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mwouts/jupytext | jupytext/cell_to_text.py | LightScriptCellExporter.explicit_start_marker | def explicit_start_marker(self, source):
"""Does the python representation of this cell requires an explicit
start of cell marker?"""
if not self.use_cell_markers:
return False
if self.metadata:
return True
if self.cell_marker_start:
start_code... | python | def explicit_start_marker(self, source):
"""Does the python representation of this cell requires an explicit
start of cell marker?"""
if not self.use_cell_markers:
return False
if self.metadata:
return True
if self.cell_marker_start:
start_code... | [
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mwouts/jupytext | jupytext/cell_to_text.py | LightScriptCellExporter.remove_eoc_marker | def remove_eoc_marker(self, text, next_text):
"""Remove end of cell marker when next cell has an explicit start marker"""
if self.cell_marker_start:
return text
if self.is_code() and text[-1] == self.comment + ' -':
# remove end of cell marker when redundant with next ex... | python | def remove_eoc_marker(self, text, next_text):
"""Remove end of cell marker when next cell has an explicit start marker"""
if self.cell_marker_start:
return text
if self.is_code() and text[-1] == self.comment + ' -':
# remove end of cell marker when redundant with next ex... | [
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mwouts/jupytext | jupytext/cell_to_text.py | LightScriptCellExporter.simplify_soc_marker | def simplify_soc_marker(self, text, prev_text):
"""Simplify start of cell marker when previous line is blank"""
if self.cell_marker_start:
return text
if self.is_code() and text and text[0] == self.comment + ' + {}':
if not prev_text or not prev_text[-1].strip():
... | python | def simplify_soc_marker(self, text, prev_text):
"""Simplify start of cell marker when previous line is blank"""
if self.cell_marker_start:
return text
if self.is_code() and text and text[0] == self.comment + ' + {}':
if not prev_text or not prev_text[-1].strip():
... | [
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mwouts/jupytext | jupytext/cell_to_text.py | RScriptCellExporter.code_to_text | def code_to_text(self):
"""Return the text representation of a code cell"""
active = is_active(self.ext, self.metadata)
source = copy(self.source)
escape_code_start(source, self.ext, self.language)
if active:
comment_magic(source, self.language, self.comment_magics)
... | python | def code_to_text(self):
"""Return the text representation of a code cell"""
active = is_active(self.ext, self.metadata)
source = copy(self.source)
escape_code_start(source, self.ext, self.language)
if active:
comment_magic(source, self.language, self.comment_magics)
... | [
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mwouts/jupytext | jupytext/cell_to_text.py | DoublePercentCellExporter.cell_to_text | def cell_to_text(self):
"""Return the text representation for the cell"""
if self.cell_type != 'code':
self.metadata['cell_type'] = self.cell_type
active = is_active('py', self.metadata)
if self.language != self.default_language and 'active' not in self.metadata:
... | python | def cell_to_text(self):
"""Return the text representation for the cell"""
if self.cell_type != 'code':
self.metadata['cell_type'] = self.cell_type
active = is_active('py', self.metadata)
if self.language != self.default_language and 'active' not in self.metadata:
... | [
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mwouts/jupytext | jupytext/cell_to_text.py | SphinxGalleryCellExporter.cell_to_text | def cell_to_text(self):
"""Return the text representation for the cell"""
if self.cell_type == 'code':
source = copy(self.source)
return comment_magic(source, self.language, self.comment_magics)
if 'cell_marker' in self.metadata:
cell_marker = self.metadata.p... | python | def cell_to_text(self):
"""Return the text representation for the cell"""
if self.cell_type == 'code':
source = copy(self.source)
return comment_magic(source, self.language, self.comment_magics)
if 'cell_marker' in self.metadata:
cell_marker = self.metadata.p... | [
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mwouts/jupytext | jupytext/pandoc.py | pandoc | def pandoc(args, filein=None, fileout=None):
"""Execute pandoc with the given arguments"""
cmd = [u'pandoc']
if filein:
cmd.append(filein)
if fileout:
cmd.append('-o')
cmd.append(fileout)
cmd.extend(args.split())
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE)
... | python | def pandoc(args, filein=None, fileout=None):
"""Execute pandoc with the given arguments"""
cmd = [u'pandoc']
if filein:
cmd.append(filein)
if fileout:
cmd.append('-o')
cmd.append(fileout)
cmd.extend(args.split())
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE)
... | [
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mwouts/jupytext | jupytext/pandoc.py | pandoc_version | def pandoc_version():
"""Pandoc's version number"""
version = pandoc(u'--version').splitlines()[0].split()[1]
if parse_version(version) < parse_version('2.7.2'):
raise PandocError('Please install pandoc>=2.7.2 (found version {})'.format(version))
return version | python | def pandoc_version():
"""Pandoc's version number"""
version = pandoc(u'--version').splitlines()[0].split()[1]
if parse_version(version) < parse_version('2.7.2'):
raise PandocError('Please install pandoc>=2.7.2 (found version {})'.format(version))
return version | [
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mwouts/jupytext | jupytext/pandoc.py | md_to_notebook | def md_to_notebook(text):
"""Convert a Markdown text to a Jupyter notebook, using Pandoc"""
tmp_file = tempfile.NamedTemporaryFile(delete=False)
tmp_file.write(text.encode('utf-8'))
tmp_file.close()
pandoc(u'--from markdown --to ipynb -s --atx-headers --wrap=preserve --preserve-tabs', tmp_file.name... | python | def md_to_notebook(text):
"""Convert a Markdown text to a Jupyter notebook, using Pandoc"""
tmp_file = tempfile.NamedTemporaryFile(delete=False)
tmp_file.write(text.encode('utf-8'))
tmp_file.close()
pandoc(u'--from markdown --to ipynb -s --atx-headers --wrap=preserve --preserve-tabs', tmp_file.name... | [
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mwouts/jupytext | jupytext/pandoc.py | notebook_to_md | def notebook_to_md(notebook):
"""Convert a notebook to its Markdown representation, using Pandoc"""
tmp_file = tempfile.NamedTemporaryFile(delete=False)
tmp_file.write(ipynb_writes(notebook).encode('utf-8'))
tmp_file.close()
pandoc(u'--from ipynb --to markdown -s --atx-headers --wrap=preserve --pre... | python | def notebook_to_md(notebook):
"""Convert a notebook to its Markdown representation, using Pandoc"""
tmp_file = tempfile.NamedTemporaryFile(delete=False)
tmp_file.write(ipynb_writes(notebook).encode('utf-8'))
tmp_file.close()
pandoc(u'--from ipynb --to markdown -s --atx-headers --wrap=preserve --pre... | [
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facebook/watchman | python/pywatchman/pybser.py | _int_size | def _int_size(x):
"""Return the smallest size int that can store the value"""
if -0x80 <= x <= 0x7F:
return 1
elif -0x8000 <= x <= 0x7FFF:
return 2
elif -0x80000000 <= x <= 0x7FFFFFFF:
return 4
elif long(-0x8000000000000000) <= x <= long(0x7FFFFFFFFFFFFFFF):
return 8
... | python | def _int_size(x):
"""Return the smallest size int that can store the value"""
if -0x80 <= x <= 0x7F:
return 1
elif -0x8000 <= x <= 0x7FFF:
return 2
elif -0x80000000 <= x <= 0x7FFFFFFF:
return 4
elif long(-0x8000000000000000) <= x <= long(0x7FFFFFFFFFFFFFFF):
return 8
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facebook/watchman | python/pywatchman/pybser.py | loads | def loads(buf, mutable=True, value_encoding=None, value_errors=None):
"""Deserialize a BSER-encoded blob.
@param buf: The buffer to deserialize.
@type buf: bytes
@param mutable: Whether to return mutable results.
@type mutable: bool
@param value_encoding: Optional codec to use to decode value... | python | def loads(buf, mutable=True, value_encoding=None, value_errors=None):
"""Deserialize a BSER-encoded blob.
@param buf: The buffer to deserialize.
@type buf: bytes
@param mutable: Whether to return mutable results.
@type mutable: bool
@param value_encoding: Optional codec to use to decode value... | [
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facebook/watchman | build/fbcode_builder/utils.py | run_command | def run_command(*cmd, **kwargs):
'The stdout of most fbcode_builder utilities is meant to be parsed.'
logging.debug('Running: {0} with {1}'.format(cmd, kwargs))
kwargs['stdout'] = sys.stderr
subprocess.check_call(cmd, **kwargs) | python | def run_command(*cmd, **kwargs):
'The stdout of most fbcode_builder utilities is meant to be parsed.'
logging.debug('Running: {0} with {1}'.format(cmd, kwargs))
kwargs['stdout'] = sys.stderr
subprocess.check_call(cmd, **kwargs) | [
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facebook/watchman | build/fbcode_builder/utils.py | _inner_read_config | def _inner_read_config(path):
'''
Helper to read a named config file.
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https://bugs.python.org/issue21591
The bug prevents us from defining either a local function or a lambda
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... | python | def _inner_read_config(path):
'''
Helper to read a named config file.
The grossness with the global is a workaround for this python bug:
https://bugs.python.org/issue21591
The bug prevents us from defining either a local function or a lambda
in the scope of read_fbcode_builder_config below.
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facebook/watchman | build/fbcode_builder/utils.py | steps_for_spec | def steps_for_spec(builder, spec, processed_modules=None):
'''
Sets `builder` configuration, and returns all the builder steps
necessary to build `spec` and its dependencies.
Traverses the dependencies in depth-first order, honoring the sequencing
in each 'depends_on' list.
'''
if processed... | python | def steps_for_spec(builder, spec, processed_modules=None):
'''
Sets `builder` configuration, and returns all the builder steps
necessary to build `spec` and its dependencies.
Traverses the dependencies in depth-first order, honoring the sequencing
in each 'depends_on' list.
'''
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facebook/watchman | getdeps.py | vcpkg_dir | def vcpkg_dir():
""" Figure out where vcpkg is installed.
vcpkg-exported is populated in some flavors of FB internal builds.
C:/tools/vcpkg is the appveyor location.
C:/open/vcpkg is my local location.
"""
for p in ["vcpkg-exported", "C:/tools/vcpkg", "C:/open/vcpkg"]:
if os.path.isdir(p... | python | def vcpkg_dir():
""" Figure out where vcpkg is installed.
vcpkg-exported is populated in some flavors of FB internal builds.
C:/tools/vcpkg is the appveyor location.
C:/open/vcpkg is my local location.
"""
for p in ["vcpkg-exported", "C:/tools/vcpkg", "C:/open/vcpkg"]:
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facebook/watchman | python/pywatchman/capabilities.py | synthesize | def synthesize(vers, opts):
""" Synthesize a capability enabled version response
This is a very limited emulation for relatively recent feature sets
"""
parsed_version = parse_version(vers["version"])
vers["capabilities"] = {}
for name in opts["optional"]:
vers["capabilities"][name] ... | python | def synthesize(vers, opts):
""" Synthesize a capability enabled version response
This is a very limited emulation for relatively recent feature sets
"""
parsed_version = parse_version(vers["version"])
vers["capabilities"] = {}
for name in opts["optional"]:
vers["capabilities"][name] ... | [
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facebook/watchman | build/fbcode_builder/shell_quoting.py | shell_quote | def shell_quote(s):
'Quotes a string if it is not already quoted'
return s if isinstance(s, ShellQuoted) \
else ShellQuoted("'" + str(s).replace("'", "'\\''") + "'") | python | def shell_quote(s):
'Quotes a string if it is not already quoted'
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facebook/watchman | build/fbcode_builder/shell_quoting.py | raw_shell | def raw_shell(s):
'Not a member of ShellQuoted so we get a useful error for raw strings'
if isinstance(s, ShellQuoted):
return s.do_not_use_raw_str
raise RuntimeError('{0} should have been ShellQuoted'.format(s)) | python | def raw_shell(s):
'Not a member of ShellQuoted so we get a useful error for raw strings'
if isinstance(s, ShellQuoted):
return s.do_not_use_raw_str
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facebook/watchman | build/fbcode_builder/shell_quoting.py | shell_join | def shell_join(delim, it):
'Joins an iterable of ShellQuoted with a delimiter between each two'
return ShellQuoted(delim.join(raw_shell(s) for s in it)) | python | def shell_join(delim, it):
'Joins an iterable of ShellQuoted with a delimiter between each two'
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facebook/watchman | build/fbcode_builder/shell_quoting.py | path_join | def path_join(*args):
'Joins ShellQuoted and raw pieces of paths to make a shell-quoted path'
return ShellQuoted(os.path.join(*[
raw_shell(shell_quote(s)) for s in args
])) | python | def path_join(*args):
'Joins ShellQuoted and raw pieces of paths to make a shell-quoted path'
return ShellQuoted(os.path.join(*[
raw_shell(shell_quote(s)) for s in args
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facebook/watchman | build/fbcode_builder/shell_quoting.py | shell_comment | def shell_comment(c):
'Do not shell-escape raw strings in comments, but do handle line breaks.'
return ShellQuoted('# {c}').format(c=ShellQuoted(
(raw_shell(c) if isinstance(c, ShellQuoted) else c)
.replace('\n', '\n# ')
)) | python | def shell_comment(c):
'Do not shell-escape raw strings in comments, but do handle line breaks.'
return ShellQuoted('# {c}').format(c=ShellQuoted(
(raw_shell(c) if isinstance(c, ShellQuoted) else c)
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facebook/watchman | winbuild/copy-dyn-deps.py | State.resolve_dep | def resolve_dep(self, depname):
""" Locate dep in the search path; if found, return its path.
If not found in the search path, and the dep is not a system-provided
dep, raise an error """
for d in self._search_path:
name = os.path.join(d, depname)
if self._mock:
... | python | def resolve_dep(self, depname):
""" Locate dep in the search path; if found, return its path.
If not found in the search path, and the dep is not a system-provided
dep, raise an error """
for d in self._search_path:
name = os.path.join(d, depname)
if self._mock:
... | [
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facebook/watchman | winbuild/copy-dyn-deps.py | State.resolve_dep_from_path | def resolve_dep_from_path(self, depname):
""" If we can find the dep in the PATH, then we consider it to
be a system dependency that we should not bundle in the package """
if is_system_dep(depname):
return True
for d in self._path:
name = os.path.join(d, depname... | python | def resolve_dep_from_path(self, depname):
""" If we can find the dep in the PATH, then we consider it to
be a system dependency that we should not bundle in the package """
if is_system_dep(depname):
return True
for d in self._path:
name = os.path.join(d, depname... | [
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facebook/watchman | python/pywatchman_aio/__init__.py | AsyncBserCodec._loads | def _loads(self, response):
""" Parse the BSER packet """
return bser.loads(
response,
True,
value_encoding=encoding.get_local_encoding(),
value_errors=encoding.default_local_errors,
) | python | def _loads(self, response):
""" Parse the BSER packet """
return bser.loads(
response,
True,
value_encoding=encoding.get_local_encoding(),
value_errors=encoding.default_local_errors,
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facebook/watchman | python/pywatchman_aio/__init__.py | AIOClient.receive_bilateral_response | async def receive_bilateral_response(self):
"""Receive the response to a request made to the Watchman service."""
self._check_receive_loop()
resp = await self.bilateral_response_queue.get()
self._check_error(resp)
return resp | python | async def receive_bilateral_response(self):
"""Receive the response to a request made to the Watchman service."""
self._check_receive_loop()
resp = await self.bilateral_response_queue.get()
self._check_error(resp)
return resp | [
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facebook/watchman | python/pywatchman_aio/__init__.py | AIOClient.query | async def query(self, *args):
"""Send a query to the Watchman service and return the response."""
self._check_receive_loop()
try:
await self.connection.send(args)
return await self.receive_bilateral_response()
except CommandError as ex:
ex.setCommand(... | python | async def query(self, *args):
"""Send a query to the Watchman service and return the response."""
self._check_receive_loop()
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await self.connection.send(args)
return await self.receive_bilateral_response()
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facebook/watchman | python/pywatchman_aio/__init__.py | AIOClient.capability_check | async def capability_check(self, optional=None, required=None):
"""Perform a server capability check."""
self._check_receive_loop()
# If the returned response is an error, self.query will raise an error
await self.query(
"version", {"optional": optional or [], "required": re... | python | async def capability_check(self, optional=None, required=None):
"""Perform a server capability check."""
self._check_receive_loop()
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facebook/watchman | python/pywatchman_aio/__init__.py | AIOClient.get_subscription | async def get_subscription(self, name, root):
""" Retrieve the data associated with a named subscription
Returns None if there is no data associated with `name`
If root is not None, then only return the subscription
data that matches both root and name. When used in this way,
... | python | async def get_subscription(self, name, root):
""" Retrieve the data associated with a named subscription
Returns None if there is no data associated with `name`
If root is not None, then only return the subscription
data that matches both root and name. When used in this way,
... | [
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facebook/watchman | python/pywatchman_aio/__init__.py | AIOClient.pop_log | async def pop_log(self):
"""Get one log from the log queue."""
self._check_receive_loop()
res = self.log_queue.get()
self._check_error(res)
return res | python | async def pop_log(self):
"""Get one log from the log queue."""
self._check_receive_loop()
res = self.log_queue.get()
self._check_error(res)
return res | [
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facebook/watchman | python/pywatchman_aio/__init__.py | AIOClient.close | def close(self):
"""Close the underlying connection."""
self._closed = True
if self.receive_task:
self.receive_task.cancel()
if self.connection:
self.connection.close() | python | def close(self):
"""Close the underlying connection."""
self._closed = True
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facebook/watchman | python/pywatchman_aio/__init__.py | AIOClient.enable_receiving | def enable_receiving(self, loop=None):
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facebook/watchman | python/pywatchman_aio/__init__.py | AIOClient.from_socket | async def from_socket(cls, sockname: typing.Optional[str] = None) -> "AIOClient":
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facebook/watchman | python/pywatchman_aio/__init__.py | AIOClient._receive_loop | async def _receive_loop(self):
"""Receive the response to a request made to the Watchman service.
Note that when trying to receive a PDU from the Watchman service,
we might get a unilateral response to a subscription or log, so these
are processed and queued up for later retrieval. This... | python | async def _receive_loop(self):
"""Receive the response to a request made to the Watchman service.
Note that when trying to receive a PDU from the Watchman service,
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facebook/watchman | python/pywatchman/__init__.py | _win32_strerror | def _win32_strerror(err):
""" expand a win32 error code into a human readable message """
# FormatMessage will allocate memory and assign it here
buf = ctypes.c_char_p()
FormatMessage(
FORMAT_MESSAGE_FROM_SYSTEM
| FORMAT_MESSAGE_ALLOCATE_BUFFER
| FORMAT_MESSAGE_IGNORE_INSERTS,
... | python | def _win32_strerror(err):
""" expand a win32 error code into a human readable message """
# FormatMessage will allocate memory and assign it here
buf = ctypes.c_char_p()
FormatMessage(
FORMAT_MESSAGE_FROM_SYSTEM
| FORMAT_MESSAGE_ALLOCATE_BUFFER
| FORMAT_MESSAGE_IGNORE_INSERTS,
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facebook/watchman | python/pywatchman/__init__.py | _get_overlapped_result_ex_impl | def _get_overlapped_result_ex_impl(pipe, olap, nbytes, millis, alertable):
""" Windows 7 and earlier does not support GetOverlappedResultEx. The
alternative is to use GetOverlappedResult and wait for read or write
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WaitForSingleObjectEx. Create... | python | def _get_overlapped_result_ex_impl(pipe, olap, nbytes, millis, alertable):
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facebook/watchman | python/pywatchman/__init__.py | Transport.readLine | def readLine(self):
""" read a line
Maintains its own buffer, callers of the transport should not mix
calls to readBytes and readLine.
"""
if self.buf is None:
self.buf = []
# Buffer may already have a line if we've received unilateral
# response(s) f... | python | def readLine(self):
""" read a line
Maintains its own buffer, callers of the transport should not mix
calls to readBytes and readLine.
"""
if self.buf is None:
self.buf = []
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facebook/watchman | python/pywatchman/__init__.py | WindowsNamedPipeTransport.readBytes | def readBytes(self, size):
""" A read can block for an unbounded amount of time, even if the
kernel reports that the pipe handle is signalled, so we need to
always perform our reads asynchronously
"""
# try to satisfy the read from any buffered data
if self._iobu... | python | def readBytes(self, size):
""" A read can block for an unbounded amount of time, even if the
kernel reports that the pipe handle is signalled, so we need to
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"""
# try to satisfy the read from any buffered data
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facebook/watchman | python/pywatchman/__init__.py | client._connect | def _connect(self):
""" establish transport connection """
if self.recvConn:
if self.pid != os.getpid():
raise UseAfterFork(
"do not re-use a connection after fork; open a new client instead"
)
return
if self.sockpath ... | python | def _connect(self):
""" establish transport connection """
if self.recvConn:
if self.pid != os.getpid():
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facebook/watchman | python/pywatchman/__init__.py | client.receive | def receive(self):
""" receive the next PDU from the watchman service
If the client has activated subscriptions or logs then
this PDU may be a unilateral PDU sent by the service to
inform the client of a log event or subscription change.
It may also simply be the response porti... | python | def receive(self):
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facebook/watchman | python/pywatchman/__init__.py | client.getLog | def getLog(self, remove=True):
""" Retrieve buffered log data
If remove is true the data will be removed from the buffer.
Otherwise it will be left in the buffer
"""
res = self.logs
if remove:
self.logs = []
return res | python | def getLog(self, remove=True):
""" Retrieve buffered log data
If remove is true the data will be removed from the buffer.
Otherwise it will be left in the buffer
"""
res = self.logs
if remove:
self.logs = []
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facebook/watchman | python/pywatchman/__init__.py | client.getSubscription | def getSubscription(self, name, remove=True, root=None):
""" Retrieve the data associated with a named subscription
If remove is True (the default), the subscription data is removed
from the buffer. Otherwise the data is returned but left in
the buffer.
Returns None if there i... | python | def getSubscription(self, name, remove=True, root=None):
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facebook/watchman | python/pywatchman/__init__.py | client.query | def query(self, *args):
""" Send a query to the watchman service and return the response
This call will block until the response is returned.
If any unilateral responses are sent by the service in between
the request-response they will be buffered up in the client object
and NOT... | python | def query(self, *args):
""" Send a query to the watchman service and return the response
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facebook/watchman | python/pywatchman/__init__.py | client.capabilityCheck | def capabilityCheck(self, optional=None, required=None):
""" Perform a server capability check """
res = self.query(
"version", {"optional": optional or [], "required": required or []}
)
if not self._hasprop(res, "capabilities"):
# Server doesn't support capabili... | python | def capabilityCheck(self, optional=None, required=None):
""" Perform a server capability check """
res = self.query(
"version", {"optional": optional or [], "required": required or []}
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facebook/watchman | python/pywatchman/load.py | _read_bytes | def _read_bytes(fp, buf):
"""Read bytes from a file-like object
@param fp: File-like object that implements read(int)
@type fp: file
@param buf: Buffer to read into
@type buf: bytes
@return: buf
"""
# Do the first read without resizing the input buffer
offset = 0
remaining = ... | python | def _read_bytes(fp, buf):
"""Read bytes from a file-like object
@param fp: File-like object that implements read(int)
@type fp: file
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@type buf: bytes
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facebook/watchman | python/pywatchman/load.py | load | def load(fp, mutable=True, value_encoding=None, value_errors=None):
"""Deserialize a BSER-encoded blob.
@param fp: The file-object to deserialize.
@type file:
@param mutable: Whether to return mutable results.
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"""Deserialize a BSER-encoded blob.
@param fp: The file-object to deserialize.
@type file:
@param mutable: Whether to return mutable results.
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facebook/watchman | build/fbcode_builder/make_docker_context.py | make_docker_context | def make_docker_context(
get_steps_fn, github_project, opts=None, default_context_dir=None
):
'''
Returns a path to the Docker context directory. See parse_args.py.
Helper for making a command-line utility that writes your project's
Dockerfile and associated data into a (temporary) directory. Your... | python | def make_docker_context(
get_steps_fn, github_project, opts=None, default_context_dir=None
):
'''
Returns a path to the Docker context directory. See parse_args.py.
Helper for making a command-line utility that writes your project's
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facebook/watchman | build/fbcode_builder/fbcode_builder.py | FBCodeBuilder.diagnostics | def diagnostics(self):
'Log some system diagnostics before/after setup for ease of debugging'
# The builder's repr is not used in a command to avoid pointlessly
# invalidating Docker's build cache.
return self.step('Diagnostics', [
self.comment('Builder {0}'.format(repr(self)... | python | def diagnostics(self):
'Log some system diagnostics before/after setup for ease of debugging'
# The builder's repr is not used in a command to avoid pointlessly
# invalidating Docker's build cache.
return self.step('Diagnostics', [
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facebook/watchman | build/fbcode_builder/fbcode_builder.py | FBCodeBuilder.fb_github_project_workdir | def fb_github_project_workdir(self, project_and_path, github_org='facebook'):
'This helper lets Facebook-internal CI special-cases FB projects'
project, path = project_and_path.split('/', 1)
return self.github_project_workdir(github_org + '/' + project, path) | python | def fb_github_project_workdir(self, project_and_path, github_org='facebook'):
'This helper lets Facebook-internal CI special-cases FB projects'
project, path = project_and_path.split('/', 1)
return self.github_project_workdir(github_org + '/' + project, path) | [
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HIPS/autograd | examples/gaussian_process.py | make_gp_funs | def make_gp_funs(cov_func, num_cov_params):
"""Functions that perform Gaussian process regression.
cov_func has signature (cov_params, x, x')"""
def unpack_kernel_params(params):
mean = params[0]
cov_params = params[2:]
noise_scale = np.exp(params[1]) + 0.0001
ret... | python | def make_gp_funs(cov_func, num_cov_params):
"""Functions that perform Gaussian process regression.
cov_func has signature (cov_params, x, x')"""
def unpack_kernel_params(params):
mean = params[0]
cov_params = params[2:]
noise_scale = np.exp(params[1]) + 0.0001
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HIPS/autograd | autograd/core.py | def_linear | def def_linear(fun):
"""Flags that a function is linear wrt all args"""
defjvp_argnum(fun, lambda argnum, g, ans, args, kwargs:
fun(*subval(args, argnum, g), **kwargs)) | python | def def_linear(fun):
"""Flags that a function is linear wrt all args"""
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HIPS/autograd | examples/fluidsim/fluidsim.py | project | def project(vx, vy):
"""Project the velocity field to be approximately mass-conserving,
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p = np.zeros(vx.shape)
h = 1.0/vx.shape[0]
div = -0.5 * h * (np.roll(vx, -1, axis=0) - np.roll(vx, 1, axis=0)
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"""Project the velocity field to be approximately mass-conserving,
using a few iterations of Gauss-Seidel."""
p = np.zeros(vx.shape)
h = 1.0/vx.shape[0]
div = -0.5 * h * (np.roll(vx, -1, axis=0) - np.roll(vx, 1, axis=0)
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HIPS/autograd | autograd/tracer.py | primitive | def primitive(f_raw):
"""
Wraps a function so that its gradient can be specified and its invocation
can be recorded. For examples, see the docs."""
@wraps(f_raw)
def f_wrapped(*args, **kwargs):
boxed_args, trace, node_constructor = find_top_boxed_args(args)
if boxed_args:
... | python | def primitive(f_raw):
"""
Wraps a function so that its gradient can be specified and its invocation
can be recorded. For examples, see the docs."""
@wraps(f_raw)
def f_wrapped(*args, **kwargs):
boxed_args, trace, node_constructor = find_top_boxed_args(args)
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HIPS/autograd | examples/define_gradient.py | logsumexp | def logsumexp(x):
"""Numerically stable log(sum(exp(x))), also defined in scipy.misc"""
max_x = np.max(x)
return max_x + np.log(np.sum(np.exp(x - max_x))) | python | def logsumexp(x):
"""Numerically stable log(sum(exp(x))), also defined in scipy.misc"""
max_x = np.max(x)
return max_x + np.log(np.sum(np.exp(x - max_x))) | [
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HIPS/autograd | examples/variational_autoencoder.py | init_net_params | def init_net_params(scale, layer_sizes, rs=npr.RandomState(0)):
"""Build a (weights, biases) tuples for all layers."""
return [(scale * rs.randn(m, n), # weight matrix
scale * rs.randn(n)) # bias vector
for m, n in zip(layer_sizes[:-1], layer_sizes[1:])] | python | def init_net_params(scale, layer_sizes, rs=npr.RandomState(0)):
"""Build a (weights, biases) tuples for all layers."""
return [(scale * rs.randn(m, n), # weight matrix
scale * rs.randn(n)) # bias vector
for m, n in zip(layer_sizes[:-1], layer_sizes[1:])] | [
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HIPS/autograd | examples/hmm_em.py | build_dataset | def build_dataset(filename, max_lines=-1):
"""Loads a text file, and turns each line into an encoded sequence."""
encodings = dict(list(map(reversed, enumerate(string.printable))))
digitize = lambda char: encodings[char] if char in encodings else len(encodings)
encode_line = lambda line: np.array(list(m... | python | def build_dataset(filename, max_lines=-1):
"""Loads a text file, and turns each line into an encoded sequence."""
encodings = dict(list(map(reversed, enumerate(string.printable))))
digitize = lambda char: encodings[char] if char in encodings else len(encodings)
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HIPS/autograd | examples/fluidsim/wing.py | project | def project(vx, vy, occlusion):
"""Project the velocity field to be approximately mass-conserving,
using a few iterations of Gauss-Seidel."""
p = np.zeros(vx.shape)
div = -0.5 * (np.roll(vx, -1, axis=1) - np.roll(vx, 1, axis=1)
+ np.roll(vy, -1, axis=0) - np.roll(vy, 1, axis=0))
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"""Project the velocity field to be approximately mass-conserving,
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p = np.zeros(vx.shape)
div = -0.5 * (np.roll(vx, -1, axis=1) - np.roll(vx, 1, axis=1)
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HIPS/autograd | examples/fluidsim/wing.py | advect | def advect(f, vx, vy):
"""Move field f according to x and y velocities (u and v)
using an implicit Euler integrator."""
rows, cols = f.shape
cell_xs, cell_ys = np.meshgrid(np.arange(cols), np.arange(rows))
center_xs = (cell_xs - vx).ravel()
center_ys = (cell_ys - vy).ravel()
# Compute in... | python | def advect(f, vx, vy):
"""Move field f according to x and y velocities (u and v)
using an implicit Euler integrator."""
rows, cols = f.shape
cell_xs, cell_ys = np.meshgrid(np.arange(cols), np.arange(rows))
center_xs = (cell_xs - vx).ravel()
center_ys = (cell_ys - vy).ravel()
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HIPS/autograd | autograd/misc/optimizers.py | unflatten_optimizer | def unflatten_optimizer(optimize):
"""Takes an optimizer that operates on flat 1D numpy arrays and returns a
wrapped version that handles trees of nested containers (lists/tuples/dicts)
with arrays/scalars at the leaves."""
@wraps(optimize)
def _optimize(grad, x0, callback=None, *args, **kwargs):
... | python | def unflatten_optimizer(optimize):
"""Takes an optimizer that operates on flat 1D numpy arrays and returns a
wrapped version that handles trees of nested containers (lists/tuples/dicts)
with arrays/scalars at the leaves."""
@wraps(optimize)
def _optimize(grad, x0, callback=None, *args, **kwargs):
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HIPS/autograd | autograd/misc/optimizers.py | sgd | def sgd(grad, x, callback=None, num_iters=200, step_size=0.1, mass=0.9):
"""Stochastic gradient descent with momentum.
grad() must have signature grad(x, i), where i is the iteration number."""
velocity = np.zeros(len(x))
for i in range(num_iters):
g = grad(x, i)
if callback: callback(x,... | python | def sgd(grad, x, callback=None, num_iters=200, step_size=0.1, mass=0.9):
"""Stochastic gradient descent with momentum.
grad() must have signature grad(x, i), where i is the iteration number."""
velocity = np.zeros(len(x))
for i in range(num_iters):
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HIPS/autograd | autograd/misc/optimizers.py | rmsprop | def rmsprop(grad, x, callback=None, num_iters=100,
step_size=0.1, gamma=0.9, eps=10**-8):
"""Root mean squared prop: See Adagrad paper for details."""
avg_sq_grad = np.ones(len(x))
for i in range(num_iters):
g = grad(x, i)
if callback: callback(x, i, g)
avg_sq_grad = avg_... | python | def rmsprop(grad, x, callback=None, num_iters=100,
step_size=0.1, gamma=0.9, eps=10**-8):
"""Root mean squared prop: See Adagrad paper for details."""
avg_sq_grad = np.ones(len(x))
for i in range(num_iters):
g = grad(x, i)
if callback: callback(x, i, g)
avg_sq_grad = avg_... | [
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HIPS/autograd | autograd/misc/optimizers.py | adam | def adam(grad, x, callback=None, num_iters=100,
step_size=0.001, b1=0.9, b2=0.999, eps=10**-8):
"""Adam as described in http://arxiv.org/pdf/1412.6980.pdf.
It's basically RMSprop with momentum and some correction terms."""
m = np.zeros(len(x))
v = np.zeros(len(x))
for i in range(num_iters):... | python | def adam(grad, x, callback=None, num_iters=100,
step_size=0.001, b1=0.9, b2=0.999, eps=10**-8):
"""Adam as described in http://arxiv.org/pdf/1412.6980.pdf.
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v = np.zeros(len(x))
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HIPS/autograd | examples/ica.py | make_ica_funs | def make_ica_funs(observed_dimension, latent_dimension):
"""These functions implement independent component analysis.
The model is:
latents are drawn i.i.d. for each data point from a product of student-ts.
weights are the same across all datapoints.
each data = latents * weghts + noise."""
de... | python | def make_ica_funs(observed_dimension, latent_dimension):
"""These functions implement independent component analysis.
The model is:
latents are drawn i.i.d. for each data point from a product of student-ts.
weights are the same across all datapoints.
each data = latents * weghts + noise."""
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HIPS/autograd | examples/neural_net.py | neural_net_predict | def neural_net_predict(params, inputs):
"""Implements a deep neural network for classification.
params is a list of (weights, bias) tuples.
inputs is an (N x D) matrix.
returns normalized class log-probabilities."""
for W, b in params:
outputs = np.dot(inputs, W) + b
inputs ... | python | def neural_net_predict(params, inputs):
"""Implements a deep neural network for classification.
params is a list of (weights, bias) tuples.
inputs is an (N x D) matrix.
returns normalized class log-probabilities."""
for W, b in params:
outputs = np.dot(inputs, W) + b
inputs ... | [
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HIPS/autograd | examples/neural_net.py | l2_norm | def l2_norm(params):
"""Computes l2 norm of params by flattening them into a vector."""
flattened, _ = flatten(params)
return np.dot(flattened, flattened) | python | def l2_norm(params):
"""Computes l2 norm of params by flattening them into a vector."""
flattened, _ = flatten(params)
return np.dot(flattened, flattened) | [
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HIPS/autograd | examples/bayesian_neural_net.py | make_nn_funs | def make_nn_funs(layer_sizes, L2_reg, noise_variance, nonlinearity=np.tanh):
"""These functions implement a standard multi-layer perceptron,
vectorized over both training examples and weight samples."""
shapes = list(zip(layer_sizes[:-1], layer_sizes[1:]))
num_weights = sum((m+1)*n for m, n in shapes)
... | python | def make_nn_funs(layer_sizes, L2_reg, noise_variance, nonlinearity=np.tanh):
"""These functions implement a standard multi-layer perceptron,
vectorized over both training examples and weight samples."""
shapes = list(zip(layer_sizes[:-1], layer_sizes[1:]))
num_weights = sum((m+1)*n for m, n in shapes)
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HIPS/autograd | examples/generative_adversarial_net.py | neural_net_predict | def neural_net_predict(params, inputs):
"""Params is a list of (weights, bias) tuples.
inputs is an (N x D) matrix."""
inpW, inpb = params[0]
inputs = relu(np.dot(inputs, inpW) + inpb)
for W, b in params[1:-1]:
outputs = batch_normalize(np.dot(inputs, W) + b)
inputs = relu(outputs... | python | def neural_net_predict(params, inputs):
"""Params is a list of (weights, bias) tuples.
inputs is an (N x D) matrix."""
inpW, inpb = params[0]
inputs = relu(np.dot(inputs, inpW) + inpb)
for W, b in params[1:-1]:
outputs = batch_normalize(np.dot(inputs, W) + b)
inputs = relu(outputs... | [
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HIPS/autograd | examples/generative_adversarial_net.py | adam_minimax | def adam_minimax(grad_both, init_params_max, init_params_min, callback=None, num_iters=100,
step_size_max=0.001, step_size_min=0.001, b1=0.9, b2=0.999, eps=10**-8):
"""Adam modified to do minimiax optimization, for instance to help with
training generative adversarial networks."""
x_max, unflatten... | python | def adam_minimax(grad_both, init_params_max, init_params_min, callback=None, num_iters=100,
step_size_max=0.001, step_size_min=0.001, b1=0.9, b2=0.999, eps=10**-8):
"""Adam modified to do minimiax optimization, for instance to help with
training generative adversarial networks."""
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HIPS/autograd | autograd/differential_operators.py | elementwise_grad | def elementwise_grad(fun, x):
"""
Returns a function that computes the sum of each column of the Jacobian of
`fun`, in one pass. If the Jacobian is diagonal, then this is the diagonal
of the Jacobian.
"""
vjp, ans = _make_vjp(fun, x)
if vspace(ans).iscomplex:
raise TypeError("Element... | python | def elementwise_grad(fun, x):
"""
Returns a function that computes the sum of each column of the Jacobian of
`fun`, in one pass. If the Jacobian is diagonal, then this is the diagonal
of the Jacobian.
"""
vjp, ans = _make_vjp(fun, x)
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HIPS/autograd | autograd/differential_operators.py | jacobian | def jacobian(fun, x):
"""
Returns a function which computes the Jacobian of `fun` with respect to
positional argument number `argnum`, which must be a scalar or array. Unlike
`grad` it is not restricted to scalar-output functions, but also it cannot
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"""
Returns a function which computes the Jacobian of `fun` with respect to
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HIPS/autograd | autograd/differential_operators.py | grad_named | def grad_named(fun, argname):
'''Takes gradients with respect to a named argument.
Doesn't work on *args or **kwargs.'''
arg_index = getargspec(fun).args.index(argname)
return grad(fun, arg_index) | python | def grad_named(fun, argname):
'''Takes gradients with respect to a named argument.
Doesn't work on *args or **kwargs.'''
arg_index = getargspec(fun).args.index(argname)
return grad(fun, arg_index) | [
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HIPS/autograd | autograd/differential_operators.py | hessian_tensor_product | def hessian_tensor_product(fun, argnum=0):
"""Builds a function that returns the exact Hessian-tensor product.
The returned function has arguments (*args, tensor, **kwargs), and for
vectors takes roughly 4x as long to evaluate as the original function."""
fun_grad = grad(fun, argnum)
def vector_dot_... | python | def hessian_tensor_product(fun, argnum=0):
"""Builds a function that returns the exact Hessian-tensor product.
The returned function has arguments (*args, tensor, **kwargs), and for
vectors takes roughly 4x as long to evaluate as the original function."""
fun_grad = grad(fun, argnum)
def vector_dot_... | [
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HIPS/autograd | autograd/differential_operators.py | tensor_jacobian_product | def tensor_jacobian_product(fun, argnum=0):
"""Builds a function that returns the exact tensor-Jacobian product, that
is the Jacobian matrix left-multiplied by tensor. The returned function
has arguments (*args, tensor, **kwargs)."""
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"""Builds a function that returns the exact tensor-Jacobian product, that
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HIPS/autograd | autograd/differential_operators.py | make_jvp_reversemode | def make_jvp_reversemode(fun, x):
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HIPS/autograd | autograd/differential_operators.py | make_ggnvp | def make_ggnvp(f, g=lambda x: 1./2*np.sum(x**2, axis=-1), f_argnum=0):
"""Builds a function for evaluating generalized-Gauss-Newton-vector products
at a point. Slightly more expensive than mixed-mode."""
@unary_to_nary
def _make_ggnvp(f, x):
f_vjp, f_x = _make_vjp(f, x)
g_hvp, grad_g_x =... | python | def make_ggnvp(f, g=lambda x: 1./2*np.sum(x**2, axis=-1), f_argnum=0):
"""Builds a function for evaluating generalized-Gauss-Newton-vector products
at a point. Slightly more expensive than mixed-mode."""
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def _make_ggnvp(f, x):
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HIPS/autograd | autograd/differential_operators.py | value_and_grad | def value_and_grad(fun, x):
"""Returns a function that returns both value and gradient. Suitable for use
in scipy.optimize"""
vjp, ans = _make_vjp(fun, x)
if not vspace(ans).size == 1:
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"""Returns a function that returns both value and gradient. Suitable for use
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vjp, ans = _make_vjp(fun, x)
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HIPS/autograd | autograd/differential_operators.py | grad_and_aux | def grad_and_aux(fun, x):
"""Builds a function that returns the gradient of the first output and the
(unmodified) second output of a function that returns two outputs."""
vjp, (ans, aux) = _make_vjp(lambda x: atuple(fun(x)), x)
return vjp((vspace(ans).ones(), vspace(aux).zeros())), aux | python | def grad_and_aux(fun, x):
"""Builds a function that returns the gradient of the first output and the
(unmodified) second output of a function that returns two outputs."""
vjp, (ans, aux) = _make_vjp(lambda x: atuple(fun(x)), x)
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HIPS/autograd | autograd/differential_operators.py | multigrad_dict | def multigrad_dict(fun):
"Takes gradients wrt all arguments simultaneously,"
"returns a dict mapping 'argname' to 'gradval'"
import funcsigs
sig = funcsigs.signature(fun)
def select(preds, lst):
idx = lambda item: next(
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sig = funcsigs.signature(fun)
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HIPS/autograd | autograd/differential_operators.py | checkpoint | def checkpoint(fun):
"""Returns a checkpointed version of `fun`, where intermediate values
computed during the forward pass of `fun` are discarded and then recomputed
for the backward pass. Useful to save memory, effectively trading off time
and memory. See e.g. arxiv.org/abs/1604.06174.
"""
def... | python | def checkpoint(fun):
"""Returns a checkpointed version of `fun`, where intermediate values
computed during the forward pass of `fun` are discarded and then recomputed
for the backward pass. Useful to save memory, effectively trading off time
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HIPS/autograd | examples/rnn.py | string_to_one_hot | def string_to_one_hot(string, maxchar):
"""Converts an ASCII string to a one-of-k encoding."""
ascii = np.array([ord(c) for c in string]).T
return np.array(ascii[:,None] == np.arange(maxchar)[None, :], dtype=int) | python | def string_to_one_hot(string, maxchar):
"""Converts an ASCII string to a one-of-k encoding."""
ascii = np.array([ord(c) for c in string]).T
return np.array(ascii[:,None] == np.arange(maxchar)[None, :], dtype=int) | [
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HIPS/autograd | examples/rnn.py | build_dataset | def build_dataset(filename, sequence_length, alphabet_size, max_lines=-1):
"""Loads a text file, and turns each line into an encoded sequence."""
with open(filename) as f:
content = f.readlines()
content = content[:max_lines]
content = [line for line in content if len(line) > 2] # Remove blank... | python | def build_dataset(filename, sequence_length, alphabet_size, max_lines=-1):
"""Loads a text file, and turns each line into an encoded sequence."""
with open(filename) as f:
content = f.readlines()
content = content[:max_lines]
content = [line for line in content if len(line) > 2] # Remove blank... | [
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HIPS/autograd | examples/ode_net.py | init_nn_params | def init_nn_params(scale, layer_sizes, rs=npr.RandomState(0)):
"""Build a list of (weights, biases) tuples, one for each layer."""
return [(rs.randn(insize, outsize) * scale, # weight matrix
rs.randn(outsize) * scale) # bias vector
for insize, outsize in zip(layer_sizes[:-1]... | python | def init_nn_params(scale, layer_sizes, rs=npr.RandomState(0)):
"""Build a list of (weights, biases) tuples, one for each layer."""
return [(rs.randn(insize, outsize) * scale, # weight matrix
rs.randn(outsize) * scale) # bias vector
for insize, outsize in zip(layer_sizes[:-1]... | [
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HIPS/autograd | autograd/numpy/fft.py | make_rfft_factors | def make_rfft_factors(axes, resshape, facshape, normshape, norm):
""" make the compression factors and compute the normalization
for irfft and rfft.
"""
N = 1.0
for n in normshape: N = N * n
# inplace modification is fine because we produce a constant
# which doesn't go into autograd.
... | python | def make_rfft_factors(axes, resshape, facshape, normshape, norm):
""" make the compression factors and compute the normalization
for irfft and rfft.
"""
N = 1.0
for n in normshape: N = N * n
# inplace modification is fine because we produce a constant
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HIPS/autograd | examples/mixture_variational_inference.py | variational_lower_bound | def variational_lower_bound(params, t, logprob, sampler, log_density,
num_samples, rs):
"""Provides a stochastic estimate of the variational lower bound,
for any variational family and model density."""
samples = sampler(params, num_samples, rs)
log_qs = log_density(params... | python | def variational_lower_bound(params, t, logprob, sampler, log_density,
num_samples, rs):
"""Provides a stochastic estimate of the variational lower bound,
for any variational family and model density."""
samples = sampler(params, num_samples, rs)
log_qs = log_density(params... | [
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HIPS/autograd | autograd/numpy/linalg.py | grad_eigh | def grad_eigh(ans, x, UPLO='L'):
"""Gradient for eigenvalues and vectors of a symmetric matrix."""
N = x.shape[-1]
w, v = ans # Eigenvalues, eigenvectors.
def vjp(g):
wg, vg = g # Gradient w.r.t. eigenvalues, eigenvectors.
w_repeated = anp.repeat(w[..., anp.newaxis]... | python | def grad_eigh(ans, x, UPLO='L'):
"""Gradient for eigenvalues and vectors of a symmetric matrix."""
N = x.shape[-1]
w, v = ans # Eigenvalues, eigenvectors.
def vjp(g):
wg, vg = g # Gradient w.r.t. eigenvalues, eigenvectors.
w_repeated = anp.repeat(w[..., anp.newaxis]... | [
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HIPS/autograd | examples/black_box_svi.py | black_box_variational_inference | def black_box_variational_inference(logprob, D, num_samples):
"""Implements http://arxiv.org/abs/1401.0118, and uses the
local reparameterization trick from http://arxiv.org/abs/1506.02557"""
def unpack_params(params):
# Variational dist is a diagonal Gaussian.
mean, log_std = params[:D], p... | python | def black_box_variational_inference(logprob, D, num_samples):
"""Implements http://arxiv.org/abs/1401.0118, and uses the
local reparameterization trick from http://arxiv.org/abs/1506.02557"""
def unpack_params(params):
# Variational dist is a diagonal Gaussian.
mean, log_std = params[:D], p... | [
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HIPS/autograd | autograd/numpy/numpy_vjps.py | repeat_to_match_shape | def repeat_to_match_shape(g, shape, dtype, axis, keepdims):
"""Returns the array g repeated along axis to fit vector space vs.
Also returns the number of repetitions of the array."""
if shape == ():
return g, 1
axis = list(axis) if isinstance(axis, tuple) else axis
new_shape = onp.array(sha... | python | def repeat_to_match_shape(g, shape, dtype, axis, keepdims):
"""Returns the array g repeated along axis to fit vector space vs.
Also returns the number of repetitions of the array."""
if shape == ():
return g, 1
axis = list(axis) if isinstance(axis, tuple) else axis
new_shape = onp.array(sha... | [
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HIPS/autograd | examples/data.py | plot_images | def plot_images(images, ax, ims_per_row=5, padding=5, digit_dimensions=(28, 28),
cmap=matplotlib.cm.binary, vmin=None, vmax=None):
"""Images should be a (N_images x pixels) matrix."""
N_images = images.shape[0]
N_rows = (N_images - 1) // ims_per_row + 1
pad_value = np.min(images.ravel())... | python | def plot_images(images, ax, ims_per_row=5, padding=5, digit_dimensions=(28, 28),
cmap=matplotlib.cm.binary, vmin=None, vmax=None):
"""Images should be a (N_images x pixels) matrix."""
N_images = images.shape[0]
N_rows = (N_images - 1) // ims_per_row + 1
pad_value = np.min(images.ravel())... | [
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HIPS/autograd | examples/data.py | make_pinwheel | def make_pinwheel(radial_std, tangential_std, num_classes, num_per_class, rate,
rs=npr.RandomState(0)):
"""Based on code by Ryan P. Adams."""
rads = np.linspace(0, 2*np.pi, num_classes, endpoint=False)
features = rs.randn(num_classes*num_per_class, 2) \
* np.array([radial_std, tan... | python | def make_pinwheel(radial_std, tangential_std, num_classes, num_per_class, rate,
rs=npr.RandomState(0)):
"""Based on code by Ryan P. Adams."""
rads = np.linspace(0, 2*np.pi, num_classes, endpoint=False)
features = rs.randn(num_classes*num_per_class, 2) \
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dxa4481/truffleHog | truffleHog/truffleHog.py | shannon_entropy | def shannon_entropy(data, iterator):
"""
Borrowed from http://blog.dkbza.org/2007/05/scanning-data-for-entropy-anomalies.html
"""
if not data:
return 0
entropy = 0
for x in iterator:
p_x = float(data.count(x))/len(data)
if p_x > 0:
entropy += - p_x*math.log(p_... | python | def shannon_entropy(data, iterator):
"""
Borrowed from http://blog.dkbza.org/2007/05/scanning-data-for-entropy-anomalies.html
"""
if not data:
return 0
entropy = 0
for x in iterator:
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agermanidis/autosub | autosub/formatters.py | srt_formatter | def srt_formatter(subtitles, padding_before=0, padding_after=0):
"""
Serialize a list of subtitles according to the SRT format, with optional time padding.
"""
sub_rip_file = pysrt.SubRipFile()
for i, ((start, end), text) in enumerate(subtitles, start=1):
item = pysrt.SubRipItem()
it... | python | def srt_formatter(subtitles, padding_before=0, padding_after=0):
"""
Serialize a list of subtitles according to the SRT format, with optional time padding.
"""
sub_rip_file = pysrt.SubRipFile()
for i, ((start, end), text) in enumerate(subtitles, start=1):
item = pysrt.SubRipItem()
it... | [
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agermanidis/autosub | autosub/formatters.py | vtt_formatter | def vtt_formatter(subtitles, padding_before=0, padding_after=0):
"""
Serialize a list of subtitles according to the VTT format, with optional time padding.
"""
text = srt_formatter(subtitles, padding_before, padding_after)
text = 'WEBVTT\n\n' + text.replace(',', '.')
return text | python | def vtt_formatter(subtitles, padding_before=0, padding_after=0):
"""
Serialize a list of subtitles according to the VTT format, with optional time padding.
"""
text = srt_formatter(subtitles, padding_before, padding_after)
text = 'WEBVTT\n\n' + text.replace(',', '.')
return text | [
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agermanidis/autosub | autosub/formatters.py | json_formatter | def json_formatter(subtitles):
"""
Serialize a list of subtitles as a JSON blob.
"""
subtitle_dicts = [
{
'start': start,
'end': end,
'content': text,
}
for ((start, end), text)
in subtitles
]
return json.dumps(subtitle_dicts) | python | def json_formatter(subtitles):
"""
Serialize a list of subtitles as a JSON blob.
"""
subtitle_dicts = [
{
'start': start,
'end': end,
'content': text,
}
for ((start, end), text)
in subtitles
]
return json.dumps(subtitle_dicts) | [
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agermanidis/autosub | autosub/__init__.py | percentile | def percentile(arr, percent):
"""
Calculate the given percentile of arr.
"""
arr = sorted(arr)
index = (len(arr) - 1) * percent
floor = math.floor(index)
ceil = math.ceil(index)
if floor == ceil:
return arr[int(index)]
low_value = arr[int(floor)] * (ceil - index)
high_val... | python | def percentile(arr, percent):
"""
Calculate the given percentile of arr.
"""
arr = sorted(arr)
index = (len(arr) - 1) * percent
floor = math.floor(index)
ceil = math.ceil(index)
if floor == ceil:
return arr[int(index)]
low_value = arr[int(floor)] * (ceil - index)
high_val... | [
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agermanidis/autosub | autosub/__init__.py | extract_audio | def extract_audio(filename, channels=1, rate=16000):
"""
Extract audio from an input file to a temporary WAV file.
"""
temp = tempfile.NamedTemporaryFile(suffix='.wav', delete=False)
if not os.path.isfile(filename):
print("The given file does not exist: {}".format(filename))
raise Ex... | python | def extract_audio(filename, channels=1, rate=16000):
"""
Extract audio from an input file to a temporary WAV file.
"""
temp = tempfile.NamedTemporaryFile(suffix='.wav', delete=False)
if not os.path.isfile(filename):
print("The given file does not exist: {}".format(filename))
raise Ex... | [
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agermanidis/autosub | autosub/__init__.py | find_speech_regions | def find_speech_regions(filename, frame_width=4096, min_region_size=0.5, max_region_size=6): # pylint: disable=too-many-locals
"""
Perform voice activity detection on a given audio file.
"""
reader = wave.open(filename)
sample_width = reader.getsampwidth()
rate = reader.getframerate()
n_chan... | python | def find_speech_regions(filename, frame_width=4096, min_region_size=0.5, max_region_size=6): # pylint: disable=too-many-locals
"""
Perform voice activity detection on a given audio file.
"""
reader = wave.open(filename)
sample_width = reader.getsampwidth()
rate = reader.getframerate()
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agermanidis/autosub | autosub/__init__.py | generate_subtitles | def generate_subtitles( # pylint: disable=too-many-locals,too-many-arguments
source_path,
output=None,
concurrency=DEFAULT_CONCURRENCY,
src_language=DEFAULT_SRC_LANGUAGE,
dst_language=DEFAULT_DST_LANGUAGE,
subtitle_file_format=DEFAULT_SUBTITLE_FORMAT,
api_key=None... | python | def generate_subtitles( # pylint: disable=too-many-locals,too-many-arguments
source_path,
output=None,
concurrency=DEFAULT_CONCURRENCY,
src_language=DEFAULT_SRC_LANGUAGE,
dst_language=DEFAULT_DST_LANGUAGE,
subtitle_file_format=DEFAULT_SUBTITLE_FORMAT,
api_key=None... | [
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agermanidis/autosub | autosub/__init__.py | validate | def validate(args):
"""
Check that the CLI arguments passed to autosub are valid.
"""
if args.format not in FORMATTERS:
print(
"Subtitle format not supported. "
"Run with --list-formats to see all supported formats."
)
return False
if args.src_languag... | python | def validate(args):
"""
Check that the CLI arguments passed to autosub are valid.
"""
if args.format not in FORMATTERS:
print(
"Subtitle format not supported. "
"Run with --list-formats to see all supported formats."
)
return False
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agermanidis/autosub | autosub/__init__.py | main | def main():
"""
Run autosub as a command-line program.
"""
parser = argparse.ArgumentParser()
parser.add_argument('source_path', help="Path to the video or audio file to subtitle",
nargs='?')
parser.add_argument('-C', '--concurrency', help="Number of concurrent API reques... | python | def main():
"""
Run autosub as a command-line program.
"""
parser = argparse.ArgumentParser()
parser.add_argument('source_path', help="Path to the video or audio file to subtitle",
nargs='?')
parser.add_argument('-C', '--concurrency', help="Number of concurrent API reques... | [
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palantir/python-language-server | pyls/plugins/pylint_lint.py | PylintLinter.lint | def lint(cls, document, is_saved, flags=''):
"""Plugin interface to pyls linter.
Args:
document: The document to be linted.
is_saved: Whether or not the file has been saved to disk.
flags: Additional flags to pass to pylint. Not exposed to
pyls_lint, ... | python | def lint(cls, document, is_saved, flags=''):
"""Plugin interface to pyls linter.
Args:
document: The document to be linted.
is_saved: Whether or not the file has been saved to disk.
flags: Additional flags to pass to pylint. Not exposed to
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is_saved: Whether or not the file has been saved to disk.
flags: Additional flags to pass to pylint. Not exposed to
pyls_lint, but used for testing.
Returns:
A li... | [
"Plugin",
"interface",
"to",
"pyls",
"linter",
"."
] | 96e08d85635382d17024c352306c4759f124195d | https://github.com/palantir/python-language-server/blob/96e08d85635382d17024c352306c4759f124195d/pyls/plugins/pylint_lint.py#L15-L131 | train |
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