content
stringlengths 35
762k
| sha1
stringlengths 40
40
| id
int64 0
3.66M
|
|---|---|---|
def get_post_count(user):
"""
Get number of posts published by the requst user.
Parameters
------------
user: The request user
Returns
-------
count: int
The number of posts published by the requst user.
"""
count = Post.objects.filter(publisher=user).count()
return count | 6000bcd43ef2b8edf3c1dd04df89dcef38f110d5 | 12,459 |
from config import employee_required_fields
def create_new_employee(employees):
"""
Create a new employee record with the employees dictionary
Use the employee_sections dictionary template to create a
new employee record.
"""
subsidiary = input('Employee Subsidiary (SK, CZ):')
employee_id = generate_employee_id(subsidiary, employees)
employee = {} # Storage for new employee
print('Please, enter records for new employee ID: ' + employee_id)
# Iterating over 'employee_sections'
for section in employee_sections['<employee_id>']:
# Inserting empty section
employee[section] = {}
for field in employee_sections['<employee_id>'][section]:
_input = ''
while not _input:
_input = input(section + '/' + field + ': ')
if not _input and field in employee_required_fields:
print('This field is required, please enter the value.')
else:
employee[section][field] = _input
break
print(employee)
employees[employee_id] = employee
print('Thank you, entry has been completed for ID: ' + employee_id)
input('Press ENTER to continue')
commit_changes(file_with_employees, str(employees))
return employees | aa5d0981c2b81ad65ed5ad0368fd1b3b79796a40 | 12,460 |
def gather_squares_triangles(p1,p2,depth):
""" Draw Square and Right Triangle given 2 points,
Recurse on new points
args:
p1,p2 (float,float) : absolute position on base vertices
depth (int) : decrementing counter that terminates recursion
return:
squares [(float,float,float,float)...] : absolute positions of
vertices of squares
triangles [(float,float,float)...] : absolute positions of
vertices of right triangles
"""
# Break Recursion if depth is met
if depth == 0:
return [],[]
# Generate Points
pd = (p2[0] - p1[0]),(p1[1] - p2[1])
p3 = (p2[0] - pd[1]),(p2[1] - pd[0])
p4 = (p1[0] - pd[1]),(p1[1] - pd[0])
p5 = (p4[0] + (pd[0] - pd[1])/2),(p4[1] - (pd[0] + pd[1])/2)
# Gather Points further down the tree
squares_left,triangles_left = gather_squares_triangles(p4,p5,depth-1)
squares_right,triangles_right = gather_squares_triangles(p5,p3,depth-1)
# Merge and Return
squares = [[p1,p2,p3,p4]]+squares_left+squares_right
triangles = [[p3,p4,p5]]+triangles_left+triangles_right
return squares,triangles | de4e720eb10cb378f00086a6e8e45886746055c0 | 12,461 |
def update_node(node_name, node_type, root=None):
"""
! Node is assumed to have only one input and one output port with a maximum
of one connection for each.
Returns:
NodegraphAPI.Node: newly created node
"""
new = NodegraphAPI.CreateNode(node_type, root or NodegraphAPI.GetRootNode())
if new.getType() == "Group":
new_in = new.addInputPort("in")
new_out = new.addOutputPort("out")
else:
new_in = new.getInputPortByIndex(0)
new_out = new.getOutputPortByIndex(0)
existingn = NodegraphAPI.GetNode(node_name)
if existingn:
# we assume there is only 1 input/output port with only one connection
in_port = existingn.getInputPorts()[0]
in_port = in_port.getConnectedPort(0)
out_port = existingn.getOutputPorts()[0]
out_port = out_port.getConnectedPort(0)
pos = NodegraphAPI.GetNodePosition(existingn) # type: tuple
existingn.delete()
NodegraphAPI.SetNodePosition(new, pos)
if in_port:
in_port.connect(new_in)
if out_port:
out_port.connect(new_out)
logger.info("[update_node] Found existing node, it has been updated.")
new.setName(node_name)
logger.info("[update_node] Finished for node <{}>".format(node_name))
return new | 916beec7de527ee56d5326061aa2c367af17434f | 12,462 |
def dan_acf(x, axis=0, fast=False):
"""
Estimate the autocorrelation function of a time series using the FFT.
Args:
x (array): The time series. If multidimensional, set the time axis
using the ``axis`` keyword argument and the function will be
computed for every other axis.
axis (Optional[int]): The time axis of ``x``. Assumed to be the first
axis if not specified.
fast (Optional[bool]): If ``True``, only use the largest ``2^n``
entries for efficiency. (default: False)
Returns:
acf (array): The acf array.
"""
x = np.atleast_1d(x)
m = [slice(None), ] * len(x.shape)
# For computational efficiency, crop the chain to the largest power of
# two if requested.
if fast:
n = int(2**np.floor(np.log2(x.shape[axis])))
m[axis] = slice(0, n)
x = x
else:
n = x.shape[axis]
# Compute the FFT and then (from that) the auto-correlation function.
f = np.fft.fft(x-np.mean(x, axis=axis), n=2*n, axis=axis)
m[axis] = slice(0, n)
acf = np.fft.ifft(f * np.conjugate(f), axis=axis)[tuple(m)].real
m[axis] = 0
return acf / acf[m] | 85273d95564f0e8c0afb9ff00ac23dc04539f291 | 12,463 |
from datetime import datetime
def schedule_decision():
"""最適化の実行と結果の表示を行う関数"""
# トップページを表示する（GETリクエストがきた場合）
if request.method == "GET":
return render_template("scheduler/schedule_decision.html", solution_html=None)
# POSTリクエストである「最適化を実行」ボタンが押された場合に実行
# データがアップロードされているかチェックする。適切でなければ元のページに戻る
if not check_request(request):
return redirect(request.url)
# 前処理（データ読み込み）
df_kagisime, df_gomisute = preprocess(request)
# 最適化実行
prob = KandGProblem(df_kagisime, df_gomisute)
solution_df = prob.solve()
L_gomisute_members = list(prob.L_gomisute_members)
# ログインしている場合，DBに決定した予定表を追加．
if current_user.is_authenticated:
yyyy, mm, _ = solution_df.index[0].split("/")
user_id = session["_user_id"]
print(user_id)
print("currentuser:", current_user)
is_new_schedule = not ScheduleLists.query.filter_by(
user_id=user_id, yyyymm=yyyy + mm
).all()
if is_new_schedule:
schedule_list = ScheduleLists(user_id=user_id, yyyymm=yyyy + mm)
db.session.add(schedule_list)
db.session.commit()
schedulelist_id = (
ScheduleLists.query.filter_by(user_id=user_id, yyyymm=yyyy + mm)
.group_by("id")
.first()
)
print(schedulelist_id.id)
for row in solution_df.itertuples():
if not is_new_schedule:
print(datetime.strptime(row[0], "%Y/%m/%d"))
old_schedule = Schedules.query.filter_by(
schedulelist_id=schedulelist_id.id,
date=datetime.strptime(row[0], "%Y/%m/%d"),
).first()
print(old_schedule)
if old_schedule:
old_schedule.k_members = row[1]
old_schedule.g_members = row[2]
db.session.add(old_schedule)
db.session.commit()
else:
schedule = Schedules(
schedulelist_id=schedulelist_id.id,
date=datetime.strptime(row[0], "%Y/%m/%d"),
k_members=row[1],
g_members=row[2],
)
db.session.add(schedule)
db.session.commit()
# 後処理（最適化結果をHTMLに表示できる形式にする）
solution_html = postprocess(solution_df)
return render_template(
"scheduler/schedule_decision.html",
solution_html=solution_html,
solution_df=solution_df,
L_gomisute_members=" ".join(L_gomisute_members),
) | 6f259961d027b6e4a3dc88289a5ba62b162705f6 | 12,464 |
def infection_rate_asymptomatic_30x40():
"""
Real Name: b'infection rate asymptomatic 30x40'
Original Eqn: b'contact infectivity asymptomatic 30x40*(social distancing policy SWITCH self 40*social distancing policy 40\\\\ +(1-social distancing policy SWITCH self 40))*Infected asymptomatic 30x40*Susceptible 40\\\\ /non controlled pop 30x40'
Units: b'person/Day'
Limits: (None, None)
Type: component
b''
"""
return contact_infectivity_asymptomatic_30x40() * (
social_distancing_policy_switch_self_40() * social_distancing_policy_40() +
(1 - social_distancing_policy_switch_self_40())
) * infected_asymptomatic_30x40() * susceptible_40() / non_controlled_pop_30x40() | 16aebdca2259933dcdab1a00ed8d37b10d5b8714 | 12,465 |
def slug(hans, style=Style.NORMAL, heteronym=False, separator='-',
errors='default', strict=True):
"""将汉字转换为拼音，然后生成 slug 字符串.
:param hans: 汉字字符串( ``'你好吗'`` )或列表( ``['你好', '吗']`` ).
可以使用自己喜爱的分词模块对字符串进行分词处理,
只需将经过分词处理的字符串列表传进来就可以了。
:type hans: unicode 字符串或字符串列表
:param style: 指定拼音风格，默认是 :py:attr:`~pypinyin.Style.NORMAL` 风格。
更多拼音风格详见 :class:`~pypinyin.Style`
:param heteronym: 是否启用多音字
:param separator: 两个拼音间的分隔符/连接符
:param errors: 指定如何处理没有拼音的字符，详情请参考
:py:func:`~pypinyin.pinyin`
:param strict: 只获取声母或只获取韵母相关拼音风格的返回结果
是否严格遵照《汉语拼音方案》来处理声母和韵母，
详见 :ref:`strict`
:return: slug 字符串.
:raise AssertionError: 当传入的字符串不是 unicode 字符时会抛出这个异常
::
>>> import pypinyin
>>> from pypinyin import Style
>>> pypinyin.slug('中国人')
'zhong-guo-ren'
>>> pypinyin.slug('中国人', separator=' ')
'zhong guo ren'
>>> pypinyin.slug('中国人', style=Style.FIRST_LETTER)
'z-g-r'
>>> pypinyin.slug('中国人', style=Style.CYRILLIC)
'чжун1-го2-жэнь2'
"""
return separator.join(
chain(
*_default_pinyin.pinyin(
hans, style=style, heteronym=heteronym,
errors=errors, strict=strict
)
)
) | 124431e3ea8747dfdc024f93e88f692746797013 | 12,466 |
def A_weight(signal, fs):
"""
Return the given signal after passing through an A-weighting filter
signal : array_like
Input signal
fs : float
Sampling frequency
"""
b, a = A_weighting(fs)
return lfilter(b, a, signal) | 1c6abdd90b85762db4383972de7508d00b561065 | 12,467 |
from typing import Tuple
from typing import Union
import traceback
def send_task_to_executor(task_tuple: TaskInstanceInCelery) \
-> Tuple[TaskInstanceKey, CommandType, Union[AsyncResult, ExceptionWithTraceback]]:
"""Sends task to executor."""
key, _, command, queue, task_to_run = task_tuple
try:
with timeout(seconds=OPERATION_TIMEOUT):
result = task_to_run.apply_async(args=[command], queue=queue)
except Exception as e: # pylint: disable=broad-except
exception_traceback = "Celery Task ID: {}\n{}".format(key, traceback.format_exc())
result = ExceptionWithTraceback(e, exception_traceback)
return key, command, result | cbc93ac3a3c146b748c0ec88eaa9cb2cd631ac85 | 12,470 |
def geometries_from_bbox(north, south, east, west, tags):
"""
Create a GeoDataFrame of OSM entities within a N, S, E, W bounding box.
Parameters
----------
north : float
northern latitude of bounding box
south : float
southern latitude of bounding box
east : float
eastern longitude of bounding box
west : float
western longitude of bounding box
tags : dict
Dict of tags used for finding objects in the selected area. Results
returned are the union, not intersection of each individual tag.
Each result matches at least one given tag. The dict keys should be
OSM tags, (e.g., `building`, `landuse`, `highway`, etc) and the dict
values should be either `True` to retrieve all items with the given
tag, or a string to get a single tag-value combination, or a list of
strings to get multiple values for the given tag. For example,
`tags = {'building': True}` would return all building footprints in
the area. `tags = {'amenity':True, 'landuse':['retail','commercial'],
'highway':'bus_stop'}` would return all amenities, landuse=retail,
landuse=commercial, and highway=bus_stop.
Returns
-------
gdf : geopandas.GeoDataFrame
Notes
-----
You can configure the Overpass server timeout, memory allocation, and
other custom settings via ox.config().
"""
# convert bounding box to a polygon
polygon = utils_geo.bbox_to_poly(north, south, east, west)
# create GeoDataFrame of geometries within this polygon
gdf = geometries_from_polygon(polygon, tags)
return gdf | 32aeebe7f644df00b613ef6e0d4f30baef1a5743 | 12,473 |
def dBzdtAnalCircT(a, t, sigma):
"""
Hz component of analytic solution for half-space (Circular-loop source)
Src and Rx are on the surface and receiver is located at the center of the loop.
Src waveform here is step-off.
.. math::
\\frac{\partial h_z}{\partial t} = -\\frac{I}{\mu_0\sigma a^3} \
\left( 3erf(\\theta a) - \\frac{2}{\sqrt{\pi}}\\theta a (3+2\\theta^2 a^2) e^{-\\theta^2a^2}\\right)
.. math::
\\theta = \sqrt{\\frac{\sigma\mu}{4t}}
"""
theta = np.sqrt((sigma*mu_0)/(4*t))
const = -1/(mu_0*sigma*a**3)
ta = theta*a
eta = erf(ta)
t1 = 3*eta
t2 = -2/(np.pi**0.5)*ta*(3+2*ta**2)*np.exp(-ta**2)
dhzdt = const*(t1+t2)
return mu_0*dhzdt | 18b9428528ed11a121ad01578d2bfc35faceae21 | 12,474 |
def count_increasing(ratings, n):
"""
Only considering the increasing case
"""
arr = [1] * n
cnt = 1
for i in range(1, n):
cnt = cnt + 1 if ratings[i - 1] < ratings[i] else 1
arr[i] = cnt
return arr | 9fe274527fbba505467a195bf555c77d2f3e6aed | 12,475 |
import copy
def load_train_data_frame(train_small, target, keras_options, model_options, verbose=0):
"""
### CAUTION: TF2.4 Still cannot load a DataFrame with Nulls in string or categoricals!
############################################################################
#### TF 2.4 still cannot load tensor_slices into ds if an object or string column
#### that has nulls in it! So we need to find other ways to load tensor_slices by
#### first filling dataframe with pandas fillna() function!
#############################################################################
"""
train_small = copy.deepcopy(train_small)
DS_LEN = model_options['DS_LEN']
#### do this for dataframes ##################
try:
batch_size = keras_options["batchsize"]
if isinstance(keras_options["batchsize"], str):
batch_size = find_batch_size(DS_LEN)
except:
#### If it is not given find it here ####
batch_size = find_batch_size(DS_LEN)
######### Modify or Convert column names to fit tensorflow rules of no space in names!
sel_preds = ["_".join(x.split(" ")) for x in list(train_small) ]
#### This can also be a problem with other special characters ###
sel_preds = ["_".join(x.split("(")) for x in sel_preds ]
sel_preds = ["_".join(x.split(")")) for x in sel_preds ]
sel_preds = ["_".join(x.split("/")) for x in sel_preds ]
sel_preds = ["_".join(x.split("\\")) for x in sel_preds ]
sel_preds = ["_".join(x.split("?")) for x in sel_preds ]
sel_preds = [x.lower() for x in sel_preds ]
if isinstance(target, str):
target = "_".join(target.split(" "))
target = "_".join(target.split("("))
target = "_".join(target.split(")"))
target = "_".join(target.split("/"))
target = "_".join(target.split("\\"))
target = "_".join(target.split("?"))
target = target.lower()
model_label = 'Single_Label'
else:
target = ["_".join(x.split(" ")) for x in target ]
target = ["_".join(x.split("(")) for x in target ]
target = ["_".join(x.split(")")) for x in target ]
target = ["_".join(x.split("/")) for x in target ]
target = ["_".join(x.split("\\")) for x in target ]
target = ["_".join(x.split("?")) for x in target ]
target = [x.lower() for x in target ]
model_label = 'Multi_Label'
train_small.columns = sel_preds
print('Alert! Modified column names to satisfy rules for column names in Tensorflow...')
#### if target is changed you must send that modified target back to other processes ######
### usecols is basically target in a list format. Very handy to know when target is a list.
try:
modeltype = model_options["modeltype"]
if model_options["modeltype"] == '':
### usecols is basically target in a list format. Very handy to know when target is a list.
modeltype, model_label, usecols = find_problem_type(train_small, target, model_options, verbose)
else:
if isinstance(target, str):
usecols = [target]
else:
usecols = copy.deepcopy(target)
except:
### if modeltype is given, then do not find the model type using this function
modeltype, model_label, usecols = find_problem_type(train_small, target, model_options, verbose)
### Cat_Vocab_Dict contains all info about vocabulary in each variable and their size
print(' Classifying variables using data sample in pandas...')
train_small, var_df, cat_vocab_dict = classify_features_using_pandas(train_small, target, model_options, verbose=verbose)
########## Just transfer all the values from var_df to cat_vocab_dict ##################################
for each_key in var_df:
cat_vocab_dict[each_key] = var_df[each_key]
############################################################################################################
model_options['modeltype'] = modeltype
model_options['model_label'] = model_label
cat_vocab_dict['target_variables'] = usecols
cat_vocab_dict['modeltype'] = modeltype
model_options['batch_size'] = batch_size
########## Find small details about the data to help create the right model ###
target_transformed = False
if modeltype != 'Regression':
if isinstance(target, str):
#### This is for Single Label Problems ######
if train_small[target].dtype == 'object' or str(train_small[target].dtype).lower() == 'category':
target_transformed = True
target_vocab = train_small[target].unique()
num_classes = len(target_vocab)
else:
if 0 not in np.unique(train_small[target]):
target_transformed = True ### label encoding must be done since no zero class!
target_vocab = train_small[target].unique()
num_classes = len(train_small[target].value_counts())
elif isinstance(target, list):
#### This is for Multi-Label Problems #######
copy_target = copy.deepcopy(target)
num_classes = []
for each_target in copy_target:
if train_small[target[0]].dtype == 'object' or str(train_small[target[0]].dtype).lower() == 'category':
target_transformed = True
target_vocab = train_small[target].unique().tolist()
num_classes_each = len(target_vocab)
else:
if 0 not in np.unique(train_small[target[0]]):
target_transformed = True ### label encoding must be done since no zero class!
target_vocab = train_small[target[0]].unique()
num_classes_each = train_small[target].apply(np.unique).apply(len).max()
num_classes.append(int(num_classes_each))
else:
num_classes = 1
target_vocab = []
########### find the number of labels in data ####
if isinstance(target, str):
num_labels = 1
elif isinstance(target, list):
if len(target) == 1:
num_labels = 1
else:
num_labels = len(target)
#### This is where we set the model_options for num_classes and num_labels #########
model_options['num_labels'] = num_labels
model_options['num_classes'] = num_classes
cat_vocab_dict['num_labels'] = num_labels
cat_vocab_dict['num_classes'] = num_classes
cat_vocab_dict["target_transformed"] = target_transformed
#### fill missing values using this function ##############
train_small = fill_missing_values_for_TF2(train_small, cat_vocab_dict)
##### Do the deletion of cols after filling with missing values since otherwise fill errors!
drop_cols = var_df['cols_delete']
cat_vocab_dict['columns_deleted'] = drop_cols
if len(drop_cols) > 0: ### drop cols that have been identified for deletion ###
print(' Dropping %s columns marked for deletion...' %drop_cols)
train_small.drop(drop_cols,axis=1,inplace=True)
######### Now load the train Dataframe into a tf.data.dataset #############
if target_transformed:
####################### T R A N S F O R M I N G T A R G E T ########################
train_small[target], cat_vocab_dict = transform_train_target(train_small, target, modeltype,
model_label, cat_vocab_dict)
if isinstance(target, str):
#### For single label do this: labels can be without names since there is only one label
if target != '':
labels = train_small[target]
features = train_small.drop(target, axis=1)
ds = tf.data.Dataset.from_tensor_slices((dict(features), labels))
else:
print('target variable is blank - please fix input and try again')
return
elif isinstance(target, list):
#### For multi label do this: labels must be dict and hence with names since there are many targets
labels = train_small[target]
features = train_small.drop(target, axis=1)
ds = tf.data.Dataset.from_tensor_slices((dict(features), dict(labels)))
else:
ds = tf.data.Dataset.from_tensor_slices(dict(train_small))
###### Now save some defaults in cat_vocab_dict ##########################
try:
keras_options["batchsize"] = batch_size
cat_vocab_dict['batch_size'] = batch_size
except:
batch_size = find_batch_size(DS_LEN)
keras_options["batchsize"] = batch_size
cat_vocab_dict['batch_size'] = batch_size
##########################################################################
#### C H E C K F O R I N F I N I T E V A L U E S H E R E ##########
##########################################################################
cols_with_infinity = find_columns_with_infinity(train_small)
if cols_with_infinity:
train_small = drop_rows_with_infinity(train_small, cols_with_infinity, fill_value=True)
return train_small, ds, var_df, cat_vocab_dict, keras_options, model_options | 85c496b485bbc26afbadf181a2231e3f5bd93706 | 12,476 |
def stat_float_times(space, newval=-1):
"""stat_float_times([newval]) -> oldval
Determine whether os.[lf]stat represents time stamps as float objects.
If newval is True, future calls to stat() return floats, if it is False,
future calls return ints.
If newval is omitted, return the current setting.
"""
state = space.fromcache(StatState)
if newval == -1:
return space.newbool(state.stat_float_times)
else:
state.stat_float_times = (newval != 0) | e183f0cc2ce56bc7b4ac6ce95d8cb671a963422f | 12,477 |
def decorate(rvecs):
"""Output range vectors into some desired string format"""
return ', '.join(['{%s}' % ','.join([str(x) for x in rvec]) for rvec in rvecs]) | 31a3d4414b0b88ffd92a5ddd8eb09aaf90ef3742 | 12,478 |
def update_topic_collection_items(request_ctx, collection_item_id, topic_id, **request_kwargs):
"""
Accepts the same parameters as create
:param request_ctx: The request context
:type request_ctx: :class:RequestContext
:param collection_item_id: (required) ID
:type collection_item_id: string
:param topic_id: (required) ID
:type topic_id: string
:return: Update a topic
:rtype: requests.Response (with void data)
"""
path = '/v1/collection_items/{collection_item_id}/discussion_topics/{topic_id}'
url = request_ctx.base_api_url + path.format(collection_item_id=collection_item_id, topic_id=topic_id)
response = client.put(request_ctx, url, **request_kwargs)
return response | 06b0709f5fa4acf189baef8f2665bee81b3c4993 | 12,479 |
def upsample(inputs, factor=(2, 2), interpolation='nearest'):
"""
Upsampling layer by factor
Parameters
----------
inputs: Input tensor
factor: The upsampling factors for (height, width). One integer or tuple of
two integers
interpolation: A string, one of [`nearest`, `bilinear`, 'bicubic', 'area'].
"""
# get new_size
_, height, width, _ = inputs.get_shape().as_list()
factor = _make_pair(factor)
new_height = height * factor[0]
new_width = width * factor[1]
new_size = (new_height, new_width)
# get interpolation type
interp_types = {
'nearest': tf.image.ResizeMethod.NEAREST_NEIGHBOR,
'bilinear': tf.image.ResizeMethod.BILINEAR,
'bicubic': tf.image.ResizeMethod.BICUBIC,
'area': tf.image.ResizeMethod.AREA,
}
if interpolation not in interp_types.keys():
raise ValueError("interpolation must be one of "
"['nearest', 'bilinear', 'bicubic', 'area']")
interp_type = interp_types.get(interpolation)
return tf.image.resize_images(inputs, size=new_size, method=interp_type) | dfbd42871e63cb685f9cfbf9185da38839a9ee4e | 12,480 |
def root_mean_squared_error(*args, **kwargs):
"""
Returns the square-root of ``scikit-learn``'s ``mean_squared_error`` metric.
All arguments are forwarded to that function.
"""
return np.sqrt(mean_squared_error(*args, **kwargs)) | 51084b2ec55d14657fa128f0df2bd3f438c2367b | 12,481 |
def idwt2(Wimg, level=4):
""" inverse 2d wavelet transform
:param Wimg: 2d array
wavelet coefficients
:param level: int
level of wavelet transform - image shape has to be multiples of 2**level
:return: 2d array
image
"""
coeffs = _from_img_to_coeffs(Wimg, levels=level)
return pywt.waverec2(coeffs, wavelet='db4', mode='per') | 521ceca879b0961730b1efd6dac54772a2b41ca3 | 12,482 |
def get_color(card):
"""Returns the card's color
Args:
card (webelement): a visible card
Returns:
str: card's color
"""
color = card.find_element_by_xpath(".//div/*[name()='svg']/*[name()='use'][2]").get_attribute("stroke")
# both light and dark theme
if (color == "#ff0101" or color == "#ffb047"):
color = "red"
elif (color == "#800080" or color == "#ff47ff"):
color = "purple"
else:
color = "green"
return color | 452266b81d70973149fed4ab2e6cbc9c93591180 | 12,483 |
from typing import Dict
from typing import Any
def is_valid_path(parameters: Dict[str, Any]) -> bool:
"""Single "." chars and empty strings "" are excluded from path by urllib3.
A path containing to "/" or "%2F" will lead to ambiguous path resolution in
many frameworks and libraries, such behaviour have been observed in both
WSGI and ASGI applications.
In this case one variable in the path template will be empty, which will lead to 404 in most of the cases.
Because of it this case doesn't bring much value and might lead to false positives results of Schemathesis runs.
"""
path_parameter_blacklist = (".", SLASH, "")
return not any(
(value in path_parameter_blacklist or is_illegal_surrogate(value) or isinstance(value, str) and SLASH in value)
for value in parameters.values()
) | 5f80ff76c535b3913efc7ba83e04c4c049a9e50b | 12,484 |
import torch
def to_tensor(x):
"""
Arguments:
x: an instance of PIL image.
Returns:
a float tensor with shape [3, h, w],
it represents a RGB image with
pixel values in [0, 1] range.
"""
x = np.array(x)
x = torch.FloatTensor(x)
return x.permute(2, 0, 1).unsqueeze(0).div(255.0) | 6ff19bd7549a4fce455f03559420216020658c44 | 12,485 |
def fetch_data(fold_path):
"""Fetch data saving in fold path.
Convert data into suitable format, using csv files in fold path.
:param fold_path: String. The fold in which data files are saved.
:return:
training_data: Dataframe. Combined dataframe to create training data.
testing_data: Dataframe. Combined dataframe to create testing data.
"""
# Read all the data from target fold path.
pokemon = pd.read_csv(fold_path+'/pokemon.csv')
combats = pd.read_csv(fold_path+'/combats.csv')
test_data = pd.read_csv(fold_path+'/tests.csv')
# Convert data into suitable format for training and testing.
training_data = convert_data(combats, pokemon, win_column='Winner')
testing_data = convert_data(test_data, pokemon)
return training_data, testing_data | 42ea9ea6d1d9d597acc4ed1a14099711642608f4 | 12,488 |
def add_chr_prefix(band):
"""
Return the band string with chr prefixed
"""
return ''.join(['chr', band]) | 08a99220023f10d79bdacdb062a27efcb51086ce | 12,489 |
def disable_text_recog_aug_test(cfg, set_types=None):
"""Remove aug_test from test pipeline of text recognition.
Args:
cfg (mmcv.Config): Input config.
set_types (list[str]): Type of dataset source. Should be
None or sublist of ['test', 'val']
Returns:
cfg (mmcv.Config): Output config removing
`MultiRotateAugOCR` in test pipeline.
"""
assert set_types is None or isinstance(set_types, list)
if set_types is None:
set_types = ['val', 'test']
for set_type in set_types:
if cfg.data[set_type].pipeline[1].type == 'MultiRotateAugOCR':
cfg.data[set_type].pipeline = [
cfg.data[set_type].pipeline[0],
*cfg.data[set_type].pipeline[1].transforms
]
return cfg | bda3a5420d32d55062b23a6af27cee3e203b878c | 12,490 |
def layer_svg(svg_bottom, svg_top, offset: list = [0.0, 0.0]):
"""
Adds one SVG over another. Modifies the bottom SVG in place.
:param svg_bottom: The bottom SVG, in in xml.etree.ElementTree form
:param svg_top: The top SVG, in in xml.etree.ElementTree form
:param offset: How far to offset the top SVG elements
"""
if svg_top is None:
return
# print(svg_top.tag)
for child in list(svg_top):
apply_offset(child, offset, offset_children=True)
svg_bottom.append(child)
return svg_bottom | 6c6a8151d17f4aff9f1491d1ed71772d9434ae4c | 12,491 |
def utxo_cmd(ctx, dry_run):
"""Get the node's current UTxO with the option of filtering by address(es)"""
try:
CardanoCli.execute(cmd=["cardano-cli", "query", "utxo"], dry_run=dry_run, include_network=True)
except CardanoPyError as cpe:
ctx.fail(cpe.message)
return cpe.return_code | 52807294a445fc2f641c1b921807bba898ad8c34 | 12,493 |
def delta_in_ms(delta):
"""
Convert a timedelta object to milliseconds.
"""
return delta.seconds*1000.0+delta.microseconds/1000.0 | 4ed048155daf4a4891488e28c674e905e1bbe947 | 12,494 |
import slicer, collections, fnmatch
def getNodes(pattern="*", scene=None, useLists=False):
"""Return a dictionary of nodes where the name or id matches the ``pattern``.
By default, ``pattern`` is a wildcard and it returns all nodes associated
with ``slicer.mrmlScene``.
If multiple node share the same name, using ``useLists=False`` (default behavior)
returns only the last node with that name. If ``useLists=True``, it returns
a dictionary of lists of nodes.
"""
nodes = collections.OrderedDict()
if scene is None:
scene = slicer.mrmlScene
count = scene.GetNumberOfNodes()
for idx in range(count):
node = scene.GetNthNode(idx)
name = node.GetName()
id = node.GetID()
if (fnmatch.fnmatchcase(name, pattern) or
fnmatch.fnmatchcase(id, pattern)):
if useLists:
nodes.setdefault(node.GetName(), []).append(node)
else:
nodes[node.GetName()] = node
return nodes | 6d6c44987a800f361d45f4538167acb65e738418 | 12,495 |
from typing import Union
from typing import Type
from re import X
from typing import Mapping
from typing import Optional
def get_cls(
query: Union[None, str, Type[X]],
base: Type[X],
lookup_dict: Mapping[str, Type[X]],
lookup_dict_synonyms: Optional[Mapping[str, Type[X]]] = None,
default: Optional[Type[X]] = None,
suffix: Optional[str] = None,
) -> Type[X]:
"""Get a class by string, default, or implementation."""
if query is None:
if default is None:
raise ValueError(f'No default {base.__name__} set')
return default
elif not isinstance(query, (str, type)):
raise TypeError(f'Invalid {base.__name__} type: {type(query)} - {query}')
elif isinstance(query, str):
key = normalize_string(query, suffix=suffix)
if key in lookup_dict:
return lookup_dict[key]
if lookup_dict_synonyms is not None and key in lookup_dict_synonyms:
return lookup_dict_synonyms[key]
raise ValueError(f'Invalid {base.__name__} name: {query}')
elif issubclass(query, base):
return query
raise TypeError(f'Not subclass of {base.__name__}: {query}') | e5f805df5ef19de9939344beee21834e3f2556ab | 12,496 |
def selection_sort(data):
"""Sort a list of unique numbers in ascending order using selection sort. O(n^2).
The process includes repeatedly iterating through a list, finding the smallest element, and sorting that element.
Args:
data: data to sort (list of int)
Returns:
sorted list
"""
sorted_data = data[:]
for i, value in enumerate(sorted_data):
# find smallest value in unsorted subset
min_value = min(sorted_data[i:])
index_min = sorted_data.index(min_value)
# place smallest value at start of unsorted subset
sorted_data[i], sorted_data[index_min] = min_value, value
return sorted_data | 8b745be41c857669aedecb25b3006bbdc1ef04eb | 12,497 |
def _conv(args, filter_size, num_features, bias, reuse, w_init=None, b_init=0.0, scope='_conv'):
"""convolution:
Args:
args: a Tensor or a list of Tensors of dimension 3D, 4D or 5D
batch x n, Tensors.
filter_size: int tuple of filter height and width.
reuse: None/True, whether to reuse variables
w_init: weights initializer object
b_init: a `int`, bias initializer value
num_features: int, number of features.
bias_start: starting value to initialize the bias; 0 by default.
Returns:
A 3D, 4D, or 5D Tensor with shape [batch ... num_features]
Raises:
ValueError: if some of the arguments has unspecified or wrong shape.
"""
# Calculate the total size of arguments on dimension 1.
total_arg_size_depth = 0
shapes = [a.get_shape().as_list() for a in args]
shape_length = len(shapes[0])
for shape in shapes:
if len(shape) not in [3, 4, 5]:
raise ValueError("Conv Linear expects 3D, 4D or 5D arguments: %s" % str(shapes))
if len(shape) != len(shapes[0]):
raise ValueError("Conv Linear expects all args to be of same Dimensiton: %s" % str(shapes))
else:
total_arg_size_depth += shape[-1]
dtype = [a.dtype for a in args][0]
# determine correct conv operation
if shape_length == 3:
conv_op = tf.nn.conv1d
strides = 1
elif shape_length == 4:
conv_op = tf.nn.conv2d
strides = shape_length * [1]
elif shape_length == 5:
conv_op = tf.nn.conv3d
strides = shape_length * [1]
# Now the computation.
with tf.variable_scope(scope, reuse=reuse):
kernel = tf.get_variable(
"W", filter_size + [total_arg_size_depth, num_features], dtype=dtype, initializer=w_init)
if len(args) == 1:
res = conv_op(args[0], kernel, strides, padding='SAME')
else:
res = conv_op(tf.concat(axis=shape_length - 1, values=args), kernel, strides, padding='SAME')
if not bias:
return res
bias_term = tf.get_variable(
"biases", [num_features],
dtype=dtype,
initializer=tf.constant_initializer(b_init, dtype=dtype))
return res + bias_term | 104d91623949e4506c4b72001c23b6ab7fb312ca | 12,498 |
def _feature_normalization(features, method, feature_type):
"""Normalize the given feature vector `y`, with the stated normalization `method`.
Args:
features (np.ndarray): The signal array
method (str): Normalization method.
'global': Uses global mean and standard deviation values from `train.txt`.
The normalization is being applied element wise.
([sample] - [mean]^T) / [std]^T
Where brackets denote matrices or vectors.
'local': Use local (in sample) mean and standard deviation values, and apply the
normalization element wise, like in `global`.
'local_scalar': Uses only the mean and standard deviation of the current sample.
The normalization is being applied by ([sample] - mean_scalar) / std_scalar
'none': No normalization is being applied.
feature_type (str): Feature type, see `load_sample` for details.
Returns:
np.ndarray: The normalized feature vector.
"""
if method == 'none':
return features
elif method == 'global':
# Option 'global' is applied element wise.
if feature_type == 'mel':
global_mean = __global_mean_mel
global_std = __global_std_mel
elif feature_type == 'mfcc':
global_mean = __global_mean_mfcc
global_std = __global_std_mfcc
else:
raise ValueError('Unsupported global feature type: {}'.format(feature_type))
return (features - global_mean) / global_std
elif method == 'local':
return (features - np.mean(features, axis=0)) / np.std(features, axis=0)
elif method == 'local_scalar':
# Option 'local' uses scalar values.
return (features - np.mean(features)) / np.std(features)
else:
raise ValueError('Invalid normalization method: {}'.format(method)) | 0479363651a4bcf1622e7bdb0906b55e3adb1cce | 12,500 |
def get_constraint(name):
"""
Lookup table of default weight constraint functions.
Parameters
----------
name : Constraint, None, str
Constraint to look up. Must be one of:
- 'l1' : L1 weight-decay.
- 'l2' : L2 weight-decay.
- 'l1-l2' : Combined L1-L2 weight-decay.
- Constraint : A custom implementation.
- None : Return None.
Custom Constraint must implement `constrain`
function.
Returns
-------
constraint : Constraint or None
The constraint function.
"""
if name == 'unit' : return UnitNorm
elif name == 'maxnorm' : return MaxNorm
elif name == 'minmax' : return MinMaxNorm
elif isinstance(name, (None, Constraint)) : return name
else : raise ValueError("Invalid regularizer") | 09927531f4c6770e86ad603063e4edb0b0c4ff48 | 12,501 |
def player_count(conn, team_id):
"""Returns the number of players associated with a particular team"""
c = conn.cursor()
c.execute("SELECT id FROM players WHERE team_id=?", (team_id,))
return len(c.fetchall()) | cfced6da6c8927db2ccf331dca7d23bba0ce67e5 | 12,502 |
def _RedisClient(address):
"""
Return a connection object connected to the socket given by `address`
"""
h1, h2 = get_handle_pair(conn_type=REDIS_LIST_CONN)
c = _RedisConnection(h1)
#redis_client = util.get_redis_client()
redis_client = util.get_cache_client()
ip, port = address
chan = '{}:{}'.format(ip, port)
redis_client.publish(chan, bytes(h2, 'utf-8'))
ack = c.recv()
assert ack == 'OK'
return c | fc8bab786bb521fbd0715da3ab690575d1df865e | 12,503 |
import math
def format_timedelta(value,
time_format="{days} days, {hours2}:{minutes2}:{seconds2}"):
"""Format a datetie.timedelta. See """
if hasattr(value, 'seconds'):
seconds = value.seconds + value.days * 24 * 3600
else:
seconds = int(value)
seconds_total = seconds
minutes = int(math.floor(seconds / 60))
minutes_total = minutes
seconds -= minutes * 60
hours = int(math.floor(minutes / 60))
hours_total = hours
minutes -= hours * 60
days = int(math.floor(hours / 24))
days_total = days
hours -= days * 24
years = int(math.floor(days / 365))
years_total = years
days -= years * 365
return time_format.format(
**{
'seconds': seconds,
'seconds2': str(seconds).zfill(2),
'minutes': minutes,
'minutes2': str(minutes).zfill(2),
'hours': hours,
'hours2': str(hours).zfill(2),
'days': days,
'years': years,
'seconds_total': seconds_total,
'minutes_total': minutes_total,
'hours_total': hours_total,
'days_total': days_total,
'years_total': years_total,
}) | 19dc2b175beb1d030f14ae7fe96cb16d66f6c219 | 12,504 |
def random_account_user(account):
"""Get a random user for an account."""
account_user = AccountUser.objects.filter(account=account).order_by("?").first()
return account_user.user if account_user else None | 5fe918af67710d0d1519f56eee15811430a0e139 | 12,505 |
def overwrite(main_config_obj, args):
"""
Overwrites parameters with input flags
Args:
main_config_obj (ConfigClass): config instance
args (dict): arguments used to overwrite
Returns:
ConfigClass: config instance
"""
# Sort on nested level to override shallow items first
args = dict(sorted(args.items(), key=lambda item: item[0].count('.')))
for argument_key, val in args.items():
# Seperate nested keys into outer and inner
outer_keys = argument_key.split('.')
inner_key = outer_keys.pop(-1)
base_err_msg = f"Can't set '{argument_key} = {val}'"
# Check that the nested config has the attribute and is a config class
config_obj = main_config_obj
config_class = type(config_obj).__name__
for key_idx, key_part in enumerate(argument_key.split('.')):
err_msg = f"{base_err_msg}. '{key_part}' isn't an attribute in '{config_class}'"
assert hasattr(config_obj, key_part), err_msg
# Check if the config allows the argument
figutils.check_allowed_input_argument(config_obj, key_part, argument_key)
# Check if the outer attributes are config classes
if key_idx < len(outer_keys):
config_obj = getattr(config_obj, key_part)
config_class = type(config_obj).__name__
err_msg = f"{base_err_msg}. '{'.'.join(outer_keys)}' isn't a registered Anyfig config class"
assert figutils.is_config_class(config_obj), err_msg
value_class = type(getattr(config_obj, inner_key))
base_err_msg = f"Input argument '{argument_key}' with value {val} can't create an object of the expected type"
# Create new anyfig class object
if figutils.is_config_class(value_class):
value_obj = create_config(val)
# Create new object that follows the InterfaceField's rules
elif issubclass(value_class, fields.InterfaceField):
field = getattr(config_obj, inner_key)
if isinstance(value_class, fields.InputField):
value_class = field.type_pattern
else:
value_class = type(field.value)
try:
val = value_class(val)
except Exception as e:
err_msg = f"{base_err_msg} {field.type_pattern}. {e}"
raise RuntimeError(err_msg) from None
field = field.update_value(inner_key, val, config_class)
value_obj = field.finish_wrapping_phase(inner_key, config_class)
# Create new object of previous value type with new value
else:
try:
if isinstance(val, dict): # Keyword specified cli-arguments
value_obj = value_class(**val)
else:
value_obj = value_class(val)
except Exception as e:
err_msg = f"{base_err_msg} {value_class}. {e}"
raise RuntimeError(err_msg) from None
# Overwrite old value
setattr(config_obj, inner_key, value_obj)
return main_config_obj | 98ee9cf034a9b714ae18e737761b06bfd669bfa4 | 12,506 |
def max_delta(model, new_model):
"""Return the largest difference between any two corresponding
values in the models"""
return max( [(abs(model[i] - new_model[i])).max() for i in range(len(model))] ) | faf4a9fb2b24f7e7b4f357eef195e435950ea218 | 12,507 |
def wiener_khinchin_transform(power_spectrum, frequency, time):
"""
A function to transform the power spectrum to a correlation function by the Wiener Khinchin transformation
** Input:**
* **power_spectrum** (`list or numpy.array`):
The power spectrum of the signal.
* **frequency** (`list or numpy.array`):
The frequency discretizations of the power spectrum.
* **time** (`list or numpy.array`):
The time discretizations of the signal.
**Output/Returns:**
* **correlation_function** (`list or numpy.array`):
The correlation function of the signal.
"""
frequency_interval = frequency[1] - frequency[0]
fac = np.ones(len(frequency))
fac[1: len(frequency) - 1: 2] = 4
fac[2: len(frequency) - 2: 2] = 2
fac = fac * frequency_interval / 3
correlation_function = np.zeros(len(time))
for i in range(len(time)):
correlation_function[i] = 2 * np.dot(fac, power_spectrum * np.cos(frequency * time[i]))
return correlation_function | 3cf8916c75632e3a0db52f907ce180eb766f9f2e | 12,508 |
def child_is_flat(children, level=1):
"""
Check if all children in section is in same level.
children - list of section children.
level - integer, current level of depth.
Returns True if all children in the same level, False otherwise.
"""
return all(
len(child) <= level + 1 or child[(level + 1) :][0].isalpha()
for child in children
) | e14f9210a90b40b419d21fffa1542212429d80be | 12,509 |
from pathlib import Path
def load_dataset(name, other_paths=[]):
"""Load a dataset with given (file) name."""
if isinstance(name, Dataset):
return name
path = Path(name)
# First, try if you have passed a fully formed dataset path
if path.is_file():
return _from_npy(name, classes=classes)
# Go through the dataset paths, return the first dataset found
all_paths = dataset_path + other_paths
for p in all_paths:
try:
file = p / path
return _from_npy(file, classes=classes)
except FileNotFoundError:
pass
raise FileNotFoundError(
"Could not find dataset {} in paths {}".format(name, all_paths)
) | 3f3d2e7e7ec577098e1a1599c74638ced5d3c103 | 12,510 |
def isqrtcovresnet101b(**kwargs):
"""
iSQRT-COV-ResNet-101 model with stride at the second convolution in bottleneck block from 'Towards Faster Training
of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization,'
https://arxiv.org/abs/1712.01034.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
"""
return get_isqrtcovresnet(blocks=101, conv1_stride=False, model_name="isqrtcovresnet101b", **kwargs) | fdf166fa3ce9e893e8e97d1057dac89d084d2217 | 12,511 |
def get_data(name: str, level: int, max_level: int) -> str:
"""從維基頁面爬取資料
參數:
name: 程式或節點名稱
level: 欲查詢的等級
回傳:
爬到的資料
"""
reply_msg = []
for dataframe in read_html(generate_url(name)):
if (max_level < dataframe.shape[0] < max_level + 3 and
dataframe.iloc[level, 0].isdigit() and
level == int(dataframe.iloc[level, 0])):
reply_msg.append(zip(*dataframe.iloc[[0, level], 1:].values))
return '\n'.join('：'.join(pair) for data in reply_msg for pair in data) | 4e0f11a33c81993132d45f3fdad5f42c1288bbe5 | 12,512 |
def insert_data(context, data_dict):
"""
:raises InvalidDataError: if there is an invalid value in the given data
"""
data_dict['method'] = _INSERT
result = upsert_data(context, data_dict)
return result | c631016be36f1988bfa9c98cea42a7f63fddc276 | 12,514 |
import time
def timestamp():
"""Get the unix timestamp now and retuen it.
Attention: It's a floating point number."""
timestamp = time.time()
return timestamp | 8e56a61659da657da9d5dda364d4d9e8f3d58ed2 | 12,515 |
from datetime import datetime
def _n64_to_datetime(n64):
"""Convert Numpy 64 bit timestamps to datetime objects. Units in seconds"""
return datetime.utcfromtimestamp(n64.tolist() / 1e9) | a25327f2cd0093635f86f3145f5674cc1945d3f8 | 12,516 |
import itertools
def cycle(iterable):
"""Make an iterator returning elements from the iterable and saving a copy of each.
When the iterable is exhausted, return elements from the saved copy. Repeats indefinitely.
This function uses single dispatch.
.. seealso:: :func:`itertools.cycle`
"""
return itertools.cycle(iterable) | 13f479fca709dffa77eeca3d32ff7265c81588bf | 12,517 |
def get_availability_zone(name=None,state=None,zone_id=None,opts=None):
"""
`.getAvailabilityZone` provides details about a specific availability zone (AZ)
in the current region.
This can be used both to validate an availability zone given in a variable
and to split the AZ name into its component parts of an AWS region and an
AZ identifier letter. The latter may be useful e.g. for implementing a
consistent subnet numbering scheme across several regions by mapping both
the region and the subnet letter to network numbers.
This is different from the `.getAvailabilityZones` (plural) data source,
which provides a list of the available zones.
:param str name: The full name of the availability zone to select.
:param str state: A specific availability zone state to require. May
be any of `"available"`, `"information"` or `"impaired"`.
:param str zone_id: The zone ID of the availability zone to select.
> This content is derived from https://github.com/terraform-providers/terraform-provider-aws/blob/master/website/docs/d/availability_zone.html.markdown.
"""
__args__ = dict()
__args__['name'] = name
__args__['state'] = state
__args__['zoneId'] = zone_id
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = utilities.get_version()
__ret__ = pulumi.runtime.invoke('aws:index/getAvailabilityZone:getAvailabilityZone', __args__, opts=opts).value
return AwaitableGetAvailabilityZoneResult(
name=__ret__.get('name'),
name_suffix=__ret__.get('nameSuffix'),
region=__ret__.get('region'),
state=__ret__.get('state'),
zone_id=__ret__.get('zoneId'),
id=__ret__.get('id')) | 6cb20524c1e0a2539e221711f1153949ab72f8e1 | 12,518 |
def _add_u_eq(blk, uex=0.8):
"""Add heat transfer coefficent adjustment for feed water flow rate.
This is based on knowing the heat transfer coefficent at a particular flow
and assuming the heat transfer coefficent is porportial to feed water
flow rate raised to certain power (typically 0.8)
Args:
blk: Heat exchanger block to add correlation to
uex: Correlation parameter value (defalut 0.8)
Returns:
None
"""
ti = blk.flowsheet().time
blk.U0 = pyo.Var(ti)
blk.f0 = pyo.Var(ti)
blk.uex = pyo.Var(ti, initialize=uex)
for t in ti:
blk.U0[t].value = blk.overall_heat_transfer_coefficient[t].value
blk.f0[t].value = blk.tube.properties_in[t].flow_mol.value
blk.overall_heat_transfer_coefficient.unfix()
blk.U0.fix()
blk.uex.fix()
blk.f0.fix()
@blk.Constraint(ti)
def U_eq(b, t):
return (
b.overall_heat_transfer_coefficient[t] ==
b.U0[t]*(b.tube.properties_in[t].flow_mol/b.f0[t])**b.uex[t]
) | f6b34a8e75367b43dbe759d273aa4be7dc371c12 | 12,519 |
def find_process_in_list( proclist, pid ):
"""
Searches for the given 'pid' in 'proclist' (which should be the output
from get_process_list(). If not found, None is returned. Otherwise a
list
[ user, pid, ppid ]
"""
for L in proclist:
if pid == L[1]:
return L
return None | 19eab54b4d04b40a54a39a44e50ae28fbff9457c | 12,520 |
def solution(s, start_pos, end_pos):
"""
Find the minimal nucleotide from a range of sequence DNA.
:param s: String consisting of the letters A, C, G and T,
which correspond to the types of successive nucleotides in the sequence
:param start_pos: array with the start indexes for the intervals to check
:param end_pos: array with the end indexes for the intervals to check
:return: a list with the minimal nucleotide for each interval defined by start_pos and end_pos
"""
highest_class = 'T'
highest_class_value = 4
# The array below must be in ascending order regarding the value assigned to the classes in the challenge description
# (not necessarily in alphabetic order)
other_classes = ['A', 'C', 'G']
other_classes_values = [1, 2, 3]
# We create a prefix_sum list for each class, so we can identify when a range has that specific class
prefix_sums = __class_based_prefix_sums(s, other_classes)
result = []
for i in range(len(start_pos)):
# We don't need to create a prefix_sum list for the class with highest value,
# because we can always use it as a fallback
current_result = highest_class_value
for j in range(len(other_classes)):
if __class_is_present(prefix_sums, j, start_pos[i], end_pos[i]):
current_result = other_classes_values[j]
break
result.append(current_result)
return result | 25ef2f7e9b009de0534f8dde132c0eb44e3fe374 | 12,521 |
def validate_address(value: str, context: dict = {}) -> str:
"""
Default address validator function. Can be overriden by providing a
dotted path to a function in ``SALESMAN_ADDRESS_VALIDATOR`` setting.
Args:
value (str): Address text to be validated
context (dict, optional): Validator context data.
Raises:
ValidationError: In case address is not valid
Returns:
str: Validated value
"""
if not value:
raise ValidationError(_("Address is required."))
return value | 65e04a4780432608aa049687da98bd05a527fbad | 12,522 |
from pathlib import Path
def _get_hg_repo(path_dir):
"""Parse `hg paths` command to find remote path."""
if path_dir == "":
return ""
hgrc = Path(path_dir) / ".hg" / "hgrc"
if hgrc.exists():
config = ConfigParser()
config.read(str(hgrc))
if "paths" in config:
return config["paths"].get("default", "hgrc: no default path?")
else:
return "hgrc: no [paths] section?"
else:
return "not a hg repo" | 773ab4b45ba6883446c8e4a7725b7ac9d707440f | 12,525 |
def array_to_string(array,
col_delim=' ',
row_delim='\n',
digits=8,
value_format='{}'):
"""
Convert a 1 or 2D array into a string with a specified number
of digits and delimiter. The reason this exists is that the
basic numpy array to string conversions are surprisingly bad.
Parameters
------------
array : (n,) or (n, d) float or int
Data to be converted
If shape is (n,) only column delimiter will be used
col_delim : str
What string should separate values in a column
row_delim : str
What string should separate values in a row
digits : int
How many digits should floating point numbers include
value_format : str
Format string for each value or sequence of values
If multiple values per value_format it must divide
into array evenly.
Returns
----------
formatted : str
String representation of original array
"""
# convert inputs to correct types
array = np.asanyarray(array)
digits = int(digits)
row_delim = str(row_delim)
col_delim = str(col_delim)
value_format = str(value_format)
# abort for non-flat arrays
if len(array.shape) > 2:
raise ValueError('conversion only works on 1D/2D arrays not %s!',
str(array.shape))
# allow a value to be repeated in a value format
repeats = value_format.count('{}')
if array.dtype.kind == 'i':
# integer types don't need a specified precision
format_str = value_format + col_delim
elif array.dtype.kind == 'f':
# add the digits formatting to floats
format_str = value_format.replace(
'{}', '{:.' + str(digits) + 'f}') + col_delim
else:
raise ValueError('dtype %s not convertible!',
array.dtype.name)
# length of extra delimiters at the end
end_junk = len(col_delim)
# if we have a 2D array add a row delimiter
if len(array.shape) == 2:
format_str *= array.shape[1]
# cut off the last column delimiter and add a row delimiter
format_str = format_str[:-len(col_delim)] + row_delim
end_junk = len(row_delim)
# expand format string to whole array
format_str *= len(array)
# if an array is repeated in the value format
# do the shaping here so we don't need to specify indexes
shaped = np.tile(array.reshape((-1, 1)),
(1, repeats)).reshape(-1)
# run the format operation and remove the extra delimiters
formatted = format_str.format(*shaped)[:-end_junk]
return formatted | 9e7f189049b1ad3eff3679568a84e7151e2c643c | 12,526 |
def get_dp_logs(logs):
"""Get only the list of data point logs, filter out the rest."""
filtered = []
compute_bias_for_types = [
"mouseout",
"add_to_list_via_card_click",
"add_to_list_via_scatterplot_click",
"select_from_list",
"remove_from_list",
]
for log in logs:
if log["type"] in compute_bias_for_types:
filtered.append(log)
return filtered | e0a7c579fa9218edbf942afdbdb8e6cf940d1a0c | 12,527 |
from typing import List
from typing import Dict
def assign_reports_to_watchlist(cb: CbThreatHunterAPI, watchlist_id: str, reports: List[Dict]) -> Dict:
"""Set a watchlist report IDs attribute to the passed reports.
Args:
cb: Cb PSC object
watchlist_id: The Watchlist ID to update.
reports: The Intel Reports.
Returns:
The Watchlist in dict form.
"""
watchlist_data = get_watchlist(cb, watchlist_id)
if not watchlist_data:
return None
watchlist_data["report_ids"] = [r["id"] for r in reports]
watchlist_data = update_watchlist(cb, watchlist_data)
if not watchlist_data:
LOGGER.error(f"unexpected problem updating watchlist with report IDs.")
return False
return watchlist_data | 92bb0369211c1720fa4d9baa7a4e3965851339f2 | 12,528 |
def visualize_filter(
image,
model,
layer,
filter_index,
optimization_parameters,
transformation=None,
regularization=None,
threshold=None,
):
"""Create a feature visualization for a filter in a layer of the model.
Args:
image (array): the image to be modified by the feature vis process.
model (object): the model to be used for the feature visualization.
layer (string): the name of the layer to be used in the visualization.
filter_index (number): the index of the filter to be visualized.
optimization_parameters (OptimizationParameters): the optimizer class to be applied.
transformations (function): a function defining the transformations to be perfromed.
regularization (function): customized regularizers to be applied. Defaults to None.
threshold (list): Intermediate steps for visualization. Defaults to None.
Returns:
tuple: activation and result image for the process.
"""
image = tf.Variable(image)
feature_extractor = get_feature_extractor(model, layer)
_threshold_figures = figure(figsize=(15, 10), dpi=200)
print("Starting Feature Vis Process")
for iteration in range(optimization_parameters.iterations):
pctg = int(iteration / optimization_parameters.iterations * 100)
if transformation:
if not callable(transformation):
raise ValueError("The transformations need to be a function.")
image = transformation(image)
else:
image = trans.standard_transformation(image)
activation, image = gradient_ascent_step(
image, feature_extractor, filter_index, regularization,
optimization_parameters
)
print('>>', pctg, '%', end="\r", flush=True)
# Routine for creating a threshold image for Jupyter Notebooks
if isinstance(threshold, list) and (iteration in threshold):
threshold_image = _threshold_figures.add_subplot(
1, len(threshold), threshold.index(iteration) + 1
)
threshold_image.title.set_text(f"Step {iteration}")
threshold_view(image)
print('>> 100 %')
if image.shape[1] < 299 or image.shape[2] < 299:
image = tf.image.resize(image, [299, 299])
# Decode the resulting input image
image = imgs.deprocess_image(image[0].numpy())
return activation, image | 09940c0484361240929f61f04c9a96771b440033 | 12,529 |
def subtraction(x, y):
"""
Subtraction x and y
>>> subtraction(-20, 80)
-100
"""
assert isinstance(x, (int, float)), "The x value must be an int or float"
assert isinstance(y, (int, float)), "The y value must be an int or float"
return x - y | 203233897d31cb5bc79fca0f8c911b03d7deb5ba | 12,530 |
import aiohttp
async def paste(text: str) -> str:
"""Return an online bin of given text."""
session = aiohttp.ClientSession()
async with session.post("https://hasteb.in/documents", data=text) as post:
if post.status == 200:
response = await post.text()
return f"https://hasteb.in/{response[8:-2]}"
post = await session.post("https://bin.drlazor.be", data={"val": text})
if post.status == 200:
return post.url | d204f6f1db3aa33c98c4ebeae9888acc438f7dc3 | 12,531 |
def lr_step(base_lr, curr_iter, decay_iters, warmup_iter=0):
"""Stepwise exponential-decay learning rate policy.
Args:
base_lr: A scalar indicates initial learning rate.
curr_iter: A scalar indicates current iteration.
decay_iter: A list of scalars indicates the numbers of
iteration when the learning rate is decayed.
warmup_iter: A scalar indicates the number of iteration
before which the learning rate is not adjusted.
Return:
A scalar indicates the current adjusted learning rate.
"""
if curr_iter < warmup_iter:
alpha = curr_iter / warmup_iter
return base_lr * (1 / 10.0 * (1 - alpha) + alpha)
else:
return base_lr * (0.1 ** get_step_index(curr_iter, decay_iters)) | b8cfe670aba0bed1f84ae09c6271e681fad42864 | 12,532 |
def apo(coalg):
"""
Extending an anamorphism with the ability to halt.
In this version, a boolean is paired with the value that indicates halting.
"""
def run(a):
stop, fa = coalg(a)
return fa if stop else fa.map(run)
return run | a1e64d9ed49a8641095c8a8c20ae08c1cc6e9c19 | 12,533 |
def cat_sample(ps):
"""
sample from categorical distribution
ps is a 2D array whose rows are vectors of probabilities
"""
r = nr.rand(len(ps))
out = np.zeros(len(ps),dtype='i4')
cumsums = np.cumsum(ps, axis=1)
for (irow,csrow) in enumerate(cumsums):
for (icol, csel) in enumerate(csrow):
if csel > r[irow]:
out[irow] = icol
break
return out | 30009b31dba0eff23010bfe6d531e8c55e46873c | 12,534 |
def extract_text(text):
""" """
l = []
res = []
i = 0
while i < len(text) - 2:
h, i, _ = next_token(text, i)
obj = text[h:i]
l.append(obj)
for j, tok in enumerate(l):
if tok == b'Tf':
font = l[j-2]
fsize = float(l[j-1])
elif tok == b'Td':
x = float(l[j-2])
y = float(l[j-1])
elif tok == b'Tj':
text = l[j-1]
res.append((x, y, font, fsize, text[1:-1]))
return res | 9b0746be6f6fa39548fd34f3bffda7e8baf4a6ef | 12,536 |
def add_pruning_arguments_to_parser(parser):
"""Add pruning arguments to existing argparse parser"""
parser.add_argument('--do_prune', action='store_true',
help="Perform pruning when training a model")
parser.add_argument('--pruning_config', type=str,
default='', help="Path to a pruning config")
parser.add_argument('--pruning_override', type=str, nargs='*', action=ConcatenateStringAction,
default='', help="JSON string to override pruning configuration file")
return parser | 2a94e0986564f4af8fe580ca3500f06c04598f14 | 12,537 |
def read_ult_meta(filebase):
"""Convenience fcn for output of targeted metadata."""
meta = _parse_ult_meta(filebase)
return (meta["NumVectors"],
meta["PixPerVector"],
meta["ZeroOffset"],
meta["Angle"],
meta["PixelsPerMm"],
meta["FramesPerSec"],
meta["TimeInSecsOfFirstFrame"]) | b2237a2dab9faf98179f69de9e9a5f1dc7289f78 | 12,539 |
from typing import Iterable
from typing import List
def safe_identifiers_iterable(val_list: Iterable[str]) -> List[str]:
"""
Returns new list, all with safe identifiers.
"""
return [safe_identifier(val) for val in val_list] | 6b80d90cfac2ea527ace38cc6550571b5f120a7f | 12,540 |
def encode_varint(value, write):
""" Encode an integer to a varint presentation. See
https://developers.google.com/protocol-buffers/docs/encoding?csw=1#varints
on how those can be produced.
Arguments:
value (int): Value to encode
write (function): Called per byte that needs to be writen
Returns:
int: Number of bytes written
"""
value = (value << 1) ^ (value >> 63)
if value <= 0x7f: # 1 byte
write(value)
return 1
if value <= 0x3fff: # 2 bytes
write(0x80 | (value & 0x7f))
write(value >> 7)
return 2
if value <= 0x1fffff: # 3 bytes
write(0x80 | (value & 0x7f))
write(0x80 | ((value >> 7) & 0x7f))
write(value >> 14)
return 3
if value <= 0xfffffff: # 4 bytes
write(0x80 | (value & 0x7f))
write(0x80 | ((value >> 7) & 0x7f))
write(0x80 | ((value >> 14) & 0x7f))
write(value >> 21)
return 4
if value <= 0x7ffffffff: # 5 bytes
write(0x80 | (value & 0x7f))
write(0x80 | ((value >> 7) & 0x7f))
write(0x80 | ((value >> 14) & 0x7f))
write(0x80 | ((value >> 21) & 0x7f))
write(value >> 28)
return 5
else:
# Return to general algorithm
bits = value & 0x7f
value >>= 7
i = 0
while value:
write(0x80 | bits)
bits = value & 0x7f
value >>= 7
i += 1
write(bits)
return i | 075286208008a0b7507eafe19158eebdb2af66b7 | 12,541 |
def heap_sort(li):
""" [list of int] => [list of int]
Heap sort: divides its input into a sorted and an unsorted region,
and it iteratively shrinks the unsorted region by extracting the
largest element from it and inserting it into the sorted region.
It does not waste time with a linear-time scan of the unsorted region;
rather, heap sort maintains the unsorted region in a heap data structure
to more quickly find the largest element in each step.
To implement a heap using arrays, we will use the rule
li[k] >= li[2*k+1] and li[k] >= li[2*k+2] (left child and right child
respectively).
More generally, the array must satisfy the heap quality:
For any given node C, if P is a parent node of C, then
the value of P is greater than or equal to the key of C
(for max heaps)
Graphically, this would look like:
0
1 2
3 4 5 6
7 8 9 10 11 12 13 14
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
"""
def heapify(lst, heap_size, root):
""" ([list of int], int, int) => [list of int]
Rearranges the list to satisfy the heap quality.
Root is index of the largest element in the lst.
"""
# the largest node
largest = root
left_child = 2 * largest + 1
right_child = 2 * largest + 2
# check if left_child and root need to be swapped
if left_child < heap_size and lst[largest] < lst[left_child]:
largest = left_child
# check if right_child and root need to be swapped
if right_child < heap_size and lst[largest] < lst[right_child]:
largest = right_child
# change root, if needed
if largest != root:
lst[root], lst[largest] = lst[largest], lst[root]
# continue to heapify the root
heapify(lst, heap_size, largest)
# Build a maxheap by iterating through the list backwards
for i in range(len(li), -1, -1):
heapify(li, len(li), i)
print(li)
# extract elements one by one
for i in range(len(li) - 1, 0, -1):
"""remember, heap sort differs from insertion sort in that
# it searches for the maximum, rather than minimum, element.
li[0:end] is a heap (like a tree, but elements are not guaranteed
to be sorted) and li[end:len(li)] is in sorted order."""
li[i], li[0] = li[0], li[i]
# return to heap, since the heap was messed up by swapping
heapify(li, i, 0)
return li | a72be31e5256c880c157636aa7a15df013ce651d | 12,542 |
def vector_field(v, t, inf_mat, state_meta):
"""vector_field returns the temporal derivative of a flatten state vector
:param v: array of shape (1,mmax+1+(nmax+1)**2) for the flatten state vector
:param t: float for time (unused)
:param inf_mat: array of shape (nmax+1,nmax+1) representing the infection rate
:param state_meta: tuple of arrays encoding information of the structure.
:returns vec_field: array of shape (1,(nmax+1)**2) for the flatten
vector field.
"""
mmax = state_meta[0]
nmax = state_meta[1]
m = state_meta[2]
gm = state_meta[3]
pn = state_meta[4]
imat = state_meta[5]
nmat = state_meta[6]
pnmat = state_meta[7]
sm = v[:mmax+1]
fni = v[mmax+1:].reshape(nmax+1,nmax+1)
fni_field = np.zeros(fni.shape) #matrix field
sm_field = np.zeros(sm.shape)
#calculate mean-field quantities
r = np.sum(inf_mat[2:,:]*(nmat[2:,:]-imat[2:,:])*fni[2:,:]*pnmat[2:,:])
r /= np.sum((nmat[2:,:]-imat[2:,:])*fni[2:,:]*pnmat[2:,:])
rho = r*excess_susceptible_membership(m,gm,sm)
#contribution for nodes
#------------------------
sm_field = 1 - sm - sm*m*r
#contribution for groups
#------------------------
#contribution from above
fni_field[2:,:nmax] += imat[2:,1:]*fni[2:,1:]
#contribution from equal
fni_field[2:,:] += (-imat[2:,:]
-(nmat[2:,:] - imat[2:,:])
*(inf_mat[2:,:] + rho))*fni[2:,:]
#contribution from below
fni_field[2:,1:nmax+1] += ((nmat[2:,:nmax] - imat[2:,:nmax])
*(inf_mat[2:,:nmax] + rho))*fni[2:,:nmax]
return np.concatenate((sm_field,fni_field.reshape((nmax+1)**2))) | 31c8023966fd3e5c35b734759a3747f0d2752390 | 12,543 |
def newton(start, loss_fn, *args, lower=0, upper=None, epsilon=1e-9):
"""
Newton's Method!
"""
theta, origin, destination = args[0], args[1], args[2]
if upper is None:
upper = 1
start = lower
while True:
if loss_fn(start, theta, origin, destination) > 0:
start = (upper+start)/2
else:
start = (lower+start)/2
# print("START", start)
x_cur = start
x_prev = -1
try:
while np.abs(x_cur-x_prev) >= epsilon:
# print(x)
x_prev = x_cur
x_cur = newton_single(x_cur, loss_fn, theta, origin, destination)
# print(x, x-x_prev, np.abs(x-x_prev)>=epsilon)
if np.isnan(x_cur):
continue
return x_cur
except ZeroDivisionError:
print(start, x_cur) | bbd04297639fbc964c55a8c964e5bd5fb24d6e22 | 12,544 |
import torch
def eval_det_cls(pred, gt, iou_thr=None):
"""Generic functions to compute precision/recall for object detection for a
single class.
Args:
pred (dict): Predictions mapping from image id to bounding boxes \
and scores.
gt (dict): Ground truths mapping from image id to bounding boxes.
iou_thr (list[float]): A list of iou thresholds.
Return:
tuple (np.ndarray, np.ndarray, float): Recalls, precisions and \
average precision.
"""
# {img_id: {'bbox': box structure, 'det': matched list}}
class_recs = {}
npos = 0
img_id_npos = {}
for img_id in gt.keys():
cur_gt_num = len(gt[img_id])
if cur_gt_num != 0:
gt_cur = torch.zeros([cur_gt_num, 7], dtype=torch.float32)
for i in range(cur_gt_num):
gt_cur[i] = gt[img_id][i].tensor
bbox = gt[img_id][0].new_box(gt_cur)
else:
bbox = gt[img_id]
det = [[False] * len(bbox) for i in iou_thr]
npos += len(bbox)
img_id_npos[img_id] = img_id_npos.get(img_id, 0) + len(bbox)
class_recs[img_id] = {'bbox': bbox, 'det': det}
# construct dets
image_ids = []
confidence = []
ious = []
for img_id in pred.keys():
cur_num = len(pred[img_id])
if cur_num == 0:
continue
pred_cur = torch.zeros((cur_num, 7), dtype=torch.float32)
box_idx = 0
for box, score in pred[img_id]:
image_ids.append(img_id)
confidence.append(score)
pred_cur[box_idx] = box.tensor
box_idx += 1
pred_cur = box.new_box(pred_cur)
gt_cur = class_recs[img_id]['bbox']
if len(gt_cur) > 0:
# calculate iou in each image
iou_cur = pred_cur.overlaps(pred_cur, gt_cur)
for i in range(cur_num):
ious.append(iou_cur[i])
else:
for i in range(cur_num):
ious.append(np.zeros(1))
confidence = np.array(confidence)
# sort by confidence
sorted_ind = np.argsort(-confidence)
image_ids = [image_ids[x] for x in sorted_ind]
ious = [ious[x] for x in sorted_ind]
# go down dets and mark TPs and FPs
nd = len(image_ids)
tp_thr = [np.zeros(nd) for i in iou_thr]
fp_thr = [np.zeros(nd) for i in iou_thr]
for d in range(nd):
R = class_recs[image_ids[d]]
iou_max = -np.inf
BBGT = R['bbox']
cur_iou = ious[d]
if len(BBGT) > 0:
# compute overlaps
for j in range(len(BBGT)):
# iou = get_iou_main(get_iou_func, (bb, BBGT[j,...]))
iou = cur_iou[j]
if iou > iou_max:
iou_max = iou
jmax = j
for iou_idx, thresh in enumerate(iou_thr):
if iou_max > thresh:
if not R['det'][iou_idx][jmax]:
tp_thr[iou_idx][d] = 1.
R['det'][iou_idx][jmax] = 1
else:
fp_thr[iou_idx][d] = 1.
else:
fp_thr[iou_idx][d] = 1.
ret = []
# Return additional information for custom metrics.
new_ret = {}
new_ret["image_ids"] = image_ids
new_ret["iou_thr"] = iou_thr
new_ret["ious"] = [max(x.tolist()) for x in ious]
new_ret["fp_thr"] = [x.tolist() for x in fp_thr]
new_ret["tp_thr"] = [x.tolist() for x in tp_thr]
new_ret["img_id_npos"] = img_id_npos
for iou_idx, thresh in enumerate(iou_thr):
# compute precision recall
fp = np.cumsum(fp_thr[iou_idx])
tp = np.cumsum(tp_thr[iou_idx])
recall = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
precision = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = average_precision(recall, precision)
ret.append((recall, precision, ap))
return ret, new_ret | 762f70d95261509778a1b015af30eab68f951b15 | 12,545 |
import pathlib
from typing import List
from typing import Dict
import tqdm
def parse_g2o(path: pathlib.Path, pose_count_limit: int = 100000) -> G2OData:
"""Parse a G2O file. Creates a list of factors and dictionary of initial poses."""
with open(path) as file:
lines = [line.strip() for line in file.readlines()]
pose_variables: List[jaxfg.geometry.LieVariableBase] = []
initial_poses: Dict[jaxfg.geometry.LieVariableBase, jaxlie.MatrixLieGroup] = {}
factors: List[jaxfg.core.FactorBase] = []
for line in tqdm(lines):
parts = [part for part in line.split(" ") if part != ""]
variable: jaxfg.geometry.LieVariableBase
between: jaxlie.MatrixLieGroup
if parts[0] == "VERTEX_SE2":
if len(pose_variables) > pose_count_limit:
continue
# Create SE(2) variable
_, index, x, y, theta = parts
index = int(index)
x, y, theta = map(float, [x, y, theta])
assert len(initial_poses) == index
variable = jaxfg.geometry.SE2Variable()
initial_poses[variable] = jaxlie.SE2.from_xy_theta(x, y, theta)
pose_variables.append(variable)
elif parts[0] == "EDGE_SE2":
# Create relative offset between pair of SE(2) variables
before_index = int(parts[1])
after_index = int(parts[2])
if before_index > pose_count_limit or after_index > pose_count_limit:
continue
between = jaxlie.SE2.from_xy_theta(*(float(p) for p in parts[3:6]))
precision_matrix_components = onp.array(list(map(float, parts[6:])))
precision_matrix = onp.zeros((3, 3))
precision_matrix[onp.triu_indices(3)] = precision_matrix_components
precision_matrix = precision_matrix.T
precision_matrix[onp.triu_indices(3)] = precision_matrix_components
sqrt_precision_matrix = onp.linalg.cholesky(precision_matrix).T
factors.append(
jaxfg.geometry.BetweenFactor.make(
variable_T_world_a=pose_variables[before_index],
variable_T_world_b=pose_variables[after_index],
T_a_b=between,
noise_model=jaxfg.noises.Gaussian(
sqrt_precision_matrix=sqrt_precision_matrix
),
)
)
elif parts[0] == "VERTEX_SE3:QUAT":
# Create SE(3) variable
_, index, x, y, z, qx, qy, qz, qw = parts
index = int(index)
assert len(initial_poses) == index
variable = jaxfg.geometry.SE3Variable()
initial_poses[variable] = jaxlie.SE3(
wxyz_xyz=onp.array(list(map(float, [qw, qx, qy, qz, x, y, z])))
)
pose_variables.append(variable)
elif parts[0] == "EDGE_SE3:QUAT":
# Create relative offset between pair of SE(3) variables
before_index = int(parts[1])
after_index = int(parts[2])
numerical_parts = list(map(float, parts[3:]))
assert len(numerical_parts) == 7 + 21
# between = jaxlie.SE3.from_xy_theta(*(float(p) for p in parts[3:6]))
xyz = numerical_parts[0:3]
quaternion = numerical_parts[3:7]
between = jaxlie.SE3.from_rotation_and_translation(
rotation=jaxlie.SO3.from_quaternion_xyzw(onp.array(quaternion)),
translation=onp.array(xyz),
)
precision_matrix = onp.zeros((6, 6))
precision_matrix[onp.triu_indices(6)] = numerical_parts[7:]
precision_matrix = precision_matrix.T
precision_matrix[onp.triu_indices(6)] = numerical_parts[7:]
sqrt_precision_matrix = onp.linalg.cholesky(precision_matrix).T
factors.append(
jaxfg.geometry.BetweenFactor.make(
variable_T_world_a=pose_variables[before_index],
variable_T_world_b=pose_variables[after_index],
T_a_b=between,
noise_model=jaxfg.noises.Gaussian(
sqrt_precision_matrix=sqrt_precision_matrix
),
)
)
else:
assert False, f"Unexpected line type: {parts[0]}"
# Anchor start pose
factors.append(
jaxfg.geometry.PriorFactor.make(
variable=pose_variables[0],
mu=initial_poses[pose_variables[0]],
noise_model=jaxfg.noises.DiagonalGaussian(
jnp.ones(pose_variables[0].get_local_parameter_dim()) * 100.0
),
)
)
return G2OData(factors=factors, initial_poses=initial_poses) | 6c766401220849e337279e8b465f9d67477a1599 | 12,546 |
def _som_actor(env):
"""
Construct the actor part of the model and return it.
"""
nactions = np.product(env.action_shape)
model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape=(1,) + env.observation_space.shape))
model.add(keras.layers.Dense(400))
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dense(200))
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dense(nactions))
model.add(keras.layers.Activation('sigmoid'))
return model | e3bc1f675b16b2d728b1c070324139f0d99071a7 | 12,547 |
def sendEmail():
"""email sender"""
send_email('Registration ATS',
['[email protected]'],
'Thanks for registering ATS!',
'<h3>Thanks for registering with ATS!</h3>')
return "email sent to [email protected]" | e9125c32adac8267aaa550e59e27db4a10746ace | 12,548 |
import scipy
def Pvalue(chi2, df):
"""Returns the p-value of getting chi2 from a chi-squared distribution.
chi2: observed chi-squared statistic
df: degrees of freedom
"""
return 1 - scipy.stats.chi2.cdf(chi2, df) | 1a2198e5d47396fc785a627d96513ded1d6894e0 | 12,549 |
def template(template_lookup_key: str) -> str:
"""Return template as string."""
with open(template_path(template_lookup_key), "r") as filepath:
template = filepath.read()
return template | d03bbc2baa8cb18174a468579bdea1da906de09d | 12,550 |
def filter_rows(df, condition, reason):
"""
:param reason:
:param df:
:param condition: boolean, true for row to keep
:return: filter country_city_codes df
"""
n_dropped = (condition == False).sum()
print(
f"\nexcluding {n_dropped} locations ({n_dropped / df.shape[0]:.1%}) due to {reason}"
)
return df[condition] | 7e5e6925bfb7d90bc90b42fda202d80e8ef5e3f6 | 12,551 |
def parse_projected_dos(f):
"""Parse `projected_dos.dat` output file."""
data = np.loadtxt(f)
projected_dos = {"frequency_points": data[:, 0], "projected_dos": data[:, 1:].T}
pdos = orm.XyData()
pdos_list = [pd for pd in projected_dos["projected_dos"]]
pdos.set_x(projected_dos["frequency_points"], "Frequency", "THz")
pdos.set_y(
pdos_list,
[
"Projected DOS",
]
* len(pdos_list),
[
"1/THz",
]
* len(pdos_list),
)
pdos.label = "Projected DOS"
return pdos | 89c280e92c7598e3947d8ccda20b921c601c9b10 | 12,552 |
def get_from_parameters(a, b, c, alpha, beta, gamma):
"""
Create a Lattice using unit cell lengths and angles (in degrees).
This code is modified from the pymatgen source code [1]_.
Parameters
----------
a : :class:`float`:
*a* lattice parameter.
b : :class:`float`:
*b* lattice parameter.
c : :class:`float`:
*c* lattice parameter.
alpha : :class:`float`:
*alpha* angle in degrees.
beta : :class:`float`:
*beta* angle in degrees.
gamma : :class:`float`:
*gamma* angle in degrees.
Returns
-------
:class:`tuple` of three :class:`numpy.ndarray`
Tuple of cell lattice vectors of shape (3, ) in Angstrom.
"""
angles_r = np.radians([alpha, beta, gamma])
cos_alpha, cos_beta, cos_gamma = np.cos(angles_r)
sin_alpha, sin_beta, sin_gamma = np.sin(angles_r)
val = (cos_alpha * cos_beta - cos_gamma) / (sin_alpha * sin_beta)
# Sometimes rounding errors result in values slightly > 1.
val = cap_absolute_value(val)
gamma_star = np.arccos(val)
vector_a = np.array([a * sin_beta, 0.0, a * cos_beta])
vector_b = np.array([
-b * sin_alpha * np.cos(gamma_star),
b * sin_alpha * np.sin(gamma_star),
b * cos_alpha,
])
vector_c = np.array([0.0, 0.0, float(c)])
return tuple([vector_a, vector_b, vector_c]) | 076763f30da86b12747ede930993d99fc3b742d8 | 12,553 |
import random
def random_chinese_name():
"""生成随机中文名字
包括的名字格式：2个字名字**，3个字名字***，4个字名字****
:return:
"""
name_len = random.choice([i for i in range(4)])
if name_len == 0:
name = random_two_name()
elif name_len == 1:
name = random_three_name()
elif name_len == 2:
name = random_three_names()
else:
name = random_four_name()
return name | c86232cb81c492e2301837f5e330e6140ee503f3 | 12,554 |
def power_list(lists: [list]) -> list:
""" power set across the options of all lists """
if len(lists) == 1:
return [[v] for v in lists[0]]
grids = power_list(lists[:-1])
new_grids = []
for v in lists[-1]:
for g in grids:
new_grids.append(g + [v])
return new_grids | 135e3cde20388d999456e2e8a2fed4d98fac581d | 12,555 |
import time
def send_email(from_email, to, subject, message, html=True):
"""
Send emails to the given recipients
:param from_email:
:param to:
:param subject:
:param message:
:param html:
:return: Boolean value
"""
try:
email = EmailMessage(subject, message, from_email, to)
print("Sending email..")
if html:
email.content_subtype = 'html'
email.send()
return True
except Exception as e:
print("Error in sending email: {0}".format(str(e)))
if 'rate exceeded' in str(e):
time.sleep(2)
send_email(from_email, to, subject, message)
return False | 28751bc30f51148c0389d4127229e6352a18cacb | 12,556 |
import random
def attack(health, power, percent_to_hit):
"""Calculates health from percent to hit and power of hit
Parameters:
health - integer defining health of attackee
power - integer defining damage of attacker
percent to hit - float defining percent chance to hit of attacker
Returns: new health
"""
random_number = random.random() # number between 0.0 and 1.0
# if our random number falls between 0 and percent to hit
if random_number <= percent_to_hit:
# then a hit occurred so we reduce health by power
health = health - power
# return the new health value
return health | 83a74908f76f389c798b28c5d3f9035d2d8aff6a | 12,557 |
def signal_requests_mock_factory(requests_mock: Mocker) -> Mocker:
"""Create signal service mock from factory."""
def _signal_requests_mock_factory(
success_send_result: bool = True, content_length_header: str = None
) -> Mocker:
requests_mock.register_uri(
"GET",
"http://127.0.0.1:8080/v1/about",
status_code=HTTPStatus.OK,
json={"versions": ["v1", "v2"]},
)
if success_send_result:
requests_mock.register_uri(
"POST",
"http://127.0.0.1:8080" + SIGNAL_SEND_PATH_SUFIX,
status_code=HTTPStatus.CREATED,
)
else:
requests_mock.register_uri(
"POST",
"http://127.0.0.1:8080" + SIGNAL_SEND_PATH_SUFIX,
status_code=HTTPStatus.BAD_REQUEST,
)
if content_length_header is not None:
requests_mock.register_uri(
"GET",
URL_ATTACHMENT,
status_code=HTTPStatus.OK,
content=CONTENT,
headers={"Content-Length": content_length_header},
)
else:
requests_mock.register_uri(
"GET",
URL_ATTACHMENT,
status_code=HTTPStatus.OK,
content=CONTENT,
)
return requests_mock
return _signal_requests_mock_factory | 543f73ec004911c87e9986cbd940a733f03287bf | 12,558 |
def test_dwt_denoise_trace():
""" Check that sample data fed into dwt_denoise_trace() can be processed
and that the returned signal is reasonable (for just one trace)"""
# Loma Prieta test station (nc216859)
data_files, origin = read_data_dir('geonet', 'us1000778i', '*.V1A')
trace = []
trace = read_data(data_files[0])
dataOut = dwt.denoise_trace(tr=trace)
# Look at frequency content? Samples?
return dataOut | 4c526e7e76c8672322bec0323974ca2ee20e25dd | 12,559 |
def get_networks(project_id=None,
auth_token=None):
"""
Get a list of all routed networks
"""
url = CATALOG_HOST + "/routednetwork"
try:
response_body = _api_request(url=url,
http_method="GET",
project_id=project_id,
auth_token=auth_token)
except CommandExecutionError as e:
log.exception(e)
return None
networks = [
network
for network
in response_body
if network['internalDeploymentStatus']['phase'] in list(map(str, POSITIVE_PHASES))
]
return networks | c2c9bfe05cfa416c9e37d04aefcc640d5d2250f7 | 12,560 |
def feature_registration(source,target, MIN_MATCH_COUNT = 12):
"""
Obtain the rigid transformation from source to target
first find correspondence of color images by performing fast registration
using SIFT features on color images.
The corresponding depth values of the matching keypoints is then used to
obtain rigid transformation through a ransac process.
Parameters
----------
source : ((n,m) uint8, (n,m) float)
The source color image and the corresponding 3d pointcloud combined in a list
target : ((n,m) uint8, (n,m) float)
The target color image and the corresponding 3d pointcloud combined in a list
MIN_MATCH_COUNT : int
The minimum number of good corresponding feature points for the algorithm to
trust the pairwise registration result with feature matching only
Returns
----------
transform: (4,4) float or None
The homogeneous rigid transformation that transforms source to the target's
frame
if None, registration result using feature matching only cannot be trusted
either due to no enough good matching feature points are found, or the ransac
process does not return a solution
"""
cad_src, depth_src = source
cad_des, depth_des = target
# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create()
# find the keypoints and descripto rs with SIFT
kp1, des1 = sift.detectAndCompute(cad_src,None)
kp2, des2 = sift.detectAndCompute(cad_des,None)
# find good mathces
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1,des2, k=2)
good = []
for m,n in matches:
if m.distance < 0.7*n.distance:
good.append(m)
# if number of good matching feature point is greater than the MIN_MATCH_COUNT
if len(good)>MIN_MATCH_COUNT:
src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
matchesMask = mask.ravel().tolist()
bad_match_index = np.where(np.array(matchesMask) == 0)
src_index=np.vstack(src_pts).squeeze()
src_index = np.delete(src_index, tuple(bad_match_index[0]), axis=0)
src_index[:,[0, 1]] = src_index[:,[1, 0]]
src_index = tuple(src_index.T.astype(np.int32))
src_depths = depth_src[src_index]
dst_index=np.vstack(dst_pts).squeeze()
dst_index = np.delete(dst_index, tuple(bad_match_index[0]), axis=0)
dst_index[:,[0, 1]] = dst_index[:,[1, 0]]
dst_index = tuple(dst_index.T.astype(np.int32))
dst_depths = depth_des[dst_index]
dst_good=[]
src_good=[]
dst_depths=dst_depths[matchesMask>0][0]
src_depths=src_depths[matchesMask>0][0]
for i in xrange(len(dst_depths)):
if np.sum(dst_depths[i])!=0 and np.sum(src_depths[i])!=0:
dst_good.append(dst_depths[i].tolist())
src_good.append(src_depths[i].tolist())
# get rigid transforms between 2 set of feature points through ransac
transform = match_ransac(np.asarray(src_good),np.asarray(dst_good))
return transform
else:
return None | d5839ef3586acd84c57341f19700de38660f9a9f | 12,561 |
def set_metadata(testbench_config, testbench):
"""
Perform the direct substitutions from the sonar testbench metadata into the
the testbench
Args:
testbench_config (Testbench): Sonar testbench description
testbench (str): The testbench template
"""
for key, value in testbench_config.metadata.items():
if value is None:
replace_str = ""
else:
replace_str = str(value)
search_str = "SONAR_" + key.upper()
testbench = replace_in_testbenches(testbench, search_str, replace_str)
return testbench | 375712b92f7467ee4d49e5d9e91250464c81337d | 12,562 |
def index(a, x):
"""Locate the leftmost value exactly equal to x"""
i = bisect_left(a, x)
if i != len(a) and a[i] == x:
return i
raise ValueError | f77aed5c55750b848fdf51b66b38f3774c812e23 | 12,563 |
def convert_secondary_type_list(obj):
"""
:type obj: :class:`[mbdata.models.ReleaseGroupSecondaryType]`
"""
type_list = models.secondary_type_list()
[type_list.add_secondary_type(convert_secondary_type(t)) for t in obj]
return type_list | d84d20f6d82b462bda5bf04f6784effea47a0265 | 12,564 |
import json
def load_data(path):
"""Load JSON data."""
with open(path) as inf:
return json.load(inf) | 531fc2b27a6ab9588b1f047e25758f359dc21b6d | 12,566 |
from pathlib import Path
def get_extension(file_path):
"""
get_extension(file)
Gets the extension of the given file.
Parameters
----------
file_path
A path to a file
Returns
-------
str
Returns the extension of the file if it exists or None otherwise.
The Returning extension contains a dot. Ex: .csv
"""
if exists(file_path):
return Path(file_path).suffix
else:
return None | 7b1c4ba4f20ac913bb38292d4a704869cab6937e | 12,567 |
def rank_in_group(df, group_col, rank_col, rank_method="first"):
"""Ranks a column in each group which is grouped by another column
Args:
df (pandas.DataFrame): dataframe to rank-in-group its column
group_col (str): column to be grouped by
rank_col (str): column to be ranked for
rank_method (str): rank method to be the "method" argument of pandas.rank() function
Returns:
pandas.DataFrame: dataframe after the rank-in-group operation
"""
df = df.copy()
df_slice = df[[group_col, rank_col]].drop_duplicates()
df_slice["ranked_{}".format(rank_col)] = df_slice[rank_column].rank(
method=rank_method
)
df = pd.merge(
df,
df_slice[[group_col, "ranked_{}".format(rank_col)]],
how="left",
on=group_col,
)
return df | f2ae45641339bf4bc71bc48a415a28602ccf8da3 | 12,568 |
import six
def get_layer_options(layer_options, local_options):
"""
Get parameters belonging to a certain type of layer.
Parameters
----------
layer_options : list of String
Specifies parameters of the layer.
local_options : list of dictionary
Specifies local parameters in a model function.
"""
layer_options_dict = {}
for key, value in six.iteritems(local_options):
if key in layer_options:
layer_options_dict[key] = value
return layer_options_dict | e40945395c4a96c0a0b9447eeb1d0b50cf661bd7 | 12,569 |
def expr(term:Vn,add:Vt,expr:Vn)->Vn:
"""
expr -> term + expr
"""
return {"add":[term,expr]} | f66475ecbd255ac4c4a04b0d705f1c052c4ee123 | 12,570 |
import json
def gene_box(cohort, order='median', percentage=False):
"""Box plot with counts of filtered mutations by gene.
percentage computes fitness as the increase with respect to
the self-renewing replication rate lambda=1.3.
Color allows you to use a dictionary of colors by gene.
Returns a figure."""
# Load gene color dictionary
with open('../Resources/gene_color_dict.json') as json_file:
color_dict = json.load(json_file)
# Create a dictionary with all filtered genes
gene_list = []
for traj in cohort:
gene_list.append(traj.gene)
gene_dict = {element: [] for element in set(gene_list)}
# update the counts for each gene
if percentage is False:
y_label = 'Fitness'
for traj in cohort:
fitness = traj.fitness
gene_dict[traj.gene].append(fitness)
if percentage is True:
y_label = 'fitness_percentage'
for traj in cohort:
fitness = traj.fitness_percentage
gene_dict[traj.gene].append(fitness)
# sort dictionary in descending order
if order == 'mean':
gene_dict = dict(sorted(gene_dict.items(),
key=lambda item: np.mean(item[1]),
reverse=True))
if order == 'median':
gene_dict = dict(sorted(gene_dict.items(),
key=lambda item: np.median(item[1]),
reverse=True))
if order == 'max':
gene_dict = dict(sorted(gene_dict.items(),
key=lambda item: np.max(item[1]),
reverse=True))
# Bar plot
fig = go.Figure()
# color_dict = dict()
# if isinstance(color, dict):
# color_dict = color
for i, key in enumerate(gene_dict):
fig.add_trace(
go.Box(y=gene_dict[key],
marker_color=color_dict[key],
name=key, boxpoints='all', showlegend=False))
fig.update_layout(title='Gene distribution of filtered mutations',
yaxis_title=y_label,
template="simple_white")
fig.update_xaxes(linewidth=2)
fig.update_yaxes(linewidth=2)
if percentage is False:
fig.update_yaxes(type='log', tickvals=[0.05, 0.1, 0.2, 0.4])
fig.update_layout(xaxis_tickangle=-45)
return fig, gene_dict | 851c166246144b14d51863b4c775baa88ab87205 | 12,571 |
from typing import Union
from typing import List
def _clip_and_count(
adata: AnnData,
target_col: str,
*,
groupby: Union[str, None, List[str]] = None,
clip_at: int = 3,
inplace: bool = True,
key_added: Union[str, None] = None,
fraction: bool = True,
) -> Union[None, np.ndarray]:
"""Counts the number of identical entries in `target_col`
for each group in `group_by`.
"""
if target_col not in adata.obs.columns:
raise ValueError("`target_col` not found in obs.")
groupby = [groupby] if isinstance(groupby, str) else groupby
groupby_cols = [target_col] if groupby is None else groupby + [target_col]
clonotype_counts = (
adata.obs.groupby(groupby_cols, observed=True)
.size()
.reset_index(name="tmp_count")
.assign(
tmp_count=lambda X: [
">= {}".format(min(n, clip_at)) if n >= clip_at else str(n)
for n in X["tmp_count"].values
]
)
)
clipped_count = adata.obs.merge(clonotype_counts, how="left", on=groupby_cols)[
"tmp_count"
].values
if inplace:
key_added = (
"{}_clipped_count".format(target_col) if key_added is None else key_added
)
adata.obs[key_added] = clipped_count
else:
return clipped_count | 20673965557afdcf75b3201cf743fff100981ec3 | 12,572 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.