id stringlengths 9 9 | text stringlengths 312 2.4k | title stringclasses 1
value |
|---|---|---|
000000000 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
V, x = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
V.data += x
#print(V)
with open('result/result_{}.pkl'.format(args.test_ca... | |
000000001 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = (sa.count_nonzero()==0)
#print(result)
with open('result/result_{}... | |
000000002 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
x, mu, stddev = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = stats.lognorm(s=stddev, scale=np.exp(mu)).... | |
000000003 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
x, shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.ndimage.zoom(x, zoom=(shape[0]/x.shape[0], ... | |
000000004 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
... | |
000000005 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
sa, sb = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sa.multiply(sb)
#print(result)
with open('r... | |
000000006 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize
import numpy as np
a, x_true, y, x0, x_lower_bounds = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def residual_ans(x, a, y):
s =... | |
000000007 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
c, low, high = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(c=5, low=0, high=1):
result = scipy.integrate.quadrature(lam... | |
000000008 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
import numpy as np
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
indices = [('1415777_at Pnlipr... | |
000000009 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
kurtosis_result = (sum((a - np.mean(a)) ** 4)/len(a)) / np.std(a)**4
#print(kurtosis... | |
000000010 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy
x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = np.polyfit(np.log(x), y, 1)[::-1]
#print(result)
with open... | |
000000011 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy as sp
from scipy import integrate,stats
def bekkers(x, a, m, d):
p = a*np.exp((-1*(x**(1/3) - m)**2)/(2*d**2))*x**(-2/3)
return(p... | |
000000012 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = pd.DataFrame(data=stats.zscore(df, axis ... | |
000000013 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial.distance
example_array = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(example_array):
import itertools
... | |
000000014 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize as optimize
from math import *
initial_guess = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def g(params):
import numpy as np
... | |
000000015 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize as sciopt
import numpy as np
import pandas as pd
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
weights = (a.values / a.values.sum... | |
000000016 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.optimize import fsolve
def eqn(x, a, b):
return x + 2*a - b**2
xdata, bdata = pickle.load(open(f"input/input{args.test_case}.pkl", "rb... | |
000000017 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
... | |
000000018 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
arr = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
M = csr_matrix(arr)
result = M.A.diagonal(0)
#p... | |
000000019 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
... | |
000000020 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy
import scipy.optimize
import numpy as np
def test_func(x):
return (x[0])**2+(x[1])**2
def test_grad(x):
return [2*x[0],2*x[1]]
starting_point, dire... | |
000000021 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
z_scores, mu, sigma = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
temp = np.array(z_scores)
p_values = scipy.stats... | |
000000022 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import lil_matrix
from scipy import sparse
M = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
rows, cols = M.nonzero(... | |
000000023 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
c1, c2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Feature = sparse.vstack((c1, c2))
#print(Feature)
with open('result/resu... | |
000000024 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.spatial import distance
shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
xs, ys = np.indices(shape)
xs = xs.reshape(shap... | |
000000025 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial.distance
example_array = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
import itertools
n = example_array.max()+1
in... | |
000000026 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import numpy as np
import scipy.stats as stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
import itertools as IT
for col1, col2... | |
000000027 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
sA, sB = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(sA, sB):
result = sA.multiply(sB)
return ... | |
000000028 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
square = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def filter_isolated_cells(array, struct):
filtered_array... | |
000000029 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
c1, c2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Feature = sparse.hstack((c1, c2)).tocsr()
#print(Feature)
with open('res... | |
000000030 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy
x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = np.polyfit(np.log(x), y, 1)
#print(result)
with open('resu... | |
000000031 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
col = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Max, Min = col.max(), col.min()
#print(Max)
#p... | |
000000032 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = (sa.count_nonzero()==0)
#print(result)
with open('result/result_{}... | |
000000033 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy.optimize import curve_fit
import numpy as np
z, Ua, tau, degree = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def fourier(x, *a):
ret = a[... | |
000000034 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.optimize
x, y, p0 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.optimize.curve_fit(lambda t,a,b, c: a*np.e... | |
000000035 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy
import numpy as np
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
a = 1-np.sign(a)
#print(a)
with open('result/result_{}.pkl'.format(ar... | |
000000036 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
import numpy as np
centroids, data, k = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def find_k_closest(centroids, data, k=1, distan... | |
000000037 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
b = scipy.ndimage.median_filter(a, size=(3, 3), origin=(0, 1))
... | |
000000038 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa, sb = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.hstack((sa, sb)).tocsr()
#print(result)
with open('resu... | |
000000039 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
c1, c2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Feature = sparse.hstack((c1, c2)).tocsr()
#print(Feature)
with open('res... | |
000000040 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
N, p = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
n = np.arange(N + 1, dtype=np.int64)
dist = scipy.stats.binom(p=... | |
000000041 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats as ss
x1, x2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
s, c_v, s_l = ss.anderson_ksamp([x1,x2])
result = c_v[2] ... | |
000000042 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial
import scipy.optimize
np.random.seed(100)
points1, N, points2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
C = s... | |
000000043 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.optimize as sciopt
fp = lambda p, x: p[0]*x[0]+p[1]*x[1]
e = lambda p, x, y: ((fp(p,x)-y)**2).sum()
pmin, pmax, x, y = pickle.load(op... | |
000000044 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import signal
arr, n = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
res = signal.argrelextrema(arr, np.less_equal, order=n, a... | |
000000045 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial
centroids, data = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def find_k_closest(centroids, data, k=1, distance_n... | |
000000046 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize
import numpy as np
a, y, x0 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def residual_ans(x, a, y):
s = ((y - a.dot(x**2))**2)... | |
000000047 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
points, extraPoints = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
vor = scipy.spatial.Voronoi(points)
kdtree = scipy.spatial.cKDTree(... | |
000000048 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
import numpy as np
centroids, data = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def find_k_closest(centroids, data, k=1, distance... | |
000000049 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
s, t = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
x, y = np.ogrid[-1:1:10j,-2:0:10j]
z = (x + y)*np.exp(-6.0 ... | |
000000050 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
points, extraPoints = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
vor = scipy.spatial.Voronoi(points)
kdtree = scipy.spatial.cKDTree(... | |
000000051 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import numpy as np
N0, time_span = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def dN1_dt (t, N1):
return -100 * N1 + np.sin(t)... | |
000000052 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
x, array, x_new = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
new_array = scipy.interpolate.interp1d(x, array,... | |
000000053 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy
import numpy as np
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
a = np.sign(a)
#print(a)
with open('result/result_{}.pkl'.format(args... | |
000000054 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
points, V, request = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.interpolate.griddata(points,... | |
000000055 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
z_scores = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
temp = np.array(z_scores)
p_values = scipy.stats.norm.cdf(te... | |
000000056 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import numpy as np
N0, time_span = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def dN1_dt (t, N1):
return -100 * N1 + np.sin(t... | |
000000057 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.sparse as sparse
vectors, max_vector_size = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.lil_matrix((len(... | |
000000058 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.spatial import distance
shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(shape = (6, 6)):
xs, ys = np.indices(... | |
000000059 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
matrix = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.spdiags(matrix, (1, 0, -1), 5, 5).T.A
... | |
000000060 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.optimize import minimize
def function(x):
return -1*(18*x[0]+16*x[1]+12*x[2]+11*x[3])
I = pickle.load(open(f"input/input{args.test_cas... | |
000000061 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import integrate
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
df.Time = pd.to_datetime(df.Time, format='%Y-%m... | |
000000062 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
pre_course_scores, during_course_scores = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
p_value = stats.ranksums... | |
000000063 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
V, x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
V = V.copy()
V.data += x
V.eliminate_zeros()
V.data += y
V.eliminate_zeros... | |
000000064 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import lil_matrix
def f(sA):
rows, cols = sA.nonzero()
sA[cols, rows] = sA[rows, cols]
return sA
sA = pickle.load(open... | |
000000065 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
def f(pre_course_scores, during_course_scores):
p_value = stats.ranksums(pre_course_scores, during_course_scores).pvalue
... | |
000000066 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa, sb = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.vstack((sa, sb)).tocsr()
#print(result)
with open('resu... | |
000000067 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
square = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def filter_isolated_cells(array, struct):
filtered_array... | |
000000068 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats as ss
x1, x2, x3, x4 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
statistic, critical_values, significance_level = ... | |
000000069 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = pd.DataFrame(data=stats.zscore(df, axis =... | |
000000070 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import math
import numpy as np
x, u, o2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def NDfx(x):
return((1/math.sqrt((2*math.... | |
000000071 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
s, t = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(s, t):
x, y = np.ogrid[-1:1:10j,-2:0:10j]
z =... | |
000000072 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
kurtosis_result = scipy.stats.kurtosis(a)
#print(kurtosis_result)... | |
000000073 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy as sp
from scipy import integrate,stats
def bekkers(x, a, m, d):
p = a*np.exp((-1*(x**(1/3) - m)**2)/(2*d**2))*x**(-2/3)
return(p... | |
000000074 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import ndimage
def f(img):
threshold = 0.75
blobs = img > threshold
labels, result = ndimage.label(blobs)
return result
i... | |
000000075 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import signal
arr, n = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = signal.argrelextrema(arr, np.less_equal, order=n... | |
000000076 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
col = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
n = col.shape[0]
val = col.data
for i in range... | |
000000077 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.spatial import distance
shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
xs, ys = np.indices(shape)
xs = xs.reshape(shap... | |
000000078 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import sparse
V, x = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
V._update(zip(V.keys(), np.array(list(V.values())) + x))
#... | |
000000079 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial
import scipy.optimize
points1, N, points2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
C = scipy.spatial.distanc... | |
000000080 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.optimize import fsolve
def eqn(x, a, b):
return x + 2*a - b**2
xdata, adata = pickle.load(open(f"input/input{args.test_case}.pkl", "rb... | |
000000081 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
import math
sa = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
sa = sparse.csr_matrix(sa.toarray() / np.sqrt(np.... | |
000000082 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
mu, stddev = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
expected_value = np.exp(mu + stddev ** 2 / 2)
median =... | |
000000083 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import ndimage
img = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
threshold = 0.75
blobs = img < threshold
labels, result =... | |
000000084 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import ndimage
img = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
threshold = 0.75
blobs = img > threshold
labels, nlabels ... | |
000000085 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
points, V, request = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.interpolate.griddata(points,... | |
000000086 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import numpy as np
x, y, alpha = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
s, p = stats.ks_2samp(x, y)
result = (p <= alpha)
... | |
000000087 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
a, b = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
b = sparse.csr_matrix(a)
b.setdiag(0)
b.eliminate_zeros()
... | |
000000088 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import misc
from scipy.ndimage import rotate
import numpy as np
data_orig, x0, y0, angle = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def rot... | |
000000089 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import numpy as np
np.random.seed(42)
x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
statistic, p_value = stats.ks_2samp(x, y)... | |
000000090 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
indices = [('1415777_at Pnliprp1', 'data'), ('141... | |
000000091 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.interpolate
x, y, eval = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.interpolate.griddata(x, y, eval)
#print(result)
with... | |
000000092 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import pandas as pd
import numpy as np
LETTERS = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ')
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"... | |
000000093 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import math
import numpy as np
x, u, o2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def NDfx(x):
return((1/math.sqrt((2*math.... | |
000000094 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial.distance
example_array = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
import itertools
n = example_array.max()+1
in... | |
000000095 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import interpolate
import numpy as np
x, y, x_val = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = np.zeros((5, 100))
for i in range(5):
... | |
000000096 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
c, low, high = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.integrate.quadrature(lambda x: 2*c*x, low, high)[0]
#p... | |
000000097 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
col = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
mean = col.mean()
N = col.shape[0]
sqr = col.c... | |
000000098 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import numpy as np
N0, time_span = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def dN1_dt(t, N1):
input = 1-np.cos(t) if 0<t<2*... | |
000000099 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
p_values = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
z_scores = scipy.stats.norm.ppf(p_values)
#print(z_scores)
... |
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