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import pickle
def load_pickle(filename):
"""Load Pickfle file"""
filehandler = open(filename, 'rb')
return pickle.load(filehandler) | f93b13616f94c31bc2673232de14b834a8163c5f | 970 |
def is_str_str_dict(x):
"""Tests if something is a str:str dictionary"""
return isinstance(x, dict) and all(
isinstance(k, str) and isinstance(v, str) for k, v in x.items()
) | ce6230714c0526764f2cc67e4dedf598acd28169 | 982 |
def _ensureListLike(item):
"""
Return the item if it is a list or tuple, otherwise add it to a list and
return that.
"""
return item if (isinstance(item, list) or isinstance(item, tuple)) \
else [item] | 1c602a1fcf8dd6a5b4583264e63e38747f5b0d50 | 983 |
import io
def get_file_from_gitlab(gitpkg, path, ref="master"):
"""Retrieves a file from a Gitlab repository, returns a (StringIO) file."""
return io.StringIO(gitpkg.files.get(file_path=path, ref=ref).decode()) | 7eccad01a538bdd99651b0792aff150f73e82cdd | 984 |
def count_disordered(arr, size):
"""Counts the number of items that are out of the expected
order (monotonous increase) in the given list."""
counter = 0
state = {
"expected": next(item for item in range(size) if item in arr),
"checked": []
}
def advance_state():
state["expected"] += 1
while True:
in_arr = state["expected"] in arr
is_overflow = state["expected"] > size
not_checked = state["expected"] not in state["checked"]
if not_checked and (in_arr or is_overflow):
return
state["expected"] += 1
for val in arr:
if val == state["expected"]:
advance_state()
else:
counter += 1
state["checked"].append(val)
return counter | bb708e7d862ea55e81207cd7ee85e634675b3992 | 986 |
def every(n_steps):
"""Returns True every n_steps, for use as *_at functions in various places."""
return lambda step: step % n_steps == 0 | 02fc6bc59fa6f223b681539baeae32c40bd9577e | 991 |
def calc_batch_size(num_examples, batches_per_loop, batch_size):
"""Reduce the batch size if needed to cover all examples without a remainder."""
assert batch_size > 0
assert num_examples % batches_per_loop == 0
while num_examples % (batch_size * batches_per_loop) != 0:
batch_size -= 1
return batch_size | 3c394813a98a8414645f633a519001937247e8b0 | 992 |
def has_admin_access(user):
"""Check if a user has admin access."""
return user == 'admin' | d178861bee504f6f3026c9e495d56cc8d2d7c3d3 | 993 |
def flatten_mock_calls(mock):
"""
Flatten the calls performed on a particular mock object,
into a list of calls with arguments.
"""
result = []
for call in mock.mock_calls:
call = list(call)
call_name = call[0]
if '.' in str(call_name):
call_name = str(call_name).split('.')[-1]
result.append([call_name] + call[1:])
return result | 7c41025382f4ca25db1ccd328e9eb17e1d72a01a | 995 |
def update_not_existing_kwargs(to_update, update_from):
"""
This function updates the keyword aguments from update_from in
to_update, only if the keys are not set in to_update.
This is used for updated kwargs from the default dicts.
"""
if to_update is None:
to_update = {}
to_update.update({k:v for k,v in update_from.items() if k not in to_update})
return to_update | a66de151e6bc6d8f5b2f1b0ff32e30d2c8cb5277 | 996 |
from typing import List
def add_multiple_package(package_list: List[str]) -> str:
"""
Generate latex code to add multiple package to preamble
:param package_list: List of package to add in preamble
"""
usepackage_command_list = []
for package in package_list:
usepackage_command_list.append(rf"""\usepackage{{{package}}}""")
return "\n".join(usepackage_command_list) | 90bdd0a521c094d92c35ef92e62d6b43f6b135b4 | 1,002 |
def commit_veto(environ, status, headers):
"""Veto a commit.
This hook is called by repoze.tm in case we want to veto a commit
for some reason. Return True to force a rollback.
By default we veto if the response's status code is an error code.
Override this method, or monkey patch the instancemethod, to fine
tune this behaviour.
"""
return not 200 <= int(status.split(None, 1)[0]) < 400 | 9fc96fe8cdbedde20cb325e189b71d9df94cf176 | 1,004 |
import torch
def one_hot(y, num_dim=10):
"""
One Hot Encoding, similar to `torch.eye(num_dim).index_select(dim=0, index=y)`
:param y: N-dim tenser
:param num_dim: do one-hot labeling from `0` to `num_dim-1`
:return: shape = (batch_size, num_dim)
"""
one_hot_y = torch.zeros(y.size(0), num_dim)
if y.is_cuda:
one_hot_y = one_hot_y.cuda()
return one_hot_y.scatter_(1, y.view(-1, 1), 1.) | 694bfea18ecbb5c5737e0d38c0aa0f5f52a82a55 | 1,007 |
def extend_dict(x, *y):
"""Similar to Object.assign() / _.extend() in Javascript, using
'dict.update()'
Args:
x (dict): the base dict to merge into with 'update()'
*y (dict, iter): any number of dictionary or iterable key/value
pairs to be sequentially merged into 'x'. Skipped if None.
"""
z = x.copy()
for d in [d for d in y if d is not None]:
z.update(d)
return z | f10a5bc7d5ed3646e6a9f8f9535a16bd800c7fcd | 1,012 |
def make_general_csv_rows(general_csv_dict):
"""
Method for make list of metrics from general metrics dict.
Rows using in general metrics writer
:param general_csv_dict: dict with all metrics
:type general_csv_dict: dict
:return: all metrics as rows
:rtype: list
"""
rows = []
for key, value in general_csv_dict.items():
row = [key[0], key[1]]
row.extend(value)
rows.append(row)
return rows | 45ca165d312b39cd0b7088e0bcbfb402a92e7e2b | 1,015 |
def get_speakable_timestamp(timestamp):
"""Return a 'speakable' timestamp, e.g. 8am, noon, 9pm, etc."""
speakable = f"{timestamp.strftime('%I').lstrip('0')} {timestamp.strftime('%p')}"
if speakable == '12 PM':
return 'noon'
elif speakable == '12 AM':
return 'midnight'
return speakable | 0b724686ebd5d3152d9017dc456d2945c78be0ee | 1,016 |
import torch
def _featurize(inputs,model):
"""
Helper function used to featurize exemplars before feeding into
buffer.
"""
with torch.no_grad():
# Forward pass
outputs = model(*inputs).detach() #Featurize raw exem
return outputs | 191fd1b362f38309a35618284fcf3f1910a06bd6 | 1,017 |
def make_file_prefix(run, component_name):
"""
Compose the run number and component name into string prefix
to use with filenames.
"""
return "{}_{}".format(component_name, run) | 73ef37d75d9e187ee49ee058958c3b8701185585 | 1,022 |
import base64
def multibase_b64decode(data):
"""
Follow forge's base64 urlsafe encode convention to decode string
Args:
data(string): encoded string
Returns: bytes
Examples:
>>> multibase_b64decode('aGVsbG8')
b'hello'
"""
if isinstance(data, str):
data = data.encode()
return base64.urlsafe_b64decode(
(data + b'=' * (-len(data) % 4))) | fdbc0f937e33d7994737a3a515973598cac3debd | 1,025 |
import hashlib
def sha1_file(filename):
"""
Return the hex string representation of the SHA1 checksum of the filename
"""
s = hashlib.sha1()
with open(filename, "rb") as f:
for line in f:
s.update(line)
return s.hexdigest() | b993ac9f025d69124962905f87b1968617bb33f5 | 1,032 |
def read_from_file(file_path):
"""
Read a file and return a list with all the lines in the file
"""
file_in_list = []
with open(file_path, 'r') as f:
for line in f.readlines():
file_in_list.append(line)
return file_in_list | 5fef3a3f50528c1a9786451666ae7e43be282bf9 | 1,033 |
import sqlite3
def cn(DB):
"""Return the cursor and connection object."""
conn = sqlite3.connect(DB)
c = conn.cursor()
return (c,conn) | 76abbec283d45732213f8b94031242146cdb4ee0 | 1,043 |
import copy
def partially_matched_crossover(random, mom, dad, args):
"""Return the offspring of partially matched crossover on the candidates.
This function performs partially matched crossover (PMX). This type of
crossover assumes that candidates are composed of discrete values that
are permutations of a given set (typically integers). It produces offspring
that are themselves permutations of the set.
.. Arguments:
random -- the random number generator object
mom -- the first parent candidate
dad -- the second parent candidate
args -- a dictionary of keyword arguments
Optional keyword arguments in args:
- *crossover_rate* -- the rate at which crossover is performed
(default 1.0)
"""
crossover_rate = args.setdefault('crossover_rate', 1.0)
if random.random() < crossover_rate:
size = len(mom)
points = random.sample(range(size), 2)
x, y = min(points), max(points)
bro = copy.copy(dad)
bro[x:y+1] = mom[x:y+1]
sis = copy.copy(mom)
sis[x:y+1] = dad[x:y+1]
for parent, child in zip([dad, mom], [bro, sis]):
for i in range(x, y+1):
if parent[i] not in child[x:y+1]:
spot = i
while x <= spot <= y:
spot = parent.index(child[spot])
child[spot] = parent[i]
return [bro, sis]
else:
return [mom, dad] | b0d5132cf4ca14095f3d7c637cb50db3fe37d244 | 1,044 |
import re
def regex_trim(input, regex, replace=''):
"""
Trims or replaces the regex match in an input string.
input (string): the input string to search for matches
regex (string): regex to match
replace (string - optional): a string to replace any matches with. Defaults to trimming the match.
"""
return re.sub(regex, replace, input) | 169bfaa0d2bfd7a1f32c1e05a63b41993f82bf4b | 1,045 |
def prepare_definitions(defs, prefix=None):
"""
prepares definitions from a dictionary
With a provided dictionary of definitions in key-value pairs and builds them
into an definition list. For example, if a dictionary contains a key ``foo``
with a value ``bar``, the returns definitions will be a list with the values
``['foo=bar']``. If a key contains a value of ``None``, the key will be
ignored and will not be part of the final definition list. If a ``prefix``
value is provided, each definition entry will be prefixed with the provided
value.
Args:
defs: the arguments to process
prefix (optional): prefix value to prefix each definition
Returns:
list of arguments
"""
final = []
if defs:
for key, val in defs.items():
if val is None:
continue
if prefix:
key = prefix + key
if val:
final.append('{}={}'.format(key, val))
else:
final.append(key)
return final | ddc6d14cc18f8afba766efee65ab365df1d226c2 | 1,048 |
def extract_job_url(job):
"""
parse the job data and extract the str for the URL of the job posted
params:
job str: html str representation from bs4
returns:
url str: relative URL path of the job ad
"""
return job.a["href"] | 7517badcc2814e641c04a8f880353d897d434b7f | 1,049 |
import random
def check_random_state(seed):
"""
Turn seed into a random.Random instance
If seed is None, return the Random singleton used by random.
If seed is an int, return a new Random instance seeded with seed.
If seed is already a Random instance, return it.
Otherwise raise ValueError.
"""
# Code slightly adjusted from scikit-learn utils/validation.py
if seed is None or isinstance(seed, int):
rng = random.Random(seed)
elif isinstance(seed, random.Random):
rng = seed
else:
raise ValueError(
"### error: '{}' cannot be used to seed random.Random instance.".format(
seed
)
)
return rng | 347481de01f4a3bba59bc9a2c484c10d4857e1e2 | 1,055 |
def get_user_playlists(spotipy_obj, username):
"""Gets and returns all Spotify playlists owned by the username specified.
Parameters:
spotipy_obj: Spotipy object
username: Spotify username
Returns:
List of dictionaries, each dictionary a Spotify playlist object.
"""
# Grab all user playlists, including private ones
initial_playlists = spotipy_obj.user_playlists(username)
final_playlists = []
while initial_playlists:
for playlist in initial_playlists["items"]:
if playlist["owner"]["id"] == username:
final_playlists.append(playlist)
if initial_playlists["next"]:
initial_playlists = spotipy_obj.next(initial_playlists)
else:
initial_playlists = None
return final_playlists | 90c06e0ddd91a7a84f4d905dd9334f9b4c27f890 | 1,060 |
def compute_median_survival_time(times, surv_function):
"""
Computes a median survival time estimate by looking for where the survival
function crosses 1/2.
Parameters
----------
times : 1D numpy array
Sorted list of unique times (in ascending order).
surv_function : 1D numpy array
A survival function evaluated at each of time in `times`, in the same
order.
Returns
-------
output : float
Median survival time estimate.
"""
t_left = times[0]
t_right = times[-1]
if surv_function[-1] > 1/2:
# survival function never crosses 1/2; just output this last time point
return t_right
for t, s in zip(times, surv_function):
if s >= 0.5:
t_left = t
for t, s in zip(reversed(times), reversed(surv_function)):
if s <= 0.5:
t_right = t
return (t_left + t_right) / 2. | 22103bc705acb791c0937a403aa9c34e9145e1c2 | 1,063 |
def aggregate_ant(data, sub_num, response_type="full"):
"""
Aggregate data from the ANT task.
Calculates various summary statistics for the ANT task for a given subject.
Parameters
----------
data : dataframe
Pandas dataframe containing a single subjects trial data for the task.
sub_num : str
Subject number to which the data file belongs.
response_type : {'full', 'correct', 'incorrect'}, optional
Should the summary data be calculated using all trials? Only correct
trials? Or only incorrect trials? This is not supported in all tasks.
Returns
-------
stats : list
List containing the calculated data for the subject.
"""
# Calculate times following errors and correct responses
df = data
follow_error_rt = df.loc[df.correct.shift() == 0, "RT"].mean()
follow_correct_rt = df.loc[df.correct.shift() == 1, "RT"].mean()
if response_type == "correct":
df = data[data["correct"] == 1]
elif response_type == "incorrect":
df = data[data["correct"] == 0]
elif response_type == "full":
df = data
# Aggregated descriptives
## congruency conditions
grouped_congruency = df.groupby("congruency")
neutral_rt = grouped_congruency.mean().get_value("neutral", "RT")
congruent_rt = grouped_congruency.mean().get_value("congruent", "RT")
incongruent_rt = grouped_congruency.mean().get_value("incongruent", "RT")
neutral_rtsd = grouped_congruency.std().get_value("neutral", "RT")
congruent_rtsd = grouped_congruency.std().get_value("congruent", "RT")
incongruent_rtsd = grouped_congruency.std().get_value("incongruent", "RT")
neutral_rtcov = neutral_rtsd / neutral_rt
congruent_rtcov = congruent_rtsd / congruent_rt
incongruent_rtcov = incongruent_rtsd / incongruent_rt
neutral_correct = grouped_congruency.sum().get_value("neutral", "correct")
congruent_correct = grouped_congruency.sum().get_value("congruent", "correct")
incongruent_correct = grouped_congruency.sum().get_value("incongruent", "correct")
## cue conditions
grouped_cue = df.groupby("cue")
nocue_rt = grouped_cue.mean().get_value("nocue", "RT")
center_rt = grouped_cue.mean().get_value("center", "RT")
spatial_rt = grouped_cue.mean().get_value("spatial", "RT")
double_rt = grouped_cue.mean().get_value("double", "RT")
nocue_rtsd = grouped_cue.std().get_value("nocue", "RT")
center_rtsd = grouped_cue.std().get_value("center", "RT")
spatial_rtsd = grouped_cue.std().get_value("spatial", "RT")
double_rtsd = grouped_cue.std().get_value("double", "RT")
nocue_rtcov = nocue_rtsd / nocue_rt
center_rtcov = center_rtsd / center_rt
spatial_rtcov = spatial_rtsd / spatial_rt
double_rtcov = double_rtsd / double_rt
nocue_correct = grouped_cue.sum().get_value("nocue", "correct")
center_correct = grouped_cue.sum().get_value("center", "correct")
spatial_correct = grouped_cue.sum().get_value("spatial", "correct")
double_correct = grouped_cue.sum().get_value("double", "correct")
# OLS regression
conflict_intercept, conflict_slope = congruent_rt, incongruent_rt - congruent_rt
conflict_slope_norm = conflict_slope / congruent_rt
alerting_intercept, alerting_slope = double_rt, nocue_rt - double_rt
alerting_slope_norm = alerting_slope / double_rt
orienting_intercept, orienting_slope = spatial_rt, center_rt - spatial_rt
orienting_slope_norm = orienting_slope / spatial_rt
return [
sub_num,
follow_error_rt,
follow_correct_rt,
neutral_rt,
congruent_rt,
incongruent_rt,
neutral_rtsd,
congruent_rtsd,
incongruent_rtsd,
neutral_rtcov,
congruent_rtcov,
incongruent_rtcov,
neutral_correct,
congruent_correct,
incongruent_correct,
nocue_rt,
center_rt,
spatial_rt,
double_rt,
nocue_rtsd,
center_rtsd,
spatial_rtsd,
double_rtsd,
nocue_rtcov,
center_rtcov,
spatial_rtcov,
double_rtcov,
nocue_correct,
center_correct,
spatial_correct,
double_correct,
conflict_intercept,
conflict_slope,
conflict_slope_norm,
alerting_intercept,
alerting_slope,
alerting_slope_norm,
orienting_intercept,
orienting_slope,
orienting_slope_norm,
] | be01651d450560a5c36bc6240025fe59352d6347 | 1,064 |
def display_ordinal_value(glyph: str):
"""Displays the integer value of the given glyph
Examples:
>>> display_ordinal_value('🐍')\n
128013
>>> display_ordinal_value('G')\n
71
>>> display_ordinal_value('g')\n
103
"""
return ord(glyph) | 7daa53180023bfec2968308d463ac615a83a4e55 | 1,072 |
import re
def tokens(s):
"""Return a list of strings containing individual words from string s.
This function splits on whitespace transitions, and captures apostrophes
(for contractions).
>>> tokens("I'm fine, how are you?")
["I'm", 'fine', 'how', 'are', 'you']
"""
words = re.findall(r"\b[\w']+\b", s)
return words | aee0b6fad2f9107c893496f1f3807e80c9d2e44b | 1,079 |
def potatoes(p0, w0, p1):
"""
- p1/100 = water1 / water1 + (1 - p0/100) * w0
=> water1 = w0 * p1/100 * (1 - p0/100) / (1 - p1/100)
- dry = w0 * (1 - p0/100)
- w1 = water1 + dry = w0 * (100 - p0) / (100 - p1)
Example:
98/100 = water1 / water1 + (1- 99/100) * 100
water1 = 49
w1 = 49 + 1 = 50
"""
w1 = w0 * (100 - p0) / (100 - p1)
return int(w1) | f2955a58db3a48c64b6acc4980e663f33332aeea | 1,084 |
def relacao(lista):
"""Crie uma função que recebe uma lista de números reais e retorna uma outra lista de tamanho 3 em que
(i) o primeiro elemento é a quantidade de números maiores que zero,
(ii) o segundo elemento é a quantidade de números menores que zero e
(iii) o último elemento é a quantidade de zeros da lista inicial.
Args:
lista (list): lista recebida para ser processada pela funcao
Returns:
list: lista com tamanho três na ordem (maiores, menores e iguais a zero)
"""
maior = menor = igual = 0
for i in lista:
if i > 0:
maior += 1
elif i < 0:
menor += 1
else:
igual += 1
return f'[{maior},{menor},{igual}]' | 39e45d8221d5d5b7322ebec5aa3f761d9e2ef413 | 1,087 |
def german_weekday_name(date):
"""Return the german weekday name for a given date."""
days = [u'Montag', u'Dienstag', u'Mittwoch', u'Donnerstag', u'Freitag', u'Samstag', u'Sonntag']
return days[date.weekday()] | 7d2919c61438ec913abe38cccd924bb69f866655 | 1,089 |
def identity_func(x):
"""The identify (a.k.a. transparent) function that returns it's input as is."""
return x | 06e0296c338d68663aa87d08b21f84919be3f85e | 1,090 |
def make_choice_validator(
choices, default_key=None, normalizer=None):
"""
Returns a callable that accepts the choices provided.
Choices should be provided as a list of 2-tuples, where the first
element is a string that should match user input (the key); the
second being the value associated with the key.
The callable by default will match, upon complete match the first
value associated with the result will be returned. Partial matches
are supported.
If a default is provided, that value will be returned if the user
provided input is empty, i.e. the value that is mapped to the empty
string.
Finally, a normalizer function can be passed. This normalizes all
keys and validation value.
"""
def normalize_all(_choices):
# normalize all the keys for easier comparison
if normalizer:
_choices = [(normalizer(key), value) for key, value in choices]
return _choices
choices = normalize_all(choices)
def choice_validator(value):
if normalizer:
value = normalizer(value)
if not value and default_key:
value = choices[default_key][0]
results = []
for choice, mapped in choices:
if value == choice:
return mapped
if choice.startswith(value):
results.append((choice, mapped))
if len(results) == 1:
return results[0][1]
elif not results:
raise ValueError('Invalid choice.')
else:
raise ValueError(
'Choice ambiguous between (%s)' % ', '.join(
k for k, v in normalize_all(results))
)
return choice_validator | 65ac672f16a1031a9051bc4f6769c6b1b88db727 | 1,091 |
def gen_event_type_entry_str(event_type_name, event_type, event_config):
"""
return string like:
{"cpu-cycles", PERF_TYPE_HARDWARE, PERF_COUNT_HW_CPU_CYCLES},
"""
return '{"%s", %s, %s},\n' % (event_type_name, event_type, event_config) | ca89c19b45f182b8a7ae74ab76f3f42bddf46811 | 1,092 |
from pathlib import Path
def get_force_charge() -> str:
"""
Gets the command object for the force charge command
Returns:
The command object as a json string
"""
force_charge = Path('force_charge.json').read_text()
return force_charge | c67277c62664419c3b4a19ae57ea6de027c60416 | 1,093 |
def _non_string_elements(x):
"""
Simple helper to check that all values of x are string. Returns all non string elements as (position, element).
:param x: Iterable
:return: [(int, !String), ...]
"""
problems = []
for i in range(0, len(x)):
if not isinstance(x[i], str):
problems.append((i, x[i]))
return problems | 974715622949157693084823a52a88973b51d100 | 1,095 |
def get_filename_pair(filename):
"""
Given the name of a VASF data file (*.rsd) or parameter file (*.rsp) return
a tuple of (parameters_filename, data_filename). It doesn't matter if the
filename is a fully qualified path or not.
- assumes extensions are all caps or all lower
"""
param_filename = data_filename = filename[:-1]
if filename[-1:].isupper():
data_filename += 'D'
param_filename += 'P'
else:
data_filename += 'd'
param_filename += 'p'
return (param_filename, data_filename) | f6eb5a64cf472f230c5806447d9c2ee8ae43a71d | 1,097 |
def addBenchmark(df):
"""Add benchmark to df."""
# Compute the inverse of the distance
distance_inv = (1. / df.filter(regex='^distance*', axis=1)).values
# Extract the value at the nearest station
values = df.filter(regex='value_*', axis=1)
# Compute the benchmark
numer = (distance_inv * values).sum(axis=1)
denom = (distance_inv * (values != 0)).sum(axis=1)
# Compute the benchmark
benchmark = numer / denom
df["Benchmark"] = benchmark
return df | 62c63215d622c46bed8200f97ad55b985e2beb20 | 1,100 |
def is_file_like(f):
"""Check to see if ```f``` has a ```read()``` method."""
return hasattr(f, 'read') and callable(f.read) | 9eee8c8f4a6966d1db67fb4aa9149e2fbd390fb9 | 1,101 |
def check_protocol(protocol):
"""
Check if a given protocol works by computing the qubit excitation probabilities
"""
qubit_weight = {}
qubit_weight[protocol[0][0][0]] = 1.0
for pair_set in protocol:
for i, j, p in pair_set:
qubit_weight[j] = qubit_weight[i] * (1.0 - p)
qubit_weight[i] *= p
return qubit_weight | 8b9d0a8e329a340718d37bc79066be4a05cf2d20 | 1,102 |
def choose_first_not_none(*args):
""" Choose first non None alternative in args.
:param args: alternative list
:return: the first non None alternative.
"""
for a in args:
if a is not None:
return a
return None | fe3efba85251161cd0a6ecb50583cc443cd04dc0 | 1,103 |
def add_dict(dct1, dct2):
"""Returns a new dictionaries where the content of the dictionaries `dct1`
and `dct2` are merged together."""
result = dct1.copy()
result.update(dct2)
return result | eba785e4d00534e94c1bdde413603d64e18aac05 | 1,105 |
def mapdict(itemfunc, dictionary):
"""
Much like the builtin function 'map', but works on dictionaries.
*itemfunc* should be a function which takes one parameter, a (key,
value) pair, and returns a new (or same) (key, value) pair to go in
the dictionary.
"""
return dict(map(itemfunc, dictionary.items())) | 1f0573410f82acb1f3c06029cf4bfaccd295e1ac | 1,110 |
def get_classpath(obj):
"""
Return the full module and class path of the obj. For instance,
kgof.density.IsotropicNormal
Return a string.
"""
return obj.__class__.__module__ + "." + obj.__class__.__name__ | bf986e2b27dd8a216a2cc2cdb2fb2b8a83b361cc | 1,112 |
def _get_should_cache_fn(conf, group):
"""Build a function that returns a config group's caching status.
For any given object that has caching capabilities, a boolean config option
for that object's group should exist and default to ``True``. This
function will use that value to tell the caching decorator if caching for
that object is enabled. To properly use this with the decorator, pass this
function the configuration group and assign the result to a variable.
Pass the new variable to the caching decorator as the named argument
``should_cache_fn``.
:param conf: config object, must have had :func:`configure` called on it.
:type conf: oslo_config.cfg.ConfigOpts
:param group: name of the configuration group to examine
:type group: string
:returns: function reference
"""
def should_cache(value):
if not conf.cache.enabled:
return False
conf_group = getattr(conf, group)
return getattr(conf_group, 'caching', True)
return should_cache | 7a11124c640bfb3ced28e2d9395593b70dc85a0a | 1,128 |
def horizontal_move(t, h_speed=-2/320):
"""Probe moves horizontally at h_speed [cm/s]"""
return 0.*t, h_speed*t, 2/16 + 0*t | d9cf0e5b968e7d8319b7f63f7d1d7a4666484ad3 | 1,134 |
def categories_report(x):
"""Returns value counts report.
Parameters
----------
x: pd.Series
The series with the values
Returns
-------
string
The value counts report.
str1 = False 22 | True 20 | nan 34
str2 = False (22) | True (20) | nan (34)
"""
# Do counting and sorting
counts = x.value_counts(dropna=False)
counts.index = counts.index.map(str)
counts = counts.sort_index()
# Create different strings
str1 = ' | '.join(str(counts).split("\n")[:-1])
str2 = ' | '.join("%s (%s)" % (i, counts[i]) for i in counts.index)
# Return
return str2 | 695ccd73ee73a13e92edbdf0eb242121d136ddbb | 1,135 |
def euler(step, y0):
"""
Implements Euler's method for the differential equation dy/dx = 1/(2(y-1)) on the interval [0,4]
"""
x = [0]
index_x = 0
while x[index_x] < 4:
x.append(x[index_x] + step)
index_x += 1
index_y = 0
y = [y0]
def yprime(y):
yprime = 1 / (2 * (y - 1))
return yprime
while index_y < index_x:
y.append(y[index_y] + step * yprime(y[index_y]))
index_y += 1
return x, y | 89c6e6409a1c43ce4766507fba2f401bb01cfbb8 | 1,142 |
import logging
def update_softwaretitle_packages(api, jssid, pkgs):
"""
Update packages of software title
:param jssid: Patch Software Title ID
:param pkgs: dict of {version: package, ...}
:returns: None
"""
logger = logging.getLogger(__name__)
data = api.get(f"patchsoftwaretitles/id/{jssid}")
title = data['patch_software_title']
title_name = title['name']
logger.info(f"updating patch software title: {title_name} ({jssid})")
# single version (dict), multiple versions (list)
version = title['versions']['version']
_modified = False
try:
# access key of single version and count on TypeError being raised
v = version['software_version']
if v in pkgs.keys():
version['package'] = {'name': pkgs[v]}
_modified = True
except TypeError:
# looks like it was actually a list
for _version in version:
v = _version['software_version']
if v in pkgs.keys():
_version['package'] = {'name': pkgs[v]}
_modified = True
if _modified:
result = api.put(f"patchsoftwaretitles/id/{jssid}", data)
logger.info(f"succesfully updated: {title_name}")
return result
else:
logger.info(f"software title was not modified") | 0acb3dfbff0e85a2e8a876d5e5d484c4d1e52068 | 1,143 |
from typing import Sequence
def _table(*rows: Sequence) -> str:
"""
>>> _table(['a', 1, 'c', 1.23])
'|a|1|c|1.23|'
>>> _table(['foo', 0, None])
'|foo|||'
>>> print(_table(['multiple', 'rows', 0], ['each', 'a', 'list']))
|multiple|rows||
|each|a|list|
"""
return '\n'.join([
'|'.join(['', *[str(cell or '') for cell in row], '']) for row in rows
]) | d566da2ad9240e73b60af00d3e4b4e25607234b4 | 1,146 |
import difflib
def lines_diff(lines1, lines2):
"""Show difference between lines."""
is_diff = False
diffs = list()
for line in difflib.ndiff(lines1, lines2):
if not is_diff and line[0] in ('+', '-'):
is_diff = True
diffs.append(line)
return is_diff, diffs | 50916d46871980fadfd854dc698481a4b0f35834 | 1,150 |
import re
def countBasesInFasta(fastaFile):
"""
Given a fasta file, return a dict where the number of records and
the total number of bases are given by 'records' and 'bases' respectively.
"""
recordRE = re.compile(r'^>')
whiteSpaceRE = re.compile(r'\s+')
total_bases = 0
total_seqs = 0
with open(fastaFile) as f:
for line in f:
if recordRE.match(line):
total_seqs += 1
continue
total_bases += len(whiteSpaceRE.sub('', line))
return {'records': total_seqs, 'bases': total_bases} | 45eaa5b8d36b4bae6b97bb29fdead1efc0aed8c2 | 1,156 |
def linear_search(iterable, item):
"""Returns the index of the item in the unsorted iterable.
Iterates through a collection, comparing each item to the target item, and
returns the index of the first item that is equal to the target item.
* O(n) time complexity
* O(1) space complexity
Args:
iterable: A collection that is iterable.
item: An object to search for.
Returns:
The index of the item in the sorted iterable, or -1 if not found.
Raises:
TypeError: If iterable is not iterable.
"""
try:
_ = iter(iterable)
except TypeError:
raise TypeError('\'{}\' object is not iterable'.format(
type(iterable).__name__))
for index, _item in enumerate(iterable):
if _item == item:
return index
return -1 | bdbd7e70cea79deef1375648bde61067df1d2221 | 1,158 |
def create_MD_tag(reference_seq, query_seq):
"""Create MD tag
Args:
reference_seq (str) : reference sequence of alignment
query_seq (str) : query bases of alignment
Returns:
md_tag(str) : md description of the alignment
"""
no_change = 0
md = []
for ref_base, query_base in zip(reference_seq, query_seq):
if ref_base.upper() == query_base:
no_change += 1
else:
if no_change > 0:
md.append(str(no_change))
md.append(ref_base)
no_change = 0
if no_change > 0:
md.append(str(no_change))
return ''.join(md) | 4b711521d00af132e8e29fe4fc44785b985c2607 | 1,159 |
import re
def calc_word_frequency(my_string, my_word):
"""Calculate the number of occurrences of a given word in a given string.
Args:
my_string (str): String to search
my_word (str): The word to search for
Returns:
int: The number of occurrences of the given word in the given string.
"""
# Remove all non alphanumeric characters from the string
filtered_string = re.sub(r'[^A-Za-z0-9 ]+', '', my_string)
# Return the number of occurrences of my_word in the filtered string
return filtered_string.split().count(my_word) | 15ff723dd2ff089fb12cccb38283f1f75e37079d | 1,160 |
import hashlib
def intmd5(source: str, nbytes=4) -> int:
"""
Generate a predictive random integer of nbytes*8 bits based on a source string.
:param source:
seed string to generate random integer.
:param nbytes:
size of the integer.
"""
hashobj = hashlib.md5(source.encode())
return int.from_bytes(hashobj.digest()[:nbytes], byteorder="big", signed=False) | c03eb99a67af00a4a081423ecca3a724111514e1 | 1,161 |
def _partition_at_level(dendrogram, level) :
"""Return the partition of the nodes at the given level
A dendrogram is a tree and each level is a partition of the graph nodes.
Level 0 is the first partition, which contains the smallest snapshot_affiliations, and the best is len(dendrogram) - 1.
The higher the level is, the bigger are the snapshot_affiliations
"""
partition = dendrogram[0].copy()
for index in range(1, level + 1) :
for node, community in partition.items() :
partition[node] = dendrogram[index][community]
return partition | b179127076c386480c31a18a0956eb30d5f4ef2a | 1,167 |
def _format_stages_summary(stage_results):
"""
stage_results (list of (tuples of
(success:boolean, stage_name:string, status_msg:string)))
returns a string of a report, one line per stage.
Something like:
Stage: <stage x> :: SUCCESS
Stage: <stage y> :: FAILED
Stage: <stage z> :: SUCCESS
"""
#find the longest stage name to pad report lines
max_name_len = 0
for entry in stage_results:
x, stage_name, y = entry
name_len = len(stage_name)
if name_len > max_name_len:
max_name_len = name_len
summary = ""
for entry in stage_results:
x, stage_name, status_msg = entry
summary += 'Stage: ' + stage_name.ljust(max_name_len) + ":: "
summary += status_msg + '\n'
return summary | 2f5c757342e98ab258bdeaf7ffdc0c5d6d4668ca | 1,174 |
import socket
def canonical_ipv4_address(ip_addr):
"""Return the IPv4 address in a canonical format"""
return socket.inet_ntoa(socket.inet_aton(ip_addr)) | edacc70ccc3eef12030c4c597c257775d3ed5fa4 | 1,177 |
def make_chained_transformation(tran_fns, *args, **kwargs):
"""Returns a dataset transformation function that applies a list of
transformations sequentially.
Args:
tran_fns (list): A list of dataset transformation.
*args: Extra arguments for each of the transformation function.
**kwargs: Extra keyword arguments for each of the transformation
function.
Returns:
A transformation function to be used in
:tf_main:`tf.data.Dataset.map <data/Dataset#map>`.
"""
def _chained_fn(data):
for tran_fns_i in tran_fns:
data = tran_fns_i(data, *args, **kwargs)
return data
return _chained_fn | 5f24e030df74a0617e633ca8f8d4a3954674b001 | 1,179 |
def generate_outlier_bounds_iqr(df, column, multiplier=1.5):
"""
Takes in a dataframe, the column name, and can specify a multiplier (default=1.5). Returns the upper and lower bounds for the
values in that column that signify outliers.
"""
q1 = df[column].quantile(.25)
q3 = df[column].quantile(.75)
iqr = q3 - q1
upper = q3 + (multiplier * iqr)
lower = q1 - (multiplier * iqr)
return upper, lower | 7f096d5f5cf2417cbc161713715a39560efd140a | 1,182 |
import math
from PIL import ImageColor
def indexedcolor(i, num, npersat=15, lightness=60):
"""Returns an rgb color triplet for a given index, with a finite max 'num'.
Thus if you need 10 colors and want to get color #5, you would call this with (5, 10).
The colors are "repeatable".
"""
nsats = int(math.ceil(num/float(npersat)))
sat = 100 - int((i//npersat)*(100/nsats))
l = lightness
nhues = int(math.ceil(num/float(nsats)))
hue = (i % nhues) * (360//nhues)
#print >>sys.stderr, 'For i %d, num %d, got %d sats, %d hues -> %d, %d, %d' % (i, num, nsats, nhues, hue, sat, l)
return ImageColor.getrgb('hsl(%d,%d%%,%d%%)' % (hue, sat, l)) | 418a875bc8ae50ce21f9667f46718863ba0f55e3 | 1,185 |
def dot_to_dict(values):
"""Convert dot notation to a dict. For example: ["token.pos", "token._.xyz"]
become {"token": {"pos": True, "_": {"xyz": True }}}.
values (iterable): The values to convert.
RETURNS (dict): The converted values.
"""
result = {}
for value in values:
path = result
parts = value.lower().split(".")
for i, item in enumerate(parts):
is_last = i == len(parts) - 1
path = path.setdefault(item, True if is_last else {})
return result | a2c56a01b179d27eabc728d6ff2ec979885d5feb | 1,186 |
from pathlib import Path
def delta_path(base_path: Path, item_path: Path, new_base_path: Path) -> Path:
"""
Removes a base path from an item, and appends result to a new path
:param base_path: The :py:class:`pathlib.Path` to be removed from `item_path`
:param item_path: The :py:class:`pathlib.Path` to be delta-ed
:param new_base_path: The new base :py:class:`pathlib.Path` for `item_path`.
:raises ValueError: If base_path is not a sub-path of item_path.
:return: The new combined path.
"""
path_stub = item_path.relative_to(base_path)
new_item_path = new_base_path / path_stub
return new_item_path | ec531a011e36f053a8092525faae2047f5f66ccc | 1,189 |
import random
def describe_current_subtask(subtask, prefix=True):
"""
Make a 'natural' language description of subtask name
"""
to_verb = {"AnswerQuestion": "answering a question",
"ArmGoal": "moving my arm",
"DemoPresentation": "giving a demo",
"Find": "finding",
"Follow": "following",
"Guide": "guiding",
"GripperGoal": "moving my gripper",
"HandOver": "handing something over",
"Inspect": "inspecting",
"LookAt": "looking",
"NavigateTo": "navigating",
"PickUp": "picking up",
"Place": "placing",
"ResetWM": "resetting my world model",
"Say": "speaking",
"SendPicture": "sending a picture",
"TurnTowardSound": "turning towards a sound"}
description = to_verb.get(subtask, subtask + "ing")
if prefix:
description = random.choice(["I'm busy", "I'm"]) + " " + description
return description | 628c699201c26242bd72c6066cba07cce54b14ca | 1,197 |
import re
def parse_date(deadline_date):
"""
Given a date in the form MM/DD/YY or MM/DD/YYYY, returns
the integers MM, DD, and YYYY (or YY) in this order.
"""
deadline_split = re.split('\\/|\\-', deadline_date)
return int(deadline_split[0]), int(deadline_split[1]), int(deadline_split[2]) | 0ded6bccce8437aad61cfa5ff121c5ed0595849b | 1,199 |
import re
def get_file_name(part):
"""get file name using regex from fragment ID"""
return re.findall(r"='(.*\-[a-z]+).*", part)[0] | 30c8867d8e14b04c593359f1c16d9bf324711ba0 | 1,201 |
def black_color_func(word, font_size, position, orientation,
random_state=None, **kwargs):
"""Make word cloud black and white."""
return("hsl(0,100%, 1%)") | d5e874a4f62d30abcba29476d0ba7fc3a31b0ca6 | 1,210 |
def detect_label_column(column_names):
""" Detect the label column - which we display as the label for a joined column.
If a table has two columns, one of which is ID, then label_column is the other one.
"""
if (column_names and len(column_names) == 2 and "id" in column_names):
return [c for c in column_names if c != "id"][0]
return None | 40524e7ed0878316564ad8fd66a2c09fc892e979 | 1,211 |
def table(custom_headings, col_headings_formatted, rows, spec):
"""
Create a LaTeX table
Parameters
----------
custom_headings : None, dict
optional dictionary of custom table headings
col_headings_formatted : list
formatted column headings
rows : list of lists of cell-strings
Data in the table, pre-formatted
spec : dict
options for the formatter
Returns
-------
dict : contains key 'latex', which corresponds to a latex string representing the table
"""
longtables = spec['longtables']
table = "longtable" if longtables else "tabular"
if custom_headings is not None \
and "latex" in custom_headings:
latex = custom_headings['latex']
else:
latex = "\\begin{%s}[l]{%s}\n\hline\n" % \
(table, "|c" * len(col_headings_formatted) + "|")
latex += ("%s \\\\ \hline\n"
% (" & ".join(col_headings_formatted)))
for formatted_rowData in rows:
if len(formatted_rowData) > 0:
formatted_rowData_latex = [
(formatted_cell['latex'] if isinstance(formatted_cell, dict)
else formatted_cell) for formatted_cell in formatted_rowData]
latex += " & ".join(formatted_rowData_latex)
#MULTI-ROW support for *data* (non-col-header) rows of table. Currently
# unused (unneeded) - see multirow formatter that is commented out in formatters.py
#multirows = [ ("multirow" in el) for el in formatted_rowData_latex ]
#if any(multirows):
# latex += " \\\\ "
# last = True; lineStart = None; col = 1
# for multi,data in zip(multirows,formatted_rowData_latex):
# if last == True and multi == False:
# lineStart = col #line start
# elif last == False and multi == True:
# latex += "\cline{%d-%d} " % (lineStart,col) #line end
# last=multi
# res = _re.search("multicolumn{([0-9])}",data)
# if res: col += int(res.group(1))
# else: col += 1
# if last == False: #need to end last line
# latex += "\cline{%d-%d} "%(lineStart,col-1)
# latex += "\n"
#else:
latex += " \\\\ \hline\n"
latex += "\end{%s}\n" % table
return {'latex': latex} | 0ca28fce26fc7476aa5b88a621c5476ae8d381ce | 1,213 |
from typing import List
def split_to_sublists(initial_list:list, n:int, strict:bool=True) -> List[list]:
"""Takes a list and splits it into sublists of size n
Parameters
----------
initial_list : list
The initial list to split into sublists
n : int
The size of each sublist
strict: bool
Whether to force an error if the length of the initial list is not divisible by n (split into even groups), default True
Returns
-------
List[list]
A list of lists of size n (unless strict is False, then the last list may be > n)
Examples
--------
### Split gallery images into sublists of 3
#### JINJA USAGE
```jinja2
{% if gallery|length % 3 == 0 %}
{% for sublist in gallery|split_to_sublists(3) %}
<div class="row">
<div class="col-md-4">
<img src="{{ sublist.0[0]['file_path'] }}" alt="{{ sublist.0[0]['file_path'].split()[-1] }}">
</div>
<div class="col-md-4">
<img src="{{ sublist.1[0]['file_path'] }}" alt="{{ sublist.1[0]['file_path'].split()[-1]}}">
</div>
<div class="col-md-4">
<img src="{{ sublist.2[0]['file_path'] }}" alt="{{ sublist.2[0]['file_path'].split()[-1] }}">
</div>
</div>
{% endfor %}
{% endif }
```
The above jinja is roughly equivalent to something like this in pure python:
```python
gallery = ["image 1" , "image 2", "image 3", "image 4" , "image 5", "image 6"]
if len(images) % 3 == 0:
for sublist in split_to_sublists(gallery, 3): # Returns [["image 1" , "image 2", "image 3"], ["image 4" , "image 5", "image 6"]]
... # Do stuff with each sublist
```
"""
if strict:
if not len(initial_list) % n == 0:
raise ValueError(f"Provided list was not of correct size: \n\tList: {initial_list}\n\tSegment size {n}")
result = []
for i in range(0, len(initial_list), n): # Create sublists up to size n
result.append( initial_list[i:i + n])
return result | fcca74f9814020c99aaf8b31f092ca3ca9533216 | 1,215 |
def sectionize(parts, first_is_heading=False):
"""Join parts of the text after splitting into sections with headings.
This function assumes that a text was splitted at section headings,
so every two list elements after the first one is a heading-section pair.
This assumption is used to join sections with their corresponding headings.
Parameters
----------
parts : list of str
List of text parts.
first_is_heading : bool
Should first element be treated as heading in lists of length greater than 1.
"""
parts = parts.copy()
if len(parts) <= 1:
return parts
first = []
if not first_is_heading:
first.append(parts[0])
del parts[0]
sections = first + [ "\n".join(parts[i:i+2]) for i in range(0, len(parts), 2) ]
return sections | 402832d55268dc808888f94b95e3a1c991394041 | 1,217 |
def byte_compare(stream_a, stream_b):
"""Byte compare two files (early out on first difference).
Returns:
(bool, int): offset of first mismatch or 0 if equal
"""
bufsize = 16 * 1024
equal = True
ofs = 0
while True:
b1 = stream_a.read(bufsize)
b2 = stream_b.read(bufsize)
if b1 != b2:
equal = False
if b1 and b2:
# we have two different buffers: find first mismatch
for a, b in zip(b1, b2):
if a != b:
break
ofs += 1
break
ofs += len(b1)
if not b1: # both buffers empty
break
return (equal, ofs) | 59adfe50fefdb79edd082a35437018d4b954ec75 | 1,218 |
import re
def is_regex(param):
"""
判断参数是否是合法正则表达式字符串
:param param: {String} 参数
:return: {Boolean} 是否是合法正则表达式
"""
try:
re.compile(param)
return True
except re.error:
return False | 6a3ee33e68e33d3557db546beadc005235360080 | 1,219 |
def min_count1(lst):
"""
Get minimal value of list, version 1
:param lst: Numbers list
:return: Minimal value and its count on the list
"""
if len(lst) == 0:
return []
count = 0
min_value = lst[0]
for num in lst:
if num == min_value:
count += 1
elif num < min_value:
count = 1
min_value = num
return [min_value, count] | b441d0a37534909e9a990b91a953d4022698c04b | 1,220 |
def exactly_one_topping(ketchup, mustard, onion):
"""Return whether the customer wants exactly one of the three available toppings
on their hot dog.
"""
return True if int(ketchup) + int(mustard) + int(onion) == 1 else False | 214c95d35c116993dc78740d5d16b874122960ed | 1,221 |
def strip_line_endings(data: list) -> list:
"""Removes line endings(\n). Removes item if only contains \n."""
return [i.rstrip("\n") for i in data if i != "\n"] | 5383b1bc3884395459ca63b6f15c0a1091eaaaf0 | 1,222 |
def taxon_id(_):
"""
Always returns 10090, the mouse taxon id.
"""
return 10090 | 117fe7f8d56eb9be4ee2b0f4d782b806576faedf | 1,225 |
def getLogisticModelNames(config):
"""
Get the names of the models present in the configobj
Args:
config: configobj object defining the model and its inputs.
Returns:
list: list of model names.
"""
names = []
lmodel_space = config
for key, value in lmodel_space.items():
if isinstance(value, str):
continue
else: # this is a model
names.append(key)
return names | f7f82b12eb50a58c92970b5c2a8f99eb01945523 | 1,227 |
def mp0(g0):
"""Return 0th order free energy."""
return g0.sum() | 5aa3580fec1322bd7b4e357ec6bee4d52fae592e | 1,228 |
import socket
import fcntl
import struct
def get_ip_address(dev="eth0"):
"""Retrieves the IP address via SIOCGIFADDR - only tested on Linux."""
try:
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
return socket.inet_ntoa(fcntl.ioctl(s.fileno(),0x8915,struct.pack('256s', dev[:15]))[20:24])
except:
return None | 96f59f17937543ed9cd4652af4703eaf975b8069 | 1,234 |
def gen_fov_chan_names(num_fovs, num_chans, return_imgs=False, use_delimiter=False):
"""Generate fov and channel names
Names have the format 'fov0', 'fov1', ..., 'fovN' for fovs and 'chan0', 'chan1', ...,
'chanM' for channels.
Args:
num_fovs (int):
Number of fov names to create
num_chans (int):
Number of channel names to create
return_imgs (bool):
Return 'chanK.tiff' as well if True. Default is False
use_delimiter (bool):
Appends '_otherinfo' to the first fov. Useful for testing fov id extraction from
filenames. Default is False
Returns:
tuple (list, list) or (list, list, list):
If return_imgs is False, only fov and channel names are returned
If return_imgs is True, image names will also be returned
"""
fovs = [f'fov{i}' for i in range(num_fovs)]
if use_delimiter:
fovs[0] = f'{fovs[0]}_otherinfo'
chans = [f'chan{i}' for i in range(num_chans)]
if return_imgs:
imgs = [f'{chan}.tiff' for chan in chans]
return fovs, chans, imgs
else:
return fovs, chans | 417490259c42a52c58aab418fbb63185602e6750 | 1,240 |
def flatten_list(a_list, parent_list=None):
"""Given a list/tuple as entry point, return a flattened list version.
EG:
>>> flatten_list([1, 2, [3, 4]])
[1, 2, 3, 4]
NB: The kwargs are only for internal use of the function and should not be
used by the caller.
"""
if parent_list is None:
parent_list = []
for element in a_list:
if isinstance(element, list):
flatten_list(element, parent_list=parent_list)
elif isinstance(element, tuple):
flatten_list(element, parent_list=parent_list)
else:
parent_list.append(element)
return parent_list | dd6c9c66a370e65744ede40dfdc295b0ec63379a | 1,243 |
def unpad_pkcs7(data):
"""
Strips PKCS#7 padding from data.
Raises ValueError if padding is invalid.
"""
if len(data) == 0:
raise ValueError("Error: Empty input.")
pad_value = data[-1]
if pad_value == 0 or pad_value > 16:
raise ValueError("Error: Invalid padding.")
for i in range(1, pad_value + 1):
if data[-i] != pad_value:
raise ValueError("Error: Invalid padding.")
unpadded = data[: (len(data) - pad_value)]
return unpadded | 27e59b8a880c130997f19814135c09cb6e94354d | 1,247 |
import re
def remove_multispaces(text):
""" Replace multiple spaces with only 1 space """
return [re.sub(r' +', " ",word) for word in text] | 0b87f6a4b0d49931b3f4bec6f9c313be05d476f0 | 1,252 |
def num_fixed_points(permutation):
"""
Compute the number of fixed points (elements mapping to themselves) of a permutation.
:param permutation: Permutation in one-line notation (length n tuple of the numbers 0, 1, ..., n-1).
:return: Number of fixed points in the permutation.
.. rubric:: Examples
>>> num_fixed_points((0, 2, 1))
1
"""
n = 0
for i in range(len(permutation)):
if permutation[i] == i:
n += 1
return n | 124713cd4c90988c43630a74881e7107ff748682 | 1,257 |
def iscode(c):
"""
Tests if argument type could be lines of code,
i.e. list of strings
"""
if type(c) == type([]):
if c:
return type(c[0]) == type('')
else:
return True
else: return False | e60da6c05922ff1e67db15fa4caa1500a8f470c7 | 1,259 |
import re
def split_bucket(s3_key):
"""
Returns the bucket name and the key from an s3 location string.
"""
match = re.match(r'(?:s3://)?([^/]+)/(.*)', s3_key, re.IGNORECASE)
if not match:
return None, s3_key
return match.group(1), match.group(2) | 6b854bdc9d105643a9fa528e6fefd19672451e63 | 1,261 |
def create_dictionary(names, months, years, max_sustained_winds, areas_affected, updated_damages, deaths):
"""Create dictionary of hurricanes with hurricane name as the key and a dictionary of hurricane data as the value."""
hurricanes = dict()
num_hurricanes = len(names)
for i in range(num_hurricanes):
hurricanes[names[i]] = {"Name": names[i],
"Month": months[i],
"Year": years[i],
"Max Sustained Wind": max_sustained_winds[i],
"Areas Affected": areas_affected[i],
"Damage": updated_damages[i],
"Deaths": deaths[i]}
return hurricanes | 5a27d5349113f29d2af55df27a2ee2c2cc524549 | 1,266 |
def generate_file_prefix(bin_params):
""" Use the bin params to generate a file prefix."""
prefix = "bin_"
for j in range(0, len(bin_params)):
if (j + 1) % 2 != 0:
prefix += str(bin_params[j]) + "-"
else:
prefix += str(bin_params[j]) + "_"
return prefix | cc058a64fcab77f6a4794a8bf7edb1e0e86c040c | 1,270 |
def create_scale(tonic, pattern, octave=1):
"""
Create an octave-repeating scale from a tonic note
and a pattern of intervals
Args:
tonic: root note (midi note number)
pattern: pattern of intervals (list of numbers representing
intervals in semitones)
octave: span of scale (in octaves)
Returns:
list of midi notes in the scale
"""
assert(sum(pattern)==12)
scale = [tonic]
note = tonic
for o in range(octave):
for i in pattern:
note += i
if note <= 127:
scale.append(note)
return scale | f9337289fda2e1b08cd371d3e91cc5a23c9c9822 | 1,276 |
def get_subgraph_pos(G, pos):
""" Returns the filtered positions for subgraph G. If subgraph = original graph then pos will be returned.
Parameters
----------
G : nx.Graph
A graph object.
Pos : dict
A dictionary with nodes as keys and positions as values.
Example
-------
>>> pos = nx.spring_layout(G)
>>> subgraph_nodes = ['1','2','3']
>>> subgraph = G.subgraph(subgraph_nodes)
>>> subgraph_positions = get_subgraph_pos(subgraph,pos)
Returns
-------
dict
Assuming positions were generated earlier for a larger graph with some layout algorithm
this functions returns the filtered positions by the subgraph.
"""
return {k: v for k, v in pos.items() if k in G.nodes()} | ca7fc389cc51aaace7a751f2107fe5cfbfd22e6c | 1,280 |
def pds3_label_gen_date(file):
"""Returns the creation date of a given PDS3 label.
:param path: File path
:type path: str
:return: Creation date
:rtype: str
"""
generation_date = "N/A"
with open(file, "r") as f:
for line in f:
if "PRODUCT_CREATION_TIME" in line:
generation_date = line.split("=")[1].strip()
return generation_date | c2877fa9246dd0c12c6ea47635ab248dc038b179 | 1,283 |
def harmony(*args):
"""
Takes an arbitrary number of floats and prints their harmonic
medium value. Calculation is done with formula:
number_of_args \ (1 \ item1 + 1 \ item2 + ...)
Args:
*args (tuple): number of arguments with a type: float, integer
Returns:
float: harmonic medium value
"""
result = 0
if 0 in args:
return 0.0
for item in args:
result += 1 / item
return len(args) / result | bc66276b3ef27ef0bfd059afa8ca7afd5d9cbb82 | 1,284 |
def pack(pieces=()):
"""
Join a sequence of strings together.
:param list pieces: list of strings
:rtype: bytes
"""
return b''.join(pieces) | ffd0852a16c6292f921e5cf205301171e3a96fd3 | 1,286 |
import inspect
def getsource(obj,is_binary=False):
"""Wrapper around inspect.getsource.
This can be modified by other projects to provide customized source
extraction.
Inputs:
- obj: an object whose source code we will attempt to extract.
Optional inputs:
- is_binary: whether the object is known to come from a binary source.
This implementation will skip returning any output for binary objects, but
custom extractors may know how to meaningfully process them."""
if is_binary:
return None
else:
return inspect.getsource(obj) | 9e97a030c695b9ea50d27abc5253e47be7d4c06a | 1,292 |
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