3 :\\n total += 1\\n return float(total)/n\",\n \"def probability_of_all_successes(p: float, r: int, n: int) -> float:\\n\\n if r == 1:\\n return pow(p, n)\\n elif n == 0:\\n return 1\\n else:\\n result = 0\\n for x in range(0, n+1):\\n result += pow(p, x) * pow(1-p, n-x) * probability_of_all_successes(p, r-1, n-x)\\n return result\",\n \"def prob1(n):\\n\\n # create a giant draw from a normal distribution\\n random_draws = np.random.normal(loc= 0, scale = 1, size = n)\\n\\n # mask the values\\n mask = random_draws > 3\\n\\n return np.sum(mask)/float(n)\",\n \"def bernoulli_trial(p: float) -> int:\\n return 1 if random.random() < p else 0\",\n \"def prior(n=10):\\r\\n p = []\\r\\n trials = 0\\r\\n acc = 0\\r\\n while acc < n:\\r\\n trials += 1\\r\\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\\r\\n # print(\\\"r: \\\", r)\\r\\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\\r\\n p.append(r)\\r\\n acc += 1\\r\\n # print(\\\"trials: \\\", trials, \\\", acc: \\\", acc)\\r\\n return p\",\n \"def chance(n, p):\\n total = 0.0\\n for k in range(n+1):\\n total += comb(n, k, exact=False) * p**k * (1-p) ** (n-k)\\n return total\",\n \"def test_probability_of_all_successes():\\n\\n assert(probability_of_all_successes(1/2,1,2) == 0.25)\\n assert(are_close(probability_of_all_successes(1/6,1,2), 1/36, 0.001))\\n assert(are_close(probability_of_all_successes(1/2,2,2), 7/16, 0.001))\",\n \"def expected_value_of_outcome(n, p):\\r\\n expected = n * p\\r\\n print(\\\"expected value / mean = \\\", expected)\\r\\n return expected\",\n \"def prob(throw, n, d=6, type='classical'):\\n count = 0\\n table = throw_table(n, d, type)\\n for t in table:\\n if sum(t) == throw:\\n count += 1\\n \\n return float(count)/len(table)\",\n \"def probability(p):\\n return p > random.uniform(0.0, 1.0)\",\n \"def probability(n, k, p):\\n prob = 0\\n power = expotentation_by_squaring((1-p), n)\\n count_mult = math.log(n, 2)\\n p_fraction = p/(1-p)\\n count_mult += 1\\n for i in range(0, k+1):\\n element = newton(n, i)*power\\n prob += element\\n power *= p_fraction\\n count_mult += 2\\n return prob, count_mult\",\n \"def binomial(n: int, p: float) -> int:\\n return sum(bernoulli_trial(p) for _ in range(n))\",\n \"def game1(n):\\r\\n\\twin=0\\r\\n\\tfor i in range(n):\\r\\n\\t\\tif game(1)==1:\\r\\n\\t\\t\\twin+=1\\r\\n\\tprob1=win/n\\r\\n\\treturn prob1\",\n \"def find_trials_required(number_of_sequences, sigma, p=0.9):\\n return ceil((p * (1 - p)) / (number_of_sequences * sigma ** 2) - (1 / number_of_sequences))\",\n \"def simulate_rerolling(p: float, n: int) -> int:\\n\\n counter = 0\\n new_n = n\\n while new_n > 0:\\n for _ in range(new_n):\\n ran = random.random()\\n if ran < p:\\n new_n -= 1\\n counter += 1\\n return counter\",\n \"def multiple_comparisons(p, n):\\r\\n if p > 1e-6: # if p is large and n small, calculate directly\\r\\n return 1 - (1 - p) ** n\\r\\n else:\\r\\n return one_minus_exp(-n * p)\",\n \"def prob_choice(p):\\n \\n return np.random.random_sample() < p\",\n \"def is_prime(n, number_of_tests=5):\\n passes = 0\\n prime = True #assume prime\\n for i in xrange(number_of_tests):\\n passes += 1\\n random_int = random.randint(2, n-1)\\n test = pow(random_int, n-1, n)\\n if test != 1:\\n prime = False\\n break\\n if prime:\\n return 0\\n else:\\n return passes\",\n \"def compute_prob_mle(X: np.ndarray, n: int) -> float:\\n\\n assert n > 1, \\\"for n = 1 use Bernoulli distribution.\\\"\\n Binomial._check_input_data(X=X)\\n Binomial._check_support(X=X, n=n)\\n\\n prob = X.mean() / n\\n return prob\",\n \"def probability(prob):\\n return random.random() <= prob\",\n \"def binomialTest(k, n, p = 0.5, exact = False):\\n\\tassert(k <= n)\\n\\tassert(k >= 0 and n > 0)\\n\\tn = int(n)\\n\\tk = int(k)\\n\\tp_value = 1.0\\n\\n\\t# Trivial cases where p = 0 or p = 1\\n\\tif p == 0.0: # Must then have k = 0\\n\\t\\tif k > 0:\\n\\t\\t\\treturn 0.0\\n\\t\\telse:\\n\\t\\t\\treturn 1.0\\n\\tif p == 1.0: # Must then have k = n\\n\\t\\tif k <= n:\\n\\t\\t\\treturn 1.0\\n\\n\\tif k == 0:\\n\\t\\t# Probability of at least zero successes is 1\\n\\t\\tp_value = 1.0\\n\\telif k == n:\\n\\t\\t# Probability of all successes\\n\\t\\tp_value = p**n\\n\\telse:\\n\\t\\tif not exact and n*p > 30 and n*(1-p) > 30:\\n\\t\\t\\t# Use normal approximation\\n\\t\\t\\tmu = n*p\\n\\t\\t\\tsd = math.sqrt(n*p*(1-p))\\n\\t\\t\\tz = (k-mu)/sd\\n\\t\\t\\tif z < 0.0:\\n\\t\\t\\t\\tp_value = 1-Prob_Z(z)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tp_value = Prob_Z(z)\\n\\t\\telse:\\n\\t\\t\\tp_value = p**n # The last term in the sum\\n\\t\\t\\tfor j in range(k,n):\\n\\t\\t\\t\\t# Compute logarithm of (n choose j) p^j (1-p)^ (n-j), the\\n\\t\\t\\t\\t# binomial probability. Use logarithm to avoid overflow\\n\\t\\t\\t\\t# problems with potentially enormous factorials.\\n\\t\\t\\t\\tlog_p = logChoose(n,j) + j*math.log(p) + (n-j)*math.log(1-p)\\n\\t\\t\\t\\tp_value += math.exp(log_p)\\n\\t\\t\\tif p_value > 1.0:\\n\\t\\t\\t\\tp_value = 1.0\\n\\treturn p_value\",\n \"def is_probable_prime( n, trials = 5 ):\\n\\n if n in smallprimeset: return True\\n\\n if n < 2: return False\\n # special case 2\\n if n == 2:\\n return True\\n # ensure n is odd\\n if n % 2 == 0:\\n return False\\n # write n-1 as 2**s * d\\n # repeatedly try to divide n-1 by 2\\n s = 0\\n d = n - 1\\n while True:\\n quotient, remainder = divmod( d, 2 )\\n if remainder == 1:\\n break\\n s += 1\\n d = quotient\\n assert( ( 2 ** s ) * d == n - 1 )\\n\\n # test the base a to see whether it is a witness for the compositeness of n\\n def try_composite( a ):\\n if pow( a, d, n ) == 1:\\n return False\\n for i in range( s ):\\n if pow( a, 2 ** i * d, n ) == n - 1:\\n return False\\n return True # n is definitely composite\\n\\n for i in range( trials ):\\n a = random.randrange( 2, n )\\n if try_composite( a ):\\n return False\\n\\n return True # no base tested showed n as composite\",\n \"def likelihood(x, n, P):\\n if not isinstance(n, int) or (n <= 0):\\n raise ValueError('n must be a positive integer')\\n if not isinstance(x, int) or (x < 0):\\n raise ValueError(\\n 'x must be an integer that is greater than or equal to 0')\\n if x > n:\\n raise ValueError('x cannot be greater than n')\\n if not isinstance(P, np.ndarray) or len(P.shape) != 1:\\n raise TypeError('P must be a 1D numpy.ndarray')\\n if not np.all((P >= 0) & (P <= 1)):\\n raise ValueError('All values in P must be in the range [0, 1]')\\n nume = np.math.factorial(n)\\n deno = (np.math.factorial(x) * (np.math.factorial(n - x)))\\n fact = nume / deno\\n P_likelihood = fact * (np.power(P, x)) * (np.power((1 - P), (n - x)))\\n return P_likelihood\",\n \"def p(self) -> Probability:\\n ...\",\n \"def binomial(n, p):\\n sum_ans = 0\\n for k in range(n):\\n sum_ans = sum_ans + bernoulli(p)\\n return sum_ans\",\n \"def bernoulli(p):\\r\\n if np.random.random() < p:\\r\\n return 0\\r\\n else:\\r\\n return 1\",\n \"def logp(value, n, p):\\n\\n res = factln(n) + pt.sum(-factln(value) + logpow(p, value), axis=-1)\\n res = pt.switch(\\n pt.or_(pt.any(pt.lt(value, 0), axis=-1), pt.neq(pt.sum(value, axis=-1), n)),\\n -np.inf,\\n res,\\n )\\n return check_parameters(\\n res,\\n 0 <= p,\\n p <= 1,\\n pt.isclose(pt.sum(p, axis=-1), 1),\\n pt.ge(n, 0),\\n msg=\\\"0 <= p <= 1, sum(p) = 1, n >= 0\\\",\\n )\",\n \"def factorPR(n):\\n\\tnumsteps=2*math.floor(math.sqrt(math.sqrt(n)))\\n\\tfor additive in range(1,5):\\n\\t\\tfast=slow=1; i=1\\n\\t\\twhile i<numsteps:\\n\\t\\t\\tslow = (slow*slow + additive) % n\\n\\t\\t\\ti = i + 1\\n\\t\\t\\tfast = (fast*fast + additive) % n\\n\\t\\t\\tfast = (fast*fast + additive) % n\\n\\t\\t\\tg = gcd(fast-slow,n)\\n\\t\\t\\tif (g != 1):\\n\\t\\t\\t\\tif (g == n):\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\treturn g\\n\\treturn 1\",\n \"def game3(n):\\r\\n\\twin=0\\r\\n\\tfor i in range(n):\\r\\n\\t\\tif game(3)==1:\\r\\n\\t\\t\\twin+=1\\t\\r\\n\\tprob3=win/n\\r\\n\\treturn prob3\",\n \"def prob_no_match(n):\\n return math.factorial(n)*math.comb(365,n)/(365**n)\",\n \"def Poisson(n, k):\\n\\tp = math.exp(-k) * math.pow(k, n) / float(Factorial(n))\\n\\tassert 0.0 <= p <= 1.0, \\\"Error, value of p is invalid probability: \\\" + str(p)\\n\\treturn p\",\n \"def prbs(m, n):\\n return np.array(np.random.rand(m, n) > 0.5, dtype=np.int) - 0.5\",\n \"def success_p_dist(n_trials, n_meanings, n_signals, input_meaning_p = None):\\r\\n if input_meaning_p is None:\\r\\n input_meaning_p = uniform_p(n_meanings)\\r\\n y = []\\r\\n for i in range(n_trials):\\r\\n dyad = ProductSystem.rand_dyad(n_meanings, n_signals)\\r\\n y.append(dyad.success_p(input_meaning_p))\\r\\n sns.distplot(np.asarray(y))\",\n \"def probability_at_least_d_fail_equal_reliability(d, chunk_count, reliability):\\n \\n return sum([loss_probability(reliability, k, chunk_count) for k in range(d, chunk_count + 1)])\",\n \"def lnprob(theta, observables):\\n prior = lnprior(theta)\\n if not np.isfinite(prior):\\n return -inf\\n return prior + lnlike(theta, observables)\",\n \"def precision_n(test_scores, num_true, n):\\n precisions = []\\n for i in range(n):\\n precision = 0\\n for k, item in enumerate(test_scores):\\n ranks = len(item) - rankdata(item, method='ordinal').astype(int)\\n precision += min(1, len(set(ranks[:i+1]) & set(range(num_true[k]))))\\n precision /= float(len(test_scores))\\n precision *= 100\\n precisions.append(precision)\\n return precisions\",\n \"def pred_prob(self, t, x, **kwargs):\\n raise NotImplementedError\",\n \"def compute_expected_return_(domain, N, policy, p_init, s_init):\\r\\n pos = p_init\\r\\n speed = s_init\\r\\n expected_return = 0\\r\\n\\r\\n for i in range(N):\\r\\n if domain.terminalState(pos, speed):\\r\\n break\\r\\n action = policy((pos, speed))\\r\\n if isinstance(action,str):\\r\\n action = domain.getAction(action)\\r\\n new_pos, new_speed = domain.getNextState(pos, speed, action)\\r\\n r = domain.getReward(pos, speed, action, new_pos, new_speed)\\r\\n expected_return += ((domain.discount_factor)**i)*r\\r\\n pos = new_pos\\r\\n speed = new_speed\\r\\n \\r\\n return expected_return\",\n \"def greater_than_n_wins(wins_req: int, total_chances: int, win_rate: float) -> float:\\n overall_prob = 0\\n loss_rate = 1 - win_rate\\n for num in range(wins_req, total_chances+1, 1):\\n numerator = comb(total_chances, num)\\n denominator = (win_rate ** num) * (loss_rate ** (total_chances - num))\\n overall_prob += numerator * denominator\\n return overall_prob\",\n \"def game2(n):\\r\\n\\twin=0\\r\\n\\tfor i in range(n):\\r\\n\\t\\tif game(2)==1:\\r\\n\\t\\t\\twin+=1\\r\\n\\tprob2=win/n\\r\\n\\treturn prob2\",\n \"def probit(x):\\n from tensorflow_probability import distributions\\n return distributions.Normal(0, 1).cdf(x)\",\n \"def prob(x):\\n\\treturn 1. * bivariate_normal(x, (0., 1.2), (1., 1.), .8) + \\\\\\n\\t 1.05 * bivariate_normal(x, (.6, -1.), (1.3, .7), -.6)\",\n \"def empirical_distribution(data, x, n):\\n i = 0\\n while i < n and data[i] <= x:\\n i+=1\\n return float(i)/n\",\n \"def prob4():\\n\\n\\n N = 500000\\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\\n\\n h = lambda x: x[0] < -1 and x[1] > 1\\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\\n\\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\\n\\n return 1./N * np.sum(probability)\",\n \"def tpr(positive, negative, fpr):\\n threshold = np.percentile(np.asarray(negative), 100 - fpr)\\n total_true_positives = sum(positive > threshold)\\n\\n return total_true_positives / len(positive)\",\n \"def initial_probabilities_from_trajectories(n_states, trajectories):\\n p = np.zeros(n_states)\\n\\n for t in trajectories:\\n p[t.transitions()[0][0]] += 1.0\\n\\n return p / len(trajectories)\",\n \"def potential_witnesses(n):\\n if n < 1373653: return [2, 3] # n < 1,373,653\\n if n < 9080191: return [31, 73] # n < 9,080,191\\n if n < 4759123141: return [2, 7, 61] # n < 4,759,123,141\\n if n < 2152302898747: return [2, 3, 5, 7, 11] # n < 2,152,302,898,747\\n if n < 3474749660383: return [2, 3, 5, 7, 11, 13] # n < 3,474,749,660,383\\n if n < 341550071728321: return [2, 3, 5, 7, 11, 13, 17] # n < 341,550,071,728,321\\n return [random.randint(1,n-1) for _ in xrange(0,20)] # 99.999999999909051% (1 - .25**20) accuracy for n >= 341,550,071,728,321\",\n \"def expectancyPcnt(self, p):\\n return (2 * p) - 1\",\n \"def _basic_probability(count: int, sequence_total_count: int) -> float:\\n return float(count) / sequence_total_count\",\n \"def prob_m_of_n(m, n, T, l):\\n PFD_one_unit = l*T\\n m_of_n = binom(n, m) * (PFD_one_unit)**(n-m) * (1-PFD_one_unit)**m\\n return m_of_n\",\n \"def prove_N() -> Proof:\\n # Optional Task 6.8\",\n \"def test_samples_close_to_inclusion_probability_priority(self):\\n # The range we allow around 0.5n\\n distance_from_half = 0.01\\n # The number of elements we use (computed using Chernoff bounds)\\n n = int((6.0 / (distance_from_half**2)) *\\n math.log(2 * FAILURE_PROBABILITY_INVERSE, math.e) + 1)\\n s = private_sampling.ThresholdSample(\\n 0.5, private_sampling.PrioritySamplingMethod)\\n for i in range(n):\\n s.process(i, 1.0)\\n self.assertGreaterEqual(len(s.elements), (0.5 - distance_from_half) * n)\\n self.assertLessEqual(len(s.elements), (0.5 + distance_from_half) * n)\",\n \"def expected_return(self, n_step):\\r\\n value = 0\\r\\n n_experiences = 50\\r\\n for i in range(n_experiences):\\r\\n trajectory = self.domain_exploration(n_step)\\r\\n value += self.compute_j(trajectory)\\r\\n return value/n_experiences\",\n \"def isprime(n):\\n if n!=int(n):\\n return False\\n n=int(n)\\n #Miller-Rabin test for prime\\n if n==0 or n==1 or n==4 or n==6 or n==8 or n==9:\\n return False\\n\\n if n==2 or n==3 or n==5 or n==7:\\n return True\\n s = 0\\n d = n-1\\n while d%2==0:\\n d>>=1\\n s+=1\\n assert(2**s * d == n-1)\\n\\n def trial_composite(a):\\n if pow(a, d, n) == 1:\\n return False\\n for i in range(s):\\n if pow(a, 2**i * d, n) == n-1:\\n return False\\n return True\\n\\n for i in range(8):#number of trials\\n a = random.randrange(2, n)\\n if trial_composite(a):\\n return False\\n\\n return True\",\n \"def p(n):\\n def p_(x):\\n return np.percentile(x, n)\\n\\n p_.__name__ = \\\"p_%s\\\" % n\\n return p_\",\n \"def random_walk(n, p):\\n random_array = np.random.uniform(0, 1, n)\\n left = random_array[random_array > p].size\\n right = n - left\\n \\n return (right-left)\",\n \"def pareto_distribution(v, p=0.8):\\n thr = np.sum(v)*p\\n cumsum = 0\\n for i, _v in enumerate(v, 1):\\n cumsum += _v\\n if cumsum >= thr:\\n return i * 1.0 / len(v)\",\n \"def puissance(x: float, n: int) -> float:\\n resultat: float = 1\\n signe: int = 1\\n if n != 0:\\n if n <= 0:\\n n = -n\\n signe = -1\\n for cpt in range(1, n + 1):\\n resultat = resultat * x\\n if signe < 0:\\n resultat = 1 / resultat\\n return resultat\",\n \"def generate_bits(n, p):\\n try:\\n return (np.random.rand(*n) > p).astype(int)\\n except TypeError:\\n return (np.random.rand(n) > p).astype(int)\",\n \"def solution(n,p):\\n \\n a=pow(n, (p - 1) // 2, p)\\n if(a==1):\\n return True\\n else :\\n return False\",\n \"def infection_probability(mu, sigma, n=1):\\n prob = np.abs(np.random.normal(mu, sigma, n))\\n \\n if (prob + prob) <0 or (prob + prob) >1: \\n prob = np.array([mu])\\n prob = prob.tolist()[0]\\n return prob\",\n \"def Probability(rating1, rating2):\\n return 1.0 * 1.0 / (1 + 1.0 * math.pow(10, 1.0 * (rating1 - rating2) / 400))\",\n \"def test(numTrials):\\n # Your Code Here\\n hits = 0.0\\n for i in range(numTrials):\\n result = trial()\\n #print result\\n hits += result\\n return hits / numTrials\",\n \"def is_prime(number, num_trials=200):\\n if number < 2:\\n return False\\n if number != 2 and number % 2 == 0:\\n return False\\n\\n # Find largest odd factor of n-1.\\n exp = number - 1\\n while exp % 2 == 0:\\n exp //= 2\\n\\n for _ in range(num_trials):\\n rand_val = int(random.SystemRandom().randrange(1, number))\\n new_exp = exp\\n power = pow(rand_val, new_exp, number)\\n while new_exp != number - 1 and power != 1 and power != number - 1:\\n power = (power * power) % number\\n new_exp *= 2\\n if power != number - 1 and new_exp % 2 == 0:\\n return False\\n\\n return True\",\n \"def getrawpval(teststat, statlist):\\n \\n nstats = len(statlist)\\n highend = lowend = 0 # specifies the range of the p-value\\n \\n for i in range(nstats):\\n \\n if teststat == statlist[i]:\\n lowend = 1 - (i + 1)/float(nstats)\\n if highend < lowend:\\n highend = 1 - i/float(nstats)\\n \\n elif teststat < statlist[i]:\\n if highend == 0:\\n return 1 - i/float(nstats)\\n \\n return random.uniform(lowend, highend)\",\n \"def bin_dist(n, p, x):\\n def factorial(x):\\n if x >= 0:\\n \\n factorial = 1\\n\\n for i in range(1, x + 1):\\n factorial = float(factorial * i)\\n # print(f' The factorial of {x} is {factorial}') \\n return factorial\\n\\n else:\\n raise ValueError(\\\"Sorry x cannot be a negative number\\\")\\n\\n def combination(n, r):\\n \\\"\\\"\\\"\\n Given n total number of items,\\n what is the number of possible ways\\n to choose r items from it?\\n\\n :param n: total number of items (integer)\\n :param r: number of items to arrange (int)\\n :return: number of combinations\\n :rtype: integer\\n \\\"\\\"\\\"\\n\\n \\n\\n \\n numerator = factorial(n)\\n denominator = factorial(r)\\n subtracted_answer = factorial(n-r)\\n \\n\\n answer = numerator/(denominator * subtracted_answer)\\n print(answer)\\n return answer \\n\\n # from scipy.special import comb\\n if x > n:\\n raise ValueError(\\\"Error, x must be less than n\\\")\\n else:\\n\\n\\n prob_success = float((combination(n, x)) * ((p**x)*((1-p)**(n-x))))\\n\\n print(prob_success)\\n return prob_success\",\n \"def solution(n: int = 2000000) -> int:\\n\\n return sum(takewhile(lambda x: x < n, prime_generator()))\",\n \"def probability_at_least_d_fail(disks, d, chunk_count, reliability):\\n #print(disks, d, chunk_count, reliability)\\n \\n return sum([disk_loss_probability(disks, reliability, k) for k in range(d, chunk_count + 1)])\\n # return sum([disk_loss_probability(k) for k in range(recovery_threshold, chunk_count + 1)])\",\n \"def compute_perplexity(self, train_corpus: str, test_corpus: str, n: int):\\n N = len(''.join(test_corpus.split()))\\n y = -1 / N\\n likelihood = self.compute_likelihood(train_corpus, test_corpus, n)\\n return likelihood ** y\",\n \"def get_success_probability(self):\\n\\t\\treturn min(self.get_raw_probability(), RunOrder.MAX_PERCENTS)\",\n \"def Ballie_PSW_test(n, max_trivial_trials=100):\\n for i in range(max_trivial_trials):\\n if primes[i] == n:\\n return True\\n if n % primes[i] == 0:\\n return False\\n if primes[i] ** 2 >= n:\\n return True\\n if not fermat_strong_test(n, 2):\\n return False\\n if not lucas_selfridge_test(n):\\n return False\\n return True\",\n \"def logp(value, n, a):\\n sum_a = a.sum(axis=-1)\\n const = (gammaln(n + 1) + gammaln(sum_a)) - gammaln(n + sum_a)\\n series = gammaln(value + a) - (gammaln(value + 1) + gammaln(a))\\n res = const + series.sum(axis=-1)\\n\\n res = pt.switch(\\n pt.or_(\\n pt.any(pt.lt(value, 0), axis=-1),\\n pt.neq(pt.sum(value, axis=-1), n),\\n ),\\n -np.inf,\\n res,\\n )\\n\\n return check_parameters(\\n res,\\n a > 0,\\n n >= 0,\\n msg=\\\"a > 0, n >= 0\\\",\\n )\",\n \"def Bernoulli(p, succ=1, fail=0, symbol=None):\\n\\n return BernoulliPSpace(p, succ, fail, symbol).value\",\n \"def tourney_prob(k, N, m):\\n\\n if N < m:\\n print \\\"The second argument cannot be smaller than the third one.\\\"\\n sys.exit()\\n\\n if m < 1 or k <= 0:\\n return 0.0\\n elif m == 1:\\n return 1.0 / N\\n else:\\n return float(N - k) * m / (N * (m - 1)) * tourney_prob(k, N - 1, m - 1)\",\n \"def prob4():\\n#raise NotImplementedError(\\\"Problem 4 Incomplete\\\")\\n h = lambda x : x[0] < -1 and x[1] > 1\\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\\n X = np.random.multivariate_normal(mean=np.array([-1,1]),cov=np.eye(2),size=10000)\\n return 1./10000*np.sum(np.apply_along_axis(h,1,X)*np.apply_along_axis(f,1,X)/np.apply_along_axis(g,1,X))\",\n \"def payoff_n_p(p, n=3,\\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\\n distribution = []\\n for n_makers in range(n + 1):\\n if n_makers == 0:\\n payoff_prob = [1 - prob for prob in p.values()]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((minor_contract, payoff_prob))\\n elif n_makers == n:\\n payoff_prob = [prob for prob in p.values()]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((MLB_contract, payoff_prob))\\n else:\\n makers = list(combinations(range(1, n + 1), n_makers))\\n for maker_set in makers:\\n if 1 in maker_set:\\n payoff = MLB_contract - 0.1*(MLB_contract-thresh)\\n payoff += (n_makers-1)*0.1*(MLB_contract-thresh)/(n-1)\\n payoff_prob = [p[player] for player in maker_set]\\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((payoff, payoff_prob))\\n else:\\n payoff = minor_contract\\n payoff += n_makers*0.1*(MLB_contract-thresh)/(n-1)\\n payoff_prob = [p[player] for player in maker_set]\\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((payoff, payoff_prob))\\n E_payoff = [a*b for (a, b) in distribution]\\n E_payoff = sum(E_payoff)\\n var_payoff = [((a-E_payoff)**2)*b for (a, b) in distribution]\\n var_payoff = sum(var_payoff)\\n return E_payoff, var_payoff**0.5\",\n \"def factorPR(n):\\r\\n\\tfor slow in [2,3,4,6]:\\r\\n\\t\\tnumsteps=2*math.floor(math.sqrt(math.sqrt(n))); fast=slow; i=1\\r\\n\\t\\twhile i<numsteps:\\r\\n\\t\\t\\tslow = (slow*slow + 1) % n\\r\\n\\t\\t\\ti = i + 1\\r\\n\\t\\t\\tfast = (fast*fast + 1) % n\\r\\n\\t\\t\\tfast = (fast*fast + 1) % n\\r\\n\\t\\t\\tg = gcd(fast-slow,n)\\r\\n\\t\\t\\tif (g != 1):\\r\\n\\t\\t\\t\\tif (g == n):\\r\\n\\t\\t\\t\\t\\tbreak\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\treturn g\\r\\n\\treturn 1\",\n \"def p(x):\\n if x<0 or x>1:\\n return 0\\n else:\\n return 1\",\n \"def prob_t_N(genotype, base):\\n cnter = Counter(genotype)\\n return cnter.get(base, 0) * 1/len(genotype)\",\n \"def payoff_n(n=3, p=0.06,\\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\\n distribution = []\\n for n_makers in range(n + 1): # For every number of possible players who make it\\n if n_makers == 0:\\n distribution.append((minor_contract, (1-p)**n))\\n elif n_makers == n:\\n distribution.append((MLB_contract, p**n))\\n else:\\n # number of combinations of players who make it\\n n_combinations = factorial(n)\\n n_combinations /= (factorial(n-n_makers)*factorial(n_makers))\\n n_combinations = int(n_combinations)\\n\\n # number of combinations where player 1 makes it\\n n_indv_inmajors = factorial(n-1)\\n n_indv_inmajors /= (factorial((n-1)-(n_makers-1))*factorial(n_makers-1))\\n n_indv_inmajors = int(n_indv_inmajors)\\n\\n # probability that n_makers of players make it\\n payoff_prob = p**n_makers * (1-p)**(n-n_makers)\\n\\n # payoff when player 1 is one of the players who makes it\\n payoff = MLB_contract - 0.1*(MLB_contract-thresh)\\n payoff += (n_makers-1)*0.1*(MLB_contract-thresh)/(n-1)\\n distribution.append((payoff, payoff_prob*n_indv_inmajors))\\n\\n # payoff when player 1 is not one of the players who makes it\\n payoff = minor_contract\\n payoff += n_makers*0.1*(MLB_contract-thresh)/(n-1)\\n distribution.append((payoff, payoff_prob*(n_combinations-n_indv_inmajors)))\\n E_payoff = [a*b for (a, b) in distribution]\\n E_payoff = sum(E_payoff)\\n var_payoff = [((a-E_payoff)**2)*b for (a, b) in distribution]\\n var_payoff = sum(var_payoff)\\n return E_payoff, var_payoff**0.5\",\n \"def func_with_prob(f, p):\\n def g(x):\\n choice = tf.random_uniform(shape=(), minval=0, maxval=1)\\n return tf.cond(choice < p, lambda: f(x), lambda: x)\\n return g\",\n \"def get_probable_prime(n: int) -> [int]:\\n return [6*n-1, 6*n+1]\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n probabilities = dict()\\n samples = []\\n\\n # Random first sample\\n page = random.choice(list(corpus.keys()))\\n samples.append(page)\\n \\n # Remaining samples after first\\n for i in range(n-1):\\n p = transition_model(corpus, page, damping_factor)\\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\\n samples.append(page)\\n\\n # Count\\n for p in corpus.keys():\\n probabilities[p] = samples.count(p) / n\\n\\n return probabilities\",\n \"def calc_p(ran):\\n import random\\n from math import sqrt\\n\\n p = None; hits = 0; misses = 0\\n\\n for i in range(ran):\\n x = random.random() * 2 - 1\\n y = random.random() * 2 - 1\\n r = sqrt(x**2 + y**2)\\n if r <= 1:\\n hits += 1\\n \\n p = 4 * hits / ran\\n\\n return p\",\n \"def prime_dist(func, n):\\n x = func(n)\\n y = list(range(len(x)))\\n for i in range(len(x)):\\n y[i] = (i+1)*np.log(x[i])\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.set_ylabel(r'k $\\\\cdot$ log(p$_k$)')\\n ax.set_xlabel(r'p$_k$')\\n plt.suptitle('Distribution of primes', fontsize=15)\\n ax.plot(x, y, lw=2, color='#FE4365')\\n plt.show()\",\n \"def random_round(x):\\r\\n prob = x - int(x)\\r\\n int_x = int(x)\\r\\n return int_x + (prob > np.random.rand())\",\n \"def test_success_formula(n_meanings, n_signals, n_trials, n_runs):\\r\\n input_meaning_p = rand_p(n_meanings)\\r\\n print(input_meaning_p)\\r\\n print()\\r\\n dyad = ProductSystem.rand_dyad(n_meanings, n_signals)\\r\\n print(dyad.psys.matrix)\\r\\n print()\\r\\n print(dyad.rsys.matrix)\\r\\n print()\\r\\n data = tuple(cummean(tuple(dyad.randsuccess(input_meaning_p) \\\\\\r\\n for j in range(n_trials))) \\\\\\r\\n for i in range(n_runs))\\r\\n graph = sns.tsplot(data, err_style='unit_traces')\\r\\n graph.set(xlabel='Number of trials', ylabel='Success proportion', ylim=[0,1])\\r\\n success_p = dyad.success_p(input_meaning_p)\\r\\n print(success_p)\\r\\n sns.tsplot((success_p,) * n_trials, color=sns.color_palette()[1])\",\n \"def prob_list(n, p):\\n ...\\n#pn is the probability of seeing a pair of aces two hands in a row in n hands.\\n#qn = 1 - pn\\n#thus qn is the probability of NOT seeing a par of aces two hands in a row in n hands.\\n list = []\\n qn_a = 1\\n qn_b = 1\\n list.append(qn_a)\\n list.append(qn_b)\\n\\n for i in range(n-2):\\n i += 0\\n qn_next = (1-p)*qn_b + p*(1-p)*qn_a\\n list.append(qn_next)\\n qn_a = qn_b\\n qn_b = qn_next\\n return list\\n\\n\\n # returns list of probs. [q0,... qn]\",\n \"def rbernoulli(p):\\n # The idea is to sample a random real r in the unit interval, one\\n # bit (i.e. binary decimal place) at a time, until we are sure\\n # that either r < p or r > p.\\n hi = 1.0\\n lo = 0.0\\n d = -1\\n while lo < p < hi:\\n if random.getrandbits(1):\\n lo = (hi + lo)/2\\n else:\\n hi = (hi + lo)/2\\n print lo,hi\\n if p > hi:\\n return 1\\n else:\\n return 0\",\n \"def prob_distr(self, x):\\n return 1.0/x\",\n \"def priorProb(self, state):\\n actions = []\\n for i in range(0, 10):\\n actions.append(((i, i+1), random.uniform(0, 1))) \\n \\n return actions\",\n \"def bernoulli(n):\\n\\n x, res, s, c = Rat(0), Rat(0), Rat(0), Rat(-1)\\n for k in range(1, n+2):\\n c *= 1 - Rat(n + 2)/k\\n s += x**n\\n x += 1\\n res += c*s/k\\n return res\",\n \"def prob(self, sequence):\\n prob = 1\\n for event, context in self.extract_ngrams(sequence):\\n prob *= self.cond_prob(event, context)\\n return prob\",\n \"def test(n,trials):\\n for trial in range(trials):\\n lst = list(range(n))\\n my_shuffle(lst)\\n ans = lst.pop() if n>0 else 0\\n \\n res = f1(lst)\\n \\n try:\\n assert ans == res\\n except:\\n st = f\\\"Incorrect output {res} on {lst} w/ missing {ans}.\\\"\\n raise AssertionError(st)\",\n \"def _calculate_p(vector: pd.DataFrame, n: int) -> pd.DataFrame:\\r\\n\\r\\n ## Isolate permuted walk scores (these fields begin w/ p_), identify permuted scores\\r\\n ## of equal or greater magnitude than the one originally observed, sum the scores,\\r\\n ## then calculate the p-value.\\r\\n vector['p'] = (\\r\\n vector.filter(regex=r'p_\\\\d+')\\r\\n .apply(lambda x: x >= vector.probability)\\r\\n .select_dtypes(include=['bool'])\\r\\n .sum(axis=1)\\r\\n )\\r\\n vector['p'] = (vector.p + 1) / (n + 1)\\r\\n\\r\\n ## Get rid of all the permuted score columns\\r\\n return vector[['node_from', 'node_to', 'probability', 'p']]\",\n \"def slow_kp(p, f):\\n return sum(1 for n in range(p) if f(n) % p == 0)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8051699","0.80322367","0.8030806","0.80230564","0.7710508","0.7604674","0.7147401","0.7144347","0.69072294","0.68672615","0.6867023","0.6762666","0.6749791","0.67391","0.67163926","0.67021275","0.6657762","0.65597177","0.65574616","0.6515296","0.65025866","0.6491918","0.6448943","0.63730633","0.63622284","0.63423043","0.6336658","0.6289481","0.6284959","0.6263841","0.6242011","0.6219305","0.62159556","0.6207555","0.61981076","0.6179615","0.6175351","0.6112214","0.60892516","0.6080384","0.6077555","0.6065975","0.6065502","0.60648286","0.6054373","0.6048346","0.60431975","0.6040837","0.60301733","0.60166144","0.60110384","0.6003902","0.5996792","0.5986918","0.5976713","0.59762585","0.5964676","0.59561694","0.595551","0.5954251","0.5953838","0.59433454","0.5936724","0.59272784","0.59231824","0.59075135","0.5901891","0.5898163","0.58975345","0.5886233","0.58825266","0.5881258","0.5880054","0.58731157","0.586875","0.5863932","0.58616847","0.5854559","0.585373","0.5841648","0.58409196","0.5828682","0.5822867","0.58184737","0.58147866","0.5812468","0.5807581","0.58018386","0.58004856","0.5799742","0.57901424","0.57737195","0.5763038","0.5750472","0.57501066","0.5748215","0.5738069","0.5737268","0.5736806","0.57320476"],"string":"[\n \"0.8051699\",\n \"0.80322367\",\n \"0.8030806\",\n \"0.80230564\",\n \"0.7710508\",\n \"0.7604674\",\n \"0.7147401\",\n \"0.7144347\",\n \"0.69072294\",\n \"0.68672615\",\n \"0.6867023\",\n \"0.6762666\",\n \"0.6749791\",\n \"0.67391\",\n \"0.67163926\",\n \"0.67021275\",\n \"0.6657762\",\n \"0.65597177\",\n \"0.65574616\",\n \"0.6515296\",\n \"0.65025866\",\n \"0.6491918\",\n \"0.6448943\",\n \"0.63730633\",\n \"0.63622284\",\n \"0.63423043\",\n \"0.6336658\",\n \"0.6289481\",\n \"0.6284959\",\n \"0.6263841\",\n \"0.6242011\",\n \"0.6219305\",\n \"0.62159556\",\n \"0.6207555\",\n \"0.61981076\",\n \"0.6179615\",\n \"0.6175351\",\n \"0.6112214\",\n \"0.60892516\",\n \"0.6080384\",\n \"0.6077555\",\n \"0.6065975\",\n \"0.6065502\",\n \"0.60648286\",\n \"0.6054373\",\n \"0.6048346\",\n \"0.60431975\",\n \"0.6040837\",\n \"0.60301733\",\n \"0.60166144\",\n \"0.60110384\",\n \"0.6003902\",\n \"0.5996792\",\n \"0.5986918\",\n \"0.5976713\",\n \"0.59762585\",\n \"0.5964676\",\n \"0.59561694\",\n \"0.595551\",\n \"0.5954251\",\n \"0.5953838\",\n \"0.59433454\",\n \"0.5936724\",\n \"0.59272784\",\n \"0.59231824\",\n \"0.59075135\",\n \"0.5901891\",\n \"0.5898163\",\n \"0.58975345\",\n \"0.5886233\",\n \"0.58825266\",\n \"0.5881258\",\n \"0.5880054\",\n \"0.58731157\",\n \"0.586875\",\n \"0.5863932\",\n \"0.58616847\",\n \"0.5854559\",\n \"0.585373\",\n \"0.5841648\",\n \"0.58409196\",\n \"0.5828682\",\n \"0.5822867\",\n \"0.58184737\",\n \"0.58147866\",\n \"0.5812468\",\n \"0.5807581\",\n \"0.58018386\",\n \"0.58004856\",\n \"0.5799742\",\n \"0.57901424\",\n \"0.57737195\",\n \"0.5763038\",\n \"0.5750472\",\n \"0.57501066\",\n \"0.5748215\",\n \"0.5738069\",\n \"0.5737268\",\n \"0.5736806\",\n \"0.57320476\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5732656"},"document_rank":{"kind":"string","value":"99"}}},{"rowIdx":25292,"cells":{"query":{"kind":"string","value":"Given n number of trials, p the probability of success, what is the probability of having x successes? Your function should raise a ValueError if x is higher than n. If you need to compute combinations, you can import the function \"comb\" from the package \"scipy.special\""},"document":{"kind":"string","value":"def bin_dist(n, p, x):\n def factorial(x):\n if x >= 0:\n \n factorial = 1\n\n for i in range(1, x + 1):\n factorial = float(factorial * i)\n # print(f' The factorial of {x} is {factorial}') \n return factorial\n\n else:\n raise ValueError(\"Sorry x cannot be a negative number\")\n\n def combination(n, r):\n \"\"\"\n Given n total number of items,\n what is the number of possible ways\n to choose r items from it?\n\n :param n: total number of items (integer)\n :param r: number of items to arrange (int)\n :return: number of combinations\n :rtype: integer\n \"\"\"\n\n \n\n \n numerator = factorial(n)\n denominator = factorial(r)\n subtracted_answer = factorial(n-r)\n \n\n answer = numerator/(denominator * subtracted_answer)\n print(answer)\n return answer \n\n # from scipy.special import comb\n if x > n:\n raise ValueError(\"Error, x must be less than n\")\n else:\n\n\n prob_success = float((combination(n, x)) * ((p**x)*((1-p)**(n-x))))\n\n print(prob_success)\n return prob_success"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n\n\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n\n # If less than p, it's a success so add one to n_success\n if random_number< p:\n n_success += 1\n\n return n_success","def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n\n # If less than p, it's a success so add one to n_success\n if random_number < p:\n n_success += 1\n\n return n_success","def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n # If less than p, it's a success so add one to n_success\n if random_number < p:\n n_success += 1\n\n return n_success","def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n\n\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n\n\n # If less than p, it's a success so add one to n_success\n if random_number < p:\n n_success += 1\n\n return n_success","def probability_of_all_successes(p: float, r: int, n: int) -> float:\n\n if r == 1:\n return pow(p, n)\n elif n == 0:\n return 1\n else:\n result = 0\n for x in range(0, n+1):\n result += pow(p, x) * pow(1-p, n-x) * probability_of_all_successes(p, r-1, n-x)\n return result","def chance(n, p):\n total = 0.0\n for k in range(n+1):\n total += comb(n, k, exact=False) * p**k * (1-p) ** (n-k)\n return total","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def binomial(n: int, p: float) -> int:\n return sum(bernoulli_trial(p) for _ in range(n))","def prob1(n):\n\n # create a giant draw from a normal distribution\n random_draws = np.random.normal(loc= 0, scale = 1, size = n)\n\n # mask the values\n mask = random_draws > 3\n\n return np.sum(mask)/float(n)","def bin_cdf(n, p, x):\n\n # p C (bin_dist) ** 0 ) *(1-bin_dist)** p\n\n # n = (p)=20\n # x = x = 1 = r\n # nCr = n! / r!(n-r)\n\n \n\n\n\n\n\n\n\n\n def bin_dist(n, p, x):\n \"\"\"\n Given n number of trials, p the probability of success,\n what is the probability of having x successes?\n\n Your function should raise a ValueError if x is higher\n than n.\n\n If you need to compute combinations, you can import the\n function \"comb\" from the package \"scipy.special\"\n\n :param n: number of trials (int)\n :param p: probability of success\n :param x: number of successes (int)\n :return: probability of having x successes\n :rtype: float\n :raise ValueError: if x > n\n \"\"\"\n def factorial(x):\n if x >= 0:\n \n factorial = 1\n\n for i in range(1, x + 1):\n factorial = float(factorial * i)\n # print(f' The factorial of {x} is {factorial}') \n return factorial\n\n else:\n raise ValueError(\"Sorry x cannot be a negative number\")\n\n def combination(n, r):\n \"\"\"\n Given n total number of items,\n what is the number of possible ways\n to choose r items from it?\n\n :param n: total number of items (integer)\n :param r: number of items to arrange (int)\n :return: number of combinations\n :rtype: integer\n \"\"\"\n\n \n\n \n numerator = factorial(n)\n denominator = factorial(r)\n subtracted_answer = factorial(n-r)\n \n\n answer = numerator/(denominator * subtracted_answer)\n print(answer)\n return answer \n\n # from scipy.special import comb\n if x > n:\n raise ValueError(\"Error, x must be less than n\")\n else:\n\n\n prob_success = float((combination(n, x)) * ((p**x)*((1-p)**(n-x))))\n\n print(prob_success)\n return prob_success \n \n # an= 1-bin_dist(n,p,x)\n # print(f'word{an}')\n # n= 12\n # p=0.25\n # # x=0??\n # ((n!)/ (x!*(n-x)!)) * (p**x) * (1-p)**(n-x)\n sum_prob = []\n for i in range(x+1):\n print(i)\n prob = bin_dist(n,p,x=i)\n sum_prob.append(prob)\n print(sum_prob)\n total =sum(sum_prob)\n print(total)","def is_probable_prime( n, trials = 5 ):\n\n if n in smallprimeset: return True\n\n if n < 2: return False\n # special case 2\n if n == 2:\n return True\n # ensure n is odd\n if n % 2 == 0:\n return False\n # write n-1 as 2**s * d\n # repeatedly try to divide n-1 by 2\n s = 0\n d = n - 1\n while True:\n quotient, remainder = divmod( d, 2 )\n if remainder == 1:\n break\n s += 1\n d = quotient\n assert( ( 2 ** s ) * d == n - 1 )\n\n # test the base a to see whether it is a witness for the compositeness of n\n def try_composite( a ):\n if pow( a, d, n ) == 1:\n return False\n for i in range( s ):\n if pow( a, 2 ** i * d, n ) == n - 1:\n return False\n return True # n is definitely composite\n\n for i in range( trials ):\n a = random.randrange( 2, n )\n if try_composite( a ):\n return False\n\n return True # no base tested showed n as composite","def probability(n, k, p):\n prob = 0\n power = expotentation_by_squaring((1-p), n)\n count_mult = math.log(n, 2)\n p_fraction = p/(1-p)\n count_mult += 1\n for i in range(0, k+1):\n element = newton(n, i)*power\n prob += element\n power *= p_fraction\n count_mult += 2\n return prob, count_mult","def binomial(n, p):\n sum_ans = 0\n for k in range(n):\n sum_ans = sum_ans + bernoulli(p)\n return sum_ans","def factorPR(n):\n\tnumsteps=2*math.floor(math.sqrt(math.sqrt(n)))\n\tfor additive in range(1,5):\n\t\tfast=slow=1; i=1\n\t\twhile i<numsteps:\n\t\t\tslow = (slow*slow + additive) % n\n\t\t\ti = i + 1\n\t\t\tfast = (fast*fast + additive) % n\n\t\t\tfast = (fast*fast + additive) % n\n\t\t\tg = gcd(fast-slow,n)\n\t\t\tif (g != 1):\n\t\t\t\tif (g == n):\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\treturn g\n\treturn 1","def game1(n):\r\n\twin=0\r\n\tfor i in range(n):\r\n\t\tif game(1)==1:\r\n\t\t\twin+=1\r\n\tprob1=win/n\r\n\treturn prob1","def binomialTest(k, n, p = 0.5, exact = False):\n\tassert(k <= n)\n\tassert(k >= 0 and n > 0)\n\tn = int(n)\n\tk = int(k)\n\tp_value = 1.0\n\n\t# Trivial cases where p = 0 or p = 1\n\tif p == 0.0: # Must then have k = 0\n\t\tif k > 0:\n\t\t\treturn 0.0\n\t\telse:\n\t\t\treturn 1.0\n\tif p == 1.0: # Must then have k = n\n\t\tif k <= n:\n\t\t\treturn 1.0\n\n\tif k == 0:\n\t\t# Probability of at least zero successes is 1\n\t\tp_value = 1.0\n\telif k == n:\n\t\t# Probability of all successes\n\t\tp_value = p**n\n\telse:\n\t\tif not exact and n*p > 30 and n*(1-p) > 30:\n\t\t\t# Use normal approximation\n\t\t\tmu = n*p\n\t\t\tsd = math.sqrt(n*p*(1-p))\n\t\t\tz = (k-mu)/sd\n\t\t\tif z < 0.0:\n\t\t\t\tp_value = 1-Prob_Z(z)\n\t\t\telse:\n\t\t\t\tp_value = Prob_Z(z)\n\t\telse:\n\t\t\tp_value = p**n # The last term in the sum\n\t\t\tfor j in range(k,n):\n\t\t\t\t# Compute logarithm of (n choose j) p^j (1-p)^ (n-j), the\n\t\t\t\t# binomial probability. Use logarithm to avoid overflow\n\t\t\t\t# problems with potentially enormous factorials.\n\t\t\t\tlog_p = logChoose(n,j) + j*math.log(p) + (n-j)*math.log(1-p)\n\t\t\t\tp_value += math.exp(log_p)\n\t\t\tif p_value > 1.0:\n\t\t\t\tp_value = 1.0\n\treturn p_value","def bernoulli_trial(p: float) -> int:\n return 1 if random.random() < p else 0","def combinations(n) -> float:\r\n c = math.factorial(n) / (math.factorial(2) * math.factorial(n - 2))\r\n return c","def test_probability_of_all_successes():\n\n assert(probability_of_all_successes(1/2,1,2) == 0.25)\n assert(are_close(probability_of_all_successes(1/6,1,2), 1/36, 0.001))\n assert(are_close(probability_of_all_successes(1/2,2,2), 7/16, 0.001))","def multiple_comparisons(p, n):\r\n if p > 1e-6: # if p is large and n small, calculate directly\r\n return 1 - (1 - p) ** n\r\n else:\r\n return one_minus_exp(-n * p)","def prior(n=10):\r\n p = []\r\n trials = 0\r\n acc = 0\r\n while acc < n:\r\n trials += 1\r\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\r\n # print(\"r: \", r)\r\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\r\n p.append(r)\r\n acc += 1\r\n # print(\"trials: \", trials, \", acc: \", acc)\r\n return p","def success_p_dist(n_trials, n_meanings, n_signals, input_meaning_p = None):\r\n if input_meaning_p is None:\r\n input_meaning_p = uniform_p(n_meanings)\r\n y = []\r\n for i in range(n_trials):\r\n dyad = ProductSystem.rand_dyad(n_meanings, n_signals)\r\n y.append(dyad.success_p(input_meaning_p))\r\n sns.distplot(np.asarray(y))","def prob(throw, n, d=6, type='classical'):\n count = 0\n table = throw_table(n, d, type)\n for t in table:\n if sum(t) == throw:\n count += 1\n \n return float(count)/len(table)","def prob_list(n, p):\n ...\n#pn is the probability of seeing a pair of aces two hands in a row in n hands.\n#qn = 1 - pn\n#thus qn is the probability of NOT seeing a par of aces two hands in a row in n hands.\n list = []\n qn_a = 1\n qn_b = 1\n list.append(qn_a)\n list.append(qn_b)\n\n for i in range(n-2):\n i += 0\n qn_next = (1-p)*qn_b + p*(1-p)*qn_a\n list.append(qn_next)\n qn_a = qn_b\n qn_b = qn_next\n return list\n\n\n # returns list of probs. [q0,... qn]","def find_trials_required(number_of_sequences, sigma, p=0.9):\n return ceil((p * (1 - p)) / (number_of_sequences * sigma ** 2) - (1 / number_of_sequences))","def factorPR(n):\r\n\tfor slow in [2,3,4,6]:\r\n\t\tnumsteps=2*math.floor(math.sqrt(math.sqrt(n))); fast=slow; i=1\r\n\t\twhile i<numsteps:\r\n\t\t\tslow = (slow*slow + 1) % n\r\n\t\t\ti = i + 1\r\n\t\t\tfast = (fast*fast + 1) % n\r\n\t\t\tfast = (fast*fast + 1) % n\r\n\t\t\tg = gcd(fast-slow,n)\r\n\t\t\tif (g != 1):\r\n\t\t\t\tif (g == n):\r\n\t\t\t\t\tbreak\r\n\t\t\t\telse:\r\n\t\t\t\t\treturn g\r\n\treturn 1","def is_prime(n, number_of_tests=5):\n passes = 0\n prime = True #assume prime\n for i in xrange(number_of_tests):\n passes += 1\n random_int = random.randint(2, n-1)\n test = pow(random_int, n-1, n)\n if test != 1:\n prime = False\n break\n if prime:\n return 0\n else:\n return passes","def prob_choice(p):\n \n return np.random.random_sample() < p","def isprime(n):\n if n!=int(n):\n return False\n n=int(n)\n #Miller-Rabin test for prime\n if n==0 or n==1 or n==4 or n==6 or n==8 or n==9:\n return False\n\n if n==2 or n==3 or n==5 or n==7:\n return True\n s = 0\n d = n-1\n while d%2==0:\n d>>=1\n s+=1\n assert(2**s * d == n-1)\n\n def trial_composite(a):\n if pow(a, d, n) == 1:\n return False\n for i in range(s):\n if pow(a, 2**i * d, n) == n-1:\n return False\n return True\n\n for i in range(8):#number of trials\n a = random.randrange(2, n)\n if trial_composite(a):\n return False\n\n return True","def probit(x):\n from tensorflow_probability import distributions\n return distributions.Normal(0, 1).cdf(x)","def expected_value_of_outcome(n, p):\r\n expected = n * p\r\n print(\"expected value / mean = \", expected)\r\n return expected","def game3(n):\r\n\twin=0\r\n\tfor i in range(n):\r\n\t\tif game(3)==1:\r\n\t\t\twin+=1\t\r\n\tprob3=win/n\r\n\treturn prob3","def compute_prob_mle(X: np.ndarray, n: int) -> float:\n\n assert n > 1, \"for n = 1 use Bernoulli distribution.\"\n Binomial._check_input_data(X=X)\n Binomial._check_support(X=X, n=n)\n\n prob = X.mean() / n\n return prob","def prob_no_match(n):\n return math.factorial(n)*math.comb(365,n)/(365**n)","def get_probable_prime(n: int) -> [int]:\n return [6*n-1, 6*n+1]","def calculate_probability(k: int, m: int, n: int) -> float:\n population = [\"AA\" for _ in range(k)] + [\"Aa\" for _ in range(m)] + [\"aa\" for _ in range(n)]\n pairings = it.combinations(population, 2)\n probabilities = [PROBABILITIES[pairing] for pairing in pairings]\n output = sum(probabilities) / len(probabilities)\n\n return output","def potential_witnesses(n):\n if n < 1373653: return [2, 3] # n < 1,373,653\n if n < 9080191: return [31, 73] # n < 9,080,191\n if n < 4759123141: return [2, 7, 61] # n < 4,759,123,141\n if n < 2152302898747: return [2, 3, 5, 7, 11] # n < 2,152,302,898,747\n if n < 3474749660383: return [2, 3, 5, 7, 11, 13] # n < 3,474,749,660,383\n if n < 341550071728321: return [2, 3, 5, 7, 11, 13, 17] # n < 341,550,071,728,321\n return [random.randint(1,n-1) for _ in xrange(0,20)] # 99.999999999909051% (1 - .25**20) accuracy for n >= 341,550,071,728,321","def initial_probabilities_from_trajectories(n_states, trajectories):\n p = np.zeros(n_states)\n\n for t in trajectories:\n p[t.transitions()[0][0]] += 1.0\n\n return p / len(trajectories)","def payoff_n(n=3, p=0.06,\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\n distribution = []\n for n_makers in range(n + 1): # For every number of possible players who make it\n if n_makers == 0:\n distribution.append((minor_contract, (1-p)**n))\n elif n_makers == n:\n distribution.append((MLB_contract, p**n))\n else:\n # number of combinations of players who make it\n n_combinations = factorial(n)\n n_combinations /= (factorial(n-n_makers)*factorial(n_makers))\n n_combinations = int(n_combinations)\n\n # number of combinations where player 1 makes it\n n_indv_inmajors = factorial(n-1)\n n_indv_inmajors /= (factorial((n-1)-(n_makers-1))*factorial(n_makers-1))\n n_indv_inmajors = int(n_indv_inmajors)\n\n # probability that n_makers of players make it\n payoff_prob = p**n_makers * (1-p)**(n-n_makers)\n\n # payoff when player 1 is one of the players who makes it\n payoff = MLB_contract - 0.1*(MLB_contract-thresh)\n payoff += (n_makers-1)*0.1*(MLB_contract-thresh)/(n-1)\n distribution.append((payoff, payoff_prob*n_indv_inmajors))\n\n # payoff when player 1 is not one of the players who makes it\n payoff = minor_contract\n payoff += n_makers*0.1*(MLB_contract-thresh)/(n-1)\n distribution.append((payoff, payoff_prob*(n_combinations-n_indv_inmajors)))\n E_payoff = [a*b for (a, b) in distribution]\n E_payoff = sum(E_payoff)\n var_payoff = [((a-E_payoff)**2)*b for (a, b) in distribution]\n var_payoff = sum(var_payoff)\n return E_payoff, var_payoff**0.5","def payoff_n_p(p, n=3,\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\n distribution = []\n for n_makers in range(n + 1):\n if n_makers == 0:\n payoff_prob = [1 - prob for prob in p.values()]\n payoff_prob = np.prod(payoff_prob)\n distribution.append((minor_contract, payoff_prob))\n elif n_makers == n:\n payoff_prob = [prob for prob in p.values()]\n payoff_prob = np.prod(payoff_prob)\n distribution.append((MLB_contract, payoff_prob))\n else:\n makers = list(combinations(range(1, n + 1), n_makers))\n for maker_set in makers:\n if 1 in maker_set:\n payoff = MLB_contract - 0.1*(MLB_contract-thresh)\n payoff += (n_makers-1)*0.1*(MLB_contract-thresh)/(n-1)\n payoff_prob = [p[player] for player in maker_set]\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\n payoff_prob = np.prod(payoff_prob)\n distribution.append((payoff, payoff_prob))\n else:\n payoff = minor_contract\n payoff += n_makers*0.1*(MLB_contract-thresh)/(n-1)\n payoff_prob = [p[player] for player in maker_set]\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\n payoff_prob = np.prod(payoff_prob)\n distribution.append((payoff, payoff_prob))\n E_payoff = [a*b for (a, b) in distribution]\n E_payoff = sum(E_payoff)\n var_payoff = [((a-E_payoff)**2)*b for (a, b) in distribution]\n var_payoff = sum(var_payoff)\n return E_payoff, var_payoff**0.5","def prob4():\n\n\n N = 500000\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\n\n h = lambda x: x[0] < -1 and x[1] > 1\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\n\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\n\n return 1./N * np.sum(probability)","def r_combinations(n,r):\n return r_permutations(n,r) / math.factorial(r)","def bernoulli(n):\n\n x, res, s, c = Rat(0), Rat(0), Rat(0), Rat(-1)\n for k in range(1, n+2):\n c *= 1 - Rat(n + 2)/k\n s += x**n\n x += 1\n res += c*s/k\n return res","def simulate_rerolling(p: float, n: int) -> int:\n\n counter = 0\n new_n = n\n while new_n > 0:\n for _ in range(new_n):\n ran = random.random()\n if ran < p:\n new_n -= 1\n counter += 1\n return counter","def compute_perplexity(self, train_corpus: str, test_corpus: str, n: int):\n N = len(''.join(test_corpus.split()))\n y = -1 / N\n likelihood = self.compute_likelihood(train_corpus, test_corpus, n)\n return likelihood ** y","def prbs(m, n):\n return np.array(np.random.rand(m, n) > 0.5, dtype=np.int) - 0.5","def solution(n,p):\n \n a=pow(n, (p - 1) // 2, p)\n if(a==1):\n return True\n else :\n return False","def game2(n):\r\n\twin=0\r\n\tfor i in range(n):\r\n\t\tif game(2)==1:\r\n\t\t\twin+=1\r\n\tprob2=win/n\r\n\treturn prob2","def prob4():\n#raise NotImplementedError(\"Problem 4 Incomplete\")\n h = lambda x : x[0] < -1 and x[1] > 1\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\n X = np.random.multivariate_normal(mean=np.array([-1,1]),cov=np.eye(2),size=10000)\n return 1./10000*np.sum(np.apply_along_axis(h,1,X)*np.apply_along_axis(f,1,X)/np.apply_along_axis(g,1,X))","def dbinom(self, x, n, p):\n f = math.factorial\n C = Decimal(f(n) / (f(x) * f(n-x)))\n return C * p**x * (1-p)**(n-x)","def factor(n):\n from random import randrange\n while True:\n i = 1\n x = randrange(0, n)\n y = x\n while True:\n i += 1\n x = ((x ** 2) - 1) % n\n d = gcd(y - x, n)\n if d != 1 and d != n:\n return d\n if i > 10000:\n break","def solve(n=100):\n # Build the board.\n single_vals = list(range(1, 21)) + [25]\n double_vals = [2*x for x in single_vals]\n triple_vals = [3*x for x in range(1, 21)]\n total_vals = single_vals + double_vals + triple_vals + [0]\n\n return sum(1 for other_throws, double_throw\n in product(combinations_with_replacement(total_vals, 2), double_vals, repeat=1)\n if sum(other_throws) + double_throw < n)","def probability(series, params):\n\n prob = 1\n\n for result in series:\n\n prob *= params[result]\n\n return prob * params[\"die\"]","def is_prime(number, num_trials=200):\n if number < 2:\n return False\n if number != 2 and number % 2 == 0:\n return False\n\n # Find largest odd factor of n-1.\n exp = number - 1\n while exp % 2 == 0:\n exp //= 2\n\n for _ in range(num_trials):\n rand_val = int(random.SystemRandom().randrange(1, number))\n new_exp = exp\n power = pow(rand_val, new_exp, number)\n while new_exp != number - 1 and power != 1 and power != number - 1:\n power = (power * power) % number\n new_exp *= 2\n if power != number - 1 and new_exp % 2 == 0:\n return False\n\n return True","def Ballie_PSW_test(n, max_trivial_trials=100):\n for i in range(max_trivial_trials):\n if primes[i] == n:\n return True\n if n % primes[i] == 0:\n return False\n if primes[i] ** 2 >= n:\n return True\n if not fermat_strong_test(n, 2):\n return False\n if not lucas_selfridge_test(n):\n return False\n return True","def p(self) -> Probability:\n ...","def prob_m_of_n(m, n, T, l):\n PFD_one_unit = l*T\n m_of_n = binom(n, m) * (PFD_one_unit)**(n-m) * (1-PFD_one_unit)**m\n return m_of_n","def prob(x):\n\treturn 1. * bivariate_normal(x, (0., 1.2), (1., 1.), .8) + \\\n\t 1.05 * bivariate_normal(x, (.6, -1.), (1.3, .7), -.6)","def Pollard_pm1(n, primes, max_B=1000000):\n B = 10\n g = 1\n while B < max_B and g < n:\n a = randint(2, n - 2)\n g = gcd(a, n)\n if g != 1:\n return g\n for p in primes:\n if p >= B:\n break\n pd = 1 # p^d\n while pd * p <= B:\n pd *= p\n a = powmod(a, pd, n)\n g = gcd(a - 1, n)\n if g != 1 and g != n:\n return g\n B *= 2\n return 1","def test_success_formula(n_meanings, n_signals, n_trials, n_runs):\r\n input_meaning_p = rand_p(n_meanings)\r\n print(input_meaning_p)\r\n print()\r\n dyad = ProductSystem.rand_dyad(n_meanings, n_signals)\r\n print(dyad.psys.matrix)\r\n print()\r\n print(dyad.rsys.matrix)\r\n print()\r\n data = tuple(cummean(tuple(dyad.randsuccess(input_meaning_p) \\\r\n for j in range(n_trials))) \\\r\n for i in range(n_runs))\r\n graph = sns.tsplot(data, err_style='unit_traces')\r\n graph.set(xlabel='Number of trials', ylabel='Success proportion', ylim=[0,1])\r\n success_p = dyad.success_p(input_meaning_p)\r\n print(success_p)\r\n sns.tsplot((success_p,) * n_trials, color=sns.color_palette()[1])","def logp(value, n, p):\n\n res = factln(n) + pt.sum(-factln(value) + logpow(p, value), axis=-1)\n res = pt.switch(\n pt.or_(pt.any(pt.lt(value, 0), axis=-1), pt.neq(pt.sum(value, axis=-1), n)),\n -np.inf,\n res,\n )\n return check_parameters(\n res,\n 0 <= p,\n p <= 1,\n pt.isclose(pt.sum(p, axis=-1), 1),\n pt.ge(n, 0),\n msg=\"0 <= p <= 1, sum(p) = 1, n >= 0\",\n )","def easy_solve(n=30):\n # Use p1 and p2 to store expected payoffs per player\n p_1 = []\n p_2 = []\n # Get each possible dice roll in order\n arr = list(range(1, n + 1))\n x = (1/2)*(np.sqrt(2*n**2 + 2*n - 3) - 1)\n for i in range(n):\n # Let player 1 choose ith possible integer\n player_1 = arr[i]\n # Use aforementioned strategy\n if arr[i] <= int(x):\n player_2 = arr[i] + 1\n # Append expected value per player according to which integer is larger\n p_1.append((1/n)*sum(arr[:player_1]))\n p_2.append((1/n)*sum(arr[player_2-1:]))\n else:\n player_2 = arr[i] - 1\n p_2.append((1/n)*sum(arr[:player_2]))\n p_1.append((1/n)*sum(arr[player_1-1:]))\n return p_1, p_2","def probability(p):\n return p > random.uniform(0.0, 1.0)","def bernoulli(p):\r\n if np.random.random() < p:\r\n return 0\r\n else:\r\n return 1","def generate_bits(n, p):\n try:\n return (np.random.rand(*n) > p).astype(int)\n except TypeError:\n return (np.random.rand(n) > p).astype(int)","def get_prob_for_distributions(p):\n w1 = p[0]\n mu1 = p[1]\n sigma1 = p[2]\n w2 = p[3]\n mu2 = p[4]\n sigma2 = p[5]\n w3 = p[6]\n mu3 = p[7]\n sigma3 = p[8]\n dist_range = (0, 4.330310991999920844e+01)\n x = np.linspace(dist_range[0], dist_range[1], 1000)\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\n p1 = A1 / (A1 + A2 + A3)\n p2 = A2 / (A1 + A2 + A3)\n p3 = A3 / (A1 + A2 + A3)\n return p1, p2, p3","def prodi( iterable ):\n p= 1\n for n in iterable:\n p *= n\n return p","def P(N,L) :\n accum = 0.0\n sign = 1.0\n for i in xrange(len(L)+1) :\n accum2 = 0.0\n for combin in combinations(L,i) :\n term = 1.0\n j = 0.0\n for Li in combin :\n term *= Li/(N-j)\n j += 1\n accum2 += term\n accum += sign*accum2\n sign *= -1.0\n return accum","def Binomial(n, p, succ=1, fail=0, symbol=None):\n\n return BinomialPSpace(n, p, succ, fail, symbol).value","def discrete_sample(p, n_samples=None, rng=np.random):\n\n # check distribution\n # assert isdistribution(p), 'Probabilities must be non-negative and sum to one.'\n\n one_sample = n_samples is None\n\n # cumulative distribution\n c = np.cumsum(p[:-1])[np.newaxis, :]\n\n # get the samples\n r = rng.rand(1 if one_sample else n_samples, 1)\n samples = np.sum((r > c).astype(int), axis=1)\n\n return samples[0] if one_sample else samples","def get_success_probabilities_from_results(results: Sequence[Sequence[Sequence[int]]]) \\\n -> Sequence[float]:\n num_shots = len(results[0])\n n_bits = len(results[0][0]) - 1\n\n probabilities = []\n # loop over all binary strings of length n_bits\n for result, bits in zip(results, all_bitstrings(2 * n_bits)):\n # Input nums are written from (MSB .... LSB) = (a_n, ..., a_1, a_0)\n num_a = bit_array_to_int(bits[:n_bits])\n num_b = bit_array_to_int(bits[n_bits:])\n\n # add the numbers\n ans = num_a + num_b\n ans_bits = int_to_bit_array(ans, n_bits + 1)\n\n # a success occurs if a shot matches the expected ans bit for bit\n probability = 0\n for shot in result:\n if np.array_equal(ans_bits, shot):\n probability += 1. / num_shots\n probabilities.append(probability)\n\n return probabilities","def _fair_sharing_mp(n=2,decimals=100,pratio=15,debug=False):\n import operator\n import itertools\n from mpmath import mp\n mp.dps=decimals\n p=mp.mpf(10)**-(decimals/pratio)\n acc=mp.mpf(0)\n wins=[mp.mpf(0)]*n\n if debug:\n print('p = '+str(p))\n for i in itertools.count():\n index,_ = min(enumerate(wins),key=operator.itemgetter(1))\n yield index\n inc=p*(1-acc)\n wins[index]+=inc\n acc+=inc\n if debug:\n print('acc = '+str(acc))","def sample_discrete(probs):\r\n q = np.random.rand()\r\n i = 0\r\n p_sum = 0.0\r\n while p_sum < q:\r\n p_sum += probs[i]\r\n i += 1\r\n return i - 1","def puissance(x: float, n: int) -> float:\n resultat: float = 1\n signe: int = 1\n if n != 0:\n if n <= 0:\n n = -n\n signe = -1\n for cpt in range(1, n + 1):\n resultat = resultat * x\n if signe < 0:\n resultat = 1 / resultat\n return resultat","def find_pcts_multi(P, start_b = [], iter = 10000):\n assert len(P) >= 2\n wins_per_player = [0] * len(P)\n all_hole = reduce(lambda x,y: x+y, P)\n for i in range(iter):\n deck = Deck()\n need = 5 - len(start_b)\n b2 = draw_sure(deck, need, all_hole+start_b)\n s = [evaluator.evaluate(start_b+b2, h) for h in P]\n for i, e in enumerate(s):\n if e == min(s):\n wins_per_player[i] += 1\n return [float(x) / sum(wins_per_player) for x in wins_per_player]","def Poisson(n, k):\n\tp = math.exp(-k) * math.pow(k, n) / float(Factorial(n))\n\tassert 0.0 <= p <= 1.0, \"Error, value of p is invalid probability: \" + str(p)\n\treturn p","def compute_expected_return_(domain, N, policy, p_init, s_init):\r\n pos = p_init\r\n speed = s_init\r\n expected_return = 0\r\n\r\n for i in range(N):\r\n if domain.terminalState(pos, speed):\r\n break\r\n action = policy((pos, speed))\r\n if isinstance(action,str):\r\n action = domain.getAction(action)\r\n new_pos, new_speed = domain.getNextState(pos, speed, action)\r\n r = domain.getReward(pos, speed, action, new_pos, new_speed)\r\n expected_return += ((domain.discount_factor)**i)*r\r\n pos = new_pos\r\n speed = new_speed\r\n \r\n return expected_return","def prob_t_N(genotype, base):\n cnter = Counter(genotype)\n return cnter.get(base, 0) * 1/len(genotype)","def Mn_plus_distribution(n, x):\n b_bounds = Mn_plus_bounds(n, x)\n return 1.0 - crossprob.ecdf1_new_b(b_bounds)\n #return 1.0 - crossprob.ecdf2(b_bounds, [1]*len(b_bounds), True)","def permutations_(n, r):\n return factorial(n) / factorial(n-r)","def test(n,trials):\n for trial in range(trials):\n lst = list(range(n))\n my_shuffle(lst)\n ans = lst.pop() if n>0 else 0\n \n res = f1(lst)\n \n try:\n assert ans == res\n except:\n st = f\"Incorrect output {res} on {lst} w/ missing {ans}.\"\n raise AssertionError(st)","def prob_extinct(Z0, t, n, s, u):\n p = 1\n for k in range(len(Z0)):\n p *= Phi_k(Phit(t - 1, List([0.] * n), s, u), k, s, u) ** Z0[k]\n return p","def probability(cpts, term, obs):\r\n \r\n \r\n # term is a list e.g., ['x_1', '0']\r\n # flip refers to the assignment either '0' false or '1' true\r\n flip = term[1]\r\n # the term itself\r\n term = term[0]\r\n # accumulator variable\r\n answer = 0\r\n # this loop locates where in the CPT we're looking\r\n for clause in range(len(cpts)):\r\n if cpts[clause][0] == term:\r\n index = clause\r\n # focus on our term\r\n cpt = cpts[index]\r\n # this loop checks if there are no preconditions\r\n # if not, then we immediately know the probability and can return\r\n for m in range(len(cpt[1])):\r\n if cpt[1][m][-2][1] == '1':\r\n if cpt[1][m][0] == [[]]:\r\n answer = cpt[1][m][-1]\r\n # list of the variables we have observed\r\n have = []\r\n if obs != []:\r\n for k in obs:\r\n have.append(k[0])\r\n # list of variables we need to know in order to calculate the probability\r\n needed = []\r\n for prob in range(len(cpt[1])):\r\n for j in cpt[1][prob][0]:\r\n if j != []:\r\n if j[0] not in needed:\r\n needed.append(j[0])\r\n # conditional logic based on the known variables\r\n for required in needed:\r\n if required not in have:\r\n # deep copy our observations list\r\n obs2 = []\r\n obs3 = []\r\n for observs in obs:\r\n obs2.append(observs)\r\n obs3.append(observs)\r\n # if we need to know a variable but don't have it\r\n # then we allow it to be either 0 or 1\r\n obs3.append([required,'1'])\r\n obs2.append([required,'0'])\r\n # computes probability if the unknown term is true, times \r\n # the probability that the unknown term is true, plus the\r\n # probability if the unknown term is false, times the \r\n # probability that the unknown term is false\r\n answer = (probability(cpts, [term,flip], obs3) * probability(cpts, [required,'1'], obs)) + (probability(cpts, [term,flip], obs2) * (probability(cpts, [required,'0'], obs)))\r\n # this loop looks complicated but all it's doing is finding the correct\r\n # line in the CPT\r\n if cpt[1][prob][-2][1] == '1':\r\n count = 1\r\n for i in range(len(cpt[1][prob][0])):\r\n if cpt[1][prob][0][i] in obs:\r\n count *= 1\r\n else:\r\n count = 0\r\n if count == 1:\r\n answer += cpt[1][prob][-1]\r\n\r\n\r\n # this computes the probability that the term is true, so if we asked \r\n # for the probability that it is false, just return 1 - answer\r\n if flip == '0':\r\n return 1 - answer\r\n return answer","def prove_N() -> Proof:\n # Optional Task 6.8","def probability(prob):\n return random.random() <= prob","def PN(self, n):\n if not self.isVaild():\n pass\n if n < self.C:\n return self.P0()*(self.r()**n)/math.factorial(n)\n else:\n return self.P0()*(self.r()**n)/(math.factorial(self.C)*self.C**(n-self.C))","def likelihood(x, n, P):\n if not isinstance(n, int) or (n <= 0):\n raise ValueError('n must be a positive integer')\n if not isinstance(x, int) or (x < 0):\n raise ValueError(\n 'x must be an integer that is greater than or equal to 0')\n if x > n:\n raise ValueError('x cannot be greater than n')\n if not isinstance(P, np.ndarray) or len(P.shape) != 1:\n raise TypeError('P must be a 1D numpy.ndarray')\n if not np.all((P >= 0) & (P <= 1)):\n raise ValueError('All values in P must be in the range [0, 1]')\n nume = np.math.factorial(n)\n deno = (np.math.factorial(x) * (np.math.factorial(n - x)))\n fact = nume / deno\n P_likelihood = fact * (np.power(P, x)) * (np.power((1 - P), (n - x)))\n return P_likelihood","def test(numTrials):\n # Your Code Here\n hits = 0.0\n for i in range(numTrials):\n result = trial()\n #print result\n hits += result\n return hits / numTrials","def pe_prob_003(num):\n factors = get_unique_factors(num)\n\n return factors[-1] if factors else 1","def greater_than_n_wins(wins_req: int, total_chances: int, win_rate: float) -> float:\n overall_prob = 0\n loss_rate = 1 - win_rate\n for num in range(wins_req, total_chances+1, 1):\n numerator = comb(total_chances, num)\n denominator = (win_rate ** num) * (loss_rate ** (total_chances - num))\n overall_prob += numerator * denominator\n return overall_prob","def generate_n(k_problem, n):\n return [generate_first_random(k_problem) for i in range(n)]","def is_prime(n):\n assert n > 3\n k = int(log2(n))\n m = n - 1\n d = 0\n while(m % 2 == 0):\n m //= 2\n d += 1\n for _ in range(k):\n a = randint(2, n - 2)\n x = pow(a, m, n)\n if x == 1 or x == n - 1:\n continue\n for _ in range(d - 1):\n x = pow(x, 2, n)\n if x == 1:\n return 0\n if x == n - 1:\n break\n if x != n - 1:\n return 0\n return 1","def sample(a, p):\n if (len(a) != len(p)):\n raise Exception('a != p')\n p = np.array(p)\n p = p / p.sum()\n r = random.random()\n n = len(a)\n total = 0 # range: [0,1]\n for i in xrange(n):\n total += p[i]\n if total > r:\n return a[i]\n return a[i]","def prob1(L):\n return min(L), max(L), sum(L)/len(L)\n raise NotImplementedError(\"Problem 1 Incomplete\")","def main():\n\n import sys\n sys.setrecursionlimit(10**7)\n from itertools import accumulate, combinations, permutations, product # https://docs.python.org/ja/3/library/itertools.html\n # accumulate() returns iterator! to get list: list(accumulate())\n from math import factorial, ceil, floor\n def factorize(n):\n \"\"\"return the factors of the Arg and count of each factor\n \n Args:\n n (long): number to be resolved into factors\n \n Returns:\n list of tuples: factorize(220) returns [(2, 2), (5, 1), (11, 1)]\n \"\"\"\n fct = [] # prime factor\n b, e = 2, 0 # base, exponent\n while b * b <= n:\n while n % b == 0:\n n = n // b\n e = e + 1\n if e > 0:\n fct.append((b, e))\n b, e = b + 1, 0\n if n > 1:\n fct.append((n, 1))\n return fct\n def combinations_count(n, r):\n \"\"\"Return the number of selecting r pieces of items from n kinds of items.\n \n Args:\n n (long): number\n r (long): number\n \n Raises:\n Exception: not defined when n or r is negative\n \n Returns:\n long: number\n \"\"\"\n # TODO: How should I do when n - r is negative?\n if n < 0 or r < 0:\n raise Exception('combinations_count(n, r) not defined when n or r is negative')\n if n - r < r: r = n - r\n if r < 0: return 0\n if r == 0: return 1\n if r == 1: return n\n numerator = [n - r + k + 1 for k in range(r)]\n denominator = [k + 1 for k in range(r)]\n for p in range(2,r+1):\n pivot = denominator[p - 1]\n if pivot > 1:\n offset = (n - r) % p\n for k in range(p-1,r,p):\n numerator[k - offset] /= pivot\n denominator[k] /= pivot\n result = 1\n for k in range(r):\n if numerator[k] > 1:\n result *= int(numerator[k])\n return result\n def combinations_with_replacement_count(n, r):\n \"\"\"Return the number of selecting r pieces of items from n kinds of items allowing individual elements to be repeated more than once.\n \n Args:\n n (long): number\n r (long): number\n \n Raises:\n Exception: not defined when n or r is negative\n \n Returns:\n long: number\n \"\"\"\n if n < 0 or r < 0:\n raise Exception('combinations_with_replacement_count(n, r) not defined when n or r is negative')\n elif n == 0:\n return 1\n else:\n return combinations_count(n + r - 1, r)\n from bisect import bisect_left, bisect_right\n from collections import deque, Counter, defaultdict # https://docs.python.org/ja/3/library/collections.html#collections.deque\n from heapq import heapify, heappop, heappush, heappushpop, heapreplace,nlargest,nsmallest # https://docs.python.org/ja/3/library/heapq.html\n from copy import deepcopy, copy # https://docs.python.org/ja/3/library/copy.html\n from operator import itemgetter\n # ex1: List.sort(key=itemgetter(1))\n # ex2: sorted(tuples, key=itemgetter(1,2))\n from functools import reduce\n def chmin(x, y):\n \"\"\"change minimum\n if x > y, x = y and return (x, True).\n convenient when solving problems of dp[i]\n \n Args:\n x (long): current minimum value\n y (long): potential minimum value\n \n Returns:\n (x, bool): (x, True) when updated, else (x, False)\n \"\"\"\n if x > y:\n x = y\n return (x, True)\n else:\n return (x, False)\n def chmax(x, y):\n \"\"\"change maximum\n if x < y, x = y and return (x, True).\n convenient when solving problems of dp[i]\n \n Args:\n x (long): current maximum value\n y (long): potential maximum value\n \n Returns:\n (x, bool): (x, True) when updated, else (x, False)\n \"\"\"\n if x < y:\n x = y\n return (x, True)\n else:\n return (x, False)\n\n from fractions import gcd # Deprecated since version 3.5: Use math.gcd() instead.\n def gcds(numbers):\n return reduce(gcd, numbers)\n def lcm(x, y):\n return (x * y) // gcd(x, y)\n def lcms(numbers):\n return reduce(lcm, numbers, 1)\n\n # first create factorial_list\n # fac_list = mod_factorial_list(n)\n INF = 10 ** 18\n MOD = 10 ** 9 + 7\n modpow = lambda a, n, p = MOD: pow(a, n, p) # Recursive function in python is slow!\n def modinv(a, p = MOD):\n # evaluate reciprocal using Fermat's little theorem:\n # a**(p-1) is identical to 1 (mod p) when a and p is coprime\n return modpow(a, p-2, p)\n def modinv_list(n, p = MOD):\n if n <= 1:\n return [0,1][:n+1]\n else:\n inv_t = [0,1]\n for i in range(2, n+1):\n inv_t += [inv_t[p % i] * (p - int(p / i)) % p]\n return inv_t\n def modfactorial_list(n, p = MOD):\n if n == 0:\n return [1]\n else:\n l = [0] * (n+1)\n tmp = 1\n for i in range(1, n+1):\n tmp = tmp * i % p\n l[i] = tmp\n return l\n def modcomb(n, k, fac_list = [], p = MOD):\n # fac_list = modfactorial_list(100)\n # print(modcomb(100, 5, modfactorial_list(100)))\n from math import factorial\n if n < 0 or k < 0 or n < k: return 0\n if n == 0 or k == 0: return 1\n if len(fac_list) <= n:\n a = factorial(n) % p\n b = factorial(k) % p\n c = factorial(n-k) % p\n else:\n a = fac_list[n]\n b = fac_list[k]\n c = fac_list[n-k]\n return (a * modpow(b, p-2, p) * modpow(c, p-2, p)) % p\n def modadd(a, b, p = MOD):\n return (a + b) % MOD\n def modsub(a, b, p = MOD):\n return (a - b) % p\n def modmul(a, b, p = MOD):\n return ((a % p) * (b % p)) % p\n def moddiv(a, b, p = MOD):\n return modmul(a, modpow(b, p-2, p))\n\n \"\"\" initialize variables and set inputs\n # initialize variables\n # to initialize list, use [0] * n\n # to initialize two dimentional array, use [[0] * N for _ in range(N)]\n # set inputs\n # open(0).read() is a convenient method:\n # ex) n, m, *x = map(int, open(0).read().split())\n # min(x[::2]) - max(x[1::2])\n # ex2) *x, = map(int, open(0).read().split())\n # don't forget to add comma after *x if only one variable is used\n # preprocessing\n # transpose = [x for x in zip(*data)]\n # ex) [[1, 2, 3], [4, 5, 6], [7, 8, 9]] => [(1, 4, 7), (2, 5, 8), (3, 6, 9)]\n # flat = [flatten for inner in data for flatten in inner]\n # ex) [[1, 2, 3], [4, 5, 6], [7, 8, 9]] => [1, 2, 3, 4, 5, 6, 7, 8, 9]\n # calculate and output\n # output pattern\n # ex1) print(*l) => when l = [2, 5, 6], printed 2 5 6\n \"\"\"\n\n # functions used\n r = lambda: sys.stdin.readline().strip()\n r_int = lambda: int(r())\n R = lambda: list(map(int, r().split()))\n Rfloat = lambda: list(map(float, r().split()))\n Rtuple = lambda: tuple(map(int, r().split()))\n Rmap = lambda: map(int, r().split())\n\n \"\"\" how to treat input\n # single int: int(r())\n # single string: r()\n # single float: float(r())\n # line int: R()\n # line string: r().split()\n # line (str, int, int): [j if i == 0 else int(j) for i, j in enumerate(r().split())]\n # lines int: [R() for _ in range(n)]\n \"\"\"\n\n # main\n N, Q = R()\n STX = [R() for _ in range(N)]\n STX.sort(key=itemgetter(2))\n\n D = [int(r()) for _ in range(Q)]\n Stopped = [-1] * Q\n ans = [-1] * Q\n\n for s, t, x in STX:\n l = bisect_left(D, s-x)\n r = bisect_left(D,t-x)\n a = l\n while a < r:\n if Stopped[a] == -1:\n ans[a] = x\n Stopped[a] = r\n a += 1\n else:\n a = Stopped[a]\n\n for i in ans:\n print(i)\n\n \"\"\"memo: how to use defaultdict of list\n # initialize\n Dic = defaultdict(list)\n # append / extend\n Dic[x].append(y)\n # for\n for k, v in Dic.items():\n \"\"\"","def expected_return(self, n_step):\r\n value = 0\r\n n_experiences = 50\r\n for i in range(n_experiences):\r\n trajectory = self.domain_exploration(n_step)\r\n value += self.compute_j(trajectory)\r\n return value/n_experiences","def uniform_but_one_dataset_no_weight(n, p):\n elements = []\n for i in range(n):\n elements.append(i)\n for i in range(int(n**(1.0 / p)) - 1):\n elements.append(1)\n return elements","def prime_dist(func, n):\n x = func(n)\n y = list(range(len(x)))\n for i in range(len(x)):\n y[i] = (i+1)*np.log(x[i])\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.set_ylabel(r'k $\\cdot$ log(p$_k$)')\n ax.set_xlabel(r'p$_k$')\n plt.suptitle('Distribution of primes', fontsize=15)\n ax.plot(x, y, lw=2, color='#FE4365')\n plt.show()","def get_rank_probabilities(n: int) -> List[float]:\n alpha = 3.5\n ranks = [1 / i**alpha for i in range(1, n + 1)]\n\n return [r / sum(ranks) for r in ranks]","def probability_at_least_d_fail_equal_reliability(d, chunk_count, reliability):\n \n return sum([loss_probability(reliability, k, chunk_count) for k in range(d, chunk_count + 1)])"],"string":"[\n \"def perform_bernoulli_trials(n, p):\\n # Initialize number of successes: n_success\\n n_success = 0\\n\\n\\n # Perform trials\\n for i in range(n):\\n # Choose random number between zero and one: random_number\\n random_number = np.random.random()\\n\\n # If less than p, it's a success so add one to n_success\\n if random_number< p:\\n n_success += 1\\n\\n return n_success\",\n \"def perform_bernoulli_trials(n, p):\\n # Initialize number of successes: n_success\\n n_success = 0\\n\\n # Perform trials\\n for i in range(n):\\n # Choose random number between zero and one: random_number\\n random_number = np.random.random()\\n\\n # If less than p, it's a success so add one to n_success\\n if random_number < p:\\n n_success += 1\\n\\n return n_success\",\n \"def perform_bernoulli_trials(n, p):\\n # Initialize number of successes: n_success\\n n_success = 0\\n # Perform trials\\n for i in range(n):\\n # Choose random number between zero and one: random_number\\n random_number = np.random.random()\\n # If less than p, it's a success so add one to n_success\\n if random_number < p:\\n n_success += 1\\n\\n return n_success\",\n \"def perform_bernoulli_trials(n, p):\\n # Initialize number of successes: n_success\\n n_success = 0\\n\\n\\n # Perform trials\\n for i in range(n):\\n # Choose random number between zero and one: random_number\\n random_number = np.random.random()\\n\\n\\n # If less than p, it's a success so add one to n_success\\n if random_number < p:\\n n_success += 1\\n\\n return n_success\",\n \"def probability_of_all_successes(p: float, r: int, n: int) -> float:\\n\\n if r == 1:\\n return pow(p, n)\\n elif n == 0:\\n return 1\\n else:\\n result = 0\\n for x in range(0, n+1):\\n result += pow(p, x) * pow(1-p, n-x) * probability_of_all_successes(p, r-1, n-x)\\n return result\",\n \"def chance(n, p):\\n total = 0.0\\n for k in range(n+1):\\n total += comb(n, k, exact=False) * p**k * (1-p) ** (n-k)\\n return total\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def binomial(n: int, p: float) -> int:\\n return sum(bernoulli_trial(p) for _ in range(n))\",\n \"def prob1(n):\\n\\n # create a giant draw from a normal distribution\\n random_draws = np.random.normal(loc= 0, scale = 1, size = n)\\n\\n # mask the values\\n mask = random_draws > 3\\n\\n return np.sum(mask)/float(n)\",\n \"def bin_cdf(n, p, x):\\n\\n # p C (bin_dist) ** 0 ) *(1-bin_dist)** p\\n\\n # n = (p)=20\\n # x = x = 1 = r\\n # nCr = n! / r!(n-r)\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n def bin_dist(n, p, x):\\n \\\"\\\"\\\"\\n Given n number of trials, p the probability of success,\\n what is the probability of having x successes?\\n\\n Your function should raise a ValueError if x is higher\\n than n.\\n\\n If you need to compute combinations, you can import the\\n function \\\"comb\\\" from the package \\\"scipy.special\\\"\\n\\n :param n: number of trials (int)\\n :param p: probability of success\\n :param x: number of successes (int)\\n :return: probability of having x successes\\n :rtype: float\\n :raise ValueError: if x > n\\n \\\"\\\"\\\"\\n def factorial(x):\\n if x >= 0:\\n \\n factorial = 1\\n\\n for i in range(1, x + 1):\\n factorial = float(factorial * i)\\n # print(f' The factorial of {x} is {factorial}') \\n return factorial\\n\\n else:\\n raise ValueError(\\\"Sorry x cannot be a negative number\\\")\\n\\n def combination(n, r):\\n \\\"\\\"\\\"\\n Given n total number of items,\\n what is the number of possible ways\\n to choose r items from it?\\n\\n :param n: total number of items (integer)\\n :param r: number of items to arrange (int)\\n :return: number of combinations\\n :rtype: integer\\n \\\"\\\"\\\"\\n\\n \\n\\n \\n numerator = factorial(n)\\n denominator = factorial(r)\\n subtracted_answer = factorial(n-r)\\n \\n\\n answer = numerator/(denominator * subtracted_answer)\\n print(answer)\\n return answer \\n\\n # from scipy.special import comb\\n if x > n:\\n raise ValueError(\\\"Error, x must be less than n\\\")\\n else:\\n\\n\\n prob_success = float((combination(n, x)) * ((p**x)*((1-p)**(n-x))))\\n\\n print(prob_success)\\n return prob_success \\n \\n # an= 1-bin_dist(n,p,x)\\n # print(f'word{an}')\\n # n= 12\\n # p=0.25\\n # # x=0??\\n # ((n!)/ (x!*(n-x)!)) * (p**x) * (1-p)**(n-x)\\n sum_prob = []\\n for i in range(x+1):\\n print(i)\\n prob = bin_dist(n,p,x=i)\\n sum_prob.append(prob)\\n print(sum_prob)\\n total =sum(sum_prob)\\n print(total)\",\n \"def is_probable_prime( n, trials = 5 ):\\n\\n if n in smallprimeset: return True\\n\\n if n < 2: return False\\n # special case 2\\n if n == 2:\\n return True\\n # ensure n is odd\\n if n % 2 == 0:\\n return False\\n # write n-1 as 2**s * d\\n # repeatedly try to divide n-1 by 2\\n s = 0\\n d = n - 1\\n while True:\\n quotient, remainder = divmod( d, 2 )\\n if remainder == 1:\\n break\\n s += 1\\n d = quotient\\n assert( ( 2 ** s ) * d == n - 1 )\\n\\n # test the base a to see whether it is a witness for the compositeness of n\\n def try_composite( a ):\\n if pow( a, d, n ) == 1:\\n return False\\n for i in range( s ):\\n if pow( a, 2 ** i * d, n ) == n - 1:\\n return False\\n return True # n is definitely composite\\n\\n for i in range( trials ):\\n a = random.randrange( 2, n )\\n if try_composite( a ):\\n return False\\n\\n return True # no base tested showed n as composite\",\n \"def probability(n, k, p):\\n prob = 0\\n power = expotentation_by_squaring((1-p), n)\\n count_mult = math.log(n, 2)\\n p_fraction = p/(1-p)\\n count_mult += 1\\n for i in range(0, k+1):\\n element = newton(n, i)*power\\n prob += element\\n power *= p_fraction\\n count_mult += 2\\n return prob, count_mult\",\n \"def binomial(n, p):\\n sum_ans = 0\\n for k in range(n):\\n sum_ans = sum_ans + bernoulli(p)\\n return sum_ans\",\n \"def factorPR(n):\\n\\tnumsteps=2*math.floor(math.sqrt(math.sqrt(n)))\\n\\tfor additive in range(1,5):\\n\\t\\tfast=slow=1; i=1\\n\\t\\twhile i<numsteps:\\n\\t\\t\\tslow = (slow*slow + additive) % n\\n\\t\\t\\ti = i + 1\\n\\t\\t\\tfast = (fast*fast + additive) % n\\n\\t\\t\\tfast = (fast*fast + additive) % n\\n\\t\\t\\tg = gcd(fast-slow,n)\\n\\t\\t\\tif (g != 1):\\n\\t\\t\\t\\tif (g == n):\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\treturn g\\n\\treturn 1\",\n \"def game1(n):\\r\\n\\twin=0\\r\\n\\tfor i in range(n):\\r\\n\\t\\tif game(1)==1:\\r\\n\\t\\t\\twin+=1\\r\\n\\tprob1=win/n\\r\\n\\treturn prob1\",\n \"def binomialTest(k, n, p = 0.5, exact = False):\\n\\tassert(k <= n)\\n\\tassert(k >= 0 and n > 0)\\n\\tn = int(n)\\n\\tk = int(k)\\n\\tp_value = 1.0\\n\\n\\t# Trivial cases where p = 0 or p = 1\\n\\tif p == 0.0: # Must then have k = 0\\n\\t\\tif k > 0:\\n\\t\\t\\treturn 0.0\\n\\t\\telse:\\n\\t\\t\\treturn 1.0\\n\\tif p == 1.0: # Must then have k = n\\n\\t\\tif k <= n:\\n\\t\\t\\treturn 1.0\\n\\n\\tif k == 0:\\n\\t\\t# Probability of at least zero successes is 1\\n\\t\\tp_value = 1.0\\n\\telif k == n:\\n\\t\\t# Probability of all successes\\n\\t\\tp_value = p**n\\n\\telse:\\n\\t\\tif not exact and n*p > 30 and n*(1-p) > 30:\\n\\t\\t\\t# Use normal approximation\\n\\t\\t\\tmu = n*p\\n\\t\\t\\tsd = math.sqrt(n*p*(1-p))\\n\\t\\t\\tz = (k-mu)/sd\\n\\t\\t\\tif z < 0.0:\\n\\t\\t\\t\\tp_value = 1-Prob_Z(z)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tp_value = Prob_Z(z)\\n\\t\\telse:\\n\\t\\t\\tp_value = p**n # The last term in the sum\\n\\t\\t\\tfor j in range(k,n):\\n\\t\\t\\t\\t# Compute logarithm of (n choose j) p^j (1-p)^ (n-j), the\\n\\t\\t\\t\\t# binomial probability. Use logarithm to avoid overflow\\n\\t\\t\\t\\t# problems with potentially enormous factorials.\\n\\t\\t\\t\\tlog_p = logChoose(n,j) + j*math.log(p) + (n-j)*math.log(1-p)\\n\\t\\t\\t\\tp_value += math.exp(log_p)\\n\\t\\t\\tif p_value > 1.0:\\n\\t\\t\\t\\tp_value = 1.0\\n\\treturn p_value\",\n \"def bernoulli_trial(p: float) -> int:\\n return 1 if random.random() < p else 0\",\n \"def combinations(n) -> float:\\r\\n c = math.factorial(n) / (math.factorial(2) * math.factorial(n - 2))\\r\\n return c\",\n \"def test_probability_of_all_successes():\\n\\n assert(probability_of_all_successes(1/2,1,2) == 0.25)\\n assert(are_close(probability_of_all_successes(1/6,1,2), 1/36, 0.001))\\n assert(are_close(probability_of_all_successes(1/2,2,2), 7/16, 0.001))\",\n \"def multiple_comparisons(p, n):\\r\\n if p > 1e-6: # if p is large and n small, calculate directly\\r\\n return 1 - (1 - p) ** n\\r\\n else:\\r\\n return one_minus_exp(-n * p)\",\n \"def prior(n=10):\\r\\n p = []\\r\\n trials = 0\\r\\n acc = 0\\r\\n while acc < n:\\r\\n trials += 1\\r\\n r = np.random.rand(2) * np.array([4, 2]) + np.array([-2, -1])\\r\\n # print(\\\"r: \\\", r)\\r\\n if r[1] + r[0] >= -1 and r[1] - r[0] >= -1:\\r\\n p.append(r)\\r\\n acc += 1\\r\\n # print(\\\"trials: \\\", trials, \\\", acc: \\\", acc)\\r\\n return p\",\n \"def success_p_dist(n_trials, n_meanings, n_signals, input_meaning_p = None):\\r\\n if input_meaning_p is None:\\r\\n input_meaning_p = uniform_p(n_meanings)\\r\\n y = []\\r\\n for i in range(n_trials):\\r\\n dyad = ProductSystem.rand_dyad(n_meanings, n_signals)\\r\\n y.append(dyad.success_p(input_meaning_p))\\r\\n sns.distplot(np.asarray(y))\",\n \"def prob(throw, n, d=6, type='classical'):\\n count = 0\\n table = throw_table(n, d, type)\\n for t in table:\\n if sum(t) == throw:\\n count += 1\\n \\n return float(count)/len(table)\",\n \"def prob_list(n, p):\\n ...\\n#pn is the probability of seeing a pair of aces two hands in a row in n hands.\\n#qn = 1 - pn\\n#thus qn is the probability of NOT seeing a par of aces two hands in a row in n hands.\\n list = []\\n qn_a = 1\\n qn_b = 1\\n list.append(qn_a)\\n list.append(qn_b)\\n\\n for i in range(n-2):\\n i += 0\\n qn_next = (1-p)*qn_b + p*(1-p)*qn_a\\n list.append(qn_next)\\n qn_a = qn_b\\n qn_b = qn_next\\n return list\\n\\n\\n # returns list of probs. [q0,... qn]\",\n \"def find_trials_required(number_of_sequences, sigma, p=0.9):\\n return ceil((p * (1 - p)) / (number_of_sequences * sigma ** 2) - (1 / number_of_sequences))\",\n \"def factorPR(n):\\r\\n\\tfor slow in [2,3,4,6]:\\r\\n\\t\\tnumsteps=2*math.floor(math.sqrt(math.sqrt(n))); fast=slow; i=1\\r\\n\\t\\twhile i<numsteps:\\r\\n\\t\\t\\tslow = (slow*slow + 1) % n\\r\\n\\t\\t\\ti = i + 1\\r\\n\\t\\t\\tfast = (fast*fast + 1) % n\\r\\n\\t\\t\\tfast = (fast*fast + 1) % n\\r\\n\\t\\t\\tg = gcd(fast-slow,n)\\r\\n\\t\\t\\tif (g != 1):\\r\\n\\t\\t\\t\\tif (g == n):\\r\\n\\t\\t\\t\\t\\tbreak\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\treturn g\\r\\n\\treturn 1\",\n \"def is_prime(n, number_of_tests=5):\\n passes = 0\\n prime = True #assume prime\\n for i in xrange(number_of_tests):\\n passes += 1\\n random_int = random.randint(2, n-1)\\n test = pow(random_int, n-1, n)\\n if test != 1:\\n prime = False\\n break\\n if prime:\\n return 0\\n else:\\n return passes\",\n \"def prob_choice(p):\\n \\n return np.random.random_sample() < p\",\n \"def isprime(n):\\n if n!=int(n):\\n return False\\n n=int(n)\\n #Miller-Rabin test for prime\\n if n==0 or n==1 or n==4 or n==6 or n==8 or n==9:\\n return False\\n\\n if n==2 or n==3 or n==5 or n==7:\\n return True\\n s = 0\\n d = n-1\\n while d%2==0:\\n d>>=1\\n s+=1\\n assert(2**s * d == n-1)\\n\\n def trial_composite(a):\\n if pow(a, d, n) == 1:\\n return False\\n for i in range(s):\\n if pow(a, 2**i * d, n) == n-1:\\n return False\\n return True\\n\\n for i in range(8):#number of trials\\n a = random.randrange(2, n)\\n if trial_composite(a):\\n return False\\n\\n return True\",\n \"def probit(x):\\n from tensorflow_probability import distributions\\n return distributions.Normal(0, 1).cdf(x)\",\n \"def expected_value_of_outcome(n, p):\\r\\n expected = n * p\\r\\n print(\\\"expected value / mean = \\\", expected)\\r\\n return expected\",\n \"def game3(n):\\r\\n\\twin=0\\r\\n\\tfor i in range(n):\\r\\n\\t\\tif game(3)==1:\\r\\n\\t\\t\\twin+=1\\t\\r\\n\\tprob3=win/n\\r\\n\\treturn prob3\",\n \"def compute_prob_mle(X: np.ndarray, n: int) -> float:\\n\\n assert n > 1, \\\"for n = 1 use Bernoulli distribution.\\\"\\n Binomial._check_input_data(X=X)\\n Binomial._check_support(X=X, n=n)\\n\\n prob = X.mean() / n\\n return prob\",\n \"def prob_no_match(n):\\n return math.factorial(n)*math.comb(365,n)/(365**n)\",\n \"def get_probable_prime(n: int) -> [int]:\\n return [6*n-1, 6*n+1]\",\n \"def calculate_probability(k: int, m: int, n: int) -> float:\\n population = [\\\"AA\\\" for _ in range(k)] + [\\\"Aa\\\" for _ in range(m)] + [\\\"aa\\\" for _ in range(n)]\\n pairings = it.combinations(population, 2)\\n probabilities = [PROBABILITIES[pairing] for pairing in pairings]\\n output = sum(probabilities) / len(probabilities)\\n\\n return output\",\n \"def potential_witnesses(n):\\n if n < 1373653: return [2, 3] # n < 1,373,653\\n if n < 9080191: return [31, 73] # n < 9,080,191\\n if n < 4759123141: return [2, 7, 61] # n < 4,759,123,141\\n if n < 2152302898747: return [2, 3, 5, 7, 11] # n < 2,152,302,898,747\\n if n < 3474749660383: return [2, 3, 5, 7, 11, 13] # n < 3,474,749,660,383\\n if n < 341550071728321: return [2, 3, 5, 7, 11, 13, 17] # n < 341,550,071,728,321\\n return [random.randint(1,n-1) for _ in xrange(0,20)] # 99.999999999909051% (1 - .25**20) accuracy for n >= 341,550,071,728,321\",\n \"def initial_probabilities_from_trajectories(n_states, trajectories):\\n p = np.zeros(n_states)\\n\\n for t in trajectories:\\n p[t.transitions()[0][0]] += 1.0\\n\\n return p / len(trajectories)\",\n \"def payoff_n(n=3, p=0.06,\\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\\n distribution = []\\n for n_makers in range(n + 1): # For every number of possible players who make it\\n if n_makers == 0:\\n distribution.append((minor_contract, (1-p)**n))\\n elif n_makers == n:\\n distribution.append((MLB_contract, p**n))\\n else:\\n # number of combinations of players who make it\\n n_combinations = factorial(n)\\n n_combinations /= (factorial(n-n_makers)*factorial(n_makers))\\n n_combinations = int(n_combinations)\\n\\n # number of combinations where player 1 makes it\\n n_indv_inmajors = factorial(n-1)\\n n_indv_inmajors /= (factorial((n-1)-(n_makers-1))*factorial(n_makers-1))\\n n_indv_inmajors = int(n_indv_inmajors)\\n\\n # probability that n_makers of players make it\\n payoff_prob = p**n_makers * (1-p)**(n-n_makers)\\n\\n # payoff when player 1 is one of the players who makes it\\n payoff = MLB_contract - 0.1*(MLB_contract-thresh)\\n payoff += (n_makers-1)*0.1*(MLB_contract-thresh)/(n-1)\\n distribution.append((payoff, payoff_prob*n_indv_inmajors))\\n\\n # payoff when player 1 is not one of the players who makes it\\n payoff = minor_contract\\n payoff += n_makers*0.1*(MLB_contract-thresh)/(n-1)\\n distribution.append((payoff, payoff_prob*(n_combinations-n_indv_inmajors)))\\n E_payoff = [a*b for (a, b) in distribution]\\n E_payoff = sum(E_payoff)\\n var_payoff = [((a-E_payoff)**2)*b for (a, b) in distribution]\\n var_payoff = sum(var_payoff)\\n return E_payoff, var_payoff**0.5\",\n \"def payoff_n_p(p, n=3,\\n MLB_contract=4158333, minor_contract=6600, thresh=1500000):\\n distribution = []\\n for n_makers in range(n + 1):\\n if n_makers == 0:\\n payoff_prob = [1 - prob for prob in p.values()]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((minor_contract, payoff_prob))\\n elif n_makers == n:\\n payoff_prob = [prob for prob in p.values()]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((MLB_contract, payoff_prob))\\n else:\\n makers = list(combinations(range(1, n + 1), n_makers))\\n for maker_set in makers:\\n if 1 in maker_set:\\n payoff = MLB_contract - 0.1*(MLB_contract-thresh)\\n payoff += (n_makers-1)*0.1*(MLB_contract-thresh)/(n-1)\\n payoff_prob = [p[player] for player in maker_set]\\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((payoff, payoff_prob))\\n else:\\n payoff = minor_contract\\n payoff += n_makers*0.1*(MLB_contract-thresh)/(n-1)\\n payoff_prob = [p[player] for player in maker_set]\\n payoff_prob += [1-p[player] for player in p.keys() if player not in maker_set]\\n payoff_prob = np.prod(payoff_prob)\\n distribution.append((payoff, payoff_prob))\\n E_payoff = [a*b for (a, b) in distribution]\\n E_payoff = sum(E_payoff)\\n var_payoff = [((a-E_payoff)**2)*b for (a, b) in distribution]\\n var_payoff = sum(var_payoff)\\n return E_payoff, var_payoff**0.5\",\n \"def prob4():\\n\\n\\n N = 500000\\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\\n\\n h = lambda x: x[0] < -1 and x[1] > 1\\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\\n\\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\\n\\n return 1./N * np.sum(probability)\",\n \"def r_combinations(n,r):\\n return r_permutations(n,r) / math.factorial(r)\",\n \"def bernoulli(n):\\n\\n x, res, s, c = Rat(0), Rat(0), Rat(0), Rat(-1)\\n for k in range(1, n+2):\\n c *= 1 - Rat(n + 2)/k\\n s += x**n\\n x += 1\\n res += c*s/k\\n return res\",\n \"def simulate_rerolling(p: float, n: int) -> int:\\n\\n counter = 0\\n new_n = n\\n while new_n > 0:\\n for _ in range(new_n):\\n ran = random.random()\\n if ran < p:\\n new_n -= 1\\n counter += 1\\n return counter\",\n \"def compute_perplexity(self, train_corpus: str, test_corpus: str, n: int):\\n N = len(''.join(test_corpus.split()))\\n y = -1 / N\\n likelihood = self.compute_likelihood(train_corpus, test_corpus, n)\\n return likelihood ** y\",\n \"def prbs(m, n):\\n return np.array(np.random.rand(m, n) > 0.5, dtype=np.int) - 0.5\",\n \"def solution(n,p):\\n \\n a=pow(n, (p - 1) // 2, p)\\n if(a==1):\\n return True\\n else :\\n return False\",\n \"def game2(n):\\r\\n\\twin=0\\r\\n\\tfor i in range(n):\\r\\n\\t\\tif game(2)==1:\\r\\n\\t\\t\\twin+=1\\r\\n\\tprob2=win/n\\r\\n\\treturn prob2\",\n \"def prob4():\\n#raise NotImplementedError(\\\"Problem 4 Incomplete\\\")\\n h = lambda x : x[0] < -1 and x[1] > 1\\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\\n X = np.random.multivariate_normal(mean=np.array([-1,1]),cov=np.eye(2),size=10000)\\n return 1./10000*np.sum(np.apply_along_axis(h,1,X)*np.apply_along_axis(f,1,X)/np.apply_along_axis(g,1,X))\",\n \"def dbinom(self, x, n, p):\\n f = math.factorial\\n C = Decimal(f(n) / (f(x) * f(n-x)))\\n return C * p**x * (1-p)**(n-x)\",\n \"def factor(n):\\n from random import randrange\\n while True:\\n i = 1\\n x = randrange(0, n)\\n y = x\\n while True:\\n i += 1\\n x = ((x ** 2) - 1) % n\\n d = gcd(y - x, n)\\n if d != 1 and d != n:\\n return d\\n if i > 10000:\\n break\",\n \"def solve(n=100):\\n # Build the board.\\n single_vals = list(range(1, 21)) + [25]\\n double_vals = [2*x for x in single_vals]\\n triple_vals = [3*x for x in range(1, 21)]\\n total_vals = single_vals + double_vals + triple_vals + [0]\\n\\n return sum(1 for other_throws, double_throw\\n in product(combinations_with_replacement(total_vals, 2), double_vals, repeat=1)\\n if sum(other_throws) + double_throw < n)\",\n \"def probability(series, params):\\n\\n prob = 1\\n\\n for result in series:\\n\\n prob *= params[result]\\n\\n return prob * params[\\\"die\\\"]\",\n \"def is_prime(number, num_trials=200):\\n if number < 2:\\n return False\\n if number != 2 and number % 2 == 0:\\n return False\\n\\n # Find largest odd factor of n-1.\\n exp = number - 1\\n while exp % 2 == 0:\\n exp //= 2\\n\\n for _ in range(num_trials):\\n rand_val = int(random.SystemRandom().randrange(1, number))\\n new_exp = exp\\n power = pow(rand_val, new_exp, number)\\n while new_exp != number - 1 and power != 1 and power != number - 1:\\n power = (power * power) % number\\n new_exp *= 2\\n if power != number - 1 and new_exp % 2 == 0:\\n return False\\n\\n return True\",\n \"def Ballie_PSW_test(n, max_trivial_trials=100):\\n for i in range(max_trivial_trials):\\n if primes[i] == n:\\n return True\\n if n % primes[i] == 0:\\n return False\\n if primes[i] ** 2 >= n:\\n return True\\n if not fermat_strong_test(n, 2):\\n return False\\n if not lucas_selfridge_test(n):\\n return False\\n return True\",\n \"def p(self) -> Probability:\\n ...\",\n \"def prob_m_of_n(m, n, T, l):\\n PFD_one_unit = l*T\\n m_of_n = binom(n, m) * (PFD_one_unit)**(n-m) * (1-PFD_one_unit)**m\\n return m_of_n\",\n \"def prob(x):\\n\\treturn 1. * bivariate_normal(x, (0., 1.2), (1., 1.), .8) + \\\\\\n\\t 1.05 * bivariate_normal(x, (.6, -1.), (1.3, .7), -.6)\",\n \"def Pollard_pm1(n, primes, max_B=1000000):\\n B = 10\\n g = 1\\n while B < max_B and g < n:\\n a = randint(2, n - 2)\\n g = gcd(a, n)\\n if g != 1:\\n return g\\n for p in primes:\\n if p >= B:\\n break\\n pd = 1 # p^d\\n while pd * p <= B:\\n pd *= p\\n a = powmod(a, pd, n)\\n g = gcd(a - 1, n)\\n if g != 1 and g != n:\\n return g\\n B *= 2\\n return 1\",\n \"def test_success_formula(n_meanings, n_signals, n_trials, n_runs):\\r\\n input_meaning_p = rand_p(n_meanings)\\r\\n print(input_meaning_p)\\r\\n print()\\r\\n dyad = ProductSystem.rand_dyad(n_meanings, n_signals)\\r\\n print(dyad.psys.matrix)\\r\\n print()\\r\\n print(dyad.rsys.matrix)\\r\\n print()\\r\\n data = tuple(cummean(tuple(dyad.randsuccess(input_meaning_p) \\\\\\r\\n for j in range(n_trials))) \\\\\\r\\n for i in range(n_runs))\\r\\n graph = sns.tsplot(data, err_style='unit_traces')\\r\\n graph.set(xlabel='Number of trials', ylabel='Success proportion', ylim=[0,1])\\r\\n success_p = dyad.success_p(input_meaning_p)\\r\\n print(success_p)\\r\\n sns.tsplot((success_p,) * n_trials, color=sns.color_palette()[1])\",\n \"def logp(value, n, p):\\n\\n res = factln(n) + pt.sum(-factln(value) + logpow(p, value), axis=-1)\\n res = pt.switch(\\n pt.or_(pt.any(pt.lt(value, 0), axis=-1), pt.neq(pt.sum(value, axis=-1), n)),\\n -np.inf,\\n res,\\n )\\n return check_parameters(\\n res,\\n 0 <= p,\\n p <= 1,\\n pt.isclose(pt.sum(p, axis=-1), 1),\\n pt.ge(n, 0),\\n msg=\\\"0 <= p <= 1, sum(p) = 1, n >= 0\\\",\\n )\",\n \"def easy_solve(n=30):\\n # Use p1 and p2 to store expected payoffs per player\\n p_1 = []\\n p_2 = []\\n # Get each possible dice roll in order\\n arr = list(range(1, n + 1))\\n x = (1/2)*(np.sqrt(2*n**2 + 2*n - 3) - 1)\\n for i in range(n):\\n # Let player 1 choose ith possible integer\\n player_1 = arr[i]\\n # Use aforementioned strategy\\n if arr[i] <= int(x):\\n player_2 = arr[i] + 1\\n # Append expected value per player according to which integer is larger\\n p_1.append((1/n)*sum(arr[:player_1]))\\n p_2.append((1/n)*sum(arr[player_2-1:]))\\n else:\\n player_2 = arr[i] - 1\\n p_2.append((1/n)*sum(arr[:player_2]))\\n p_1.append((1/n)*sum(arr[player_1-1:]))\\n return p_1, p_2\",\n \"def probability(p):\\n return p > random.uniform(0.0, 1.0)\",\n \"def bernoulli(p):\\r\\n if np.random.random() < p:\\r\\n return 0\\r\\n else:\\r\\n return 1\",\n \"def generate_bits(n, p):\\n try:\\n return (np.random.rand(*n) > p).astype(int)\\n except TypeError:\\n return (np.random.rand(n) > p).astype(int)\",\n \"def get_prob_for_distributions(p):\\n w1 = p[0]\\n mu1 = p[1]\\n sigma1 = p[2]\\n w2 = p[3]\\n mu2 = p[4]\\n sigma2 = p[5]\\n w3 = p[6]\\n mu3 = p[7]\\n sigma3 = p[8]\\n dist_range = (0, 4.330310991999920844e+01)\\n x = np.linspace(dist_range[0], dist_range[1], 1000)\\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\\n p1 = A1 / (A1 + A2 + A3)\\n p2 = A2 / (A1 + A2 + A3)\\n p3 = A3 / (A1 + A2 + A3)\\n return p1, p2, p3\",\n \"def prodi( iterable ):\\n p= 1\\n for n in iterable:\\n p *= n\\n return p\",\n \"def P(N,L) :\\n accum = 0.0\\n sign = 1.0\\n for i in xrange(len(L)+1) :\\n accum2 = 0.0\\n for combin in combinations(L,i) :\\n term = 1.0\\n j = 0.0\\n for Li in combin :\\n term *= Li/(N-j)\\n j += 1\\n accum2 += term\\n accum += sign*accum2\\n sign *= -1.0\\n return accum\",\n \"def Binomial(n, p, succ=1, fail=0, symbol=None):\\n\\n return BinomialPSpace(n, p, succ, fail, symbol).value\",\n \"def discrete_sample(p, n_samples=None, rng=np.random):\\n\\n # check distribution\\n # assert isdistribution(p), 'Probabilities must be non-negative and sum to one.'\\n\\n one_sample = n_samples is None\\n\\n # cumulative distribution\\n c = np.cumsum(p[:-1])[np.newaxis, :]\\n\\n # get the samples\\n r = rng.rand(1 if one_sample else n_samples, 1)\\n samples = np.sum((r > c).astype(int), axis=1)\\n\\n return samples[0] if one_sample else samples\",\n \"def get_success_probabilities_from_results(results: Sequence[Sequence[Sequence[int]]]) \\\\\\n -> Sequence[float]:\\n num_shots = len(results[0])\\n n_bits = len(results[0][0]) - 1\\n\\n probabilities = []\\n # loop over all binary strings of length n_bits\\n for result, bits in zip(results, all_bitstrings(2 * n_bits)):\\n # Input nums are written from (MSB .... LSB) = (a_n, ..., a_1, a_0)\\n num_a = bit_array_to_int(bits[:n_bits])\\n num_b = bit_array_to_int(bits[n_bits:])\\n\\n # add the numbers\\n ans = num_a + num_b\\n ans_bits = int_to_bit_array(ans, n_bits + 1)\\n\\n # a success occurs if a shot matches the expected ans bit for bit\\n probability = 0\\n for shot in result:\\n if np.array_equal(ans_bits, shot):\\n probability += 1. / num_shots\\n probabilities.append(probability)\\n\\n return probabilities\",\n \"def _fair_sharing_mp(n=2,decimals=100,pratio=15,debug=False):\\n import operator\\n import itertools\\n from mpmath import mp\\n mp.dps=decimals\\n p=mp.mpf(10)**-(decimals/pratio)\\n acc=mp.mpf(0)\\n wins=[mp.mpf(0)]*n\\n if debug:\\n print('p = '+str(p))\\n for i in itertools.count():\\n index,_ = min(enumerate(wins),key=operator.itemgetter(1))\\n yield index\\n inc=p*(1-acc)\\n wins[index]+=inc\\n acc+=inc\\n if debug:\\n print('acc = '+str(acc))\",\n \"def sample_discrete(probs):\\r\\n q = np.random.rand()\\r\\n i = 0\\r\\n p_sum = 0.0\\r\\n while p_sum < q:\\r\\n p_sum += probs[i]\\r\\n i += 1\\r\\n return i - 1\",\n \"def puissance(x: float, n: int) -> float:\\n resultat: float = 1\\n signe: int = 1\\n if n != 0:\\n if n <= 0:\\n n = -n\\n signe = -1\\n for cpt in range(1, n + 1):\\n resultat = resultat * x\\n if signe < 0:\\n resultat = 1 / resultat\\n return resultat\",\n \"def find_pcts_multi(P, start_b = [], iter = 10000):\\n assert len(P) >= 2\\n wins_per_player = [0] * len(P)\\n all_hole = reduce(lambda x,y: x+y, P)\\n for i in range(iter):\\n deck = Deck()\\n need = 5 - len(start_b)\\n b2 = draw_sure(deck, need, all_hole+start_b)\\n s = [evaluator.evaluate(start_b+b2, h) for h in P]\\n for i, e in enumerate(s):\\n if e == min(s):\\n wins_per_player[i] += 1\\n return [float(x) / sum(wins_per_player) for x in wins_per_player]\",\n \"def Poisson(n, k):\\n\\tp = math.exp(-k) * math.pow(k, n) / float(Factorial(n))\\n\\tassert 0.0 <= p <= 1.0, \\\"Error, value of p is invalid probability: \\\" + str(p)\\n\\treturn p\",\n \"def compute_expected_return_(domain, N, policy, p_init, s_init):\\r\\n pos = p_init\\r\\n speed = s_init\\r\\n expected_return = 0\\r\\n\\r\\n for i in range(N):\\r\\n if domain.terminalState(pos, speed):\\r\\n break\\r\\n action = policy((pos, speed))\\r\\n if isinstance(action,str):\\r\\n action = domain.getAction(action)\\r\\n new_pos, new_speed = domain.getNextState(pos, speed, action)\\r\\n r = domain.getReward(pos, speed, action, new_pos, new_speed)\\r\\n expected_return += ((domain.discount_factor)**i)*r\\r\\n pos = new_pos\\r\\n speed = new_speed\\r\\n \\r\\n return expected_return\",\n \"def prob_t_N(genotype, base):\\n cnter = Counter(genotype)\\n return cnter.get(base, 0) * 1/len(genotype)\",\n \"def Mn_plus_distribution(n, x):\\n b_bounds = Mn_plus_bounds(n, x)\\n return 1.0 - crossprob.ecdf1_new_b(b_bounds)\\n #return 1.0 - crossprob.ecdf2(b_bounds, [1]*len(b_bounds), True)\",\n \"def permutations_(n, r):\\n return factorial(n) / factorial(n-r)\",\n \"def test(n,trials):\\n for trial in range(trials):\\n lst = list(range(n))\\n my_shuffle(lst)\\n ans = lst.pop() if n>0 else 0\\n \\n res = f1(lst)\\n \\n try:\\n assert ans == res\\n except:\\n st = f\\\"Incorrect output {res} on {lst} w/ missing {ans}.\\\"\\n raise AssertionError(st)\",\n \"def prob_extinct(Z0, t, n, s, u):\\n p = 1\\n for k in range(len(Z0)):\\n p *= Phi_k(Phit(t - 1, List([0.] * n), s, u), k, s, u) ** Z0[k]\\n return p\",\n \"def probability(cpts, term, obs):\\r\\n \\r\\n \\r\\n # term is a list e.g., ['x_1', '0']\\r\\n # flip refers to the assignment either '0' false or '1' true\\r\\n flip = term[1]\\r\\n # the term itself\\r\\n term = term[0]\\r\\n # accumulator variable\\r\\n answer = 0\\r\\n # this loop locates where in the CPT we're looking\\r\\n for clause in range(len(cpts)):\\r\\n if cpts[clause][0] == term:\\r\\n index = clause\\r\\n # focus on our term\\r\\n cpt = cpts[index]\\r\\n # this loop checks if there are no preconditions\\r\\n # if not, then we immediately know the probability and can return\\r\\n for m in range(len(cpt[1])):\\r\\n if cpt[1][m][-2][1] == '1':\\r\\n if cpt[1][m][0] == [[]]:\\r\\n answer = cpt[1][m][-1]\\r\\n # list of the variables we have observed\\r\\n have = []\\r\\n if obs != []:\\r\\n for k in obs:\\r\\n have.append(k[0])\\r\\n # list of variables we need to know in order to calculate the probability\\r\\n needed = []\\r\\n for prob in range(len(cpt[1])):\\r\\n for j in cpt[1][prob][0]:\\r\\n if j != []:\\r\\n if j[0] not in needed:\\r\\n needed.append(j[0])\\r\\n # conditional logic based on the known variables\\r\\n for required in needed:\\r\\n if required not in have:\\r\\n # deep copy our observations list\\r\\n obs2 = []\\r\\n obs3 = []\\r\\n for observs in obs:\\r\\n obs2.append(observs)\\r\\n obs3.append(observs)\\r\\n # if we need to know a variable but don't have it\\r\\n # then we allow it to be either 0 or 1\\r\\n obs3.append([required,'1'])\\r\\n obs2.append([required,'0'])\\r\\n # computes probability if the unknown term is true, times \\r\\n # the probability that the unknown term is true, plus the\\r\\n # probability if the unknown term is false, times the \\r\\n # probability that the unknown term is false\\r\\n answer = (probability(cpts, [term,flip], obs3) * probability(cpts, [required,'1'], obs)) + (probability(cpts, [term,flip], obs2) * (probability(cpts, [required,'0'], obs)))\\r\\n # this loop looks complicated but all it's doing is finding the correct\\r\\n # line in the CPT\\r\\n if cpt[1][prob][-2][1] == '1':\\r\\n count = 1\\r\\n for i in range(len(cpt[1][prob][0])):\\r\\n if cpt[1][prob][0][i] in obs:\\r\\n count *= 1\\r\\n else:\\r\\n count = 0\\r\\n if count == 1:\\r\\n answer += cpt[1][prob][-1]\\r\\n\\r\\n\\r\\n # this computes the probability that the term is true, so if we asked \\r\\n # for the probability that it is false, just return 1 - answer\\r\\n if flip == '0':\\r\\n return 1 - answer\\r\\n return answer\",\n \"def prove_N() -> Proof:\\n # Optional Task 6.8\",\n \"def probability(prob):\\n return random.random() <= prob\",\n \"def PN(self, n):\\n if not self.isVaild():\\n pass\\n if n < self.C:\\n return self.P0()*(self.r()**n)/math.factorial(n)\\n else:\\n return self.P0()*(self.r()**n)/(math.factorial(self.C)*self.C**(n-self.C))\",\n \"def likelihood(x, n, P):\\n if not isinstance(n, int) or (n <= 0):\\n raise ValueError('n must be a positive integer')\\n if not isinstance(x, int) or (x < 0):\\n raise ValueError(\\n 'x must be an integer that is greater than or equal to 0')\\n if x > n:\\n raise ValueError('x cannot be greater than n')\\n if not isinstance(P, np.ndarray) or len(P.shape) != 1:\\n raise TypeError('P must be a 1D numpy.ndarray')\\n if not np.all((P >= 0) & (P <= 1)):\\n raise ValueError('All values in P must be in the range [0, 1]')\\n nume = np.math.factorial(n)\\n deno = (np.math.factorial(x) * (np.math.factorial(n - x)))\\n fact = nume / deno\\n P_likelihood = fact * (np.power(P, x)) * (np.power((1 - P), (n - x)))\\n return P_likelihood\",\n \"def test(numTrials):\\n # Your Code Here\\n hits = 0.0\\n for i in range(numTrials):\\n result = trial()\\n #print result\\n hits += result\\n return hits / numTrials\",\n \"def pe_prob_003(num):\\n factors = get_unique_factors(num)\\n\\n return factors[-1] if factors else 1\",\n \"def greater_than_n_wins(wins_req: int, total_chances: int, win_rate: float) -> float:\\n overall_prob = 0\\n loss_rate = 1 - win_rate\\n for num in range(wins_req, total_chances+1, 1):\\n numerator = comb(total_chances, num)\\n denominator = (win_rate ** num) * (loss_rate ** (total_chances - num))\\n overall_prob += numerator * denominator\\n return overall_prob\",\n \"def generate_n(k_problem, n):\\n return [generate_first_random(k_problem) for i in range(n)]\",\n \"def is_prime(n):\\n assert n > 3\\n k = int(log2(n))\\n m = n - 1\\n d = 0\\n while(m % 2 == 0):\\n m //= 2\\n d += 1\\n for _ in range(k):\\n a = randint(2, n - 2)\\n x = pow(a, m, n)\\n if x == 1 or x == n - 1:\\n continue\\n for _ in range(d - 1):\\n x = pow(x, 2, n)\\n if x == 1:\\n return 0\\n if x == n - 1:\\n break\\n if x != n - 1:\\n return 0\\n return 1\",\n \"def sample(a, p):\\n if (len(a) != len(p)):\\n raise Exception('a != p')\\n p = np.array(p)\\n p = p / p.sum()\\n r = random.random()\\n n = len(a)\\n total = 0 # range: [0,1]\\n for i in xrange(n):\\n total += p[i]\\n if total > r:\\n return a[i]\\n return a[i]\",\n \"def prob1(L):\\n return min(L), max(L), sum(L)/len(L)\\n raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\",\n \"def main():\\n\\n import sys\\n sys.setrecursionlimit(10**7)\\n from itertools import accumulate, combinations, permutations, product # https://docs.python.org/ja/3/library/itertools.html\\n # accumulate() returns iterator! to get list: list(accumulate())\\n from math import factorial, ceil, floor\\n def factorize(n):\\n \\\"\\\"\\\"return the factors of the Arg and count of each factor\\n \\n Args:\\n n (long): number to be resolved into factors\\n \\n Returns:\\n list of tuples: factorize(220) returns [(2, 2), (5, 1), (11, 1)]\\n \\\"\\\"\\\"\\n fct = [] # prime factor\\n b, e = 2, 0 # base, exponent\\n while b * b <= n:\\n while n % b == 0:\\n n = n // b\\n e = e + 1\\n if e > 0:\\n fct.append((b, e))\\n b, e = b + 1, 0\\n if n > 1:\\n fct.append((n, 1))\\n return fct\\n def combinations_count(n, r):\\n \\\"\\\"\\\"Return the number of selecting r pieces of items from n kinds of items.\\n \\n Args:\\n n (long): number\\n r (long): number\\n \\n Raises:\\n Exception: not defined when n or r is negative\\n \\n Returns:\\n long: number\\n \\\"\\\"\\\"\\n # TODO: How should I do when n - r is negative?\\n if n < 0 or r < 0:\\n raise Exception('combinations_count(n, r) not defined when n or r is negative')\\n if n - r < r: r = n - r\\n if r < 0: return 0\\n if r == 0: return 1\\n if r == 1: return n\\n numerator = [n - r + k + 1 for k in range(r)]\\n denominator = [k + 1 for k in range(r)]\\n for p in range(2,r+1):\\n pivot = denominator[p - 1]\\n if pivot > 1:\\n offset = (n - r) % p\\n for k in range(p-1,r,p):\\n numerator[k - offset] /= pivot\\n denominator[k] /= pivot\\n result = 1\\n for k in range(r):\\n if numerator[k] > 1:\\n result *= int(numerator[k])\\n return result\\n def combinations_with_replacement_count(n, r):\\n \\\"\\\"\\\"Return the number of selecting r pieces of items from n kinds of items allowing individual elements to be repeated more than once.\\n \\n Args:\\n n (long): number\\n r (long): number\\n \\n Raises:\\n Exception: not defined when n or r is negative\\n \\n Returns:\\n long: number\\n \\\"\\\"\\\"\\n if n < 0 or r < 0:\\n raise Exception('combinations_with_replacement_count(n, r) not defined when n or r is negative')\\n elif n == 0:\\n return 1\\n else:\\n return combinations_count(n + r - 1, r)\\n from bisect import bisect_left, bisect_right\\n from collections import deque, Counter, defaultdict # https://docs.python.org/ja/3/library/collections.html#collections.deque\\n from heapq import heapify, heappop, heappush, heappushpop, heapreplace,nlargest,nsmallest # https://docs.python.org/ja/3/library/heapq.html\\n from copy import deepcopy, copy # https://docs.python.org/ja/3/library/copy.html\\n from operator import itemgetter\\n # ex1: List.sort(key=itemgetter(1))\\n # ex2: sorted(tuples, key=itemgetter(1,2))\\n from functools import reduce\\n def chmin(x, y):\\n \\\"\\\"\\\"change minimum\\n if x > y, x = y and return (x, True).\\n convenient when solving problems of dp[i]\\n \\n Args:\\n x (long): current minimum value\\n y (long): potential minimum value\\n \\n Returns:\\n (x, bool): (x, True) when updated, else (x, False)\\n \\\"\\\"\\\"\\n if x > y:\\n x = y\\n return (x, True)\\n else:\\n return (x, False)\\n def chmax(x, y):\\n \\\"\\\"\\\"change maximum\\n if x < y, x = y and return (x, True).\\n convenient when solving problems of dp[i]\\n \\n Args:\\n x (long): current maximum value\\n y (long): potential maximum value\\n \\n Returns:\\n (x, bool): (x, True) when updated, else (x, False)\\n \\\"\\\"\\\"\\n if x < y:\\n x = y\\n return (x, True)\\n else:\\n return (x, False)\\n\\n from fractions import gcd # Deprecated since version 3.5: Use math.gcd() instead.\\n def gcds(numbers):\\n return reduce(gcd, numbers)\\n def lcm(x, y):\\n return (x * y) // gcd(x, y)\\n def lcms(numbers):\\n return reduce(lcm, numbers, 1)\\n\\n # first create factorial_list\\n # fac_list = mod_factorial_list(n)\\n INF = 10 ** 18\\n MOD = 10 ** 9 + 7\\n modpow = lambda a, n, p = MOD: pow(a, n, p) # Recursive function in python is slow!\\n def modinv(a, p = MOD):\\n # evaluate reciprocal using Fermat's little theorem:\\n # a**(p-1) is identical to 1 (mod p) when a and p is coprime\\n return modpow(a, p-2, p)\\n def modinv_list(n, p = MOD):\\n if n <= 1:\\n return [0,1][:n+1]\\n else:\\n inv_t = [0,1]\\n for i in range(2, n+1):\\n inv_t += [inv_t[p % i] * (p - int(p / i)) % p]\\n return inv_t\\n def modfactorial_list(n, p = MOD):\\n if n == 0:\\n return [1]\\n else:\\n l = [0] * (n+1)\\n tmp = 1\\n for i in range(1, n+1):\\n tmp = tmp * i % p\\n l[i] = tmp\\n return l\\n def modcomb(n, k, fac_list = [], p = MOD):\\n # fac_list = modfactorial_list(100)\\n # print(modcomb(100, 5, modfactorial_list(100)))\\n from math import factorial\\n if n < 0 or k < 0 or n < k: return 0\\n if n == 0 or k == 0: return 1\\n if len(fac_list) <= n:\\n a = factorial(n) % p\\n b = factorial(k) % p\\n c = factorial(n-k) % p\\n else:\\n a = fac_list[n]\\n b = fac_list[k]\\n c = fac_list[n-k]\\n return (a * modpow(b, p-2, p) * modpow(c, p-2, p)) % p\\n def modadd(a, b, p = MOD):\\n return (a + b) % MOD\\n def modsub(a, b, p = MOD):\\n return (a - b) % p\\n def modmul(a, b, p = MOD):\\n return ((a % p) * (b % p)) % p\\n def moddiv(a, b, p = MOD):\\n return modmul(a, modpow(b, p-2, p))\\n\\n \\\"\\\"\\\" initialize variables and set inputs\\n # initialize variables\\n # to initialize list, use [0] * n\\n # to initialize two dimentional array, use [[0] * N for _ in range(N)]\\n # set inputs\\n # open(0).read() is a convenient method:\\n # ex) n, m, *x = map(int, open(0).read().split())\\n # min(x[::2]) - max(x[1::2])\\n # ex2) *x, = map(int, open(0).read().split())\\n # don't forget to add comma after *x if only one variable is used\\n # preprocessing\\n # transpose = [x for x in zip(*data)]\\n # ex) [[1, 2, 3], [4, 5, 6], [7, 8, 9]] => [(1, 4, 7), (2, 5, 8), (3, 6, 9)]\\n # flat = [flatten for inner in data for flatten in inner]\\n # ex) [[1, 2, 3], [4, 5, 6], [7, 8, 9]] => [1, 2, 3, 4, 5, 6, 7, 8, 9]\\n # calculate and output\\n # output pattern\\n # ex1) print(*l) => when l = [2, 5, 6], printed 2 5 6\\n \\\"\\\"\\\"\\n\\n # functions used\\n r = lambda: sys.stdin.readline().strip()\\n r_int = lambda: int(r())\\n R = lambda: list(map(int, r().split()))\\n Rfloat = lambda: list(map(float, r().split()))\\n Rtuple = lambda: tuple(map(int, r().split()))\\n Rmap = lambda: map(int, r().split())\\n\\n \\\"\\\"\\\" how to treat input\\n # single int: int(r())\\n # single string: r()\\n # single float: float(r())\\n # line int: R()\\n # line string: r().split()\\n # line (str, int, int): [j if i == 0 else int(j) for i, j in enumerate(r().split())]\\n # lines int: [R() for _ in range(n)]\\n \\\"\\\"\\\"\\n\\n # main\\n N, Q = R()\\n STX = [R() for _ in range(N)]\\n STX.sort(key=itemgetter(2))\\n\\n D = [int(r()) for _ in range(Q)]\\n Stopped = [-1] * Q\\n ans = [-1] * Q\\n\\n for s, t, x in STX:\\n l = bisect_left(D, s-x)\\n r = bisect_left(D,t-x)\\n a = l\\n while a < r:\\n if Stopped[a] == -1:\\n ans[a] = x\\n Stopped[a] = r\\n a += 1\\n else:\\n a = Stopped[a]\\n\\n for i in ans:\\n print(i)\\n\\n \\\"\\\"\\\"memo: how to use defaultdict of list\\n # initialize\\n Dic = defaultdict(list)\\n # append / extend\\n Dic[x].append(y)\\n # for\\n for k, v in Dic.items():\\n \\\"\\\"\\\"\",\n \"def expected_return(self, n_step):\\r\\n value = 0\\r\\n n_experiences = 50\\r\\n for i in range(n_experiences):\\r\\n trajectory = self.domain_exploration(n_step)\\r\\n value += self.compute_j(trajectory)\\r\\n return value/n_experiences\",\n \"def uniform_but_one_dataset_no_weight(n, p):\\n elements = []\\n for i in range(n):\\n elements.append(i)\\n for i in range(int(n**(1.0 / p)) - 1):\\n elements.append(1)\\n return elements\",\n \"def prime_dist(func, n):\\n x = func(n)\\n y = list(range(len(x)))\\n for i in range(len(x)):\\n y[i] = (i+1)*np.log(x[i])\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.set_ylabel(r'k $\\\\cdot$ log(p$_k$)')\\n ax.set_xlabel(r'p$_k$')\\n plt.suptitle('Distribution of primes', fontsize=15)\\n ax.plot(x, y, lw=2, color='#FE4365')\\n plt.show()\",\n \"def get_rank_probabilities(n: int) -> List[float]:\\n alpha = 3.5\\n ranks = [1 / i**alpha for i in range(1, n + 1)]\\n\\n return [r / sum(ranks) for r in ranks]\",\n \"def probability_at_least_d_fail_equal_reliability(d, chunk_count, reliability):\\n \\n return sum([loss_probability(reliability, k, chunk_count) for k in range(d, chunk_count + 1)])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7992995","0.7988638","0.7983567","0.7979973","0.78188634","0.7754114","0.7721174","0.70337445","0.7027335","0.69186896","0.6905595","0.69036245","0.6807177","0.6772495","0.6591704","0.65804875","0.65346736","0.6518955","0.65020025","0.6491071","0.647253","0.6464994","0.6436011","0.64119565","0.63691026","0.6367758","0.6340869","0.63292146","0.63161266","0.629052","0.62669885","0.62628967","0.6211393","0.61855304","0.6176248","0.6153868","0.6134419","0.61253375","0.6122377","0.61164564","0.61018455","0.61009955","0.6088401","0.6086985","0.6077799","0.6074581","0.6063363","0.60600215","0.60463375","0.6042409","0.6035486","0.60330456","0.6010004","0.6009267","0.6005945","0.60015804","0.59884214","0.5986605","0.59838706","0.5983596","0.5975814","0.5947047","0.59286344","0.59239924","0.591262","0.5908121","0.59077924","0.586508","0.5858683","0.58575386","0.5855478","0.58502656","0.58465964","0.58241624","0.5821346","0.5819044","0.5814005","0.5811805","0.5808601","0.57966334","0.579457","0.57944715","0.5788377","0.57826513","0.57782054","0.57719165","0.577062","0.57631487","0.57630664","0.5760711","0.5757256","0.5745856","0.5742307","0.57407105","0.5739276","0.57385194","0.5736728","0.57315105","0.57295954","0.5725301"],"string":"[\n \"0.7992995\",\n \"0.7988638\",\n \"0.7983567\",\n \"0.7979973\",\n \"0.78188634\",\n \"0.7754114\",\n \"0.7721174\",\n \"0.70337445\",\n \"0.7027335\",\n \"0.69186896\",\n \"0.6905595\",\n \"0.69036245\",\n \"0.6807177\",\n \"0.6772495\",\n \"0.6591704\",\n \"0.65804875\",\n \"0.65346736\",\n \"0.6518955\",\n \"0.65020025\",\n \"0.6491071\",\n \"0.647253\",\n \"0.6464994\",\n \"0.6436011\",\n \"0.64119565\",\n \"0.63691026\",\n \"0.6367758\",\n \"0.6340869\",\n \"0.63292146\",\n \"0.63161266\",\n \"0.629052\",\n \"0.62669885\",\n \"0.62628967\",\n \"0.6211393\",\n \"0.61855304\",\n \"0.6176248\",\n \"0.6153868\",\n \"0.6134419\",\n \"0.61253375\",\n \"0.6122377\",\n \"0.61164564\",\n \"0.61018455\",\n \"0.61009955\",\n \"0.6088401\",\n \"0.6086985\",\n \"0.6077799\",\n \"0.6074581\",\n \"0.6063363\",\n \"0.60600215\",\n \"0.60463375\",\n \"0.6042409\",\n \"0.6035486\",\n \"0.60330456\",\n \"0.6010004\",\n \"0.6009267\",\n \"0.6005945\",\n \"0.60015804\",\n \"0.59884214\",\n \"0.5986605\",\n \"0.59838706\",\n \"0.5983596\",\n \"0.5975814\",\n \"0.5947047\",\n \"0.59286344\",\n \"0.59239924\",\n \"0.591262\",\n \"0.5908121\",\n \"0.59077924\",\n \"0.586508\",\n \"0.5858683\",\n \"0.58575386\",\n \"0.5855478\",\n \"0.58502656\",\n \"0.58465964\",\n \"0.58241624\",\n \"0.5821346\",\n \"0.5819044\",\n \"0.5814005\",\n \"0.5811805\",\n \"0.5808601\",\n \"0.57966334\",\n \"0.579457\",\n \"0.57944715\",\n \"0.5788377\",\n \"0.57826513\",\n \"0.57782054\",\n \"0.57719165\",\n \"0.577062\",\n \"0.57631487\",\n \"0.57630664\",\n \"0.5760711\",\n \"0.5757256\",\n \"0.5745856\",\n \"0.5742307\",\n \"0.57407105\",\n \"0.5739276\",\n \"0.57385194\",\n \"0.5736728\",\n \"0.57315105\",\n \"0.57295954\",\n \"0.5725301\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66633165"},"document_rank":{"kind":"string","value":"14"}}},{"rowIdx":25293,"cells":{"query":{"kind":"string","value":"Given n total number of items, what is the number of possible ways to choose r items from it?"},"document":{"kind":"string","value":"def combination(n, r):\n\n \n\n \n numerator = factorial(n)\n denominator = factorial(r)\n subtracted_answer = factorial(n-r)\n \n\n answer = numerator/(denominator * subtracted_answer)\n print(answer)\n return answer"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def number_of_ways(n):\r\n return number_of_ways_helper([1, 5, 10, 25], n)","def main():\n\n import sys\n sys.setrecursionlimit(10**7)\n from itertools import accumulate, combinations, permutations, product # https://docs.python.org/ja/3/library/itertools.html\n # accumulate() returns iterator! to get list: list(accumulate())\n from math import factorial, ceil, floor\n def factorize(n):\n \"\"\"return the factors of the Arg and count of each factor\n \n Args:\n n (long): number to be resolved into factors\n \n Returns:\n list of tuples: factorize(220) returns [(2, 2), (5, 1), (11, 1)]\n \"\"\"\n fct = [] # prime factor\n b, e = 2, 0 # base, exponent\n while b * b <= n:\n while n % b == 0:\n n = n // b\n e = e + 1\n if e > 0:\n fct.append((b, e))\n b, e = b + 1, 0\n if n > 1:\n fct.append((n, 1))\n return fct\n def combinations_count(n, r):\n \"\"\"Return the number of selecting r pieces of items from n kinds of items.\n \n Args:\n n (long): number\n r (long): number\n \n Raises:\n Exception: not defined when n or r is negative\n \n Returns:\n long: number\n \"\"\"\n # TODO: How should I do when n - r is negative?\n if n < 0 or r < 0:\n raise Exception('combinations_count(n, r) not defined when n or r is negative')\n if n - r < r: r = n - r\n if r < 0: return 0\n if r == 0: return 1\n if r == 1: return n\n numerator = [n - r + k + 1 for k in range(r)]\n denominator = [k + 1 for k in range(r)]\n for p in range(2,r+1):\n pivot = denominator[p - 1]\n if pivot > 1:\n offset = (n - r) % p\n for k in range(p-1,r,p):\n numerator[k - offset] /= pivot\n denominator[k] /= pivot\n result = 1\n for k in range(r):\n if numerator[k] > 1:\n result *= int(numerator[k])\n return result\n def combinations_with_replacement_count(n, r):\n \"\"\"Return the number of selecting r pieces of items from n kinds of items allowing individual elements to be repeated more than once.\n \n Args:\n n (long): number\n r (long): number\n \n Raises:\n Exception: not defined when n or r is negative\n \n Returns:\n long: number\n \"\"\"\n if n < 0 or r < 0:\n raise Exception('combinations_with_replacement_count(n, r) not defined when n or r is negative')\n elif n == 0:\n return 1\n else:\n return combinations_count(n + r - 1, r)\n from bisect import bisect_left, bisect_right\n from collections import deque, Counter, defaultdict # https://docs.python.org/ja/3/library/collections.html#collections.deque\n from heapq import heapify, heappop, heappush, heappushpop, heapreplace,nlargest,nsmallest # https://docs.python.org/ja/3/library/heapq.html\n from copy import deepcopy, copy # https://docs.python.org/ja/3/library/copy.html\n from operator import itemgetter\n # ex1: List.sort(key=itemgetter(1))\n # ex2: sorted(tuples, key=itemgetter(1,2))\n from functools import reduce\n def chmin(x, y):\n \"\"\"change minimum\n if x > y, x = y and return (x, True).\n convenient when solving problems of dp[i]\n \n Args:\n x (long): current minimum value\n y (long): potential minimum value\n \n Returns:\n (x, bool): (x, True) when updated, else (x, False)\n \"\"\"\n if x > y:\n x = y\n return (x, True)\n else:\n return (x, False)\n def chmax(x, y):\n \"\"\"change maximum\n if x < y, x = y and return (x, True).\n convenient when solving problems of dp[i]\n \n Args:\n x (long): current maximum value\n y (long): potential maximum value\n \n Returns:\n (x, bool): (x, True) when updated, else (x, False)\n \"\"\"\n if x < y:\n x = y\n return (x, True)\n else:\n return (x, False)\n\n from fractions import gcd # Deprecated since version 3.5: Use math.gcd() instead.\n def gcds(numbers):\n return reduce(gcd, numbers)\n def lcm(x, y):\n return (x * y) // gcd(x, y)\n def lcms(numbers):\n return reduce(lcm, numbers, 1)\n\n # first create factorial_list\n # fac_list = mod_factorial_list(n)\n INF = 10 ** 18\n MOD = 10 ** 9 + 7\n modpow = lambda a, n, p = MOD: pow(a, n, p) # Recursive function in python is slow!\n def modinv(a, p = MOD):\n # evaluate reciprocal using Fermat's little theorem:\n # a**(p-1) is identical to 1 (mod p) when a and p is coprime\n return modpow(a, p-2, p)\n def modinv_list(n, p = MOD):\n if n <= 1:\n return [0,1][:n+1]\n else:\n inv_t = [0,1]\n for i in range(2, n+1):\n inv_t += [inv_t[p % i] * (p - int(p / i)) % p]\n return inv_t\n def modfactorial_list(n, p = MOD):\n if n == 0:\n return [1]\n else:\n l = [0] * (n+1)\n tmp = 1\n for i in range(1, n+1):\n tmp = tmp * i % p\n l[i] = tmp\n return l\n def modcomb(n, k, fac_list = [], p = MOD):\n # fac_list = modfactorial_list(100)\n # print(modcomb(100, 5, modfactorial_list(100)))\n from math import factorial\n if n < 0 or k < 0 or n < k: return 0\n if n == 0 or k == 0: return 1\n if len(fac_list) <= n:\n a = factorial(n) % p\n b = factorial(k) % p\n c = factorial(n-k) % p\n else:\n a = fac_list[n]\n b = fac_list[k]\n c = fac_list[n-k]\n return (a * modpow(b, p-2, p) * modpow(c, p-2, p)) % p\n def modadd(a, b, p = MOD):\n return (a + b) % MOD\n def modsub(a, b, p = MOD):\n return (a - b) % p\n def modmul(a, b, p = MOD):\n return ((a % p) * (b % p)) % p\n def moddiv(a, b, p = MOD):\n return modmul(a, modpow(b, p-2, p))\n\n \"\"\" initialize variables and set inputs\n # initialize variables\n # to initialize list, use [0] * n\n # to initialize two dimentional array, use [[0] * N for _ in range(N)]\n # set inputs\n # open(0).read() is a convenient method:\n # ex) n, m, *x = map(int, open(0).read().split())\n # min(x[::2]) - max(x[1::2])\n # ex2) *x, = map(int, open(0).read().split())\n # don't forget to add comma after *x if only one variable is used\n # preprocessing\n # transpose = [x for x in zip(*data)]\n # ex) [[1, 2, 3], [4, 5, 6], [7, 8, 9]] => [(1, 4, 7), (2, 5, 8), (3, 6, 9)]\n # flat = [flatten for inner in data for flatten in inner]\n # ex) [[1, 2, 3], [4, 5, 6], [7, 8, 9]] => [1, 2, 3, 4, 5, 6, 7, 8, 9]\n # calculate and output\n # output pattern\n # ex1) print(*l) => when l = [2, 5, 6], printed 2 5 6\n \"\"\"\n\n # functions used\n r = lambda: sys.stdin.readline().strip()\n r_int = lambda: int(r())\n R = lambda: list(map(int, r().split()))\n Rfloat = lambda: list(map(float, r().split()))\n Rtuple = lambda: tuple(map(int, r().split()))\n Rmap = lambda: map(int, r().split())\n\n \"\"\" how to treat input\n # single int: int(r())\n # single string: r()\n # single float: float(r())\n # line int: R()\n # line string: r().split()\n # line (str, int, int): [j if i == 0 else int(j) for i, j in enumerate(r().split())]\n # lines int: [R() for _ in range(n)]\n \"\"\"\n\n # main\n N, Q = R()\n STX = [R() for _ in range(N)]\n STX.sort(key=itemgetter(2))\n\n D = [int(r()) for _ in range(Q)]\n Stopped = [-1] * Q\n ans = [-1] * Q\n\n for s, t, x in STX:\n l = bisect_left(D, s-x)\n r = bisect_left(D,t-x)\n a = l\n while a < r:\n if Stopped[a] == -1:\n ans[a] = x\n Stopped[a] = r\n a += 1\n else:\n a = Stopped[a]\n\n for i in ans:\n print(i)\n\n \"\"\"memo: how to use defaultdict of list\n # initialize\n Dic = defaultdict(list)\n # append / extend\n Dic[x].append(y)\n # for\n for k, v in Dic.items():\n \"\"\"","def r_combinations(n,r):\n return r_permutations(n,r) / math.factorial(r)","def marbles(n: int, k: int) -> int:\n # return (n-1) Choose (k - 1)\n # which is the number of possibilities with the given constraints.\n return n_choose_k(n - 1, k - 1)","def bruteForcePopulation(N):\n return list(itertools.permutations(range(N), N))","def r_permutations(n, r):\n return math.factorial(n) / math.factorial(n - r)","def permutations_(n, r):\n return factorial(n) / factorial(n-r)","def test_n_choose_n(self):\n self.assertEqual(wordlib.combinations(5, 5), 1)","def get_total_combo(pool, size):\n return len(pool) ** argp.length","def count_ways(n):\n if n < 0:\n return 0\n elif n == 0:\n return 1\n else:\n total = 0\n for i in range(1, min(n, 3) + 1):\n total += count_ways(n - i)\n return total","def combinations_with_replacement_count(n, r):\n if n < 0 or r < 0:\n raise Exception('combinations_with_replacement_count(n, r) not defined when n or r is negative')\n elif n == 0:\n return 1\n else:\n return combinations_count(n + r - 1, r)","def chooseItems(member, items, item_count):\r\n # citation https://en.wikipedia.org/wiki/Knapsack_problem#Dynamic_programming_in-advance_algorithm\r\n # used the pseudocode provided\r\n # https://www.geeksforgeeks.org/python-program-for-dynamic-programming-set-10-0-1-knapsack-problem/\r\n # for how to set up opt\r\n opt = [[0 for x in range(member[0]+1)] for x in range(item_count+1)]\r\n for i in range(len(items)+1):\r\n # for each item...\r\n for w in range(member[0]+1):\r\n # find best value at buttom-up approach to carrying capacity of the family member\r\n if i == 0 or w == 0:\r\n # initialize at 0\r\n opt[i][w] = 0\r\n elif items[i-1][1] <= w:\r\n # compare current value and weight to that of it added with current item info\r\n if items[i-1][0] + opt[i-1][w-items[i-1][1]] > opt[i-1][w]:\r\n opt[i][w] = items[i-1][0] + opt[i-1][w-items[i-1][1]]\r\n else:\r\n opt[i][w] = opt[i-1][w]\r\n else:\r\n opt[i][w] = opt[i-1][w]\r\n\r\n carrying_val = max(opt[-1])\r\n track_items = carrying_val\r\n carried_items = []\r\n track_weight = member[0]\r\n # citation https://www.geeksforgeeks.org/printing-items-01-knapsack/ only for finding items carried\r\n for i in range(len(items), 0, -1):\r\n if track_items <= 0:\r\n break\r\n if track_items == opt[i-1][track_weight]:\r\n continue\r\n else:\r\n carried_items.append(items[i - 1][2])\r\n track_items -= items[i - 1][0]\r\n track_weight -= items[i - 1][1]\r\n data_for_member = [carrying_val, carried_items]\r\n return data_for_member","def solution(resources, args):\n retval = 0\n repeats = [3, 5, 6, 9, 10, 12, 15]\n\n i = 0\n n = args.number - 1\n\n while n > 15:\n retval += sum(repeats)\n retval += 15*len(repeats)*i\n n -= 15\n i += 1\n while n >= 3:\n if n % 3 == 0 or n % 5 == 0:\n retval += 15*i + n\n n -= 1\n return retval","def partial_permutations(n, k):\n return int((factorial(n) / factorial(n - k)) % 1000000)","def permutations(n, r):\n result = 1\n for i in range(n, n-r, -1):\n result *= i\n return result","def compute_combinations(items: List[Union[List[Any], Tuple]], n: int) -> List[List[Any]]:\n return [chunks[i:i + n] for chunks in items for i in range(len(chunks) - (n - 1))]","def possible_rolls(D1,n):\n possibilities = []\n for D2 in range(1,7):\n for D3 in range(1,7):\n if D1+D2+D3 == n:\n possibilities.append((D1,D2,D3))\n return possibilities","def choose(n: int, k: int) -> int:\n return permute(n, k) // factorial(k)","def n_choose_r(n, r):\n if r == 1:\n return n\n elif r == n:\n return 1\n elif r == 0 or n == 0:\n return 0\n else:\n return n_choose_r(n-1, r-1) + n_choose_r(n-1, r)","def combinations(iterable, r):\n pool = tuple(iterable)\n n = len(pool)\n if r > n:\n return\n indices = list(range(r))\n yield tuple(pool[i] for i in indices)\n while True:\n for i in reversed(range(r)):\n if indices[i] != i + n - r:\n break\n else:\n return\n indices[i] += 1\n for j in range(i+1, r):\n indices[j] = indices[j-1] + 1\n yield tuple(pool[i] for i in indices)","def cyclen(n, iterable):\n return chain.from_iterable(repeat(tuple(iterable), n))","def get_n_best(self):\n pass","def linear_combination(n):\n weighs = (1, 3, 9, 27)\n\n for factors in factors_set():\n sum = 0\n for i in range(len(factors)):\n sum += factors[i] * weighs[i]\n if sum == n:\n return factors","def countArrangement(self, n: int) -> int:\n def iter_digit(n):\n while n:\n yield n % 2\n n //= 2\n\n @lru_cache(None)\n def dfs(i, remains):\n if i == n+1:\n return 1\n cnt = 0\n for j, d in enumerate(iter_digit(remains)):\n if d == 0:\n continue\n if j%i == 0 or i%j == 0:\n remains ^= 2**j\n cnt += dfs(i+1, remains)\n remains ^= 2**j\n return cnt\n\n # starting from 11..10 (length is n+1)\n return dfs(1, 2**(n+1)-2)","def many(t, n, seed= 937162211):\n random.seed(seed)\n return random.choices(t, k=n)","def big_selections(lst: List[int], n: int) -> List[List[int]]:\n if not lst:\n return [[]]\n else:\n holder = [lst.copy()]\n for i in range(len(lst)):\n l2 = lst.copy()\n l2.pop(i)\n for item in selections(l2):\n if item not in holder and sum(item) >= n:\n holder.append(item)\n return holder","def part_2():\n return itertools.permutations(range(5, 10))","def test_combinations(self):\r\n self.assertFloatEqual(combinations(5, 3), 10)\r\n self.assertFloatEqual(combinations(5, 2), 10)\r\n # only one way to pick no items or the same number of items\r\n self.assertFloatEqual(combinations(123456789, 0), 1)\r\n self.assertFloatEqual(combinations(123456789, 123456789), 1)\r\n # n ways to pick one item\r\n self.assertFloatEqual(combinations(123456789, 1), 123456789)\r\n # n(n-1)/2 ways to pick 2 items\r\n self.assertFloatEqual(\r\n combinations(\r\n 123456789,\r\n 2),\r\n 123456789 *\r\n 123456788 /\r\n 2)\r\n # check an arbitrary value in R\r\n self.assertFloatEqual(combinations(1234567, 12), 2.617073e64)","def integer_partitions(n, **kwargs):\n if 'parts' in kwargs:\n parts = sorted(kwargs['parts'], reverse=True)\n custom_parts = True\n else:\n parts = range(n, 0, -1)\n custom_parts = False\n total_number = len(parts)\n\n if 'distinct' in kwargs and kwargs['distinct']:\n distinct = 1\n else:\n distinct = 0\n\n if 'num_parts' in kwargs:\n num_parts = kwargs['num_parts']\n if num_parts > n:\n yield []\n return\n else:\n num_parts = 0\n\n def algorithm_p(n):\n \"\"\"\n Generates all partitions of n. This is Algorithm P from 7.2.1.4 of\n Knuth, Vol. 4.\n \"\"\"\n partition = [0]*n\n last_replaced = 0\n partition[last_replaced] = n\n idx = last_replaced - (n == 1)\n\n while True:\n yield partition[0:last_replaced + 1]\n if idx < 0:\n return\n if partition[idx] == 2:\n partition[idx] = 1\n idx -= 1\n last_replaced += 1\n partition[last_replaced] = 1\n else:\n replacement = partition[idx] - 1\n partition[idx] = replacement\n n = last_replaced - idx + 1\n last_replaced = idx + 1\n while n > replacement:\n partition[last_replaced] = replacement\n last_replaced += 1\n n -= replacement\n partition[last_replaced] = n\n idx = last_replaced - (n == 1)\n\n def algorithm_h(n, m):\n \"\"\"\n Generates all partitions of n into m parts. This is Algorithm H from\n 7.2.1.4 of Knuth, Vol. 4.\n \"\"\"\n partition = [1]*m\n partition[0] = n - m + 1\n\n while True:\n yield partition[:]\n if partition[1] < partition[0] - 1:\n partition[0] -= 1\n partition[1] += 1\n else:\n j = 2\n s = partition[0] + partition[1] - 1\n while j < m and partition[j] >= partition[0] - 1:\n s += partition[j]\n j += 1\n if j >= m:\n return\n replacement = partition[j] + 1\n partition[j] = replacement\n j -= 1\n while j > 0:\n partition[j] = replacement\n s -= replacement\n j -= 1\n partition[0] = s\n\n def backtrack(partial_sum, used, num_used, last_idx):\n if partial_sum == n:\n if not num_parts or (num_parts and num_used == num_parts):\n yield used\n elif partial_sum < n:\n if num_parts and num_used >= num_parts:\n return\n idx = 0\n if last_idx != 0:\n idx = last_idx + distinct\n for i in xrange(idx, total_number):\n part = parts[i]\n for partition in backtrack(partial_sum + part,\n used + [part], num_used + 1, i):\n yield partition\n\n if distinct or custom_parts:\n partition_gen = backtrack(0, [], 0, 0)\n elif not distinct and not custom_parts and num_parts != 0:\n partition_gen = algorithm_h(n, num_parts)\n else:\n partition_gen = algorithm_p(n)\n\n for partition in partition_gen:\n yield partition","def task3(nums):\n if len(nums) < 2:\n return nums\n pivot = random.choice(nums)\n l_nums = [n for n in nums if n < pivot]\n e_nums = [pivot] * nums.count(pivot)\n r_nums = [n for n in nums if n > pivot]\n return task3(l_nums) + e_nums + task3(r_nums)","def countTriplets3(arr, r):\n from collections import Counter\n from math import factorial\n arr_dict = Counter()\n n0 = arr[0]\n\n # If the ratio 'r' is 1 then this is a special case of combinations\n if r == 1:\n for x in arr:\n if x == n0: arr_dict[x] += 1\n n = arr_dict[n0]\n r2 = 3\n return factorial(n)//(factorial(r2)*factorial(n-r2)), arr_dict\n\n # Main variables for the rest of the function\n max_arr = max(arr)\n ratio_range = [n0]\n triplets = 0\n\n # Build all possible values\n index = n0 \n counter = 0\n while index < max_arr:\n index *= r\n ratio_range.append(index)\n counter += 1\n if ratio_range[-1] > max_arr: ratio_range.pop(-1)\n \n # Build the counter\n for x in arr:\n if x in ratio_range: arr_dict[x] += 1\n\n # With the 1 special case removed, there now cannot be triplets if there are not 3 items in the dict\n if len(arr_dict) < 3: return triplets, arr_dict, ratio_range\n\n for y in range(len(ratio_range)-2):\n firstkey = ratio_range[y]\n secondkey = ratio_range[y+1]\n thirdkey = ratio_range[y+2]\n \n # If there are no triplets then the loop will exit without incrementing triplets \n if firstkey not in arr_dict or secondkey not in arr_dict or thirdkey not in arr_dict: \n continue\n else:\n firstvalue = arr_dict[firstkey]\n secondvalue = arr_dict[secondkey]\n thirdvalue = arr_dict[thirdkey]\n \n triplet_count = (firstvalue) * (secondvalue) * (thirdvalue)\n triplets += triplet_count\n\n return triplets, arr_dict","def num_of_ways(n):\n if n == 0 or n == 1:\n return 1\n \n n_minus_2_step = 1\n n_minus_1_step = 1\n n_step = None\n\n #num_of_ways(n) = num_of_ways(n-1) + num_of_ways(n-2)\n for i in range(n-1):\n n_step = n_minus_1_step + n_minus_2_step\n n_minus_2_step = n_minus_1_step\n n_minus_1_step = n_step\n \n return n_step","def get_n_chains(self): \n res_id_cnt = 0\n tot_n_res = len(self.res_ids)\n n_chns = 0\n for res_id in self.res_ids:\n res_chn_i = res_id[2]\n if res_id_cnt > 1:\n if res_chn_i == self.res_ids[res_id_cnt-1][2]:\n pass\n else:\n n_chns+=1\n res_id_cnt+=1\n return n_chns","def combinations(n, k):\n return factorial(n) / (factorial(k) * factorial(n - k))","def combinations_count(n, r):\n # TODO: How should I do when n - r is negative?\n if n < 0 or r < 0:\n raise Exception('combinations_count(n, r) not defined when n or r is negative')\n if n - r < r: r = n - r\n if r < 0: return 0\n if r == 0: return 1\n if r == 1: return n\n numerator = [n - r + k + 1 for k in range(r)]\n denominator = [k + 1 for k in range(r)]\n for p in range(2,r+1):\n pivot = denominator[p - 1]\n if pivot > 1:\n offset = (n - r) % p\n for k in range(p-1,r,p):\n numerator[k - offset] /= pivot\n denominator[k] /= pivot\n result = 1\n for k in range(r):\n if numerator[k] > 1:\n result *= int(numerator[k])\n return result","def combinations(n) -> float:\r\n c = math.factorial(n) / (math.factorial(2) * math.factorial(n - 2))\r\n return c","def n_choose_k(N,K):\n return factorial(N) // (factorial(N - K) * factorial(K))","def knapsack(n=4, W=6, items=[[3, 4], [2, 3], [4, 2], [4, 3]]):\n A = np.zeros(shape=(n+1, W+1))\n A = calculate_optimal_solution(A, W, items, n)\n return A","def test_select_N_of(self):\n pop0 = []\n pop1 = [1]\n popmany = range(10)\n self.assertEqual(set(), select_N_of(pop0, 0))\n self.assertEqual(set(), select_N_of(pop0, 1))\n self.assertEqual(set(), select_N_of(pop0, 1e7))\n self.assertEqual(set([]), select_N_of(pop1, 0))\n self.assertEqual(set([1]), select_N_of(pop1, 1))\n self.assertEqual(set([1]), select_N_of(pop1, 1e7))\n self.assertEqual(set([]), select_N_of(popmany, 0))\n popmany_1 = select_N_of(popmany, 1)\n self.assertEqual(len(popmany_1), 1)\n self.assertTrue(popmany_1.pop() in popmany)\n self.assertEqual(len(select_N_of(popmany, 1e7)), 10)","def powerset(n):\n # chain r-combinations generator for r=0, 1,..., n\n return chain.from_iterable(combinations(range(n), r) for r in range(n+1))","def _pop_random_n(entities: np.array, weights: np.array, count: int = 3):\n for _ in range(count):\n if not len(entities):\n return\n\n choice, entities, weights = _pop_random(entities, weights)\n yield choice","def cycles(n, support, randomize=False):\n support = np.array(support)\n\n def gen(p):\n g = combinations(support, n)\n if randomize:\n g = list(g)\n random.shuffle(g)\n\n for local_support in g:\n for output_p in all_permutations(local_support)(p):\n yield output_p\n\n return gen","def test_get_n_bits_combinations():\n # Check n=1 - Pass\n assert layer_util.get_n_bits_combinations(1) == [[0], [1]]\n # Check n=2 - Pass\n assert layer_util.get_n_bits_combinations(2) == [[0, 0], [0, 1], [1, 0], [1, 1]]\n\n # Check n=3 - Pass\n assert layer_util.get_n_bits_combinations(3) == [\n [0, 0, 0],\n [0, 0, 1],\n [0, 1, 0],\n [0, 1, 1],\n [1, 0, 0],\n [1, 0, 1],\n [1, 1, 0],\n [1, 1, 1],\n ]","def get_n_solutions(self, n):\n return [self.get_solution() for _ in range(n)]","def part_1():\n return itertools.permutations(range(5))","def dynamic_programming_solution():\n num_combos = [1] * (TOTAL + 1)\n\n # ignore 1p coin since we are initializing num_combos with that value\n for coin in COINS[1:]:\n for total in range(TOTAL + 1):\n # if coin value is greater than total then num_combos cannot change\n if coin <= total: \n num_combos[total] += num_combos[total - coin]\n\n return num_combos[TOTAL]","def countArrangement(self, n: int) -> int:\n @lru_cache(None)\n def dfs(i, remains: Set[int]):\n if i == n+1:\n return 1\n cnt = 0\n for j in remains:\n if i%j == 0 or j%i == 0:\n cnt += dfs(i+1, remains - {j})\n return cnt\n\n return dfs(1, frozenset(range(1, n+1)))","def countTriplets2(arr, r):\n from collections import Counter\n from math import factorial\n\n # If the ratio 'r' is 1 then this is a special case of combinations\n if r == 1:\n n = len(arr)\n r = 3\n return factorial(n)//(factorial(r)*factorial(n-r))\n\n arr_dict = Counter()\n ratio_range = []\n index = 0\n counter = 0\n triplets = 0\n \n # Build the counter\n for x in arr:\n arr_dict[x] += 1\n max_arr_dict = max(arr_dict)\n\n # With the 1 special case removed, there now cannot be triplets if there are not 3 items in the dict\n if len(arr_dict) < 3: return triplets\n \n # There is now the potential for triplets so build all possible values\n while index < max_arr_dict:\n index = r**counter\n ratio_range.append(index)\n counter += 1\n if ratio_range[-1] > max_arr_dict: ratio_range.pop(-1)\n\n for y in range(len(ratio_range)-2):\n firstkey = ratio_range[y]\n secondkey = ratio_range[y+1]\n thirdkey = ratio_range[y+2]\n \n # If there are no triplets then the loop will exit without incrementing triplets \n if firstkey not in arr_dict or secondkey not in arr_dict or thirdkey not in arr_dict: \n continue\n else:\n firstvalue = arr_dict[firstkey]\n secondvalue = arr_dict[secondkey]\n thirdvalue = arr_dict[thirdkey]\n \n summedvalue = (firstvalue + secondvalue + thirdvalue) - 3\n triplet_count = 2**summedvalue\n triplets += triplet_count\n\n return triplets","def _n_choose_2(n):\n return (n * (n - 1)) // 2","def choose(n,r):\n \tc=0\n \tif (n>=0) and (0 <= r <= n):\n\t \tc = 1L\n\t \tdenom = 1\n\t \tfor (num,denom) in zip(xrange(n,n-r,-1), xrange(1,r+1,1)):\n\t\t\tc = (c * num) // denom","def choose_m_n(li,min,max):\n n_items = random.randrange(min,max+1)\n if n_items == 0:\n return [ ]\n sample=random.sample(li,n_items) # Should it be sorted?\n return sample","def nth_permutation(elems, n):\n pos, summ = 0, 0\n permutation = \"\"\n for i in reversed(range(1,len(elems))):\n fact_i = fact(i)\n while summ+fact_i < n:\n summ += fact_i\n pos += 1\n permutation += str(elems[pos])\n del(elems[pos])\n pos = 0\n return permutation + str(elems[0])","def random_sampling(elements, n):\r\n import random\r\n return [random.choice(elements) for i in range(n)]","def countTriplets(x, n):\n x = tuple(x)\n if not x or not n: return\n return deep(x, n, 0, None)","def take(iterable, n):\n\n def taking(iterable_):\n for i, e in enumerate(iterable_):\n if i < n:\n yield e\n\n return taking(iterable)","def choose(n, k):\n ans, k = 1, min(k, n-k)\n for i in range(k):\n ans *= n-i\n ans //= i+1\n return ans","def countTriplets4(arr, r):\n from collections import Counter\n arr_dict = {}\n n0 = arr[0]\n max_arr = max(arr)\n ratio_range = {n0: 0}\n triplets = 0\n\n # Build all possible values\n index = n0 \n counter = 1\n while index < max_arr:\n index *= r\n ratio_range[index] = counter\n counter += 1\n if index > max_arr: ratio_range.pop(index)\n \n # Remove anything that isn't a possible value and build the dictionary\n for x in range(len(arr)-1, -1, -1):\n if arr[x] not in ratio_range: \n arr.pop(x)\n continue\n if arr[x] in arr_dict:\n arr_dict[arr[x]] = [x] + arr_dict[arr[x]]\n else:\n arr_dict[arr[x]] = [x]\n if len(arr) < 3: return triplets # return 0 if there are not enough items left in arr to make a triplet\n\n # Iterate backwards through arr starting at index arr[-2]\n for n in range(len(arr)-2, -1, -1):\n item = arr[n]\n item_before = item // r if item // r in ratio_range else 0 # Set to 0 if the next value in the progression does not appear in the input\n item_after = item * r if item * r in ratio_range else 0 # Set to 0 if the previous value in the progression does not appear in the input\n if not item_before or not item_after: continue # Continue in the loop if triplets are not possible with 'item' as 'j'\n \n counter_before = sum(1 for x in arr_dict[item_before] if x < n)\n counter_after = sum(1 for x in arr_dict[item_after] if x > n)\n triplets += counter_before * counter_after\n return triplets","def countArrangement(self, n: int) -> int:\n def dfs(i, remains: List[int]):\n if i == n+1:\n return 1\n cnt = 0\n for j in range(1, n+1):\n if remains[j] is None and (i%j == 0 or j%i == 0):\n remains[j] = i\n cnt += dfs(i+1, remains)\n remains[j] = None\n return cnt\n\n return dfs(1, [None]*(n+1))","def partitions(items: int) -> int:\n return bell_number(items)","def roulette(weights, n):\n if n > len(weights):\n raise Exception(\"Can't choose {} samples from {} items\".format(n, len(weights)))\n if any(map(lambda w: w <= 0, weights.values())):\n raise Exception(\"The weight can't be a non-positive number.\")\n items = weights.items()\n chosen = set()\n for i in range(n):\n total = sum(list(zip(*items))[1])\n dice = random.random() * total\n running_weight = 0\n chosen_item = None\n for item, weight in items:\n if dice < running_weight + weight:\n chosen_item = item\n break\n running_weight += weight\n chosen.add(chosen_item)\n items = [(i, w) for (i, w) in items if i != chosen_item]\n return list(chosen)","def permute(n, r):\n\n product = 1\n for i in range(n - r + 1, n + 1):\n product *= i\n return product","def number_of_trees_of_order(n):\n if n < 2:\n return n\n result = 0\n for k in range(1, n):\n result += k * number_of_trees_of_order(k) * _s(n-1, k)\n return result // (n - 1)","def choose(n, a):\n return torch.as_tensor([a[idx] for idx in torch.randperm(len(a))[:n]])","def sixn(m):\n if m <= 2:\n return ()\n if m > 2:\n yield 2\n if m > 3:\n yield 3\n for n in count(1):\n x = 6 * n - 1\n y = x + 2\n if x < m:\n yield x\n else:\n break\n if y < m:\n yield y\n else:\n break","def solve1(n):\n for queens in itertools.permutations(range(n)):\n if is_valid(queens):\n yield queens","def select(individuals, n):\r\n # return selBest(individuals, n)\r\n return individuals[:n]","def n_choose_kv(newK):\n values = np.zeros((1,newK+1))\n ks = np.arange(newK+1)\n \n for i in range(newK+1):\n values[i] = scipy.misc.comb(newK, ks[i])\n\n return values","def chain(n, used, used_list, cmem, fmem):\n if n in used:\n i = used_list.index(n)\n chain_length = len(used_list) - i\n cmem[n] = chain_length\n return chain_length, chain_length\n if n in cmem:\n return cmem[n], 0\n next = digits_factorial(n, fmem)\n used.add(n)\n used_list.append(n)\n chain_length, loop = chain(next, used, used_list, cmem, fmem)\n if loop > 0:\n loop -= 1\n cmem[n] = chain_length\n return chain_length, loop\n chain_length += 1\n cmem[n] = chain_length\n return chain_length, 0","def at_least_n_ver(seq, n):\r\n num_AB = 0\r\n for item in seq:\r\n if np.array_equal(item, Quantifier.AB):\r\n if num_AB == n-1:\r\n return Quantifier.T\r\n else:\r\n num_AB += 1\r\n return Quantifier.F","def potential_witnesses(n):\n if n < 1373653: return [2, 3] # n < 1,373,653\n if n < 9080191: return [31, 73] # n < 9,080,191\n if n < 4759123141: return [2, 7, 61] # n < 4,759,123,141\n if n < 2152302898747: return [2, 3, 5, 7, 11] # n < 2,152,302,898,747\n if n < 3474749660383: return [2, 3, 5, 7, 11, 13] # n < 3,474,749,660,383\n if n < 341550071728321: return [2, 3, 5, 7, 11, 13, 17] # n < 341,550,071,728,321\n return [random.randint(1,n-1) for _ in xrange(0,20)] # 99.999999999909051% (1 - .25**20) accuracy for n >= 341,550,071,728,321","def choices(N, M):\n #yield from itertools.combinations(range(2, M+1), N)\n # Use stars and bars method\n allbars = itertools.combinations( range(1,N+M-1), M-2 )\n for bars in allbars:\n bars = [0] + list(bars) + [N+M-1]\n yi = [ y-x for x, y in zip(bars, bars[1:]) ]\n yield [ y-1 for y in yi ]","def get_perms(n):\n \n from itertools import permutations\n bases = 'CATGN'\n return [''.join(perm) for perm in permutations(bases, n)]","def totalSolutions(n:int):\n\n memo = [0]*6\n memo[5] = 1\n if n == 1:\n return 1\n\n for i in range(n):\n solutions = sum(memo)\n memo.pop(0)\n memo.append(solutions)\n\n return memo.pop()","def solve(n=100):\n # Build the board.\n single_vals = list(range(1, 21)) + [25]\n double_vals = [2*x for x in single_vals]\n triple_vals = [3*x for x in range(1, 21)]\n total_vals = single_vals + double_vals + triple_vals + [0]\n\n return sum(1 for other_throws, double_throw\n in product(combinations_with_replacement(total_vals, 2), double_vals, repeat=1)\n if sum(other_throws) + double_throw < n)","def test_permutation(n: int) -> List[int]:\n permutation = [i + 1 for i in range(n)]\n cycle_length = 2\n cycle_index = 0\n\n while cycle_length + cycle_index < n:\n permutation[cycle_index + cycle_length - 1] = cycle_index\n cycle_index += cycle_length\n cycle_length += 1\n\n if n > 0:\n permutation[n - 1] = cycle_index\n\n return permutation","def permutations(k: int) -> int:\n return factorial(k)","def binary_compositions(n):\n return productrange(*[2]*(n-1))","def nchoosek(n, k):\n if n < k:\n return 0\n return partition(n, [k, n - k])","def combinations(s, n):\n return (\"\".join(x) for x in tuples(s,n))","def knapsack(items, m, n):\r\n population = generate_pop(items, population=[])\r\n i = 0\r\n while i < MAX_ITERATIONS:\r\n # sorted population\r\n # population = sorted(\r\n # population,\r\n # key=lambda child: fitness_function(child, m),\r\n # reverse=True)\r\n\r\n # selected parents\r\n population = select_parents(population, m)\r\n\r\n # the best couple of items\r\n population = do_crossover(population[:2])\r\n\r\n # Step 5\r\n population = generate_pop(items, population=population)\r\n\r\n # Step 6:\r\n population = mutate_population(population, m)\r\n\r\n total_value = is_solution(population, m, n)\r\n if total_value:\r\n print('Found solution: {}'.format(population[0]))\r\n return total_value\r\n\r\n print('No solution found...')\r\n return False","def unique_climbs(N, X={1, 2}):\n result = 0\n\n if N == 0:\n return 1\n if N < 0:\n return 0\n\n for cnt in X:\n result += unique_climbs(N-cnt, X)\n\n return result","def choose(n, k):\n # http://stackoverflow.com/a/3025547/313967\n if 0 <= k <= n:\n ntok = 1\n ktok = 1\n for t in xrange(1, min(k, n - k) + 1):\n ntok *= n\n ktok *= t\n n -= 1\n return ntok // ktok\n else:\n return 0","def get_num_pairs(seq):\n n = len(seq)\n return int(n * (n-1)/2) # sum of arphmetic progression (n-1)...1","def buyable(n: int) -> bool:\n if n in [4, 6, 25]:\n return True\n elif n < 4:\n return False\n else:\n buyability = False\n for size in [4, 6, 25]:\n buyability |= buyable(n - size)\n return buyability","def generate_objects_of_size(\n self, n: int, **parameters: int\n ) -> Iterator[CombinatorialObjectType]:","def greedy_partition(counts, n):\r\n\r\n buckets = [[] for i in range(n)]\r\n fill_levels = [0 for i in range(n)]\r\n\r\n for key in sorted(counts, reverse=True,\r\n key=lambda c: counts[c]):\r\n smallest = fill_levels.index(min(fill_levels))\r\n buckets[smallest].append(key)\r\n fill_levels[smallest] += counts[key]\r\n\r\n return buckets, fill_levels","def solve(self):\n\n n = 1\n\n # Get the n-length combinations (if any) that can accommodate the\n # defined load.\n combos_n = self.fit_n_bins(n=n)\n\n while True:\n # Get the n+1-length combinations (if any) that can accommodate the\n # defined load.\n combos_np1 = self.fit_n_bins(n=n + 1)\n\n # If no viable combinations were found for both n and n + 1 lengths\n # the increment the defined length, swap the combinations and skip\n # to the next iteration\n if not combos_n or not combos_np1:\n combos_n = combos_np1\n n += 1\n continue\n\n # if the n + 1 length combinations happen to offer decreased\n # capacity then prefer those combinations over the shorter yet more\n # wasteful combinations\n if sum(combos_n[0]) > sum(combos_np1[0]):\n return combos_np1\n else:\n return combos_n","def main(size, item, answer):\n item = list(map(int, item))\n item.sort()\n for number in item:\n if size - number >= 0:\n size -= number\n answer += 1\n print(answer)","def knapsack(items, capacity):\r\n pass","def choose(n, k):\n if 0 <= k <= n:\n ntok = 1\n ktok = 1\n for t in xrange(1, min(k, n - k) + 1):\n ntok *= n\n ktok *= t\n n -= 1\n return ntok // ktok\n else:\n return 0","def choose(n, k):\n if 0 <= k <= n:\n ntok = 1\n ktok = 1\n for t in xrange(1, min(k, n - k) + 1):\n ntok *= n\n ktok *= t\n n -= 1\n return ntok // ktok\n else:\n return 0","def solve(n=10):\n return sum(M_N_S(n, d)[2] for d in range(10))","def nCr(\n n: int,\n r: int = 0,\n l: list = [],\n ) -> int:\n\n # Initialisations\n c = 0\n\n # Check if the list is not empty\n if l != []:\n\n # Loop through the list\n for i in l:\n\n # Cumulatively calculate the n Choose r value for each list item\n c += comb(n, i, exact = True)\n\n # Check if r is not zero\n elif r != 0:\n\n # Calculate the n Choose r value\n c = comb(n, r, exact = True)\n\n else:\n\n # Loop through all possible r\n for i in range(0, n):\n\n # Cumulatively calculate the n Choose r value\n c += comb(n, i, exact = True)\n\n return c","def windows_of_permutations(n, step):\n def gen(p):\n for i in range(0, NB_AVIONS-n, step):\n for perm in all_permutations(range(i, i+n))(p):\n yield perm\n return gen","def get_witnesses(n):\n assert (n > 2) and (n % 2 == 1)\n if n < 2047:\n # References: [1], [2], [4]\n witnesses = (2,)\n elif n < 1373653: # ~1.3 million\n # References: [1], [2], [3], [4]\n witnesses = (2, 3)\n elif n < 9080191: # ~9.0 million\n # References: [3], [4]\n witnesses = (31, 73)\n elif n < 25326001: # ~25.3 million\n # References: [1], [2], [3], [4]\n witnesses = (2, 3, 5)\n elif n < 3215031751: # ~3.2 billion\n # References: [1], [2], [3], [4]\n witnesses = (2, 3, 5, 7)\n elif n < 4759123141: # ~4.7 billion\n # References: [3], [4]\n witnesses = (2, 7, 61)\n elif n < 2152302898747: # ~2.1 trillion\n # References: [1], [2], [3], [4]\n witnesses = (2, 3, 5, 7, 11)\n elif n < 3474749660383: # ~3.4 trillion\n # References: [1], [2], [3], [4]\n witnesses = (2, 3, 5, 7, 11, 13)\n elif n < 341550071728321: # ~341.5 trillion\n # References: [1], [2], [3], [4]\n witnesses = (2, 3, 5, 7, 11, 13, 17)\n elif n < 3825123056546413051: # ~3.8 million trillion\n # References: [1], [4]\n witnesses = (2, 3, 5, 7, 11, 13, 17, 19, 23)\n elif n <= 2**64:\n witnesses = (2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37)\n else:\n witnesses = None\n \n return witnesses","def generate_n(k_problem, n):\n return [generate_first_random(k_problem) for i in range(n)]","def possibleTuples(nDifferentNodes):\n numberOfPossibleTuples = {}\n for nodeName, numberOfDifferent in nDifferentNodes.iteritems():\n numberOfPossibleTuples[nodeName] = sum([comb(numberOfDifferent,i,exact=True)for i in range(0,4)])\n return numberOfPossibleTuples","def generate_products(n=30):\n products = []\n for i in range(n):\n adj = choice(ADJECTIVES)\n item = choice(ITEMS)\n name = f'{adj} {item}'\n product = Product(name,\n randint(5,101),\n randint(5,101),\n uniform(0,2.5),\n randint(100000,1000000))\n products.append(product)\n return products","def calculate_image_possibilities():\n\n # Reordering the color ramps in the palette yields 3! combinations\n palette_reorder_possibilities = 6\n\n return len(palettes) * palette_reorder_possibilities * len(grips) * len(pommels) * len(crossguards) * len(blades)","def subsets(n):\n binary = lambda x: x>0 and binary(x>>1) + [x&1] or []\n pad = lambda l: [0]*(n-len(l)) + l #Always returns a list of length 'n'\n return [pad(binary(i)) for i in range(1, 2**n)]","def get_max_combination(total_cuts):\n max_pieces = 0\n for i in range(total_cuts):\n result = i * (total_cuts - i)\n if result > max_pieces:\n max_pieces = result\n print(max_pieces)"],"string":"[\n \"def number_of_ways(n):\\r\\n return number_of_ways_helper([1, 5, 10, 25], n)\",\n \"def main():\\n\\n import sys\\n sys.setrecursionlimit(10**7)\\n from itertools import accumulate, combinations, permutations, product # https://docs.python.org/ja/3/library/itertools.html\\n # accumulate() returns iterator! to get list: list(accumulate())\\n from math import factorial, ceil, floor\\n def factorize(n):\\n \\\"\\\"\\\"return the factors of the Arg and count of each factor\\n \\n Args:\\n n (long): number to be resolved into factors\\n \\n Returns:\\n list of tuples: factorize(220) returns [(2, 2), (5, 1), (11, 1)]\\n \\\"\\\"\\\"\\n fct = [] # prime factor\\n b, e = 2, 0 # base, exponent\\n while b * b <= n:\\n while n % b == 0:\\n n = n // b\\n e = e + 1\\n if e > 0:\\n fct.append((b, e))\\n b, e = b + 1, 0\\n if n > 1:\\n fct.append((n, 1))\\n return fct\\n def combinations_count(n, r):\\n \\\"\\\"\\\"Return the number of selecting r pieces of items from n kinds of items.\\n \\n Args:\\n n (long): number\\n r (long): number\\n \\n Raises:\\n Exception: not defined when n or r is negative\\n \\n Returns:\\n long: number\\n \\\"\\\"\\\"\\n # TODO: How should I do when n - r is negative?\\n if n < 0 or r < 0:\\n raise Exception('combinations_count(n, r) not defined when n or r is negative')\\n if n - r < r: r = n - r\\n if r < 0: return 0\\n if r == 0: return 1\\n if r == 1: return n\\n numerator = [n - r + k + 1 for k in range(r)]\\n denominator = [k + 1 for k in range(r)]\\n for p in range(2,r+1):\\n pivot = denominator[p - 1]\\n if pivot > 1:\\n offset = (n - r) % p\\n for k in range(p-1,r,p):\\n numerator[k - offset] /= pivot\\n denominator[k] /= pivot\\n result = 1\\n for k in range(r):\\n if numerator[k] > 1:\\n result *= int(numerator[k])\\n return result\\n def combinations_with_replacement_count(n, r):\\n \\\"\\\"\\\"Return the number of selecting r pieces of items from n kinds of items allowing individual elements to be repeated more than once.\\n \\n Args:\\n n (long): number\\n r (long): number\\n \\n Raises:\\n Exception: not defined when n or r is negative\\n \\n Returns:\\n long: number\\n \\\"\\\"\\\"\\n if n < 0 or r < 0:\\n raise Exception('combinations_with_replacement_count(n, r) not defined when n or r is negative')\\n elif n == 0:\\n return 1\\n else:\\n return combinations_count(n + r - 1, r)\\n from bisect import bisect_left, bisect_right\\n from collections import deque, Counter, defaultdict # https://docs.python.org/ja/3/library/collections.html#collections.deque\\n from heapq import heapify, heappop, heappush, heappushpop, heapreplace,nlargest,nsmallest # https://docs.python.org/ja/3/library/heapq.html\\n from copy import deepcopy, copy # https://docs.python.org/ja/3/library/copy.html\\n from operator import itemgetter\\n # ex1: List.sort(key=itemgetter(1))\\n # ex2: sorted(tuples, key=itemgetter(1,2))\\n from functools import reduce\\n def chmin(x, y):\\n \\\"\\\"\\\"change minimum\\n if x > y, x = y and return (x, True).\\n convenient when solving problems of dp[i]\\n \\n Args:\\n x (long): current minimum value\\n y (long): potential minimum value\\n \\n Returns:\\n (x, bool): (x, True) when updated, else (x, False)\\n \\\"\\\"\\\"\\n if x > y:\\n x = y\\n return (x, True)\\n else:\\n return (x, False)\\n def chmax(x, y):\\n \\\"\\\"\\\"change maximum\\n if x < y, x = y and return (x, True).\\n convenient when solving problems of dp[i]\\n \\n Args:\\n x (long): current maximum value\\n y (long): potential maximum value\\n \\n Returns:\\n (x, bool): (x, True) when updated, else (x, False)\\n \\\"\\\"\\\"\\n if x < y:\\n x = y\\n return (x, True)\\n else:\\n return (x, False)\\n\\n from fractions import gcd # Deprecated since version 3.5: Use math.gcd() instead.\\n def gcds(numbers):\\n return reduce(gcd, numbers)\\n def lcm(x, y):\\n return (x * y) // gcd(x, y)\\n def lcms(numbers):\\n return reduce(lcm, numbers, 1)\\n\\n # first create factorial_list\\n # fac_list = mod_factorial_list(n)\\n INF = 10 ** 18\\n MOD = 10 ** 9 + 7\\n modpow = lambda a, n, p = MOD: pow(a, n, p) # Recursive function in python is slow!\\n def modinv(a, p = MOD):\\n # evaluate reciprocal using Fermat's little theorem:\\n # a**(p-1) is identical to 1 (mod p) when a and p is coprime\\n return modpow(a, p-2, p)\\n def modinv_list(n, p = MOD):\\n if n <= 1:\\n return [0,1][:n+1]\\n else:\\n inv_t = [0,1]\\n for i in range(2, n+1):\\n inv_t += [inv_t[p % i] * (p - int(p / i)) % p]\\n return inv_t\\n def modfactorial_list(n, p = MOD):\\n if n == 0:\\n return [1]\\n else:\\n l = [0] * (n+1)\\n tmp = 1\\n for i in range(1, n+1):\\n tmp = tmp * i % p\\n l[i] = tmp\\n return l\\n def modcomb(n, k, fac_list = [], p = MOD):\\n # fac_list = modfactorial_list(100)\\n # print(modcomb(100, 5, modfactorial_list(100)))\\n from math import factorial\\n if n < 0 or k < 0 or n < k: return 0\\n if n == 0 or k == 0: return 1\\n if len(fac_list) <= n:\\n a = factorial(n) % p\\n b = factorial(k) % p\\n c = factorial(n-k) % p\\n else:\\n a = fac_list[n]\\n b = fac_list[k]\\n c = fac_list[n-k]\\n return (a * modpow(b, p-2, p) * modpow(c, p-2, p)) % p\\n def modadd(a, b, p = MOD):\\n return (a + b) % MOD\\n def modsub(a, b, p = MOD):\\n return (a - b) % p\\n def modmul(a, b, p = MOD):\\n return ((a % p) * (b % p)) % p\\n def moddiv(a, b, p = MOD):\\n return modmul(a, modpow(b, p-2, p))\\n\\n \\\"\\\"\\\" initialize variables and set inputs\\n # initialize variables\\n # to initialize list, use [0] * n\\n # to initialize two dimentional array, use [[0] * N for _ in range(N)]\\n # set inputs\\n # open(0).read() is a convenient method:\\n # ex) n, m, *x = map(int, open(0).read().split())\\n # min(x[::2]) - max(x[1::2])\\n # ex2) *x, = map(int, open(0).read().split())\\n # don't forget to add comma after *x if only one variable is used\\n # preprocessing\\n # transpose = [x for x in zip(*data)]\\n # ex) [[1, 2, 3], [4, 5, 6], [7, 8, 9]] => [(1, 4, 7), (2, 5, 8), (3, 6, 9)]\\n # flat = [flatten for inner in data for flatten in inner]\\n # ex) [[1, 2, 3], [4, 5, 6], [7, 8, 9]] => [1, 2, 3, 4, 5, 6, 7, 8, 9]\\n # calculate and output\\n # output pattern\\n # ex1) print(*l) => when l = [2, 5, 6], printed 2 5 6\\n \\\"\\\"\\\"\\n\\n # functions used\\n r = lambda: sys.stdin.readline().strip()\\n r_int = lambda: int(r())\\n R = lambda: list(map(int, r().split()))\\n Rfloat = lambda: list(map(float, r().split()))\\n Rtuple = lambda: tuple(map(int, r().split()))\\n Rmap = lambda: map(int, r().split())\\n\\n \\\"\\\"\\\" how to treat input\\n # single int: int(r())\\n # single string: r()\\n # single float: float(r())\\n # line int: R()\\n # line string: r().split()\\n # line (str, int, int): [j if i == 0 else int(j) for i, j in enumerate(r().split())]\\n # lines int: [R() for _ in range(n)]\\n \\\"\\\"\\\"\\n\\n # main\\n N, Q = R()\\n STX = [R() for _ in range(N)]\\n STX.sort(key=itemgetter(2))\\n\\n D = [int(r()) for _ in range(Q)]\\n Stopped = [-1] * Q\\n ans = [-1] * Q\\n\\n for s, t, x in STX:\\n l = bisect_left(D, s-x)\\n r = bisect_left(D,t-x)\\n a = l\\n while a < r:\\n if Stopped[a] == -1:\\n ans[a] = x\\n Stopped[a] = r\\n a += 1\\n else:\\n a = Stopped[a]\\n\\n for i in ans:\\n print(i)\\n\\n \\\"\\\"\\\"memo: how to use defaultdict of list\\n # initialize\\n Dic = defaultdict(list)\\n # append / extend\\n Dic[x].append(y)\\n # for\\n for k, v in Dic.items():\\n \\\"\\\"\\\"\",\n \"def r_combinations(n,r):\\n return r_permutations(n,r) / math.factorial(r)\",\n \"def marbles(n: int, k: int) -> int:\\n # return (n-1) Choose (k - 1)\\n # which is the number of possibilities with the given constraints.\\n return n_choose_k(n - 1, k - 1)\",\n \"def bruteForcePopulation(N):\\n return list(itertools.permutations(range(N), N))\",\n \"def r_permutations(n, r):\\n return math.factorial(n) / math.factorial(n - r)\",\n \"def permutations_(n, r):\\n return factorial(n) / factorial(n-r)\",\n \"def test_n_choose_n(self):\\n self.assertEqual(wordlib.combinations(5, 5), 1)\",\n \"def get_total_combo(pool, size):\\n return len(pool) ** argp.length\",\n \"def count_ways(n):\\n if n < 0:\\n return 0\\n elif n == 0:\\n return 1\\n else:\\n total = 0\\n for i in range(1, min(n, 3) + 1):\\n total += count_ways(n - i)\\n return total\",\n \"def combinations_with_replacement_count(n, r):\\n if n < 0 or r < 0:\\n raise Exception('combinations_with_replacement_count(n, r) not defined when n or r is negative')\\n elif n == 0:\\n return 1\\n else:\\n return combinations_count(n + r - 1, r)\",\n \"def chooseItems(member, items, item_count):\\r\\n # citation https://en.wikipedia.org/wiki/Knapsack_problem#Dynamic_programming_in-advance_algorithm\\r\\n # used the pseudocode provided\\r\\n # https://www.geeksforgeeks.org/python-program-for-dynamic-programming-set-10-0-1-knapsack-problem/\\r\\n # for how to set up opt\\r\\n opt = [[0 for x in range(member[0]+1)] for x in range(item_count+1)]\\r\\n for i in range(len(items)+1):\\r\\n # for each item...\\r\\n for w in range(member[0]+1):\\r\\n # find best value at buttom-up approach to carrying capacity of the family member\\r\\n if i == 0 or w == 0:\\r\\n # initialize at 0\\r\\n opt[i][w] = 0\\r\\n elif items[i-1][1] <= w:\\r\\n # compare current value and weight to that of it added with current item info\\r\\n if items[i-1][0] + opt[i-1][w-items[i-1][1]] > opt[i-1][w]:\\r\\n opt[i][w] = items[i-1][0] + opt[i-1][w-items[i-1][1]]\\r\\n else:\\r\\n opt[i][w] = opt[i-1][w]\\r\\n else:\\r\\n opt[i][w] = opt[i-1][w]\\r\\n\\r\\n carrying_val = max(opt[-1])\\r\\n track_items = carrying_val\\r\\n carried_items = []\\r\\n track_weight = member[0]\\r\\n # citation https://www.geeksforgeeks.org/printing-items-01-knapsack/ only for finding items carried\\r\\n for i in range(len(items), 0, -1):\\r\\n if track_items <= 0:\\r\\n break\\r\\n if track_items == opt[i-1][track_weight]:\\r\\n continue\\r\\n else:\\r\\n carried_items.append(items[i - 1][2])\\r\\n track_items -= items[i - 1][0]\\r\\n track_weight -= items[i - 1][1]\\r\\n data_for_member = [carrying_val, carried_items]\\r\\n return data_for_member\",\n \"def solution(resources, args):\\n retval = 0\\n repeats = [3, 5, 6, 9, 10, 12, 15]\\n\\n i = 0\\n n = args.number - 1\\n\\n while n > 15:\\n retval += sum(repeats)\\n retval += 15*len(repeats)*i\\n n -= 15\\n i += 1\\n while n >= 3:\\n if n % 3 == 0 or n % 5 == 0:\\n retval += 15*i + n\\n n -= 1\\n return retval\",\n \"def partial_permutations(n, k):\\n return int((factorial(n) / factorial(n - k)) % 1000000)\",\n \"def permutations(n, r):\\n result = 1\\n for i in range(n, n-r, -1):\\n result *= i\\n return result\",\n \"def compute_combinations(items: List[Union[List[Any], Tuple]], n: int) -> List[List[Any]]:\\n return [chunks[i:i + n] for chunks in items for i in range(len(chunks) - (n - 1))]\",\n \"def possible_rolls(D1,n):\\n possibilities = []\\n for D2 in range(1,7):\\n for D3 in range(1,7):\\n if D1+D2+D3 == n:\\n possibilities.append((D1,D2,D3))\\n return possibilities\",\n \"def choose(n: int, k: int) -> int:\\n return permute(n, k) // factorial(k)\",\n \"def n_choose_r(n, r):\\n if r == 1:\\n return n\\n elif r == n:\\n return 1\\n elif r == 0 or n == 0:\\n return 0\\n else:\\n return n_choose_r(n-1, r-1) + n_choose_r(n-1, r)\",\n \"def combinations(iterable, r):\\n pool = tuple(iterable)\\n n = len(pool)\\n if r > n:\\n return\\n indices = list(range(r))\\n yield tuple(pool[i] for i in indices)\\n while True:\\n for i in reversed(range(r)):\\n if indices[i] != i + n - r:\\n break\\n else:\\n return\\n indices[i] += 1\\n for j in range(i+1, r):\\n indices[j] = indices[j-1] + 1\\n yield tuple(pool[i] for i in indices)\",\n \"def cyclen(n, iterable):\\n return chain.from_iterable(repeat(tuple(iterable), n))\",\n \"def get_n_best(self):\\n pass\",\n \"def linear_combination(n):\\n weighs = (1, 3, 9, 27)\\n\\n for factors in factors_set():\\n sum = 0\\n for i in range(len(factors)):\\n sum += factors[i] * weighs[i]\\n if sum == n:\\n return factors\",\n \"def countArrangement(self, n: int) -> int:\\n def iter_digit(n):\\n while n:\\n yield n % 2\\n n //= 2\\n\\n @lru_cache(None)\\n def dfs(i, remains):\\n if i == n+1:\\n return 1\\n cnt = 0\\n for j, d in enumerate(iter_digit(remains)):\\n if d == 0:\\n continue\\n if j%i == 0 or i%j == 0:\\n remains ^= 2**j\\n cnt += dfs(i+1, remains)\\n remains ^= 2**j\\n return cnt\\n\\n # starting from 11..10 (length is n+1)\\n return dfs(1, 2**(n+1)-2)\",\n \"def many(t, n, seed= 937162211):\\n random.seed(seed)\\n return random.choices(t, k=n)\",\n \"def big_selections(lst: List[int], n: int) -> List[List[int]]:\\n if not lst:\\n return [[]]\\n else:\\n holder = [lst.copy()]\\n for i in range(len(lst)):\\n l2 = lst.copy()\\n l2.pop(i)\\n for item in selections(l2):\\n if item not in holder and sum(item) >= n:\\n holder.append(item)\\n return holder\",\n \"def part_2():\\n return itertools.permutations(range(5, 10))\",\n \"def test_combinations(self):\\r\\n self.assertFloatEqual(combinations(5, 3), 10)\\r\\n self.assertFloatEqual(combinations(5, 2), 10)\\r\\n # only one way to pick no items or the same number of items\\r\\n self.assertFloatEqual(combinations(123456789, 0), 1)\\r\\n self.assertFloatEqual(combinations(123456789, 123456789), 1)\\r\\n # n ways to pick one item\\r\\n self.assertFloatEqual(combinations(123456789, 1), 123456789)\\r\\n # n(n-1)/2 ways to pick 2 items\\r\\n self.assertFloatEqual(\\r\\n combinations(\\r\\n 123456789,\\r\\n 2),\\r\\n 123456789 *\\r\\n 123456788 /\\r\\n 2)\\r\\n # check an arbitrary value in R\\r\\n self.assertFloatEqual(combinations(1234567, 12), 2.617073e64)\",\n \"def integer_partitions(n, **kwargs):\\n if 'parts' in kwargs:\\n parts = sorted(kwargs['parts'], reverse=True)\\n custom_parts = True\\n else:\\n parts = range(n, 0, -1)\\n custom_parts = False\\n total_number = len(parts)\\n\\n if 'distinct' in kwargs and kwargs['distinct']:\\n distinct = 1\\n else:\\n distinct = 0\\n\\n if 'num_parts' in kwargs:\\n num_parts = kwargs['num_parts']\\n if num_parts > n:\\n yield []\\n return\\n else:\\n num_parts = 0\\n\\n def algorithm_p(n):\\n \\\"\\\"\\\"\\n Generates all partitions of n. This is Algorithm P from 7.2.1.4 of\\n Knuth, Vol. 4.\\n \\\"\\\"\\\"\\n partition = [0]*n\\n last_replaced = 0\\n partition[last_replaced] = n\\n idx = last_replaced - (n == 1)\\n\\n while True:\\n yield partition[0:last_replaced + 1]\\n if idx < 0:\\n return\\n if partition[idx] == 2:\\n partition[idx] = 1\\n idx -= 1\\n last_replaced += 1\\n partition[last_replaced] = 1\\n else:\\n replacement = partition[idx] - 1\\n partition[idx] = replacement\\n n = last_replaced - idx + 1\\n last_replaced = idx + 1\\n while n > replacement:\\n partition[last_replaced] = replacement\\n last_replaced += 1\\n n -= replacement\\n partition[last_replaced] = n\\n idx = last_replaced - (n == 1)\\n\\n def algorithm_h(n, m):\\n \\\"\\\"\\\"\\n Generates all partitions of n into m parts. This is Algorithm H from\\n 7.2.1.4 of Knuth, Vol. 4.\\n \\\"\\\"\\\"\\n partition = [1]*m\\n partition[0] = n - m + 1\\n\\n while True:\\n yield partition[:]\\n if partition[1] < partition[0] - 1:\\n partition[0] -= 1\\n partition[1] += 1\\n else:\\n j = 2\\n s = partition[0] + partition[1] - 1\\n while j < m and partition[j] >= partition[0] - 1:\\n s += partition[j]\\n j += 1\\n if j >= m:\\n return\\n replacement = partition[j] + 1\\n partition[j] = replacement\\n j -= 1\\n while j > 0:\\n partition[j] = replacement\\n s -= replacement\\n j -= 1\\n partition[0] = s\\n\\n def backtrack(partial_sum, used, num_used, last_idx):\\n if partial_sum == n:\\n if not num_parts or (num_parts and num_used == num_parts):\\n yield used\\n elif partial_sum < n:\\n if num_parts and num_used >= num_parts:\\n return\\n idx = 0\\n if last_idx != 0:\\n idx = last_idx + distinct\\n for i in xrange(idx, total_number):\\n part = parts[i]\\n for partition in backtrack(partial_sum + part,\\n used + [part], num_used + 1, i):\\n yield partition\\n\\n if distinct or custom_parts:\\n partition_gen = backtrack(0, [], 0, 0)\\n elif not distinct and not custom_parts and num_parts != 0:\\n partition_gen = algorithm_h(n, num_parts)\\n else:\\n partition_gen = algorithm_p(n)\\n\\n for partition in partition_gen:\\n yield partition\",\n \"def task3(nums):\\n if len(nums) < 2:\\n return nums\\n pivot = random.choice(nums)\\n l_nums = [n for n in nums if n < pivot]\\n e_nums = [pivot] * nums.count(pivot)\\n r_nums = [n for n in nums if n > pivot]\\n return task3(l_nums) + e_nums + task3(r_nums)\",\n \"def countTriplets3(arr, r):\\n from collections import Counter\\n from math import factorial\\n arr_dict = Counter()\\n n0 = arr[0]\\n\\n # If the ratio 'r' is 1 then this is a special case of combinations\\n if r == 1:\\n for x in arr:\\n if x == n0: arr_dict[x] += 1\\n n = arr_dict[n0]\\n r2 = 3\\n return factorial(n)//(factorial(r2)*factorial(n-r2)), arr_dict\\n\\n # Main variables for the rest of the function\\n max_arr = max(arr)\\n ratio_range = [n0]\\n triplets = 0\\n\\n # Build all possible values\\n index = n0 \\n counter = 0\\n while index < max_arr:\\n index *= r\\n ratio_range.append(index)\\n counter += 1\\n if ratio_range[-1] > max_arr: ratio_range.pop(-1)\\n \\n # Build the counter\\n for x in arr:\\n if x in ratio_range: arr_dict[x] += 1\\n\\n # With the 1 special case removed, there now cannot be triplets if there are not 3 items in the dict\\n if len(arr_dict) < 3: return triplets, arr_dict, ratio_range\\n\\n for y in range(len(ratio_range)-2):\\n firstkey = ratio_range[y]\\n secondkey = ratio_range[y+1]\\n thirdkey = ratio_range[y+2]\\n \\n # If there are no triplets then the loop will exit without incrementing triplets \\n if firstkey not in arr_dict or secondkey not in arr_dict or thirdkey not in arr_dict: \\n continue\\n else:\\n firstvalue = arr_dict[firstkey]\\n secondvalue = arr_dict[secondkey]\\n thirdvalue = arr_dict[thirdkey]\\n \\n triplet_count = (firstvalue) * (secondvalue) * (thirdvalue)\\n triplets += triplet_count\\n\\n return triplets, arr_dict\",\n \"def num_of_ways(n):\\n if n == 0 or n == 1:\\n return 1\\n \\n n_minus_2_step = 1\\n n_minus_1_step = 1\\n n_step = None\\n\\n #num_of_ways(n) = num_of_ways(n-1) + num_of_ways(n-2)\\n for i in range(n-1):\\n n_step = n_minus_1_step + n_minus_2_step\\n n_minus_2_step = n_minus_1_step\\n n_minus_1_step = n_step\\n \\n return n_step\",\n \"def get_n_chains(self): \\n res_id_cnt = 0\\n tot_n_res = len(self.res_ids)\\n n_chns = 0\\n for res_id in self.res_ids:\\n res_chn_i = res_id[2]\\n if res_id_cnt > 1:\\n if res_chn_i == self.res_ids[res_id_cnt-1][2]:\\n pass\\n else:\\n n_chns+=1\\n res_id_cnt+=1\\n return n_chns\",\n \"def combinations(n, k):\\n return factorial(n) / (factorial(k) * factorial(n - k))\",\n \"def combinations_count(n, r):\\n # TODO: How should I do when n - r is negative?\\n if n < 0 or r < 0:\\n raise Exception('combinations_count(n, r) not defined when n or r is negative')\\n if n - r < r: r = n - r\\n if r < 0: return 0\\n if r == 0: return 1\\n if r == 1: return n\\n numerator = [n - r + k + 1 for k in range(r)]\\n denominator = [k + 1 for k in range(r)]\\n for p in range(2,r+1):\\n pivot = denominator[p - 1]\\n if pivot > 1:\\n offset = (n - r) % p\\n for k in range(p-1,r,p):\\n numerator[k - offset] /= pivot\\n denominator[k] /= pivot\\n result = 1\\n for k in range(r):\\n if numerator[k] > 1:\\n result *= int(numerator[k])\\n return result\",\n \"def combinations(n) -> float:\\r\\n c = math.factorial(n) / (math.factorial(2) * math.factorial(n - 2))\\r\\n return c\",\n \"def n_choose_k(N,K):\\n return factorial(N) // (factorial(N - K) * factorial(K))\",\n \"def knapsack(n=4, W=6, items=[[3, 4], [2, 3], [4, 2], [4, 3]]):\\n A = np.zeros(shape=(n+1, W+1))\\n A = calculate_optimal_solution(A, W, items, n)\\n return A\",\n \"def test_select_N_of(self):\\n pop0 = []\\n pop1 = [1]\\n popmany = range(10)\\n self.assertEqual(set(), select_N_of(pop0, 0))\\n self.assertEqual(set(), select_N_of(pop0, 1))\\n self.assertEqual(set(), select_N_of(pop0, 1e7))\\n self.assertEqual(set([]), select_N_of(pop1, 0))\\n self.assertEqual(set([1]), select_N_of(pop1, 1))\\n self.assertEqual(set([1]), select_N_of(pop1, 1e7))\\n self.assertEqual(set([]), select_N_of(popmany, 0))\\n popmany_1 = select_N_of(popmany, 1)\\n self.assertEqual(len(popmany_1), 1)\\n self.assertTrue(popmany_1.pop() in popmany)\\n self.assertEqual(len(select_N_of(popmany, 1e7)), 10)\",\n \"def powerset(n):\\n # chain r-combinations generator for r=0, 1,..., n\\n return chain.from_iterable(combinations(range(n), r) for r in range(n+1))\",\n \"def _pop_random_n(entities: np.array, weights: np.array, count: int = 3):\\n for _ in range(count):\\n if not len(entities):\\n return\\n\\n choice, entities, weights = _pop_random(entities, weights)\\n yield choice\",\n \"def cycles(n, support, randomize=False):\\n support = np.array(support)\\n\\n def gen(p):\\n g = combinations(support, n)\\n if randomize:\\n g = list(g)\\n random.shuffle(g)\\n\\n for local_support in g:\\n for output_p in all_permutations(local_support)(p):\\n yield output_p\\n\\n return gen\",\n \"def test_get_n_bits_combinations():\\n # Check n=1 - Pass\\n assert layer_util.get_n_bits_combinations(1) == [[0], [1]]\\n # Check n=2 - Pass\\n assert layer_util.get_n_bits_combinations(2) == [[0, 0], [0, 1], [1, 0], [1, 1]]\\n\\n # Check n=3 - Pass\\n assert layer_util.get_n_bits_combinations(3) == [\\n [0, 0, 0],\\n [0, 0, 1],\\n [0, 1, 0],\\n [0, 1, 1],\\n [1, 0, 0],\\n [1, 0, 1],\\n [1, 1, 0],\\n [1, 1, 1],\\n ]\",\n \"def get_n_solutions(self, n):\\n return [self.get_solution() for _ in range(n)]\",\n \"def part_1():\\n return itertools.permutations(range(5))\",\n \"def dynamic_programming_solution():\\n num_combos = [1] * (TOTAL + 1)\\n\\n # ignore 1p coin since we are initializing num_combos with that value\\n for coin in COINS[1:]:\\n for total in range(TOTAL + 1):\\n # if coin value is greater than total then num_combos cannot change\\n if coin <= total: \\n num_combos[total] += num_combos[total - coin]\\n\\n return num_combos[TOTAL]\",\n \"def countArrangement(self, n: int) -> int:\\n @lru_cache(None)\\n def dfs(i, remains: Set[int]):\\n if i == n+1:\\n return 1\\n cnt = 0\\n for j in remains:\\n if i%j == 0 or j%i == 0:\\n cnt += dfs(i+1, remains - {j})\\n return cnt\\n\\n return dfs(1, frozenset(range(1, n+1)))\",\n \"def countTriplets2(arr, r):\\n from collections import Counter\\n from math import factorial\\n\\n # If the ratio 'r' is 1 then this is a special case of combinations\\n if r == 1:\\n n = len(arr)\\n r = 3\\n return factorial(n)//(factorial(r)*factorial(n-r))\\n\\n arr_dict = Counter()\\n ratio_range = []\\n index = 0\\n counter = 0\\n triplets = 0\\n \\n # Build the counter\\n for x in arr:\\n arr_dict[x] += 1\\n max_arr_dict = max(arr_dict)\\n\\n # With the 1 special case removed, there now cannot be triplets if there are not 3 items in the dict\\n if len(arr_dict) < 3: return triplets\\n \\n # There is now the potential for triplets so build all possible values\\n while index < max_arr_dict:\\n index = r**counter\\n ratio_range.append(index)\\n counter += 1\\n if ratio_range[-1] > max_arr_dict: ratio_range.pop(-1)\\n\\n for y in range(len(ratio_range)-2):\\n firstkey = ratio_range[y]\\n secondkey = ratio_range[y+1]\\n thirdkey = ratio_range[y+2]\\n \\n # If there are no triplets then the loop will exit without incrementing triplets \\n if firstkey not in arr_dict or secondkey not in arr_dict or thirdkey not in arr_dict: \\n continue\\n else:\\n firstvalue = arr_dict[firstkey]\\n secondvalue = arr_dict[secondkey]\\n thirdvalue = arr_dict[thirdkey]\\n \\n summedvalue = (firstvalue + secondvalue + thirdvalue) - 3\\n triplet_count = 2**summedvalue\\n triplets += triplet_count\\n\\n return triplets\",\n \"def _n_choose_2(n):\\n return (n * (n - 1)) // 2\",\n \"def choose(n,r):\\n \\tc=0\\n \\tif (n>=0) and (0 <= r <= n):\\n\\t \\tc = 1L\\n\\t \\tdenom = 1\\n\\t \\tfor (num,denom) in zip(xrange(n,n-r,-1), xrange(1,r+1,1)):\\n\\t\\t\\tc = (c * num) // denom\",\n \"def choose_m_n(li,min,max):\\n n_items = random.randrange(min,max+1)\\n if n_items == 0:\\n return [ ]\\n sample=random.sample(li,n_items) # Should it be sorted?\\n return sample\",\n \"def nth_permutation(elems, n):\\n pos, summ = 0, 0\\n permutation = \\\"\\\"\\n for i in reversed(range(1,len(elems))):\\n fact_i = fact(i)\\n while summ+fact_i < n:\\n summ += fact_i\\n pos += 1\\n permutation += str(elems[pos])\\n del(elems[pos])\\n pos = 0\\n return permutation + str(elems[0])\",\n \"def random_sampling(elements, n):\\r\\n import random\\r\\n return [random.choice(elements) for i in range(n)]\",\n \"def countTriplets(x, n):\\n x = tuple(x)\\n if not x or not n: return\\n return deep(x, n, 0, None)\",\n \"def take(iterable, n):\\n\\n def taking(iterable_):\\n for i, e in enumerate(iterable_):\\n if i < n:\\n yield e\\n\\n return taking(iterable)\",\n \"def choose(n, k):\\n ans, k = 1, min(k, n-k)\\n for i in range(k):\\n ans *= n-i\\n ans //= i+1\\n return ans\",\n \"def countTriplets4(arr, r):\\n from collections import Counter\\n arr_dict = {}\\n n0 = arr[0]\\n max_arr = max(arr)\\n ratio_range = {n0: 0}\\n triplets = 0\\n\\n # Build all possible values\\n index = n0 \\n counter = 1\\n while index < max_arr:\\n index *= r\\n ratio_range[index] = counter\\n counter += 1\\n if index > max_arr: ratio_range.pop(index)\\n \\n # Remove anything that isn't a possible value and build the dictionary\\n for x in range(len(arr)-1, -1, -1):\\n if arr[x] not in ratio_range: \\n arr.pop(x)\\n continue\\n if arr[x] in arr_dict:\\n arr_dict[arr[x]] = [x] + arr_dict[arr[x]]\\n else:\\n arr_dict[arr[x]] = [x]\\n if len(arr) < 3: return triplets # return 0 if there are not enough items left in arr to make a triplet\\n\\n # Iterate backwards through arr starting at index arr[-2]\\n for n in range(len(arr)-2, -1, -1):\\n item = arr[n]\\n item_before = item // r if item // r in ratio_range else 0 # Set to 0 if the next value in the progression does not appear in the input\\n item_after = item * r if item * r in ratio_range else 0 # Set to 0 if the previous value in the progression does not appear in the input\\n if not item_before or not item_after: continue # Continue in the loop if triplets are not possible with 'item' as 'j'\\n \\n counter_before = sum(1 for x in arr_dict[item_before] if x < n)\\n counter_after = sum(1 for x in arr_dict[item_after] if x > n)\\n triplets += counter_before * counter_after\\n return triplets\",\n \"def countArrangement(self, n: int) -> int:\\n def dfs(i, remains: List[int]):\\n if i == n+1:\\n return 1\\n cnt = 0\\n for j in range(1, n+1):\\n if remains[j] is None and (i%j == 0 or j%i == 0):\\n remains[j] = i\\n cnt += dfs(i+1, remains)\\n remains[j] = None\\n return cnt\\n\\n return dfs(1, [None]*(n+1))\",\n \"def partitions(items: int) -> int:\\n return bell_number(items)\",\n \"def roulette(weights, n):\\n if n > len(weights):\\n raise Exception(\\\"Can't choose {} samples from {} items\\\".format(n, len(weights)))\\n if any(map(lambda w: w <= 0, weights.values())):\\n raise Exception(\\\"The weight can't be a non-positive number.\\\")\\n items = weights.items()\\n chosen = set()\\n for i in range(n):\\n total = sum(list(zip(*items))[1])\\n dice = random.random() * total\\n running_weight = 0\\n chosen_item = None\\n for item, weight in items:\\n if dice < running_weight + weight:\\n chosen_item = item\\n break\\n running_weight += weight\\n chosen.add(chosen_item)\\n items = [(i, w) for (i, w) in items if i != chosen_item]\\n return list(chosen)\",\n \"def permute(n, r):\\n\\n product = 1\\n for i in range(n - r + 1, n + 1):\\n product *= i\\n return product\",\n \"def number_of_trees_of_order(n):\\n if n < 2:\\n return n\\n result = 0\\n for k in range(1, n):\\n result += k * number_of_trees_of_order(k) * _s(n-1, k)\\n return result // (n - 1)\",\n \"def choose(n, a):\\n return torch.as_tensor([a[idx] for idx in torch.randperm(len(a))[:n]])\",\n \"def sixn(m):\\n if m <= 2:\\n return ()\\n if m > 2:\\n yield 2\\n if m > 3:\\n yield 3\\n for n in count(1):\\n x = 6 * n - 1\\n y = x + 2\\n if x < m:\\n yield x\\n else:\\n break\\n if y < m:\\n yield y\\n else:\\n break\",\n \"def solve1(n):\\n for queens in itertools.permutations(range(n)):\\n if is_valid(queens):\\n yield queens\",\n \"def select(individuals, n):\\r\\n # return selBest(individuals, n)\\r\\n return individuals[:n]\",\n \"def n_choose_kv(newK):\\n values = np.zeros((1,newK+1))\\n ks = np.arange(newK+1)\\n \\n for i in range(newK+1):\\n values[i] = scipy.misc.comb(newK, ks[i])\\n\\n return values\",\n \"def chain(n, used, used_list, cmem, fmem):\\n if n in used:\\n i = used_list.index(n)\\n chain_length = len(used_list) - i\\n cmem[n] = chain_length\\n return chain_length, chain_length\\n if n in cmem:\\n return cmem[n], 0\\n next = digits_factorial(n, fmem)\\n used.add(n)\\n used_list.append(n)\\n chain_length, loop = chain(next, used, used_list, cmem, fmem)\\n if loop > 0:\\n loop -= 1\\n cmem[n] = chain_length\\n return chain_length, loop\\n chain_length += 1\\n cmem[n] = chain_length\\n return chain_length, 0\",\n \"def at_least_n_ver(seq, n):\\r\\n num_AB = 0\\r\\n for item in seq:\\r\\n if np.array_equal(item, Quantifier.AB):\\r\\n if num_AB == n-1:\\r\\n return Quantifier.T\\r\\n else:\\r\\n num_AB += 1\\r\\n return Quantifier.F\",\n \"def potential_witnesses(n):\\n if n < 1373653: return [2, 3] # n < 1,373,653\\n if n < 9080191: return [31, 73] # n < 9,080,191\\n if n < 4759123141: return [2, 7, 61] # n < 4,759,123,141\\n if n < 2152302898747: return [2, 3, 5, 7, 11] # n < 2,152,302,898,747\\n if n < 3474749660383: return [2, 3, 5, 7, 11, 13] # n < 3,474,749,660,383\\n if n < 341550071728321: return [2, 3, 5, 7, 11, 13, 17] # n < 341,550,071,728,321\\n return [random.randint(1,n-1) for _ in xrange(0,20)] # 99.999999999909051% (1 - .25**20) accuracy for n >= 341,550,071,728,321\",\n \"def choices(N, M):\\n #yield from itertools.combinations(range(2, M+1), N)\\n # Use stars and bars method\\n allbars = itertools.combinations( range(1,N+M-1), M-2 )\\n for bars in allbars:\\n bars = [0] + list(bars) + [N+M-1]\\n yi = [ y-x for x, y in zip(bars, bars[1:]) ]\\n yield [ y-1 for y in yi ]\",\n \"def get_perms(n):\\n \\n from itertools import permutations\\n bases = 'CATGN'\\n return [''.join(perm) for perm in permutations(bases, n)]\",\n \"def totalSolutions(n:int):\\n\\n memo = [0]*6\\n memo[5] = 1\\n if n == 1:\\n return 1\\n\\n for i in range(n):\\n solutions = sum(memo)\\n memo.pop(0)\\n memo.append(solutions)\\n\\n return memo.pop()\",\n \"def solve(n=100):\\n # Build the board.\\n single_vals = list(range(1, 21)) + [25]\\n double_vals = [2*x for x in single_vals]\\n triple_vals = [3*x for x in range(1, 21)]\\n total_vals = single_vals + double_vals + triple_vals + [0]\\n\\n return sum(1 for other_throws, double_throw\\n in product(combinations_with_replacement(total_vals, 2), double_vals, repeat=1)\\n if sum(other_throws) + double_throw < n)\",\n \"def test_permutation(n: int) -> List[int]:\\n permutation = [i + 1 for i in range(n)]\\n cycle_length = 2\\n cycle_index = 0\\n\\n while cycle_length + cycle_index < n:\\n permutation[cycle_index + cycle_length - 1] = cycle_index\\n cycle_index += cycle_length\\n cycle_length += 1\\n\\n if n > 0:\\n permutation[n - 1] = cycle_index\\n\\n return permutation\",\n \"def permutations(k: int) -> int:\\n return factorial(k)\",\n \"def binary_compositions(n):\\n return productrange(*[2]*(n-1))\",\n \"def nchoosek(n, k):\\n if n < k:\\n return 0\\n return partition(n, [k, n - k])\",\n \"def combinations(s, n):\\n return (\\\"\\\".join(x) for x in tuples(s,n))\",\n \"def knapsack(items, m, n):\\r\\n population = generate_pop(items, population=[])\\r\\n i = 0\\r\\n while i < MAX_ITERATIONS:\\r\\n # sorted population\\r\\n # population = sorted(\\r\\n # population,\\r\\n # key=lambda child: fitness_function(child, m),\\r\\n # reverse=True)\\r\\n\\r\\n # selected parents\\r\\n population = select_parents(population, m)\\r\\n\\r\\n # the best couple of items\\r\\n population = do_crossover(population[:2])\\r\\n\\r\\n # Step 5\\r\\n population = generate_pop(items, population=population)\\r\\n\\r\\n # Step 6:\\r\\n population = mutate_population(population, m)\\r\\n\\r\\n total_value = is_solution(population, m, n)\\r\\n if total_value:\\r\\n print('Found solution: {}'.format(population[0]))\\r\\n return total_value\\r\\n\\r\\n print('No solution found...')\\r\\n return False\",\n \"def unique_climbs(N, X={1, 2}):\\n result = 0\\n\\n if N == 0:\\n return 1\\n if N < 0:\\n return 0\\n\\n for cnt in X:\\n result += unique_climbs(N-cnt, X)\\n\\n return result\",\n \"def choose(n, k):\\n # http://stackoverflow.com/a/3025547/313967\\n if 0 <= k <= n:\\n ntok = 1\\n ktok = 1\\n for t in xrange(1, min(k, n - k) + 1):\\n ntok *= n\\n ktok *= t\\n n -= 1\\n return ntok // ktok\\n else:\\n return 0\",\n \"def get_num_pairs(seq):\\n n = len(seq)\\n return int(n * (n-1)/2) # sum of arphmetic progression (n-1)...1\",\n \"def buyable(n: int) -> bool:\\n if n in [4, 6, 25]:\\n return True\\n elif n < 4:\\n return False\\n else:\\n buyability = False\\n for size in [4, 6, 25]:\\n buyability |= buyable(n - size)\\n return buyability\",\n \"def generate_objects_of_size(\\n self, n: int, **parameters: int\\n ) -> Iterator[CombinatorialObjectType]:\",\n \"def greedy_partition(counts, n):\\r\\n\\r\\n buckets = [[] for i in range(n)]\\r\\n fill_levels = [0 for i in range(n)]\\r\\n\\r\\n for key in sorted(counts, reverse=True,\\r\\n key=lambda c: counts[c]):\\r\\n smallest = fill_levels.index(min(fill_levels))\\r\\n buckets[smallest].append(key)\\r\\n fill_levels[smallest] += counts[key]\\r\\n\\r\\n return buckets, fill_levels\",\n \"def solve(self):\\n\\n n = 1\\n\\n # Get the n-length combinations (if any) that can accommodate the\\n # defined load.\\n combos_n = self.fit_n_bins(n=n)\\n\\n while True:\\n # Get the n+1-length combinations (if any) that can accommodate the\\n # defined load.\\n combos_np1 = self.fit_n_bins(n=n + 1)\\n\\n # If no viable combinations were found for both n and n + 1 lengths\\n # the increment the defined length, swap the combinations and skip\\n # to the next iteration\\n if not combos_n or not combos_np1:\\n combos_n = combos_np1\\n n += 1\\n continue\\n\\n # if the n + 1 length combinations happen to offer decreased\\n # capacity then prefer those combinations over the shorter yet more\\n # wasteful combinations\\n if sum(combos_n[0]) > sum(combos_np1[0]):\\n return combos_np1\\n else:\\n return combos_n\",\n \"def main(size, item, answer):\\n item = list(map(int, item))\\n item.sort()\\n for number in item:\\n if size - number >= 0:\\n size -= number\\n answer += 1\\n print(answer)\",\n \"def knapsack(items, capacity):\\r\\n pass\",\n \"def choose(n, k):\\n if 0 <= k <= n:\\n ntok = 1\\n ktok = 1\\n for t in xrange(1, min(k, n - k) + 1):\\n ntok *= n\\n ktok *= t\\n n -= 1\\n return ntok // ktok\\n else:\\n return 0\",\n \"def choose(n, k):\\n if 0 <= k <= n:\\n ntok = 1\\n ktok = 1\\n for t in xrange(1, min(k, n - k) + 1):\\n ntok *= n\\n ktok *= t\\n n -= 1\\n return ntok // ktok\\n else:\\n return 0\",\n \"def solve(n=10):\\n return sum(M_N_S(n, d)[2] for d in range(10))\",\n \"def nCr(\\n n: int,\\n r: int = 0,\\n l: list = [],\\n ) -> int:\\n\\n # Initialisations\\n c = 0\\n\\n # Check if the list is not empty\\n if l != []:\\n\\n # Loop through the list\\n for i in l:\\n\\n # Cumulatively calculate the n Choose r value for each list item\\n c += comb(n, i, exact = True)\\n\\n # Check if r is not zero\\n elif r != 0:\\n\\n # Calculate the n Choose r value\\n c = comb(n, r, exact = True)\\n\\n else:\\n\\n # Loop through all possible r\\n for i in range(0, n):\\n\\n # Cumulatively calculate the n Choose r value\\n c += comb(n, i, exact = True)\\n\\n return c\",\n \"def windows_of_permutations(n, step):\\n def gen(p):\\n for i in range(0, NB_AVIONS-n, step):\\n for perm in all_permutations(range(i, i+n))(p):\\n yield perm\\n return gen\",\n \"def get_witnesses(n):\\n assert (n > 2) and (n % 2 == 1)\\n if n < 2047:\\n # References: [1], [2], [4]\\n witnesses = (2,)\\n elif n < 1373653: # ~1.3 million\\n # References: [1], [2], [3], [4]\\n witnesses = (2, 3)\\n elif n < 9080191: # ~9.0 million\\n # References: [3], [4]\\n witnesses = (31, 73)\\n elif n < 25326001: # ~25.3 million\\n # References: [1], [2], [3], [4]\\n witnesses = (2, 3, 5)\\n elif n < 3215031751: # ~3.2 billion\\n # References: [1], [2], [3], [4]\\n witnesses = (2, 3, 5, 7)\\n elif n < 4759123141: # ~4.7 billion\\n # References: [3], [4]\\n witnesses = (2, 7, 61)\\n elif n < 2152302898747: # ~2.1 trillion\\n # References: [1], [2], [3], [4]\\n witnesses = (2, 3, 5, 7, 11)\\n elif n < 3474749660383: # ~3.4 trillion\\n # References: [1], [2], [3], [4]\\n witnesses = (2, 3, 5, 7, 11, 13)\\n elif n < 341550071728321: # ~341.5 trillion\\n # References: [1], [2], [3], [4]\\n witnesses = (2, 3, 5, 7, 11, 13, 17)\\n elif n < 3825123056546413051: # ~3.8 million trillion\\n # References: [1], [4]\\n witnesses = (2, 3, 5, 7, 11, 13, 17, 19, 23)\\n elif n <= 2**64:\\n witnesses = (2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37)\\n else:\\n witnesses = None\\n \\n return witnesses\",\n \"def generate_n(k_problem, n):\\n return [generate_first_random(k_problem) for i in range(n)]\",\n \"def possibleTuples(nDifferentNodes):\\n numberOfPossibleTuples = {}\\n for nodeName, numberOfDifferent in nDifferentNodes.iteritems():\\n numberOfPossibleTuples[nodeName] = sum([comb(numberOfDifferent,i,exact=True)for i in range(0,4)])\\n return numberOfPossibleTuples\",\n \"def generate_products(n=30):\\n products = []\\n for i in range(n):\\n adj = choice(ADJECTIVES)\\n item = choice(ITEMS)\\n name = f'{adj} {item}'\\n product = Product(name,\\n randint(5,101),\\n randint(5,101),\\n uniform(0,2.5),\\n randint(100000,1000000))\\n products.append(product)\\n return products\",\n \"def calculate_image_possibilities():\\n\\n # Reordering the color ramps in the palette yields 3! combinations\\n palette_reorder_possibilities = 6\\n\\n return len(palettes) * palette_reorder_possibilities * len(grips) * len(pommels) * len(crossguards) * len(blades)\",\n \"def subsets(n):\\n binary = lambda x: x>0 and binary(x>>1) + [x&1] or []\\n pad = lambda l: [0]*(n-len(l)) + l #Always returns a list of length 'n'\\n return [pad(binary(i)) for i in range(1, 2**n)]\",\n \"def get_max_combination(total_cuts):\\n max_pieces = 0\\n for i in range(total_cuts):\\n result = i * (total_cuts - i)\\n if result > max_pieces:\\n max_pieces = result\\n print(max_pieces)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72152823","0.6937321","0.6853795","0.67474264","0.67017275","0.6627513","0.6490569","0.64309037","0.6399863","0.6383735","0.6378631","0.63440573","0.63363504","0.6292473","0.6291534","0.6279435","0.62197953","0.6189228","0.6186623","0.61849743","0.6137126","0.61306256","0.611709","0.6107852","0.6098089","0.6094521","0.6092483","0.6083829","0.60807353","0.6070493","0.60700405","0.6067269","0.60413504","0.6039713","0.6028433","0.60245806","0.6005966","0.600576","0.59985644","0.59888804","0.59823066","0.597868","0.5978458","0.59741104","0.5951501","0.5938304","0.59267795","0.5922852","0.5920776","0.5918624","0.5915689","0.590636","0.5898082","0.58970666","0.58803624","0.58750945","0.5868389","0.58649796","0.58508146","0.58492976","0.5842562","0.5842253","0.58284986","0.5828101","0.58186924","0.58185726","0.58177596","0.5810799","0.5805744","0.5805538","0.57886106","0.5783833","0.57822794","0.57773745","0.5773033","0.5771499","0.5770422","0.5764172","0.57629126","0.57559067","0.5739762","0.5737059","0.5733379","0.57277423","0.57245183","0.5724095","0.5712108","0.5708344","0.5706674","0.57005894","0.57005894","0.57001686","0.5698051","0.5696326","0.568986","0.56850284","0.56830966","0.56756794","0.5673525","0.56721103","0.56706434"],"string":"[\n \"0.72152823\",\n \"0.6937321\",\n \"0.6853795\",\n \"0.67474264\",\n \"0.67017275\",\n \"0.6627513\",\n \"0.6490569\",\n \"0.64309037\",\n \"0.6399863\",\n \"0.6383735\",\n \"0.6378631\",\n \"0.63440573\",\n \"0.63363504\",\n \"0.6292473\",\n \"0.6291534\",\n \"0.6279435\",\n \"0.62197953\",\n \"0.6189228\",\n \"0.6186623\",\n \"0.61849743\",\n \"0.6137126\",\n \"0.61306256\",\n \"0.611709\",\n \"0.6107852\",\n \"0.6098089\",\n \"0.6094521\",\n \"0.6092483\",\n \"0.6083829\",\n \"0.60807353\",\n \"0.6070493\",\n \"0.60700405\",\n \"0.6067269\",\n \"0.60413504\",\n \"0.6039713\",\n \"0.6028433\",\n \"0.60245806\",\n \"0.6005966\",\n \"0.600576\",\n \"0.59985644\",\n \"0.59888804\",\n \"0.59823066\",\n \"0.597868\",\n \"0.5978458\",\n \"0.59741104\",\n \"0.5951501\",\n \"0.5938304\",\n \"0.59267795\",\n \"0.5922852\",\n \"0.5920776\",\n \"0.5918624\",\n \"0.5915689\",\n \"0.590636\",\n \"0.5898082\",\n \"0.58970666\",\n \"0.58803624\",\n \"0.58750945\",\n \"0.5868389\",\n \"0.58649796\",\n \"0.58508146\",\n \"0.58492976\",\n \"0.5842562\",\n \"0.5842253\",\n \"0.58284986\",\n \"0.5828101\",\n \"0.58186924\",\n \"0.58185726\",\n \"0.58177596\",\n \"0.5810799\",\n \"0.5805744\",\n \"0.5805538\",\n \"0.57886106\",\n \"0.5783833\",\n \"0.57822794\",\n \"0.57773745\",\n \"0.5773033\",\n \"0.5771499\",\n \"0.5770422\",\n \"0.5764172\",\n \"0.57629126\",\n \"0.57559067\",\n \"0.5739762\",\n \"0.5737059\",\n \"0.5733379\",\n \"0.57277423\",\n \"0.57245183\",\n \"0.5724095\",\n \"0.5712108\",\n \"0.5708344\",\n \"0.5706674\",\n \"0.57005894\",\n \"0.57005894\",\n \"0.57001686\",\n \"0.5698051\",\n \"0.5696326\",\n \"0.568986\",\n \"0.56850284\",\n \"0.56830966\",\n \"0.56756794\",\n \"0.5673525\",\n \"0.56721103\",\n \"0.56706434\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":25294,"cells":{"query":{"kind":"string","value":"Move a die through a list of positions."},"document":{"kind":"string","value":"def move(self, *positions, show_length=True) -> str:\n move_parts = []\n move_count = len(positions)\n pips = prev_x = prev_y = 0\n for i, (x, y) in enumerate(positions):\n if i == 0:\n pips = self.dice.pop((x, y))\n else:\n dx = x - prev_x\n dy = y - prev_y\n if 0 < dx:\n move_part = f'R{dx}'\n elif dx < 0:\n move_part = f'L{-dx}'\n elif 0 < dy:\n move_part = f'U{dy}'\n else:\n move_part = f'D{-dy}'\n if not show_length:\n move_part = move_part[0]\n if i == 1:\n move_part = f'{pips}{move_part}'\n if i == move_count - 1:\n self.dice[x, y] = pips\n move_parts.append(move_part)\n prev_x = x\n prev_y = y\n return ''.join(move_parts)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def throw(self, move):\n for dice_index in move:\n self.dice[dice_index - 1] = random.randint(1,6)","def move_tie_fighters(self):\n for i in range(len(self.tie_fighters)):\n self.tie_fighters[i].move_tie_fighter()","def migration(self):\n\n coordinates = self.get_random_coordinates()\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cell_move_herbivores(coordinate)\n self.cell_move_carnivores(coordinate)\n\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cells[coordinate].move_new_animals()","def move(self, direction, cycles):\n\t\tpass","def move(self, direction: str) -> int:\n (i, j), _ = self.position.popitem(last=True) # current position\n self.position[(i, j)] = 0 # add back \n if direction == \"U\": i -= 1\n elif direction == \"L\": j -= 1\n elif direction == \"R\": j += 1\n else: i += 1\n if self.food and self.food[0] == [i, j]: self.food.popleft()\n else: self.position.popitem(last=False)\n if not (0 <= i < self.height and 0 <= j < self.width) or (i, j) in self.position: return -1 # game over \n self.position[(i, j)] = 0\n return len(self.position)-1","def roll_the_dice(self, dice):\n if type(dice) == list:\n for die in dice:\n die.roll()","def move(self):\n for agent in self.agents:\n if not agent.fidelity:\n options = agent.get_move_options(agent.hex, self.kernel_size, None, extend=True)\n target = random36.choices(population=options,weights=[x.quality**2 for x in options])\n agent.move(target[0])","def one_player_leg(length, pos):\n throw = dice()\n moves = []\n while throw == 6:\n if pos + throw < length:\n pos += throw\n moves.append(throw)\n elif pos + throw == length:\n pos += throw\n moves.append(throw)\n return pos, moves\n else:\n moves.append(throw)\n throw = dice()\n else:\n if pos + throw < length:\n pos += throw\n moves.append(throw)\n elif pos + throw == length:\n pos += throw\n moves.append(throw)\n return pos, moves\n else:\n moves.append(throw)\n return pos, moves","def roll_dices(self):\n dice1 = random.randint(1, 6)\n dice2 = random.randint(1, 6)\n\n self.client.send_player_end_dices()\n self.game.player_rolled_dices([dice1, dice2])\n asyncio.ensure_future(self.move(dice1 + dice2))","def test_move():\n human = Human()\n coordinates = [2, 1]\n dimensions = [3, 4]\n\n new_coordinates = human.move(coordinates, dimensions)\n\n possible_new_coordinates = [[2, 0], [3, 0], [3, 1], [3, 2], [2, 2], [1, 2], [1, 1], [1, 0]]\n\n assert new_coordinates in possible_new_coordinates","def move(self, direction):\n head = self.snake[0]\n delta = self.dirs[direction]\n nextMove = [head[0] + delta[0], head[1] + delta[1]]\n if not self.isValidMove(nextMove):\n return -1\n\n if self.food and nextMove == self.food[0]:\n self.food.popleft()\n else:\n self.snake.pop()\n\n self.snake.appendleft(nextMove)\n\n return len(self.snake) - 1","def move(self, direction=0):\n moves = [\n [3, 1],\n [2, 2],\n [1, 3],\n [0, 0]\n ]\n\n self._rotate(moves[direction][0])\n\n for row in range(4):\n r = [i for i in self.map[row] if i != 0]\n\n r_result = []\n while(len(r)):\n num = r.pop(0)\n if len(r) and num == r[0]:\n num += r.pop(0)\n # TODO: Do a 2048 check here to see if the player won?\n # this might not be the best place because we could use\n # this method to run tests to see if the player has any valid moves\n r_result.append(num)\n \n self.map[row] = r_result + [0]*(4-len(r_result))\n\n self._add_random_piece()\n\n self._rotate(moves[direction][1])\n self.print_map()","def eat(bots, food_list):\n \n # iterates through bot positions\n for bot in bots:\n # iterates through food positions\n for food_loc in food_list:\n # compare bot and food positions\n first_pos = bot.position[0] == food_loc[0]\n second_pos = bot.position[1] == food_loc[1]\n \n # if food and bot in same spot, food is removed\n if first_pos and second_pos:\n food_list.remove(food_loc)","def move(self, coordinates, direction):\n pass","def move(t, length):\n pu(t)\n\t\n fd(t, length)\n pd(t)","def move(exits, direction):\r\n return rooms[exits[direction]]","def actions_turn(positions, player1, player2, creatures):\n\n attacking = []\n moving = []\n\n # Split the orders within attacks and movements\n for character in positions:\n if character in creatures:\n if positions[character]['order'] == 'attack':\n attacking += character\n else:\n moving += character\n elif character in player1:\n if positions[character]['order'] == 'attack':\n attacking += character\n else:\n moving += character\n\n else:\n if positions[character]['order'] == 'attack':\n attacking += character\n else:\n moving += character\n\n # Execute the attacks\n for character in attacking:\n # First attacking : the creatures\n if character in creatures:\n hero_name = character\n name_attack = positions[character]['name_attack']\n attack_coord = positions[character]['where']\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\n # Then the heroes of the first player\n elif character in player1:\n hero_name = character\n name_attack = positions[character]['name_attack']\n attack_coord = positions[character]['where']\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\n # Finally the ones of the second player\n else:\n hero_name = character\n name_attack = positions[character]['name_attack']\n attack_coord = positions[character]['where']\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\n\n for character in moving:\n print(character, moving)\n # First moving : the creatures\n if character in creatures:\n hero_name = character\n movement_coord = positions[character]['where']\n move(hero_name, positions, movement_coord)\n\n # Then the heroes of the first player\n elif character in player1:\n hero_name = character\n movement_coord = positions[character]['where']\n move(hero_name, positions, movement_coord)\n\n # Finally the ones of the second player\n else:\n hero_name = character\n movement_coord = positions[character]['where']\n move(hero_name, positions, movement_coord)\n\n return player1, player2, positions, creatures","def move(self):\n if self.adjustment > 0:\n # The player moves up a ladder\n old_position = self.position\n # In the next move, player on top of ladder\n\n self.position = old_position - self.dropped_steps\n # player drops steps\n super().move()\n # player move\n die = self.position - old_position + self.dropped_steps - \\\n self.adjustment\n \"\"\" \n the current position of the player is: old position - \n dropped_steps + die + adjustment. Rearranging to find the die.\n \n \"\"\"\n if die < self.dropped_steps:\n self.position = old_position\n # if die is less than dropped_steps, the player will stand still.\n else:\n super().move()\n # if not climbling a ladder, then the player make a regular move","def temp_swap_dice(self, move, new_faces):\n assert len(move) == len(new_faces)\n output = list(self.dice)\n for face in move:\n output.remove(face)\n for face in new_faces:\n output.append(face)\n return tuple(sorted(output))","def find_moveable_pieces(self, die, p1): \n moveable = []\n if (p1):\n #must we re-enter?\n if (self.p1vec[0] > 0):\n if (self.free_spot(0, die, p1)):\n b = Board(self.p1vec[:],self.p2vec[:])\n b.move(0, die, p1)\n moveable.append(b)\n #no? ok then generate the moves\n else:\n for i in range(1, 25):\n if (self.p1vec[i] > 0):\n if (self.free_spot(i, die, p1)):\n b = Board(self.p1vec[:],self.p2vec[:])\n b.move(i, die, p1)\n moveable.append(b)\n else:\n #must we re-enter?\n if (self.p2vec[0] > 0):\n if (self.free_spot(0, die, p1)):\n b = Board(self.p1vec[:],self.p2vec[:])\n b.move(0, die, p1)\n moveable.append(b)\n #no? ok then generate the moves\n else:\n for i in range(1, 25):\n if (self.p2vec[i] > 0):\n if (self.free_spot(i, die, p1)):\n b = Board(self.p1vec[:],self.p2vec[:])\n b.move(i, die, p1)\n moveable.append(b)\n return moveable","def on_die(self, head_position, board, score, body_parts):","def move(self,move):\n for x in range(len(self.coord)):\n self.coord[x] = np.array([y+np.array(move) for y in self.coord[x]])\n return self","def DoMove(position, move):\n return position - move","def move(self, direction):\n # replace with your code\n\n indices = self.direction_indices[direction]\n for coordinate in indices:\n merged_coordinate_list = self.get_list(direction, coordinate)\n self.change_board(merged_coordinate_list, coordinate, direction)\n print(self.__str__())\n if self.board_is_not_full():\n self.new_tile()","def move(self):\r\n segments = len(self.all_turtles) - 1\r\n for i in range(len(self.all_turtles)):\r\n if segments == 0:\r\n self.all_turtles[segments].forward(MOVE_DISTANCE)\r\n else:\r\n new_x = self.all_turtles[segments - 1].xcor()\r\n new_y = self.all_turtles[segments - 1].ycor()\r\n self.all_turtles[segments].goto(new_x, new_y)\r\n segments -= 1","def postion_fleet(self, ships_positions, board):\n for cell in ships_positions:\n row = ord(cell[:1]) - ord('A')\n col = int(cell[1:]) - 1\n for i in range(row, row + self.total_rows_req):\n for j in range(col, col + self.total_column_req):\n self.position_ship(\n board.grid[i][j],\n constants.FLEET_P_CLASS,\n constants.P_CLASS_HIT_POWER\n )","def move(self):\n assert self.is_alive, \"Sprite is dead, and should not be able to move\"\n if self.health > 3:\n self.y += random.randint(-1, 1) # change by -1, 0, 1\n self.x += random.randint(-1, 1) # change by -1, 0, 1\n print(self.name, \"moves to position\", str(self.x), \",\", str(self.y))","def move_all_animals(self):\n\n y_lim, x_lim = np.shape(self.map)\n for y in range(y_lim):\n for x in range(x_lim):\n loc = y, x\n self.map[loc].migration(self.get_neighbour((y, x)))","def postion_fleet(self, ships_positions, board):\n for cell in ships_positions:\n row = ord(cell[:1]) - ord('A')\n col = int(cell[1:]) - 1\n for i in range(row, row + self.total_rows_req):\n for j in range(col, col + self.total_column_req):\n self.position_ship(\n board.grid[i][j],\n constants.FLEET_Q_CLASS,\n constants.Q_CLASS_HIT_POWER\n )","def move(self):\n # neighbor offsets\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\n for i in range(len(offset)):\n x = self.x + offset[i][0] # neighboring coordinates\n y = self.y + offset[i][1]\n if self.island.animal(x, y) == 0: # neighboring spot is open\n self.island.remove(self) # remove from current spot\n self.x = x # new coordinates\n self.y = y\n self.island.register(self) # register new coordinates\n break # finished with move","def move(self):\n # neighbor offsets\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\n for i in range(len(offset)):\n x = self.x + offset[i][0] # neighboring coordinates\n y = self.y + offset[i][1]\n if self.island.animal(x, y) == 0: # neighboring spot is open\n self.island.remove(self) # remove from current spot\n self.x = x # new coordinates\n self.y = y\n self.island.register(self) # register new coordinates\n break # finished with move","def move(camel_dict, tiles_dict, camel, roll):\n camel_space = camel_dict[camel][\"space\"]\n camel_height = camel_dict[camel][\"height\"]\n min_height_movers, num_movers, movers = camels_to_move(\n camel_dict, camel_space, camel_height\n )\n destination = camel_space + roll\n block = False\n give_points = None\n if destination in tiles_dict.keys():\n if tiles_dict[destination][\"tile_type\"] == \"skip\":\n give_points = destination\n destination += 1\n elif tiles_dict[destination][\"tile_type\"] == \"block\":\n give_points = destination\n destination -= 1\n block = True\n max_height_dest, destinationers = camels_in_dest(camel_dict, destination)\n if block:\n block_move(\n camel_dict,\n movers,\n destinationers,\n destination,\n min_height_movers,\n num_movers,\n )\n else:\n reg_move(camel_dict, movers, destination, min_height_movers, max_height_dest)\n return give_points","def cpu_move(moves_list):\n \n move = randint(0,3)\n return move","def AeroMove(self, pos):\r\n\r\n pass","def move(self):\n self._move_range_shuffle(3)\n self._move_satisfy_random_constraint()\n # self._move_range_shuffle(3)\n #if (curr_energy > 50):\n # self._move_satisfy_random_constraint()\n #else:\n # self._move_range_shuffle(3)","def move(self, pos_to_move):\n if (self.current_pos + 1) - pos_to_move == 1: #Move to the left\n direction = 4\n elif (self.current_pos + 1) - pos_to_move == -1: #Move to the right\n direction = 2\n elif (self.current_pos + 1) - pos_to_move == 5: #Move to the top\n direction = 1\n else: #Move to the bottom\n direction = 3\n return [MOVE, direction]","def move_humans(self, zombie_distance):\n updated_human_list = []\n for human in self.humans():\n # find the largest distance in 8 neighbor cells\n max_distance = 0\n max_dist_cell = [(human[0], human[1])]\n # loop through neighbor cells\n for cell in self.eight_neighbors(human[0], human[1]):\n if self.is_empty(cell[0], cell[1]):\n distance = zombie_distance[cell[0]][cell[1]]\n if distance == max_distance:\n max_dist_cell.append(cell)\n elif distance > max_distance:\n max_dist_cell = [cell]\n max_distance = distance\n # decide which cell to move to\n updated_human_list.append(max_dist_cell[random.randrange(len(max_dist_cell))])\n self._human_list = updated_human_list","def move_fish_list(fishes: FishList, width: int):\n if fishes is not None:\n move_swimming_fish(fishes.first, width)\n move_fish_list(fishes.rest, width)","def move(degs, i, j, n):\n if n > 0:\n temp = 3 - i - j\n move(degs, i, temp, n - 1) \n degs[j].append(degs[i].pop(-1))\n print(degs)\n move(degs, temp, j, n - 1)","def turn(self):\r\n # 1 throw the dice\r\n if (\r\n not self.player_list[self.current_player].is_in_jail()\r\n or self.try_leave_jail()\r\n ):\r\n thr = Throw()\r\n while thr is not None:\r\n # check in where the current player will land\r\n new_position = self.compute_new_position_from_dice(\r\n self.current_player, thr\r\n )\r\n self.move_player_to(self.current_player, new_position, thr=thr)\r\n\r\n if thr.is_double():\r\n thr = Throw()\r\n else:\r\n thr = None\r\n print(\"------------------------------\")\r\n\r\n self.player_observable_variables()\r\n\r\n # move turn to next player\r\n self.current_player += 1\r\n if self.current_player >= len(self.player_list):\r\n self.current_player = 0\r\n self.full_turn_count += 1\r\n print(\"**********************\")\r\n print(\r\n \"Full turn:\",\r\n self.full_turn_count,\r\n \"\\n\",\r\n \"\\n\".join(map(lambda x: x.full(), self.player_list)),\r\n )\r\n print(\"**********************\")","def move2(self):\n\n options = self.location.exits.keys()\n for key in options:\n if self.location.exits[key] == p.location:\n self.location.objects.remove(a)\n self.location = p.location\n self.location.objects.append(a)\n print('fred entered the room')\n self.attack(['attack', str(p.name)])\n break\n else:\n self.move1()","def move(x,y):\r\n pass","def step(self, move):","def move(self, direction):\r\n # replace with your code\r\n row_dir = OFFSETS[direction][0]\r\n col_dir = OFFSETS[direction][1]\r\n \r\n if row_dir == 0:\r\n new_cells = self._cells\r\n new_dir = col_dir\r\n else:\r\n new_tuples = zip(*self._cells)\r\n new_cells = [list(item) for item in new_tuples]\r\n new_dir = row_dir\r\n \r\n tmp_cells = []\r\n for lists in new_cells:\r\n lists = lists[::new_dir]\r\n merge_lists = merge(lists)\r\n tmp_cells.append(merge_lists[::new_dir])\r\n \r\n if row_dir == 0:\r\n self._cells = tmp_cells\r\n else:\r\n new_tuples = zip(*tmp_cells)\r\n new_cells = [list(item) for item in new_tuples]\r\n self._cells = new_cells\r\n \r\n self.new_tile()","def move(self, direction: str) -> int:\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\n # print(x,y)\n # went out bound\n if x >= self.n or y >= self.m or x < 0 or y < 0:\n return -1\n tail = self.snake.popleft()\n self.snake_set.discard(tail)\n if (x, y) in self.snake_set:\n return -1\n\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\n self.food.popleft()\n self.snake.appendleft(tail)\n self.snake_set.add(tail)\n self.snake.append((x, y))\n self.snake_set.add((x, y))\n\n return len(self.snake) - 1","def move(self):\n\n choices = []\n if self.game.green_apples > 0:\n choices.append(self.game.move_green)\n if self.game.red_apples > 0:\n choices.append(self.game.move_red)\n if self.game.blue_plums > 0:\n choices.append(self.game.move_blue)\n if self.game.yellow_pears > 0:\n choices.append(self.game.move_yellow)\n\n random_index = random.randint(0, len(choices) - 1)\n f = choices[random_index]\n f(True)","def move1(self):\n\n options = self.location.exits.keys()\n self.location.objects.remove(a)\n print('fred is moving..')\n self.location = self.location.exits[random.choice(list(options))]\n self.location.objects.append(a)","def step(self, crowd):\n\n for boid in crowd:\n random_int = random.randint(0, 5)\n\n # if random_int > 4:\n # random_int = random.randint(0, 5)\n # if random_int > 4:\n # for i in range (1, 500):\n # goalX, goalY = self.goals[boid.goalNr]\n # x, y = boid.position\n\n # if (goalX + 10 >= x >= goalX - 10) and (goalY + 10 >= y >= goalY - 10):\n # boid.reached_goal(goalX + 10, goalY + 10)\n\n # dx = random.randint(0, self.width) - x\n # dy = random.randint(0, self.height) - y\n\n # # Unit vector in the same direction\n # distance = math.sqrt(dx * dx + dy * dy)\n # dx /= distance\n # dy /= distance\n\n # # And now we move:\n # x += dx\n # y += dy\n\n # boid.set_goal(dx, dy)\n\n # boid.position += boid.velocity\n #else:\n # boid.position += boid.velocity\n \n # Vector from me to cursor\n\n\n goalX, goalY = self.goals[boid.goalNr]\n x, y = boid.position\n\n if (goalX + 10 >= x >= goalX - 10) and (goalY + 10 >= y >= goalY - 10):\n boid.reached_goal(goalX + 10, goalY + 10)\n\n else:\n dx = goalX - x\n dy = goalY - y\n\n # Unit vector in the same direction\n # distance = np.linalg.norm(dx * dx + dy * dy)\n distance = math.sqrt(dx * dx + dy * dy)\n dx /= distance\n dy /= distance\n\n # And now we move:\n x += dx\n y += dy\n\n boid.set_goal(dx, dy)\n\n boid.position += boid.velocity","def execute_move(self, game_state):\n # Set new location based on which ghost this is\n game_state.ghosts_pos[self.ghost_id] = self.next_move","def execute_move(self, game_state):\n # Set new location based on which ghost this is\n game_state.ghosts_pos[self.ghost_id] = self.next_move","def move(self, direction: str) -> int:\n # O(1) per move\n\n cur_pos = self.positions[-1]\n\n move = self.moves[direction]\n new_pos = cur_pos[0] + move[0], cur_pos[1] + move[1]\n\n if new_pos[0] == self.height or new_pos[0] == -1 or new_pos[1] == self.width or new_pos[1] == -1 or (new_pos in self.positions_set and new_pos != self.positions[0]):\n return -1\n\n self.positions.append(new_pos)\n self.positions_set.add(new_pos)\n\n if self.eaten < len(self.food) and new_pos == self.food[self.eaten]:\n self.eaten += 1\n else:\n tail = self.positions.popleft()\n\n if tail != self.positions[-1]:\n self.positions_set.remove(tail)\n\n return self.eaten","def mass_move(self,\r\n entrylist1,\r\n entrylist2):\r\n\r\n\r\n if len(entrylist1) > len(entrylist2):\r\n entrylist2 = entrylist2+list(range(entrylist2[-1]+1,\r\n (entrylist2[-1]\r\n +len(entrylist1)\r\n -len(entrylist2))))\r\n lastindexto = entrylist2[0]\r\n\r\n e1 = iter(entrylist1)\r\n e2 = iter(entrylist2)\r\n gofurther = True\r\n\r\n while gofurther:\r\n try:\r\n indexfrom = next(e1)\r\n except StopIteration:\r\n indexfrom = StopIteration\r\n try:\r\n indexto = self.find_space(next(e2), entrylist2)\r\n except StopIteration:\r\n indexto = StopIteration\r\n if indexto != StopIteration:\r\n lastindexto = indexto\r\n if indexto == StopIteration:\r\n indexto = self.find_space(lastindexto)\r\n\r\n if indexfrom != StopIteration:\r\n self.display_buffer.append(alerts.MOVING_FROM\r\n +index_reduce(str(indexfrom))\r\n +queries.TOTO+str(indexto))\r\n self.move(indexfrom, indexto)\r\n else:\r\n gofurther = False","def get_steps_from_position_list(position_list):\n\n step_list = []\n\n for i in range(len(position_list) - 1):\n current_position = position_list[i]\n next_position = position_list[i+1]\n if (current_position.x == next_position.x and \n current_position.y == next_position.y):\n continue\n\n #Cardinal directions\n if (next_position.x - current_position.x == 1 and \n next_position.y - current_position.y == 0):\n next_action = \"right\"\n elif (next_position.x - current_position.x == -1 and \n next_position.y - current_position.y == 0):\n next_action = \"left\"\n elif (next_position.x - current_position.x == 0 and \n next_position.y - current_position.y == 1):\n next_action = \"up\"\n elif (next_position.x - current_position.x == 0 and \n next_position.y - current_position.y == -1):\n next_action = \"down\"\n\n #Extended directions\n elif (next_position.x - current_position.x == 1 and \n next_position.y - current_position.y == 1):\n next_action = \"up_right\"\n elif (next_position.x - current_position.x == -1 and \n next_position.y - current_position.y == 1):\n next_action = \"up_left\"\n elif (next_position.x - current_position.x == -1 and \n next_position.y - current_position.y == -1):\n next_action = \"down_left\"\n elif (next_position.x - current_position.x == 1 and \n next_position.y - current_position.y == -1):\n next_action = \"down_right\"\n next_step = Step(current_position, next_action)\n step_list.append(next_step)\n \n if len(step_list) == 0:\n next_step = Step(position_list[0], \"stay\")\n step_list.append(next_step)\n\n return step_list","def move_humans(self, zombie_distance_field):\r\n blocked = self.get_grid_height() * self.get_grid_width() #getting the distance value of obstacles\r\n new_positions = []\r\n for human in self.humans(): #calculate move for each human\r\n moves = self.eight_neighbors(human[0], human[1]) #getting list of up to 8 possible moves\r\n moves.append((human[0], human[1]))\r\n potential_moves = []\r\n distance = zombie_distance_field[human[0]][human[1]]\r\n for move in moves: #storing potential move if the distance is the max but not that of an obstacle\r\n if zombie_distance_field[move[0]][move[1]] < blocked:\r\n if zombie_distance_field[move[0]][move[1]] > distance:\r\n potential_moves = [move]\r\n distance = zombie_distance_field[move[0]][move[1]]\r\n elif zombie_distance_field[move[0]][move[1]] == distance: #getting multiple moves if valid\r\n potential_moves.append(move) \r\n \r\n new_positions.append(random.choice(potential_moves))\r\n self._human_list = new_positions","def turn_around():\n for i in range(2):\n turn_left()","def test_move_default_dropped_steps(self):\n player = ss.LazyPlayer()\n random.seed(2)\n player.move()\n random.seed(5)\n player.move()\n assert player.position == 44","def move_me_on_spawn(self):\r\n\t\tif self.points_to_go:\r\n\t\t\tself.start_pos = self.points_to_go[0]\r\n\t\t\tfor point in self.points_to_go[1:]:\r\n\t\t\t\tfor i in range(len(self.points_to_go[1:])):\r\n\t\t\t\t\tself.goal_pos = self.points_to_go[i]\r\n\t\t\t\t\t\r\n\t\t\t\t\tself.move_me()\r\n\t\t\t\t\t#self.start_pos = \r\n\t\t\t\t\t#print(self.goal_pos)\r\n\t\t\t\t\t#if self.move_me():\r\n\t\t\t\t\t#\ti += 1\r\n\t\t\t\t\t#\tprint('switch')\r","def roll_die(self, number_of_rolls):\n\t\tfor roll in range(0, number_of_rolls):\n\t\t\tprint(str(randint(1, self.sides)), end = \", \")\n\t\tprint()","def test_fourth_move(self):\n\t\tdata = {'moves': [('r','s'), ('r','p'), ('s','p')],\n\t\t\t\t'player_number': 2}\n\t\tfor AI in self.list_of_AIs:\n\t\t\tself.assertTrue(AI.move(data) in ['r', 'p', 's'])","def move(self):\n pass","def person_move(self, pos1=None, pos2=None):\n if self.available_combinations() != []:\n if pos1 is None and pos2 is None:\n comb = random.choice(self.available_combinations())\n self.board[comb[0]][comb[1]] = 5\n else:\n if (pos1, pos2) in self.available_combinations():\n self.board[pos1][pos2] = 5\n else:\n print(\"This position is not available\")\n raise TextException\n self.last_move = 5","def move_marble(self, coordinates, direction):\n pass","def move_zombies(self, human_distance_field): #essentially the same as move_humans, but in 4 directions not 8\r\n blocked = self.get_grid_height() * self.get_grid_width()\r\n new_positions = []\r\n for zombie in self.zombies():\r\n moves = self.four_neighbors(zombie[0], zombie[1])\r\n moves.append((zombie[0], zombie[1]))\r\n potential_moves = [moves[0]]\r\n distance = human_distance_field[moves[0][0]][moves[0][1]]\r\n \r\n for move in moves:\r\n if human_distance_field[move[0]][move[1]] < blocked:\r\n if human_distance_field[move[0]][move[1]] < distance:\r\n potential_moves = [move]\r\n distance = human_distance_field[move[0]][move[1]]\r\n elif human_distance_field[move[0]][move[1]] == distance:\r\n potential_moves.append(move)\r\n \r\n new_positions.append(random.choice(potential_moves))\r\n \r\n self._zombie_list = new_positions","def move_asteroids(self):\n for i in range(len(self.asteroid_type)):\n self.game.move(self.asteroid_type[i], -self.asteroid_speed[i], 0)\n for i in range(len(self.asteroid_id_e)):\n self.game.move(self.asteroid_id_e[i], -self.asteroid_speed_e[i], 0)","def move(self):\n \n # checks for bots nearby\n next_move = self.follow()\n \n # finds a random move if no bot\n if next_move is self.position:\n self.position = self.wander()\n else:\n self.position = next_move","def move_zombies(self, human_distance):\n updated_zombie_list = []\n for zombie in self.zombies():\n # find the smallest distance in 4 neighbor cells\n min_distance = human_distance[zombie[0]][zombie[1]]\n min_dist_cell = [zombie]\n # loop through neighbor cells\n for cell in self.four_neighbors(zombie[0], zombie[1]):\n if self.is_empty(cell[0], cell[1]):\n distance = human_distance[cell[0]][cell[1]]\n if distance == min_distance:\n min_dist_cell.append(cell)\n elif distance < min_distance:\n min_dist_cell = [cell]\n min_distance = distance\n # decide which cell to move to\n updated_zombie_list.append(min_dist_cell[random.randrange(len(min_dist_cell))])\n self._zombie_list = updated_zombie_list","def run_instructions(self) -> None:\n for item in self._instructions:\n for _ in range(item[1]):\n self.move_head(item[0])","def step(self,action_list,verbose=False):\n rewards,dones = np.zeros((self.n_snakes),dtype=np.float), np.zeros((self.n_snakes)).astype('bool')\n for i in range(self.n_snakes):\n eaten_food = self.snakes[i].move(action_list[i],self.food_pos)\n if eaten_food == True : \n self.update_food()\n rewards[i]=1; # Eaten food \n if self.hasCollided() == True:\n self.snakes[i].reset(self.sample_empty_pos())\n rewards[i]=-1;\n dones[i]=True;\n\n self.display(verbose=verbose)\n observation = self.get_observation();\n observation = np.repeat(observation,repeats=self.n_snakes,axis=0)\n states = self.get_state()\n return observation,rewards,dones,states;","def throw_dice(self):\n self.dice = []\n for i in range (6):\n die = random.randint(1, 6)\n self.dice.append(die)\n self.num_throws += 1","def move(self, move):\n raise NotImplementedError()","def move_repeatedly():\n check = check50.run(run_command)\n check.stdin(\"WEST\").stdout(room_2_description)\n check.stdin(\"EAST\").stdout(room_1_name)\n check.stdin(\"WEST\").stdout(room_2_name)","def main():\n move()\n move()\n pick_beeper()\n move()\n turn_left()\n for i in range(2):\n move()\n put_beeper()\n turn_around()\n move_to_wall()\n turn_right()\n move_to_wall()\n turn_around()","def move(self, step):\n for point in self.points:\n l = min(len(step), len(point.position))\n for i in range(l):\n point.position[i] = step[i]","def __snake_move(self):\n self.__eat_candy()\n # move tail and body\n n_snake = len(self.__snake)\n for i in range(1, n_snake):\n s2 = self.__snake[n_snake - i]\n s1 = self.__snake[n_snake - i - 1]\n s2.move(s1.pos())\n # move head\n pos = self.__snake[0].pos()\n tmp_snake = Snake(self, direction=self.__h_direction, position=pos)\n h_pos = self.__get_next_head_pos(tmp_snake)\n tmp_snake.remove()\n icon = f'resources/{self.__directions[self.__h_direction]}.svg'\n new_head = Snake(self, icon, self.__h_direction, self.__cell_edge, h_pos)\n old_head = self.__snake[0]\n self.__snake[0] = new_head\n old_head.remove()\n self.__crash_check()","def move(self, direction, cycles):\n\t\tself.planet.tiles[self.y][self.x].set_occupant() # set occupant to the initial tile\n\t\tif direction == \"N\": # unit vector (0, -1)\n\t\t\ty_symbol = -1\n\t\t\tx_symbol = 0\n\t\tif direction == \"S\": # unit vector (0, 1)\n\t\t\ty_symbol = 1\n\t\t\tx_symbol = 0\n\t\tif direction == \"W\": # unit vector (-1, 0)\n\t\t\tx_symbol = -1\n\t\t\ty_symbol = 0\n\t\tif direction == \"E\": # unit vector (1, 0)\n\t\t\tx_symbol = 1\n\t\t\ty_symbol = 0\n\t\ti = 0\n\t\twhile i < int(cycles):\n\t\t\tnext_x = self.x + x_symbol # change x coordinate\n\t\t\tnext_y = self.y + y_symbol # change y coordinate\n\t\t\tnext_x, next_y = self.spherical(next_x, next_y) # get the next tile's coordinate\n\t\t\tif self.can_move(next_x, next_y): # check whether rover can move\n\t\t\t\t#reduce battery\n\t\t\t\tif self.planet.tiles[next_y][next_x].is_shaded():\n\t\t\t\t\tself.battery -= 1\n\t\t\t\tself.x = next_x\n\t\t\t\tself.y = next_y\n\t\t\t\ttile = self.planet.tiles[next_y][next_x]\n\t\t\t\ttile.set_occupant()\n\t\t\t\ti += 1\n\t\t\telse:\n\t\t\t\tbreak","def move_random(self, board: Board) -> None:\n rnd_move_idx = randint(0,4)\n # moves: stay, up, left, right, down\n moves = [[0,0], [0,-1], [-1,0], [1,0], [0,1]]\n\n if board.can_position_at(self.x + moves[rnd_move_idx][0], self.y + moves[rnd_move_idx][1]):\n board.set_element_at_position(0, self.x, self.y)\n self.x += moves[rnd_move_idx][0]\n self.y += moves[rnd_move_idx][1]\n board.set_element_at_position(3, self.x, self.y)\n print(\"Bomberman moved to [\", self.x, \",\", self.y, \"]\")","def move_peers(self, data):\n for peer_id, row, col in data:\n if peer_id in self.peers:\n self.peers[peer_id].move(row, col)\n return","def test_move(self, data, moving_indices, move_offset):\n assume(np.max(moving_indices) < data.shape[0])\n\n unmoved = copy(data)\n layer = Points(data)\n\n move_offset = move_offset[: data.shape[1]]\n\n layer._move(moving_indices, list(np.zeros(data.shape[1])))\n layer._move(moving_indices, list(move_offset))\n layer._drag_start = None\n\n assert_almost_equal(\n layer.data[moving_indices], unmoved[moving_indices] + move_offset\n )\n\n unmoved_indices = list(range(data.shape[0]))\n for mi in moving_indices:\n if mi in unmoved_indices:\n unmoved_indices.remove(mi)\n assert_almost_equal(layer.data[unmoved_indices], unmoved[unmoved_indices])","def random_move(turtle, distance):\n angle = uniform(-90,90)\n d = uniform(0,distance)\n turtle.left(angle)\n turtle.forward(d)","def move_creatures(self):\r\n auto = self.state\r\n for y, line in enumerate(auto):\r\n for x, cell in enumerate(line):\r\n 'no creature cell'\r\n if cell == 0:\r\n continue\r\n next_cell = np.random.randint(0, 8)\r\n 'Stay in place'\r\n if next_cell == 0:\r\n continue\r\n ' check next cell'\r\n auto = self.check_next_cell(cell, auto, y, x, next_cell)\r\n\r\n self.nHealthy = np.sum(self.state == 1)\r\n self.nSick = np.sum(self.state == 2)\r\n return auto","def move(self):\n \n self.position = self.wander()","def move_pawn(pos, game):\n #Convert coordinates to row and column\n row = int(pos[1]//(SQUARESIZE+FENCEWIDTH))\n col = int(pos[0]//(SQUARESIZE+FENCEWIDTH))\n #Make move\n game.move_pawn(game.get_player_turn(), (col,row))","def _move(self, direction, count, step_size):\n for _ in range(count):\n if not self.has_hit_edge():\n self.position = min(self.scene.step_count - 1, max(0,\n self.position + direction * step_size))\n self._take_measure()\n self.steps_taken += 1\n self.visited_positions.append(self.position)","def move_girl(girl):\n global trainer_speed, girlcount, girl_left\n if girl_left:\n for i in girl:\n i.x -= trainer_speed\n i.move_speed()\n girlcount += 0.5\n girl_frame = int(girlcount) % 3\n camera.draw(girl[girl_frame])\n else:\n for i in girl:\n i.x += trainer_speed\n i.move_speed()\n girlcount += 0.5\n girl_frame = int(girlcount) % 3\n camera.draw(girl[girl_frame + 3])\n for each in girl:\n if each.x < 20:\n girl_left = False\n if each.x > 780:\n girl_left = True","def move(self, rel_pos):\n self.pos = (self.pos[0] + rel_pos[0] * GRID, self.pos[1] + rel_pos[1] * GRID)","def move_draught_end(event):\n global red_draughts, white_draughts\n global board_array\n global old_point\n global die_1_num, die_2_num, doubles\n draught = board.find_withtag(CURRENT)[0]\n #Figure out which point they want to put it on\n bottom = (event.y-click_offset[1] >= board_height//2)\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\n is_red = draught in red_draughts\n if bottom == False:\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\n else:\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \n #Check legality\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\n draw_draughts()\n return\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\n draw_draughts()\n return\n if(new_point == 0 and not is_red): #if white trying to bear off\n for i in xrange(7,26):\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\n draw_draughts()\n return\n if(new_point == 25 and is_red): #if red trying to bear off\n for i in xrange(0,18):\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\n draw_draughts()\n return \n \n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\n if(doubles == False) or (die_2_num != 0):\n die_1_num = 0\n else: \n die_2_num = die_1_num\n doubles = False\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\n if(doubles == False) or (die_1_num != 0):\n die_2_num = 0\n else: \n die_1_num = die_2_num\n doubles = False\n else: #Can't move there on this roll\n draw_draughts()\n return\n update_dice()\n #Update board_array\n if is_red:\n board_array[old_point][0] -= 1\n board_array[new_point][0] += 1\n if(board_array[new_point][1] == 1): #Handle hits\n board_array[new_point][1] -= 1\n board_array[25][1] += 1\n else:\n board_array[old_point][1] -= 1\n board_array[new_point][1] += 1\n if(board_array[new_point][0] == 1): #Handle hits\n board_array[new_point][0] -= 1\n board_array[0][0] += 1\n\n draw_draughts()\n if(die_1_num == 0 and die_2_num == 0):\n comp_turn()","def pickup_dominoes(self, num_dominoes, player):\n\n for domino in range(num_dominoes):\n shuffle(self.boneyard)\n player.hand.append(self.boneyard.pop(0))","def turnPoints(angles, points):\n l = len(points)\n for i in range(l - 1):\n points[i].turn(angles[i], points[i + 1:])","def make_move(self, playername, coordinates, direction):\n\n pass","def move(self):\r\n for index in range(self.size):\r\n self.values[index] = self.values[index] + self.velocities[index]\r\n \r\n # Adjust values to keep particle inside boundaries.\r\n if self.values[index] < Particle.MIN_VALUE:\r\n self.values[index] = (-self.values[index] % Particle.MAX_VALUE)\r\n elif self.values[index] > Particle.MAX_VALUE:\r\n self.values[index] = (self.values[index] % Particle.MAX_VALUE)","def set_move(self, position: int) -> None:\n if position not in range(1, 10):\n raise IllegalMoveException\n elif position in self.moves:\n raise DuplicateMoveException\n self.moves[position] = self.turn\n if self.turn == self.players[0]:\n self.turn = self.players[1]\n else:\n self.turn = self.players[0]","def move(self, mapa: Map, bomberman: Bomberman, bombs: list, enemies: dict, moves: int=1) -> list:\n safety_pos = self.pos\n advance_pos = [self.pos]\n if self.pos != self.enemy.lastpos:\n self.enemy.lastdir = vector2dir(self.pos[0]-self.enemy.lastpos[0], self.pos[1]-self.enemy.lastpos[1])\n for x in range(moves):\n self.enemy.move(mapa, bomberman, bombs, list(enemies.values()))\n if self.pos != advance_pos[-1]: advance_pos.append(self.pos)\n self.pos = safety_pos\n return advance_pos","def move(self, direction):\n pass","def move_humans(self, zombie_distance_field):\n self.move(zombie_distance_field, self._human_list, self.eight_neighbors, \"lt\")","def generate_moves(self, board: Board) -> typing.Iterator[MoveDescription]:\n dominoes = set(board.dominoes)\n for domino in dominoes:\n dx, dy = domino.direction\n yield from self.try_move(domino, dx, dy)\n yield from self.try_move(domino, -dx, -dy)","def move_ghosts(self):\n temp_ghosts = []\n for ghost in self.ghosts:\n if (self.player.x - ghost.x > 0 and\n board.board[ghost.y][ghost.x+1 if ghost.x < 34 else ghost.x] == '.' or\n board.board[ghost.y][ghost.x+1 if ghost.x < 34 else ghost.x] == 'P'\n ):\n board.board[ghost.y][ghost.x] = '.'\n if board.board[ghost.y][ghost.x+1] == 'P':\n print 'Lost the game'\n self.player.die = 1\n exit()\n board.board[ghost.y][ghost.x+1] = 'G'\n ghost.x += 1\n elif (self.player.x - ghost.x < 0 and\n board.board[ghost.y][ghost.x-1 if ghost.x > 0 else ghost.x] == '.'\n or board.board[ghost.y][ghost.x-1 if ghost.x > 0 else ghost.x] == 'P'\n ):\n board.board[ghost.y][ghost.x] = '.'\n if board.board[ghost.y][ghost.x-1] == 'P':\n print 'Lost the game'\n self.player.die = 1\n exit()\n board.board[ghost.y][ghost.x-1] = 'G'\n ghost.x -= 1\n elif (self.player.y - ghost.y > 0\n and board.board[ghost.y+1 if ghost.y < 14 else ghost.y][ghost.x] == '.'\n or board.board[ghost.y+1 if ghost.y < 14 else ghost.y][ghost.x] == 'P'\n ):\n board.board[ghost.y][ghost.x] = '.'\n if board.board[ghost.y+1][ghost.x] == 'P':\n print 'Lost the game'\n self.player.die = 1\n exit()\n board.board[ghost.y+1][ghost.x] = 'G'\n ghost.y += 1\n elif (board.board[ghost.y-1 if ghost.y > 0 else ghost.y][ghost.x] == '.'\n or board.board[ghost.y-1 if ghost.y > 0 else ghost.y][ghost.x] == 'P'\n ):\n board.board[ghost.y][ghost.x] = '.'\n if board.board[ghost.y-1][ghost.x] == 'P':\n print 'Lost the game'\n self.player.die = 1\n exit()\n board.board[ghost.y-1][ghost.x] = 'G'\n ghost.y -= 1\n temp_ghosts.append(ghost)\n self.ghosts = temp_ghosts","def hslArray_move(self, hslList, delay):\n # res\n\n res = self.hslArrayOfs_move(0,hslList, delay)\n return res","def move_randomly(self, with_fight=False):\n delta = [(-1, -1), (-1, 0), (-1, 1), (0, 1), (0, -1), (1, -1), (1, 0), (1, 1)]\n rd.shuffle(delta)\n x, y = self.owner.pos\n while len(delta) > 0:\n dx, dy = delta.pop()\n if self.move_towards_position((x + dx, y + dy)):\n return","def test_moves(self):\n self.herb.fitness = 1\n Herbivore.set_parameters({\"mu\": 1})\n original_position = (1, 1)\n self.herb.coordinates = self.herb.migrate(self.list)\n nt.assert_not_equal(self.herb.coordinates, original_position)","def move(self, heaps: Tuple[int, ...], misere: bool) -> Tuple[int, int]:\r\n pass"],"string":"[\n \"def throw(self, move):\\n for dice_index in move:\\n self.dice[dice_index - 1] = random.randint(1,6)\",\n \"def move_tie_fighters(self):\\n for i in range(len(self.tie_fighters)):\\n self.tie_fighters[i].move_tie_fighter()\",\n \"def migration(self):\\n\\n coordinates = self.get_random_coordinates()\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cell_move_herbivores(coordinate)\\n self.cell_move_carnivores(coordinate)\\n\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cells[coordinate].move_new_animals()\",\n \"def move(self, direction, cycles):\\n\\t\\tpass\",\n \"def move(self, direction: str) -> int:\\n (i, j), _ = self.position.popitem(last=True) # current position\\n self.position[(i, j)] = 0 # add back \\n if direction == \\\"U\\\": i -= 1\\n elif direction == \\\"L\\\": j -= 1\\n elif direction == \\\"R\\\": j += 1\\n else: i += 1\\n if self.food and self.food[0] == [i, j]: self.food.popleft()\\n else: self.position.popitem(last=False)\\n if not (0 <= i < self.height and 0 <= j < self.width) or (i, j) in self.position: return -1 # game over \\n self.position[(i, j)] = 0\\n return len(self.position)-1\",\n \"def roll_the_dice(self, dice):\\n if type(dice) == list:\\n for die in dice:\\n die.roll()\",\n \"def move(self):\\n for agent in self.agents:\\n if not agent.fidelity:\\n options = agent.get_move_options(agent.hex, self.kernel_size, None, extend=True)\\n target = random36.choices(population=options,weights=[x.quality**2 for x in options])\\n agent.move(target[0])\",\n \"def one_player_leg(length, pos):\\n throw = dice()\\n moves = []\\n while throw == 6:\\n if pos + throw < length:\\n pos += throw\\n moves.append(throw)\\n elif pos + throw == length:\\n pos += throw\\n moves.append(throw)\\n return pos, moves\\n else:\\n moves.append(throw)\\n throw = dice()\\n else:\\n if pos + throw < length:\\n pos += throw\\n moves.append(throw)\\n elif pos + throw == length:\\n pos += throw\\n moves.append(throw)\\n return pos, moves\\n else:\\n moves.append(throw)\\n return pos, moves\",\n \"def roll_dices(self):\\n dice1 = random.randint(1, 6)\\n dice2 = random.randint(1, 6)\\n\\n self.client.send_player_end_dices()\\n self.game.player_rolled_dices([dice1, dice2])\\n asyncio.ensure_future(self.move(dice1 + dice2))\",\n \"def test_move():\\n human = Human()\\n coordinates = [2, 1]\\n dimensions = [3, 4]\\n\\n new_coordinates = human.move(coordinates, dimensions)\\n\\n possible_new_coordinates = [[2, 0], [3, 0], [3, 1], [3, 2], [2, 2], [1, 2], [1, 1], [1, 0]]\\n\\n assert new_coordinates in possible_new_coordinates\",\n \"def move(self, direction):\\n head = self.snake[0]\\n delta = self.dirs[direction]\\n nextMove = [head[0] + delta[0], head[1] + delta[1]]\\n if not self.isValidMove(nextMove):\\n return -1\\n\\n if self.food and nextMove == self.food[0]:\\n self.food.popleft()\\n else:\\n self.snake.pop()\\n\\n self.snake.appendleft(nextMove)\\n\\n return len(self.snake) - 1\",\n \"def move(self, direction=0):\\n moves = [\\n [3, 1],\\n [2, 2],\\n [1, 3],\\n [0, 0]\\n ]\\n\\n self._rotate(moves[direction][0])\\n\\n for row in range(4):\\n r = [i for i in self.map[row] if i != 0]\\n\\n r_result = []\\n while(len(r)):\\n num = r.pop(0)\\n if len(r) and num == r[0]:\\n num += r.pop(0)\\n # TODO: Do a 2048 check here to see if the player won?\\n # this might not be the best place because we could use\\n # this method to run tests to see if the player has any valid moves\\n r_result.append(num)\\n \\n self.map[row] = r_result + [0]*(4-len(r_result))\\n\\n self._add_random_piece()\\n\\n self._rotate(moves[direction][1])\\n self.print_map()\",\n \"def eat(bots, food_list):\\n \\n # iterates through bot positions\\n for bot in bots:\\n # iterates through food positions\\n for food_loc in food_list:\\n # compare bot and food positions\\n first_pos = bot.position[0] == food_loc[0]\\n second_pos = bot.position[1] == food_loc[1]\\n \\n # if food and bot in same spot, food is removed\\n if first_pos and second_pos:\\n food_list.remove(food_loc)\",\n \"def move(self, coordinates, direction):\\n pass\",\n \"def move(t, length):\\n pu(t)\\n\\t\\n fd(t, length)\\n pd(t)\",\n \"def move(exits, direction):\\r\\n return rooms[exits[direction]]\",\n \"def actions_turn(positions, player1, player2, creatures):\\n\\n attacking = []\\n moving = []\\n\\n # Split the orders within attacks and movements\\n for character in positions:\\n if character in creatures:\\n if positions[character]['order'] == 'attack':\\n attacking += character\\n else:\\n moving += character\\n elif character in player1:\\n if positions[character]['order'] == 'attack':\\n attacking += character\\n else:\\n moving += character\\n\\n else:\\n if positions[character]['order'] == 'attack':\\n attacking += character\\n else:\\n moving += character\\n\\n # Execute the attacks\\n for character in attacking:\\n # First attacking : the creatures\\n if character in creatures:\\n hero_name = character\\n name_attack = positions[character]['name_attack']\\n attack_coord = positions[character]['where']\\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\\n # Then the heroes of the first player\\n elif character in player1:\\n hero_name = character\\n name_attack = positions[character]['name_attack']\\n attack_coord = positions[character]['where']\\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\\n # Finally the ones of the second player\\n else:\\n hero_name = character\\n name_attack = positions[character]['name_attack']\\n attack_coord = positions[character]['where']\\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\\n\\n for character in moving:\\n print(character, moving)\\n # First moving : the creatures\\n if character in creatures:\\n hero_name = character\\n movement_coord = positions[character]['where']\\n move(hero_name, positions, movement_coord)\\n\\n # Then the heroes of the first player\\n elif character in player1:\\n hero_name = character\\n movement_coord = positions[character]['where']\\n move(hero_name, positions, movement_coord)\\n\\n # Finally the ones of the second player\\n else:\\n hero_name = character\\n movement_coord = positions[character]['where']\\n move(hero_name, positions, movement_coord)\\n\\n return player1, player2, positions, creatures\",\n \"def move(self):\\n if self.adjustment > 0:\\n # The player moves up a ladder\\n old_position = self.position\\n # In the next move, player on top of ladder\\n\\n self.position = old_position - self.dropped_steps\\n # player drops steps\\n super().move()\\n # player move\\n die = self.position - old_position + self.dropped_steps - \\\\\\n self.adjustment\\n \\\"\\\"\\\" \\n the current position of the player is: old position - \\n dropped_steps + die + adjustment. Rearranging to find the die.\\n \\n \\\"\\\"\\\"\\n if die < self.dropped_steps:\\n self.position = old_position\\n # if die is less than dropped_steps, the player will stand still.\\n else:\\n super().move()\\n # if not climbling a ladder, then the player make a regular move\",\n \"def temp_swap_dice(self, move, new_faces):\\n assert len(move) == len(new_faces)\\n output = list(self.dice)\\n for face in move:\\n output.remove(face)\\n for face in new_faces:\\n output.append(face)\\n return tuple(sorted(output))\",\n \"def find_moveable_pieces(self, die, p1): \\n moveable = []\\n if (p1):\\n #must we re-enter?\\n if (self.p1vec[0] > 0):\\n if (self.free_spot(0, die, p1)):\\n b = Board(self.p1vec[:],self.p2vec[:])\\n b.move(0, die, p1)\\n moveable.append(b)\\n #no? ok then generate the moves\\n else:\\n for i in range(1, 25):\\n if (self.p1vec[i] > 0):\\n if (self.free_spot(i, die, p1)):\\n b = Board(self.p1vec[:],self.p2vec[:])\\n b.move(i, die, p1)\\n moveable.append(b)\\n else:\\n #must we re-enter?\\n if (self.p2vec[0] > 0):\\n if (self.free_spot(0, die, p1)):\\n b = Board(self.p1vec[:],self.p2vec[:])\\n b.move(0, die, p1)\\n moveable.append(b)\\n #no? ok then generate the moves\\n else:\\n for i in range(1, 25):\\n if (self.p2vec[i] > 0):\\n if (self.free_spot(i, die, p1)):\\n b = Board(self.p1vec[:],self.p2vec[:])\\n b.move(i, die, p1)\\n moveable.append(b)\\n return moveable\",\n \"def on_die(self, head_position, board, score, body_parts):\",\n \"def move(self,move):\\n for x in range(len(self.coord)):\\n self.coord[x] = np.array([y+np.array(move) for y in self.coord[x]])\\n return self\",\n \"def DoMove(position, move):\\n return position - move\",\n \"def move(self, direction):\\n # replace with your code\\n\\n indices = self.direction_indices[direction]\\n for coordinate in indices:\\n merged_coordinate_list = self.get_list(direction, coordinate)\\n self.change_board(merged_coordinate_list, coordinate, direction)\\n print(self.__str__())\\n if self.board_is_not_full():\\n self.new_tile()\",\n \"def move(self):\\r\\n segments = len(self.all_turtles) - 1\\r\\n for i in range(len(self.all_turtles)):\\r\\n if segments == 0:\\r\\n self.all_turtles[segments].forward(MOVE_DISTANCE)\\r\\n else:\\r\\n new_x = self.all_turtles[segments - 1].xcor()\\r\\n new_y = self.all_turtles[segments - 1].ycor()\\r\\n self.all_turtles[segments].goto(new_x, new_y)\\r\\n segments -= 1\",\n \"def postion_fleet(self, ships_positions, board):\\n for cell in ships_positions:\\n row = ord(cell[:1]) - ord('A')\\n col = int(cell[1:]) - 1\\n for i in range(row, row + self.total_rows_req):\\n for j in range(col, col + self.total_column_req):\\n self.position_ship(\\n board.grid[i][j],\\n constants.FLEET_P_CLASS,\\n constants.P_CLASS_HIT_POWER\\n )\",\n \"def move(self):\\n assert self.is_alive, \\\"Sprite is dead, and should not be able to move\\\"\\n if self.health > 3:\\n self.y += random.randint(-1, 1) # change by -1, 0, 1\\n self.x += random.randint(-1, 1) # change by -1, 0, 1\\n print(self.name, \\\"moves to position\\\", str(self.x), \\\",\\\", str(self.y))\",\n \"def move_all_animals(self):\\n\\n y_lim, x_lim = np.shape(self.map)\\n for y in range(y_lim):\\n for x in range(x_lim):\\n loc = y, x\\n self.map[loc].migration(self.get_neighbour((y, x)))\",\n \"def postion_fleet(self, ships_positions, board):\\n for cell in ships_positions:\\n row = ord(cell[:1]) - ord('A')\\n col = int(cell[1:]) - 1\\n for i in range(row, row + self.total_rows_req):\\n for j in range(col, col + self.total_column_req):\\n self.position_ship(\\n board.grid[i][j],\\n constants.FLEET_Q_CLASS,\\n constants.Q_CLASS_HIT_POWER\\n )\",\n \"def move(self):\\n # neighbor offsets\\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\\n for i in range(len(offset)):\\n x = self.x + offset[i][0] # neighboring coordinates\\n y = self.y + offset[i][1]\\n if self.island.animal(x, y) == 0: # neighboring spot is open\\n self.island.remove(self) # remove from current spot\\n self.x = x # new coordinates\\n self.y = y\\n self.island.register(self) # register new coordinates\\n break # finished with move\",\n \"def move(self):\\n # neighbor offsets\\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\\n for i in range(len(offset)):\\n x = self.x + offset[i][0] # neighboring coordinates\\n y = self.y + offset[i][1]\\n if self.island.animal(x, y) == 0: # neighboring spot is open\\n self.island.remove(self) # remove from current spot\\n self.x = x # new coordinates\\n self.y = y\\n self.island.register(self) # register new coordinates\\n break # finished with move\",\n \"def move(camel_dict, tiles_dict, camel, roll):\\n camel_space = camel_dict[camel][\\\"space\\\"]\\n camel_height = camel_dict[camel][\\\"height\\\"]\\n min_height_movers, num_movers, movers = camels_to_move(\\n camel_dict, camel_space, camel_height\\n )\\n destination = camel_space + roll\\n block = False\\n give_points = None\\n if destination in tiles_dict.keys():\\n if tiles_dict[destination][\\\"tile_type\\\"] == \\\"skip\\\":\\n give_points = destination\\n destination += 1\\n elif tiles_dict[destination][\\\"tile_type\\\"] == \\\"block\\\":\\n give_points = destination\\n destination -= 1\\n block = True\\n max_height_dest, destinationers = camels_in_dest(camel_dict, destination)\\n if block:\\n block_move(\\n camel_dict,\\n movers,\\n destinationers,\\n destination,\\n min_height_movers,\\n num_movers,\\n )\\n else:\\n reg_move(camel_dict, movers, destination, min_height_movers, max_height_dest)\\n return give_points\",\n \"def cpu_move(moves_list):\\n \\n move = randint(0,3)\\n return move\",\n \"def AeroMove(self, pos):\\r\\n\\r\\n pass\",\n \"def move(self):\\n self._move_range_shuffle(3)\\n self._move_satisfy_random_constraint()\\n # self._move_range_shuffle(3)\\n #if (curr_energy > 50):\\n # self._move_satisfy_random_constraint()\\n #else:\\n # self._move_range_shuffle(3)\",\n \"def move(self, pos_to_move):\\n if (self.current_pos + 1) - pos_to_move == 1: #Move to the left\\n direction = 4\\n elif (self.current_pos + 1) - pos_to_move == -1: #Move to the right\\n direction = 2\\n elif (self.current_pos + 1) - pos_to_move == 5: #Move to the top\\n direction = 1\\n else: #Move to the bottom\\n direction = 3\\n return [MOVE, direction]\",\n \"def move_humans(self, zombie_distance):\\n updated_human_list = []\\n for human in self.humans():\\n # find the largest distance in 8 neighbor cells\\n max_distance = 0\\n max_dist_cell = [(human[0], human[1])]\\n # loop through neighbor cells\\n for cell in self.eight_neighbors(human[0], human[1]):\\n if self.is_empty(cell[0], cell[1]):\\n distance = zombie_distance[cell[0]][cell[1]]\\n if distance == max_distance:\\n max_dist_cell.append(cell)\\n elif distance > max_distance:\\n max_dist_cell = [cell]\\n max_distance = distance\\n # decide which cell to move to\\n updated_human_list.append(max_dist_cell[random.randrange(len(max_dist_cell))])\\n self._human_list = updated_human_list\",\n \"def move_fish_list(fishes: FishList, width: int):\\n if fishes is not None:\\n move_swimming_fish(fishes.first, width)\\n move_fish_list(fishes.rest, width)\",\n \"def move(degs, i, j, n):\\n if n > 0:\\n temp = 3 - i - j\\n move(degs, i, temp, n - 1) \\n degs[j].append(degs[i].pop(-1))\\n print(degs)\\n move(degs, temp, j, n - 1)\",\n \"def turn(self):\\r\\n # 1 throw the dice\\r\\n if (\\r\\n not self.player_list[self.current_player].is_in_jail()\\r\\n or self.try_leave_jail()\\r\\n ):\\r\\n thr = Throw()\\r\\n while thr is not None:\\r\\n # check in where the current player will land\\r\\n new_position = self.compute_new_position_from_dice(\\r\\n self.current_player, thr\\r\\n )\\r\\n self.move_player_to(self.current_player, new_position, thr=thr)\\r\\n\\r\\n if thr.is_double():\\r\\n thr = Throw()\\r\\n else:\\r\\n thr = None\\r\\n print(\\\"------------------------------\\\")\\r\\n\\r\\n self.player_observable_variables()\\r\\n\\r\\n # move turn to next player\\r\\n self.current_player += 1\\r\\n if self.current_player >= len(self.player_list):\\r\\n self.current_player = 0\\r\\n self.full_turn_count += 1\\r\\n print(\\\"**********************\\\")\\r\\n print(\\r\\n \\\"Full turn:\\\",\\r\\n self.full_turn_count,\\r\\n \\\"\\\\n\\\",\\r\\n \\\"\\\\n\\\".join(map(lambda x: x.full(), self.player_list)),\\r\\n )\\r\\n print(\\\"**********************\\\")\",\n \"def move2(self):\\n\\n options = self.location.exits.keys()\\n for key in options:\\n if self.location.exits[key] == p.location:\\n self.location.objects.remove(a)\\n self.location = p.location\\n self.location.objects.append(a)\\n print('fred entered the room')\\n self.attack(['attack', str(p.name)])\\n break\\n else:\\n self.move1()\",\n \"def move(x,y):\\r\\n pass\",\n \"def step(self, move):\",\n \"def move(self, direction):\\r\\n # replace with your code\\r\\n row_dir = OFFSETS[direction][0]\\r\\n col_dir = OFFSETS[direction][1]\\r\\n \\r\\n if row_dir == 0:\\r\\n new_cells = self._cells\\r\\n new_dir = col_dir\\r\\n else:\\r\\n new_tuples = zip(*self._cells)\\r\\n new_cells = [list(item) for item in new_tuples]\\r\\n new_dir = row_dir\\r\\n \\r\\n tmp_cells = []\\r\\n for lists in new_cells:\\r\\n lists = lists[::new_dir]\\r\\n merge_lists = merge(lists)\\r\\n tmp_cells.append(merge_lists[::new_dir])\\r\\n \\r\\n if row_dir == 0:\\r\\n self._cells = tmp_cells\\r\\n else:\\r\\n new_tuples = zip(*tmp_cells)\\r\\n new_cells = [list(item) for item in new_tuples]\\r\\n self._cells = new_cells\\r\\n \\r\\n self.new_tile()\",\n \"def move(self, direction: str) -> int:\\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\\n # print(x,y)\\n # went out bound\\n if x >= self.n or y >= self.m or x < 0 or y < 0:\\n return -1\\n tail = self.snake.popleft()\\n self.snake_set.discard(tail)\\n if (x, y) in self.snake_set:\\n return -1\\n\\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\\n self.food.popleft()\\n self.snake.appendleft(tail)\\n self.snake_set.add(tail)\\n self.snake.append((x, y))\\n self.snake_set.add((x, y))\\n\\n return len(self.snake) - 1\",\n \"def move(self):\\n\\n choices = []\\n if self.game.green_apples > 0:\\n choices.append(self.game.move_green)\\n if self.game.red_apples > 0:\\n choices.append(self.game.move_red)\\n if self.game.blue_plums > 0:\\n choices.append(self.game.move_blue)\\n if self.game.yellow_pears > 0:\\n choices.append(self.game.move_yellow)\\n\\n random_index = random.randint(0, len(choices) - 1)\\n f = choices[random_index]\\n f(True)\",\n \"def move1(self):\\n\\n options = self.location.exits.keys()\\n self.location.objects.remove(a)\\n print('fred is moving..')\\n self.location = self.location.exits[random.choice(list(options))]\\n self.location.objects.append(a)\",\n \"def step(self, crowd):\\n\\n for boid in crowd:\\n random_int = random.randint(0, 5)\\n\\n # if random_int > 4:\\n # random_int = random.randint(0, 5)\\n # if random_int > 4:\\n # for i in range (1, 500):\\n # goalX, goalY = self.goals[boid.goalNr]\\n # x, y = boid.position\\n\\n # if (goalX + 10 >= x >= goalX - 10) and (goalY + 10 >= y >= goalY - 10):\\n # boid.reached_goal(goalX + 10, goalY + 10)\\n\\n # dx = random.randint(0, self.width) - x\\n # dy = random.randint(0, self.height) - y\\n\\n # # Unit vector in the same direction\\n # distance = math.sqrt(dx * dx + dy * dy)\\n # dx /= distance\\n # dy /= distance\\n\\n # # And now we move:\\n # x += dx\\n # y += dy\\n\\n # boid.set_goal(dx, dy)\\n\\n # boid.position += boid.velocity\\n #else:\\n # boid.position += boid.velocity\\n \\n # Vector from me to cursor\\n\\n\\n goalX, goalY = self.goals[boid.goalNr]\\n x, y = boid.position\\n\\n if (goalX + 10 >= x >= goalX - 10) and (goalY + 10 >= y >= goalY - 10):\\n boid.reached_goal(goalX + 10, goalY + 10)\\n\\n else:\\n dx = goalX - x\\n dy = goalY - y\\n\\n # Unit vector in the same direction\\n # distance = np.linalg.norm(dx * dx + dy * dy)\\n distance = math.sqrt(dx * dx + dy * dy)\\n dx /= distance\\n dy /= distance\\n\\n # And now we move:\\n x += dx\\n y += dy\\n\\n boid.set_goal(dx, dy)\\n\\n boid.position += boid.velocity\",\n \"def execute_move(self, game_state):\\n # Set new location based on which ghost this is\\n game_state.ghosts_pos[self.ghost_id] = self.next_move\",\n \"def execute_move(self, game_state):\\n # Set new location based on which ghost this is\\n game_state.ghosts_pos[self.ghost_id] = self.next_move\",\n \"def move(self, direction: str) -> int:\\n # O(1) per move\\n\\n cur_pos = self.positions[-1]\\n\\n move = self.moves[direction]\\n new_pos = cur_pos[0] + move[0], cur_pos[1] + move[1]\\n\\n if new_pos[0] == self.height or new_pos[0] == -1 or new_pos[1] == self.width or new_pos[1] == -1 or (new_pos in self.positions_set and new_pos != self.positions[0]):\\n return -1\\n\\n self.positions.append(new_pos)\\n self.positions_set.add(new_pos)\\n\\n if self.eaten < len(self.food) and new_pos == self.food[self.eaten]:\\n self.eaten += 1\\n else:\\n tail = self.positions.popleft()\\n\\n if tail != self.positions[-1]:\\n self.positions_set.remove(tail)\\n\\n return self.eaten\",\n \"def mass_move(self,\\r\\n entrylist1,\\r\\n entrylist2):\\r\\n\\r\\n\\r\\n if len(entrylist1) > len(entrylist2):\\r\\n entrylist2 = entrylist2+list(range(entrylist2[-1]+1,\\r\\n (entrylist2[-1]\\r\\n +len(entrylist1)\\r\\n -len(entrylist2))))\\r\\n lastindexto = entrylist2[0]\\r\\n\\r\\n e1 = iter(entrylist1)\\r\\n e2 = iter(entrylist2)\\r\\n gofurther = True\\r\\n\\r\\n while gofurther:\\r\\n try:\\r\\n indexfrom = next(e1)\\r\\n except StopIteration:\\r\\n indexfrom = StopIteration\\r\\n try:\\r\\n indexto = self.find_space(next(e2), entrylist2)\\r\\n except StopIteration:\\r\\n indexto = StopIteration\\r\\n if indexto != StopIteration:\\r\\n lastindexto = indexto\\r\\n if indexto == StopIteration:\\r\\n indexto = self.find_space(lastindexto)\\r\\n\\r\\n if indexfrom != StopIteration:\\r\\n self.display_buffer.append(alerts.MOVING_FROM\\r\\n +index_reduce(str(indexfrom))\\r\\n +queries.TOTO+str(indexto))\\r\\n self.move(indexfrom, indexto)\\r\\n else:\\r\\n gofurther = False\",\n \"def get_steps_from_position_list(position_list):\\n\\n step_list = []\\n\\n for i in range(len(position_list) - 1):\\n current_position = position_list[i]\\n next_position = position_list[i+1]\\n if (current_position.x == next_position.x and \\n current_position.y == next_position.y):\\n continue\\n\\n #Cardinal directions\\n if (next_position.x - current_position.x == 1 and \\n next_position.y - current_position.y == 0):\\n next_action = \\\"right\\\"\\n elif (next_position.x - current_position.x == -1 and \\n next_position.y - current_position.y == 0):\\n next_action = \\\"left\\\"\\n elif (next_position.x - current_position.x == 0 and \\n next_position.y - current_position.y == 1):\\n next_action = \\\"up\\\"\\n elif (next_position.x - current_position.x == 0 and \\n next_position.y - current_position.y == -1):\\n next_action = \\\"down\\\"\\n\\n #Extended directions\\n elif (next_position.x - current_position.x == 1 and \\n next_position.y - current_position.y == 1):\\n next_action = \\\"up_right\\\"\\n elif (next_position.x - current_position.x == -1 and \\n next_position.y - current_position.y == 1):\\n next_action = \\\"up_left\\\"\\n elif (next_position.x - current_position.x == -1 and \\n next_position.y - current_position.y == -1):\\n next_action = \\\"down_left\\\"\\n elif (next_position.x - current_position.x == 1 and \\n next_position.y - current_position.y == -1):\\n next_action = \\\"down_right\\\"\\n next_step = Step(current_position, next_action)\\n step_list.append(next_step)\\n \\n if len(step_list) == 0:\\n next_step = Step(position_list[0], \\\"stay\\\")\\n step_list.append(next_step)\\n\\n return step_list\",\n \"def move_humans(self, zombie_distance_field):\\r\\n blocked = self.get_grid_height() * self.get_grid_width() #getting the distance value of obstacles\\r\\n new_positions = []\\r\\n for human in self.humans(): #calculate move for each human\\r\\n moves = self.eight_neighbors(human[0], human[1]) #getting list of up to 8 possible moves\\r\\n moves.append((human[0], human[1]))\\r\\n potential_moves = []\\r\\n distance = zombie_distance_field[human[0]][human[1]]\\r\\n for move in moves: #storing potential move if the distance is the max but not that of an obstacle\\r\\n if zombie_distance_field[move[0]][move[1]] < blocked:\\r\\n if zombie_distance_field[move[0]][move[1]] > distance:\\r\\n potential_moves = [move]\\r\\n distance = zombie_distance_field[move[0]][move[1]]\\r\\n elif zombie_distance_field[move[0]][move[1]] == distance: #getting multiple moves if valid\\r\\n potential_moves.append(move) \\r\\n \\r\\n new_positions.append(random.choice(potential_moves))\\r\\n self._human_list = new_positions\",\n \"def turn_around():\\n for i in range(2):\\n turn_left()\",\n \"def test_move_default_dropped_steps(self):\\n player = ss.LazyPlayer()\\n random.seed(2)\\n player.move()\\n random.seed(5)\\n player.move()\\n assert player.position == 44\",\n \"def move_me_on_spawn(self):\\r\\n\\t\\tif self.points_to_go:\\r\\n\\t\\t\\tself.start_pos = self.points_to_go[0]\\r\\n\\t\\t\\tfor point in self.points_to_go[1:]:\\r\\n\\t\\t\\t\\tfor i in range(len(self.points_to_go[1:])):\\r\\n\\t\\t\\t\\t\\tself.goal_pos = self.points_to_go[i]\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tself.move_me()\\r\\n\\t\\t\\t\\t\\t#self.start_pos = \\r\\n\\t\\t\\t\\t\\t#print(self.goal_pos)\\r\\n\\t\\t\\t\\t\\t#if self.move_me():\\r\\n\\t\\t\\t\\t\\t#\\ti += 1\\r\\n\\t\\t\\t\\t\\t#\\tprint('switch')\\r\",\n \"def roll_die(self, number_of_rolls):\\n\\t\\tfor roll in range(0, number_of_rolls):\\n\\t\\t\\tprint(str(randint(1, self.sides)), end = \\\", \\\")\\n\\t\\tprint()\",\n \"def test_fourth_move(self):\\n\\t\\tdata = {'moves': [('r','s'), ('r','p'), ('s','p')],\\n\\t\\t\\t\\t'player_number': 2}\\n\\t\\tfor AI in self.list_of_AIs:\\n\\t\\t\\tself.assertTrue(AI.move(data) in ['r', 'p', 's'])\",\n \"def move(self):\\n pass\",\n \"def person_move(self, pos1=None, pos2=None):\\n if self.available_combinations() != []:\\n if pos1 is None and pos2 is None:\\n comb = random.choice(self.available_combinations())\\n self.board[comb[0]][comb[1]] = 5\\n else:\\n if (pos1, pos2) in self.available_combinations():\\n self.board[pos1][pos2] = 5\\n else:\\n print(\\\"This position is not available\\\")\\n raise TextException\\n self.last_move = 5\",\n \"def move_marble(self, coordinates, direction):\\n pass\",\n \"def move_zombies(self, human_distance_field): #essentially the same as move_humans, but in 4 directions not 8\\r\\n blocked = self.get_grid_height() * self.get_grid_width()\\r\\n new_positions = []\\r\\n for zombie in self.zombies():\\r\\n moves = self.four_neighbors(zombie[0], zombie[1])\\r\\n moves.append((zombie[0], zombie[1]))\\r\\n potential_moves = [moves[0]]\\r\\n distance = human_distance_field[moves[0][0]][moves[0][1]]\\r\\n \\r\\n for move in moves:\\r\\n if human_distance_field[move[0]][move[1]] < blocked:\\r\\n if human_distance_field[move[0]][move[1]] < distance:\\r\\n potential_moves = [move]\\r\\n distance = human_distance_field[move[0]][move[1]]\\r\\n elif human_distance_field[move[0]][move[1]] == distance:\\r\\n potential_moves.append(move)\\r\\n \\r\\n new_positions.append(random.choice(potential_moves))\\r\\n \\r\\n self._zombie_list = new_positions\",\n \"def move_asteroids(self):\\n for i in range(len(self.asteroid_type)):\\n self.game.move(self.asteroid_type[i], -self.asteroid_speed[i], 0)\\n for i in range(len(self.asteroid_id_e)):\\n self.game.move(self.asteroid_id_e[i], -self.asteroid_speed_e[i], 0)\",\n \"def move(self):\\n \\n # checks for bots nearby\\n next_move = self.follow()\\n \\n # finds a random move if no bot\\n if next_move is self.position:\\n self.position = self.wander()\\n else:\\n self.position = next_move\",\n \"def move_zombies(self, human_distance):\\n updated_zombie_list = []\\n for zombie in self.zombies():\\n # find the smallest distance in 4 neighbor cells\\n min_distance = human_distance[zombie[0]][zombie[1]]\\n min_dist_cell = [zombie]\\n # loop through neighbor cells\\n for cell in self.four_neighbors(zombie[0], zombie[1]):\\n if self.is_empty(cell[0], cell[1]):\\n distance = human_distance[cell[0]][cell[1]]\\n if distance == min_distance:\\n min_dist_cell.append(cell)\\n elif distance < min_distance:\\n min_dist_cell = [cell]\\n min_distance = distance\\n # decide which cell to move to\\n updated_zombie_list.append(min_dist_cell[random.randrange(len(min_dist_cell))])\\n self._zombie_list = updated_zombie_list\",\n \"def run_instructions(self) -> None:\\n for item in self._instructions:\\n for _ in range(item[1]):\\n self.move_head(item[0])\",\n \"def step(self,action_list,verbose=False):\\n rewards,dones = np.zeros((self.n_snakes),dtype=np.float), np.zeros((self.n_snakes)).astype('bool')\\n for i in range(self.n_snakes):\\n eaten_food = self.snakes[i].move(action_list[i],self.food_pos)\\n if eaten_food == True : \\n self.update_food()\\n rewards[i]=1; # Eaten food \\n if self.hasCollided() == True:\\n self.snakes[i].reset(self.sample_empty_pos())\\n rewards[i]=-1;\\n dones[i]=True;\\n\\n self.display(verbose=verbose)\\n observation = self.get_observation();\\n observation = np.repeat(observation,repeats=self.n_snakes,axis=0)\\n states = self.get_state()\\n return observation,rewards,dones,states;\",\n \"def throw_dice(self):\\n self.dice = []\\n for i in range (6):\\n die = random.randint(1, 6)\\n self.dice.append(die)\\n self.num_throws += 1\",\n \"def move(self, move):\\n raise NotImplementedError()\",\n \"def move_repeatedly():\\n check = check50.run(run_command)\\n check.stdin(\\\"WEST\\\").stdout(room_2_description)\\n check.stdin(\\\"EAST\\\").stdout(room_1_name)\\n check.stdin(\\\"WEST\\\").stdout(room_2_name)\",\n \"def main():\\n move()\\n move()\\n pick_beeper()\\n move()\\n turn_left()\\n for i in range(2):\\n move()\\n put_beeper()\\n turn_around()\\n move_to_wall()\\n turn_right()\\n move_to_wall()\\n turn_around()\",\n \"def move(self, step):\\n for point in self.points:\\n l = min(len(step), len(point.position))\\n for i in range(l):\\n point.position[i] = step[i]\",\n \"def __snake_move(self):\\n self.__eat_candy()\\n # move tail and body\\n n_snake = len(self.__snake)\\n for i in range(1, n_snake):\\n s2 = self.__snake[n_snake - i]\\n s1 = self.__snake[n_snake - i - 1]\\n s2.move(s1.pos())\\n # move head\\n pos = self.__snake[0].pos()\\n tmp_snake = Snake(self, direction=self.__h_direction, position=pos)\\n h_pos = self.__get_next_head_pos(tmp_snake)\\n tmp_snake.remove()\\n icon = f'resources/{self.__directions[self.__h_direction]}.svg'\\n new_head = Snake(self, icon, self.__h_direction, self.__cell_edge, h_pos)\\n old_head = self.__snake[0]\\n self.__snake[0] = new_head\\n old_head.remove()\\n self.__crash_check()\",\n \"def move(self, direction, cycles):\\n\\t\\tself.planet.tiles[self.y][self.x].set_occupant() # set occupant to the initial tile\\n\\t\\tif direction == \\\"N\\\": # unit vector (0, -1)\\n\\t\\t\\ty_symbol = -1\\n\\t\\t\\tx_symbol = 0\\n\\t\\tif direction == \\\"S\\\": # unit vector (0, 1)\\n\\t\\t\\ty_symbol = 1\\n\\t\\t\\tx_symbol = 0\\n\\t\\tif direction == \\\"W\\\": # unit vector (-1, 0)\\n\\t\\t\\tx_symbol = -1\\n\\t\\t\\ty_symbol = 0\\n\\t\\tif direction == \\\"E\\\": # unit vector (1, 0)\\n\\t\\t\\tx_symbol = 1\\n\\t\\t\\ty_symbol = 0\\n\\t\\ti = 0\\n\\t\\twhile i < int(cycles):\\n\\t\\t\\tnext_x = self.x + x_symbol # change x coordinate\\n\\t\\t\\tnext_y = self.y + y_symbol # change y coordinate\\n\\t\\t\\tnext_x, next_y = self.spherical(next_x, next_y) # get the next tile's coordinate\\n\\t\\t\\tif self.can_move(next_x, next_y): # check whether rover can move\\n\\t\\t\\t\\t#reduce battery\\n\\t\\t\\t\\tif self.planet.tiles[next_y][next_x].is_shaded():\\n\\t\\t\\t\\t\\tself.battery -= 1\\n\\t\\t\\t\\tself.x = next_x\\n\\t\\t\\t\\tself.y = next_y\\n\\t\\t\\t\\ttile = self.planet.tiles[next_y][next_x]\\n\\t\\t\\t\\ttile.set_occupant()\\n\\t\\t\\t\\ti += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbreak\",\n \"def move_random(self, board: Board) -> None:\\n rnd_move_idx = randint(0,4)\\n # moves: stay, up, left, right, down\\n moves = [[0,0], [0,-1], [-1,0], [1,0], [0,1]]\\n\\n if board.can_position_at(self.x + moves[rnd_move_idx][0], self.y + moves[rnd_move_idx][1]):\\n board.set_element_at_position(0, self.x, self.y)\\n self.x += moves[rnd_move_idx][0]\\n self.y += moves[rnd_move_idx][1]\\n board.set_element_at_position(3, self.x, self.y)\\n print(\\\"Bomberman moved to [\\\", self.x, \\\",\\\", self.y, \\\"]\\\")\",\n \"def move_peers(self, data):\\n for peer_id, row, col in data:\\n if peer_id in self.peers:\\n self.peers[peer_id].move(row, col)\\n return\",\n \"def test_move(self, data, moving_indices, move_offset):\\n assume(np.max(moving_indices) < data.shape[0])\\n\\n unmoved = copy(data)\\n layer = Points(data)\\n\\n move_offset = move_offset[: data.shape[1]]\\n\\n layer._move(moving_indices, list(np.zeros(data.shape[1])))\\n layer._move(moving_indices, list(move_offset))\\n layer._drag_start = None\\n\\n assert_almost_equal(\\n layer.data[moving_indices], unmoved[moving_indices] + move_offset\\n )\\n\\n unmoved_indices = list(range(data.shape[0]))\\n for mi in moving_indices:\\n if mi in unmoved_indices:\\n unmoved_indices.remove(mi)\\n assert_almost_equal(layer.data[unmoved_indices], unmoved[unmoved_indices])\",\n \"def random_move(turtle, distance):\\n angle = uniform(-90,90)\\n d = uniform(0,distance)\\n turtle.left(angle)\\n turtle.forward(d)\",\n \"def move_creatures(self):\\r\\n auto = self.state\\r\\n for y, line in enumerate(auto):\\r\\n for x, cell in enumerate(line):\\r\\n 'no creature cell'\\r\\n if cell == 0:\\r\\n continue\\r\\n next_cell = np.random.randint(0, 8)\\r\\n 'Stay in place'\\r\\n if next_cell == 0:\\r\\n continue\\r\\n ' check next cell'\\r\\n auto = self.check_next_cell(cell, auto, y, x, next_cell)\\r\\n\\r\\n self.nHealthy = np.sum(self.state == 1)\\r\\n self.nSick = np.sum(self.state == 2)\\r\\n return auto\",\n \"def move(self):\\n \\n self.position = self.wander()\",\n \"def move_pawn(pos, game):\\n #Convert coordinates to row and column\\n row = int(pos[1]//(SQUARESIZE+FENCEWIDTH))\\n col = int(pos[0]//(SQUARESIZE+FENCEWIDTH))\\n #Make move\\n game.move_pawn(game.get_player_turn(), (col,row))\",\n \"def _move(self, direction, count, step_size):\\n for _ in range(count):\\n if not self.has_hit_edge():\\n self.position = min(self.scene.step_count - 1, max(0,\\n self.position + direction * step_size))\\n self._take_measure()\\n self.steps_taken += 1\\n self.visited_positions.append(self.position)\",\n \"def move_girl(girl):\\n global trainer_speed, girlcount, girl_left\\n if girl_left:\\n for i in girl:\\n i.x -= trainer_speed\\n i.move_speed()\\n girlcount += 0.5\\n girl_frame = int(girlcount) % 3\\n camera.draw(girl[girl_frame])\\n else:\\n for i in girl:\\n i.x += trainer_speed\\n i.move_speed()\\n girlcount += 0.5\\n girl_frame = int(girlcount) % 3\\n camera.draw(girl[girl_frame + 3])\\n for each in girl:\\n if each.x < 20:\\n girl_left = False\\n if each.x > 780:\\n girl_left = True\",\n \"def move(self, rel_pos):\\n self.pos = (self.pos[0] + rel_pos[0] * GRID, self.pos[1] + rel_pos[1] * GRID)\",\n \"def move_draught_end(event):\\n global red_draughts, white_draughts\\n global board_array\\n global old_point\\n global die_1_num, die_2_num, doubles\\n draught = board.find_withtag(CURRENT)[0]\\n #Figure out which point they want to put it on\\n bottom = (event.y-click_offset[1] >= board_height//2)\\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\\n is_red = draught in red_draughts\\n if bottom == False:\\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\\n else:\\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \\n #Check legality\\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\\n draw_draughts()\\n return\\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\\n draw_draughts()\\n return\\n if(new_point == 0 and not is_red): #if white trying to bear off\\n for i in xrange(7,26):\\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\\n draw_draughts()\\n return\\n if(new_point == 25 and is_red): #if red trying to bear off\\n for i in xrange(0,18):\\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\\n draw_draughts()\\n return \\n \\n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\\n if(doubles == False) or (die_2_num != 0):\\n die_1_num = 0\\n else: \\n die_2_num = die_1_num\\n doubles = False\\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\\n if(doubles == False) or (die_1_num != 0):\\n die_2_num = 0\\n else: \\n die_1_num = die_2_num\\n doubles = False\\n else: #Can't move there on this roll\\n draw_draughts()\\n return\\n update_dice()\\n #Update board_array\\n if is_red:\\n board_array[old_point][0] -= 1\\n board_array[new_point][0] += 1\\n if(board_array[new_point][1] == 1): #Handle hits\\n board_array[new_point][1] -= 1\\n board_array[25][1] += 1\\n else:\\n board_array[old_point][1] -= 1\\n board_array[new_point][1] += 1\\n if(board_array[new_point][0] == 1): #Handle hits\\n board_array[new_point][0] -= 1\\n board_array[0][0] += 1\\n\\n draw_draughts()\\n if(die_1_num == 0 and die_2_num == 0):\\n comp_turn()\",\n \"def pickup_dominoes(self, num_dominoes, player):\\n\\n for domino in range(num_dominoes):\\n shuffle(self.boneyard)\\n player.hand.append(self.boneyard.pop(0))\",\n \"def turnPoints(angles, points):\\n l = len(points)\\n for i in range(l - 1):\\n points[i].turn(angles[i], points[i + 1:])\",\n \"def make_move(self, playername, coordinates, direction):\\n\\n pass\",\n \"def move(self):\\r\\n for index in range(self.size):\\r\\n self.values[index] = self.values[index] + self.velocities[index]\\r\\n \\r\\n # Adjust values to keep particle inside boundaries.\\r\\n if self.values[index] < Particle.MIN_VALUE:\\r\\n self.values[index] = (-self.values[index] % Particle.MAX_VALUE)\\r\\n elif self.values[index] > Particle.MAX_VALUE:\\r\\n self.values[index] = (self.values[index] % Particle.MAX_VALUE)\",\n \"def set_move(self, position: int) -> None:\\n if position not in range(1, 10):\\n raise IllegalMoveException\\n elif position in self.moves:\\n raise DuplicateMoveException\\n self.moves[position] = self.turn\\n if self.turn == self.players[0]:\\n self.turn = self.players[1]\\n else:\\n self.turn = self.players[0]\",\n \"def move(self, mapa: Map, bomberman: Bomberman, bombs: list, enemies: dict, moves: int=1) -> list:\\n safety_pos = self.pos\\n advance_pos = [self.pos]\\n if self.pos != self.enemy.lastpos:\\n self.enemy.lastdir = vector2dir(self.pos[0]-self.enemy.lastpos[0], self.pos[1]-self.enemy.lastpos[1])\\n for x in range(moves):\\n self.enemy.move(mapa, bomberman, bombs, list(enemies.values()))\\n if self.pos != advance_pos[-1]: advance_pos.append(self.pos)\\n self.pos = safety_pos\\n return advance_pos\",\n \"def move(self, direction):\\n pass\",\n \"def move_humans(self, zombie_distance_field):\\n self.move(zombie_distance_field, self._human_list, self.eight_neighbors, \\\"lt\\\")\",\n \"def generate_moves(self, board: Board) -> typing.Iterator[MoveDescription]:\\n dominoes = set(board.dominoes)\\n for domino in dominoes:\\n dx, dy = domino.direction\\n yield from self.try_move(domino, dx, dy)\\n yield from self.try_move(domino, -dx, -dy)\",\n \"def move_ghosts(self):\\n temp_ghosts = []\\n for ghost in self.ghosts:\\n if (self.player.x - ghost.x > 0 and\\n board.board[ghost.y][ghost.x+1 if ghost.x < 34 else ghost.x] == '.' or\\n board.board[ghost.y][ghost.x+1 if ghost.x < 34 else ghost.x] == 'P'\\n ):\\n board.board[ghost.y][ghost.x] = '.'\\n if board.board[ghost.y][ghost.x+1] == 'P':\\n print 'Lost the game'\\n self.player.die = 1\\n exit()\\n board.board[ghost.y][ghost.x+1] = 'G'\\n ghost.x += 1\\n elif (self.player.x - ghost.x < 0 and\\n board.board[ghost.y][ghost.x-1 if ghost.x > 0 else ghost.x] == '.'\\n or board.board[ghost.y][ghost.x-1 if ghost.x > 0 else ghost.x] == 'P'\\n ):\\n board.board[ghost.y][ghost.x] = '.'\\n if board.board[ghost.y][ghost.x-1] == 'P':\\n print 'Lost the game'\\n self.player.die = 1\\n exit()\\n board.board[ghost.y][ghost.x-1] = 'G'\\n ghost.x -= 1\\n elif (self.player.y - ghost.y > 0\\n and board.board[ghost.y+1 if ghost.y < 14 else ghost.y][ghost.x] == '.'\\n or board.board[ghost.y+1 if ghost.y < 14 else ghost.y][ghost.x] == 'P'\\n ):\\n board.board[ghost.y][ghost.x] = '.'\\n if board.board[ghost.y+1][ghost.x] == 'P':\\n print 'Lost the game'\\n self.player.die = 1\\n exit()\\n board.board[ghost.y+1][ghost.x] = 'G'\\n ghost.y += 1\\n elif (board.board[ghost.y-1 if ghost.y > 0 else ghost.y][ghost.x] == '.'\\n or board.board[ghost.y-1 if ghost.y > 0 else ghost.y][ghost.x] == 'P'\\n ):\\n board.board[ghost.y][ghost.x] = '.'\\n if board.board[ghost.y-1][ghost.x] == 'P':\\n print 'Lost the game'\\n self.player.die = 1\\n exit()\\n board.board[ghost.y-1][ghost.x] = 'G'\\n ghost.y -= 1\\n temp_ghosts.append(ghost)\\n self.ghosts = temp_ghosts\",\n \"def hslArray_move(self, hslList, delay):\\n # res\\n\\n res = self.hslArrayOfs_move(0,hslList, delay)\\n return res\",\n \"def move_randomly(self, with_fight=False):\\n delta = [(-1, -1), (-1, 0), (-1, 1), (0, 1), (0, -1), (1, -1), (1, 0), (1, 1)]\\n rd.shuffle(delta)\\n x, y = self.owner.pos\\n while len(delta) > 0:\\n dx, dy = delta.pop()\\n if self.move_towards_position((x + dx, y + dy)):\\n return\",\n \"def test_moves(self):\\n self.herb.fitness = 1\\n Herbivore.set_parameters({\\\"mu\\\": 1})\\n original_position = (1, 1)\\n self.herb.coordinates = self.herb.migrate(self.list)\\n nt.assert_not_equal(self.herb.coordinates, original_position)\",\n \"def move(self, heaps: Tuple[int, ...], misere: bool) -> Tuple[int, int]:\\r\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66328007","0.596524","0.5870962","0.58515584","0.5819032","0.57985187","0.5759108","0.57562107","0.574448","0.57078034","0.56926847","0.56588614","0.5642412","0.5605683","0.5596673","0.5586302","0.55856097","0.5582289","0.5575625","0.55608356","0.55554855","0.5535301","0.55272806","0.55162615","0.55116314","0.55028677","0.5497211","0.5493248","0.5492864","0.549278","0.549278","0.5492294","0.54682726","0.54630786","0.5434286","0.5432944","0.5424205","0.5412741","0.541158","0.5411338","0.54051256","0.54050434","0.54014254","0.5387991","0.53866655","0.5382255","0.5375748","0.53638893","0.53490514","0.53490514","0.5336116","0.5334558","0.532847","0.5328228","0.53102493","0.53100324","0.530685","0.5306827","0.5300871","0.5296214","0.5294268","0.5283535","0.52804404","0.52801543","0.5275297","0.5274978","0.52695936","0.5261961","0.52568525","0.52472156","0.5246552","0.52416444","0.5240666","0.52378726","0.5230398","0.52293384","0.52288747","0.5227627","0.52258754","0.5222245","0.52210593","0.521976","0.52065957","0.5203256","0.5201678","0.51991594","0.51970255","0.5192387","0.51870865","0.51854354","0.5183236","0.5177846","0.5176564","0.51712966","0.5166224","0.5166","0.5162003","0.51503485","0.5149858","0.5146311"],"string":"[\n \"0.66328007\",\n \"0.596524\",\n \"0.5870962\",\n \"0.58515584\",\n \"0.5819032\",\n \"0.57985187\",\n \"0.5759108\",\n \"0.57562107\",\n \"0.574448\",\n \"0.57078034\",\n \"0.56926847\",\n \"0.56588614\",\n \"0.5642412\",\n \"0.5605683\",\n \"0.5596673\",\n \"0.5586302\",\n \"0.55856097\",\n \"0.5582289\",\n \"0.5575625\",\n \"0.55608356\",\n \"0.55554855\",\n \"0.5535301\",\n \"0.55272806\",\n \"0.55162615\",\n \"0.55116314\",\n \"0.55028677\",\n \"0.5497211\",\n \"0.5493248\",\n \"0.5492864\",\n \"0.549278\",\n \"0.549278\",\n \"0.5492294\",\n \"0.54682726\",\n \"0.54630786\",\n \"0.5434286\",\n \"0.5432944\",\n \"0.5424205\",\n \"0.5412741\",\n \"0.541158\",\n \"0.5411338\",\n \"0.54051256\",\n \"0.54050434\",\n \"0.54014254\",\n \"0.5387991\",\n \"0.53866655\",\n \"0.5382255\",\n \"0.5375748\",\n \"0.53638893\",\n \"0.53490514\",\n \"0.53490514\",\n \"0.5336116\",\n \"0.5334558\",\n \"0.532847\",\n \"0.5328228\",\n \"0.53102493\",\n \"0.53100324\",\n \"0.530685\",\n \"0.5306827\",\n \"0.5300871\",\n \"0.5296214\",\n \"0.5294268\",\n \"0.5283535\",\n \"0.52804404\",\n \"0.52801543\",\n \"0.5275297\",\n \"0.5274978\",\n \"0.52695936\",\n \"0.5261961\",\n \"0.52568525\",\n \"0.52472156\",\n \"0.5246552\",\n \"0.52416444\",\n \"0.5240666\",\n \"0.52378726\",\n \"0.5230398\",\n \"0.52293384\",\n \"0.52288747\",\n \"0.5227627\",\n \"0.52258754\",\n \"0.5222245\",\n \"0.52210593\",\n \"0.521976\",\n \"0.52065957\",\n \"0.5203256\",\n \"0.5201678\",\n \"0.51991594\",\n \"0.51970255\",\n \"0.5192387\",\n \"0.51870865\",\n \"0.51854354\",\n \"0.5183236\",\n \"0.5177846\",\n \"0.5176564\",\n \"0.51712966\",\n \"0.5166224\",\n \"0.5166\",\n \"0.5162003\",\n \"0.51503485\",\n \"0.5149858\",\n \"0.5146311\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63560057"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":25295,"cells":{"query":{"kind":"string","value":"Record the joint character between a pair of cells."},"document":{"kind":"string","value":"def add_joint(joint: str, x1: int, y1: int, x2: int, y2: int) -> str:\n return joint"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def junction_char(self):\n ...","def _put_chr_at(self, char, row, col, color, adjustment_x=.19, adjustment_y=.19):\n self._goto_piece_xy(row, col, adjustment_x, adjustment_y)\n self.pen.color(color)\n self.pen.write(char, font=(\"Courier\", round(self.square_side_size * .7),\n \"normal\"))","def encryptPair(self):\n # Locate the characters in the matrix\n self.deselectCanvasses()\n (row1, col1) = self.matrix.find(self.plainText[self.cursor])\n (row2, col2) = self.matrix.find(self.plainText[self.cursor + 1])\n self.selectCanvas(row1, col1, \"gray\")\n self.selectCanvas(row2, col2, \"gray\")\n # Swap them if they are in the same row or column\n if row1 == row2 or col1 == col2:\n return self.plainText[self.cursor + 1] + self.plainText[self.cursor]\n # Otherwise, use the characters at the opposite\n # corners of the rectangle in the matrix\n else:\n self.selectCanvas(row2, col1, \"pink\")\n self.selectCanvas(row1, col2, \"pink\")\n ch1 = self.matrix[row2][col1]\n ch2 = self.matrix[row1][col2]\n return ch1 + ch2","def _get_letters_adjacent_cells(self, grid, x0, y0):\n letters = ''\n for cell in grid.get_adjacent_cells(x0, y0):\n letters = letters + cell['letter']\n return ''.join(sorted(letters))","def joint_pairs(self):\n return ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))","def en_passant(self, from_row, from_col, to_row, to_col):\n t = self.data[from_row][from_col]\n self.data[from_row][from_col] = '.'\n self.data[to_row][to_col] = t \n self.data[from_row][to_col] = '.'","def display_board(board: list, character: list):\n for index in range(len(board)):\n for index_two in range(len(board[index])):\n current_position = [index, index_two]\n if current_position == character:\n if index_two == len(board[index]) - 1:\n print('c')\n else:\n print('c', end=\" \")\n else:\n if index_two == len(board[index]) - 1:\n print('x')\n else:\n print('x', end=\" \")","def createperson(self, board, x_ind, y_ind, char):\n self = self\n for i in range(4):\n for j in range(2):\n board[x_ind + j][y_ind + i] = char\n return board","def cell_value(self, x, y):\n if x == 8 and y == 0:\n return \"--\"\n (r, g) = self[(x, y)]\n return \"%s%s\" % (r, g)","def label_joints():\n side_dict = {'C': 0,\n 'L': 1,\n 'R': 2}\n for jnt in mc.ls(type='joint'):\n mc.setAttr('{}.side'.format(jnt), side_dict[jnt.split('_')[0]])\n mc.setAttr('{}.type'.format(jnt), 18)\n mc.setAttr('{}.otherType'.format(jnt), jnt.split('_')[1], type=\"string\")","def display_position(game_board: list, character: list):\n print(\"You are currently here:\")\n for position in game_board:\n if position[0] == character[0] and position[1] == character[1]:\n print('C', end=\" \")\n else:\n print(\"*\", end=\" \")\n if position[1] == game_board[-1][1]:\n print(\"\")","def writechar(self, char=\" \"):\n\n font=self.selectfont(char)\n t = self.canvas.beginText(self.x, self.y)\n t.setFont(font, self.fontsize)\n t.setFillGray(self.gray)\n t.textOut(char)\n self.canvas.drawText(t)\n return t","def dummy_junction24():\n return 'junction:chr1:251-399:+'","def insertJoint(*args, **kwargs)->AnyStr:\n pass","def __str__(self) -> str:\n output = self.columns * \" __\" + \"\\n\"\n for i in range(self.rows):\n for j in range(self.columns):\n filling = \"__\"\n if len(self.cells[i][j].agents) == 1:\n filling = \"_§\"\n elif len(self.cells[i][j].agents) == 2:\n filling = \"§§\"\n elif len(self.cells[i][j].agents) > 2:\n filling = \"++\"\n output += \"|\" + filling\n if j == self.columns - 1:\n output += \"|\"\n output += \"\\n\"\n return output","def dummy_junction23():\n return 'junction:chr1:251-299:+'","def jointext(firststring, secondstring):\n\n # Return the joined strings\n return str(firststring) + str(secondstring)","def coord_char(coord, matrix):\n row_index, column_index = coord\n\n return matrix[row_index][column_index]","def dummy_junction13():\n return 'junction:chr1:176-299:+'","def simple_cell_string(self, values):\n if len(values) == 0:\n return '!'\n elif len(values) == 1:\n return self.number_strings[values[0]]\n else:\n return '.'","def encode(self, char):\n\n if char == self.pair[0]:\n return self.pair[1]\n elif char == self.pair[1]:\n return self.pair[0]\n else:\n return char","def joint_callback(data):\n joints[0] = data.position[9]\n joints[1] = data.position[10]\n joints[2] = data.position[11]\n joints[3] = data.position[12]\n joints[4] = data.position[13]\n global position_geted\n position_geted = True","def joint_pairs(self):\n return [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16], #17 body keypoints\n [20-3, 23-3], [21-3, 24-3], [22-3, 25-3], [26-3, 42-3], [27-3, 41-3], [28-3, 40-3], [29-3, 39-3], [30-3, 38-3], \n [31-3, 37-3], [32-3, 36-3], [33-3, 35-3], [43-3, 52-3], [44-3, 51-3], [45-3, 50-3], [46-3, 49-3], [47-3, 48-3], \n [62-3, 71-3], [63-3, 70-3], [64-3, 69-3], [65-3, 68-3], [66-3, 73-3], [67-3, 72-3], [57-3, 61-3], [58-3, 60-3],\n [74-3, 80-3], [75-3, 79-3], [76-3, 78-3], [87-3, 89-3], [93-3, 91-3], [86-3, 90-3], [85-3, 81-3], [84-3, 82-3],\n [94-3, 115-3], [95-3, 116-3], [96-3, 117-3], [97-3, 118-3], [98-3, 119-3], [99-3, 120-3], [100-3, 121-3],\n [101-3, 122-3], [102-3, 123-3], [103-3, 124-3], [104-3, 125-3], [105-3, 126-3], [106-3, 127-3], [107-3, 128-3],\n [108-3, 129-3], [109-3, 130-3], [110-3, 131-3], [111-3, 132-3], [112-3, 133-3], [113-3, 134-3], [114-3, 135-3]]","def test_two_cell_repel():\n space = c6.Space()\n c6.Cell(space, [0, 0], 1)\n c6.Cell(space, [0, 1.9], 1)\n for i in range(2):\n space.step()","def dummy_junction14():\n return \"junction:chr1:176-324:+\"","def add_char(self, coord, char, modify=False):\n if modify:\n range_y, range_x = self._map_dims\n new_coord = [coord[0]+range_y[0]-1, coord[1]+range_x[0]-1]\n self._screen.addch(new_coord[0], new_coord[1], char)\n self._screen.refresh()\n return new_coord\n else:\n self._screen.addch(coord[0], coord[1], char)\n self._screen.refresh()\n return coord","def interact(self,cell1,cell2):\n difference = math.fabs(cell1-cell2)\n if difference<=self.t: #if both cells are close enough: \n if cell1>cell2:\n # print \"Case 1\"\n return (cell1-difference/2.0,cell2+difference/2.0)\n else:\n # print \"Case 2\"\n return (cell1+difference/2.0,cell2-difference/2.0)\n else: #cells too far apart, they radicalize\n if cell1>cell2:\n # print \"Case 3\"\n return (cell1+(1-cell1)/2.0,cell2/2.0) #the higher one moves toward 1, the lower one moves toward 0\n else:\n # print \"Case 4\"\n return (cell1/2.0,cell2+(1-cell2)/2.0)","def __createkey__(self):\n return str(self.currentCol) + \",\" + str(self.currentRow)","def getCellStr(self, x, y): # TODO: refactor regarding issue #11\n c = self.board.getCell(x, y)\n\n if c == 0:\n return '.' if self.__azmode else ' .'\n\n elif self.__azmode:\n az = {}\n for i in range(1, int(math.log(self.board.goal(), 2))):\n az[2 ** i] = chr(i + 96)\n\n if c not in az:\n return '?'\n s = az[c]\n elif c == 1024:\n s = ' 1k'\n elif c == 2048:\n s = ' 2k'\n else:\n s = '%3d' % c\n\n return self.__colors.get(c, Fore.RESET) + s + Style.RESET_ALL","def teleport_pair(cell, game_coords):\n # get cell display\n item = search_coords(game_coords, cell)\n\n if item in ['1', '2', '3', '4', '5', '6', '7', '8', '9']:\n for coords in game_coords[item]:\n # partner pad found\n if coords != cell:\n return coords\n\n # not a teleport pad\n return -1","def generate_joint_labels(self):\r\n correspondence = \"SPEED CORRESPONDENCE:\\n\"\r\n self.correspondence = []\r\n for speed_idx, speed in enumerate(self.speed_set):\r\n correspondence += \"{}: {}\\n\".format(speed_idx, speed)\r\n self.correspondence.append(speed)\r\n logger.info(correspondence)\r\n return {\r\n \"move_joint\": torch.linspace(\r\n 0, len(self.speed_set)-1, len(self.speed_set), dtype=torch.int64\r\n )\r\n }","def encrypt_digraph(self, dg):\n\n first, second = dg\n\n if first == second:\n second = 'X'\n\n frow, fcol = self.location_of(first)\n srow, scol = self.location_of(second)\n\n # If the letters appear on the same row of your table, replace them with the\n # letters to their immediate right respectively (wrapping around to the left\n # side of the row if a letter in the original pair was on the right side of the\n # row).\n if frow == srow:\n fcol, scol = (fcol + 1) % 5, (scol + 1) % 5\n\n # If the letters appear on the same column of your table, replace them with the\n # letters immediately below respectively (wrapping around to the top side of\n # the column if a letter in the original pair was on the bottom side of the\n # column).\n if fcol == scol:\n frow, srow = (frow - 1) % 5, (srow - 1) % 5\n\n # If the letters are not on the same row or column, replace them with\n # the letters on the same row respectively but at the other pair of\n # corners of the rectangle defined by the original pair. The order is\n # important – the first letter of the encrypted pair is the one that\n # lies on the same row as the first letter of the plaintext pair\n if frow != srow and fcol != scol:\n fcol, scol = scol, fcol\n\n return self.create_digraph(frow, fcol, srow, scol)","def dummy_junction34():\n return 'junction:chr1:351-399:+'","def _generate_joint_string(self, joint, skeleton, joint_level):\n joint_string = \"\"\n temp_level = 0\n tab_string = \"\"\n while temp_level < joint_level:\n tab_string += \"\\t\"\n temp_level += 1\n\n # determine joint type\n if joint_level == 0:\n joint_string += tab_string + \"ROOT \" + joint + \"\\n\"\n else:\n if len(skeleton.nodes[joint].children) > 0:\n joint_string += tab_string + \"JOINT \" + joint + \"\\n\"\n else:\n joint_string += tab_string + \"End Site\" + \"\\n\"\n\n # open bracket add offset\n joint_string += tab_string + \"{\" + \"\\n\"\n offset = skeleton.nodes[joint].offset\n joint_string += tab_string + \"\\t\" + \"OFFSET \" + \"\\t \" + \\\n str(offset[0]) + \"\\t \" + str(offset[1]) + \"\\t \" + str(offset[2]) + \"\\n\"\n\n if len(skeleton.nodes[joint].children) > 0:\n # channel information\n channels = skeleton.nodes[joint].channels\n joint_string += tab_string + \"\\t\" + \\\n \"CHANNELS \" + str(len(channels)) + \" \"\n for tok in channels:\n joint_string += tok + \" \"\n joint_string += \"\\n\"\n\n joint_level += 1\n # recursive call for all children\n for child in skeleton.nodes[joint].children:\n joint_string += self._generate_joint_string(child.node_name, skeleton, joint_level)\n\n # close the bracket\n joint_string += tab_string + \"}\" + \"\\n\"\n return joint_string","def connect_cells(dfte,vari):\n # Create the variabel cell for mother, grand mother and grand grand mother\n if 'g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'parent_cell') #Create genealogy\n if 'g_g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'g_parent_cell')\n if 'g_g_g_parent_cell' not in dfte.columns:\n dfte = rl.genalogy(dfte,'g_g_parent_cell')\n #give unique index to all cells\n dfte['uid'] = dfte['cell']+dfte['time_sec'].apply(lambda x: str(x))\n vac=[];sc=[];uid = []\n # Create a vecotor for the variable of interest of cell,mother,grand mother and grand grand mother and an unique identifier of it\n for c,idx in enumerate(dfte['cell'].unique()):\n dau = dfte.loc[dfte['cell']==idx]\n pc = dau['parent_cell'].iloc[0]\n mum = dfte.loc[dfte['cell']==pc]\n gpc = dau['g_parent_cell'].iloc[0]\n gmum = dfte.loc[dfte['cell']==gpc]\n ggpc = dau['g_g_parent_cell'].iloc[0]\n ggmum = dfte.loc[dfte['cell']==ggpc]\n gggpc = dau['g_g_g_parent_cell'].iloc[0]\n gggmum = dfte.loc[dfte['cell']==gggpc]\n fte = lambda x: x[['{}'.format(vari),'uid']].values\n tmp = np.vstack([fte(gggmum),fte(ggmum),fte(gmum),fte(mum),fte(dau)])\n vac.append(tmp[:,0])\n uid.append(tmp[:,1])\n sc.append(['super_cell_{}'.format(c)]*len(tmp))\n return pd.DataFrame({'super_cell':np.hstack(sc),'uid':np.hstack(uid),'{}'.format(vari):np.hstack(vac)})","def output_char(char):\n # If the last printed character has the same token, it also has the\n # same style, so we don't output it.\n if last_char[0] and last_char[0].token == char.token:\n write(char.char)\n else:\n style = get_style_for_token(char.token)\n\n if style:\n output.set_attributes(style['color'], style['bgcolor'],\n bold=style.get('bold', False),\n underline=style.get('underline', False))\n\n # If we print something with a background color, remember that.\n background_turned_on[0] = bool(style['bgcolor'])\n else:\n # Reset previous style and output.\n output.reset_attributes()\n\n write(char.char)\n\n last_char[0] = char","def printBoard2( A ):\r\n for row in A:\r\n for col in row:\r\n \tif col == 1:\r\n \t\tsys.stdout.write(\"@\")\r\n \telse:\r\n \t\tsys.stdout.write(\" \")\r\n sys.stdout.write( '\\n' )","def point(self, x, y, char):\n assert len(char) == 1\n assert x >= 0\n assert x < self.cols\n assert y >= 0\n assert y < self.lines\n\n self.canvas[y][x] = char","def join_with_prev(self, prev):\n if prev._cell_input.getText() == \"\":\n new_text = self._cell_input.getText()\n else:\n new_text = prev._cell_input.getText()\n if self._cell_input.getText() != \"\":\n new_text += \"\\n\" + self._cell_input.getText()\n y_new = prev._cell_input.rows()\n if prev._cell_input.getText() == \"\":\n y_new -= 1\n prev._cell_input.setText(new_text)\n prev._cell_input.set_cursor_coordinates(0, y_new)\n prev.set_focus()","def write_to_cell(table, row, col, text):\n\n\ttable.cell(row, col).paragraphs[0].runs[0].text = text","def deg_locations_writer(cell,width,(x,y)):\n if np.any(np.isnan(cell)):\n sys.stdout.write(' ' * width)\n else:\n sys.stdout.write(\n \"{0:^{width}s}\".format(\n \"{0:.2f},{1:.2f}\".format(cell[0],cell[1]),\n width=width\n )\n )","def addch(self, posy, posx, character, color_pair):\r\n if posy < 0 or posy > self.height - 1:\r\n return\r\n if posx < 0 or posx > self.width - 1:\r\n return\r\n if posx == self.width - 1 and posy == self.height - 1:\r\n return\r\n self.win.addch(posy, posx, character, color_pair)","def _insChar(self, char, pos, color):\n char, vertices, glyph = self._extractGlyph(char, glm.vec4(color))\n if not self.text:\n off, kern = self._updateMetric(pos, char)\n if char in self.NO_GLYPH_CHARS:\n self.colors.insert(pos, [char, None])\n else:\n vertices['vtx'] += off + glyph['offset']\n self.allVertices = np.hstack(vertices)\n self.allIndices = self._baseInd\n self.colors.insert(pos, [char, color])\n self.text += char\n else:\n self.logger.debug(\"Inserting %r at %d\" % (char, pos))\n nonGlyph = countInSet(self.text[:pos], self.NO_GLYPH_CHARS)\n # Arrange vertices\n if pos < len(self.text):\n self.allVertices = self.allVertices[:(pos - nonGlyph) * 4]\n self.allIndices = self.allIndices[:pos - nonGlyph]\n\n # Set the metric\n off, kern = self._updateMetric(pos, char)\n if char in self.NO_GLYPH_CHARS:\n color = None\n else:\n vertices['vtx'] += off + kern + glyph['offset']\n if self.allVertices is None:\n self.allVertices = np.hstack(vertices)\n else:\n self.allVertices = np.append(self.allVertices, vertices)\n if self.allIndices is None:\n self.allIndices = self._baseInd\n else:\n self.allIndices = np.vstack((self.allIndices,\n self._baseInd + (pos - nonGlyph) * 4))\n\n self.colors.insert(pos, [char, color])\n if pos < len(self.text):\n self.text = self.text[:pos] + char + self.text[pos:]\n self._updateGlyphs(pos, char)\n else:\n self.text += char","def compute_pair_frequencies(text, alphabet_size=256):\n\n curr = text[1:]\n prev = text[:-1]\n pairs = np.stack([prev, curr])\n\n print(\"Counting pairs . . .\")\n (char1, char2), counts = np.unique(pairs, return_counts=True, axis=1)\n print(\"Done.\\n\")\n\n joints = np.zeros((alphabet_size, alphabet_size))\n joints[char1, char2] = counts / np.sum(counts)\n assert np.isclose(np.sum(joints), 1.0)\n\n return joints","def drawcell(self,cell):\n \n # get top left and center co-ords of cell\n left, top = self.getlefttop(cell.xpos,cell.ypos)\n\n # draw connections to other cells (if these exist)\n # done first since this is 'bottom layer'\n for c in cell.conns:\n self.drawconn(left,top,c)\n \n # outer circle\n pygame.draw.circle(self.screen, cell.ocol,\n (left + CELLSIZE/2,top + CELLSIZE/2),\n CELLSIZE/2)\n # inner circle\n pygame.draw.circle(self.screen, cell.icol,\n (left + CELLSIZE/2,top + CELLSIZE/2),\n CELLSIZE/2 - TSIZE)\n \n # draw arrows\n for t in enumerate(cell.current):\n if t[1] == 1:\n points = self.gettpoints(left,top,t[0])\n pygame.draw.polygon(self.screen, cell.tcol, points)","def add_cell(self, connections=[], cell = None):\n # Create new cell\n idx = len(self.cells)\n position = np.random.randn(3) if cell == None else cell.position + 1\n connections = connections if len(connections) != 0 else [idx - 1]\n new_cell = Cell(idx=idx, connections=connections, position=position)\n # Connect new cell with connected cells in plant \"\"\"\n for old_cell in self.cells:\n if old_cell.idx in new_cell.connections: \n old_cell.connections.append(new_cell.idx)\n # Add new cell to plant\n self.cells.append(new_cell)","def write_row(prefix, address1, address2, row, output):\n values = ' '.join('{:02x}'.format(x) for x in row)\n ascii = ''.join(chr(x) if 32 <= x < 128 else '.' for x in row)\n # pad width\n values += ' ' * (47 - len(values))\n ascii += ' ' * (16 - len(values))\n # output line, insert gap at 8\n output.write('{} {:^8s} {:^8s}: {} {} {} {}\\n'.format(\n prefix,\n '{:08x}'.format(address1) if address1 is not None else '--------',\n '{:08x}'.format(address2) if address2 is not None else '--------',\n values[:24], values[24:],\n ascii[:8], ascii[8:]))","def createCell(self, space):\n\t\tself.createCellEntity(space)\n\t\tDEBUG_MSG(\"Avatar::createCell\")","def write_char(self, char, token, string_index=None,\n set_cursor_position=False, z_index=False):\n assert len(char) == 1\n\n char_obj = Char(char, token, z_index)\n char_width = char_obj.get_width()\n\n # In case there is no more place left at this line, go first to the\n # following line. (Also in case of double-width characters.)\n if self._x + char_width > self.size.columns:\n self._y += 1\n self._x = 0\n\n insert_pos = self._y, self._x # XXX: make a Point of this?\n\n if string_index is not None:\n self._cursor_mappings[string_index] = insert_pos\n\n if set_cursor_position:\n self.cursor_position = Point(y=self._y, x=self._x)\n\n # Insertion of newline\n if char == '\\n':\n self._y += 1\n self._x = 0\n self._line_number += 1\n\n # Insertion of a 'visible' character.\n else:\n if char_obj.z_index >= self._buffer[self._y][self._x].z_index:\n self._buffer[self._y][self._x] = char_obj\n\n # When we have a double width character, store this byte in the\n # second cell. So that if this character gets deleted afterwarsd,\n # the ``output_screen_diff`` will notice that this byte is also\n # gone and redraw both cells.\n if char_width > 1:\n self._buffer[self._y][self._x+1] = Char(six.unichr(0))\n\n # Move position\n self._x += char_width\n\n return insert_pos","def print_room(room):\r\n\r\n for row in room:\r\n for cell in row:\r\n if cell == \"obstacle\":\r\n print(\"O\", end=\"\")\r\n elif cell == \"robot\":\r\n print(\"R\", end=\"\")\r\n elif cell == \"empty\":\r\n print(\" \", end=\"\")\r\n elif cell == \"dirt\":\r\n print(\"*\", end=\"\")\r\n\r\n print()","def character(self) -> str:\r\n return self.char if self.was_guessed else '_'","def writechar(self, char: int, /) -> None:","def drawconn(self,left,top,conn):\n # x and y are left and top of cell\n COFF = 0 # offset at edge of circle\n if conn == 'u':\n x = left\n y = top - COFF\n width = CELLSIZE\n height = CWIDTH\n elif conn == 'd':\n x = left\n y = top + CELLSIZE - CWIDTH\n width = CELLSIZE\n height = CWIDTH - COFF\n elif conn == 'l':\n x = left + COFF\n y = top\n width = CWIDTH\n height = CELLSIZE\n elif conn == 'r':\n x = left + CELLSIZE - CWIDTH\n y = top\n width = CWIDTH - COFF\n height = CELLSIZE\n pygame.draw.rect(self.screen,CONNCOLOUR,(x,y,width,height))\n pygame.draw.rect(self.screen,BLACK,(x,y,width,height),CBOX)\n return","def forward_character():\r\n set_point(point()+1)","def Pos(row, col):\n return ESC + str(row) + ';' + str(col) + 'H'","def set_j(cmd, limb, joints, index, delta):\n joint = joints[index]\n cmd[joint] = delta + limb.joint_angle(joint)","def placeKeeper (self, keeper_char, row, column):\n self.maze[row][column] = keeper_char","def inInteractionCellStr(atoms, atom):\r\n \r\n def inDesiredCell(atom):\r\n if atom.pos[0] >= Na and atom.pos[0] < (Na + 1):\r\n if atom.pos[1] >= Nb and atom.pos[1] < (Nb + 1):\r\n if atom.pos[2] >= Nc and atom.pos[2] < (Nc + 1):\r\n return True\r\n return False\r\n \r\n #First check if the atom is in the first crystallographic cell\r\n if inDesiredCell(atom):\r\n return \"X\"\r\n #If not, check if it bonds to an atom that is\r\n for i in range(len(atom.interactions)):\r\n if inDesiredCell(atoms[atom.interactions[i][0]]):\r\n return \"X\"\r\n \r\n for interaction in atom.interCellInteractions:\r\n interactingAtom = atoms[interaction[0]]\r\n if inDesiredCell(interactingAtom):\r\n return \"X\"\r\n \r\n return \"O\"","def dummy_junction12():\n return \"junction:chr1:176-224:+\"","def bend_connection(self, room_1, room_2):\n bend_point = Point(random.randint(room_1.left, room_1.right),\n random.randint(room_2.top, room_2.bottom))\n if room_1.bottom < room_2.top:\n draw_point = bend_point\n while self.get_tile(draw_point) == '#':\n self.set_tile(draw_point, glyph='|')\n draw_point = Point(draw_point.x, draw_point.y-1)\n else:\n draw_point = bend_point\n while self.get_tile(draw_point) == '#':\n self.set_tile(draw_point, glyph='|')\n draw_point = Point(draw_point.x, draw_point.y+1)\n if room_1.left < room_2.right:\n # Move off our starting point, so we start on a wall\n draw_point = Point(bend_point.x+1, bend_point.y)\n while self.get_tile(draw_point) == '#':\n self.set_tile(draw_point, glyph='-')\n draw_point = Point(draw_point.x+1, draw_point.y)\n else:\n # Move off our starting point, so we start on a wall\n draw_point = Point(bend_point.x-1, bend_point.y)\n while self.get_tile(draw_point) == '#':\n self.set_tile(draw_point, glyph='-')\n draw_point = Point(draw_point.x-1, draw_point.y)","def draw(grid):\n\n for row in grid:\n for char in row:\n if char is None:\n sys.stdout.write(\" \")\n else:\n sys.stdout.write(char)\n sys.stdout.write(\"\\n\")","def read_join(stream: Stream):\n join_bin = stream.read_uchar()\n if join_bin == 0:\n return 'miter'\n elif join_bin == 1:\n return 'round'\n elif join_bin == 2:\n return 'bevel'\n else:\n raise UnreadableSymbolException('unknown join style {}'.format(join_bin))","def formatJointTableForLaTeX(joints):\n (varList, atoms, probs) = joints\n cols = len(varList) + len (probs[0][1])\n with open(\"table1.tex\",\"w\") as out:\n out.write (\"\\\\begin{tabular}{|\" + \"|\".join([\"c\"]*(cols-2))+\"||c|c|}\\n\")\n out.write (\"\\\\hline\\n\")\n # Table header\n out.write (\" & \".join(varList) + \" & \" + \" & \".join([a for a in atoms]) + \" & Joint $p$ & ln~$p$ \\\\\\\\ \\\\hline\\n\")\n # Table rows\n logps = []\n for (grounding, probs) in probs:\n out.write (\" & \".join([val for (var, val) in grounding.varList]) + \" & \" +\n \" & \".join([str(n.val)+\" ({:.1f})\".format(p) for (n,p) in probs[:-2]]) +\n \" & {:.2f}\".format(probs[-2]) + \" & {:.2f}\".format(probs[-1]) + \"\\\\\\\\\\n\")\n logps.append(probs[-1])\n # A line to indicate there are further entries in the DB\n out.write(\" & \".join([\"\\ldots\"]*cols) + \"\\\\\\\\\\n\")\n # Close environment\n out.write (\"\\\\hline\\n\\\\end{tabular}\\n\")\n\n with open(\"tab1plogp.tex\",\"w\") as plogp:\n plogp.write(\"\\\\newcommand{\\\\pseudologp}{\"+\"{:.2f}\".format(sum(logps)/len(logps))+\"}\\n\")","def set_character(self, y_pos, x_pos):\n self.map[y_pos][x_pos] = 'G'","def writeContactPoint(cp):\n return ' '.join(str(v) for v in (cp.x+cp.n+[cp.kFriction]))","def __cross_cord(self,A, B):\n table_dict = {}\n for s_idx, s in enumerate(A):\n for t_idx, t in enumerate(B):\n table_dict[s+t] = (s_idx,t_idx)\n\n return table_dict","def __repr__(self) -> str:\n\t\t\n\t\trepr = \"\"\n\t\tfor row in self.board:\n\t\t\tfor element in row:\n\t\t\t\tif element:\n\t\t\t\t\trepr = repr + \"o \"\n\t\t\t\telse:\n\t\t\t\t\trepr = repr + \"@ \"\n\t\t\trepr = repr + \"\\n\"\n\t\treturn repr","def insertChar(self, ch):\n word, cx = self.edCursor.getPos()\n string = word.string[:cx] + ch + word.string[cx:]\n word.setString(string)\n # Re-render from tline:\n self.rsubject.linify(word.tline)\n self.edCursor.setPos(word, cx+1)","def putchar(self, col, row, char, color=GREEN):\n for j in range(FONT_HEIGHT - 5):\n v = self.font[ord(char)][3 + j]\n for i in range(FONT_WIDTH):\n if v & (1 << (7 - i)):\n self.putpixel(col + i, row + j, color)\n else:\n self.putpixel(col + i, row + j, BLACK)","def rotateconnections(self,cells,cellkey):\n cell = cells[cellkey]\n for c in cell.conns:\n if c == 'u':\n key = str(cell.xpos) + str(cell.ypos - 1)\n elif c == 'd':\n key = str(cell.xpos) + str(cell.ypos + 1)\n elif c == 'l':\n key = str(cell.xpos-1) + str(cell.ypos)\n elif c == 'r':\n key = str(cell.xpos +1) + str(cell.ypos) \n self.rotatecell(cells[key])","def placeCrate (self, crates_char, row, column):","def __repr__(self):\n r = \"\"\n for row in self.board:\n for cell in row:\n if cell == \"\":\n cell = color_magenta(\"_\")\n r += cell + \" \" # for all the empty strings, we will replace it with an '_'.\n r += \"\\n\"\n return r","def __str__(self):\n if self.__style == 'default':\n if self.living:\n return Cell.liveChar\n else:\n return Cell.deadChar","def hasJunction(junc, chr, leftEdge, rightEdge, wiggle=0):\n\n for i in range(leftEdge-wiggle, leftEdge+wiggle+1):\n for j in range(rightEdge-wiggle, rightEdge+wiggle+1):\n try:\n if junc[chr].has_key( (i, j) ):\n return True\n except KeyError:\n return False\n\n return False","def pair(handIn, indx = 0):","def display_char(self) -> None:\r\n print(self.char if self.was_guessed else '_', end=' ')","def move_character(self, old_y, old_x, y_pos, x_pos):\n self.map[old_y][old_x] = ' '\n self.map[y_pos][x_pos] = 'G'","def joint_entropy(column_i, column_j):\n\tif len(column_i) != len(column_j):\n\t\traise IndexError(\"The two MSA should have the same number of related sequences (same species)\")\n\tfreq_ij = dict()\n\ttotal = len(column_i)\n\tentropy = 0\n\tfor index in range(total):\n\t\ti = column_i[index]\n\t\tj = column_j[index]\n\t\tif i+j in freq_ij:\n\t\t\tfreq_ij[i+j] +=1\n\t\telse:\n\t\t\tfreq_ij[i+j] = 1\n\n\tfor key in freq_ij:\n\t\tfreq_ij[key] /= total\n\t\tentropy += freq_ij[key]*math.log(freq_ij[key], 2)\n\tif entropy != 0.0:\n\t\treturn -entropy\n\telse:\n\t\treturn entropy","def input(self, dots):\n\t\t# Insert the newly entered cell into the buffer at the cursor position.\n\t\tpos = self.untranslatedStart + self.untranslatedCursorPos\n\t\tself.bufferBraille.insert(pos, dots)\n\t\tself.untranslatedCursorPos += 1\n\t\t# Space ends the word.\n\t\tendWord = dots == 0\n\t\t# For uncontracted braille, translate the buffer for each cell added.\n\t\t# Any new characters produced are then sent immediately.\n\t\t# For contracted braille, translate the buffer only when a word is ended (i.e. a space is typed).\n\t\t# This is because later cells can change characters produced by previous cells.\n\t\t# For example, in English grade 2, \"tg\" produces just \"tg\",\n\t\t# but \"tgr\" produces \"together\".\n\t\tif not self.useContractedForCurrentFocus or endWord:\n\t\t\tif self._translate(endWord):\n\t\t\t\tif not endWord:\n\t\t\t\t\tself.cellsWithText.add(pos)\n\t\t\telif self.bufferText and not self.useContractedForCurrentFocus:\n\t\t\t\t# Translators: Reported when translation didn't succeed due to unsupported input.\n\t\t\t\tspeech.speakMessage(_(\"Unsupported input\"))\n\t\t\t\tself.flushBuffer()\n\t\t\telse:\n\t\t\t\t# This cell didn't produce any text; e.g. number sign.\n\t\t\t\tself._reportUntranslated(pos)\n\t\telse:\n\t\t\tself._reportUntranslated(pos)","def join(self) -> str:\n\n return _ZWJ.join(e.emoji for e in self.emojis)","def character(self):\n if self.is_alive():\n return \"[*]\"\n return \"[ ]\"","def make_board() -> dict:\n coordinates = {(x, y): ' o ' for x in range(5) for y in range(5)}\n coordinates[(0, 0)] = \" 🚶 \"\n coordinates[(4, 4)] = \" x \"\n return coordinates","def write(branch=None, branch_track=None, inner=True, connect_to=None):\n global _tracks, glyphs\n if inner:\n result = \" \"\n else:\n if branch == 'start':\n result = glyphs[\">\"]\n elif branch == 'end':\n result = glyphs[\"<\"]\n else:\n result = glyphs[\"-\"]\n\n for n, track in enumerate(_tracks):\n if track:\n if branch:\n if n < branch_track:\n if n == connect_to:\n result += glyphs[\"}\"]\n elif inner or branch == 'mid':\n result += glyphs[\"|\"]\n else:\n result += glyphs[\"+\"]\n elif n == branch_track:\n if branch == 'start':\n if inner:\n result += glyphs[\",\"]\n else:\n result += glyphs[\"T\"]\n elif branch == 'end':\n if inner:\n result += glyphs[\"`\"]\n else:\n result += glyphs[\"^\"]\n elif branch == 'mid':\n if inner:\n result += glyphs[\"}\"]\n else:\n result += glyphs[\"|\"]\n else:\n result += glyphs[\".\"]\n elif n > branch_track:\n result += glyphs[\"+\"]\n else:\n result += glyphs[\"|\"]\n else:\n if branch:\n if n < branch_track:\n if inner or branch == 'mid':\n result += \" \"\n else:\n result += glyphs[\"-\"]\n elif n == branch_track:\n result += \"?\"\n elif n > branch_track:\n result += glyphs[\"-\"]\n else:\n result += \" \"\n if inner:\n if branch:\n if branch == 'start' or branch == '<':\n result += ligatures['<-']\n elif branch == 'end' or branch == '>':\n result += ligatures['->']\n else:\n result += glyphs[\"-\"]+glyphs[\"-\"]\n else:\n result += \" \"\n else:\n if branch == '<':\n result += ligatures['<-']\n elif branch == '>':\n result += ligatures['->']\n else:\n result += glyphs[\"-\"]+glyphs[\"-\"]\n return result+\":\"","def print(self, assignment):\n letters = self.letter_grid(assignment)\n for i in range(self.crossword.height):\n for j in range(self.crossword.width):\n if self.crossword.structure[i][j]:\n print(letters[i][j] or \" \", end=\"\")\n else:\n print(\"█\", end=\"\")\n print()","def print(self, assignment):\n letters = self.letter_grid(assignment)\n for i in range(self.crossword.height):\n for j in range(self.crossword.width):\n if self.crossword.structure[i][j]:\n print(letters[i][j] or \" \", end=\"\")\n else:\n print(\"█\", end=\"\")\n print()","def print(self, assignment):\n letters = self.letter_grid(assignment)\n for i in range(self.crossword.height):\n for j in range(self.crossword.width):\n if self.crossword.structure[i][j]:\n print(letters[i][j] or \" \", end=\"\")\n else:\n print(\"█\", end=\"\")\n print()","def test_print_cell(self):\n self.assertEqual(str(self.deadCell), \" \")\n self.assertEqual(str(self.livingCell), \"X\")","def write_char(self, char=' '):\n integer = ord(char)\n self.instruction(integer, True)","def _pipe_segment_with_colons(align, colwidth):\n w = colwidth\n if align in [\"right\", \"decimal\"]:\n return (\"-\" * (w - 1)) + \":\"\n elif align == \"center\":\n return \":\" + (\"-\" * (w - 2)) + \":\"\n elif align == \"left\":\n return \":\" + (\"-\" * (w - 1))\n else:\n return \"-\" * w","def writeContactPoint(cp):\n return ' '.join([str(v) for v in cp.x+cp.n+[cp.kFriction]])","def change(coor):\n return chr(coor[0] + 65), coor[1] + 1","def crossover(mother: str, father: str) -> Tuple[str, str]:\r\n position = random.randrange(0, DNA_SIZE, 1)\r\n return mother[:position] + father[position:], father[:position] + mother[position:]","def markCell(self, mark, i, j):\n if 0 <= i < len(self.board) and 0 <= j < len(self.board) and self.empty_cells > 0:\n if self.board[i][j] != \" \":\n return False\n else:\n self.board[i][j] = mark\n self.empty_cells -= 1\n return True\n else:\n return False","def motd(\r\n padding=61,\r\n corner=\"*\",\r\n left_and_right_column=\"|\",\r\n top_and_bottom_row=\"-\",\r\n white_space=\" \"\r\n\r\n):\r\n for i in message:\r\n\r\n if i == message[0]:\r\n print(corner,\r\n top_and_bottom_row * padding,\r\n corner\r\n )\r\n print(left_and_right_column,\r\n white_space * padding,\r\n left_and_right_column\r\n )\r\n\r\n if i == message[-1]:\r\n print(\r\n left_and_right_column,\r\n i.center(padding),\r\n left_and_right_column\r\n )\r\n print(\r\n left_and_right_column,\r\n white_space * padding,\r\n left_and_right_column\r\n )\r\n print(\r\n corner,\r\n top_and_bottom_row * padding,\r\n corner\r\n )\r\n\r\n else:\r\n print(left_and_right_column,\r\n i.center(padding),\r\n left_and_right_column\r\n )","def exchange_keys(self, client_sock, obtained_cell):\n\n private_key = ec.generate_private_key(\n ec.SECP384R1(), default_backend()) # elliptic curve\n public_key = private_key.public_key() # duh same.\n serialised_public_key = public_key.public_bytes(\n encoding=serialization.Encoding.PEM,\n format=serialization.PublicFormat.SubjectPublicKeyInfo\n )\n # serialise the public key that I'm going to send them\n their_key = serialization.load_pem_public_key(\n obtained_cell.payload, backend=default_backend())\n shared_key = private_key.exchange(ec.ECDH(), their_key)\n salty = os.urandom(8)\n derived_key = HKDF(\n algorithm=hashes.SHA256(),\n length=32,\n salt=salty,\n info=None,\n backend=default_backend()\n ).derive(shared_key)\n reply_cell = Cell(serialised_public_key,\n salt=salty, ctype=CellType.CONNECT_RESP)\n signature = self.sign(salty) # sign the random bytes\n reply_cell.signature = signature # assign the signature.\n if util.RELAY_DEBUG:\n print(\"reply cell\")\n print(pickle.dumps(reply_cell))\n # send them the serialised version.\n client_sock.send(pickle.dumps(reply_cell))\n return private_key, derived_key","def append_cand():\n if col_i == down_col:\n return (lastrow[col_i] +\n [[key + [append_key(lastrow[col_i], left_struc, key)],\n right_elem]])","def addInterCellInteraction(self, atom2, jMat, direction):\r\n #Check for repeats (necessary for method used to translate these bonds)\r\n for interaction in self.interCellInteractions:\r\n if interaction[0] == atom2 and interaction[1] == jMat:\r\n return #contains this interaction already\r\n self.interCellInteractions.append((atom2, jMat, direction))","def joint_adr(self, joint_name):\n jntadr = mjlib.mj_name2id(self.ptr, C.mjOBJ_JOINT, joint_name)\n assert (jntadr >= 0)\n dofmap = {C.mjJNT_FREE: 7,\n C.mjJNT_BALL: 4,\n C.mjJNT_SLIDE: 1,\n C.mjJNT_HINGE: 1}\n qposadr = self.jnt_qposadr[jntadr][0]\n qveladr = self.jnt_dofadr[jntadr][0]\n dof = dofmap[self.jnt_type[jntadr][0]]\n return (qposadr, qveladr, dof)","def jogador(self, msg, match):\n\n linha, coluna, posição = self.posicao_do_jogador()\n yield \"Posição no mapa: [\" + str(linha) + \",\" + str(coluna) + \"]\"\n yield \"Sentido (ponto cardeal): \" + posição","def render(state, c_choice, h_choice):\r\n\r\n chars = {\r\n 2: h_choice,\r\n 1: c_choice,\r\n 0: ' '\r\n }\r\n str_line = '--------------------------------------------------'\r\n\r\n print('\\n' + str_line)\r\n for row in state:\r\n for cell in row:\r\n #print(cell)\r\n symbol = chars[cell]\r\n print(f'| {symbol} |', end='')\r\n print('\\n' + str_line)"],"string":"[\n \"def junction_char(self):\\n ...\",\n \"def _put_chr_at(self, char, row, col, color, adjustment_x=.19, adjustment_y=.19):\\n self._goto_piece_xy(row, col, adjustment_x, adjustment_y)\\n self.pen.color(color)\\n self.pen.write(char, font=(\\\"Courier\\\", round(self.square_side_size * .7),\\n \\\"normal\\\"))\",\n \"def encryptPair(self):\\n # Locate the characters in the matrix\\n self.deselectCanvasses()\\n (row1, col1) = self.matrix.find(self.plainText[self.cursor])\\n (row2, col2) = self.matrix.find(self.plainText[self.cursor + 1])\\n self.selectCanvas(row1, col1, \\\"gray\\\")\\n self.selectCanvas(row2, col2, \\\"gray\\\")\\n # Swap them if they are in the same row or column\\n if row1 == row2 or col1 == col2:\\n return self.plainText[self.cursor + 1] + self.plainText[self.cursor]\\n # Otherwise, use the characters at the opposite\\n # corners of the rectangle in the matrix\\n else:\\n self.selectCanvas(row2, col1, \\\"pink\\\")\\n self.selectCanvas(row1, col2, \\\"pink\\\")\\n ch1 = self.matrix[row2][col1]\\n ch2 = self.matrix[row1][col2]\\n return ch1 + ch2\",\n \"def _get_letters_adjacent_cells(self, grid, x0, y0):\\n letters = ''\\n for cell in grid.get_adjacent_cells(x0, y0):\\n letters = letters + cell['letter']\\n return ''.join(sorted(letters))\",\n \"def joint_pairs(self):\\n return ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))\",\n \"def en_passant(self, from_row, from_col, to_row, to_col):\\n t = self.data[from_row][from_col]\\n self.data[from_row][from_col] = '.'\\n self.data[to_row][to_col] = t \\n self.data[from_row][to_col] = '.'\",\n \"def display_board(board: list, character: list):\\n for index in range(len(board)):\\n for index_two in range(len(board[index])):\\n current_position = [index, index_two]\\n if current_position == character:\\n if index_two == len(board[index]) - 1:\\n print('c')\\n else:\\n print('c', end=\\\" \\\")\\n else:\\n if index_two == len(board[index]) - 1:\\n print('x')\\n else:\\n print('x', end=\\\" \\\")\",\n \"def createperson(self, board, x_ind, y_ind, char):\\n self = self\\n for i in range(4):\\n for j in range(2):\\n board[x_ind + j][y_ind + i] = char\\n return board\",\n \"def cell_value(self, x, y):\\n if x == 8 and y == 0:\\n return \\\"--\\\"\\n (r, g) = self[(x, y)]\\n return \\\"%s%s\\\" % (r, g)\",\n \"def label_joints():\\n side_dict = {'C': 0,\\n 'L': 1,\\n 'R': 2}\\n for jnt in mc.ls(type='joint'):\\n mc.setAttr('{}.side'.format(jnt), side_dict[jnt.split('_')[0]])\\n mc.setAttr('{}.type'.format(jnt), 18)\\n mc.setAttr('{}.otherType'.format(jnt), jnt.split('_')[1], type=\\\"string\\\")\",\n \"def display_position(game_board: list, character: list):\\n print(\\\"You are currently here:\\\")\\n for position in game_board:\\n if position[0] == character[0] and position[1] == character[1]:\\n print('C', end=\\\" \\\")\\n else:\\n print(\\\"*\\\", end=\\\" \\\")\\n if position[1] == game_board[-1][1]:\\n print(\\\"\\\")\",\n \"def writechar(self, char=\\\" \\\"):\\n\\n font=self.selectfont(char)\\n t = self.canvas.beginText(self.x, self.y)\\n t.setFont(font, self.fontsize)\\n t.setFillGray(self.gray)\\n t.textOut(char)\\n self.canvas.drawText(t)\\n return t\",\n \"def dummy_junction24():\\n return 'junction:chr1:251-399:+'\",\n \"def insertJoint(*args, **kwargs)->AnyStr:\\n pass\",\n \"def __str__(self) -> str:\\n output = self.columns * \\\" __\\\" + \\\"\\\\n\\\"\\n for i in range(self.rows):\\n for j in range(self.columns):\\n filling = \\\"__\\\"\\n if len(self.cells[i][j].agents) == 1:\\n filling = \\\"_§\\\"\\n elif len(self.cells[i][j].agents) == 2:\\n filling = \\\"§§\\\"\\n elif len(self.cells[i][j].agents) > 2:\\n filling = \\\"++\\\"\\n output += \\\"|\\\" + filling\\n if j == self.columns - 1:\\n output += \\\"|\\\"\\n output += \\\"\\\\n\\\"\\n return output\",\n \"def dummy_junction23():\\n return 'junction:chr1:251-299:+'\",\n \"def jointext(firststring, secondstring):\\n\\n # Return the joined strings\\n return str(firststring) + str(secondstring)\",\n \"def coord_char(coord, matrix):\\n row_index, column_index = coord\\n\\n return matrix[row_index][column_index]\",\n \"def dummy_junction13():\\n return 'junction:chr1:176-299:+'\",\n \"def simple_cell_string(self, values):\\n if len(values) == 0:\\n return '!'\\n elif len(values) == 1:\\n return self.number_strings[values[0]]\\n else:\\n return '.'\",\n \"def encode(self, char):\\n\\n if char == self.pair[0]:\\n return self.pair[1]\\n elif char == self.pair[1]:\\n return self.pair[0]\\n else:\\n return char\",\n \"def joint_callback(data):\\n joints[0] = data.position[9]\\n joints[1] = data.position[10]\\n joints[2] = data.position[11]\\n joints[3] = data.position[12]\\n joints[4] = data.position[13]\\n global position_geted\\n position_geted = True\",\n \"def joint_pairs(self):\\n return [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16], #17 body keypoints\\n [20-3, 23-3], [21-3, 24-3], [22-3, 25-3], [26-3, 42-3], [27-3, 41-3], [28-3, 40-3], [29-3, 39-3], [30-3, 38-3], \\n [31-3, 37-3], [32-3, 36-3], [33-3, 35-3], [43-3, 52-3], [44-3, 51-3], [45-3, 50-3], [46-3, 49-3], [47-3, 48-3], \\n [62-3, 71-3], [63-3, 70-3], [64-3, 69-3], [65-3, 68-3], [66-3, 73-3], [67-3, 72-3], [57-3, 61-3], [58-3, 60-3],\\n [74-3, 80-3], [75-3, 79-3], [76-3, 78-3], [87-3, 89-3], [93-3, 91-3], [86-3, 90-3], [85-3, 81-3], [84-3, 82-3],\\n [94-3, 115-3], [95-3, 116-3], [96-3, 117-3], [97-3, 118-3], [98-3, 119-3], [99-3, 120-3], [100-3, 121-3],\\n [101-3, 122-3], [102-3, 123-3], [103-3, 124-3], [104-3, 125-3], [105-3, 126-3], [106-3, 127-3], [107-3, 128-3],\\n [108-3, 129-3], [109-3, 130-3], [110-3, 131-3], [111-3, 132-3], [112-3, 133-3], [113-3, 134-3], [114-3, 135-3]]\",\n \"def test_two_cell_repel():\\n space = c6.Space()\\n c6.Cell(space, [0, 0], 1)\\n c6.Cell(space, [0, 1.9], 1)\\n for i in range(2):\\n space.step()\",\n \"def dummy_junction14():\\n return \\\"junction:chr1:176-324:+\\\"\",\n \"def add_char(self, coord, char, modify=False):\\n if modify:\\n range_y, range_x = self._map_dims\\n new_coord = [coord[0]+range_y[0]-1, coord[1]+range_x[0]-1]\\n self._screen.addch(new_coord[0], new_coord[1], char)\\n self._screen.refresh()\\n return new_coord\\n else:\\n self._screen.addch(coord[0], coord[1], char)\\n self._screen.refresh()\\n return coord\",\n \"def interact(self,cell1,cell2):\\n difference = math.fabs(cell1-cell2)\\n if difference<=self.t: #if both cells are close enough: \\n if cell1>cell2:\\n # print \\\"Case 1\\\"\\n return (cell1-difference/2.0,cell2+difference/2.0)\\n else:\\n # print \\\"Case 2\\\"\\n return (cell1+difference/2.0,cell2-difference/2.0)\\n else: #cells too far apart, they radicalize\\n if cell1>cell2:\\n # print \\\"Case 3\\\"\\n return (cell1+(1-cell1)/2.0,cell2/2.0) #the higher one moves toward 1, the lower one moves toward 0\\n else:\\n # print \\\"Case 4\\\"\\n return (cell1/2.0,cell2+(1-cell2)/2.0)\",\n \"def __createkey__(self):\\n return str(self.currentCol) + \\\",\\\" + str(self.currentRow)\",\n \"def getCellStr(self, x, y): # TODO: refactor regarding issue #11\\n c = self.board.getCell(x, y)\\n\\n if c == 0:\\n return '.' if self.__azmode else ' .'\\n\\n elif self.__azmode:\\n az = {}\\n for i in range(1, int(math.log(self.board.goal(), 2))):\\n az[2 ** i] = chr(i + 96)\\n\\n if c not in az:\\n return '?'\\n s = az[c]\\n elif c == 1024:\\n s = ' 1k'\\n elif c == 2048:\\n s = ' 2k'\\n else:\\n s = '%3d' % c\\n\\n return self.__colors.get(c, Fore.RESET) + s + Style.RESET_ALL\",\n \"def teleport_pair(cell, game_coords):\\n # get cell display\\n item = search_coords(game_coords, cell)\\n\\n if item in ['1', '2', '3', '4', '5', '6', '7', '8', '9']:\\n for coords in game_coords[item]:\\n # partner pad found\\n if coords != cell:\\n return coords\\n\\n # not a teleport pad\\n return -1\",\n \"def generate_joint_labels(self):\\r\\n correspondence = \\\"SPEED CORRESPONDENCE:\\\\n\\\"\\r\\n self.correspondence = []\\r\\n for speed_idx, speed in enumerate(self.speed_set):\\r\\n correspondence += \\\"{}: {}\\\\n\\\".format(speed_idx, speed)\\r\\n self.correspondence.append(speed)\\r\\n logger.info(correspondence)\\r\\n return {\\r\\n \\\"move_joint\\\": torch.linspace(\\r\\n 0, len(self.speed_set)-1, len(self.speed_set), dtype=torch.int64\\r\\n )\\r\\n }\",\n \"def encrypt_digraph(self, dg):\\n\\n first, second = dg\\n\\n if first == second:\\n second = 'X'\\n\\n frow, fcol = self.location_of(first)\\n srow, scol = self.location_of(second)\\n\\n # If the letters appear on the same row of your table, replace them with the\\n # letters to their immediate right respectively (wrapping around to the left\\n # side of the row if a letter in the original pair was on the right side of the\\n # row).\\n if frow == srow:\\n fcol, scol = (fcol + 1) % 5, (scol + 1) % 5\\n\\n # If the letters appear on the same column of your table, replace them with the\\n # letters immediately below respectively (wrapping around to the top side of\\n # the column if a letter in the original pair was on the bottom side of the\\n # column).\\n if fcol == scol:\\n frow, srow = (frow - 1) % 5, (srow - 1) % 5\\n\\n # If the letters are not on the same row or column, replace them with\\n # the letters on the same row respectively but at the other pair of\\n # corners of the rectangle defined by the original pair. The order is\\n # important – the first letter of the encrypted pair is the one that\\n # lies on the same row as the first letter of the plaintext pair\\n if frow != srow and fcol != scol:\\n fcol, scol = scol, fcol\\n\\n return self.create_digraph(frow, fcol, srow, scol)\",\n \"def dummy_junction34():\\n return 'junction:chr1:351-399:+'\",\n \"def _generate_joint_string(self, joint, skeleton, joint_level):\\n joint_string = \\\"\\\"\\n temp_level = 0\\n tab_string = \\\"\\\"\\n while temp_level < joint_level:\\n tab_string += \\\"\\\\t\\\"\\n temp_level += 1\\n\\n # determine joint type\\n if joint_level == 0:\\n joint_string += tab_string + \\\"ROOT \\\" + joint + \\\"\\\\n\\\"\\n else:\\n if len(skeleton.nodes[joint].children) > 0:\\n joint_string += tab_string + \\\"JOINT \\\" + joint + \\\"\\\\n\\\"\\n else:\\n joint_string += tab_string + \\\"End Site\\\" + \\\"\\\\n\\\"\\n\\n # open bracket add offset\\n joint_string += tab_string + \\\"{\\\" + \\\"\\\\n\\\"\\n offset = skeleton.nodes[joint].offset\\n joint_string += tab_string + \\\"\\\\t\\\" + \\\"OFFSET \\\" + \\\"\\\\t \\\" + \\\\\\n str(offset[0]) + \\\"\\\\t \\\" + str(offset[1]) + \\\"\\\\t \\\" + str(offset[2]) + \\\"\\\\n\\\"\\n\\n if len(skeleton.nodes[joint].children) > 0:\\n # channel information\\n channels = skeleton.nodes[joint].channels\\n joint_string += tab_string + \\\"\\\\t\\\" + \\\\\\n \\\"CHANNELS \\\" + str(len(channels)) + \\\" \\\"\\n for tok in channels:\\n joint_string += tok + \\\" \\\"\\n joint_string += \\\"\\\\n\\\"\\n\\n joint_level += 1\\n # recursive call for all children\\n for child in skeleton.nodes[joint].children:\\n joint_string += self._generate_joint_string(child.node_name, skeleton, joint_level)\\n\\n # close the bracket\\n joint_string += tab_string + \\\"}\\\" + \\\"\\\\n\\\"\\n return joint_string\",\n \"def connect_cells(dfte,vari):\\n # Create the variabel cell for mother, grand mother and grand grand mother\\n if 'g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'parent_cell') #Create genealogy\\n if 'g_g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'g_parent_cell')\\n if 'g_g_g_parent_cell' not in dfte.columns:\\n dfte = rl.genalogy(dfte,'g_g_parent_cell')\\n #give unique index to all cells\\n dfte['uid'] = dfte['cell']+dfte['time_sec'].apply(lambda x: str(x))\\n vac=[];sc=[];uid = []\\n # Create a vecotor for the variable of interest of cell,mother,grand mother and grand grand mother and an unique identifier of it\\n for c,idx in enumerate(dfte['cell'].unique()):\\n dau = dfte.loc[dfte['cell']==idx]\\n pc = dau['parent_cell'].iloc[0]\\n mum = dfte.loc[dfte['cell']==pc]\\n gpc = dau['g_parent_cell'].iloc[0]\\n gmum = dfte.loc[dfte['cell']==gpc]\\n ggpc = dau['g_g_parent_cell'].iloc[0]\\n ggmum = dfte.loc[dfte['cell']==ggpc]\\n gggpc = dau['g_g_g_parent_cell'].iloc[0]\\n gggmum = dfte.loc[dfte['cell']==gggpc]\\n fte = lambda x: x[['{}'.format(vari),'uid']].values\\n tmp = np.vstack([fte(gggmum),fte(ggmum),fte(gmum),fte(mum),fte(dau)])\\n vac.append(tmp[:,0])\\n uid.append(tmp[:,1])\\n sc.append(['super_cell_{}'.format(c)]*len(tmp))\\n return pd.DataFrame({'super_cell':np.hstack(sc),'uid':np.hstack(uid),'{}'.format(vari):np.hstack(vac)})\",\n \"def output_char(char):\\n # If the last printed character has the same token, it also has the\\n # same style, so we don't output it.\\n if last_char[0] and last_char[0].token == char.token:\\n write(char.char)\\n else:\\n style = get_style_for_token(char.token)\\n\\n if style:\\n output.set_attributes(style['color'], style['bgcolor'],\\n bold=style.get('bold', False),\\n underline=style.get('underline', False))\\n\\n # If we print something with a background color, remember that.\\n background_turned_on[0] = bool(style['bgcolor'])\\n else:\\n # Reset previous style and output.\\n output.reset_attributes()\\n\\n write(char.char)\\n\\n last_char[0] = char\",\n \"def printBoard2( A ):\\r\\n for row in A:\\r\\n for col in row:\\r\\n \\tif col == 1:\\r\\n \\t\\tsys.stdout.write(\\\"@\\\")\\r\\n \\telse:\\r\\n \\t\\tsys.stdout.write(\\\" \\\")\\r\\n sys.stdout.write( '\\\\n' )\",\n \"def point(self, x, y, char):\\n assert len(char) == 1\\n assert x >= 0\\n assert x < self.cols\\n assert y >= 0\\n assert y < self.lines\\n\\n self.canvas[y][x] = char\",\n \"def join_with_prev(self, prev):\\n if prev._cell_input.getText() == \\\"\\\":\\n new_text = self._cell_input.getText()\\n else:\\n new_text = prev._cell_input.getText()\\n if self._cell_input.getText() != \\\"\\\":\\n new_text += \\\"\\\\n\\\" + self._cell_input.getText()\\n y_new = prev._cell_input.rows()\\n if prev._cell_input.getText() == \\\"\\\":\\n y_new -= 1\\n prev._cell_input.setText(new_text)\\n prev._cell_input.set_cursor_coordinates(0, y_new)\\n prev.set_focus()\",\n \"def write_to_cell(table, row, col, text):\\n\\n\\ttable.cell(row, col).paragraphs[0].runs[0].text = text\",\n \"def deg_locations_writer(cell,width,(x,y)):\\n if np.any(np.isnan(cell)):\\n sys.stdout.write(' ' * width)\\n else:\\n sys.stdout.write(\\n \\\"{0:^{width}s}\\\".format(\\n \\\"{0:.2f},{1:.2f}\\\".format(cell[0],cell[1]),\\n width=width\\n )\\n )\",\n \"def addch(self, posy, posx, character, color_pair):\\r\\n if posy < 0 or posy > self.height - 1:\\r\\n return\\r\\n if posx < 0 or posx > self.width - 1:\\r\\n return\\r\\n if posx == self.width - 1 and posy == self.height - 1:\\r\\n return\\r\\n self.win.addch(posy, posx, character, color_pair)\",\n \"def _insChar(self, char, pos, color):\\n char, vertices, glyph = self._extractGlyph(char, glm.vec4(color))\\n if not self.text:\\n off, kern = self._updateMetric(pos, char)\\n if char in self.NO_GLYPH_CHARS:\\n self.colors.insert(pos, [char, None])\\n else:\\n vertices['vtx'] += off + glyph['offset']\\n self.allVertices = np.hstack(vertices)\\n self.allIndices = self._baseInd\\n self.colors.insert(pos, [char, color])\\n self.text += char\\n else:\\n self.logger.debug(\\\"Inserting %r at %d\\\" % (char, pos))\\n nonGlyph = countInSet(self.text[:pos], self.NO_GLYPH_CHARS)\\n # Arrange vertices\\n if pos < len(self.text):\\n self.allVertices = self.allVertices[:(pos - nonGlyph) * 4]\\n self.allIndices = self.allIndices[:pos - nonGlyph]\\n\\n # Set the metric\\n off, kern = self._updateMetric(pos, char)\\n if char in self.NO_GLYPH_CHARS:\\n color = None\\n else:\\n vertices['vtx'] += off + kern + glyph['offset']\\n if self.allVertices is None:\\n self.allVertices = np.hstack(vertices)\\n else:\\n self.allVertices = np.append(self.allVertices, vertices)\\n if self.allIndices is None:\\n self.allIndices = self._baseInd\\n else:\\n self.allIndices = np.vstack((self.allIndices,\\n self._baseInd + (pos - nonGlyph) * 4))\\n\\n self.colors.insert(pos, [char, color])\\n if pos < len(self.text):\\n self.text = self.text[:pos] + char + self.text[pos:]\\n self._updateGlyphs(pos, char)\\n else:\\n self.text += char\",\n \"def compute_pair_frequencies(text, alphabet_size=256):\\n\\n curr = text[1:]\\n prev = text[:-1]\\n pairs = np.stack([prev, curr])\\n\\n print(\\\"Counting pairs . . .\\\")\\n (char1, char2), counts = np.unique(pairs, return_counts=True, axis=1)\\n print(\\\"Done.\\\\n\\\")\\n\\n joints = np.zeros((alphabet_size, alphabet_size))\\n joints[char1, char2] = counts / np.sum(counts)\\n assert np.isclose(np.sum(joints), 1.0)\\n\\n return joints\",\n \"def drawcell(self,cell):\\n \\n # get top left and center co-ords of cell\\n left, top = self.getlefttop(cell.xpos,cell.ypos)\\n\\n # draw connections to other cells (if these exist)\\n # done first since this is 'bottom layer'\\n for c in cell.conns:\\n self.drawconn(left,top,c)\\n \\n # outer circle\\n pygame.draw.circle(self.screen, cell.ocol,\\n (left + CELLSIZE/2,top + CELLSIZE/2),\\n CELLSIZE/2)\\n # inner circle\\n pygame.draw.circle(self.screen, cell.icol,\\n (left + CELLSIZE/2,top + CELLSIZE/2),\\n CELLSIZE/2 - TSIZE)\\n \\n # draw arrows\\n for t in enumerate(cell.current):\\n if t[1] == 1:\\n points = self.gettpoints(left,top,t[0])\\n pygame.draw.polygon(self.screen, cell.tcol, points)\",\n \"def add_cell(self, connections=[], cell = None):\\n # Create new cell\\n idx = len(self.cells)\\n position = np.random.randn(3) if cell == None else cell.position + 1\\n connections = connections if len(connections) != 0 else [idx - 1]\\n new_cell = Cell(idx=idx, connections=connections, position=position)\\n # Connect new cell with connected cells in plant \\\"\\\"\\\"\\n for old_cell in self.cells:\\n if old_cell.idx in new_cell.connections: \\n old_cell.connections.append(new_cell.idx)\\n # Add new cell to plant\\n self.cells.append(new_cell)\",\n \"def write_row(prefix, address1, address2, row, output):\\n values = ' '.join('{:02x}'.format(x) for x in row)\\n ascii = ''.join(chr(x) if 32 <= x < 128 else '.' for x in row)\\n # pad width\\n values += ' ' * (47 - len(values))\\n ascii += ' ' * (16 - len(values))\\n # output line, insert gap at 8\\n output.write('{} {:^8s} {:^8s}: {} {} {} {}\\\\n'.format(\\n prefix,\\n '{:08x}'.format(address1) if address1 is not None else '--------',\\n '{:08x}'.format(address2) if address2 is not None else '--------',\\n values[:24], values[24:],\\n ascii[:8], ascii[8:]))\",\n \"def createCell(self, space):\\n\\t\\tself.createCellEntity(space)\\n\\t\\tDEBUG_MSG(\\\"Avatar::createCell\\\")\",\n \"def write_char(self, char, token, string_index=None,\\n set_cursor_position=False, z_index=False):\\n assert len(char) == 1\\n\\n char_obj = Char(char, token, z_index)\\n char_width = char_obj.get_width()\\n\\n # In case there is no more place left at this line, go first to the\\n # following line. (Also in case of double-width characters.)\\n if self._x + char_width > self.size.columns:\\n self._y += 1\\n self._x = 0\\n\\n insert_pos = self._y, self._x # XXX: make a Point of this?\\n\\n if string_index is not None:\\n self._cursor_mappings[string_index] = insert_pos\\n\\n if set_cursor_position:\\n self.cursor_position = Point(y=self._y, x=self._x)\\n\\n # Insertion of newline\\n if char == '\\\\n':\\n self._y += 1\\n self._x = 0\\n self._line_number += 1\\n\\n # Insertion of a 'visible' character.\\n else:\\n if char_obj.z_index >= self._buffer[self._y][self._x].z_index:\\n self._buffer[self._y][self._x] = char_obj\\n\\n # When we have a double width character, store this byte in the\\n # second cell. So that if this character gets deleted afterwarsd,\\n # the ``output_screen_diff`` will notice that this byte is also\\n # gone and redraw both cells.\\n if char_width > 1:\\n self._buffer[self._y][self._x+1] = Char(six.unichr(0))\\n\\n # Move position\\n self._x += char_width\\n\\n return insert_pos\",\n \"def print_room(room):\\r\\n\\r\\n for row in room:\\r\\n for cell in row:\\r\\n if cell == \\\"obstacle\\\":\\r\\n print(\\\"O\\\", end=\\\"\\\")\\r\\n elif cell == \\\"robot\\\":\\r\\n print(\\\"R\\\", end=\\\"\\\")\\r\\n elif cell == \\\"empty\\\":\\r\\n print(\\\" \\\", end=\\\"\\\")\\r\\n elif cell == \\\"dirt\\\":\\r\\n print(\\\"*\\\", end=\\\"\\\")\\r\\n\\r\\n print()\",\n \"def character(self) -> str:\\r\\n return self.char if self.was_guessed else '_'\",\n \"def writechar(self, char: int, /) -> None:\",\n \"def drawconn(self,left,top,conn):\\n # x and y are left and top of cell\\n COFF = 0 # offset at edge of circle\\n if conn == 'u':\\n x = left\\n y = top - COFF\\n width = CELLSIZE\\n height = CWIDTH\\n elif conn == 'd':\\n x = left\\n y = top + CELLSIZE - CWIDTH\\n width = CELLSIZE\\n height = CWIDTH - COFF\\n elif conn == 'l':\\n x = left + COFF\\n y = top\\n width = CWIDTH\\n height = CELLSIZE\\n elif conn == 'r':\\n x = left + CELLSIZE - CWIDTH\\n y = top\\n width = CWIDTH - COFF\\n height = CELLSIZE\\n pygame.draw.rect(self.screen,CONNCOLOUR,(x,y,width,height))\\n pygame.draw.rect(self.screen,BLACK,(x,y,width,height),CBOX)\\n return\",\n \"def forward_character():\\r\\n set_point(point()+1)\",\n \"def Pos(row, col):\\n return ESC + str(row) + ';' + str(col) + 'H'\",\n \"def set_j(cmd, limb, joints, index, delta):\\n joint = joints[index]\\n cmd[joint] = delta + limb.joint_angle(joint)\",\n \"def placeKeeper (self, keeper_char, row, column):\\n self.maze[row][column] = keeper_char\",\n \"def inInteractionCellStr(atoms, atom):\\r\\n \\r\\n def inDesiredCell(atom):\\r\\n if atom.pos[0] >= Na and atom.pos[0] < (Na + 1):\\r\\n if atom.pos[1] >= Nb and atom.pos[1] < (Nb + 1):\\r\\n if atom.pos[2] >= Nc and atom.pos[2] < (Nc + 1):\\r\\n return True\\r\\n return False\\r\\n \\r\\n #First check if the atom is in the first crystallographic cell\\r\\n if inDesiredCell(atom):\\r\\n return \\\"X\\\"\\r\\n #If not, check if it bonds to an atom that is\\r\\n for i in range(len(atom.interactions)):\\r\\n if inDesiredCell(atoms[atom.interactions[i][0]]):\\r\\n return \\\"X\\\"\\r\\n \\r\\n for interaction in atom.interCellInteractions:\\r\\n interactingAtom = atoms[interaction[0]]\\r\\n if inDesiredCell(interactingAtom):\\r\\n return \\\"X\\\"\\r\\n \\r\\n return \\\"O\\\"\",\n \"def dummy_junction12():\\n return \\\"junction:chr1:176-224:+\\\"\",\n \"def bend_connection(self, room_1, room_2):\\n bend_point = Point(random.randint(room_1.left, room_1.right),\\n random.randint(room_2.top, room_2.bottom))\\n if room_1.bottom < room_2.top:\\n draw_point = bend_point\\n while self.get_tile(draw_point) == '#':\\n self.set_tile(draw_point, glyph='|')\\n draw_point = Point(draw_point.x, draw_point.y-1)\\n else:\\n draw_point = bend_point\\n while self.get_tile(draw_point) == '#':\\n self.set_tile(draw_point, glyph='|')\\n draw_point = Point(draw_point.x, draw_point.y+1)\\n if room_1.left < room_2.right:\\n # Move off our starting point, so we start on a wall\\n draw_point = Point(bend_point.x+1, bend_point.y)\\n while self.get_tile(draw_point) == '#':\\n self.set_tile(draw_point, glyph='-')\\n draw_point = Point(draw_point.x+1, draw_point.y)\\n else:\\n # Move off our starting point, so we start on a wall\\n draw_point = Point(bend_point.x-1, bend_point.y)\\n while self.get_tile(draw_point) == '#':\\n self.set_tile(draw_point, glyph='-')\\n draw_point = Point(draw_point.x-1, draw_point.y)\",\n \"def draw(grid):\\n\\n for row in grid:\\n for char in row:\\n if char is None:\\n sys.stdout.write(\\\" \\\")\\n else:\\n sys.stdout.write(char)\\n sys.stdout.write(\\\"\\\\n\\\")\",\n \"def read_join(stream: Stream):\\n join_bin = stream.read_uchar()\\n if join_bin == 0:\\n return 'miter'\\n elif join_bin == 1:\\n return 'round'\\n elif join_bin == 2:\\n return 'bevel'\\n else:\\n raise UnreadableSymbolException('unknown join style {}'.format(join_bin))\",\n \"def formatJointTableForLaTeX(joints):\\n (varList, atoms, probs) = joints\\n cols = len(varList) + len (probs[0][1])\\n with open(\\\"table1.tex\\\",\\\"w\\\") as out:\\n out.write (\\\"\\\\\\\\begin{tabular}{|\\\" + \\\"|\\\".join([\\\"c\\\"]*(cols-2))+\\\"||c|c|}\\\\n\\\")\\n out.write (\\\"\\\\\\\\hline\\\\n\\\")\\n # Table header\\n out.write (\\\" & \\\".join(varList) + \\\" & \\\" + \\\" & \\\".join([a for a in atoms]) + \\\" & Joint $p$ & ln~$p$ \\\\\\\\\\\\\\\\ \\\\\\\\hline\\\\n\\\")\\n # Table rows\\n logps = []\\n for (grounding, probs) in probs:\\n out.write (\\\" & \\\".join([val for (var, val) in grounding.varList]) + \\\" & \\\" +\\n \\\" & \\\".join([str(n.val)+\\\" ({:.1f})\\\".format(p) for (n,p) in probs[:-2]]) +\\n \\\" & {:.2f}\\\".format(probs[-2]) + \\\" & {:.2f}\\\".format(probs[-1]) + \\\"\\\\\\\\\\\\\\\\\\\\n\\\")\\n logps.append(probs[-1])\\n # A line to indicate there are further entries in the DB\\n out.write(\\\" & \\\".join([\\\"\\\\ldots\\\"]*cols) + \\\"\\\\\\\\\\\\\\\\\\\\n\\\")\\n # Close environment\\n out.write (\\\"\\\\\\\\hline\\\\n\\\\\\\\end{tabular}\\\\n\\\")\\n\\n with open(\\\"tab1plogp.tex\\\",\\\"w\\\") as plogp:\\n plogp.write(\\\"\\\\\\\\newcommand{\\\\\\\\pseudologp}{\\\"+\\\"{:.2f}\\\".format(sum(logps)/len(logps))+\\\"}\\\\n\\\")\",\n \"def set_character(self, y_pos, x_pos):\\n self.map[y_pos][x_pos] = 'G'\",\n \"def writeContactPoint(cp):\\n return ' '.join(str(v) for v in (cp.x+cp.n+[cp.kFriction]))\",\n \"def __cross_cord(self,A, B):\\n table_dict = {}\\n for s_idx, s in enumerate(A):\\n for t_idx, t in enumerate(B):\\n table_dict[s+t] = (s_idx,t_idx)\\n\\n return table_dict\",\n \"def __repr__(self) -> str:\\n\\t\\t\\n\\t\\trepr = \\\"\\\"\\n\\t\\tfor row in self.board:\\n\\t\\t\\tfor element in row:\\n\\t\\t\\t\\tif element:\\n\\t\\t\\t\\t\\trepr = repr + \\\"o \\\"\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\trepr = repr + \\\"@ \\\"\\n\\t\\t\\trepr = repr + \\\"\\\\n\\\"\\n\\t\\treturn repr\",\n \"def insertChar(self, ch):\\n word, cx = self.edCursor.getPos()\\n string = word.string[:cx] + ch + word.string[cx:]\\n word.setString(string)\\n # Re-render from tline:\\n self.rsubject.linify(word.tline)\\n self.edCursor.setPos(word, cx+1)\",\n \"def putchar(self, col, row, char, color=GREEN):\\n for j in range(FONT_HEIGHT - 5):\\n v = self.font[ord(char)][3 + j]\\n for i in range(FONT_WIDTH):\\n if v & (1 << (7 - i)):\\n self.putpixel(col + i, row + j, color)\\n else:\\n self.putpixel(col + i, row + j, BLACK)\",\n \"def rotateconnections(self,cells,cellkey):\\n cell = cells[cellkey]\\n for c in cell.conns:\\n if c == 'u':\\n key = str(cell.xpos) + str(cell.ypos - 1)\\n elif c == 'd':\\n key = str(cell.xpos) + str(cell.ypos + 1)\\n elif c == 'l':\\n key = str(cell.xpos-1) + str(cell.ypos)\\n elif c == 'r':\\n key = str(cell.xpos +1) + str(cell.ypos) \\n self.rotatecell(cells[key])\",\n \"def placeCrate (self, crates_char, row, column):\",\n \"def __repr__(self):\\n r = \\\"\\\"\\n for row in self.board:\\n for cell in row:\\n if cell == \\\"\\\":\\n cell = color_magenta(\\\"_\\\")\\n r += cell + \\\" \\\" # for all the empty strings, we will replace it with an '_'.\\n r += \\\"\\\\n\\\"\\n return r\",\n \"def __str__(self):\\n if self.__style == 'default':\\n if self.living:\\n return Cell.liveChar\\n else:\\n return Cell.deadChar\",\n \"def hasJunction(junc, chr, leftEdge, rightEdge, wiggle=0):\\n\\n for i in range(leftEdge-wiggle, leftEdge+wiggle+1):\\n for j in range(rightEdge-wiggle, rightEdge+wiggle+1):\\n try:\\n if junc[chr].has_key( (i, j) ):\\n return True\\n except KeyError:\\n return False\\n\\n return False\",\n \"def pair(handIn, indx = 0):\",\n \"def display_char(self) -> None:\\r\\n print(self.char if self.was_guessed else '_', end=' ')\",\n \"def move_character(self, old_y, old_x, y_pos, x_pos):\\n self.map[old_y][old_x] = ' '\\n self.map[y_pos][x_pos] = 'G'\",\n \"def joint_entropy(column_i, column_j):\\n\\tif len(column_i) != len(column_j):\\n\\t\\traise IndexError(\\\"The two MSA should have the same number of related sequences (same species)\\\")\\n\\tfreq_ij = dict()\\n\\ttotal = len(column_i)\\n\\tentropy = 0\\n\\tfor index in range(total):\\n\\t\\ti = column_i[index]\\n\\t\\tj = column_j[index]\\n\\t\\tif i+j in freq_ij:\\n\\t\\t\\tfreq_ij[i+j] +=1\\n\\t\\telse:\\n\\t\\t\\tfreq_ij[i+j] = 1\\n\\n\\tfor key in freq_ij:\\n\\t\\tfreq_ij[key] /= total\\n\\t\\tentropy += freq_ij[key]*math.log(freq_ij[key], 2)\\n\\tif entropy != 0.0:\\n\\t\\treturn -entropy\\n\\telse:\\n\\t\\treturn entropy\",\n \"def input(self, dots):\\n\\t\\t# Insert the newly entered cell into the buffer at the cursor position.\\n\\t\\tpos = self.untranslatedStart + self.untranslatedCursorPos\\n\\t\\tself.bufferBraille.insert(pos, dots)\\n\\t\\tself.untranslatedCursorPos += 1\\n\\t\\t# Space ends the word.\\n\\t\\tendWord = dots == 0\\n\\t\\t# For uncontracted braille, translate the buffer for each cell added.\\n\\t\\t# Any new characters produced are then sent immediately.\\n\\t\\t# For contracted braille, translate the buffer only when a word is ended (i.e. a space is typed).\\n\\t\\t# This is because later cells can change characters produced by previous cells.\\n\\t\\t# For example, in English grade 2, \\\"tg\\\" produces just \\\"tg\\\",\\n\\t\\t# but \\\"tgr\\\" produces \\\"together\\\".\\n\\t\\tif not self.useContractedForCurrentFocus or endWord:\\n\\t\\t\\tif self._translate(endWord):\\n\\t\\t\\t\\tif not endWord:\\n\\t\\t\\t\\t\\tself.cellsWithText.add(pos)\\n\\t\\t\\telif self.bufferText and not self.useContractedForCurrentFocus:\\n\\t\\t\\t\\t# Translators: Reported when translation didn't succeed due to unsupported input.\\n\\t\\t\\t\\tspeech.speakMessage(_(\\\"Unsupported input\\\"))\\n\\t\\t\\t\\tself.flushBuffer()\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# This cell didn't produce any text; e.g. number sign.\\n\\t\\t\\t\\tself._reportUntranslated(pos)\\n\\t\\telse:\\n\\t\\t\\tself._reportUntranslated(pos)\",\n \"def join(self) -> str:\\n\\n return _ZWJ.join(e.emoji for e in self.emojis)\",\n \"def character(self):\\n if self.is_alive():\\n return \\\"[*]\\\"\\n return \\\"[ ]\\\"\",\n \"def make_board() -> dict:\\n coordinates = {(x, y): ' o ' for x in range(5) for y in range(5)}\\n coordinates[(0, 0)] = \\\" 🚶 \\\"\\n coordinates[(4, 4)] = \\\" x \\\"\\n return coordinates\",\n \"def write(branch=None, branch_track=None, inner=True, connect_to=None):\\n global _tracks, glyphs\\n if inner:\\n result = \\\" \\\"\\n else:\\n if branch == 'start':\\n result = glyphs[\\\">\\\"]\\n elif branch == 'end':\\n result = glyphs[\\\"<\\\"]\\n else:\\n result = glyphs[\\\"-\\\"]\\n\\n for n, track in enumerate(_tracks):\\n if track:\\n if branch:\\n if n < branch_track:\\n if n == connect_to:\\n result += glyphs[\\\"}\\\"]\\n elif inner or branch == 'mid':\\n result += glyphs[\\\"|\\\"]\\n else:\\n result += glyphs[\\\"+\\\"]\\n elif n == branch_track:\\n if branch == 'start':\\n if inner:\\n result += glyphs[\\\",\\\"]\\n else:\\n result += glyphs[\\\"T\\\"]\\n elif branch == 'end':\\n if inner:\\n result += glyphs[\\\"`\\\"]\\n else:\\n result += glyphs[\\\"^\\\"]\\n elif branch == 'mid':\\n if inner:\\n result += glyphs[\\\"}\\\"]\\n else:\\n result += glyphs[\\\"|\\\"]\\n else:\\n result += glyphs[\\\".\\\"]\\n elif n > branch_track:\\n result += glyphs[\\\"+\\\"]\\n else:\\n result += glyphs[\\\"|\\\"]\\n else:\\n if branch:\\n if n < branch_track:\\n if inner or branch == 'mid':\\n result += \\\" \\\"\\n else:\\n result += glyphs[\\\"-\\\"]\\n elif n == branch_track:\\n result += \\\"?\\\"\\n elif n > branch_track:\\n result += glyphs[\\\"-\\\"]\\n else:\\n result += \\\" \\\"\\n if inner:\\n if branch:\\n if branch == 'start' or branch == '<':\\n result += ligatures['<-']\\n elif branch == 'end' or branch == '>':\\n result += ligatures['->']\\n else:\\n result += glyphs[\\\"-\\\"]+glyphs[\\\"-\\\"]\\n else:\\n result += \\\" \\\"\\n else:\\n if branch == '<':\\n result += ligatures['<-']\\n elif branch == '>':\\n result += ligatures['->']\\n else:\\n result += glyphs[\\\"-\\\"]+glyphs[\\\"-\\\"]\\n return result+\\\":\\\"\",\n \"def print(self, assignment):\\n letters = self.letter_grid(assignment)\\n for i in range(self.crossword.height):\\n for j in range(self.crossword.width):\\n if self.crossword.structure[i][j]:\\n print(letters[i][j] or \\\" \\\", end=\\\"\\\")\\n else:\\n print(\\\"█\\\", end=\\\"\\\")\\n print()\",\n \"def print(self, assignment):\\n letters = self.letter_grid(assignment)\\n for i in range(self.crossword.height):\\n for j in range(self.crossword.width):\\n if self.crossword.structure[i][j]:\\n print(letters[i][j] or \\\" \\\", end=\\\"\\\")\\n else:\\n print(\\\"█\\\", end=\\\"\\\")\\n print()\",\n \"def print(self, assignment):\\n letters = self.letter_grid(assignment)\\n for i in range(self.crossword.height):\\n for j in range(self.crossword.width):\\n if self.crossword.structure[i][j]:\\n print(letters[i][j] or \\\" \\\", end=\\\"\\\")\\n else:\\n print(\\\"█\\\", end=\\\"\\\")\\n print()\",\n \"def test_print_cell(self):\\n self.assertEqual(str(self.deadCell), \\\" \\\")\\n self.assertEqual(str(self.livingCell), \\\"X\\\")\",\n \"def write_char(self, char=' '):\\n integer = ord(char)\\n self.instruction(integer, True)\",\n \"def _pipe_segment_with_colons(align, colwidth):\\n w = colwidth\\n if align in [\\\"right\\\", \\\"decimal\\\"]:\\n return (\\\"-\\\" * (w - 1)) + \\\":\\\"\\n elif align == \\\"center\\\":\\n return \\\":\\\" + (\\\"-\\\" * (w - 2)) + \\\":\\\"\\n elif align == \\\"left\\\":\\n return \\\":\\\" + (\\\"-\\\" * (w - 1))\\n else:\\n return \\\"-\\\" * w\",\n \"def writeContactPoint(cp):\\n return ' '.join([str(v) for v in cp.x+cp.n+[cp.kFriction]])\",\n \"def change(coor):\\n return chr(coor[0] + 65), coor[1] + 1\",\n \"def crossover(mother: str, father: str) -> Tuple[str, str]:\\r\\n position = random.randrange(0, DNA_SIZE, 1)\\r\\n return mother[:position] + father[position:], father[:position] + mother[position:]\",\n \"def markCell(self, mark, i, j):\\n if 0 <= i < len(self.board) and 0 <= j < len(self.board) and self.empty_cells > 0:\\n if self.board[i][j] != \\\" \\\":\\n return False\\n else:\\n self.board[i][j] = mark\\n self.empty_cells -= 1\\n return True\\n else:\\n return False\",\n \"def motd(\\r\\n padding=61,\\r\\n corner=\\\"*\\\",\\r\\n left_and_right_column=\\\"|\\\",\\r\\n top_and_bottom_row=\\\"-\\\",\\r\\n white_space=\\\" \\\"\\r\\n\\r\\n):\\r\\n for i in message:\\r\\n\\r\\n if i == message[0]:\\r\\n print(corner,\\r\\n top_and_bottom_row * padding,\\r\\n corner\\r\\n )\\r\\n print(left_and_right_column,\\r\\n white_space * padding,\\r\\n left_and_right_column\\r\\n )\\r\\n\\r\\n if i == message[-1]:\\r\\n print(\\r\\n left_and_right_column,\\r\\n i.center(padding),\\r\\n left_and_right_column\\r\\n )\\r\\n print(\\r\\n left_and_right_column,\\r\\n white_space * padding,\\r\\n left_and_right_column\\r\\n )\\r\\n print(\\r\\n corner,\\r\\n top_and_bottom_row * padding,\\r\\n corner\\r\\n )\\r\\n\\r\\n else:\\r\\n print(left_and_right_column,\\r\\n i.center(padding),\\r\\n left_and_right_column\\r\\n )\",\n \"def exchange_keys(self, client_sock, obtained_cell):\\n\\n private_key = ec.generate_private_key(\\n ec.SECP384R1(), default_backend()) # elliptic curve\\n public_key = private_key.public_key() # duh same.\\n serialised_public_key = public_key.public_bytes(\\n encoding=serialization.Encoding.PEM,\\n format=serialization.PublicFormat.SubjectPublicKeyInfo\\n )\\n # serialise the public key that I'm going to send them\\n their_key = serialization.load_pem_public_key(\\n obtained_cell.payload, backend=default_backend())\\n shared_key = private_key.exchange(ec.ECDH(), their_key)\\n salty = os.urandom(8)\\n derived_key = HKDF(\\n algorithm=hashes.SHA256(),\\n length=32,\\n salt=salty,\\n info=None,\\n backend=default_backend()\\n ).derive(shared_key)\\n reply_cell = Cell(serialised_public_key,\\n salt=salty, ctype=CellType.CONNECT_RESP)\\n signature = self.sign(salty) # sign the random bytes\\n reply_cell.signature = signature # assign the signature.\\n if util.RELAY_DEBUG:\\n print(\\\"reply cell\\\")\\n print(pickle.dumps(reply_cell))\\n # send them the serialised version.\\n client_sock.send(pickle.dumps(reply_cell))\\n return private_key, derived_key\",\n \"def append_cand():\\n if col_i == down_col:\\n return (lastrow[col_i] +\\n [[key + [append_key(lastrow[col_i], left_struc, key)],\\n right_elem]])\",\n \"def addInterCellInteraction(self, atom2, jMat, direction):\\r\\n #Check for repeats (necessary for method used to translate these bonds)\\r\\n for interaction in self.interCellInteractions:\\r\\n if interaction[0] == atom2 and interaction[1] == jMat:\\r\\n return #contains this interaction already\\r\\n self.interCellInteractions.append((atom2, jMat, direction))\",\n \"def joint_adr(self, joint_name):\\n jntadr = mjlib.mj_name2id(self.ptr, C.mjOBJ_JOINT, joint_name)\\n assert (jntadr >= 0)\\n dofmap = {C.mjJNT_FREE: 7,\\n C.mjJNT_BALL: 4,\\n C.mjJNT_SLIDE: 1,\\n C.mjJNT_HINGE: 1}\\n qposadr = self.jnt_qposadr[jntadr][0]\\n qveladr = self.jnt_dofadr[jntadr][0]\\n dof = dofmap[self.jnt_type[jntadr][0]]\\n return (qposadr, qveladr, dof)\",\n \"def jogador(self, msg, match):\\n\\n linha, coluna, posição = self.posicao_do_jogador()\\n yield \\\"Posição no mapa: [\\\" + str(linha) + \\\",\\\" + str(coluna) + \\\"]\\\"\\n yield \\\"Sentido (ponto cardeal): \\\" + posição\",\n \"def render(state, c_choice, h_choice):\\r\\n\\r\\n chars = {\\r\\n 2: h_choice,\\r\\n 1: c_choice,\\r\\n 0: ' '\\r\\n }\\r\\n str_line = '--------------------------------------------------'\\r\\n\\r\\n print('\\\\n' + str_line)\\r\\n for row in state:\\r\\n for cell in row:\\r\\n #print(cell)\\r\\n symbol = chars[cell]\\r\\n print(f'| {symbol} |', end='')\\r\\n print('\\\\n' + str_line)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6033239","0.5642616","0.5578127","0.5315496","0.52856386","0.5230224","0.5207506","0.51857436","0.51733494","0.5166131","0.51407605","0.5093636","0.5047172","0.50447404","0.50364846","0.50296426","0.5018322","0.499095","0.49901915","0.49538672","0.49527854","0.49503666","0.49114814","0.49036044","0.49035886","0.49032792","0.4896659","0.48891604","0.48668414","0.48373324","0.48308587","0.48249325","0.4819467","0.4811443","0.48049918","0.48025358","0.47722688","0.4771127","0.47634003","0.47564402","0.47532034","0.47197908","0.4713787","0.47057718","0.47057253","0.46988174","0.4688677","0.4686376","0.4685415","0.46832025","0.46787497","0.466441","0.46643713","0.46615645","0.46543723","0.4650444","0.4650029","0.4647478","0.4645886","0.46412927","0.46392515","0.46371403","0.46203312","0.4614413","0.46073478","0.46066245","0.4599642","0.4598963","0.45921132","0.45899037","0.45810097","0.45762077","0.45544916","0.45524213","0.45372227","0.45288765","0.45266318","0.45263416","0.45155215","0.45144364","0.45120534","0.4505515","0.45018372","0.45011234","0.45011234","0.45011234","0.44974503","0.4490221","0.44832543","0.4478043","0.447567","0.44715244","0.44655028","0.4461479","0.44592327","0.44569767","0.4449113","0.44472983","0.44462857","0.44462612"],"string":"[\n \"0.6033239\",\n \"0.5642616\",\n \"0.5578127\",\n \"0.5315496\",\n \"0.52856386\",\n \"0.5230224\",\n \"0.5207506\",\n \"0.51857436\",\n \"0.51733494\",\n \"0.5166131\",\n \"0.51407605\",\n \"0.5093636\",\n \"0.5047172\",\n \"0.50447404\",\n \"0.50364846\",\n \"0.50296426\",\n \"0.5018322\",\n \"0.499095\",\n \"0.49901915\",\n \"0.49538672\",\n \"0.49527854\",\n \"0.49503666\",\n \"0.49114814\",\n \"0.49036044\",\n \"0.49035886\",\n \"0.49032792\",\n \"0.4896659\",\n \"0.48891604\",\n \"0.48668414\",\n \"0.48373324\",\n \"0.48308587\",\n \"0.48249325\",\n \"0.4819467\",\n \"0.4811443\",\n \"0.48049918\",\n \"0.48025358\",\n \"0.47722688\",\n \"0.4771127\",\n \"0.47634003\",\n \"0.47564402\",\n \"0.47532034\",\n \"0.47197908\",\n \"0.4713787\",\n \"0.47057718\",\n \"0.47057253\",\n \"0.46988174\",\n \"0.4688677\",\n \"0.4686376\",\n \"0.4685415\",\n \"0.46832025\",\n \"0.46787497\",\n \"0.466441\",\n \"0.46643713\",\n \"0.46615645\",\n \"0.46543723\",\n \"0.4650444\",\n \"0.4650029\",\n \"0.4647478\",\n \"0.4645886\",\n \"0.46412927\",\n \"0.46392515\",\n \"0.46371403\",\n \"0.46203312\",\n \"0.4614413\",\n \"0.46073478\",\n \"0.46066245\",\n \"0.4599642\",\n \"0.4598963\",\n \"0.45921132\",\n \"0.45899037\",\n \"0.45810097\",\n \"0.45762077\",\n \"0.45544916\",\n \"0.45524213\",\n \"0.45372227\",\n \"0.45288765\",\n \"0.45266318\",\n \"0.45263416\",\n \"0.45155215\",\n \"0.45144364\",\n \"0.45120534\",\n \"0.4505515\",\n \"0.45018372\",\n \"0.45011234\",\n \"0.45011234\",\n \"0.45011234\",\n \"0.44974503\",\n \"0.4490221\",\n \"0.44832543\",\n \"0.4478043\",\n \"0.447567\",\n \"0.44715244\",\n \"0.44655028\",\n \"0.4461479\",\n \"0.44592327\",\n \"0.44569767\",\n \"0.4449113\",\n \"0.44472983\",\n \"0.44462857\",\n \"0.44462612\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5760053"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":25296,"cells":{"query":{"kind":"string","value":"Split all dominoes into separate cells. Useful for Dominosa."},"document":{"kind":"string","value":"def split_all(self):\n for domino in self.dominoes[:]:\n self.split(domino)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_split_cell_splits_neighbours(mock_amg):\n\n # split the centre cell in the mock grid\n # this will create 4 more cells at tier 1\n mock_amg.cells[4].split()\n\n # now split the bottom right of these cells\n # this should force the east and south cells to also be split\n mock_amg.cells[4].children['br'].split()\n\n assert mock_amg.cells[5].has_children\n assert mock_amg.cells[1].has_children","def test_split_adds_children(mock_amg):\n\n mock_amg.cells[0].split()\n assert mock_amg.cells[0].children['bl'] is mock_amg.cells[-4]\n assert mock_amg.cells[0].children['br'] is mock_amg.cells[-3]\n assert mock_amg.cells[0].children['tl'] is mock_amg.cells[-2]\n assert mock_amg.cells[0].children['tr'] is mock_amg.cells[-1]","def pushDominoes(self, dominoes: str) -> str:\n\n N = len(dominoes)\n dist_R = [-1] * N\n dist_L = [-1] * N\n\n count = -1\n for i in range(N):\n if dominoes[i] == 'R':\n count = 0\n elif dominoes[i] == 'L':\n count = -1\n elif count >= 0:\n count += 1\n dist_R[i] = count if count >= 0 else 2 * N\n\n count = -1\n for i in reversed(range(N)):\n if dominoes[i] == 'L':\n count = 0\n elif dominoes[i] == 'R':\n count = -1\n elif count >= 0:\n count += 1\n dist_L[i] = count if count >= 0 else 2 * N\n\n # print(dist_R)\n # print(dist_L)\n\n out = [\".\"] * N\n for i in range(N):\n if dist_R[i] < dist_L[i]:\n out[i] = \"R\"\n elif dist_R[i] > dist_L[i]:\n out[i] = \"L\"\n return \"\".join(out)","def all_cells(self):\n \"\"\"\n Note that we use the convention that the first cell is (1,1)\n \"\"\"\n spart_star = self.circle_star()\n part = Partition(list(spart_star))\n coordinates = part.cells()\n coordinates = [(x+1, y+1) for x, y in coordinates]\n return coordinates","def cells(self):\n \"\"\"\n Note that we use the convention that the first cell is (1,1)\n \"\"\"\n part = Partition(list(self))\n coordinates = part.cells()\n coordinates = [(x+1, y+1) for x, y in coordinates]\n return coordinates","def cells(self):\n \"\"\"\n Note that we use the convention that the first cell is (1,1)\n \"\"\"\n spart_star = self.star()\n part = Partition(list(spart_star))\n coordinates = part.cells()\n coordinates = [(x+1, y+1) for x, y in coordinates]\n return coordinates","def get_cells(self):\n return [\n cell for column in self.children for cell in column.get_cells()]","def get_numbered_cells_adjacent_closed_cells(self):\r\n numbered_cells_adjacent_closed_cells = []\r\n numbered_cells = self.get_all_numbered_cells()\r\n for row, col in numbered_cells:\r\n _, closed_cells, _, _ = self.get_cells(row, col)\r\n if closed_cells:\r\n numbered_cells_adjacent_closed_cells.append((row, col))\r\n return numbered_cells_adjacent_closed_cells","def delete_cells(self):\n cells_dict_base, cell_list_act, common_cells = self.cells_list()\n while(len(common_cells) != 0):\n cell_index = cells_dict_base[common_cells[0]]['index']\n if cell_index != len(cells_dict_base)-1:\n cell_start = cells_dict_base[common_cells[0]]['start']\n next_cell_start = [values['start'] for cell_name, values in cells_dict_base.items() if values['index'] == (cell_index+1)][0]\n cell_content = self.base_doc[cell_start: next_cell_start]\n self.base_doc = self.base_doc.replace(cell_content, '')\n else:\n cell_start = cells_dict_base[common_cells[0]]['start']\n cell_content = self.base_doc[cell_start:]\n self.base_doc = self.base_doc.replace(cell_content, '')\n self.removed_last_bracket = True\n cells_dict_base, cell_list_act, common_cells = self.cells_list()","def bosonic_cells(self):\n cells = self.cells()\n fermionic_cells = self.fermionic_cells()\n coords = [x for x in cells if x not in fermionic_cells]\n return coords","def to_cdo_grid(self, outfile):","def simple_solution(self, dominoes: str) -> str:\n dominoes = list(dominoes)\n nodes = [(-1, 'L')] + [(i, x) for i, x in enumerate(dominoes) if x != '.'] + [\n (len(dominoes), 'R')]\n for (i, x), (j, y) in zip(nodes, nodes[1:]):\n if x == y:\n for k in range(i + 1, j):\n dominoes[k] = x\n elif x == 'R' and y == 'L':\n k, l = i, j\n while k < l:\n dominoes[k] = x\n dominoes[l] = y\n k, l = k + 1, l - 1\n if ((j - i + 1) % 2) != 0:\n dominoes[(j + i) // 2] = '.'\n return ''.join(dominoes)","def _init_pagoda_clses(self):\n a = []; b = [] #IGNORE:C0321\n c = []; d = [] #IGNORE:C0321\n row_selector = ((a, b), (c, d))\n for cell in self.compact:\n row_selector[cell.y % 2][cell.x % 2].append(cell)\n cell.pvalue = cell.y % 2 + 1 + cell.x % 2 \n return a, b, c, d","def partition_domain(self, dom):\r\n split = len(dom) // 2\r\n dom1 = set(list(dom)[0:split])\r\n dom2 = dom - dom1\r\n return dom1, dom2","def _cells(notebook):\n if notebook.nbformat < 4:\n for ws in notebook.worksheets:\n for cell in ws.cells:\n yield cell\n else:\n for cell in notebook.cells:\n yield cell","def make_cells(self, fields: Dict[str, Any]) -> List[Cell]:\n result: List[Cell] = []\n for column_title, field_value in fields.items():\n result.append(self.make_cell(column_title, field_value))\n return result","def test_split_cell_east_sets_neighbours(mock_amg):\n\n mock_amg.cells[4].split() # middle cell\n mock_amg.cells[5].split() # east cell\n\n east = mock_amg.cells[5]\n west = mock_amg.cells[4]\n\n assert west.children['tr'].east == east.children['tl']\n assert west.children['br'].east == east.children['bl']\n assert east.children['tl'].west == west.children['tr']\n assert east.children['bl'].west == west.children['br']","def test_split_cell_creates_four_more_cells(mock_amg):\n\n init_n_windows = mock_amg.n_windows\n init_n_cells = mock_amg.n_cells\n\n mock_amg.cells[0].split()\n\n assert init_n_windows + 5 == mock_amg.n_windows\n assert init_n_cells + 4 == mock_amg.n_cells","def pushDominoes(self, dominoes: str) -> str:\n return self.simple_solution(dominoes)","def split(self, dim, bins):\n if type(bins) in (int, complex):\n bins = np.r_[self.min(dim):self.max(dim):bins]\n p = self.get_cols(dim)[0]\n idx = np.digitize(p, bins)\n splitted = []\n for i in xrange(1,len(bins)):\n new_data = self.data[idx==i]\n splitted.append(DataTable(\n new_data,\n self.dims,\n self.legends,\n self.tags,\n self.sub_name('%.2f<=%s<%.2f' % (bins[i-1], dim, bins[i]))))\n return splitted","def _generate_cells(self) -> None:\n for i in range(15):\n for j in range(15):\n c = Cell(x=i, y=j)\n c.answer = self.puzzle.solution[j*self.width+i]\n self.cells[(j, i)] = c # row, col","def test_split_cell_south_sets_neighbours(mock_amg):\n\n mock_amg.cells[4].split() # middle cell\n mock_amg.cells[1].split() # south cell\n\n south = mock_amg.cells[1]\n north = mock_amg.cells[4]\n\n assert south.children['tl'].north == north.children['bl']\n assert south.children['tr'].north == north.children['br']\n assert north.children['bl'].south == south.children['tl']\n assert north.children['br'].south == south.children['tr']","def merge_cells(self):\n\n for rownum, row in enumerate(self.cells):\n for colnum, cell in enumerate(row):\n if not isinstance(cell, Cell):\n continue\n cols_to_merge = 0\n for i in range(colnum+1, len(row)):\n if isinstance(self.cells[rownum][i], Cell) and self.cells[rownum][i].event == cell.event:\n cols_to_merge += 1\n if cols_to_merge > 0:\n cell.colspan = cols_to_merge + 1\n for i in range(1, cols_to_merge + 1):\n self.cells[rownum][colnum + i] = SpanCell(rownum, colnum + i)","def cells_list(self):\n xx, yy = np.meshgrid(self.x_spacings, self.y_spacings)\n return np.vstack([yy.ravel(), xx.ravel()]).transpose()","def test_split_adds_known_neighbours(mock_amg):\n\n mock_amg.cells[4].split()\n # bl\n assert mock_amg.cells[-4].north is mock_amg.cells[-2]\n assert mock_amg.cells[-4].east is mock_amg.cells[-3]\n\n # br\n assert mock_amg.cells[-3].north is mock_amg.cells[-1]\n assert mock_amg.cells[-3].west is mock_amg.cells[-4]\n\n # tl\n assert mock_amg.cells[-2].south is mock_amg.cells[-4]\n assert mock_amg.cells[-2].east is mock_amg.cells[-1]\n\n # tr\n assert mock_amg.cells[-1].south is mock_amg.cells[-3]\n assert mock_amg.cells[-1].west is mock_amg.cells[-2]","def fill_cells(self, latitudes, longitudes, businesses):\n if len(businesses) == 0:\n # There are no businesses in this area\n return\n if longitudes.size > 2:\n # First we devide the collection along the center of longitudes\n center = longitudes[longitudes.size / 2]\n fill_cells(self, latitudes,\n longitudes[:(longitudes.size / 2 + 1)],\n businesses[businesses.longitude <= center])\n fill_cells(self, latitudes, longitudes[(longitudes.size / 2):],\n businesses[businesses.longitude > center])\n elif latitudes.size > 2:\n # Now we devide along the center of latitudes\n center = latitudes[latitudes.size / 2]\n fill_cells(self, latitudes[:(latitudes.size / 2 + 1)],\n longitudes,\n businesses[businesses.latitude <= center])\n fill_cells(self, latitudes[(latitudes.size / 2):], longitudes,\n businesses[businesses.latitude > center])\n else:\n # Now we are in one Cell! Let's store our businesses\n bizs = []\n for idx, business in businesses.iterrows():\n # For performance, we only store index and coordinates\n bizs.append({'index': idx,\n 'latitude': business.latitude,\n 'longitude': business.longitude})\n coord = (self.longitudes.searchsorted(longitudes[0]),\n self.latitudes.searchsorted(latitudes[0]))\n if coord[0] not in self.cells:\n self.cells[coord[0]] = {}\n self.cells[coord[0]].update({coord[1]: bizs})","def visit_table(self, sytable):\n def index(sytable):\n try:\n return sytable.get_column(self.input_index)\n except:\n return np.arange(sytable.number_of_rows())\n\n def slices_using_group_array(group_array):\n \"\"\"Return the slices to split by.\n A group array is made of strictly increasing group identifiers.\n\n >>> slices_using_group_array(np.array([0, 0, 0, 1, 1, 2, 3, 3, 3]))\n [(0, 3), (3, 5), (5, 6), (6, 9)]\n \"\"\"\n unique_elements = np.unique(group_array)\n slices = []\n for unique_element in unique_elements:\n indexes = np.flatnonzero(group_array == unique_element)\n low, high = (indexes[0], indexes[-1] + 1)\n slices.append((unique_element, slice(low, high)))\n return slices\n\n def indices_using_group_array(group_array):\n \"\"\"\n Return list of index lists, ordered by first occurance of value.\n \"\"\"\n unique_elements = np.unique(group_array)\n indices = []\n for unique_element in unique_elements:\n indices.append((unique_element,\n np.flatnonzero(group_array == unique_element)))\n return indices\n\n columns = sytable.columns()\n # Perform the split and append the new tables to output.\n slice_indices = indices_using_group_array(index(sytable))\n column_attrs = {}\n\n for unique_element, slice_index in slice_indices:\n # Sets of all columns except for the INDEX columns.\n result = type(sytable)(sytable.container_type)\n self.output_list.append((unique_element, result))\n\n for column in columns:\n array = sytable.get_column(column)[slice_index]\n if self.remove_fill and len(array):\n kind = array.dtype.kind\n if kind in ['S', 'U']:\n if np.all(array == ''):\n continue\n else:\n if not len(array) or np.isnan(np.min(array)):\n continue\n\n result.set_column(column, array)\n if column in column_attrs:\n attrs = column_attrs[column]\n else:\n attrs = dict(\n sytable.get_column_attributes(column).get())\n column_attrs[column] = attrs\n result.get_column_attributes(column).set(attrs)","def choose_extra_dominoes(self, random):\n dominoes = self.extra_dominoes[:]\n count = len(dominoes)\n start = random.randrange(count)\n for i in range(count):\n yield dominoes[(i + start) % count]","def test_split_cell_west_sets_neighbours(mock_amg):\n\n mock_amg.cells[4].split() # middle cell\n mock_amg.cells[3].split() # west cell\n\n west = mock_amg.cells[3]\n east = mock_amg.cells[4]\n\n assert west.children['tr'].east == east.children['tl']\n assert west.children['br'].east == east.children['bl']\n assert east.children['tl'].west == west.children['tr']\n assert east.children['bl'].west == west.children['br']","def split_into_codons(dna):\n dna_split = []\n length = math.ceil(len(dna)/3)\n for i in range(0, length):\n j = 3*i\n codon = dna[j:j+3]\n dna_split += [codon]\n return dna_split","def fermionic_cells(self):\n cells = self.cells()\n cells_and_circles = self.all_cells()\n circles = [x for x in cells_and_circles if x not in cells]\n coords = [(i, jprime)\n for iprime, jprime in circles\n for i, j in circles\n if iprime > i\n ]\n coords.sort()\n return coords","def create_cells(self, N):\n self.cells = []\n r = 50 # Radius of cell locations from origin (0,0,0) in microns\n N = self._N\n for i in range(N):\n cell = BallAndStick()\n # When cells are created, the soma location is at (0,0,0) and\n # the dendrite extends along the X-axis.\n # First, at the origin, rotate about Z.\n cell.rotateZ(i * 2 * pi / N)\n # Then reposition\n x_loc = cos(i * 2 * pi / N) * r\n y_loc = sin(i * 2 * pi / N) * r\n cell.set_position(x_loc, y_loc, 0)\n self.cells.append(cell)","def test_split_cell_north_sets_neighbours(mock_amg):\n\n mock_amg.cells[4].split() # middle cell\n mock_amg.cells[7].split() # north cell\n\n north = mock_amg.cells[7]\n south = mock_amg.cells[4]\n\n assert south.children['tl'].north == north.children['bl']\n assert south.children['tr'].north == north.children['br']\n assert north.children['bl'].south == south.children['tl']\n assert north.children['br'].south == south.children['tr']","def make_filiation(self):\n if self.cells is not None:\n filiate_from_bpointer(self.cells)\n# for cell in self.cells:\n# childs = []\n# for cc in self.cells:\n# if cc.bpointer == cell.identifier:\n# childs.append(cc)\n# cc.parent = cell\n# cc.set_division_event()\n# cell.childs = childs\n return","def pushDominoes(self, dominoes):\n if len(dominoes) < 2:\n return dominoes\n\n state = dominoes\n while True:\n next_state = self.domino_fall(state)\n if next_state == state:\n break\n state = next_state\n return state","def markdown_cells(self):\n for cell in self.content.cells:\n if cell.cell_type == \"markdown\" and not cell.source.startswith(NOTEBOOK_HEADER_TAG) \\\n and not cell.source.startswith(NAVBAR_TAG):\n yield cell","def autosplit(self):\n result = RowSet()\n for row in self:\n result.append(row.autosplit())\n return result","def splitColumns(self, numColumns):\n if numColumns < 2:\n return [self]\n if len(self) <= numColumns:\n return [OutputGroup([item]) for item in self]\n groupList = []\n numEach = len(self) // numColumns\n for colNum in range(numColumns - 1):\n groupList.append(self[colNum * numEach : (colNum+1) * numEach])\n groupList.append(self[(numColumns-1) * numEach : ])\n numChanges = 1\n while numChanges:\n numChanges = 0\n for colNum in range(numColumns - 1):\n if groupList[colNum].totalHeight() > groupList[colNum+1].\\\n totalHeight() + groupList[colNum][-1].height:\n groupList[colNum+1].insert(0, groupList[colNum][-1])\n groupList[colNum] = groupList[colNum][:-1]\n numChanges += 1\n if groupList[colNum].totalHeight() + groupList[colNum+1][0].\\\n height <= groupList[colNum+1].totalHeight():\n groupList[colNum].append(groupList[colNum+1][0])\n groupList[colNum+1] = groupList[colNum+1][1:]\n numChanges += 1\n return groupList","def create_grid_and_edges(data, drone_altitude, safety_distance):\n # minimum and maximum north coordinates\n north_min = np.floor(np.min(data[:, 0] - data[:, 3]))\n north_max = np.ceil(np.max(data[:, 0] + data[:, 3]))\n\n # minimum and maximum east coordinates\n east_min = np.floor(np.min(data[:, 1] - data[:, 4]))\n east_max = np.ceil(np.max(data[:, 1] + data[:, 4]))\n\n # given the minimum and maximum coordinates we can\n # calculate the size of the grid.\n north_size = int(np.ceil(north_max - north_min))\n east_size = int(np.ceil(east_max - east_min))\n\n # Initialize an empty grid\n grid = np.zeros((north_size, east_size))\n # Initialize an empty list for Voronoi points\n points = []\n # Populate the grid with obstacles\n for i in range(data.shape[0]):\n north, east, alt, d_north, d_east, d_alt = data[i, :]\n if alt + d_alt + safety_distance > drone_altitude:\n obstacle = [\n int(np.clip(north - d_north - safety_distance - north_min, 0, north_size-1)),\n int(np.clip(north + d_north + safety_distance - north_min, 0, north_size-1)),\n int(np.clip(east - d_east - safety_distance - east_min, 0, east_size-1)),\n int(np.clip(east + d_east + safety_distance - east_min, 0, east_size-1)),\n ]\n grid[obstacle[0]:obstacle[1]+1, obstacle[2]:obstacle[3]+1] = 1\n # add center of obstacles to points list\n points.append([north - north_min, east - east_min])\n\n graph = Voronoi(points)\n\n edges = []\n for v in graph.ridge_vertices:\n p1 = graph.vertices[v[0]]\n p2 = graph.vertices[v[1]]\n cells = list(bresenham(int(p1[0]), int(p1[1]), int(p2[0]), int(p2[1])))\n hit = False\n\n for c in cells:\n if np.amin(c) < 0 or c[0] >= grid.shape[0] or c[1] >= grid.shape[1]:\n hit = True\n break\n if grid[c[0], c[1]] == 1:\n hit = True\n break\n\n if not hit:\n p1 = (p1[0], p1[1])\n p2 = (p2[0], p2[1])\n edges.append((p1, p2))\n\n return grid, edges, int(north_min), int(east_min)","def get_states_as_cells(self, oids=None, symbols=None, tokens=None):\n return [CharacterDataCell(value=s) for \\\n s in self.get_states(oids=oids, symbols=symbols, tokens=tokens)]","def cells(self):\n return ((row, col) for row in self.rows for col in self.cols)","def get_cells(self):\n self.list = [self.origin]\n\n for i in range(1, self.size):\n if(self.direction ==self.direction.RIGHT):\n self.list.append((self.origin[0], self.origin[1]+i))\n elif(self.direction ==self.direction.DOWN):\n self.list.append((self.origin[0]-i, self.origin[1]))\n\n return self.list","def split_board(board):\n row1 = board[0]\n row2 = board[1]\n row3 = board[2]\n diag1 = [row1[0], row2[1], row3[2]]\n diag2 = [row1[2], row2[1], row3[0]]\n col1 = [row1[0], row2[0], row3[0]]\n col2 = [row1[1], row2[1], row3[1]]\n col3 = [row1[2], row2[2], row3[2]]\n return [row1, row2, row3, diag1, diag2, col1, col2, col3]","def DivideDF(df_all):\n return df_all.iloc[:df_all.trn_len], df_all.iloc[df_all.trn_len:]","def cells(self) -> Iterable[\"RenderableType\"]:\n yield from self._cells","def get_all_numbered_cells(self):\r\n num_rows = len(self.mine_field)\r\n num_cols = len(self.mine_field[0])\r\n numbered_cells = [(row, col)\r\n for row in range(num_rows)\r\n for col in range(num_cols)\r\n if self.mine_field[row][col] != '?' and \\\r\n self.mine_field[row][col] != 'x']\r\n return numbered_cells","def pickup_dominoes(self, num_dominoes, player):\n\n for domino in range(num_dominoes):\n shuffle(self.boneyard)\n player.hand.append(self.boneyard.pop(0))","def edges_as_nodes_cells_mark(self):\n ptm_sides = np.zeros([len(self.edges),5],np.int32)\n ptm_sides[:,0:2] = self.edges['nodes']\n ptm_sides[:,2:4] = self.edges['cells']\n ptm_sides[:,4] = self.edges['mark']\n return ptm_sides","def split(self, X):","def calculate_divided_differences_row(nodes_to_compute):\n divided_differences = []\n\n if len(nodes_to_compute) == 1:\n return None\n\n for i in range(0, len(nodes_to_compute) - 1):\n child = DividedDifferenceNode.create_child_node(nodes_to_compute[i], nodes_to_compute[i + 1])\n child.calculate_value()\n divided_differences.append(child)\n\n for node in divided_differences:\n print(node, end='')\n\n print('\\n')\n return divided_differences","def test_split_adds_parents(mock_amg):\n\n mock_amg.cells[0].split()\n assert mock_amg.cells[-4].parent is mock_amg.cells[0]\n assert mock_amg.cells[-3].parent is mock_amg.cells[0]\n assert mock_amg.cells[-2].parent is mock_amg.cells[0]\n assert mock_amg.cells[-1].parent is mock_amg.cells[0]","def _chunks(l, ncols):\n assert isinstance(ncols, int), \"ncols must be an integer\"\n for i in range(0, len(l), ncols):\n yield l[i: i+ncols]","def collect_rows():\n return ((x, y) for x in range(80) for y in range(x + 1, 9 + (x//9)*9))","def cells(self):\n return chain.from_iterable(self.cols)","def sq_wtd_voxels(ndomains, pos, rcrit, ngrid=None, log=null_log):\n\n if ngrid is None:\n # To make each cell non-ghost dominated would really like\n # 0.1/rcrit, but I rely a bit on adjacent cells being in \n # the same domain.\n ngrid = int(0.15/rcrit)\n\n if ndomains>64:\n raise Exception('{:,}>64, maximum domains for single master'.format(ndomains))\n\n tot_cells = ngrid**3\n cells = get_cells(pos, ngrid, log)\n assert(cells.min()>=0)\n assert(cells.max()<tot_cells)\n counts = np.bincount(cells, minlength=tot_cells)\n cell_ids = np.arange(tot_cells)\n\n doms = domain_region_grow(np.reshape(counts, (ngrid,ngrid,ngrid)), ndomains)-1\n assert((doms>=0).all()) # check we covered all the domains\n cells_per_dmn = np.bincount(doms.ravel())\n tot_per_dmn = np.bincount(doms.ravel()[cells])\n print(\"Domain totals:\", ', '.join('%d'%npts for npts in tot_per_dmn), file=log)\n\n cell_masks = 1<<(doms.astype(np.uint64))\n pos_masks = cell_masks.ravel()[cells].copy()\n\n prettyprint_cellmasks(cell_masks, log)\n # 'Bleed' the cell masks to find ghosts\n ngb_masks = adjacent_cell_masks(pos, cell_masks, rcrit, log)\n\n for dom, mask in enumerate(np.unique(pos_masks)):\n # find all the positions needed in this domain\n idx_domain = np.flatnonzero(ngb_masks & mask)\n # which of these are non-ghosts\n idx_non_ghosts = np.flatnonzero(np.equal(pos_masks[idx_domain], mask))\n\n pct_ghosts = (100*(len(idx_domain)-len(idx_non_ghosts)))//len(idx_domain)\n\n txt = 'Domain %d: '%dom + \\\n '{:,} particles of which {:,}% are ghosts ({:,} domain cells)'.format(len(idx_domain),\n pct_ghosts, cells_per_dmn[dom])\n # count parts+ghosts\n if pct_ghosts<50:\n print(txt, file=log)\n else:\n print(MarkUp.WARNING+txt+MarkUp.ENDC, file=log) \n yield idx_domain, idx_non_ghosts","def createGridNodesinFRF(x0, y0, dx, dy, ni, nj):\n assert dx.shape[0] == ni-1, 'This function assumes that there are n-1 dx values'\n\n if np.mean(np.diff(dx)) != np.mean(dx): # vairable spacing cell array\n icoord = np.zeros(ni) # assume\n jcoord = np.zeros(nj)\n icoord[0] = x0\n jcoord[0] = y0\n for xx, dxx in enumerate(dx):\n icoord[xx+1] = icoord[xx] - dxx # assumes offshore origin\n for yy, dyy in enumerate(dy):\n jcoord[yy+1] = jcoord[yy] - dyy\n else:\n raise NotImplementedError\n\n return icoord, jcoord","def separar_vardado(self,numvar=4):\n c = 0\n\n for i in range(0,len(self.dado),numvar):\n self.h.insert(c,self.dado[i])\n \n self.nO.insert(c,self.dado[i+1]*1e6)\n self.nN2.insert(c,self.dado[i+2]*1e6) # O produto por 1e6 é para passar de cm^-3 para m^-3\n self.nO2.insert(c,self.dado[i+3]*1e6)\n \n #print(c,self.h2[c],self.nN2[c],self.nO2[c],self.nO[c],\"\\n gv_MSISE\")\n #print(\"\\n\",i,\"\\n\")\n\n c = c + 1","def split_data(data):\n data_values = []\n target_drugs = []\n cell_lines = []\n states = []\n replicates = []\n \n for i in data.columns:\n target_drug = i.split()[-2]\n cell_line = i.split()[-1].split(\"_\")[0]\n state = i.split()[-1].split(\"_\")[1]\n replicate = i.split()[-1].split(\"_\")[2]\n \n data_values.append(list(data[i]))\n target_drugs.append(int(target_drug))\n cell_lines.append(cell_line)\n states.append(state)\n replicates.append(replicate)\n \n data_values = np.array(data_values)\n target_drugs = np.array(target_drugs)\n cell_lines = np.array(cell_lines)\n states = np.array(states)\n replicates = np.array(replicates)\n \n return data_values, target_drugs, cell_lines, states, replicates","def _do_split_single_haghverdi16(self, Dseg, tips):\n # compute splits using different starting points the first index of\n # tips is the starting point for the other two, the order does not\n # matter\n ssegs = []\n # permutations of tip cells\n ps = [[0, 1, 2], # start by computing distances from the first tip\n [1, 2, 0], # -\"- second tip\n [2, 0, 1]] # -\"- third tip\n # import matplotlib.pyplot as pl\n for i, p in enumerate(ps):\n ssegs.append(self.__do_split_haghverdi16(Dseg, tips[p]))\n return ssegs","def get_cells(self, row, col):\r\n surrounding_cells = self.get_surrounding_cells(row, col)\r\n closed_cells = self.filter_cells(surrounding_cells, '?')\r\n mine_cells = self.filter_cells(surrounding_cells, 'x')\r\n numbered_cells = list(set(surrounding_cells).difference(closed_cells))\r\n numbered_cells = list(set(numbered_cells).difference(mine_cells))\r\n return surrounding_cells, closed_cells, mine_cells, numbered_cells","def cells(self):\r\n return Cells(self)","def split_dataframe(df, split_elements_list):\n y = df.filter(split_elements_list)\n x = df.drop(split_elements_list, axis=1)\n\n return x, y","def subdivide_ngons(faces: Iterable[Sequence[Union[Vector, Vec2]]]) -> Iterable[\n List[Vector]]:\n for face in faces:\n if len(face) < 5:\n yield face\n else:\n mid_pos = sum(face) / len(face)\n for index, vertex in enumerate(face):\n yield face[index - 1], vertex, mid_pos","def make_grid(self):\n grid = []\n for index, _ in enumerate(self.font[self.chars[0]]):\n grid.append([])\n\n for char in self.chars:\n for index in range(config()[\"panel\"][\"height\"]):\n grid[index].extend(self.font[char][index])\n\n return grid","def empty_cells(self) -> List[Cell]:\n return list(ob.pos[0] for ob in self.new_obs())","def _create_grid_with_cells(self, width, height):\n grid = []\n for row in range(height):\n grid.append([])\n for column in range(width):\n if column % 2 == 1 and row % 2 == 1:\n grid[row].append(TILE_EMPTY)\n elif (\n column == 0 or row == 0 or column == width - 1 or row == height - 1\n ):\n grid[row].append(TILE_CRATE)\n else:\n grid[row].append(TILE_CRATE)\n grid[-2][-3] = TILE_EMPTY\n grid[1][0] = TILE_EMPTY\n return grid","def split(self):\n sub_images = []\n\n for region in regionprops(self.cells):\n minr, minc, maxr, maxc = region.bbox\n sub_image = self.image_raw[max(0, minr - 10):maxr, max(0, minc - 10):maxc, :]\n\n sub_images.append(FQimage(data=sub_image))\n\n return sub_images","def _get_cells(\n self, console: \"Console\", column_index: int, column: Column\n ) -> Iterable[_Cell]:\n\n collapse_padding = self.collapse_padding\n pad_edge = self.pad_edge\n padding = self.padding\n any_padding = any(padding)\n\n first_column = column_index == 0\n last_column = column_index == len(self.columns) - 1\n\n _padding_cache: Dict[Tuple[bool, bool], Tuple[int, int, int, int]] = {}\n\n def get_padding(first_row: bool, last_row: bool) -> Tuple[int, int, int, int]:\n cached = _padding_cache.get((first_row, last_row))\n if cached:\n return cached\n top, right, bottom, left = padding\n\n if collapse_padding:\n if not first_column:\n left = max(0, left - right)\n if not last_row:\n bottom = max(0, top - bottom)\n\n if not pad_edge:\n if first_column:\n left = 0\n if last_column:\n right = 0\n if first_row:\n top = 0\n if last_row:\n bottom = 0\n _padding = (top, right, bottom, left)\n _padding_cache[(first_row, last_row)] = _padding\n return _padding\n\n raw_cells: List[Tuple[StyleType, \"RenderableType\"]] = []\n _append = raw_cells.append\n get_style = console.get_style\n if self.show_header:\n header_style = get_style(self.header_style or \"\") + get_style(\n column.header_style\n )\n _append((header_style, column.header))\n cell_style = get_style(column.style or \"\")\n for cell in column.cells:\n _append((cell_style, cell))\n if self.show_footer:\n footer_style = get_style(self.footer_style or \"\") + get_style(\n column.footer_style\n )\n _append((footer_style, column.footer))\n\n if any_padding:\n _Padding = Padding\n for first, last, (style, renderable) in loop_first_last(raw_cells):\n yield _Cell(\n style,\n _Padding(renderable, get_padding(first, last)),\n getattr(renderable, \"vertical\", None) or column.vertical,\n )\n else:\n for (style, renderable) in raw_cells:\n yield _Cell(\n style,\n renderable,\n getattr(renderable, \"vertical\", None) or column.vertical,\n )","def _cells(self):\n\n # Matrix size\n size = self.specification['DIMENSION']\n # Edge weight format\n edge_format = self.specification['EDGE_WEIGHT_FORMAT'].split('_')\n # Matrix type: FULL, UPPER, LOWER\n matrix = edge_format[0]\n\n # Data direction: ROW, COL; diagonal offset\n offset = 0\n if matrix != 'FULL':\n if edge_format[1] in ['ROW', 'COL']:\n direction = edge_format[1]\n # No diagonal entries, matrix needs to be offseted by 1\n offset = 1\n else:\n direction = edge_format[2]\n\n # Initial position in the matrix\n row = col = -1\n\n # Increments `b`. If `b` is larger than `bound` increments `a` and\n # sets `b` to `ret`.\n def calc(a, b, bound, ret):\n a = max(a, 0)\n b += 1\n if b > bound:\n a += 1\n b = ret\n return a, max(b, ret)\n\n # Generate the coordinates\n while True:\n if matrix == 'FULL':\n row, col = calc(row, col, size - 1, 0)\n elif matrix == 'UPPER' and direction == 'ROW':\n row, col = calc(row, col, size - 1, row + 1 + offset)\n elif matrix == 'LOWER' and direction == 'COL':\n col, row = calc(col, row, size - 1, col + 1 + offset)\n elif matrix == 'LOWER' and direction == 'ROW':\n row, col = calc(\n row, col, row - offset if offset else max(row, 0), 0)\n elif matrix == 'UPPER' and direction == 'COL':\n col, row = calc(\n col, row, col - offset if offset else max(col, 0), 0)\n\n # End when all coordinates are generated\n if row >= size or col >= size:\n break\n\n yield row, col","def grid(self, northeast, southwest, density=200):\n grid = []\n\n # Determine the bounds of a standard, positive quadrant plot\n y_max, y_min = int(northeast[0]), int(southwest[0])\n x_max, x_min = int(northeast[1]), int(southwest[1])\n\n # Construct a sequence of boxes each moving clockwise from southwest corner\n master = []\n for x in range(x_min, x_max, density):\n for y in range(y_min, y_max, density):\n polygon = [\n (x, y),\n (x, y + density),\n (x + density, y + density),\n (x + density, y),\n (x, y)\n ]\n master.append(polygon)\n\n return master","def set_datas_from_labyrinth(self):\n\n node_number = 1\n\n for i, line in enumerate(self.labyrinth):\n row = list()\n for j, column in enumerate(line):\n node = Node(node_number, (i,j))\n\n #If it is an empty space\n if column == \"\":\n element = Element(\"Empty\", \" \", False)\n node.elements = [element]\n self.list_empty_nodes.append(node)\n\n #If this is the start point\n elif column == \"S\":\n element = Element(\"Start\", \"S\", False)\n agent = Element(\"Agent\", \"A\", False)\n node.elements = [element, agent]\n self.start_point = node\n self.agent_node = node\n self.list_empty_nodes.append(node)\n\n #If this is the exit\n elif column == \"E\":\n element = Element(\"Exit\", \"E\", False)\n node.elements = [element]\n self.exit_point = node\n self.list_empty_nodes.append(node)\n\n #If this is a wall\n elif column == \"X\":\n element = Element(\"Wall\", \"X\", True)\n node.elements = [element]\n self.list_wall_nodes.append(node)\n\n row.append(node)\n node_number += 1\n self.labyrinth_statistics[\"number_of_nodes\"] += 1\n self.labyrinth_nodes.append(row)","def generate_voronoi_diagram(\n num_cells, width, height\n) -> Tuple[Voronoi, List[List[int]]]:\n # Make up data points\n points = np.random.rand(num_cells - 4, 2)\n default = np.array(\n [\n np.array([0.0, 0.0]),\n np.array([1.0, 0.0]),\n np.array([0.0, 1.0]),\n np.array([1.0, 1.0]),\n ]\n )\n points = np.concatenate((points, default), axis=0)\n # Scale them\n points = scale_points(points, width, height)\n # Compute Voronoi tesselation\n vor = Voronoi(points)\n # Plot\n voronoi_plot_2d(vor)\n return vor, points","def getColumns(self, stripped):\n noheader = stripped[7:]\n entries = list(chunkify(noheader, 5))\n columns = []\n coverage = [False for x in range(self.rowLength)] #makes a string of bytes covered (0 or 1)\n for entry in entries:\n offset = int(entry[1])\n length = int(entry[2])\n assert offset+length<=self.rowLength, \"Error: entry length does not match row length in:\\n\"+ self.path +\".\"\n if coverage[offset] == True: # in the case where NMM entries are overlapping\n entry[0] = \"##OVERLAP WARNING## \" + entry[0]\n for x in range(offset, offset+length):\n coverage[x] = True #set bytes as covered.\n columns.append(NightmareEntry(entry))\n #at this point you have a list [True, True, False] or whatever\n fillerEntries = []\n count = 0\n for offset,val in enumerate(coverage):\n if val==False:\n count += 1\n if count == 1:\n fillerEntry = [\"##UNKNOWN##\",offset,1,\"HEXA\",\"NULL\"]\n fillerEntries.append(fillerEntry)\n else:\n fillerEntries[-1][2] = count\n else:\n count = 0\n for fillerEntry in fillerEntries:\n columns.append(NightmareEntry(fillerEntry))\n columns.sort(key=lambda col: col.offset) #sort columns by offset\n return columns","def _regions(self, voronoi_diagram, unique_id, ids, crs):\n vertices = pd.Series(voronoi_diagram.regions).take(voronoi_diagram.point_region)\n polygons = []\n for region in vertices:\n if -1 not in region:\n polygons.append(pygeos.polygons(voronoi_diagram.vertices[region]))\n else:\n polygons.append(None)\n\n regions_gdf = gpd.GeoDataFrame(\n {unique_id: ids}, geometry=polygons, crs=crs\n ).dropna()\n regions_gdf = regions_gdf.loc[\n regions_gdf[unique_id] != -1\n ] # delete hull-based cells\n\n return regions_gdf","async def divide_into_horizontal_columns(content_list, column_amount):\n column_strings = []\n for i, content in enumerate(content_list):\n column_index = i % column_amount\n if len(column_strings) < column_amount:\n column_strings.append(content)\n else:\n column_strings[column_index] += \"\\n\" + content\n\n return column_strings","def cells_list(self):\n # self.cell_list_base = []\n # self.cell_list_act = []\n\n pattern = \"cell \\(.*?\\)\"\n \n cell_list_base = [cell for cell in re.findall(pattern, self.base_doc) if 'cell ()' not in cell] # List of cells present in base lib\n cellformat_base = [cell.replace('(','\\(').replace(')','\\)') for cell in cell_list_base] # Formated string for regex pattern\n cells_group_base = [re.search(cell, self.base_doc) for cell in cellformat_base] # Group of regex matches\n cells_dict_base = {base_cell.group(0):{'index': index, 'start': base_cell.start(), 'end': base_cell.end()} for index, base_cell in enumerate(cells_group_base)}\n\n cell_list_act = [cell for cell in re.findall(pattern, self.cell_doc) if 'cell ()' not in cell] # List of cells present in the active lib\n common_cells = [cell for cell in cell_list_act if cell in cell_list_base] # Common cells needs to be deleted\n\n return cells_dict_base, cell_list_act, common_cells","def addSeparatorFeature(self):\n \n # graphical separators\n dNS = {\"pc\":PageXml.NS_PAGE_XML}\n someNode = self.lNode[0]\n ndPage = someNode.node.xpath(\"ancestor::pc:Page\", namespaces=dNS)[0]\n lNdSep = ndPage.xpath(\".//pc:SeparatorRegion\", namespaces=dNS)\n loSep = [ShapeLoader.node_to_LineString(_nd) for _nd in lNdSep]\n \n if self.bVerbose: traceln(\" %d graphical separators\"%len(loSep))\n\n # make an indexed rtree\n idx = index.Index()\n for i, oSep in enumerate(loSep):\n idx.insert(i, oSep.bounds)\n \n # take each edge in turn and list the separators it crosses\n nCrossing = 0\n for edge in self.lEdge:\n # bottom-left corner to bottom-left corner\n oEdge = geom.LineString([(edge.A.x1, edge.A.y1), (edge.B.x1, edge.B.y1)])\n prepO = prep(oEdge)\n lCrossingPoints = []\n fSepTotalLen = 0\n for i in idx.intersection(oEdge.bounds):\n # check each candidate in turn\n oSep = loSep[i]\n if prepO.intersects(oSep):\n fSepTotalLen += oSep.length\n oPt = oEdge.intersection(oSep)\n if type(oPt) != geom.Point:\n traceln('Intersection in not a point: skipping it')\n else:\n lCrossingPoints.append(oPt)\n \n if lCrossingPoints:\n nCrossing += 1\n edge.bCrossingSep = True\n edge.sep_NbCrossing = len(lCrossingPoints)\n minx, miny, maxx, maxy = geom.MultiPoint(lCrossingPoints).bounds\n edge.sep_SpanLen = abs(minx-maxx) + abs(miny-maxy)\n edge.sep_AvgSpanSgmt = edge.sep_SpanLen / len(lCrossingPoints) \n edge.sep_AvgSepLen = fSepTotalLen / len(lCrossingPoints)\n else:\n edge.bCrossingSep = False\n edge.sep_NbCrossing = 0\n edge.sep_SpanLen = 0\n edge.sep_AvgSpanSgmt = 0 \n edge.sep_AvgSepLen = 0\n \n #traceln((edge.A.domid, edge.B.domid, edge.bCrossingSep, edge.sep_NbCrossing, edge.sep_SpanLen, edge.sep_AvgSpanSgmt, edge.sep_AvgSepLen))\n \n \n if self.bVerbose: \n traceln(\" %d (/ %d) edges crossing at least one graphical separator\"%(nCrossing, len(self.lEdge)))","def make_cells(self, limits):\n low, high = limits\n xs = range(low[0], high[0])\n ys = range(low[1], high[1])\n for x in xs:\n col = []\n for y in ys:\n indices = (x, y)\n self.cells[indices] = Patch(self, indices)","def _regions(self, voronoi_diagram, unique_id, ids, crs):\n # generate DataFrame of results\n regions = pd.DataFrame()\n regions[unique_id] = ids # add unique id\n regions[\"region\"] = voronoi_diagram.point_region # add region id for each point\n\n # add vertices of each polygon\n vertices = []\n for region in regions.region:\n vertices.append(voronoi_diagram.regions[region])\n regions[\"vertices\"] = vertices\n\n # convert vertices to Polygons\n polygons = []\n for region in tqdm(regions.vertices, desc=\"Vertices to Polygons\"):\n if -1 not in region:\n polygons.append(Polygon(voronoi_diagram.vertices[region]))\n else:\n polygons.append(None)\n # save polygons as geometry column\n regions[\"geometry\"] = polygons\n\n # generate GeoDataFrame\n regions_gdf = gpd.GeoDataFrame(regions.dropna(), geometry=\"geometry\")\n regions_gdf = regions_gdf.loc[\n regions_gdf[\"geometry\"].length < 1000000\n ] # delete errors\n regions_gdf = regions_gdf.loc[\n regions_gdf[unique_id] != -1\n ] # delete hull-based cells\n regions_gdf.crs = crs\n return regions_gdf","def cell_division(waitlist, celllist, AgEpitope, tnow, mut_list, RNs):\n for cell in celllist:\n # get list of 0 to 2 daughters\n dlist, mut_list = divide(cell, AgEpitope, tnow, mut_list, RNs)\n # add daughters to waitlist\n waitlist = waitlist + dlist\n return waitlist, mut_list","def create_cells(self):\n raise NotImplementedError(\n \"create_cells function not reimplemented from base class\")","def separate_adjacent_pins(self, separation):\n # Commented out to debug with SCMOS\n #if separation==0:\n # return\n\n pin_names = self.pin_groups.keys()\n for i,pin_name1 in enumerate(pin_names):\n for j,pin_name2 in enumerate(pin_names):\n if i==j:\n continue\n if i>j:\n return\n self.separate_adjacent_pin(pin_name1, pin_name2, separation)","def create_cells(self, blocks):\n cells = []\n for block in blocks:\n if (block['type'] == self.code) and (block['IO'] == 'input'):\n code_cell = self.create_code_cell(block)\n cells.append(code_cell)\n\n elif (block['type'] == self.code and\n block['IO'] == 'output' and\n cells[-1].cell_type == 'code'):\n cells[-1].outputs = self.create_outputs(block)\n\n elif block['type'] == self.markdown:\n markdown_cell = self.create_markdown_cell(block)\n cells.append(markdown_cell)\n\n else:\n raise NotImplementedError(\"{} is not supported as a cell\"\n \"type\".format(block['type']))\n\n return cells","def get_columns_with_rule():\n skip_next = True\n for child in box.children:\n if child.style['column_span'] == 'all':\n skip_next = True\n elif skip_next:\n skip_next = False\n else:\n yield child","def split_features(data):\n X = data.copy()\n #split nucleotid string (len=60) into a list of independent characters (DNA nucleotids)\n X['dna'] = X['dna'].map(lambda x : list(str(x).strip()))\n #create 60 new attributes (columns) for each DNA nucleotide index\n #each attribute has name dna_idx where idx is index (1-based) in the list above\n for idx in range(60):\n X['dna_%d' % (idx+1)] = X['dna'].map(lambda x : x[idx])\n #remove the old dna column (redundant information)\n del X['dna']\n #remove descriptor\n del X['id']\n \n return X","def get_child_nodes(self):\n parts = []\n for child in sorted(self.children, key=lambda c: c.start):\n child_part = (child.start, child.end, child)\n parts.append(child_part)\n return parts","def calculate_divided_differences(nodes):\n nodes_to_compute = []\n divided_differences = []\n for node in nodes:\n nodes_to_compute.append(DividedDifferenceNode(x=node[0], divided_difference=node[1]))\n\n divided_differences.append(tuple(nodes_to_compute))\n\n while len(nodes_to_compute) > 1:\n next_node_row = calculate_divided_differences_row(nodes_to_compute)\n divided_differences.append(tuple(next_node_row))\n nodes_to_compute = next_node_row\n\n return divided_differences","def split_per(folderin, folderout, split_col='ECO_ID', colNms=['i_h100','i_cd',\n 'doy','i_wflen','i_acqdate','b1','vcf','ECO_NAME','ECO_ID','BIOME','geometry']):\n\n split_files = glob.glob(folderin + '*.shp')\n\n for filename in split_files:\n print(filename)\n basename = os.path.splitext(os.path.basename(filename))[0]\n dfa = gpd.read_file(filename)\n df = dfa.astype({split_col: 'int32'}) \n ecoNames = list(np.unique(df[split_col]))#get list of unique ecoregions \n \n for eco in ecoNames:\n #create new df with just columns I want\n df2 = gpd.GeoDataFrame(df, columns=colNms)\n ID = str(eco)\n df_eco = df.loc[df2[split_col]==eco, colNms]\n df_eco.to_file(folderout + '/{}_eco_{}.shp'.format(basename, ID))","async def divide_into_vertical_columns(content_list, column_amount):\n base_column_size, extras = divmod(len(content_list), column_amount)\n\n column_strings = []\n content_list_index = 0\n for i in range(column_amount):\n column_size = base_column_size\n if extras > i:\n column_size += 1\n\n for j in range(column_size):\n content = content_list[content_list_index]\n if len(column_strings) <= i:\n column_strings.append(content)\n else:\n column_strings[i] += \"\\n\" + content\n\n content_list_index += 1\n\n return column_strings","def __divideset(rows, column, value):\n split_function = None #Initialize a variable split function.\n if isinstance(value, int) or isinstance(value, float): #Check if value is a number\n #True = the observation's value >= to the splitting criteria. False otherwise\n split_function = lambda row: row[column] >= value\n else:\n #If value is a string, True is where the observation's value == the criteria\n split_function = lambda row:row[column] == value\n \n #Divide the rows into two sets and return them\n set1 = [row for row in rows if split_function(row)]\n set2 = [row for row in rows if not split_function(row)]\n return (set1, set2)","def split(gt, isotropic):\n edges = get_edges(gt, isotropic)\n cytosol = edges == 0\n membrane = edges != 0\n return cytosol, membrane","def choose_and_flip_extra_dominoes(self, random):\n for domino in self.choose_extra_dominoes(random):\n if domino.head.pips == domino.tail.pips:\n yield domino, False\n else:\n flip_first = random.randint(0, 1)\n for j in range(2):\n yield domino, flip_first + j == 1","def grid(plots):\n ydim, xdim = plots.shape\n w = mean_width(plots)\n columns = []\n for i in plots.identifiers:\n r = plots.region_by_identifier(i)\n r.identifier = i\n if len(columns) == 0:\n c = Column(w, xdim)\n c.append(r)\n columns.append(c)\n continue\n\n included = False\n for c in columns:\n if c.in_column(r):\n c.append(r)\n included = True\n break\n\n if not included:\n c = Column(w, xdim)\n c.append(r)\n columns.append(c)\n\n # Sort the columns left to right.\n columns.sort(key=lambda i: i.x_mean)\n\n for c in columns:\n # Sort the rows top to bottom.\n c.sort(key=lambda i: i.centroid[0])\n\n return columns","def make_space_elements(space):\n return [\n html.Div(html.Hr()),\n html.Div(\"%s\" % space.title(), id=\"%s_title\" % space, style={\"font-size\": \"200%\"}),\n dcc.Graph(id=\"histogram_%s\" % space, figure=show_histogram(space, 0)),\n make_space_table(space),\n ]","def create_grid_and_edges(data, drone_altitude, safety_distance):\n\n # minimum and maximum north coordinates\n north_min = np.floor(np.min(data[:, 0] - data[:, 3]))\n north_max = np.ceil(np.max(data[:, 0] + data[:, 3]))\n\n # minimum and maximum east coordinates\n east_min = np.floor(np.min(data[:, 1] - data[:, 4]))\n east_max = np.ceil(np.max(data[:, 1] + data[:, 4]))\n\n # given the minimum and maximum coordinates we can\n # calculate the size of the grid.\n north_size = int(np.ceil((north_max - north_min)))\n east_size = int(np.ceil((east_max - east_min)))\n\n # Initialize an empty grid\n grid = np.zeros((north_size, east_size))\n\n # Define a list to hold Voronoi points\n points = []\n # Populate the grid with obstacles\n for i in range(data.shape[0]):\n north, east, alt, d_north, d_east, d_alt = data[i, :]\n\n if alt + d_alt + safety_distance > drone_altitude:\n obstacle = [\n int(north - d_north - safety_distance - north_min),\n int(north + d_north + safety_distance - north_min),\n int(east - d_east - safety_distance - east_min),\n int(east + d_east + safety_distance - east_min),\n ]\n grid[obstacle[0]:obstacle[1] + 1, obstacle[2]:obstacle[3] + 1] = 1\n\n # add center of obstacles to points list\n points.append([north - north_min, east - east_min])\n\n # create a voronoi graph based on\n # location of obstacle centres\n graph = Voronoi(points)\n # check each edge from graph.ridge_vertices for collision\n edges = []\n for v in graph.ridge_vertices:\n p1 = graph.vertices[v[0]].astype(int)\n p2 = graph.vertices[v[1]].astype(int)\n # test each pair p1 and p2 for collision using Bresenham\n # If the edge does not hit an obstacle add it to the list\n in_collision = False\n ridgeline = bresenham(p1[0], p1[1], p2[0], p2[1])\n for b in ridgeline:\n # eliminate out of range points in the line\n if b[0] < 0 or b[0] >= grid.shape[0]:\n in_collision = True\n break\n if b[1] < 0 or b[1] >= grid.shape[1]:\n in_collision = True\n break\n # check if grid cell is an obstacle\n if grid[b[0], b[1]] == 1:\n in_collision = True\n break\n # keep ridge points not in collision\n if not in_collision:\n p1 = (p1[0], p1[1])\n p2 = (p2[0], p2[1])\n edges.append((p1, p2))\n\n return grid, edges","def create_cells(self, N):\n\t\tself.stn_cells = []\n\t\tself.gpe_cells = []\n\t\tfor i in range(N):\n\t\t\t# create cell pair\n\t\t\tident = str(i+1)\n\t\t\tstn = STN()\n\t\t\tgpe = GPe(delay=self.delay,dur=self.dur,amp=self.amp,loc=self.loc,cur=True)\n\t\t\tstn.ident = 'stn%s' % (ident)\n\t\t\tgpe.ident = 'gpe%s' % (ident)\n\t\t\t# reposition cell pair\n\t\t\tstn.set_position(i*25,-10,0)\n\t\t\tgpe.set_position(i*25,10,0)\n\t\t\t# add pair to cell lists\n\t\t\tself.stn_cells.append(stn)\n\t\t\tself.gpe_cells.append(gpe)","def make_free_cell_list():\r\n for row in range(9):\r\n for col in range(9):\r\n if (application.ui.__getattribute__(f'cell{col+1}{row+1}')).text() == \"\":\r\n lst_free_cells.append(Point(row, col))","def splitPlace(self):\r\n \r\n \r\n \r\n nodeSortedIter = sorted(self.G.degree_iter(),key=itemgetter(1),reverse=True)\r\n \r\n placeCnt = 0\r\n \r\n for node in nodeSortedIter:\r\n if placeCnt<self.cells/2:\r\n self.sitesA.append(node[0])\r\n self.G.node[node[0]][\"part\"] = 'A'\r\n else:\r\n self.sitesB.append(node[0])\r\n self.G.node[node[0]][\"part\"] = 'B'\r\n placeCnt+=1","def splitbylo(self,lo):\n ind=np.where(self.lo==lo)[0]\n self.splitbyind(ind)","def get_donnees(self):\r\n \r\n return self._donnees"],"string":"[\n \"def test_split_cell_splits_neighbours(mock_amg):\\n\\n # split the centre cell in the mock grid\\n # this will create 4 more cells at tier 1\\n mock_amg.cells[4].split()\\n\\n # now split the bottom right of these cells\\n # this should force the east and south cells to also be split\\n mock_amg.cells[4].children['br'].split()\\n\\n assert mock_amg.cells[5].has_children\\n assert mock_amg.cells[1].has_children\",\n \"def test_split_adds_children(mock_amg):\\n\\n mock_amg.cells[0].split()\\n assert mock_amg.cells[0].children['bl'] is mock_amg.cells[-4]\\n assert mock_amg.cells[0].children['br'] is mock_amg.cells[-3]\\n assert mock_amg.cells[0].children['tl'] is mock_amg.cells[-2]\\n assert mock_amg.cells[0].children['tr'] is mock_amg.cells[-1]\",\n \"def pushDominoes(self, dominoes: str) -> str:\\n\\n N = len(dominoes)\\n dist_R = [-1] * N\\n dist_L = [-1] * N\\n\\n count = -1\\n for i in range(N):\\n if dominoes[i] == 'R':\\n count = 0\\n elif dominoes[i] == 'L':\\n count = -1\\n elif count >= 0:\\n count += 1\\n dist_R[i] = count if count >= 0 else 2 * N\\n\\n count = -1\\n for i in reversed(range(N)):\\n if dominoes[i] == 'L':\\n count = 0\\n elif dominoes[i] == 'R':\\n count = -1\\n elif count >= 0:\\n count += 1\\n dist_L[i] = count if count >= 0 else 2 * N\\n\\n # print(dist_R)\\n # print(dist_L)\\n\\n out = [\\\".\\\"] * N\\n for i in range(N):\\n if dist_R[i] < dist_L[i]:\\n out[i] = \\\"R\\\"\\n elif dist_R[i] > dist_L[i]:\\n out[i] = \\\"L\\\"\\n return \\\"\\\".join(out)\",\n \"def all_cells(self):\\n \\\"\\\"\\\"\\n Note that we use the convention that the first cell is (1,1)\\n \\\"\\\"\\\"\\n spart_star = self.circle_star()\\n part = Partition(list(spart_star))\\n coordinates = part.cells()\\n coordinates = [(x+1, y+1) for x, y in coordinates]\\n return coordinates\",\n \"def cells(self):\\n \\\"\\\"\\\"\\n Note that we use the convention that the first cell is (1,1)\\n \\\"\\\"\\\"\\n part = Partition(list(self))\\n coordinates = part.cells()\\n coordinates = [(x+1, y+1) for x, y in coordinates]\\n return coordinates\",\n \"def cells(self):\\n \\\"\\\"\\\"\\n Note that we use the convention that the first cell is (1,1)\\n \\\"\\\"\\\"\\n spart_star = self.star()\\n part = Partition(list(spart_star))\\n coordinates = part.cells()\\n coordinates = [(x+1, y+1) for x, y in coordinates]\\n return coordinates\",\n \"def get_cells(self):\\n return [\\n cell for column in self.children for cell in column.get_cells()]\",\n \"def get_numbered_cells_adjacent_closed_cells(self):\\r\\n numbered_cells_adjacent_closed_cells = []\\r\\n numbered_cells = self.get_all_numbered_cells()\\r\\n for row, col in numbered_cells:\\r\\n _, closed_cells, _, _ = self.get_cells(row, col)\\r\\n if closed_cells:\\r\\n numbered_cells_adjacent_closed_cells.append((row, col))\\r\\n return numbered_cells_adjacent_closed_cells\",\n \"def delete_cells(self):\\n cells_dict_base, cell_list_act, common_cells = self.cells_list()\\n while(len(common_cells) != 0):\\n cell_index = cells_dict_base[common_cells[0]]['index']\\n if cell_index != len(cells_dict_base)-1:\\n cell_start = cells_dict_base[common_cells[0]]['start']\\n next_cell_start = [values['start'] for cell_name, values in cells_dict_base.items() if values['index'] == (cell_index+1)][0]\\n cell_content = self.base_doc[cell_start: next_cell_start]\\n self.base_doc = self.base_doc.replace(cell_content, '')\\n else:\\n cell_start = cells_dict_base[common_cells[0]]['start']\\n cell_content = self.base_doc[cell_start:]\\n self.base_doc = self.base_doc.replace(cell_content, '')\\n self.removed_last_bracket = True\\n cells_dict_base, cell_list_act, common_cells = self.cells_list()\",\n \"def bosonic_cells(self):\\n cells = self.cells()\\n fermionic_cells = self.fermionic_cells()\\n coords = [x for x in cells if x not in fermionic_cells]\\n return coords\",\n \"def to_cdo_grid(self, outfile):\",\n \"def simple_solution(self, dominoes: str) -> str:\\n dominoes = list(dominoes)\\n nodes = [(-1, 'L')] + [(i, x) for i, x in enumerate(dominoes) if x != '.'] + [\\n (len(dominoes), 'R')]\\n for (i, x), (j, y) in zip(nodes, nodes[1:]):\\n if x == y:\\n for k in range(i + 1, j):\\n dominoes[k] = x\\n elif x == 'R' and y == 'L':\\n k, l = i, j\\n while k < l:\\n dominoes[k] = x\\n dominoes[l] = y\\n k, l = k + 1, l - 1\\n if ((j - i + 1) % 2) != 0:\\n dominoes[(j + i) // 2] = '.'\\n return ''.join(dominoes)\",\n \"def _init_pagoda_clses(self):\\n a = []; b = [] #IGNORE:C0321\\n c = []; d = [] #IGNORE:C0321\\n row_selector = ((a, b), (c, d))\\n for cell in self.compact:\\n row_selector[cell.y % 2][cell.x % 2].append(cell)\\n cell.pvalue = cell.y % 2 + 1 + cell.x % 2 \\n return a, b, c, d\",\n \"def partition_domain(self, dom):\\r\\n split = len(dom) // 2\\r\\n dom1 = set(list(dom)[0:split])\\r\\n dom2 = dom - dom1\\r\\n return dom1, dom2\",\n \"def _cells(notebook):\\n if notebook.nbformat < 4:\\n for ws in notebook.worksheets:\\n for cell in ws.cells:\\n yield cell\\n else:\\n for cell in notebook.cells:\\n yield cell\",\n \"def make_cells(self, fields: Dict[str, Any]) -> List[Cell]:\\n result: List[Cell] = []\\n for column_title, field_value in fields.items():\\n result.append(self.make_cell(column_title, field_value))\\n return result\",\n \"def test_split_cell_east_sets_neighbours(mock_amg):\\n\\n mock_amg.cells[4].split() # middle cell\\n mock_amg.cells[5].split() # east cell\\n\\n east = mock_amg.cells[5]\\n west = mock_amg.cells[4]\\n\\n assert west.children['tr'].east == east.children['tl']\\n assert west.children['br'].east == east.children['bl']\\n assert east.children['tl'].west == west.children['tr']\\n assert east.children['bl'].west == west.children['br']\",\n \"def test_split_cell_creates_four_more_cells(mock_amg):\\n\\n init_n_windows = mock_amg.n_windows\\n init_n_cells = mock_amg.n_cells\\n\\n mock_amg.cells[0].split()\\n\\n assert init_n_windows + 5 == mock_amg.n_windows\\n assert init_n_cells + 4 == mock_amg.n_cells\",\n \"def pushDominoes(self, dominoes: str) -> str:\\n return self.simple_solution(dominoes)\",\n \"def split(self, dim, bins):\\n if type(bins) in (int, complex):\\n bins = np.r_[self.min(dim):self.max(dim):bins]\\n p = self.get_cols(dim)[0]\\n idx = np.digitize(p, bins)\\n splitted = []\\n for i in xrange(1,len(bins)):\\n new_data = self.data[idx==i]\\n splitted.append(DataTable(\\n new_data,\\n self.dims,\\n self.legends,\\n self.tags,\\n self.sub_name('%.2f<=%s<%.2f' % (bins[i-1], dim, bins[i]))))\\n return splitted\",\n \"def _generate_cells(self) -> None:\\n for i in range(15):\\n for j in range(15):\\n c = Cell(x=i, y=j)\\n c.answer = self.puzzle.solution[j*self.width+i]\\n self.cells[(j, i)] = c # row, col\",\n \"def test_split_cell_south_sets_neighbours(mock_amg):\\n\\n mock_amg.cells[4].split() # middle cell\\n mock_amg.cells[1].split() # south cell\\n\\n south = mock_amg.cells[1]\\n north = mock_amg.cells[4]\\n\\n assert south.children['tl'].north == north.children['bl']\\n assert south.children['tr'].north == north.children['br']\\n assert north.children['bl'].south == south.children['tl']\\n assert north.children['br'].south == south.children['tr']\",\n \"def merge_cells(self):\\n\\n for rownum, row in enumerate(self.cells):\\n for colnum, cell in enumerate(row):\\n if not isinstance(cell, Cell):\\n continue\\n cols_to_merge = 0\\n for i in range(colnum+1, len(row)):\\n if isinstance(self.cells[rownum][i], Cell) and self.cells[rownum][i].event == cell.event:\\n cols_to_merge += 1\\n if cols_to_merge > 0:\\n cell.colspan = cols_to_merge + 1\\n for i in range(1, cols_to_merge + 1):\\n self.cells[rownum][colnum + i] = SpanCell(rownum, colnum + i)\",\n \"def cells_list(self):\\n xx, yy = np.meshgrid(self.x_spacings, self.y_spacings)\\n return np.vstack([yy.ravel(), xx.ravel()]).transpose()\",\n \"def test_split_adds_known_neighbours(mock_amg):\\n\\n mock_amg.cells[4].split()\\n # bl\\n assert mock_amg.cells[-4].north is mock_amg.cells[-2]\\n assert mock_amg.cells[-4].east is mock_amg.cells[-3]\\n\\n # br\\n assert mock_amg.cells[-3].north is mock_amg.cells[-1]\\n assert mock_amg.cells[-3].west is mock_amg.cells[-4]\\n\\n # tl\\n assert mock_amg.cells[-2].south is mock_amg.cells[-4]\\n assert mock_amg.cells[-2].east is mock_amg.cells[-1]\\n\\n # tr\\n assert mock_amg.cells[-1].south is mock_amg.cells[-3]\\n assert mock_amg.cells[-1].west is mock_amg.cells[-2]\",\n \"def fill_cells(self, latitudes, longitudes, businesses):\\n if len(businesses) == 0:\\n # There are no businesses in this area\\n return\\n if longitudes.size > 2:\\n # First we devide the collection along the center of longitudes\\n center = longitudes[longitudes.size / 2]\\n fill_cells(self, latitudes,\\n longitudes[:(longitudes.size / 2 + 1)],\\n businesses[businesses.longitude <= center])\\n fill_cells(self, latitudes, longitudes[(longitudes.size / 2):],\\n businesses[businesses.longitude > center])\\n elif latitudes.size > 2:\\n # Now we devide along the center of latitudes\\n center = latitudes[latitudes.size / 2]\\n fill_cells(self, latitudes[:(latitudes.size / 2 + 1)],\\n longitudes,\\n businesses[businesses.latitude <= center])\\n fill_cells(self, latitudes[(latitudes.size / 2):], longitudes,\\n businesses[businesses.latitude > center])\\n else:\\n # Now we are in one Cell! Let's store our businesses\\n bizs = []\\n for idx, business in businesses.iterrows():\\n # For performance, we only store index and coordinates\\n bizs.append({'index': idx,\\n 'latitude': business.latitude,\\n 'longitude': business.longitude})\\n coord = (self.longitudes.searchsorted(longitudes[0]),\\n self.latitudes.searchsorted(latitudes[0]))\\n if coord[0] not in self.cells:\\n self.cells[coord[0]] = {}\\n self.cells[coord[0]].update({coord[1]: bizs})\",\n \"def visit_table(self, sytable):\\n def index(sytable):\\n try:\\n return sytable.get_column(self.input_index)\\n except:\\n return np.arange(sytable.number_of_rows())\\n\\n def slices_using_group_array(group_array):\\n \\\"\\\"\\\"Return the slices to split by.\\n A group array is made of strictly increasing group identifiers.\\n\\n >>> slices_using_group_array(np.array([0, 0, 0, 1, 1, 2, 3, 3, 3]))\\n [(0, 3), (3, 5), (5, 6), (6, 9)]\\n \\\"\\\"\\\"\\n unique_elements = np.unique(group_array)\\n slices = []\\n for unique_element in unique_elements:\\n indexes = np.flatnonzero(group_array == unique_element)\\n low, high = (indexes[0], indexes[-1] + 1)\\n slices.append((unique_element, slice(low, high)))\\n return slices\\n\\n def indices_using_group_array(group_array):\\n \\\"\\\"\\\"\\n Return list of index lists, ordered by first occurance of value.\\n \\\"\\\"\\\"\\n unique_elements = np.unique(group_array)\\n indices = []\\n for unique_element in unique_elements:\\n indices.append((unique_element,\\n np.flatnonzero(group_array == unique_element)))\\n return indices\\n\\n columns = sytable.columns()\\n # Perform the split and append the new tables to output.\\n slice_indices = indices_using_group_array(index(sytable))\\n column_attrs = {}\\n\\n for unique_element, slice_index in slice_indices:\\n # Sets of all columns except for the INDEX columns.\\n result = type(sytable)(sytable.container_type)\\n self.output_list.append((unique_element, result))\\n\\n for column in columns:\\n array = sytable.get_column(column)[slice_index]\\n if self.remove_fill and len(array):\\n kind = array.dtype.kind\\n if kind in ['S', 'U']:\\n if np.all(array == ''):\\n continue\\n else:\\n if not len(array) or np.isnan(np.min(array)):\\n continue\\n\\n result.set_column(column, array)\\n if column in column_attrs:\\n attrs = column_attrs[column]\\n else:\\n attrs = dict(\\n sytable.get_column_attributes(column).get())\\n column_attrs[column] = attrs\\n result.get_column_attributes(column).set(attrs)\",\n \"def choose_extra_dominoes(self, random):\\n dominoes = self.extra_dominoes[:]\\n count = len(dominoes)\\n start = random.randrange(count)\\n for i in range(count):\\n yield dominoes[(i + start) % count]\",\n \"def test_split_cell_west_sets_neighbours(mock_amg):\\n\\n mock_amg.cells[4].split() # middle cell\\n mock_amg.cells[3].split() # west cell\\n\\n west = mock_amg.cells[3]\\n east = mock_amg.cells[4]\\n\\n assert west.children['tr'].east == east.children['tl']\\n assert west.children['br'].east == east.children['bl']\\n assert east.children['tl'].west == west.children['tr']\\n assert east.children['bl'].west == west.children['br']\",\n \"def split_into_codons(dna):\\n dna_split = []\\n length = math.ceil(len(dna)/3)\\n for i in range(0, length):\\n j = 3*i\\n codon = dna[j:j+3]\\n dna_split += [codon]\\n return dna_split\",\n \"def fermionic_cells(self):\\n cells = self.cells()\\n cells_and_circles = self.all_cells()\\n circles = [x for x in cells_and_circles if x not in cells]\\n coords = [(i, jprime)\\n for iprime, jprime in circles\\n for i, j in circles\\n if iprime > i\\n ]\\n coords.sort()\\n return coords\",\n \"def create_cells(self, N):\\n self.cells = []\\n r = 50 # Radius of cell locations from origin (0,0,0) in microns\\n N = self._N\\n for i in range(N):\\n cell = BallAndStick()\\n # When cells are created, the soma location is at (0,0,0) and\\n # the dendrite extends along the X-axis.\\n # First, at the origin, rotate about Z.\\n cell.rotateZ(i * 2 * pi / N)\\n # Then reposition\\n x_loc = cos(i * 2 * pi / N) * r\\n y_loc = sin(i * 2 * pi / N) * r\\n cell.set_position(x_loc, y_loc, 0)\\n self.cells.append(cell)\",\n \"def test_split_cell_north_sets_neighbours(mock_amg):\\n\\n mock_amg.cells[4].split() # middle cell\\n mock_amg.cells[7].split() # north cell\\n\\n north = mock_amg.cells[7]\\n south = mock_amg.cells[4]\\n\\n assert south.children['tl'].north == north.children['bl']\\n assert south.children['tr'].north == north.children['br']\\n assert north.children['bl'].south == south.children['tl']\\n assert north.children['br'].south == south.children['tr']\",\n \"def make_filiation(self):\\n if self.cells is not None:\\n filiate_from_bpointer(self.cells)\\n# for cell in self.cells:\\n# childs = []\\n# for cc in self.cells:\\n# if cc.bpointer == cell.identifier:\\n# childs.append(cc)\\n# cc.parent = cell\\n# cc.set_division_event()\\n# cell.childs = childs\\n return\",\n \"def pushDominoes(self, dominoes):\\n if len(dominoes) < 2:\\n return dominoes\\n\\n state = dominoes\\n while True:\\n next_state = self.domino_fall(state)\\n if next_state == state:\\n break\\n state = next_state\\n return state\",\n \"def markdown_cells(self):\\n for cell in self.content.cells:\\n if cell.cell_type == \\\"markdown\\\" and not cell.source.startswith(NOTEBOOK_HEADER_TAG) \\\\\\n and not cell.source.startswith(NAVBAR_TAG):\\n yield cell\",\n \"def autosplit(self):\\n result = RowSet()\\n for row in self:\\n result.append(row.autosplit())\\n return result\",\n \"def splitColumns(self, numColumns):\\n if numColumns < 2:\\n return [self]\\n if len(self) <= numColumns:\\n return [OutputGroup([item]) for item in self]\\n groupList = []\\n numEach = len(self) // numColumns\\n for colNum in range(numColumns - 1):\\n groupList.append(self[colNum * numEach : (colNum+1) * numEach])\\n groupList.append(self[(numColumns-1) * numEach : ])\\n numChanges = 1\\n while numChanges:\\n numChanges = 0\\n for colNum in range(numColumns - 1):\\n if groupList[colNum].totalHeight() > groupList[colNum+1].\\\\\\n totalHeight() + groupList[colNum][-1].height:\\n groupList[colNum+1].insert(0, groupList[colNum][-1])\\n groupList[colNum] = groupList[colNum][:-1]\\n numChanges += 1\\n if groupList[colNum].totalHeight() + groupList[colNum+1][0].\\\\\\n height <= groupList[colNum+1].totalHeight():\\n groupList[colNum].append(groupList[colNum+1][0])\\n groupList[colNum+1] = groupList[colNum+1][1:]\\n numChanges += 1\\n return groupList\",\n \"def create_grid_and_edges(data, drone_altitude, safety_distance):\\n # minimum and maximum north coordinates\\n north_min = np.floor(np.min(data[:, 0] - data[:, 3]))\\n north_max = np.ceil(np.max(data[:, 0] + data[:, 3]))\\n\\n # minimum and maximum east coordinates\\n east_min = np.floor(np.min(data[:, 1] - data[:, 4]))\\n east_max = np.ceil(np.max(data[:, 1] + data[:, 4]))\\n\\n # given the minimum and maximum coordinates we can\\n # calculate the size of the grid.\\n north_size = int(np.ceil(north_max - north_min))\\n east_size = int(np.ceil(east_max - east_min))\\n\\n # Initialize an empty grid\\n grid = np.zeros((north_size, east_size))\\n # Initialize an empty list for Voronoi points\\n points = []\\n # Populate the grid with obstacles\\n for i in range(data.shape[0]):\\n north, east, alt, d_north, d_east, d_alt = data[i, :]\\n if alt + d_alt + safety_distance > drone_altitude:\\n obstacle = [\\n int(np.clip(north - d_north - safety_distance - north_min, 0, north_size-1)),\\n int(np.clip(north + d_north + safety_distance - north_min, 0, north_size-1)),\\n int(np.clip(east - d_east - safety_distance - east_min, 0, east_size-1)),\\n int(np.clip(east + d_east + safety_distance - east_min, 0, east_size-1)),\\n ]\\n grid[obstacle[0]:obstacle[1]+1, obstacle[2]:obstacle[3]+1] = 1\\n # add center of obstacles to points list\\n points.append([north - north_min, east - east_min])\\n\\n graph = Voronoi(points)\\n\\n edges = []\\n for v in graph.ridge_vertices:\\n p1 = graph.vertices[v[0]]\\n p2 = graph.vertices[v[1]]\\n cells = list(bresenham(int(p1[0]), int(p1[1]), int(p2[0]), int(p2[1])))\\n hit = False\\n\\n for c in cells:\\n if np.amin(c) < 0 or c[0] >= grid.shape[0] or c[1] >= grid.shape[1]:\\n hit = True\\n break\\n if grid[c[0], c[1]] == 1:\\n hit = True\\n break\\n\\n if not hit:\\n p1 = (p1[0], p1[1])\\n p2 = (p2[0], p2[1])\\n edges.append((p1, p2))\\n\\n return grid, edges, int(north_min), int(east_min)\",\n \"def get_states_as_cells(self, oids=None, symbols=None, tokens=None):\\n return [CharacterDataCell(value=s) for \\\\\\n s in self.get_states(oids=oids, symbols=symbols, tokens=tokens)]\",\n \"def cells(self):\\n return ((row, col) for row in self.rows for col in self.cols)\",\n \"def get_cells(self):\\n self.list = [self.origin]\\n\\n for i in range(1, self.size):\\n if(self.direction ==self.direction.RIGHT):\\n self.list.append((self.origin[0], self.origin[1]+i))\\n elif(self.direction ==self.direction.DOWN):\\n self.list.append((self.origin[0]-i, self.origin[1]))\\n\\n return self.list\",\n \"def split_board(board):\\n row1 = board[0]\\n row2 = board[1]\\n row3 = board[2]\\n diag1 = [row1[0], row2[1], row3[2]]\\n diag2 = [row1[2], row2[1], row3[0]]\\n col1 = [row1[0], row2[0], row3[0]]\\n col2 = [row1[1], row2[1], row3[1]]\\n col3 = [row1[2], row2[2], row3[2]]\\n return [row1, row2, row3, diag1, diag2, col1, col2, col3]\",\n \"def DivideDF(df_all):\\n return df_all.iloc[:df_all.trn_len], df_all.iloc[df_all.trn_len:]\",\n \"def cells(self) -> Iterable[\\\"RenderableType\\\"]:\\n yield from self._cells\",\n \"def get_all_numbered_cells(self):\\r\\n num_rows = len(self.mine_field)\\r\\n num_cols = len(self.mine_field[0])\\r\\n numbered_cells = [(row, col)\\r\\n for row in range(num_rows)\\r\\n for col in range(num_cols)\\r\\n if self.mine_field[row][col] != '?' and \\\\\\r\\n self.mine_field[row][col] != 'x']\\r\\n return numbered_cells\",\n \"def pickup_dominoes(self, num_dominoes, player):\\n\\n for domino in range(num_dominoes):\\n shuffle(self.boneyard)\\n player.hand.append(self.boneyard.pop(0))\",\n \"def edges_as_nodes_cells_mark(self):\\n ptm_sides = np.zeros([len(self.edges),5],np.int32)\\n ptm_sides[:,0:2] = self.edges['nodes']\\n ptm_sides[:,2:4] = self.edges['cells']\\n ptm_sides[:,4] = self.edges['mark']\\n return ptm_sides\",\n \"def split(self, X):\",\n \"def calculate_divided_differences_row(nodes_to_compute):\\n divided_differences = []\\n\\n if len(nodes_to_compute) == 1:\\n return None\\n\\n for i in range(0, len(nodes_to_compute) - 1):\\n child = DividedDifferenceNode.create_child_node(nodes_to_compute[i], nodes_to_compute[i + 1])\\n child.calculate_value()\\n divided_differences.append(child)\\n\\n for node in divided_differences:\\n print(node, end='')\\n\\n print('\\\\n')\\n return divided_differences\",\n \"def test_split_adds_parents(mock_amg):\\n\\n mock_amg.cells[0].split()\\n assert mock_amg.cells[-4].parent is mock_amg.cells[0]\\n assert mock_amg.cells[-3].parent is mock_amg.cells[0]\\n assert mock_amg.cells[-2].parent is mock_amg.cells[0]\\n assert mock_amg.cells[-1].parent is mock_amg.cells[0]\",\n \"def _chunks(l, ncols):\\n assert isinstance(ncols, int), \\\"ncols must be an integer\\\"\\n for i in range(0, len(l), ncols):\\n yield l[i: i+ncols]\",\n \"def collect_rows():\\n return ((x, y) for x in range(80) for y in range(x + 1, 9 + (x//9)*9))\",\n \"def cells(self):\\n return chain.from_iterable(self.cols)\",\n \"def sq_wtd_voxels(ndomains, pos, rcrit, ngrid=None, log=null_log):\\n\\n if ngrid is None:\\n # To make each cell non-ghost dominated would really like\\n # 0.1/rcrit, but I rely a bit on adjacent cells being in \\n # the same domain.\\n ngrid = int(0.15/rcrit)\\n\\n if ndomains>64:\\n raise Exception('{:,}>64, maximum domains for single master'.format(ndomains))\\n\\n tot_cells = ngrid**3\\n cells = get_cells(pos, ngrid, log)\\n assert(cells.min()>=0)\\n assert(cells.max()<tot_cells)\\n counts = np.bincount(cells, minlength=tot_cells)\\n cell_ids = np.arange(tot_cells)\\n\\n doms = domain_region_grow(np.reshape(counts, (ngrid,ngrid,ngrid)), ndomains)-1\\n assert((doms>=0).all()) # check we covered all the domains\\n cells_per_dmn = np.bincount(doms.ravel())\\n tot_per_dmn = np.bincount(doms.ravel()[cells])\\n print(\\\"Domain totals:\\\", ', '.join('%d'%npts for npts in tot_per_dmn), file=log)\\n\\n cell_masks = 1<<(doms.astype(np.uint64))\\n pos_masks = cell_masks.ravel()[cells].copy()\\n\\n prettyprint_cellmasks(cell_masks, log)\\n # 'Bleed' the cell masks to find ghosts\\n ngb_masks = adjacent_cell_masks(pos, cell_masks, rcrit, log)\\n\\n for dom, mask in enumerate(np.unique(pos_masks)):\\n # find all the positions needed in this domain\\n idx_domain = np.flatnonzero(ngb_masks & mask)\\n # which of these are non-ghosts\\n idx_non_ghosts = np.flatnonzero(np.equal(pos_masks[idx_domain], mask))\\n\\n pct_ghosts = (100*(len(idx_domain)-len(idx_non_ghosts)))//len(idx_domain)\\n\\n txt = 'Domain %d: '%dom + \\\\\\n '{:,} particles of which {:,}% are ghosts ({:,} domain cells)'.format(len(idx_domain),\\n pct_ghosts, cells_per_dmn[dom])\\n # count parts+ghosts\\n if pct_ghosts<50:\\n print(txt, file=log)\\n else:\\n print(MarkUp.WARNING+txt+MarkUp.ENDC, file=log) \\n yield idx_domain, idx_non_ghosts\",\n \"def createGridNodesinFRF(x0, y0, dx, dy, ni, nj):\\n assert dx.shape[0] == ni-1, 'This function assumes that there are n-1 dx values'\\n\\n if np.mean(np.diff(dx)) != np.mean(dx): # vairable spacing cell array\\n icoord = np.zeros(ni) # assume\\n jcoord = np.zeros(nj)\\n icoord[0] = x0\\n jcoord[0] = y0\\n for xx, dxx in enumerate(dx):\\n icoord[xx+1] = icoord[xx] - dxx # assumes offshore origin\\n for yy, dyy in enumerate(dy):\\n jcoord[yy+1] = jcoord[yy] - dyy\\n else:\\n raise NotImplementedError\\n\\n return icoord, jcoord\",\n \"def separar_vardado(self,numvar=4):\\n c = 0\\n\\n for i in range(0,len(self.dado),numvar):\\n self.h.insert(c,self.dado[i])\\n \\n self.nO.insert(c,self.dado[i+1]*1e6)\\n self.nN2.insert(c,self.dado[i+2]*1e6) # O produto por 1e6 é para passar de cm^-3 para m^-3\\n self.nO2.insert(c,self.dado[i+3]*1e6)\\n \\n #print(c,self.h2[c],self.nN2[c],self.nO2[c],self.nO[c],\\\"\\\\n gv_MSISE\\\")\\n #print(\\\"\\\\n\\\",i,\\\"\\\\n\\\")\\n\\n c = c + 1\",\n \"def split_data(data):\\n data_values = []\\n target_drugs = []\\n cell_lines = []\\n states = []\\n replicates = []\\n \\n for i in data.columns:\\n target_drug = i.split()[-2]\\n cell_line = i.split()[-1].split(\\\"_\\\")[0]\\n state = i.split()[-1].split(\\\"_\\\")[1]\\n replicate = i.split()[-1].split(\\\"_\\\")[2]\\n \\n data_values.append(list(data[i]))\\n target_drugs.append(int(target_drug))\\n cell_lines.append(cell_line)\\n states.append(state)\\n replicates.append(replicate)\\n \\n data_values = np.array(data_values)\\n target_drugs = np.array(target_drugs)\\n cell_lines = np.array(cell_lines)\\n states = np.array(states)\\n replicates = np.array(replicates)\\n \\n return data_values, target_drugs, cell_lines, states, replicates\",\n \"def _do_split_single_haghverdi16(self, Dseg, tips):\\n # compute splits using different starting points the first index of\\n # tips is the starting point for the other two, the order does not\\n # matter\\n ssegs = []\\n # permutations of tip cells\\n ps = [[0, 1, 2], # start by computing distances from the first tip\\n [1, 2, 0], # -\\\"- second tip\\n [2, 0, 1]] # -\\\"- third tip\\n # import matplotlib.pyplot as pl\\n for i, p in enumerate(ps):\\n ssegs.append(self.__do_split_haghverdi16(Dseg, tips[p]))\\n return ssegs\",\n \"def get_cells(self, row, col):\\r\\n surrounding_cells = self.get_surrounding_cells(row, col)\\r\\n closed_cells = self.filter_cells(surrounding_cells, '?')\\r\\n mine_cells = self.filter_cells(surrounding_cells, 'x')\\r\\n numbered_cells = list(set(surrounding_cells).difference(closed_cells))\\r\\n numbered_cells = list(set(numbered_cells).difference(mine_cells))\\r\\n return surrounding_cells, closed_cells, mine_cells, numbered_cells\",\n \"def cells(self):\\r\\n return Cells(self)\",\n \"def split_dataframe(df, split_elements_list):\\n y = df.filter(split_elements_list)\\n x = df.drop(split_elements_list, axis=1)\\n\\n return x, y\",\n \"def subdivide_ngons(faces: Iterable[Sequence[Union[Vector, Vec2]]]) -> Iterable[\\n List[Vector]]:\\n for face in faces:\\n if len(face) < 5:\\n yield face\\n else:\\n mid_pos = sum(face) / len(face)\\n for index, vertex in enumerate(face):\\n yield face[index - 1], vertex, mid_pos\",\n \"def make_grid(self):\\n grid = []\\n for index, _ in enumerate(self.font[self.chars[0]]):\\n grid.append([])\\n\\n for char in self.chars:\\n for index in range(config()[\\\"panel\\\"][\\\"height\\\"]):\\n grid[index].extend(self.font[char][index])\\n\\n return grid\",\n \"def empty_cells(self) -> List[Cell]:\\n return list(ob.pos[0] for ob in self.new_obs())\",\n \"def _create_grid_with_cells(self, width, height):\\n grid = []\\n for row in range(height):\\n grid.append([])\\n for column in range(width):\\n if column % 2 == 1 and row % 2 == 1:\\n grid[row].append(TILE_EMPTY)\\n elif (\\n column == 0 or row == 0 or column == width - 1 or row == height - 1\\n ):\\n grid[row].append(TILE_CRATE)\\n else:\\n grid[row].append(TILE_CRATE)\\n grid[-2][-3] = TILE_EMPTY\\n grid[1][0] = TILE_EMPTY\\n return grid\",\n \"def split(self):\\n sub_images = []\\n\\n for region in regionprops(self.cells):\\n minr, minc, maxr, maxc = region.bbox\\n sub_image = self.image_raw[max(0, minr - 10):maxr, max(0, minc - 10):maxc, :]\\n\\n sub_images.append(FQimage(data=sub_image))\\n\\n return sub_images\",\n \"def _get_cells(\\n self, console: \\\"Console\\\", column_index: int, column: Column\\n ) -> Iterable[_Cell]:\\n\\n collapse_padding = self.collapse_padding\\n pad_edge = self.pad_edge\\n padding = self.padding\\n any_padding = any(padding)\\n\\n first_column = column_index == 0\\n last_column = column_index == len(self.columns) - 1\\n\\n _padding_cache: Dict[Tuple[bool, bool], Tuple[int, int, int, int]] = {}\\n\\n def get_padding(first_row: bool, last_row: bool) -> Tuple[int, int, int, int]:\\n cached = _padding_cache.get((first_row, last_row))\\n if cached:\\n return cached\\n top, right, bottom, left = padding\\n\\n if collapse_padding:\\n if not first_column:\\n left = max(0, left - right)\\n if not last_row:\\n bottom = max(0, top - bottom)\\n\\n if not pad_edge:\\n if first_column:\\n left = 0\\n if last_column:\\n right = 0\\n if first_row:\\n top = 0\\n if last_row:\\n bottom = 0\\n _padding = (top, right, bottom, left)\\n _padding_cache[(first_row, last_row)] = _padding\\n return _padding\\n\\n raw_cells: List[Tuple[StyleType, \\\"RenderableType\\\"]] = []\\n _append = raw_cells.append\\n get_style = console.get_style\\n if self.show_header:\\n header_style = get_style(self.header_style or \\\"\\\") + get_style(\\n column.header_style\\n )\\n _append((header_style, column.header))\\n cell_style = get_style(column.style or \\\"\\\")\\n for cell in column.cells:\\n _append((cell_style, cell))\\n if self.show_footer:\\n footer_style = get_style(self.footer_style or \\\"\\\") + get_style(\\n column.footer_style\\n )\\n _append((footer_style, column.footer))\\n\\n if any_padding:\\n _Padding = Padding\\n for first, last, (style, renderable) in loop_first_last(raw_cells):\\n yield _Cell(\\n style,\\n _Padding(renderable, get_padding(first, last)),\\n getattr(renderable, \\\"vertical\\\", None) or column.vertical,\\n )\\n else:\\n for (style, renderable) in raw_cells:\\n yield _Cell(\\n style,\\n renderable,\\n getattr(renderable, \\\"vertical\\\", None) or column.vertical,\\n )\",\n \"def _cells(self):\\n\\n # Matrix size\\n size = self.specification['DIMENSION']\\n # Edge weight format\\n edge_format = self.specification['EDGE_WEIGHT_FORMAT'].split('_')\\n # Matrix type: FULL, UPPER, LOWER\\n matrix = edge_format[0]\\n\\n # Data direction: ROW, COL; diagonal offset\\n offset = 0\\n if matrix != 'FULL':\\n if edge_format[1] in ['ROW', 'COL']:\\n direction = edge_format[1]\\n # No diagonal entries, matrix needs to be offseted by 1\\n offset = 1\\n else:\\n direction = edge_format[2]\\n\\n # Initial position in the matrix\\n row = col = -1\\n\\n # Increments `b`. If `b` is larger than `bound` increments `a` and\\n # sets `b` to `ret`.\\n def calc(a, b, bound, ret):\\n a = max(a, 0)\\n b += 1\\n if b > bound:\\n a += 1\\n b = ret\\n return a, max(b, ret)\\n\\n # Generate the coordinates\\n while True:\\n if matrix == 'FULL':\\n row, col = calc(row, col, size - 1, 0)\\n elif matrix == 'UPPER' and direction == 'ROW':\\n row, col = calc(row, col, size - 1, row + 1 + offset)\\n elif matrix == 'LOWER' and direction == 'COL':\\n col, row = calc(col, row, size - 1, col + 1 + offset)\\n elif matrix == 'LOWER' and direction == 'ROW':\\n row, col = calc(\\n row, col, row - offset if offset else max(row, 0), 0)\\n elif matrix == 'UPPER' and direction == 'COL':\\n col, row = calc(\\n col, row, col - offset if offset else max(col, 0), 0)\\n\\n # End when all coordinates are generated\\n if row >= size or col >= size:\\n break\\n\\n yield row, col\",\n \"def grid(self, northeast, southwest, density=200):\\n grid = []\\n\\n # Determine the bounds of a standard, positive quadrant plot\\n y_max, y_min = int(northeast[0]), int(southwest[0])\\n x_max, x_min = int(northeast[1]), int(southwest[1])\\n\\n # Construct a sequence of boxes each moving clockwise from southwest corner\\n master = []\\n for x in range(x_min, x_max, density):\\n for y in range(y_min, y_max, density):\\n polygon = [\\n (x, y),\\n (x, y + density),\\n (x + density, y + density),\\n (x + density, y),\\n (x, y)\\n ]\\n master.append(polygon)\\n\\n return master\",\n \"def set_datas_from_labyrinth(self):\\n\\n node_number = 1\\n\\n for i, line in enumerate(self.labyrinth):\\n row = list()\\n for j, column in enumerate(line):\\n node = Node(node_number, (i,j))\\n\\n #If it is an empty space\\n if column == \\\"\\\":\\n element = Element(\\\"Empty\\\", \\\" \\\", False)\\n node.elements = [element]\\n self.list_empty_nodes.append(node)\\n\\n #If this is the start point\\n elif column == \\\"S\\\":\\n element = Element(\\\"Start\\\", \\\"S\\\", False)\\n agent = Element(\\\"Agent\\\", \\\"A\\\", False)\\n node.elements = [element, agent]\\n self.start_point = node\\n self.agent_node = node\\n self.list_empty_nodes.append(node)\\n\\n #If this is the exit\\n elif column == \\\"E\\\":\\n element = Element(\\\"Exit\\\", \\\"E\\\", False)\\n node.elements = [element]\\n self.exit_point = node\\n self.list_empty_nodes.append(node)\\n\\n #If this is a wall\\n elif column == \\\"X\\\":\\n element = Element(\\\"Wall\\\", \\\"X\\\", True)\\n node.elements = [element]\\n self.list_wall_nodes.append(node)\\n\\n row.append(node)\\n node_number += 1\\n self.labyrinth_statistics[\\\"number_of_nodes\\\"] += 1\\n self.labyrinth_nodes.append(row)\",\n \"def generate_voronoi_diagram(\\n num_cells, width, height\\n) -> Tuple[Voronoi, List[List[int]]]:\\n # Make up data points\\n points = np.random.rand(num_cells - 4, 2)\\n default = np.array(\\n [\\n np.array([0.0, 0.0]),\\n np.array([1.0, 0.0]),\\n np.array([0.0, 1.0]),\\n np.array([1.0, 1.0]),\\n ]\\n )\\n points = np.concatenate((points, default), axis=0)\\n # Scale them\\n points = scale_points(points, width, height)\\n # Compute Voronoi tesselation\\n vor = Voronoi(points)\\n # Plot\\n voronoi_plot_2d(vor)\\n return vor, points\",\n \"def getColumns(self, stripped):\\n noheader = stripped[7:]\\n entries = list(chunkify(noheader, 5))\\n columns = []\\n coverage = [False for x in range(self.rowLength)] #makes a string of bytes covered (0 or 1)\\n for entry in entries:\\n offset = int(entry[1])\\n length = int(entry[2])\\n assert offset+length<=self.rowLength, \\\"Error: entry length does not match row length in:\\\\n\\\"+ self.path +\\\".\\\"\\n if coverage[offset] == True: # in the case where NMM entries are overlapping\\n entry[0] = \\\"##OVERLAP WARNING## \\\" + entry[0]\\n for x in range(offset, offset+length):\\n coverage[x] = True #set bytes as covered.\\n columns.append(NightmareEntry(entry))\\n #at this point you have a list [True, True, False] or whatever\\n fillerEntries = []\\n count = 0\\n for offset,val in enumerate(coverage):\\n if val==False:\\n count += 1\\n if count == 1:\\n fillerEntry = [\\\"##UNKNOWN##\\\",offset,1,\\\"HEXA\\\",\\\"NULL\\\"]\\n fillerEntries.append(fillerEntry)\\n else:\\n fillerEntries[-1][2] = count\\n else:\\n count = 0\\n for fillerEntry in fillerEntries:\\n columns.append(NightmareEntry(fillerEntry))\\n columns.sort(key=lambda col: col.offset) #sort columns by offset\\n return columns\",\n \"def _regions(self, voronoi_diagram, unique_id, ids, crs):\\n vertices = pd.Series(voronoi_diagram.regions).take(voronoi_diagram.point_region)\\n polygons = []\\n for region in vertices:\\n if -1 not in region:\\n polygons.append(pygeos.polygons(voronoi_diagram.vertices[region]))\\n else:\\n polygons.append(None)\\n\\n regions_gdf = gpd.GeoDataFrame(\\n {unique_id: ids}, geometry=polygons, crs=crs\\n ).dropna()\\n regions_gdf = regions_gdf.loc[\\n regions_gdf[unique_id] != -1\\n ] # delete hull-based cells\\n\\n return regions_gdf\",\n \"async def divide_into_horizontal_columns(content_list, column_amount):\\n column_strings = []\\n for i, content in enumerate(content_list):\\n column_index = i % column_amount\\n if len(column_strings) < column_amount:\\n column_strings.append(content)\\n else:\\n column_strings[column_index] += \\\"\\\\n\\\" + content\\n\\n return column_strings\",\n \"def cells_list(self):\\n # self.cell_list_base = []\\n # self.cell_list_act = []\\n\\n pattern = \\\"cell \\\\(.*?\\\\)\\\"\\n \\n cell_list_base = [cell for cell in re.findall(pattern, self.base_doc) if 'cell ()' not in cell] # List of cells present in base lib\\n cellformat_base = [cell.replace('(','\\\\(').replace(')','\\\\)') for cell in cell_list_base] # Formated string for regex pattern\\n cells_group_base = [re.search(cell, self.base_doc) for cell in cellformat_base] # Group of regex matches\\n cells_dict_base = {base_cell.group(0):{'index': index, 'start': base_cell.start(), 'end': base_cell.end()} for index, base_cell in enumerate(cells_group_base)}\\n\\n cell_list_act = [cell for cell in re.findall(pattern, self.cell_doc) if 'cell ()' not in cell] # List of cells present in the active lib\\n common_cells = [cell for cell in cell_list_act if cell in cell_list_base] # Common cells needs to be deleted\\n\\n return cells_dict_base, cell_list_act, common_cells\",\n \"def addSeparatorFeature(self):\\n \\n # graphical separators\\n dNS = {\\\"pc\\\":PageXml.NS_PAGE_XML}\\n someNode = self.lNode[0]\\n ndPage = someNode.node.xpath(\\\"ancestor::pc:Page\\\", namespaces=dNS)[0]\\n lNdSep = ndPage.xpath(\\\".//pc:SeparatorRegion\\\", namespaces=dNS)\\n loSep = [ShapeLoader.node_to_LineString(_nd) for _nd in lNdSep]\\n \\n if self.bVerbose: traceln(\\\" %d graphical separators\\\"%len(loSep))\\n\\n # make an indexed rtree\\n idx = index.Index()\\n for i, oSep in enumerate(loSep):\\n idx.insert(i, oSep.bounds)\\n \\n # take each edge in turn and list the separators it crosses\\n nCrossing = 0\\n for edge in self.lEdge:\\n # bottom-left corner to bottom-left corner\\n oEdge = geom.LineString([(edge.A.x1, edge.A.y1), (edge.B.x1, edge.B.y1)])\\n prepO = prep(oEdge)\\n lCrossingPoints = []\\n fSepTotalLen = 0\\n for i in idx.intersection(oEdge.bounds):\\n # check each candidate in turn\\n oSep = loSep[i]\\n if prepO.intersects(oSep):\\n fSepTotalLen += oSep.length\\n oPt = oEdge.intersection(oSep)\\n if type(oPt) != geom.Point:\\n traceln('Intersection in not a point: skipping it')\\n else:\\n lCrossingPoints.append(oPt)\\n \\n if lCrossingPoints:\\n nCrossing += 1\\n edge.bCrossingSep = True\\n edge.sep_NbCrossing = len(lCrossingPoints)\\n minx, miny, maxx, maxy = geom.MultiPoint(lCrossingPoints).bounds\\n edge.sep_SpanLen = abs(minx-maxx) + abs(miny-maxy)\\n edge.sep_AvgSpanSgmt = edge.sep_SpanLen / len(lCrossingPoints) \\n edge.sep_AvgSepLen = fSepTotalLen / len(lCrossingPoints)\\n else:\\n edge.bCrossingSep = False\\n edge.sep_NbCrossing = 0\\n edge.sep_SpanLen = 0\\n edge.sep_AvgSpanSgmt = 0 \\n edge.sep_AvgSepLen = 0\\n \\n #traceln((edge.A.domid, edge.B.domid, edge.bCrossingSep, edge.sep_NbCrossing, edge.sep_SpanLen, edge.sep_AvgSpanSgmt, edge.sep_AvgSepLen))\\n \\n \\n if self.bVerbose: \\n traceln(\\\" %d (/ %d) edges crossing at least one graphical separator\\\"%(nCrossing, len(self.lEdge)))\",\n \"def make_cells(self, limits):\\n low, high = limits\\n xs = range(low[0], high[0])\\n ys = range(low[1], high[1])\\n for x in xs:\\n col = []\\n for y in ys:\\n indices = (x, y)\\n self.cells[indices] = Patch(self, indices)\",\n \"def _regions(self, voronoi_diagram, unique_id, ids, crs):\\n # generate DataFrame of results\\n regions = pd.DataFrame()\\n regions[unique_id] = ids # add unique id\\n regions[\\\"region\\\"] = voronoi_diagram.point_region # add region id for each point\\n\\n # add vertices of each polygon\\n vertices = []\\n for region in regions.region:\\n vertices.append(voronoi_diagram.regions[region])\\n regions[\\\"vertices\\\"] = vertices\\n\\n # convert vertices to Polygons\\n polygons = []\\n for region in tqdm(regions.vertices, desc=\\\"Vertices to Polygons\\\"):\\n if -1 not in region:\\n polygons.append(Polygon(voronoi_diagram.vertices[region]))\\n else:\\n polygons.append(None)\\n # save polygons as geometry column\\n regions[\\\"geometry\\\"] = polygons\\n\\n # generate GeoDataFrame\\n regions_gdf = gpd.GeoDataFrame(regions.dropna(), geometry=\\\"geometry\\\")\\n regions_gdf = regions_gdf.loc[\\n regions_gdf[\\\"geometry\\\"].length < 1000000\\n ] # delete errors\\n regions_gdf = regions_gdf.loc[\\n regions_gdf[unique_id] != -1\\n ] # delete hull-based cells\\n regions_gdf.crs = crs\\n return regions_gdf\",\n \"def cell_division(waitlist, celllist, AgEpitope, tnow, mut_list, RNs):\\n for cell in celllist:\\n # get list of 0 to 2 daughters\\n dlist, mut_list = divide(cell, AgEpitope, tnow, mut_list, RNs)\\n # add daughters to waitlist\\n waitlist = waitlist + dlist\\n return waitlist, mut_list\",\n \"def create_cells(self):\\n raise NotImplementedError(\\n \\\"create_cells function not reimplemented from base class\\\")\",\n \"def separate_adjacent_pins(self, separation):\\n # Commented out to debug with SCMOS\\n #if separation==0:\\n # return\\n\\n pin_names = self.pin_groups.keys()\\n for i,pin_name1 in enumerate(pin_names):\\n for j,pin_name2 in enumerate(pin_names):\\n if i==j:\\n continue\\n if i>j:\\n return\\n self.separate_adjacent_pin(pin_name1, pin_name2, separation)\",\n \"def create_cells(self, blocks):\\n cells = []\\n for block in blocks:\\n if (block['type'] == self.code) and (block['IO'] == 'input'):\\n code_cell = self.create_code_cell(block)\\n cells.append(code_cell)\\n\\n elif (block['type'] == self.code and\\n block['IO'] == 'output' and\\n cells[-1].cell_type == 'code'):\\n cells[-1].outputs = self.create_outputs(block)\\n\\n elif block['type'] == self.markdown:\\n markdown_cell = self.create_markdown_cell(block)\\n cells.append(markdown_cell)\\n\\n else:\\n raise NotImplementedError(\\\"{} is not supported as a cell\\\"\\n \\\"type\\\".format(block['type']))\\n\\n return cells\",\n \"def get_columns_with_rule():\\n skip_next = True\\n for child in box.children:\\n if child.style['column_span'] == 'all':\\n skip_next = True\\n elif skip_next:\\n skip_next = False\\n else:\\n yield child\",\n \"def split_features(data):\\n X = data.copy()\\n #split nucleotid string (len=60) into a list of independent characters (DNA nucleotids)\\n X['dna'] = X['dna'].map(lambda x : list(str(x).strip()))\\n #create 60 new attributes (columns) for each DNA nucleotide index\\n #each attribute has name dna_idx where idx is index (1-based) in the list above\\n for idx in range(60):\\n X['dna_%d' % (idx+1)] = X['dna'].map(lambda x : x[idx])\\n #remove the old dna column (redundant information)\\n del X['dna']\\n #remove descriptor\\n del X['id']\\n \\n return X\",\n \"def get_child_nodes(self):\\n parts = []\\n for child in sorted(self.children, key=lambda c: c.start):\\n child_part = (child.start, child.end, child)\\n parts.append(child_part)\\n return parts\",\n \"def calculate_divided_differences(nodes):\\n nodes_to_compute = []\\n divided_differences = []\\n for node in nodes:\\n nodes_to_compute.append(DividedDifferenceNode(x=node[0], divided_difference=node[1]))\\n\\n divided_differences.append(tuple(nodes_to_compute))\\n\\n while len(nodes_to_compute) > 1:\\n next_node_row = calculate_divided_differences_row(nodes_to_compute)\\n divided_differences.append(tuple(next_node_row))\\n nodes_to_compute = next_node_row\\n\\n return divided_differences\",\n \"def split_per(folderin, folderout, split_col='ECO_ID', colNms=['i_h100','i_cd',\\n 'doy','i_wflen','i_acqdate','b1','vcf','ECO_NAME','ECO_ID','BIOME','geometry']):\\n\\n split_files = glob.glob(folderin + '*.shp')\\n\\n for filename in split_files:\\n print(filename)\\n basename = os.path.splitext(os.path.basename(filename))[0]\\n dfa = gpd.read_file(filename)\\n df = dfa.astype({split_col: 'int32'}) \\n ecoNames = list(np.unique(df[split_col]))#get list of unique ecoregions \\n \\n for eco in ecoNames:\\n #create new df with just columns I want\\n df2 = gpd.GeoDataFrame(df, columns=colNms)\\n ID = str(eco)\\n df_eco = df.loc[df2[split_col]==eco, colNms]\\n df_eco.to_file(folderout + '/{}_eco_{}.shp'.format(basename, ID))\",\n \"async def divide_into_vertical_columns(content_list, column_amount):\\n base_column_size, extras = divmod(len(content_list), column_amount)\\n\\n column_strings = []\\n content_list_index = 0\\n for i in range(column_amount):\\n column_size = base_column_size\\n if extras > i:\\n column_size += 1\\n\\n for j in range(column_size):\\n content = content_list[content_list_index]\\n if len(column_strings) <= i:\\n column_strings.append(content)\\n else:\\n column_strings[i] += \\\"\\\\n\\\" + content\\n\\n content_list_index += 1\\n\\n return column_strings\",\n \"def __divideset(rows, column, value):\\n split_function = None #Initialize a variable split function.\\n if isinstance(value, int) or isinstance(value, float): #Check if value is a number\\n #True = the observation's value >= to the splitting criteria. False otherwise\\n split_function = lambda row: row[column] >= value\\n else:\\n #If value is a string, True is where the observation's value == the criteria\\n split_function = lambda row:row[column] == value\\n \\n #Divide the rows into two sets and return them\\n set1 = [row for row in rows if split_function(row)]\\n set2 = [row for row in rows if not split_function(row)]\\n return (set1, set2)\",\n \"def split(gt, isotropic):\\n edges = get_edges(gt, isotropic)\\n cytosol = edges == 0\\n membrane = edges != 0\\n return cytosol, membrane\",\n \"def choose_and_flip_extra_dominoes(self, random):\\n for domino in self.choose_extra_dominoes(random):\\n if domino.head.pips == domino.tail.pips:\\n yield domino, False\\n else:\\n flip_first = random.randint(0, 1)\\n for j in range(2):\\n yield domino, flip_first + j == 1\",\n \"def grid(plots):\\n ydim, xdim = plots.shape\\n w = mean_width(plots)\\n columns = []\\n for i in plots.identifiers:\\n r = plots.region_by_identifier(i)\\n r.identifier = i\\n if len(columns) == 0:\\n c = Column(w, xdim)\\n c.append(r)\\n columns.append(c)\\n continue\\n\\n included = False\\n for c in columns:\\n if c.in_column(r):\\n c.append(r)\\n included = True\\n break\\n\\n if not included:\\n c = Column(w, xdim)\\n c.append(r)\\n columns.append(c)\\n\\n # Sort the columns left to right.\\n columns.sort(key=lambda i: i.x_mean)\\n\\n for c in columns:\\n # Sort the rows top to bottom.\\n c.sort(key=lambda i: i.centroid[0])\\n\\n return columns\",\n \"def make_space_elements(space):\\n return [\\n html.Div(html.Hr()),\\n html.Div(\\\"%s\\\" % space.title(), id=\\\"%s_title\\\" % space, style={\\\"font-size\\\": \\\"200%\\\"}),\\n dcc.Graph(id=\\\"histogram_%s\\\" % space, figure=show_histogram(space, 0)),\\n make_space_table(space),\\n ]\",\n \"def create_grid_and_edges(data, drone_altitude, safety_distance):\\n\\n # minimum and maximum north coordinates\\n north_min = np.floor(np.min(data[:, 0] - data[:, 3]))\\n north_max = np.ceil(np.max(data[:, 0] + data[:, 3]))\\n\\n # minimum and maximum east coordinates\\n east_min = np.floor(np.min(data[:, 1] - data[:, 4]))\\n east_max = np.ceil(np.max(data[:, 1] + data[:, 4]))\\n\\n # given the minimum and maximum coordinates we can\\n # calculate the size of the grid.\\n north_size = int(np.ceil((north_max - north_min)))\\n east_size = int(np.ceil((east_max - east_min)))\\n\\n # Initialize an empty grid\\n grid = np.zeros((north_size, east_size))\\n\\n # Define a list to hold Voronoi points\\n points = []\\n # Populate the grid with obstacles\\n for i in range(data.shape[0]):\\n north, east, alt, d_north, d_east, d_alt = data[i, :]\\n\\n if alt + d_alt + safety_distance > drone_altitude:\\n obstacle = [\\n int(north - d_north - safety_distance - north_min),\\n int(north + d_north + safety_distance - north_min),\\n int(east - d_east - safety_distance - east_min),\\n int(east + d_east + safety_distance - east_min),\\n ]\\n grid[obstacle[0]:obstacle[1] + 1, obstacle[2]:obstacle[3] + 1] = 1\\n\\n # add center of obstacles to points list\\n points.append([north - north_min, east - east_min])\\n\\n # create a voronoi graph based on\\n # location of obstacle centres\\n graph = Voronoi(points)\\n # check each edge from graph.ridge_vertices for collision\\n edges = []\\n for v in graph.ridge_vertices:\\n p1 = graph.vertices[v[0]].astype(int)\\n p2 = graph.vertices[v[1]].astype(int)\\n # test each pair p1 and p2 for collision using Bresenham\\n # If the edge does not hit an obstacle add it to the list\\n in_collision = False\\n ridgeline = bresenham(p1[0], p1[1], p2[0], p2[1])\\n for b in ridgeline:\\n # eliminate out of range points in the line\\n if b[0] < 0 or b[0] >= grid.shape[0]:\\n in_collision = True\\n break\\n if b[1] < 0 or b[1] >= grid.shape[1]:\\n in_collision = True\\n break\\n # check if grid cell is an obstacle\\n if grid[b[0], b[1]] == 1:\\n in_collision = True\\n break\\n # keep ridge points not in collision\\n if not in_collision:\\n p1 = (p1[0], p1[1])\\n p2 = (p2[0], p2[1])\\n edges.append((p1, p2))\\n\\n return grid, edges\",\n \"def create_cells(self, N):\\n\\t\\tself.stn_cells = []\\n\\t\\tself.gpe_cells = []\\n\\t\\tfor i in range(N):\\n\\t\\t\\t# create cell pair\\n\\t\\t\\tident = str(i+1)\\n\\t\\t\\tstn = STN()\\n\\t\\t\\tgpe = GPe(delay=self.delay,dur=self.dur,amp=self.amp,loc=self.loc,cur=True)\\n\\t\\t\\tstn.ident = 'stn%s' % (ident)\\n\\t\\t\\tgpe.ident = 'gpe%s' % (ident)\\n\\t\\t\\t# reposition cell pair\\n\\t\\t\\tstn.set_position(i*25,-10,0)\\n\\t\\t\\tgpe.set_position(i*25,10,0)\\n\\t\\t\\t# add pair to cell lists\\n\\t\\t\\tself.stn_cells.append(stn)\\n\\t\\t\\tself.gpe_cells.append(gpe)\",\n \"def make_free_cell_list():\\r\\n for row in range(9):\\r\\n for col in range(9):\\r\\n if (application.ui.__getattribute__(f'cell{col+1}{row+1}')).text() == \\\"\\\":\\r\\n lst_free_cells.append(Point(row, col))\",\n \"def splitPlace(self):\\r\\n \\r\\n \\r\\n \\r\\n nodeSortedIter = sorted(self.G.degree_iter(),key=itemgetter(1),reverse=True)\\r\\n \\r\\n placeCnt = 0\\r\\n \\r\\n for node in nodeSortedIter:\\r\\n if placeCnt<self.cells/2:\\r\\n self.sitesA.append(node[0])\\r\\n self.G.node[node[0]][\\\"part\\\"] = 'A'\\r\\n else:\\r\\n self.sitesB.append(node[0])\\r\\n self.G.node[node[0]][\\\"part\\\"] = 'B'\\r\\n placeCnt+=1\",\n \"def splitbylo(self,lo):\\n ind=np.where(self.lo==lo)[0]\\n self.splitbyind(ind)\",\n \"def get_donnees(self):\\r\\n \\r\\n return self._donnees\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.54147345","0.53187776","0.5316432","0.53141034","0.50757587","0.50472033","0.5007801","0.49147692","0.49121788","0.4892322","0.48909703","0.48512492","0.48459086","0.48424825","0.48366407","0.48032853","0.47910866","0.47892538","0.47808278","0.47771978","0.47716329","0.4771325","0.47523955","0.47449994","0.47403342","0.47374755","0.47298428","0.4729653","0.4727065","0.4720182","0.47166204","0.47099584","0.47084057","0.46803463","0.46642366","0.46618348","0.46457756","0.4616883","0.461251","0.46107674","0.45888197","0.45412675","0.4538422","0.45383143","0.45307344","0.45302433","0.4528726","0.45213684","0.45154446","0.45067728","0.4478685","0.44716665","0.44675675","0.44644704","0.44616836","0.44506437","0.44322607","0.44304648","0.44286034","0.44224012","0.4417593","0.44169694","0.4411633","0.44105583","0.44045553","0.43996716","0.43993825","0.4392979","0.43877754","0.43731818","0.43690497","0.43670046","0.4363747","0.43599504","0.43484464","0.43446872","0.43266293","0.43230018","0.43190947","0.4314868","0.43143666","0.4314148","0.43137288","0.43099385","0.4307764","0.43053967","0.43028456","0.43023664","0.43001452","0.42980722","0.42941692","0.42931417","0.42918122","0.4288153","0.42861757","0.42846268","0.4282221","0.42794505","0.42738816","0.42732653"],"string":"[\n \"0.54147345\",\n \"0.53187776\",\n \"0.5316432\",\n \"0.53141034\",\n \"0.50757587\",\n \"0.50472033\",\n \"0.5007801\",\n \"0.49147692\",\n \"0.49121788\",\n \"0.4892322\",\n \"0.48909703\",\n \"0.48512492\",\n \"0.48459086\",\n \"0.48424825\",\n \"0.48366407\",\n \"0.48032853\",\n \"0.47910866\",\n \"0.47892538\",\n \"0.47808278\",\n \"0.47771978\",\n \"0.47716329\",\n \"0.4771325\",\n \"0.47523955\",\n \"0.47449994\",\n \"0.47403342\",\n \"0.47374755\",\n \"0.47298428\",\n \"0.4729653\",\n \"0.4727065\",\n \"0.4720182\",\n \"0.47166204\",\n \"0.47099584\",\n \"0.47084057\",\n \"0.46803463\",\n \"0.46642366\",\n \"0.46618348\",\n \"0.46457756\",\n \"0.4616883\",\n \"0.461251\",\n \"0.46107674\",\n \"0.45888197\",\n \"0.45412675\",\n \"0.4538422\",\n \"0.45383143\",\n \"0.45307344\",\n \"0.45302433\",\n \"0.4528726\",\n \"0.45213684\",\n \"0.45154446\",\n \"0.45067728\",\n \"0.4478685\",\n \"0.44716665\",\n \"0.44675675\",\n \"0.44644704\",\n \"0.44616836\",\n \"0.44506437\",\n \"0.44322607\",\n \"0.44304648\",\n \"0.44286034\",\n \"0.44224012\",\n \"0.4417593\",\n \"0.44169694\",\n \"0.4411633\",\n \"0.44105583\",\n \"0.44045553\",\n \"0.43996716\",\n \"0.43993825\",\n \"0.4392979\",\n \"0.43877754\",\n \"0.43731818\",\n \"0.43690497\",\n \"0.43670046\",\n \"0.4363747\",\n \"0.43599504\",\n \"0.43484464\",\n \"0.43446872\",\n \"0.43266293\",\n \"0.43230018\",\n \"0.43190947\",\n \"0.4314868\",\n \"0.43143666\",\n \"0.4314148\",\n \"0.43137288\",\n \"0.43099385\",\n \"0.4307764\",\n \"0.43053967\",\n \"0.43028456\",\n \"0.43023664\",\n \"0.43001452\",\n \"0.42980722\",\n \"0.42941692\",\n \"0.42931417\",\n \"0.42918122\",\n \"0.4288153\",\n \"0.42861757\",\n \"0.42846268\",\n \"0.4282221\",\n \"0.42794505\",\n \"0.42738816\",\n \"0.42732653\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7638271"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":25297,"cells":{"query":{"kind":"string","value":"Build a display string for the board's current state."},"document":{"kind":"string","value":"def display(self, cropped=False, cropping_bounds=None):\n xmin, xmax, ymin, ymax = self.get_bounds(cropped)\n if cropping_bounds is not None:\n cropping_bounds[:] = [xmin, ymin, xmax, ymax]\n width = xmax-xmin+1\n height = ymax-ymin+1\n marker_display = self.display_markers()\n are_markers_unique = not marker_display.startswith('(')\n display = [[' '] * (width*2-1) for _ in range(height*2-1)]\n\n for y in range(height):\n for x in range(width):\n row = (height - y - 1)*2\n col = x*2\n cell = self[x+xmin][y+ymin]\n cell_display = self.display_cell(cell,\n x+xmin,\n y+ymin,\n are_markers_unique)\n display[row][col] = cell_display\n if (cell is not None and\n cell.domino is not None and\n cell.domino.head == cell):\n dx, dy = cell.domino.direction\n divider = '|' if dx else '-'\n display[row-dy][col+dx] = divider\n self.adjust_display(display)\n main_display = ''.join(''.join(row).rstrip() + '\\n' for row in display)\n if marker_display:\n main_display = f'{main_display}---\\n{marker_display}\\n'\n if self.dice_set:\n dice_text = self.dice_set.crop_text(xmin, ymin)\n main_display = f'{main_display}---\\ndice:{dice_text}\\n'\n\n return main_display"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __str__(self):\n board = ''\n board_2 = ''\n\n for row in self.from_grid:\n for space in row:\n board += ' ' + space\n board += '\\n'\n\n for row in self.to_grid:\n for space in row:\n board_2 += ' ' + space\n board_2 += '\\n'\n\n return 'Current State:\\n' + board + 'Target State:\\n' + board_2","def macro_str(self):\n str = '-' * (2 * self.SIZE + 1) + '\\n'\n for row in self.boards:\n str += ' '\n for board in row:\n str += board.state.value + ' '\n str += '\\n' + '-' * (2 * self.SIZE + 1) + '\\n'\n return str","def __str__(self):\n s = \" 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\\n\"\n board = initial_board()\n count = 1\n for i in self.occupied:\n board[i[0]][i[1]] = self.occupied[i]\n space = ''\n for i in range(0, 16):\n space += ' '\n start = '---'.join(space)\n s += start+'\\n|'\n for row in range(1,16):\n for col in range(1,16):\n if use_color and (row, col) == self.action:\n s += '\\033[91m'\n if board[row][col] == 0:\n s += ' |'\n elif board[row][col] == 1:\n s += ' O |'\n else:\n s += ' X |'\n if use_color and (row, col) == self.action:\n s += '\\033[0m'\n s += '\\033[0m'\n s+=str(count)+'\\n'+start+'\\n|'\n count += 1\n\n s = s[:len(s)-1]\n s += \"\\n*****************************************************************************\"\n return s[:len(s)-1]","def show_board(self):\n board_vis = f\"\\n{'*' * 22}Board state{'*' * 23}\\n\"\n str_p2_store=\" \"+str(self.p2_store()) if self.p2_store()<10 else str(self.p2_store())\n board_vis += (f\" {str_p2_store} - | \" +\n \" || \".join(\n [i if len(i) == 2 else ' ' + i for i in list(map(str, self.p2_pits()[::-1]))]) + \" | \\n\")\n board_vis += f\"{'-------' * (self.M + 2)}\\n\"\n board_vis += (\" | \" + \" || \".join(\n [i if len(i) == 2 else ' ' + i for i in list(map(str, self.p1_pits()))]) +\n f\" | - {self.p1_store()}\\n\")\n board_vis += f\"{'*' * 56}\\n\"\n print(board_vis)","def __str__(self):\n def align_column(grid):\n board = \"\"\n for i in range(self.n):\n board += str(grid[i]) + \"\\n\"\n return board.strip()\n return (\"===Current Stage===\\n\"\n \"{}\\n\"\n \"====Goal Board=====\\n\"\n \"{}\".format(align_column(self.from_grid),\n align_column(self.to_grid)))","def get_board_state_pretty(self):\n\n board_state = ''\n for i in range(0, 3):\n board_state += ' | '.join([self.board['{}{}'.format(i, j)] for j in range(0, 3)])\n board_state += '\\n'\n return board_state","def __str__(self):\n s=\"\"\n for y in range(0,HEIGHT):\n for x in range(0,WIDTH):\n s+=str(self.gameState[x,y])\n return s","def __str__(self):\n\t\tboardString = \"\\n{0}|{1}|{2}\\n-----\\n{3}|{4}|{5}\\n-----\\n{6}|{7}|{8}\\n\"\n\t\treturn boardString.format(self.board[0], self.board[1], self.board[2], self.board[3],\n\t\t\tself.board[4], self.board[5], self.board[6], self.board[7], self.board[8])","def __repr__(self):\n string = \"Current state: \\n\"\n if self.state[0] == 0: # We're on the left side\n string += \"M: \"\n string += str(self.state[1]).ljust(10)\n string += \"M: \"\n string += str(TOTAL_NO_MISSIONARIES - self.state[1]).ljust(10)\n string += \"\\n\"\n\n string += \"C: \"\n string += str(self.state[2]).ljust(10)\n string += \"C: \"\n string += str(TOTAL_NO_CANNIBALS - self.state[2]).ljust(10)\n string += \"\\n\"\n\n string += \"Boat position: left\\n\"\n else: # We're on the right side\n string += \"M: \"\n string += str(TOTAL_NO_MISSIONARIES - self.state[1]).ljust(10)\n string += \"M: \"\n string += str(self.state[1])\n string += \"\\n\"\n\n string += \"C: \"\n string += str(TOTAL_NO_CANNIBALS - self.state[2]).ljust(10)\n string += \"C: \"\n string += str(self.state[2]).ljust(10)\n string += \"\\n\"\n\n string += \"Boat position: right\\n\"\n string += \"\\n\"\n return string","def display_state(self):\r\n\r\n print('\\n')\r\n print('>>CURRENT STATE')\r\n ct = 0\r\n for i in self.state:\r\n for j in i:\r\n if j == -1:\r\n val = 'X'\r\n else:\r\n val = str(ct)\r\n if len(val) == 1:\r\n print(' ' + val + ' ', end='')\r\n else:\r\n print(val + ' ', end='')\r\n ct += 1\r\n print('\\n')","def _display_board(state: game.GameState) -> None:\n for row in range(state.get_rows()):\n rowString = \"|\"\n for col in range(state.get_columns()):\n cellValue = state.get_cell_contents(row, col)\n cellState = state.get_cell_state(row, col)\n if cellState == game.EMPTY_CELL:\n rowString += ' '\n elif cellState == game.OCCUPIED_CELL:\n rowString += (' ' + cellValue + ' ')\n elif cellState == game.FALLER_MOVING_CELL:\n rowString += ('[' + cellValue + ']')\n elif cellState == game.FALLER_STOPPED_CELL:\n rowString += ('|' + cellValue + '|')\n elif cellState == game.MATCHED_CELL:\n rowString += ('*' + cellValue + '*')\n rowString += '|'\n print(rowString)\n finalLine = ' '\n for col in range(state.get_columns()):\n finalLine += '---'\n finalLine += ' '\n print(finalLine)","def __repr__(self):\n s = \"\"\n for y in range(0,HEIGHT):\n temp=\"\"\n for x in range(0,WIDTH):\n temp = temp+ str(self.gameState[x,y])\n s += temp+\"\\n\"\n return s","def __str__(self):\n s = \"\"\n for r in range(1,self.size+1):\n for c in range(1,self.size+1):\n s += str(self.gameState[r,c])\n return s","def __str__(self):\n return ''.join(str(e) + ' ' for e in self.state)","def __str__(self):\n state = ''\n state += ' '.join([str(x) for x in self.pos]) + ' '\n state += ''.join([str(x) + ' ' + str(y) + ' ' for x,\n y in zip(self.BU, self.BD)])\n for e in self.BF:\n state += ' '.join([str(x) for x in e])\n state += ' '\n state += ' '.join([str(x) for x in self.LU]) + ' '\n state += ' '.join([str(x) for x in self.LD]) + ' '\n\n return state","def __repr__(self):\n representantion = ''\n\n for i in range(3):\n for j in range(3):\n representantion += str(self.state[3 * i + j])\n\n if j == 2 and i != 2:\n representantion += '\\n'\n else:\n representantion += ' '\n\n return representantion","def board_string(self):\n s = \"\"\n for i, v in enumerate(self.board):\n # if i % 81 == 0:\n # s += \"\\n\"\n if v is None:\n s += \"0\"\n else:\n if v.color == StoneColor.black:\n s += \"1\"\n else:\n s += \"2\"\n return s","def __str__(self):\n # Build the string line by line. Join at the end.\n lines = []\n lines.append(\"Initial State: {{{}}}\".format(self.initial_state))\n lines.append(\n \"Final States: {{{}}}\".format(\n \",\".join(map(str, self.final_states))))\n\n # column headers\n lines.append(\n \"State\\t{}\".format(\"\\t\".join(self.alphabet)))\n\n # For each state, print transitions\n for state_name in range(1, len(self.transitions) + 1):\n line = \"{}\".format(state_name)\n for symbol in self.alphabet:\n line += \"\\t{{{}}}\".format(\n \",\".join(map(str, self.transitions.get(\n state_name, dict()).get(symbol, []))))\n lines.append(line)\n\n return \"\\n\".join(lines)","def __str__(self):\n line = ''\n line += self.board_state.__str__()\n line += self.move.__str__()\n line += '\\n'\n return line","def __str__(self):\n #formatting board correctly\n formatted_board = \"\"\n for i in range(self.size):\n formatted_board += str(self.board[i]) + \"\\n\"\n return \"Board size: \" + str(self.size) + \"\\n\" + \"Number of Queens placed: \" + str(self.num_queens_placed) + \"\\n\" + str(formatted_board)","def __str__(self):\n board = ''\n for row in range(self.height):\n if row > 0:\n board += '\\n'\n for col in range(self.width):\n if self.board[row][col] == '':\n board += '| '\n else:\n board += ('|' + self.board[row][col])\n board += '|'\n board += ('\\n' + '-' * 2 * self.width + '\\n')\n for i in range(self.width):\n board += (' ' + str(i))\n return board","def __repr__(self):\n W = self.width\n H = self.height\n\n s = '' # the string to return\n for row in range(0, H):\n s += '|'\n for col in range(0, W):\n s += self.data[row][col] + '|'\n s += '\\n'\n\n s += (2 * W + 1) * '-' # bottom of the board\n s += '\\n'\n\n x = -1\n for i in range(W):\n if x == 9:\n x = 0\n s += \" \" + str(x)\n else:\n x += 1\n s += \" \" + str(x)\n\n return s # the board is complete, return it","def displayBoard(self):\n res = ''\n for i in range(0, self.size):\n res += '|'\n for j in range(0, self.size):\n res += ' ' + str(self.board[i][j])\n res += '\\n'\n res += '+'\n for i in range(0, self.size * 2):\n res += '-'\n res += '\\n '\n for i in range(1, (self.size + 1)):\n res += (' ' + str(i))\n return res","def __str__(self):\n b = ''\n for i in range(7): # 7 steps in the board\n if i == self.chaser_i: # chaser position\n b += '|' + str(i) + '| chaser |\\n'\n elif i == self.player_i: # player position\n b += '|' + str(i) + '| player |\\n'\n else:\n b += '|' + str(i) + '| |\\n'\n b += '|7| bank |\\n' # bank position\n return b","def __draw_board(self, state=None):\n if not state:\n state = self.state\n if self.game_started:\n print('\\r\\033[8A')\n print(emoji.emojize(' {state[0]} | {state[1]} | {state[2]} \\n___|___|___\\n'\n ' {state[3]} | {state[4]} | {state[5]} \\n___|___|___\\n'\n ' {state[6]} | {state[7]} | {state[8]} \\n | | \\n'.format(state=state)))","def get_board_state(self):\n\n board_state = ''\n for i in range(0, 3):\n board_state += ''.join([self.board['{}{}'.format(i, j)] for j in range(0, 3)])\n return board_state","def show( self):\n def symbol( i):\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\n \n X, Y = np.max( self.board.positions, 0)\n # -2 to indicate outside board.\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \n for x, y in self.board.positions:\n display[x, y] = -1 # -1 to indicate unoccupied\n for p, i in self.occupation.items():\n x, y = self.board.positions[p]\n display[x, y] = i\n for x in xrange(X+1):\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\n print s","def __str__(self):\n s=''\n for r in range(self.n):\n for c in range(self.n):\n s += str(self.state[r][c]) + ' '\n s += '\\n'\n s += str('hcost') + ' : ' + str(self.hcost)\n s += '\\n'\n return s","def the_display(self):\r\n return f\"\"\"\r\n {self.display[0]}\\n\r\n {self.display[1]}\\n\r\n {self.display[2]}\\n\r\n {self.display[3]}\\n\r\n {self.display[4]}\\n\r\n \"\"\"","def __str__(self):\n width = int(np.prod(self.no_parent_states)) #multiplies the elements in list, here tot num of states\n grid = np.meshgrid(*[range(i) for i in self.no_parent_states]) \n s = \"\"\n for (i, e) in enumerate(self.parents):\n s += '+----------+' + '----------+' * width + '\\n'\n gi = grid[i].reshape(-1)\n s += f'|{e:^10}|' + '|'.join([f'{e + \"(\"+str(j)+\")\":^10}' for j in gi])\n s += '|\\n'\n\n for i in range(self.no_states):\n s += '+----------+' + '----------+' * width + '\\n'\n state_name = self.name + f'({i})'\n s += f'|{state_name:^10}|' + '|'.join([f'{p:^10.4f}' for p in self.table[i]])\n s += '|\\n'\n\n s += '+----------+' + '----------+' * width + '\\n'\n\n return s","def __str__(self):\n rep = \"\"\n for row in range(self._dim):\n for col in range(self._dim):\n rep += STRMAP[self._board[row][col]]\n if col == self._dim - 1:\n rep += \"\\n\"\n else:\n rep += \" | \"\n if row != self._dim - 1:\n rep += \"-\" * (4 * self._dim - 3)\n rep += \"\\n\"\n return rep","def render_state(self, state, is_first_player_black):\n MOVES_STR = {\n -1 if is_first_player_black else 1: '▒▒',\n 0: ' ',\n 1 if is_first_player_black else -1: '██',\n }\n\n numbers = ' ' + ''.join(\n str(i) + (' ' if i < 10 else '')\n for i in range(self.SIZE)\n )\n top = ' ╔' + ('══' * self.SIZE) + '╗'\n bottom = ' ╚' + ('══' * self.SIZE) + '╝'\n\n collated_string = '{}\\n{}\\n{}\\n{}'.format(\n numbers,\n top,\n '\\n'.join([\n '{}║{}║'.format(\n (' ' if i < 10 else '') + str(i),\n ''.join([\n MOVES_STR[state[self.SIZE * i + j]]\n for j in range(self.SIZE)\n ]),\n )\n for i in range(self.SIZE)\n ]),\n bottom,\n )\n return collated_string","def __str__(self):\n board = \"\"\" 0 1 2 3 4 5\\n\"\"\"\n\n for y in range(Board.board_size):\n board += str(y) + \" \"\n for x in range(Board.board_size):\n piece = self.board[x][y] if self.board[x][y] is not None else \".\"\n\n piece = str(piece).lower() if piece in self.player_1_pieces else str(piece)\n\n board += piece\n board += \" \"\n board += \"\\n\"\n return board","def display(self):\n for r in range(1, self.size+1):\n print(\"+\" + (\"-+\"*self.size))\n print(\"|\", end=\"\")\n for c in range(1, self.size+1):\n print(self.gameState[r,c], end=\"\")\n print(\"|\",end=\"\")\n print()\n print(\"+\" + (\"-+\"*self.size))","def __str__(self):\n # replace with your code\n board = \"\"\n for index in range(self.grid_height):\n board += \"[\"\n for inner_index in range(self.grid_width):\n board += str(self.board[index][inner_index]) + \" \"\n else:\n board += \"]\\n\"\n return board","def __repr__(self):\n t = ''\n for x in range(len(self.board)):\n for y in range(len(self.board[0])):\n t += str(self.board[x][y]) + ' '\n t += '\\n'\n return t","def printBoard(self):\n\t\tkey = [' ', 'X', 'O']\n\t\tprint(' | |')\n\t\tprint(' ' + key[self.state[0][0]] + ' | ' + key[self.state[0][1]] + ' | ' + key[self.state[0][2]])\n\t\tprint(' | |')\n\t\tprint('-----------')\n\t\tprint(' | |')\n\t\tprint(' ' + key[self.state[1][0]] + ' | ' + key[self.state[1][1]] + ' | ' + key[self.state[1][2]])\n\t\tprint(' | |')\n\t\tprint('-----------')\n\t\tprint(' | |')\n\t\tprint(' ' + key[self.state[2][0]] + ' | ' + key[self.state[2][1]] + ' | ' + key[self.state[2][2]])\n\t\tprint(' | |')","def __str__(self):\n return \"\".join(list(map(lambda row: ''.join(row), self.state)))","def __repr__(self):\r\n numLetters = self.numLetters\r\n S = ''\r\n S += 3*'\\n'\r\n S += ' '\r\n for i in range(numLetters):\r\n S += self.currentBoard[i] + ' '\r\n\r\n return S","def __repr__(self):\n s = dashes = \"\".join([ ' -' for i in range(self.BoardSize) ])\n for row in range( self.BoardSize ):\n sRow = '|'\n for col in range( self.BoardSize ):\n sRow += str(self.CurrentGameboard[row][col]) + '|'\n s += '\\n' + sRow + '\\n' + dashes\n return s","def __str__(self) -> str:\n return self.board","def __str__(self):\r\n \r\n #return \"The 2048 board is \" + str(self._cells)\r\n string = \"\"\r\n for row in range(self._grid_height):\r\n for column in range(self._grid_width):\r\n if column == self._grid_width -1:\r\n string += str(self._cells[row][column]) + \"\\n\"\r\n else:\r\n string += str(self._cells[row][column]) +\", \"\r\n return \"The 2048 board is \"+ str(self._grid_height) + \"x\" + str(self._grid_width) + \" and contains: \" + \"\\n\" + string","def showBoard(self):\n \n brd = \"\\n | | \\n\" + \\\n \" \" + self.squares[0] + \" | \" + self.squares[1] + \" | \" + self.squares[2] + \" \\n\" + \\\n \"___|___|___\\n\" + \\\n \" | | \\n\" + \\\n \" \" + self.squares[3] + \" | \" + self.squares[4] + \" | \" + self.squares[5] + \" \\n\" + \\\n \"___|___|___\\n\" + \\\n \" | | \\n\" + \\\n \" \" + self.squares[6] + \" | \" + self.squares[7] + \" | \" + self.squares[8] + \" \\n\" + \\\n \" | | \\n\"\n\n return brd","def __repr__(self):\n r = \"\"\n for row in self.board:\n for cell in row:\n if cell == \"\":\n cell = color_magenta(\"_\")\n r += cell + \" \" # for all the empty strings, we will replace it with an '_'.\n r += \"\\n\"\n return r","def __display(self,state: dict):\n width = 1+max(len(state[s]) for s in self.__boxes)\n line = '+'.join(['-'*(width*3)]*3)\n for r in self.__rows:\n print(''.join( state[r+c].center(width)+ ('|' if c in '36' else '')\n for c in self.__cols))\n if r in 'CF': print(line)","def print_state(self):\n grid = [[\".\" for _ in range(self.width)] for _ in range(self.height)]\n #icons = [\"^\", \"/\", \">\", \"\\\\\", \"|\", \"/\", \"<\", \"\\\\\"] # NON-UNICODE, uncomment if problems\n icons = [chr(0x2191), chr(0x2197), chr(0x2192), chr(0x2198), \\\n chr(0x2193), chr(0x2199), chr(0x2190), chr(0x2196)]\n for robot in self.robots:\n grid[robot[1]][robot[0]] = icons[(robot[2]+robot[3]) % 8]\n for item in self.items:\n if item[2] == 1:\n grid[item[1]][item[0]] = \"O\"\n elif item[2] == 2:\n grid[item[1]][item[0]] = \"*\"\n print(\"-\"*(self.width+2))\n for i in range(self.height):\n print(\"|\", end=\"\")\n for j in range(self.width):\n print(grid[i][j], end=\"\")\n print(\"|\")\n print(\"-\"*(self.width+2))","def __str__(self):\n board_lists = [['_']*self.__width for rows in range(self.__height)]\n for car in self.__cars:\n car_coords = car.car_coordinates()\n for item in car_coords:\n if item == (3,7):\n pass\n else:\n board_lists[item[0]][item[1]] = car.get_name()\n board_str = '\\n'.join(' '.join(sub) for sub in board_lists)\n return board_str","def __str__(self):\n str = '-' * (self.SIZE ** 2 + self.SIZE + 1) + '\\n'\n for row in self.boards:\n for i in range(self.SIZE):\n str += '|'\n for board in row:\n for square in board.export_grid()[i]:\n str += square.value\n str += '|'\n str += '\\n'\n str += '-' * (self.SIZE ** 2 + self.SIZE + 1) + '\\n'\n return str","def __str__(self):\n result = \"\"\n for i in range(3):\n for j in range(3):\n if self.board[i][j] == 5:\n result += \" x\"\n elif self.board[i][j] == 7:\n result += \" о\"\n else:\n result += \" #\"\n result += \"\\n\"\n return result","def __str__(self):\n card_str = ['-' * CARD_WIDTH]\n card_str.append('{} (Game {})'.format(self.name, self.game.number).center(CARD_WIDTH))\n card_str.append('-' * CARD_WIDTH)\n card_str.append(''.center(CARD_WIDTH))\n card_str.append('Money: {}'.format(self.money).center(CARD_WIDTH))\n card_str.append('Turnover: {}'.format(self.turnover).center(CARD_WIDTH))\n card_str.append(''.center(CARD_WIDTH))\n card_str.append('-' * CARD_WIDTH)\n return '\\n'.join('|{}|'.format(l) for l in card_str)","def __str__(self):\r\n grid_text = \"\\n-------------------\\n|\"\r\n for i in range(len(self.grid)):\r\n grid_text = grid_text + ' %s '%(self.grid[i][-1])\r\n\r\n if i%3 == 2:\r\n grid_text = grid_text + '|\\n-------------------\\n|'\r\n else:\r\n grid_text = grid_text + '|'\r\n return grid_text[0:len(grid_text)-1]","def __str__(self) -> str:\n if self.write_back is black.WriteBack.CHECK:\n reformatted = \"would be reformatted\"\n unchanged = \"would be left unchanged\"\n failed = \"would fail to reformat\"\n cleared = \"would be cleared\"\n else:\n reformatted = \"reformatted\"\n unchanged = \"left unchanged\"\n failed = \"failed to reformat\"\n cleared = \"cleared\"\n report = []\n if self.change_count:\n s = \"s\" if self.change_count > 1 else \"\"\n report.append(\n click.style(\n f\"{self.change_count} cell{s} {reformatted}\", bold=True\n )\n )\n if self.same_count:\n s = \"s\" if self.same_count > 1 else \"\"\n report.append(f\"{self.same_count} cell{s} {unchanged}\")\n if self.failure_count:\n s = \"s\" if self.failure_count > 1 else \"\"\n report.append(\n click.style(f\"{self.failure_count} cell{s} {failed}\", fg=\"red\")\n )\n if self.output_change_count:\n s = \"s\" if self.change_count > 1 else \"\"\n report.append(\n click.style(\n f\"{self.output_change_count} output{s} {cleared}\",\n bold=True,\n )\n )\n if self.output_same_count:\n s = \"s\" if self.same_count > 1 else \"\"\n report.append(f\"{self.output_same_count} output{s} {unchanged}\")\n return \", \".join(report) + \".\"","def __str__(self):\r\n # replace with your code\r\n return str(self._board[0]) + \"\\n\" + str(self._board[1]) + \"\\n\" + str(self._board[2]) + \"\\n\" + str(self._board[3]) + \"\\n\\n\"","def __str__(self):\n \n # top row\n result = ' '\n result = '\\n ' + '-' * (self.DIM*2+5) + '\\n'\n \n # board rows\n for row in range(self.DIM):\n if row is 3 or row is 6:\n result += '|' + '-' * (self.DIM*2+5) + '|' + '\\n'\n # result += '|-------+-------+-------|\\n'\n result += '| '\n for col in range(self.DIM):\n if col is 3 or col is 6:\n result += '| '\n if self.board[row][col] == SudokuConfig.EMPTY:\n result += '.'\n else:\n result += str(str(self.board[row][col]))\n if col != self.DIM-1:\n result += ' '\n result += ' |' + '\\n'\n \n # bottom row\n result += ' ' + '-' * (self.DIM*2+5) + '\\n'\n result += ' '\n result += '\\n'\n \n return result","def print_board(self):\n \n # How to show empty/p1/p2\n VALS = \".XO\"\n\n print(\"\\n a b c d e f g\")\n print(\" /--+-+-+-+-+-+--\\\\\")\n for r in range(_HEIGHT - 1, -1, -1):\n s = \"%s |\" % r\n for c in range(_WIDTH):\n # Print mark next to most recent move\n mark = \">\" if self.last_play_rc == (r, c) else \" \"\n s += mark + VALS[self.board[r * 7 + c]]\n print(s + \" |\")\n print(\" \\\\--+-+-+-+-+-+--/\")\n print(\" a b c d e f g\\n\")","def __str__(self):\n if self._active_player:\n def piece_to_index(piece):\n return (piece & 0xF)\n else:\n def piece_to_index(piece):\n return (piece & 0xE) | (0 if piece & 1 else 1)\n\n return '\\n'.join(map(\n lambda posY, row: ''.join(map(\n lambda posX, piece: self.EMOJI[\n piece_to_index(piece)\n if piece else\n 14 + ((posY + posX) % 2)],\n count(), row)),\n count(),\n self.board if self._active_player else reversed(\n [reversed(row) for row in self.board])))","def printboard(state):\n cells = []\n for i in range(3):\n for j in range(3):\n cells.append(NAMES[state[i][j]].center(6))\n print(cells)\n print(*cells)\n print(BOARD_FORMAT.format(*cells))","def __str__(self):\n string = ''\n for row in self.board:\n for item in row:\n if item == None:\n string += \"_ \"\n else:\n string += f\"{item.name:<2}\"\n string += '\\n'\n \n return string","def __str__(self) -> str:\n return self._color + str(self._value) + SudokuTile.C_END","def print(self):\n print(\" a b c d e f g h \")\n print(\" ┼───┼───┼───┼───┼───┼───┼───┼───┼\")\n for row in range(8, 0, -1):\n pieces = \" │ \".join(self.state[row - 1])\n print(f\"{row} │ {pieces} │ {row}\")\n print(\" ┼───┼───┼───┼───┼───┼───┼───┼───┼\")\n print(\" a b c d e f g h \")","def draw_board(self):\n board = \"-------------------\\n\"\n board += \"| %s | %s | %s |\\n\" % (self.board_values[1], self.board_values[2], self.board_values[3])\n board += \"-------------------\\n\"\n board += \"| %s | %s | %s |\\n\" % (self.board_values[4], self.board_values[5], self.board_values[6])\n board += \"-------------------\\n\"\n board += \"| %s | %s | %s |\\n\" % (self.board_values[7], self.board_values[8], self.board_values[9])\n board += \"-------------------\\n\"\n return board","def show(self):\n print('\\n'+'\\n'.join([' '.join([['.', 'O', 'X'][self.board[3*j + i]]\n for i in range(3)]) for j in range(3)]))","def display(self, update_board=1):\n\tM,N = self.X, self.Y \n\tif not update_board:\n\t for i in range(0, M):\n\t\tfor j in range(0, N):\n\t\t\tif self.state.has_key( (i,j) ): \n\t\t\t self.scr.addch(j+1, i+1, self.char)\n\t\t\telse:\n\t\t\t self.scr.addch(j+1, i+1, ' ')\n\t self.scr.refresh()\n\t return\n\n\td={} ; self.boring=1\n\tfor i in range(0, M):\n\t L=range( max(0, i-1), min(M, i+2) )\n\t for j in range(0, N):\n\t\ts=0\n\t\tlive=self.state.has_key( (i,j) )\n\t\tfor k in range( max(0, j-1), min(N, j+2) ):\n\t\t for l in L:\n\t\t\tif self.state.has_key( (l,k) ):\n\t\t\t s=s+1\n\t\ts=s-live\n\t\tif s==3: \n\t\t # Birth\n\t\t d[i,j]=1 \n\t\t self.scr.addch(j+1, i+1, self.char)\n\t\t if not live: self.boring=0 \n\t\telif s==2 and live: d[i,j]=1 # Survival\n\t\telif live: \n\t\t # Death\n\t\t self.scr.addch(j+1, i+1, ' ')\n\t\t self.boring=0\n\tself.state=d\n\tself.scr.refresh()","def print_game_state(board):\r\n print(board)\r\n illegal_moves = [(0, 0), (2, 0), (0, 4), (2, 4)]\r\n for i in range(board.shape[0]):\r\n buffer = ''\r\n for j in range(board.shape[1]):\r\n if board[i][j] == 1:\r\n buffer += 'X\\t'\r\n elif board[i][j] == 2:\r\n buffer += '0\\t'\r\n elif (i, j) in illegal_moves:\r\n buffer += ' \\t'\r\n else:\r\n buffer += '-\\t'\r\n print (buffer)","def display(self):\n lines = []\n lines.append(\" \".join([str(x + 1) for x in range(self.board.columns)]))\n for row in self.board.board:\n new_row = \"\".join([c for c in row])\n new_row = new_row.replace(\"0\", \" \")\n new_row = new_row.replace(\"1\", colored(f\"{PLAYER1_CHR} \", PLAYER1_COLOR))\n new_row = new_row.replace(\"2\", colored(f\"{PLAYER2_CHR} \", PLAYER2_COLOR))\n lines.append(new_row)\n return \"\\n\".join(lines)","def __repr__(self):\n s = ''\n nums = (self.mines + 1) * self.clicked\n for row in range(0, self.rows):\n for col in range(0, self.cols):\n num = nums[row, col]\n if self.isFlagged(row, col):\n s += str(Board.FLAGGED) + ' '\n elif num == 0:\n s += str(Board.HIDDEN) + ' '\n else:\n if self.isMine(row, col):\n s += str(Board.MINE) + ' '\n else:\n s += str(int(num - 1)) + ' '\n s += '\\n'\n return s","def board(self) -> str:\n divider = \"+\" + \"-\" * 23 + \"+\"\n b = [divider]\n for i in range(9):\n r = []\n for j in range(3):\n s = tool.index_counter(i, j * 3)\n r.append(' '.join(str(i) if i > 0 else ' '\n for i in self.grid[s:s+3]))\n b.append(f\"| {r[0]} | {r[1]} | {r[2]} |\")\n if (i + 1) % 3 == 0:\n b.append(divider)\n return \"\\n\".join(b)","def __str__(self):\r\n return str(self.board)","def __str__(self):\n return '\\n'.join(str(self._board[j]) for j in range(self._n))","def display_state_cmd(self):\n self.__display(self.state)","def print(self) -> str:\n if self.is_unoccupied():\n return \"\"\n return str(\"%s-%s\" % (self.piece.color.name, self.piece.name.name))","def __repr__(self):\r\n # Initialize the return string\r\n s = ''\r\n\r\n for row in range(self.height):\r\n # Print the index of the row\r\n s = s + str(row % 10) + ' |'\r\n\r\n for col in range(self.width):\r\n s += self.grid[row][col]\r\n s += '|'\r\n\r\n s += '\\n'\r\n s += '--' * (self.width + 1)\r\n s += '-'\r\n s += '\\n'\r\n \r\n s += ' '\r\n for i in range(self.width):\r\n s += ' ' + str(i % 10) \r\n \r\n return s","def display_board(board_state):\n\n if type(board_state) != str:\n raise TypeError('Given board input must be String')\n\n if len(board_state) != 9:\n raise Exception(\"Input board string length is not 9\")\n\n counter = 0\n # print()\n for position in board_state:\n counter += 1\n if counter % 3 == 0:\n \n if counter != 9:\n paddingString = \"\\n---------\\n\"\n else:\n paddingString = ''\n else:\n paddingString = \" | \"\n\n if position.isnumeric():\n print(\" \", end=paddingString)\n\n else:\n print(position, end=paddingString)\n\n print(\"\\n\\n\")","def __str__(self):\n\n # Create grid headers for the table\n headers = [letter for letter in string.ascii_uppercase[:self.width]]\n\n board_state = []\n board_state.extend([[value for value in row] for i, row in enumerate(self.board_state)])\n\n for idx, row in enumerate(board_state):\n row.insert(0, idx + 1)\n\n return tabulate(board_state, headers, tablefmt=\"grid\")","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self) -> str:\n not_actual = self.current_board\n representation = self.current_board\n\n for index in range(len(not_actual)):\n if not_actual[index: index + 2] in ['31', '32', '33', '34', '36',\n '37', '38']:\n representation = representation.replace(\n not_actual[index: index + 2], '@')\n if not_actual[index: index + 2] in ['41', '42', '43', '44', '45',\n '46', '47', '48']:\n representation = representation.replace(\n not_actual[index: index + 2], '@')\n if not_actual[index: index + 2] in ['51', '52', '53', '54', '55',\n '56', '57', '58']:\n representation = representation.replace(\n not_actual[index: index + 2], '@')\n return representation","def display_board(self):\n print(f\"{BREAK_STRING}\\n\")\n print(f\"STOCK \\t WASTE \\t\\t\\t\\tFOUNDATION\\n\")\n print(f\"{self.stock}\\t{self.waste}\\t\\t\\t\\t{self.foundations['clubs']}\\t{self.foundations['diamonds']}\"\n f\"\\t{self.foundations['hearts']}\\t{self.foundations['spades']}\\n\")\n print(f\"\\nTABLEAU\\n\")\n for num in range(1, 8):\n print(f\"{num} {self.tableaus[num-1]}\")\n print(f\"{BREAK_STRING}\\n\")","def __str__(self):\n return \"Board: \" + str(self._board) + \" Workers: \" + str(self._workers.items())","def get_state_display(self, obj):\n return obj.get_state_display()","def __str__(self):\r\n ans = \"\"\r\n for row in range(self._height):\r\n ans += str(self._grid[row])\r\n ans += \"\\n\"\r\n return ans","def __str__(self):\r\n ans = \"\"\r\n for row in range(self._height):\r\n ans += str(self._grid[row])\r\n ans += \"\\n\"\r\n return ans","def __str__(self):\r\n ans = \"\"\r\n for row in range(self._height):\r\n ans += str(self._grid[row])\r\n ans += \"\\n\"\r\n return ans","def show_board(self):\n for i in range(self.num_rows):\n print(' ----'*8)\n s = \"\"\n for j in range(self.num_cols):\n s += '| {} '.format(self._show_piece(i, j))\n print(\"{}|\".format(s))\n print(' ----'*8)","def display(self) -> str:\n lines, _, _, _ = self._display_aux()\n return '\\n'.join(lines)","def __repr__(self):\n return f'Board({ self.board !r})'","def _repr_(self):\n if len(self._states_)==0:\n return \"Empty finite state machine\"\n if len(self._states_)==1:\n return \"Finite state machine with 1 state\"\n else:\n return \"Finite state machine with %s states\" % len(self._states_)","def print_board(self):\n board = \"\"\n for i in range(3):#need to change this in the future\n for j in range(3):#need to change this in the future\n board += self.board[i][j]\n if j != 2:#need to change this in the future\n board += \" | \"\n board += \"\\n\"\n return board","def __str__(self):\n out = \"{}.\".format(self.move_number)\n if self.white.san != \"\":\n out += \" \" + str(self.white)\n else:\n out += \"..\"\n if self.black.san != \"\":\n out += \" \" + str(self.black)\n if self.comment:\n out += \" {\" + self.comment + \"}\"\n return out","def print(self):\n board_string = ''\n for y in range(self.size):\n if y == 0:\n board_string += '+ '\n for x in range(self.size):\n board_string += str(x+1) + ' '\n board_string += '\\n'\n board_string += (1+3*self.size)*'-'\n board_string += '\\n'\n board_string += str(y+1)+'|'+y*' '\n \n for x in range(self.size):\n board_string += ' '\n if self.board[y,x] == HexBoard.BLUE:\n board_string += self.char_player1\n elif self.board[y,x] == HexBoard.RED:\n board_string += self.char_player2\n else: \n board_string += self.char_empty\n board_string += '\\n'\n board_string = board_string.strip()\n\n print(board_string)","def draw(self):\n res = ''\n # ANSI code to clear the screen\n #res += chr(27) + \"[2J\"\n for position, value in enumerate(self.board.tttboard):\n if value is None:\n res += str(position)\n #sys.stdout.write(str(position))\n else:\n res += str(value)\n #sys.stdout.write(str(value))\n\n if (position + 1) % 3 != 0:\n res += str('|')\n #sys.stdout.write('|')\n else:\n #print ''\n\n res += str('\\n')\n if position == 2 or position == 5:\n #print '-' * 5\n\n res += '-' * 5\n res += str('\\n')\n return res","def state(self) -> str:","def display(self): \n print ' ' \n print 'Connect ', NWIN, ' Board '\n print ' ' \n for r in reversed(range(self.getHeight())):\n for c in range(self.getWidth()):\n if self.cell[c][r] == BLACK:\n print '+',\n elif self.cell[c][r] == WHITE:\n print '-',\n else:\n print '.',\n print ' '\n for c in range(self.getWidth()):\n print c,\n print ' '\n print ' '","def __repr__(self):\n\n # Creates a deep copy of _board and changes all None values to underscores.\n temp_board = copy.deepcopy(self.get_board())\n\n for row_index, row in enumerate(temp_board):\n\n for column_index, column in enumerate(row):\n\n if column is None:\n\n row[column_index] = '____' + self.reverse_position(column_index, row_index) + '_____'\n\n # str() with position is mapped onto each list within _board list,\n # converted to string, and then joined by a nextline character.\n return '\\n\\n'.join(map(str, temp_board))","def __str__(self) -> str:\n string = ''\n for row in self._grid_display:\n string += row.__str__() + '\\n'\n return string","def stateString(self):\n return self._mdp.stateString(self._cur_state);","def __str__(self):\r\n out = \"##\"*(self.width+1)+\"\\n\"\r\n for i in range(self.height):\r\n out += \"#\"\r\n for j in range(self.width):\r\n if self.grid[i][j] == 0:\r\n out += \"##\"\r\n else:\r\n if not self.showSolution:\r\n out += \" \"\r\n elif (i,j) in self.solution:\r\n out += \"**\"\r\n else:\r\n out += \" \"\r\n out += \"#\\n\"\r\n return out + \"##\"*(self.width+1)","def pretty_state(state, was_changed):\n pstate = state + \" \"\n if was_changed:\n pstate = state + \"*\"\n if not uses_rich:\n return pstate\n\n scols = {\n # forks\n 'wg' : 'gray',\n 'wu' : 'yellow',\n 'wd' : 'blue',\n\n # philosophers\n 'sl' : 'green',\n 'wt' : 'yellow',\n 'gl' : 'cyan',\n 'gr' : 'bright_cyan',\n 'es' : 'purple',\n 'dl' : 'yellow',\n 'dr' : 'bright_yellow',\n 'xx' : 'white', # done\n }\n if was_changed:\n return f\"[red][bold]{pstate}[/bold][/red]\"\n col = scols.get(state, 'white')\n return f\"[{col}]{pstate}[/{col}]\"","def display_board(self):\n print(self.game_board)"],"string":"[\n \"def __str__(self):\\n board = ''\\n board_2 = ''\\n\\n for row in self.from_grid:\\n for space in row:\\n board += ' ' + space\\n board += '\\\\n'\\n\\n for row in self.to_grid:\\n for space in row:\\n board_2 += ' ' + space\\n board_2 += '\\\\n'\\n\\n return 'Current State:\\\\n' + board + 'Target State:\\\\n' + board_2\",\n \"def macro_str(self):\\n str = '-' * (2 * self.SIZE + 1) + '\\\\n'\\n for row in self.boards:\\n str += ' '\\n for board in row:\\n str += board.state.value + ' '\\n str += '\\\\n' + '-' * (2 * self.SIZE + 1) + '\\\\n'\\n return str\",\n \"def __str__(self):\\n s = \\\" 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\\\\n\\\"\\n board = initial_board()\\n count = 1\\n for i in self.occupied:\\n board[i[0]][i[1]] = self.occupied[i]\\n space = ''\\n for i in range(0, 16):\\n space += ' '\\n start = '---'.join(space)\\n s += start+'\\\\n|'\\n for row in range(1,16):\\n for col in range(1,16):\\n if use_color and (row, col) == self.action:\\n s += '\\\\033[91m'\\n if board[row][col] == 0:\\n s += ' |'\\n elif board[row][col] == 1:\\n s += ' O |'\\n else:\\n s += ' X |'\\n if use_color and (row, col) == self.action:\\n s += '\\\\033[0m'\\n s += '\\\\033[0m'\\n s+=str(count)+'\\\\n'+start+'\\\\n|'\\n count += 1\\n\\n s = s[:len(s)-1]\\n s += \\\"\\\\n*****************************************************************************\\\"\\n return s[:len(s)-1]\",\n \"def show_board(self):\\n board_vis = f\\\"\\\\n{'*' * 22}Board state{'*' * 23}\\\\n\\\"\\n str_p2_store=\\\" \\\"+str(self.p2_store()) if self.p2_store()<10 else str(self.p2_store())\\n board_vis += (f\\\" {str_p2_store} - | \\\" +\\n \\\" || \\\".join(\\n [i if len(i) == 2 else ' ' + i for i in list(map(str, self.p2_pits()[::-1]))]) + \\\" | \\\\n\\\")\\n board_vis += f\\\"{'-------' * (self.M + 2)}\\\\n\\\"\\n board_vis += (\\\" | \\\" + \\\" || \\\".join(\\n [i if len(i) == 2 else ' ' + i for i in list(map(str, self.p1_pits()))]) +\\n f\\\" | - {self.p1_store()}\\\\n\\\")\\n board_vis += f\\\"{'*' * 56}\\\\n\\\"\\n print(board_vis)\",\n \"def __str__(self):\\n def align_column(grid):\\n board = \\\"\\\"\\n for i in range(self.n):\\n board += str(grid[i]) + \\\"\\\\n\\\"\\n return board.strip()\\n return (\\\"===Current Stage===\\\\n\\\"\\n \\\"{}\\\\n\\\"\\n \\\"====Goal Board=====\\\\n\\\"\\n \\\"{}\\\".format(align_column(self.from_grid),\\n align_column(self.to_grid)))\",\n \"def get_board_state_pretty(self):\\n\\n board_state = ''\\n for i in range(0, 3):\\n board_state += ' | '.join([self.board['{}{}'.format(i, j)] for j in range(0, 3)])\\n board_state += '\\\\n'\\n return board_state\",\n \"def __str__(self):\\n s=\\\"\\\"\\n for y in range(0,HEIGHT):\\n for x in range(0,WIDTH):\\n s+=str(self.gameState[x,y])\\n return s\",\n \"def __str__(self):\\n\\t\\tboardString = \\\"\\\\n{0}|{1}|{2}\\\\n-----\\\\n{3}|{4}|{5}\\\\n-----\\\\n{6}|{7}|{8}\\\\n\\\"\\n\\t\\treturn boardString.format(self.board[0], self.board[1], self.board[2], self.board[3],\\n\\t\\t\\tself.board[4], self.board[5], self.board[6], self.board[7], self.board[8])\",\n \"def __repr__(self):\\n string = \\\"Current state: \\\\n\\\"\\n if self.state[0] == 0: # We're on the left side\\n string += \\\"M: \\\"\\n string += str(self.state[1]).ljust(10)\\n string += \\\"M: \\\"\\n string += str(TOTAL_NO_MISSIONARIES - self.state[1]).ljust(10)\\n string += \\\"\\\\n\\\"\\n\\n string += \\\"C: \\\"\\n string += str(self.state[2]).ljust(10)\\n string += \\\"C: \\\"\\n string += str(TOTAL_NO_CANNIBALS - self.state[2]).ljust(10)\\n string += \\\"\\\\n\\\"\\n\\n string += \\\"Boat position: left\\\\n\\\"\\n else: # We're on the right side\\n string += \\\"M: \\\"\\n string += str(TOTAL_NO_MISSIONARIES - self.state[1]).ljust(10)\\n string += \\\"M: \\\"\\n string += str(self.state[1])\\n string += \\\"\\\\n\\\"\\n\\n string += \\\"C: \\\"\\n string += str(TOTAL_NO_CANNIBALS - self.state[2]).ljust(10)\\n string += \\\"C: \\\"\\n string += str(self.state[2]).ljust(10)\\n string += \\\"\\\\n\\\"\\n\\n string += \\\"Boat position: right\\\\n\\\"\\n string += \\\"\\\\n\\\"\\n return string\",\n \"def display_state(self):\\r\\n\\r\\n print('\\\\n')\\r\\n print('>>CURRENT STATE')\\r\\n ct = 0\\r\\n for i in self.state:\\r\\n for j in i:\\r\\n if j == -1:\\r\\n val = 'X'\\r\\n else:\\r\\n val = str(ct)\\r\\n if len(val) == 1:\\r\\n print(' ' + val + ' ', end='')\\r\\n else:\\r\\n print(val + ' ', end='')\\r\\n ct += 1\\r\\n print('\\\\n')\",\n \"def _display_board(state: game.GameState) -> None:\\n for row in range(state.get_rows()):\\n rowString = \\\"|\\\"\\n for col in range(state.get_columns()):\\n cellValue = state.get_cell_contents(row, col)\\n cellState = state.get_cell_state(row, col)\\n if cellState == game.EMPTY_CELL:\\n rowString += ' '\\n elif cellState == game.OCCUPIED_CELL:\\n rowString += (' ' + cellValue + ' ')\\n elif cellState == game.FALLER_MOVING_CELL:\\n rowString += ('[' + cellValue + ']')\\n elif cellState == game.FALLER_STOPPED_CELL:\\n rowString += ('|' + cellValue + '|')\\n elif cellState == game.MATCHED_CELL:\\n rowString += ('*' + cellValue + '*')\\n rowString += '|'\\n print(rowString)\\n finalLine = ' '\\n for col in range(state.get_columns()):\\n finalLine += '---'\\n finalLine += ' '\\n print(finalLine)\",\n \"def __repr__(self):\\n s = \\\"\\\"\\n for y in range(0,HEIGHT):\\n temp=\\\"\\\"\\n for x in range(0,WIDTH):\\n temp = temp+ str(self.gameState[x,y])\\n s += temp+\\\"\\\\n\\\"\\n return s\",\n \"def __str__(self):\\n s = \\\"\\\"\\n for r in range(1,self.size+1):\\n for c in range(1,self.size+1):\\n s += str(self.gameState[r,c])\\n return s\",\n \"def __str__(self):\\n return ''.join(str(e) + ' ' for e in self.state)\",\n \"def __str__(self):\\n state = ''\\n state += ' '.join([str(x) for x in self.pos]) + ' '\\n state += ''.join([str(x) + ' ' + str(y) + ' ' for x,\\n y in zip(self.BU, self.BD)])\\n for e in self.BF:\\n state += ' '.join([str(x) for x in e])\\n state += ' '\\n state += ' '.join([str(x) for x in self.LU]) + ' '\\n state += ' '.join([str(x) for x in self.LD]) + ' '\\n\\n return state\",\n \"def __repr__(self):\\n representantion = ''\\n\\n for i in range(3):\\n for j in range(3):\\n representantion += str(self.state[3 * i + j])\\n\\n if j == 2 and i != 2:\\n representantion += '\\\\n'\\n else:\\n representantion += ' '\\n\\n return representantion\",\n \"def board_string(self):\\n s = \\\"\\\"\\n for i, v in enumerate(self.board):\\n # if i % 81 == 0:\\n # s += \\\"\\\\n\\\"\\n if v is None:\\n s += \\\"0\\\"\\n else:\\n if v.color == StoneColor.black:\\n s += \\\"1\\\"\\n else:\\n s += \\\"2\\\"\\n return s\",\n \"def __str__(self):\\n # Build the string line by line. Join at the end.\\n lines = []\\n lines.append(\\\"Initial State: {{{}}}\\\".format(self.initial_state))\\n lines.append(\\n \\\"Final States: {{{}}}\\\".format(\\n \\\",\\\".join(map(str, self.final_states))))\\n\\n # column headers\\n lines.append(\\n \\\"State\\\\t{}\\\".format(\\\"\\\\t\\\".join(self.alphabet)))\\n\\n # For each state, print transitions\\n for state_name in range(1, len(self.transitions) + 1):\\n line = \\\"{}\\\".format(state_name)\\n for symbol in self.alphabet:\\n line += \\\"\\\\t{{{}}}\\\".format(\\n \\\",\\\".join(map(str, self.transitions.get(\\n state_name, dict()).get(symbol, []))))\\n lines.append(line)\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"def __str__(self):\\n line = ''\\n line += self.board_state.__str__()\\n line += self.move.__str__()\\n line += '\\\\n'\\n return line\",\n \"def __str__(self):\\n #formatting board correctly\\n formatted_board = \\\"\\\"\\n for i in range(self.size):\\n formatted_board += str(self.board[i]) + \\\"\\\\n\\\"\\n return \\\"Board size: \\\" + str(self.size) + \\\"\\\\n\\\" + \\\"Number of Queens placed: \\\" + str(self.num_queens_placed) + \\\"\\\\n\\\" + str(formatted_board)\",\n \"def __str__(self):\\n board = ''\\n for row in range(self.height):\\n if row > 0:\\n board += '\\\\n'\\n for col in range(self.width):\\n if self.board[row][col] == '':\\n board += '| '\\n else:\\n board += ('|' + self.board[row][col])\\n board += '|'\\n board += ('\\\\n' + '-' * 2 * self.width + '\\\\n')\\n for i in range(self.width):\\n board += (' ' + str(i))\\n return board\",\n \"def __repr__(self):\\n W = self.width\\n H = self.height\\n\\n s = '' # the string to return\\n for row in range(0, H):\\n s += '|'\\n for col in range(0, W):\\n s += self.data[row][col] + '|'\\n s += '\\\\n'\\n\\n s += (2 * W + 1) * '-' # bottom of the board\\n s += '\\\\n'\\n\\n x = -1\\n for i in range(W):\\n if x == 9:\\n x = 0\\n s += \\\" \\\" + str(x)\\n else:\\n x += 1\\n s += \\\" \\\" + str(x)\\n\\n return s # the board is complete, return it\",\n \"def displayBoard(self):\\n res = ''\\n for i in range(0, self.size):\\n res += '|'\\n for j in range(0, self.size):\\n res += ' ' + str(self.board[i][j])\\n res += '\\\\n'\\n res += '+'\\n for i in range(0, self.size * 2):\\n res += '-'\\n res += '\\\\n '\\n for i in range(1, (self.size + 1)):\\n res += (' ' + str(i))\\n return res\",\n \"def __str__(self):\\n b = ''\\n for i in range(7): # 7 steps in the board\\n if i == self.chaser_i: # chaser position\\n b += '|' + str(i) + '| chaser |\\\\n'\\n elif i == self.player_i: # player position\\n b += '|' + str(i) + '| player |\\\\n'\\n else:\\n b += '|' + str(i) + '| |\\\\n'\\n b += '|7| bank |\\\\n' # bank position\\n return b\",\n \"def __draw_board(self, state=None):\\n if not state:\\n state = self.state\\n if self.game_started:\\n print('\\\\r\\\\033[8A')\\n print(emoji.emojize(' {state[0]} | {state[1]} | {state[2]} \\\\n___|___|___\\\\n'\\n ' {state[3]} | {state[4]} | {state[5]} \\\\n___|___|___\\\\n'\\n ' {state[6]} | {state[7]} | {state[8]} \\\\n | | \\\\n'.format(state=state)))\",\n \"def get_board_state(self):\\n\\n board_state = ''\\n for i in range(0, 3):\\n board_state += ''.join([self.board['{}{}'.format(i, j)] for j in range(0, 3)])\\n return board_state\",\n \"def show( self):\\n def symbol( i):\\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\\n \\n X, Y = np.max( self.board.positions, 0)\\n # -2 to indicate outside board.\\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \\n for x, y in self.board.positions:\\n display[x, y] = -1 # -1 to indicate unoccupied\\n for p, i in self.occupation.items():\\n x, y = self.board.positions[p]\\n display[x, y] = i\\n for x in xrange(X+1):\\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\\n print s\",\n \"def __str__(self):\\n s=''\\n for r in range(self.n):\\n for c in range(self.n):\\n s += str(self.state[r][c]) + ' '\\n s += '\\\\n'\\n s += str('hcost') + ' : ' + str(self.hcost)\\n s += '\\\\n'\\n return s\",\n \"def the_display(self):\\r\\n return f\\\"\\\"\\\"\\r\\n {self.display[0]}\\\\n\\r\\n {self.display[1]}\\\\n\\r\\n {self.display[2]}\\\\n\\r\\n {self.display[3]}\\\\n\\r\\n {self.display[4]}\\\\n\\r\\n \\\"\\\"\\\"\",\n \"def __str__(self):\\n width = int(np.prod(self.no_parent_states)) #multiplies the elements in list, here tot num of states\\n grid = np.meshgrid(*[range(i) for i in self.no_parent_states]) \\n s = \\\"\\\"\\n for (i, e) in enumerate(self.parents):\\n s += '+----------+' + '----------+' * width + '\\\\n'\\n gi = grid[i].reshape(-1)\\n s += f'|{e:^10}|' + '|'.join([f'{e + \\\"(\\\"+str(j)+\\\")\\\":^10}' for j in gi])\\n s += '|\\\\n'\\n\\n for i in range(self.no_states):\\n s += '+----------+' + '----------+' * width + '\\\\n'\\n state_name = self.name + f'({i})'\\n s += f'|{state_name:^10}|' + '|'.join([f'{p:^10.4f}' for p in self.table[i]])\\n s += '|\\\\n'\\n\\n s += '+----------+' + '----------+' * width + '\\\\n'\\n\\n return s\",\n \"def __str__(self):\\n rep = \\\"\\\"\\n for row in range(self._dim):\\n for col in range(self._dim):\\n rep += STRMAP[self._board[row][col]]\\n if col == self._dim - 1:\\n rep += \\\"\\\\n\\\"\\n else:\\n rep += \\\" | \\\"\\n if row != self._dim - 1:\\n rep += \\\"-\\\" * (4 * self._dim - 3)\\n rep += \\\"\\\\n\\\"\\n return rep\",\n \"def render_state(self, state, is_first_player_black):\\n MOVES_STR = {\\n -1 if is_first_player_black else 1: '▒▒',\\n 0: ' ',\\n 1 if is_first_player_black else -1: '██',\\n }\\n\\n numbers = ' ' + ''.join(\\n str(i) + (' ' if i < 10 else '')\\n for i in range(self.SIZE)\\n )\\n top = ' ╔' + ('══' * self.SIZE) + '╗'\\n bottom = ' ╚' + ('══' * self.SIZE) + '╝'\\n\\n collated_string = '{}\\\\n{}\\\\n{}\\\\n{}'.format(\\n numbers,\\n top,\\n '\\\\n'.join([\\n '{}║{}║'.format(\\n (' ' if i < 10 else '') + str(i),\\n ''.join([\\n MOVES_STR[state[self.SIZE * i + j]]\\n for j in range(self.SIZE)\\n ]),\\n )\\n for i in range(self.SIZE)\\n ]),\\n bottom,\\n )\\n return collated_string\",\n \"def __str__(self):\\n board = \\\"\\\"\\\" 0 1 2 3 4 5\\\\n\\\"\\\"\\\"\\n\\n for y in range(Board.board_size):\\n board += str(y) + \\\" \\\"\\n for x in range(Board.board_size):\\n piece = self.board[x][y] if self.board[x][y] is not None else \\\".\\\"\\n\\n piece = str(piece).lower() if piece in self.player_1_pieces else str(piece)\\n\\n board += piece\\n board += \\\" \\\"\\n board += \\\"\\\\n\\\"\\n return board\",\n \"def display(self):\\n for r in range(1, self.size+1):\\n print(\\\"+\\\" + (\\\"-+\\\"*self.size))\\n print(\\\"|\\\", end=\\\"\\\")\\n for c in range(1, self.size+1):\\n print(self.gameState[r,c], end=\\\"\\\")\\n print(\\\"|\\\",end=\\\"\\\")\\n print()\\n print(\\\"+\\\" + (\\\"-+\\\"*self.size))\",\n \"def __str__(self):\\n # replace with your code\\n board = \\\"\\\"\\n for index in range(self.grid_height):\\n board += \\\"[\\\"\\n for inner_index in range(self.grid_width):\\n board += str(self.board[index][inner_index]) + \\\" \\\"\\n else:\\n board += \\\"]\\\\n\\\"\\n return board\",\n \"def __repr__(self):\\n t = ''\\n for x in range(len(self.board)):\\n for y in range(len(self.board[0])):\\n t += str(self.board[x][y]) + ' '\\n t += '\\\\n'\\n return t\",\n \"def printBoard(self):\\n\\t\\tkey = [' ', 'X', 'O']\\n\\t\\tprint(' | |')\\n\\t\\tprint(' ' + key[self.state[0][0]] + ' | ' + key[self.state[0][1]] + ' | ' + key[self.state[0][2]])\\n\\t\\tprint(' | |')\\n\\t\\tprint('-----------')\\n\\t\\tprint(' | |')\\n\\t\\tprint(' ' + key[self.state[1][0]] + ' | ' + key[self.state[1][1]] + ' | ' + key[self.state[1][2]])\\n\\t\\tprint(' | |')\\n\\t\\tprint('-----------')\\n\\t\\tprint(' | |')\\n\\t\\tprint(' ' + key[self.state[2][0]] + ' | ' + key[self.state[2][1]] + ' | ' + key[self.state[2][2]])\\n\\t\\tprint(' | |')\",\n \"def __str__(self):\\n return \\\"\\\".join(list(map(lambda row: ''.join(row), self.state)))\",\n \"def __repr__(self):\\r\\n numLetters = self.numLetters\\r\\n S = ''\\r\\n S += 3*'\\\\n'\\r\\n S += ' '\\r\\n for i in range(numLetters):\\r\\n S += self.currentBoard[i] + ' '\\r\\n\\r\\n return S\",\n \"def __repr__(self):\\n s = dashes = \\\"\\\".join([ ' -' for i in range(self.BoardSize) ])\\n for row in range( self.BoardSize ):\\n sRow = '|'\\n for col in range( self.BoardSize ):\\n sRow += str(self.CurrentGameboard[row][col]) + '|'\\n s += '\\\\n' + sRow + '\\\\n' + dashes\\n return s\",\n \"def __str__(self) -> str:\\n return self.board\",\n \"def __str__(self):\\r\\n \\r\\n #return \\\"The 2048 board is \\\" + str(self._cells)\\r\\n string = \\\"\\\"\\r\\n for row in range(self._grid_height):\\r\\n for column in range(self._grid_width):\\r\\n if column == self._grid_width -1:\\r\\n string += str(self._cells[row][column]) + \\\"\\\\n\\\"\\r\\n else:\\r\\n string += str(self._cells[row][column]) +\\\", \\\"\\r\\n return \\\"The 2048 board is \\\"+ str(self._grid_height) + \\\"x\\\" + str(self._grid_width) + \\\" and contains: \\\" + \\\"\\\\n\\\" + string\",\n \"def showBoard(self):\\n \\n brd = \\\"\\\\n | | \\\\n\\\" + \\\\\\n \\\" \\\" + self.squares[0] + \\\" | \\\" + self.squares[1] + \\\" | \\\" + self.squares[2] + \\\" \\\\n\\\" + \\\\\\n \\\"___|___|___\\\\n\\\" + \\\\\\n \\\" | | \\\\n\\\" + \\\\\\n \\\" \\\" + self.squares[3] + \\\" | \\\" + self.squares[4] + \\\" | \\\" + self.squares[5] + \\\" \\\\n\\\" + \\\\\\n \\\"___|___|___\\\\n\\\" + \\\\\\n \\\" | | \\\\n\\\" + \\\\\\n \\\" \\\" + self.squares[6] + \\\" | \\\" + self.squares[7] + \\\" | \\\" + self.squares[8] + \\\" \\\\n\\\" + \\\\\\n \\\" | | \\\\n\\\"\\n\\n return brd\",\n \"def __repr__(self):\\n r = \\\"\\\"\\n for row in self.board:\\n for cell in row:\\n if cell == \\\"\\\":\\n cell = color_magenta(\\\"_\\\")\\n r += cell + \\\" \\\" # for all the empty strings, we will replace it with an '_'.\\n r += \\\"\\\\n\\\"\\n return r\",\n \"def __display(self,state: dict):\\n width = 1+max(len(state[s]) for s in self.__boxes)\\n line = '+'.join(['-'*(width*3)]*3)\\n for r in self.__rows:\\n print(''.join( state[r+c].center(width)+ ('|' if c in '36' else '')\\n for c in self.__cols))\\n if r in 'CF': print(line)\",\n \"def print_state(self):\\n grid = [[\\\".\\\" for _ in range(self.width)] for _ in range(self.height)]\\n #icons = [\\\"^\\\", \\\"/\\\", \\\">\\\", \\\"\\\\\\\\\\\", \\\"|\\\", \\\"/\\\", \\\"<\\\", \\\"\\\\\\\\\\\"] # NON-UNICODE, uncomment if problems\\n icons = [chr(0x2191), chr(0x2197), chr(0x2192), chr(0x2198), \\\\\\n chr(0x2193), chr(0x2199), chr(0x2190), chr(0x2196)]\\n for robot in self.robots:\\n grid[robot[1]][robot[0]] = icons[(robot[2]+robot[3]) % 8]\\n for item in self.items:\\n if item[2] == 1:\\n grid[item[1]][item[0]] = \\\"O\\\"\\n elif item[2] == 2:\\n grid[item[1]][item[0]] = \\\"*\\\"\\n print(\\\"-\\\"*(self.width+2))\\n for i in range(self.height):\\n print(\\\"|\\\", end=\\\"\\\")\\n for j in range(self.width):\\n print(grid[i][j], end=\\\"\\\")\\n print(\\\"|\\\")\\n print(\\\"-\\\"*(self.width+2))\",\n \"def __str__(self):\\n board_lists = [['_']*self.__width for rows in range(self.__height)]\\n for car in self.__cars:\\n car_coords = car.car_coordinates()\\n for item in car_coords:\\n if item == (3,7):\\n pass\\n else:\\n board_lists[item[0]][item[1]] = car.get_name()\\n board_str = '\\\\n'.join(' '.join(sub) for sub in board_lists)\\n return board_str\",\n \"def __str__(self):\\n str = '-' * (self.SIZE ** 2 + self.SIZE + 1) + '\\\\n'\\n for row in self.boards:\\n for i in range(self.SIZE):\\n str += '|'\\n for board in row:\\n for square in board.export_grid()[i]:\\n str += square.value\\n str += '|'\\n str += '\\\\n'\\n str += '-' * (self.SIZE ** 2 + self.SIZE + 1) + '\\\\n'\\n return str\",\n \"def __str__(self):\\n result = \\\"\\\"\\n for i in range(3):\\n for j in range(3):\\n if self.board[i][j] == 5:\\n result += \\\" x\\\"\\n elif self.board[i][j] == 7:\\n result += \\\" о\\\"\\n else:\\n result += \\\" #\\\"\\n result += \\\"\\\\n\\\"\\n return result\",\n \"def __str__(self):\\n card_str = ['-' * CARD_WIDTH]\\n card_str.append('{} (Game {})'.format(self.name, self.game.number).center(CARD_WIDTH))\\n card_str.append('-' * CARD_WIDTH)\\n card_str.append(''.center(CARD_WIDTH))\\n card_str.append('Money: {}'.format(self.money).center(CARD_WIDTH))\\n card_str.append('Turnover: {}'.format(self.turnover).center(CARD_WIDTH))\\n card_str.append(''.center(CARD_WIDTH))\\n card_str.append('-' * CARD_WIDTH)\\n return '\\\\n'.join('|{}|'.format(l) for l in card_str)\",\n \"def __str__(self):\\r\\n grid_text = \\\"\\\\n-------------------\\\\n|\\\"\\r\\n for i in range(len(self.grid)):\\r\\n grid_text = grid_text + ' %s '%(self.grid[i][-1])\\r\\n\\r\\n if i%3 == 2:\\r\\n grid_text = grid_text + '|\\\\n-------------------\\\\n|'\\r\\n else:\\r\\n grid_text = grid_text + '|'\\r\\n return grid_text[0:len(grid_text)-1]\",\n \"def __str__(self) -> str:\\n if self.write_back is black.WriteBack.CHECK:\\n reformatted = \\\"would be reformatted\\\"\\n unchanged = \\\"would be left unchanged\\\"\\n failed = \\\"would fail to reformat\\\"\\n cleared = \\\"would be cleared\\\"\\n else:\\n reformatted = \\\"reformatted\\\"\\n unchanged = \\\"left unchanged\\\"\\n failed = \\\"failed to reformat\\\"\\n cleared = \\\"cleared\\\"\\n report = []\\n if self.change_count:\\n s = \\\"s\\\" if self.change_count > 1 else \\\"\\\"\\n report.append(\\n click.style(\\n f\\\"{self.change_count} cell{s} {reformatted}\\\", bold=True\\n )\\n )\\n if self.same_count:\\n s = \\\"s\\\" if self.same_count > 1 else \\\"\\\"\\n report.append(f\\\"{self.same_count} cell{s} {unchanged}\\\")\\n if self.failure_count:\\n s = \\\"s\\\" if self.failure_count > 1 else \\\"\\\"\\n report.append(\\n click.style(f\\\"{self.failure_count} cell{s} {failed}\\\", fg=\\\"red\\\")\\n )\\n if self.output_change_count:\\n s = \\\"s\\\" if self.change_count > 1 else \\\"\\\"\\n report.append(\\n click.style(\\n f\\\"{self.output_change_count} output{s} {cleared}\\\",\\n bold=True,\\n )\\n )\\n if self.output_same_count:\\n s = \\\"s\\\" if self.same_count > 1 else \\\"\\\"\\n report.append(f\\\"{self.output_same_count} output{s} {unchanged}\\\")\\n return \\\", \\\".join(report) + \\\".\\\"\",\n \"def __str__(self):\\r\\n # replace with your code\\r\\n return str(self._board[0]) + \\\"\\\\n\\\" + str(self._board[1]) + \\\"\\\\n\\\" + str(self._board[2]) + \\\"\\\\n\\\" + str(self._board[3]) + \\\"\\\\n\\\\n\\\"\",\n \"def __str__(self):\\n \\n # top row\\n result = ' '\\n result = '\\\\n ' + '-' * (self.DIM*2+5) + '\\\\n'\\n \\n # board rows\\n for row in range(self.DIM):\\n if row is 3 or row is 6:\\n result += '|' + '-' * (self.DIM*2+5) + '|' + '\\\\n'\\n # result += '|-------+-------+-------|\\\\n'\\n result += '| '\\n for col in range(self.DIM):\\n if col is 3 or col is 6:\\n result += '| '\\n if self.board[row][col] == SudokuConfig.EMPTY:\\n result += '.'\\n else:\\n result += str(str(self.board[row][col]))\\n if col != self.DIM-1:\\n result += ' '\\n result += ' |' + '\\\\n'\\n \\n # bottom row\\n result += ' ' + '-' * (self.DIM*2+5) + '\\\\n'\\n result += ' '\\n result += '\\\\n'\\n \\n return result\",\n \"def print_board(self):\\n \\n # How to show empty/p1/p2\\n VALS = \\\".XO\\\"\\n\\n print(\\\"\\\\n a b c d e f g\\\")\\n print(\\\" /--+-+-+-+-+-+--\\\\\\\\\\\")\\n for r in range(_HEIGHT - 1, -1, -1):\\n s = \\\"%s |\\\" % r\\n for c in range(_WIDTH):\\n # Print mark next to most recent move\\n mark = \\\">\\\" if self.last_play_rc == (r, c) else \\\" \\\"\\n s += mark + VALS[self.board[r * 7 + c]]\\n print(s + \\\" |\\\")\\n print(\\\" \\\\\\\\--+-+-+-+-+-+--/\\\")\\n print(\\\" a b c d e f g\\\\n\\\")\",\n \"def __str__(self):\\n if self._active_player:\\n def piece_to_index(piece):\\n return (piece & 0xF)\\n else:\\n def piece_to_index(piece):\\n return (piece & 0xE) | (0 if piece & 1 else 1)\\n\\n return '\\\\n'.join(map(\\n lambda posY, row: ''.join(map(\\n lambda posX, piece: self.EMOJI[\\n piece_to_index(piece)\\n if piece else\\n 14 + ((posY + posX) % 2)],\\n count(), row)),\\n count(),\\n self.board if self._active_player else reversed(\\n [reversed(row) for row in self.board])))\",\n \"def printboard(state):\\n cells = []\\n for i in range(3):\\n for j in range(3):\\n cells.append(NAMES[state[i][j]].center(6))\\n print(cells)\\n print(*cells)\\n print(BOARD_FORMAT.format(*cells))\",\n \"def __str__(self):\\n string = ''\\n for row in self.board:\\n for item in row:\\n if item == None:\\n string += \\\"_ \\\"\\n else:\\n string += f\\\"{item.name:<2}\\\"\\n string += '\\\\n'\\n \\n return string\",\n \"def __str__(self) -> str:\\n return self._color + str(self._value) + SudokuTile.C_END\",\n \"def print(self):\\n print(\\\" a b c d e f g h \\\")\\n print(\\\" ┼───┼───┼───┼───┼───┼───┼───┼───┼\\\")\\n for row in range(8, 0, -1):\\n pieces = \\\" │ \\\".join(self.state[row - 1])\\n print(f\\\"{row} │ {pieces} │ {row}\\\")\\n print(\\\" ┼───┼───┼───┼───┼───┼───┼───┼───┼\\\")\\n print(\\\" a b c d e f g h \\\")\",\n \"def draw_board(self):\\n board = \\\"-------------------\\\\n\\\"\\n board += \\\"| %s | %s | %s |\\\\n\\\" % (self.board_values[1], self.board_values[2], self.board_values[3])\\n board += \\\"-------------------\\\\n\\\"\\n board += \\\"| %s | %s | %s |\\\\n\\\" % (self.board_values[4], self.board_values[5], self.board_values[6])\\n board += \\\"-------------------\\\\n\\\"\\n board += \\\"| %s | %s | %s |\\\\n\\\" % (self.board_values[7], self.board_values[8], self.board_values[9])\\n board += \\\"-------------------\\\\n\\\"\\n return board\",\n \"def show(self):\\n print('\\\\n'+'\\\\n'.join([' '.join([['.', 'O', 'X'][self.board[3*j + i]]\\n for i in range(3)]) for j in range(3)]))\",\n \"def display(self, update_board=1):\\n\\tM,N = self.X, self.Y \\n\\tif not update_board:\\n\\t for i in range(0, M):\\n\\t\\tfor j in range(0, N):\\n\\t\\t\\tif self.state.has_key( (i,j) ): \\n\\t\\t\\t self.scr.addch(j+1, i+1, self.char)\\n\\t\\t\\telse:\\n\\t\\t\\t self.scr.addch(j+1, i+1, ' ')\\n\\t self.scr.refresh()\\n\\t return\\n\\n\\td={} ; self.boring=1\\n\\tfor i in range(0, M):\\n\\t L=range( max(0, i-1), min(M, i+2) )\\n\\t for j in range(0, N):\\n\\t\\ts=0\\n\\t\\tlive=self.state.has_key( (i,j) )\\n\\t\\tfor k in range( max(0, j-1), min(N, j+2) ):\\n\\t\\t for l in L:\\n\\t\\t\\tif self.state.has_key( (l,k) ):\\n\\t\\t\\t s=s+1\\n\\t\\ts=s-live\\n\\t\\tif s==3: \\n\\t\\t # Birth\\n\\t\\t d[i,j]=1 \\n\\t\\t self.scr.addch(j+1, i+1, self.char)\\n\\t\\t if not live: self.boring=0 \\n\\t\\telif s==2 and live: d[i,j]=1 # Survival\\n\\t\\telif live: \\n\\t\\t # Death\\n\\t\\t self.scr.addch(j+1, i+1, ' ')\\n\\t\\t self.boring=0\\n\\tself.state=d\\n\\tself.scr.refresh()\",\n \"def print_game_state(board):\\r\\n print(board)\\r\\n illegal_moves = [(0, 0), (2, 0), (0, 4), (2, 4)]\\r\\n for i in range(board.shape[0]):\\r\\n buffer = ''\\r\\n for j in range(board.shape[1]):\\r\\n if board[i][j] == 1:\\r\\n buffer += 'X\\\\t'\\r\\n elif board[i][j] == 2:\\r\\n buffer += '0\\\\t'\\r\\n elif (i, j) in illegal_moves:\\r\\n buffer += ' \\\\t'\\r\\n else:\\r\\n buffer += '-\\\\t'\\r\\n print (buffer)\",\n \"def display(self):\\n lines = []\\n lines.append(\\\" \\\".join([str(x + 1) for x in range(self.board.columns)]))\\n for row in self.board.board:\\n new_row = \\\"\\\".join([c for c in row])\\n new_row = new_row.replace(\\\"0\\\", \\\" \\\")\\n new_row = new_row.replace(\\\"1\\\", colored(f\\\"{PLAYER1_CHR} \\\", PLAYER1_COLOR))\\n new_row = new_row.replace(\\\"2\\\", colored(f\\\"{PLAYER2_CHR} \\\", PLAYER2_COLOR))\\n lines.append(new_row)\\n return \\\"\\\\n\\\".join(lines)\",\n \"def __repr__(self):\\n s = ''\\n nums = (self.mines + 1) * self.clicked\\n for row in range(0, self.rows):\\n for col in range(0, self.cols):\\n num = nums[row, col]\\n if self.isFlagged(row, col):\\n s += str(Board.FLAGGED) + ' '\\n elif num == 0:\\n s += str(Board.HIDDEN) + ' '\\n else:\\n if self.isMine(row, col):\\n s += str(Board.MINE) + ' '\\n else:\\n s += str(int(num - 1)) + ' '\\n s += '\\\\n'\\n return s\",\n \"def board(self) -> str:\\n divider = \\\"+\\\" + \\\"-\\\" * 23 + \\\"+\\\"\\n b = [divider]\\n for i in range(9):\\n r = []\\n for j in range(3):\\n s = tool.index_counter(i, j * 3)\\n r.append(' '.join(str(i) if i > 0 else ' '\\n for i in self.grid[s:s+3]))\\n b.append(f\\\"| {r[0]} | {r[1]} | {r[2]} |\\\")\\n if (i + 1) % 3 == 0:\\n b.append(divider)\\n return \\\"\\\\n\\\".join(b)\",\n \"def __str__(self):\\r\\n return str(self.board)\",\n \"def __str__(self):\\n return '\\\\n'.join(str(self._board[j]) for j in range(self._n))\",\n \"def display_state_cmd(self):\\n self.__display(self.state)\",\n \"def print(self) -> str:\\n if self.is_unoccupied():\\n return \\\"\\\"\\n return str(\\\"%s-%s\\\" % (self.piece.color.name, self.piece.name.name))\",\n \"def __repr__(self):\\r\\n # Initialize the return string\\r\\n s = ''\\r\\n\\r\\n for row in range(self.height):\\r\\n # Print the index of the row\\r\\n s = s + str(row % 10) + ' |'\\r\\n\\r\\n for col in range(self.width):\\r\\n s += self.grid[row][col]\\r\\n s += '|'\\r\\n\\r\\n s += '\\\\n'\\r\\n s += '--' * (self.width + 1)\\r\\n s += '-'\\r\\n s += '\\\\n'\\r\\n \\r\\n s += ' '\\r\\n for i in range(self.width):\\r\\n s += ' ' + str(i % 10) \\r\\n \\r\\n return s\",\n \"def display_board(board_state):\\n\\n if type(board_state) != str:\\n raise TypeError('Given board input must be String')\\n\\n if len(board_state) != 9:\\n raise Exception(\\\"Input board string length is not 9\\\")\\n\\n counter = 0\\n # print()\\n for position in board_state:\\n counter += 1\\n if counter % 3 == 0:\\n \\n if counter != 9:\\n paddingString = \\\"\\\\n---------\\\\n\\\"\\n else:\\n paddingString = ''\\n else:\\n paddingString = \\\" | \\\"\\n\\n if position.isnumeric():\\n print(\\\" \\\", end=paddingString)\\n\\n else:\\n print(position, end=paddingString)\\n\\n print(\\\"\\\\n\\\\n\\\")\",\n \"def __str__(self):\\n\\n # Create grid headers for the table\\n headers = [letter for letter in string.ascii_uppercase[:self.width]]\\n\\n board_state = []\\n board_state.extend([[value for value in row] for i, row in enumerate(self.board_state)])\\n\\n for idx, row in enumerate(board_state):\\n row.insert(0, idx + 1)\\n\\n return tabulate(board_state, headers, tablefmt=\\\"grid\\\")\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self) -> str:\\n not_actual = self.current_board\\n representation = self.current_board\\n\\n for index in range(len(not_actual)):\\n if not_actual[index: index + 2] in ['31', '32', '33', '34', '36',\\n '37', '38']:\\n representation = representation.replace(\\n not_actual[index: index + 2], '@')\\n if not_actual[index: index + 2] in ['41', '42', '43', '44', '45',\\n '46', '47', '48']:\\n representation = representation.replace(\\n not_actual[index: index + 2], '@')\\n if not_actual[index: index + 2] in ['51', '52', '53', '54', '55',\\n '56', '57', '58']:\\n representation = representation.replace(\\n not_actual[index: index + 2], '@')\\n return representation\",\n \"def display_board(self):\\n print(f\\\"{BREAK_STRING}\\\\n\\\")\\n print(f\\\"STOCK \\\\t WASTE \\\\t\\\\t\\\\t\\\\tFOUNDATION\\\\n\\\")\\n print(f\\\"{self.stock}\\\\t{self.waste}\\\\t\\\\t\\\\t\\\\t{self.foundations['clubs']}\\\\t{self.foundations['diamonds']}\\\"\\n f\\\"\\\\t{self.foundations['hearts']}\\\\t{self.foundations['spades']}\\\\n\\\")\\n print(f\\\"\\\\nTABLEAU\\\\n\\\")\\n for num in range(1, 8):\\n print(f\\\"{num} {self.tableaus[num-1]}\\\")\\n print(f\\\"{BREAK_STRING}\\\\n\\\")\",\n \"def __str__(self):\\n return \\\"Board: \\\" + str(self._board) + \\\" Workers: \\\" + str(self._workers.items())\",\n \"def get_state_display(self, obj):\\n return obj.get_state_display()\",\n \"def __str__(self):\\r\\n ans = \\\"\\\"\\r\\n for row in range(self._height):\\r\\n ans += str(self._grid[row])\\r\\n ans += \\\"\\\\n\\\"\\r\\n return ans\",\n \"def __str__(self):\\r\\n ans = \\\"\\\"\\r\\n for row in range(self._height):\\r\\n ans += str(self._grid[row])\\r\\n ans += \\\"\\\\n\\\"\\r\\n return ans\",\n \"def __str__(self):\\r\\n ans = \\\"\\\"\\r\\n for row in range(self._height):\\r\\n ans += str(self._grid[row])\\r\\n ans += \\\"\\\\n\\\"\\r\\n return ans\",\n \"def show_board(self):\\n for i in range(self.num_rows):\\n print(' ----'*8)\\n s = \\\"\\\"\\n for j in range(self.num_cols):\\n s += '| {} '.format(self._show_piece(i, j))\\n print(\\\"{}|\\\".format(s))\\n print(' ----'*8)\",\n \"def display(self) -> str:\\n lines, _, _, _ = self._display_aux()\\n return '\\\\n'.join(lines)\",\n \"def __repr__(self):\\n return f'Board({ self.board !r})'\",\n \"def _repr_(self):\\n if len(self._states_)==0:\\n return \\\"Empty finite state machine\\\"\\n if len(self._states_)==1:\\n return \\\"Finite state machine with 1 state\\\"\\n else:\\n return \\\"Finite state machine with %s states\\\" % len(self._states_)\",\n \"def print_board(self):\\n board = \\\"\\\"\\n for i in range(3):#need to change this in the future\\n for j in range(3):#need to change this in the future\\n board += self.board[i][j]\\n if j != 2:#need to change this in the future\\n board += \\\" | \\\"\\n board += \\\"\\\\n\\\"\\n return board\",\n \"def __str__(self):\\n out = \\\"{}.\\\".format(self.move_number)\\n if self.white.san != \\\"\\\":\\n out += \\\" \\\" + str(self.white)\\n else:\\n out += \\\"..\\\"\\n if self.black.san != \\\"\\\":\\n out += \\\" \\\" + str(self.black)\\n if self.comment:\\n out += \\\" {\\\" + self.comment + \\\"}\\\"\\n return out\",\n \"def print(self):\\n board_string = ''\\n for y in range(self.size):\\n if y == 0:\\n board_string += '+ '\\n for x in range(self.size):\\n board_string += str(x+1) + ' '\\n board_string += '\\\\n'\\n board_string += (1+3*self.size)*'-'\\n board_string += '\\\\n'\\n board_string += str(y+1)+'|'+y*' '\\n \\n for x in range(self.size):\\n board_string += ' '\\n if self.board[y,x] == HexBoard.BLUE:\\n board_string += self.char_player1\\n elif self.board[y,x] == HexBoard.RED:\\n board_string += self.char_player2\\n else: \\n board_string += self.char_empty\\n board_string += '\\\\n'\\n board_string = board_string.strip()\\n\\n print(board_string)\",\n \"def draw(self):\\n res = ''\\n # ANSI code to clear the screen\\n #res += chr(27) + \\\"[2J\\\"\\n for position, value in enumerate(self.board.tttboard):\\n if value is None:\\n res += str(position)\\n #sys.stdout.write(str(position))\\n else:\\n res += str(value)\\n #sys.stdout.write(str(value))\\n\\n if (position + 1) % 3 != 0:\\n res += str('|')\\n #sys.stdout.write('|')\\n else:\\n #print ''\\n\\n res += str('\\\\n')\\n if position == 2 or position == 5:\\n #print '-' * 5\\n\\n res += '-' * 5\\n res += str('\\\\n')\\n return res\",\n \"def state(self) -> str:\",\n \"def display(self): \\n print ' ' \\n print 'Connect ', NWIN, ' Board '\\n print ' ' \\n for r in reversed(range(self.getHeight())):\\n for c in range(self.getWidth()):\\n if self.cell[c][r] == BLACK:\\n print '+',\\n elif self.cell[c][r] == WHITE:\\n print '-',\\n else:\\n print '.',\\n print ' '\\n for c in range(self.getWidth()):\\n print c,\\n print ' '\\n print ' '\",\n \"def __repr__(self):\\n\\n # Creates a deep copy of _board and changes all None values to underscores.\\n temp_board = copy.deepcopy(self.get_board())\\n\\n for row_index, row in enumerate(temp_board):\\n\\n for column_index, column in enumerate(row):\\n\\n if column is None:\\n\\n row[column_index] = '____' + self.reverse_position(column_index, row_index) + '_____'\\n\\n # str() with position is mapped onto each list within _board list,\\n # converted to string, and then joined by a nextline character.\\n return '\\\\n\\\\n'.join(map(str, temp_board))\",\n \"def __str__(self) -> str:\\n string = ''\\n for row in self._grid_display:\\n string += row.__str__() + '\\\\n'\\n return string\",\n \"def stateString(self):\\n return self._mdp.stateString(self._cur_state);\",\n \"def __str__(self):\\r\\n out = \\\"##\\\"*(self.width+1)+\\\"\\\\n\\\"\\r\\n for i in range(self.height):\\r\\n out += \\\"#\\\"\\r\\n for j in range(self.width):\\r\\n if self.grid[i][j] == 0:\\r\\n out += \\\"##\\\"\\r\\n else:\\r\\n if not self.showSolution:\\r\\n out += \\\" \\\"\\r\\n elif (i,j) in self.solution:\\r\\n out += \\\"**\\\"\\r\\n else:\\r\\n out += \\\" \\\"\\r\\n out += \\\"#\\\\n\\\"\\r\\n return out + \\\"##\\\"*(self.width+1)\",\n \"def pretty_state(state, was_changed):\\n pstate = state + \\\" \\\"\\n if was_changed:\\n pstate = state + \\\"*\\\"\\n if not uses_rich:\\n return pstate\\n\\n scols = {\\n # forks\\n 'wg' : 'gray',\\n 'wu' : 'yellow',\\n 'wd' : 'blue',\\n\\n # philosophers\\n 'sl' : 'green',\\n 'wt' : 'yellow',\\n 'gl' : 'cyan',\\n 'gr' : 'bright_cyan',\\n 'es' : 'purple',\\n 'dl' : 'yellow',\\n 'dr' : 'bright_yellow',\\n 'xx' : 'white', # done\\n }\\n if was_changed:\\n return f\\\"[red][bold]{pstate}[/bold][/red]\\\"\\n col = scols.get(state, 'white')\\n return f\\\"[{col}]{pstate}[/{col}]\\\"\",\n \"def display_board(self):\\n print(self.game_board)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7867957","0.7523896","0.73911226","0.73753697","0.7373144","0.7246394","0.7220203","0.7215452","0.7110438","0.71008956","0.7092637","0.70639235","0.7059323","0.7039391","0.7023496","0.7022453","0.7006166","0.69822073","0.6949946","0.6925835","0.69216865","0.69214165","0.68730223","0.6862658","0.6852694","0.6833031","0.68274957","0.6818402","0.6811945","0.6777108","0.6774134","0.67714363","0.67578256","0.67525053","0.6748707","0.6742789","0.6723193","0.67203826","0.67201763","0.6713812","0.6712293","0.6707471","0.66989034","0.66923743","0.6690522","0.66861176","0.66526246","0.6647448","0.6643837","0.66284394","0.66120267","0.6600266","0.6582181","0.6580375","0.6575507","0.6554835","0.65446544","0.65424955","0.6540245","0.6539516","0.65360254","0.6532429","0.65321213","0.65205073","0.6520049","0.6519601","0.6516077","0.6515585","0.6511669","0.6511268","0.6471322","0.6470173","0.6464306","0.6445652","0.6436559","0.6436559","0.6436559","0.6436559","0.6430978","0.64288855","0.64280105","0.64277095","0.6422821","0.6422821","0.6422821","0.64129776","0.6403673","0.63847363","0.6383602","0.63799095","0.6377035","0.6363749","0.6361767","0.6346627","0.6338524","0.6332258","0.6331275","0.63292253","0.6318199","0.6307882","0.6306965"],"string":"[\n \"0.7867957\",\n \"0.7523896\",\n \"0.73911226\",\n \"0.73753697\",\n \"0.7373144\",\n \"0.7246394\",\n \"0.7220203\",\n \"0.7215452\",\n \"0.7110438\",\n \"0.71008956\",\n \"0.7092637\",\n \"0.70639235\",\n \"0.7059323\",\n \"0.7039391\",\n \"0.7023496\",\n \"0.7022453\",\n \"0.7006166\",\n \"0.69822073\",\n \"0.6949946\",\n \"0.6925835\",\n \"0.69216865\",\n \"0.69214165\",\n \"0.68730223\",\n \"0.6862658\",\n \"0.6852694\",\n \"0.6833031\",\n \"0.68274957\",\n \"0.6818402\",\n \"0.6811945\",\n \"0.6777108\",\n \"0.6774134\",\n \"0.67714363\",\n \"0.67578256\",\n \"0.67525053\",\n \"0.6748707\",\n \"0.6742789\",\n \"0.6723193\",\n \"0.67203826\",\n \"0.67201763\",\n \"0.6713812\",\n \"0.6712293\",\n \"0.6707471\",\n \"0.66989034\",\n \"0.66923743\",\n \"0.6690522\",\n \"0.66861176\",\n \"0.66526246\",\n \"0.6647448\",\n \"0.6643837\",\n \"0.66284394\",\n \"0.66120267\",\n \"0.6600266\",\n \"0.6582181\",\n \"0.6580375\",\n \"0.6575507\",\n \"0.6554835\",\n \"0.65446544\",\n \"0.65424955\",\n \"0.6540245\",\n \"0.6539516\",\n \"0.65360254\",\n \"0.6532429\",\n \"0.65321213\",\n \"0.65205073\",\n \"0.6520049\",\n \"0.6519601\",\n \"0.6516077\",\n \"0.6515585\",\n \"0.6511669\",\n \"0.6511268\",\n \"0.6471322\",\n \"0.6470173\",\n \"0.6464306\",\n \"0.6445652\",\n \"0.6436559\",\n \"0.6436559\",\n \"0.6436559\",\n \"0.6436559\",\n \"0.6430978\",\n \"0.64288855\",\n \"0.64280105\",\n \"0.64277095\",\n \"0.6422821\",\n \"0.6422821\",\n \"0.6422821\",\n \"0.64129776\",\n \"0.6403673\",\n \"0.63847363\",\n \"0.6383602\",\n \"0.63799095\",\n \"0.6377035\",\n \"0.6363749\",\n \"0.6361767\",\n \"0.6346627\",\n \"0.6338524\",\n \"0.6332258\",\n \"0.6331275\",\n \"0.63292253\",\n \"0.6318199\",\n \"0.6307882\",\n \"0.6306965\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":25298,"cells":{"query":{"kind":"string","value":"Adjust the display grid before it gets assembled."},"document":{"kind":"string","value":"def adjust_display(self, display: typing.List[typing.List[str]]):"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _prepare_grid(self):\n raise NotImplementedError","def update_positions(self, grid):\r\n self.grid = grid","def apply(self):\n self.grid_size = self.values[0]","def configure_grid(self):\r\n\r\n for r in range(3):\r\n self.rowconfigure(r, weight=1)\r\n for c in range(3):\r\n self.columnconfigure(c, weight=1)","def prepare_screen_grid_display(grid, screen):\n screen.blit(grid.prepare_screen_menu_grid, grid.prepare_screen_menu_grid_rect)\n screen.blit(grid.prepare_screen_pick_deck_grid, grid.prepare_screen_pick_deck_grid_rect)\n screen.blit(grid.prepare_screen_ai_setup_grid, grid.prepare_screen_ai_setup_grid_rect)","def _update_dimensions(self):\n _, self.width = self.window.getmaxyx()\n self.spacing = self.width // self.total_columns","def relayout(self): \n\t\t#self.urmaswin.Layout()\n\t\t#wx.CallAfter(self.urmaswin.Layout)\n\t\t#wx.CallAfter(self.visualizer.OnSize)","def recalculate_layout(self):\n self.max_y, self.max_x = self.scr.getmaxyx()\n self.num_columns = (1 + max(0, self.max_x - self.column_width)\n // (self.column_width + self.column_gap))\n if (self.num_columns * self.column_width +\n self.num_columns * self.column_gap) < self.max_x - 3:\n self.vis_columns = self.num_columns + 1\n else:\n self.vis_columns = self.num_columns\n\n if self.x >= self.num_columns:\n # reposition x\n ox = self.win_x + self.x\n self.win_x = max(ox - self.num_columns + 1, 0)\n self.x = self.num_columns - 1\n if self.y >= self.max_y - self.header_offset:\n # reposition y\n oy = self.win_y + self.y\n self.win_y = max(oy - (self.max_y - self.header_offset) + 1, 0)\n self.y = self.max_y - self.header_offset - 1","def _arrange_displays (self):\n if self.locked:\n self._changed = True\n return []\n n = len(self.displays)\n if n == 0:\n return []\n # find the optimal number of initial splits (rows/columns)\n data = []\n # if not given, check splitting into both rows and columns\n for axis in ((0, 1) if self.split is None else (self.split,)):\n data.append(self._optimal_splits(axis) + (axis,))\n error, splits, axis = min(data)\n # determine distribution of displays over splits\n per_split = int(ceil(float(n) / splits))\n num_unfilled = splits * per_split - n\n num_filled = splits - num_unfilled\n # decide display sizes\n screen_size = self.screen.get_size()\n split_sizes = split(screen_size[not axis], splits)\n to_fill = screen_size[axis]\n filled_sizes = split(to_fill, per_split)\n if self.expand:\n unfilled_sizes = split(to_fill, per_split - 1)\n else:\n unfilled_sizes = filled_sizes[:-1]\n # decide where unfilled splits go\n if self.pos < 0:\n filled = [False] * num_unfilled + [True] * num_filled\n elif self.pos < 0:\n filled = [True] * num_filled + [False] * num_unfilled\n else:\n filled = ([True] * (num_filled / 2) + [False] * num_unfilled +\n [True] * (num_filled - num_filled / 2))\n # determine allocation of padding in unfilled splits\n padding = to_fill - sum(unfilled_sizes)\n inner_padding = int(self.padding * padding)\n outer_padding = padding - inner_padding\n padding = [outer_padding / 2] + split(inner_padding, per_split - 2)\n # construct rects and assign to displays\n displays = self.displays[:]\n changed = []\n x = 0\n #print n, axis, splits, split_sizes, filled_sizes, unfilled_sizes, filled\n for i in xrange(splits):\n y = 0\n for j in xrange(per_split + filled[i] - 1):\n display = displays.pop()\n if not filled[i]:\n # this split is unfilled: include padding\n y += padding[j]\n sizes = (unfilled_sizes, filled_sizes)[filled[i]]\n # construct rect\n if axis == 1:\n rect = [x, y, split_sizes[i], sizes[j]]\n else:\n rect = [y, x, sizes[j], split_sizes[i]]\n rect = pygame.Rect(rect)\n if rect != display.rect:\n changed.append(display)\n display.rect = rect\n y += sizes[j]\n x += split_sizes[i]\n return changed","def set_grid(self,ug):\n self.grd=ug\n self.set_topology()","def adjustSize( self ):\n cell = self.scene().cellWidth() * 2\n minheight = cell\n minwidth = 2 * cell\n \n # fit to the grid size\n metrics = QFontMetrics(QApplication.font())\n width = metrics.width(self.displayName()) + 20\n width = ((width/cell) * cell) + (cell % width)\n \n height = self.rect().height()\n \n # adjust for the icon\n icon = self.icon()\n if icon and not icon.isNull():\n width += self.iconSize().width() + 2\n height = max(height, self.iconSize().height() + 2)\n \n self.setMinimumWidth(max(width, minwidth))\n self.setMinimumHeight(max(height, minheight))\n \n self.rebuild()","def recreate_grid(self):\n\n self.print_numlist = arcade.SpriteList()\n for row in range(ROW_COUNT):\n for column in range(COLUMN_COUNT):\n sprite = arcade.Sprite(\n f\"Numbers/{self.grid[row][column]}.png\", scale=0.2\n )\n x = (MARGIN + WIDTH) * column + MARGIN + WIDTH // 2\n y = (MARGIN + HEIGHT) * row + MARGIN + HEIGHT // 2\n sprite.center_x = x\n sprite.center_y = y\n self.print_numlist.append(sprite)\n # Check to see if all squares have been filled in\n if 0 not in self.grid:\n # if Cameron.Check_for_Completion(self.grid) == True:\n self.done = True","def give_space(self):\n for child in self.winfo_children():\n child.grid_configure(padx=5, pady=5)","def resetgrid(self):\n self.remainingminecount = self.minecount\n self.board.reset()\n self.button_array = [[QtGui.QPushButton() \\\n for col in range(self.cols)] for row in range(self.rows)]\n self.game_in_progress = True\n self.first_click = True\n for row in range(self.rows):\n for col in range(self.cols):\n self.button_array[row][col].setFixedSize(self.cell_size, self.cell_size)\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/unopenedsquare.png\"))\n self.button_array[row][col].setIconSize(QtCore.QSize(self.cell_size,\\\n self.cell_size))\n self.button_array[row][col].clicked.connect(self.handle_left_click)\n self.button_array[row][col].setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n self.button_array[row][col].customContextMenuRequested.connect(\\\n self.handle_right_click)\n self.cell_grid_layout.addWidget(self.button_array[row][col], row, col)\n self.mines_lcd.display(str(self.remainingminecount))\n self.status_button.setIcon(QtGui.QIcon(\"icons/smiley1.ico\"))\n self.time = 0\n self.time_lcd.display(self.time)","def drawGrid(self):\n for div in range(NBCELL):\n sec = SSIZE*div\n self.can.create_line(0, sec, GSIZE, sec, width=3, fill=GFILL)\n self.can.create_line(sec, 0, sec, GSIZE, width=3, fill=GFILL)","def _reset_display(self, game):\n for coord in game.board.grid.keys():\n canvas_coord = self._havannah_coord_to_canvas_coord(coord)\n self._draw_hex(canvas_coord, self.HEX_BLANK)","def normalize(self, redraw=True):\n if self.screen_rect is not None:\n self.relative_sizes = []\n\n height = self.screen_rect.height\n left, right = self._get_columns()\n\n if left.count > 0:\n self.relative_sizes += self._split_integer(height, left.count)\n if right.count > 0:\n self.relative_sizes += self._split_integer(height, right.count)\n\n if redraw:\n self.group.layout_all()\n self.do_normalize = False","def initialize_grid(self) -> None:\n for i in range(self.grid_size[0]):\n for j in range(self.grid_size[1]):\n self.set(i, j, self.base_color)","def adjust_visual(self):\n\n if (self.direction is bs.Direction.LEFT):\n self.rect.x -= 0.5 * CELL_SIZE","def reset(self):\n # replace with your code\n self._grid = [[0 for dummy_column in range(self._grid_width)] for dummy_row in range(self._grid_height)]\n for dummy_num in range(2):\n self.new_tile()","def show_grid(self):\n for ax in (self.time_velocity, self.time_power, self.power_velocity):\n ax.grid(True)","def _do_layout(self):\n return","def draw_grid(self):\n self.screen.draw_many_tiles(tile for tile in self.iter_grid_tiles())\n pass","def reset(self):\r\n self.grid = [[0 for dummy_col in range(self.grid_width)] for dummy_row in range(self.grid_height)]\r\n self.new_tile()\r\n self.new_tile()","def _align_toplevel_grid(self):\n\n # align origin with nearest multple of 128\n self.mins[0] -= self.mins[0] % 128\n self.mins[1] -= self.mins[1] % 128\n\n width = self.maxs[0] - self.mins[0]\n height = self.maxs[1] - self.mins[1]\n greatest_dim = max(width, height)\n nearest_pow_two = int(2 ** np.ceil(np.log2(greatest_dim)))\n width_adjustment = (nearest_pow_two - width)\n height_adjustment = (nearest_pow_two - height)\n\n self.maxs[0] += width_adjustment\n self.maxs[1] += height_adjustment","def set_grid(self, rows, columns):\n self.get_words()\n self.word_search_grid = []\n for r in range(rows):\n rlist = []\n for c in range(columns):\n rlist += [LetterField(self.centrepiece)]\n rlist[-1].entry.grid(row = r, column = c)\n self.word_search_grid += [rlist]\n self.customiser.destroy()\n \n self.action_button.config(text = \"Solve Word Search\", command = self.solve, state = NORMAL)","def resize (self):\n return self._arrange_displays()","def reset(self):\n\n for _ in range(self.grid_height):\n row = [0 for _ in range(self.grid_width)]\n self.grid.append(row)","def updateDisplay(self):\n if self._displayPjt:\n self._displayPjt.updateim()\n if self._displayUsr:\n self._displayUsr.updateim()\n if self._displayVtk:\n self._displayVtk.updateim()","def reset(self):\n # replace with your code\n self._grid = [[0] * self._width for _ in xrange(self._height)]\n self.new_tile()\n self.new_tile()","def reset(self):\r\n # replace with your code\r\n self._cells = [[0 for dummy_col in range(self._grid_width)] for dummy_row in range(self._grid_height)]\r\n self.new_tile()\r\n self.new_tile()","def updateGrid(self) -> None:\n emu = self.emulator\n arch = self.root.arch\n registers = arch.registers\n self.__values.setRowCount(len(registers))\n for i, reg in enumerate(registers):\n self.__values.setRowHeight(i, self.__row_size)\n name = QTableWidgetItem(reg)\n name.setFlags(Qt.NoItemFlags)\n val = emu.get_register_value(reg) if emu.vm else 0\n old_val = self.__old_register_values.get(reg, 0)\n if type(val) in (int, int):\n value = format_address(val, arch)\n else:\n value = str(val)\n value = QTableWidgetItem( value )\n if old_val != val:\n self.__old_register_values[reg] = val\n value.setForeground(QColor(Qt.red))\n value.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable | Qt.ItemIsEditable)\n self.__values.setItem(i, 0, name)\n self.__values.setItem(i, 1, value)\n return","def draw_grid(self):\n for x in range(0, WIDTH, TILESIZE):\n pg.draw.line(self.screen, LIGHTGREY, (x, 0), (x, HEIGHT))\n \n for y in range(0, HEIGHT, TILESIZE):\n pg.draw.line(self.screen, LIGHTGREY, (0, y), (WIDTH, y))","def gen_grids(self):\n self.dx = self.grid_width / self.grid_resol\n self.dk = 2 * np.pi/self.grid_width\n self.grid_x_shifted = -self.grid_width/2 + self.dx * np.arange(0, self.grid_resol)\n self.grid_x = self.grid_x_shifted + self.grid_center\n self.grid_k = - (np.pi * self.grid_resol)/self.grid_width + self.dk * np.arange(0, self.grid_resol)\n self.grid_k = np.roll(self.grid_k, int((self.grid_resol)/2))\n self.grid_kin = np.square(self.h)/ (2*self.m) * np.square(self.grid_k)","def layout_board(self):\n raise NotImplementedError","def reset(self):\n # replace with your code\n dummy_row = self._grid_height\n dummy_col = self._grid_width\n self._cells = [ [0 for dummy_col in range(self._grid_width)] \n for dummy_row in range(self._grid_height)]\n \n self.new_tile()\n self.new_tile()","def get_grid(self):\n self.fullws = []\n for row in self.word_search_grid:\n rowdata = []\n for column in row:\n rowdata += [column.entry.get()]\n self.fullws += [rowdata]\n self.logic.set_grid(self.fullws)","def reset(self):\n # self.grid = [[0] * self.grid_width] * self.grid_height\n self.grid = []\n for dummy_row in range(self.grid_height):\n new_row = []\n for dummy_col in range(self.grid_width):\n new_row.append(0)\n self.grid.append(new_row)\n self.new_tile()\n self.new_tile()","def fill_grid_np(self):\n\n self.grid_np = [None for i in range(GRID_HEIGHT*GRID_HEIGHT*MAX_CELL_SIZE)]\n grid = self.grid_np\n # cell_size = self.cell_size\n for obj in self.levels[self.curient_level].objects:\n obj.position_grid[X], obj.position_grid[Y] = get_grid_xy(obj.position_np, ZOMBIE_SIZE)\n x, y = obj.position_grid[X], obj.position_grid[Y]\n grid[y*GRID_WIDTH + x] = obj\n # if cell_size[y*GRID_WIDTH + x] < MAX_CELL_SIZE:\n # cell_size[y*GRID_WIDTH + x] += 1","def update_grid(self):\n if self.game_over:\n return\n if self.active_piece is None:\n self.place_new_piece()\n if self.piece_collision_exists(self.active_piece):\n self.handle_active_piece_collision()\n self.place_new_piece()\n self.shift_cells(self.active_piece, self.current_direction)\n self.active_piece = TransformPiece.shift_coordinates(self.active_piece, self.current_direction)\n self.merge_with_completed_rows()\n if self.is_game_won():\n self.game_over = True","def update_grid(self):\n # Check to see if we have moved squares\n _new_grid = self.calc_grid()\n if _new_grid == self._grid:\n return\n # Remove from old square and add to new square\n self.target._grid[self._grid][self._type].discard(self)\n self.target._grid[_new_grid][self._type].add(self)\n # Update coordinates\n self._grid = _new_grid","def update_grid(self):\n def rgb_color(rgb):\n \"\"\"RGB to HEX\"\"\"\n return '#%02x%02x%02x' % rgb\n\n # clear on widgets preexisting on frame\n for widget in self.main_grid.winfo_children():\n widget.destroy()\n\n BORDER_WIDTH = 8\n\n # draw tiles\n for i in range(len(self.main_grid_values)):\n for j in range(len(self.main_grid_values[i])):\n if self.main_grid_values[i][j] == 0:\n # ignore 0 tiles for text\n tile = tk.Label(self.main_grid, bg='red', text='', font=('Arial', 18))\n else:\n # give tiles a different color\n exponent = int(math.log(int(self.main_grid_values[i][j]), 2))\n tile = tk.Label(\n self.main_grid,\n bg=rgb_color((255, 255 - exponent*31, 0)),\n text=self.main_grid_values[i][j],\n font=('Arial', 20)\n )\n\n # color the 0 tiles with grey\n if tile['text'] == '':\n tile.config(bg=rgb_color((210, 210, 210)))\n\n # border configuration - so that all borders are not overlapping\n if j == len(self.main_grid_values) - 1 and i == len(self.main_grid_values) - 1:\n tile.grid(row=i, column=j, padx=BORDER_WIDTH, pady=BORDER_WIDTH, sticky='news')\n\n elif j == len(self.main_grid_values) - 1:\n tile.grid(row=i, column=j, padx=BORDER_WIDTH, pady=(BORDER_WIDTH, 0), sticky='news')\n\n elif i == len(self.main_grid_values) - 1:\n tile.grid(row=i, column=j, padx=(BORDER_WIDTH, 0), pady=BORDER_WIDTH, sticky='news')\n\n else:\n tile.grid(row=i, column=j, padx=(BORDER_WIDTH, 0), pady=(BORDER_WIDTH, 0), sticky='news')","def do_relayout(self):\n # This method is called whenever a relayout is requested. By\n # default, this is when the layout children change. In that case\n # we just need to update the min and max sizes. We are a top\n # level window, so no one really cares about our size hint. \n self.update_minimum_size()\n self.update_maximum_size()","def draw_grid(self) -> None:\n for x in range(0, WIDTH, TILE_SIZE):\n pg.draw.line(self.screen, LIGHT_GREY, (x, INFO_HEIGHT), (x, HEIGHT))\n for y in range(INFO_HEIGHT, INFO_HEIGHT + HEIGHT, TILE_SIZE):\n pg.draw.line(self.screen, LIGHT_GREY, (0, y), (WIDTH, y))","def _reset_display(self, game):\n for r, orow in enumerate(self.inner_boards):\n for c, board in enumerate(orow):\n for ir, irow in enumerate(board):\n for ic, _ in enumerate(irow):\n self._update_label((r,c), (ir,ic), TTTMove.BLANK)","def grid(self, grid):\n\n self._grid = grid","def update(self, grid, colRamp = ['white', 'blue']):\n \n # update the cell colors\n for y in range(len(grid)):\n yl = y + 1\n for x in range(len(grid[y])):\n xl = x + 1\n color = colRamp[int(grid[y][x])]\n self.displayWindow.update((xl, yl), color)\n\n # refresh the window\n self.displayWindow.tkupdate()","def reset(self):\r\n self._cells = [ [0 for dummy_col in range(self._grid_width)] \r\n for dummy_row in range(self._grid_height) ]\r\n \r\n \r\n self.new_tile()\r\n self.new_tile()","def show_grid(self) -> None:\n from pymol import cmd\n from PyQt5 import QtWidgets\n\n global x, y, z\n\n if self.input.count() > 0:\n # Get minimum and maximum dimensions of target PDB\n pdb = self.input.currentText()\n ([min_x, min_y, min_z], [max_x, max_y, max_z]) = cmd.get_extent(pdb)\n\n # Get Probe Out value\n probe_out = self.probe_out.value()\n probe_out = round(probe_out - round(probe_out, 4) % round(0.6, 4), 1)\n\n # Prepare dimensions\n min_x = round(min_x - (min_x % 0.6), 1) - probe_out\n min_y = round(min_y - (min_y % 0.6), 1) - probe_out\n min_z = round(min_z - (min_z % 0.6), 1) - probe_out\n max_x = round(max_x - (max_x % 0.6) + 0.6, 1) + probe_out\n max_y = round(max_y - (max_y % 0.6) + 0.6, 1) + probe_out\n max_z = round(max_z - (max_z % 0.6) + 0.6, 1) + probe_out\n\n # Get center of each dimension (x, y, z)\n x = (min_x + max_x) / 2\n y = (min_y + max_y) / 2\n z = (min_z + max_z) / 2\n\n # Draw Grid\n self.draw_grid(min_x, max_x, min_y, max_y, min_z, max_z)\n else:\n QtWidgets.QMessageBox.critical(self, \"Error\", \"Select an input PDB!\")\n return","def rescale(self, event: tkinter.Event) -> None:\n # the properties which are linked to the event of reconfiguration\n # contain all the new sizes of the panel :\n self.width, self.height = event.width - 4, event.height - 4\n # The subtraction of 4 pixels is here to compensate the width\n # of the 'highlight bordure' rolling the canvas)\n self.draw_board()","def reset(self):\r\n self.grid = np.array([[' '] * self.width for row in range(self.height)])\r\n self.num_checkers = 0","def get_grid(self):\n self._export_mode = 'grid'\n self._counter_update_data += 1","def play(self):\n self.populateGrid()\n self.displayGrid()","def adjust_columns(self):\r\n for col in range(3):\r\n self.resizeColumnToContents(col)","def reset(self):\n self._grid = [[0] * self._width for _ in range(self._height)]\n self.new_tile()\n self.new_tile()","def reset(self):\r\n # creating the grid with the values all initialized to zero\r\n \r\n self._grid = [[ 0 for dummy_col in range(self._width)]\r\n for dummy_row in range(self._height)]\r\n # introducing the two initial tiles\r\n self.new_tile()\r\n self.new_tile()\r\n #for testing purposes\r\n #print self.grid\r\n #print self\r","def adjust_screen_size(self) -> None:\n if self.screen:\n max_row, max_cols = self.screen.getmaxyx()\n if max_row < MIN_SIZE + len(self.all_items):\n self.screen.resize(self.menu_height, max_cols)\n self.draw()","def reset(self):\n self._grid = [[0 for dummy_col in range(self._width)]\n for dummy_row in range(self._height)]\n self.new_tile()\n self.new_tile()","def gridDisplay(self):\n\n if self.griddButton.isCheckable():\n self.photo_grid.setVisible(False)\n self.griddButton.setCheckable(False)\n self.griddButton.setDown(False)\n self.statustext.setText(\"Hide Grid\")\n else:\n self.griddButton.setCheckable(True)\n self.photo_grid.setVisible(True)\n self.griddButton.setDown(True)\n self.statustext.setText(\"Display Grid - Rule of thirds\")","def initialize(self, frame):\n self.grid_size = 5\n\n Label(frame, text=\"Grid Size:\").grid(row=0)\n\n self.e1 = Scale(frame, from_=self.grid_size, to=25, orient=HORIZONTAL)\n self.e1.grid(row=0, column=1)\n\n return self.e1","def customise_grid(self):\n\n # Agent colours, agent tooltips and grid initialisation\n mapper = linear_cmap(field_name='category',\n palette=['blue', 'red', 'green', 'orange', 'purple'] ,\n low=0,\n high=4)\n TOOLTIPS = [(\"Residential utility\", \"@res_utility\"),\n ('Local composition', '@local_comp'),\n ('Neighbourhood composition', '@n_comp'),\n (\"School utility\", \"@school_utility\"),\n ('Distance', '@dist_school'),\n ('School composition', '@s_comp'),\n ('School composition utility', '@school_comp_utility')]\n hover = HoverTool(names=[\"households\", \"schools\"], tooltips=TOOLTIPS)\n self.grid = figure(x_range=(self.model.grid.x_min - 1,\n self.model.grid.x_max),\n y_range=(self.model.grid.y_min - 1,\n self.model.grid.y_max),\n tools=[hover, 'tap', 'pan',\n WheelZoomTool()],\n tooltips=TOOLTIPS, output_backend=\"webgl\")\n\n # Set WheelZoomTool active by default if not lattice\n if self.params['case'].lower() != 'lattice':\n self.grid.toolbar.active_scroll = self.grid.select_one(\n WheelZoomTool)\n\n # Add a background map using OpenStreetMap (Google Maps is too\n # computationally expensive and cannot zoom properly)\n self.grid.add_tile(get_provider('OSM'))\n\n self.grid.axis.visible = False\n self.grid.grid.visible = False\n # Function to highlight all households that are currently enrolled in\n # the same school.\n self.source.selected.on_change(\"indices\", self.select_households)\n\n # Plot households\n self.grid.circle(x=\"x\",\n y=\"y\",\n size=5,\n view=self.household_view,\n source=self.source,\n fill_color=mapper,\n line_color='black',\n alpha=0.8,\n nonselection_fill_alpha=0.2,\n selection_fill_alpha=1,\n name='households')\n\n # Plot schools\n self.grid.circle(x=\"x\",\n y=\"y\",\n size=7,\n source=self.source,\n view=self.school_view,\n fill_color='yellow',\n line_color='black',\n name='schools')\n\n # Plot neighbourhoods\n self.grid.patches('x',\n 'y',\n source=self.source,\n view=self.neighbourhood_view,\n fill_color=None,\n line_color=\"black\",\n line_width=2,\n hover_alpha=0,\n hover_line_color=None,\n name='neighbourhoods',\n selection_fill_alpha=0)","def _grid_hint_size(self) -> int:","def update_grid_pos(self):\n self.grid_pos = self.get_tile_of_position(self.tank.body.position)","def configureWidgetGrid(self, widget, horiz_split, vert_split):\n for i in range(vert_split):\n widget.grid_columnconfigure(i, weight = 1)\n\n for j in range(horiz_split):\n widget.grid_rowconfigure(j, weight = 1)","def grid(self):\r\n dimA = self.dimA ; dimC = self.dimA ; W_grid = self.W_grid\r\n \r\n self.tol = 10e-5\r\n self.Niter = 10000\r\n \r\n a0 = 100 / self.dimA\r\n c0 = 100 / self.dimA\r\n a_grid = np.mgrid[0:(dimA):1] ; a_grid = a0 * a_grid ; self.a_grid = a_grid\r\n c_grid = np.mgrid[0:(dimC):1] ; c_grid = c0 * c_grid ; self.c_grid = c_grid\r\n self.W_grid = W_grid","def reset(self):\n # replace with your code\n self.board = [[0 for dummy_index in range(self.grid_width)] for dummy_inner_index in range(self.grid_height)]","def setSystemGrid(self):\n fromSystem = self.myGalaxy.systems[self.fromSystem]\n toSystem = self.myGalaxy.systems[self.toSystem]\n self.systemGrid = funcs.getMapQuadrant(toSystem, self, fromSystem.x, fromSystem.y,\n toSystem.x, toSystem.y)","def build_deck_screen_grid_display(grid, screen):\n screen.blit(grid.build_deck_screen_card_gallery_grid, grid.build_deck_screen_card_gallery_grid_rect)\n screen.blit(grid.build_deck_screen_deck_grid, grid.build_deck_screen_deck_grid_rect)","def get_grid_size(self, ui, res_dir):\r\n print_it('determining grid size', PrintOpts.lvl1.value)\r\n self.sun.simple_clone()\r\n self.sun.clone.make_profile(PreSol.res_x.value, PreSol.res_y.value,\r\n self.init_force)\r\n self.planet.simple_clone()\r\n self.planet.clone.make_slave_to(self.sun.clone)\r\n\r\n init_displ = hertz_displ(self.sun.clone.e, self.planet.e,\r\n self.sun.clone.ny, self.planet.ny,\r\n self.sun.clone.r_hertz_x,\r\n self.sun.clone.r_hertz_y,\r\n self.planet.clone.r_hertz_x,\r\n self.planet.clone.r_hertz_y,\r\n self.sun.norm_forces[0])\r\n too_many_els_in_y = 1\r\n too_many_els_in_x = 1\r\n contact_width_y = 0.05\r\n contact_width_x = 0.05\r\n while too_many_els_in_y != 0 or \\\r\n too_many_els_in_x != 0:\r\n self.sun.clone.make_profile(self.sun.clone.res_x,\r\n self.sun.clone.res_y, self.init_force,\r\n contact_width=contact_width_y,\r\n contact_length=contact_width_x)\r\n self.planet.clone.make_slave_to(self.sun.clone)\r\n\r\n pressure, init_displ = \\\r\n pre_solve_half_space(self.sun.clone.profile,\r\n self.planet.clone.profile,\r\n self.sun.clone.x_axis,\r\n self.sun.clone.y_axis,\r\n self.sun.clone.res_x, self.sun.clone.res_y,\r\n self.sun.clone.delta_x,\r\n self.sun.clone.delta_y, self.sun.clone.e,\r\n self.planet.clone.e, self.sun.clone.ny,\r\n self.planet.clone.ny,\r\n self.sun.norm_forces[0],\r\n init_displ=init_displ, print_prog=False)\r\n\r\n pressure_els_y = sum(\r\n pressure[math.floor(self.sun.clone.res_y / 2), :] > 0)\r\n too_many_els_in_y = self.sun.clone.res_y - pressure_els_y - 2\r\n if too_many_els_in_y:\r\n contact_width_y += -np.sign(\r\n too_many_els_in_y) * contact_width_y / 25\r\n\r\n pressure_els_x = sum(\r\n pressure[:, math.floor(self.sun.clone.res_x / 2)] > 0)\r\n too_many_els_in_x = self.sun.clone.res_x - pressure_els_x - 2\r\n if too_many_els_in_x:\r\n contact_width_x += -np.sign(\r\n too_many_els_in_x) * contact_width_x / 25\r\n\r\n self.sun.make_profile(self.sun.res_x, self.sun.res_y, self.init_force,\r\n contact_width=contact_width_y,\r\n contact_length=contact_width_x)\r\n self.planet.make_slave_to(self.sun)\r\n return init_displ","def displayGrid(self, toggled):\n self.scene.setGridVisible(visible=toggled)","def battle_screen_grid_display(grid, screen):\n screen.blit(grid.battle_screen_menu_grid, grid.battle_screen_menu_grid_rect)\n screen.blit(grid.battle_screen_deck_grid, grid.battle_screen_deck_grid_rect)\n screen.blit(grid.battle_screen_character_1_grid, grid.battle_screen_character_1_grid_rect)\n screen.blit(grid.battle_screen_character_2_grid, grid.battle_screen_character_2_grid_rect)\n screen.blit(grid.battle_screen_battle_1_grid, grid.battle_screen_battle_1_grid_rect)\n screen.blit(grid.battle_screen_battle_2_grid, grid.battle_screen_battle_2_grid_rect)\n screen.blit(grid.battle_screen_item_1_grid, grid.battle_screen_item_1_grid_rect)\n screen.blit(grid.battle_screen_item_2_grid, grid.battle_screen_item_2_grid_rect)\n screen.blit(grid.battle_screen_instruction_bar_grid, grid.battle_screen_instruction_bar_grid_rect)","def _layout(self):\n top = 2.0\n y = 0.0\n x = 0.0\n maxend = 0.0\n for track in self._tracks:\n track.set_view(self.view.species, self.view.seqname, \n self.view.start, self.view.end)\n tracksize = track.get_size()\n y -= tracksize[1]\n track.set_pos(track.pos_offset[0] + x, track.pos_offset[1] + y)\n self.size = [self.view.end - self.view.start + 1, 0 - y]","def process_grid(self, grid: Grid) -> Grid:","def __update_visible(self) -> None:\n for i in range(0, 8):\n visible_row = self.__row_position + Labyrinth.ALL_ROW_MOVE[i]\n visible_col = self.__col_position + Labyrinth.ALL_COL_MOVE[i]\n if 0 <= visible_row < self.__labyrinth.labyrinth_height and \\\n 0 <= visible_col < self.__labyrinth.labyrinth_width:\n self.__labyrinth.visible_cells[visible_row][visible_col] = 1","def resize(self):\n\t\tself.win.erase()\n\t\tfor c in self.components:\n\t\t\tc.resize()\n\t\tself.draw(True)","def initialize_global_variables():\n\n # Screen Padding\n global BottomPadding, TopPadding, LeftPadding, RightPadding\n # Window Decoration\n global WinTitle, WinBorder\n # Grid Layout\n global CORNER_WIDTHS, CENTER_WIDTHS, Monitors, WidthAdjustment\n # Simple Layout\n global MwFactor\n # System Desktop and Screen Information\n global MaxWidth, MaxHeight, OrigX, OrigY, Desktop, WinList, OldWinList\n # Miscellaneous \n global TempFile, WindowFilter\n \n Config = initconfig()\n cfgSection=\"DEFAULT\"\n \n # use \"default\" for configurations written using the original stiler\n if Config.has_section(\"default\"):\n cfgSection=\"default\"\n\n BottomPadding = Config.getint(cfgSection,\"BottomPadding\")\n TopPadding = Config.getint(cfgSection,\"TopPadding\")\n LeftPadding = Config.getint(cfgSection,\"LeftPadding\")\n RightPadding = Config.getint(cfgSection,\"RightPadding\")\n WinTitle = Config.getint(cfgSection,\"WinTitle\")\n WinBorder = Config.getint(cfgSection,\"WinBorder\")\n MwFactor = Config.getfloat(cfgSection,\"MwFactor\")\n TempFile = Config.get(cfgSection,\"TempFile\")\n Monitors = Config.getint(cfgSection,\"Monitors\")\n WidthAdjustment = Config.getfloat(cfgSection,\"WidthAdjustment\")\n WindowFilter = Config.getboolean(cfgSection,\"WindowFilter\")\n CORNER_WIDTHS = map(lambda y:float(y),Config.get(cfgSection,\"GridWidths\").split(\",\"))\n\n # create the opposite section for each corner_width\n opposite_widths = []\n for width in CORNER_WIDTHS:\n opposite_widths.append(round(abs(1.0-width),2))\n\n # add the opposites\n CORNER_WIDTHS.extend(opposite_widths)\n\n CORNER_WIDTHS=list(set(CORNER_WIDTHS)) # filter out any duplicates\n CORNER_WIDTHS.sort()\n\n CENTER_WIDTHS = filter(lambda y: y < 0.5, CORNER_WIDTHS)\n CENTER_WIDTHS = map(lambda y:round(abs(y*2-1.0),2),CENTER_WIDTHS)\n CENTER_WIDTHS.append(1.0)\t\t\t\t # always allow max for centers\n CENTER_WIDTHS = list(set(CENTER_WIDTHS)) # filter dups\n CENTER_WIDTHS.sort()\n\n # Handle multiple monitors\n CORNER_WIDTHS=map(lambda y:round(y/Monitors,2)+WidthAdjustment,CORNER_WIDTHS)\n CENTER_WIDTHS=map(lambda y:round(y/Monitors,2)+WidthAdjustment,CENTER_WIDTHS)\n\n logging.debug(\"corner widths: %s\" % CORNER_WIDTHS)\n logging.debug(\"center widths: %s\" % CENTER_WIDTHS)\n\n (Desktop,OrigXstr,OrigYstr,MaxWidthStr,MaxHeightStr,WinList) = initialize()\n MaxWidth = int(MaxWidthStr) - LeftPadding - RightPadding\n MaxHeight = int(MaxHeightStr) - TopPadding - BottomPadding\n OrigX = int(OrigXstr) + LeftPadding\n OrigY = int(OrigYstr) + TopPadding \n OldWinList = retrieve(TempFile)","def configure_grid(node: tkt.Misc, colnum: int, rownum: int):\n for i in range(colnum):\n node.grid_columnconfigure(i, weight=1)\n\n for i in range(rownum):\n node.grid_rowconfigure(i, weight=1)","def __initialize_grid_dimensions(self) -> None:\n if len(self.players) > 3:\n self.num_columns = len(self.players)\n self.num_rows = self.num_cells // len(self.players)\n else:\n self.num_columns = self.num_cells // len(self.players)\n self.num_rows = len(self.players)\n self.grid_handler = GridHandler(self.num_columns, self.num_rows)","def init_grid(self):\n self.pts = np.array(\n np.meshgrid(\n np.arange(self.net_dim[0]) + 1,\n np.arange(self.net_dim[1]) + 1\n )\n ).reshape(2, np.prod(self.net_dim)).T\n if self.topo == \"hexagonal\":\n self.pts[:, 0] = self.pts[:, 0] + .5 * (self.pts[:, 1] % 2)\n self.pts[:, 1] = np.sqrt(3) / 2 * self.pts[:, 1]","def on_resize(self, *args):\n\n self.page_current.width = terminal.width # Give page new terminal width\n self.render_buffer = []\n\n self.render() # Re-render buffer","def fill_grid(self):\n\n for row_margin, row in enumerate(range(self.rows)):\n self.grid.append([])\n\n for col_margin, col in enumerate(range(self.cols)):\n x = col*self.cell_size + col_margin\n y = row*self.cell_size + row_margin\n\n rect = pygame.Rect(x, y, self.cell_size, self.cell_size)\n\n cell = Cell(row, col, rect)\n\n if row == 7 and col == 3:\n cell.root = True\n self.root = cell\n elif row == 7 and col == 16:\n cell.goal = True\n self.goal = cell\n\n self.grid[row].append(cell)","def project_grid(self):\n\n # option 1: no shared axes. Trees are scaled(?)\n\n\n # option 2: shared axes. Spacing of tree","def draw(self):\n\t\tfor i in range(0, self.size):\n\t\t\tprint('\\n' + \"----\" * self.size)\n\t\t\tfor j in range(0, self.size):\n\t\t\t\tprint(self.grid[i][j] + ' |', end=\" \")\n\t\tprint('\\n'+ \"----\" * self.size + '\\n')","def update_grid(self, player: str, letter: str) -> None:\n self.cells = [cell.update_cell(player, letter) for cell in self.cells]\n i = 0\n while i <= self.size:\n self.leylines[i].update_line(player, letter)\n self.rights[i].update_line(player, letter)\n self.lefts[i].update_line(player, letter)\n i += 1","def draw_grid(self, verbosity=0):\n log.debug(\"Drawing grid\")\n (x0, y0) = self.origin\n color = (191, 191, 191)\n\n (w, h) = self.surface.get_size()\n\n i = x0\n while True:\n (x, ignore) = self.map_to_screen((i, 0))\n if x > w:\n break\n pygame.draw.line(self.surface, color, (x, 0), (x, h), 1)\n i += 10\n\n j = y0\n while True:\n (ignore, y) = self.map_to_screen((0, j))\n if y > h:\n break\n pygame.draw.line(self.surface, color, (0, y), (w, y), 1)\n j -= 10","def paint(self, grid):\n grid[self.y, self.x] = self.appearance\n return grid","def draw(self):\n for x in range(self.numRows):\n print self.grid[x]","def draw(self):\n\n super().draw()\n \n self.dim = self.getdim()\n start_x, start_y, = self.x(), self.y()\n\n for y in range(self.r):\n for x in range(self.c):\n x_pos, y_pos = start_x + (self.dim * x), start_y + (self.dim * y)\n self.tiles[y][x].resize(x_pos, y_pos, self.dim, self.dim)","def print_grid(self):\r\n\t\tprint self.grid","def get_grid_width(self):\n # replace with your code\n return 0","def __update_table(self):\n\n headlines = [\"\", ]\n headlines += range(1, + 1)\n headlines = [\" \"] + [str(x) for x in range(1, self.find_table_length() + 1)]\n self.__main_display_table.config(columns=headlines)\n\n for headline in headlines:\n self.__main_display_table.heading(headline, text=headline)\n self.__main_display_table.column(headline, anchor=\"center\", width=35)\n\n data = self.__display_buses_location()\n\n for i in self.__main_display_table.get_children():\n # deletes all the data in the chart\n self.__main_display_table.delete(i)\n for line in data:\n # inserts new data into the chart, goes line by line\n self.__main_display_table.insert(\"\", END, values=line)","def set_global_layout_itc(self, h):\n\n #global rows\n self.layout[h, 0:self.num_global_blocks, :] = 1\n\n #global columns\n self.layout[h, :, 0:self.num_global_blocks] = 1","def grid_init(self):\n # draw.line(surface, color, start_pos, end_pos, width/thickness)\n pygame.draw.line(\n self.game_screen,\n GameData.line_color,\n (0, GameData.square_size),\n (GameData.screen_dim, GameData.square_size),\n GameData.line_width\n )\n # # 2 horizontal\n pygame.draw.line(\n self.game_screen,\n GameData.line_color,\n (0, 2 * GameData.square_size),\n (GameData.screen_dim,2 * GameData.square_size),\n GameData.line_width\n )\n\n # # 1 vertical\n pygame.draw.line(\n self.game_screen,\n GameData.line_color,\n (GameData.square_size, 0),\n (GameData.square_size, GameData.screen_dim),\n GameData.line_width\n )\n # # 2 vertical\n pygame.draw.line(\n self.game_screen,\n GameData.line_color,\n (2 * GameData.square_size, 0),\n (2 * GameData.square_size, GameData.screen_dim),\n GameData.line_width)","def update_grid(comp, grid):\n while not comp.waiting and not comp.halt_status:\n time.sleep(0.001)\n\n while not comp.output.empty():\n # Get 3 results\n x = comp.output.get()\n y = comp.output.get()\n b = comp.output.get()\n\n if x == -1 and y == 0:\n print(f\"Score = {b}\")\n\n grid[x][y] = b","def setLayout():\n global layout\n\n if p.GetString(\"Layout\") == \"Grid\":\n layoutFlow.setEnabled(False)\n layoutGrid.setEnabled(True)\n layoutStretch.setEnabled(True)\n layout = layoutGrid\n else:\n layoutGrid.setEnabled(False)\n layoutStretch.setEnabled(False)\n layoutFlow.setEnabled(True)\n layout = layoutFlow","def update_dimensions(self):\r\n # stores the old screen height for cleaning the screen\r\n old_w_height = self.w_height\r\n\r\n self.w_width, self.w_height = get_terminal_size()\r\n # see __init__\r\n self.w_width -= self.w_width % 2\r\n self.w_height -= self.w_height % 2\r\n\r\n # no need to clear screen if window size hasn't changed\r\n if old_w_height != self.w_height:\r\n self.clear_screen(old_w_height)","def Init_Adaptive_Grid(self,):\n self.Indicator[0] = 0\n self.Old[0] = 0\n \n for i in range(self.num_dim):\n self.Active[i] = i+1\n self.Indicator[i+1] = 1\n self.N_Forward[i,0] = i+2\n self.N_Backward[i,i+1] = 1\n self.Idx[i,i+1] = 1","def do_layout(self):\n self.define_panel_structure()\n self.layout_selection()\n self.layout_data_list()\n self.layout_batch()\n self.layout_button()","def show(self):\r\n display(self.grid_part)","def grow_main(self):\n self._grow_main(self.change_ratio)\n self.group.layout_all()","def drawGrid(self):\n\n if self.orientation == \"isometric\":\n for vline in range(0, self.map_array.shape[0]):\n line = self.canvas.create_line(iso(vline*self.cell_width, 0),\n iso(vline*self.cell_width, self.map_array.shape[0]*self.cell_height))\n self.canvas_objects.append(line)\n\n for hline in (range(0, self.map_array.shape[1])):\n line = self.canvas.create_line(iso(0, hline*self.cell_height),\n iso(self.map_array.shape[1]*self.cell_width, hline*self.cell_height))\n self.canvas_objects.append(line)\n self.canvas.bind(\"<Button-1>\", self.paintCells)\n self.canvas.bind(\"<Enter>\", self.drawFrame)\n self.canvas.bind(\"<Leave>\", self.killFrame)\n self.canvas.bind(\"<Motion>\", self.showFrame)"],"string":"[\n \"def _prepare_grid(self):\\n raise NotImplementedError\",\n \"def update_positions(self, grid):\\r\\n self.grid = grid\",\n \"def apply(self):\\n self.grid_size = self.values[0]\",\n \"def configure_grid(self):\\r\\n\\r\\n for r in range(3):\\r\\n self.rowconfigure(r, weight=1)\\r\\n for c in range(3):\\r\\n self.columnconfigure(c, weight=1)\",\n \"def prepare_screen_grid_display(grid, screen):\\n screen.blit(grid.prepare_screen_menu_grid, grid.prepare_screen_menu_grid_rect)\\n screen.blit(grid.prepare_screen_pick_deck_grid, grid.prepare_screen_pick_deck_grid_rect)\\n screen.blit(grid.prepare_screen_ai_setup_grid, grid.prepare_screen_ai_setup_grid_rect)\",\n \"def _update_dimensions(self):\\n _, self.width = self.window.getmaxyx()\\n self.spacing = self.width // self.total_columns\",\n \"def relayout(self): \\n\\t\\t#self.urmaswin.Layout()\\n\\t\\t#wx.CallAfter(self.urmaswin.Layout)\\n\\t\\t#wx.CallAfter(self.visualizer.OnSize)\",\n \"def recalculate_layout(self):\\n self.max_y, self.max_x = self.scr.getmaxyx()\\n self.num_columns = (1 + max(0, self.max_x - self.column_width)\\n // (self.column_width + self.column_gap))\\n if (self.num_columns * self.column_width +\\n self.num_columns * self.column_gap) < self.max_x - 3:\\n self.vis_columns = self.num_columns + 1\\n else:\\n self.vis_columns = self.num_columns\\n\\n if self.x >= self.num_columns:\\n # reposition x\\n ox = self.win_x + self.x\\n self.win_x = max(ox - self.num_columns + 1, 0)\\n self.x = self.num_columns - 1\\n if self.y >= self.max_y - self.header_offset:\\n # reposition y\\n oy = self.win_y + self.y\\n self.win_y = max(oy - (self.max_y - self.header_offset) + 1, 0)\\n self.y = self.max_y - self.header_offset - 1\",\n \"def _arrange_displays (self):\\n if self.locked:\\n self._changed = True\\n return []\\n n = len(self.displays)\\n if n == 0:\\n return []\\n # find the optimal number of initial splits (rows/columns)\\n data = []\\n # if not given, check splitting into both rows and columns\\n for axis in ((0, 1) if self.split is None else (self.split,)):\\n data.append(self._optimal_splits(axis) + (axis,))\\n error, splits, axis = min(data)\\n # determine distribution of displays over splits\\n per_split = int(ceil(float(n) / splits))\\n num_unfilled = splits * per_split - n\\n num_filled = splits - num_unfilled\\n # decide display sizes\\n screen_size = self.screen.get_size()\\n split_sizes = split(screen_size[not axis], splits)\\n to_fill = screen_size[axis]\\n filled_sizes = split(to_fill, per_split)\\n if self.expand:\\n unfilled_sizes = split(to_fill, per_split - 1)\\n else:\\n unfilled_sizes = filled_sizes[:-1]\\n # decide where unfilled splits go\\n if self.pos < 0:\\n filled = [False] * num_unfilled + [True] * num_filled\\n elif self.pos < 0:\\n filled = [True] * num_filled + [False] * num_unfilled\\n else:\\n filled = ([True] * (num_filled / 2) + [False] * num_unfilled +\\n [True] * (num_filled - num_filled / 2))\\n # determine allocation of padding in unfilled splits\\n padding = to_fill - sum(unfilled_sizes)\\n inner_padding = int(self.padding * padding)\\n outer_padding = padding - inner_padding\\n padding = [outer_padding / 2] + split(inner_padding, per_split - 2)\\n # construct rects and assign to displays\\n displays = self.displays[:]\\n changed = []\\n x = 0\\n #print n, axis, splits, split_sizes, filled_sizes, unfilled_sizes, filled\\n for i in xrange(splits):\\n y = 0\\n for j in xrange(per_split + filled[i] - 1):\\n display = displays.pop()\\n if not filled[i]:\\n # this split is unfilled: include padding\\n y += padding[j]\\n sizes = (unfilled_sizes, filled_sizes)[filled[i]]\\n # construct rect\\n if axis == 1:\\n rect = [x, y, split_sizes[i], sizes[j]]\\n else:\\n rect = [y, x, sizes[j], split_sizes[i]]\\n rect = pygame.Rect(rect)\\n if rect != display.rect:\\n changed.append(display)\\n display.rect = rect\\n y += sizes[j]\\n x += split_sizes[i]\\n return changed\",\n \"def set_grid(self,ug):\\n self.grd=ug\\n self.set_topology()\",\n \"def adjustSize( self ):\\n cell = self.scene().cellWidth() * 2\\n minheight = cell\\n minwidth = 2 * cell\\n \\n # fit to the grid size\\n metrics = QFontMetrics(QApplication.font())\\n width = metrics.width(self.displayName()) + 20\\n width = ((width/cell) * cell) + (cell % width)\\n \\n height = self.rect().height()\\n \\n # adjust for the icon\\n icon = self.icon()\\n if icon and not icon.isNull():\\n width += self.iconSize().width() + 2\\n height = max(height, self.iconSize().height() + 2)\\n \\n self.setMinimumWidth(max(width, minwidth))\\n self.setMinimumHeight(max(height, minheight))\\n \\n self.rebuild()\",\n \"def recreate_grid(self):\\n\\n self.print_numlist = arcade.SpriteList()\\n for row in range(ROW_COUNT):\\n for column in range(COLUMN_COUNT):\\n sprite = arcade.Sprite(\\n f\\\"Numbers/{self.grid[row][column]}.png\\\", scale=0.2\\n )\\n x = (MARGIN + WIDTH) * column + MARGIN + WIDTH // 2\\n y = (MARGIN + HEIGHT) * row + MARGIN + HEIGHT // 2\\n sprite.center_x = x\\n sprite.center_y = y\\n self.print_numlist.append(sprite)\\n # Check to see if all squares have been filled in\\n if 0 not in self.grid:\\n # if Cameron.Check_for_Completion(self.grid) == True:\\n self.done = True\",\n \"def give_space(self):\\n for child in self.winfo_children():\\n child.grid_configure(padx=5, pady=5)\",\n \"def resetgrid(self):\\n self.remainingminecount = self.minecount\\n self.board.reset()\\n self.button_array = [[QtGui.QPushButton() \\\\\\n for col in range(self.cols)] for row in range(self.rows)]\\n self.game_in_progress = True\\n self.first_click = True\\n for row in range(self.rows):\\n for col in range(self.cols):\\n self.button_array[row][col].setFixedSize(self.cell_size, self.cell_size)\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/unopenedsquare.png\\\"))\\n self.button_array[row][col].setIconSize(QtCore.QSize(self.cell_size,\\\\\\n self.cell_size))\\n self.button_array[row][col].clicked.connect(self.handle_left_click)\\n self.button_array[row][col].setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\\n self.button_array[row][col].customContextMenuRequested.connect(\\\\\\n self.handle_right_click)\\n self.cell_grid_layout.addWidget(self.button_array[row][col], row, col)\\n self.mines_lcd.display(str(self.remainingminecount))\\n self.status_button.setIcon(QtGui.QIcon(\\\"icons/smiley1.ico\\\"))\\n self.time = 0\\n self.time_lcd.display(self.time)\",\n \"def drawGrid(self):\\n for div in range(NBCELL):\\n sec = SSIZE*div\\n self.can.create_line(0, sec, GSIZE, sec, width=3, fill=GFILL)\\n self.can.create_line(sec, 0, sec, GSIZE, width=3, fill=GFILL)\",\n \"def _reset_display(self, game):\\n for coord in game.board.grid.keys():\\n canvas_coord = self._havannah_coord_to_canvas_coord(coord)\\n self._draw_hex(canvas_coord, self.HEX_BLANK)\",\n \"def normalize(self, redraw=True):\\n if self.screen_rect is not None:\\n self.relative_sizes = []\\n\\n height = self.screen_rect.height\\n left, right = self._get_columns()\\n\\n if left.count > 0:\\n self.relative_sizes += self._split_integer(height, left.count)\\n if right.count > 0:\\n self.relative_sizes += self._split_integer(height, right.count)\\n\\n if redraw:\\n self.group.layout_all()\\n self.do_normalize = False\",\n \"def initialize_grid(self) -> None:\\n for i in range(self.grid_size[0]):\\n for j in range(self.grid_size[1]):\\n self.set(i, j, self.base_color)\",\n \"def adjust_visual(self):\\n\\n if (self.direction is bs.Direction.LEFT):\\n self.rect.x -= 0.5 * CELL_SIZE\",\n \"def reset(self):\\n # replace with your code\\n self._grid = [[0 for dummy_column in range(self._grid_width)] for dummy_row in range(self._grid_height)]\\n for dummy_num in range(2):\\n self.new_tile()\",\n \"def show_grid(self):\\n for ax in (self.time_velocity, self.time_power, self.power_velocity):\\n ax.grid(True)\",\n \"def _do_layout(self):\\n return\",\n \"def draw_grid(self):\\n self.screen.draw_many_tiles(tile for tile in self.iter_grid_tiles())\\n pass\",\n \"def reset(self):\\r\\n self.grid = [[0 for dummy_col in range(self.grid_width)] for dummy_row in range(self.grid_height)]\\r\\n self.new_tile()\\r\\n self.new_tile()\",\n \"def _align_toplevel_grid(self):\\n\\n # align origin with nearest multple of 128\\n self.mins[0] -= self.mins[0] % 128\\n self.mins[1] -= self.mins[1] % 128\\n\\n width = self.maxs[0] - self.mins[0]\\n height = self.maxs[1] - self.mins[1]\\n greatest_dim = max(width, height)\\n nearest_pow_two = int(2 ** np.ceil(np.log2(greatest_dim)))\\n width_adjustment = (nearest_pow_two - width)\\n height_adjustment = (nearest_pow_two - height)\\n\\n self.maxs[0] += width_adjustment\\n self.maxs[1] += height_adjustment\",\n \"def set_grid(self, rows, columns):\\n self.get_words()\\n self.word_search_grid = []\\n for r in range(rows):\\n rlist = []\\n for c in range(columns):\\n rlist += [LetterField(self.centrepiece)]\\n rlist[-1].entry.grid(row = r, column = c)\\n self.word_search_grid += [rlist]\\n self.customiser.destroy()\\n \\n self.action_button.config(text = \\\"Solve Word Search\\\", command = self.solve, state = NORMAL)\",\n \"def resize (self):\\n return self._arrange_displays()\",\n \"def reset(self):\\n\\n for _ in range(self.grid_height):\\n row = [0 for _ in range(self.grid_width)]\\n self.grid.append(row)\",\n \"def updateDisplay(self):\\n if self._displayPjt:\\n self._displayPjt.updateim()\\n if self._displayUsr:\\n self._displayUsr.updateim()\\n if self._displayVtk:\\n self._displayVtk.updateim()\",\n \"def reset(self):\\n # replace with your code\\n self._grid = [[0] * self._width for _ in xrange(self._height)]\\n self.new_tile()\\n self.new_tile()\",\n \"def reset(self):\\r\\n # replace with your code\\r\\n self._cells = [[0 for dummy_col in range(self._grid_width)] for dummy_row in range(self._grid_height)]\\r\\n self.new_tile()\\r\\n self.new_tile()\",\n \"def updateGrid(self) -> None:\\n emu = self.emulator\\n arch = self.root.arch\\n registers = arch.registers\\n self.__values.setRowCount(len(registers))\\n for i, reg in enumerate(registers):\\n self.__values.setRowHeight(i, self.__row_size)\\n name = QTableWidgetItem(reg)\\n name.setFlags(Qt.NoItemFlags)\\n val = emu.get_register_value(reg) if emu.vm else 0\\n old_val = self.__old_register_values.get(reg, 0)\\n if type(val) in (int, int):\\n value = format_address(val, arch)\\n else:\\n value = str(val)\\n value = QTableWidgetItem( value )\\n if old_val != val:\\n self.__old_register_values[reg] = val\\n value.setForeground(QColor(Qt.red))\\n value.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable | Qt.ItemIsEditable)\\n self.__values.setItem(i, 0, name)\\n self.__values.setItem(i, 1, value)\\n return\",\n \"def draw_grid(self):\\n for x in range(0, WIDTH, TILESIZE):\\n pg.draw.line(self.screen, LIGHTGREY, (x, 0), (x, HEIGHT))\\n \\n for y in range(0, HEIGHT, TILESIZE):\\n pg.draw.line(self.screen, LIGHTGREY, (0, y), (WIDTH, y))\",\n \"def gen_grids(self):\\n self.dx = self.grid_width / self.grid_resol\\n self.dk = 2 * np.pi/self.grid_width\\n self.grid_x_shifted = -self.grid_width/2 + self.dx * np.arange(0, self.grid_resol)\\n self.grid_x = self.grid_x_shifted + self.grid_center\\n self.grid_k = - (np.pi * self.grid_resol)/self.grid_width + self.dk * np.arange(0, self.grid_resol)\\n self.grid_k = np.roll(self.grid_k, int((self.grid_resol)/2))\\n self.grid_kin = np.square(self.h)/ (2*self.m) * np.square(self.grid_k)\",\n \"def layout_board(self):\\n raise NotImplementedError\",\n \"def reset(self):\\n # replace with your code\\n dummy_row = self._grid_height\\n dummy_col = self._grid_width\\n self._cells = [ [0 for dummy_col in range(self._grid_width)] \\n for dummy_row in range(self._grid_height)]\\n \\n self.new_tile()\\n self.new_tile()\",\n \"def get_grid(self):\\n self.fullws = []\\n for row in self.word_search_grid:\\n rowdata = []\\n for column in row:\\n rowdata += [column.entry.get()]\\n self.fullws += [rowdata]\\n self.logic.set_grid(self.fullws)\",\n \"def reset(self):\\n # self.grid = [[0] * self.grid_width] * self.grid_height\\n self.grid = []\\n for dummy_row in range(self.grid_height):\\n new_row = []\\n for dummy_col in range(self.grid_width):\\n new_row.append(0)\\n self.grid.append(new_row)\\n self.new_tile()\\n self.new_tile()\",\n \"def fill_grid_np(self):\\n\\n self.grid_np = [None for i in range(GRID_HEIGHT*GRID_HEIGHT*MAX_CELL_SIZE)]\\n grid = self.grid_np\\n # cell_size = self.cell_size\\n for obj in self.levels[self.curient_level].objects:\\n obj.position_grid[X], obj.position_grid[Y] = get_grid_xy(obj.position_np, ZOMBIE_SIZE)\\n x, y = obj.position_grid[X], obj.position_grid[Y]\\n grid[y*GRID_WIDTH + x] = obj\\n # if cell_size[y*GRID_WIDTH + x] < MAX_CELL_SIZE:\\n # cell_size[y*GRID_WIDTH + x] += 1\",\n \"def update_grid(self):\\n if self.game_over:\\n return\\n if self.active_piece is None:\\n self.place_new_piece()\\n if self.piece_collision_exists(self.active_piece):\\n self.handle_active_piece_collision()\\n self.place_new_piece()\\n self.shift_cells(self.active_piece, self.current_direction)\\n self.active_piece = TransformPiece.shift_coordinates(self.active_piece, self.current_direction)\\n self.merge_with_completed_rows()\\n if self.is_game_won():\\n self.game_over = True\",\n \"def update_grid(self):\\n # Check to see if we have moved squares\\n _new_grid = self.calc_grid()\\n if _new_grid == self._grid:\\n return\\n # Remove from old square and add to new square\\n self.target._grid[self._grid][self._type].discard(self)\\n self.target._grid[_new_grid][self._type].add(self)\\n # Update coordinates\\n self._grid = _new_grid\",\n \"def update_grid(self):\\n def rgb_color(rgb):\\n \\\"\\\"\\\"RGB to HEX\\\"\\\"\\\"\\n return '#%02x%02x%02x' % rgb\\n\\n # clear on widgets preexisting on frame\\n for widget in self.main_grid.winfo_children():\\n widget.destroy()\\n\\n BORDER_WIDTH = 8\\n\\n # draw tiles\\n for i in range(len(self.main_grid_values)):\\n for j in range(len(self.main_grid_values[i])):\\n if self.main_grid_values[i][j] == 0:\\n # ignore 0 tiles for text\\n tile = tk.Label(self.main_grid, bg='red', text='', font=('Arial', 18))\\n else:\\n # give tiles a different color\\n exponent = int(math.log(int(self.main_grid_values[i][j]), 2))\\n tile = tk.Label(\\n self.main_grid,\\n bg=rgb_color((255, 255 - exponent*31, 0)),\\n text=self.main_grid_values[i][j],\\n font=('Arial', 20)\\n )\\n\\n # color the 0 tiles with grey\\n if tile['text'] == '':\\n tile.config(bg=rgb_color((210, 210, 210)))\\n\\n # border configuration - so that all borders are not overlapping\\n if j == len(self.main_grid_values) - 1 and i == len(self.main_grid_values) - 1:\\n tile.grid(row=i, column=j, padx=BORDER_WIDTH, pady=BORDER_WIDTH, sticky='news')\\n\\n elif j == len(self.main_grid_values) - 1:\\n tile.grid(row=i, column=j, padx=BORDER_WIDTH, pady=(BORDER_WIDTH, 0), sticky='news')\\n\\n elif i == len(self.main_grid_values) - 1:\\n tile.grid(row=i, column=j, padx=(BORDER_WIDTH, 0), pady=BORDER_WIDTH, sticky='news')\\n\\n else:\\n tile.grid(row=i, column=j, padx=(BORDER_WIDTH, 0), pady=(BORDER_WIDTH, 0), sticky='news')\",\n \"def do_relayout(self):\\n # This method is called whenever a relayout is requested. By\\n # default, this is when the layout children change. In that case\\n # we just need to update the min and max sizes. We are a top\\n # level window, so no one really cares about our size hint. \\n self.update_minimum_size()\\n self.update_maximum_size()\",\n \"def draw_grid(self) -> None:\\n for x in range(0, WIDTH, TILE_SIZE):\\n pg.draw.line(self.screen, LIGHT_GREY, (x, INFO_HEIGHT), (x, HEIGHT))\\n for y in range(INFO_HEIGHT, INFO_HEIGHT + HEIGHT, TILE_SIZE):\\n pg.draw.line(self.screen, LIGHT_GREY, (0, y), (WIDTH, y))\",\n \"def _reset_display(self, game):\\n for r, orow in enumerate(self.inner_boards):\\n for c, board in enumerate(orow):\\n for ir, irow in enumerate(board):\\n for ic, _ in enumerate(irow):\\n self._update_label((r,c), (ir,ic), TTTMove.BLANK)\",\n \"def grid(self, grid):\\n\\n self._grid = grid\",\n \"def update(self, grid, colRamp = ['white', 'blue']):\\n \\n # update the cell colors\\n for y in range(len(grid)):\\n yl = y + 1\\n for x in range(len(grid[y])):\\n xl = x + 1\\n color = colRamp[int(grid[y][x])]\\n self.displayWindow.update((xl, yl), color)\\n\\n # refresh the window\\n self.displayWindow.tkupdate()\",\n \"def reset(self):\\r\\n self._cells = [ [0 for dummy_col in range(self._grid_width)] \\r\\n for dummy_row in range(self._grid_height) ]\\r\\n \\r\\n \\r\\n self.new_tile()\\r\\n self.new_tile()\",\n \"def show_grid(self) -> None:\\n from pymol import cmd\\n from PyQt5 import QtWidgets\\n\\n global x, y, z\\n\\n if self.input.count() > 0:\\n # Get minimum and maximum dimensions of target PDB\\n pdb = self.input.currentText()\\n ([min_x, min_y, min_z], [max_x, max_y, max_z]) = cmd.get_extent(pdb)\\n\\n # Get Probe Out value\\n probe_out = self.probe_out.value()\\n probe_out = round(probe_out - round(probe_out, 4) % round(0.6, 4), 1)\\n\\n # Prepare dimensions\\n min_x = round(min_x - (min_x % 0.6), 1) - probe_out\\n min_y = round(min_y - (min_y % 0.6), 1) - probe_out\\n min_z = round(min_z - (min_z % 0.6), 1) - probe_out\\n max_x = round(max_x - (max_x % 0.6) + 0.6, 1) + probe_out\\n max_y = round(max_y - (max_y % 0.6) + 0.6, 1) + probe_out\\n max_z = round(max_z - (max_z % 0.6) + 0.6, 1) + probe_out\\n\\n # Get center of each dimension (x, y, z)\\n x = (min_x + max_x) / 2\\n y = (min_y + max_y) / 2\\n z = (min_z + max_z) / 2\\n\\n # Draw Grid\\n self.draw_grid(min_x, max_x, min_y, max_y, min_z, max_z)\\n else:\\n QtWidgets.QMessageBox.critical(self, \\\"Error\\\", \\\"Select an input PDB!\\\")\\n return\",\n \"def rescale(self, event: tkinter.Event) -> None:\\n # the properties which are linked to the event of reconfiguration\\n # contain all the new sizes of the panel :\\n self.width, self.height = event.width - 4, event.height - 4\\n # The subtraction of 4 pixels is here to compensate the width\\n # of the 'highlight bordure' rolling the canvas)\\n self.draw_board()\",\n \"def reset(self):\\r\\n self.grid = np.array([[' '] * self.width for row in range(self.height)])\\r\\n self.num_checkers = 0\",\n \"def get_grid(self):\\n self._export_mode = 'grid'\\n self._counter_update_data += 1\",\n \"def play(self):\\n self.populateGrid()\\n self.displayGrid()\",\n \"def adjust_columns(self):\\r\\n for col in range(3):\\r\\n self.resizeColumnToContents(col)\",\n \"def reset(self):\\n self._grid = [[0] * self._width for _ in range(self._height)]\\n self.new_tile()\\n self.new_tile()\",\n \"def reset(self):\\r\\n # creating the grid with the values all initialized to zero\\r\\n \\r\\n self._grid = [[ 0 for dummy_col in range(self._width)]\\r\\n for dummy_row in range(self._height)]\\r\\n # introducing the two initial tiles\\r\\n self.new_tile()\\r\\n self.new_tile()\\r\\n #for testing purposes\\r\\n #print self.grid\\r\\n #print self\\r\",\n \"def adjust_screen_size(self) -> None:\\n if self.screen:\\n max_row, max_cols = self.screen.getmaxyx()\\n if max_row < MIN_SIZE + len(self.all_items):\\n self.screen.resize(self.menu_height, max_cols)\\n self.draw()\",\n \"def reset(self):\\n self._grid = [[0 for dummy_col in range(self._width)]\\n for dummy_row in range(self._height)]\\n self.new_tile()\\n self.new_tile()\",\n \"def gridDisplay(self):\\n\\n if self.griddButton.isCheckable():\\n self.photo_grid.setVisible(False)\\n self.griddButton.setCheckable(False)\\n self.griddButton.setDown(False)\\n self.statustext.setText(\\\"Hide Grid\\\")\\n else:\\n self.griddButton.setCheckable(True)\\n self.photo_grid.setVisible(True)\\n self.griddButton.setDown(True)\\n self.statustext.setText(\\\"Display Grid - Rule of thirds\\\")\",\n \"def initialize(self, frame):\\n self.grid_size = 5\\n\\n Label(frame, text=\\\"Grid Size:\\\").grid(row=0)\\n\\n self.e1 = Scale(frame, from_=self.grid_size, to=25, orient=HORIZONTAL)\\n self.e1.grid(row=0, column=1)\\n\\n return self.e1\",\n \"def customise_grid(self):\\n\\n # Agent colours, agent tooltips and grid initialisation\\n mapper = linear_cmap(field_name='category',\\n palette=['blue', 'red', 'green', 'orange', 'purple'] ,\\n low=0,\\n high=4)\\n TOOLTIPS = [(\\\"Residential utility\\\", \\\"@res_utility\\\"),\\n ('Local composition', '@local_comp'),\\n ('Neighbourhood composition', '@n_comp'),\\n (\\\"School utility\\\", \\\"@school_utility\\\"),\\n ('Distance', '@dist_school'),\\n ('School composition', '@s_comp'),\\n ('School composition utility', '@school_comp_utility')]\\n hover = HoverTool(names=[\\\"households\\\", \\\"schools\\\"], tooltips=TOOLTIPS)\\n self.grid = figure(x_range=(self.model.grid.x_min - 1,\\n self.model.grid.x_max),\\n y_range=(self.model.grid.y_min - 1,\\n self.model.grid.y_max),\\n tools=[hover, 'tap', 'pan',\\n WheelZoomTool()],\\n tooltips=TOOLTIPS, output_backend=\\\"webgl\\\")\\n\\n # Set WheelZoomTool active by default if not lattice\\n if self.params['case'].lower() != 'lattice':\\n self.grid.toolbar.active_scroll = self.grid.select_one(\\n WheelZoomTool)\\n\\n # Add a background map using OpenStreetMap (Google Maps is too\\n # computationally expensive and cannot zoom properly)\\n self.grid.add_tile(get_provider('OSM'))\\n\\n self.grid.axis.visible = False\\n self.grid.grid.visible = False\\n # Function to highlight all households that are currently enrolled in\\n # the same school.\\n self.source.selected.on_change(\\\"indices\\\", self.select_households)\\n\\n # Plot households\\n self.grid.circle(x=\\\"x\\\",\\n y=\\\"y\\\",\\n size=5,\\n view=self.household_view,\\n source=self.source,\\n fill_color=mapper,\\n line_color='black',\\n alpha=0.8,\\n nonselection_fill_alpha=0.2,\\n selection_fill_alpha=1,\\n name='households')\\n\\n # Plot schools\\n self.grid.circle(x=\\\"x\\\",\\n y=\\\"y\\\",\\n size=7,\\n source=self.source,\\n view=self.school_view,\\n fill_color='yellow',\\n line_color='black',\\n name='schools')\\n\\n # Plot neighbourhoods\\n self.grid.patches('x',\\n 'y',\\n source=self.source,\\n view=self.neighbourhood_view,\\n fill_color=None,\\n line_color=\\\"black\\\",\\n line_width=2,\\n hover_alpha=0,\\n hover_line_color=None,\\n name='neighbourhoods',\\n selection_fill_alpha=0)\",\n \"def _grid_hint_size(self) -> int:\",\n \"def update_grid_pos(self):\\n self.grid_pos = self.get_tile_of_position(self.tank.body.position)\",\n \"def configureWidgetGrid(self, widget, horiz_split, vert_split):\\n for i in range(vert_split):\\n widget.grid_columnconfigure(i, weight = 1)\\n\\n for j in range(horiz_split):\\n widget.grid_rowconfigure(j, weight = 1)\",\n \"def grid(self):\\r\\n dimA = self.dimA ; dimC = self.dimA ; W_grid = self.W_grid\\r\\n \\r\\n self.tol = 10e-5\\r\\n self.Niter = 10000\\r\\n \\r\\n a0 = 100 / self.dimA\\r\\n c0 = 100 / self.dimA\\r\\n a_grid = np.mgrid[0:(dimA):1] ; a_grid = a0 * a_grid ; self.a_grid = a_grid\\r\\n c_grid = np.mgrid[0:(dimC):1] ; c_grid = c0 * c_grid ; self.c_grid = c_grid\\r\\n self.W_grid = W_grid\",\n \"def reset(self):\\n # replace with your code\\n self.board = [[0 for dummy_index in range(self.grid_width)] for dummy_inner_index in range(self.grid_height)]\",\n \"def setSystemGrid(self):\\n fromSystem = self.myGalaxy.systems[self.fromSystem]\\n toSystem = self.myGalaxy.systems[self.toSystem]\\n self.systemGrid = funcs.getMapQuadrant(toSystem, self, fromSystem.x, fromSystem.y,\\n toSystem.x, toSystem.y)\",\n \"def build_deck_screen_grid_display(grid, screen):\\n screen.blit(grid.build_deck_screen_card_gallery_grid, grid.build_deck_screen_card_gallery_grid_rect)\\n screen.blit(grid.build_deck_screen_deck_grid, grid.build_deck_screen_deck_grid_rect)\",\n \"def get_grid_size(self, ui, res_dir):\\r\\n print_it('determining grid size', PrintOpts.lvl1.value)\\r\\n self.sun.simple_clone()\\r\\n self.sun.clone.make_profile(PreSol.res_x.value, PreSol.res_y.value,\\r\\n self.init_force)\\r\\n self.planet.simple_clone()\\r\\n self.planet.clone.make_slave_to(self.sun.clone)\\r\\n\\r\\n init_displ = hertz_displ(self.sun.clone.e, self.planet.e,\\r\\n self.sun.clone.ny, self.planet.ny,\\r\\n self.sun.clone.r_hertz_x,\\r\\n self.sun.clone.r_hertz_y,\\r\\n self.planet.clone.r_hertz_x,\\r\\n self.planet.clone.r_hertz_y,\\r\\n self.sun.norm_forces[0])\\r\\n too_many_els_in_y = 1\\r\\n too_many_els_in_x = 1\\r\\n contact_width_y = 0.05\\r\\n contact_width_x = 0.05\\r\\n while too_many_els_in_y != 0 or \\\\\\r\\n too_many_els_in_x != 0:\\r\\n self.sun.clone.make_profile(self.sun.clone.res_x,\\r\\n self.sun.clone.res_y, self.init_force,\\r\\n contact_width=contact_width_y,\\r\\n contact_length=contact_width_x)\\r\\n self.planet.clone.make_slave_to(self.sun.clone)\\r\\n\\r\\n pressure, init_displ = \\\\\\r\\n pre_solve_half_space(self.sun.clone.profile,\\r\\n self.planet.clone.profile,\\r\\n self.sun.clone.x_axis,\\r\\n self.sun.clone.y_axis,\\r\\n self.sun.clone.res_x, self.sun.clone.res_y,\\r\\n self.sun.clone.delta_x,\\r\\n self.sun.clone.delta_y, self.sun.clone.e,\\r\\n self.planet.clone.e, self.sun.clone.ny,\\r\\n self.planet.clone.ny,\\r\\n self.sun.norm_forces[0],\\r\\n init_displ=init_displ, print_prog=False)\\r\\n\\r\\n pressure_els_y = sum(\\r\\n pressure[math.floor(self.sun.clone.res_y / 2), :] > 0)\\r\\n too_many_els_in_y = self.sun.clone.res_y - pressure_els_y - 2\\r\\n if too_many_els_in_y:\\r\\n contact_width_y += -np.sign(\\r\\n too_many_els_in_y) * contact_width_y / 25\\r\\n\\r\\n pressure_els_x = sum(\\r\\n pressure[:, math.floor(self.sun.clone.res_x / 2)] > 0)\\r\\n too_many_els_in_x = self.sun.clone.res_x - pressure_els_x - 2\\r\\n if too_many_els_in_x:\\r\\n contact_width_x += -np.sign(\\r\\n too_many_els_in_x) * contact_width_x / 25\\r\\n\\r\\n self.sun.make_profile(self.sun.res_x, self.sun.res_y, self.init_force,\\r\\n contact_width=contact_width_y,\\r\\n contact_length=contact_width_x)\\r\\n self.planet.make_slave_to(self.sun)\\r\\n return init_displ\",\n \"def displayGrid(self, toggled):\\n self.scene.setGridVisible(visible=toggled)\",\n \"def battle_screen_grid_display(grid, screen):\\n screen.blit(grid.battle_screen_menu_grid, grid.battle_screen_menu_grid_rect)\\n screen.blit(grid.battle_screen_deck_grid, grid.battle_screen_deck_grid_rect)\\n screen.blit(grid.battle_screen_character_1_grid, grid.battle_screen_character_1_grid_rect)\\n screen.blit(grid.battle_screen_character_2_grid, grid.battle_screen_character_2_grid_rect)\\n screen.blit(grid.battle_screen_battle_1_grid, grid.battle_screen_battle_1_grid_rect)\\n screen.blit(grid.battle_screen_battle_2_grid, grid.battle_screen_battle_2_grid_rect)\\n screen.blit(grid.battle_screen_item_1_grid, grid.battle_screen_item_1_grid_rect)\\n screen.blit(grid.battle_screen_item_2_grid, grid.battle_screen_item_2_grid_rect)\\n screen.blit(grid.battle_screen_instruction_bar_grid, grid.battle_screen_instruction_bar_grid_rect)\",\n \"def _layout(self):\\n top = 2.0\\n y = 0.0\\n x = 0.0\\n maxend = 0.0\\n for track in self._tracks:\\n track.set_view(self.view.species, self.view.seqname, \\n self.view.start, self.view.end)\\n tracksize = track.get_size()\\n y -= tracksize[1]\\n track.set_pos(track.pos_offset[0] + x, track.pos_offset[1] + y)\\n self.size = [self.view.end - self.view.start + 1, 0 - y]\",\n \"def process_grid(self, grid: Grid) -> Grid:\",\n \"def __update_visible(self) -> None:\\n for i in range(0, 8):\\n visible_row = self.__row_position + Labyrinth.ALL_ROW_MOVE[i]\\n visible_col = self.__col_position + Labyrinth.ALL_COL_MOVE[i]\\n if 0 <= visible_row < self.__labyrinth.labyrinth_height and \\\\\\n 0 <= visible_col < self.__labyrinth.labyrinth_width:\\n self.__labyrinth.visible_cells[visible_row][visible_col] = 1\",\n \"def resize(self):\\n\\t\\tself.win.erase()\\n\\t\\tfor c in self.components:\\n\\t\\t\\tc.resize()\\n\\t\\tself.draw(True)\",\n \"def initialize_global_variables():\\n\\n # Screen Padding\\n global BottomPadding, TopPadding, LeftPadding, RightPadding\\n # Window Decoration\\n global WinTitle, WinBorder\\n # Grid Layout\\n global CORNER_WIDTHS, CENTER_WIDTHS, Monitors, WidthAdjustment\\n # Simple Layout\\n global MwFactor\\n # System Desktop and Screen Information\\n global MaxWidth, MaxHeight, OrigX, OrigY, Desktop, WinList, OldWinList\\n # Miscellaneous \\n global TempFile, WindowFilter\\n \\n Config = initconfig()\\n cfgSection=\\\"DEFAULT\\\"\\n \\n # use \\\"default\\\" for configurations written using the original stiler\\n if Config.has_section(\\\"default\\\"):\\n cfgSection=\\\"default\\\"\\n\\n BottomPadding = Config.getint(cfgSection,\\\"BottomPadding\\\")\\n TopPadding = Config.getint(cfgSection,\\\"TopPadding\\\")\\n LeftPadding = Config.getint(cfgSection,\\\"LeftPadding\\\")\\n RightPadding = Config.getint(cfgSection,\\\"RightPadding\\\")\\n WinTitle = Config.getint(cfgSection,\\\"WinTitle\\\")\\n WinBorder = Config.getint(cfgSection,\\\"WinBorder\\\")\\n MwFactor = Config.getfloat(cfgSection,\\\"MwFactor\\\")\\n TempFile = Config.get(cfgSection,\\\"TempFile\\\")\\n Monitors = Config.getint(cfgSection,\\\"Monitors\\\")\\n WidthAdjustment = Config.getfloat(cfgSection,\\\"WidthAdjustment\\\")\\n WindowFilter = Config.getboolean(cfgSection,\\\"WindowFilter\\\")\\n CORNER_WIDTHS = map(lambda y:float(y),Config.get(cfgSection,\\\"GridWidths\\\").split(\\\",\\\"))\\n\\n # create the opposite section for each corner_width\\n opposite_widths = []\\n for width in CORNER_WIDTHS:\\n opposite_widths.append(round(abs(1.0-width),2))\\n\\n # add the opposites\\n CORNER_WIDTHS.extend(opposite_widths)\\n\\n CORNER_WIDTHS=list(set(CORNER_WIDTHS)) # filter out any duplicates\\n CORNER_WIDTHS.sort()\\n\\n CENTER_WIDTHS = filter(lambda y: y < 0.5, CORNER_WIDTHS)\\n CENTER_WIDTHS = map(lambda y:round(abs(y*2-1.0),2),CENTER_WIDTHS)\\n CENTER_WIDTHS.append(1.0)\\t\\t\\t\\t # always allow max for centers\\n CENTER_WIDTHS = list(set(CENTER_WIDTHS)) # filter dups\\n CENTER_WIDTHS.sort()\\n\\n # Handle multiple monitors\\n CORNER_WIDTHS=map(lambda y:round(y/Monitors,2)+WidthAdjustment,CORNER_WIDTHS)\\n CENTER_WIDTHS=map(lambda y:round(y/Monitors,2)+WidthAdjustment,CENTER_WIDTHS)\\n\\n logging.debug(\\\"corner widths: %s\\\" % CORNER_WIDTHS)\\n logging.debug(\\\"center widths: %s\\\" % CENTER_WIDTHS)\\n\\n (Desktop,OrigXstr,OrigYstr,MaxWidthStr,MaxHeightStr,WinList) = initialize()\\n MaxWidth = int(MaxWidthStr) - LeftPadding - RightPadding\\n MaxHeight = int(MaxHeightStr) - TopPadding - BottomPadding\\n OrigX = int(OrigXstr) + LeftPadding\\n OrigY = int(OrigYstr) + TopPadding \\n OldWinList = retrieve(TempFile)\",\n \"def configure_grid(node: tkt.Misc, colnum: int, rownum: int):\\n for i in range(colnum):\\n node.grid_columnconfigure(i, weight=1)\\n\\n for i in range(rownum):\\n node.grid_rowconfigure(i, weight=1)\",\n \"def __initialize_grid_dimensions(self) -> None:\\n if len(self.players) > 3:\\n self.num_columns = len(self.players)\\n self.num_rows = self.num_cells // len(self.players)\\n else:\\n self.num_columns = self.num_cells // len(self.players)\\n self.num_rows = len(self.players)\\n self.grid_handler = GridHandler(self.num_columns, self.num_rows)\",\n \"def init_grid(self):\\n self.pts = np.array(\\n np.meshgrid(\\n np.arange(self.net_dim[0]) + 1,\\n np.arange(self.net_dim[1]) + 1\\n )\\n ).reshape(2, np.prod(self.net_dim)).T\\n if self.topo == \\\"hexagonal\\\":\\n self.pts[:, 0] = self.pts[:, 0] + .5 * (self.pts[:, 1] % 2)\\n self.pts[:, 1] = np.sqrt(3) / 2 * self.pts[:, 1]\",\n \"def on_resize(self, *args):\\n\\n self.page_current.width = terminal.width # Give page new terminal width\\n self.render_buffer = []\\n\\n self.render() # Re-render buffer\",\n \"def fill_grid(self):\\n\\n for row_margin, row in enumerate(range(self.rows)):\\n self.grid.append([])\\n\\n for col_margin, col in enumerate(range(self.cols)):\\n x = col*self.cell_size + col_margin\\n y = row*self.cell_size + row_margin\\n\\n rect = pygame.Rect(x, y, self.cell_size, self.cell_size)\\n\\n cell = Cell(row, col, rect)\\n\\n if row == 7 and col == 3:\\n cell.root = True\\n self.root = cell\\n elif row == 7 and col == 16:\\n cell.goal = True\\n self.goal = cell\\n\\n self.grid[row].append(cell)\",\n \"def project_grid(self):\\n\\n # option 1: no shared axes. Trees are scaled(?)\\n\\n\\n # option 2: shared axes. Spacing of tree\",\n \"def draw(self):\\n\\t\\tfor i in range(0, self.size):\\n\\t\\t\\tprint('\\\\n' + \\\"----\\\" * self.size)\\n\\t\\t\\tfor j in range(0, self.size):\\n\\t\\t\\t\\tprint(self.grid[i][j] + ' |', end=\\\" \\\")\\n\\t\\tprint('\\\\n'+ \\\"----\\\" * self.size + '\\\\n')\",\n \"def update_grid(self, player: str, letter: str) -> None:\\n self.cells = [cell.update_cell(player, letter) for cell in self.cells]\\n i = 0\\n while i <= self.size:\\n self.leylines[i].update_line(player, letter)\\n self.rights[i].update_line(player, letter)\\n self.lefts[i].update_line(player, letter)\\n i += 1\",\n \"def draw_grid(self, verbosity=0):\\n log.debug(\\\"Drawing grid\\\")\\n (x0, y0) = self.origin\\n color = (191, 191, 191)\\n\\n (w, h) = self.surface.get_size()\\n\\n i = x0\\n while True:\\n (x, ignore) = self.map_to_screen((i, 0))\\n if x > w:\\n break\\n pygame.draw.line(self.surface, color, (x, 0), (x, h), 1)\\n i += 10\\n\\n j = y0\\n while True:\\n (ignore, y) = self.map_to_screen((0, j))\\n if y > h:\\n break\\n pygame.draw.line(self.surface, color, (0, y), (w, y), 1)\\n j -= 10\",\n \"def paint(self, grid):\\n grid[self.y, self.x] = self.appearance\\n return grid\",\n \"def draw(self):\\n for x in range(self.numRows):\\n print self.grid[x]\",\n \"def draw(self):\\n\\n super().draw()\\n \\n self.dim = self.getdim()\\n start_x, start_y, = self.x(), self.y()\\n\\n for y in range(self.r):\\n for x in range(self.c):\\n x_pos, y_pos = start_x + (self.dim * x), start_y + (self.dim * y)\\n self.tiles[y][x].resize(x_pos, y_pos, self.dim, self.dim)\",\n \"def print_grid(self):\\r\\n\\t\\tprint self.grid\",\n \"def get_grid_width(self):\\n # replace with your code\\n return 0\",\n \"def __update_table(self):\\n\\n headlines = [\\\"\\\", ]\\n headlines += range(1, + 1)\\n headlines = [\\\" \\\"] + [str(x) for x in range(1, self.find_table_length() + 1)]\\n self.__main_display_table.config(columns=headlines)\\n\\n for headline in headlines:\\n self.__main_display_table.heading(headline, text=headline)\\n self.__main_display_table.column(headline, anchor=\\\"center\\\", width=35)\\n\\n data = self.__display_buses_location()\\n\\n for i in self.__main_display_table.get_children():\\n # deletes all the data in the chart\\n self.__main_display_table.delete(i)\\n for line in data:\\n # inserts new data into the chart, goes line by line\\n self.__main_display_table.insert(\\\"\\\", END, values=line)\",\n \"def set_global_layout_itc(self, h):\\n\\n #global rows\\n self.layout[h, 0:self.num_global_blocks, :] = 1\\n\\n #global columns\\n self.layout[h, :, 0:self.num_global_blocks] = 1\",\n \"def grid_init(self):\\n # draw.line(surface, color, start_pos, end_pos, width/thickness)\\n pygame.draw.line(\\n self.game_screen,\\n GameData.line_color,\\n (0, GameData.square_size),\\n (GameData.screen_dim, GameData.square_size),\\n GameData.line_width\\n )\\n # # 2 horizontal\\n pygame.draw.line(\\n self.game_screen,\\n GameData.line_color,\\n (0, 2 * GameData.square_size),\\n (GameData.screen_dim,2 * GameData.square_size),\\n GameData.line_width\\n )\\n\\n # # 1 vertical\\n pygame.draw.line(\\n self.game_screen,\\n GameData.line_color,\\n (GameData.square_size, 0),\\n (GameData.square_size, GameData.screen_dim),\\n GameData.line_width\\n )\\n # # 2 vertical\\n pygame.draw.line(\\n self.game_screen,\\n GameData.line_color,\\n (2 * GameData.square_size, 0),\\n (2 * GameData.square_size, GameData.screen_dim),\\n GameData.line_width)\",\n \"def update_grid(comp, grid):\\n while not comp.waiting and not comp.halt_status:\\n time.sleep(0.001)\\n\\n while not comp.output.empty():\\n # Get 3 results\\n x = comp.output.get()\\n y = comp.output.get()\\n b = comp.output.get()\\n\\n if x == -1 and y == 0:\\n print(f\\\"Score = {b}\\\")\\n\\n grid[x][y] = b\",\n \"def setLayout():\\n global layout\\n\\n if p.GetString(\\\"Layout\\\") == \\\"Grid\\\":\\n layoutFlow.setEnabled(False)\\n layoutGrid.setEnabled(True)\\n layoutStretch.setEnabled(True)\\n layout = layoutGrid\\n else:\\n layoutGrid.setEnabled(False)\\n layoutStretch.setEnabled(False)\\n layoutFlow.setEnabled(True)\\n layout = layoutFlow\",\n \"def update_dimensions(self):\\r\\n # stores the old screen height for cleaning the screen\\r\\n old_w_height = self.w_height\\r\\n\\r\\n self.w_width, self.w_height = get_terminal_size()\\r\\n # see __init__\\r\\n self.w_width -= self.w_width % 2\\r\\n self.w_height -= self.w_height % 2\\r\\n\\r\\n # no need to clear screen if window size hasn't changed\\r\\n if old_w_height != self.w_height:\\r\\n self.clear_screen(old_w_height)\",\n \"def Init_Adaptive_Grid(self,):\\n self.Indicator[0] = 0\\n self.Old[0] = 0\\n \\n for i in range(self.num_dim):\\n self.Active[i] = i+1\\n self.Indicator[i+1] = 1\\n self.N_Forward[i,0] = i+2\\n self.N_Backward[i,i+1] = 1\\n self.Idx[i,i+1] = 1\",\n \"def do_layout(self):\\n self.define_panel_structure()\\n self.layout_selection()\\n self.layout_data_list()\\n self.layout_batch()\\n self.layout_button()\",\n \"def show(self):\\r\\n display(self.grid_part)\",\n \"def grow_main(self):\\n self._grow_main(self.change_ratio)\\n self.group.layout_all()\",\n \"def drawGrid(self):\\n\\n if self.orientation == \\\"isometric\\\":\\n for vline in range(0, self.map_array.shape[0]):\\n line = self.canvas.create_line(iso(vline*self.cell_width, 0),\\n iso(vline*self.cell_width, self.map_array.shape[0]*self.cell_height))\\n self.canvas_objects.append(line)\\n\\n for hline in (range(0, self.map_array.shape[1])):\\n line = self.canvas.create_line(iso(0, hline*self.cell_height),\\n iso(self.map_array.shape[1]*self.cell_width, hline*self.cell_height))\\n self.canvas_objects.append(line)\\n self.canvas.bind(\\\"<Button-1>\\\", self.paintCells)\\n self.canvas.bind(\\\"<Enter>\\\", self.drawFrame)\\n self.canvas.bind(\\\"<Leave>\\\", self.killFrame)\\n self.canvas.bind(\\\"<Motion>\\\", self.showFrame)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67337763","0.65539414","0.6454927","0.64203435","0.63934195","0.63931537","0.63745236","0.63429075","0.6283407","0.6222548","0.62183297","0.620335","0.6120996","0.6119752","0.6101794","0.60955167","0.6091233","0.60688007","0.6063356","0.6054027","0.60358065","0.6035793","0.6024286","0.6022243","0.59848416","0.5978418","0.5975005","0.5972734","0.59693515","0.59466535","0.5934746","0.5925724","0.59223145","0.5905698","0.5900853","0.5897937","0.58968246","0.58957404","0.5892263","0.5890441","0.5889452","0.5885783","0.58811563","0.58759975","0.58684886","0.58636683","0.5839126","0.5838472","0.583206","0.5817308","0.5816574","0.5805289","0.57856596","0.576975","0.57585734","0.57564974","0.57558817","0.5754961","0.574815","0.57457775","0.57318485","0.57314664","0.57276714","0.57191455","0.57186973","0.57182336","0.5709386","0.5706915","0.57032835","0.5701165","0.56963104","0.56924915","0.5681741","0.56773883","0.5676914","0.56731904","0.5671045","0.5664039","0.5662867","0.56612843","0.56536746","0.5644512","0.5641598","0.5641479","0.56381464","0.5637555","0.56291777","0.5627213","0.5615653","0.5611437","0.56088847","0.5608156","0.5605189","0.5601834","0.55930966","0.5592856","0.55927634","0.55923855","0.55898726","0.55888456","0.5585994"],"string":"[\n \"0.67337763\",\n \"0.65539414\",\n \"0.6454927\",\n \"0.64203435\",\n \"0.63934195\",\n \"0.63931537\",\n \"0.63745236\",\n \"0.63429075\",\n \"0.6283407\",\n \"0.6222548\",\n \"0.62183297\",\n \"0.620335\",\n \"0.6120996\",\n \"0.6119752\",\n \"0.6101794\",\n \"0.60955167\",\n \"0.6091233\",\n \"0.60688007\",\n \"0.6063356\",\n \"0.6054027\",\n \"0.60358065\",\n \"0.6035793\",\n \"0.6024286\",\n \"0.6022243\",\n \"0.59848416\",\n \"0.5978418\",\n \"0.5975005\",\n \"0.5972734\",\n \"0.59693515\",\n \"0.59466535\",\n \"0.5934746\",\n \"0.5925724\",\n \"0.59223145\",\n \"0.5905698\",\n \"0.5900853\",\n \"0.5897937\",\n \"0.58968246\",\n \"0.58957404\",\n \"0.5892263\",\n \"0.5890441\",\n \"0.5889452\",\n \"0.5885783\",\n \"0.58811563\",\n \"0.58759975\",\n \"0.58684886\",\n \"0.58636683\",\n \"0.5839126\",\n \"0.5838472\",\n \"0.583206\",\n \"0.5817308\",\n \"0.5816574\",\n \"0.5805289\",\n \"0.57856596\",\n \"0.576975\",\n \"0.57585734\",\n \"0.57564974\",\n \"0.57558817\",\n \"0.5754961\",\n \"0.574815\",\n \"0.57457775\",\n \"0.57318485\",\n \"0.57314664\",\n \"0.57276714\",\n \"0.57191455\",\n \"0.57186973\",\n \"0.57182336\",\n \"0.5709386\",\n \"0.5706915\",\n \"0.57032835\",\n \"0.5701165\",\n \"0.56963104\",\n \"0.56924915\",\n \"0.5681741\",\n \"0.56773883\",\n \"0.5676914\",\n \"0.56731904\",\n \"0.5671045\",\n \"0.5664039\",\n \"0.5662867\",\n \"0.56612843\",\n \"0.56536746\",\n \"0.5644512\",\n \"0.5641598\",\n \"0.5641479\",\n \"0.56381464\",\n \"0.5637555\",\n \"0.56291777\",\n \"0.5627213\",\n \"0.5615653\",\n \"0.5611437\",\n \"0.56088847\",\n \"0.5608156\",\n \"0.5605189\",\n \"0.5601834\",\n \"0.55930966\",\n \"0.5592856\",\n \"0.55927634\",\n \"0.55923855\",\n \"0.55898726\",\n \"0.55888456\",\n \"0.5585994\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":25299,"cells":{"query":{"kind":"string","value":"Iterate through self.extra_dominoes, start at random position. a generator of dominoes."},"document":{"kind":"string","value":"def choose_extra_dominoes(self, random):\n dominoes = self.extra_dominoes[:]\n count = len(dominoes)\n start = random.randrange(count)\n for i in range(count):\n yield dominoes[(i + start) % count]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def choose_and_flip_extra_dominoes(self, random):\n for domino in self.choose_extra_dominoes(random):\n if domino.head.pips == domino.tail.pips:\n yield domino, False\n else:\n flip_first = random.randint(0, 1)\n for j in range(2):\n yield domino, flip_first + j == 1","def pickup_dominoes(self, num_dominoes, player):\n\n for domino in range(num_dominoes):\n shuffle(self.boneyard)\n player.hand.append(self.boneyard.pop(0))","def derandomize(self, xs, noises):\n raise NotImplementedError","def _all_donors(self, include_background=True):\n sheets = self.shortcut_sheets\n if not include_background:\n sheets = filter(is_not_background, sheets)\n for sheet in sheets:\n for entity in sheet.bio_entities.values():\n yield entity","def __dice_generator(self):\n self.current_dice = np.random.randint(1, 6 + 1)","def get_seeds(self, start: float, num: int) -> np.ndarray:\r\n\r\n if self.integral:\r\n if self.domain[1] - self.domain[0] + 1 <= num:\r\n return np.arange(self.domain[0], self.domain[1] + 1)\r\n result = np.random.choice(\r\n np.arange(self.domain[0], self.domain[1] + 1), num, replace=False)\r\n if start not in result:\r\n result[0] = start\r\n return result\r\n\r\n\r\n result = np.zeros((num,), dtype='float64')\r\n result[0] = start\r\n min_sep = (self.domain[1] - self.domain[0]) * min(0.05, 0.25 / num)\r\n\r\n for i in range(1, num):\r\n rejections = 0\r\n while True:\r\n _pt = np.random.uniform(self.domain[0], self.domain[1], 1)\r\n if sdist.cdist(_pt.reshape(1, 1), result[:i].reshape(i, 1)).min() < min_sep:\r\n rejections += 1\r\n if rejections > 50000:\r\n raise ValueError(\r\n f'failed to sample! domain might be singleton: {self.domain}')\r\n else:\r\n result[i] = _pt\r\n break\r\n\r\n return result","def generate_random_walker():\n # must have seeds that generate known problems\n must_have_seeds = [112, 308, 393]\n for seed in must_have_seeds:\n print(\"Last used seed: {}\".format(seed))\n detections = detections_simple_tracking(seed)\n yield simple_walker(data_simple_tracking(detections)), detections\n while True:\n seed = random.randint(0, 2**10)\n print(\"Last used seed: {}\".format(seed))\n detections = detections_simple_tracking(seed)\n yield simple_walker(data_simple_tracking(detections)), detections","def iter_dist(self):\n self.makeTree()\n coords = self.coords\n sd = selfdistance\n for i in self.loopindices:\n dists, inds = self.nntree.query(coords[i], self.nnmaxcount,\n distance_upper_bound=self.nncutoff)\n yield coords[i], dists.compress((dists > sd) & ~np.isinf(dists))","def get_examples():\n symbols = get_symbols()\n symbol_start_indices, total_examples = _index_symbols(symbols)\n selection_order = np.arange(total_examples)\n np.random.shuffle(selection_order)\n for sample_index in selection_order:\n # use a binary search to determine which symbol to use given the sample_index\n start_index_index = bisect.bisect_left(symbol_start_indices, sample_index + 1) - 1\n start_index = symbol_start_indices[start_index_index]\n offset = sample_index - start_index\n symbol = symbols[start_index_index]\n time_series_data = get_time_series_data(symbol)\n next_day = time_series_data[offset]\n previous_days = time_series_data[offset + 1: offset + 1 + EXAMPLE_SIZE]\n previous_days.reverse()\n metadata = get_metadata_for_symbol(symbol)\n yield previous_days, next_day, metadata","def split_all(self):\n for domino in self.dominoes[:]:\n self.split(domino)","def nextGen(self):\n\n p = []\n while len(p) < len(self.p):\n #select mates and produce offspring\n p1, p2 = self.select()\n offspring = self.mate(p1, p2)\n\n #put the offspring in the next generation (with mutation)\n for child in offspring:\n child=self.mutate(child)\n p.append(child)\n \n\n # the world belongs to the new generation\n return p","def getRandomCoordinates( self, size ):\n if not self.mIsLoaded: self.__loadIndex()\n\n token = random.choice( self.mIndex.keys() ) \n strand = random.choice( (\"+\", \"-\") )\n pos_id, pos_seq, lcontig = self.mIndex[token][:3]\n rpos = random.randint( 0, lcontig )\n if random.choice( (\"True\", \"False\") ):\n start = rpos\n end = min(rpos + size, lcontig)\n else:\n start = max(0, rpos - size)\n end = rpos\n \n return token, strand, start, end","def insere_n_nos(self, num_nos):\n for i in range(num_nos):\n index = random.randint(1, 11 ** 4)\n elem = random.randint(1, 11 ** 4)\n self.insere(index, elem)","def create_next_gen(self, parents_sreprs_couple):\n child0, child1 = self.recombine(parents_sreprs_couple[0], parents_sreprs_couple[1])\n if random.random() < self.mutate_prob:\n child0 = self.mutate(child0)\n if random.random() < self.mutate_prob:\n child1 = self.mutate(child1)\n\n return child0, child1","def generate_random_tomogram_set(templates, criteria, number_of_tomograms, dim, seed=None, noise=False):\n if (seed == None):\n seed = random.randint(0, 2 ** 31 - 1)\n print('Using random seed: ', seed)\n random.seed(int(seed))\n np.random.seed(int(seed))\n\n for i in range(number_of_tomograms):\n yield generate_random_tomogram(templates, criteria, dim, noise)","def play_dominoes(draw=draw_init, save_file=None, num_games=1000, save=True):\n list_victories = [0]*(NUM_PLAYERS+1)\n game = Dominoes(NUM_PLAYERS, HAND_SIZE, TOPNUM, save_file=save_file)\n \n # Setups the game. This includes initializing the player instances.\n player1 = RandomDominoPlayer(1, game)\n player2 = RandomDominoPlayer(2, game)\n player3 = RandomDominoPlayer(3, game)\n player4 = RandomDominoPlayer(4, game)\n list_players = (player1, player2, player3, player4)\n game.setup(list_players)\n\n # Play!\n for i in range(num_games):\n # Cleans the game state\n game.reset()\n \n # Draws the starting dominoes.\n starting_dominoes = draw(NUM_PLAYERS, DOMINO, HAND_SIZE)\n# print('\\nStarting_dominoes')\n# print(starting_dominoes[0])\n# print(starting_dominoes[1])\n# print(starting_dominoes[2])\n# print(starting_dominoes[3])\n \n # Assigns the starting dominoes to each player\n for player, dominoes in zip(list_players, starting_dominoes):\n player.reset(start_dominoes=list(dominoes))\n \n # Rolls the game!\n winner = roll_game(game, save=save)\n list_victories[winner] += 1\n if (i*20) % num_games == 0:\n print(i*100/num_games, '% processed.')\n \n# print('Salida:', game.salida)\n \n print('\\n', list_victories, '\\t', list_victories[1]+list_victories[3], \n list_victories[2]+list_victories[4])","def random_iterator(self, batch_size):\n all_indices = np.nonzero(np.logical_not(self._dones))[0]\n np.random.shuffle(all_indices)\n\n states = np.asarray(self._states)\n actions = np.asarray(self._actions)\n next_states = np.asarray(self._next_states)\n rewards = np.asarray(self._rewards)\n dones = np.asarray(self._dones)\n\n i = 0\n while i < len(all_indices):\n indices = all_indices[i:i+batch_size]\n\n yield states[indices], actions[indices], next_states[indices], rewards[indices], dones[indices]\n\n i += batch_size","def _build_genotypes(self):\n x = np.zeros(self.n)\n \n # Frequencies derived from HWE.\n num_hetero = 2 * self.maf * (1 - self.maf) * self.n\n num_homo_minor = self.maf ** 2 * self.n\n \n x[:num_hetero] = 1\n x[num_hetero:num_hetero+num_homo_minor] = 2\n np.random.shuffle(x)\n \n # Add noise for dosage values if needed.\n if self.dosage_var:\n x[x == 0] += np.abs(\n np.random.normal(0, self.dosage_var, len(x[x == 0]))\n )\n x[x == 1] += np.random.normal(0, self.dosage_var, len(x[x == 1]))\n x[x == 2] -= np.abs(\n np.random.normal(0, self.dosage_var, len(x[x == 2]))\n )\n\n # Mask some values if the call rate is not 1.\n if self.call_rate < 1:\n missing_rate = 1 - self.call_rate\n missing_number = missing_rate * self.n\n missing_idx = np.arange(0, self.n)\n np.random.shuffle(missing_idx)\n missing_idx = missing_idx[:missing_number]\n x[missing_idx] = np.nan\n \n return x","def _iterate_domains(self):\n\n class DomainIter:\n # Indices refer to positions between the nucleotides, as usual for \n # slices in python.\n\n def __init__(self, domain, cursor, rel_start, rel_end):\n self.domain = domain\n self.start = cursor\n self.rel_start = rel_start\n self.rel_end = rel_end\n\n def __repr__(self):\n return ('DomainIter('\n 'domain={0.domain!r}, '\n 'start={0.start}, '\n 'rel_start={0.rel_start}, '\n 'rel_end={0.rel_end})'.format(self))\n @property\n def len(self):\n return self.rel_end - self.rel_start\n\n @property\n def end(self):\n return self.start + self.len\n\n def rel_index(self, index):\n return index - self.start + self.rel_start\n\n def abs_index(self, rel_index):\n return self.start + rel_index - self.rel_start\n\n domain_cursor = 0\n index_cursor = 0\n \n while domain_cursor < len(self._domains):\n domain = self._domains[domain_cursor]\n\n # If this domain doesn't have anything attached to it, then we can \n # just yield the whole thing right away.\n\n if domain not in self._attachments:\n yield DomainIter(domain, index_cursor, 0, len(domain))\n index_cursor += len(domain)\n\n # If this domain does have something attached to it, then we need \n # to carefully yield only the parts of it that aren't covered by \n # the attachment.\n\n else:\n attachment = self._attachments[domain]\n\n # Yield whatever fraction of this domain comes before the \n # attachment.\n\n yield DomainIter(domain,\n index_cursor, 0, attachment.start_index)\n index_cursor += attachment.start_index\n\n # Yield the domains in the attachment itself by recursively \n # calling this method.\n\n for domain_iter in attachment.construct._iterate_domains():\n domain_iter.start += index_cursor\n yield domain_iter\n index_cursor += len(attachment.construct)\n\n # Skip domains until we reach the one where the attachment \n # ends.\n\n while domain is not attachment.end_domain:\n domain_cursor += 1\n domain = self._domains[domain_cursor]\n\n # Yield whatever fraction of that domain comes after the \n # attachment.\n\n yield DomainIter(domain,\n index_cursor, attachment.end_index, len(domain))\n index_cursor += len(domain) - attachment.end_index\n\n domain_cursor += 1","def mutate_random(self, n=1):\n mutated_dna = self._dna\n for i in range(n):\n mutated_dna = mutate(mutated_dna)\n return Gene(mutated_dna, self._exon_regions)","def get_next_articles(self):\n\t\tarticles = Blog.objects.all()\\\n\t\t\t.filter(publication_date__lte=datetime.date.today())\\\n\t\t\t.order_by('publication_date')\n\n\t\tif articles.count() <= 4:\n\t\t\treturn articles\n\n\t\ti, j, k, l = random.sample(range(0, articles.count()-1), 4)\n\t\treturn [articles[i], articles[j], articles[k], articles[l]]","def generate_dimino(self, af=False):\n idn = list(range(self.degree))\n order = 0\n element_list = [idn]\n set_element_list = {tuple(idn)}\n if af:\n yield idn\n else:\n yield _af_new(idn)\n gens = [p._array_form for p in self.generators]\n\n for i in range(len(gens)):\n # D elements of the subgroup G_i generated by gens[:i]\n D = element_list[:]\n N = [idn]\n while N:\n A = N\n N = []\n for a in A:\n for g in gens[:i + 1]:\n ag = _af_rmul(a, g)\n if tuple(ag) not in set_element_list:\n # produce G_i*g\n for d in D:\n order += 1\n ap = _af_rmul(d, ag)\n if af:\n yield ap\n else:\n p = _af_new(ap)\n yield p\n element_list.append(ap)\n set_element_list.add(tuple(ap))\n N.append(ap)\n self._order = len(element_list)","def generate_examples_serial(self):\n # Now we have a Gensim Dictionary to work with\n self._build_dictionary()\n # Remove any tokens with a frequency less than 10\n self.dictionary.filter_extremes(no_below=10, no_above=0.75)\n\n self.examples = []\n for file in tqdm(self.load_files(), desc=\"Generating Examples (serial)\"):\n file = self.dictionary.doc2idx(file)\n self.examples.extend(self._generate_examples_from_file(file))","def __iter__(self) -> Generator:\n\t\treturn (article for article in self._articles)","def random_population():\n pop = []\n for i in range(POP_SIZE):\n dna = \"\"\n for c in range(DNA_SIZE):\n dna += random_char()\n pop.append(dna)\n return pop","def _generate_pores(self):\n logger.info(\"Place randomly located pores in the domain\")\n #Original Random Point Generator\n #coords = sp.rand(self._Np,3)*[self._Lx,self._Ly,self._Lz]\n #Seeding Code\n coords = np.zeros([self._Np,3])\n #reject points close to boundaries - if False there will be slightly more\n rejection = [False,False,True]\n for j in range(3):\n i = 0\n while i < self._Np:\n coord = np.random.uniform(0,1,1)\n if self._reject(coord) == rejection[j]:\n coords[i][j]=coord\n i += 1\n coords*=np.array([self._Lx,self._Ly,self._Lz])\n #Seeding Code\n #Uniform Random Generator\n #coords = np.array([np.random.uniform(0,self._Lx,self._Np),np.random.uniform(0,self._Ly,self._Np),np.random.uniform(0,self._Lz,self._Np)]).T\n\n self['pore.coords'] = coords\n logger.debug(\"End of method\")","def generate_moves(self, board: Board) -> typing.Iterator[MoveDescription]:\n dominoes = set(board.dominoes)\n for domino in dominoes:\n dx, dy = domino.direction\n yield from self.try_move(domino, dx, dy)\n yield from self.try_move(domino, -dx, -dy)","def generate_units(self, num_soldados, num_magos):\n self.soldados = [soldier(randrange(*self.race.rango_atq_soldado),\n randrange(*self.race.rango_vid_soldado))\n for i in range(num_soldados)]\n\n self.magos = [mage(randrange(*self.race.rango_atq_mago),\n randrange(*self.race.rango_vid_mago))\n for i in range(num_magos)]\n\n self._initial_life_set = False","def sequence(self):\n for tn in self._testnodes:\n yield tn","def newGeneration(self):\n for i in range(0, len(self.population)):\n [ind1, ind2] = self.randomSelection()\n child = self.crossover(ind1, ind2)\n self.population[i].setGene(child)\n self.mutation(self.population[i])","def generator(self, random, args):\r\n locations = [i for i in range(len(self.weights))]\r\n random.shuffle(locations)\r\n return locations","def node_gen(self):\n for n in self.child_list:\n yield from n.node_gen\n yield self","def _setup():\n for item in dominos:\n queue.append([item])","def gen_donor():\n# <<<<<<< master\n return [donor for donor in donor_data]","def random_population() -> List:\r\n pop = []\r\n for _ in range(POPULATION_SIZE):\r\n dna = \"\"\r\n for c in range(DNA_SIZE):\r\n dna += random_charachter()\r\n pop.append(dna)\r\n return pop","def GeneratorWithNoise(dataset,\n batch_size,\n noise_size,\n which_batches=range(0,1),\n mode='shuffled_sequential',\n num_batches=-1):\n\n for b in dataset.iterator(\n batch_size,\n dataset.get_num_examples()/batch_size,\n mode=mode,\n data_specs=dataset.get_data_specs(),\n return_tuple=True\n ):\n b = [b[i] for i in which_batches]\n t, bsize, dim = b[0].shape # time length, batch size, dim\n eps = np.random.normal(0,1,size=(t, bsize, noise_size)).astype(floatX)\n b.append(eps)\n yield b","def _generate_random_population(self, pop_size):\n\n random_population = []\n for agent in range(pop_size):\n random_population.append(self._generate_random_agent())\n return random_population","def generar_orden_botones():\r\n lista_orden = [] # Lista con orden de los botones\r\n while len(lista_orden) != 4:\r\n num_random = num_rand(0,3)\r\n if num_random not in lista_orden:\r\n lista_orden.append(num_random)\r\n return lista_orden","def random_positions(mini, maxi):\n x_cord = (maxi - mini)*np.random.random(SIZE) + mini\n y_cord = (maxi - mini)*np.random.random(SIZE) + mini\n return np.column_stack([x_cord, y_cord])","def random(self, n=1):\n # self.num_generated += n","def generator(self, args, gen):\n import random\n\n if args.seed:\n random.seed(args.seed)\n seqs = [s for s in gen]\n sample_indices = random.sample(range(len(seqs)), min(len(seqs), args.number))\n for i in sample_indices:\n yield seqs[i]","def deng_random(seeds):\n\n x = seeds[0]\n y = seeds[1]\n\n while 0<1:\n temp = x\n x = (x + y) % 1.0\n y = temp\n yield x","def sample_personas(self):\n persona = self.personas_list\n n = constants.CURATED_PERSONA_CHOICES\n logging.info(\n f'Randomly choosing {n} personas from {len(persona)} available ones.'\n )\n if self.persona_replacement:\n return random.sample(persona, k=n)\n else:\n return [persona.pop() for _ in range(n)]","def dice_gen():\r\n import random\r\n\r\n rdm = random.Random()\r\n rdm_gen = []\r\n\r\n table_txt = open(\"dice-table.txt\", \"r\")\r\n while True:\r\n line = table_txt.readline()\r\n if len(line) == 0:\r\n break\r\n line = list(map(int, line.split()))\r\n rdm_gen.append(line[rdm.randint(0,10)])\r\n\r\n table_txt.close()\r\n return(rdm_gen)","def generator():\n mygenerator = (x for x in range(3))\n for element in mygenerator:\n print 'poprve = ', element\n\n for element in mygenerator:\n print 'podruhe = ', element","def randomDays(self):\n flag = False\n while not flag:\n r = np.floor(np.random.rand() * len(self.df.index))\n start = self.df.index[r]\n if r+self.daysHeld < len(self.df.index)-1:\n end = self.df.index[r+self.daysHeld]\n flag = True\n return (start,end)","def pick_gynodioecious_parents(simu, config):\n rng = simu.getRNG()\n runif = rng.randUniform\n rint = rng.randInt\n try:\n sstar = config.sstar\n H = config.H\n def compound_generator(pop):\n \"\"\"\n Picks up parent(s) under gynodioecy using compound parameters.\n \"\"\"\n gen = -1\n while True:\n ngen = pop.dvars().gen\n if gen != ngen:\n # At the beginning of a generation, extract the\n # sex-specific subpopulations from a parental\n # population. The sex-specific subpopulations are used\n # throughout mating events in one generation.\n gen = ngen\n h = pop.extractSubPops(subPops=[(0, 0)])\n f = pop.extractSubPops(subPops=[(0, 1)])\n Nh = h.popSize()\n Nf = f.popSize()\n\n if runif() < sstar: # uniparental\n yield h.individual(rint(Nh))\n else: # biparental\n if runif() < H: # having a hermaphroditic seed parent\n first, second = rint(Nh), rint(Nh)\n while first == second:\n second = rint(Nh)\n yield [h.individual(first), h.individual(second)]\n else: # female seed parent\n yield [h.individual(rint(Nh)), f.individual(rint(Nf))]\n return compound_generator\n except KeyError:\n a = config.a\n sigma = config.sigma\n tau = config.tau\n def fundamental_generator(pop):\n \"\"\"\n Picks up parent(s) under gynodioecy using fundamental parameters.\n \"\"\"\n gen = -1\n while True:\n ngen = pop.dvars().gen\n if gen != ngen:\n # At the beginning of a generation, extract the\n # sex-specific subpopulations from a parental\n # population. The sex-specific subpopulations are used\n # throughout mating events in one generation.\n gen = ngen\n h = pop.extractSubPops(subPops=[(0, 0)])\n f = pop.extractSubPops(subPops=[(0, 1)])\n Nh = h.popSize()\n Nf = f.popSize()\n hermseed = Nh / (Nh * Nf * sigma)\n\n if runif() < hermseed: # hermaphroditic seed parent\n if runif() < a: # self-pollen\n yield h.individual(rint(Nh))\n else: # non self-pollen\n first, second = rint(Nh), rint(Nh)\n while first == second:\n second = rint(Nh)\n yield [h.individual(first), h.individual(second)]\n else: # female seed parent\n if runif() < tau:\n yield [h.individual(rint(Nh)), f.individual(rint(Nf))]\n return fundamental_generator","def DOIs(self, default=[{}]):\n tmp = self.data.get('metadata', {}).get('dois', default)\n return [HEP.DOIObject(i) for i in tmp]","def seq_data_iter_random(corpus, batch_size, num_steps): #@save\n # Start with a random offset (inclusive of `num_steps - 1`) to partition a\n # sequence\n corpus = corpus[random.randint(0, num_steps - 1):]\n # Subtract 1 since we need to account for labels\n num_subseqs = (len(corpus) - 1) // num_steps\n # The starting indices for subsequences of length `num_steps`\n initial_indices = list(range(0, num_subseqs * num_steps, num_steps))\n # In random sampling, the subsequences from two adjacent random\n # minibatches during iteration are not necessarily adjacent on the\n # original sequence\n random.shuffle(initial_indices)\n\n def data(pos):\n # Return a sequence of length `num_steps` starting from `pos`\n return corpus[pos: pos + num_steps]\n\n num_batches = num_subseqs // batch_size\n for i in range(0, batch_size * num_batches, batch_size):\n # Here, `initial_indices` contains randomized starting indices for\n # subsequences\n initial_indices_per_batch = initial_indices[i: i + batch_size]\n X = [data(j) for j in initial_indices_per_batch]\n Y = [data(j + 1) for j in initial_indices_per_batch]\n yield torch.tensor(X), torch.tensor(Y)","def get_donnees(self):\r\n \r\n return self._donnees","def random_coordinates():\n return Coordinates(random.randint(0, 14), random.randint(0, 14))","def getFirstTorsionInds(self, extended):\n offset = 6 if extended else 0\n torsionInds = np.array(range(offset + 5, self.natoms * 3, 3))\n primaryTorsions = sorted(list(set(self._firstTorsionTInd)))\n return list(torsionInds[primaryTorsions])","def generate_random_numbers(self):\r\n #random.seed(seed=self.seed)\r\n #err = random.random((3,1))\r\n #f = open('test_res', 'a')\r\n #f.write('probability - %s' %self.seed)\r\n #f.write(str(list(err[:3,:])))\r\n #f.write('\\n')\r\n #f.close()\r\n\r\n dist = RandomDistribution(self.seed)\r\n rand_numbers = dist.return_random_variables(self.num_agents)\r\n return rand_numbers","def Generate_Random( self ):\n print( 'Generating Random coordinates' )\n stands = self.Data.Stand.keys()\n stands.sort()\n for s in stands:\n trees = self.Data.Stand[s].Tree.keys()\n trees.sort()\n for t in trees:\n self.Data.Stand[s].Tree[t].X = random.uniform( 0, 208.71 )\n self.Data.Stand[s].Tree[t].Y = random.uniform( 0, 208.71 )","def in_random_order(theta):\r\n indexes = [i for i,_ in enumerate(data)] # creates a list of indices\r\n random.shuffle(indexes) # shuffles them\r\n for i in indexes:\r\n yield data[i] # return data in that order\r","def data_over_diamond(only_valid_monster=True) -> Generator[GameData, None, None]:\n\ttarget = collection.find({\"tier\": {\"$in\": elo[-4:]}, \"game_duration\": {\"$gte\": 1200}})\n\tfor data in target:\n\t\tgame_data = GameData(data)\n\t\tif not only_valid_monster or (only_valid_monster and game_data.is_monster_log_valid()):\n\t\t\tyield game_data","def test_generation(self):\n generator = math_helpers.pentagonal_number_generator()\n first_ten_pentagonal_numbers = [next(generator) for _ in range(10)]\n canonical_values = [1, 5, 12, 22, 35, 51, 70, 92, 117, 145]\n self.assertEqual(canonical_values, first_ten_pentagonal_numbers)","def build(self):\n self._start = np.zeros_like(self._rooms.shape)\n self._start[0] = random.randrange(self._rooms.shape[0])\n position = self._start\n egress = Direction.South\n distance = 1\n while position in self:\n room = self[position]\n room.egress = egress\n room.distance = distance\n yield position\n options = {}\n for direction in Direction.range():\n if direction != egress:\n new_position = position + direction.offset()\n if new_position in self:\n if self.is_sealed(new_position):\n options[direction] = new_position\n if options:\n direction = random.choice(tuple(options.keys()))\n room.remove_wall(direction)\n position = options[direction]\n egress = direction.reverse()\n distance += 1\n else:\n position += egress.offset()\n if position in self:\n egress = self[position].egress\n distance = self[position].distance","def test_generator_method(self):\n for i in range(0, 4):\n yield self.try_odd, i","def pick_androdioecious_parents(simu, config):\n rng = simu.getRNG()\n runif = rng.randUniform\n rint = rng.randInt\n try:\n sstar = config.sstar\n def compound_generator(pop):\n \"\"\"\n Picks up parent(s) under androdioecy using a compound parameter.\n \"\"\"\n gen = -1\n while True:\n ngen = pop.dvars().gen\n if gen != ngen:\n # At the beginning of a generation, extract the\n # sex-specific subpopulations from a parental\n # population. The sex-specific subpopulations are used\n # throughout mating events in one generation.\n gen = ngen\n males = pop.extractSubPops(subPops=[(0, 0)])\n herms = pop.extractSubPops(subPops=[(0, 1)])\n nmale = males.popSize()\n nherm = herms.popSize()\n\n if runif() < sstar: # uniparental\n yield herms.individual(rint(nherm))\n else: # biparental\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\n return compound_generator\n except KeyError:\n stilde = config.stilde\n tau = config.tau\n def compound_generator(pop):\n \"\"\"\n Picks up parent(s) under androdioecy using fundamental parameters.\n \"\"\"\n gen = -1\n while True:\n ngen = pop.dvars().gen\n if gen != ngen:\n # At the beginning of a generation, extract the\n # sex-specific subpopulations from a parental\n # population. The sex-specific subpopulations are used\n # throughout mating events in one generation.\n gen = ngen\n males = pop.extractSubPops(subPops=[(0, 0)])\n herms = pop.extractSubPops(subPops=[(0, 1)])\n nmale = males.popSize()\n nherm = herms.popSize()\n\n if runif() < stilde: # proportion of self-fertlized egg\n if runif() < tau: # survival rate of a uniparental zygote rel to a biparental z.\n yield herms.individual(rint(nherm))\n else: # biparental\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\n return compound_generator","def xrndSphere(n):\n for i in xrange(n):\n yield rndSphere()","def get_nodes(self, gid, sid, did, scid, doid):\n yield self.generate_browser_collection_node(doid)","def _create_random_tetrino(self):\n shape_index = randrange(constant.NUM_SHAPES)\n shape = constant.SHAPES[shape_index]\n shape_locations = self.translate_shape(shape[0], 0, 0)\n num_blocks = len(shape_locations)\n location = self._create_random_offsets(shape_locations)\n new_tetrino = Tetrino(location, shape_index, \\\n num_blocks, self.tetrino_id, self.game_size)\n self.tetrino_set[self.tetrino_id] = new_tetrino\n self.tetrino_id += 1\n return new_tetrino","def generate(self):\n start = self.nu_idx\n stop = start + self.score\n return self.orig_nu[start:stop]","def get_randoms_to_generate(self):\n return self._randoms_to_generate","def generate_individual(size):\n individual = []\n \n for i in range(size):\n individual.append(random.randint(0,1))\n\n return individual","def __iterate_minibatches(self, dataset, batchsize, shuffle=False):\n n = len(dataset[0])\n assert len(dataset[1]) == n\n batchsize = min(batchsize, n)\n indices = numpy.arange(n)\n numpy.random.shuffle(indices)\n for start_idx in range(0, n - batchsize + 1, batchsize):\n excerpt = indices[start_idx:start_idx + batchsize]\n yield dataset[0][excerpt], dataset[1][excerpt]","def _next_generation(self, ranks):\n replace = ranks[:int(self.population_size * self.culling)]\n for idx in replace:\n self.population[idx] = self._create_offspring()","def gen_seq(self,ntrials=20,pm_trial_position=None):\n # insert ranomly positioned pm trials\n if type(pm_trial_position)==type(None):\n ntrials -= 1+self.num_pm_trials\n pm_trial_position = np.random.randint(self.min_start_trials,ntrials,self.num_pm_trials) \n else:\n ntrials -= 1+len(pm_trial_position)\n pm_trial_position = pm_trial_position\n # generate og stim\n seq = np.random.randint(0,self.ntokens_og,ntrials)\n X = np.insert(seq,[0,*pm_trial_position],self.pm_token)\n # form Y \n Xroll = np.roll(X,self.nback)\n Y = (X == Xroll).astype(int) # nback trials\n Y[X==self.pm_token]=2 # pm trials\n return X,Y","def remove_randomico(lista, qtd_remocao):\n for i in range(qtd_remocao):\n lista.pop(random.randrange(len(lista))) \n return lista","def generate_observations(self, eval_mode, augment_frames=None):\n episode_idx = 0\n augment_frames = (\n augment_frames if augment_frames is not None\n else self._augment_frames and not eval_mode)\n for t, obs in enumerate(self._flat_observations):\n if augment_frames:\n obs = image_utils.random_crop_image(obs)\n if self._split_by_episodes:\n yield obs, episode_idx\n else:\n yield obs\n if self._is_terminal[t]:\n episode_idx += 1","def get_random_population():\r\n return [ get_random_individual() for _ in range(POPULATION_COUNT) ]","def __generate_random_gene_sequence(self):\n genes = []\n for j in range(self.chromosome_size):\n genes.append(random.choice(self.gene_pool))\n\n return genes","def iter_segments_(self, X):\n\n # heuristic that tries to avoid highly-overlapping sub-segments\n # (i.e. with more than 50% overlap on average) for short speech turns\n n_samples = len(X)\n n = (n_samples - self.n_samples_) // (self.n_samples_ // 2) + 1\n n = min(n, self.per_turn)\n\n # choose (and yield) n sub-segments at random\n for i in np.random.randint(0, n_samples - self.n_samples_, n):\n yield X[i: i + self.n_samples_]","def generate_test():\n o = []\n pos = [384, 288]\n note_group_size = GAN_PARAMS[\"note_group_size\"]\n generate_set(begin=3 * note_group_size, start_pos=pos,\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)","def totem_random():\n random_head()\n random_head()\n random_head()","def generatePopulation(self,size):\n return [ self._individual(self._individualSize) for x in range(size) ]","def create_enemies(self):\n if not self.ENEMY_DIST:\n raise ValueError('You must initialize ENEMY_DIST. Import turnable.rooms.FightRoom and '\n 'call FightRoom.set_enemy_dist(your_dist).')\n\n amount = random.randint(1, 3)\n for c in range(amount):\n en = self._get_enemy()(pos=self.pos)\n en.game = self.game\n self.enemies.append(en)","def random_by_year_list(self):\n\n from_year = xbmcgui.Dialog().input(\n \"From year\", type=xbmcgui.INPUT_NUMERIC)\n to_year = xbmcgui.Dialog().input(\n \"To year\", type=xbmcgui.INPUT_NUMERIC)\n\n xbmcplugin.setContent(self.addon_handle, \"songs\")\n\n for track in self.connection.walk_random_songs(\n size=self.random_count, from_year=from_year, to_year=to_year):\n self.add_track(track, show_artist=True)\n\n xbmcplugin.endOfDirectory(self.addon_handle)","def _random_doi():\n return \"10.\" + _random_string(3, 4) + \"/\" + _random_string(5, 10)","def iter_coordinates(self):\n for coord in self.position:\n yield coord","def generator(self, random, args):\r\n raise NotImplementedError","def _add_dominance_relation__to__nodes(self):\n dominating_dict = defaultdict(list)\n dominated_dict = defaultdict(list)\n for dom_rel_id in self._dominance_relation_ids:\n dominated_node_id = self.edges[dom_rel_id].target\n dominating_node_id = self.edges[dom_rel_id].source\n dominating_dict[dominating_node_id].append(dominated_node_id)\n dominated_dict[dominated_node_id].append(dominating_node_id)\n\n for dominating_node_id in dominating_dict:\n self.nodes[dominating_node_id].dominates = \\\n dominating_dict[dominating_node_id]\n for dominated_node_id in dominated_dict:\n self.nodes[dominated_node_id].dominated_by = \\\n dominated_dict[dominated_node_id]","def insere_randomico(lista, qtd_insercao):\n for i in range(qtd_insercao):\n lista.append(random.randint(1,10000))\n random.shuffle(lista)\n return lista","def _pop_random_n(entities: np.array, weights: np.array, count: int = 3):\n for _ in range(count):\n if not len(entities):\n return\n\n choice, entities, weights = _pop_random(entities, weights)\n yield choice","def generate_next(self, direction =\"+\", choose= None):\n import random\n if direction == \"+\":\n links = self.links_f\n elif direction == \"-\":\n links = self.links_b\n else:\n raise ValueError(\"Undefined direction argument\")\n \n if len(links.link) == 0:\n raise EndOfStory(\"No carry ons\")\n \n if choose == None:\n choose = random.randrange(len(links.link))\n conflicts = links.link[choose]\n \n for conflict in conflicts:\n\n yield Conflict(self.soup, conflict[0], conflict[1], self.update_transforms(self.transpositions, conflict[2]), self.characters, self.storyline)","def generate_examples(gen, truncation=0.7, n=100):\n gen.eval()\n alpha = 1.0\n for i in range(n):\n with torch.no_grad():\n noise = torch.tensor(truncnorm.rvs(-truncation, truncation, size=(1, config.Z_DIM, 1, 1)), device=config.DEVICE, dtype=torch.float32)\n img = gen(noise, alpha, 279)\n save_image(img*0.5+0.5, f\"saved_examples/{i+2000}.jpg\")","def random_girl(self):\n return [result for result in self._db.girls.find().limit(1) \\\n .skip(random.randrange(self._db.girls.count()))][0]","def star_topology(random, population, args):\r\n for _ in range(len(population)):\r\n yield population[:]","def iter_trios(self):\n for row in self._ped_tab.itertuples():\n if row.father == '0':\n continue\n if row.mother == '0':\n continue\n yield (row.sample_id, row.father, row.mother)","def set_random_musicians(self, num:int):\n try:\n self.cursor.execute(\"insert into musicians (name, birthday, role, group_id) \"\n \"select rand.name, rand.birthday, rand.role, rand.group_id \"\n \"from (SELECT groups.id as group_id, \"\n \"(md5(random()::text)) as name, \"\n \"(md5(random()::text)) as role, \"\n \"((current_date - '100 years'::interval) + trunc(random() * 365) * '1 day'::interval + trunc(random() * 3) * '1 year'::interval ) as birthday \"\n f\"from generate_series(1, 10), groups ORDER BY random() limit {num}) as rand\")\n self.connection.commit()\n if self.cursor.rowcount:\n return \"generated musicians\"\n else:\n return \"NULL\"\n except(Exception, psycopg2.Error) as error:\n self.connect.rollback()\n print(\"error in generate\", error)","def generate(self, words=5):\n seed()\n return sorted(self.keywords, key=lambda *args: random())[0:words]","def _seed_population(self):\n return [self._generate_weights() for x in range(self.population_size)]","def yatzy_dice():\n return [random_die() for _ in range(5)]","def get_dossiers_shared_with_group(self):\n dossier_brains = api.content.find(allowedRolesAndUsers=u'user:{}'.format(self.group_id))\n ndossiers = len(dossier_brains)\n print(\"found {} dossiers\".format(ndossiers))\n for i, dossier_brain in enumerate(dossier_brains):\n if i % 5000 == 0:\n self.collect_garbage(self.context)\n rss = self.get_rss() / 1024.0\n print(\"done with {}/{}; {:.1f}%; Memory: {}\".format(\n i, ndossiers, 100. * i / ndossiers, rss))\n dossier = dossier_brain.getObject()\n assignments = RoleAssignmentManager(dossier).get_assignments_by_principal_id(self.group_id)\n sharing_assignment = self._find_sharing_assignment(assignments)\n if sharing_assignment:\n yield (dossier, sharing_assignment)","def _ontonotes_subset(ontonotes_reader: Ontonotes,\r\n file_path: str,\r\n domain_identifier: str) -> Iterable[OntonotesSentence]:\r\n for conll_file in ontonotes_reader.dataset_path_iterator(file_path):\r\n if domain_identifier is None or f\"/{domain_identifier}/\" in conll_file:\r\n yield from ontonotes_reader.sentence_iterator(conll_file)","def randNotes(self, value, chord):\n aChord = mgChord(value, chord)\n self.data = []\n for i in range(len(self.rhythm)):\n note = random.choice(aChord).copy()\n note.setDuration(self.rhythm[i])\n self.data.append(note)","def with_coordinates(self):\n for tweet in self.having(coordinates=self.default_coordinates):\n yield tweet","def make_parents(self):\r\n self.parents = []\r\n \r\n for loopindex in range(0, int(self.population_size * 0.6)):\r\n while True:\r\n if loopindex < int(self.population_size * 6 / 15):\r\n parent = random.choice(self.best_districts)\r\n else:\r\n parent = random.choice(self.worst_districts)\r\n \r\n if parent not in self.parents:\r\n self.parents.append(parent)\r\n break","def _ontonotes_subset(ontonotes_reader: Ontonotes,\n file_path: str,\n domain_identifier: str) -> Iterable[OntonotesSentence]:\n for conll_file in ontonotes_reader.dataset_path_iterator(file_path):\n if domain_identifier is None or f\"/{domain_identifier}/\" in conll_file:\n yield from ontonotes_reader.sentence_iterator(conll_file)"],"string":"[\n \"def choose_and_flip_extra_dominoes(self, random):\\n for domino in self.choose_extra_dominoes(random):\\n if domino.head.pips == domino.tail.pips:\\n yield domino, False\\n else:\\n flip_first = random.randint(0, 1)\\n for j in range(2):\\n yield domino, flip_first + j == 1\",\n \"def pickup_dominoes(self, num_dominoes, player):\\n\\n for domino in range(num_dominoes):\\n shuffle(self.boneyard)\\n player.hand.append(self.boneyard.pop(0))\",\n \"def derandomize(self, xs, noises):\\n raise NotImplementedError\",\n \"def _all_donors(self, include_background=True):\\n sheets = self.shortcut_sheets\\n if not include_background:\\n sheets = filter(is_not_background, sheets)\\n for sheet in sheets:\\n for entity in sheet.bio_entities.values():\\n yield entity\",\n \"def __dice_generator(self):\\n self.current_dice = np.random.randint(1, 6 + 1)\",\n \"def get_seeds(self, start: float, num: int) -> np.ndarray:\\r\\n\\r\\n if self.integral:\\r\\n if self.domain[1] - self.domain[0] + 1 <= num:\\r\\n return np.arange(self.domain[0], self.domain[1] + 1)\\r\\n result = np.random.choice(\\r\\n np.arange(self.domain[0], self.domain[1] + 1), num, replace=False)\\r\\n if start not in result:\\r\\n result[0] = start\\r\\n return result\\r\\n\\r\\n\\r\\n result = np.zeros((num,), dtype='float64')\\r\\n result[0] = start\\r\\n min_sep = (self.domain[1] - self.domain[0]) * min(0.05, 0.25 / num)\\r\\n\\r\\n for i in range(1, num):\\r\\n rejections = 0\\r\\n while True:\\r\\n _pt = np.random.uniform(self.domain[0], self.domain[1], 1)\\r\\n if sdist.cdist(_pt.reshape(1, 1), result[:i].reshape(i, 1)).min() < min_sep:\\r\\n rejections += 1\\r\\n if rejections > 50000:\\r\\n raise ValueError(\\r\\n f'failed to sample! domain might be singleton: {self.domain}')\\r\\n else:\\r\\n result[i] = _pt\\r\\n break\\r\\n\\r\\n return result\",\n \"def generate_random_walker():\\n # must have seeds that generate known problems\\n must_have_seeds = [112, 308, 393]\\n for seed in must_have_seeds:\\n print(\\\"Last used seed: {}\\\".format(seed))\\n detections = detections_simple_tracking(seed)\\n yield simple_walker(data_simple_tracking(detections)), detections\\n while True:\\n seed = random.randint(0, 2**10)\\n print(\\\"Last used seed: {}\\\".format(seed))\\n detections = detections_simple_tracking(seed)\\n yield simple_walker(data_simple_tracking(detections)), detections\",\n \"def iter_dist(self):\\n self.makeTree()\\n coords = self.coords\\n sd = selfdistance\\n for i in self.loopindices:\\n dists, inds = self.nntree.query(coords[i], self.nnmaxcount,\\n distance_upper_bound=self.nncutoff)\\n yield coords[i], dists.compress((dists > sd) & ~np.isinf(dists))\",\n \"def get_examples():\\n symbols = get_symbols()\\n symbol_start_indices, total_examples = _index_symbols(symbols)\\n selection_order = np.arange(total_examples)\\n np.random.shuffle(selection_order)\\n for sample_index in selection_order:\\n # use a binary search to determine which symbol to use given the sample_index\\n start_index_index = bisect.bisect_left(symbol_start_indices, sample_index + 1) - 1\\n start_index = symbol_start_indices[start_index_index]\\n offset = sample_index - start_index\\n symbol = symbols[start_index_index]\\n time_series_data = get_time_series_data(symbol)\\n next_day = time_series_data[offset]\\n previous_days = time_series_data[offset + 1: offset + 1 + EXAMPLE_SIZE]\\n previous_days.reverse()\\n metadata = get_metadata_for_symbol(symbol)\\n yield previous_days, next_day, metadata\",\n \"def split_all(self):\\n for domino in self.dominoes[:]:\\n self.split(domino)\",\n \"def nextGen(self):\\n\\n p = []\\n while len(p) < len(self.p):\\n #select mates and produce offspring\\n p1, p2 = self.select()\\n offspring = self.mate(p1, p2)\\n\\n #put the offspring in the next generation (with mutation)\\n for child in offspring:\\n child=self.mutate(child)\\n p.append(child)\\n \\n\\n # the world belongs to the new generation\\n return p\",\n \"def getRandomCoordinates( self, size ):\\n if not self.mIsLoaded: self.__loadIndex()\\n\\n token = random.choice( self.mIndex.keys() ) \\n strand = random.choice( (\\\"+\\\", \\\"-\\\") )\\n pos_id, pos_seq, lcontig = self.mIndex[token][:3]\\n rpos = random.randint( 0, lcontig )\\n if random.choice( (\\\"True\\\", \\\"False\\\") ):\\n start = rpos\\n end = min(rpos + size, lcontig)\\n else:\\n start = max(0, rpos - size)\\n end = rpos\\n \\n return token, strand, start, end\",\n \"def insere_n_nos(self, num_nos):\\n for i in range(num_nos):\\n index = random.randint(1, 11 ** 4)\\n elem = random.randint(1, 11 ** 4)\\n self.insere(index, elem)\",\n \"def create_next_gen(self, parents_sreprs_couple):\\n child0, child1 = self.recombine(parents_sreprs_couple[0], parents_sreprs_couple[1])\\n if random.random() < self.mutate_prob:\\n child0 = self.mutate(child0)\\n if random.random() < self.mutate_prob:\\n child1 = self.mutate(child1)\\n\\n return child0, child1\",\n \"def generate_random_tomogram_set(templates, criteria, number_of_tomograms, dim, seed=None, noise=False):\\n if (seed == None):\\n seed = random.randint(0, 2 ** 31 - 1)\\n print('Using random seed: ', seed)\\n random.seed(int(seed))\\n np.random.seed(int(seed))\\n\\n for i in range(number_of_tomograms):\\n yield generate_random_tomogram(templates, criteria, dim, noise)\",\n \"def play_dominoes(draw=draw_init, save_file=None, num_games=1000, save=True):\\n list_victories = [0]*(NUM_PLAYERS+1)\\n game = Dominoes(NUM_PLAYERS, HAND_SIZE, TOPNUM, save_file=save_file)\\n \\n # Setups the game. This includes initializing the player instances.\\n player1 = RandomDominoPlayer(1, game)\\n player2 = RandomDominoPlayer(2, game)\\n player3 = RandomDominoPlayer(3, game)\\n player4 = RandomDominoPlayer(4, game)\\n list_players = (player1, player2, player3, player4)\\n game.setup(list_players)\\n\\n # Play!\\n for i in range(num_games):\\n # Cleans the game state\\n game.reset()\\n \\n # Draws the starting dominoes.\\n starting_dominoes = draw(NUM_PLAYERS, DOMINO, HAND_SIZE)\\n# print('\\\\nStarting_dominoes')\\n# print(starting_dominoes[0])\\n# print(starting_dominoes[1])\\n# print(starting_dominoes[2])\\n# print(starting_dominoes[3])\\n \\n # Assigns the starting dominoes to each player\\n for player, dominoes in zip(list_players, starting_dominoes):\\n player.reset(start_dominoes=list(dominoes))\\n \\n # Rolls the game!\\n winner = roll_game(game, save=save)\\n list_victories[winner] += 1\\n if (i*20) % num_games == 0:\\n print(i*100/num_games, '% processed.')\\n \\n# print('Salida:', game.salida)\\n \\n print('\\\\n', list_victories, '\\\\t', list_victories[1]+list_victories[3], \\n list_victories[2]+list_victories[4])\",\n \"def random_iterator(self, batch_size):\\n all_indices = np.nonzero(np.logical_not(self._dones))[0]\\n np.random.shuffle(all_indices)\\n\\n states = np.asarray(self._states)\\n actions = np.asarray(self._actions)\\n next_states = np.asarray(self._next_states)\\n rewards = np.asarray(self._rewards)\\n dones = np.asarray(self._dones)\\n\\n i = 0\\n while i < len(all_indices):\\n indices = all_indices[i:i+batch_size]\\n\\n yield states[indices], actions[indices], next_states[indices], rewards[indices], dones[indices]\\n\\n i += batch_size\",\n \"def _build_genotypes(self):\\n x = np.zeros(self.n)\\n \\n # Frequencies derived from HWE.\\n num_hetero = 2 * self.maf * (1 - self.maf) * self.n\\n num_homo_minor = self.maf ** 2 * self.n\\n \\n x[:num_hetero] = 1\\n x[num_hetero:num_hetero+num_homo_minor] = 2\\n np.random.shuffle(x)\\n \\n # Add noise for dosage values if needed.\\n if self.dosage_var:\\n x[x == 0] += np.abs(\\n np.random.normal(0, self.dosage_var, len(x[x == 0]))\\n )\\n x[x == 1] += np.random.normal(0, self.dosage_var, len(x[x == 1]))\\n x[x == 2] -= np.abs(\\n np.random.normal(0, self.dosage_var, len(x[x == 2]))\\n )\\n\\n # Mask some values if the call rate is not 1.\\n if self.call_rate < 1:\\n missing_rate = 1 - self.call_rate\\n missing_number = missing_rate * self.n\\n missing_idx = np.arange(0, self.n)\\n np.random.shuffle(missing_idx)\\n missing_idx = missing_idx[:missing_number]\\n x[missing_idx] = np.nan\\n \\n return x\",\n \"def _iterate_domains(self):\\n\\n class DomainIter:\\n # Indices refer to positions between the nucleotides, as usual for \\n # slices in python.\\n\\n def __init__(self, domain, cursor, rel_start, rel_end):\\n self.domain = domain\\n self.start = cursor\\n self.rel_start = rel_start\\n self.rel_end = rel_end\\n\\n def __repr__(self):\\n return ('DomainIter('\\n 'domain={0.domain!r}, '\\n 'start={0.start}, '\\n 'rel_start={0.rel_start}, '\\n 'rel_end={0.rel_end})'.format(self))\\n @property\\n def len(self):\\n return self.rel_end - self.rel_start\\n\\n @property\\n def end(self):\\n return self.start + self.len\\n\\n def rel_index(self, index):\\n return index - self.start + self.rel_start\\n\\n def abs_index(self, rel_index):\\n return self.start + rel_index - self.rel_start\\n\\n domain_cursor = 0\\n index_cursor = 0\\n \\n while domain_cursor < len(self._domains):\\n domain = self._domains[domain_cursor]\\n\\n # If this domain doesn't have anything attached to it, then we can \\n # just yield the whole thing right away.\\n\\n if domain not in self._attachments:\\n yield DomainIter(domain, index_cursor, 0, len(domain))\\n index_cursor += len(domain)\\n\\n # If this domain does have something attached to it, then we need \\n # to carefully yield only the parts of it that aren't covered by \\n # the attachment.\\n\\n else:\\n attachment = self._attachments[domain]\\n\\n # Yield whatever fraction of this domain comes before the \\n # attachment.\\n\\n yield DomainIter(domain,\\n index_cursor, 0, attachment.start_index)\\n index_cursor += attachment.start_index\\n\\n # Yield the domains in the attachment itself by recursively \\n # calling this method.\\n\\n for domain_iter in attachment.construct._iterate_domains():\\n domain_iter.start += index_cursor\\n yield domain_iter\\n index_cursor += len(attachment.construct)\\n\\n # Skip domains until we reach the one where the attachment \\n # ends.\\n\\n while domain is not attachment.end_domain:\\n domain_cursor += 1\\n domain = self._domains[domain_cursor]\\n\\n # Yield whatever fraction of that domain comes after the \\n # attachment.\\n\\n yield DomainIter(domain,\\n index_cursor, attachment.end_index, len(domain))\\n index_cursor += len(domain) - attachment.end_index\\n\\n domain_cursor += 1\",\n \"def mutate_random(self, n=1):\\n mutated_dna = self._dna\\n for i in range(n):\\n mutated_dna = mutate(mutated_dna)\\n return Gene(mutated_dna, self._exon_regions)\",\n \"def get_next_articles(self):\\n\\t\\tarticles = Blog.objects.all()\\\\\\n\\t\\t\\t.filter(publication_date__lte=datetime.date.today())\\\\\\n\\t\\t\\t.order_by('publication_date')\\n\\n\\t\\tif articles.count() <= 4:\\n\\t\\t\\treturn articles\\n\\n\\t\\ti, j, k, l = random.sample(range(0, articles.count()-1), 4)\\n\\t\\treturn [articles[i], articles[j], articles[k], articles[l]]\",\n \"def generate_dimino(self, af=False):\\n idn = list(range(self.degree))\\n order = 0\\n element_list = [idn]\\n set_element_list = {tuple(idn)}\\n if af:\\n yield idn\\n else:\\n yield _af_new(idn)\\n gens = [p._array_form for p in self.generators]\\n\\n for i in range(len(gens)):\\n # D elements of the subgroup G_i generated by gens[:i]\\n D = element_list[:]\\n N = [idn]\\n while N:\\n A = N\\n N = []\\n for a in A:\\n for g in gens[:i + 1]:\\n ag = _af_rmul(a, g)\\n if tuple(ag) not in set_element_list:\\n # produce G_i*g\\n for d in D:\\n order += 1\\n ap = _af_rmul(d, ag)\\n if af:\\n yield ap\\n else:\\n p = _af_new(ap)\\n yield p\\n element_list.append(ap)\\n set_element_list.add(tuple(ap))\\n N.append(ap)\\n self._order = len(element_list)\",\n \"def generate_examples_serial(self):\\n # Now we have a Gensim Dictionary to work with\\n self._build_dictionary()\\n # Remove any tokens with a frequency less than 10\\n self.dictionary.filter_extremes(no_below=10, no_above=0.75)\\n\\n self.examples = []\\n for file in tqdm(self.load_files(), desc=\\\"Generating Examples (serial)\\\"):\\n file = self.dictionary.doc2idx(file)\\n self.examples.extend(self._generate_examples_from_file(file))\",\n \"def __iter__(self) -> Generator:\\n\\t\\treturn (article for article in self._articles)\",\n \"def random_population():\\n pop = []\\n for i in range(POP_SIZE):\\n dna = \\\"\\\"\\n for c in range(DNA_SIZE):\\n dna += random_char()\\n pop.append(dna)\\n return pop\",\n \"def _generate_pores(self):\\n logger.info(\\\"Place randomly located pores in the domain\\\")\\n #Original Random Point Generator\\n #coords = sp.rand(self._Np,3)*[self._Lx,self._Ly,self._Lz]\\n #Seeding Code\\n coords = np.zeros([self._Np,3])\\n #reject points close to boundaries - if False there will be slightly more\\n rejection = [False,False,True]\\n for j in range(3):\\n i = 0\\n while i < self._Np:\\n coord = np.random.uniform(0,1,1)\\n if self._reject(coord) == rejection[j]:\\n coords[i][j]=coord\\n i += 1\\n coords*=np.array([self._Lx,self._Ly,self._Lz])\\n #Seeding Code\\n #Uniform Random Generator\\n #coords = np.array([np.random.uniform(0,self._Lx,self._Np),np.random.uniform(0,self._Ly,self._Np),np.random.uniform(0,self._Lz,self._Np)]).T\\n\\n self['pore.coords'] = coords\\n logger.debug(\\\"End of method\\\")\",\n \"def generate_moves(self, board: Board) -> typing.Iterator[MoveDescription]:\\n dominoes = set(board.dominoes)\\n for domino in dominoes:\\n dx, dy = domino.direction\\n yield from self.try_move(domino, dx, dy)\\n yield from self.try_move(domino, -dx, -dy)\",\n \"def generate_units(self, num_soldados, num_magos):\\n self.soldados = [soldier(randrange(*self.race.rango_atq_soldado),\\n randrange(*self.race.rango_vid_soldado))\\n for i in range(num_soldados)]\\n\\n self.magos = [mage(randrange(*self.race.rango_atq_mago),\\n randrange(*self.race.rango_vid_mago))\\n for i in range(num_magos)]\\n\\n self._initial_life_set = False\",\n \"def sequence(self):\\n for tn in self._testnodes:\\n yield tn\",\n \"def newGeneration(self):\\n for i in range(0, len(self.population)):\\n [ind1, ind2] = self.randomSelection()\\n child = self.crossover(ind1, ind2)\\n self.population[i].setGene(child)\\n self.mutation(self.population[i])\",\n \"def generator(self, random, args):\\r\\n locations = [i for i in range(len(self.weights))]\\r\\n random.shuffle(locations)\\r\\n return locations\",\n \"def node_gen(self):\\n for n in self.child_list:\\n yield from n.node_gen\\n yield self\",\n \"def _setup():\\n for item in dominos:\\n queue.append([item])\",\n \"def gen_donor():\\n# <<<<<<< master\\n return [donor for donor in donor_data]\",\n \"def random_population() -> List:\\r\\n pop = []\\r\\n for _ in range(POPULATION_SIZE):\\r\\n dna = \\\"\\\"\\r\\n for c in range(DNA_SIZE):\\r\\n dna += random_charachter()\\r\\n pop.append(dna)\\r\\n return pop\",\n \"def GeneratorWithNoise(dataset,\\n batch_size,\\n noise_size,\\n which_batches=range(0,1),\\n mode='shuffled_sequential',\\n num_batches=-1):\\n\\n for b in dataset.iterator(\\n batch_size,\\n dataset.get_num_examples()/batch_size,\\n mode=mode,\\n data_specs=dataset.get_data_specs(),\\n return_tuple=True\\n ):\\n b = [b[i] for i in which_batches]\\n t, bsize, dim = b[0].shape # time length, batch size, dim\\n eps = np.random.normal(0,1,size=(t, bsize, noise_size)).astype(floatX)\\n b.append(eps)\\n yield b\",\n \"def _generate_random_population(self, pop_size):\\n\\n random_population = []\\n for agent in range(pop_size):\\n random_population.append(self._generate_random_agent())\\n return random_population\",\n \"def generar_orden_botones():\\r\\n lista_orden = [] # Lista con orden de los botones\\r\\n while len(lista_orden) != 4:\\r\\n num_random = num_rand(0,3)\\r\\n if num_random not in lista_orden:\\r\\n lista_orden.append(num_random)\\r\\n return lista_orden\",\n \"def random_positions(mini, maxi):\\n x_cord = (maxi - mini)*np.random.random(SIZE) + mini\\n y_cord = (maxi - mini)*np.random.random(SIZE) + mini\\n return np.column_stack([x_cord, y_cord])\",\n \"def random(self, n=1):\\n # self.num_generated += n\",\n \"def generator(self, args, gen):\\n import random\\n\\n if args.seed:\\n random.seed(args.seed)\\n seqs = [s for s in gen]\\n sample_indices = random.sample(range(len(seqs)), min(len(seqs), args.number))\\n for i in sample_indices:\\n yield seqs[i]\",\n \"def deng_random(seeds):\\n\\n x = seeds[0]\\n y = seeds[1]\\n\\n while 0<1:\\n temp = x\\n x = (x + y) % 1.0\\n y = temp\\n yield x\",\n \"def sample_personas(self):\\n persona = self.personas_list\\n n = constants.CURATED_PERSONA_CHOICES\\n logging.info(\\n f'Randomly choosing {n} personas from {len(persona)} available ones.'\\n )\\n if self.persona_replacement:\\n return random.sample(persona, k=n)\\n else:\\n return [persona.pop() for _ in range(n)]\",\n \"def dice_gen():\\r\\n import random\\r\\n\\r\\n rdm = random.Random()\\r\\n rdm_gen = []\\r\\n\\r\\n table_txt = open(\\\"dice-table.txt\\\", \\\"r\\\")\\r\\n while True:\\r\\n line = table_txt.readline()\\r\\n if len(line) == 0:\\r\\n break\\r\\n line = list(map(int, line.split()))\\r\\n rdm_gen.append(line[rdm.randint(0,10)])\\r\\n\\r\\n table_txt.close()\\r\\n return(rdm_gen)\",\n \"def generator():\\n mygenerator = (x for x in range(3))\\n for element in mygenerator:\\n print 'poprve = ', element\\n\\n for element in mygenerator:\\n print 'podruhe = ', element\",\n \"def randomDays(self):\\n flag = False\\n while not flag:\\n r = np.floor(np.random.rand() * len(self.df.index))\\n start = self.df.index[r]\\n if r+self.daysHeld < len(self.df.index)-1:\\n end = self.df.index[r+self.daysHeld]\\n flag = True\\n return (start,end)\",\n \"def pick_gynodioecious_parents(simu, config):\\n rng = simu.getRNG()\\n runif = rng.randUniform\\n rint = rng.randInt\\n try:\\n sstar = config.sstar\\n H = config.H\\n def compound_generator(pop):\\n \\\"\\\"\\\"\\n Picks up parent(s) under gynodioecy using compound parameters.\\n \\\"\\\"\\\"\\n gen = -1\\n while True:\\n ngen = pop.dvars().gen\\n if gen != ngen:\\n # At the beginning of a generation, extract the\\n # sex-specific subpopulations from a parental\\n # population. The sex-specific subpopulations are used\\n # throughout mating events in one generation.\\n gen = ngen\\n h = pop.extractSubPops(subPops=[(0, 0)])\\n f = pop.extractSubPops(subPops=[(0, 1)])\\n Nh = h.popSize()\\n Nf = f.popSize()\\n\\n if runif() < sstar: # uniparental\\n yield h.individual(rint(Nh))\\n else: # biparental\\n if runif() < H: # having a hermaphroditic seed parent\\n first, second = rint(Nh), rint(Nh)\\n while first == second:\\n second = rint(Nh)\\n yield [h.individual(first), h.individual(second)]\\n else: # female seed parent\\n yield [h.individual(rint(Nh)), f.individual(rint(Nf))]\\n return compound_generator\\n except KeyError:\\n a = config.a\\n sigma = config.sigma\\n tau = config.tau\\n def fundamental_generator(pop):\\n \\\"\\\"\\\"\\n Picks up parent(s) under gynodioecy using fundamental parameters.\\n \\\"\\\"\\\"\\n gen = -1\\n while True:\\n ngen = pop.dvars().gen\\n if gen != ngen:\\n # At the beginning of a generation, extract the\\n # sex-specific subpopulations from a parental\\n # population. The sex-specific subpopulations are used\\n # throughout mating events in one generation.\\n gen = ngen\\n h = pop.extractSubPops(subPops=[(0, 0)])\\n f = pop.extractSubPops(subPops=[(0, 1)])\\n Nh = h.popSize()\\n Nf = f.popSize()\\n hermseed = Nh / (Nh * Nf * sigma)\\n\\n if runif() < hermseed: # hermaphroditic seed parent\\n if runif() < a: # self-pollen\\n yield h.individual(rint(Nh))\\n else: # non self-pollen\\n first, second = rint(Nh), rint(Nh)\\n while first == second:\\n second = rint(Nh)\\n yield [h.individual(first), h.individual(second)]\\n else: # female seed parent\\n if runif() < tau:\\n yield [h.individual(rint(Nh)), f.individual(rint(Nf))]\\n return fundamental_generator\",\n \"def DOIs(self, default=[{}]):\\n tmp = self.data.get('metadata', {}).get('dois', default)\\n return [HEP.DOIObject(i) for i in tmp]\",\n \"def seq_data_iter_random(corpus, batch_size, num_steps): #@save\\n # Start with a random offset (inclusive of `num_steps - 1`) to partition a\\n # sequence\\n corpus = corpus[random.randint(0, num_steps - 1):]\\n # Subtract 1 since we need to account for labels\\n num_subseqs = (len(corpus) - 1) // num_steps\\n # The starting indices for subsequences of length `num_steps`\\n initial_indices = list(range(0, num_subseqs * num_steps, num_steps))\\n # In random sampling, the subsequences from two adjacent random\\n # minibatches during iteration are not necessarily adjacent on the\\n # original sequence\\n random.shuffle(initial_indices)\\n\\n def data(pos):\\n # Return a sequence of length `num_steps` starting from `pos`\\n return corpus[pos: pos + num_steps]\\n\\n num_batches = num_subseqs // batch_size\\n for i in range(0, batch_size * num_batches, batch_size):\\n # Here, `initial_indices` contains randomized starting indices for\\n # subsequences\\n initial_indices_per_batch = initial_indices[i: i + batch_size]\\n X = [data(j) for j in initial_indices_per_batch]\\n Y = [data(j + 1) for j in initial_indices_per_batch]\\n yield torch.tensor(X), torch.tensor(Y)\",\n \"def get_donnees(self):\\r\\n \\r\\n return self._donnees\",\n \"def random_coordinates():\\n return Coordinates(random.randint(0, 14), random.randint(0, 14))\",\n \"def getFirstTorsionInds(self, extended):\\n offset = 6 if extended else 0\\n torsionInds = np.array(range(offset + 5, self.natoms * 3, 3))\\n primaryTorsions = sorted(list(set(self._firstTorsionTInd)))\\n return list(torsionInds[primaryTorsions])\",\n \"def generate_random_numbers(self):\\r\\n #random.seed(seed=self.seed)\\r\\n #err = random.random((3,1))\\r\\n #f = open('test_res', 'a')\\r\\n #f.write('probability - %s' %self.seed)\\r\\n #f.write(str(list(err[:3,:])))\\r\\n #f.write('\\\\n')\\r\\n #f.close()\\r\\n\\r\\n dist = RandomDistribution(self.seed)\\r\\n rand_numbers = dist.return_random_variables(self.num_agents)\\r\\n return rand_numbers\",\n \"def Generate_Random( self ):\\n print( 'Generating Random coordinates' )\\n stands = self.Data.Stand.keys()\\n stands.sort()\\n for s in stands:\\n trees = self.Data.Stand[s].Tree.keys()\\n trees.sort()\\n for t in trees:\\n self.Data.Stand[s].Tree[t].X = random.uniform( 0, 208.71 )\\n self.Data.Stand[s].Tree[t].Y = random.uniform( 0, 208.71 )\",\n \"def in_random_order(theta):\\r\\n indexes = [i for i,_ in enumerate(data)] # creates a list of indices\\r\\n random.shuffle(indexes) # shuffles them\\r\\n for i in indexes:\\r\\n yield data[i] # return data in that order\\r\",\n \"def data_over_diamond(only_valid_monster=True) -> Generator[GameData, None, None]:\\n\\ttarget = collection.find({\\\"tier\\\": {\\\"$in\\\": elo[-4:]}, \\\"game_duration\\\": {\\\"$gte\\\": 1200}})\\n\\tfor data in target:\\n\\t\\tgame_data = GameData(data)\\n\\t\\tif not only_valid_monster or (only_valid_monster and game_data.is_monster_log_valid()):\\n\\t\\t\\tyield game_data\",\n \"def test_generation(self):\\n generator = math_helpers.pentagonal_number_generator()\\n first_ten_pentagonal_numbers = [next(generator) for _ in range(10)]\\n canonical_values = [1, 5, 12, 22, 35, 51, 70, 92, 117, 145]\\n self.assertEqual(canonical_values, first_ten_pentagonal_numbers)\",\n \"def build(self):\\n self._start = np.zeros_like(self._rooms.shape)\\n self._start[0] = random.randrange(self._rooms.shape[0])\\n position = self._start\\n egress = Direction.South\\n distance = 1\\n while position in self:\\n room = self[position]\\n room.egress = egress\\n room.distance = distance\\n yield position\\n options = {}\\n for direction in Direction.range():\\n if direction != egress:\\n new_position = position + direction.offset()\\n if new_position in self:\\n if self.is_sealed(new_position):\\n options[direction] = new_position\\n if options:\\n direction = random.choice(tuple(options.keys()))\\n room.remove_wall(direction)\\n position = options[direction]\\n egress = direction.reverse()\\n distance += 1\\n else:\\n position += egress.offset()\\n if position in self:\\n egress = self[position].egress\\n distance = self[position].distance\",\n \"def test_generator_method(self):\\n for i in range(0, 4):\\n yield self.try_odd, i\",\n \"def pick_androdioecious_parents(simu, config):\\n rng = simu.getRNG()\\n runif = rng.randUniform\\n rint = rng.randInt\\n try:\\n sstar = config.sstar\\n def compound_generator(pop):\\n \\\"\\\"\\\"\\n Picks up parent(s) under androdioecy using a compound parameter.\\n \\\"\\\"\\\"\\n gen = -1\\n while True:\\n ngen = pop.dvars().gen\\n if gen != ngen:\\n # At the beginning of a generation, extract the\\n # sex-specific subpopulations from a parental\\n # population. The sex-specific subpopulations are used\\n # throughout mating events in one generation.\\n gen = ngen\\n males = pop.extractSubPops(subPops=[(0, 0)])\\n herms = pop.extractSubPops(subPops=[(0, 1)])\\n nmale = males.popSize()\\n nherm = herms.popSize()\\n\\n if runif() < sstar: # uniparental\\n yield herms.individual(rint(nherm))\\n else: # biparental\\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\\n return compound_generator\\n except KeyError:\\n stilde = config.stilde\\n tau = config.tau\\n def compound_generator(pop):\\n \\\"\\\"\\\"\\n Picks up parent(s) under androdioecy using fundamental parameters.\\n \\\"\\\"\\\"\\n gen = -1\\n while True:\\n ngen = pop.dvars().gen\\n if gen != ngen:\\n # At the beginning of a generation, extract the\\n # sex-specific subpopulations from a parental\\n # population. The sex-specific subpopulations are used\\n # throughout mating events in one generation.\\n gen = ngen\\n males = pop.extractSubPops(subPops=[(0, 0)])\\n herms = pop.extractSubPops(subPops=[(0, 1)])\\n nmale = males.popSize()\\n nherm = herms.popSize()\\n\\n if runif() < stilde: # proportion of self-fertlized egg\\n if runif() < tau: # survival rate of a uniparental zygote rel to a biparental z.\\n yield herms.individual(rint(nherm))\\n else: # biparental\\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\\n return compound_generator\",\n \"def xrndSphere(n):\\n for i in xrange(n):\\n yield rndSphere()\",\n \"def get_nodes(self, gid, sid, did, scid, doid):\\n yield self.generate_browser_collection_node(doid)\",\n \"def _create_random_tetrino(self):\\n shape_index = randrange(constant.NUM_SHAPES)\\n shape = constant.SHAPES[shape_index]\\n shape_locations = self.translate_shape(shape[0], 0, 0)\\n num_blocks = len(shape_locations)\\n location = self._create_random_offsets(shape_locations)\\n new_tetrino = Tetrino(location, shape_index, \\\\\\n num_blocks, self.tetrino_id, self.game_size)\\n self.tetrino_set[self.tetrino_id] = new_tetrino\\n self.tetrino_id += 1\\n return new_tetrino\",\n \"def generate(self):\\n start = self.nu_idx\\n stop = start + self.score\\n return self.orig_nu[start:stop]\",\n \"def get_randoms_to_generate(self):\\n return self._randoms_to_generate\",\n \"def generate_individual(size):\\n individual = []\\n \\n for i in range(size):\\n individual.append(random.randint(0,1))\\n\\n return individual\",\n \"def __iterate_minibatches(self, dataset, batchsize, shuffle=False):\\n n = len(dataset[0])\\n assert len(dataset[1]) == n\\n batchsize = min(batchsize, n)\\n indices = numpy.arange(n)\\n numpy.random.shuffle(indices)\\n for start_idx in range(0, n - batchsize + 1, batchsize):\\n excerpt = indices[start_idx:start_idx + batchsize]\\n yield dataset[0][excerpt], dataset[1][excerpt]\",\n \"def _next_generation(self, ranks):\\n replace = ranks[:int(self.population_size * self.culling)]\\n for idx in replace:\\n self.population[idx] = self._create_offspring()\",\n \"def gen_seq(self,ntrials=20,pm_trial_position=None):\\n # insert ranomly positioned pm trials\\n if type(pm_trial_position)==type(None):\\n ntrials -= 1+self.num_pm_trials\\n pm_trial_position = np.random.randint(self.min_start_trials,ntrials,self.num_pm_trials) \\n else:\\n ntrials -= 1+len(pm_trial_position)\\n pm_trial_position = pm_trial_position\\n # generate og stim\\n seq = np.random.randint(0,self.ntokens_og,ntrials)\\n X = np.insert(seq,[0,*pm_trial_position],self.pm_token)\\n # form Y \\n Xroll = np.roll(X,self.nback)\\n Y = (X == Xroll).astype(int) # nback trials\\n Y[X==self.pm_token]=2 # pm trials\\n return X,Y\",\n \"def remove_randomico(lista, qtd_remocao):\\n for i in range(qtd_remocao):\\n lista.pop(random.randrange(len(lista))) \\n return lista\",\n \"def generate_observations(self, eval_mode, augment_frames=None):\\n episode_idx = 0\\n augment_frames = (\\n augment_frames if augment_frames is not None\\n else self._augment_frames and not eval_mode)\\n for t, obs in enumerate(self._flat_observations):\\n if augment_frames:\\n obs = image_utils.random_crop_image(obs)\\n if self._split_by_episodes:\\n yield obs, episode_idx\\n else:\\n yield obs\\n if self._is_terminal[t]:\\n episode_idx += 1\",\n \"def get_random_population():\\r\\n return [ get_random_individual() for _ in range(POPULATION_COUNT) ]\",\n \"def __generate_random_gene_sequence(self):\\n genes = []\\n for j in range(self.chromosome_size):\\n genes.append(random.choice(self.gene_pool))\\n\\n return genes\",\n \"def iter_segments_(self, X):\\n\\n # heuristic that tries to avoid highly-overlapping sub-segments\\n # (i.e. with more than 50% overlap on average) for short speech turns\\n n_samples = len(X)\\n n = (n_samples - self.n_samples_) // (self.n_samples_ // 2) + 1\\n n = min(n, self.per_turn)\\n\\n # choose (and yield) n sub-segments at random\\n for i in np.random.randint(0, n_samples - self.n_samples_, n):\\n yield X[i: i + self.n_samples_]\",\n \"def generate_test():\\n o = []\\n pos = [384, 288]\\n note_group_size = GAN_PARAMS[\\\"note_group_size\\\"]\\n generate_set(begin=3 * note_group_size, start_pos=pos,\\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)\",\n \"def totem_random():\\n random_head()\\n random_head()\\n random_head()\",\n \"def generatePopulation(self,size):\\n return [ self._individual(self._individualSize) for x in range(size) ]\",\n \"def create_enemies(self):\\n if not self.ENEMY_DIST:\\n raise ValueError('You must initialize ENEMY_DIST. Import turnable.rooms.FightRoom and '\\n 'call FightRoom.set_enemy_dist(your_dist).')\\n\\n amount = random.randint(1, 3)\\n for c in range(amount):\\n en = self._get_enemy()(pos=self.pos)\\n en.game = self.game\\n self.enemies.append(en)\",\n \"def random_by_year_list(self):\\n\\n from_year = xbmcgui.Dialog().input(\\n \\\"From year\\\", type=xbmcgui.INPUT_NUMERIC)\\n to_year = xbmcgui.Dialog().input(\\n \\\"To year\\\", type=xbmcgui.INPUT_NUMERIC)\\n\\n xbmcplugin.setContent(self.addon_handle, \\\"songs\\\")\\n\\n for track in self.connection.walk_random_songs(\\n size=self.random_count, from_year=from_year, to_year=to_year):\\n self.add_track(track, show_artist=True)\\n\\n xbmcplugin.endOfDirectory(self.addon_handle)\",\n \"def _random_doi():\\n return \\\"10.\\\" + _random_string(3, 4) + \\\"/\\\" + _random_string(5, 10)\",\n \"def iter_coordinates(self):\\n for coord in self.position:\\n yield coord\",\n \"def generator(self, random, args):\\r\\n raise NotImplementedError\",\n \"def _add_dominance_relation__to__nodes(self):\\n dominating_dict = defaultdict(list)\\n dominated_dict = defaultdict(list)\\n for dom_rel_id in self._dominance_relation_ids:\\n dominated_node_id = self.edges[dom_rel_id].target\\n dominating_node_id = self.edges[dom_rel_id].source\\n dominating_dict[dominating_node_id].append(dominated_node_id)\\n dominated_dict[dominated_node_id].append(dominating_node_id)\\n\\n for dominating_node_id in dominating_dict:\\n self.nodes[dominating_node_id].dominates = \\\\\\n dominating_dict[dominating_node_id]\\n for dominated_node_id in dominated_dict:\\n self.nodes[dominated_node_id].dominated_by = \\\\\\n dominated_dict[dominated_node_id]\",\n \"def insere_randomico(lista, qtd_insercao):\\n for i in range(qtd_insercao):\\n lista.append(random.randint(1,10000))\\n random.shuffle(lista)\\n return lista\",\n \"def _pop_random_n(entities: np.array, weights: np.array, count: int = 3):\\n for _ in range(count):\\n if not len(entities):\\n return\\n\\n choice, entities, weights = _pop_random(entities, weights)\\n yield choice\",\n \"def generate_next(self, direction =\\\"+\\\", choose= None):\\n import random\\n if direction == \\\"+\\\":\\n links = self.links_f\\n elif direction == \\\"-\\\":\\n links = self.links_b\\n else:\\n raise ValueError(\\\"Undefined direction argument\\\")\\n \\n if len(links.link) == 0:\\n raise EndOfStory(\\\"No carry ons\\\")\\n \\n if choose == None:\\n choose = random.randrange(len(links.link))\\n conflicts = links.link[choose]\\n \\n for conflict in conflicts:\\n\\n yield Conflict(self.soup, conflict[0], conflict[1], self.update_transforms(self.transpositions, conflict[2]), self.characters, self.storyline)\",\n \"def generate_examples(gen, truncation=0.7, n=100):\\n gen.eval()\\n alpha = 1.0\\n for i in range(n):\\n with torch.no_grad():\\n noise = torch.tensor(truncnorm.rvs(-truncation, truncation, size=(1, config.Z_DIM, 1, 1)), device=config.DEVICE, dtype=torch.float32)\\n img = gen(noise, alpha, 279)\\n save_image(img*0.5+0.5, f\\\"saved_examples/{i+2000}.jpg\\\")\",\n \"def random_girl(self):\\n return [result for result in self._db.girls.find().limit(1) \\\\\\n .skip(random.randrange(self._db.girls.count()))][0]\",\n \"def star_topology(random, population, args):\\r\\n for _ in range(len(population)):\\r\\n yield population[:]\",\n \"def iter_trios(self):\\n for row in self._ped_tab.itertuples():\\n if row.father == '0':\\n continue\\n if row.mother == '0':\\n continue\\n yield (row.sample_id, row.father, row.mother)\",\n \"def set_random_musicians(self, num:int):\\n try:\\n self.cursor.execute(\\\"insert into musicians (name, birthday, role, group_id) \\\"\\n \\\"select rand.name, rand.birthday, rand.role, rand.group_id \\\"\\n \\\"from (SELECT groups.id as group_id, \\\"\\n \\\"(md5(random()::text)) as name, \\\"\\n \\\"(md5(random()::text)) as role, \\\"\\n \\\"((current_date - '100 years'::interval) + trunc(random() * 365) * '1 day'::interval + trunc(random() * 3) * '1 year'::interval ) as birthday \\\"\\n f\\\"from generate_series(1, 10), groups ORDER BY random() limit {num}) as rand\\\")\\n self.connection.commit()\\n if self.cursor.rowcount:\\n return \\\"generated musicians\\\"\\n else:\\n return \\\"NULL\\\"\\n except(Exception, psycopg2.Error) as error:\\n self.connect.rollback()\\n print(\\\"error in generate\\\", error)\",\n \"def generate(self, words=5):\\n seed()\\n return sorted(self.keywords, key=lambda *args: random())[0:words]\",\n \"def _seed_population(self):\\n return [self._generate_weights() for x in range(self.population_size)]\",\n \"def yatzy_dice():\\n return [random_die() for _ in range(5)]\",\n \"def get_dossiers_shared_with_group(self):\\n dossier_brains = api.content.find(allowedRolesAndUsers=u'user:{}'.format(self.group_id))\\n ndossiers = len(dossier_brains)\\n print(\\\"found {} dossiers\\\".format(ndossiers))\\n for i, dossier_brain in enumerate(dossier_brains):\\n if i % 5000 == 0:\\n self.collect_garbage(self.context)\\n rss = self.get_rss() / 1024.0\\n print(\\\"done with {}/{}; {:.1f}%; Memory: {}\\\".format(\\n i, ndossiers, 100. * i / ndossiers, rss))\\n dossier = dossier_brain.getObject()\\n assignments = RoleAssignmentManager(dossier).get_assignments_by_principal_id(self.group_id)\\n sharing_assignment = self._find_sharing_assignment(assignments)\\n if sharing_assignment:\\n yield (dossier, sharing_assignment)\",\n \"def _ontonotes_subset(ontonotes_reader: Ontonotes,\\r\\n file_path: str,\\r\\n domain_identifier: str) -> Iterable[OntonotesSentence]:\\r\\n for conll_file in ontonotes_reader.dataset_path_iterator(file_path):\\r\\n if domain_identifier is None or f\\\"/{domain_identifier}/\\\" in conll_file:\\r\\n yield from ontonotes_reader.sentence_iterator(conll_file)\",\n \"def randNotes(self, value, chord):\\n aChord = mgChord(value, chord)\\n self.data = []\\n for i in range(len(self.rhythm)):\\n note = random.choice(aChord).copy()\\n note.setDuration(self.rhythm[i])\\n self.data.append(note)\",\n \"def with_coordinates(self):\\n for tweet in self.having(coordinates=self.default_coordinates):\\n yield tweet\",\n \"def make_parents(self):\\r\\n self.parents = []\\r\\n \\r\\n for loopindex in range(0, int(self.population_size * 0.6)):\\r\\n while True:\\r\\n if loopindex < int(self.population_size * 6 / 15):\\r\\n parent = random.choice(self.best_districts)\\r\\n else:\\r\\n parent = random.choice(self.worst_districts)\\r\\n \\r\\n if parent not in self.parents:\\r\\n self.parents.append(parent)\\r\\n break\",\n \"def _ontonotes_subset(ontonotes_reader: Ontonotes,\\n file_path: str,\\n domain_identifier: str) -> Iterable[OntonotesSentence]:\\n for conll_file in ontonotes_reader.dataset_path_iterator(file_path):\\n if domain_identifier is None or f\\\"/{domain_identifier}/\\\" in conll_file:\\n yield from ontonotes_reader.sentence_iterator(conll_file)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6895019","0.62940025","0.5725973","0.53913283","0.51478684","0.5122216","0.5106027","0.51026565","0.5055542","0.50077224","0.49957895","0.4972248","0.49301994","0.49140477","0.49055457","0.48926997","0.4876082","0.48698455","0.48647448","0.48630825","0.48471773","0.48347035","0.47879037","0.4787243","0.47839093","0.47767895","0.47700804","0.4763562","0.47510606","0.47437888","0.4743771","0.47407603","0.4734424","0.47244877","0.47116652","0.47070092","0.46918133","0.468641","0.46837443","0.46697122","0.46594408","0.46540272","0.4648755","0.46478024","0.46450815","0.4640494","0.4638057","0.46379557","0.4636234","0.4627677","0.46157587","0.4612547","0.46116847","0.46075085","0.46064025","0.4606039","0.4605828","0.46020135","0.45974255","0.45955414","0.45928252","0.45926592","0.4574796","0.4570613","0.4566679","0.45637256","0.45608595","0.45489752","0.45342404","0.45339394","0.45297906","0.4519275","0.4516926","0.4515052","0.45136404","0.4506803","0.45059156","0.45057","0.45037204","0.45024619","0.449786","0.4491928","0.44872013","0.4486053","0.44859385","0.44805443","0.4476459","0.44763684","0.4474348","0.4471783","0.44709265","0.44678915","0.4466688","0.44661677","0.44583678","0.44581193","0.4455813","0.44506755","0.44485936","0.44481742"],"string":"[\n \"0.6895019\",\n \"0.62940025\",\n \"0.5725973\",\n \"0.53913283\",\n \"0.51478684\",\n \"0.5122216\",\n \"0.5106027\",\n \"0.51026565\",\n \"0.5055542\",\n \"0.50077224\",\n \"0.49957895\",\n \"0.4972248\",\n \"0.49301994\",\n \"0.49140477\",\n \"0.49055457\",\n \"0.48926997\",\n \"0.4876082\",\n \"0.48698455\",\n \"0.48647448\",\n \"0.48630825\",\n \"0.48471773\",\n \"0.48347035\",\n \"0.47879037\",\n \"0.4787243\",\n \"0.47839093\",\n \"0.47767895\",\n \"0.47700804\",\n \"0.4763562\",\n \"0.47510606\",\n \"0.47437888\",\n \"0.4743771\",\n \"0.47407603\",\n \"0.4734424\",\n \"0.47244877\",\n \"0.47116652\",\n \"0.47070092\",\n \"0.46918133\",\n \"0.468641\",\n \"0.46837443\",\n \"0.46697122\",\n \"0.46594408\",\n \"0.46540272\",\n \"0.4648755\",\n \"0.46478024\",\n \"0.46450815\",\n \"0.4640494\",\n \"0.4638057\",\n \"0.46379557\",\n \"0.4636234\",\n \"0.4627677\",\n \"0.46157587\",\n \"0.4612547\",\n \"0.46116847\",\n \"0.46075085\",\n \"0.46064025\",\n \"0.4606039\",\n \"0.4605828\",\n \"0.46020135\",\n \"0.45974255\",\n \"0.45955414\",\n \"0.45928252\",\n \"0.45926592\",\n \"0.4574796\",\n \"0.4570613\",\n \"0.4566679\",\n \"0.45637256\",\n \"0.45608595\",\n \"0.45489752\",\n \"0.45342404\",\n \"0.45339394\",\n \"0.45297906\",\n \"0.4519275\",\n \"0.4516926\",\n \"0.4515052\",\n \"0.45136404\",\n \"0.4506803\",\n \"0.45059156\",\n \"0.45057\",\n \"0.45037204\",\n \"0.45024619\",\n \"0.449786\",\n \"0.4491928\",\n \"0.44872013\",\n \"0.4486053\",\n \"0.44859385\",\n \"0.44805443\",\n \"0.4476459\",\n \"0.44763684\",\n \"0.4474348\",\n \"0.4471783\",\n \"0.44709265\",\n \"0.44678915\",\n \"0.4466688\",\n \"0.44661677\",\n \"0.44583678\",\n \"0.44581193\",\n \"0.4455813\",\n \"0.44506755\",\n \"0.44485936\",\n \"0.44481742\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8736248"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":252,"numItemsPerPage":100,"numTotalItems":95772,"offset":25200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNjc1Mzc2ODJkMmE2Mjg0NzVhMWJmZmNkIl0sImlhdCI6MTc5MTA5Nzg4MCwianRpIjoiMGJkZGE0OGQtOGRjMy00MjMzLTgyMmItNzM1MWUzYzRiMDA5Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3OTExMDE0ODAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.-diyKOkIW4opVarVd-tWAmGNS8SOKEa_WJ07de_nosPz9J9FAA2rHUHVgKc9-2pFhrSACnk3mf5DDbE9sV_ZAg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Returns a query result dictionary, given an ACE Record instance and a dictionary of the XPath queries that need to be executed on this record. | def query_dict_for_record(record, touched_queries):
result = dict()
if len(touched_queries) > 0:
parsed_record = etree.parse(StringIO(record.test_data_xml()))
result.update(dict((q_name, {'query': q_value,
'result': list(x.text for x in etree.ETXPath(q_value)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute_records_query(query):\n result, hits, output = execute_basic(TYPE_RECORD, query)\n for rec in hits.get('hits', []):\n record = rec.get('_source')\n record['score'] = rec.get('_score')\n record['text'] = rec.get('highlight', {}).get('text')\n output['results'].append(re... | [
"0.57072085",
"0.5547496",
"0.5542535",
"0.55252427",
"0.5523193",
"0.5461427",
"0.5346732",
"0.53411245",
"0.53293097",
"0.53058386",
"0.5295658",
"0.5276403",
"0.52639025",
"0.51895535",
"0.51621974",
"0.5137885",
"0.51368797",
"0.51255596",
"0.51243746",
"0.512335",
"0.510... | 0.755422 | 0 |
Returns list for each ACERecord supplied, each specifying fromto which node+terminal between which this record was obtained, together with a query result dictionary. | def perform_queries(records, project_type, project, msgflow):
all_queries = subdirs_file_content_to_dict(os.path.join(data_dir, project_type, project, msgflow),
split_by_line=False, subdict_by_path=True)
result = list({'from': {'node': record.source_node, 'terminal... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_records(self):\n logging.debug('Return all records in table')\n if not self._dbconnect or not self._cursor:\n raise Exception('Invalid call to Context Manager method!')\n\n self._cursor.execute(\"\"\"SELECT * FROM {}\"\"\".format(self._name))\n rows = self._cursor.fet... | [
"0.6234349",
"0.59395957",
"0.5783132",
"0.5760269",
"0.56434655",
"0.5625844",
"0.5620573",
"0.5597441",
"0.55879647",
"0.549597",
"0.5477377",
"0.53048503",
"0.52551764",
"0.51955223",
"0.51800114",
"0.5148783",
"0.5139155",
"0.513071",
"0.51090914",
"0.51068676",
"0.510563... | 0.54963076 | 9 |
Endpoint to exercise a message. The message is injected into the flow. For this recording and injection must be temporarily enabled on the flow. Test data is obtained, after which instances of ACERecord are created and sorted on flowSequenceNumber. For each record, an object is created with the fromto node+terminal inf... | def post(self, project_type, project, msgflow, node):
result = dict()
try:
ace_conn.start_recording(project_type, project, msgflow)
ace_conn.start_injection(project_type, project, msgflow)
ace_conn.inject(project_type, project, msgflow, node, request.data)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_echo(self):\n self.add_item(\"skill\", \"fetchai/echo:0.5.0\")\n\n process = self.run_agent()\n is_running = self.is_running(process)\n assert is_running, \"AEA not running within timeout!\"\n\n # add sending and receiving envelope from input/output files\n sender... | [
"0.5372232",
"0.5337886",
"0.5229061",
"0.51248443",
"0.5084246",
"0.5082173",
"0.5059913",
"0.50520146",
"0.5048308",
"0.50415814",
"0.5012191",
"0.5003331",
"0.49835372",
"0.49795404",
"0.49769887",
"0.49708155",
"0.49585548",
"0.49475357",
"0.49267948",
"0.48812303",
"0.48... | 0.5756279 | 0 |
Ask a yes/no question via raw_input() and return their answer. "question" is a string that is presented to the user. "default" is the presumed answer if the user just hits . It must be "yes" (the default), "no" or None (meaning an answer is required of the user). The force option simply sets the answer to default. The ... | def query_yes_no(question, default="yes", force=False):
valid = {"yes":True, "y":True, "ye":True,
"no":False, "n":False}
if default == None:
prompt = " [y/n] "
elif default == "yes":
prompt = " [Y/n] "
elif default == "no":
prompt = " [y/N] "
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query_yes_no(question, default=\"yes\"):\n valid = {\"yes\":\"yes\", \"y\":\"yes\", \"ye\":\"yes\",\n \"no\":\"no\", \"n\":\"no\"}\n if default == None:\n prompt = \" [y/n] \"\n elif default == \"yes\":\n prompt = \" [Y/n] \"\n elif default == \"no\":\n promp... | [
"0.82480913",
"0.8201619",
"0.8161579",
"0.81534576",
"0.81376886",
"0.81355315",
"0.81342065",
"0.8133191",
"0.8133191",
"0.8130005",
"0.8128684",
"0.8128684",
"0.8128684",
"0.8128684",
"0.8128684",
"0.81278366",
"0.81278366",
"0.81278366",
"0.81278366",
"0.81278366",
"0.812... | 0.85351175 | 0 |
Prompt a statement requiring an interactive from the user. | def query_ok(statement="", force=False):
prompt = " [press <enter> to continue] "
while True:
sys.stdout.write(statement + prompt)
if not force:
ok = raw_input().lower()
else:
ok = ""
if ok == "":
return True
else:
sys.stdou... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ask(self, prompt: str) -> str:\n raise NotImplementedError",
"def prompt_user(prompt):\r\n # raw_input returns the empty string for \"enter\"\r\n yes = set(['yes', 'y'])\r\n no = set(['no','n'])\r\n\r\n try:\r\n print(prompt)\r\n choice = raw_input().lower()\r\n # woul... | [
"0.6755771",
"0.67360723",
"0.6700402",
"0.66866815",
"0.66583824",
"0.66383564",
"0.65155077",
"0.65145904",
"0.6495161",
"0.64846927",
"0.6434562",
"0.63812006",
"0.6373911",
"0.63394517",
"0.63238543",
"0.62432694",
"0.6237048",
"0.62323844",
"0.6148552",
"0.61435086",
"0.... | 0.62230843 | 18 |
Perform a directory walk | def walk(rootdir):
flist = []
for root, dirs, files in os.walk(rootdir):
flist = flist + [os.path.join(root, x) for x in files]
return flist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def walk(dir, callback):\n\n dir = abspath(dir)\n for file in listdir(dir):\n nfile = join(dir, file)\n if isdir(nfile):\n walk(nfile, callback)\n else:\n callback(nfile)",
"def walk(dirname): \n for name in os.listdir(dirname):\n path = os.path.join(dir... | [
"0.76540864",
"0.7646833",
"0.75663054",
"0.73219824",
"0.72194135",
"0.7084689",
"0.68280494",
"0.68234414",
"0.67714447",
"0.67714447",
"0.6764872",
"0.66485465",
"0.6637909",
"0.6593982",
"0.6566981",
"0.6565008",
"0.6515065",
"0.6490184",
"0.6445862",
"0.6416494",
"0.6369... | 0.5934327 | 50 |
Perform a filtered directory walk. | def filtered_walk(rootdir, filter_fn, include_dirs=None, exclude_dirs=None, get_dirs=False):
flist = []
dlist = []
for root, dirs, files in os.walk(rootdir):
if include_dirs and len(set(root.split(os.sep)).intersection(set(include_dirs))) == 0:
## Also try re.search in case we have patte... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter(self):\n self._printer('Standard Walk')\n count = Counter(length=3)\n for directory in self.directory:\n self._printer('Searching ' + directory)\n for root, directories, files in os.walk(directory, topdown=self.topdown):\n root = root[len(str(dir... | [
"0.7196122",
"0.6588966",
"0.6396584",
"0.6380269",
"0.6349008",
"0.63149124",
"0.61969244",
"0.61103547",
"0.60762525",
"0.6074421",
"0.60585856",
"0.6046986",
"0.60449713",
"0.6044598",
"0.60307413",
"0.6027791",
"0.60122037",
"0.599719",
"0.5978354",
"0.5976603",
"0.597657... | 0.6661728 | 1 |
Make a directory if it doesn't exist, handling concurrent race conditions. | def safe_makedir(dname):
if not os.path.exists(dname):
# we could get an error here if multiple processes are creating
# the directory at the same time. Grr, concurrency.
try:
os.makedirs(dname)
except OSError:
if not os.path.isdir(dname):
rais... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_dir_if_needed(dir) :\n\tif not exists(dir) :\n\t\tos.makedirs(dir)",
"def mkDir(path):\n if not os.path.exists(path):\n try:\n os.makedirs(path)\n except OSError:\n # In a race between two threads, this thread may have lost,\n # in which case the directo... | [
"0.8121099",
"0.7892072",
"0.788025",
"0.7782501",
"0.7757828",
"0.7722888",
"0.7703332",
"0.7641113",
"0.7607421",
"0.75588185",
"0.75113726",
"0.7501842",
"0.7485724",
"0.74761015",
"0.74720377",
"0.74711245",
"0.74672055",
"0.74672055",
"0.74634916",
"0.74364537",
"0.74195... | 0.7507635 | 11 |
Context manager to temporarily change to a new directory. | def chdir(new_dir):
cur_dir = os.getcwd()
# FIXME: currently assuming directory exists
safe_makedir(new_dir)
os.chdir(new_dir)
try:
yield
finally:
os.chdir(cur_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def use_dir(new_dir):\n owd = os.getcwd()\n os.chdir(new_dir)\n\n try:\n yield\n finally:\n os.chdir(owd)",
"def cd_manager(self, new_wd):\n old_wd = self.cwd\n self.cd(new_wd)\n yield self.cwd\n self.cd(old_wd)",
"def __enter__(self):\n self.savedPa... | [
"0.7735854",
"0.75990963",
"0.752381",
"0.7449731",
"0.7449731",
"0.744947",
"0.73910314",
"0.727928",
"0.71958107",
"0.71416557",
"0.71343964",
"0.71328884",
"0.7042217",
"0.70238966",
"0.701297",
"0.69625574",
"0.6961247",
"0.69471306",
"0.692629",
"0.6924177",
"0.6918348",... | 0.74439114 | 6 |
Transform option list to a dictionary. | def opt_to_dict(opts):
if isinstance(opts, dict):
return
args = list(itertools.chain.from_iterable([x.split("=") for x in opts]))
opt_d = {k: True if v.startswith('-') else v
for k,v in zip(args, args[1:]+["--"]) if k.startswith('-')}
return opt_d | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_options(option_list: List[str]) -> Dict[str, Union[int, float, str]]:\n d = dict()\n for o in option_list:\n o = o.split('=')\n if len(o) != 3:\n raise OptionParsingError(\"Not enough elements in the parsed options. Need 3 elements.\")\n key = o[0]\n val = o[1... | [
"0.6948613",
"0.661204",
"0.6580496",
"0.65647084",
"0.65088916",
"0.6373278",
"0.6370471",
"0.63431555",
"0.6230479",
"0.6225121",
"0.61512506",
"0.6135905",
"0.60848534",
"0.6042554",
"0.6006351",
"0.5991005",
"0.59683454",
"0.58970153",
"0.5887909",
"0.5887085",
"0.5885839... | 0.6884359 | 1 |
Remove unwanted options from an option list. | def prune_option_list(opts, keys):
opt_d = opt_to_dict(opts)
for k in keys:
if k in opt_d:
del opt_d[k]
return [k for item in opt_d.iteritems() for k in item] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _sanitize(self, opts_list):\n for opt in opts_list:\n if len(opt.strip()) == 0:\n opts_list.remove(opt)\n return opts_list",
"def remove_all(self):\n self._options.clear()\n self._programs.clear()",
"def replace_unacceptable_options(options, is_request)... | [
"0.7164903",
"0.62534535",
"0.62313527",
"0.6177054",
"0.6156464",
"0.61167467",
"0.598523",
"0.5968531",
"0.5958963",
"0.5934466",
"0.5840218",
"0.5644665",
"0.5616773",
"0.55490786",
"0.55408776",
"0.551189",
"0.5459334",
"0.54491204",
"0.54466665",
"0.54283345",
"0.5392538... | 0.7166121 | 0 |
Make a paper plot for the Ohmic (or linear) mobility of the RTA, lowfield, and fulldrift solutions. | def linear_mobility_paperplot(fieldVector,df):
vcm = np.array(fieldVector) * 1e-2
lw = 1.5
mu_1 = []
mu_2 = []
mu_3 = []
meanE_1 = []
meanE_2 = []
meanE_3 = []
for ee in fieldVector:
chi_1_i = np.load(pp.outputLoc + 'Steady/' + 'chi_' + '1_' + "E_{:.1e}.npy".format(ee))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def small_signal_mobility_paperplot(fieldVector, freqVector, df):\n vcm = np.array(fieldVector)*1e-2\n n = utilities.calculate_density(df)\n lw = 1.5\n fig, ax = plt.subplots()\n for freq in freqVector:\n cond = []\n mu_3 = []\n for ee in fieldVector:\n chi_3_i = np.l... | [
"0.6821178",
"0.64246243",
"0.6286595",
"0.626816",
"0.6249688",
"0.6224722",
"0.6198306",
"0.6154236",
"0.613358",
"0.61210513",
"0.6096487",
"0.6086529",
"0.6069941",
"0.598622",
"0.59854215",
"0.5982351",
"0.59644985",
"0.59618616",
"0.59519553",
"0.59491116",
"0.59424275"... | 0.65401083 | 1 |
Make and save a paper plot for the small signal AC conductivity and save to file. | def small_signal_mobility_paperplot(fieldVector, freqVector, df):
vcm = np.array(fieldVector)*1e-2
n = utilities.calculate_density(df)
lw = 1.5
fig, ax = plt.subplots()
for freq in freqVector:
cond = []
mu_3 = []
for ee in fieldVector:
chi_3_i = np.load(pp.outputL... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(file_name):\n setup()\n plt.savefig(file_name)",
"def sac_paper_plot(log_dir, det_policy_sublocation,act_0,act_1,act_2,is_tri,actions_to_plot_large=10,\n actions_to_plot_small = 100,actions_per_log = 6000, custom_colors=None,prot_linewidth=2.7,\n reward_linewi... | [
"0.63829935",
"0.61786145",
"0.61507136",
"0.6035337",
"0.6032561",
"0.6008468",
"0.59910804",
"0.588599",
"0.58742714",
"0.5847295",
"0.5822006",
"0.57927346",
"0.57869285",
"0.57778376",
"0.57364345",
"0.57148474",
"0.570858",
"0.5702769",
"0.5675583",
"0.56593865",
"0.5654... | 0.55942696 | 25 |
Make a paper plot for the momentum KDE of the lowfield, and fulldrift solutions. | def momentum_kde_paperplot(fields):
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True)
axisList = [ax1,ax2,ax3]
i =0
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
for ee in fields:
ee_Vcm = ee/100
textstr = r'$E_{k_x}\, = \, %.1f \, V \, cm^{-1}$' % ee_Vcm
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def momentum_kde2_paperplot(fields):\n plt.figure(figsize=(2.65, 2.5))\n ax = plt.axes([0.18, 0.17, 0.8, 0.8])\n colorList = [med_color, high_color]\n lw = 1.5\n i = 0\n meankx_2 = []\n meankx_3 = []\n k_ax = np.load(pp.outputLoc + 'Momentum_KDE/' + 'k_ax_' + '2_' + \"E_{:.1e}.npy\".format(... | [
"0.7816318",
"0.70826715",
"0.6468687",
"0.61942935",
"0.6098648",
"0.60919285",
"0.59449416",
"0.58786696",
"0.5867492",
"0.58451384",
"0.5830002",
"0.58245164",
"0.5811453",
"0.5785101",
"0.5761999",
"0.57525295",
"0.57412106",
"0.5740107",
"0.5730716",
"0.5714857",
"0.5704... | 0.73915446 | 1 |
Make a paper plot for the momentum KDE of the lowfield, and fulldrift solutions. | def momentum_kde2_paperplot(fields):
plt.figure(figsize=(2.65, 2.5))
ax = plt.axes([0.18, 0.17, 0.8, 0.8])
colorList = [med_color, high_color]
lw = 1.5
i = 0
meankx_2 = []
meankx_3 = []
k_ax = np.load(pp.outputLoc + 'Momentum_KDE/' + 'k_ax_' + '2_' + "E_{:.1e}.npy".format(fields[0]))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def momentum_kde_paperplot(fields):\n fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True)\n axisList = [ax1,ax2,ax3]\n i =0\n\n props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n for ee in fields:\n ee_Vcm = ee/100\n textstr = r'$E_{k_x}\\, = \\, %.1f \\, V \\, cm^{-1... | [
"0.73927927",
"0.7082678",
"0.64685386",
"0.6194481",
"0.60984546",
"0.6091529",
"0.5944429",
"0.5879228",
"0.58678186",
"0.5845622",
"0.5829439",
"0.5824676",
"0.58108956",
"0.5784534",
"0.57618827",
"0.57524467",
"0.57417613",
"0.57405406",
"0.57309103",
"0.57143843",
"0.57... | 0.7817756 | 0 |
Make a energy plot for the momentum KDE of the lowfield, and fulldrift solutions. | def energy_kde_paperplot(fields,df):
plt.figure()
i = 0
colorList = ['dodgerblue','tomato']
lw = 2
meanE_2 = []
meanE_3 = []
mup = np.min(df['energy [eV]']) - pp.mu
chi_0 = np.load(pp.outputLoc + 'Steady/' + 'chi_' + '2_' + "E_{:.1e}.npy".format(fields[0]))
g_en_axis, _, _, _, _, _,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def momentum_kde2_paperplot(fields):\n plt.figure(figsize=(2.65, 2.5))\n ax = plt.axes([0.18, 0.17, 0.8, 0.8])\n colorList = [med_color, high_color]\n lw = 1.5\n i = 0\n meankx_2 = []\n meankx_3 = []\n k_ax = np.load(pp.outputLoc + 'Momentum_KDE/' + 'k_ax_' + '2_' + \"E_{:.1e}.npy\".format(... | [
"0.7072073",
"0.6860298",
"0.6784533",
"0.6767711",
"0.6717085",
"0.64930975",
"0.6264038",
"0.62380916",
"0.6227457",
"0.62017035",
"0.6190444",
"0.61265624",
"0.60596615",
"0.6039065",
"0.60338074",
"0.59825134",
"0.59809995",
"0.5952686",
"0.5921822",
"0.5920559",
"0.59139... | 0.7414247 | 0 |
Data from square 1 is carried 0 steps, since it's at the access port. | def test_first_pos() -> None:
assert sw.walk_to(1) == sw.Coordinate(0, 0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self):\n self.dev.write(1, 'A0')\n digit1, digit2 = self.dev.read(0x81, 64)[:2]\n # Save the data as voltage between 0.0 and 5.0\n self.data0.append((digit1 + 256*digit2)*5.0/1024)",
"def readOneData(self):\n\t\tpass",
"def test_op_one_int(self):\n\n device = pym... | [
"0.5967737",
"0.55366534",
"0.54499793",
"0.5377072",
"0.5333435",
"0.515353",
"0.5136547",
"0.5088798",
"0.50717396",
"0.5071443",
"0.5071443",
"0.5071443",
"0.50584936",
"0.50543225",
"0.5038344",
"0.5021923",
"0.49890968",
"0.49433732",
"0.49217415",
"0.49207532",
"0.49158... | 0.0 | -1 |
Data from square 1024 must be carried 31 steps. | def test_pos_1024() -> None:
assert sw.walk_to(1024).distance == 31 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processing_data(raw_data):\n data = np.frombuffer(raw_data, np.uint8)\n data = np.reshape(data, [data.shape[0]//1029, -1])\n data = data[:, 5:]\n data = np.reshape(data, [1, -1])\n data = 256 * data[0, 0::2] + data[0, 1::2]\n data = 10 * (data / 65535)\n data = np.reshape(data, [-1, 8]).T\... | [
"0.63460886",
"0.5817549",
"0.5816994",
"0.5768104",
"0.5681826",
"0.566487",
"0.5664062",
"0.566259",
"0.56563985",
"0.5567269",
"0.5552219",
"0.5543385",
"0.55432326",
"0.551347",
"0.550619",
"0.5491778",
"0.54823196",
"0.54610217",
"0.5449745",
"0.5424971",
"0.5361107",
... | 0.0 | -1 |
Returns the nth item | def nth(iterable, index):
return next(itertools.islice(iterable, index, None)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nth(n, seq):\n try:\n return seq[n]\n except TypeError:\n return next(itertools.islice(seq, n, None))",
"def nth(iterable, n, default=None):\n return next(islice(iterable, n, None), default)",
"def nth(iterable, n, default=None):\n return next(islice(iterable, n, None), default)",... | [
"0.7396352",
"0.72852194",
"0.72852194",
"0.72852194",
"0.7229926",
"0.7180901",
"0.70936525",
"0.69702876",
"0.6887761",
"0.6603851",
"0.6541124",
"0.6488166",
"0.6476991",
"0.64401025",
"0.64200073",
"0.6405436",
"0.63758516",
"0.6363515",
"0.63572127",
"0.62999797",
"0.629... | 0.7512134 | 0 |
Square 2 has one adjacent filled square (with 1), so it stores 1. | def test_sum_pos_2() -> None:
# Note: We take 1 step (first item) - thus end up on square 2
# (square 1 being home).
assert nth(sw.sum_walk(), 0) == 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def square2_checker(self, x, y, row2, col2):\n \n self.x = x\n self.y = y\n self.row2 = row2\n self.col2 = col2\n\n return abs(self.x - self.row2) == 1 and self.col2 == self.y \\\n or abs(self.y - self.col2) == 1 and self.row2 == self.x",
"def secondMove(bo... | [
"0.6059381",
"0.5955183",
"0.584613",
"0.56877613",
"0.5649856",
"0.5645991",
"0.5626221",
"0.55715334",
"0.55336505",
"0.551319",
"0.5499531",
"0.5469189",
"0.5469189",
"0.5468453",
"0.54598105",
"0.54315805",
"0.5426739",
"0.5424086",
"0.5397284",
"0.53963614",
"0.5382417",... | 0.56117094 | 7 |
Square 3 has both of the above squares as neighbors and stores 2. | def test_sum_pos_3() -> None:
# 2nd step - 3rd square
assert nth(sw.sum_walk(), 1) == 2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def neighbours2((u,v)):\r\n\r\n return ((u-1, v+1), (u,v+1), (u+1,v+1), \r\n (u-1,v), (u+1,v),\r\n (u-1,v-1), (u,v-1), (u+1,v-1))",
"def get_neighbors(self):\n return list(map(self.game.square, [self.position - self.game.rules[\"row_len\"], self.position + 1, self.position + self.... | [
"0.647665",
"0.6442566",
"0.63085395",
"0.6278189",
"0.6266447",
"0.62343496",
"0.62111956",
"0.6209179",
"0.61452353",
"0.61067104",
"0.60986245",
"0.6090197",
"0.60811675",
"0.6070315",
"0.60668266",
"0.6024939",
"0.60212654",
"0.6002938",
"0.6001121",
"0.59865355",
"0.5985... | 0.0 | -1 |
Square 4 has all three squares as neighbors and stores 4. | def test_sum_pos_4() -> None:
# Third step, 4th square.
assert nth(sw.sum_walk(), 2) == 4 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def neighbors_of_4(mapdata, x, y):\n four_neigh = set()\n # if PathPlanner.is_cell_walkable(mapdata, x, y) == True:\n if (PathPlanner.is_cell_walkable(mapdata, x+1, y)):\n four_neigh |= {(x+1,y)}\n if (PathPlanner.is_cell_walkable(mapdata, x-1, y)):\n four_neigh |=... | [
"0.7066604",
"0.69850945",
"0.6465638",
"0.6417407",
"0.62720263",
"0.61062175",
"0.6052148",
"0.6043467",
"0.6038454",
"0.60289276",
"0.6002066",
"0.59915555",
"0.5958216",
"0.59563166",
"0.5903847",
"0.5899874",
"0.58866215",
"0.5883123",
"0.58689153",
"0.58309054",
"0.5781... | 0.0 | -1 |
Square 5 has the first and fourth squares as neighbors, so stores 5. | def test_sum_pos_5() -> None:
# Fourth step, 5th square.
assert nth(sw.sum_walk(), 3) == 5 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def problem5(self, s):\n points = 0\n\n points = self.neighbor( 10, 10, s.nearest_neighbor)*3\n points += self.neighbor(100, 10, s.nearest_neighbor)*3\n points += self.neighbor( 10, 100, s.nearest_neighbor)*3\n points += self.neighbor(100, 100, s.nearest_neighbor)*3\n p... | [
"0.6525291",
"0.64248365",
"0.64212275",
"0.63307184",
"0.6300695",
"0.6289579",
"0.6257782",
"0.62313396",
"0.6211605",
"0.6194239",
"0.6168775",
"0.6109721",
"0.6094891",
"0.60934365",
"0.6090234",
"0.60810983",
"0.60609305",
"0.6054976",
"0.6004933",
"0.5993476",
"0.596606... | 0.6007456 | 18 |
First value larger than 4 in a location is 5, in location 5. | def test_sum_larger_than_4() -> None:
assert sw.sum_bigger_than(4) == 5 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _replace_center_with_one_if_five_neighbors_are_different_than_0(values):\n greater_than_0 = 0\n for entry in values:\n if entry > 0:\n greater_than_0 += 1\n if greater_than_0 >= 5:\n return 1\n else:\n return 0",
"def test_nearest_lo... | [
"0.58578753",
"0.576571",
"0.5746794",
"0.550913",
"0.5417282",
"0.535983",
"0.5356827",
"0.53275746",
"0.527239",
"0.52406675",
"0.52343464",
"0.5229549",
"0.51876897",
"0.5177736",
"0.5175734",
"0.5174075",
"0.5165948",
"0.5136667",
"0.5136667",
"0.51265985",
"0.51261836",
... | 0.0 | -1 |
Count amount of calls of a method | def count_calls(method: Callable) -> Callable:
k = method.__qualname__
@wraps(method)
def wrapper(self, *args, **kwargs):
"""
Wrapper method
"""
self._redis.incr(k)
return method(self, *args, **kwargs)
return wrapper | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def call_count(self):\n return len(self.mock_calls)",
"def count_calls(func):\n\n @functools.wraps(func)\n def wrapper_count_calls(*args, **kwargs):\n wrapper_count_calls.num_calls += 1\n print(f\"Call {wrapper_count_calls.num_calls} of {func.__name__!r}\")\n return func(*args, ... | [
"0.7700084",
"0.76074284",
"0.75858516",
"0.7440649",
"0.7427044",
"0.7417465",
"0.733295",
"0.7280602",
"0.7223752",
"0.7223752",
"0.7223752",
"0.7223752",
"0.72111034",
"0.71857244",
"0.7170684",
"0.7122875",
"0.7097699",
"0.70863014",
"0.6910949",
"0.6898251",
"0.6898251",... | 0.7264558 | 8 |
Store history of inputs and outputs for a function | def call_history(method: Callable) -> Callable:
i_keys = method.__qualname__ + ":inputs"
o_keys = method.__qualname__ + ":outputs"
@wraps(method)
def wrapper(self, *args, **kwargs):
"""
Set list keys to wrapped function
"""
self._redis.rpush(i_keys, str(args))
r ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def history():",
"def storeState(self):\n\n self.action_history[self.trial] = self.action\n self.ball_history[self.trial] = self.ballcolor",
"def show_input_history(self):\n # copy with user multifilter\n pass",
"def fit_history(self) -> FitResultHelper:\n pass",
"def sav... | [
"0.6308154",
"0.61945885",
"0.60563385",
"0.6010466",
"0.5976015",
"0.5948027",
"0.5906661",
"0.5841546",
"0.5819957",
"0.57499707",
"0.5747726",
"0.5735471",
"0.57344973",
"0.56674623",
"0.5651438",
"0.5611321",
"0.56106305",
"0.56016344",
"0.55927086",
"0.5590723",
"0.55736... | 0.58278376 | 8 |
Set list keys to wrapped function | def wrapper(self, *args, **kwargs):
self._redis.rpush(i_keys, str(args))
r = method(self, *args, **kwargs)
self._redis.rpush(o_keys, str(r))
return r | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_handler(key):\n def wrapper(func):\n func.set_key = key\n return func\n\n return wrapper",
"def set_function_list(self, L):\n\t\tself.function_list = L",
"def __call__(self, f):\n def w(*args, **kwargs):\n if not isinstance(self.keys, list):\n ... | [
"0.6521831",
"0.65043116",
"0.63858986",
"0.636023",
"0.6356911",
"0.6136244",
"0.6033099",
"0.6012478",
"0.600189",
"0.5885888",
"0.5881756",
"0.5840017",
"0.58237225",
"0.5802475",
"0.57107884",
"0.5709828",
"0.5692243",
"0.566112",
"0.56496793",
"0.56439",
"0.561974",
"0... | 0.5244141 | 56 |
Ouput log of actions taken on method | def replay(method: Callable) -> None:
counter_key = method.__qualname__
i_keys = method.__qualname__ + ':inputs'
o_keys = method.__qualname__ + ':outputs'
this = method.__self__
counter = this.get_str(counter_key)
history = list(zip(this.get_list(i_keys),
this.get_list(o_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logtool(self, action, **options):\n pass",
"def _info(self, func):\n self.logger.info(\"llamando a %s\" % func)",
"def on_a(self):\r\n self.log()",
"def log(self, message):",
"def LogCommand(self): \n # Retrieve the args for the command.\n posname, kwname, args... | [
"0.70196855",
"0.6802665",
"0.67890334",
"0.67452294",
"0.66154486",
"0.6548533",
"0.6539033",
"0.6537761",
"0.6509257",
"0.64813614",
"0.6400591",
"0.6360071",
"0.63514966",
"0.63387394",
"0.63154525",
"0.6285869",
"0.6239835",
"0.6236979",
"0.6204017",
"0.6194011",
"0.61785... | 0.0 | -1 |
Instantiate a empty Redis | def __init__(self):
self._redis = redis.Redis(host="localhost", port=6379)
self._redis.flushdb() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, host, port):\n self.r = redis.StrictRedis(host=host, port=port)",
"def __init__(self, host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD):\n self.db = redis.StrictRedis(host=host, port=port, password=password, decode_responses=True)",
"def __init__(self, config):\n ... | [
"0.77647334",
"0.7516936",
"0.7490148",
"0.7289205",
"0.72216445",
"0.7121275",
"0.69119596",
"0.68894404",
"0.6873963",
"0.6847636",
"0.6805552",
"0.6770867",
"0.6766439",
"0.67155826",
"0.6714582",
"0.6684755",
"0.66187555",
"0.6604865",
"0.6604865",
"0.65435404",
"0.649048... | 0.749138 | 2 |
Store any type of data in Redis | def store(self, data: Union[str, bytes, int, float]) -> str:
k = str(uuid.uuid4())
self._redis[k] = data
return k | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def store(self, data: Union[str, bytes, int, float]) -> str:\n key = str(uuid.uuid4())\n self._redis.set(key, data)\n return key",
"def redis_save(key: object, value: object) -> object:\n if key is not None and value is not None:\n red.redis.set(json.dumps(key), json.dumps(value))"... | [
"0.74398786",
"0.7203963",
"0.68926376",
"0.64535683",
"0.6436974",
"0.6225524",
"0.61990386",
"0.61871934",
"0.61752015",
"0.6131745",
"0.6131207",
"0.6122489",
"0.6113348",
"0.6100349",
"0.6039475",
"0.5939632",
"0.59025615",
"0.5878599",
"0.58582723",
"0.5839085",
"0.58303... | 0.7362231 | 1 |
Get value in db with callback format | def get(self, key: str, fn: Optional[Callable] = None) ->\
Union[str, bytes, int, float]:
return fn(self._redis.get(key)) if fn else self._redis.get(key) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_callback(self, value):\n if value:\n # Should be a DBRef\n return self.model.find_one({\"_id\": value.id})\n return value",
"def db_values(self, db):",
"def _db(self, value):",
"def get_value(self):",
"def getvalue(self):\n ...",
"def getvalue(self):\n ... | [
"0.76099515",
"0.6539641",
"0.645696",
"0.63717",
"0.62489164",
"0.62489164",
"0.61767685",
"0.61688983",
"0.6150886",
"0.6079202",
"0.6064559",
"0.5916899",
"0.58600366",
"0.5854786",
"0.58326894",
"0.58202755",
"0.57832086",
"0.57406497",
"0.57007426",
"0.5685605",
"0.56768... | 0.0 | -1 |
bytes from store to list | def get_list(self, k: str) -> List:
return self._redis.lrange(k, 0, -1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def network_bytes(self) -> List[bytes]:",
"def get_list_of_bytes2(self):\n pass",
"def flatten_list_bytes(list_of_data):\n if not PY34:\n list_of_data = (bytes(data) if isinstance(data, memoryview) else\n data for data in list_of_data)\n return b''.join(list_of_data)",
"def get... | [
"0.70149946",
"0.66737",
"0.64794904",
"0.64504474",
"0.6314454",
"0.6131867",
"0.6043757",
"0.5994261",
"0.59164953",
"0.5910397",
"0.59022194",
"0.5876766",
"0.5868104",
"0.5842442",
"0.5833972",
"0.5811272",
"0.580573",
"0.57865405",
"0.5774859",
"0.5770557",
"0.57333845",... | 0.0 | -1 |
method function for test | def add(left: int, right: int) -> int:
return left + right | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _test(self):",
"def _test(self):",
"def _test(self):",
"def _test(self):",
"def _test(self):",
"def test():",
"def test():",
"def test(self):",
"def test(self):",
"def test(self):\n pass",
"def tests():",
"def _test(self):\n pass",
"def _test(self):\n pass",
"def ... | [
"0.85272825",
"0.85272825",
"0.85272825",
"0.85272825",
"0.85272825",
"0.823295",
"0.823295",
"0.8142115",
"0.8142115",
"0.8089903",
"0.8033258",
"0.80181545",
"0.80181545",
"0.80181545",
"0.7949432",
"0.7901211",
"0.78332204",
"0.78264356",
"0.7823471",
"0.7760016",
"0.77569... | 0.0 | -1 |
test of Server class | def test_server():
logger = PythonLogger(LogLevel.TRACE)
name = 'add'
logger = PythonLogger()
executor = FunctionMethodExecutor(logger, name, add)
method_server = SimpleMethodServer(logger, [executor])
server_config = ServerConfig()
server_config.tcp_acceptors = [TCPAcceptorConfig()]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_server_info(self):\n pass",
"def server():",
"def server():",
"def test_setup_server(self):\r\n\r\n self.server_socket = setup_server()\r\n host, port = self.server_socket.getsockname()\r\n # Not much to test here, if we get something back it should work\r\n self.a... | [
"0.7765244",
"0.7660035",
"0.7660035",
"0.7556732",
"0.7359465",
"0.7205023",
"0.7127771",
"0.71062976",
"0.7015855",
"0.7012502",
"0.69685245",
"0.69648224",
"0.693817",
"0.68814826",
"0.6859551",
"0.68455327",
"0.6837512",
"0.6823868",
"0.6778921",
"0.67439336",
"0.6717929"... | 0.65524065 | 36 |
Return range of a native type. | def native_type_range(fmt):
if fmt == 'c':
lh = (0, 256)
elif fmt == '?':
lh = (0, 2)
elif fmt == 'f':
lh = (-(1<<63), 1<<63)
elif fmt == 'd':
lh = (-(1<<1023), 1<<1023)
else:
for exp in (128, 127, 64, 63, 32, 31, 16, 15, 8, 7):
try:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def detect_range(self) -> Union[int, float]:\n return self.proto.detect_range",
"def get_range(self, rel_name):\n return self._declaration[rel_name].range_type",
"def GetScalarRange(self):\n ...",
"def range(self):\n return self.timerange()",
"def native_type_range(fmt):\n if fmt... | [
"0.7174355",
"0.689528",
"0.68078554",
"0.6694855",
"0.66533893",
"0.6612708",
"0.6558848",
"0.65285164",
"0.6527802",
"0.65069115",
"0.65069115",
"0.6480364",
"0.63700145",
"0.6348342",
"0.6348342",
"0.62516916",
"0.62408125",
"0.61799943",
"0.61763644",
"0.6166519",
"0.6139... | 0.6626005 | 5 |
Return random item for a type specified by a mode and a single format character. | def randrange_fmt(mode, char, obj):
x = randrange(*fmtdict[mode][char])
if char == 'c':
x = bytes([x])
if obj == 'numpy' and x == b'\x00':
# http://projects.scipy.org/numpy/ticket/1925
x = b'\x01'
if char == '?':
x = bool(x)
if char == 'f' or char == 'd':
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randitems(n, obj='ndarray', mode=None, char=None):\n if mode is None:\n mode = choice(cap[obj][MODE])\n if char is None:\n char = choice(tuple(fmtdict[mode]))\n multiplier = choice(cap[obj][MULT])\n fmt = mode + '#' + char * int(multiplier if multiplier else 1)\n items = gen_items(... | [
"0.6796878",
"0.6796878",
"0.60390544",
"0.60390544",
"0.5697757",
"0.56245166",
"0.55712277",
"0.5493748",
"0.5452205",
"0.537199",
"0.5334354",
"0.5267052",
"0.52179533",
"0.5095594",
"0.5086428",
"0.5081015",
"0.5081015",
"0.5054717",
"0.50137657",
"0.50027424",
"0.4957444... | 0.5423713 | 9 |
Return single random item. | def gen_item(fmt, obj):
mode, chars = fmt.split('#')
x = []
for c in chars:
x.append(randrange_fmt(mode, c, obj))
return x[0] if len(x) == 1 else tuple(x) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getRandom(self) -> int:\n return random.choice(self.items)",
"def random(self):\n try:\n return self.order_by('?')[0]\n except IndexError:\n raise self.model.DoesNotExist",
"def read_random_item(user_id: int, db: Session = Depends(get_db)):\n # Call function to... | [
"0.75377405",
"0.7337129",
"0.7262384",
"0.7241162",
"0.72338086",
"0.72156364",
"0.7190311",
"0.71842796",
"0.7049892",
"0.70360816",
"0.7027471",
"0.70067525",
"0.6985511",
"0.697375",
"0.697375",
"0.697375",
"0.697375",
"0.69379413",
"0.6930757",
"0.69288605",
"0.6924486",... | 0.0 | -1 |
Return a list of random items (or a scalar). | def gen_items(n, fmt, obj):
if n == 0:
return gen_item(fmt, obj)
lst = [0] * n
for i in range(n):
lst[i] = gen_item(fmt, obj)
return lst | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample(self):\n return self.items[self.np_random.choice(len(self.items))]",
"def generator(self, random, args):\r\n if self.duplicates:\r\n max_count = [self.capacity // item[0] for item in self.items]\r\n return [random.randint(0, m) for m in max_count]\r\n else:\r... | [
"0.7186447",
"0.68290174",
"0.67713726",
"0.6744953",
"0.6688288",
"0.6684551",
"0.661171",
"0.6610131",
"0.6610131",
"0.6610131",
"0.6610131",
"0.65993875",
"0.6582984",
"0.6574758",
"0.65567404",
"0.6546218",
"0.65396774",
"0.6507699",
"0.65028495",
"0.65009904",
"0.6481836... | 0.0 | -1 |
Return random format, items, item. | def randitems(n, obj='ndarray', mode=None, char=None):
if mode is None:
mode = choice(cap[obj][MODE])
if char is None:
char = choice(tuple(fmtdict[mode]))
multiplier = choice(cap[obj][MULT])
fmt = mode + '#' + char * int(multiplier if multiplier else 1)
items = gen_items(n, fmt, obj)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen_item(fmt, obj):\n mode, chars = fmt.split('#')\n x = []\n for c in chars:\n x.append(randrange_fmt(mode, c, obj))\n return x[0] if len(x) == 1 else tuple(x)",
"def gen_item(fmt, obj):\n mode, chars = fmt.split('#')\n x = []\n for c in chars:\n x.append(randrange_fmt(mod... | [
"0.6775782",
"0.6775782",
"0.6505952",
"0.6253222",
"0.6138426",
"0.60967493",
"0.5961111",
"0.59302914",
"0.592502",
"0.5770306",
"0.5770306",
"0.5770306",
"0.57691205",
"0.5754582",
"0.5659331",
"0.5654171",
"0.562061",
"0.5578145",
"0.557014",
"0.55524075",
"0.5522926",
... | 0.64387965 | 3 |
Iterate through supported mode/char combinations. | def iter_mode(n, obj='ndarray'):
for mode in cap[obj][MODE]:
for char in fmtdict[mode]:
yield randitems(n, obj, mode, char) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __iter__(self):\n return iter([v for k, v in sorted(self._modes.items())])",
"def modes(self):\n try:\n order = self._current_order\n except AttributeError:\n raise AttributeError('Cannot iterate over modes without iterating over orders!') from None\n mode = ... | [
"0.62502277",
"0.60794157",
"0.594942",
"0.5905437",
"0.5903358",
"0.5762716",
"0.56345135",
"0.5598813",
"0.5586852",
"0.5512914",
"0.550523",
"0.5503041",
"0.54652876",
"0.5426214",
"0.54166454",
"0.5404549",
"0.53661305",
"0.5359288",
"0.5355193",
"0.53164005",
"0.5308114"... | 0.68199724 | 0 |
Yield (format, items, item) for all possible modes and format characters plus one random compound format string. | def iter_format(nitems, testobj='ndarray'):
for t in iter_mode(nitems, testobj):
yield t
if testobj != 'ndarray':
return
yield struct_items(nitems, testobj) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeNamesFromFormats(formats):\n i = getIter(formats)\n if not i:\n return\n\n try:\n c = 0\n item = i.next()\n while item:\n c = c +1\n name = 'c%s' % c\n if isinstance(item, str):\n yield name\n else:\n ... | [
"0.59636045",
"0.55950534",
"0.5526537",
"0.5526537",
"0.5512344",
"0.5512344",
"0.5500678",
"0.5500678",
"0.54459256",
"0.5404356",
"0.54026484",
"0.5386869",
"0.5325779",
"0.52617586",
"0.52564883",
"0.5248751",
"0.5190863",
"0.5118963",
"0.50866807",
"0.50587744",
"0.50531... | 0.64417255 | 0 |
format suitable for memoryview | def is_memoryview_format(fmt):
x = len(fmt)
return ((x == 1 or (x == 2 and fmt[0] == '@')) and
fmt[x-1] in MEMORYVIEW) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format(self, data):",
"def asformat(self, format):",
"def format(self):\n ...",
"def format_data(self, data):",
"def test_repr_format(self):\n shape = (3, 4, 5)\n index = (0, 2, 1)\n t = View(shape=shape, index=index)\n assert t.repr_format(1.0) == \"View(shape=(3, 4,... | [
"0.6637037",
"0.6513249",
"0.6481153",
"0.6379912",
"0.6364625",
"0.61014384",
"0.5993504",
"0.59295833",
"0.5911117",
"0.5828893",
"0.5821409",
"0.5821409",
"0.57990885",
"0.5798701",
"0.5766792",
"0.576159",
"0.5759765",
"0.5703525",
"0.5594841",
"0.55925876",
"0.55827713",... | 0.5712799 | 17 |
Tuple items (representing structs) are regarded as atoms. | def atomp(lst):
return not isinstance(lst, list) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tuple(x):\n pass",
"def as_tuple(self):\n return (self.oid, self.type, self.value)",
"def astuple(self):\n try:\n return tuple([x.astuple() for x in self])\n except Exception:\n pass\n return tuple([x for x in self])",
"def single_element_tuple():\n single = (1,)\n print(... | [
"0.62056684",
"0.6184957",
"0.6157319",
"0.6060547",
"0.60536045",
"0.6001506",
"0.5986729",
"0.5916221",
"0.58694434",
"0.5864544",
"0.584824",
"0.5791348",
"0.57505023",
"0.57495034",
"0.57297975",
"0.5720588",
"0.5687215",
"0.5687215",
"0.56623316",
"0.5627032",
"0.5608446... | 0.0 | -1 |
Product of list elements. | def prod(lst):
if len(lst) == 0:
return 0
x = lst[0]
for v in lst[1:]:
x *= v
return x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prod(lst):\n return reduce(mul, lst, 1)",
"def listMul(l):\n \n # initialization for variable prod\n prod = 1.0\n \n for element in l:\n prod *= element # a *= b is equivalent to a = a * b\n \n return prod",
"def list_product(lst):\n prod = 1\n for val in lst:\n ... | [
"0.7628697",
"0.7593564",
"0.75456065",
"0.7424486",
"0.72949135",
"0.725921",
"0.72151756",
"0.7156667",
"0.71171826",
"0.71168196",
"0.7099029",
"0.70157486",
"0.70081353",
"0.7006188",
"0.69586205",
"0.69586205",
"0.69586205",
"0.69568986",
"0.68286026",
"0.6814557",
"0.68... | 0.7144101 | 8 |
Calculate strides of a contiguous array. Layout is 'C' or 'F' (Fortran). | def strides_from_shape(ndim, shape, itemsize, layout):
if ndim == 0:
return ()
if layout == 'C':
strides = list(shape[1:]) + [itemsize]
for i in range(ndim-2, -1, -1):
strides[i] *= strides[i+1]
else:
strides = [itemsize] + list(shape[:-1])
for i in range(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strides_from_shape(ndim, shape, itemsize, layout):\n if ndim == 0:\n return ()\n if layout == 'C':\n strides = list(shape[1:]) + [itemsize]\n for i in range(ndim - 2, -1, -1):\n strides[i] *= strides[i + 1]\n else:\n strides = [itemsize] + list(shape[:-1])\n ... | [
"0.6671998",
"0.6667623",
"0.63901645",
"0.62995565",
"0.6209812",
"0.62079126",
"0.6108434",
"0.60595614",
"0.60080725",
"0.5996222",
"0.5996222",
"0.5860129",
"0.57919973",
"0.57440704",
"0.56975204",
"0.56885684",
"0.56631756",
"0.5634464",
"0.56175673",
"0.5605618",
"0.55... | 0.6674238 | 0 |
Convert flat item list to the nested list representation of a multidimensional C array with shape 's'. | def _ca(items, s):
if atomp(items):
return items
if len(s) == 0:
return items[0]
lst = [0] * s[0]
stride = len(items) // s[0] if s[0] else 0
for i in range(s[0]):
start = i*stride
lst[i] = _ca(items[start:start+stride], s[1:])
return lst | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flatten_2D_list(list_2d):\n return [item for sub in list_2d for item in sub]",
"def flatten_list(l):\n obj = []\n\n def recurse(ll):\n if isinstance(ll, list) or isinstance(ll, np.ndarray):\n for i, _ in enumerate(ll):\n recurse(ll[i])\n else:\n obj... | [
"0.59607273",
"0.5784534",
"0.573234",
"0.56969345",
"0.56966835",
"0.5667547",
"0.5642063",
"0.56420296",
"0.56246406",
"0.56184787",
"0.5595709",
"0.5550527",
"0.55375654",
"0.55223006",
"0.55164844",
"0.5515455",
"0.55008477",
"0.549687",
"0.549088",
"0.5466289",
"0.544795... | 0.5428784 | 22 |
Convert flat item list to the nested list representation of a multidimensional Fortran array with shape 's'. | def _fa(items, s):
if atomp(items):
return items
if len(s) == 0:
return items[0]
lst = [0] * s[0]
stride = s[0]
for i in range(s[0]):
lst[i] = _fa(items[i::stride], s[1:])
return lst | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flatten_2D_list(list_2d):\n return [item for sub in list_2d for item in sub]",
"def _unflatten(updates, flat):\n updates_flat, treedef = tree_flatten(updates)\n offsets = []\n for update in updates_flat:\n size = np.prod(update.shape)\n if offsets:\n offsets.append(size + offsets... | [
"0.58835673",
"0.5798904",
"0.57985276",
"0.57891816",
"0.57352287",
"0.57273215",
"0.5620075",
"0.5616092",
"0.56024605",
"0.5555752",
"0.5553258",
"0.5551791",
"0.5545441",
"0.5536632",
"0.5493388",
"0.549334",
"0.54808545",
"0.547685",
"0.546228",
"0.5439881",
"0.54281837"... | 0.52800167 | 43 |
Generate all possible tuples of indices. | def indices(shape):
iterables = [range(v) for v in shape]
return product(*iterables) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_indices(max_passes: int) -> Iterable[Tuple[int, int]]:\n base_indices = (0, max_passes)\n pw_indices = (max_passes, max_passes * 2)\n ip_indices = (max_passes * 2, max_passes * 3)\n strand_indices = (max_passes * 3, max_passes * 4)\n ccs_indices = (max_passes * 4, max_passes * 4 + 1)\n sn_indices = (... | [
"0.67532384",
"0.674835",
"0.6640834",
"0.6628575",
"0.65287274",
"0.6371146",
"0.6330858",
"0.6324179",
"0.62465256",
"0.6200598",
"0.6156113",
"0.6150643",
"0.60295343",
"0.6006623",
"0.60026",
"0.5961674",
"0.59433466",
"0.59391856",
"0.59216404",
"0.59216404",
"0.5907448"... | 0.6601521 | 4 |
Convert multidimensional index to the position in the flat list. | def getindex(ndim, ind, strides):
ret = 0
for i in range(ndim):
ret += strides[i] * ind[i]
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ind2coord(self, index):\n\n # assert (index >= 0)\n # assert(index < self.n - 1)\n\n col = index // self.rows\n row = index % self.rows\n\n return [row, col]",
"def mapping(index: Union[int, List[int]]) -> Union[int, List[int]]:\n if isinstance(index, int):\n ... | [
"0.6243093",
"0.6141841",
"0.6127711",
"0.6079915",
"0.6027663",
"0.59269977",
"0.59215206",
"0.5921272",
"0.59085566",
"0.5885764",
"0.5852488",
"0.5829429",
"0.578314",
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"0.5765309",
"0.5744594",
"0.57069933",
"0.56776106",
"0.5675322",
"0.5675175",
"0.56736",
... | 0.0 | -1 |
Transpose flat item list that is regarded as a multidimensional | def transpose(src, shape):
if not shape:
return src
ndim = len(shape)
sstrides = strides_from_shape(ndim, shape, 1, 'C')
dstrides = strides_from_shape(ndim, shape[::-1], 1, 'C')
dest = [0] * len(src)
for ind in indices(shape):
fr = getindex(ndim, ind, sstrides)
to = getin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transpose(lst):\n return list(zip(*lst))",
"def transpose(a: List[List[T]]) -> List[List[T]]:\n return list(map(list, zip(*a)))",
"def transpose(grid):\n return map(list, zip(*grid))",
"def _transpose(self, result_list):\n first_result = result_list[0]\n\n # The case where op i... | [
"0.7321097",
"0.70771873",
"0.7029112",
"0.6656462",
"0.66194856",
"0.6589029",
"0.6555851",
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"0.6380234",
"0.6366075",
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"0.6261174",
"0.62221223",
"0.6198896",
"0.6186384",
"0.61600924",
"0.61566144",
"0.61444175",
"0.61380... | 0.5523381 | 92 |
flatten list or return scalar | def flatten(lst):
if atomp(lst): # scalar
return lst
return _flatten(lst) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lflatten(*lst):\n return flatten(list(lst))",
"def _flatten_one(x):\n return x[0] if is_iterable(x) else x",
"def flatten(x): # przerobić na lambda?\n if x==[]:\n return None\n else:\n return x[0]",
"def flatten():",
"def flatten(lst):\n if atomp(lst):\n return lst\n... | [
"0.7351641",
"0.7303014",
"0.71548724",
"0.7148735",
"0.7075203",
"0.69926786",
"0.6866925",
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"0.68152994",
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"0.6666743",
"0.6657045",
"0.6656961",
"0.66309655",
"0.66255593",
"0.6615",
"0.6612183... | 0.83005923 | 0 |
Compare the structure of llst[lslices] and rlst[rslices]. | def cmp_structure(llst, rlst, lslices, rslices):
lshape = slice_shape(llst, lslices)
rshape = slice_shape(rlst, rslices)
if (len(lshape) != len(rshape)):
return -1
for i in range(len(lshape)):
if lshape[i] != rshape[i]:
return -1
if lshape[i] == 0:
return ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cmp_structure(llst, rlst, lslices, rslices):\n lshape = slice_shape(llst, lslices)\n rshape = slice_shape(rlst, rslices)\n if len(lshape) != len(rshape):\n return -1\n for i in range(len(lshape)):\n if lshape[i] != rshape[i]:\n return -1\n if lshape[i] == 0:\n ... | [
"0.8107347",
"0.5830058",
"0.5787684",
"0.57273674",
"0.5708086",
"0.547884",
"0.54676497",
"0.5341118",
"0.53267294",
"0.5325244",
"0.5295086",
"0.52857417",
"0.52715236",
"0.52042264",
"0.5128856",
"0.50830114",
"0.50252223",
"0.5011563",
"0.50000906",
"0.49984854",
"0.4990... | 0.8097147 | 1 |
Verify that the parameters represent a valid array within | def verify_structure(memlen, itemsize, ndim, shape, strides, offset):
if offset % itemsize:
return False
if offset < 0 or offset+itemsize > memlen:
return False
if any(v % itemsize for v in strides):
return False
if ndim <= 0:
return ndim == 0 and not shape and not strid... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate_array(t):\n basetype = t.type\n if is_array(basetype):\n raise ArrayOfArrayError(t)\n validate(basetype)",
"def _validate_array_params(array_params):\n if isinstance(array_params, dict):\n # Shallow check; make sure each antenna position is a 3-vector.\n if all(len(... | [
"0.7270341",
"0.69448113",
"0.683705",
"0.6811981",
"0.6776789",
"0.67708015",
"0.6727861",
"0.6655488",
"0.6578328",
"0.6572302",
"0.65476936",
"0.6494485",
"0.64726275",
"0.64687735",
"0.64408755",
"0.6395904",
"0.6385423",
"0.635954",
"0.63554",
"0.63529444",
"0.63336235",... | 0.0 | -1 |
Location of an item in the underlying memory. | def memory_index(indices, t):
memlen, itemsize, ndim, shape, strides, offset = t
p = offset
for i in range(ndim):
p += strides[i]*indices[i]
return p | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loc(self):\n return self._loc",
"def loc(self):\n return self._loc",
"def item_location(self, item_location):\n\n self._item_location = item_location",
"def location(self, item):\n return reverse(item)",
"def findItem(self, item):\n found_location = self.__find(item)\n\n ... | [
"0.64741904",
"0.64741904",
"0.6460164",
"0.630083",
"0.6235623",
"0.61925596",
"0.61581695",
"0.61156976",
"0.6103149",
"0.6092022",
"0.60529405",
"0.6024192",
"0.6024192",
"0.6024192",
"0.60195667",
"0.59354365",
"0.59073675",
"0.5875071",
"0.5874959",
"0.58549565",
"0.5850... | 0.0 | -1 |
The structure 't' is overlapping if at least one memory location is visited twice while iterating through all possible tuples of indices. | def is_overlapping(t):
memlen, itemsize, ndim, shape, strides, offset = t
visited = 1<<memlen
for ind in indices(shape):
i = memory_index(ind, t)
bit = 1<<i
if visited & bit:
return True
visited |= bit
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_overlapping(t):\n memlen, itemsize, ndim, shape, strides, offset = t\n visited = 1 << memlen\n for ind in indices(shape):\n i = memory_index(ind, t)\n bit = 1 << i\n if visited & bit:\n return True\n visited |= bit\n return False",
"def listOfOverlappingT... | [
"0.71436214",
"0.6230163",
"0.54177374",
"0.53693205",
"0.526794",
"0.52386534",
"0.5171279",
"0.5169592",
"0.5149605",
"0.5122925",
"0.5052315",
"0.50437593",
"0.5038537",
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"0.5033946",
"0.49886644",
"0.49637598",
"0.49573925",
"0.495004",
"0.49486208",
"0.493795... | 0.7153341 | 0 |
Create a random slice of len slicelen that fits into listlen. | def randslice_from_slicelen(slicelen, listlen):
maxstart = listlen - slicelen
start = randrange(maxstart+1)
maxstep = (listlen - start) // slicelen if slicelen else 1
step = randrange(1, maxstep+1)
stop = start + slicelen * step
s = slice(start, stop, step)
_, _, _, control = slice_indices(s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randslice_from_slicelen(slicelen, listlen):\n maxstart = listlen - slicelen\n start = randrange(maxstart + 1)\n maxstep = (listlen - start) // slicelen if slicelen else 1\n step = randrange(1, maxstep + 1)\n stop = start + slicelen * step\n s = slice(start, stop, step)\n _, _, _, control =... | [
"0.83833927",
"0.80226773",
"0.714893",
"0.7132235",
"0.6567857",
"0.6413113",
"0.62118304",
"0.61636114",
"0.6134608",
"0.6101278",
"0.61010647",
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"0.6016087",
"0.59929234",
"0.5939068",
"0.59296066",
"0.59249735",
"0.59247345",
"0.5907804",
"0.5882... | 0.8387383 | 0 |
Create two sets of slices for an array x with shape 'shape' such that shapeof(x[lslices]) == shapeof(x[rslices]). | def randslice_from_shape(ndim, shape):
lslices = [0] * ndim
rslices = [0] * ndim
for n in range(ndim):
l = shape[n]
slicelen = randrange(1, l+1) if l > 0 else 0
lslices[n] = randslice_from_slicelen(slicelen, l)
rslices[n] = randslice_from_slicelen(slicelen, l)
return tupl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randslice_from_shape(ndim, shape):\n lslices = [0] * ndim\n rslices = [0] * ndim\n for n in range(ndim):\n l = shape[n]\n slicelen = randrange(1, l + 1) if l > 0 else 0\n lslices[n] = randslice_from_slicelen(slicelen, l)\n rslices[n] = randslice_from_slicelen(slicelen, l)\n... | [
"0.6905628",
"0.65749097",
"0.6485385",
"0.6485385",
"0.6428866",
"0.64170295",
"0.64149487",
"0.6344559",
"0.6343766",
"0.6329222",
"0.62940896",
"0.6255896",
"0.62288624",
"0.6182799",
"0.59876955",
"0.5903534",
"0.58359647",
"0.5818925",
"0.5786385",
"0.5780439",
"0.572824... | 0.6896135 | 1 |
Create (lshape, rshape, tuple(lslices), tuple(rslices)) such that shapeof(x[lslices]) == shapeof(y[rslices]), where x is an array with shape 'lshape' and y is an array with shape 'rshape'. | def rand_aligned_slices(maxdim=5, maxshape=16):
ndim = randrange(1, maxdim+1)
minshape = 2
n = randrange(100)
if n >= 95:
minshape = 0
elif n >= 90:
minshape = 1
all_random = True if randrange(100) >= 80 else False
lshape = [0]*ndim; rshape = [0]*ndim
lslices = [0]*ndim; ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def match_shapes(arrs):\n #temp = [(name, np.asarray(a), deg) for name, a, deg in arrs]\n #ndim = max([a.ndim - deg for _, a, deg in arrs])\n\n temp = [a for name, a, deg in arrs]\n for i in range(len(temp)):\n if np.isscalar(temp[i]):\n temp[i] = np.array(temp[i])\n ndim = max([a.... | [
"0.6327064",
"0.6313743",
"0.6305719",
"0.63030237",
"0.6290705",
"0.6188022",
"0.6066026",
"0.60396975",
"0.60346824",
"0.59923345",
"0.59665436",
"0.590952",
"0.58448064",
"0.5815266",
"0.5795022",
"0.57507616",
"0.57300574",
"0.57165897",
"0.56608945",
"0.56530553",
"0.561... | 0.52202415 | 53 |
Return a list of random items for structure 't' with format 'fmtchar'. | def randitems_from_structure(fmt, t):
memlen, itemsize, _, _, _, _ = t
return gen_items(memlen//itemsize, '#'+fmt, 'numpy') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randitems_from_structure(fmt, t):\n memlen, itemsize, _, _, _, _ = t\n return gen_items(memlen // itemsize, '#' + fmt, 'numpy')",
"def gen_item(fmt, obj):\n mode, chars = fmt.split('#')\n x = []\n for c in chars:\n x.append(randrange_fmt(mode, c, obj))\n return x[0] if len(x) == 1 el... | [
"0.75925416",
"0.6998413",
"0.6998413",
"0.61594206",
"0.61594206",
"0.5678781",
"0.55998796",
"0.55998796",
"0.5551217",
"0.55419385",
"0.5459458",
"0.5367428",
"0.53579396",
"0.53160495",
"0.5218872",
"0.51369077",
"0.5077376",
"0.5070219",
"0.5048697",
"0.50483674",
"0.503... | 0.7437101 | 1 |
Return ndarray from the tuple returned by rand_structure() | def ndarray_from_structure(items, fmt, t, flags=0):
memlen, itemsize, ndim, shape, strides, offset = t
return ndarray(items, shape=shape, strides=strides, format=fmt,
offset=offset, flags=ND_WRITABLE|flags) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_rand_array(self):\n return np.random.random((self.w + 1, self.h + 1, 2))",
"def random_array(size_tuple):\n\n output = np.random.rand(\n size_tuple[0], size_tuple[1])\n\n return output",
"def _get_rand_array(self):\n return np.random.random((self.w, self.h))",
"def _get_ra... | [
"0.7160795",
"0.6981562",
"0.687871",
"0.68413705",
"0.6571761",
"0.6536121",
"0.64408153",
"0.62637174",
"0.6254054",
"0.6222539",
"0.61559474",
"0.6148007",
"0.6124988",
"0.61171156",
"0.6101302",
"0.6100603",
"0.60837346",
"0.6055601",
"0.60541236",
"0.604905",
"0.6042505"... | 0.0 | -1 |
Return numpy_array from the tuple returned by rand_structure() | def numpy_array_from_structure(items, fmt, t):
memlen, itemsize, ndim, shape, strides, offset = t
buf = bytearray(memlen)
for j, v in enumerate(items):
struct.pack_into(fmt, buf, j*itemsize, v)
return numpy_array(buffer=buf, shape=shape, strides=strides,
dtype=fmt, offset=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_array(size_tuple):\n\n output = np.random.rand(\n size_tuple[0], size_tuple[1])\n\n return output",
"def _get_rand_array(self):\n return np.random.random((self.w + 1, self.h + 1, 2))",
"def _get_rand_array(self):\n pass",
"def _get_rand_array(self):\n return np.ra... | [
"0.715556",
"0.71340054",
"0.70975256",
"0.6925855",
"0.6733625",
"0.6368195",
"0.6337332",
"0.63088566",
"0.6258198",
"0.60780334",
"0.607233",
"0.6022399",
"0.6017903",
"0.59449095",
"0.5937947",
"0.59133595",
"0.58726776",
"0.5857207",
"0.58105093",
"0.57880354",
"0.573006... | 0.0 | -1 |
Interpret the raw memory of 'exporter' as a list of items with size 'itemsize'. If shape=None, the new structure is assumed to be 1D with n itemsize = bytelen. If shape is given, the usual constraint for contiguous arrays prod(shape) itemsize = bytelen applies. On success, return (items, shape). If the constraints cann... | def cast_items(exporter, fmt, itemsize, shape=None):
bytelen = exporter.nbytes
if shape:
if prod(shape) * itemsize != bytelen:
return None, shape
elif shape == []:
if exporter.ndim == 0 or itemsize != bytelen:
return None, shape
else:
n, r = divmod(bytelen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cast_items(exporter, fmt, itemsize, shape=None):\n bytelen = exporter.nbytes\n if shape:\n if prod(shape) * itemsize != bytelen:\n return None, shape\n elif shape == []:\n if exporter.ndim == 0 or itemsize != bytelen:\n return None, shape\n else:\n n, r = ... | [
"0.77506953",
"0.49568605",
"0.48241737",
"0.48023552",
"0.47398067",
"0.4713811",
"0.47077888",
"0.46982223",
"0.4669957",
"0.4669957",
"0.4669957",
"0.4669957",
"0.46594286",
"0.4643557",
"0.459365",
"0.4521161",
"0.45037562",
"0.44983464",
"0.4494086",
"0.4490511",
"0.4454... | 0.77280533 | 1 |
Generate shapes to test casting. | def gencastshapes():
for n in range(32):
yield [n]
ndim = randrange(4, 6)
minshape = 1 if randrange(100) > 80 else 2
yield [randrange(minshape, 5) for _ in range(ndim)]
ndim = randrange(2, 4)
minshape = 1 if randrange(100) > 80 else 2
yield [randrange(minshape, 5) for _ in range(ndim... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_shapes(data_dir):\n dataset.create_shapes(10, 10, 1, data_dir=data_dir)\n img_path = os.path.join(data_dir, \"ellipse/0.png\")\n assert os.path.exists(img_path)\n img = imageio.imread(img_path)\n assert img.shape == (10, 10, 4)",
"def create_random_shapes(shapesList):\n for _ in... | [
"0.67064095",
"0.66619724",
"0.6262542",
"0.6234531",
"0.6208814",
"0.61593676",
"0.6048804",
"0.60143715",
"0.60028",
"0.5992972",
"0.59387314",
"0.5908916",
"0.5902789",
"0.586213",
"0.58594203",
"0.5833996",
"0.5833996",
"0.582378",
"0.58225274",
"0.58054274",
"0.5756805",... | 0.6170885 | 5 |
Generate all possible slices for a single dimension. | def genslices(n):
return product(range(-n, n+1), range(-n, n+1), range(-n, n+1)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_slices(self, dim=None, sample=None):\n \n if dim is None:\n dim = self.D\n if sample is None:\n sample = range(1, int(self.poolSize)+1)\n labelsCube = self.labelledCube(dim, sample)\n init = [range(3)] * (dim-1)\n slice_colors = ['yellow', ... | [
"0.68956304",
"0.6782944",
"0.6318284",
"0.61906445",
"0.6138602",
"0.606376",
"0.6022408",
"0.6019132",
"0.5987078",
"0.5982537",
"0.5982537",
"0.5952053",
"0.5952053",
"0.5930457",
"0.59301263",
"0.59170926",
"0.5881405",
"0.5880028",
"0.58595836",
"0.5842746",
"0.58289135"... | 0.55072 | 39 |
Generate all possible slice tuples for 'shape'. | def genslices_ndim(ndim, shape):
iterables = [genslices(shape[n]) for n in range(ndim)]
return product(*iterables) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randslice_from_shape(ndim, shape):\n lslices = [0] * ndim\n rslices = [0] * ndim\n for n in range(ndim):\n l = shape[n]\n slicelen = randrange(1, l + 1) if l > 0 else 0\n lslices[n] = randslice_from_slicelen(slicelen, l)\n rslices[n] = randslice_from_slicelen(slicelen, l)\n... | [
"0.7245465",
"0.7242757",
"0.6539281",
"0.6419186",
"0.64133",
"0.6405746",
"0.6356059",
"0.63555807",
"0.6354344",
"0.62147045",
"0.6181078",
"0.6152246",
"0.6074758",
"0.6030818",
"0.60114497",
"0.60114497",
"0.59410995",
"0.5884168",
"0.5851655",
"0.57941407",
"0.57577944"... | 0.61784625 | 12 |
Generate random slice for a single dimension of length n. If zero=True, the slices may be empty, otherwise they will be nonempty. | def rslice(n, allow_empty=False):
minlen = 0 if allow_empty or n == 0 else 1
slicelen = randrange(minlen, n+1)
return randslice_from_slicelen(slicelen, n) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rslice(n, allow_empty=False):\n minlen = 0 if allow_empty or n == 0 else 1\n slicelen = randrange(minlen, n + 1)\n return randslice_from_slicelen(slicelen, n)",
"def get_slice(self, n):\n if n == 0:\n return slice(self._lo_atom, self._lo_atom + self._n_atoms)\n raise IndexEr... | [
"0.77326006",
"0.70148605",
"0.6813906",
"0.6813906",
"0.6412858",
"0.63864595",
"0.62999004",
"0.6264508",
"0.61941475",
"0.6164201",
"0.616065",
"0.6156945",
"0.6141035",
"0.61246413",
"0.61231834",
"0.605221",
"0.60043675",
"0.60013884",
"0.59873164",
"0.5930526",
"0.59282... | 0.77208936 | 1 |
Generate random slices for a single dimension. | def rslices(n, allow_empty=False):
for _ in range(5):
yield rslice(n, allow_empty) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def samples_multidimensional_uniform(bounds, points_count):\n dim = len(bounds)\n Z_rand = np.zeros(shape=(points_count, dim))\n for k in range(0,dim):\n Z_rand[:,k] = np.random.uniform(low=bounds[k][0], high=bounds[k][1], size=points_count)\n print('shape: ', Z_rand.shape)\n return Z_rand",
... | [
"0.6748864",
"0.66025615",
"0.6602007",
"0.65602344",
"0.6382453",
"0.637084",
"0.6325425",
"0.63170785",
"0.63101554",
"0.6257017",
"0.62307423",
"0.6230162",
"0.61685413",
"0.6137612",
"0.6112374",
"0.60909176",
"0.6086134",
"0.6047355",
"0.6043131",
"0.6034215",
"0.6028377... | 0.57534003 | 41 |
Generate random slice tuples for 'shape'. | def rslices_ndim(ndim, shape, iterations=5):
# non-empty slices
for _ in range(iterations):
yield tuple(rslice(shape[n]) for n in range(ndim))
# possibly empty slices
for _ in range(iterations):
yield tuple(rslice(shape[n], allow_empty=True) for n in range(ndim))
# invalid slices
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randslice_from_shape(ndim, shape):\n lslices = [0] * ndim\n rslices = [0] * ndim\n for n in range(ndim):\n l = shape[n]\n slicelen = randrange(1, l+1) if l > 0 else 0\n lslices[n] = randslice_from_slicelen(slicelen, l)\n rslices[n] = randslice_from_slicelen(slicelen, l)\n ... | [
"0.81737244",
"0.8173164",
"0.70365703",
"0.6893058",
"0.66185147",
"0.6588831",
"0.65197295",
"0.64492506",
"0.64458674",
"0.6420841",
"0.6409542",
"0.6335667",
"0.63094866",
"0.6286346",
"0.62749714",
"0.62746143",
"0.62283",
"0.6214",
"0.6178123",
"0.6170837",
"0.6170837",... | 0.59845704 | 36 |
Print ndarray for debugging. | def ndarray_print(nd):
try:
x = nd.tolist()
except (TypeError, NotImplementedError):
x = nd.tobytes()
if isinstance(nd, ndarray):
offset = nd.offset
flags = nd.flags
else:
offset = 'unknown'
flags = 'unknown'
print("ndarray(%s, shape=%s, strides=%s, su... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ndarray_print(nd):\n try:\n x = nd.tolist()\n except (TypeError, NotImplementedError):\n x = nd.tobytes()\n if isinstance(nd, ndarray):\n offset = nd.offset\n flags = nd.flags\n else:\n offset = 'unknown'\n flags = 'unknown'\n print(\n \"ndarray(%... | [
"0.7679877",
"0.68368405",
"0.6631245",
"0.6609683",
"0.65994364",
"0.65967774",
"0.6263622",
"0.62557095",
"0.62557095",
"0.62557095",
"0.62557095",
"0.6235029",
"0.6219768",
"0.6178411",
"0.61287254",
"0.6089198",
"0.6083977",
"0.60370946",
"0.60179627",
"0.6011618",
"0.598... | 0.7686053 | 0 |
idf should be genespecific | def process_arc_df(idf, ymax, y_interval=0):
odf = idf.copy()
odf['y_interval'] = y_interval
odf['mid_point'] = (odf['sc_t'] + odf['aclv_t']) / 2
order = odf['alen'].argsort().argsort()
odf['order'] = order
# take log to squash the difference in between
# arc_height = np.log(order + 1) + 0.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_idf(term, documents):\n\n number_of_docs = len(documents)\n documents_containing_term = len([document for document in documents if term in document])\n\n idf = math.log10(number_of_docs / documents_containing_term)\n\n return round(idf, 5)",
"def calc_idf(self, nd):\n # collect idf sum to calc... | [
"0.6475487",
"0.6212292",
"0.6194341",
"0.6188523",
"0.6082829",
"0.60702246",
"0.6049595",
"0.6043377",
"0.60023904",
"0.59861237",
"0.59745646",
"0.59393996",
"0.5885963",
"0.58711284",
"0.5846038",
"0.57972324",
"0.5784424",
"0.5780035",
"0.57797086",
"0.57674223",
"0.5752... | 0.0 | -1 |
connect annotated clv to the below ax where predicted clvs are | def plot_connector(ax, arc_df):
# gene = get_property(arc_df, 'gene_name')
clvs = arc_df['aclv_t'].values.tolist()
scs = arc_df['sc_t'].values.tolist()
# lowest = -0.1 if (gene, dise) in MAIN_PLOT_GD_PAIRS_COMPLEX else -0.8
lowest = -1
# for i in clvs + scs:
for i in clvs:
# -0.25 i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lca(self, v, w):",
"def get_vshape(self, dmax, plot=True):\n '''\n\n '''\n dists_label = np.linspace(0.001, dmax, 200)\n dists = np.concatenate([[0], np.linspace(0.001, dmax, 200)])/np.sqrt(2)\n dists_xy = np.stack([dists, dists], 1)\n dist_mat = self.get_distance_ma... | [
"0.56138206",
"0.5608647",
"0.54186684",
"0.5302264",
"0.5283774",
"0.52464485",
"0.5216876",
"0.5174231",
"0.5168294",
"0.51151377",
"0.5091749",
"0.5072296",
"0.5069256",
"0.5067856",
"0.50639397",
"0.50111675",
"0.50003016",
"0.5000205",
"0.49753287",
"0.49463645",
"0.4932... | 0.0 | -1 |
Is (x0, y0) on a shared diagonal with (x1, y1)? | def share_diagonal(x0,y0,x1,y1):
return abs(x0 - x1) == abs(y0 - y1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def share_diagonal(x0, y0, x1, y1):\r\n dy = abs(y1 - y0) # Calc the absolute y distance\r\n dx = abs(x1 - x0) # CXalc the absolute x distance\r\n return dx == dy # They clash if dx == dy\r",
"def share_diagonal(x0, y0, x1, y1):\r\n dy = abs(y1 - y0) # Calc the absolute ... | [
"0.8518046",
"0.8518046",
"0.8502754",
"0.84337777",
"0.7760696",
"0.70689964",
"0.70689964",
"0.706408",
"0.6565794",
"0.65179807",
"0.6373702",
"0.63458717",
"0.63303655",
"0.6296012",
"0.6293987",
"0.62332195",
"0.62295103",
"0.61589813",
"0.61407775",
"0.6136831",
"0.6124... | 0.88636863 | 0 |
Return True if the queen at column c clashes with any queen to its left. | def col_clashes(bs, c):
for i in range(c):
if share_diagonal(i,bs[i], c,bs[c]):
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isfree(col,queens):\n if col in queens:\n return False\n elif any([ abs(col-col1)==len(queens)-index for index,col1 in enumerate(queens)]):\n #c[r]==c[j]; r-j==c[r]-c[j]; r-j==c[j]-c[r]\n # col is the colomn to check; len(queens) just be the row index of col, dont subtract 1\n ... | [
"0.77055407",
"0.72922397",
"0.7145801",
"0.70153964",
"0.6985228",
"0.6902115",
"0.66958386",
"0.65576535",
"0.6551649",
"0.6548673",
"0.6523149",
"0.6523149",
"0.6522317",
"0.6519715",
"0.65155363",
"0.6512403",
"0.6512403",
"0.65036315",
"0.6501785",
"0.64987516",
"0.64938... | 0.0 | -1 |
Determine whether we have any queens clashing on the diagonals. We're assuming here that the_board is a permutation of column numbers, so we're not explicitly checking row or column clashes. | def has_clashes(the_board):
for c in range(1, len(the_board)):
if col_clashes(the_board, c):
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def multipleQueensAlongDiagonals(board):\n if (multipleQueensOnRightDiagonals(board) or\n multipleQueensOnLeftDiagonals(board)):\n return True\n\n return False",
"def diag_win(board):\n\tif board[1][1] != EMPTY and (board[1][1] == board[0][2] == board[2][0] or board[1][1] == board[0][0] =... | [
"0.7737763",
"0.77164614",
"0.76054054",
"0.75930977",
"0.75839365",
"0.75839365",
"0.75591165",
"0.75591165",
"0.7456546",
"0.73946005",
"0.7344994",
"0.7342945",
"0.72782034",
"0.71962905",
"0.7145917",
"0.7118199",
"0.70307064",
"0.6995394",
"0.6990177",
"0.69780976",
"0.6... | 0.7583776 | 6 |
Return the schema of a Data Type. | def __call__(self, cls_or_name: Union[str, Type]) -> Type[DTSchema]:
if isinstance(cls_or_name, type):
n = cls_or_name.__name__
else:
n = cls_or_name
if hasattr(self, n):
return getattr(self, n)
raise ValueError(f"Could not find type {cls_or_name}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _schema_type(self) -> Optional[type]:\n return SeriesSchema",
"def get_schema(self):\n response = self.client.get(self._get_collection_url('schema'))\n\n return response.get('schema', {})",
"def get_schema(self):\r\n return self.__schema",
"def get_schema_type(arg_schema: Dict... | [
"0.72283876",
"0.70753324",
"0.69580424",
"0.6946455",
"0.69358844",
"0.69223475",
"0.68721735",
"0.6850955",
"0.68285525",
"0.68234205",
"0.6798246",
"0.67978346",
"0.6782011",
"0.6762986",
"0.67275816",
"0.6727343",
"0.67157215",
"0.6694789",
"0.6689674",
"0.66346025",
"0.6... | 0.0 | -1 |
Parse a formatted string and return the names of the args and their types. Will raise a ValueError if the type is not a pyopenapi3 `Field` or an already defined Component Parameter type. In the case that the type represents a `Field`, then its type will be returned, respectively. Otherwise, if it is an already defined ... | def parse_name_and_type_from_fmt_str(
formatted_str: str,
allowed_types: Optional[Dict[str, Component]] = None
) -> Generator[Tuple[str, Type[Field]], None, None]:
for _, arg_name, _type_name, _ in Formatter().parse(formatted_str):
if arg_name is not None:
try:
as... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_type(self, param):\n\n def evaluate(instance):\n if isinstance(instance, (Struct, Enum)):\n return instance.name\n if isinstance(instance, (Integer, Float)):\n return 'Number'\n return type(instance).__name__\n\n if isinstance... | [
"0.5762772",
"0.56126136",
"0.5612374",
"0.5606245",
"0.5510904",
"0.5498231",
"0.54389805",
"0.5416661",
"0.5379599",
"0.53538597",
"0.5281033",
"0.52633864",
"0.522637",
"0.51833683",
"0.5179722",
"0.5152138",
"0.51198363",
"0.51118517",
"0.50726426",
"0.5072116",
"0.507044... | 0.74992687 | 0 |
Convert a custom object to a schema. This is done by create a reference to the object. Any nonreference object should be created by the Components builder. param `obj` must be a subtype of `data_types.Component`. Its type will determine what kind of component it is, e.g. '/components/ schemas/...' or '/components/param... | def convert_objects_to_schema(obj: Type[Component]) -> ReferenceObject:
cmp_type: str = 'schemas' # default component type
if hasattr(obj, '__cmp_type__'):
cmp_type = obj.__cmp_type__.lower() # type: ignore
return create_reference(obj.__name__, cmp_type) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_schema(obj):\n\n if not isinstance(obj, Schema):\n if isinstance(obj, dict):\n return DictStructure(obj)\n elif isinstance(obj, list):\n return ListStructure(obj)\n elif isinstance(obj, (int, float, str, bool)) or (obj is None):\n return Value(obj)\... | [
"0.6243453",
"0.5825912",
"0.57944256",
"0.57603896",
"0.56610686",
"0.5654507",
"0.5639352",
"0.5597519",
"0.5585299",
"0.5489156",
"0.5368194",
"0.53657424",
"0.5364657",
"0.5353328",
"0.5295786",
"0.5216217",
"0.5193709",
"0.516745",
"0.51386046",
"0.5135405",
"0.5110169",... | 0.81769264 | 0 |
'Inject' the `Component` class into the custom, user defined, soontobe Component, class. This will help when building a property that involves a user defined custom Component. param `cmp_type` is some subtype of `data_types.Component`, e.g. whether it is a Schema component or Parameter component. | def inject_component(cls, cmp_type: Type[ComponentType]):
if issubclass(cls, Component):
return cls
else:
injected = type(
"Injected",
(cls, cmp_type),
{attr_name: attr for attr_name, attr in cls.__dict__.items()}
)
injected.__qualname__ = f'Co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_comp(self, name, ctype):\n\n name = self.name + '.' + name\n\n assert name not in self.components, 'A component named \\'{}\\' already exists for node \\'{}\\''.format(\n name, self.name)\n\n try:\n cls = co.str_to_comp(ctype)\n except AttributeError:\n ... | [
"0.6341976",
"0.60763973",
"0.60763973",
"0.5894654",
"0.5816849",
"0.5718252",
"0.56145835",
"0.56028533",
"0.5554072",
"0.55418164",
"0.5510925",
"0.54735297",
"0.54278535",
"0.5406879",
"0.5326886",
"0.5326886",
"0.5322498",
"0.52880853",
"0.518145",
"0.51773274",
"0.51618... | 0.7875679 | 0 |
Init by server url | def init(server_host_and_port):
global __obj_starter_api
__obj_starter_api = PrivateApi(api_url=server_host_and_port) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, server_url='http://192.241.215.92:8011'):\n assert isinstance(server_url, str)\n self.server_url = server_url",
"def __init__(self, url):\n self.url = url\n self.admin_url = os.path.join(url, \"__admin\")\n self.admin_mapping_url = os.path.join(self.admin_url... | [
"0.7656898",
"0.70661646",
"0.6904823",
"0.689395",
"0.67538166",
"0.66310674",
"0.66245764",
"0.6576854",
"0.65653795",
"0.6529327",
"0.6493813",
"0.63935435",
"0.6388721",
"0.6376249",
"0.63753974",
"0.6375212",
"0.63577634",
"0.6355919",
"0.6355919",
"0.6355919",
"0.635494... | 0.68095416 | 4 |
Build and submit task to server | def build_submit(service_id, data=None):
return submit(
build_task(service_id, data)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_task():",
"def task():",
"def run(self):\n if self.type_task == \"Api-request\":\n self.config = ConfigApiRequestTask(**self.dynamic_configs)\n self.task = ApiRequestTask(\n priority=0, # fixed priority\n config=self.config\n )\n ... | [
"0.70684713",
"0.66794336",
"0.6674396",
"0.66026026",
"0.6517576",
"0.65075755",
"0.6490517",
"0.64522195",
"0.6393312",
"0.6379747",
"0.63695216",
"0.6331631",
"0.6319318",
"0.62970674",
"0.6277401",
"0.62158525",
"0.6213745",
"0.6209379",
"0.6209379",
"0.6204134",
"0.62009... | 0.66046375 | 3 |
If the algorithm is allowed to modify the lists. | def add_numbers(head1,head2):
if not head2 and head1 :
return head1
elif not head1 and head2 :
return head2
elif not head1 and not head2 :
return head2
else :
return reverse(add(reverse(head1),reverse(head2))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkLists(self):\n self.x = self.checkList(self.x)\n self.y = self.checkList(self.y)\n return",
"def can_be_modified(self):\n return self.state in {RequestState.pending, RequestState.accepted}",
"def _set_listonly(self, value):\n if not value and self.__listonly:\n ... | [
"0.6362258",
"0.61513126",
"0.61318016",
"0.60871756",
"0.59612465",
"0.5935387",
"0.5869912",
"0.5833893",
"0.5829071",
"0.5793278",
"0.5785526",
"0.5719487",
"0.5713861",
"0.57039505",
"0.57035685",
"0.56947637",
"0.5652734",
"0.56144464",
"0.56124794",
"0.55669487",
"0.556... | 0.0 | -1 |
Determine all of the local ip addresses for this machine This allows us to flag traffic as inbound or outbound. | def detect_local_ips(self):
result = set()
for ifaceName in interfaces():
try:
address = [i['addr'] for i in ifaddresses(ifaceName)[AF_INET]]
except:
pass
result.add(address[0])
return tuple(result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_ips():\r\n local_ips = []\r\n public_ips = []\r\n \r\n # list of iface names, 'lo0', 'eth0', etc.\r\n for iface in netifaces.interfaces():\r\n # list of ipv4 addrinfo dicts\r\n ipv4s = netifaces.ifaddresses(iface).get(netifaces.AF_INET, [])\r\n for entry in ipv4s:\r\n ... | [
"0.73083735",
"0.72061265",
"0.704093",
"0.6923851",
"0.6890017",
"0.6762811",
"0.6715329",
"0.67115986",
"0.66833586",
"0.6657584",
"0.66189605",
"0.66133195",
"0.66041005",
"0.6584781",
"0.6563698",
"0.6518723",
"0.64804894",
"0.647476",
"0.645703",
"0.63811326",
"0.6353452... | 0.74796903 | 0 |
Sorts an iterable of packets and removes the duplicates | def iter_packets(iterable):
prev = None
for i in sorted(iterable, key=attrgetter('seq')):
if prev is None or prev.seq != i.seq:
prev = i
yield i | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sort_grouped_packets(self, grouped_packets):\n for group in grouped_packets:\n group.sort(key=lambda x: x.time, reverse=False)\n return grouped_packets",
"def insertOrderedPacket(self, packet:Rudp.Packet, packets:list) -> list:\n i = 0\n for i in range(len(packets)):\n ... | [
"0.6647256",
"0.61278707",
"0.60170823",
"0.5986823",
"0.596562",
"0.5906093",
"0.5628137",
"0.5617985",
"0.560249",
"0.5601702",
"0.5557772",
"0.5519489",
"0.55179644",
"0.55005574",
"0.5492273",
"0.54918206",
"0.5472187",
"0.5472187",
"0.54687566",
"0.543962",
"0.54305553",... | 0.6355191 | 1 |
Hashes a packet to determine the tcp stream it is part of | def hash_packet(eth, outbound=False):
ip = eth.data
tcp = ip.data
return '%s:%i' % (ipaddr_string(ip.dst if outbound else ip.src),
tcp.sport if outbound else tcp.dport
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hash_packet(packet):\n if packet.proto == 6:\n #*** Is TCP:\n packet_tuple = (packet.ip_src,\n packet.ip_dst,\n packet.proto,\n packet.tp_src,\n packet.tp_dst,\n packet.tp_seq_src,\n ... | [
"0.7895792",
"0.661901",
"0.63492525",
"0.6196242",
"0.6103663",
"0.6019841",
"0.5995078",
"0.5981094",
"0.59252846",
"0.5890868",
"0.58816797",
"0.58208543",
"0.5804756",
"0.5796329",
"0.5782937",
"0.5778316",
"0.574992",
"0.568126",
"0.56439674",
"0.5617276",
"0.5610319",
... | 0.72170126 | 1 |
Looks in the buffer to see if we have the next packet, if so append it and continue till there are no packets left. | def _check_buffer(self):
count = 0
for packet in self.remove_buffered_packets():
self._append_packet(packet)
count += 1
if count > 0:
logging.debug('Removed %i items from the buffer, %i left.' % (count, len(self.buffer))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_to_buffer(self, packet):\n # If the buffer is currently empty, the we should send the next packet\n # (i.e. this packet) as soon as it finishes transmitting (which will take\n # time + sizeofpacket/transmissionrate)\n if (len(self.buffer) == 0):\n self.next_packet_sen... | [
"0.74713373",
"0.71250594",
"0.6726249",
"0.6618854",
"0.6465137",
"0.6395793",
"0.6097378",
"0.60572255",
"0.60321057",
"0.596553",
"0.5958994",
"0.5956344",
"0.594581",
"0.5886289",
"0.5877437",
"0.5875217",
"0.5834494",
"0.5769007",
"0.5697798",
"0.56975275",
"0.56848884",... | 0.6629113 | 3 |
Iterates over next packets in the buffer and removes them | def remove_buffered_packets(self):
seq = self.next_seq
while True:
p = self.buffer.pop(seq, None)
if p is None:
break
else:
seq += len(p.data)
yield p | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drop_packets(self, verbose=False):\n while True:\n try:\n packet, address = self._socket.recvfrom(10240)\n except:\n break\n\n if verbose:\n logger.debug(\"dropped %d bytes from %s:%d\", len(packet), address[0], address[1])",
... | [
"0.67283076",
"0.66058624",
"0.63236773",
"0.6317739",
"0.6310936",
"0.62291014",
"0.61791795",
"0.60786915",
"0.60678726",
"0.59578645",
"0.5947683",
"0.5928341",
"0.5889969",
"0.5796768",
"0.5776283",
"0.5721672",
"0.571722",
"0.57118416",
"0.5706474",
"0.5685877",
"0.56655... | 0.7841867 | 0 |
Appends a packet to the end of the list of received packets and processes it | def _append_packet(self, packet):
self.next_seq += len(packet.data)
if self.headers is not None:
if self.packets is None:
self.packets = StringIO.StringIO()
self.packets.write(packet.data)
self.http_bytes_loaded += len(packet.data)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def packet_queue(self, pkt):\n \n pkt.pos = 0\n pkt.to_process = pkt.packet_length\n \n self.out_packet.append(pkt)\n return NC.ERR_SUCCESS",
"def process(self, packet):\n pass",
"def add_to_buffer(self, packet):\n # If the buffer is currently empty, the ... | [
"0.7200842",
"0.6791505",
"0.65107197",
"0.6466896",
"0.6292419",
"0.62873334",
"0.6270652",
"0.6237618",
"0.62123734",
"0.6207645",
"0.6195065",
"0.6191263",
"0.60909575",
"0.60606736",
"0.60586536",
"0.6052133",
"0.60464114",
"0.60459393",
"0.60107774",
"0.5988574",
"0.5982... | 0.63934094 | 4 |
This will eventually provide a way for a callback receive packets in order | def _handle_ordered_packet(self, packet):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def callback(self, packet, sender=None):\n pass",
"def _pkt_handle(self, pkt):\n\n # snag any left over data from last read()\n # Parse the header to get type\n offset, payload_len, subtype, nxp_sniffer = lowpan.message.parse_header(pkt[0])\n\n\n # Extract the raw message bytes... | [
"0.7107678",
"0.64932233",
"0.644447",
"0.6367621",
"0.6333254",
"0.6190142",
"0.61311525",
"0.6129073",
"0.6126829",
"0.61010754",
"0.60605866",
"0.6048082",
"0.6015961",
"0.6012906",
"0.59732354",
"0.59732354",
"0.59718686",
"0.5969053",
"0.5905559",
"0.5835373",
"0.5826865... | 0.5721915 | 28 |
Check the status of a job. | def check_status(self, job_uuid):
project = self._project
project_id = project.get_uuid() if type(project) is Project else project
code, response = self._rest.get(f'/projects/{project_id}/jobs/{job_uuid}/status')
if code != 200:
raise RuntimeError("Server status code: %s; Res... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_job_status(self, jobid=None):\n\n if jobid is None:\n if hasattr(self, 'current_job'):\n jobid = self.current_job\n else:\n jobid = self.current_job\n\n response = self._request(\n 'GET', CosmoSim.QUERY_URL + '/{}'.format(jobid)... | [
"0.79731226",
"0.7944931",
"0.7796605",
"0.77694833",
"0.775386",
"0.77262664",
"0.76045054",
"0.748808",
"0.7468982",
"0.737767",
"0.72872686",
"0.72710776",
"0.7249173",
"0.7246472",
"0.7232544",
"0.7220014",
"0.7208316",
"0.71444625",
"0.7134333",
"0.7080739",
"0.70677483"... | 0.71159524 | 19 |
Get the results of a job. | def get_results(self, job_uuid):
project = self._project
project_id = project.get_uuid() if type(project) is Project else project
code, response = self._rest.get(f'/projects/{project_id}/jobs/{job_uuid}/result')
if code != 200:
raise RuntimeError("Server status code: %s; Res... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_results(self, job_id):\n ujs = self.__ujs_client()\n res = ujs.get_results(job_id)\n return res",
"def getResults(self):\n if self.__jobInfo.results is not None:\n return json.loads(self.__jobInfo.results)\n else:\n return None",
"def get_results(self, job, ... | [
"0.76884747",
"0.7678061",
"0.7445121",
"0.74429166",
"0.7372573",
"0.735825",
"0.70753413",
"0.70528066",
"0.70358944",
"0.69135725",
"0.6910701",
"0.67281175",
"0.67259216",
"0.6709659",
"0.6699838",
"0.6654707",
"0.6602226",
"0.659859",
"0.659859",
"0.65819",
"0.6562158",
... | 0.6844862 | 11 |
rss232 standard baud rates 300 1200 2400 4800 9600 14400 19200 28800 38400 57600 115200 230400 | def BaudGen(clk, reset, rx_tick, tx_tick, clk_freq=100000000, baudrate=9600, rx_div=16):
tx_tick_max = int(round(clk_freq/baudrate))
tick_count_reg = Signal(intbv(0, min=0, max=tx_tick_max))
tick_count_next = Signal(intbv(0, min=0, max=tx_tick_max))
rx_div_count_reg = Signal(intbv(0, min=0, max=rx_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, port, baudrate):\n return",
"def __init__(self, id: int, baudrate: int=9600, bits: int=8, parity: int=None, stop: int=1, tx: Pin=None, rx: Pin=None):",
"def baudrate_cmd(self, baudrate):\r\n if baudrate is 19200:\r\n baudrate = \"F\"\r\n else:\r\n b... | [
"0.6666518",
"0.65934765",
"0.63405526",
"0.6223233",
"0.61509335",
"0.61282706",
"0.6121142",
"0.6112411",
"0.60013664",
"0.5970437",
"0.591027",
"0.58561224",
"0.58513325",
"0.58400947",
"0.58149344",
"0.5779933",
"0.5740304",
"0.5728582",
"0.5727158",
"0.5721995",
"0.57059... | 0.5508608 | 39 |
Given n number of trials, p the probability of successes, what is the probability of having less than or equal to x successes? Your function should raise a ValueError if x is higher than n. | def bin_cdf(n, p, x):
# p C (bin_dist) ** 0 ) *(1-bin_dist)** p
# n = (p)=20
# x = x = 1 = r
# nCr = n! / r!(n-r)
def bin_dist(n, p, x):
"""
Given n number of trials, p the probability of success,
what is the probability of having x successes?
Your function shoul... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n\n\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n\n # If less than p, it's a success so ad... | [
"0.8051699",
"0.80322367",
"0.8030806",
"0.80230564",
"0.7710508",
"0.7604674",
"0.7147401",
"0.7144347",
"0.69072294",
"0.68672615",
"0.6867023",
"0.6762666",
"0.6749791",
"0.67391",
"0.67163926",
"0.67021275",
"0.6657762",
"0.65597177",
"0.65574616",
"0.6515296",
"0.6502586... | 0.5732656 | 99 |
Given n number of trials, p the probability of success, what is the probability of having x successes? Your function should raise a ValueError if x is higher than n. If you need to compute combinations, you can import the function "comb" from the package "scipy.special" | def bin_dist(n, p, x):
def factorial(x):
if x >= 0:
factorial = 1
for i in range(1, x + 1):
factorial = float(factorial * i)
# print(f' The factorial of {x} is {factorial}')
return factorial
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perform_bernoulli_trials(n, p):\n # Initialize number of successes: n_success\n n_success = 0\n\n\n # Perform trials\n for i in range(n):\n # Choose random number between zero and one: random_number\n random_number = np.random.random()\n\n # If less than p, it's a success so ad... | [
"0.7992995",
"0.7988638",
"0.7983567",
"0.7979973",
"0.78188634",
"0.7754114",
"0.7721174",
"0.70337445",
"0.7027335",
"0.69186896",
"0.6905595",
"0.69036245",
"0.6807177",
"0.6772495",
"0.6591704",
"0.65804875",
"0.65346736",
"0.6518955",
"0.65020025",
"0.6491071",
"0.647253... | 0.66633165 | 14 |
Given n total number of items, what is the number of possible ways to choose r items from it? | def combination(n, r):
numerator = factorial(n)
denominator = factorial(r)
subtracted_answer = factorial(n-r)
answer = numerator/(denominator * subtracted_answer)
print(answer)
return answer | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def number_of_ways(n):\r\n return number_of_ways_helper([1, 5, 10, 25], n)",
"def main():\n\n import sys\n sys.setrecursionlimit(10**7)\n from itertools import accumulate, combinations, permutations, product # https://docs.python.org/ja/3/library/itertools.html\n # accumulate() returns iterator! t... | [
"0.72152823",
"0.6937321",
"0.6853795",
"0.67474264",
"0.67017275",
"0.6627513",
"0.6490569",
"0.64309037",
"0.6399863",
"0.6383735",
"0.6378631",
"0.63440573",
"0.63363504",
"0.6292473",
"0.6291534",
"0.6279435",
"0.62197953",
"0.6189228",
"0.6186623",
"0.61849743",
"0.61371... | 0.0 | -1 |
Move a die through a list of positions. | def move(self, *positions, show_length=True) -> str:
move_parts = []
move_count = len(positions)
pips = prev_x = prev_y = 0
for i, (x, y) in enumerate(positions):
if i == 0:
pips = self.dice.pop((x, y))
else:
dx = x - prev_x
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def throw(self, move):\n for dice_index in move:\n self.dice[dice_index - 1] = random.randint(1,6)",
"def move_tie_fighters(self):\n for i in range(len(self.tie_fighters)):\n self.tie_fighters[i].move_tie_fighter()",
"def migration(self):\n\n coordinates = self.get_ra... | [
"0.66328007",
"0.596524",
"0.5870962",
"0.58515584",
"0.5819032",
"0.57985187",
"0.5759108",
"0.57562107",
"0.574448",
"0.57078034",
"0.56926847",
"0.56588614",
"0.5642412",
"0.5605683",
"0.5596673",
"0.5586302",
"0.55856097",
"0.5582289",
"0.5575625",
"0.55608356",
"0.555548... | 0.63560057 | 1 |
Record the joint character between a pair of cells. | def add_joint(joint: str, x1: int, y1: int, x2: int, y2: int) -> str:
return joint | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def junction_char(self):\n ...",
"def _put_chr_at(self, char, row, col, color, adjustment_x=.19, adjustment_y=.19):\n self._goto_piece_xy(row, col, adjustment_x, adjustment_y)\n self.pen.color(color)\n self.pen.write(char, font=(\"Courier\", round(self.square_side_size * .7),\n ... | [
"0.6033239",
"0.5642616",
"0.5578127",
"0.5315496",
"0.52856386",
"0.5230224",
"0.5207506",
"0.51857436",
"0.51733494",
"0.5166131",
"0.51407605",
"0.5093636",
"0.5047172",
"0.50447404",
"0.50364846",
"0.50296426",
"0.5018322",
"0.499095",
"0.49901915",
"0.49538672",
"0.49527... | 0.5760053 | 1 |
Split all dominoes into separate cells. Useful for Dominosa. | def split_all(self):
for domino in self.dominoes[:]:
self.split(domino) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_split_cell_splits_neighbours(mock_amg):\n\n # split the centre cell in the mock grid\n # this will create 4 more cells at tier 1\n mock_amg.cells[4].split()\n\n # now split the bottom right of these cells\n # this should force the east and south cells to also be split\n mock_amg.cells[4]... | [
"0.54147345",
"0.53187776",
"0.5316432",
"0.53141034",
"0.50757587",
"0.50472033",
"0.5007801",
"0.49147692",
"0.49121788",
"0.4892322",
"0.48909703",
"0.48512492",
"0.48459086",
"0.48424825",
"0.48366407",
"0.48032853",
"0.47910866",
"0.47892538",
"0.47808278",
"0.47771978",
... | 0.7638271 | 0 |
Build a display string for the board's current state. | def display(self, cropped=False, cropping_bounds=None):
xmin, xmax, ymin, ymax = self.get_bounds(cropped)
if cropping_bounds is not None:
cropping_bounds[:] = [xmin, ymin, xmax, ymax]
width = xmax-xmin+1
height = ymax-ymin+1
marker_display = self.display_markers()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n board = ''\n board_2 = ''\n\n for row in self.from_grid:\n for space in row:\n board += ' ' + space\n board += '\\n'\n\n for row in self.to_grid:\n for space in row:\n board_2 += ' ' + space\n ... | [
"0.7867957",
"0.7523896",
"0.73911226",
"0.73753697",
"0.7373144",
"0.7246394",
"0.7220203",
"0.7215452",
"0.7110438",
"0.71008956",
"0.7092637",
"0.70639235",
"0.7059323",
"0.7039391",
"0.7023496",
"0.7022453",
"0.7006166",
"0.69822073",
"0.6949946",
"0.6925835",
"0.69216865... | 0.0 | -1 |
Adjust the display grid before it gets assembled. | def adjust_display(self, display: typing.List[typing.List[str]]): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _prepare_grid(self):\n raise NotImplementedError",
"def update_positions(self, grid):\r\n self.grid = grid",
"def apply(self):\n self.grid_size = self.values[0]",
"def configure_grid(self):\r\n\r\n for r in range(3):\r\n self.rowconfigure(r, weight=1)\r\n for... | [
"0.67337763",
"0.65539414",
"0.6454927",
"0.64203435",
"0.63934195",
"0.63931537",
"0.63745236",
"0.63429075",
"0.6283407",
"0.6222548",
"0.62183297",
"0.620335",
"0.6120996",
"0.6119752",
"0.6101794",
"0.60955167",
"0.6091233",
"0.60688007",
"0.6063356",
"0.6054027",
"0.6035... | 0.0 | -1 |
Iterate through self.extra_dominoes, start at random position. a generator of dominoes. | def choose_extra_dominoes(self, random):
dominoes = self.extra_dominoes[:]
count = len(dominoes)
start = random.randrange(count)
for i in range(count):
yield dominoes[(i + start) % count] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def choose_and_flip_extra_dominoes(self, random):\n for domino in self.choose_extra_dominoes(random):\n if domino.head.pips == domino.tail.pips:\n yield domino, False\n else:\n flip_first = random.randint(0, 1)\n for j in range(2):\n ... | [
"0.6895019",
"0.62940025",
"0.5725973",
"0.53913283",
"0.51478684",
"0.5122216",
"0.5106027",
"0.51026565",
"0.5055542",
"0.50077224",
"0.49957895",
"0.4972248",
"0.49301994",
"0.49140477",
"0.49055457",
"0.48926997",
"0.4876082",
"0.48698455",
"0.48647448",
"0.48630825",
"0.... | 0.8736248 | 0 |
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