rem stringlengths 0 322k | add stringlengths 0 2.05M | context stringlengths 8 228k |
|---|---|---|
row = tmp + [[code, str(date)] for code, date in row['single']] | if 'single' in row: tmp.extend([[code, str(date)] for code, date in row['single']]) row = tmp | def write_element(writer, number, name, element): if isinstance(element, Exception): row = [number, name, 'ERROR', element.message, element.detail] else: row = [number, name, element] |
writer.writerows(iter_element_values(20, 'Limitations on public access', metadata.access_limits)) | row = metadata.access_limits if row and not isinstance(row, Exception): tmp = [] for defn in row: entry = vocab2row(defn, []) tmp.append(entry) row = tmp writer.writerows(iter_element_values(20, 'Limitations on public access', row)) | def write_element(writer, number, name, element): if isinstance(element, Exception): row = [number, name, 'ERROR', element.message, element.detail] else: row = [number, name, element] |
writer.writerows(iter_element_values(23, 'Data format', metadata.data_format)) | row = metadata.data_format if row and not isinstance(row, Exception): tmp = [] for keyword, defn in row.items(): entry = vocab2row(defn, [keyword]) tmp.append(entry) row = tmp writer.writerows(iter_element_values(23, 'Data format', row)) | def write_element(writer, number, name, element): if isinstance(element, Exception): row = [number, name, 'ERROR', element.message, element.detail] else: row = [number, name, element] |
buf.write(line) | buf.write(str(line)) | def handleException(self, exception, environ, start_response): from mako.exceptions import RichTraceback from cStringIO import StringIO traceback = RichTraceback() buf = StringIO() for (filename, lineno, function, line) in traceback.traceback: buf.write("File %s, line %s, in %s\n" % (filename, lineno, function)) buf.... |
r = self.request(gid) | areas = get_areas(environ) r = self.request(gid, areas) | def __call__(self, environ, start_response): import os.path import medin.spatial |
tmp.append((key.capitalize(), row[key])) | tmp.append([key.capitalize(), row[key]]) | def vocab2row(vocab, default=None): if vocab: if 'error' in vocab: return ['ERROR', vocab['error']] else: return [vocab['short'], vocab['long'], vocab['defn']] |
entry = vocab2row(defn, []) | if 'other' in defn: entry = ['other', defn['other']] else: entry = vocab2row(defn, []) | def vocab2row(vocab, default=None): if vocab: if 'error' in vocab: return ['ERROR', vocab['error']] else: return [vocab['short'], vocab['long'], vocab['defn']] |
return TemplateContext('Search the Data Archive Centres', tvars=tvars) | return TemplateContext('Search the MEDIN Data Archive Centres', tvars=tvars) | def setup(self, environ): q = get_query(environ) errors = q.verify() if errors: for error in errors: msg_error(environ, error) search_term = q.getSearchTerm(cast=False) count = q.getCount() sort = q.getSort(cast=False) bbox = q.getBBOX() start_date = q.getStartDate(cast=False) end_date = q.getEndDate(cast=False) area ... |
date = self[key][0] | date = self[key][0].strip() | def asDate(self, key, cast, default, is_start): try: date = self[key][0] except KeyError, AttributeError: return default |
scl = logging.getLogger('suds.client') scl.setLevel(logging.DEBUG) scl.addHandler(logging.StreamHandler()) | def error_renderer(exception, environ, start_response): app = TemplateChooser(default_template) view = views.ErrorRenderer(exception) app.addContentTypes(view, 'light', light_types, light_default) app.addContentTypes(view, 'full', full_types) return app(environ, start_response) | |
if count > 0: | start_index = query.getStartIndex() if start_index < 1: if count != 0: dws_count = count - (abs(start_index) + 1) else: dws_count = 1 start_index = 1 elif count > 0: | def __call__(self, query, result_type, logger): try: ResponseClass = self._result_map[result_type] except KeyError: raise ValueError('Unknown result type: %s' % str(result_type)) |
dws_count = 1; | dws_count = 1 | def __call__(self, query, result_type, logger): try: ResponseClass = self._result_map[result_type] except KeyError: raise ValueError('Unknown result type: %s' % str(result_type)) |
query.getStartIndex(), | start_index, | def __call__(self, query, result_type, logger): try: ResponseClass = self._result_map[result_type] except KeyError: raise ValueError('Unknown result type: %s' % str(result_type)) |
docid = list(r)[0] | try: docid = list(r)[0] except IndexError: docid = 'none' | def setup(self, environ): from medin.dws import RESULT_SIMPLE from medin.terms import VocabError areas = get_areas(environ) q = get_query(environ) errors = q.verify() if errors: for error in errors: msg_error(environ, error) |
_field_map = {'updated': 'DatasetMetadataUpdateOrder', | _field_map = {'updated': 'DatasetMetadataUpdateDate', | def _processDocument(self, doc): |
field = 'DatasetMetadataUpdateOrder' | field = 'DatasetMetadataUpdateDate' | def __call__(self, query, result_type, logger): try: ResponseClass = self._result_map[result_type] except KeyError: raise ValueError('Unknown result type: %s' % str(result_type)) |
dates['range'] = [] | pass | def temporal_reference(self): dates = {} try: begin = self.xpath.xpathEval('//gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent//gml:beginPosition')[0].content.strip() end = self.xpath.xpathEval('//gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent//gml:endPosition')[0].content.strip() except IndexError: dat... |
dates['single'] = [] | single = [] | def temporal_reference(self): dates = {} try: begin = self.xpath.xpathEval('//gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent//gml:beginPosition')[0].content.strip() end = self.xpath.xpathEval('//gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent//gml:endPosition')[0].content.strip() except IndexError: dat... |
dates['single'].append((code, date)) | single.append((code, date)) if single: dates['single'] = single | def temporal_reference(self): dates = {} try: begin = self.xpath.xpathEval('//gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent//gml:beginPosition')[0].content.strip() end = self.xpath.xpathEval('//gmd:EX_Extent/gmd:temporalElement/gmd:EX_TemporalExtent//gml:endPosition')[0].content.strip() except IndexError: dat... |
from urllib2 import URLError | def _callService(self, method, *args, **kwargs): """ Wrap the call to the SOAP service with some error checking """ | |
from urllib2 import URLError | def __call__(self, query, logger): from urllib2 import URLError from query import QueryError | |
from urllib2 import URLError | def getMetadataFormats(self): from urllib2 import URLError | |
from urllib2 import URLError | def __call__(self, gid): """ Connect to the DWS and retrieve a metadata entry by its ID """ | |
self.client = suds.client.Client(wsdl, timeout=15) | self.client = suds.client.Client(wsdl, timeout=10) | def __init__(self, wsdl=None): if wsdl is None: wsdl = 'file://%s' % os.path.abspath(os.path.join(os.path.dirname(__file__), 'data', 'dws.wsdl')) |
except ValueError, IndexError: | except (ValueError, IndexError): | def _callService(self, method, *args, **kwargs): """ Wrap the call to the SOAP service with some error checking """ from urllib2 import URLError try: return method(*args, **kwargs) except URLError, e: try: status, msg = e.reason except ValueError: status = 500 msg = str(e.reason) raise DWSError('Connecting to the Dis... |
self.add(TopLevelLookupException, self.handleMakoError) | self.add(TopLevelLookupException, self.handleTemplateLookupException) self.add(Exception, self.handleException) | def __init__(self, *args, **kwargs): super(ErrorHandler, self).__init__(*args, **kwargs) |
def handleMakoError(self, exception, environ, start_response): | def handleTemplateLookupException(self, exception, environ, start_response): | def handleMakoError(self, exception, environ, start_response): from mako.exceptions import TopLevelLookupException message = 'The template could not be found: %s' % str(exception) e = errata.HTTPError('404 Not Found', message) try: return self.handleHTTPError(e, environ, start_response) except TopLevelLookupException:... |
mediator = self.templates[template] | mediator = self.templates[template].app | def addContentTypes(self, app, template, content_types, default=None): """ Associate a WSGI app and a template with one or more content-types """ try: mediator = self.templates[template] except KeyError: from mediator import Mediator mediator = self.templates[template] = Mediator() |
mediator = self.templates[template] = Mediator() | mediator = Mediator() self.templates[template] = EnvironNormalise(mediator) | def addContentTypes(self, app, template, content_types, default=None): """ Associate a WSGI app and a template with one or more content-types """ try: mediator = self.templates[template] except KeyError: from mediator import Mediator mediator = self.templates[template] = Mediator() |
if self and self.count: | if self and self.hits: | def __len__(self): if self and self.count: return len(getattr(self.reply.Documents, self.doc_type)) return 0 |
if self and self.count: | if self and self.hits: | def __iter__(self): if self and self.count: docs = getattr(self.reply.Documents, self.doc_type) else: docs = [] |
Class to split an input expression into one or more pieces. | Node to select a portion of the input. See the `split` function for a user-friendly version. | def __getitem__(self, slice): r""" Returns the slice argument. """ return slice |
The regions are specified by a list of tuples representing indexes into the input. The split regions may overlap. The first split is the output of the main class and the other will be the outputs of the subnodes accessible though `helpers`. Note: To use the convinient syntax in the examples below import `i` from this... | def __getitem__(self, slice): r""" Returns the slice argument. """ return slice | |
Initialize fields. | def __init__(self, input, split, name=None): r""" Initialize fields. Tests: >>> x = T.fmatrix('x') >>> s = SplitNode(x, i[1:,:]) >>> s.split (slice(1, None, None), slice(None, None, None)) >>> s.params [] """ self.split = split BaseNode.__init__(self, [input], name) | |
Build node with input expression `input`. | def transform(self, inp): r""" Build node with input expression `input`. Tests: >>> x = T.fmatrix('x') >>> s = SplitNode(x, i[1:3]) >>> theano.pp(s.output) 'x[1:3]' """ return inp[self.split] | |
>>> x_n = InputNode(x) | def __init__(self, input, subgraph_builder, mem_init=None, outshp=None, dtype=theano.config.floatX, name=None): r""" Tests: >>> x = T.fmatrix('x') >>> x_n = InputNode(x) >>> r = RecurrentWrapper(x, lambda x_n: SimpleNode(x_n, 10, 5, dtype='float32'), ... outshp=(5,), dtype='float32') >>> r.memory.v... | |
Resets the memory to initial value. | Resets the memory to the initial value. | def clear(self): r""" Resets the memory to initial value. """ self.memory.value = self.mem_init.copy() |
self.layers[-1].filter = self.layer.filter self.layers[-1].b = self.layer.b | self.layers[-2].filter = self.layer.filter self.layers[-2].b = self.layer.b | def _load_(self, file): s = file.read(4) if s != 'CAE1': raise ValueError('wrong cookie for ConvAutoencoder') self.layer = ConvLayer.loadf(file) self.layers[-1].filter = self.layer.filter self.layers[-1].b = self.layer.b |
'<theano.scan.Scan object at ...>(?_steps, x, memory, W, b)[:, 0, :]' | '<theano.scan.Scan object at ...>(?_steps, x, memory, cost, W, b)[:, 0, :]' | def build(self, input, input_shape=None): r""" Builds the layer with input expresstion `input`. |
def f(inp, mem): | if self.memory.dtype == 'float32': cost = theano.shared(numpy.float32(0.0), name='cost') else: cost = theano.shared(numpy.float64(0.0), name='cost') def f(inp, mem, cc): | def f(inp, mem): self.base_layer.build(T.join(1, T.unbroadcast(T.shape_padleft(inp),0), mem), inp_shape) return self.base_layer.output |
return self.base_layer.output | if hasattr(self.base_layer, 'cost'): cost = self.base_layer.cost else: cost = 0.0 return self.base_layer.output, cost | def f(inp, mem): self.base_layer.build(T.join(1, T.unbroadcast(T.shape_padleft(inp),0), mem), inp_shape) return self.base_layer.output |
outs, upds = theano.scan(f, sequences=[input], outputs_info=[self.memory]) | outs, upds = theano.scan(f, sequences=[input], outputs_info=[self.memory, cost]) | def f(inp, mem): self.base_layer.build(T.join(1, T.unbroadcast(T.shape_padleft(inp),0), mem), inp_shape) return self.base_layer.output |
self.output = outs[:,0,:] | self.output = outs[0][:,0,:] | def f(inp, mem): self.base_layer.build(T.join(1, T.unbroadcast(T.shape_padleft(inp),0), mem), inp_shape) return self.base_layer.output |
self.memory.default_update = outs[-1] | self.memory.default_update = outs[0][-1] if hasattr(self.base_layer, 'cost'): self.cost = T.mean(outs[1]) | def f(inp, mem): self.base_layer.build(T.join(1, T.unbroadcast(T.shape_padleft(inp),0), mem), inp_shape) return self.base_layer.output |
class RecurrentAutoencoder(RecurrentWrapper): r""" Specialized version of RecurrentWrapper to deal with autoencoder pretraining. See the documentation for `Autoencoder` for details on the semantics of the parameters. | def recurrent_autoencoder(n_in, n_out, tied=True, nlin=sigmoid, noise=0.0, error=cross_entropy, name=None, dtype=theano.config.floatX, rng=numpy.random, noise_rng=RandomStreams()): r""" Utility function to create a recurrent autoencoder. See the documentation for `Autoencoder` for details on the semantics of the param... | def recurrent_layer(n_in, n_out, nlin=sigmoid, rng=numpy.random, name=None, dtype=theano.config.floatX): r""" Utility function to create a recurrent layer. See the documentation for `SimpleLayer` for details on the semantics of the parameters. Examples: >>> rl = recurrent_layer(3, 2) """ from pynnet.layers import Sim... |
>>> rae = RecurrentAutoencoder(3, 2) >>> rae = RecurrentAutoencoder(4, 4, tied=False) | >>> rae = recurrent_autoencoder(3, 2) >>> rae = recurrent_autoencoder(4, 4, tied=False) | def recurrent_layer(n_in, n_out, nlin=sigmoid, rng=numpy.random, name=None, dtype=theano.config.floatX): r""" Utility function to create a recurrent layer. See the documentation for `SimpleLayer` for details on the semantics of the parameters. Examples: >>> rl = recurrent_layer(3, 2) """ from pynnet.layers import Sim... |
def __init__(self, n_in, n_out, tied=True, nlin=sigmoid, noise=0.0, error=cross_entropy, name=None, rng=numpy.random, dtype=theano.config.floatX, noise_rng=RandomStreams()): r""" Tests: >>> rae = RecurrentAutoencoder(10, 5, dtype='float32') >>> r2 = test_saveload(rae) """ from pynnet.layers import Autoencoder ae = Auto... | from pynnet.layers import Autoencoder ae = Autoencoder(n_in+n_out, n_out, tied=tied, nlin=nlin, noise=noise, err=error, dtype=dtype, rng=rng, noise_rng=noise_rng) return RecurrentWrapper(ae, (n_out,), name=name, dtype=dtype) | def __init__(self, n_in, n_out, tied=True, nlin=sigmoid, noise=0.0, error=cross_entropy, name=None, rng=numpy.random, dtype=theano.config.floatX, noise_rng=RandomStreams()): r""" Tests: >>> rae = RecurrentAutoencoder(10, 5, dtype='float32') >>> r2 = test_saveload(rae) """ from pynnet.layers import Autoencoder ae = Auto... |
['<RandomStateType>', '<RandomStateType>', 'W', 'b', 'c'] | ['<RandomStateType>', '<RandomStateType>', '<RandomStateType>', 'W', 'b', 'c'] | def pretrain_helper(self, lr=0.1): r""" Returns the cost function and the updates that should be used for pretraining. |
"((((sum(((tanh((ConvOp{('imshp', None),('kshp', (3, 3)),('nkern', 2),('bsize', None),('dx', 1),('dy', 1),('out_mode', 'valid'),('unroll_batch', 0),('unroll_kern', 0),('unroll_patch', True),('imshp_logical', None),('kshp_logical', (3, 3)),('kshp_logical_top_aligned', True)}(tanh((ConvOp{('imshp', None),('kshp', (3, 3))... | "((((sum(((tanh((ConvOp{('imshp', None),('kshp', (3, 3)),('nkern', 1),('bsize', None),('dx', 1),('dy', 1),('out_mode', 'valid'),('unroll_batch', 0),('unroll_kern', 0),('unroll_patch', True),('imshp_logical', None),('kshp_logical', (3, 3)),('kshp_logical_top_aligned', True)}(tanh((ConvOp{('imshp', None),('kshp', (3, 3))... | def build(self, input): r""" Builds the layer with input expression `input` |
numpy.save(file, self.b) | numpy.save(file, self._b.value) | def _save_(self, file): file.write('AE2') psave(self.tied, file) if self.tied: numpy.save(file, self.b) |
self.module = sys.modules[module] | r self.module = module | def __init__(self, module, name, shared_class=MemoryDataset): self.module = sys.modules[module] self.name = name self.shared_class = shared_class self._set_data() |
object.__setstate__(self, d) | r self.__dict__.update(d) | def __setstate__(self, d): object.__setstate__(self, d) self._set_data() |
self.data = getattr(self.module, self.name) | r self.data = getattr(sys.modules[self.module], self.name) | def _set_data(self): self.data = getattr(self.module, self.name) |
return ((Broad(self.gate_in.output)*cell_inp) + \ (Broad(self.gate_forget.output)*outp)) | return ((Broad0(self.gate_in.output)*cell_inp) + \ (Broad0(self.gate_forget.output)*outp)) | def block(inp, cell_inp, outp): inp = T.join(0, inp, outp) if input_shape is None: ishp = None else: ishp = (1, self.map_in.output_shape[1]+input_shape[1]) self.gate_in.build(inp, ishp) self.gate_forget.build(inp, ishp) return ((Broad(self.gate_in.output)*cell_inp) + \ (Broad(self.gate_forget.output)*outp)) |
>>> r = RBMLayer(4, 3) | >>> r = RBMLayer(4, 3, dtype='float32') | def free_energy(self, v_sample): r""" Returns the free energy expression for sample `v_sample`. Tests: >>> r = RBMLayer(4, 3) >>> x = T.matrix('sample') >>> e = r.free_energy(x) >>> theano.pp(e) '(sum(((-Sum{1}(log((1 + exp(((sample \\dot W) + b)))))) - (sample \\dot c))) / ((-Sum{1}(log((1 + exp(((sample \\dot W) + b... |
'(sum(((-Sum{1}(log((1 + exp(((sample \\dot W) + b)))))) - (sample \\dot c))) / ((-Sum{1}(log((1 + exp(((sample \\dot W) + b)))))) - (sample \\dot c)).shape[0])' | '(sum(((-Sum{1}(log((1 + exp(((sample \\dot W) + b)))))) - (sample \\dot c))) / float32(((-Sum{1}(log((1 + exp(((sample \\dot W) + b)))))) - (sample \\dot c)).shape)[0])' | def free_energy(self, v_sample): r""" Returns the free energy expression for sample `v_sample`. Tests: >>> r = RBMLayer(4, 3) >>> x = T.matrix('sample') >>> e = r.free_energy(x) >>> theano.pp(e) '(sum(((-Sum{1}(log((1 + exp(((sample \\dot W) + b)))))) - (sample \\dot c))) / ((-Sum{1}(log((1 + exp(((sample \\dot W) + b... |
>>> a.layers [CorruptLayer..., SimpleLayer..., SharedLayer...] | >>> a.noise 0.01 >>> a.W.value.shape (20, 16) >>> a.b.value.shape (16,) >>> a.activation <function tanh at ...> | def __init__(self, n_in, n_out, tied=False, nlin=tanh, noise=0.0, err=mse, dtype=theano.config.floatX, name=None): r""" Tests: >>> a = Autoencoder(20, 16, tied=True, noise=0.01) >>> a.layers [CorruptLayer..., SimpleLayer..., SharedLayer...] >>> a2 = test_saveload(a) >>> a2.layers [CorruptLayer..., SimpleLayer..., Share... |
>>> a2.layers [CorruptLayer..., SimpleLayer..., SharedLayer...] >>> theano.pp(a2.layers[-1].W) | >>> a2.noise 0.01 >>> theano.pp(a2.W2) | def __init__(self, n_in, n_out, tied=False, nlin=tanh, noise=0.0, err=mse, dtype=theano.config.floatX, name=None): r""" Tests: >>> a = Autoencoder(20, 16, tied=True, noise=0.01) >>> a.layers [CorruptLayer..., SimpleLayer..., SharedLayer...] >>> a2 = test_saveload(a) >>> a2.layers [CorruptLayer..., SimpleLayer..., Share... |
>>> a2.layers[-1].b | >>> a2.b2 | def __init__(self, n_in, n_out, tied=False, nlin=tanh, noise=0.0, err=mse, dtype=theano.config.floatX, name=None): r""" Tests: >>> a = Autoencoder(20, 16, tied=True, noise=0.01) >>> a.layers [CorruptLayer..., SimpleLayer..., SharedLayer...] >>> a2 = test_saveload(a) >>> a2.layers [CorruptLayer..., SimpleLayer..., Share... |
>>> ca.layers [CorruptLayer..., SharedConvLayer..., ConvLayer...] | >>> ca.noise 0.0 >>> ca.filter_shape (3, 1, 5, 5) >>> ca.filter.value.shape (3, 1, 5, 5) >>> ca.b.value.shape (3,) >>> ca.filter2_shape (1, 3, 5, 5) >>> ca.b2.value.shape (1,) >>> ca.nlin <function tanh at ...> | def __init__(self, filter_size, num_filt, num_in=1, rng=numpy.random, nlin=tanh, err=mse, noise=0.0, dtype=theano.config.floatX, name=None): r""" Tests: >>> ca = ConvAutoencoder((5,5), 3) >>> ca.layers [CorruptLayer..., SharedConvLayer..., ConvLayer...] >>> ca2 = test_saveload(ca) >>> ca2.layers [CorruptLayer..., Share... |
>>> ca2.layers [CorruptLayer..., SharedConvLayer..., ConvLayer...] >>> ca2.layers[0].filter.value.shape | >>> ca2.filter.value.shape | def __init__(self, filter_size, num_filt, num_in=1, rng=numpy.random, nlin=tanh, err=mse, noise=0.0, dtype=theano.config.floatX, name=None): r""" Tests: >>> ca = ConvAutoencoder((5,5), 3) >>> ca.layers [CorruptLayer..., SharedConvLayer..., ConvLayer...] >>> ca2 = test_saveload(ca) >>> ca2.layers [CorruptLayer..., Share... |
NNet.__init__(self, [CorruptLayer(noisyness), layer1, layer2], | NNet.__init__(self, [CorruptLayer(noise), layer1, layer2], | def __init__(self, filter_size, num_filt, num_in=1, rng=numpy.random, nlin=tanh, err=mse, noise=0.0, dtype=theano.config.floatX, name=None): r""" Tests: >>> ca = ConvAutoencoder((5,5), 3) >>> ca.layers [CorruptLayer..., SharedConvLayer..., ConvLayer...] >>> ca2 = test_saveload(ca) >>> ca2.layers [CorruptLayer..., Share... |
psave((self.layers, [l.__class__ for l in self.layers]), file) | psave((self.err, [l.__class__ for l in self.layers]), file) | def _save_(self, file): r"""save state to a file""" file.write('NN2') psave((self.layers, [l.__class__ for l in self.layers]), file) for l in self.layers: l.savef(file) |
result = dict (get_result (pdu.contents)) | result = get_result (pdu.contents) | def callback (self, pdu): reqid = pdu.contents.reqid result = dict (get_result (pdu.contents)) if reqid in self.async_requests: rtype, timeout = self.async_requests.pop (reqid, (None, None)) slot = self.COMMAND_TO_SLOT.get (rtype, None) b = self._process_waiting_async_request (reqid, result) else: slot = self.COMMAND_... |
if not self._process_waiting_async_request (reqid, 'timeout'): | if self._process_waiting_async_request (reqid, 'timeout'): | def timeout (self, reqid): if not self._process_waiting_async_request (reqid, 'timeout'): self.manager.emit ('timeout', self.name, reqid) |
cbuff = create_string_buffer (s, len (s)) | def str_to_oid (s): if isinstance (s, basestring): if s[:2] == '.1': #s = 'iso' + s[2:] r = map (int, filter (None, s.split ('.'))) else: an_oid = (oid * MAX_OID_LEN)() cbuff = create_string_buffer (s, len (s)) length = c_size_t (len (an_oid)) r = lib.get_node (cbuff, an_oid, byref (length)) if r: r = an_oid[:length.va... | |
r = lib.get_node (cbuff, an_oid, byref (length)) | r = lib.get_node (s, an_oid, byref (length)) | def str_to_oid (s): if isinstance (s, basestring): if s[:2] == '.1': #s = 'iso' + s[2:] r = map (int, filter (None, s.split ('.'))) else: an_oid = (oid * MAX_OID_LEN)() cbuff = create_string_buffer (s, len (s)) length = c_size_t (len (an_oid)) r = lib.get_node (cbuff, an_oid, byref (length)) if r: r = an_oid[:length.va... |
n = 1 | self.bits_per = 1 | def init_class (self, name='SNMPManager', log=None, max_fd=1024, threaded_processor=True, process_sessions_sleep=0.01, local_dir=None): self.name = name self.local_dir = local_dir self.__log = log self.mutex = threading.RLock () self.sessions = {} self._signal_handlers = {} self._quit = False |
n = 32 | self.bits_per = struct.calcsize(c_long._type_) * 8 print "bit_per", self.bits_per | def init_class (self, name='SNMPManager', log=None, max_fd=1024, threaded_processor=True, process_sessions_sleep=0.01, local_dir=None): self.name = name self.local_dir = local_dir self.__log = log self.mutex = threading.RLock () self.sessions = {} self._signal_handlers = {} self._quit = False |
self._fdset = c_long * (max_fd / n) | self._fdset = c_long * (max_fd / self.bits_per) | def init_class (self, name='SNMPManager', log=None, max_fd=1024, threaded_processor=True, process_sessions_sleep=0.01, local_dir=None): self.name = name self.local_dir = local_dir self.__log = log self.mutex = threading.RLock () self.sessions = {} self._signal_handlers = {} self._quit = False |
for j in range (0, 32): bit = 0x00000001 << (j % 32) | for j in range (0, self.bits_per): bit = 0x00000001 << (j % self.bits_per) | def _fdset2list_unix (self, rd, n, cnt): result = [] #for i in range (cnt): for i in range (len (rd)): if rd[i]: for j in range (0, 32): bit = 0x00000001 << (j % 32) if rd[i] & bit: result.append (i * 32 + j) return result |
result.append (i * 32 + j) | result.append (i * self.bits_per + j) | def _fdset2list_unix (self, rd, n, cnt): result = [] #for i in range (cnt): for i in range (len (rd)): if rd[i]: for j in range (0, 32): bit = 0x00000001 << (j % 32) if rd[i] & bit: result.append (i * 32 + j) return result |
rd[fd / 32] |= 1 << (fd % 32) | rd[fd / self.bits_per] |= 1 << (fd % self.bits_per) | def _snmp_read_unix (self, d): for fd in d: rd = self._fdset () rd[fd / 32] |= 1 << (fd % 32) lib.snmp_read (byref (rd)) |
except: pass | except Exception, e: self.log (LOG_DEBUG, 'callback exception: %s' % str(e)) | def emit (self, slot, session, reqid, *args, **kargs): if slot in self._signal_handlers: handlers = [] handlers.extend (self._signal_handlers[slot].get (session, {}).values ()) handlers.extend (self._signal_handlers[slot].get (None, {}).values ()) for cb in (h['callback'] for h in handlers): try: cb (self, slot, sessio... |
return tp == 0 | return tp != 0 | def read_module (self, name): tp = lib.netsnmp_read_module (name) return tp == 0 |
return True | return self.surfaceEditor.ui.checkBoxUseRadius.isChecked() | def setCenter(self, center): [xx, yy, zz] = self.worldToObjectCoordinates(center) self.renderer.setDisplayRadiusOrigin(xx, yy, zz) return True |
rigidInit = engine.getPairRigidTransform(alignmentIx, helixIx, helixIx) | rigidInit = engine.getPairRigidTransform(alignmentIx, self.chainHelixMapping[helixIx], self.chainHelixMapping[helixIx]) | def doFlexibleDeformationPiecewise(self, alignmentIx): engine = self.engine # Getting all helix transformations transforms = {} origChain = [] count = 0 for chain in self.cAlphaViewer.loadedChains: origChain.append({}) for i in chain.residueRange(): origChain[count][i] = chain[i].getAtom('CA').getPosition() count = cou... |
for i in range(helix.startIndex,maxIndex): | for i in range(helix.startIndex,maxIndex+1): | def doFlexibleDeformationPiecewise(self, alignmentIx): engine = self.engine # Getting all helix transformations transforms = {} origChain = [] count = 0 for chain in self.cAlphaViewer.loadedChains: origChain.append({}) for i in chain.residueRange(): origChain[count][i] = chain[i].getAtom('CA').getPosition() count = cou... |
for k in range(helix.startIndex, maxIndex): if k != maxIndex and k not in range(helix.startIndex, helix.stopIndex+1): | for k in range(helix.startIndex, maxIndex+1): if k not in range(helix.startIndex, helix.stopIndex+1): | def doFlexibleDeformationPiecewise(self, alignmentIx): engine = self.engine # Getting all helix transformations transforms = {} origChain = [] count = 0 for chain in self.cAlphaViewer.loadedChains: origChain.append({}) for i in chain.residueRange(): origChain[count][i] = chain[i].getAtom('CA').getPosition() count = cou... |
rigidInit = engine.getPairRigidTransform(alignmentIx, helixIx, nextHelixIx) | rigidInit = engine.getPairRigidTransform(alignmentIx, self.chainHelixMapping[helixIx], self.chainHelixMapping[nextHelixIx]) | def doFlexibleDeformationPiecewise(self, alignmentIx): engine = self.engine # Getting all helix transformations transforms = {} origChain = [] count = 0 for chain in self.cAlphaViewer.loadedChains: origChain.append({}) for i in chain.residueRange(): origChain[count][i] = chain[i].getAtom('CA').getPosition() count = cou... |
visibility = [self.atomsVisible, self.bondsVisible, not self.atomsVisible] | visibility = [self.atomsVisible, self.bondsVisible, (not self.atomsVisible) and self.bondsVisible] | def modelChanged(self): self.updateActionsAndMenus() if self.gllist != 0: glDeleteLists(self.gllist,1) self.gllist = glGenLists(1) glNewList(self.gllist, GL_COMPILE) if(self.displayStyle == self.DisplayStyleBackbone): visibility = [self.atomsVisible, self.bondsVisible, not self.atomsVisible] colors = [self.getAtomColo... |
pass | print "loaded chains:", len(self.loadedChains) self.renderer.cleanSecondaryStructures() for chain in self.loadedChains: print "helices in chain: ", len(filter(lambda x: x.type == "helix", chain.secelList.values())) print "strands in chain: ", len(filter(lambda x: x.type == "strand", chain.secelList.values())) print "lo... | def setAtomColorsAndVisibility(self, displayStyle): if displayStyle == self.DisplayStyleBackbone: self.setAllAtomColor(self.getAtomColor()) elif displayStyle == self.DisplayStyleRibbon: #Pass into c++ layer all data needed to draw ribbon diagram here! pass elif displayStyle == self.DisplayStyleSideChain: self.setSpecif... |
rawAtom.setTempFactor(temp_factor) | rawAtom.setTempFactor(tempFactor) | def addAtom(self, atomName, x, y, z, element="", serialNo=None, occupancy=None, tempFactor=None, charge="" ): '''Adds a new PDBAtom to the residue.''' residueIndex=self.chain.findIndexForRes(self) rawAtom=PDBAtom(self.chain.getPdbID(), self.chain.getChainID() , residueIndex, atomName) rawAtom.setPosition(Vector3DFloat(... |
visibility = [self.atomsVisible, self.bondsVisible, (not self.atomsVisible) and self.bondsVisible] | visibility = [self.atomsVisible, self.bondsVisible, self.bondsVisible and (not self.atomsVisible)] | def modelChanged(self): self.updateActionsAndMenus() if self.gllist != 0: glDeleteLists(self.gllist,1) self.gllist = glGenLists(1) glNewList(self.gllist, GL_COMPILE) if(self.displayStyle == self.DisplayStyleBackbone): visibility = [self.atomsVisible, self.bondsVisible, not self.atomsVisible] colors = [self.getAtomColo... |
print "loaded chains:", len(self.loadedChains) self.renderer.cleanSecondaryStructures() for chain in self.loadedChains: print "helices in chain: ", len(filter(lambda x: x.type == "helix", chain.secelList.values())) print "strands in chain: ", len(filter(lambda x: x.type == "strand", chain.secelList.values())) print "lo... | pass | def setAtomColorsAndVisibility(self, displayStyle): if displayStyle == self.DisplayStyleBackbone: self.setAllAtomColor(self.getAtomColor()) elif displayStyle == self.DisplayStyleRibbon: #Pass into c++ layer all data needed to draw ribbon diagram here! print "loaded chains:", len(self.loadedChains) self.renderer.cleanSe... |
@property def form_fields(self): fields = form.FormFields(IUserDataSchema) fields['portrait'].custom_widget = FileUploadWidget props = getToolByName(self, 'portal_properties').site_properties if props.getProperty('use_email_as_login'): return fields.omit('email') else: return fields | form_fields = form.FormFields(IUserDataSchema) form_fields['portrait'].custom_widget = FileUploadWidget | def set_pdelete(self, value): if value: context = aq_inner(self.context) context.portal_membership.deletePersonalPortrait() |
return self.context.getProperty('fullname', '') | return self._getProperty('fullname') | def get_fullname(self): return self.context.getProperty('fullname', '') |
return self.context.getProperty('email', '') | return self._getProperty('email') | def get_email(self): return self.context.getProperty('email', '') |
return self.context.getProperty('home_page', '') | return self._getProperty('home_page') | def get_home_page(self): return self.context.getProperty('home_page', '') |
return self.context.getProperty('description', '') | return self._getProperty('description') | def get_description(self): return self.context.getProperty('description', '') |
return self.context.getProperty('location', '') | return self._getProperty('location') | def get_location(self): return self.context.getProperty('location', '') |
groupData = [] | terms = [] | def getGroupIds(context): site = getSite() groups_tool = getToolByName(site, 'portal_groups') groups = groups_tool.listGroups() # Get group id, title tuples for each, omitting virtual group 'AuthenticatedUsers' groupData = [] for g in groups: if g.id == 'AuthenticatedUsers': continue is_zope_manager = getSecurityManage... |
groupData.append(('%s (%s)' % (g.getGroupTitleOrName(), g.id), g.id)) | title = u'%s (%s)' % (safe_unicode(g.getGroupTitleOrName()), g.id) terms.append(SimpleTerm(g.id, g.id, title)) | def getGroupIds(context): site = getSite() groups_tool = getToolByName(site, 'portal_groups') groups = groups_tool.listGroups() # Get group id, title tuples for each, omitting virtual group 'AuthenticatedUsers' groupData = [] for g in groups: if g.id == 'AuthenticatedUsers': continue is_zope_manager = getSecurityManage... |
groupData.sort(key=lambda x: normalizeString(x[0])) return SimpleVocabulary.fromItems(groupData) | terms.sort(key=lambda x: normalizeString(x.title)) return SimpleVocabulary(terms) | def getGroupIds(context): site = getSite() groups_tool = getToolByName(site, 'portal_groups') groups = groups_tool.listGroups() # Get group id, title tuples for each, omitting virtual group 'AuthenticatedUsers' groupData = [] for g in groups: if g.id == 'AuthenticatedUsers': continue is_zope_manager = getSecurityManage... |
groupData.sort(key=lambda x: x[0].lower()) | groupData.sort(key=lambda x: normalizeString(x[0])) | def getGroupIds(context): site = getSite() groups_tool = getToolByName(site, 'portal_groups') groups = groups_tool.listGroups() # Get group id, title tuples for each, omitting virtual group 'AuthenticatedUsers' groupData = [] for g in groups: if g.id == 'AuthenticatedUsers': continue is_zope_manager = getSecurityManage... |
result = self.handle_join_success(data) | self.handle_join_success(data) | def action_join(self, action, data): result = self.handle_join_success(data) # XXX Return somewhere else, depending on what # handle_join_success returns? return self.context.unrestrictedTraverse('registered')() |
if portal.validate_email or data.get('mail_me'): | if data.get('mail_me') or (portal.validate_email and not data.get('password')): | def handle_join_success(self, data): portal = getUtility(ISiteRoot) registration = getToolByName(self.context, 'portal_registration') portal_props = getToolByName(self.context, 'portal_properties') props = portal_props.site_properties use_email_as_login = props.getProperty('use_email_as_login') |
print "XXX portrait: %s" % portrait | def get_portrait(self): mtool = getToolByName(self.context, 'portal_membership') member = mtool.getAuthenticatedMember() portrait = mtool.getPersonalPortrait(member.id) print "XXX portrait: %s" % portrait return portrait | |
return value.decode('utf-8') | return safe_unicode(value) | def _getProperty(self, name): """ PlonePAS encodes all unicode coming from PropertySheets. Decode before sending to formlib. """ value = self.context.getProperty(name, '') if value: return value.decode('utf-8') return value |
member = mtool.getAuthenticatedMember() portrait = mtool.getPersonalPortrait(member.id) | portrait = mtool.getPersonalPortrait(self.context.id) | def get_portrait(self): mtool = getToolByName(self.context, 'portal_membership') member = mtool.getAuthenticatedMember() portrait = mtool.getPersonalPortrait(member.id) return portrait |
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