def create_variable(self,variable,type,dimensions=None):
"""
create_variable() [public]
Purpose: Creates a variable in the file.
Parameters: variable [type=string]
Name of NetCDF variable to be created.
type [type=string]
The variable primitive datatypes correspond to the dtype attribute of a numpy array. You can specify the
datatype as a numpy dtype object, or anything that can be converted to a numpy dtype object. Valid
datatype specifiers include: 'f4' (32-bit floating point), 'f8' (64-bit floating point), 'i4' (32-bit
signed integer), 'i2' (16-bit signed integer), 'i8' (64-bit singed integer), 'i1' (8-bit signed integer),
'u1' (8-bit unsigned integer), 'u2' (16-bit unsigned integer), 'u4' (32-bit unsigned integer), 'u8'
(64-bit unsigned integer), or 'S1' (single-character string). The old Numeric single-character typecodes
('f','d','h', 's','b','B','c','i','l'), corresponding to ('f4','f8','i2','i2','i1','i1','S1','i4','i4'),
will also work.
dimensions [type=tuple of strings]
The dimensions of the variable as a tuple of strings that name dimensions already created in the netCDF file.
If the variable is a scalar, only specify the variable and type.
Returns: An instance of the Variable class that points to the variable in the file.
"""
if variable not in self._df.variables.keys():
if reader.__name__ == "Nio":
print 'Creating variable for Nio'
if dimensions == None:
self._df.create_variable(variable,type,dimensions=())
else:
self._df.create_variable(variable,type,dimensions)
elif reader.__name__ in ['scipy.io.netcdf','Dataset']:
print 'Creating variable for netcdf'
if dimensions == None:
self._df.createVariable(variable,type)
else:
self._df.createVariable(variable,type,dimensions)
else:
warning("DataIO: Variable '%s' already exists in file '%s'." % (variable, self._file_name))
def __init__(self, value):
try:
self.value = float(value)
except:
fatal("invalid argument: %r" % time)
warning("'max_time' filter is deprecated, use "
"'user_time < %.4f' filter expression" % self.value)
def post_process(self, buf):
"""Post-process the FFFF header
Process the FFFF header, assigning unspecified element locations to
be contiguous (on erase-block-size boundaries), and read the TFTF
files into the buffer at those locations.
(Called by "create-ffff" after processing all arguments)
"""
# Revalidate the erase block size
self.erase_block_mask = self.erase_block_size - 1
# Scan the elements and fill in missing start locations.
# Elements are concatenated at the granuarity of the erase block size
location = self.elements[0].element_location
for index, element in enumerate(self.elements):
element.index = index
if element.element_type != FFFF_ELEMENT_END_OF_ELEMENT_TABLE:
if element.element_location == 0:
element.element_location = location
error("Note: Assuming element [{0:d}]"
" loads at {1:08x}".format(element.index, location))
if self.flash_image_length != 0 and \
element.element_location + element.element_length >= \
self.flash_image_length:
error("--element-location " +
format(element.element_location, "#x") +
" + --element-length " +
format(element.element_length, "#x") +
" exceeds --image-length " +
format(self.flash_image_length, "#x"))
sys.exit(PROGRAM_ERRORS)
location = next_boundary(element.element_location +
element.element_length,
self.erase_block_size)
if element.element_type == FFFF_ELEMENT_END_OF_ELEMENT_TABLE:
break
if self.flash_image_length == 0:
self.flash_image_length = location
self.validate_element_table()
# fill in and/or trim selected FFFF fields
self.sentinel = FFFF_SENTINEL
self.timestamp = strftime("%Y%m%d %H%M%S", gmtime())
if len(self.flash_image_name) >= FFFF_FLASH_IMAGE_NAME_LENGTH:
self.flash_image_name = \
self.flash_image_name[0:FFFF_FLASH_IMAGE_NAME_LENGTH - 1]
warning("flash_image_name truncated to '{0:s}'".
format(self.flash_image_name))
self.tail_sentinel = FFFF_SENTINEL
# Flush the structure elements to the FFFF buffer and do a final
# sniff test on the results
self.pack()
self.validate_ffff_header()
def set_type(driveletter, drive_type):
# Make sure we use the same caps for the driveletter as the reg
typesraw = registry.get("HKEY_LOCAL_MACHINE\\Software\\Wine\\Drives", quiet=True, shallow=True)
for key, value in typesraw.iteritems():
if key.lower == driveletter.lower():
driverletter = key
# Save it
if drive_type not in DRIVE_TYPES or drive_type != None:
util.warning("Setting drive type to unknown value: %s" % drive_type)
registry.set({"HKEY_LOCAL_MACHINE\\Software\\Wine\\Drives": {"%s:" % driveletter: drive_type}})
def client(self, conn):
try:
data = util.get_bytearray(conn)
if len(data) > 0:
reply = self.handler(data)
if reply is not None:
util.send_bytearray(conn, reply)
except Exception as e:
util.warning('Server handler threw "%s"' % e)
traceback.print_exc(file=sys.stdout)
conn.close()
def build(smbuildfile, compiler, plugins, packages, flags='', output_dir='builds', nosource=False):
"""Performs the entire build process."""
# setup directory structure, execute user-configurations
plugin_build_dir = os.path.join(output_dir, 'plugins')
util.mkdir(output_dir)
util.mkdir(plugin_build_dir)
# scan deps for what we need to do
packages_to_build = set()
for name, package in packages.items():
if smbuildfile == package.smbuildfile:
packages_to_build.add(name)
plugins_to_compile = set()
for name in packages_to_build:
for_this_package = base.find_plugin_deps(packages[name], packages)
for plugin_name in for_this_package:
plugins_to_compile.add(plugin_name)
if plugin_name not in plugins:
err = 'Package {} uses plugin {}, but it does not exist'.format(name, plugin_name)
raise ValueError(err)
# also make sure plugin dependencies are met by the package
for dep in plugins[plugin_name].deps:
if dep not in for_this_package:
msg = 'Plugin {} depends on {}, but is not part of package {}'
msg = msg.format(plugin_name, dep, name)
util.warning(msg)
# also compile any plugins from this smbuildfile
for plugin_name in plugins:
if plugins[plugin_name].smbuildfile == smbuildfile:
plugins_to_compile.add(plugin_name)
# compile plugins
compiled_count = 0
for name in plugins_to_compile:
plugin = plugins[name]
if plugin.compile(compiler, plugin_build_dir, flags):
compiled_count += 1
# build packages
for name in packages_to_build:
package = packages[name]
print('Building package {}'.format(name))
package.create(output_dir, packages, plugins, nosource)
if len(plugins) == 0:
util.warning('No plugins were found in {}.'.format(smbuildfile))
elif compiled_count == 0:
print('All plugins up to date.')
def set_file_attribute(self, attribute, value):
"""
set_file_attribute() [public]
Purpose: Set a file attribute, but only if the write flag is set.
Parameters: attribute [type=string]
The name of the attribute to set.
value [type=int,float,char,string]
The value to put in the file.
Returns: [nothing]
"""
if not self._write:
warning("DataIO: Write flag has not been set on file '%s'. No action taken." % self._file_name)
return
setattr(self._df, attribute, value)
return
def client(self, conn):
try:
while True:
json = util.receive_json(conn)
if json is None:
break
reply = self.handler(json)
if reply is not None:
util.send_json(conn, reply)
except Exception as e:
util.warning('Server handler threw "%s"' % e)
traceback.print_exc(file=sys.stdout)
pass
conn.close()
def glob_files(file_list, name, warn_on_empty=False):
"""
Support function for pattern matching on files that
returns a list of matching files.
"""
output = []
current_path = os.path.join(*DirectoryStack)
for pattern in file_list:
matches = glob.glob(os.path.join(current_path, pattern))
if matches:
for f in matches:
output.append(f)
if not matches and warn_on_empty:
util.warning('No files matched pattern {}'.format(pattern))
return output
def set_variable(self, variable, value):
"""
set_variable() [public]
Purpose: Put an array of data in the file, but only if write flag has been set.
Parameters: variable [type=string]
Name of the variable to set.
value [type=np.array]
Array to put in the file.
Returns: [nothing]
"""
if not self._write:
warning("DataIO: Write flag has not been set on file '%s'. No action taken." % self._file_name)
return
self._ov_variables[variable] = value
return
def remove_variable(self, variable):
"""
remove_variable() [public]
Purpose: Set a file variable to the remove list, to be "removed" on close.
Parameters: variable [type=string]
Name of the variable to remove.
Returns: [nothing]
"""
if not self._write:
warning("DataIO: Write flag has not been set on file '%s'. No action taken." % self._file_name)
return
if variable not in self._rm_variables:
self._rm_variables.append(variable)
else:
warning("DataIO: Variable '%s' has already been removed from file '%s'." % (variable, self._file_name))
return
def remove_file_attribute(self, attribute):
"""
remove_file_attribute() [public]
Purpose: Set a file attribute to the remove list, to be "removed" on close.
Parameters: attribute [type=string]
Name of the attribute to remove.
Returns: [nothing]
"""
if not self._write:
warning("DataIO: Write flag has not been set on file '%s'. No action taken." % self._file_name)
return
if attribute not in self._rm_attributes['__file__']:
self._rm_attributes['__file__'].append(attribute)
else:
warning("DataIO: Attribute '%s' has already been removed from file '%s'." % (attribute, self._file_name))
return
def streamsearch(ofile, text, max_pages=10, results_per_page=100):
"""Stream the results of searching for 'text' to the 'ofile' output file
Args:
ofile str, the name of a file where we will write any tweets
we find. Tweets are written in JSON format, with every
tweet being stored in a separate line as a Python dict.
text str, the text to search for in Twitter. This can
be a plain text string or a '#hashtag' to look
for tweets of this topic only.
max_pages int, maximum number of result 'pages' to obtain
from Twitter's backlog of archived tweets. When
not specified, default to 10 pages.
results_per_page int, maximum number of results per page to fetch
from Twitter's backlog of archived tweets. When
not specified, default to 100 tweets per page.
Returns:
None
"""
# Load the id of already seen tweets, if there are any.
ofilename = ofile or 'standard output'
seen = ofile and preload_tweets(ofile) or set()
if seen:
message('%d tweets preloaded from %s', len(seen), ofilename)
try:
ostream = ofile and file(ofile, 'a+') or sys.stdout
for matches in search(text, max_pages=max_pages,
results_per_page=results_per_page):
newmatches = 0
for tweet in matches:
(tid, tuser, text) = (tweet['id'], tweet['from_user'],
tweet['text'])
if not tid in seen:
newmatches += 1
seen.add(tid)
print >> ostream, json.dumps(tweet)
if newmatches > 0:
message('%d new tweets logged at %s', newmatches, ofilename)
ostream.close()
except IOError, e:
if ostream and ostream != sys.stdout:
ostream.close()
warning('Error writing at file "%s". %s', ofilename, e)
def set_variable_attribute(self, variable, attribute, value):
"""
set_variable_attribute() [public]
Purpose: Set a variable attriute, but only if the write flag is set.
Parameters: variable [type=string]
Name of the variable whose attribute to set.
attribute [type=string]
Name of the attribute to set.
value [type=int,float,char,string]
The value to put in the file.
Returns: [nothing]
"""
if not self._write:
warning("DataIO: Write flag has not been set on file '%s'. No action taken." % self._file_name)
return
if reader.__name__ in ["Nio","scipy.io.netcdf"]:
setattr(self._df.variables[variable], attribute, value)
elif reader.__name__ == "Dataset":
setattr(self._df.variables[variable],attribute,value)
return
def load_from_dict(dict_):
from os.path import isfile, sep
global _triggers, _hooks
if 'hooks' not in dict_:
return
for trigger, fnames in dict_['hooks'].items():
if trigger not in _triggers:
raise ValueError("unknown trigger: '" + str(trigger) + "'")
for fname in fnames:
if fname in _hooks[trigger]:
raise ValueError("duplicate hook: '" + str(fname) + "'")
if not isfile(config.hooks_dir + sep + fname):
util.warning("could not find hook file: '" + \
str(fname) + "'")
_hooks[trigger].append(fname)
def __should_fail(self, result):
"""Called before an eval test is about to be failed, but the criteria
specifies that a human should confirm the failure before taking
any points. If this method returns True, the points are ultimately
taken.
"""
import sys
from grader import write_results
from prompt import prompt
util.warning("about to fail a test")
write_results(sys.stdout, [result])
points = result['deduction']
s = util.plural("point", points)
fail_msg = "fail this test (-{} {})".format(points, s)
choices = [fail_msg, "do not fail this test"]
return prompt(choices, '1') == [0]
def remove_variable_attribute(self, variable, attribute):
"""
remove_variable_attribute () [public]
Purpose: Set a variable attribute to the remove list, to be "removed" on close.
Parameters: variable [type=string]
Name of the variable whose attribute to remove.
attribute [type=string]
Name of the attribute to remove.
Returns: [nothing]
"""
if not self._write:
warning("DataIO: Write flag has not been set on file '%s'. No action taken." % self._file_name)
return
try:
if attribute not in self._rm_attributes[variable]:
self._rm_attributes[variable].append(attribute)
else:
warning("DataIO: Attribute '%s' has already been removed from variable '%s' in file '%s'." % (attribute, variable, self._file_name))
except KeyError:
self._rm_attributes[variable] = []
self._rm_attributes[variable].append(attribute)
return
def openFile(self, filename):
# Sanitize name
root, ext = os.path.splitext(filename)
if not ext:
filename += '.xml'
if not os.path.isfile(filename):
return util.warning(self,
'File({0}) does not exist'.format(filename))
# Check for doubles
for idx, config in enumerate(self.getConfigs()):
if config.getFilename() == filename:
self.switchTab(idx)
return util.warning(
self, 'File "{0}" is already open'.format(filename))
# New config
config = SirannonConfiguration()
config.loadFromFile(filename)
# Check for outdated parameters and ask user for removal
if config.proofRead() < 0:
config.sync()
dialog2 = self.getWidget('QuestionDialog')
dialog2.set_markup(
'Configuration contains outdated or unkown parameters for some components. Do you wish to remove them?'
)
status = dialog2.run()
dialog2.hide()
sys.stdout.flush()
if status == -8:
config.proofRead(correct=True)
# Show the opened XML
self.newTab(config)
self.switchTab(-1, 1)
return True
def build(self, jobs='1'):
packagelist = importer.get_modulelist(builder)
packagelist.remove('base')
self.builders = {}
self.projects = {}
for p in packagelist:
self.builders[p] = importer.get_module(builder, p)
importer.require(p)
self.files = {}
self.files['bin'] = []
self.files['lib'] = []
self.files['share'] = []
for r in self.config['repo']:
if 'root' in r:
path = os.path.join(r['path'], r['root'])
else:
path = r['path']
if not 'exclude' in r:
r['exclude'] = []
if 'type' in r:
print r['type']
self.projects[r['path']] = self.builders[r['type']].Builder(
path, self.repos[r['path']].get_version())
outfiles = self.projects[r['path']].build(jobs, r['exclude'])
for f in self.files:
outfiles[f] = [x for x in outfiles[f] if x]
self.files[f].extend(outfiles[f])
else:
util.warning("No type declared for", r['path'], "; skipping")
def __processSplit(command, keys, attributes, timestamp, source, backfill,
processCoid, processSecid):
if "SPLIT" not in attributes:
return
secid = database.getSecidFromCsid(keys["GVKEY"], keys["IID"], timestamp)
if not processSecid(secid):
return
if secid is None:
secid = database.createNewCsid(keys["GVKEY"], keys["IID"], timestamp)
util.warning("Created new secid: {}.{}=>{}".format(
keys['GVKEY'], keys['IID'], secid))
if backfill:
timestamp = min(
timestamp,
util.convert_date_to_millis(__datePlusOne(keys["DATADATE"])))
code = database.getAttributeType("SPLIT", source, "n", database.SPLIT)
updates = (0, 0)
if command in ("I", "C"):
updates = database.insertTimelineRow(database.SPLIT, {
"secid": secid,
"date": int(keys["DATADATE"])
}, {
"backfill": backfill,
"rate": float(attributes["SPLIT"])
}, timestamp)
elif command in ("D", "R"):
updates = database.killOrDeleteTimelineRow(
database.SPLIT, {
"secid": secid,
"date": int(keys["DATADATE"])
}, timestamp)
database.updateAttributeStats(code, *updates)
def __verifySymbols(rows, start, end, data, warnings, stats):
#check that born<died for each row. report rows with born=died and born>died
for i in range(start, end):
if rows[i]["born"] == rows[i]["died"]:
if warnings: util.warning("born=died: {}".format(rows[i]))
stats["row born=died"] = stats["row born=died"] + 1
stats["warnings"] = stats["warnings"] + 1
elif rows[i]["died"] is not None and rows[i]["born"] > rows[i]["died"]:
util.error("born>died: {}".format(rows[i]))
stats["row born>died"] = stats["row born>died"] + 1
stats["errors"] = stats["errors"] + 1
#check that each row was born at the point the other died
#chck if consecutive rows have the same data
for i in range(start + 1, end):
if rows[i - 1]["died"] is None or rows[
i - 1]["died"] > rows[i]["born"]: #overlapping rows
#do a more thorough check
if rows[i - 1]["country"] != rows[i]["country"] or rows[
i - 1]["coid"] == rows[i]["coid"]:
if warnings:
util.warning("benign overlap: {} | {}".format(
rows[i - 1], rows[i]))
stats["benign overlap"] = stats["benign overlap"] + 1
stats["warnings"] = stats["warnings"] + 1
else:
util.error("overlapping rows: {} | {}".format(
rows[i - 1], rows[i]))
stats["overlapping row"] = stats["overlapping row"] + 1
stats["errors"] = stats["errors"] + 1
elif rows[i - 1]["died"] < rows[i]["born"]:
if warnings:
util.warning("gap in the timeline: {} | {}".format(
rows[i - 1], rows[i])) #timeline gap
stats["gap"] = stats["gap"] + 1
stats["warnings"] = stats["warnings"] + 1
elif util.dict_fields_eq(rows[i - 1], rows[i], data):
if warnings:
util.warning("consecutive rows with same data: {} | {}".format(
rows[i - 1], rows[i]))
stats["consecutive with same data"] = stats[
"consecutive with same data"] + 1
stats["warnings"] = stats["warnings"] + 1
def statesCode(xmlTypeNode):
if len(xmlTypeNode.findall("case")) != 1:
util.warning(
"func/type : Unsupported number of \"case\" tags (expected 1) for \""
+ xmlTypeNode.find("title").text.encode("utf-8") +
"\" node. This profile will be ignored.")
return " return m_historizers;\n"
code = ""
for xmlDataFieldNode in xmlHelper.findUsefulDataFieldNodes(
inXmlNode=xmlTypeNode.find("case")):
if isLinearValue(xmlDataFieldNode):
dataText = xmlDataFieldNode.find("data").text
if dataText == "Temperature" or \
dataText == "Humidity" or \
dataText == "Barometer" or \
dataText == "Supply voltage" or \
dataText == "Illumination" or \
dataText == "Illuminance":
code += statesCodeForLinearValue(xmlDataFieldNode)
elif str(dataText.encode("utf-8")) == "Sun – West" \
or str(dataText.encode("utf-8")) == "Sun – South" \
or str(dataText.encode("utf-8")) == "Sun – East": # Provided as kilo-lux when Yadoms knows only lux
code += statesCodeForLinearValue(
xmlDataFieldNode, 1000)
elif dataText == "Energy Storage":
code += statesCodeForLinearValue(xmlDataFieldNode,
applyCoef=None,
finalCast="int")
else:
util.warning(
"func/type : Unsupported linear data type \"" +
str(dataText.encode("utf-8")) + "\" for \"" +
str(
xmlTypeNode.find("title").text.encode(
"utf-8")) +
"\" node. This data will be ignored.")
continue
elif isBoolValue(xmlDataFieldNode):
code += statesCodeForBoolValue(xmlDataFieldNode)
else:
util.warning("func/type : Unsupported data type \"" +
str(
xmlDataFieldNode.find(
"data").text.encode("utf-8")) +
"\" for \"" + str(
xmlTypeNode.find("title").text.encode(
"utf-8")) +
"\" node. This data will be ignored.")
continue
code += " return m_historizers;"
return code
def remove_unicode(text):
"""
Cleans ingredient text from allrecipes
:param text: text with unicode
:return: text without unicode
"""
# TODO: Make sure jalapeno works with this encoding
try:
decoded_text = text.decode('unicode_escape')
except UnicodeDecodeError:
util.warning('UnicodeDecodeError on decode: '+text)
decoded_text = ''
except UnicodeEncodeError:
util.warning('UnicodeEncodeError on decode: '+text)
decoded_text = ''
try:
encoded_text = decoded_text.encode('utf-8')
except UnicodeDecodeError:
util.warning('UnicodeDecodeError on encode: '+text)
encoded_text = ''
except UnicodeEncodeError:
util.warning('UnicodeEncodeError on encode: '+text)
encoded_text = ''
return regex.uni.sub('', encoded_text)
def __processDividend(command, keys, attributes, timestamp, source, backfill,
global_cs):
if global_cs and not keys["IID"].endswith("W"):
return
elif not global_cs and keys["IID"].endswith("W"):
return
secid = database.getSecidFromCsid(keys["GVKEY"], keys["IID"], timestamp)
if secid is None:
secid = database.createNewCsid(keys["GVKEY"], keys["IID"], timestamp)
util.warning("Created new secid: {}.{}=>{}".format(
keys['GVKEY'], keys['IID'], secid))
if backfill == 1:
timestamp = min(
timestamp,
util.convert_date_to_millis(__datePlusOne(keys["DATADATE"])))
code = database.getAttributeType("DIVIDEND", source, "n",
database.DIVIDEND)
updates = (0, 0)
if command in ("I"):
data = {
"backfill": backfill,
"currency": database.getCurrencyType(keys["CURCDDV"])
}
#get the data that we track and translate them to our own names. make also sure that you get the attribute types right
for k, v in __usToCsDividendTranslate.iteritems():
value = attributes.get(v, None)
if value is not None:
data[k] = float(value)
else:
data[k] = value #i.e., None
#finally do the insertion
updates = database.insertTimelineRow(database.DIVIDEND, {
"secid": secid,
"date": int(keys["DATADATE"])
}, data, timestamp)
elif command in ("R"):
updates = database.killOrDeleteTimelineRow(
database.DIVIDEND, {
"secid": secid,
"date": int(keys["DATADATE"])
}, timestamp)
elif command in ("C", "D"):
data = {
"backfill": backfill,
"currency": database.getCurrencyType(keys["CURCDDV"])
}
for n, v in attributes.iteritems(
): #for each attribute from the compustat line
if n in __csToUsDividendTranslate: #if it is among the ones we track
ourName = __csToUsDividendTranslate[n]
if v is not None: #else
data[ourName] = float(v)
else:
data[ourName] = None #i.e None
updates = database.updateTimelineRow(database.DIVIDEND, {
"secid": secid,
"date": int(keys["DATADATE"])
}, data, timestamp)
database.updateAttributeStats(code, *updates)
else:
util.set_log_file("all", True)
if options.db == "pri":
newdb.init_db()
database = newdb.get_db()
elif options.db == "sec":
newdb.init_db(os.environ["SEC_DB_CONFIG_FILE"])
database = newdb.get_db()
else:
util.error("Valid database choices are [pri|sec]")
sys.exit(1)
# Check for previously running instance
if not database.getProcessedFilesLock():
util.warning("Not processing, previous instance running")
sys.exit(1)
#XXX may want to precache seen files for speed in loading
try:
for source in options.source.split("+"):
util.info("Processing source %s" % source)
from data_sources.file_source import FileSource
util.info("Indexing new files for %s" % source)
fs = FileSource()
files = []
sconfig = config.load_source_config(source)
# List files
fs.cwd("%s/%s" % (os.environ['DATA_DIR'], sconfig['local_dir']))
def evaluate(self, command):
warning("'negate' filter is deprecated, use 'not result' "
"filter expression")
command.result = not command.result
def parse_entry(tokens, meet):
"""
Parses a "D-Line" in a Hytek entry file that corresponds to an athlete's
entry into a particular event within a meet.
D-Line format
<Token#> <Data> <MaxChar> <Description>
0 D 1 Individual Entry Record
1 Last Name 20 (Required)
2 First Name 20 (Required)
3 Initial 1 (Optional)
4 Gender 1 M = Male, F = Female (Required)
5 Birth Date 10 MM/DD/YYYY (Optional)
6 Team Code 4 4 characters max; use UNA if unknown (Required)
7 Team Name 30 Use Unattached if unknown (Required)
8 Age 3 Age is optional if birth date provided
9 School Year 2 (Optional for HyTek, but not for TMS)
10 Event Code 10 Examples: 100, 5000S, 10000W, SP, HJ, DEC
11 Entry Mark 11 Time: hh:mm:ss.tt (1:23.44.55, 1:19.14, 58.83, 13.4h)
Field Metric: 12.33, 1233;
Field English: 12-10.25", 12', 121025, 12' 10
Combined-event: 3020 (points)
12 Mark measure 1 M for Metric, E for English (Required if Entry Mark
provided)
"""
if len(tokens) < 11:
error(f"HyTek D-Line requires at least 11 fields. <{tokens}>")
(_junk1, last_name, first_name, middle, gender, _junk2, team_code,
team_name, _junk3, grade, ht_event_code) = tokens[0:11]
athlete = Athlete.add_athlete_to_db(first_name, middle, last_name, gender,
grade, team_code, team_name)
# If the athlete's record in file was bad, add_athlete_to_db returns None.
if athlete is None:
warning(f"Skipping athlete {first_name} {last_name}")
return
# translate the HyTek event names into TMS event codes
event_code = ht_event_translator.get(ht_event_code, ht_event_code)
q = MeetDivisionEvent.query
try:
mde = q.filter_by(meet_id=meet.id,
event_code=event_code,
div_id=athlete.division.id).one()
except NoResultFound:
raise TmsError("MDE doesn't exist: meet #{}, event {}, div {}".format(
meet.id, event_code, athlete.division.code))
entry = Entry(athlete=athlete, mde=mde)
# we need to commit here, or else we can't see the event in the below call
# of entry.set_mark method.
db.session.add(entry)
db.session.commit()
info(f"Added entry: {entry}")
# If the athlete's entry includes a seed mark for this event, set it
if len(tokens[11:13]) == 2:
entry.set_mark(mark_string=tokens[11], mark_measure_type=tokens[12])
# I don't understand why, but at this point the entry thinks its "event"
# attribute is "None", even after I setup the relationship with the mde,
# which should also get me the event. I believe this shoudl be possible
# without adding the entry to the session and commiting.
db.session.commit()
info("Set entry's mark. Entry: {}. Mark:{}".format(entry.event.code,
entry.mark_to_string()))
cppHistorizerClass.addConstructor(cppClass.CppClassConstructor("const std::string& keywordName", \
"CSingleHistorizableData<" + historizerEnumName + ">(keywordName, yApi::CStandardCapacity(\"" + historizerEnumName + "\", yApi::CStandardUnits::NoUnit(), yApi::EKeywordDataType::kNoData), yApi::EKeywordAccessMode::kGet)"))
cppHistorizerClass.addDependency(
cppClass.CppExtendedEnumType(historizerEnumName,
enumValues))
return cppHistorizerClass
def printCtorExtraParameters(ctorExtraParameters):
if not ctorExtraParameters:
return ""
return ", ".join(ctorExtraParameters)
historizersCppName = []
if len(xmlTypeNode.findall("case")) != 1:
util.warning(
"func/type : Unsupported number of \"case\" tags (expected 1) for \""
+ xmlTypeNode.find("title").text.encode("utf-8") +
"\" node. This profile will be ignored.")
else:
for xmlDataFieldNode in xmlHelper.findUsefulDataFieldNodes(
inXmlNode=xmlTypeNode.find("case")):
dataText = xmlDataFieldNode.find("data").text
keywordName = xmlDataFieldNode.find(
"shortcut").text + " - " + dataText
historizerCppName = "m_" + cppHelper.toCppName(keywordName)
cppHistorizerClassName = ""
ctorExtraParameters = []
if isLinearValue(xmlDataFieldNode):
if dataText == "Temperature":
if not supportedUnit(xmlDataFieldNode, u"°C"):
continue
cppHistorizerClassName = "yApi::historization::CTemperature"
def _parseFile(filepath):
#this should only happen when we process the first file ever
if filepath is None:
return {},None,None,None
info = datafiles.read_info_file(filepath)
if os.path.basename(filepath).startswith("yearn_archive.txt"):
backfill = 1
archive = True
elif info['date_last_absent'] is None:
timestamp = util.convert_date_to_millis(info['date_modified'])
backfill = 1
archive = False
else:
timestamp = util.convert_date_to_millis(info['date_first_present'])
backfill = 0
archive = False
file = open(filepath, "r")
data={}
for line in file:
line = line.rstrip("\n")
# Parse date
# XXX all dates need to be in UTC based on exchange of stock
annDate, name, ticker, value, time = line.split("\t")
if time == 'Time Not Supplied':
exactAnnDate = annDate + ' 00:00 UTC'
elif time == 'Before Market Open':
exactAnnDate = annDate + ' 08:00 EST'
elif time == 'After Market Close':
exactAnnDate = annDate + ' 17:00 EST'
else:
exactAnnDate = annDate +" "+ time.replace("ET", "EST")
#annDate to millis
try:
exactAnnDate = util.convert_date_to_millis(exactAnnDate)
except:
util.warning("Failed to parse {}".format(exactAnnDate))
print "Failed to parse {}".format(exactAnnDate)
continue
if archive:
timestamp = util.convert_date_to_millis(annDate) - util.convert_date_to_millis(datetime.timedelta(days=30))
secid = database.getSecidFromXref("TIC", ticker, timestamp, "compustat_idhist", newdb.xrefsolve.preferUS)
if secid is None:
util.warning("Failed to map ticker {}".format(ticker))
continue
coid, issueid = database.getCsidFromSecid(secid)
assert coid is not None
data[(coid,exactAnnDate,backfill)]=annDate
#data.append((coid,exactAnnDate,backfill,timestamp))
file.close()
#get the file start date from the filename
if not archive:
startDate=os.path.normpath(filepath).split("/")[-1][0:8] #split the filepath, take last token and its first 8 chars
else:
startDate="20060101"
return (data,archive,startDate,timestamp)
def run(self, _):
import io
import sys
util.info("running HMMM test")
if self.input is not None:
in_buf = io.StringIO(self.input)
sys.stdin = in_buf
if self.output is not None:
out_buf = io.StringIO()
sys.stdout = out_buf
try:
hmc.run(self.file.binary_name, debug=False)
except KeyboardInterrupt:
sys.stdin, sys.stdout = sys.__stdin__, sys.__stdout__
desc = "test failed because the grader halted the program"
util.warning(desc)
err = filter(_not_boring, out_buf.getvalue().split('\n')[-5:-1])
return {'deduction': self.deduction,
'description': self.description,
'notes': [desc] + list(err)}
except SystemExit:
sys.stdin, sys.stdout = sys.__stdin__, sys.__stdout__
util.warning("failing test because the simulator exited uncleanly")
err = filter(_not_boring, out_buf.getvalue().split('\n')[-5:-1])
return {'deduction': self.deduction,
'description': "simluator exited with an error",
'notes': err}
# restore default standard in/out
sys.stdin, sys.stdout = sys.__stdin__, sys.__stdout__
if self.output is not None:
output = out_buf.getvalue()
passed = True
if self.output is not None:
passed = passed and self.__output_matches(output)
if passed:
return None
else:
result = {'deduction': self.deduction,
'description': self.description,
'notes': []}
if self.output is not None and type(self.output) is str:
eo = self.output.split('\n')
po = output.split('\n')
result['notes'].append("expected output:")
result['notes'] += eo
result['notes'].append("produced output:")
result['notes'] += po
return result
def run_tests(self):
import sys
import io
import os
results = dict()
results[self] = []
actual_setrecursionlimit = sys.setrecursionlimit
def intercept_stacksize_change(new_val):
util.info("intercepting call to sys.setrecursionlimit()")
old_val = sys.getrecursionlimit()
if new_val < old_val:
util.info("keeping stack size at " + str(old_val))
return
if new_val > MAX_STACK_SIZE:
util.info("code wants to set stack size too large")
util.info("keeping stack size at " + str(old_val))
return
else:
util.info("growing stack size to " + str(new_val))
actual_setrecursionlimit(new_val)
sys.setrecursionlimit = intercept_stacksize_change
try:
directory, name = os.path.split(self.path)
mod_name = name[:name.index('.py')] if '.py' in name else name
sys.path.append(directory)
util.info("importing module '{}'".format(mod_name))
# redirect standard out to empty buffer to "mute" the program
#sys.stdout = io.StringIO()
module_context = __import__(mod_name)
#sys.stdout = sys.__stdout__
util.info("finished importing module".format(mod_name))
except:
# "un-mute" the program and give socrates access to stdout
#sys.stdout = sys.__stdout__
import traceback
err = sys.exc_info()
util.error("encountered an error importing "
"'{}' module ({})".format(mod_name, err[0].__name__))
traceback.print_exc()
if self.error_deduction:
deduction = self.error_deduction
else:
deduction = self.point_value
util.warning("deducting {} points for import "
"error".format(deduction))
return [{'deduction': deduction,
'description': "error importing '{}'".format(self.path),
'notes': ["encountered {}".format(err[0].__name__)]}]
found_functions = self.__get_members(module_context, 'functions')
found_classes = self.__get_members(module_context, 'classes')
found_variables = self.__get_members(module_context, 'variables')
for test in self.tests:
result = test.run(module_context)
if result is not None:
util.add_to(result, results[self])
for func in self.functions:
results[func] = []
if func not in found_functions:
results[func].append({'deduction': func.point_value,
'description': "missing "
"{}".format(func)})
continue
for test in func.tests:
# TODO fix this hacky thing
if type(test) is TestSet:
for m in test.members:
m.target = func
result = test.run(module_context)
if result is not None:
util.add_to(result, results[func])
for cls in self.classes:
results[cls] = []
if cls not in found_classes:
results[cls].append({'deduction': cls.point_value,
'description': "missing "
"{}".format(cls)})
continue
# TODO move this into __get_members
cls_obj = _find_class_from_cxt(module_context, cls.name)
import inspect
found_methods = []
for m in inspect.getmembers(cls_obj, inspect.isfunction):
for method in cls.methods:
if method.name == m[0]:
found_methods.append(method)
for method in cls.methods:
results[method] = []
if method not in found_methods:
results[method].append({'deduction': method.point_value,
'description': "missing "
"{}".format(method)})
continue
for test in method.tests:
# TODO fix this hacky thing
if type(test) is TestSet:
for m in test.members:
m.target = method
result = test.run(module_context)
if result is not None:
util.add_to(result, results[method])
for var in self.variables:
results[var] = []
if var not in found_variables:
results[var].append({'deduction': var.point_value,
'description': "missing "
"{}".format(var)})
continue
for test in var.tests:
# TODO fix this hacky thing
if type(test) is TestSet:
for m in test.members:
m.target = var
result = test.run(module_context)
if result is not None:
util.add_to(result, results[var])
for target, failures in results.items():
sum = 0
for f in failures:
if 'deduction' in f:
# deduction is at top level
sum += f['deduction']
if sum > target.point_value:
f['deduction'] = 0
elif 'subresults' in f:
# deduction for this failure is made up of subresults
for subfailure in f['subresults']:
sum += subfailure['deduction']
if sum > target.point_value:
subfailure['deduction'] = 0
return [item for subl in results.values() for item in subl]
row = parse(line)
# Trim whitespace in each field
row = [field.strip() for field in row]
# Convert to dict using header
row = dict(zip(header, row))
# Filter out records where action code is not 'A'
if row['action_code'] != 'A':
continue
# Use full certification year instead of last 2 chars
if row['year']:
row['year'] = '20' + row['year']
# Enforce numeric fields
for field in numeric_fields:
try:
row[field] = int(row[field])
except ValueError:
warning('[{0}] Invalid integer conversion of {1} for "{2}"'.format(
row['parcel_number'], field, row[field]))
row[field] = 0
# Construct unique record id from property id + certification year
row['record_id'] = '{0}{1}'.format(row['parcel_number'], row['year'])
# Filter out
writer.writerow(row)
def __run_function(self, context):
import sys
import io
import random
mod_name = context.__name__
fn_name = self.target.name
testing_method = type(self.target) is PythonMethod
before = self.before
if before and type(before) is CriteriaObject:
before = _convert_using_cxt(context, before)
args = self.__get_args(mod_name, context)
if type(args) is tuple:
return {'deduction': self.deduction,
'description': self.description,
'notes': ["cannot test function",
"unexpected number of parameters "
"(expected {}, submission has {})".format(
args[0], args[1])]}
locals = {mod_name: context}
vars = util.ALPHABET[:len(args)]
args_strings = []
for i in range(len(vars)):
locals[vars[i]] = args[i][1]
args_strings.append("{}={}".format(args[i][0], vars[i]))
fn_call = "{}({})".format(fn_name, ', '.join(args_strings))
if testing_method:
locals["obj"] = before
code = "obj." + fn_call
else:
code = mod_name + "." + fn_call
if not self.description:
self.description = self.__build_description()
# redirect standard in to buffer
if self.input is not None:
in_buf = io.StringIO(self.input)
else:
in_buf = io.StringIO()
sys.stdin = in_buf
# redirect standard out to buffer
out_buf = io.StringIO()
sys.stdout = out_buf
if self.random_seed:
random.seed(self.random_seed)
try:
return_value = eval(code, globals(), locals)
except KeyboardInterrupt:
sys.stdin, sys.stdout = sys.__stdin__, sys.__stdout__
util.warning("interrupting a test")
return {'deduction': self.deduction,
'description': self.description,
'notes': ["test was interrupted by the grader"]}
except:
import sys
err = sys.exc_info()
sys.stdin, sys.stdout = sys.__stdin__, sys.__stdout__
util.warning("failing a test due to an error ({})".format(err[1]))
return {'deduction': self.deduction,
'description': self.description,
'notes': [str(err[1]) + " (" + str(err[0].__name__) + ")"]}
# restore default standard in/out
sys.stdin, sys.stdout = sys.__stdin__, sys.__stdout__
if self.output is not None:
output = out_buf.getvalue()
passed = True
if self.value is not None:
passed = passed and self.value == return_value
if self.output is not None:
passed = passed and self.__output_matches(output)
# if we are testing a method and there are post-state requirements
# for the method, fail the test if the object doesn't match the
# required post-state
if testing_method and self.after is not None:
if not _attributes_equal(locals["obj"], self.after):
passed = False
if passed:
return None
else:
result = {'deduction': self.deduction,
'description': self.description,
'notes': []}
if self.arguments:
for arg, val in self.arguments:
s = "where '{}' is {}".format(arg, _safe_str(val))
result['notes'].append(s)
if testing_method and before is not None:
result['notes'].append("called object before "
"the method call: "
"{}".format(_safe_str(before)))
if self.value is not None:
result['notes'].append("expected value: " + \
_safe_str(self.value))
result['notes'].append("produced value: " + \
_safe_str(return_value))
if self.output is not None and type(self.output) is str:
result['notes'].append("expected output:")
result['notes'].extend(self.output.split('\n'))
result['notes'].append("produced output:")
result['notes'].extend(output.split('\n'))
if testing_method and self.after is not None:
result['notes'].append("expected object after "
"the method runs: "
"{}".format(_safe_str(self.after)))
if self.prompt:
if self.__should_fail(result):
return result
else:
return None
else:
return result
ostream.close()
except IOError, e:
if ostream and ostream != sys.stdout:
ostream.close()
warning('Error writing at file "%s". %s', ofilename, e)
if __name__ == '__main__':
json_filename = None # Where to store matching tweets
lookup_text = None # Text to search for
# Parse command-line args for output file name.
parser = argparse.ArgumentParser(description=(
'Collect tweets matching a text pattern and store them'
'continuously in JSON-formatted lines of a local file.'))
parser.add_argument('-o', '--output', metavar='FILE', type=str,
default=None, help='output file name')
parser.add_argument('TEXT', nargs='+', type=str, default=None,
help='text to search for in tweet content')
args = parser.parse_args()
json_filename = args.output # Where to store matching tweets
lookup_text = ' '.join(args.TEXT) # Text to search for
# Keep searching for tweets, until manually interrupted.
while True:
try:
streamsearch(json_filename, lookup_text)
except TwitterHTTPError, e:
warning('Skipping HTTP error %s [...]', str(e).split('\n')[0])
pass
def run_tests(self):
from logisim.errors import NoValueGivenError
logisim_file = logisim.load(self.path)
broken = logisim_file.broken
results = dict()
for c in self.circuits:
results[c] = []
# check if circuit couldn't be parsed
if c.name in broken:
desc = str(c) + " has major wiring issues"
util.warning(desc)
results[c].append({'deduction': c.error_deduction,
'description': desc})
continue
circuit = logisim_file.get_circuit(c.name)
# check if circuit is missing
if circuit is None:
util.warning("missing " + str(c))
results[c].append({'deduction': c.point_value,
'description': "missing " + str(c)})
continue
# check if circuit's output pins have any input
for pin in circuit.output_pins:
if len(pin.input_from) > 0:
break
else:
desc = "output pins of " + str(c) + " are not connected " + \
"to anything"
util.warning(desc)
results[c].append({'deduction': c.error_deduction,
'description': desc})
continue
# check that the circuit's pins have labels
without_labels = []
for pin in circuit.input_pins + circuit.output_pins:
if not hasattr(pin, 'label'):
without_labels.append(repr(pin))
if without_labels:
if len(without_labels) == 1:
desc = "a pin is missing a label in " + str(c)
else:
desc = "pins are missing labels in " + str(c)
util.warning(desc)
results[c].append({'deduction': c.error_deduction,
'description': desc,
'notes': without_labels})
continue
# check that the circuit has the pins we require
label_errors = _check_labels(c, circuit)
if label_errors:
desc = str(c) + " is missing required pins"
util.warning(desc)
results[c].append({'deduction': c.error_deduction,
'description': desc,
'notes': label_errors})
continue
# actually run any tests
try:
for t in c.tests:
result = t.run(circuit)
if result:
outer_result = {'description': str(c) + \
" failed a test"}
outer_result['subresults'] = [result]
results[c].append(outer_result)
except NoValueGivenError as e:
desc = str(c) + " could not be tested"
results[c].append({'deduction': c.error_deduction,
'description': desc,
'notes': [str(e)]})
return [item for subl in results.values() for item in subl]
def deploy_concourse_landscape(
config_set: ConfigurationSet,
deployment_name: str='concourse',
timeout_seconds: int=180,
):
ensure_helm_setup()
# Fetch all the necessary config
config_factory = global_ctx().cfg_factory()
concourse_cfg = config_set.concourse()
# Kubernetes cluster config
kubernetes_config_name = concourse_cfg.kubernetes_cluster_config()
kubernetes_config = config_factory.kubernetes(kubernetes_config_name)
# Container-registry config
image_pull_secret_name = concourse_cfg.image_pull_secret()
container_registry = config_factory.container_registry(image_pull_secret_name)
cr_credentials = container_registry.credentials()
# TLS config
tls_config_name = concourse_cfg.tls_config()
tls_config = config_factory.tls_config(tls_config_name)
tls_secret_name = concourse_cfg.tls_secret_name()
# Helm config
helm_chart_default_values_name = concourse_cfg.helm_chart_default_values_config()
default_helm_values = config_factory.concourse_helmchart(helm_chart_default_values_name).raw
helm_chart_values_name = concourse_cfg.helm_chart_values()
custom_helm_values = config_factory.concourse_helmchart(helm_chart_values_name).raw
# Proxy config
if concourse_cfg.proxy():
proxy_cfg_name = concourse_cfg.proxy()
proxy_cfg = config_factory.proxy(proxy_cfg_name)
info('Creating config-maps for the mitm proxy ...')
create_proxy_configmaps(
proxy_cfg=proxy_cfg,
namespace=deployment_name,
)
info('Creating default image-pull-secret ...')
create_image_pull_secret(
credentials=cr_credentials,
image_pull_secret_name=image_pull_secret_name,
namespace=deployment_name,
)
info('Creating tls-secret ...')
create_tls_secret(
tls_config=tls_config,
tls_secret_name=tls_secret_name,
namespace=deployment_name,
)
warning(
'Teams will not be set up properly on Concourse if the deployment times out, '
'even if Helm eventually succeeds. In this case, run the deployment command again after '
'Concourse is available.'
)
instance_specific_helm_values = create_instance_specific_helm_values(
concourse_cfg=concourse_cfg, config_factory=config_factory,
)
chart_version = concourse_cfg.helm_chart_version()
# Add proxy sidecars to instance specific values.
# NOTE: Only works for helm chart version 3.8.0 or greater
if concourse_cfg.proxy():
chart_version_semver = semver.parse_version_info(concourse_cfg.helm_chart_version())
min_version = semver.parse_version_info('3.8.0')
if chart_version_semver >= min_version:
instance_specific_helm_values = add_proxy_values(
config_set=config_set,
instance_specific_values=instance_specific_helm_values,
)
else:
fail('Proxy deployment requires the configured helm chart version to be at least 3.8.0')
execute_helm_deployment(
kubernetes_config,
deployment_name,
'stable/concourse',
deployment_name,
default_helm_values,
custom_helm_values,
instance_specific_helm_values,
chart_version=chart_version,
)
info('Waiting until the webserver can be reached ...')
deployment_helper = kube_ctx.deployment_helper()
is_web_deployment_available = deployment_helper.wait_until_deployment_available(
namespace=deployment_name,
name='concourse-web',
timeout_seconds=timeout_seconds,
)
if not is_web_deployment_available:
fail(
dedent(
"""No Concourse webserver reachable after {t} second(s).
Check status of Pods created by "concourse-web"-deployment in namespace {ns}
"""
).format(
t = timeout_seconds,
ns = deployment_name,
)
)
info('Webserver became accessible.')
# Even though the deployment is available, the ingress might need a few seconds to update.
time.sleep(3)
info('Setting teams on Concourse ...')
set_teams(config=concourse_cfg)
parser.print_help(util.LOGFILE)
exit(1)
#Only single day should be output to screen
if (args.singleDate is None) and (args.report is False):
parser.print_help(util.LOGFILE)
exit(1)
date = fromDate
while date < toDate:
try:
reco = backoffice.singleDayReconcile(date, args.old)
if args.report:
output = open(
os.environ["REPORT_DIR"] + "/borecon/" +
date.strftime("%Y%m%d") + ".txt", "w")
else:
output = sys.stdout
output.write(reco)
if output != sys.stdout:
output.close()
if args.email:
email(reco, date.strftime("%Y%m%d"))
util.info("Reconciled day {}".format(date.strftime("%Y%m%d")))
except backoffice.PositionSourceError:
util.warning("Data to reconcile day not found: {}\n".format(
date.strftime("%Y%m%d")))
finally:
date = date + dayDelta
def evaluate(self, command):
warning("'negate' filter is deprecated, use 'not result' "
"filter expression")
command.result = not command.result
import numpy as np
from util import warning, fatalError
#from callCount import FunctionCallCount
try:
# Look for PyNIO ...
import Nio as reader
print "Using PyNIO as data reader."
except ImportError:
# PyNIO is not installed, so ...
try:
from netCDF4 import Dataset as reader
warning("Using netCDF4-python as data reader. NetCDF format supported only.")
except ImportError:
try:
# Look for scipy's NetCDF reader ...
import scipy.io.netcdf as reader
warning("Using scipy.io as data reader. NetCDF format supported only.")
except ImportError:
# scipy's NetCDF reader isn't installed either. Uhoh ...
fatalError("No scientific data reader found. Exiting ...")
class DataIO:
"""
DataIO
Purpose: Handles the reading and writing of data for HootPy using whichever reader is installed
Started: 30 September 2010 by Tim Supinie (tsupinie#@ou.edu)
Completed: [not yet]
Modified: [not yet]
"""
def process_common_data(self, pkt):
pkt = pkt[10:-1]
arfcn = 0
if pkt[0] == 0x03: # Common Signalling Info
#util.xxd(pkt)
# pkt[1] - pkt[4] == ts
chan_type = pkt[5]
chan_subtype = pkt[6]
direction = pkt[7] # 2 - DL, 1 - UL
msg_len = pkt[8] | (pkt[9] << 8)
msg_content = pkt[10:]
if chan_type == 0x30: # UMTS RRC
chan_map_ul = {
0x30: util.gsmtap_umts_rrc_types.UL_CCCH,
0x31: util.gsmtap_umts_rrc_types.UL_DCCH
}
chan_map_dl = {
0x30: util.gsmtap_umts_rrc_types.DL_CCCH,
0x31: util.gsmtap_umts_rrc_types.DL_DCCH,
0x32: util.gsmtap_umts_rrc_types.BCCH_BCH,
0x34: util.gsmtap_umts_rrc_types.PCCH
}
subtype = 0
if direction == 2:
subtype = chan_map_dl[chan_subtype]
arfcn = self.umts_last_uarfcn_dl
elif direction == 1:
subtype = chan_map_ul[chan_subtype]
arfcn = self.umts_last_uarfcn_ul
else:
print('Unknown direction %02x' % direction)
return b''
gsmtap_hdr = util.create_gsmtap_header(
version=2,
payload_type=util.gsmtap_type.UMTS_RRC,
arfcn=arfcn,
sub_type=subtype)
return gsmtap_hdr + msg_content
elif chan_type == 0x01: # UMTS NAS
if direction == 2:
arfcn = self.umts_last_uarfcn_dl
elif direction == 1:
arfcn = self.umts_last_uarfcn_ul
gsmtap_hdr = util.create_gsmtap_header(
version=2, payload_type=util.gsmtap_type.ABIS, arfcn=arfcn)
return gsmtap_hdr + msg_content
elif chan_type == 0x20: # GSM RR
# TODO: CCCH and SACCH are not distinguished by headers!
# Some values are RR message, some are RR_short_PD
if direction == 2: # RR DL w/ pseudo length
lapdm_address = b'\x01'
# Control field
lapdm_control = b'\x03'
# length field
if msg_len > 63:
util.warning('message length longer than 63 (%s)' %
msg_len)
return b''
lapdm_len = bytes([(msg_len << 2) | 0x01])
#msg_content = lapdm_address + lapdm_control + lapdm_len + msg_content
gsmtap_hdr = util.create_gsmtap_header(
version=2,
payload_type=util.gsmtap_type.UM,
sub_type=util.gsmtap_channel.CCCH
) # Subtype (XXX: All CCCH)
return gsmtap_hdr + msg_content
elif direction == 1: # Only RR
gsmtap_hdr = util.create_gsmtap_header(
version=2, payload_type=util.gsmtap_type.ABIS)
return gsmtap_hdr + msg_content
elif chan_type == 0x21: # GSM RLC/MAC
arfcn = 1
if direction == 1:
arfcn = arfcn | (1 << 14)
gsmtap_hdr = util.create_gsmtap_header(
version=2,
payload_type=util.gsmtap_type.UM,
arfcn=arfcn,
sub_type=util.gsmtap_channel.PACCH
) # Subtype (PACCH dissects as MAC)
#return gsmtap_hdr + msg_content
return b''
else:
print('Unknown channel type %02x for subcommand 0x03' %
chan_type)
return b''
else:
print('Unknown subcommand %02x for command 0x21' % pkt[0])
#util.xxd(pkt)
return b''
return b''
def process(filePath, source, verifyOnly=False):
#process the RSK files for now
if filePath.find(".RSK.") < 0:
return
file = open(filePath, "r")
#The first 2 lines should be the pricedate and the modeldate
tokens = file.readline().strip().split(":")
if tokens[0] != "PriceDate":
util.error("It doesn't seem like a barra daily format")
raise Exception
else:
priceDate = __barraDateToCompact(tokens[1].strip())
tokens = file.readline().strip().split(":")
if tokens[0] != "ModelDate":
util.error("It doesn't seem like a barra daily format")
raise Exception
else:
#pass
modelDate = __barraDateToCompact(tokens[1].strip())
# If we have acquisition times, use these for real born time.
# Else, use the priceDate + 1 day
fileInfo = datafiles.read_info_file(filePath)
if fileInfo['date_last_absent'] is not None:
timestamp = util.convert_date_to_millis(fileInfo['date_first_present'])
backfill = 0
database.setAttributeAutoCreate(True)
else:
date = priceDate + datetime.timedelta(days=1)
timestamp = util.convert_date_to_millis(date.strftime("%Y%m%d"))
backfill = 1
database.setAttributeAutoCreate(True)
#get the header names. comma separated, surrounded by double quotes
line = file.readline()
headers = __getListFromBarraLine(line)
#init the dabase
#database.dropXrefCache()
#database.addXrefCache(timestamp) #cache xrefs
#######MAPPING VERIFICATION CODE########
inconcistentMappings = []
########################################
for line in file:
data = __getListFromBarraLine(line)
if len(data) != len(headers):
util.warning("Skipping bad line: {}".format(line))
continue
data = dict(zip(headers, data))
#######MAPPING VERIFICATION CODE########
if verifyOnly:
result = __verifyMapping(
data["BARRID"], util.cusip8to9(data["CUSIP"]), data["TICKER"],
source, timestamp,
newdb.xrefsolve.preferUS) #mirror the getSecid call
if result is not None: inconcistentMappings.append(result)
continue
########################################
secid = __getSecId(data["BARRID"], util.cusip8to9(data["CUSIP"]),
data["TICKER"], source, timestamp,
newdb.xrefsolve.preferUS, filePath)
if secid is None:
continue
#Now, insert barra attributes and attribute values
__removeUnwantedAttributes(data)
for attributeName, attributeValue in data.iteritems():
if isinstance(attributeValue, str):
table = "s"
elif isinstance(attributeValue, int):
table = "n"
elif isinstance(attributeValue, float):
table = "n"
else:
util.error(
"Dude, attribute values should be either int,float or str")
raise
#assert attributeName.startswith("INDNAME") and table=="s"
#With the exeption of capitalization and price, the other barra attributes
#are attributes that are evaluated monthly. for them, the date should be the
#model date. price we ignore, while capitatlization, we only create a new tuple
#if the capitalization has changed more than a threshould since the last date
#for which we have a tuple
if attributeName == "PRICE":
continue
elif attributeName == "CAPITALIZATION":
database.insertAttribute(
"sec", "n", secid, util.convert_date_to_millis(priceDate),
source, attributeName, attributeValue, timestamp, None,
backfill, False, True, __capEquals)
else:
database.insertAttribute(
"sec", table, secid,
util.convert_date_to_millis(modelDate), source,
attributeName, attributeValue, timestamp, None, backfill)
file.close()
#######MAPPING VERIFICATION CODE########
if verifyOnly:
return inconcistentMappings
def process(filepath, source):
#if full
if "full" in source or "load" in source:
#timestamp=util.convert_date_to_millis("18000101");
fileInfo = datafiles.read_info_file(filepath)
timestamp = util.convert_date_to_millis(fileInfo['date_modified'])
backfill = 1
database.setAttributeAutoCreate(True)
optimize = False
else:
fileInfo = datafiles.read_info_file(filepath)
if fileInfo["date_last_absent"] is None:
timestamp = util.convert_date_to_millis(fileInfo['date_modified'])
backfill = 0
else:
timestamp = util.convert_date_to_millis(
fileInfo['date_first_present'])
backfill = 0
database.setAttributeAutoCreate(False)
optimize = True
if "_g" in source:
global_cs = True
else:
global_cs = False
database.setAttributeAutoCreate(True)
database.setCurrencyAutoCreate(True)
#open the zipped file
zf = zipfile.ZipFile(filepath)
names = zf.namelist()
assert len(names) == 1
file = zf.open(names[0])
#variables that persist through loop
#presented here for clarity only
table = None
keyNames = None
attributeNames = None
numOfKeys = None
if optimize:
parsedLines = __optimize(file)
#process lines
counter = 0
while True:
if optimize:
if len(parsedLines) == 0: break
line = parsedLines.pop(0)
if len(line) == 3:
(command, keyValues,
attributeValues) = line[0], line[1], line[2]
elif len(line) == 4:
(table, numOfKeys, keyNames,
attributeNames) = line[0], line[1], line[2], line[3]
continue
else:
continue
else:
line = __getSplitLine(file)
if line is None: break
if line[0] in ("T", "F", "E"):
continue
elif line[0] in ("H"):
(table, numOfKeys, keyNames,
attributeNames) = __parseHeaderLine(line)
continue
elif line[0] in ("I,C,D,R"):
(command, keyValues,
attributeValues) = __parseDataLine(line, numOfKeys)
else:
util.warning("Oh no! a K command on table {}: {}".format(
table, line))
continue
#progress
counter = counter + 1
if counter % 10000 == 0:
util.info("{}: Processing line {}k".format(datetime.datetime.now(),
counter / 1000))
#remove keys that are replicated in attributes
keys = {}
keys.update(zip(keyNames, keyValues))
attributes = {}
if command in ("I", "C"):
for n, v in zip(attributeNames, attributeValues):
if n not in keys and v != "": attributes[n] = v
elif command in ("D"):
for n, v in zip(attributeNames, attributeValues):
if n not in keys and v == " ": attributes[n] = None
elif command in ("R"):
for n, v in zip(attributeNames, attributeValues):
if n not in keys: attributes[n] = None
if table == "security":
__processSecurity(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table == "sec_dprc":
__processPrice(command, keys, attributes, timestamp, source,
backfill, global_cs)
__processCSHOC(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table == "company":
__processCompany(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table == "sec_divid":
__processDividend(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table == "sec_split":
__processSplit(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table == "co_industry":
__processIndustry(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table == "co_hgic":
__processHgic(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table in ("co_afnd1", "co_afnd2", "co_ifndq", "co_ifndsa",
"co_ifndytd"):
__processFundamental(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table in ("co_idesind", 'co_adesind'):
__processDesind(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table in ("co_amkt", 'co_imkt'):
__processMkt(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table == "co_filedate":
__processFiledate(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table == "adsprate":
__processCredit(command, keys, attributes, timestamp, source,
backfill, global_cs)
elif table == "exrt_dly":
__processExchange(command, keys, attributes, timestamp, source,
backfill, global_cs)
else:
continue
#__processBufferedFundamentals(source, backfill, buffer)
file.close()
zf.close()
os.path.join(args.location, datestr,
"exec_order_ts.txt"), "w") as outfile:
outfile.write("\n".join(res))
outfile.write("\n")
if args.fills:
data = getLogs(datestr, r"fills\.txt")
ticker2secid = getTicker2secid(
os.path.join(args.location, datestr, "tickers.txt"))
res = parseFills(ticker2secid, data)
with open(
os.path.join(args.location, datestr,
"fills.{}.txt.tmp".format(datestr)),
"w") as outfile:
outfile.write("\n".join(res))
outfile.write("\n")
if args.infraRecon:
ticker2secid = getTicker2secid(
os.path.join(args.location, datestr, "tickers.txt"))
res = doInfraRecon(ticker2secid, datestr)
with open(
os.path.join(args.location, datestr,
"fills.{}.txt.tmp".format(datestr)),
"w") as outfile:
outfile.write("\n".join(res))
outfile.write("\n")
except Exception, e:
util.warning(e)
pass
finally:
date = date + dayDelta
def mfclean(options):
clark_options = {}
clark_options["gain"] = options.gain
clark_options["iterations"] = options.iterations
clark_options["cycle_speedup"] = options.cycle_speedup
max_baseline = options.max_baseline if options.max_baseline > 0.0 else \
10000.0
processor_options = {}
processor_options["processor"] = options.processor
processor_options["w_max"] = max_baseline
processor_options["padding"] = 1.0
processor_options["image"] = options.image
processor_options["threads"] = options.threads
processor_options["weighttype"] = options.weighttype
processor_options["rmode"] = options.rmode
processor_options["noise"] = options.noise
processor_options["robustness"] = options.robustness
processor_options["profile"] = options.profile
processor = processors.create_data_processor(options.ms, processor_options)
channel_freq = processor.channel_frequency()
channel_width = processor.channel_width()
max_freq = numpy.max(channel_freq)
image_size = 2.0 * util.full_width_half_max(70.0, max_freq)
# TODO: Cyril mentioned above image size estimation is too conservative.
# Need to check this and find a better estimate if necessary. For now, will
# just multiply estimated FOV by 2.0.
image_size *= 2.0
(n_px, delta_px) = util.image_configuration(image_size, max_freq,
max_baseline)
util.notice("image configuration:")
util.notice(" size: %d x %d pixel" % (n_px, n_px))
util.notice(" angular size: %.2f deg"
% (image_size * 180.0 / numpy.pi))
util.notice(" angular resolution @ 3 pixel/beam: %.2f arcsec/pixel"
% (3600.0 * delta_px * 180.0 / numpy.pi))
# TODO: Need to implement support for multiple channel images. Currently,
# all data channels are combined into a single MFS image per correlation.
image_shape = (1, 4, n_px, n_px)
image_coordinates = pyrap.images.coordinates.coordinatesystem(
casaimwrap.make_coordinate_system(image_shape[2:], [delta_px,
delta_px], processor.phase_reference(), channel_freq, channel_width))
n_model = 1
# TODO: Check code for n_model > 1!
assert(n_model == 1)
# Comment from CASA source code:
#
# Set to search for peak in I^2+Q^2+U^2+V^2 domain or each stokes plane
# seperately. Ignored for hogbom and msclean for now.
# join_stokes = False
join_stokes = True
# Compute approximate PSFs.
util.notice("computing approximate point spread functions...")
psf = [None for i in range(n_model)]
beam = [None for i in range(n_model)]
for i in range(n_model):
psf[i] = processor.point_spread_function(image_coordinates, image_shape)
fit = casaimwrap.fit_gaussian_psf(image_coordinates.dict(),
psf[i])
assert(fit["ok"])
beam[i] = BeamParameters((fit["major"] * numpy.pi) / (3600.0 * 180.0),
(fit["minor"] * numpy.pi) / (3600.0 * 180.0), (fit["angle"]
* numpy.pi) / 180.0)
util.notice("model %d/%d: major axis: %f arcsec, minor axis: %f arcsec,"
" position angle: %f deg" % (i, n_model - 1, abs(fit["major"]),
abs(fit["minor"]), fit["angle"]))
# Validate PSFs.
(min_psf, max_psf, max_psf_outer, psf_patch_size, max_sidelobe) = \
validate_psf(image_coordinates, psf, beam)
clark_options["psf_patch_size"] = psf_patch_size
updated = [False for i in range(n_model)]
weight = [None for i in range(n_model)]
model = [numpy.zeros(image_shape) for i in range(n_model)]
delta = [numpy.zeros(image_shape) for i in range(n_model)]
residual = [numpy.zeros(image_shape) for i in range(n_model)]
if join_stokes:
iterations = numpy.zeros((n_model, 1, image_shape[0]))
stokes = ["JOINT"]
cr_slices = [slice(None)]
else:
iterations = numpy.zeros((n_model, image_shape[1], image_shape[0]))
stokes = image_coordinates.get_coordinate("stokes").get_stokes()
cr_slices = [slice(i, i + 1) for i in range(4)]
cycle = 0
diverged = False
absmax = options.threshold
previous_absmax = 1e30
while absmax >= options.threshold and numpy.max(iterations) \
< options.iterations and (cycle == 0 or any(updated)):
util.notice(">> starting major cycle: %d <<" % cycle)
# Comment from CASA source code:
#
# Make the residual images. We do an incremental update for cycles after
# the first one. If we have only one model then we use convolutions to
# speed the processing
util.notice("computing residuals...")
# TODO: If n_models > 1, need to compute residuals from the sum of
# the degridded visibilities (see LofarCubeSkyEquation.cc).
assert(n_model == 1)
if cycle == 0:
# Assuming the initial models are zero, the residual visibilities
# equal the observed visibilities and therefore we only need to
# grid them.
for i in range(n_model):
residual[i], weight[i] = processor.grid(image_coordinates,
image_shape, processors.Normalization.FLAT_NOISE)
else:
for i in range(n_model):
if updated[i]:
residual[i], weight[i] = \
processor.residual(image_coordinates, model[i],
processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_NOISE)
updated[i] = False
# Compute residual statistics.
(absmax, resmin, resmax) = max_field(residual, weight)
# Print some statistics.
for i in range(n_model):
util.notice("model %d/%d: min residual: %f, max residual: %f"
% (i, n_model - 1, resmin[i], resmax[i]))
util.notice("peak residual: %f" % absmax)
# Comment from CASA source code:
#
# Check if absmax is 5% above its previous value.
#
# TODO: Value used does not look like 5%?
if absmax >= 1.000005 * previous_absmax:
diverged = True
break
# Store absmax of this major cycle for later reference.
previous_absmax = absmax
# Check stop criterium.
if absmax < options.threshold:
break
# TODO: What is this really used for? And does the max weight indeed
# correspond to sensitivity in Jy/beam?
if cycle == 0:
max_weight = 0.0
for i in range(n_model):
max_weight = max(max_weight, numpy.max(weight[i]))
util.notice("maximum sensitivity: %f Jy/beam" % (1.0
/ numpy.sqrt(max_weight)))
# Comment from CASA source code:
#
# Calculate the threshold for this cycle. Add a safety factor
#
# fractionOfPsf controls how deep the cleaning should go.
# There are two user-controls.
# cycleFactor_p : scale factor for the PSF sidelobe level.
# 1 : clean down to the psf sidelobe level
# <1 : go deeper
# >1 : shallower : stop sooner.
# Default : 1.5
# cycleMaxPsfFraction_p : scale factor as a fraction of the PSF peak
# must be 0.0 < xx < 1.0 (obviously)
# Default : 0.8
fraction_of_psf = min(options.cycle_max_psf_fraction,
options.cycle_factor * max_sidelobe)
if fraction_of_psf > 0.8:
util.warning("PSF fraction for threshold computation is too"
" high: %f. Forcing to 0.8 to ensure that the threshold is"
" smaller than the peak residual!" % fraction_of_psf)
fraction_of_psf = 0.8 # painfully slow!
# Update cycle threshold.
cycle_threshold = max(0.95 * options.threshold, fraction_of_psf
* absmax)
clark_options["cycle_threshold"] = cycle_threshold
util.notice("minor cycle threshold max(0.95 * %f, peak residual * %f):"
" %f" % (options.threshold, fraction_of_psf, cycle_threshold))
# Execute the minor cycle (Clark clean) for each channel of each model.
util.notice("starting minor cycle...")
for i in range(n_model):
if max(abs(resmin[i]), abs(resmax[i])) < cycle_threshold:
util.notice("model %d/%d: peak residual below threshold"
% (i, n_model - 1))
continue
if max_psf[i] <= 0.0:
util.warning("model %d/%d: point spread function negative or"
" zero" % (i, n_model - 1))
continue
# Zero the delta image for this model.
delta[i].fill(0.0)
for (cr, cr_slice) in enumerate(cr_slices):
for ch in range(len(residual[i])):
# TODO: The value of max_weight is only updated during
# cycle 0. Is this correct?
#
assert(len(weight[i].shape) == 2
and weight[i].shape[:2] == residual[i].shape[:2])
plane_weight = numpy.sqrt(weight[i][ch, cr_slice]
/ max_weight)
if numpy.any(plane_weight > 0.01):
weight_mask = numpy.ones((residual[i].shape[2:]))
else:
weight_mask = numpy.zeros((residual[i].shape[2:]))
# Call CASA Clark clean implementation (minor cycle).
# TODO: When cleaning each Stokes parameter separately,
# the PSF of Stokes I is used for all others as well?
#
# Comment from CASA source code:
#
# We only want the PSF for the first polarization so we
# iterate over polarization LAST.
#
result = casaimwrap.clark_clean(psf[i][ch,0,:,:],
residual[i][ch,cr_slice,:,:], weight_mask,
iterations[i,cr,ch], clark_options)
if result["iterations"] > iterations[i,cr,ch]:
updated[i] = True
delta[i][ch,cr_slice,:,:] = result["delta"]
iterations[i,cr,ch] = result["iterations"]
else:
assert(numpy.all(result["delta"] == 0.0))
util.notice("model %d/%d: stokes: %s, cleaned: %f Jy, "
"iterations per channel: %s" % (i, n_model - 1,
stokes[cr], numpy.sum(delta[i][ch,cr_slice,:,:]),
str(iterations[i,cr,:])))
# Update model images if required.
for i in range(n_model):
if updated[i]:
model[i] += delta[i]
# Update major cycle counter.
cycle += 1
if any(updated):
util.notice("finalizing residual images for all fields...")
for i in range(n_model):
if updated[i]:
residual[i], weight[i] = processor.residual(image_coordinates,
model[i], processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_NOISE)
(absmax, resmin, resmax) = max_field(residual, weight)
# Print some statistics.
for i in range(n_model):
util.notice("model %d/%d: min residual: %f, max residual: %f"
% (i, n_model - 1, resmin[i], resmax[i]))
util.notice("peak residual: %f" % absmax)
else:
util.notice("residual images for all fields are up-to-date...")
# Store output images.
util.notice("storing average response...")
util.store_image(options.image + ".response", image_coordinates,
processor.response(image_coordinates, image_shape))
util.notice("storing model images...")
for i in range(n_model):
util.store_image(options.image + ".model.flat_noise",
image_coordinates, model[i])
util.store_image(options.image + ".model", image_coordinates,
processor.normalize(image_coordinates, model[i],
processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_GAIN))
util.notice("storing residual images...")
for i in range(n_model):
util.store_image(options.image + ".residual.flat_noise",
image_coordinates, residual[i])
util.store_image(options.image + ".residual", image_coordinates,
processor.normalize(image_coordinates, residual[i],
processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_GAIN))
util.notice("storing restored images...")
for i in range(n_model):
restored = restore_image(image_coordinates.dict(), model[i],
residual[i], beam[i])
util.store_image(options.image + ".restored.flat_noise",
image_coordinates, restored)
util.store_image(options.image + ".restored", image_coordinates,
processor.normalize(image_coordinates, restored,
processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_GAIN))
# Print some statistics.
for i in range(n_model):
util.notice("model %d/%d: clean flux: %f, residual rms: %f" % (i,
n_model - 1, numpy.sum(model[i]), numpy.std(residual[i])))
if diverged:
util.error("clean diverged.")
elif absmax < options.threshold:
util.notice("clean converged.")
else:
util.warning("clean did not reach threshold: %f Jy."
% options.threshold)
def doInfraRecon(ticker2secid, datestr):
(seqnum2Info, orderid2Info) = getSeqnumOrderidMaps(datestr)
execFills = readInfraExecLogs(datestr)
processedExecFills = associateOrderidToExecFills(execFills, seqnum2Info)
orderid2fills = readOurFillsFile(datestr)
retVal = [
"type|date|strat|seqnum|secid|ticker|ts_received|ts_exchange|shares|price|exchange|liquidity|orderID|tactic"
]
seqnum = 0
missingFills = []
for eFill in processedExecFills:
seqnum += 1
orderid = eFill['orderid']
if orderid not in orderid2fills:
matchIndex = -1
else:
matchIndex = matchFill(eFill, orderid2fills[orderid],
MAX_TIMESTAMP_DIFF)
if matchIndex < 0:
#util.warning('Did not find a match. Creating missing fill: ' + str(eFill))
missingFills.append(eFill)
# add the fill
retVal.append(
"F|{date}|1|{seqnum}|{secid}|{ticker}|{ts}|{ts}|{qty}|{price}|{exch}|{liq}|{orderid}|{tactic}"
.format(date=datestr,
seqnum=seqnum,
secid=ticker2secid[eFill['ticker']],
ticker=eFill['ticker'],
ts=eFill['timestamp'],
qty=eFill['qty'],
price=eFill['price'],
exch=eFill['exch'],
liq=eFill['liq'],
orderid=eFill['orderid'],
tactic=eFill['tactic']))
continue
matchedFill = orderid2fills[orderid][matchIndex]
matchedFill['date'] = datestr
matchedFill['seqnum'] = seqnum
retVal.append(
"F|{date}|1|{seqnum}|{secid}|{ticker}|{ts_received}|{ts_exchange}|{qty}|{price}|{exch}|{liq}|{orderid}|{tactic}"
.format(**matchedFill))
nDelayedFills = 0
leftOverMissingFills = []
for eFill in missingFills:
orderid = eFill['orderid']
if orderid not in orderid2fills:
matchIndex = -1
else:
matchIndex = matchFill(eFill, orderid2fills[orderid], 1000)
if matchIndex < 0:
#util.warning('Did not find a match. Creating missing fill: ' + str(eFill))
leftOverMissingFills.append(eFill)
else:
nDelayedFills += 1
util.info(
'Substituted timestamps for {} fills, since they had a delayed timestamp.'
.format(nDelayedFills))
nSuperDelayedFills = 0
actualMissingFills = 0
for eFill in leftOverMissingFills:
orderid = eFill['orderid']
if orderid not in orderid2fills:
matchIndex = -1
else:
matchIndex = matchFill(eFill, orderid2fills[orderid], 3000)
if matchIndex < 0:
util.warning('Did not find a match. Creating missing fill: ' +
str(eFill))
actualMissingFills += 1
else:
nSuperDelayedFills += 1
util.info(
'Substituted timestamps for {} fills, since they had a delayed timestamp of over 1 second.'
.format(nSuperDelayedFills))
util.info('Actual missing fills = {}'.format(actualMissingFills))
for orderid in orderid2fills:
for fill in orderid2fills[orderid]:
if fill['matched'] != True:
util.warning("Could not match fill: " + str(fill))
return retVal
def __optimize(file):
organizer = {}
table = None
keyNames = None
attributeNames = None
numOfKeys = None
while True:
line = __getSplitLine(file)
if line is None: break
if line[0] in ("T", "F", "E"):
continue
elif line[0] in ("H"):
(table, numOfKeys, keyNames,
attributeNames) = __parseHeaderLine(line)
continue
elif line[0] in ("I,C,D,R"):
d = (command, keyValues,
attributeValues) = __parseDataLine(line, numOfKeys)
t = (table, numOfKeys, keyNames, attributeNames)
if t in organizer:
lines = organizer[t]
else:
lines = []
organizer[t] = lines
lines.append(d)
continue
else:
util.warning("Oh no! a K command on table {}: {}".format(
table, line))
continue
allLines = []
for header, datalines in organizer.iteritems():
datalines.sort(key=lambda x: x[1])
#remove some redundancies from datalines
start = 0
end = 0
while start != len(datalines):
(start, end) = __consecutiveEqual(datalines, start, lambda x: x[1])
for i in xrange(start, end - 1, 1):
if datalines[i][0] == "D" and datalines[i + 1][0] == "R":
datalines[i] = ()
elif (datalines[i][0] == "D" or datalines[i][0]
== "C") and datalines[i + 1][0] == "C":
same = True
for x, y in zip(datalines[i][2], datalines[i + 1][2]):
if len(x) == 0 and len(y) == 0:
continue
elif len(x) > 0 and len(y) > 0:
continue
else:
same = False
break
if same:
datalines[i] = ()
else:
newdataline = []
for x, y in zip(datalines[i][2], datalines[i + 1][2]):
if len(y) > 0:
newdataline.append('')
else:
newdataline.append(x)
datalines[i] = (datalines[i][0], datalines[i][1],
tuple(newdataline))
start = end
allLines.append(header)
allLines.extend(datalines)
return allLines
def _load_style_substitutions(self):
"""
Loads Italian, Mexican, South Asian, vegan, AND vegetarian text files into fields
"""
# TODO: I feel really bad about the use of copied code, so a helper function could be good to write sometime.
mexican_to_italian = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#mexican_to_italian", "#end_mexican_to_italian")
mexican_to_asian = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#mexican_to_asian", "#end_mexican_to_asian")
asian_to_italian = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#asian_to_italian", "#end_asian_to_italian")
asian_to_mexican = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#asian_to_mexican", "#end_asian_to_mexican")
italian_to_mexican = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#italian_to_mexican", "#end_italian_to_mexican")
italian_to_asian = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#italian_to_asian", "#end_italian_to_asian")
italian_spices_subs = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#italian_spices_subs", "#end_italian_spices_subs")
italian_spices = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#italian_spices_subs", "#end_italian_spices_subs")
asian_spices = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#asian_spices", "#end_asian_spices")
asian_spices_subs = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#asian_spices_subs", "#end_asian_spices_subs")
mexican_spices = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#mexican_spices", "#end_mexican_spices")
mexican_spices_subs = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#mexican_spices_subs", "#end_mexican_spices_subs")
neutral_to_asian = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#neutral_to_asian", "#end_neutral_to_asian")
neutral_to_mexican = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#neutral_to_mexican", "#end_neutral_to_mexican")
neutral_to_italian = read_specific_lines(
util.relative_path("kb_data/style_substitutions.txt"),
"#neutral_to_italian", "#end_neutral_to_italian")
vegan_sub_list = read_txt_lines_into_list(
'kb_data/vegan_substitutions.txt')
vegetarian_sub_list = read_txt_lines_into_list(
'kb_data/vegetarian_substitutions.txt')
for raw_sub in italian_spices:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.italian_spices_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'italian'))
for raw_sub in italian_spices_subs:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.italian_spices_subs.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'italian'))
for raw_sub in asian_spices_subs:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.asian_spices_subs.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'asian'))
for raw_sub in mexican_spices_subs:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.mexican_spices_subs.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'mexican'))
for spice in mexican_spices:
self.mexican_spices_list.append(self.lookup_single_food(spice))
for spice in asian_spices:
self.asian_spices_list.append(self.lookup_single_food(spice))
for raw_sub in mexican_to_italian:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.mexican_to_italian_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'mexican_to_italian'))
for raw_sub in mexican_to_asian:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.mexican_to_asian_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'mexican_to_asian'))
for raw_sub in asian_to_italian:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.asian_to_italian_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'asian_to_italian'))
for raw_sub in asian_to_mexican:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.asian_to_mexican_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'asian_to_mexican'))
for raw_sub in italian_to_asian:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.italian_to_asian_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'italian_to_asian'))
for raw_sub in italian_to_mexican:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.italian_to_mexican_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'italian_to_mexican'))
for raw_sub in vegan_sub_list:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.vegan_substitutions.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'vegan'))
for raw_sub in vegetarian_sub_list:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.vegetarian_substitutions.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'vegetarian'))
for raw_sub in neutral_to_italian:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.neutral_to_italian_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'neutral_to_italian'))
for raw_sub in neutral_to_asian:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.neutral_to_asian_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'neutral_to_asian'))
for raw_sub in neutral_to_mexican:
parsed_in_out = [thing.strip() for thing in raw_sub.split('=')]
if len(parsed_in_out) != 2:
util.warning('Incorrect substitution string: ' + raw_sub)
continue
self.neutral_to_mexican_list.append(
self._format_raw_sub(parsed_in_out[0], parsed_in_out[1],
'neutral_to_mexican'))
def filter_and_display_upload_results(
upload_results: typing.Sequence[UploadResult],
cve_threshold=7,
ignore_if_triaged=True,
) -> typing.Iterable[typing.Tuple[UploadResult, int]]:
# we only require the analysis_results for now
results_without_components = []
results_below_cve_thresh = []
results_above_cve_thresh = []
for upload_result in upload_results:
result = upload_result.result
components = result.components()
if not components:
results_without_components.append(upload_result)
continue
greatest_cve = -1
for component in components:
vulnerabilities = filter(lambda v: not v.historical(),
component.vulnerabilities())
if ignore_if_triaged:
vulnerabilities = filter(lambda v: not v.has_triage(),
vulnerabilities)
greatest_cve_candidate = highest_major_cve_severity(
vulnerabilities)
if greatest_cve_candidate > greatest_cve:
greatest_cve = greatest_cve_candidate
if greatest_cve >= cve_threshold:
results_above_cve_thresh.append((upload_result, greatest_cve))
continue
else:
results_below_cve_thresh.append((upload_result, greatest_cve))
continue
if results_without_components:
warning(
f'Protecode did not identify components for {len(results_without_components)}:\n'
)
for result in results_without_components:
print(result.result.display_name())
print('')
def render_results_table(
upload_results: typing.Sequence[typing.Tuple[UploadResult, int]]):
header = ('Component Name', 'Greatest CVE')
results = sorted(upload_results, key=lambda e: e[1])
result = tabulate.tabulate(
map(lambda r: (r[0].result.display_name(), r[1]), results),
headers=header,
tablefmt='fancy_grid',
)
print(result)
if results_below_cve_thresh:
info(
f'The following components were below configured cve threshold {cve_threshold}'
)
render_results_table(upload_results=results_below_cve_thresh)
print('')
if results_above_cve_thresh:
warning('The following components have critical vulnerabilities:')
render_results_table(upload_results=results_above_cve_thresh)
return results_above_cve_thresh
def grade(criteria, submissions, filename,
assume_missing=False, late_check=True):
found = []
num_missing = 0
total = criteria.total_points
for f in criteria.files:
crit_dir, crit_name = os.path.split(f.path)
for s in submissions:
sub_dir, sub_name = os.path.split(s)
if crit_name == sub_name:
found.append(f)
break
else:
util.warning("could not find file '{}'".format(f.path))
if len(submissions) < 1:
continue
if not assume_missing:
# find the submission directory (it could be the
# current working directory, but maybe not)
submission_dir, _ = os.path.split(submissions[0])
if not submission_dir:
submission_dir = os.path.abspath(os.curdir)
choices = [f for f in os.listdir(submission_dir)
if os.path.isfile(os.path.join(submission_dir, f))]
choices.append("skip grading this submission now")
choices.append("mark the file as missing")
util.info("this student may have named the file incorrectly")
# we prompt the grader for zero or one choice
got = prompt(choices, '1')
got = got[0]
if got == len(choices) - 1:
# declare the file missing
num_missing += 1
continue
elif got == len(choices) - 2:
util.info("skipping this submission")
util.exit(util.EXIT_WITH_DEFER)
else:
# get absolute path to the old and new files
sname = choices[got]
opath = os.path.join(submission_dir, sname)
npath = os.path.join(submission_dir, crit_name)
try:
os.rename(opath, npath)
except:
util.error("error renaming incorrectly named file")
util.print_traceback()
util.exit(util.ERR_GRADING_MISC)
found.append(f)
out = io.StringIO()
try:
for f in criteria.files:
out.write(util.heading("{} [{} points]".format(f, f.point_value),
level=2))
if f not in found:
total -= f.point_value
out.write("-{}\tnot submitted\n".format(f.point_value))
out.write("\n\n")
continue
util.info("running tests for " + str(f))
points_taken = 0
points_taken += write_results(out, f.run_tests())
if late_check:
file_stat = os.stat(f.path)
mtime = datetime.datetime.fromtimestamp(file_stat.st_mtime)
mult = criteria.get_late_penalty(mtime)
late_penalty = f.point_value * mult
if late_penalty != 0:
util.warning("taking {}% late penalty".format(mult * 100))
adjusted = min(f.point_value - points_taken, late_penalty)
out.write("-{}\tsubmitted late\n".format(adjusted))
points_taken += adjusted
total -= min(f.point_value, points_taken)
out.write("\n")
out.write("\nTotal: {}\n".format(total))
except KeyboardInterrupt:
out.close()
util.warning("stopping (received interrupt)")
util.exit(util.ERR_INTERRUPTED)
except:
out.close()
util.exit(util.ERR_GRADING_MISC)
with open(filename, 'w') as f:
out.seek(0)
f.write(out.read())
return num_missing
continue
f = open(p)
data = f.read()
f.close()
if data:
print(
"-- command %s (note: suppressed by default, "
"see sandbox dir for log files) --" % (type))
print "--\n%s--\n" % data
test_result = cmd_object.result
if not test_result:
break
if not interpolated_variables:
warning('no substitutions found. Fetched root ignored?')
# Remove the temporary directory.
if is_temp:
if shell.execute(['rm', '-rf', sandbox]) != 0:
note('unable to remove sandbox dir %r' % path)
return test_result, command_objects
def get_best_match(builds, name, key=lambda x: x):
builds = list(builds)
builds.sort(key=key)
if name is None and builds:
return builds[-1]
sdir = config["remote_dir"] + "/" + sub_dir
util.info("Looking in %s" % sdir)
source.cwd(sdir)
listing = source.list(config["regex"])
filecnt = 0
for info in listing:
filename = info[0]
size = info[1]
util.info("Looking at file: %s" % filename)
# Ingore exceptions because sometimes FTP mod time failed when new files were being uploaded
mod_time = source.modtime(filename)
if mod_time is None:
util.warning(
"Could not get mod time of %s, skipping file" %
filename)
continue
local_filename = mod_time.strftime(config["prefix"])
local_filename += filename if options.source != "newsscope" else filename.split(
"/")[-1] + ".xml.gz"
if options.source == "bberg2" and filename.count(".enc") > 0:
local_filename = local_filename.replace(".enc", "")
# Add a hash of the timestamp for uniqueness/detecting modified files
hash = hashlib.md5()
hash.update(mod_time.__str__())
hash.update(size.__str__())
local_filename += ".%s" % hash.hexdigest()[0:8]
local_path = "%s/%s/%s/%s/%s" % (
os.environ["DATA_DIR"], config["local_dir"],
def _check_http_code(self, result, url):
if result.status_code < 200 or result.status_code >= 300:
warning('{c} - {m}: {u}'.format(c=result.status_code, m=result.content, u=url))
raise RuntimeError()
def verify_backups(self, only='') -> None:
"""Verify all, or only the latest, backup(s) in this repository.
Iterate through every 'new' and 'old' entry in every (or latest)
backup, and verify that (1) the hash exists, and (2) its contents
matches its name.
"""
if only == '':
bkup_list = self.all_backups
else:
bkup_list = [only]
num_backups = len(bkup_list)
# entries = 0
num_files = 0
hits = 0
all_hashes: Set[str] = set()
good: Set[str] = set()
bad: Set[str] = set()
missing: Set[str] = set()
for b in bkup_list:
util.msg(f'Checking: {b}')
bkup = os.path.join(self.backups, b)
with open(bkup, 'rt') as f:
line_num = 0
saw_end = False
for line in f:
line_num += 1
if line_num == 1 and not line.startswith('start'):
util.fatal(f'Backup {bkup} corrupted. No start.')
if line[0:3] in ('new', 'old'):
num_files += 1
flds = line.split('\t')
h = flds[1]
this_file = flds[3]
all_hashes.add(h)
if h in good:
hits += 1
# continue
elif h in bad:
hits += 1
util.error(f'invalid hash {h} for {this_file}')
# continue
else:
# We haven't seen this hash before
(d, ff) = self.fname_from_hash(h)
if self.find_file(os.path.join(d, ff)):
if _verify_hash(os.path.join(self.objects, d),
ff):
good.add(h)
else:
bad.add(h)
t = this_file
util.error(f'invalid hash {h} for {t}')
else:
missing.add(h)
util.error(f'missing {h} for {this_file}')
else:
# print(line_num, line)
# This should be a trailer line
if line.startswith('end'):
saw_end = True
if not saw_end:
util.warning(f'Backup {bkup} has no end marker')
if len(all_hashes) != len(good) + len(bad) + len(missing):
util.fatal(f'hash bug: {len(all_hashes)} \
{len(good)} {len(bad)} {len(missing)}')
util.msg('Verify results:')
util.msg(f'backups checked = {num_backups}')
util.msg(f'files checked = {num_files}')
util.msg(f'invalid = {len(bad)}')
util.msg(f'missing = {len(missing)}')
util.msg(f'cache hits = {hits}')
if not os.path.exists(p):
continue
f = open(p)
data = f.read()
f.close()
if data:
print ("-- command %s (note: suppressed by default, "
"see sandbox dir for log files) --" % (type))
print "--\n%s--\n" % data
test_result = cmd_object.result
if not test_result:
break
if not interpolated_variables:
warning('no substitutions found. Fetched root ignored?')
# Remove the temporary directory.
if is_temp:
if shell.execute(['rm', '-rf', sandbox]) != 0:
note('unable to remove sandbox dir %r' % path)
return test_result, command_objects
def get_best_match(builds, name, key=lambda x: x):
builds = list(builds)
builds.sort(key=key)
if name is None and builds:
return builds[-1]
def process_results(self, results):
# collect pipelines by concourse target (concourse_cfg, team_name) as key
concourse_target_results = {}
for result in results:
definition_descriptor = result.definition_descriptor
concourse_target_key = definition_descriptor.concourse_target_key()
if concourse_target_key not in concourse_target_results:
concourse_target_results[concourse_target_key] = set()
concourse_target_results[concourse_target_key].add(result)
for concourse_target_key, concourse_results in concourse_target_results.items():
# TODO: implement eq for concourse_cfg
concourse_cfg, concourse_team = next(iter(
concourse_results)).definition_descriptor.concourse_target()
concourse_results = concourse_target_results[concourse_target_key]
concourse_api = client.from_cfg(
concourse_cfg=concourse_cfg,
team_name=concourse_team,
)
# find pipelines to remove
deployed_pipeline_names = set(map(
lambda r: r.definition_descriptor.pipeline_name, concourse_results
))
pipelines_to_remove = set(concourse_api.pipelines()) - deployed_pipeline_names
for pipeline_name in pipelines_to_remove:
info('removing pipeline: {p}'.format(p=pipeline_name))
concourse_api.delete_pipeline(pipeline_name)
# trigger resource checks in new pipelines
self._initialise_new_pipeline_resources(concourse_api, concourse_results)
# order pipelines alphabetically
pipeline_names = list(concourse_api.pipelines())
pipeline_names.sort()
concourse_api.order_pipelines(pipeline_names)
# evaluate results
failed_descriptors = [
d for d in results
if not d.deploy_status & DeployStatus.SUCCEEDED
]
failed_count = len(failed_descriptors)
info('Successfully replicated {d} pipeline(s)'.format(d=len(results) - failed_count))
if failed_count == 0:
return True
warning('Errors occurred whilst replicating {d} pipeline(s):'.format(
d=failed_count,
)
)
all_notifications_succeeded = True
for failed_descriptor in failed_descriptors:
warning(failed_descriptor.definition_descriptor.pipeline_name)
try:
self._notify_broken_definition_owners(failed_descriptor)
except Exception:
warning('an error occurred whilst trying to send error notifications')
traceback.print_exc()
all_notifications_succeeded = False
# signall error only if error notifications failed
return all_notifications_succeeded
# the current working directory at the time socrates started
_cwd = os.getcwd()
# the absolute path to this config.py file while running
_conf = os.path.abspath(inspect.getfile(inspect.currentframe()))
# SOCRATES_DIR should contain the running socrates.py file
SOCRATES_DIR, _ = os.path.split(_conf)
SOCRATES_CONFIG = SOCRATES_DIR + os.sep + 'socrates.ini'
_parser = configparser.ConfigParser()
if os.path.isfile(SOCRATES_CONFIG):
_parser.read(SOCRATES_CONFIG)
if len(_parser) < 2:
util.warning("found config file, but it looks incomplete")
hooks_dir = _parser.get('socrates', 'hooks_dir',
fallback=SOCRATES_DIR + os.sep + 'hooks')
scripts_dir = _parser.get('socrates', 'scripts_dir',
fallback=SOCRATES_DIR + os.sep + 'scripts')
static_dir = _parser.get('socrates', 'static_dir',
fallback=SOCRATES_DIR + os.sep + 'static')
dropbox_dir = _parser.get('socrates', 'dropbox_dir',
fallback=SOCRATES_DIR + os.sep + 'dropbox')
criteria_dir = _parser.get('socrates', 'criteria_dir',
fallback=SOCRATES_DIR + os.sep + 'criteria')
from datetime import timedelta as _td
if _parser.has_option('socrates', 'grace_period'):
_grace_str = _parser.get('socrates', 'grace_period')
def _notify_broken_definition_owners(self, failed_descriptor):
definition_descriptor = failed_descriptor.definition_descriptor
main_repo = definition_descriptor.main_repo
github_cfg = github_cfg_for_hostname(self._cfg_set, main_repo['hostname'])
github_api = _create_github_api_object(github_cfg)
repo_owner, repo_name = main_repo['path'].split('/')
githubrepobranch = GitHubRepoBranch(
github_config=github_cfg,
repo_owner=repo_owner,
repo_name=repo_name,
branch=main_repo['branch'],
)
repo_helper = GitHubRepositoryHelper.from_githubrepobranch(
githubrepobranch=githubrepobranch,
)
codeowners_enumerator = CodeownersEnumerator()
codeowners_resolver = CodeOwnerEntryResolver(github_api=github_api)
recipients = set(codeowners_resolver.resolve_email_addresses(
codeowners_enumerator.enumerate_remote_repo(github_repo_helper=repo_helper)
))
# in case no codeowners are available, resort to using the committer
if not recipients:
head_commit = repo_helper.repository.commit(main_repo['branch'])
user_ids = {
user_info.get('login')
for user_info
in (head_commit.committer, head_commit.author)
if user_info.get('login')
}
for user_id in user_ids:
user = github_api.user(user_id)
if user.email:
recipients.add(user.email)
# if there are still no recipients available print a warning
if not recipients:
warning(textwrap.dedent(
f"""
Unable to determine recipient for pipeline '{definition_descriptor.pipeline_name}'
found in branch '{main_repo['branch']}' ({main_repo['path']}). Please make sure that
CODEOWNERS and committers have exposed a public e-mail address in their profile.
"""
))
else:
info(f'Sending notification e-mail to {recipients} ({main_repo["path"]})')
email_cfg = self._cfg_set.email()
_send_mail(
email_cfg=email_cfg,
recipients=recipients,
subject='Your pipeline definition in {repo} is erroneous'.format(
repo=main_repo['path'],
),
mail_template=(
f"The pipeline definition for pipeline '{definition_descriptor.pipeline_name}' "
f" on branch '{main_repo['branch']}' contains errors.\n\n"
f"Error details:\n{str(failed_descriptor.error_details)}"
)
)
def bail(self, error):
self.ctx.getConfig().purge()
util.warning(self.ctx, error)
def mfclean(options):
clark_options = {}
clark_options["gain"] = options.gain
clark_options["iterations"] = options.iterations
clark_options["cycle_speedup"] = options.cycle_speedup
max_baseline = options.max_baseline if options.max_baseline > 0.0 else \
10000.0
processor_options = {}
processor_options["processor"] = options.processor
processor_options["w_max"] = max_baseline
processor_options["padding"] = 1.0
processor_options["image"] = options.image
processor_options["threads"] = options.threads
processor_options["weighttype"] = options.weighttype
processor_options["rmode"] = options.rmode
processor_options["noise"] = options.noise
processor_options["robustness"] = options.robustness
processor_options["profile"] = options.profile
processor = processors.create_data_processor(options.ms, processor_options)
channel_freq = processor.channel_frequency()
channel_width = processor.channel_width()
max_freq = numpy.max(channel_freq)
image_size = 2.0 * util.full_width_half_max(70.0, max_freq)
# TODO: Cyril mentioned above image size estimation is too conservative.
# Need to check this and find a better estimate if necessary. For now, will
# just multiply estimated FOV by 2.0.
image_size *= 2.0
(n_px, delta_px) = util.image_configuration(image_size, max_freq,
max_baseline)
util.notice("image configuration:")
util.notice(" size: %d x %d pixel" % (n_px, n_px))
util.notice(" angular size: %.2f deg" % (image_size * 180.0 / numpy.pi))
util.notice(" angular resolution @ 3 pixel/beam: %.2f arcsec/pixel" %
(3600.0 * delta_px * 180.0 / numpy.pi))
# TODO: Need to implement support for multiple channel images. Currently,
# all data channels are combined into a single MFS image per correlation.
image_shape = (1, 4, n_px, n_px)
image_coordinates = pyrap.images.coordinates.coordinatesystem(
casaimwrap.make_coordinate_system(image_shape[2:],
[delta_px, delta_px],
processor.phase_reference(),
channel_freq, channel_width))
n_model = 1
# TODO: Check code for n_model > 1!
assert (n_model == 1)
# Comment from CASA source code:
#
# Set to search for peak in I^2+Q^2+U^2+V^2 domain or each stokes plane
# seperately. Ignored for hogbom and msclean for now.
# join_stokes = False
join_stokes = True
# Compute approximate PSFs.
util.notice("computing approximate point spread functions...")
psf = [None for i in range(n_model)]
beam = [None for i in range(n_model)]
for i in range(n_model):
psf[i] = processor.point_spread_function(image_coordinates,
image_shape)
fit = casaimwrap.fit_gaussian_psf(image_coordinates.dict(), psf[i])
assert (fit["ok"])
beam[i] = BeamParameters((fit["major"] * numpy.pi) / (3600.0 * 180.0),
(fit["minor"] * numpy.pi) / (3600.0 * 180.0),
(fit["angle"] * numpy.pi) / 180.0)
util.notice(
"model %d/%d: major axis: %f arcsec, minor axis: %f arcsec,"
" position angle: %f deg" % (i, n_model - 1, abs(
fit["major"]), abs(fit["minor"]), fit["angle"]))
# Validate PSFs.
(min_psf, max_psf, max_psf_outer, psf_patch_size, max_sidelobe) = \
validate_psf(image_coordinates, psf, beam)
clark_options["psf_patch_size"] = psf_patch_size
updated = [False for i in range(n_model)]
weight = [None for i in range(n_model)]
model = [numpy.zeros(image_shape) for i in range(n_model)]
delta = [numpy.zeros(image_shape) for i in range(n_model)]
residual = [numpy.zeros(image_shape) for i in range(n_model)]
if join_stokes:
iterations = numpy.zeros((n_model, 1, image_shape[0]))
stokes = ["JOINT"]
cr_slices = [slice(None)]
else:
iterations = numpy.zeros((n_model, image_shape[1], image_shape[0]))
stokes = image_coordinates.get_coordinate("stokes").get_stokes()
cr_slices = [slice(i, i + 1) for i in range(4)]
cycle = 0
diverged = False
absmax = options.threshold
previous_absmax = 1e30
while absmax >= options.threshold and numpy.max(iterations) \
< options.iterations and (cycle == 0 or any(updated)):
util.notice(">> starting major cycle: %d <<" % cycle)
# Comment from CASA source code:
#
# Make the residual images. We do an incremental update for cycles after
# the first one. If we have only one model then we use convolutions to
# speed the processing
util.notice("computing residuals...")
# TODO: If n_models > 1, need to compute residuals from the sum of
# the degridded visibilities (see LofarCubeSkyEquation.cc).
assert (n_model == 1)
if cycle == 0:
# Assuming the initial models are zero, the residual visibilities
# equal the observed visibilities and therefore we only need to
# grid them.
for i in range(n_model):
residual[i], weight[i] = processor.grid(
image_coordinates, image_shape,
processors.Normalization.FLAT_NOISE)
else:
for i in range(n_model):
if updated[i]:
residual[i], weight[i] = \
processor.residual(image_coordinates, model[i],
processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_NOISE)
updated[i] = False
# Compute residual statistics.
(absmax, resmin, resmax) = max_field(residual, weight)
# Print some statistics.
for i in range(n_model):
util.notice("model %d/%d: min residual: %f, max residual: %f" %
(i, n_model - 1, resmin[i], resmax[i]))
util.notice("peak residual: %f" % absmax)
# Comment from CASA source code:
#
# Check if absmax is 5% above its previous value.
#
# TODO: Value used does not look like 5%?
if absmax >= 1.000005 * previous_absmax:
diverged = True
break
# Store absmax of this major cycle for later reference.
previous_absmax = absmax
# Check stop criterium.
if absmax < options.threshold:
break
# TODO: What is this really used for? And does the max weight indeed
# correspond to sensitivity in Jy/beam?
if cycle == 0:
max_weight = 0.0
for i in range(n_model):
max_weight = max(max_weight, numpy.max(weight[i]))
util.notice("maximum sensitivity: %f Jy/beam" %
(1.0 / numpy.sqrt(max_weight)))
# Comment from CASA source code:
#
# Calculate the threshold for this cycle. Add a safety factor
#
# fractionOfPsf controls how deep the cleaning should go.
# There are two user-controls.
# cycleFactor_p : scale factor for the PSF sidelobe level.
# 1 : clean down to the psf sidelobe level
# <1 : go deeper
# >1 : shallower : stop sooner.
# Default : 1.5
# cycleMaxPsfFraction_p : scale factor as a fraction of the PSF peak
# must be 0.0 < xx < 1.0 (obviously)
# Default : 0.8
fraction_of_psf = min(options.cycle_max_psf_fraction,
options.cycle_factor * max_sidelobe)
if fraction_of_psf > 0.8:
util.warning(
"PSF fraction for threshold computation is too"
" high: %f. Forcing to 0.8 to ensure that the threshold is"
" smaller than the peak residual!" % fraction_of_psf)
fraction_of_psf = 0.8 # painfully slow!
# Update cycle threshold.
cycle_threshold = max(0.95 * options.threshold,
fraction_of_psf * absmax)
clark_options["cycle_threshold"] = cycle_threshold
util.notice("minor cycle threshold max(0.95 * %f, peak residual * %f):"
" %f" %
(options.threshold, fraction_of_psf, cycle_threshold))
# Execute the minor cycle (Clark clean) for each channel of each model.
util.notice("starting minor cycle...")
for i in range(n_model):
if max(abs(resmin[i]), abs(resmax[i])) < cycle_threshold:
util.notice("model %d/%d: peak residual below threshold" %
(i, n_model - 1))
continue
if max_psf[i] <= 0.0:
util.warning("model %d/%d: point spread function negative or"
" zero" % (i, n_model - 1))
continue
# Zero the delta image for this model.
delta[i].fill(0.0)
for (cr, cr_slice) in enumerate(cr_slices):
for ch in range(len(residual[i])):
# TODO: The value of max_weight is only updated during
# cycle 0. Is this correct?
#
assert (len(weight[i].shape) == 2
and weight[i].shape[:2] == residual[i].shape[:2])
plane_weight = numpy.sqrt(weight[i][ch, cr_slice] /
max_weight)
if numpy.any(plane_weight > 0.01):
weight_mask = numpy.ones((residual[i].shape[2:]))
else:
weight_mask = numpy.zeros((residual[i].shape[2:]))
# Call CASA Clark clean implementation (minor cycle).
# TODO: When cleaning each Stokes parameter separately,
# the PSF of Stokes I is used for all others as well?
#
# Comment from CASA source code:
#
# We only want the PSF for the first polarization so we
# iterate over polarization LAST.
#
result = casaimwrap.clark_clean(
psf[i][ch, 0, :, :], residual[i][ch, cr_slice, :, :],
weight_mask, iterations[i, cr, ch], clark_options)
if result["iterations"] > iterations[i, cr, ch]:
updated[i] = True
delta[i][ch, cr_slice, :, :] = result["delta"]
iterations[i, cr, ch] = result["iterations"]
else:
assert (numpy.all(result["delta"] == 0.0))
util.notice("model %d/%d: stokes: %s, cleaned: %f Jy, "
"iterations per channel: %s" %
(i, n_model - 1, stokes[cr],
numpy.sum(delta[i][ch, cr_slice, :, :]),
str(iterations[i, cr, :])))
# Update model images if required.
for i in range(n_model):
if updated[i]:
model[i] += delta[i]
# Update major cycle counter.
cycle += 1
if any(updated):
util.notice("finalizing residual images for all fields...")
for i in range(n_model):
if updated[i]:
residual[i], weight[i] = processor.residual(
image_coordinates, model[i],
processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_NOISE)
(absmax, resmin, resmax) = max_field(residual, weight)
# Print some statistics.
for i in range(n_model):
util.notice("model %d/%d: min residual: %f, max residual: %f" %
(i, n_model - 1, resmin[i], resmax[i]))
util.notice("peak residual: %f" % absmax)
else:
util.notice("residual images for all fields are up-to-date...")
# Store output images.
util.notice("storing average response...")
util.store_image(options.image + ".response", image_coordinates,
processor.response(image_coordinates, image_shape))
util.notice("storing model images...")
for i in range(n_model):
util.store_image(options.image + ".model.flat_noise",
image_coordinates, model[i])
util.store_image(
options.image + ".model", image_coordinates,
processor.normalize(image_coordinates, model[i],
processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_GAIN))
util.notice("storing residual images...")
for i in range(n_model):
util.store_image(options.image + ".residual.flat_noise",
image_coordinates, residual[i])
util.store_image(
options.image + ".residual", image_coordinates,
processor.normalize(image_coordinates, residual[i],
processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_GAIN))
util.notice("storing restored images...")
for i in range(n_model):
restored = restore_image(image_coordinates.dict(), model[i],
residual[i], beam[i])
util.store_image(options.image + ".restored.flat_noise",
image_coordinates, restored)
util.store_image(
options.image + ".restored", image_coordinates,
processor.normalize(image_coordinates, restored,
processors.Normalization.FLAT_NOISE,
processors.Normalization.FLAT_GAIN))
# Print some statistics.
for i in range(n_model):
util.notice(
"model %d/%d: clean flux: %f, residual rms: %f" %
(i, n_model - 1, numpy.sum(model[i]), numpy.std(residual[i])))
if diverged:
util.error("clean diverged.")
elif absmax < options.threshold:
util.notice("clean converged.")
else:
util.warning("clean did not reach threshold: %f Jy." %
options.threshold)
