Python numpy.loadtxt() Examples
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Example #1
Source File: main.py From transferlearning with MIT License | 14 votes |
def classify_1nn(data_train, data_test): ''' Classification using 1NN Inputs: data_train, data_test: train and test csv file path Outputs: yprediction and accuracy ''' from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score from sklearn.preprocessing import StandardScaler data = {'src': np.loadtxt(data_train, delimiter=','), 'tar': np.loadtxt(data_test, delimiter=','), } Xs, Ys, Xt, Yt = data['src'][:, :-1], data['src'][:, - 1], data['tar'][:, :-1], data['tar'][:, -1] Xs = StandardScaler(with_mean=0, with_std=1).fit_transform(Xs) Xt = StandardScaler(with_mean=0, with_std=1).fit_transform(Xt) clf = KNeighborsClassifier(n_neighbors=1) clf.fit(Xs, Ys) ypred = clf.predict(Xt) acc = accuracy_score(y_true=Yt, y_pred=ypred) print('Acc: {:.4f}'.format(acc)) return ypred, acc
Example #2
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 7 votes |
def plot_evolution_results(hyp): # from utils.utils import *; plot_evolution_results(hyp) # Plot hyperparameter evolution results in evolve.txt x = np.loadtxt('evolve.txt', ndmin=2) f = fitness(x) weights = (f - f.min()) ** 2 # for weighted results fig = plt.figure(figsize=(12, 10)) matplotlib.rc('font', **{'size': 8}) for i, (k, v) in enumerate(hyp.items()): y = x[:, i + 5] # mu = (y * weights).sum() / weights.sum() # best weighted result mu = y[f.argmax()] # best single result plt.subplot(4, 5, i + 1) plt.plot(mu, f.max(), 'o', markersize=10) plt.plot(y, f, '.') plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters print('%15s: %.3g' % (k, mu)) fig.tight_layout() plt.savefig('evolve.png', dpi=200)
Example #3
Source File: m_phonons.py From pyscf with Apache License 2.0 | 6 votes |
def read_vibra_vectors(fname=None): """ Reads siesta.vectors file --- output of VIBRA utility """ from io import StringIO # StringIO behaves like a file object if fname is None: fname = 'siesta.vectors' with open(fname, 'r') as content_file: content = content_file.readlines() na = -1 na_prev = -1 for i,l in enumerate(content): if 'Eigenmode (real part)' in l: l1 = i if 'Eigenmode (imaginary part)' in l: na = i - l1 - 1 if na!=na_prev and na_prev>0: raise RuntimeError('na!=na_prev') na_prev = na ma2xyz = list() m2e = list() for i,l in enumerate(content): if 'Frequency' in l: m2e.append(float(l.split()[2])) if 'Eigenmode (real part)' in l: ma2xyz.append(np.loadtxt(StringIO(''.join(str(k) for k in content[i+1:i+1+na])))) return np.array(m2e),np.array(ma2xyz)
Example #4
Source File: test_0161_bse_h2b_spin1_uhf_cis.py From pyscf with Apache License 2.0 | 6 votes |
def test_161_bse_h2b_spin1_uhf_cis(self): """ This """ mol = gto.M(verbose=1,atom='B 0 0 0; H 0 0.489 1.074; H 0 0.489 -1.074',basis='cc-pvdz',spin=1) gto_mf = scf.UHF(mol) gto_mf.kernel() gto_td = tddft.TDHF(gto_mf) gto_td.nstates = 150 gto_td.kernel() omegas = np.arange(0.0, 2.0, 0.01) + 1j*0.03 p_ave = -polariz_freq_osc_strength(gto_td.e, gto_td.oscillator_strength(), omegas).imag data = np.array([omegas.real*HARTREE2EV, p_ave]) np.savetxt('test_0161_bse_h2b_spin1_uhf_cis_pyscf.txt', data.T, fmt=['%f','%f']) #data_ref = np.loadtxt('test_0159_bse_h2b_uhf_cis_pyscf.txt-ref').T #self.assertTrue(np.allclose(data_ref, data, atol=1e-6, rtol=1e-3)) nao_td = bse_iter(mf=gto_mf, gto=mol, verbosity=0, xc_code='CIS') polariz = -nao_td.comp_polariz_inter_ave(omegas).imag data = np.array([omegas.real*HARTREE2EV, polariz]) np.savetxt('test_0161_bse_h2b_spin1_uhf_cis_nao.txt', data.T, fmt=['%f','%f']) #data_ref = np.loadtxt('test_0161_bse_h2b_spin1_uhf_cis_nao.txt-ref').T #self.assertTrue(np.allclose(data_ref, data, atol=1e-6, rtol=1e-3))
Example #5
Source File: test_0091_tddft_x_zip_na20.py From pyscf with Apache License 2.0 | 6 votes |
def test_x_zip_feature_na20_chain(self): """ This a test for compression of the eigenvectos at higher energies """ dname = dirname(abspath(__file__)) siesd = dname+'/sodium_20' x = td_c(label='siesta', cd=siesd,x_zip=True, x_zip_emax=0.25,x_zip_eps=0.05,jcutoff=7,xc_code='RPA',nr=128, fermi_energy=-0.0913346431431985) eps = 0.005 ww = np.arange(0.0, 0.5, eps/2.0)+1j*eps data = np.array([ww.real*27.2114, -x.comp_polariz_inter_ave(ww).imag]) fname = 'na20_chain.tddft_iter_rpa.omega.inter.ave.x_zip.txt' np.savetxt(fname, data.T, fmt=['%f','%f']) #print(__file__, fname) data_ref = np.loadtxt(dname+'/'+fname+'-ref') #print(' x.rf0_ncalls ', x.rf0_ncalls) #print(' x.matvec_ncalls ', x.matvec_ncalls) self.assertTrue(np.allclose(data_ref,data.T, rtol=1.0e-1, atol=1e-06))
Example #6
Source File: test_0040_bse_rpa_nao.py From pyscf with Apache License 2.0 | 6 votes |
def test_0040_bse_rpa_nao(self): """ Compute polarization with RPA via 2-point non-local potentials (BSE solver) """ from timeit import default_timer as timer dname = os.path.dirname(os.path.abspath(__file__)) bse = bse_iter(label='water', cd=dname, xc_code='RPA', verbosity=0) omegas = np.linspace(0.0,2.0,150)+1j*0.01 #print(__name__, omegas.shape) pxx = np.zeros(len(omegas)) for iw,omega in enumerate(omegas): for ixyz in range(1): dip = bse.dip_ab[ixyz] vab = bse.apply_l(dip, omega) pxx[iw] = pxx[iw] - (vab.imag*dip.reshape(-1)).sum() data = np.array([omegas.real*27.2114, pxx]) np.savetxt('water.bse_iter_rpa.omega.inter.pxx.txt', data.T, fmt=['%f','%f']) data_ref = np.loadtxt(dname+'/water.bse_iter_rpa.omega.inter.pxx.txt-ref') #print(' bse.l0_ncalls ', bse.l0_ncalls) self.assertTrue(np.allclose(data_ref,data.T, rtol=1.0, atol=1e-05))
Example #7
Source File: generate_rotations.py From pointnet-registration-framework with MIT License | 6 votes |
def get_datasets(args): cinfo = None if args.categoryfile: #categories = numpy.loadtxt(args.categoryfile, dtype=str, delimiter="\n").tolist() categories = [line.rstrip('\n') for line in open(args.categoryfile)] categories.sort() c_to_idx = {categories[i]: i for i in range(len(categories))} cinfo = (categories, c_to_idx) if args.dataset_type == 'modelnet': transform = torchvision.transforms.Compose([\ ptlk.data.transforms.Mesh2Points(),\ ptlk.data.transforms.OnUnitCube(),\ ]) testdata = ptlk.data.datasets.ModelNet(args.dataset_path, train=0, transform=transform, classinfo=cinfo) return testdata
Example #8
Source File: test_0157_bse_h2o_rhf_cis.py From pyscf with Apache License 2.0 | 6 votes |
def test_157_bse_h2o_rhf_cis(self): """ This """ mol = gto.M(verbose=1,atom='O 0 0 0; H 0 0.489 1.074; H 0 0.489 -1.074',basis='cc-pvdz') gto_mf = scf.RHF(mol) gto_mf.kernel() gto_td = tddft.TDDFT(gto_mf) gto_td.nstates = 190 gto_td.kernel() omegas = np.arange(0.0, 2.0, 0.01) + 1j*0.03 p_ave = -polariz_freq_osc_strength(gto_td.e, gto_td.oscillator_strength(), omegas).imag data = np.array([omegas.real*HARTREE2EV, p_ave]) np.savetxt('test_0157_bse_h2o_rhf_cis_pyscf.txt', data.T, fmt=['%f','%f']) data_ref = np.loadtxt('test_0157_bse_h2o_rhf_cis_pyscf.txt-ref').T self.assertTrue(np.allclose(data_ref, data, atol=1e-6, rtol=1e-3)) nao_td = bse_iter(mf=gto_mf, gto=mol, verbosity=0, xc_code='CIS') polariz = -nao_td.comp_polariz_inter_ave(omegas).imag data = np.array([omegas.real*HARTREE2EV, polariz]) np.savetxt('test_0157_bse_h2o_rhf_cis_nao.txt', data.T, fmt=['%f','%f']) data_ref = np.loadtxt('test_0157_bse_h2o_rhf_cis_nao.txt-ref').T self.assertTrue(np.allclose(data_ref, data, atol=5e-5, rtol=5e-2), msg="{}".format(abs(data_ref-data).sum()/data.size ) )
Example #9
Source File: test_0035_bse_nonin_nao.py From pyscf with Apache License 2.0 | 6 votes |
def test_bse_iter_nonin(self): """ Compute polarization with LDA TDDFT """ from timeit import default_timer as timer dname = os.path.dirname(os.path.abspath(__file__)) bse = bse_iter(label='water', cd=dname, iter_broadening=1e-2, xc_code='RPA', verbosity=0) omegas = np.linspace(0.0,2.0,500)+1j*bse.eps pxx = np.zeros(len(omegas)) for iw,omega in enumerate(omegas): for ixyz in range(1): vab = bse.apply_l0(bse.dip_ab[ixyz], omega) pxx[iw] = pxx[iw] - (vab.imag*bse.dip_ab[ixyz].reshape(-1)).sum() data = np.array([omegas.real*27.2114, pxx]) np.savetxt('water.bse_iter_rpa.omega.nonin.pxx.txt', data.T, fmt=['%f','%f']) data_ref = np.loadtxt(dname+'/water.bse_iter_rpa.omega.nonin.pxx.txt-ref') #print(' bse.l0_ncalls ', bse.l0_ncalls) self.assertTrue(np.allclose(data_ref,data.T, rtol=1.0, atol=1e-05))
Example #10
Source File: DataManager.py From Caffe-Python-Data-Layer with BSD 2-Clause "Simplified" License | 6 votes |
def load_all(self): """The function to load all data and labels Give: data: the list of raw data, needs to be decompressed (e.g., raw JPEG string) labels: numpy array, with each element is a string """ start = time.time() print("Start Loading Data from BCF {}".format( 'MEMORY' if self._bcf_mode == 'MEM' else 'FILE')) self._labels = np.loadtxt(self._label_fn).astype(str) if self._bcf.size() != self._labels.shape[0]: raise Exception("Number of samples in data" "and labels are not equal") else: for idx in range(self._bcf.size()): datum_str = self._bcf.get(idx) self._data.append(datum_str) end = time.time() print("Loading {} samples Done: Time cost {} seconds".format( len(self._data), end - start)) return self._data, self._labels
Example #11
Source File: test_performance_indicator.py From pymoo with Apache License 2.0 | 6 votes |
def test_values_of_indicators(self): l = [ (GD, "gd"), (IGD, "igd") ] folder = os.path.join(get_pymoo(), "tests", "performance_indicator") pf = np.loadtxt(os.path.join(folder, "performance_indicators.pf")) for indicator, ext in l: for i in range(1, 5): F = np.loadtxt(os.path.join(folder, "performance_indicators_%s.f" % i)) val = indicator(pf).calc(F) correct = np.loadtxt(os.path.join(folder, "performance_indicators_%s.%s" % (i, ext))) self.assertTrue(correct == val)
Example #12
Source File: test_0152_bse_h2b_uks_pz.py From pyscf with Apache License 2.0 | 6 votes |
def test_152_bse_h2b_uks_pz(self): """ This """ mol = gto.M(verbose=1,atom='B 0 0 0; H 0 0.489 1.074; H 0 0.489 -1.074',basis='cc-pvdz',spin=3) gto_mf = scf.UKS(mol) gto_mf.kernel() gto_td = tddft.TDDFT(gto_mf) gto_td.nstates = 190 gto_td.kernel() omegas = np.arange(0.0, 2.0, 0.01) + 1j*0.03 p_ave = -polariz_freq_osc_strength(gto_td.e, gto_td.oscillator_strength(), omegas).imag data = np.array([omegas.real*HARTREE2EV, p_ave]) np.savetxt('test_0152_bse_h2b_uks_pz_pyscf.txt', data.T, fmt=['%f','%f']) data_ref = np.loadtxt('test_0152_bse_h2b_uks_pz_pyscf.txt-ref').T self.assertTrue(np.allclose(data_ref, data, atol=1e-6, rtol=1e-3)) nao_td = bse_iter(mf=gto_mf, gto=mol, verbosity=0) polariz = -nao_td.comp_polariz_inter_ave(omegas).imag data = np.array([omegas.real*HARTREE2EV, polariz]) np.savetxt('test_0152_bse_h2b_uks_pz_nao.txt', data.T, fmt=['%f','%f']) data_ref = np.loadtxt('test_0152_bse_h2b_uks_pz_nao.txt-ref').T self.assertTrue(np.allclose(data_ref, data, atol=1e-6, rtol=1e-3))
Example #13
Source File: datasets.py From discomll with Apache License 2.0 | 6 votes |
def breastcancer_cont(replication=2): f = open(path + "breast_cancer_wisconsin_cont.txt", "r") data = np.loadtxt(f, delimiter=",", dtype=np.string0) x_train = np.array(data[:, range(0, 9)]) y_train = np.array(data[:, 9]) for j in range(replication - 1): x_train = np.vstack([x_train, data[:, range(0, 9)]]) y_train = np.hstack([y_train, data[:, 9]]) x_train = np.array(x_train, dtype=np.float) f = open(path + "breast_cancer_wisconsin_cont_test.txt") data = np.loadtxt(f, delimiter=",", dtype=np.string0) x_test = np.array(data[:, range(0, 9)]) y_test = np.array(data[:, 9]) for j in range(replication - 1): x_test = np.vstack([x_test, data[:, range(0, 9)]]) y_test = np.hstack([y_test, data[:, 9]]) x_test = np.array(x_test, dtype=np.float) return x_train, y_train, x_test, y_test
Example #14
Source File: test_0162_bse_h2o_spin2_uhf_cis.py From pyscf with Apache License 2.0 | 6 votes |
def test_0162_bse_h2o_spin2_uhf_cis(self): """ This """ mol = gto.M(verbose=1,atom='O 0 0 0; H 0 0.489 1.074; H 0 0.489 -1.074',basis='cc-pvdz',spin=2) gto_mf = scf.UHF(mol) gto_mf.kernel() gto_td = tddft.TDDFT(gto_mf) gto_td.nstates = 190 gto_td.kernel() omegas = np.arange(0.0, 2.0, 0.01) + 1j*0.03 p_ave = -polariz_freq_osc_strength(gto_td.e, gto_td.oscillator_strength(), omegas).imag data = np.array([omegas.real*HARTREE2EV, p_ave]) np.savetxt('test_0162_bse_h2o_spin2_uhf_cis_pyscf.txt', data.T, fmt=['%f','%f']) #data_ref = np.loadtxt('test_0162_bse_h2o_spin2_uhf_cis_pyscf.txt-ref').T #self.assertTrue(np.allclose(data_ref, data, atol=1e-6, rtol=1e-3)) nao_td = bse_iter(mf=gto_mf, gto=mol, verbosity=0, xc_code='CIS') polariz = -nao_td.comp_polariz_inter_ave(omegas).imag data = np.array([omegas.real*HARTREE2EV, polariz]) np.savetxt('test_0162_bse_h2o_spin2_uhf_cis_nao.txt', data.T, fmt=['%f','%f']) #data_ref = np.loadtxt('test_0162_bse_h2o_spin2_uhf_cis_nao.txt-ref').T #self.assertTrue(np.allclose(data_ref, data, atol=1e-6, rtol=1e-3), \ # msg="{}".format(abs(data_ref-data).sum()/data.size))
Example #15
Source File: datasets.py From discomll with Apache License 2.0 | 6 votes |
def iris(replication=2): f = open(path + "iris.txt") data = np.loadtxt(f, delimiter=",", dtype=np.string0) x_train = np.array(data[:, range(0, 4)], dtype=np.float) y_train = data[:, 4] for j in range(replication - 1): x_train = np.vstack([x_train, data[:, range(0, 4)]]) y_train = np.hstack([y_train, data[:, 4]]) x_train = np.array(x_train, dtype=np.float) f = open(path + "iris_test.txt") data = np.loadtxt(f, delimiter=",", dtype=np.string0) x_test = np.array(data[:, range(0, 4)], dtype=np.float) y_test = data[:, 4] for j in range(replication - 1): x_test = np.vstack([x_test, data[:, range(0, 4)]]) y_test = np.hstack([y_test, data[:, 4]]) x_test = np.array(x_test, dtype=np.float) return x_train, y_train, x_test, y_test
Example #16
Source File: datasets.py From discomll with Apache License 2.0 | 6 votes |
def regression_data(): f = open(path + "regression_data1.txt") data = np.loadtxt(f, delimiter=",") x1 = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1) y1 = data[:, 1] f = open(path + "regression_data2.txt") data = np.loadtxt(f, delimiter=",") x2 = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1) y2 = data[:, 1] x1 = np.vstack((x1, x2)) y1 = np.hstack((y1, y2)) f = open(path + "regression_data_test1.txt") data = np.loadtxt(f, delimiter=",") x1_test = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1) y1_test = data[:, 1] f = open(path + "regression_data_test2.txt") data = np.loadtxt(f, delimiter=",") x2_test = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1) y2_test = data[:, 1] x1_test = np.vstack((x1_test, x2_test)) y1_test = np.hstack((y1_test, y2_test)) return x1, y1, x1_test, y1_test
Example #17
Source File: script_preprocess_annoations_S3DIS.py From DOTA_models with Apache License 2.0 | 6 votes |
def collect_room(building_name, room_name): room_dir = os.path.join(DATA_DIR, 'Stanford3dDataset_v1.2', building_name, room_name, 'Annotations') files = glob.glob1(room_dir, '*.txt') files = sorted(files, key=lambda s: s.lower()) vertexs = []; colors = []; for f in files: file_name = os.path.join(room_dir, f) logging.info(' %s', file_name) a = np.loadtxt(file_name) vertex = a[:,:3]*1. color = a[:,3:]*1 color = color.astype(np.uint8) vertexs.append(vertex) colors.append(color) files = [f.split('.')[0] for f in files] out = {'vertexs': vertexs, 'colors': colors, 'names': files} return out
Example #18
Source File: tddft_iter.py From pyscf with Apache License 2.0 | 6 votes |
def load_kernel_method(self, kernel_fname, kernel_format="npy", kernel_path_hdf5=None, **kw): """ Loads from file and initializes .kernel field... Useful? Rewrite?""" if kernel_format == "npy": self.kernel = self.dtype(np.load(kernel_fname)) elif kernel_format == "txt": self.kernel = np.loadtxt(kernel_fname, dtype=self.dtype) elif kernel_format == "hdf5": import h5py if kernel_path_hdf5 is None: raise ValueError("kernel_path_hdf5 not set while trying to read kernel from hdf5 file.") self.kernel = h5py.File(kernel_fname, "r")[kernel_path_hdf5].value else: raise ValueError("Wrong format for loading kernel, must be: npy, txt or hdf5, got " + kernel_format) if len(self.kernel.shape) > 1: raise ValueError("The kernel must be saved in packed format in order to be loaded!") assert self.nprod*(self.nprod+1)//2 == self.kernel.size, "wrong size for loaded kernel: %r %r "%(self.nprod*(self.nprod+1)//2, self.kernel.size) self.kernel_dim = self.nprod
Example #19
Source File: Stark.py From EXOSIMS with BSD 3-Clause "New" or "Revised" License | 6 votes |
def calcfbetaInput(self): # table 17 in Leinert et al. (1998) # Zodiacal Light brightness function of solar LON (rows) and LAT (columns) # values given in W m−2 sr−1 μm−1 for a wavelength of 500 nm path = os.path.split(inspect.getfile(self.__class__))[0] Izod = np.loadtxt(os.path.join(path, 'Leinert98_table17.txt'))*1e-8 # W/m2/sr/um # create data point coordinates lon_pts = np.array([0., 5, 10, 15, 20, 25, 30, 35, 40, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180]) # deg lat_pts = np.array([0., 5, 10, 15, 20, 25, 30, 45, 60, 75, 90]) # deg y_pts, x_pts = np.meshgrid(lat_pts, lon_pts) points = np.array(list(zip(np.concatenate(x_pts), np.concatenate(y_pts)))) # create data values, normalized by (90,0) value z = Izod/Izod[12,0] values = z.reshape(z.size) return points, values
Example #20
Source File: m_dos_pdos_eigenvalues.py From pyscf with Apache License 2.0 | 6 votes |
def dosplot (filename = None, data = None, fermi = None): if (filename is not None): data = np.loadtxt(filename) elif (data is not None): data = data import matplotlib.pyplot as plt from matplotlib import rc plt.rc('text', usetex=True) plt.rc('font', family='serif') plt.plot(data.T[0], data.T[1], label='MF Spin-UP', linestyle=':',color='r') plt.fill_between(data.T[0], 0, data.T[1], facecolor='r',alpha=0.1, interpolate=True) plt.plot(data.T[0], data.T[2], label='QP Spin-UP',color='r') plt.fill_between(data.T[0], 0, data.T[2], facecolor='r',alpha=0.5, interpolate=True) plt.plot(data.T[0],-data.T[3], label='MF Spin-DN', linestyle=':',color='b') plt.fill_between(data.T[0], 0, -data.T[3], facecolor='b',alpha=0.1, interpolate=True) plt.plot(data.T[0],-data.T[4], label='QP Spin-DN',color='b') plt.fill_between(data.T[0], 0, -data.T[4], facecolor='b',alpha=0.5, interpolate=True) if (fermi!=None): plt.axvline(x=fermi ,color='k', linestyle='--') #label='Fermi Energy' plt.axhline(y=0,color='k') plt.title('Total DOS', fontsize=20) plt.xlabel('Energy (eV)', fontsize=15) plt.ylabel('Density of States (electron/eV)', fontsize=15) plt.legend() plt.savefig("dos_eigen.svg", dpi=900) plt.show()
Example #21
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 6 votes |
def print_mutation(hyp, results, bucket=''): # Print mutation results to evolve.txt (for use with train.py --evolve) a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values c = '%10.3g' * len(results) % results # results (P, R, mAP, F1, test_loss) print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) if bucket: os.system('gsutil cp gs://%s/evolve.txt .' % bucket) # download evolve.txt with open('evolve.txt', 'a') as f: # append result f.write(c + b + '\n') x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows np.savetxt('evolve.txt', x[np.argsort(-fitness(x))], '%10.3g') # save sort by fitness if bucket: os.system('gsutil cp evolve.txt gs://%s' % bucket) # upload evolve.txt
Example #22
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 6 votes |
def plot_results(start=0, stop=0): # from utils.utils import *; plot_results() # Plot training results files 'results*.txt' fig, ax = plt.subplots(2, 5, figsize=(14, 7)) ax = ax.ravel() s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall', 'val GIoU', 'val Objectness', 'val Classification', 'mAP', 'F1'] for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T n = results.shape[1] # number of rows x = range(start, min(stop, n) if stop else n) for i in range(10): y = results[i, x] if i in [0, 1, 2, 5, 6, 7]: y[y == 0] = np.nan # dont show zero loss values ax[i].plot(x, y, marker='.', label=f.replace('.txt', '')) ax[i].set_title(s[i]) if i in [5, 6, 7]: # share train and val loss y axes ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) fig.tight_layout() ax[1].legend() fig.savefig('results.png', dpi=200)
Example #23
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 6 votes |
def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_results_overlay() # Plot training results files 'results*.txt', overlaying train and val losses s = ['train', 'train', 'train', 'Precision', 'mAP', 'val', 'val', 'val', 'Recall', 'F1'] # legends t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T n = results.shape[1] # number of rows x = range(start, min(stop, n) if stop else n) fig, ax = plt.subplots(1, 5, figsize=(14, 3.5)) ax = ax.ravel() for i in range(5): for j in [i, i + 5]: y = results[j, x] if i in [0, 1, 2]: y[y == 0] = np.nan # dont show zero loss values ax[i].plot(x, y, marker='.', label=s[j]) ax[i].set_title(t[i]) ax[i].legend() ax[i].set_ylabel(f) if i == 0 else None # add filename fig.tight_layout() fig.savefig(f.replace('.txt', '.png'), dpi=200)
Example #24
Source File: data_io.py From Kaggler with MIT License | 6 votes |
def load_csv(path): """Load data from a CSV file. Args: path (str): A path to the CSV format file containing data. dense (boolean): An optional variable indicating if the return matrix should be dense. By default, it is false. Returns: Data matrix X and target vector y """ with open(path) as f: line = f.readline().strip() X = np.loadtxt(path, delimiter=',', skiprows=0 if is_number(line.split(',')[0]) else 1) y = np.array(X[:, 0]).flatten() X = X[:, 1:] return X, y
Example #25
Source File: test_topfarm.py From TOPFARM with GNU Affero General Public License v3.0 | 6 votes |
def test_2x3(self): # Loading the water depth map dat = loadtxt('data/WaterDepth1.dat') X, Y = meshgrid(linspace(0., 1000., 50), linspace(0., 1000., 50)) depth = array(zip(X.flatten(), Y.flatten(), dat.flatten())) borders = array([[200, 200], [150, 500], [200, 800], [600, 900], [700, 700], [900, 500], [800, 200], [500, 100], [200, 200]]) baseline = array([[587.5, 223.07692308], [525., 346.15384615], [837.5, 530.76923077], [525., 530.76923077], [525., 838.46153846], [837.5, 469.23076923]]) wt_desc = WTDescFromWTG('data/V80-2MW-offshore.wtg').wt_desc wt_layout = GenericWindFarmTurbineLayout([WTPC(wt_desc=wt_desc, position=pos) for pos in baseline]) t = Topfarm( baseline_layout = wt_layout, borders = borders, depth_map = depth, dist_WT_D = 5.0, distribution='spiral', wind_speeds=[4., 8., 20.], wind_directions=linspace(0., 360., 36)[:-1] ) t.run() self.fail('make save function') t.save()
Example #26
Source File: datasets.py From discomll with Apache License 2.0 | 6 votes |
def breastcancer_disc(replication=2): f = open(path + "breast_cancer_wisconsin_disc.txt") data = np.loadtxt(f, delimiter=",") x_train = data[:, range(1, 10)] y_train = data[:, 10] for j in range(replication - 1): x_train = np.vstack([x_train, data[:, range(1, 10)]]) y_train = np.hstack([y_train, data[:, 10]]) f = open(path + "breast_cancer_wisconsin_disc_test.txt") data = np.loadtxt(f, delimiter=",") x_test = data[:, range(1, 10)] y_test = data[:, 10] for j in range(replication - 1): x_test = np.vstack([x_test, data[:, range(1, 10)]]) y_test = np.hstack([y_test, data[:, 10]]) return x_train, y_train, x_test, y_test
Example #27
Source File: test_problems_dascmop.py From pymoo with Apache License 2.0 | 5 votes |
def load(name, diff): path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "resources", "DASCMOP", str(diff)) X = np.loadtxt(os.path.join(path, "%s.x" % name)) F = np.loadtxt(os.path.join(path, "%s.f" % name)) CV = np.loadtxt(os.path.join(path, "%s.cv" % name))[:, None] return X, F, -CV
Example #28
Source File: test_ctaea.py From pymoo with Apache License 2.0 | 5 votes |
def setUpClass(cls): cls.ref_dirs = np.loadtxt(path_to_test_resources('ctaea', 'weights.txt')) cls.evaluator = Evaluator()
Example #29
Source File: test_decomposition.py From pymoo with Apache License 2.0 | 5 votes |
def test_perp_dist(self): np.random.seed(1) F = np.random.random((100, 3)) weights = np.random.random((10, 3)) D = PerpendicularDistance(_type="python").do(F, weights, _type="many_to_many") np.testing.assert_allclose(D, np.loadtxt(path_to_test_resources("perp_dist"))) D = PerpendicularDistance(_type="cython").do(F, weights, _type="many_to_many") np.testing.assert_allclose(D, np.loadtxt(path_to_test_resources("perp_dist")))
Example #30
Source File: m_dos_pdos_eigenvalues.py From pyscf with Apache License 2.0 | 5 votes |
def pdosplot (filename = None, data = None, size = None, fermi = None): if (filename is not None): data = np.loadtxt(filename).T elif (data is not None): data = data if (size is None): print('Please give number of resolved angular momentum!') if (fermi is None): print ('Please give fermi energy') import matplotlib.pyplot as plt from matplotlib import rc plt.rc('text', usetex=True) plt.rc('font', family='serif') orb_name = ['$s$','$p$','$d$','$f$','$g$','$h$','$i$','$k$'] orb_colo = ['r','g','b','y','k','m','c','w'] for i, (n,c) in enumerate(zip(orb_name[0:size],orb_colo[0:size])): #GW_spin_UP plt.plot(data[0], data[i+1], label='QP- '+n,color=c) plt.fill_between(data[0], 0, data[i+1], facecolor=c, alpha=0.5, interpolate=True) #MF_spin_UP plt.plot(data[0], data[i+size+1], label='MF- '+n, linestyle=':',color=c) plt.fill_between(data[0], 0, data[i+size+1], facecolor=c, alpha=0.1, interpolate=True) #GW_spin_DN plt.plot(data[0], -data[i+2*size+1], label='_nolegend_',color=c) plt.fill_between(data[0], 0, -data[i+2*size+1], facecolor=c, alpha=0.5, interpolate=True) #MF_spin_DN plt.plot(data[0], -data[i+3*size+1], label='_nolegend_', linestyle=':',color=c) plt.fill_between(data[0], 0, -data[i+3*size+1], facecolor=c, alpha=0.1, interpolate=True) plt.axvline(x=fermi, color='k', linestyle='--') #label='Fermi Energy' plt.axhline(y=0,color='k') plt.title('PDOS', fontsize=20) plt.xlabel('Energy (eV)', fontsize=15) plt.ylabel('Projected Density of States (electron/eV)', fontsize=15) plt.legend() plt.savefig("pdos.svg", dpi=900) plt.show()