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def expected_shd(posterior, ground_truth):
"""Compute the Expected Structural Hamming Distance.
This function computes the Expected SHD between a posterior approximation
given as a collection of samples from the posterior, and the ground-truth
graph used in the original data generation process.
Parameters
----------
posterior : np.ndarray instance
Posterior approximation. The array must have size `(B, N, N)`, where `B`
is the number of sample graphs from the posterior approximation, and `N`
is the number of variables in the graphs.
ground_truth : np.ndarray instance
Adjacency matrix of the ground-truth graph. The array must have size
`(N, N)`, where `N` is the number of variables in the graph.
Returns
-------
e_shd : float
The Expected SHD.
"""
# Compute the pairwise differences
diff = np.abs(posterior - np.expand_dims(ground_truth, axis=0))
diff = diff + diff.transpose((0, 2, 1))
# Ignore double edges
diff = np.minimum(diff, 1)
shds = np.sum(diff, axis=(1, 2)) / 2
return np.mean(shds)
| 5,500 |
def package_list_read(pkgpath):
"""Read package list"""
try:
with open(PACKAGE_LIST_FILE, 'r') as pkglistfile:
return json.loads(pkglistfile.read())
except Exception:
return []
| 5,501 |
def hpat_pandas_series_le(self, other, level=None, fill_value=None, axis=0):
"""
Pandas Series method :meth:`pandas.Series.le` implementation.
.. only:: developer
Test: python -m hpat.runtests hpat.tests.test_series.TestSeries.test_series_op8
Parameters
----------
self: :class:`pandas.Series`
input arg
other: :obj:`pandas.Series`, :obj:`int` or :obj:`float`
input arg
level: :obj:`int` or name
*unsupported*
fill_value: :obj:`float` or None, default None
*unsupported*
axis: default 0
*unsupported*
Returns
-------
:obj:`pandas.Series`
returns :obj:`pandas.Series` object
"""
_func_name = 'Method le().'
if not isinstance(self, SeriesType):
raise TypingError('{} The object must be a pandas.series. Given: {}'.format(_func_name, self))
if level is not None or fill_value is not None or axis != 0:
raise TypingError('{} Unsupported parameters. Given level: {}, fill_value: {}, axis: {}'.format(_func_name, level, fill_value, axis))
if isinstance(other, SeriesType):
def hpat_pandas_series_le_impl(self, other):
"""
Test: python -m hpat.runtests hpat.tests.test_series.TestSeries.test_series_op8
"""
return pandas.Series(self._data <= other._data)
return hpat_pandas_series_le_impl
if isinstance(other, types.Integer) or isinstance(other, types.Float):
def hpat_pandas_series_le_impl(self, other):
"""
Test: python -m hpat.runtests hpat.tests.test_series.TestSeries.test_series_op8_integer_scalar
Test: python -m hpat.runtests hpat.tests.test_series.TestSeries.test_series_op8_float_scalar
"""
return pandas.Series(self._data <= other)
return hpat_pandas_series_le_impl
raise TypingError('{} The object must be a pandas.series and argument must be a number. Given: {} and other: {}'.format(_func_name, self, other))
| 5,502 |
def add_obs_info(telem, obs_stats):
"""
Add observation-specific information to a telemetry table (ok flag, and outlier flag).
This is done as part of get_agasc_id_stats. It is a convenience for writing reports.
:param telem: list of tables
One or more telemetry tables (potentially many observations)
:param obs_stats: table
The result of calc_obs_stats.
:return:
"""
logger.debug(' Adding observation info to telemetry...')
obs_stats['obs_ok'] = (
(obs_stats['n'] > 10)
& (obs_stats['f_track'] > 0.3)
& (obs_stats['lf_variability_100s'] < 1)
)
obs_stats['comments'] = np.zeros(len(obs_stats), dtype='<U80')
telem = vstack(telem)
telem['obs_ok'] = True
telem['obs_outlier'] = False
for s in obs_stats:
obsid = s['obsid']
o = (telem['obsid'] == obsid)
telem['obs_ok'][o] = np.ones(np.sum(o), dtype=bool) * s['obs_ok']
if (np.any(telem['ok'][o]) and s['f_track'] > 0
and np.isfinite(s['q75']) and np.isfinite(s['q25'])):
iqr = s['q75'] - s['q25']
telem['obs_outlier'][o] = (
telem[o]['ok'] & (iqr > 0)
& ((telem[o]['mags'] < s['q25'] - 1.5 * iqr)
| (telem[o]['mags'] > s['q75'] + 1.5 * iqr))
)
logger.debug(f' Adding observation info to telemetry {obsid=}')
return telem
| 5,503 |
def plot_greedy_kde_interval_2d(pts, levels, xmin=None, xmax=None, ymin=None, ymax=None, Nx=100, Ny=100, cmap=None, colors=None, *args, **kwargs):
"""Plots the given probability interval contours, using a greedy
selection algorithm. Additional arguments passed to
:func:`pp.contour`.
The algorithm uses a two-step process (see `this document
<https://dcc.ligo.org/LIGO-P1400054/public>`_) so that the
resulting credible areas will be unbiased.
:param pts: Array of shape ``(Npts, 2)`` that contains the points
in question.
:param levels: Sequence of levels (between 0 and 1) of probability
intervals to plot.
:param xmin: Minimum value in x. If ``None``, use minimum data
value.
:param xmax: Maximum value in x. If ``None``, use minimum data
value.
:param ymin: Minimum value in y. If ``None``, use minimum data
value.
:param ymax: Maximum value in y. If ``None``, use minimum data
value.
:param Nx: Number of subdivisions in x for contour plot. (Default
100.)
:param Ny: Number of subdivisions in y for contour plot. (Default
100.)
:param cmap: See :func:`pp.contour`.
:param colors: See :func:`pp.contour`.
"""
Npts=pts.shape[0]
kde_pts = pts[:Npts/2,:]
den_pts = pts[Npts/2:,:]
Nkde = kde_pts.shape[0]
Nden = den_pts.shape[0]
kde=ss.gaussian_kde(kde_pts.T)
den=kde(den_pts.T)
densort=np.sort(den)[::-1]
if xmin is None:
xmin = np.min(pts[:,0])
if xmax is None:
xmax = np.max(pts[:,0])
if ymin is None:
ymin = np.min(pts[:,1])
if ymax is None:
ymax = np.max(pts[:,1])
xs = np.linspace(xmin, xmax, Nx)
ys = np.linspace(ymin, ymax, Ny)
XS,YS=np.meshgrid(xs,ys)
ZS=np.reshape(kde(np.row_stack((XS.flatten(), YS.flatten()))), (Nx, Ny))
zvalues=[]
for level in levels:
ilevel = int(Nden*level + 0.5)
if ilevel >= Nden:
ilevel = Nden-1
zvalues.append(densort[ilevel])
pp.contour(XS, YS, ZS, zvalues, colors=colors, cmap=cmap, *args, **kwargs)
| 5,504 |
def map_view(request):
"""
Place to show off the new map view
"""
# Define view options
view_options = MVView(
projection='EPSG:4326',
center=[-100, 40],
zoom=3.5,
maxZoom=18,
minZoom=2
)
# Define drawing options
drawing_options = MVDraw(
controls=['Modify', 'Delete', 'Move', 'Point', 'LineString', 'Polygon', 'Box'],
initial='Point',
output_format='GeoJSON'
)
# Define GeoJSON layer
geojson_object = {
'type': 'FeatureCollection',
'crs': {
'type': 'name',
'properties': {
'name': 'EPSG:3857'
}
},
'features': [
{
'type': 'Feature',
'geometry': {
'type': 'Point',
'coordinates': [0, 0]
}
},
{
'type': 'Feature',
'geometry': {
'type': 'LineString',
'coordinates': [[4e6, -2e6], [8e6, 2e6]]
}
},
{
'type': 'Feature',
'geometry': {
'type': 'Polygon',
'coordinates': [[[-5e6, -1e6], [-4e6, 1e6], [-3e6, -1e6]]]
}
}
]
}
# Define layers
map_layers = []
geojson_layer = MVLayer(source='GeoJSON',
options=geojson_object,
editable=False,
legend_title='Test GeoJSON',
legend_extent=[-46.7, -48.5, 74, 59],
legend_classes=[
MVLegendClass('polygon', 'Polygons', fill='rgba(255,255,255,0.8)', stroke='#3d9dcd'),
MVLegendClass('line', 'Lines', stroke='#3d9dcd')
])
map_layers.append(geojson_layer)
if get_geoserver_wms():
# Define GeoServer Layer
geoserver_layer = MVLayer(source='ImageWMS',
options={'url': get_geoserver_wms(),
'params': {'LAYERS': 'topp:states'},
'serverType': 'geoserver'},
legend_title='USA Population',
legend_extent=[-126, 24.5, -66.2, 49],
legend_classes=[
MVLegendClass('polygon', 'Low Density', fill='#00ff00', stroke='#000000'),
MVLegendClass('polygon', 'Medium Density', fill='#ff0000', stroke='#000000'),
MVLegendClass('polygon', 'High Density', fill='#0000ff', stroke='#000000')
])
map_layers.append(geoserver_layer)
# Define KML Layer
kml_layer = MVLayer(source='KML',
options={'url': '/static/tethys_gizmos/data/model.kml'},
legend_title='Park City Watershed',
legend_extent=[-111.60, 40.57, -111.43, 40.70],
legend_classes=[
MVLegendClass('polygon', 'Watershed Boundary', fill='#ff8000'),
MVLegendClass('line', 'Stream Network', stroke='#0000ff'),
])
map_layers.append(kml_layer)
# Tiled ArcGIS REST Layer
arc_gis_layer = MVLayer(source='TileArcGISRest',
options={'url': 'http://sampleserver1.arcgisonline.com/ArcGIS/rest/services/' +
'Specialty/ESRI_StateCityHighway_USA/MapServer'},
legend_title='ESRI USA Highway',
legend_extent=[-173, 17, -65, 72])
map_layers.append(arc_gis_layer)
# Define map view options
map_view_options = MapView(
height='600px',
width='100%',
controls=['ZoomSlider', 'Rotate', 'FullScreen',
{'MousePosition': {'projection': 'EPSG:4326'}},
{'ZoomToExtent': {'projection': 'EPSG:4326', 'extent': [-130, 22, -65, 54]}}],
layers=map_layers,
view=view_options,
basemap='OpenStreetMap',
draw=drawing_options,
legend=True
)
submitted_geometry = request.POST.get('geometry', None)
if submitted_geometry is not None:
messages.info(request, submitted_geometry)
context = {'map_view': map_view_options}
return render(request, 'tethys_gizmos/gizmo_showcase/map_view.html', context)
| 5,505 |
def test_auto_linebreaks_no_ignore_lf(get_lcd):
"""
Do not ignore manual \n after auto linebreak.
"""
lcd = get_lcd(16, 2, True)
lcd.write_string('a' * 16) # Fill up line
lcd.write_string('\nb')
assert lcd._content[0] == [98, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97]
assert lcd._content[1] == [SP, SP, SP, SP, SP, SP, SP, SP, SP, SP, SP, SP, SP, SP, SP, SP]
| 5,506 |
def decompose_jamo(compound):
"""Return a tuple of jamo character constituents of a compound.
Note: Non-compound characters are echoed back.
WARNING: Archaic jamo compounds will raise NotImplementedError.
"""
if len(compound) != 1:
raise TypeError("decompose_jamo() expects a single character,",
"but received", type(compound), "length",
len(compound))
if compound not in JAMO_COMPOUNDS:
# Strict version:
# raise TypeError("decompose_jamo() expects a compound jamo,",
# "but received", compound)
return compound
return _JAMO_TO_COMPONENTS.get(compound, compound)
| 5,507 |
def test_wrong_input():
"""Test all kinds of wrong inputs."""
with pytest.raises(ValueError):
HiddenLayerHandle(method="potato")(n_in=10, n_out=10, n_sample=100)
| 5,508 |
def cp_in_drive(
source_id: str,
dest_title: Optional[str] = None,
parent_dir_id: Optional[str] = None,
) -> DiyGDriveFile:
"""Copy a specified file in Google Drive and return the created file."""
drive = create_diy_gdrive()
if dest_title is None:
dest_title = build_dest_title(drive, source_id)
return drive.copy_file(source_id, dest_title, parent_dir_id)
| 5,509 |
def label_tuning(
text_embeddings,
text_labels,
label_embeddings,
n_steps: int,
reg_coefficient: float,
learning_rate: float,
dropout: float,
) -> np.ndarray:
"""
With N as number of examples, K as number of classes, k as embedding dimension.
Args:
'text_embeddings': float[N,k] of embedded texts
'text_labels': float[N,K] class score for each example.
'label_embeddings': float[K,k] class embeddings
Returns:
float[K,k] updated class embeddings
"""
if text_embeddings.shape[0] == 0:
raise ValueError(text_embeddings.shape)
if label_embeddings.shape[0] == 0:
raise ValueError(label_embeddings.shape)
text_embeddings = tf.constant(text_embeddings)
text_labels = tf.constant(text_labels)
label_embeddings = tf.constant(label_embeddings)
init_label_embeddings = label_embeddings
for i in range(n_steps):
with tf.GradientTape() as tape:
tape.watch(label_embeddings)
dot_loss = _get_loss(
text_embeddings,
text_labels,
label_embeddings,
dropout=dropout,
)
drift_loss = tf.reduce_mean(
(label_embeddings - init_label_embeddings) ** 2
)
total_loss = dot_loss + reg_coefficient * drift_loss
gradient = tape.gradient(total_loss + drift_loss, label_embeddings)
label_embeddings = label_embeddings - (learning_rate * gradient)
label_embeddings = label_embeddings.numpy()
return label_embeddings
| 5,510 |
def create_nan_filter(tensor):
"""Creates a layer which replace NaN's with zero's."""
return tf.where(tf.is_nan(tensor), tf.zeros_like(tensor), tensor)
| 5,511 |
def requestor_is_superuser(requestor):
"""Return True if requestor is superuser."""
return getattr(requestor, "is_superuser", False)
| 5,512 |
def run_inital_basgra(basali, weed_dm_frac, harv_targ, harv_trig, freq):
"""
run an intial test
:param basali:
:param weed_dm_frac:
:return:
"""
params, matrix_weather, days_harvest, doy_irr = get_input_data(basali, weed_dm_frac, harv_targ=harv_targ,
harv_trig=harv_trig, freq=freq)
temp = run_basgra_nz(params, matrix_weather, days_harvest, doy_irr, verbose=False)
out = {'temp': temp}
temp.to_csv(r"C:\Users\Matt Hanson\Downloads\test_get_time.csv")
plot_multiple_results(out, out_vars=['DM', 'YIELD', 'DMH_RYE', 'DM_RYE_RM', 'IRRIG', 'PAW', 'DMH','BASAL'])
| 5,513 |
def process(business: Business, # pylint: disable=too-many-branches
filing: Dict,
filing_rec: Filing,
filing_meta: FilingMeta): # pylint: disable=too-many-branches
"""Process the incoming historic conversion filing."""
# Extract the filing information for incorporation
if not (conversion_filing := filing.get('filing', {}).get('conversion')):
raise QueueException(f'CONVL legal_filing:conversion missing from {filing_rec.id}')
if business:
raise QueueException(f'Business Already Exist: CONVL legal_filing:conversion {filing_rec.id}')
if not (corp_num := filing.get('filing', {}).get('business', {}).get('identifier')):
raise QueueException(f'conversion {filing_rec.id} missing the business idnetifier.')
# Initial insert of the business record
business_info_obj = conversion_filing.get('nameRequest')
if not (business := business_info.update_business_info(corp_num, Business(), business_info_obj, filing_rec)):
raise QueueException(f'CONVL conversion {filing_rec.id}, Unable to create business.')
if offices := conversion_filing.get('offices'):
update_offices(business, offices)
if parties := conversion_filing.get('parties'):
update_parties(business, parties)
if share_structure := conversion_filing.get('shareStructure'):
shares.update_share_structure(business, share_structure)
if name_translations := conversion_filing.get('nameTranslations'):
aliases.update_aliases(business, name_translations)
return business, filing_rec
| 5,514 |
def pid(text):
"""Print text if global debug flag set to true"""
if global_debug:
print(text)
| 5,515 |
def est_const_bsl(bsl,starttime=None,endtime=None,intercept=False,val_tw=None):
"""Performs a linear regression (assuming the intercept at the origin).
The corresponding formula is tt-S*1/v-c = 0 in which tt is the travel
time of the acoustic signal in seconds and 1/v is the reciprocal of the
harmonic mean of the sound speed. The slope S is equal to the constant
baseline length and by default c is assumed to be 0, but can optionally
also be determined (intercept=True).
It needs:
bsl ... pandas.Dataframe with ID of beacon 1 ('ID'), ID of beacon 2
('range_ID'), calculated baseline lengths in metres ('bsl'), one
way traveltime in seconds ('tt'), sound speed at beacon 1 ('ssp1')
in metres per second, sound speed at beacon 2 ('ssp2') in metres per
second, measured traveltime in milliseconds ('range'), turn around
time in milliseconds ('TAT')(eventually harmonic mean of 'ssp1' and
'ssp2' ('hmssp') and reciprocal of harmonic mean of 'ssp1' and
'ssp2' ('1/v'); if they do not exist, they will be calculated) with
corresponding times of measurement for beacon pair.
starttime (optional) ... string with starttime of time window for
estimation of constant baseline length (format: 'YYYY-mm-dd
HH:MM:SS', default: first entry in bsl)
endtime (optional) ... string with endtime of time window for estimation
of constant baseline length (format: 'YYYY-mm-dd HH:MM:SS', default:
last entry in bsl)
intercept (optional) ... specify whether intercept should be set to
0 [False] or should be calculated [True] (default is False)
val_tw (optional) ... specify time window for which estimated constant
baseline length and standard deviation (as well as intercept) will be
stored in returned pandas.Dataframe (format: ['YYYY-mm-dd HH:MM:SS',
'YYYY-mm-dd HH:MM:SS'], default is starttime and endtime)
It returns:
bsl ... pandas.Dataframe with ID of beacon 1 ('ID'), ID of beacon 2
('range_ID'), calculated baseline lengths in metres ('bsl'), one
way traveltime in seconds ('tt'), sound speed at beacon 1 ('ssp1')
in metres per second, sound speed at beacon 2 ('ssp2') in metres per
second, measured traveltime in milliseconds ('range'), turn around
time in milliseconds ('TAT'), harmonic mean of 'ssp1' and 'ssp2'
('hmssp'), reciprocal of harmonic mean of 'ssp1' and 'ssp2' ('1/v'),
constant baseline length ('bsl_const') in given time window and
standard deviation of the measurements compared to the fitted line
in seconds (sigma = sqrt(sum((tt-S*1/v)^2)/(len(1/v)-1)),
'std_dev_tt') in given time window (and intercept ('intercept') )
with corresponding times of measurement for beacon pair.
"""
# check if columns 'hmssp' and '1/v' (harmonic mean of sound speeds and its
# reciprocal already exist in bsl and if not then calculate them
if not set(['hmssp','1/v']).issubset(bsl.columns):
bsl = calc_hmssp_recp_v(bsl)
# end if not set(['hmssp','1/v']).issubset(bsl.columns):
# copy bsl to new pandas.Dataframe to cut it in time
bsl_new = bsl.copy()
# check if time window for estimation of constant baseline length is given
if starttime is not None:
bsl_new = bsl_new.loc[starttime:]
else:
# set startime to first index in bsl
starttime = bsl_new.index[0]
# end if starttime is not None:
if endtime is not None:
bsl_new = bsl_new.loc[:endtime]
else:
# set endtime to last index in bsl
endtime = bsl_new.index[-1]
# end if endtime is not None:
# the numpy function numpy.linalg.lstsq() needs x as (M,N) matrix
if not intercept:
x = bsl_new['1/v'][:,np.newaxis]
else:
x = np.array(([[bsl_new['1/v'][j], 1] for j in range(len(bsl_new))]))
# end if not intercept:
S,residuals,_,_ = np.linalg.lstsq(x,bsl_new['tt'])
sigma = np.sqrt(residuals/(len(x)-1))
# set column 'bsl_const' for values between starttime and endtime to S and
# column 'std_dev_tt' to estimated sigma in bsl
if val_tw is not None:
starttime = val_tw[0]
endtime = val_tw[1]
# end if val_tw is not None:
if not intercept:
bsl.loc[starttime:endtime,'bsl_const'] = S
else:
bsl.loc[starttime:endtime,'bsl_const'] = S[0]
bsl.loc[starttime:endtime,'intercept'] = S[1]
# end if not intercept:
bsl.loc[starttime:endtime,'std_dev_tt'] = sigma
return(bsl)
| 5,516 |
def GetExtractedFiles(Directory):
"""A generator that outputs all files in a diretory"""
for Thing in os.listdir(Directory):
PathThing = os.path.join(Directory, Thing)
if os.path.isdir(PathThing):
for File in GetExtractedFiles(PathThing):
yield File
else:
yield PathThing
| 5,517 |
def print_all(key = None):
"""
Prints out the complete list of physical_constants to the screen or
one single value
Parameters
----------
key : Python string or unicode
Key in dictionary `physical_constants`
Returns
-------
None
See Also
--------
_constants : Contains the description of `physical_constants`, which, as a
dictionary literal object, does not itself possess a docstring.
"""
column_width = [25, 20, 20, 20]
table_width = (column_width[0] + column_width[1] + column_width[2]
+ column_width[3])
format_string = ('{0:<' + str(column_width[0]) + '}' + '{1:>' +
str(column_width[1]) + '}' + '{2:>' + str(column_width[2])
+ '}' + '{3:>' + str(column_width[3]) + '}')
print(format_string.format('Name', 'Value', 'Units', 'Error'))
print(('{:-^' + str(table_width) + '}').format(''))
if key is None:
for key in physical_constants:
print(format_string.format(key, str(value(key)), unit(key),
str(uncertainty(key))))
else:
print(format_string.format(key, str(value(key)), unit(key),
str(uncertainty(key))))
| 5,518 |
def SeasonUPdate(temp):
""" Update appliance characteristics given the change in season
Parameters
----------
temp (obj): appliance set object for an individual season
Returns
----------
app_expected_load (float): expected load power in Watts
app_expected_dur (float): expected duration in hours
appliance_set (list of applience objects): applience list for a given season
t_delta_exp_dur (pandas datetime): expected appliance duration
app_index (array): index for each applience
"""
app_expected_load = temp.app_expected_load
app_expected_dur = temp.app_expected_dur
appliance_set = temp.appliance_set
t_delta_exp_dur = temp.t_delta_exp_dur
app_index = np.arange(0,len(temp.appliance_set))
return app_expected_load,app_expected_dur,appliance_set,t_delta_exp_dur,app_index
| 5,519 |
def pad_and_reshape(instr_spec, frame_length, F):
"""
:param instr_spec:
:param frame_length:
:param F:
:returns:
"""
spec_shape = tf.shape(instr_spec)
extension_row = tf.zeros((spec_shape[0], spec_shape[1], 1, spec_shape[-1]))
n_extra_row = (frame_length) // 2 + 1 - F
extension = tf.tile(extension_row, [1, 1, n_extra_row, 1])
extended_spec = tf.concat([instr_spec, extension], axis=2)
old_shape = tf.shape(extended_spec)
new_shape = tf.concat([
[old_shape[0] * old_shape[1]],
old_shape[2:]],
axis=0)
processed_instr_spec = tf.reshape(extended_spec, new_shape)
return processed_instr_spec
| 5,520 |
def GetExclusiveStorageForNodes(cfg, node_uuids):
"""Return the exclusive storage flag for all the given nodes.
@type cfg: L{config.ConfigWriter}
@param cfg: cluster configuration
@type node_uuids: list or tuple
@param node_uuids: node UUIDs for which to read the flag
@rtype: dict
@return: mapping from node uuids to exclusive storage flags
@raise errors.OpPrereqError: if any given node name has no corresponding
node
"""
getflag = lambda n: _GetExclusiveStorageFlag(cfg, n)
flags = map(getflag, node_uuids)
return dict(zip(node_uuids, flags))
| 5,521 |
def get_read_data(file, dic, keys):
""" Assigns reads to labels"""
r = csv.reader(open(file))
lines = list(r)
vecs_forwards = []
labels_forwards = []
vecs_reverse = []
labels_reverse = []
for key in keys:
for i in dic[key]:
for j in lines:
if i in j[0]:
if '_2.fq' in j[0] or '_R2_' in j[0]:
vecs_reverse.append(j[2:])
labels_reverse.append(key)
else:
vecs_forwards.append(j[2:])
labels_forwards.append(key)
return np.array(vecs_forwards), np.array(labels_forwards), np.array(vecs_reverse), np.array(labels_reverse)
| 5,522 |
def removeDuplicates(listToRemoveFrom: list[str]):
"""Given list, returns list without duplicates"""
listToReturn: list[str] = []
for item in listToRemoveFrom:
if item not in listToReturn:
listToReturn.append(item)
return listToReturn
| 5,523 |
def check_hms_angle(value):
"""
Validating function for angle sexagesimal representation in hours.
Used in the rich_validator
"""
if isinstance(value, list):
raise validate.ValidateError("expected value angle, found list")
match = hms_angle_re.match(value)
if not match:
raise VdtAngleError("not a valid hour angle: %s" % value)
return hms_to_angle(match.groups())
| 5,524 |
def adjust_learning_rate(optimizer, epoch, default_lr=0.1):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = default_lr * (0.1 ** (epoch // 30))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
| 5,525 |
def test_matcher_without_allure(
request: SubRequest,
pytester: Pytester,
):
"""Test matcher without allure"""
*_, not_implemented = test_cases()
percent = (len(scenarios) - len(not_implemented)) * 100 // len(scenarios)
with allure_unloaded(request):
pytester_result = run_tests(
pytester=pytester,
testfile_path="matcher_pytester_test.py",
additional_opts=["--sc-type", "test", "--sc-only", "--sc-target", percent],
outcomes={"passed": 0},
)
with allure.step("Check summary for coverage percent"):
# Last one line will be greetings while previous one with stats
assert any(
f"{percent}%" in outline for outline in pytester_result.outlines
), f'Should be "{percent}%" in outlines'
| 5,526 |
def test_merge_configs_extend_two():
"""Ensure extension on first level is fine"""
a = {'a': 1, 'b': 2}
b = {'c': 3}
m = resolve_config.merge_configs(a, b)
assert m == {'a': 1, 'b': 2, 'c': 3}
| 5,527 |
def get_logger(module_name):
"""Generates a logger for each module of the project.
By default, the logger logs debug-level information into a
newscrapy.log file and info-level information in console.
Parameters
----------
module_name: str
The name of the module for which the logger should
be generated, in snakecase.
Returns
-------
Logger
A logger for a specific module.
"""
logger = logging.getLogger('%s_logger' % (module_name))
file_handler = logging.FileHandler('newscrapy.log')
console_handler = logging.StreamHandler()
file_formatter = logging.Formatter('%(asctime)s - %(name)s - '
'%(levelname)s - %(message)s')
console_formatter = logging.Formatter('%(message)s')
logger.setLevel(logging.DEBUG)
file_handler.setLevel(logging.DEBUG)
console_handler.setLevel(logging.INFO)
file_handler.setFormatter(file_formatter)
console_handler.setFormatter(console_formatter)
logger.addHandler(file_handler)
logger.addHandler(console_handler)
return logger
| 5,528 |
def stats():
"""Retrives the count of each object type.
Returns:
JSON object with the number of objects by type."""
return jsonify({
"amenities": storage.count("Amenity"),
"cities": storage.count("City"),
"places": storage.count("Place"),
"reviews": storage.count("Review"),
"states": storage.count("State"),
"users": storage.count("User")
})
| 5,529 |
def test_task_persist(_task):
""" show that the task object is the same throughout """
_task.on_success = False # start False
def on_success(task, result):
task.on_success = True # change to True in success function
task.callback(0, result)
_task.call(
task_callback,
on_success=on_success,
)
assert _task.on_success
| 5,530 |
def addflux2pix(px,py,pixels,fmod):
"""Usage: pixels=addflux2pix(px,py,pixels,fmod)
Drizel Flux onto Pixels using a square PSF of pixel size unity
px,py are the pixel position (integers)
fmod is the flux calculated for (px,py) pixel
and it has the same length as px and py
pixels is the image.
"""
xmax = pixels.shape[0] #Size of pixel array
ymax = pixels.shape[1]
pxmh = px-0.5 #location of reference corner of PSF square
pymh = py-0.5
dx = np.floor(px+0.5)-pxmh
dy = np.floor(py+0.5)-pymh
# Supposing right-left as x axis and up-down as y axis:
# Lower left pixel
npx = int(pxmh) #Numpy arrays start at zero
npy = int(pymh)
#print('n',npx,npy)
if (npx >= 0) & (npx < xmax) & (npy >= 0) & (npy < ymax) :
pixels[npx,npy]=pixels[npx,npy]+fmod*dx*dy
#Same operations are done for the 3 pixels other neighbouring pixels
# Lower right pixel
npx = int(pxmh)+1 #Numpy arrays start at zero
npy = int(pymh)
if (npx >= 0) & (npx < xmax) & (npy >= 0) & (npy < ymax) :
pixels[npx,npy]=pixels[npx,npy]+fmod*(1.0-dx)*dy
# Upper left pixel
npx = int(pxmh) #Numpy arrays start at zero
npy = int(pymh)+1
if (npx >= 0) & (npx < xmax) & (npy >= 0) & (npy < ymax) :
pixels[npx,npy]=pixels[npx,npy]+fmod*dx*(1.0-dy)
# Upper right pixel
npx = int(pxmh)+1 #Numpy arrays start at zero
npy = int(pymh)+1
if (npx >= 0) & (npx < xmax) & (npy >= 0) & (npy < ymax) :
pixels[npx,npy]=pixels[npx,npy]+fmod*(1.0-dx)*(1.0-dy)
return pixels;
| 5,531 |
def get_dea_landsat_vrt_dict(feat_list):
"""
this func is designed to take all releveant landsat bands
on the dea public database for each scene in stac query.
it results in a list of vrts for each band seperately and maps
them to a dict where band name is the key, list is the value pair.
"""
# notify
print('Getting landsat vrts for each relevant bands.')
# check features type, length
if not isinstance(feat_list, list):
raise TypeError('Features must be a list of xml objects.')
elif not len(feat_list) > 0:
raise ValueError('No features provided.')
# required dea landsat ard band names
bands = [
'nbart_blue',
'nbart_green',
'nbart_red',
'nbart_nir',
'nbart_swir_1',
'nbart_swir_2',
'oa_fmask'
]
# iter each band name and build associated vrt list
band_vrts_dict = {}
for band in bands:
print('Building landsat vrt list for band: {}.'.format(band))
band_vrts_dict[band] = make_vrt_list(feat_list, band=band)
# notify and return
print('Got {} landsat vrt band lists successfully.'.format(len(band_vrts_dict)))
return band_vrts_dict
| 5,532 |
def test_validate_skip_inputs_fq_files_not_found(tmp_file):
"""
Test that non-existent :py:const:`riboviz.params.FQ_FILES` files
in the presence of both :py:const:`riboviz.params.VALIDATE_ONLY`
and :py:const:`riboviz.params.SKIP_INPUTS` returns a zero exit
code.
:param tmp_file: Path to temporary file, to write configuration to
:type tmp_file: str or unicode
"""
with open(riboviz.test.VIGNETTE_CONFIG, 'r') as f:
config = yaml.load(f, yaml.SafeLoader)
config[params.FQ_FILES] = {
"foo1": "foo1.fq", "foo2": "foo2.fq"
}
config[params.VALIDATE_ONLY] = True
config[params.SKIP_INPUTS] = True
with open(tmp_file, 'w') as f:
yaml.dump(config, f)
exit_code = run_nextflow(tmp_file)
assert exit_code == 0, \
"Unexpected exit code %d" % exit_code
| 5,533 |
def test_spammers_error(mailchimp, mailchimp_member, err_response):
"""Integration test to validate an error is thrown when mailchimp returns error during subscription"""
mailchimp.http_mock.post('https://us05.api.mailchimp.com/3.0/lists/test-list-id', json=err_response)
with pytest.raises(MailchimpSubscriptionFailed) as exception:
mailchimp.mass_subscribe(
list_id='test-list-id',
members=[mailchimp_member],
)
assert 'f+localmachinetest@f213.in has' in str(exception)
assert '(ERROR_GENERIC)' in str(exception)
| 5,534 |
def load_json() -> tuple[list["Team"], list["User"]]:
"""Load the Json file."""
logging.debug("Starting to load data file.")
with open(".example.json") as file:
data = json.load(file)
if any(field not in data for field in REQUIRED_DATA_FIELDS):
raise ValueError("Required field is missing.")
team_mapping = {}
users = []
for uid, user_data in data["users"].items():
if any(field not in user_data for field in REQUIRED_USER_FIELDS):
raise ValueError("Required field is missing.")
user = User(uid, **user_data)
users.append(user)
if user_data["team"] not in team_mapping:
team_mapping[user_data["team"]] = []
team_mapping[user_data["team"]].append(user)
teams = []
for tid, team_data in data["teams"].items():
if any(field not in team_data for field in REQUIRED_TEAM_FIELDS):
raise ValueError("Required field is missing.")
team = Team(tid, team_mapping.get(tid, []), None, **team_data)
teams.append(team)
for user in users:
if user.team == tid:
user.team = team
if user.leader:
if team.leader is not None:
raise ValueError(f"Team {tid!r} has more than one leader.")
team.leader = user
for user in users:
if isinstance(user.team, str):
raise ValueError(f"Unknown team {user.team!r}")
logging.debug("Data loaded.")
return teams, users
| 5,535 |
def is_zh_l_bracket(uni_ch):
"""判断一个 unicode 是否是中文左括号。"""
if uni_ch == u'\uff08':
return True
else:
return False
| 5,536 |
def test_archive__ArchiveForm__3(search_data, browser, role):
"""It cannot be accessed by non-admin users."""
browser.login(role)
browser.keyword_search('church')
# There is no archive option which can be applied:
assert (search_result_handlers_without_archive_for_editor
== browser.getControl('Apply on selected persons').displayOptions)
browser.assert_forbidden(browser.SEARCH_ARCHIVE_URL)
| 5,537 |
def petlink32_to_dynamic_projection_mMR(filename,n_packets,n_radial_bins,n_angles,n_sinograms,time_bins,n_axial,n_azimuthal,angles_axial,angles_azimuthal,size_u,size_v,n_u,n_v,span,n_segments,segments_sizes,michelogram_segments,michelogram_planes, status_callback):
"""Make dynamic compressed projection from list-mode data. """
descriptor = [ {'name':'filename', 'type':'string', 'value':filename ,'size':len(filename)},
{'name':'n_packets', 'type':'long', 'value':n_packets },
{'name':'n_radial_bins', 'type':'uint', 'value':n_radial_bins },
{'name':'n_angles', 'type':'uint', 'value':n_angles },
{'name':'n_sinograms', 'type':'uint', 'value':n_sinograms },
{'name':'n_time_bins', 'type':'uint', 'value':len(time_bins)-1 },
{'name':'time_bins', 'type':'array', 'value':np.int32(time_bins) },
{'name':'n_axial', 'type':'uint', 'value':n_axial },
{'name':'n_azimuthal', 'type':'uint', 'value':n_azimuthal },
{'name':'angles_axial', 'type':'array', 'value':angles_axial },
{'name':'angles_azimuthal', 'type':'array', 'value':angles_azimuthal },
{'name':'size_u', 'type':'float', 'value':size_u },
{'name':'size_v', 'type':'float', 'value':size_v },
{'name':'n_u', 'type':'uint', 'value':n_u },
{'name':'n_v', 'type':'uint', 'value':n_v },
{'name':'span', 'type':'uint', 'value':span },
{'name':'n_segments', 'type':'uint', 'value':n_segments },
{'name':'segments_sizes', 'type':'array', 'value':np.int32(segments_sizes) },
{'name':'michelogram_segments', 'type':'array', 'value':np.int32(michelogram_segments) },
{'name':'michelogram_planes', 'type':'array', 'value':np.int32(michelogram_planes) },
{'name':'status_callback', 'type':'function','value':status_callback, 'arg_types':['uint'] }, ]
r = call_c_function( mMR_c.petlink32_to_dynamic_projection_mMR_michelogram, descriptor )
if not r.status == petlink.status_success():
raise ErrorInCFunction("The execution of 'petlink32_to_dynamic_projection_mMR_michelogram' was unsuccessful.",r.status,'mMR_c.petlink32_to_dynamic_projection_mMR')
return r.dictionary
| 5,538 |
def is_core_recipe(component: Dict) -> bool:
"""
Returns True if a recipe component contains a "Core Recipe"
preparation.
"""
preparations = component.get('recipeItem', {}).get('preparations') or []
return any(prep.get('id') == PreparationEnum.CORE_RECIPE.value for prep in preparations)
| 5,539 |
def login_submit_step(context):
"""
The cognito signin form is rendered in HTML twice for difference screen sizes.
The small screen version appears first in the HTML but is hidden by CSS.
Without the .visible-md class this resolves the hidden form element and
is unable to interact with the form.
"""
elem = context.browser.find_element_by_css_selector(
".visible-md .modal-body #signInFormPassword"
)
elem.submit()
| 5,540 |
def build_estimator(output_dir, first_layer_size, num_layers, dropout,
learning_rate, save_checkpoints_steps):
"""Builds and returns a DNN Estimator, defined by input parameters.
Args:
output_dir: string, directory to save Estimator.
first_layer_size: int, size of first hidden layer of DNN.
num_layers: int, number of hidden layers.
dropout: float, dropout rate used in training.
learning_rate: float, learning_rate used in training.
save_checkpoints_steps: int, training steps to save Estimator.
Returns:
`Estimator` instance.
"""
# Sets head to default head for DNNClassifier with two classes.
model_params = {
'head':
head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss(),
'feature_columns': [
tf.feature_column.numeric_column(c, shape=[])
for c in constants.FEATURE_COLUMNS
],
'hidden_units': [
max(int(first_layer_size / (pow(2, i))), 2)
for i in range(int(num_layers))
],
'dropout':
dropout,
'optimizer':
tf.train.AdagradOptimizer(learning_rate)
}
def _model_fn(features, labels, mode, params):
"""Build TF graph based on canned DNN classifier."""
key_column = features.pop(constants.KEY_COLUMN, None)
if key_column is None:
raise ValueError('Key is missing from features.')
spec = _dnn_model_fn(features=features, labels=labels, mode=mode, **params)
predictions = spec.predictions
if predictions:
predictions[constants.KEY_COLUMN] = tf.convert_to_tensor_or_sparse_tensor(
key_column)
spec = spec._replace(predictions=predictions)
spec = spec._replace(export_outputs={
'classes': tf.estimator.export.PredictOutput(predictions)
})
return spec
config = tf.estimator.RunConfig(save_checkpoints_steps=save_checkpoints_steps)
return tf.estimator.Estimator(
model_fn=_model_fn,
model_dir=output_dir,
config=config,
params=model_params)
| 5,541 |
def importConfig():
"""設定ファイルの読み込み
Returns:
tuple:
str: interface,
str: alexa_remote_control.sh path
list: device list
"""
with open("config.json", "r", encoding="utf-8") as f:
config = json.load(f)
interface = config["interface"]
if not interface:
return False
arc_path = config["arc_path"]
devices = config["device_list"]
return (interface, arc_path, devices)
| 5,542 |
def create_dataset(project_id):
"""Creates a dataset for the given Google Cloud project."""
from google.cloud import datalabeling_v1beta1 as datalabeling
client = datalabeling.DataLabelingServiceClient()
# [END datalabeling_create_dataset_beta]
# If provided, use a provided test endpoint - this will prevent tests on
# this snippet from triggering any action by a real human
if 'DATALABELING_ENDPOINT' in os.environ:
opts = ClientOptions(api_endpoint=os.getenv('DATALABELING_ENDPOINT'))
client = datalabeling.DataLabelingServiceClient(client_options=opts)
# [START datalabeling_create_dataset_beta]
formatted_project_name = client.project_path(project_id)
dataset = datalabeling.types.Dataset(
display_name='YOUR_DATASET_SET_DISPLAY_NAME',
description='YOUR_DESCRIPTION'
)
response = client.create_dataset(formatted_project_name, dataset)
# The format of resource name:
# project_id/{project_id}/datasets/{dataset_id}
print('The dataset resource name: {}'.format(response.name))
print('Display name: {}'.format(response.display_name))
print('Description: {}'.format(response.description))
print('Create time:')
print('\tseconds: {}'.format(response.create_time.seconds))
print('\tnanos: {}\n'.format(response.create_time.nanos))
return response
| 5,543 |
def create_local_command(opts: Options, jobs: List[Dict[str, Any]], jobs_metadata: List[Options]) -> str:
"""Create a terminal command to run the jobs locally."""
cmd = ""
for meta, job in zip(jobs_metadata, jobs):
input_file = meta.input.absolute().as_posix()
workdir = meta.workdir.absolute().as_posix()
# Run locally
cmd += f'cd {workdir} && {opts.command} {input_file} & '
return cmd
| 5,544 |
def test_write_tags(tmpdir):
"""Test writing tags from a FLAC to mp3 file."""
# Prepare.
flac = tmpdir.mkdir('flac').join('song.flac').ensure(file=True)
mp3 = tmpdir.mkdir('mp3').join('song.mp3').ensure(file=True)
with open(os.path.join(os.path.dirname(__file__), '1khz_sine.flac'), 'rb') as f:
flac.write(f.read(), 'wb')
with open(os.path.join(os.path.dirname(__file__), '1khz_sine.mp3'), 'rb') as f:
mp3.write(f.read(), 'wb')
flac, mp3 = str(flac.realpath()), str(mp3.realpath())
tags = FLAC(flac)
tags.update(dict(artist='Artist2', date='2012', album='Album', tracknumber='01', title='Title', unsyncedlyrics='L'))
image = Picture()
image.type, image.mime = 3, 'image/jpeg'
with open(os.path.join(os.path.dirname(__file__), '1_album_art.jpg'), 'rb') as f:
image.data = f.read()
tags.add_picture(image)
tags.save()
# Test.
ConvertFiles.write_tags(flac, mp3)
# Check.
id3 = ID3(mp3)
assert 'Artist2' == id3['TPE1']
assert '2012' == id3['TDRC']
assert 'Album' == id3['TALB']
assert '01' == id3['TRCK']
assert 'Title' == id3['TIT2']
assert 'L' == id3["USLT:Lyrics:'eng'"].text
with open(os.path.join(os.path.dirname(__file__), '1_album_art.jpg'), 'rb') as f:
assert f.read() == id3['APIC:'].data
assert ({}, [], [], []) == find_files(str(tmpdir.join('flac')), str(tmpdir.join('mp3')))
| 5,545 |
def time_for_log() -> str:
"""Function that print the current time for bot prints"""
return time.strftime("%d/%m %H:%M:%S - ")
| 5,546 |
def reset_session(self, request):
"""
Resets the session present in the current request,
to reset the session is to unset it from the request.
This method is useful for situation where a new session
context is required or one is meant to be created always.
:type request: Request
:param request: The request to be used.
"""
# resets the session removing it from the request
# this allows subsequent calls to create a new session
request.reset_session()
| 5,547 |
def _is_int(n) -> bool:
"""
is_int 是判断给定数字 n 是否为整数，
在判断中 n 小于epsilon的小数部分将被忽略，
是则返回 True，否则 False
:param n: 待判断的数字
:return: True if n is A_ub integer, False else
"""
return (n - math.floor(n) < _epsilon) or (math.ceil(n) - n < _epsilon)
| 5,548 |
def _cpp_het_stat(A, t_stop, rates, t_start=0. * pq.ms):
"""
Generate a Compound Poisson Process (CPP) with amplitude distribution
A and heterogeneous firing rates r=r[0], r[1], ..., r[-1].
Parameters
----------
A : np.ndarray
CPP's amplitude distribution. A[j] represents the probability of
a synchronous event of size j among the generated spike trains.
The sum over all entries of A must be equal to one.
t_stop : pq.Quantity
The end time of the output spike trains
rates : pq.Quantity
Array of firing rates of each spike train generated with
t_start : pq.Quantity, optional
The start time of the output spike trains
Default: 0 pq.ms
Returns
-------
list of neo.SpikeTrain
List of neo.SpikeTrains with different firing rates, forming
a CPP with amplitude distribution `A`.
"""
# Computation of Parameters of the two CPPs that will be merged
# (uncorrelated with heterog. rates + correlated with homog. rates)
n_spiketrains = len(rates) # number of output spike trains
# amplitude expectation
expected_amplitude = np.dot(A, np.arange(n_spiketrains + 1))
r_sum = np.sum(rates) # sum of all output firing rates
r_min = np.min(rates) # minimum of the firing rates
# rate of the uncorrelated CPP
r_uncorrelated = r_sum - n_spiketrains * r_min
# rate of the correlated CPP
r_correlated = r_sum / expected_amplitude - r_uncorrelated
# rate of the hidden mother process
r_mother = r_uncorrelated + r_correlated
# Check the analytical constraint for the amplitude distribution
if A[1] < (r_uncorrelated / r_mother).rescale(
pq.dimensionless).magnitude:
raise ValueError('A[1] too small / A[i], i>1 too high')
# Compute the amplitude distribution of the correlated CPP, and generate it
A = A * (r_mother / r_correlated).magnitude
A[1] = A[1] - r_uncorrelated / r_correlated
compound_poisson_spiketrains = _cpp_hom_stat(
A, t_stop, r_min, t_start)
# Generate the independent heterogeneous Poisson processes
poisson_spiketrains = \
[homogeneous_poisson_process(rate - r_min, t_start, t_stop)
for rate in rates]
# Pool the correlated CPP and the corresponding Poisson processes
return [_pool_two_spiketrains(compound_poisson_spiketrain,
poisson_spiketrain)
for compound_poisson_spiketrain, poisson_spiketrain
in zip(compound_poisson_spiketrains, poisson_spiketrains)]
| 5,549 |
def return_bad_parameter_config() -> CloudSettings:
"""Return a wrongly configured cloud config class."""
CloudSettingsTest = CloudSettings( # noqa: N806
settings_order=[
"init_settings",
"aws_parameter_setting",
"file_secret_settings",
"env_settings",
]
) # noqa: N806
class AWSSettings(CloudSettingsTest): # type: ignore
test: str = "Cool"
prefix_test_store: str = ""
return AWSSettings()
| 5,550 |
def update(isamAppliance, instance_id, id, filename=None, contents=None, check_mode=False, force=False):
"""
Update a file in the administration pages root
:param isamAppliance:
:param instance_id:
:param id:
:param name:
:param contents:
:param check_mode:
:param force:
:return:
"""
if force is True or _check_file(isamAppliance, instance_id, id) is True:
if check_mode is True:
return isamAppliance.create_return_object(changed=True)
else:
if filename is not None:
return isamAppliance.invoke_put_files(
"Update a file in the administration page root",
"/wga/reverseproxy/{0}/management_root/{1}".format(instance_id, id),
[
{
'file_formfield': 'file',
'filename': filename,
'mimetype': 'application/octet-stream'
}
],
{
'file': filename,
'type': 'file'
})
elif contents is not None:
return isamAppliance.invoke_put_files(
"Update a file in the administration page root",
"/wga/reverseproxy/{0}/management_root/{1}".format(instance_id, id),
{
'contents': contents,
'type': 'file'
})
else:
return isamAppliance.create_return_object(
warnings=["Either contents or filename parameter need to be provided. Skipping update request."])
| 5,551 |
def view_deflate_encoded_content():
"""Returns Deflate-encoded data.
---
tags:
- Response formats
produces:
- application/json
responses:
200:
description: Defalte-encoded data.
"""
return jsonify(get_dict("origin", "headers", method=request.method, deflated=True))
| 5,552 |
def predict_from_word_vectors_matrix(tokens, matrix, nlp, POS="NOUN", top_number=constants.DEFAULT_TOP_ASSOCIATIONS):
"""
Make a prediction based on the word vectors
:param tokens:
:param matrix:
:param nlp:
:param POS:
:param top_number:
:return:
"""
vector_results = collect_word_vector_associations(tokens, matrix)
top_results = get_top_results(vector_results, nlp, top_number, POS)
return top_results
| 5,553 |
def get_luis_keys():
"""Retrieve Keys for LUIS app"""
load_dotenv()
key = os.getenv("LUIS_KEY")
region = os.getenv("LUIS_REGION")
app_id = os.getenv("LUIS_APP_ID")
return key, region, app_id
| 5,554 |
def xls_to_dict(path_or_file):
"""
Return a Python dictionary with a key for each worksheet
name. For each sheet there is a list of dictionaries, each
dictionary corresponds to a single row in the worksheet. A
dictionary has keys taken from the column headers and values
equal to the cell value for that row and column.
All the keys and leaf elements are unicode text.
"""
try:
if isinstance(path_or_file, basestring):
workbook = xlrd.open_workbook(filename=path_or_file)
else:
workbook = xlrd.open_workbook(file_contents=path_or_file.read())
except XLRDError as error:
raise PyXFormError("Error reading .xls file: %s" % error)
def xls_to_dict_normal_sheet(sheet):
def iswhitespace(string):
return isinstance(string, basestring) and len(string.strip()) == 0
# Check for duplicate column headers
column_header_list = list()
for column in range(0, sheet.ncols):
column_header = sheet.cell_value(0, column)
if column_header in column_header_list:
raise PyXFormError("Duplicate column header: %s" % column_header)
# xls file with 3 columns mostly have a 3 more columns that are
# blank by default or something, skip during check
if column_header is not None:
if not iswhitespace(column_header):
# strip whitespaces from the header
clean_header = re.sub(r"( )+", " ", column_header.strip())
column_header_list.append(clean_header)
result = []
for row in range(1, sheet.nrows):
row_dict = OrderedDict()
for column in range(0, sheet.ncols):
# Changing to cell_value function
# convert to string, in case it is not string
key = "%s" % sheet.cell_value(0, column)
key = key.strip()
value = sheet.cell_value(row, column)
# remove whitespace at the beginning and end of value
if isinstance(value, basestring):
value = value.strip()
value_type = sheet.cell_type(row, column)
if value is not None:
if not iswhitespace(value):
try:
row_dict[key] = xls_value_to_unicode(
value, value_type, workbook.datemode
)
except XLDateAmbiguous:
raise PyXFormError(
XL_DATE_AMBIGOUS_MSG % (sheet.name, column_header, row)
)
# Taking this condition out so I can get accurate row numbers.
# TODO: Do the same for csvs
# if row_dict != {}:
result.append(row_dict)
return result, _list_to_dict_list(column_header_list)
def xls_value_from_sheet(sheet, row, column):
value = sheet.cell_value(row, column)
value_type = sheet.cell_type(row, column)
if value is not None and value != "":
try:
return xls_value_to_unicode(value, value_type, workbook.datemode)
except XLDateAmbiguous:
raise PyXFormError(XL_DATE_AMBIGOUS_MSG % (sheet.name, column, row))
else:
raise PyXFormError("Empty Value")
result = OrderedDict()
for sheet in workbook.sheets():
# Note that the sheet exists but do no further processing here.
result[sheet.name] = []
# Do not process sheets that have nothing to do with XLSForm.
if sheet.name not in constants.SUPPORTED_SHEET_NAMES:
if len(workbook.sheets()) == 1:
(
result[constants.SURVEY],
result["%s_header" % constants.SURVEY],
) = xls_to_dict_normal_sheet(sheet)
else:
continue
else:
(
result[sheet.name],
result["%s_header" % sheet.name],
) = xls_to_dict_normal_sheet(sheet)
return result
| 5,555 |
def blendImg(img_a, img_b, α=0.8, β=1., γ=0.):
"""
The result image is computed as follows:
img_a * α + img_b * β + γ
"""
return cv2.addWeighted(img_a, α, img_b, β, γ)
| 5,556 |
def setup(app):
"""Sets up the extension"""
app.add_autodocumenter(documenters.FunctionDocumenter)
app.add_config_value(
"autoclass_content", "class", True, ENUM("both", "class", "init")
)
app.add_config_value(
"autodoc_member_order",
"alphabetical",
True,
ENUM("alphabetic", "alphabetical", "bysource", "groupwise"),
)
app.add_config_value("autodoc_default_options", {}, True)
app.add_config_value("autodoc_docstring_signature", True, True)
app.add_config_value("autodoc_mock_imports", [], True)
app.add_config_value(
"autodoc_typehints", "signature", True, ENUM("signature", "description", "none")
)
app.add_config_value("autodoc_type_aliases", {}, True)
app.add_config_value("autodoc_warningiserror", True, True)
app.add_config_value("autodoc_inherit_docstrings", True, True)
app.add_event("autodoc-before-process-signature")
app.add_event("autodoc-process-docstring")
app.add_event("autodoc-process-signature")
app.add_event("autodoc-skip-member")
app.connect("config-inited", migrate_autodoc_member_order, priority=800)
app.setup_extension("sphinx.ext.autodoc.type_comment")
app.setup_extension("sphinx.ext.autodoc.typehints")
return {"version": sphinx.__display_version__, "parallel_read_safe": True}
| 5,557 |
def test_register_invalid_transfer(raiden_network, settle_timeout):
""" Regression test for registration of invalid transfer.
The bug occurred if a transfer with an invalid allowance but a valid secret
was registered, when the local end registered the transfer it would
"unlock" the partners' token, but the transfer wouldn't be sent because the
allowance check failed, leaving the channel in an inconsistent state.
"""
app0, app1 = raiden_network # pylint: disable=unbalanced-tuple-unpacking
graph0 = app0.raiden.channelgraphs.values()[0]
graph1 = app1.raiden.channelgraphs.values()[0]
channel0 = graph0.partneraddress_channel.values()[0]
channel1 = graph1.partneraddress_channel.values()[0]
balance0 = channel0.balance
balance1 = channel1.balance
amount = 10
block_number = app0.raiden.chain.block_number()
expiration = block_number + settle_timeout - 1
secret = 'secret'
hashlock = sha3(secret)
transfer1 = channel0.create_mediatedtransfer(
block_number,
transfer_initiator=app0.raiden.address,
transfer_target=app1.raiden.address,
fee=0,
amount=amount,
identifier=1,
expiration=expiration,
hashlock=hashlock,
)
# register a locked transfer
app0.raiden.sign(transfer1)
channel0.register_transfer(
app0.raiden.chain.block_number(),
transfer1,
)
channel1.register_transfer(
app1.raiden.chain.block_number(),
transfer1,
)
# assert the locked transfer is registered
assert_synched_channels(
channel0, balance0, [],
channel1, balance1, [transfer1.lock],
)
# handcrafted transfer because channel.create_transfer won't create it
transfer2 = DirectTransfer(
1, # TODO: fill in identifier
nonce=channel0.our_state.nonce,
token=channel0.token_address,
transferred_amount=channel1.balance + balance0 + amount,
recipient=channel0.partner_state.address,
locksroot=channel0.partner_state.balance_proof.merkleroot_for_unclaimed(),
)
app0.raiden.sign(transfer2)
# this need to fail because the allowance is incorrect
with pytest.raises(Exception):
channel0.register_transfer(
app0.raiden.chain.block_number(),
transfer2,
)
with pytest.raises(Exception):
channel1.register_transfer(
app1.raiden.chain.block_number(),
transfer2,
)
# the registration of a bad transfer need fail equaly on both channels
assert_synched_channels(
channel0, balance0, [],
channel1, balance1, [transfer1.lock],
)
| 5,558 |
def genoimc_dup4_loc():
"""Create genoimc dup4 sequence location"""
return {
"_id": "ga4gh:VSL.us51izImAQQWr-Hu6Q7HQm-vYvmb-jJo",
"sequence_id": "ga4gh:SQ.-A1QmD_MatoqxvgVxBLZTONHz9-c7nQo",
"interval": {
"type": "SequenceInterval",
"start": {
"value": 30417575,
"comparator": "<=",
"type": "IndefiniteRange"
},
"end": {
"value": 31394018,
"comparator": ">=",
"type": "IndefiniteRange"
}
},
"type": "SequenceLocation"
}
| 5,559 |
def compare_versions(a, b):
"""Return 0 if a == b, 1 if a > b, else -1."""
a, b = version_to_ints(a), version_to_ints(b)
for i in range(min(len(a), len(b))):
if a[i] > b[i]:
return 1
elif a[i] < b[i]:
return -1
return 0
| 5,560 |
def check_ast_schema_is_valid(ast: DocumentNode) -> None:
"""Check the schema satisfies structural requirements for rename and merge.
In particular, check that the schema contains no mutations, no subscriptions, no
InputObjectTypeDefinitions, no TypeExtensionDefinitions, all type names are valid and not
reserved (not starting with double underscores), and all query type field names match the
types they query.
Args:
ast: represents schema
Raises:
- SchemaStructureError if the AST cannot be built into a valid schema, if the schema
contains mutations, subscriptions, InputObjectTypeDefinitions, TypeExtensionsDefinitions,
or if any query type field does not match the queried type.
- InvalidNameError if a type has a type name that is invalid or reserved
"""
schema = build_ast_schema(ast)
if schema.mutation_type is not None:
raise SchemaStructureError(
"Renaming schemas that contain mutations is currently not supported."
)
if schema.subscription_type is not None:
raise SchemaStructureError(
"Renaming schemas that contain subscriptions is currently not supported."
)
visit(ast, CheckValidTypesAndNamesVisitor())
query_type = get_query_type_name(schema)
visit(ast, CheckQueryTypeFieldsNameMatchVisitor(query_type))
| 5,561 |
def get_machine_action_data(machine_action_response):
"""Get machine raw response and returns the machine action info in context and human readable format.
Notes:
Machine action is a collection of actions you can apply on the machine, for more info
https://docs.microsoft.com/en-us/windows/security/threat-protection/microsoft-defender-atp/machineaction
Returns:
dict. Machine action's info
"""
action_data = \
{
"ID": machine_action_response.get('id'),
"Type": machine_action_response.get('type'),
"Scope": machine_action_response.get('scope'),
"Requestor": machine_action_response.get('requestor'),
"RequestorComment": machine_action_response.get('requestorComment'),
"Status": machine_action_response.get('status'),
"MachineID": machine_action_response.get('machineId'),
"ComputerDNSName": machine_action_response.get('computerDnsName'),
"CreationDateTimeUtc": machine_action_response.get('creationDateTimeUtc'),
"LastUpdateTimeUtc": machine_action_response.get('lastUpdateTimeUtc'),
"RelatedFileInfo": {
"FileIdentifier": machine_action_response.get('fileIdentifier'),
"FileIdentifierType": machine_action_response.get('fileIdentifierType')
},
"Commands": machine_action_response.get('commands')
}
return action_data
| 5,562 |
def test_validate_cabling_invalid_ip_file():
"""Test that the `canu validate network cabling` command errors on invalid IPs from a file."""
invalid_ip = "999.999.999.999"
with runner.isolated_filesystem():
with open("test.txt", "w") as f:
f.write(invalid_ip)
result = runner.invoke(
cli,
[
"--cache",
cache_minutes,
"validate",
"network",
"cabling",
"--architecture",
architecture,
"--ips-file",
"test.txt",
"--username",
username,
"--password",
password,
],
)
assert result.exit_code == 2
assert "Error: Invalid value:" in str(result.output)
| 5,563 |
def do_zone_validation(domain):
"""Preform validation on domain. This function calls the following
functions::
check_for_soa_partition
check_for_master_delegation
validate_zone_soa
.. note::
The type of the domain that is passed is determined
dynamically
:param domain: The domain/reverse_domain being validated.
:type domain: :class:`Domain` or :class:`ReverseDomain`
The following code is an example of how to call this function during
*domain* introspection.
>>> do_zone_validation(self, self.master_domain)
The following code is an example of how to call this function during
*reverse_domain* introspection.
>>> do_zone_validation(self, self.master_reverse_domain)
"""
check_for_master_delegation(domain, domain.master_domain)
validate_zone_soa(domain, domain.master_domain)
check_for_soa_partition(domain, domain.domain_set.all())
| 5,564 |
def BulkRemove(fname,masterfile=None,edlfile=None):
"""
Given a file with one IP per line, remove the given IPs from the EDL if they are in there
"""
global AutoSave
success = True
removes = list()
if os.path.exists(fname):
with open(fname,"rt") as ip_list:
for ip in ip_list:
removes.append(ip.strip())
Remove(removes,masterfile,edlfile)
else:
success = False
return success
| 5,565 |
def log(message, level_in=0, tag=PLUGIN_NAME):
"""
Writes to QGIS inbuilt logger accessible through panel.
:param message: logging message to write, error or URL.
:type message: str
:param level_in: integer representation of logging level.
:type level_in: int
@param tag: if relevant give tag name.
"""
if level_in == 0:
level = Qgis.Info
elif level_in == 1:
level = Qgis.Warning
elif level_in == 2:
level = Qgis.Critical
else:
level = Qgis.Info
QgsMessageLog.logMessage(message, tag.strip(), level)
| 5,566 |
def convolutionalize(modules, input_size):
"""
Recast `modules` into fully convolutional form.
The conversion transfers weights and infers kernel sizes from the
`input_size` and modules' action on it.
n.b. This only handles the conversion of linear/fully-connected modules,
although other module types could require conversion for correctness.
"""
fully_conv_modules = []
x = torch.zeros((1, ) + input_size)
for m in modules:
if isinstance(m, nn.Linear):
n = nn.Conv2d(x.size(1), m.weight.size(0), kernel_size=(x.size(2), x.size(3)))
n.weight.data.view(-1).copy_(m.weight.data.view(-1))
n.bias.data.view(-1).copy_(m.bias.data.view(-1))
m = n
fully_conv_modules.append(m)
x = m(x)
return fully_conv_modules
| 5,567 |
def is_unique2(s):
"""
Use a list and the int of the character will tell if that character has
already appeared once
"""
d = []
for t in s:
if d[int(t)]:
return False
d[int(t)] = True
return True
| 5,568 |
def generate_wav(audio, file_name, sample_rate=41000):
"""
Generate .wav file from recorded audio
:param audio: Numpy array of audio samples
:param file_name: File name
:param sample_rate: Audio sample rate. (Default = 41000)
:return: None
"""
wavio.write(file_name, audio, sample_rate, sampwidth=3)
| 5,569 |
def uncomplete_tree_parallel(x:ATree, mode="full"):
""" Input is tuple (nl, fl, split)
Output is a randomly uncompleted tree,
every node annotated whether it's terminated and what actions are good at that node
"""
fl = x
fl.parent = None
add_descendants_ancestors(fl)
y = ATree("@START@", [])
y.align = fl
y.is_open = True
i = 0
y = assign_gold_actions(y, mode=mode)
choices = [deepcopy(y)] # !! can't cache because different choices !
while not all_terminated(y):
y = mark_for_execution(y, mode=mode)
y = execute_chosen_actions(y, mode=mode)
y = assign_gold_actions(y, mode=mode)
y = adjust_gold(y, mode=mode)
choices.append(deepcopy(y))
i += 1
ret = random.choice(choices[:-1])
return ret
| 5,570 |
def stations_by_river(stations):
"""Give a dictionary to hold the rivers name as keys and their corresponding stations' name as values"""
rivers_name = []
for i in stations:
if i.river not in rivers_name:
rivers_name.append(i.river)
elif i.river in rivers_name:
continue
big_list = []
for n in rivers_name:
lists = []
for y in stations:
if n == y.river:
lists.append(y.name)
elif n != y.river:
continue
lists = sorted(lists)
big_list.append(lists)
dictionary = dict(zip(rivers_name, big_list))
dicti = {}
for key in sorted(dictionary):
dicti.update({key : dictionary[key]})
assert dicti != {}
return dicti
| 5,571 |
def clean():
"""Delete Generated Documentation"""
with lcd("docs"):
pip(requirements="requirements.txt")
local("make clean")
| 5,572 |
def QA_SU_save_huobi(frequency):
"""
Save huobi kline "smart"
"""
if (frequency not in ["1d", "1day", "day"]):
return QA_SU_save_huobi_min(frequency)
else:
return QA_SU_save_huobi_day(frequency)
| 5,573 |
def new_users():
"""
I have the highland! Create some users.
"""
| 5,574 |
def get_cachefile(filename=None):
"""Resolve cachefile path
"""
if filename is None:
for f in FILENAMES:
if os.path.exists(f):
return f
return IDFILE
else:
return filename
| 5,575 |
def inverse(a):
"""
[description]
calculating the inverse of the number of characters,
we do this to be able to find our departure when we arrive.
this part will be used to decrypt the message received.
:param a: it is an Int
:return: x -> it is an Int
"""
x = 0
while a * x % 97 != 1:
x = x + 1
return x
| 5,576 |
def currency_column_to_numeric(
df: pd.DataFrame,
column_name: str,
cleaning_style: Optional[str] = None,
cast_non_numeric: Optional[dict] = None,
fill_all_non_numeric: Optional[Union[float, int]] = None,
remove_non_numeric: bool = False,
) -> pd.DataFrame:
"""Convert currency column to numeric.
This method does not mutate the original DataFrame.
This method allows one to take a column containing currency values,
inadvertently imported as a string, and cast it as a float. This is
usually the case when reading CSV files that were modified in Excel.
Empty strings (i.e. `''`) are retained as `NaN` values.
Example:
>>> import pandas as pd
>>> import janitor
>>> df = pd.DataFrame({
... "a_col": [" 24.56", "-", "(12.12)", "1,000,000"],
... "d_col": ["", "foo", "1.23 dollars", "-1,000 yen"],
... })
>>> df # doctest: +NORMALIZE_WHITESPACE
a_col d_col
0 24.56
1 - foo
2 (12.12) 1.23 dollars
3 1,000,000 -1,000 yen
The default cleaning style.
>>> df.currency_column_to_numeric("d_col")
a_col d_col
0 24.56 NaN
1 - NaN
2 (12.12) 1.23
3 1,000,000 -1000.00
The accounting cleaning style.
>>> df.currency_column_to_numeric("a_col", cleaning_style="accounting") # doctest: +NORMALIZE_WHITESPACE
a_col d_col
0 24.56
1 0.00 foo
2 -12.12 1.23 dollars
3 1000000.00 -1,000 yen
Valid cleaning styles are:
- `None`: Default cleaning is applied. Empty strings are always retained as
`NaN`. Numbers, `-`, `.` are extracted and the resulting string
is cast to a float.
- `'accounting'`: Replaces numbers in parentheses with negatives, removes commas.
:param df: The pandas DataFrame.
:param column_name: The column containing currency values to modify.
:param cleaning_style: What style of cleaning to perform.
:param cast_non_numeric: A dict of how to coerce certain strings to numeric
type. For example, if there are values of 'REORDER' in the DataFrame,
`{'REORDER': 0}` will cast all instances of 'REORDER' to 0.
Only takes effect in the default cleaning style.
:param fill_all_non_numeric: Similar to `cast_non_numeric`, but fills all
strings to the same value. For example, `fill_all_non_numeric=1`, will
make everything that doesn't coerce to a currency `1`.
Only takes effect in the default cleaning style.
:param remove_non_numeric: If set to True, rows of `df` that contain
non-numeric values in the `column_name` column will be removed.
Only takes effect in the default cleaning style.
:raises ValueError: If `cleaning_style` is not one of the accepted styles.
:returns: A pandas DataFrame.
""" # noqa: E501
check("column_name", column_name, [str])
check_column(df, column_name)
column_series = df[column_name]
if cleaning_style == "accounting":
df.loc[:, column_name] = df[column_name].apply(
_clean_accounting_column
)
return df
if cleaning_style is not None:
raise ValueError(
"`cleaning_style` is expected to be one of ('accounting', None). "
f"Got {cleaning_style!r} instead."
)
if cast_non_numeric:
check("cast_non_numeric", cast_non_numeric, [dict])
_make_cc_patrial = partial(
_currency_column_to_numeric,
cast_non_numeric=cast_non_numeric,
)
column_series = column_series.apply(_make_cc_patrial)
if remove_non_numeric:
df = df.loc[column_series != "", :]
# _replace_empty_string_with_none is applied here after the check on
# remove_non_numeric since "" is our indicator that a string was coerced
# in the original column
column_series = _replace_empty_string_with_none(column_series)
if fill_all_non_numeric is not None:
check("fill_all_non_numeric", fill_all_non_numeric, [int, float])
column_series = column_series.fillna(fill_all_non_numeric)
column_series = _replace_original_empty_string_with_none(column_series)
df = df.assign(**{column_name: pd.to_numeric(column_series)})
return df
| 5,577 |
async def subreddit_type_submissions(sub="wallstreetbets", kind="new"):
"""
"""
comments = []
articles = []
red = await reddit_instance()
subreddit = await red.subreddit(sub)
if kind == "hot":
submissions = subreddit.hot()
elif kind == "top":
submissions = subreddit.top()
elif kind == "new":
submissions = subreddit.new()
elif kind == "random_rising":
submissions = subreddit.random_rising()
else:
submissions = subreddit.random()
async for submission in submissions:
article = clean_submission(submission)
article['subreddit'] = sub
articles.append(article)
top_level_comments = await submission.comments()
print(f"📗 Looking at submission: {article['title'][:40]}...")
for top_level_comment in top_level_comments:
if isinstance(top_level_comment, MoreComments):
continue
comment = clean_comment(top_level_comment)
print(f"🗯️ ... {comment['author']} said {comment['body'][:40]}")
comment['article_id'] = article['id']
comments.append(comment)
return (articles, comments)
| 5,578 |
def get_args():
"""Get argument"""
try:
opts, args = getopt.getopt(
sys.argv[1:],
"i:s:t:o:rvh",
["ibam=",
"snp=",
"tag=",
"output=",
"rstat", "verbose", "help"])
except getopt.GetoptError as err:
print(str(err))
usage()
sys.exit(-1)
return opts
| 5,579 |
def _get_bundle_manifest(
uuid: str,
replica: Replica,
version: typing.Optional[str],
*,
bucket: typing.Optional[str] = None) -> typing.Optional[dict]:
"""
Return the contents of the bundle manifest file from cloud storage, subject to the rules of tombstoning. If version
is None, return the latest version, once again, subject to the rules of tombstoning.
If the bundle cannot be found, return None
"""
uuid = uuid.lower()
handle = Config.get_blobstore_handle(replica)
default_bucket = replica.bucket
# need the ability to use fixture bucket for testing
bucket = default_bucket if bucket is None else bucket
def tombstone_exists(uuid: str, version: typing.Optional[str]):
return test_object_exists(handle, bucket, BundleTombstoneID(uuid=uuid, version=version).to_key())
# handle the following deletion cases
# 1. the whole bundle is deleted
# 2. the specific version of the bundle is deleted
if tombstone_exists(uuid, None) or (version and tombstone_exists(uuid, version)):
return None
# handle the following deletion case
# 3. no version is specified, we want the latest _non-deleted_ version
if version is None:
# list the files and find the one that is the most recent.
prefix = f"bundles/{uuid}."
object_names = handle.list(bucket, prefix)
version = _latest_version_from_object_names(object_names)
if version is None:
# no matches!
return None
bundle_fqid = BundleFQID(uuid=uuid, version=version)
# retrieve the bundle metadata.
try:
bundle_manifest_blob = handle.get(bucket, bundle_fqid.to_key()).decode("utf-8")
return json.loads(bundle_manifest_blob)
except BlobNotFoundError:
return None
| 5,580 |
def handler400(request, exception):
"""
This is a Django handler function for 400 Bad Request error
:param request: The Django Request object
:param exception: The exception caught
:return: The 400 error page
"""
context = get_base_context(request)
context.update({
'message': {
'title': '400 Bad Request',
'description': 'Your client has issued a malformed or illegal request.'
}
})
return render(request, 'velarium/base.html', context=context, status=400)
| 5,581 |
def object_trajectory_proposal(vid, fstart, fend, gt=False, verbose=False):
"""
Set gt=True for providing groundtruth bounding box trajectories and
predicting classme feature only
"""
vsig = get_segment_signature(vid, fstart, fend)
name = 'traj_cls_gt' if gt else 'traj_cls'
path = get_feature_path(name, vid)
path = os.path.join(path, '{}-{}.json'.format(vsig, name))
if os.path.exists(path):
if verbose:
print('loading object {} proposal for video segment {}'.format(name, vsig))
with open(path, 'r') as fin:
trajs = json.load(fin)
trajs = [Trajectory(**traj) for traj in trajs]
else:
if verbose:
print('no object {} proposal for video segment {}'.format(name, vsig))
trajs = []
return trajs
| 5,582 |
def _gather_topk_beams(nested, score_or_log_prob, batch_size, beam_size):
"""Gather top beams from nested structure."""
_, topk_indexes = tf.nn.top_k(score_or_log_prob, k=beam_size)
return _gather_beams(nested, topk_indexes, batch_size, beam_size)
| 5,583 |
def _traceinv_exact(K, B, C, matrix, gram, exponent):
"""
Finds traceinv directly for the purpose of comparison.
"""
# Exact solution of traceinv for band matrix
if B is not None:
if scipy.sparse.isspmatrix(K):
K_ = K.toarray()
B_ = B.toarray()
if C is not None:
C_ = C.toarray()
else:
K_ = K
B_ = B
if C is not None:
C_ = C
if exponent == 0:
if C is not None:
traceinv_exact = numpy.trace(C_ @ B_)
else:
traceinv_exact = numpy.trace(B_)
else:
if gram:
K_ = numpy.matmul(K_.T, K_)
if exponent > 1:
K1 = K_.copy()
for i in range(1, exponent):
K_ = numpy.matmul(K_, K1)
Kinv = numpy.linalg.inv(K_)
Op = numpy.matmul(Kinv, B_)
if C is not None:
Op = Kinv @ C_ @ Op
traceinv_exact = numpy.trace(Op)
elif exponent == 1 and not gram:
# B is identity. Using analytic formula.
traceinv_exact = band_matrix_traceinv(matrix['a'], matrix['b'],
matrix['size'], True)
else:
# B and C are identity. Compute traceinv directly.
if scipy.sparse.isspmatrix(K):
K_ = K.toarray()
else:
K_ = K
if exponent == 0:
traceinv_exact = K_.shape[0]
else:
if gram:
K_ = numpy.matmul(K_.T, K_)
K_temp = K_.copy()
for i in range(1, exponent):
K_ = numpy.matmul(K_, K_temp)
Kinv = numpy.linalg.inv(K_)
traceinv_exact = numpy.trace(Kinv)
return traceinv_exact
| 5,584 |
def create_feature_vector_of_mean_mfcc_for_song(song_file_path: str) -> ndarray:
"""
Takes in a file path to a song segment and returns a numpy array containing the mean mfcc values
:param song_file_path: str
:return: ndarray
"""
song_segment, sample_rate = librosa.load(song_file_path)
mfccs = librosa.feature.mfcc(y=song_segment, sr=sample_rate, n_mfcc=NUMBER_OF_MFCC)
mfccs_processed = np.mean(mfccs.T, axis=0)
df = pd.DataFrame(mfccs_processed)
z_score_normalized_mfccs = (df.values - df.values.mean()) / df.values.std()
z_score_normalized_mfccs = np.array([i[0] for i in z_score_normalized_mfccs])
return z_score_normalized_mfccs
| 5,585 |
def stations_highest_rel_level(stations, N):
"""Returns a list containing the names of the N stations
with the highest water level relative to the typical range"""
names = [] # create list for names
levels = [] # create list for levels
for i in range(len(stations)): # iterate through stations
if stations[i].relative_water_level() is not None:
# ^checks for valid relative water level
names.append(stations[i].name)
levels.append(stations[i].relative_water_level())
# ^adds names and levels to respective lists
combined = list(zip(names, levels)) # combines names and levels
combined.sort(key=lambda x: x[1], reverse=1) # sorts in reverse
output = [] # create output list
for i in range(N): # iterate up to N
output.append(combined[i][0]) # add station name to output
return output
| 5,586 |
def read_image(file_name, format=None):
"""
Read an image into the given format.
Will apply rotation and flipping if the image has such exif information.
Args:
file_name (str): image file path
format (str): one of the supported image modes in PIL, or "BGR"
Returns:
image (np.ndarray): an HWC image in the given format.
"""
with PathManager.open(file_name, "rb") as f:
image = Image.open(f)
# capture and ignore this bug: https://github.com/python-pillow/Pillow/issues/3973
try:
image = ImageOps.exif_transpose(image)
except Exception:
pass
if format is not None:
# PIL only supports RGB, so convert to RGB and flip channels over below
conversion_format = format
if format == "BGR":
conversion_format = "RGB"
image = image.convert(conversion_format)
image = np.asarray(image)
if format == "BGR":
# flip channels if needed
image = image[:, :, ::-1]
# PIL squeezes out the channel dimension for "L", so make it HWC
if format == "L":
image = np.expand_dims(image, -1)
return image
| 5,587 |
def reduce_opacity_filter(image, opacity_level):
"""Filter divides each pixel by the specified amount
of opacity_level
"""
data = []
image_data = get_image_data(image)
# creating new opacity level pixels
for i in range(len(image_data)):
current_tuple = list(image_data[i])
current_tuple[0] = round(current_tuple[0] / opacity_level)
current_tuple[1] = round(current_tuple[1] / opacity_level)
current_tuple[2] = round(current_tuple[2] / opacity_level)
data.append(tuple(current_tuple))
# saving the image
footer(image, data, "reduce_opacity_filter")
| 5,588 |
def add_global_nodes_edges(g_nx : nx.Graph, feat_data: np.ndarray, adj_list: np.ndarray,
g_feat_data: np.ndarray, g_adj_list: np.ndarray):
"""
:param g_nx:
:param feat_data:
:param adj_list:
:param g_feat_data:
:param g_adj_list:
:return:
"""
feat_data = np.concatenate([feat_data, g_feat_data], 0)
# adj_list.update((k, adj_list[k].union(g_adj_list[k])) for k in range(len(g_adj_list)))
adj_list.update((k, adj_list[k].union(g_adj_list[k])) for k in range(len(feat_data)))
g_edge_list = [[[k, v] for v in vs] for k, vs in g_adj_list.items()]
g_edge_list = [x for sublist in g_edge_list for x in sublist]
g_nx.add_edges_from(g_edge_list)
return g_nx, feat_data, adj_list
| 5,589 |
def _load_readme(file_name: str = "README.md") -> str:
"""
Load readme from a text file.
Args:
file_name (str, optional): File name that contains the readme. Defaults to "README.md".
Returns:
str: Readme text.
"""
with open(os.path.join(_PATH_ROOT, file_name), "r", encoding="utf-8") as file:
readme = file.read()
return readme
| 5,590 |
def get_data_collector_instance(args, config):
"""Get the instance of the data
:param args: arguments of the script
:type args: Namespace
:raises NotImplementedError: no data collector implemented for given data source
:return: instance of the specific data collector
:rtype: subclass of BaseDataCollector
"""
if args.data_source == DATA_SOURCE_RSS:
return RssDataCollector(args.base_url, config[CONFIG_RSS_HEADER])
elif args.data_source == DATA_SOURCE_REDDIT:
return RedditDataCollector(REDDIT_CLIENT_ID, REDDIT_CLIENT_SECRET)
elif args.data_source == DATA_SOURCE_TWITTER:
return TwitterDataCollector(TWITTER_CONSUMER_KEY, TWITTER_CONSUMER_SECRET, TWITTER_BEARER_TOKEN)
else:
raise NotImplementedError
| 5,591 |
def timeIntegration(params):
"""Sets up the parameters for time integration
:param params: Parameter dictionary of the model
:type params: dict
:return: Integrated activity variables of the model
:rtype: (numpy.ndarray,)
"""
dt = params["dt"] # Time step for the Euler intergration (ms)
duration = params["duration"] # imulation duration (ms)
RNGseed = params["seed"] # seed for RNG
# ------------------------------------------------------------------------
# local parameters
# See Papadopoulos et al., Relations between large-scale brain connectivity and effects of regional stimulation
# depend on collective dynamical state, arXiv, 2020
tau_exc = params["tau_exc"] #
tau_inh = params["tau_inh"] #
c_excexc = params["c_excexc"] #
c_excinh = params["c_excinh"] #
c_inhexc = params["c_inhexc"] #
c_inhinh = params["c_inhinh"] #
a_exc = params["a_exc"] #
a_inh = params["a_inh"] #
mu_exc = params["mu_exc"] #
mu_inh = params["mu_inh"] #
# external input parameters:
# Parameter of the Ornstein-Uhlenbeck process for the external input(ms)
tau_ou = params["tau_ou"]
# Parameter of the Ornstein-Uhlenbeck (OU) process for the external input ( mV/ms/sqrt(ms) )
sigma_ou = params["sigma_ou"]
# Mean external excitatory input (OU process) (mV/ms)
exc_ou_mean = params["exc_ou_mean"]
# Mean external inhibitory input (OU process) (mV/ms)
inh_ou_mean = params["inh_ou_mean"]
# ------------------------------------------------------------------------
# global coupling parameters
# Connectivity matrix
# Interareal relative coupling strengths (values between 0 and 1), Cmat(i,j) connection from jth to ith
Cmat = params["Cmat"]
N = len(Cmat) # Number of nodes
K_gl = params["K_gl"] # global coupling strength
# Interareal connection delay
lengthMat = params["lengthMat"]
signalV = params["signalV"]
if N == 1:
Dmat = np.zeros((N, N))
else:
# Interareal connection delays, Dmat(i,j) Connnection from jth node to ith (ms)
Dmat = dp.computeDelayMatrix(lengthMat, signalV)
Dmat[np.eye(len(Dmat)) == 1] = np.zeros(len(Dmat))
Dmat_ndt = np.around(Dmat / dt).astype(int) # delay matrix in multiples of dt
params["Dmat_ndt"] = Dmat_ndt
# ------------------------------------------------------------------------
# Initialization
# Floating point issue in np.arange() workaraound: use integers in np.arange()
t = np.arange(1, round(duration, 6) / dt + 1) * dt # Time variable (ms)
sqrt_dt = np.sqrt(dt)
max_global_delay = np.max(Dmat_ndt)
startind = int(max_global_delay + 1) # timestep to start integration at
exc_ou = params["exc_ou"]
inh_ou = params["inh_ou"]
exc_ext = params["exc_ext"]
inh_ext = params["inh_ext"]
# state variable arrays, have length of t + startind
# they store initial conditions AND simulated data
excs = np.zeros((N, startind + len(t)))
inhs = np.zeros((N, startind + len(t)))
# ------------------------------------------------------------------------
# Set initial values
# if initial values are just a Nx1 array
if np.shape(params["exc_init"])[1] == 1:
exc_init = np.dot(params["exc_init"], np.ones((1, startind)))
inh_init = np.dot(params["inh_init"], np.ones((1, startind)))
# if initial values are a Nxt array
else:
exc_init = params["exc_init"][:, -startind:]
inh_init = params["inh_init"][:, -startind:]
# xsd = np.zeros((N,N)) # delayed activity
exc_input_d = np.zeros(N) # delayed input to x
inh_input_d = np.zeros(N) # delayed input to y
np.random.seed(RNGseed)
# Save the noise in the activity array to save memory
excs[:, startind:] = np.random.standard_normal((N, len(t)))
inhs[:, startind:] = np.random.standard_normal((N, len(t)))
excs[:, :startind] = exc_init
inhs[:, :startind] = inh_init
noise_exc = np.zeros((N,))
noise_inh = np.zeros((N,))
# ------------------------------------------------------------------------
return timeIntegration_njit_elementwise(
startind,
t,
dt,
sqrt_dt,
N,
Cmat,
K_gl,
Dmat_ndt,
excs,
inhs,
exc_input_d,
inh_input_d,
exc_ext,
inh_ext,
tau_exc,
tau_inh,
a_exc,
a_inh,
mu_exc,
mu_inh,
c_excexc,
c_excinh,
c_inhexc,
c_inhinh,
noise_exc,
noise_inh,
exc_ou,
inh_ou,
exc_ou_mean,
inh_ou_mean,
tau_ou,
sigma_ou,
)
| 5,592 |
def test_set_stage_invalid_buttons(memory):
"""Test setting the stage with invalid buttons."""
stage = 0
display = 1
expected_exception = 'buttons must be of type list'
with pytest.raises(Exception, message=expected_exception):
memory.set_stage(stage, display, '1, 2, 3, 4')
expected_exception = 'buttons list must contain exactly 4 items'
with pytest.raises(Exception, message=expected_exception):
memory.set_stage(stage, display, [1, 2, 3])
expected_exception = 'buttons list must contain one each of 1, 2, 3, 4'
with pytest.raises(Exception, message=expected_exception):
memory.set_stage(stage, display, [1, 2, 3, 1])
expected_exception = 'buttons items must be of type int'
with pytest.raises(Exception, message=expected_exception):
memory.set_stage(stage, display, [1, 2, 3, '4'])
expected_exception = 'buttons items must be between 1 and 4'
with pytest.raises(Exception, message=expected_exception):
memory.set_stage(stage, display, [1, 2, 3, 5])
| 5,593 |
def is_role_user(session, user=None, group=None):
# type: (Session, User, Group) -> bool
"""
Takes in a User or a Group and returns a boolean indicating whether
that User/Group is a component of a service account.
Args:
session: the database session
user: a User object to check
group: a Group object to check
Throws:
AssertionError if neither a user nor a group is provided
Returns:
whether the User/Group is a component of a service account
"""
if user is not None:
return user.role_user
assert group is not None
user = User.get(session, name=group.groupname)
if not user:
return False
return user.role_user
| 5,594 |
def argCOM(y):
"""argCOM(y) returns the location of COM of y."""
idx = np.round(np.sum(y/np.sum(y)*np.arange(len(y))))
return int(idx)
| 5,595 |
def fringe(z, z1, z2, rad, a1):
"""
Approximation to the longitudinal profile of a multipole from a permanent magnet assembly.
see Wan et al. 2018 for definition and Enge functions paper (Enge 1964)
"""
zz1 = (z - z1) / (2 * rad / pc.pi)
zz2 = (z - z2) / (2 * rad / pc.pi)
fout = ( (1 / ( 2 * np.tanh((z2 - z1) / (4 * rad / pc.pi)) ) )
* (np.tanh(zz1 + a1 * zz1**2 )
- np.tanh(zz2 - a1 * zz2**2) )
)
return fout
| 5,596 |
def random_param_shift(vals, sigmas):
"""Add a random (normal) shift to a parameter set, for testing"""
assert len(vals) == len(sigmas)
shifts = [random.gauss(0, sd) for sd in sigmas]
newvals = [(x + y) for x, y in zip(vals, shifts)]
return newvals
| 5,597 |
def compute_encrypted_request_hash(caller):
"""
This function will compute encrypted request Hash
:return: encrypted request hash
"""
first_string = get_parameter(caller.params_obj, "requesterNonce") or ""
worker_order_id = get_parameter(caller.params_obj, "workOrderId") or ""
worker_id = get_parameter(caller.params_obj, "workerId") or ""
workload_id = get_parameter(caller.params_obj, "workloadId") or ""
requester_id = get_parameter(caller.params_obj, "requesterId") or ""
requester_id = str(requester_id)
first_string += \
worker_order_id + worker_id + workload_id + requester_id
concat_hash = first_string.encode("UTF-8")
hash_1 = crypto_utils.compute_message_hash(concat_hash)
in_data = get_parameter(caller.params_obj, "inData")
out_data = get_parameter(caller.params_obj, "outData")
hash_2 = bytearray()
if in_data is not None:
hash_2 = compute_hash_string(in_data)
hash_3 = bytearray()
if out_data is not None:
hash_3 = compute_hash_string(out_data)
final_string = hash_1 + hash_2 + hash_3
caller.final_hash = crypto_utils.compute_message_hash(final_string)
encrypted_request_hash = crypto_utils.byte_array_to_hex(
crypto_utils.encrypt_data(
caller.final_hash, caller.session_key,
caller.session_iv))
return encrypted_request_hash
| 5,598 |
def test_num_samples(res_surf):
""" Verify dimension value."""
assert res_surf.num_samples == 47
| 5,599 |
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