def test_unaryFunctions(self): for func,inOutData in TEST_CASES_UNARY.items(): print('testing univar_polyops.%s with numpy: ' % func.__name__, end='') for output,input in inOutData: self.assertTrue(na_eq(na(output), func(na(input))), 'testing %s = %s(%s)' % (output,func.__name__,input)) print('.', end='') print()
def add_ids(json_list): ids = list( map( bson.Int64, list( na([get_max_id()] * len(json_list)) + na(list(range(1, len(json_list) + 1)))))) return list(map(add_id_, list(zip(ids, json_list))))
def getFitForN(N): a = [] for i in range(1, N + 1): row = [] for j in range(N - 1, -1, -1): row.append(pow(i, j)) a.append(row) a = na(a) b = na([sequence(i) for i in range(1, N + 1)]) coeffs = solve(a, b) fit = pv(coeffs, N + 1) return fit
def get_duplicates(update=False): dups, _ = get_duplicates_for_range(1900, 2017) duplicates = na(list(map(lambda x: x[0], dups))) if not update: return duplicates with open(duplicate_fname, 'wb') as f: np.save(f, duplicates) return duplicates
def save_processed_pmids(): existing = get_existing_pmids() downloaded = get_downloaded_pmids() result = existing.union(downloaded) arr_to_save = na(list(result)) with open(processed_fname, 'wb') as f: np.save(f, arr_to_save) return result
def get_data_from_dcm(self, idx): dcms = [] parent_path = self.ids[idx] for file in os.listdir(parent_path): if not file.endswith('dcm'): continue image, dcm_data = imread(opjoin(parent_path, file)) if not has_slice_location(dcm_data): continue dcms.append((image, dcm_data)) dcms.sort(key=lambda dcm: dcm[1].SliceLocation) nodules = parseXML(parent_path) id2roi = create_map_from_nodules(nodules) imgs = na([dcm[0] for dcm in dcms]) imgs = imgs[np.newaxis, :] bbox = resolve_bbox(na(dcms), id2roi) return imgs, bbox
def basiccube(): dir = na([[1,0,0]]) k = 15 maxvol = 0.02 g = dg3d.impedance(dir,k) gg = dg3d.planeWaves(dir,k)[0] s = dg3d.solver(dg3d.cubemesh(maxvol), 2, 12, k).solve(g) v = dg3d.Visualiser(s,gg)
def numpysum(): import time n = 1000 A = numpy.mat(numpy.reshape(range(n*n), (n,n)), dtype=numpy.float64) t = [] t.append(time.time()) B1 = A + A +A + A t.append(time.time()) A2 = [A,A,A,A] t.append(time.time()) B2 = numpy.sum(A2, axis=0) t.append(time.time()) l = len(A2) B3 = numpy.reshape(numpy.dot(numpy.ones(l), numpy.reshape(A2, (l, -1))), (n,n)) t.append(time.time()) op = lambda a,b:a+b B4 = reduce(op, A2) t.append(time.time()) print na(t[1:]) - na(t[:-1])
def save_downloaded_pmids(): downloaded_pmids = set(get_downloaded_pmids_for_range(1900, 2017)) arr_to_save = na(list(downloaded_pmids)) with open(downloaded_fname, 'wb') as f: np.save(f, arr_to_save) return downloaded_pmids
def getU(t): if t < 5: return u elif 20 < t < 22: return -u / 2 else: return 0 deltaT = 1 D = 100 M = 1e4 # 1 ton # system state F = na([[1, deltaT], [0, M / (M + deltaT * D)]]) # input matrix G = nvect(.5 * deltaT * deltaT, deltaT) # observation matrix H = na([[1, 0]]) # process noise covariance Q = np.eye(2) * .1 # measurement noise covar. R = na([100]) # true init state x_0 = nvect(0, 0) # assumed init state x_0_hat = nvect(50, 0) # assumed init state err covar matrix P = na([[1000, 0], [0, 1]])
from numpy.random import randn import numpy as np import matplotlib.pyplot as plt def nvect(*argv): return np.array([argv]).T cmap = plt.get_cmap("tab20c") """ process model """ u = 10. #m/s^2 deltaT = 1e-2 # system state F = na([[1, deltaT], [0, 1]]) # input matrix G = nvect(-.5 * deltaT * deltaT, -deltaT) # observation matrix H = na([[1, 0]]) """ uncertainty """ n = 2 # number of states q = 0.5 # std of process r = 2 # std of measurement # process noise covariance Q = q**2 * np.eye(n) # covariance of process #measurement noise covar. R = r**2 # covariance of measurement #Q=np.zeros(2) #R=na([4])
A2 = [A,A,A,A] t.append(time.time()) B2 = numpy.sum(A2, axis=0) t.append(time.time()) l = len(A2) B3 = numpy.reshape(numpy.dot(numpy.ones(l), numpy.reshape(A2, (l, -1))), (n,n)) t.append(time.time()) op = lambda a,b:a+b B4 = reduce(op, A2) t.append(time.time()) print na(t[1:]) - na(t[:-1]) if __name__ == '__main__': print "hello" dir = na([[1,0,0]]) k = 20 maxvol = 0.04 print k, maxvol g = dg3d.impedance(dir,k) gg = dg3d.planeWaves(dir,k)[0] s = dg3d.solver(dg3d.cubemesh(maxvol), 4, 10, k) s.solve(g) v = dg3d.Visualiser(s,gg) v.showuaveragereal() sys.exit(0)
- Code By Michael Sherif Naguib - license: MIT open source - Date: 12/20/18 - @University of Tulsa - Description: for storing constants of functions... ''' # ========================== Predefined Transformations ======================= # Predefined Transformations: [MATRIX as [[A,B],[C,D]], SHIFTS as [X,Y], ROTATION ,PROBABILITY] from numpy import array as na # NOT NP... NOTE: Numpy Array as na from math import * constants = {} #Barnsley Fern constants["bfern"] = [ [na([[0, 0], [0, 0.16]]), na([0, 0]), 0, 0.01], [na([[0.85, 0.04], [0 - 0.04, 0.85]]), na([0, 1.6]), 0, 0.85], [na([[0.2, 0 - 0.26], [0.23, 0.22]]), na([0, 1.6]), 0, 0.07], [na([[0 - 0.15, 0.28], [0.26, 0.24]]), na([0, 0.44]), 0, 0.07] ] #Rose(like) constants["rose"] = [ [na([[0, 0], [0, 0.16]]), na([0, 0]), 0, 0.01], [na([[0.85, 0.04], [0 - 0.04, 0.85]]), na([0, 1.6]), 45, 0.85], [na([[0.2, 0 - 0.26], [0.23, 0.22]]), na([0, 1.6]), 3, 0.10],