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def parse_metadata(metadata_filepath: Union[str, Path]) -> Dict:
"""Parse the metadata file retreived from the BEACO2N site
Args:
metadata_filepath: Path of raw CSV metadata file
pipeline: Are we running as part of the pipeline? If True
return the parsed site information dictionary.
Returns:
dict: Dictionary of site metadata
"""
metadata_filepath = Path(metadata_filepath).resolve()
raw_metadata = pd.read_csv(metadata_filepath)
site_metadata = aDict()
try:
for index, row in raw_metadata.iterrows():
site_name = row["node_name_long"].lower().replace(" ", "")
site_data = site_metadata[site_name]
site_data["long_name"] = row["node_name_long"]
site_data["id"] = row["id"]
site_data["latitude"] = round(row["lat"], 5)
site_data["longitude"] = round(row["lng"], 5)
site_data["magl"] = check_nan(row["height_above_ground"])
site_data["masl"] = check_nan(row["height_above_sea"])
site_data["deployed"] = check_date(row["deployed"])
site_data["node_folder_id"] = row["node_folder_id"]
except Exception as e:
raise ValueError(f"Can't read metadata file, please ensure it has expected columns. Error: {e}")
# Convert to a normal dict
metadata: Dict = site_metadata.to_dict()
return metadata | 100 |
def unit_norm(model,axis=0):
"""
Constrains the weights incident to each hidden unit to have unit norm.
Args:
axis (int):axis along which to calculate weight norms.
model : the model contains weights need to setting the constraints.
"""
def apply_constraint(t: Tensor):
w_data = None
if isinstance(t, tf.Variable):
w_data = t.value().detach()
else:
w_data = t.copy().detach()
param_applied = w_data/ (epsilon() +sqrt(reduce_sum(square(w_data),axis=axis,keepdims=True)))
param_applied = param_applied.detach()
return param_applied
if is_tensor(model):
model = apply_constraint(model)
elif isinstance(model, Layer):
for name, param in model.named_parameters():
if 'bias' not in name and param is not None and param.trainable == True:
param.assign(apply_constraint(param)) | 101 |
def responsive_units(spike_times, spike_clusters, event_times,
pre_time=[0.5, 0], post_time=[0, 0.5], alpha=0.05):
"""
Determine responsive neurons by doing a Wilcoxon Signed-Rank test between a baseline period
before a certain task event (e.g. stimulus onset) and a period after the task event.
Parameters
----------
spike_times : 1D array
spike times (in seconds)
spike_clusters : 1D array
cluster ids corresponding to each event in `spikes`
event_times : 1D array
times (in seconds) of the events from the two groups
pre_time : two-element array
time (in seconds) preceding the event to get the baseline (e.g. [0.5, 0.2] would be a
window starting 0.5 seconds before the event and ending at 0.2 seconds before the event)
post_time : two-element array
time (in seconds) to follow the event times
alpha : float
alpha to use for statistical significance
Returns
-------
significant_units : ndarray
an array with the indices of clusters that are significatly modulated
stats : 1D array
the statistic of the test that was performed
p_values : ndarray
the p-values of all the clusters
cluster_ids : ndarray
cluster ids of the p-values
"""
# Get spike counts for baseline and event timewindow
baseline_times = np.column_stack(((event_times - pre_time[0]), (event_times - pre_time[1])))
baseline_counts, cluster_ids = get_spike_counts_in_bins(spike_times, spike_clusters,
baseline_times)
times = np.column_stack(((event_times + post_time[0]), (event_times + post_time[1])))
spike_counts, cluster_ids = get_spike_counts_in_bins(spike_times, spike_clusters, times)
# Do statistics
p_values = np.empty(spike_counts.shape[0])
stats = np.empty(spike_counts.shape[0])
for i in range(spike_counts.shape[0]):
if np.sum(baseline_counts[i, :] - spike_counts[i, :]) == 0:
p_values[i] = 1
stats[i] = 0
else:
stats[i], p_values[i] = wilcoxon(baseline_counts[i, :], spike_counts[i, :])
# Perform FDR correction for multiple testing
sig_units, p_values, _, _ = multipletests(p_values, alpha, method='fdr_bh')
significant_units = cluster_ids[sig_units]
return significant_units, stats, p_values, cluster_ids | 102 |
def create_link(seconds, image_name, size):
"""
Function returns temporary link to the image
"""
token = signing.dumps([str(timezone.now() + timedelta(seconds=int(seconds))), image_name, size])
return settings.SERVER_PATH + reverse("image:dynamic-image", kwargs={"token": token}) | 103 |
def test_paragraph_series_m_tb_ul_t_nl_ul_t_nl_ulb_nl_tb():
"""
Test case: Unordered list text newline unordered list (b) new line thematic break
"""
# Arrange
source_markdown = """- abc
- def
*
---
"""
expected_tokens = [
"[ulist(1,1):-::2:: ]",
"[para(1,3):]",
"[text(1,3):abc:]",
"[end-para:::True]",
"[li(2,1):2::]",
"[para(2,3):\n]",
"[text(2,3):def\n::\n]",
"[text(3,1):*:]",
"[end-para:::True]",
"[end-ulist:::True]",
"[tbreak(4,1):-::---]",
"[BLANK(5,1):]",
]
expected_gfm = """<ul>
<li>abc</li>
<li>def
*</li>
</ul>
<hr />"""
# Act & Assert
act_and_assert(source_markdown, expected_gfm, expected_tokens) | 104 |
def read_one_hot_labels(filename):
"""Read topic labels from file in one-hot form
:param filename: name of input file
:return: topic labels (one-hot DataFrame, M x N)
"""
return pd.read_csv(filename, dtype=np.bool) | 105 |
def make_randint_list(start, stop, length=10):
""" Makes a list of randomly generated integers
Args:
start: lowest integer to be generated randomly.
stop: highest integer to be generated randomly.
length: length of generated list.
Returns:
list of random numbers between start and stop of length length
"""
return [randint(start, stop) for i in range(length)] | 106 |
def merge(intervals: list[list[int]]) -> list[list[int]]:
"""Generate a new schedule with non-overlapping intervals by merging intervals which overlap
Complexity:
n = len(intervals)
Time: O(nlogn) for the initial sort
Space: O(n) for the worst case of no overlapping intervals
Examples:
>>> merge(intervals=[[1,3],[2,6],[8,10],[15,18]])
[[1, 6], [8, 10], [15, 18]]
>>> merge(intervals=[[1,4],[4,5]])
[[1, 5]]
>>> merge(intervals=[[1,4]])
[[1, 4]]
"""
## EDGE CASES ##
if len(intervals) <= 1:
return intervals
"""ALGORITHM"""
## INITIALIZE VARS ##
intervals.sort(key=lambda k: k[0]) # sort on start times
# DS's/res
merged_intervals = []
# MERGE INTERVALS
prev_interval, remaining_intervals = intervals[0], intervals[1:]
for curr_interval in remaining_intervals:
# if prev interval end >= curr interval start
if prev_interval[1] >= curr_interval[0]:
# adjust new prev interval
prev_interval[1] = max(prev_interval[1], curr_interval[1])
else:
merged_intervals.append(prev_interval)
prev_interval = curr_interval
merged_intervals.append(prev_interval)
return merged_intervals | 107 |
def df_drop_duplicates(df, ignore_key_pattern="time"):
"""
Drop duplicates from dataframe ignore columns with keys containing defined pattern.
:param df:
:param noinfo_key_pattern:
:return:
"""
ks = df_drop_keys_contains(df, ignore_key_pattern)
df = df.drop_duplicates(ks)
return df | 108 |
def get_mediawiki_flow_graph(limit, period):
"""
:type limit int
:type period int
:rtype: list[dict]
"""
# https://kibana5.wikia-inc.com/goto/e6ab16f694b625d5b87833ae794f5989
# goreplay is running in RES (check SJC logs only)
rows = ElasticsearchQuery(
es_host=ELASTICSEARCH_HOST,
period=period,
index_prefix='logstash-mediawiki'
).query_by_string(
query='"Wikia internal request" AND @fields.environment: "prod" '
'AND @fields.datacenter: "sjc" '
'AND @fields.http_url_path: *',
fields=[
'@context.source',
'@fields.http_url_path',
],
limit=limit
)
# extract required fields only
# (u'user-permissions', 'api:query::users')
# (u'1', 'nirvana:EmailControllerDiscussionReply::handle')
rows = [
(
row.get('@context', {})['source'],
normalize_mediawiki_url(row.get('@fields', {})['http_url_path'])
)
for row in rows
if row.get('@context', {}).get('source') is not None
]
# process the logs
def _map(item):
return '{}-{}'.format(item[0], item[1])
def _reduce(items):
first = items[0]
source = first[0]
target = first[1]
return {
'source': source if source != '1' else 'internal',
'edge': 'http',
'target': target,
# the following is optional
'metadata': '{:.3f} reqs per sec'.format(1. * len(items) / period)
}
return logs_map_and_reduce(rows, _map, _reduce) | 109 |
def showsounding(ab2, rhoa, resp=None, mn2=None, islog=True, xlab=None):
"""
Display a sounding curve (rhoa over ab/2) and an additional response.
"""
if xlab is None:
xlab = r'$\rho_a$ in $\Omega$m'
ab2a = N.asarray(ab2)
rhoa = N.asarray(rhoa)
if mn2 is None:
if islog:
l1 = P.loglog(rhoa, ab2, 'rx-', label='observed')
else:
l1 = P.semilogy(rhoa, ab2, 'rx-', label='observed')
P.hold(True)
if resp is not None:
if islog:
l2 = P.loglog(resp, ab2, 'bo-', label='simulated')
else:
l2 = P.semilogy(resp, ab2, 'bo-', label='simulated')
P.legend((l1, l2), ('obs', 'sim'), loc=0)
else:
for unmi in N.unique(mn2):
if islog:
l1 = P.loglog(rhoa[mn2 == unmi], ab2a[mn2 == unmi],
'rx-', label='observed')
else:
l1 = P.semilogy(rhoa[mn2 == unmi], ab2a[mn2 == unmi],
'rx-', label='observed')
P.hold(True)
if resp is not None:
l2 = P.loglog(resp[mn2 == unmi], ab2a[mn2 == unmi],
'bo-', label='simulated')
P.legend((l1, l2), ('obs', 'sim'))
P.axis('tight')
P.ylim((max(ab2), min(ab2)))
locs = P.yticks()[0]
if len(locs) < 2:
locs = N.hstack((min(ab2), locs, max(ab2)))
else:
locs[0] = max(locs[0], min(ab2))
locs[-1] = min(locs[-1], max(ab2))
a = []
for l in locs:
a.append('%g' % rndig(l))
P.yticks(locs, a)
locs = P.xticks()[0]
a = []
for l in locs:
a.append('%g' % rndig(l))
P.xticks(locs, a)
P.grid(which='both')
P.xlabel(xlab)
P.ylabel('AB/2 in m')
# P.legend()
P.show()
return | 110 |
def cfg_from_list(cfg_list):
"""Set config keys via list (e.g., from command line)."""
from ast import literal_eval
assert len(cfg_list) % 2 == 0
for k, v in zip(cfg_list[0::2], cfg_list[1::2]):
key_list = k.split('.')
d = __C
for subkey in key_list[:-1]:
assert d.has_key(subkey)
d = d[subkey]
subkey = key_list[-1]
assert d.has_key(subkey)
try:
value = literal_eval(v)
except:
# handle the case when v is a string literal
value = v
assert type(value) == type(d[subkey]), \
'type {} does not match original type {}'.format(
type(value), type(d[subkey]))
d[subkey] = value | 111 |
def bsplslib_Unperiodize(*args):
"""
:param UDirection:
:type UDirection: bool
:param Degree:
:type Degree: int
:param Mults:
:type Mults: TColStd_Array1OfInteger &
:param Knots:
:type Knots: TColStd_Array1OfReal &
:param Poles:
:type Poles: TColgp_Array2OfPnt
:param Weights:
:type Weights: TColStd_Array2OfReal &
:param NewMults:
:type NewMults: TColStd_Array1OfInteger &
:param NewKnots:
:type NewKnots: TColStd_Array1OfReal &
:param NewPoles:
:type NewPoles: TColgp_Array2OfPnt
:param NewWeights:
:type NewWeights: TColStd_Array2OfReal &
:rtype: void
"""
return _BSplSLib.bsplslib_Unperiodize(*args) | 112 |
def genomic_del3_abs_37(genomic_del3_37_loc):
"""Create test fixture absolute copy number variation"""
return {
"type": "AbsoluteCopyNumber",
"_id": "ga4gh:VAC.Pv9I4Dqk69w-tX0axaikVqid-pozxU74",
"subject": genomic_del3_37_loc,
"copies": {"type": "Number", "value": 2}
} | 113 |
def set_atommap(mol, num=0):
"""Set the atom map number for all atoms in the molecule.
Parameters
----------
mol : rdkit.Chem.rdchem.Mol
A molecule.
num : int
The atom map number to set for all atoms. If 0, it will clear the atom map.
"""
for atom in mol.GetAtoms():
atom.SetAtomMapNum(num) | 114 |
def write_data_to_file(training_data_files, out_file, image_shape, truth_dtype=np.uint8, subject_ids=None,
normalize=True, crop=True):
"""
Takes in a set of training images and writes those images to an hdf5 file.
:param training_data_files: List of tuples containing the training data files. The modalities should be listed in
the same order in each tuple. The last item in each tuple must be the labeled image.
Example: [('sub1-T1.nii.gz', 'sub1-T2.nii.gz', 'sub1-truth.nii.gz'),
('sub2-T1.nii.gz', 'sub2-T2.nii.gz', 'sub2-truth.nii.gz')]
:param out_file: Where the hdf5 file will be written to.
:param image_shape: Shape of the images that will be saved to the hdf5 file.
:param truth_dtype: Default is 8-bit unsigned integer.
:return: Location of the hdf5 file with the image data written to it.
"""
n_samples = len(training_data_files)
n_channels = len(training_data_files[0]) - 1 # n_channels is actually the number of modalities we have
try:
if not normalize:
hdf5_file, data_storage, truth_storage, affine_storage, normalization_storage = \
create_data_file(out_file,
n_channels=n_channels,
n_samples=n_samples,
image_shape=image_shape,
normalize=normalize,
storage_names=('data', 'truth', 'index', 'normalization'))
else:
hdf5_file, data_storage, truth_storage, affine_storage = create_data_file(out_file,
n_channels=n_channels,
n_samples=n_samples,
image_shape=image_shape,
normalize=normalize)
normalization_storage = None
except Exception as e:
# If something goes wrong, delete the incomplete data file
os.remove(out_file)
raise e
write_image_data_to_file(training_data_files, data_storage, truth_storage, image_shape,
truth_dtype=truth_dtype, n_channels=n_channels, affine_storage=affine_storage, crop=crop,
normalization_storage=normalization_storage)
if subject_ids:
hdf5_file.create_array(hdf5_file.root, 'subject_ids', obj=subject_ids)
if normalize:
normalize_data_storage(data_storage)
hdf5_file.close()
return out_file | 115 |
def get_configinfo(env):
"""Returns a list of dictionaries containing the `name` and `options`
of each configuration section. The value of `options` is a list of
dictionaries containing the `name`, `value` and `modified` state of
each configuration option. The `modified` value is True if the value
differs from its default.
:since: version 1.1.2
"""
all_options = {}
for (section, name), option in \
Option.get_registry(env.compmgr).iteritems():
all_options.setdefault(section, {})[name] = option
sections = []
for section in env.config.sections(env.compmgr):
options = []
for name, value in env.config.options(section, env.compmgr):
registered = all_options.get(section, {}).get(name)
if registered:
default = registered.default
normalized = registered.normalize(value)
else:
default = u''
normalized = unicode(value)
options.append({'name': name, 'value': value,
'modified': normalized != default})
options.sort(key=lambda o: o['name'])
sections.append({'name': section, 'options': options})
sections.sort(key=lambda s: s['name'])
return sections | 116 |
def fabric_wired(ctx, obj):
"""DNA Center Fabric Wired API (version: 1.3.1).
Wraps the DNA Center Fabric Wired API and exposes the API as native Python commands.
"""
ctx.obj = obj.fabric_wired | 117 |
def main():
"""
Run all the tests
"""
files = utils.get_files(PATH)
with open('results.txt', 'w') as f:
f.write(f'{sys.version}\n')
f.write(LINE)
for exe in ['./sort1', './sort2', './sort3']:
for file in files:
cmd = subprocess.run(['time', exe, file], capture_output=True, text=True)
if cmd.returncode != 0:
print("Error")
sys.exit(cmd.returncode)
time = PATTERN.findall(cmd.stderr)[0]
f.write(f'time {exe} {file} : {time}\n')
f.write(LINE) | 118 |
def given_energy(n, ef_energy):
"""
Calculate and return the value of given energy using given values of the params
How to Use:
Give arguments for ef_energy and n parameters
*USE KEYWORD ARGUMENTS FOR EASY USE, OTHERWISE
IT'LL BE HARD TO UNDERSTAND AND USE.'
Parameters:
ef_energy (int):effective energy in Joule
n (int): efficiency
Returns:
int: the value of given energy in Joule
"""
gv_energy = ef_energy / n
return gv_energy | 119 |
def departures(stop: location.Stop, day: date, fname: str, days: int = 1) -> None:
"""Departures from a given stop for a given date (range)"""
servinglines = set((c.line, c.id) for c in stop.courses)
deps: List[Tuple[datetime, schedule.TripStop]] = []
for currdate in (day + timedelta(days=n) for n in range(days)):
tripquery = schedule.Trip.query_for_date(stop._session, currdate) \
.filter(
(schedule.Trip.version_id == stop.version_id)
& tuple_(schedule.Trip.line, schedule.Trip.course_id).in_(servinglines)
).options(
load_only('version_id'), load_only('line'), load_only('id'), load_only('course_id'), load_only('line_dir'), load_only('timing_group'), load_only('departure_time'), load_only('arr_stop_id'),
joinedload('course', innerjoin=True).load_only('version_id').load_only('line').load_only('id').load_only('line_dir').load_only('name'),
joinedload('arr_stop').load_only('name')
)
trips = [(t, t.trip_stops(simple=True)) for t in tripquery.all()]
# todo: eventuell mal das trip_stops sich sparen koennen.
for trip, tripstops in trips:
deps.extend(
(datetime.combine(currdate, datetime.min.time()) + ts.dep_time, ts)
for n, ts
in filter(
lambda n_ts: n_ts[1].stop == stop and n_ts[0] != len(tripstops),
enumerate(tripstops, start=1)
))
rows = [("date", "time", "plat", "linenum", "direction")]
rows.extend(
(
deptime.strftime("%Y-%m-%d"), deptime.strftime("%H:%M:%S"),
ts.stop_point.name if ts.stop_point is not None else "",
ts.trip.course.name, ts.trip.arr_stop.name
) for deptime, ts in sorted(deps, key=lambda d: d[0]))
with open(fname, 'w', encoding='utf-8') as f:
writer(f, delimiter=";", lineterminator='\n').writerows(rows) | 120 |
def dt_getreferences_rmap_na():
"""
>>> old_state = test_config.setup(cache=None, url="https://jwst-crds-dev.stsci.edu")
>>> os.environ["CRDS_MAPPATH_SINGLE"] = test_config.TEST_DATA
>>> heavy_client.getreferences({"META.INSTRUMENT.NAME":"NIRISS", "META.INSTRUMENT.DETECTOR":"NIS", "META.INSTRUMENT.FILTER":"BOGUS2"},
... observatory="jwst", context="jwst_na_omit.pmap", ignore_cache=False)
{'flat': 'NOT FOUND n/a'}
>>> test_config.cleanup(old_state)
>> config.get_crds_state()
""" | 121 |
def sequence_sigmoid_cross_entropy(labels,
logits,
sequence_length,
average_across_batch=True,
average_across_timesteps=False,
average_across_classes=True,
sum_over_batch=False,
sum_over_timesteps=True,
sum_over_classes=False,
time_major=False,
stop_gradient_to_label=False,
name=None):
"""Computes sigmoid cross entropy for each time step of sequence
predictions.
Args:
labels: Target class distributions.
- If :attr:`time_major` is `False` (default), this must be a\
Tensor of shape `[batch_size, max_time(, num_classes)]`.
- If `time_major` is `True`, this must be a Tensor of shape\
`[max_time, batch_size(, num_classes)]`.
Each row of `labels` should be a valid probability
distribution, otherwise, the computation of the gradient will be
incorrect.
logits: Unscaled log probabilities having the same shape as with
:attr:`labels`.
sequence_length: A Tensor of shape `[batch_size]`. Time steps beyond
the respective sequence lengths will have zero losses.
average_across_timesteps (bool): If set, average the loss across
the time dimension. Must not set `average_across_timesteps`
and `sum_over_timesteps` at the same time.
average_across_batch (bool): If set, average the loss across the
batch dimension. Must not set `average_across_batch`'
and `sum_over_batch` at the same time.
average_across_classes (bool): If set, average the loss across the
class dimension (if exists). Must not set
`average_across_classes`' and `sum_over_classes` at
the same time. Ignored if :attr:`logits` is a 2D Tensor.
sum_over_timesteps (bool): If set, sum the loss across the
time dimension. Must not set `average_across_timesteps`
and `sum_over_timesteps` at the same time.
sum_over_batch (bool): If set, sum the loss across the
batch dimension. Must not set `average_across_batch`
and `sum_over_batch` at the same time.
sum_over_classes (bool): If set, sum the loss across the
class dimension. Must not set `average_across_classes`
and `sum_over_classes` at the same time. Ignored if
:attr:`logits` is a 2D Tensor.
time_major (bool): The shape format of the inputs. If `True`,
:attr:`labels` and :attr:`logits` must have shape
`[max_time, batch_size, ...]`. If `False`
(default), they must have shape `[batch_size, max_time, ...]`.
stop_gradient_to_label (bool): If set, gradient propagation to
:attr:`labels` will be disabled.
name (str, optional): A name for the operation.
Returns:
A Tensor containing the loss, of rank 0, 1, or 2 depending on the
arguments
:attr:`{average_across}/{sum_over}_{timesteps}/{batch}/{classes}`.
For example, if the class dimension does not exist, and
- If :attr:`sum_over_timesteps` and :attr:`average_across_batch` \
are `True` (default), the return Tensor is of rank 0.
- If :attr:`average_across_batch` is `True` and other arguments are \
`False`, the return Tensor is of shape `[max_time]`.
"""
with tf.name_scope(name, "sequence_sigmoid_cross_entropy"):
if stop_gradient_to_label:
labels = tf.stop_gradient(labels)
losses = tf.nn.sigmoid_cross_entropy_with_logits(
labels=labels, logits=logits)
rank = shapes.get_rank(logits) or shapes.get_rank(labels)
if rank is None:
raise ValueError(
'Cannot determine the rank of `logits` or `labels`.')
losses = mask_and_reduce(
losses,
sequence_length,
rank=rank,
average_across_batch=average_across_batch,
average_across_timesteps=average_across_timesteps,
average_across_remaining=average_across_classes,
sum_over_batch=sum_over_batch,
sum_over_timesteps=sum_over_timesteps,
sum_over_remaining=sum_over_classes,
time_major=time_major)
return losses | 122 |
def stats(func):
"""Stats printing and exception handling decorator"""
def inner(*args):
try:
code, decoded, res = func(*args)
except ValueError as err:
print(err)
else:
if FORMATTING:
code_length = 0
for el in code:
code_length += len(el)
compression_rate = 24 * img.shape[0] * img.shape[1] / code_length
print(f"Code length: {code_length}")
else:
compression_rate = 24 * img.shape[0] * img.shape[1] / len(code)
code_length = len(code)
print(f"Code length: {code_length}")
#Convert RGB to YCbCr
color_conv = RGBYCbCr()
img_ycbcr = color_conv.forward(img)
decoded_ycbcr = color_conv.forward(decoded)
#Calculate MSE and PSNR, Y:U:V = 6:1:1
MSE_y = ((img_ycbcr[:,:,0].astype(int)-decoded_ycbcr[:,:,0].astype(int))**2).mean()
MSE_u = ((img_ycbcr[:,:,1].astype(int)-decoded_ycbcr[:,:,1].astype(int))**2).mean()
MSE_v = ((img_ycbcr[:,:,2].astype(int)-decoded_ycbcr[:,:,2].astype(int))**2).mean()
PSNR_y = 10 * math.log10((255*255)/MSE_y)
PSNR_u = 10 * math.log10((255*255)/MSE_u)
PSNR_v = 10 * math.log10((255*255)/MSE_v)
PSNR = (PSNR_y * 6 + PSNR_u + PSNR_v)/8
#Call the functions of SSIM, MS-SSIM, VIF
D_1 = SSIM(channels=1)
D_2 = MS_SSIM(channels=1)
D_3 = VIFs(channels=3) # spatial domain VIF
#To get 4-dimension torch tensors, (N, 3, H, W), divide by 255 to let the range between (0,1)
torch_decoded = torch.FloatTensor(decoded.astype(int).swapaxes(0,2).swapaxes(1,2)).unsqueeze(0)/255
torch_img = torch.FloatTensor(img.astype(int).swapaxes(0,2).swapaxes(1,2)).unsqueeze(0)/255
torch_decoded_ycbcr = torch.FloatTensor(decoded_ycbcr.astype(int).swapaxes(0,2).swapaxes(1,2)).unsqueeze(0)/255
torch_img_ycbcr = torch.FloatTensor(img_ycbcr.astype(int).swapaxes(0,2).swapaxes(1,2)).unsqueeze(0)/255
#Calculate SSIM, MS-SSIM, VIF
#SSIM on luma channel
SSIM_value = D_1(torch_decoded_ycbcr[:, [0], :, :] , torch_img_ycbcr[:, [0], :, :], as_loss=False)
#MS-SSIM on luma channel
MS_SSIM_value = D_2(torch_decoded_ycbcr[:, [0], :, :], torch_img_ycbcr[:, [0], :, :], as_loss=False)
#VIF on spatial domain
VIF_value = D_3(torch_decoded, torch_img, as_loss=False)
#print(D_3(torch_img, torch_img, as_loss=False))
#Print out the results
#print(f"Mean squared error: {MSE}")
print(f"General PSNR: {PSNR}")
print(f"SSIM: {SSIM_value}")
print(f"MS_SSIM: {MS_SSIM_value}")
print(f"VIF: {VIF_value}")
print(f"Compression rate: {compression_rate} bits/nt")
# plt.imshow(decoded)
# plt.show()
# io.imsave(str(compression_rate) + ".png", decoded)
return code, decoded, res, compression_rate, PSNR, SSIM_value, MS_SSIM_value, VIF_value
return inner | 123 |
async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Unload a config entry."""
unload_ok = all(
await asyncio.gather(
*[
hass.config_entries.async_forward_entry_unload(entry, platform)
for platform in PLATFORMS
]
)
)
if unload_ok:
config_data = hass.data[DOMAIN].pop(entry.entry_id)
await config_data[CONF_CLIENT].async_client_close()
return unload_ok | 124 |
def get_ucp_worker_info():
"""Gets information on the current UCX worker, obtained from
`ucp_worker_print_info`.
"""
return _get_ctx().ucp_worker_info() | 125 |
def check_can_collect_payment(id):
"""
Check if participant can collect payment this is true if :
- They have been signed up for a year
- They have never collected payment before or their last collection was more than 5 months ago
"""
select = "SELECT time_sign_up FROM SESSION_INFO WHERE user_id = (%s)"
time_sign_up = db.execute(select, (id,), 1)
one_year_after_sign_up = time_sign_up[0][0] + timedelta(weeks=43)
select = "SELECT date_collected,next_collection from TASK_COMPLETED WHERE user_id = (%s)"
date_collected = db.execute(select, (id,), 1)
can_collect_payment = False
#if one_year_after_sign_up < datetime.now() and user_type and next_collection[0][0] and next_collection[0][0] < datetime.now():
if one_year_after_sign_up < datetime.now() and len(date_collected) >= 1 and (date_collected[0][0] == None or date_collected[0][0] < (datetime.now() - timedelta(weeks=22))):
can_collect_payment = True
date_collected = date_collected[0][0]
elif len(date_collected) > 1:
date_collected = date_collected[0][0]
return (can_collect_payment,date_collected,time_sign_up) | 126 |
def plus_tensor(wx, wy, wz=np.array([0, 0, 1])):
"""Calculate the plus polarization tensor for some basis.c.f., eq. 2 of https://arxiv.org/pdf/1710.03794.pdf"""
e_plus = np.outer(wx, wx) - np.outer(wy, wy)
return e_plus | 127 |
def duplicate_objects(dup_infos):
"""Duplicate an object with optional transformations.
Args:
dup_infos (list[dict]): A list of duplication infos.
Each info is a dictionary, containing the following data:
original (str): Name of the object to duplicate.
name (str): Desired name for the duplicate.
translation (f,f,f): Translation float tuple or None if not
to change.
rotation (f,f,f): Rotation float tuple or None if not to
change.
scale (f,f,f): 3d scale float tuple or None if not to change.
Returns:
list[tuple (str, str)]: The first element of each tuple
contains the return 'code' of the operation, which can be
- 'Ok' If no problem occured.
- 'NotFound' If the original could not be found.
- 'Renamed' If the name was changed by the editor.
- 'Failed' If something else problematic happened.
The second element is None, unless the editor 'Renamed' the
object, in which case it contains the editor-assigned name.
If the return value is 'Renamed', the calling function must assign
the returned name to the original object in the Program or find a
new fitting name and assign it to the duplicated object using the
:func:`renameObject` function with the returned string as name.
.. seealso:: :func:`renameObject` :func:`getFreeName`
"""
infos_str = json.dumps(dup_infos)
msg = "DuplicateObjects " + infos_str
result = connection.send_message(msg)
results = json.parse(result)
return results | 128 |
def determineOptimalStartingPoint(options,logger_proxy,logging_mutex):
"""
If options.checkpoint is set to -1 then operations will start from the beginning but will skip steps to regenerate files
For all other values of options.checkpoint, pre-generated data will be overwritten
"""
flag = checkForNewData(options)
#options.checkpoint = 0 # Nothing will be deleted and nothing will be regenerated. Computation will start from the beginning and skip the steps already computed.
if flag == 2:
pass # No new RNA-Seq samples. PsiCLASS, FINDER and BRAKER2 runs have completed successfully. No need to change `options.checkpoint` can go ahead with user request
elif flag == 3:
if options.checkpoint>4:
options.checkpoint = 4 # No new RNA-Seq samples, PsiCLASS, FINDER done but BRAKER NOT done.
elif flag == 4:
if options.checkpoint>3:
options.checkpoint=3 # No new RNA-Seq samples, PsiCLASS done but FINDER and BRAKER NOT done
elif flag == 5:
if options.checkpoint>2:
options.checkpoint=2
if options.checkpoint == 0:
pass # Do Nothing
elif options.checkpoint == 1: # Align reads to reference genome (Will trigger removal of all alignments and start from beginning)
os.system(f"rm -rf {options.output_star}/*") # Delete contents of alignment folder
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/*") # Delete contents of assembly folder
os.system(f"rm -rf {options.output_braker}/*") # Delete contents of BRAKER2 folder
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/braker.gtf")
elif options.checkpoint == 2: # Assemble with PsiCLASS (Will remove all assemblies)
os.system(f"rm -rf {options.output_star}/*_counts_all_info.pkl") # Removing genomic read counts file
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/*") # Delete contents of assembly folder
os.system(f"rm -rf {options.output_braker}/*") # Delete contents of BRAKER2 folder
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/braker.gtf")
elif options.checkpoint == 3: # Find genes with FINDER (entails changepoint detection)
os.system(f"rm -rf {options.output_star}/*_counts_all_info.pkl") # Removing genomic read counts file
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/combined/combined_*gtf") # Delete contents of assembly folder
os.system(f"rm -rf {options.output_braker}/*") # Delete contents of BRAKER2 folder
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/braker.gtf")
elif options.checkpoint == 4: # Predict genes using BRAKER2
os.system(f"rm -rf {options.output_braker}/*") # Delete contents of BRAKER2 folder
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/combined/combined_with_CDS*.gtf")
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/braker.gtf")
elif options.checkpoint == 5: # Annotate coding regions
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/combined/combined_with_CDS*.gtf")
elif options.checkpoint == 6: # Merge FINDER annotations with BRAKER2 predictions and protein sequences
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/combined/combined_with_CDS_*.gtf")
os.system(f"rm -rf {options.output_assemblies_psiclass_terminal_exon_length_modified}/combined/FINDER_BRAKER_PROT.gtf")
with logging_mutex:
logger_proxy.info(f"Starting FINDER from {options.checkpoint} checkpoint") | 129 |
def _list_data_objects(request, model, serializer):
"""a factory method for querying and receiving database objects"""
obj = model.objects.all()
ser = serializer(obj, many=True)
return Response(ser.data, status=status.HTTP_200_OK) | 130 |
def load_conf(file='./config', section='SYNTH_DATA'):
"""load configuration
Args:
file (str, optional): path to conf file. Defaults to './config'.
section (str, optional): name of section. Defaults to 'SYNTH_DATA'.
Returns:
[str]: params of configuration
"""
log_message('Load configuration.')
config = configparser.ConfigParser()
resource = config.read(file)
if 0 == resource:
log_message('Error: cannot read configuration file.')
exit(1)
params = {}
options = config.options(section)
for opt in options:
params[opt] = config.get(section, opt)
log_message(' - %s: %s' % (opt, params[opt]))
return params | 131 |
def plot_proper_motion(df):
"""Plot proper motion.
df: DataFrame with `pm_phi1` and `pm_phi2`
"""
x = df['pm_phi1']
y = df['pm_phi2']
plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)
plt.xlabel('Proper motion phi1 (mas/yr)')
plt.ylabel('Proper motion phi2 (mas/yr)')
plt.xlim(-12, 8)
plt.ylim(-10, 10) | 132 |
def consume(context, state):
"""
*musicpd.org, playback section:*
``consume {STATE}``
Sets consume state to ``STATE``, ``STATE`` should be 0 or
1. When consume is activated, each song played is removed from
playlist.
"""
if int(state):
context.core.tracklist.consume = True
else:
context.core.tracklist.consume = False | 133 |
def distance(a, b):
"""
Computes a
:param a:
:param b:
:return:
"""
x = a[0] - b[0]
y = a[1] - b[1]
return math.sqrt(x ** 2 + y ** 2) | 134 |
def test_immunization_3(base_settings):
"""No. 3 tests collection for Immunization.
Test File: immunization-example-refused.json
"""
filename = base_settings["unittest_data_dir"] / "immunization-example-refused.json"
inst = immunization.Immunization.parse_file(
filename, content_type="application/json", encoding="utf-8"
)
assert "Immunization" == inst.resource_type
impl_immunization_3(inst)
# testing reverse by generating data from itself and create again.
data = inst.dict()
assert "Immunization" == data["resourceType"]
inst2 = immunization.Immunization(**data)
impl_immunization_3(inst2) | 135 |
def approve_pipelines_for_publishing(pipeline_ids): # noqa: E501
"""approve_pipelines_for_publishing
:param pipeline_ids: Array of pipeline IDs to be approved for publishing.
:type pipeline_ids: List[str]
:rtype: None
"""
pipe_exts: [ApiPipelineExtension] = load_data(ApiPipelineExtension)
pipe_ext_ids = {p.id for p in pipe_exts}
missing_pipe_ext_ids = set(pipeline_ids) - pipe_ext_ids
for id in missing_pipe_ext_ids:
store_data(ApiPipelineExtension(id=id))
update_multiple(ApiPipelineExtension, [], "publish_approved", False)
if pipeline_ids:
update_multiple(ApiPipelineExtension, pipeline_ids, "publish_approved", True)
return None, 200 | 136 |
def test_k8s_plugin_gets_raw_metrics_empty(postfix, mocked_http):
"""
K8sCollector returns a empty dict in case of problems.
GIVEN: K8s stats server won't return a JSON response for some reason
(Unauthorized, exceptions, internal errors, etc).
WHEN: K8sCollector method `_get_raw_metrics` is used.
THEN: It returns an empty dict.
"""
url = f"https://10.1.18.1:10250/stats/{postfix}"
cert = ("client.crt", "client.key")
result = K8sCollector._get_raw_metrics(url, cert)
assert result == {} | 137 |
def make_tokenizer_module(tokenizer):
"""tokenizer module"""
tokenizers = {}
cursors = {}
@ffi.callback("int(int, const char *const*, sqlite3_tokenizer **)")
def xcreate(argc, argv, ppTokenizer):
if hasattr(tokenizer, "__call__"):
args = [ffi.string(x).decode("utf-8") for x in argv[0:argc]]
tk = tokenizer(args)
else:
tk = tokenizer
th = ffi.new_handle(tk)
tkn = ffi.new("sqlite3_tokenizer *")
tkn.t = th
tokenizers[tkn] = th
ppTokenizer[0] = tkn
return SQLITE_OK
@ffi.callback("int(sqlite3_tokenizer *)")
def xdestroy(pTokenizer):
tkn = pTokenizer
del tokenizers[tkn]
return SQLITE_OK
@ffi.callback(
"int(sqlite3_tokenizer*, const char *, int, sqlite3_tokenizer_cursor **)"
)
def xopen(pTokenizer, pInput, nInput, ppCursor):
cur = ffi.new("sqlite3_tokenizer_cursor *")
tokenizer = ffi.from_handle(pTokenizer.t)
i = ffi.string(pInput).decode("utf-8")
tokens = [(n.encode("utf-8"), b, e) for n, b, e in tokenizer.tokenize(i) if n]
tknh = ffi.new_handle(iter(tokens))
cur.pTokenizer = pTokenizer
cur.tokens = tknh
cur.pos = 0
cur.offset = 0
cursors[cur] = tknh
ppCursor[0] = cur
return SQLITE_OK
@ffi.callback(
"int(sqlite3_tokenizer_cursor*, const char **, int *, int *, int *, int *)"
)
def xnext(pCursor, ppToken, pnBytes, piStartOffset, piEndOffset, piPosition):
try:
cur = pCursor[0]
tokens = ffi.from_handle(cur.tokens)
normalized, inputBegin, inputEnd = next(tokens)
ppToken[0] = ffi.from_buffer(normalized)
pnBytes[0] = len(normalized)
piStartOffset[0] = inputBegin
piEndOffset[0] = inputEnd
cur.offset = inputEnd
piPosition[0] = cur.pos
cur.pos += 1
except StopIteration:
return SQLITE_DONE
return SQLITE_OK
@ffi.callback("int(sqlite3_tokenizer_cursor *)")
def xclose(pCursor):
tk = ffi.from_handle(pCursor.pTokenizer.t)
on_close = getattr(tk, "on_close", None)
if on_close and hasattr(on_close, "__call__"):
on_close()
del cursors[pCursor]
return SQLITE_OK
tokenizer_module = ffi.new(
"sqlite3_tokenizer_module*", [0, xcreate, xdestroy, xopen, xclose, xnext]
)
tokenizer_modules[tokenizer] = (
tokenizer_module,
xcreate,
xdestroy,
xopen,
xclose,
xnext,
)
return tokenizer_module | 138 |
def looping_call(interval, callable):
"""
Returns a greenlet running your callable in a loop and an Event you can set
to terminate the loop cleanly.
"""
ev = Event()
def loop(interval, callable):
while not ev.wait(timeout=interval):
callable()
return gevent.spawn(loop, interval, callable), ev | 139 |
def obj2sta(obj, sta_filename, workshop=None):
"""
Convert an object(json format) to sta file
:param obj: ready to convert
:param sta_filename: output sta filename
:param workshop: means path to read binary file
:return:
"""
if workshop is None:
workshop = ''
with open(sta_filename, 'wb') as ofile:
kwargs = {}
kwargs['workshop'] = workshop
byte = pack_obj(obj, **kwargs)
# write header
ofile.write(struct.pack('i', STA_MARK))
# write content
ofile.write(byte) | 140 |
def print_twin_results_vec(vec_dat, labels):
"""Print out comparison results, stored in 1d vector.
Parameters
----------
vec_dat : 1d array
Vector of data to print out.
labels : list of str
Labels for what data each row corresponds to.
"""
for ind, label in enumerate(labels):
print('\t', label, '\t : ', '{:5.4f}'.format(vec_dat[ind])) | 141 |
def rsquared_adj(r, nobs, df_res, has_constant=True):
"""
Compute the adjusted R^2, coefficient of determination.
Args:
r (float): rsquared value
nobs (int): number of observations the model was fit on
df_res (int): degrees of freedom of the residuals (nobs - number of model params)
has_constant (bool): whether the fitted model included a constant (intercept)
Returns:
float: adjusted coefficient of determination
"""
if has_constant:
return 1.0 - (nobs - 1) / df_res * (1.0 - r)
else:
return 1.0 - nobs / df_res * (1.0 - r) | 142 |
def run_tests(test_folder: str, build_folder: str) -> None:
""" Discover and run tests. """
sys.path.insert(0, '.')
# Make sure log messages are not shown on stdout/stderr. We can't simply
# increase the log level since some unit tests expect logging to happen.
logging.getLogger().addHandler(logging.StreamHandler(io.StringIO()))
# Run the unit test with the XML test runner so that the test output
# can be processed by Sonar.
my_dir = pathlib.Path(__file__).resolve().parent
tests_dir = my_dir / test_folder
results_dir = my_dir.parent / "build" / build_folder
results_dir.mkdir(parents=True, exist_ok=True)
unittest.main(module=None, testRunner=xmlrunner.XMLTestRunner(output=str(results_dir)),
argv=[sys.argv[0], "discover", "-s", str(tests_dir), "-p", "*_tests.py"]) | 143 |
def metadata_factory(repo, json=False, **kwargs):
"""
This generates a layout you would expect for metadata storage with files.
:type json: bool
:param json: if True, will return string instead.
"""
output = {
"baseline_filename": None,
"crontab": "0 0 * * *",
"exclude_regex": None,
"plugins": {
"AWSKeyDetector": {},
"ArtifactoryDetector": {},
"Base64HighEntropyString": {
"base64_limit": 4.5,
},
"BasicAuthDetector": {},
"HexHighEntropyString": {
"hex_limit": 3,
},
"KeywordDetector": {
'keyword_exclude': None
},
"MailchimpDetector": {},
"PrivateKeyDetector": {},
"SlackDetector": {},
"StripeDetector": {},
},
"repo": repo,
"sha": 'sha256-hash',
}
output.update(kwargs)
if json:
return json_module.dumps(output, indent=2, sort_keys=True)
return output | 144 |
def saveTextData(file_name, data, fmt):
"""Save data array in text format
Args:
file_name (str): output file name
data (ndarray): numpy ndarray data
fmt (str): format string, e.g. "%.2e"
"""
numpy.savetxt(file_name, data, fmt=fmt) | 145 |
def inpand(clip: vs.VideoNode, sw: int, sh: Optional[int] = None, mode: XxpandMode = XxpandMode.RECTANGLE,
thr: Optional[int] = None, planes: int | Sequence[int] | None = None) -> vs.VideoNode:
"""
Calls std.Minimum in order to shrink each pixel with the smallest value in its 3x3 neighbourhood
from the desired width and height.
:param clip: Source clip.
:param sw: Shrinking shape width.
:param sh: Shrinking shape height. If not specified, default to sw.
:param mode: Shape form. Ellipses are combinations of rectangles and losanges
and look more like octogons.
Losanges are truncated (not scaled) when sw and sh are not equal.
:param thr: Allows to limit how much pixels are changed.
Output pixels will not become less than ``input - threshold``.
The default is no limit.
:param planes: Specifies which planes will be processed. Any unprocessed planes will be simply copied.
:return: Transformed clip
"""
return morpho_transfo(clip, core.std.Minimum, sw, sh, mode, thr, planes) | 146 |
def pre_update(ctx, ref=settings.UPDATE_REF):
"""Update code to pick up changes to this file."""
update_code(ref)
update_info() | 147 |
def test_instantiation(adb_executable):
"""
Just make sure that we can instantiate the ADB object
:return: None
"""
try:
adb = ADB(adb_executable) # noqa: W0612
except: # noqa
pytest.fail("No failure should be raised") | 148 |
def _extract_aggregate_functions(before_aggregate):
"""Converts `before_aggregate` to aggregation functions.
Args:
before_aggregate: The first result of splitting `after_broadcast` on
`intrinsic_defs.FEDERATED_AGGREGATE`.
Returns:
`zero`, `accumulate`, `merge` and `report` as specified by
`canonical_form.CanonicalForm`. All are instances of
`building_blocks.CompiledComputation`.
Raises:
transformations.CanonicalFormCompilationError: If we extract an ASTs of the
wrong type.
"""
# See `get_iterative_process_for_canonical_form()` above for the meaning of
# variable names used in the code below.
zero_index_in_before_aggregate_result = 1
zero_tff = transformations.select_output_from_lambda(
before_aggregate, zero_index_in_before_aggregate_result).result
accumulate_index_in_before_aggregate_result = 2
accumulate_tff = transformations.select_output_from_lambda(
before_aggregate, accumulate_index_in_before_aggregate_result).result
merge_index_in_before_aggregate_result = 3
merge_tff = transformations.select_output_from_lambda(
before_aggregate, merge_index_in_before_aggregate_result).result
report_index_in_before_aggregate_result = 4
report_tff = transformations.select_output_from_lambda(
before_aggregate, report_index_in_before_aggregate_result).result
zero = transformations.consolidate_and_extract_local_processing(zero_tff)
accumulate = transformations.consolidate_and_extract_local_processing(
accumulate_tff)
merge = transformations.consolidate_and_extract_local_processing(merge_tff)
report = transformations.consolidate_and_extract_local_processing(report_tff)
return zero, accumulate, merge, report | 149 |
def process_site_eb(err, product_id, sid, data):
"""Errorback from process_site transaction."""
msg = f"process_site({product_id}, {sid}, {data}) got {err}"
common.email_error(err, msg) | 150 |
def _make_system(A, M, x0, b):
"""Make a linear system Ax = b
Args:
A (cupy.ndarray or cupyx.scipy.sparse.spmatrix or
cupyx.scipy.sparse.LinearOperator): sparse or dense matrix.
M (cupy.ndarray or cupyx.scipy.sparse.spmatrix or
cupyx.scipy.sparse.LinearOperator): preconditioner.
x0 (cupy.ndarray): initial guess to iterative method.
b (cupy.ndarray): right hand side.
Returns:
tuple:
It returns (A, M, x, b).
A (LinaerOperator): matrix of linear system
M (LinearOperator): preconditioner
x (cupy.ndarray): initial guess
b (cupy.ndarray): right hand side.
"""
fast_matvec = _make_fast_matvec(A)
A = _interface.aslinearoperator(A)
if fast_matvec is not None:
A = _interface.LinearOperator(A.shape, matvec=fast_matvec,
rmatvec=A.rmatvec, dtype=A.dtype)
if A.shape[0] != A.shape[1]:
raise ValueError('expected square matrix (shape: {})'.format(A.shape))
if A.dtype.char not in 'fdFD':
raise TypeError('unsupprted dtype (actual: {})'.format(A.dtype))
n = A.shape[0]
if not (b.shape == (n,) or b.shape == (n, 1)):
raise ValueError('b has incompatible dimensions')
b = b.astype(A.dtype).ravel()
if x0 is None:
x = cupy.zeros((n,), dtype=A.dtype)
else:
if not (x0.shape == (n,) or x0.shape == (n, 1)):
raise ValueError('x0 has incompatible dimensions')
x = x0.astype(A.dtype).ravel()
if M is None:
M = _interface.IdentityOperator(shape=A.shape, dtype=A.dtype)
else:
fast_matvec = _make_fast_matvec(M)
M = _interface.aslinearoperator(M)
if fast_matvec is not None:
M = _interface.LinearOperator(M.shape, matvec=fast_matvec,
rmatvec=M.rmatvec, dtype=M.dtype)
if A.shape != M.shape:
raise ValueError('matrix and preconditioner have different shapes')
return A, M, x, b | 151 |
def merge_intersecting_segments(segments: List[Segment]) -> List[Segment]:
"""
Merges intersecting segments from the list.
"""
sorted_by_start = sorted(segments, key=lambda segment: segment.start)
merged = []
for segment in sorted_by_start:
if not merged:
merged.append(Segment(segment.start, segment.end))
continue
last_merged = merged[-1]
if segment.start <= last_merged.end:
last_merged.end = max(last_merged.end, segment.end)
else:
merged.append(Segment(segment.start, segment.end))
return merged | 152 |
def test_set_dids_metadata_bulk_multi(did_factory):
""" DID (CORE) : Test setting metadata in bulk with multiple key-values on multiple dids"""
skip_without_json()
nb_dids = 5
dids = [did_factory.make_dataset() for _ in range(nb_dids)]
for did in dids:
testkeys = list(map(lambda i: 'testkey' + generate_uuid(), range(3)))
testmeta = {key: key + 'value' for key in testkeys}
did['meta'] = testmeta
print(dids)
set_dids_metadata_bulk(dids=dids, recursive=False)
for did in dids:
testmeta = did['meta']
print('Metadata:', testmeta)
meta = get_metadata(plugin="ALL", scope=did['scope'], name=did['name'])
print('Metadata:', meta)
for testkey in testmeta:
assert testkey in meta and meta[testkey] == testmeta[testkey] | 153 |
def change_log_root_key():
"""Root key of an entity group with change log."""
# Bump ID to rebuild the change log from *History entities.
return ndb.Key('AuthDBLog', 'v1') | 154 |
def load_file(filename):
"""Loads a TESS *spoc* FITS file and returns TIME, PDCSAP_FLUX"""
hdu = fits.open(filename)
time = hdu[1].data['TIME']
flux = hdu[1].data['PDCSAP_FLUX']
flux[flux == 0] = numpy.nan
return time, flux | 155 |
def AcProgRanlib(context, selection=None):
"""Corresponds to AC_PROG_RANLIB_ autoconf macro
:Parameters:
context
SCons configuration context.
selection
If ``None`` (default), the program will be found automatically,
otherwise the method will return the value of **selection**.
.. _AC_PROG_RANLIB: http://www.gnu.org/software/autoconf/manual/autoconf.html#index-AC_005fPROG_005fRANLIB-291
"""
raise NotImplementedError("not implemented") | 156 |
def blit_anchors(dest, dest_anchor, src, src_anchor):
""" Blits the source onto the destination such that their anchors align.
src_anchor and dest_anchor can be strings of one of the point attributes (topleft, center,
midbottom, etc.) or a position on their respective surfaces (e.g [100, 200]).
"""
try:
src_anchor = get_anchor(src, src_anchor)
except ValueError:
pass # Assume src_anchor is already a point. If not, it will fail in the map().
try:
dest_anchor = get_anchor(dest, dest_anchor)
except ValueError:
pass # Assume dest_anchor is already a point. If not, it will fail in the map().
topleft = list(map(lambda a,b,c: a - b + c, src.get_rect().topleft, src_anchor, dest_anchor))
dest.blit(src, topleft) | 157 |
def updateGraph( Graph, GraphNumber ):
"""
The function calls an appropriate plot-function based on the mode (value) of
the radio-button
:param Graph: an instance of VibroP_GraphObject class
:param GraphNumber: int
:return:
"""
# Update the graph ID ( GraphNumber - it's a built-in bohek variable that
# belongs to the RadioButton widget )
Graph.setPlottingGraphNumber( GraphNumber )
plotEigenfrequenciesPlate( Graph )
# Depict coresponding lines based on the graph chosen by the user
if (GraphNumber == 0):
plotWaveSpeedGraph( Graph )
if (GraphNumber == 1):
plotWaveSpeedGraphWithLimits( Graph )
if (GraphNumber == 2):
plotModesInBand( Graph )
if (GraphNumber == 3):
plotModalDensity( Graph )
if (GraphNumber == 4):
plotModalOverlapFactor( Graph )
if (GraphNumber == 5):
plotMaximumElementSize( Graph ) | 158 |
def create_insight_id_extension(
insight_id_value: str, insight_system: str
) -> Extension:
"""Creates an extension for an insight-id with a valueIdentifier
The insight id extension is defined in the IG at:
https://alvearie.io/alvearie-fhir-ig/StructureDefinition-insight-id.html
Args:
insight_id_value - the value of the insight id
insight_system - urn for the system used to create the insight
Returns: The insight id extension
Example:
>>> ext = create_insight_id_extension("insight-1", "urn:id:alvearie.io/patterns/QuickUMLS_v1.4.0")
>>> print(ext.json(indent=2))
{
"url": "http://ibm.com/fhir/cdm/StructureDefinition/insight-id",
"valueIdentifier": {
"system": "urn:id:alvearie.io/patterns/QuickUMLS_v1.4.0",
"value": "insight-1"
}
}
"""
insight_id_ext = Extension.construct()
insight_id_ext.url = alvearie_ext_url.INSIGHT_ID_URL
insight_id = Identifier.construct()
insight_id.system = insight_system
insight_id.value = insight_id_value
insight_id_ext.valueIdentifier = insight_id
return insight_id_ext | 159 |
def ReadNotifyResponseHeader(payload_size, data_type, data_count, sid, ioid):
"""
Construct a ``MessageHeader`` for a ReadNotifyResponse command.
Read value of a channel. Sent over TCP.
Parameters
----------
payload_size : integer
Size of DBR formatted data in payload.
data_type : integer
Payload format.
data_count : integer
Payload element count.
sid : integer
SID of the channel.
ioid : integer
IOID of this operation.
"""
struct_args = (15, payload_size, data_type, data_count, sid, ioid)
# If payload_size or data_count cannot fit into a 16-bit integer, use the
# extended header.
return (ExtendedMessageHeader(*struct_args)
if any((payload_size > 0xffff, data_count > 0xffff, ))
else MessageHeader(*struct_args)) | 160 |
def substitute_T5_cols(c, cols, nlu_identifier=True):
"""
rename cols with base name either <t5> or if not unique <t5_<task>>
"""
new_cols = {}
new_base_name = 't5' if nlu_identifier=='UNIQUE' else f't5_{nlu_identifier}'
for col in cols :
if '_results' in col : new_cols[col] = new_base_name
elif '_beginnings' in col : new_cols[col] = f'{new_base_name}_begin'
elif '_endings' in col : new_cols[col] = f'{new_base_name}_end'
elif '_embeddings' in col : continue # Token never stores Embeddings new_cols[col] = f'{new_base_name}_embedding'
elif '_types' in col : continue # new_cols[col] = f'{new_base_name}_type'
elif 'meta' in col:
if '_sentence' in col : new_cols[col] = f'{new_base_name}_origin_sentence' # maps to which sentence token comes from
else : logger.info(f'Dropping unmatched metadata_col={col} for c={c}')
# new_cols[col]= f"{new_base_name}_confidence"
return new_cols | 161 |
def _get_trigger_func(name, trigger_name):
"""
Given a valid vulnerability name, get the trigger function corresponding
to the vulnerability name and trigger name.
If the trigger function isn't found, raise NotFound.
"""
try:
return get_trigger(name, trigger_name)
except AttributeError:
raise NotFound() | 162 |
def _get_rotated_bounding_box(size, quaternion):
"""Calculates the bounding box of a rotated 3D box.
Args:
size: An array of length 3 specifying the half-lengths of a box.
quaternion: A unit quaternion specifying the box's orientation.
Returns:
An array of length 3 specifying the half-lengths of the bounding box of
the rotated box.
"""
corners = ((size[0], size[1], size[2]),
(size[0], size[1], -size[2]),
(size[0], -size[1], size[2]),
(-size[0], size[1], size[2]))
rotated_corners = tuple(
transformations.quat_rotate(quaternion, corner) for corner in corners)
return np.amax(np.abs(rotated_corners), axis=0) | 163 |
def Cos(
a: float = 1.,
b: float = 1.,
c: float = 0.) -> InternalLayer:
"""Affine transform of `Cos` nonlinearity, i.e. `a cos(b*x + c)`.
Args:
a: output scale.
b: input scale.
c: input phase shift.
Returns:
`(init_fn, apply_fn, kernel_fn)`.
"""
return Sin(a=a, b=b, c=c + np.pi / 2) | 164 |
def key_make_model(chip):
""" Given a chip, return make and model string.
Make and model are extracted from chip.misc using the keys "make" and
"model". If they are missing it returns None for that value. If misc
missing or not a dictionary, (None, None) is returned.
Args:
chip: A chip named tuple
Returns:
string: "make_model" from the chip. The string "None" may be returned
for one of the positions (or both) if it is missing in the chip.
"""
output = [None, None]
# Ensure we have a misc dictionary
if hasattr(chip, "misc"):
misc = chip.misc
if hasattr(misc, "get"):
output[0] = misc.get("make", None)
output[1] = misc.get("model", None)
return tuple_to_string(output) | 165 |
def check_concentration(R,D):
"""
check the concentration of a halo
by finding where the power law is most similar to alpha^-2
return 1./radius, which is the concentration.
(so find the scale radius by taking 1./concentration)
"""
func = np.log10(R**-2.)-np.log10(D)
print('Concentration={}'.format(1./R[np.nanargmin(func)])) | 166 |
def _mergesort_space_optimized(nums: list, start: int, end: int) -> None:
"""Performing merge operation in-place by overwriting associated indexes of input array
Complexity:
n = len(nums)
Space: n = O(n)
for (2 * n/2) copies of sorted left/right subarrays
Examples:
>>> _mergesort_space_optimized([], 0, 0)
"""
## EDGE CASES ##
if not nums:
return
"""Algorithm"""
## BASE CASE ##
if start >= end:
return
## INITIALIZE VARS##
mid = (start + end + 1) // 2
## RECURSIVELY SORT SUBARRAYS ##
_mergesort_space_optimized(nums, start, mid - 1)
left = nums[start:mid] # n/2 copy
_mergesort_space_optimized(nums, mid, end)
right = nums[mid : end + 1] # n/2 copy
## MERGE SORTED SUBARRAYS ##
curr_left_idx = curr_right_idx = 0
insertion_idx = start
# Merge until a single subarray is exhausted
while curr_left_idx < len(left) and curr_right_idx < len(right):
if left[curr_left_idx] <= right[curr_right_idx]:
nums[insertion_idx] = left[curr_left_idx]
curr_left_idx += 1
else:
nums[insertion_idx] = right[curr_right_idx]
curr_right_idx += 1
insertion_idx += 1
# Merge remaining subarray
if curr_left_idx == len(left):
while curr_right_idx < len(right):
nums[insertion_idx] = right[curr_right_idx]
curr_right_idx += 1
insertion_idx += 1
else:
while curr_left_idx < len(left):
nums[insertion_idx] = left[curr_left_idx]
curr_left_idx += 1
insertion_idx += 1 | 167 |
def test_has_object_destroy_permission_owner(api_rf, km_user_accessor_factory):
"""
The Know Me user the accessor grants access on should be able to
destroy the accessor.
"""
accessor = km_user_accessor_factory()
api_rf.user = accessor.km_user.user
request = api_rf.get("/")
assert accessor.has_object_destroy_permission(request) | 168 |
def alternating_epsilons_actor_core(
policy_network: EpsilonPolicy, epsilons: Sequence[float],
) -> actor_core_lib.ActorCore[EpsilonActorState, None]:
"""Returns actor components for alternating epsilon exploration.
Args:
policy_network: A feedforward action selecting function.
epsilons: epsilons to alternate per-episode for epsilon-greedy exploration.
Returns:
A feedforward policy.
"""
epsilons = jnp.array(epsilons)
def apply_and_sample(params: networks_lib.Params,
observation: networks_lib.Observation,
state: EpsilonActorState):
random_key, key = jax.random.split(state.rng)
actions = policy_network(params, key, observation, state.epsilon)
return (actions.astype(jnp.int32),
EpsilonActorState(rng=random_key, epsilon=state.epsilon))
def policy_init(random_key: networks_lib.PRNGKey):
random_key, key = jax.random.split(random_key)
epsilon = jax.random.choice(key, epsilons)
return EpsilonActorState(rng=random_key, epsilon=epsilon)
return actor_core_lib.ActorCore(
init=policy_init, select_action=apply_and_sample,
get_extras=lambda _: None) | 169 |
def get_keywords(
current_user: models.User = Depends(deps.get_current_active_user),
controller_client: ControllerClient = Depends(deps.get_controller_client),
labels: List = Depends(deps.get_personal_labels),
q: Optional[str] = Query(None, description="query keywords"),
offset: int = Query(0),
limit: Optional[int] = Query(None),
) -> Dict:
"""
Get keywords and aliases
"""
filter_f = partial(filter_keyword, q) if q else None
items = list(labels_to_keywords(labels, filter_f))
if settings.REVERSE_KEYWORDS_OUTPUT:
items.reverse()
res = {"total": len(items), "items": paginate(items, offset, limit)}
return {"result": res} | 170 |
def _compile_unit(i):
"""Append gas to unit and update CO2e for pint/iam-unit compatibility"""
if " equivalent" in i["unit"]:
return i["unit"].replace("CO2 equivalent", "CO2e")
if i["unit"] in ["kt", "t"]:
return " ".join([i["unit"], i["gas"]])
else:
return i["unit"] | 171 |
def get_fdfs_url(file):
"""
上传文件或图片到FastDFS
:param file: 文件或图片对象，二进制数据或本地文件
:return: 文件或图片在FastDFS中的url
"""
# 创建FastDFS连接对象
fdfs_client = Fdfs_client(settings.FASTDFS_CONF_PATH)
"""
client.upload_by_filename(文件名),
client.upload_by_buffer(文件bytes数据)
"""
# 上传文件或图片到fastDFS
if isinstance(file, InMemoryUploadedFile):
result = fdfs_client.upload_by_buffer(file.read())
else:
result = fdfs_client.upload_by_filename(file)
"""
result = {
'Group name': 'group1', # FastDFS服务端Storage组名
'Remote file_id': 'group1/M00/00/00/wKgThF0LMsmATQGSAAExf6lt6Ck10.jpeg', # 文件存储的位置(索引)，可用于下载
'Status': 'Upload successed.', # 文件上传结果反馈
'Local file name': '/home/python/Desktop/upload_Images/02.jpeg', # 所上传文件的真实路径
'Uploaded size': '76.00KB', # 文件大小
'Storage IP': '192.168.19.132'} # FastDFS服务端Storage的IP
"""
# 判断是否上传成功，result为一个字典
if result['Status'] != 'Upload successed.':
return Response(status=403)
# 获取文件或图片上传后的路径
file_url = result['Remote file_id']
return file_url | 172 |
def test_download(mocker: MockFixture):
"""Test happy Path"""
# Mocking cmd line arguments
testArgs = Namespace(output="file.out", slot="Test")
mocker.patch("argparse.ArgumentParser.parse_args", return_value=testArgs)
mocker.patch("requests.request", side_effect=fakeQnAMakerAPI)
fileOut = mock_open()
mocker.patch.object(builtins, "open", fileOut)
# run as module (start from if __name__ == "__main__")
runpy.run_module("kb.scripts.download-kb", run_name="__main__")
fileOut.assert_called_once_with("file.out", "w", encoding="utf-8")
fileOut().write.assert_called_with(
json.dumps(jsonKB["qnaDocuments"], sort_keys=True, indent=4)
) | 173 |
def updateDb(dbObj, resultTupleList):
"""
Write the results to the dbOj.
"""
for (benchmarkInfo, benchmarkResults) in resultTupleList:
dbObj.addResults(benchmarkInfo, benchmarkResults) | 174 |
def get_all_edge_detects(clip: vs.VideoNode, **kwargs: Any) -> List[vs.VideoNode]:
"""Allows you to get all masks inheriting from EdgeDetect.
Args:
clip (vs.VideoNode):
Source clip.
kwargs:
Arguments passed to EdgeDetect().get_mask
Returns:
List[vs.VideoNode]: List of masks.
Example:
from vardefunc.mask import get_all_edge_detect
clip.set_output(0)
for i, mask in enumerate(get_all_edge_detect(get_y(clip)), start=1):
mask.set_output(i)
"""
masks = [
edge_detect().get_mask(clip, **kwargs).text.Text(edge_detect.__name__) # type: ignore
for edge_detect in EdgeDetect.__subclasses__()
]
return masks | 175 |
def add_overwrite_arg(parser):
"""
Add overwrite option to the parser.
Parameters
----------
parser: argparse.ArgumentParser object
"""
parser.add_argument(
'-f', dest='overwrite', action='store_true',
help='Force overwriting of the output files.') | 176 |
def get_ustz(localdt, timezone):
"""
Returns the timezone associated to a local datetime and an IANA timezone.
There are two common timezone conventions. One is the Olson/IANA and
the other is the Microsoft convention. For example, the closest IANA
timezone for Boston, MA is America/New_York. More commonly, this is
known as Eastern time zone. The goal of this function is to return the
common name for a timezone in the contiguous US.
Note that Arizona has its own IANA timezone and does not observe daylight
savings. So depending on the time of year, the offset for Arizona will
correspond to either Pacific or Mountain time.
Parameters
----------
localdt : datetime
The local datetime instance.
timezone : str
The IANA timezone associated with `localdt`. This should be a timezone
for the contiguous US.
use_noon : bool
If `True`, ignore the time for the incoming datetime and use noon
instead. This is nice for quick checks, but undesirable when accurate
timezone identification is needed late at night or early morning
Returns
------
tz : str
The common name for the timezone. This will be one of Pacific, Mountain,
Central, or Eastern.
"""
# Use noon to guarantee that we have the same day in each timezone.
# This is desirable in the sense that we don't want someone's tweet jumping
# from Eastern to Central, for example, at the end of daylight savings time.
localdt = datetime.datetime(localdt.year, localdt.month, localdt.day, 12)
timezone = pytz.timezone(timezone)
dt = timezone.localize(localdt)
for tz, tz_ref in TIMEZONES:
dt_new = dt.astimezone(tz_ref)
if dt_new.utcoffset() == dt.utcoffset():
return tz | 177 |
def plot_rolling_sharpe(returns, rolling_window=APPROX_BDAYS_PER_MONTH * 6,
**kwargs):
"""
Plots the rolling Sharpe ratio versus date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
rolling_window : int, optional
The days window over which to compute the sharpe ratio.
**kwargs, optional
Passed to plotting function.
Returns
-------
ax : matplotlib.Axes
The axes that were plotted on.
"""
rolling_sharpe_ts = timeseries.rolling_sharpe(
returns, rolling_window)
return rolling_sharpe_ts | 178 |
def test_data_format_avro(sdc_builder, sdc_executor, gcp):
"""
Write data to Google cloud storage using Avro format.
The pipeline looks like:
google_cloud_storage_origin >> wiretap
"""
DATA = {'name': 'boss', 'age': 60, 'emails': ['[email protected]', '[email protected]'], 'boss': None}
SCHEMA = {'namespace': 'example.avro',
'type': 'record',
'name': 'Employee',
'fields': [{'name': 'name', 'type': 'string'},
{'name': 'age', 'type': 'int'},
{'name': 'emails', 'type': {'type': 'array', 'items': 'string'}},
{'name': 'boss', 'type': ['Employee', 'null']}]}
pipeline_builder = sdc_builder.get_pipeline_builder()
storage_client = gcp.storage_client
bucket_name = "stf_std_" + get_random_string(string.ascii_lowercase, 20)
dev_raw_data_source = pipeline_builder.add_stage('Dev Raw Data Source')
dev_raw_data_source.set_attributes(data_format='JSON',
stop_after_first_batch=True,
raw_data=json.dumps(DATA))
google_cloud_storage = pipeline_builder.add_stage('Google Cloud Storage', type='destination')
google_cloud_storage.set_attributes(bucket=bucket_name,
common_prefix='gcs-test',
partition_prefix='test',
data_format='AVRO',
avro_schema=json.dumps(SCHEMA),
avro_schema_location='INLINE')
dev_raw_data_source >> google_cloud_storage
pipeline = pipeline_builder.build(
title=f'Google Cloud Storage Destination Data Format Avro').configure_for_environment(gcp)
sdc_executor.add_pipeline(pipeline)
try:
created_bucket = gcp.retry_429(storage_client.create_bucket)(bucket_name)
logger.info('Starting GCS Origin pipeline and wait until the information is read ...')
sdc_executor.start_pipeline(pipeline).wait_for_finished()
# To verify that the Avro format has been successfully stored, we read the data from GCS using an auxiliary
# pipeline. This pipeline is set to read data in Avro format.
result = read_avro_data(bucket_name, sdc_builder, sdc_executor, gcp)
# We compare the results read by the GCS Origin pipeline and check that the data is equal to the original data stored
assert [record.field for record in result] == [DATA]
finally:
logger.info('Deleting bucket %s ...', created_bucket.name)
gcp.retry_429(created_bucket.delete)(force=True) | 179 |
def saveSolution(name, C):
"""save set C to file name as a VertexCover solution"""
f = open(name, "w")
s = ",".join([str(c) for c in C])
f.write(s)
f.close() | 180 |
def ResolveNamespace(namespace):
"""Validate app namespace, providing a default.
If the argument is None, namespace_manager.get_namespace() is substituted.
Args:
namespace: The namespace argument value to be validated.
Returns:
The value of namespace, or the substituted default.
Always a non-empty string or None.
Raises:
BadArgumentError if the value is not a string.
"""
if namespace is None:
namespace = namespace_manager.get_namespace()
else:
namespace_manager.validate_namespace(
namespace, datastore_errors.BadArgumentError)
return namespace | 181 |
def Calculo_por_etapas(Diccionario):
"""Calculo de la hornilla por etapas"""
Lista_Contenido=[]
Lista_columnas=[]
#Normalización de la capacidad de la hornilla
#Mem_dias=float(Diccionario['¿Cada cuantos días quiere moler? (días)'])
#Mem_Temp=Normalizar_Capacidad(float(Diccionario['Capacidad estimada de la hornilla']),Mem_dias)
#print(float(Diccionario['Capacidad estimada de la hornilla']))
#print(Mem_Temp)
Etapas=int(float(Diccionario['Etapas']))#Mem_Temp[1]
#Etapas=12
#Saturador "minimo son dos etapas"
if (Etapas>2):
Factor_Division=Etapas-2
else:
Factor_Division=2
Etapas=2
#Caracteristicas de las celdas de cada columna (Lista_columnas)
#Fila 0 concentración de solidos inicial
#Fila 1 Concentración de solidos final
#Fila 2 Concentración promedio
#Fila 3 Masa de jugo de entrada
#Fila 4 Calor Especifico P Cte jugo
#Fila 5 Densidad del Jugo
#Fila 6 Volumen de jugo kg
#Fila 7 Volumen de jugo en L
#Fila 8 Temperatura de Entrada
#Fila 9 Temperatura de Salida
#Fila 10 Entalpia de Vaporización
#Fila 11 Masa de Agua a Evaporar
#Fila 12 Calor Nece Calc por Etapa
for i in range(13):
for j in range (Etapas):
Lista_columnas.append(float(i+j))
Lista_Contenido.append(Lista_columnas)
Lista_columnas=[]
Lista_Contenido[0][0]=float(Diccionario['CSS del jugo pos-evaporación']) #Concentracion_solidos_inicial (CSS02)
Lista_Contenido[1][0]=float(Diccionario['CSS panela']) #Concentracion_solidos_final (CSSF1)
Lista_Contenido[0][Etapas-1]=float(Diccionario['CSS del jugo de Caña']) #Concentracion_solidos_inicial (CSS01)
Lista_Contenido[1][Etapas-1]=float(Diccionario['CSS del jugo clarificado']) #Concentracion_solidos_final (CSSF1)
if(Etapas>2):
ite=0
for i in range(Etapas-2,0,-1):
Lista_Contenido[0][i]=Lista_Contenido[1][i+1]
if(ite==0):
Lista_Contenido[1][i]=((Lista_Contenido[0][0]-Lista_Contenido[0][i])/Factor_Division)+Lista_Contenido[0][i]
ite=ite+1
else:
Lista_Contenido[1][i]=((Lista_Contenido[0][0]-Lista_Contenido[0][Etapas-2])/Factor_Division)+Lista_Contenido[0][i]
for i in range(Etapas-1,-1,-1):
#Concentración promedio=(Concentracion_solidos_inicial+Concentracion_solidos_final)/2
Lista_Contenido[2][i]=(Lista_Contenido[0][i]+Lista_Contenido[1][i])/2
if(i==Etapas-1):
#Masa de jugo de entrada
Lista_Contenido[3][i]=float(Diccionario['A clarificación'])
else:
#Masa de jugo de entrada=(Masa de jugo etapa anterior*CCS inicial etapa anterior)/CCS Final etapa anterior
Lista_Contenido[3][i]=Lista_Contenido[3][i+1]*Lista_Contenido[0][i+1]/Lista_Contenido[1][i+1]
#Calor_Especifico_P_Cte_jugo=4.18*(1-(0.006*Concetracion_promedio))
Lista_Contenido[4][i]=4.18*(1-(0.006*Lista_Contenido[2][i]))
#Densidad_del_Jugo=997.39+(4.46*Concetracion_promedio)
Lista_Contenido[5][i]=997.39+(4.46*Lista_Contenido[2][i])
#Volumen_jugo=Masa_jugo_de_entrada/Densidad_del_Jugo
Lista_Contenido[6][i]=Lista_Contenido[3][i]/Lista_Contenido[5][i]
#Volumen_jugo_L=Volumen_jugo*1000
Lista_Contenido[7][i]=Lista_Contenido[6][i]*1000.0
if(i==Etapas-1):
#Temperatura_Entrada=Temperatura ambiente
Lista_Contenido[8][i]=float(Diccionario['Temperatura del ambiente'])
else:
#Temperatura_Entrada=Temperatura_ebullición_agua+0.2209*math.exp(0.0557*Concentracion_solidos_inicial)
Lista_Contenido[8][i]=Lista_Contenido[9][i+1]
#Temperatura_Salida=G37+0.2209*math.exp(0.0557*Concentracion_solidos_final)
Lista_Contenido[9][i]=float(Diccionario['Temperatura de ebullición del agua'])+0.2209*math.exp(0.0557*Lista_Contenido[1][i])
#Entalpia_Vaporizacion=(2492.9-(2.0523*Temperatura_Entrada))-(0.0030752*(Temperatura_Entrada**2))
Lista_Contenido[10][i]=(2492.9-(2.0523*Lista_Contenido[8][i]))-(0.0030752*(Lista_Contenido[8][i]**2))
#Masa_Agua_Evaporar=Masa_jugo_de_entrada-(Masa_jugo_de_entrada*Concentracion_solidos_inicial/Concentracion_solidos_final)
Lista_Contenido[11][i]=Lista_Contenido[3][i]-(Lista_Contenido[3][i]*Lista_Contenido[0][i]/Lista_Contenido[1][i])
#Calor_por_Etapa=(Masa_jugo_de_entrada*Calor_Especifico_P_Cte_jugo*(Temperatura_Salida-Temperatura_Entrada)+Masa_Agua_Evaporar*Entalpia_Vaporizacion)/3600
Lista_Contenido[12][i]=(Lista_Contenido[3][i]*Lista_Contenido[4][i]*(Lista_Contenido[9][i]-Lista_Contenido[8][i])+Lista_Contenido[11][i]*Lista_Contenido[10][i])/3600.0
#Fijar decimales en 3
for j in range (13):
for i in range (Etapas):
Lista_Contenido[j][i]=round(Lista_Contenido[j][i],3)
#Cambiar la salida o posicion de la paila de punteo a la paila 3 o 4
Lista_contenido_2=[]
L_aux=[]
for i in Lista_Contenido:
inio=3
if (Etapas!=7):
L_aux.append(i[2])
L_aux.append(i[1])
L_aux.append(i[0])
inio=3
else:
L_aux.append(i[3])
L_aux.append(i[2])
L_aux.append(i[1])
L_aux.append(i[0])
inio=4
for t in range(inio,len(i)):
L_aux.append(i[t])
Lista_contenido_2.append(L_aux)
L_aux=[]
Lista_Contenido=Lista_contenido_2
Etiquetas=[
'Concentracion de Solidos Inicial [ºBrix]',
'Concentracion de Solidos Final [ºBrix]',
'Concentracion de Solidos Promedio [ºBrix]',
'Masa de Jugo Entrada [Kg]',
'Calor Especifico P Cte jugo [kJ/Kg °C]',
'Densidad del Jugo [kg/m3]',
'Volumen de jugo [m^3/kg]',
'Volumen de jugo [L]',
'Temperatura de Entrada [ºC]',
'Temperatura de Salida [ºC]',
'Entalpia de Vaporización [kJ/kg]',
'Masa de Agua a Evaporar [kg]',
'Calor Nece Calc por Etapa [kW]'
]
Dict_aux=dict(zip(Etiquetas,Lista_Contenido))
Dict_aux_2=dict(zip(['Etapas'],[Etapas]))
Dict_aux.update(Dict_aux_2)
return Dict_aux | 182 |
def venus_equ_proportional_minute(x, y, e, R):
"""
Venus equation proportional minute
:param x: true argument (av) in degree
:param y: mean center (cm) in degree
:param e: eccentricity
:param R: radius of the epicycle
:return: minutes proportional in degree
"""
return utils.minuta_proportionalia(x, R, e, y) | 183 |
def e2_cond(p, m, sigma, alpha, mu):
"""
This function is dependent from the gamma function.
Conditional mean of the square of the normal distribution.
See the article for more informations.
Parameters
----------
p : float
Proportion of persistent species.
m : float
Mean of the persistent species.
sigma : float
Root square mean of the persistent species.
alpha : float
Parameter of the model - Interaction strength.
mu : float
Parameter of the model - Interaction drift.
Returns
-------
float
Conditional mean associated to the system.
"""
# The value delta is similar in the article.
delta = alpha/(sigma*np.sqrt(p))*(1+mu*p*m)
p_1 = np.exp(-delta**2/2)
p_2 = 1-stats.norm.cdf(-delta)
return (1/np.sqrt(2*np.pi))*-delta*p_1/p_2+1 | 184 |
def concline_generator(matches, idxs, df, metadata,
add_meta, category, fname, preserve_case=False):
"""
Get all conclines
:param matches: a list of formatted matches
:param idxs: their (sent, word) idx
"""
conc_res = []
# potential speedup: turn idxs into dict
from collections import defaultdict
mdict = defaultdict(list)
# if remaking idxs here, don't need to do it earlier
idxs = list(matches.index)
for mid, (s, i) in zip(matches, idxs):
#for s, i in matches:
mdict[s].append((i, mid))
# shorten df to just relevant sents to save lookup time
df = df.loc[list(mdict.keys())]
# don't look up the same sentence multiple times
for s, tup in sorted(mdict.items()):
sent = df.loc[s]
if not preserve_case:
sent = sent.str.lower()
meta = metadata[s]
sname = meta.get('speaker', 'none')
for i, mid in tup:
if not preserve_case:
mid = mid.lower()
ix = '%d,%d' % (s, i)
start = ' '.join(sent.loc[:i-1].values)
end = ' '.join(sent.loc[i+1:].values)
lin = [ix, category, fname, sname, start, mid, end]
if add_meta:
for k, v in sorted(meta.items()):
if k in ['speaker', 'parse', 'sent_id']:
continue
if isinstance(add_meta, list):
if k in add_meta:
lin.append(v)
elif add_meta is True:
lin.append(v)
conc_res.append(lin)
return conc_res | 185 |
def main():
"""Create a flow that uses the 'say_hello' task and run it.
This creates a Prefect flow and runs it a couple times with
different inputs.
"""
with Flow("My First Flow") as flow:
name = Parameter('name')
say_hello(name)
flow.run(name='World')
flow.run(name='NH Python Meetup Group') | 186 |
def match_detections(predicted_data, gt_data, min_iou):
"""Carry out matching between detected and ground truth bboxes.
:param predicted_data: List of predicted bboxes
:param gt_data: List of ground truth bboxes
:param min_iou: Min IoU value to match bboxes
:return: List of matches
"""
all_matches = {}
total_gt_bbox_num = 0
matched_gt_bbox_num = 0
frame_ids = gt_data.keys()
for frame_id in tqdm(frame_ids, desc='Matching detections'):
if frame_id not in predicted_data.keys():
all_matches[frame_id] = []
continue
gt_bboxes = gt_data[frame_id]
predicted_bboxes = predicted_data[frame_id]
total_gt_bbox_num += len(gt_bboxes)
similarity_matrix = calculate_similarity_matrix(gt_bboxes, predicted_bboxes)
matches = []
for _ in xrange(len(gt_bboxes)):
best_match_pos = np.unravel_index(similarity_matrix.argmax(), similarity_matrix.shape)
best_match_value = similarity_matrix[best_match_pos]
if best_match_value <= min_iou:
break
gt_id = best_match_pos[0]
predicted_id = best_match_pos[1]
similarity_matrix[gt_id, :] = 0.0
similarity_matrix[:, predicted_id] = 0.0
matches.append((gt_id, predicted_id))
matched_gt_bbox_num += 1
all_matches[frame_id] = matches
print('Matched gt bbox: {} / {} ({:.2f}%)'
.format(matched_gt_bbox_num, total_gt_bbox_num,
100. * float(matched_gt_bbox_num) / float(max(1, total_gt_bbox_num))))
return all_matches | 187 |
def map_string(affix_string: str, punctuation: str, whitespace_only: bool = False) -> str:
"""Turn affix string into type char representation. Types are 'w' for non-whitespace char,
and 's' for whitespace char.
:param affix_string: a string
:type: str
:param punctuation: the set of characters to treat as punctuation
:type punctuation: str
:param whitespace_only: whether to treat only whitespace as word boundary or also include (some) punctuation
:type whitespace_only: bool
:return: the type char representation
:rtype: str
"""
if whitespace_only:
return "".join(["s" if char == " " else "w" for char in affix_string])
else:
return "".join(["s" if char == " " or char in punctuation else "w" for char in affix_string]) | 188 |
def info(input, verbose, pyformat, **kwargs):
"""
Provides info about the input. Requires valid input.
"""
if not input:
input = '-'
with click.open_file(input, mode='rb') as f:
data = json_utils.load_ordered(f)
d = {
'length': len(data),
'keys': sorted(data.keys())
}
if verbose:
d['_object'] = {
'type': type(data),
# 'repr': repr(data),
# 'vars': sorted(vars(data)),
# 'dir': sorted(dir(data)),
'members': sorted(varsdict(data).keys())
}
# click.echo(d)
# click.echo(sorted(d.items()))
if pyformat:
s = pformat(d)
else:
s = json.dumps(d, indent=2, sort_keys=True)
click.echo(s) | 189 |
def config_loads(cfg_text, from_cfg=None, whitelist_keys=None):
"""Same as config_load but load from a string
"""
try:
cfg = AttrDict(yaml.load(cfg_text))
except TypeError:
# empty string
cfg = AttrDict()
if from_cfg:
if not whitelist_keys:
whitelist_keys = []
_validate_config(cfg, from_cfg, whitelist_keys)
return from_cfg + cfg
return cfg | 190 |
def align_column ( table , index , align = 'left') :
"""Aling the certain column of the table
>>> aligned = align_column ( table , 1 , 'left' )
"""
nrows = [ list ( row ) for row in table ]
lmax = 0
for row in nrows :
if index <= len ( row ) :
item = decolorize ( row [ index ] )
lmax = max ( lmax , len ( item ) )
if not lmax : return table
aleft = align.lower() in left
aright = not aleft and align.lower() in right
new_table = []
for row in nrows :
if index <= len ( row ) :
item = decolorize ( row [ index ] )
nspace = lmax - len ( item )
if aleft :
item = row [ index ] + nspace * ' '
elif aright:
item = nspace * ' ' + row [ index ]
else :
sl = nspace / 2
sr = nspace - sl
item = sl * ' ' + row [ index ] + sr * ' '
row[ index ] = item
new_table.append ( row )
return [ tuple ( row ) for row in new_table ] | 191 |
def calculate_shared_private_key(partner):
"""
Calculate a shared private key
:param partner: Name of partner
"""
print('generating {}'.format(get_private_filename(partner)))
private_key = get_key(private_filename)
public_key = get_key(public_filename)
shared_modified_key = get_key(get_modified_filename(partner))
shared_private_key = pow(shared_modified_key, private_key, public_key)
save_key(shared_private_key, get_private_filename(partner)) | 192 |
def make_form(domain, parent, data, existing=None):
"""simulate a POST payload from the location create/edit page"""
location = existing or Location(domain=domain, parent=parent)
def make_payload(k, v):
if hasattr(k, '__iter__'):
prefix, propname = k
prefix = 'props_%s' % prefix
else:
prefix, propname = 'main', k
return ('%s-%s' % (prefix, propname), v)
payload = dict(make_payload(k, v) for k, v in data.iteritems())
return LocationForm(location, payload) | 193 |
def score_auroc(y_true: Iterable[int], y_prob: Iterable[Iterable[float]]) -> float:
"""
Computes the Area Under ROC curve (AUROC).
Parameters
----------
y_true : List
TODO
y_prob : List
TODO
References
----------
.. [1] `Wikipedia entry for the Receiver operating characteristic
<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
.. [2] `Analyzing a portion of the ROC curve. McClish, 1989
<https://www.ncbi.nlm.nih.gov/pubmed/2668680>`_
"""
assert len(y_true) == len(y_prob)
tpr, fpr = roc_curve_multiclass(y_true, y_prob)
fpr_diffs = [fpr[i] - fpr[i - 1] for i in range(1, len(fpr))]
tpr_means = [(tpr[i] + tpr[i - 1]) / 2.0 for i in range(1, len(tpr))]
return sum([tpr_i * fpr_i for tpr_i, fpr_i in zip(tpr_means, fpr_diffs)]) | 194 |
def set_group(pathname,group_name):
"""Change the group for the given file or directory"""
#group=grp.getgrnam(group_name)
#gid = group.gr_gid
#os.lchown(pathname, -1, gid)#To leave one of the ids unchanged, set it to -1. This function will not follow symbolic links.
shutil.chown(path, group=group_name) | 195 |
def test_add_item_db(session):
"""Test if through session we can add an item
Args:
session (SQLAlchemy Object Session): It's the session object from SQLALchemy Instance
"""
add_item = Items("Conjured Mana Cake", 5, 8)
session.add(add_item)
session.commit()
assert add_item.name == "Conjured Mana Cake"
assert add_item.sell_in == 5
assert add_item.quality == 8 | 196 |
def abs_densitye_seed(model, inputs, args, tokenizer, **kwargs):
"""Maximum density sampling by calculating information density for
example when passed through [model]"""
# print('getting embedding_a')
X_a = load_and_embed_examples(args, model, tokenizer, evaluate=True, text = 'text_a')
# print('getting embedding_b')
X_b = load_and_embed_examples(args, model, tokenizer, evaluate=True, text = 'text_b')
X = np.absolute(X_a - X_b)
similarity_mtx = 1 / (1 + pairwise_distances(X, X, metric='euclidean'))
scores = torch.tensor(similarity_mtx.mean(axis=1))
return scores | 197 |
def get_marketplace_image_output(instance_type: Optional[pulumi.Input[Optional[str]]] = None,
label: Optional[pulumi.Input[str]] = None,
zone: Optional[pulumi.Input[Optional[str]]] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetMarketplaceImageResult]:
"""
Gets local image ID of an image from its label name.
## Example Usage
```python
import pulumi
import pulumi_scaleway as scaleway
my_image = scaleway.get_marketplace_image(label="ubuntu_focal")
```
:param str instance_type: The instance type the image is compatible with.
You find all the available types on the [pricing page](https://www.scaleway.com/en/pricing/).
:param str label: Exact label of the desired image. You can use [this endpoint](https://api-marketplace.scaleway.com/images?page=1&per_page=100)
to find the right `label`.
:param str zone: `zone`) The zone in which the image exists.
"""
... | 198 |
def read_capacity_from_file(experiment_name):
""" Read and return the min capacity, max capacity, interpolation, gamma as a tuple if the capacity
is variable. Otherwise return the constant capacity as is.
TODO: This is a bit brittle at the moment - We should take a look at fixing this for static beta later.
Parameters
----------
experiment_name : str
The name of the experiment, which is the name of the folder that the model is expected to be in.
"""
meta_data = load_metadata(os.path.join(RES_DIR, experiment_name))
min_capacity = meta_data['betaB_initC']
max_capacity = meta_data['betaB_finC']
interp_capacity = meta_data['betaB_stepsC']
gamma = meta_data['betaB_G']
return (min_capacity, max_capacity, interp_capacity, gamma) | 199 |
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