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compressor = lzma.LZMACompressor( check=lzma.CHECK_CRC64, filters=[ {"id": lzma.FILTER_X86}, {"id": lzma.FILTER_LZMA2, "preset": lzma.PRESET_DEFAULT}, ]) for block in src: encoded = compressor.compress(block) if encoded: ...
def xz_compress_stream(src)
Compress data from `src`. Args: src (iterable): iterable that yields blocks of data to compress Yields: blocks of compressed data
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dec = lzma.LZMADecompressor() for block in src: decoded = dec.decompress(block) if decoded: yield decoded if dec.unused_data: # pragma: nocover; can't figure out how to test this raise IOError('Read unused data at end of compressed stream')
def xz_decompress_stream(src)
Decompress data from `src`. Args: src (iterable): iterable that yields blocks of compressed data Yields: blocks of uncompressed data
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block = next(src) compression = guess_compression(block) if compression == 'bz2': src = bz2_decompress_stream(chain([block], src)) elif compression == 'xz': src = xz_decompress_stream(chain([block], src)) else: src = chain([block], src) for block in src: yie...
def auto_decompress_stream(src)
Decompress data from `src` if required. If the first block of `src` appears to be compressed, then the entire stream will be uncompressed. Otherwise the stream will be passed along as-is. Args: src (iterable): iterable that yields blocks of data Yields: blocks of uncompressed data
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path = os.path.abspath(path) dirname = os.path.abspath(dirname) while len(path) >= len(dirname): if path == dirname: return True newpath = os.path.dirname(path) if newpath == path: return False path = newpath return False
def path_is_inside(path, dirname)
Return True if path is under dirname.
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# TODO: do we really want to be absolute here? base = os.path.abspath(base) path = os.path.join(base, *elements) path = os.path.normpath(path) if not path_is_inside(path, base): raise ValueError('target path is outside of the base path') return path
def safejoin(base, *elements)
Safely joins paths together. The result will always be a subdirectory under `base`, otherwise ValueError is raised. Args: base (str): base path elements (list of strings): path elements to join to base Returns: elements joined to base
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current = fileobj.tell() fileobj.seek(0, 2) end = fileobj.tell() fileobj.seek(current) return end
def filesize(fileobj)
Return the number of bytes in the fileobj. This function seeks to the end of the file, and then back to the original position.
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evt = WeakEvent(auto_reset=False) # first ensure that any pending callbacks from worker threads have been delivered # these are calls of _fromMain() Callback(evt.Signal) evt.Wait(timeout=timeout) evt.Reset() # reuse # grab the current set of inprogress cothreads/events wait4 = se...
def _sync(timeout=None)
I will wait until all pending handlers cothreads have completed
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_SharedPV.close(self, destroy) if sync: # TODO: still not syncing PVA workers... _sync() self._disconnected.Wait(timeout=timeout)
def close(self, destroy=False, sync=False, timeout=None)
Close PV, disconnecting any clients. :param bool destroy: Indicate "permanent" closure. Current clients will not see subsequent open(). :param bool sync: When block until any pending onLastDisconnect() is delivered (timeout applies). :param float timeout: Applies only when sync=True. None for...
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while True: # TODO: Queue.get() (and anything using thread.allocate_lock # ignores signals :( so timeout periodically to allow delivery try: callable = None # ensure no lingering references to past work while blocking callable =...
def handle(self)
Process queued work until interrupt() is called
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lvl = _lvlmap.get(lvl, lvl) assert lvl in _lvls, lvl _ClientProvider.set_debug(lvl)
def set_debug(lvl)
Set PVA global debug print level. This prints directly to stdout, bypassing eg. sys.stdout. :param lvl: logging.* level or logLevel*
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_log.debug("P4P atexit begins") # clean provider registry from .server import clearProviders, _cleanup_servers clearProviders() # close client contexts from .client.raw import _cleanup_contexts _cleanup_contexts() # stop servers _cleanup_servers() # shutdown default work ...
def cleanup()
P4P sequenced shutdown. Intended to be atexit. Idenpotent.
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with klass(*args, **kws): _log.info("Running server") try: while True: time.sleep(100) except KeyboardInterrupt: pass finally: _log.info("Stopping server")
def forever(klass, *args, **kws)
Create a server and block the calling thread until KeyboardInterrupt. Shorthand for: :: with Server(*args, **kws): try; time.sleep(99999999) except KeyboardInterrupt: pass
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isarray = valtype[:1] == 'a' F = [ ('value', valtype), ('alarm', alarm), ('timeStamp', timeStamp), ] _metaHelper(F, valtype, display=display, control=control, valueAlarm=valueAlarm) F.extend(extra) return Type(id="epics:nt/NTSc...
def buildType(valtype, extra=[], display=False, control=False, valueAlarm=False)
Build a Type :param str valtype: A type code to be used with the 'value' field. See :ref:`valuecodes` :param list extra: A list of tuples describing additional non-standard fields :param bool display: Include optional fields for display meta-data :param bool control: Include optional f...
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if isinstance(value, Value): return value elif isinstance(value, ntwrappercommon): return value.raw elif isinstance(value, dict): return self.Value(self.type, value) else: S, NS = divmod(float(timestamp or time.time()), 1.0) ...
def wrap(self, value, timestamp=None)
Pack python value into Value Accepts dict to explicitly initialize fields be name. Any other type is assigned to the 'value' field.
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assert isinstance(value, Value), value V = value.value try: T = klass.typeMap[type(V)] except KeyError: raise ValueError("Can't unwrap value of type %s" % type(V)) try: return T(value.value)._store(value) except Exception as e:...
def unwrap(klass, value)
Unpack a Value into an augmented python type (selected from the 'value' field)
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S = super(Value, self).changed for fld in fields or (None,): # no args tests for any change if S(fld): return True return False
def changed(self, *fields)
Test if one or more fields have changed. A field is considered to have changed if it has been marked as changed, or if any of its parent, or child, fields have been marked as changed.
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return ValueBase.changedSet(self, expand, parents)
def changedSet(self, expand=False, parents=False)
:param bool expand: Whether to expand when entire sub-structures are marked as changed. If True, then sub-structures are expanded and only leaf fields will be included. If False, then a direct translation is made, which may include both leaf and sub-structure fiel...
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attrib = getattr(value, 'attrib', {}) S, NS = divmod(time.time(), 1.0) value = numpy.asarray(value) # loses any special/augmented attributes dims = list(value.shape) dims.reverse() # inner-most sent as left if 'ColorMode' not in attrib: # attempt to...
def wrap(self, value)
Wrap numpy.ndarray as Value
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V = value.value if V is None: # Union empty. treat as zero-length char array V = numpy.zeros((0,), dtype=numpy.uint8) return V.view(klass.ntndarray)._store(value)
def unwrap(klass, value)
Unwrap Value as NTNDArray
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def dounwrap(code, msg, val): _log.debug("Handler (%s, %s, %s) -> %s", code, msg, LazyRepr(val), handler) try: if code == 0: handler(RemoteError(msg)) elif code == 1: handler(Cancelled()) else: if val is not Non...
def unwrapHandler(handler, nt)
Wrap get/rpc handler to unwrap Value
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if callable(value): def logbuilder(V): try: value(V) except: _log.exception("Error in Builder") raise # will be logged again return logbuilder def builder(V): try: if isinstance(value, Value): ...
def defaultBuilder(value, nt)
Reasonably sensible default handling of put builder
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if name is None: self._channels = {} else: self._channels.pop(name) if self._ctxt is not None: self._ctxt.disconnect(name)
def disconnect(self, name=None)
Clear internal Channel cache, allowing currently unused channels to be implictly closed. :param str name: None, to clear the entire cache, or a name string to clear only a certain entry.
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opts = [] if process is not None: opts.append('process=%s' % process) if wait is not None: if wait: opts.append('wait=true') else: opts.append('wait=false') return 'field()record[%s]' % (','.join(opts))
def _request(self, process=None, wait=None)
helper for building pvRequests :param str process: Control remote processing. May be 'true', 'false', 'passive', or None. :param bool wait: Wait for all server processing to complete.
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chan = self._channel(name) return _p4p.ClientOperation(chan, handler=unwrapHandler(handler, self._nt), pvRequest=wrapRequest(request), get=True, put=False)
def get(self, name, handler, request=None)
Begin Fetch of current value of a PV :param name: A single name string or list of name strings :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. :param callable handler: Completion notification. Called with a Value, RemoteError, or Cancelled ...
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chan = self._channel(name) return _p4p.ClientOperation(chan, handler=unwrapHandler(handler, self._nt), builder=defaultBuilder(builder, self._nt), pvRequest=wrapRequest(request), get=get, put=True)
def put(self, name, handler, builder=None, request=None, get=True)
Write a new value to a PV. :param name: A single name string or list of name strings :param callable handler: Completion notification. Called with None (success), RemoteError, or Cancelled :param callable builder: Called when the PV Put type is known. A builder is responsible ...
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chan = self._channel(name) if value is None: value = Value(Type([])) return _p4p.ClientOperation(chan, handler=unwrapHandler(handler, self._nt), value=value, pvRequest=wrapRequest(request), rpc=True)
def rpc(self, name, handler, value, request=None)
Perform RPC operation on PV :param name: A single name string or list of name strings :param callable handler: Completion notification. Called with a Value, RemoteError, or Cancelled :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. :...
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chan = self._channel(name) return Subscription(context=self, nt=self._nt, channel=chan, handler=monHandler(handler), pvRequest=wrapRequest(request), **kws)
def monitor(self, name, handler, request=None, **kws)
Begin subscription to named PV :param str name: PV name string :param callable handler: Completion notification. Called with None (FIFO not empty), RemoteError, Cancelled, or Disconnected :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. ...
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_SharedPV.close(self, destroy) if sync: return self._wait_closed()
def close(self, destroy=False, sync=False)
Close PV, disconnecting any clients. :param bool destroy: Indicate "permanent" closure. Current clients will not see subsequent open(). :param bool sync: When block until any pending onLastDisconnect() is delivered (timeout applies). :param float timeout: Applies only when sync=True. None for...
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def decorate(fn): assert asyncio.iscoroutinefunction(fn), "Place @timesout before @coroutine" @wraps(fn) @asyncio.coroutine def wrapper(*args, timeout=deftimeout, **kws): loop = kws.get('loop') fut = fn(*args, **kws) if timeout is None: ...
def timesout(deftimeout=5.0)
Decorate a coroutine to implement an overall timeout. The decorated coroutine will have an additional keyword argument 'timeout=' which gives a timeout in seconds, or None to disable timeout. :param float deftimeout: The default timeout= for the decorated coroutine. It is suggested perform one ov...
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singlepv = isinstance(name, (bytes, str)) if singlepv: return (yield from self._get_one(name, request=request)) elif request is None: request = [None] * len(name) assert len(name) == len(request), (name, request) futs = [self._get_one(N, reques...
def get(self, name, request=None)
Fetch current value of some number of PVs. :param name: A single name string or list of name strings :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. :returns: A p4p.Value, or list of same. Subject to :py:ref:`unwrap`. When invoked ...
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if request and (process or wait is not None): raise ValueError("request= is mutually exclusive to process= or wait=") elif process or wait is not None: request = 'field()record[block=%s,process=%s]' % ('true' if wait else 'false', process or 'passive') singlepv ...
def put(self, name, values, request=None, process=None, wait=None, get=True)
Write a new value of some number of PVs. :param name: A single name string or list of name strings :param values: A single value, a list of values, a dict, a `Value`. May be modified by the constructor nt= argument. :param request: A :py:class:`p4p.Value` or string to qualify this request, or ...
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assert asyncio.iscoroutinefunction(cb), "monitor callback must be coroutine" R = Subscription(name, cb, notify_disconnect=notify_disconnect, loop=self.loop) cb = partial(self.loop.call_soon_threadsafe, R._event) R._S = super(Context, self).monitor(name, cb, request) ret...
def monitor(self, name, cb, request=None, notify_disconnect=False)
Create a subscription. :param str name: PV name string :param callable cb: Processing callback :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. :param bool notify_disconnect: In additional to Values, the callback may also be call with ...
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if self._S is not None: # after .close() self._event should never be called self._S.close() self._S = None self._Q.put_nowait(None)
def close(self)
Begin closing subscription.
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if self._S is not None: # after .close() self._event should never be called self._S.close() # wait for Cancelled to be delivered self._evt.wait() self._S = None
def close(self)
Close subscription.
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if self._Q is not None: for T in self._T: self._Q.interrupt() for n, T in enumerate(self._T): _log.debug('Join Context worker %d', n) T.join() _log.debug('Joined Context workers') self._Q, self._T = None, No...
def close(self)
Force close all Channels and cancel all Operations
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singlepv = isinstance(name, (bytes, unicode)) if singlepv: name = [name] request = [request] elif request is None: request = [None] * len(name) assert len(name) == len(request), (name, request) # use Queue instead of Event to allow ...
def get(self, name, request=None, timeout=5.0, throw=True)
Fetch current value of some number of PVs. :param name: A single name string or list of name strings :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. :param float timeout: Operation timeout in seconds :param bool throw: When true, oper...
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if request and (process or wait is not None): raise ValueError("request= is mutually exclusive to process= or wait=") elif process or wait is not None: request = 'field()record[block=%s,process=%s]' % ('true' if wait else 'false', process or 'passive') singlepv ...
def put(self, name, values, request=None, timeout=5.0, throw=True, process=None, wait=None, get=True)
Write a new value of some number of PVs. :param name: A single name string or list of name strings :param values: A single value, a list of values, a dict, a `Value`. May be modified by the constructor nt= argument. :param request: A :py:class:`p4p.Value` or string to qualify this request, or ...
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done = Queue() op = super(Context, self).rpc(name, done.put_nowait, value, request=request) try: try: result = done.get(timeout=timeout) except Empty: result = TimeoutError() if throw and isinstance(result, Exception)...
def rpc(self, name, value, request=None, timeout=5.0, throw=True)
Perform a Remote Procedure Call (RPC) operation :param str name: PV name string :param Value value: Arguments. Must be Value instance :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. :param float timeout: Operation timeout in seconds ...
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R = Subscription(self, name, cb, notify_disconnect=notify_disconnect, queue=queue) R._S = super(Context, self).monitor(name, R._event, request) return R
def monitor(self, name, cb, request=None, notify_disconnect=False, queue=None)
Create a subscription. :param str name: PV name string :param callable cb: Processing callback :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. :param bool notify_disconnect: In additional to Values, the callback may also be call with ...
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self._wrap = wrap or (nt and nt.wrap) or self._wrap self._unwrap = unwrap or (nt and nt.unwrap) or self._unwrap _SharedPV.open(self, self._wrap(value))
def open(self, value, nt=None, wrap=None, unwrap=None)
Mark the PV as opened an provide its initial value. This initial value is later updated with post(). :param value: A Value, or appropriate object (see nt= and wrap= of the constructor). Any clients which have begun connecting which began connecting while this PV was in the close'd sta...
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wrap = None if rtype is None or isinstance(rtype, Type): pass elif isinstance(type, (list, tuple)): rtype = Type(rtype) elif hasattr(rtype, 'type'): # eg. one of the NT* helper classes wrap = rtype.wrap rtype = rtype.type else: raise TypeError("Not suppo...
def rpc(rtype=None)
Decorator marks a method for export. :param type: Specifies which :py:class:`Type` this method will return. The return type (rtype) must be one of: - An instance of :py:class:`p4p.Type` - None, in which case the method must return a :py:class:`p4p.Value` - One of the NT helper classes (eg :py:cla...
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def wrapper(fn): fn._call_PV = pvname fn._call_Request = request fn._reply_Type = rtype return fn return wrapper
def rpccall(pvname, request=None, rtype=None)
Decorator marks a client proxy method. :param str pvname: The PV name, which will be formated using the 'format' argument of the proxy class constructor. :param request: A pvRequest string or :py:class:`p4p.Value` passed to eg. :py:meth:`p4p.client.thread.Context.rpc`. The method to be decorated must have...
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from p4p.server import Server import time queue = ThreadedWorkQueue(maxsize=maxsize, workers=workers) provider = NTURIDispatcher(queue, target=target, prefix=prefix, name=provider) threads = [] server = Server(providers=[provider], useenv=useenv, conf=conf, isolate=isolate) with server,...
def quickRPCServer(provider, prefix, target, maxsize=20, workers=1, useenv=True, conf=None, isolate=False)
Run an RPC server in the current thread Calls are handled sequentially, and always in the current thread, if workers=1 (the default). If workers>1 then calls are handled concurrently by a pool of worker threads. Requires NTURI style argument encoding. :param str provider: A provider name. Must be uni...
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# inject our ctor first so we don't have to worry about super() non-sense. def _proxyinit(self, context=None, format={}, **kws): assert context is not None, context self.context = context self.format = format spec.__init__(self, **kws) obj = {'__init__': _proxyinit} ...
def rpcproxy(spec)
Decorator to enable this class to proxy RPC client calls The decorated class constructor takes two additional arguments, `context=` is required to be a :class:`~p4p.client.thread.Context`. `format`= can be a string, tuple, or dictionary and is applied to PV name strings given to :py:func:`rpcall`. ...
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if val.getID()!=self.id: self._update(val) return self._unwrap(val)
def unwrap(self, val)
Unpack a Value as some other python type
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assert valtype[:1] == 'a', 'valtype must be an array' return Type(id="epics:nt/NTMultiChannel:1.0", spec=[ ('value', valtype), ('channelName', 'as'), ('descriptor', 's'), ('alarm', alarm), ...
def buildType(valtype, extra=[])
Build a Type :param str valtype: A type code to be used with the 'value' field. Must be an array :param list extra: A list of tuples describing additional non-standard fields :returns: A :py:class:`Type`
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return Type(id="epics:nt/NTTable:1.0", spec=[ ('labels', 'as'), ('value', ('S', None, columns)), ('descriptor', 's'), ('alarm', alarm), ('timeStamp', timeStamp), ]...
def buildType(columns=[], extra=[])
Build a table :param list columns: List of column names and types. eg [('colA', 'd')] :param list extra: A list of tuples describing additional non-standard fields :returns: A :py:class:`Type`
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if isinstance(values, Value): return values cols = dict([(L, []) for L in self.labels]) try: # unzip list of dict for V in values: for L in self.labels: try: cols[L].append(V[L]) ...
def wrap(self, values)
Pack an iterable of dict into a Value >>> T=NTTable([('A', 'ai'), ('B', 'as')]) >>> V = T.wrap([ {'A':42, 'B':'one'}, {'A':43, 'B':'two'}, ])
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ret = [] # build lists of column names, and value lbl, cols = [], [] for cname, cval in value.value.items(): lbl.append(cname) cols.append(cval) # zip together column arrays to iterate over rows for rval in izip(*cols): # zip...
def unwrap(value)
Iterate an NTTable :returns: An iterator yielding an OrderedDict for each column
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try: return Type(id="epics:nt/NTURI:1.0", spec=[ ('scheme', 's'), ('authority', 's'), ('path', 's'), ('query', ('S', None, args)), ]) except Exception as e: raise ValueError('Unable to build NTUR...
def buildType(args)
Build NTURI :param list args: A list of tuples of query argument name and PVD type code. >>> I = NTURI([ ('arg_a', 'I'), ('arg_two', 's'), ])
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# build dict of argument name+value AV = {} AV.update([A for A in kws.items() if A[1] is not None]) AV.update([(N, V) for (N, _T), V in zip(self._args, args)]) # list of argument name+type tuples for which a value was provided AT = [A for A in self._args if A[0]...
def wrap(self, path, args=(), kws={}, scheme='', authority='')
Wrap argument values (tuple/list with optional dict) into Value :param str path: The PV name to which this call is made :param tuple args: Ordered arguments :param dict kws: Keyword arguments :rtype: Value
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V = self.type() S, NS = divmod(float(timestamp or time.time()), 1.0) V.timeStamp = { 'secondsPastEpoch': S, 'nanoseconds': NS * 1e9, } if isinstance(value, dict): # assume dict of index and choices list V.value = value ...
def wrap(self, value, timestamp=None)
Pack python value into Value
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if value.changed('value.choices'): self._choices = value['value.choices'] idx = value['value.index'] ret = ntenum(idx)._store(value) try: ret.choice = self._choices[idx] except IndexError: pass # leave it as None return ret
def unwrap(self, value)
Unpack a Value into an augmented python type (selected from the 'value' field)
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if isinstance(py, (bytes, unicode)): for i,C in enumerate(V['value.choices'] or self._choices): if py==C: V['value.index'] = i return # attempt to parse as integer V['value.index'] = py
def assign(self, V, py)
Store python value in Value
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_SharedPV.close(self, destroy) if sync: # TODO: still not syncing PVA workers... self._queue.sync() self._disconnected.wait()
def close(self, destroy=False, sync=False, timeout=None)
Close PV, disconnecting any clients. :param bool destroy: Indicate "permanent" closure. Current clients will not see subsequent open(). :param bool sync: When block until any pending onLastDisconnect() is delivered (timeout applies). :param float timeout: Applies only when sync=True. None for...
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all = gc.get_objects() _stats = {} for obj in all: K = type(obj) if K is StatsDelta: continue # avoid counting ourselves elif K is InstanceType: # instance of an old-style class K = getattr(obj, '__class__', K) # Track types as strings to avo...
def gcstats()
Count the number of instances of each type/class :returns: A dict() mapping type (as a string) to an integer number of references
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import threading import time S = _StatsThread(period=period, file=file) T = threading.Thread(target=S) T.daemon = True T.start()
def periodic(period=60.0, file=sys.stderr)
Start a daemon thread which will periodically print GC stats :param period: Update period in seconds :param file: A writable file-like object
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cur = gcstats() Ncur = len(cur) if self.stats is not None and file is not None: prev = self.stats Nprev = self.ntypes # may be less than len(prev) if Ncur != Nprev: print("# Types %d -> %d" % (Nprev, Ncur), file=file) Sc...
def collect(self, file=sys.stderr)
Collect stats and print results to file :param file: A writable file-like object
3.125672
3.247894
0.962369
singlepv = isinstance(name, (bytes, unicode)) if singlepv: return self._get_one(name, request=request, timeout=timeout, throw=throw) elif request is None: request = [None] * len(name) assert len(name) == len(request), (name, request) return cot...
def get(self, name, request=None, timeout=5.0, throw=True)
Fetch current value of some number of PVs. :param name: A single name string or list of name strings :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. :param float timeout: Operation timeout in seconds :param bool throw: When true, oper...
3.976739
4.42289
0.899127
if request and (process or wait is not None): raise ValueError("request= is mutually exclusive to process= or wait=") elif process or wait is not None: request = 'field()record[block=%s,process=%s]' % ('true' if wait else 'false', process or 'passive') singlepv ...
def put(self, name, values, request=None, process=None, wait=None, timeout=5.0, get=True, throw=True)
Write a new value of some number of PVs. :param name: A single name string or list of name strings :param values: A single value, a list of values, a dict, a `Value`. May be modified by the constructor nt= argument. :param request: A :py:class:`p4p.Value` or string to qualify this request, or ...
4.052863
3.912603
1.035848
R = Subscription(name, cb, notify_disconnect=notify_disconnect) cb = partial(cothread.Callback, R._event) R._S = super(Context, self).monitor(name, cb, request) return R
def monitor(self, name, cb, request=None, notify_disconnect=False)
Create a subscription. :param str name: PV name string :param callable cb: Processing callback :param request: A :py:class:`p4p.Value` or string to qualify this request, or None to use a default. :param bool notify_disconnect: In additional to Values, the callback may also be call with ...
9.767743
11.640135
0.839143
if self._S is not None: # after .close() self._event should never be called self._S.close() self._S = None self._Q.Signal(None) self._T.Wait()
def close(self)
Close subscription.
12.167932
10.877541
1.118629
check_chamber(chamber) kwargs.update(chamber=chamber, congress=congress) if 'state' in kwargs and 'district' in kwargs: path = ("members/{chamber}/{state}/{district}/" "current.json").format(**kwargs) elif 'state' in kwargs: path = ...
def filter(self, chamber, congress=CURRENT_CONGRESS, **kwargs)
Takes a chamber and Congress, OR state and district, returning a list of members
2.726276
2.562781
1.063796
"Same as BillsClient.by_member" path = "members/{0}/bills/{1}.json".format(member_id, type) return self.fetch(path)
def bills(self, member_id, type='introduced')
Same as BillsClient.by_member
7.477584
4.230176
1.767677
check_chamber(chamber) path = "members/{first}/{type}/{second}/{congress}/{chamber}.json" path = path.format(first=first, second=second, type=type, congress=congress, chamber=chamber) return self.fetch(path)
def compare(self, first, second, chamber, type='votes', congress=CURRENT_CONGRESS)
See how often two members voted together in a given Congress. Takes two member IDs, a chamber and a Congress number.
3.134657
2.986945
1.049453
path = "members/{member_id}/bills/{type}.json".format( member_id=member_id, type=type) return self.fetch(path)
def by_member(self, member_id, type='introduced')
Takes a bioguide ID and a type: (introduced|updated|cosponsored|withdrawn) Returns recent bills
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3.471364
1.063463
"Shortcut for upcoming bills" path = "bills/upcoming/{chamber}.json".format(chamber=chamber) return self.fetch(path)
def upcoming(self, chamber, congress=CURRENT_CONGRESS)
Shortcut for upcoming bills
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5.554422
1.352273
check_chamber(chamber) now = datetime.datetime.now() year = year or now.year month = month or now.month path = "{chamber}/votes/{year}/{month}.json".format( chamber=chamber, year=year, month=month) return self.fetch(path, parse=lambda r: r['results'...
def by_month(self, chamber, year=None, month=None)
Return votes for a single month, defaulting to the current month.
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2.679508
1.138442
check_chamber(chamber) start, end = parse_date(start), parse_date(end) if start > end: start, end = end, start path = "{chamber}/votes/{start:%Y-%m-%d}/{end:%Y-%m-%d}.json".format( chamber=chamber, start=start, end=end) return self.fetch(path, p...
def by_range(self, chamber, start, end)
Return votes cast in a chamber between two dates, up to one month apart.
2.605852
2.488953
1.046967
"Return votes cast in a chamber on a single day" date = parse_date(date) return self.by_range(chamber, date, date)
def by_date(self, chamber, date)
Return votes cast in a chamber on a single day
5.711632
3.734192
1.52955
"Return today's votes in a given chamber" now = datetime.date.today() return self.by_range(chamber, now, now)
def today(self, chamber)
Return today's votes in a given chamber
6.530143
5.703763
1.144883
"Return votes by type: missed, party, lone no, perfect" check_chamber(chamber) path = "{congress}/{chamber}/votes/{type}.json".format( congress=congress, chamber=chamber, type=type) return self.fetch(path)
def by_type(self, chamber, type, congress=CURRENT_CONGRESS)
Return votes by type: missed, party, lone no, perfect
7.221801
2.859424
2.525614
"Return votes on nominations from a given Congress" path = "{congress}/nominations.json".format(congress=congress) return self.fetch(path)
def nominations(self, congress=CURRENT_CONGRESS)
Return votes on nominations from a given Congress
6.364917
4.015357
1.585143
url = self.BASE_URI + path headers = {'X-API-Key': self.apikey} log.debug(url) resp, content = self.http.request(url, headers=headers) content = u(content) content = json.loads(content) # handle errors if not content.get('status') == 'OK': ...
def fetch(self, path, parse=lambda r: r['results'][0])
Make an API request, with authentication. This method can be used directly to fetch new endpoints or customize parsing. :: >>> from congress import Congress >>> client = Congress() >>> senate = client.fetch('115/senate/members.json') >>> print(s...
3.722414
3.919763
0.949653
if isinstance(s, (datetime.datetime, datetime.date)): return s try: from dateutil.parser import parse except ImportError: parse = lambda d: datetime.datetime.strptime(d, "%Y-%m-%d") return parse(s)
def parse_date(s)
Parse a date using dateutil.parser.parse if available, falling back to datetime.datetime.strptime if not
2.318273
2.323321
0.997828
''' >>> d = DiskVarArray('/tmp/test3', dtype='uint32') >>> d.append([1, 2, 3, 4]) >>> d.__getitem__(0) memmap([1, 2, 3, 4], dtype=uint32) >>> d.append([5, 6, 7, 8]) >>> d.__getitem__(1) memmap([5, 6, 7, 8], dtype=uint32) >>> shutil.rmtree('/tmp/tes...
def append(self, v)
>>> d = DiskVarArray('/tmp/test3', dtype='uint32') >>> d.append([1, 2, 3, 4]) >>> d.__getitem__(0) memmap([1, 2, 3, 4], dtype=uint32) >>> d.append([5, 6, 7, 8]) >>> d.__getitem__(1) memmap([5, 6, 7, 8], dtype=uint32) >>> shutil.rmtree('/tmp/test3', ignore_errors=T...
2.728655
1.381803
1.974706
''' >>> import numpy as np >>> d = DiskVarArray('/tmp/test4', dtype='uint32') >>> d.append([1, 2, 3, 4]) >>> d.destroy # doctest:+ELLIPSIS <bound method DiskVarArray.destroy of <diskarray.vararray.DiskVarArray object at 0x...>> >>> shutil.rmtree('/tmp/test4', igno...
def destroy(self)
>>> import numpy as np >>> d = DiskVarArray('/tmp/test4', dtype='uint32') >>> d.append([1, 2, 3, 4]) >>> d.destroy # doctest:+ELLIPSIS <bound method DiskVarArray.destroy of <diskarray.vararray.DiskVarArray object at 0x...>> >>> shutil.rmtree('/tmp/test4', ignore_errors=True)
3.857712
1.510887
2.553276
''' >>> import numpy as np >>> da = DiskArray('/tmp/test.array', shape=(0, 3), growby=3, dtype=np.float32) >>> print(da[:]) [] >>> data = np.array([[2,3,4], [1, 2, 3]]) >>> da.append(data[0]) >>> print(da[:]) [[2. 3. 4.] [0. 0. 0.] [0. 0. 0.]] ...
def append(self, v)
>>> import numpy as np >>> da = DiskArray('/tmp/test.array', shape=(0, 3), growby=3, dtype=np.float32) >>> print(da[:]) [] >>> data = np.array([[2,3,4], [1, 2, 3]]) >>> da.append(data[0]) >>> print(da[:]) [[2. 3. 4.] [0. 0. 0.] [0. 0. 0.]]
3.785548
2.513223
1.506252
''' >>> import numpy as np >>> da = DiskArray('/tmp/test.array', shape=(0, 3), capacity=(10, 3), dtype=np.float32) >>> print(da[:]) [[2. 3. 4.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] >>> data = np.arr...
def extend(self, v)
>>> import numpy as np >>> da = DiskArray('/tmp/test.array', shape=(0, 3), capacity=(10, 3), dtype=np.float32) >>> print(da[:]) [[2. 3. 4.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] >>> data = np.array([[2,3,4], [1, 2, ...
1.959003
1.405483
1.393829
# Strip the index info non_index_columns = filter(lambda x: x not in self._prdd._index_names, self._prdd._column_names()) self._grouped_spark_sql = (self._prdd.to_spark_sql() .select(non_index_columns) ...
def _prep_spark_sql_groupby(self)
Used Spark SQL group approach
5.304926
5.126567
1.034791
myargs = self._myargs mykwargs = self._mykwargs def extract_keys(groupedFrame): for key, group in groupedFrame: yield (key, group) def group_and_extract(frame): return extract_keys(frame.groupby(*myargs, **mykwargs)) self._baseR...
def _prep_pandas_groupby(self)
Prepare the old school pandas group by based approach.
6.027497
5.732729
1.051419
return rdd.reduceByKey(lambda x, y: x.append(y))
def _group(self, rdd)
Group together the values with the same key.
5.560222
3.902011
1.424963
self._prep_pandas_groupby() def extract_group_labels(frame): return (frame[0], frame[1].index.values) return self._mergedRDD.map(extract_group_labels).collectAsMap()
def groups(self)
Returns dict {group name -> group labels}.
10.953206
8.761948
1.250088
if self._can_use_new_school(): return self._grouped_spark_sql.count() self._prep_pandas_groupby() return self._mergedRDD.count()
def ngroups(self)
Number of groups.
20.676849
19.488468
1.060979
self._prep_pandas_groupby() def extract_group_indices(frame): return (frame[0], frame[1].index) return self._mergedRDD.map(extract_group_indices).collectAsMap()
def indices(self)
Returns dict {group name -> group indices}.
10.669307
8.412019
1.268341
self._prep_pandas_groupby() return DataFrame.fromDataFrameRDD( self._regroup_mergedRDD().values().map( lambda x: x.median()), self.sql_ctx)
def median(self)
Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex.
19.510612
17.972054
1.085608
if self._can_use_new_school(): self._prep_spark_sql_groupby() import pyspark.sql.functions as func return self._use_aggregation(func.mean) self._prep_pandas_groupby() return DataFrame.fromDataFrameRDD( self._regroup_mergedRDD().values().ma...
def mean(self)
Compute mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex.
10.609351
9.958145
1.065394
self._prep_pandas_groupby() return DataFrame.fromDataFrameRDD( self._regroup_mergedRDD().values().map( lambda x: x.var(ddof=ddof)), self.sql_ctx)
def var(self, ddof=1)
Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex.
13.80889
14.83413
0.930886
if self._can_use_new_school(): self._prep_spark_sql_groupby() import pyspark.sql.functions as func return self._use_aggregation(func.sum) self._prep_pandas_groupby() myargs = self._myargs mykwargs = self._mykwargs def create_combiner(...
def sum(self)
Compute the sum for each group.
5.864257
5.565516
1.053677
expressions = map(lambda c: f(c).alias(c), self._columns) return expressions
def _create_exprs_using_func(self, f, columns)
Create aggregate expressions using the provided function with the result coming back as the original column name.
9.94746
9.223942
1.078439
if not columns: columns = self._columns from pyspark.sql import functions as F aggs = map(lambda column: agg(column).alias(column), self._columns) aggRdd = self._grouped_spark_sql.agg(*aggs) df = DataFrame.from_schema_rdd(aggRdd, self._by) return df
def _use_aggregation(self, agg, columns=None)
Compute the result using the aggregation function provided. The aggregation name must also be provided so we can strip of the extra name that Spark SQL adds.
5.345678
5.241404
1.019894
myargs = self._myargs mykwargs = self._mykwargs self._prep_pandas_groupby() def regroup(df): return df.groupby(*myargs, **mykwargs) return self._mergedRDD.mapValues(regroup)
def _regroup_mergedRDD(self)
A common pattern is we want to call groupby again on the dataframes so we can use the groupby functions.
5.682367
5.013714
1.133365
# TODO: Stop collecting the entire frame for each key. self._prep_pandas_groupby() myargs = self._myargs mykwargs = self._mykwargs nthRDD = self._regroup_mergedRDD().mapValues( lambda r: r.nth( n, *args, **kwargs)).values() return Data...
def nth(self, n, *args, **kwargs)
Take the nth element of each grouby.
11.909569
10.438528
1.140924
if self._can_use_new_school() and f == pd.Series.kurtosis: self._prep_spark_sql_groupby() import custom_functions as CF return self._use_aggregation(CF.kurtosis) else: self._prep_pandas_groupby() return DataFrame.fromDataFrameRDD( ...
def aggregate(self, f)
Apply the aggregation function. Note: This implementation does note take advantage of partial aggregation unless we have one of the special cases. Currently the only special case is Series.kurtosis - and even that doesn't properly do partial aggregations, but we can improve it to...
11.415647
9.680187
1.17928
self._prep_pandas_groupby() def key_by_index(data): # TODO: Is there a better way to do this? for key, row in data.iterrows(): yield (key, pd.DataFrame.from_dict( dict([(key, row)]), orient='index')) myargs = sel...
def apply(self, func, *args, **kwargs)
Apply the provided function and combine the results together in the same way as apply from groupby in pandas. This returns a DataFrame.
5.161427
5.283984
0.976806
def _(col): spark_ctx = SparkContext._active_spark_context java_ctx = (getattr(spark_ctx._jvm.com.sparklingpandas.functions, name) (col._java_ctx if isinstance(col, Column) else col)) return Column(java_ctx) _.__name__ = name _.__d...
def _create_function(name, doc="")
Create a function for aggregator by name
5.027864
4.951739
1.015373
for column, values in frame.iteritems(): # Temporary hack, fix later counter = self._counters.get(column) for value in values: if counter is not None: counter.merge(value)
def merge(self, frame)
Add another DataFrame to the PStatCounter.
7.30637
5.195086
1.4064
if not isinstance(other, PStatCounter): raise Exception("Can only merge PStatcounters!") for column, counter in self._counters.items(): other_counter = other._counters.get(column) self._counters[column] = counter.mergeStats(other_counter) return sel...
def merge_pstats(self, other)
Merge all of the stats counters of the other PStatCounter with our counters.
3.911093
3.246351
1.204766
for column_name, _ in self._column_stats.items(): data_arr = frame[[column_name]].values count, min_max_tup, mean, _, _, _ = \ scistats.describe(data_arr) stats_counter = StatCounter() stats_counter.n = count stats_counter.mu =...
def merge(self, frame)
Add another DataFrame to the accumulated stats for each column. Parameters ---------- frame: pandas DataFrame we will update our stats counter with.
3.77852
3.48472
1.084311
for column_name, _ in self._column_stats.items(): self._column_stats[column_name] = self._column_stats[column_name] \ .mergeStats(other_col_counters._column_stats[column_name]) return self
def merge_stats(self, other_col_counters)
Merge statistics from a different column stats counter in to this one. Parameters ---------- other_column_counters: Other col_stat_counter to marge in to this one.
2.65907
2.609235
1.019099