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def create_training_patches(images, patch_size, patches_per_image=1, patch_stride=None):
"""
Returns a batch of image patches, given a batch of images.
Args:
images (list, numpy.array): Batch of images.
patch_size (tuple, list): The (width, height) of the patch to
return.
patches_per_image (int): Number of random patches to
generate from each image in the input batch. Default is 1.
patch_stride (int): Stride to use in strided patching. Default
is None, which does not use strided patching. If integer is passed
then strided patching will be used regardless of what is passed
to 'patches_per_image'.
Returns:
(numpy.array): Batch of image patches.
"""
image_patches = []
for im in images:
if patch_stride is None:
for i in range(patches_per_image):
image_patches.append(get_random_patch(im, patch_size))
else:
image_patches += list(get_stride_patches(im, patch_size, patch_stride, 2))
return np.array(image_patches) | 5fce19d2d13f790500e0cbd42934dd6e83c6b084 | 12,573 |
import time
def get_prover_options(prover_round_tag='manual',
prover_round=-1) -> deephol_pb2.ProverOptions:
"""Returns a ProverOptions proto based on FLAGS."""
if not FLAGS.prover_options:
tf.logging.fatal('Mandatory flag --prover_options is not specified.')
if not tf.gfile.Exists(FLAGS.prover_options):
tf.logging.fatal('Required prover options file "%s" does not exist.',
FLAGS.prover_options)
prover_options = deephol_pb2.ProverOptions()
if FLAGS.max_theorem_parameters is not None:
tf.logging.warning(
'Overring max_theorem_parameters in prover options to %d.',
FLAGS.max_theorem_parameters)
prover_options.action_generator_options.max_theorem_parameters = (
FLAGS.max_theorem_parameters)
with tf.gfile.Open(FLAGS.prover_options) as f:
text_format.MergeLines(f, prover_options)
if prover_options.builtin_library:
tf.logging.warning('builtin_library is deprecated. Do not provide.')
if str(prover_options.builtin_library) not in ['core']:
tf.logging.fatal('Unsupported built in library: %s',
prover_options.builtin_library)
if FLAGS.timeout_seconds is not None:
prover_options.timeout_seconds = FLAGS.timeout_seconds
if not FLAGS.output:
tf.logging.fatal('Missing flag --output [recordio_pattern]')
prover_options.prover_round = deephol_pb2.ProverRound(
start_seconds=int(round(time.time())),
tag=prover_round_tag,
round=prover_round)
_verify_prover_options(prover_options)
# Log prover options.
tf.logging.info('Using prover_options:\n %s', str(prover_options))
return prover_options | ee1ee9fb7ce573c543f0750d6b8fd1eed98deec9 | 12,574 |
def bracketpy(pystring):
"""Find CEDICT-style pinyin in square brackets and correct pinyin.
Looks for square brackets in the string and tries to convert its
contents to correct pinyin. It is assumed anything in square
brackets is CC-CEDICT-format pinyin.
e.g.: "拼音[pin1 yin1]" will be converted into "拼音 pīnyīn".
"""
if len(findall("(\[.+?\])", pystring)) >= 1:
cedpylist = findall("(\[.+?\])", pystring)
for item in cedpylist:
pystring = pystring.replace(item, " " + pyjoin(item[1:-1]))
return pystring
if len(findall("(\[.+?\])", pystring)) < 1:
return pystring
else:
return None | 0499047ec45e6b9c66c27dd89663667b13ddbfb1 | 12,575 |
import psutil
def get_ram_usage_bytes(size_format: str = 'M'):
"""
Size formats include K = Kilobyte, M = Megabyte, G = Gigabyte
"""
total = psutil.virtual_memory().total
available = psutil.virtual_memory().available
used = total - available
# Apply size
if size_format == 'K':
used = used / 1024
if size_format == 'M':
used = used / 1024 / 1024
if size_format == 'G':
used = used / 1024 / 1024 / 1024
return int(used) | 990987078b0ad3c2ac2ee76dcf96b7cdf01f0354 | 12,576 |
def weighted_crossentropy(weights, name='anonymous'):
"""A weighted version of tensorflow.keras.objectives.categorical_crossentropy
Arguments:
weights = np.array([0.5,2,10]) # Class one at 0.5, class 2 twice the normal weights, class 3 10x.
name: string identifying the loss to differentiate when models have multiple losses
Returns:
keras loss function named name+'_weighted_loss'
"""
string_globe = 'global ' + name + '_weights\n'
string_globe += 'global ' + name + '_kweights\n'
string_globe += name + '_weights = np.array(weights)\n'
string_globe += name + '_kweights = K.variable('+name+'_weights)\n'
exec(string_globe, globals(), locals())
fxn_postfix = '_weighted_loss'
string_fxn = 'def ' + name + fxn_postfix + '(y_true, y_pred):\n'
string_fxn += '\ty_pred /= K.sum(y_pred, axis=-1, keepdims=True)\n'
string_fxn += '\ty_pred = K.clip(y_pred, K.epsilon(), 1 - K.epsilon())\n'
string_fxn += '\tloss = y_true * K.log(y_pred) * ' + name + '_kweights\n'
string_fxn += '\tloss = -K.sum(loss, -1)\n'
string_fxn += '\treturn loss\n'
exec(string_fxn, globals(), locals())
loss_fxn = eval(name + fxn_postfix, globals(), locals())
return loss_fxn | 071ab00a723d54194b4b27c889c638debe82f10a | 12,577 |
def _get_package_type(id):
"""
Given the id of a package this method will return the type of the
package, or 'dataset' if no type is currently set
"""
pkg = model.Package.get(id)
if pkg:
return pkg.type or u'dataset'
return None | c84e137e2b0adaf8719d757f178aa47d4a63c46a | 12,578 |
def _find_protruding_dimensions(f, care, fol):
"""Return variables along which `f` violates `care`."""
vrs = joint_support([f, care], fol)
dims = set()
for var in vrs:
other_vars = vrs - {var}
f_proj = fol.exist(other_vars, f)
care_proj = fol.exist(other_vars, care)
if (care_proj | ~ f_proj) != fol.true:
dims.add(var)
return dims | 02da5718645652288c9fa6fcedc07198afe49a58 | 12,579 |
import random
def simulate_relatedness(genotypes, relatedness=.5, n_iter=1000, copy=True):
"""
Simulate relatedness by randomly copying genotypes between individuals.
Parameters
----------
genotypes : array_like
An array of shape (n_variants, n_samples, ploidy) where each
element of the array is an integer corresponding to an allele index
(-1 = missing, 0 = reference allele, 1 = first alternate allele,
2 = second alternate allele, etc.).
relatedness : float, optional
Fraction of variants to copy genotypes for.
n_iter : int, optional
Number of times to randomly copy genotypes between individuals.
copy : bool, optional
If False, modify `genotypes` in place.
Returns
-------
genotypes : ndarray, shape (n_variants, n_samples, ploidy)
The input genotype array but with relatedness simulated.
"""
# check genotypes array
genotypes = np.asarray(genotypes)
assert genotypes.ndim >= 2
n_variants = genotypes.shape[0]
n_samples = genotypes.shape[1]
# copy input array
if copy:
genotypes = genotypes.copy()
else:
# modify in place
pass
# determine the number of variants to copy genotypes for
n_copy = int(relatedness * n_variants)
# iteratively introduce relatedness
for i in range(n_iter):
# randomly choose donor and recipient
donor_index = random.randint(0, n_samples-1)
donor = genotypes[:, donor_index]
recip_index = random.randint(0, n_samples-1)
recip = genotypes[:, recip_index]
# randomly pick a set of variants to copy
variant_indices = random.sample(range(n_variants), n_copy)
# copy across genotypes
recip[variant_indices] = donor[variant_indices]
return genotypes | e319f4e15c4c08eb90260b77efc25ea330aac4c9 | 12,581 |
def pages_substitute(content):
"""
Substitute tags in pages source.
"""
if TAG_USERGROUPS in content:
usergroups = UserGroup.objects.filter(is_active=True).order_by('name')
replacement = ", ".join(f"[{u.name}]({u.webpage_url})" for u in usergroups)
content = content.replace(TAG_USERGROUPS, replacement)
return content | 13c2138256a0e1afa0ad376994849f2716020540 | 12,582 |
def vcfanno(vcf, out_file, conf_fns, data, basepath=None, lua_fns=None):
"""
annotate a VCF file using vcfanno (https://github.com/brentp/vcfanno)
"""
if utils.file_exists(out_file):
return out_file
if lua_fns is None:
lua_fns = []
vcfanno = config_utils.get_program("vcfanno", data)
with file_transaction(out_file) as tx_out_file:
conffn = _combine_files(conf_fns, tx_out_file)
luafn = _combine_files(lua_fns, tx_out_file)
luaflag = "-lua {0}".format(luafn) if luafn and utils.file_exists(luafn) else ""
basepathflag = "-base-path {0}".format(basepath) if basepath else ""
cores = dd.get_num_cores(data)
cmd = "{vcfanno} -p {cores} {luaflag} {basepathflag} {conffn} {vcf} | sed -e 's/Number=A/Number=1/g' | bgzip -c > {tx_out_file}"
message = "Annotating {vcf} with vcfanno, using {conffn}".format(**locals())
do.run(cmd.format(**locals()), message)
return out_file | 808488bd07c56b541694715193df4ae1cb51869c | 12,583 |
def clean(params: dict) -> str:
"""
Build clean rules for Makefile
"""
clean = "\t@$(RM) -rf $(BUILDDIR)\n"
if params["library_libft"]:
clean += "\t@make $@ -C " + params["folder_libft"] + "\n"
if params["library_mlx"] and params["compile_mlx"]:
clean += "\t@make $@ -C " + params["folder_mlx"] + "\n"
return clean | fb7dd0e7a2fbb080dd8b0d5d4489e9c5ef1367ec | 12,584 |
import re
def mathematica(quero: str, meta: str = '') -> bool:
"""mathematica
Rudimentar mathematical operations (boolean result)
Args:
quero (_type_, optional): _description_. Defaults to str.
Returns:
bool: True if evaluate to True.
"""
# neo_quero = quero.replace(' ', '').replace('(', '').replace(')', '')
neo_quero = quero.replace(' ', '')
if quero == 'True':
return True
if quero == 'False':
return False
if neo_quero.find('&&') > -1:
parts = neo_quero.split('&&')
# print(parts)
# return bool(parts[0]) and bool(parts[1])
return logicum(parts[0]) and logicum(parts[1])
if neo_quero.find('||') > -1:
parts = neo_quero.split('||')
# return bool(parts[0]) or bool(parts[1])
return logicum(parts[0]) or logicum(parts[1])
# regula = r"(\d*)(.{1,2})(\d*)"
regula = r"(?P<n1>(\d*))(?P<op>(\D{1,2}))(?P<n2>(\d*))"
r1 = re.match(regula, neo_quero)
if r1.group('op') == '==':
return int(r1.group('n1')) == int(r1.group('n2'))
if r1.group('op') == '!=':
return int(r1.group('n1')) != int(r1.group('n2'))
if r1.group('op') == '<=':
return int(r1.group('n1')) <= int(r1.group('n2'))
if r1.group('op') == '>=':
return int(r1.group('n1')) >= int(r1.group('n2'))
if r1.group('op') == '<':
return int(r1.group('n1')) < int(r1.group('n2'))
if r1.group('op') == '>':
return int(r1.group('n1')) > int(r1.group('n2'))
raise ValueError(
'mathematica: <quaero> [{1}] <op>? [{0}]'.format(str(quero), meta)) | b05777e880688d8f3fc90ba9d54098f341054bd7 | 12,585 |
def time_pet(power,energy):
"""Usage: time_pet(power,energy)"""
return energy/power | 11e9c82b8c1be84995f9517e04ed5e1270801e27 | 12,587 |
def compute_sigma0(
T,
S,
**kwargs,
):
"""
compute the density anomaly referenced to the surface
"""
return compute_rho(T, S, 0, **kwargs) - 1000 | 300d5552c70e6fd6d8708345aa3eed53795309cf | 12,588 |
def get_neighbors_radius(nelx, nely, coord, connect, radius):
""" Check neighboring elements that have the centroid within the predetermined radius.
Args:
nelx (:obj:`int`): Number of elements on the x axis.
nely (:obj:`int`): Number of elements on the x axis
coord (:obj:`numpy.array`): Coordinates of the element.
connect (:obj:`numpy.array`): Element connectivity.
radius (:obj:`float`): Radius to get elements in the vicinity of each element.
Returns:
neighbors, H, centroids
"""
el_number = nelx * nely
centroids = np.empty((el_number, 2))
idx = connect[:, 1:] - 1
centroids[:, 0] = np.sum(coord[idx, 1], axis = 1)/4
centroids[:, 1] = np.sum(coord[idx, 2], axis = 1)/4
ind_rows = []
ind_cols = []
data = []
cols = 0
neighbors = []
for el in range(el_number):
distance = np.sqrt(np.sum((centroids[el] - centroids)**2, axis=1))
mask = distance <= radius
neighbor = mask.nonzero()[0] + 1
neighbors.extend(neighbor - 1)
hi = radius - distance
hi_max = np.maximum(0, hi)
data.extend(hi_max[mask])
aux = len(hi_max[mask])
rows = np.repeat(el, aux) #.tolist()
columns = np.arange(0, aux)
ind_rows.extend(rows)
ind_cols.extend(columns)
if aux > cols:
cols = aux
H = csc_matrix((data, (ind_rows, ind_cols)), shape=(nelx*nely, cols)).toarray()
neighbors = csc_matrix((neighbors, (ind_rows, ind_cols)), shape=(nelx*nely, cols), dtype='int').toarray()
return neighbors, H, centroids | 669e11d3a2890f1485e33e021b2671ff6f197c03 | 12,589 |
import copy
def merge_with(obj, *sources, **kwargs):
"""
This method is like :func:`merge` except that it accepts customizer which is invoked to produce
the merged values of the destination and source properties. If customizer returns ``None``,
merging is handled by this method instead. The customizer is invoked with five arguments:
``(obj_value, src_value, key, obj, source)``.
Args:
obj (dict): Destination object to merge source(s) into.
sources (dict): Source objects to merge from. subsequent sources
overwrite previous ones.
Keyword Args:
iteratee (callable, optional): Iteratee function to handle merging
(must be passed in as keyword argument).
Returns:
dict: Merged object.
Warning:
`obj` is modified in place.
Example:
>>> cbk = lambda obj_val, src_val: obj_val + src_val
>>> obj1 = {'a': [1], 'b': [2]}
>>> obj2 = {'a': [3], 'b': [4]}
>>> res = merge_with(obj1, obj2, cbk)
>>> obj1 == {'a': [1, 3], 'b': [2, 4]}
True
.. versionadded:: 4.0.0
.. versionchanged:: 4.9.3
Fixed regression in v4.8.0 that caused exception when `obj` was ``None``.
"""
if obj is None:
return None
sources = list(sources)
iteratee = kwargs.pop("iteratee", None)
if iteratee is None and sources and callable(sources[-1]):
iteratee = sources.pop()
sources = [copy.deepcopy(source) for source in sources]
if callable(iteratee):
iteratee = partial(callit, iteratee, argcount=getargcount(iteratee, maxargs=5))
else:
iteratee = None
return _merge_with(obj, *sources, iteratee=iteratee, **kwargs) | 94a16ae7d3f3e73ef8e27b32cd38a09c61ad1b2b | 12,590 |
def count_class_nbr_patent_cnt(base_data_list, calculate_type):
"""
统计在所有数据中不同分类号对应的专利数量
:param base_data_list:
:return:
"""
class_number_patent_cnt_dict = dict()
for base_data in base_data_list:
class_number_value = base_data[const.CLASS_NBR]
calculate_class_number_patent_count_dict(class_number_value, class_number_patent_cnt_dict, calculate_type)
return class_number_patent_cnt_dict | 6dfe06c2233fbfafc8083dc968d32520564319f8 | 12,591 |
def plot_pta_L(df):
"""
INPUTS
-df: pandas dataframe containing the data to plot
OUTPUTS
-saves pta graphs in .html
"""
title = generate_title_run_PTA(df, "Left Ear", df.index[0])
labels = {"title": title,
"x": "Frequency (Hz)",
"y": "Hearing Threshold (dB HL)"}
fig = go.Figure()
fig.update_layout(title=labels["title"],
xaxis_title=labels["x"],
yaxis_title=labels["y"],
xaxis_type="log",
xaxis_range=[np.log10(100), np.log10(20000)],
yaxis_range=[80, -20],
yaxis_dtick=10,
xaxis_showline=True,
xaxis_linecolor="black",
yaxis_showline=True,
yaxis_linecolor="black",
yaxis_zeroline=True,
yaxis_zerolinewidth=1,
yaxis_zerolinecolor="black")
x, y = data_to_plot_PTA(df, "LE_")
fig.add_trace(go.Scatter(x=x,
y=y,
line_color="blue",
mode='lines+markers',
name=labels["title"],
hovertemplate="%{x:1.0f} Hz<br>" +
"%{y:1.0f} dB HL"))
completed = save_graph_PTA(fig, df, "Left Ear")
if completed is True:
return True
else:
return False | ec82b58a6a476bee5e864b8678bb90d2998d4a02 | 12,592 |
def create_graph(edge_num: int, edge_list: list) -> dict:
"""
Create a graph expressed with adjacency list
:dict_key : int (a vertex)
:dict_value : set (consisted of vertices adjacent to key vertex)
"""
a_graph = {i: set() for i in range(edge_num)}
for a, b in edge_list:
a_graph[a - 1].add(b - 1) # All graphs always need this line
a_graph[b - 1].add(a - 1) # Only undirected graph needs this line
return a_graph | 6ec1a71cf82a3a669090df42ac7d53e1286fda2d | 12,593 |
def get_current_version() -> str:
"""Read the version of the package.
See https://packaging.python.org/guides/single-sourcing-package-version
"""
version_exports = {}
with open(VERSION_FILE) as file:
exec(file.read(), version_exports) # pylint: disable=exec-used
return version_exports["VERSION"] | c283d58881aa381503bb3500bd7d745f25df0f7e | 12,595 |
import random
def seed_story(text_dict):
"""Generate random seed for story."""
story_seed = random.choice(list(text_dict.keys()))
return story_seed | 0c0f41186f6eaab84a1d197e9335b4c28fd83785 | 12,596 |
def _get_rel_att_inputs(d_model, n_heads): # pylint: disable=invalid-name
"""Global relative attentions bias initialization shared across the layers."""
assert d_model % n_heads == 0 and d_model % 2 == 0
d_head = d_model // n_heads
bias_initializer = init.RandomNormalInitializer(1e-6)
context_bias_layer = core.Weights(bias_initializer,
shape=(1, n_heads, 1, d_head))
location_bias_layer = core.Weights(bias_initializer,
shape=(1, n_heads, 1, d_head))
return context_bias_layer, location_bias_layer | 57f58f29a586571f1cc8fa1fc69956a4168cbf16 | 12,598 |
def two_time_pad():
"""A one-time pad simply involves the xor of a message with a key to produce a ciphertext: c = m ^ k.
It is essential that the key be as long as the message, or in other words that the key not be repeated for two distinct message blocks.
Your task:
In this problem you will break a cipher when the one-time pad is re-used.
c_1 = 3801025f45561a49131a1e180702
c_2 = 07010051455001060e551c571106
These are two hex-encoded ciphertexts that were formed by applying a “one-time pad” to two different messages with
the same key. Find the two corresponding messages m_1 and m_2.
Okay, to make your search simpler, let me lay out a few ground rules. First, every character in the text is either
a lowercase letter or a space, aside from perhaps the first character in the first message which might be capitalized.
As a consequence, no punctuation appears in the messages. Second, the messages consist of English words in ASCII.
Finally, all of the words within each message is guaranteed to come from the set of the 100 most
common English words: https://en.wikipedia.org/wiki/Most_common_words_in_English.
Returns:
Output the concatenation of strings m_1 and m_2. (Don't worry if words get smashed together as a result.)
"""
c_1 = '3801025f45561a49131a1e180702'
c_2 = '07010051455001060e551c571106'
# converting the hexadecimal representaiton to integers for every 2 bytes since it xor operations become on integers
c_1_int = [int(c_1[i] + c_1[i+1], 16) for i in range(0, len(c_1), 2)]
c_2_int = [int(c_2[i] + c_2[i+1], 16) for i in range(0, len(c_1), 2)]
xord = [c_1_int[i] ^ c_2_int[i] for i in range(len(c_1_int))] #xor of the two lists which are integer representations
result = construct('',xord)
if result == None:
return None
else:
print(result)
new_string = ''.join([chr(ord(result[i]) ^ xord[i]) for i in range(len(result))])
return new_string + result | d4c45312f32b372a065365c78a991969e2bc53be | 12,599 |
def same_datatypes(lst):
"""
Überprüft für eine Liste, ob sie nur Daten vom selben Typ enthält. Dabei spielen Keys, Länge der Objekte etc. eine Rolle
:param lst: Liste, die überprüft werden soll
:type lst: list
:return: Boolean, je nach Ausgang der Überprüfung
"""
datatype = type(lst[0]).__name__
for item in lst:
if type(item).__name__ != datatype: # return False, wenn die Liste verschiedene Datentypen enthält
return False
# Datentypen sind gleich, aber sind deren Strukturen auch gleich? (für komplexe Datentypen)
if datatype == "dict":
keys = lst[0].keys()
for item in lst:
if item.keys() != keys: # return False, wenn die Keys der Dictionaries verschieden sind
return False
elif datatype == "list":
if sum([len(x) for x in lst]) / len(lst) != len(lst[0]): # return False, falls die Listen in der Liste verschiedene Längen haben
return False
datatypes = list(map(lambda x: type(x).__name__, lst[0]))
for item in lst:
if list(map(lambda x: type(x).__name__, item)) != datatypes: # return False, falls die Elemente der inneren Listen verschiedene Datenytpen haben
return False
return True | 9c49376ec34ed0970171597f77de4c4c224350b4 | 12,600 |
def _show_stat_wrapper_Progress(count, last_count, start_time, max_count, speed_calc_cycles,
width, q, last_speed, prepend, show_stat_function, add_args,
i, lock):
"""
calculate
"""
count_value, max_count_value, speed, tet, ttg, = Progress._calc(count,
last_count,
start_time,
max_count,
speed_calc_cycles,
q,
last_speed,
lock)
return show_stat_function(count_value, max_count_value, prepend, speed, tet, ttg, width, i, **add_args) | 3c98f44acc8de94573ba37a7785df18fc8e72966 | 12,601 |
def _to_base58_string(prefixed_key: bytes):
"""
Convert prefixed_key bytes into Es/EC strings with a checksum
:param prefixed_key: the EC private key or EC address prefixed with the appropriate bytes
:return: a EC private key string or EC address
"""
prefix = prefixed_key[:PREFIX_LENGTH]
assert prefix == ECAddress.PREFIX or prefix == ECPrivateKey.PREFIX, 'Invalid key prefix.'
temp_hash = sha256(prefixed_key[:BODY_LENGTH]).digest()
checksum = sha256(temp_hash).digest()[:CHECKSUM_LENGTH]
return base58.encode(prefixed_key + checksum) | 326580e714d6489a193347498c68ef9d90f6f651 | 12,602 |
def round_int(n, d):
"""Round a number (float/int) to the closest multiple of a divisor (int)."""
return round(n / float(d)) * d | 372c0f8845994aaa03f99ebb2f65243e6490b341 | 12,603 |
def merge_array_list(arg):
"""
Merge multiple arrays into a single array
:param arg: lists
:type arg: list
:return: The final array
:rtype: list
"""
# Check if arg is a list
if type(arg) != list:
raise errors.AnsibleFilterError('Invalid value type, should be array')
final_list = []
for cur_list in arg:
final_list += cur_list
return final_list | 649412488655542f27a1e7d377252c060107b57e | 12,604 |
def load_callbacks(boot, bootstrap, jacknife,
out, keras_verbose, patience):
"""
Specifies Keras callbacks, including checkpoints, early stopping,
and reducing learning rate.
Parameters
----------
boot
bootstrap
jacknife
out
keras_verbose
patience
batch_size
Returns
-------
checkpointer
earlystop
reducelr
"""
if bootstrap or jacknife:
checkpointer = tf.keras.callbacks.ModelCheckpoint(
filepath=out + "_boot" + str(boot) + "_weights.hdf5",
verbose=keras_verbose,
save_best_only=True,
save_weights_only=True,
monitor="val_loss",
save_freq="epoch",
)
else:
checkpointer = tf.keras.callbacks.ModelCheckpoint(
filepath=out + "_weights.hdf5",
verbose=keras_verbose,
save_best_only=True,
save_weights_only=True,
monitor="val_loss",
save_freq="epoch",
)
earlystop = tf.keras.callbacks.EarlyStopping(
monitor="val_loss", min_delta=0, patience=patience
)
reducelr = tf.keras.callbacks.ReduceLROnPlateau(
monitor="val_loss",
factor=0.5,
patience=int(patience / 6),
verbose=keras_verbose,
mode="auto",
min_delta=0,
cooldown=0,
min_lr=0,
)
return checkpointer, earlystop, reducelr | 0ca09ccaca4424c1a546caade3d809b7f69cbb5e | 12,605 |
def build_sentence_model(cls, vocab_size, seq_length, tokens, transitions,
num_classes, training_mode, ground_truth_transitions_visible, vs,
initial_embeddings=None, project_embeddings=False, ss_mask_gen=None, ss_prob=0.0):
"""
Construct a classifier which makes use of some hard-stack model.
Args:
cls: Hard stack class to use (from e.g. `spinn.fat_stack`)
vocab_size:
seq_length: Length of each sequence provided to the stack model
tokens: Theano batch (integer matrix), `batch_size * seq_length`
transitions: Theano batch (integer matrix), `batch_size * seq_length`
num_classes: Number of output classes
training_mode: A Theano scalar indicating whether to act as a training model
with dropout (1.0) or to act as an eval model with rescaling (0.0).
ground_truth_transitions_visible: A Theano scalar. If set (1.0), allow the model access
to ground truth transitions. This can be disabled at evaluation time to force Model 1
(or 2S) to evaluate in the Model 2 style with predicted transitions. Has no effect on Model 0.
vs: Variable store.
"""
# Prepare layer which performs stack element composition.
if cls is spinn.plain_rnn.RNN:
if FLAGS.use_gru:
compose_network = partial(util.GRULayer,
initializer=util.HeKaimingInitializer())
else:
compose_network = partial(util.LSTMLayer,
initializer=util.HeKaimingInitializer())
embedding_projection_network = None
elif cls is spinn.cbow.CBOW:
compose_network = None
embedding_projection_network = None
else:
if FLAGS.lstm_composition:
if FLAGS.use_gru:
compose_network = partial(util.TreeGRULayer,
initializer=util.HeKaimingInitializer())
else:
compose_network = partial(util.TreeLSTMLayer,
initializer=util.HeKaimingInitializer())
else:
assert not FLAGS.connect_tracking_comp, "Can only connect tracking and composition unit while using TreeLSTM"
compose_network = partial(util.ReLULayer,
initializer=util.HeKaimingInitializer())
if project_embeddings:
embedding_projection_network = util.Linear
else:
assert FLAGS.word_embedding_dim == FLAGS.model_dim, \
"word_embedding_dim must equal model_dim unless a projection layer is used."
embedding_projection_network = util.IdentityLayer
# Build hard stack which scans over input sequence.
sentence_model = cls(
FLAGS.model_dim, FLAGS.word_embedding_dim, vocab_size, seq_length,
compose_network, embedding_projection_network, training_mode, ground_truth_transitions_visible, vs,
predict_use_cell=FLAGS.predict_use_cell,
use_tracking_lstm=FLAGS.use_tracking_lstm,
tracking_lstm_hidden_dim=FLAGS.tracking_lstm_hidden_dim,
X=tokens,
transitions=transitions,
initial_embeddings=initial_embeddings,
embedding_dropout_keep_rate=FLAGS.embedding_keep_rate,
ss_mask_gen=ss_mask_gen,
ss_prob=ss_prob,
connect_tracking_comp=FLAGS.connect_tracking_comp,
context_sensitive_shift=FLAGS.context_sensitive_shift,
context_sensitive_use_relu=FLAGS.context_sensitive_use_relu,
use_input_batch_norm=False)
# Extract top element of final stack timestep.
if FLAGS.lstm_composition or cls is spinn.plain_rnn.RNN:
sentence_vector = sentence_model.final_representations[:,:FLAGS.model_dim / 2].reshape((-1, FLAGS.model_dim / 2))
sentence_vector_dim = FLAGS.model_dim / 2
else:
sentence_vector = sentence_model.final_representations.reshape((-1, FLAGS.model_dim))
sentence_vector_dim = FLAGS.model_dim
sentence_vector = util.BatchNorm(sentence_vector, sentence_vector_dim, vs, "sentence_vector", training_mode)
sentence_vector = util.Dropout(sentence_vector, FLAGS.semantic_classifier_keep_rate, training_mode)
# Feed forward through a single output layer
logits = util.Linear(
sentence_vector, sentence_vector_dim, num_classes, vs,
name="semantic_classifier", use_bias=True)
return sentence_model.transitions_pred, logits | 085d6a0538bfa06a34c543c27efd651c4c46168a | 12,606 |
def read_xls_as_dict(filename, header="top"):
"""
Read a xls file as dictionary.
@param filename File name (*.xls or *.xlsx)
@param header Header position. Options: "top", "left"
@return Dictionary with header as key
"""
table = read_xls(filename)
if (header == "top"):
return read_table_header_top(table)
elif (header == "left"):
return read_table_header_left(table)
else:
return {} | 9ed410e42a11ee898466bb2f36b6d02e051b21ec | 12,607 |
def check_hostgroup(zapi, region_name, cluster_id):
"""check hostgroup from region name if exists
:region_name: region name of hostgroup
:returns: true or false
"""
return zapi.hostgroup.exists(name="Region [%s %s]" % (region_name, cluster_id)) | b237b544ac59331ce94dd1ac471187a60d527a1b | 12,608 |
import tempfile
def matlab_to_tt(ttemps, eng, is_orth=True, backend="numpy", mode="l"):
"""Load matlab.object representing TTeMPS into Python as TT"""
_, f = tempfile.mkstemp(suffix=".mat")
eng.TTeMPS_to_Py(f, ttemps, nargout=0)
tt = load_matlab_tt(f, is_orth=is_orth, mode=mode, backend=backend)
return tt | e21087e2587368a55ece7a50f576573c5284373a | 12,609 |
def encode_mecab(tagger, string):
"""
string을 mecab을 이용해서 형태소 분석
:param tagger: 형태소 분석기 객체
:param string: input text
:return tokens: 형태소 분석 결과
:return indexs: 띄어쓰기 위치
"""
string = string.strip()
if len(string) == 0:
return [], []
words = string.split()
nodes = tagger.pos(" ".join(words))
tokens = []
for node in nodes:
surface = node[0].strip()
if 0 < len(surface):
for s in surface.split(): # mecab 출력 중 '영치기 영차' 처리
tokens.append(s)
indexs = []
index, start, end = -1, 0, 100000
for i, token in enumerate(tokens): # 분류가 잘 되었는지 검증
if end < len(words[index]):
start = end
end += len(token)
else:
index += 1
start = 0
end = len(token)
indexs.append(i) # values 중 실제 시작 위치 기록
assert words[index][start:end] == token, f"{words[index][start:end]} != {token}"
return tokens, indexs | 847278728ebe7790d8aef2a125a420d5779adc6b | 12,610 |
def nutrient_limited_growth(X,idx_A,idx_B,growth_rate,half_saturation):
""" non-linear response with respect to *destination/predator* compartment
Similar to holling_type_II and is a reparameterization of holling II.
The response with respect to the origin compartment 'B' is approximately
linear for small 'B' and converges towards an upper limit governed by the
'growth_rate' for large 'B'.
For examples see:
`Examples <https://gist.github.com/465b/cce390f58d64d70613a593c8038d4dc6>`_
Parameters
----------
X : np.array
containing the current state of the contained quantity of each
compartment
idx_A : integer
index of the element representing the destination/predator compartment
idx_B : integer
index of the element representing the origin/pray compartment
growth_rate : float
first parameter of the interaction.
governs the upper limit of the response.
half_saturation : float
second parameter of the interaction.
governs the slope of the response.
Returns
-------
df : float
change in the origin and destitnation compartment. Calculated by
consumption_rate = ((hunting_rate * origin_compartment) / (1 +
hunting_rate * food_processing_time * origin_compartment)) *
destination_compartment
"""
A = X[idx_A] # quantity of compartment A (predator/consumer)
B = X[idx_B] # quantity of compartment B (prey/nutrient)
df = growth_rate*(B/(half_saturation+B))*A
return df | 05e66a0e426a404a5356f04f8568ab23548b6dbe | 12,611 |
def aes128_decrypt(AES_KEY, _data):
"""
AES 128 位解密
:param requestData:
:return:
"""
# 秘钥实例
newAes = getAesByKey(AES_KEY)
# 解密
data = newAes.decrypt(_data)
rawDataLength = len(data)
# 剔除掉数据后面的补齐位
paddingNum = ord(data[rawDataLength - 1])
if paddingNum > 0 and paddingNum <= 16:
data = data[0:(rawDataLength - paddingNum)]
return data | 520c03a509f63807a62ccb0385e99bc9b674fd67 | 12,612 |
def human_readable_size(size, decimals=1):
"""Transform size in bytes into human readable text."""
for unit in ["B", "KB", "MB", "GB", "TB"]:
if size < 1000:
break
size /= 1000
return f"{size:.{decimals}f} {unit}" | 5fb0dc79162d0bc0a945061aa0889735b24fff7b | 12,613 |
def generichash_blake2b_final(statebuf, digest_size):
"""Finalize the blake2b hash state and return the digest.
:param statebuf:
:type statebuf: bytes
:param digest_size:
:type digest_size: int
:return: the blake2 digest of the passed-in data stream
:rtype: bytes
"""
_digest = ffi.new("unsigned char[]", crypto_generichash_BYTES_MAX)
rc = lib.crypto_generichash_blake2b_final(statebuf, _digest, digest_size)
ensure(rc == 0, 'Unexpected failure',
raising=exc.RuntimeError)
return ffi.buffer(_digest, digest_size)[:] | a81da8346bafb2f7d8fd40b0d9ff204689d002f8 | 12,614 |
def walker_input_formatter(t, obs):
"""
This function formats the data to give as input to the controller
:param t:
:param obs:
:return: None
"""
return obs | 651038cd4dc0e8c8ccb89a10a5b20f6031e17ba8 | 12,615 |
def build_url_base(url):
"""Normalize and build the final url
:param url: The given url
:type url: str
:return: The final url
:rtype: str
"""
normalize = normalize_url(url=url)
final_url = "{url}/api".format(url=normalize)
return final_url | a500a6d96ab637182abab966817209324ddc670a | 12,616 |
from typing import Tuple
from typing import List
def build_decoder(
latent_dim: int,
input_shape: Tuple,
encoder_shape: Tuple,
filters: List[int],
kernels: List[Tuple[int, int]],
strides: List[int]
) -> Model:
"""Return decoder model.
Parameters
----------
latent_dim:int,
Size of the latent vector.
encoder_shape:Tuple,
Output shape of the last convolutional layer
of the encoder model.
filters:List[int],
List of filters for the convolutional layer.
kernels:List[Tuple[int, int]],
List of kernel sizes for the convolutional layer.
strides:List[int]
List of strides for the convolutional layer.
"""
decoder_input = Input(
shape=(latent_dim,),
name='decoder_input'
)
x = Dense(np.prod(encoder_shape))(decoder_input)
x = Reshape(encoder_shape)(x)
x = decoder_blocks(
x,
reversed(filters),
reversed(kernels),
reversed(strides)
)
decoder_output = Conv2DTranspose(
filters=1,
kernel_size=kernels[0],
activation=axis_softmax,
padding='same',
name='decoder_output'
)(x)
reshape = Reshape(input_shape)(decoder_output)
# Instantiate Decoder Model
return Model(
decoder_input,
reshape,
name='decoder'
) | efdac0fa9df81249e531b2568cd8f91816c209a6 | 12,617 |
def execute_with_python_values(executable, arguments=(), backend=None):
"""Execute on one replica with Python values as arguments and output."""
backend = backend or get_local_backend()
def put(arg):
return Buffer.from_pyval(
arg, device=executable.DeviceOrdinals()[0], backend=backend)
arguments = [put(arg) for arg in arguments]
return executable.Execute(arguments).to_py() | 26c9352feb2e5c7e6fb46702105245f582218e91 | 12,618 |
def _get_label_members(X, labels, cluster):
"""
Helper function to get samples of a specified cluster.
Args:
X (np.ndarray): ndarray with dimensions [n_samples, n_features]
data to check validity of clustering
labels (np.array): clustering assignments for data X
cluster (int): cluster of interest
Returns: members (np.ndarray)
array of dimensions (n_samples, n_features) of samples of the
specified cluster.
"""
indices = np.where(labels == cluster)[0]
members = X[indices]
return members | 18c213f88816108f93ddd38cdd2c934f431ea35a | 12,620 |
from typing import Tuple
def get_spectrum_by_close_values(
mz: list,
it: list,
left_border: float,
right_border: float,
*,
eps: float = 0.0
) -> Tuple[list, list, int, int]:
"""int
Function to get segment of spectrum by left and right
border
:param mz: m/z array
:param it: it intensities
:param left_border: left border
:param right_border: right border
:param eps: epsilon to provide regulation of borders
:return: closest to left and right border values of spectrum, left and right
"""
mz, it = mz.copy(), it.copy()
left = bisect_left(mz, left_border - eps)
right = bisect_right(mz, right_border + eps)
return mz[left:right].copy(), it[left:right].copy(), left, right | 0ec34b044b9105fe1c232baa9b51760cbb96b9d9 | 12,621 |
def refresh_wrapper(trynum, maxtries, *args, **kwargs):
"""A @retry argmod_func to refresh a Wrapper, which must be the first arg.
When using @retry to decorate a method which modifies a Wrapper, a common
cause of retry is etag mismatch. In this case, the retry should refresh
the wrapper before attempting the modifications again. This method may be
passed to @retry's argmod_func argument to effect such a refresh.
Note that the decorated method must be defined such that the wrapper is its
first argument.
"""
arglist = list(args)
# If we get here, we *usually* have an etag mismatch, so specifying
# use_etag=False *should* be redundant. However, for scenarios where we're
# retrying for some other reason, we want to guarantee a fresh fetch to
# obliterate any local changes we made to the wrapper (because the retry
# should be making those changes again).
arglist[0] = arglist[0].refresh(use_etag=False)
return arglist, kwargs | 089b859964e89d54def0058abc9cc7536f5d8877 | 12,623 |
def compute_frames_per_animation(
attacks_per_second: float,
base_animation_length: int,
speed_coefficient: float = 1.0,
engine_tick_rate: int = 60,
is_channeling: bool = False) -> int:
"""Calculates frames per animation needed to resolve a certain ability at attacks_per_second.
Args:
attacks_per_second: attacks per second of character
base_animation_length: animation length of ability
speed_coefficient: speed-up scalar of ability
engine_tick_rate: server tick rate
is_channeling: whether or not the ability is a channeling skill
Returns:
int: number of frames one casts needs to resolve for
"""
_coeff = engine_tick_rate / (attacks_per_second * speed_coefficient)
if is_channeling:
return np.floor(_coeff)
else:
return np.ceil((base_animation_length - 1) / base_animation_length * _coeff) | 44427cf28152de21de42f0220e75f87717235275 | 12,624 |
def pad_rect(rect, move):
"""Returns padded rectangles given specified padding"""
if rect['dx'] > 2:
rect['x'] += move[0]
rect['dx'] -= 1*move[0]
if rect['dy'] > 2:
rect['y'] += move[1]
rect['dy'] -= 1*move[1]
return rect | 48bdbdc9d4736e372afc983ab5966fc80a221d4d | 12,625 |
import asyncio
async def yes_no(ctx: commands.Context,
message: str="Are you sure? Type **yes** within 10 seconds to confirm. o.o"):
"""Yes no helper. Ask a confirmation message with a timeout of 10 seconds.
ctx - The context in which the question is being asked.
message - Optional messsage that the question should ask.
"""
await ctx.send(message)
try:
message = await ctx.bot.wait_for("message", timeout=10,
check=lambda message: message.author == ctx.message.author)
except asyncio.TimeoutError:
await ctx.send("Timed out waiting. :<")
return False
if message.clean_content.lower() not in ["yes", "y"]:
await ctx.send("Command cancelled. :<")
return False
return True | 2b8ab0bfc51d4be68a42507bad6dbb945465d2e4 | 12,626 |
def __validation(size: int, it1: int, it2: int, it3: int, it4: int) -> bool:
""" Проверка на корректность тура
size: размер маршрута
it1, it2, it3, it4: индексы городов: t1, t2i, t2i+1, t2i+2
return: корректен или нет
"""
return between(size, it1, it3, it4) and between(size, it4, it2, it1) | 5fcb29f45c456115e8b87f0313e05f327c702849 | 12,627 |
def get_type_associations(base_type, generic_base_type): # type: (t.Type[TType], t.Type[TValue]) -> t.List[t.Tuple[t.Type[TValue], t.Type[TType]]]
"""Create and return a list of tuples associating generic_base_type derived types with a corresponding base_type derived type."""
return [item for item in [(get_generic_type(sc_type, generic_base_type), sc_type) for sc_type in get_subclasses(base_type)] if item[1]] | fe18bf72a96d6dfa8fad2c625732e781d54cae4d | 12,628 |
import rpy2.robjects as robj
from rpy2.robjects.packages import importr
import anndata2ri
def identify_empty_droplets(data, min_cells=3, **kw):
"""Detect empty droplets using DropletUtils
"""
importr("DropletUtils")
adata = data.copy()
col_sum = adata.X.sum(0)
if hasattr(col_sum, 'A'):
col_sum = col_sum.A.squeeze()
keep = col_sum > min_cells
adata = adata[:,keep]
#adata.X = adata.X.tocsc()
anndata2ri.activate()
robj.globalenv["X"] = adata
res = robj.r('res <- emptyDrops(assay(X))')
anndata2ri.deactivate()
keep = res.loc[res.FDR<0.01,:]
data = data[keep.index,:]
data.obs['empty_FDR'] = keep['FDR']
return data | 9c2d532d75afb6044836249eb525e86c60511c9b | 12,629 |
def catalog_category_RSS(category_id):
"""
Return an RSS feed containing all items in the specified category_id
"""
items = session.query(Item).filter_by(
category_id=category_id).all()
doc = jaxml.XML_document()
doc.category(str(category_id))
for item in items:
doc._push()
doc.item()
doc.id(item.id)
doc.name(item.name)
doc.description(item.description)
doc.imagepath('"' + item.image + '"')
doc.category_id(item.category_id)
doc.user_id(item.user_id)
doc._pop()
return doc.__repr__() | 13554cf1eba3a83c0fb23a6f848751721579dfea | 12,630 |
def get_caller_name(N=0, allow_genexpr=True):
"""
get the name of the function that called you
Args:
N (int): (defaults to 0) number of levels up in the stack
allow_genexpr (bool): (default = True)
Returns:
str: a function name
CommandLine:
python -m utool.util_dbg get_caller_name
python -m utool get_caller_name
python ~/code/utool/utool/__main__.py get_caller_name
python ~/code/utool/utool/__init__.py get_caller_name
python ~/code/utool/utool/util_dbg.py get_caller_name
Example:
>>> # ENABLE_DOCTEST
>>> from utool.util_dbg import * # NOQA
>>> import utool as ut
>>> N = list(range(0, 13))
>>> allow_genexpr = True
>>> caller_name = get_caller_name(N, allow_genexpr)
>>> print(caller_name)
"""
if isinstance(N, (list, tuple, range)):
name_list = []
for N_ in N:
try:
name_list.append(get_caller_name(N_))
except AssertionError:
name_list.append('X')
return '[' + ']['.join(name_list) + ']'
parent_frame = get_stack_frame(N=N + 2)
caller_name = parent_frame.f_code.co_name
co_filename = parent_frame.f_code.co_filename
if not allow_genexpr:
count = 0
while True:
count += 1
if caller_name == '<genexpr>':
parent_frame = get_stack_frame(N=N + 1 + count)
caller_name = parent_frame.f_code.co_name
else:
break
#try:
# if 'func' in parent_frame.f_locals:
# caller_name += '(' + meta_util_six.get_funcname(parent_frame.f_locals['func']) + ')'
#except Exception:
# pass
if caller_name == '<module>':
# Make the caller name the filename
caller_name = splitext(split(co_filename)[1])[0]
if caller_name in {'__init__', '__main__'}:
# Make the caller name the filename
caller_name = basename(dirname(co_filename)) + '.' + caller_name
return caller_name | 6c6ce7690d1bc4bd51037056e27f5dbd73085e29 | 12,631 |
from typing import Any
def create_user(
*,
db: Session = Depends(deps.get_db),
user_in: schema_in.UserCreateIn,
) -> Any:
"""
Create new user.
"""
new_user = User(**{k: v for k, v in user_in.dict().items() if k != 'password'})
new_user.hashed_password = get_password_hash(user_in.password)
new_user.gid = -1
try:
db.add(new_user)
db.commit()
except IntegrityError:
db.rollback()
raise HTTPException(
status_code=400,
detail="The user with this username already exists in the system.",
)
new_role = Role(uid=new_user.uid, nickname=new_user.nickname, avatar=new_user.avatar, gid=-1)
new_role.reset()
db.add(new_role)
db.commit()
return GameEnum.OK.digest() | cd8c3036026639d3e29e6dc030335f328d11c144 | 12,634 |
import math
def number_format(interp, num_args, number, decimals=0, dec_point='.',
thousands_sep=','):
"""Format a number with grouped thousands."""
if num_args == 3:
return interp.space.w_False
ino = int(number)
dec = abs(number - ino)
rest = ""
if decimals == 0 and dec >= 0.5:
if number > 0:
ino += 1
else:
ino -= 1
elif decimals > 0:
s_dec = str(dec)
if decimals + 2 < len(s_dec):
if ord(s_dec[decimals + 2]) >= ord('5'):
dec += math.pow(10, -decimals)
if dec >= 1:
if number > 0:
ino += 1
else:
ino -= 1
rest = "0" * decimals
else:
s_dec = str(dec)
if not rest:
rest = s_dec[2:decimals + 2]
else:
rest = s_dec[2:] + "0" * (decimals - len(s_dec) + 2)
s = str(ino)
res = []
i = 0
while i < len(s):
res.append(s[i])
if s[i] != '-' and i != len(s) - 1 and (len(s) - i - 1) % 3 == 0:
for item in thousands_sep:
res.append(item)
i += 1
if decimals > 0:
for item in dec_point:
res.append(item)
return interp.space.wrap("".join(res) + rest) | 9d5ab0b9ed5dd6054ce4f356e6811c1b155e2062 | 12,635 |
from typing import Optional
def _serialization_expr(value_expr: str, a_type: mapry.Type,
py: mapry.Py) -> Optional[str]:
"""
Generate the expression of the serialization of the given value.
If no serialization expression can be generated (e.g., in case of nested
structures such as arrays and maps), None is returned.
:param value_expr: Python expression of the value to be serialized
:param a_type: the mapry type of the value
:param py: Python settings
:return: generated expression, or None if not possible
"""
result = None # type: Optional[str]
if isinstance(a_type,
(mapry.Boolean, mapry.Integer, mapry.Float, mapry.String)):
result = value_expr
elif isinstance(a_type, mapry.Path):
if py.path_as == 'str':
result = value_expr
elif py.path_as == 'pathlib.Path':
result = 'str({})'.format(value_expr)
else:
raise NotImplementedError(
"Unhandled path_as: {}".format(py.path_as))
elif isinstance(a_type, (mapry.Date, mapry.Datetime, mapry.Time)):
result = '{value_expr}.strftime({dt_format!r})'.format(
value_expr=value_expr, dt_format=a_type.format)
elif isinstance(a_type, mapry.TimeZone):
if py.timezone_as == 'str':
result = value_expr
elif py.timezone_as == 'pytz.timezone':
result = 'str({})'.format(value_expr)
else:
raise NotImplementedError(
'Unhandled timezone_as: {}'.format(py.timezone_as))
elif isinstance(a_type, mapry.Duration):
result = '_duration_to_string({})'.format(value_expr)
elif isinstance(a_type, mapry.Array):
result = None
elif isinstance(a_type, mapry.Map):
result = None
elif isinstance(a_type, mapry.Class):
result = "{}.id".format(value_expr)
elif isinstance(a_type, mapry.Embed):
result = "serialize_{}({})".format(
mapry.py.naming.as_variable(a_type.name), value_expr)
else:
raise NotImplementedError(
"Unhandled serialization expression of type: {}".format(a_type))
return result | 5920a4c10dabe2ff061a1f141cd9c9f10faebafa | 12,636 |
def get_init_hash():
""" 获得一个初始、空哈希值 """
return imagehash.ImageHash(np.zeros([8, 8]).astype(bool)) | cd4665e6b5cdf232883093dab660aafcc2109a44 | 12,637 |
def get_vertex_between_points(point1, point2, at_distance):
"""Returns vertex between point1 and point2 at a distance from point1.
Args:
point1: First vertex having tuple (x,y) co-ordinates.
point2: Second vertex having tuple (x,y) co-ordinates.
at_distance: A distance at which to locate the vertex on the line joining point1 and point2.
Returns:
A Point object.
"""
line = LineString([point1, point2])
new_point = line.interpolate(at_distance)
return new_point | acb5cd76ef7dd3a16592c5fbaf74d6d777ab338c | 12,638 |
def disable_cache(response: Response) -> Response:
"""Prevents cached responses"""
response.headers["Cache-Control"] = "no-cache, no-store, must-revalidate, public, max-age=0"
response.headers["Expires"] = 0
response.headers["Pragma"] = "no-cache"
return response | 6f63c7e93a7c354c85171652dca51162e15b7137 | 12,639 |
def get_dir(src_point, rot_rad):
"""Rotate the point by `rot_rad` degree."""
sn, cs = np.sin(rot_rad), np.cos(rot_rad)
src_result = [0, 0]
src_result[0] = src_point[0] * cs - src_point[1] * sn
src_result[1] = src_point[0] * sn + src_point[1] * cs
return src_result | 40b36671c50a6b6b8905eca9915901cd613c2aaa | 12,640 |
def sparse_gauss_seidel(A,b,maxiters=100,tol=1e-8):
"""Returns the solution to the system Ax = b using the Gauss-Seidel method.
Inputs:
A (array) - 2D scipy.sparse matrix
b (array) - 1D NumPy array
maxiters (int, optional) - maximum iterations for algorithm to perform.
tol (float) - tolerance for convergence
Returns:
x (array) - solution to system Ax = b.
x_approx (list) - list of approximations at each iteration.
"""
if type(A) != spar.csr_matrix:
A = spar.csr_matrix(A)
n = A.shape[0]
x0 = np.zeros(n)
x = np.ones(n)
x_approx = []
for k in xrange(maxiters):
x = x0.copy()
diag = A.diagonal()
for i in xrange(n):
rowstart = A.indptr[i]
rowend = A.indptr[i+1]
Aix = np.dot(A.data[rowstart:rowend],
x[A.indices[rowstart:rowend]])
x[i] += (b[i] - Aix)/diag[i]
if np.max(np.abs(x0-x)) < tol:
return x, x_approx
x0 = x
x_approx.append(x)
print "Maxiters hit!"
return x, x_approx | 139fefa8e45d14f32ea9bb4dd25df03762737090 | 12,641 |
def delete_user(user_id):
"""Delete user from Users database and their permissions
from SurveyPermissions and ReportPermissions.
:Route: /api/user/<int:user_id>
:Methods: DELETE
:Roles: s
:param user_id: user id
:return dict: {"delete": user_id}
"""
user = database.get_user(user_id)
database.delete_user(user)
return {"delete": user.id} | c8f86dc20db67a5e3511e082f6308903b1acdaa2 | 12,642 |
def selectTopFive(sortedList):
"""
从sortedList中选出前五，返回对应的名字与commit数量列成的列表
:param sortedList:按值从大到小进行排序的authorDict
:return:size -- [commit数量]
labels -- [名字]
"""
size = []
labels = []
for i in range(5):
labels.append(sortedList[i][0])
size.append(sortedList[i][1])
return size, labels | 747ad379ed73aeb6ccb48487b48dc6150350204e | 12,643 |
def get_license(file):
"""Returns the license from the input file.
"""
# Collect the license
lic = ''
for line in file:
if line.startswith('#include') or line.startswith('#ifndef'):
break
else:
lic += line
return lic | 126fff2dd0464ef1987f3ab672f6b36b8fa962f7 | 12,644 |
def quote_query_string(chars):
"""
Multibyte charactor string is quoted by double quote.
Because english analyzer of Elasticsearch decomposes
multibyte character strings with OR expression.
e.g. 神保町 -> 神 OR 保 OR 町
"神保町"-> 神保町
"""
if not isinstance(chars, unicode):
chars = chars.decode('utf-8')
token = u''
qs = u''
in_escape = False
in_quote = False
in_token = False
for c in chars:
# backslash escape
if in_escape:
token += c
in_escape = False
continue
if c == u'\\':
token += c
in_escape = True
continue
# quote
if c != u'"' and in_quote:
token += c
continue
if c == u'"' and in_quote:
token += c
qs += token
token = u''
in_quote = False
continue
# otherwise: not in_quote
if _is_delimiter(c) or c == u'"':
if in_token:
qs += _quote_token(token)
token = u''
in_token = False
if c == u'"':
token += c
in_quote = True
else:
qs += c
continue
# otherwise: not _is_delimiter(c)
token += c
in_token = True
if token:
qs += _quote_token(token)
return qs | 8d1888df17a617d42e6a0d1b909e08e4f84fa4c9 | 12,645 |
def copy_params(params: ParamsDict) -> ParamsDict:
"""copy a parameter dictionary
Args:
params: the parameter dictionary to copy
Returns:
the copied parameter dictionary
Note:
this copy function works recursively on all subdictionaries of the params
dictionary but does NOT copy any non-dictionary values.
"""
validate_params(params)
params = {**params}
if all(isinstance(v, dict) for v in params.values()):
return {k: copy_params(params[k]) for k in params}
return params | 8248b31698f6b51103dc34bad7b13373591b10cd | 12,646 |
def watch_list(request):
"""
Get watchlist or create a watchlist, or delete from watchlist
:param request:
:return:
"""
if request.method == 'GET':
watchlist = WatchList.objects.filter(user=request.user)
serializer = WatchListSerializer(watchlist, many=True)
return Response(data=serializer.data, status=status.HTTP_200_OK)
elif request.method == 'POST':
movie_id = request.data.get('movie_id')
if movie_id is not None:
# check if movie is in db
try:
movie = Movie_Collected.objects.get(pk=movie_id)
watchlist = WatchList.objects.filter(user=request.user, movie=movie).exists()
if watchlist:
message = {"error": "Movie already in watchlist"}
return Response(data=message, status=status.HTTP_400_BAD_REQUEST)
else:
watchlist = WatchList.objects.create(user=request.user, movie=movie)
serializer = WatchListSerializer(watchlist)
return Response(data=serializer.data, status=status.HTTP_201_CREATED)
except Movie_Collected.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
message = {'error': 'Movie id is required'}
return Response(data=message, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE':
movie_id = request.data.get('movie_id')
if movie_id is not None:
try:
movie = Movie_Collected.objects.get(pk=movie_id)
WatchList.objects.filter(user=request.user, movie=movie).delete()
return Response(status=status.HTTP_204_NO_CONTENT)
except Movie_Collected.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
message = {'error': 'Movie id is required'}
return Response(data=message, status=status.HTTP_400_BAD_REQUEST) | c92d39da05546fea9330ffe44cea5dd0c30f6427 | 12,647 |
async def user_has_pl(api, room_id, mxid, pl=100):
"""
Determine if a user is admin in a given room.
"""
pls = await api.get_power_levels(room_id)
users = pls["users"]
user_pl = users.get(mxid, 0)
return user_pl == pl | 5678af17469202e0b0a0232e066e7ed5c8212ee6 | 12,648 |
import cgi
from typing import Optional
def get_cgi_parameter_bool_or_default(form: cgi.FieldStorage,
key: str,
default: bool = None) -> Optional[bool]:
"""
Extracts a boolean parameter from a CGI form (``"1"`` = ``True``,
other string = ``False``, absent/zero-length string = default value).
"""
s = get_cgi_parameter_str(form, key)
if s is None or len(s) == 0:
return default
return is_1(s) | 905dfa96628414e3b076fd3345113588f3f6ef08 | 12,649 |
def loss_function_1(y_true, y_pred):
""" Probabilistic output loss """
a = tf.clip_by_value(y_pred, 1e-20, 1)
b = tf.clip_by_value(tf.subtract(1.0, y_pred), 1e-20, 1)
cross_entropy = - tf.multiply(y_true, tf.log(a)) - tf.multiply(tf.subtract(1.0, y_true), tf.log(b))
cross_entropy = tf.reduce_mean(cross_entropy, 0)
loss = tf.reduce_mean(cross_entropy)
return loss | 8426ef13bd56fa3ff11226556d37bf738333a165 | 12,650 |
def sanitize_for_json(tag):
"""eugh the tags text is in comment strings"""
return tag.text.replace('<!--', '').replace('-->', '') | 211c07864af825ad29dfc806844927db977e6ce0 | 12,651 |
def load_data_and_labels(dataset_name):
"""
Loads MR polarity data from files, splits the data into words and generates labels.
Returns split sentences and labels.
"""
for i in [1]:
# Load data from files
positive_examples = list(open('data/'+str(dataset_name)+'/'+str(dataset_name)+'.pos',encoding="utf-8").readlines())
# positive_examples = positive_examples[0:1000]
positive_examples = [s.strip() for s in positive_examples]
negative_examples = list(open('data/'+str(dataset_name)+'/'+str(dataset_name)+'.neg',encoding="utf-8").readlines())
# negative_examples = negative_examples[0:1000]
negative_examples = [s.strip() for s in negative_examples]
# Split by words
x_text = positive_examples + negative_examples
x_text = [clean_str(sent) for sent in x_text]
x_text = [s.split(" ") for s in x_text]
# Generate labels
positive_labels = [[0, 1] for _ in positive_examples]
negative_labels = [[1, 0] for _ in negative_examples]
y = np.concatenate([positive_labels, negative_labels], 0)
return [x_text, y] | d753494f3a614850c07f40230c3373eab13b0c6b | 12,652 |
def tileswrap(ihtORsize, numtilings, floats, wrapwidths, ints=[], readonly=False):
"""Returns num-tilings tile indices corresponding to the floats and ints, wrapping some floats"""
qfloats = [floor(f * numtilings) for f in floats]
Tiles = []
for tiling in range(numtilings):
tilingX2 = tiling * 2
coords = [tiling]
b = tiling
for q, width in zip_longest(qfloats, wrapwidths):
c = (q + b % numtilings) // numtilings
coords.append(c % width if width else c)
b += tilingX2
coords.extend(ints)
Tiles.append(hashcoords(coords, ihtORsize, readonly))
return Tiles | e9a9dc439307fc114c9abc939f642ea411acd26e | 12,653 |
def coerce(data, egdata):
"""Coerce a python object to another type using the AE coercers"""
pdata = pack(data)
pegdata = pack(egdata)
pdata = pdata.AECoerceDesc(pegdata.type)
return unpack(pdata) | dc7499530b77a25c8b51537e2e21115d3ce3ccee | 12,654 |
from typing import Union
from typing import List
def _write_mkaero1(model: Union[BDF, OP2Geom], name: str,
mkaero1s: List[MKAERO1], ncards: int,
op2_file, op2_ascii, endian: bytes, nastran_format: str='nx') -> int:
"""writes the MKAERO1
data = (1.3, -1, -1, -1, -1, -1, -1, -1,
0.03, 0.04, 0.05, -1, -1, -1, -1, -1)
"""
key = (3802, 38, 271)
makero1s_temp = []
makero1s_final = []
for mkaero in mkaero1s:
nmachs = len(mkaero.machs)
nkfreqs = len(mkaero.reduced_freqs)
assert nmachs > 0, mkaero
assert nkfreqs > 0, mkaero
if nmachs <= 8 and nkfreqs <= 8:
# no splitting required
makero1s_final.append((mkaero.machs, mkaero.reduced_freqs))
elif nmachs <= 8 or nkfreqs <= 8:
# one of machs or kfreqs < 8
makero1s_temp.append((mkaero.machs, mkaero.reduced_freqs))
else:
# both machs and kfreqs > 8
nloops_mach = int(np.ceil(nmachs/8))
for i in range(nloops_mach):
machs_temp = _makero_temp(mkaero.machs, i, nloops_mach)
assert len(machs_temp) > 0, (i, nloops_mach, machs_temp)
makero1s_temp.append((machs_temp, mkaero.reduced_freqs))
for (machs, reduced_freqs) in makero1s_temp:
nmachs = len(machs)
nkfreqs = len(reduced_freqs)
assert nmachs > 0, nmachs
assert nkfreqs > 0, nkfreqs
if nmachs <= 8 and nkfreqs <= 8: # pragma: no cover
raise RuntimeError(f'this should never happen...nmachs={nmachs} knfreqs={nkfreqs}')
if nmachs <= 8:
# nkfreqs > 8
nloops = int(np.ceil(nkfreqs/8))
for i in range(nloops):
reduced_freqs_temp = _makero_temp(reduced_freqs, i, nloops)
makero1s_final.append((machs, reduced_freqs_temp))
elif nkfreqs <= 8:
# nmachs > 8
nloops = int(np.ceil(nmachs/8))
for i in range(nloops):
machs_temp = _makero_temp(machs, i, nloops)
assert len(machs_temp) > 0, (i, nloops_mach, machs_temp)
makero1s_final.append((machs_temp, reduced_freqs))
else: # pragma: no cover
raise RuntimeError(f'this should never happen...nmachs={nmachs} knfreqs={nkfreqs}')
#raise RuntimeError((nmachs, nkfreqs))
ncards = len(makero1s_final)
nfields = 16
nbytes = write_header(name, nfields, ncards, key, op2_file, op2_ascii)
for machs, reduced_freqs in makero1s_final:
data = []
nmachs = len(machs)
nkfreqs = len(reduced_freqs)
assert nmachs > 0, machs
assert nkfreqs > 0, reduced_freqs
nint_mach = 8 - nmachs
nint_kfreq = 8 - nkfreqs
fmt1 = b'%if' % nmachs + b'i' * nint_mach
fmt2 = b'%if' % nkfreqs + b'i' * nint_kfreq
spack = Struct(endian + fmt1 + fmt2)
data.extend(machs.tolist())
assert nint_mach < 8, nint_mach
if nint_mach:
data.extend([-1]*nint_mach)
data.extend(reduced_freqs.tolist())
if nint_kfreq:
data.extend([-1]*nint_kfreq)
op2_ascii.write(f' mkaero1 data={data}\n')
op2_file.write(spack.pack(*data))
return nbytes | ad45ec25989714685a6a2b2e61d6833a9ab56a6d | 12,656 |
def _mesh_obj_large():
"""build a large, random mesh model/dataset"""
n_tri, n_pts = 400, 1000
node = np.random.randn(n_pts, 2)
element = np.array([np.random.permutation(n_pts)[:3] for _ in range(n_tri)])
perm = np.random.randn(n_tri)
np.random.seed(0)
el_pos = np.random.permutation(n_pts)[:16]
return PyEITMesh(node=node, element=element, perm=perm, el_pos=el_pos, ref_node=0) | c2db6a3484dc4923d92519488d0b10d7a7cd75bb | 12,657 |
def cursor():
"""Return a database cursor."""
return util.get_dbconn("mesosite").cursor() | 516cf2a1716204487dd4cff4f063397365b21fa1 | 12,658 |
def custom_field_check(issue_in, attrib, name=None):
""" This method allows the user to get in the comments customfiled that are not common
to all the project, in case the customfiled does not existe the method returns an
empty string.
"""
if hasattr(issue_in.fields, attrib):
value = str(eval('issue_in.fields.%s'%str(attrib)))
if name != None:
return str("%s : %s"%(name,value))
else:
return str(value)
else:
return str("") | d9c051fa922f34242d3b5e94e8534b4dc8038f19 | 12,659 |
def header(text, color='black', gen_text=None):
"""Create an HTML header"""
if gen_text:
raw_html = f'<h1 style="margin-top:16px;color: {color};font-size:54px"><center>' + str(
text) + '<span style="color: red">' + str(gen_text) + '</center></h1>'
else:
raw_html = f'<h1 style="margin-top:12px;color: {color};font-size:54px"><center>' + str(
text) + '</center></h1>'
return raw_html | 646b0a16b35cd4350feadd75674eea6ab6da6404 | 12,660 |
def block_pose(detection, block_size=0.05):
# type: (AprilTagDetection, float) -> PoseStamped
"""Given a tag detection (id == 0), return the block's pose. The block pose
has the same orientation as the tag detection, but it's position is
translated to be at the cube's center.
Args:
detection: The AprilTagDetection.
block_size: The block's side length in meters.
"""
transform = tf.transformations.concatenate_matrices(
tf.transformations.translation_matrix(
[detection.pose.pose.position.x,
detection.pose.pose.position.y,
detection.pose.pose.position.z]
),
tf.transformations.quaternion_matrix(
[detection.pose.pose.orientation.x,
detection.pose.pose.orientation.y,
detection.pose.pose.orientation.z,
detection.pose.pose.orientation.w]
),
tf.transformations.translation_matrix(
[0, 0, -block_size / 2]
)
)
t = tf.transformations.translation_from_matrix(transform)
q = tf.transformations.quaternion_from_matrix(transform)
ps = PoseStamped()
ps.header.frame_id = detection.pose.header.frame_id
ps.header.stamp = detection.pose.header.stamp
ps.pose.position = Point(*t)
ps.pose.orientation = Quaternion(*q)
return ps | da6ee3bb1bf8a071ea5859d17dcad07ecd8781a3 | 12,661 |
async def batch_omim_similarity(
data: models.POST_OMIM_Batch,
method: str = 'graphic',
combine: str = 'funSimAvg',
kind: str = 'omim'
) -> dict:
"""
Similarity score between one HPOSet and several OMIM Diseases
"""
other_sets = []
for other in data.omim_diseases:
try:
disease = Omim.get(other)
hpos = ','.join([str(x) for x in disease.hpo])
except KeyError:
hpos = ''
other_sets.append(
models.POST_HPOSet(
set2=hpos,
name=other
)
)
res = await terms.batch_similarity(
data=models.POST_Batch(
set1=data.set1,
other_sets=other_sets
),
method=method,
combine=combine,
kind=kind
)
return res | 2da8dc25d867f132ec0f571b4d9dff3d7de38c21 | 12,662 |
def vector(*,
unit: _Union[_cpp.Unit, str, None] = default_unit,
value: _Union[_np.ndarray, list]):
"""Constructs a zero dimensional :class:`Variable` holding a single length-3
vector.
:param value: Initial value, a list or 1-D numpy array.
:param unit: Optional, unit. Default=dimensionless
:returns: A scalar (zero-dimensional) Variable.
:seealso: :py:func:`scipp.vectors`
"""
return _cpp.vectors(dims=[], unit=unit, values=value) | dda09f89ba00ffab789c7ed9f6f6713a45c9bd03 | 12,663 |
import laspy
def read_lidar(filename, **kwargs):
"""Read a LAS file.
Args:
filename (str): Path to a LAS file.
Returns:
LasData: The LasData object return by laspy.read.
"""
try:
except ImportError:
print(
"The laspy package is required for this function. Use pip install laspy to install it."
)
return
return laspy.read(filename, **kwargs) | 5336c34223216d4a1857cc5c858ccca704508e22 | 12,664 |
def get_gene_starting_with(gene_symbol: str, verbose: bool = True):
""" get the genes that start with the symbol given
Args:
- gene_symbol: str
- verbose: bool
Returns:
- list of str
- None
"""
gene_symbol = gene_symbol.strip().upper()
ext = "search/symbol/{}*".format(gene_symbol)
data = get_api_response("{}/{}".format(URL, ext))
res = data["response"]["docs"]
if res == []:
if verbose:
print("No gene found starting with {}".format(gene_symbol))
return
else:
gene_symbols = [res[i]["symbol"] for i in range(len(res))]
if verbose:
print("Found these genes starting with {}:".format(gene_symbol))
for symbol in gene_symbols:
print(symbol)
return gene_symbols | c0f092a93d44dd264f6b251ff3eba565b29abda0 | 12,665 |
import time
def gen_timestamp():
"""
Generates a unique (let's hope!), whole-number, unix-time timestamp.
"""
return int(time() * 1e6) | cb044e7428c062660eb998856245d4cd2c692a7e | 12,666 |
def learningCurve(X, y, Xval, yval, Lambda):
"""returns the train and
cross validation set errors for a learning curve. In particular,
it returns two vectors of the same length - error_train and
error_val. Then, error_train(i) contains the training error for
i examples (and similarly for error_val(i)).
In this function, you will compute the train and test errors for
dataset sizes from 1 up to m. In practice, when working with larger
datasets, you might want to do this in larger intervals.
"""
# Number of training examples
m, _ = X.shape
# You need to return these values correctly
error_train = np.zeros(m)
error_val = np.zeros(m)
for i in range(m):
theta = trainLinearReg(X[:i + 1], y[:i + 1], Lambda)
error_train[i], _ = linearRegCostFunction(X[:i + 1], y[:i + 1], theta, 0)
error_val[i], _ = linearRegCostFunction(Xval, yval, theta, 0)
return error_train, error_val | 8cdfdec694cbfadef92375c7cf8eba4da012be59 | 12,667 |
def decode_xml(text):
"""Parse an XML document into a dictionary. This assume that the
document is only 1 level, i.e.:
<top>
<child1>content</child1>
<child2>content</child2>
</top>
will be parsed as: child1=content, child2=content"""
xmldoc = minidom.parseString(text)
return dict([(x.tagName, x.firstChild.nodeValue)
for x in xmldoc.documentElement.childNodes
if x.childNodes.length == 1]) | 826bdc1ff0c4df503fdbc6f7e76b013d907b208b | 12,668 |
def _qcheminputfile(ccdata, templatefile, inpfile):
"""
Generate input file from geometry (list of lines) depending on job type
:ccdata: ccData object
:templatefile: templatefile - tells us which template file to use
:inpfile: OUTPUT - expects a path/to/inputfile to write inpfile
"""
string = ''
if hasattr(ccdata, 'charge'):
charge = ccdata.charge
else:
charge = 0
if hasattr(ccdata, 'mult'):
mult = ccdata.mult
else:
print('Multiplicity not found, set to 1 by default')
mult = 1
# $molecule
string += '$molecule\n'
string += '{0} {1}\n'.format(charge, mult)
# Geometry (Maybe a cleaner way to do this..)
atomnos = [pt.Element[x] for x in ccdata.atomnos]
atomcoords = ccdata.atomcoords[-1]
if not type(atomcoords) is list:
atomcoords = atomcoords.tolist()
for i in range(len(atomcoords)):
atomcoords[i].insert(0, atomnos[i])
for atom in atomcoords:
string += ' {0} {1:10.8f} {2:10.8f} {3:10.8f}\n'.format(*atom)
string += '$end\n\n'
# $end
# $rem
with open(templates.get(templatefile), 'r') as templatehandle:
templatelines = [x for x in templatehandle.readlines()]
for line in templatelines:
string += line
# $end
return string | 3ca565c4c599bccfd3916a0003126b3085cc7254 | 12,669 |
def arrangements(ns):
"""
prime factors of 19208 lead to the "tribonacci" dict;
only needed up to trib(4)
"""
trib = {0: 1, 1: 1, 2: 2, 3: 4, 4: 7}
count = 1
one_seq = 0
for n in ns:
if n == 1:
one_seq += 1
if n == 3:
count *= trib[one_seq]
one_seq = 0
return count
# # one-liner...
# return reduce(lambda c, n: (c[0]*trib[c[1]], 0) if n == 3 else (c[0], c[1]+1), ns, (1,0))[0] | 01f3defb25624d7a801be87c7336ddf72479e489 | 12,670 |
def vtpnt(x, y, z=0):
"""坐标点转化为浮点数"""
return win32com.client.VARIANT (pythoncom.VT_ARRAY | pythoncom.VT_R8, (x, y, z)) | 1e7c79353d010d4dd8daa4b7fa7c39b841ff8ffe | 12,671 |
from datetime import datetime
def get_time_delta(pre_date: datetime):
"""
获取给定时间与当前时间的差值
Args:
pre_date:
Returns:
"""
date_delta = datetime.datetime.now() - pre_date
return date_delta.days | 4f8894b06dc667b166ab0ee6d86b484e967501ac | 12,672 |
from bs4 import BeautifulSoup
def render_checkbox_list(soup_body: object) -> object:
"""As the chosen markdown processor does not support task lists (lists with checkboxes), this function post-processes
a bs4 object created from outputted HTML, replacing instances of '[ ]' (or '[]') at the beginning of a list item
with an unchecked box, and instances of '[x]' (or '[X]') at the beginning of a list item with a checked box.
Args:
soup_body: bs4 object input
Returns:
modified bs4 object
"""
if not isinstance(soup_body, BeautifulSoup):
raise TypeError('Input must be a bs4.BeautifulSoup object')
for ul in soup_body.find_all('ul'):
for li in ul.find_all('li', recursive=False):
if (li.contents[0].string[:2] == '[]') or (li.contents[0].string[:3] == '[ ]'):
unchecked = soup_body.new_tag("input", disabled="", type="checkbox")
li.contents[0].string.replace_with(li.contents[0].string.replace('[] ', u'\u2002'))
li.contents[0].string.replace_with(li.contents[0].string.replace('[ ] ', u'\u2002'))
li.contents[0].insert_before(unchecked)
li.find_parent('ul')['style'] = 'list-style-type: none; padding-left: 0.5em; margin-left: 0.25em;'
elif (li.contents[0].string[:3] == '[x]') or (li.contents[0].string[:3] == '[X]'):
checked = soup_body.new_tag("input", disabled="", checked="", type="checkbox")
li.contents[0].string.replace_with(li.contents[0].string.replace('[x] ', u'\u2002'))
li.contents[0].string.replace_with(li.contents[0].string.replace('[X] ', u'\u2002'))
li.contents[0].insert_before(checked)
li.find_parent('ul')['style'] = 'list-style-type: none; padding-left: 0.5em; margin-left: 0.25em;'
return soup_body | 640f00d726a1268eb71134e29dbde53ef0ec44f5 | 12,673 |
def slowness_to_velocity(slowness):
"""
Convert a slowness log in µs per unit depth, to velocity in unit depth
per second.
Args:
slowness (ndarray): A value or sequence of values.
Returns:
ndarray: The velocity.
"""
return 1e6 / np.array(slowness) | dbfc3b4206ddf615da634e328328c4b8588e5c7a | 12,674 |
def SingleDetectorLogLikelihoodModelViaArray(lookupNKDict,ctUArrayDict,ctVArrayDict, tref, RA,DEC, thS,phiS,psi, dist,det):
"""
DOCUMENT ME!!!
"""
global distMpcRef
# N.B.: The Ylms are a function of - phiref b/c we are passively rotating
# the source frame, rather than actively rotating the binary.
# Said another way, the m^th harmonic of the waveform should transform as
# e^{- i m phiref}, but the Ylms go as e^{+ i m phiref}, so we must give
# - phiref as an argument so Y_lm h_lm has the proper phiref dependence
U = ctUArrayDict[det]
V = ctVArrayDict[det]
Ylms = ComputeYlmsArray(lookupNKDict[det], thS,-phiS)
if (det == "Fake"):
F=np.exp(-2.*1j*psi) # psi is applied through *F* in our model
else:
F = ComplexAntennaFactor(det, RA,DEC,psi,tref)
distMpc = dist/(lal.PC_SI*1e6)
# Term 2 part 1 : conj(Ylms*F)*crossTermsU*F*Ylms
# Term 2 part 2: Ylms*F*crossTermsV*F*Ylms
term2 = 0.j
term2 += F*np.conj(F)*(np.dot(np.conj(Ylms), np.dot(U,Ylms)))
term2 += F*F*np.dot(Ylms,np.dot(V,Ylms))
term2 = np.sum(term2)
term2 = -np.real(term2) / 4. /(distMpc/distMpcRef)**2
return term2 | 35c27e53833cb54f856adde6815bf51c3feca019 | 12,675 |
import numpy as np
def manualcropping(I, pointsfile):
"""This function crops a copy of image I according to points stored
in a text file (pointsfile) and corresponding to aponeuroses (see
Args section).
Args:
I (array): 3-canal image
pointsfile (text file): contains points' coordinates. Pointsfile must be
organized such that:
- column 0 is the ID of each point
- column 1 is the X coordinate of each point, that is the corresponding
column in I
- column 2 is the Y coordinate, that is the row in I
- row 0 is for txt columns' names
- rows 1 and 2 are for two points of the scale
- rows 3 to 13 are aponeuroses' points in panoramic images // raws 3
to 10 in simple images
- following rows are for muscle fascicles (and are optional for this
function)
Other requirements: pointsfile's name must 1) include extension
2) indicates whether I is panoramic or simple by having 'p' or
's' just before the point of the extension.
Returns:
I2 (array) : array of same type than I. It is the cropped image of I according
to the aponeuroses' points manually picked and stored in pointsfile.
point_of_intersect (tuple) : point at right
of the image; should correspond to the point of intersection of deep
and upper aponeuroses.
min_raw, max_raw, min_col, max_col: indices of the location of the cropped image
in the input raw image
"""
data = open(pointsfile, 'r')
#finds whether the image is panoramic or simple
search_point = -1
while (pointsfile[search_point] != '.') and (search_point > (-len(pointsfile))):
search_point = search_point-1
if (search_point == -len(pointsfile)):
raise TypeError("Input pointsfile's name is not correct. Check extension.")
else:
imagetype = pointsfile[search_point-1]
#extract points from the input file
picked_points = []
for line in data:
line = line.strip('\n')
x = line.split('\t')
picked_points.append((x[1], x[2]))
#keep aponeuroses points according to image type
if imagetype == 'p': #keep points 3 to 13 included
apos = np.asarray(picked_points[3:14], dtype=np.float64, order='C')
elif imagetype == 's': #keep points 3 to 10 included
apos = np.asarray(picked_points[3:11], dtype=np.float64, order='C')
else:
raise ValueError("pointsfile's name does not fulfill conditions. See docstrings")
#find max and min indexes for columns and raws to crop image I
#with a margin of 10 pixels (5 pixels for min_raw).
#Coordinates are inverted in apos
min_raw = max(0, np.min(apos[:, 1])-10)
max_raw = min(I.shape[0], np.max(apos[:, 1])+20)
min_col = max(0, np.min(apos[:, 0])-10)
max_col = min(I.shape[1], np.max(apos[:, 0])+10)
i_cropped = np.copy(I[int(min_raw):int(max_raw), int(min_col):int(max_col), :])
index = np.argmax(apos[:, 0])
point_of_intersect = (apos[index][1] - min_raw, apos[index][0] - min_col)
#close file
data.close()
return i_cropped, point_of_intersect, int(min_raw), int(max_raw), int(min_col), int(max_col) | eb3f49b5b46d1966946fc3d00bcae113f51c60d1 | 12,676 |
from datetime import datetime
def prepare_time_micros(data, schema):
"""Convert datetime.time to int timestamp with microseconds"""
if isinstance(data, datetime.time):
return int(data.hour * MCS_PER_HOUR + data.minute * MCS_PER_MINUTE
+ data.second * MCS_PER_SECOND + data.microsecond)
else:
return data | bfdfe40065db66417bf2b641a24b195f4114687e | 12,677 |
def get_configs_path_mapping():
"""
Gets a dictionary mapping directories to back up to their destination path.
"""
return {
"Library/Application Support/Sublime Text 2/Packages/User/": "sublime_2",
"Library/Application Support/Sublime Text 3/Packages/User/": "sublime_3",
"Library/Preferences/IntelliJIdea2018.2/": "intellijidea_2018.2",
"Library/Preferences/PyCharm2018.2/": "pycharm_2018.2",
"Library/Preferences/CLion2018.2/": "clion_2018.2",
"Library/Preferences/PhpStorm2018.2": "phpstorm_2018.2",
} | d7617139a36ca2e1d4df57379d6af73e3b075c84 | 12,678 |
def num_from_bins(bins, cls, reg):
"""
:param bins: list
The bins
:param cls: int
Classification result
:param reg:
Regression result
:return: computed value
"""
bin_width = bins[0][1] - bins[0][0]
bin_center = float(bins[cls][0] + bins[cls][1]) / 2
return bin_center + reg * bin_width | 468e56075cf214f88d87298b259f7253d013a3f3 | 12,680 |
def rotate90(matrix: list) -> tuple:
"""return the matrix rotated by 90"""
return tuple(''.join(column)[::-1] for column in zip(*matrix)) | 770a8a69513c4f88c185778ad9203976d5ee6147 | 12,681 |
def get_aspect(jdate, body1, body2):
"""
Return the aspect and orb between two bodies for a certain date
Return None if there's no aspect
"""
if body1 > body2:
body1, body2 = body2, body1
dist = distance(long(jdate, body1),
long(jdate, body2))
for i_asp, aspect in enumerate(aspects['value']):
orb = get_orb(body1, body2, i_asp)
if i_asp == 0 and dist <= orb:
return body1, body2, i_asp, dist
elif aspect - orb <= dist <= aspect + orb:
return body1, body2, i_asp, aspect - dist
return None | 11a9b05fbc924290e390329395361da0c856541e | 12,682 |
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