Datasets:

Anonymous2023

/

dataset2python

like 0

rwl/pylon

pylon/ac_pf.py

FastDecoupledPF._q_iteration

def _q_iteration(self, Q, Bpp_solver, Vm, Va, pq): """ Performs a Q iteration, updates Vm. """ dVm = -Bpp_solver.solve(Q) # Update voltage. Vm[pq] = Vm[pq] + dVm V = Vm * exp(1j * Va) return V, Vm, Va

python

def _q_iteration(self, Q, Bpp_solver, Vm, Va, pq): """ Performs a Q iteration, updates Vm. """ dVm = -Bpp_solver.solve(Q) # Update voltage. Vm[pq] = Vm[pq] + dVm V = Vm * exp(1j * Va) return V, Vm, Va

Performs a Q iteration, updates Vm.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/ac_pf.py#L494-L503

train

melizalab/libtfr

examples/tfr_tm.py

fmsin

def fmsin(N, fnormin=0.05, fnormax=0.45, period=None, t0=None, fnorm0=0.25, pm1=1): """ Signal with sinusoidal frequency modulation. generates a frequency modulation with a sinusoidal frequency. This sinusoidal modulation is designed such that the instantaneous frequency at time T0 is equal to FNORM0, and the ambiguity between increasing or decreasing frequency is solved by PM1. N : number of points. FNORMIN : smallest normalized frequency (default: 0.05) FNORMAX : highest normalized frequency (default: 0.45) PERIOD : period of the sinusoidal fm (default: N ) T0 : time reference for the phase (default: N/2 ) FNORM0 : normalized frequency at time T0 (default: 0.25) PM1 : frequency direction at T0 (-1 or +1) (default: +1 ) Returns: Y : signal IFLAW : its instantaneous frequency law Example: z,i=fmsin(140,0.05,0.45,100,20,0.3,-1.0) Original MATLAB code F. Auger, July 1995. (note: Licensed under GPL; see main LICENSE file) """ if period==None: period = N if t0==None: t0 = N/2 pm1 = nx.sign(pm1) fnormid=0.5*(fnormax+fnormin); delta =0.5*(fnormax-fnormin); phi =-pm1*nx.arccos((fnorm0-fnormid)/delta); time =nx.arange(1,N)-t0; phase =2*nx.pi*fnormid*time+delta*period*(nx.sin(2*nx.pi*time/period+phi)-nx.sin(phi)); y =nx.exp(1j*phase) iflaw =fnormid+delta*nx.cos(2*nx.pi*time/period+phi); return y,iflaw

python

def fmsin(N, fnormin=0.05, fnormax=0.45, period=None, t0=None, fnorm0=0.25, pm1=1): """ Signal with sinusoidal frequency modulation. generates a frequency modulation with a sinusoidal frequency. This sinusoidal modulation is designed such that the instantaneous frequency at time T0 is equal to FNORM0, and the ambiguity between increasing or decreasing frequency is solved by PM1. N : number of points. FNORMIN : smallest normalized frequency (default: 0.05) FNORMAX : highest normalized frequency (default: 0.45) PERIOD : period of the sinusoidal fm (default: N ) T0 : time reference for the phase (default: N/2 ) FNORM0 : normalized frequency at time T0 (default: 0.25) PM1 : frequency direction at T0 (-1 or +1) (default: +1 ) Returns: Y : signal IFLAW : its instantaneous frequency law Example: z,i=fmsin(140,0.05,0.45,100,20,0.3,-1.0) Original MATLAB code F. Auger, July 1995. (note: Licensed under GPL; see main LICENSE file) """ if period==None: period = N if t0==None: t0 = N/2 pm1 = nx.sign(pm1) fnormid=0.5*(fnormax+fnormin); delta =0.5*(fnormax-fnormin); phi =-pm1*nx.arccos((fnorm0-fnormid)/delta); time =nx.arange(1,N)-t0; phase =2*nx.pi*fnormid*time+delta*period*(nx.sin(2*nx.pi*time/period+phi)-nx.sin(phi)); y =nx.exp(1j*phase) iflaw =fnormid+delta*nx.cos(2*nx.pi*time/period+phi); return y,iflaw

Signal with sinusoidal frequency modulation. generates a frequency modulation with a sinusoidal frequency. This sinusoidal modulation is designed such that the instantaneous frequency at time T0 is equal to FNORM0, and the ambiguity between increasing or decreasing frequency is solved by PM1. N : number of points. FNORMIN : smallest normalized frequency (default: 0.05) FNORMAX : highest normalized frequency (default: 0.45) PERIOD : period of the sinusoidal fm (default: N ) T0 : time reference for the phase (default: N/2 ) FNORM0 : normalized frequency at time T0 (default: 0.25) PM1 : frequency direction at T0 (-1 or +1) (default: +1 ) Returns: Y : signal IFLAW : its instantaneous frequency law Example: z,i=fmsin(140,0.05,0.45,100,20,0.3,-1.0) Original MATLAB code F. Auger, July 1995. (note: Licensed under GPL; see main LICENSE file)

9f7e7705793d258a0b205f185b20e3bbcda473da

https://github.com/melizalab/libtfr/blob/9f7e7705793d258a0b205f185b20e3bbcda473da/examples/tfr_tm.py#L14-L56

train

rwl/pylon

pylon/io/rdf.py

RDFReader._parse_rdf

def _parse_rdf(self, file): """ Returns a case from the given file. """ store = Graph() store.parse(file) print len(store)

python

def _parse_rdf(self, file): """ Returns a case from the given file. """ store = Graph() store.parse(file) print len(store)

Returns a case from the given file.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/rdf.py#L72-L78

train

finklabs/metrics

metrics/plugins.py

load_plugins

def load_plugins(group='metrics.plugin.10'): """Load and installed metrics plugins. """ # on using entrypoints: # http://stackoverflow.com/questions/774824/explain-python-entry-points file_processors = [] build_processors = [] for ep in pkg_resources.iter_entry_points(group, name=None): log.debug('loading \'%s\'', ep) plugin = ep.load() # load the plugin if hasattr(plugin, 'get_file_processors'): file_processors.extend(plugin.get_file_processors()) if hasattr(plugin, 'get_build_processors'): build_processors.extend(plugin.get_build_processors()) return file_processors, build_processors

python

def load_plugins(group='metrics.plugin.10'): """Load and installed metrics plugins. """ # on using entrypoints: # http://stackoverflow.com/questions/774824/explain-python-entry-points file_processors = [] build_processors = [] for ep in pkg_resources.iter_entry_points(group, name=None): log.debug('loading \'%s\'', ep) plugin = ep.load() # load the plugin if hasattr(plugin, 'get_file_processors'): file_processors.extend(plugin.get_file_processors()) if hasattr(plugin, 'get_build_processors'): build_processors.extend(plugin.get_build_processors()) return file_processors, build_processors

Load and installed metrics plugins.

fd9974af498831664b9ae8e8f3834e1ec2e8a699

https://github.com/finklabs/metrics/blob/fd9974af498831664b9ae8e8f3834e1ec2e8a699/metrics/plugins.py#L11-L25

train

rwl/pylon

pylon/main.py

read_case

def read_case(input, format=None): """ Returns a case object from the given input file object. The data format may be optionally specified. """ # Map of data file types to readers. format_map = {"matpower": MATPOWERReader, "psse": PSSEReader, "pickle": PickleReader} # Read case data. if format_map.has_key(format): reader_klass = format_map[format] reader = reader_klass() case = reader.read(input) else: # Try each of the readers at random. for reader_klass in format_map.values(): reader = reader_klass() try: case = reader.read(input) if case is not None: break except: pass else: case = None return case

python

def read_case(input, format=None): """ Returns a case object from the given input file object. The data format may be optionally specified. """ # Map of data file types to readers. format_map = {"matpower": MATPOWERReader, "psse": PSSEReader, "pickle": PickleReader} # Read case data. if format_map.has_key(format): reader_klass = format_map[format] reader = reader_klass() case = reader.read(input) else: # Try each of the readers at random. for reader_klass in format_map.values(): reader = reader_klass() try: case = reader.read(input) if case is not None: break except: pass else: case = None return case

Returns a case object from the given input file object. The data format may be optionally specified.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/main.py#L48-L74

train

rwl/pylon

pylon/main.py

detect_data_file

def detect_data_file(input, file_name=""): """ Detects the format of a network data file according to the file extension and the header. """ _, ext = os.path.splitext(file_name) if ext == ".m": line = input.readline() # first line if line.startswith("function"): type = "matpower" logger.info("Recognised MATPOWER data file.") elif line.startswith("Bus.con" or line.startswith("%")): type = "psat" logger.info("Recognised PSAT data file.") else: type = "unrecognised" input.seek(0) # reset buffer for parsing elif (ext == ".raw") or (ext == ".psse"): type = "psse" logger.info("Recognised PSS/E data file.") elif (ext == ".pkl") or (ext == ".pickle"): type = "pickle" logger.info("Recognised pickled case.") else: type = None return type

python

def detect_data_file(input, file_name=""): """ Detects the format of a network data file according to the file extension and the header. """ _, ext = os.path.splitext(file_name) if ext == ".m": line = input.readline() # first line if line.startswith("function"): type = "matpower" logger.info("Recognised MATPOWER data file.") elif line.startswith("Bus.con" or line.startswith("%")): type = "psat" logger.info("Recognised PSAT data file.") else: type = "unrecognised" input.seek(0) # reset buffer for parsing elif (ext == ".raw") or (ext == ".psse"): type = "psse" logger.info("Recognised PSS/E data file.") elif (ext == ".pkl") or (ext == ".pickle"): type = "pickle" logger.info("Recognised pickled case.") else: type = None return type

Detects the format of a network data file according to the file extension and the header.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/main.py#L80-L109

train

rwl/pylon

pylon/io/dot.py

DotWriter.write

def write(self, file_or_filename, prog=None, format='xdot'): """ Writes the case data in Graphviz DOT language. The format 'raw' is used to dump the Dot representation of the Case object, without further processing. The output can be processed by any of graphviz tools, defined in 'prog'. """ if prog is None: file = super(DotWriter, self).write(file_or_filename) else: buf = StringIO.StringIO() super(DotWriter, self).write(buf) buf.seek(0) data = self.create(buf.getvalue(), prog, format) if isinstance(file_or_filename, basestring): file = None try: file = open(file_or_filename, "wb") except: logger.error("Error opening %s." % file_or_filename) finally: if file is not None: file.write(data) file.close() else: file = file_or_filename file.write(data) return file

python

def write(self, file_or_filename, prog=None, format='xdot'): """ Writes the case data in Graphviz DOT language. The format 'raw' is used to dump the Dot representation of the Case object, without further processing. The output can be processed by any of graphviz tools, defined in 'prog'. """ if prog is None: file = super(DotWriter, self).write(file_or_filename) else: buf = StringIO.StringIO() super(DotWriter, self).write(buf) buf.seek(0) data = self.create(buf.getvalue(), prog, format) if isinstance(file_or_filename, basestring): file = None try: file = open(file_or_filename, "wb") except: logger.error("Error opening %s." % file_or_filename) finally: if file is not None: file.write(data) file.close() else: file = file_or_filename file.write(data) return file

Writes the case data in Graphviz DOT language. The format 'raw' is used to dump the Dot representation of the Case object, without further processing. The output can be processed by any of graphviz tools, defined in 'prog'.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/dot.py#L71-L100

train

rwl/pylon

pylon/io/dot.py

DotWriter.write_bus_data

def write_bus_data(self, file, padding=" "): """ Writes bus data to file. """ for bus in self.case.buses: attrs = ['%s="%s"' % (k, v) for k, v in self.bus_attr.iteritems()] # attrs.insert(0, 'label="%s"' % bus.name) attr_str = ", ".join(attrs) file.write("%s%s [%s];\n" % (padding, bus.name, attr_str))

python

def write_bus_data(self, file, padding=" "): """ Writes bus data to file. """ for bus in self.case.buses: attrs = ['%s="%s"' % (k, v) for k, v in self.bus_attr.iteritems()] # attrs.insert(0, 'label="%s"' % bus.name) attr_str = ", ".join(attrs) file.write("%s%s [%s];\n" % (padding, bus.name, attr_str))

Writes bus data to file.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/dot.py#L114-L122

train

rwl/pylon

pylon/io/dot.py

DotWriter.write_branch_data

def write_branch_data(self, file, padding=" "): """ Writes branch data in Graphviz DOT language. """ attrs = ['%s="%s"' % (k,v) for k,v in self.branch_attr.iteritems()] attr_str = ", ".join(attrs) for br in self.case.branches: file.write("%s%s -> %s [%s];\n" % \ (padding, br.from_bus.name, br.to_bus.name, attr_str))

python

def write_branch_data(self, file, padding=" "): """ Writes branch data in Graphviz DOT language. """ attrs = ['%s="%s"' % (k,v) for k,v in self.branch_attr.iteritems()] attr_str = ", ".join(attrs) for br in self.case.branches: file.write("%s%s -> %s [%s];\n" % \ (padding, br.from_bus.name, br.to_bus.name, attr_str))

Writes branch data in Graphviz DOT language.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/dot.py#L125-L133

train

rwl/pylon

pylon/io/dot.py

DotWriter.write_generator_data

def write_generator_data(self, file, padding=" "): """ Write generator data in Graphviz DOT language. """ attrs = ['%s="%s"' % (k, v) for k, v in self.gen_attr.iteritems()] attr_str = ", ".join(attrs) edge_attrs = ['%s="%s"' % (k,v) for k,v in {}.iteritems()] edge_attr_str = ", ".join(edge_attrs) for g in self.case.generators: # Generator node. file.write("%s%s [%s];\n" % (padding, g.name, attr_str)) # Edge connecting generator and bus. file.write("%s%s -> %s [%s];\n" % \ (padding, g.name, g.bus.name, edge_attr_str))

python

def write_generator_data(self, file, padding=" "): """ Write generator data in Graphviz DOT language. """ attrs = ['%s="%s"' % (k, v) for k, v in self.gen_attr.iteritems()] attr_str = ", ".join(attrs) edge_attrs = ['%s="%s"' % (k,v) for k,v in {}.iteritems()] edge_attr_str = ", ".join(edge_attrs) for g in self.case.generators: # Generator node. file.write("%s%s [%s];\n" % (padding, g.name, attr_str)) # Edge connecting generator and bus. file.write("%s%s -> %s [%s];\n" % \ (padding, g.name, g.bus.name, edge_attr_str))

Write generator data in Graphviz DOT language.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/dot.py#L136-L151

train

rwl/pylon

pylon/io/dot.py

DotWriter.create

def create(self, dotdata, prog="dot", format="xdot"): """ Creates and returns a representation of the graph using the Graphviz layout program given by 'prog', according to the given format. Writes the graph to a temporary dot file and processes it with the program given by 'prog' (which defaults to 'dot'), reading the output and returning it as a string if the operation is successful. On failure None is returned. Based on PyDot by Ero Carrera. """ import os, tempfile from dot2tex.dotparsing import find_graphviz # Map Graphviz executable names to their paths. progs = find_graphviz() if progs is None: logger.warning("GraphViz executables not found.") return None if not progs.has_key(prog): logger.warning('Invalid program [%s]. Available programs are: %s' % \ (prog, progs.keys())) return None # Make a temporary file ... tmp_fd, tmp_name = tempfile.mkstemp() os.close(tmp_fd) # ... and save the graph to it. dot_fd = file(tmp_name, "w+b") dot_fd.write(dotdata) # DOT language. dot_fd.close() # Get the temporary file directory name. tmp_dir = os.path.dirname(tmp_name) # Process the file using the layout program, specifying the format. p = subprocess.Popen((progs[prog], '-T'+format, tmp_name), cwd=tmp_dir, stderr=subprocess.PIPE, stdout=subprocess.PIPE) stderr = p.stderr stdout = p.stdout # Make sense of the standard output form the process. stdout_output = list() while True: data = stdout.read() if not data: break stdout_output.append(data) stdout.close() if stdout_output: stdout_output = ''.join(stdout_output) # Similarly so for any standard error. if not stderr.closed: stderr_output = list() while True: data = stderr.read() if not data: break stderr_output.append(data) stderr.close() if stderr_output: stderr_output = ''.join(stderr_output) status = p.wait() if status != 0 : logger.error("Program [%s] terminated with status: %d. stderr " \ "follows: %s" % ( prog, status, stderr_output ) ) elif stderr_output: logger.error( "%s", stderr_output ) # Remove the temporary file. os.unlink(tmp_name) return stdout_output

python

def create(self, dotdata, prog="dot", format="xdot"): """ Creates and returns a representation of the graph using the Graphviz layout program given by 'prog', according to the given format. Writes the graph to a temporary dot file and processes it with the program given by 'prog' (which defaults to 'dot'), reading the output and returning it as a string if the operation is successful. On failure None is returned. Based on PyDot by Ero Carrera. """ import os, tempfile from dot2tex.dotparsing import find_graphviz # Map Graphviz executable names to their paths. progs = find_graphviz() if progs is None: logger.warning("GraphViz executables not found.") return None if not progs.has_key(prog): logger.warning('Invalid program [%s]. Available programs are: %s' % \ (prog, progs.keys())) return None # Make a temporary file ... tmp_fd, tmp_name = tempfile.mkstemp() os.close(tmp_fd) # ... and save the graph to it. dot_fd = file(tmp_name, "w+b") dot_fd.write(dotdata) # DOT language. dot_fd.close() # Get the temporary file directory name. tmp_dir = os.path.dirname(tmp_name) # Process the file using the layout program, specifying the format. p = subprocess.Popen((progs[prog], '-T'+format, tmp_name), cwd=tmp_dir, stderr=subprocess.PIPE, stdout=subprocess.PIPE) stderr = p.stderr stdout = p.stdout # Make sense of the standard output form the process. stdout_output = list() while True: data = stdout.read() if not data: break stdout_output.append(data) stdout.close() if stdout_output: stdout_output = ''.join(stdout_output) # Similarly so for any standard error. if not stderr.closed: stderr_output = list() while True: data = stderr.read() if not data: break stderr_output.append(data) stderr.close() if stderr_output: stderr_output = ''.join(stderr_output) status = p.wait() if status != 0 : logger.error("Program [%s] terminated with status: %d. stderr " \ "follows: %s" % ( prog, status, stderr_output ) ) elif stderr_output: logger.error( "%s", stderr_output ) # Remove the temporary file. os.unlink(tmp_name) return stdout_output

Creates and returns a representation of the graph using the Graphviz layout program given by 'prog', according to the given format. Writes the graph to a temporary dot file and processes it with the program given by 'prog' (which defaults to 'dot'), reading the output and returning it as a string if the operation is successful. On failure None is returned. Based on PyDot by Ero Carrera.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/dot.py#L157-L235

train

finklabs/metrics

metrics/outputformat_xml.py

format

def format(file_metrics, build_metrics): """compute output in XML format.""" def indent(elem, level=0): i = "\n" + level*" " if len(elem): if not elem.text or not elem.text.strip(): elem.text = i + " " if not elem.tail or not elem.tail.strip(): elem.tail = i for elem in elem: indent(elem, level+1) if not elem.tail or not elem.tail.strip(): elem.tail = i else: if level and (not elem.tail or not elem.tail.strip()): elem.tail = i root = ET.Element('metrics') # file_metrics files = ET.Element('files') root.append(files) for key in file_metrics.keys(): tmp_file = ET.SubElement(files, "file", {'name': key, 'language': file_metrics[key]['language']}) for name in file_metrics[key].keys(): if name == 'language': continue tmp_metric = ET.SubElement(tmp_file, "metric", {'name': name, 'value': str(file_metrics[key][name])}) # build_metrics if build_metrics: build = ET.Element('build') root.append(build) # TODO indent(root) if PY3: body = ET.tostring(root, encoding='unicode') else: body = ET.tostring(root) return body

python

def format(file_metrics, build_metrics): """compute output in XML format.""" def indent(elem, level=0): i = "\n" + level*" " if len(elem): if not elem.text or not elem.text.strip(): elem.text = i + " " if not elem.tail or not elem.tail.strip(): elem.tail = i for elem in elem: indent(elem, level+1) if not elem.tail or not elem.tail.strip(): elem.tail = i else: if level and (not elem.tail or not elem.tail.strip()): elem.tail = i root = ET.Element('metrics') # file_metrics files = ET.Element('files') root.append(files) for key in file_metrics.keys(): tmp_file = ET.SubElement(files, "file", {'name': key, 'language': file_metrics[key]['language']}) for name in file_metrics[key].keys(): if name == 'language': continue tmp_metric = ET.SubElement(tmp_file, "metric", {'name': name, 'value': str(file_metrics[key][name])}) # build_metrics if build_metrics: build = ET.Element('build') root.append(build) # TODO indent(root) if PY3: body = ET.tostring(root, encoding='unicode') else: body = ET.tostring(root) return body

compute output in XML format.

fd9974af498831664b9ae8e8f3834e1ec2e8a699

https://github.com/finklabs/metrics/blob/fd9974af498831664b9ae8e8f3834e1ec2e8a699/metrics/outputformat_xml.py#L16-L59

train

brunobord/tdaemon

tdaemon.py

ask

def ask(message='Are you sure? [y/N]'): """Asks the user his opinion.""" agree = False answer = raw_input(message).lower() if answer.startswith('y'): agree = True return agree

python

def ask(message='Are you sure? [y/N]'): """Asks the user his opinion.""" agree = False answer = raw_input(message).lower() if answer.startswith('y'): agree = True return agree

Asks the user his opinion.

733b5bddb4b12bc3db326a192ce5606f28768307

https://github.com/brunobord/tdaemon/blob/733b5bddb4b12bc3db326a192ce5606f28768307/tdaemon.py#L45-L51

train

brunobord/tdaemon

tdaemon.py

main

def main(prog_args=None): """ What do you expect? """ if prog_args is None: prog_args = sys.argv parser = optparse.OptionParser() parser.usage = """Usage: %[prog] [options] [<path>]""" parser.add_option("-t", "--test-program", dest="test_program", default="nose", help="specifies the test-program to use. Valid values" " include `nose` (or `nosetests`), `django`, `py` (for `py.test`), " '`symfony`, `jelix` `phpunit` and `tox`') parser.add_option("-d", "--debug", dest="debug", action="store_true", default=False) parser.add_option('-s', '--size-max', dest='size_max', default=25, type="int", help="Sets the maximum size (in MB) of files.") parser.add_option('--custom-args', dest='custom_args', default='', type="str", help="Defines custom arguments to pass after the test program command") parser.add_option('--ignore-dirs', dest='ignore_dirs', default='', type="str", help="Defines directories to ignore. Use a comma-separated list.") parser.add_option('-y', '--quiet', dest='quiet', action="store_true", default=False, help="Don't ask for any input.") opt, args = parser.parse_args(prog_args) if args[1:]: path = args[1] else: path = '.' try: watcher = Watcher(path, opt.test_program, opt.debug, opt.custom_args, opt.ignore_dirs, opt.quiet) watcher_file_size = watcher.file_sizes() if watcher_file_size > opt.size_max: message = "It looks like the total file size (%dMb) is larger than the `max size` option (%dMb).\nThis may slow down the file comparison process, and thus the daemon performances.\nDo you wish to continue? [y/N] " % (watcher_file_size, opt.size_max) if not opt.quiet and not ask(message): raise CancelDueToUserRequest('Ok, thx, bye...') print "Ready to watch file changes..." watcher.loop() except (KeyboardInterrupt, SystemExit): # Ignore when you exit via Crtl-C pass except Exception, msg: print msg print "Bye"

python

def main(prog_args=None): """ What do you expect? """ if prog_args is None: prog_args = sys.argv parser = optparse.OptionParser() parser.usage = """Usage: %[prog] [options] [<path>]""" parser.add_option("-t", "--test-program", dest="test_program", default="nose", help="specifies the test-program to use. Valid values" " include `nose` (or `nosetests`), `django`, `py` (for `py.test`), " '`symfony`, `jelix` `phpunit` and `tox`') parser.add_option("-d", "--debug", dest="debug", action="store_true", default=False) parser.add_option('-s', '--size-max', dest='size_max', default=25, type="int", help="Sets the maximum size (in MB) of files.") parser.add_option('--custom-args', dest='custom_args', default='', type="str", help="Defines custom arguments to pass after the test program command") parser.add_option('--ignore-dirs', dest='ignore_dirs', default='', type="str", help="Defines directories to ignore. Use a comma-separated list.") parser.add_option('-y', '--quiet', dest='quiet', action="store_true", default=False, help="Don't ask for any input.") opt, args = parser.parse_args(prog_args) if args[1:]: path = args[1] else: path = '.' try: watcher = Watcher(path, opt.test_program, opt.debug, opt.custom_args, opt.ignore_dirs, opt.quiet) watcher_file_size = watcher.file_sizes() if watcher_file_size > opt.size_max: message = "It looks like the total file size (%dMb) is larger than the `max size` option (%dMb).\nThis may slow down the file comparison process, and thus the daemon performances.\nDo you wish to continue? [y/N] " % (watcher_file_size, opt.size_max) if not opt.quiet and not ask(message): raise CancelDueToUserRequest('Ok, thx, bye...') print "Ready to watch file changes..." watcher.loop() except (KeyboardInterrupt, SystemExit): # Ignore when you exit via Crtl-C pass except Exception, msg: print msg print "Bye"

What do you expect?

733b5bddb4b12bc3db326a192ce5606f28768307

https://github.com/brunobord/tdaemon/blob/733b5bddb4b12bc3db326a192ce5606f28768307/tdaemon.py#L235-L288

train

brunobord/tdaemon

tdaemon.py

Watcher.check_configuration

def check_configuration(self, file_path, test_program, custom_args): """Checks if configuration is ok.""" # checking filepath if not os.path.isdir(file_path): raise InvalidFilePath("INVALID CONFIGURATION: file path %s is not a directory" % os.path.abspath(file_path) ) if not test_program in IMPLEMENTED_TEST_PROGRAMS: raise InvalidTestProgram('The `%s` is unknown, or not yet implemented. Please chose another one.' % test_program) if custom_args: if not self.quiet and not ask("WARNING!!!\nYou are about to run the following command\n\n $ %s\n\nAre you sure you still want to proceed [y/N]? " % self.get_cmd()): raise CancelDueToUserRequest('Test cancelled...')

python

def check_configuration(self, file_path, test_program, custom_args): """Checks if configuration is ok.""" # checking filepath if not os.path.isdir(file_path): raise InvalidFilePath("INVALID CONFIGURATION: file path %s is not a directory" % os.path.abspath(file_path) ) if not test_program in IMPLEMENTED_TEST_PROGRAMS: raise InvalidTestProgram('The `%s` is unknown, or not yet implemented. Please chose another one.' % test_program) if custom_args: if not self.quiet and not ask("WARNING!!!\nYou are about to run the following command\n\n $ %s\n\nAre you sure you still want to proceed [y/N]? " % self.get_cmd()): raise CancelDueToUserRequest('Test cancelled...')

Checks if configuration is ok.

733b5bddb4b12bc3db326a192ce5606f28768307

https://github.com/brunobord/tdaemon/blob/733b5bddb4b12bc3db326a192ce5606f28768307/tdaemon.py#L88-L101

train

brunobord/tdaemon

tdaemon.py

Watcher.check_dependencies

def check_dependencies(self): "Checks if the test program is available in the python environnement" if self.test_program == 'nose': try: import nose except ImportError: sys.exit('Nosetests is not available on your system. Please install it and try to run it again') if self.test_program == 'py': try: import py except: sys.exit('py.test is not available on your system. Please install it and try to run it again') if self.test_program == 'django': try: import django except: sys.exit('django is not available on your system. Please install it and try to run it again') if self.test_program == 'phpunit': try: process = subprocess.check_call(['phpunit','--version']) except: sys.exit('phpunit is not available on your system. Please install it and try to run it again') if self.test_program == 'tox': try: import tox except ImportError: sys.exit('tox is not available on your system. Please install it and try to run it again')

python

def check_dependencies(self): "Checks if the test program is available in the python environnement" if self.test_program == 'nose': try: import nose except ImportError: sys.exit('Nosetests is not available on your system. Please install it and try to run it again') if self.test_program == 'py': try: import py except: sys.exit('py.test is not available on your system. Please install it and try to run it again') if self.test_program == 'django': try: import django except: sys.exit('django is not available on your system. Please install it and try to run it again') if self.test_program == 'phpunit': try: process = subprocess.check_call(['phpunit','--version']) except: sys.exit('phpunit is not available on your system. Please install it and try to run it again') if self.test_program == 'tox': try: import tox except ImportError: sys.exit('tox is not available on your system. Please install it and try to run it again')

Checks if the test program is available in the python environnement

733b5bddb4b12bc3db326a192ce5606f28768307

https://github.com/brunobord/tdaemon/blob/733b5bddb4b12bc3db326a192ce5606f28768307/tdaemon.py#L103-L129

train

brunobord/tdaemon

tdaemon.py

Watcher.get_cmd

def get_cmd(self): """Returns the full command to be executed at runtime""" cmd = None if self.test_program in ('nose', 'nosetests'): cmd = "nosetests %s" % self.file_path elif self.test_program == 'django': executable = "%s/manage.py" % self.file_path if os.path.exists(executable): cmd = "python %s/manage.py test" % self.file_path else: cmd = "django-admin.py test" elif self.test_program == 'py': cmd = 'py.test %s' % self.file_path elif self.test_program == 'symfony': cmd = 'symfony test-all' elif self.test_program == 'jelix': # as seen on http://jelix.org/articles/fr/manuel-1.1/tests_unitaires cmd = 'php tests.php' elif self.test_program == 'phpunit': cmd = 'phpunit' elif self.test_program == 'sphinx': cmd = 'make html' elif self.test_program == 'tox': cmd = 'tox' if not cmd: raise InvalidTestProgram("The test program %s is unknown. Valid options are: `nose`, `django` and `py`" % self.test_program) # adding custom args if self.custom_args: cmd = '%s %s' % (cmd, self.custom_args) return cmd

python

def get_cmd(self): """Returns the full command to be executed at runtime""" cmd = None if self.test_program in ('nose', 'nosetests'): cmd = "nosetests %s" % self.file_path elif self.test_program == 'django': executable = "%s/manage.py" % self.file_path if os.path.exists(executable): cmd = "python %s/manage.py test" % self.file_path else: cmd = "django-admin.py test" elif self.test_program == 'py': cmd = 'py.test %s' % self.file_path elif self.test_program == 'symfony': cmd = 'symfony test-all' elif self.test_program == 'jelix': # as seen on http://jelix.org/articles/fr/manuel-1.1/tests_unitaires cmd = 'php tests.php' elif self.test_program == 'phpunit': cmd = 'phpunit' elif self.test_program == 'sphinx': cmd = 'make html' elif self.test_program == 'tox': cmd = 'tox' if not cmd: raise InvalidTestProgram("The test program %s is unknown. Valid options are: `nose`, `django` and `py`" % self.test_program) # adding custom args if self.custom_args: cmd = '%s %s' % (cmd, self.custom_args) return cmd

Returns the full command to be executed at runtime

733b5bddb4b12bc3db326a192ce5606f28768307

https://github.com/brunobord/tdaemon/blob/733b5bddb4b12bc3db326a192ce5606f28768307/tdaemon.py#L132-L164

train

brunobord/tdaemon

tdaemon.py

Watcher.include

def include(self, path): """Returns `True` if the file is not ignored""" for extension in IGNORE_EXTENSIONS: if path.endswith(extension): return False parts = path.split(os.path.sep) for part in parts: if part in self.ignore_dirs: return False return True

python

def include(self, path): """Returns `True` if the file is not ignored""" for extension in IGNORE_EXTENSIONS: if path.endswith(extension): return False parts = path.split(os.path.sep) for part in parts: if part in self.ignore_dirs: return False return True

Returns `True` if the file is not ignored

733b5bddb4b12bc3db326a192ce5606f28768307

https://github.com/brunobord/tdaemon/blob/733b5bddb4b12bc3db326a192ce5606f28768307/tdaemon.py#L167-L176

train

brunobord/tdaemon

tdaemon.py

Watcher.diff_list

def diff_list(self, list1, list2): """Extracts differences between lists. For debug purposes""" for key in list1: if key in list2 and list2[key] != list1[key]: print key elif key not in list2: print key

python

def diff_list(self, list1, list2): """Extracts differences between lists. For debug purposes""" for key in list1: if key in list2 and list2[key] != list1[key]: print key elif key not in list2: print key

Extracts differences between lists. For debug purposes

733b5bddb4b12bc3db326a192ce5606f28768307

https://github.com/brunobord/tdaemon/blob/733b5bddb4b12bc3db326a192ce5606f28768307/tdaemon.py#L205-L211

train

brunobord/tdaemon

tdaemon.py

Watcher.run

def run(self, cmd): """Runs the appropriate command""" print datetime.datetime.now() output = subprocess.Popen(cmd, shell=True) output = output.communicate()[0] print output

python

def run(self, cmd): """Runs the appropriate command""" print datetime.datetime.now() output = subprocess.Popen(cmd, shell=True) output = output.communicate()[0] print output

Runs the appropriate command

733b5bddb4b12bc3db326a192ce5606f28768307

https://github.com/brunobord/tdaemon/blob/733b5bddb4b12bc3db326a192ce5606f28768307/tdaemon.py#L213-L218

train

brunobord/tdaemon

tdaemon.py

Watcher.loop

def loop(self): """Main loop daemon.""" while True: sleep(1) new_file_list = self.walk(self.file_path, {}) if new_file_list != self.file_list: if self.debug: self.diff_list(new_file_list, self.file_list) self.run_tests() self.file_list = new_file_list

python

def loop(self): """Main loop daemon.""" while True: sleep(1) new_file_list = self.walk(self.file_path, {}) if new_file_list != self.file_list: if self.debug: self.diff_list(new_file_list, self.file_list) self.run_tests() self.file_list = new_file_list

Main loop daemon.

733b5bddb4b12bc3db326a192ce5606f28768307

https://github.com/brunobord/tdaemon/blob/733b5bddb4b12bc3db326a192ce5606f28768307/tdaemon.py#L224-L233

train

finklabs/metrics

metrics/outputformat_json.py

format

def format(file_metrics, build_metrics): """compute output in JSON format.""" metrics = {'files': file_metrics} if build_metrics: metrics['build'] = build_metrics body = json.dumps(metrics, sort_keys=True, indent=4) + '\n' return body

python

def format(file_metrics, build_metrics): """compute output in JSON format.""" metrics = {'files': file_metrics} if build_metrics: metrics['build'] = build_metrics body = json.dumps(metrics, sort_keys=True, indent=4) + '\n' return body

compute output in JSON format.

fd9974af498831664b9ae8e8f3834e1ec2e8a699

https://github.com/finklabs/metrics/blob/fd9974af498831664b9ae8e8f3834e1ec2e8a699/metrics/outputformat_json.py#L9-L15

train

rwl/pylon

contrib/cvxopf.py

split_linear_constraints

def split_linear_constraints(A, l, u): """ Returns the linear equality and inequality constraints. """ ieq = [] igt = [] ilt = [] ibx = [] for i in range(len(l)): if abs(u[i] - l[i]) <= EPS: ieq.append(i) elif (u[i] > 1e10) and (l[i] > -1e10): igt.append(i) elif (l[i] <= -1e10) and (u[i] < 1e10): ilt.append(i) elif (abs(u[i] - l[i]) > EPS) and (u[i] < 1e10) and (l[i] > -1e10): ibx.append(i) else: raise ValueError Ae = A[ieq, :] Ai = sparse([A[ilt, :], -A[igt, :], A[ibx, :], -A[ibx, :]]) be = u[ieq, :] bi = matrix([u[ilt], -l[igt], u[ibx], -l[ibx]]) return Ae, be, Ai, bi

python

def split_linear_constraints(A, l, u): """ Returns the linear equality and inequality constraints. """ ieq = [] igt = [] ilt = [] ibx = [] for i in range(len(l)): if abs(u[i] - l[i]) <= EPS: ieq.append(i) elif (u[i] > 1e10) and (l[i] > -1e10): igt.append(i) elif (l[i] <= -1e10) and (u[i] < 1e10): ilt.append(i) elif (abs(u[i] - l[i]) > EPS) and (u[i] < 1e10) and (l[i] > -1e10): ibx.append(i) else: raise ValueError Ae = A[ieq, :] Ai = sparse([A[ilt, :], -A[igt, :], A[ibx, :], -A[ibx, :]]) be = u[ieq, :] bi = matrix([u[ilt], -l[igt], u[ibx], -l[ibx]]) return Ae, be, Ai, bi

Returns the linear equality and inequality constraints.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/cvxopf.py#L472-L496

train

rwl/pylon

contrib/cvxopf.py

dSbus_dV

def dSbus_dV(Y, V): """ Computes the partial derivative of power injection w.r.t. voltage. References: Ray Zimmerman, "dSbus_dV.m", MATPOWER, version 3.2, PSERC (Cornell), http://www.pserc.cornell.edu/matpower/ """ I = Y * V diagV = spdiag(V) diagIbus = spdiag(I) diagVnorm = spdiag(div(V, abs(V))) # Element-wise division. dS_dVm = diagV * conj(Y * diagVnorm) + conj(diagIbus) * diagVnorm dS_dVa = 1j * diagV * conj(diagIbus - Y * diagV) return dS_dVm, dS_dVa

python

def dSbus_dV(Y, V): """ Computes the partial derivative of power injection w.r.t. voltage. References: Ray Zimmerman, "dSbus_dV.m", MATPOWER, version 3.2, PSERC (Cornell), http://www.pserc.cornell.edu/matpower/ """ I = Y * V diagV = spdiag(V) diagIbus = spdiag(I) diagVnorm = spdiag(div(V, abs(V))) # Element-wise division. dS_dVm = diagV * conj(Y * diagVnorm) + conj(diagIbus) * diagVnorm dS_dVa = 1j * diagV * conj(diagIbus - Y * diagV) return dS_dVm, dS_dVa

Computes the partial derivative of power injection w.r.t. voltage. References: Ray Zimmerman, "dSbus_dV.m", MATPOWER, version 3.2, PSERC (Cornell), http://www.pserc.cornell.edu/matpower/

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/cvxopf.py#L502-L518

train

rwl/pylon

contrib/cvxopf.py

dIbr_dV

def dIbr_dV(Yf, Yt, V): """ Computes partial derivatives of branch currents w.r.t. voltage. Ray Zimmerman, "dIbr_dV.m", MATPOWER, version 4.0b1, PSERC (Cornell), http://www.pserc.cornell.edu/matpower/ """ # nb = len(V) Vnorm = div(V, abs(V)) diagV = spdiag(V) diagVnorm = spdiag(Vnorm) dIf_dVa = Yf * 1j * diagV dIf_dVm = Yf * diagVnorm dIt_dVa = Yt * 1j * diagV dIt_dVm = Yt * diagVnorm # Compute currents. If = Yf * V It = Yt * V return dIf_dVa, dIf_dVm, dIt_dVa, dIt_dVm, If, It

python

def dIbr_dV(Yf, Yt, V): """ Computes partial derivatives of branch currents w.r.t. voltage. Ray Zimmerman, "dIbr_dV.m", MATPOWER, version 4.0b1, PSERC (Cornell), http://www.pserc.cornell.edu/matpower/ """ # nb = len(V) Vnorm = div(V, abs(V)) diagV = spdiag(V) diagVnorm = spdiag(Vnorm) dIf_dVa = Yf * 1j * diagV dIf_dVm = Yf * diagVnorm dIt_dVa = Yt * 1j * diagV dIt_dVm = Yt * diagVnorm # Compute currents. If = Yf * V It = Yt * V return dIf_dVa, dIf_dVm, dIt_dVa, dIt_dVm, If, It

Computes partial derivatives of branch currents w.r.t. voltage. Ray Zimmerman, "dIbr_dV.m", MATPOWER, version 4.0b1, PSERC (Cornell), http://www.pserc.cornell.edu/matpower/

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/cvxopf.py#L524-L544

train

rwl/pylon

contrib/cvxopf.py

dSbr_dV

def dSbr_dV(Yf, Yt, V, buses, branches): """ Computes the branch power flow vector and the partial derivative of branch power flow w.r.t voltage. """ nl = len(branches) nb = len(V) f = matrix([l.from_bus._i for l in branches]) t = matrix([l.to_bus._i for l in branches]) # Compute currents. If = Yf * V It = Yt * V Vnorm = div(V, abs(V)) diagVf = spdiag(V[f]) diagIf = spdiag(If) diagVt = spdiag(V[t]) diagIt = spdiag(It) diagV = spdiag(V) diagVnorm = spdiag(Vnorm) ibr = range(nl) size = (nl, nb) # Partial derivative of S w.r.t voltage phase angle. dSf_dVa = 1j * (conj(diagIf) * spmatrix(V[f], ibr, f, size) - diagVf * conj(Yf * diagV)) dSt_dVa = 1j * (conj(diagIt) * spmatrix(V[t], ibr, t, size) - diagVt * conj(Yt * diagV)) # Partial derivative of S w.r.t. voltage amplitude. dSf_dVm = diagVf * conj(Yf * diagVnorm) + conj(diagIf) * \ spmatrix(Vnorm[f], ibr, f, size) dSt_dVm = diagVt * conj(Yt * diagVnorm) + conj(diagIt) * \ spmatrix(Vnorm[t], ibr, t, size) # Compute power flow vectors. Sf = mul(V[f], conj(If)) St = mul(V[t], conj(It)) return dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St

python

def dSbr_dV(Yf, Yt, V, buses, branches): """ Computes the branch power flow vector and the partial derivative of branch power flow w.r.t voltage. """ nl = len(branches) nb = len(V) f = matrix([l.from_bus._i for l in branches]) t = matrix([l.to_bus._i for l in branches]) # Compute currents. If = Yf * V It = Yt * V Vnorm = div(V, abs(V)) diagVf = spdiag(V[f]) diagIf = spdiag(If) diagVt = spdiag(V[t]) diagIt = spdiag(It) diagV = spdiag(V) diagVnorm = spdiag(Vnorm) ibr = range(nl) size = (nl, nb) # Partial derivative of S w.r.t voltage phase angle. dSf_dVa = 1j * (conj(diagIf) * spmatrix(V[f], ibr, f, size) - diagVf * conj(Yf * diagV)) dSt_dVa = 1j * (conj(diagIt) * spmatrix(V[t], ibr, t, size) - diagVt * conj(Yt * diagV)) # Partial derivative of S w.r.t. voltage amplitude. dSf_dVm = diagVf * conj(Yf * diagVnorm) + conj(diagIf) * \ spmatrix(Vnorm[f], ibr, f, size) dSt_dVm = diagVt * conj(Yt * diagVnorm) + conj(diagIt) * \ spmatrix(Vnorm[t], ibr, t, size) # Compute power flow vectors. Sf = mul(V[f], conj(If)) St = mul(V[t], conj(It)) return dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St

Computes the branch power flow vector and the partial derivative of branch power flow w.r.t voltage.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/cvxopf.py#L550-L593

train

rwl/pylon

contrib/cvxopf.py

dAbr_dV

def dAbr_dV(dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St): """ Partial derivatives of squared flow magnitudes w.r.t voltage. Computes partial derivatives of apparent power w.r.t active and reactive power flows. Partial derivative must equal 1 for lines with zero flow to avoid division by zero errors (1 comes from L'Hopital). """ dAf_dPf = spdiag(2 * Sf.real()) dAf_dQf = spdiag(2 * Sf.imag()) dAt_dPt = spdiag(2 * St.real()) dAt_dQt = spdiag(2 * St.imag()) # Partial derivative of apparent power magnitude w.r.t voltage # phase angle. dAf_dVa = dAf_dPf * dSf_dVa.real() + dAf_dQf * dSf_dVa.imag() dAt_dVa = dAt_dPt * dSt_dVa.real() + dAt_dQt * dSt_dVa.imag() # Partial derivative of apparent power magnitude w.r.t. voltage # amplitude. dAf_dVm = dAf_dPf * dSf_dVm.real() + dAf_dQf * dSf_dVm.imag() dAt_dVm = dAt_dPt * dSt_dVm.real() + dAt_dQt * dSt_dVm.imag() return dAf_dVa, dAf_dVm, dAt_dVa, dAt_dVm

python

def dAbr_dV(dSf_dVa, dSf_dVm, dSt_dVa, dSt_dVm, Sf, St): """ Partial derivatives of squared flow magnitudes w.r.t voltage. Computes partial derivatives of apparent power w.r.t active and reactive power flows. Partial derivative must equal 1 for lines with zero flow to avoid division by zero errors (1 comes from L'Hopital). """ dAf_dPf = spdiag(2 * Sf.real()) dAf_dQf = spdiag(2 * Sf.imag()) dAt_dPt = spdiag(2 * St.real()) dAt_dQt = spdiag(2 * St.imag()) # Partial derivative of apparent power magnitude w.r.t voltage # phase angle. dAf_dVa = dAf_dPf * dSf_dVa.real() + dAf_dQf * dSf_dVa.imag() dAt_dVa = dAt_dPt * dSt_dVa.real() + dAt_dQt * dSt_dVa.imag() # Partial derivative of apparent power magnitude w.r.t. voltage # amplitude. dAf_dVm = dAf_dPf * dSf_dVm.real() + dAf_dQf * dSf_dVm.imag() dAt_dVm = dAt_dPt * dSt_dVm.real() + dAt_dQt * dSt_dVm.imag() return dAf_dVa, dAf_dVm, dAt_dVa, dAt_dVm

Partial derivatives of squared flow magnitudes w.r.t voltage. Computes partial derivatives of apparent power w.r.t active and reactive power flows. Partial derivative must equal 1 for lines with zero flow to avoid division by zero errors (1 comes from L'Hopital).

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/cvxopf.py#L599-L621

train

rwl/pylon

contrib/cvxopf.py

d2Sbus_dV2

def d2Sbus_dV2(Ybus, V, lam): """ Computes 2nd derivatives of power injection w.r.t. voltage. """ n = len(V) Ibus = Ybus * V diaglam = spdiag(lam) diagV = spdiag(V) A = spmatrix(mul(lam, V), range(n), range(n)) B = Ybus * diagV C = A * conj(B) D = Ybus.H * diagV E = conj(diagV) * (D * diaglam - spmatrix(D*lam, range(n), range(n))) F = C - A * spmatrix(conj(Ibus), range(n), range(n)) G = spmatrix(div(matrix(1.0, (n, 1)), abs(V)), range(n), range(n)) Gaa = E + F Gva = 1j * G * (E - F) Gav = Gva.T Gvv = G * (C + C.T) * G return Gaa, Gav, Gva, Gvv

python

def d2Sbus_dV2(Ybus, V, lam): """ Computes 2nd derivatives of power injection w.r.t. voltage. """ n = len(V) Ibus = Ybus * V diaglam = spdiag(lam) diagV = spdiag(V) A = spmatrix(mul(lam, V), range(n), range(n)) B = Ybus * diagV C = A * conj(B) D = Ybus.H * diagV E = conj(diagV) * (D * diaglam - spmatrix(D*lam, range(n), range(n))) F = C - A * spmatrix(conj(Ibus), range(n), range(n)) G = spmatrix(div(matrix(1.0, (n, 1)), abs(V)), range(n), range(n)) Gaa = E + F Gva = 1j * G * (E - F) Gav = Gva.T Gvv = G * (C + C.T) * G return Gaa, Gav, Gva, Gvv

Computes 2nd derivatives of power injection w.r.t. voltage.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/cvxopf.py#L627-L648

train

rwl/pylon

contrib/cvxopf.py

d2Ibr_dV2

def d2Ibr_dV2(Ybr, V, lam): """ Computes 2nd derivatives of complex branch current w.r.t. voltage. """ nb = len(V) diaginvVm = spdiag(div(matrix(1.0, (nb, 1)), abs(V))) Haa = spdiag(mul(-(Ybr.T * lam), V)) Hva = -1j * Haa * diaginvVm Hav = Hva Hvv = spmatrix([], [], [], (nb, nb)) return Haa, Hav, Hva, Hvv

python

def d2Ibr_dV2(Ybr, V, lam): """ Computes 2nd derivatives of complex branch current w.r.t. voltage. """ nb = len(V) diaginvVm = spdiag(div(matrix(1.0, (nb, 1)), abs(V))) Haa = spdiag(mul(-(Ybr.T * lam), V)) Hva = -1j * Haa * diaginvVm Hav = Hva Hvv = spmatrix([], [], [], (nb, nb)) return Haa, Hav, Hva, Hvv

Computes 2nd derivatives of complex branch current w.r.t. voltage.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/cvxopf.py#L654-L665

train

rwl/pylon

contrib/cvxopf.py

d2Sbr_dV2

def d2Sbr_dV2(Cbr, Ybr, V, lam): """ Computes 2nd derivatives of complex power flow w.r.t. voltage. """ nb = len(V) diaglam = spdiag(lam) diagV = spdiag(V) A = Ybr.H * diaglam * Cbr B = conj(diagV) * A * diagV D = spdiag(mul((A*V), conj(V))) E = spdiag(mul((A.T * conj(V)), V)) F = B + B.T G = spdiag(div(matrix(1.0, (nb, 1)), abs(V))) Haa = F - D - E Hva = 1j * G * (B - B.T - D + E) Hav = Hva.T Hvv = G * F * G return Haa, Hav, Hva, Hvv

python

def d2Sbr_dV2(Cbr, Ybr, V, lam): """ Computes 2nd derivatives of complex power flow w.r.t. voltage. """ nb = len(V) diaglam = spdiag(lam) diagV = spdiag(V) A = Ybr.H * diaglam * Cbr B = conj(diagV) * A * diagV D = spdiag(mul((A*V), conj(V))) E = spdiag(mul((A.T * conj(V)), V)) F = B + B.T G = spdiag(div(matrix(1.0, (nb, 1)), abs(V))) Haa = F - D - E Hva = 1j * G * (B - B.T - D + E) Hav = Hva.T Hvv = G * F * G return Haa, Hav, Hva, Hvv

Computes 2nd derivatives of complex power flow w.r.t. voltage.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/cvxopf.py#L671-L691

train

rwl/pylon

contrib/cvxopf.py

tocvx

def tocvx(B): """ Converts a sparse SciPy matrix into a sparse CVXOPT matrix. """ Bcoo = B.tocoo() return spmatrix(Bcoo.data, Bcoo.row.tolist(), Bcoo.col.tolist())

python

def tocvx(B): """ Converts a sparse SciPy matrix into a sparse CVXOPT matrix. """ Bcoo = B.tocoo() return spmatrix(Bcoo.data, Bcoo.row.tolist(), Bcoo.col.tolist())

Converts a sparse SciPy matrix into a sparse CVXOPT matrix.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/cvxopf.py#L741-L745

train

rwl/pylon

pyreto/discrete/experiment.py

MarketExperiment.doInteractions

def doInteractions(self, number=1): """ Directly maps the agents and the tasks. """ t0 = time.time() for _ in range(number): self._oneInteraction() elapsed = time.time() - t0 logger.info("%d interactions executed in %.3fs." % (number, elapsed)) return self.stepid

python

def doInteractions(self, number=1): """ Directly maps the agents and the tasks. """ t0 = time.time() for _ in range(number): self._oneInteraction() elapsed = time.time() - t0 logger.info("%d interactions executed in %.3fs." % (number, elapsed)) return self.stepid

Directly maps the agents and the tasks.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/discrete/experiment.py#L72-L83

train

rwl/pylon

pylon/dyn.py

DynamicCase.exciter

def exciter(self, Xexc, Pexc, Vexc): """ Exciter model. Based on Exciter.m from MatDyn by Stijn Cole, developed at Katholieke Universiteit Leuven. See U{http://www.esat.kuleuven.be/electa/teaching/ matdyn/} for more information. """ exciters = self.exciters F = zeros(Xexc.shape) typ1 = [e.generator._i for e in exciters if e.model ==CONST_EXCITATION] typ2 = [e.generator._i for e in exciters if e.model == IEEE_DC1A] # Exciter type 1: constant excitation F[typ1, :] = 0.0 # Exciter type 2: IEEE DC1A Efd = Xexc[typ2, 0] Uf = Xexc[typ2, 1] Ur = Xexc[typ2, 2] Ka = Pexc[typ2, 0] Ta = Pexc[typ2, 1] Ke = Pexc[typ2, 2] Te = Pexc[typ2, 3] Kf = Pexc[typ2, 4] Tf = Pexc[typ2, 5] Aex = Pexc[typ2, 6] Bex = Pexc[typ2, 7] Ur_min = Pexc[typ2, 8] Ur_max = Pexc[typ2, 9] Uref = Pexc[typ2, 10] Uref2 = Pexc[typ2, 11] U = Vexc[typ2, 1] Ux = Aex * exp(Bex * Efd) dUr = 1 / Ta * (Ka * (Uref - U + Uref2 - Uf) - Ur) dUf = 1 / Tf * (Kf / Te * (Ur - Ux - Ke * Efd) - Uf) if sum(flatnonzero(Ur > Ur_max)) >= 1: Ur2 = Ur_max elif sum(flatnonzero(Ur < Ur_max)) >= 1: Ur2 = Ur_min else: Ur2 = Ur dEfd = 1 / Te * (Ur2 - Ux - Ke * Efd) F[typ2, :] = c_[dEfd, dUf, dUr] # Exciter type 3: # Exciter type 4: return F

python

def exciter(self, Xexc, Pexc, Vexc): """ Exciter model. Based on Exciter.m from MatDyn by Stijn Cole, developed at Katholieke Universiteit Leuven. See U{http://www.esat.kuleuven.be/electa/teaching/ matdyn/} for more information. """ exciters = self.exciters F = zeros(Xexc.shape) typ1 = [e.generator._i for e in exciters if e.model ==CONST_EXCITATION] typ2 = [e.generator._i for e in exciters if e.model == IEEE_DC1A] # Exciter type 1: constant excitation F[typ1, :] = 0.0 # Exciter type 2: IEEE DC1A Efd = Xexc[typ2, 0] Uf = Xexc[typ2, 1] Ur = Xexc[typ2, 2] Ka = Pexc[typ2, 0] Ta = Pexc[typ2, 1] Ke = Pexc[typ2, 2] Te = Pexc[typ2, 3] Kf = Pexc[typ2, 4] Tf = Pexc[typ2, 5] Aex = Pexc[typ2, 6] Bex = Pexc[typ2, 7] Ur_min = Pexc[typ2, 8] Ur_max = Pexc[typ2, 9] Uref = Pexc[typ2, 10] Uref2 = Pexc[typ2, 11] U = Vexc[typ2, 1] Ux = Aex * exp(Bex * Efd) dUr = 1 / Ta * (Ka * (Uref - U + Uref2 - Uf) - Ur) dUf = 1 / Tf * (Kf / Te * (Ur - Ux - Ke * Efd) - Uf) if sum(flatnonzero(Ur > Ur_max)) >= 1: Ur2 = Ur_max elif sum(flatnonzero(Ur < Ur_max)) >= 1: Ur2 = Ur_min else: Ur2 = Ur dEfd = 1 / Te * (Ur2 - Ux - Ke * Efd) F[typ2, :] = c_[dEfd, dUf, dUr] # Exciter type 3: # Exciter type 4: return F

Exciter model. Based on Exciter.m from MatDyn by Stijn Cole, developed at Katholieke Universiteit Leuven. See U{http://www.esat.kuleuven.be/electa/teaching/ matdyn/} for more information.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/dyn.py#L409-L464

train

rwl/pylon

pylon/dyn.py

DynamicCase.governor

def governor(self, Xgov, Pgov, Vgov): """ Governor model. Based on Governor.m from MatDyn by Stijn Cole, developed at Katholieke Universiteit Leuven. See U{http://www.esat.kuleuven.be/electa/teaching/ matdyn/} for more information. """ governors = self.governors omegas = 2 * pi * self.freq F = zeros(Xgov.shape) typ1 = [g.generator._i for g in governors if g.model == CONST_POWER] typ2 = [g.generator._i for g in governors if g.model == GENERAL_IEEE] # Governor type 1: constant power F[typ1, 0] = 0 # Governor type 2: IEEE general speed-governing system Pm = Xgov[typ2, 0] P = Xgov[typ2, 1] x = Xgov[typ2, 2] z = Xgov[typ2, 3] K = Pgov[typ2, 0] T1 = Pgov[typ2, 1] T2 = Pgov[typ2, 2] T3 = Pgov[typ2, 3] Pup = Pgov[typ2, 4] Pdown = Pgov[typ2, 5] Pmax = Pgov[typ2, 6] Pmin = Pgov[typ2, 7] P0 = Pgov[typ2, 8] omega = Vgov[typ2, 0] dx = K * (-1 / T1 * x + (1 - T2 / T1) * (omega - omegas)) dP = 1 / T1 * x + T2 / T1 * (omega - omegas) y = 1 / T3 * (P0 - P - Pm) y2 = y if sum(flatnonzero(y > Pup)) >= 1: y2 = (1 - flatnonzero(y > Pup)) * y2 + flatnonzero(y > Pup) * Pup if sum(flatnonzero(y < Pdown)) >= 1: y2 = (1 - flatnonzero(y<Pdown)) * y2 + flatnonzero(y<Pdown) * Pdown dz = y2 dPm = y2 if sum(flatnonzero(z > Pmax)) >= 1: dPm = (1 - flatnonzero(z > Pmax)) * dPm + flatnonzero(z > Pmax) * 0 if sum(flatnonzero(z < Pmin)) >= 1: dPm = (1 - flatnonzero(z < Pmin)) * dPm + flatnonzero(z < Pmin) * 0 F[typ2, :] = c_[dPm, dP, dx, dz] # Governor type 3: # Governor type 4: return F

python

def governor(self, Xgov, Pgov, Vgov): """ Governor model. Based on Governor.m from MatDyn by Stijn Cole, developed at Katholieke Universiteit Leuven. See U{http://www.esat.kuleuven.be/electa/teaching/ matdyn/} for more information. """ governors = self.governors omegas = 2 * pi * self.freq F = zeros(Xgov.shape) typ1 = [g.generator._i for g in governors if g.model == CONST_POWER] typ2 = [g.generator._i for g in governors if g.model == GENERAL_IEEE] # Governor type 1: constant power F[typ1, 0] = 0 # Governor type 2: IEEE general speed-governing system Pm = Xgov[typ2, 0] P = Xgov[typ2, 1] x = Xgov[typ2, 2] z = Xgov[typ2, 3] K = Pgov[typ2, 0] T1 = Pgov[typ2, 1] T2 = Pgov[typ2, 2] T3 = Pgov[typ2, 3] Pup = Pgov[typ2, 4] Pdown = Pgov[typ2, 5] Pmax = Pgov[typ2, 6] Pmin = Pgov[typ2, 7] P0 = Pgov[typ2, 8] omega = Vgov[typ2, 0] dx = K * (-1 / T1 * x + (1 - T2 / T1) * (omega - omegas)) dP = 1 / T1 * x + T2 / T1 * (omega - omegas) y = 1 / T3 * (P0 - P - Pm) y2 = y if sum(flatnonzero(y > Pup)) >= 1: y2 = (1 - flatnonzero(y > Pup)) * y2 + flatnonzero(y > Pup) * Pup if sum(flatnonzero(y < Pdown)) >= 1: y2 = (1 - flatnonzero(y<Pdown)) * y2 + flatnonzero(y<Pdown) * Pdown dz = y2 dPm = y2 if sum(flatnonzero(z > Pmax)) >= 1: dPm = (1 - flatnonzero(z > Pmax)) * dPm + flatnonzero(z > Pmax) * 0 if sum(flatnonzero(z < Pmin)) >= 1: dPm = (1 - flatnonzero(z < Pmin)) * dPm + flatnonzero(z < Pmin) * 0 F[typ2, :] = c_[dPm, dP, dx, dz] # Governor type 3: # Governor type 4: return F

Governor model. Based on Governor.m from MatDyn by Stijn Cole, developed at Katholieke Universiteit Leuven. See U{http://www.esat.kuleuven.be/electa/teaching/ matdyn/} for more information.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/dyn.py#L467-L530

train

rwl/pylon

pylon/dyn.py

DynamicCase.generator

def generator(self, Xgen, Xexc, Xgov, Vgen): """ Generator model. Based on Generator.m from MatDyn by Stijn Cole, developed at Katholieke Universiteit Leuven. See U{http://www.esat.kuleuven.be/electa/teaching/ matdyn/} for more information. """ generators = self.dyn_generators omegas = 2 * pi * self.freq F = zeros(Xgen.shape) typ1 = [g._i for g in generators if g.model == CLASSICAL] typ2 = [g._i for g in generators if g.model == FOURTH_ORDER] # Generator type 1: classical model omega = Xgen[typ1, 1] Pm0 = Xgov[typ1, 0] H = array([g.h for g in generators])[typ1] D = array([g.d for g in generators])[typ1] Pe = Vgen[typ1, 2] ddelta = omega = omegas domega = pi * self.freq / H * (-D * (omega - omegas) + Pm0 - Pe) dEq = zeros(len(typ1)) F[typ1, :] = c_[ddelta, domega, dEq] # Generator type 2: 4th order model omega = Xgen[typ2, 1] Eq_tr = Xgen[typ2, 2] Ed_tr = Xgen[typ2, 3] H = array([g.h for g in generators]) D = array([g.d for g in generators]) xd = array([g.xd for g in generators]) xq = array([g.xq for g in generators]) xd_tr = array([g.xd_tr for g in generators]) xq_tr = array([g.xq_tr for g in generators]) Td0_tr = array([g.td for g in generators]) Tq0_tr = array([g.tq for g in generators]) Id = Vgen[typ2, 0] Iq = Vgen[typ2, 1] Pe = Vgen[typ2, 2] Efd = Xexc[typ2, 0] Pm = Xgov[typ2, 0] ddelta = omega - omegas domega = pi * self.freq / H * (-D * (omega - omegas) + Pm - Pe) dEq = 1 / Td0_tr * (Efd - Eq_tr + (xd - xd_tr) * Id) dEd = 1 / Tq0_tr * (-Ed_tr - (xq - xq_tr) * Iq) F[typ2, :] = c_[ddelta, domega, dEq, dEd] # Generator type 3: # Generator type 4: return F

python

def generator(self, Xgen, Xexc, Xgov, Vgen): """ Generator model. Based on Generator.m from MatDyn by Stijn Cole, developed at Katholieke Universiteit Leuven. See U{http://www.esat.kuleuven.be/electa/teaching/ matdyn/} for more information. """ generators = self.dyn_generators omegas = 2 * pi * self.freq F = zeros(Xgen.shape) typ1 = [g._i for g in generators if g.model == CLASSICAL] typ2 = [g._i for g in generators if g.model == FOURTH_ORDER] # Generator type 1: classical model omega = Xgen[typ1, 1] Pm0 = Xgov[typ1, 0] H = array([g.h for g in generators])[typ1] D = array([g.d for g in generators])[typ1] Pe = Vgen[typ1, 2] ddelta = omega = omegas domega = pi * self.freq / H * (-D * (omega - omegas) + Pm0 - Pe) dEq = zeros(len(typ1)) F[typ1, :] = c_[ddelta, domega, dEq] # Generator type 2: 4th order model omega = Xgen[typ2, 1] Eq_tr = Xgen[typ2, 2] Ed_tr = Xgen[typ2, 3] H = array([g.h for g in generators]) D = array([g.d for g in generators]) xd = array([g.xd for g in generators]) xq = array([g.xq for g in generators]) xd_tr = array([g.xd_tr for g in generators]) xq_tr = array([g.xq_tr for g in generators]) Td0_tr = array([g.td for g in generators]) Tq0_tr = array([g.tq for g in generators]) Id = Vgen[typ2, 0] Iq = Vgen[typ2, 1] Pe = Vgen[typ2, 2] Efd = Xexc[typ2, 0] Pm = Xgov[typ2, 0] ddelta = omega - omegas domega = pi * self.freq / H * (-D * (omega - omegas) + Pm - Pe) dEq = 1 / Td0_tr * (Efd - Eq_tr + (xd - xd_tr) * Id) dEd = 1 / Tq0_tr * (-Ed_tr - (xq - xq_tr) * Iq) F[typ2, :] = c_[ddelta, domega, dEq, dEd] # Generator type 3: # Generator type 4: return F

Generator model. Based on Generator.m from MatDyn by Stijn Cole, developed at Katholieke Universiteit Leuven. See U{http://www.esat.kuleuven.be/electa/teaching/ matdyn/} for more information.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/dyn.py#L533-L595

train

rwl/pylon

pylon/io/rst.py

ReSTWriter._write_data

def _write_data(self, file): """ Writes case data to file in ReStructuredText format. """ self.write_case_data(file) file.write("Bus Data\n") file.write("-" * 8 + "\n") self.write_bus_data(file) file.write("\n") file.write("Branch Data\n") file.write("-" * 11 + "\n") self.write_branch_data(file) file.write("\n") file.write("Generator Data\n") file.write("-" * 14 + "\n") self.write_generator_data(file) file.write("\n")

python

def _write_data(self, file): """ Writes case data to file in ReStructuredText format. """ self.write_case_data(file) file.write("Bus Data\n") file.write("-" * 8 + "\n") self.write_bus_data(file) file.write("\n") file.write("Branch Data\n") file.write("-" * 11 + "\n") self.write_branch_data(file) file.write("\n") file.write("Generator Data\n") file.write("-" * 14 + "\n") self.write_generator_data(file) file.write("\n")

Writes case data to file in ReStructuredText format.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/rst.py#L40-L58

train

rwl/pylon

pylon/io/rst.py

ReSTWriter.write_bus_data

def write_bus_data(self, file): """ Writes bus data to a ReST table. """ report = CaseReport(self.case) buses = self.case.buses col_width = 8 col_width_2 = col_width * 2 + 1 col1_width = 6 sep = "=" * 6 + " " + ("=" * col_width + " ") * 6 + "\n" file.write(sep) # Line one of column headers file.write("Name".center(col1_width) + " ") file.write("Voltage (pu)".center(col_width_2) + " ") file.write("Generation".center(col_width_2) + " ") file.write("Load".center(col_width_2) + " ") file.write("\n") file.write("-" * col1_width +" "+ ("-" * col_width_2 + " ") * 3 + "\n") # Line two of column header file.write("..".ljust(col1_width) + " ") file.write("Amp".center(col_width) + " ") file.write("Phase".center(col_width) + " ") file.write("P (MW)".center(col_width) + " ") file.write("Q (MVAr)".center(col_width) + " ") file.write("P (MW)".center(col_width) + " ") file.write("Q (MVAr)".center(col_width) + " ") file.write("\n") file.write(sep) # Bus rows for bus in buses: file.write(bus.name[:col1_width].ljust(col1_width)) file.write(" %8.3f" % bus.v_magnitude) file.write(" %8.3f" % bus.v_angle) file.write(" %8.2f" % self.case.s_supply(bus).real) file.write(" %8.2f" % self.case.s_supply(bus).imag) file.write(" %8.2f" % self.case.s_demand(bus).real) file.write(" %8.2f" % self.case.s_demand(bus).imag) file.write("\n") # Totals # file.write("..".ljust(col1_width) + " ") # file.write(("..".ljust(col_width) + " ")*2) # file.write(("_"*col_width + " ")*4 + "\n") file.write("..".ljust(col1_width) + " " + "..".ljust(col_width) + " ") file.write("*Total:*".rjust(col_width) + " ") ptot = report.actual_pgen qtot = report.actual_qgen file.write("%8.2f " % ptot) file.write("%8.2f " % qtot) file.write("%8.2f " % report.p_demand) file.write("%8.2f " % report.q_demand) file.write("\n") file.write(sep) del report

python

def write_bus_data(self, file): """ Writes bus data to a ReST table. """ report = CaseReport(self.case) buses = self.case.buses col_width = 8 col_width_2 = col_width * 2 + 1 col1_width = 6 sep = "=" * 6 + " " + ("=" * col_width + " ") * 6 + "\n" file.write(sep) # Line one of column headers file.write("Name".center(col1_width) + " ") file.write("Voltage (pu)".center(col_width_2) + " ") file.write("Generation".center(col_width_2) + " ") file.write("Load".center(col_width_2) + " ") file.write("\n") file.write("-" * col1_width +" "+ ("-" * col_width_2 + " ") * 3 + "\n") # Line two of column header file.write("..".ljust(col1_width) + " ") file.write("Amp".center(col_width) + " ") file.write("Phase".center(col_width) + " ") file.write("P (MW)".center(col_width) + " ") file.write("Q (MVAr)".center(col_width) + " ") file.write("P (MW)".center(col_width) + " ") file.write("Q (MVAr)".center(col_width) + " ") file.write("\n") file.write(sep) # Bus rows for bus in buses: file.write(bus.name[:col1_width].ljust(col1_width)) file.write(" %8.3f" % bus.v_magnitude) file.write(" %8.3f" % bus.v_angle) file.write(" %8.2f" % self.case.s_supply(bus).real) file.write(" %8.2f" % self.case.s_supply(bus).imag) file.write(" %8.2f" % self.case.s_demand(bus).real) file.write(" %8.2f" % self.case.s_demand(bus).imag) file.write("\n") # Totals # file.write("..".ljust(col1_width) + " ") # file.write(("..".ljust(col_width) + " ")*2) # file.write(("_"*col_width + " ")*4 + "\n") file.write("..".ljust(col1_width) + " " + "..".ljust(col_width) + " ") file.write("*Total:*".rjust(col_width) + " ") ptot = report.actual_pgen qtot = report.actual_qgen file.write("%8.2f " % ptot) file.write("%8.2f " % qtot) file.write("%8.2f " % report.p_demand) file.write("%8.2f " % report.q_demand) file.write("\n") file.write(sep) del report

Writes bus data to a ReST table.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/rst.py#L87-L146

train

rwl/pylon

pylon/io/rst.py

ReSTWriter.write_how_many

def write_how_many(self, file): """ Writes component numbers to a table. """ report = CaseReport(self.case) # Map component labels to attribute names components = [("Bus", "n_buses"), ("Generator", "n_generators"), ("Committed Generator", "n_online_generators"), ("Load", "n_loads"), ("Fixed Load", "n_fixed_loads"), ("Despatchable Load", "n_online_vloads"), ("Shunt", "n_shunts"), ("Branch", "n_branches"), ("Transformer", "n_transformers"), ("Inter-tie", "n_interties"), ("Area", "n_areas") ] # Column 1 width longest = max([len(c[0]) for c in components]) col1_header = "Object" col1_width = longest col2_header = "Quantity" col2_width = len(col2_header) # Row separator sep = "="*col1_width + " " + "="*col2_width + "\n" # Row headers file.write(sep) file.write(col1_header.center(col1_width)) file.write(" ") file.write("%s\n" % col2_header.center(col2_width)) file.write(sep) # Rows for label, attr in components: col2_value = str(getattr(report, attr)) file.write("%s %s\n" % (label.ljust(col1_width), col2_value.rjust(col2_width))) else: file.write(sep) file.write("\n") del report

python

def write_how_many(self, file): """ Writes component numbers to a table. """ report = CaseReport(self.case) # Map component labels to attribute names components = [("Bus", "n_buses"), ("Generator", "n_generators"), ("Committed Generator", "n_online_generators"), ("Load", "n_loads"), ("Fixed Load", "n_fixed_loads"), ("Despatchable Load", "n_online_vloads"), ("Shunt", "n_shunts"), ("Branch", "n_branches"), ("Transformer", "n_transformers"), ("Inter-tie", "n_interties"), ("Area", "n_areas") ] # Column 1 width longest = max([len(c[0]) for c in components]) col1_header = "Object" col1_width = longest col2_header = "Quantity" col2_width = len(col2_header) # Row separator sep = "="*col1_width + " " + "="*col2_width + "\n" # Row headers file.write(sep) file.write(col1_header.center(col1_width)) file.write(" ") file.write("%s\n" % col2_header.center(col2_width)) file.write(sep) # Rows for label, attr in components: col2_value = str(getattr(report, attr)) file.write("%s %s\n" % (label.ljust(col1_width), col2_value.rjust(col2_width))) else: file.write(sep) file.write("\n") del report

Writes component numbers to a table.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/rst.py#L312-L355

train

rwl/pylon

pylon/io/rst.py

ReSTWriter.write_min_max

def write_min_max(self, file): """ Writes minimum and maximum values to a table. """ report = CaseReport(self.case) col1_header = "Attribute" col1_width = 19 col2_header = "Minimum" col3_header = "Maximum" col_width = 22 sep = "="*col1_width +" "+ "="*col_width +" "+ "="*col_width + "\n" # Row headers file.write(sep) file.write("%s" % col1_header.center(col1_width)) file.write(" ") file.write("%s" % col2_header.center(col_width)) file.write(" ") file.write("%s" % col3_header.center(col_width)) file.write("\n") file.write(sep) # Rows min_val, min_i = getattr(report, "min_v_magnitude") max_val, max_i = getattr(report, "max_v_magnitude") file.write("%s %7.3f p.u. @ bus %2d %7.3f p.u. @ bus %2d\n" % ("Voltage Amplitude".ljust(col1_width), min_val, min_i, max_val, max_i)) min_val, min_i = getattr(report, "min_v_angle") max_val, max_i = getattr(report, "max_v_angle") file.write("%s %16.3f %16.3f\n" % ("Voltage Phase Angle".ljust(col1_width), min_val, max_val)) file.write(sep) file.write("\n") del report

python

def write_min_max(self, file): """ Writes minimum and maximum values to a table. """ report = CaseReport(self.case) col1_header = "Attribute" col1_width = 19 col2_header = "Minimum" col3_header = "Maximum" col_width = 22 sep = "="*col1_width +" "+ "="*col_width +" "+ "="*col_width + "\n" # Row headers file.write(sep) file.write("%s" % col1_header.center(col1_width)) file.write(" ") file.write("%s" % col2_header.center(col_width)) file.write(" ") file.write("%s" % col3_header.center(col_width)) file.write("\n") file.write(sep) # Rows min_val, min_i = getattr(report, "min_v_magnitude") max_val, max_i = getattr(report, "max_v_magnitude") file.write("%s %7.3f p.u. @ bus %2d %7.3f p.u. @ bus %2d\n" % ("Voltage Amplitude".ljust(col1_width), min_val, min_i, max_val, max_i)) min_val, min_i = getattr(report, "min_v_angle") max_val, max_i = getattr(report, "max_v_angle") file.write("%s %16.3f %16.3f\n" % ("Voltage Phase Angle".ljust(col1_width), min_val, max_val)) file.write(sep) file.write("\n") del report

Writes minimum and maximum values to a table.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/rst.py#L438-L478

train

rwl/pylon

pylon/io/parsing_util.py

make_unique_name

def make_unique_name(base, existing=[], format="%s_%s"): """ Return a name, unique within a context, based on the specified name. @param base: the desired base name of the generated unique name. @param existing: a sequence of the existing names to avoid returning. @param format: a formatting specification for how the name is made unique. """ count = 2 name = base while name in existing: name = format % (base, count) count += 1 return name

python

def make_unique_name(base, existing=[], format="%s_%s"): """ Return a name, unique within a context, based on the specified name. @param base: the desired base name of the generated unique name. @param existing: a sequence of the existing names to avoid returning. @param format: a formatting specification for how the name is made unique. """ count = 2 name = base while name in existing: name = format % (base, count) count += 1 return name

Return a name, unique within a context, based on the specified name. @param base: the desired base name of the generated unique name. @param existing: a sequence of the existing names to avoid returning. @param format: a formatting specification for how the name is made unique.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/parsing_util.py#L167-L180

train

pymoca/pymoca

setup.py

call_antlr4

def call_antlr4(arg): "calls antlr4 on grammar file" # pylint: disable=unused-argument, unused-variable antlr_path = os.path.join(ROOT_DIR, "java", "antlr-4.7-complete.jar") classpath = os.pathsep.join([".", "{:s}".format(antlr_path), "$CLASSPATH"]) generated = os.path.join(ROOT_DIR, 'src', 'pymoca', 'generated') cmd = "java -Xmx500M -cp \"{classpath:s}\" org.antlr.v4.Tool {arg:s}" \ " -o {generated:s} -visitor -Dlanguage=Python3".format(**locals()) print(cmd) proc = subprocess.Popen(cmd.split(), cwd=os.path.join(ROOT_DIR, 'src', 'pymoca')) proc.communicate() with open(os.path.join(generated, '__init__.py'), 'w') as fid: fid.write('')

python

def call_antlr4(arg): "calls antlr4 on grammar file" # pylint: disable=unused-argument, unused-variable antlr_path = os.path.join(ROOT_DIR, "java", "antlr-4.7-complete.jar") classpath = os.pathsep.join([".", "{:s}".format(antlr_path), "$CLASSPATH"]) generated = os.path.join(ROOT_DIR, 'src', 'pymoca', 'generated') cmd = "java -Xmx500M -cp \"{classpath:s}\" org.antlr.v4.Tool {arg:s}" \ " -o {generated:s} -visitor -Dlanguage=Python3".format(**locals()) print(cmd) proc = subprocess.Popen(cmd.split(), cwd=os.path.join(ROOT_DIR, 'src', 'pymoca')) proc.communicate() with open(os.path.join(generated, '__init__.py'), 'w') as fid: fid.write('')

calls antlr4 on grammar file

14b5eb7425e96689de6cc5c10f400895d586a978

https://github.com/pymoca/pymoca/blob/14b5eb7425e96689de6cc5c10f400895d586a978/setup.py#L74-L86

train

pymoca/pymoca

setup.py

setup_package

def setup_package(): """ Setup the package. """ with open('requirements.txt', 'r') as req_file: install_reqs = req_file.read().split('\n') cmdclass_ = {'antlr': AntlrBuildCommand} cmdclass_.update(versioneer.get_cmdclass()) setup( version=versioneer.get_version(), name='pymoca', maintainer="James Goppert", maintainer_email="[email protected]", description=DOCLINES[0], long_description="\n".join(DOCLINES[2:]), url='https://github.com/pymoca/pymoca', author='James Goppert', author_email='[email protected]', download_url='https://github.com/pymoca/pymoca', license='BSD', classifiers=[_f for _f in CLASSIFIERS.split('\n') if _f], platforms=["Windows", "Linux", "Solaris", "Mac OS-X", "Unix"], install_requires=install_reqs, tests_require=['coverage >= 3.7.1', 'nose >= 1.3.1'], test_suite='nose.collector', python_requires='>=3.5', packages=find_packages("src"), package_dir={"": "src"}, include_package_data=True, cmdclass=cmdclass_ )

python

def setup_package(): """ Setup the package. """ with open('requirements.txt', 'r') as req_file: install_reqs = req_file.read().split('\n') cmdclass_ = {'antlr': AntlrBuildCommand} cmdclass_.update(versioneer.get_cmdclass()) setup( version=versioneer.get_version(), name='pymoca', maintainer="James Goppert", maintainer_email="[email protected]", description=DOCLINES[0], long_description="\n".join(DOCLINES[2:]), url='https://github.com/pymoca/pymoca', author='James Goppert', author_email='[email protected]', download_url='https://github.com/pymoca/pymoca', license='BSD', classifiers=[_f for _f in CLASSIFIERS.split('\n') if _f], platforms=["Windows", "Linux", "Solaris", "Mac OS-X", "Unix"], install_requires=install_reqs, tests_require=['coverage >= 3.7.1', 'nose >= 1.3.1'], test_suite='nose.collector', python_requires='>=3.5', packages=find_packages("src"), package_dir={"": "src"}, include_package_data=True, cmdclass=cmdclass_ )

Setup the package.

14b5eb7425e96689de6cc5c10f400895d586a978

https://github.com/pymoca/pymoca/blob/14b5eb7425e96689de6cc5c10f400895d586a978/setup.py#L89-L121

train

rwl/pylon

contrib/pylontk.py

CaseProperties.body

def body(self, frame): """ Creates the dialog body. Returns the widget that should have initial focus. """ master = Frame(self) master.pack(padx=5, pady=0, expand=1, fill=BOTH) title = Label(master, text="Buses") title.pack(side=TOP) bus_lb = self.bus_lb = Listbox(master, selectmode=SINGLE, width=10) bus_lb.pack(side=LEFT) for bus in self.case.buses: bus_lb.insert(END, bus.name) bus_lb.bind("<<ListboxSelect>>", self.on_bus) self.bus_params = BusProperties(master) return bus_lb

python

def body(self, frame): """ Creates the dialog body. Returns the widget that should have initial focus. """ master = Frame(self) master.pack(padx=5, pady=0, expand=1, fill=BOTH) title = Label(master, text="Buses") title.pack(side=TOP) bus_lb = self.bus_lb = Listbox(master, selectmode=SINGLE, width=10) bus_lb.pack(side=LEFT) for bus in self.case.buses: bus_lb.insert(END, bus.name) bus_lb.bind("<<ListboxSelect>>", self.on_bus) self.bus_params = BusProperties(master) return bus_lb

Creates the dialog body. Returns the widget that should have initial focus.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/pylontk.py#L538-L558

train

rwl/pylon

pylon/opf.py

OPF.solve

def solve(self, solver_klass=None): """ Solves an optimal power flow and returns a results dictionary. """ # Start the clock. t0 = time() # Build an OPF model with variables and constraints. om = self._construct_opf_model(self.case) if om is None: return {"converged": False, "output": {"message": "No Ref Bus."}} # Call the specific solver. # if self.opt["verbose"]: # print '\nPYLON Version %s, %s', "0.4.2", "April 2010" if solver_klass is not None: result = solver_klass(om, opt=self.opt).solve() elif self.dc: # if self.opt["verbose"]: # print ' -- DC Optimal Power Flow\n' result = DCOPFSolver(om, opt=self.opt).solve() else: # if self.opt["verbose"]: # print ' -- AC Optimal Power Flow\n' result = PIPSSolver(om, opt=self.opt).solve() result["elapsed"] = time() - t0 if self.opt.has_key("verbose"): if self.opt["verbose"]: logger.info("OPF completed in %.3fs." % result["elapsed"]) return result

python

def solve(self, solver_klass=None): """ Solves an optimal power flow and returns a results dictionary. """ # Start the clock. t0 = time() # Build an OPF model with variables and constraints. om = self._construct_opf_model(self.case) if om is None: return {"converged": False, "output": {"message": "No Ref Bus."}} # Call the specific solver. # if self.opt["verbose"]: # print '\nPYLON Version %s, %s', "0.4.2", "April 2010" if solver_klass is not None: result = solver_klass(om, opt=self.opt).solve() elif self.dc: # if self.opt["verbose"]: # print ' -- DC Optimal Power Flow\n' result = DCOPFSolver(om, opt=self.opt).solve() else: # if self.opt["verbose"]: # print ' -- AC Optimal Power Flow\n' result = PIPSSolver(om, opt=self.opt).solve() result["elapsed"] = time() - t0 if self.opt.has_key("verbose"): if self.opt["verbose"]: logger.info("OPF completed in %.3fs." % result["elapsed"]) return result

Solves an optimal power flow and returns a results dictionary.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L79-L110

train

rwl/pylon

pylon/opf.py

OPF._construct_opf_model

def _construct_opf_model(self, case): """ Returns an OPF model. """ # Zero the case result attributes. self.case.reset() base_mva = case.base_mva # Check for one reference bus. oneref, refs = self._ref_check(case) if not oneref: #return {"status": "error"} None # Remove isolated components. bs, ln, gn = self._remove_isolated(case) # Update bus indexes. self.case.index_buses(bs) # Convert single-block piecewise-linear costs into linear polynomial. gn = self._pwl1_to_poly(gn) # Set-up initial problem variables. Va = self._get_voltage_angle_var(refs, bs) Pg = self._get_pgen_var(gn, base_mva) if self.dc: # DC model. # Get the susceptance matrices and phase shift injection vectors. B, Bf, Pbusinj, Pfinj = self.case.makeBdc(bs, ln) # Power mismatch constraints (B*Va + Pg = Pd). Pmis = self._power_mismatch_dc(bs, gn, B, Pbusinj, base_mva) # Branch flow limit constraints. Pf, Pt = self._branch_flow_dc(ln, Bf, Pfinj, base_mva) else: # Set-up additional AC-OPF problem variables. Vm = self._get_voltage_magnitude_var(bs, gn) Qg = self._get_qgen_var(gn, base_mva) Pmis, Qmis, Sf, St = self._nln_constraints(len(bs), len(ln)) vl = self._const_pf_constraints(gn, base_mva) # TODO: Generator PQ capability curve constraints. # PQh, PQl = self._pq_capability_curve_constraints(gn) # Branch voltage angle difference limits. ang = self._voltage_angle_diff_limit(bs, ln) if self.dc: vars = [Va, Pg] constraints = [Pmis, Pf, Pt, ang] else: vars = [Va, Vm, Pg, Qg] constraints = [Pmis, Qmis, Sf, St, #PQh, PQL, vl, ang] # Piece-wise linear generator cost constraints. y, ycon = self._pwl_gen_costs(gn, base_mva) if ycon is not None: vars.append(y) constraints.append(ycon) # Add variables and constraints to the OPF model object. opf = OPFModel(case) opf.add_vars(vars) opf.add_constraints(constraints) if self.dc: # user data opf._Bf = Bf opf._Pfinj = Pfinj return opf

python

def _construct_opf_model(self, case): """ Returns an OPF model. """ # Zero the case result attributes. self.case.reset() base_mva = case.base_mva # Check for one reference bus. oneref, refs = self._ref_check(case) if not oneref: #return {"status": "error"} None # Remove isolated components. bs, ln, gn = self._remove_isolated(case) # Update bus indexes. self.case.index_buses(bs) # Convert single-block piecewise-linear costs into linear polynomial. gn = self._pwl1_to_poly(gn) # Set-up initial problem variables. Va = self._get_voltage_angle_var(refs, bs) Pg = self._get_pgen_var(gn, base_mva) if self.dc: # DC model. # Get the susceptance matrices and phase shift injection vectors. B, Bf, Pbusinj, Pfinj = self.case.makeBdc(bs, ln) # Power mismatch constraints (B*Va + Pg = Pd). Pmis = self._power_mismatch_dc(bs, gn, B, Pbusinj, base_mva) # Branch flow limit constraints. Pf, Pt = self._branch_flow_dc(ln, Bf, Pfinj, base_mva) else: # Set-up additional AC-OPF problem variables. Vm = self._get_voltage_magnitude_var(bs, gn) Qg = self._get_qgen_var(gn, base_mva) Pmis, Qmis, Sf, St = self._nln_constraints(len(bs), len(ln)) vl = self._const_pf_constraints(gn, base_mva) # TODO: Generator PQ capability curve constraints. # PQh, PQl = self._pq_capability_curve_constraints(gn) # Branch voltage angle difference limits. ang = self._voltage_angle_diff_limit(bs, ln) if self.dc: vars = [Va, Pg] constraints = [Pmis, Pf, Pt, ang] else: vars = [Va, Vm, Pg, Qg] constraints = [Pmis, Qmis, Sf, St, #PQh, PQL, vl, ang] # Piece-wise linear generator cost constraints. y, ycon = self._pwl_gen_costs(gn, base_mva) if ycon is not None: vars.append(y) constraints.append(ycon) # Add variables and constraints to the OPF model object. opf = OPFModel(case) opf.add_vars(vars) opf.add_constraints(constraints) if self.dc: # user data opf._Bf = Bf opf._Pfinj = Pfinj return opf

Returns an OPF model.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L116-L190

train

rwl/pylon

pylon/opf.py

OPF._ref_check

def _ref_check(self, case): """ Checks that there is only one reference bus. """ refs = [bus._i for bus in case.buses if bus.type == REFERENCE] if len(refs) == 1: return True, refs else: logger.error("OPF requires a single reference bus.") return False, refs

python

def _ref_check(self, case): """ Checks that there is only one reference bus. """ refs = [bus._i for bus in case.buses if bus.type == REFERENCE] if len(refs) == 1: return True, refs else: logger.error("OPF requires a single reference bus.") return False, refs

Checks that there is only one reference bus.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L193-L202

train

rwl/pylon

pylon/opf.py

OPF._remove_isolated

def _remove_isolated(self, case): """ Returns non-isolated case components. """ # case.deactivate_isolated() buses = case.connected_buses branches = case.online_branches gens = case.online_generators return buses, branches, gens

python

def _remove_isolated(self, case): """ Returns non-isolated case components. """ # case.deactivate_isolated() buses = case.connected_buses branches = case.online_branches gens = case.online_generators return buses, branches, gens

Returns non-isolated case components.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L205-L213

train

rwl/pylon

pylon/opf.py

OPF._pwl1_to_poly

def _pwl1_to_poly(self, generators): """ Converts single-block piecewise-linear costs into linear polynomial. """ for g in generators: if (g.pcost_model == PW_LINEAR) and (len(g.p_cost) == 2): g.pwl_to_poly() return generators

python

def _pwl1_to_poly(self, generators): """ Converts single-block piecewise-linear costs into linear polynomial. """ for g in generators: if (g.pcost_model == PW_LINEAR) and (len(g.p_cost) == 2): g.pwl_to_poly() return generators

Converts single-block piecewise-linear costs into linear polynomial.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L216-L224

train

rwl/pylon

pylon/opf.py

OPF._get_voltage_angle_var

def _get_voltage_angle_var(self, refs, buses): """ Returns the voltage angle variable set. """ Va = array([b.v_angle * (pi / 180.0) for b in buses]) Vau = Inf * ones(len(buses)) Val = -Vau Vau[refs] = Va[refs] Val[refs] = Va[refs] return Variable("Va", len(buses), Va, Val, Vau)

python

def _get_voltage_angle_var(self, refs, buses): """ Returns the voltage angle variable set. """ Va = array([b.v_angle * (pi / 180.0) for b in buses]) Vau = Inf * ones(len(buses)) Val = -Vau Vau[refs] = Va[refs] Val[refs] = Va[refs] return Variable("Va", len(buses), Va, Val, Vau)

Returns the voltage angle variable set.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L230-L240

train

rwl/pylon

pylon/opf.py

OPF._get_voltage_magnitude_var

def _get_voltage_magnitude_var(self, buses, generators): """ Returns the voltage magnitude variable set. """ Vm = array([b.v_magnitude for b in buses]) # For buses with generators initialise Vm from gen data. for g in generators: Vm[g.bus._i] = g.v_magnitude Vmin = array([b.v_min for b in buses]) Vmax = array([b.v_max for b in buses]) return Variable("Vm", len(buses), Vm, Vmin, Vmax)

python

def _get_voltage_magnitude_var(self, buses, generators): """ Returns the voltage magnitude variable set. """ Vm = array([b.v_magnitude for b in buses]) # For buses with generators initialise Vm from gen data. for g in generators: Vm[g.bus._i] = g.v_magnitude Vmin = array([b.v_min for b in buses]) Vmax = array([b.v_max for b in buses]) return Variable("Vm", len(buses), Vm, Vmin, Vmax)

Returns the voltage magnitude variable set.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L243-L255

train

rwl/pylon

pylon/opf.py

OPF._get_pgen_var

def _get_pgen_var(self, generators, base_mva): """ Returns the generator active power set-point variable. """ Pg = array([g.p / base_mva for g in generators]) Pmin = array([g.p_min / base_mva for g in generators]) Pmax = array([g.p_max / base_mva for g in generators]) return Variable("Pg", len(generators), Pg, Pmin, Pmax)

python

def _get_pgen_var(self, generators, base_mva): """ Returns the generator active power set-point variable. """ Pg = array([g.p / base_mva for g in generators]) Pmin = array([g.p_min / base_mva for g in generators]) Pmax = array([g.p_max / base_mva for g in generators]) return Variable("Pg", len(generators), Pg, Pmin, Pmax)

Returns the generator active power set-point variable.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L258-L266

train

rwl/pylon

pylon/opf.py

OPF._get_qgen_var

def _get_qgen_var(self, generators, base_mva): """ Returns the generator reactive power variable set. """ Qg = array([g.q / base_mva for g in generators]) Qmin = array([g.q_min / base_mva for g in generators]) Qmax = array([g.q_max / base_mva for g in generators]) return Variable("Qg", len(generators), Qg, Qmin, Qmax)

python

def _get_qgen_var(self, generators, base_mva): """ Returns the generator reactive power variable set. """ Qg = array([g.q / base_mva for g in generators]) Qmin = array([g.q_min / base_mva for g in generators]) Qmax = array([g.q_max / base_mva for g in generators]) return Variable("Qg", len(generators), Qg, Qmin, Qmax)

Returns the generator reactive power variable set.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L269-L277

train

rwl/pylon

pylon/opf.py

OPF._nln_constraints

def _nln_constraints(self, nb, nl): """ Returns non-linear constraints for OPF. """ Pmis = NonLinearConstraint("Pmis", nb) Qmis = NonLinearConstraint("Qmis", nb) Sf = NonLinearConstraint("Sf", nl) St = NonLinearConstraint("St", nl) return Pmis, Qmis, Sf, St

python

def _nln_constraints(self, nb, nl): """ Returns non-linear constraints for OPF. """ Pmis = NonLinearConstraint("Pmis", nb) Qmis = NonLinearConstraint("Qmis", nb) Sf = NonLinearConstraint("Sf", nl) St = NonLinearConstraint("St", nl) return Pmis, Qmis, Sf, St

Returns non-linear constraints for OPF.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L283-L291

train

rwl/pylon

pylon/opf.py

OPF._const_pf_constraints

def _const_pf_constraints(self, gn, base_mva): """ Returns a linear constraint enforcing constant power factor for dispatchable loads. The power factor is derived from the original value of Pmin and either Qmin (for inductive loads) or Qmax (for capacitive loads). If both Qmin and Qmax are zero, this implies a unity power factor without the need for an additional constraint. """ ivl = array([i for i, g in enumerate(gn) if g.is_load and (g.q_min != 0.0 or g.q_max != 0.0)]) vl = [gn[i] for i in ivl] nvl = len(vl) ng = len(gn) Pg = array([g.p for g in vl]) / base_mva Qg = array([g.q for g in vl]) / base_mva Pmin = array([g.p_min for g in vl]) / base_mva Qmin = array([g.q_min for g in vl]) / base_mva Qmax = array([g.q_max for g in vl]) / base_mva # At least one of the Q limits must be zero (corresponding to Pmax==0). for g in vl: if g.qmin != 0.0 and g.q_max != 0.0: logger.error("Either Qmin or Qmax must be equal to zero for " "each dispatchable load.") # Initial values of PG and QG must be consistent with specified power # factor. This is to prevent a user from unknowingly using a case file # which would have defined a different power factor constraint under a # previous version which used PG and QG to define the power factor. Qlim = (Qmin == 0.0) * Qmax + (Qmax == 0.0) * Qmin if any( abs(Qg - Pg * Qlim / Pmin) > 1e-6 ): logger.error("For a dispatchable load, PG and QG must be " "consistent with the power factor defined by " "PMIN and the Q limits.") # Make Avl, lvl, uvl, for lvl <= Avl * r_[Pg, Qg] <= uvl if nvl > 0: xx = Pmin yy = Qlim pftheta = arctan2(yy, xx) pc = sin(pftheta) qc = -cos(pftheta) ii = array([range(nvl), range(nvl)]) jj = r_[ivl, ivl + ng] Avl = csr_matrix(r_[pc, qc], (ii, jj), (nvl, 2 * ng)) lvl = zeros(nvl) uvl = lvl else: Avl = zeros((0, 2 * ng)) lvl = array([]) uvl = array([]) return LinearConstraint("vl", Avl, lvl, uvl, ["Pg", "Qg"])

python

def _const_pf_constraints(self, gn, base_mva): """ Returns a linear constraint enforcing constant power factor for dispatchable loads. The power factor is derived from the original value of Pmin and either Qmin (for inductive loads) or Qmax (for capacitive loads). If both Qmin and Qmax are zero, this implies a unity power factor without the need for an additional constraint. """ ivl = array([i for i, g in enumerate(gn) if g.is_load and (g.q_min != 0.0 or g.q_max != 0.0)]) vl = [gn[i] for i in ivl] nvl = len(vl) ng = len(gn) Pg = array([g.p for g in vl]) / base_mva Qg = array([g.q for g in vl]) / base_mva Pmin = array([g.p_min for g in vl]) / base_mva Qmin = array([g.q_min for g in vl]) / base_mva Qmax = array([g.q_max for g in vl]) / base_mva # At least one of the Q limits must be zero (corresponding to Pmax==0). for g in vl: if g.qmin != 0.0 and g.q_max != 0.0: logger.error("Either Qmin or Qmax must be equal to zero for " "each dispatchable load.") # Initial values of PG and QG must be consistent with specified power # factor. This is to prevent a user from unknowingly using a case file # which would have defined a different power factor constraint under a # previous version which used PG and QG to define the power factor. Qlim = (Qmin == 0.0) * Qmax + (Qmax == 0.0) * Qmin if any( abs(Qg - Pg * Qlim / Pmin) > 1e-6 ): logger.error("For a dispatchable load, PG and QG must be " "consistent with the power factor defined by " "PMIN and the Q limits.") # Make Avl, lvl, uvl, for lvl <= Avl * r_[Pg, Qg] <= uvl if nvl > 0: xx = Pmin yy = Qlim pftheta = arctan2(yy, xx) pc = sin(pftheta) qc = -cos(pftheta) ii = array([range(nvl), range(nvl)]) jj = r_[ivl, ivl + ng] Avl = csr_matrix(r_[pc, qc], (ii, jj), (nvl, 2 * ng)) lvl = zeros(nvl) uvl = lvl else: Avl = zeros((0, 2 * ng)) lvl = array([]) uvl = array([]) return LinearConstraint("vl", Avl, lvl, uvl, ["Pg", "Qg"])

Returns a linear constraint enforcing constant power factor for dispatchable loads. The power factor is derived from the original value of Pmin and either Qmin (for inductive loads) or Qmax (for capacitive loads). If both Qmin and Qmax are zero, this implies a unity power factor without the need for an additional constraint.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L330-L384

train

rwl/pylon

pylon/opf.py

OPF._voltage_angle_diff_limit

def _voltage_angle_diff_limit(self, buses, branches): """ Returns the constraint on the branch voltage angle differences. """ nb = len(buses) if not self.ignore_ang_lim: iang = [i for i, b in enumerate(branches) if (b.ang_min and (b.ang_min > -360.0)) or (b.ang_max and (b.ang_max < 360.0))] iangl = array([i for i, b in enumerate(branches) if b.ang_min is not None])[iang] iangh = array([i for i, b in enumerate(branches) if b.ang_max is not None])[iang] nang = len(iang) if nang > 0: ii = range(nang) + range(nang) jjf = array([b.from_bus._i for b in branches])[iang] jjt = array([b.to_bus._i for b in branches])[iang] jj = r_[jjf, jjt] Aang = csr_matrix(r_[ones(nang), -ones(nang)], (ii, jj)) uang = Inf * ones(nang) lang = -uang lang[iangl] = array([b.ang_min * (pi / 180.0) for b in branches])[iangl] uang[iangh] = array([b.ang_max * (pi / 180.0) for b in branches])[iangh] else: # Aang = csr_matrix((0, nb), dtype=float64) # lang = array([], dtype=float64) # uang = array([], dtype=float64) Aang = zeros((0, nb)) lang = array([]) uang = array([]) else: # Aang = csr_matrix((0, nb), dtype=float64) # lang = array([], dtype=float64) # uang = array([], dtype=float64) # iang = array([], dtype=float64) Aang = zeros((0, nb)) lang = array([]) uang = array([]) return LinearConstraint("ang", Aang, lang, uang, ["Va"])

python

def _voltage_angle_diff_limit(self, buses, branches): """ Returns the constraint on the branch voltage angle differences. """ nb = len(buses) if not self.ignore_ang_lim: iang = [i for i, b in enumerate(branches) if (b.ang_min and (b.ang_min > -360.0)) or (b.ang_max and (b.ang_max < 360.0))] iangl = array([i for i, b in enumerate(branches) if b.ang_min is not None])[iang] iangh = array([i for i, b in enumerate(branches) if b.ang_max is not None])[iang] nang = len(iang) if nang > 0: ii = range(nang) + range(nang) jjf = array([b.from_bus._i for b in branches])[iang] jjt = array([b.to_bus._i for b in branches])[iang] jj = r_[jjf, jjt] Aang = csr_matrix(r_[ones(nang), -ones(nang)], (ii, jj)) uang = Inf * ones(nang) lang = -uang lang[iangl] = array([b.ang_min * (pi / 180.0) for b in branches])[iangl] uang[iangh] = array([b.ang_max * (pi / 180.0) for b in branches])[iangh] else: # Aang = csr_matrix((0, nb), dtype=float64) # lang = array([], dtype=float64) # uang = array([], dtype=float64) Aang = zeros((0, nb)) lang = array([]) uang = array([]) else: # Aang = csr_matrix((0, nb), dtype=float64) # lang = array([], dtype=float64) # uang = array([], dtype=float64) # iang = array([], dtype=float64) Aang = zeros((0, nb)) lang = array([]) uang = array([]) return LinearConstraint("ang", Aang, lang, uang, ["Va"])

Returns the constraint on the branch voltage angle differences.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L387-L430

train

rwl/pylon

pylon/opf.py

OPFModel.add_var

def add_var(self, var): """ Adds a variable to the model. """ if var.name in [v.name for v in self.vars]: logger.error("Variable set named '%s' already exists." % var.name) return var.i1 = self.var_N var.iN = self.var_N + var.N - 1 self.vars.append(var)

python

def add_var(self, var): """ Adds a variable to the model. """ if var.name in [v.name for v in self.vars]: logger.error("Variable set named '%s' already exists." % var.name) return var.i1 = self.var_N var.iN = self.var_N + var.N - 1 self.vars.append(var)

Adds a variable to the model.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L731-L740

train

rwl/pylon

pylon/opf.py

OPFModel.get_var

def get_var(self, name): """ Returns the variable set with the given name. """ for var in self.vars: if var.name == name: return var else: raise ValueError

python

def get_var(self, name): """ Returns the variable set with the given name. """ for var in self.vars: if var.name == name: return var else: raise ValueError

Returns the variable set with the given name.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L750-L757

train

rwl/pylon

pylon/opf.py

OPFModel.linear_constraints

def linear_constraints(self): """ Returns the linear constraints. """ if self.lin_N == 0: return None, array([]), array([]) A = lil_matrix((self.lin_N, self.var_N), dtype=float64) l = -Inf * ones(self.lin_N) u = -l for lin in self.lin_constraints: if lin.N: # non-zero number of rows to add Ak = lin.A # A for kth linear constrain set i1 = lin.i1 # starting row index iN = lin.iN # ending row index vsl = lin.vs # var set list kN = -1 # initialize last col of Ak used Ai = lil_matrix((lin.N, self.var_N), dtype=float64) for v in vsl: var = self.get_var(v) j1 = var.i1 # starting column in A jN = var.iN # ending column in A k1 = kN + 1 # starting column in Ak kN = kN + var.N # ending column in Ak if j1 == jN: # FIXME: Single column slicing broken in lil. for i in range(Ai.shape[0]): Ai[i, j1] = Ak[i, k1] else: Ai[:, j1:jN + 1] = Ak[:, k1:kN + 1] A[i1:iN + 1, :] = Ai l[i1:iN + 1] = lin.l u[i1:iN + 1] = lin.u return A.tocsr(), l, u

python

def linear_constraints(self): """ Returns the linear constraints. """ if self.lin_N == 0: return None, array([]), array([]) A = lil_matrix((self.lin_N, self.var_N), dtype=float64) l = -Inf * ones(self.lin_N) u = -l for lin in self.lin_constraints: if lin.N: # non-zero number of rows to add Ak = lin.A # A for kth linear constrain set i1 = lin.i1 # starting row index iN = lin.iN # ending row index vsl = lin.vs # var set list kN = -1 # initialize last col of Ak used Ai = lil_matrix((lin.N, self.var_N), dtype=float64) for v in vsl: var = self.get_var(v) j1 = var.i1 # starting column in A jN = var.iN # ending column in A k1 = kN + 1 # starting column in Ak kN = kN + var.N # ending column in Ak if j1 == jN: # FIXME: Single column slicing broken in lil. for i in range(Ai.shape[0]): Ai[i, j1] = Ak[i, k1] else: Ai[:, j1:jN + 1] = Ak[:, k1:kN + 1] A[i1:iN + 1, :] = Ai l[i1:iN + 1] = lin.l u[i1:iN + 1] = lin.u return A.tocsr(), l, u

Returns the linear constraints.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L782-L818

train

rwl/pylon

pylon/opf.py

OPFModel.add_constraint

def add_constraint(self, con): """ Adds a constraint to the model. """ if isinstance(con, LinearConstraint): N, M = con.A.shape if con.name in [c.name for c in self.lin_constraints]: logger.error("Constraint set named '%s' already exists." % con.name) return False else: con.i1 = self.lin_N# + 1 con.iN = self.lin_N + N - 1 nv = 0 for vs in con.vs: nv = nv + self.get_var_N(vs) if M != nv: logger.error("Number of columns of A does not match number" " of variables, A is %d x %d, nv = %d", N, M, nv) self.lin_constraints.append(con) elif isinstance(con, NonLinearConstraint): N = con.N if con.name in [c.name for c in self.nln_constraints]: logger.error("Constraint set named '%s' already exists." % con.name) return False else: con.i1 = self.nln_N# + 1 con.iN = self.nln_N + N self.nln_constraints.append(con) else: raise ValueError return True

python

def add_constraint(self, con): """ Adds a constraint to the model. """ if isinstance(con, LinearConstraint): N, M = con.A.shape if con.name in [c.name for c in self.lin_constraints]: logger.error("Constraint set named '%s' already exists." % con.name) return False else: con.i1 = self.lin_N# + 1 con.iN = self.lin_N + N - 1 nv = 0 for vs in con.vs: nv = nv + self.get_var_N(vs) if M != nv: logger.error("Number of columns of A does not match number" " of variables, A is %d x %d, nv = %d", N, M, nv) self.lin_constraints.append(con) elif isinstance(con, NonLinearConstraint): N = con.N if con.name in [c.name for c in self.nln_constraints]: logger.error("Constraint set named '%s' already exists." % con.name) return False else: con.i1 = self.nln_N# + 1 con.iN = self.nln_N + N self.nln_constraints.append(con) else: raise ValueError return True

Adds a constraint to the model.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/opf.py#L821-L854

train

rwl/pylon

pylon/ipopf.py

IPOPFSolver._solve

def _solve(self, x0, A, l, u, xmin, xmax): """ Solves using the Interior Point OPTimizer. """ # Indexes of constrained lines. il = [i for i,ln in enumerate(self._ln) if 0.0 < ln.rate_a < 1e10] nl2 = len(il) neqnln = 2 * self._nb # no. of non-linear equality constraints niqnln = 2 * len(il) # no. of lines with constraints user_data = {"A": A, "neqnln": neqnln, "niqnln": niqnln} self._f(x0) Jdata = self._dg(x0, False, user_data) # Hdata = self._h(x0, ones(neqnln + niqnln), None, False, user_data) lmbda = {"eqnonlin": ones(neqnln), "ineqnonlin": ones(niqnln)} H = tril(self._hessfcn(x0, lmbda), format="coo") self._Hrow, self._Hcol = H.row, H.col n = len(x0) # the number of variables xl = xmin xu = xmax gl = r_[zeros(2 * self._nb), -Inf * ones(2 * nl2), l] gu = r_[zeros(2 * self._nb), zeros(2 * nl2), u] m = len(gl) # the number of constraints nnzj = len(Jdata) # the number of nonzeros in Jacobian matrix nnzh = 0#len(H.data) # the number of non-zeros in Hessian matrix f_fcn, df_fcn, g_fcn, dg_fcn, h_fcn = \ self._f, self._df, self._g, self._dg, self._h nlp = pyipopt.create(n, xl, xu, m, gl, gu, nnzj, nnzh, f_fcn, df_fcn, g_fcn, dg_fcn)#, h_fcn) # print dir(nlp) # nlp.str_option("print_options_documentation", "yes") # nlp.int_option("max_iter", 10) # x, zl, zu, obj = nlp.solve(x0) success = nlp.solve(x0, user_data) nlp.close()

python

def _solve(self, x0, A, l, u, xmin, xmax): """ Solves using the Interior Point OPTimizer. """ # Indexes of constrained lines. il = [i for i,ln in enumerate(self._ln) if 0.0 < ln.rate_a < 1e10] nl2 = len(il) neqnln = 2 * self._nb # no. of non-linear equality constraints niqnln = 2 * len(il) # no. of lines with constraints user_data = {"A": A, "neqnln": neqnln, "niqnln": niqnln} self._f(x0) Jdata = self._dg(x0, False, user_data) # Hdata = self._h(x0, ones(neqnln + niqnln), None, False, user_data) lmbda = {"eqnonlin": ones(neqnln), "ineqnonlin": ones(niqnln)} H = tril(self._hessfcn(x0, lmbda), format="coo") self._Hrow, self._Hcol = H.row, H.col n = len(x0) # the number of variables xl = xmin xu = xmax gl = r_[zeros(2 * self._nb), -Inf * ones(2 * nl2), l] gu = r_[zeros(2 * self._nb), zeros(2 * nl2), u] m = len(gl) # the number of constraints nnzj = len(Jdata) # the number of nonzeros in Jacobian matrix nnzh = 0#len(H.data) # the number of non-zeros in Hessian matrix f_fcn, df_fcn, g_fcn, dg_fcn, h_fcn = \ self._f, self._df, self._g, self._dg, self._h nlp = pyipopt.create(n, xl, xu, m, gl, gu, nnzj, nnzh, f_fcn, df_fcn, g_fcn, dg_fcn)#, h_fcn) # print dir(nlp) # nlp.str_option("print_options_documentation", "yes") # nlp.int_option("max_iter", 10) # x, zl, zu, obj = nlp.solve(x0) success = nlp.solve(x0, user_data) nlp.close()

Solves using the Interior Point OPTimizer.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/ipopf.py#L44-L86

train

rwl/pylon

pyreto/continuous/experiment.py

MarketExperiment.doOutages

def doOutages(self): """ Applies branch outtages. """ assert len(self.branchOutages) == len(self.market.case.branches) weights = [[(False, r), (True, 1 - (r))] for r in self.branchOutages] for i, ln in enumerate(self.market.case.branches): ln.online = weighted_choice(weights[i]) if ln.online == False: print "Branch outage [%s] in period %d." %(ln.name,self.stepid)

python

def doOutages(self): """ Applies branch outtages. """ assert len(self.branchOutages) == len(self.market.case.branches) weights = [[(False, r), (True, 1 - (r))] for r in self.branchOutages] for i, ln in enumerate(self.market.case.branches): ln.online = weighted_choice(weights[i]) if ln.online == False: print "Branch outage [%s] in period %d." %(ln.name,self.stepid)

Applies branch outtages.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/continuous/experiment.py#L133-L143

train

rwl/pylon

pyreto/continuous/experiment.py

MarketExperiment.reset_case

def reset_case(self): """ Returns the case to its original state. """ for bus in self.market.case.buses: bus.p_demand = self.pdemand[bus] for task in self.tasks: for g in task.env.generators: g.p = task.env._g0[g]["p"] g.p_max = task.env._g0[g]["p_max"] g.p_min = task.env._g0[g]["p_min"] g.q = task.env._g0[g]["q"] g.q_max = task.env._g0[g]["q_max"] g.q_min = task.env._g0[g]["q_min"] g.p_cost = task.env._g0[g]["p_cost"] g.pcost_model = task.env._g0[g]["pcost_model"] g.q_cost = task.env._g0[g]["q_cost"] g.qcost_model = task.env._g0[g]["qcost_model"] g.c_startup = task.env._g0[g]["startup"] g.c_shutdown = task.env._g0[g]["shutdown"]

python

def reset_case(self): """ Returns the case to its original state. """ for bus in self.market.case.buses: bus.p_demand = self.pdemand[bus] for task in self.tasks: for g in task.env.generators: g.p = task.env._g0[g]["p"] g.p_max = task.env._g0[g]["p_max"] g.p_min = task.env._g0[g]["p_min"] g.q = task.env._g0[g]["q"] g.q_max = task.env._g0[g]["q_max"] g.q_min = task.env._g0[g]["q_min"] g.p_cost = task.env._g0[g]["p_cost"] g.pcost_model = task.env._g0[g]["pcost_model"] g.q_cost = task.env._g0[g]["q_cost"] g.qcost_model = task.env._g0[g]["qcost_model"] g.c_startup = task.env._g0[g]["startup"] g.c_shutdown = task.env._g0[g]["shutdown"]

Returns the case to its original state.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/continuous/experiment.py#L146-L164

train

rwl/pylon

pyreto/continuous/experiment.py

MarketExperiment.doEpisodes

def doEpisodes(self, number=1): """ Do the given numer of episodes, and return the rewards of each step as a list. """ for episode in range(number): print "Starting episode %d." % episode # Initialise the profile cycle. if len(self.profile.shape) == 1: # 1D array self._pcycle = cycle(self.profile) else: assert self.profile.shape[0] >= number self._pcycle = cycle(self.profile[episode, :]) # Scale the initial load. c = self._pcycle.next() for bus in self.market.case.buses: bus.p_demand = self.pdemand[bus] * c # Initialise agents and their tasks. for task, agent in zip(self.tasks, self.agents): agent.newEpisode() task.reset() while False in [task.isFinished() for task in self.tasks]: if True in [task.isFinished() for task in self.tasks]: raise ValueError self._oneInteraction() self.reset_case()

python

def doEpisodes(self, number=1): """ Do the given numer of episodes, and return the rewards of each step as a list. """ for episode in range(number): print "Starting episode %d." % episode # Initialise the profile cycle. if len(self.profile.shape) == 1: # 1D array self._pcycle = cycle(self.profile) else: assert self.profile.shape[0] >= number self._pcycle = cycle(self.profile[episode, :]) # Scale the initial load. c = self._pcycle.next() for bus in self.market.case.buses: bus.p_demand = self.pdemand[bus] * c # Initialise agents and their tasks. for task, agent in zip(self.tasks, self.agents): agent.newEpisode() task.reset() while False in [task.isFinished() for task in self.tasks]: if True in [task.isFinished() for task in self.tasks]: raise ValueError self._oneInteraction() self.reset_case()

Do the given numer of episodes, and return the rewards of each step as a list.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/continuous/experiment.py#L170-L199

train

rwl/pylon

pyreto/continuous/experiment.py

MarketExperiment.reset

def reset(self): """ Sets initial conditions for the experiment. """ self.stepid = 0 for task, agent in zip(self.tasks, self.agents): task.reset() agent.module.reset() agent.history.reset()

python

def reset(self): """ Sets initial conditions for the experiment. """ self.stepid = 0 for task, agent in zip(self.tasks, self.agents): task.reset() agent.module.reset() agent.history.reset()

Sets initial conditions for the experiment.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/continuous/experiment.py#L249-L258

train

rwl/pylon

pyreto/roth_erev.py

RothErev._updatePropensities

def _updatePropensities(self, lastState, lastAction, reward): """ Update the propensities for all actions. The propensity for last action chosen will be updated using the feedback value that resulted from performing the action. If j is the index of the last action chosen, r_j is the reward received for performing j, i is the current action being updated, q_i is the propensity for i, and phi is the recency parameter, then this update function can be expressed as:: q_i = (1-phi) * q_i + E(i, r_j) """ phi = self.recency for action in range(self.module.numActions): carryOver = (1 - phi) * self.module.getValue(lastState, action) experience = self._experience(lastState, action, lastAction,reward) self.module.updateValue(lastState, action, carryOver + experience)

python

def _updatePropensities(self, lastState, lastAction, reward): """ Update the propensities for all actions. The propensity for last action chosen will be updated using the feedback value that resulted from performing the action. If j is the index of the last action chosen, r_j is the reward received for performing j, i is the current action being updated, q_i is the propensity for i, and phi is the recency parameter, then this update function can be expressed as:: q_i = (1-phi) * q_i + E(i, r_j) """ phi = self.recency for action in range(self.module.numActions): carryOver = (1 - phi) * self.module.getValue(lastState, action) experience = self._experience(lastState, action, lastAction,reward) self.module.updateValue(lastState, action, carryOver + experience)

Update the propensities for all actions. The propensity for last action chosen will be updated using the feedback value that resulted from performing the action. If j is the index of the last action chosen, r_j is the reward received for performing j, i is the current action being updated, q_i is the propensity for i, and phi is the recency parameter, then this update function can be expressed as:: q_i = (1-phi) * q_i + E(i, r_j)

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/roth_erev.py#L136-L154

train

rwl/pylon

pyreto/roth_erev.py

ProportionalExplorer._forwardImplementation

def _forwardImplementation(self, inbuf, outbuf): """ Proportional probability method. """ assert self.module propensities = self.module.getActionValues(0) summedProps = sum(propensities) probabilities = propensities / summedProps action = eventGenerator(probabilities) # action = drawIndex(probabilities) outbuf[:] = scipy.array([action])

python

def _forwardImplementation(self, inbuf, outbuf): """ Proportional probability method. """ assert self.module propensities = self.module.getActionValues(0) summedProps = sum(propensities) probabilities = propensities / summedProps action = eventGenerator(probabilities) # action = drawIndex(probabilities) outbuf[:] = scipy.array([action])

Proportional probability method.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/roth_erev.py#L254-L267

train

rwl/pylon

pylon/io/excel.py

ExcelWriter.write

def write(self, file_or_filename): """ Writes case data to file in Excel format. """ self.book = Workbook() self._write_data(None) self.book.save(file_or_filename)

python

def write(self, file_or_filename): """ Writes case data to file in Excel format. """ self.book = Workbook() self._write_data(None) self.book.save(file_or_filename)

Writes case data to file in Excel format.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/excel.py#L38-L43

train

rwl/pylon

pylon/io/excel.py

ExcelWriter.write_bus_data

def write_bus_data(self, file): """ Writes bus data to an Excel spreadsheet. """ bus_sheet = self.book.add_sheet("Buses") for i, bus in enumerate(self.case.buses): for j, attr in enumerate(BUS_ATTRS): bus_sheet.write(i, j, getattr(bus, attr))

python

def write_bus_data(self, file): """ Writes bus data to an Excel spreadsheet. """ bus_sheet = self.book.add_sheet("Buses") for i, bus in enumerate(self.case.buses): for j, attr in enumerate(BUS_ATTRS): bus_sheet.write(i, j, getattr(bus, attr))

Writes bus data to an Excel spreadsheet.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/excel.py#L56-L63

train

rwl/pylon

pylon/io/excel.py

ExcelWriter.write_branch_data

def write_branch_data(self, file): """ Writes branch data to an Excel spreadsheet. """ branch_sheet = self.book.add_sheet("Branches") for i, branch in enumerate(self.case.branches): for j, attr in enumerate(BRANCH_ATTRS): branch_sheet.write(i, j, getattr(branch, attr))

python

def write_branch_data(self, file): """ Writes branch data to an Excel spreadsheet. """ branch_sheet = self.book.add_sheet("Branches") for i, branch in enumerate(self.case.branches): for j, attr in enumerate(BRANCH_ATTRS): branch_sheet.write(i, j, getattr(branch, attr))

Writes branch data to an Excel spreadsheet.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/excel.py#L66-L73

train

rwl/pylon

pylon/io/excel.py

ExcelWriter.write_generator_data

def write_generator_data(self, file): """ Write generator data to file. """ generator_sheet = self.book.add_sheet("Generators") for j, generator in enumerate(self.case.generators): i = generator.bus._i for k, attr in enumerate(GENERATOR_ATTRS): generator_sheet.write(j, 0, i)

python

def write_generator_data(self, file): """ Write generator data to file. """ generator_sheet = self.book.add_sheet("Generators") for j, generator in enumerate(self.case.generators): i = generator.bus._i for k, attr in enumerate(GENERATOR_ATTRS): generator_sheet.write(j, 0, i)

Write generator data to file.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/excel.py#L76-L84

train

rwl/pylon

pylon/io/excel.py

CSVWriter.write

def write(self, file_or_filename): """ Writes case data as CSV. """ if isinstance(file_or_filename, basestring): file = open(file_or_filename, "wb") else: file = file_or_filename self.writer = csv.writer(file) super(CSVWriter, self).write(file)

python

def write(self, file_or_filename): """ Writes case data as CSV. """ if isinstance(file_or_filename, basestring): file = open(file_or_filename, "wb") else: file = file_or_filename self.writer = csv.writer(file) super(CSVWriter, self).write(file)

Writes case data as CSV.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/excel.py#L112-L122

train

rwl/pylon

pylon/io/excel.py

CSVWriter.write_case_data

def write_case_data(self, file): """ Writes the case data as CSV. """ writer = self._get_writer(file) writer.writerow(["Name", "base_mva"]) writer.writerow([self.case.name, self.case.base_mva])

python

def write_case_data(self, file): """ Writes the case data as CSV. """ writer = self._get_writer(file) writer.writerow(["Name", "base_mva"]) writer.writerow([self.case.name, self.case.base_mva])

Writes the case data as CSV.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/excel.py#L125-L130

train

rwl/pylon

pylon/io/excel.py

CSVWriter.write_bus_data

def write_bus_data(self, file): """ Writes bus data as CSV. """ writer = self._get_writer(file) writer.writerow(BUS_ATTRS) for bus in self.case.buses: writer.writerow([getattr(bus, attr) for attr in BUS_ATTRS])

python

def write_bus_data(self, file): """ Writes bus data as CSV. """ writer = self._get_writer(file) writer.writerow(BUS_ATTRS) for bus in self.case.buses: writer.writerow([getattr(bus, attr) for attr in BUS_ATTRS])

Writes bus data as CSV.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/excel.py#L133-L139

train

rwl/pylon

pylon/io/excel.py

CSVWriter.write_branch_data

def write_branch_data(self, file): """ Writes branch data as CSV. """ writer = self._get_writer(file) writer.writerow(BRANCH_ATTRS) for branch in self.case.branches: writer.writerow([getattr(branch, a) for a in BRANCH_ATTRS])

python

def write_branch_data(self, file): """ Writes branch data as CSV. """ writer = self._get_writer(file) writer.writerow(BRANCH_ATTRS) for branch in self.case.branches: writer.writerow([getattr(branch, a) for a in BRANCH_ATTRS])

Writes branch data as CSV.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/excel.py#L142-L148

train

rwl/pylon

pylon/io/excel.py

CSVWriter.write_generator_data

def write_generator_data(self, file): """ Write generator data as CSV. """ writer = self._get_writer(file) writer.writerow(["bus"] + GENERATOR_ATTRS) for g in self.case.generators: i = g.bus._i writer.writerow([i] + [getattr(g,a) for a in GENERATOR_ATTRS])

python

def write_generator_data(self, file): """ Write generator data as CSV. """ writer = self._get_writer(file) writer.writerow(["bus"] + GENERATOR_ATTRS) for g in self.case.generators: i = g.bus._i writer.writerow([i] + [getattr(g,a) for a in GENERATOR_ATTRS])

Write generator data as CSV.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/excel.py#L151-L159

train

rwl/pylon

pyreto/smart_market.py

SmartMarket.run

def run(self): """ Computes cleared offers and bids. """ # Start the clock. t0 = time.time() # Manage reactive power offers/bids. haveQ = self._isReactiveMarket() # Withhold offers/bids outwith optional price limits. self._withholdOffbids() # Convert offers/bids to pwl functions and update limits. self._offbidToCase() # Compute dispatch points and LMPs using OPF. success = self._runOPF() if success: # Get nodal marginal prices from OPF. gteeOfferPrice, gteeBidPrice = self._nodalPrices(haveQ) # Determine quantity and price for each offer/bid. self._runAuction(gteeOfferPrice, gteeBidPrice, haveQ) logger.info("SmartMarket cleared in %.3fs" % (time.time() - t0)) else: for offbid in self.offers + self.bids: offbid.clearedQuantity = 0.0 offbid.clearedPrice = 0.0 offbid.accepted = False offbid.generator.p = 0.0 logger.error("Non-convergent market OPF. Blackout!") return self.offers, self.bids

python

def run(self): """ Computes cleared offers and bids. """ # Start the clock. t0 = time.time() # Manage reactive power offers/bids. haveQ = self._isReactiveMarket() # Withhold offers/bids outwith optional price limits. self._withholdOffbids() # Convert offers/bids to pwl functions and update limits. self._offbidToCase() # Compute dispatch points and LMPs using OPF. success = self._runOPF() if success: # Get nodal marginal prices from OPF. gteeOfferPrice, gteeBidPrice = self._nodalPrices(haveQ) # Determine quantity and price for each offer/bid. self._runAuction(gteeOfferPrice, gteeBidPrice, haveQ) logger.info("SmartMarket cleared in %.3fs" % (time.time() - t0)) else: for offbid in self.offers + self.bids: offbid.clearedQuantity = 0.0 offbid.clearedPrice = 0.0 offbid.accepted = False offbid.generator.p = 0.0 logger.error("Non-convergent market OPF. Blackout!") return self.offers, self.bids

Computes cleared offers and bids.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/smart_market.py#L131-L166

train

rwl/pylon

pyreto/smart_market.py

SmartMarket._runOPF

def _runOPF(self): """ Computes dispatch points and LMPs using OPF. """ if self.decommit: solver = UDOPF(self.case, dc=(self.locationalAdjustment == "dc")) elif self.locationalAdjustment == "dc": solver = OPF(self.case, dc=True) else: solver = OPF(self.case, dc=False, opt={"verbose": True}) self._solution = solver.solve() # for ob in self.offers + self.bids: # ob.f = solution["f"] return self._solution["converged"]

python

def _runOPF(self): """ Computes dispatch points and LMPs using OPF. """ if self.decommit: solver = UDOPF(self.case, dc=(self.locationalAdjustment == "dc")) elif self.locationalAdjustment == "dc": solver = OPF(self.case, dc=True) else: solver = OPF(self.case, dc=False, opt={"verbose": True}) self._solution = solver.solve() # for ob in self.offers + self.bids: # ob.f = solution["f"] return self._solution["converged"]

Computes dispatch points and LMPs using OPF.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/smart_market.py#L266-L281

train

rwl/pylon

contrib/public/services/simplejson/encoder.py

JSONEncoder.encode

def encode(self, o): """ Return a JSON string representation of a Python data structure. >>> JSONEncoder().encode({"foo": ["bar", "baz"]}) '{"foo":["bar", "baz"]}' """ # This doesn't pass the iterator directly to ''.join() because it # sucks at reporting exceptions. It's going to do this internally # anyway because it uses PySequence_Fast or similar. chunks = list(self.iterencode(o)) return ''.join(chunks)

python

def encode(self, o): """ Return a JSON string representation of a Python data structure. >>> JSONEncoder().encode({"foo": ["bar", "baz"]}) '{"foo":["bar", "baz"]}' """ # This doesn't pass the iterator directly to ''.join() because it # sucks at reporting exceptions. It's going to do this internally # anyway because it uses PySequence_Fast or similar. chunks = list(self.iterencode(o)) return ''.join(chunks)

Return a JSON string representation of a Python data structure. >>> JSONEncoder().encode({"foo": ["bar", "baz"]}) '{"foo":["bar", "baz"]}'

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/contrib/public/services/simplejson/encoder.py#L278-L289

train

finklabs/metrics

metrics/compute.py

compute_file_metrics

def compute_file_metrics(processors, language, key, token_list): """use processors to compute file metrics.""" # multiply iterator tli = itertools.tee(token_list, len(processors)) metrics = OrderedDict() # reset all processors for p in processors: p.reset() # process all tokens for p, tl in zip(processors, tli): p.process_file(language, key, tl) # collect metrics from all processors for p in processors: metrics.update(p.metrics) return metrics

python

def compute_file_metrics(processors, language, key, token_list): """use processors to compute file metrics.""" # multiply iterator tli = itertools.tee(token_list, len(processors)) metrics = OrderedDict() # reset all processors for p in processors: p.reset() # process all tokens for p, tl in zip(processors, tli): p.process_file(language, key, tl) # collect metrics from all processors for p in processors: metrics.update(p.metrics) return metrics

use processors to compute file metrics.

fd9974af498831664b9ae8e8f3834e1ec2e8a699

https://github.com/finklabs/metrics/blob/fd9974af498831664b9ae8e8f3834e1ec2e8a699/metrics/compute.py#L8-L26

train

Liebeck/IWNLP-py

iwnlp/iwnlp_wrapper.py

IWNLPWrapper.load

def load(self, lemmatizer_path): """ This methods load the IWNLP.Lemmatizer json file and creates a dictionary of lowercased forms which maps each form to its possible lemmas. """ self.lemmatizer = {} with io.open(lemmatizer_path, encoding='utf-8') as data_file: raw = json.load(data_file) for entry in raw: self.lemmatizer[entry["Form"]] = entry["Lemmas"] self.apply_blacklist()

python

def load(self, lemmatizer_path): """ This methods load the IWNLP.Lemmatizer json file and creates a dictionary of lowercased forms which maps each form to its possible lemmas. """ self.lemmatizer = {} with io.open(lemmatizer_path, encoding='utf-8') as data_file: raw = json.load(data_file) for entry in raw: self.lemmatizer[entry["Form"]] = entry["Lemmas"] self.apply_blacklist()

This methods load the IWNLP.Lemmatizer json file and creates a dictionary of lowercased forms which maps each form to its possible lemmas.

fd4b81769317476eac0487396cce0faf482a1913

https://github.com/Liebeck/IWNLP-py/blob/fd4b81769317476eac0487396cce0faf482a1913/iwnlp/iwnlp_wrapper.py#L14-L24

train

rwl/pylon

pylon/io/common.py

_CaseWriter.write

def write(self, file_or_filename): """ Writes the case data to file. """ if isinstance(file_or_filename, basestring): file = None try: file = open(file_or_filename, "wb") except Exception, detail: logger.error("Error opening %s." % detail) finally: if file is not None: self._write_data(file) file.close() else: file = file_or_filename self._write_data(file) return file

python

def write(self, file_or_filename): """ Writes the case data to file. """ if isinstance(file_or_filename, basestring): file = None try: file = open(file_or_filename, "wb") except Exception, detail: logger.error("Error opening %s." % detail) finally: if file is not None: self._write_data(file) file.close() else: file = file_or_filename self._write_data(file) return file

Writes the case data to file.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/common.py#L64-L81

train

rwl/pylon

pyreto/discrete/task.py

ProfitTask.performAction

def performAction(self, action): """ The action vector is stripped and the only element is cast to integer and given to the super class. """ self.t += 1 super(ProfitTask, self).performAction(int(action[0])) self.samples += 1

python

def performAction(self, action): """ The action vector is stripped and the only element is cast to integer and given to the super class. """ self.t += 1 super(ProfitTask, self).performAction(int(action[0])) self.samples += 1

The action vector is stripped and the only element is cast to integer and given to the super class.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/discrete/task.py#L120-L126

train

rwl/pylon

pyreto/discrete/task.py

ProfitTask.addReward

def addReward(self, r=None): """ A filtered mapping towards performAction of the underlying environment. """ r = self.getReward() if r is None else r # by default, the cumulative reward is just the sum over the episode if self.discount: self.cumulativeReward += power(self.discount, self.samples) * r else: self.cumulativeReward += r

python

def addReward(self, r=None): """ A filtered mapping towards performAction of the underlying environment. """ r = self.getReward() if r is None else r # by default, the cumulative reward is just the sum over the episode if self.discount: self.cumulativeReward += power(self.discount, self.samples) * r else: self.cumulativeReward += r

A filtered mapping towards performAction of the underlying environment.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/discrete/task.py#L156-L166

train

rwl/pylon

pylon/estimator.py

StateEstimator.getV0

def getV0(self, v_mag_guess, buses, generators, type=CASE_GUESS): """ Returns the initial voltage profile. """ if type == CASE_GUESS: Va = array([b.v_angle * (pi / 180.0) for b in buses]) Vm = array([b.v_magnitude for b in buses]) V0 = Vm * exp(1j * Va) elif type == FLAT_START: V0 = ones(len(buses)) elif type == FROM_INPUT: V0 = v_mag_guess else: raise ValueError # Set the voltages of PV buses and the reference bus in the guess. # online = [g for g in self.case.generators if g.online] gbus = [g.bus._i for g in generators] Vg = array([g.v_magnitude for g in generators]) V0[gbus] = Vg * abs(V0[gbus]) / V0[gbus] return V0

python

def getV0(self, v_mag_guess, buses, generators, type=CASE_GUESS): """ Returns the initial voltage profile. """ if type == CASE_GUESS: Va = array([b.v_angle * (pi / 180.0) for b in buses]) Vm = array([b.v_magnitude for b in buses]) V0 = Vm * exp(1j * Va) elif type == FLAT_START: V0 = ones(len(buses)) elif type == FROM_INPUT: V0 = v_mag_guess else: raise ValueError # Set the voltages of PV buses and the reference bus in the guess. # online = [g for g in self.case.generators if g.online] gbus = [g.bus._i for g in generators] Vg = array([g.v_magnitude for g in generators]) V0[gbus] = Vg * abs(V0[gbus]) / V0[gbus] return V0

Returns the initial voltage profile.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/estimator.py#L300-L321

train

rwl/pylon

pylon/estimator.py

StateEstimator.output_solution

def output_solution(self, fd, z, z_est, error_sqrsum): """ Prints comparison of measurements and their estimations. """ col_width = 11 sep = ("=" * col_width + " ") * 4 + "\n" fd.write("State Estimation\n") fd.write("-" * 16 + "\n") fd.write(sep) fd.write("Type".center(col_width) + " ") fd.write("Name".center(col_width) + " ") fd.write("Measurement".center(col_width) + " ") fd.write("Estimation".center(col_width) + " ") fd.write("\n") fd.write(sep) c = 0 for t in [PF, PT, QF, QT, PG, QG, VM, VA]: for meas in self.measurements: if meas.type == t: n = meas.b_or_l.name[:col_width].ljust(col_width) fd.write(t.ljust(col_width) + " ") fd.write(n + " ") fd.write("%11.5f " % z[c]) fd.write("%11.5f\n" % z_est[c]) # fd.write("%s\t%s\t%.3f\t%.3f\n" % (t, n, z[c], z_est[c])) c += 1 fd.write("\nWeighted sum of error squares = %.4f\n" % error_sqrsum)

python

def output_solution(self, fd, z, z_est, error_sqrsum): """ Prints comparison of measurements and their estimations. """ col_width = 11 sep = ("=" * col_width + " ") * 4 + "\n" fd.write("State Estimation\n") fd.write("-" * 16 + "\n") fd.write(sep) fd.write("Type".center(col_width) + " ") fd.write("Name".center(col_width) + " ") fd.write("Measurement".center(col_width) + " ") fd.write("Estimation".center(col_width) + " ") fd.write("\n") fd.write(sep) c = 0 for t in [PF, PT, QF, QT, PG, QG, VM, VA]: for meas in self.measurements: if meas.type == t: n = meas.b_or_l.name[:col_width].ljust(col_width) fd.write(t.ljust(col_width) + " ") fd.write(n + " ") fd.write("%11.5f " % z[c]) fd.write("%11.5f\n" % z_est[c]) # fd.write("%s\t%s\t%.3f\t%.3f\n" % (t, n, z[c], z_est[c])) c += 1 fd.write("\nWeighted sum of error squares = %.4f\n" % error_sqrsum)

Prints comparison of measurements and their estimations.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/estimator.py#L324-L352

train

rwl/pylon

pyreto/auction.py

Auction.run

def run(self): """ Clears a set of bids and offers. """ # Compute cleared offer/bid quantities from total dispatched quantity. self._clearQuantities() # Compute shift values to add to lam to get desired pricing. # lao, fro, lab, frb = self._first_rejected_last_accepted() # Clear offer/bid prices according to auction type. self._clearPrices() # self._clear_prices(lao, fro, lab, frb) # Clip cleared prices according to guarantees and limits. self._clipPrices() self._logClearances() return self.offers, self.bids

python

def run(self): """ Clears a set of bids and offers. """ # Compute cleared offer/bid quantities from total dispatched quantity. self._clearQuantities() # Compute shift values to add to lam to get desired pricing. # lao, fro, lab, frb = self._first_rejected_last_accepted() # Clear offer/bid prices according to auction type. self._clearPrices() # self._clear_prices(lao, fro, lab, frb) # Clip cleared prices according to guarantees and limits. self._clipPrices() self._logClearances() return self.offers, self.bids

Clears a set of bids and offers.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/auction.py#L85-L103

train

rwl/pylon

pyreto/auction.py

Auction._clearQuantity

def _clearQuantity(self, offbids, gen): """ Computes the cleared bid quantity from total dispatched quantity. """ # Filter out offers/bids not applicable to the generator in question. gOffbids = [offer for offer in offbids if offer.generator == gen] # Offers/bids within valid price limits (not withheld). valid = [ob for ob in gOffbids if not ob.withheld] # Sort offers by price in ascending order and bids in decending order. valid.sort(key=lambda ob: ob.price, reverse=[False, True][gen.is_load]) acceptedQty = 0.0 for ob in valid: # Compute the fraction of the block accepted. accepted = (ob.totalQuantity - acceptedQty) / ob.quantity # Clip to the range 0-1. if accepted > 1.0: accepted = 1.0 elif accepted < 1.0e-05: accepted = 0.0 ob.clearedQuantity = accepted * ob.quantity ob.accepted = (accepted > 0.0) # Log the event. # if ob.accepted: # logger.info("%s [%s, %.3f, %.3f] accepted at %.2f MW." % # (ob.__class__.__name__, ob.generator.name, ob.quantity, # ob.price, ob.clearedQuantity)) # else: # logger.info("%s [%s, %.3f, %.3f] rejected." % # (ob.__class__.__name__, ob.generator.name, ob.quantity, # ob.price)) # Increment the accepted quantity. acceptedQty += ob.quantity

python

def _clearQuantity(self, offbids, gen): """ Computes the cleared bid quantity from total dispatched quantity. """ # Filter out offers/bids not applicable to the generator in question. gOffbids = [offer for offer in offbids if offer.generator == gen] # Offers/bids within valid price limits (not withheld). valid = [ob for ob in gOffbids if not ob.withheld] # Sort offers by price in ascending order and bids in decending order. valid.sort(key=lambda ob: ob.price, reverse=[False, True][gen.is_load]) acceptedQty = 0.0 for ob in valid: # Compute the fraction of the block accepted. accepted = (ob.totalQuantity - acceptedQty) / ob.quantity # Clip to the range 0-1. if accepted > 1.0: accepted = 1.0 elif accepted < 1.0e-05: accepted = 0.0 ob.clearedQuantity = accepted * ob.quantity ob.accepted = (accepted > 0.0) # Log the event. # if ob.accepted: # logger.info("%s [%s, %.3f, %.3f] accepted at %.2f MW." % # (ob.__class__.__name__, ob.generator.name, ob.quantity, # ob.price, ob.clearedQuantity)) # else: # logger.info("%s [%s, %.3f, %.3f] rejected." % # (ob.__class__.__name__, ob.generator.name, ob.quantity, # ob.price)) # Increment the accepted quantity. acceptedQty += ob.quantity

Computes the cleared bid quantity from total dispatched quantity.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/auction.py#L120-L158

train

rwl/pylon

pyreto/auction.py

Auction._clearPrices

def _clearPrices(self): """ Clears prices according to auction type. """ for offbid in self.offers + self.bids: if self.auctionType == DISCRIMINATIVE: offbid.clearedPrice = offbid.price elif self.auctionType == FIRST_PRICE: offbid.clearedPrice = offbid.lmbda else: raise ValueError

python

def _clearPrices(self): """ Clears prices according to auction type. """ for offbid in self.offers + self.bids: if self.auctionType == DISCRIMINATIVE: offbid.clearedPrice = offbid.price elif self.auctionType == FIRST_PRICE: offbid.clearedPrice = offbid.lmbda else: raise ValueError

Clears prices according to auction type.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/auction.py#L219-L228

train

rwl/pylon

pyreto/auction.py

Auction._clipPrices

def _clipPrices(self): """ Clip cleared prices according to guarantees and limits. """ # Guarantee that cleared offer prices are >= offers. if self.guaranteeOfferPrice: for offer in self.offers: if offer.accepted and offer.clearedPrice < offer.price: offer.clearedPrice = offer.price # Guarantee that cleared bid prices are <= bids. if self.guaranteeBidPrice: for bid in self.bids: if bid.accepted and bid.clearedPrice > bid.price: bid.clearedPrice = bid.price # Clip cleared offer prices. if self.limits.has_key("maxClearedOffer"): maxClearedOffer = self.limits["maxClearedOffer"] for offer in self.offers: if offer.clearedPrice > maxClearedOffer: offer.clearedPrice = maxClearedOffer # Clip cleared bid prices. if self.limits.has_key("minClearedBid"): minClearedBid = self.limits["minClearedBid"] for bid in self.bids: if bid.clearedPrice < minClearedBid: bid.clearedPrice = minClearedBid # Make prices uniform across all offers/bids for each generator after # clipping (except for discrim auction) since clipping may only affect # a single block of a multi-block generator. if self.auctionType != DISCRIMINATIVE: for g in self.case.generators: gOffers = [of for of in self.offers if of.generator == g] if gOffers: uniformPrice = max([of.clearedPrice for of in gOffers]) for of in gOffers: of.clearedPrice = uniformPrice gBids = [bid for bid in self.bids if bid.vLoad == g] if gBids: uniformPrice = min([bid.cleared_price for bid in gBids]) for bid in gBids: bid.clearedPrice = uniformPrice

python

def _clipPrices(self): """ Clip cleared prices according to guarantees and limits. """ # Guarantee that cleared offer prices are >= offers. if self.guaranteeOfferPrice: for offer in self.offers: if offer.accepted and offer.clearedPrice < offer.price: offer.clearedPrice = offer.price # Guarantee that cleared bid prices are <= bids. if self.guaranteeBidPrice: for bid in self.bids: if bid.accepted and bid.clearedPrice > bid.price: bid.clearedPrice = bid.price # Clip cleared offer prices. if self.limits.has_key("maxClearedOffer"): maxClearedOffer = self.limits["maxClearedOffer"] for offer in self.offers: if offer.clearedPrice > maxClearedOffer: offer.clearedPrice = maxClearedOffer # Clip cleared bid prices. if self.limits.has_key("minClearedBid"): minClearedBid = self.limits["minClearedBid"] for bid in self.bids: if bid.clearedPrice < minClearedBid: bid.clearedPrice = minClearedBid # Make prices uniform across all offers/bids for each generator after # clipping (except for discrim auction) since clipping may only affect # a single block of a multi-block generator. if self.auctionType != DISCRIMINATIVE: for g in self.case.generators: gOffers = [of for of in self.offers if of.generator == g] if gOffers: uniformPrice = max([of.clearedPrice for of in gOffers]) for of in gOffers: of.clearedPrice = uniformPrice gBids = [bid for bid in self.bids if bid.vLoad == g] if gBids: uniformPrice = min([bid.cleared_price for bid in gBids]) for bid in gBids: bid.clearedPrice = uniformPrice

Clip cleared prices according to guarantees and limits.

916514255db1ae1661406f0283df756baf960d14

https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pyreto/auction.py#L265-L311

train

praekeltfoundation/seaworthy

seaworthy/client.py

wait_for_response

def wait_for_response(client, timeout, path='/', expected_status_code=None): """ Try make a GET request with an HTTP client against a certain path and return once any response has been received, ignoring any errors. :param ContainerHttpClient client: The HTTP client to use to connect to the container. :param timeout: Timeout value in seconds. :param path: HTTP path to request. :param int expected_status_code: If set, wait until a response with this status code is received. If not set, the status code will not be checked. :raises TimeoutError: If a request fails to be made within the timeout period. """ # We want time.monotonic on Pythons that have it, otherwise time.time will # have to do. get_time = getattr(time, 'monotonic', time.time) deadline = get_time() + timeout while True: try: # Don't care what the response is, as long as we get one time_left = deadline - get_time() response = client.get( path, timeout=max(time_left, 0.001), allow_redirects=False) if (expected_status_code is None or response.status_code == expected_status_code): return except requests.exceptions.Timeout: # Requests timed out, our time must be up break except Exception: # Ignore other exceptions pass if get_time() >= deadline: break time.sleep(0.1) raise TimeoutError('Timeout waiting for HTTP response.')

python

def wait_for_response(client, timeout, path='/', expected_status_code=None): """ Try make a GET request with an HTTP client against a certain path and return once any response has been received, ignoring any errors. :param ContainerHttpClient client: The HTTP client to use to connect to the container. :param timeout: Timeout value in seconds. :param path: HTTP path to request. :param int expected_status_code: If set, wait until a response with this status code is received. If not set, the status code will not be checked. :raises TimeoutError: If a request fails to be made within the timeout period. """ # We want time.monotonic on Pythons that have it, otherwise time.time will # have to do. get_time = getattr(time, 'monotonic', time.time) deadline = get_time() + timeout while True: try: # Don't care what the response is, as long as we get one time_left = deadline - get_time() response = client.get( path, timeout=max(time_left, 0.001), allow_redirects=False) if (expected_status_code is None or response.status_code == expected_status_code): return except requests.exceptions.Timeout: # Requests timed out, our time must be up break except Exception: # Ignore other exceptions pass if get_time() >= deadline: break time.sleep(0.1) raise TimeoutError('Timeout waiting for HTTP response.')

Try make a GET request with an HTTP client against a certain path and return once any response has been received, ignoring any errors. :param ContainerHttpClient client: The HTTP client to use to connect to the container. :param timeout: Timeout value in seconds. :param path: HTTP path to request. :param int expected_status_code: If set, wait until a response with this status code is received. If not set, the status code will not be checked. :raises TimeoutError: If a request fails to be made within the timeout period.

6f10a19b45d4ea1dc3bd0553cc4d0438696c079c

https://github.com/praekeltfoundation/seaworthy/blob/6f10a19b45d4ea1dc3bd0553cc4d0438696c079c/seaworthy/client.py#L205-L248

train

praekeltfoundation/seaworthy

seaworthy/client.py

ContainerHttpClient.request

def request(self, method, path=None, url_kwargs=None, **kwargs): """ Make a request against a container. :param method: The HTTP method to use. :param list path: The HTTP path (either absolute or relative). :param dict url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param kwargs: Any other parameters to pass to Requests. """ return self._session.request( method, self._url(path, url_kwargs), **kwargs)

python

def request(self, method, path=None, url_kwargs=None, **kwargs): """ Make a request against a container. :param method: The HTTP method to use. :param list path: The HTTP path (either absolute or relative). :param dict url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param kwargs: Any other parameters to pass to Requests. """ return self._session.request( method, self._url(path, url_kwargs), **kwargs)

Make a request against a container. :param method: The HTTP method to use. :param list path: The HTTP path (either absolute or relative). :param dict url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param kwargs: Any other parameters to pass to Requests.

6f10a19b45d4ea1dc3bd0553cc4d0438696c079c

https://github.com/praekeltfoundation/seaworthy/blob/6f10a19b45d4ea1dc3bd0553cc4d0438696c079c/seaworthy/client.py#L90-L104

train

praekeltfoundation/seaworthy

seaworthy/client.py

ContainerHttpClient.options

def options(self, path=None, url_kwargs=None, **kwargs): """ Sends an OPTIONS request. :param path: The HTTP path (either absolute or relative). :param url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param **kwargs: Optional arguments that ``request`` takes. :return: response object """ return self._session.options(self._url(path, url_kwargs), **kwargs)

python

def options(self, path=None, url_kwargs=None, **kwargs): """ Sends an OPTIONS request. :param path: The HTTP path (either absolute or relative). :param url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param **kwargs: Optional arguments that ``request`` takes. :return: response object """ return self._session.options(self._url(path, url_kwargs), **kwargs)

Sends an OPTIONS request. :param path: The HTTP path (either absolute or relative). :param url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param **kwargs: Optional arguments that ``request`` takes. :return: response object

6f10a19b45d4ea1dc3bd0553cc4d0438696c079c

https://github.com/praekeltfoundation/seaworthy/blob/6f10a19b45d4ea1dc3bd0553cc4d0438696c079c/seaworthy/client.py#L120-L132

train

praekeltfoundation/seaworthy

seaworthy/client.py

ContainerHttpClient.head

def head(self, path=None, url_kwargs=None, **kwargs): """ Sends a HEAD request. :param path: The HTTP path (either absolute or relative). :param url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param **kwargs: Optional arguments that ``request`` takes. :return: response object """ return self._session.head(self._url(path, url_kwargs), **kwargs)

python

def head(self, path=None, url_kwargs=None, **kwargs): """ Sends a HEAD request. :param path: The HTTP path (either absolute or relative). :param url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param **kwargs: Optional arguments that ``request`` takes. :return: response object """ return self._session.head(self._url(path, url_kwargs), **kwargs)

Sends a HEAD request. :param path: The HTTP path (either absolute or relative). :param url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param **kwargs: Optional arguments that ``request`` takes. :return: response object

6f10a19b45d4ea1dc3bd0553cc4d0438696c079c

https://github.com/praekeltfoundation/seaworthy/blob/6f10a19b45d4ea1dc3bd0553cc4d0438696c079c/seaworthy/client.py#L134-L146

train

praekeltfoundation/seaworthy

seaworthy/client.py

ContainerHttpClient.post

def post(self, path=None, url_kwargs=None, **kwargs): """ Sends a POST request. :param path: The HTTP path (either absolute or relative). :param url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param **kwargs: Optional arguments that ``request`` takes. :return: response object """ return self._session.post(self._url(path, url_kwargs), **kwargs)

python

def post(self, path=None, url_kwargs=None, **kwargs): """ Sends a POST request. :param path: The HTTP path (either absolute or relative). :param url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param **kwargs: Optional arguments that ``request`` takes. :return: response object """ return self._session.post(self._url(path, url_kwargs), **kwargs)

Sends a POST request. :param path: The HTTP path (either absolute or relative). :param url_kwargs: Parameters to override in the generated URL. See `~hyperlink.URL`. :param **kwargs: Optional arguments that ``request`` takes. :return: response object

6f10a19b45d4ea1dc3bd0553cc4d0438696c079c

https://github.com/praekeltfoundation/seaworthy/blob/6f10a19b45d4ea1dc3bd0553cc4d0438696c079c/seaworthy/client.py#L148-L160

train

ratt-ru/PyMORESANE

pymoresane/iuwt.py

iuwt_decomposition

def iuwt_decomposition(in1, scale_count, scale_adjust=0, mode='ser', core_count=2, store_smoothed=False, store_on_gpu=False): """ This function serves as a handler for the different implementations of the IUWT decomposition. It allows the different methods to be used almost interchangeably. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_count (no default): Maximum scale to be considered. scale_adjust (default=0): Adjustment to scale value if first scales are of no interest. mode (default='ser'): Implementation of the IUWT to be used - 'ser', 'mp' or 'gpu'. core_count (default=1): Additional option for multiprocessing - specifies core count. store_smoothed (default=False): Boolean specifier for whether the smoothed image is stored or not. store_on_gpu (default=False): Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: Returns the decomposition with the additional smoothed coefficients if specified. """ if mode=='ser': return ser_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed) elif mode=='mp': return mp_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed, core_count) elif mode=='gpu': return gpu_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed, store_on_gpu)

python

def iuwt_decomposition(in1, scale_count, scale_adjust=0, mode='ser', core_count=2, store_smoothed=False, store_on_gpu=False): """ This function serves as a handler for the different implementations of the IUWT decomposition. It allows the different methods to be used almost interchangeably. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_count (no default): Maximum scale to be considered. scale_adjust (default=0): Adjustment to scale value if first scales are of no interest. mode (default='ser'): Implementation of the IUWT to be used - 'ser', 'mp' or 'gpu'. core_count (default=1): Additional option for multiprocessing - specifies core count. store_smoothed (default=False): Boolean specifier for whether the smoothed image is stored or not. store_on_gpu (default=False): Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: Returns the decomposition with the additional smoothed coefficients if specified. """ if mode=='ser': return ser_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed) elif mode=='mp': return mp_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed, core_count) elif mode=='gpu': return gpu_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed, store_on_gpu)

This function serves as a handler for the different implementations of the IUWT decomposition. It allows the different methods to be used almost interchangeably. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_count (no default): Maximum scale to be considered. scale_adjust (default=0): Adjustment to scale value if first scales are of no interest. mode (default='ser'): Implementation of the IUWT to be used - 'ser', 'mp' or 'gpu'. core_count (default=1): Additional option for multiprocessing - specifies core count. store_smoothed (default=False): Boolean specifier for whether the smoothed image is stored or not. store_on_gpu (default=False): Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: Returns the decomposition with the additional smoothed coefficients if specified.

b024591ad0bbb69320d08841f28a2c27f62ae1af

https://github.com/ratt-ru/PyMORESANE/blob/b024591ad0bbb69320d08841f28a2c27f62ae1af/pymoresane/iuwt.py#L17-L41

train

ratt-ru/PyMORESANE

pymoresane/iuwt.py

iuwt_recomposition

def iuwt_recomposition(in1, scale_adjust=0, mode='ser', core_count=1, store_on_gpu=False, smoothed_array=None): """ This function serves as a handler for the different implementations of the IUWT recomposition. It allows the different methods to be used almost interchangeably. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_adjust (no default): Number of omitted scales. mode (default='ser') Implementation of the IUWT to be used - 'ser', 'mp' or 'gpu'. core_count (default=1) Additional option for multiprocessing - specifies core count. store_on_gpu (default=False): Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: Returns the recomposition. """ if mode=='ser': return ser_iuwt_recomposition(in1, scale_adjust, smoothed_array) elif mode=='mp': return mp_iuwt_recomposition(in1, scale_adjust, core_count, smoothed_array) elif mode=='gpu': return gpu_iuwt_recomposition(in1, scale_adjust, store_on_gpu, smoothed_array)

python

def iuwt_recomposition(in1, scale_adjust=0, mode='ser', core_count=1, store_on_gpu=False, smoothed_array=None): """ This function serves as a handler for the different implementations of the IUWT recomposition. It allows the different methods to be used almost interchangeably. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_adjust (no default): Number of omitted scales. mode (default='ser') Implementation of the IUWT to be used - 'ser', 'mp' or 'gpu'. core_count (default=1) Additional option for multiprocessing - specifies core count. store_on_gpu (default=False): Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: Returns the recomposition. """ if mode=='ser': return ser_iuwt_recomposition(in1, scale_adjust, smoothed_array) elif mode=='mp': return mp_iuwt_recomposition(in1, scale_adjust, core_count, smoothed_array) elif mode=='gpu': return gpu_iuwt_recomposition(in1, scale_adjust, store_on_gpu, smoothed_array)

This function serves as a handler for the different implementations of the IUWT recomposition. It allows the different methods to be used almost interchangeably. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_adjust (no default): Number of omitted scales. mode (default='ser') Implementation of the IUWT to be used - 'ser', 'mp' or 'gpu'. core_count (default=1) Additional option for multiprocessing - specifies core count. store_on_gpu (default=False): Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: Returns the recomposition.

b024591ad0bbb69320d08841f28a2c27f62ae1af

https://github.com/ratt-ru/PyMORESANE/blob/b024591ad0bbb69320d08841f28a2c27f62ae1af/pymoresane/iuwt.py#L43-L64

train

ratt-ru/PyMORESANE

pymoresane/iuwt.py

ser_iuwt_decomposition

def ser_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed): """ This function calls the a trous algorithm code to decompose the input into its wavelet coefficients. This is the isotropic undecimated wavelet transform implemented for a single CPU core. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_count (no default): Maximum scale to be considered. scale_adjust (default=0): Adjustment to scale value if first scales are of no interest. store_smoothed (default=False):Boolean specifier for whether the smoothed image is stored or not. OUTPUTS: detail_coeffs Array containing the detail coefficients. C0 (optional): Array containing the smoothest version of the input. """ wavelet_filter = (1./16)*np.array([1,4,6,4,1]) # Filter-bank for use in the a trous algorithm. # Initialises an empty array to store the coefficients. detail_coeffs = np.empty([scale_count-scale_adjust, in1.shape[0], in1.shape[1]]) C0 = in1 # Sets the initial value to be the input array. # The following loop, which iterates up to scale_adjust, applies the a trous algorithm to the scales which are # considered insignificant. This is important as each set of wavelet coefficients depends on the last smoothed # version of the input. if scale_adjust>0: for i in range(0, scale_adjust): C0 = ser_a_trous(C0, wavelet_filter, i) # The meat of the algorithm - two sequential applications fo the a trous followed by determination and storing of # the detail coefficients. C0 is reassigned the value of C on each loop - C0 is always the smoothest version of the # input image. for i in range(scale_adjust,scale_count): C = ser_a_trous(C0, wavelet_filter, i) # Approximation coefficients. C1 = ser_a_trous(C, wavelet_filter, i) # Approximation coefficients. detail_coeffs[i-scale_adjust,:,:] = C0 - C1 # Detail coefficients. C0 = C if store_smoothed: return detail_coeffs, C0 else: return detail_coeffs

python

def ser_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed): """ This function calls the a trous algorithm code to decompose the input into its wavelet coefficients. This is the isotropic undecimated wavelet transform implemented for a single CPU core. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_count (no default): Maximum scale to be considered. scale_adjust (default=0): Adjustment to scale value if first scales are of no interest. store_smoothed (default=False):Boolean specifier for whether the smoothed image is stored or not. OUTPUTS: detail_coeffs Array containing the detail coefficients. C0 (optional): Array containing the smoothest version of the input. """ wavelet_filter = (1./16)*np.array([1,4,6,4,1]) # Filter-bank for use in the a trous algorithm. # Initialises an empty array to store the coefficients. detail_coeffs = np.empty([scale_count-scale_adjust, in1.shape[0], in1.shape[1]]) C0 = in1 # Sets the initial value to be the input array. # The following loop, which iterates up to scale_adjust, applies the a trous algorithm to the scales which are # considered insignificant. This is important as each set of wavelet coefficients depends on the last smoothed # version of the input. if scale_adjust>0: for i in range(0, scale_adjust): C0 = ser_a_trous(C0, wavelet_filter, i) # The meat of the algorithm - two sequential applications fo the a trous followed by determination and storing of # the detail coefficients. C0 is reassigned the value of C on each loop - C0 is always the smoothest version of the # input image. for i in range(scale_adjust,scale_count): C = ser_a_trous(C0, wavelet_filter, i) # Approximation coefficients. C1 = ser_a_trous(C, wavelet_filter, i) # Approximation coefficients. detail_coeffs[i-scale_adjust,:,:] = C0 - C1 # Detail coefficients. C0 = C if store_smoothed: return detail_coeffs, C0 else: return detail_coeffs

This function calls the a trous algorithm code to decompose the input into its wavelet coefficients. This is the isotropic undecimated wavelet transform implemented for a single CPU core. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_count (no default): Maximum scale to be considered. scale_adjust (default=0): Adjustment to scale value if first scales are of no interest. store_smoothed (default=False):Boolean specifier for whether the smoothed image is stored or not. OUTPUTS: detail_coeffs Array containing the detail coefficients. C0 (optional): Array containing the smoothest version of the input.

b024591ad0bbb69320d08841f28a2c27f62ae1af

https://github.com/ratt-ru/PyMORESANE/blob/b024591ad0bbb69320d08841f28a2c27f62ae1af/pymoresane/iuwt.py#L66-L111

train

ratt-ru/PyMORESANE

pymoresane/iuwt.py

ser_iuwt_recomposition

def ser_iuwt_recomposition(in1, scale_adjust, smoothed_array): """ This function calls the a trous algorithm code to recompose the input into a single array. This is the implementation of the isotropic undecimated wavelet transform recomposition for a single CPU core. INPUTS: in1 (no default): Array containing wavelet coefficients. scale_adjust (no default): Indicates the number of truncated array pages. smoothed_array (default=None): For a complete inverse transform, this must be the smoothest approximation. OUTPUTS: recomposition Array containing the reconstructed image. """ wavelet_filter = (1./16)*np.array([1,4,6,4,1]) # Filter-bank for use in the a trous algorithm. # Determines scale with adjustment and creates a zero array to store the output, unless smoothed_array is given. max_scale = in1.shape[0] + scale_adjust if smoothed_array is None: recomposition = np.zeros([in1.shape[1], in1.shape[2]]) else: recomposition = smoothed_array # The following loops call the a trous algorithm code to recompose the input. The first loop assumes that there are # non-zero wavelet coefficients at scales above scale_adjust, while the second loop completes the recomposition # on the scales less than scale_adjust. for i in range(max_scale-1, scale_adjust-1, -1): recomposition = ser_a_trous(recomposition, wavelet_filter, i) + in1[i-scale_adjust,:,:] if scale_adjust>0: for i in range(scale_adjust-1, -1, -1): recomposition = ser_a_trous(recomposition, wavelet_filter, i) return recomposition

python

def ser_iuwt_recomposition(in1, scale_adjust, smoothed_array): """ This function calls the a trous algorithm code to recompose the input into a single array. This is the implementation of the isotropic undecimated wavelet transform recomposition for a single CPU core. INPUTS: in1 (no default): Array containing wavelet coefficients. scale_adjust (no default): Indicates the number of truncated array pages. smoothed_array (default=None): For a complete inverse transform, this must be the smoothest approximation. OUTPUTS: recomposition Array containing the reconstructed image. """ wavelet_filter = (1./16)*np.array([1,4,6,4,1]) # Filter-bank for use in the a trous algorithm. # Determines scale with adjustment and creates a zero array to store the output, unless smoothed_array is given. max_scale = in1.shape[0] + scale_adjust if smoothed_array is None: recomposition = np.zeros([in1.shape[1], in1.shape[2]]) else: recomposition = smoothed_array # The following loops call the a trous algorithm code to recompose the input. The first loop assumes that there are # non-zero wavelet coefficients at scales above scale_adjust, while the second loop completes the recomposition # on the scales less than scale_adjust. for i in range(max_scale-1, scale_adjust-1, -1): recomposition = ser_a_trous(recomposition, wavelet_filter, i) + in1[i-scale_adjust,:,:] if scale_adjust>0: for i in range(scale_adjust-1, -1, -1): recomposition = ser_a_trous(recomposition, wavelet_filter, i) return recomposition

This function calls the a trous algorithm code to recompose the input into a single array. This is the implementation of the isotropic undecimated wavelet transform recomposition for a single CPU core. INPUTS: in1 (no default): Array containing wavelet coefficients. scale_adjust (no default): Indicates the number of truncated array pages. smoothed_array (default=None): For a complete inverse transform, this must be the smoothest approximation. OUTPUTS: recomposition Array containing the reconstructed image.

b024591ad0bbb69320d08841f28a2c27f62ae1af

https://github.com/ratt-ru/PyMORESANE/blob/b024591ad0bbb69320d08841f28a2c27f62ae1af/pymoresane/iuwt.py#L113-L149

train

ratt-ru/PyMORESANE

pymoresane/iuwt.py

mp_iuwt_recomposition

def mp_iuwt_recomposition(in1, scale_adjust, core_count, smoothed_array): """ This function calls the a trous algorithm code to recompose the input into a single array. This is the implementation of the isotropic undecimated wavelet transform recomposition for multiple CPU cores. INPUTS: in1 (no default): Array containing wavelet coefficients. scale_adjust (no default): Indicates the number of omitted array pages. core_count (no default): Indicates the number of cores to be used. smoothed_array (default=None): For a complete inverse transform, this must be the smoothest approximation. OUTPUTS: recomposiiton Array containing the reconstructed image. """ wavelet_filter = (1./16)*np.array([1,4,6,4,1]) # Filter-bank for use in the a trous algorithm. # Determines scale with adjustment and creates a zero array to store the output, unless smoothed_array is given. max_scale = in1.shape[0] + scale_adjust if smoothed_array is None: recomposition = np.zeros([in1.shape[1], in1.shape[2]]) else: recomposition = smoothed_array # The following loops call the a trous algorithm code to recompose the input. The first loop assumes that there are # non-zero wavelet coefficients at scales above scale_adjust, while the second loop completes the recomposition # on the scales less than scale_adjust. for i in range(max_scale-1, scale_adjust-1, -1): recomposition = mp_a_trous(recomposition, wavelet_filter, i, core_count) + in1[i-scale_adjust,:,:] if scale_adjust>0: for i in range(scale_adjust-1, -1, -1): recomposition = mp_a_trous(recomposition, wavelet_filter, i, core_count) return recomposition

python

def mp_iuwt_recomposition(in1, scale_adjust, core_count, smoothed_array): """ This function calls the a trous algorithm code to recompose the input into a single array. This is the implementation of the isotropic undecimated wavelet transform recomposition for multiple CPU cores. INPUTS: in1 (no default): Array containing wavelet coefficients. scale_adjust (no default): Indicates the number of omitted array pages. core_count (no default): Indicates the number of cores to be used. smoothed_array (default=None): For a complete inverse transform, this must be the smoothest approximation. OUTPUTS: recomposiiton Array containing the reconstructed image. """ wavelet_filter = (1./16)*np.array([1,4,6,4,1]) # Filter-bank for use in the a trous algorithm. # Determines scale with adjustment and creates a zero array to store the output, unless smoothed_array is given. max_scale = in1.shape[0] + scale_adjust if smoothed_array is None: recomposition = np.zeros([in1.shape[1], in1.shape[2]]) else: recomposition = smoothed_array # The following loops call the a trous algorithm code to recompose the input. The first loop assumes that there are # non-zero wavelet coefficients at scales above scale_adjust, while the second loop completes the recomposition # on the scales less than scale_adjust. for i in range(max_scale-1, scale_adjust-1, -1): recomposition = mp_a_trous(recomposition, wavelet_filter, i, core_count) + in1[i-scale_adjust,:,:] if scale_adjust>0: for i in range(scale_adjust-1, -1, -1): recomposition = mp_a_trous(recomposition, wavelet_filter, i, core_count) return recomposition

This function calls the a trous algorithm code to recompose the input into a single array. This is the implementation of the isotropic undecimated wavelet transform recomposition for multiple CPU cores. INPUTS: in1 (no default): Array containing wavelet coefficients. scale_adjust (no default): Indicates the number of omitted array pages. core_count (no default): Indicates the number of cores to be used. smoothed_array (default=None): For a complete inverse transform, this must be the smoothest approximation. OUTPUTS: recomposiiton Array containing the reconstructed image.

b024591ad0bbb69320d08841f28a2c27f62ae1af

https://github.com/ratt-ru/PyMORESANE/blob/b024591ad0bbb69320d08841f28a2c27f62ae1af/pymoresane/iuwt.py#L242-L279

train

ratt-ru/PyMORESANE

pymoresane/iuwt.py

gpu_iuwt_decomposition

def gpu_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed, store_on_gpu): """ This function calls the a trous algorithm code to decompose the input into its wavelet coefficients. This is the isotropic undecimated wavelet transform implemented for a GPU. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_count (no default): Maximum scale to be considered. scale_adjust (no default): Adjustment to scale value if first scales are of no interest. store_smoothed (no default): Boolean specifier for whether the smoothed image is stored or not. store_on_gpu (no default): Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: detail_coeffs Array containing the detail coefficients. C0 (optional): Array containing the smoothest version of the input. """ # The following simple kernel just allows for the construction of a 3D decomposition on the GPU. ker = SourceModule(""" __global__ void gpu_store_detail_coeffs(float *in1, float *in2, float* out1, int *scale, int *adjust) { const int len = gridDim.x*blockDim.x; const int i = (blockDim.x * blockIdx.x + threadIdx.x); const int j = (blockDim.y * blockIdx.y + threadIdx.y)*len; const int k = (blockDim.z * blockIdx.z + threadIdx.z)*(len*len); const int tid2 = i + j; const int tid3 = i + j + k; if ((blockIdx.z + adjust[0])==scale[0]) { out1[tid3] = in1[tid2] - in2[tid2]; } } """) wavelet_filter = (1./16)*np.array([1,4,6,4,1], dtype=np.float32) # Filter-bank for use in the a trous algorithm. wavelet_filter = gpuarray.to_gpu_async(wavelet_filter) # Initialises an empty array to store the detail coefficients. detail_coeffs = gpuarray.empty([scale_count-scale_adjust, in1.shape[0], in1.shape[1]], np.float32) # Determines whether the array is already on the GPU or not. If not, moves it to the GPU. try: gpu_in1 = gpuarray.to_gpu_async(in1.astype(np.float32)) except: gpu_in1 = in1 # Sets up some working arrays on the GPU to prevent memory transfers. gpu_tmp = gpuarray.empty_like(gpu_in1) gpu_out1 = gpuarray.empty_like(gpu_in1) gpu_out2 = gpuarray.empty_like(gpu_in1) # Sets up some parameters required by the algorithm on the GPU. gpu_scale = gpuarray.zeros([1], np.int32) gpu_adjust = gpuarray.zeros([1], np.int32) gpu_adjust += scale_adjust # Fetches the a trous kernels and sets up the unique storing kernel. gpu_a_trous_row_kernel, gpu_a_trous_col_kernel = gpu_a_trous() gpu_store_detail_coeffs = ker.get_function("gpu_store_detail_coeffs") grid_rows = int(in1.shape[0]//32) grid_cols = int(in1.shape[1]//32) # The following loop, which iterates up to scale_adjust, applies the a trous algorithm to the scales which are # considered insignificant. This is important as each set of wavelet coefficients depends on the last smoothed # version of the input. if scale_adjust>0: for i in range(0, scale_adjust): gpu_a_trous_row_kernel(gpu_in1, gpu_tmp, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) gpu_a_trous_col_kernel(gpu_tmp, gpu_out1, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) gpu_in1, gpu_out1 = gpu_out1, gpu_in1 gpu_scale += 1 # The meat of the algorithm - two sequential applications fo the a trous followed by determination and storing of # the detail coefficients. C0 is reassigned the value of C on each loop - C0 is always the smoothest version of the # input image. for i in range(scale_adjust, scale_count): gpu_a_trous_row_kernel(gpu_in1, gpu_tmp, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) gpu_a_trous_col_kernel(gpu_tmp, gpu_out1, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) # Approximation coefficients. gpu_a_trous_row_kernel(gpu_out1, gpu_tmp, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) gpu_a_trous_col_kernel(gpu_tmp, gpu_out2, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) # Approximation coefficients. gpu_store_detail_coeffs(gpu_in1, gpu_out2, detail_coeffs, gpu_scale, gpu_adjust, block=(32,32,1), grid=(grid_cols, grid_rows, int(scale_count))) # Detail coefficients. gpu_in1, gpu_out1 = gpu_out1, gpu_in1 gpu_scale += 1 # Return values depend on mode. NOTE: store_smoothed does not work if the result stays on the gpu. if store_on_gpu: return detail_coeffs elif store_smoothed: return detail_coeffs.get(), gpu_in1.get() else: return detail_coeffs.get()

python

def gpu_iuwt_decomposition(in1, scale_count, scale_adjust, store_smoothed, store_on_gpu): """ This function calls the a trous algorithm code to decompose the input into its wavelet coefficients. This is the isotropic undecimated wavelet transform implemented for a GPU. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_count (no default): Maximum scale to be considered. scale_adjust (no default): Adjustment to scale value if first scales are of no interest. store_smoothed (no default): Boolean specifier for whether the smoothed image is stored or not. store_on_gpu (no default): Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: detail_coeffs Array containing the detail coefficients. C0 (optional): Array containing the smoothest version of the input. """ # The following simple kernel just allows for the construction of a 3D decomposition on the GPU. ker = SourceModule(""" __global__ void gpu_store_detail_coeffs(float *in1, float *in2, float* out1, int *scale, int *adjust) { const int len = gridDim.x*blockDim.x; const int i = (blockDim.x * blockIdx.x + threadIdx.x); const int j = (blockDim.y * blockIdx.y + threadIdx.y)*len; const int k = (blockDim.z * blockIdx.z + threadIdx.z)*(len*len); const int tid2 = i + j; const int tid3 = i + j + k; if ((blockIdx.z + adjust[0])==scale[0]) { out1[tid3] = in1[tid2] - in2[tid2]; } } """) wavelet_filter = (1./16)*np.array([1,4,6,4,1], dtype=np.float32) # Filter-bank for use in the a trous algorithm. wavelet_filter = gpuarray.to_gpu_async(wavelet_filter) # Initialises an empty array to store the detail coefficients. detail_coeffs = gpuarray.empty([scale_count-scale_adjust, in1.shape[0], in1.shape[1]], np.float32) # Determines whether the array is already on the GPU or not. If not, moves it to the GPU. try: gpu_in1 = gpuarray.to_gpu_async(in1.astype(np.float32)) except: gpu_in1 = in1 # Sets up some working arrays on the GPU to prevent memory transfers. gpu_tmp = gpuarray.empty_like(gpu_in1) gpu_out1 = gpuarray.empty_like(gpu_in1) gpu_out2 = gpuarray.empty_like(gpu_in1) # Sets up some parameters required by the algorithm on the GPU. gpu_scale = gpuarray.zeros([1], np.int32) gpu_adjust = gpuarray.zeros([1], np.int32) gpu_adjust += scale_adjust # Fetches the a trous kernels and sets up the unique storing kernel. gpu_a_trous_row_kernel, gpu_a_trous_col_kernel = gpu_a_trous() gpu_store_detail_coeffs = ker.get_function("gpu_store_detail_coeffs") grid_rows = int(in1.shape[0]//32) grid_cols = int(in1.shape[1]//32) # The following loop, which iterates up to scale_adjust, applies the a trous algorithm to the scales which are # considered insignificant. This is important as each set of wavelet coefficients depends on the last smoothed # version of the input. if scale_adjust>0: for i in range(0, scale_adjust): gpu_a_trous_row_kernel(gpu_in1, gpu_tmp, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) gpu_a_trous_col_kernel(gpu_tmp, gpu_out1, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) gpu_in1, gpu_out1 = gpu_out1, gpu_in1 gpu_scale += 1 # The meat of the algorithm - two sequential applications fo the a trous followed by determination and storing of # the detail coefficients. C0 is reassigned the value of C on each loop - C0 is always the smoothest version of the # input image. for i in range(scale_adjust, scale_count): gpu_a_trous_row_kernel(gpu_in1, gpu_tmp, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) gpu_a_trous_col_kernel(gpu_tmp, gpu_out1, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) # Approximation coefficients. gpu_a_trous_row_kernel(gpu_out1, gpu_tmp, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) gpu_a_trous_col_kernel(gpu_tmp, gpu_out2, wavelet_filter, gpu_scale, block=(32,32,1), grid=(grid_cols, grid_rows)) # Approximation coefficients. gpu_store_detail_coeffs(gpu_in1, gpu_out2, detail_coeffs, gpu_scale, gpu_adjust, block=(32,32,1), grid=(grid_cols, grid_rows, int(scale_count))) # Detail coefficients. gpu_in1, gpu_out1 = gpu_out1, gpu_in1 gpu_scale += 1 # Return values depend on mode. NOTE: store_smoothed does not work if the result stays on the gpu. if store_on_gpu: return detail_coeffs elif store_smoothed: return detail_coeffs.get(), gpu_in1.get() else: return detail_coeffs.get()

This function calls the a trous algorithm code to decompose the input into its wavelet coefficients. This is the isotropic undecimated wavelet transform implemented for a GPU. INPUTS: in1 (no default): Array on which the decomposition is to be performed. scale_count (no default): Maximum scale to be considered. scale_adjust (no default): Adjustment to scale value if first scales are of no interest. store_smoothed (no default): Boolean specifier for whether the smoothed image is stored or not. store_on_gpu (no default): Boolean specifier for whether the decomposition is stored on the gpu or not. OUTPUTS: detail_coeffs Array containing the detail coefficients. C0 (optional): Array containing the smoothest version of the input.

b024591ad0bbb69320d08841f28a2c27f62ae1af

https://github.com/ratt-ru/PyMORESANE/blob/b024591ad0bbb69320d08841f28a2c27f62ae1af/pymoresane/iuwt.py#L384-L499

train