforge.net/ Example ------- >>> html_text = ''' ... ====== ===== ====== ... Inputs Output ... ------------- ------ ... A B A or B ... ====== ===== ====== ... False False False ... True False True ... False True True ... True True True ... ====== ===== ====== ... ''' >>> from dashtable import simple2data >>> table, spans, use_headers, headers_row = simple2data(html_text) >>> from pprint import pprint >>> pprint(table) [['Inputs', 'Output', ''], ['A', 'B', 'A or B'], ['False', 'False', 'False'], ['True, 'False', 'True'], ['False', 'True', 'True'], ['True', 'True', 'True']] >>> print(spans) [[[0, 0], [0, 1]]] >>> print(use_headers) True >>> print(headers_row) 1 """ try: import docutils.statemachine import docutils.parsers.rst.tableparser except ImportError: print("ERROR: You must install the docutils library to use simple2data") return lines = text.split('\n') lines = truncate_empty_lines(lines) leading_space = lines[0].replace(lines[0].lstrip(), '') for i in range(len(lines)): lines[i] = lines[i][len(leading_space)::] parser = docutils.parsers.rst.tableparser.SimpleTableParser() block = docutils.statemachine.StringList(list(lines)) simple_data = list(parser.parse(block)) column_widths = simple_data.pop(0) column_count = len(column_widths) headers_row = 0 if len(simple_data[0]) > 0: use_headers = True headers_row = len(simple_data[0]) - 1 headers = simple_data[0][0] row_count = len(simple_data[1]) + len(simple_data[0]) while len(simple_data[0]) > 0: simple_data[1].insert(0, simple_data[0][-1]) simple_data[0].pop(-1) simple_data.pop(0) else: use_headers = False simple_data.pop(0) row_count = len(simple_data[0]) simple_data = simple_data[0] table = make_empty_table(row_count, column_count) spans = [] for row in range(len(simple_data)): for column in range(len(simple_data[row])): try: text = '\n'.join(simple_data[row][column][3]).rstrip() table[row][column] = text extra_rows = simple_data[row][column][0] extra_columns = simple_data[row][column][1] span = make_span(row, column, extra_rows, extra_columns) span = sorted(span) span = list(span for span,_ in itertools.groupby(span)) if not len(span) == 1: spans.append(span) except TypeError: pass spans = sorted(spans) return table, spans, use_headers, headers_row

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Convert a simple table to data (the kind used by DashTable) Parameters ---------- text : str A valid simple rst table Returns ------- table : list of lists of str spans : list of lists of lists of int A span is a [row, column] pair that defines a group of merged cells in the table. In a simple rst table, spans can only be colspans. use_headers : bool Whether or not this table uses headers headers_row : int The row where headers are located Notes ----- This function requires docutils_. .. _docutils: http://docutils.sourceforge.net/ Example ------- >>> html_text = ''' ... ====== ===== ====== ... Inputs Output ... ------------- ------ ... A B A or B ... ====== ===== ====== ... False False False ... True False True ... False True True ... True True True ... ====== ===== ====== ... ''' >>> from dashtable import simple2data >>> table, spans, use_headers, headers_row = simple2data(html_text) >>> from pprint import pprint >>> pprint(table) [['Inputs', 'Output', ''], ['A', 'B', 'A or B'], ['False', 'False', 'False'], ['True, 'False', 'True'], ['False', 'True', 'True'], ['True', 'True', 'True']] >>> print(spans) [[[0, 0], [0, 1]]] >>> print(use_headers) True >>> print(headers_row) 1

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train

https://github.com/doakey3/DashTable/blob/744cfb6a717fa75a8092c83ebcd49b2668023681/dashtable/simple2data/simple2data.py#L9-L121

HPAC/matchpy

matchpy/expressions/substitution.py

Substitution.rename

def rename(self, renaming: Dict[str, str]) -> 'Substitution': """Return a copy of the substitution with renamed variables. Example: Rename the variable *x* to *y*: >>> subst = Substitution({'x': a}) >>> subst.rename({'x': 'y'}) {'y': Symbol('a')} Args: renaming: A dictionary mapping old variable names to new ones. Returns: A copy of the substitution where variable names have been replaced according to the given renaming dictionary. Names that are not contained in the dictionary are left unchanged. """ return Substitution((renaming.get(name, name), value) for name, value in self.items())

python

def rename(self, renaming: Dict[str, str]) -> 'Substitution': """Return a copy of the substitution with renamed variables. Example: Rename the variable *x* to *y*: >>> subst = Substitution({'x': a}) >>> subst.rename({'x': 'y'}) {'y': Symbol('a')} Args: renaming: A dictionary mapping old variable names to new ones. Returns: A copy of the substitution where variable names have been replaced according to the given renaming dictionary. Names that are not contained in the dictionary are left unchanged. """ return Substitution((renaming.get(name, name), value) for name, value in self.items())

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train

https://github.com/HPAC/matchpy/blob/06b2ec50ee0efdf3dd183768c0ffdb51b7efc393/matchpy/expressions/substitution.py#L199-L218

jxtech/wechatpy

wechatpy/client/api/message.py

WeChatMessage.send_mass_video

def send_mass_video(self, group_or_users, media_id, title=None, description=None, is_to_all=False, preview=False, send_ignore_reprint=0, client_msg_id=None): """ 群发视频消息 详情请参考 https://mp.weixin.qq.com/wiki?id=mp1481187827_i0l21 :param group_or_users: 值为整型数字时为按分组群发，值为列表/元组时为按 OpenID 列表群发 当 is_to_all 为 True 时，传入 None 即对所有用户发送。 :param media_id: 视频的媒体 ID。可以通过 :func:`upload_video` 上传。 :param title: 视频标题 :param description: 视频描述 :param is_to_all: 用于设定是否向全部用户发送，值为true或false，选择true该消息群发给所有用户 选择false可根据group_id发送给指定群组的用户 :type is_to_all: bool :param preview: 是否发送预览，此时 group_or_users 参数应为一个openid字符串 :type preview: bool :param send_ignore_reprint: 指定待群发的文章被判定为转载时，是否继续群发。 当 send_ignore_reprint 参数设置为1时，文章被判定为转载时，且原创文允许转载时，将继续进行群发操作。 当 send_ignore_reprint 参数设置为0时，文章被判定为转载时，将停止群发操作。 send_ignore_reprint 默认为0。 :type send_ignore_reprint: int :param client_msg_id: 开发者侧群发 msgid，长度限制 64 字节 :type client_msg_id: str :return: 返回的 JSON 数据包 """ video_data = { 'media_id': media_id } if title: video_data['title'] = title if description: video_data['description'] = description return self._send_mass_message( group_or_users, 'mpvideo', { 'mpvideo': video_data }, is_to_all, preview, send_ignore_reprint, client_msg_id, )

python

def send_mass_video(self, group_or_users, media_id, title=None, description=None, is_to_all=False, preview=False, send_ignore_reprint=0, client_msg_id=None): """ 群发视频消息 详情请参考 https://mp.weixin.qq.com/wiki?id=mp1481187827_i0l21 :param group_or_users: 值为整型数字时为按分组群发，值为列表/元组时为按 OpenID 列表群发 当 is_to_all 为 True 时，传入 None 即对所有用户发送。 :param media_id: 视频的媒体 ID。可以通过 :func:`upload_video` 上传。 :param title: 视频标题 :param description: 视频描述 :param is_to_all: 用于设定是否向全部用户发送，值为true或false，选择true该消息群发给所有用户 选择false可根据group_id发送给指定群组的用户 :type is_to_all: bool :param preview: 是否发送预览，此时 group_or_users 参数应为一个openid字符串 :type preview: bool :param send_ignore_reprint: 指定待群发的文章被判定为转载时，是否继续群发。 当 send_ignore_reprint 参数设置为1时，文章被判定为转载时，且原创文允许转载时，将继续进行群发操作。 当 send_ignore_reprint 参数设置为0时，文章被判定为转载时，将停止群发操作。 send_ignore_reprint 默认为0。 :type send_ignore_reprint: int :param client_msg_id: 开发者侧群发 msgid，长度限制 64 字节 :type client_msg_id: str :return: 返回的 JSON 数据包 """ video_data = { 'media_id': media_id } if title: video_data['title'] = title if description: video_data['description'] = description return self._send_mass_message( group_or_users, 'mpvideo', { 'mpvideo': video_data }, is_to_all, preview, send_ignore_reprint, client_msg_id, )

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train

https://github.com/jxtech/wechatpy/blob/4df0da795618c0895a10f1c2cde9e9d5c0a93aaa/wechatpy/client/api/message.py#L450-L496

BerkeleyAutomation/autolab_core

autolab_core/points.py

NormalCloud._check_valid_data

def _check_valid_data(self, data): """Checks that the incoming data is a 3 x #elements ndarray of normal vectors. Parameters ---------- data : :obj:`numpy.ndarray` The data to verify. Raises ------ ValueError If the data is not of the correct shape or type, or if the vectors therein are not normalized. """ if data.dtype.type != np.float32 and data.dtype.type != np.float64: raise ValueError('Must initialize normals clouds with a numpy float ndarray') if data.shape[0] != 3: raise ValueError('Illegal data array passed to normal cloud. Must have 3 coordinates') if len(data.shape) > 2: raise ValueError('Illegal data array passed to normal cloud. Must have 1 or 2 dimensions') if np.any((np.abs(np.linalg.norm(data, axis=0) - 1) > 1e-4) & (np.linalg.norm(data, axis=0) != 0)): raise ValueError('Illegal data array passed to normal cloud. Must have norm=1.0 or norm=0.0')

python

def _check_valid_data(self, data): """Checks that the incoming data is a 3 x #elements ndarray of normal vectors. Parameters ---------- data : :obj:`numpy.ndarray` The data to verify. Raises ------ ValueError If the data is not of the correct shape or type, or if the vectors therein are not normalized. """ if data.dtype.type != np.float32 and data.dtype.type != np.float64: raise ValueError('Must initialize normals clouds with a numpy float ndarray') if data.shape[0] != 3: raise ValueError('Illegal data array passed to normal cloud. Must have 3 coordinates') if len(data.shape) > 2: raise ValueError('Illegal data array passed to normal cloud. Must have 1 or 2 dimensions') if np.any((np.abs(np.linalg.norm(data, axis=0) - 1) > 1e-4) & (np.linalg.norm(data, axis=0) != 0)): raise ValueError('Illegal data array passed to normal cloud. Must have norm=1.0 or norm=0.0')

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train

https://github.com/BerkeleyAutomation/autolab_core/blob/8f3813f6401972868cc5e3981ba1b4382d4418d5/autolab_core/points.py#L852-L874

alvations/pywsd

pywsd/similarity.py

sim

def sim(sense1: "wn.Synset", sense2: "wn.Synset", option: str = "path") -> float: """ Calculates similarity based on user's choice. :param sense1: A synset. :param sense2: A synset. :param option: String, one of ('path', 'wup', 'lch', 'res', 'jcn', 'lin'). :return: A float, similarity measurement. """ option = option.lower() if option.lower() in ["path", "path_similarity", "wup", "wupa", "wu-palmer", "wu-palmer", 'lch', "leacock-chordorow"]: return similarity_by_path(sense1, sense2, option) elif option.lower() in ["res", "resnik", "jcn","jiang-conrath", "lin"]: return similarity_by_infocontent(sense1, sense2, option)

python

def sim(sense1: "wn.Synset", sense2: "wn.Synset", option: str = "path") -> float: """ Calculates similarity based on user's choice. :param sense1: A synset. :param sense2: A synset. :param option: String, one of ('path', 'wup', 'lch', 'res', 'jcn', 'lin'). :return: A float, similarity measurement. """ option = option.lower() if option.lower() in ["path", "path_similarity", "wup", "wupa", "wu-palmer", "wu-palmer", 'lch', "leacock-chordorow"]: return similarity_by_path(sense1, sense2, option) elif option.lower() in ["res", "resnik", "jcn","jiang-conrath", "lin"]: return similarity_by_infocontent(sense1, sense2, option)

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train

https://github.com/alvations/pywsd/blob/4c12394c8adbcfed71dd912bdbef2e36370821bf/pywsd/similarity.py#L73-L90

senaite/senaite.jsonapi

src/senaite/jsonapi/fieldmanagers.py

DateTimeFieldManager.set

def set(self, instance, value, **kw): """Converts the value into a DateTime object before setting. """ if value: try: value = DateTime(value) except SyntaxError: logger.warn("Value '{}' is not a valid DateTime string" .format(value)) return False self._set(instance, value, **kw)

python

def set(self, instance, value, **kw): """Converts the value into a DateTime object before setting. """ if value: try: value = DateTime(value) except SyntaxError: logger.warn("Value '{}' is not a valid DateTime string" .format(value)) return False self._set(instance, value, **kw)

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train

https://github.com/senaite/senaite.jsonapi/blob/871959f4b1c9edbb477e9456325527ca78e13ec6/src/senaite/jsonapi/fieldmanagers.py#L326-L337

wummel/linkchecker

scripts/update_iana_uri_schemes.py

parse_csv_file

def parse_csv_file(url, res): """Parse given URL and write res with {scheme -> description}""" response = requests.get(url, stream=True) reader = csv.reader(response.iter_lines()) first_row = True for row in reader: if first_row: # skip first row first_row = False else: scheme, template, description, reference = row res[scheme] = description

python

def parse_csv_file(url, res): """Parse given URL and write res with {scheme -> description}""" response = requests.get(url, stream=True) reader = csv.reader(response.iter_lines()) first_row = True for row in reader: if first_row: # skip first row first_row = False else: scheme, template, description, reference = row res[scheme] = description

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train

https://github.com/wummel/linkchecker/blob/c2ce810c3fb00b895a841a7be6b2e78c64e7b042/scripts/update_iana_uri_schemes.py#L91-L102

apache/incubator-mxnet

python/mxnet/ndarray/ndarray.py

NDArray._at

def _at(self, idx): """Returns a view of the array sliced at `idx` in the first dim. This is called through ``x[idx]``. Parameters ---------- idx : int index for slicing the `NDArray` in the first dim. Returns ------- NDArray `NDArray` sharing the memory with the current one sliced at `idx` in the first dim. Examples -------- >>> a = mx.nd.array([[1,2], [3, 4]]) >>> a[1].asnumpy() array([ 3., 4.], dtype=float32) >>> b = mx.nd.array([1, 2, 3, 4]) >>> b[0].asnumpy() array([ 1.], dtype=float32) """ handle = NDArrayHandle() if idx < 0: length = self.shape[0] idx += length if idx < 0: raise IndexError('index %d is out of bounds for axis 0 with size %d' % (idx-length, length)) check_call(_LIB.MXNDArrayAt( self.handle, mx_uint(idx), ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable)

python

def _at(self, idx): """Returns a view of the array sliced at `idx` in the first dim. This is called through ``x[idx]``. Parameters ---------- idx : int index for slicing the `NDArray` in the first dim. Returns ------- NDArray `NDArray` sharing the memory with the current one sliced at `idx` in the first dim. Examples -------- >>> a = mx.nd.array([[1,2], [3, 4]]) >>> a[1].asnumpy() array([ 3., 4.], dtype=float32) >>> b = mx.nd.array([1, 2, 3, 4]) >>> b[0].asnumpy() array([ 1.], dtype=float32) """ handle = NDArrayHandle() if idx < 0: length = self.shape[0] idx += length if idx < 0: raise IndexError('index %d is out of bounds for axis 0 with size %d' % (idx-length, length)) check_call(_LIB.MXNDArrayAt( self.handle, mx_uint(idx), ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable)

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train

https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L912-L944

reiinakano/xcessiv

xcessiv/functions.py

import_object_from_path

def import_object_from_path(path, object): """Used to import an object from an absolute path. This function takes an absolute path and imports it as a Python module. It then returns the object with name `object` from the imported module. Args: path (string): Absolute file path of .py file to import object (string): Name of object to extract from imported module """ with open(path) as f: return import_object_from_string_code(f.read(), object)

python

def import_object_from_path(path, object): """Used to import an object from an absolute path. This function takes an absolute path and imports it as a Python module. It then returns the object with name `object` from the imported module. Args: path (string): Absolute file path of .py file to import object (string): Name of object to extract from imported module """ with open(path) as f: return import_object_from_string_code(f.read(), object)

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train

https://github.com/reiinakano/xcessiv/blob/a48dff7d370c84eb5c243bde87164c1f5fd096d5/xcessiv/functions.py#L36-L48

pbrisk/unicum

unicum/linkedobject.py

LinkedObject.register_link

def register_link(self, obj, attr=None): """ creates link from obj.attr to self :param obj: object to register link to :param attr: attribute name to register link to """ name = repr(self) if not name: return self l = self.__class__._get_links() if name not in l: l[name] = set() v = WeakAttrLink(obj, attr) if v not in l[name]: l[name].add(v) return self

python

def register_link(self, obj, attr=None): """ creates link from obj.attr to self :param obj: object to register link to :param attr: attribute name to register link to """ name = repr(self) if not name: return self l = self.__class__._get_links() if name not in l: l[name] = set() v = WeakAttrLink(obj, attr) if v not in l[name]: l[name].add(v) return self

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train

https://github.com/pbrisk/unicum/blob/24bfa7355f36847a06646c58e9fd75bd3b689bfe/unicum/linkedobject.py#L80-L95

miku/gluish

gluish/format.py

iter_tsv

def iter_tsv(input_stream, cols=None, encoding='utf-8'): """ If a tuple is given in cols, use the elements as names to construct a namedtuple. Columns can be marked as ignored by using ``X`` or ``0`` as column name. Example (ignore the first four columns of a five column TSV): :: def run(self): with self.input().open() as handle: for row in handle.iter_tsv(cols=('X', 'X', 'X', 'X', 'iln')): print(row.iln) """ if cols: cols = [c if not c in ('x', 'X', 0, None) else random_string(length=5) for c in cols] Record = collections.namedtuple('Record', cols) for line in input_stream: yield Record._make(line.decode(encoding).rstrip('\n').split('\t')) else: for line in input_stream: yield tuple(line.decode(encoding).rstrip('\n').split('\t'))

python

def iter_tsv(input_stream, cols=None, encoding='utf-8'): """ If a tuple is given in cols, use the elements as names to construct a namedtuple. Columns can be marked as ignored by using ``X`` or ``0`` as column name. Example (ignore the first four columns of a five column TSV): :: def run(self): with self.input().open() as handle: for row in handle.iter_tsv(cols=('X', 'X', 'X', 'X', 'iln')): print(row.iln) """ if cols: cols = [c if not c in ('x', 'X', 0, None) else random_string(length=5) for c in cols] Record = collections.namedtuple('Record', cols) for line in input_stream: yield Record._make(line.decode(encoding).rstrip('\n').split('\t')) else: for line in input_stream: yield tuple(line.decode(encoding).rstrip('\n').split('\t'))

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If a tuple is given in cols, use the elements as names to construct a namedtuple. Columns can be marked as ignored by using ``X`` or ``0`` as column name. Example (ignore the first four columns of a five column TSV): :: def run(self): with self.input().open() as handle: for row in handle.iter_tsv(cols=('X', 'X', 'X', 'X', 'iln')): print(row.iln)

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train

https://github.com/miku/gluish/blob/56d3ac4f41a944e31ecac0aa3b6d1dc2ce705e29/gluish/format.py#L65-L89

ngmarchant/oasis

oasis/passive.py

PassiveSampler.reset

def reset(self): """Resets the sampler to its initial state Note ---- This will destroy the label cache and history of estimates. """ self._TP = np.zeros(self._n_class) self._FP = np.zeros(self._n_class) self._FN = np.zeros(self._n_class) self._TN = np.zeros(self._n_class) self.t_ = 0 self._queried_oracle = np.repeat(False, self._max_iter) self.cached_labels_ = np.repeat(np.nan, self._n_items) self._estimate = np.tile(np.nan, [self._max_iter, self._n_class])

python

def reset(self): """Resets the sampler to its initial state Note ---- This will destroy the label cache and history of estimates. """ self._TP = np.zeros(self._n_class) self._FP = np.zeros(self._n_class) self._FN = np.zeros(self._n_class) self._TN = np.zeros(self._n_class) self.t_ = 0 self._queried_oracle = np.repeat(False, self._max_iter) self.cached_labels_ = np.repeat(np.nan, self._n_items) self._estimate = np.tile(np.nan, [self._max_iter, self._n_class])

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Resets the sampler to its initial state Note ---- This will destroy the label cache and history of estimates.

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train

https://github.com/ngmarchant/oasis/blob/28a037a8924b85ae97db8a93960a910a219d6a4a/oasis/passive.py#L141-L155

petl-developers/petl

petl/transform/setops.py

recorddiff

def recorddiff(a, b, buffersize=None, tempdir=None, cache=True, strict=False): """ Find the difference between records in two tables. E.g.:: >>> import petl as etl >>> a = [['foo', 'bar', 'baz'], ... ['A', 1, True], ... ['C', 7, False], ... ['B', 2, False], ... ['C', 9, True]] >>> b = [['bar', 'foo', 'baz'], ... [2, 'B', False], ... [9, 'A', False], ... [3, 'B', True], ... [9, 'C', True]] >>> added, subtracted = etl.recorddiff(a, b) >>> added +-----+-----+-------+ | bar | foo | baz | +=====+=====+=======+ | 3 | 'B' | True | +-----+-----+-------+ | 9 | 'A' | False | +-----+-----+-------+ >>> subtracted +-----+-----+-------+ | foo | bar | baz | +=====+=====+=======+ | 'A' | 1 | True | +-----+-----+-------+ | 'C' | 7 | False | +-----+-----+-------+ Convenient shorthand for ``(recordcomplement(b, a), recordcomplement(a, b))``. See also :func:`petl.transform.setops.recordcomplement`. See also the discussion of the `buffersize`, `tempdir` and `cache` arguments under the :func:`petl.transform.sorts.sort` function. .. versionchanged:: 1.1.0 If `strict` is `True` then strict set-like behaviour is used. """ added = recordcomplement(b, a, buffersize=buffersize, tempdir=tempdir, cache=cache, strict=strict) subtracted = recordcomplement(a, b, buffersize=buffersize, tempdir=tempdir, cache=cache, strict=strict) return added, subtracted

python

def recorddiff(a, b, buffersize=None, tempdir=None, cache=True, strict=False): """ Find the difference between records in two tables. E.g.:: >>> import petl as etl >>> a = [['foo', 'bar', 'baz'], ... ['A', 1, True], ... ['C', 7, False], ... ['B', 2, False], ... ['C', 9, True]] >>> b = [['bar', 'foo', 'baz'], ... [2, 'B', False], ... [9, 'A', False], ... [3, 'B', True], ... [9, 'C', True]] >>> added, subtracted = etl.recorddiff(a, b) >>> added +-----+-----+-------+ | bar | foo | baz | +=====+=====+=======+ | 3 | 'B' | True | +-----+-----+-------+ | 9 | 'A' | False | +-----+-----+-------+ >>> subtracted +-----+-----+-------+ | foo | bar | baz | +=====+=====+=======+ | 'A' | 1 | True | +-----+-----+-------+ | 'C' | 7 | False | +-----+-----+-------+ Convenient shorthand for ``(recordcomplement(b, a), recordcomplement(a, b))``. See also :func:`petl.transform.setops.recordcomplement`. See also the discussion of the `buffersize`, `tempdir` and `cache` arguments under the :func:`petl.transform.sorts.sort` function. .. versionchanged:: 1.1.0 If `strict` is `True` then strict set-like behaviour is used. """ added = recordcomplement(b, a, buffersize=buffersize, tempdir=tempdir, cache=cache, strict=strict) subtracted = recordcomplement(a, b, buffersize=buffersize, tempdir=tempdir, cache=cache, strict=strict) return added, subtracted

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Find the difference between records in two tables. E.g.:: >>> import petl as etl >>> a = [['foo', 'bar', 'baz'], ... ['A', 1, True], ... ['C', 7, False], ... ['B', 2, False], ... ['C', 9, True]] >>> b = [['bar', 'foo', 'baz'], ... [2, 'B', False], ... [9, 'A', False], ... [3, 'B', True], ... [9, 'C', True]] >>> added, subtracted = etl.recorddiff(a, b) >>> added +-----+-----+-------+ | bar | foo | baz | +=====+=====+=======+ | 3 | 'B' | True | +-----+-----+-------+ | 9 | 'A' | False | +-----+-----+-------+ >>> subtracted +-----+-----+-------+ | foo | bar | baz | +=====+=====+=======+ | 'A' | 1 | True | +-----+-----+-------+ | 'C' | 7 | False | +-----+-----+-------+ Convenient shorthand for ``(recordcomplement(b, a), recordcomplement(a, b))``. See also :func:`petl.transform.setops.recordcomplement`. See also the discussion of the `buffersize`, `tempdir` and `cache` arguments under the :func:`petl.transform.sorts.sort` function. .. versionchanged:: 1.1.0 If `strict` is `True` then strict set-like behaviour is used.

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train

https://github.com/petl-developers/petl/blob/1d33ca055f7e04e0d28a772041c9fd30c8d415d6/petl/transform/setops.py#L293-L344

ZELLMECHANIK-DRESDEN/dclab

dclab/kde_methods.py

kde_none

def kde_none(events_x, events_y, xout=None, yout=None): """ No Kernel Density Estimation Parameters ---------- events_x, events_y: 1D ndarray The input points for kernel density estimation. Input is flattened automatically. xout, yout: ndarray The coordinates at which the KDE should be computed. If set to none, input coordinates are used. Returns ------- density: ndarray, same shape as `xout` The KDE for the points in (xout, yout) Notes ----- This method is a convenience method that always returns ones in the shape that the other methods in this module produce. """ valid_combi = ((xout is None and yout is None) or (xout is not None and yout is not None) ) if not valid_combi: raise ValueError("Both `xout` and `yout` must be (un)set.") if yout is None and yout is None: xout = events_x yout = events_y return np.ones(xout.shape)

python

def kde_none(events_x, events_y, xout=None, yout=None): """ No Kernel Density Estimation Parameters ---------- events_x, events_y: 1D ndarray The input points for kernel density estimation. Input is flattened automatically. xout, yout: ndarray The coordinates at which the KDE should be computed. If set to none, input coordinates are used. Returns ------- density: ndarray, same shape as `xout` The KDE for the points in (xout, yout) Notes ----- This method is a convenience method that always returns ones in the shape that the other methods in this module produce. """ valid_combi = ((xout is None and yout is None) or (xout is not None and yout is not None) ) if not valid_combi: raise ValueError("Both `xout` and `yout` must be (un)set.") if yout is None and yout is None: xout = events_x yout = events_y return np.ones(xout.shape)

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['No', 'Kernel', 'Density', 'Estimation']

train

https://github.com/ZELLMECHANIK-DRESDEN/dclab/blob/79002c4356e7020c2ba73ab0a3819c9abd4affec/dclab/kde_methods.py#L177-L209

saltstack/salt

salt/fileserver/s3fs.py

_get_cached_file_name

def _get_cached_file_name(bucket_name, saltenv, path): ''' Return the cached file name for a bucket path file ''' file_path = os.path.join(_get_cache_dir(), saltenv, bucket_name, path) # make sure bucket and saltenv directories exist if not os.path.exists(os.path.dirname(file_path)): os.makedirs(os.path.dirname(file_path)) return file_path

python

def _get_cached_file_name(bucket_name, saltenv, path): ''' Return the cached file name for a bucket path file ''' file_path = os.path.join(_get_cache_dir(), saltenv, bucket_name, path) # make sure bucket and saltenv directories exist if not os.path.exists(os.path.dirname(file_path)): os.makedirs(os.path.dirname(file_path)) return file_path

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train

https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/fileserver/s3fs.py#L380-L391

tanghaibao/goatools

goatools/godag_plot.py

GODagSmallPlot._get_item_str

def _get_item_str(self, res): """Return genes in any of these formats: 1. 19264, 17319, 12520, 12043, 74131, 22163, 12575 2. Ptprc, Mif, Cd81, Bcl2, Sash3, Tnfrsf4, Cdkn1a 3. 7: Ptprc, Mif, Cd81, Bcl2, Sash3... """ npl = self.pltvars.items_p_line # Number of items Per Line prt_items = sorted([self.__get_genestr(itemid) for itemid in res.study_items]) prt_multiline = [prt_items[i:i+npl] for i in range(0, len(prt_items), npl)] num_items = len(prt_items) if self.study_items_max is None: genestr = "\n".join([", ".join(str(e) for e in sublist) for sublist in prt_multiline]) return "{N}) {GENES}".format(N=num_items, GENES=genestr) else: if num_items <= self.study_items_max: strs = [", ".join(str(e) for e in sublist) for sublist in prt_multiline] genestr = "\n".join([", ".join(str(e) for e in sublist) for sublist in prt_multiline]) return genestr else: short_list = prt_items[:self.study_items_max] short_mult = [short_list[i:i+npl] for i in range(0, len(short_list), npl)] short_str = "\n".join([", ".join(str(e) for e in sublist) for sublist in short_mult]) return "".join(["{N} genes; ".format(N=num_items), short_str, "..."])

python

def _get_item_str(self, res): """Return genes in any of these formats: 1. 19264, 17319, 12520, 12043, 74131, 22163, 12575 2. Ptprc, Mif, Cd81, Bcl2, Sash3, Tnfrsf4, Cdkn1a 3. 7: Ptprc, Mif, Cd81, Bcl2, Sash3... """ npl = self.pltvars.items_p_line # Number of items Per Line prt_items = sorted([self.__get_genestr(itemid) for itemid in res.study_items]) prt_multiline = [prt_items[i:i+npl] for i in range(0, len(prt_items), npl)] num_items = len(prt_items) if self.study_items_max is None: genestr = "\n".join([", ".join(str(e) for e in sublist) for sublist in prt_multiline]) return "{N}) {GENES}".format(N=num_items, GENES=genestr) else: if num_items <= self.study_items_max: strs = [", ".join(str(e) for e in sublist) for sublist in prt_multiline] genestr = "\n".join([", ".join(str(e) for e in sublist) for sublist in prt_multiline]) return genestr else: short_list = prt_items[:self.study_items_max] short_mult = [short_list[i:i+npl] for i in range(0, len(short_list), npl)] short_str = "\n".join([", ".join(str(e) for e in sublist) for sublist in short_mult]) return "".join(["{N} genes; ".format(N=num_items), short_str, "..."])

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train

https://github.com/tanghaibao/goatools/blob/407682e573a108864a79031f8ca19ee3bf377626/goatools/godag_plot.py#L255-L277

atztogo/phonopy

phonopy/file_IO.py

write_force_constants_to_hdf5

def write_force_constants_to_hdf5(force_constants, filename='force_constants.hdf5', p2s_map=None, physical_unit=None, compression=None): """Write force constants in hdf5 format. Parameters ---------- force_constants: ndarray Force constants shape=(n_satom,n_satom,3,3) or (n_patom,n_satom,3,3) dtype=double filename: str Filename to be saved p2s_map: ndarray Primitive atom indices in supercell index system shape=(n_patom,) dtype=intc physical_unit : str, optional Physical unit used for force contants. Default is None. compression : str or int, optional h5py's lossless compression filters (e.g., "gzip", "lzf"). See the detail at docstring of h5py.Group.create_dataset. Default is None. """ try: import h5py except ImportError: raise ModuleNotFoundError("You need to install python-h5py.") with h5py.File(filename, 'w') as w: w.create_dataset('force_constants', data=force_constants, compression=compression) if p2s_map is not None: w.create_dataset('p2s_map', data=p2s_map) if physical_unit is not None: dset = w.create_dataset('physical_unit', (1,), dtype='S%d' % len(physical_unit)) dset[0] = np.string_(physical_unit)

python

def write_force_constants_to_hdf5(force_constants, filename='force_constants.hdf5', p2s_map=None, physical_unit=None, compression=None): """Write force constants in hdf5 format. Parameters ---------- force_constants: ndarray Force constants shape=(n_satom,n_satom,3,3) or (n_patom,n_satom,3,3) dtype=double filename: str Filename to be saved p2s_map: ndarray Primitive atom indices in supercell index system shape=(n_patom,) dtype=intc physical_unit : str, optional Physical unit used for force contants. Default is None. compression : str or int, optional h5py's lossless compression filters (e.g., "gzip", "lzf"). See the detail at docstring of h5py.Group.create_dataset. Default is None. """ try: import h5py except ImportError: raise ModuleNotFoundError("You need to install python-h5py.") with h5py.File(filename, 'w') as w: w.create_dataset('force_constants', data=force_constants, compression=compression) if p2s_map is not None: w.create_dataset('p2s_map', data=p2s_map) if physical_unit is not None: dset = w.create_dataset('physical_unit', (1,), dtype='S%d' % len(physical_unit)) dset[0] = np.string_(physical_unit)

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Write force constants in hdf5 format. Parameters ---------- force_constants: ndarray Force constants shape=(n_satom,n_satom,3,3) or (n_patom,n_satom,3,3) dtype=double filename: str Filename to be saved p2s_map: ndarray Primitive atom indices in supercell index system shape=(n_patom,) dtype=intc physical_unit : str, optional Physical unit used for force contants. Default is None. compression : str or int, optional h5py's lossless compression filters (e.g., "gzip", "lzf"). See the detail at docstring of h5py.Group.create_dataset. Default is None.

['Write', 'force', 'constants', 'in', 'hdf5', 'format', '.']

train

https://github.com/atztogo/phonopy/blob/869cc2ba9e7d495d5f4cf6942415ab3fc9e2a10f/phonopy/file_IO.py#L285-L326

DLR-RM/RAFCON

source/rafcon/core/execution/execution_engine.py

ExecutionEngine.step_into

def step_into(self): """Take a forward step (into) for all active states in the state machine """ logger.debug("Execution step into ...") self.run_to_states = [] if self.finished_or_stopped(): self.set_execution_mode(StateMachineExecutionStatus.FORWARD_INTO) self._run_active_state_machine() else: self.set_execution_mode(StateMachineExecutionStatus.FORWARD_INTO)

python

def step_into(self): """Take a forward step (into) for all active states in the state machine """ logger.debug("Execution step into ...") self.run_to_states = [] if self.finished_or_stopped(): self.set_execution_mode(StateMachineExecutionStatus.FORWARD_INTO) self._run_active_state_machine() else: self.set_execution_mode(StateMachineExecutionStatus.FORWARD_INTO)

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train

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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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robertmartin8/PyPortfolioOpt

pypfopt/objective_functions.py

negative_sharpe

def negative_sharpe( weights, expected_returns, cov_matrix, gamma=0, risk_free_rate=0.02 ): """ Calculate the negative Sharpe ratio of a portfolio :param weights: asset weights of the portfolio :type weights: np.ndarray :param expected_returns: expected return of each asset :type expected_returns: pd.Series :param cov_matrix: the covariance matrix of asset returns :type cov_matrix: pd.DataFrame :param gamma: L2 regularisation parameter, defaults to 0. Increase if you want more non-negligible weights :type gamma: float, optional :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02 :type risk_free_rate: float, optional :return: negative Sharpe ratio :rtype: float """ mu = weights.dot(expected_returns) sigma = np.sqrt(np.dot(weights, np.dot(cov_matrix, weights.T))) L2_reg = gamma * (weights ** 2).sum() return -(mu - risk_free_rate) / sigma + L2_reg

python

def negative_sharpe( weights, expected_returns, cov_matrix, gamma=0, risk_free_rate=0.02 ): """ Calculate the negative Sharpe ratio of a portfolio :param weights: asset weights of the portfolio :type weights: np.ndarray :param expected_returns: expected return of each asset :type expected_returns: pd.Series :param cov_matrix: the covariance matrix of asset returns :type cov_matrix: pd.DataFrame :param gamma: L2 regularisation parameter, defaults to 0. Increase if you want more non-negligible weights :type gamma: float, optional :param risk_free_rate: risk-free rate of borrowing/lending, defaults to 0.02 :type risk_free_rate: float, optional :return: negative Sharpe ratio :rtype: float """ mu = weights.dot(expected_returns) sigma = np.sqrt(np.dot(weights, np.dot(cov_matrix, weights.T))) L2_reg = gamma * (weights ** 2).sum() return -(mu - risk_free_rate) / sigma + L2_reg

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census-instrumentation/opencensus-python

opencensus/trace/span.py

Span.add_time_event

def add_time_event(self, time_event): """Add a TimeEvent. :type time_event: :class: `~opencensus.trace.time_event.TimeEvent` :param time_event: A TimeEvent object. """ if isinstance(time_event, time_event_module.TimeEvent): self.time_events.append(time_event) else: raise TypeError("Type Error: received {}, but requires TimeEvent.". format(type(time_event).__name__))

python

def add_time_event(self, time_event): """Add a TimeEvent. :type time_event: :class: `~opencensus.trace.time_event.TimeEvent` :param time_event: A TimeEvent object. """ if isinstance(time_event, time_event_module.TimeEvent): self.time_events.append(time_event) else: raise TypeError("Type Error: received {}, but requires TimeEvent.". format(type(time_event).__name__))

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richardcornish/django-applepodcast

podcast/templatetags/podcast_tags.py

show_url

def show_url(context, **kwargs): """Return the show feed URL with different protocol.""" if len(kwargs) != 2: raise TemplateSyntaxError(_('"show_url" tag takes exactly two keyword arguments.')) request = context['request'] current_site = get_current_site(request) url = add_domain(current_site.domain, kwargs['url']) return re.sub(r'https?:\/\/', '%s://' % kwargs['protocol'], url)

python

def show_url(context, **kwargs): """Return the show feed URL with different protocol.""" if len(kwargs) != 2: raise TemplateSyntaxError(_('"show_url" tag takes exactly two keyword arguments.')) request = context['request'] current_site = get_current_site(request) url = add_domain(current_site.domain, kwargs['url']) return re.sub(r'https?:\/\/', '%s://' % kwargs['protocol'], url)

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mcieslik-mctp/papy

src/papy/core.py

Plumber.stop

def stop(self): """ Stops a paused pipeline. This will a trigger a ``StopIteration`` in the inputs of the pipeline. And retrieve the buffered results. This will stop all ``Pipers`` and ``NuMaps``. Python will not terminate cleanly if a pipeline is running or paused. """ if self._started.isSet() and \ not self._running.isSet() and \ not self._pausing.isSet(): # stops the dagger super(Plumber, self).stop() # disconnects all pipers self.disconnect() self.stats['run_time'] = time() - self.stats['start_time'] self._started.clear() else: raise PlumberError

python

def stop(self): """ Stops a paused pipeline. This will a trigger a ``StopIteration`` in the inputs of the pipeline. And retrieve the buffered results. This will stop all ``Pipers`` and ``NuMaps``. Python will not terminate cleanly if a pipeline is running or paused. """ if self._started.isSet() and \ not self._running.isSet() and \ not self._pausing.isSet(): # stops the dagger super(Plumber, self).stop() # disconnects all pipers self.disconnect() self.stats['run_time'] = time() - self.stats['start_time'] self._started.clear() else: raise PlumberError

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LogicalDash/LiSE

ELiDE/ELiDE/app.py

ELiDEApp.build_config

def build_config(self, config): """Set config defaults""" for sec in 'LiSE', 'ELiDE': config.adddefaultsection(sec) config.setdefaults( 'LiSE', { 'world': 'sqlite:///LiSEworld.db', 'language': 'eng', 'logfile': '', 'loglevel': 'info' } ) config.setdefaults( 'ELiDE', { 'boardchar': 'physical', 'debugger': 'no', 'inspector': 'no', 'user_kv': 'yes', 'play_speed': '1', 'thing_graphics': json.dumps([ ("Marsh Davies' Island", 'marsh_davies_island_fg.atlas'), ('RLTiles: Body', 'base.atlas'), ('RLTiles: Basic clothes', 'body.atlas'), ('RLTiles: Armwear', 'arm.atlas'), ('RLTiles: Legwear', 'leg.atlas'), ('RLTiles: Right hand', 'hand1.atlas'), ('RLTiles: Left hand', 'hand2.atlas'), ('RLTiles: Boots', 'boot.atlas'), ('RLTiles: Hair', 'hair.atlas'), ('RLTiles: Beard', 'beard.atlas'), ('RLTiles: Headwear', 'head.atlas') ]), 'place_graphics': json.dumps([ ("Marsh Davies' Island", 'marsh_davies_island_bg.atlas'), ("Marsh Davies' Crypt", 'marsh_davies_crypt.atlas'), ('RLTiles: Dungeon', 'dungeon.atlas') ]) } ) config.write()

python

def build_config(self, config): """Set config defaults""" for sec in 'LiSE', 'ELiDE': config.adddefaultsection(sec) config.setdefaults( 'LiSE', { 'world': 'sqlite:///LiSEworld.db', 'language': 'eng', 'logfile': '', 'loglevel': 'info' } ) config.setdefaults( 'ELiDE', { 'boardchar': 'physical', 'debugger': 'no', 'inspector': 'no', 'user_kv': 'yes', 'play_speed': '1', 'thing_graphics': json.dumps([ ("Marsh Davies' Island", 'marsh_davies_island_fg.atlas'), ('RLTiles: Body', 'base.atlas'), ('RLTiles: Basic clothes', 'body.atlas'), ('RLTiles: Armwear', 'arm.atlas'), ('RLTiles: Legwear', 'leg.atlas'), ('RLTiles: Right hand', 'hand1.atlas'), ('RLTiles: Left hand', 'hand2.atlas'), ('RLTiles: Boots', 'boot.atlas'), ('RLTiles: Hair', 'hair.atlas'), ('RLTiles: Beard', 'beard.atlas'), ('RLTiles: Headwear', 'head.atlas') ]), 'place_graphics': json.dumps([ ("Marsh Davies' Island", 'marsh_davies_island_bg.atlas'), ("Marsh Davies' Crypt", 'marsh_davies_crypt.atlas'), ('RLTiles: Dungeon', 'dungeon.atlas') ]) } ) config.write()

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train

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ssalentin/plip

plip/modules/supplemental.py

ring_is_planar

def ring_is_planar(ring, r_atoms): """Given a set of ring atoms, check if the ring is sufficiently planar to be considered aromatic""" normals = [] for a in r_atoms: adj = pybel.ob.OBAtomAtomIter(a.OBAtom) # Check for neighboring atoms in the ring n_coords = [pybel.Atom(neigh).coords for neigh in adj if ring.IsMember(neigh)] vec1, vec2 = vector(a.coords, n_coords[0]), vector(a.coords, n_coords[1]) normals.append(np.cross(vec1, vec2)) # Given all normals of ring atoms and their neighbors, the angle between any has to be 5.0 deg or less for n1, n2 in itertools.product(normals, repeat=2): arom_angle = vecangle(n1, n2) if all([arom_angle > config.AROMATIC_PLANARITY, arom_angle < 180.0 - config.AROMATIC_PLANARITY]): return False return True

python

def ring_is_planar(ring, r_atoms): """Given a set of ring atoms, check if the ring is sufficiently planar to be considered aromatic""" normals = [] for a in r_atoms: adj = pybel.ob.OBAtomAtomIter(a.OBAtom) # Check for neighboring atoms in the ring n_coords = [pybel.Atom(neigh).coords for neigh in adj if ring.IsMember(neigh)] vec1, vec2 = vector(a.coords, n_coords[0]), vector(a.coords, n_coords[1]) normals.append(np.cross(vec1, vec2)) # Given all normals of ring atoms and their neighbors, the angle between any has to be 5.0 deg or less for n1, n2 in itertools.product(normals, repeat=2): arom_angle = vecangle(n1, n2) if all([arom_angle > config.AROMATIC_PLANARITY, arom_angle < 180.0 - config.AROMATIC_PLANARITY]): return False return True

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Accelize/pycosio

pycosio/storage/oss.py

_OSSSystem._remove

def _remove(self, client_kwargs): """ Remove an object. args: client_kwargs (dict): Client arguments. """ with _handle_oss_error(): bucket = self._get_bucket(client_kwargs) # Object if 'key' in client_kwargs: return bucket.delete_object(key=client_kwargs['key']) # Bucket return bucket.delete_bucket()

python

def _remove(self, client_kwargs): """ Remove an object. args: client_kwargs (dict): Client arguments. """ with _handle_oss_error(): bucket = self._get_bucket(client_kwargs) # Object if 'key' in client_kwargs: return bucket.delete_object(key=client_kwargs['key']) # Bucket return bucket.delete_bucket()

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train

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ambitioninc/newrelic-api

newrelic_api/application_hosts.py

ApplicationHosts.show

def show(self, application_id, host_id): """ This API endpoint returns a single application host, identified by its ID. :type application_id: int :param application_id: Application ID :type host_id: int :param host_id: Application host ID :rtype: dict :return: The JSON response of the API :: { "application_host": { "id": "integer", "application_name": "string", "host": "string", "language": "integer", "health_status": "string", "application_summary": { "response_time": "float", "throughput": "float", "error_rate": "float", "apdex_score": "float" }, "end_user_summary": { "response_time": "float", "throughput": "float", "apdex_score": "float" }, "links": { "application": "integer", "application_instances": [ "integer" ], "server": "integer" } } } """ return self._get( url='{root}applications/{application_id}/hosts/{host_id}.json'.format( root=self.URL, application_id=application_id, host_id=host_id ), headers=self.headers, )

python

def show(self, application_id, host_id): """ This API endpoint returns a single application host, identified by its ID. :type application_id: int :param application_id: Application ID :type host_id: int :param host_id: Application host ID :rtype: dict :return: The JSON response of the API :: { "application_host": { "id": "integer", "application_name": "string", "host": "string", "language": "integer", "health_status": "string", "application_summary": { "response_time": "float", "throughput": "float", "error_rate": "float", "apdex_score": "float" }, "end_user_summary": { "response_time": "float", "throughput": "float", "apdex_score": "float" }, "links": { "application": "integer", "application_instances": [ "integer" ], "server": "integer" } } } """ return self._get( url='{root}applications/{application_id}/hosts/{host_id}.json'.format( root=self.URL, application_id=application_id, host_id=host_id ), headers=self.headers, )

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This API endpoint returns a single application host, identified by its ID. :type application_id: int :param application_id: Application ID :type host_id: int :param host_id: Application host ID :rtype: dict :return: The JSON response of the API :: { "application_host": { "id": "integer", "application_name": "string", "host": "string", "language": "integer", "health_status": "string", "application_summary": { "response_time": "float", "throughput": "float", "error_rate": "float", "apdex_score": "float" }, "end_user_summary": { "response_time": "float", "throughput": "float", "apdex_score": "float" }, "links": { "application": "integer", "application_instances": [ "integer" ], "server": "integer" } } }

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train

https://github.com/ambitioninc/newrelic-api/blob/07b4430aa6ae61e4704e2928a6e7a24c76f0f424/newrelic_api/application_hosts.py#L91-L143

MichaelAquilina/S4

s4/clients/local.py

LocalSyncClient.unlock

def unlock(self): """ Unlock the active advisory lock. """ logger.debug("Releasing lock %s", self.lock_file) self._lock.release() try: os.unlink(self.lock_file) except FileNotFoundError: pass

python

def unlock(self): """ Unlock the active advisory lock. """ logger.debug("Releasing lock %s", self.lock_file) self._lock.release() try: os.unlink(self.lock_file) except FileNotFoundError: pass

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Unlock the active advisory lock.

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train

https://github.com/MichaelAquilina/S4/blob/05d74697e6ec683f0329c983f7c3f05ab75fd57e/s4/clients/local.py#L77-L86

letuananh/chirptext

chirptext/dekomecab.py

parse

def parse(content, *args, **kwargs): ''' Use mecab-python3 by default to parse JP text. Fall back to mecab binary app if needed ''' global MECAB_PYTHON3 if 'mecab_loc' not in kwargs and MECAB_PYTHON3 and 'MeCab' in globals(): return MeCab.Tagger(*args).parse(content) else: return run_mecab_process(content, *args, **kwargs)

python

def parse(content, *args, **kwargs): ''' Use mecab-python3 by default to parse JP text. Fall back to mecab binary app if needed ''' global MECAB_PYTHON3 if 'mecab_loc' not in kwargs and MECAB_PYTHON3 and 'MeCab' in globals(): return MeCab.Tagger(*args).parse(content) else: return run_mecab_process(content, *args, **kwargs)

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Use mecab-python3 by default to parse JP text. Fall back to mecab binary app if needed

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train

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usc-isi-i2/etk

etk/extractors/spacy_rule_extractor.py

SpacyRuleExtractor._filter_match

def _filter_match(self, span: span, relations: Dict, patterns: List) -> bool: """ Filter the match result according to prefix, suffix, min, max ... Args: span: span relations: Dict patterns: List of pattern Returns: bool """ for pattern_id, a_pattern in enumerate(patterns): token_range = relations[pattern_id] if token_range: tokens = [x for x in span[token_range[0]:token_range[1]]] if a_pattern.type == "word": if not self._pre_suf_fix_filter(tokens, a_pattern.prefix, a_pattern.suffix): return False if a_pattern.type == "shape": if not (self._full_shape_filter(tokens, a_pattern.full_shape) and self._pre_suf_fix_filter(tokens, a_pattern.prefix,a_pattern.suffix)): return False if a_pattern.type == "number": if not self._min_max_filter(tokens, a_pattern.min, a_pattern.max): return False return True

python

def _filter_match(self, span: span, relations: Dict, patterns: List) -> bool: """ Filter the match result according to prefix, suffix, min, max ... Args: span: span relations: Dict patterns: List of pattern Returns: bool """ for pattern_id, a_pattern in enumerate(patterns): token_range = relations[pattern_id] if token_range: tokens = [x for x in span[token_range[0]:token_range[1]]] if a_pattern.type == "word": if not self._pre_suf_fix_filter(tokens, a_pattern.prefix, a_pattern.suffix): return False if a_pattern.type == "shape": if not (self._full_shape_filter(tokens, a_pattern.full_shape) and self._pre_suf_fix_filter(tokens, a_pattern.prefix,a_pattern.suffix)): return False if a_pattern.type == "number": if not self._min_max_filter(tokens, a_pattern.min, a_pattern.max): return False return True

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Filter the match result according to prefix, suffix, min, max ... Args: span: span relations: Dict patterns: List of pattern Returns: bool

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train

https://github.com/usc-isi-i2/etk/blob/aab077c984ea20f5e8ae33af622fe11d3c4df866/etk/extractors/spacy_rule_extractor.py#L198-L223

ome/omego

omego/external.py

External.set_server_dir

def set_server_dir(self, dir): """ Set the directory of the server to be controlled """ self.dir = os.path.abspath(dir) config = os.path.join(self.dir, 'etc', 'grid', 'config.xml') self.configured = os.path.exists(config)

python

def set_server_dir(self, dir): """ Set the directory of the server to be controlled """ self.dir = os.path.abspath(dir) config = os.path.join(self.dir, 'etc', 'grid', 'config.xml') self.configured = os.path.exists(config)

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Set the directory of the server to be controlled

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train

https://github.com/ome/omego/blob/2dadbf3c6342b6c995f9e0dceaf3c0b7fab030fb/omego/external.py#L55-L61

wummel/dosage

dosagelib/director.py

ComicGetter.getStrips

def getStrips(self, scraperobj): """Download comic strips.""" with lock: host_lock = get_host_lock(scraperobj.url) with host_lock: self._getStrips(scraperobj)

python

def getStrips(self, scraperobj): """Download comic strips.""" with lock: host_lock = get_host_lock(scraperobj.url) with host_lock: self._getStrips(scraperobj)

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Download comic strips.

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train

https://github.com/wummel/dosage/blob/a0109c3a46219f280e6e5e77183674e40da0f304/dosagelib/director.py#L91-L96

bcbio/bcbio-nextgen

bcbio/variation/effects.py

_installed_snpeff_genome

def _installed_snpeff_genome(base_name, config): """Find the most recent installed genome for snpEff with the given name. """ snpeff_config_file = os.path.join(config_utils.get_program("snpeff", config, "dir"), "snpEff.config") if os.path.exists(snpeff_config_file): data_dir = _find_snpeff_datadir(snpeff_config_file) dbs = [d for d in sorted(glob.glob(os.path.join(data_dir, "%s*" % base_name)), reverse=True) if os.path.isdir(d)] else: data_dir = None dbs = [] if len(dbs) == 0: raise ValueError("No database found in %s for %s" % (data_dir, base_name)) else: return data_dir, os.path.split(dbs[0])[-1]

python

def _installed_snpeff_genome(base_name, config): """Find the most recent installed genome for snpEff with the given name. """ snpeff_config_file = os.path.join(config_utils.get_program("snpeff", config, "dir"), "snpEff.config") if os.path.exists(snpeff_config_file): data_dir = _find_snpeff_datadir(snpeff_config_file) dbs = [d for d in sorted(glob.glob(os.path.join(data_dir, "%s*" % base_name)), reverse=True) if os.path.isdir(d)] else: data_dir = None dbs = [] if len(dbs) == 0: raise ValueError("No database found in %s for %s" % (data_dir, base_name)) else: return data_dir, os.path.split(dbs[0])[-1]

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Find the most recent installed genome for snpEff with the given name.

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train

https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/effects.py#L400-L415

DataDog/integrations-core

datadog_checks_dev/datadog_checks/dev/tooling/github.py

parse_pr_numbers

def parse_pr_numbers(git_log_lines): """ Parse PR numbers from commit messages. At GitHub those have the format: `here is the message (#1234)` being `1234` the PR number. """ prs = [] for line in git_log_lines: pr_number = parse_pr_number(line) if pr_number: prs.append(pr_number) return prs

python

def parse_pr_numbers(git_log_lines): """ Parse PR numbers from commit messages. At GitHub those have the format: `here is the message (#1234)` being `1234` the PR number. """ prs = [] for line in git_log_lines: pr_number = parse_pr_number(line) if pr_number: prs.append(pr_number) return prs

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Parse PR numbers from commit messages. At GitHub those have the format: `here is the message (#1234)` being `1234` the PR number.

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train

https://github.com/DataDog/integrations-core/blob/ebd41c873cf9f97a8c51bf9459bc6a7536af8acd/datadog_checks_dev/datadog_checks/dev/tooling/github.py#L98-L111

saulpw/visidata

visidata/canvas.py

Canvas.setCursorSize

def setCursorSize(self, p): 'sets width based on diagonal corner p' self.cursorBox = BoundingBox(self.cursorBox.xmin, self.cursorBox.ymin, p.x, p.y) self.cursorBox.w = max(self.cursorBox.w, self.canvasCharWidth) self.cursorBox.h = max(self.cursorBox.h, self.canvasCharHeight)

python

def setCursorSize(self, p): 'sets width based on diagonal corner p' self.cursorBox = BoundingBox(self.cursorBox.xmin, self.cursorBox.ymin, p.x, p.y) self.cursorBox.w = max(self.cursorBox.w, self.canvasCharWidth) self.cursorBox.h = max(self.cursorBox.h, self.canvasCharHeight)

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sets width based on diagonal corner p

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train

https://github.com/saulpw/visidata/blob/32771e0cea6c24fc7902683d14558391395c591f/visidata/canvas.py#L371-L375

hubo1016/namedstruct

namedstruct/namedstruct.py

NamedStruct._tobytes

def _tobytes(self, skipprepack = False): ''' Convert the struct to bytes. This is the standard way to convert a NamedStruct to bytes. :param skipprepack: if True, the prepack stage is skipped. For parser internal use. :returns: converted bytes ''' stream = BytesIO() self._tostream(stream, skipprepack) return stream.getvalue()

python

def _tobytes(self, skipprepack = False): ''' Convert the struct to bytes. This is the standard way to convert a NamedStruct to bytes. :param skipprepack: if True, the prepack stage is skipped. For parser internal use. :returns: converted bytes ''' stream = BytesIO() self._tostream(stream, skipprepack) return stream.getvalue()

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Convert the struct to bytes. This is the standard way to convert a NamedStruct to bytes. :param skipprepack: if True, the prepack stage is skipped. For parser internal use. :returns: converted bytes

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train

https://github.com/hubo1016/namedstruct/blob/5039026e0df4ce23003d212358918dbe1a6e1d76/namedstruct/namedstruct.py#L146-L156

saltstack/salt

salt/modules/parted_partition.py

disk_toggle

def disk_toggle(device, flag): ''' Toggle the state of <flag> on <device>. Valid flags are the same as the disk_set command. CLI Example: .. code-block:: bash salt '*' partition.disk_toggle /dev/sda pmbr_boot ''' _validate_device(device) if flag not in VALID_DISK_FLAGS: raise CommandExecutionError('Invalid flag passed to partition.disk_toggle') cmd = ['parted', '-m', '-s', device, 'disk_toggle', flag] out = __salt__['cmd.run'](cmd).splitlines() return out

python

def disk_toggle(device, flag): ''' Toggle the state of <flag> on <device>. Valid flags are the same as the disk_set command. CLI Example: .. code-block:: bash salt '*' partition.disk_toggle /dev/sda pmbr_boot ''' _validate_device(device) if flag not in VALID_DISK_FLAGS: raise CommandExecutionError('Invalid flag passed to partition.disk_toggle') cmd = ['parted', '-m', '-s', device, 'disk_toggle', flag] out = __salt__['cmd.run'](cmd).splitlines() return out

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Toggle the state of <flag> on <device>. Valid flags are the same as the disk_set command. CLI Example: .. code-block:: bash salt '*' partition.disk_toggle /dev/sda pmbr_boot

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train

https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/parted_partition.py#L761-L779

vxgmichel/aiostream

aiostream/stream/time.py

delay

async def delay(source, delay): """Delay the iteration of an asynchronous sequence.""" await asyncio.sleep(delay) async with streamcontext(source) as streamer: async for item in streamer: yield item

python

async def delay(source, delay): """Delay the iteration of an asynchronous sequence.""" await asyncio.sleep(delay) async with streamcontext(source) as streamer: async for item in streamer: yield item

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Delay the iteration of an asynchronous sequence.

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train

https://github.com/vxgmichel/aiostream/blob/43bdf04ab19108a3f1b5a472062e1392a26cbcf8/aiostream/stream/time.py#L60-L65

apache/incubator-mxnet

python/mxnet/ndarray/ndarray.py

arange

def arange(start, stop=None, step=1.0, repeat=1, infer_range=None, ctx=None, dtype=mx_real_t): """Returns evenly spaced values within a given interval. Values are generated within the half-open interval [`start`, `stop`). In other words, the interval includes `start` but excludes `stop`. The function is similar to the built-in Python function `range` and to `numpy.arange`, but returns an `NDArray`. Parameters ---------- start : number, optional Start of interval. The default start value is 0. stop : number End of interval. step : number, optional Spacing between values. The default step size is 1. repeat : int, optional Number of times to repeat each element. The default repeat count is 1. infer_range : boolean, optional When set to True, infer the stop position from the start, step, repeat, and output tensor size. ctx : Context, optional Device context. Default context is the current default context. dtype : str or numpy.dtype, optional The data type of the `NDArray`. The default datatype is `np.float32`. Returns ------- NDArray `NDArray` of evenly spaced values in the specified range. Examples -------- >>> mx.nd.arange(3).asnumpy() array([ 0., 1., 2.], dtype=float32) >>> mx.nd.arange(2, 6).asnumpy() array([ 2., 3., 4., 5.], dtype=float32) >>> mx.nd.arange(2, 6, step=2).asnumpy() array([ 2., 4.], dtype=float32) >>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy() array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32) >>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy() array([2, 2, 2, 4, 4, 4], dtype=int32) """ if infer_range is not None: warnings.warn('`infer_range` argument has been deprecated', DeprecationWarning) if ctx is None: ctx = current_context() return _internal._arange(start=start, stop=stop, step=step, repeat=repeat, infer_range=False, dtype=dtype, ctx=str(ctx))

python

def arange(start, stop=None, step=1.0, repeat=1, infer_range=None, ctx=None, dtype=mx_real_t): """Returns evenly spaced values within a given interval. Values are generated within the half-open interval [`start`, `stop`). In other words, the interval includes `start` but excludes `stop`. The function is similar to the built-in Python function `range` and to `numpy.arange`, but returns an `NDArray`. Parameters ---------- start : number, optional Start of interval. The default start value is 0. stop : number End of interval. step : number, optional Spacing between values. The default step size is 1. repeat : int, optional Number of times to repeat each element. The default repeat count is 1. infer_range : boolean, optional When set to True, infer the stop position from the start, step, repeat, and output tensor size. ctx : Context, optional Device context. Default context is the current default context. dtype : str or numpy.dtype, optional The data type of the `NDArray`. The default datatype is `np.float32`. Returns ------- NDArray `NDArray` of evenly spaced values in the specified range. Examples -------- >>> mx.nd.arange(3).asnumpy() array([ 0., 1., 2.], dtype=float32) >>> mx.nd.arange(2, 6).asnumpy() array([ 2., 3., 4., 5.], dtype=float32) >>> mx.nd.arange(2, 6, step=2).asnumpy() array([ 2., 4.], dtype=float32) >>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy() array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32) >>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy() array([2, 2, 2, 4, 4, 4], dtype=int32) """ if infer_range is not None: warnings.warn('`infer_range` argument has been deprecated', DeprecationWarning) if ctx is None: ctx = current_context() return _internal._arange(start=start, stop=stop, step=step, repeat=repeat, infer_range=False, dtype=dtype, ctx=str(ctx))

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train

https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/ndarray/ndarray.py#L2560-L2610

watson-developer-cloud/python-sdk

ibm_watson/natural_language_classifier_v1.py

NaturalLanguageClassifierV1.classify

def classify(self, classifier_id, text, **kwargs): """ Classify a phrase. Returns label information for the input. The status must be `Available` before you can use the classifier to classify text. :param str classifier_id: Classifier ID to use. :param str text: The submitted phrase. The maximum length is 2048 characters. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if classifier_id is None: raise ValueError('classifier_id must be provided') if text is None: raise ValueError('text must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) sdk_headers = get_sdk_headers('natural_language_classifier', 'V1', 'classify') headers.update(sdk_headers) data = {'text': text} url = '/v1/classifiers/{0}/classify'.format( *self._encode_path_vars(classifier_id)) response = self.request( method='POST', url=url, headers=headers, json=data, accept_json=True) return response

python

def classify(self, classifier_id, text, **kwargs): """ Classify a phrase. Returns label information for the input. The status must be `Available` before you can use the classifier to classify text. :param str classifier_id: Classifier ID to use. :param str text: The submitted phrase. The maximum length is 2048 characters. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if classifier_id is None: raise ValueError('classifier_id must be provided') if text is None: raise ValueError('text must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) sdk_headers = get_sdk_headers('natural_language_classifier', 'V1', 'classify') headers.update(sdk_headers) data = {'text': text} url = '/v1/classifiers/{0}/classify'.format( *self._encode_path_vars(classifier_id)) response = self.request( method='POST', url=url, headers=headers, json=data, accept_json=True) return response

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train

https://github.com/watson-developer-cloud/python-sdk/blob/4c2c9df4466fcde88975da9ecd834e6ba95eb353/ibm_watson/natural_language_classifier_v1.py#L97-L133

gagneurlab/concise

concise/legacy/concise.py

Concise._set_var_res

def _set_var_res(self, weights): """ Transform the weights to var_res """ if weights is None: return # layer 1 motif_base_weights_raw = np.swapaxes(weights["motif_base_weights"], 2, 0) motif_base_weights = motif_base_weights_raw[np.newaxis] motif_bias = weights["motif_bias"] feature_weights = weights["feature_weights"] spline_weights = weights["spline_weights"] # filter motif_weights = weights["motif_weights"] final_bias = weights["final_bias"] var_res = { "motif_base_weights": motif_base_weights, "motif_bias": motif_bias, "spline_weights": spline_weights, "feature_weights": feature_weights, "motif_weights": motif_weights, "final_bias": final_bias } # cast everything to float32 var_res = {key: value.astype(np.float32) if value is not None else None for key, value in var_res.items()} self._var_res = var_res

python

def _set_var_res(self, weights): """ Transform the weights to var_res """ if weights is None: return # layer 1 motif_base_weights_raw = np.swapaxes(weights["motif_base_weights"], 2, 0) motif_base_weights = motif_base_weights_raw[np.newaxis] motif_bias = weights["motif_bias"] feature_weights = weights["feature_weights"] spline_weights = weights["spline_weights"] # filter motif_weights = weights["motif_weights"] final_bias = weights["final_bias"] var_res = { "motif_base_weights": motif_base_weights, "motif_bias": motif_bias, "spline_weights": spline_weights, "feature_weights": feature_weights, "motif_weights": motif_weights, "final_bias": final_bias } # cast everything to float32 var_res = {key: value.astype(np.float32) if value is not None else None for key, value in var_res.items()} self._var_res = var_res

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train

https://github.com/gagneurlab/concise/blob/d15262eb1e590008bc96ba31e93bfbdbfa1a9fd4/concise/legacy/concise.py#L1028-L1059

bpython/curtsies

examples/tttplaybitboard.py

tictactoe

def tictactoe(w, i, player, opponent, grid=None): "Put two strategies to a classic battle of wits." grid = grid or empty_grid while True: w.render_to_terminal(w.array_from_text(view(grid))) if is_won(grid): print(whose_move(grid), "wins.") break if not successors(grid): print("A draw.") break grid = player(w, i, grid) player, opponent = opponent, player

python

def tictactoe(w, i, player, opponent, grid=None): "Put two strategies to a classic battle of wits." grid = grid or empty_grid while True: w.render_to_terminal(w.array_from_text(view(grid))) if is_won(grid): print(whose_move(grid), "wins.") break if not successors(grid): print("A draw.") break grid = player(w, i, grid) player, opponent = opponent, player

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train

https://github.com/bpython/curtsies/blob/223e42b97fbf6c86b479ed4f0963a067333c5a63/examples/tttplaybitboard.py#L40-L52

Erotemic/utool

utool/util_graph.py

nx_gen_edge_values

def nx_gen_edge_values(G, key, edges=None, default=util_const.NoParam, on_missing='error', on_keyerr='default'): """ Generates attributes values of specific edges Args: on_missing (str): Strategy for handling nodes missing from G. Can be {'error', 'default'}. defaults to 'error'. on_keyerr (str): Strategy for handling keys missing from node dicts. Can be {'error', 'default'}. defaults to 'default' if default is specified, otherwise defaults to 'error'. """ if edges is None: edges = G.edges() if on_missing is None: on_missing = 'error' if on_keyerr is None: on_keyerr = 'default' if default is util_const.NoParam and on_keyerr == 'default': on_keyerr = 'error' # Generate `data_iter` edges and data dictionary if on_missing == 'error': data_iter = (G.adj[u][v] for u, v in edges) elif on_missing == 'default': data_iter = (G.adj[u][v] if G.has_edge(u, v) else {} for u, v in edges) else: raise KeyError('on_missing={} must be error, filter or default'.format( on_missing)) # Get `value_iter` desired value out of dictionary if on_keyerr == 'error': value_iter = (d[key] for d in data_iter) elif on_keyerr == 'default': value_iter = (d.get(key, default) for d in data_iter) else: raise KeyError('on_keyerr={} must be error or default'.format(on_keyerr)) return value_iter

python

def nx_gen_edge_values(G, key, edges=None, default=util_const.NoParam, on_missing='error', on_keyerr='default'): """ Generates attributes values of specific edges Args: on_missing (str): Strategy for handling nodes missing from G. Can be {'error', 'default'}. defaults to 'error'. on_keyerr (str): Strategy for handling keys missing from node dicts. Can be {'error', 'default'}. defaults to 'default' if default is specified, otherwise defaults to 'error'. """ if edges is None: edges = G.edges() if on_missing is None: on_missing = 'error' if on_keyerr is None: on_keyerr = 'default' if default is util_const.NoParam and on_keyerr == 'default': on_keyerr = 'error' # Generate `data_iter` edges and data dictionary if on_missing == 'error': data_iter = (G.adj[u][v] for u, v in edges) elif on_missing == 'default': data_iter = (G.adj[u][v] if G.has_edge(u, v) else {} for u, v in edges) else: raise KeyError('on_missing={} must be error, filter or default'.format( on_missing)) # Get `value_iter` desired value out of dictionary if on_keyerr == 'error': value_iter = (d[key] for d in data_iter) elif on_keyerr == 'default': value_iter = (d.get(key, default) for d in data_iter) else: raise KeyError('on_keyerr={} must be error or default'.format(on_keyerr)) return value_iter

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['Generates', 'attributes', 'values', 'of', 'specific', 'edges']

train

https://github.com/Erotemic/utool/blob/3b27e1f4e6e6fb23cd8744af7b7195b57d99e03a/utool/util_graph.py#L880-L916

datastax/python-driver

cassandra/cqlengine/query.py

DMLQuery.save

def save(self): """ Creates / updates a row. This is a blind insert call. All validation and cleaning needs to happen prior to calling this. """ if self.instance is None: raise CQLEngineException("DML Query intance attribute is None") assert type(self.instance) == self.model nulled_fields = set() if self.instance._has_counter or self.instance._can_update(): if self.instance._has_counter: warn("'create' and 'save' actions on Counters are deprecated. It will be disallowed in 4.0. " "Use the 'update' mechanism instead.", DeprecationWarning) return self.update() else: insert = InsertStatement(self.column_family_name, ttl=self._ttl, timestamp=self._timestamp, if_not_exists=self._if_not_exists) static_save_only = False if len(self.instance._clustering_keys) == 0 else True for name, col in self.instance._clustering_keys.items(): static_save_only = static_save_only and col._val_is_null(getattr(self.instance, name, None)) for name, col in self.instance._columns.items(): if static_save_only and not col.static and not col.partition_key: continue val = getattr(self.instance, name, None) if col._val_is_null(val): if self.instance._values[name].changed: nulled_fields.add(col.db_field_name) continue if col.has_default and not self.instance._values[name].changed: # Ensure default columns included in a save() are marked as explicit, to get them *persisted* properly self.instance._values[name].explicit = True insert.add_assignment(col, getattr(self.instance, name, None)) # skip query execution if it's empty # caused by pointless update queries if not insert.is_empty: self._execute(insert) # delete any nulled columns if not static_save_only: self._delete_null_columns()

python

def save(self): """ Creates / updates a row. This is a blind insert call. All validation and cleaning needs to happen prior to calling this. """ if self.instance is None: raise CQLEngineException("DML Query intance attribute is None") assert type(self.instance) == self.model nulled_fields = set() if self.instance._has_counter or self.instance._can_update(): if self.instance._has_counter: warn("'create' and 'save' actions on Counters are deprecated. It will be disallowed in 4.0. " "Use the 'update' mechanism instead.", DeprecationWarning) return self.update() else: insert = InsertStatement(self.column_family_name, ttl=self._ttl, timestamp=self._timestamp, if_not_exists=self._if_not_exists) static_save_only = False if len(self.instance._clustering_keys) == 0 else True for name, col in self.instance._clustering_keys.items(): static_save_only = static_save_only and col._val_is_null(getattr(self.instance, name, None)) for name, col in self.instance._columns.items(): if static_save_only and not col.static and not col.partition_key: continue val = getattr(self.instance, name, None) if col._val_is_null(val): if self.instance._values[name].changed: nulled_fields.add(col.db_field_name) continue if col.has_default and not self.instance._values[name].changed: # Ensure default columns included in a save() are marked as explicit, to get them *persisted* properly self.instance._values[name].explicit = True insert.add_assignment(col, getattr(self.instance, name, None)) # skip query execution if it's empty # caused by pointless update queries if not insert.is_empty: self._execute(insert) # delete any nulled columns if not static_save_only: self._delete_null_columns()

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train

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SheffieldML/GPyOpt

GPyOpt/interface/driver.py

BODriver._get_obj

def _get_obj(self,space): """ Imports the acquisition function. """ obj_func = self.obj_func from ..core.task import SingleObjective return SingleObjective(obj_func, self.config['resources']['cores'], space=space, unfold_args=True)

python

def _get_obj(self,space): """ Imports the acquisition function. """ obj_func = self.obj_func from ..core.task import SingleObjective return SingleObjective(obj_func, self.config['resources']['cores'], space=space, unfold_args=True)

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train

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tensorpack/tensorpack

examples/GAN/InfoGAN-mnist.py

shapeless_placeholder

def shapeless_placeholder(x, axis, name): """ Make the static shape of a tensor less specific. If you want to feed to a tensor, the shape of the feed value must match the tensor's static shape. This function creates a placeholder which defaults to x if not fed, but has a less specific static shape than x. See also `tensorflow#5680 <https://github.com/tensorflow/tensorflow/issues/5680>`_. Args: x: a tensor axis(int or list of ints): these axes of ``x.get_shape()`` will become None in the output. name(str): name of the output tensor Returns: a tensor equal to x, but shape information is partially cleared. """ shp = x.get_shape().as_list() if not isinstance(axis, list): axis = [axis] for a in axis: if shp[a] is None: raise ValueError("Axis {} of shape {} is already unknown!".format(a, shp)) shp[a] = None x = tf.placeholder_with_default(x, shape=shp, name=name) return x

python

def shapeless_placeholder(x, axis, name): """ Make the static shape of a tensor less specific. If you want to feed to a tensor, the shape of the feed value must match the tensor's static shape. This function creates a placeholder which defaults to x if not fed, but has a less specific static shape than x. See also `tensorflow#5680 <https://github.com/tensorflow/tensorflow/issues/5680>`_. Args: x: a tensor axis(int or list of ints): these axes of ``x.get_shape()`` will become None in the output. name(str): name of the output tensor Returns: a tensor equal to x, but shape information is partially cleared. """ shp = x.get_shape().as_list() if not isinstance(axis, list): axis = [axis] for a in axis: if shp[a] is None: raise ValueError("Axis {} of shape {} is already unknown!".format(a, shp)) shp[a] = None x = tf.placeholder_with_default(x, shape=shp, name=name) return x

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train

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Cornices/cornice.ext.sphinx

cornice_sphinx/__init__.py

ServiceDirective._get_attributes

def _get_attributes(schema, location): """Return the schema's children, filtered by location.""" schema = DottedNameResolver(__name__).maybe_resolve(schema) def _filter(attr): if not hasattr(attr, "location"): valid_location = 'body' in location else: valid_location = attr.location in to_list(location) return valid_location return list(filter(_filter, schema().children))

python

def _get_attributes(schema, location): """Return the schema's children, filtered by location.""" schema = DottedNameResolver(__name__).maybe_resolve(schema) def _filter(attr): if not hasattr(attr, "location"): valid_location = 'body' in location else: valid_location = attr.location in to_list(location) return valid_location return list(filter(_filter, schema().children))

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train

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mlperf/training

translation/tensorflow/transformer/utils/tokenizer.py

_save_vocab_file

def _save_vocab_file(vocab_file, subtoken_list): """Save subtokens to file.""" with tf.gfile.Open(vocab_file, mode="w") as f: for subtoken in subtoken_list: f.write("'%s'\n" % _unicode_to_native(subtoken))

python

def _save_vocab_file(vocab_file, subtoken_list): """Save subtokens to file.""" with tf.gfile.Open(vocab_file, mode="w") as f: for subtoken in subtoken_list: f.write("'%s'\n" % _unicode_to_native(subtoken))

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train

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StackStorm/pybind

pybind/slxos/v17r_1_01a/system_monitor/__init__.py

system_monitor._set_MM

def _set_MM(self, v, load=False): """ Setter method for MM, mapped from YANG variable /system_monitor/MM (container) If this variable is read-only (config: false) in the source YANG file, then _set_MM is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_MM() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=MM.MM, is_container='container', presence=False, yang_name="MM", rest_name="MM", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure threshold setting for \n component:MM', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-system-monitor', defining_module='brocade-system-monitor', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """MM must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=MM.MM, is_container='container', presence=False, yang_name="MM", rest_name="MM", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure threshold setting for \n component:MM', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-system-monitor', defining_module='brocade-system-monitor', yang_type='container', is_config=True)""", }) self.__MM = t if hasattr(self, '_set'): self._set()

python

def _set_MM(self, v, load=False): """ Setter method for MM, mapped from YANG variable /system_monitor/MM (container) If this variable is read-only (config: false) in the source YANG file, then _set_MM is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_MM() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=MM.MM, is_container='container', presence=False, yang_name="MM", rest_name="MM", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure threshold setting for \n component:MM', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-system-monitor', defining_module='brocade-system-monitor', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """MM must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=MM.MM, is_container='container', presence=False, yang_name="MM", rest_name="MM", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure threshold setting for \n component:MM', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-system-monitor', defining_module='brocade-system-monitor', yang_type='container', is_config=True)""", }) self.__MM = t if hasattr(self, '_set'): self._set()

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train

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mitsei/dlkit

dlkit/primordium/transport/objects.py

DataInputStream.read

def read(self, buf=None, n=None): """Reads a specified number of ``bytes`` from this stream. arg: n (cardinal): the number of ``bytes`` to read return: (integer) - the ``bytes`` read raise: IllegalState - this stream has been closed or ``at_end_of_stream()`` is ``true`` raise: InvalidArgument - the size of ``buf`` is less than ``n`` raise: OperationFailed - unable to complete request *compliance: mandatory -- This method must be implemented.* """ if n is not None: return self._my_data.read(n) else: return self._my_data.read()

python

def read(self, buf=None, n=None): """Reads a specified number of ``bytes`` from this stream. arg: n (cardinal): the number of ``bytes`` to read return: (integer) - the ``bytes`` read raise: IllegalState - this stream has been closed or ``at_end_of_stream()`` is ``true`` raise: InvalidArgument - the size of ``buf`` is less than ``n`` raise: OperationFailed - unable to complete request *compliance: mandatory -- This method must be implemented.* """ if n is not None: return self._my_data.read(n) else: return self._my_data.read()

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train

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materialsproject/pymatgen

pymatgen/analysis/graphs.py

MoleculeGraph.with_empty_graph

def with_empty_graph(cls, molecule, name="bonds", edge_weight_name=None, edge_weight_units=None): """ Constructor for MoleculeGraph, returns a MoleculeGraph object with an empty graph (no edges, only nodes defined that correspond to Sites in Molecule). :param molecule (Molecule): :param name (str): name of graph, e.g. "bonds" :param edge_weight_name (str): name of edge weights, e.g. "bond_length" or "exchange_constant" :param edge_weight_units (str): name of edge weight units e.g. "Å" or "eV" :return (MoleculeGraph): """ if edge_weight_name and (edge_weight_units is None): raise ValueError("Please specify units associated " "with your edge weights. Can be " "empty string if arbitrary or " "dimensionless.") # construct graph with one node per site # graph attributes don't change behavior of graph, # they're just for book-keeping graph = nx.MultiDiGraph(edge_weight_name=edge_weight_name, edge_weight_units=edge_weight_units, name=name) graph.add_nodes_from(range(len(molecule))) graph_data = json_graph.adjacency_data(graph) return cls(molecule, graph_data=graph_data)

python

def with_empty_graph(cls, molecule, name="bonds", edge_weight_name=None, edge_weight_units=None): """ Constructor for MoleculeGraph, returns a MoleculeGraph object with an empty graph (no edges, only nodes defined that correspond to Sites in Molecule). :param molecule (Molecule): :param name (str): name of graph, e.g. "bonds" :param edge_weight_name (str): name of edge weights, e.g. "bond_length" or "exchange_constant" :param edge_weight_units (str): name of edge weight units e.g. "Å" or "eV" :return (MoleculeGraph): """ if edge_weight_name and (edge_weight_units is None): raise ValueError("Please specify units associated " "with your edge weights. Can be " "empty string if arbitrary or " "dimensionless.") # construct graph with one node per site # graph attributes don't change behavior of graph, # they're just for book-keeping graph = nx.MultiDiGraph(edge_weight_name=edge_weight_name, edge_weight_units=edge_weight_units, name=name) graph.add_nodes_from(range(len(molecule))) graph_data = json_graph.adjacency_data(graph) return cls(molecule, graph_data=graph_data)

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train

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QUANTAXIS/QUANTAXIS

QUANTAXIS/QAFetch/QATdx.py

QA_fetch_get_future_transaction_realtime

def QA_fetch_get_future_transaction_realtime(code, ip=None, port=None): '期货历史成交分笔' ip, port = get_extensionmarket_ip(ip, port) apix = TdxExHq_API() global extension_market_list extension_market_list = QA_fetch_get_extensionmarket_list( ) if extension_market_list is None else extension_market_list code_market = extension_market_list.query( 'code=="{}"'.format(code)).iloc[0] with apix.connect(ip, port): data = pd.DataFrame() data = pd.concat([apix.to_df(apix.get_transaction_data( int(code_market.market), code, (30 - i) * 1800)) for i in range(31)], axis=0) return data.assign(datetime=pd.to_datetime(data['date'])).assign(date=lambda x: str(x)[0:10]) \ .assign(code=str(code)).assign(order=range(len(data.index))).set_index('datetime', drop=False, inplace=False)

python

def QA_fetch_get_future_transaction_realtime(code, ip=None, port=None): '期货历史成交分笔' ip, port = get_extensionmarket_ip(ip, port) apix = TdxExHq_API() global extension_market_list extension_market_list = QA_fetch_get_extensionmarket_list( ) if extension_market_list is None else extension_market_list code_market = extension_market_list.query( 'code=="{}"'.format(code)).iloc[0] with apix.connect(ip, port): data = pd.DataFrame() data = pd.concat([apix.to_df(apix.get_transaction_data( int(code_market.market), code, (30 - i) * 1800)) for i in range(31)], axis=0) return data.assign(datetime=pd.to_datetime(data['date'])).assign(date=lambda x: str(x)[0:10]) \ .assign(code=str(code)).assign(order=range(len(data.index))).set_index('datetime', drop=False, inplace=False)

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期货历史成交分笔

['期货历史成交分笔']

train

https://github.com/QUANTAXIS/QUANTAXIS/blob/bb1fe424e4108b62a1f712b81a05cf829297a5c0/QUANTAXIS/QAFetch/QATdx.py#L2112-L2128

beerfactory/hbmqtt

hbmqtt/broker.py

Broker.authenticate

def authenticate(self, session: Session, listener): """ This method call the authenticate method on registered plugins to test user authentication. User is considered authenticated if all plugins called returns True. Plugins authenticate() method are supposed to return : - True if user is authentication succeed - False if user authentication fails - None if authentication can't be achieved (then plugin result is then ignored) :param session: :param listener: :return: """ auth_plugins = None auth_config = self.config.get('auth', None) if auth_config: auth_plugins = auth_config.get('plugins', None) returns = yield from self.plugins_manager.map_plugin_coro( "authenticate", session=session, filter_plugins=auth_plugins) auth_result = True if returns: for plugin in returns: res = returns[plugin] if res is False: auth_result = False self.logger.debug("Authentication failed due to '%s' plugin result: %s" % (plugin.name, res)) else: self.logger.debug("'%s' plugin result: %s" % (plugin.name, res)) # If all plugins returned True, authentication is success return auth_result

python

def authenticate(self, session: Session, listener): """ This method call the authenticate method on registered plugins to test user authentication. User is considered authenticated if all plugins called returns True. Plugins authenticate() method are supposed to return : - True if user is authentication succeed - False if user authentication fails - None if authentication can't be achieved (then plugin result is then ignored) :param session: :param listener: :return: """ auth_plugins = None auth_config = self.config.get('auth', None) if auth_config: auth_plugins = auth_config.get('plugins', None) returns = yield from self.plugins_manager.map_plugin_coro( "authenticate", session=session, filter_plugins=auth_plugins) auth_result = True if returns: for plugin in returns: res = returns[plugin] if res is False: auth_result = False self.logger.debug("Authentication failed due to '%s' plugin result: %s" % (plugin.name, res)) else: self.logger.debug("'%s' plugin result: %s" % (plugin.name, res)) # If all plugins returned True, authentication is success return auth_result

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train

https://github.com/beerfactory/hbmqtt/blob/4aa6fe982141abc3c54e9f4d7b981ab3eba0a13c/hbmqtt/broker.py#L532-L562

datastax/python-driver

cassandra/cluster.py

ControlConnection._set_new_connection

def _set_new_connection(self, conn): """ Replace existing connection (if there is one) and close it. """ with self._lock: old = self._connection self._connection = conn if old: log.debug("[control connection] Closing old connection %r, replacing with %r", old, conn) old.close()

python

def _set_new_connection(self, conn): """ Replace existing connection (if there is one) and close it. """ with self._lock: old = self._connection self._connection = conn if old: log.debug("[control connection] Closing old connection %r, replacing with %r", old, conn) old.close()

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Replace existing connection (if there is one) and close it.

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train

https://github.com/datastax/python-driver/blob/30a80d0b798b1f45f8cb77163b1fa791f3e3ca29/cassandra/cluster.py#L2888-L2898

mikhaildubov/AST-text-analysis

east/asts/ast_naive.py

NaiveAnnotatedSuffixTree._construct

def _construct(self, strings_collection): """ Naive generalized suffix tree construction algorithm, with quadratic [O(n_1^2 + ... + n_m^2)] worst-case time complexity, where m is the number of strings in collection. """ # 0. Add a unique character to each string in the collection, # to preserve simplicity while building the tree strings_collection = utils.make_unique_endings(strings_collection) root = ast.AnnotatedSuffixTree.Node() root.strings_collection = strings_collection # For each string in the collection... for string_ind in xrange(len(strings_collection)): string = strings_collection[string_ind] # For each suffix of that string... # (do not handle unique last characters as suffixes) for suffix_start in xrange(len(string)-1): suffix = string[suffix_start:] # ... first try to find maximal matching path node = root child_node = node.chose_arc(suffix) while child_node: (str_ind, substr_start, substr_end) = child_node.arc() match = utils.match_strings( suffix, strings_collection[str_ind][substr_start:substr_end]) if match == substr_end-substr_start: # matched the arc, proceed with child node suffix = suffix[match:] suffix_start += match node = child_node node.weight += 1 child_node = node.chose_arc(suffix) else: # ... then, where the matching path ends; # create new inner node # (that's the only possible alternative # since we have unique string endings) node.remove_child(child_node) new_node = node.add_new_child(string_ind, suffix_start, suffix_start+match) new_leaf = new_node.add_new_child(string_ind, suffix_start+match, len(string)) (osi, oss, ose) = child_node._arc child_node._arc = (osi, oss+match, ose) new_node.add_child(child_node) new_leaf.weight = 1 new_node.weight = 1 + child_node.weight suffix = '' break # ... or create new leaf if there was no appropriate arc to proceed if suffix: new_leaf = node.add_new_child(string_ind, suffix_start, len(string)) new_leaf.weight = 1 # Root will also be annotated by the weight of its children, # to preserve simplicity while calculating string matching for k in root.children: root.weight += root.children[k].weight return root

python

def _construct(self, strings_collection): """ Naive generalized suffix tree construction algorithm, with quadratic [O(n_1^2 + ... + n_m^2)] worst-case time complexity, where m is the number of strings in collection. """ # 0. Add a unique character to each string in the collection, # to preserve simplicity while building the tree strings_collection = utils.make_unique_endings(strings_collection) root = ast.AnnotatedSuffixTree.Node() root.strings_collection = strings_collection # For each string in the collection... for string_ind in xrange(len(strings_collection)): string = strings_collection[string_ind] # For each suffix of that string... # (do not handle unique last characters as suffixes) for suffix_start in xrange(len(string)-1): suffix = string[suffix_start:] # ... first try to find maximal matching path node = root child_node = node.chose_arc(suffix) while child_node: (str_ind, substr_start, substr_end) = child_node.arc() match = utils.match_strings( suffix, strings_collection[str_ind][substr_start:substr_end]) if match == substr_end-substr_start: # matched the arc, proceed with child node suffix = suffix[match:] suffix_start += match node = child_node node.weight += 1 child_node = node.chose_arc(suffix) else: # ... then, where the matching path ends; # create new inner node # (that's the only possible alternative # since we have unique string endings) node.remove_child(child_node) new_node = node.add_new_child(string_ind, suffix_start, suffix_start+match) new_leaf = new_node.add_new_child(string_ind, suffix_start+match, len(string)) (osi, oss, ose) = child_node._arc child_node._arc = (osi, oss+match, ose) new_node.add_child(child_node) new_leaf.weight = 1 new_node.weight = 1 + child_node.weight suffix = '' break # ... or create new leaf if there was no appropriate arc to proceed if suffix: new_leaf = node.add_new_child(string_ind, suffix_start, len(string)) new_leaf.weight = 1 # Root will also be annotated by the weight of its children, # to preserve simplicity while calculating string matching for k in root.children: root.weight += root.children[k].weight return root

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train

https://github.com/mikhaildubov/AST-text-analysis/blob/055ad8d2492c100bbbaa25309ec1074bdf1dfaa5/east/asts/ast_naive.py#L12-L76

bsmurphy/PyKrige

pykrige/core.py

_krige

def _krige(X, y, coords, variogram_function, variogram_model_parameters, coordinates_type): """Sets up and solves the ordinary kriging system for the given coordinate pair. This function is only used for the statistics calculations. Parameters ---------- X: ndarray float array [n_samples, n_dim], the input array of coordinates y: ndarray float array [n_samples], the input array of measurement values coords: ndarray float array [1, n_dim], point at which to evaluate the kriging system variogram_function: callable function that will be called to evaluate variogram model variogram_model_parameters: list user-specified parameters for variogram model coordinates_type: str type of coordinates in X array, can be 'euclidean' for standard rectangular coordinates or 'geographic' if the coordinates are lat/lon Returns ------- zinterp: float kriging estimate at the specified point sigmasq: float mean square error of the kriging estimate """ zero_index = None zero_value = False # calculate distance between points... need a square distance matrix # of inter-measurement-point distances and a vector of distances between # measurement points (X) and the kriging point (coords) if coordinates_type == 'euclidean': d = squareform(pdist(X, metric='euclidean')) bd = np.squeeze(cdist(X, coords[None, :], metric='euclidean')) # geographic coordinate distances still calculated in the old way... # assume X[:, 0] ('x') => lon, X[:, 1] ('y') => lat # also assume problem is 2D; check done earlier in initializing variogram elif coordinates_type == 'geographic': x1, x2 = np.meshgrid(X[:, 0], X[:, 0], sparse=True) y1, y2 = np.meshgrid(X[:, 1], X[:, 1], sparse=True) d = great_circle_distance(x1, y1, x2, y2) bd = great_circle_distance(X[:, 0], X[:, 1], coords[0] * np.ones(X.shape[0]), coords[1] * np.ones(X.shape[0])) # this check is done when initializing variogram, but kept here anyways... else: raise ValueError("Specified coordinate type '%s' " "is not supported." % coordinates_type) # check if kriging point overlaps with measurement point if np.any(np.absolute(bd) <= 1e-10): zero_value = True zero_index = np.where(bd <= 1e-10)[0][0] # set up kriging matrix n = X.shape[0] a = np.zeros((n+1, n+1)) a[:n, :n] = - variogram_function(variogram_model_parameters, d) np.fill_diagonal(a, 0.0) a[n, :] = 1.0 a[:, n] = 1.0 a[n, n] = 0.0 # set up RHS b = np.zeros((n+1, 1)) b[:n, 0] = - variogram_function(variogram_model_parameters, bd) if zero_value: b[zero_index, 0] = 0.0 b[n, 0] = 1.0 # solve res = np.linalg.solve(a, b) zinterp = np.sum(res[:n, 0] * y) sigmasq = np.sum(res[:, 0] * -b[:, 0]) return zinterp, sigmasq

python

def _krige(X, y, coords, variogram_function, variogram_model_parameters, coordinates_type): """Sets up and solves the ordinary kriging system for the given coordinate pair. This function is only used for the statistics calculations. Parameters ---------- X: ndarray float array [n_samples, n_dim], the input array of coordinates y: ndarray float array [n_samples], the input array of measurement values coords: ndarray float array [1, n_dim], point at which to evaluate the kriging system variogram_function: callable function that will be called to evaluate variogram model variogram_model_parameters: list user-specified parameters for variogram model coordinates_type: str type of coordinates in X array, can be 'euclidean' for standard rectangular coordinates or 'geographic' if the coordinates are lat/lon Returns ------- zinterp: float kriging estimate at the specified point sigmasq: float mean square error of the kriging estimate """ zero_index = None zero_value = False # calculate distance between points... need a square distance matrix # of inter-measurement-point distances and a vector of distances between # measurement points (X) and the kriging point (coords) if coordinates_type == 'euclidean': d = squareform(pdist(X, metric='euclidean')) bd = np.squeeze(cdist(X, coords[None, :], metric='euclidean')) # geographic coordinate distances still calculated in the old way... # assume X[:, 0] ('x') => lon, X[:, 1] ('y') => lat # also assume problem is 2D; check done earlier in initializing variogram elif coordinates_type == 'geographic': x1, x2 = np.meshgrid(X[:, 0], X[:, 0], sparse=True) y1, y2 = np.meshgrid(X[:, 1], X[:, 1], sparse=True) d = great_circle_distance(x1, y1, x2, y2) bd = great_circle_distance(X[:, 0], X[:, 1], coords[0] * np.ones(X.shape[0]), coords[1] * np.ones(X.shape[0])) # this check is done when initializing variogram, but kept here anyways... else: raise ValueError("Specified coordinate type '%s' " "is not supported." % coordinates_type) # check if kriging point overlaps with measurement point if np.any(np.absolute(bd) <= 1e-10): zero_value = True zero_index = np.where(bd <= 1e-10)[0][0] # set up kriging matrix n = X.shape[0] a = np.zeros((n+1, n+1)) a[:n, :n] = - variogram_function(variogram_model_parameters, d) np.fill_diagonal(a, 0.0) a[n, :] = 1.0 a[:, n] = 1.0 a[n, n] = 0.0 # set up RHS b = np.zeros((n+1, 1)) b[:n, 0] = - variogram_function(variogram_model_parameters, bd) if zero_value: b[zero_index, 0] = 0.0 b[n, 0] = 1.0 # solve res = np.linalg.solve(a, b) zinterp = np.sum(res[:n, 0] * y) sigmasq = np.sum(res[:, 0] * -b[:, 0]) return zinterp, sigmasq

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Sets up and solves the ordinary kriging system for the given coordinate pair. This function is only used for the statistics calculations. Parameters ---------- X: ndarray float array [n_samples, n_dim], the input array of coordinates y: ndarray float array [n_samples], the input array of measurement values coords: ndarray float array [1, n_dim], point at which to evaluate the kriging system variogram_function: callable function that will be called to evaluate variogram model variogram_model_parameters: list user-specified parameters for variogram model coordinates_type: str type of coordinates in X array, can be 'euclidean' for standard rectangular coordinates or 'geographic' if the coordinates are lat/lon Returns ------- zinterp: float kriging estimate at the specified point sigmasq: float mean square error of the kriging estimate

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train

https://github.com/bsmurphy/PyKrige/blob/a4db3003b0b5688658c12faeb95a5a8b2b14b433/pykrige/core.py#L585-L666