content stringlengths 39 14.9k | sha1 stringlengths 40 40 | id int64 0 710k |
|---|---|---|
def dump_feature(feature):
"""
Gets a single feature as a dictionary, and returns it rendered as a string
"""
s = "|f "
for _key in sorted(feature.keys()):
s += "{}:{} ".format(_key, feature[_key])
return s | b20dff368943aecbe485778b3cded552db56ffb0 | 686,435 |
def remove_padding(im, pad):
"""
Function for removing padding from an image.
:param im: image to remove padding from
:param pad: number of pixels of padding to remove
:return:
"""
return im[pad:-pad, pad:-pad] | 58d822dc9ab587f63d1a98eb19e17268d75b2ab4 | 686,436 |
def about_view(request):
"""Return about page."""
return {} | 70a2324c5e7d578c83f6603f8fe81bbb6c258a97 | 686,437 |
import torch
def split_with_shape(tensor_flatten, mask_flatten, tensor_shape):
"""
Params:
:tensor_flatten: (B, L, C)
:mask_flatten: (B, L)
:tensor_shape: (N, 2)
Return:
:tensor_list: [(B, H1 * W1, C), ..., (B, HN * WN, C)]
:mask_list: [(B, H1 * W1), ..., (B, HN * WN)]
"""
chunk_sizes = (tensor_shape[:, 0] * tensor_shape[:, 1]).tolist()
if tensor_flatten is None and mask_flatten is None:
raise ValueError("Both tensor and mask are None")
if tensor_flatten is not None:
tensor_list = torch.split(tensor_flatten, chunk_sizes, dim=1)
else:
tensor_list = None
if mask_flatten is not None:
mask_list = torch.split(mask_flatten, chunk_sizes, dim=1)
else:
mask_list = None
return tensor_list, mask_list | 9fd537ac1261ce13e7faef2078fa4c2960699169 | 686,438 |
def uniqueArrayName(dataset, name0):
""" Generate a unique name for a DataArray in a dataset """
ii = 0
name = name0
while name in dataset.arrays:
name = name0 + '_%d' % ii
ii = ii + 1
if ii > 1000:
raise Exception('too many arrays in DataSet')
return name | d7da95a9e3a9d959012c101bbc40495a1e085dfd | 686,439 |
import importlib
def _import_module(mocker):
"""Fixture providing import_module mock."""
return mocker.Mock(spec_set=importlib.import_module) | 67db797fdfe5602134b2ef0feaa6efded8865938 | 686,442 |
def bin2int(bin):
"""convert the binary (as string) to integer"""
#print('bin conversion', bin, int(bin, 2))
return int(bin, 2) | def903c6015c13629b6c1778c69e747a82bb32be | 686,443 |
def _ci_to_hgvs_coord(s, e):
""" Convert continuous interbase (right-open) coordinates (..,-2,-1,0,1,..) to
discontinuous HGVS coordinates (..,-2,-1,1,2,..)
"""
def _ci_to_hgvs(c):
return c + 1 if c >= 0 else c
return (None if s is None else _ci_to_hgvs(s),
None if e is None else _ci_to_hgvs(e) - 1) | a25cee5f587b59fd52779f6eeba363f3ddda77b0 | 686,444 |
def num_sevens(n):
"""Returns the number of times 7 appears as a digit of n.
>>> num_sevens(3)
0
>>> num_sevens(7)
1
>>> num_sevens(7777777)
7
>>> num_sevens(2637)
1
>>> num_sevens(76370)
2
>>> num_sevens(12345)
0
>>> from construct_check import check
>>> # ban all assignment statements
>>> check(HW_SOURCE_FILE, 'num_sevens',
... ['Assign', 'AugAssign'])
True
"""
if n == 0:
return 0
if n % 10 == 7:
return num_sevens(n // 10) + 1
else:
return num_sevens(n // 10) | 67e3da342208fa130cd0d1400acfbe3d27585d0b | 686,445 |
def _recurse_on_subclasses(klass):
"""Return as a set all subclasses in a classes' subclass
hierarchy."""
return set(klass.__subclasses__()).union(
[
sub
for cls in klass.__subclasses__()
for sub in _recurse_on_subclasses(cls)
]
) | 4fb399c5362ab0019e07c76e71cf16139c0be49a | 686,447 |
def average_word_length(tweet):
"""
Return the average length of the tweet
:param tweet: raw text tweet
:return: the float number of character count divided by the word count
"""
character_count = 0
word_count = 0
for c in tweet:
character_count += 1
word_count = len(tweet.split())
return float(character_count)/float(word_count) | 4077e531c69b51f6df74546b0e84fc41720ffbe5 | 686,449 |
def flatten_dataframe_for_JSON(df):
"""Flatten a pandas dataframe to a plain list, suitable for JSON serialisation"""
return df.values.flatten().tolist() | ef4ad28904f083f1d86daefab25ce13faf36a344 | 686,452 |
def _indent(text, amount):
"""Indent a multiline string by some number of spaces.
Parameters
----------
text: str
The text to be indented
amount: int
The number of spaces to indent the text
Returns
-------
indented_text
"""
indentation = amount * ' '
return indentation + ('\n' + indentation).join(text.split('\n')) | cf571a3d616e99547757c8ec7d69307985362280 | 686,455 |
def normalize(x, xmin, xmax):
"""Normalize `x` given explicit min/max values. """
return (x - xmin) / (xmax - xmin) | 92d9e0d7e5aad9ee9ff83f3adb042161a3702aec | 686,456 |
def _translate_snapshot_summary_view(context, vol):
"""Maps keys for snapshots summary view."""
d = {}
d['id'] = vol['id']
d['volumeId'] = vol['volume_id']
d['status'] = vol['status']
# NOTE(gagupta): We map volume_size as the snapshot size
d['size'] = vol['volume_size']
d['createdAt'] = vol['created_at']
d['displayName'] = vol['display_name']
d['displayDescription'] = vol['display_description']
return d | 9e0fd90e7c6cd702b8309fae3f8122032e1ec854 | 686,457 |
from typing import List
from typing import Dict
import csv
def read_csv(path: str) -> List[Dict[str, str]]:
"""Reads a csv file, and returns the content inside a list of dictionaries.
Args:
path: The path to the csv file.
Returns:
A list of dictionaries. Each row in the csv file will be a list entry. The
dictionary is keyed by the column names.
"""
with open(path, 'r') as f:
return list(csv.DictReader(f)) | 4051c851fc8919e5c6cb2c6bfd52793e6ab7f639 | 686,458 |
def correct_date(date):
"""
Converts the date format to one accepted by SWA
SWA form cannot accept slashes for dates and is in the format YYYY-MM-DD
:param date: Date string to correct
:return: Corrected date string
"""
if date is None:
return ""
else:
a, b, c = date.split("/")
if len(a) == 4:
# Assumed format is year, month, day
return "%s-%s-%s"%(a, b, c)
else:
# Assumed format is month, day, year
return "%s-%s-%s" % (c, a, b) | 6b17124b4b5e4a3740b56210fc88ea79ca816434 | 686,459 |
def dns_name_encode(name):
"""
DNS domain name encoder (string to bytes)
name -- example: "www.example.com"
return -- example: b'\x03www\x07example\x03com\x00'
"""
name_encoded = [b""]
# "www" -> b"www"
labels = [part.encode() for part in name.split(".") if len(part) != 0]
for label in labels:
# b"www" -> "\x03www"
name_encoded.append(chr(len(label)).encode() + label)
return b"".join(name_encoded) + b"\x00" | 48b298ade17d232caf533d462ea5ef125ce78622 | 686,461 |
def TestingResources(network_ref, subnet_ref, region, zones):
"""Get the resources necessary to test an internal load balancer.
This creates a test service, and a standalone client.
Args:
network_ref: A reference to a GCE network for resources to act in.
subnet_ref: A reference to a GCE subnetwork for resources to act in.
region: The region to deploy the load balancer.
zones: A list of zones to deploy the service.
Returns:
A list of resource definitions.
"""
return [{
'name': 'test-service',
'type': 'test_service.py',
'properties': {
'network': network_ref,
'subnet': subnet_ref,
'region': region,
'zones': zones
}
}, {
'name': 'standalone-client',
'type': 'standalone_test_instance.py',
'properties': {
'network': network_ref,
'subnet': subnet_ref,
'zone': zones[0]
}
}] | 80545f5c4b842bee788a9b03aa9f975ba073d251 | 686,464 |
def compute_bigram(input, unigram_count, bigram_count):
"""
compute_bigram - function to compute bigram probability
Inputs:
input : tuple
bigram
unigram_count : defaultdict(int)
Occurence hashmap of single words/tokens
bigram_count : defaultdict(int)
Occurence hasmap of bi-words/tokens
Outputs:
output : float
Bigram probability
"""
return bigram_count[input] / unigram_count[input[0]] | 0e8efd792171ae48b3627779e7cf51a6d60d774f | 686,469 |
def addQuotes(s):
"""wrap a strings with quotes"""
return "\"" + s + "\"" | b7895bb9ef872585bfcf9bab2103803dab5b676a | 686,476 |
def remove_matching_braces(latex):
"""
If `latex` is surrounded by matching braces, remove them. They are not
necessary.
Parameters
----------
latex : string
Returns
-------
string
Examples
--------
>>> remove_matching_braces('{2+2}')
'2+2'
>>> remove_matching_braces('{2+2')
'{2+2'
"""
if latex.startswith("{") and latex.endswith("}"):
opened = 1
matches = True
for char in latex[1:-1]:
if char == "{":
opened += 1
elif char == "}":
opened -= 1
if opened == 0:
matches = False
if matches:
latex = latex[1:-1]
return latex | 67adfaa0962c94515cea43b6f393a910443e32f8 | 686,479 |
def point_avg(points):
"""
Accepts a list of points, each with the same number of dimensions.
NB. points can have more dimensions than 2
Returns a new point which is the center of all the points.
"""
dimensions = len(points[0])
new_center = []
for dimension in range(dimensions):
dim_sum = 0 # dimension sum
for p in points:
dim_sum += p[dimension]
# average of each dimension
new_center.append(dim_sum / float(len(points)))
return new_center | ac82a036ce5b6fdea0e8c842fba065acd16160a4 | 686,480 |
def _check_errors(results):
"""
<SEMI-PRIVATE> Checks whether the results from the Azure API contain errors or not.
_check_errors(results)
results: [str]
The results from the Azure translation API call
Returns: bool; True if the API results contain errors.
"""
errors = False
for result in results:
if 'translations' not in result:
errors = True
break
return errors | 4e35ede6f5146b91022e9a799ed1954897200eab | 686,482 |
from typing import List
def join_sublist_element(input_list: List[List[str]]) -> List[str]:
"""Join each sublist of chars into string.
This function joins all the element(chars) in each sub-lists together, and
turns every sub-lists to one element in the overall list.
The sublist will turned into a string with all the same elements as before.
:param input_list: the returned list after cut
:return: the list that contains all the segments as strings.
"""
return ["".join(chars) for chars in input_list] | 0594b85d264db84faf39b4b4de3e438dd90d4c11 | 686,483 |
import json
def make_split_change_event(change_number):
"""Make a split change event."""
return {
'event': 'message',
'data': json.dumps({
'id':'TVUsxaabHs:0:0',
'clientId':'pri:MzM0ODI1MTkxMw==',
'timestamp': change_number-1,
'encoding':'json',
'channel':'MTYyMTcxOTQ4Mw==_MjA4MzczNDU1Mg==_splits',
'data': json.dumps({
'type': 'SPLIT_UPDATE',
'changeNumber': change_number
})
})
} | 38c79f7b53fd63b83255deea987ebe1c9f4359ed | 686,486 |
import logging
def _get_logger(fp, enabled=True, log_level="INFO"):
"""Create a logger that outputs to supplied filepath
Args:
enabled (bool): Whether logging is enabled. This function is called
in scripts that call it with suppied arguments, and allows the
scripts this utility blindly.
fp (str): filename of the log
log_level (str): Python logging level to set
Returns:
(logging.logger) logging handle
"""
# no matter what a fangless logger is created (for cleaner code)
logger = logging.getLogger(fp.split("/")[-1].split(".")[0])
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s %(levelname)-8s %(message)s \n',
datefmt="%Y-%m-%d %H:%M:%S")
if enabled:
# set filehandler
fh = logging.FileHandler(fp, mode='w')
fh.setFormatter(formatter)
fh.setLevel(getattr(logging, log_level.upper()))
logger.addHandler(fh)
return logger | 1fbbca246b9b235e74ee3d0ee5e1d591c165d322 | 686,490 |
def assemble(current_lst):
"""
:param current_lst: list of index for the string list
:return: str, assemble list as string
"""
string = ''
for alphabet in current_lst:
string += alphabet
return string | 86f7b4fc9a72f2cd2ea17600d1bc0e31fcd03c97 | 686,492 |
import random
def dataset_creation(labeled_data):
"""Split labeled data into train and test"""
one_class = []
zero_class = []
for data_point in labeled_data:
if data_point[2] == 0:
zero_class.append(data_point)
else:
one_class.append(data_point)
random.shuffle(zero_class)
random.shuffle(one_class)
training_data = one_class[:len(one_class) // 2] + zero_class[:len(one_class) // 2]
test_data = one_class[len(one_class) // 2:] + zero_class[len(one_class) // 2:]
return training_data, test_data | e94a6b289d133866b2a4f2a48ded626aeb6228e5 | 686,495 |
def is_power_of_two(value: int) -> bool:
"""
Determine if the given value is a power of 2.
Negative numbers and 0 cannot be a power of 2 and will thus return `False`.
:param value: The value to check.
:return: `True` if the value is a power of two, 0 otherwise.
"""
if value <= 0:
return False
# If value is a power of two, its highest bit that is set to 1 will toggle to 0 when subtracting 1 because all
# other bits are 0; all other bits will thus be flipped to 1. Therefore, value and (value - 1) don't have any bits
# in common and the bitwise AND will return 0.
# On the other hand, if value is not a power of two, the highest bit set to 1 will not be flipped when subtracting 1
# and thus, value and (value - 1) will have bits in common and the bitwise AND will not return 0.
# Therefore, if the result is 0, value is a power of two.
return (value & (value - 1)) == 0 | 306702ee4291fdcc2a7d4871e7ec4f2e24c8ffd6 | 686,499 |
def get_ctx_result(result):
""" This function get's called for every result object. The result object represents every ActionResult object that
you've added in the action handler. Usually this is one per action. This function converts the result object into a
context dictionary.
:param result: ActionResult object
:param provides: action name
:return: context dictionary
"""
ctx_result = {}
# param = result.get_param()
summary = result.get_summary()
ctx_result['vault_id'] = summary.get('vault_id')
ctx_result['vault_file_name'] = summary.get('name')
ctx_result['vault_file_path'] = summary.get('vault_file_path')
try:
ctx_result[' '] = result.get_message()
except:
pass
return ctx_result | ac6f0a4d4e4632a69703f036fbb6e5ffc3444df9 | 686,505 |
def pintensity(policies, weights):
"""Get the total intensity of a given bin of policies"""
total = 0
for p in policies:
if p == "nan":
continue
if p not in weights.keys():
raise ValueError(f"Missing intensity group: {p}")
else:
total += weights[p]
return total | 86b305f75a8027aedacc52709e83b316c3233e88 | 686,509 |
def get(seq, ind, defVal=None):
"""Return seq[ind] if available, else defVal"""
try:
return seq[ind]
except LookupError:
return defVal | 9e9f64145fb1780b8c956737e080dcddb7e4e0d5 | 686,524 |
def get_bot_in_location(location, game):
"""Returns the bot in the given location."""
bots = game.get('robots')
if location in bots.keys():
return bots[location]
else:
return None | bcc4f909dc2fd936fe83a641d2966c475e58ffa6 | 686,526 |
def is_mutable_s(text: str) -> bool:
"""
Checks if the word starts with a mutable 's'.
('s' is mutable when followed by a vowel, n, r, or l)
:param text: the string to check
:return: true if the input starts with a mutable s
"""
lc = text.lower()
return len(lc) >= 2 and lc[0] == 's' and lc[1] in "rnlaeiouáéíóú" | e45ddd8b7cc060d39944fc6590f6aa2eb86bb379 | 686,528 |
def NBR(ds):
"""
Computes the Normalized Burn Ratio for an `xarray.Dataset`.
The formula is (NIR - SWIR2) / (NIR + SWIR2).
Values should be in the range [-1,1] for valid LANDSAT data (nir and swir2 are positive).
Parameters
----------
ds: xarray.Dataset
An `xarray.Dataset` that must contain 'nir' and 'swir2' `DataArrays`.
Returns
-------
nbr: xarray.DataArray
An `xarray.DataArray` with the same shape as `ds` - the same coordinates in
the same order.
"""
return (ds.nir - ds.swir2) / (ds.nir + ds.swir2) | 2030cde88eaf8e4e2999d4db09ba4d3ea7e93260 | 686,533 |
def epsIndicator(frontOld, frontNew):
"""
This function computes the epsilon indicator
:param frontOld: Old Pareto front
:type frontOld: list
:param frontNew: New Pareto front
:type frontNew: list
:return: epsilon indicator between the old and new Pareto fronts
:rtype: float
"""
epsInd = 0
firstValueAll = True
for indNew in frontNew:
tempEpsInd = 0
firstValue = True
for indOld in frontOld:
(aOld, bOld, cOld) = indOld.fitness.values
(aNew, bNew, cNew) = indNew.fitness.values
compare = max(aOld-aNew, bOld-bNew, cOld-cNew)
if firstValue:
tempEpsInd = compare
firstValue = False
if compare < tempEpsInd:
tempEpsInd = compare
if firstValueAll:
epsInd = tempEpsInd
firstValueAll = False
if tempEpsInd > epsInd:
epsInd = tempEpsInd
return epsInd | 8ec7b18b36a32b963c148aa7592557c2724bde0d | 686,534 |
from typing import Optional
from typing import Tuple
import cgi
def _parse_content_type(content_type: Optional[str]) -> Tuple[Optional[str], str]:
"""Tease out the content-type and character encoding.
A default character encoding of UTF-8 is used, so the content-type
must be used to determine if any decoding is necessary to begin
with.
"""
if not content_type:
return None, "utf-8"
else:
type_, parameters = cgi.parse_header(content_type)
encoding = parameters.get("charset", "utf-8")
return type_, encoding | e6ae570ff6bf65fce4000d8d5216f8977f6a19a0 | 686,541 |
def ranked_vaccine_peptides(variant_to_vaccine_peptides_dict):
"""
This function returns a sorted list whose first element is a Variant and whose second
element is a list of VaccinePeptide objects.
Parameters
----------
variant_to_vaccine_peptides_dict : dict
Dictionary from varcode.Variant to list of VaccinePeptide
Returns list of (varcode.Variant, VaccinePeptide list) tuples
"""
result_list = list(variant_to_vaccine_peptides_dict.items())
def sort_key(variant_and_vaccine_peptides_pair):
vaccine_peptides = variant_and_vaccine_peptides_pair[1]
if len(vaccine_peptides) == 0:
return 0.0
else:
top_vaccine_peptide = vaccine_peptides[0]
return top_vaccine_peptide.combined_score
# sort in descending order of combined (expression * mhc binding) scores
result_list.sort(key=sort_key, reverse=True)
return result_list | e775eff9c84a0404dfd5165b3116c5a661c475e3 | 686,544 |
def get_coord_by_index(da, indx):
"""
Take an Xarray DataArray, return a coordinate by its index in the
order of the data structure. E.g. if var is defined on: ('time', 'lat'),
indx=0 will return the coordinate labelled as 'time'.
"""
coord_name = da.dims[indx]
return da.coords[coord_name] | c6b2710351e71f6d17f5b50a8759a30811e4a41c | 686,546 |
def variance(hist:dict):
"""Compute the variance of an histogram.
:param hist: the histogram
:type hist: dict
:return: the variance of the histogram
:rtype: float
"""
vl = list(hist.values())
m = sum(vl) / float(len(vl))
return sum([(m - v)**2 for v in vl]) / float(len(vl)) | 87124db6bc41757fd899177fb935c0023d007140 | 686,548 |
import base64
import io
def mpl_png(fig) -> str:
"""Return the base64 encoded png string of a matplotlib figure.
Parameters
----------
fig : Figure
Matplotlib figure
Returns
-------
str
Figure base64 encoding
"""
f = io.BytesIO()
fig.savefig(f, format="png", bbox_inches="tight")
f.seek(0)
b = base64.b64encode(f.getvalue()).decode("utf-8").replace("\n", "")
return '<img class="mpl-figure-png" align="center" src="data:image/png;base64,%s">' % b | 7e11fdeeceef128141ff63cfd4cbbacfa40cca8f | 686,553 |
import collections
def compute_f1(hypothesis_list, reference_list, eps=1e-8):
"""
Computes token F1 given a hypothesis and reference. This is defined as
F1 = 2 * ((P * R) / (P + R + eps)) where P = precision, R = recall, and eps
= epsilon for smoothing zero denominators. By default, eps = 1e-8.
"""
hypothesis_set = collections.Counter(hypothesis_list)
reference_set = collections.Counter(reference_list)
overlapping_set = hypothesis_set & reference_set
hypothesis_count = len(hypothesis_list)
reference_count = len(reference_list)
overlapping_count = sum(overlapping_set.values())
precision = overlapping_count / hypothesis_count if hypothesis_count > 0 else 0
recall = overlapping_count / reference_count if reference_count > 0 else 0
f1 = (2.0 * precision * recall) / (precision + recall + eps)
return f1 | 0d4c4cec2dcd91791b2e00c8792ed2ea5d9e814b | 686,556 |
def get_p_key(episode_info):
""" create the primary key field by concatenating episode information
:param episode_info: Dictionary of a single episode
"""
return f'{episode_info["show_stub"]}S{episode_info["season"]}E{episode_info["episode"]}' | f1f7b497aae69a715c312c2de4384f53ee1755d8 | 686,560 |
from typing import List
def maxCrossingSum(arr: List[int], start: int, mid: int, stop: int):
"""Helper Function - Find the max crossing sum w.r.t. middle index"""
leftSum = arr[mid] # start point
leftMaxSum = arr[mid] # Keep track of maximum sum
# Traverse in reverse direction from (mid-1) to start
for i in range(mid - 1, start - 1, -1):
leftSum = leftSum + arr[i]
if leftSum > leftMaxSum:
leftMaxSum = leftSum
rightSum = arr[mid + 1] # start point
rightMaxSum = arr[mid + 1] # keep track of maximum sum
# Traverse in forward direction from (mid+2) to stop
for j in range(mid + 2, stop + 1):
rightSum = rightSum + arr[j]
if rightSum > rightMaxSum:
rightMaxSum = rightSum
return rightMaxSum + leftMaxSum | 61c334b5a9d1857fb54a0518a992ef9c756f57a2 | 686,567 |
def get_attack_name(node):
"""Returns the first of the three '|'-delimited fields in the 'text' field of the child 'attack' node."""
for child in node.iterchildren():
if child.tag == 'attack':
return child.text.split('|')[0] | 5ecdb060814fd94ff749718d50d9b8601351e5b4 | 686,572 |
def clean_html(html_text):
""" Converts <1 to 1 in html text"""
return html_text.replace("><1<", ">less than 1<") | d07db5cf23445aa9a6e6782a8de089299ec30dc4 | 686,573 |
def occupancy_accuracy(gt, pred):
"""Compute occupancy accuracy of numpy tensors."""
return (gt == pred).sum() / float(gt.size) | e3eb56650978d33f1e1c61d0128695ec2fc399d8 | 686,574 |
def _get_winsdk_full_version(repository_ctx):
"""Return the value of BAZEL_WINSDK_FULL_VERSION if defined, otherwise an empty string."""
return repository_ctx.os.environ.get("BAZEL_WINSDK_FULL_VERSION", default = "") | bc14431495312ee910bb9e9e69eb05b571f63deb | 686,579 |
def asses_quality(spec, negative_threshold_percent=20):
"""
// default
- 0 = default
// good
- 1: to be defined
- 2: to be defined
// bad
- 3: Pointing Problem
- 4: more than `negative_threshold_percent` of the flux is negative
- 5: A science target with no WCS
"""
if "STD" not in spec.header.get("OBJECT") and spec.header.get("SRCPOS",None) =="auto":
return 5
if not spec.header.get("POSOK",True):
return 3
flagnegative = spec.data<0
if len(spec.data[flagnegative]) / len(spec.data) > negative_threshold_percent/100:
return 4
return 0 | 752c97be8f0752ae7aad02b93afde77f8a36fd39 | 686,582 |
def extendImages(center, left, right, steering, correction):
"""
Extend the image paths from `center`, `left` and `right` using the correction factor `correction`
Returns ([imagePaths], [steerings])
"""
imagePaths = []
imagePaths.extend(center)
imagePaths.extend(left)
imagePaths.extend(right)
steerings = []
steerings.extend(steering)
steerings.extend([x + correction for x in steering])
steerings.extend([x - correction for x in steering])
return (imagePaths, steerings) | 235883b4b04ed33cf36e8c0fbdb6f47c4645d95a | 686,590 |
from typing import Callable
def linear_schedule(
initial_value: float, final_value: float, end: float = 0
) -> Callable[[float], float]:
"""
Linear learning rate schedule.
:param initial_value: Initial learning rate.
:param final_value: Final learning rate
:param end: Progress remaining where final value will be reached.
:return: schedule that computes
current learning rate depending on remaining progress
"""
assert 0 < end < 1
def func(progress_remaining: float) -> float:
"""
Progress will decrease from 1 (beginning) to 0.
:param progress_remaining:
:return: current learning rate
"""
if progress_remaining < end:
lr = final_value
else:
x0 = end
x1 = 1
y0 = final_value
y1 = initial_value
a = (y1 - y0) / (x1 - x0)
b = y1 - a * x1
lr = a * progress_remaining + b
return lr
return func | 0363718b080ee9c111e25e1d32b96245d18ec0fc | 686,591 |
def is_target_platform(ctx, platform):
"""
Determine if the platform is a target platform or a configure/platform generator platform
:param ctx: Context
:param platform: Platform to check
:return: True if it is a target platform, False if not
"""
return platform and platform not in ('project_generator', []) | 7c48e886f15beba0f2350aee4a6b1a8aef408537 | 686,595 |
def convert_station_group_to_dictionary(group):
"""
Takes station group in format: [[station, component, network, location, start, end], [..], ...]
and returns its dictionary representation:
{
station: <station>,
components: [component1, .., componentN],
network: <network>,
location: <location>,
start: <start>,
end: <end>
}
"""
components = [x[1] for x in group]
station = group[0]
return {
'station': station[0],
'components': components,
'network': station[2],
'location': station[3],
'start': station[4],
'end': station[5]
} | f06e83188fe81f59fe6a18dc02704b47f17fbe6d | 686,596 |
def get_max_lat(shape):
"""
It returns the maximum latitude given a shape
:param shape: country shape
:return: the max latitude
"""
return shape.bounds[3] | f165ac6158a874afb454bdca5fc2be59b64730d1 | 686,599 |
def get_card_group(card, idolized):
"""
Returns an identifier for a card's group, to help separate them by rarities
:param card: card data from schoolido.lu api
:param idolized: True if idolized version
:return: card group name
"""
return "%s-%s%d" % (card['rarity'], card['attribute'], 1 if idolized else 0) | d675a1c575b5e212172116eb23d97fab95ab9d32 | 686,600 |
def parse(address):
"""Parses the given MAC address and transforms it into "xx:xx:xx:xx:xx:xx"
form. This form of expressing MAC addresses is the most standard one.
In general, the "address" parameter accepts three forms of MAC address i
input:
- '1234567890af',
- '12-34-56-78-90-af' (windows-like) and
- '12:34:56:78:90:af' (the most used form).
The 'address' parameter must be string, of course.
"""
assert address is not None
temp = address
if "-" in temp:
temp = temp.replace("-", ":")
if ":" not in temp:
lst = list(temp)
for cnt in range(10, 0, -2):
lst.insert(cnt, ":")
temp = ""
for c in lst:
temp = temp + c
return temp.lower() | 5bf3e63e35872b01c01f3462354bb33f27c8f7b9 | 686,601 |
def parse_mode(cipher_nme):
"""
Parse the cipher mode from cipher name
e.g. aes-128-gcm, the mode is gcm
:param cipher_nme: str cipher name, aes-128-cfb, aes-128-gcm ...
:return: str/None The mode, cfb, gcm ...
"""
hyphen = cipher_nme.rfind('-')
if hyphen > 0:
return cipher_nme[hyphen:]
return None | 0c5f82465f3ab3ec4e4735ef86c825880c1db203 | 686,606 |
def get_coordinates(read):
""" return external coordinates of aligned read bases """
fivep = read.aend if read.is_reverse else read.pos
threep = read.pos if read.is_reverse else read.aend
return fivep, threep | fa77cc9498598ab9e64c9f008a671e56636a932a | 686,607 |
def add_drip_columns(cave_results_df):
"""
Calculates Drips per min
Calculates Drip Rate Error
Adds all as columns ad returns input df.
Parameters
----------
cave_results_df : pandas dataframe
Returns
-------
cave_results_df : pandas dataframe
"""
# Data Conversions
cave_results_df['DripsPerMin'] = 60.0/cave_results_df['DripInterval']
# Error Propagation
cave_results_df['DripRate_Err'] = (60*cave_results_df['DripInterval_err'] /
cave_results_df['DripInterval'].pow(2))
return cave_results_df | c418af1c806eee9fd09a28ffb2e6cf92bc139f0b | 686,608 |
import calendar
def datetime_to_ms(dt):
"""
Converts a datetime to a millisecond accuracy timestamp
"""
seconds = calendar.timegm(dt.utctimetuple())
return seconds * 1000 + dt.microsecond / 1000 | cd2ad394c478905988b145c5b8a2c242f7165674 | 686,609 |
def temp_gradient(x, y):
"""Calculate the temperature gradient for a point
defined by its cartesian coordinates: x, y.
""" # Could also use SymPy to calculate derivative tbh
fx = -x / (x**2 + y**2)
fy = -y / (x**2 + y**2)
return fx, fy | e21e95133cfd803bb63b14aabe6cd27ab0a08d87 | 686,610 |
import math
def get_tile_size(num_pixels, tile_size=400):
"""
num_pixels is the number of pixels in a dimension of the image.
tile_size is the desired tile-size.
"""
# How many times can we repeat a tile of the desired size.
num_tiles = int(round(num_pixels / tile_size))
# Ensure that there is at least 1 tile.
num_tiles = max(1, num_tiles)
# The actual tile-size.
actual_tile_size = math.ceil(num_pixels / num_tiles)
return actual_tile_size | a139fdb8f4548dd2f8576f116943473475294bae | 686,613 |
def b(b1, b2):
"""
Return the difference between b1 and b2.
"""
return b1 - b2 | 7073bd8bceea55e130709db6f8f5ec722d3670c6 | 686,614 |
import csv
def load_examples(input_file):
"""Load data that we'll learn from"""
with open(input_file) as f:
reader = csv.reader(f)
header = next(reader)
assert header == ["From", "To"]
examples_to_learn_from = [l for l in reader]
return examples_to_learn_from | e49f9f81f6993aa100378a0777166ef2efbcecdc | 686,615 |
def sublist(full_list, index):
""" returns a sub-list of contiguous non-emtpy lines starting at index """
sub_list = []
while index < len(full_list):
line = full_list[index].strip()
if line != "":
sub_list.append(line)
index += 1
else: break
return sub_list | ee4ed5e730829a22cb825e39aa95b19e037dda00 | 686,617 |
import imp
def load_model(model_name, X_train, y_train, optimization_parameters, sklearn_model=None):
"""
Loads the base model model with the specific parameters
:param model_name: the name of the model
:param X_train: training data (# of samples x # of features)
:param y_train: labels for the training data (# of samples * 1)
:return: model object
"""
model_source = imp.load_source(model_name, 'models/%s.py' % (model_name))
model = model_source.Model(X_train, y_train, optimization_parameters, sklearn_model)
return model | 201c0b66e5cce4236f9757e781e7a9428c53840e | 686,621 |
def is_int(n):
"""Determines if a value is a valid integer"""
try:
int(n)
return True
except ValueError:
return False | 1e99dcc056971e030853fef23b10340b2d4bf3fb | 686,622 |
def _token_to_dict(name, prefix=None):
"""
Generates function that converts a token to a dict containing the token name as key.
The length of prefix is stripped from the key.
"""
if prefix is not None:
return lambda x: {name[len(prefix) :]: x[0]}
return lambda x: {name: x[0]} | 2c141bb780c45c3d1bd387ed1423f900cc8a3bdb | 686,625 |
def keypoint_outside_bbox(kp, bb):
"""
Check if keypoint is outside box
:param kp: co ordinates of keypoint(x, y)
:param bb: bounding box details (x_top_left, y_top_left, x_bottom_right, y_bottom_right)
:return: True if keypoint is outside of bounding box, else False
"""
x = kp.pt[0]
y = kp.pt[1]
x_top_left, y_top_left, x_bottom_right, y_bottom_right = bb
return not ((x > x_top_left) and (x < x_bottom_right)
and (y > y_top_left) and (y < y_bottom_right)) | a95c02dae09468bfadd5d88f16fdf40848ef343e | 686,626 |
def isInt(string):
"""
Determine whether a string is an int
@param string: the string to check
@return True if the string is an int, False otherwise
"""
try: int(string)
except: return False
return True | 400eebfd4c0eeac78e56fe026e5e7b5846f9df31 | 686,628 |
def is_unlinked_account(user):
"""
Returns whether the provided user has authenticated via
an InstitutionAccount not yet linked to a Uniauth profile.
"""
return user.username and user.username.startswith('cas-') | 6090c8fc6e1eacb3063bc39178f3f8dc4757274c | 686,631 |
def add_prefix_safely(token, prefix):
"""Attaches the passed in prefix argument to the front of the token, unless the token
is an empty string in which case nothing happens and the token is returned unchaged.
This can be important for avoiding accidentally making a meaningless token meaningful
by modifying it with an additional text component.
Args:
token (str): Any arbitrary string.
prefix (str): Any arbitrary string.
Returns:
str: The token with the prefix added to the beginning of the string.
"""
if len(token) > 0:
return("{}{}".format(prefix, token))
else:
return("") | bccf22b4ecbac2de40796e0b46c2707142ab3a70 | 686,632 |
def in_bounds(lat, lon, corners):
"""
Return true if the lat lon is within the corners.
"""
return \
lat >= corners[0] and lat <= corners[2] and \
lon >= corners[1] and lon <= corners[3] | 680512f122f7110ff20fdf85891d0f0b87df1b29 | 686,634 |
def hours2days(input_hours):
""" Receive the hours and will return ow many days and hours there is in the input """
# Floor division to get the days but not the extra hours
days = input_hours // 24
# Modulus to get the left hours after calculating the days
hours = input_hours % 24
# Returns tuples
return days, hours | fcd04352aa990154d55bd8c3352c717d06ad50f0 | 686,636 |
def class_name(obj):
"""Fetch the class name of an object (class or instance).
Another cosmetic shortcut. The builtin way of getting an instance's class
name is pretty disgusting (and long), accessing two hidden attributes in
a row just feels wrong.
:param any obj: The object you want the class name of
:returns str: The class name
"""
if isinstance(obj, type):
# It's a class.
return obj.__name__
else:
# It's an instance of a class.
return obj.__class__.__name__ | 6e6e41df7d8eb4fe83f4bea7e763446805a55477 | 686,640 |
import typing
import re
def extract_ints(raw: str) -> typing.List[int]:
"""Utility function to extract all integers from some string.
Many inputs can be directly parsed with this function.
"""
return list(map(int, re.findall(r"((?:-|\+)?\d+)", raw))) | 6385360142bfa9ba39467a2f534404e48b48c528 | 686,641 |
def tokenize_description(description, has_name):
"""
Splits comma-separated resource descriptions into tokens.
:param description: String describing a resource, as described in the ion-hash-test-driver CLI help.
:param has_name: If True, there may be three tokens, the first of which must be the resource's name. Otherwise,
there may be a maximum of two tokens, which represent the location and optional revision.
:return: If `has_name` is True, three components (name, location, revision). Otherwise, two components
(name, location)
"""
components = description.split(',')
max_components = 3
if not has_name:
max_components = 2
if len(components) < max_components:
revision = 'master'
else:
revision = components[max_components - 1]
if len(components) < max_components - 1:
raise ValueError("Invalid implementation description.")
if has_name:
return components[0], components[max_components - 2], revision
else:
return components[max_components - 2], revision | 34c27f892a1c3e08782c33a28f0ce76a13934044 | 686,642 |
from typing import Counter
def _get_object_count_by_type(objects):
"""Counts Python objects by type."""
return Counter(map(type, objects)) | 49b29527e08b404dd5a56090c70c82d84768ad6a | 686,645 |
def Sqr_Chord_Len_C3V(vec1, vec2):
"""Computes the square length of the difference between the two arguments.
The arguments are assumed to be 3d vectors (indexable items of length 3)."""
diff0 = vec1[0] - vec2[0];
diff1 = vec1[1] - vec2[1];
diff2 = vec1[2] - vec2[2];
result = diff0 * diff0 + diff1 * diff1 + diff2 * diff2;
return result | 2d89ddaa35aba5cc7ee9d30d07086288c3b6194f | 686,646 |
def set_binary_labels(input_examples, positive_label):
"""
Replaces the class labels with 1.0 or -1.0, depending on whether
the class label matches 'positive_label'.
Returns an array of tuples, where the first element is the label number
and the second is a path to the image file.
"""
examples = []
for example in input_examples:
if example[0] == positive_label:
examples.append(("1.0", example[1]))
else:
examples.append(("-1.0", example[1]))
return examples | e610ffb8abf6ec5e672b0a5615e219e1489ff59a | 686,647 |
def _ExtractCommentText(comment, users_by_id):
"""Return a string with all the searchable text of the given Comment PB."""
commenter_email = users_by_id[comment.user_id].email
return '%s %s %s' % (
commenter_email,
comment.content,
' '.join(attach.filename
for attach in comment.attachments
if not attach.deleted)) | 7c372eb15805ca3f47b54c32ba9afb64208815dc | 686,649 |
import torch
def inv_quad_chol_lower(x,L,y):
"""
Computes x (L L^T) y
x : (m x n)
L : (n x n)
y : (n x k)
"""
m = x.shape[0]
if m == 1:
xy = torch.cat([x.reshape(-1,1),y],dim=1)
return torch.triangular_solve(xy,L,upper=False)[0].prod(dim=1).sum(dim=0).reshape(1,1)
else:
z1 = torch.triangular_solve(x.transpose(1,0),L,upper=False)[0].transpose(1,0) #m x n
z2 = torch.triangular_solve(y,L,upper=False)[0] #n x k
return torch.matmul(z1,z2) | 5f89775b2c735c71e904562a110a1d550e9d7665 | 686,655 |
import re
def get_original_image_link(url):
"""
Parse the image URL to cut off the 150x150 from the RSS feed
Args:
url (str): The image URL
Returns:
str: The updated URL if it matches the regex, else the original URL
"""
match = re.match(r"^(?P<prefix>.+)-150x150\.(?P<suffix>.+)$", url)
if match:
return f"{match.group('prefix')}.{match.group('suffix')}"
return url | 31b9a0e42b1a1852e2afe974aa0b799e266cd4cd | 686,656 |
def rgb_to_hex(rgb_color: tuple) -> str:
"""
Convert rgb color to hex
example: (255, 255, 255) to #FFFFFF
:param rgb_color:
:return:
"""
r, g, b = rgb_color
return '#%02x%02x%02x' % (r, g, b) | a382d69fb4d12c6ae977fa30b4e097d4da675840 | 686,657 |
def _slugify(value: str) -> str:
"""
Converts the value to a slugified version reducing the str down to just
alpha-numeric values and removing white-space
:param value: value as a str
:return: slugified version of the value
"""
return ''.join(s for s in value if s.isalnum()).lower() | dd0e28381f1033c4fea9bb4b368a715f2ba132fe | 686,664 |
def sort_nodes_by_priority(g):
"""
Sort node ids in ascending order of priority.
:param g: a game graph.
:return: the node ids sorted in ascending order of priority.
"""
# x is a key of the dictionary a value (1, 4) means player 1, priority 4
# we return the node id, sorted by priority incrementally
return sorted(g.nodes.iterkeys(), key=lambda x: g.nodes[x][1]) | 3018606b8bb49c217b106f957b47c41f4680b22c | 686,665 |
def notcontains(value, arg):
"""
Test whether a value does not contain any of a given set of strings.
`arg` should be a comma-separated list of strings.
"""
for s in arg.split(','):
if s in value:
return False
return True | d7d2b5bf4b3b28b771083677b0817537cdeea455 | 686,666 |
def power_time_series(series, scalar):
""" Multiply a series by itself X times where X is a scalar """
s = str(series)
return f"multiply([{','.join([s for _ in range(scalar)])}])" | 1d76ccf684d5ed2380be6cf3f7dad846b36f1733 | 686,669 |
def TraceAgent(agent):
"""Wrap the agent's program to print its input and output. This will let
you see what the agent is doing in the environment."""
old_program = agent.program
def new_program(percept):
action = old_program(percept)
print('{} perceives {} and does {}'.format(agent, percept, action))
return action
agent.program = new_program
return agent | ef6c2bdf48e16645f8374e85a19b2060ca450537 | 686,670 |
def is_valid_id(id):
"""Check if id is valid. """
parts = id.split(':')
if len(parts) == 3:
group, artifact, version = parts
if group and artifact and version:
return True
return False | c81ff28cbca39e601ddc7ea0786db0e62779096a | 686,681 |
def STD_DEV_SAMP(*expression):
"""
Calculates the sample standard deviation of the input values.
Use if the values encompass a sample of a population of data from which to generalize about the population.
See https://docs.mongodb.com/manual/reference/operator/aggregation/stdDevSamp/
for more details
:param expression: expression/expressions
:return: Aggregation operator
"""
return {'$stdDevSamp': list(expression)} if len(expression) > 1 else {'$stdDevSamp': expression[0]} if expression else {} | cfd4b27cf75e014f8c7debda0dfd97dfa118d7b9 | 686,685 |
def misclassification_percentage(y_true, y_pred):
"""
Returns misclassification percentage ( misclassified_examples / total_examples * 100.0)
"""
misclassified_examples = list(y_true == y_pred).count(False) * 1.
total_examples = y_true.shape[0]
return (misclassified_examples / total_examples) * 100.0 | 262057b506ea3e3edbefc9bc019f36b67b485ee1 | 686,687 |
import socket
def int_to_ip(ip):
"""Convert 32-bit integer to an IP"""
return socket.inet_ntoa(ip.to_bytes(4, 'big')) | 4263848279ba9dd5b45c04ede7ac20e38ab9dd60 | 686,688 |
from typing import Iterable
def _unique(*args: Iterable) -> list:
"""Return a list of all unique values, in order of first appearance.
Args:
args: Iterables of values.
Examples:
>>> _unique([0, 2], (2, 1))
[0, 2, 1]
>>> _unique([{'x': 0, 'y': 1}, {'y': 1, 'x': 0}], [{'z': 2}])
[{'x': 0, 'y': 1}, {'z': 2}]
"""
values = []
for parent in args:
for child in parent:
if child not in values:
values.append(child)
return values | 5d43dfa3d13b04b40c2ae37fac25df44d4d37295 | 686,691 |
def linear_function(m, x, b):
""" A linear function of one variable x
with slope m and and intercept b.
"""
return m * x + b | f61b62d36314483029a3f668a8aaa2b98b1a765c | 686,692 |
import functools
def dgetattr(obj, name, is_dict=False):
"""
get deep attribute
operates the same as getattr(obj, name) but can use '.' for nested attributes
e.g. dgetattr(my_object, 'a.b') would return value of my_object.a.b
"""
atr = dict.__getitem__ if is_dict else getattr
names = name.split('.')
names = [obj] + names
return functools.reduce(atr, names) | 0bde83bf68c031d7ea27bb42ffde6c9f751b94fa | 686,693 |
import json
from pathlib import Path
def get_total_epochs(save_path, run, last_gen):
"""
Compute the total number of performed epochs.
Parameters
----------
save_path: str
path where the ojects needed to resume evolution are stored.
run : int
current evolutionary run
last_gen : int
count the number of performed epochs until the last_gen generation
Returns
-------
total_epochs : int
sum of the number of epochs performed by all trainings
"""
total_epochs = 0
for gen in range(0, last_gen+1):
j = json.load(open(Path('%s/run_%d/gen_%d.csv' % (save_path, run, gen))))
num_epochs = [elm['num_epochs'] for elm in j]
total_epochs += sum(num_epochs)
return total_epochs | a9f046640b2502ae5057ab9cfc88ea37d895863e | 686,701 |
def filter_same_tokens(tokens):
"""
Function for filtering repetitions in tokens.
:param tokens: list with str
List of tokens for filtering.
:return:
Filtered list of tokens without repetitions.
"""
if not isinstance(tokens, list):
raise TypeError
for token in tokens:
if not isinstance(token, str):
raise TypeError
tmp_set = set(tokens)
return [token for token in tmp_set] | bb489aea2dd7c05b2c359555fcc8e7f619859d0c | 686,702 |
import json
def loads(json_str):
"""Load an object from a JSON string"""
return json.loads(json_str) | 2a6d810349e99db6f5169b9e50df7f070f3de70a | 686,704 |
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