def test_warnings(self):
with warnings.catch_warnings():
logging.captureWarnings(True)
try:
warnings.filterwarnings("always", category=UserWarning)
file = io.StringIO()
h = logging.StreamHandler(file)
logger = logging.getLogger("py.warnings")
logger.addHandler(h)
warnings.warn("I'm warning you...")
logger.removeHandler(h)
s = file.getvalue()
h.close()
self.assertTrue(s.find("UserWarning: I'm warning you...\n") > 0)
#See if an explicit file uses the original implementation
file = io.StringIO()
warnings.showwarning("Explicit", UserWarning, "dummy.py", 42,
file, "Dummy line")
s = file.getvalue()
file.close()
self.assertEqual(s,
"dummy.py:42: UserWarning: Explicit\n Dummy line\n")
finally:
logging.captureWarnings(False)
def __init__(self, pin=None, pull_up=False):
if pin in (2, 3) and not pull_up:
raise InputDeviceError(
'GPIO pins 2 and 3 are fitted with physical pull up '
'resistors; you cannot initialize them with pull_up=False'
)
# _pull_up should be assigned first as __repr__ relies upon it to
# support the case where __repr__ is called during debugging of an
# instance that has failed to initialize (due to an exception in the
# super-class __init__)
self._pull_up = pull_up
super(InputDevice, self).__init__(pin)
self._active_edge = GPIO.FALLING if pull_up else GPIO.RISING
self._inactive_edge = GPIO.RISING if pull_up else GPIO.FALLING
self._active_state = GPIO.LOW if pull_up else GPIO.HIGH
self._inactive_state = GPIO.HIGH if pull_up else GPIO.LOW
pull = GPIO.PUD_UP if pull_up else GPIO.PUD_DOWN
try:
# NOTE: catch_warnings isn't thread-safe but hopefully no-one's
# messing around with GPIO init within background threads...
with warnings.catch_warnings(record=True) as w:
GPIO.setup(pin, GPIO.IN, pull)
# The only warning we want to squash is a RuntimeWarning that is
# thrown when setting pins 2 or 3. Anything else should be replayed
for warning in w:
if warning.category != RuntimeWarning or pin not in (2, 3):
warnings.showwarning(
warning.message, warning.category, warning.filename,
warning.lineno, warning.file, warning.line
)
except:
self.close()
raise
def sigma_envelopes(self, steps = 1):
"""
Envelopes and twiss-functions from sigma-matrix mathod
"""
# initials
bx,ax,gx,epsx = PARAMS['twiss_x_i']()
by,ay,gy,epsy = PARAMS['twiss_y_i']()
bz,az,gz,epsz = PARAMS['twiss_z_i']()
twv0 = NP.array([bx,ax,gx,by,ay,gy,bz,az,gz]) # twiss vector IN lattice
sg0 = Sigma(twv0,epsx,epsy,epsz) # sigma object IN lattice
# sigma envelopes as function of distance s
sigma_fun = Functions(('s','bx','ax','gax','by','ay','gy','bz','az','gz'))
for node in iter(self): # loop nodes
# sigma-matrices for a single node
sigmas = node.sigma_beam(steps = steps, sg = sg0)
# prep plot list of ftn's
for sg,s in sigmas:
v = sg.twiss() # twiss from Sigma object
flist = v.tolist()
sigma_fun.append(s,tuple(flist))
sg0 = sg # loop back nodes
# aperture check
if FLAGS['useaper']:
nbsigma = PARAMS['nbsigma']
if node.aperture != None:
aperture = node.aperture
sigx, sigxp, sigy, sigyp = node['sigxy']
si,sm,sf = node.position
if(aperture < nbsigma*sigx or aperture < nbsigma*sigy):
warnings.showwarning(
'{} sigma aperture hit @ s={:.1f} [m]'.format(nbsigma,sm),
UserWarning,'lattice.py',
'sigma_functions()')
return sigma_fun
def __init__(self, pin=None, active_high=True, initial_value=False):
self._active_high = active_high
super(OutputDevice, self).__init__(pin)
self._active_state = GPIO.HIGH if active_high else GPIO.LOW
self._inactive_state = GPIO.LOW if active_high else GPIO.HIGH
try:
# NOTE: catch_warnings isn't thread-safe but hopefully no-one's
# messing around with GPIO init within background threads...
with warnings.catch_warnings(record=True) as w:
# This is horrid, but we can't specify initial=None with setup
if initial_value is None:
GPIO.setup(pin, GPIO.OUT)
else:
GPIO.setup(pin, GPIO.OUT, initial=
[self._inactive_state, self._active_state][bool(initial_value)])
GPIO.setup(pin, GPIO.OUT)
# The only warning we want to squash is a RuntimeWarning that is
# thrown when setting pins 2 or 3. Anything else should be replayed
for warning in w:
if warning.category != RuntimeWarning or pin not in (2, 3):
warnings.showwarning(
warning.message, warning.category, warning.filename,
warning.lineno, warning.file, warning.line
)
except:
self.close()
raise
def test2(input_file):
print('---------------------------------TEST2')
with open(input_file,'r') as fileobject:
try:
indat = yaml.load(fileobject)
except Exception as ex:
warnings.showwarning(
'InputError: {} - STOP'.format(str(ex)),
UserWarning,
'lattice_generator.py',
'factory()',
)
sys.exit(1)
fileobject.close()
ky = indat.keys()
for k in ky:
print()
print(k)
klist = indat[k]
print(klist)
if not klist: continue
nlist = flatten(klist)
if k == 'LATTICE':
N = nlist[0]
plist = nlist[1:]
qlist = plist.copy()
for i in range(N-1):
qlist += plist
nlist=qlist
print(nlist)
def Normal(**kwargs):
'''
Devuelve la instancia de Union de tipo JOIN
parametros => diccionario de tipo {'alias':ClaseOTD}
'''
import warnings
#advertencia de componente deprecado
warnings.showwarning('Union.Normal(): deprecado, recurrir directamente al constructor de la clase!'
, DeprecationWarning
, __file__
, 0
)
# si el parametro NO es un diccionario
if type(kwargs) is not dict:
raise Exception(Union.NOMBRE_CLASE
+ ".Normal: El parametro se debe pasar "
+ "{'alias':ClaseOTD}!"
)
return Union(cTipo = Union.NORMAL, **kwargs)
def new_func(*args, **kwargs):
warnings.showwarning(
"Call to deprecated function {}.".format(func.__name__),
category=DeprecationWarning,
filename=func.__code__.co_filename,
lineno=func.__code__.co_firstlineno + 1
)
return func(*args, **kwargs)
def ignore_deprecation_warnings():
with warnings.catch_warnings(record=True) as warning_list: # catch all warnings
yield
# rethrow all warnings that were not DeprecationWarnings
for w in warning_list:
if not issubclass(w.category, DeprecationWarning):
warnings.showwarning(message=w.message, category=w.category, filename=w.filename, lineno=w.lineno, file=w.file, line=w.line)
def _handle_caught_warnings(caught_warnings, filename):
logger = logging.getLogger(__name__)
for warning in caught_warnings:
if warning.category == PILImage.DecompressionBombWarning:
logger.info('PILImage reported a possible DecompressionBomb'
' for file {}'.format(filename))
else:
warnings.showwarning(warning.message, warning.category,
warning.filename, warning.lineno)
def _read_config(rcfile):
"""Read configuration information from a file"""
global config
defaults = {'enable_deprecation_warning': str(True),
'ncpus': str(multiprocessing.cpu_count()),
'qindenton_url': 'http://virbo.org/ftp/QinDenton/hour/merged/latest/WGhour-latest.d.zip',
'omni2_url': 'http://virbo.org/ftp/OMNI/OMNI2/merged/latest/OMNI_OMNI2-latest.cdf.zip',
'leapsec_url': 'http://maia.usno.navy.mil/ser7/tai-utc.dat',
'psddata_url': 'http://spacepy.lanl.gov/repository/psd_dat.sqlite',
'support_notice': str(True),
'apply_plot_styles': str(True),
}
#Functions to cast a config value; if not specified, value is a string
str2bool = lambda x: x.lower() in ('1', 'yes', 'true', 'on')
caster = {'enable_deprecation_warning': str2bool,
'ncpus': int,
'support_notice': str2bool,
'apply_plot_styles': str2bool,
}
#SafeConfigParser deprecated in 3.2. And this is hideous, but...
if hasattr(ConfigParser, 'SafeConfigParser'):
cp_class = ConfigParser.SafeConfigParser
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings(
'always', 'The SafeConfigParser class has been renamed.*',
DeprecationWarning,
'^spacepy$') #configparser lies about source of warnings
ConfigParser.SafeConfigParser()
for this_w in w:
if isinstance(this_w.message, DeprecationWarning):
cp_class = ConfigParser.ConfigParser
else:
warnings.showwarning(this_w.message, this_w.category,
this_w.filename, this_w.lineno,
this_w.file, this_w.line)
else:
cp_class = ConfigParser.ConfigParser
cp = cp_class(defaults)
try:
successful = cp.read([rcfile])
except ConfigParser.Error:
successful = []
if successful: #New file structure
config = dict(cp.items('spacepy'))
else: #old file structure, wipe it out
cp = cp_class()
cp.add_section('spacepy')
with open(rcfile, 'w') as cf:
cp.write(cf)
for k in defaults:
if not k in config:
config[k] = defaults[k]
_write_defaults(rcfile, defaults)
for k in caster:
config[k] = caster[k](config[k])
def ready(self):
""" Performs an DB engine check, as we maintain some engine specific queries. """
if (connection.vendor not in settings.DSMR_SUPPORTED_DB_VENDORS):
# Temporary for backwards compatibility
warnings.showwarning(
_(
'Unsupported database engine "{}" active, '
'some features might not work properly'.format(connection.vendor)
),
RuntimeWarning, __file__, 0
)
def get_mandatory(attributes,key,item):
try:
res = attributes[key]
except KeyError:
warnings.showwarning(
'InputError: Mandatory attribute "{}" missing for element "{}" - STOP'.format(key,item),
UserWarning,
'lattice_generator.py',
'get_mandatory()',
)
sys.exit(1)
return res
def _read_image(file_path):
with warnings.catch_warnings(record=True) as caught_warnings:
im = PILImage.open(file_path)
for warning in caught_warnings:
if warning.category == PILImage.DecompressionBombWarning:
logger = logging.getLogger(__name__)
logger.info('PILImage reported a possible DecompressionBomb'
' for file {}'.format(file_path))
else:
warnings.showwarning(warning.message, warning.category,
warning.filename, warning.lineno)
return im
def __init__(self, pin=None):
super(OutputDevice, self).__init__(pin)
# NOTE: catch_warnings isn't thread-safe but hopefully no-one's messing
# around with GPIO init within background threads...
with warnings.catch_warnings(record=True) as w:
GPIO.setup(pin, GPIO.OUT)
# The only warning we want to squash is a RuntimeWarning that is thrown
# when setting pins 2 or 3. Anything else should be replayed
for warning in w:
if warning.category != RuntimeWarning or pin not in (2, 3):
warnings.showwarning(
warning.message, warning.category, warning.filename,
warning.lineno, warning.file, warning.line
)
def __init__(self, pin=None, active_high=True):
self._active_high = active_high
super(OutputDevice, self).__init__(pin)
self._active_state = GPIO.HIGH if active_high else GPIO.LOW
self._inactive_state = GPIO.LOW if active_high else GPIO.HIGH
try:
# NOTE: catch_warnings isn't thread-safe but hopefully no-one's
# messing around with GPIO init within background threads...
with warnings.catch_warnings(record=True) as w:
GPIO.setup(pin, GPIO.OUT)
# The only warning we want to squash is a RuntimeWarning that is
# thrown when setting pins 2 or 3. Anything else should be replayed
for warning in w:
if warning.category != RuntimeWarning or pin not in (2, 3):
warnings.showwarning(warning.message, warning.category,
warning.filename, warning.lineno,
warning.file, warning.line)
except:
self.close()
raise
def test_import_error_in_warning_logging():
"""
Regression test for https://github.com/astropy/astropy/issues/2671
This test actually puts a goofy fake module into ``sys.modules`` to test
this problem.
"""
class FakeModule(object):
def __getattr__(self, attr):
raise ImportError('_showwarning should ignore any exceptions '
'here')
log.enable_warnings_logging()
sys.modules['<test fake module>'] = FakeModule()
try:
warnings.showwarning(AstropyWarning('Regression test for #2671'),
AstropyWarning, '<this is only a test>', 1)
finally:
del sys.modules['<test fake module>']
def transform(self, samples):
components = [
'C' + str.zfill(x, 2)
for x in np.arange(self.components).astype('str')
]
timenames = select.getsamplingnames(samples)
subcolix = pd.MultiIndex.from_product([components, timenames],
names=['component', 'sample'])
ica_samples = pd.DataFrame(index=samples.index,
columns=subcolix,
dtype=samples.values.dtype)
chanlen = len(select.getchannelnames(samples))
with catch_warnings(record=True) as w:
simplefilter('ignore', UserWarning)
for index, sample in samples.iterrows():
self.update_status(len(samples))
data = sample.reshape(chanlen, -1)
ica = FastICA(max_iter=self.maxiter,
n_components=self.components)
ica.fit(data)
# FIXME:
# how do we get the same ordering (EEGlab is sorting by
# "mean projected variance")
# also we need to check the correct sign of the components
# this is the point where I realized ICA might not be useful at all
for compindex, component in enumerate(components):
ica_samples.loc[index].loc[
component].values[:] = ica.components_[compindex]
if w:
showwarning("recorded {0} warnings (non-convergence)".format(
len(w)))
return ica_samples.astype('float')
def iv__sim900__sweep_current__read_voltage(current_range, current_step,
bias_resistance,
measurement_delay):
if np.abs(current_applied_vec[0]) >= 0.01 or np.abs(
current_applied_vec[-1]) >= 0.01:
warnings.showwarning(
'The SIM928 can only output in the range [-10,10]mA. You have attempted to exceed this range.',
UserWarning, os.path.basename(__file__),
inspect.currentframe().f_back.f_lineno)
from instruments.srs_sim970 import SIM970
from instruments.srs_sim928 import SIM928
voltage_meter = SIM970(
'GPIB0::2',
7) #voltmeter = SIM970(visa_name = 'GPIB0::2', sim900port = 2)
voltage_source = SIM928('GPIB0::2', 5)
current_applied_vec = np.arange(current_range[0],
current_range[1] + current_step,
current_step)
#SIM928 max voltage = [-20,20]V
voltage_applied_vec = current_applied_vec * bias_resistance
if np.abs(voltage_applied_vec[0]) >= 20 or np.abs(
voltage_applied_vec[-1]) >= 20:
warnings.showwarning(
'The SIM928 can only output in the range [-20,20]V. You have attempted to exceed this range.',
UserWarning, os.path.basename(__file__),
inspect.currentframe().f_back.f_lineno)
voltage_read_vec = []
voltage_source.reset()
voltage_source.set_output(True)
for ii in range(len(voltage_applied_vec)):
voltage_source.set_voltage(voltage=voltage_applied_vec[ii])
time.sleep(measurement_delay)
voltage_read_vec.append(voltage_meter.read_voltage(channel=1))
voltage_source.set_output(False)
return current_applied_vec, voltage_read_vec
def __exit__(self, *exc_info):
"""Exit context manager, called at exit of block"""
retval = super(assertWarns, self).__exit__(*exc_info)
if exc_info[0] is not None: # Exception in handling, show all warnings
for w in self._log:
warnings.showwarning(w.message, w.category, w.filename,
w.lineno)
return retval
n_match = 0 # Number of matching warnings
msg_pat = re.compile(self._filterspec[1], re.I)
cat = self._filterspec[2]
mod_pat = self._filterspec[3]
matchall = not mod_pat #Empty pattern, match all modules
mod_pat = re.compile(mod_pat)
lineno = int(self._filterspec[4])
# show_warnings isn't given the module, just the filename,
# so find filenames of desired modules.
mod_files = []
for m in list(sys.modules):
if mod_pat.match(m) and hasattr(sys.modules[m], '__file__'):
fnl = sys.modules[m].__file__
if fnl is None:
continue
if fnl.lower().endswith(('.pyc', '.pyo')):
fnl = fnl[:-1]
mod_files.append(fnl)
for w in self._log:
if issubclass(w.category, cat) \
and (matchall or w.filename in mod_files) \
and msg_pat.match(str(w.message)) and lineno in (0, w.lineno):
n_match += 1
else:
warnings.showwarning(w.message, w.category, w.filename,
w.lineno)
if self.requireWarning:
if n_match == 0:
self._testcase.fail('Warning not issued.')
elif n_match > 1:
self._testcase.fail('Warning issued {} times.'.format(n_match))
elif n_match:
self._testcase.fail('Warning was issued.')
def annotate_vcf(self, outfile, random=False, vaf_cutoff=None):
'''
Read in a VCF, add annotation and write it back out again.
'''
warningcount = 0
with open(outfile, 'w') as out_fh:
vcf_writer = vcf.Writer(out_fh, self.reader)
for record in self.reader:
if vaf_cutoff is not None:
vafs = [ self.calculate_vaf(call, record) for call in record.samples ]
fvafs = [ x for x in vafs if x is not None ]
if max(fvafs) < vaf_cutoff:
continue
if random:
record = self._randomise_record(record)
with catch_warnings(record=True) as warnlist:
newrecs = self._annotate_record(record)
for rec in newrecs:
vcf_writer.write_record(rec)
# Count AnnotationWarnings, show all others.
annwarns = 0
for wrn in warnlist:
if issubclass(wrn.category, AnnotationWarning):
annwarns += 1
else:
showwarning(wrn.message, wrn.category,
wrn.filename, wrn.lineno,
wrn.file, wrn.line)
if annwarns > 0:
warningcount += 1
if warningcount > 0:
sys.stderr.write("Detected AnnotationWarnings for %d variants.\n"
% warningcount)
def twiss_envelopes(self,steps=1):
"""
Calulate envelopes from initial twiss-vector with beta-matrices
"""
twfun = Functions(('s','bx','ax','gx','by','ay','gy','bz','az','gz'))
bx,ax,gx,epsx = PARAMS['twiss_x_i']()
by,ay,gy,epsy = PARAMS['twiss_y_i']()
bz,az,gz,epsz = PARAMS['twiss_z_i']()
twv0 = NP.array([bx,ax,gx,by,ay,gy,bz,az,gz]) # initial
B_matrix = NP.eye(9) # cumulated beta-matrix
for node in iter(self):
slices = node.make_slices(anz = steps)
means = []
s,sm,sf = node.position
for slice in slices:
beta_matrix = slice.beta_matrix()
B_matrix = NP.dot(beta_matrix,B_matrix)
twv = NP.dot(B_matrix,twv0) # track twiss-vector
s += slice.length
twfun.append(s,tuple(twv))
bx = twv[Ktw.bx]; ax = twv[Ktw.ax]; gx = twv[Ktw.gx]
by = twv[Ktw.by]; ay = twv[Ktw.ay]; gy = twv[Ktw.gy]
sigxy = (*sigmas(ax,bx,epsx),*sigmas(ay,by,epsy))
means.append(sigxy)
# means = NP.array(means)
means = NP.mean(means,axis=0)
# each node has its tuple of average sigmas
node['sigxy'] = tuple(means)
# aperture check
if FLAGS['useaper']:
nbsigma = PARAMS['nbsigma']
if node.aperture != None:
aperture = node.aperture
sigx, sigxp, sigy, sigyp = node['sigxy']
if(aperture < nbsigma*sigx or aperture < nbsigma*sigy):
warnings.showwarning(
'{} sigma aperture hit @ s={:.1f} [m]'.format(nbsigma,sm),
UserWarning,'lattice.py',
'twiss_functions()')
return twfun
def test_import_error_in_warning_logging():
"""
Regression test for https://github.com/astropy/astropy/issues/2671
This test actually puts a goofy fake module into ``sys.modules`` to test
this problem.
"""
class FakeModule(object):
def __getattr__(self, attr):
raise ImportError('_showwarning should ignore any exceptions '
'here')
log.enable_warnings_logging()
sys.modules['<test fake module>'] = FakeModule()
try:
warnings.showwarning(AstropyWarning('Regression test for #2671'),
AstropyWarning, '<this is only a test>', 1)
finally:
del sys.modules['<test fake module>']
def __init__(self, klass=None, model=None):
frame, filename, line_number, function_name, lines, index = inspect.stack()[1]
warnings.showwarning('Serializers are now directly implemented in models. Please use the model class directly instead"', DeprecationWarning,
filename, line_number)
self.local_path = None
self.remote_path = None
self.fitting_path = None
self.model_path = None
self.remote_model_path = None
self.model = model
self._fitting = None
if model is not None:
self.local_path = join(lore.env.models_dir, model.__module__, model.__class__.__name__)
self.remote_path = join(model.__module__, model.__class__.__name__)
elif klass is not None:
self.local_path = join(lore.env.models_dir, klass.__module__, klass.__name__)
self.remote_path = join(klass.__module__, klass.__name__)
else:
raise ValueError('You must pass name or model')
self.fitting = self.last_fitting()
def download(cls, fitting=None):
frame, filename, line_number, function_name, lines, index = inspect.stack(
)[1]
warnings.showwarning(
'Please start using explicit fitting number when downloading the model ex "Keras.download(10)". Default Keras.download() will be deprecated in 0.7.0',
DeprecationWarning, filename, line_number)
model = cls(None, None)
if not fitting:
fitting = model.last_fitting()
model.fitting = int(fitting)
try:
lore.io.download(model.remote_model_path(),
model.model_path(),
cache=True)
except botocore.exceptions.ClientError as e:
if e.response['Error']['Code'] == "404":
model.fitting = None
lore.io.download(model.remote_model_path(),
model.model_path(),
cache=True)
return cls.load(fitting)
def qryTransaction(self, contract=None, count=None):
"""
:param contract: 建议填写。但是对于部分交易所，此项不填可以返回所有成交。
:param count: 返回成交记录的条数
:return:
"""
params = {}
self.reqID += 1
if contract is not None:
params['contract'] = contract
else:
warnings.showwarning(":param contract: 建议填写。但是对于部分交易所，此项不填可以返回所有成交。")
if count is not None:
params['count'] = count
data = {'reqID': self.reqID,
'callback': self.onQryTransaction,
'params': params}
self.reqQueue.put(data)
def __ignore_warning(*args, **kwargs):
# Execute the code while recording all warnings
with warnings.catch_warnings(record=True) as ws:
# Catch all warnings of this type
warnings.simplefilter('always', warning)
# Execute the function
result = function(*args, **kwargs)
# Now that all code was executed and the warnings
# collected, re-send all warnings that are beyond our
# expected number of warnings
if count is not None:
for w in ws[count:]:
warnings.showwarning(
message=w.message,
category=w.category,
filename=w.filename,
lineno=w.lineno,
file=w.file,
line=w.line,
)
return result
def _runtime_warning_context(): # type: ignore
"""
Context manager which checks for RuntimeWarnings, specifically to
avoid "coroutine 'X' was never awaited" warnings being missed.
If RuntimeWarnings occur in the context a RuntimeError is raised.
"""
with warnings.catch_warnings(record=True) as _warnings:
yield
rw = [
'{w.filename}:{w.lineno}:{w.message}'.format(w=w)
for w in _warnings if w.category == RuntimeWarning
]
if rw:
raise RuntimeError('{} Runtime Warning{},\n{}'.format(
len(rw), '' if len(rw) == 1 else 's', '\n'.join(rw)))
# Propagate warnings to pytest
for msg in _warnings:
warnings.showwarning(msg.message, msg.category, msg.filename,
msg.lineno, msg.file, msg.line)
def get_number_and_suffix(string):
"""Return number and suffix of a string.
args:
string (str): String.
returns:
(tuple): number, suffix
examples:
```
>>> get_number_and_suffix('1khz')
> (1.0, 'khz')
```
"""
warnings.showwarning(
"get_number_and_suffix will be deprecated. Please use ekpmeasure.universal.get_number_and_suffix",
DeprecationWarning, '', 0)
return _get_number_and_suffix(string)
def __init__(self, owner = None, occupancy = None, tenure = None, address = None, longitude = None,
latitude = None, value = None, cost = None, area = None,
bedrooms = None, bathrooms = None, listed = False, damage_state = None,
building_stock = None):
"""
Keyword Arguments:
owner -- entities.Owner or subclass that represents building owner.
occupancy -- The buildings occupancy. DESaster currently supports SFR and mobile home.
address -- Building's address
longitude -- Building's longitude
latitude -- Building's latitude
value -- Building's value in $
cost -- Building's monthly cost in $ (e.g., mortgage or rent)
bedrooms -- Building's number of bedrooms
bathrooms -- Building's number of bathrooms
area -- Building's area in sf
listed -- Whether building is for rent or sale.
damage_state -- Building's damage state (e.g., HAZUS damage states)
building_stock -- The the building's associated building stock FilterStore
Modified Attributes:
self.damage_value -- Calculated using setStructuralDamageValueHAZUS()
Inheritance:
structures.Building
"""
Building.__init__(self, owner, occupancy, tenure, address, longitude,
latitude, value, cost, area,
listed, damage_state, building_stock)
self.bedrooms = bedrooms # Number of bedrooms in building
self.bathrooms = bathrooms # Number of bedrooms in building
# Verify that building dataframe has expected occupancy types
# Raise warning, if not (but continue with simulation)
if not occupancy.lower() in ('single family dwelling', 'mobile home'):
warnings.showwarning('Warning: SingleFamilyResidential not compatible with given occupancy type: {0}'.format(
occupancy.title()), DeprecationWarning, filename = sys.stderr,
lineno=661)
def field_limit(Bx, By, Bz, max_field_strength=1.5) -> bool:
""" Calculate the absolute field amplitude and check against the max_field_strength.
For use with QCoDeS oxford.MercuryiPS_VISA driver.
Inputs:
Bx, By, Bz (float): magnetic field components
Outputs:
bool: true, if sqrt(Bx^2+By^2+Bz^2) < max_field_strength; else false
"""
if max_field_strength > 1.5:
showwarning(
"Be aware that mu-metal shields are saturated by too large magnetic fields and will not work afterwards. "
"Are you sure you want to go to more than 1.5 T?",
ResourceWarning,
"cqed/cqed/custom_pysweep_functions/magnet",
20,
)
if np.sqrt(Bx**2 + By**2 + Bz**2) > max_field_strength:
return bool(False)
else:
return bool(True)
def _get_number_and_suffix(string):
"""Return number and suffix of a string. e.g. 1khz will return (1.0, 'khz').
args:
string (str): String.
returns:
(tuple): number, suffix
"""
warnings.showwarning("_get_number_and_suffix is deprecated. Please use get_number_and_suffix instead.", DeprecationWarning, '', '')
iteration = 0
number = np.nan
while np.isnan(number):
if iteration >= len(string):
raise ValueError('unable to find a valid number in str: {}'.format(string))
try:
number = float(string[:-(1+iteration)])
except ValueError:
iteration+=1
return number, string[-(iteration + 1):]
def read(self):
for line in self.fstream:
line = line.strip().lower()
if line == 'specgrid':
self.n = self.read_specgrid()
elif line == 'coord':
self.coord = self.read_coord()
elif line == 'zcorn':
self.zcorn = self.read_zcorn()
elif line in {'actnum', 'permx', 'permy', 'permz', 'poro', 'satnum', 'rho', 'kx', 'kz', 'emodulus25', 'poissonratio25', 'pressure', }:
dtype = np.int32 if line in {'actnum', 'satnum'} else np.float64
self.attribute[line] = self.cell_property(dtype)
elif line in {'grid', '/', ''} or line.startswith('--'):
pass
elif not re.match('[0-9]', line[0]):
warnings.showwarning(
'Unkown keyword "{}" encountered in file'.format(line.split()[0]),
SyntaxWarning, self.filename, self.line_number, line=[],
)
else:
pass # silently skip large number blocks
self.raw = DiscontBoxMesh(self.n, self.coord, self.zcorn)
def sweep_z(self, points, max_field_strength=1.5):
''' Generate a pysweep.SweepObject to sweep field z amplitude at fixed x and y.
Inputs:
points (float): sweep values for z, i.e. the magentic field strength, unit: T, range: -max_field_strength <= z <= max_field_strength
max_field_strength (float): maximum magnetic field strength, unit: T
Output:
pysweep.SweepObject
'''
if max_field_strength > 1.5:
showwarning(
'Be aware that mu-metal shields are saturated by too large magnetic fields and will not work afterwards.'
'Are you sure you want to go to more than 1.5 T?',
ResourceWarning, 'cqed/cqed/custom_pysweep_functions/magnet',
162)
@MakeMeasurementFunction([])
def point_function(d):
return points, []
@MakeMeasurementFunction([])
def set_function(z, d):
assert max_field_strength > np.abs(z) >= 0, 'The field amplitude must not exceed {} and be lager than 0.' \
' Upper limit can be adjusted with kwarg: max_field_strength.' \
' Proceed with caution (Mu-metal shields do not appreciate high fields!)'.format(
max_field_strength)
self.magnet.z_target(z)
self.magnet.ramp(mode="safe")
return []
return SweepObject(set_function=set_function,
unit="T",
label="z_field",
point_function=point_function,
dataparameter=None)
def __init__(
self,
model=None,
embed_size=10,
sequence_embedding='flatten',
sequence_embed_size=10,
hidden_width=1024,
hidden_layers=4,
layer_shrink=0.5,
dropout=0,
batch_size=32,
learning_rate=0.001,
decay=0.,
optimizer=None,
hidden_activation='relu',
hidden_activity_regularizer=None,
hidden_bias_regularizer=None,
hidden_kernel_regularizer=None,
output_activation='sigmoid',
monitor='val_loss',
loss='categorical_crossentropy',
towers=1,
cudnn=True,
multi_gpu_model=True,
):
kwargs = locals()
kwargs.pop('self')
kwargs.pop('__class__')
super(Keras, self).__init__(**kwargs)
frame, filename, line_number, function_name, lines, index = inspect.stack(
)[1]
warnings.showwarning(
'lore.estimators.keras.Keras is deprecated. Please use "from lore.estimators.keras import Base"',
DeprecationWarning, filename, line_number)
def load_Dataset(path, meta_data=None, readfileby=read_ekpy_data):
"""
Load a dataset from path. Path must contain (pickle or .csv) file ``'meta_data'``.
args:
path (str): Path to data
meta_data (pandas.DataFrame): meta_data if one wishes to provide different meta_data from that provided in path.
readfileby (callable): Method for reading data.
returns:
(Dataset): Dataset
"""
files = list(os.listdir(path))
existing_ekpds = []
for file in files:
if '.ekpds' in file:
existing_ekpds.append(file)
if len(existing_ekpds) != 0:
warnings.showwarning(
'There exist .ekpds files ({}) in this directory. If you want to load those Datasets, be sure to use ``.read_ekpds``'
.format(existing_ekpds), UserWarning, '', 0)
return Dataset(path, _build_df(path, meta_data), readfileby=readfileby)
except ValueError as e:
raise e
f.close()
default_settings.update(**settings_from_file)
class Settings:
def __init__(self):
for k, v in default_settings.items():
setattr(self, k, v)
settings = Settings()
if not settings.REMOTE_REPO:
warnings.showwarning(
'Missed remote repository parameter',
UserWarning,
settings_path,
1
)
sys.exit(1)
if not getattr(settings, 'REPO_PATH', None):
git_name = settings.REMOTE_REPO.split('/')[-1]
repo_name = git_name.split('.')[0]
settings.REPO_PATH = os.path.join(settings.PROCYON_PATH, repo_name)
sys.path.append(settings.REPO_PATH)
from StreamManager.StreamManager4 import StreamManager
from webgraphic.webgraphic import webgraphic
try:
import threading
except ImportError:
import dummy_threading as threading
# CPU number
try: # try first
import multiprocessing
except ImportError:
multiprocessing = None
proc_cnt = 0
warnings.showwarning('current system has not multiprocessing support',
ResourceWarning,
filename=__file__,
lineno=87)
else: # CPU number if multiprocessing supported
proc_cnt = ast.literal_eval(os.environ['PROC_CNT'])
finally:
CPU_CNT = proc_cnt
# PID
PID = os.getpid()
# file root path
ROOT = os.path.dirname(os.path.abspath(__file__))
# 1-initialisation; 2-migration; 3-prediction; 4-adaptation; 5-regeneration
MODE = 3
# path of input data
PATH = '/mad/pcap'
# latest processing file name
def new_warn_explicit(message, category, filename, lineno,
module=None, registry=None, module_globals=None,
emit_module=None):
lineno = int(lineno)
if module is None:
module = filename or "<unknown>"
if module[-3:].lower() == ".py":
module = module[:-3] # XXX What about leading pathname?
if registry is None:
registry = {}
if registry.get('version', 0) != warnings._filters_version:
registry.clear()
registry['version'] = warnings._filters_version
if isinstance(message, Warning):
text = str(message)
category = message.__class__
else:
text = message
message = category(message)
key = (text, category, lineno)
# Quick test for common case
if registry.get(key):
return
# Search the filters
for item in warnings.filters:
item = _get_proxy_filter(item)
if len(item) == 5:
action, msg, cat, mod, ln = item
emod = None
else:
action, msg, cat, mod, ln, emod = item
if ((msg is None or msg.match(text)) and
issubclass(category, cat) and
(mod is None or mod.match(module)) and
(emod is None or emod.match(emit_module)) and
(ln == 0 or lineno == ln)):
break
else:
action = defaultaction
# Early exit actions
if action == "ignore":
registry[key] = 1
return
# Prime the linecache for formatting, in case the
# "file" is actually in a zipfile or something.
import linecache
linecache.getlines(filename, module_globals)
if action == "error":
raise message
# Other actions
if action == "once":
registry[key] = 1
oncekey = (text, category)
if onceregistry.get(oncekey):
return
onceregistry[oncekey] = 1
elif action == "always":
pass
elif action == "module":
registry[key] = 1
altkey = (text, category, 0)
if registry.get(altkey):
return
registry[altkey] = 1
elif action == "default":
registry[key] = 1
elif action == "custom":
pass
else:
# Unrecognized actions are errors
raise RuntimeError(
"Unrecognized action (%r) in warnings.filters:\n %s" %
(action, item))
if not callable(showwarning):
raise TypeError("warnings.showwarning() must be set to a "
"function or method")
# Print message and context
showwarning(message, category, filename, lineno)
def instanciate_element(item):
DEBUG_MODULE('instanciate_element: instanciate {}'.format(item))
key = item[0]
attributes = item[1]
# aperture = PARAMS['aperture'] # default aperture
if key == 'D':
label = attributes['ID']
length = get_mandatory(attributes,'length',label)
aperture = attributes['aperture'] if 'aperture' in attributes else None
instance = ELM.D(length=length,label=label,aperture=aperture)
instance['label'] = label
instance['length'] = length
instance['aperture'] = aperture
elif key == 'SIXD':
label = attributes['ID']+'#'
length = get_mandatory(attributes,'length',label)
aperture = attributes['aperture'] if 'aperture' in attributes else None
instance = ELM.SIXD(length=length,label=label,aperture=aperture)
instance['label'] = label
instance['length'] = length
instance['aperture'] = aperture
elif key == 'QF':
label = attributes['ID']
length = get_mandatory(attributes,'length',label)
dBdz = get_mandatory(attributes,"B'",label)
slices = get_mandatory(attributes,'slices',label)
aperture = get_mandatory(attributes,'aperture',label)
kq = dBdz/PARAMS['sollteilchen'].brho
Bpole = dBdz*aperture
if slices > 1:
instance = replace_QF_with_QFth_lattice(slices,kq,length,label,PARAMS['sollteilchen'],aperture)
elif slices <= 1:
instance = ELM.QF(k0=kq,length=length,label=label,aperture=aperture)
instance['label'] = label
instance['dBdz'] = dBdz
instance['bore'] = aperture
instance['Bpole'] = Bpole
pass
elif key == 'QD':
label = attributes['ID']
length = get_mandatory(attributes,'length',label)
dBdz = get_mandatory(attributes,"B'",label)
slices = get_mandatory(attributes,'slices',label)
aperture = get_mandatory(attributes,'aperture',label)
kq = dBdz/PARAMS['sollteilchen'].brho
Bpole = dBdz*aperture
if slices > 1:
instance = replace_QD_with_QDth_lattice(slices,kq,length,label,PARAMS['sollteilchen'],aperture)
elif slices <= 1:
instance = ELM.QD(k0=kq,length=length,label=label,aperture=aperture)
instance['label'] = label
instance['dBdz'] = dBdz
instance['bore'] = aperture
instance['Bpole'] = Bpole
elif key == 'RFG':
label = attributes['ID']
PhiSoll = radians(get_mandatory(attributes,"PhiSync",label))
freq = float(get_mandatory(attributes,"freq",label))
gap = get_mandatory(attributes,'gap',label)
aperture = get_mandatory(attributes,'aperture',label)
dWf = FLAGS['dWf']
mapping = PARAMS['mapping']
EzPeak = get_mandatory(attributes,"EzPeak",label)
EzAvg = get_mandatory(attributes,"EzAvg",label)
if mapping == None:
mapping = 't3d'
EzPeak = EzAvg
if mapping == 'ttf' or mapping == 'dyn' or mapping == 'oxal': # SF-data
fname = get_mandatory(attributes,"SFdata",label)
if fname not in PARAMS:
PARAMS[fname] = SFdata(fname,EzPeak=EzPeak)
EzAvg = PARAMS[fname].EzAvg
instance = ELM.RFG(EzAvg=EzAvg,PhiSoll=PhiSoll,fRF=freq,label=label,gap=gap,mapping=mapping,dWf=dWf,aperture=aperture,SFdata=PARAMS[fname])
pass
else:
EzPeak = EzAvg
instance = ELM.RFG(EzAvg=EzAvg,PhiSoll=PhiSoll,fRF=freq,label=label,gap=gap,mapping=mapping,dWf=dWf,aperture=aperture)
instance['EzAvg'] = EzAvg
instance['EzPeak'] = EzPeak
instance['label'] = label
instance['PhiSoll'] = PhiSoll
instance['freq'] = freq
instance['gap'] = gap
instance['aperture'] = aperture
instance['dWf'] = dWf
instance['mapping'] = mapping
elif key == 'RFC':
label = attributes['ID']
PhiSoll = radians(get_mandatory(attributes,"PhiSync",label))
freq = float(get_mandatory(attributes,"freq",label))
gap = get_mandatory(attributes,'gap',label)
aperture = get_mandatory(attributes,'aperture',label)
dWf = FLAGS['dWf']
length = get_mandatory(attributes,'length',label)
mapping = PARAMS['mapping']
EzPeak = get_mandatory(attributes,"EzPeak",label)
EzAvg = get_mandatory(attributes,"EzAvg",label)
if mapping == None:
mapping = 't3d'
EzPeak = EzAvg
if mapping == 'ttf' or mapping == 'dyn' or mapping == 'oxal': # SF-data
fname = get_mandatory(attributes,"SFdata",label)
if fname not in PARAMS:
PARAMS[fname] = SFdata(fname,EzPeak=EzPeak)
EzAvg = PARAMS[fname].EzAvg
instance = ELM.RFC(EzAvg=EzAvg,label=label,PhiSoll=PhiSoll,fRF=freq,gap=gap,aperture=aperture,dWf=dWf,length=length,mapping=mapping,SFdata=PARAMS[fname])
pass
else:
EzPeak = EzAvg
instance = ELM.RFC(EzAvg=EzAvg,label=label,PhiSoll=PhiSoll,fRF=freq,gap=gap,aperture=aperture,dWf=dWf,length=length,mapping=mapping)
instance['EzAvg'] = EzAvg
instance['EzPeak'] = EzPeak
instance['label'] = label
instance['PhiSoll'] = PhiSoll
instance['freq'] = freq
instance['gap'] = gap
instance['aperture'] = aperture
instance['dWf'] = dWf
instance['length'] = length
instance['mapping'] = mapping
elif key == 'GAP':
label = attributes['ID']
gap = get_mandatory(attributes,'gap',label)
EzAvg = get_mandatory(attributes,"EzAvg",label)
PhiSoll = radians(get_mandatory(attributes,"PhiSync",label))
freq = float(get_mandatory(attributes,"freq",label))
dWf = FLAGS['dWf']
aperture = get_mandatory(attributes,'aperture',label)
instance = ELM.GAP(EzAvg=EzAvg,PhiSoll=PhiSoll,fRF=freq,label=label,gap=gap,dWf=dWf,aperture=aperture)
instance['EzAvg'] = EzAvg
instance['EzPeak'] = EzAvg
instance['label'] = label
instance['gap'] = gap
instance['PhiSoll'] = PhiSoll
instance['freq'] = freq
instance['dWf'] = dWf
elif key == 'MRK':
label = attributes['ID']
action = get_mandatory(attributes,'action',label)
if 'scatter' == action:
prefix = attributes['prefix'] if 'prefix' in attributes else ''
abszisse = attributes['abscissa'] if 'abscissa' in attributes else 'z'
ordinate = attributes['ordinate'] if 'ordinate' in attributes else 'zp'
instance = MRK.PoincareAction(label=label, prefix=prefix, abszisse=abszisse, ordinate=ordinate)
instance['prefix'] = prefix
instance['abszisse'] = abszisse
instance['ordinate'] = ordinate
else:
instance = ELM.MRK(label=label,action=action)
instance['label'] = label
instance['action'] = action
else:
warnings.showwarning(
'InputError: Unknown element type encountered: "{}" - STOP'.format(key),
UserWarning,
'lattice_generator.py',
'instanciate_element()',
)
sys.exit(1)
try:
sec = attributes['sec'] #can fail because sections are not mandatory
except:
pass
else:
instance.section = sec
return (label,instance)
# Fit chebyshev polynomials.
print("Working on objects %d to %d in timespan %f to %f" % (n, n + nObj, t, t + tSpan), file=log)
cheb = ChebyFits(subsetOrbits, t, tSpan, skyTolerance=args.skyTol,
nDecimal=args.nDecimal, nCoeff_position=args.nCoeff,
ngran=64, nCoeff_vmag=9, nCoeff_delta=5, nCoeff_elongation=6,
obscode=807, timeScale='TAI')
try:
cheb.calcSegmentLength(length=args.length)
except ValueError as ve:
cheb.length = None
for objId in subsetOrbits.orbits['objId'].as_matrix():
cheb.failed.append((objId, tStart, tEnd))
warnings.showwarning("Objid %s to %s (n %d to %d), segment %f to %f - error: %s"
% (subsetOrbits.orbits.objId.iloc[0],
subsetOrbits.orbits.objId.iloc[-1],
n, n + nObj, t, t + tSpan, ve.message),
UserWarning, "generateCoefficients.py", 132, file=log)
# Put this in a separate try/except block, because errors here can mask errors in the previous
# length determination stage otherwise.
if cheb.length is not None:
try:
cheb.calcSegments()
except ValueError as ve:
for objId in subsetOrbits.orbits['objId'].as_matrix():
cheb.failed.append((objId, tStart, tEnd))
warnings.showwarning("Objid %s to %s (n %d to %d), segment %f to %f - error: %s"
% (subsetOrbits.orbits.objId.iloc[0],
subsetOrbits.orbits.objId.iloc[-1],
n, n + nObj, t, t + tSpan, ve.message),
else:
new_lis.append(item)
return new_lis
##--------- MAIN
if __name__ == '__main__':
input_file = 'simuINstat.yml'
# input_file = 'learnyaml.yaml'
with open(input_file,'r') as fileobject:
try:
indat = yaml.load(fileobject)
except Exception as ex:
warnings.showwarning(
'InputError: {} - STOP'.format(str(ex)),
UserWarning,
'lattice_generator.py',
'factory()',
)
sys.exit(1)
fileobject.close()
ky = indat.keys()
for k in ky:
print()
print(k)
klist = indat[k]
print(klist)
if not klist: continue
nlist = flatten(klist)
if k == 'LATTICE':
N = nlist[0]
plist = nlist[1:]
def _scan_all_plugins(
modules: List[Any],
) -> Tuple[Dict[Type[Plugin], List[Type[Plugin]]], ScanStats]:
stats = ScanStats()
stats.total_time = timer()
ret: Dict[Type[Plugin], List[Type[Plugin]]] = defaultdict(list)
plugin_types: List[Type[Plugin]] = [
Plugin,
ConfigSource,
CompletionPlugin,
Launcher,
Sweeper,
SearchPathPlugin,
]
for mdl in modules:
for importer, modname, ispkg in pkgutil.walk_packages(
path=mdl.__path__,
prefix=mdl.__name__ + ".",
onerror=lambda x: None):
try:
module_name = modname.rsplit(".", 1)[-1]
# If module's name starts with "_", do not load the module.
# But if the module's name starts with a "__", then load the
# module.
if module_name.startswith(
"_") and not module_name.startswith("__"):
continue
import_time = timer()
m = importer.find_module(modname)
with warnings.catch_warnings(
record=True) as recorded_warnings:
loaded_mod = m.load_module(modname)
import_time = timer() - import_time
if len(recorded_warnings) > 0:
sys.stderr.write(
f"[Hydra plugins scanner] : warnings from '{modname}'. Please report to plugin author.\n"
)
for w in recorded_warnings:
warnings.showwarning(
message=w.message, # type: ignore
category=w.category,
filename=w.filename,
lineno=w.lineno,
file=w.file,
line=w.line,
)
stats.total_modules_import_time += import_time
assert modname not in stats.modules_import_time
stats.modules_import_time[modname] = import_time
if loaded_mod is not None:
for name, obj in inspect.getmembers(loaded_mod):
if (inspect.isclass(obj)
and issubclass(obj, Plugin)
and not inspect.isabstract(obj)):
for plugin_type in plugin_types:
if issubclass(obj, plugin_type):
ret[plugin_type].append(obj)
except ImportError as e:
warnings.warn(
message=f"\n"
f"\tError importing '{modname}'.\n"
f"\tPlugin is incompatible with this Hydra version or buggy.\n"
f"\tRecommended to uninstall or upgrade plugin.\n"
f"\t\t{type(e).__name__} : {e}",
category=UserWarning,
)
stats.total_time = timer() - stats.total_time
return ret, stats
def validate_nwbs():
global TOTAL, FAILURES, ERRORS
logging.info('running validation tests on NWB files')
examples_nwbs = glob.glob('*.nwb')
import pynwb
for nwb in examples_nwbs:
try:
logging.info("Validating file %s" % nwb)
ws = list()
with warnings.catch_warnings(record=True) as tmp:
logging.info("Validating with pynwb.validate method.")
with pynwb.NWBHDF5IO(nwb, mode='r') as io:
errors = pynwb.validate(io)
TOTAL += 1
if errors:
FAILURES += 1
ERRORS += 1
for err in errors:
print("Error: %s" % err)
def get_namespaces(nwbfile):
comp = run([
"python", "-m", "pynwb.validate", "--list-namespaces",
"--cached-namespace", nwb
],
stdout=PIPE,
stderr=STDOUT,
universal_newlines=True,
timeout=20)
if comp.returncode != 0:
return []
return comp.stdout.split()
namespaces = get_namespaces(nwb)
if len(namespaces) == 0:
FAILURES += 1
ERRORS += 1
cmds = []
cmds += [["python", "-m", "pynwb.validate", nwb]]
cmds += [[
"python", "-m", "pynwb.validate", "--cached-namespace", nwb
]]
cmds += [[
"python", "-m", "pynwb.validate", "--no-cached-namespace",
nwb
]]
for ns in namespaces:
cmds += [[
"python", "-m", "pynwb.validate", "--cached-namespace",
"--ns", ns, nwb
]]
for cmd in cmds:
logging.info("Validating with \"%s\"." %
(" ".join(cmd[:-1])))
comp = run(cmd,
stdout=PIPE,
stderr=STDOUT,
universal_newlines=True,
timeout=20)
TOTAL += 1
if comp.returncode != 0:
FAILURES += 1
ERRORS += 1
print("Error: %s" % comp.stdout)
for w in tmp: # ignore RunTimeWarnings about importing
if isinstance(w.message,
RuntimeWarning) and not warning_re.match(
str(w.message)):
ws.append(w)
for w in ws:
warnings.showwarning(w.message, w.category, w.filename,
w.lineno, w.line)
except Exception:
print(traceback.format_exc())
FAILURES += 1
ERRORS += 1
def solve_mbar_once(u_kn_nonzero,
N_k_nonzero,
f_k_nonzero,
method="hybr",
tol=1E-12,
options=None):
"""Solve MBAR self-consistent equations using some form of equation solver.
Parameters
----------
u_kn_nonzero : np.ndarray, shape=(n_states, n_samples), dtype='float'
The reduced potential energies, i.e. -log unnormalized probabilities
for the nonempty states
N_k_nonzero : np.ndarray, shape=(n_states), dtype='int'
The number of samples in each state for the nonempty states
f_k_nonzero : np.ndarray, shape=(n_states), dtype='float'
The reduced free energies for the nonempty states
method : str, optional, default="hybr"
The optimization routine to use. This can be any of the methods
available via scipy.optimize.minimize() or scipy.optimize.root().
tol : float, optional, default=1E-14
The convergance tolerance for minimize() or root()
verbose: bool
Whether to print information about the solution method.
options: dict, optional, default=None
Optional dictionary of algorithm-specific parameters. See
scipy.optimize.root or scipy.optimize.minimize for details.
Returns
-------
f_k : np.ndarray
The converged reduced free energies.
results : dict
Dictionary containing entire results of optimization routine, may
be useful when debugging convergence.
Notes
-----
This function requires that N_k_nonzero > 0--that is, you should have
already dropped all the states for which you have no samples.
Internally, this function works in a reduced coordinate system defined
by subtracting off the first component of f_k and fixing that component
to be zero.
For fast but precise convergence, we recommend calling this function
multiple times to polish the result. `solve_mbar()` facilitates this.
"""
u_kn_nonzero, N_k_nonzero, f_k_nonzero = validate_inputs(
u_kn_nonzero, N_k_nonzero, f_k_nonzero)
f_k_nonzero = f_k_nonzero - f_k_nonzero[
0] # Work with reduced dimensions with f_k[0] := 0
u_kn_nonzero = precondition_u_kn(u_kn_nonzero, N_k_nonzero, f_k_nonzero)
pad = lambda x: np.pad(
x, (1, 0), mode='constant'
) # Helper function inserts zero before first element
unpad_second_arg = lambda obj, grad: (obj, grad[
1:]) # Helper function drops first element of gradient
# Create objective functions / nonlinear equations to send to scipy.optimize, fixing f_0 = 0
grad = lambda x: mbar_gradient(u_kn_nonzero, N_k_nonzero, pad(x))[
1:] # Objective function gradient
grad_and_obj = lambda x: unpad_second_arg(*mbar_objective_and_gradient(
u_kn_nonzero, N_k_nonzero, pad(x)
)) # Objective function gradient and objective function
hess = lambda x: mbar_hessian(u_kn_nonzero, N_k_nonzero, pad(x))[
1:][:, 1:] # Hessian of objective function
with warnings.catch_warnings(record=True) as w:
if method in [
"L-BFGS-B", "dogleg", "CG", "BFGS", "Newton-CG", "TNC",
"trust-ncg", "SLSQP"
]:
if method in ["L-BFGS-B", "CG"]:
hess = None # To suppress warning from passing a hessian function.
results = scipy.optimize.minimize(grad_and_obj,
f_k_nonzero[1:],
jac=True,
hess=hess,
method=method,
tol=tol,
options=options)
f_k_nonzero = pad(results["x"])
elif method == 'adaptive':
results = adaptive(u_kn_nonzero,
N_k_nonzero,
f_k_nonzero,
tol=tol,
options=options)
f_k_nonzero = results # they are the same for adaptive, until we decide to return more.
else:
results = scipy.optimize.root(grad,
f_k_nonzero[1:],
jac=hess,
method=method,
tol=tol,
options=options)
f_k_nonzero = pad(results["x"])
#If there were runtime warnings, show the messages
if len(w) > 0:
for warn_msg in w:
warnings.showwarning(warn_msg.message, warn_msg.category,
warn_msg.filename, warn_msg.lineno,
warn_msg.file, "")
#Ensure MBAR solved correctly
W_nk_check = mbar_W_nk(u_kn_nonzero, N_k_nonzero, f_k_nonzero)
check_w_normalized(W_nk_check, N_k_nonzero)
print(
"MBAR weights converged within tolerance, despite the SciPy Warnings. Please validate your results."
)
return f_k_nonzero, results
dump utilities for `pcapkit` implementation
"""
import os
import warnings
import tbtrim
from pcapkit.utilities.exceptions import DEVMODE, BaseError
from pcapkit.utilities.warnings import DevModeWarning
# set up sys.excepthook
if DEVMODE:
warnings.showwarning(
'development mode enabled',
DevModeWarning,
filename=__file__,
lineno=0,
line=f"PCAPKIT_DEVMODE={os.environ['PCAPKIT_DEVMODE']}")
else:
ROOT = os.path.dirname(os.path.realpath(__file__))
tbtrim.set_trim_rule(lambda filename: ROOT in os.path.realpath(filename),
exception=BaseError,
strict=False)
# All Reference
import pcapkit.all
# Interface
from pcapkit.interface import *
# ToolKit
# now the convergence should be stricter and more accurate.
# !!! fixed bug in ellipsoid: dr2**POW --> dr2**(POW/2.)
# v10.8
# netRads accepting node and link labels
# !!!! bugfix on networkBase get_json:
# new params were ignored. Now JSON updates params before making network.
#import tensorflow as tf
from numpy import *
import json, os, time
from warnings import showwarning
try:
import tensorflow as tf
except ImportError:
showwarning("Can't import tensorflow!", ImportWarning, 'net3d_v9_4_10', 8)
try:
from matplotlib.pyplot import *
#from matplotlib.mlab import movavg
except ImportError:
showwarning("Can't import matplotlib.pyplot!", ImportWarning,
'net3d_v9_4_10', 8)
############################
__version__ = 'v10.7'
############################
PAIRS = 100
POW = 2
POWn = 2
def solve_mbar_once(u_kn_nonzero, N_k_nonzero, f_k_nonzero, method="hybr", tol=1E-12, options=None):
"""Solve MBAR self-consistent equations using some form of equation solver.
Parameters
----------
u_kn_nonzero : np.ndarray, shape=(n_states, n_samples), dtype='float'
The reduced potential energies, i.e. -log unnormalized probabilities
for the nonempty states
N_k_nonzero : np.ndarray, shape=(n_states), dtype='int'
The number of samples in each state for the nonempty states
f_k_nonzero : np.ndarray, shape=(n_states), dtype='float'
The reduced free energies for the nonempty states
method : str, optional, default="hybr"
The optimization routine to use. This can be any of the methods
available via scipy.optimize.minimize() or scipy.optimize.root().
tol : float, optional, default=1E-14
The convergance tolerance for minimize() or root()
verbose: bool
Whether to print information about the solution method.
options: dict, optional, default=None
Optional dictionary of algorithm-specific parameters. See
scipy.optimize.root or scipy.optimize.minimize for details.
Returns
-------
f_k : np.ndarray
The converged reduced free energies.
results : dict
Dictionary containing entire results of optimization routine, may
be useful when debugging convergence.
Notes
-----
This function requires that N_k_nonzero > 0--that is, you should have
already dropped all the states for which you have no samples.
Internally, this function works in a reduced coordinate system defined
by subtracting off the first component of f_k and fixing that component
to be zero.
For fast but precise convergence, we recommend calling this function
multiple times to polish the result. `solve_mbar()` facilitates this.
"""
u_kn_nonzero, N_k_nonzero, f_k_nonzero = validate_inputs(u_kn_nonzero, N_k_nonzero, f_k_nonzero)
f_k_nonzero = f_k_nonzero - f_k_nonzero[0] # Work with reduced dimensions with f_k[0] := 0
u_kn_nonzero = precondition_u_kn(u_kn_nonzero, N_k_nonzero, f_k_nonzero)
pad = lambda x: np.pad(x, (1, 0), mode='constant') # Helper function inserts zero before first element
unpad_second_arg = lambda obj, grad: (obj, grad[1:]) # Helper function drops first element of gradient
# Create objective functions / nonlinear equations to send to scipy.optimize, fixing f_0 = 0
grad = lambda x: mbar_gradient(u_kn_nonzero, N_k_nonzero, pad(x))[1:] # Objective function gradient
grad_and_obj = lambda x: unpad_second_arg(*mbar_objective_and_gradient(u_kn_nonzero, N_k_nonzero, pad(x))) # Objective function gradient and objective function
hess = lambda x: mbar_hessian(u_kn_nonzero, N_k_nonzero, pad(x))[1:][:, 1:] # Hessian of objective function
with warnings.catch_warnings(record=True) as w:
if method in ["L-BFGS-B", "dogleg", "CG", "BFGS", "Newton-CG", "TNC", "trust-ncg", "SLSQP"]:
if method in ["L-BFGS-B", "CG"]:
hess = None # To suppress warning from passing a hessian function.
results = scipy.optimize.minimize(grad_and_obj, f_k_nonzero[1:], jac=True, hess=hess, method=method, tol=tol, options=options)
f_k_nonzero = pad(results["x"])
elif method == 'adaptive':
results = adaptive(u_kn_nonzero, N_k_nonzero, f_k_nonzero, tol=tol, options=options)
f_k_nonzero = results # they are the same for adaptive, until we decide to return more.
else:
results = scipy.optimize.root(grad, f_k_nonzero[1:], jac=hess, method=method, tol=tol, options=options)
f_k_nonzero = pad(results["x"])
# If there were runtime warnings, show the messages
if len(w) > 0:
can_ignore = True
for warn_msg in w:
if "Unknown solver options" in str(warn_msg.message):
continue
warnings.showwarning(warn_msg.message, warn_msg.category,
warn_msg.filename, warn_msg.lineno, warn_msg.file, "")
can_ignore = False # If any warning is not just unknown options, can ]not skip check
if not can_ignore:
# Ensure MBAR solved correctly
w_nk_check = mbar_W_nk(u_kn_nonzero, N_k_nonzero, f_k_nonzero)
check_w_normalized(w_nk_check, N_k_nonzero)
print("MBAR weights converged within tolerance, despite the SciPy Warnings. Please validate your results.")
return f_k_nonzero, results
import yfinance
__version__ = 'yfinance v. ' + yfinance.__version__
__author__ = yfinance.__author__
Ticker = yfinance.download
download = yfinance.download
pdr_override = yfinance.download
def get_yahoo_crumb(*args, **kwargs):
pass
def parse_ticker_csv(*args, **kwargs):
pass
__all__ = yfinance.__all__ + ['get_yahoo_crumb', 'parse_ticker_csv']
import warnings
warnings.showwarning(
"""
*** `fix_yahoo_finance` was renamed to `yfinance`. ***
Please install and use `yfinance` directly using `pip install yfinance -U`
More information: https://github.com/ranaroussi/yfinance
""", DeprecationWarning, __file__, 0)
def initialize_model(
rng_key,
model,
*,
init_strategy=init_to_uniform,
dynamic_args=False,
model_args=(),
model_kwargs=None,
forward_mode_differentiation=False,
validate_grad=True,
):
"""
(EXPERIMENTAL INTERFACE) Helper function that calls :func:`~numpyro.infer.util.get_potential_fn`
and :func:`~numpyro.infer.util.find_valid_initial_params` under the hood
to return a tuple of (`init_params_info`, `potential_fn`, `postprocess_fn`, `model_trace`).
:param jax.random.PRNGKey rng_key: random number generator seed to
sample from the prior. The returned `init_params` will have the
batch shape ``rng_key.shape[:-1]``.
:param model: Python callable containing Pyro primitives.
:param callable init_strategy: a per-site initialization function.
See :ref:`init_strategy` section for available functions.
:param bool dynamic_args: if `True`, the `potential_fn` and
`constraints_fn` are themselves dependent on model arguments.
When provided a `*model_args, **model_kwargs`, they return
`potential_fn` and `constraints_fn` callables, respectively.
:param tuple model_args: args provided to the model.
:param dict model_kwargs: kwargs provided to the model.
:param bool forward_mode_differentiation: whether to use forward-mode differentiation
or reverse-mode differentiation. By default, we use reverse mode but the forward
mode can be useful in some cases to improve the performance. In addition, some
control flow utility on JAX such as `jax.lax.while_loop` or `jax.lax.fori_loop`
only supports forward-mode differentiation. See
`JAX's The Autodiff Cookbook <https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html>`_
for more information.
:param bool validate_grad: whether to validate gradient of the initial params.
Defaults to True.
:return: a namedtupe `ModelInfo` which contains the fields
(`param_info`, `potential_fn`, `postprocess_fn`, `model_trace`), where
`param_info` is a namedtuple `ParamInfo` containing values from the prior
used to initiate MCMC, their corresponding potential energy, and their gradients;
`postprocess_fn` is a callable that uses inverse transforms
to convert unconstrained HMC samples to constrained values that
lie within the site's support, in addition to returning values
at `deterministic` sites in the model.
"""
model_kwargs = {} if model_kwargs is None else model_kwargs
substituted_model = substitute(
seed(model, rng_key if jnp.ndim(rng_key) == 1 else rng_key[0]),
substitute_fn=init_strategy,
)
(
inv_transforms,
replay_model,
has_enumerate_support,
model_trace,
) = _get_model_transforms(substituted_model, model_args, model_kwargs)
# substitute param sites from model_trace to model so
# we don't need to generate again parameters of `numpyro.module`
model = substitute(
model,
data={
k: site["value"]
for k, site in model_trace.items() if site["type"] in ["param"]
},
)
constrained_values = {
k: v["value"]
for k, v in model_trace.items() if v["type"] == "sample"
and not v["is_observed"] and not v["fn"].is_discrete
}
if has_enumerate_support:
from numpyro.contrib.funsor import config_enumerate, enum
if not isinstance(model, enum):
max_plate_nesting = _guess_max_plate_nesting(model_trace)
_validate_model(model_trace)
model = enum(config_enumerate(model), -max_plate_nesting - 1)
potential_fn, postprocess_fn = get_potential_fn(
model,
inv_transforms,
replay_model=replay_model,
enum=has_enumerate_support,
dynamic_args=dynamic_args,
model_args=model_args,
model_kwargs=model_kwargs,
)
init_strategy = (init_strategy if isinstance(init_strategy, partial) else
init_strategy())
if (init_strategy.func is init_to_value) and not replay_model:
init_values = init_strategy.keywords.get("values")
unconstrained_values = transform_fn(inv_transforms,
init_values,
invert=True)
init_strategy = _init_to_unconstrained_value(
values=unconstrained_values)
prototype_params = transform_fn(inv_transforms,
constrained_values,
invert=True)
(init_params, pe, grad), is_valid = find_valid_initial_params(
rng_key,
substitute(
model,
data={
k: site["value"]
for k, site in model_trace.items()
if site["type"] in ["plate"]
},
),
init_strategy=init_strategy,
enum=has_enumerate_support,
model_args=model_args,
model_kwargs=model_kwargs,
prototype_params=prototype_params,
forward_mode_differentiation=forward_mode_differentiation,
validate_grad=validate_grad,
)
if not_jax_tracer(is_valid):
if device_get(~jnp.all(is_valid)):
with numpyro.validation_enabled(), trace() as tr:
# validate parameters
substituted_model(*model_args, **model_kwargs)
# validate values
for site in tr.values():
if site["type"] == "sample":
with warnings.catch_warnings(record=True) as ws:
site["fn"]._validate_sample(site["value"])
if len(ws) > 0:
for w in ws:
# at site information to the warning message
w.message.args = ("Site {}: {}".format(
site["name"],
w.message.args[0]), ) + w.message.args[1:]
warnings.showwarning(
w.message,
w.category,
w.filename,
w.lineno,
file=w.file,
line=w.line,
)
raise RuntimeError(
"Cannot find valid initial parameters. Please check your model again."
)
return ModelInfo(ParamInfo(init_params, pe, grad), potential_fn,
postprocess_fn, model_trace)
def parse_imageinfo(gpath):
""" Worker function: gpath must be in UNIX-PATH format!
Args:
tup (tuple): a tuple or one argument
(so the function can be parallelized easily)
(here it is just gpath, no tuple, sorry for confusion)
Returns:
tuple: param_tup -
if successful returns a tuple of image parameters which are values
for SQL columns on else returns None
CommandLine:
python -m ibeis.algo.preproc.preproc_image --exec-parse_imageinfo
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.preproc.preproc_image import * # NOQA
>>> gpath = ('/media/raid/work/lynx/_ibsdb/images/f6c84c6d-55ca-fd02-d0b4-1c7c9c27c894.jpg')
>>> param_tup = parse_imageinfo(tup)
>>> result = ('param_tup = %s' % (str(param_tup),))
>>> print(result)
"""
# Parse arguments from tuple
#print('[ginfo] gpath=%r' % gpath)
# Try to open the image
with warnings.catch_warnings(record=True) as w:
try:
pil_img = Image.open(gpath, 'r') # Open PIL Image
except IOError as ex:
print('[preproc] IOError: %s' % (str(ex),))
return None
if len(w) > 0:
for warn in w:
warnings.showwarning(warn.message, warn.category,
warn.filename, warn.lineno, warn.file,
warn.line)
#warnstr = warnings.formatwarning
#print(warnstr)
print('Warnings issued by %r' % (gpath,))
# Parse out the data
width, height = pil_img.size # Read width, height
time, lat, lon = parse_exif(pil_img) # Read exif tags
# We cannot use pixel data as libjpeg is not determenistic (even for reads!)
image_uuid = ut.get_file_uuid(gpath) # Read file ]-hash-> guid = gid
#orig_gpath = gpath
orig_gname = basename(gpath)
ext = get_standard_ext(gpath)
notes = ''
# Build parameters tuple
param_tup = (
image_uuid,
gpath,
gpath,
orig_gname,
#orig_gpath,
ext,
width,
height,
time,
lat,
lon,
notes
)
#print('[ginfo] %r %r' % (image_uuid, orig_gname))
return param_tup
def _read_config(rcfile):
"""Read configuration information from a file"""
global config
defaults = {
'enable_deprecation_warning': str(True),
'keepalive': str(True),
'ncpus': str(multiprocessing.cpu_count()),
'qindenton_url':
'http://virbo.org/ftp/QinDenton/hour/merged/latest/WGhour-latest.d.zip',
'qd_daily_url':
'https://rbsp-ect.newmexicoconsortium.org/data_pub/QinDenton/',
'omni2_url':
'https://spdf.gsfc.nasa.gov/pub/data/omni/omni_cdaweb/hourly/',
'leapsec_url':
'https://oceandata.sci.gsfc.nasa.gov/Ancillary/LUTs/modis/leapsec.dat',
'psddata_url': 'http://spacepy.lanl.gov/repository/psd_dat.sqlite',
'support_notice': str(True),
'apply_plot_styles': str(True),
}
#Functions to cast a config value; if not specified, value is a string
str2bool = lambda x: x.lower() in ('1', 'yes', 'true', 'on')
caster = {
'enable_deprecation_warning': str2bool,
'keepalive': str2bool,
'ncpus': int,
'support_notice': str2bool,
'apply_plot_styles': str2bool,
}
#SafeConfigParser deprecated in 3.2. And this is hideous, but...
if hasattr(ConfigParser, 'SafeConfigParser'):
cp_class = ConfigParser.SafeConfigParser
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings(
'always', 'The SafeConfigParser class has been renamed.*',
DeprecationWarning,
'^spacepy$') #configparser lies about source of warnings
ConfigParser.SafeConfigParser()
for this_w in w:
if isinstance(this_w.message, DeprecationWarning):
cp_class = ConfigParser.ConfigParser
else:
warnings.showwarning(this_w.message, this_w.category,
this_w.filename, this_w.lineno,
this_w.file, this_w.line)
else:
cp_class = ConfigParser.ConfigParser
cp = cp_class(defaults)
try:
successful = cp.read([rcfile])
except ConfigParser.Error:
successful = []
if successful: #New file structure
config = dict(cp.items('spacepy'))
else: #old file structure, wipe it out
cp = cp_class()
cp.add_section('spacepy')
with open(rcfile, 'w') as cf:
cp.write(cf)
for k in defaults:
if not k in config:
config[k] = defaults[k]
_write_defaults(rcfile, defaults)
for k in caster:
config[k] = caster[k](config[k])
import time
import pickle
import asyncio
import warnings
import aiohttp.web
try:
import aioredis
except ImportError:
warnings.showwarning("aioredis library not found. Redis cache client not available")
class BaseStaticBuilder(object):
def __init__(self, expiration: int = 300, base_path: str = None):
self.base_path = base_path
self.expiration = expiration
async def write(self, content: str, path: str) -> object:
raise NotImplementedError()
def make_key(self, request: aiohttp.web.Request) -> str:
key = "{method}{host}{path}{postdata}{ctype}".format(method=request.method,
path=request.path_qs,
host=request.host,
postdata="".join(request.post()),
ctype=request.content_type)
return key
# Double-Ellipsoid forces
import tensorflow as tf
from numpy import *
import json, os, time
from warnings import showwarning
try:
from matplotlib.pyplot import *
#from matplotlib.mlab import movavg
except ImportError:
showwarning("Can't import matplotlib.pyplot!", ImportWarning,
'net3d_v9_4_6', 8)
PAIRS = 100
POW = 2
POWn = 2
POW_SN = 2 # power extra factors of r_i+r_j in node repulsion
#pairs = array([i for i in itertools.combinations(arange(400),2)])
def expand(pts, n):
s = pts.shape
x = linspace(0, s[0] - 1, n)
xp = arange(s[0])
return array([interp(x, xp, i) for i in pts.T]).T
def dvecz(x, y):
#assert (len(x.shape)==2) and (x.shape[1] == y.shape[1])
xT_ = tf.constant(x[:, :, newaxis].T, dtype=tf.float32)
def factory(input_file):
""" factory creates a lattice from input-file """
#--------
def read_elements(in_data):
element_list = in_data['ELEMENTS']
elements = liofd2d(element_list)
# add {'ID':key} to attribute list
IDs = elements.keys()
for ID in IDs:
attList = elements[ID]
attList.append({'ID':ID})
return elements
# --------
def read_flags(in_data):
"""returns a dict of flags"""
flags_list = in_data['FLAGS']
flags = liofd2d(flags_list) if flags_list != None else {}
if 'accON' in flags:
if flags['accON']:
FLAGS['dWf'] = 1.
SUMMARY['accON'] = True
else:
FLAGS['dWf'] = 0.
SUMMARY['accON'] = False
if 'periodic' in flags: FLAGS['periodic'] = flags['periodic']
if 'egf' in flags: FLAGS['egf'] = flags['egf']
if 'sigma' in flags: FLAGS['sigma'] = flags['sigma']
if 'KVout' in flags: FLAGS['KVout'] = flags['KVout']
if 'verbose' in flags: FLAGS['verbose'] = flags['verbose']
if 'express' in flags: FLAGS['express'] = flags['express']
if 'useaper' in flags: FLAGS['useaper'] = flags['useaper']
if 'bucket' in flags: FLAGS['bucket'] = flags['bucket']
if 'csTrak' in flags: FLAGS['csTrak'] = flags['csTrak']
if 'pspace' in flags: FLAGS['pspace'] = flags['pspace']
return flags
# --------
def read_sections(in_data):
""" returns a list of section names """
sec_list = []
use_sections = True
try: ## can fail because sections are not mandatory
sec_list = in_data['SECTIONS']
sec_list = flatten(sec_list)
except:
use_sections = False
PARAMS['sections'] = sec_list
FLAGS['sections'] = use_sections
return sec_list
# --------
def read_parameters(in_data):
""" returns a dict of parameters """
parameter_list = in_data['PARAMETERS']
parameters = liofd2d(parameter_list)
if 'Tkin' in parameters: PARAMS['injection_energy'] = parameters['Tkin']
if 'phi_sync' in parameters: PARAMS['phisoll'] = parameters['phi_sync']
if 'gap' in parameters: PARAMS['gap'] = parameters['gap']
if 'cav_len' in parameters: PARAMS['cavity_laenge'] = parameters['cav_len']
if 'ql' in parameters: PARAMS['ql'] = parameters['ql']
if 'windings' in parameters: PARAMS['nbwindgs'] = parameters['windings']
if 'nbsigma' in parameters: PARAMS['nbsigma'] = parameters['nbsigma']
if 'aperture' in parameters: PARAMS['aperture'] = parameters['aperture']
if 'emitx_i' in parameters: PARAMS['emitx_i'] = parameters['emitx_i']
if 'emity_i' in parameters: PARAMS['emity_i'] = parameters['emity_i']
if 'betax_i' in parameters: PARAMS['betax_i'] = parameters['betax_i']
if 'betay_i' in parameters: PARAMS['betay_i'] = parameters['betay_i']
if 'alfax_i' in parameters: PARAMS['alfax_i'] = parameters['alfax_i']
if 'alfay_i' in parameters: PARAMS['alfay_i'] = parameters['alfay_i']
if 'mapping' in parameters: PARAMS['mapping'] = parameters['mapping']
if 'DT2T' in parameters: PARAMS['DT2T'] = parameters['DT2T']
if 'lattvers' in parameters: PARAMS['lattice_version'] = parameters['lattvers']
return parameters
#--------
def get_flattened_lattice_list(in_data):
""" read_and_flatten lattice from (in_data)."""
lattice_list = in_data['LATTICE']
lattice_list = flatten(lattice_list)
N = lattice_list[0] # Duplikator
plist = lattice_list[1:]
qlist = plist.copy()
for i in range(N-1):
qlist += plist
lattice_list=qlist
return lattice_list
#--------
def make_lattice(latticeList,in_data):
""" instanciate all elements from flattened node list"""
lattice = Lattice()
DEBUG_OFF('make_lattice for sollteilchen\n'+PARAMS['sollteilchen'].string())
elements = read_elements(in_data)
for ID in lattice_list:
element = elements[ID]
element = liofd2d(element)
elementClass = element['type']
elmItem = (elementClass,element)
# !!INSTANCIATE!!
(label,instance) = instanciate_element(elmItem)
section = instance.section if FLAGS['sections'] else '*'
DEBUG_MODULE('instance {} {} {}'.format(label,instance,section))
# add element instance to lattice
if isinstance(instance,ELM._Node):
lattice.add_element(instance)
# elif isinstance(instance,Lattice):
# lattice.concat(instance) # concatenate partial with lattice
return lattice # the complete lattice
## factory body --------
SUMMARY['input file'] = PARAMS['input_file'] = input_file
with open(input_file,'r') as fileobject:
try:
in_data = yaml.load(fileobject)
except Exception as ex:
warnings.showwarning(
'InputError: {} - STOP'.format(str(ex)),
UserWarning,
'lattice_generator.py',
'factory()',
)
sys.exit(1)
fileobject.close()
read_flags(in_data)
read_sections(in_data)
read_parameters(in_data)
DEBUG_MODULE('PARAMS after read_parameters()',PARAMS)
# lattice_list is a flat list of node IDs
lattice_list = get_flattened_lattice_list(in_data)
DEBUG_MODULE('latticeList in factory()',lattice_list)
# __call__ sollteilchen energy
PARAMS['sollteilchen'](tkin=PARAMS['injection_energy'])
lattice = make_lattice(lattice_list,in_data)
# DEBUG_MODULE('lattice_generator >>\n',lattice.string())
SUMMARY['lattice length [m]'] = PARAMS['lattice_length'] = lattice.length
DEBUG_OFF('SUMMARY in factory()',SUMMARY)
# end of factory(...)
return lattice
