@pustam_egr

import pandas as pd import matplotlib.pyplot as plt import numpy as np # ---------------------------- # Team xG Data # ---------------------------- teams = [ "Spain", "Portugal", "Morocco", "Canada", "France", "Paraguay", "Brazil", "Norway"

import matplotlib.pyplot as plt import numpy as np # Match order (Latest to Earliest) matches = ["Final/\nBronze", "SF", "QF", "R16", "R32", "GS3", "GS2", "GS1"] x = np.arange(len(matches)) data = { "Spain":{ "xGF":[2.29,1.63,1.96,1.69

import matplotlib.pyplot as plt import numpy as np # Match order (Latest to Earliest) matches = ["Final/\nBronze","SF","QF","R16","R32","GS3","GS2","GS1"] data = { "Spain":{ "xGF":[2.29,1.63,1.96,1.69,2.32,0.86,2.30,2.10], "xGA

import matplotlib.pyplot as plt import numpy as np # Match order (Latest to Earliest) matches = ["Final/\nBronze", "SF", "QF", "R16", "R32", "GS3", "GS2", "GS1"] data = { "Spain": { "xGF":[2.29,1.63,1.96,1.69,2.32,0.86,2.30,2.10],

import matplotlib.pyplot as plt # ============================================================ # Match order (Latest to Earliest) # ============================================================ matches = ["Final/\nBronze", "SF", "QF", "R16", "R32",

import matplotlib.pyplot as plt # =========================================== # 2026 FIFA World Cup # Attack vs Defence (Expected Goals) # X-axis : xG Conceded per Match (lower = better defence) # Y-axis : xG Created per Match (higher = better atta

import math from functools import lru_cache import matplotlib.pyplot as plt def count_max_less_than(m, n, k): @lru_cache(maxsize=None) def dp(people_left, cats_left): if cats_left == 0: return 1 if people_left == 0 else

import numpy as np import matplotlib.pyplot as plt from scipy.integrate import quad # ---------------------------- # Physical constants # ---------------------------- g = 9.81 a_out = 0.01 H0 = 1.0

import numpy as np import matplotlib.pyplot as plt from scipy.integrate import solve_ivp, cumulative_trapezoid from scipy.interpolate import interp1d # ---------------------------- # Physical parameters # ---------------------------- g = 9.81 a_out

import numpy as np import matplotlib.pyplot as plt from scipy.integrate import solve_ivp, cumulative_trapezoid from scipy.interpolate import interp1d # ---------------------------- # Physical parameters # ---------------------------- g = 9.81 a_out

import numpy as np import matplotlib.pyplot as plt from scipy.integrate import solve_ivp, cumulative_trapezoid from scipy.interpolate import interp1d # ---------------------------- # Physical parameters # ---------------------------- g = 9.81

import csv import datetime import urllib.request import json import matplotlib.pyplot as plt import numpy as np AWARD_DAY = 10 AWARD_MONTH = 12

import csv import datetime import urllib.request import json import matplotlib.pyplot as plt import numpy as np AWARD_DAY = 10 AWARD_MONTH = 12 CSV_FILENAME = "physics_nobel_laureates_ages_1901_2025.csv"

import numpy as np from scipy.integrate import dblquad def integrand_u(x, y): rsq = x**2 + y**2 num = y * (1 - np.exp(-rsq)) denom = 2 * rsq return -num/ denom if rsq != 0 else 0 def integrand_v(x, y):

library(ggplot2) a <- 150 b <- 75 atan_ba <- atan(b / a) theta1 <- seq(-atan_ba, atan_ba, length.out = 100) theta2 <- seq(atan_ba, pi - atan_ba, length.out = 100) theta3 <- seq(pi - atan_ba, pi + atan_ba, length.out = 100) theta4 <- seq(pi + atan_

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D a, b, c = 150, 75, 50 vertices = np.array([ [-a, -b, -c], [-a, -b, c], [-a, b, -c],

a = 150; b = 75; vertices = [ -a, -b; -a, b; a, b; a, -b ];

import numpy as np import matplotlib.pyplot as plt a, b = 150, 75 vertices = np.array([ [-a, -b], [-a, b], [ a, b], [ a, -b]

import numpy as np import matplotlib.pyplot as plt a = 150 b = 75 theta1 = np.linspace(-np.arctan(b/a), np.arctan(b/a), 100) theta2 = np.linspace(np.arctan(b/a), np.pi - np.arctan(b/a), 100) theta3 = np.linspace(np.pi - np.arctan(b/a), np.pi + np.

a = 150; b = 75; theta1 = linspace(-atan(b/a), atan(b/a), 100); theta2 = linspace(atan(b/a), pi - atan(b/a), 100); theta3 = linspace(pi - atan(b/a), pi + atan(b/a), 100); theta4 = linspace(pi + atan(b/a), 2*pi - atan(b/a), 100); r1 = a ./ abs(co