From scipy-svn at scipy.org Thu Aug 3 10:39:04 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 3 Aug 2006 09:39:04 -0500 (CDT) Subject: [Scipy-svn] r2143 - in trunk/Lib/sandbox/svm: . tests Message-ID: <20060803143904.5BB1039C045@new.scipy.org> Author: fullung Date: 2006-08-03 09:38:13 -0500 (Thu, 03 Aug 2006) New Revision: 2143 Modified: trunk/Lib/sandbox/svm/classification.py trunk/Lib/sandbox/svm/oneclass.py trunk/Lib/sandbox/svm/predict.py trunk/Lib/sandbox/svm/tests/test_classification.py trunk/Lib/sandbox/svm/tests/test_oneclass.py trunk/Lib/sandbox/svm/tests/test_regression.py Log: Model compaction. Modified: trunk/Lib/sandbox/svm/classification.py =================================================================== --- trunk/Lib/sandbox/svm/classification.py 2006-07-28 00:10:27 UTC (rev 2142) +++ trunk/Lib/sandbox/svm/classification.py 2006-08-03 14:38:13 UTC (rev 2143) @@ -42,7 +42,7 @@ For training data with nr_class classes, this function returns nr_class*(nr_class-1)/2 decision values in a dictionary for each item in the test dataset. The keys of the dictionary are - 2-tuples, one for each combination of two class labels. + 2-tuples, one for each permutation of two class labels. """ n = self.nr_class * (self.nr_class - 1) / 2 def p(v): Modified: trunk/Lib/sandbox/svm/oneclass.py =================================================================== --- trunk/Lib/sandbox/svm/oneclass.py 2006-07-28 00:10:27 UTC (rev 2142) +++ trunk/Lib/sandbox/svm/oneclass.py 2006-08-03 14:38:13 UTC (rev 2143) @@ -33,7 +33,7 @@ distribution, while a non-positive value indicates that is is not. """ - return [self.predictor.predict_values(x, 1)[0] for x in dataset] + return [self.predictor.predict_values(x, 1) for x in dataset] def compact(self): self.predictor.compact() Modified: trunk/Lib/sandbox/svm/predict.py =================================================================== --- trunk/Lib/sandbox/svm/predict.py 2006-07-28 00:10:27 UTC (rev 2142) +++ trunk/Lib/sandbox/svm/predict.py 2006-08-03 14:38:13 UTC (rev 2143) @@ -1,4 +1,5 @@ -from ctypes import POINTER, c_double, addressof +from ctypes import POINTER, c_double, addressof, byref +from itertools import izip import numpy as N from dataset import svm_node_dot @@ -44,7 +45,10 @@ v = N.empty((n,), dtype=N.float64) vptr = v.ctypes.data_as(POINTER(c_double)) libsvm.svm_predict_values(self.model, xptr, vptr) - return v + if n == 1: + return v[0] + else: + return v def predict_probability(self, x, n): if not self.model.contents.param.probability: @@ -88,6 +92,7 @@ ids = [int(modelc.SV[i][0].value) for i in range(modelc.l)] support_vectors = [dataset[id] for id in ids] self.support_vectors = support_vectors + self.is_compact = False libsvm.svm_destroy_model(model) def predict(self, x): @@ -107,7 +112,7 @@ else: return self.predict_values(x, 1) - def predict_values(self, x, n): + def _predict_values_sparse(self, x, n): if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: kvalue = N.empty((len(self.support_vectors),)) for i, sv in enumerate(self.support_vectors): @@ -121,12 +126,12 @@ ci, cj = self.nSV[i], self.nSV[j] coef1 = self.sv_coef[j - 1] coef2 = self.sv_coef[i] - sum = -self.rho[p] + sum = 0. for k in range(ci): sum += coef1[si + k] * kvalue[si + k] for k in range(cj): sum += coef2[sj + k] * kvalue[sj + k] - dec_values[p] = sum + dec_values[p] = sum - self.rho[p] p += 1 return dec_values else: @@ -135,8 +140,52 @@ z += sv_coef * self.kernel(x, sv, svm_node_dot) return z + def _predict_values_compact(self, x, n): + if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: + for i, sv in enumerate(self.support_vectors): + kvalue = N.empty((len(self.support_vectors),)) + kvalue[i] = self.kernel(x, sv, svm_node_dot) + return kvalue - self.rho + else: + sv = self.support_vectors[0] + return self.kernel(x, sv, svm_node_dot) - self.rho + + def predict_values(self, x, n): + if self.is_compact: + return self._predict_values_compact(x, n) + else: + return self._predict_values_sparse(x, n) + def predict_probability(self, x, n): raise NotImplementedError + def _compact_svs(self, svs, coefs): + maxlen = 0 + for sv in svs: + maxlen = N.maximum(maxlen, sv['index'].max()) + csv = N.zeros((maxlen + 1,), libsvm.svm_node_dtype) + csv['index'][:-1] = N.arange(1, maxlen + 1) + csv['index'][-1] = -1 + for coef, sv in izip(coefs, svs): + idx = sv['index'][:-1] - 1 + csv['value'][idx] += coef*sv['value'][:-1] + return csv + def compact(self): - raise NotImplementedError + if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: + compact_support_vectors = [] + for i in range(self.nr_class): + for j in range(i + 1, self.nr_class): + si, sj = self.start[i], self.start[j] + ci, cj = self.nSV[i], self.nSV[j] + svi = self.support_vectors[si:si + ci] + svj = self.support_vectors[sj:sj + cj] + coef1 = self.sv_coef[j - 1][si:si + ci] + coef2 = self.sv_coef[i][sj:sj + cj] + csv = self._compact_svs(svi + svj, coef1 + coef2) + compact_support_vectors.append(csv) + self.support_vectors = compact_support_vectors + else: + csv = self._compact_svs(self.support_vectors, self.sv_coef) + self.support_vectors = [csv] + self.is_compact = True Modified: trunk/Lib/sandbox/svm/tests/test_classification.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_classification.py 2006-07-28 00:10:27 UTC (rev 2142) +++ trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-03 14:38:13 UTC (rev 2143) @@ -139,7 +139,7 @@ refx = N.vstack([x1, x2]) trndata = LibSvmClassificationDataSet(zip(reflabels, refx)) testdata = LibSvmTestDataSet(refx) - return trndata, trndata1, trndata2, testdata + return trndata, testdata, trndata1, trndata2 def _make_kernels(self): def kernelf(x, y, dot): @@ -158,7 +158,7 @@ return kernels def check_all(self): - trndata, trndata1, trndata2, testdata = self._make_datasets() + trndata, testdata, trndata1, trndata2 = self._make_datasets() kernels = self._make_kernels() weights = [(0, 2.0), (1, 5.0), (2, 3.0)] for kernel in kernels: @@ -226,5 +226,20 @@ p = results.predict(testdata) assert_array_equal(p, refp) + def check_compact(self): + traindata, testdata = self._make_basic_datasets() + kernel = LinearKernel() + cost = 10.0 + weights = [(1, 10.0)] + model = LibSvmCClassificationModel(kernel, cost, weights) + results = model.fit(traindata, LibSvmPythonPredictor) + refvs = results.predict_values(testdata) + results.compact() + vs = results.predict_values(testdata) + print vs + for refv, v in zip(refvs, vs): + for key, value in refv.iteritems(): + self.assertEqual(value, v[key]) + if __name__ == '__main__': NumpyTest().run() Modified: trunk/Lib/sandbox/svm/tests/test_oneclass.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_oneclass.py 2006-07-28 00:10:27 UTC (rev 2142) +++ trunk/Lib/sandbox/svm/tests/test_oneclass.py 2006-08-03 14:38:13 UTC (rev 2143) @@ -5,6 +5,7 @@ from svm.dataset import LibSvmOneClassDataSet, LibSvmTestDataSet from svm.kernel import * from svm.oneclass import * +from svm.predict import * restore_path() class test_oneclass(NumpyTestCase): @@ -24,9 +25,8 @@ return traindata, testdata def check_train(self): - ModelType = LibSvmOneClassModel traindata, testdata = self._make_basic_datasets() - model = ModelType(LinearKernel()) + model = LibSvmOneClassModel(LinearKernel()) results = model.fit(traindata) p = results.predict(testdata) assert_array_equal(p, [False, False, False, True]) @@ -56,5 +56,14 @@ for p, v in zip(pred, values): self.assertEqual(v > 0, p) + def check_compact(self): + traindata, testdata = self._make_basic_datasets() + model = LibSvmOneClassModel(LinearKernel()) + results = model.fit(traindata, LibSvmPythonPredictor) + refv = results.predict_values(testdata) + results.compact() + v = results.predict_values(testdata) + assert_array_equal(refv, v) + if __name__ == '__main__': NumpyTest().run() Modified: trunk/Lib/sandbox/svm/tests/test_regression.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_regression.py 2006-07-28 00:10:27 UTC (rev 2142) +++ trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-03 14:38:13 UTC (rev 2143) @@ -163,5 +163,15 @@ p = results.predict(testdata) assert_array_almost_equal(refp, p) + def check_compact(self): + traindata, testdata = self._make_basic_datasets() + kernel = LinearKernel() + model = LibSvmEpsilonRegressionModel(LinearKernel()) + results = model.fit(traindata, LibSvmPythonPredictor) + refp = results.predict(testdata) + results.compact() + p = results.predict(testdata) + assert_array_equal(refp, p) + if __name__ == '__main__': NumpyTest().run() From scipy-svn at scipy.org Thu Aug 3 11:12:32 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 3 Aug 2006 10:12:32 -0500 (CDT) Subject: [Scipy-svn] r2144 - in trunk/Lib/sandbox/svm: . tests Message-ID: <20060803151232.D3A0239C093@new.scipy.org> Author: fullung Date: 2006-08-03 10:12:13 -0500 (Thu, 03 Aug 2006) New Revision: 2144 Modified: trunk/Lib/sandbox/svm/dataset.py trunk/Lib/sandbox/svm/tests/test_classification.py trunk/Lib/sandbox/svm/tests/test_dataset.py trunk/Lib/sandbox/svm/tests/test_regression.py Log: Move zipping inside the datasets. Modified: trunk/Lib/sandbox/svm/dataset.py =================================================================== --- trunk/Lib/sandbox/svm/dataset.py 2006-08-03 14:38:13 UTC (rev 2143) +++ trunk/Lib/sandbox/svm/dataset.py 2006-08-03 15:12:13 UTC (rev 2144) @@ -139,22 +139,20 @@ param.kernel_type = libsvm.PRECOMPUTED class LibSvmRegressionDataSet(LibSvmDataSet): - def __init__(self, origdata): - data = map(lambda x: (x[0], convert_to_svm_node(x[1])), origdata) + def __init__(self, y, x): + origdata = zip(y, x) + data = [(x[0], convert_to_svm_node(x[1])) for x in origdata] LibSvmDataSet.__init__(self, data) class LibSvmClassificationDataSet(LibSvmDataSet): - def __init__(self, origdata): - labels = N.array(map(lambda x: x[0], origdata), dtype=N.intc) - labels.sort() - self.labels = labels - - data = map(lambda x: (x[0],convert_to_svm_node(x[1])), origdata) + def __init__(self, labels, x): + origdata = zip(labels, x) + data = [(x[0], convert_to_svm_node(x[1])) for x in origdata] LibSvmDataSet.__init__(self, data) class LibSvmOneClassDataSet(LibSvmDataSet): - def __init__(self, origdata): - data = map(lambda x: tuple([0,convert_to_svm_node(x)]), origdata) + def __init__(self, x): + data = [(0, convert_to_svm_node(y)) for y in x] LibSvmDataSet.__init__(self, data) class LibSvmTestDataSet: Modified: trunk/Lib/sandbox/svm/tests/test_classification.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-03 14:38:13 UTC (rev 2143) +++ trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-03 15:12:13 UTC (rev 2144) @@ -32,7 +32,7 @@ N.array([0, 1]), N.array([1, 0]), N.array([1, 1])] - traindata = LibSvmClassificationDataSet(zip(labels, x)) + traindata = LibSvmClassificationDataSet(labels, x) testdata = LibSvmTestDataSet(x) return traindata, testdata @@ -100,7 +100,7 @@ def check_cross_validate(self): labels = ([-1] * 50) + ([1] * 50) x = N.random.randn(len(labels), 10) - traindata = LibSvmClassificationDataSet(zip(labels, x)) + traindata = LibSvmClassificationDataSet(labels, x) kernel = LinearKernel() model = LibSvmCClassificationModel(kernel) nr_fold = 10 @@ -133,11 +133,11 @@ x1 = N.random.randn(len(labels1), 10) labels2 = N.random.random_integers(0, 2, 10) x2 = N.random.randn(len(labels2), x1.shape[1]) - trndata1 = LibSvmClassificationDataSet(zip(labels1, x1)) - trndata2 = LibSvmClassificationDataSet(zip(labels2, x2)) + trndata1 = LibSvmClassificationDataSet(labels1, x1) + trndata2 = LibSvmClassificationDataSet(labels2, x2) reflabels = N.concatenate([labels1, labels2]) refx = N.vstack([x1, x2]) - trndata = LibSvmClassificationDataSet(zip(reflabels, refx)) + trndata = LibSvmClassificationDataSet(reflabels, refx) testdata = LibSvmTestDataSet(refx) return trndata, testdata, trndata1, trndata2 Modified: trunk/Lib/sandbox/svm/tests/test_dataset.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_dataset.py 2006-08-03 14:38:13 UTC (rev 2143) +++ trunk/Lib/sandbox/svm/tests/test_dataset.py 2006-08-03 15:12:13 UTC (rev 2144) @@ -93,7 +93,7 @@ ] y = N.random.randn(10) x = N.random.randn(len(y), 10) - origdata = LibSvmRegressionDataSet(zip(y, x)) + origdata = LibSvmRegressionDataSet(y, x) for kernel in kernels: # calculate expected Gram matrix @@ -112,12 +112,12 @@ y1 = N.random.randn(10) x1 = N.random.randn(len(y1), 10) - origdata = LibSvmRegressionDataSet(zip(y1, x1)) + origdata = LibSvmRegressionDataSet(y1, x1) pcdata = origdata.precompute(kernel) y2 = N.random.randn(5) x2 = N.random.randn(len(y2), x1.shape[1]) - moredata = LibSvmRegressionDataSet(zip(y2, x2)) + moredata = LibSvmRegressionDataSet(y2, x2) morepcdata = pcdata.combine(moredata) expt_grammat = N.empty((len(y1) + len(y2),)*2) Modified: trunk/Lib/sandbox/svm/tests/test_regression.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-03 14:38:13 UTC (rev 2143) +++ trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-03 15:12:13 UTC (rev 2144) @@ -32,7 +32,7 @@ N.array([0, 1]), N.array([1, 0]), N.array([1, 1])] - traindata = LibSvmRegressionDataSet(zip(y, x)) + traindata = LibSvmRegressionDataSet(y, x) testdata = LibSvmTestDataSet(x) model = ModelType(LinearKernel(), probability=True) results = model.fit(traindata) @@ -45,7 +45,7 @@ N.array([0, 1]), N.array([1, 0]), N.array([1, 1])] - traindata = LibSvmRegressionDataSet(zip(labels, x)) + traindata = LibSvmRegressionDataSet(labels, x) testdata = LibSvmTestDataSet(x) return traindata, testdata @@ -85,7 +85,7 @@ def check_cross_validate(self): y = N.random.randn(100) x = N.random.randn(len(y), 10) - traindata = LibSvmRegressionDataSet(zip(y, x)) + traindata = LibSvmRegressionDataSet(y, x) kernel = LinearKernel() model = LibSvmEpsilonRegressionModel(kernel) nr_fold = 10 @@ -108,11 +108,11 @@ x1 = N.random.randn(len(y1), 10) y2 = N.random.randn(5) x2 = N.random.randn(len(y2), x1.shape[1]) - trndata1 = LibSvmRegressionDataSet(zip(y1, x1)) - trndata2 = LibSvmRegressionDataSet(zip(y2, x2)) + trndata1 = LibSvmRegressionDataSet(y1, x1) + trndata2 = LibSvmRegressionDataSet(y2, x2) refy = N.concatenate([y1, y2]) refx = N.vstack([x1, x2]) - trndata = LibSvmRegressionDataSet(zip(refy, refx)) + trndata = LibSvmRegressionDataSet(refy, refx) testdata = LibSvmTestDataSet(refx) return trndata, trndata1, trndata2, testdata From scipy-svn at scipy.org Thu Aug 3 11:14:30 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 3 Aug 2006 10:14:30 -0500 (CDT) Subject: [Scipy-svn] r2145 - trunk/Lib/sandbox/svm/tests Message-ID: <20060803151430.2738339C093@new.scipy.org> Author: fullung Date: 2006-08-03 10:14:19 -0500 (Thu, 03 Aug 2006) New Revision: 2145 Added: trunk/Lib/sandbox/svm/tests/test_speed.py Log: Benchmark with large test dataset. Added: trunk/Lib/sandbox/svm/tests/test_speed.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_speed.py 2006-08-03 15:12:13 UTC (rev 2144) +++ trunk/Lib/sandbox/svm/tests/test_speed.py 2006-08-03 15:14:19 UTC (rev 2145) @@ -0,0 +1,28 @@ +from numpy.testing import * +import numpy as N + +set_local_path('../..') +from svm.classification import * +from svm.dataset import * +from svm.kernel import * +from svm.predict import * +restore_path() + +class test_classification_speed(NumpyTestCase): + def check_large_test_dataset(self): + x = N.random.randn(150, 5) + labels = N.random.random_integers(1, 5, x.shape[0]) + traindata = LibSvmClassificationDataSet(labels, x) + + kernel = RBFKernel(traindata.gamma) + model = LibSvmCClassificationModel(kernel) + results = model.fit(traindata, LibSvmPythonPredictor) + results.compact() + + xdim, ydim = 32, 32 + img = N.random.randn(xdim, ydim, 3) + testdata = LibSvmTestDataSet(img.reshape(xdim*ydim, 3)) + v = results.predict_values(testdata) + +if __name__ == '__main__': + NumpyTest().run() From scipy-svn at scipy.org Thu Aug 3 16:57:55 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 3 Aug 2006 15:57:55 -0500 (CDT) Subject: [Scipy-svn] r2146 - in trunk/Lib: . sandbox Message-ID: <20060803205755.2A21F39C088@new.scipy.org> Author: oliphant Date: 2006-08-03 15:57:51 -0500 (Thu, 03 Aug 2006) New Revision: 2146 Added: trunk/Lib/stsci/ Removed: trunk/Lib/sandbox/stsci/ Modified: trunk/Lib/setup.py Log: Move stsci to main tree. Modified: trunk/Lib/setup.py =================================================================== --- trunk/Lib/setup.py 2006-08-03 15:14:19 UTC (rev 2145) +++ trunk/Lib/setup.py 2006-08-03 20:57:51 UTC (rev 2146) @@ -20,6 +20,7 @@ config.add_subpackage('special') config.add_subpackage('stats') config.add_subpackage('ndimage') + config.add_subpackage('stsci') config.add_subpackage('weave') config.make_svn_version_py() # installs __svn_version__.py config.make_config_py() Copied: trunk/Lib/stsci (from rev 2142, trunk/Lib/sandbox/stsci) From scipy-svn at scipy.org Thu Aug 3 19:44:13 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 3 Aug 2006 18:44:13 -0500 (CDT) Subject: [Scipy-svn] r2147 - trunk/Lib/io Message-ID: <20060803234413.04AEE39C0A7@new.scipy.org> Author: oliphant Date: 2006-08-03 18:44:12 -0500 (Thu, 03 Aug 2006) New Revision: 2147 Modified: trunk/Lib/io/mio.py Log: Fix exact type-checking for array Modified: trunk/Lib/io/mio.py =================================================================== --- trunk/Lib/io/mio.py 2006-08-03 20:57:51 UTC (rev 2146) +++ trunk/Lib/io/mio.py 2006-08-03 23:44:12 UTC (rev 2147) @@ -857,7 +857,7 @@ O = 0 for variable in dict.keys(): var = dict[variable] - if type(var) is not ArrayType: + if not isinstance(var, ArrayType): continue if var.dtype.char == 'S1': T = 1 From scipy-svn at scipy.org Fri Aug 4 06:13:26 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 4 Aug 2006 05:13:26 -0500 (CDT) Subject: [Scipy-svn] r2148 - in trunk/Lib: io sandbox/odr sandbox/xplt stats/tests Message-ID: <20060804101326.99D9F39C0C8@new.scipy.org> Author: edschofield Date: 2006-08-04 05:13:12 -0500 (Fri, 04 Aug 2006) New Revision: 2148 Modified: trunk/Lib/io/mio.py trunk/Lib/io/mmio.py trunk/Lib/sandbox/odr/odrpack.py trunk/Lib/sandbox/xplt/ezplot.py trunk/Lib/sandbox/xplt/graph.py trunk/Lib/sandbox/xplt/shapetest.py trunk/Lib/stats/tests/test_distributions.py Log: Some conversions ArrayType -> ndarray Modified: trunk/Lib/io/mio.py =================================================================== --- trunk/Lib/io/mio.py 2006-08-03 23:44:12 UTC (rev 2147) +++ trunk/Lib/io/mio.py 2006-08-04 10:13:12 UTC (rev 2148) @@ -3,7 +3,7 @@ # Author: Travis Oliphant from numpy import squeeze -from numpy import ndarray as ArrayType +from numpy import ndarray from numpy import * import numpyio import struct, os, sys @@ -857,7 +857,7 @@ O = 0 for variable in dict.keys(): var = dict[variable] - if not isinstance(var, ArrayType): + if not isinstance(var, ndarray): continue if var.dtype.char == 'S1': T = 1 Modified: trunk/Lib/io/mmio.py =================================================================== --- trunk/Lib/io/mmio.py 2006-08-03 23:44:12 UTC (rev 2147) +++ trunk/Lib/io/mmio.py 2006-08-04 10:13:12 UTC (rev 2148) @@ -13,9 +13,7 @@ # TODO: support for sparse matrices, need spmatrix.tocoo(). import os -from types import ListType, TupleType -from numpy import asarray, real,imag,conj,zeros -from numpy import ndarray as ArrayType +from numpy import asarray, real, imag, conj, zeros, ndarray __all__ = ['mminfo','mmread','mmwrite'] @@ -239,7 +237,7 @@ target = open(target,'w') close_it = 1 - if type(a) in [ListType,ArrayType,TupleType] or hasattr(a,'__array__'): + if isinstance(a, list) or isinstance(a, ndarray) or isinstance(a, tuple) or hasattr(a,'__array__'): rep = 'array' a = asarray(a) if len(a.shape) != 2: Modified: trunk/Lib/sandbox/odr/odrpack.py =================================================================== --- trunk/Lib/sandbox/odr/odrpack.py 2006-08-03 23:44:12 UTC (rev 2147) +++ trunk/Lib/sandbox/odr/odrpack.py 2006-08-04 10:13:12 UTC (rev 2148) @@ -378,7 +378,7 @@ if len(cov.shape) == 2: return linalg.inverse(cov) else: - weights = numpy.zeros(cov.shape, Float) + weights = numpy.zeros(cov.shape, float) for i in range(cov.shape[-1]): # n weights[:,:,i] = linalg.inv(cov[:,:,i]) @@ -740,7 +740,7 @@ x_s = list(self.data.x.shape) - if type(self.data.y) is numpy.ArrayType: + if isinstance(self.data.y, numpy.ndarray): y_s = list(self.data.y.shape) if self.model.implicit: raise odr_error, "an implicit model cannot use response data" @@ -853,12 +853,12 @@ lwork = (18 + 11*p + p*p + m + m*m + 4*n*q + 2*n*m + 2*n*q*p + 5*q + q*(p+m) + ldwe*ld2we*q) - if type(self.work) is numpy.ArrayType and self.work.shape == (lwork,)\ - and self.work.dtype == numpy.Float: + if isinstance(self.work, numpy.ndarray) and self.work.shape == (lwork,)\ + and self.work.dtype == numpy.Float: # the existing array is fine return else: - self.work = numpy.zeros((lwork,), numpy.Float) + self.work = numpy.zeros((lwork,), float) def set_job(self, fit_type=None, deriv=None, var_calc=None, del_init=None, restart=None): Modified: trunk/Lib/sandbox/xplt/ezplot.py =================================================================== --- trunk/Lib/sandbox/xplt/ezplot.py 2006-08-03 23:44:12 UTC (rev 2147) +++ trunk/Lib/sandbox/xplt/ezplot.py 2006-08-04 10:13:12 UTC (rev 2148) @@ -158,6 +158,7 @@ import shapetest from scipy import * +from numpy import ndarray from numpy.core.umath import * _ezdict_ = {'t': 'true' , 'T': 'true', 'y': 'true', 'Y': 'true', @@ -934,7 +935,7 @@ col = [col] * no_of_coords elif len (col) < no_of_coords : col = col + [_color_] * (no_of_coords - len (col)) - if x is None or type (x) == ArrayType and len (shape (x)) == 1 : + if x is None or isinstance(x, ndarray) and x.ndim == 1: x = [x] * no_of_coords elif shape (x) [0] < no_of_coords : x = x + [None] * no_of_coords - shape (x) [0] Modified: trunk/Lib/sandbox/xplt/graph.py =================================================================== --- trunk/Lib/sandbox/xplt/graph.py 2006-08-03 23:44:12 UTC (rev 2147) +++ trunk/Lib/sandbox/xplt/graph.py 2006-08-04 10:13:12 UTC (rev 2148) @@ -1,5 +1,3 @@ -## Automatically adapted for scipy Oct 31, 2005 by - # Copyright (c) 1996, 1997, The Regents of the University of California. # All rights reserved. See Legal.htm for full text and disclaimer. @@ -31,6 +29,7 @@ from scipy import * from numpy.core.umath import * from shapetest import * +from numpy import ndarray class Graph : @@ -197,7 +196,7 @@ elif is_scalar (axl) : raise self._AxisSpecError , \ "Axis limits must be a point." - elif type (axl) == ListType or type (axl) == ArrayType : + elif isinstance(axl, list) or isinstance(axl, ndarray): if type (axl [0]) != ListType and type (axl [0]) != ArrayType : self._axis_limits [0] = axl else : Modified: trunk/Lib/sandbox/xplt/shapetest.py =================================================================== --- trunk/Lib/sandbox/xplt/shapetest.py 2006-08-03 23:44:12 UTC (rev 2147) +++ trunk/Lib/sandbox/xplt/shapetest.py 2006-08-04 10:13:12 UTC (rev 2148) @@ -1,11 +1,10 @@ -## Automatically adapted for scipy Oct 31, 2005 by - # Copyright (c) 1996, 1997, The Regents of the University of California. # All rights reserved. See Legal.htm for full text and disclaimer. # I've felt the need for such a test for a long time; # this tells you whether an item is a scalar or not. from types import * +from numpy import ndarray from scipy import * def is_scalar (x) : @@ -19,8 +18,8 @@ # This routine should be able to tell you the size of any object: def no_of_dims (x) : if x == None : return 0 - if (type (x) == ArrayType) : return len (x.shape) - if (type (x) == ListType or type (x) == TupleType) : return 1 + if (isinstance(x, ndarray)) : return len (x.shape) + if (isinstance(x, list) or isinstance(x, tuple) : return 1 # I don't know if there are any other possibilities. for i in range (10) : if is_scalar (x) : return i Modified: trunk/Lib/stats/tests/test_distributions.py =================================================================== --- trunk/Lib/stats/tests/test_distributions.py 2006-08-03 23:44:12 UTC (rev 2147) +++ trunk/Lib/stats/tests/test_distributions.py 2006-08-04 10:13:12 UTC (rev 2148) @@ -103,7 +103,7 @@ assert(numpy.shape(vals) == (2, 50)) assert(vals.dtype.char in numpy.typecodes['AllInteger']) val = stats.binom.rvs(10, 0.75) - assert(isinstance(val, numpy.ArrayType)) + assert(isinstance(val, numpy.ndarray)) assert(val.dtype.char in numpy.typecodes['AllInteger']) @@ -114,7 +114,7 @@ assert(numpy.shape(vals) == (2, 50)) assert(vals.dtype.char in numpy.typecodes['AllInteger']) val = stats.bernoulli.rvs(0.75) - assert(isinstance(val, numpy.ArrayType)) + assert(isinstance(val, numpy.ndarray)) assert(val.dtype.char in numpy.typecodes['AllInteger']) class test_nbinom(ScipyTestCase): @@ -124,7 +124,7 @@ assert(numpy.shape(vals) == (2, 50)) assert(vals.dtype.char in numpy.typecodes['AllInteger']) val = stats.nbinom.rvs(10, 0.75) - assert(isinstance(val, numpy.ArrayType)) + assert(isinstance(val, numpy.ndarray)) assert(val.dtype.char in numpy.typecodes['AllInteger']) class test_geom(ScipyTestCase): @@ -134,7 +134,7 @@ assert(numpy.shape(vals) == (2, 50)) assert(vals.dtype.char in numpy.typecodes['AllInteger']) val = stats.geom.rvs(0.75) - assert(isinstance(val, numpy.ArrayType)) + assert(isinstance(val, numpy.ndarray)) assert(val.dtype.char in numpy.typecodes['AllInteger']) class test_hypergeom(ScipyTestCase): @@ -145,7 +145,7 @@ assert(numpy.shape(vals) == (2, 50)) assert(vals.dtype.char in numpy.typecodes['AllInteger']) val = stats.hypergeom.rvs(20, 3, 10) - assert(isinstance(val, numpy.ArrayType)) + assert(isinstance(val, numpy.ndarray)) assert(val.dtype.char in numpy.typecodes['AllInteger']) class test_logser(ScipyTestCase): @@ -155,7 +155,7 @@ assert(numpy.shape(vals) == (2, 50)) assert(vals.dtype.char in numpy.typecodes['AllInteger']) val = stats.logser.rvs(0.75) - assert(isinstance(val, numpy.ArrayType)) + assert(isinstance(val, numpy.ndarray)) assert(val.dtype.char in numpy.typecodes['AllInteger']) class test_poisson(ScipyTestCase): @@ -165,7 +165,7 @@ assert(numpy.shape(vals) == (2, 50)) assert(vals.dtype.char in numpy.typecodes['AllInteger']) val = stats.poisson.rvs(0.5) - assert(isinstance(val, numpy.ArrayType)) + assert(isinstance(val, numpy.ndarray)) assert(val.dtype.char in numpy.typecodes['AllInteger']) class test_zipf(ScipyTestCase): @@ -175,7 +175,7 @@ assert(numpy.shape(vals) == (2, 50)) assert(vals.dtype.char in numpy.typecodes['AllInteger']) val = stats.zipf.rvs(1.5) - assert(isinstance(val, numpy.ArrayType)) + assert(isinstance(val, numpy.ndarray)) assert(val.dtype.char in numpy.typecodes['AllInteger']) class test_dlaplace(ScipyTestCase): @@ -184,7 +184,7 @@ assert(numpy.shape(vals) == (2, 50)) assert(vals.dtype.char in numpy.typecodes['AllInteger']) val = stats.dlaplace.rvs(1.5) - assert(isinstance(val, numpy.ArrayType)) + assert(isinstance(val, numpy.ndarray)) assert(val.dtype.char in numpy.typecodes['AllInteger']) if __name__ == "__main__": From scipy-svn at scipy.org Sat Aug 5 03:28:01 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 5 Aug 2006 02:28:01 -0500 (CDT) Subject: [Scipy-svn] r2149 - in trunk/Lib: fftpack fftpack/tests ndimage ndimage/tests sandbox/xplt stats stats/tests Message-ID: <20060805072801.452E239C06B@new.scipy.org> Author: oliphant Date: 2006-08-05 02:27:52 -0500 (Sat, 05 Aug 2006) New Revision: 2149 Modified: trunk/Lib/fftpack/helper.py trunk/Lib/fftpack/tests/test_basic.py trunk/Lib/ndimage/filters.py trunk/Lib/ndimage/measurements.py trunk/Lib/ndimage/morphology.py trunk/Lib/ndimage/tests/test_ndimage.py trunk/Lib/sandbox/xplt/shapetest.py trunk/Lib/stats/distributions.py trunk/Lib/stats/tests/test_distributions.py Log: Updates to work with NumPy 1.0b2 Modified: trunk/Lib/fftpack/helper.py =================================================================== --- trunk/Lib/fftpack/helper.py 2006-08-04 10:13:12 UTC (rev 2148) +++ trunk/Lib/fftpack/helper.py 2006-08-05 07:27:52 UTC (rev 2149) @@ -1,7 +1,7 @@ __all__ = ['fftshift', 'ifftshift', 'fftfreq', 'rfftfreq'] from numpy import array -from numpy.dft.helper import fftshift, ifftshift, fftfreq +from numpy.fft.helper import fftshift, ifftshift, fftfreq def rfftfreq(n,d=1.0): """ rfftfreq(n, d=1.0) -> f Modified: trunk/Lib/fftpack/tests/test_basic.py =================================================================== --- trunk/Lib/fftpack/tests/test_basic.py 2006-08-04 10:13:12 UTC (rev 2148) +++ trunk/Lib/fftpack/tests/test_basic.py 2006-08-05 07:27:52 UTC (rev 2149) @@ -19,7 +19,7 @@ from numpy import arange, add, array, asarray, zeros, dot, exp, pi,\ swapaxes, double, cdouble -import numpy.dft +import numpy.fft from numpy.random import rand def random(size): @@ -121,13 +121,13 @@ n = 2**i x = range(n) y = fftpack.zfft(x) - y2 = numpy.dft.fft(x) + y2 = numpy.fft.fft(x) assert_array_almost_equal(y,y2) y = fftpack.zrfft(x) assert_array_almost_equal(y,y2) def bench_random(self,level=5): - from numpy.dft import fft as numpy_fft + from numpy.fft import fft as numpy_fft print print ' Fast Fourier Transform' print '=================================================' @@ -183,7 +183,7 @@ n = 2**i x = range(n) y = fftpack.zfft(x,direction=-1) - y2 = numpy.dft.ifft(x) + y2 = numpy.fft.ifft(x) assert_array_almost_equal(y,y2) y = fftpack.zrfft(x,direction=-1) assert_array_almost_equal(y,y2) @@ -202,7 +202,7 @@ assert_array_almost_equal (fft(ifft(x)),x) def bench_random(self,level=5): - from numpy.dft import ifft as numpy_ifft + from numpy.fft import ifft as numpy_ifft print print ' Inverse Fast Fourier Transform' print '===============================================' @@ -250,7 +250,7 @@ assert_array_almost_equal(y,y1) def check_djbfft(self): - from numpy.dft import fft as numpy_fft + from numpy.fft import fft as numpy_fft for i in range(2,14): n = 2**i x = range(n) @@ -265,7 +265,7 @@ assert_array_almost_equal(y,y1) def bench_random(self,level=5): - from numpy.dft import rfft as numpy_rfft + from numpy.fft import rfft as numpy_rfft print print 'Fast Fourier Transform (real data)' print '==================================' @@ -309,7 +309,7 @@ assert_array_almost_equal(y,ifft(x1)) def check_djbfft(self): - from numpy.dft import ifft as numpy_ifft + from numpy.fft import ifft as numpy_ifft for i in range(2,14): n = 2**i x = range(n) @@ -330,7 +330,7 @@ assert_array_almost_equal (rfft(irfft(x)),x) def bench_random(self,level=5): - from numpy.dft import irfft as numpy_irfft + from numpy.fft import irfft as numpy_irfft print print 'Inverse Fast Fourier Transform (real data)' @@ -496,7 +496,7 @@ fftn(swapaxes(large_x1,-1,-2)),-1,-2)) def bench_random(self,level=5): - from numpy.dft import fftn as numpy_fftn + from numpy.fft import fftn as numpy_fftn print print ' Multi-dimensional Fast Fourier Transform' print '===================================================' Modified: trunk/Lib/ndimage/filters.py =================================================================== --- trunk/Lib/ndimage/filters.py 2006-08-04 10:13:12 UTC (rev 2148) +++ trunk/Lib/ndimage/filters.py 2006-08-05 07:27:52 UTC (rev 2149) @@ -429,7 +429,7 @@ separable= True else: footprint = numarray.asarray(footprint) - footprint = footprint.astype(numarray.Bool) + footprint = footprint.astype(bool) if numarray.alltrue(numarray.ravel(footprint)): size = footprint.shape footprint = None @@ -440,10 +440,10 @@ structure = numarray.asarray(structure, dtype = numarray.Float64) separable = False if footprint is None: - footprint = numarray.ones(structure.shape, numarray.Bool) + footprint = numarray.ones(structure.shape, bool) else: footprint = numarray.asarray(footprint) - footprint = footprint.astype(numarray.Bool) + footprint = footprint.astype(bool) input = numarray.asarray(input) if numarray.iscomplexobj(input): raise TypeError, 'Complex type not supported' @@ -520,9 +520,9 @@ if size == None: raise RuntimeError, "no footprint or filter size provided" sizes = _ni_support._normalize_sequence(size, input.ndim) - footprint = numarray.ones(sizes, dtype = numarray.Bool) + footprint = numarray.ones(sizes, dtype = bool) else: - footprint = numarray.asarray(footprint, dtype = numarray.Bool) + footprint = numarray.asarray(footprint, dtype = bool) fshape = [ii for ii in footprint.shape if ii > 0] if len(fshape) != input.ndim: raise RuntimeError, 'filter footprint array has incorrect shape.' @@ -656,10 +656,10 @@ if size == None: raise RuntimeError, "no footprint or filter size provided" sizes = _ni_support._normalize_sequence(size, input.ndim) - footprint = numarray.ones(size, dtype = numarray.Bool) + footprint = numarray.ones(size, dtype = bool) else: footprint = numarray.asarray(footprint) - footprint = footprint.astype(numarray.Bool) + footprint = footprint.astype(bool) fshape = [ii for ii in footprint.shape if ii > 0] if len(fshape) != input.ndim: raise RuntimeError, 'filter footprint array has incorrect shape.' Modified: trunk/Lib/ndimage/measurements.py =================================================================== --- trunk/Lib/ndimage/measurements.py 2006-08-04 10:13:12 UTC (rev 2148) +++ trunk/Lib/ndimage/measurements.py 2006-08-05 07:27:52 UTC (rev 2149) @@ -31,6 +31,7 @@ import types import math import numpy.oldnumeric as numarray +import numpy import _ni_support import _nd_image import morphology @@ -50,7 +51,7 @@ raise TypeError, 'Complex type not supported' if structure == None: structure = morphology.generate_binary_structure(input.ndim, 1) - structure = numarray.asarray(structure, dtype = numarray.Bool) + structure = numarray.asarray(structure, dtype = bool) if structure.ndim != input.ndim: raise RuntimeError, 'structure and input must have equal rank' for ii in structure.shape: @@ -58,7 +59,7 @@ raise RuntimeError, 'structure dimensions must be equal to 3' if not structure.flags.contiguous: structure = structure.copy() - if isinstance(output, numarray.ndarray): + if isinstance(output, numpy.ndarray): if output.dtype.type != numarray.int32: raise RuntimeError, 'output type must be Int32' else: @@ -344,7 +345,7 @@ raise TypeError, 'only 8 and 16 unsigned inputs are supported' if structure == None: structure = morphology.generate_binary_structure(input.ndim, 1) - structure = numarray.asarray(structure, dtype = numarray.Bool) + structure = numarray.asarray(structure, dtype = bool) if structure.ndim != input.ndim: raise RuntimeError, 'structure and input must have equal rank' for ii in structure.shape: @@ -356,18 +357,18 @@ if input.shape != markers.shape: raise RuntimeError, 'input and markers must have equal shape' - integral_types = [numarray.int0, - numarray.int8, - numarray.int16, - numarray.int32, - numarray.int_, - numarray.int64, - numarray.intc, - numarray.intp] + integral_types = [numpy.int0, + numpy.int8, + numpy.int16, + numpy.int32, + numpy.int_, + numpy.int64, + numpy.intc, + numpy.intp] if markers.dtype.type not in integral_types: raise RuntimeError, 'marker should be of integer type' - if isinstance(output, numarray.ndarray): + if isinstance(output, numpy.ndarray): if output.dtype.type not in integral_types: raise RuntimeError, 'output should be of integer type' else: Modified: trunk/Lib/ndimage/morphology.py =================================================================== --- trunk/Lib/ndimage/morphology.py 2006-08-04 10:13:12 UTC (rev 2148) +++ trunk/Lib/ndimage/morphology.py 2006-08-05 07:27:52 UTC (rev 2149) @@ -29,6 +29,7 @@ # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import numpy.oldnumeric as numarray +import numpy import _ni_support import _nd_image import filters @@ -56,7 +57,7 @@ pos = [ni * (structure.shape[ii] / 2) for ii in range(len(shape))] slc = [slice(pos[ii], pos[ii] + structure.shape[ii], None) for ii in range(len(shape))] - out = numarray.zeros(shape, numarray.Bool) + out = numarray.zeros(shape, bool) out[slc] = structure != 0 out = binary_dilation(out, structure, iterations = ni) if origin is None: @@ -76,13 +77,13 @@ connectivity = 1 if rank < 1: if connectivity < 1: - return numarray.array(0, dtype = numarray.Bool) + return numarray.array(0, dtype = bool) else: - return numarray.array(1, dtype = numarray.Bool) - output = numarray.zeros([3] * rank, numarray.Bool) + return numarray.array(1, dtype = bool) + output = numarray.zeros([3] * rank, bool) output = numarray.fabs(numarray.indices([3] * rank) - 1) output = numarray.add.reduce(output, 0) - return numarray.asarray(output <= connectivity, dtype = numarray.Bool) + return numarray.asarray(output <= connectivity, dtype = bool) def _binary_erosion(input, structure, iterations, mask, output, @@ -94,7 +95,7 @@ structure = generate_binary_structure(input.ndim, 1) else: structure = numarray.asarray(structure) - structure = structure.astype(numarray.Bool) + structure = structure.astype(bool) if structure.ndim != input.ndim: raise RuntimeError, 'structure rank must equal input rank' if not structure.flags.contiguous: @@ -107,11 +108,11 @@ raise RuntimeError, 'mask and input must have equal sizes' origin = _ni_support._normalize_sequence(origin, input.ndim) cit = _center_is_true(structure, origin) - if isinstance(output, numarray.ndarray): + if isinstance(output, numpy.ndarray): if numarray.iscomplexobj(output): raise TypeError, 'Complex output type not supported' else: - output = numarray.Bool + output = bool output, return_value = _ni_support._get_output(output, input) @@ -140,11 +141,11 @@ origin, invert, coordinate_list) return return_value else: - tmp_in = numarray.zeros(input.shape, numarray.Bool) + tmp_in = numarray.zeros(input.shape, bool) if return_value == None: tmp_out = output else: - tmp_out = numarray.zeros(input.shape, numarray.Bool) + tmp_out = numarray.zeros(input.shape, bool) if not iterations & 1: tmp_in, tmp_out = tmp_out, tmp_in changed = _nd_image.binary_erosion(input, structure, mask, @@ -269,7 +270,7 @@ tmp1 = _binary_erosion(input, structure1, 1, None, None, 0, origin1, 0, False) - inplace = isinstance(output, numarray.ndarray) + inplace = isinstance(output, numpy.ndarray) result = _binary_erosion(input, structure2, 1, None, output, 0, origin2, 1, False) if inplace: @@ -305,8 +306,8 @@ to one. """ mask = numarray.logical_not(input) - tmp = numarray.zeros(mask.shape, numarray.Bool) - inplace = isinstance(output, numarray.ndarray) + tmp = numarray.zeros(mask.shape, bool) + inplace = isinstance(output, numpy.ndarray) if inplace: binary_dilation(tmp, structure, -1, mask, output, 1, origin) numarray.logical_not(output, output) @@ -407,7 +408,7 @@ """ tmp = grey_dilation(input, size, footprint, structure, None, mode, cval, origin) - if isinstance(output, numarray.ndarray): + if isinstance(output, numpy.ndarray): grey_erosion(input, size, footprint, structure, output, mode, cval, origin) return numarray.subtract(tmp, output, output) @@ -429,7 +430,7 @@ """ tmp1 = grey_dilation(input, size, footprint, structure, None, mode, cval, origin) - if isinstance(output, numarray.ndarray): + if isinstance(output, numpy.ndarray): grey_erosion(input, size, footprint, structure, output, mode, cval, origin) numarray.add(tmp1, output, output) @@ -458,7 +459,7 @@ """ tmp = grey_erosion(input, size, footprint, structure, None, mode, cval, origin) - if isinstance(output, numarray.ndarray): + if isinstance(output, numpy.ndarray): grey_dilation(tmp, size, footprint, structure, output, mode, cval, origin) del tmp @@ -482,7 +483,7 @@ """ tmp = grey_dilation(input, size, footprint, structure, None, mode, cval, origin) - if isinstance(output, numarray.ndarray): + if isinstance(output, numpy.ndarray): grey_erosion(tmp, size, footprint, structure, output, mode, cval, origin) del tmp @@ -573,7 +574,7 @@ dt = None _nd_image.distance_transform_bf(tmp1, metric, sampling, dt, ft) if return_indices: - if isinstance(indices, numarray.ndarray): + if isinstance(indices, numpy.ndarray): if indices.dtype.type != numarray.int32: raise RuntimeError, 'indices must of Int32 type' if indices.shape != (tmp1.ndim,) + tmp1.shape: @@ -589,9 +590,9 @@ ft = tmp2 # construct and return the result result = [] - if return_distances and not isinstance(distances, numarray.ndarray): + if return_distances and not isinstance(distances, numpy.ndarray): result.append(dt) - if return_indices and not isinstance(indices, numarray.ndarray): + if return_indices and not isinstance(indices, numpy.ndarray): result.append(ft) if len(result) == 2: return tuple(result) @@ -628,8 +629,8 @@ if (not return_distances) and (not return_indices): msg = 'at least one of distances/indices must be specified' raise RuntimeError, msg - ft_inplace = isinstance(indices, numarray.ndarray) - dt_inplace = isinstance(distances, numarray.ndarray) + ft_inplace = isinstance(indices, numpy.ndarray) + dt_inplace = isinstance(distances, numpy.ndarray) input = numarray.asarray(input) if structure == 'cityblock': rank = input.ndim @@ -725,8 +726,8 @@ if (not return_distances) and (not return_indices): msg = 'at least one of distances/indices must be specified' raise RuntimeError, msg - ft_inplace = isinstance(indices, numarray.ndarray) - dt_inplace = isinstance(distances, numarray.ndarray) + ft_inplace = isinstance(indices, numpy.ndarray) + dt_inplace = isinstance(distances, numpy.ndarray) # calculate the feature transform input = numarray.where(input, 1, 0).astype(numarray.Int8) if sampling is not None: Modified: trunk/Lib/ndimage/tests/test_ndimage.py =================================================================== --- trunk/Lib/ndimage/tests/test_ndimage.py 2006-08-04 10:13:12 UTC (rev 2148) +++ trunk/Lib/ndimage/tests/test_ndimage.py 2006-08-05 07:27:52 UTC (rev 2149) @@ -32,7 +32,8 @@ import unittest import math import numpy.oldnumeric as numarray -from numpy import dft +import numpy +from numpy import fft from numpy.testing import * set_package_path() import scipy.ndimage as ndimage @@ -42,9 +43,9 @@ eps = 1e-12 def diff(a, b): - if not isinstance(a, numarray.ndarray): + if not isinstance(a, numpy.ndarray): a = numarray.asarray(a) - if not isinstance(b, numarray.ndarray): + if not isinstance(b, numpy.ndarray): b = numarray.asarray(b) if (0 in a.shape) and (0 in b.shape): return 0.0 @@ -1302,12 +1303,12 @@ for type in [numarray.float32, numarray.float64]: a = numarray.zeros(shape, type) a[0, 0] = 1.0 - a = dft.rfft(a, shape[0], 0) - a = dft.fft(a, shape[1], 1) + a = fft.rfft(a, shape[0], 0) + a = fft.fft(a, shape[1], 1) a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0) - a = dft.ifft(a, shape[1], 1) - a = dft.irfft(a, shape[0], 0) + a = fft.ifft(a, shape[1], 1) + a = fft.irfft(a, shape[0], 0) self.failUnless(diff(ndimage.sum(a), 1.0) < eps) def test_fourier_gaussian_complex01(self): @@ -1316,12 +1317,12 @@ for type in [numarray.complex64, numarray.complex128]: a = numarray.zeros(shape, type) a[0, 0] = 1.0 - a = dft.fft(a, shape[0], 0) - a = dft.fft(a, shape[1], 1) + a = fft.fft(a, shape[0], 0) + a = fft.fft(a, shape[1], 1) a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0) - a = dft.ifft(a, shape[1], 1) - a = dft.ifft(a, shape[0], 0) + a = fft.ifft(a, shape[1], 1) + a = fft.ifft(a, shape[0], 0) error = diff(ndimage.sum(a.real), 1.0) self.failUnless(error < eps) @@ -1331,12 +1332,12 @@ for type in [numarray.float32, numarray.float64]: a = numarray.zeros(shape, type) a[0, 0] = 1.0 - a = dft.rfft(a, shape[0], 0) - a = dft.fft(a, shape[1], 1) + a = fft.rfft(a, shape[0], 0) + a = fft.fft(a, shape[1], 1) a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0) - a = dft.ifft(a, shape[1], 1) - a = dft.irfft(a, shape[0], 0) + a = fft.ifft(a, shape[1], 1) + a = fft.irfft(a, shape[0], 0) self.failUnless(diff(ndimage.sum(a), 1.0) < eps) def test_fourier_uniform_complex01(self): @@ -1345,11 +1346,11 @@ for type in [numarray.complex64, numarray.complex128]: a = numarray.zeros(shape, type) a[0, 0] = 1.0 - a = dft.fft(a, shape[0], 0) - a = dft.fft(a, shape[1], 1) + a = fft.fft(a, shape[0], 0) + a = fft.fft(a, shape[1], 1) a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0) - a = dft.ifft(a, shape[1], 1) - a = dft.ifft(a, shape[0], 0) + a = fft.ifft(a, shape[1], 1) + a = fft.ifft(a, shape[0], 0) error = diff(ndimage.sum(a.real), 1.0) self.failUnless(error < eps) @@ -1359,11 +1360,11 @@ for dtype in [numarray.float32, numarray.float64]: true = numarray.arange(shape[0] * shape[1], dtype = dtype) true.shape = shape - a = dft.rfft(true, shape[0], 0) - a = dft.fft(a, shape[1], 1) + a = fft.rfft(true, shape[0], 0) + a = fft.fft(a, shape[1], 1) a = ndimage.fourier_shift(a, [1, 1], shape[0], 0) - a = dft.ifft(a, shape[1], 1) - a = dft.irfft(a, shape[0], 0) + a = fft.ifft(a, shape[1], 1) + a = fft.irfft(a, shape[0], 0) error1 = diff(a[1:, 1:], true[:-1, :-1]) error2 = diff(a.imag, numarray.zeros(shape)) self.failUnless(error1 < 1e-10 and error2 < 1e-10) @@ -1375,11 +1376,11 @@ true = numarray.arange(shape[0] * shape[1], dtype = type) true.shape = shape - a = dft.fft(true, shape[0], 0) - a = dft.fft(a, shape[1], 1) + a = fft.fft(true, shape[0], 0) + a = fft.fft(a, shape[1], 1) a = ndimage.fourier_shift(a, [1, 1], -1, 0) - a = dft.ifft(a, shape[1], 1) - a = dft.ifft(a, shape[0], 0) + a = fft.ifft(a, shape[1], 1) + a = fft.ifft(a, shape[0], 0) error1 = diff(a.real[1:, 1:], true[:-1, :-1]) error2 = diff(a.imag, numarray.zeros(shape)) self.failUnless(error1 < 1e-10 and error2 < 1e-10) @@ -1390,12 +1391,12 @@ for type in [numarray.float32, numarray.float64]: a = numarray.zeros(shape, type) a[0, 0] = 1.0 - a = dft.rfft(a, shape[0], 0) - a = dft.fft(a, shape[1], 1) + a = fft.rfft(a, shape[0], 0) + a = fft.fft(a, shape[1], 1) a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], shape[0], 0) - a = dft.ifft(a, shape[1], 1) - a = dft.irfft(a, shape[0], 0) + a = fft.ifft(a, shape[1], 1) + a = fft.irfft(a, shape[0], 0) self.failUnless(diff(ndimage.sum(a), 1.0) < eps) def test_fourier_ellipsoid_complex01(self): @@ -1404,12 +1405,12 @@ for type in [numarray.complex64, numarray.complex128]: a = numarray.zeros(shape, type) a[0, 0] = 1.0 - a = dft.fft(a, shape[0], 0) - a = dft.fft(a, shape[1], 1) + a = fft.fft(a, shape[0], 0) + a = fft.fft(a, shape[1], 1) a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0) - a = dft.ifft(a, shape[1], 1) - a = dft.ifft(a, shape[0], 0) + a = fft.ifft(a, shape[1], 1) + a = fft.ifft(a, shape[0], 0) error = diff(ndimage.sum(a.real), 1.0) self.failUnless(error < eps) @@ -2745,7 +2746,7 @@ def test_sum06(self): "sum 6" - labels = numarray.array([], numarray.Bool) + labels = numarray.array([], bool) for type in self.types: input = numarray.array([], type) output = ndimage.sum(input, labels = labels) @@ -2753,7 +2754,7 @@ def test_sum07(self): "sum 7" - labels = numarray.ones([0, 4], numarray.Bool) + labels = numarray.ones([0, 4], bool) for type in self.types: input = numarray.zeros([0, 4], type) output = ndimage.sum(input, labels = labels) @@ -2761,7 +2762,7 @@ def test_sum08(self): "sum 8" - labels = numarray.array([1, 0], numarray.Bool) + labels = numarray.array([1, 0], bool) for type in self.types: input = numarray.array([1, 2], type) output = ndimage.sum(input, labels = labels) @@ -2769,7 +2770,7 @@ def test_sum09(self): "sum 9" - labels = numarray.array([1, 0], numarray.Bool) + labels = numarray.array([1, 0], bool) for type in self.types: input = numarray.array([[1, 2], [3, 4]], type) output = ndimage.sum(input, labels = labels) @@ -2777,8 +2778,8 @@ def test_sum10(self): "sum 10" - labels = numarray.array([1, 0], numarray.Bool) - input = numarray.array([[1, 2], [3, 4]], numarray.Bool) + labels = numarray.array([1, 0], bool) + input = numarray.array([[1, 2], [3, 4]], bool) output = ndimage.sum(input, labels = labels) self.failUnless(output == 2.0) @@ -2802,7 +2803,7 @@ def test_mean01(self): "mean 1" - labels = numarray.array([1, 0], numarray.Bool) + labels = numarray.array([1, 0], bool) for type in self.types: input = numarray.array([[1, 2], [3, 4]], type) output = ndimage.mean(input, labels = labels) @@ -2810,8 +2811,8 @@ def test_mean02(self): "mean 2" - labels = numarray.array([1, 0], numarray.Bool) - input = numarray.array([[1, 2], [3, 4]], numarray.Bool) + labels = numarray.array([1, 0], bool) + input = numarray.array([[1, 2], [3, 4]], bool) output = ndimage.mean(input, labels = labels) self.failUnless(output == 1.0) @@ -2835,7 +2836,7 @@ def test_minimum01(self): "minimum 1" - labels = numarray.array([1, 0], numarray.Bool) + labels = numarray.array([1, 0], bool) for type in self.types: input = numarray.array([[1, 2], [3, 4]], type) output = ndimage.minimum(input, labels = labels) @@ -2843,8 +2844,8 @@ def test_minimum02(self): "minimum 2" - labels = numarray.array([1, 0], numarray.Bool) - input = numarray.array([[2, 2], [2, 4]], numarray.Bool) + labels = numarray.array([1, 0], bool) + input = numarray.array([[2, 2], [2, 4]], bool) output = ndimage.minimum(input, labels = labels) self.failUnless(output == 1.0) @@ -2868,7 +2869,7 @@ def test_maximum01(self): "maximum 1" - labels = numarray.array([1, 0], numarray.Bool) + labels = numarray.array([1, 0], bool) for type in self.types: input = numarray.array([[1, 2], [3, 4]], type) output = ndimage.maximum(input, labels = labels) @@ -2876,8 +2877,8 @@ def test_maximum02(self): "maximum 2" - labels = numarray.array([1, 0], numarray.Bool) - input = numarray.array([[2, 2], [2, 4]], numarray.Bool) + labels = numarray.array([1, 0], bool) + input = numarray.array([[2, 2], [2, 4]], bool) output = ndimage.maximum(input, labels = labels) self.failUnless(output == 1.0) @@ -2922,7 +2923,7 @@ def test_variance04(self): "variance 4" - input = numarray.array([1, 0], numarray.Bool) + input = numarray.array([1, 0], bool) output = ndimage.variance(input) self.failUnless(output == 0.5) @@ -2965,7 +2966,7 @@ def test_standard_deviation04(self): "standard deviation 4" - input = numarray.array([1, 0], numarray.Bool) + input = numarray.array([1, 0], bool) output = ndimage.standard_deviation(input) self.failUnless(output == math.sqrt(0.5)) @@ -2989,7 +2990,7 @@ def test_minimum_position01(self): "minimum position 1" - labels = numarray.array([1, 0], numarray.Bool) + labels = numarray.array([1, 0], bool) for type in self.types: input = numarray.array([[1, 2], [3, 4]], type) output = ndimage.minimum_position(input, @@ -3009,7 +3010,7 @@ "minimum position 3" input = numarray.array([[5, 4, 2, 5], [3, 7, 0, 2], - [1, 5, 1, 1]], numarray.Bool) + [1, 5, 1, 1]], bool) output = ndimage.minimum_position(input) self.failUnless(output == (1, 2)) @@ -3017,7 +3018,7 @@ "minimum position 4" input = numarray.array([[5, 4, 2, 5], [3, 7, 1, 2], - [1, 5, 1, 1]], numarray.Bool) + [1, 5, 1, 1]], bool) output = ndimage.minimum_position(input) self.failUnless(output == (0, 0)) @@ -3054,7 +3055,7 @@ def test_maximum_position01(self): "maximum position 1" - labels = numarray.array([1, 0], numarray.Bool) + labels = numarray.array([1, 0], bool) for type in self.types: input = numarray.array([[1, 2], [3, 4]], type) output = ndimage.maximum_position(input, @@ -3074,7 +3075,7 @@ "maximum position 3" input = numarray.array([[5, 4, 2, 5], [3, 7, 8, 2], - [1, 5, 1, 1]], numarray.Bool) + [1, 5, 1, 1]], bool) output = ndimage.maximum_position(input) self.failUnless(output == (0, 0)) @@ -3111,7 +3112,7 @@ def test_extrema01(self): "extrema 1" - labels = numarray.array([1, 0], numarray.Bool) + labels = numarray.array([1, 0], bool) for type in self.types: input = numarray.array([[1, 2], [3, 4]], type) output1 = ndimage.extrema(input, labels = labels) @@ -3225,7 +3226,7 @@ def test_center_of_mass06(self): "center of mass 6" true = [0.5, 0.5] - input = numarray.array([[1, 2], [3, 1]], numarray.Bool) + input = numarray.array([[1, 2], [3, 1]], bool) output = ndimage.center_of_mass(input) e = diff(true, output) self.failUnless(e < eps) @@ -3234,7 +3235,7 @@ "center of mass 7" labels = [1, 0] true = [0.5, 0.0] - input = numarray.array([[1, 2], [3, 1]], numarray.Bool) + input = numarray.array([[1, 2], [3, 1]], bool) output = ndimage.center_of_mass(input, labels) e = diff(true, output) self.failUnless(e < eps) @@ -3243,7 +3244,7 @@ "center of mass 8" labels = [1, 2] true = [0.5, 1.0] - input = numarray.array([[5, 2], [3, 1]], numarray.Bool) + input = numarray.array([[5, 2], [3, 1]], bool) output = ndimage.center_of_mass(input, labels, 2) e = diff(true, output) self.failUnless(e < eps) @@ -3253,7 +3254,7 @@ "center of mass 9" labels = [1, 2] true = [(0.5, 0.0), (0.5, 1.0)] - input = numarray.array([[1, 2], [1, 1]], numarray.Bool) + input = numarray.array([[1, 2], [1, 1]], bool) output = ndimage.center_of_mass(input, labels, [1, 2]) e = diff(true, output) self.failUnless(e < eps) @@ -4163,7 +4164,7 @@ [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, iterations = 2) self.failUnless(diff(out, true) < eps) @@ -4186,8 +4187,8 @@ [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0]], numarray.Bool) - out = numarray.zeros(data.shape, numarray.Bool) + [0, 0, 0, 0, 0, 0, 0]], bool) + out = numarray.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value = 1, iterations = 2, output = out) self.failUnless(diff(out, true) < eps) @@ -4210,7 +4211,7 @@ [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], - [0, 0, 0, 1, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 1, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, iterations = 3) self.failUnless(diff(out, true) < eps) @@ -4233,8 +4234,8 @@ [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], - [0, 0, 0, 1, 0, 0, 0]], numarray.Bool) - out = numarray.zeros(data.shape, numarray.Bool) + [0, 0, 0, 1, 0, 0, 0]], bool) + out = numarray.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value = 1, iterations = 3, output = out) self.failUnless(diff(out, true) < eps) @@ -4257,8 +4258,8 @@ [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], - [0, 0, 0, 1, 0, 0, 0]], numarray.Bool) - out = numarray.zeros(data.shape, numarray.Bool) + [0, 0, 0, 1, 0, 0, 0]], bool) + out = numarray.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value = 1, iterations = 1, output = out, origin = (-1, -1)) self.failUnless(diff(out, true) < eps) @@ -4281,7 +4282,7 @@ [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, iterations = 2) self.failUnless(diff(out, true) < eps) @@ -4311,7 +4312,7 @@ [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, mask = mask, iterations = -1) self.failUnless(diff(out, true) < eps) @@ -4341,7 +4342,7 @@ [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, mask = mask) self.failUnless(diff(out, true) < eps) @@ -4364,7 +4365,7 @@ [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], - [0, 0, 0, 1, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 1, 0, 0, 0]], bool) tmp = [[0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 1], @@ -4375,7 +4376,7 @@ true = numarray.logical_and(tmp, mask) tmp = numarray.logical_and(data, numarray.logical_not(mask)) true = numarray.logical_or(true, tmp) - out = numarray.zeros(data.shape, numarray.Bool) + out = numarray.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value = 1, iterations = 1, output = out, origin = (-1, -1), mask = mask) @@ -4762,7 +4763,7 @@ [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], - [0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0]], bool) out = ndimage.binary_dilation(data, struct, iterations = 2) self.failUnless(diff(out, true) < eps) @@ -4781,8 +4782,8 @@ [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], - [0, 0, 0, 0, 0]], numarray.Bool) - out = numarray.zeros(data.shape, numarray.Bool) + [0, 0, 0, 0, 0]], bool) + out = numarray.zeros(data.shape, bool) ndimage.binary_dilation(data, struct, iterations = 2, output = out) self.failUnless(diff(out, true) < eps) @@ -4801,7 +4802,7 @@ [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], - [0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0]], bool) out = ndimage.binary_dilation(data, struct, iterations = 3) self.failUnless(diff(out, true) < eps) @@ -4820,8 +4821,8 @@ [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], - [0, 0, 0, 0, 0]], numarray.Bool) - out = numarray.zeros(data.shape, numarray.Bool) + [0, 0, 0, 0, 0]], bool) + out = numarray.zeros(data.shape, bool) ndimage.binary_dilation(data, struct, iterations = 3, output = out) self.failUnless(diff(out, true) < eps) @@ -4838,7 +4839,7 @@ [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) mask = numarray.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], @@ -4846,7 +4847,7 @@ [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numarray.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], @@ -4854,7 +4855,7 @@ [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_dilation(data, struct, iterations = -1, mask = mask, border_value = 0) @@ -4880,8 +4881,8 @@ [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) - data = numarray.zeros(mask.shape, numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) + data = numarray.zeros(mask.shape, bool) out = ndimage.binary_dilation(data, struct, iterations = -1, mask = mask, border_value = 1) self.failUnless(diff(out, true) < eps) @@ -4940,7 +4941,7 @@ [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) mask = numarray.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], @@ -4948,7 +4949,7 @@ [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numarray.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], @@ -4956,7 +4957,7 @@ [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_propagation(data, struct, mask = mask, border_value = 0) @@ -4982,8 +4983,8 @@ [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) - data = numarray.zeros(mask.shape, numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) + data = numarray.zeros(mask.shape, bool) out = ndimage.binary_propagation(data, struct, mask = mask, border_value = 1) self.failUnless(diff(out, true) < eps) @@ -5086,14 +5087,14 @@ [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numarray.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_fill_holes(data) self.failUnless(diff(out, true) < eps) @@ -5105,14 +5106,14 @@ [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numarray.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_fill_holes(data) self.failUnless(diff(out, true) < eps) @@ -5124,14 +5125,14 @@ [0, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 1, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1, 1, 1], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numarray.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 1, 1, 1], [0, 1, 0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 0, 1, 0, 1], [0, 0, 1, 0, 0, 1, 1, 1], - [0, 0, 0, 0, 0, 0, 0, 0]], numarray.Bool) + [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_fill_holes(data) self.failUnless(diff(out, true) < eps) @@ -5412,7 +5413,7 @@ [0, 1, 1, 1, 1], [0, 1, 1, 1, 1], [0, 0, 0, 0, 0]], type) - out = numarray.zeros(data.shape, numarray.Bool) + out = numarray.zeros(data.shape, bool) ndimage.binary_hit_or_miss(data, struct, output = out) self.failUnless(diff(true, out) < eps) Modified: trunk/Lib/sandbox/xplt/shapetest.py =================================================================== --- trunk/Lib/sandbox/xplt/shapetest.py 2006-08-04 10:13:12 UTC (rev 2148) +++ trunk/Lib/sandbox/xplt/shapetest.py 2006-08-05 07:27:52 UTC (rev 2149) @@ -18,8 +18,8 @@ # This routine should be able to tell you the size of any object: def no_of_dims (x) : if x == None : return 0 - if (isinstance(x, ndarray)) : return len (x.shape) - if (isinstance(x, list) or isinstance(x, tuple) : return 1 + if isinstance(x, ndarray) : return len (x.shape) + if isinstance(x, (list, tuple)) : return 1 # I don't know if there are any other possibilities. for i in range (10) : if is_scalar (x) : return i Modified: trunk/Lib/stats/distributions.py =================================================================== --- trunk/Lib/stats/distributions.py 2006-08-04 10:13:12 UTC (rev 2148) +++ trunk/Lib/stats/distributions.py 2006-08-05 07:27:52 UTC (rev 2149) @@ -9,7 +9,7 @@ import scipy.special as special import scipy.optimize as optimize import inspect -from numpy import alltrue, where, arange, put, putmask, nonzero, \ +from numpy import alltrue, where, arange, put, putmask, \ ravel, take, ones, sum, shape, product, repeat, reshape, \ zeros, floor, logical_and, log, sqrt, exp, arctanh, tan, sin, arcsin, \ arctan, tanh, ndarray, cos, cosh, sinh, newaxis @@ -17,6 +17,7 @@ any, argsort, argmax, vectorize, r_, asarray, nan, inf, pi, isnan, isinf import numpy import numpy.random as mtrand +from numpy.oldnumeric import nonzero __all__ = [ 'rv_continuous', Modified: trunk/Lib/stats/tests/test_distributions.py =================================================================== --- trunk/Lib/stats/tests/test_distributions.py 2006-08-04 10:13:12 UTC (rev 2148) +++ trunk/Lib/stats/tests/test_distributions.py 2006-08-05 07:27:52 UTC (rev 2149) @@ -7,6 +7,7 @@ set_package_path() import numpy.oldnumeric as numpy +from numpy import typecodes import stats restore_path() @@ -77,11 +78,11 @@ assert(len(vals) == 100) vals = stats.randint.rvs(5,30,size=(2,50)) assert(numpy.shape(vals) == (2,50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.randint.rvs(15,46) assert((val >= 15) & (val < 46)) assert isinstance(val, numpy.ScalarType),`type(val)` - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) def check_pdf(self): k = numpy.r_[0:36] @@ -101,10 +102,10 @@ vals = stats.binom.rvs(10, 0.75, size=(2, 50)) assert(numpy.all(vals >= 0) & numpy.all(vals <= 10)) assert(numpy.shape(vals) == (2, 50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.binom.rvs(10, 0.75) assert(isinstance(val, numpy.ndarray)) - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) class test_bernoulli(ScipyTestCase): @@ -112,30 +113,30 @@ vals = stats.bernoulli.rvs(0.75, size=(2, 50)) assert(numpy.all(vals >= 0) & numpy.all(vals <= 1)) assert(numpy.shape(vals) == (2, 50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.bernoulli.rvs(0.75) assert(isinstance(val, numpy.ndarray)) - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) class test_nbinom(ScipyTestCase): def check_rvs(self): vals = stats.nbinom.rvs(10, 0.75, size=(2, 50)) assert(numpy.all(vals >= 0)) assert(numpy.shape(vals) == (2, 50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.nbinom.rvs(10, 0.75) assert(isinstance(val, numpy.ndarray)) - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) class test_geom(ScipyTestCase): def check_rvs(self): vals = stats.geom.rvs(0.75, size=(2, 50)) assert(numpy.all(vals >= 0)) assert(numpy.shape(vals) == (2, 50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.geom.rvs(0.75) assert(isinstance(val, numpy.ndarray)) - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) class test_hypergeom(ScipyTestCase): def check_rvs(self): @@ -143,49 +144,49 @@ assert(numpy.all(vals >= 0) & numpy.all(vals <= 3)) assert(numpy.shape(vals) == (2, 50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.hypergeom.rvs(20, 3, 10) assert(isinstance(val, numpy.ndarray)) - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) class test_logser(ScipyTestCase): def check_rvs(self): vals = stats.logser.rvs(0.75, size=(2, 50)) assert(numpy.all(vals >= 1)) assert(numpy.shape(vals) == (2, 50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.logser.rvs(0.75) assert(isinstance(val, numpy.ndarray)) - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) class test_poisson(ScipyTestCase): def check_rvs(self): vals = stats.poisson.rvs(0.5, size=(2, 50)) assert(numpy.all(vals >= 0)) assert(numpy.shape(vals) == (2, 50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.poisson.rvs(0.5) assert(isinstance(val, numpy.ndarray)) - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) class test_zipf(ScipyTestCase): def check_rvs(self): vals = stats.zipf.rvs(1.5, size=(2, 50)) assert(numpy.all(vals >= 1)) assert(numpy.shape(vals) == (2, 50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.zipf.rvs(1.5) assert(isinstance(val, numpy.ndarray)) - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) class test_dlaplace(ScipyTestCase): def check_rvs(self): vals = stats.dlaplace.rvs(1.5 , size=(2, 50)) assert(numpy.shape(vals) == (2, 50)) - assert(vals.dtype.char in numpy.typecodes['AllInteger']) + assert(vals.dtype.char in typecodes['AllInteger']) val = stats.dlaplace.rvs(1.5) assert(isinstance(val, numpy.ndarray)) - assert(val.dtype.char in numpy.typecodes['AllInteger']) + assert(val.dtype.char in typecodes['AllInteger']) if __name__ == "__main__": ScipyTest('stats.distributions').run() From scipy-svn at scipy.org Sat Aug 5 03:52:54 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 5 Aug 2006 02:52:54 -0500 (CDT) Subject: [Scipy-svn] r2150 - in trunk/Lib: linalg misc sandbox/models sandbox/xplt stats Message-ID: <20060805075254.15F0839C072@new.scipy.org> Author: oliphant Date: 2006-08-05 02:52:47 -0500 (Sat, 05 Aug 2006) New Revision: 2150 Modified: trunk/Lib/linalg/decomp.py trunk/Lib/misc/pilutil.py trunk/Lib/sandbox/models/bsplines.py trunk/Lib/sandbox/models/utils.py trunk/Lib/sandbox/xplt/colorbar.py trunk/Lib/sandbox/xplt/slice3.py trunk/Lib/stats/distributions.py Log: Make use of flatnonzero Modified: trunk/Lib/linalg/decomp.py =================================================================== --- trunk/Lib/linalg/decomp.py 2006-08-05 07:27:52 UTC (rev 2149) +++ trunk/Lib/linalg/decomp.py 2006-08-05 07:52:47 UTC (rev 2150) @@ -32,7 +32,7 @@ _I = cast['F'](1j) def _make_complex_eigvecs(w,vin,cmplx_tcode): v = numpy.array(vin,dtype=cmplx_tcode) - ind = numpy.nonzero(numpy.not_equal(w.imag,0.0)) + ind = numpy.flatnonzero(numpy.not_equal(w.imag,0.0)) vnew = numpy.zeros((v.shape[0],len(ind)>>1),cmplx_tcode) vnew.real = numpy.take(vin,ind[::2],1) vnew.imag = numpy.take(vin,ind[1::2],1) Modified: trunk/Lib/misc/pilutil.py =================================================================== --- trunk/Lib/misc/pilutil.py 2006-08-05 07:27:52 UTC (rev 2149) +++ trunk/Lib/misc/pilutil.py 2006-08-05 07:52:47 UTC (rev 2150) @@ -157,9 +157,9 @@ # Check for 3 in datacube shape --- 'RGB' or 'YCbCr' if channel_axis is None: if (3 in shape): - ca = numpy.nonzero(asarray(shape) == 3)[0] + ca = numpy.flatnonzero(asarray(shape) == 3)[0] else: - ca = numpy.nonzero(asarray(shape) == 4) + ca = numpy.flatnonzero(asarray(shape) == 4) if len(ca): ca = ca[0] else: Modified: trunk/Lib/sandbox/models/bsplines.py =================================================================== --- trunk/Lib/sandbox/models/bsplines.py 2006-08-05 07:27:52 UTC (rev 2149) +++ trunk/Lib/sandbox/models/bsplines.py 2006-08-05 07:52:47 UTC (rev 2150) @@ -161,7 +161,7 @@ # throw out rows with zeros (this happens at boundary points!) - mask = N.nonzero(1 - N.alltrue(N.equal(bt, 0), axis=0)) + mask = N.flatnonzero(1 - N.alltrue(N.equal(bt, 0), axis=0)) bt = bt[:,mask] y = y[mask] Modified: trunk/Lib/sandbox/models/utils.py =================================================================== --- trunk/Lib/sandbox/models/utils.py 2006-08-05 07:27:52 UTC (rev 2149) +++ trunk/Lib/sandbox/models/utils.py 2006-08-05 07:52:47 UTC (rev 2150) @@ -40,10 +40,8 @@ """ Erase columns of zeros: can save some time in pseudoinverse. """ - colsum = N.add.reduce(matrix**2, 0) - - val = [matrix[:,i] for i in N.nonzero(colsum)] + val = [matrix[:,i] for i in N.flatnonzero(colsum)] return N.array(N.transpose(val)) def rank(X, cond=1.0e-12): Modified: trunk/Lib/sandbox/xplt/colorbar.py =================================================================== --- trunk/Lib/sandbox/xplt/colorbar.py 2006-08-05 07:27:52 UTC (rev 2149) +++ trunk/Lib/sandbox/xplt/colorbar.py 2006-08-05 07:52:47 UTC (rev 2150) @@ -9,6 +9,7 @@ from numpy import * from gist import * from slice3 import * +from numpy.oldnumeric import nonzero def nice_levels (z, n = 8) : """nice_levels(z, n = 8) finds approximately n "nice values" Modified: trunk/Lib/sandbox/xplt/slice3.py =================================================================== --- trunk/Lib/sandbox/xplt/slice3.py 2006-08-05 07:27:52 UTC (rev 2149) +++ trunk/Lib/sandbox/xplt/slice3.py 2006-08-05 07:52:47 UTC (rev 2150) @@ -22,6 +22,7 @@ from numpy import * from gistC import * from gistfuncs import * +from numpy.oldnumeric import nonzero # # Caveats: Modified: trunk/Lib/stats/distributions.py =================================================================== --- trunk/Lib/stats/distributions.py 2006-08-05 07:27:52 UTC (rev 2149) +++ trunk/Lib/stats/distributions.py 2006-08-05 07:52:47 UTC (rev 2150) @@ -17,7 +17,7 @@ any, argsort, argmax, vectorize, r_, asarray, nan, inf, pi, isnan, isinf import numpy import numpy.random as mtrand -from numpy.oldnumeric import nonzero +from numpy import flatnonzero as nonzero __all__ = [ 'rv_continuous', From scipy-svn at scipy.org Sat Aug 5 16:24:47 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 5 Aug 2006 15:24:47 -0500 (CDT) Subject: [Scipy-svn] r2151 - trunk/Lib/misc Message-ID: <20060805202447.B19CC39C0B6@new.scipy.org> Author: oliphant Date: 2006-08-05 15:24:43 -0500 (Sat, 05 Aug 2006) New Revision: 2151 Modified: trunk/Lib/misc/__init__.py trunk/Lib/misc/common.py Log: Move source, info, and who to NumPy Modified: trunk/Lib/misc/__init__.py =================================================================== --- trunk/Lib/misc/__init__.py 2006-08-05 07:52:47 UTC (rev 2150) +++ trunk/Lib/misc/__init__.py 2006-08-05 20:24:43 UTC (rev 2151) @@ -1,12 +1,18 @@ from info import __doc__ -__all__ = ['limits'] +__all__ = ['limits', 'who', 'source', 'info'] import limits from common import * -from helpmod import * +from numpy import who, source, info as _info +import sys +def info(object=None,maxwidth=76,output=sys.stdout,toplevel='scipy'): + return _info(object, maxwidth, output, toplevel) +info.__doc__ = _info.__doc__ +del sys + try: from pilutil import * __all__ += pilutil.__all__ @@ -14,7 +20,6 @@ pass __all__ += common.__all__ -__all__ += helpmod.__all__ from numpy.testing import ScipyTest test = ScipyTest().test Modified: trunk/Lib/misc/common.py =================================================================== --- trunk/Lib/misc/common.py 2006-08-05 07:52:47 UTC (rev 2150) +++ trunk/Lib/misc/common.py 2006-08-05 20:24:43 UTC (rev 2151) @@ -11,7 +11,9 @@ newaxis, hstack, product, array, where, \ zeros, extract, insert, pi, sqrt, eye, poly1d, dot, r_ -__all__ = ['factorial','factorial2','factorialk','comb','who', +from numpy import who + +__all__ = ['factorial','factorial2','factorialk','comb', 'central_diff_weights', 'derivative', 'pade', 'lena'] # XXX: the factorial functions could move to scipy.special, and the others @@ -223,7 +225,6 @@ q = r_[1.0,pq[n+1:]] return poly1d(p[::-1]), poly1d(q[::-1]) -# XXX: broken since plt is gone def lena(): import cPickle, os fname = os.path.join(os.path.dirname(__file__),'lena.dat') @@ -232,65 +233,3 @@ f.close() return lena - -#----------------------------------------------------------------------------- -# Matlab like functions for output and information on the variables used. -#----------------------------------------------------------------------------- - -def who(vardict=None): - """Print the scipy arrays in the given dictionary (or globals() if None). - """ - if vardict is None: - frame = sys._getframe().f_back - vardict = frame.f_globals - sta = [] - cache = {} - for name in vardict.keys(): - if isinstance(vardict[name],numpy.ndarray): - var = vardict[name] - idv = id(var) - if idv in cache.keys(): - namestr = name + " (%s)" % cache[idv] - original=0 - else: - cache[idv] = name - namestr = name - original=1 - shapestr = " x ".join(map(str, var.shape)) - bytestr = str(var.itemsize*numpy.product(var.shape)) - sta.append([namestr, shapestr, bytestr, var.dtype.name, - original]) - - maxname = 0 - maxshape = 0 - maxbyte = 0 - totalbytes = 0 - for k in range(len(sta)): - val = sta[k] - if maxname < len(val[0]): - maxname = len(val[0]) - if maxshape < len(val[1]): - maxshape = len(val[1]) - if maxbyte < len(val[2]): - maxbyte = len(val[2]) - if val[4]: - totalbytes += int(val[2]) - - max = numpy.maximum - if len(sta) > 0: - sp1 = max(10,maxname) - sp2 = max(10,maxshape) - sp3 = max(10,maxbyte) - prval = "Name %s Shape %s Bytes %s Type" % (sp1*' ', sp2*' ', sp3*' ') - print prval + "\n" + "="*(len(prval)+5) + "\n" - - for k in range(len(sta)): - val = sta[k] - print "%s %s %s %s %s %s %s" % (val[0], ' '*(sp1-len(val[0])+4), - val[1], ' '*(sp2-len(val[1])+5), - val[2], ' '*(sp3-len(val[2])+5), - val[3]) - print "\nUpper bound on total bytes = %d" % totalbytes - return - -#----------------------------------------------------------------------------- From scipy-svn at scipy.org Wed Aug 9 05:47:11 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 9 Aug 2006 04:47:11 -0500 (CDT) Subject: [Scipy-svn] r2152 - in trunk/Lib/linsolve: . umfpack Message-ID: <20060809094711.0629639C02F@new.scipy.org> Author: rc Date: 2006-08-09 04:47:04 -0500 (Wed, 09 Aug 2006) New Revision: 2152 Modified: trunk/Lib/linsolve/__init__.py trunk/Lib/linsolve/info.py trunk/Lib/linsolve/linsolve.py trunk/Lib/linsolve/umfpack/__init__.py trunk/Lib/linsolve/umfpack/info.py trunk/Lib/linsolve/umfpack/umfpack.py Log: updated docs Modified: trunk/Lib/linsolve/__init__.py =================================================================== --- trunk/Lib/linsolve/__init__.py 2006-08-05 20:24:43 UTC (rev 2151) +++ trunk/Lib/linsolve/__init__.py 2006-08-09 09:47:04 UTC (rev 2152) @@ -2,6 +2,10 @@ from info import __doc__ +import umfpack +__doc__ = '\n\n'.join( (__doc__, umfpack.__doc__) ) +del umfpack + from linsolve import * __all__ = filter(lambda s:not s.startswith('_'),dir()) Modified: trunk/Lib/linsolve/info.py =================================================================== --- trunk/Lib/linsolve/info.py 2006-08-05 20:24:43 UTC (rev 2151) +++ trunk/Lib/linsolve/info.py 2006-08-09 09:47:04 UTC (rev 2152) @@ -2,5 +2,50 @@ Linear Solvers ============== +The default solver is SuperLU (included in the scipy distribution), which can +solve real or complex linear systems in both single and double precisions. It +is automatically replaced by UMFPACK, if available. Note that UMFPACK works in +double precision only, so switch it off by +>>> use_solver( use = {'useUmfpack': False} ) +to solve in the single precision. + +Example session: + +>>> from scipy.sparse import csc_matrix +>>> from numpy import array +>>> from scipy.linsolve import spdiags, spsolve, use_solver +>>> +>>> print "Inverting a sparse linear system:" +>>> print "The sparse matrix (constructed from diagonals):" +>>> a = spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]], [0, 1], 5, 5) +>>> b = array([1, 2, 3, 4, 5]) +>>> print "Solve: single precision complex:" +>>> use_solver( use = {'useUmfpack' : False} ) +>>> a = a.astype('F') +>>> x = spsolve(a, b) +>>> print x +>>> print "Error: ", a*x-b +>>> +>>> print "Solve: double precision complex:" +>>> use_solver( use = {'useUmfpack' : True} ) +>>> a = a.astype('D') +>>> x = spsolve(a, b) +>>> print x +>>> print "Error: ", a*x-b +>>> +>>> print "Solve: double precision:" +>>> a = a.astype('d') +>>> x = spsolve(a, b) +>>> print x +>>> print "Error: ", a*x-b +>>> +>>> print "Solve: single precision:" +>>> use_solver( use = {'useUmfpack' : False} ) +>>> a = a.astype('f') +>>> x = spsolve(a, b.astype('f')) +>>> print x +>>> print "Error: ", a*x-b + + """ postpone_import = 1 Modified: trunk/Lib/linsolve/linsolve.py =================================================================== --- trunk/Lib/linsolve/linsolve.py 2006-08-05 20:24:43 UTC (rev 2151) +++ trunk/Lib/linsolve/linsolve.py 2006-08-09 09:47:04 UTC (rev 2152) @@ -90,22 +90,23 @@ diag_pivot_thresh, drop_tol, relax, panel_size) def _testme(): - from scipy.sparse import csc_matrix, dok_matrix - from numpy import transpose, array, arange - + from scipy.sparse import csc_matrix + from numpy import array + from scipy.linsolve import spdiags, spsolve, use_solver + print "Inverting a sparse linear system:" print "The sparse matrix (constructed from diagonals):" a = spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]], [0, 1], 5, 5) b = array([1, 2, 3, 4, 5]) print "Solve: single precision complex:" - globals()['useUmfpack'] = False + use_solver( use = {'useUmfpack' : False} ) a = a.astype('F') x = spsolve(a, b) print x print "Error: ", a*x-b print "Solve: double precision complex:" - globals()['useUmfpack'] = True + use_solver( use = {'useUmfpack' : True} ) a = a.astype('D') x = spsolve(a, b) print x @@ -118,7 +119,7 @@ print "Error: ", a*x-b print "Solve: single precision:" - globals()['useUmfpack'] = False + use_solver( use = {'useUmfpack' : False} ) a = a.astype('f') x = spsolve(a, b.astype('f')) print x Modified: trunk/Lib/linsolve/umfpack/__init__.py =================================================================== --- trunk/Lib/linsolve/umfpack/__init__.py 2006-08-05 20:24:43 UTC (rev 2151) +++ trunk/Lib/linsolve/umfpack/__init__.py 2006-08-09 09:47:04 UTC (rev 2152) @@ -1,5 +1,7 @@ +from info import __doc__ + from umfpack import * +__all__ = filter(lambda s:not s.startswith('_'),dir()) from numpy.testing import ScipyTest test = ScipyTest().test - Modified: trunk/Lib/linsolve/umfpack/info.py =================================================================== --- trunk/Lib/linsolve/umfpack/info.py 2006-08-05 20:24:43 UTC (rev 2151) +++ trunk/Lib/linsolve/umfpack/info.py 2006-08-09 09:47:04 UTC (rev 2152) @@ -1,5 +1,119 @@ """ -UMFPACK v4.4 wrappers. +Interface to the UMFPACK library. +================================= + +Routines for symbolic and numeric LU factorization of sparse +matrices and for solving systems of linear equations with sparse matrices. + +Tested with UMFPACK V4.4 (Jan. 28, 2005), V5.0 (May 5, 2006) +Copyright (c) 2005 by Timothy A. Davis. All Rights Reserved. +UMFPACK homepage: http://www.cise.ufl.edu/research/sparse/umfpack + +Contains: UmfpackContext class + +Use 'print UmfpackContext().funs' to see all UMFPACK library functions the +module exposes, if you need something not covered by the examples below. + +Installation: +============= +Example site.cfg entry: + +UMFPACK v4.4 in : + +[amd] +library_dirs = /UMFPACK/AMD/Lib +include_dirs = /UMFPACK/AMD/Include +amd_libs = amd + +[umfpack] +library_dirs = /UMFPACK/UMFPACK/Lib +include_dirs = /UMFPACK/UMFPACK/Include +umfpack_libs = umfpack + +UMFPACK v5.0 (as part of UFsparse package) in : + +[amd] +library_dirs = /UFsparse/AMD/Lib +include_dirs = /UFsparse/AMD/Include, /UFsparse/UFconfig +amd_libs = amd + +[umfpack] +library_dirs = /UFsparse/UMFPACK/Lib +include_dirs = /UFsparse/UMFPACK/Include, /UFsparse/UFconfig +umfpack_libs = umfpack + +Examples: +========= + +Assuming this module imported as um (import scipy.linsolve.umfpack as um) + +Sparse matrix in CSR or CSC format: mtx +Righthand-side: rhs +Solution: sol + +# Contruct the solver. +umfpack = um.UmfpackContext() # Use default 'di' family of UMFPACK routines. + +# One-shot solution. +sol = umfpack( um.UMFPACK_A, mtx, rhs, autoTranspose = True ) +# same as: +sol = umfpack.linsolve( um.UMFPACK_A, mtx, rhs, autoTranspose = True ) + +-or- + +# Make LU decomposition. +umfpack.numeric( mtx ) +... +# Use already LU-decomposed matrix. +sol1 = umfpack( um.UMFPACK_A, mtx, rhs1, autoTranspose = True ) +sol2 = umfpack( um.UMFPACK_A, mtx, rhs2, autoTranspose = True ) +# same as: +sol1 = umfpack.solve( um.UMFPACK_A, mtx, rhs1, autoTranspose = True ) +sol2 = umfpack.solve( um.UMFPACK_A, mtx, rhs2, autoTranspose = True ) + +-or- + +# Make symbolic decomposition. +umfpack.symbolic( mtx0 ) +# Print statistics. +umfpack.report_symbolic() + +... + +# Make LU decomposition of mtx1 which has same structure as mtx0. +umfpack.numeric( mtx1 ) +# Print statistics. +umfpack.report_numeric() + +# Use already LU-decomposed matrix. +sol1 = umfpack( um.UMFPACK_A, mtx1, rhs1, autoTranspose = True ) + +... + +# Make LU decomposition of mtx2 which has same structure as mtx0. +umfpack.numeric( mtx2 ) +sol2 = umfpack.solve( um.UMFPACK_A, mtx2, rhs2, autoTranspose = True ) + +# Print all statistics. +umfpack.report_info() + +Setting control parameters: +=========================== +Assuming this module imported as um: + +List of control parameter names is accessible as 'um.umfControls' - their +meaning and possible values are described in the UMFPACK documentation. +To each name corresponds an attribute of the 'um' module, such as, +for example 'um.UMFPACK_PRL' (controlling the verbosity of umfpack report +functions). These attributes are in fact indices into the control array +- to set the corresponding control array value, just do the following: + +umfpack = um.UmfpackContext() +umfpack.control[um.UMFPACK_PRL] = 4 # Let's be more verbose. + +-- +Author: Robert Cimrman """ +postpone_import = 1 global_symbols = ['UmfpackContext'] Modified: trunk/Lib/linsolve/umfpack/umfpack.py =================================================================== --- trunk/Lib/linsolve/umfpack/umfpack.py 2006-08-05 20:24:43 UTC (rev 2151) +++ trunk/Lib/linsolve/umfpack/umfpack.py 2006-08-09 09:47:04 UTC (rev 2152) @@ -1,3 +1,11 @@ +""" +Interface to the UMFPACK library. + +-- +Author: Robert Cimrman +""" + + #from base import Struct, pause import numpy as nm import scipy.sparse as sp @@ -7,93 +15,6 @@ except: _um = None -__doc__ = """ -Interface to the UMFPACK library. -Routines for symbolic and numeric LU factorization of sparse -matrices and for solving systems of linear equations with sparse matrices. - -Tested with UMFPACK V4.4 (Jan. 28, 2005), -Copyright (c) 2005 by Timothy A. Davis. All Rights Reserved. -UMFPACK homepage: http://www.cise.ufl.edu/research/sparse/umfpack - -Contains: UmfpackContext class - -Use 'print UmfpackContext().funs' to see all UMFPACK library functions the -module exposes, if you need something not covered by the examples below. - -Examples: -========= - -Assuming this module imported as um - -Sparse matrix in CSR or CSC format: mtx -Righthand-side: rhs -Solution: sol - -# Contruct the solver. -umfpack = um.UmfpackContext() # Use default 'di' family of UMFPACK routines. - -# One-shot solution. -sol = umfpack( um.UMFPACK_A, mtx, rhs, autoTranspose = True ) -# same as: -sol = umfpack.linsolve( um.UMFPACK_A, mtx, rhs, autoTranspose = True ) - --or- - -# Make LU decomposition. -umfpack.numeric( mtx ) -... -# Use already LU-decomposed matrix. -sol1 = umfpack( um.UMFPACK_A, mtx, rhs1, autoTranspose = True ) -sol2 = umfpack( um.UMFPACK_A, mtx, rhs2, autoTranspose = True ) -# same as: -sol1 = umfpack.solve( um.UMFPACK_A, mtx, rhs1, autoTranspose = True ) -sol2 = umfpack.solve( um.UMFPACK_A, mtx, rhs2, autoTranspose = True ) - --or- - -# Make symbolic decomposition. -umfpack.symbolic( mtx0 ) -# Print statistics. -umfpack.report_symbolic() - -... - -# Make LU decomposition of mtx1 which has same structure as mtx0. -umfpack.numeric( mtx1 ) -# Print statistics. -umfpack.report_numeric() - -# Use already LU-decomposed matrix. -sol1 = umfpack( um.UMFPACK_A, mtx1, rhs1, autoTranspose = True ) - -... - -# Make LU decomposition of mtx2 which has same structure as mtx0. -umfpack.numeric( mtx2 ) -sol2 = umfpack.solve( um.UMFPACK_A, mtx2, rhs2, autoTranspose = True ) - -# Print all statistics. -umfpack.report_info() - -Setting control parameters: -=========================== -Assuming this module imported as um: - -List of control parameter names is accessible as 'um.umfControls' - their -meaning and possible values are described in the UMFPACK documentation. -To each name corresponds an attribute of the 'um' module, such as, -for example 'um.UMFPACK_PRL' (controlling the verbosity of umfpack report -functions). These attributes are in fact indices into the control array -- to set the corresponding control array value, just do the following: - -umfpack = um.UmfpackContext() -umfpack.control[um.UMFPACK_PRL] = 4 # Let's be more verbose. - --- -Author: Robert Cimrman -""" - ## # 30.11.2005, c def updateDictWithVars( adict, module, pattern, group = None ): From scipy-svn at scipy.org Wed Aug 9 16:53:24 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 9 Aug 2006 15:53:24 -0500 (CDT) Subject: [Scipy-svn] r2153 - trunk/Lib/special/cephes Message-ID: <20060809205324.037B739C044@new.scipy.org> Author: cookedm Date: 2006-08-09 15:53:22 -0500 (Wed, 09 Aug 2006) New Revision: 2153 Modified: trunk/Lib/special/cephes/mconf.h Log: Fix #12: Lib\special\cephes\const.c doesn't compile with Visual Studio Modified: trunk/Lib/special/cephes/mconf.h =================================================================== --- trunk/Lib/special/cephes/mconf.h 2006-08-09 09:47:04 UTC (rev 2152) +++ trunk/Lib/special/cephes/mconf.h 2006-08-09 20:53:22 UTC (rev 2153) @@ -106,11 +106,16 @@ defined(__decvax__) || defined(pro350) || defined(pdp11) #define DEC 1 -#elif defined(ns32000) || defined(sun386) || \ - defined(i386) || defined(MIPSEL) || defined(_MIPSEL) || \ - defined(BIT_ZERO_ON_RIGHT) || defined(__alpha__) || defined(__alpha) || \ - defined(sequent) || defined(i386) || \ - defined(__ns32000__) || defined(__sun386__) || defined(__i386__) +#elif defined(ns32000) || defined(__ns32000__) || \ + defined(sun386) || defined(__sun386__) || \ + defined(__i386__) || defined(i386) || \ + defined(_M_IX86) || defined(_X86_) || defined(__THW_INTEL__) || \ + defined(__I86__) || defined(__INTEL__) || \ + defined(__amd64__) || defined(__ia64__) || defined(_M_IA64) || \ + defined(MIPSEL) || defined(_MIPSEL) || \ + defined(BIT_ZERO_ON_RIGHT) || \ + defined(__alpha__) || defined(__alpha) || \ + defined(sequent) #define IBMPC 1 /* Intel IEEE, low order words come first */ #define BIGENDIAN 0 From scipy-svn at scipy.org Wed Aug 9 17:10:09 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 9 Aug 2006 16:10:09 -0500 (CDT) Subject: [Scipy-svn] r2154 - trunk/Lib/special/cephes Message-ID: <20060809211009.DB15D39C0B8@new.scipy.org> Author: cookedm Date: 2006-08-09 16:10:08 -0500 (Wed, 09 Aug 2006) New Revision: 2154 Modified: trunk/Lib/special/cephes/cephes_names.h Log: special.cephes: add to so that it doesn't conflict with system's Modified: trunk/Lib/special/cephes/cephes_names.h =================================================================== --- trunk/Lib/special/cephes/cephes_names.h 2006-08-09 20:53:22 UTC (rev 2153) +++ trunk/Lib/special/cephes/cephes_names.h 2006-08-09 21:10:08 UTC (rev 2154) @@ -22,6 +22,7 @@ #define exp1m cephes_exp1m #define exp2 cephes_exp2 #define expn cephes_expn +#define fabs cephes_fabs #define fdtrc cephes_fdtrc #define fdtr cephes_fdtr #define fdtri cephes_fdtri From scipy-svn at scipy.org Wed Aug 9 17:59:14 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 9 Aug 2006 16:59:14 -0500 (CDT) Subject: [Scipy-svn] r2155 - trunk/Lib/optimize/lbfgsb Message-ID: <20060809215914.40F5C39C016@new.scipy.org> Author: cookedm Date: 2006-08-09 16:59:12 -0500 (Wed, 09 Aug 2006) New Revision: 2155 Modified: trunk/Lib/optimize/lbfgsb/routines.f Log: Fix #210: L-BFGS-B performs very badly due to silent failures. The LAPACK routine `dpotrs` was being used for the linpack routine `dtrsl`, instead of `dtrtrs`. Modified: trunk/Lib/optimize/lbfgsb/routines.f =================================================================== --- trunk/Lib/optimize/lbfgsb/routines.f 2006-08-09 21:10:08 UTC (rev 2154) +++ trunk/Lib/optimize/lbfgsb/routines.f 2006-08-09 21:59:12 UTC (rev 2155) @@ -1,4 +1,7 @@ c Modified for SciPy by removing dependency on linpack +c - dnrm2, daxpy, dcopy, ddot, dscal are the same in linpack and +c LAPACK +c - wrappers that call LAPACK are used for dtrsl and dpofa c================ L-BFGS-B (version 2.1) ========================== subroutine setulb(n, m, x, l, u, nbd, f, g, factr, pgtol, wa, iwa, @@ -1100,7 +1103,6 @@ c c Subprograms called: c -c CHANGED to use dpotrs from LAPACK c Linpack ... dtrsl. c c @@ -1135,8 +1137,7 @@ p(i2) = v(i2) + sum 20 continue c Solve the triangular system -c call dtrsl(wt,m,col,p(col+1),11,info) - call dpotrs('L',col,1,wt,m,p(col+1),col,info) + call dtrsl(wt,m,col,p(col+1),11,info) if (info .ne. 0) return c solve D^(1/2)p1=v1. @@ -1148,8 +1149,7 @@ c [ 0 J' ] [ p2 ] [ p2 ]. c solve J^Tp2=p2. -c call dtrsl(wt,m,col,p(col+1),01,info) - call dpotrs('U',col,1,wt,m,p(col+1),col,info) + call dtrsl(wt,m,col,p(col+1),01,info) if (info .ne. 0) return c compute p1=-D^(-1/2)(p1-D^(-1/2)L'p2) @@ -1902,7 +1902,6 @@ c c Subprograms called: c -c Changed to LAPACK and BLAS ROUTINES ... dcopy, dpotrf, c Linpack ... dcopy, dpofa, dtrsl. c c @@ -2088,7 +2087,7 @@ c first Cholesky factor (1,1) block of wn to get LL' c with L' stored in the upper triangle of wn. - call dpotrf('U',col,wn,m2,info) + call dpofa(wn,m2,col,info) if (info .ne. 0) then info = -1 return @@ -2096,8 +2095,7 @@ c then form L^-1(-L_a'+R_z') in the (1,2) block. col2 = 2*col do 71 js = col+1 ,col2 -c call dtrsl(wn,m2,col,wn(1,js),11,info) - call dpotrs('L',col,1,wn,m2,wn(1,js),col,info) + call dtrsl(wn,m2,col,wn(1,js),11,info) 71 continue c Form S'AA'S*theta + (L^-1(-L_a'+R_z'))'L^-1(-L_a'+R_z') in the @@ -2112,7 +2110,7 @@ c Cholesky factorization of (2,2) block of wn. - call dpotrf('U',col,wn(col+1,col+1),m2,info) + call dpofa(wn(col+1,col+1),m2,col,info) if (info .ne. 0) then info = -2 return @@ -2140,7 +2138,6 @@ c c Subprograms called: c -c CHANGED to LAPACK -- dpotrf c Linpack ... dpofa. c c @@ -2182,7 +2179,7 @@ c Cholesky factorize T to J*J' with c J' stored in the upper triangle of wt. - call dpotrf('U',col,wt,m,info) + call dpofa(wt,m,col,info) if (info .ne. 0) then info = -3 endif @@ -3125,14 +3122,12 @@ m2 = 2*m col2 = 2*col -c call dtrsl(wn,m2,col2,wv,11,info) - call dpotrs('L',col2,1,wn,m2,wv,col2,info) + call dtrsl(wn,m2,col2,wv,11,info) if (info .ne. 0) return do 25 i = 1, col wv(i) = -wv(i) 25 continue -c call dtrsl(wn,m2,col2,wv,01,info) - call dpotrs('U',col2,1,wn,m2,wv,col2,info) + call dtrsl(wn,m2,col2,wv,01,info) if (info .ne. 0) return c Compute d = (1/theta)d + (1/theta**2)Z'W wv. @@ -3960,5 +3955,120 @@ 70 return end - + c====================== The end of dpmeps ============================== + + subroutine dpofa(a,lda,n,info) + integer lda,n,info + double precision a(lda,1) +c +c dpofa factors a double precision symmetric positive definite +c matrix. +c +c dpofa is usually called by dpoco, but it can be called +c directly with a saving in time if rcond is not needed. +c (time for dpoco) = (1 + 18/n)*(time for dpofa) . +c +c on entry +c +c a double precision(lda, n) +c the symmetric matrix to be factored. only the +c diagonal and upper triangle are used. +c +c lda integer +c the leading dimension of the array a . +c +c n integer +c the order of the matrix a . +c +c on return +c +c a an upper triangular matrix r so that a = trans(r)*r +c where trans(r) is the transpose. +c the strict lower triangle is unaltered. +c if info .ne. 0 , the factorization is not complete. +c +c info integer +c = 0 for normal return. +c = k signals an error condition. the leading minor +c of order k is not positive definite. +c +c This is just a wrapper that calls LAPACK, but with the LINPACK +c calling convention. + + call dpotrf('U', n, a, lda, info) + end + +c====================== The end of dpofa =============================== + + subroutine dtrsl(t, ldt, n, b, job, info) + integer ldt, n, job, info + double precision t(ldt,1), b(1) +c +c +c dtrsl solves systems of the form +c +c t * x = b +c or +c trans(t) * x = b +c +c where t is a triangular matrix of order n. here trans(t) +c denotes the transpose of the matrix t. +c +c on entry +c +c t double precision(ldt,n) +c t contains the matrix of the system. the zero +c elements of the matrix are not referenced, and +c the corresponding elements of the array can be +c used to store other information. +c +c ldt integer +c ldt is the leading dimension of the array t. +c +c n integer +c n is the order of the system. +c +c b double precision(n). +c b contains the right hand side of the system. +c +c job integer +c job specifies what kind of system is to be solved. +c if job is +c +c 00 solve t*x=b, t lower triangular, +c 01 solve t*x=b, t upper triangular, +c 10 solve trans(t)*x=b, t lower triangular, +c 11 solve trans(t)*x=b, t upper triangular. +c +c on return +c +c b b contains the solution, if info .eq. 0. +c otherwise b is unaltered. +c +c info integer +c info contains zero if the system is nonsingular. +c otherwise info contains the index of +c the first zero diagonal element of t. +c +c This is just a wrapper that calls LAPACK, but with the LINPACK +c calling convention. + + character*1 uplo, trans + + if (job .eq. 00) then + uplo = 'L' + trans = 'N' + else if (job .eq. 01) then + uplo = 'U' + trans = 'N' + else if (job .eq. 10) then + uplo = 'L' + trans = 'T' + else if (job .eq. 11) then + uplo = 'U' + trans = 'T' + endif + call dtrtrs(uplo, trans, 'N', n, 1, t, ldt, b, n, info) + end +c====================== The end of dtrsl ============================== From scipy-svn at scipy.org Wed Aug 9 18:43:19 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 9 Aug 2006 17:43:19 -0500 (CDT) Subject: [Scipy-svn] r2156 - trunk/Lib/weave/scxx Message-ID: <20060809224319.BE41239C00C@new.scipy.org> Author: oliphant Date: 2006-08-09 17:43:16 -0500 (Wed, 09 Aug 2006) New Revision: 2156 Modified: trunk/Lib/weave/scxx/object.h Log: Fix up weave to understand more objects in conversion to Python scalars. Modified: trunk/Lib/weave/scxx/object.h =================================================================== --- trunk/Lib/weave/scxx/object.h 2006-08-09 21:59:12 UTC (rev 2155) +++ trunk/Lib/weave/scxx/object.h 2006-08-09 22:43:16 UTC (rev 2156) @@ -126,25 +126,41 @@ }; operator int () const { - if (!PyInt_Check(_obj)) - fail(PyExc_TypeError, "cannot convert value to integer"); - return PyInt_AsLong(_obj); + int _val; + _val = PyInt_AsLong(_obj); + if (PyErr_Occurred()) { + PyErr_Clear(); + fail(PyExc_TypeError, "cannot convert value to integer"); + } + return _val; }; operator float () const { - if (!PyFloat_Check(_obj)) + float _val; + _val = (float) PyFloat_AsDouble(_obj); + if (PyErr_Occurred()) { + PyErr_Clear(); fail(PyExc_TypeError, "cannot convert value to float"); - return (float) PyFloat_AsDouble(_obj); + } + return _val; }; operator double () const { - if (!PyFloat_Check(_obj)) + double _val; + _val = PyFloat_AsDouble(_obj); + if (PyErr_Occurred()) { + PyErr_Clear(); fail(PyExc_TypeError, "cannot convert value to double"); - return PyFloat_AsDouble(_obj); + } + return _val; }; operator std::complex () const { - if (!PyComplex_Check(_obj)) + double real, imag; + real = PyComplex_RealAsDouble(_obj); + imag = PyComplex_ImagAsDouble(_obj); + if (PyErr_Occurred()) { + PyErr_Clear(); fail(PyExc_TypeError, "cannot convert value to complex"); - return std::complex(PyComplex_RealAsDouble(_obj), - PyComplex_ImagAsDouble(_obj)); + } + return std::complex(real, imag); }; operator std::string () const { if (!PyString_Check(_obj)) From scipy-svn at scipy.org Sat Aug 12 17:37:55 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 12 Aug 2006 16:37:55 -0500 (CDT) Subject: [Scipy-svn] r2157 - in trunk/Lib/io: . tests tests/data Message-ID: <20060812213755.3D62639C05D@new.scipy.org> Author: stefan Date: 2006-08-12 16:37:25 -0500 (Sat, 12 Aug 2006) New Revision: 2157 Added: trunk/Lib/io/tests/data/ trunk/Lib/io/tests/data/test3dmatrix_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testcell_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testcellnest_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testcomplex_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testdouble_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testmatrix_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testminus_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testobject_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testonechar_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testsparse_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/testsparsecomplex_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/teststring_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/teststringarray_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/teststruct_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/teststructarr_6.5.1_GLNX86.mat trunk/Lib/io/tests/data/teststructnest_6.5.1_GLNX86.mat trunk/Lib/io/tests/test_mio.py Modified: trunk/Lib/io/mio.py Log: Improved MATLAB file reading [for Matthew Brett and Nick Fotopoulos]. Modified: trunk/Lib/io/mio.py =================================================================== --- trunk/Lib/io/mio.py 2006-08-09 22:43:16 UTC (rev 2156) +++ trunk/Lib/io/mio.py 2006-08-12 21:37:25 UTC (rev 2157) @@ -2,22 +2,21 @@ # Author: Travis Oliphant -from numpy import squeeze -from numpy import ndarray -from numpy import * -import numpyio import struct, os, sys import types +from tempfile import mkstemp +import zlib + +from numpy import array, asarray, empty, obj2sctype, product, reshape, \ + squeeze, transpose, zeros, vstack, ndarray, shape, diff, where, uint8 +import numpyio + try: import scipy.sparse have_sparse = 1 except ImportError: have_sparse = 0 -if sys.version_info[0] < 2 or sys.version_info[1] < 3: - False = 0 - True = 1 - LittleEndian = (sys.byteorder == 'little') _unit_imag = {'f': array(1j,'F'), 'd': 1j} @@ -29,7 +28,7 @@ mtype = 'B' elif mtype in ['S1', 'char', 'char*1']: mtype = 'B' - elif mtype in ['h','schar', 'signed char']: + elif mtype in ['b', 'schar', 'signed char']: mtype = 'b' elif mtype in ['h','short','int16','integer*2']: mtype = 'h' @@ -37,7 +36,7 @@ mtype = 'H' elif mtype in ['i','int']: mtype = 'i' - elif mtype in ['i','uint','uint32','unsigned int']: + elif mtype in ['I','uint','uint32','unsigned int']: mtype = 'I' elif mtype in ['l','long','int32','integer*4']: mtype = 'l' @@ -55,57 +54,9 @@ newarr = empty((1,),mtype) return newarr.itemsize, newarr.dtype.char -if sys.version[:3] < "2.2": - class file: - def __init__(self, name, mode='r', bufsize=-1): - self.fid = open(name, mode, bufsize) +class fopen(object): + """Class for reading and writing binary files into numpy arrays. - def close(self): - self.fid.close() - - def flush(self): - self.fid.flush() - - def fileno(self): - return self.fid.fileno() - - def isatty(self): - return self.fid.isatty() - - def read(size=-1): - return self.fid.read(size) - - def readline(size=-1): - return self.fid.readlines() - - def readlines(sizehint=None): - if sizehint is None: - return self.fid.readlines() - else: - return self.fid.readlines(sizehint) - - def seek(offset, whence=0): - self.fid.seek(offset, whence) - - def tell(): - return self.fid.tell() - - def truncate(size=None): - if size is None: - self.fid.truncate() - else: - self.fid.truncate(size) - - def write(str): - self.fid.write(str) - - def writelines(sequence): - self.fid.write(sequence) - - -class fopen(file): - """Class for reading and writing binary files into Numeric arrays. - Inputs: file_name -- The complete path name to the file to open. @@ -127,8 +78,8 @@ # Methods: # -# read -- read data from file and return Numeric array -# write -- write to file from Numeric array +# read -- read data from file and return numpy array +# write -- write to file from numpy array # fort_read -- read Fortran-formatted binary data from the file. # fort_write -- write Fortran-formatted binary data to the file. # rewind -- rewind to beginning of file @@ -136,67 +87,63 @@ # seek -- seek to some position in the file # tell -- return current position in file # close -- close the file -# -# -# def __init__(self,file_name,permission='rb',format='n'): - if 'B' not in permission: permission += 'B' - if type(file_name) in (types.StringType, types.UnicodeType): - file.__init__(self, file_name, permission) - elif 'fileno' in file_name.__methods__: # first argument is an open file - self = file_name - - if format in ['native','n','default']: - self.__dict__['bs'] = 0 - self.__dict__['format'] = 'native' - elif format in ['ieee-le','l','little-endian','le']: - self.__dict__['bs'] = not LittleEndian - self.__dict__['format'] = 'ieee-le' - elif format in ['ieee-be','B','big-endian','be']: - self.__dict__['bs'] = LittleEndian - self.__dict__['format'] = 'ieee-be' + if 'b' not in permission: permission += 'b' + if isinstance(file_name, basestring): + self.file = file(file_name, permission) + elif isinstance(file_name, file) and not file_name.closed: + # first argument is an open file + self.file = file_name else: - raise ValueError, "Unrecognized format: " + format - -# def __setattr__(self, attribute): -# raise SyntaxError, "There are no user-settable attributes." - + raise TypeError, 'Need filename or open file as input' + self.setformat(format) + self.zbuffer = None + def __del__(self): try: - self.close() + self.file.close() except: pass + def close(self): + self.file.close() + + def seek(self, *args): + self.file.seek(*args) + + def tell(self): + self.file.tell() + def raw_read(self, size=-1): """Read raw bytes from file as string.""" - return file.read(self, size) + return self.file.read(size) def raw_write(self, str): """Write string to file as raw bytes.""" - return file.read(self, str) + return self.file.write(str) def setformat(self, format): """Set the byte-order of the file.""" if format in ['native','n','default']: - self.__dict__['bs'] = False - self.__dict__['format'] = 'native' + self.bs = False + self.format = 'native' elif format in ['ieee-le','l','little-endian','le']: - self.__dict__['bs'] = not LittleEndian - self.__dict__['format'] = 'ieee-le' + self.bs = not LittleEndian + self.format = 'ieee-le' elif format in ['ieee-be','B','big-endian','be']: - self.__dict__['bs'] = LittleEndian - self.__dict__['format'] = 'ieee-be' + self.bs = LittleEndian + self.format = 'ieee-be' else: raise ValueError, "Unrecognized format: " + format return def write(self,data,mtype=None,bs=None): - """Write to open file object the flattened Numeric array data. + """Write to open file object the flattened numpy array data. Inputs: - data -- the Numeric array to write. + data -- the numpy array to write. mtype -- a string indicating the binary type to write. The default is the type of data. If necessary a cast is made. unsigned byte : 'B', 'uchar', 'byte' 'unsigned char', 'int8', @@ -227,13 +174,13 @@ mtype = data.dtype.char howmany,mtype = getsize_type(mtype) count = product(data.shape) - numpyio.fwrite(self,count,data,mtype,bs) + numpyio.fwrite(self.file,count,data,mtype,bs) return fwrite = write def read(self,count,stype,rtype=None,bs=None,c_is_b=0): - """Read data from file and return it in a Numeric array. + """Read data from file and return it in a numpy array. Inputs: @@ -249,7 +196,7 @@ Outputs: (output,) - output -- a Numeric array of type rtype. + output -- a numpy array of type rtype. """ if bs is None: bs = self.bs @@ -291,9 +238,7 @@ howmany,rtype = getsize_type(rtype) if count == 0: return zeros(0,rtype) - retval = numpyio.fread(self, count, stype, rtype, bs) - if len(retval) == 1: - retval = retval[0] + retval = numpyio.fread(self.file, count, stype, rtype, bs) if shape is not None: retval = resize(retval, shape) return retval @@ -301,7 +246,7 @@ fread = read def rewind(self,howmany=None): - """Rewind a file to it's beginning or by a specified amount. + """Rewind a file to its beginning or by a specified amount. """ if howmany is None: self.seek(0) @@ -318,7 +263,7 @@ self.seek(0,2) sz = self.tell() self.seek(curpos) - self.__dict__['thesize'] = sz + self.thesize = sz return sz def fort_write(self,fmt,*args): @@ -341,7 +286,7 @@ nfmt = ">i" else: nfmt = "i" - if type(fmt) in (types.StringType, types.UnicodeType): + if isinstance(fmt, basestring): if self.format == 'ieee-le': fmt = "<"+fmt elif self.format == 'ieee-be': @@ -359,7 +304,7 @@ count = product(fmt.shape) strlen = struct.pack(nfmt,count*sz) self.write(strlen) - numpyio.fwrite(self.fid,count,fmt,mtype,self.bs) + numpyio.fwrite(self.file,count,fmt,mtype,self.bs) self.write(strlen) else: raise TypeError, "Unknown type in first argument" @@ -412,14 +357,42 @@ raise ValueError, "Negative number of bytes to read:\n file is probably not opened with correct endian-ness." if ncount == 0: raise ValueError, "End of file? Zero-bytes to read." - retval = numpyio.fread(self, ncount, dtype, dtype, self.bs) + retval = numpyio.fread(self.file, ncount, dtype, dtype, self.bs) if len(retval) == 1: retval = retval[0] if (self.raw_read(nn) == ''): raise ValueError, "Unexpected end of file..." return retval + +class CompressedFopen(fopen): + """ File container for temporary buffer to decompress data """ + def __init__(self, *args, **kwargs): + fd, fname = mkstemp() + super(CompressedFopen, self).__init__( + os.fdopen(fd, 'w+b'), *args, **kwargs) + self.file_name = fname + + def fill(self, bytes): + """ Uncompress buffer in @bytes and write to file """ + self.rewind() + self.raw_write(zlib.decompress(bytes)) + self.rewind() + def __del__(self): + try: + self.file.truncate(0) + except: + pass + try: + self.close() + except: + pass + try: + os.remove(self.file_name) + except: + pass + #### MATLAB Version 5 Support ########### # Portions of code borrowed and (heavily) adapted @@ -465,6 +438,10 @@ miINT64 =12 miUINT64 = 13 miMATRIX = 14 +miCOMPRESSED = 15 +miUTF8 = 16 +miUTF16 = 17 +miUTF32 = 18 miNumbers = ( miINT8, @@ -491,8 +468,23 @@ miINT64 : ('miINT64',8,'q'), miUINT64 : ('miUINT64',8,'Q'), miMATRIX : ('miMATRIX',0,None), + miUTF8 : ('miUTF8',1,'b'), + miUTF16 : ('miUTF16',2,'h'), + miUTF32 : ('miUTF32',4,'l'), } +''' Before release v7.1 (release 14) matlab used the system default +character encoding scheme padded out to 16-bits. Release 14 and later +use Unicode. When saving character data, matlab R14 checks if it can +be encoded in 7-bit ascii, and saves in that format if so.''' +miCodecs = { + miUINT8: 'ascii', + miUINT16: sys.getdefaultencoding(), + miUTF8: 'utf8', + miUTF16: 'utf16', + miUTF32: 'utf32', + } + mxCELL_CLASS = 1 mxSTRUCT_CLASS = 2 mxOBJECT_CLASS = 3 @@ -518,8 +510,8 @@ mxINT32_CLASS, mxUINT32_CLASS, ) - -def _parse_header(fid, dict): + +def _parse_header(fid, hdict): correct_endian = (ord('M')<<8) + ord('I') # if this number is read no BS fid.seek(126) # skip to endian detector @@ -531,12 +523,19 @@ else: openstr = 'l' fid.setformat(openstr) # change byte-order if necessary fid.rewind() - dict['__header__'] = fid.raw_read(124).strip(' \t\n\000') + hdict['__header__'] = fid.raw_read(124).strip(' \t\n\000') vers = fid.read(1,'int16') - dict['__version__'] = '%d.%d' % (vers >> 8, vers & 255) + hdict['__version__'] = '%d.%d' % (vers >> 8, vers & 0xFF) fid.seek(2,1) # move to start of data return +def _skip_padding(fid, numbytes, rowsize): + """ Skip to next row or @rowsize after previous read of @numbytes """ + mod = numbytes % rowsize + if mod: + skip = rowsize-mod + fid.seek(skip,1) + def _parse_array_flags(fid): # first 8 bytes are always miUINT32 and 8 --- just a check dtype, nbytes = fid.read(2,'I') @@ -545,8 +544,8 @@ # read array flags. rawflags = fid.read(2,'I') - class_ = rawflags[0] & 255 - flags = (rawflags[0] & 65535) >> 8 + class_ = rawflags[0] & 0xFF + flags = (rawflags[0] & 0xFFFF) >> 8 # Global and logical fields are currently ignored if (flags & 8): cmplx = 1 else: cmplx = 0 @@ -558,16 +557,34 @@ def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) - dims = _get_element(fid)[0] - name = asarray(_get_element(fid)[0]).tostring() + dims = _get_element(fid) + name = _get_element(fid).tostring() tupdims = tuple(dims[::-1]) if dclass in mxArrays: - result, unused =_get_element(fid) + result, unused, dtype =_get_element(fid, return_name_dtype=True) if dclass == mxCHAR_CLASS: - result = ''.join(asarray(result).astype('S1')) + en = miCodecs[dtype] + try: + " ".encode(en) + except LookupError: + raise ValueError, 'Character encoding %s not supported' % en + if dtype == miUINT16: + char_len = len(" ".encode(en)) - len(" ".encode(en)) + if char_len == 1: # Need to downsample from 16 bit + result = result.astype(uint8) + result = squeeze(transpose(reshape(result,tupdims))) + dims = result.shape + if len(dims) >= 2: # return array of strings + n_dims = dims[:-1] + string_arr = reshape(result, (product(n_dims), dims[-1])) + result = empty(n_dims, dtype=object) + for i in range(0, n_dims[-1]): + result[...,i] = string_arr[i].tostring().decode(en) + else: # return string + result = result.tostring().decode(en) else: if cmplx: - imag, unused =_get_element(fid) + imag =_get_element(fid) try: result = result + _unit_imag[imag.dtype.char] * imag except KeyError: @@ -576,139 +593,167 @@ elif dclass == mxCELL_CLASS: length = product(dims) - result = zeros(length, PyObject) + result = empty(length, dtype=object) for i in range(length): - sa, unused = _get_element(fid) - result[i]= sa + result[i] = _get_element(fid) result = squeeze(transpose(reshape(result,tupdims))) - if rank(result)==0: result = result.item() + if not result.shape: + result = result.item() elif dclass == mxSTRUCT_CLASS: length = product(dims) - result = zeros(length, PyObject) - namelength = _get_element(fid)[0] + result = zeros(length, object) + namelength = _get_element(fid) # get field names - names = _get_element(fid)[0] + names = _get_element(fid) splitnames = [names[i:i+namelength] for i in \ xrange(0,len(names),namelength)] - fieldnames = [''.join(asarray(x).astype('S1')).strip('\x00') + fieldnames = [x.tostring().strip('\x00') for x in splitnames] for i in range(length): result[i] = mat_struct() for element in fieldnames: - val,unused = _get_element(fid) - result[i].__dict__[element] = val + result[i].__dict__[element] = _get_element(fid) result = squeeze(transpose(reshape(result,tupdims))) - if rank(result)==0: result = result.item() + if not result.shape: + result = result.item() # object is like a structure with but with a class name elif dclass == mxOBJECT_CLASS: - class_name = ''.join(asarray(_get_element(fid)[0]).astype('S1')) + class_name = _get_element(fid).tostring() length = product(dims) - result = zeros(length, PyObject) - namelength = _get_element(fid)[0] + result = zeros(length, object) + namelength = _get_element(fid) # get field names - names = _get_element(fid)[0] + names = _get_element(fid) splitnames = [names[i:i+namelength] for i in \ xrange(0,len(names),namelength)] - fieldnames = [''.join(asarray(x).astype('S1')).strip('\x00') + fieldnames = [x.tostring().strip('\x00') for x in splitnames] for i in range(length): result[i] = mat_obj() result[i]._classname = class_name for element in fieldnames: - val,unused = _get_element(fid) - result[i].__dict__[element] = val + result[i].__dict__[element] = _get_element(fid) result = squeeze(transpose(reshape(result,tupdims))) - if rank(result)==0: result = result.item() + if not result.shape: + result = result.item() elif dclass == mxSPARSE_CLASS: - rowind, unused = _get_element(fid) - colind, unused = _get_element(fid) - res, unused = _get_element(fid) + rowind = _get_element(fid) + colind = _get_element(fid) + res = _get_element(fid) if cmplx: - imag, unused = _get_element(fid) + imag = _get_element(fid) try: res = res + _unit_imag[imag.dtype.char] * imag except (KeyError,AttributeError): res = res + 1j*imag + ''' From the matlab API documentation, last found here: + http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_external/ + @rowind are simply the row indices for all the (@res) non-zero + entries in the sparse array. @rowind has nzmax entries, so + may well have more entries than len(@res), the actual number + of non-zero entries, but @rowind[len(res):] can be discarded + and should be 0. @colind has length (number of columns + 1), + and is such that, if D = diff(@colind), D[j] gives the number + of non-zero entries in column j. Because @rowind values are + stored in column order, this gives the column corresponding to + each @rowind + ''' + cols = empty((len(res)), dtype=rowind.dtype) + col_counts = diff(colind) + start_row = 0 + for i in where(col_counts)[0]: + end_row = start_row + col_counts[i] + cols[start_row:end_row] = i + start_row = end_row + ij = vstack((rowind[:len(res)], cols)) if have_sparse: - spmat = scipy.sparse.csc_matrix(res, (rowind[:len(res)], colind), - M=dims[0],N=dims[1]) - result = spmat + result = scipy.sparse.csc_matrix((res,ij), [dims[0],dims[1]]) else: - result = (dims, rowind, colind, res) + result = (dims, ij, res) return result, name # Return a Python object for the element -def _get_element(fid): +def _get_element(fid, return_name_dtype=False): + """ Return a python object from next element in @fid + @fid - fopen object for matfile + @return_name_dtype - if True, return tuple of (element, name, dtype) + if False, return element only + """ + name = None test = fid.raw_read(1) if len(test) == 0: # nothing left raise EOFError else: fid.rewind(1) # get the data tag - raw_tag = fid.read(1,'I') - - # check for compressed + raw_tag = int(fid.read(1,'I')) + + # check for small data element format numbytes = raw_tag >> 16 - if numbytes > 0: # compressed format + if numbytes > 0: # small data element format if numbytes > 4: raise IOError, "Problem with MAT file: " \ - "too many bytes in compressed format." - dtype = raw_tag & 65535 + "too many bytes in small data element format." + dtype = int(raw_tag & 0xFFFF) el = fid.read(numbytes,miDataTypes[dtype][2],c_is_b=1) fid.seek(4-numbytes,1) # skip padding - return el, None + else: + # otherwise parse tag + dtype = raw_tag + numbytes = fid.read(1,'I') + + if dtype == miCOMPRESSED: # compressed data type + if not fid.zbuffer: + fid.zbuffer = CompressedFopen(format=fid.format) + fid.zbuffer.fill(fid.raw_read(numbytes)) + _skip_padding(fid, numbytes, 8) + return _get_element(fid.zbuffer, return_name_dtype) + if dtype != miMATRIX: # basic data type + try: + el = fid.read(numbytes,miDataTypes[dtype][2],c_is_b=1) + except KeyError: + raise ValueError, "Unknown data type" + _skip_padding(fid, numbytes, 8) + else: + # handle miMatrix type + el, name = _parse_mimatrix(fid,numbytes) - # otherwise parse tag - dtype = raw_tag - numbytes = fid.read(1,'I') - if dtype != miMATRIX: # basic data type - try: - outarr = fid.read(numbytes,miDataTypes[dtype][2],c_is_b=1) - except KeyError: - raise ValueError, "Unknown data type" - mod8 = numbytes%8 - if mod8: # skip past padding - skip = 8-mod8 - fid.seek(skip,1) - return outarr, None + if return_name_dtype: + return el, name, dtype + return el - # handle miMatrix type - el, name = _parse_mimatrix(fid,numbytes) - return el, name - def _loadv5(fid,basename): - # return a dictionary from a Matlab version 5 file + # return a dictionary from a Matlab version 5-7.1 file # always contains the variable __header__ - dict = {} - _parse_header(fid,dict) + mdict = {} + _parse_header(fid,mdict) var = 0 while 1: # file pointer to start of next data try: var = var + 1 - el, varname = _get_element(fid) + el, varname, unused = _get_element(fid, return_name_dtype=True) if varname is None: varname = '%s_%04d' % (basename,var) - dict[varname] = el + mdict[varname] = el except EOFError: break - return dict + return mdict ### END MATLAB v5 support ############# -def loadmat(name, dict=None, appendmat=1, basename='raw'): +def loadmat(name, mdict=None, appendmat=1, basename='raw'): """Load the MATLAB(tm) mat file. If name is a full path name load it in. Otherwise search for the file on the sys.path list and load the first one found (the current directory is searched first). - Both v4 (Level 1.0) and v6 matfiles are supported. Version 7.0 files - are not yet supported. + v4 (Level 1.0), v6 and v7.1 matfiles are supported. Inputs: @@ -754,13 +799,13 @@ if not (0 in test_vals): # MATLAB version 5 format fid.rewind() thisdict = _loadv5(fid,basename) - if dict is not None: - dict.update(thisdict) + if mdict is not None: + mdict.update(thisdict) return else: return thisdict - - + + # The remainder of this function is the v4 codepath testtype = struct.unpack('i',test_vals.tostring()) # Check to see if the number is positive and less than 5000. if testtype[0] < 0 or testtype[0] > 4999: @@ -816,12 +861,13 @@ data = atleast_1d(fid.fread(numels,storage)) if header[3]: # imaginary data data2 = fid.fread(numels,storage) - new = zeros(data.shape,data.dtype.char.capitalize()) - new.real = data - new.imag = data2 - data = new - del(new) - del(data2) + if data.dtype.char == 'f' and data2.dtype.char == 'f': + new = empty(data.shape,'F') + new.real = data + new.imag = data2 + data = new + del(new) + del(data2) if len(data) > 1: data=data.reshape((header[2], header[1]) ) thisdict[varname] = transpose(squeeze(data)) @@ -836,14 +882,14 @@ thisdict[varname] = data fid.close() - if dict is not None: + if mdict is not None: print "Names defined = ", defnames - dict.update(thisdict) + mdict.update(thisdict) else: return thisdict -def savemat(filename, dict): +def savemat(filename, mdict): """Save a dictionary of names and arrays into the MATLAB-style .mat file. This saves the arrayobjects in the given dictionary to a matlab Version 4 @@ -855,8 +901,8 @@ fid = fopen(filename,'wb') M = not LittleEndian O = 0 - for variable in dict.keys(): - var = dict[variable] + for variable in mdict.keys(): + var = mdict[variable] if not isinstance(var, ndarray): continue if var.dtype.char == 'S1': Added: trunk/Lib/io/tests/data/test3dmatrix_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/test3dmatrix_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testcell_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testcell_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testcellnest_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testcellnest_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testcomplex_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testcomplex_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testdouble_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testdouble_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testmatrix_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testmatrix_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testminus_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testminus_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testobject_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testobject_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testonechar_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testonechar_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testsparse_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testsparse_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/testsparsecomplex_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testsparsecomplex_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/teststring_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/teststring_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/teststringarray_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/teststringarray_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/teststruct_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/teststruct_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/teststructarr_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/teststructarr_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/data/teststructnest_6.5.1_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/teststructnest_6.5.1_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Added: trunk/Lib/io/tests/test_mio.py =================================================================== --- trunk/Lib/io/tests/test_mio.py 2006-08-09 22:43:16 UTC (rev 2156) +++ trunk/Lib/io/tests/test_mio.py 2006-08-12 21:37:25 UTC (rev 2157) @@ -0,0 +1,195 @@ +#!/usr/bin/env python + +import os +from glob import glob +from numpy.testing import set_package_path, restore_path, ScipyTestCase, ScipyTest +from numpy.testing import assert_equal, assert_array_almost_equal +from numpy import arange, array, eye, pi, cos, exp, sin, sqrt, ndarray, \ + zeros, reshape, transpose, empty +import scipy.sparse as SP + +set_package_path() +from scipy.io.mio import loadmat, mat_obj, mat_struct +restore_path() + +try: # Python 2.3 support + from sets import Set as set +except: + pass + +class test_mio_array(ScipyTestCase): + def __init__(self, *args, **kwargs): + super(test_mio_array, self).__init__(*args, **kwargs) + self.test_data_path = os.path.join(os.path.dirname(__file__), './data') + + def _check_level(self, label, expected, actual): + """ Check one level of a potentially nested dictionary / list """ + # object array is returned from cell array in mat file + if isinstance(expected, ndarray) and expected.dtype.hasobject == 1: + assert type(expected) is type(actual), "Different types at %s" % label + assert len(expected) == len(actual), "Different list lengths at %s" % label + for i, ev in enumerate(expected): + level_label = "%s, [%d], " % (label, i) + self._check_level(level_label, ev, actual[i]) + return + # object, as container for matlab structs and objects + elif isinstance(expected, mat_struct) or isinstance(expected, mat_obj): + assert isinstance(actual, type(expected)), \ + "Different types %s and %s at %s" % label + ex_fields = dir(expected) + ac_fields = dir(actual) + for k in ex_fields: + if k.startswith('__') and k.endswith('__'): + continue + assert k in ac_fields, "Missing field at %s" % label + ev = expected.__dict__[k] + v = actual.__dict__[k] + level_label = "%s, field %s, " % (label, k) + self._check_level(level_label, ev, v) + return + # hoping this is a single value, which might be an array + if SP.issparse(expected): + assert SP.issparse(actual), "Expected sparse at %s" % label + assert_array_almost_equal(actual.todense(), + expected.todense(), + err_msg = label) + elif isinstance(expected, ndarray): + assert isinstance(actual, ndarray), "Expected ndarray at %s" % label + assert_array_almost_equal(actual, expected, err_msg=label) + else: + assert isinstance(expected, type(actual)), \ + "Types %s and %s do not match at %s" % (type(expected), type(actual), label) + assert_equal(actual, expected, err_msg=label) + + def _check_case(self, name, case): + filt = os.path.join(self.test_data_path, 'test%s_*.mat' % name) + files = glob(filt) + assert files, "No files for test %s using filter %s" % (name, filt) + for f in files: + matdict = loadmat(f) + label = "Test '%s', file:%s" % (name, f) + for k, expected in case.items(): + k_label = "%s, variable %s" % (label, k) + assert k in matdict, "Missing key at %s" % k_label + self._check_level(k_label, expected, matdict[k]) + + # Add the actual tests dynamically, with given parameters + def _make_check_case(name, expected): + def cc(self): + self._check_case(name, expected) + cc.__doc__ = "check loadmat case %s" % name + return cc + + # Define cases to test + theta = pi/4*arange(9,dtype=float) + case_table = [ + {'name': 'double', + 'expected': {'testdouble': theta} + }] + case_table.append( + {'name': 'string', + 'expected': {'teststring': u'"Do nine men interpret?" "Nine men," I nod.'}, + }) + case_table.append( + {'name': 'complex', + 'expected': {'testcomplex': cos(theta) + 1j*sin(theta)} + }) + case_table.append( + {'name': 'cell', + 'expected': {'testcell': + array([u'This cell contains this string and 3 arrays of '+\ + 'increasing length', + array([1]), array([1,2]), array([1,2,3])], + dtype=object)} + }) + st = mat_struct() + st.stringfield = u'Rats live on no evil star.' + st.doublefield = array([sqrt(2),exp(1),pi]) + st.complexfield = (1+1j)*array([sqrt(2),exp(1),pi]) + case_table.append( + {'name': 'struct', + 'expected': {'teststruct': st} + }) + A = zeros((3,5)) + A[0] = range(1,6) + A[:,0] = range(1,4) + case_table.append( + {'name': 'matrix', + 'expected': {'testmatrix': A}, + }) + case_table.append( + {'name': '3dmatrix', + 'expected': {'test3dmatrix': transpose(reshape(range(1,25), (4,3,2)))} + }) + case_table.append( + {'name': 'sparse', + 'expected': {'testsparse': SP.csc_matrix(A)}, + }) + B = A.astype(complex) + B[0,0] += 1j + case_table.append( + {'name': 'sparsecomplex', + 'expected': {'testsparsecomplex': SP.csc_matrix(B)}, + }) + case_table.append( + {'name': 'minus', + 'expected': {'testminus': array([-1])}, + }) + case_table.append( + {'name': 'onechar', + 'expected': {'testonechar': u'r'}, + }) + case_table.append( + {'name': 'stringarray', + 'expected': {'teststringarray': array([u'one ', u'two ', u'three'], dtype=object)}, + }) + case_table.append( + {'name': 'cellnest', + 'expected': {'testcellnest': array([array([1]), + array([array([2]), array([3]), + array([array([4]), array([5])], + dtype=object)], + dtype=object)], + dtype=object)}, + }) + st = mat_struct() + st.one = array([1]) + st.two = mat_struct() + st.two.three = u'number 3' + case_table.append( + {'name': 'structnest', + 'expected': {'teststructnest': st} + }) + a = empty((2), dtype=object) + a[0], a[1] = mat_struct(), mat_struct() + a[0].one = array([1]) + a[0].two = array([2]) + a[1].one = u'number 1' + a[1].two = u'number 2' + case_table.append( + {'name': 'structarr', + 'expected': {'teststructarr': a} + }) + + a = mat_obj() + a._classname = 'inline' + a.expr = u'x' + a.inputExpr = u' x = INLINE_INPUTS_{1};' + a.args = u'x' + a.isEmpty = array([0]) + a.numArgs = array([1]) + a.version = array([1]) + case_table.append( + {'name': 'object', + 'expected': {'testobject': a} + }) + + # add tests + for case in case_table: + name = case['name'] + expected = case['expected'] + exec 'check_%s = _make_check_case(name, expected)' % name + +if __name__ == "__main__": + ScipyTest().run() + From scipy-svn at scipy.org Sat Aug 12 19:04:37 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 12 Aug 2006 18:04:37 -0500 (CDT) Subject: [Scipy-svn] r2158 - trunk/Lib/interpolate Message-ID: <20060812230437.E011339C051@new.scipy.org> Author: stefan Date: 2006-08-12 18:04:32 -0500 (Sat, 12 Aug 2006) New Revision: 2158 Modified: trunk/Lib/interpolate/fitpack.py Log: Improve user-friendliness of splrep [for David Huard]. Closes ticket #199. Modified: trunk/Lib/interpolate/fitpack.py =================================================================== --- trunk/Lib/interpolate/fitpack.py 2006-08-12 21:37:25 UTC (rev 2157) +++ trunk/Lib/interpolate/fitpack.py 2006-08-12 23:04:32 UTC (rev 2158) @@ -49,7 +49,7 @@ fp gives the upper bound fp0 for the smoothing factor s""",None], 1:["""\ The required storage space exceeds the available strorage space. - Probably causes: nest to small or s is too small. (fp>s)""",ValueError], + Probably causes: data (x,y) size is too small or smoothing parameter s is too small (fp>s).""",ValueError], 2:["""\ A theoretically impossible results when finding a smoothin spline with fp = s. Probably causes: s too small. (abs(fp-s)/s>0.001)""",ValueError], @@ -246,7 +246,7 @@ _curfit_cache = {'t': array([],float), 'wrk': array([],float), 'iwrk':array([],int)} -def splrep(x,y,w=None,xb=None,xe=None,k=3,task=0,s=None,t=None, +def splrep(x,y,w=None,xb=None,xe=None,k=3,task=0,s=1e-3,t=None, full_output=0,per=0,quiet=1): """Find the B-spline representation of 1-D curve. @@ -308,7 +308,17 @@ Remarks: - SEE splev for evaluation of the spline and its derivatives. + See splev for evaluation of the spline and its derivatives. + + Example: + + x = linspace(0, 10, 10) + y = sin(x) + tck = splrep(x, y) + x2 = linspace(0, 10, 200) + y2 = splev(x2, tck) + plot(x, y, 'o', x2, y2) + """ if task<=0: _curfit_cache = {} From scipy-svn at scipy.org Sun Aug 13 21:27:12 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 13 Aug 2006 20:27:12 -0500 (CDT) Subject: [Scipy-svn] r2159 - in trunk/Lib: integrate/odepack integrate/quadpack interpolate/fitpack Message-ID: <20060814012712.9EB5139C049@new.scipy.org> Author: cookedm Date: 2006-08-13 20:26:54 -0500 (Sun, 13 Aug 2006) New Revision: 2159 Modified: trunk/Lib/integrate/odepack/intdy.f trunk/Lib/integrate/odepack/lsoda.f trunk/Lib/integrate/odepack/lsodar.f trunk/Lib/integrate/odepack/lsode.f trunk/Lib/integrate/odepack/lsodes.f trunk/Lib/integrate/odepack/lsodi.f trunk/Lib/integrate/odepack/lsoibt.f trunk/Lib/integrate/odepack/xerrwv.f trunk/Lib/integrate/quadpack/dqag.f trunk/Lib/integrate/quadpack/dqagi.f trunk/Lib/integrate/quadpack/dqagp.f trunk/Lib/integrate/quadpack/dqags.f trunk/Lib/integrate/quadpack/dqawc.f trunk/Lib/integrate/quadpack/dqawf.f trunk/Lib/integrate/quadpack/dqawo.f trunk/Lib/integrate/quadpack/dqaws.f trunk/Lib/integrate/quadpack/dqng.f trunk/Lib/interpolate/fitpack/fppola.f trunk/Lib/interpolate/fitpack/fpsphe.f trunk/Lib/interpolate/fitpack/fpsurf.f Log: Move some Fortran code out of the 1960's: remove Hollerith constants Modified: trunk/Lib/integrate/odepack/intdy.f =================================================================== --- trunk/Lib/integrate/odepack/intdy.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/odepack/intdy.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -69,14 +69,14 @@ 60 dky(i) = r*dky(i) return c - 80 call xerrwv(30hintdy-- k (=i1) illegal , + 80 call xerrwv('intdy-- k (=i1) illegal ', 1 30, 51, 0, 1, k, 0, 0, 0.0d0, 0.0d0) iflag = -1 return - 90 call xerrwv(30hintdy-- t (=r1) illegal , + 90 call xerrwv('intdy-- t (=r1) illegal ', 1 30, 52, 0, 0, 0, 0, 1, t, 0.0d0) call xerrwv( - 1 60h t not in interval tcur - hu (= r1) to tcur (=r2) , + 1 ' t not in interval tcur - hu (= r1) to tcur (=r2) ', 1 60, 52, 0, 0, 0, 0, 2, tp, tn) iflag = -2 return Modified: trunk/Lib/integrate/odepack/lsoda.f =================================================================== --- trunk/Lib/integrate/odepack/lsoda.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/odepack/lsoda.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -164,15 +164,15 @@ c call lsoda(fex,neq,y,t,tout,itol,rtol,atol,itask,istate, c 1 iopt,rwork,lrw,iwork,liw,jdum,jt) c write(6,20)t,y(1),y(2),y(3) -c 20 format(7h at t =,e12.4,6h y =,3e14.6) +c 20 format(' at t =',e12.4,' y =',3e14.6) c if (istate .lt. 0) go to 80 c 40 tout = tout*10.0d0 c write(6,60)iwork(11),iwork(12),iwork(13),iwork(19),rwork(15) -c 60 format(/12h no. steps =,i4,11h no. f-s =,i4,11h no. j-s =,i4/ -c 1 19h method last used =,i2,25h last switch was at t =,e12.4) +c 60 format(/' no. steps =',i4,' no. f-s =',i4,' no. j-s =',i4/ +c 1 ' method last used =',i2,' last switch was at t =',e12.4) c stop c 80 write(6,90)istate -c 90 format(///22h error halt.. istate =,i3) +c 90 format(///' error halt.. istate =',i3) c stop c end c @@ -1134,13 +1134,13 @@ insufr = 2 lewt = len1c + 1 call xerrwv( - 1 60hlsoda-- warning.. rwork length is sufficient for now, but , + 1 'lsoda-- warning.. rwork length is sufficient for now, but ', 1 60, 103, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h may not be later. integration will proceed anyway. , + 1 ' may not be later. integration will proceed anyway. ', 1 60, 103, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 50h length needed is lenrw = i1, while lrw = i2., + 1 ' length needed is lenrw = i1, while lrw = i2.', 1 50, 103, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) 65 lsavf = lewt + n lacor = lsavf + n @@ -1148,13 +1148,13 @@ if (liw .ge. leniw) go to 70 insufi = 2 call xerrwv( - 1 60hlsoda-- warning.. iwork length is sufficient for now, but , + 1 'lsoda-- warning.. iwork length is sufficient for now, but ', 1 60, 104, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h may not be later. integration will proceed anyway. , + 1 ' may not be later. integration will proceed anyway. ', 1 60, 104, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 50h length needed is leniw = i1, while liw = i2., + 1 ' length needed is leniw = i1, while liw = i2.', 1 50, 104, 0, 2, leniw, liw, 0, 0.0d0, 0.0d0) 70 continue c check rtol and atol for legality. ------------------------------------ @@ -1334,17 +1334,17 @@ 280 if ((tn + h) .ne. tn) go to 290 nhnil = nhnil + 1 if (nhnil .gt. mxhnil) go to 290 - call xerrwv(50hlsoda-- warning..internal t (=r1) and h (=r2) are, + call xerrwv('lsoda-- warning..internal t (=r1) and h (=r2) are', 1 50, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h such that in the machine, t + h = t on the next step , + 1 ' such that in the machine, t + h = t on the next step ', 1 60, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h (h = step size). solver will continue anyway, + call xerrwv(' (h = step size). solver will continue anyway', 1 50, 101, 0, 0, 0, 0, 2, tn, h) if (nhnil .lt. mxhnil) go to 290 - call xerrwv(50hlsoda-- above warning has been issued i1 times. , + call xerrwv('lsoda-- above warning has been issued i1 times. ', 1 50, 102, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h it will not be issued again for this problem, + call xerrwv(' it will not be issued again for this problem', 1 50, 102, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) 290 continue c----------------------------------------------------------------------- @@ -1375,13 +1375,13 @@ jstart = -1 if (ixpr .eq. 0) go to 310 if (meth .eq. 2) call xerrwv( - 1 60hlsoda-- a switch to the bdf (stiff) method has occurred , + 1 'lsoda-- a switch to the bdf (stiff) method has occurred ', 1 60, 105, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) if (meth .eq. 1) call xerrwv( - 1 60hlsoda-- a switch to the adams (nonstiff) method has occurred, + 1 'lsoda-- a switch to the adams (nonstiff) method has occurred', 1 60, 106, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h at t = r1, tentative step size h = r2, step nst = i1 , + 1 ' at t = r1, tentative step size h = r2, step nst = i1 ', 1 60, 107, 0, 1, nst, 0, 2, tn, h) 310 go to (320, 400, 330, 340, 350), itask c itask = 1. if tout has been reached, interpolate. ------------------- @@ -1440,7 +1440,7 @@ 430 ntrep = ntrep + 1 if (ntrep .lt. 5) return call xerrwv( - 1 60hlsoda-- repeated calls with istate = 1 and tout = t (=r1) , + 1 'lsoda-- repeated calls with istate = 1 and tout = t (=r1) ', 1 60, 301, 0, 0, 0, 0, 1, t, 0.0d0) go to 800 c----------------------------------------------------------------------- @@ -1453,55 +1453,55 @@ c the work arrays before returning. c----------------------------------------------------------------------- c the maximum number of steps was taken before reaching tout. ---------- - 500 call xerrwv(50hlsoda-- at current t (=r1), mxstep (=i1) steps , + 500 call xerrwv('lsoda-- at current t (=r1), mxstep (=i1) steps ', 1 50, 201, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h taken on this call before reaching tout , + call xerrwv(' taken on this call before reaching tout ', 1 50, 201, 0, 1, mxstep, 0, 1, tn, 0.0d0) istate = -1 go to 580 c ewt(i) .le. 0.0 for some i (not at start of problem). ---------------- 510 ewti = rwork(lewt+i-1) - call xerrwv(50hlsoda-- at t (=r1), ewt(i1) has become r2 .le. 0., + call xerrwv('lsoda-- at t (=r1), ewt(i1) has become r2 .le. 0.', 1 50, 202, 0, 1, i, 0, 2, tn, ewti) istate = -6 go to 580 c too much accuracy requested for machine precision. ------------------- - 520 call xerrwv(50hlsoda-- at t (=r1), too much accuracy requested , + 520 call xerrwv('lsoda-- at t (=r1), too much accuracy requested ', 1 50, 203, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h for precision of machine.. see tolsf (=r2) , + call xerrwv(' for precision of machine.. see tolsf (=r2) ', 1 50, 203, 0, 0, 0, 0, 2, tn, tolsf) rwork(14) = tolsf istate = -2 go to 580 c kflag = -1. error test failed repeatedly or with abs(h) = hmin. ----- - 530 call xerrwv(50hlsoda-- at t(=r1) and step size h(=r2), the error, + 530 call xerrwv('lsoda-- at t(=r1) and step size h(=r2), the error', 1 50, 204, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h test failed repeatedly or with abs(h) = hmin, + call xerrwv(' test failed repeatedly or with abs(h) = hmin', 1 50, 204, 0, 0, 0, 0, 2, tn, h) istate = -4 go to 560 c kflag = -2. convergence failed repeatedly or with abs(h) = hmin. ---- - 540 call xerrwv(50hlsoda-- at t (=r1) and step size h (=r2), the , + 540 call xerrwv('lsoda-- at t (=r1) and step size h (=r2), the ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h corrector convergence failed repeatedly , + call xerrwv(' corrector convergence failed repeatedly ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(30h or with abs(h) = hmin , + call xerrwv(' or with abs(h) = hmin ', 1 30, 205, 0, 0, 0, 0, 2, tn, h) istate = -5 go to 560 c rwork length too small to proceed. ----------------------------------- - 550 call xerrwv(50hlsoda-- at current t(=r1), rwork length too small, + 550 call xerrwv('lsoda-- at current t(=r1), rwork length too small', 1 50, 206, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h to proceed. the integration was otherwise successful., + 1 ' to proceed. the integration was otherwise successful.', 1 60, 206, 0, 0, 0, 0, 1, tn, 0.0d0) istate = -7 go to 580 c iwork length too small to proceed. ----------------------------------- - 555 call xerrwv(50hlsoda-- at current t(=r1), iwork length too small, + 555 call xerrwv('lsoda-- at current t(=r1), iwork length too small', 1 50, 207, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h to proceed. the integration was otherwise successful., + 1 ' to proceed. the integration was otherwise successful.', 1 60, 207, 0, 0, 0, 0, 1, tn, 0.0d0) istate = -7 go to 580 @@ -1540,114 +1540,114 @@ c 5 consecutive such returns just before this call to the solver, c the run is halted. c----------------------------------------------------------------------- - 601 call xerrwv(30hlsoda-- istate (=i1) illegal , + 601 call xerrwv('lsoda-- istate (=i1) illegal ', 1 30, 1, 0, 1, istate, 0, 0, 0.0d0, 0.0d0) go to 700 - 602 call xerrwv(30hlsoda-- itask (=i1) illegal , + 602 call xerrwv('lsoda-- itask (=i1) illegal ', 1 30, 2, 0, 1, itask, 0, 0, 0.0d0, 0.0d0) go to 700 - 603 call xerrwv(50hlsoda-- istate .gt. 1 but lsoda not initialized , + 603 call xerrwv('lsoda-- istate .gt. 1 but lsoda not initialized ', 1 50, 3, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) go to 700 - 604 call xerrwv(30hlsoda-- neq (=i1) .lt. 1 , + 604 call xerrwv('lsoda-- neq (=i1) .lt. 1 ', 1 30, 4, 0, 1, neq(1), 0, 0, 0.0d0, 0.0d0) go to 700 - 605 call xerrwv(50hlsoda-- istate = 3 and neq increased (i1 to i2) , + 605 call xerrwv('lsoda-- istate = 3 and neq increased (i1 to i2) ', 1 50, 5, 0, 2, n, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 606 call xerrwv(30hlsoda-- itol (=i1) illegal , + 606 call xerrwv('lsoda-- itol (=i1) illegal ', 1 30, 6, 0, 1, itol, 0, 0, 0.0d0, 0.0d0) go to 700 - 607 call xerrwv(30hlsoda-- iopt (=i1) illegal , + 607 call xerrwv('lsoda-- iopt (=i1) illegal ', 1 30, 7, 0, 1, iopt, 0, 0, 0.0d0, 0.0d0) go to 700 - 608 call xerrwv(30hlsoda-- jt (=i1) illegal , + 608 call xerrwv('lsoda-- jt (=i1) illegal ', 1 30, 8, 0, 1, jt, 0, 0, 0.0d0, 0.0d0) go to 700 - 609 call xerrwv(50hlsoda-- ml (=i1) illegal.. .lt.0 or .ge.neq (=i2), + 609 call xerrwv('lsoda-- ml (=i1) illegal.. .lt.0 or .ge.neq (=i2)', 1 50, 9, 0, 2, ml, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 610 call xerrwv(50hlsoda-- mu (=i1) illegal.. .lt.0 or .ge.neq (=i2), + 610 call xerrwv('lsoda-- mu (=i1) illegal.. .lt.0 or .ge.neq (=i2)', 1 50, 10, 0, 2, mu, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 611 call xerrwv(30hlsoda-- ixpr (=i1) illegal , + 611 call xerrwv('lsoda-- ixpr (=i1) illegal ', 1 30, 11, 0, 1, ixpr, 0, 0, 0.0d0, 0.0d0) go to 700 - 612 call xerrwv(30hlsoda-- mxstep (=i1) .lt. 0 , + 612 call xerrwv('lsoda-- mxstep (=i1) .lt. 0 ', 1 30, 12, 0, 1, mxstep, 0, 0, 0.0d0, 0.0d0) go to 700 - 613 call xerrwv(30hlsoda-- mxhnil (=i1) .lt. 0 , + 613 call xerrwv('lsoda-- mxhnil (=i1) .lt. 0 ', 1 30, 13, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) go to 700 - 614 call xerrwv(40hlsoda-- tout (=r1) behind t (=r2) , + 614 call xerrwv('lsoda-- tout (=r1) behind t (=r2) ', 1 40, 14, 0, 0, 0, 0, 2, tout, t) - call xerrwv(50h integration direction is given by h0 (=r1) , + call xerrwv(' integration direction is given by h0 (=r1) ', 1 50, 14, 0, 0, 0, 0, 1, h0, 0.0d0) go to 700 - 615 call xerrwv(30hlsoda-- hmax (=r1) .lt. 0.0 , + 615 call xerrwv('lsoda-- hmax (=r1) .lt. 0.0 ', 1 30, 15, 0, 0, 0, 0, 1, hmax, 0.0d0) go to 700 - 616 call xerrwv(30hlsoda-- hmin (=r1) .lt. 0.0 , + 616 call xerrwv('lsoda-- hmin (=r1) .lt. 0.0 ', 1 30, 16, 0, 0, 0, 0, 1, hmin, 0.0d0) go to 700 617 call xerrwv( - 1 60hlsoda-- rwork length needed, lenrw (=i1), exceeds lrw (=i2), + 1 'lsoda-- rwork length needed, lenrw (=i1), exceeds lrw (=i2)', 1 60, 17, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 618 call xerrwv( - 1 60hlsoda-- iwork length needed, leniw (=i1), exceeds liw (=i2), + 1 'lsoda-- iwork length needed, leniw (=i1), exceeds liw (=i2)', 1 60, 18, 0, 2, leniw, liw, 0, 0.0d0, 0.0d0) go to 700 - 619 call xerrwv(40hlsoda-- rtol(i1) is r1 .lt. 0.0 , + 619 call xerrwv('lsoda-- rtol(i1) is r1 .lt. 0.0 ', 1 40, 19, 0, 1, i, 0, 1, rtoli, 0.0d0) go to 700 - 620 call xerrwv(40hlsoda-- atol(i1) is r1 .lt. 0.0 , + 620 call xerrwv('lsoda-- atol(i1) is r1 .lt. 0.0 ', 1 40, 20, 0, 1, i, 0, 1, atoli, 0.0d0) go to 700 621 ewti = rwork(lewt+i-1) - call xerrwv(40hlsoda-- ewt(i1) is r1 .le. 0.0 , + call xerrwv('lsoda-- ewt(i1) is r1 .le. 0.0 ', 1 40, 21, 0, 1, i, 0, 1, ewti, 0.0d0) go to 700 622 call xerrwv( - 1 60hlsoda-- tout (=r1) too close to t(=r2) to start integration, + 1 'lsoda-- tout (=r1) too close to t(=r2) to start integration', 1 60, 22, 0, 0, 0, 0, 2, tout, t) go to 700 623 call xerrwv( - 1 60hlsoda-- itask = i1 and tout (=r1) behind tcur - hu (= r2) , + 1 'lsoda-- itask = i1 and tout (=r1) behind tcur - hu (= r2) ', 1 60, 23, 0, 1, itask, 0, 2, tout, tp) go to 700 624 call xerrwv( - 1 60hlsoda-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) , + 1 'lsoda-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) ', 1 60, 24, 0, 0, 0, 0, 2, tcrit, tn) go to 700 625 call xerrwv( - 1 60hlsoda-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) , + 1 'lsoda-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) ', 1 60, 25, 0, 0, 0, 0, 2, tcrit, tout) go to 700 - 626 call xerrwv(50hlsoda-- at start of problem, too much accuracy , + 626 call xerrwv('lsoda-- at start of problem, too much accuracy ', 1 50, 26, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h requested for precision of machine.. see tolsf (=r1) , + 1 ' requested for precision of machine.. see tolsf (=r1) ', 1 60, 26, 0, 0, 0, 0, 1, tolsf, 0.0d0) rwork(14) = tolsf go to 700 - 627 call xerrwv(50hlsoda-- trouble from intdy. itask = i1, tout = r1, + 627 call xerrwv('lsoda-- trouble from intdy. itask = i1, tout = r1', 1 50, 27, 0, 1, itask, 0, 1, tout, 0.0d0) go to 700 - 628 call xerrwv(30hlsoda-- mxordn (=i1) .lt. 0 , + 628 call xerrwv('lsoda-- mxordn (=i1) .lt. 0 ', 1 30, 28, 0, 1, mxordn, 0, 0, 0.0d0, 0.0d0) go to 700 - 629 call xerrwv(30hlsoda-- mxords (=i1) .lt. 0 , + 629 call xerrwv('lsoda-- mxords (=i1) .lt. 0 ', 1 30, 29, 0, 1, mxords, 0, 0, 0.0d0, 0.0d0) c 700 if (illin .eq. 5) go to 710 illin = illin + 1 istate = -3 return - 710 call xerrwv(50hlsoda-- repeated occurrences of illegal input , + 710 call xerrwv('lsoda-- repeated occurrences of illegal input ', 1 50, 302, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) c - 800 call xerrwv(50hlsoda-- run aborted.. apparent infinite loop , + 800 call xerrwv('lsoda-- run aborted.. apparent infinite loop ', 1 50, 303, 2, 0, 0, 0, 0, 0.0d0, 0.0d0) return c----------------------- end of subroutine lsoda ----------------------- Modified: trunk/Lib/integrate/odepack/lsodar.f =================================================================== --- trunk/Lib/integrate/odepack/lsodar.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/odepack/lsodar.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -206,22 +206,22 @@ c 10 call lsodar(fex,neq,y,t,tout,itol,rtol,atol,itask,istate, c 1 iopt,rwork,lrw,iwork,liw,jdum,jt,gex,ng,jroot) c write(6,20)t,y(1),y(2),y(3) -c 20 format(7h at t =,e12.4,6h y =,3e14.6) +c 20 format(' at t =',e12.4,' y =',3e14.6) c if (istate .lt. 0) go to 80 c if (istate .eq. 2) go to 40 c write(6,30)jroot(1),jroot(2) -c 30 format(5x,35h the above line is a root, jroot =,2i5) +c 30 format(5x,' the above line is a root, jroot =',2i5) c istate = 2 c go to 10 c 40 tout = tout*10.0d0 c write(6,60)iwork(11),iwork(12),iwork(13),iwork(10), c 1 iwork(19),rwork(15) -c 60 format(/12h no. steps =,i4,11h no. f-s =,i4,11h no. j-s =,i4, -c 1 11h no. g-s =,i4/ -c 2 19h method last used =,i2,25h last switch was at t =,e12.4) +c 60 format(/' no. steps =',i4,' no. f-s =',i4,' no. j-s =',i4, +c 1 ' no. g-s =',i4/ +c 2 ' method last used =',i2,' last switch was at t =',e12.4) c stop c 80 write(6,90)istate -c 90 format(///22h error halt.. istate =,i3) +c 90 format(///' error halt.. istate =',i3) c stop c end c @@ -1277,13 +1277,13 @@ insufr = 2 lewt = len1c + 1 call xerrwv( - 1 60hlsodar- warning.. rwork length is sufficient for now, but , + 1 'lsodar- warning.. rwork length is sufficient for now, but ', 1 60, 103, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h may not be later. integration will proceed anyway. , + 1 ' may not be later. integration will proceed anyway. ', 1 60, 103, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 50h length needed is lenrw = i1, while lrw = i2., + 1 ' length needed is lenrw = i1, while lrw = i2.', 1 50, 103, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) 65 lsavf = lewt + n lacor = lsavf + n @@ -1291,13 +1291,13 @@ if (liw .ge. leniw) go to 70 insufi = 2 call xerrwv( - 1 60hlsodar- warning.. iwork length is sufficient for now, but , + 1 'lsodar- warning.. iwork length is sufficient for now, but ', 1 60, 104, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h may not be later. integration will proceed anyway. , + 1 ' may not be later. integration will proceed anyway. ', 1 60, 104, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 50h length needed is leniw = i1, while liw = i2., + 1 ' length needed is leniw = i1, while liw = i2.', 1 50, 104, 0, 2, leniw, liw, 0, 0.0d0, 0.0d0) 70 continue c check rtol and atol for legality. ------------------------------------ @@ -1503,17 +1503,17 @@ 280 if ((tn + h) .ne. tn) go to 290 nhnil = nhnil + 1 if (nhnil .gt. mxhnil) go to 290 - call xerrwv(50hlsodar- warning..internal t (=r1) and h (=r2) are, + call xerrwv('lsodar- warning..internal t (=r1) and h (=r2) are', 1 50, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h such that in the machine, t + h = t on the next step , + 1 ' such that in the machine, t + h = t on the next step ', 1 60, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h (h = step size). solver will continue anyway, + call xerrwv(' (h = step size). solver will continue anyway', 1 50, 101, 0, 0, 0, 0, 2, tn, h) if (nhnil .lt. mxhnil) go to 290 - call xerrwv(50hlsodar- above warning has been issued i1 times. , + call xerrwv('lsodar- above warning has been issued i1 times. ', 1 50, 102, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h it will not be issued again for this problem, + call xerrwv(' it will not be issued again for this problem', 1 50, 102, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) 290 continue c----------------------------------------------------------------------- @@ -1545,13 +1545,13 @@ jstart = -1 if (ixpr .eq. 0) go to 310 if (meth .eq. 2) call xerrwv( - 1 60hlsodar- a switch to the bdf (stiff) method has occurred , + 1 'lsodar- a switch to the bdf (stiff) method has occurred ', 1 60, 105, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) if (meth .eq. 1) call xerrwv( - 1 60hlsodar- a switch to the adams (nonstiff) method has occurred, + 1 'lsodar- a switch to the adams (nonstiff) method has occurred', 1 60, 106, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h at t = r1, tentative step size h = r2, step nst = i1 , + 1 ' at t = r1, tentative step size h = r2, step nst = i1 ', 1 60, 107, 0, 1, nst, 0, 2, tn, h) 310 continue c @@ -1625,7 +1625,7 @@ 430 ntrep = ntrep + 1 if (ntrep .lt. 5) return call xerrwv( - 1 60hlsodar- repeated calls with istate = 1 and tout = t (=r1) , + 1 'lsodar- repeated calls with istate = 1 and tout = t (=r1) ', 1 60, 301, 0, 0, 0, 0, 1, t, 0.0d0) go to 800 c----------------------------------------------------------------------- @@ -1638,55 +1638,55 @@ c the work arrays before returning. c----------------------------------------------------------------------- c the maximum number of steps was taken before reaching tout. ---------- - 500 call xerrwv(50hlsodar- at current t (=r1), mxstep (=i1) steps , + 500 call xerrwv('lsodar- at current t (=r1), mxstep (=i1) steps ', 1 50, 201, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h taken on this call before reaching tout , + call xerrwv(' taken on this call before reaching tout ', 1 50, 201, 0, 1, mxstep, 0, 1, tn, 0.0d0) istate = -1 go to 580 c ewt(i) .le. 0.0 for some i (not at start of problem). ---------------- 510 ewti = rwork(lewt+i-1) - call xerrwv(50hlsodar- at t (=r1), ewt(i1) has become r2 .le. 0., + call xerrwv('lsodar- at t (=r1), ewt(i1) has become r2 .le. 0.', 1 50, 202, 0, 1, i, 0, 2, tn, ewti) istate = -6 go to 580 c too much accuracy requested for machine precision. ------------------- - 520 call xerrwv(50hlsodar- at t (=r1), too much accuracy requested , + 520 call xerrwv('lsodar- at t (=r1), too much accuracy requested ', 1 50, 203, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h for precision of machine.. see tolsf (=r2) , + call xerrwv(' for precision of machine.. see tolsf (=r2) ', 1 50, 203, 0, 0, 0, 0, 2, tn, tolsf) rwork(14) = tolsf istate = -2 go to 580 c kflag = -1. error test failed repeatedly or with abs(h) = hmin. ----- - 530 call xerrwv(50hlsodar- at t(=r1) and step size h(=r2), the error, + 530 call xerrwv('lsodar- at t(=r1) and step size h(=r2), the error', 1 50, 204, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h test failed repeatedly or with abs(h) = hmin, + call xerrwv(' test failed repeatedly or with abs(h) = hmin', 1 50, 204, 0, 0, 0, 0, 2, tn, h) istate = -4 go to 560 c kflag = -2. convergence failed repeatedly or with abs(h) = hmin. ---- - 540 call xerrwv(50hlsodar- at t (=r1) and step size h (=r2), the , + 540 call xerrwv('lsodar- at t (=r1) and step size h (=r2), the ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h corrector convergence failed repeatedly , + call xerrwv(' corrector convergence failed repeatedly ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(30h or with abs(h) = hmin , + call xerrwv(' or with abs(h) = hmin ', 1 30, 205, 0, 0, 0, 0, 2, tn, h) istate = -5 go to 560 c rwork length too small to proceed. ----------------------------------- - 550 call xerrwv(50hlsodar- at current t(=r1), rwork length too small, + 550 call xerrwv('lsodar- at current t(=r1), rwork length too small', 1 50, 206, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h to proceed. the integration was otherwise successful., + 1 ' to proceed. the integration was otherwise successful.', 1 60, 206, 0, 0, 0, 0, 1, tn, 0.0d0) istate = -7 go to 580 c iwork length too small to proceed. ----------------------------------- - 555 call xerrwv(50hlsodar- at current t(=r1), iwork length too small, + 555 call xerrwv('lsodar- at current t(=r1), iwork length too small', 1 50, 207, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h to proceed. the integration was otherwise successful., + 1 ' to proceed. the integration was otherwise successful.', 1 60, 207, 0, 0, 0, 0, 1, tn, 0.0d0) istate = -7 go to 580 @@ -1727,117 +1727,117 @@ c 5 consecutive such returns just before this call to the solver, c the run is halted. c----------------------------------------------------------------------- - 601 call xerrwv(30hlsodar- istate (=i1) illegal , + 601 call xerrwv('lsodar- istate (=i1) illegal ', 1 30, 1, 0, 1, istate, 0, 0, 0.0d0, 0.0d0) go to 700 - 602 call xerrwv(30hlsodar- itask (=i1) illegal , + 602 call xerrwv('lsodar- itask (=i1) illegal ', 1 30, 2, 0, 1, itask, 0, 0, 0.0d0, 0.0d0) go to 700 - 603 call xerrwv(50hlsodar- istate .gt. 1 but lsodar not initialized , + 603 call xerrwv('lsodar- istate .gt. 1 but lsodar not initialized ', 1 50, 3, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) go to 700 - 604 call xerrwv(30hlsodar- neq (=i1) .lt. 1 , + 604 call xerrwv('lsodar- neq (=i1) .lt. 1 ', 1 30, 4, 0, 1, neq(1), 0, 0, 0.0d0, 0.0d0) go to 700 - 605 call xerrwv(50hlsodar- istate = 3 and neq increased (i1 to i2) , + 605 call xerrwv('lsodar- istate = 3 and neq increased (i1 to i2) ', 1 50, 5, 0, 2, n, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 606 call xerrwv(30hlsodar- itol (=i1) illegal , + 606 call xerrwv('lsodar- itol (=i1) illegal ', 1 30, 6, 0, 1, itol, 0, 0, 0.0d0, 0.0d0) go to 700 - 607 call xerrwv(30hlsodar- iopt (=i1) illegal , + 607 call xerrwv('lsodar- iopt (=i1) illegal ', 1 30, 7, 0, 1, iopt, 0, 0, 0.0d0, 0.0d0) go to 700 - 608 call xerrwv(30hlsodar- jt (=i1) illegal , + 608 call xerrwv('lsodar- jt (=i1) illegal ', 1 30, 8, 0, 1, jt, 0, 0, 0.0d0, 0.0d0) go to 700 - 609 call xerrwv(50hlsodar- ml (=i1) illegal.. .lt.0 or .ge.neq (=i2), + 609 call xerrwv('lsodar- ml (=i1) illegal.. .lt.0 or .ge.neq (=i2)', 1 50, 9, 0, 2, ml, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 610 call xerrwv(50hlsodar- mu (=i1) illegal.. .lt.0 or .ge.neq (=i2), + 610 call xerrwv('lsodar- mu (=i1) illegal.. .lt.0 or .ge.neq (=i2)', 1 50, 10, 0, 2, mu, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 611 call xerrwv(30hlsodar- ixpr (=i1) illegal , + 611 call xerrwv('lsodar- ixpr (=i1) illegal ', 1 30, 11, 0, 1, ixpr, 0, 0, 0.0d0, 0.0d0) go to 700 - 612 call xerrwv(30hlsodar- mxstep (=i1) .lt. 0 , + 612 call xerrwv('lsodar- mxstep (=i1) .lt. 0 ', 1 30, 12, 0, 1, mxstep, 0, 0, 0.0d0, 0.0d0) go to 700 - 613 call xerrwv(30hlsodar- mxhnil (=i1) .lt. 0 , + 613 call xerrwv('lsodar- mxhnil (=i1) .lt. 0 ', 1 30, 13, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) go to 700 - 614 call xerrwv(40hlsodar- tout (=r1) behind t (=r2) , + 614 call xerrwv('lsodar- tout (=r1) behind t (=r2) ', 1 40, 14, 0, 0, 0, 0, 2, tout, t) - call xerrwv(50h integration direction is given by h0 (=r1) , + call xerrwv(' integration direction is given by h0 (=r1) ', 1 50, 14, 0, 0, 0, 0, 1, h0, 0.0d0) go to 700 - 615 call xerrwv(30hlsodar- hmax (=r1) .lt. 0.0 , + 615 call xerrwv('lsodar- hmax (=r1) .lt. 0.0 ', 1 30, 15, 0, 0, 0, 0, 1, hmax, 0.0d0) go to 700 - 616 call xerrwv(30hlsodar- hmin (=r1) .lt. 0.0 , + 616 call xerrwv('lsodar- hmin (=r1) .lt. 0.0 ', 1 30, 16, 0, 0, 0, 0, 1, hmin, 0.0d0) go to 700 617 call xerrwv( - 1 60hlsodar- rwork length needed, lenrw (=i1), exceeds lrw (=i2), + 1 'lsodar- rwork length needed, lenrw (=i1), exceeds lrw (=i2)', 1 60, 17, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 618 call xerrwv( - 1 60hlsodar- iwork length needed, leniw (=i1), exceeds liw (=i2), + 1 'lsodar- iwork length needed, leniw (=i1), exceeds liw (=i2)', 1 60, 18, 0, 2, leniw, liw, 0, 0.0d0, 0.0d0) go to 700 - 619 call xerrwv(40hlsodar- rtol(i1) is r1 .lt. 0.0 , + 619 call xerrwv('lsodar- rtol(i1) is r1 .lt. 0.0 ', 1 40, 19, 0, 1, i, 0, 1, rtoli, 0.0d0) go to 700 - 620 call xerrwv(40hlsodar- atol(i1) is r1 .lt. 0.0 , + 620 call xerrwv('lsodar- atol(i1) is r1 .lt. 0.0 ', 1 40, 20, 0, 1, i, 0, 1, atoli, 0.0d0) go to 700 621 ewti = rwork(lewt+i-1) - call xerrwv(40hlsodar- ewt(i1) is r1 .le. 0.0 , + call xerrwv('lsodar- ewt(i1) is r1 .le. 0.0 ', 1 40, 21, 0, 1, i, 0, 1, ewti, 0.0d0) go to 700 622 call xerrwv( - 1 60hlsodar- tout (=r1) too close to t(=r2) to start integration, + 1 'lsodar- tout (=r1) too close to t(=r2) to start integration', 1 60, 22, 0, 0, 0, 0, 2, tout, t) go to 700 623 call xerrwv( - 1 60hlsodar- itask = i1 and tout (=r1) behind tcur - hu (= r2) , + 1 'lsodar- itask = i1 and tout (=r1) behind tcur - hu (= r2) ', 1 60, 23, 0, 1, itask, 0, 2, tout, tp) go to 700 624 call xerrwv( - 1 60hlsodar- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) , + 1 'lsodar- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) ', 1 60, 24, 0, 0, 0, 0, 2, tcrit, tn) go to 700 625 call xerrwv( - 1 60hlsodar- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) , + 1 'lsodar- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) ', 1 60, 25, 0, 0, 0, 0, 2, tcrit, tout) go to 700 - 626 call xerrwv(50hlsodar- at start of problem, too much accuracy , + 626 call xerrwv('lsodar- at start of problem, too much accuracy ', 1 50, 26, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h requested for precision of machine.. see tolsf (=r1) , + 1 ' requested for precision of machine.. see tolsf (=r1) ', 1 60, 26, 0, 0, 0, 0, 1, tolsf, 0.0d0) rwork(14) = tolsf go to 700 - 627 call xerrwv(50hlsodar- trouble from intdy. itask = i1, tout = r1, + 627 call xerrwv('lsodar- trouble from intdy. itask = i1, tout = r1', 1 50, 27, 0, 1, itask, 0, 1, tout, 0.0d0) go to 700 - 628 call xerrwv(30hlsodar- mxordn (=i1) .lt. 0 , + 628 call xerrwv('lsodar- mxordn (=i1) .lt. 0 ', 1 30, 28, 0, 1, mxordn, 0, 0, 0.0d0, 0.0d0) go to 700 - 629 call xerrwv(30hlsodar- mxords (=i1) .lt. 0 , + 629 call xerrwv('lsodar- mxords (=i1) .lt. 0 ', 1 30, 29, 0, 1, mxords, 0, 0, 0.0d0, 0.0d0) go to 700 - 630 call xerrwv(30hlsodar- ng (=i1) .lt. 0 , + 630 call xerrwv('lsodar- ng (=i1) .lt. 0 ', 1 30, 30, 0, 1, ng, 0, 0, 0.0d0, 0.0d0) go to 700 - 631 call xerrwv(50hlsodar- ng changed (from i1 to i2) illegally, , + 631 call xerrwv('lsodar- ng changed (from i1 to i2) illegally, ', 1 50, 31, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h i.e. not immediately after a root was found , + call xerrwv(' i.e. not immediately after a root was found ', 1 50, 31, 0, 2, ngc, ng, 0, 0.0d0, 0.0d0) go to 700 - 632 call xerrwv(50hlsodar- one or more components of g has a root , + 632 call xerrwv('lsodar- one or more components of g has a root ', 1 50, 32, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(40h too near to the initial point , + call xerrwv(' too near to the initial point ', 1 40, 32, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) c 700 if (illin .eq. 5) go to 710 @@ -1845,10 +1845,10 @@ tlast = t istate = -3 return - 710 call xerrwv(50hlsodar- repeated occurrences of illegal input , + 710 call xerrwv('lsodar- repeated occurrences of illegal input ', 1 50, 302, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) c - 800 call xerrwv(50hlsodar- run aborted.. apparent infinite loop , + 800 call xerrwv('lsodar- run aborted.. apparent infinite loop ', 1 50, 303, 2, 0, 0, 0, 0, 0.0d0, 0.0d0) return c----------------------- end of subroutine lsodar ---------------------- Modified: trunk/Lib/integrate/odepack/lsode.f =================================================================== --- trunk/Lib/integrate/odepack/lsode.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/odepack/lsode.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -178,14 +178,14 @@ c call lsode(fex,neq,y,t,tout,itol,rtol,atol,itask,istate, c 1 iopt,rwork,lrw,iwork,liw,jex,mf) c write(6,20)t,y(1),y(2),y(3) -c 20 format(7h at t =,e12.4,6h y =,3e14.6) +c 20 format(' at t =',e12.4,' y =',3e14.6) c if (istate .lt. 0) go to 80 c 40 tout = tout*10.d0 c write(6,60)iwork(11),iwork(12),iwork(13) -c 60 format(/12h no. steps =,i4,11h no. f-s =,i4,11h no. j-s =,i4) +c 60 format(/' no. steps =',i4,' no. f-s =',i4,' no. j-s =',i4) c stop c 80 write(6,90)istate -c 90 format(///22h error halt.. istate =,i3) +c 90 format(///' error halt.. istate =',i3) c stop c end c @@ -1243,17 +1243,17 @@ 280 if ((tn + h) .ne. tn) go to 290 nhnil = nhnil + 1 if (nhnil .gt. mxhnil) go to 290 - call xerrwv(50hlsode-- warning..internal t (=r1) and h (=r2) are, + call xerrwv('lsode-- warning..internal t (=r1) and h (=r2) are', 1 50, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h such that in the machine, t + h = t on the next step , + 1 ' such that in the machine, t + h = t on the next step ', 1 60, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h (h = step size). solver will continue anyway, + call xerrwv(' (h = step size). solver will continue anyway', 1 50, 101, 0, 0, 0, 0, 2, tn, h) if (nhnil .lt. mxhnil) go to 290 - call xerrwv(50hlsode-- above warning has been issued i1 times. , + call xerrwv('lsode-- above warning has been issued i1 times. ', 1 50, 102, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h it will not be issued again for this problem, + call xerrwv(' it will not be issued again for this problem', 1 50, 102, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) 290 continue c----------------------------------------------------------------------- @@ -1324,7 +1324,7 @@ 430 ntrep = ntrep + 1 if (ntrep .lt. 5) return call xerrwv( - 1 60hlsode-- repeated calls with istate = 1 and tout = t (=r1) , + 1 'lsode-- repeated calls with istate = 1 and tout = t (=r1) ', 1 60, 301, 0, 0, 0, 0, 1, t, 0.0d0) go to 800 c----------------------------------------------------------------------- @@ -1337,39 +1337,39 @@ c the work arrays before returning. c----------------------------------------------------------------------- c the maximum number of steps was taken before reaching tout. ---------- - 500 call xerrwv(50hlsode-- at current t (=r1), mxstep (=i1) steps , + 500 call xerrwv('lsode-- at current t (=r1), mxstep (=i1) steps ', 1 50, 201, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h taken on this call before reaching tout , + call xerrwv(' taken on this call before reaching tout ', 1 50, 201, 0, 1, mxstep, 0, 1, tn, 0.0d0) istate = -1 go to 580 c ewt(i) .le. 0.0 for some i (not at start of problem). ---------------- 510 ewti = rwork(lewt+i-1) - call xerrwv(50hlsode-- at t (=r1), ewt(i1) has become r2 .le. 0., + call xerrwv('lsode-- at t (=r1), ewt(i1) has become r2 .le. 0.', 1 50, 202, 0, 1, i, 0, 2, tn, ewti) istate = -6 go to 580 c too much accuracy requested for machine precision. ------------------- - 520 call xerrwv(50hlsode-- at t (=r1), too much accuracy requested , + 520 call xerrwv('lsode-- at t (=r1), too much accuracy requested ', 1 50, 203, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h for precision of machine.. see tolsf (=r2) , + call xerrwv(' for precision of machine.. see tolsf (=r2) ', 1 50, 203, 0, 0, 0, 0, 2, tn, tolsf) rwork(14) = tolsf istate = -2 go to 580 c kflag = -1. error test failed repeatedly or with abs(h) = hmin. ----- - 530 call xerrwv(50hlsode-- at t(=r1) and step size h(=r2), the error, + 530 call xerrwv('lsode-- at t(=r1) and step size h(=r2), the error', 1 50, 204, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h test failed repeatedly or with abs(h) = hmin, + call xerrwv(' test failed repeatedly or with abs(h) = hmin', 1 50, 204, 0, 0, 0, 0, 2, tn, h) istate = -4 go to 560 c kflag = -2. convergence failed repeatedly or with abs(h) = hmin. ---- - 540 call xerrwv(50hlsode-- at t (=r1) and step size h (=r2), the , + 540 call xerrwv('lsode-- at t (=r1) and step size h (=r2), the ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h corrector convergence failed repeatedly , + call xerrwv(' corrector convergence failed repeatedly ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(30h or with abs(h) = hmin , + call xerrwv(' or with abs(h) = hmin ', 1 30, 205, 0, 0, 0, 0, 2, tn, h) istate = -5 c compute imxer if relevant. ------------------------------------------- @@ -1404,108 +1404,108 @@ c 5 consecutive such returns just before this call to the solver, c the run is halted. c----------------------------------------------------------------------- - 601 call xerrwv(30hlsode-- istate (=i1) illegal , + 601 call xerrwv('lsode-- istate (=i1) illegal ', 1 30, 1, 0, 1, istate, 0, 0, 0.0d0, 0.0d0) go to 700 - 602 call xerrwv(30hlsode-- itask (=i1) illegal , + 602 call xerrwv('lsode-- itask (=i1) illegal ', 1 30, 2, 0, 1, itask, 0, 0, 0.0d0, 0.0d0) go to 700 - 603 call xerrwv(50hlsode-- istate .gt. 1 but lsode not initialized , + 603 call xerrwv('lsode-- istate .gt. 1 but lsode not initialized ', 1 50, 3, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) go to 700 - 604 call xerrwv(30hlsode-- neq (=i1) .lt. 1 , + 604 call xerrwv('lsode-- neq (=i1) .lt. 1 ', 1 30, 4, 0, 1, neq(1), 0, 0, 0.0d0, 0.0d0) go to 700 - 605 call xerrwv(50hlsode-- istate = 3 and neq increased (i1 to i2) , + 605 call xerrwv('lsode-- istate = 3 and neq increased (i1 to i2) ', 1 50, 5, 0, 2, n, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 606 call xerrwv(30hlsode-- itol (=i1) illegal , + 606 call xerrwv('lsode-- itol (=i1) illegal ', 1 30, 6, 0, 1, itol, 0, 0, 0.0d0, 0.0d0) go to 700 - 607 call xerrwv(30hlsode-- iopt (=i1) illegal , + 607 call xerrwv('lsode-- iopt (=i1) illegal ', 1 30, 7, 0, 1, iopt, 0, 0, 0.0d0, 0.0d0) go to 700 - 608 call xerrwv(30hlsode-- mf (=i1) illegal , + 608 call xerrwv('lsode-- mf (=i1) illegal ', 1 30, 8, 0, 1, mf, 0, 0, 0.0d0, 0.0d0) go to 700 - 609 call xerrwv(50hlsode-- ml (=i1) illegal.. .lt.0 or .ge.neq (=i2), + 609 call xerrwv('lsode-- ml (=i1) illegal.. .lt.0 or .ge.neq (=i2)', 1 50, 9, 0, 2, ml, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 610 call xerrwv(50hlsode-- mu (=i1) illegal.. .lt.0 or .ge.neq (=i2), + 610 call xerrwv('lsode-- mu (=i1) illegal.. .lt.0 or .ge.neq (=i2)', 1 50, 10, 0, 2, mu, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 611 call xerrwv(30hlsode-- maxord (=i1) .lt. 0 , + 611 call xerrwv('lsode-- maxord (=i1) .lt. 0 ', 1 30, 11, 0, 1, maxord, 0, 0, 0.0d0, 0.0d0) go to 700 - 612 call xerrwv(30hlsode-- mxstep (=i1) .lt. 0 , + 612 call xerrwv('lsode-- mxstep (=i1) .lt. 0 ', 1 30, 12, 0, 1, mxstep, 0, 0, 0.0d0, 0.0d0) go to 700 - 613 call xerrwv(30hlsode-- mxhnil (=i1) .lt. 0 , + 613 call xerrwv('lsode-- mxhnil (=i1) .lt. 0 ', 1 30, 13, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) go to 700 - 614 call xerrwv(40hlsode-- tout (=r1) behind t (=r2) , + 614 call xerrwv('lsode-- tout (=r1) behind t (=r2) ', 1 40, 14, 0, 0, 0, 0, 2, tout, t) - call xerrwv(50h integration direction is given by h0 (=r1) , + call xerrwv(' integration direction is given by h0 (=r1) ', 1 50, 14, 0, 0, 0, 0, 1, h0, 0.0d0) go to 700 - 615 call xerrwv(30hlsode-- hmax (=r1) .lt. 0.0 , + 615 call xerrwv('lsode-- hmax (=r1) .lt. 0.0 ', 1 30, 15, 0, 0, 0, 0, 1, hmax, 0.0d0) go to 700 - 616 call xerrwv(30hlsode-- hmin (=r1) .lt. 0.0 , + 616 call xerrwv('lsode-- hmin (=r1) .lt. 0.0 ', 1 30, 16, 0, 0, 0, 0, 1, hmin, 0.0d0) go to 700 617 call xerrwv( - 1 60hlsode-- rwork length needed, lenrw (=i1), exceeds lrw (=i2), + 1 'lsode-- rwork length needed, lenrw (=i1), exceeds lrw (=i2)', 1 60, 17, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 618 call xerrwv( - 1 60hlsode-- iwork length needed, leniw (=i1), exceeds liw (=i2), + 1 'lsode-- iwork length needed, leniw (=i1), exceeds liw (=i2)', 1 60, 18, 0, 2, leniw, liw, 0, 0.0d0, 0.0d0) go to 700 - 619 call xerrwv(40hlsode-- rtol(i1) is r1 .lt. 0.0 , + 619 call xerrwv('lsode-- rtol(i1) is r1 .lt. 0.0 ', 1 40, 19, 0, 1, i, 0, 1, rtoli, 0.0d0) go to 700 - 620 call xerrwv(40hlsode-- atol(i1) is r1 .lt. 0.0 , + 620 call xerrwv('lsode-- atol(i1) is r1 .lt. 0.0 ', 1 40, 20, 0, 1, i, 0, 1, atoli, 0.0d0) go to 700 621 ewti = rwork(lewt+i-1) - call xerrwv(40hlsode-- ewt(i1) is r1 .le. 0.0 , + call xerrwv('lsode-- ewt(i1) is r1 .le. 0.0 ', 1 40, 21, 0, 1, i, 0, 1, ewti, 0.0d0) go to 700 622 call xerrwv( - 1 60hlsode-- tout (=r1) too close to t(=r2) to start integration, + 1 'lsode-- tout (=r1) too close to t(=r2) to start integration', 1 60, 22, 0, 0, 0, 0, 2, tout, t) go to 700 623 call xerrwv( - 1 60hlsode-- itask = i1 and tout (=r1) behind tcur - hu (= r2) , + 1 'lsode-- itask = i1 and tout (=r1) behind tcur - hu (= r2) ', 1 60, 23, 0, 1, itask, 0, 2, tout, tp) go to 700 624 call xerrwv( - 1 60hlsode-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) , + 1 'lsode-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) ', 1 60, 24, 0, 0, 0, 0, 2, tcrit, tn) go to 700 625 call xerrwv( - 1 60hlsode-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) , + 1 'lsode-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) ', 1 60, 25, 0, 0, 0, 0, 2, tcrit, tout) go to 700 - 626 call xerrwv(50hlsode-- at start of problem, too much accuracy , + 626 call xerrwv('lsode-- at start of problem, too much accuracy ', 1 50, 26, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h requested for precision of machine.. see tolsf (=r1) , + 1 ' requested for precision of machine.. see tolsf (=r1) ', 1 60, 26, 0, 0, 0, 0, 1, tolsf, 0.0d0) rwork(14) = tolsf go to 700 - 627 call xerrwv(50hlsode-- trouble from intdy. itask = i1, tout = r1, + 627 call xerrwv('lsode-- trouble from intdy. itask = i1, tout = r1', 1 50, 27, 0, 1, itask, 0, 1, tout, 0.0d0) c 700 if (illin .eq. 5) go to 710 illin = illin + 1 istate = -3 return - 710 call xerrwv(50hlsode-- repeated occurrences of illegal input , + 710 call xerrwv('lsode-- repeated occurrences of illegal input ', 1 50, 302, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) c - 800 call xerrwv(50hlsode-- run aborted.. apparent infinite loop , + 800 call xerrwv('lsode-- run aborted.. apparent infinite loop ', 1 50, 303, 2, 0, 0, 0, 0, 0.0d0, 0.0d0) return c----------------------- end of subroutine lsode ----------------------- Modified: trunk/Lib/integrate/odepack/lsodes.f =================================================================== --- trunk/Lib/integrate/odepack/lsodes.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/odepack/lsodes.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -217,9 +217,9 @@ c call lsodes (fex, neq, y, t, tout, itol, rtol, atol, c 1 itask, istate, iopt, rwork, lrw, iwork, liw, jex, mf) c write(6,30)t,iwork(11),rwork(11),(y(i),i=1,neq) -c 30 format(//7h at t =,e11.3,4x, -c 1 12h no. steps =,i5,4x,12h last step =,e11.3/ -c 2 13h y array = ,4e14.5/13x,4e14.5/13x,4e14.5) +c 30 format(//' at t =',e11.3,4x, +c 1 ' no. steps =',i5,4x,' last step =',e11.3/ +c 2 ' y array = ',4e14.5/13x,4e14.5/13x,4e14.5) c if (istate .lt. 0) go to 80 c tout = tout*10.0d0 c 40 continue @@ -232,13 +232,13 @@ c nnz = iwork(19) c nnzlu = iwork(25) + iwork(26) + neq c write (6,70) lenrw,leniw,nst,nfe,nje,nlu,nnz,nnzlu -c 70 format(//22h required rwork size =,i4,15h iwork size =,i4/ -c 1 12h no. steps =,i4,12h no. f-s =,i4,12h no. j-s =,i4, -c 2 13h no. lu-s =,i4/23h no. of nonzeros in j =,i5, -c 3 26h no. of nonzeros in lu =,i5) +c 70 format(//' required rwork size =',i4,' iwork size =',i4/ +c 1 ' no. steps =',i4,' no. f-s =',i4,' no. j-s =',i4, +c 2 ' no. lu-s =',i4/' no. of nonzeros in j =',i5, +c 3 ' no. of nonzeros in lu =',i5) c stop c 80 write(6,90)istate -c 90 format(///22h error halt.. istate =,i3) +c 90 format(///' error halt.. istate =',i3) c stop c end c @@ -1646,17 +1646,17 @@ 280 if ((tn + h) .ne. tn) go to 290 nhnil = nhnil + 1 if (nhnil .gt. mxhnil) go to 290 - call xerrwv(50hlsodes-- warning..internal t (=r1) and h (=r2) are, + call xerrwv('lsodes-- warning..internal t (=r1) and h (=r2) are', 1 50, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h such that in the machine, t + h = t on the next step , + 1 ' such that in the machine, t + h = t on the next step ', 1 60, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h (h = step size). solver will continue anyway, + call xerrwv(' (h = step size). solver will continue anyway', 1 50, 101, 0, 0, 0, 0, 2, tn, h) if (nhnil .lt. mxhnil) go to 290 - call xerrwv(50hlsodes-- above warning has been issued i1 times. , + call xerrwv('lsodes-- above warning has been issued i1 times. ', 1 50, 102, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h it will not be issued again for this problem, + call xerrwv(' it will not be issued again for this problem', 1 50, 102, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) 290 continue c----------------------------------------------------------------------- @@ -1732,7 +1732,7 @@ 430 ntrep = ntrep + 1 if (ntrep .lt. 5) return call xerrwv( - 1 60hlsodes-- repeated calls with istate = 1 and tout = t (=r1) , + 1 'lsodes-- repeated calls with istate = 1 and tout = t (=r1) ', 1 60, 301, 0, 0, 0, 0, 1, t, 0.0d0) go to 800 c----------------------------------------------------------------------- @@ -1745,48 +1745,48 @@ c the work arrays before returning. c----------------------------------------------------------------------- c the maximum number of steps was taken before reaching tout. ---------- - 500 call xerrwv(50hlsodes-- at current t (=r1), mxstep (=i1) steps , + 500 call xerrwv('lsodes-- at current t (=r1), mxstep (=i1) steps ', 1 50, 201, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h taken on this call before reaching tout , + call xerrwv(' taken on this call before reaching tout ', 1 50, 201, 0, 1, mxstep, 0, 1, tn, 0.0d0) istate = -1 go to 580 c ewt(i) .le. 0.0 for some i (not at start of problem). ---------------- 510 ewti = rwork(lewt+i-1) - call xerrwv(50hlsodes-- at t (=r1), ewt(i1) has become r2 .le. 0., + call xerrwv('lsodes-- at t (=r1), ewt(i1) has become r2 .le. 0.', 1 50, 202, 0, 1, i, 0, 2, tn, ewti) istate = -6 go to 580 c too much accuracy requested for machine precision. ------------------- - 520 call xerrwv(50hlsodes-- at t (=r1), too much accuracy requested , + 520 call xerrwv('lsodes-- at t (=r1), too much accuracy requested ', 1 50, 203, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h for precision of machine.. see tolsf (=r2) , + call xerrwv(' for precision of machine.. see tolsf (=r2) ', 1 50, 203, 0, 0, 0, 0, 2, tn, tolsf) rwork(14) = tolsf istate = -2 go to 580 c kflag = -1. error test failed repeatedly or with abs(h) = hmin. ----- - 530 call xerrwv(50hlsodes-- at t(=r1) and step size h(=r2), the error, + 530 call xerrwv('lsodes-- at t(=r1) and step size h(=r2), the error', 1 50, 204, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h test failed repeatedly or with abs(h) = hmin, + call xerrwv(' test failed repeatedly or with abs(h) = hmin', 1 50, 204, 0, 0, 0, 0, 2, tn, h) istate = -4 go to 560 c kflag = -2. convergence failed repeatedly or with abs(h) = hmin. ---- - 540 call xerrwv(50hlsodes-- at t (=r1) and step size h (=r2), the , + 540 call xerrwv('lsodes-- at t (=r1) and step size h (=r2), the ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h corrector convergence failed repeatedly , + call xerrwv(' corrector convergence failed repeatedly ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(30h or with abs(h) = hmin , + call xerrwv(' or with abs(h) = hmin ', 1 30, 205, 0, 0, 0, 0, 2, tn, h) istate = -5 go to 560 c kflag = -3. fatal error flag returned by prjs or slss (cdrv). ------- - 550 call xerrwv(50hlsodes-- at t (=r1) and step size h (=r2), a fatal, + 550 call xerrwv('lsodes-- at t (=r1) and step size h (=r2), a fatal', 1 50, 207, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h error flag was returned by cdrv (by way of , + call xerrwv(' error flag was returned by cdrv (by way of ', 1 50, 207, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(30h subroutine prjs or slss), + call xerrwv(' subroutine prjs or slss)', 1 30, 207, 0, 0, 0, 0, 2, tn, h) istate = -7 go to 580 @@ -1827,161 +1827,161 @@ c 5 consecutive such returns just before this call to the solver, c the run is halted. c----------------------------------------------------------------------- - 601 call xerrwv(30hlsodes-- istate (=i1) illegal , + 601 call xerrwv('lsodes-- istate (=i1) illegal ', 1 30, 1, 0, 1, istate, 0, 0, 0.0d0, 0.0d0) go to 700 - 602 call xerrwv(30hlsodes-- itask (=i1) illegal , + 602 call xerrwv('lsodes-- itask (=i1) illegal ', 1 30, 2, 0, 1, itask, 0, 0, 0.0d0, 0.0d0) go to 700 - 603 call xerrwv(50hlsodes-- istate .gt. 1 but lsodes not initialized , + 603 call xerrwv('lsodes-- istate .gt. 1 but lsodes not initialized ', 1 50, 3, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) go to 700 - 604 call xerrwv(30hlsodes-- neq (=i1) .lt. 1 , + 604 call xerrwv('lsodes-- neq (=i1) .lt. 1 ', 1 30, 4, 0, 1, neq(1), 0, 0, 0.0d0, 0.0d0) go to 700 - 605 call xerrwv(50hlsodes-- istate = 3 and neq increased (i1 to i2) , + 605 call xerrwv('lsodes-- istate = 3 and neq increased (i1 to i2) ', 1 50, 5, 0, 2, n, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 606 call xerrwv(30hlsodes-- itol (=i1) illegal , + 606 call xerrwv('lsodes-- itol (=i1) illegal ', 1 30, 6, 0, 1, itol, 0, 0, 0.0d0, 0.0d0) go to 700 - 607 call xerrwv(30hlsodes-- iopt (=i1) illegal , + 607 call xerrwv('lsodes-- iopt (=i1) illegal ', 1 30, 7, 0, 1, iopt, 0, 0, 0.0d0, 0.0d0) go to 700 - 608 call xerrwv(30hlsodes-- mf (=i1) illegal , + 608 call xerrwv('lsodes-- mf (=i1) illegal ', 1 30, 8, 0, 1, mf, 0, 0, 0.0d0, 0.0d0) go to 700 - 609 call xerrwv(30hlsodes-- seth (=r1) .lt. 0.0 , + 609 call xerrwv('lsodes-- seth (=r1) .lt. 0.0 ', 1 30, 9, 0, 0, 0, 0, 1, seth, 0.0d0) go to 700 - 611 call xerrwv(30hlsodes-- maxord (=i1) .lt. 0 , + 611 call xerrwv('lsodes-- maxord (=i1) .lt. 0 ', 1 30, 11, 0, 1, maxord, 0, 0, 0.0d0, 0.0d0) go to 700 - 612 call xerrwv(30hlsodes-- mxstep (=i1) .lt. 0 , + 612 call xerrwv('lsodes-- mxstep (=i1) .lt. 0 ', 1 30, 12, 0, 1, mxstep, 0, 0, 0.0d0, 0.0d0) go to 700 - 613 call xerrwv(30hlsodes-- mxhnil (=i1) .lt. 0 , + 613 call xerrwv('lsodes-- mxhnil (=i1) .lt. 0 ', 1 30, 13, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) go to 700 - 614 call xerrwv(40hlsodes-- tout (=r1) behind t (=r2) , + 614 call xerrwv('lsodes-- tout (=r1) behind t (=r2) ', 1 40, 14, 0, 0, 0, 0, 2, tout, t) - call xerrwv(50h integration direction is given by h0 (=r1) , + call xerrwv(' integration direction is given by h0 (=r1) ', 1 50, 14, 0, 0, 0, 0, 1, h0, 0.0d0) go to 700 - 615 call xerrwv(30hlsodes-- hmax (=r1) .lt. 0.0 , + 615 call xerrwv('lsodes-- hmax (=r1) .lt. 0.0 ', 1 30, 15, 0, 0, 0, 0, 1, hmax, 0.0d0) go to 700 - 616 call xerrwv(30hlsodes-- hmin (=r1) .lt. 0.0 , + 616 call xerrwv('lsodes-- hmin (=r1) .lt. 0.0 ', 1 30, 16, 0, 0, 0, 0, 1, hmin, 0.0d0) go to 700 - 617 call xerrwv(50hlsodes-- rwork length is insufficient to proceed. , + 617 call xerrwv('lsodes-- rwork length is insufficient to proceed. ', 1 50, 17, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h length needed is .ge. lenrw (=i1), exceeds lrw (=i2), + 1 ' length needed is .ge. lenrw (=i1), exceeds lrw (=i2)', 1 60, 17, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 - 618 call xerrwv(50hlsodes-- iwork length is insufficient to proceed. , + 618 call xerrwv('lsodes-- iwork length is insufficient to proceed. ', 1 50, 18, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h length needed is .ge. leniw (=i1), exceeds liw (=i2), + 1 ' length needed is .ge. leniw (=i1), exceeds liw (=i2)', 1 60, 18, 0, 2, leniw, liw, 0, 0.0d0, 0.0d0) go to 700 - 619 call xerrwv(40hlsodes-- rtol(i1) is r1 .lt. 0.0 , + 619 call xerrwv('lsodes-- rtol(i1) is r1 .lt. 0.0 ', 1 40, 19, 0, 1, i, 0, 1, rtoli, 0.0d0) go to 700 - 620 call xerrwv(40hlsodes-- atol(i1) is r1 .lt. 0.0 , + 620 call xerrwv('lsodes-- atol(i1) is r1 .lt. 0.0 ', 1 40, 20, 0, 1, i, 0, 1, atoli, 0.0d0) go to 700 621 ewti = rwork(lewt+i-1) - call xerrwv(40hlsodes-- ewt(i1) is r1 .le. 0.0 , + call xerrwv('lsodes-- ewt(i1) is r1 .le. 0.0 ', 1 40, 21, 0, 1, i, 0, 1, ewti, 0.0d0) go to 700 622 call xerrwv( - 1 60hlsodes-- tout (=r1) too close to t(=r2) to start integration, + 1 'lsodes-- tout (=r1) too close to t(=r2) to start integration', 1 60, 22, 0, 0, 0, 0, 2, tout, t) go to 700 623 call xerrwv( - 1 60hlsodes-- itask = i1 and tout (=r1) behind tcur - hu (= r2) , + 1 'lsodes-- itask = i1 and tout (=r1) behind tcur - hu (= r2) ', 1 60, 23, 0, 1, itask, 0, 2, tout, tp) go to 700 624 call xerrwv( - 1 60hlsodes-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) , + 1 'lsodes-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) ', 1 60, 24, 0, 0, 0, 0, 2, tcrit, tn) go to 700 625 call xerrwv( - 1 60hlsodes-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) , + 1 'lsodes-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) ', 1 60, 25, 0, 0, 0, 0, 2, tcrit, tout) go to 700 - 626 call xerrwv(50hlsodes-- at start of problem, too much accuracy , + 626 call xerrwv('lsodes-- at start of problem, too much accuracy ', 1 50, 26, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h requested for precision of machine.. see tolsf (=r1) , + 1 ' requested for precision of machine.. see tolsf (=r1) ', 1 60, 26, 0, 0, 0, 0, 1, tolsf, 0.0d0) rwork(14) = tolsf go to 700 - 627 call xerrwv(50hlsodes-- trouble from intdy. itask = i1, tout = r1, + 627 call xerrwv('lsodes-- trouble from intdy. itask = i1, tout = r1', 1 50, 27, 0, 1, itask, 0, 1, tout, 0.0d0) go to 700 628 call xerrwv( - 1 60hlsodes-- rwork length insufficient (for subroutine prep). , + 1 'lsodes-- rwork length insufficient (for subroutine prep). ', 1 60, 28, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h length needed is .ge. lenrw (=i1), exceeds lrw (=i2), + 1 ' length needed is .ge. lenrw (=i1), exceeds lrw (=i2)', 1 60, 28, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 629 call xerrwv( - 1 60hlsodes-- rwork length insufficient (for subroutine jgroup). , + 1 'lsodes-- rwork length insufficient (for subroutine jgroup). ', 1 60, 29, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h length needed is .ge. lenrw (=i1), exceeds lrw (=i2), + 1 ' length needed is .ge. lenrw (=i1), exceeds lrw (=i2)', 1 60, 29, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 630 call xerrwv( - 1 60hlsodes-- rwork length insufficient (for subroutine odrv). , + 1 'lsodes-- rwork length insufficient (for subroutine odrv). ', 1 60, 30, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h length needed is .ge. lenrw (=i1), exceeds lrw (=i2), + 1 ' length needed is .ge. lenrw (=i1), exceeds lrw (=i2)', 1 60, 30, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 631 call xerrwv( - 1 60hlsodes-- error from odrv in yale sparse matrix package , + 1 'lsodes-- error from odrv in yale sparse matrix package ', 1 60, 31, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) imul = (iys - 1)/n irem = iys - imul*n call xerrwv( - 1 60h at t (=r1), odrv returned error flag = i1*neq + i2. , + 1 ' at t (=r1), odrv returned error flag = i1*neq + i2. ', 1 60, 31, 0, 2, imul, irem, 1, tn, 0.0d0) go to 700 632 call xerrwv( - 1 60hlsodes-- rwork length insufficient (for subroutine cdrv). , + 1 'lsodes-- rwork length insufficient (for subroutine cdrv). ', 1 60, 32, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h length needed is .ge. lenrw (=i1), exceeds lrw (=i2), + 1 ' length needed is .ge. lenrw (=i1), exceeds lrw (=i2)', 1 60, 32, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 633 call xerrwv( - 1 60hlsodes-- error from cdrv in yale sparse matrix package , + 1 'lsodes-- error from cdrv in yale sparse matrix package ', 1 60, 33, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) imul = (iys - 1)/n irem = iys - imul*n call xerrwv( - 1 60h at t (=r1), cdrv returned error flag = i1*neq + i2. , + 1 ' at t (=r1), cdrv returned error flag = i1*neq + i2. ', 1 60, 33, 0, 2, imul, irem, 1, tn, 0.0d0) if (imul .eq. 2) call xerrwv( - 1 60h duplicate entry in sparsity structure descriptors , + 1 ' duplicate entry in sparsity structure descriptors ', 1 60, 33, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) if (imul .eq. 3 .or. imul .eq. 6) call xerrwv( - 1 60h insufficient storage for nsfc (called by cdrv) , + 1 ' insufficient storage for nsfc (called by cdrv) ', 1 60, 33, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) c 700 if (illin .eq. 5) go to 710 illin = illin + 1 istate = -3 return - 710 call xerrwv(50hlsodes-- repeated occurrences of illegal input , + 710 call xerrwv('lsodes-- repeated occurrences of illegal input ', 1 50, 302, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) c - 800 call xerrwv(50hlsodes-- run aborted.. apparent infinite loop , + 800 call xerrwv('lsodes-- run aborted.. apparent infinite loop ', 1 50, 303, 2, 0, 0, 0, 0, 0.0d0, 0.0d0) return c----------------------- end of subroutine lsodes ---------------------- Modified: trunk/Lib/integrate/odepack/lsodi.f =================================================================== --- trunk/Lib/integrate/odepack/lsodi.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/odepack/lsodi.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -247,15 +247,15 @@ c call lsodi(resid, aplusp, dgbydy, neq, y, ydoti, t, tout, itol, c 1 rtol, atol, itask, istate, iopt, rwork, lrw, iwork, liw, mf) c write (6,20) t, y(1), y(2), y(3) -c 20 format(7h at t =,e12.4,6h y =,3e14.6) +c 20 format(' at t =',e12.4,' y =',3e14.6) c if (istate .lt. 0 ) go to 80 c 40 tout = tout*10.d0 c write (6,60) iwork(11), iwork(12), iwork(13) -c 60 format(/12h no. steps =,i4,11h no. r-s =,i4, -c 1 11h no. j-s =,i4) +c 60 format(/' no. steps =',i4,' no. r-s =',i4, +c 1 ' no. j-s =',i4) c stop c 80 write (6,90) istate -c 90 format(///22h error halt.. istate =,i3) +c 90 format(///' error halt.. istate =',i3) c stop c end c @@ -1446,17 +1446,17 @@ 280 if ((tn + h) .ne. tn) go to 290 nhnil = nhnil + 1 if (nhnil .gt. mxhnil) go to 290 - call xerrwv(50hlsodi-- warning..internal t (=r1) and h (=r2) are, + call xerrwv('lsodi-- warning..internal t (=r1) and h (=r2) are', 1 50, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h such that in the machine, t + h = t on the next step , + 1 ' such that in the machine, t + h = t on the next step ', 1 60, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h (h = step size). solver will continue anyway, + call xerrwv(' (h = step size). solver will continue anyway', 1 50, 101, 0, 0, 0, 0, 2, tn, h) if (nhnil .lt. mxhnil) go to 290 - call xerrwv(50hlsodi-- above warning has been issued i1 times. , + call xerrwv('lsodi-- above warning has been issued i1 times. ', 1 50, 102, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h it will not be issued again for this problem, + call xerrwv(' it will not be issued again for this problem', 1 50, 102, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) 290 continue c----------------------------------------------------------------------- @@ -1533,7 +1533,7 @@ 430 ntrep = ntrep + 1 if (ntrep .lt. 5) return call xerrwv( - 1 60hlsodi-- repeated calls with istate= 0 or 1 and tout= t(=r1), + 1 'lsodi-- repeated calls with istate= 0 or 1 and tout= t(=r1)', 1 60, 301, 0, 0, 0, 0, 1, t, 0.0d0) go to 800 c----------------------------------------------------------------------- @@ -1546,64 +1546,64 @@ c the work arrays before returning. c----------------------------------------------------------------------- c the maximum number of steps was taken before reaching tout. ---------- - 500 call xerrwv(50hlsodi-- at current t (=r1), mxstep (=i1) steps , + 500 call xerrwv('lsodi-- at current t (=r1), mxstep (=i1) steps ', 1 50, 201, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h taken on this call before reaching tout , + call xerrwv(' taken on this call before reaching tout ', 1 50, 201, 0, 1, mxstep, 0, 1, tn, 0.0d0) istate = -1 go to 580 c ewt(i) .le. 0.0 for some i (not at start of problem). ---------------- 510 ewti = rwork(lewt+i-1) - call xerrwv(50hlsodi-- at t (=r1), ewt(i1) has become r2 .le. 0., + call xerrwv('lsodi-- at t (=r1), ewt(i1) has become r2 .le. 0.', 1 50, 202, 0, 1, i, 0, 2, tn, ewti) istate = -6 go to 590 c too much accuracy requested for machine precision. ------------------- - 520 call xerrwv(50hlsodi-- at t (=r1), too much accuracy requested , + 520 call xerrwv('lsodi-- at t (=r1), too much accuracy requested ', 1 50, 203, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h for precision of machine.. see tolsf (=r2) , + call xerrwv(' for precision of machine.. see tolsf (=r2) ', 1 50, 203, 0, 0, 0, 0, 2, tn, tolsf) rwork(14) = tolsf istate = -2 go to 590 c kflag = -1. error test failed repeatedly or with abs(h) = hmin. ----- - 530 call xerrwv(50hlsodi-- at t(=r1) and step size h(=r2), the error, + 530 call xerrwv('lsodi-- at t(=r1) and step size h(=r2), the error', 1 50, 204, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h test failed repeatedly or with abs(h) = hmin, + call xerrwv(' test failed repeatedly or with abs(h) = hmin', 1 50, 204, 0, 0, 0, 0, 2, tn, h) istate = -4 go to 570 c kflag = -2. convergence failed repeatedly or with abs(h) = hmin. ---- - 540 call xerrwv(50hlsodi-- at t (=r1) and step size h (=r2), the , + 540 call xerrwv('lsodi-- at t (=r1) and step size h (=r2), the ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h corrector convergence failed repeatedly , + call xerrwv(' corrector convergence failed repeatedly ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(30h or with abs(h) = hmin , + call xerrwv(' or with abs(h) = hmin ', 1 30, 205, 0, 0, 0, 0, 2, tn, h) istate = -5 go to 570 c ires = 3 returned by res, despite retries by stodi. ------------------ - 550 call xerrwv(50hlsodi-- at t (=r1) residual routine returned , + 550 call xerrwv('lsodi-- at t (=r1) residual routine returned ', 1 50, 206, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(40h error ires = 3 repeatedly , + call xerrwv(' error ires = 3 repeatedly ', 1 40, 206, 0, 0, 0, 0, 1, tn, 0.0d0) istate = -7 go to 590 c ainvg failed because a-matrix was singular. -------------------------- 560 ier = -ier call xerrwv( - 1 60hlsodi-- attempt to initialize dy/dt failed.. matrix a is , + 1 'lsodi-- attempt to initialize dy/dt failed.. matrix a is ', 1 60, 207, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h singular. sgefa or sgbfa returned info=(i1), + call xerrwv(' singular. sgefa or sgbfa returned info=(i1)', 2 50, 207, 0, 1, ier, 0, 0, 0.0d0, 0.0d0) istate = -8 return c ainvg failed because res set ires to 2 or 3. ------------------------- - 565 call xerrwv(50hlsodi-- attempt to initialize dy/dt failed , + 565 call xerrwv('lsodi-- attempt to initialize dy/dt failed ', 1 50, 208, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h because residual routine set its error flag , + call xerrwv(' because residual routine set its error flag ', 1 50, 208, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(20h to ires = (i1), + call xerrwv(' to ires = (i1)', 1 20, 208, 0, 1, ier, 0, 0, 0.0d0, 0.0d0) istate = -8 return @@ -1626,9 +1626,9 @@ call res ( neq, tn, y, rwork(lsavr), ydoti, ires ) nre = nre + 1 if (ires .le. 1) go to 595 - call xerrwv(50hlsodi-- residual routine set its flag ires , + call xerrwv('lsodi-- residual routine set its flag ires ', 1 50, 210, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h to (i1) when called for final output. , + call xerrwv(' to (i1) when called for final output. ', 1 50, 210, 0, 1, ires, 0, 0, 0.0d0, 0.0d0) go to 595 c set y vector, t, illin, and optional outputs. ------------------------ @@ -1653,108 +1653,108 @@ c 5 consecutive such returns just before this call to the solver, c the run is halted. c----------------------------------------------------------------------- - 601 call xerrwv(30hlsodi-- istate (=i1) illegal , + 601 call xerrwv('lsodi-- istate (=i1) illegal ', 1 30, 1, 0, 1, istate, 0, 0, 0.0d0, 0.0d0) go to 700 - 602 call xerrwv(30hlsodi-- itask (=i1) illegal , + 602 call xerrwv('lsodi-- itask (=i1) illegal ', 1 30, 2, 0, 1, itask, 0, 0, 0.0d0, 0.0d0) go to 700 - 603 call xerrwv(50hlsodi-- istate .gt. 1 but lsodi not initialized , + 603 call xerrwv('lsodi-- istate .gt. 1 but lsodi not initialized ', 1 50, 3, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) go to 700 - 604 call xerrwv(30hlsodi-- neq (=i1) .lt. 1 , + 604 call xerrwv('lsodi-- neq (=i1) .lt. 1 ', 1 30, 4, 0, 1, neq(1), 0, 0, 0.0d0, 0.0d0) go to 700 - 605 call xerrwv(50hlsodi-- istate = 3 and neq increased (i1 to i2) , + 605 call xerrwv('lsodi-- istate = 3 and neq increased (i1 to i2) ', 1 50, 5, 0, 2, n, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 606 call xerrwv(30hlsodi-- itol (=i1) illegal , + 606 call xerrwv('lsodi-- itol (=i1) illegal ', 1 30, 6, 0, 1, itol, 0, 0, 0.0d0, 0.0d0) go to 700 - 607 call xerrwv(30hlsodi-- iopt (=i1) illegal , + 607 call xerrwv('lsodi-- iopt (=i1) illegal ', 1 30, 7, 0, 1, iopt, 0, 0, 0.0d0, 0.0d0) go to 700 - 608 call xerrwv(30hlsodi-- mf (=i1) illegal , + 608 call xerrwv('lsodi-- mf (=i1) illegal ', 1 30, 8, 0, 1, mf, 0, 0, 0.0d0, 0.0d0) go to 700 - 609 call xerrwv(50hlsodi-- ml(=i1) illegal.. .lt. 0 or .ge. neq(=i2), + 609 call xerrwv('lsodi-- ml(=i1) illegal.. .lt. 0 or .ge. neq(=i2)', 1 50, 9, 0, 2, ml, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 610 call xerrwv(50hlsodi-- mu(=i1) illegal.. .lt. 0 or .ge. neq(=i2), + 610 call xerrwv('lsodi-- mu(=i1) illegal.. .lt. 0 or .ge. neq(=i2)', 1 50, 10, 0, 2, mu, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 611 call xerrwv(30hlsodi-- maxord (=i1) .lt. 0 , + 611 call xerrwv('lsodi-- maxord (=i1) .lt. 0 ', 1 30, 11, 0, 1, maxord, 0, 0, 0.0d0, 0.0d0) go to 700 - 612 call xerrwv(30hlsodi-- mxstep (=i1) .lt. 0 , + 612 call xerrwv('lsodi-- mxstep (=i1) .lt. 0 ', 1 30, 12, 0, 1, mxstep, 0, 0, 0.0d0, 0.0d0) go to 700 - 613 call xerrwv(30hlsodi-- mxhnil (=i1) .lt. 0 , + 613 call xerrwv('lsodi-- mxhnil (=i1) .lt. 0 ', 1 30, 13, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) go to 700 - 614 call xerrwv(40hlsodi-- tout (=r1) behind t (=r2) , + 614 call xerrwv('lsodi-- tout (=r1) behind t (=r2) ', 1 40, 14, 0, 0, 0, 0, 2, tout, t) - call xerrwv(50h integration direction is given by h0 (=r1) , + call xerrwv(' integration direction is given by h0 (=r1) ', 1 50, 14, 0, 0, 0, 0, 1, h0, 0.0d0) go to 700 - 615 call xerrwv(30hlsodi-- hmax (=r1) .lt. 0.0 , + 615 call xerrwv('lsodi-- hmax (=r1) .lt. 0.0 ', 1 30, 15, 0, 0, 0, 0, 1, hmax, 0.0d0) go to 700 - 616 call xerrwv(30hlsodi-- hmin (=r1) .lt. 0.0 , + 616 call xerrwv('lsodi-- hmin (=r1) .lt. 0.0 ', 1 30, 16, 0, 0, 0, 0, 1, hmin, 0.0d0) go to 700 617 call xerrwv( - 1 60hlsodi-- rwork length needed, lenrw (=i1), exceeds lrw (=i2), + 1 'lsodi-- rwork length needed, lenrw (=i1), exceeds lrw (=i2)', 1 60, 17, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 618 call xerrwv( - 1 60hlsodi-- iwork length needed, leniw (=i1), exceeds liw (=i2), + 1 'lsodi-- iwork length needed, leniw (=i1), exceeds liw (=i2)', 1 60, 18, 0, 2, leniw, liw, 0, 0.0d0, 0.0d0) go to 700 - 619 call xerrwv(40hlsodi-- rtol(=i1) is r1 .lt. 0.0 , + 619 call xerrwv('lsodi-- rtol(=i1) is r1 .lt. 0.0 ', 1 40, 19, 0, 1, i, 0, 1, rtoli, 0.0d0) go to 700 - 620 call xerrwv(40hlsodi-- atol(=i1) is r1 .lt. 0.0 , + 620 call xerrwv('lsodi-- atol(=i1) is r1 .lt. 0.0 ', 1 40, 20, 0, 1, i, 0, 1, atoli, 0.0d0) go to 700 621 ewti = rwork(lewt+i-1) - call xerrwv(40hlsodi-- ewt(=i1) is r1 .le. 0.0 , + call xerrwv('lsodi-- ewt(=i1) is r1 .le. 0.0 ', 1 40, 21, 0, 1, i, 0, 1, ewti, 0.0d0) go to 700 622 call xerrwv( - 1 60hlsodi-- tout (=r1) too close to t(=r2) to start integration, + 1 'lsodi-- tout (=r1) too close to t(=r2) to start integration', 1 60, 22, 0, 0, 0, 0, 2, tout, t) go to 700 623 call xerrwv( - 1 60hlsodi-- itask = i1 and tout (=r1) behind tcur - hu (= r2) , + 1 'lsodi-- itask = i1 and tout (=r1) behind tcur - hu (= r2) ', 1 60, 23, 0, 1, itask, 0, 2, tout, tp) go to 700 624 call xerrwv( - 1 60hlsodi-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) , + 1 'lsodi-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) ', 1 60, 24, 0, 0, 0, 0, 2, tcrit, tn) go to 700 625 call xerrwv( - 1 60hlsodi-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) , + 1 'lsodi-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) ', 1 60, 25, 0, 0, 0, 0, 2, tcrit, tout) go to 700 - 626 call xerrwv(50hlsodi-- at start of problem, too much accuracy , + 626 call xerrwv('lsodi-- at start of problem, too much accuracy ', 1 50, 26, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h requested for precision of machine.. see tolsf (=r1) , + 1 ' requested for precision of machine.. see tolsf (=r1) ', 1 60, 26, 0, 0, 0, 0, 1, tolsf, 0.0d0) rwork(14) = tolsf go to 700 - 627 call xerrwv(50hlsodi-- trouble from intdy. itask = i1, tout = r1, + 627 call xerrwv('lsodi-- trouble from intdy. itask = i1, tout = r1', 1 50, 27, 0, 1, itask, 0, 1, tout, 0.0d0) c 700 if (illin .eq. 5) go to 710 illin = illin + 1 istate = -3 return - 710 call xerrwv(50hlsodi-- repeated occurrences of illegal input , + 710 call xerrwv('lsodi-- repeated occurrences of illegal input ', 1 50, 302, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) c - 800 call xerrwv(50hlsodi-- run aborted.. apparent infinite loop , + 800 call xerrwv('lsodi-- run aborted.. apparent infinite loop ', 1 50, 303, 2, 0, 0, 0, 0, 0.0d0, 0.0d0) return c----------------------- end of subroutine lsodi ----------------------- Modified: trunk/Lib/integrate/odepack/lsoibt.f =================================================================== --- trunk/Lib/integrate/odepack/lsoibt.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/odepack/lsoibt.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -280,16 +280,16 @@ c call lsoibt (resid, addabt, jacbt, neq, y, ydoti, t, tout, c 1 itol,rtol,atol, itask, istate, iopt, rwork,lrw,iwork,liw, mf) c write (6,30) t, iwork(11), iwork(12), iwork(13) -c 30 format(7h at t =,f5.2,14h no. steps =,i4,11h no. r-s =,i4, -c 1 11h no. j-s =,i3) +c 30 format(' at t =',f5.2,' no. steps =',i4,' no. r-s =',i4, +c 1 ' no. j-s =',i3) c if (istate .ne. 2) go to 90 c tout = tout + 0.1d0 c 40 continue c write(6,50) (y(i),i=1,neq) -c 50 format(/24h final solution values../9(5e12.4/)) +c 50 format(/' final solution values..'/9(5e12.4/)) c stop c 90 write(6,95) istate -c 95 format(///22h error halt.. istate =,i3) +c 95 format(///' error halt.. istate =',i3) c stop c end c @@ -1500,17 +1500,17 @@ 280 if ((tn + h) .ne. tn) go to 290 nhnil = nhnil + 1 if (nhnil .gt. mxhnil) go to 290 - call xerrwv(50hlsoibt-- warning..internal t (=r1) and h (=r2) are, + call xerrwv('lsoibt-- warning..internal t (=r1) and h (=r2) are', 1 50, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h such that in the machine, t + h = t on the next step , + 1 ' such that in the machine, t + h = t on the next step ', 1 60, 101, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h (h = step size). solver will continue anyway, + call xerrwv(' (h = step size). solver will continue anyway', 1 50, 101, 0, 0, 0, 0, 2, tn, h) if (nhnil .lt. mxhnil) go to 290 - call xerrwv(50hlsoibt-- above warning has been issued i1 times. , + call xerrwv('lsoibt-- above warning has been issued i1 times. ', 1 50, 102, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h it will not be issued again for this problem, + call xerrwv(' it will not be issued again for this problem', 1 50, 102, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) 290 continue c----------------------------------------------------------------------- @@ -1587,7 +1587,7 @@ 430 ntrep = ntrep + 1 if (ntrep .lt. 5) return call xerrwv( - 1 60hlsoibt-- repeated calls with istate= 0 or 1 and tout= t(=r1), + 1 'lsoibt-- repeated calls with istate= 0 or 1 and tout= t(=r1)', 1 60, 301, 0, 0, 0, 0, 1, t, 0.0d0) go to 800 c----------------------------------------------------------------------- @@ -1600,64 +1600,64 @@ c the work arrays before returning. c----------------------------------------------------------------------- c the maximum number of steps was taken before reaching tout. ---------- - 500 call xerrwv(50hlsoibt-- at current t (=r1), mxstep (=i1) steps , + 500 call xerrwv('lsoibt-- at current t (=r1), mxstep (=i1) steps ', 1 50, 201, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h taken on this call before reaching tout , + call xerrwv(' taken on this call before reaching tout ', 1 50, 201, 0, 1, mxstep, 0, 1, tn, 0.0d0) istate = -1 go to 580 c ewt(i) .le. 0.0 for some i (not at start of problem). ---------------- 510 ewti = rwork(lewt+i-1) - call xerrwv(50hlsoibt-- at t (=r1), ewt(i1) has become r2 .le. 0., + call xerrwv('lsoibt-- at t (=r1), ewt(i1) has become r2 .le. 0.', 1 50, 202, 0, 1, i, 0, 2, tn, ewti) istate = -6 go to 590 c too much accuracy requested for machine precision. ------------------- - 520 call xerrwv(50hlsoibt-- at t (=r1), too much accuracy requested , + 520 call xerrwv('lsoibt-- at t (=r1), too much accuracy requested ', 1 50, 203, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h for precision of machine.. see tolsf (=r2) , + call xerrwv(' for precision of machine.. see tolsf (=r2) ', 1 50, 203, 0, 0, 0, 0, 2, tn, tolsf) rwork(14) = tolsf istate = -2 go to 590 c kflag = -1. error test failed repeatedly or with abs(h) = hmin. ----- - 530 call xerrwv(50hlsoibt-- at t(=r1) and step size h(=r2), the error, + 530 call xerrwv('lsoibt-- at t(=r1) and step size h(=r2), the error', 1 50, 204, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h test failed repeatedly or with abs(h) = hmin, + call xerrwv(' test failed repeatedly or with abs(h) = hmin', 1 50, 204, 0, 0, 0, 0, 2, tn, h) istate = -4 go to 570 c kflag = -2. convergence failed repeatedly or with abs(h) = hmin. ---- - 540 call xerrwv(50hlsoibt-- at t (=r1) and step size h (=r2), the , + 540 call xerrwv('lsoibt-- at t (=r1) and step size h (=r2), the ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h corrector convergence failed repeatedly , + call xerrwv(' corrector convergence failed repeatedly ', 1 50, 205, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(30h or with abs(h) = hmin , + call xerrwv(' or with abs(h) = hmin ', 1 30, 205, 0, 0, 0, 0, 2, tn, h) istate = -5 go to 570 c ires = 3 returned by res, despite retries by stodi. ------------------ - 550 call xerrwv(50hlsoibt-- at t (=r1) residual routine returned , + 550 call xerrwv('lsoibt-- at t (=r1) residual routine returned ', 1 50, 206, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(40h error ires = 3 repeatedly , + call xerrwv(' error ires = 3 repeatedly ', 1 40, 206, 0, 0, 0, 0, 1, tn, 0.0d0) istate = -7 go to 590 c aigbt failed because a diagonal block of a-matrix was singular. ------ 560 ier = -ier call xerrwv( - 1 60hlsoibt-- attempt to initialize dy/dt failed.. matrix a has a, + 1 'lsoibt-- attempt to initialize dy/dt failed.. matrix a has a', 1 60, 207, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h singular diagonal block, block no. = (i1) , + call xerrwv(' singular diagonal block, block no. = (i1) ', 2 50, 207, 0, 1, ier, 0, 0, 0.0d0, 0.0d0) istate = -8 return c aigbt failed because res set ires to 2 or 3. ------------------------- - 565 call xerrwv(50hlsoibt-- attempt to initialize dy/dt failed , + 565 call xerrwv('lsoibt-- attempt to initialize dy/dt failed ', 1 50, 208, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h because residual routine set its error flag , + call xerrwv(' because residual routine set its error flag ', 1 50, 208, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(20h to ires = (i1), + call xerrwv(' to ires = (i1)', 1 20, 208, 0, 1, ier, 0, 0, 0.0d0, 0.0d0) istate = -8 return @@ -1680,9 +1680,9 @@ call res ( neq, tn, y, rwork(lsavr), ydoti, ires ) nre = nre + 1 if ( ires .le. 1 ) go to 595 - call xerrwv(50hlsoibt-- residual routine set its flag ires , + call xerrwv('lsoibt-- residual routine set its flag ires ', 1 50, 210, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) - call xerrwv(50h to (i1) when called for final output. , + call xerrwv(' to (i1) when called for final output. ', 1 50, 210, 0, 1, ires, 0, 0, 0.0d0, 0.0d0) go to 595 c set y vector, t, illin, and optional outputs. ------------------------ @@ -1707,108 +1707,108 @@ c 5 consecutive such returns just before this call to the solver, c the run is halted. c----------------------------------------------------------------------- - 601 call xerrwv(30hlsoibt-- istate (=i1) illegal , + 601 call xerrwv('lsoibt-- istate (=i1) illegal ', 1 30, 1, 0, 1, istate, 0, 0, 0.0d0, 0.0d0) go to 700 - 602 call xerrwv(30hlsoibt-- itask (=i1) illegal , + 602 call xerrwv('lsoibt-- itask (=i1) illegal ', 1 30, 2, 0, 1, itask, 0, 0, 0.0d0, 0.0d0) go to 700 - 603 call xerrwv(50hlsoibt-- istate .gt. 1 but lsoibt not initialized , + 603 call xerrwv('lsoibt-- istate .gt. 1 but lsoibt not initialized ', 1 50, 3, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) go to 700 - 604 call xerrwv(30hlsoibt-- neq (=i1) .lt. 1 , + 604 call xerrwv('lsoibt-- neq (=i1) .lt. 1 ', 1 30, 4, 0, 1, neq(1), 0, 0, 0.0d0, 0.0d0) go to 700 - 605 call xerrwv(50hlsoibt-- istate = 3 and neq increased (i1 to i2) , + 605 call xerrwv('lsoibt-- istate = 3 and neq increased (i1 to i2) ', 1 50, 5, 0, 2, n, neq(1), 0, 0.0d0, 0.0d0) go to 700 - 606 call xerrwv(30hlsoibt-- itol (=i1) illegal , + 606 call xerrwv('lsoibt-- itol (=i1) illegal ', 1 30, 6, 0, 1, itol, 0, 0, 0.0d0, 0.0d0) go to 700 - 607 call xerrwv(30hlsoibt-- iopt (=i1) illegal , + 607 call xerrwv('lsoibt-- iopt (=i1) illegal ', 1 30, 7, 0, 1, iopt, 0, 0, 0.0d0, 0.0d0) go to 700 - 608 call xerrwv(30hlsoibt-- mf (=i1) illegal , + 608 call xerrwv('lsoibt-- mf (=i1) illegal ', 1 30, 8, 0, 1, mf, 0, 0, 0.0d0, 0.0d0) go to 700 - 609 call xerrwv(40hlsoibt-- mb (=i1) or nb (=i2) illegal , + 609 call xerrwv('lsoibt-- mb (=i1) or nb (=i2) illegal ', 1 50, 9, 0, 2, mb, nb, 0, 0.0d0, 0.0d0) go to 700 - 610 call xerrwv(40hlsoibt-- nb(=i1) illegal.. .lt. 4 , + 610 call xerrwv('lsoibt-- nb(=i1) illegal.. .lt. 4 ', 1 50, 10, 0, 1, nb, 0, 0, 0.0d0, 0.0d0) go to 700 - 611 call xerrwv(30hlsoibt-- maxord (=i1) .lt. 0 , + 611 call xerrwv('lsoibt-- maxord (=i1) .lt. 0 ', 1 30, 11, 0, 1, maxord, 0, 0, 0.0d0, 0.0d0) go to 700 - 612 call xerrwv(30hlsoibt-- mxstep (=i1) .lt. 0 , + 612 call xerrwv('lsoibt-- mxstep (=i1) .lt. 0 ', 1 30, 12, 0, 1, mxstep, 0, 0, 0.0d0, 0.0d0) go to 700 - 613 call xerrwv(30hlsoibt-- mxhnil (=i1) .lt. 0 , + 613 call xerrwv('lsoibt-- mxhnil (=i1) .lt. 0 ', 1 30, 13, 0, 1, mxhnil, 0, 0, 0.0d0, 0.0d0) go to 700 - 614 call xerrwv(40hlsoibt-- tout (=r1) behind t (=r2) , + 614 call xerrwv('lsoibt-- tout (=r1) behind t (=r2) ', 1 40, 14, 0, 0, 0, 0, 2, tout, t) - call xerrwv(50h integration direction is given by h0 (=r1) , + call xerrwv(' integration direction is given by h0 (=r1) ', 1 50, 14, 0, 0, 0, 0, 1, h0, 0.0d0) go to 700 - 615 call xerrwv(30hlsoibt-- hmax (=r1) .lt. 0.0 , + 615 call xerrwv('lsoibt-- hmax (=r1) .lt. 0.0 ', 1 30, 15, 0, 0, 0, 0, 1, hmax, 0.0d0) go to 700 - 616 call xerrwv(30hlsoibt-- hmin (=r1) .lt. 0.0 , + 616 call xerrwv('lsoibt-- hmin (=r1) .lt. 0.0 ', 1 30, 16, 0, 0, 0, 0, 1, hmin, 0.0d0) go to 700 617 call xerrwv( - 1 60hlsoibt-- rwork length needed, lenrw (=i1), exceeds lrw (=i2), + 1 'lsoibt-- rwork length needed, lenrw (=i1), exceeds lrw (=i2)', 1 60, 17, 0, 2, lenrw, lrw, 0, 0.0d0, 0.0d0) go to 700 618 call xerrwv( - 1 60hlsoibt-- iwork length needed, leniw (=i1), exceeds liw (=i2), + 1 'lsoibt-- iwork length needed, leniw (=i1), exceeds liw (=i2)', 1 60, 18, 0, 2, leniw, liw, 0, 0.0d0, 0.0d0) go to 700 - 619 call xerrwv(40hlsoibt-- rtol(=i1) is r1 .lt. 0.0 , + 619 call xerrwv('lsoibt-- rtol(=i1) is r1 .lt. 0.0 ', 1 40, 19, 0, 1, i, 0, 1, rtoli, 0.0d0) go to 700 - 620 call xerrwv(40hlsoibt-- atol(=i1) is r1 .lt. 0.0 , + 620 call xerrwv('lsoibt-- atol(=i1) is r1 .lt. 0.0 ', 1 40, 20, 0, 1, i, 0, 1, atoli, 0.0d0) go to 700 621 ewti = rwork(lewt+i-1) - call xerrwv(40hlsoibt-- ewt(=i1) is r1 .le. 0.0 , + call xerrwv('lsoibt-- ewt(=i1) is r1 .le. 0.0 ', 1 40, 21, 0, 1, i, 0, 1, ewti, 0.0d0) go to 700 622 call xerrwv( - 1 60hlsoibt-- tout (=r1) too close to t(=r2) to start integration, + 1 'lsoibt-- tout (=r1) too close to t(=r2) to start integration', 1 60, 22, 0, 0, 0, 0, 2, tout, t) go to 700 623 call xerrwv( - 1 60hlsoibt-- itask = i1 and tout (=r1) behind tcur - hu (= r2) , + 1 'lsoibt-- itask = i1 and tout (=r1) behind tcur - hu (= r2) ', 1 60, 23, 0, 1, itask, 0, 2, tout, tp) go to 700 624 call xerrwv( - 1 60hlsoibt-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) , + 1 'lsoibt-- itask = 4 or 5 and tcrit (=r1) behind tcur (=r2) ', 1 60, 24, 0, 0, 0, 0, 2, tcrit, tn) go to 700 625 call xerrwv( - 1 60hlsoibt-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) , + 1 'lsoibt-- itask = 4 or 5 and tcrit (=r1) behind tout (=r2) ', 1 60, 25, 0, 0, 0, 0, 2, tcrit, tout) go to 700 - 626 call xerrwv(50hlsoibt-- at start of problem, too much accuracy , + 626 call xerrwv('lsoibt-- at start of problem, too much accuracy ', 1 50, 26, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) call xerrwv( - 1 60h requested for precision of machine.. see tolsf (=r1) , + 1 ' requested for precision of machine.. see tolsf (=r1) ', 1 60, 26, 0, 0, 0, 0, 1, tolsf, 0.0d0) rwork(14) = tolsf go to 700 - 627 call xerrwv(50hlsoibt-- trouble from intdy. itask = i1, tout = r1, + 627 call xerrwv('lsoibt-- trouble from intdy. itask = i1, tout = r1', 1 50, 27, 0, 1, itask, 0, 1, tout, 0.0d0) c 700 if (illin .eq. 5) go to 710 illin = illin + 1 istate = -3 return - 710 call xerrwv(50hlsoibt-- repeated occurrences of illegal input , + 710 call xerrwv('lsoibt-- repeated occurrences of illegal input ', 1 50, 302, 0, 0, 0, 0, 0, 0.0d0, 0.0d0) c - 800 call xerrwv(50hlsoibt-- run aborted.. apparent infinite loop , + 800 call xerrwv('lsoibt-- run aborted.. apparent infinite loop ', 1 50, 303, 2, 0, 0, 0, 0, 0.0d0, 0.0d0) return c----------------------- end of subroutine lsoibt ---------------------- Modified: trunk/Lib/integrate/odepack/xerrwv.f =================================================================== --- trunk/Lib/integrate/odepack/xerrwv.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/odepack/xerrwv.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -100,13 +100,13 @@ c 10 format(1x,30a2) c----------------------------------------------------------------------- if (ni .eq. 1) write (lun, 20) i1 - 20 format(6x,23hin above message, i1 =,i10) + 20 format(6x,'in above message, i1 =',i10) if (ni .eq. 2) write (lun, 30) i1,i2 - 30 format(6x,23hin above message, i1 =,i10,3x,4hi2 =,i10) + 30 format(6x,'in above message, i1 =',i10,3x,'i2 =',i10) if (nr .eq. 1) write (lun, 40) r1 - 40 format(6x,23hin above message, r1 =,d21.13) + 40 format(6x,'in above message, r1 =',d21.13) if (nr .eq. 2) write (lun, 50) r1,r2 - 50 format(6x,15hin above, r1 =,d21.13,3x,4hr2 =,d21.13) + 50 format(6x,'in above, r1 =',d21.13,3x,'r2 =',d21.13) c abort the run if level = 2. ------------------------------------------ 100 if (level .ne. 2) return stop Modified: trunk/Lib/integrate/quadpack/dqag.f =================================================================== --- trunk/Lib/integrate/quadpack/dqag.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/quadpack/dqag.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -177,6 +177,6 @@ c lvl = 0 10 if(ier.eq.6) lvl = 1 - if(ier.ne.0) call xerror(26habnormal return from dqag ,26,ier,lvl) + if(ier.ne.0) call xerror('abnormal return from dqag' ,26,ier,lvl) return end Modified: trunk/Lib/integrate/quadpack/dqagi.f =================================================================== --- trunk/Lib/integrate/quadpack/dqagi.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/quadpack/dqagi.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -186,6 +186,6 @@ c lvl = 0 10 if(ier.eq.6) lvl = 1 - if(ier.ne.0) call xerror(26habnormal return from dqagi,26,ier,lvl) + if(ier.ne.0) call xerror('abnormal return from dqagi',26,ier,lvl) return end Modified: trunk/Lib/integrate/quadpack/dqagp.f =================================================================== --- trunk/Lib/integrate/quadpack/dqagp.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/quadpack/dqagp.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -220,6 +220,6 @@ c lvl = 0 10 if(ier.eq.6) lvl = 1 - if(ier.ne.0) call xerror(26habnormal return from dqagp,26,ier,lvl) + if(ier.ne.0) call xerror('abnormal return from dqagp',26,ier,lvl) return end Modified: trunk/Lib/integrate/quadpack/dqags.f =================================================================== --- trunk/Lib/integrate/quadpack/dqags.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/quadpack/dqags.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -183,6 +183,6 @@ c lvl = 0 10 if(ier.eq.6) lvl = 1 - if(ier.ne.0) call xerror(26habnormal return from dqags,26,ier,lvl) + if(ier.ne.0) call xerror('abnormal return from dqags',26,ier,lvl) return end Modified: trunk/Lib/integrate/quadpack/dqawc.f =================================================================== --- trunk/Lib/integrate/quadpack/dqawc.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/quadpack/dqawc.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -173,6 +173,6 @@ c lvl = 0 10 if(ier.eq.6) lvl = 1 - if(ier.ne.0) call xerror(26habnormal return from dqawc,26,ier,lvl) + if(ier.ne.0) call xerror('abnormal return from dqawc',26,ier,lvl) return end Modified: trunk/Lib/integrate/quadpack/dqawf.f =================================================================== --- trunk/Lib/integrate/quadpack/dqawf.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/quadpack/dqawf.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -226,6 +226,6 @@ c lvl = 0 10 if(ier.eq.6) lvl = 1 - if(ier.ne.0) call xerror(26habnormal return from dqawf,26,ier,lvl) + if(ier.ne.0) call xerror('abnormal return from dqawf',26,ier,lvl) return end Modified: trunk/Lib/integrate/quadpack/dqawo.f =================================================================== --- trunk/Lib/integrate/quadpack/dqawo.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/quadpack/dqawo.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -220,6 +220,6 @@ c lvl = 0 10 if(ier.eq.6) lvl = 0 - if(ier.ne.0) call xerror(26habnormal return from dqawo,26,ier,lvl) + if(ier.ne.0) call xerror('abnormal return from dqawo',26,ier,lvl) return end Modified: trunk/Lib/integrate/quadpack/dqaws.f =================================================================== --- trunk/Lib/integrate/quadpack/dqaws.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/quadpack/dqaws.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -195,6 +195,6 @@ c lvl = 0 10 if(ier.eq.6) lvl = 1 - if(ier.ne.0) call xerror(26habnormal return from dqaws,26,ier,lvl) + if(ier.ne.0) call xerror('abnormal return from dqaws',26,ier,lvl) return end Modified: trunk/Lib/integrate/quadpack/dqng.f =================================================================== --- trunk/Lib/integrate/quadpack/dqng.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/integrate/quadpack/dqng.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -369,6 +369,6 @@ c ***jump out of do-loop if (ier.eq.0) go to 999 70 continue - 80 call xerror(26habnormal return from dqng ,26,ier,0) + 80 call xerror('abnormal return from dqng ',26,ier,0) 999 return end Modified: trunk/Lib/interpolate/fitpack/fppola.f =================================================================== --- trunk/Lib/interpolate/fitpack/fppola.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/interpolate/fitpack/fppola.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -51,7 +51,9 @@ c calculation of acc, the absolute tolerance for the root of f(p)=s. acc = tol*s if(iopt1.eq.0) go to 10 - if(s.lt.sup) if(nv-nvmin) 70,90,90 + if(s.lt.sup) then + if(nv-nvmin) 70,90,90 + endif c if iopt1 = 0 we begin by computing the weighted least-squares c polymomial of the form c s(u,v) = f(1)*(1-u**3)+f(2)*u**3+f(3)*(u**2-u**3)+f(4)*(u-u**3) @@ -416,9 +418,13 @@ c the spline. 430 call fprppo(nu,nv,iopt2,iopt3,cosi,ratio,c,ff,ncoff) c test whether the least-squares spline is an acceptable solution. - if(iopt1.lt.0) if(fp) 970,970,980 + if(iopt1.lt.0) then + if(fp) 970,970,980 + endif fpms = fp-s - if(abs(fpms).le.acc) if(fp) 970,970,980 + if(abs(fpms).le.acc) then + if(fp) 970,970,980 + endif c if f(p=inf) < s, accept the choice of knots. if(fpms.lt.0.) go to 580 c test whether we cannot further increase the number of knots @@ -634,7 +640,9 @@ do 710 irot=jrot,ncof piv = h(1) i2 = min0(iband1,ncof-irot) - if(piv.eq.0.) if(i2) 720,720,690 + if(piv.eq.0.) then + if(i2) 720,720,690 + endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),co,si) c apply that givens transformation to the right hand side. @@ -697,7 +705,9 @@ do 800 irot=jrot,ncof piv = h(1) i2 = min0(iband3,ncof-irot) - if(piv.eq.0.) if(i2) 810,810,780 + if(piv.eq.0.) then + if(i2) 810,810,780 + endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),co,si) c apply that givens transformation to the right hand side. Modified: trunk/Lib/interpolate/fitpack/fpsphe.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpsphe.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/interpolate/fitpack/fpsphe.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -44,7 +44,9 @@ c calculation of acc, the absolute tolerance for the root of f(p)=s. acc = tol*s if(iopt.eq.0) go to 10 - if(s.lt.sup) if(np-11) 60,70,70 + if(s.lt.sup) then + if(np-11) 60,70,70 + endif c if iopt=0 we begin by computing the weighted least-squares polynomial c of the form c s(teta,phi) = c1*f1(teta) + cn*fn(teta) @@ -366,9 +368,13 @@ c the spherical spline. 390 call fprpsp(nt,np,coco,cosi,c,ff,ncoff) c test whether the least-squares spline is an acceptable solution. - if(iopt.lt.0) if(fp) 970,970,980 + if(iopt.lt.0) then + if(fp) 970,970,980 + endif fpms = fp-s - if(abs(fpms).le.acc) if(fp) 970,970,980 + if(abs(fpms).le.acc) then + if(fp) 970,970,980 + endif c if f(p=inf) < s, accept the choice of knots. if(fpms.lt.0.) go to 580 c test whether we cannot further increase the number of knots. @@ -567,7 +573,9 @@ do 710 irot=jrot,ncof piv = h(1) i2 = min0(iband1,ncof-irot) - if(piv.eq.0.) if(i2) 720,720,690 + if(piv.eq.0.) then + if(i2) 720,720,690 + endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),co,si) c apply that givens transformation to the right hand side. @@ -622,7 +630,9 @@ do 800 irot=jrot,ncof piv = h(1) i2 = min0(iband3,ncof-irot) - if(piv.eq.0.) if(i2) 810,810,780 + if(piv.eq.0.) then + if(i2) 810,810,780 + endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),co,si) c apply that givens transformation to the right hand side. Modified: trunk/Lib/interpolate/fitpack/fpsurf.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpsurf.f 2006-08-12 23:04:32 UTC (rev 2158) +++ trunk/Lib/interpolate/fitpack/fpsurf.f 2006-08-14 01:26:54 UTC (rev 2159) @@ -286,7 +286,9 @@ c test whether the least-squares spline is an acceptable solution. if(iopt.lt.0) go to 820 fpms = fp-s - if(abs(fpms).le.acc) if(fp) 815,815,820 + if(abs(fpms).le.acc) then + if(fp) 815,815,820 + endif c test whether we can accept the choice of knots. if(fpms.lt.0.) go to 430 c test whether we cannot further increase the number of knots. @@ -464,7 +466,9 @@ do 540 irot=jrot,ncof piv = h(1) i2 = min0(iband1,ncof-irot) - if(piv.eq.0.) if(i2) 550,550,520 + if(piv.eq.0.) then + if(i2) 550,550,520 + endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),cos,sin) c apply that givens transformation to the right hand side. @@ -504,7 +508,9 @@ do 620 irot=jrot,ncof piv = h(1) i2 = min0(iband3,ncof-irot) - if(piv.eq.0.) if(i2) 630,630,600 + if(piv.eq.0.) then + if(i2) 630,630,600 + endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),cos,sin) c apply that givens transformation to the right hand side. From scipy-svn at scipy.org Mon Aug 14 00:23:06 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 13 Aug 2006 23:23:06 -0500 (CDT) Subject: [Scipy-svn] r2160 - in trunk/Lib: fftpack/dfftpack fftpack/fftpack integrate/odepack interpolate/fitpack sandbox/odr/odrpack special/cdflib Message-ID: <20060814042306.5CD6739C093@new.scipy.org> Author: cookedm Date: 2006-08-13 23:22:28 -0500 (Sun, 13 Aug 2006) New Revision: 2160 Modified: trunk/Lib/fftpack/dfftpack/dcosqb.f trunk/Lib/fftpack/dfftpack/dcosqf.f trunk/Lib/fftpack/dfftpack/dcost.f trunk/Lib/fftpack/dfftpack/dfftb1.f trunk/Lib/fftpack/dfftpack/dfftf1.f trunk/Lib/fftpack/dfftpack/dffti1.f trunk/Lib/fftpack/dfftpack/dsint1.f trunk/Lib/fftpack/dfftpack/zffti1.f trunk/Lib/fftpack/fftpack/cffti1.f trunk/Lib/fftpack/fftpack/cosqb.f trunk/Lib/fftpack/fftpack/cosqf.f trunk/Lib/fftpack/fftpack/cost.f trunk/Lib/fftpack/fftpack/rfftb1.f trunk/Lib/fftpack/fftpack/rfftf1.f trunk/Lib/fftpack/fftpack/rffti1.f trunk/Lib/fftpack/fftpack/sint1.f trunk/Lib/integrate/odepack/cntnzu.f trunk/Lib/integrate/odepack/lsodes.f trunk/Lib/integrate/odepack/lsodi.f trunk/Lib/integrate/odepack/lsoibt.f trunk/Lib/integrate/odepack/mdi.f trunk/Lib/integrate/odepack/mdu.f trunk/Lib/integrate/odepack/nsfc.f trunk/Lib/interpolate/fitpack/bispev.f trunk/Lib/interpolate/fitpack/clocur.f trunk/Lib/interpolate/fitpack/cocosp.f trunk/Lib/interpolate/fitpack/concur.f trunk/Lib/interpolate/fitpack/curev.f trunk/Lib/interpolate/fitpack/curfit.f trunk/Lib/interpolate/fitpack/fpadno.f trunk/Lib/interpolate/fitpack/fpchep.f trunk/Lib/interpolate/fitpack/fpfrno.f trunk/Lib/interpolate/fitpack/fpintb.f trunk/Lib/interpolate/fitpack/fppasu.f trunk/Lib/interpolate/fitpack/fppogr.f trunk/Lib/interpolate/fitpack/fppola.f trunk/Lib/interpolate/fitpack/fpregr.f trunk/Lib/interpolate/fitpack/fpseno.f trunk/Lib/interpolate/fitpack/fpspgr.f trunk/Lib/interpolate/fitpack/fpsphe.f trunk/Lib/interpolate/fitpack/fpsurf.f trunk/Lib/interpolate/fitpack/parcur.f trunk/Lib/interpolate/fitpack/parder.f trunk/Lib/interpolate/fitpack/parsur.f trunk/Lib/interpolate/fitpack/percur.f trunk/Lib/interpolate/fitpack/pogrid.f trunk/Lib/interpolate/fitpack/regrid.f trunk/Lib/interpolate/fitpack/spgrid.f trunk/Lib/interpolate/fitpack/surev.f trunk/Lib/sandbox/odr/odrpack/d_lpkbls.f trunk/Lib/special/cdflib/gam1.f trunk/Lib/special/cdflib/gamma_fort.f trunk/Lib/special/cdflib/gratio.f Log: More 1960's Fortran removal: replace arithmetic IF's. This cuts down the warnings from gfortran by a lot. Modified: trunk/Lib/fftpack/dfftpack/dcosqb.f =================================================================== --- trunk/Lib/fftpack/dfftpack/dcosqb.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/dfftpack/dcosqb.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -2,7 +2,9 @@ IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION X(*) ,WSAVE(*) DATA TSQRT2 /2.82842712474619009760D0/ - IF (N-2) 101,102,103 + IF (N.lt.2) GO TO 101 + IF (N.eq.2) GO TO 102 + GO TO 103 101 X(1) = 4.0D0*X(1) RETURN 102 X1 = 4.0D0*(X(1)+X(2)) Modified: trunk/Lib/fftpack/dfftpack/dcosqf.f =================================================================== --- trunk/Lib/fftpack/dfftpack/dcosqf.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/dfftpack/dcosqf.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -2,7 +2,9 @@ IMPLICIT DOUBLE PRECISION (A-H,O-Z) DIMENSION X(*) ,WSAVE(*) DATA SQRT2 /1.41421356237309504880D0/ - IF (N-2) 102,101,103 + IF (N.lt.2) GO TO 102 + IF (N.eq.2) GO TO 101 + GO TO 103 101 TSQX = SQRT2*X(2) X(2) = X(1)-TSQX X(1) = X(1)+TSQX Modified: trunk/Lib/fftpack/dfftpack/dcost.f =================================================================== --- trunk/Lib/fftpack/dfftpack/dcost.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/dfftpack/dcost.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -4,7 +4,9 @@ NM1 = N-1 NP1 = N+1 NS2 = N/2 - IF (N-2) 106,101,102 + IF (N.lt.2) GO TO 106 + IF (N.eq.2) GO TO 101 + GO TO 102 101 X1H = X(1)+X(2) X(2) = X(1)-X(2) X(1) = X1H Modified: trunk/Lib/fftpack/dfftpack/dfftb1.f =================================================================== --- trunk/Lib/fftpack/dfftpack/dfftb1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/dfftpack/dfftb1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -229,7 +229,9 @@ CH(1,K,1) = CC(1,1,K)+CC(IDO,2,K) CH(1,K,2) = CC(1,1,K)-CC(IDO,2,K) 101 CONTINUE - IF (IDO-2) 107,105,102 + IF (IDO.lt.2) GO TO 107 + IF (IDO.eq.2) GO TO 105 + GO TO 102 102 IDP2 = IDO+2 DO 104 K=1,L1 DO 103 I=3,IDO,2 @@ -305,7 +307,9 @@ CH(1,K,3) = TR2-TR3 CH(1,K,4) = TR1+TR4 101 CONTINUE - IF (IDO-2) 107,105,102 + IF (IDO.lt.2) GO TO 107 + IF (IDO.eq.2) GO TO 105 + GO TO 102 102 IDP2 = IDO+2 DO 104 K=1,L1 DO 103 I=3,IDO,2 Modified: trunk/Lib/fftpack/dfftpack/dfftf1.f =================================================================== --- trunk/Lib/fftpack/dfftpack/dfftf1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/dfftpack/dfftf1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -236,7 +236,9 @@ CH(1,1,K) = CC(1,K,1)+CC(1,K,2) CH(IDO,2,K) = CC(1,K,1)-CC(1,K,2) 101 CONTINUE - IF (IDO-2) 107,105,102 + IF (IDO.lt.2) GO TO 107 + IF (IDO.eq.2) GO TO 105 + GO TO 102 102 IDP2 = IDO+2 DO 104 K=1,L1 DO 103 I=3,IDO,2 @@ -308,7 +310,9 @@ CH(IDO,2,K) = CC(1,K,1)-CC(1,K,3) CH(1,3,K) = CC(1,K,4)-CC(1,K,2) 101 CONTINUE - IF (IDO-2) 107,105,102 + IF (IDO.lt.2) GO TO 107 + IF (IDO.eq.2) GO TO 105 + GO TO 102 102 IDP2 = IDO+2 DO 104 K=1,L1 DO 103 I=3,IDO,2 Modified: trunk/Lib/fftpack/dfftpack/dffti1.f =================================================================== --- trunk/Lib/fftpack/dfftpack/dffti1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/dfftpack/dffti1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -6,13 +6,15 @@ NF = 0 J = 0 101 J = J+1 - IF (J-4) 102,102,103 + IF (J.le.4) GO TO 102 + GO TO 103 102 NTRY = NTRYH(J) GO TO 104 103 NTRY = NTRY+2 104 NQ = NL/NTRY NR = NL-NTRY*NQ - IF (NR) 101,105,101 + IF (NR.eq.0) GO TO 105 + GO TO 101 105 NF = NF+1 IFAC(NF+2) = NTRY NL = NQ Modified: trunk/Lib/fftpack/dfftpack/dsint1.f =================================================================== --- trunk/Lib/fftpack/dfftpack/dsint1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/dfftpack/dsint1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -6,7 +6,9 @@ XH(I) = WAR(I) WAR(I) = X(I) 100 CONTINUE - IF (N-2) 101,102,103 + IF (N.lt.2) GO TO 101 + IF (N.eq.2) GO TO 102 + GO TO 103 101 XH(1) = XH(1)+XH(1) GO TO 106 102 XHOLD = SQRT3*(XH(1)+XH(2)) Modified: trunk/Lib/fftpack/dfftpack/zffti1.f =================================================================== --- trunk/Lib/fftpack/dfftpack/zffti1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/dfftpack/zffti1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -6,13 +6,15 @@ NF = 0 J = 0 101 J = J+1 - IF (J-4) 102,102,103 + IF (J.le.4) GO TO 102 + GO TO 103 102 NTRY = NTRYH(J) GO TO 104 103 NTRY = NTRY+2 104 NQ = NL/NTRY NR = NL-NTRY*NQ - IF (NR) 101,105,101 + IF (NR.eq.0) GO TO 105 + GO TO 101 105 NF = NF+1 IFAC(NF+2) = NTRY NL = NQ Modified: trunk/Lib/fftpack/fftpack/cffti1.f =================================================================== --- trunk/Lib/fftpack/fftpack/cffti1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/fftpack/cffti1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -5,13 +5,15 @@ NF = 0 J = 0 101 J = J+1 - IF (J-4) 102,102,103 + IF (J.le.4) GO TO 102 + GO TO 103 102 NTRY = NTRYH(J) GO TO 104 103 NTRY = NTRY+2 104 NQ = NL/NTRY NR = NL-NTRY*NQ - IF (NR) 101,105,101 + IF (NR.eq.0) GO TO 105 + GO TO 101 105 NF = NF+1 IFAC(NF+2) = NTRY NL = NQ Modified: trunk/Lib/fftpack/fftpack/cosqb.f =================================================================== --- trunk/Lib/fftpack/fftpack/cosqb.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/fftpack/cosqb.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -1,7 +1,9 @@ SUBROUTINE COSQB (N,X,WSAVE) DIMENSION X(*) ,WSAVE(*) DATA TSQRT2 /2.82842712474619/ - IF (N-2) 101,102,103 + IF (N.lt.2) GO TO 101 + IF (N.eq.2) GO TO 102 + GO TO 103 101 X(1) = 4.*X(1) RETURN 102 X1 = 4.*(X(1)+X(2)) Modified: trunk/Lib/fftpack/fftpack/cosqf.f =================================================================== --- trunk/Lib/fftpack/fftpack/cosqf.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/fftpack/cosqf.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -1,7 +1,9 @@ SUBROUTINE COSQF (N,X,WSAVE) DIMENSION X(*) ,WSAVE(*) DATA SQRT2 /1.4142135623731/ - IF (N-2) 102,101,103 + IF (N.lt.2) GO TO 102 + IF (N.eq.2) GO TO 101 + GO TO 103 101 TSQX = SQRT2*X(2) X(2) = X(1)-TSQX X(1) = X(1)+TSQX Modified: trunk/Lib/fftpack/fftpack/cost.f =================================================================== --- trunk/Lib/fftpack/fftpack/cost.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/fftpack/cost.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -3,7 +3,9 @@ NM1 = N-1 NP1 = N+1 NS2 = N/2 - IF (N-2) 106,101,102 + IF (N.lt.2) GO TO 106 + IF (N.eq.2) GO TO 101 + GO TO 102 101 X1H = X(1)+X(2) X(2) = X(1)-X(2) X(1) = X1H Modified: trunk/Lib/fftpack/fftpack/rfftb1.f =================================================================== --- trunk/Lib/fftpack/fftpack/rfftb1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/fftpack/rfftb1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -64,7 +64,9 @@ CH(1,K,1) = CC(1,1,K)+CC(IDO,2,K) CH(1,K,2) = CC(1,1,K)-CC(IDO,2,K) 101 CONTINUE - IF (IDO-2) 107,105,102 + IF (IDO.lt.2) GO TO 107 + IF (IDO.eq.2) GO TO 105 + GO TO 102 102 IDP2 = IDO+2 DO 104 K=1,L1 DO 103 I=3,IDO,2 @@ -135,7 +137,9 @@ CH(1,K,3) = TR2-TR3 CH(1,K,4) = TR1+TR4 101 CONTINUE - IF (IDO-2) 107,105,102 + IF (IDO.lt.2) GO TO 107 + IF (IDO.eq.2) GO TO 105 + GO TO 102 102 IDP2 = IDO+2 DO 104 K=1,L1 DO 103 I=3,IDO,2 Modified: trunk/Lib/fftpack/fftpack/rfftf1.f =================================================================== --- trunk/Lib/fftpack/fftpack/rfftf1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/fftpack/rfftf1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -64,7 +64,9 @@ CH(1,1,K) = CC(1,K,1)+CC(1,K,2) CH(IDO,2,K) = CC(1,K,1)-CC(1,K,2) 101 CONTINUE - IF (IDO-2) 107,105,102 + IF (IDO.lt.2) GO TO 107 + IF (IDO.eq.2) GO TO 105 + GO TO 102 102 IDP2 = IDO+2 DO 104 K=1,L1 DO 103 I=3,IDO,2 @@ -131,7 +133,9 @@ CH(IDO,2,K) = CC(1,K,1)-CC(1,K,3) CH(1,3,K) = CC(1,K,4)-CC(1,K,2) 101 CONTINUE - IF (IDO-2) 107,105,102 + IF (IDO.lt.2) GO TO 107 + IF (IDO.eq.2) GO TO 105 + GO TO 102 102 IDP2 = IDO+2 DO 104 K=1,L1 DO 103 I=3,IDO,2 Modified: trunk/Lib/fftpack/fftpack/rffti1.f =================================================================== --- trunk/Lib/fftpack/fftpack/rffti1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/fftpack/rffti1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -5,13 +5,15 @@ NF = 0 J = 0 101 J = J+1 - IF (J-4) 102,102,103 + IF (J.le.4) GO TO 102 + GO TO 103 102 NTRY = NTRYH(J) GO TO 104 103 NTRY = NTRY+2 104 NQ = NL/NTRY NR = NL-NTRY*NQ - IF (NR) 101,105,101 + IF (NR.eq.0) GO TO 105 + GO TO 101 105 NF = NF+1 IFAC(NF+2) = NTRY NL = NQ Modified: trunk/Lib/fftpack/fftpack/sint1.f =================================================================== --- trunk/Lib/fftpack/fftpack/sint1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/fftpack/fftpack/sint1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -5,7 +5,9 @@ XH(I) = WAR(I) WAR(I) = X(I) 100 CONTINUE - IF (N-2) 101,102,103 + IF (N.lt.2) GO TO 101 + IF (N.eq.2) GO TO 102 + GO TO 103 101 XH(1) = XH(1)+XH(1) GO TO 106 102 XHOLD = SQRT3*(XH(1)+XH(2)) Modified: trunk/Lib/integrate/odepack/cntnzu.f =================================================================== --- trunk/Lib/integrate/odepack/cntnzu.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/integrate/odepack/cntnzu.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -16,7 +16,9 @@ jmax = ia(ii+1) - 1 if (jmin .gt. jmax) go to 50 do 40 j = jmin,jmax - if (ja(j) - ii) 10, 40, 30 + if (ja(j).lt.ii) go to 10 + if (ja(j).eq.ii) go to 40 + go to 30 10 jj =ja(j) kmin = ia(jj) kmax = ia(jj+1) - 1 Modified: trunk/Lib/integrate/odepack/lsodes.f =================================================================== --- trunk/Lib/integrate/odepack/lsodes.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/integrate/odepack/lsodes.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -1433,7 +1433,9 @@ lyhd = lyh - lyhn imax = lyhn - 1 + lenyhm c move yh. branch for move right, no move, or move left. -------------- - if (lyhd) 70,80,74 + if (lyhd.lt.0) go to 70 + if (lyhd.eq.0) go to 80 + go to 74 70 do 72 i = lyhn,imax j = imax + lyhn - i 72 rwork(j) = rwork(j+lyhd) Modified: trunk/Lib/integrate/odepack/lsodi.f =================================================================== --- trunk/Lib/integrate/odepack/lsodi.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/integrate/odepack/lsodi.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -1325,7 +1325,9 @@ call ainvg( res, adda, neq, t, y, rwork(lyd0), miter, 1 ml, mu, rwork(lp), iwork(21), ier ) nre = nre + 1 - if (ier) 560,110,565 + if (ier.lt.0) go to 560 + if (ier.eq.0) go to 110 + go to 565 110 continue do 115 i = 1,n 115 rwork(i+lyh-1) = y(i) Modified: trunk/Lib/integrate/odepack/lsoibt.f =================================================================== --- trunk/Lib/integrate/odepack/lsoibt.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/integrate/odepack/lsoibt.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -1379,7 +1379,9 @@ call aigbt( res, adda, neq, t, y, rwork(lyd0), 1 mb, nb, rwork(lp), iwork(21), ier ) nre = nre + 1 - if (ier) 560,110,565 + if (ier.lt.0) go to 560 + if (ier.eq.0) go to 110 + go to 565 110 continue do 115 i = 1,n 115 rwork(i+lyh-1) = y(i) Modified: trunk/Lib/integrate/odepack/mdi.f =================================================================== --- trunk/Lib/integrate/odepack/mdi.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/integrate/odepack/mdi.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -22,7 +22,9 @@ if (jmin.gt.jmax) go to 6 do 5 j=jmin,jmax vj = ja(j) - if (vj-vi) 2, 5, 4 + if (vj.lt.vi) go to 2 + if (vj.eq.vi) go to 5 + go to 4 c c------if a(vi,vj) is in strict lower triangle c------check for previous occurrence of a(vj,vi) Modified: trunk/Lib/integrate/odepack/mdu.f =================================================================== --- trunk/Lib/integrate/odepack/mdu.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/integrate/odepack/mdu.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -19,7 +19,9 @@ do 10 ilp=1,ilpmax i = l(i) vi = v(i) - if (last(vi)) 1, 10, 8 + if (last(vi).lt.0) go to 1 + if (last(vi).eq.0) go to 10 + go to 8 c c------if vi neither prototype nor duplicate vertex, then merge elements c------to compute degree Modified: trunk/Lib/integrate/odepack/nsfc.f =================================================================== --- trunk/Lib/integrate/odepack/nsfc.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/integrate/odepack/nsfc.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -148,7 +148,9 @@ 11 if (jlmin .gt. jlptr) go to 15 qm = q(qm) do 12 j=jlmin,jlptr - if (jl(j) - qm) 12, 13, 15 + if (jl(j).lt.qm) go to 12 + if (jl(j).eq.qm) go to 13 + go to 15 12 continue go to 15 13 ijl(k) = j @@ -250,7 +252,9 @@ 28 if (jumin .gt. juptr) go to 32 qm = q(qm) do 29 j=jumin,juptr - if (ju(j) - qm) 29, 30, 32 + if (ju(j).lt.qm) go to 29 + if (ju(j).eq.qm) go to 30 + go to 32 29 continue go to 32 30 iju(k) = j Modified: trunk/Lib/interpolate/fitpack/bispev.f =================================================================== --- trunk/Lib/interpolate/fitpack/bispev.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/bispev.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -83,11 +83,15 @@ lwest = (kx+1)*mx+(ky+1)*my if(lwrk.lt.lwest) go to 100 if(kwrk.lt.(mx+my)) go to 100 - if(mx-1) 100,30,10 + if (mx.lt.1) go to 100 + if (mx.eq.1) go to 30 + go to 10 10 do 20 i=2,mx if(x(i).lt.x(i-1)) go to 100 20 continue - 30 if(my-1) 100,60,40 + 30 if (my.lt.1) go to 100 + if (my.eq.1) go to 60 + go to 40 40 do 50 i=2,my if(y(i).lt.y(i-1)) go to 100 50 continue Modified: trunk/Lib/interpolate/fitpack/clocur.f =================================================================== --- trunk/Lib/interpolate/fitpack/clocur.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/clocur.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -331,7 +331,8 @@ t(i1) = t(j1)+per 60 continue call fpchep(u,m,t,n,k,ier) - if(ier) 90,80,90 + if (ier.eq.0) go to 80 + go to 90 70 if(s.lt.0.) go to 90 if(s.eq.0. .and. nest.lt.(m+2*k)) go to 90 ier = 0 Modified: trunk/Lib/interpolate/fitpack/cocosp.f =================================================================== --- trunk/Lib/interpolate/fitpack/cocosp.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/cocosp.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -148,7 +148,8 @@ if(x(i-1).ge.x(i) .or. w(i).le.0.) go to 40 10 continue call fpchec(x,m,t,n,3,ier) - if(ier) 40,20,40 + if (ier.eq.0) go to 20 + go to 40 c set numbers e(i) 20 n6 = n-6 do 30 i=1,n6 Modified: trunk/Lib/interpolate/fitpack/concur.f =================================================================== --- trunk/Lib/interpolate/fitpack/concur.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/concur.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -330,7 +330,8 @@ j = j-1 20 continue call fpched(u,m,t,n,k,ib,ie,ier) - if(ier) 90,40,90 + if (ier.eq.0) go to 40 + go to 90 30 if(s.lt.0.) go to 90 nmax = m+k1+ib1+ie1 if(s.eq.0. .and. nest.lt.nmax) go to 90 Modified: trunk/Lib/interpolate/fitpack/curev.f =================================================================== --- trunk/Lib/interpolate/fitpack/curev.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/curev.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -63,7 +63,9 @@ c before starting computations a data check is made. if the input data c are invalid control is immediately repassed to the calling program. ier = 10 - if(m-1) 100,30,10 + if (m.lt.1) go to 100 + if (m.eq.1) go to 30 + go to 10 10 do 20 i=2,m if(u(i).lt.u(i-1)) go to 100 20 continue Modified: trunk/Lib/interpolate/fitpack/curfit.f =================================================================== --- trunk/Lib/interpolate/fitpack/curfit.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/curfit.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -243,7 +243,8 @@ j = j-1 20 continue call fpchec(x,m,t,n,k,ier) - if(ier) 50,40,50 + if (ier.eq.0) go to 40 + go to 50 30 if(s.lt.0.) go to 50 if(s.eq.0. .and. nest.lt.(m+k1)) go to 50 ier = 0 Modified: trunk/Lib/interpolate/fitpack/fpadno.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpadno.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fpadno.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -25,7 +25,9 @@ 10 k = left(point) bool = .true. 20 if(k.eq.0) go to 50 - if(info(k)-jbind(niveau)) 30,40,50 + if (info(k)-jbind(niveau).lt.0) go to 30 + if (info(k)-jbind(niveau).eq.0) go to 40 + go to 50 30 point = k k = right(point) bool = .false. Modified: trunk/Lib/interpolate/fitpack/fpchep.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpchep.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fpchep.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -66,7 +66,8 @@ 70 i = i+1 if(i.gt.mm) go to 120 i2 = i-m1 - if(i2) 80,80,90 + if (i2.le.0) go to 80 + go to 90 80 xi = x(i) go to 100 90 xi = x(i2)+per Modified: trunk/Lib/interpolate/fitpack/fpfrno.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpfrno.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fpfrno.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -26,7 +26,9 @@ i = l j = j+1 go to 20 - 30 if(i-count) 110,100,40 + 30 if (i.lt.count) go to 110 + if (i.eq.count) go to 100 + go to 40 40 if(up(count).eq.0) go to 50 count = count+1 go to 30 Modified: trunk/Lib/interpolate/fitpack/fpintb.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpintb.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fpintb.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -39,7 +39,9 @@ a = x b = y min = 0 - if(a-b) 30,160,20 + if (a.lt.b) go to 30 + if (a.eq.b) go to 160 + go to 20 20 a = y b = x min = 1 Modified: trunk/Lib/interpolate/fitpack/fppasu.f =================================================================== --- trunk/Lib/interpolate/fitpack/fppasu.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fppasu.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -256,7 +256,9 @@ ier = 0 c adjust the parameter reducu or reducv according to the direction c in which the last added knots were located. - if(lastdi) 150,170,160 + if (lastdi.lt.0) go to 150 + if (lastdi.eq.0) go to 170 + go to 160 150 reducu = fpold-fp go to 170 160 reducv = fpold-fp @@ -276,7 +278,9 @@ rn = nplusv if(reducv.gt.acc) npl1 = rn*fpms/reducv nplv = min0(nplusv*2,max0(npl1,nplusv/2,1)) - 190 if(nplu-nplv) 210,200,230 + 190 if (nplu.lt.nplv) go to 210 + if (nplu.eq.nplv) go to 200 + go to 230 200 if(lastdi.lt.0) go to 230 210 if(nu.eq.nue) go to 230 c addition in the u-direction. Modified: trunk/Lib/interpolate/fitpack/fppogr.f =================================================================== --- trunk/Lib/interpolate/fitpack/fppogr.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fppogr.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -266,7 +266,9 @@ ier = 0 c adjust the parameter reducu or reducv according to the direction c in which the last added knots were located. - if(lastdi) 160,155,170 + if (lastdi.lt.0) go to 160 + if (lastdi.eq.0) go to 155 + go to 170 155 nplv = 3 idd(2) = ider(2) fpold = fp @@ -291,7 +293,9 @@ if(reducv.gt.acc) npl1 = rn*fpms/reducv nplv = min0(nplusv*2,max0(npl1,nplusv/2,1)) c test whether we are going to add knots in the u- or v-direction. - 190 if(nplu-nplv) 210,200,230 + 190 if (nplu.lt.nplv) go to 210 + if (nplu.eq.nplv) go to 200 + go to 230 200 if(lastdi.lt.0) go to 230 210 if(nu.eq.nue) go to 230 c addition in the u-direction. Modified: trunk/Lib/interpolate/fitpack/fppola.f =================================================================== --- trunk/Lib/interpolate/fitpack/fppola.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fppola.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -52,7 +52,8 @@ acc = tol*s if(iopt1.eq.0) go to 10 if(s.lt.sup) then - if(nv-nvmin) 70,90,90 + if (nv.lt.nvmin) go to 70 + go to 90 endif c if iopt1 = 0 we begin by computing the weighted least-squares c polymomial of the form @@ -419,11 +420,13 @@ 430 call fprppo(nu,nv,iopt2,iopt3,cosi,ratio,c,ff,ncoff) c test whether the least-squares spline is an acceptable solution. if(iopt1.lt.0) then - if(fp) 970,970,980 + if (fp.le.0) go to 970 + go to 980 endif fpms = fp-s if(abs(fpms).le.acc) then - if(fp) 970,970,980 + if (fp.le.0) go to 970 + go to 980 endif c if f(p=inf) < s, accept the choice of knots. if(fpms.lt.0.) go to 580 @@ -641,7 +644,8 @@ piv = h(1) i2 = min0(iband1,ncof-irot) if(piv.eq.0.) then - if(i2) 720,720,690 + if (i2.le.0) go to 720 + go to 690 endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),co,si) @@ -706,7 +710,8 @@ piv = h(1) i2 = min0(iband3,ncof-irot) if(piv.eq.0.) then - if(i2) 810,810,780 + if (i2.le.0) go to 810 + go to 780 endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),co,si) Modified: trunk/Lib/interpolate/fitpack/fpregr.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpregr.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fpregr.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -230,7 +230,9 @@ ier = 0 c adjust the parameter reducx or reducy according to the direction c in which the last added knots were located. - if(lastdi) 150,170,160 + if (lastdi.lt.0) go to 150 + if (lastdi.eq.0) go to 170 + go to 160 150 reducx = fpold-fp go to 170 160 reducy = fpold-fp @@ -250,7 +252,9 @@ rn = nplusy if(reducy.gt.acc) npl1 = rn*fpms/reducy nply = min0(nplusy*2,max0(npl1,nplusy/2,1)) - 190 if(nplx-nply) 210,200,230 + 190 if (nplx.lt.nply) go to 210 + if (nplx.eq.nply) go to 200 + go to 230 200 if(lastdi.lt.0) go to 230 210 if(nx.eq.nxe) go to 230 c addition in the x-direction. Modified: trunk/Lib/interpolate/fitpack/fpseno.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpseno.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fpseno.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -25,7 +25,8 @@ 20 k = right(merk) if(k.ne.0) go to 30 merk = up(merk) - if(merk-1) 40,40,20 + if (merk.le.1) go to 40 + go to 20 30 merk = k k = left(merk) if(k.ne.0) go to 30 Modified: trunk/Lib/interpolate/fitpack/fpspgr.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpspgr.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fpspgr.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -294,7 +294,9 @@ ier = 0 c adjust the parameter reducu or reducv according to the direction c in which the last added knots were located. - if(lastdi) 160,155,170 + if (lastdi.lt.0) go to 160 + if (lastdi.eq.0) go to 155 + go to 170 155 nplv = 3 idd(2) = ider(2) idd(4) = ider(4) @@ -320,7 +322,9 @@ if(reducv.gt.acc) npl1 = rn*fpms/reducv nplv = min0(nplusv*2,max0(npl1,nplusv/2,1)) c test whether we are going to add knots in the u- or v-direction. - 190 if(nplu-nplv) 210,200,230 + 190 if (nplu.lt.nplv) go to 210 + if (nplu.eq.nplv) go to 200 + go to 230 200 if(lastdi.lt.0) go to 230 210 if(nu.eq.nue) go to 230 c addition in the u-direction. Modified: trunk/Lib/interpolate/fitpack/fpsphe.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpsphe.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fpsphe.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -45,7 +45,8 @@ acc = tol*s if(iopt.eq.0) go to 10 if(s.lt.sup) then - if(np-11) 60,70,70 + if (np.lt.11) go to 60 + go to 70 endif c if iopt=0 we begin by computing the weighted least-squares polynomial c of the form @@ -369,11 +370,13 @@ 390 call fprpsp(nt,np,coco,cosi,c,ff,ncoff) c test whether the least-squares spline is an acceptable solution. if(iopt.lt.0) then - if(fp) 970,970,980 + if (fp.le.0) go to 970 + go to 980 endif fpms = fp-s if(abs(fpms).le.acc) then - if(fp) 970,970,980 + if (fp.le.0) go to 970 + go to 980 endif c if f(p=inf) < s, accept the choice of knots. if(fpms.lt.0.) go to 580 @@ -574,7 +577,8 @@ piv = h(1) i2 = min0(iband1,ncof-irot) if(piv.eq.0.) then - if(i2) 720,720,690 + if (i2.le.0) go to 720 + go to 690 endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),co,si) @@ -631,7 +635,8 @@ piv = h(1) i2 = min0(iband3,ncof-irot) if(piv.eq.0.) then - if(i2) 810,810,780 + if (i2.le.0) go to 810 + go to 780 endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),co,si) Modified: trunk/Lib/interpolate/fitpack/fpsurf.f =================================================================== --- trunk/Lib/interpolate/fitpack/fpsurf.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/fpsurf.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -135,7 +135,9 @@ ty(i) = store 70 continue n1 = n+1 - if(nx-ny) 80,120,100 + if (nx.lt.ny) go to 80 + if (nx.eq.ny) go to 120 + go to 100 80 do 90 i=n1,ny tx(i) = ty(i) 90 continue @@ -287,7 +289,8 @@ if(iopt.lt.0) go to 820 fpms = fp-s if(abs(fpms).le.acc) then - if(fp) 815,815,820 + if (fp.le.0) go to 815 + go to 820 endif c test whether we can accept the choice of knots. if(fpms.lt.0.) go to 430 @@ -467,7 +470,8 @@ piv = h(1) i2 = min0(iband1,ncof-irot) if(piv.eq.0.) then - if(i2) 550,550,520 + if (i2.le.0) go to 550 + go to 520 endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),cos,sin) @@ -509,7 +513,8 @@ piv = h(1) i2 = min0(iband3,ncof-irot) if(piv.eq.0.) then - if(i2) 630,630,600 + if (i2.le.0) go to 630 + go to 600 endif c calculate the parameters of the givens transformation. call fpgivs(piv,q(irot,1),cos,sin) @@ -654,7 +659,9 @@ ty(i) = store 870 continue n1 = n+1 - if(nx-ny) 880,920,900 + if (nx.lt.ny) go to 880 + if (nx.eq.ny) go to 920 + go to 900 880 do 890 i=n1,ny tx(i) = ty(i) 890 continue Modified: trunk/Lib/interpolate/fitpack/parcur.f =================================================================== --- trunk/Lib/interpolate/fitpack/parcur.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/parcur.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -315,7 +315,8 @@ j = j-1 60 continue call fpchec(u,m,t,n,k,ier) - if(ier) 90,80,90 + if (ier.eq.0) go to 80 + go to 90 70 if(s.lt.0.) go to 90 if(s.eq.0. .and. nest.lt.(m+k1)) go to 90 ier = 0 Modified: trunk/Lib/interpolate/fitpack/parder.f =================================================================== --- trunk/Lib/interpolate/fitpack/parder.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/parder.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -94,11 +94,15 @@ lwest = nc +(kx1-nux)*mx+(ky1-nuy)*my if(lwrk.lt.lwest) go to 400 if(kwrk.lt.(mx+my)) go to 400 - if(mx-1) 400,30,10 + if (mx.lt.1) go to 400 + if (mx.eq.1) go to 30 + go to 10 10 do 20 i=2,mx if(x(i).lt.x(i-1)) go to 400 20 continue - 30 if(my-1) 400,60,40 + 30 if (my.lt.1) go to 400 + if (my.eq.1) go to 60 + go to 40 40 do 50 i=2,my if(y(i).lt.y(i-1)) go to 400 50 continue Modified: trunk/Lib/interpolate/fitpack/parsur.f =================================================================== --- trunk/Lib/interpolate/fitpack/parsur.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/parsur.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -367,7 +367,8 @@ tv(l3) = tv(l1)+perv 90 continue call fpchep(v,mv,tv,nv,3,ier) - if(ier) 200,150,200 + if (ier.eq.0) go to 150 + go to 200 100 if(s.lt.0.) go to 200 if(s.eq.0. .and. (nuest.lt.(mu+4+2*ipar(1)) .or. * nvest.lt.(mv+4+2*ipar(2))) )go to 200 Modified: trunk/Lib/interpolate/fitpack/percur.f =================================================================== --- trunk/Lib/interpolate/fitpack/percur.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/percur.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -253,7 +253,8 @@ t(i1) = t(j1)+per 20 continue call fpchep(x,m,t,n,k,ier) - if(ier) 50,40,50 + if (ier.eq.0) go to 40 + go to 50 30 if(s.lt.0.) go to 50 if(s.eq.0. .and. nest.lt.(m+2*k)) go to 50 ier = 0 Modified: trunk/Lib/interpolate/fitpack/pogrid.f =================================================================== --- trunk/Lib/interpolate/fitpack/pogrid.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/pogrid.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -442,7 +442,8 @@ 135 continue wrk(l) = ve call fpchep(wrk(9),mv+1,tv,nv,3,ier) - if(ier) 200,150,200 + if (ier.eq.0) go to 150 + go to 200 140 if(s.lt.0.) go to 200 if(s.eq.0. .and. (nuest.lt.(mu+5+iopt(2)+iopt(3)) .or. * nvest.lt.(mv+7)) ) go to 200 Modified: trunk/Lib/interpolate/fitpack/regrid.f =================================================================== --- trunk/Lib/interpolate/fitpack/regrid.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/regrid.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -329,7 +329,8 @@ j = j-1 40 continue call fpchec(y,my,ty,ny,ky,ier) - if(ier) 70,60,70 + if (ier.eq.0) go to 60 + go to 70 50 if(s.lt.0.) go to 70 if(s.eq.0. .and. (nxest.lt.(mx+kx1) .or. nyest.lt.(my+ky1)) ) * go to 70 Modified: trunk/Lib/interpolate/fitpack/spgrid.f =================================================================== --- trunk/Lib/interpolate/fitpack/spgrid.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/spgrid.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -476,7 +476,8 @@ 135 continue wrk(l) = ve call fpchep(wrk(13),mv+1,tv,nv,3,ier) - if(ier) 200,150,200 + if (ier.eq.0) go to 150 + go to 200 140 if(s.lt.0.) go to 200 if(s.eq.0. .and. (nuest.lt.(mu+6+iopt(2)+iopt(3)) .or. * nvest.lt.(mv+7)) ) go to 200 Modified: trunk/Lib/interpolate/fitpack/surev.f =================================================================== --- trunk/Lib/interpolate/fitpack/surev.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/interpolate/fitpack/surev.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -87,11 +87,15 @@ muv = mu+mv if(lwrk.lt.4*muv) go to 100 if(kwrk.lt.muv) go to 100 - if(mu-1) 100,30,10 + if (mu.lt.1) go to 100 + if (mu.eq.1) go to 30 + go to 10 10 do 20 i=2,mu if(u(i).lt.u(i-1)) go to 100 20 continue - 30 if(mv-1) 100,60,40 + 30 if (mv.lt.1) go to 100 + if (mv.eq.1) go to 60 + go to 40 40 do 50 i=2,mv if(v(i).lt.v(i-1)) go to 100 50 continue Modified: trunk/Lib/sandbox/odr/odrpack/d_lpkbls.f =================================================================== --- trunk/Lib/sandbox/odr/odrpack/d_lpkbls.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/sandbox/odr/odrpack/d_lpkbls.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -136,7 +136,9 @@ IF(N.LE.0.OR.DA.EQ.0.D0) RETURN - IF(INCX.EQ.INCY) IF(INCX-1) 5,20,60 + IF (INCX.EQ.INCY) IF(INCX.lt.1) GOTO 5 + IF (INCX.EQ.INCY) IF(INCX.eq.1) GOTO 20 + GOTO 60 5 CONTINUE C CODE FOR NONEQUAL OR NONPOSITIVE INCREMENTS. @@ -481,7 +483,9 @@ IF(N.LE.0)RETURN - IF(INCX.EQ.INCY) IF(INCX-1) 5,20,60 + IF (INCX.EQ.INCY) IF(INCX.lt.1) GOTO 5 + IF (INCX.EQ.INCY) IF(INCX.eq.1) GOTO 20 + GOTO 60 5 CONTINUE C CODE FOR UNEQUAL OR NONPOSITIVE INCREMENTS. @@ -585,7 +589,9 @@ DDOT = 0.D0 IF(N.LE.0)RETURN - IF(INCX.EQ.INCY) IF(INCX-1) 5,20,60 + IF (INCX.EQ.INCY) IF(INCX.lt.1) GOTO 5 + IF (INCX.EQ.INCY) IF(INCX.eq.1) GOTO 20 + GOTO 60 5 CONTINUE C CODE FOR UNEQUAL OR NONPOSITIVE INCREMENTS. @@ -1766,7 +1772,9 @@ IF(N.LE.0)RETURN - IF(INCX.EQ.INCY) IF(INCX-1) 5,20,60 + IF (INCX.EQ.INCY) IF(INCX.lt.1) GOTO 5 + IF (INCX.EQ.INCY) IF(INCX.eq.1) GOTO 20 + GOTO 60 5 CONTINUE C CODE FOR UNEQUAL OR NONPOSITIVE INCREMENTS. Modified: trunk/Lib/special/cdflib/gam1.f =================================================================== --- trunk/Lib/special/cdflib/gam1.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/special/cdflib/gam1.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -35,7 +35,9 @@ t = a d = a - 0.5D0 IF (d.GT.0.0D0) t = d - 0.5D0 - IF (t) 40,10,20 + IF (t.lt.0) GO TO 40 + IF (t.eq.0) GO TO 10 + GO TO 20 C 10 gam1 = 0.0D0 RETURN Modified: trunk/Lib/special/cdflib/gamma_fort.f =================================================================== --- trunk/Lib/special/cdflib/gamma_fort.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/special/cdflib/gamma_fort.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -63,7 +63,9 @@ C C LET T BE THE PRODUCT OF A-J WHEN A .GE. 2 C - IF (m) 40,30,10 + IF (m.lt.0) GO TO 40 + IF (m.eq.0) GO TO 30 + GO TO 10 10 DO 20 j = 1,m x = x - 1.0D0 t = x*t Modified: trunk/Lib/special/cdflib/gratio.f =================================================================== --- trunk/Lib/special/cdflib/gratio.f 2006-08-14 01:26:54 UTC (rev 2159) +++ trunk/Lib/special/cdflib/gratio.f 2006-08-14 04:22:28 UTC (rev 2160) @@ -317,7 +317,9 @@ u = 1.0D0/a z = sqrt(z+z) IF (l.LT.1.0D0) z = -z - IF (iop-2) 280,290,300 + IF (iop.lt.2) GO TO 280 + IF (iop.eq.2) GO TO 290 + GO TO 300 C 280 IF (abs(s).LE.1.D-3) GO TO 340 c0 = ((((((((((((d0(13)*z+d0(12))*z+d0(11))*z+d0(10))*z+d0(9))*z+ @@ -363,7 +365,9 @@ u = 1.0D0/a z = sqrt(z+z) IF (l.LT.1.0D0) z = -z - IF (iop-2) 340,350,360 + IF (iop.lt.2) GO TO 340 + IF (iop.eq.2) GO TO 350 + GO TO 360 C 340 c0 = ((((((d0(7)*z+d0(6))*z+d0(5))*z+d0(4))*z+d0(3))*z+d0(2))*z+ + d0(1))*z - third From scipy-svn at scipy.org Mon Aug 14 17:03:08 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 14 Aug 2006 16:03:08 -0500 (CDT) Subject: [Scipy-svn] r2161 - in trunk/Lib/stsci: convolve/lib image/lib Message-ID: <20060814210308.1FFC039C045@new.scipy.org> Author: oliphant Date: 2006-08-14 16:02:53 -0500 (Mon, 14 Aug 2006) New Revision: 2161 Modified: trunk/Lib/stsci/convolve/lib/Convolve.py trunk/Lib/stsci/image/lib/_image.py Log: Fix up imports in stsci Modified: trunk/Lib/stsci/convolve/lib/Convolve.py =================================================================== --- trunk/Lib/stsci/convolve/lib/Convolve.py 2006-08-14 04:22:28 UTC (rev 2160) +++ trunk/Lib/stsci/convolve/lib/Convolve.py 2006-08-14 21:02:53 UTC (rev 2161) @@ -1,6 +1,6 @@ import numpy as num import _correlate -import numpy.dft as dft +import numpy.fft as dft import iraf_frame VALID = 0 Modified: trunk/Lib/stsci/image/lib/_image.py =================================================================== --- trunk/Lib/stsci/image/lib/_image.py 2006-08-14 04:22:28 UTC (rev 2160) +++ trunk/Lib/stsci/image/lib/_image.py 2006-08-14 21:02:53 UTC (rev 2161) @@ -1,6 +1,6 @@ import numpy as num -import scipy.sandbox.stsci.convolve as convolve -import scipy.sandbox.stsci.convolve._correlate as _correlate +import scipy.stsci.convolve as convolve +import scipy.stsci.convolve._correlate as _correlate MLab=num def _translate(a, dx, dy, output=None, mode="nearest", cval=0.0): From scipy-svn at scipy.org Mon Aug 14 19:19:29 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 14 Aug 2006 18:19:29 -0500 (CDT) Subject: [Scipy-svn] r2162 - trunk/Lib/io Message-ID: <20060814231929.3AB9839C044@new.scipy.org> Author: stefan Date: 2006-08-14 18:19:23 -0500 (Mon, 14 Aug 2006) New Revision: 2162 Modified: trunk/Lib/io/mio.py Log: Add 64-bit support to mio [for Matthew Brett]. Modified: trunk/Lib/io/mio.py =================================================================== --- trunk/Lib/io/mio.py 2006-08-14 21:02:53 UTC (rev 2161) +++ trunk/Lib/io/mio.py 2006-08-14 23:19:23 UTC (rev 2162) @@ -38,8 +38,8 @@ mtype = 'i' elif mtype in ['I','uint','uint32','unsigned int']: mtype = 'I' - elif mtype in ['l','long','int32','integer*4']: - mtype = 'l' + elif mtype in ['u4','int32','integer*4']: + mtype = 'u4' elif mtype in ['f','float','float32','real*4', 'real']: mtype = 'f' elif mtype in ['d','double','float64','real*8', 'double precision']: @@ -154,7 +154,7 @@ unsigned short : 'H', 'ushort','uint16','unsigned short' int : 'i', 'int' unsigned int : 'I', 'uint32','uint','unsigned int' - long : 'l', 'long', 'int32', 'integer*4' + int32 : 'u4', 'int32', 'integer*4' float : 'f', 'float', 'float32', 'real*4' double : 'd', 'double', 'float64', 'real*8' complex float : 'F', 'complex float', 'complex*8', 'complex64' @@ -461,7 +461,7 @@ miUINT8 : ('miUINT8', 1,'B'), miINT16 : ('miINT16', 2,'h'), miUINT16 :('miUINT16',2,'H'), - miINT32 : ('miINT32',4,'l'), + miINT32 : ('miINT32',4,'u4'), miUINT32 : ('miUINT32',4,'I'), miSINGLE : ('miSINGLE',4,'f'), miDOUBLE : ('miDOUBLE',8,'d'), @@ -470,7 +470,7 @@ miMATRIX : ('miMATRIX',0,None), miUTF8 : ('miUTF8',1,'b'), miUTF16 : ('miUTF16',2,'h'), - miUTF32 : ('miUTF32',4,'l'), + miUTF32 : ('miUTF32',4,'u4'), } ''' Before release v7.1 (release 14) matlab used the system default @@ -567,11 +567,13 @@ try: " ".encode(en) except LookupError: - raise ValueError, 'Character encoding %s not supported' % en + raise TypeError, 'Character encoding %s not supported' % en if dtype == miUINT16: char_len = len(" ".encode(en)) - len(" ".encode(en)) if char_len == 1: # Need to downsample from 16 bit result = result.astype(uint8) + elif char_len != 2: + raise TypeError, 'miUNIT16 type cannot use >2 bytes encoding' result = squeeze(transpose(reshape(result,tupdims))) dims = result.shape if len(dims) >= 2: # return array of strings @@ -590,7 +592,7 @@ except KeyError: result = result + 1j*imag result = squeeze(transpose(reshape(result,tupdims))) - + elif dclass == mxCELL_CLASS: length = product(dims) result = empty(length, dtype=object) @@ -713,7 +715,7 @@ fid.zbuffer.fill(fid.raw_read(numbytes)) _skip_padding(fid, numbytes, 8) return _get_element(fid.zbuffer, return_name_dtype) - if dtype != miMATRIX: # basic data type + if dtype != miMATRIX: # therefore basic data type try: el = fid.read(numbytes,miDataTypes[dtype][2],c_is_b=1) except KeyError: @@ -895,7 +897,7 @@ This saves the arrayobjects in the given dictionary to a matlab Version 4 style .mat file. """ - storage = {'D':0,'d':0,'F':1,'f':1,'l':2,'i':2,'h':3,'B':5} + storage = {'D':0,'d':0,'F':1,'f':1,'u4':2,'i':2,'h':3,'B':5} if filename[-4:] != ".mat": filename = filename + ".mat" fid = fopen(filename,'wb') From scipy-svn at scipy.org Tue Aug 15 08:01:49 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 15 Aug 2006 07:01:49 -0500 (CDT) Subject: [Scipy-svn] r2163 - trunk/Lib/signal Message-ID: <20060815120149.80E7F39C010@new.scipy.org> Author: stefan Date: 2006-08-15 07:01:44 -0500 (Tue, 15 Aug 2006) New Revision: 2163 Modified: trunk/Lib/signal/signaltools.py Log: Convert remez input argument to array. Modified: trunk/Lib/signal/signaltools.py =================================================================== --- trunk/Lib/signal/signaltools.py 2006-08-14 23:19:23 UTC (rev 2162) +++ trunk/Lib/signal/signaltools.py 2006-08-15 12:01:44 UTC (rev 2163) @@ -422,7 +422,7 @@ if weight is None: weight = [1] * len(desired) - bands = bands.copy() + bands = asarray(bands).copy() return sigtools._remez(numtaps, bands, desired, weight, tnum, Hz, maxiter, grid_density) From scipy-svn at scipy.org Fri Aug 18 11:32:50 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 18 Aug 2006 10:32:50 -0500 (CDT) Subject: [Scipy-svn] r2164 - trunk/Lib/sparse Message-ID: <20060818153250.8A01539C107@new.scipy.org> Author: edschofield Date: 2006-08-18 10:32:46 -0500 (Fri, 18 Aug 2006) New Revision: 2164 Modified: trunk/Lib/sparse/sparse.py Log: Removed stray breakpoint in sparse.py Modified: trunk/Lib/sparse/sparse.py =================================================================== --- trunk/Lib/sparse/sparse.py 2006-08-15 12:01:44 UTC (rev 2163) +++ trunk/Lib/sparse/sparse.py 2006-08-18 15:32:46 UTC (rev 2164) @@ -2497,8 +2497,6 @@ elif operator.isSequenceType(i): seq = i else: - import pdb - pdb.set_trace() raise IndexError, "invalid index" try: if not len(x) == len(seq): From scipy-svn at scipy.org Fri Aug 18 12:39:23 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 18 Aug 2006 11:39:23 -0500 (CDT) Subject: [Scipy-svn] r2165 - trunk/Lib/sandbox/ann Message-ID: <20060818163923.9ED7039C089@new.scipy.org> Author: fred.mailhot Date: 2006-08-18 11:39:21 -0500 (Fri, 18 Aug 2006) New Revision: 2165 Added: trunk/Lib/sandbox/ann/rbf.py Log: New start with RBF net. Will finish this Sunday. Added: trunk/Lib/sandbox/ann/rbf.py =================================================================== --- trunk/Lib/sandbox/ann/rbf.py 2006-08-18 15:32:46 UTC (rev 2164) +++ trunk/Lib/sandbox/ann/rbf.py 2006-08-18 16:39:21 UTC (rev 2165) @@ -0,0 +1,39 @@ +# rbf2.py +# tilde +# 2006/08/18 +# - new attempt at RBF net to get my ideas straight...deadline is fast approaching! + +from numpy import * + +class rbf: + + _type = 'rbf' + + def __init__(nin,nhid,nout,trndata): + # set easy params + self.nin = nin + self.nhid = nhid + self.nout = nout + # choose subset (1/5?) of training data for basis fxn centers and + self.centers = [] + for i in trndata: + if random.random < 0.2: + self.centers.append(i) + # set common variance proportional to max dist between centers + d_max = 0.0 + for i in self.centers: + for j in self.centers: + tmp = sqrt((i-j)**2) + if tmp > d_max: + d_max = tmp + self.variance = d_max/2.0*size(trndata) + + + def fwd(self,inputs): + """ Propagate values forward through the net. + Inputs: + inputs - vector of input values + """ + z = exp((-1.0/(2*self.variance))* + o = dot(z,self.w) + dot(ones((len(z),1)),self.b) + From scipy-svn at scipy.org Fri Aug 18 12:40:57 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 18 Aug 2006 11:40:57 -0500 (CDT) Subject: [Scipy-svn] r2166 - trunk/Lib/sandbox/ann Message-ID: <20060818164057.92F8C39C0D4@new.scipy.org> Author: fred.mailhot Date: 2006-08-18 11:40:55 -0500 (Fri, 18 Aug 2006) New Revision: 2166 Modified: trunk/Lib/sandbox/ann/mlp.py Log: Heavily refactored MLP. Only leastsq used for optimization. Modified: trunk/Lib/sandbox/ann/mlp.py =================================================================== --- trunk/Lib/sandbox/ann/mlp.py 2006-08-18 16:39:21 UTC (rev 2165) +++ trunk/Lib/sandbox/ann/mlp.py 2006-08-18 16:40:55 UTC (rev 2166) @@ -1,8 +1,10 @@ # mlp.py # by: Fred Mailhot -# last mod: 2006-06-21 +# last mod: 2006-08-18 -from scipy import * +from scipy import * # I'll want to change this for numpy eventually +from scipy.optimize import leastsq +import copy class mlp: """Class to define, train and test a multilayer perceptron.""" @@ -10,7 +12,6 @@ _type = 'mlp' _outfxns = ('linear','logistic','softmax') _algs = ('simplex','powell','bfgs','cg','ncg','leastsq') - _options = zeros(18) # don't know if I'll use this or not def __init__(self,nin,nhid,nout,fxn,alg='leastsq',w=None): """ Set up instance of mlp. Initial weights are drawn from a @@ -47,7 +48,6 @@ self.w2 = randn(nhid,nout)/sqrt(nhid+1) self.b2 = randn(1,nout)/sqrt(nhid+1) self.packwts() - self.options = self._options def unpackwts(self): """ Decompose 1-d vector of weights w into appropriate weight @@ -96,138 +96,55 @@ return array(y) def errfxn(self,w,x,t): - """ Implementing 'canonical' error fxns for each of the output - activation fxns (see Nabney pp.123-128,156-158 for more info). - Borrowing heavily from the Netlab implementations for now. + """ Return vector of squared-errors for the leastsq optimizer """ y = self.fwd(x,w) - if self.alg == 'leastsq': - return sum(array(y-t)**2,axis=1) - if self.outfxn == 'linear': - # calculate & return SSE - return 0.5*sum(sum(array(y-t)**2,axis=1)) - elif self.outfxn == 'logistic': - # calculate & return x-entropy - return -1.0*sum(sum(t*log2(y)+(1-t)*log2(1-y),axis=1)) - elif self.outfxn == 'softmax': - # calculate & return entropy - return -1.0*sum(sum(t*log2(y),axis=1)) - else: - # this shouldn't happen...return SSE as a reasonable default - return 0.5*sum(sum((y - t)**2,axis=1)) - - def errgrad(self,w,x,t): - """ Error gradient fxns for canonical error fxns (see above, and - Nabney pp.127-128,156-158) - ** Includes error-backpropagation (Netlab splits these fxns) - Inputs: - w - weight vector (don't really know why I pass this around...) - x - input patterns - t - targets - Outputs: - g - gradient - - - ***N.B.********************************************************* - I'M DOING SOMETHING WRONG HERE, EVIDENTLY, AS THE OPTIMIZATION - FXNS THAT DEPEND ON A GRADIENT FXN AREN'T DOING WHAT THEY'RE - SUPPOSED TO (i.e. they're not optimizing anything) - **************************************************************** - """ - # get output and hidden activation patterns for a full forward pass - y,z = self.fwd(x,w,True) - outdeltas = y-t - - # compute second-layer weight and bias gradients - # THIS IS AN AWFUL-LOOKING HACK, BUT I HAVEN'T FOUND A BETTER - # WAY TO DO IT, YET... - w2grad = zeros((shape(x)[0],shape(z)[1]*shape(outdeltas)[1]),dtype=Float) - for i in range(shape(w2grad)[0]): - w2grad[i] = outer(outdeltas[i],z[i]).reshape(size(outdeltas[i])*size(z[i])) - w2grad = sum(w2grad) - b2grad = sum(outdeltas) - # backpropagate...AGAIN WITH THE FUGLY HACK...PLUS I HAVE TO EXPLICITLY - # LOOP OVER ALL INPUT PATTERNS....*bleah*... - hiddeltas = zeros((shape(x)[0],self.nhid),dtype=Float) - for i in range(shape(hiddeltas)[0]): - for j in range(shape(hiddeltas)[1]): - hiddeltas[i][j] = (1-z[i][j]**2)*sum(diag(outer(self.w2[j],outdeltas[i]))) - # compute first-layer weight and bias gradients - w1grad = zeros((shape(x)[0],shape(x)[1]*shape(hiddeltas)[1]),dtype=Float) - for i in range(shape(w1grad)[0]): - w1grad[i] = outer(hiddeltas[i],x[i]).reshape(size(hiddeltas[i])*size(x[i])) - w1grad = sum(w1grad) - b1grad = sum(hiddeltas) - # pack into a single vector and return it - g = hstack([w1grad.reshape(size(w1grad)), - b1grad.reshape(size(b1grad)), - w2grad.reshape(size(w2grad)), - b2grad.reshape(size(b2grad))]) - return g - + return sum(array(y-t)**2,axis=1) + def train(self,x,t): - """ Train a multilayer perceptron with a user-specified algorithm. - Inputs: + """ Train a multilayer perceptron using scipy's leastsq optimizer + Input: x - matrix of input data t - matrix of target outputs - alg - training algorithm, one of {simplex,bfgs,ncg,leastsq} - Outputs: - w - post-optimization weight vector + Returns: + post-optimization weight vector """ - # N.B. doing nothing with the specified algorithm for now, - # just optimizing with the leastsq fxn in scipy.optimize - if self.alg == 'simplex': - from scipy.optimize import fmin - w = fmin(self.errfxn,self.w_packed,args=(x,t),full_output=True) - elif self.alg == 'bfgs': - from scipy.optimize import fmin_bfgs - # version of this that uses errgrad doesn't converge - #w = fmin_bfgs(self.errfxn,self.w_packed,fprime=self.errgrad,args=(x,t),full_output=True) - w = fmin_bfgs(self.errfxn,self.w_packed,args=(x,t),full_output=True) - elif self.alg == 'cg': - from scipy.optimize import fmin_cg - #w = fmin_cg(self.errfxn,self.w_packed,self.errgrad,args=(x,t),full_output=True) - w = fmin_cg(self.errfxn,self.w_packed,args=(x,t),full_output=True) - elif self.alg == 'ncg': - from scipy.optimize import fmin_ncg - w = fmin_ncg(self.errfxn,self.w_packed,self.errgrad,args=(x,t),\ - full_output=True) - else: - # leastsq, or undef'd algorithm, in which case use leastsq as - # a reasonable default - if self.alg != 'leastsq': - import sys - print "Undefined algorithm, using least-squares" - sys.stdout.flush() - from scipy.optimize import leastsq - w = leastsq(self.errfxn,self.w_packed,args=(x,t),\ - full_output=True) - return w + # something's going wrong w/ the full_output option + # return leastsq(self.errfxn,self.w_packed,args=(x,t),full_output=True) + return leastsq(self.errfxn,self.w_packed,args=(x,t)) def main(): - import os,sys,copy - from scipy.io import read_array, write_array """ Approx test of module, using the oilTrn/oilTst data files that are - distributed with Netlab. Prints a bunch of info about weight vector and - error measures before and after optimization. """ - opt = raw_input("\nEnter desired optimizer (simplex,bfgs,cg,ncg,leastsq): ") + from scipy.io import read_array, write_array + # build the net print "\nCreating 12-5-2 MLP with linear outputs" - net = mlp(12,5,2,'linear',opt) + net = mlp(12,5,2,'linear') w_init = copy.copy(net.w_packed) + # prep the train/test data print "\nLoading training and test sets...", trn_input = read_array('data/oilTrn.dat',lines=(3,-1),columns=(0,(1,12))) trn_targs = read_array('data/oilTrn.dat',lines=(3,-1),columns=(12,-1)) tst_input = read_array('data/oilTst.dat',lines=(3,-1),columns=(0,(1,12))) tst_targs = read_array('data/oilTst.dat',lines=(3,-1),columns=(12,-1)) print "done." - sys.stdout.flush() - - print "\nInitial error: ",net.errfxn(net.w_packed,tst_input,tst_targs) - retval = net.train(trn_input,trn_targs) - net.w_packed = retval[0] - print "\nFinal error: ",net.errfxn(net.w_packed,tst_input,tst_targs) - + # initial squared-error + print "\nInitial SSE on training set: ",\ + sum(net.errfxn(net.w_packed,trn_input,trn_targs)) + print "\nInitial SSE on testing set: ",\ + sum(net.errfxn(net.w_packed,tst_input,tst_targs)) + # train the net + net.w_packed = net.train(trn_input,trn_targs)[0] + # final squared-error + print "\nFinal SSE on training set: ",\ + sum(net.errfxn(net.w_packed,trn_input,trn_targs)) + print "\nFinal SSE on testing set: ",\ + sum(net.errfxn(net.w_packed,tst_input,tst_targs)) + # view extended output? + # REMOVING THIS OPTION FOR NOW + #if raw_input("Do you want to see the full training output? (y/n").lower() == 'y': + # print retval[1] + if __name__ == '__main__': main() From scipy-svn at scipy.org Fri Aug 18 12:41:56 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 18 Aug 2006 11:41:56 -0500 (CDT) Subject: [Scipy-svn] r2167 - trunk/Lib/sandbox/ann Message-ID: <20060818164156.7C90939C089@new.scipy.org> Author: fred.mailhot Date: 2006-08-18 11:41:53 -0500 (Fri, 18 Aug 2006) New Revision: 2167 Modified: trunk/Lib/sandbox/ann/srn.py Log: Refactoring SRN based on MLP. Simple 1-step backprop only. Modified: trunk/Lib/sandbox/ann/srn.py =================================================================== --- trunk/Lib/sandbox/ann/srn.py 2006-08-18 16:40:55 UTC (rev 2166) +++ trunk/Lib/sandbox/ann/srn.py 2006-08-18 16:41:53 UTC (rev 2167) @@ -8,19 +8,13 @@ class srn: """Class to define, train and test a simple recurrent network a.k.a. 'Elman net' (cf. Elman 1991's Machine Learnig paper,inter alia) - - ************************* NOTA BENE 2006-06-23 ************************ - * This is obviously still very incomplete. The initial implementation * - * will only have straightforward backprop-through-time (with the * - * option to truncate). * - *********************************************************************** """ _type = 'srn' _outfxns = ('linear','logistic','softmax') - _algs = ('bptt') # hopefully eventually RTRL and EKF + _alg = ('srn') - def __init__(self,ni,nh,no,f,tau=-1,w=None): + def __init__(self,ni,nh,no,f,h=-1,w=None): """ Set up instance of srn. Initial weights are drawn from a zero-mean Gaussian w/ variance is scaled by fan-in. (see Bishop 1995 for justification) @@ -31,7 +25,6 @@ f - string description of output unit activation fxn; one of {'linear','logistic','softmax'} (n.b. hidden/context units use tanh) - h - truncation constant for bptt(h) w - initialized 1-d weight vector """ if f not in self._outfxns: @@ -39,14 +32,15 @@ self.outfxn = 'linear' else: self.outfxn = f + # set up layers of units self.ni = ni self.nh = nh - self.nc = nh # context units + self.nc = nh self.no = no - self.alg = self._algs[1] self.z = zeros((h,nh),dtype=Float) # hidden activations for 1 epoch self.c = zeros((h,nh),dtype=Float) # context activations for 1 epoch self.o = zeros((h,no),dtype=Float) # output activiation for 1 epoch + self.p = zeros((nh,nw,nw),dtype=Float) if w: self.nw = size(w) self.wp = w @@ -95,10 +89,11 @@ self.b2.reshape(size(self.b2))]) def fwd(self,x,w=None,hid=False): - """ Propagate values forward through the net. This (i) feeds the current input - and values of the context units (i.e. hidden vals from previous time step) - into the hidden layer, which is then (ii) fed to the output layer, and - (iii) copied to the context layer + """ Propagate values forward through the net. + This involves the following steps: + (i) feeds the current input and context values to the hidden layer, + (ii) hidden layer net input is transformed and then sent to the outputs + (iii) output values are copied to the context layer Inputs: x - matrix of all input patterns w - 1-d vector of weights @@ -111,14 +106,11 @@ if wts is not None: self.wp = w self.unpack() - - # compute hidden activations + # compute net input to hiddens and then squash it self.z = tanh(dot(x,self.w1) + dot(self.c,self.wc) + dot(ones((len(x),1)),self.b1)) - # copy hidden vals to context units + # send hidden vals to output and copy to context + o = dot(self.z,self.w2) + dot(ones((len(self.z),1)),self.b2) self.c = copy.copy(self.z) - # compute net input to output units - o = dot(self.z,self.w2) + dot(ones((len(self.z),1)),self.b2) - # compute output activations if self.outfxn == 'linear': y = o @@ -134,7 +126,7 @@ return array(y) def train(self,x,t,N): - """ The calls to the various trainig algorithms. + """ Train net by standard backpropagation Inputs: x - all input patterns t - all target patterns @@ -142,30 +134,47 @@ Outputs: w - new weight vector """ - pass + for i in range(N): + def errfxn(self,w,x,t): """ Error functions for each of the output-unit activation functions. Inputs: w - current weight vector - x - current pattern input(s) (len(x) == tau) + x - current pattern input(s) (len(x) == self.h) t - current pattern target(s) """ - pass + y,z = self.fwd(w,x,True) + if self.outfxn == 'linear': + # calculate & return SSE + err = 0.5*sum(sum(array(y-t)**2,axis=1)) + elif self.outfxn == 'logistic': + # calculate & return x-entropy + err = -1.0*sum(sum(t*log2(y)+(1-t)*log2(1-y),axis=1)) + elif self.outfxn == 'softmax': + # calculate & return entropy + err = -1.0*sum(sum(t*log2(y),axis=1)) + else: + # this shouldn't happen, return SSE as safe default + err = 0.5*sum(sum(array(y-t)**2,axis=1)) + + # returning a tuple of info for now...not sure why + return err,y,z def main(): """ Set up a 1-2-1 SRN to solve the temporal-XOR problem from Elman 1990. """ from scipy.io import read_array, write_array - print "Creating 1-2-1 SRN for 'temporal-XOR' (sent net.trunc to 2)" + print "Creating 1-2-1 SRN for 'temporal-XOR' (net.h = 2)" net = srn(1,2,1,'logistic',2) + print net print "\nLoading training and test sets...", trn_input = read_array('data/t-xor1.dat') trn_targs = hstack([trn_input[1:],trn_input[0]]) tst_input = read_array('data/t-xor2.dat') tst_targs = hstack([tst_input[1:],tst_input[0]]) print "done." - N = input("Number of times to see all patterns: ") + N = input("Number of iterations over training set: ") print "\nInitial error: ",net.errfxn(net.wp,tst_input,tst_targs) net.train(trn_input,trn_targs,N) From scipy-svn at scipy.org Sun Aug 20 02:29:30 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 20 Aug 2006 01:29:30 -0500 (CDT) Subject: [Scipy-svn] r2168 - trunk/Lib/sandbox/ann/data Message-ID: <20060820062930.1D6FB39C0B2@new.scipy.org> Author: fred.mailhot Date: 2006-08-20 01:29:23 -0500 (Sun, 20 Aug 2006) New Revision: 2168 Added: trunk/Lib/sandbox/ann/data/xor-trn.dat Log: data files for testing Added: trunk/Lib/sandbox/ann/data/xor-trn.dat =================================================================== --- trunk/Lib/sandbox/ann/data/xor-trn.dat 2006-08-18 16:41:53 UTC (rev 2167) +++ trunk/Lib/sandbox/ann/data/xor-trn.dat 2006-08-20 06:29:23 UTC (rev 2168) @@ -0,0 +1,15 @@ +nin 2 +nout 1 +ndata 12 +1 0 1 +0 1 1 +0 0 0 +1 1 0 +1 0 1 +0 1 1 +0 0 0 +1 1 0 +1 0 1 +0 1 1 +0 0 0 +1 1 0 From scipy-svn at scipy.org Sun Aug 20 02:30:16 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 20 Aug 2006 01:30:16 -0500 (CDT) Subject: [Scipy-svn] r2169 - trunk/Lib/sandbox/ann/data Message-ID: <20060820063016.531E939C016@new.scipy.org> Author: fred.mailhot Date: 2006-08-20 01:30:09 -0500 (Sun, 20 Aug 2006) New Revision: 2169 Added: trunk/Lib/sandbox/ann/data/oil-trn.dat trunk/Lib/sandbox/ann/data/oil-tst.dat trunk/Lib/sandbox/ann/data/txor-trn.dat trunk/Lib/sandbox/ann/data/txor-tst.dat trunk/Lib/sandbox/ann/data/xor-tst.dat Log: more data files for testing Added: trunk/Lib/sandbox/ann/data/oil-trn.dat =================================================================== --- trunk/Lib/sandbox/ann/data/oil-trn.dat 2006-08-20 06:29:23 UTC (rev 2168) +++ trunk/Lib/sandbox/ann/data/oil-trn.dat 2006-08-20 06:30:09 UTC (rev 2169) @@ -0,0 +1,503 @@ + nin 12 + nout 2 + ndata 500 + 3.315000e-01 2.156000e-01 6.802000e-01 1.434000e-01 6.825000e-01 2.720000e-01 6.223000e-01 2.092000e-01 7.961000e-01 1.530000e-01 5.856000e-01 2.573000e-01 3.440000e-01 1.401000e-01 + 9.390000e-02 1.008900e+00 3.650000e-02 6.944000e-01 9.080000e-02 4.961000e-01 7.220000e-02 6.521000e-01 -1.300000e-02 6.085000e-01 6.310000e-02 6.597000e-01 5.140000e-02 4.459000e-01 + 5.184000e-01 2.283000e-01 5.300000e-01 6.884000e-01 7.456000e-01 6.171000e-01 6.136000e-01 5.928000e-01 7.678000e-01 6.130000e-01 6.705000e-01 5.202000e-01 3.710000e-01 3.214000e-01 + 4.208000e-01 6.740000e-01 1.651000e-01 7.592000e-01 1.810000e-01 5.448000e-01 1.707000e-01 7.554000e-01 1.635000e-01 5.492000e-01 2.598000e-01 6.455000e-01 1.667000e-01 4.177000e-01 + 3.130000e-01 6.465000e-01 5.908000e-01 6.924000e-01 7.664000e-01 6.262000e-01 1.717700e+00 1.500000e-02 8.510000e-02 1.904600e+00 -1.650000e-02 2.210000e-02 3.378000e-01 4.184000e-01 + 1.145800e+00 -4.670000e-02 4.056000e-01 5.662000e-01 3.123000e-01 4.580000e-01 3.636000e-01 6.134000e-01 3.305000e-01 4.132000e-01 4.167000e-01 5.514000e-01 3.249000e-01 2.790000e-01 +-1.900000e-03 1.732000e-01 5.700000e-03 4.882000e-01 2.076000e-01 3.910000e-01 8.600000e-03 1.719800e+00 2.150000e-02 -2.580000e-02 6.730000e-02 -8.290000e-02 5.100000e-02 2.123000e-01 + 7.800000e-03 4.615000e-01 1.181000e-01 6.590000e-01 2.587000e-01 6.352000e-01 -1.910000e-02 1.749100e+00 1.098000e-01 -1.315000e-01 4.070000e-02 -4.850000e-02 6.210000e-02 3.856000e-01 + 7.305000e-01 1.189000e-01 1.062600e+00 1.013000e-01 1.138500e+00 1.486000e-01 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2006 01:37:07 -0500 (CDT) Subject: [Scipy-svn] r2170 - trunk/Lib/sandbox/ann Message-ID: <20060820063707.D90D139C016@new.scipy.org> Author: fred.mailhot Date: 2006-08-20 01:37:03 -0500 (Sun, 20 Aug 2006) New Revision: 2170 Modified: trunk/Lib/sandbox/ann/mlp.py trunk/Lib/sandbox/ann/srn.py Log: Heavy refactoring. These are more or less complete now. Small additions remain to make the interface more obvious for users. Modified: trunk/Lib/sandbox/ann/mlp.py =================================================================== --- trunk/Lib/sandbox/ann/mlp.py 2006-08-20 06:30:09 UTC (rev 2169) +++ trunk/Lib/sandbox/ann/mlp.py 2006-08-20 06:37:03 UTC (rev 2170) @@ -1,149 +1,146 @@ # mlp.py # by: Fred Mailhot -# last mod: 2006-08-18 +# last mod: 2006-08-19 -from scipy import * # I'll want to change this for numpy eventually +import numpy as N from scipy.optimize import leastsq -import copy class mlp: - """Class to define, train and test a multilayer perceptron.""" + """Class to define, train and test a multilayer perceptron. + """ _type = 'mlp' _outfxns = ('linear','logistic','softmax') - _algs = ('simplex','powell','bfgs','cg','ncg','leastsq') - def __init__(self,nin,nhid,nout,fxn,alg='leastsq',w=None): + def __init__(self,ni,nh,no,f='linear',w=None): """ Set up instance of mlp. Initial weights are drawn from a zero-mean Gaussian w/ variance is scaled by fan-in. - (see Bishop 1995 for justification) - Inputs: - nin/nhid/nout - integer number of input/hidden/output units, - respectively - fxn - string description of output unit activation - fxn (hidden units use tanh); can be 'linear', - 'logistic' or 'softmax' + Input: + ni - # of inputs + nh - # of hidden & context units + no - # of outputs + f - output activation fxn + w - weight vector """ - if fxn not in self._outfxns: + if f not in self._outfxns: print "Undefined activation fxn. Using linear" self.outfxn = 'linear' else: - self.outfxn = fxn - self.nin = nin - self.nhid = nhid - self.nout = nout - self.alg = alg + self.outfxn = f + self.ni = ni + self.nh = nh + self.no = no + #self.alg = alg if w: - self.nwts = size(w) - self.w_packed = w - self.w1 = zeros((nin,nhid),dtype=Float) - self.b1 = zeros((1,nhid),dtype=Float) - self.w2 = zeros((nhid,nout),dtype=Float) - self.b2 = zeros((1,nout),dtype=Float) - self.unpackwts() + self.nw = N.size(w) + self.wp = w + self.w1 = N.zeros((ni,nh),dtype=Float) + self.b1 = N.zeros((1,nh),dtype=Float) + self.w2 = N.zeros((nh,no),dtype=Float) + self.b2 = N.zeros((1,no),dtype=Float) + self.unpack() else: - self.nwts = (nin+1)*nhid + (nhid+1)*nout - self.w1 = randn(nin,nhid)/sqrt(nin+1) - self.b1 = randn(1,nhid)/sqrt(nin+1) - self.w2 = randn(nhid,nout)/sqrt(nhid+1) - self.b2 = randn(1,nout)/sqrt(nhid+1) - self.packwts() + self.nw = (ni+1)*nh + (nh+1)*no + self.w1 = N.random.randn(ni,nh)/N.sqrt(ni+1) + self.b1 = N.random.randn(1,nh)/N.sqrt(ni+1) + self.w2 = N.random.randn(nh,no)/N.sqrt(nh+1) + self.b2 = N.random.randn(1,no)/N.sqrt(nh+1) + self.pack() - def unpackwts(self): + def unpack(self): """ Decompose 1-d vector of weights w into appropriate weight matrices (w1,b1,w2,b2) and reinsert them into net """ - self.w1 = reshape(array(self.w_packed)[:self.nin*self.nhid],(self.nin,self.nhid)) - self.b1 = reshape(array(self.w_packed)[(self.nin*self.nhid):(self.nin*self.nhid)+self.nhid],(1,self.nhid)) - self.w2 = reshape(array(self.w_packed)[(self.nin*self.nhid)+self.nhid:\ - (self.nin*self.nhid)+self.nhid+(self.nhid*self.nout)],(self.nhid,self.nout)) - self.b2 = reshape(array(self.w_packed)[(self.nin*self.nhid)+self.nhid+(self.nhid*self.nout):],(1,self.nout)) + self.w1 = N.array(self.wp)[:self.ni*self.nh].reshape(self.ni,self.nh) + self.b1 = N.array(self.wp)[(self.ni*self.nh):(self.ni*self.nh)+self.nh].reshape(1,self.nh) + self.w2 = N.array(self.wp)[(self.ni*self.nh)+self.nh:(self.ni*self.nh)+self.nh+(self.nh*self.no)].reshape(self.nh,self.no) + self.b2 = N.array(self.wp)[(self.ni*self.nh)+self.nh+(self.nh*self.no):].reshape(1,self.no) - def packwts(self): + def pack(self): """ Compile weight matrices w1,b1,w2,b2 from net into a single vector, suitable for optimization routines. """ - self.w_packed = hstack([self.w1.reshape(size(self.w1)), - self.b1.reshape(size(self.b1)), - self.w2.reshape(size(self.w2)), - self.b2.reshape(size(self.b2))]) + self.wp = N.hstack([self.w1.reshape(N.size(self.w1)), + self.b1.reshape(N.size(self.b1)), + self.w2.reshape(N.size(self.w2)), + self.b2.reshape(N.size(self.b2))]) - def fwd(self,inputs,wts=None,hid=False): - """ Propagate values forward through the net. - Inputs: - inputs - self.nin*1 vector of inputs - hid - boolean specifying whether or not to return hidden - unit activations, False by default + def fwd_all(self,x,w=None): + """ Propagate values forward through the net. + Input: + x - array (size>1) of input patterns + w - optional 1-d vector of weights + Returns: + y - array of outputs for all input patterns """ - if wts is not None: - self.w_packed = wts - self.unpackwts() - - z = tanh(dot(inputs,self.w1) + dot(ones((len(inputs),1)),self.b1)) - o = dot(z,self.w2) + dot(ones((len(z),1)),self.b2) - + if w is not None: + self.wp = w + self.unpack() + # compute vector of hidden unit values + z = N.tanh(N.dot(x,self.w1) + N.dot(N.ones((len(x),1)),self.b1)) + # compute vector of net outputs + o = N.dot(z,self.w2) + N.dot(N.ones((len(z),1)),self.b2) + # compute final output activations if self.outfxn == 'linear': y = o elif self.outfxn == 'logistic': # TODO: check for overflow here... - y = 1/(1+exp(-o)) + y = 1/(1+N.exp(-o)) elif self.outfxn == 'softmax': # TODO: and here... - tmp = exp(o) - y = tmp/(sum(temp,1)*ones((1,self.nout))) + tmp = N.exp(o) + y = tmp/(N.sum(temp,1)*N.ones((1,self.no))) - if hid: - return array(y),array(z) - else: - return array(y) + return N.array(y) def errfxn(self,w,x,t): """ Return vector of squared-errors for the leastsq optimizer """ - y = self.fwd(x,w) - return sum(array(y-t)**2,axis=1) + y = self.fwd_all(x,w) + return N.sum(N.array(y-t)**2,axis=1) def train(self,x,t): - """ Train a multilayer perceptron using scipy's leastsq optimizer + """ Train network using scipy's leastsq optimizer Input: - x - matrix of input data - t - matrix of target outputs + x - array of input data + t - array of targets + + N.B. x and t comprise the *entire* collection of training data + Returns: post-optimization weight vector """ - # something's going wrong w/ the full_output option - # return leastsq(self.errfxn,self.w_packed,args=(x,t),full_output=True) - return leastsq(self.errfxn,self.w_packed,args=(x,t)) + return leastsq(self.errfxn,self.wp,args=(x,t)) + def test_all(self,x,t): + """ Test network on an array (size>1) of patterns + Input: + x - array of input data + t - array of targets + Returns: + sum-squared-error over all data + """ + return N.sum(self.errfxn(self.wp,x,t)) + def main(): - """ Approx test of module, using the oilTrn/oilTst data files that are + """ Build/train/test MLP """ from scipy.io import read_array, write_array - # build the net - print "\nCreating 12-5-2 MLP with linear outputs" - net = mlp(12,5,2,'linear') - w_init = copy.copy(net.w_packed) - # prep the train/test data + print "\nCreating 2-2-1 MLP with logistic outputs" + net = mlp(2,2,1,'logistic') print "\nLoading training and test sets...", - trn_input = read_array('data/oilTrn.dat',lines=(3,-1),columns=(0,(1,12))) - trn_targs = read_array('data/oilTrn.dat',lines=(3,-1),columns=(12,-1)) - tst_input = read_array('data/oilTst.dat',lines=(3,-1),columns=(0,(1,12))) - tst_targs = read_array('data/oilTst.dat',lines=(3,-1),columns=(12,-1)) + trn_input = read_array('data/xor-trn.dat',lines=(3,-1),columns=(0,(1,2))) + trn_targs = read_array('data/xor-trn.dat',lines=(3,-1),columns=(2,-1)) + trn_targs = trn_targs.reshape(N.size(trn_targs),1) + tst_input = read_array('data/xor-tst.dat',lines=(3,-1),columns=(0,(1,2))) + tst_targs = read_array('data/xor-tst.dat',lines=(3,-1),columns=(2,-1)) + tst_targs = tst_targs.reshape(N.size(tst_targs),1) print "done." - # initial squared-error - print "\nInitial SSE on training set: ",\ - sum(net.errfxn(net.w_packed,trn_input,trn_targs)) - print "\nInitial SSE on testing set: ",\ - sum(net.errfxn(net.w_packed,tst_input,tst_targs)) - # train the net - net.w_packed = net.train(trn_input,trn_targs)[0] - # final squared-error - print "\nFinal SSE on training set: ",\ - sum(net.errfxn(net.w_packed,trn_input,trn_targs)) - print "\nFinal SSE on testing set: ",\ - sum(net.errfxn(net.w_packed,tst_input,tst_targs)) - # view extended output? - # REMOVING THIS OPTION FOR NOW - #if raw_input("Do you want to see the full training output? (y/n").lower() == 'y': - # print retval[1] + print "\nInitial SSE:\n" + print "\ttraining set: ",net.test_all(trn_input,trn_targs) + print "\ttesting set: ",net.test_all(tst_input,tst_targs),"\n" + net.wp = net.train(trn_input,trn_targs)[0] + print "\nFinal SSE:\n" + print "\ttraining set: ",net.test_all(trn_input,trn_targs) + print "\ttesting set: ",net.test_all(tst_input,tst_targs),"\n" if __name__ == '__main__': main() Modified: trunk/Lib/sandbox/ann/srn.py =================================================================== --- trunk/Lib/sandbox/ann/srn.py 2006-08-20 06:30:09 UTC (rev 2169) +++ trunk/Lib/sandbox/ann/srn.py 2006-08-20 06:37:03 UTC (rev 2170) @@ -1,54 +1,44 @@ # srn.py # by: Fred Mailhot -# last mod: 2006-06-22 +# last mod: 2006-08-18 -from scipy import * -import copy +import numpy as N +from scipy.optimize import leastsq class srn: """Class to define, train and test a simple recurrent network - a.k.a. 'Elman net' (cf. Elman 1991's Machine Learnig paper,inter alia) """ _type = 'srn' _outfxns = ('linear','logistic','softmax') - _alg = ('srn') - def __init__(self,ni,nh,no,f,h=-1,w=None): + def __init__(self,ni,nh,no,f='linear',w=None): """ Set up instance of srn. Initial weights are drawn from a zero-mean Gaussian w/ variance is scaled by fan-in. - (see Bishop 1995 for justification) - Inputs: - ni - integer number of input units - nh - integer number of hiden & context units - no - integer number of output units, - f - string description of output unit activation fxn; - one of {'linear','logistic','softmax'} - (n.b. hidden/context units use tanh) - w - initialized 1-d weight vector + Input: + ni - # of inputs + nh - # of hidden & context units + no - # of outputs + f - output activation fxn + w - weight vector """ if f not in self._outfxns: print "Undefined activation fxn. Using linear" self.outfxn = 'linear' else: self.outfxn = f - # set up layers of units self.ni = ni self.nh = nh self.nc = nh self.no = no - self.z = zeros((h,nh),dtype=Float) # hidden activations for 1 epoch - self.c = zeros((h,nh),dtype=Float) # context activations for 1 epoch - self.o = zeros((h,no),dtype=Float) # output activiation for 1 epoch - self.p = zeros((nh,nw,nw),dtype=Float) if w: - self.nw = size(w) + self.nw = N.size(w) self.wp = w - self.w1 = zeros((ni,nh),dtype=Float) # input-hidden wts - self.b1 = zeros((1,nh),dtype=Float) # input biases - self.wc = zeros((nh,nh),dtype=Float) # context wts - self.w2 = zeros((nh,no),dtype=Float) # hidden-output wts - self.b2 = zeros((1,no),dtype=Float) # hidden biases + self.w1 = N.zeros((ni,nh),dtype=Float) # input-hidden wts + self.b1 = N.zeros((1,nh),dtype=Float) # input biases + self.wc = N.zeros((nh,nh),dtype=Float) # context wts + self.w2 = N.zeros((nh,no),dtype=Float) # hidden-output wts + self.b2 = N.zeros((1,no),dtype=Float) # hidden biases self.unpack() else: # N.B. I just understood something about the way reshape() works @@ -57,129 +47,113 @@ # propagation. # I'll implement this next week. self.nw = (ni+1)*nh + (nh*nh) + (nh+1)*no - self.w1 = randn(ni,nh)/sqrt(ni+1) - self.b1 = randn(1,nh)/sqrt(ni+1) - self.wc = randn(nh,nh)/sqrt(nh+1) - self.w2 = randn(nh,no)/sqrt(nh+1) - self.b2 = randn(1,no)/sqrt(nh+1) + self.w1 = N.random.randn(ni,nh)/N.sqrt(ni+1) + self.b1 = N.random.randn(1,nh)/N.sqrt(ni+1) + self.wc = N.random.randn(nh,nh)/N.sqrt(nh+1) + self.w2 = N.random.randn(nh,no)/N.sqrt(nh+1) + self.b2 = N.random.randn(1,no)/N.sqrt(nh+1) self.pack() - if size(self.wp) != self.nw: - raise ValueError, "Unexpected number of weights" def unpack(self): """ Decompose 1-d vector of weights w into appropriate weight matrices (w1,b1,w2,b2) and reinsert them into net """ - self.w1 = reshape(array(self.wp)[:self.ni*self.nh],(self.ni,self.nh)) - self.b1 = reshape(array(self.wp)[(self.ni*self.nh):(self.ni*self.nh)+self.nh],(1,self.nh)) - self.wc = reshape(array(self.wp)[(self.ni*self.nh)+self.nh:\ - (self.ni*self.nh)+self.nh+(self.nh*self.nh)],(self.nh,self.nh)) - self.w2 = reshape(array(self.wp)[(self.ni*self.nh)+self.nh+(self.nh*self.nh):\ - (self.ni*self.nh)+self.nh+(self.nh*self.nh)+(self.nh*self.no)],(self.nh,self.no)) - self.b2 = reshape(array(self.wp)[(self.ni*self.nh)+self.nh+(self.nh*self.no):],(1,self.no)) + self.w1 = N.array(self.wp)[:self.ni*self.nh].reshape(self.ni,self.nh) + self.b1 = N.array(self.wp)[(self.ni*self.nh):(self.ni*self.nh)+self.nh].reshape(1,self.nh) + self.wc = N.array(self.wp)[(self.ni*self.nh)+self.nh:(self.ni*self.nh)+self.nh+(self.nh*self.nh)].reshape(self.nh,self.nh) + self.w2 = N.array(self.wp)[(self.ni*self.nh)+self.nh+(self.nh*self.nh):(self.ni*self.nh)+self.nh+(self.nh*self.nh)+(self.nh*self.no)].reshape(self.nh,self.no) + self.b2 = N.array(self.wp)[(self.ni*self.nh)+self.nh+(self.nh*self.nh)+(self.nh*self.no):].reshape(1,self.no) def pack(self): """ Compile weight matrices w1,b1,wc,w2,b2 from net into a single vector, suitable for optimization routines. """ - self.wp = hstack([self.w1.reshape(size(self.w1)), - self.b1.reshape(size(self.b1)), - self.wc.reshape(size(self.wc)), - self.w2.reshape(size(self.w2)), - self.b2.reshape(size(self.b2))]) + self.wp = N.hstack([self.w1.reshape(N.size(self.w1)), + self.b1.reshape(N.size(self.b1)), + self.wc.reshape(N.size(self.wc)), + self.w2.reshape(N.size(self.w2)), + self.b2.reshape(N.size(self.b2))]) - def fwd(self,x,w=None,hid=False): + def fwd_all(self,x,w=None): """ Propagate values forward through the net. - This involves the following steps: - (i) feeds the current input and context values to the hidden layer, - (ii) hidden layer net input is transformed and then sent to the outputs - (iii) output values are copied to the context layer - Inputs: + Input: x - matrix of all input patterns w - 1-d vector of weights - hid - boolean specifying whether or not to return hidden - unit activations, False by default - Outputs: + Returns: y - matrix of all outputs - z - matrix of all hidden activations (if hid=True) """ - if wts is not None: + if w is not None: self.wp = w self.unpack() - # compute net input to hiddens and then squash it - self.z = tanh(dot(x,self.w1) + dot(self.c,self.wc) + dot(ones((len(x),1)),self.b1)) - # send hidden vals to output and copy to context - o = dot(self.z,self.w2) + dot(ones((len(self.z),1)),self.b2) - self.c = copy.copy(self.z) - # compute output activations + # compute vector of context values for current weight matrix + c = N.tanh(N.dot(x,self.w1) + N.dot(N.ones((len(x),1)),self.b1)) + c = N.vstack([c[1:],c[0]]) + # compute vector of hidden unit values + z = N.tanh(N.dot(x,self.w1) + N.dot(c,self.wc) + N.dot(N.ones((len(x),1)),self.b1)) + # compute vector of net outputs + o = N.dot(z,self.w2) + N.dot(N.ones((len(z),1)),self.b2) + # compute final output activations if self.outfxn == 'linear': y = o elif self.outfxn == 'logistic': # TODO: check for overflow here... - y = 1/(1+exp(-o)) + y = 1/(1+N.exp(-o)) elif self.outfxn == 'softmax': # TODO: and here... - tmp = exp(o) - y = tmp/(sum(temp,1)*ones((1,self.no))) + tmp = N.exp(o) + y = tmp/(N.sum(temp,1)*N.ones((1,self.no))) - if hid: - return array(y),array(z) - else: - return array(y) - - def train(self,x,t,N): - """ Train net by standard backpropagation - Inputs: - x - all input patterns - t - all target patterns - N - number of times to go over patterns - Outputs: - w - new weight vector + return N.array(y) + + def errfxn(self,w,x,t): + """ Return vector of squared-errors for the leastsq optimizer """ - for i in range(N): - + y = self.fwd_all(x,w) + return N.sum(N.array(y-t)**2,axis=1) - def errfxn(self,w,x,t): - """ Error functions for each of the output-unit activation functions. - Inputs: - w - current weight vector - x - current pattern input(s) (len(x) == self.h) - t - current pattern target(s) + def train(self,x,t): + """ Train a multilayer perceptron using scipy's leastsq optimizer + Input: + x - matrix of input data + t - matrix of target outputs + Returns: + post-optimization weight vector """ - y,z = self.fwd(w,x,True) - if self.outfxn == 'linear': - # calculate & return SSE - err = 0.5*sum(sum(array(y-t)**2,axis=1)) - elif self.outfxn == 'logistic': - # calculate & return x-entropy - err = -1.0*sum(sum(t*log2(y)+(1-t)*log2(1-y),axis=1)) - elif self.outfxn == 'softmax': - # calculate & return entropy - err = -1.0*sum(sum(t*log2(y),axis=1)) - else: - # this shouldn't happen, return SSE as safe default - err = 0.5*sum(sum(array(y-t)**2,axis=1)) - - # returning a tuple of info for now...not sure why - return err,y,z + return leastsq(self.errfxn,self.wp,args=(x,t)) + def test_all(self,x,t): + """ Test network on an array (size>1) of patterns + Input: + x - array of input data + t - array of targets + Returns: + sum-squared-error over all data + """ + return N.sum(self.errfxn(self.wp,x,t)) + + def main(): """ Set up a 1-2-1 SRN to solve the temporal-XOR problem from Elman 1990. """ from scipy.io import read_array, write_array - print "Creating 1-2-1 SRN for 'temporal-XOR' (net.h = 2)" - net = srn(1,2,1,'logistic',2) - print net + print "\nCreating 1-2-1 SRN for 'temporal-XOR'" + net = srn(1,2,1,'logistic') print "\nLoading training and test sets...", - trn_input = read_array('data/t-xor1.dat') - trn_targs = hstack([trn_input[1:],trn_input[0]]) - tst_input = read_array('data/t-xor2.dat') - tst_targs = hstack([tst_input[1:],tst_input[0]]) + trn_input = read_array('data/txor-trn.dat') + trn_targs = N.hstack([trn_input[1:],trn_input[0]]) + trn_input = trn_input.reshape(N.size(trn_input),1) + trn_targs = trn_targs.reshape(N.size(trn_targs),1) + tst_input = read_array('data/txor-tst.dat') + tst_targs = N.hstack([tst_input[1:],tst_input[0]]) + tst_input = tst_input.reshape(N.size(tst_input),1) + tst_targs = tst_targs.reshape(N.size(tst_targs),1) print "done." - N = input("Number of iterations over training set: ") + print "\nInitial SSE:\n" + print "\ttraining set: ",net.test_all(trn_input,trn_targs) + print "\ttesting set: ",net.test_all(tst_input,tst_targs),"\n" + net.wp = net.train(trn_input,trn_targs)[0] + print "\nFinal SSE:\n" + print "\ttraining set: ",net.test_all(trn_input,trn_targs) + print "\ttesting set: ",net.test_all(tst_input,tst_targs),"\n" - print "\nInitial error: ",net.errfxn(net.wp,tst_input,tst_targs) - net.train(trn_input,trn_targs,N) - print "\nFinal error: ",net.errfxn(net.wp,tst_input,tst_targs) - if __name__ == '__main__': main() From scipy-svn at scipy.org Sun Aug 20 21:02:49 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 20 Aug 2006 20:02:49 -0500 (CDT) Subject: [Scipy-svn] r2171 - trunk/Lib/sandbox/ann Message-ID: <20060821010249.D155839C030@new.scipy.org> Author: fred.mailhot Date: 2006-08-20 20:02:46 -0500 (Sun, 20 Aug 2006) New Revision: 2171 Modified: trunk/Lib/sandbox/ann/mlp.py trunk/Lib/sandbox/ann/rbf.py trunk/Lib/sandbox/ann/srn.py Log: Updates/refactorings for mlp and srn, addition of rbf. Modified: trunk/Lib/sandbox/ann/mlp.py =================================================================== --- trunk/Lib/sandbox/ann/mlp.py 2006-08-20 06:37:03 UTC (rev 2170) +++ trunk/Lib/sandbox/ann/mlp.py 2006-08-21 01:02:46 UTC (rev 2171) @@ -17,10 +17,10 @@ zero-mean Gaussian w/ variance is scaled by fan-in. Input: ni - # of inputs - nh - # of hidden & context units + nh - # of hidden units no - # of outputs f - output activation fxn - w - weight vector + w - vector of initial weights """ if f not in self._outfxns: print "Undefined activation fxn. Using linear" Modified: trunk/Lib/sandbox/ann/rbf.py =================================================================== --- trunk/Lib/sandbox/ann/rbf.py 2006-08-20 06:37:03 UTC (rev 2170) +++ trunk/Lib/sandbox/ann/rbf.py 2006-08-21 01:02:46 UTC (rev 2171) @@ -1,39 +1,137 @@ # rbf2.py # tilde -# 2006/08/18 -# - new attempt at RBF net to get my ideas straight...deadline is fast approaching! +# 2006/08/20 -from numpy import * +import numpy as N +from scipy.optimize import leastsq class rbf: + """Class to define/train/test a radial basis function network + """ _type = 'rbf' - - def __init__(nin,nhid,nout,trndata): - # set easy params - self.nin = nin - self.nhid = nhid - self.nout = nout - # choose subset (1/5?) of training data for basis fxn centers and - self.centers = [] - for i in trndata: - if random.random < 0.2: - self.centers.append(i) - # set common variance proportional to max dist between centers + _outfxns = ('linear','logistic','softmax') + + + def __init__(self,ni,no,f='linear'): + """ Set up instance of RBF net. N.B. RBF centers and variance are selected at training time + Input: + ni - # of inputs + no - # of outputs + f - output activation fxn + """ + + self.ni = ni + self.no = no + self.outfxn = f + + def unpack(self): + """ Decompose 1-d vector of weights w into appropriate weight + matrices (self.{w/b}) and reinsert them into net + """ + self.w = N.array(self.wp)[:self.centers.shape[0]*self.no].reshape(self.centers.shape[0],self.no) + self.b = N.array(self.wp)[(self.centers.shape[0]*self.no):].reshape(1,self.no) + + def pack(self): + """ Compile weight matrices w,b from net into a + single vector, suitable for optimization routines. + """ + self.wp = N.hstack([self.w.reshape(N.size(self.w)), + self.b.reshape(N.size(self.b))]) + + def fwd_all(self,X,w=None): + """ Propagate values forward through the net. + Inputs: + inputs - vector of input values + w - packed array of weights + Returns: + array of outputs for all input patterns + """ + if w is not None: + self.wp = w + self.unpack() + # compute hidden unit values + z = N.zeros((len(X),self.centers.shape[0])) + for i in range(len(X)): + z[i] = N.exp((-1.0/(2*self.variance))*(N.sum((X[i]-self.centers)**2,axis=1))) + # compute net outputs + o = N.dot(z,self.w) + N.dot(N.ones((len(z),1)),self.b) + # compute final output activations + if self.outfxn == 'linear': + y = o + elif self.outfxn == 'logistic': # TODO: check for overflow here... + y = 1/(1+N.exp(-o)) + elif self.outfxn == 'softmax': # TODO: and here... + tmp = N.exp(o) + y = tmp/(N.sum(temp,1)*N.ones((1,self.no))) + + return N.array(y) + + + def err_fxn(self,w,X,Y): + """ Return vector of squared-errors for the leastsq optimizer + """ + O = self.fwd_all(X,w) + return N.sum(N.array(O-Y)**2,axis=1) + + def train(self,X,Y): + """ Train RBF network: + (i) select fixed centers randomly from input data (10%) + (ii) set fixed variance from max dist between centers + (iii) learn output weights using scipy's leastsq optimizer + """ + # set centers + self.centers = N.zeros((len(X)/10,X.shape[1])) + for i in range(len(X)): + if i%10 == 0: + self.centers[i/10] = X[i] + # set variance d_max = 0.0 for i in self.centers: for j in self.centers: - tmp = sqrt((i-j)**2) + tmp = N.sum(N.sqrt((i-j)**2)) if tmp > d_max: d_max = tmp - self.variance = d_max/2.0*size(trndata) - - - def fwd(self,inputs): - """ Propagate values forward through the net. - Inputs: - inputs - vector of input values + self.variance = d_max/(2.0*len(X)) + # train weights + self.nw = self.centers.shape[0]*self.no + self.w = N.random.randn(self.centers.shape[0],self.no)/N.sqrt(self.centers.shape[0]+1) + self.b = N.random.randn(1,self.no)/N.sqrt(self.centers.shape[0]+1) + self.pack() + self.wp = leastsq(self.err_fxn,self.wp,args=(X,Y))[0] + + def test_all(self,X,Y): + """ Test network on an array (size>1) of patterns + Input: + x - array of input data + t - array of targets + Returns: + sum-squared-error over all data """ - z = exp((-1.0/(2*self.variance))* - o = dot(z,self.w) + dot(ones((len(z),1)),self.b) + return N.sum(self.err_fxn(self.wp,X,Y)) +def main(): + """ Build/train/test RBF net + """ + from scipy.io import read_array + print "\nCreating RBF net" + net = rbf(12,2) + print "\nLoading training and test sets...", + X_trn = read_array('data/oil-trn.dat',columns=(0,(1,12)),lines=(3,-1)) + Y_trn = read_array('data/oil-trn.dat',columns=(12,-1),lines=(3,-1)) + X_tst = read_array('data/oil-tst.dat',columns=(0,(1,12)),lines=(3,-1)) + Y_tst = read_array('data/oil-tst.dat',columns=(12,-1),lines=(3,-1)) + print "done." + #print "\nInitial SSE:\n" + #print "\ttraining set: ",net.test_all(X_trn,Y_trn) + #print "\ttesting set: ",net.test_all(X_tst,Y_tst),"\n" + print "Training...", + net.train(X_trn,Y_trn) + print "done." + print "\nFinal SSE:\n" + print "\ttraining set: ",net.test_all(X_trn,Y_trn) + print "\ttesting set: ",net.test_all(X_tst,Y_tst),"\n" + + +if __name__ == '__main__': + main() Modified: trunk/Lib/sandbox/ann/srn.py =================================================================== --- trunk/Lib/sandbox/ann/srn.py 2006-08-20 06:37:03 UTC (rev 2170) +++ trunk/Lib/sandbox/ann/srn.py 2006-08-21 01:02:46 UTC (rev 2171) @@ -85,13 +85,22 @@ if w is not None: self.wp = w self.unpack() + + ### NEW ATTEMPT ### + z = N.array(N.ones(self.nh)*0.5) # init to 0.5, it will be updated on-the-fly + o = N.zeros((x.shape[0],self.no)) # this will hold the non-squashed outputs + for i in range(len(x)): + z = N.tanh(N.dot(x[i],self.w1) + N.dot(z,self.wc) + self.b1) + o[i] = (N.dot(z,self.w2) + self.b2)[0] + # compute vector of context values for current weight matrix - c = N.tanh(N.dot(x,self.w1) + N.dot(N.ones((len(x),1)),self.b1)) - c = N.vstack([c[1:],c[0]]) + #c = N.tanh(N.dot(x,self.w1) + N.dot(N.ones((len(x),1)),self.b1)) + #c = N.vstack([c[1:],c[0]]) # compute vector of hidden unit values - z = N.tanh(N.dot(x,self.w1) + N.dot(c,self.wc) + N.dot(N.ones((len(x),1)),self.b1)) + #z = N.tanh(N.dot(x,self.w1) + N.dot(c,self.wc) + N.dot(N.ones((len(x),1)),self.b1)) # compute vector of net outputs - o = N.dot(z,self.w2) + N.dot(N.ones((len(z),1)),self.b2) + #o = N.dot(z,self.w2) + N.dot(N.ones((len(z),1)),self.b2) + # compute final output activations if self.outfxn == 'linear': y = o @@ -101,7 +110,7 @@ tmp = N.exp(o) y = tmp/(N.sum(temp,1)*N.ones((1,self.no))) - return N.array(y) + return y def errfxn(self,w,x,t): """ Return vector of squared-errors for the leastsq optimizer From scipy-svn at scipy.org Sun Aug 20 21:40:22 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 20 Aug 2006 20:40:22 -0500 (CDT) Subject: [Scipy-svn] r2172 - in trunk/Lib/sandbox/ann: . data Message-ID: <20060821014022.0BD1439C030@new.scipy.org> Author: fred.mailhot Date: 2006-08-20 20:40:20 -0500 (Sun, 20 Aug 2006) New Revision: 2172 Removed: trunk/Lib/sandbox/ann/data/oilTrn.dat trunk/Lib/sandbox/ann/data/oilTst.dat Modified: trunk/Lib/sandbox/ann/README Log: Removed two data files. Modified: trunk/Lib/sandbox/ann/README =================================================================== --- trunk/Lib/sandbox/ann/README 2006-08-21 01:02:46 UTC (rev 2171) +++ trunk/Lib/sandbox/ann/README 2006-08-21 01:40:20 UTC (rev 2172) @@ -5,8 +5,9 @@ fred.mailhot at gmail.com -A "neural" network module for scipy, adding standard feedforward architectures -(MLP and RBF), as well as some recurrent architectures (e.g. SRN) and -optimization algorithms. +An artificial neural network module for scipy, adding standard feedforward architectures +(MLP and RBF), as well as some recurrent architectures (e.g. SRN). +Each of {mlp,srn,rbf}.py contains a class to define, train and test a network, +along with a main() functions that demos on a toy dataset. Deleted: trunk/Lib/sandbox/ann/data/oilTrn.dat =================================================================== --- trunk/Lib/sandbox/ann/data/oilTrn.dat 2006-08-21 01:02:46 UTC (rev 2171) +++ trunk/Lib/sandbox/ann/data/oilTrn.dat 2006-08-21 01:40:20 UTC (rev 2172) @@ -1,503 +0,0 @@ - nin 12 - nout 2 - ndata 500 - 3.315000e-01 2.156000e-01 6.802000e-01 1.434000e-01 6.825000e-01 2.720000e-01 6.223000e-01 2.092000e-01 7.961000e-01 1.530000e-01 5.856000e-01 2.573000e-01 3.440000e-01 1.401000e-01 - 9.390000e-02 1.008900e+00 3.650000e-02 6.944000e-01 9.080000e-02 4.961000e-01 7.220000e-02 6.521000e-01 -1.300000e-02 6.085000e-01 6.310000e-02 6.597000e-01 5.140000e-02 4.459000e-01 - 5.184000e-01 2.283000e-01 5.300000e-01 6.884000e-01 7.456000e-01 6.171000e-01 6.136000e-01 5.928000e-01 7.678000e-01 6.130000e-01 6.705000e-01 5.202000e-01 3.710000e-01 3.214000e-01 - 4.208000e-01 6.740000e-01 1.651000e-01 7.592000e-01 1.810000e-01 5.448000e-01 1.707000e-01 7.554000e-01 1.635000e-01 5.492000e-01 2.598000e-01 6.455000e-01 1.667000e-01 4.177000e-01 - 3.130000e-01 6.465000e-01 5.908000e-01 6.924000e-01 7.664000e-01 6.262000e-01 1.717700e+00 1.500000e-02 8.510000e-02 1.904600e+00 -1.650000e-02 2.210000e-02 3.378000e-01 4.184000e-01 - 1.145800e+00 -4.670000e-02 4.056000e-01 5.662000e-01 3.123000e-01 4.580000e-01 3.636000e-01 6.134000e-01 3.305000e-01 4.132000e-01 4.167000e-01 5.514000e-01 3.249000e-01 2.790000e-01 --1.900000e-03 1.732000e-01 5.700000e-03 4.882000e-01 2.076000e-01 3.910000e-01 8.600000e-03 1.719800e+00 2.150000e-02 -2.580000e-02 6.730000e-02 -8.290000e-02 5.100000e-02 2.123000e-01 - 7.800000e-03 4.615000e-01 1.181000e-01 6.590000e-01 2.587000e-01 6.352000e-01 -1.910000e-02 1.749100e+00 1.098000e-01 -1.315000e-01 4.070000e-02 -4.850000e-02 6.210000e-02 3.856000e-01 - 7.305000e-01 1.189000e-01 1.062600e+00 1.013000e-01 1.138500e+00 1.486000e-01 1.763100e+00 -4.160000e-02 2.073100e+00 -8.720000e-02 -4.390000e-02 5.300000e-02 6.070000e-01 8.130000e-02 - 6.199000e-01 4.779000e-01 9.528000e-01 7.463000e-01 9.741000e-01 8.633000e-01 1.677100e+00 6.200000e-02 2.027100e+00 -3.010000e-02 1.510000e-02 1.714700e+00 5.065000e-01 4.589000e-01 - 1.130800e+00 -2.860000e-02 1.000500e+00 7.387000e-01 8.390000e-01 1.874000e-01 1.084800e+00 6.374000e-01 8.534000e-01 1.373000e-01 1.042200e+00 6.836000e-01 6.215000e-01 1.323000e-01 - 5.093000e-01 6.038000e-01 2.874000e-01 8.162000e-01 2.267000e-01 6.181000e-01 2.646000e-01 8.490000e-01 3.365000e-01 4.648000e-01 2.815000e-01 8.254000e-01 2.280000e-01 4.243000e-01 - 7.600000e-02 5.010000e-01 1.870000e-01 7.011000e-01 1.728000e-01 8.475000e-01 2.300000e-01 6.536000e-01 1.616000e-01 8.732000e-01 1.603000e-01 7.331000e-01 9.550000e-02 4.203000e-01 - 4.662000e-01 1.209000e-01 7.657000e-01 1.390000e-01 8.886000e-01 1.304000e-01 1.694000e+00 4.530000e-02 -1.170000e-02 2.011100e+00 -1.940000e-02 2.170000e-02 4.422000e-01 7.900000e-02 - 1.125400e+00 -2.520000e-02 6.776000e-01 1.793000e-01 4.287000e-01 2.804000e-01 6.003000e-01 2.737000e-01 4.541000e-01 2.370000e-01 6.775000e-01 1.850000e-01 4.369000e-01 1.284000e-01 - 3.334000e-01 5.468000e-01 6.072000e-01 7.549000e-01 6.294000e-01 9.322000e-01 5.850000e-01 7.859000e-01 6.324000e-01 9.568000e-01 6.026000e-01 7.616000e-01 3.277000e-01 4.629000e-01 - 3.760000e-02 7.113000e-01 4.992000e-01 5.377000e-01 6.049000e-01 5.530000e-01 1.663500e+00 8.210000e-02 -1.890000e-02 2.024100e+00 1.400000e-03 -3.000000e-03 2.534000e-01 3.568000e-01 - 1.703000e-01 2.234000e-01 2.677000e-01 3.477000e-01 2.734000e-01 4.324000e-01 1.222000e-01 5.213000e-01 2.333000e-01 4.926000e-01 2.044000e-01 4.215000e-01 1.407000e-01 2.175000e-01 - 1.328000e-01 2.144000e-01 2.466000e-01 2.912000e-01 3.244000e-01 2.815000e-01 1.586000e-01 3.982000e-01 3.165000e-01 3.005000e-01 3.038000e-01 2.239000e-01 1.300000e-01 1.831000e-01 - 1.106900e+00 -1.500000e-03 1.787100e+00 -7.230000e-02 1.038900e+00 7.086000e-01 1.672800e+00 7.010000e-02 1.008300e+00 5.901000e-01 1.740500e+00 -1.880000e-02 7.498000e-01 2.096000e-01 - 1.083700e+00 2.940000e-02 6.675000e-01 5.421000e-01 5.040000e-01 3.997000e-01 7.271000e-01 4.673000e-01 6.061000e-01 2.526000e-01 7.957000e-01 3.861000e-01 4.794000e-01 1.997000e-01 - 6.379000e-01 3.709000e-01 9.139000e-01 6.785000e-01 1.117100e+00 6.825000e-01 9.074000e-01 6.917000e-01 1.152700e+00 6.739000e-01 9.866000e-01 5.957000e-01 5.590000e-01 3.562000e-01 - 6.520000e-02 4.575000e-01 5.170000e-01 2.900000e-01 6.257000e-01 3.041000e-01 1.700700e+00 3.410000e-02 -1.700000e-03 2.400000e-03 -4.430000e-02 5.270000e-02 2.800000e-01 1.838000e-01 - 5.460000e-02 4.317000e-01 2.260000e-02 8.026000e-01 2.228000e-01 7.077000e-01 1.370000e-02 1.717800e+00 -5.640000e-02 6.540000e-02 -9.100000e-03 1.070000e-02 7.470000e-02 3.874000e-01 - 6.250000e-02 1.036100e+00 -3.160000e-02 9.835000e-01 1.020000e-02 7.331000e-01 -5.260000e-02 1.005500e+00 -5.360000e-02 7.915000e-01 -2.270000e-02 9.754000e-01 3.200000e-03 5.922000e-01 - 8.813000e-01 2.321000e-01 4.637000e-01 1.117000e-01 2.749000e-01 2.270000e-01 4.328000e-01 1.497000e-01 3.223000e-01 1.589000e-01 4.639000e-01 1.134000e-01 2.964000e-01 1.282000e-01 - 2.830000e-02 1.087400e+00 1.069000e-01 6.261000e-01 1.089000e-01 4.879000e-01 -3.670000e-02 7.978000e-01 -6.910000e-02 6.871000e-01 4.170000e-02 7.048000e-01 3.750000e-02 4.698000e-01 - 1.116000e+00 -1.490000e-02 1.075900e+00 6.281000e-01 6.518000e-01 9.179000e-01 9.002000e-01 8.356000e-01 6.325000e-01 8.442000e-01 8.748000e-01 8.595000e-01 5.796000e-01 3.492000e-01 - 3.371000e-01 7.714000e-01 7.103000e-01 8.678000e-01 8.329000e-01 8.652000e-01 1.701600e+00 3.270000e-02 -1.300000e-01 2.157200e+00 -8.280000e-02 1.834700e+00 3.773000e-01 5.367000e-01 - 7.327000e-01 3.195000e-01 9.908000e-01 6.799000e-01 1.154500e+00 7.437000e-01 1.039000e+00 6.200000e-01 1.251800e+00 6.617000e-01 9.425000e-01 7.395000e-01 5.773000e-01 3.877000e-01 - 3.568000e-01 4.784000e-01 7.597000e-01 3.709000e-01 7.185000e-01 5.614000e-01 1.689700e+00 4.820000e-02 -5.500000e-02 2.069800e+00 1.110000e-02 -1.070000e-02 3.974000e-01 2.757000e-01 - 8.451000e-01 2.537000e-01 1.175900e+00 3.158000e-01 1.157700e+00 4.833000e-01 1.690800e+00 5.060000e-02 1.869400e+00 1.534000e-01 -8.700000e-02 1.834000e+00 6.629000e-01 2.139000e-01 - 1.053400e+00 6.240000e-02 9.920000e-01 7.462000e-01 6.545000e-01 9.606000e-01 9.666000e-01 7.742000e-01 7.771000e-01 7.086000e-01 9.877000e-01 7.512000e-01 6.243000e-01 3.112000e-01 - 7.150000e-02 1.045000e+00 1.133000e-01 8.252000e-01 4.200000e-02 7.057000e-01 1.016000e-01 8.422000e-01 7.360000e-02 6.540000e-01 7.840000e-02 8.680000e-01 7.220000e-02 5.227000e-01 - 1.002600e+00 1.198000e-01 6.942000e-01 1.048700e+00 5.669000e-01 5.787000e-01 7.905000e-01 9.402000e-01 6.875000e-01 3.929000e-01 7.361000e-01 1.000800e+00 5.116000e-01 2.856000e-01 - 4.843000e-01 6.102000e-01 2.827000e-01 1.441700e+00 2.176000e-01 8.856000e-01 3.216000e-01 1.399100e+00 8.600000e-02 1.002300e+00 2.466000e-01 1.485100e+00 1.914000e-01 5.923000e-01 - 3.972000e-01 1.078000e-01 5.178000e-01 2.948000e-01 5.923000e-01 3.344000e-01 5.037000e-01 3.121000e-01 6.118000e-01 3.270000e-01 5.425000e-01 2.633000e-01 3.099000e-01 1.574000e-01 - 3.522000e-01 3.868000e-01 5.201000e-01 6.377000e-01 5.522000e-01 7.776000e-01 5.076000e-01 6.553000e-01 6.258000e-01 7.105000e-01 4.515000e-01 7.196000e-01 3.029000e-01 3.665000e-01 - 2.230000e-01 8.879000e-01 1.593000e-01 7.129000e-01 1.089000e-01 5.914000e-01 1.294000e-01 7.501000e-01 1.590000e-01 5.180000e-01 1.132000e-01 7.677000e-01 1.232000e-01 4.428000e-01 - 4.409000e-01 -1.820000e-02 6.190000e-01 5.930000e-02 5.689000e-01 2.286000e-01 4.717000e-01 2.311000e-01 5.974000e-01 2.056000e-01 4.849000e-01 2.192000e-01 3.067000e-01 9.330000e-02 - 7.310000e-02 4.657000e-01 1.022000e-01 7.503000e-01 5.510000e-02 9.277000e-01 7.300000e-02 7.852000e-01 1.180000e-01 8.651000e-01 1.219000e-01 7.260000e-01 5.260000e-02 4.397000e-01 - 6.670000e-02 5.461000e-01 4.370000e-01 4.789000e-01 5.523000e-01 4.830000e-01 1.668600e+00 7.700000e-02 1.280000e-02 1.978700e+00 3.340000e-02 -4.030000e-02 2.290000e-01 3.043000e-01 - 8.427000e-01 2.270000e-01 1.094300e+00 3.023000e-01 1.294900e+00 2.022000e-01 1.886300e+00 -1.803000e-01 2.044700e+00 -4.780000e-02 2.900000e-03 1.732800e+00 6.900000e-01 1.222000e-01 - 1.126600e+00 -2.560000e-02 5.215000e-01 1.210700e+00 4.669000e-01 6.685000e-01 5.571000e-01 1.170500e+00 4.676000e-01 6.308000e-01 4.948000e-01 1.244900e+00 3.906000e-01 4.083000e-01 - 5.600000e-01 3.640000e-01 8.255000e-01 6.307000e-01 9.831000e-01 6.679000e-01 8.842000e-01 5.626000e-01 9.357000e-01 7.488000e-01 8.266000e-01 6.312000e-01 4.738000e-01 3.681000e-01 - 4.810000e-02 1.007400e+00 4.296000e-01 9.304000e-01 4.478000e-01 1.047200e+00 1.756000e+00 -3.380000e-02 -7.480000e-02 2.089300e+00 -4.250000e-02 5.140000e-02 2.391000e-01 5.596000e-01 - 1.095200e+00 1.350000e-02 8.862000e-01 8.475000e-01 7.144000e-01 3.892000e-01 9.740000e-01 7.529000e-01 7.114000e-01 3.571000e-01 9.248000e-01 8.077000e-01 5.859000e-01 1.979000e-01 - 2.006000e-01 3.454000e-01 3.986000e-01 4.469000e-01 3.417000e-01 6.387000e-01 2.808000e-01 5.793000e-01 4.392000e-01 5.374000e-01 3.424000e-01 5.096000e-01 2.043000e-01 2.865000e-01 --2.480000e-02 8.079000e-01 3.514000e-01 7.237000e-01 4.603000e-01 7.386000e-01 8.570000e-02 1.632300e+00 6.240000e-02 1.922700e+00 -2.600000e-02 3.120000e-02 1.639000e-01 4.770000e-01 - 4.297000e-01 1.825000e-01 5.939000e-01 3.794000e-01 7.233000e-01 3.816000e-01 6.731000e-01 2.893000e-01 7.694000e-01 3.435000e-01 7.308000e-01 2.192000e-01 3.835000e-01 1.722000e-01 - 6.357000e-01 4.547000e-01 2.637000e-01 7.217000e-01 2.175000e-01 5.563000e-01 2.751000e-01 7.127000e-01 1.261000e-01 6.451000e-01 2.460000e-01 7.426000e-01 2.248000e-01 3.850000e-01 - 1.124800e+00 -2.590000e-02 4.916000e-01 1.229300e+00 3.932000e-01 9.462000e-01 5.271000e-01 1.185500e+00 4.211000e-01 8.591000e-01 5.142000e-01 1.206400e+00 3.246000e-01 5.502000e-01 - 1.131100e+00 -2.980000e-02 1.013700e+00 7.144000e-01 8.036000e-01 2.317000e-01 1.076000e+00 6.498000e-01 7.927000e-01 2.099000e-01 9.650000e-01 7.713000e-01 6.183000e-01 1.358000e-01 - 3.026000e-01 4.889000e-01 6.114000e-01 4.941000e-01 6.889000e-01 5.464000e-01 1.615600e+00 1.361000e-01 5.000000e-04 2.004300e+00 3.290000e-02 -3.970000e-02 3.152000e-01 3.408000e-01 --1.580000e-02 4.553000e-01 1.158000e-01 6.442000e-01 3.486000e-01 5.106000e-01 -1.019000e-01 1.854600e+00 2.450000e-02 -3.050000e-02 6.260000e-02 -7.460000e-02 1.096000e-01 3.147000e-01 --8.610000e-02 1.207600e+00 3.407000e-01 1.090100e+00 5.373000e-01 9.981000e-01 -3.530000e-02 1.776400e+00 -1.200000e-03 2.001700e+00 2.530000e-02 1.701800e+00 1.707000e-01 6.720000e-01 - 3.425000e-01 1.699000e-01 6.039000e-01 1.853000e-01 5.608000e-01 3.606000e-01 5.748000e-01 2.185000e-01 6.062000e-01 3.205000e-01 4.801000e-01 3.313000e-01 3.026000e-01 1.613000e-01 - 8.624000e-01 2.389000e-01 1.129600e+00 5.381000e-01 1.292500e+00 4.896000e-01 1.810500e+00 -9.300000e-02 2.041200e+00 -4.980000e-02 4.800000e-03 1.730400e+00 6.792000e-01 2.692000e-01 --2.360000e-02 6.987000e-01 1.493000e-01 8.415000e-01 2.933000e-01 8.121000e-01 -3.410000e-02 1.772800e+00 -7.390000e-02 2.085800e+00 -5.190000e-02 6.100000e-02 1.021000e-01 4.729000e-01 - 1.140600e+00 -3.760000e-02 5.592000e-01 1.172900e+00 3.989000e-01 8.188000e-01 6.176000e-01 1.108100e+00 4.037000e-01 7.717000e-01 5.580000e-01 1.172800e+00 4.104000e-01 4.151000e-01 - 4.442000e-01 2.989000e-01 6.370000e-01 5.436000e-01 8.215000e-01 5.003000e-01 7.841000e-01 3.647000e-01 7.794000e-01 5.730000e-01 6.997000e-01 4.653000e-01 3.936000e-01 2.806000e-01 - 7.410000e-02 3.967000e-01 4.133000e-01 3.645000e-01 4.413000e-01 4.767000e-01 -4.360000e-02 1.781800e+00 4.830000e-02 -5.760000e-02 1.240000e-02 -1.260000e-02 1.716000e-01 2.843000e-01 - 3.583000e-01 7.460000e-01 1.489000e-01 6.681000e-01 1.327000e-01 5.254000e-01 1.705000e-01 6.434000e-01 1.733000e-01 4.643000e-01 1.570000e-01 6.552000e-01 1.646000e-01 3.708000e-01 - 5.144000e-01 7.100000e-03 7.310000e-01 1.166000e-01 8.438000e-01 1.292000e-01 1.745400e+00 -2.040000e-02 5.240000e-02 -6.220000e-02 2.000000e-04 -1.200000e-03 4.207000e-01 6.780000e-02 - 6.726000e-01 1.323000e-01 1.144800e+00 9.590000e-02 1.362000e+00 3.870000e-02 1.169700e+00 6.840000e-02 1.369600e+00 5.110000e-02 1.182100e+00 5.370000e-02 6.784000e-01 3.530000e-02 - 1.674000e-01 3.897000e-01 4.111000e-01 4.741000e-01 6.153000e-01 3.688000e-01 1.697900e+00 3.140000e-02 -5.520000e-02 2.065200e+00 -4.320000e-02 5.020000e-02 2.435000e-01 2.632000e-01 - 3.785000e-01 7.282000e-01 1.824000e-01 7.266000e-01 1.605000e-01 5.580000e-01 1.435000e-01 7.714000e-01 1.922000e-01 5.056000e-01 3.317000e-01 5.522000e-01 1.777000e-01 3.997000e-01 - 3.145000e-01 7.818000e-01 1.241000e-01 5.976000e-01 1.230000e-01 4.677000e-01 1.220000e-01 6.060000e-01 1.649000e-01 4.058000e-01 7.690000e-02 6.546000e-01 1.322000e-01 3.602000e-01 - 2.918000e-01 2.719000e-01 6.459000e-01 2.241000e-01 7.378000e-01 2.574000e-01 1.683400e+00 6.250000e-02 2.860000e-02 1.964400e+00 -5.600000e-03 6.500000e-03 3.631000e-01 1.400000e-01 - 1.151300e+00 -5.120000e-02 6.183000e-01 3.049000e-01 6.627000e-01 3.790000e-02 7.937000e-01 9.420000e-02 5.698000e-01 1.317000e-01 8.385000e-01 4.390000e-02 4.815000e-01 9.850000e-02 - 1.269000e-01 9.731000e-01 4.120000e-01 1.036700e+00 5.733000e-01 9.856000e-01 1.699400e+00 3.680000e-02 -2.470000e-02 2.024300e+00 -1.100000e-02 1.743800e+00 2.299000e-01 6.160000e-01 --9.200000e-03 6.616000e-01 5.080000e-02 9.117000e-01 1.767000e-01 9.075000e-01 2.000000e-02 1.709800e+00 -6.340000e-02 2.076700e+00 -2.020000e-02 2.450000e-02 4.400000e-02 5.188000e-01 - 3.096000e-01 3.670000e-01 4.894000e-01 5.677000e-01 5.287000e-01 6.841000e-01 4.190000e-01 6.544000e-01 5.844000e-01 6.375000e-01 5.973000e-01 4.401000e-01 2.783000e-01 3.331000e-01 - 5.330000e-02 8.748000e-01 1.818000e-01 1.072900e+00 2.553000e-01 1.123700e+00 -2.450000e-02 1.760300e+00 -3.000000e-03 2.009000e+00 4.320000e-02 -5.350000e-02 1.072000e-01 6.315000e-01 - 1.054700e+00 5.700000e-02 9.906000e-01 9.390000e-02 8.846000e-01 -8.220000e-02 1.024500e+00 4.920000e-02 7.418000e-01 6.930000e-02 1.035600e+00 3.830000e-02 6.174000e-01 2.600000e-02 - 4.399000e-01 3.581000e-01 8.365000e-01 3.885000e-01 9.079000e-01 4.923000e-01 7.725000e-01 4.573000e-01 7.929000e-01 6.507000e-01 7.882000e-01 4.434000e-01 4.279000e-01 2.874000e-01 - 4.167000e-01 5.007000e-01 6.382000e-01 7.986000e-01 8.046000e-01 8.237000e-01 7.404000e-01 6.831000e-01 7.403000e-01 9.233000e-01 6.321000e-01 8.118000e-01 3.938000e-01 4.348000e-01 - 1.641000e-01 5.116000e-01 5.404000e-01 4.425000e-01 5.521000e-01 5.661000e-01 1.699300e+00 3.610000e-02 2.810000e-02 1.964300e+00 6.130000e-02 -7.280000e-02 2.508000e-01 3.306000e-01 - 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1.152100e+00 -5.580000e-02 7.192000e-01 1.030300e+00 7.694000e-01 4.801000e-01 8.318000e-01 8.979000e-01 6.653000e-01 5.582000e-01 9.178000e-01 7.947000e-01 5.415000e-01 3.093000e-01 - 5.044000e-01 1.540000e-02 8.126000e-01 -2.700000e-03 8.906000e-01 4.030000e-02 7.963000e-01 1.780000e-02 9.854000e-01 -5.490000e-02 7.248000e-01 1.000000e-01 4.532000e-01 1.820000e-02 --2.180000e-02 8.299000e-01 1.599000e-01 9.649000e-01 3.780000e-01 8.483000e-01 2.180000e-02 1.707200e+00 -4.570000e-02 2.054800e+00 -1.020000e-02 1.240000e-02 1.121000e-01 5.458000e-01 - 9.160000e-02 1.036900e+00 1.755000e-01 6.179000e-01 8.330000e-02 5.723000e-01 1.340000e-01 6.709000e-01 5.480000e-02 5.914000e-01 6.290000e-02 7.533000e-01 1.001000e-01 4.355000e-01 - 3.507000e-01 4.682000e-01 6.535000e-01 4.860000e-01 7.487000e-01 5.119000e-01 1.678900e+00 5.970000e-02 -5.820000e-02 2.068200e+00 -2.410000e-02 2.870000e-02 3.420000e-01 3.332000e-01 - 1.034700e+00 8.190000e-02 5.016000e-01 1.232800e+00 4.562000e-01 1.218400e+00 4.686000e-01 1.269500e+00 4.205000e-01 1.132200e+00 5.104000e-01 1.223000e+00 3.742000e-01 5.762000e-01 - 6.282000e-01 1.157000e-01 8.571000e-01 3.314000e-01 1.049100e+00 2.918000e-01 8.451000e-01 3.437000e-01 1.130400e+00 2.179000e-01 9.457000e-01 2.238000e-01 5.257000e-01 1.544000e-01 - 1.087900e+00 1.780000e-02 5.849000e-01 9.080000e-02 4.400000e-01 1.295000e-01 5.795000e-01 1.014000e-01 4.978000e-01 4.730000e-02 6.493000e-01 2.220000e-02 4.243000e-01 4.870000e-02 - 1.043200e+00 7.570000e-02 3.362000e-01 8.387000e-01 3.922000e-01 4.720000e-01 4.663000e-01 6.843000e-01 3.137000e-01 5.435000e-01 4.333000e-01 7.240000e-01 3.211000e-01 3.463000e-01 - 1.763000e-01 5.283000e-01 4.547000e-01 5.701000e-01 6.420000e-01 4.928000e-01 1.767600e+00 -4.320000e-02 3.570000e-02 1.956900e+00 -3.270000e-02 3.990000e-02 2.514000e-01 3.485000e-01 - 4.510000e-01 6.430000e-02 6.271000e-01 2.025000e-01 7.699000e-01 1.555000e-01 7.216000e-01 8.540000e-02 8.444000e-01 8.560000e-02 8.332000e-01 -4.750000e-02 4.062000e-01 6.300000e-02 - 7.900000e-02 5.267000e-01 8.690000e-02 8.697000e-01 1.671000e-01 9.115000e-01 5.880000e-02 9.035000e-01 6.870000e-02 1.042700e+00 8.080000e-02 8.789000e-01 6.730000e-02 4.822000e-01 - 2.188000e-01 3.369000e-01 3.669000e-01 5.010000e-01 3.112000e-01 6.960000e-01 2.375000e-01 6.537000e-01 3.345000e-01 6.777000e-01 2.797000e-01 6.031000e-01 1.660000e-01 3.429000e-01 --9.450000e-02 1.214000e+00 -7.660000e-02 1.790700e+00 1.769000e-01 1.628300e+00 -1.730000e-02 1.752100e+00 2.320000e-02 1.976100e+00 -8.350000e-02 1.829900e+00 2.030000e-02 9.413000e-01 - 1.486000e-01 6.692000e-01 1.849000e-01 1.112100e+00 2.417000e-01 1.229800e+00 3.131000e-01 9.606000e-01 3.608000e-01 1.105200e+00 1.707000e-01 1.123700e+00 1.341000e-01 6.086000e-01 - 1.151900e+00 -5.420000e-02 6.338000e-01 3.940000e-01 4.542000e-01 3.527000e-01 5.905000e-01 4.522000e-01 5.814000e-01 1.835000e-01 6.035000e-01 4.320000e-01 4.249000e-01 2.042000e-01 - 1.479000e-01 2.251000e-01 1.986000e-01 3.961000e-01 2.674000e-01 3.986000e-01 1.340000e-01 4.690000e-01 2.304000e-01 4.534000e-01 1.999000e-01 3.911000e-01 1.029000e-01 2.407000e-01 - 5.890000e-02 1.864000e-01 3.178000e-01 2.535000e-01 4.522000e-01 2.345000e-01 9.190000e-02 1.622500e+00 -7.700000e-03 9.000000e-03 -7.670000e-02 9.070000e-02 1.755000e-01 1.388000e-01 - 9.400000e-02 6.238000e-01 3.969000e-01 6.334000e-01 4.998000e-01 6.599000e-01 1.790100e+00 -6.620000e-02 -7.930000e-02 2.093800e+00 2.700000e-03 -4.400000e-03 2.079000e-01 3.997000e-01 - 2.319000e-01 5.927000e-01 3.022000e-01 1.001700e+00 3.991000e-01 1.072700e+00 4.055000e-01 8.795000e-01 4.504000e-01 1.036000e+00 3.112000e-01 9.839000e-01 1.925000e-01 5.553000e-01 - 3.815000e-01 3.332000e-01 6.462000e-01 3.913000e-01 7.058000e-01 4.557000e-01 1.709500e+00 2.520000e-02 -6.140000e-02 2.076500e+00 2.690000e-02 -3.110000e-02 3.664000e-01 2.403000e-01 - 1.078300e+00 3.410000e-02 8.201000e-01 9.103000e-01 6.938000e-01 1.278200e+00 7.104000e-01 1.039200e+00 5.932000e-01 1.278000e+00 7.361000e-01 1.003500e+00 5.368000e-01 4.586000e-01 - 9.140000e-02 5.724000e-01 3.504000e-01 6.380000e-01 4.148000e-01 7.028000e-01 1.752300e+00 -2.260000e-02 -2.290000e-02 2.024300e+00 -1.770000e-02 1.910000e-02 2.003000e-01 3.734000e-01 - 4.475000e-01 6.681000e-01 3.219000e-01 5.937000e-01 2.144000e-01 5.219000e-01 2.775000e-01 6.470000e-01 1.795000e-01 5.440000e-01 2.650000e-01 6.645000e-01 2.055000e-01 3.837000e-01 - 1.052400e+00 6.480000e-02 5.225000e-01 8.387000e-01 4.788000e-01 4.712000e-01 5.038000e-01 8.560000e-01 3.832000e-01 5.550000e-01 5.543000e-01 7.987000e-01 3.811000e-01 3.338000e-01 - 9.849000e-01 1.433000e-01 5.062000e-01 2.203000e-01 5.441000e-01 2.260000e-02 5.696000e-01 1.404000e-01 4.719000e-01 9.920000e-02 4.997000e-01 2.260000e-01 3.843000e-01 1.097000e-01 - 5.748000e-01 1.579000e-01 9.100000e-01 2.326000e-01 9.980000e-01 3.141000e-01 9.007000e-01 2.419000e-01 9.729000e-01 3.628000e-01 8.942000e-01 2.515000e-01 5.003000e-01 1.643000e-01 - 4.395000e-01 4.679000e-01 8.962000e-01 2.943000e-01 9.701000e-01 3.516000e-01 1.817900e+00 -9.650000e-02 2.730000e-02 1.960900e+00 2.100000e-03 -1.700000e-03 4.865000e-01 2.228000e-01 - 4.840000e-01 2.094000e-01 7.491000e-01 3.479000e-01 9.198000e-01 3.134000e-01 7.565000e-01 3.362000e-01 7.934000e-01 4.768000e-01 6.991000e-01 4.020000e-01 4.212000e-01 2.110000e-01 - 4.958000e-01 1.443000e-01 8.970000e-01 8.420000e-02 8.679000e-01 2.843000e-01 7.258000e-01 2.898000e-01 8.606000e-01 3.076000e-01 7.934000e-01 2.101000e-01 4.459000e-01 1.359000e-01 - 1.079100e+00 3.290000e-02 5.166000e-01 7.448000e-01 4.626000e-01 4.487000e-01 5.806000e-01 6.720000e-01 4.977000e-01 3.901000e-01 5.378000e-01 7.246000e-01 4.012000e-01 2.923000e-01 - 5.550000e-02 7.274000e-01 3.617000e-01 7.326000e-01 5.602000e-01 6.382000e-01 1.931200e+00 -2.333000e-01 7.000000e-03 1.992700e+00 -7.110000e-02 8.280000e-02 2.292000e-01 4.078000e-01 - 1.089100e+00 2.140000e-02 4.152000e-01 8.053000e-01 3.788000e-01 5.185000e-01 4.597000e-01 7.495000e-01 3.572000e-01 5.192000e-01 4.332000e-01 7.823000e-01 3.524000e-01 3.287000e-01 - 5.581000e-01 5.780000e-02 7.462000e-01 2.461000e-01 8.369000e-01 2.983000e-01 8.010000e-01 1.832000e-01 9.550000e-01 1.747000e-01 8.798000e-01 8.960000e-02 4.791000e-01 8.590000e-02 - 1.096500e+00 3.900000e-03 4.117000e-01 4.921000e-01 3.664000e-01 3.479000e-01 4.433000e-01 4.572000e-01 3.868000e-01 3.088000e-01 4.363000e-01 4.653000e-01 3.154000e-01 2.638000e-01 - 1.103600e+00 -9.000000e-04 5.596000e-01 1.163800e+00 4.281000e-01 9.927000e-01 5.554000e-01 1.170500e+00 4.449000e-01 9.107000e-01 5.766000e-01 1.139700e+00 3.750000e-01 5.223000e-01 - 4.741000e-01 2.965000e-01 7.502000e-01 4.499000e-01 7.687000e-01 6.150000e-01 6.862000e-01 5.282000e-01 8.357000e-01 5.573000e-01 6.292000e-01 5.912000e-01 4.165000e-01 2.794000e-01 - 3.807000e-01 4.936000e-01 5.540000e-01 8.272000e-01 8.500000e-01 6.754000e-01 6.042000e-01 7.640000e-01 7.195000e-01 8.542000e-01 6.354000e-01 7.275000e-01 3.726000e-01 4.133000e-01 - 8.364000e-01 3.270000e-02 1.186400e+00 1.968000e-01 1.436000e+00 1.226000e-01 1.236400e+00 1.442000e-01 1.383800e+00 2.110000e-01 1.171300e+00 2.177000e-01 7.280000e-01 6.490000e-02 --1.560000e-02 4.196000e-01 4.650000e-02 5.764000e-01 6.000000e-02 6.460000e-01 3.050000e-02 5.935000e-01 4.570000e-02 6.741000e-01 -3.280000e-02 6.699000e-01 1.860000e-02 3.420000e-01 - 5.736000e-01 5.383000e-01 2.750000e-01 1.314900e+00 2.674000e-01 7.521000e-01 3.316000e-01 1.253500e+00 2.474000e-01 7.465000e-01 2.832000e-01 1.306000e+00 2.464000e-01 4.982000e-01 - 7.007000e-01 3.733000e-01 9.031000e-01 7.899000e-01 1.038900e+00 7.671000e-01 1.784000e+00 -5.930000e-02 2.500000e-02 1.970800e+00 2.790000e-02 1.699700e+00 4.946000e-01 4.653000e-01 - 1.101300e+00 3.700000e-03 4.778000e-01 5.740000e-02 3.817000e-01 6.810000e-02 3.378000e-01 2.228000e-01 3.602000e-01 8.800000e-02 3.097000e-01 2.582000e-01 3.379000e-01 5.670000e-02 --5.860000e-02 7.000000e-02 2.469000e-01 5.640000e-02 2.655000e-01 1.748000e-01 -4.000000e-03 5.900000e-03 2.730000e-02 -3.150000e-02 5.900000e-03 -7.100000e-03 1.159000e-01 4.810000e-02 - 1.120600e+00 -1.640000e-02 5.178000e-01 1.212100e+00 3.926000e-01 1.017600e+00 5.052000e-01 1.224700e+00 3.007000e-01 1.063800e+00 4.734000e-01 1.256600e+00 3.547000e-01 5.385000e-01 - 2.983000e-01 8.036000e-01 2.176000e-01 9.156000e-01 1.722000e-01 6.839000e-01 1.845000e-01 9.522000e-01 1.204000e-01 7.224000e-01 2.503000e-01 8.755000e-01 1.339000e-01 5.293000e-01 - 1.063100e+00 5.430000e-02 8.058000e-01 9.270000e-01 5.826000e-01 9.213000e-01 7.334000e-01 1.003300e+00 6.819000e-01 7.321000e-01 7.287000e-01 1.006000e+00 5.168000e-01 4.008000e-01 - 4.254000e-01 3.643000e-01 7.034000e-01 5.253000e-01 6.943000e-01 7.253000e-01 8.134000e-01 4.001000e-01 7.932000e-01 6.344000e-01 7.578000e-01 4.625000e-01 4.057000e-01 3.053000e-01 - 4.290000e-02 7.170000e-01 2.490000e-01 8.308000e-01 4.208000e-01 7.661000e-01 -2.320000e-02 1.762900e+00 1.860000e-02 1.978800e+00 -6.870000e-02 8.130000e-02 1.143000e-01 5.234000e-01 --3.980000e-02 6.514000e-01 1.588000e-01 7.603000e-01 2.672000e-01 7.724000e-01 -1.780000e-02 1.750400e+00 1.890000e-02 1.978200e+00 -5.620000e-02 6.840000e-02 9.140000e-02 4.407000e-01 - 2.062000e-01 4.098000e-01 2.551000e-01 7.148000e-01 4.466000e-01 6.359000e-01 2.715000e-01 6.969000e-01 2.490000e-01 8.829000e-01 2.201000e-01 7.591000e-01 1.590000e-01 4.010000e-01 - 3.410000e-01 4.307000e-01 7.206000e-01 3.524000e-01 7.949000e-01 4.069000e-01 1.602900e+00 1.539000e-01 -1.459000e-01 2.171600e+00 1.000000e-02 -1.250000e-02 3.856000e-01 2.496000e-01 --2.410000e-02 6.119000e-01 2.913000e-01 5.930000e-01 2.959000e-01 7.341000e-01 6.990000e-02 1.644200e+00 -4.070000e-02 2.050400e+00 9.500000e-02 -1.145000e-01 1.324000e-01 3.856000e-01 - 8.864000e-01 1.457000e-01 1.449500e+00 1.612000e-01 1.558100e+00 2.872000e-01 1.467800e+00 1.383000e-01 1.645400e+00 2.126000e-01 1.511700e+00 8.060000e-02 8.334000e-01 9.520000e-02 - 4.516000e-01 3.031000e-01 6.539000e-01 4.383000e-01 8.696000e-01 3.244000e-01 1.766400e+00 -3.990000e-02 -4.020000e-02 2.044000e+00 5.800000e-02 -6.990000e-02 3.998000e-01 2.355000e-01 - 1.500000e-03 1.039600e+00 -6.270000e-02 1.707800e+00 -1.400000e-02 1.880800e+00 6.470000e-02 1.564000e+00 1.572000e-01 1.698000e+00 1.540000e-02 1.620800e+00 1.990000e-02 9.212000e-01 - 4.896000e-01 2.424000e-01 8.585000e-01 1.687000e-01 1.021900e+00 1.227000e-01 1.728400e+00 -1.600000e-03 3.230000e-02 1.961700e+00 -3.790000e-02 4.570000e-02 4.665000e-01 1.439000e-01 - 1.028500e+00 2.200000e-03 1.519100e+00 1.153000e-01 1.891400e+00 -6.680000e-02 1.574600e+00 4.710000e-02 1.882900e+00 -2.460000e-02 1.660200e+00 -5.800000e-02 9.215000e-01 1.180000e-02 - 5.700000e-01 1.949000e-01 6.662000e-01 5.775000e-01 7.901000e-01 6.218000e-01 7.056000e-01 5.306000e-01 7.961000e-01 6.351000e-01 7.592000e-01 4.700000e-01 4.177000e-01 2.949000e-01 - 2.964000e-01 2.839000e-01 3.532000e-01 5.773000e-01 4.329000e-01 6.187000e-01 3.322000e-01 6.025000e-01 4.204000e-01 6.566000e-01 3.175000e-01 6.181000e-01 2.053000e-01 3.326000e-01 - 1.110100e+00 -6.900000e-03 5.430000e-01 7.135000e-01 3.813000e-01 5.501000e-01 4.775000e-01 7.901000e-01 4.455000e-01 4.477000e-01 5.519000e-01 7.022000e-01 3.929000e-01 3.006000e-01 - 4.494000e-01 3.016000e-01 6.661000e-01 5.118000e-01 8.272000e-01 5.039000e-01 7.380000e-01 4.324000e-01 8.066000e-01 5.531000e-01 7.674000e-01 3.959000e-01 4.210000e-01 2.538000e-01 - 1.201700e+00 -1.156000e-01 3.379000e-01 6.891000e-01 3.519000e-01 4.317000e-01 4.171000e-01 5.947000e-01 2.714000e-01 5.115000e-01 3.865000e-01 6.303000e-01 3.125000e-01 3.083000e-01 - 4.175000e-01 6.920000e-01 7.238000e-01 8.324000e-01 8.213000e-01 8.528000e-01 1.684100e+00 5.680000e-02 9.100000e-03 1.987300e+00 -1.150000e-02 1.749900e+00 4.226000e-01 4.766000e-01 - 4.360000e-01 3.713000e-01 6.214000e-01 6.574000e-01 7.203000e-01 7.235000e-01 5.825000e-01 6.956000e-01 7.206000e-01 7.458000e-01 6.731000e-01 5.917000e-01 3.731000e-01 3.595000e-01 - 3.848000e-01 2.801000e-01 5.886000e-01 4.593000e-01 7.417000e-01 4.387000e-01 5.876000e-01 4.592000e-01 7.156000e-01 4.903000e-01 6.483000e-01 3.882000e-01 3.588000e-01 2.423000e-01 - 1.096400e+00 1.260000e-02 8.966000e-01 3.421000e-01 6.822000e-01 2.301000e-01 8.664000e-01 3.788000e-01 6.956000e-01 1.860000e-01 8.982000e-01 3.447000e-01 5.707000e-01 1.193000e-01 - 4.805000e-01 5.688000e-01 5.959000e-01 1.079200e+00 6.938000e-01 1.213100e+00 6.770000e-01 9.835000e-01 7.859000e-01 1.129700e+00 7.537000e-01 8.900000e-01 3.702000e-01 5.925000e-01 - 1.090700e+00 1.510000e-02 4.532000e-01 8.947000e-01 3.829000e-01 5.780000e-01 4.569000e-01 8.886000e-01 3.970000e-01 5.348000e-01 5.703000e-01 7.581000e-01 3.893000e-01 3.213000e-01 - 4.580000e-02 5.139000e-01 -1.840000e-02 3.196000e-01 -3.710000e-02 3.004000e-01 9.080000e-02 1.905000e-01 -1.390000e-02 2.675000e-01 1.530000e-02 2.785000e-01 9.600000e-03 2.236000e-01 - 1.131200e+00 -3.020000e-02 8.929000e-01 7.040000e-02 7.703000e-01 -2.560000e-02 8.951000e-01 6.580000e-02 6.570000e-01 8.810000e-02 8.834000e-01 8.170000e-02 5.877000e-01 1.170000e-02 --5.820000e-02 8.150000e-01 3.495000e-01 7.002000e-01 4.372000e-01 7.411000e-01 -8.800000e-02 1.829700e+00 6.840000e-02 1.920900e+00 -3.980000e-02 4.890000e-02 1.860000e-01 4.308000e-01 - 3.479000e-01 6.417000e-01 6.093000e-01 7.009000e-01 7.565000e-01 6.621000e-01 1.628200e+00 1.224000e-01 1.492000e-01 1.828500e+00 -1.960000e-02 2.300000e-02 3.743000e-01 3.924000e-01 - 4.160000e-01 1.654000e-01 6.349000e-01 2.783000e-01 6.370000e-01 4.172000e-01 6.487000e-01 2.577000e-01 6.379000e-01 4.257000e-01 6.367000e-01 2.739000e-01 3.315000e-01 2.005000e-01 - 7.600000e-02 1.032000e+00 3.440000e-02 1.042800e+00 -1.570000e-02 8.486000e-01 4.830000e-02 1.026200e+00 9.170000e-02 7.007000e-01 -1.420000e-02 1.099300e+00 4.420000e-02 5.973000e-01 - 6.870000e-02 6.446000e-01 4.410000e-01 5.699000e-01 4.881000e-01 6.571000e-01 1.674200e+00 6.730000e-02 1.860000e-02 1.976100e+00 2.750000e-02 -3.180000e-02 2.199000e-01 3.772000e-01 - 1.838000e-01 9.222000e-01 1.668000e-01 8.231000e-01 1.093000e-01 6.645000e-01 1.597000e-01 8.303000e-01 1.604000e-01 5.899000e-01 1.905000e-01 7.935000e-01 9.970000e-02 5.155000e-01 - 5.985000e-01 2.114000e-01 8.599000e-01 2.747000e-01 8.155000e-01 4.715000e-01 1.754300e+00 -2.590000e-02 -1.890000e-02 2.017000e+00 -6.550000e-02 7.960000e-02 4.398000e-01 2.384000e-01 - 9.812000e-01 1.464000e-01 9.873000e-01 7.388000e-01 7.230000e-01 1.253800e+00 9.414000e-01 7.927000e-01 7.645000e-01 1.026800e+00 9.855000e-01 7.375000e-01 6.030000e-01 3.864000e-01 - 2.771000e-01 4.934000e-01 4.557000e-01 7.448000e-01 4.533000e-01 9.261000e-01 3.633000e-01 8.519000e-01 3.589000e-01 1.055700e+00 4.432000e-01 7.585000e-01 2.485000e-01 4.471000e-01 - 2.765000e-01 5.446000e-01 4.058000e-01 8.792000e-01 5.765000e-01 8.749000e-01 4.498000e-01 8.381000e-01 6.530000e-01 8.068000e-01 4.016000e-01 8.954000e-01 2.742000e-01 4.652000e-01 - 8.580000e-01 2.206000e-01 3.943000e-01 1.329800e+00 3.000000e-01 7.321000e-01 4.228000e-01 1.296500e+00 3.049000e-01 6.911000e-01 3.720000e-01 1.352200e+00 2.668000e-01 4.835000e-01 - 4.178000e-01 5.168000e-01 5.407000e-01 9.438000e-01 6.523000e-01 1.029500e+00 6.214000e-01 8.466000e-01 7.659000e-01 9.212000e-01 5.444000e-01 9.391000e-01 3.318000e-01 5.214000e-01 - 5.398000e-01 3.048000e-01 8.779000e-01 4.404000e-01 1.011700e+00 4.854000e-01 8.146000e-01 5.131000e-01 9.718000e-01 5.539000e-01 8.771000e-01 4.340000e-01 4.961000e-01 2.664000e-01 - 1.737000e-01 1.020000e-01 2.218000e-01 2.243000e-01 3.025000e-01 1.940000e-01 2.421000e-01 1.973000e-01 2.738000e-01 2.345000e-01 1.940000e-01 2.585000e-01 1.483000e-01 1.043000e-01 - 2.552000e-01 6.242000e-01 5.095000e-01 6.932000e-01 7.394000e-01 5.597000e-01 1.650500e+00 1.011000e-01 -2.090000e-02 2.025200e+00 4.510000e-02 -5.310000e-02 3.175000e-01 3.858000e-01 - 6.043000e-01 4.847000e-01 2.698000e-01 7.133000e-01 1.207000e-01 6.650000e-01 1.966000e-01 7.995000e-01 3.183000e-01 4.146000e-01 3.367000e-01 6.292000e-01 2.181000e-01 3.906000e-01 - 7.783000e-01 4.700000e-03 1.048700e+00 2.047000e-01 1.169700e+00 2.627000e-01 1.024000e+00 2.338000e-01 1.229200e+00 2.135000e-01 1.067300e+00 1.827000e-01 5.997000e-01 1.258000e-01 - 1.056900e+00 5.520000e-02 7.505000e-01 1.643000e-01 5.627000e-01 1.735000e-01 7.464000e-01 1.701000e-01 5.599000e-01 1.603000e-01 7.443000e-01 1.746000e-01 4.886000e-01 9.790000e-02 - 1.082000e+00 2.940000e-02 6.796000e-01 1.778000e-01 5.755000e-01 1.053000e-01 7.075000e-01 1.409000e-01 5.595000e-01 1.105000e-01 8.247000e-01 -2.300000e-03 5.124000e-01 3.890000e-02 - 5.710000e-02 1.050300e+00 -1.100000e-03 4.328000e-01 1.623000e-01 1.709000e-01 -3.400000e-02 4.719000e-01 -1.950000e-02 3.805000e-01 -9.330000e-02 5.434000e-01 3.350000e-02 2.892000e-01 - 1.309000e-01 3.577000e-01 1.534000e-01 6.240000e-01 1.369000e-01 7.509000e-01 2.430000e-01 5.122000e-01 2.289000e-01 6.538000e-01 2.045000e-01 5.573000e-01 1.022000e-01 3.412000e-01 - 3.363000e-01 4.877000e-01 6.284000e-01 5.175000e-01 6.639000e-01 6.105000e-01 1.634700e+00 1.160000e-01 -7.740000e-02 2.089800e+00 1.650000e-02 -2.060000e-02 3.415000e-01 3.319000e-01 - 8.430000e-02 3.520000e-01 5.280000e-02 6.516000e-01 6.420000e-02 7.334000e-01 5.900000e-02 6.430000e-01 6.910000e-02 7.387000e-01 1.051000e-01 5.891000e-01 3.390000e-02 3.710000e-01 - 2.419000e-01 2.554000e-01 3.898000e-01 3.872000e-01 4.288000e-01 4.575000e-01 3.504000e-01 4.362000e-01 4.903000e-01 3.988000e-01 3.969000e-01 3.784000e-01 2.171000e-01 2.331000e-01 - 6.307000e-01 4.740000e-01 9.798000e-01 5.770000e-01 9.152000e-01 7.924000e-01 1.912900e+00 -2.140000e-01 2.007500e+00 -1.050000e-02 -5.000000e-04 1.730100e+00 5.307000e-01 3.768000e-01 - 4.460000e-01 -1.960000e-02 7.009000e-01 -2.960000e-02 7.206000e-01 5.770000e-02 6.415000e-01 4.150000e-02 7.693000e-01 1.520000e-02 7.258000e-01 -5.950000e-02 3.754000e-01 1.790000e-02 - 1.226000e-01 8.072000e-01 5.387000e-01 6.823000e-01 6.626000e-01 6.829000e-01 1.689300e+00 4.540000e-02 -1.990000e-02 2.019300e+00 -3.810000e-02 4.740000e-02 2.932000e-01 4.304000e-01 - 7.825000e-01 9.250000e-02 1.127900e+00 2.562000e-01 1.325700e+00 2.437000e-01 1.183100e+00 1.954000e-01 1.290500e+00 3.130000e-01 1.240500e+00 1.253000e-01 6.916000e-01 1.009000e-01 - 4.689000e-01 1.481000e-01 7.357000e-01 2.329000e-01 7.442000e-01 3.792000e-01 6.927000e-01 2.833000e-01 8.208000e-01 3.054000e-01 7.793000e-01 1.818000e-01 4.031000e-01 1.613000e-01 - 6.073000e-01 1.505000e-01 8.918000e-01 3.078000e-01 1.123600e+00 2.238000e-01 8.878000e-01 3.141000e-01 1.023600e+00 3.635000e-01 8.919000e-01 3.122000e-01 4.959000e-01 2.008000e-01 - 1.084500e+00 2.560000e-02 1.255700e+00 4.653000e-01 9.001000e-01 1.065200e+00 1.240200e+00 4.783000e-01 9.773000e-01 7.541000e-01 1.280700e+00 4.358000e-01 6.714000e-01 3.143000e-01 - 1.122400e+00 -2.090000e-02 6.288000e-01 1.111500e+00 5.239000e-01 8.923000e-01 7.640000e-01 9.513000e-01 6.113000e-01 7.272000e-01 6.948000e-01 1.037500e+00 4.859000e-01 4.066000e-01 - 8.826000e-01 2.210000e-01 3.047000e-01 6.431000e-01 3.247000e-01 4.102000e-01 4.222000e-01 5.054000e-01 3.085000e-01 4.134000e-01 3.644000e-01 5.701000e-01 2.931000e-01 2.968000e-01 --7.180000e-02 6.558000e-01 2.116000e-01 6.708000e-01 3.328000e-01 6.744000e-01 3.570000e-02 1.688300e+00 5.100000e-02 1.946700e+00 -7.030000e-02 8.540000e-02 1.075000e-01 4.049000e-01 - 3.613000e-01 7.365000e-01 2.189000e-01 5.576000e-01 1.782000e-01 4.514000e-01 2.020000e-01 5.775000e-01 1.855000e-01 4.275000e-01 2.536000e-01 5.144000e-01 1.511000e-01 3.725000e-01 - 1.074900e+00 4.140000e-02 9.121000e-01 1.876000e-01 7.667000e-01 5.760000e-02 9.258000e-01 1.722000e-01 7.291000e-01 8.450000e-02 8.942000e-01 2.083000e-01 5.681000e-01 8.440000e-02 - 2.760000e-02 1.086800e+00 -4.250000e-02 5.219000e-01 3.250000e-02 3.607000e-01 2.460000e-02 4.429000e-01 8.690000e-02 2.859000e-01 -1.100000e-03 4.726000e-01 3.560000e-02 3.120000e-01 --4.950000e-02 5.970000e-02 5.990000e-02 1.809000e-01 1.120000e-02 3.660000e-01 1.340000e-02 -1.290000e-02 7.630000e-02 -9.200000e-02 5.320000e-02 -6.370000e-02 6.000000e-03 1.293000e-01 --7.640000e-02 1.198800e+00 3.247000e-01 1.192000e+00 3.850000e-01 1.257300e+00 -8.280000e-02 1.832100e+00 4.500000e-03 1.991200e+00 -1.990000e-02 1.757700e+00 1.692000e-01 7.139000e-01 - 3.735000e-01 7.355000e-01 1.927000e-01 5.480000e-01 2.143000e-01 3.824000e-01 2.458000e-01 4.837000e-01 1.067000e-01 4.974000e-01 1.687000e-01 5.756000e-01 1.769000e-01 3.251000e-01 - 2.741000e-01 5.164000e-01 3.749000e-01 8.773000e-01 4.355000e-01 9.832000e-01 4.299000e-01 8.103000e-01 5.146000e-01 9.149000e-01 3.877000e-01 8.587000e-01 2.248000e-01 4.949000e-01 --7.800000e-03 7.603000e-01 -4.110000e-02 1.110900e+00 -3.300000e-03 1.193900e+00 -7.110000e-02 1.819100e+00 6.250000e-02 1.926700e+00 4.510000e-02 -5.140000e-02 1.150000e-02 6.132000e-01 - 1.184000e+00 -9.540000e-02 4.044000e-01 6.970000e-01 2.919000e-01 5.550000e-01 5.266000e-01 5.560000e-01 3.209000e-01 5.018000e-01 3.657000e-01 7.450000e-01 3.112000e-01 3.396000e-01 - 4.732000e-01 3.046000e-01 6.683000e-01 4.455000e-01 8.095000e-01 4.224000e-01 1.736500e+00 1.300000e-03 3.190000e-02 1.964000e+00 -2.400000e-03 3.500000e-03 4.121000e-01 2.376000e-01 - 1.384000e-01 6.302000e-01 1.225000e-01 1.096600e+00 2.138000e-01 1.162500e+00 1.260000e-01 1.090700e+00 2.173000e-01 1.177400e+00 2.158000e-01 9.860000e-01 1.137000e-01 5.832000e-01 - 5.437000e-01 1.828000e-01 8.222000e-01 3.264000e-01 1.067400e+00 2.131000e-01 8.137000e-01 3.349000e-01 1.033500e+00 2.770000e-01 8.017000e-01 3.519000e-01 5.158000e-01 1.387000e-01 - 8.353000e-01 1.856000e-01 1.130800e+00 2.115000e-01 1.142400e+00 3.409000e-01 1.648800e+00 9.690000e-02 1.939400e+00 7.250000e-02 -3.960000e-02 4.650000e-02 6.589000e-01 1.338000e-01 - 5.665000e-01 4.000000e-01 8.040000e-01 4.914000e-01 9.725000e-01 4.351000e-01 1.770100e+00 -5.090000e-02 -6.530000e-02 2.077400e+00 8.140000e-02 -9.730000e-02 4.666000e-01 2.948000e-01 - 1.780000e-02 6.441000e-01 1.934000e-01 8.153000e-01 2.080000e-01 9.453000e-01 2.370000e-01 7.618000e-01 2.103000e-01 9.556000e-01 8.110000e-02 9.460000e-01 8.990000e-02 4.970000e-01 - 3.716000e-01 5.727000e-01 5.762000e-01 9.028000e-01 7.666000e-01 8.992000e-01 6.574000e-01 8.045000e-01 8.085000e-01 8.724000e-01 6.284000e-01 8.446000e-01 3.867000e-01 4.575000e-01 - 9.650000e-02 8.800000e-02 5.990000e-02 2.461000e-01 1.445000e-01 1.877000e-01 1.420000e-01 1.516000e-01 1.662000e-01 1.680000e-01 1.162000e-01 1.799000e-01 6.860000e-02 1.019000e-01 - 5.024000e-01 5.639000e-01 7.328000e-01 9.492000e-01 8.707000e-01 1.036400e+00 6.174000e-01 1.084500e+00 8.037000e-01 1.142800e+00 7.794000e-01 8.958000e-01 4.507000e-01 5.156000e-01 - 5.465000e-01 4.334000e-01 9.130000e-01 6.068000e-01 8.931000e-01 8.647000e-01 8.490000e-01 6.866000e-01 9.978000e-01 7.642000e-01 8.058000e-01 7.353000e-01 4.696000e-01 4.187000e-01 - 1.090000e-01 7.328000e-01 2.592000e-01 1.042400e+00 3.453000e-01 1.126100e+00 2.120000e-01 1.099000e+00 3.552000e-01 1.139400e+00 2.400000e-01 1.065500e+00 1.622000e-01 5.869000e-01 - 3.045000e-01 4.043000e-01 4.679000e-01 6.469000e-01 5.784000e-01 6.798000e-01 4.442000e-01 6.740000e-01 5.287000e-01 7.581000e-01 4.444000e-01 6.719000e-01 2.540000e-01 3.910000e-01 - 5.597000e-01 2.298000e-01 8.849000e-01 3.551000e-01 1.034300e+00 3.724000e-01 1.094000e+00 1.069000e-01 1.116900e+00 3.034000e-01 8.565000e-01 3.830000e-01 5.458000e-01 1.637000e-01 - 1.135400e+00 -3.660000e-02 9.930000e-01 7.328000e-01 7.396000e-01 4.829000e-01 1.096200e+00 6.153000e-01 7.750000e-01 4.016000e-01 1.057200e+00 6.568000e-01 6.032000e-01 2.289000e-01 - 2.453000e-01 4.808000e-01 4.855000e-01 6.343000e-01 6.267000e-01 6.316000e-01 5.035000e-01 6.100000e-01 6.372000e-01 6.430000e-01 4.382000e-01 6.838000e-01 2.897000e-01 3.544000e-01 - 1.590000e-01 9.624000e-01 5.616000e-01 1.167600e+00 7.067000e-01 1.198000e+00 1.750800e+00 -2.140000e-02 1.230000e-02 1.983100e+00 1.710000e-02 1.712500e+00 3.037000e-01 6.852000e-01 - 1.016200e+00 1.034000e-01 4.259000e-01 4.574000e-01 3.615000e-01 3.437000e-01 3.998000e-01 4.866000e-01 3.650000e-01 3.214000e-01 4.585000e-01 4.220000e-01 3.193000e-01 2.518000e-01 --6.100000e-02 1.181100e+00 1.040000e-02 1.661100e+00 9.170000e-02 1.701800e+00 1.160000e-02 1.715900e+00 -5.730000e-02 2.068800e+00 -1.860000e-02 1.750300e+00 4.010000e-02 9.064000e-01 - 1.116900e+00 -1.100000e-02 1.668300e+00 7.910000e-02 1.079600e+00 2.620000e-02 1.753600e+00 -3.070000e-02 9.317000e-01 1.614000e-01 1.719400e+00 1.680000e-02 7.741000e-01 1.160000e-02 - 5.425000e-01 5.662000e-01 4.065000e-01 7.119000e-01 1.688000e-01 7.062000e-01 3.222000e-01 8.160000e-01 7.430000e-02 7.979000e-01 1.885000e-01 9.741000e-01 2.290000e-01 4.339000e-01 - 1.092100e+00 1.910000e-02 6.402000e-01 6.883000e-01 4.099000e-01 5.550000e-01 5.433000e-01 8.057000e-01 4.109000e-01 5.229000e-01 5.550000e-01 7.881000e-01 4.032000e-01 3.093000e-01 - 4.276000e-01 5.982000e-01 8.580000e-01 4.580000e-01 9.666000e-01 4.712000e-01 1.702400e+00 3.460000e-02 9.500000e-03 1.983900e+00 3.450000e-02 -4.050000e-02 4.654000e-01 3.163000e-01 - 1.115200e+00 -1.240000e-02 8.336000e-01 9.390000e-02 7.419000e-01 -2.350000e-02 7.926000e-01 1.436000e-01 6.870000e-01 2.140000e-02 8.857000e-01 3.260000e-02 5.579000e-01 2.920000e-02 - 6.695000e-01 2.592000e-01 1.030400e+00 1.922000e-01 1.123800e+00 2.317000e-01 1.701900e+00 3.900000e-02 1.941600e+00 6.780000e-02 -6.900000e-03 9.600000e-03 5.872000e-01 1.409000e-01 - 1.106000e+00 1.000000e-04 5.493000e-01 1.184000e+00 5.373000e-01 6.650000e-01 4.143000e-01 1.344100e+00 4.731000e-01 6.990000e-01 5.029000e-01 1.236600e+00 4.045000e-01 4.258000e-01 - 2.478000e-01 6.458000e-01 4.043000e-01 8.337000e-01 7.193000e-01 6.084000e-01 1.761800e+00 -2.940000e-02 3.350000e-02 1.959700e+00 -3.270000e-02 4.190000e-02 2.817000e-01 4.376000e-01 - 7.105000e-01 3.832000e-01 9.548000e-01 4.648000e-01 1.065400e+00 4.734000e-01 1.780600e+00 -5.460000e-02 1.931100e+00 7.980000e-02 -8.090000e-02 1.827600e+00 5.788000e-01 2.513000e-01 - 9.968000e-01 1.254000e-01 3.771000e-01 8.535000e-01 3.739000e-01 5.202000e-01 4.012000e-01 8.273000e-01 3.794000e-01 4.933000e-01 3.189000e-01 9.200000e-01 3.228000e-01 3.621000e-01 - 8.350000e-02 5.562000e-01 -2.360000e-02 9.990000e-01 1.625000e-01 8.986000e-01 -2.440000e-02 1.758700e+00 9.700000e-02 1.884700e+00 -6.000000e-03 8.400000e-03 4.000000e-03 5.609000e-01 - 7.489000e-01 2.026000e-01 1.055900e+00 4.588000e-01 1.076900e+00 6.637000e-01 9.010000e-01 6.377000e-01 1.106200e+00 6.628000e-01 9.712000e-01 5.537000e-01 5.625000e-01 3.189000e-01 - 6.852000e-01 4.216000e-01 2.922000e-01 1.446000e+00 2.703000e-01 1.491500e+00 3.944000e-01 1.328700e+00 2.530000e-01 1.362000e+00 3.464000e-01 1.382300e+00 2.611000e-01 7.005000e-01 - 1.096600e+00 1.160000e-02 5.389000e-01 6.236000e-01 4.291000e-01 4.441000e-01 4.939000e-01 6.776000e-01 5.015000e-01 3.356000e-01 5.481000e-01 6.099000e-01 3.832000e-01 2.869000e-01 - 4.674000e-01 3.366000e-01 6.300000e-01 6.509000e-01 8.707000e-01 5.648000e-01 8.028000e-01 4.547000e-01 7.896000e-01 6.800000e-01 6.882000e-01 5.842000e-01 3.931000e-01 3.419000e-01 - 3.740000e-02 3.977000e-01 1.968000e-01 5.588000e-01 3.630000e-01 5.027000e-01 5.700000e-03 1.723800e+00 5.880000e-02 -6.790000e-02 4.370000e-02 -5.240000e-02 9.980000e-02 3.309000e-01 --8.640000e-02 7.606000e-01 1.386000e-01 8.285000e-01 1.761000e-01 9.310000e-01 1.890000e-02 1.707500e+00 8.900000e-02 1.899300e+00 -2.260000e-02 2.660000e-02 7.470000e-02 4.927000e-01 - 9.280000e-02 4.511000e-01 2.259000e-01 6.111000e-01 2.024000e-01 7.561000e-01 2.166000e-01 6.206000e-01 2.474000e-01 7.191000e-01 3.004000e-01 5.213000e-01 1.155000e-01 3.700000e-01 - 1.129200e+00 -2.850000e-02 1.839300e+00 -1.256000e-01 1.044800e+00 3.002000e-01 1.690200e+00 4.770000e-02 1.060200e+00 2.250000e-01 1.756400e+00 -2.550000e-02 7.967000e-01 7.160000e-02 - 4.576000e-01 6.481000e-01 8.842000e-01 6.654000e-01 8.400000e-01 8.585000e-01 1.776800e+00 -5.000000e-02 -2.600000e-02 2.027400e+00 -3.770000e-02 1.776600e+00 4.504000e-01 4.539000e-01 - 9.950000e-01 1.179000e-01 4.039000e-01 5.380000e-02 3.093000e-01 8.520000e-02 3.899000e-01 6.880000e-02 3.294000e-01 5.420000e-02 3.797000e-01 8.240000e-02 3.046000e-01 4.090000e-02 - 3.339000e-01 6.875000e-01 4.558000e-01 1.153400e+00 6.172000e-01 1.192000e+00 5.502000e-01 1.038800e+00 6.202000e-01 1.225200e+00 5.811000e-01 1.007600e+00 3.185000e-01 6.004000e-01 --4.820000e-02 6.359000e-01 1.800000e-02 8.810000e-01 2.713000e-01 7.287000e-01 -7.900000e-03 1.742900e+00 -7.300000e-02 2.082400e+00 1.420000e-02 -1.830000e-02 5.610000e-02 4.595000e-01 - 2.457000e-01 8.729000e-01 5.142000e-01 1.241100e+00 6.712000e-01 1.218500e+00 1.593200e+00 1.650000e-01 -7.070000e-02 2.076500e+00 -4.660000e-02 1.782600e+00 3.449000e-01 6.335000e-01 - 1.229000e-01 4.821000e-01 1.893000e-01 7.599000e-01 1.586000e-01 9.281000e-01 1.126000e-01 8.487000e-01 1.617000e-01 9.455000e-01 1.438000e-01 8.120000e-01 8.710000e-02 4.641000e-01 - 1.070200e+00 4.060000e-02 9.576000e-01 7.759000e-01 7.389000e-01 6.740000e-01 9.694000e-01 7.624000e-01 6.454000e-01 7.210000e-01 8.924000e-01 8.504000e-01 5.947000e-01 3.001000e-01 - 1.169300e+00 -7.410000e-02 4.090000e-01 1.405000e-01 3.912000e-01 5.770000e-02 3.398000e-01 2.218000e-01 3.632000e-01 8.360000e-02 3.703000e-01 1.829000e-01 3.115000e-01 8.760000e-02 - 1.105400e+00 -1.500000e-03 8.807000e-01 8.586000e-01 7.397000e-01 7.915000e-01 8.299000e-01 9.165000e-01 6.437000e-01 8.200000e-01 8.560000e-01 8.872000e-01 5.826000e-01 3.420000e-01 - 7.364000e-01 3.606000e-01 3.379000e-01 1.395600e+00 3.809000e-01 6.602000e-01 3.742000e-01 1.349900e+00 2.766000e-01 7.495000e-01 3.453000e-01 1.385800e+00 2.625000e-01 5.014000e-01 - 2.111000e-01 4.445000e-01 3.171000e-01 7.100000e-01 2.735000e-01 9.087000e-01 2.507000e-01 7.881000e-01 3.714000e-01 8.123000e-01 3.026000e-01 7.286000e-01 1.915000e-01 3.995000e-01 - 1.103900e+00 1.500000e-03 1.563900e+00 1.408000e-01 1.042800e+00 2.625000e-01 1.342200e+00 4.042000e-01 9.119000e-01 3.649000e-01 1.385600e+00 3.508000e-01 7.253000e-01 1.404000e-01 - 4.195000e-01 8.830000e-02 7.209000e-01 9.850000e-02 9.006000e-01 3.060000e-02 1.774500e+00 -4.860000e-02 4.910000e-02 -5.700000e-02 -4.180000e-02 4.740000e-02 4.286000e-01 3.880000e-02 - 5.653000e-01 2.091000e-01 8.336000e-01 2.673000e-01 9.022000e-01 3.250000e-01 1.754900e+00 -2.750000e-02 -4.610000e-02 2.045700e+00 9.900000e-03 -1.070000e-02 4.766000e-01 1.708000e-01 - 1.073400e+00 3.650000e-02 1.302600e+00 4.334000e-01 9.583000e-01 1.296000e-01 1.387600e+00 3.254000e-01 9.933000e-01 4.790000e-02 1.318700e+00 4.104000e-01 7.018000e-01 7.740000e-02 - 1.012300e+00 9.170000e-02 1.274500e+00 4.656000e-01 1.394300e+00 5.197000e-01 1.596500e+00 1.580000e-01 1.963400e+00 4.470000e-02 -3.710000e-02 1.775800e+00 7.757000e-01 2.109000e-01 - 3.312000e-01 4.549000e-01 4.088000e-01 8.564000e-01 4.991000e-01 9.237000e-01 3.774000e-01 8.868000e-01 4.258000e-01 1.034300e+00 4.400000e-01 8.127000e-01 2.453000e-01 4.791000e-01 - 2.755000e-01 5.100000e-02 5.709000e-01 7.640000e-02 6.809000e-01 8.630000e-02 1.552000e+00 2.105000e-01 1.029000e-01 -1.244000e-01 4.730000e-02 -5.740000e-02 3.188000e-01 4.080000e-02 - 1.982000e-01 4.741000e-01 5.539000e-01 4.219000e-01 5.941000e-01 5.179000e-01 1.645400e+00 9.840000e-02 -2.010000e-02 2.023500e+00 2.800000e-03 -4.000000e-03 2.789000e-01 2.966000e-01 - 5.457000e-01 5.523000e-01 2.855000e-01 1.444900e+00 2.507000e-01 9.536000e-01 2.293000e-01 1.511200e+00 1.937000e-01 9.833000e-01 2.684000e-01 1.465500e+00 2.168000e-01 6.102000e-01 --1.760000e-02 7.540000e-01 2.606000e-01 7.783000e-01 3.203000e-01 8.502000e-01 3.800000e-02 1.688500e+00 -8.700000e-03 2.010800e+00 1.600000e-03 -5.200000e-03 1.225000e-01 4.917000e-01 - 4.159000e-01 3.145000e-01 5.517000e-01 6.108000e-01 6.129000e-01 7.097000e-01 5.831000e-01 5.751000e-01 5.852000e-01 7.686000e-01 6.010000e-01 5.550000e-01 3.130000e-01 3.593000e-01 - 9.423000e-01 8.490000e-02 1.512500e+00 9.520000e-02 1.424700e+00 4.486000e-01 1.395900e+00 2.259000e-01 1.504800e+00 3.934000e-01 1.365400e+00 2.641000e-01 7.831000e-01 1.587000e-01 - 4.414000e-01 3.166000e-01 7.875000e-01 2.755000e-01 8.969000e-01 2.846000e-01 1.744600e+00 -1.050000e-02 2.500000e-02 1.969800e+00 6.880000e-02 -8.310000e-02 4.572000e-01 1.650000e-01 - 4.897000e-01 6.166000e-01 7.778000e-01 7.313000e-01 9.998000e-01 6.125000e-01 1.702800e+00 3.800000e-02 1.101000e-01 1.871500e+00 6.670000e-02 1.654300e+00 4.621000e-01 4.153000e-01 - 3.592000e-01 1.203000e-01 5.896000e-01 1.628000e-01 7.070000e-01 1.390000e-01 6.235000e-01 1.224000e-01 6.721000e-01 1.972000e-01 6.086000e-01 1.439000e-01 3.612000e-01 6.870000e-02 - 1.177800e+00 -8.270000e-02 1.425000e+00 2.890000e-02 1.075300e+00 -1.025000e-01 1.426900e+00 3.190000e-02 9.603000e-01 8.000000e-04 1.419000e+00 4.170000e-02 7.187000e-01 1.360000e-02 - 2.602000e-01 7.164000e-01 7.322000e-01 5.280000e-01 7.810000e-01 6.129000e-01 1.754400e+00 -2.570000e-02 -4.170000e-02 2.048600e+00 -7.420000e-02 8.850000e-02 3.622000e-01 3.902000e-01 - 9.853000e-01 1.293000e-01 4.345000e-01 2.152000e-01 3.437000e-01 1.954000e-01 4.099000e-01 2.471000e-01 3.284000e-01 2.032000e-01 4.200000e-01 2.330000e-01 3.041000e-01 1.577000e-01 - 9.117000e-01 1.948000e-01 1.158700e+00 4.074000e-01 1.335200e+00 3.364000e-01 1.714900e+00 1.760000e-02 2.016600e+00 -2.550000e-02 -1.041000e-01 1.856400e+00 7.200000e-01 1.807000e-01 - 1.140600e+00 -3.580000e-02 6.300000e-01 1.801000e-01 3.542000e-01 3.308000e-01 5.145000e-01 3.221000e-01 4.868000e-01 1.562000e-01 5.023000e-01 3.346000e-01 4.021000e-01 1.433000e-01 - 6.267000e-01 4.142000e-01 9.552000e-01 6.795000e-01 1.028300e+00 8.437000e-01 9.689000e-01 6.655000e-01 1.223900e+00 6.474000e-01 9.202000e-01 7.226000e-01 5.495000e-01 3.955000e-01 - 2.915000e-01 2.898000e-01 3.733000e-01 5.469000e-01 3.424000e-01 7.258000e-01 3.200000e-01 6.140000e-01 4.248000e-01 6.449000e-01 2.811000e-01 6.647000e-01 1.979000e-01 3.389000e-01 - 7.741000e-01 1.946000e-01 1.068400e+00 4.755000e-01 1.394300e+00 3.300000e-01 1.170700e+00 3.506000e-01 1.343000e+00 4.205000e-01 1.218300e+00 2.957000e-01 6.731000e-01 2.077000e-01 --3.680000e-02 4.017000e-01 6.590000e-02 6.122000e-01 2.108000e-01 5.843000e-01 -7.320000e-02 1.821100e+00 -7.040000e-02 8.380000e-02 -1.620000e-02 1.770000e-02 6.800000e-02 3.089000e-01 - 3.511000e-01 8.330000e-02 6.243000e-01 4.320000e-02 6.950000e-01 7.220000e-02 6.273000e-01 3.880000e-02 8.073000e-01 -5.250000e-02 6.866000e-01 -2.930000e-02 3.668000e-01 1.780000e-02 --2.200000e-03 4.328000e-01 9.620000e-02 5.566000e-01 1.032000e-01 6.397000e-01 7.780000e-02 5.799000e-01 1.418000e-01 6.060000e-01 1.540000e-02 6.526000e-01 4.010000e-02 3.400000e-01 - 1.157000e-01 3.932000e-01 2.233000e-01 5.684000e-01 3.136000e-01 5.740000e-01 2.221000e-01 5.673000e-01 1.697000e-01 7.595000e-01 1.936000e-01 6.002000e-01 1.201000e-01 3.379000e-01 - 3.699000e-01 7.311000e-01 7.281000e-01 9.934000e-01 6.734000e-01 1.311100e+00 1.734800e+00 -5.500000e-03 1.186000e-01 1.858800e+00 -1.470000e-02 1.746300e+00 3.813000e-01 6.166000e-01 - 3.891000e-01 2.215000e-01 6.282000e-01 3.252000e-01 7.111000e-01 3.700000e-01 6.102000e-01 3.434000e-01 7.287000e-01 3.707000e-01 5.959000e-01 3.620000e-01 3.536000e-01 1.964000e-01 - 1.167600e+00 -7.010000e-02 3.770000e-01 1.359000e+00 2.343000e-01 1.332700e+00 4.709000e-01 1.250900e+00 2.915000e-01 1.174700e+00 3.580000e-01 1.382700e+00 3.080000e-01 6.191000e-01 - 4.259000e-01 4.327000e-01 6.630000e-01 6.835000e-01 6.497000e-01 9.032000e-01 6.208000e-01 7.378000e-01 6.525000e-01 9.307000e-01 6.688000e-01 6.760000e-01 3.576000e-01 4.249000e-01 - 4.507000e-01 5.850000e-01 7.612000e-01 8.484000e-01 8.878000e-01 9.469000e-01 7.216000e-01 8.978000e-01 7.942000e-01 1.079400e+00 7.377000e-01 8.773000e-01 4.067000e-01 5.303000e-01 - 1.700000e-01 9.436000e-01 1.486000e-01 1.091900e+00 5.420000e-02 8.650000e-01 2.837000e-01 9.306000e-01 1.378000e-01 7.436000e-01 1.412000e-01 1.099300e+00 1.027000e-01 5.879000e-01 - 3.313000e-01 2.213000e-01 5.529000e-01 3.276000e-01 7.630000e-01 2.247000e-01 1.775100e+00 -4.960000e-02 -1.123000e-01 2.130400e+00 3.450000e-02 -4.120000e-02 3.109000e-01 1.995000e-01 --3.970000e-02 8.653000e-01 2.490000e-02 1.106400e+00 1.539000e-01 1.077600e+00 2.170000e-02 1.703100e+00 -3.390000e-02 2.043400e+00 4.890000e-02 -5.940000e-02 9.900000e-03 6.578000e-01 - 4.160000e-02 2.492000e-01 3.630000e-02 5.778000e-01 2.428000e-01 4.730000e-01 -2.500000e-03 1.733400e+00 -5.520000e-02 6.750000e-02 4.880000e-02 -5.870000e-02 4.570000e-02 2.934000e-01 - 1.156100e+00 -6.180000e-02 4.096000e-01 1.040000e+00 4.179000e-01 5.648000e-01 4.842000e-01 9.480000e-01 3.375000e-01 6.273000e-01 3.995000e-01 1.051100e+00 3.377000e-01 3.912000e-01 - 1.151400e+00 -5.380000e-02 5.146000e-01 1.212200e+00 4.147000e-01 8.332000e-01 5.307000e-01 1.194600e+00 3.870000e-01 8.223000e-01 5.730000e-01 1.144800e+00 3.725000e-01 4.698000e-01 - 8.854000e-01 2.118000e-01 2.810000e-01 5.846000e-01 3.241000e-01 3.542000e-01 3.327000e-01 5.222000e-01 3.007000e-01 3.679000e-01 4.131000e-01 4.262000e-01 2.879000e-01 2.674000e-01 - 3.638000e-01 3.804000e-01 7.445000e-01 2.988000e-01 8.495000e-01 3.202000e-01 1.906100e+00 -2.026000e-01 0.000000e+00 2.001200e+00 -8.700000e-03 9.600000e-03 3.821000e-01 2.389000e-01 - 2.077000e-01 4.487000e-01 2.482000e-01 7.953000e-01 3.955000e-01 7.727000e-01 3.535000e-01 6.678000e-01 4.896000e-01 6.782000e-01 3.969000e-01 6.183000e-01 2.022000e-01 3.902000e-01 - 2.329000e-01 6.285000e-01 5.094000e-01 8.113000e-01 6.033000e-01 8.956000e-01 5.496000e-01 7.599000e-01 6.561000e-01 8.529000e-01 5.272000e-01 7.862000e-01 3.007000e-01 4.596000e-01 - 2.791000e-01 3.898000e-01 3.058000e-01 7.622000e-01 5.019000e-01 6.861000e-01 4.306000e-01 6.145000e-01 4.562000e-01 7.582000e-01 4.978000e-01 5.388000e-01 2.469000e-01 3.571000e-01 - 7.123000e-01 4.088000e-01 4.206000e-01 5.596000e-01 2.809000e-01 4.953000e-01 3.921000e-01 5.953000e-01 3.269000e-01 4.263000e-01 3.688000e-01 6.182000e-01 2.817000e-01 3.309000e-01 - 4.560000e-01 6.668000e-01 9.033000e-01 7.476000e-01 1.013600e+00 7.590000e-01 1.754300e+00 -2.050000e-02 1.967200e+00 3.600000e-02 -5.360000e-02 1.796700e+00 5.007000e-01 4.429000e-01 - 8.890000e-02 1.325000e-01 4.074000e-01 1.244000e-01 5.965000e-01 4.450000e-02 1.626600e+00 1.235000e-01 -4.290000e-02 5.310000e-02 4.770000e-02 -5.700000e-02 2.280000e-01 6.230000e-02 - 1.146600e+00 -4.920000e-02 7.559000e-01 2.360000e-02 5.875000e-01 5.060000e-02 7.465000e-01 3.910000e-02 6.143000e-01 2.300000e-03 8.245000e-01 -5.390000e-02 5.154000e-01 7.700000e-03 - 8.170000e-02 2.001000e-01 1.828000e-01 2.480000e-01 5.410000e-02 4.648000e-01 1.310000e-01 3.071000e-01 6.150000e-02 4.636000e-01 1.292000e-01 3.150000e-01 6.720000e-02 1.890000e-01 - 3.180000e-02 6.007000e-01 9.280000e-02 8.930000e-01 6.660000e-02 1.058400e+00 1.871000e-01 7.811000e-01 1.862000e-01 9.383000e-01 1.827000e-01 7.888000e-01 4.380000e-02 5.275000e-01 - 4.072000e-01 3.659000e-01 5.085000e-01 7.296000e-01 5.936000e-01 8.117000e-01 6.028000e-01 6.189000e-01 5.598000e-01 8.765000e-01 6.076000e-01 6.148000e-01 3.187000e-01 3.925000e-01 - 2.243000e-01 6.124000e-01 4.186000e-01 7.552000e-01 5.787000e-01 7.057000e-01 1.668500e+00 7.700000e-02 -8.110000e-02 2.092600e+00 -5.970000e-02 7.010000e-02 2.660000e-01 4.185000e-01 - 1.119900e+00 -1.730000e-02 7.295000e-01 1.010000e+00 6.374000e-01 6.944000e-01 7.769000e-01 9.545000e-01 7.149000e-01 5.439000e-01 7.168000e-01 1.024200e+00 5.167000e-01 3.524000e-01 - 4.520000e-01 4.722000e-01 6.902000e-01 7.582000e-01 9.625000e-01 6.599000e-01 7.994000e-01 6.382000e-01 8.570000e-01 8.108000e-01 7.531000e-01 6.950000e-01 4.221000e-01 4.123000e-01 - 1.081400e+00 2.580000e-02 7.381000e-01 8.548000e-01 5.455000e-01 4.843000e-01 8.089000e-01 7.721000e-01 6.447000e-01 3.352000e-01 7.806000e-01 8.049000e-01 5.102000e-01 2.349000e-01 --3.530000e-02 9.139000e-01 3.109000e-01 8.767000e-01 3.875000e-01 9.291000e-01 1.760000e-02 1.708500e+00 5.190000e-02 1.937300e+00 -2.270000e-02 2.460000e-02 1.649000e-01 5.366000e-01 - 9.807000e-01 1.179000e-01 4.309000e-01 2.824000e-01 3.117000e-01 2.823000e-01 4.382000e-01 2.777000e-01 3.246000e-01 2.548000e-01 4.180000e-01 3.002000e-01 3.007000e-01 1.943000e-01 - 3.250000e-01 2.790000e-01 7.360000e-01 1.607000e-01 7.521000e-01 2.878000e-01 1.784500e+00 -6.330000e-02 -7.240000e-02 2.082300e+00 3.470000e-02 -4.150000e-02 3.443000e-01 1.906000e-01 - 5.688000e-01 9.040000e-02 8.446000e-01 1.970000e-01 9.281000e-01 2.649000e-01 8.492000e-01 1.929000e-01 8.796000e-01 3.376000e-01 7.574000e-01 3.030000e-01 4.745000e-01 1.294000e-01 - 3.120000e-01 4.004000e-01 6.671000e-01 3.566000e-01 5.774000e-01 6.072000e-01 1.690600e+00 5.000000e-02 1.290000e-02 1.987400e+00 -1.380000e-02 1.650000e-02 3.382000e-01 2.667000e-01 - 1.663000e-01 6.186000e-01 1.562000e-01 1.084300e+00 1.519000e-01 1.267800e+00 1.862000e-01 1.049100e+00 3.050000e-01 1.105300e+00 1.669000e-01 1.073600e+00 1.202000e-01 5.912000e-01 - 3.376000e-01 5.206000e-01 4.022000e-01 9.626000e-01 6.526000e-01 8.635000e-01 5.593000e-01 7.763000e-01 5.713000e-01 9.856000e-01 6.018000e-01 7.242000e-01 3.129000e-01 4.596000e-01 - 4.579000e-01 6.553000e-01 2.936000e-01 8.654000e-01 2.920000e-01 5.680000e-01 2.777000e-01 8.882000e-01 2.360000e-01 6.118000e-01 2.499000e-01 9.187000e-01 2.159000e-01 4.540000e-01 - 3.084000e-01 5.384000e-01 6.887000e-01 4.607000e-01 7.591000e-01 5.193000e-01 1.674400e+00 6.190000e-02 -2.330000e-02 2.028300e+00 -1.730000e-02 2.100000e-02 3.601000e-01 3.235000e-01 - 2.808000e-01 2.563000e-01 4.488000e-01 3.873000e-01 5.796000e-01 3.615000e-01 4.806000e-01 3.485000e-01 5.999000e-01 3.518000e-01 4.774000e-01 3.545000e-01 2.802000e-01 1.986000e-01 - 8.380000e-02 1.481000e-01 1.604000e-01 1.943000e-01 2.514000e-01 1.399000e-01 2.079000e-01 1.384000e-01 2.309000e-01 1.706000e-01 1.796000e-01 1.728000e-01 1.045000e-01 9.790000e-02 - 5.270000e-02 4.620000e-01 3.844000e-01 4.432000e-01 5.097000e-01 4.337000e-01 -2.350000e-02 1.761600e+00 -9.900000e-03 1.100000e-02 3.140000e-02 -3.780000e-02 1.804000e-01 3.003000e-01 - 4.784000e-01 -3.000000e-03 7.456000e-01 2.300000e-03 7.309000e-01 1.364000e-01 6.966000e-01 5.930000e-02 7.600000e-01 1.187000e-01 7.205000e-01 3.540000e-02 4.033000e-01 3.320000e-02 - 3.585000e-01 6.260000e-01 7.743000e-01 5.065000e-01 7.985000e-01 6.149000e-01 1.737500e+00 -1.090000e-02 4.900000e-02 1.945400e+00 -8.970000e-02 1.065000e-01 4.380000e-01 3.180000e-01 - 5.082000e-01 5.910000e-01 7.890000e-01 7.576000e-01 8.673000e-01 8.025000e-01 1.677600e+00 6.540000e-02 -5.020000e-02 2.058300e+00 8.400000e-02 1.632800e+00 4.487000e-01 4.481000e-01 - 3.315000e-01 5.409000e-01 6.587000e-01 5.250000e-01 6.879000e-01 6.287000e-01 1.730300e+00 0.000000e+00 8.930000e-02 1.898600e+00 4.910000e-02 -5.990000e-02 3.776000e-01 3.192000e-01 - 7.500000e-03 7.326000e-01 2.217000e-01 8.010000e-01 2.055000e-01 9.643000e-01 6.180000e-02 1.656800e+00 -5.870000e-02 2.070400e+00 -2.560000e-02 3.100000e-02 4.460000e-02 5.755000e-01 --4.240000e-02 7.591000e-01 3.428000e-01 6.647000e-01 3.545000e-01 7.933000e-01 5.000000e-02 1.675500e+00 -9.690000e-02 2.112200e+00 -1.800000e-03 2.400000e-03 1.535000e-01 4.441000e-01 - 3.393000e-01 5.055000e-01 6.030000e-01 7.080000e-01 7.056000e-01 7.814000e-01 6.102000e-01 7.022000e-01 7.245000e-01 7.864000e-01 5.652000e-01 7.547000e-01 3.327000e-01 4.272000e-01 - 6.824000e-01 4.344000e-01 1.050700e+00 6.378000e-01 1.118400e+00 7.004000e-01 1.775100e+00 -5.320000e-02 1.875000e+00 1.442000e-01 3.940000e-02 1.686500e+00 6.244000e-01 3.308000e-01 - 3.648000e-01 5.643000e-01 7.458000e-01 4.867000e-01 8.342000e-01 5.187000e-01 1.737500e+00 -6.500000e-03 -9.810000e-02 2.117800e+00 -5.920000e-02 7.120000e-02 3.983000e-01 3.318000e-01 - 1.129600e+00 -3.020000e-02 6.534000e-01 1.097600e+00 6.021000e-01 4.781000e-01 7.221000e-01 1.015500e+00 6.298000e-01 4.144000e-01 7.729000e-01 9.528000e-01 5.045000e-01 2.708000e-01 - 4.052000e-01 3.131000e-01 6.783000e-01 4.436000e-01 7.699000e-01 5.071000e-01 6.499000e-01 4.775000e-01 8.500000e-01 4.314000e-01 7.182000e-01 3.959000e-01 4.053000e-01 2.391000e-01 - 1.131800e+00 -3.150000e-02 6.668000e-01 5.677000e-01 5.198000e-01 3.855000e-01 6.295000e-01 6.142000e-01 4.733000e-01 4.171000e-01 7.019000e-01 5.303000e-01 4.325000e-01 2.588000e-01 --1.350000e-02 9.692000e-01 6.860000e-02 1.201600e+00 2.007000e-01 1.184700e+00 -1.011000e-01 1.850400e+00 2.550000e-02 1.973700e+00 6.100000e-02 -7.320000e-02 5.340000e-02 6.952000e-01 - 1.009400e+00 1.134000e-01 4.213000e-01 8.066000e-01 3.547000e-01 5.525000e-01 4.474000e-01 7.713000e-01 4.188000e-01 4.510000e-01 5.455000e-01 6.543000e-01 3.627000e-01 3.198000e-01 - 4.146000e-01 4.600000e-02 7.304000e-01 4.780000e-02 8.895000e-01 -9.000000e-04 1.766900e+00 -4.180000e-02 1.940000e-02 -2.560000e-02 1.600000e-03 -2.100000e-03 4.215000e-01 2.020000e-02 - 4.200000e-03 7.406000e-01 4.619000e-01 5.687000e-01 5.006000e-01 6.651000e-01 1.647100e+00 9.630000e-02 -8.380000e-02 2.093600e+00 1.680000e-02 -2.180000e-02 2.127000e-01 3.993000e-01 - 8.960000e-01 2.122000e-01 1.236100e+00 2.560000e-01 1.376200e+00 2.300000e-01 1.731100e+00 5.000000e-04 1.994500e+00 4.600000e-03 -2.330000e-02 1.759100e+00 7.241000e-01 1.468000e-01 - 1.125000e+00 -2.640000e-02 1.185100e+00 5.672000e-01 9.300000e-01 2.285000e-01 1.218800e+00 5.273000e-01 8.381000e-01 2.953000e-01 1.288500e+00 4.426000e-01 7.039000e-01 1.022000e-01 - 9.570000e-02 2.974000e-01 4.062000e-01 1.635000e-01 4.607000e-01 1.812000e-01 3.996000e-01 1.672000e-01 4.821000e-01 1.683000e-01 3.296000e-01 2.546000e-01 2.160000e-01 1.141000e-01 - 1.066400e+00 5.060000e-02 4.352000e-01 1.309700e+00 3.315000e-01 1.332100e+00 4.690000e-01 1.277800e+00 4.950000e-01 1.028800e+00 4.517000e-01 1.293800e+00 3.870000e-01 5.589000e-01 - 9.807000e-01 1.493000e-01 1.107100e+00 6.114000e-01 7.685000e-01 2.679000e-01 9.559000e-01 7.915000e-01 7.832000e-01 2.189000e-01 1.040300e+00 6.888000e-01 6.295000e-01 1.212000e-01 - 4.976000e-01 3.970000e-01 6.387000e-01 7.825000e-01 8.251000e-01 7.792000e-01 7.457000e-01 6.585000e-01 7.633000e-01 8.793000e-01 7.247000e-01 6.854000e-01 3.927000e-01 4.256000e-01 - 7.080000e-02 8.437000e-01 1.565000e-01 1.267600e+00 2.215000e-01 1.388900e+00 2.746000e-01 1.129200e+00 2.840000e-01 1.339000e+00 1.668000e-01 1.250800e+00 1.165000e-01 7.000000e-01 - 2.704000e-01 7.288000e-01 6.289000e-01 6.757000e-01 7.274000e-01 7.037000e-01 1.694200e+00 4.620000e-02 5.750000e-02 1.930000e+00 -1.150000e-02 1.200000e-02 3.791000e-01 3.871000e-01 - 3.198000e-01 7.852000e-01 6.013000e-01 1.135000e+00 7.234000e-01 1.198300e+00 1.628900e+00 1.248000e-01 -4.420000e-02 2.053300e+00 8.170000e-02 1.635800e+00 3.488000e-01 6.410000e-01 - 8.978000e-01 1.226000e-01 1.111100e+00 2.376000e-01 1.333900e+00 1.199000e-01 1.906700e+00 -2.029000e-01 1.955700e+00 4.500000e-02 1.680000e-02 -1.970000e-02 7.193000e-01 6.620000e-02 - 3.376000e-01 4.681000e-01 7.722000e-01 3.205000e-01 7.227000e-01 5.211000e-01 1.563600e+00 1.941000e-01 5.300000e-03 1.993600e+00 -4.300000e-02 5.260000e-02 3.808000e-01 2.757000e-01 --1.780000e-02 9.781000e-01 2.480000e-01 1.004500e+00 2.617000e-01 1.130300e+00 -5.760000e-02 1.800700e+00 3.090000e-02 1.965300e+00 -6.770000e-02 7.990000e-02 9.110000e-02 6.573000e-01 - 3.065000e-01 4.802000e-01 5.440000e-01 6.745000e-01 5.246000e-01 8.795000e-01 4.652000e-01 7.694000e-01 5.639000e-01 8.570000e-01 4.735000e-01 7.587000e-01 2.864000e-01 4.227000e-01 - 7.427000e-01 7.390000e-02 1.189300e+00 8.300000e-02 1.414300e+00 2.050000e-02 1.250600e+00 6.000000e-03 1.441500e+00 1.240000e-02 1.193500e+00 7.420000e-02 7.161000e-01 1.110000e-02 - 4.666000e-01 6.226000e-01 8.800000e-01 7.976000e-01 9.467000e-01 9.688000e-01 6.725000e-01 1.039400e+00 8.872000e-01 1.067700e+00 7.361000e-01 9.614000e-01 4.290000e-01 5.515000e-01 - 3.063000e-01 6.264000e-01 7.307000e-01 4.934000e-01 8.177000e-01 5.290000e-01 1.775700e+00 -4.860000e-02 -1.500000e-02 2.013600e+00 -5.090000e-02 6.170000e-02 4.033000e-01 3.219000e-01 --2.300000e-03 9.057000e-01 2.291000e-01 9.915000e-01 4.589000e-01 8.647000e-01 5.000000e-04 1.731800e+00 4.500000e-03 1.990400e+00 -5.000000e-03 5.300000e-03 1.490000e-01 5.679000e-01 - 7.854000e-01 3.098000e-01 2.999000e-01 6.876000e-01 2.856000e-01 4.767000e-01 3.644000e-01 6.130000e-01 2.626000e-01 4.918000e-01 3.000000e-01 6.889000e-01 2.698000e-01 3.384000e-01 - 4.141000e-01 2.904000e-01 6.394000e-01 3.938000e-01 8.197000e-01 3.237000e-01 1.624800e+00 1.227000e-01 -1.530000e-02 2.018500e+00 -3.490000e-02 4.160000e-02 4.051000e-01 1.915000e-01 - 6.392000e-01 4.688000e-01 9.039000e-01 8.416000e-01 1.061400e+00 8.376000e-01 1.598700e+00 1.593000e-01 1.972200e+00 3.310000e-02 -9.980000e-02 1.852400e+00 5.675000e-01 4.143000e-01 - 6.436000e-01 2.330000e-02 1.019000e+00 -4.770000e-02 9.992000e-01 1.174000e-01 1.605600e+00 1.474000e-01 2.067800e+00 -8.150000e-02 5.890000e-02 -6.990000e-02 5.617000e-01 1.030000e-02 - 1.056800e+00 5.610000e-02 9.277000e-01 3.840000e-01 7.862000e-01 1.421000e-01 9.047000e-01 4.088000e-01 7.154000e-01 1.977000e-01 9.725000e-01 3.286000e-01 5.982000e-01 1.060000e-01 - 2.863000e-01 2.397000e-01 3.017000e-01 5.512000e-01 4.118000e-01 5.414000e-01 4.823000e-01 3.335000e-01 2.984000e-01 6.918000e-01 4.641000e-01 3.558000e-01 2.224000e-01 2.593000e-01 - 4.731000e-01 8.560000e-02 7.429000e-01 1.325000e-01 9.045000e-01 8.170000e-02 8.110000e-01 4.620000e-02 8.391000e-01 1.784000e-01 7.406000e-01 1.352000e-01 4.483000e-01 5.340000e-02 - 1.335000e-01 9.647000e-01 4.820000e-02 1.051100e+00 1.408000e-01 6.785000e-01 3.630000e-02 1.068100e+00 7.970000e-02 7.308000e-01 1.641000e-01 9.179000e-01 4.960000e-02 6.001000e-01 - 4.580000e-02 9.044000e-01 3.932000e-01 8.614000e-01 5.322000e-01 8.431000e-01 1.675300e+00 6.230000e-02 3.500000e-02 1.957700e+00 -3.200000e-03 3.000000e-03 2.104000e-01 5.312000e-01 - 1.039000e+00 7.570000e-02 7.307000e-01 1.009800e+00 5.982000e-01 1.377000e+00 7.363000e-01 1.004300e+00 6.714000e-01 1.031500e+00 8.457000e-01 8.775000e-01 5.253000e-01 4.539000e-01 - 5.764000e-01 3.032000e-01 9.577000e-01 4.120000e-01 9.673000e-01 6.153000e-01 9.352000e-01 4.392000e-01 9.752000e-01 6.314000e-01 7.572000e-01 6.480000e-01 5.075000e-01 2.932000e-01 - 1.140700e+00 -4.550000e-02 9.417000e-01 7.776000e-01 7.075000e-01 5.760000e-01 8.199000e-01 9.228000e-01 6.980000e-01 5.391000e-01 9.480000e-01 7.681000e-01 5.668000e-01 2.880000e-01 --2.900000e-02 6.733000e-01 3.202000e-01 6.315000e-01 4.043000e-01 6.685000e-01 -7.370000e-02 1.813700e+00 1.229000e-01 1.851200e+00 4.170000e-02 -5.200000e-02 1.569000e-01 3.977000e-01 - 3.265000e-01 4.894000e-01 6.227000e-01 5.130000e-01 7.186000e-01 5.417000e-01 1.765500e+00 -3.930000e-02 -4.840000e-02 2.053500e+00 9.800000e-03 -1.190000e-02 3.285000e-01 3.432000e-01 - 1.117100e+00 -8.600000e-03 7.302000e-01 3.616000e-01 6.130000e-01 2.139000e-01 6.646000e-01 4.410000e-01 6.113000e-01 1.987000e-01 7.062000e-01 3.899000e-01 4.921000e-01 1.567000e-01 - 1.141900e+00 -4.700000e-02 6.574000e-01 7.773000e-01 5.479000e-01 4.382000e-01 7.244000e-01 6.954000e-01 5.710000e-01 3.823000e-01 7.053000e-01 7.203000e-01 4.940000e-01 2.317000e-01 - 4.965000e-01 1.738000e-01 7.885000e-01 2.035000e-01 9.662000e-01 1.357000e-01 1.835300e+00 -1.154000e-01 -1.560000e-02 2.021700e+00 -1.450000e-02 1.650000e-02 4.759000e-01 9.810000e-02 - 4.544000e-01 3.766000e-01 8.270000e-01 4.520000e-01 7.125000e-01 7.868000e-01 6.932000e-01 6.157000e-01 7.669000e-01 7.423000e-01 5.673000e-01 7.614000e-01 3.870000e-01 3.691000e-01 - 4.055000e-01 3.654000e-01 7.823000e-01 3.933000e-01 7.759000e-01 5.908000e-01 7.125000e-01 4.798000e-01 9.026000e-01 4.538000e-01 7.434000e-01 4.432000e-01 4.013000e-01 2.881000e-01 - 5.728000e-01 2.989000e-01 8.306000e-01 3.644000e-01 9.304000e-01 3.902000e-01 1.696000e+00 4.100000e-02 -8.430000e-02 2.101000e+00 -7.130000e-02 8.580000e-02 5.000000e-01 2.026000e-01 - 3.376000e-01 7.775000e-01 3.464000e-01 1.362100e+00 2.232000e-01 9.473000e-01 2.115000e-01 1.521300e+00 2.551000e-01 8.705000e-01 2.435000e-01 1.486300e+00 1.729000e-01 6.418000e-01 - 1.090600e+00 1.860000e-02 6.090000e-01 1.122400e+00 4.901000e-01 5.531000e-01 5.431000e-01 1.201300e+00 5.042000e-01 5.040000e-01 5.107000e-01 1.237100e+00 4.253000e-01 3.304000e-01 - 1.114300e+00 -1.080000e-02 5.238000e-01 4.260000e-01 3.534000e-01 4.049000e-01 5.256000e-01 4.235000e-01 4.289000e-01 2.981000e-01 5.392000e-01 4.056000e-01 3.375000e-01 2.647000e-01 - 4.647000e-01 9.610000e-02 8.450000e-01 1.090000e-02 9.376000e-01 3.980000e-02 7.987000e-01 6.400000e-02 8.026000e-01 2.185000e-01 7.818000e-01 8.880000e-02 4.603000e-01 3.840000e-02 - 1.721000e-01 2.183000e-01 2.819000e-01 3.280000e-01 3.557000e-01 3.364000e-01 2.338000e-01 3.847000e-01 3.266000e-01 3.775000e-01 2.295000e-01 3.909000e-01 1.535000e-01 2.003000e-01 - 1.860000e-02 2.330000e-01 8.280000e-02 2.981000e-01 6.380000e-02 3.789000e-01 -1.660000e-02 4.184000e-01 1.610000e-01 2.707000e-01 8.580000e-02 2.988000e-01 3.820000e-02 1.848000e-01 - 4.782000e-01 4.641000e-01 8.946000e-01 3.435000e-01 8.573000e-01 5.357000e-01 1.875700e+00 -1.691000e-01 -6.900000e-03 2.014400e+00 2.090000e-02 -2.310000e-02 4.434000e-01 3.007000e-01 - 3.309000e-01 4.250000e-01 4.928000e-01 6.981000e-01 6.051000e-01 7.404000e-01 6.155000e-01 5.528000e-01 6.382000e-01 7.205000e-01 5.233000e-01 6.605000e-01 3.175000e-01 3.624000e-01 - 2.536000e-01 1.080000e-01 5.554000e-01 1.227000e-01 7.170000e-01 7.060000e-02 1.696900e+00 4.170000e-02 -1.440000e-02 1.760000e-02 -1.016000e-01 1.216000e-01 3.275000e-01 4.920000e-02 - 3.390000e-01 3.020000e-01 5.153000e-01 4.942000e-01 5.936000e-01 5.545000e-01 5.314000e-01 4.704000e-01 6.225000e-01 5.370000e-01 5.093000e-01 4.972000e-01 3.172000e-01 2.615000e-01 - 1.167900e+00 -7.670000e-02 1.151900e+00 5.657000e-01 8.234000e-01 1.057900e+00 1.061100e+00 6.778000e-01 6.292000e-01 1.053300e+00 1.050300e+00 6.942000e-01 6.490000e-01 3.211000e-01 - 4.128000e-01 6.886000e-01 7.720000e-01 6.333000e-01 8.191000e-01 7.276000e-01 1.696900e+00 4.170000e-02 -1.400000e-03 1.999100e+00 -2.770000e-02 1.763800e+00 4.395000e-01 3.880000e-01 - 2.401000e-01 8.456000e-01 2.801000e-01 1.440500e+00 3.156000e-01 1.645200e+00 4.983000e-01 1.182000e+00 4.511000e-01 1.513000e+00 4.349000e-01 1.260300e+00 2.223000e-01 7.598000e-01 - 3.190000e-02 2.327000e-01 1.329000e-01 4.503000e-01 1.820000e-01 5.382000e-01 1.320000e-01 1.579700e+00 6.880000e-02 -8.450000e-02 -1.080000e-02 1.390000e-02 8.020000e-02 2.422000e-01 - 4.039000e-01 3.573000e-01 6.585000e-01 5.334000e-01 6.388000e-01 7.320000e-01 6.293000e-01 5.603000e-01 6.803000e-01 7.047000e-01 5.667000e-01 6.358000e-01 3.484000e-01 3.435000e-01 - 1.027700e+00 8.760000e-02 5.360000e-01 1.174600e+00 3.294000e-01 7.892000e-01 3.724000e-01 1.366100e+00 2.953000e-01 7.905000e-01 4.678000e-01 1.253200e+00 3.132000e-01 4.753000e-01 - 6.950000e-02 4.076000e-01 1.708000e-01 5.636000e-01 1.306000e-01 7.205000e-01 -1.290000e-02 7.861000e-01 1.415000e-01 7.203000e-01 6.850000e-02 6.867000e-01 5.100000e-02 3.835000e-01 - 5.578000e-01 4.956000e-01 8.414000e-01 5.372000e-01 9.265000e-01 5.695000e-01 1.720000e+00 1.960000e-02 7.740000e-02 1.906200e+00 -9.720000e-02 1.850300e+00 4.662000e-01 3.445000e-01 - 2.043000e-01 1.528000e-01 3.941000e-01 1.483000e-01 4.314000e-01 1.899000e-01 4.244000e-01 1.136000e-01 5.053000e-01 1.130000e-01 4.428000e-01 8.950000e-02 2.396000e-01 7.130000e-02 - 3.115000e-01 6.206000e-01 5.196000e-01 9.349000e-01 4.160000e-01 1.275600e+00 4.773000e-01 9.806000e-01 5.755000e-01 1.103300e+00 3.650000e-01 1.116200e+00 2.662000e-01 5.800000e-01 - 1.036700e+00 8.220000e-02 8.868000e-01 8.528000e-01 7.260000e-01 4.464000e-01 1.059900e+00 6.539000e-01 6.681000e-01 4.769000e-01 9.639000e-01 7.606000e-01 5.826000e-01 2.310000e-01 - 1.069800e+00 4.080000e-02 4.507000e-01 2.553000e-01 3.520000e-01 2.347000e-01 4.403000e-01 2.715000e-01 3.898000e-01 1.776000e-01 4.880000e-01 2.127000e-01 3.393000e-01 1.490000e-01 - 9.398000e-01 9.490000e-02 1.514900e+00 9.620000e-02 1.657200e+00 1.844000e-01 1.592300e+00 4.300000e-03 1.785300e+00 7.150000e-02 1.490300e+00 1.233000e-01 8.673000e-01 6.500000e-02 - 2.916000e-01 8.154000e-01 2.496000e-01 7.557000e-01 7.570000e-02 7.302000e-01 2.293000e-01 7.797000e-01 1.223000e-01 6.540000e-01 1.639000e-01 8.590000e-01 1.455000e-01 4.776000e-01 - 1.027000e-01 1.016100e+00 1.556000e-01 1.566700e+00 1.183000e-01 9.786000e-01 1.365000e-01 1.589500e+00 1.133000e-01 9.559000e-01 1.248000e-01 1.605400e+00 8.940000e-02 6.949000e-01 - 1.103500e+00 3.500000e-03 5.619000e-01 1.930000e-01 4.270000e-01 1.961000e-01 5.009000e-01 2.624000e-01 4.615000e-01 1.391000e-01 5.809000e-01 1.712000e-01 3.970000e-01 1.167000e-01 - 1.000000e-03 9.440000e-01 3.068000e-01 9.555000e-01 4.642000e-01 9.027000e-01 -1.152000e-01 1.865900e+00 -8.080000e-02 2.098800e+00 -8.500000e-03 1.020000e-02 1.939000e-01 5.414000e-01 - 4.455000e-01 6.606000e-01 2.377000e-01 6.792000e-01 2.448000e-01 4.713000e-01 2.568000e-01 6.523000e-01 3.088000e-01 3.794000e-01 2.070000e-01 7.139000e-01 1.991000e-01 3.831000e-01 - 3.431000e-01 3.872000e-01 5.262000e-01 6.181000e-01 5.925000e-01 7.169000e-01 6.176000e-01 5.152000e-01 6.126000e-01 7.107000e-01 4.996000e-01 6.554000e-01 3.275000e-01 3.312000e-01 - 3.390000e-02 6.709000e-01 4.034000e-01 6.058000e-01 4.849000e-01 6.530000e-01 1.696000e+00 4.940000e-02 -7.460000e-02 2.088600e+00 -6.100000e-03 7.900000e-03 2.012000e-01 3.928000e-01 --3.340000e-02 1.136700e+00 8.860000e-02 1.330500e+00 2.922000e-01 1.235300e+00 1.830000e-02 1.712800e+00 -8.160000e-02 2.100200e+00 3.500000e-03 1.726300e+00 7.930000e-02 7.498000e-01 - 1.158900e+00 -6.460000e-02 4.903000e-01 1.257400e+00 5.872000e-01 4.286000e-01 5.796000e-01 1.149900e+00 5.293000e-01 4.681000e-01 5.713000e-01 1.158500e+00 4.171000e-01 3.362000e-01 - 1.061400e+00 5.460000e-02 4.624000e-01 4.138000e-01 4.538000e-01 2.395000e-01 6.261000e-01 2.194000e-01 4.383000e-01 2.373000e-01 5.559000e-01 2.980000e-01 3.813000e-01 1.803000e-01 --6.870000e-02 1.177000e+00 3.070000e-01 1.094200e+00 3.066000e-01 1.239500e+00 8.420000e-02 1.636600e+00 -2.100000e-03 1.999800e+00 -3.140000e-02 1.768700e+00 1.517000e-01 6.749000e-01 - 4.358000e-01 5.687000e-01 7.750000e-01 5.402000e-01 9.538000e-01 4.687000e-01 1.750700e+00 -2.560000e-02 1.563000e-01 1.817000e+00 6.030000e-02 -7.160000e-02 4.426000e-01 3.335000e-01 - 3.493000e-01 3.879000e-01 4.468000e-01 7.277000e-01 5.066000e-01 8.343000e-01 6.364000e-01 5.005000e-01 6.528000e-01 6.812000e-01 4.985000e-01 6.694000e-01 3.007000e-01 3.705000e-01 - 3.451000e-01 3.304000e-01 4.110000e-01 6.678000e-01 4.958000e-01 7.328000e-01 4.974000e-01 5.712000e-01 4.849000e-01 7.611000e-01 3.385000e-01 7.543000e-01 2.568000e-01 3.634000e-01 - 6.628000e-01 4.250000e-01 2.748000e-01 2.046000e-01 1.555000e-01 2.684000e-01 2.439000e-01 2.413000e-01 2.985000e-01 8.700000e-02 2.819000e-01 1.962000e-01 2.311000e-01 1.262000e-01 - 5.381000e-01 2.008000e-01 9.185000e-01 2.281000e-01 1.066400e+00 2.308000e-01 9.062000e-01 2.368000e-01 9.814000e-01 3.535000e-01 8.856000e-01 2.679000e-01 5.252000e-01 1.371000e-01 - 1.112900e+00 -9.100000e-03 8.964000e-01 8.369000e-01 6.871000e-01 7.348000e-01 8.873000e-01 8.529000e-01 7.308000e-01 6.121000e-01 8.880000e-01 8.495000e-01 5.774000e-01 3.174000e-01 - 9.408000e-01 1.572000e-01 1.180300e+00 3.368000e-01 1.296200e+00 3.425000e-01 1.684700e+00 5.910000e-02 2.001600e+00 2.300000e-03 4.320000e-02 1.685900e+00 7.190000e-01 1.595000e-01 - 1.581000e-01 3.713000e-01 3.274000e-01 4.837000e-01 2.731000e-01 6.740000e-01 4.187000e-01 3.731000e-01 3.721000e-01 5.650000e-01 3.401000e-01 4.673000e-01 1.892000e-01 2.795000e-01 - 3.393000e-01 1.388000e-01 2.235000e-01 3.140000e-02 7.320000e-02 1.648000e-01 2.143000e-01 4.110000e-02 1.466000e-01 7.950000e-02 1.840000e-01 7.610000e-02 1.639000e-01 4.260000e-02 - 2.449000e-01 1.485000e-01 1.748000e-01 3.990000e-02 7.900000e-02 1.229000e-01 1.639000e-01 5.250000e-02 1.908000e-01 -1.550000e-02 7.440000e-02 1.631000e-01 1.260000e-01 5.300000e-02 - 3.539000e-01 7.224000e-01 4.408000e-01 1.274400e+00 6.796000e-01 1.237900e+00 4.989000e-01 1.199600e+00 6.023000e-01 1.362300e+00 4.924000e-01 1.209500e+00 2.847000e-01 6.989000e-01 - 4.341000e-01 4.468000e-01 6.116000e-01 7.835000e-01 7.314000e-01 8.503000e-01 6.383000e-01 7.511000e-01 8.338000e-01 7.523000e-01 6.846000e-01 6.988000e-01 3.918000e-01 4.062000e-01 - 5.380000e-01 4.697000e-01 7.407000e-01 8.628000e-01 7.885000e-01 1.048800e+00 7.124000e-01 8.969000e-01 9.390000e-01 9.041000e-01 7.319000e-01 8.754000e-01 4.184000e-01 5.099000e-01 - 2.920000e-01 8.900000e-02 4.256000e-01 1.747000e-01 4.704000e-01 2.201000e-01 4.925000e-01 9.680000e-02 4.691000e-01 2.292000e-01 4.426000e-01 1.539000e-01 2.753000e-01 6.600000e-02 - 4.122000e-01 8.940000e-02 5.834000e-01 2.571000e-01 6.954000e-01 2.638000e-01 1.677400e+00 6.480000e-02 -2.700000e-03 2.000000e-03 1.870000e-02 -2.020000e-02 3.243000e-01 1.595000e-01 --5.000000e-04 1.120800e+00 6.350000e-02 1.665100e+00 -5.980000e-02 1.582700e+00 -7.100000e-03 1.746900e+00 8.840000e-02 1.332500e+00 1.032000e-01 1.617900e+00 3.190000e-02 8.864000e-01 - 2.250000e-02 4.186000e-01 2.250000e-02 2.158000e-01 4.480000e-02 1.556000e-01 6.030000e-02 1.744000e-01 6.030000e-02 1.389000e-01 -2.940000e-02 2.825000e-01 1.240000e-02 1.834000e-01 - 1.856000e-01 3.467000e-01 6.820000e-02 2.194000e-01 4.480000e-02 2.059000e-01 9.490000e-02 1.882000e-01 3.840000e-02 2.095000e-01 5.270000e-02 2.388000e-01 7.130000e-02 1.545000e-01 - 5.003000e-01 2.536000e-01 1.001900e+00 1.310000e-01 1.054300e+00 2.531000e-01 8.935000e-01 2.608000e-01 1.017600e+00 3.172000e-01 9.822000e-01 1.566000e-01 5.335000e-01 1.295000e-01 - 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5.929000e-01 4.753000e-01 7.672000e-01 9.424000e-01 8.572000e-01 1.086400e+00 7.978000e-01 8.958000e-01 9.469000e-01 1.005600e+00 7.537000e-01 9.506000e-01 4.475000e-01 5.341000e-01 - 1.982000e-01 7.611000e-01 4.901000e-01 7.844000e-01 6.065000e-01 7.851000e-01 1.734700e+00 -4.100000e-03 9.350000e-02 1.890800e+00 -3.790000e-02 4.530000e-02 2.528000e-01 5.018000e-01 - 1.546000e-01 4.171000e-01 2.119000e-01 6.868000e-01 2.017000e-01 8.355000e-01 3.120000e-01 5.704000e-01 2.865000e-01 7.473000e-01 2.101000e-01 6.932000e-01 1.401000e-01 3.778000e-01 - 5.030000e-02 4.674000e-01 7.340000e-02 7.643000e-01 2.045000e-01 7.558000e-01 -4.600000e-03 1.736500e+00 -6.460000e-02 7.550000e-02 -1.480000e-02 1.930000e-02 5.150000e-02 4.315000e-01 - 1.041000e-01 6.762000e-01 3.926000e-01 7.050000e-01 5.157000e-01 7.068000e-01 1.774500e+00 -5.130000e-02 3.810000e-02 1.958000e+00 -2.210000e-02 2.530000e-02 2.124000e-01 4.345000e-01 - 6.700000e-03 5.981000e-01 -6.650000e-02 1.033500e+00 -2.740000e-02 1.112100e+00 3.610000e-02 9.109000e-01 1.080000e-02 1.085600e+00 -6.430000e-02 1.026400e+00 6.900000e-03 5.412000e-01 - 2.601000e-01 8.426000e-01 1.748000e-01 5.536000e-01 1.488000e-01 4.436000e-01 2.177000e-01 4.987000e-01 1.135000e-01 4.778000e-01 1.067000e-01 6.321000e-01 1.217000e-01 3.789000e-01 - 7.495000e-01 3.638000e-01 3.087000e-01 7.132000e-01 2.289000e-01 5.704000e-01 4.013000e-01 5.987000e-01 4.147000e-01 3.338000e-01 3.795000e-01 6.300000e-01 2.800000e-01 3.398000e-01 - 2.536000e-01 3.032000e-01 3.890000e-01 4.836000e-01 4.285000e-01 5.635000e-01 4.099000e-01 4.558000e-01 5.144000e-01 4.786000e-01 3.665000e-01 5.107000e-01 2.354000e-01 2.655000e-01 - 1.108700e+00 -1.400000e-03 6.231000e-01 1.103400e+00 5.175000e-01 5.217000e-01 5.151000e-01 1.232300e+00 5.308000e-01 4.732000e-01 4.814000e-01 1.269600e+00 4.136000e-01 3.438000e-01 - 1.243000e-01 8.552000e-01 2.607000e-01 1.254300e+00 2.224000e-01 1.517600e+00 2.060000e-01 1.322800e+00 2.795000e-01 1.476000e+00 3.078000e-01 1.201100e+00 1.504000e-01 7.250000e-01 - 9.213000e-01 1.830000e-01 1.271100e+00 2.182000e-01 1.436000e+00 1.685000e-01 1.739100e+00 -6.500000e-03 2.118200e+00 -1.382000e-01 2.510000e-02 1.703800e+00 7.304000e-01 1.416000e-01 - 4.906000e-01 2.284000e-01 7.607000e-01 3.695000e-01 7.548000e-01 5.566000e-01 8.102000e-01 3.068000e-01 8.265000e-01 4.896000e-01 7.111000e-01 4.283000e-01 4.367000e-01 2.166000e-01 - 5.023000e-01 2.296000e-01 8.095000e-01 3.391000e-01 8.645000e-01 4.546000e-01 7.044000e-01 4.620000e-01 8.431000e-01 5.025000e-01 7.605000e-01 3.984000e-01 4.166000e-01 2.559000e-01 - 1.156500e+00 -5.760000e-02 6.427000e-01 1.085200e+00 4.490000e-01 9.378000e-01 5.651000e-01 1.172700e+00 4.125000e-01 9.192000e-01 6.713000e-01 1.048000e+00 4.359000e-01 4.478000e-01 - 3.471000e-01 4.969000e-01 5.672000e-01 6.026000e-01 6.693000e-01 6.248000e-01 1.642200e+00 1.036000e-01 2.180000e-02 1.976600e+00 -6.900000e-03 6.900000e-03 3.297000e-01 3.596000e-01 - 1.772000e-01 7.112000e-01 3.103000e-01 1.079600e+00 2.551000e-01 1.339400e+00 2.338000e-01 1.168600e+00 3.075000e-01 1.305000e+00 2.437000e-01 1.153100e+00 1.468000e-01 6.600000e-01 - 4.819000e-01 5.060000e-01 7.012000e-01 8.559000e-01 8.285000e-01 9.376000e-01 6.944000e-01 8.615000e-01 7.004000e-01 1.112700e+00 7.276000e-01 8.193000e-01 4.284000e-01 4.650000e-01 - 4.597000e-01 2.749000e-01 7.482000e-01 3.990000e-01 9.198000e-01 3.746000e-01 7.880000e-01 3.529000e-01 8.253000e-01 5.067000e-01 7.385000e-01 4.140000e-01 4.344000e-01 2.271000e-01 - 4.523000e-01 6.470000e-01 2.413000e-01 1.312200e+00 1.894000e-01 8.252000e-01 1.803000e-01 1.379300e+00 1.961000e-01 7.919000e-01 1.998000e-01 1.361800e+00 1.884000e-01 5.538000e-01 - 4.270000e-02 6.164000e-01 1.516000e-01 8.657000e-01 8.410000e-02 1.095600e+00 1.261000e-01 8.992000e-01 1.549000e-01 1.029100e+00 7.630000e-02 9.625000e-01 3.020000e-02 5.699000e-01 - 4.810000e-02 6.301000e-01 3.641000e-01 6.274000e-01 4.213000e-01 6.975000e-01 4.650000e-02 1.677500e+00 -2.970000e-02 2.037900e+00 1.500000e-03 -2.200000e-03 1.792000e-01 4.041000e-01 --5.410000e-02 8.424000e-01 2.830000e-01 8.134000e-01 4.024000e-01 8.135000e-01 6.600000e-03 1.724300e+00 -8.130000e-02 2.099400e+00 -3.590000e-02 4.370000e-02 1.669000e-01 4.733000e-01 - 1.492000e-01 9.433000e-01 4.581000e-01 1.263300e+00 4.340000e-01 1.517700e+00 1.692600e+00 4.630000e-02 -5.270000e-02 2.056400e+00 -1.043000e-01 1.855000e+00 2.145000e-01 7.789000e-01 - 5.950000e-01 5.037000e-01 8.418000e-01 6.593000e-01 1.035600e+00 5.684000e-01 1.736200e+00 -4.000000e-03 1.998700e+00 2.900000e-03 7.450000e-02 1.641700e+00 5.081000e-01 3.627000e-01 - 1.041200e+00 7.690000e-02 3.429000e-01 5.131000e-01 4.083000e-01 2.563000e-01 4.457000e-01 3.872000e-01 3.686000e-01 2.875000e-01 4.201000e-01 4.196000e-01 3.165000e-01 2.346000e-01 - 5.750000e-01 9.830000e-02 7.359000e-01 3.524000e-01 9.226000e-01 3.027000e-01 7.922000e-01 2.849000e-01 9.758000e-01 2.588000e-01 8.202000e-01 2.514000e-01 4.557000e-01 1.661000e-01 - 2.356000e-01 2.608000e-01 3.491000e-01 4.312000e-01 5.015000e-01 3.656000e-01 3.259000e-01 4.589000e-01 3.623000e-01 5.480000e-01 3.415000e-01 4.391000e-01 2.121000e-01 2.362000e-01 --4.400000e-03 5.750000e-02 2.999000e-01 5.590000e-02 3.436000e-01 1.495000e-01 6.220000e-02 -7.260000e-02 3.770000e-02 -4.620000e-02 -1.930000e-02 2.460000e-02 1.388000e-01 5.610000e-02 - 3.853000e-01 2.266000e-01 6.398000e-01 2.940000e-01 7.465000e-01 3.086000e-01 1.889800e+00 -1.804000e-01 2.080000e-02 1.977300e+00 -1.660000e-02 1.840000e-02 3.421000e-01 2.053000e-01 - 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6.442000e-01 4.657000e-01 2.805000e-01 9.211000e-01 3.062000e-01 5.754000e-01 3.203000e-01 8.736000e-01 3.428000e-01 5.107000e-01 2.195000e-01 9.937000e-01 2.591000e-01 4.182000e-01 - 1.083000e-01 3.704000e-01 2.198000e-01 5.239000e-01 1.973000e-01 6.579000e-01 1.038000e-01 6.641000e-01 2.724000e-01 5.815000e-01 2.600000e-01 4.734000e-01 9.710000e-02 3.372000e-01 - 1.143400e+00 -4.700000e-02 6.394000e-01 1.084000e+00 5.072000e-01 8.219000e-01 7.274000e-01 9.770000e-01 4.117000e-01 8.792000e-01 6.133000e-01 1.115600e+00 4.261000e-01 4.439000e-01 - 4.894000e-01 2.322000e-01 9.444000e-01 1.599000e-01 1.136500e+00 1.100000e-01 9.970000e-01 9.400000e-02 1.083900e+00 1.925000e-01 9.986000e-01 9.700000e-02 5.573000e-01 7.900000e-02 - 6.940000e-02 4.496000e-01 3.861000e-01 4.431000e-01 5.083000e-01 4.420000e-01 1.722500e+00 8.000000e-03 -2.180000e-02 2.580000e-02 -1.260000e-02 1.530000e-02 2.012000e-01 2.778000e-01 - 4.709000e-01 1.637000e-01 7.540000e-01 2.368000e-01 9.168000e-01 1.987000e-01 7.055000e-01 2.919000e-01 8.799000e-01 2.597000e-01 6.879000e-01 3.119000e-01 4.364000e-01 1.345000e-01 - 3.695000e-01 7.475000e-01 7.178000e-01 7.092000e-01 8.749000e-01 6.676000e-01 1.720000e+00 6.400000e-03 -9.800000e-03 2.015000e+00 5.230000e-02 1.671600e+00 4.293000e-01 4.062000e-01 - 2.833000e-01 6.929000e-01 3.343000e-01 1.207300e+00 4.848000e-01 1.259600e+00 4.201000e-01 1.108400e+00 4.528000e-01 1.313000e+00 3.483000e-01 1.200200e+00 2.231000e-01 6.640000e-01 - 3.249000e-01 2.663000e-01 2.135000e-01 8.380000e-02 2.176000e-01 3.230000e-02 1.633000e-01 1.440000e-01 1.643000e-01 8.870000e-02 2.186000e-01 7.850000e-02 1.557000e-01 8.340000e-02 - 2.509000e-01 4.415000e-01 3.253000e-01 7.774000e-01 3.237000e-01 9.379000e-01 2.093000e-01 9.146000e-01 3.470000e-01 9.313000e-01 2.318000e-01 8.854000e-01 1.571000e-01 4.836000e-01 - 4.089000e-01 3.129000e-01 5.581000e-01 5.068000e-01 8.138000e-01 3.462000e-01 1.677900e+00 6.840000e-02 2.080000e-02 1.974000e+00 -2.290000e-02 2.710000e-02 3.926000e-01 2.173000e-01 - 6.643000e-01 4.440000e-01 4.709000e-01 3.021000e-01 3.044000e-01 3.373000e-01 3.318000e-01 4.683000e-01 2.892000e-01 3.421000e-01 3.202000e-01 4.828000e-01 2.603000e-01 2.726000e-01 - 1.089800e+00 1.820000e-02 4.522000e-01 1.280700e+00 2.793000e-01 9.139000e-01 4.674000e-01 1.263400e+00 4.811000e-01 6.379000e-01 5.040000e-01 1.218100e+00 3.527000e-01 4.602000e-01 - 7.746000e-01 2.773000e-01 1.262500e+00 3.768000e-01 1.368200e+00 5.105000e-01 1.176300e+00 4.776000e-01 1.362200e+00 5.502000e-01 1.244100e+00 4.004000e-01 7.037000e-01 2.478000e-01 - 1.214400e+00 -1.345000e-01 5.728000e-01 8.045000e-01 4.732000e-01 4.932000e-01 5.432000e-01 8.459000e-01 4.365000e-01 5.079000e-01 6.445000e-01 7.233000e-01 4.171000e-01 3.019000e-01 - 1.020300e+00 1.023000e-01 1.339900e+00 3.863000e-01 8.918000e-01 4.010000e-01 1.400000e+00 3.113000e-01 9.264000e-01 3.066000e-01 1.256800e+00 4.880000e-01 7.044000e-01 1.491000e-01 --8.000000e-04 8.211000e-01 2.361000e-01 9.075000e-01 3.880000e-01 8.679000e-01 -2.060000e-02 1.757600e+00 -1.680000e-02 2.020600e+00 -5.400000e-02 6.580000e-02 1.555000e-01 5.119000e-01 - 8.590000e-02 5.093000e-01 1.094000e-01 8.284000e-01 1.888000e-01 8.672000e-01 2.009000e-01 7.242000e-01 3.171000e-01 7.291000e-01 2.109000e-01 7.078000e-01 1.055000e-01 4.283000e-01 - 5.585000e-01 4.104000e-01 9.091000e-01 6.040000e-01 9.648000e-01 7.640000e-01 8.639000e-01 6.560000e-01 9.755000e-01 7.808000e-01 9.222000e-01 5.776000e-01 4.978000e-01 3.786000e-01 - 6.952000e-01 9.790000e-02 1.119200e+00 1.273000e-01 1.299400e+00 1.131000e-01 1.206300e+00 2.280000e-02 1.367300e+00 5.920000e-02 1.061200e+00 1.961000e-01 6.713000e-01 4.250000e-02 --2.900000e-02 6.100000e-01 1.768000e-01 7.117000e-01 2.572000e-01 7.548000e-01 -4.300000e-02 1.783800e+00 -1.134000e-01 2.131000e+00 2.780000e-02 -3.580000e-02 9.120000e-02 4.239000e-01 - 6.690000e-02 1.033000e+00 5.470000e-02 4.185000e-01 6.780000e-02 3.254000e-01 1.660000e-02 4.632000e-01 3.370000e-02 3.636000e-01 6.200000e-03 4.806000e-01 1.390000e-02 3.445000e-01 - 4.392000e-01 1.730000e-01 7.467000e-01 1.972000e-01 7.673000e-01 3.279000e-01 8.360000e-01 9.120000e-02 8.220000e-01 2.830000e-01 7.616000e-01 1.845000e-01 4.333000e-01 1.134000e-01 - 1.127900e+00 -2.750000e-02 1.157800e+00 5.609000e-01 8.199000e-01 7.000000e-01 1.071100e+00 6.639000e-01 8.206000e-01 6.127000e-01 1.003100e+00 7.467000e-01 6.514000e-01 2.694000e-01 - 8.172000e-01 2.907000e-01 3.781000e-01 1.356400e+00 3.831000e-01 1.575800e+00 5.063000e-01 1.203100e+00 3.232000e-01 1.481600e+00 5.606000e-01 1.139900e+00 2.864000e-01 7.047000e-01 - 3.522000e-01 3.816000e-01 6.513000e-01 4.030000e-01 8.796000e-01 2.741000e-01 1.853000e+00 -1.437000e-01 2.020000e-02 1.981500e+00 4.100000e-02 -4.790000e-02 4.054000e-01 2.034000e-01 - 6.243000e-01 3.136000e-01 9.404000e-01 5.338000e-01 1.054800e+00 6.177000e-01 1.003000e+00 4.583000e-01 1.125700e+00 5.655000e-01 8.917000e-01 5.895000e-01 5.389000e-01 3.107000e-01 - 1.032000e-01 2.145000e-01 2.228000e-01 2.618000e-01 2.334000e-01 3.197000e-01 2.795000e-01 1.928000e-01 2.514000e-01 3.102000e-01 2.510000e-01 2.285000e-01 1.353000e-01 1.438000e-01 - 9.120000e-02 4.763000e-01 8.260000e-02 8.195000e-01 1.323000e-01 8.828000e-01 1.208000e-01 7.711000e-01 1.502000e-01 8.749000e-01 8.230000e-02 8.164000e-01 4.590000e-02 4.733000e-01 - 8.400000e-03 5.233000e-01 3.114000e-01 5.303000e-01 3.548000e-01 6.212000e-01 -7.810000e-02 1.824600e+00 -1.500000e-02 1.630000e-02 1.900000e-02 -2.190000e-02 1.548000e-01 3.324000e-01 - 3.621000e-01 3.948000e-01 5.814000e-01 5.091000e-01 7.371000e-01 4.622000e-01 1.827200e+00 -1.137000e-01 2.880000e-02 1.964000e+00 -9.400000e-03 1.210000e-02 3.026000e-01 3.417000e-01 - 3.580000e-01 2.763000e-01 4.854000e-01 5.267000e-01 6.305000e-01 5.067000e-01 6.041000e-01 3.864000e-01 6.265000e-01 5.287000e-01 5.911000e-01 4.011000e-01 3.291000e-01 2.458000e-01 - 3.187000e-01 7.551000e-01 4.209000e-01 1.268700e+00 6.258000e-01 1.271600e+00 6.451000e-01 1.001000e+00 5.475000e-01 1.395100e+00 5.579000e-01 1.113300e+00 3.066000e-01 6.586000e-01 - 1.185000e-01 7.796000e-01 2.708000e-01 1.118500e+00 3.414000e-01 1.232300e+00 2.990000e-01 1.083900e+00 4.309000e-01 1.150900e+00 3.358000e-01 1.039400e+00 1.932000e-01 6.014000e-01 - 1.116200e+00 -1.300000e-02 5.206000e-01 2.610000e-02 4.651000e-01 -1.630000e-02 5.007000e-01 4.660000e-02 4.346000e-01 1.190000e-02 4.827000e-01 6.850000e-02 3.726000e-01 2.650000e-02 - 8.306000e-01 9.530000e-02 1.104800e+00 3.818000e-01 1.345200e+00 3.360000e-01 1.102500e+00 3.846000e-01 1.274300e+00 4.425000e-01 1.127500e+00 3.547000e-01 6.521000e-01 2.045000e-01 - 1.156300e+00 -6.370000e-02 9.582000e-01 7.839000e-01 8.017000e-01 5.624000e-01 1.087200e+00 6.377000e-01 7.981000e-01 5.080000e-01 9.966000e-01 7.431000e-01 6.323000e-01 2.479000e-01 - 1.113700e+00 -1.060000e-02 5.751000e-01 8.128000e-01 5.425000e-01 4.148000e-01 5.360000e-01 8.606000e-01 6.221000e-01 2.954000e-01 6.010000e-01 7.833000e-01 4.364000e-01 2.829000e-01 --7.000000e-04 1.000200e+00 6.240000e-02 1.491200e+00 3.840000e-02 1.734700e+00 1.108000e-01 1.435400e+00 1.197000e-01 1.670000e+00 -2.700000e-03 1.567500e+00 3.070000e-02 8.685000e-01 - 1.240000e-01 9.872000e-01 1.695000e-01 5.126000e-01 1.271000e-01 4.343000e-01 1.380000e-02 6.953000e-01 2.270000e-02 5.462000e-01 1.374000e-01 5.482000e-01 7.740000e-02 4.041000e-01 - 4.141000e-01 6.719000e-01 2.314000e-01 1.497700e+00 2.280000e-01 9.137000e-01 2.178000e-01 1.504500e+00 1.092000e-01 1.019000e+00 1.247000e-01 1.614500e+00 1.445000e-01 6.616000e-01 - 6.177000e-01 1.427000e-01 9.623000e-01 1.017000e-01 1.103000e+00 7.800000e-02 1.600300e+00 1.562000e-01 1.871500e+00 1.531000e-01 2.210000e-02 -2.600000e-02 5.527000e-01 7.560000e-02 - 1.059300e+00 5.470000e-02 9.245000e-01 3.887000e-01 7.576000e-01 1.769000e-01 9.578000e-01 3.412000e-01 7.952000e-01 1.095000e-01 1.041500e+00 2.450000e-01 6.124000e-01 9.040000e-02 - 9.449000e-01 1.353000e-01 3.213000e-01 1.421500e+00 3.429000e-01 7.263000e-01 3.767000e-01 1.351400e+00 3.432000e-01 6.875000e-01 3.776000e-01 1.354700e+00 2.832000e-01 4.873000e-01 - 6.301000e-01 4.748000e-01 4.008000e-01 9.958000e-01 2.741000e-01 6.979000e-01 3.320000e-01 1.073400e+00 3.438000e-01 5.908000e-01 3.196000e-01 1.086800e+00 2.474000e-01 4.766000e-01 - 1.978000e-01 9.140000e-01 1.938000e-01 4.248000e-01 6.020000e-02 4.722000e-01 9.960000e-02 5.354000e-01 7.210000e-02 4.447000e-01 1.717000e-01 4.529000e-01 1.206000e-01 3.249000e-01 - 2.309000e-01 2.064000e-01 6.680000e-01 5.680000e-02 6.932000e-01 1.725000e-01 1.859700e+00 -1.525000e-01 -3.130000e-02 3.630000e-02 5.910000e-02 -7.020000e-02 3.368000e-01 8.280000e-02 - 2.211000e-01 7.943000e-01 6.555000e-01 6.537000e-01 6.771000e-01 7.673000e-01 1.808700e+00 -8.840000e-02 7.200000e-03 1.994200e+00 -1.680000e-02 1.970000e-02 3.111000e-01 4.701000e-01 - 2.125000e-01 6.560000e-01 4.932000e-01 6.953000e-01 6.084000e-01 6.982000e-01 1.701900e+00 3.800000e-02 -5.100000e-02 2.060000e+00 -4.300000e-03 2.300000e-03 2.587000e-01 4.444000e-01 - 5.390000e-02 1.055300e+00 1.167000e-01 5.124000e-01 -7.110000e-02 6.230000e-01 1.115000e-01 5.170000e-01 3.800000e-02 4.834000e-01 6.440000e-02 5.757000e-01 3.960000e-02 4.136000e-01 - 2.916000e-01 2.618000e-01 3.233000e-01 5.671000e-01 5.434000e-01 4.386000e-01 4.561000e-01 4.079000e-01 4.168000e-01 6.065000e-01 3.949000e-01 4.845000e-01 2.435000e-01 2.603000e-01 - 3.046000e-01 1.198000e-01 5.786000e-01 1.665000e-01 7.735000e-01 8.260000e-02 1.760100e+00 -3.140000e-02 2.880000e-02 -3.630000e-02 -2.370000e-02 2.720000e-02 3.292000e-01 9.370000e-02 - 1.812000e-01 5.192000e-01 4.632000e-01 5.597000e-01 6.344000e-01 4.976000e-01 1.709800e+00 2.520000e-02 -7.980000e-02 2.089700e+00 -4.440000e-02 5.320000e-02 2.372000e-01 3.645000e-01 - 3.179000e-01 4.891000e-01 7.155000e-01 3.883000e-01 7.770000e-01 4.547000e-01 1.566300e+00 1.949000e-01 5.830000e-02 1.927600e+00 3.480000e-02 -4.360000e-02 3.791000e-01 2.744000e-01 - 1.477000e-01 4.547000e-01 2.569000e-01 6.831000e-01 2.450000e-01 8.360000e-01 3.765000e-01 5.412000e-01 2.740000e-01 8.186000e-01 1.857000e-01 7.708000e-01 1.411000e-01 4.037000e-01 - 2.131000e-01 6.178000e-01 6.259000e-01 4.974000e-01 7.543000e-01 4.947000e-01 1.708600e+00 2.940000e-02 4.010000e-02 1.945600e+00 2.130000e-02 -2.470000e-02 3.206000e-01 3.490000e-01 - 1.050800e+00 6.520000e-02 4.232000e-01 8.030000e-02 3.003000e-01 1.362000e-01 3.865000e-01 1.220000e-01 3.645000e-01 4.690000e-02 4.060000e-01 9.850000e-02 3.132000e-01 6.030000e-02 - 2.090000e-02 7.060000e-01 2.470000e-01 8.011000e-01 3.740000e-01 7.966000e-01 -3.690000e-02 1.777300e+00 4.610000e-02 1.946200e+00 -2.460000e-02 2.910000e-02 1.503000e-01 4.630000e-01 - 4.137000e-01 2.892000e-01 4.493000e-01 6.976000e-01 7.307000e-01 5.271000e-01 5.331000e-01 5.940000e-01 5.688000e-01 7.422000e-01 5.595000e-01 5.605000e-01 3.348000e-01 3.115000e-01 - 5.298000e-01 6.420000e-02 6.824000e-01 2.788000e-01 7.870000e-01 3.054000e-01 6.489000e-01 3.206000e-01 8.537000e-01 2.453000e-01 7.167000e-01 2.387000e-01 4.121000e-01 1.402000e-01 - 4.221000e-01 3.664000e-01 7.573000e-01 4.597000e-01 8.641000e-01 5.222000e-01 7.522000e-01 4.671000e-01 8.513000e-01 5.595000e-01 7.478000e-01 4.688000e-01 4.424000e-01 2.608000e-01 - 4.622000e-01 1.290000e-01 7.078000e-01 2.222000e-01 7.751000e-01 2.817000e-01 7.012000e-01 2.305000e-01 8.362000e-01 2.308000e-01 6.095000e-01 3.373000e-01 3.959000e-01 1.415000e-01 - 6.361000e-01 8.160000e-02 9.489000e-01 1.881000e-01 1.033400e+00 2.630000e-01 9.601000e-01 1.715000e-01 1.003700e+00 3.191000e-01 1.146200e+00 -4.830000e-02 5.663000e-01 8.560000e-02 - 2.930000e-01 5.022000e-01 7.152000e-01 3.739000e-01 7.363000e-01 4.900000e-01 1.759500e+00 -3.130000e-02 -6.460000e-02 2.074000e+00 4.020000e-02 -4.750000e-02 3.524000e-01 2.979000e-01 - 4.311000e-01 1.810000e-01 8.205000e-01 9.240000e-02 1.006300e+00 1.160000e-02 1.695800e+00 3.640000e-02 -8.400000e-03 2.010900e+00 -5.130000e-02 6.280000e-02 4.713000e-01 5.760000e-02 - 3.465000e-01 1.600000e-01 6.633000e-01 1.569000e-01 7.640000e-01 1.793000e-01 1.622700e+00 1.281000e-01 1.860000e-02 -2.340000e-02 -2.300000e-03 2.000000e-03 3.697000e-01 1.015000e-01 - 9.872000e-01 -1.126000e-01 1.332000e+00 7.640000e-02 1.548500e+00 5.010000e-02 1.269100e+00 1.510000e-01 1.585700e+00 2.980000e-02 1.368100e+00 3.380000e-02 7.755000e-01 3.590000e-02 - 2.610000e-01 7.540000e-01 6.275000e-01 6.880000e-01 6.739000e-01 7.751000e-01 1.638600e+00 1.087000e-01 5.130000e-02 1.938700e+00 4.940000e-02 -5.850000e-02 3.284000e-01 4.529000e-01 - 2.109000e-01 6.045000e-01 2.995000e-01 9.821000e-01 5.221000e-01 9.051000e-01 3.581000e-01 9.091000e-01 4.162000e-01 1.046800e+00 3.561000e-01 9.154000e-01 1.993000e-01 5.354000e-01 - 5.272000e-01 1.323000e-01 1.047100e+00 -1.195000e-01 1.095700e+00 -3.110000e-02 1.817900e+00 -1.026000e-01 1.931500e+00 7.750000e-02 6.700000e-03 -8.200000e-03 5.379000e-01 1.850000e-02 - 2.795000e-01 5.918000e-01 4.382000e-01 9.261000e-01 5.119000e-01 1.035300e+00 4.693000e-01 8.914000e-01 5.577000e-01 1.006300e+00 3.827000e-01 9.897000e-01 2.396000e-01 5.500000e-01 - 7.918000e-01 -6.760000e-02 9.720000e-01 9.020000e-02 1.069100e+00 1.199000e-01 1.781500e+00 -5.860000e-02 1.962800e+00 4.390000e-02 2.400000e-02 -2.710000e-02 5.785000e-01 4.470000e-02 - 1.210900e+00 -1.261000e-01 5.953000e-01 1.149400e+00 5.685000e-01 9.512000e-01 6.746000e-01 1.050000e+00 5.015000e-01 9.448000e-01 6.418000e-01 1.093700e+00 4.564000e-01 4.655000e-01 - 4.166000e-01 -2.610000e-02 7.158000e-01 -8.600000e-03 8.091000e-01 2.500000e-02 1.774000e+00 -4.670000e-02 -6.200000e-03 6.800000e-03 -6.670000e-02 7.770000e-02 3.702000e-01 3.560000e-02 - 1.066400e+00 4.440000e-02 3.890000e-01 1.361000e+00 4.381000e-01 6.264000e-01 5.386000e-01 1.188200e+00 4.047000e-01 6.327000e-01 3.706000e-01 1.379200e+00 3.626000e-01 4.052000e-01 - 3.560000e-01 5.282000e-01 6.139000e-01 5.976000e-01 7.348000e-01 5.920000e-01 1.600500e+00 1.565000e-01 -3.680000e-02 2.045900e+00 2.100000e-03 -9.000000e-04 3.704000e-01 3.378000e-01 - 2.368000e-01 4.550000e-01 4.686000e-01 5.905000e-01 5.762000e-01 6.224000e-01 4.614000e-01 5.998000e-01 5.424000e-01 6.833000e-01 5.509000e-01 4.959000e-01 2.914000e-01 3.168000e-01 - 1.412000e-01 5.766000e-01 2.682000e-01 7.994000e-01 4.284000e-01 7.535000e-01 2.010000e-02 1.708900e+00 -5.190000e-02 2.063600e+00 -2.990000e-02 3.540000e-02 1.924000e-01 4.226000e-01 - 4.028000e-01 3.229000e-01 5.538000e-01 5.916000e-01 6.593000e-01 6.407000e-01 5.754000e-01 5.726000e-01 6.679000e-01 6.512000e-01 4.964000e-01 6.656000e-01 3.227000e-01 3.403000e-01 - 1.014100e+00 1.139000e-01 6.173000e-01 1.123200e+00 5.625000e-01 5.521000e-01 7.679000e-01 9.384000e-01 5.604000e-01 5.170000e-01 5.513000e-01 1.193400e+00 4.537000e-01 3.371000e-01 - 2.231000e-01 2.160000e-01 3.652000e-01 3.177000e-01 3.538000e-01 4.355000e-01 3.714000e-01 3.082000e-01 3.945000e-01 4.011000e-01 3.866000e-01 2.949000e-01 2.034000e-01 1.923000e-01 - 3.449000e-01 7.698000e-01 2.342000e-01 1.963000e-01 1.374000e-01 2.406000e-01 2.404000e-01 1.906000e-01 1.173000e-01 2.574000e-01 2.592000e-01 1.675000e-01 1.858000e-01 1.419000e-01 - 3.156000e-01 1.832000e-01 3.843000e-01 4.186000e-01 5.150000e-01 3.846000e-01 4.053000e-01 3.939000e-01 4.708000e-01 4.471000e-01 4.462000e-01 3.420000e-01 2.460000e-01 2.126000e-01 - 6.092000e-01 1.414000e-01 8.663000e-01 3.277000e-01 8.921000e-01 4.823000e-01 8.205000e-01 3.809000e-01 1.009700e+00 3.674000e-01 9.123000e-01 2.730000e-01 5.124000e-01 1.745000e-01 - 8.463000e-01 1.475000e-01 1.383300e+00 1.659000e-01 1.409400e+00 3.871000e-01 1.236000e+00 3.424000e-01 1.439400e+00 3.775000e-01 1.240400e+00 3.315000e-01 7.584000e-01 1.434000e-01 - 1.186000e-01 5.166000e-01 2.754000e-01 7.017000e-01 3.716000e-01 7.266000e-01 1.927000e-01 8.017000e-01 2.624000e-01 8.739000e-01 3.585000e-01 6.027000e-01 1.595000e-01 4.034000e-01 --7.150000e-02 1.194100e+00 1.877000e-01 1.441100e+00 3.201000e-01 1.431200e+00 4.790000e-02 1.673200e+00 -5.740000e-02 2.074100e+00 1.340000e-02 1.714800e+00 9.210000e-02 8.434000e-01 - 1.061600e+00 5.140000e-02 8.015000e-01 9.388000e-01 6.061000e-01 6.110000e-01 9.143000e-01 7.975000e-01 6.790000e-01 4.857000e-01 7.059000e-01 1.043000e+00 5.455000e-01 2.809000e-01 - 7.813000e-01 2.707000e-01 1.166100e+00 1.809000e-01 1.340200e+00 1.161000e-01 1.784300e+00 -6.440000e-02 2.014600e+00 -1.000000e-02 4.940000e-02 -5.860000e-02 6.698000e-01 1.289000e-01 --2.000000e-02 8.819000e-01 2.728000e-01 8.867000e-01 2.715000e-01 1.033800e+00 1.349000e-01 1.575400e+00 2.450000e-02 1.974500e+00 2.900000e-02 -3.400000e-02 1.103000e-01 5.812000e-01 - 1.124500e+00 -2.180000e-02 4.186000e-01 1.652000e-01 3.845000e-01 9.650000e-02 4.261000e-01 1.539000e-01 3.543000e-01 1.256000e-01 4.996000e-01 6.970000e-02 3.512000e-01 6.390000e-02 --6.500000e-03 7.993000e-01 3.267000e-01 7.767000e-01 4.522000e-01 7.680000e-01 -2.820000e-02 1.766800e+00 4.970000e-02 1.942100e+00 -5.330000e-02 6.490000e-02 1.645000e-01 4.867000e-01 - 6.900000e-02 7.249000e-01 9.240000e-02 1.152900e+00 4.710000e-02 1.384400e+00 6.110000e-02 1.191900e+00 4.000000e-02 1.400800e+00 1.907000e-01 1.034100e+00 6.170000e-02 6.550000e-01 - 1.428000e-01 5.786000e-01 4.101000e-01 6.276000e-01 4.227000e-01 7.570000e-01 1.796700e+00 -7.820000e-02 1.590000e-02 1.975800e+00 3.080000e-02 -3.800000e-02 2.194000e-01 3.917000e-01 - 4.230000e-01 6.180000e-01 6.667000e-01 9.600000e-01 8.423000e-01 9.991000e-01 6.821000e-01 9.383000e-01 7.610000e-01 1.118300e+00 6.837000e-01 9.371000e-01 3.987000e-01 5.377000e-01 - 1.123200e+00 -1.980000e-02 4.842000e-01 1.256400e+00 4.733000e-01 5.613000e-01 6.713000e-01 1.041900e+00 4.869000e-01 5.181000e-01 6.174000e-01 1.101800e+00 3.947000e-01 3.597000e-01 - 3.235000e-01 4.008000e-01 6.078000e-01 4.450000e-01 7.643000e-01 3.984000e-01 1.743500e+00 -1.240000e-02 7.710000e-02 1.904800e+00 -4.670000e-02 5.660000e-02 3.336000e-01 2.813000e-01 - 8.635000e-01 2.416000e-01 3.268000e-01 7.437000e-01 3.194000e-01 4.958000e-01 4.484000e-01 5.974000e-01 3.312000e-01 4.618000e-01 3.342000e-01 7.332000e-01 2.896000e-01 3.473000e-01 - 3.989000e-01 3.064000e-01 7.602000e-01 3.139000e-01 7.549000e-01 4.913000e-01 6.895000e-01 3.998000e-01 6.527000e-01 6.348000e-01 6.298000e-01 4.730000e-01 3.961000e-01 2.339000e-01 --5.620000e-02 6.196000e-01 6.960000e-02 8.052000e-01 1.929000e-01 8.025000e-01 2.680000e-02 1.702400e+00 5.080000e-02 1.938200e+00 3.790000e-02 -4.510000e-02 7.300000e-02 4.286000e-01 - 7.670000e-02 5.532000e-01 3.697000e-01 5.783000e-01 5.229000e-01 5.426000e-01 1.730300e+00 2.500000e-03 -5.700000e-03 2.006900e+00 4.780000e-02 -5.690000e-02 2.083000e-01 3.421000e-01 - 1.222200e+00 -1.385000e-01 5.873000e-01 1.141900e+00 4.907000e-01 8.950000e-01 4.724000e-01 1.276000e+00 4.236000e-01 9.068000e-01 5.406000e-01 1.195200e+00 4.016000e-01 4.845000e-01 - 2.563000e-01 6.222000e-01 3.700000e-01 1.011000e+00 4.776000e-01 1.086300e+00 4.206000e-01 9.524000e-01 4.446000e-01 1.143800e+00 3.690000e-01 1.014000e+00 2.464000e-01 5.458000e-01 - 1.840000e-01 6.729000e-01 4.951000e-01 6.792000e-01 5.736000e-01 7.248000e-01 1.651000e+00 9.460000e-02 -1.130000e-02 2.015000e+00 1.500000e-03 -2.300000e-03 2.611000e-01 4.318000e-01 - 1.102100e+00 3.900000e-03 1.025300e+00 7.218000e-01 8.517000e-01 8.106000e-01 1.152900e+00 5.722000e-01 9.200000e-01 6.121000e-01 1.224200e+00 4.924000e-01 6.570000e-01 2.914000e-01 - 4.650000e-01 1.452000e-01 7.837000e-01 1.378000e-01 9.267000e-01 1.065000e-01 1.763500e+00 -3.920000e-02 1.258000e-01 1.849400e+00 -1.710000e-02 2.010000e-02 4.704000e-01 5.990000e-02 - 1.125400e+00 -1.860000e-02 4.118000e-01 1.341000e+00 5.358000e-01 4.982000e-01 5.828000e-01 1.137600e+00 4.601000e-01 5.488000e-01 5.933000e-01 1.129500e+00 3.844000e-01 3.734000e-01 - 1.075400e+00 3.380000e-02 7.599000e-01 9.807000e-01 6.185000e-01 7.503000e-01 8.009000e-01 9.346000e-01 6.739000e-01 6.285000e-01 8.539000e-01 8.723000e-01 5.453000e-01 3.332000e-01 - 3.525000e-01 3.345000e-01 5.116000e-01 5.722000e-01 5.640000e-01 6.692000e-01 4.808000e-01 6.056000e-01 5.902000e-01 6.561000e-01 5.130000e-01 5.635000e-01 3.051000e-01 3.173000e-01 - 5.190000e-01 5.924000e-01 8.648000e-01 7.441000e-01 1.038200e+00 6.802000e-01 1.758000e+00 -3.610000e-02 1.986400e+00 1.070000e-02 -4.070000e-02 1.777700e+00 5.055000e-01 4.177000e-01 - 5.158000e-01 1.822000e-01 8.558000e-01 1.480000e-01 9.620000e-01 1.680000e-01 1.790400e+00 -7.240000e-02 -3.050000e-02 2.032800e+00 7.580000e-02 -9.170000e-02 4.772000e-01 1.143000e-01 - 5.610000e-01 2.925000e-01 7.260000e-01 4.631000e-01 9.444000e-01 3.525000e-01 1.723300e+00 7.400000e-03 3.060000e-02 1.958500e+00 3.750000e-02 -4.560000e-02 4.457000e-01 2.517000e-01 - 5.050000e-01 6.070000e-01 8.627000e-01 8.609000e-01 8.843000e-01 1.080400e+00 1.662900e+00 7.810000e-02 9.900000e-02 1.886300e+00 -5.900000e-02 1.799200e+00 4.807000e-01 5.152000e-01 - 4.083000e-01 6.277000e-01 5.078000e-01 1.137300e+00 5.682000e-01 1.305400e+00 6.229000e-01 1.002100e+00 6.980000e-01 1.180200e+00 6.487000e-01 9.710000e-01 3.243000e-01 6.188000e-01 - 5.610000e-01 1.063000e-01 7.032000e-01 3.773000e-01 7.404000e-01 4.993000e-01 7.263000e-01 3.493000e-01 8.452000e-01 3.924000e-01 6.268000e-01 4.698000e-01 4.199000e-01 2.013000e-01 --4.600000e-03 4.540000e-01 1.915000e-01 5.706000e-01 3.021000e-01 5.829000e-01 -6.530000e-02 1.807800e+00 -4.130000e-02 4.920000e-02 9.000000e-03 -1.080000e-02 1.095000e-01 3.238000e-01 - 4.558000e-01 6.417000e-01 7.385000e-01 7.060000e-01 9.107000e-01 6.386000e-01 1.640500e+00 1.095000e-01 -1.430000e-02 2.021500e+00 -6.380000e-02 1.806700e+00 4.203000e-01 4.233000e-01 - 3.749000e-01 3.478000e-01 6.614000e-01 3.763000e-01 8.617000e-01 2.874000e-01 1.680900e+00 6.080000e-02 5.110000e-02 1.937900e+00 -4.430000e-02 5.130000e-02 3.498000e-01 2.637000e-01 - 1.172000e+00 -7.900000e-02 5.569000e-01 2.045000e-01 5.080000e-01 1.028000e-01 5.461000e-01 2.194000e-01 4.582000e-01 1.502000e-01 6.272000e-01 1.214000e-01 4.147000e-01 9.840000e-02 - 1.114400e+00 -1.480000e-02 6.321000e-01 8.080000e-02 5.397000e-01 4.140000e-02 5.880000e-01 1.360000e-01 4.192000e-01 1.711000e-01 5.523000e-01 1.767000e-01 4.362000e-01 5.740000e-02 - 4.172000e-01 4.878000e-01 7.567000e-01 4.570000e-01 8.219000e-01 5.137000e-01 1.698300e+00 4.280000e-02 -2.360000e-02 2.029800e+00 -3.000000e-04 2.000000e-03 4.055000e-01 3.136000e-01 - 3.340000e-02 9.060000e-02 1.528000e-01 2.821000e-01 2.778000e-01 2.702000e-01 3.840000e-02 1.689500e+00 8.330000e-02 -1.008000e-01 7.790000e-02 -9.420000e-02 6.010000e-02 1.798000e-01 - 1.125000e+00 -1.910000e-02 6.254000e-01 1.100600e+00 3.906000e-01 8.229000e-01 6.111000e-01 1.120500e+00 4.252000e-01 7.403000e-01 6.368000e-01 1.083800e+00 4.206000e-01 4.015000e-01 - 6.356000e-01 4.044000e-01 8.279000e-01 8.315000e-01 9.620000e-01 9.261000e-01 8.210000e-01 8.397000e-01 1.001200e+00 9.082000e-01 8.058000e-01 8.573000e-01 4.849000e-01 4.709000e-01 - 1.061500e+00 5.180000e-02 7.158000e-01 1.021300e+00 6.573000e-01 4.111000e-01 7.008000e-01 1.036400e+00 5.019000e-01 5.624000e-01 7.263000e-01 1.009700e+00 4.987000e-01 2.754000e-01 - 1.755000e-01 3.300000e-01 3.664000e-01 4.088000e-01 3.820000e-01 5.115000e-01 3.903000e-01 3.836000e-01 4.519000e-01 4.405000e-01 2.869000e-01 5.086000e-01 1.966000e-01 2.555000e-01 - 2.700000e-02 1.430000e-02 3.190000e-02 3.198000e-01 3.130000e-02 4.564000e-01 -1.280000e-02 1.590000e-02 -6.610000e-02 7.780000e-02 -8.400000e-03 8.100000e-03 1.590000e-02 1.756000e-01 - 4.663000e-01 -4.600000e-03 7.408000e-01 4.170000e-02 9.221000e-01 -2.920000e-02 1.735200e+00 -1.600000e-03 7.200000e-02 -8.600000e-02 3.510000e-02 -4.170000e-02 4.131000e-01 3.500000e-02 - 3.971000e-01 1.871000e-01 6.050000e-01 3.133000e-01 6.799000e-01 3.637000e-01 5.564000e-01 3.703000e-01 7.048000e-01 3.569000e-01 5.925000e-01 3.298000e-01 3.250000e-01 2.095000e-01 - 1.881000e-01 2.852000e-01 4.351000e-01 3.640000e-01 5.826000e-01 3.299000e-01 1.650900e+00 9.930000e-02 -7.760000e-02 9.370000e-02 3.610000e-02 -4.430000e-02 2.443000e-01 2.118000e-01 - 7.071000e-01 4.006000e-01 3.163000e-01 1.421300e+00 2.512000e-01 1.208400e+00 3.451000e-01 1.385900e+00 3.214000e-01 1.050100e+00 3.445000e-01 1.383400e+00 2.696000e-01 6.342000e-01 - 6.183000e-01 4.862000e-01 8.977000e-01 8.166000e-01 1.012400e+00 8.138000e-01 1.728200e+00 4.700000e-03 2.084100e+00 -9.930000e-02 -2.800000e-03 1.733200e+00 5.319000e-01 4.307000e-01 - 7.480000e-01 2.723000e-01 1.088900e+00 5.234000e-01 1.335700e+00 4.807000e-01 1.197500e+00 3.954000e-01 1.335600e+00 5.135000e-01 1.092100e+00 5.162000e-01 6.428000e-01 2.840000e-01 - 1.220000e-02 4.463000e-01 3.817000e-01 3.752000e-01 4.660000e-01 4.161000e-01 -3.900000e-02 1.775400e+00 2.450000e-02 -2.800000e-02 -2.000000e-02 2.500000e-02 1.909000e-01 2.436000e-01 - 4.191000e-01 7.018000e-01 7.914000e-01 6.675000e-01 1.036300e+00 5.181000e-01 1.751200e+00 -2.240000e-02 -1.037000e-01 2.116700e+00 0.000000e+00 1.728200e+00 4.685000e-01 3.836000e-01 - 5.673000e-01 5.564000e-01 3.051000e-01 8.024000e-01 2.870000e-01 5.490000e-01 3.614000e-01 7.325000e-01 2.592000e-01 5.638000e-01 3.773000e-01 7.175000e-01 2.555000e-01 3.958000e-01 - 5.016000e-01 1.766000e-01 7.704000e-01 2.323000e-01 9.845000e-01 1.148000e-01 1.770000e+00 -4.560000e-02 -1.452000e-01 2.174300e+00 2.120000e-02 -2.570000e-02 4.619000e-01 1.186000e-01 - 3.840000e-02 4.844000e-01 2.104000e-01 6.335000e-01 3.165000e-01 6.526000e-01 7.100000e-03 1.721900e+00 2.400000e-02 -3.070000e-02 -3.000000e-04 -1.300000e-03 1.120000e-01 3.751000e-01 - 6.022000e-01 4.851000e-01 9.271000e-01 4.647000e-01 1.181800e+00 3.103000e-01 1.728700e+00 6.000000e-04 2.143800e+00 -1.692000e-01 2.100000e-02 1.706200e+00 6.078000e-01 2.020000e-01 - 4.198000e-01 1.360000e-01 7.111000e-01 1.498000e-01 7.691000e-01 2.240000e-01 7.126000e-01 1.501000e-01 8.195000e-01 1.747000e-01 7.925000e-01 5.360000e-02 4.255000e-01 6.960000e-02 - 4.734000e-01 3.226000e-01 7.321000e-01 5.214000e-01 6.302000e-01 8.344000e-01 7.167000e-01 5.399000e-01 8.025000e-01 6.486000e-01 7.317000e-01 5.186000e-01 3.959000e-01 3.324000e-01 - 1.976000e-01 2.874000e-01 5.407000e-01 2.456000e-01 6.124000e-01 3.106000e-01 1.765600e+00 -4.640000e-02 -8.500000e-03 1.090000e-02 -3.050000e-02 3.470000e-02 2.834000e-01 1.711000e-01 - 2.472000e-01 3.830000e-01 4.762000e-01 4.897000e-01 5.658000e-01 5.331000e-01 4.445000e-01 5.271000e-01 4.262000e-01 7.066000e-01 5.670000e-01 3.801000e-01 2.861000e-01 2.700000e-01 - 5.640000e-02 8.770000e-01 4.094000e-01 8.304000e-01 4.598000e-01 9.081000e-01 1.685500e+00 5.740000e-02 -1.160000e-02 2.014600e+00 3.930000e-02 -4.600000e-02 2.180000e-01 5.141000e-01 - 5.933000e-01 5.124000e-01 3.009000e-01 4.766000e-01 2.757000e-01 3.484000e-01 2.430000e-01 5.403000e-01 2.414000e-01 3.768000e-01 3.809000e-01 3.783000e-01 2.467000e-01 2.736000e-01 - 4.738000e-01 1.604000e-01 6.897000e-01 3.143000e-01 7.454000e-01 4.065000e-01 7.160000e-01 2.829000e-01 7.983000e-01 3.611000e-01 6.483000e-01 3.632000e-01 4.115000e-01 1.662000e-01 - 3.263000e-01 2.228000e-01 5.195000e-01 3.371000e-01 6.499000e-01 3.145000e-01 6.013000e-01 2.391000e-01 5.882000e-01 3.997000e-01 4.504000e-01 4.183000e-01 2.802000e-01 2.177000e-01 - 2.952000e-01 3.011000e-01 3.907000e-01 5.610000e-01 5.623000e-01 4.951000e-01 3.930000e-01 5.548000e-01 5.071000e-01 5.777000e-01 3.762000e-01 5.773000e-01 2.368000e-01 3.101000e-01 - 8.571000e-01 2.373000e-01 3.476000e-01 9.596000e-01 2.949000e-01 6.430000e-01 3.605000e-01 9.372000e-01 2.396000e-01 6.787000e-01 4.688000e-01 8.115000e-01 2.814000e-01 4.219000e-01 --8.300000e-02 7.175000e-01 2.840000e-02 8.961000e-01 4.980000e-02 1.014500e+00 -5.960000e-02 1.804600e+00 -2.070000e-02 2.025200e+00 1.300000e-03 -1.400000e-03 2.410000e-02 5.176000e-01 - 4.093000e-01 4.796000e-01 7.052000e-01 4.992000e-01 8.137000e-01 5.116000e-01 1.559200e+00 2.063000e-01 -9.400000e-03 2.012900e+00 -4.380000e-02 5.310000e-02 3.694000e-01 3.477000e-01 --4.070000e-02 3.429000e-01 1.610000e-01 4.522000e-01 3.372000e-01 3.839000e-01 -1.471000e-01 1.905600e+00 2.170000e-02 -2.650000e-02 1.330000e-02 -1.820000e-02 1.011000e-01 2.359000e-01 - 4.655000e-01 3.234000e-01 6.666000e-01 5.896000e-01 7.814000e-01 6.414000e-01 7.481000e-01 4.871000e-01 8.391000e-01 5.939000e-01 7.590000e-01 4.738000e-01 4.179000e-01 2.995000e-01 - 3.631000e-01 3.763000e-01 5.393000e-01 6.265000e-01 6.059000e-01 7.227000e-01 5.750000e-01 5.816000e-01 5.516000e-01 8.031000e-01 5.457000e-01 6.142000e-01 3.099000e-01 3.612000e-01 - 4.637000e-01 4.033000e-01 7.539000e-01 5.969000e-01 7.829000e-01 7.668000e-01 6.929000e-01 6.707000e-01 9.046000e-01 6.521000e-01 7.372000e-01 6.145000e-01 4.322000e-01 3.487000e-01 - 1.090400e+00 1.660000e-02 6.835000e-01 4.016000e-01 5.539000e-01 2.774000e-01 6.936000e-01 3.948000e-01 5.649000e-01 2.433000e-01 7.215000e-01 3.609000e-01 4.784000e-01 1.675000e-01 - 1.758000e-01 4.663000e-01 4.616000e-01 5.046000e-01 5.843000e-01 4.959000e-01 1.676900e+00 5.550000e-02 1.330000e-02 1.984500e+00 2.270000e-02 -2.750000e-02 2.619000e-01 2.967000e-01 - 1.214600e+00 -1.319000e-01 5.935000e-01 5.697000e-01 5.128000e-01 3.593000e-01 5.345000e-01 6.402000e-01 4.997000e-01 3.476000e-01 5.162000e-01 6.670000e-01 4.160000e-01 2.552000e-01 - 1.620000e-02 -1.880000e-02 2.976000e-01 1.450000e-02 4.584000e-01 -3.510000e-02 -5.870000e-02 6.980000e-02 9.900000e-03 -9.800000e-03 -5.190000e-02 6.120000e-02 1.608000e-01 6.200000e-03 - 9.487000e-01 1.556000e-01 4.250000e-01 9.013000e-01 2.160000e-01 7.500000e-01 5.109000e-01 8.025000e-01 3.473000e-01 5.742000e-01 4.527000e-01 8.721000e-01 2.995000e-01 4.109000e-01 - 4.073000e-01 6.996000e-01 6.978000e-01 8.732000e-01 8.389000e-01 8.391000e-01 1.652000e+00 9.270000e-02 -9.850000e-02 2.120300e+00 4.640000e-02 1.671500e+00 4.081000e-01 4.970000e-01 - 1.179000e-01 3.772000e-01 1.543000e-01 6.233000e-01 2.983000e-01 5.636000e-01 2.670000e-01 4.901000e-01 2.800000e-01 6.010000e-01 1.791000e-01 5.917000e-01 1.150000e-01 3.288000e-01 - 3.808000e-01 3.059000e-01 7.125000e-01 2.866000e-01 8.360000e-01 2.762000e-01 1.762600e+00 -4.020000e-02 -1.060000e-02 2.014400e+00 -2.190000e-02 2.570000e-02 3.894000e-01 1.950000e-01 - 1.100700e+00 6.400000e-03 6.214000e-01 1.107200e+00 5.279000e-01 5.033000e-01 6.244000e-01 1.102300e+00 4.872000e-01 5.243000e-01 4.857000e-01 1.269100e+00 4.358000e-01 3.183000e-01 - 4.452000e-01 4.062000e-01 7.953000e-01 5.247000e-01 7.304000e-01 8.046000e-01 7.100000e-01 6.177000e-01 8.594000e-01 6.745000e-01 7.237000e-01 6.055000e-01 4.072000e-01 3.626000e-01 --3.360000e-02 7.815000e-01 3.460000e-01 6.911000e-01 3.249000e-01 8.536000e-01 -7.280000e-02 1.825300e+00 1.340000e-02 1.986900e+00 1.620000e-02 -2.070000e-02 1.240000e-01 4.968000e-01 - 4.713000e-01 4.386000e-01 7.293000e-01 6.936000e-01 7.792000e-01 8.545000e-01 6.979000e-01 7.343000e-01 8.918000e-01 7.419000e-01 6.975000e-01 7.380000e-01 4.071000e-01 4.185000e-01 - 4.175000e-01 3.384000e-01 7.303000e-01 4.443000e-01 7.458000e-01 6.023000e-01 6.859000e-01 4.919000e-01 8.668000e-01 4.794000e-01 7.331000e-01 4.360000e-01 4.239000e-01 2.520000e-01 - 5.340000e-02 9.828000e-01 2.123000e-01 1.128600e+00 2.351000e-01 1.247100e+00 -7.900000e-02 1.824500e+00 -3.620000e-02 2.039300e+00 -1.320000e-02 1.450000e-02 8.350000e-02 7.143000e-01 - 3.388000e-01 3.895000e-01 4.675000e-01 6.855000e-01 4.922000e-01 8.274000e-01 5.061000e-01 6.373000e-01 6.460000e-01 6.687000e-01 4.679000e-01 6.876000e-01 2.774000e-01 3.879000e-01 - 3.247000e-01 5.262000e-01 6.185000e-01 5.524000e-01 7.928000e-01 4.811000e-01 1.705800e+00 3.450000e-02 -1.000000e-04 1.999800e+00 -2.430000e-02 2.690000e-02 3.642000e-01 3.209000e-01 - 4.667000e-01 2.944000e-01 8.114000e-01 3.655000e-01 8.536000e-01 4.975000e-01 7.835000e-01 3.983000e-01 9.240000e-01 4.428000e-01 7.512000e-01 4.366000e-01 4.402000e-01 2.459000e-01 - 1.345000e-01 6.435000e-01 3.909000e-01 7.931000e-01 5.475000e-01 7.823000e-01 3.952000e-01 7.846000e-01 3.832000e-01 9.931000e-01 3.469000e-01 8.406000e-01 2.234000e-01 4.592000e-01 - 1.940000e-01 4.096000e-01 2.225000e-01 7.397000e-01 2.921000e-01 7.992000e-01 3.331000e-01 6.114000e-01 3.428000e-01 7.537000e-01 2.425000e-01 7.173000e-01 1.560000e-01 3.954000e-01 - 5.959000e-01 1.939000e-01 9.863000e-01 2.432000e-01 1.230900e+00 1.453000e-01 1.001400e+00 2.181000e-01 1.243900e+00 1.572000e-01 9.501000e-01 2.823000e-01 5.876000e-01 1.168000e-01 - 3.336000e-01 3.599000e-01 5.074000e-01 5.793000e-01 6.561000e-01 5.657000e-01 6.266000e-01 4.343000e-01 6.604000e-01 5.796000e-01 6.106000e-01 4.549000e-01 3.467000e-01 2.701000e-01 - 4.059000e-01 3.798000e-01 7.394000e-01 3.555000e-01 8.615000e-01 3.500000e-01 1.760300e+00 -3.010000e-02 -1.800000e-02 2.019900e+00 -1.730000e-02 2.140000e-02 4.065000e-01 2.399000e-01 - 3.310000e-01 6.894000e-01 7.105000e-01 6.104000e-01 8.226000e-01 6.172000e-01 1.725000e+00 9.400000e-03 1.055000e-01 1.875200e+00 3.200000e-03 -3.000000e-03 4.023000e-01 3.763000e-01 - 7.199000e-01 3.784000e-01 2.954000e-01 7.870000e-01 2.970000e-01 5.207000e-01 2.539000e-01 8.341000e-01 2.433000e-01 5.660000e-01 3.386000e-01 7.365000e-01 2.611000e-01 3.805000e-01 - 3.351000e-01 4.388000e-01 4.206000e-01 8.098000e-01 5.920000e-01 7.924000e-01 4.709000e-01 7.497000e-01 6.010000e-01 8.065000e-01 4.283000e-01 8.027000e-01 2.862000e-01 4.179000e-01 - 2.682000e-01 2.294000e-01 5.970000e-01 1.434000e-01 7.460000e-01 8.930000e-02 5.686000e-01 1.835000e-01 6.593000e-01 2.042000e-01 6.265000e-01 1.115000e-01 3.488000e-01 8.000000e-02 - 1.968000e-01 5.983000e-01 4.334000e-01 6.884000e-01 6.950000e-01 5.214000e-01 1.668500e+00 7.710000e-02 -1.500000e-02 2.017100e+00 -9.400000e-03 1.200000e-02 2.899000e-01 3.599000e-01 - 2.743000e-01 2.707000e-01 5.182000e-01 3.226000e-01 6.576000e-01 2.817000e-01 4.334000e-01 4.222000e-01 5.786000e-01 3.969000e-01 5.792000e-01 2.490000e-01 3.026000e-01 1.813000e-01 - 1.044000e-01 8.482000e-01 2.786000e-01 1.196100e+00 3.187000e-01 1.361100e+00 2.881000e-01 1.186100e+00 2.551000e-01 1.462000e+00 2.166000e-01 1.271600e+00 1.215000e-01 7.374000e-01 - 7.940000e-02 7.657000e-01 3.944000e-01 7.673000e-01 4.722000e-01 8.130000e-01 1.623500e+00 1.296000e-01 1.566000e-01 1.815600e+00 8.910000e-02 -1.058000e-01 2.025000e-01 4.817000e-01 - 5.802000e-01 5.219000e-01 2.736000e-01 3.596000e-01 3.377000e-01 1.663000e-01 3.450000e-01 2.768000e-01 2.607000e-01 2.504000e-01 2.996000e-01 3.324000e-01 2.310000e-01 2.168000e-01 - 3.435000e-01 7.501000e-01 1.504000e-01 4.932000e-01 6.410000e-02 4.826000e-01 2.269000e-01 4.038000e-01 1.557000e-01 3.627000e-01 6.050000e-02 6.040000e-01 1.334000e-01 3.200000e-01 - 2.296000e-01 8.800000e-02 3.187000e-01 1.855000e-01 3.660000e-01 2.098000e-01 2.541000e-01 2.652000e-01 3.659000e-01 2.184000e-01 1.360000e-01 4.045000e-01 1.647000e-01 1.304000e-01 - 3.472000e-01 5.533000e-01 7.213000e-01 6.622000e-01 7.495000e-01 8.366000e-01 6.911000e-01 7.010000e-01 7.428000e-01 8.703000e-01 6.704000e-01 7.270000e-01 3.956000e-01 4.075000e-01 - 1.127300e+00 -2.910000e-02 5.484000e-01 3.926000e-01 4.000000e-01 3.540000e-01 4.938000e-01 4.571000e-01 3.574000e-01 3.831000e-01 3.600000e-01 6.188000e-01 3.658000e-01 2.323000e-01 - 3.116000e-01 2.006000e-01 6.378000e-01 1.844000e-01 8.251000e-01 1.021000e-01 1.731600e+00 -2.000000e-03 -5.400000e-03 5.500000e-03 6.160000e-02 -7.480000e-02 3.810000e-01 8.600000e-02 - 5.224000e-01 9.700000e-03 7.253000e-01 1.418000e-01 8.516000e-01 1.318000e-01 1.666600e+00 7.340000e-02 1.910000e-02 -1.910000e-02 -1.140000e-02 1.270000e-02 4.417000e-01 5.310000e-02 - 5.163000e-01 2.570000e-01 7.918000e-01 4.214000e-01 9.877000e-01 3.764000e-01 8.280000e-01 3.769000e-01 9.526000e-01 4.407000e-01 8.246000e-01 3.858000e-01 4.710000e-01 2.260000e-01 - 5.571000e-01 5.544000e-01 2.239000e-01 9.428000e-01 2.880000e-01 5.743000e-01 3.653000e-01 7.806000e-01 2.938000e-01 5.459000e-01 3.241000e-01 8.265000e-01 2.381000e-01 4.292000e-01 - 2.273000e-01 4.148000e-01 3.511000e-01 6.521000e-01 3.822000e-01 7.671000e-01 4.646000e-01 5.182000e-01 4.037000e-01 7.567000e-01 3.498000e-01 6.586000e-01 2.074000e-01 3.724000e-01 - 4.839000e-01 4.570000e-02 1.840000e-01 1.079000e-01 2.019000e-01 4.250000e-02 3.259000e-01 -6.090000e-02 2.704000e-01 -4.380000e-02 2.482000e-01 3.690000e-02 2.045000e-01 1.880000e-02 - 1.147700e+00 -5.040000e-02 1.158300e+00 1.214000e-01 9.293000e-01 -1.200000e-02 1.340100e+00 -9.310000e-02 8.479000e-01 5.720000e-02 1.220500e+00 5.150000e-02 6.836000e-01 1.350000e-02 - 6.989000e-01 3.994000e-01 1.083100e+00 3.312000e-01 1.074000e+00 4.911000e-01 1.831800e+00 -1.164000e-01 2.058700e+00 -6.400000e-02 3.980000e-02 1.682900e+00 5.699000e-01 2.715000e-01 - 3.769000e-01 4.515000e-01 1.789000e-01 1.933000e-01 1.166000e-01 2.087000e-01 1.752000e-01 1.987000e-01 1.617000e-01 1.490000e-01 1.450000e-01 2.362000e-01 1.476000e-01 1.407000e-01 - 4.429000e-01 2.770000e-01 6.559000e-01 4.770000e-01 7.533000e-01 5.333000e-01 6.636000e-01 4.679000e-01 9.272000e-01 3.484000e-01 6.942000e-01 4.315000e-01 4.358000e-01 2.080000e-01 - 2.678000e-01 5.396000e-01 5.706000e-01 5.481000e-01 7.094000e-01 5.272000e-01 1.753200e+00 -2.780000e-02 1.525000e-01 1.819500e+00 -8.100000e-03 9.300000e-03 3.374000e-01 3.192000e-01 - 5.565000e-01 2.985000e-01 9.732000e-01 1.751000e-01 1.013600e+00 2.741000e-01 1.663900e+00 7.960000e-02 1.972300e+00 3.250000e-02 -3.070000e-02 3.620000e-02 5.264000e-01 1.595000e-01 - 7.532000e-01 3.612000e-01 3.635000e-01 1.018900e+00 3.685000e-01 5.868000e-01 3.342000e-01 1.066600e+00 2.891000e-01 6.552000e-01 3.743000e-01 1.018400e+00 2.811000e-01 4.401000e-01 - 1.108600e+00 -5.300000e-03 4.635000e-01 1.283100e+00 4.084000e-01 1.442300e+00 4.793000e-01 1.265200e+00 4.490000e-01 1.193800e+00 6.262000e-01 1.087300e+00 3.890000e-01 5.791000e-01 - 4.608000e-01 2.314000e-01 8.328000e-01 2.275000e-01 8.639000e-01 3.633000e-01 8.046000e-01 2.602000e-01 8.485000e-01 3.997000e-01 7.342000e-01 3.473000e-01 4.069000e-01 2.202000e-01 - 4.410000e-02 5.566000e-01 2.636000e-01 6.394000e-01 3.755000e-01 6.360000e-01 2.398000e-01 6.638000e-01 2.537000e-01 7.974000e-01 2.936000e-01 6.030000e-01 1.496000e-01 3.718000e-01 - 5.005000e-01 3.034000e-01 7.548000e-01 5.121000e-01 1.041600e+00 3.657000e-01 8.597000e-01 3.826000e-01 1.028000e+00 4.066000e-01 7.794000e-01 4.771000e-01 4.800000e-01 2.417000e-01 - 3.198000e-01 7.901000e-01 1.934000e-01 7.084000e-01 1.126000e-01 6.119000e-01 1.950000e-01 7.055000e-01 1.826000e-01 5.124000e-01 2.665000e-01 6.217000e-01 1.639000e-01 4.130000e-01 - 5.197000e-01 2.097000e-01 8.513000e-01 1.889000e-01 9.604000e-01 2.034000e-01 1.725800e+00 3.100000e-03 -2.900000e-02 2.034500e+00 -4.180000e-02 4.840000e-02 4.906000e-01 1.207000e-01 - 2.273000e-01 7.301000e-01 3.601000e-01 1.140200e+00 3.853000e-01 1.328900e+00 3.072000e-01 1.205700e+00 4.551000e-01 1.270000e+00 3.043000e-01 1.206400e+00 2.025000e-01 6.645000e-01 --4.580000e-02 5.081000e-01 -4.810000e-02 8.260000e-01 -1.860000e-02 9.120000e-01 4.690000e-02 1.675900e+00 -5.220000e-02 6.230000e-02 -4.100000e-03 6.400000e-03 2.600000e-03 4.344000e-01 - 1.116200e+00 -1.530000e-02 5.599000e-01 5.398000e-01 3.945000e-01 4.545000e-01 5.338000e-01 5.660000e-01 4.524000e-01 3.615000e-01 5.239000e-01 5.846000e-01 3.863000e-01 2.650000e-01 - 6.918000e-01 3.518000e-01 1.041500e+00 3.028000e-01 1.083500e+00 3.950000e-01 1.622800e+00 1.234000e-01 2.023100e+00 -2.900000e-02 6.140000e-02 -7.670000e-02 6.017000e-01 1.932000e-01 - 7.017000e-01 3.974000e-01 1.022200e+00 5.651000e-01 1.199500e+00 5.025000e-01 1.701000e+00 3.670000e-02 2.096700e+00 -1.106000e-01 6.400000e-03 1.724200e+00 5.794000e-01 3.400000e-01 - 3.025000e-01 4.500000e-01 6.046000e-01 4.666000e-01 7.225000e-01 4.639000e-01 1.802600e+00 -8.280000e-02 -4.240000e-02 2.049500e+00 -3.420000e-02 4.300000e-02 3.911000e-01 2.265000e-01 - 2.405000e-01 4.124000e-01 3.764000e-01 6.427000e-01 4.999000e-01 6.475000e-01 4.117000e-01 5.978000e-01 4.728000e-01 6.944000e-01 4.349000e-01 5.751000e-01 2.347000e-01 3.495000e-01 - 3.042000e-01 4.975000e-01 5.813000e-01 5.410000e-01 5.880000e-01 6.772000e-01 1.773000e+00 -4.970000e-02 4.010000e-02 1.949800e+00 5.640000e-02 -6.680000e-02 3.048000e-01 3.605000e-01 - 5.140000e-02 6.044000e-01 1.610000e-01 8.481000e-01 1.999000e-01 9.469000e-01 1.133000e-01 9.042000e-01 1.455000e-01 1.025900e+00 1.506000e-01 8.629000e-01 8.570000e-02 4.987000e-01 - 7.159000e-01 3.887000e-01 3.221000e-01 2.428000e-01 3.203000e-01 1.404000e-01 3.725000e-01 1.797000e-01 2.981000e-01 1.584000e-01 2.153000e-01 3.692000e-01 2.653000e-01 1.412000e-01 - 1.038500e+00 8.070000e-02 6.368000e-01 2.733000e-01 5.443000e-01 1.728000e-01 5.774000e-01 3.499000e-01 5.029000e-01 2.067000e-01 6.043000e-01 3.120000e-01 4.286000e-01 1.561000e-01 - 3.442000e-01 6.581000e-01 6.663000e-01 8.721000e-01 5.898000e-01 1.199100e+00 6.507000e-01 8.913000e-01 5.997000e-01 1.210200e+00 5.873000e-01 9.664000e-01 3.414000e-01 5.564000e-01 - 4.650000e-02 1.068000e+00 1.044000e-01 4.719000e-01 1.267000e-01 3.475000e-01 2.050000e-02 5.726000e-01 7.500000e-03 4.792000e-01 5.820000e-02 5.241000e-01 4.720000e-02 3.740000e-01 - 5.320000e-02 3.194000e-01 1.602000e-01 4.124000e-01 1.449000e-01 5.132000e-01 1.762000e-01 3.928000e-01 2.111000e-01 4.435000e-01 1.781000e-01 3.896000e-01 1.014000e-01 2.271000e-01 - 1.134400e+00 -3.280000e-02 3.935000e-01 4.392000e-01 3.808000e-01 2.835000e-01 4.294000e-01 3.987000e-01 3.470000e-01 3.056000e-01 3.493000e-01 4.929000e-01 3.117000e-01 2.354000e-01 - 3.821000e-01 6.817000e-01 6.292000e-01 7.613000e-01 8.703000e-01 6.141000e-01 1.673400e+00 6.200000e-02 5.200000e-03 1.990900e+00 -8.500000e-02 1.832600e+00 4.190000e-01 3.872000e-01 - 3.013000e-01 6.641000e-01 5.217000e-01 9.826000e-01 5.665000e-01 1.154200e+00 6.320000e-01 8.539000e-01 7.991000e-01 9.070000e-01 5.886000e-01 9.031000e-01 3.344000e-01 5.291000e-01 - 1.506000e-01 7.948000e-01 1.765000e-01 1.315600e+00 2.341000e-01 1.461900e+00 2.085000e-01 1.275200e+00 2.402000e-01 1.474500e+00 1.973000e-01 1.294000e+00 1.203000e-01 7.379000e-01 - 3.685000e-01 6.324000e-01 7.840000e-01 5.156000e-01 8.288000e-01 6.059000e-01 1.723100e+00 1.340000e-02 -3.700000e-02 2.044700e+00 -1.219000e-01 1.448000e-01 4.090000e-01 3.624000e-01 - 4.675000e-01 3.226000e-01 7.764000e-01 4.498000e-01 8.159000e-01 5.908000e-01 6.720000e-01 5.726000e-01 8.785000e-01 5.377000e-01 5.608000e-01 7.012000e-01 4.061000e-01 3.096000e-01 - 1.032100e+00 8.920000e-02 7.001000e-01 1.020800e+00 4.296000e-01 1.249300e+00 5.637000e-01 1.175500e+00 5.356000e-01 1.006600e+00 5.957000e-01 1.141200e+00 4.473000e-01 5.009000e-01 - 3.537000e-01 3.750000e-01 5.846000e-01 5.609000e-01 6.021000e-01 7.155000e-01 5.300000e-01 6.204000e-01 6.224000e-01 7.085000e-01 5.285000e-01 6.264000e-01 3.126000e-01 3.522000e-01 - 1.028400e+00 5.230000e-02 3.442000e-01 1.396500e+00 2.839000e-01 1.006800e+00 3.878000e-01 1.345500e+00 4.144000e-01 8.010000e-01 3.794000e-01 1.354100e+00 2.956000e-01 5.589000e-01 - 1.255500e+00 -1.783000e-01 9.172000e-01 1.170000e-02 6.671000e-01 7.930000e-02 8.712000e-01 6.710000e-02 6.812000e-01 4.210000e-02 9.521000e-01 -2.890000e-02 5.712000e-01 2.040000e-02 - 3.895000e-01 2.033000e-01 5.606000e-01 3.758000e-01 7.402000e-01 3.101000e-01 5.242000e-01 4.181000e-01 7.393000e-01 3.271000e-01 5.231000e-01 4.183000e-01 3.386000e-01 1.988000e-01 - 3.526000e-01 7.464000e-01 6.101000e-01 9.113000e-01 7.630000e-01 8.749000e-01 1.689900e+00 4.920000e-02 1.480000e-02 1.983400e+00 -1.200000e-02 1.745600e+00 3.587000e-01 5.248000e-01 --1.300000e-03 6.041000e-01 4.347000e-01 4.496000e-01 4.004000e-01 6.332000e-01 2.100000e-03 1.727800e+00 -8.340000e-02 2.097300e+00 1.580000e-02 -1.760000e-02 1.446000e-01 3.869000e-01 - 3.791000e-01 2.530000e-01 8.544000e-01 6.310000e-02 8.729000e-01 1.838000e-01 1.780600e+00 -5.310000e-02 7.710000e-02 1.912700e+00 -3.610000e-02 4.320000e-02 4.118000e-01 1.349000e-01 - 1.102700e+00 1.900000e-03 4.964000e-01 5.009000e-01 3.975000e-01 3.810000e-01 5.711000e-01 4.126000e-01 3.578000e-01 4.157000e-01 6.194000e-01 3.533000e-01 3.989000e-01 2.126000e-01 - 3.882000e-01 4.172000e-01 6.311000e-01 6.264000e-01 8.014000e-01 6.151000e-01 7.290000e-01 5.097000e-01 8.256000e-01 6.037000e-01 7.664000e-01 4.626000e-01 4.112000e-01 3.055000e-01 - 1.168300e+00 -7.610000e-02 5.486000e-01 1.192800e+00 5.699000e-01 4.532000e-01 5.990000e-01 1.130500e+00 4.773000e-01 5.330000e-01 5.430000e-01 1.196900e+00 4.272000e-01 3.263000e-01 - 1.946000e-01 8.463000e-01 3.935000e-01 9.886000e-01 5.201000e-01 9.817000e-01 1.797000e+00 -7.150000e-02 -9.460000e-02 2.110900e+00 3.840000e-02 1.683800e+00 2.347000e-01 5.724000e-01 - 2.387000e-01 5.287000e-01 5.081000e-01 5.803000e-01 6.225000e-01 5.861000e-01 1.602700e+00 1.486000e-01 -3.100000e-03 2.002900e+00 -3.110000e-02 3.750000e-02 2.908000e-01 3.475000e-01 - 3.340000e-02 8.543000e-01 9.320000e-02 1.295600e+00 1.707000e-01 1.390800e+00 3.800000e-02 1.361300e+00 1.285000e-01 1.471100e+00 7.590000e-02 1.317000e+00 5.550000e-02 7.455000e-01 - 2.603000e-01 6.276000e-01 6.728000e-01 5.071000e-01 7.549000e-01 5.508000e-01 1.824600e+00 -1.058000e-01 -1.900000e-02 2.019000e+00 -7.000000e-04 4.000000e-04 3.338000e-01 3.718000e-01 - 5.957000e-01 4.387000e-01 8.091000e-01 8.394000e-01 9.161000e-01 9.635000e-01 7.718000e-01 8.814000e-01 1.003700e+00 8.898000e-01 7.043000e-01 9.604000e-01 4.712000e-01 4.792000e-01 - 1.171000e+00 -7.670000e-02 5.022000e-01 3.121000e-01 4.242000e-01 2.324000e-01 4.959000e-01 3.193000e-01 4.355000e-01 2.043000e-01 5.301000e-01 2.812000e-01 3.576000e-01 1.846000e-01 - 1.121500e+00 -2.200000e-02 5.811000e-01 1.162100e+00 5.564000e-01 5.510000e-01 6.498000e-01 1.077300e+00 4.685000e-01 6.165000e-01 5.802000e-01 1.157100e+00 4.429000e-01 3.451000e-01 - 1.084900e+00 2.450000e-02 3.846000e-01 1.362700e+00 4.707000e-01 7.384000e-01 4.256000e-01 1.313500e+00 4.017000e-01 7.778000e-01 4.385000e-01 1.299300e+00 3.552000e-01 4.773000e-01 - 1.153600e+00 -5.470000e-02 4.680000e-01 3.564000e-01 3.592000e-01 3.113000e-01 3.693000e-01 4.764000e-01 3.756000e-01 2.744000e-01 4.279000e-01 4.029000e-01 3.265000e-01 2.203000e-01 - 3.748000e-01 2.994000e-01 6.536000e-01 3.408000e-01 8.457000e-01 2.527000e-01 1.739800e+00 -9.200000e-03 -6.410000e-02 2.075800e+00 3.580000e-02 -4.420000e-02 4.033000e-01 1.715000e-01 - 4.363000e-01 6.118000e-01 7.460000e-01 6.139000e-01 8.352000e-01 6.512000e-01 1.741400e+00 -1.040000e-02 -3.700000e-02 2.039400e+00 4.330000e-02 -5.150000e-02 4.164000e-01 3.860000e-01 - 1.798000e-01 2.871000e-01 3.085000e-01 4.197000e-01 3.603000e-01 4.645000e-01 2.507000e-01 4.863000e-01 3.400000e-01 5.030000e-01 3.426000e-01 3.776000e-01 1.842000e-01 2.344000e-01 - 6.403000e-01 5.420000e-02 1.025400e+00 6.470000e-02 1.219600e+00 9.200000e-03 9.108000e-01 1.963000e-01 1.133800e+00 1.292000e-01 9.212000e-01 1.857000e-01 5.768000e-01 5.450000e-02 - 4.600000e-02 8.990000e-01 1.115000e-01 1.363600e+00 1.098000e-01 1.570600e+00 1.085000e-01 1.371400e+00 2.028000e-01 1.486500e+00 4.920000e-02 1.434900e+00 6.760000e-02 7.827000e-01 - 3.339000e-01 7.763000e-01 7.458000e-01 7.458000e-01 7.757000e-01 8.486000e-01 1.733500e+00 7.000000e-04 -8.480000e-02 2.099900e+00 -2.780000e-02 1.761200e+00 3.877000e-01 4.879000e-01 - 2.704000e-01 8.443000e-01 6.167000e-01 8.324000e-01 7.813000e-01 7.781000e-01 1.633800e+00 1.169000e-01 -2.190000e-02 2.026000e+00 -8.870000e-02 1.835800e+00 3.523000e-01 4.940000e-01 - 3.663000e-01 5.339000e-01 4.716000e-01 9.602000e-01 5.079000e-01 1.127100e+00 5.363000e-01 8.867000e-01 5.595000e-01 1.094100e+00 5.606000e-01 8.596000e-01 3.032000e-01 5.188000e-01 - 6.235000e-01 1.920000e-01 1.090900e+00 1.685000e-01 1.179400e+00 2.634000e-01 1.007500e+00 2.645000e-01 1.161100e+00 3.117000e-01 1.011000e+00 2.615000e-01 5.957000e-01 1.379000e-01 --1.810000e-02 6.916000e-01 2.449000e-01 7.359000e-01 4.121000e-01 6.803000e-01 -6.350000e-02 1.805700e+00 7.400000e-02 1.915100e+00 2.490000e-02 -2.860000e-02 1.347000e-01 4.371000e-01 - 5.528000e-01 5.532000e-01 3.238000e-01 7.494000e-01 2.123000e-01 6.204000e-01 2.494000e-01 8.322000e-01 2.191000e-01 5.926000e-01 2.836000e-01 7.916000e-01 2.339000e-01 4.084000e-01 - 7.864000e-01 -2.360000e-02 1.092900e+00 1.320000e-01 1.351800e+00 2.660000e-02 1.164200e+00 4.800000e-02 1.409700e+00 -1.860000e-02 1.181300e+00 2.760000e-02 6.641000e-01 3.780000e-02 - 4.138000e-01 5.768000e-01 7.693000e-01 5.243000e-01 9.692000e-01 4.379000e-01 1.803000e+00 -8.240000e-02 6.050000e-02 1.930600e+00 -1.340000e-02 1.410000e-02 4.108000e-01 3.600000e-01 - 1.148100e+00 -5.410000e-02 7.842000e-01 2.590000e-01 6.844000e-01 1.110000e-01 8.148000e-01 2.220000e-01 7.089000e-01 6.410000e-02 8.862000e-01 1.437000e-01 5.566000e-01 7.150000e-02 - 2.915000e-01 6.952000e-01 5.862000e-01 7.160000e-01 8.323000e-01 5.673000e-01 1.671200e+00 7.010000e-02 1.290000e-02 1.985300e+00 7.770000e-02 -9.110000e-02 3.706000e-01 3.907000e-01 - 1.131700e+00 -3.170000e-02 9.419000e-01 7.896000e-01 7.905000e-01 4.900000e-01 9.017000e-01 8.375000e-01 7.738000e-01 4.528000e-01 9.162000e-01 8.146000e-01 5.810000e-01 2.747000e-01 - 3.090000e-02 9.172000e-01 3.080000e-02 1.457100e+00 1.922000e-01 1.471700e+00 4.110000e-02 1.445200e+00 1.063000e-01 1.599500e+00 7.490000e-02 1.411300e+00 2.740000e-02 8.309000e-01 - 6.018000e-01 2.622000e-01 8.873000e-01 2.966000e-01 1.037300e+00 2.592000e-01 1.817400e+00 -9.650000e-02 2.013200e+00 -1.730000e-02 9.150000e-02 -1.097000e-01 5.341000e-01 1.592000e-01 - 3.616000e-01 5.395000e-01 7.610000e-01 4.352000e-01 8.018000e-01 5.294000e-01 1.749900e+00 -2.180000e-02 6.930000e-02 1.918400e+00 -5.740000e-02 6.910000e-02 4.071000e-01 3.045000e-01 - 3.879000e-01 5.093000e-01 6.455000e-01 7.533000e-01 7.869000e-01 7.986000e-01 5.291000e-01 8.947000e-01 7.951000e-01 8.101000e-01 6.323000e-01 7.764000e-01 3.686000e-01 4.411000e-01 - 8.884000e-01 2.158000e-01 1.187600e+00 5.403000e-01 1.283500e+00 5.980000e-01 1.680100e+00 5.840000e-02 2.025300e+00 -3.460000e-02 4.970000e-02 1.670900e+00 6.889000e-01 2.914000e-01 - 1.108700e+00 2.100000e-03 1.015600e+00 7.153000e-01 7.320000e-01 1.086200e+00 9.141000e-01 8.361000e-01 8.051000e-01 8.140000e-01 9.557000e-01 7.831000e-01 6.136000e-01 3.523000e-01 - 2.780000e-02 6.130000e-02 2.001000e-01 2.144000e-01 3.815000e-01 1.446000e-01 1.435000e-01 1.563000e+00 -2.340000e-02 2.930000e-02 -1.340000e-02 1.620000e-02 1.320000e-01 8.730000e-02 - 1.968000e-01 8.896000e-01 4.827000e-01 9.247000e-01 6.167000e-01 9.017000e-01 1.671100e+00 6.720000e-02 -1.130000e-02 2.014000e+00 5.820000e-02 1.666900e+00 2.653000e-01 5.603000e-01 - 5.201000e-01 5.841000e-01 3.171000e-01 7.668000e-01 3.882000e-01 4.175000e-01 2.912000e-01 8.033000e-01 1.717000e-01 6.541000e-01 2.294000e-01 8.757000e-01 2.216000e-01 4.260000e-01 - 5.786000e-01 4.950000e-02 8.336000e-01 1.201000e-01 9.328000e-01 1.453000e-01 1.627900e+00 1.184000e-01 1.938700e+00 6.730000e-02 2.700000e-03 -2.800000e-03 5.267000e-01 1.920000e-02 - 2.881000e-01 5.880000e-01 6.085000e-01 5.808000e-01 7.821000e-01 5.208000e-01 1.687500e+00 5.650000e-02 -1.156000e-01 2.135100e+00 -2.450000e-02 2.660000e-02 3.434000e-01 3.586000e-01 - 3.163000e-01 4.248000e-01 6.331000e-01 4.197000e-01 7.968000e-01 3.696000e-01 1.673900e+00 6.850000e-02 4.840000e-02 1.946100e+00 1.170000e-02 -1.470000e-02 3.255000e-01 2.971000e-01 - 1.184100e+00 -9.100000e-02 5.882000e-01 6.830000e-01 4.091000e-01 5.274000e-01 6.264000e-01 6.381000e-01 4.739000e-01 4.277000e-01 5.825000e-01 6.909000e-01 3.942000e-01 3.051000e-01 - 2.379000e-01 4.085000e-01 3.863000e-01 6.242000e-01 3.834000e-01 7.741000e-01 3.794000e-01 6.333000e-01 3.210000e-01 8.707000e-01 3.651000e-01 6.482000e-01 2.093000e-01 3.762000e-01 - 1.124500e+00 -2.220000e-02 3.990000e-01 1.027600e+00 2.620000e-01 7.330000e-01 3.240000e-01 1.115300e+00 3.763000e-01 5.714000e-01 4.534000e-01 9.653000e-01 3.213000e-01 4.047000e-01 - 1.099400e+00 8.000000e-03 8.668000e-01 8.454000e-01 5.698000e-01 1.414000e+00 6.727000e-01 1.078300e+00 5.144000e-01 1.395700e+00 6.686000e-01 1.077000e+00 5.120000e-01 4.853000e-01 - 3.698000e-01 1.077000e-01 5.224000e-01 2.341000e-01 5.438000e-01 3.333000e-01 5.149000e-01 2.459000e-01 5.892000e-01 2.902000e-01 5.628000e-01 1.885000e-01 3.137000e-01 1.224000e-01 - 1.164700e+00 -6.970000e-02 3.553000e-01 1.395300e+00 3.349000e-01 1.135600e+00 4.702000e-01 1.253800e+00 3.487000e-01 1.043500e+00 4.655000e-01 1.262100e+00 3.359000e-01 5.706000e-01 - 3.183000e-01 3.051000e-01 7.323000e-01 2.050000e-01 6.892000e-01 4.029000e-01 4.636000e-01 5.233000e-01 6.157000e-01 5.086000e-01 5.282000e-01 4.456000e-01 3.188000e-01 2.398000e-01 - 3.521000e-01 3.844000e-01 5.984000e-01 5.504000e-01 6.438000e-01 6.688000e-01 6.586000e-01 4.784000e-01 6.964000e-01 6.264000e-01 5.053000e-01 6.583000e-01 3.198000e-01 3.473000e-01 - 5.951000e-01 4.877000e-01 8.186000e-01 8.973000e-01 8.961000e-01 1.057700e+00 6.305000e-01 1.118100e+00 8.322000e-01 1.168700e+00 7.579000e-01 9.702000e-01 4.368000e-01 5.585000e-01 - 3.389000e-01 -2.500000e-03 6.462000e-01 1.600000e-03 8.299000e-01 -7.180000e-02 1.751500e+00 -2.660000e-02 6.930000e-02 -8.170000e-02 -3.820000e-02 4.560000e-02 3.626000e-01 1.000000e-03 - 5.361000e-01 6.900000e-03 8.957000e-01 -5.900000e-02 8.035000e-01 1.913000e-01 6.853000e-01 1.933000e-01 8.260000e-01 1.836000e-01 8.115000e-01 4.050000e-02 4.454000e-01 5.170000e-02 - 1.890000e-02 9.513000e-01 1.228000e-01 1.177100e+00 3.517000e-01 1.045600e+00 6.800000e-03 1.724800e+00 -4.210000e-02 2.044000e+00 6.100000e-03 -7.700000e-03 1.020000e-01 6.578000e-01 - 9.760000e-02 1.367000e-01 1.787000e-01 1.816000e-01 9.870000e-02 3.317000e-01 7.080000e-02 3.092000e-01 1.655000e-01 2.588000e-01 1.027000e-01 2.720000e-01 6.920000e-02 1.454000e-01 - 8.847000e-01 -4.760000e-02 1.263500e+00 7.040000e-02 1.474600e+00 3.880000e-02 1.329900e+00 -7.300000e-03 1.572400e+00 -5.190000e-02 1.251700e+00 8.340000e-02 7.533000e-01 1.260000e-02 - 4.102000e-01 6.910000e-01 7.255000e-01 8.216000e-01 8.329000e-01 8.367000e-01 1.705600e+00 3.830000e-02 6.300000e-02 1.926100e+00 -7.250000e-02 1.816300e+00 3.804000e-01 5.212000e-01 - 1.200200e+00 -1.123000e-01 7.355000e-01 6.722000e-01 6.381000e-01 3.311000e-01 7.752000e-01 6.237000e-01 5.633000e-01 3.925000e-01 7.433000e-01 6.622000e-01 5.182000e-01 2.040000e-01 - 1.303000e-01 5.144000e-01 2.051000e-01 8.057000e-01 2.318000e-01 9.195000e-01 1.189000e-01 9.103000e-01 2.451000e-01 9.204000e-01 6.870000e-02 9.648000e-01 9.940000e-02 4.873000e-01 --1.940000e-02 1.128400e+00 9.260000e-02 8.642000e-01 -7.870000e-02 8.619000e-01 1.940000e-02 9.523000e-01 -3.220000e-02 7.856000e-01 1.277000e-01 8.247000e-01 2.400000e-03 6.047000e-01 - 4.836000e-01 3.774000e-01 8.733000e-01 2.825000e-01 9.310000e-01 3.550000e-01 1.847400e+00 -1.350000e-01 -6.380000e-02 2.077500e+00 -3.200000e-03 5.300000e-03 4.776000e-01 2.108000e-01 - 3.880000e-01 7.093000e-01 2.357000e-01 1.496200e+00 1.546000e-01 1.279100e+00 2.154000e-01 1.517400e+00 1.865000e-01 1.174800e+00 2.110000e-01 1.518400e+00 1.589000e-01 7.401000e-01 - 9.033000e-01 1.708000e-01 1.453700e+00 2.186000e-01 1.625800e+00 2.814000e-01 1.394600e+00 2.859000e-01 1.638100e+00 3.015000e-01 1.507100e+00 1.582000e-01 8.207000e-01 1.479000e-01 - 7.640000e-02 9.569000e-01 1.070000e-02 1.348700e+00 -2.890000e-02 1.525800e+00 -6.690000e-02 1.814900e+00 5.600000e-02 1.930900e+00 -7.500000e-03 8.600000e-03 1.070000e-02 7.913000e-01 - 6.920000e-01 1.085000e-01 1.126700e+00 1.214000e-01 1.123300e+00 3.258000e-01 9.782000e-01 2.989000e-01 1.109100e+00 3.648000e-01 9.312000e-01 3.528000e-01 5.654000e-01 1.710000e-01 - 1.140900e+00 -4.250000e-02 5.855000e-01 1.156300e+00 5.839000e-01 5.531000e-01 6.872000e-01 1.039200e+00 5.730000e-01 5.251000e-01 6.184000e-01 1.117100e+00 4.544000e-01 3.476000e-01 - 2.265000e-01 6.849000e-01 5.588000e-01 6.636000e-01 5.984000e-01 7.636000e-01 1.767000e+00 -3.880000e-02 4.490000e-02 1.949400e+00 -5.390000e-02 6.500000e-02 3.104000e-01 4.117000e-01 - 1.136600e+00 -3.860000e-02 7.026000e-01 6.384000e-01 4.141000e-01 5.617000e-01 6.621000e-01 6.825000e-01 6.124000e-01 3.018000e-01 7.418000e-01 5.917000e-01 4.552000e-01 2.588000e-01 - 1.151400e+00 -5.220000e-02 7.812000e-01 9.582000e-01 6.172000e-01 8.571000e-01 8.638000e-01 8.509000e-01 6.028000e-01 8.008000e-01 8.650000e-01 8.518000e-01 5.288000e-01 3.810000e-01 - 3.512000e-01 7.155000e-01 4.879000e-01 1.195400e+00 6.452000e-01 1.257600e+00 5.178000e-01 1.157700e+00 5.995000e-01 1.341400e+00 4.382000e-01 1.253600e+00 2.947000e-01 6.734000e-01 - 3.928000e-01 2.647000e-01 5.870000e-01 4.481000e-01 6.574000e-01 5.253000e-01 6.692000e-01 3.489000e-01 7.849000e-01 3.914000e-01 6.414000e-01 3.851000e-01 3.603000e-01 2.330000e-01 - 9.810000e-02 5.790000e-01 2.610000e-02 1.055300e+00 1.204000e-01 1.094800e+00 1.193000e-01 9.473000e-01 1.539000e-01 1.071500e+00 1.487000e-01 9.104000e-01 5.580000e-02 5.622000e-01 - 3.960000e-01 5.710000e-01 6.352000e-01 8.757000e-01 6.999000e-01 1.032200e+00 6.124000e-01 9.060000e-01 7.728000e-01 9.632000e-01 6.477000e-01 8.664000e-01 3.643000e-01 5.100000e-01 - 1.056200e+00 5.920000e-02 5.754000e-01 5.725000e-01 3.995000e-01 4.736000e-01 4.606000e-01 7.063000e-01 4.667000e-01 3.719000e-01 5.426000e-01 6.059000e-01 3.744000e-01 2.928000e-01 - 1.544000e-01 3.905000e-01 1.708000e-01 6.948000e-01 1.166000e-01 8.799000e-01 1.731000e-01 6.905000e-01 2.379000e-01 7.522000e-01 1.968000e-01 6.632000e-01 1.038000e-01 3.948000e-01 - 7.474000e-01 3.670000e-01 4.144000e-01 1.308300e+00 3.011000e-01 7.628000e-01 4.681000e-01 1.247800e+00 2.852000e-01 7.465000e-01 2.409000e-01 1.516200e+00 2.805000e-01 4.849000e-01 - 5.245000e-01 1.796000e-01 8.253000e-01 2.766000e-01 9.779000e-01 2.720000e-01 8.928000e-01 1.989000e-01 1.056100e+00 1.957000e-01 8.944000e-01 1.973000e-01 4.657000e-01 1.730000e-01 - 2.587000e-01 3.300000e-01 3.558000e-01 5.741000e-01 4.228000e-01 6.357000e-01 3.226000e-01 6.190000e-01 4.366000e-01 6.308000e-01 3.088000e-01 6.295000e-01 2.096000e-01 3.275000e-01 - 4.381000e-01 3.545000e-01 7.058000e-01 5.334000e-01 7.713000e-01 6.482000e-01 6.476000e-01 6.045000e-01 8.761000e-01 5.459000e-01 7.537000e-01 4.779000e-01 4.071000e-01 3.084000e-01 - 1.175000e-01 6.417000e-01 2.160000e-02 3.414000e-01 -4.500000e-03 3.157000e-01 4.350000e-02 3.126000e-01 8.800000e-02 2.014000e-01 8.650000e-02 2.635000e-01 5.010000e-02 2.254000e-01 - 1.150900e+00 -5.330000e-02 8.148000e-01 1.712000e-01 6.775000e-01 8.840000e-02 8.537000e-01 1.243000e-01 6.137000e-01 1.453000e-01 7.788000e-01 2.154000e-01 5.315000e-01 8.070000e-02 --5.630000e-02 9.848000e-01 1.351000e-01 1.084000e+00 6.650000e-02 1.297100e+00 8.910000e-02 1.623000e+00 6.430000e-02 1.925100e+00 4.000000e-04 -6.000000e-04 4.630000e-02 6.800000e-01 - 1.074000e+00 4.070000e-02 4.828000e-01 1.259400e+00 3.677000e-01 1.607300e+00 4.678000e-01 1.268900e+00 5.239000e-01 1.195600e+00 4.787000e-01 1.261600e+00 3.748000e-01 6.080000e-01 - 3.009000e-01 2.211000e-01 5.387000e-01 3.073000e-01 6.518000e-01 3.179000e-01 1.888000e+00 -1.786000e-01 1.250000e-02 -1.310000e-02 4.310000e-02 -5.170000e-02 3.051000e-01 1.811000e-01 - 4.833000e-01 5.855000e-01 6.725000e-01 1.015700e+00 8.943000e-01 1.002800e+00 7.816000e-01 8.851000e-01 9.070000e-01 1.016200e+00 7.665000e-01 9.063000e-01 4.491000e-01 5.144000e-01 - 2.202000e-01 1.536000e-01 3.610000e-01 2.209000e-01 5.806000e-01 5.000000e-02 3.611000e-01 2.241000e-01 4.184000e-01 2.550000e-01 4.442000e-01 1.251000e-01 2.334000e-01 9.850000e-02 - 5.666000e-01 5.338000e-01 2.767000e-01 1.301500e+00 2.398000e-01 7.728000e-01 2.772000e-01 1.298300e+00 2.390000e-01 7.463000e-01 3.174000e-01 1.256700e+00 2.205000e-01 5.236000e-01 - 3.376000e-01 7.419000e-01 4.848000e-01 1.221000e+00 6.335000e-01 1.291000e+00 5.921000e-01 1.088100e+00 6.010000e-01 1.356400e+00 4.958000e-01 1.201200e+00 3.003000e-01 6.781000e-01 - 9.463000e-01 2.020000e-02 1.380900e+00 1.522000e-01 1.676300e+00 5.780000e-02 1.385100e+00 1.532000e-01 1.648500e+00 1.135000e-01 1.339300e+00 2.017000e-01 8.296000e-01 5.060000e-02 - 3.907000e-01 6.294000e-01 5.879000e-01 7.718000e-01 8.172000e-01 6.377000e-01 1.697300e+00 4.520000e-02 1.263000e-01 1.845900e+00 -1.900000e-03 1.300000e-03 4.017000e-01 3.842000e-01 - 1.724000e-01 3.817000e-01 1.702000e-01 7.203000e-01 2.873000e-01 7.049000e-01 2.921000e-01 5.731000e-01 3.515000e-01 6.442000e-01 2.986000e-01 5.659000e-01 1.455000e-01 3.580000e-01 - 1.312000e-01 7.093000e-01 3.948000e-01 7.673000e-01 5.272000e-01 7.554000e-01 1.743300e+00 -1.250000e-02 2.350000e-02 1.975500e+00 2.500000e-03 -3.900000e-03 2.266000e-01 4.576000e-01 - 1.263000e-01 6.105000e-01 2.655000e-01 8.748000e-01 2.336000e-01 1.076800e+00 1.833000e-01 9.726000e-01 3.004000e-01 1.016500e+00 1.962000e-01 9.532000e-01 1.264000e-01 5.363000e-01 - 6.043000e-01 4.914000e-01 1.090000e+00 5.961000e-01 1.056900e+00 8.966000e-01 1.027900e+00 6.694000e-01 1.212700e+00 7.549000e-01 1.065400e+00 6.278000e-01 6.046000e-01 3.749000e-01 - 1.028400e+00 8.930000e-02 4.097000e-01 9.375000e-01 2.813000e-01 6.865000e-01 3.827000e-01 9.620000e-01 2.830000e-01 6.586000e-01 3.508000e-01 1.008600e+00 3.087000e-01 4.039000e-01 --1.970000e-02 7.719000e-01 4.634000e-01 5.700000e-01 5.143000e-01 6.520000e-01 -2.580000e-02 1.762600e+00 -1.730000e-02 2.023200e+00 2.500000e-02 -2.650000e-02 1.832000e-01 4.379000e-01 From scipy-svn at scipy.org Mon Aug 21 04:36:10 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 21 Aug 2006 03:36:10 -0500 (CDT) Subject: [Scipy-svn] r2173 - in trunk/Lib/sandbox/svm: . libsvm-2.82 tests Message-ID: <20060821083610.A7E7C39C013@new.scipy.org> Author: fullung Date: 2006-08-21 03:34:44 -0500 (Mon, 21 Aug 2006) New Revision: 2173 Modified: trunk/Lib/sandbox/svm/classification.py trunk/Lib/sandbox/svm/dataset.py trunk/Lib/sandbox/svm/kernel.py trunk/Lib/sandbox/svm/libsvm-2.82/svm.h trunk/Lib/sandbox/svm/libsvm.py trunk/Lib/sandbox/svm/predict.py trunk/Lib/sandbox/svm/regression.py trunk/Lib/sandbox/svm/setup.py trunk/Lib/sandbox/svm/tests/test_classification.py trunk/Lib/sandbox/svm/tests/test_dataset.py trunk/Lib/sandbox/svm/tests/test_kernel.py trunk/Lib/sandbox/svm/tests/test_regression.py trunk/Lib/sandbox/svm/tests/test_speed.py Log: Refactoring kernels to work better in sparse and dense cases. Lots of other updates. Modified: trunk/Lib/sandbox/svm/classification.py =================================================================== --- trunk/Lib/sandbox/svm/classification.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/classification.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -1,4 +1,5 @@ from ctypes import POINTER, c_int, c_double +from itertools import izip, repeat, chain import numpy as N from model import LibSvmModel @@ -32,7 +33,11 @@ This function does classification on a test vector x and returns the label of the predicted class. """ - return [int(self.predictor.predict(x)) for x in dataset] + if self.predictor.is_compact and dataset.is_array_data(): + return [int(x) for x in + self.predictor.predict(dataset.data)] + else: + return [int(self.predictor.predict(x)) for x in dataset] def predict_values(self, dataset): """ @@ -45,16 +50,19 @@ 2-tuples, one for each permutation of two class labels. """ n = self.nr_class * (self.nr_class - 1) / 2 - def p(v): - count = 0 + def p(vv): d = {} - for i in range(len(self.labels)): - for j in range(i + 1, len(self.labels)): - d[self.labels[i], self.labels[j]] = v[count] - d[self.labels[j], self.labels[i]] = -v[count] - count += 1 + labels = self.labels + for v, (li, lj) in \ + izip(vv, chain(*[izip(repeat(x), labels[i+1:]) + for i, x in enumerate(labels[:-1])])): + d[li, lj] = v + d[lj, li] = -v return d - vs = [self.predictor.predict_values(x, n) for x in dataset] + if self.predictor.is_compact and dataset.is_array_data(): + vs = self.predictor.predict_values(dataset.data, n) + else: + vs = [self.predictor.predict_values(x, n) for x in dataset] return [p(v) for v in vs] def predict_probability(self, dataset): Modified: trunk/Lib/sandbox/svm/dataset.py =================================================================== --- trunk/Lib/sandbox/svm/dataset.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/dataset.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -63,7 +63,7 @@ for j, (yj, xj) in enumerate(origdata[i:]): # Gram matrix is symmetric, so calculate dot product # once and store it in both required locations - z = self.kernel(xi, xj, svm_node_dot) + z = svm_node_dot(xi, xj, self.kernel) # fix index so we assign to the right place j += i grammat[i][j + 1] = 0, z @@ -112,7 +112,7 @@ for i, (yi, xi) in enumerate(dataset.data): i += m for j, (yj, xj) in enumerate(self.origdata): - z = self.kernel(xi, xj, svm_node_dot) + z = svm_node_dot(xi, xj, self.kernel) newgrammat[i][j + 1] = 0, z newgrammat[j][i + 1] = 0, z for i, (yi, xi) in enumerate(dataset.data): @@ -121,7 +121,7 @@ newgrammat[k][0] = 0, id newiddatamap[id] = xi for j, (yj, xj) in enumerate(dataset.data[i:]): - z = self.kernel(xi, xj, svm_node_dot) + z = svm_node_dot(xi, xj, self.kernel) j += k newgrammat[k][j + 1] = 0, z newgrammat[j][k + 1] = 0, z @@ -156,12 +156,17 @@ LibSvmDataSet.__init__(self, data) class LibSvmTestDataSet: - def __init__(self, origdata): - self.data = map(lambda x: convert_to_svm_node(x), origdata) + def __init__(self, data): + self.data = data self.__len__ = self.data.__len__ - self.__iter__ = self.data.__iter__ - self.__getitem__ = self.data.__getitem__ + def __iter__(self): + for x in self.data: + yield convert_to_svm_node(x) + + def is_array_data(self): + return isinstance(self.data, N.ndarray) + def convert_to_svm_node(x): y = N.empty(len(x) + 1, dtype=libsvm.svm_node_dtype) y[-1] = -1, 0. @@ -179,15 +184,10 @@ 'indexes must be unique' return y -def svm_node_dot(x, y): - # associate node indexes with array indexes - xidx = dict(zip(x['index'][:-1],range(0,len(x)))) - yidx = dict(zip(y['index'][:-1],range(0,len(y)))) - # indexes in either vector - indexes = N.unique(N.hstack([x['index'],y['index']])) - z = 0. - for j in indexes: - if j in xidx and j in yidx: - # dot if index is present in both vectors - z += x['value'][xidx[j]] * y['value'][yidx[j]] - return z +def svm_node_dot(x, y, kernel): + maxlen = N.maximum(x['index'].max(), y['index'].max()) + 1 + tmpx = N.zeros((maxlen,), N.float64) + tmpy = N.zeros((maxlen,), N.float64) + tmpx[x['index'][:-1]] = x['value'][:-1] + tmpy[y['index'][:-1]] = y['value'][:-1] + return kernel(tmpx, tmpy) Modified: trunk/Lib/sandbox/svm/kernel.py =================================================================== --- trunk/Lib/sandbox/svm/kernel.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/kernel.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -14,8 +14,10 @@ def __init__(self): self.kernel_type = libsvm.LINEAR - def __call__(self, x, y, dot): - return dot(x, y) + def __call__(self, x, y): + x = N.atleast_2d(x) + y = N.atleast_2d(y) + return N.dot(x, y.T) class PolynomialKernel: def __init__(self, degree, gamma, coef0): @@ -24,8 +26,10 @@ self.gamma = gamma self.coef0 = coef0 - def __call__(self, x, y, dot): - base = self.gamma * dot(x, y) + self.coef0 + def __call__(self, x, y): + x = N.atleast_2d(x) + y = N.atleast_2d(y) + base = self.gamma * N.dot(x, y.T) + self.coef0 tmp = base ret = 1.0 t = self.degree @@ -35,28 +39,50 @@ t /= 2 return ret + def __repr__(self): + return '' % \ + (self.degree, self.gamma, self.coef0) + class RBFKernel: def __init__(self, gamma): self.kernel_type = libsvm.RBF self.gamma = gamma - def __call__(self, x, y, dot): - z = dot(x, x) + dot(y, y) - 2 * dot(x, y) + def __call__(self, x, y): + x = N.atleast_2d(x) + y = N.atleast_2d(y) + xnorm = N.atleast_2d(N.sum(x*x, axis=1)) + ynorm = N.atleast_2d(N.sum(y*y, axis=1)) + z = xnorm + ynorm - 2 * N.atleast_2d(N.dot(x, y.T).squeeze()) return N.exp(-self.gamma * z) + def __repr__(self): + return '' % (self.gamma,) + class SigmoidKernel: def __init__(self, gamma, coef0): self.kernel_type = libsvm.SIGMOID self.gamma = gamma self.coef0 = coef0 - def __call__(self, x, y, dot): - return N.tanh(self.gamma * dot(x, y) + self.coef0) + def __call__(self, x, y): + x = N.atleast_2d(x) + y = N.atleast_2d(y) + return N.tanh(self.gamma * N.dot(x, y.T) + self.coef0) + def __repr__(self): + return '' % \ + (self.gamma, self.coef0) + class CustomKernel: def __init__(self, f): self.kernel_type = libsvm.PRECOMPUTED self.f = f - def __call__(self, x, y, dot): - return self.f(x, y, dot) + def __call__(self, x, y): + x = N.atleast_2d(x) + y = N.atleast_2d(y) + return self.f(x, y) + + def __repr__(self): + return '' % str(self.f) Modified: trunk/Lib/sandbox/svm/libsvm-2.82/svm.h =================================================================== --- trunk/Lib/sandbox/svm/libsvm-2.82/svm.h 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/libsvm-2.82/svm.h 2006-08-21 08:34:44 UTC (rev 2173) @@ -1,6 +1,7 @@ #ifndef _LIBSVM_H #define _LIBSVM_H +#ifdef _WIN32 #ifdef LIBSVM_DLL #ifdef LIBSVM_EXPORTS #define LIBSVM_API __declspec(dllexport) @@ -10,6 +11,9 @@ #else #define LIBSVM_API #endif /* LIBSVM_DLL */ +#else +#define LIBSVM_API +#endif /* _WIN32 */ #ifdef __cplusplus extern "C" { @@ -73,6 +77,10 @@ LIBSVM_API const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param); LIBSVM_API int svm_check_probability_model(const struct svm_model *model); +LIBSVM_API void initlibsvm_() +{ +} + #ifdef __cplusplus } #endif Modified: trunk/Lib/sandbox/svm/libsvm.py =================================================================== --- trunk/Lib/sandbox/svm/libsvm.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/libsvm.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -7,7 +7,7 @@ 'svm_node_dtype' ] -_libsvm = N.ctypes_load_library('libsvm_', __file__) +_libsvm = N.ctypeslib.load_library('libsvm_', __file__) svm_node_dtype = \ N.dtype({'names' : ['index', 'value'], Modified: trunk/Lib/sandbox/svm/predict.py =================================================================== --- trunk/Lib/sandbox/svm/predict.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/predict.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -1,5 +1,5 @@ from ctypes import POINTER, c_double, addressof, byref -from itertools import izip +from itertools import izip, repeat, chain import numpy as N from dataset import svm_node_dot @@ -22,6 +22,7 @@ self._transform_input = self._create_gramvec else: self._transform_input = lambda x: x + self.is_compact = False def __del__(self): libsvm.svm_destroy_model(self.model) @@ -31,7 +32,7 @@ dtype=libsvm.svm_node_dtype) for sv_id in self.sv_ids: sv = self.dataset[sv_id] - gramvec[sv_id]['value'] = self.kernel(x, sv, svm_node_dot) + gramvec[sv_id]['value'] = svm_node_dot(x, sv, self.kernel) return gramvec def predict(self, x): @@ -70,7 +71,7 @@ self.svm_type = modelc.param.svm_type if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: self.nr_class = modelc.nr_class - self.labels = modelc.labels[:self.nr_class] + self.labels = N.array(modelc.labels[:self.nr_class]) nrho = self.nr_class * (self.nr_class - 1) / 2 self.rho = modelc.rho[:nrho] self.sv_coef = [modelc.sv_coef[i][:modelc.l] @@ -97,18 +98,21 @@ def predict(self, x): if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: - n = self.nr_class * (self.nr_class - 1) / 2 + nr_class = self.nr_class + n = nr_class * (nr_class - 1) / 2 dec_values = self.predict_values(x, n) - vote = N.zeros((self.nr_class,), N.intc) - pos = 0 - for i in range(self.nr_class): - for j in range(i + 1, self.nr_class): - if dec_values[pos] > 0: - vote[i] += 1 - else: - vote[j] += 1 - pos += 1 - return self.labels[vote.argmax()] + dec_values = N.atleast_2d(dec_values) + vote = N.zeros((nr_class, dec_values.shape[0]), N.uint32) + classidx = range(nr_class) + for pos, (i, j) in \ + enumerate(chain(*[izip(repeat(idx), classidx[k+1:]) + for k, idx in + enumerate(classidx[:-1])])): + ji = N.array((j, i)) + decisions = N.array(N.sign(dec_values[:,pos]) > 0, N.int8) + chosen_classes = ji[decisions] + vote[chosen_classes,:] += 1 + return self.labels[vote.argmax(axis=0)] else: return self.predict_values(x, 1) @@ -116,7 +120,7 @@ if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: kvalue = N.empty((len(self.support_vectors),)) for i, sv in enumerate(self.support_vectors): - kvalue[i] = self.kernel(x, sv, svm_node_dot) + kvalue[i] = svm_node_dot(x, sv, self.kernel) p = 0 dec_values = N.empty((n,)) for i in range(self.nr_class): @@ -136,23 +140,31 @@ return dec_values else: z = -self.rho - for sv_coef, sv in zip(self.sv_coef, self.support_vectors): - z += sv_coef * self.kernel(x, sv, svm_node_dot) + for sv_coef, sv in izip(self.sv_coef, self.support_vectors): + z += sv_coef * svm_node_dot(x, sv, self.kernel) return z def _predict_values_compact(self, x, n): if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: for i, sv in enumerate(self.support_vectors): kvalue = N.empty((len(self.support_vectors),)) - kvalue[i] = self.kernel(x, sv, svm_node_dot) + kvalue[i] = svm_node_dot(x, sv, self.kernel) return kvalue - self.rho else: sv = self.support_vectors[0] - return self.kernel(x, sv, svm_node_dot) - self.rho + return svm_node_dot(x, sv, self.kernel) - self.rho def predict_values(self, x, n): if self.is_compact: - return self._predict_values_compact(x, n) + if isinstance(x, N.ndarray) \ + and x.dtype in N.sctypes['float']: + svvals = [sv['value'][:-1] for sv in self.support_vectors] + kvalues = [self.kernel(x[:,:len(sv)], sv) for sv in svvals] + x = [kvalue - rho + for kvalue, rho in izip(kvalues, self.rho)] + return N.asarray(zip(*x)) + else: + return self._predict_values_compact(x, n) else: return self._predict_values_sparse(x, n) Modified: trunk/Lib/sandbox/svm/regression.py =================================================================== --- trunk/Lib/sandbox/svm/regression.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/regression.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -29,7 +29,8 @@ This function does regression on a test vector x and returns the function value of x calculated using the model. """ - return [self.predictor.predict(x) for x in dataset] + z = [self.predictor.predict(x) for x in dataset] + return N.asarray(z).squeeze() def get_svr_probability(self): """ Modified: trunk/Lib/sandbox/svm/setup.py =================================================================== --- trunk/Lib/sandbox/svm/setup.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/setup.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -1,3 +1,5 @@ +from os.path import join + def configuration(parent_package='', top_path=None, package_name='svm'): from numpy.distutils.misc_util import Configuration config = Configuration(package_name,parent_package,top_path) @@ -2,6 +4,12 @@ config.add_subpackage('*') + config.add_extension('libsvm_', + define_macros=[('LIBSVM_EXPORTS', None), + ('LIBSVM_DLL', None)], + sources=[join('libsvm-2.82', 'svm.cpp')], + depends=[join('libsvm-2.82', 'svm.h')]) return config if __name__ == '__main__': from numpy.distutils.core import setup - setup(**configuration(top_path='', package_name='scipy.sandbox.svm').todict()) + setup(**configuration(top_path='', + package_name='scipy.sandbox.svm').todict()) Modified: trunk/Lib/sandbox/svm/tests/test_classification.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -226,7 +226,7 @@ p = results.predict(testdata) assert_array_equal(p, refp) - def check_compact(self): + def xcheck_compact(self): traindata, testdata = self._make_basic_datasets() kernel = LinearKernel() cost = 10.0 @@ -236,7 +236,6 @@ refvs = results.predict_values(testdata) results.compact() vs = results.predict_values(testdata) - print vs for refv, v in zip(refvs, vs): for key, value in refv.iteritems(): self.assertEqual(value, v[key]) Modified: trunk/Lib/sandbox/svm/tests/test_dataset.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_dataset.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/tests/test_dataset.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -43,7 +43,9 @@ def check_regression(self): data = [(1.0, N.arange(5))] - dataset = LibSvmRegressionDataSet(data) + y = map(lambda x: x[0], data) + x = map(lambda x: x[1], data) + dataset = LibSvmRegressionDataSet(y, x) self.assertAlmostEqual(dataset.gamma, 0.2) self.assertEqual(len(dataset), len(data)) for i, x in enumerate(dataset): @@ -52,10 +54,12 @@ def check_classification(self): data = [(1, N.arange(4)), (2, N.arange(10))] - dataset = LibSvmClassificationDataSet(data) + labels = map(lambda x: x[0], data) + x = map(lambda x: x[1], data) + dataset = LibSvmClassificationDataSet(labels, x) self.assertAlmostEqual(dataset.gamma, 0.1) - self.assert_(1 in dataset.labels) - self.assert_(2 in dataset.labels) + #self.assert_(1 in dataset.labels) + #self.assert_(2 in dataset.labels) self.assertEqual(len(dataset), len(data)) for i, x in enumerate(dataset): self.assertEqual(data[i][0], x[0]) @@ -70,17 +74,19 @@ assert_array_equal(data[i], x[1]['value'][:-1]) class test_svm_node_dot(NumpyTestCase): - def check_dot(self): + def check_basics(self): + kernel = LinearKernel() + x = N.array([(-1,0.)], dtype=svm_node_dtype) - self.assertAlmostEqual(svm_node_dot(x, x), 0.) + self.assertAlmostEqual(svm_node_dot(x, x, kernel), 0.) x = N.array([(1,1.),(-1,0.)], dtype=svm_node_dtype) y = N.array([(2,2.),(-1,0.)], dtype=svm_node_dtype) - self.assertAlmostEqual(svm_node_dot(x, y), 0.) + self.assertAlmostEqual(svm_node_dot(x, y, kernel), 0.) x = N.array([(3,2.),(-1,0.)], dtype=svm_node_dtype) y = N.array([(3,2.),(-1,0.)], dtype=svm_node_dtype) - self.assertAlmostEqual(svm_node_dot(x, y), 4.) + self.assertAlmostEqual(svm_node_dot(x, y, kernel), 4.) class test_precomputed_dataset(NumpyTestCase): def check_precompute(self): @@ -100,7 +106,7 @@ expt_grammat = N.empty((len(y),)*2) for i, xi in enumerate(x): for j, xj in enumerate(x): - expt_grammat[i, j] = kernel(xi, xj, N.dot) + expt_grammat[i, j] = kernel(xi, xj) # get a new dataset containing the precomputed data pcdata = origdata.precompute(kernel) for i, row in enumerate(pcdata.grammat): @@ -124,7 +130,7 @@ x = N.vstack([x1,x2]) for i, xi in enumerate(x): for j, xj in enumerate(x): - expt_grammat[i, j] = kernel(xi, xj, N.dot) + expt_grammat[i, j] = kernel(xi, xj) for i, row in enumerate(morepcdata.grammat): valuerow = row[1:]['value'] assert_array_almost_equal(valuerow, expt_grammat[i]) Modified: trunk/Lib/sandbox/svm/tests/test_kernel.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_kernel.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/tests/test_kernel.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -5,39 +5,59 @@ from svm.kernel import * restore_path() +def kernelfunc(x, y): + return 8 * N.dot(x, y.T) + class test_kernel(NumpyTestCase): def check_linear_kernel(self): kernel = LinearKernel() - dot = N.dot x = N.array([2.]) - self.assertAlmostEqual(kernel(x, x, dot), 4.) + self.assertAlmostEqual(kernel(x, x), 4.) def check_polynomial_kernel(self): kernel = PolynomialKernel(degree=6, gamma=1.0, coef0=1.0) - dot = N.dot x = N.array([2.]) - self.assertAlmostEqual(kernel(x, x, dot), 15625.) + self.assertAlmostEqual(kernel(x, x), 15625.) def check_sigmoid_kernel(self): kernel = SigmoidKernel(gamma=0.2, coef0=0.3) - dot = N.dot x = N.array([2.]) - self.assertAlmostEqual(kernel(x, x, dot), 0.80049902) + self.assertAlmostEqual(kernel(x, x), 0.80049902) def check_rbf_kernel(self): kernel = RBFKernel(gamma=1.0) - dot = N.dot x, y = N.array([2.]), N.array([3.]) - self.assertAlmostEqual(kernel(x, y, dot), N.exp(-1.)) + self.assertAlmostEqual(kernel(x, y), N.exp(-1.)) def check_custom_kernel(self): - def f(x, y, dot): - return 4 * dot(x, y) - kernel = CustomKernel(f) - def dot(x, y): - return 2 * N.dot(x, y) + kernel = CustomKernel(kernelfunc) x = N.array([2.]) - self.assertAlmostEqual(kernel(x, x, dot), 32.0) + self.assertAlmostEqual(kernel(x, x), 32.0) + def check_multidim_input(self): + kernels = [ + LinearKernel(), + PolynomialKernel(degree=6, gamma=1.0, coef0=1.0), + SigmoidKernel(gamma=0.2, coef0=0.3), + RBFKernel(gamma=1.0), + CustomKernel(kernelfunc) + ] + args = [ + N.random.randn(10), + N.random.randn(1, 10), + N.random.randn(5, 10) + ] + for kernel in kernels: + self.assert_(type(repr(kernel)) is str) + for i, x in enumerate(args): + zshape0 = N.atleast_2d(x).shape[0] + for y in args[i:]: + zshape1 = N.atleast_2d(y).shape[0] + z = kernel(x, y) + self.assertEqual(z.shape[0], zshape0) + self.assertEqual(z.shape[1], zshape1) + u = kernel(y, x) + assert_array_equal(u.squeeze(), z.squeeze()) + if __name__ == '__main__': NumpyTest().run() Modified: trunk/Lib/sandbox/svm/tests/test_regression.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -117,10 +117,10 @@ return trndata, trndata1, trndata2, testdata def _make_kernels(self): - def kernelf(x, y, dot): - return dot(x, y) - def kernelg(x, y, dot): - return -dot(x, y) + def kernelf(x, y): + return N.dot(x, y.T) + def kernelg(x, y): + return -N.dot(x, y.T) kernels = [LinearKernel()] kernels += [RBFKernel(gamma) for gamma in [-0.1, 0.2, 0.3]] Modified: trunk/Lib/sandbox/svm/tests/test_speed.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_speed.py 2006-08-21 01:40:20 UTC (rev 2172) +++ trunk/Lib/sandbox/svm/tests/test_speed.py 2006-08-21 08:34:44 UTC (rev 2173) @@ -10,19 +10,25 @@ class test_classification_speed(NumpyTestCase): def check_large_test_dataset(self): - x = N.random.randn(150, 5) + x = N.random.randn(150, 3) labels = N.random.random_integers(1, 5, x.shape[0]) traindata = LibSvmClassificationDataSet(labels, x) kernel = RBFKernel(traindata.gamma) model = LibSvmCClassificationModel(kernel) + xdim, ydim = 2, 2 + img = N.random.randn(xdim, ydim, 3) + testdata = LibSvmTestDataSet(img.reshape(xdim*ydim, 3)) + + refresults = model.fit(traindata) results = model.fit(traindata, LibSvmPythonPredictor) results.compact() - xdim, ydim = 32, 32 - img = N.random.randn(xdim, ydim, 3) - testdata = LibSvmTestDataSet(img.reshape(xdim*ydim, 3)) + #refv = refresults.predict_values(testdata) v = results.predict_values(testdata) + #print refv + print v + if __name__ == '__main__': NumpyTest().run() From scipy-svn at scipy.org Mon Aug 21 12:32:21 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 21 Aug 2006 11:32:21 -0500 (CDT) Subject: [Scipy-svn] r2174 - trunk/Lib/sandbox/ann Message-ID: <20060821163221.DA38439C03D@new.scipy.org> Author: fred.mailhot Date: 2006-08-21 11:32:19 -0500 (Mon, 21 Aug 2006) New Revision: 2174 Modified: trunk/Lib/sandbox/ann/__init__.py Log: Updated __init__ Modified: trunk/Lib/sandbox/ann/__init__.py =================================================================== --- trunk/Lib/sandbox/ann/__init__.py 2006-08-21 08:34:44 UTC (rev 2173) +++ trunk/Lib/sandbox/ann/__init__.py 2006-08-21 16:32:19 UTC (rev 2174) @@ -1,12 +1,6 @@ -# # req'd file for SciPy package (see DEVELOPERS.txt) # Fred Mailhot # 2006-06-13 -# -# docstring -from mlp import __doc__ +__all__ = ['mlp','srn','rbf'] -# symbols -from mlp import * - From scipy-svn at scipy.org Tue Aug 22 09:46:40 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 22 Aug 2006 08:46:40 -0500 (CDT) Subject: [Scipy-svn] r2175 - trunk/Lib/sandbox/ann Message-ID: <20060822134640.7015439C04C@new.scipy.org> Author: fred.mailhot Date: 2006-08-22 08:46:37 -0500 (Tue, 22 Aug 2006) New Revision: 2175 Modified: trunk/Lib/sandbox/ann/rbf.py Log: Easier way to select random sample of data for RBF centers. Modified: trunk/Lib/sandbox/ann/rbf.py =================================================================== --- trunk/Lib/sandbox/ann/rbf.py 2006-08-21 16:32:19 UTC (rev 2174) +++ trunk/Lib/sandbox/ann/rbf.py 2006-08-22 13:46:37 UTC (rev 2175) @@ -3,6 +3,7 @@ # 2006/08/20 import numpy as N +import random from scipy.optimize import leastsq class rbf: @@ -80,12 +81,8 @@ (ii) set fixed variance from max dist between centers (iii) learn output weights using scipy's leastsq optimizer """ - # set centers - self.centers = N.zeros((len(X)/10,X.shape[1])) - for i in range(len(X)): - if i%10 == 0: - self.centers[i/10] = X[i] - # set variance + # set centers & variance + self.centers = N.array(random.sample(X,len(X)/10)) d_max = 0.0 for i in self.centers: for j in self.centers: From scipy-svn at scipy.org Tue Aug 22 10:03:05 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 22 Aug 2006 09:03:05 -0500 (CDT) Subject: [Scipy-svn] r2176 - in trunk/Lib/sandbox/ann: . doc Message-ID: <20060822140305.7C92B39C03A@new.scipy.org> Author: fred.mailhot Date: 2006-08-22 09:03:02 -0500 (Tue, 22 Aug 2006) New Revision: 2176 Added: trunk/Lib/sandbox/ann/doc/ trunk/Lib/sandbox/ann/doc/mlp.html trunk/Lib/sandbox/ann/doc/rbf.html trunk/Lib/sandbox/ann/doc/srn.html Log: HTML files for {mlp/rbf/srn}.py generated by pydoc uploaded. Added: trunk/Lib/sandbox/ann/doc/mlp.html =================================================================== --- trunk/Lib/sandbox/ann/doc/mlp.html 2006-08-22 13:46:37 UTC (rev 2175) +++ trunk/Lib/sandbox/ann/doc/mlp.html 2006-08-22 14:03:02 UTC (rev 2176) @@ -0,0 +1,93 @@ + + +Python: module mlp + + + + +
 
+ 
mlp		index
/home/tilde/programming/SoC/scipy/Lib/sandbox/ann/mlp.py
+

# mlp.py
+# by: Fred Mailhot
+# last mod: 2006-08-19

+

+ + + + + +
 
+Modules
       
numpy
+

+ + + + + +
 
+Classes
       
+
mlp +
+

+ + + + + + + +
 
+class mlp
   Class to define, train and test a multilayer perceptron.
 
 Methods defined here:
+
__init__(self, ni, nh, no, f='linear', w=None)
Set up instance of mlp. Initial weights are drawn from a 
+zero-mean Gaussian w/ variance is scaled by fan-in.
+Input:
+    ni  - <int> # of inputs
+    nh  - <int> # of hidden units
+    no  - <int> # of outputs
+    f   - <str> output activation fxn
+    w   - <array of float> vector of initial weights
+ +
errfxn(self, w, x, t)
Return vector of squared-errors for the leastsq optimizer
+ +
fwd_all(self, x, w=None)
Propagate values forward through the net. 
+Input:
+    x   - array (size>1) of input patterns
+    w   - optional 1-d vector of weights 
+Returns:
+    y   - array of outputs for all input patterns
+ +
pack(self)
Compile weight matrices w1,b1,w2,b2 from net into a
+single vector, suitable for optimization routines.
+ +
test_all(self, x, t)
Test network on an array (size>1) of patterns
+Input:
+    x   - array of input data
+    t   - array of targets
+Returns:
+    sum-squared-error over all data
+ +
train(self, x, t)
Train network using scipy's leastsq optimizer
+Input:
+    x   - array of input data 
+    t   - array of targets
+    
+    N.B. x and t comprise the *entire* collection of training data
+    
+Returns:
+    post-optimization weight vector
+ +
unpack(self)
Decompose 1-d vector of weights w into appropriate weight 
+matrices (w1,b1,w2,b2) and reinsert them into net
+ +

+ + + + + +
 
+Functions
       
main()
Build/train/test MLP
+
+ \ No newline at end of file Added: trunk/Lib/sandbox/ann/doc/rbf.html =================================================================== --- trunk/Lib/sandbox/ann/doc/rbf.html 2006-08-22 13:46:37 UTC (rev 2175) +++ trunk/Lib/sandbox/ann/doc/rbf.html 2006-08-22 14:03:02 UTC (rev 2176) @@ -0,0 +1,86 @@ + + +Python: module rbf + + + + +
 
+ 
rbf		index
/home/tilde/programming/SoC/scipy/Lib/sandbox/ann/rbf.py
+

# rbf2.py
+# tilde
+# 2006/08/20

+

+ + + + + +
 
+Modules
       
numpy
+		random
+

+ + + + + +
 
+Classes
       
+
rbf +
+

+ + + + + + + +
 
+class rbf
   Class to define/train/test a radial basis function network
 
 Methods defined here:
+
__init__(self, ni, no, f='linear')
Set up instance of RBF net. N.B. RBF centers and variance are selected at training time 
+Input:
+    ni  - <int> # of inputs
+    no  - <int> # of outputs
+    f   - <str> output activation fxn
+ +
err_fxn(self, w, X, Y)
Return vector of squared-errors for the leastsq optimizer
+ +
fwd_all(self, X, w=None)
Propagate values forward through the net.
+Inputs:
+        inputs      - vector of input values
+        w           - packed array of weights
+Returns:
+        array of outputs for all input patterns
+ +
pack(self)
Compile weight matrices w,b from net into a
+single vector, suitable for optimization routines.
+ +
test_all(self, X, Y)
Test network on an array (size>1) of patterns
+Input:
+    x   - array of input data
+    t   - array of targets
+Returns:
+    sum-squared-error over all data
+ +
train(self, X, Y)
Train RBF network:
+(i) select fixed centers randomly from input data (10%)
+(ii) set fixed variance from max dist between centers
+(iii) learn output weights using scipy's leastsq optimizer
+ +
unpack(self)
Decompose 1-d vector of weights w into appropriate weight
+matrices (self.{w/b}) and reinsert them into net
+ +

+ + + + + +
 
+Functions
       
main()
Build/train/test RBF net
+
+ \ No newline at end of file Added: trunk/Lib/sandbox/ann/doc/srn.html =================================================================== --- trunk/Lib/sandbox/ann/doc/srn.html 2006-08-22 13:46:37 UTC (rev 2175) +++ trunk/Lib/sandbox/ann/doc/srn.html 2006-08-22 14:03:02 UTC (rev 2176) @@ -0,0 +1,90 @@ + + +Python: module srn + + + + +
 
+ 
srn		index
/home/tilde/programming/SoC/scipy/Lib/sandbox/ann/srn.py
+

# srn.py
+# by: Fred Mailhot
+# last mod: 2006-08-18

+

+ + + + + +
 
+Modules
       
numpy
+

+ + + + + +
 
+Classes
       
+
srn +
+

+ + + + + + + +
 
+class srn
   Class to define, train and test a simple recurrent network
 
 Methods defined here:
+
__init__(self, ni, nh, no, f='linear', w=None)
Set up instance of srn. Initial weights are drawn from a 
+zero-mean Gaussian w/ variance is scaled by fan-in.
+Input:
+    ni  - <int> # of inputs
+    nh  - <int> # of hidden & context units
+    no  - <int> # of outputs
+    f   - <str> output activation fxn
+    w   - <array dtype=Float> weight vector
+ +
errfxn(self, w, x, t)
Return vector of squared-errors for the leastsq optimizer
+ +
fwd_all(self, x, w=None)
Propagate values forward through the net. 
+Input:
+    x   - matrix of all input patterns
+    w   - 1-d vector of weights
+Returns:
+    y   - matrix of all outputs
+ +
pack(self)
Compile weight matrices w1,b1,wc,w2,b2 from net into a
+single vector, suitable for optimization routines.
+ +
test_all(self, x, t)
Test network on an array (size>1) of patterns
+Input:
+    x   - array of input data
+    t   - array of targets
+Returns:
+    sum-squared-error over all data
+ +
train(self, x, t)
Train a multilayer perceptron using scipy's leastsq optimizer
+Input:
+    x   - matrix of input data
+    t   - matrix of target outputs
+Returns:
+    post-optimization weight vector
+ +
unpack(self)
Decompose 1-d vector of weights w into appropriate weight 
+matrices (w1,b1,w2,b2) and reinsert them into net
+ +

+ + + + + +
 
+Functions
       
main()
Set up a 1-2-1 SRN to solve the temporal-XOR problem from Elman 1990.
+
+ \ No newline at end of file From scipy-svn at scipy.org Wed Aug 23 07:06:56 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 23 Aug 2006 06:06:56 -0500 (CDT) Subject: [Scipy-svn] r2177 - in trunk/Lib/sandbox/svm: . tests Message-ID: <20060823110656.1197C39C077@new.scipy.org> Author: fullung Date: 2006-08-23 06:06:18 -0500 (Wed, 23 Aug 2006) New Revision: 2177 Added: trunk/Lib/sandbox/svm/tests/testall.py Removed: trunk/Lib/sandbox/svm/tests/test_all.py Modified: trunk/Lib/sandbox/svm/ trunk/Lib/sandbox/svm/classification.py trunk/Lib/sandbox/svm/dataset.py trunk/Lib/sandbox/svm/kernel.py trunk/Lib/sandbox/svm/predict.py trunk/Lib/sandbox/svm/tests/ trunk/Lib/sandbox/svm/tests/test_classification.py trunk/Lib/sandbox/svm/tests/test_speed.py Log: More tests, more error checks. Property changes on: trunk/Lib/sandbox/svm ___________________________________________________________________ Name: svn:ignore + *.pyc Modified: trunk/Lib/sandbox/svm/classification.py =================================================================== --- trunk/Lib/sandbox/svm/classification.py 2006-08-22 14:03:02 UTC (rev 2176) +++ trunk/Lib/sandbox/svm/classification.py 2006-08-23 11:06:18 UTC (rev 2177) @@ -51,6 +51,7 @@ """ n = self.nr_class * (self.nr_class - 1) / 2 def p(vv): + vv = N.atleast_1d(vv) d = {} labels = self.labels for v, (li, lj) in \ Modified: trunk/Lib/sandbox/svm/dataset.py =================================================================== --- trunk/Lib/sandbox/svm/dataset.py 2006-08-22 14:03:02 UTC (rev 2176) +++ trunk/Lib/sandbox/svm/dataset.py 2006-08-23 11:06:18 UTC (rev 2177) @@ -165,7 +165,8 @@ yield convert_to_svm_node(x) def is_array_data(self): - return isinstance(self.data, N.ndarray) + return isinstance(self.data, N.ndarray) and \ + self.data.dtype in N.sctypes['float'] def convert_to_svm_node(x): y = N.empty(len(x) + 1, dtype=libsvm.svm_node_dtype) Modified: trunk/Lib/sandbox/svm/kernel.py =================================================================== --- trunk/Lib/sandbox/svm/kernel.py 2006-08-22 14:03:02 UTC (rev 2176) +++ trunk/Lib/sandbox/svm/kernel.py 2006-08-23 11:06:18 UTC (rev 2177) @@ -19,6 +19,9 @@ y = N.atleast_2d(y) return N.dot(x, y.T) + def compact(self, *args): + return self + class PolynomialKernel: def __init__(self, degree, gamma, coef0): self.kernel_type = libsvm.POLY @@ -43,12 +46,17 @@ return '' % \ (self.degree, self.gamma, self.coef0) + def compact(self, *args): + raise NotImplementedError, \ + 'model compaction for PolynomialKernel not implemented' + class RBFKernel: def __init__(self, gamma): self.kernel_type = libsvm.RBF self.gamma = gamma + self.__call__ = self.evaluate - def __call__(self, x, y): + def evaluate(self, x, y): x = N.atleast_2d(x) y = N.atleast_2d(y) xnorm = N.atleast_2d(N.sum(x*x, axis=1)) @@ -56,9 +64,16 @@ z = xnorm + ynorm - 2 * N.atleast_2d(N.dot(x, y.T).squeeze()) return N.exp(-self.gamma * z) + def evaluate_compact(self, x, y): + raise NotImplementedError + def __repr__(self): return '' % (self.gamma,) + def compact(self, *args): + raise NotImplementedError, \ + 'model compaction for RBFKernel not implemented' + class SigmoidKernel: def __init__(self, gamma, coef0): self.kernel_type = libsvm.SIGMOID @@ -74,6 +89,10 @@ return '' % \ (self.gamma, self.coef0) + def compact(self, *args): + raise NotImplementedError, \ + 'model compaction for SigmoidKernel not implemented' + class CustomKernel: def __init__(self, f): self.kernel_type = libsvm.PRECOMPUTED @@ -86,3 +105,7 @@ def __repr__(self): return '' % str(self.f) + + def compact(self, *args): + raise NotImplementedError, \ + 'model compaction for CustomKernel not implemented' Modified: trunk/Lib/sandbox/svm/predict.py =================================================================== --- trunk/Lib/sandbox/svm/predict.py 2006-08-22 14:03:02 UTC (rev 2176) +++ trunk/Lib/sandbox/svm/predict.py 2006-08-23 11:06:18 UTC (rev 2177) @@ -10,11 +10,17 @@ 'LibSvmPythonPredictor' ] +def is_classification_problem(svm_type): + return svm_type in [libsvm.C_SVC, libsvm.NU_SVC] + class LibSvmPredictor: def __init__(self, model, dataset, kernel): self.model = model self.kernel = kernel modelc = model.contents + if is_classification_problem(modelc.param.svm_type) \ + and modelc.nSV[0] == 0: + raise ValueError, 'model contains no support vectors' if modelc.param.kernel_type == libsvm.PRECOMPUTED: self.dataset = dataset self.sv_ids = [int(modelc.SV[i][0].value) @@ -69,7 +75,10 @@ self.kernel = kernel modelc = model.contents self.svm_type = modelc.param.svm_type - if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: + if is_classification_problem(self.svm_type) \ + and modelc.nSV[0] == 0: + raise ValueError, 'model contains no support vectors' + if is_classification_problem(self.svm_type): self.nr_class = modelc.nr_class self.labels = N.array(modelc.labels[:self.nr_class]) nrho = self.nr_class * (self.nr_class - 1) / 2 @@ -97,7 +106,7 @@ libsvm.svm_destroy_model(model) def predict(self, x): - if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: + if is_classification_problem(self.svm_type): nr_class = self.nr_class n = nr_class * (nr_class - 1) / 2 dec_values = self.predict_values(x, n) @@ -117,7 +126,7 @@ return self.predict_values(x, 1) def _predict_values_sparse(self, x, n): - if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: + if is_classification_problem(self.svm_type): kvalue = N.empty((len(self.support_vectors),)) for i, sv in enumerate(self.support_vectors): kvalue[i] = svm_node_dot(x, sv, self.kernel) @@ -145,21 +154,26 @@ return z def _predict_values_compact(self, x, n): - if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: - for i, sv in enumerate(self.support_vectors): + if is_classification_problem(self.svm_type): + for i, (sv, kernel) in \ + enumerate(izip(self.support_vectors, self.kernels)): kvalue = N.empty((len(self.support_vectors),)) - kvalue[i] = svm_node_dot(x, sv, self.kernel) - return kvalue - self.rho + kvalue[i] = svm_node_dot(x, sv, kernel) + kvalue -= self.rho + return kvalue else: sv = self.support_vectors[0] - return svm_node_dot(x, sv, self.kernel) - self.rho + kernel = self.kernels[0] + kvalue = svm_node_dot(x, sv, kernel) - self.rho + return kvalue def predict_values(self, x, n): if self.is_compact: if isinstance(x, N.ndarray) \ and x.dtype in N.sctypes['float']: svvals = [sv['value'][:-1] for sv in self.support_vectors] - kvalues = [self.kernel(x[:,:len(sv)], sv) for sv in svvals] + kvalues = [kernel(x[:,:len(sv)], sv) + for sv, kernel in izip(svvals, self.kernels)] x = [kvalue - rho for kvalue, rho in izip(kvalues, self.rho)] return N.asarray(zip(*x)) @@ -184,8 +198,9 @@ return csv def compact(self): - if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]: + if is_classification_problem(self.svm_type): compact_support_vectors = [] + kernels = [] for i in range(self.nr_class): for j in range(i + 1, self.nr_class): si, sj = self.start[i], self.start[j] @@ -194,10 +209,22 @@ svj = self.support_vectors[sj:sj + cj] coef1 = self.sv_coef[j - 1][si:si + ci] coef2 = self.sv_coef[i][sj:sj + cj] - csv = self._compact_svs(svi + svj, coef1 + coef2) + svij = svi + svj + coef12 = coef1 + coef2 + # Create a compacted kernel. This allows a kernel + # that depends on some values that cannot be + # calculated using from the compact representation + # of the support vectors to calculate these + # values before the time. + kernels.append(self.kernel.compact(svij, coef12)) + csv = self._compact_svs(svij, coef12) compact_support_vectors.append(csv) self.support_vectors = compact_support_vectors + self.kernel = None + self.kernels = kernels else: csv = self._compact_svs(self.support_vectors, self.sv_coef) self.support_vectors = [csv] + self.kernels = [self.kernel.compact()] + self.kernel = None self.is_compact = True Property changes on: trunk/Lib/sandbox/svm/tests ___________________________________________________________________ Name: svn:ignore + *.pyc Deleted: trunk/Lib/sandbox/svm/tests/test_all.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_all.py 2006-08-22 14:03:02 UTC (rev 2176) +++ trunk/Lib/sandbox/svm/tests/test_all.py 2006-08-23 11:06:18 UTC (rev 2177) @@ -1,9 +0,0 @@ -from test_classification import * -from test_dataset import * -from test_kernel import * -from test_libsvm import * -from test_oneclass import * -from test_regression import * - -if __name__ == '__main__': - NumpyTest().run() Modified: trunk/Lib/sandbox/svm/tests/test_classification.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-22 14:03:02 UTC (rev 2176) +++ trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-23 11:06:18 UTC (rev 2177) @@ -1,3 +1,4 @@ +from itertools import izip from numpy.testing import * import numpy as N @@ -240,5 +241,71 @@ for key, value in refv.iteritems(): self.assertEqual(value, v[key]) + def _make_compact_check_datasets(self): + x = N.random.randn(150, 3) + labels = N.random.random_integers(1, 5, x.shape[0]) + traindata = LibSvmClassificationDataSet(labels, x) + xdim, ydim, zdim = 4, 4, x.shape[1] + img = N.random.randn(xdim, ydim, zdim) + testdata1 = LibSvmTestDataSet(img.reshape(xdim*ydim, zdim)) + testdata2 = LibSvmTestDataSet(list(img.reshape(xdim*ydim, zdim))) + return traindata, testdata1, testdata2 + + def check_compact_predict_values(self): + def compare_predict_values(vx, vy): + for pred1, pred2 in izip(vx, vy): + for labels, x in pred1.iteritems(): + self.assert_(labels in pred2) + self.assertAlmostEqual(x, pred2[labels]) + traindata, testdata1, testdata2 = \ + self._make_compact_check_datasets() + kernel = LinearKernel() + model = LibSvmCClassificationModel(kernel) + refresults = model.fit(traindata) + refv1 = refresults.predict_values(testdata1) + refv2 = refresults.predict_values(testdata2) + results = model.fit(traindata, LibSvmPythonPredictor) + v11 = results.predict_values(testdata1) + v12 = results.predict_values(testdata2) + results.compact() + v21 = results.predict_values(testdata1) + v22 = results.predict_values(testdata2) + compare_predict_values(refv1, refv2) + compare_predict_values(refv1, v11) + compare_predict_values(refv1, v12) + compare_predict_values(refv1, v21) + # XXX this test fails + #compare_predict_values(refv1, v22) + + def check_compact_predict(self): + traindata, testdata1, testdata2 = \ + self._make_compact_check_datasets() + kernel = LinearKernel() + model = LibSvmCClassificationModel(kernel) + refresults = model.fit(traindata) + refp1 = refresults.predict(testdata1) + refp2 = refresults.predict(testdata2) + results = model.fit(traindata, LibSvmPythonPredictor) + p11 = results.predict(testdata1) + p12 = results.predict(testdata2) + results.compact() + p21 = results.predict(testdata1) + p22 = results.predict(testdata2) + self.assertEqual(refp1, refp2) + self.assertEqual(refp1, p11) + self.assertEqual(refp1, p12) + # XXX these tests fail + #self.assertEqual(refp1, p21) + #self.assertEqual(refp1, p22) + + def check_no_support_vectors(self): + x = N.array([[10.0, 20.0]]) + labels = [1] + traindata = LibSvmClassificationDataSet(labels, x) + kernel = LinearKernel() + model = LibSvmCClassificationModel(kernel) + testdata = LibSvmTestDataSet(x) + self.assertRaises(ValueError, model.fit, traindata) + if __name__ == '__main__': NumpyTest().run() Modified: trunk/Lib/sandbox/svm/tests/test_speed.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_speed.py 2006-08-22 14:03:02 UTC (rev 2176) +++ trunk/Lib/sandbox/svm/tests/test_speed.py 2006-08-23 11:06:18 UTC (rev 2177) @@ -11,24 +11,43 @@ class test_classification_speed(NumpyTestCase): def check_large_test_dataset(self): x = N.random.randn(150, 3) + + # XXX shows bug where we can't get any support vectors + #x = N.random.randn(4, 2) + + #x = N.random.randn(10, 3) + labels = N.random.random_integers(1, 5, x.shape[0]) + #labels = N.random.random_integers(1, 2, x.shape[0]) traindata = LibSvmClassificationDataSet(labels, x) - - kernel = RBFKernel(traindata.gamma) + #kernel = RBFKernel(traindata.gamma) + kernel = LinearKernel() + #kernel = PolynomialKernel(2, 5, 10) model = LibSvmCClassificationModel(kernel) - xdim, ydim = 2, 2 - img = N.random.randn(xdim, ydim, 3) - testdata = LibSvmTestDataSet(img.reshape(xdim*ydim, 3)) + #xdim, ydim, zdim = 1, 1, x.shape[1] + xdim, ydim, zdim = 2, 2, x.shape[1] + img = N.random.randn(xdim, ydim, zdim) + testdata1 = LibSvmTestDataSet(img.reshape(xdim*ydim, zdim)) + testdata2 = LibSvmTestDataSet(list(img.reshape(xdim*ydim, zdim))) refresults = model.fit(traindata) + refv1 = refresults.predict_values(testdata1) + refv2 = refresults.predict_values(testdata2) + results = model.fit(traindata, LibSvmPythonPredictor) + #v11 = results.predict_values(testdata1) + #v12 = results.predict_values(testdata2) + results.compact() + v21 = results.predict_values(testdata1) + #v22 = results.predict_values(testdata2) - #refv = refresults.predict_values(testdata) - v = results.predict_values(testdata) + print refv1 + print refv2 + #print v11 + #print v12 + print v21 + #print v22 - #print refv - print v - if __name__ == '__main__': NumpyTest().run() Copied: trunk/Lib/sandbox/svm/tests/testall.py (from rev 2176, trunk/Lib/sandbox/svm/tests/test_all.py) From scipy-svn at scipy.org Fri Aug 25 04:09:05 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 25 Aug 2006 03:09:05 -0500 (CDT) Subject: [Scipy-svn] r2178 - in trunk/Lib/io: . tests/data Message-ID: <20060825080905.155B339C07F@new.scipy.org> Author: stefan Date: 2006-08-25 03:08:57 -0500 (Fri, 25 Aug 2006) New Revision: 2178 Added: trunk/Lib/io/tests/data/testvec_4_GLNX86.mat Modified: trunk/Lib/io/mio.py Log: Fix loadmat for v4 files and add test. Modified: trunk/Lib/io/mio.py =================================================================== --- trunk/Lib/io/mio.py 2006-08-23 11:06:18 UTC (rev 2177) +++ trunk/Lib/io/mio.py 2006-08-25 08:08:57 UTC (rev 2178) @@ -8,7 +8,8 @@ import zlib from numpy import array, asarray, empty, obj2sctype, product, reshape, \ - squeeze, transpose, zeros, vstack, ndarray, shape, diff, where, uint8 + squeeze, transpose, zeros, vstack, ndarray, shape, diff, where, uint8, \ + atleast_1d import numpyio try: @@ -113,7 +114,7 @@ self.file.seek(*args) def tell(self): - self.file.tell() + return self.file.tell() def raw_read(self, size=-1): """Read raw bytes from file as string.""" Added: trunk/Lib/io/tests/data/testvec_4_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/Lib/io/tests/data/testvec_4_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream From scipy-svn at scipy.org Fri Aug 25 04:48:17 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 25 Aug 2006 03:48:17 -0500 (CDT) Subject: [Scipy-svn] r2179 - trunk/Lib/io/tests Message-ID: <20060825084817.37E7839C07F@new.scipy.org> Author: stefan Date: 2006-08-25 03:48:12 -0500 (Fri, 25 Aug 2006) New Revision: 2179 Modified: trunk/Lib/io/tests/test_mio.py Log: In test_mio, check accuracy to 5 decimals only (allows results to be typed in code). Add vec as a testcase. Modified: trunk/Lib/io/tests/test_mio.py =================================================================== --- trunk/Lib/io/tests/test_mio.py 2006-08-25 08:08:57 UTC (rev 2178) +++ trunk/Lib/io/tests/test_mio.py 2006-08-25 08:48:12 UTC (rev 2179) @@ -52,10 +52,11 @@ assert SP.issparse(actual), "Expected sparse at %s" % label assert_array_almost_equal(actual.todense(), expected.todense(), - err_msg = label) + err_msg = label, + decimal = 5) elif isinstance(expected, ndarray): assert isinstance(actual, ndarray), "Expected ndarray at %s" % label - assert_array_almost_equal(actual, expected, err_msg=label) + assert_array_almost_equal(actual, expected, err_msg=label, decimal = 5) else: assert isinstance(expected, type(actual)), \ "Types %s and %s do not match at %s" % (type(expected), type(actual), label) @@ -183,6 +184,12 @@ {'name': 'object', 'expected': {'testobject': a} }) + + case_table.append( + {'name': 'vec', + 'expected': {'fit_params': array([1.27661364061704e+09,7.51130255826677e-03]), + 'xdot_filt': array([8.11154474752301e-13,1.28504039006994e-11])} + }) # add tests for case in case_table: From scipy-svn at scipy.org Sun Aug 27 03:44:22 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 27 Aug 2006 02:44:22 -0500 (CDT) Subject: [Scipy-svn] r2180 - in trunk/Lib: misc signal special stats Message-ID: <20060827074422.A1A0139C00D@new.scipy.org> Author: oliphant Date: 2006-08-27 02:44:15 -0500 (Sun, 27 Aug 2006) New Revision: 2180 Modified: trunk/Lib/misc/common.py trunk/Lib/signal/waveforms.py trunk/Lib/special/basic.py trunk/Lib/stats/distributions.py trunk/Lib/stats/stats.py Log: Change all uses of insert(arr, mask, axis) to place(arr, mask, axis) Modified: trunk/Lib/misc/common.py =================================================================== --- trunk/Lib/misc/common.py 2006-08-25 08:48:12 UTC (rev 2179) +++ trunk/Lib/misc/common.py 2006-08-27 07:44:15 UTC (rev 2180) @@ -9,7 +9,7 @@ from numpy import exp, asarray, arange, \ newaxis, hstack, product, array, where, \ - zeros, extract, insert, pi, sqrt, eye, poly1d, dot, r_ + zeros, extract, place, pi, sqrt, eye, poly1d, dot, r_ from numpy import who @@ -81,8 +81,8 @@ evenn = extract(cond2,n) nd2o = oddn / 2.0 nd2e = evenn / 2.0 - insert(vals,cond1,special.gamma(nd2o+1)/sqrt(pi)*pow(2.0,nd2o+0.5)) - insert(vals,cond2,special.gamma(nd2e+1) * pow(2.0,nd2e)) + place(vals,cond1,special.gamma(nd2o+1)/sqrt(pi)*pow(2.0,nd2o+0.5)) + place(vals,cond2,special.gamma(nd2e+1) * pow(2.0,nd2e)) return vals def factorialk(n,k,exact=1): Modified: trunk/Lib/signal/waveforms.py =================================================================== --- trunk/Lib/signal/waveforms.py 2006-08-25 08:48:12 UTC (rev 2179) +++ trunk/Lib/signal/waveforms.py 2006-08-27 07:44:15 UTC (rev 2180) @@ -3,7 +3,7 @@ # Author: Travis Oliphant # 2003 -from numpy import asarray, zeros, insert, nan, mod, pi, extract, log, sqrt, \ +from numpy import asarray, zeros, place, nan, mod, pi, extract, log, sqrt, \ exp, cos, sin, size, polyval, polyint, log10 def sawtooth(t,width=1): @@ -23,7 +23,7 @@ # width must be between 0 and 1 inclusive mask1 = (w > 1) | (w < 0) - insert(y,mask1,nan) + place(y,mask1,nan) # take t modulo 2*pi tmod = mod(t,2*pi) @@ -33,7 +33,7 @@ mask2 = (1-mask1) & (tmod < w*2*pi) tsub = extract(mask2,tmod) wsub = extract(mask2,w) - insert(y,mask2,tsub / (pi*wsub) - 1) + place(y,mask2,tsub / (pi*wsub) - 1) # on the interval width*2*pi to 2*pi function is # (pi*(w+1)-tmod) / (pi*(1-w)) @@ -41,7 +41,7 @@ mask3 = (1-mask1) & (1-mask2) tsub = extract(mask3,tmod) wsub = extract(mask3,w) - insert(y,mask3, (pi*(wsub+1)-tsub)/(pi*(1-wsub))) + place(y,mask3, (pi*(wsub+1)-tsub)/(pi*(1-wsub))) return y @@ -61,7 +61,7 @@ # width must be between 0 and 1 inclusive mask1 = (w > 1) | (w < 0) - insert(y,mask1,nan) + place(y,mask1,nan) # take t modulo 2*pi tmod = mod(t,2*pi) @@ -71,7 +71,7 @@ mask2 = (1-mask1) & (tmod < w*2*pi) tsub = extract(mask2,tmod) wsub = extract(mask2,w) - insert(y,mask2,1) + place(y,mask2,1) # on the interval duty*2*pi to 2*pi function is # (pi*(w+1)-tmod) / (pi*(1-w)) @@ -79,7 +79,7 @@ mask3 = (1-mask1) & (1-mask2) tsub = extract(mask3,tmod) wsub = extract(mask3,w) - insert(y,mask3,-1) + place(y,mask3,-1) return y def gausspulse(t,fc=1000,bw=0.5,bwr=-6,tpr=-60,retquad=0,retenv=0): Modified: trunk/Lib/special/basic.py =================================================================== --- trunk/Lib/special/basic.py 2006-08-25 08:48:12 UTC (rev 2179) +++ trunk/Lib/special/basic.py 2006-08-27 07:44:15 UTC (rev 2180) @@ -32,18 +32,18 @@ y = zeros(x.shape,ytype) mask1 = (n <= 0) | (n <> floor(n)) - insert(y,mask1,nan) + place(y,mask1,nan) z = asarray(x / 2.0 / pi) mask2 = (1-mask1) & (z == floor(z)) zsub = extract(mask2,z) nsub = extract(mask2,n) - insert(y,mask2,pow(-1,zsub*(nsub-1))) + place(y,mask2,pow(-1,zsub*(nsub-1))) mask = (1-mask1) & (1-mask2) xsub = extract(mask,x) nsub = extract(mask,n) - insert(y,mask,sin(nsub*xsub/2.0)/(nsub*sin(xsub/2.0))) + place(y,mask,sin(nsub*xsub/2.0)/(nsub*sin(xsub/2.0))) return y Modified: trunk/Lib/stats/distributions.py =================================================================== --- trunk/Lib/stats/distributions.py 2006-08-25 08:48:12 UTC (rev 2179) +++ trunk/Lib/stats/distributions.py 2006-08-27 07:44:15 UTC (rev 2180) @@ -13,7 +13,7 @@ ravel, take, ones, sum, shape, product, repeat, reshape, \ zeros, floor, logical_and, log, sqrt, exp, arctanh, tan, sin, arcsin, \ arctan, tanh, ndarray, cos, cosh, sinh, newaxis -from numpy import atleast_1d, polyval, angle, ceil, insert, extract, \ +from numpy import atleast_1d, polyval, angle, ceil, place, extract, \ any, argsort, argmax, vectorize, r_, asarray, nan, inf, pi, isnan, isinf import numpy import numpy.random as mtrand @@ -461,10 +461,10 @@ cond1 = (scale > 0) & (x > self.a) & (x < self.b) cond = cond0 & cond1 output = zeros(shape(cond),'d') - insert(output,(1-cond0)*(cond1==cond1),self.badvalue) + place(output,(1-cond0)*(cond1==cond1),self.badvalue) goodargs = argsreduce(cond, *((x,)+args+(scale,))) scale, goodargs = goodargs[-1], goodargs[:-1] - insert(output,cond,self._pdf(*goodargs) / scale) + place(output,cond,self._pdf(*goodargs) / scale) return output def cdf(self,x,*args,**kwds): @@ -490,10 +490,10 @@ cond2 = (x >= self.b) & cond0 cond = cond0 & cond1 output = zeros(shape(cond),'d') - insert(output,(1-cond0)*(cond1==cond1),self.badvalue) - insert(output,cond2,1.0) + place(output,(1-cond0)*(cond1==cond1),self.badvalue) + place(output,cond2,1.0) goodargs = argsreduce(cond, *((x,)+args)) - insert(output,cond,self._cdf(*goodargs)) + place(output,cond,self._cdf(*goodargs)) return output def sf(self,x,*args,**kwds): @@ -519,10 +519,10 @@ cond2 = cond0 & (x <= self.a) cond = cond0 & cond1 output = zeros(shape(cond),'d') - insert(output,(1-cond0)*(cond1==cond1),self.badvalue) - insert(output,cond2,1.0) + place(output,(1-cond0)*(cond1==cond1),self.badvalue) + place(output,cond2,1.0) goodargs = argsreduce(cond, *((x,)+args)) - insert(output,cond,self._sf(*goodargs)) + place(output,cond,self._sf(*goodargs)) return output def ppf(self,q,*args,**kwds): @@ -547,11 +547,11 @@ cond2 = (q==1) & cond0 cond = cond0 & cond1 output = valarray(shape(cond),value=self.a*scale + loc) - insert(output,(1-cond0)+(1-cond1)*(q!=0.0), self.badvalue) - insert(output,cond2,self.b*scale + loc) + place(output,(1-cond0)+(1-cond1)*(q!=0.0), self.badvalue) + place(output,cond2,self.b*scale + loc) goodargs = argsreduce(cond, *((q,)+args+(scale,loc))) scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2] - insert(output,cond,self._ppf(*goodargs)*scale + loc) + place(output,cond,self._ppf(*goodargs)*scale + loc) return output def isf(self,q,*args,**kwds): @@ -576,11 +576,11 @@ cond2 = (q==1) & cond0 cond = cond0 & cond1 output = valarray(shape(cond),value=self.b) - insert(output,(1-cond0)*(cond1==cond1), self.badvalue) - insert(output,cond2,self.a) + place(output,(1-cond0)*(cond1==cond1), self.badvalue) + place(output,cond2,self.a) goodargs = argsreduce(cond, *((1.0-q,)+args+(scale,loc))) scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2] - insert(output,cond,self._ppf(*goodargs)*scale + loc) + place(output,cond,self._ppf(*goodargs)*scale + loc) return output def stats(self,*args,**kwds): @@ -643,7 +643,7 @@ if mu is None: mu = self._munp(1.0,*goodargs) out0 = default.copy() - insert(out0,cond,mu*scale+loc) + place(out0,cond,mu*scale+loc) output.append(out0) if 'v' in moments: @@ -653,7 +653,7 @@ mu = self._munp(1.0,*goodargs) mu2 = mu2p - mu*mu out0 = default.copy() - insert(out0,cond,mu2*scale*scale) + place(out0,cond,mu2*scale*scale) output.append(out0) if 's' in moments: @@ -667,7 +667,7 @@ mu3 = mu3p - 3*mu*mu2 - mu**3 g1 = mu3 / mu2**1.5 out0 = default.copy() - insert(out0,cond,g1) + place(out0,cond,g1) output.append(out0) if 'k' in moments: @@ -684,7 +684,7 @@ mu4 = mu4p - 4*mu*mu3 - 6*mu*mu*mu2 - mu**4 g2 = mu4 / mu2**2.0 - 3.0 out0 = default.copy() - insert(out0,cond,g2) + place(out0,cond,g2) output.append(out0) if len(output) == 1: @@ -780,9 +780,9 @@ args = map(arr,args) cond0 = self._argcheck(*args) & (scale > 0) & (loc==loc) output = zeros(shape(cond0),'d') - insert(output,(1-cond0),self.badvalue) + place(output,(1-cond0),self.badvalue) goodargs = argsreduce(cond0, *args) - insert(output,cond0,self.vecentropy(*goodargs)+log(scale)) + place(output,cond0,self.vecentropy(*goodargs)+log(scale)) return output _EULER = 0.577215664901532860606512090082402431042 # -special.psi(1) @@ -2595,25 +2595,25 @@ mask = b > 1 bt = extract(mask,b) mu = valarray(shape(b),value=inf) - insert(mu, mask, bt / (bt-1.0)) + place(mu, mask, bt / (bt-1.0)) if 'v' in moments: mask = b > 2 bt = extract( mask,b) mu2 = valarray(shape(b), value=inf) - insert(mu2, mask, bt / (bt-2.0) / (bt-1.0)**2) + place(mu2, mask, bt / (bt-2.0) / (bt-1.0)**2) if 's' in moments: mask = b > 3 bt = extract( mask,b) g1 = valarray(shape(b), value=nan) vals = 2*(bt+1.0)*sqrt(b-2.0)/((b-3.0)*sqrt(b)) - insert(g1, mask, vals) + place(g1, mask, vals) if 'k' in moments: mask = b > 4 bt = extract( mask,b) g2 = valarray(shape(b), value=nan) vals = 6.0*polyval([1.0,1.0,-6,-2],bt)/ \ polyval([1.0,-7.0,12.0,0.0],bt) - insert(g2, mask, vals) + place(g2, mask, vals) return mu, mu2, g1, g2 def _entropy(self, c): return 1 + 1.0/c - log(c) @@ -3142,11 +3142,11 @@ xn = 2*pi - xn yn = tan(xn/2.0) on = 1.0-1.0/pi*arctan(valn*yn) - insert(output, c2, on) + place(output, c2, on) if (any(xp)): yp = tan(xp/2.0) op = 1.0/pi*arctan(valp*yp) - insert(output, c1, op) + place(output, c1, op) return output def _ppf(self, q, c): val = (1.0-c)/(1.0+c) @@ -3502,9 +3502,9 @@ cond1 = (k >= self.a) & (k <= self.b) & self._nonzero(k,*args) cond = cond0 & cond1 output = zeros(shape(cond),'d') - insert(output,(1-cond0)*(cond1==cond1),self.badvalue) + place(output,(1-cond0)*(cond1==cond1),self.badvalue) goodargs = argsreduce(cond, *((k,)+args)) - insert(output,cond,self._pmf(*goodargs)) + place(output,cond,self._pmf(*goodargs)) return output def cdf(self, k, *args, **kwds): @@ -3529,10 +3529,10 @@ cond2 = (k >= self.b) cond = cond0 & cond1 output = zeros(shape(cond),'d') - insert(output,(1-cond0)*(cond1==cond1),self.badvalue) - insert(output,cond2*(cond0==cond0), 1.0) + place(output,(1-cond0)*(cond1==cond1),self.badvalue) + place(output,cond2*(cond0==cond0), 1.0) goodargs = argsreduce(cond, *((k,)+args)) - insert(output,cond,self._cdf(*goodargs)) + place(output,cond,self._cdf(*goodargs)) return output def sf(self,k,*args,**kwds): @@ -3557,10 +3557,10 @@ cond2 = (k < self.a) & cond0 cond = cond0 & cond1 output = zeros(shape(cond),'d') - insert(output,(1-cond0)*(cond1==cond1),self.badvalue) - insert(output,cond2,1.0) + place(output,(1-cond0)*(cond1==cond1),self.badvalue) + place(output,cond2,1.0) goodargs = argsreduce(cond, *((k,)+args)) - insert(output,cond,self._sf(*goodargs)) + place(output,cond,self._sf(*goodargs)) return output def ppf(self,q,*args,**kwds): @@ -3584,11 +3584,11 @@ cond2 = (q==1) & cond0 cond = cond0 & cond1 output = valarray(shape(cond),value=self.a-1) - insert(output,(1-cond0)*(cond1==cond1), self.badvalue) - insert(output,cond2,self.b) + place(output,(1-cond0)*(cond1==cond1), self.badvalue) + place(output,cond2,self.b) goodargs = argsreduce(cond, *((q,)+args+(loc,))) loc, goodargs = goodargs[-1], goodargs[:-1] - insert(output,cond,self._ppf(*goodargs) + loc) + place(output,cond,self._ppf(*goodargs) + loc) return output def isf(self,q,*args,**kwds): @@ -3613,11 +3613,11 @@ cond2 = (q==1) & cond0 cond = cond0 & cond1 output = valarray(shape(cond),value=self.b) - insert(output,(1-cond0)*(cond1==cond1), self.badvalue) - insert(output,cond2,self.a-1) + place(output,(1-cond0)*(cond1==cond1), self.badvalue) + place(output,cond2,self.a-1) goodargs = argsreduce(cond, *((q,)+args+(loc,))) loc, goodargs = goodargs[-1], goodargs[:-1] - insert(output,cond,self._ppf(*goodargs) + loc) + place(output,cond,self._ppf(*goodargs) + loc) return output def stats(self, *args, **kwds): @@ -3673,7 +3673,7 @@ if mu is None: mu = self._munp(1.0,*goodargs) out0 = default.copy() - insert(out0,cond,mu+loc) + place(out0,cond,mu+loc) output.append(out0) if 'v' in moments: @@ -3683,7 +3683,7 @@ mu = self._munp(1.0,*goodargs) mu2 = mu2p - mu*mu out0 = default.copy() - insert(out0,cond,mu2) + place(out0,cond,mu2) output.append(out0) if 's' in moments: @@ -3697,7 +3697,7 @@ mu3 = mu3p - 3*mu*mu2 - mu**3 g1 = mu3 / mu2**1.5 out0 = default.copy() - insert(out0,cond,g1) + place(out0,cond,g1) output.append(out0) if 'k' in moments: @@ -3714,7 +3714,7 @@ mu4 = mu4p - 4*mu*mu3 - 6*mu*mu*mu2 - mu**4 g2 = mu4 / mu2**2.0 - 3.0 out0 = default.copy() - insert(out0,cond,g2) + place(out0,cond,g2) output.append(out0) if len(output) == 1: @@ -3779,9 +3779,9 @@ args = map(arr,args) cond0 = self._argcheck(*args) & (loc==loc) output = zeros(shape(cond0),'d') - insert(output,(1-cond0),self.badvalue) + place(output,(1-cond0),self.badvalue) goodargs = argsreduce(cond0, *args) - insert(output,cond0,self.vecentropy(*goodargs)) + place(output,cond0,self.vecentropy(*goodargs)) return output def __call__(self, *args, **kwds): Modified: trunk/Lib/stats/stats.py =================================================================== --- trunk/Lib/stats/stats.py 2006-08-25 08:48:12 UTC (rev 2179) +++ trunk/Lib/stats/stats.py 2006-08-27 07:44:15 UTC (rev 2180) @@ -692,7 +692,7 @@ m2 = np.extract(can_correct, m2) m3 = np.extract(can_correct, m3) nval = np.sqrt((n-1.0)*n)/(n-2.0)*m3/m2**1.5 - np.insert(vals, can_correct, nval) + np.place(vals, can_correct, nval) return vals def kurtosis(a, axis=0, fisher=True, bias=True): @@ -738,7 +738,7 @@ m2 = np.extract(can_correct, m2) m4 = np.extract(can_correct, m4) nval = 1.0/(n-2)/(n-3)*((n*n-1.0)*m4/m2**2.0-3*(n-1)**2.0) - np.insert(vals, can_correct, nval+3.0) + np.place(vals, can_correct, nval+3.0) if fisher: return vals - 3 else: From scipy-svn at scipy.org Mon Aug 28 23:40:34 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 28 Aug 2006 22:40:34 -0500 (CDT) Subject: [Scipy-svn] r2181 - trunk/Lib Message-ID: <20060829034034.DB70739C033@new.scipy.org> Author: oliphant Date: 2006-08-28 22:40:28 -0500 (Mon, 28 Aug 2006) New Revision: 2181 Modified: trunk/Lib/version.py Log: Use NumPy scheme for development versions of scipy. Modified: trunk/Lib/version.py =================================================================== --- trunk/Lib/version.py 2006-08-27 07:44:15 UTC (rev 2180) +++ trunk/Lib/version.py 2006-08-29 03:40:28 UTC (rev 2181) @@ -1,13 +1,15 @@ -version = '0.5.0' +version = '0.5.1' +release=False -import os -svn_version_file = os.path.join(os.path.dirname(__file__), - '__svn_version__.py') +if not release: + import os + svn_version_file = os.path.join(os.path.dirname(__file__), + '__svn_version__.py') -if os.path.isfile(svn_version_file): - import imp - svn = imp.load_module('scipy.__svn_version__', - open(svn_version_file), - svn_version_file, - ('.py','U',1)) - version += '.'+svn.version + if os.path.isfile(svn_version_file): + import imp + svn = imp.load_module('scipy.__svn_version__', + open(svn_version_file), + svn_version_file, + ('.py','U',1)) + version += '.dev'+svn.version From scipy-svn at scipy.org Tue Aug 29 03:22:25 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 29 Aug 2006 02:22:25 -0500 (CDT) Subject: [Scipy-svn] r2182 - in trunk/Lib: cluster integrate io linalg optimize sandbox/arraysetops sandbox/models sandbox/plt sandbox/stats sandbox/xplt signal special stats tests Message-ID: <20060829072225.9EB1239C03C@new.scipy.org> Author: oliphant Date: 2006-08-29 02:22:11 -0500 (Tue, 29 Aug 2006) New Revision: 2182 Modified: trunk/Lib/cluster/vq.py trunk/Lib/integrate/quadrature.py trunk/Lib/io/array_import.py trunk/Lib/linalg/basic.py trunk/Lib/optimize/minpack.py trunk/Lib/optimize/optimize.py trunk/Lib/sandbox/arraysetops/arraysetops.py trunk/Lib/sandbox/models/utils.py trunk/Lib/sandbox/plt/plot_objects.py trunk/Lib/sandbox/stats/anova.py trunk/Lib/sandbox/xplt/pl3d.py trunk/Lib/sandbox/xplt/plwf.py trunk/Lib/signal/signaltools.py trunk/Lib/signal/wavelets.py trunk/Lib/special/orthogonal.py trunk/Lib/stats/distributions.py trunk/Lib/tests/test_basic.py Log: Fix usages of take with no axis argument. Modified: trunk/Lib/cluster/vq.py =================================================================== --- trunk/Lib/cluster/vq.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/cluster/vq.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -20,7 +20,7 @@ from numpy.random import randint from scipy.stats import std, mean from numpy import shape, zeros, subtract, sqrt, argmin, minimum, array, \ - newaxis, arange, compress, equal, take, common_type, single, double + newaxis, arange, compress, equal, common_type, single, double, take def whiten(obs): """ Normalize a group of observations on a per feature basis Modified: trunk/Lib/integrate/quadrature.py =================================================================== --- trunk/Lib/integrate/quadrature.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/integrate/quadrature.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -37,7 +37,7 @@ raise ValueError, "Gaussian quadrature is only available for " \ "finite limits." y = (b-a)*(x+1)/2.0 + a - return (b-a)/2.0*sum(w*func(y,*args)), None + return (b-a)/2.0*sum(w*func(y,*args),0), None def vectorize1(func, args=(), vec_func=False): if vec_func: @@ -323,7 +323,7 @@ h = float(interval[1]-interval[0])/numtosum lox = interval[0] + 0.5 * h; points = lox + h * arange(0, numtosum) - s = sum(function(points)) + s = sum(function(points),0) return s def _romberg_diff(b, c, k): Modified: trunk/Lib/io/array_import.py =================================================================== --- trunk/Lib/io/array_import.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/io/array_import.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -197,11 +197,11 @@ if len(collist) == 1: toconvlist = arlist[::-collist[-1]] else: - toconvlist = take(arlist,collist[:-1]) + toconvlist = take(arlist,collist[:-1],0) toconvlist = concatenate((toconvlist, arlist[(collist[-2]-collist[-1])::(-collist[-1])])) else: - toconvlist = take(arlist, collist) + toconvlist = take(arlist, collist,0) return numpyio.convert_objectarray(toconvlist, atype, missing) Modified: trunk/Lib/linalg/basic.py =================================================================== --- trunk/Lib/linalg/basic.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/linalg/basic.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -472,7 +472,7 @@ rows = mgrid[rN:0:-1] indx = cols[:,newaxis]*ones((1,rN),dtype=int) + \ rows[newaxis,:]*ones((cN,1),dtype=int) - 1 - return take(vals, indx) + return take(vals, indx, 0) def hankel(c,r=None): @@ -503,7 +503,7 @@ rows = mgrid[0:rN] indx = cols[:,newaxis]*ones((1,rN),dtype=int) + \ rows[newaxis,:]*ones((cN,1),dtype=int) - 1 - return take(vals, indx) + return take(vals, indx, 0) def all_mat(*args): return map(Matrix,args) Modified: trunk/Lib/optimize/minpack.py =================================================================== --- trunk/Lib/optimize/minpack.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/optimize/minpack.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -264,7 +264,7 @@ mesg = errors[info][0] if full_output: import scipy.linalg as sl - perm = take(eye(n),retval[1]['ipvt']-1) + perm = take(eye(n),retval[1]['ipvt']-1,0) r = triu(transpose(retval[1]['fjac'])[:n,:]) R = dot(r, perm) try: Modified: trunk/Lib/optimize/optimize.py =================================================================== --- trunk/Lib/optimize/optimize.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/optimize/optimize.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -172,7 +172,8 @@ fsim[k+1] = f ind = numpy.argsort(fsim) - fsim = numpy.take(fsim,ind) # sort so sim[0,:] has the lowest function value + fsim = numpy.take(fsim,ind,0) + # sort so sim[0,:] has the lowest function value sim = numpy.take(sim,ind,0) iterations = 1 @@ -230,7 +231,7 @@ ind = numpy.argsort(fsim) sim = numpy.take(sim,ind,0) - fsim = numpy.take(fsim,ind) + fsim = numpy.take(fsim,ind,0) if callback is not None: callback(sim[0]) iterations += 1 Modified: trunk/Lib/sandbox/arraysetops/arraysetops.py =================================================================== --- trunk/Lib/sandbox/arraysetops/arraysetops.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/sandbox/arraysetops/arraysetops.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -62,7 +62,7 @@ ar = numpy.array( ar1 ).ravel() if retIndx: perm = numpy.argsort( ar ) - aux = numpy.take( ar, perm ) + aux = numpy.take( ar, perm 0) flag = ediff1d( aux, 1 ) != 0 return numpy.compress( flag, perm ), numpy.compress( flag, aux ) else: @@ -104,8 +104,8 @@ tt = numpy.concatenate( (numpy.zeros_like( ar1 ), numpy.zeros_like( ar2 ) + 1) ) perm = numpy.argsort( ar ) - aux = numpy.take( ar, perm ) - aux2 = numpy.take( tt, perm ) + aux = numpy.take( ar, perm, 0) + aux2 = numpy.take( tt, perm, 0 ) flag = ediff1d( aux, 1 ) == 0 ii = numpy.where( flag * aux2 ) @@ -115,7 +115,7 @@ indx = numpy.argsort( perm )[:len( ar1 )] - return numpy.take( flag, indx ) + return numpy.take( flag, indx, 0 ) ## # 03.11.2005, c Modified: trunk/Lib/sandbox/models/utils.py =================================================================== --- trunk/Lib/sandbox/models/utils.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/sandbox/models/utils.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -107,8 +107,8 @@ if not sorted: asort = N.argsort(self.x) - self.x = N.take(self.x, asort) - self.y = N.take(self.y, asort) + self.x = N.take(self.x, asort, 0) + self.y = N.take(self.y, asort, 0) self.n = self.x.shape[0] def __call__(self, time): Modified: trunk/Lib/sandbox/plt/plot_objects.py =================================================================== --- trunk/Lib/sandbox/plt/plot_objects.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/sandbox/plt/plot_objects.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -928,7 +928,7 @@ else: cmap = colormap - pixels = take( cmap, scaled_mag) + pixels = take( cmap, scaled_mag, 0) del scaled_mag # need to transpose pixels in memory... bitmap = pixels.astype(UnsignedInt8).tostring() Modified: trunk/Lib/sandbox/stats/anova.py =================================================================== --- trunk/Lib/sandbox/stats/anova.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/sandbox/stats/anova.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -74,7 +74,7 @@ Nwifactors = len(Wscols) - 1 # WAS len(Wcolumns) #Nwlevels = take(array(Nlevels),Wscols) # no.lvls for each w/i subj fact #Nbtwfactors = len(Bscols) - 1 # WASNfactors - Nwifactors + 1 - Nblevels = take(array(Nlevels),Bscols) + Nblevels = take(array(Nlevels),Bscols,0) Nwsources = 2**Nwifactors - 1 # num within-subject factor-combos #Nbsources = Nallsources - Nwsources @@ -159,7 +159,7 @@ new = alluniqueslist[j].index(data[i][j]) idx.append(new) DA[idx] = data[i][-1] # put this data point in proper place in DA - btwidx = take(idx,array(Bscols)) + btwidx = take(idx,array(Bscols),0) subjslots[btwidx] = 1 # DONE CREATING DATA ARRAY, DA ... #dims = numfactors+1, dim 0=subjects # dim -1=measured values, dummyval = values used to fill empty slots in DA @@ -221,7 +221,7 @@ Lwithinsourcecol = map(Lsourceandbtws.index,Lwithinsourcecol) # Now indxlist should hold a list of indices into the list of possible # coefficients, one row per combo of coefficient. Next line PRESERVES dummyval - dvarshape = array(take(mns.shape,Lwithinsourcecol[1:])) -1 + dvarshape = array(take(mns.shape,Lwithinsourcecol[1:],0)) -1 idxarray = indices(dvarshape) newshape = array([idxarray.shape[0], multiply.reduce(idxarray.shape[1:])]) @@ -379,7 +379,7 @@ ## Calc and save sums of squares for this source SS = sum((effect**2 *sourceNarray) * - multiply.reduce(take(Marray.shape,btwnonsourcedims))) + multiply.reduce(take(Marray.shape,btwnonsourcedims,0))) ## Save it so you don't have to calculate it again next time SSlist.append(SS) SSsources.append(source) @@ -687,7 +687,7 @@ # Calc and save sums of squares for this source SS = zeros((levels,levels),'f') SS = sum((effect**2 *sourceDNarray) * - multiply.reduce(take(DM[dindex].shape,btwnonsourcedims)), + multiply.reduce(take(DM[dindex].shape,btwnonsourcedims,0)), range(len(sourceDMarray.shape)-1)) # Save it so you don't have to calculate it again next time SSlist.append(SS) @@ -739,7 +739,7 @@ idx[0] = -1 # compensate for pre-increment of 1st slot in incr() # Get a list of the maximum values each factor can handle - loopcap = take(array(Nlevels),sourcedims)-1 + loopcap = take(array(Nlevels),sourcedims,0)-1 ### WHILE STILL MORE GROUPS, CALCULATE GROUP MEAN FOR EACH D-VAR while incr(idx,loopcap) != -1: # loop through source btw level-combos Modified: trunk/Lib/sandbox/xplt/pl3d.py =================================================================== --- trunk/Lib/sandbox/xplt/pl3d.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/sandbox/xplt/pl3d.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -469,13 +469,13 @@ # (reduces to above medians for quads) # (2) compute midpoints of first three sides n2 = (nxyz [0] + 1) / 2 - c0 = (take(xyz, frst) + take(xyz, frst + 1)) / 2. + c0 = (take(xyz, frst, 0) + take(xyz, frst + 1, 0)) / 2. i = frst + n2 - 1 - c1 = (take(xyz, i) + take(xyz, i + 1)) / 2. + c1 = (take(xyz, i, 0) + take(xyz, i + 1, 0)) / 2. i = n2 / 2 - c2 = (take(xyz, frst + i) + take(xyz, frst + (i + 1) % nxyz [0])) / 2. + c2 = (take(xyz, frst + i, 0) + take(xyz, frst + (i + 1) % nxyz [0], 0)) / 2. i = minimum (i + n2, nxyz [0]) - 1 - c3 = (take(xyz, frst + i) + take(xyz, frst + (i + 1) % nxyz [0])) / 2. + c3 = (take(xyz, frst + i, 0) + take(xyz, frst + (i + 1) % nxyz [0], 0)) / 2. m1 = c1 - c0 m2 = c3 - c2 @@ -847,7 +847,7 @@ array_set (vlist, list, arange (len (list), dtype = Int)) # then reset the nlist values to that pre-sorted order, so that # sort(nlist) will be the required vertex sorting list - nlist = take(vlist, nlist) + nlist = take(vlist, nlist, 0) # the final hitch is to ensure that the vertices within each polygon # remain in their initial order (sort scrambles equal values) # since the vertices of a polygon can be cyclically permuted, Modified: trunk/Lib/sandbox/xplt/plwf.py =================================================================== --- trunk/Lib/sandbox/xplt/plwf.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/sandbox/xplt/plwf.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -243,9 +243,9 @@ ravel(add.outer (adders,zeros(nj-1, Int))) + arange((ni-1)*(nj-1), dtype = Int), array ( [[0, 1], [nj + 1, nj]]))) - xyz=array([take(ravel(xyz[0]),list), - take(ravel(xyz[1]),list), - take(ravel(xyz[2]),list)]) + xyz=array([take(ravel(xyz[0]),list,0), + take(ravel(xyz[1]),list,0), + take(ravel(xyz[2]),list,0)]) nxyz= ones((ni-1)*(nj-1)) * 4; The resulting array xyz is 3-by-(4*(nj-1)*(ni-1)). xyz[0:3,4*i:4*(i+1)] are the clockwise coordinates of the Modified: trunk/Lib/signal/signaltools.py =================================================================== --- trunk/Lib/signal/signaltools.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/signal/signaltools.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -887,7 +887,7 @@ indx = argsort(abs(p)) else: indx = argsort(p) - return take(p,indx), indx + return take(p,indx,0), indx def unique_roots(p,tol=1e-3,rtype='min'): """Determine the unique roots and their multiplicities in two lists @@ -959,7 +959,7 @@ """ extra = k p, indx = cmplx_sort(p) - r = take(r,indx) + r = take(r,indx,0) pout, mult = unique_roots(p,tol=tol,rtype=rtype) p = [] for k in range(len(pout)): @@ -1130,7 +1130,7 @@ """ extra = asarray(k) p, indx = cmplx_sort(p) - r = take(r,indx) + r = take(r,indx,0) pout, mult = unique_roots(p,tol=tol,rtype=rtype) p = [] for k in range(len(pout)): @@ -1341,7 +1341,7 @@ coef,resids,rank,s = linalg.lstsq(A,newdata[sl]) newdata[sl] = newdata[sl] - dot(A,coef) # Put data back in original shape. - tdshape = take(dshape,newdims) + tdshape = take(dshape,newdims,0) ret = reshape(newdata,tuple(tdshape)) vals = range(1,rnk) olddims = vals[:axis] + [0] + vals[axis:] Modified: trunk/Lib/signal/wavelets.py =================================================================== --- trunk/Lib/signal/wavelets.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/signal/wavelets.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -111,10 +111,10 @@ indx1 = sb.clip(2*nn-kk,-1,N+1) indx2 = sb.clip(2*nn-kk+1,-1,N+1) m = sb.zeros((2,2,N,N),'d') - m[0,0] = sb.take(thk,indx1) - m[0,1] = sb.take(thk,indx2) - m[1,0] = sb.take(tgk,indx1) - m[1,1] = sb.take(tgk,indx2) + m[0,0] = sb.take(thk,indx1,0) + m[0,1] = sb.take(thk,indx2,0) + m[1,0] = sb.take(tgk,indx1,0) + m[1,1] = sb.take(tgk,indx2,0) m *= s2 # construct the grid of points Modified: trunk/Lib/special/orthogonal.py =================================================================== --- trunk/Lib/special/orthogonal.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/special/orthogonal.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -61,6 +61,7 @@ from __future__ import nested_scopes from numpy import * +from numpy.oldnumeric import take import _cephes as cephes _gam = cephes.gamma from scipy.linalg import eig Modified: trunk/Lib/stats/distributions.py =================================================================== --- trunk/Lib/stats/distributions.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/stats/distributions.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -3057,7 +3057,7 @@ c_bad = atleast_1d((b<=0) | (x != x)) indxiter = nonzero(c_xiter) - xiter = take(x, indxiter) + xiter = take(x, indxiter, 0) vals = ones(len(c_xsimple),float) putmask(vals, c_bad, nan) @@ -3066,7 +3066,7 @@ st = where(isnan(st),0.0,st) putmask(vals, c_xnormal, norm.cdf(x, scale=st)) - biter = take(atleast_1d(b)*(x==x), indxiter) + biter = take(atleast_1d(b)*(x==x), indxiter, 0) if len(xiter) > 0: fac = special.i0(biter) x2 = xiter @@ -3191,7 +3191,7 @@ qk = 1.0*qk / sum(qk) # If qk is zero anywhere, then unless pk is zero at those places # too, the relative entropy is infinite. - if any(take(pk,nonzero(qk==0.0))!=0.0): + if any(take(pk,nonzero(qk==0.0))!=0.0, 0): return inf vec = where (pk == 0, 0.0, pk*log(pk / qk)) return -sum(vec) @@ -3359,8 +3359,8 @@ self.xk, self.pk = values self.return_integers = 0 indx = argsort(ravel(self.xk)) - self.xk = take(ravel(self.xk),indx) - self.pk = take(ravel(self.pk),indx) + self.xk = take(ravel(self.xk),indx, 0) + self.pk = take(ravel(self.pk),indx, 0) self.a = self.xk[0] self.b = self.xk[-1] self.P = make_dict(self.xk, self.pk) Modified: trunk/Lib/tests/test_basic.py =================================================================== --- trunk/Lib/tests/test_basic.py 2006-08-29 03:40:28 UTC (rev 2181) +++ trunk/Lib/tests/test_basic.py 2006-08-29 07:22:11 UTC (rev 2182) @@ -273,7 +273,7 @@ b = [[3,6.0, 9.0], [4,10.0,5.0], [8,3.0,2.0]] - assert_equal(ptp(b),[5.0,7.0,7.0]) + assert_equal(ptp(b,axis=0),[5.0,7.0,7.0]) assert_equal(ptp(b,axis=1),[6.0,6.0,6.0]) From scipy-svn at scipy.org Tue Aug 29 06:31:32 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 29 Aug 2006 05:31:32 -0500 (CDT) Subject: [Scipy-svn] r2183 - in trunk/Lib: cluster fftpack fftpack/tests interpolate io io/tests linalg maxentropy misc ndimage optimize sandbox/ann sandbox/arraysetops sandbox/delaunay sandbox/ga sandbox/gplt sandbox/image sandbox/models sandbox/montecarlo/tests sandbox/numexpr/tests sandbox/odr sandbox/rkern sandbox/stats sandbox/svm sandbox/svm/tests sandbox/xplt signal sparse/tests special special/tests stats stats/tests stsci/convolve/lib stsci/image/lib tests weave weave/tests Message-ID: <20060829103132.0A5EF39C023@new.scipy.org> Author: oliphant Date: 2006-08-29 05:30:44 -0500 (Tue, 29 Aug 2006) New Revision: 2183 Modified: trunk/Lib/cluster/vq.py trunk/Lib/fftpack/basic.py trunk/Lib/fftpack/pseudo_diffs.py trunk/Lib/fftpack/tests/test_pseudo_diffs.py trunk/Lib/interpolate/fitpack.py trunk/Lib/interpolate/fitpack2.py trunk/Lib/interpolate/interpolate.py trunk/Lib/io/mio.py trunk/Lib/io/tests/test_array_import.py trunk/Lib/linalg/basic.py trunk/Lib/linalg/matfuncs.py trunk/Lib/maxentropy/maxentropy.py trunk/Lib/maxentropy/maxentutils.py trunk/Lib/misc/common.py trunk/Lib/ndimage/filters.py trunk/Lib/ndimage/interpolation.py trunk/Lib/ndimage/morphology.py trunk/Lib/optimize/minpack.py trunk/Lib/optimize/optimize.py trunk/Lib/sandbox/ann/mlp.py trunk/Lib/sandbox/ann/rbf.py trunk/Lib/sandbox/ann/srn.py trunk/Lib/sandbox/arraysetops/arraysetops.py trunk/Lib/sandbox/delaunay/interpolate.py trunk/Lib/sandbox/ga/ga_util.py trunk/Lib/sandbox/ga/parallel_pop.py trunk/Lib/sandbox/ga/selection.py trunk/Lib/sandbox/gplt/new_plot.py trunk/Lib/sandbox/image/color.py trunk/Lib/sandbox/models/bsplines.py trunk/Lib/sandbox/models/cox.py trunk/Lib/sandbox/models/regression.py trunk/Lib/sandbox/models/utils.py trunk/Lib/sandbox/montecarlo/tests/test_dictsampler.py trunk/Lib/sandbox/montecarlo/tests/test_intsampler.py trunk/Lib/sandbox/numexpr/tests/test_numexpr.py trunk/Lib/sandbox/odr/models.py trunk/Lib/sandbox/rkern/diffev.py trunk/Lib/sandbox/rkern/pink.py trunk/Lib/sandbox/stats/anova.py trunk/Lib/sandbox/stats/obsolete.py trunk/Lib/sandbox/svm/classification.py trunk/Lib/sandbox/svm/dataset.py trunk/Lib/sandbox/svm/predict.py trunk/Lib/sandbox/svm/tests/test_regression.py trunk/Lib/sandbox/xplt/Mplot.py trunk/Lib/sandbox/xplt/NarPlotter.py trunk/Lib/sandbox/xplt/demo5.py trunk/Lib/sandbox/xplt/gist3dhelp.py trunk/Lib/sandbox/xplt/gistdemo3d.py trunk/Lib/sandbox/xplt/mesh3d.py trunk/Lib/sandbox/xplt/pl3d.py trunk/Lib/sandbox/xplt/slice3.py trunk/Lib/sandbox/xplt/sphereisos.py trunk/Lib/signal/filter_design.py trunk/Lib/signal/ltisys.py trunk/Lib/signal/signaltools.py trunk/Lib/signal/wavelets.py trunk/Lib/sparse/tests/test_sparse.py trunk/Lib/special/basic.py trunk/Lib/special/orthogonal.py trunk/Lib/special/tests/Test.py trunk/Lib/special/tests/test_basic.py trunk/Lib/stats/_support.py trunk/Lib/stats/distributions.py trunk/Lib/stats/kde.py trunk/Lib/stats/morestats.py trunk/Lib/stats/stats.py trunk/Lib/stats/tests/test_stats.py trunk/Lib/stsci/convolve/lib/Convolve.py trunk/Lib/stsci/image/lib/combine.py trunk/Lib/tests/test_basic.py trunk/Lib/tests/test_basic1a.py trunk/Lib/tests/test_common.py trunk/Lib/weave/size_check.py trunk/Lib/weave/tests/test_blitz_tools.py Log: Add axis arguments to functions that changed in NumPy 1.0b2 Modified: trunk/Lib/cluster/vq.py =================================================================== --- trunk/Lib/cluster/vq.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/cluster/vq.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -152,7 +152,7 @@ for o in obs: subtract(code_book,o,diff) # faster version of --> diff = code_book - o dist = sqrt(sum(diff*diff,-1)) - code.append(argmin(dist)) + code.append(argmin(dist,axis=-1)) #something weird here dst does not work reliably because it sometime #returns an array of goofy length. Try except fixes it, but is ugly. dst = minimum.reduce(dist,0) Modified: trunk/Lib/fftpack/basic.py =================================================================== --- trunk/Lib/fftpack/basic.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/fftpack/basic.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -273,7 +273,7 @@ Optional input: shape Defines the shape of the Fourier transform. If shape is not - specified then shape=take(x.shape,axes). + specified then shape=take(x.shape,axes,axis=0). If shape[i]>x.shape[i] then the i-th dimension is padded with zeros. If shape[i] 0): + if not alltrue(t[k+1:n-k]-t[k:n-k-1] > 0,axis=0): raise ValueError,\ 'Interior knots t must satisfy Schoenberg-Whitney conditions' data = dfitpack.fpcurfm1(x,y,k,t,w=w,xb=xb,xe=xe) @@ -300,7 +300,7 @@ def get_residual(self): """ Return weighted sum of squared residuals of the spline - approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2) + approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) """ return self.fp def get_knots(self): @@ -340,7 +340,7 @@ kx,ky=3,3 - degrees of the bivariate spline. s - positive smoothing factor defined for estimation condition: - sum((w[i]*(z[i]-s(x[i],y[i])))**2) <= s + sum((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) <= s Default s=len(w) which should be a good value if 1/w[i] is an estimate of the standard deviation of z[i]. Modified: trunk/Lib/interpolate/interpolate.py =================================================================== --- trunk/Lib/interpolate/interpolate.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/interpolate/interpolate.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -15,7 +15,7 @@ def reduce_sometrue(a): all = a while len(shape(all)) > 1: - all = sometrue(all) + all = sometrue(all,axis=0) return all class interp2d: @@ -163,7 +163,7 @@ # 4. Calculate the slope of regions that each x_new value falls in. lo = x_new_indices - 1; hi = x_new_indices - # !! take() should default to the last axis (IMHO) and remove + # !! take(,axis=0) should default to the last axis (IMHO) and remove # !! the extra argument. x_lo = take(self.x,lo,axis=self.interp_axis) x_hi = take(self.x,hi,axis=self.interp_axis); @@ -204,10 +204,10 @@ above_bounds = greater(x_new,self.x[-1]) # Note: sometrue has been redefined to handle length 0 arrays # !! Could provide more information about which values are out of bounds - if self.bounds_error and sometrue(below_bounds): + if self.bounds_error and sometrue(below_bounds,axis=0): raise ValueError, " A value in x_new is below the"\ " interpolation range." - if self.bounds_error and sometrue(above_bounds): + if self.bounds_error and sometrue(above_bounds,axis=0): raise ValueError, " A value in x_new is above the"\ " interpolation range." # !! Should we emit a warning if some values are out of bounds. Modified: trunk/Lib/io/mio.py =================================================================== --- trunk/Lib/io/mio.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/io/mio.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -174,7 +174,7 @@ if mtype is None: mtype = data.dtype.char howmany,mtype = getsize_type(mtype) - count = product(data.shape) + count = product(data.shape,axis=0) numpyio.fwrite(self.file,count,data,mtype,bs) return @@ -215,20 +215,20 @@ # allow -1 to specify unknown dimension size as in reshape minus_ones = shape.count(-1) if minus_ones == 0: - count = product(shape) + count = product(shape,axis=0) elif minus_ones == 1: now = self.tell() self.seek(0,2) end = self.tell() self.seek(now) remaining_bytes = end - now - know_dimensions_size = -product(count) * getsize_type(stype)[0] + know_dimensions_size = -product(count,axis=0) * getsize_type(stype)[0] unknown_dimension_size, illegal = divmod(remaining_bytes, know_dimensions_size) if illegal: raise ValueError("unknown dimension doesn't match filesize") shape[shape.index(-1)] = unknown_dimension_size - count = product(shape) + count = product(shape,axis=0) else: raise ValueError( "illegal count; can only specify one unknown dimension") @@ -302,7 +302,7 @@ sz,mtype = getsize_type(args[0]) else: sz,mtype = getsize_type(fmt.dtype.char) - count = product(fmt.shape) + count = product(fmt.shape,axis=0) strlen = struct.pack(nfmt,count*sz) self.write(strlen) numpyio.fwrite(self.file,count,fmt,mtype,self.bs) @@ -579,7 +579,7 @@ dims = result.shape if len(dims) >= 2: # return array of strings n_dims = dims[:-1] - string_arr = reshape(result, (product(n_dims), dims[-1])) + string_arr = reshape(result, (product(n_dims,axis=0), dims[-1])) result = empty(n_dims, dtype=object) for i in range(0, n_dims[-1]): result[...,i] = string_arr[i].tostring().decode(en) @@ -595,7 +595,7 @@ result = squeeze(transpose(reshape(result,tupdims))) elif dclass == mxCELL_CLASS: - length = product(dims) + length = product(dims,axis=0) result = empty(length, dtype=object) for i in range(length): result[i] = _get_element(fid) @@ -604,7 +604,7 @@ result = result.item() elif dclass == mxSTRUCT_CLASS: - length = product(dims) + length = product(dims,axis=0) result = zeros(length, object) namelength = _get_element(fid) # get field names @@ -624,7 +624,7 @@ # object is like a structure with but with a class name elif dclass == mxOBJECT_CLASS: class_name = _get_element(fid).tostring() - length = product(dims) + length = product(dims,axis=0) result = zeros(length, object) namelength = _get_element(fid) # get field names @@ -922,7 +922,7 @@ var = transpose(var) if len(var.shape) > 2: - var=var.reshape((product(var.shape[:-1]), var.shape[-1])) + var=var.reshape((product(var.shape[:-1],axis=0), var.shape[-1])) imagf = var.dtype.char in ['F', 'D'] fid.fwrite([var.shape[1], var.shape[0], imagf, len(variable)+1],'int') Modified: trunk/Lib/io/tests/test_array_import.py =================================================================== --- trunk/Lib/io/tests/test_array_import.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/io/tests/test_array_import.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -31,7 +31,7 @@ print "\nDon't worry about a warning regarding the number of bytes read." b = numpyio.fread(fid,1000000,N.Int16,N.Int) fid.close() - assert(N.product(a.astype(N.Int16) == b)) + assert(N.product(a.astype(N.Int16) == b,axis=0)) os.remove(fname) class test_read_array(ScipyTestCase): Modified: trunk/Lib/linalg/basic.py =================================================================== --- trunk/Lib/linalg/basic.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/linalg/basic.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -242,7 +242,7 @@ For vectors ord can be any real number including Inf or -Inf. ord = Inf, computes the maximum of the magnitudes ord = -Inf, computes minimum of the magnitudes - ord is finite, computes sum(abs(x)**ord)**(1.0/ord) + ord is finite, computes sum(abs(x)**ord,axis=0)**(1.0/ord) For matrices ord can only be one of the following values: ord = 2 computes the largest singular value @@ -251,7 +251,7 @@ ord = -1 computes the smallest column sum of absolute values ord = Inf computes the largest row sum of absolute values ord = -Inf computes the smallest row sum of absolute values - ord = 'fro' computes the frobenius norm sqrt(sum(diag(X.H * X))) + ord = 'fro' computes the frobenius norm sqrt(sum(diag(X.H * X),axis=0)) For values ord < 0, the result is, strictly speaking, not a mathematical 'norm', but it may still be useful for numerical purposes. @@ -268,22 +268,22 @@ elif ord == -Inf: return numpy.amin(abs(x)) elif ord == 1: - return numpy.sum(abs(x)) # special case for speedup + return numpy.sum(abs(x),axis=0) # special case for speedup elif ord == 2: - return sqrt(numpy.sum(real((conjugate(x)*x)))) # special case for speedup + return sqrt(numpy.sum(real((conjugate(x)*x)),axis=0)) # special case for speedup else: - return numpy.sum(abs(x)**ord)**(1.0/ord) + return numpy.sum(abs(x)**ord,axis=0)**(1.0/ord) elif nd == 2: if ord == 2: return numpy.amax(decomp.svd(x,compute_uv=0)) elif ord == -2: return numpy.amin(decomp.svd(x,compute_uv=0)) elif ord == 1: - return numpy.amax(numpy.sum(abs(x))) + return numpy.amax(numpy.sum(abs(x),axis=0)) elif ord == Inf: return numpy.amax(numpy.sum(abs(x),axis=1)) elif ord == -1: - return numpy.amin(numpy.sum(abs(x))) + return numpy.amin(numpy.sum(abs(x),axis=0)) elif ord == -Inf: return numpy.amin(numpy.sum(abs(x),axis=1)) elif ord in ['fro','f']: @@ -365,7 +365,7 @@ resids = asarray([], dtype=x.dtype) if n 1000*tol: Modified: trunk/Lib/maxentropy/maxentropy.py =================================================================== --- trunk/Lib/maxentropy/maxentropy.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/maxentropy/maxentropy.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -86,7 +86,7 @@ self.verbose = False self.maxgtol = 1e-5 - # Required tolerance of gradient on average (closeness to zero) for + # Required tolerance of gradient on average (closeness to zero,axis=0) for # CG optimization: self.avegtol = 1e-3 # Default tolerance for the other optimization algorithms: @@ -1717,7 +1717,7 @@ self.external = None self.clearcache() - meandual = numpy.average(dualapprox) + meandual = numpy.average(dualapprox,axis=0) self.external_duals[self.iters] = dualapprox self.external_gradnorms[self.iters] = gradnorms @@ -1727,7 +1727,7 @@ (len(self.externalFs), meandual) print "** Mean mean square error of the (unregularized) feature" \ " expectation estimates from the external samples =" \ - " mean(|| \hat{\mu_e} - k ||) =", numpy.average(gradnorms) + " mean(|| \hat{\mu_e} - k ||,axis=0) =", numpy.average(gradnorms,axis=0) # Track the parameter vector params with the lowest mean dual estimate # so far: if meandual < self.bestdual: Modified: trunk/Lib/maxentropy/maxentutils.py =================================================================== --- trunk/Lib/maxentropy/maxentutils.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/maxentropy/maxentutils.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -34,7 +34,7 @@ complex-numbered, such as the output of robustarraylog(). So we expect: - cmath.exp(logsumexpcomplex(robustarraylog(values))) ~= sum(values) + cmath.exp(logsumexpcomplex(robustarraylog(values))) ~= sum(values,axis=0) except for a small rounding error in both real and imag components. The output is complex. (To recover just the real component, use Modified: trunk/Lib/misc/common.py =================================================================== --- trunk/Lib/misc/common.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/misc/common.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -157,7 +157,7 @@ X = x**0.0 for k in range(1,Np): X = hstack([X,x**k]) - w = product(arange(1,ndiv+1))*linalg.inv(X)[ndiv] + w = product(arange(1,ndiv+1),axis=0)*linalg.inv(X)[ndiv] return w def derivative(func,x0,dx=1.0,n=1,args=(),order=3): @@ -200,7 +200,7 @@ ho = order >> 1 for k in range(order): val += weights[k]*func(x0+(k-ho)*dx,*args) - return val / product((dx,)*n) + return val / product((dx,)*n,axis=0) def pade(an, m): """Given Taylor series coefficients in an, return a Pade approximation to Modified: trunk/Lib/ndimage/filters.py =================================================================== --- trunk/Lib/ndimage/filters.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/ndimage/filters.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -430,7 +430,7 @@ else: footprint = numarray.asarray(footprint) footprint = footprint.astype(bool) - if numarray.alltrue(numarray.ravel(footprint)): + if numarray.alltrue(numarray.ravel(footprint),axis=0): size = footprint.shape footprint = None separable = True Modified: trunk/Lib/ndimage/interpolation.py =================================================================== --- trunk/Lib/ndimage/interpolation.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/ndimage/interpolation.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -406,7 +406,7 @@ order, mode, cval, prefilter) else: coordinates = [] - size = numarray.product(input.shape) + size = numarray.product(input.shape,axis=0) size /= input.shape[axes[0]] size /= input.shape[axes[1]] for ii in range(input.ndim): Modified: trunk/Lib/ndimage/morphology.py =================================================================== --- trunk/Lib/ndimage/morphology.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/ndimage/morphology.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -100,7 +100,7 @@ raise RuntimeError, 'structure rank must equal input rank' if not structure.flags.contiguous: structure = structure.copy() - if numarray.product(structure.shape) < 1: + if numarray.product(structure.shape,axis=0) < 1: raise RuntimeError, 'structure must not be empty' if mask is not None: mask = numarray.asarray(mask) @@ -659,7 +659,7 @@ dt = numarray.where(input, -1, 0).astype(numarray.Int32) rank = dt.ndim if return_indices: - sz = numarray.product(dt.shape) + sz = numarray.product(dt.shape,axis=0) ft = numarray.arange(sz, dtype = numarray.Int32) ft.shape = dt.shape else: Modified: trunk/Lib/optimize/minpack.py =================================================================== --- trunk/Lib/optimize/minpack.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/optimize/minpack.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -145,7 +145,7 @@ (non-linear) equations in N unknowns given a starting estimate, x0, using a modification of the Levenberg-Marquardt algorithm. - x = arg min(sum(func(y)**2)) + x = arg min(sum(func(y)**2,axis=0)) y Inputs: @@ -301,7 +301,7 @@ fvecp=fvecp.reshape((m,)) _minpack._chkder(m,n,x,fvec,fjac,ldfjac,xp,fvecp,2,err) - good = (product(greater(err,0.5))) + good = (product(greater(err,0.5),axis=0)) return (good,err) Modified: trunk/Lib/optimize/optimize.py =================================================================== --- trunk/Lib/optimize/optimize.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/optimize/optimize.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -51,11 +51,11 @@ elif ord == -Inf: return numpy.amin(abs(x)) else: - return numpy.sum(abs(x)**ord)**(1.0/ord) + return numpy.sum(abs(x)**ord,axis=0)**(1.0/ord) def rosen(x): # The Rosenbrock function x = asarray(x) - return numpy.sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0) + return numpy.sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0,axis=0) def rosen_der(x): x = asarray(x) @@ -1580,7 +1580,7 @@ grid = (grid,) Jout = vecfunc(*grid) Nshape = shape(Jout) - indx = argmin(Jout.ravel()) + indx = argmin(Jout.ravel(),axis=-1) Nindx = zeros(N,int) xmin = zeros(N,float) for k in range(N-1,-1,-1): Modified: trunk/Lib/sandbox/ann/mlp.py =================================================================== --- trunk/Lib/sandbox/ann/mlp.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/ann/mlp.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -118,7 +118,7 @@ Returns: sum-squared-error over all data """ - return N.sum(self.errfxn(self.wp,x,t)) + return N.sum(self.errfxn(self.wp,x,t),axis=0) def main(): """ Build/train/test MLP Modified: trunk/Lib/sandbox/ann/rbf.py =================================================================== --- trunk/Lib/sandbox/ann/rbf.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/ann/rbf.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -86,7 +86,7 @@ d_max = 0.0 for i in self.centers: for j in self.centers: - tmp = N.sum(N.sqrt((i-j)**2)) + tmp = N.sum(N.sqrt((i-j)**2),axis=0) if tmp > d_max: d_max = tmp self.variance = d_max/(2.0*len(X)) @@ -105,7 +105,7 @@ Returns: sum-squared-error over all data """ - return N.sum(self.err_fxn(self.wp,X,Y)) + return N.sum(self.err_fxn(self.wp,X,Y),axis=0) def main(): """ Build/train/test RBF net Modified: trunk/Lib/sandbox/ann/srn.py =================================================================== --- trunk/Lib/sandbox/ann/srn.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/ann/srn.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -136,7 +136,7 @@ Returns: sum-squared-error over all data """ - return N.sum(self.errfxn(self.wp,x,t)) + return N.sum(self.errfxn(self.wp,x,t),axis=0) def main(): Modified: trunk/Lib/sandbox/arraysetops/arraysetops.py =================================================================== --- trunk/Lib/sandbox/arraysetops/arraysetops.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/arraysetops/arraysetops.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -62,19 +62,19 @@ ar = numpy.array( ar1 ).ravel() if retIndx: perm = numpy.argsort( ar ) - aux = numpy.take( ar, perm 0) + aux = numpy.take( ar, perm 0,axis=0) flag = ediff1d( aux, 1 ) != 0 - return numpy.compress( flag, perm ), numpy.compress( flag, aux ) + return numpy.compress( flag, perm ,axis=-1), numpy.compress( flag, aux ,axis=-1) else: aux = numpy.sort( ar ) - return numpy.compress( ediff1d( aux, 1 ) != 0, aux ) + return numpy.compress( ediff1d( aux, 1 ) != 0, aux ,axis=-1) ## # 01.11.2005, c def intersect1d( ar1, ar2 ): """Intersection of 1D arrays with unique elements.""" aux = numpy.sort( numpy.concatenate( (ar1, ar2 ) ) ) - return numpy.compress( (aux[1:] - aux[:-1]) == 0, aux ) + return numpy.compress( (aux[1:] - aux[:-1]) == 0, aux ,axis=-1) ## # 01.11.2005, c @@ -83,7 +83,7 @@ # Might be faster then unique1d( intersect1d( ar1, ar2 ) )? aux = numpy.sort( numpy.concatenate( (unique1d( ar1 ), unique1d( ar2 )) ) ) - return numpy.compress( (aux[1:] - aux[:-1]) == 0, aux ) + return numpy.compress( (aux[1:] - aux[:-1]) == 0, aux ,axis=-1) ## # 01.11.2005, c @@ -92,7 +92,7 @@ aux = numpy.sort( numpy.concatenate( (ar1, ar2 ) ) ) flag = ediff1d( aux, toEnd = 1, toBegin = 1 ) == 0 flag2 = ediff1d( flag, 0 ) == 0 - return numpy.compress( flag2, aux ) + return numpy.compress( flag2, aux ,axis=-1) ## # 03.11.2005, c @@ -128,7 +128,7 @@ def setdiff1d( ar1, ar2 ): """Set difference of 1D arrays with unique elements.""" aux = setmember1d( ar1, ar2 ) - return numpy.compress( aux == 0, ar1 ) + return numpy.compress( aux == 0, ar1 ,axis=-1) ## # 03.11.2005, c @@ -138,7 +138,7 @@ ec = numpy.array( [1, 2, 5, 7] ) c = unique1d( a ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) ## # 03.11.2005, c @@ -149,7 +149,7 @@ ec = numpy.array( [1, 2, 5] ) c = intersect1d( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) ## # 03.11.2005, c @@ -160,7 +160,7 @@ ec = numpy.array( [1, 2, 5] ) c = intersect1d_nu( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) ## # 03.11.2005, c @@ -171,21 +171,21 @@ ec = numpy.array( [3, 4, 7] ) c = setxor1d( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) a = numpy.array( [1, 2, 3] ) b = numpy.array( [6, 5, 4] ) ec = numpy.array( [1, 2, 3, 4, 5, 6] ) c = setxor1d( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) a = numpy.array( [1, 8, 2, 3] ) b = numpy.array( [6, 5, 4, 8] ) ec = numpy.array( [1, 2, 3, 4, 5, 6] ) c = setxor1d( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) ## @@ -197,17 +197,17 @@ ec = numpy.array( [True, False, True, True] ) c = setmember1d( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) a[0] = 8 ec = numpy.array( [False, False, True, True] ) c = setmember1d( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) a[0], a[3] = 4, 8 ec = numpy.array( [True, False, True, False] ) c = setmember1d( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) ## # 03.11.2005, c @@ -218,7 +218,7 @@ ec = numpy.array( [1, 2, 3, 4, 5, 7] ) c = union1d( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) ## # 03.11.2005, c @@ -229,7 +229,7 @@ ec = numpy.array( [6, 7] ) c = setdiff1d( a, b ) - assert numpy.alltrue( c == ec ) + assert numpy.alltrue( c == ec ,axis=0) ## # 03.11.2005, c @@ -241,7 +241,7 @@ c1 = intersect1d_nu( a, b ) c2 = unique1d( intersect1d( a, b ) ) - assert numpy.alltrue( c1 == c2 ) + assert numpy.alltrue( c1 == c2 ,axis=0) a = numpy.array( [5, 7, 1, 2, 8] ) b = numpy.array( [9, 8, 2, 4, 3, 1, 5] ) @@ -250,7 +250,7 @@ aux1 = intersect1d( a, b ) aux2 = union1d( a, b ) c2 = setdiff1d( aux2, aux1 ) - assert numpy.alltrue( c1 == c2 ) + assert numpy.alltrue( c1 == c2 ,axis=0) ## # 02.11.2005, c @@ -292,7 +292,7 @@ dt1s.append( dt1 ) dt2s.append( dt2 ) - assert numpy.alltrue( b == c ) + assert numpy.alltrue( b == c ,axis=0) print nItems Modified: trunk/Lib/sandbox/delaunay/interpolate.py =================================================================== --- trunk/Lib/sandbox/delaunay/interpolate.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/delaunay/interpolate.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -126,7 +126,7 @@ from its original Voronoi polygon, stolen[i]. We define the natural neighbors coordinates - phi[i] = stolen[i] / sum(stolen) + phi[i] = stolen[i] / sum(stolen,axis=0) We then use these phi[i] to weight the corresponding function values from the input data z to compute the interpolated value. Modified: trunk/Lib/sandbox/ga/ga_util.py =================================================================== --- trunk/Lib/sandbox/ga/ga_util.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/ga/ga_util.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -58,4 +58,4 @@ def remove_NaN(z): from numpy import isnan, isinf, compress, logical_not - return compress(logical_not( isnan(z)+isinf(z)),z) + return compress(logical_not( isnan(z)+isinf(z)),z,axis=-1) Modified: trunk/Lib/sandbox/ga/parallel_pop.py =================================================================== --- trunk/Lib/sandbox/ga/parallel_pop.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/ga/parallel_pop.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -520,7 +520,7 @@ time.sleep(self.wait) - return sum(genome.array()) + return sum(genome.array(),axis=0) Modified: trunk/Lib/sandbox/ga/selection.py =================================================================== --- trunk/Lib/sandbox/ga/selection.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/ga/selection.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -40,7 +40,7 @@ (f_max <= 0 and f_min <= 0)): raise GAError, 'srs_selector requires all fitnesses values to be either strictly positive or strictly negative' if f_max == f_min: f = ones(shape(f),typecode = Float32) - self.dart_board = add.accumulate(f / sum(f)) + self.dart_board = add.accumulate(f / sum(f,axis=0)) def select(self,pop,cnt = 1): returns = [] for i in range(cnt): @@ -63,7 +63,7 @@ if not ( (f_max >= 0. and f_min >= 0.) or (f_max <= 0. and f_min <= 0.)): raise GAError, 'srs_selector requires all fitnesses values to be either strictly positive or strictly negative - min %f, max %f' %(f_min,f_max) - f_avg = sum(f)/sz + f_avg = sum(f,axis=0)/sz if f_avg == 0.: e = ones(shape(f),typecode = Float32) else: if pop.min_or_max() == 'max': e = f/f_avg @@ -75,7 +75,7 @@ choices = [] for i in xrange(sz): choices = choices + [pop[i]] * int(garauntee[i]) #now deal with the remainder - dart_board = add.accumulate(chance / sum(chance)) + dart_board = add.accumulate(chance / sum(chance,axis=0)) for i in range(len(choices),sz): dart = rv.random() idx = 0 Modified: trunk/Lib/sandbox/gplt/new_plot.py =================================================================== --- trunk/Lib/sandbox/gplt/new_plot.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/gplt/new_plot.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -7,8 +7,8 @@ def squeeze(a): s = shape(a) - dims = len(s) - sum(equal(1,s)) - new_s = compress(not_equal(1,s),s) + dims = len(s) - sum(equal(1,s),axis=0) + new_s = compress(not_equal(1,s),s,axis=-1) return reshape(a,new_s) Modified: trunk/Lib/sandbox/image/color.py =================================================================== --- trunk/Lib/sandbox/image/color.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/image/color.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -63,7 +63,7 @@ # Vos, Estevez, and Walraven (1990) # with alteration in S-cone sensitivity from # Stockman and Sharpe (2000) -# scaled so that sum(LMS) has a peak of 1 +# scaled so that sum(LMS,axis=0) has a peak of 1 # just like LMS_from_XYZ lms_from_rgbsb = [[0.14266235473644004, 0.49009667755566039, Modified: trunk/Lib/sandbox/models/bsplines.py =================================================================== --- trunk/Lib/sandbox/models/bsplines.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/models/bsplines.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -48,7 +48,7 @@ _shape = x.shape if _shape == (): x.shape = (1,) - x.shape = (N.product(_shape),) + x.shape = (N.product(_shape,axis=0),) if i < self.tau.shape[0] - 1: ## TODO: OWNDATA flags... v = _bspline.evaluate(x, self.tau, self.m, d, i, i+1) @@ -65,7 +65,7 @@ _shape = x.shape if _shape == (): x.shape = (1,) - x.shape = (N.product(_shape),) + x.shape = (N.product(_shape,axis=0),) if upper is None: upper = self.tau.shape[0] - self.m Modified: trunk/Lib/sandbox/models/cox.py =================================================================== --- trunk/Lib/sandbox/models/cox.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/models/cox.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -101,7 +101,7 @@ else: self.design[t] = d self.risk[t] = N.compress([s.atrisk(t) for s in self.subjects], - N.arange(self.design[t].shape[0])) + N.arange(self.design[t].shape[0]),axis=-1) def __del__(self): shutil.rmtree(self.cachedir, ignore_errors=True) Modified: trunk/Lib/sandbox/models/regression.py =================================================================== --- trunk/Lib/sandbox/models/regression.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/models/regression.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -138,7 +138,7 @@ """ Return the R^2 value for each row of the response Y. """ - self.Ssq = N.std(self.Z)**2 + self.Ssq = N.std(self.Z,axis=0)**2 ratio = self.scale / self.Ssq if not adjusted: ratio *= ((Y.shape[0] - 1) / self.df_resid) return 1 - ratio Modified: trunk/Lib/sandbox/models/utils.py =================================================================== --- trunk/Lib/sandbox/models/utils.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/models/utils.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -22,7 +22,7 @@ """ _shape = a.shape - a.shape = N.product(a.shape) + a.shape = N.product(a.shape,axis=0) m = scipy.median(N.fabs(a - scipy.median(a))) / c a.shape = _shape return m @@ -123,7 +123,7 @@ """ x = N.array(values, copy=True) x.sort() - x.shape = N.product(x.shape) + x.shape = N.product(x.shape,axis=0) n = x.shape[0] y = (N.arange(n) + 1.) / n return StepFunction(x, y) Modified: trunk/Lib/sandbox/montecarlo/tests/test_dictsampler.py =================================================================== --- trunk/Lib/sandbox/montecarlo/tests/test_dictsampler.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/montecarlo/tests/test_dictsampler.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -37,7 +37,7 @@ #import pdb #pdb.set_trace() s = sampler.sample(n) - assert sum(s[i]=='b' for i in range(n))*1./n > 0.75 + assert sum(s[i]=='b' for i in range(n),axis=0)*1./n > 0.75 #lam = 10.0 #n = 35 @@ -68,8 +68,8 @@ v = x.var() assert x.max() == 4 assert x.min() == 0 - assert sum(x==3) == 0 - assert 0.08 < average(x==1) < 0.12 + assert sum(x==3,axis=0) == 0 + assert 0.08 < average(x==1,axis=0) < 0.12 # Use a normal approx for confidence intervals for the mean z = 2.5758 # = norminv(0.995), for a 1% confidence interval assert abs(m - truemean) < z * sqrt(truevar/numsamples) Modified: trunk/Lib/sandbox/montecarlo/tests/test_intsampler.py =================================================================== --- trunk/Lib/sandbox/montecarlo/tests/test_intsampler.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/montecarlo/tests/test_intsampler.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -62,8 +62,8 @@ v = x.var() assert x.max() == 4 assert x.min() == 0 - assert sum(x==3) == 0 - assert 0.08 < average(x==1) < 0.12 + assert sum(x==3,axis=0) == 0 + assert 0.08 < average(x==1,axis=0) < 0.12 # Use a normal approx for confidence intervals for the mean z = 2.5758 # = norminv(0.995), for a 1% confidence interval assert abs(m - truemean) < z * sqrt(truevar/numsamples) Modified: trunk/Lib/sandbox/numexpr/tests/test_numexpr.py =================================================================== --- trunk/Lib/sandbox/numexpr/tests/test_numexpr.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/numexpr/tests/test_numexpr.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -49,8 +49,8 @@ ('prod_ffn', 'r0', 't3', 2)]) # Check that full reductions work. x = arange(10.0) - assert_equal(evaluate("sum(x**2+2)"), sum(x**2+2)) - assert_equal(evaluate("prod(x**2+2)"), prod(x**2+2)) + assert_equal(evaluate("sum(x**2+2,axis=0)"), sum(x**2+2,axis=0)) + assert_equal(evaluate("prod(x**2+2,axis=0)"), prod(x**2+2,axis=0)) # Check that reductions along an axis work y = arange(9.0).reshape(3,3) assert_equal(evaluate("sum(y**2, axis=1)"), sum(y**2, axis=1)) @@ -61,16 +61,16 @@ assert_equal(evaluate("prod(y**2, axis=None)"), prod(y**2, axis=None)) # Check integers x = x.astype(int) - assert_equal(evaluate("sum(x**2+2)"), sum(x**2+2)) - assert_equal(evaluate("prod(x**2+2)"), prod(x**2+2)) + assert_equal(evaluate("sum(x**2+2,axis=0)"), sum(x**2+2,axis=0)) + assert_equal(evaluate("prod(x**2+2,axis=0)"), prod(x**2+2,axis=0)) # Check complex x = x + 5j - assert_equal(evaluate("sum(x**2+2)"), sum(x**2+2)) - assert_equal(evaluate("prod(x**2+2)"), prod(x**2+2)) + assert_equal(evaluate("sum(x**2+2,axis=0)"), sum(x**2+2,axis=0)) + assert_equal(evaluate("prod(x**2+2,axis=0)"), prod(x**2+2,axis=0)) # Check boolean (should cast to integer) x = (arange(10) % 2).astype(bool) - assert_equal(evaluate("prod(x)"), prod(x)) - assert_equal(evaluate("sum(x)"), sum(x)) + assert_equal(evaluate("prod(x,axis=0)"), prod(x,axis=0)) + assert_equal(evaluate("sum(x,axis=0)"), sum(x,axis=0)) def check_axis(self): y = arange(9.0).reshape(3,3) @@ -216,9 +216,9 @@ def equal(a, b, exact): if exact: - return (shape(a) == shape(b)) and alltrue(ravel(a) == ravel(b)) + return (shape(a) == shape(b)) and alltrue(ravel(a) == ravel(b),axis=0) else: - return (shape(a) == shape(b)) and (allclose(ravel(a), ravel(b)) or alltrue(ravel(a) == ravel(b))) # XXX report a bug? + return (shape(a) == shape(b)) and (allclose(ravel(a), ravel(b)) or alltrue(ravel(a) == ravel(b),axis=0)) # XXX report a bug? class Skip(Exception): pass Modified: trunk/Lib/sandbox/odr/models.py =================================================================== --- trunk/Lib/sandbox/odr/models.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/odr/models.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -9,7 +9,7 @@ a, b = B[0], B[1:] b.shape = (b.shape[0], 1) - return a + sum(x*b) + return a + sum(x*b,axis=0) def _lin_fjb(B, x, concatenate=sb.concatenate, Float=sb.Float, ones=sb.ones, ravel=sb.ravel): @@ -20,7 +20,7 @@ def _lin_fjd(B, x, repeat=sb.repeat): b = B[1:] - b = repeat(b, (x.shape[-1],)*b.shape[-1]) + b = repeat(b, (x.shape[-1],)*b.shape[-1],axis=0) b.shape = x.shape return b @@ -40,7 +40,7 @@ a, b = B[0], B[1:] b.shape = (b.shape[0], 1) - return a + sum(b * power(x, powers)) + return a + sum(b * power(x, powers),axis=0) def _poly_fjacb(B, x, powers, power=sb.power, concatenate=sb.concatenate, Float=sb.Float, ones=sb.ones): @@ -54,7 +54,7 @@ b = b * powers - return sum(b * power(x, powers-1)) + return sum(b * power(x, powers-1),axis=0) def _exp_fcn(B, x, exp=sb.exp): return B[0] + exp(B[1] * x) Modified: trunk/Lib/sandbox/rkern/diffev.py =================================================================== --- trunk/Lib/sandbox/rkern/diffev.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/rkern/diffev.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -45,7 +45,7 @@ # S[i] = y_i # else: # Sa[i] = y_i -# if alltrue(shift): +# if alltrue(shift,axis=0): # Find graph minima of f(x) using the Ng best points in S. # Do local search from each minimum. # Replace worst Ng points in S with best Ng points in Sa. Modified: trunk/Lib/sandbox/rkern/pink.py =================================================================== --- trunk/Lib/sandbox/rkern/pink.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/rkern/pink.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -9,7 +9,7 @@ m = 1 for i in range(k): p = int(ceil(float(n) / m)) - pink += repeat(rvs(size=p), m)[:n] + pink += repeat(rvs(size=p), m,axis=0)[:n] m <<= 1 return pink/k Modified: trunk/Lib/sandbox/stats/anova.py =================================================================== --- trunk/Lib/sandbox/stats/anova.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/stats/anova.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -72,7 +72,7 @@ Wscols = [0] + Wcolumns # w/i subj columns INCL col 0 Bscols = makelist(Bbetweens+1,Nfactors+1) #list of btw-subj cols,INCL col 0 Nwifactors = len(Wscols) - 1 # WAS len(Wcolumns) - #Nwlevels = take(array(Nlevels),Wscols) # no.lvls for each w/i subj fact + #Nwlevels = take(array(Nlevels),Wscols,axis=0) # no.lvls for each w/i subj fact #Nbtwfactors = len(Bscols) - 1 # WASNfactors - Nwifactors + 1 Nblevels = take(array(Nlevels),Bscols,0) @@ -203,7 +203,7 @@ # dim-numbers change as you collapse). THIS WORKS BECAUSE WE'RE # COLLAPSING ACROSS W/I SUBJECT AXES, WHICH WILL ALL HAVE THE # SAME SUBJ IN THE SAME ARRAY LOCATIONS (i.e., dummyvals will still exist - # but should remain the same value through the mean() function + # but should remain the same value through the mean(,axis=0) function for i in range(len(Lwithinnonsource)-1,-1,-1): dwsc = mean(dwsc,Lwithinnonsource[i]) mns = dwsc @@ -287,7 +287,7 @@ idx[0] = -1 loopcap = array(tsubjslots.shape[0:-1]) -1 while incr(idx,loopcap) != -1: - DNarray[idx] = float(sum(tsubjslots[idx])) + DNarray[idx] = float(sum(tsubjslots[idx],axis=0)) thismean = (add.reduce(tsubjslots[idx] * # 1=subj dim transpose(D[dcount]),1) / DNarray[idx]) @@ -354,9 +354,9 @@ ## Calculate harmonic means for each level in source sourceNarray = apply_over_axes(hmean, Narray,btwnonsourcedims) - ## Calc grand average (ga), used for ALL effects + ## Calc grand average (ga,axis=0), used for ALL effects ga = sum((sourceMarray*sourceNarray)/ - sum(sourceNarray)) + sum(sourceNarray),axis=0),axis=0)) ga = reshape(ga,ones(len(Marray.shape))) ## If GRAND interaction, use harmonic mean of ALL cell Ns @@ -379,7 +379,7 @@ ## Calc and save sums of squares for this source SS = sum((effect**2 *sourceNarray) * - multiply.reduce(take(Marray.shape,btwnonsourcedims,0))) + multiply.reduce(take(Marray.shape,btwnonsourcedims,0)),axis=0) ## Save it so you don't have to calculate it again next time SSlist.append(SS) SSsources.append(source) @@ -628,9 +628,9 @@ RSinter = zeros((levels,levels),PyObject) for i in range(levels): for j in range(i,levels): - RSw[i,j] = RSw[j,i] = sum(tworkd[i]*tworkd[j]) + RSw[i,j] = RSw[j,i] = sum(tworkd[i]*tworkd[j],axis=0) cross = all_cellmeans[i] * all_cellmeans[j] - multfirst = sum(cross*all_cellns[i]) + multfirst = sum(cross*all_cellns[i],axis=0) RSinter[i,j] = RSinter[j,i] = asarray(multfirst) SSm[i,j] = SSm[j,i] = (mean(all_cellmeans[i],None) * mean(all_cellmeans[j],None) * @@ -654,7 +654,7 @@ # Calculate harmonic means for each level in source sourceDNarray = apply_over_axes(hmean, DN[dindex],btwnonsourcedims) - # Calc grand average (ga), used for ALL effects + # Calc grand average (ga,axis=0), used for ALL effects variableNs = apply_over_axes(sum, sourceDNarray, range(len(sourceDMarray.shape)-1)) ga = apply_over_axes(sum, (sourceDMarray*sourceDNarray) / \ @@ -745,7 +745,7 @@ while incr(idx,loopcap) != -1: # loop through source btw level-combos mask = tsubjslots[idx] thisgroup = tworkd*mask[newaxis,:] - groupmns = mean(compress(mask,thisgroup),1) + groupmns = mean(compress(mask,thisgroup,axis=-1),1) ### THEN SUBTRACT THEM FROM APPROPRIATE SUBJECTS errors = errors - multiply.outer(groupmns,mask) Modified: trunk/Lib/sandbox/stats/obsolete.py =================================================================== --- trunk/Lib/sandbox/stats/obsolete.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/stats/obsolete.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -30,7 +30,7 @@ def summult(array1, array2, axis=0): """ Multiplies elements in array1 and array2, element by element, and -returns the sum (along 'axis') of all resulting multiplications. +returns the sum (along 'axis',axis=0) of all resulting multiplications. Axis can equal None (ravel array first), or an integer (the axis over which to operate), """ Modified: trunk/Lib/sandbox/svm/classification.py =================================================================== --- trunk/Lib/sandbox/svm/classification.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/svm/classification.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -55,7 +55,7 @@ d = {} labels = self.labels for v, (li, lj) in \ - izip(vv, chain(*[izip(repeat(x), labels[i+1:]) + izip(vv, chain(*[izip(repeat(x,axis=0), labels[i+1:]) for i, x in enumerate(labels[:-1])])): d[li, lj] = v d[lj, li] = -v Modified: trunk/Lib/sandbox/svm/dataset.py =================================================================== --- trunk/Lib/sandbox/svm/dataset.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/svm/dataset.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -179,7 +179,7 @@ else: y['index'][:-1] = N.arange(1,len(x) + 1) y['value'][:-1] = x - assert N.alltrue(y[:-1]['index'] >= 1), \ + assert N.alltrue(y[:-1]['index'] >= 1,axis=0), \ 'indexes must be positive' assert len(x) == len(N.unique(y[:-1]['index'])), \ 'indexes must be unique' Modified: trunk/Lib/sandbox/svm/predict.py =================================================================== --- trunk/Lib/sandbox/svm/predict.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/svm/predict.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -114,7 +114,7 @@ vote = N.zeros((nr_class, dec_values.shape[0]), N.uint32) classidx = range(nr_class) for pos, (i, j) in \ - enumerate(chain(*[izip(repeat(idx), classidx[k+1:]) + enumerate(chain(*[izip(repeat(idx,axis=0), classidx[k+1:]) for k, idx in enumerate(classidx[:-1])])): ji = N.array((j, i)) Modified: trunk/Lib/sandbox/svm/tests/test_regression.py =================================================================== --- trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -80,7 +80,7 @@ # use differences instead of assertAlmostEqual due to # compiler-dependent variations in these values diff = N.absolute(predictions - expected_y) - self.assert_(N.alltrue(diff < 1e-3)) + self.assert_(N.alltrue(diff < 1e-3,axis=0)) def check_cross_validate(self): y = N.random.randn(100) Modified: trunk/Lib/sandbox/xplt/Mplot.py =================================================================== --- trunk/Lib/sandbox/xplt/Mplot.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/xplt/Mplot.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -1218,18 +1218,18 @@ img1 = DATA[imgsl1] getdx1 = getdx.__copy__() getdx1[slice1[0]] = 0 - dx1 = compress(getdx1,dx) - xb1 = compress(getdx1,xb) - xe1 = compress(getdx1,xe) + dx1 = compress(getdx1,dx,axis=-1) + xb1 = compress(getdx1,xb,axis=-1) + xe1 = compress(getdx1,xe,axis=-1) imgsl2 = [slice(None,None),slice(None,None),slice(None,None)] imgsl2[slice2[0]] = slice2[1] img2 = DATA[imgsl2] getdx2 = getdx.__copy__() getdx2[slice2[0]] = 0 - dx2 = compress(getdx2,dx) - xb2 = compress(getdx2,xb) - xe2 = compress(getdx2,xe) + dx2 = compress(getdx2,dx,axis=-1) + xb2 = compress(getdx2,xb,axis=-1) + xe2 = compress(getdx2,xe,axis=-1) if (slice1[0] == slice2[0]): Modified: trunk/Lib/sandbox/xplt/NarPlotter.py =================================================================== --- trunk/Lib/sandbox/xplt/NarPlotter.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/xplt/NarPlotter.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -1768,11 +1768,11 @@ val = s.val elif s.plane is not None : if len(s.val) == len (s.nv) : - val = to_corners (s.val, s.nv, sum (s.nv)) + val = to_corners (s.val, s.nv, sum (s.nv,axis=0)) else : val = s.val else : - val = ones (sum (s.nv), Float) * s.iso + val = ones (sum (s.nv,axis=0), Float) * s.iso else : nv = concatenate ( (nv, s.nv)) x = concatenate ( (x, s.xyzv [:, 0])) @@ -1784,13 +1784,13 @@ elif s.plane is not None : if len(s.val) == len (s.nv) : val = concatenate ( (val, - to_corners (s.val, s.nv, sum (s.nv)))) + to_corners (s.val, s.nv, sum (s.nv,axis=0)))) else : val = concatenate ( (val, s.val)) else : - val = concatenate ( (val, ones (sum (s.nv), Float) * s.iso)) + val = concatenate ( (val, ones (sum (s.nv,axis=0), Float) * s.iso)) nc = len (nv) - nv = concatenate ( (cumsum (nv), arange (len (x)))) + nv = concatenate ( (cumsum (nv,axis=0), arange (len (x)))) ## if isosurfaces_present : ## self.set_palette (self.split_palette) self.set_color_card (graf._color_card) Modified: trunk/Lib/sandbox/xplt/demo5.py =================================================================== --- trunk/Lib/sandbox/xplt/demo5.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/xplt/demo5.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -215,7 +215,7 @@ n_zones = f.NumZones # Put vertices in right order for Gist n_z = transpose ( - take (transpose (n_z), array ( [0, 4, 3, 7, 1, 5, 2, 6]))) + take (transpose (n_z), array ( [0, 4, 3, 7, 1, 5, 2, 6]),axis=0)) m3 = mesh3 (x, y, z, funcs = [c], verts = n_z ) # [0:10]) [nv, xyzv, cv] = slice3 (m3, 1, None, None, 1, value = .9 * max (c) ) pyz = plane3 ( array ([1, 0, 0], Float ), zeros (3, Float)) @@ -358,7 +358,7 @@ npyr = ZLsc [i] # Now reorder the points (bill has the apex last instead of first) nz_pyr = transpose ( - take (transpose (nz_pyr), array ( [4, 0, 1, 2, 3]))) + take (transpose (nz_pyr), array ( [4, 0, 1, 2, 3]),axis=0)) istart = istart + ZLss [i] * ZLsc [i] elif ZLss[i] == 6 : # PRISM nz_prism = reshape (ZLsn [istart: istart + ZLss [i] * ZLsc [i]], @@ -367,7 +367,7 @@ # now reorder the points (bill goes around a square face # instead of traversing the opposite sides in the same direction. nz_prism = transpose ( - take (transpose (nz_prism), array ( [0, 1, 3, 2, 4, 5]))) + take (transpose (nz_prism), array ( [0, 1, 3, 2, 4, 5]),axis=0)) istart = istart + ZLss [i] * ZLsc [i] elif ZLss[i] == 8 : # HEXAHEDRON nz_hex = reshape (ZLsn [istart: istart + ZLss [i] * ZLsc [i]], @@ -375,7 +375,7 @@ # now reorder the points (bill goes around a square face # instead of traversing the opposite sides in the same direction. nz_hex = transpose ( - take (transpose (nz_hex), array ( [0, 1, 3, 2, 4, 5, 7, 6]))) + take (transpose (nz_hex), array ( [0, 1, 3, 2, 4, 5, 7, 6]),axis=0)) nhex = ZLsc [i] istart = istart + ZLss [i] * ZLsc [i] else : Modified: trunk/Lib/sandbox/xplt/gist3dhelp.py =================================================================== --- trunk/Lib/sandbox/xplt/gist3dhelp.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/xplt/gist3dhelp.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -217,7 +217,7 @@ or get3_light(xyz) return 3D lighting for polygons with vertices XYZ. If NXYZ is - specified, XYZ should be sum(nxyz)-by-3, with NXYZ being the + specified, XYZ should be sum(nxyz,axis=0)-by-3, with NXYZ being the list of numbers of vertices for each polygon (as for the plfp function). If NXYZ is not specified, XYZ should be a quadrilateral mesh, ni-by-nj-by-3 (as for the plf function). In the first case, @@ -235,7 +235,7 @@ or get3_normal(xyz) return 3D normals for polygons with vertices XYZ. If NXYZ is - specified, XYZ should be sum(nxyz)-by-3, with NXYZ being the + specified, XYZ should be sum(nxyz,axis=0)-by-3, with NXYZ being the list of numbers of vertices for each polygon (as for the plfp function). If NXYZ is not specified, XYZ should be a quadrilateral mesh, ni-by-nj-by-3 (as for the plf function). In the first case, @@ -257,7 +257,7 @@ or get3_centroid(xyz) return 3D centroids for polygons with vertices XYZ. If NXYZ is - specified, XYZ should be sum(nxyz)-by-3, with NXYZ being the + specified, XYZ should be sum(nxyz,axis=0)-by-3, with NXYZ being the list of numbers of vertices for each polygon (as for the plfp function). If NXYZ is not specified, XYZ should be a quadrilateral mesh, ni-by-nj-by-3 (as for the plf function). In the first case, @@ -332,7 +332,7 @@ "sort3d" : """ sort3d(z, npolys) - given Z and NPOLYS, with len(Z)==sum(npolys), return + given Z and NPOLYS, with len(Z)==sum(npolys,axis=0), return a 2-element list [LIST, VLIST] such that Z[VLIST] and NPOLYS[LIST] are sorted from smallest average Z to largest average Z, where the averages are taken over the clusters of length NPOLYS. @@ -458,9 +458,9 @@ ravel(add.outer (adders,zeros(nj-1, Int))) + arange((ni-1)*(nj-1), dtype = Int), array ( [[0, 1], [nj + 1, nj]]))) - xyz=array([take(ravel(xyz[0]),list), - take(ravel(xyz[1]),list), - take(ravel(xyz[2]),list)]) + xyz=array([take(ravel(xyz[0]),list,axis=0), + take(ravel(xyz[1]),list,axis=0), + take(ravel(xyz[2]),list,axis=0)]) nxyz= ones((ni-1)*(nj-1)) * 4; The resulting array xyz is 3-by-(4*(nj-1)*(ni-1)). xyz[0:3,4*i:4*(i+1)] are the clockwise coordinates of the @@ -523,7 +523,7 @@ the list [NVERTS, XYZVERTS, color]. Note that it is impossible to pass arguments as addresses, as yorick does in this routine. NVERTS is the number of vertices in each polygon of the slice, and - XYZVERTS is the 3-by-sum(NVERTS) list of polygon vertices. If the + XYZVERTS is the 3-by-sum(NVERTS,axis=0) list of polygon vertices. If the FCOLOR argument is present, the values of that coloring function on the polygons are returned as the value of the slice3 function (numberof(color_values) == numberof(NVERTS) == number of polygons). @@ -573,7 +573,7 @@ slice3mesh returns a triple [nverts, xyzverts, color] nverts is no_cells long and the ith entry tells how many vertices the ith cell has. - xyzverts is sum (nverts) by 3 and gives the vertex + xyzverts is sum (nverts,axis=0) by 3 and gives the vertex coordinates of the cells in order. color, if present, is len (nverts) long and contains a color value for each cell in the mesh. @@ -781,7 +781,7 @@ Perform simple 3D rendering of an object created by slice3 (possibly followed by slice2). NVERTS and XYZVERTS are polygon - lists as returned by slice3, so XYZVERTS is sum(NVERTS)-by-3, + lists as returned by slice3, so XYZVERTS is sum(NVERTS,axis=0)-by-3, where NVERTS is a list of the number of vertices in each polygon. If present, the VALUES should have the same length as NVERTS; they are used to color the polygon. If VALUES is not specified, @@ -797,7 +797,7 @@ cmin = None, cmax = None) Add the polygon list specified by NVERTS (number of vertices in - each polygon) and XYZVERTS (3-by-sum(NVERTS) vertex coordinates) + each polygon) and XYZVERTS (3-by-sum(NVERTS,axis=0) vertex coordinates) to the currently displayed b-tree. If VALUES is specified, it must have the same dimension as NVERTS, and represents the color of each polygon. If VALUES is not specified, the polygons Modified: trunk/Lib/sandbox/xplt/gistdemo3d.py =================================================================== --- trunk/Lib/sandbox/xplt/gistdemo3d.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/xplt/gistdemo3d.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -255,7 +255,7 @@ # n_zones = f.NumZones # # Put vertices in right order for Gist # n_z = transpose ( -# take (transpose (n_z), array ( [0, 4, 3, 7, 1, 5, 2, 6]))) +# take (transpose (n_z), array ( [0, 4, 3, 7, 1, 5, 2, 6]),axis=0)) # m3 = mesh3 (x, y, z, funcs = [c], verts = n_z ) # [0:10]) # [nv, xyzv, cv] = slice3 (m3, 1, None, None, 1, value = .9 * max (c) ) # pyz = plane3 ( array ([1, 0, 0], Float ), zeros (3, Float)) @@ -400,7 +400,7 @@ # npyr = ZLsc [i] # # Now reorder the points (bill has the apex last instead of first) # nz_pyr = transpose ( -# take (transpose (nz_pyr), array ( [4, 0, 1, 2, 3]))) +# take (transpose (nz_pyr), array ( [4, 0, 1, 2, 3]),axis=0)) # istart = istart + ZLss [i] * ZLsc [i] # elif ZLss[i] == 6 : # PRISM # nz_prism = reshape (ZLsn [istart: istart + ZLss [i] * ZLsc [i]], @@ -409,7 +409,7 @@ # # now reorder the points (bill goes around a square face # # instead of traversing the opposite sides in the same direction. # nz_prism = transpose ( -# take (transpose (nz_prism), array ( [0, 1, 3, 2, 4, 5]))) +# take (transpose (nz_prism), array ( [0, 1, 3, 2, 4, 5]),axis=0)) # istart = istart + ZLss [i] * ZLsc [i] # elif ZLss[i] == 8 : # HEXAHEDRON # nz_hex = reshape (ZLsn [istart: istart + ZLss [i] * ZLsc [i]], @@ -417,7 +417,7 @@ # # now reorder the points (bill goes around a square face # # instead of traversing the opposite sides in the same direction. # nz_hex = transpose ( -# take (transpose (nz_hex), array ( [0, 1, 3, 2, 4, 5, 7, 6]))) +# take (transpose (nz_hex), array ( [0, 1, 3, 2, 4, 5, 7, 6]),axis=0)) # nhex = ZLsc [i] # istart = istart + ZLss [i] * ZLsc [i] # else : Modified: trunk/Lib/sandbox/xplt/mesh3d.py =================================================================== --- trunk/Lib/sandbox/xplt/mesh3d.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/xplt/mesh3d.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -212,10 +212,10 @@ n_z = self.cell_dict[k][1] if n_z.shape [1] == 8 : verts.append ( - take (n_z, array ( [0, 1, 3, 2, 4, 5, 7, 6]))) + take (n_z, array ( [0, 1, 3, 2, 4, 5, 7, 6]),axis=0)) elif n_z.shape [1] == 6 : verts.append ( - take (n_z, array ( [3, 0, 4, 1, 5, 2]))) + take (n_z, array ( [3, 0, 4, 1, 5, 2]),axis=0)) else : verts.append (n_z) if len (verts) == 1 : @@ -317,7 +317,7 @@ where : nv is a one-dimensional integer array whose ith entry is the number of vertices of the ith face. - xyzv is a two-dimensional array dimensioned sum (nv) by 3. + xyzv is a two-dimensional array dimensioned sum (nv,axis=0) by 3. The first nv [0] triples are the vertices of face [0], the next nv [1] triples are the vertices of face [1], etc. val (if present) is an array the same length as nv whose Modified: trunk/Lib/sandbox/xplt/pl3d.py =================================================================== --- trunk/Lib/sandbox/xplt/pl3d.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/xplt/pl3d.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -219,7 +219,7 @@ theta = kw ["theta"] y = array ( [0., z [2], -z [1]]) x = dot (transpose (gr3), array ( [1., 0., 0.])) - phi = arctan2 (sum (y * x), x [0]) + phi = arctan2 (sum (y * x,axis=0), x [0]) else : phi = kw ["phi"] theta = kw ["theta"] @@ -370,7 +370,7 @@ or get3_light(xyz) return 3D lighting for polygons with vertices XYZ. If NXYZ is - specified, XYZ should be sum(nxyz)-by-3, with NXYZ being the + specified, XYZ should be sum(nxyz,axis=0)-by-3, with NXYZ being the list of numbers of vertices for each polygon (as for the plfp function). If NXYZ is not specified, XYZ should be a quadrilateral mesh, ni-by-nj-by-3 (as for the plf function). In the first case, @@ -404,7 +404,7 @@ else : view = array ( [0., 0., zc], Float) - get3_centroid (xyz, nxyz [0]) m1 = \ - sqrt ( sum (view * view)) + sqrt ( sum (view * view,axis=0)) if m1 == 0. : m1 = 1. view = view / m1 @@ -412,11 +412,11 @@ normal [2, ...] * view [2] light = ambient + diffuse * abs (nv) if specular != 0. : - sv = transpose (transpose (sdir) / sqrt (sum (transpose (sdir*sdir)))) + sv = transpose (transpose (sdir) / sqrt (sum (transpose (sdir*sdir),axis=0))) sv = dot (sv, view) if len (shape (sdir)) == 1 : sn = sum(array([sdir[0]*normal[0],sdir[1]*normal[1], - sdir[2]*normal[2]])) + sdir[2]*normal[2]]),axis=0) ####### I left out the specular_hook stuff. m1 = maximum (sn * nv -0.5 * sv + 0.5, 1.e-30) m1 = m1 ** spower @@ -426,7 +426,7 @@ nsrc = len (shape (sdir)) for i in range (nsrc) : sn = sum(array([sdir[i,0]*normal[0],sdir[i,1]*normal[1], - sdir[i,2]*normal[2]])) + sdir[i,2]*normal[2]]),axis=0) m1 = maximum (sn * nv -0.5 * sv [i] + 0.5, 1.e-30) ** spower [i] light = light + specular * m1 return light @@ -438,7 +438,7 @@ or get3_normal(xyz) return 3D normals for polygons with vertices XYZ. If NXYZ is - specified, XYZ should be sum(nxyz)-by-3, with NXYZ being the + specified, XYZ should be sum(nxyz,axis=0)-by-3, with NXYZ being the list of numbers of vertices for each polygon (as for the plfp function). If NXYZ is not specified, XYZ should be a quadrilateral mesh, ni-by-nj-by-3 (as for the plf function). In the first case, @@ -463,7 +463,7 @@ else : # with polygon list, more elaborate calculation required # (1) frst subscripts the first vertex of each polygon - frst = cumsum (nxyz [0]) - nxyz [0] + frst = cumsum (nxyz [0],axis=0) - nxyz [0] # form normal by getting two approximate diameters # (reduces to above medians for quads) @@ -508,7 +508,7 @@ or get3_centroid(xyz) return 3D centroids for polygons with vertices XYZ. If NXYZ is - specified, XYZ should be sum(nxyz)-by-3, with NXYZ being the + specified, XYZ should be sum(nxyz,axis=0)-by-3, with NXYZ being the list of numbers of vertices for each polygon (as for the plfp function). If NXYZ is not specified, XYZ should be a quadrilateral mesh, ni-by-nj-by-3 (as for the plf function). In the first case, @@ -526,9 +526,9 @@ centroid = zcen_ (zcen_ (xyz, 1), 0) else : # with polygon list, more elaborate calculation required - last = cumsum (nxyz [0]) + last = cumsum (nxyz [0],axis=0) list = histogram (1 + last) [0:-1] - list = cumsum (list) + list = cumsum (list,axis=0) k = len (nxyz [0]) l = shape (xyz) [0] centroid = zeros ( (k, 3)) @@ -807,7 +807,7 @@ """ sort3d(z, npolys) - given Z and NPOLYS, with len(Z)==sum(npolys), return + given Z and NPOLYS, with len(Z)==sum(npolys,axis=0), return a 2-element list [LIST, VLIST] such that Z[VLIST] and NPOLYS[LIST] are sorted from smallest average Z to largest average Z, where the averages are taken over the clusters of length NPOLYS. @@ -834,8 +834,8 @@ # get a list the same length as x, y, or z which is 1 for each # vertex of poly 1, 2 for each vertex of poly2, etc. # the goal is to make nlist with histogram(nlist)==npolys - nlist = histogram(cumsum (npolys)) [0:-1] - nlist = cumsum (nlist) + nlist = histogram(cumsum (npolys,axis=0)) [0:-1] + nlist = cumsum (nlist,axis=0) # now sum the vertex values and divide by the number of vertices z = histogram (nlist, z) / npolys @@ -1079,7 +1079,7 @@ # label positions: first find shortest axis xy = sqrt (x1 * x1 + y1 * y1) xysum = add.reduce (xy) - i = argmin (xy) # mnx (xy) + i = argmin (xy,axis=-1) # mnx (xy) jk = [ [1, 2], [2, 0], [0, 1]] [i] j = jk [0] k = jk [1] Modified: trunk/Lib/sandbox/xplt/slice3.py =================================================================== --- trunk/Lib/sandbox/xplt/slice3.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/xplt/slice3.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -72,8 +72,8 @@ # the normal doesn't really need to be normalized, but this # has the desirable side effect of blowing up if normal==0 newnorm = zeros (4, Float) - newnorm [0:3] = normal / sqrt (sum (normal*normal)) - newnorm [3] = sum (multiply (normal, point)) + newnorm [0:3] = normal / sqrt (sum (normal*normal,axis=0)) + newnorm [3] = sum (multiply (normal, point),axis=0) return newnorm _Mesh3Error = "Mesh3Error" @@ -255,7 +255,7 @@ the list [NVERTS, XYZVERTS, color]. Note that it is impossible to pass arguments as addresses, as yorick does in this routine. NVERTS is the number of vertices in each polygon of the slice, and - XYZVERTS is the 3-by-sum(NVERTS) list of polygon vertices. If the + XYZVERTS is the 3-by-sum(NVERTS,axis=0) list of polygon vertices. If the FCOLOR argument is present, the values of that coloring function on the polygons are returned as the value of the slice3 function (numberof(color_values) == numberof(NVERTS) == number of polygons). @@ -442,8 +442,8 @@ critical_cells = bitwise_and (add.reduce \ (reshape (ravel (transpose (less (fs [0], slice3_precision))), \ (8, dim1))), 7) - if (sum (critical_cells) != 0) : - clist = take (fs [1], nonzero (critical_cells)) + if (sum (critical_cells,axis=0) != 0) : + clist = take (fs [1], nonzero (critical_cells),axis=0) ntotal8 = ntotal8 + len (clist) else : clist = None @@ -457,8 +457,8 @@ (6, dim1))) critical_cells = logical_and (greater (critical_cells, 0), less (critical_cells, 6)) - if (sum (critical_cells) != 0) : - clist = take (fs [1], nonzero (critical_cells)) + if (sum (critical_cells,axis=0) != 0) : + clist = take (fs [1], nonzero (critical_cells),axis=0) ntotal6 = ntotal6 + len (clist) else : clist = None @@ -472,8 +472,8 @@ (5, dim1))) critical_cells = logical_and (greater (critical_cells, 0), less (critical_cells, 5)) - if (sum (critical_cells) != 0) : - clist = take (fs [1], nonzero (critical_cells)) + if (sum (critical_cells,axis=0) != 0) : + clist = take (fs [1], nonzero (critical_cells),axis=0) ntotal5 = ntotal5 + len (clist) else : clist = None @@ -485,8 +485,8 @@ critical_cells = bitwise_and (add.reduce \ (reshape (ravel (transpose (less (fs [0], slice3_precision))), \ (4, dim1))), 3) - if (sum (critical_cells) != 0) : - clist = take (fs [1], nonzero (critical_cells)) + if (sum (critical_cells,axis=0) != 0) : + clist = take (fs [1], nonzero (critical_cells),axis=0) ntotal4 = ntotal4 + len (clist) else : clist = None @@ -501,7 +501,7 @@ clist1 = ravel (zcen_ (zcen_ (zcen_ (array (less (fs, slice3_precision), Float), 0), 1), 2)) clist1 = logical_and (less (clist1, .9), greater (clist1, .1)) - if sum (clist1) > 0 : + if sum (clist1,axis=0) > 0 : clist = nonzero (clist1) ntotal = ntotal + len (clist) else : @@ -516,30 +516,30 @@ # values at the vertices of these cells if (irregular) : # extract the portions of the data indexed by clist - fs = take (fs [0], clist) + fs = take (fs [0], clist,axis=0) if got_xyz : - _xyz3 = take (_xyz3, clist) + _xyz3 = take (_xyz3, clist,axis=0) if col : - col = take (col, clist) + col = take (col, clist,axis=0) else : # extract the to_corners portions of the data indexed by clist indices = to_corners3 (clist, dims [1], dims [2]) no_cells = shape (indices) [0] indices = ravel (indices) - fs = reshape (take (ravel (fs), indices),\ + fs = reshape (take (ravel (fs), indices,axis=0),\ (no_cells, 2, 2, 2)) if got_xyz : new_xyz3 = zeros ( (no_cells, 3, 2, 2, 2), Float ) new_xyz3 [:, 0, ...] = reshape (take (ravel (_xyz3 [0, ...]),\ - indices), (no_cells, 2, 2, 2)) + indices,axis=0), (no_cells, 2, 2, 2)) new_xyz3 [:, 1, ...] = reshape (take (ravel (_xyz3 [1, ...]),\ - indices), (no_cells, 2, 2, 2)) + indices,axis=0), (no_cells, 2, 2, 2)) new_xyz3 [:, 2, ...] = reshape (take (ravel (_xyz3 [2, ...]),\ - indices), (no_cells, 2, 2, 2)) + indices,axis=0), (no_cells, 2, 2, 2)) _xyz3 = new_xyz3 del new_xyz3 if col != None : - col = reshape (take (ravel (col), indices), (no_cells, 2, 2, 2)) + col = reshape (take (ravel (col), indices,axis=0), (no_cells, 2, 2, 2)) # NB: col represents node colors, and is only used # if those are requested. # here, the iterator converts to absolute cell indices without @@ -616,27 +616,27 @@ # construct edge endpoint indices in fs, xyz arrays # the numbers are the endpoint indices corresponding to # the order of the _no_edges [i] edges in the mask array - lower = take (_lower_vert [i], edges) + _no_verts [i] * cells - upper = take (_upper_vert [i], edges) + _no_verts [i] * cells - fsl = take (ravel (fs), lower) - fsu = take (ravel (fs), upper) + lower = take (_lower_vert [i], edges,axis=0) + _no_verts [i] * cells + upper = take (_upper_vert [i], edges,axis=0) + _no_verts [i] * cells + fsl = take (ravel (fs), lower,axis=0) + fsu = take (ravel (fs), upper,axis=0) # following denominator guaranteed non-zero denom = fsu - fsl fsu = fsu / denom fsl = fsl / denom new_xyz = zeros ( (len (lower), 3), Float ) - new_xyz [:, 0] = reshape ( (take (ravel (xyz [:, 0]), lower) * fsu - \ - take (ravel (xyz [:, 0]), upper) * fsl), (len (lower),)) - new_xyz [:, 1] = reshape ( (take (ravel (xyz [:, 1]), lower) * fsu - \ - take (ravel (xyz [:, 1]), upper) * fsl), (len (lower),)) - new_xyz [:, 2] = reshape ( (take (ravel (xyz [:, 2]), lower) * fsu - \ - take (ravel (xyz [:, 2]), upper) * fsl), (len (lower),)) + new_xyz [:, 0] = reshape ( (take (ravel (xyz [:, 0]), lower,axis=0) * fsu - \ + take (ravel (xyz [:, 0]), upper,axis=0) * fsl), (len (lower),)) + new_xyz [:, 1] = reshape ( (take (ravel (xyz [:, 1]), lower,axis=0) * fsu - \ + take (ravel (xyz [:, 1]), upper,axis=0) * fsl), (len (lower),)) + new_xyz [:, 2] = reshape ( (take (ravel (xyz [:, 2]), lower,axis=0) * fsu - \ + take (ravel (xyz [:, 2]), upper,axis=0) * fsl), (len (lower),)) xyz = new_xyz del new_xyz if col != None : # Extract subset of the data the same way - col = take (ravel (col), lower) * fsu - \ - take (ravel (col), upper) * fsl + col = take (ravel (col), lower,axis=0) * fsu - \ + take (ravel (col), upper,axis=0) * fsl # The xyz array is now the output xyzverts array, # but for the order of the points within each cell. @@ -645,10 +645,10 @@ # above and below the slicing plane p2 = left_shift (ones (_no_verts [i], Int) , array ( [0, 1, 2, 3, 4, 5, 6, 7], Int) [0: _no_verts [i]]) - pattern = transpose (sum (transpose (multiply (below, p2)))) + pattern = transpose (sum (transpose (multiply (below, p2)),axis=0)) # broadcast the cell's pattern onto each of its sliced edges - pattern = take (pattern, list / _no_edges [i]) + pattern = take (pattern, list / _no_edges [i],axis=0) # Let ne represent the number of edges of this type of cell, # and nv the number of vertices. # To each pattern, there corresponds a permutation of the @@ -662,17 +662,17 @@ # Let these permutations be stored in a ne-by-2**nv - 2 array # _poly_permutations (see next comment for explanation of 4 * ne): pattern = take (ravel (transpose (_poly_permutations [i])), - _no_edges [i] * (pattern - 1) + edges) + 4 * _no_edges [i] * cells + _no_edges [i] * (pattern - 1) + edges,axis=0) + 4 * _no_edges [i] * cells order = argsort (pattern) xyz1 = zeros ( (len (order), 3), Float ) - xyz1 [:,0] = take (ravel (xyz [:,0]), order) - xyz1 [:,1] = take (ravel (xyz [:,1]), order) - xyz1 [:,2] = take (ravel (xyz [:,2]), order) + xyz1 [:,0] = take (ravel (xyz [:,0]), order,axis=0) + xyz1 [:,1] = take (ravel (xyz [:,1]), order,axis=0) + xyz1 [:,2] = take (ravel (xyz [:,2]), order,axis=0) xyz = xyz1 if col != None : - col = take (col, order) - edges = take (edges, order) - pattern = take (pattern, order) + col = take (col, order,axis=0) + edges = take (edges, order,axis=0) + pattern = take (pattern, order,axis=0) # cells(order) is same as cells by construction */ # There remains only the question of splitting the points in @@ -697,7 +697,7 @@ list [1:] = nz + 1 newpat = zeros (len (pattern) + 1, Int) newpat [0] = 1 - newpat [1:] = cumsum (not_equal (pattern, 0)) + 1 + newpat [1:] = cumsum (not_equal (pattern, 0),axis=0) + 1 pattern = newpat nverts = histogram (pattern) [1:] xyzverts = xyz @@ -709,7 +709,7 @@ # if some polys have been split, need to split clist as well if len (list) > len (clist) : - clist = take (clist, take (cells, list)) + clist = take (clist, take (cells, list, axis=0),axis=0) if col == None : if nointerp == None : if type (fcolor) == FunctionType : @@ -780,7 +780,7 @@ slice3mesh returns a triple [nverts, xyzverts, color] nverts is no_cells long and the ith entry tells how many vertices the ith cell has. - xyzverts is sum (nverts) by 3 and gives the vertex + xyzverts is sum (nverts,axis=0) by 3 and gives the vertex coordinates of the cells in order. color, if present, is len (nverts) long and contains a color value for each cell in the mesh. @@ -889,18 +889,18 @@ elif shape (color) == (ncx, ncy) and smooth == 0 : col = ravel (color) # Lower left, upper left, upper right, lower right - col = 0.25 * (take (col, ravel (add.outer ( ncxx, ncyy))) + - take (col, ravel (add.outer ( ncxx, ncyy + 1))) + - take (col, ravel (add.outer ( ncxx + ncy, ncyy + 1))) + - take (col, ravel (add.outer ( ncxx + ncy, ncyy)))) + col = 0.25 * (take (col, ravel (add.outer ( ncxx, ncyy)),axis=0) + + take (col, ravel (add.outer ( ncxx, ncyy + 1)),axis=0) + + take (col, ravel (add.outer ( ncxx + ncy, ncyy + 1)),axis=0) + + take (col, ravel (add.outer ( ncxx + ncy, ncyy)),axis=0)) elif shape (color) == (ncx, ncy) and smooth != 0 : # Node-centered colors are wanted (smooth plots) col = ravel (color) col = ravel (transpose (array ( [ - take (col, ravel (add.outer ( ncxx, ncyy))), - take (col, ravel (add.outer ( ncxx, ncyy + 1))), - take (col, ravel (add.outer ( ncxx + ncy, ncyy + 1))), - take (col, ravel (add.outer ( ncxx + ncy, ncyy)))]))) + take (col, ravel (add.outer ( ncxx, ncyy)),axis=0), + take (col, ravel (add.outer ( ncxx, ncyy + 1)),axis=0), + take (col, ravel (add.outer ( ncxx + ncy, ncyy + 1)),axis=0), + take (col, ravel (add.outer ( ncxx + ncy, ncyy)),axis=0)]))) else : raise _Slice3MeshError, \ "color must be cell-centered or vertex centered." @@ -920,22 +920,22 @@ else : newx = ravel (x) xyzverts [:, 0] = ravel (transpose (array ( [ - take (newx, ravel (add.outer ( ncxx, ncyy))), - take (newx, ravel (add.outer ( ncxx, ncyy + 1))), - take (newx, ravel (add.outer ( ncxx + ncy, ncyy + 1))), - take (newx, ravel (add.outer ( ncxx + ncy, ncyy)))]))) + take (newx, ravel (add.outer ( ncxx, ncyy)),axis=0), + take (newx, ravel (add.outer ( ncxx, ncyy + 1)),axis=0), + take (newx, ravel (add.outer ( ncxx + ncy, ncyy + 1)),axis=0), + take (newx, ravel (add.outer ( ncxx + ncy, ncyy)),axis=0)]))) newy = ravel (y) xyzverts [:, 1] = ravel (transpose (array ( [ - take (newy, ravel (add.outer ( ncxx, ncyy))), - take (newy, ravel (add.outer ( ncxx, ncyy + 1))), - take (newy, ravel (add.outer ( ncxx + ncy, ncyy + 1))), - take (newy, ravel (add.outer ( ncxx + ncy, ncyy)))]))) + take (newy, ravel (add.outer ( ncxx, ncyy)),axis=0), + take (newy, ravel (add.outer ( ncxx, ncyy + 1)),axis=0), + take (newy, ravel (add.outer ( ncxx + ncy, ncyy + 1)),axis=0), + take (newy, ravel (add.outer ( ncxx + ncy, ncyy)),axis=0)]))) newz = ravel (z) xyzverts [:, 2] = ravel (transpose (array ( [ - take (newz, ravel (add.outer ( ncxx, ncyy))), - take (newz, ravel (add.outer ( ncxx, ncyy + 1))), - take (newz, ravel (add.outer ( ncxx + ncy, ncyy + 1))), - take (newz, ravel (add.outer ( ncxx + ncy, ncyy)))]))) + take (newz, ravel (add.outer ( ncxx, ncyy)),axis=0), + take (newz, ravel (add.outer ( ncxx, ncyy + 1)),axis=0), + take (newz, ravel (add.outer ( ncxx + ncy, ncyy + 1)),axis=0), + take (newz, ravel (add.outer ( ncxx + ncy, ncyy)),axis=0)]))) return [nverts, xyzverts, col] @@ -1041,7 +1041,7 @@ offsets = array ( [njnk, nj, 1], Int) if clist != None : # add offset for this chunk to clist and return - return sum (offsets * ( chunk [0] - 1)) + clist + return sum (offsets * ( chunk [0] - 1),axis=0) + clist # increment to next chunk xi = chunk [1, 0] @@ -1229,7 +1229,7 @@ indices = to_corners3 (chunk, dims [0] + 1, dims [1] + 1) no_cells = shape (indices) [0] indices = ravel (indices) - retval = reshape (take (ravel (fi [i]), indices), (no_cells, 2, 2, 2)) + retval = reshape (take (ravel (fi [i]), indices,axis=0), (no_cells, 2, 2, 2)) return [retval, chunk] @@ -1282,13 +1282,13 @@ tc = shape (verts) [1] # ZCM 2/4/97 the array of cell numbers must be relative if tc == 8 : # hex cells - return [ reshape (take (fi [i], indices), (no_cells, 2, 2, 2)), + return [ reshape (take (fi [i], indices,axis=0), (no_cells, 2, 2, 2)), arange (0, no_cells, dtype = Int), oldstart] elif tc == 6 : # pyramids - return [ reshape (take (fi [i], indices), (no_cells, 3, 2)), + return [ reshape (take (fi [i], indices,axis=0), (no_cells, 3, 2)), arange (0, no_cells, dtype = Int), oldstart] else : # tetrahedron or pyramid - return [ reshape (take (fi [i], indices), (no_cells, tc)), + return [ reshape (take (fi [i], indices,axis=0), (no_cells, tc)), arange (0, no_cells, dtype = Int), oldstart] _Getc3Error = "Getc3Error" @@ -1362,7 +1362,7 @@ c [0, 2] - 1:1 + c [1, 2]] else : [k, l. m] = dims - return reshape (take (ravel (fi [i - 1]), chunk), + return reshape (take (ravel (fi [i - 1]), chunk,axis=0), (len (chunk), k, l, m)) else : # it is vertex-centered, so we take averages to get cell quantity @@ -1379,13 +1379,13 @@ indices = to_corners3 (chunk, dims [1] + 1, dims [2] + 1) no_cells = shape (indices) [0] indices = ravel (indices) - corners = take (ravel (fi [i - 1]), indices) + corners = take (ravel (fi [i - 1]), indices,axis=0) if l == None : - return 0.125 * sum (transpose (reshape (corners, (no_cells, 8)))) + return 0.125 * sum (transpose (reshape (corners, (no_cells, 8))),axis=0) else : # interpolate corner values to get edge values - corners = (take (corners, l) * fsu - - take (corners, u) * fsl) / (fsu -fsl) + corners = (take (corners, l,axis=0) * fsu - + take (corners, u,axis=0) * fsl) / (fsu -fsl) # average edge values (vertex values of polys) on each poly return histogram (cells, corners) / histogram (cells) @@ -1420,7 +1420,7 @@ if type (chunk) == ListType : return fi [i - 1] [chunk [0] [0]:chunk [0] [1]] elif type (chunk) == ArrayType and len (shape (chunk)) == 1 : - return take (fi [i - 1], chunk) + return take (fi [i - 1], chunk,axis=0) else : raise _Getc3Error, "chunk argument is incomprehensible." @@ -1434,7 +1434,7 @@ if type (chunk) == ListType : indices = verts [chunk [0] [0]:chunk [0] [1]] elif type (chunk) == ArrayType and len (shape (chunk)) == 1 : - indices = take (verts, chunk) + indices = take (verts, chunk,axis=0) else : raise _Getc3Error, "chunk argument is incomprehensible." else : @@ -1459,21 +1459,21 @@ ch = chunk if j > 0 : ch = chunk - totals [j - 1] - indices = take (verts, ch) + indices = take (verts, ch,axis=0) else : raise _Getc3Error, "chunk argument is incomprehensible." shp = shape (indices) no_cells = shp [0] indices = ravel (indices) - corners = take (fi [i - 1], indices) + corners = take (fi [i - 1], indices,axis=0) if l == None : return (1. / shp [1]) * transpose ((sum (transpose (reshape (corners, - (no_cells, shp [1]))) [0:shp [1]]))) + (no_cells, shp [1]))) [0:shp [1]],axis=0))) else : # interpolate corner values to get edge values - corners = (take (corners, l) * fsu - - take (corners, u) * fsl) / (fsu -fsl) + corners = (take (corners, l,axis=0) * fsu - + take (corners, u,axis=0) * fsl) / (fsu -fsl) # average edge values (vertex values of polys) on each poly return histogram (cells, corners) / histogram (cells) @@ -1913,7 +1913,7 @@ Perform simple 3D rendering of an object created by slice3 (possibly followed by slice2). NVERTS and XYZVERTS are polygon - lists as returned by slice3, so XYZVERTS is sum(NVERTS)-by-3, + lists as returned by slice3, so XYZVERTS is sum(NVERTS,axis=0)-by-3, where NVERTS is a list of the number of vertices in each polygon. If present, the VALUES should have the same length as NVERTS; they are used to color the polygon. If VALUES is not specified, @@ -1953,10 +1953,10 @@ # xyzverts [:, 2] = z values = get3_light (xyztmp, nverts) [list, vlist] = sort3d (z, nverts) - nverts = take (nverts, list) - values = take (values, list) - x = take (x, vlist) - y = take (y, vlist) + nverts = take (nverts, list,axis=0) + values = take (values, list,axis=0) + x = take (x, vlist,axis=0) + y = take (y, vlist,axis=0) _square = get_square_ ( ) [_xfactor, _yfactor] = get_factors_ () xmax = max (x) @@ -1990,7 +1990,7 @@ nverts = array (nverts, Int) xyzverts = array (xyzverts, Float ) - if shape (xyzverts) [0] != sum (nverts) or sum (less (nverts, 3)) or \ + if shape (xyzverts) [0] != sum (nverts,axis=0) or sum (less (nverts, 3),axis=0) or \ nverts.dtype != Int : raise _Pl3surfError, "illegal or inconsistent polygon list" if values != None and len (values) != len (nverts) : @@ -2023,7 +2023,7 @@ cmin = None, cmax = None) Add the polygon list specified by NVERTS (number of vertices in - each polygon) and XYZVERTS (3-by-sum(NVERTS) vertex coordinates) + each polygon) and XYZVERTS (3-by-sum(NVERTS,axis=0) vertex coordinates) to the currently displayed b-tree. If VALUES is specified, it must have the same dimension as NVERTS, and represents the color of each polygon. If VALUES is not specified, the polygons @@ -2096,22 +2096,22 @@ if plane != None : plane = plane.astype (Float) - if shape (xyzverts) [0] != sum (nverts) or sum (less (nverts, 3)) > 0 or \ + if shape (xyzverts) [0] != sum (nverts,axis=0) or sum (less (nverts, 3),axis=0) > 0 or \ type (nverts [0]) != IntType : - print "Dim1 of xyzverts ", shape (xyzverts) [0], " sum (nverts) ",\ - sum (nverts), " sum (less (nverts, 3)) ", sum (less (nverts, 3)), \ + print "Dim1 of xyzverts ", shape (xyzverts) [0], " sum (nverts,axis=0) ",\ + sum (nverts,axis=0), " sum (less (nverts, 3),axis=0) ", sum (less (nverts, 3),axis=0), \ " type (nverts [0]) ", `type (nverts [0])` raise _Pl3treeError, "illegal or inconsistent polygon list." if type (values) == ArrayType and len (values) != len (nverts) and \ - len (values) != sum (nverts) : + len (values) != sum (nverts,axis=0) : raise _Pl3treeError, "illegal or inconsistent polygon color values" - if type (values) == ArrayType and len (values) == sum (nverts) : + if type (values) == ArrayType and len (values) == sum (nverts,axis=0) : # We have vertex-centered values, which for Gist must be # averaged over each cell - list = zeros (sum (nverts), Int) - array_set (list, cumsum (nverts) [0:-1], ones (len (nverts), Int)) + list = zeros (sum (nverts,axis=0), Int) + array_set (list, cumsum (nverts,axis=0) [0:-1], ones (len (nverts), Int)) tpc = values.dtype - values = (histogram (cumsum (list), values) / nverts).astype (tpc) + values = (histogram (cumsum (list,axis=0), values) / nverts).astype (tpc) if plane != None : if (len (shape (plane)) != 1 or shape (plane) [0] != 4) : raise _Pl3treeError, "illegal plane format, try plane3 function" @@ -2694,11 +2694,11 @@ # sort the single polygon list if not_plane : [_list, _vlist] = sort3d (_z, _nverts) - _nverts = take (_nverts, _list) + _nverts = take (_nverts, _list,axis=0) if _values != "bg" : - _values = take (_values, _list) - _x = take (_x, _vlist) - _y = take (_y, _vlist) + _values = take (_values, _list,axis=0) + _x = take (_x, _vlist,axis=0) + _y = take (_y, _vlist,axis=0) _square = get_square_ ( ) [_xfactor, _yfactor] = get_factors_ () @@ -2950,7 +2950,7 @@ for leaf in list : print indent + "leaf length= " + `len (leaf)` print indent + "npolys= " + `len (leaf [0])` + \ - ", nverts= " + `sum (leaf [0])` + ", max= " + `max (leaf [0])` + ", nverts= " + `sum (leaf [0],axis=0)` + ", max= " + `max (leaf [0])` print indent + "nverts= " + `shape (leaf [1]) [0]` + \ ", nvals= " + `len (leaf [2])` @@ -3059,11 +3059,11 @@ indices = ravel (indices) retval = zeros ( (no_cells, 3, 2, 2, 2), Float ) m30 = ravel (m3 [1] [0, ...]) - retval [:, 0, ...] = reshape (take (m30, indices), (no_cells, 2, 2, 2)) + retval [:, 0, ...] = reshape (take (m30, indices,axis=0), (no_cells, 2, 2, 2)) m31 = ravel (m3 [1] [1, ...]) - retval [:, 1, ...] = reshape (take (m31, indices), (no_cells, 2, 2, 2)) + retval [:, 1, ...] = reshape (take (m31, indices,axis=0), (no_cells, 2, 2, 2)) m32 = ravel (m3 [1] [2, ...]) - retval [:, 2, ...] = reshape (take (m32, indices), (no_cells, 2, 2, 2)) + retval [:, 2, ...] = reshape (take (m32, indices,axis=0), (no_cells, 2, 2, 2)) return retval _xyz3Error = "xyz3Error" @@ -3104,45 +3104,45 @@ else : start = 0 verts = m3 [1] [0] [i] - ns = take (verts, chunk - start) + ns = take (verts, chunk - start,axis=0) shp = shape (verts) else : - ns = take (m3 [1] [0], chunk) + ns = take (m3 [1] [0], chunk,axis=0) shp = shape (m3 [1] [0]) else : raise _xyz3Error, "chunk parameter has the wrong type." if shp [1] == 8 : # hex retval = zeros ( (no_cells, 3, 2, 2, 2), Float) retval [:, 0] = \ - reshape (take (xyz [0], ravel (ns)), (no_cells, 2, 2, 2)) + reshape (take (xyz [0], ravel (ns),axis=0), (no_cells, 2, 2, 2)) retval [:, 1] = \ - reshape (take (xyz [1], ravel (ns)), (no_cells, 2, 2, 2)) + reshape (take (xyz [1], ravel (ns),axis=0), (no_cells, 2, 2, 2)) retval [:, 2] = \ - reshape (take (xyz [2], ravel (ns)), (no_cells, 2, 2, 2)) + reshape (take (xyz [2], ravel (ns),axis=0), (no_cells, 2, 2, 2)) elif shp [1] == 6 : # prism retval = zeros ( (no_cells, 3, 3, 2), Float) retval [:, 0] = \ - reshape (take (xyz [0], ravel (ns)), (no_cells, 3, 2)) + reshape (take (xyz [0], ravel (ns),axis=0), (no_cells, 3, 2)) retval [:, 1] = \ - reshape (take (xyz [1], ravel (ns)), (no_cells, 3, 2)) + reshape (take (xyz [1], ravel (ns),axis=0), (no_cells, 3, 2)) retval [:, 2] = \ - reshape (take (xyz [2], ravel (ns)), (no_cells, 3, 2)) + reshape (take (xyz [2], ravel (ns),axis=0), (no_cells, 3, 2)) elif shp [1] == 5 : # pyramid retval = zeros ( (no_cells, 3, 5), Float) retval [:, 0] = \ - reshape (take (xyz [0], ravel (ns)), (no_cells, 5)) + reshape (take (xyz [0], ravel (ns),axis=0), (no_cells, 5)) retval [:, 1] = \ - reshape (take (xyz [1], ravel (ns)), (no_cells, 5)) + reshape (take (xyz [1], ravel (ns),axis=0), (no_cells, 5)) retval [:, 2] = \ - reshape (take (xyz [2], ravel (ns)), (no_cells, 5)) + reshape (take (xyz [2], ravel (ns),axis=0), (no_cells, 5)) elif shp [1] == 4 : # tet retval = zeros ( (no_cells, 3, 4), Float) retval [:, 0] = \ - reshape (take (xyz [0], ravel (ns)), (no_cells, 4)) + reshape (take (xyz [0], ravel (ns),axis=0), (no_cells, 4)) retval [:, 1] = \ - reshape (take (xyz [1], ravel (ns)), (no_cells, 4)) + reshape (take (xyz [1], ravel (ns),axis=0), (no_cells, 4)) retval [:, 2] = \ - reshape (take (xyz [2], ravel (ns)), (no_cells, 4)) + reshape (take (xyz [2], ravel (ns),axis=0), (no_cells, 4)) else : raise _xyz3Error, "Funny number of cell faces: " + `shp [1]` return retval @@ -3211,11 +3211,11 @@ multiply.outer ( ones (n1, Float ), zz [i [2]: i[2] + n2])) else : # -- nonconsecutive values - xyz [:, 0] = reshape (take (xx, ravel (add.outer (xchunk, ijk0))), + xyz [:, 0] = reshape (take (xx, ravel (add.outer (xchunk, ijk0)),axis=0), (len (chunk), 2, 2, 2)) - xyz [:, 1] = reshape (take (yy, ravel (add.outer (ychunk, ijk1))), + xyz [:, 1] = reshape (take (yy, ravel (add.outer (ychunk, ijk1)),axis=0), (len (chunk), 2, 2, 2)) - xyz [:, 2] = reshape (take (zz, ravel (add.outer (zchunk, ijk2))), + xyz [:, 2] = reshape (take (zz, ravel (add.outer (zchunk, ijk2)),axis=0), (len (chunk), 2, 2, 2)) return xyz Modified: trunk/Lib/sandbox/xplt/sphereisos.py =================================================================== --- trunk/Lib/sandbox/xplt/sphereisos.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sandbox/xplt/sphereisos.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -29,7 +29,7 @@ n_zones = f.NumZones # Put vertices in right order for Gist ## n_z = transpose ( -## take (transpose (n_z), array ( [0, 1, 3, 2, 4, 5, 7, 6]))) +## take (transpose (n_z), array ( [0, 1, 3, 2, 4, 5, 7, 6]),axis=0)) m1 = Mesh3d (x = x, y = y, z = z, c = c, avs = 1, hex = [n_zones, n_z]) Modified: trunk/Lib/signal/filter_design.py =================================================================== --- trunk/Lib/signal/filter_design.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/signal/filter_design.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -22,7 +22,7 @@ if len(ep) == 0: ep = atleast_1d(-1000)+0j - ez = c_[numpy.compress(ep.imag >=0, ep), numpy.compress((abs(tz) < 1e5) & (tz.imag >=0),tz)] + ez = c_[numpy.compress(ep.imag >=0, ep,axis=-1), numpy.compress((abs(tz) < 1e5) & (tz.imag >=0),tz,axis=-1)] integ = abs(ez) < 1e-10 hfreq = numpy.around(numpy.log10(numpy.max(3*abs(ez.real + integ)+1.5*ez.imag))+0.5) @@ -967,7 +967,7 @@ mu = 1.0/N * numpy.log((1.0+numpy.sqrt(1+eps*eps)) / eps) theta = pi/2.0 * (2*n-1.0)/N p = -numpy.sinh(mu)*numpy.sin(theta) + 1j*numpy.cosh(mu)*numpy.cos(theta) - k = numpy.prod(-p).real + k = numpy.prod(-p,axis=0).real if N % 2 == 0: k = k / sqrt((1+eps*eps)) return z, p, k @@ -992,7 +992,7 @@ p = exp(1j*(pi*numpy.arange(1,2*N,2)/(2.0*N) + pi/2.0)) p = sinh(mu) * p.real + 1j*cosh(mu)*p.imag p = 1.0 / p - k = (numpy.prod(-p)/numpy.prod(-z)).real + k = (numpy.prod(-p,axis=0)/numpy.prod(-z,axis=0)).real return z, p, k @@ -1059,7 +1059,7 @@ jj = len(j) [s,c,d,phi] = special.ellipj(j*capk/N,m*numpy.ones(jj)) - snew = numpy.compress(abs(s) > EPSILON, s) + snew = numpy.compress(abs(s) > EPSILON, s,axis=-1) z = 1.0 / (sqrt(m)*snew) z = 1j*z z = numpy.concatenate((z,conjugate(z))) @@ -1072,12 +1072,12 @@ p = -(c*d*sv*cv + 1j*s*dv) / (1-(d*sv)**2.0) if N % 2: - newp = numpy.compress(abs(p.imag) > EPSILON*numpy.sqrt(numpy.sum(p*numpy.conjugate(p)).real), p) + newp = numpy.compress(abs(p.imag) > EPSILON*numpy.sqrt(numpy.sum(p*numpy.conjugate(p),axis=0).real), p,axis=-1) p = numpy.concatenate((p,conjugate(newp))) else: p = numpy.concatenate((p,conjugate(p))) - k = (numpy.prod(-p) / numpy.prod(-z)).real + k = (numpy.prod(-p,axis=0) / numpy.prod(-z,axis=0)).real if N % 2 == 0: k = k / numpy.sqrt((1+eps*eps)) @@ -1539,4 +1539,4 @@ alpha = N//2 m = numpy.arange(0,N) h = win*special.sinc(cutoff*(m-alpha)) - return h / sum(h) + return h / sum(h,axis=0) Modified: trunk/Lib/signal/ltisys.py =================================================================== --- trunk/Lib/signal/ltisys.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/signal/ltisys.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -141,9 +141,9 @@ den = poly(A) - if (product(B.shape) == 0) and (product(C.shape) == 0): + if (product(B.shape,axis=0) == 0) and (product(C.shape,axis=0) == 0): num = numpy.ravel(D) - if (product(D.shape) == 0) and (product(A.shape) == 0): + if (product(D.shape,axis=0) == 0) and (product(A.shape,axis=0) == 0): den = [] end return num, den Modified: trunk/Lib/signal/signaltools.py =================================================================== --- trunk/Lib/signal/signaltools.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/signal/signaltools.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -68,7 +68,7 @@ kernel = asarray(in2) if rank(volume) == rank(kernel) == 0: return volume*kernel - if (product(kernel.shape) > product(volume.shape)): + if (product(kernel.shape,axis=0) > product(volume.shape,axis=0)): temp = kernel kernel = volume volume = temp @@ -105,7 +105,7 @@ if mode == "full": return ret elif mode == "same": - if product(s1) > product(s2): + if product(s1,axis=0) > product(s2,axis=0): osize = s1 else: osize = s2 @@ -144,7 +144,7 @@ kernel = asarray(in2) if rank(volume) == rank(kernel) == 0: return volume*kernel - if (product(kernel.shape) > product(volume.shape)): + if (product(kernel.shape,axis=0) > product(volume.shape,axis=0)): temp = kernel kernel = volume volume = temp @@ -225,7 +225,7 @@ domain = ones(kernel_size) - numels = product(kernel_size) + numels = product(kernel_size,axis=0) order = int(numels/2) return sigtools._order_filterND(volume,domain,order) @@ -258,14 +258,14 @@ mysize = asarray(mysize); # Estimate the local mean - lMean = correlate(im,ones(mysize),1) / product(mysize) + lMean = correlate(im,ones(mysize),1) / product(mysize,axis=0) # Estimate the local variance - lVar = correlate(im**2,ones(mysize),1) / product(mysize) - lMean**2 + lVar = correlate(im**2,ones(mysize),1) / product(mysize,axis=0) - lMean**2 # Estimate the noise power if needed. if noise==None: - noise = mean(ravel(lVar)) + noise = mean(ravel(lVar),axis=0) res = (im - lMean) res *= (1-noise / lVar) @@ -527,10 +527,10 @@ y = r_[y,zeros(N-L)] for m in range(M): - zi[m] = sum(b[m+1:]*x[:M-m]) + zi[m] = sum(b[m+1:]*x[:M-m],axis=0) for m in range(N): - zi[m] -= sum(a[m+1:]*y[:N-m]) + zi[m] -= sum(a[m+1:]*y[:N-m],axis=0) return zi @@ -808,7 +808,7 @@ k = m[newaxis,:] AF = twoF*special.sinc(twoF*(n-k)) [lam,vec] = linalg.eig(AF) - ind = argmax(abs(lam)) + ind = argmax(abs(lam),axis=-1) w = abs(vec[:,ind]) w = w / max(w) @@ -1328,7 +1328,7 @@ rnk = len(dshape) if axis < 0: axis = axis + rnk newdims = r_[axis,0:axis,axis+1:rnk] - newdata = reshape(transpose(data,tuple(newdims)),(N,prod(dshape)/N)) + newdata = reshape(transpose(data,tuple(newdims)),(N,prod(dshape,axis=0)/N)) newdata = newdata.copy() # make sure we have a copy if newdata.dtype.char not in 'dfDF': newdata = newdata.astype(dtype) Modified: trunk/Lib/signal/wavelets.py =================================================================== --- trunk/Lib/signal/wavelets.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/signal/wavelets.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -130,7 +130,7 @@ # evaluation points x < 1 -- i.e. position is 0.xxxx v = sb.real(v[:,ind]) # need scaling function to integrate to 1 so find - # eigenvector normalized to sum(v)=1 + # eigenvector normalized to sum(v,axis=0)=1 sm = sb.sum(v) if sm < 0: # need scaling function to integrate to 1 v = -v Modified: trunk/Lib/sparse/tests/test_sparse.py =================================================================== --- trunk/Lib/sparse/tests/test_sparse.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/sparse/tests/test_sparse.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -39,7 +39,7 @@ assert_equal(self.datsp[2,1],2) def check_sum(self): - """Does the matrix's sum() method work? + """Does the matrix's sum(,axis=0) method work? """ assert_array_equal(self.dat.sum(), self.datsp.sum()) assert_array_equal(self.dat.sum(axis=None), self.datsp.sum(axis=None)) @@ -47,7 +47,7 @@ assert_array_equal(self.dat.sum(axis=1), self.datsp.sum(axis=1)) def check_mean(self): - """Does the matrix's mean() method work? + """Does the matrix's mean(,axis=0) method work? """ assert_array_equal(self.dat.mean(), self.datsp.mean()) assert_array_equal(self.dat.mean(axis=None), self.datsp.mean(axis=None)) Modified: trunk/Lib/special/basic.py =================================================================== --- trunk/Lib/special/basic.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/special/basic.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -412,7 +412,7 @@ n, x = asarray(n), asarray(x) cond = (n==0) fac2 = (-1.0)**(n+1) * gamma(n+1.0) * zeta(n+1,x) - if sometrue(cond): + if sometrue(cond,axis=0): return where(cond, psi(x), fac2) return fac2 Modified: trunk/Lib/special/orthogonal.py =================================================================== --- trunk/Lib/special/orthogonal.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/special/orthogonal.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -105,8 +105,8 @@ [x,v] = eig((diag(an)+diag(sqrt_bn,1)+diag(sqrt_bn,-1))) answer = [] sortind = argsort(real(x)) - answer.append(take(x,sortind)) - answer.append(take(mu*v[0]**2,sortind)) + answer.append(take(x,sortind,axis=0)) + answer.append(take(mu*v[0]**2,sortind,axis=0)) return answer # Jacobi Polynomials 1 P^(alpha,beta)_n(x) Modified: trunk/Lib/special/tests/Test.py =================================================================== --- trunk/Lib/special/tests/Test.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/special/tests/Test.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -89,7 +89,7 @@ for t in range(len(self.out_vars.keys())): dev=abs(self.result[t]-self.out_vars[self.out_vars.keys()[t]]) ref=abs(self.result[t]+self.out_vars[self.out_vars.keys()[t]])/2 - mx_dev_idx=Numeric.argmax(dev) + mx_dev_idx=Numeric.argmax(dev,axis=-1) if dev[mx_dev_idx] > 0.: if ref[mx_dev_idx] > 0.: self.max_rel_dev.append(dev[mx_dev_idx]/ref[mx_dev_idx]) Modified: trunk/Lib/special/tests/test_basic.py =================================================================== --- trunk/Lib/special/tests/test_basic.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/special/tests/test_basic.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -2083,7 +2083,7 @@ def check_take(self): a = array([0,1,2,3,4,5,6,7,8]) - tka = take(a,(0,4,5,8)) + tka = take(a,(0,4,5,8),axis=0) assert_array_equal(tka,array([0,4,5,8])) class test_tandg(ScipyTestCase): Modified: trunk/Lib/stats/_support.py =================================================================== --- trunk/Lib/stats/_support.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/stats/_support.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -50,7 +50,7 @@ else: # IT MUST BE A 2+D ARRAY if inarray.typecode() != 'O': # not an Object array for item in inarray[1:]: - if not N.sum(N.alltrue(N.equal(uniques,item),1)): + if not N.sum(N.alltrue(N.equal(uniques,item),1),axis=0): try: uniques = N.concatenate( [uniques,item[N.newaxis,:]] ) except TypeError: # the item to add isn't a list @@ -61,7 +61,7 @@ for item in inarray[1:]: newflag = 1 for unq in uniques: # NOTE: cmp --> 0=same, -1=<, 1=> - test = N.sum(abs(N.array(map(cmp,item,unq)))) + test = N.sum(abs(N.array(map(cmp,item,unq))),axis=0) if test == 0: # if item identical to any 1 row in uniques newflag = 0 # then not a novel item to add break @@ -172,7 +172,7 @@ N of the mean are desired, set either or both parameters to 1. Returns: unique 'conditions' specified by the contents of columns specified - by keepcols, abutted with the mean(s) of column(s) specified by + by keepcols, abutted with the mean(s,axis=0) of column(s) specified by collapsecols """ if cfcn is None: Modified: trunk/Lib/stats/distributions.py =================================================================== --- trunk/Lib/stats/distributions.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/stats/distributions.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -211,7 +211,7 @@ def valarray(shape,value=nan,typecode=None): """Return an array of all value. """ - out = reshape(repeat([value],product(shape)),shape) + out = reshape(repeat([value],product(shape,axis=0),axis=0),shape) if typecode is not None: out = out.astype(typecode) if not isinstance(out, ndarray): @@ -261,7 +261,7 @@ - inverse survival function (inverse of sf) generic.stats(,loc=0,scale=1,moments='mv') - - mean('m'), variance('v'), skew('s'), and/or kurtosis('k') + - mean('m',axis=0), variance('v'), skew('s'), and/or kurtosis('k') generic.entropy(,loc=0,scale=1) - (differential) entropy of the RV. @@ -428,7 +428,7 @@ if size is None: size = 1 else: - self._size = product(size) + self._size = product(size,axis=0) if numpy.isscalar(size): self._size = size size = (size,) @@ -720,10 +720,10 @@ return self._munp(n,*args) def _nnlf(self, x, *args): - return -sum(log(self._pdf(x, *args))) + return -sum(log(self._pdf(x, *args)),axis=0) def nnlf(self, theta, x): - # - sum (log pdf(x, theta)) + # - sum (log pdf(x, theta),axis=0) # where theta are the parameters (including loc and scale) # try: @@ -1599,7 +1599,7 @@ return vals def _munp(self, n, c): k = arange(0,n+1) - val = (-1.0/c)**n * sum(scipy.comb(n,k)*(-1)**k / (1.0-c*k)) + val = (-1.0/c)**n * sum(scipy.comb(n,k)*(-1)**k / (1.0-c*k),axis=0) return where(c*n < 1, val, inf) def _entropy(self, c): if (c > 0): @@ -1657,7 +1657,7 @@ return 1.0/c*(1-(-log(q))**c) def _munp(self, n, c): k = arange(0,n+1) - vals = 1.0/c**n * sum(scipy.comb(n,k) * (-1)**k * special.gamma(c*k + 1)) + vals = 1.0/c**n * sum(scipy.comb(n,k) * (-1)**k * special.gamma(c*k + 1),axis=0) return where(c*n > -1, vals, inf) genextreme = genextreme_gen(name='genextreme', longname="A generalized extreme value", @@ -3173,28 +3173,28 @@ """S = entropy(pk,qk=None) calculate the entropy of a distribution given the p_k values - S = -sum(pk * log(pk)) + S = -sum(pk * log(pk),axis=0) If qk is not None, then compute a relative entropy - S = -sum(pk * log(pk / qk)) + S = -sum(pk * log(pk / qk),axis=0) Routine will normalize pk and qk if they don't sum to 1 """ pk = arr(pk) - pk = 1.0* pk / sum(pk) + pk = 1.0* pk / sum(pk,axis=0) if qk is None: vec = where(pk == 0, 0.0, pk*log(pk)) else: qk = arr(qk) if len(qk) != len(pk): raise ValueError, "qk and pk must have same length." - qk = 1.0*qk / sum(qk) + qk = 1.0*qk / sum(qk,axis=0) # If qk is zero anywhere, then unless pk is zero at those places # too, the relative entropy is infinite. - if any(take(pk,nonzero(qk==0.0))!=0.0, 0): + if any(take(pk,nonzero(qk==0.0),axis=0)!=0.0, 0): return inf vec = where (pk == 0, 0.0, pk*log(pk / qk)) - return -sum(vec) + return -sum(vec,axis=0) ## Handlers for generic case where xk and pk are given @@ -3208,11 +3208,11 @@ return 0.0 def _drv_cdf(self, xk, *args): - indx = argmax((self.xk>xk))-1 + indx = argmax((self.xk>xk),axis=-1)-1 return self.F[self.xk[indx]] def _drv_ppf(self, q, *args): - indx = argmax((self.qvals>=q)) + indx = argmax((self.qvals>=q),axis=-1) return self.Finv[self.qvals[indx]] def _drv_nonzero(self, k, *args): @@ -3320,7 +3320,7 @@ - inverse survival function (inverse of sf) generic.stats(,loc=0,moments='mv') - - mean('m'), variance('v'), skew('s'), and/or kurtosis('k') + - mean('m',axis=0), variance('v'), skew('s'), and/or kurtosis('k') generic.entropy(,loc=0) - entropy of the RV @@ -3364,7 +3364,7 @@ self.a = self.xk[0] self.b = self.xk[-1] self.P = make_dict(self.xk, self.pk) - self.qvals = numpy.cumsum(self.pk) + self.qvals = numpy.cumsum(self.pk,axis=0) self.F = make_dict(self.xk, self.qvals) self.Finv = reverse_dict(self.F) self._ppf = new.instancemethod(sgf(_drv_ppf,otypes='d'), @@ -3437,7 +3437,7 @@ def _cdfsingle(self, k, *args): m = arange(int(self.a),k+1) - return sum(self._pmf(m,*args)) + return sum(self._pmf(m,*args),axis=0) def _cdf(self, x, *args): k = floor(x) @@ -3469,7 +3469,7 @@ if size is None: size = 1 else: - self._size = product(size) + self._size = product(size,axis=0) if numpy.isscalar(size): self._size = size size = (size,) @@ -3818,7 +3818,7 @@ k = r_[0:n+1] vals = self._pmf(k,n,pr) lvals = where(vals==0,0.0,log(vals)) - return -sum(vals*lvals) + return -sum(vals*lvals,axis=0) binom = binom_gen(name='binom',shapes="n,pr",extradoc=""" Binomial distribution @@ -3969,7 +3969,7 @@ k = r_[N-(M-n):min(n,N)+1] vals = self.pmf(k,M,n,N) lvals = where(vals==0.0,0.0,log(vals)) - return -sum(vals*lvals) + return -sum(vals*lvals,axis=0) hypergeom = hypergeom_gen(name='hypergeom',longname="A hypergeometric", shapes="M,n,N", extradoc=""" Modified: trunk/Lib/stats/kde.py =================================================================== --- trunk/Lib/stats/kde.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/stats/kde.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -108,15 +108,15 @@ for i in range(self.n): diff = self.dataset[:,i,newaxis] - points tdiff = dot(self.inv_cov, diff) - energy = sum(diff*tdiff)/2.0 + energy = sum(diff*tdiff,axis=0)/2.0 result += exp(-energy) else: # loop over points for i in range(m): diff = self.dataset - points[:,i,newaxis] tdiff = dot(self.inv_cov, diff) - energy = sum(diff*tdiff)/2.0 - result[i] = sum(exp(-energy)) + energy = sum(diff*tdiff,axis=0)/2.0 + result[i] = sum(exp(-energy),axis=0) det_cov = linalg.det(2*pi*self.covariance) result /= sqrt(det_cov)*self.n @@ -159,8 +159,8 @@ diff = self.dataset - mean tdiff = dot(linalg.inv(sum_cov), diff) - energies = sum(diff*tdiff)/2.0 - result = sum(exp(-energies))/sqrt(linalg.det(2*pi*sum_cov))/self.n + energies = sum(diff*tdiff,axis=0)/2.0 + result = sum(exp(-energies),axis=0)/sqrt(linalg.det(2*pi*sum_cov))/self.n return result @@ -256,8 +256,8 @@ diff = large.dataset - mean tdiff = dot(linalg.inv(sum_cov), diff) - energies = sum(diff*tdiff)/2.0 - result += sum(exp(-energies)) + energies = sum(diff*tdiff,axis=0)/2.0 + result += sum(exp(-energies),axis=0) result /= sqrt(linalg.det(2*pi*sum_cov))*large.n*small.n Modified: trunk/Lib/stats/morestats.py =================================================================== --- trunk/Lib/stats/morestats.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/stats/morestats.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -39,7 +39,7 @@ ### Bayesian confidence intervals for mean, variance, std ########################################################## -## Assumes all is known is that mean, and std (variance) exist +## Assumes all is known is that mean, and std (variance,axis=0) exist ## and are the same for all the data. Uses Jeffrey's prior ## ## Returns alpha confidence interval for the mean, variance, @@ -142,7 +142,7 @@ data = ravel(data) N = len(data) for k in range(1,n+1): - S[k] = sum(data**k) + S[k] = sum(data**k,axis=0) if n==1: return S[1]*1.0/N elif n==2: @@ -309,8 +309,8 @@ N = len(data) y = boxcox(data,lmb) my = stats.mean(y) - f = (lmb-1)*sum(log(data)) - f -= N/2.0*log(sum((y-my)**2.0/N)) + f = (lmb-1)*sum(log(data),axis=0) + f -= N/2.0*log(sum((y-my)**2.0/N,axis=0)) return f def _boxcox_conf_interval(x, lmax, alpha): @@ -497,8 +497,8 @@ a,b = ab tmp = (xj-a)/b tmp2 = exp(tmp) - val = [sum(1.0/(1+tmp2))-0.5*N, - sum(tmp*(1.0-tmp2)/(1+tmp2))+N] + val = [sum(1.0/(1+tmp2),axis=0)-0.5*N, + sum(tmp*(1.0-tmp2)/(1+tmp2),axis=0)+N] return array(val) sol0=array([xbar,stats.std(x)]) sol = optimize.fsolve(rootfunc,sol0,args=(x,N),xtol=1e-5) @@ -510,17 +510,17 @@ def fixedsolve(th,xj,N): val = stats.sum(xj)*1.0/N tmp = exp(-xj/th) - term = sum(xj*tmp) - term /= sum(tmp) + term = sum(xj*tmp,axis=0) + term /= sum(tmp,axis=0) return val - term s = optimize.fixed_point(fixedsolve, 1.0, args=(x,N),xtol=1e-5) - xbar = -s*log(sum(exp(-x/s))*1.0/N) + xbar = -s*log(sum(exp(-x/s),axis=0)*1.0/N) w = (y-xbar)/s z = distributions.gumbel_l.cdf(w) sig = array([25,10,5,2.5,1]) critical = around(_Avals_gumbel / (1.0 + 0.2/sqrt(N)),3) i = arange(1,N+1) - S = sum((2*i-1.0)/N*(log(z)+log(1-z[::-1]))) + S = sum((2*i-1.0)/N*(log(z)+log(1-z[::-1])),axis=0) A2 = -N-S return A2, critical, sig @@ -575,7 +575,7 @@ xy = r_[x,y] # combine rank = stats.rankdata(xy) symrank = amin(array((rank,N-rank+1)),0) - AB = sum(symrank[:n]) + AB = sum(symrank[:n],axis=0) uxy = unique(xy) repeats = (len(uxy) != len(xy)) exact = ((m<55) and (n<55) and not repeats) @@ -584,19 +584,19 @@ if exact: astart, a1, ifault = statlib.gscale(n,m) ind = AB-astart - total = sum(a1) + total = sum(a1,axis=0) if ind < len(a1)/2.0: cind = int(ceil(ind)) if (ind == cind): - pval = 2.0*sum(a1[:cind+1])/total + pval = 2.0*sum(a1[:cind+1],axis=0)/total else: - pval = 2.0*sum(a1[:cind])/total + pval = 2.0*sum(a1[:cind],axis=0)/total else: find = int(floor(ind)) if (ind == floor(ind)): - pval = 2.0*sum(a1[find:])/total + pval = 2.0*sum(a1[find:],axis=0)/total else: - pval = 2.0*sum(a1[find+1:])/total + pval = 2.0*sum(a1[find+1:],axis=0)/total return AB, min(1.0,pval) # otherwise compute normal approximation @@ -607,8 +607,8 @@ mnAB = n*(N+2.0)/4.0 varAB = m*n*(N+2)*(N-2.0)/48/(N-1.0) if repeats: # adjust variance estimates - # compute sum(tj * rj**2) - fac = sum(symrank**2) + # compute sum(tj * rj**2,axis=0) + fac = sum(symrank**2,axis=0) if N % 2: # N odd varAB = m*n*(16*N*fac-(N+1)**4)/(16.0 * N**2 * (N-1)) else: # N even @@ -648,10 +648,10 @@ for j in range(k): Ni[j] = len(args[j]) ssq[j] = stats.var(args[j]) - Ntot = sum(Ni) - spsq = sum((Ni-1)*ssq)/(1.0*(Ntot-k)) - numer = (Ntot*1.0-k)*log(spsq) - sum((Ni-1.0)*log(ssq)) - denom = 1.0 + (1.0/(3*(k-1)))*((sum(1.0/(Ni-1.0)))-1.0/(Ntot-k)) + Ntot = sum(Ni,axis=0) + spsq = sum((Ni-1)*ssq,axis=0)/(1.0*(Ntot-k)) + numer = (Ntot*1.0-k)*log(spsq) - sum((Ni-1.0)*log(ssq),axis=0) + denom = 1.0 + (1.0/(3*(k-1)))*((sum(1.0/(Ni-1.0),axis=0))-1.0/(Ntot-k)) T = numer / denom pval = distributions.chi2.sf(T,k-1) # 1 - cdf return T, pval @@ -707,7 +707,7 @@ for j in range(k): Ni[j] = len(args[j]) Yci[j] = func(args[j]) - Ntot = sum(Ni) + Ntot = sum(Ni,axis=0) # compute Zij's Zij = [None]*k @@ -721,12 +721,12 @@ Zbar += Zbari[i]*Ni[i] Zbar /= Ntot - numer = (Ntot-k)*sum(Ni*(Zbari-Zbar)**2) + numer = (Ntot-k)*sum(Ni*(Zbari-Zbar)**2,axis=0) # compute denom_variance dvar = 0.0 for i in range(k): - dvar += sum((Zij[i]-Zbari[i])**2) + dvar += sum((Zij[i]-Zbari[i])**2,axis=0) denom = (k-1.0)*dvar @@ -767,11 +767,11 @@ rerr = 1+1e-7 if (x < p*n): i = arange(x+1,n+1) - y = sum(distributions.binom.pmf(i,n,p) <= d*rerr) + y = sum(distributions.binom.pmf(i,n,p) <= d*rerr,axis=0) pval = distributions.binom.cdf(x,n,p) + distributions.binom.sf(n-y,n,p) else: i = arange(0,x) - y = sum(distributions.binom.pmf(i,n,p) <= d*rerr) + y = sum(distributions.binom.pmf(i,n,p) <= d*rerr,axis=0) pval = distributions.binom.cdf(y-1,n,p) + distributions.binom.sf(x-1,n,p) return min(1.0,pval) @@ -828,7 +828,7 @@ Ni = asarray([len(args[j]) for j in range(k)]) Yci = asarray([func(args[j]) for j in range(k)]) - Ntot = sum(Ni) + Ntot = sum(Ni,axis=0) # compute Zij's Zij = [abs(asarray(args[i])-Yci[i]) for i in range(k)] allZij = [] @@ -844,7 +844,7 @@ anbar = stats.mean(a) varsq = stats.var(a) - Xsq = sum(Ni*(asarray(Aibar)-anbar)**2.0)/varsq + Xsq = sum(Ni*(asarray(Aibar)-anbar)**2.0,axis=0)/varsq pval = distributions.chi2.sf(Xsq,k-1) # 1 - cdf return Xsq, pval @@ -869,7 +869,7 @@ raise ValueError, "Not enough observations." ranks = stats.rankdata(xy) Ri = ranks[:n] - M = sum((Ri - (N+1.0)/2)**2) + M = sum((Ri - (N+1.0)/2)**2,axis=0) # Approx stat. mnM = n*(N*N-1.0)/12 varM = m*n*(N+1.0)*(N+2)*(N-2)/180 @@ -903,16 +903,16 @@ Mi = array([stats.mean(args[i]) for i in range(k)]) Vi = array([stats.var(args[i]) for i in range(k)]) Wi = Ni / Vi - swi = sum(Wi) - N = sum(Ni) - my = sum(Mi*Ni)*1.0/N - tmp = sum((1-Wi/swi)**2 / (Ni-1.0))/(k*k-1.0) + swi = sum(Wi,axis=0) + N = sum(Ni,axis=0) + my = sum(Mi*Ni,axis=0)*1.0/N + tmp = sum((1-Wi/swi)**2 / (Ni-1.0),axis=0)/(k*k-1.0) if evar: - F = ((sum(Ni*(Mi-my)**2) / (k-1.0)) / (sum((Ni-1.0)*Vi) / (N-k))) + F = ((sum(Ni*(Mi-my)**2,axis=0) / (k-1.0)) / (sum((Ni-1.0)*Vi,axis=0) / (N-k))) pval = distributions.f.sf(F,k-1,N-k) # 1-cdf else: - m = sum(Wi*Mi)*1.0/swi - F = sum(Wi*(Mi-m)**2) / ((k-1.0)*(1+2*(k-2)*tmp)) + m = sum(Wi*Mi,axis=0)*1.0/swi + F = sum(Wi*(Mi-m)**2,axis=0) / ((k-1.0)*(1+2*(k-2)*tmp)) pval = distributions.f.sf(F,k-1.0,1.0/(3*tmp)) return F, pval @@ -932,13 +932,13 @@ if len(x) <> len(y): raise ValueError, 'Unequal N in wilcoxon. Aborting.' d = x-y - d = compress(not_equal(d,0),d) # Keep all non-zero differences + d = compress(not_equal(d,0),d,axis=-1) # Keep all non-zero differences count = len(d) if (count < 10): print "Warning: sample size too small for normal approximation." r = stats.rankdata(abs(d)) - r_plus = sum((d > 0)*r) - r_minus = sum((d < 0)*r) + r_plus = sum((d > 0)*r,axis=0) + r_minus = sum((d < 0)*r,axis=0) T = min(r_plus, r_minus) mn = count*(count+1.0)*0.25 se = math.sqrt(count*(count+1)*(2*count+1.0)/24) Modified: trunk/Lib/stats/stats.py =================================================================== --- trunk/Lib/stats/stats.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/stats/stats.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -284,7 +284,7 @@ def _nanmedian(arr1d): # This only works on 1d arrays cond = 1-isnan(arr1d) - x = sort(compress(cond,arr1d)) + x = sort(compress(cond,arr1d,axis=-1)) return median(x) def nanmedian(x, axis=0): @@ -340,7 +340,7 @@ return size / np.sum(1.0/a, axis) def mean(a, axis=0): - # fixme: This seems to be redundant with numpy.mean() or even + # fixme: This seems to be redundant with numpy.mean(,axis=0) or even # the ndarray.mean() method. """Returns the arithmetic mean of m along the given dimension. @@ -945,7 +945,7 @@ Returns: percentile-position of score (0-100) relative to a """ h, lrl, binsize, extras = histogram(a,histbins,defaultlimits) - cumhist = cumsum(h*1) + cumhist = cumsum(h*1,axis=0) i = int((score - lrl)/float(binsize)) pct = (cumhist[i-1]+((score-(lrl+binsize*i))/float(binsize))*h[i])/float(len(a)) * 100 return pct @@ -1031,7 +1031,7 @@ Returns: array of cumfreq bin values, lowerreallimit, binsize, extrapoints """ h,l,b,e = histogram(a,numbins,defaultreallimits) - cumhist = cumsum(h*1) + cumhist = cumsum(h*1,axis=0) return cumhist,l,b,e @@ -1267,7 +1267,7 @@ upper tails. """ newa = trimboth(sort(a),proportiontocut) - return mean(newa) + return mean(newa,axis=0) @@ -1734,7 +1734,7 @@ f_obs = asarray(f_obs) k = len(f_obs) if f_exp is None: - f_exp = array([sum(f_obs)/float(k)] * len(f_obs),Float) + f_exp = array([sum(f_obs,axis=0)/float(k)] * len(f_obs),Float) f_exp = f_exp.astype(Float) chisq = add.reduce((f_obs-f_exp)**2 / f_exp) return chisq, chisqprob(chisq, k-1) @@ -1797,7 +1797,7 @@ ranked = rankdata(concatenate((x,y))) rankx = ranked[0:n1] # get the x-ranks #ranky = ranked[n1:] # the rest are y-ranks - u1 = n1*n2 + (n1*(n1+1))/2.0 - sum(rankx) # calc U for x + u1 = n1*n2 + (n1*(n1+1))/2.0 - sum(rankx,axis=0) # calc U for x u2 = n1*n2 - u1 # remainder is U for y bigu = max(u1,u2) smallu = min(u1,u2) @@ -1848,7 +1848,7 @@ ranked = rankdata(alldata) x = ranked[:n1] y = ranked[n1:] - s = sum(x) + s = sum(x,axis=0) expected = n1*(n1+n2+1) / 2.0 z = (s - expected) / math.sqrt(n1*n2*(n1+n2+1)/12.0) prob = 2*(1.0 -zprob(abs(z))) @@ -1878,10 +1878,10 @@ del ranked[0:n[i]] rsums = [] for i in range(len(args)): - rsums.append(sum(args[i])**2) + rsums.append(sum(args[i],axis=0)**2) rsums[i] = rsums[i] / float(n[i]) - ssbn = sum(rsums) - totaln = sum(n) + ssbn = sum(rsums,axis=0) + totaln = sum(n,axis=0) h = 12.0 / (totaln*(totaln+1)) * ssbn - 3*(totaln+1) df = len(args) - 1 if T == 0: @@ -1909,7 +1909,7 @@ data = data.astype(Float) for i in range(len(data)): data[i] = rankdata(data[i]) - ssbn = sum(sum(args,1)**2) + ssbn = sum(sum(args,1)**2,axis=0) chisq = 12.0 / (k*n*(k+1)) * ssbn - 3*n*(k+1) return chisq, chisqprob(chisq,k-1) @@ -1998,7 +1998,7 @@ if len(p) == 2: # ttest_ind c = array([1,-1]) df = n-2 - fact = sum(1.0/sum(x,0)) # i.e., 1/n1 + 1/n2 + 1/n3 ... + fact = sum(1.0/sum(x,0),axis=0) # i.e., 1/n1 + 1/n2 + 1/n3 ... t = dot(c,b) / sqrt(s_sq*fact) probs = betai(0.5*df,0.5,float(df)/(df+t*t)) return t, probs Modified: trunk/Lib/stats/tests/test_stats.py =================================================================== --- trunk/Lib/stats/tests/test_stats.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/stats/tests/test_stats.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -725,7 +725,7 @@ def check_signaltonoise(self): """ this is not in R, so used - mean(testcase)/(sqrt(var(testcase)*3/4)) """ + mean(testcase,axis=0)/(sqrt(var(testcase)*3/4)) """ #y = scipy.stats.signaltonoise(self.shoes[0]) #assert_approx_equal(y,4.5709967) y = scipy.stats.signaltonoise(self.testcase) @@ -753,7 +753,7 @@ def check_z(self): """ not in R, so used - (10-mean(testcase))/sqrt(var(testcase)*3/4) + (10-mean(testcase,axis=0))/sqrt(var(testcase)*3/4) """ y = scipy.stats.z(self.testcase,scipy.stats.mean(self.testcase)) assert_almost_equal(y,0.0) @@ -761,7 +761,7 @@ def check_zs(self): """ not in R, so tested by using - (testcase[i]-mean(testcase))/sqrt(var(testcase)*3/4) + (testcase[i]-mean(testcase,axis=0))/sqrt(var(testcase)*3/4) """ y = scipy.stats.zs(self.testcase) desired = ([-1.3416407864999, -0.44721359549996 , 0.44721359549996 , 1.3416407864999]) @@ -783,7 +783,7 @@ testmathworks = [1.165 , 0.6268, 0.0751, 0.3516, -0.6965] def check_moment(self): """ - mean((testcase-mean(testcase))**power))""" + mean((testcase-mean(testcase))**power,axis=0),axis=0))**power))""" y = scipy.stats.moment(self.testcase,1) assert_approx_equal(y,0.0,10) y = scipy.stats.moment(self.testcase,2) @@ -802,7 +802,7 @@ def check_skewness(self): """ - sum((testmathworks-mean(testmathworks))**3)/((sqrt(var(testmathworks)*4/5))**3)/5 + sum((testmathworks-mean(testmathworks,axis=0))**3,axis=0)/((sqrt(var(testmathworks)*4/5))**3)/5 """ y = scipy.stats.skew(self.testmathworks) assert_approx_equal(y,-0.29322304336607,10) @@ -812,8 +812,8 @@ assert_approx_equal(y,0.0,10) def check_kurtosis(self): """ - sum((testcase-mean(testcase))**4)/((sqrt(var(testcase)*3/4))**4)/4 - sum((test2-mean(testmathworks))**4)/((sqrt(var(testmathworks)*4/5))**4)/5 + sum((testcase-mean(testcase,axis=0))**4,axis=0)/((sqrt(var(testcase)*3/4))**4)/4 + sum((test2-mean(testmathworks,axis=0))**4,axis=0)/((sqrt(var(testmathworks)*4/5))**4)/5 Set flags for axis = 0 and fisher=0 (Pearson's defn of kurtosis for compatiability with Matlab) """ Modified: trunk/Lib/stsci/convolve/lib/Convolve.py =================================================================== --- trunk/Lib/stsci/convolve/lib/Convolve.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/stsci/convolve/lib/Convolve.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -374,7 +374,7 @@ def _fbroadcast(f, N, shape, args, params=()): """_fbroadcast(f, N, args, shape, params=()) calls 'f' for each of the 'N'-dimensional inner subnumarray of 'args'. Each subarray has - .shape == 'shape'[-N:]. There are a total of product(shape[:-N]) + .shape == 'shape'[-N:]. There are a total of product(shape[:-N],axis=0) calls to 'f'. """ if len(shape) == N: Modified: trunk/Lib/stsci/image/lib/combine.py =================================================================== --- trunk/Lib/stsci/image/lib/combine.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/stsci/image/lib/combine.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -71,7 +71,7 @@ return _combine_f("median", arrays, output, outtype, nlow, nhigh, badmasks) def average( arrays, output=None, outtype=None, nlow=0, nhigh=0, badmasks=None): - """average() nominally computes the average pixel value for a stack of + """average(,axis=0) nominally computes the average pixel value for a stack of identically shaped images. arrays specifies a sequence of inputs arrays, which are nominally a @@ -97,7 +97,7 @@ >>> a = num.arange(4) >>> a = a.reshape((2,2)) >>> arrays = [a*16, a*4, a*2, a*8] - >>> average(arrays) + >>> average(arrays,axis=0) array([[ 0, 7], [15, 22]]) >>> average(arrays, nhigh=1) Modified: trunk/Lib/tests/test_basic.py =================================================================== --- trunk/Lib/tests/test_basic.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/tests/test_basic.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -269,7 +269,7 @@ class test_ptp(unittest.TestCase): def check_basic(self): a = [3,4,5,10,-3,-5,6.0] - assert_equal(ptp(a),15.0) + assert_equal(ptp(a,axis=0),15.0) b = [[3,6.0, 9.0], [4,10.0,5.0], [8,3.0,2.0]] @@ -284,8 +284,8 @@ for ctype in ['1','b','s','i','l','f','d','F','D']: a = array(ba,ctype) a2 = array(ba2,ctype) - assert_array_equal(cumsum(a), array([1,3,13,24,30,35,39],ctype)) - assert_array_equal(cumsum(a2), + assert_array_equal(cumsum(a,axis=0), array([1,3,13,24,30,35,39],ctype)) + assert_array_equal(cumsum(a2,axis=0), array([[1,2,3,4],[6,8,10,13], [16,11,14,18]],ctype)) assert_array_equal(cumsum(a2,axis=1), @@ -305,8 +305,8 @@ self.failUnlessRaises(ArithmeticError, prod, a2, 1) self.failUnlessRaises(ArithmeticError, prod, a) else: - assert_equal(prod(a),26400) - assert_array_equal(prod(a2), array([50,36,84,180],ctype)) + assert_equal(prod(a,axis=0),26400) + assert_array_equal(prod(a2,axis=0), array([50,36,84,180],ctype)) assert_array_equal(prod(a2,axis=1), array([24, 1890, 600],ctype)) @@ -322,10 +322,10 @@ self.failUnlessRaises(ArithmeticError, cumprod, a2, 1) self.failUnlessRaises(ArithmeticError, cumprod, a) else: - assert_array_equal(cumprod(a), + assert_array_equal(cumprod(a,axis=0), array([1, 2, 20, 220, 1320, 6600, 26400],ctype)) - assert_array_equal(cumprod(a2), + assert_array_equal(cumprod(a2,axis=0), array([[ 1, 2, 3, 4], [ 5, 12, 21, 36], [50, 36, 84, 180]],ctype)) Modified: trunk/Lib/tests/test_basic1a.py =================================================================== --- trunk/Lib/tests/test_basic1a.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/tests/test_basic1a.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -27,7 +27,7 @@ class test_factorial(unittest.TestCase): def check_basic(self): for k in range(0,13): - assert_equal(factorial(k),product(grid[1:k+1])) + assert_equal(factorial(k),product(grid[1:k+1],axis=0)) self.failUnlessRaises(ValueError, factorial, -10) def check_exact(self): @@ -41,7 +41,7 @@ def check_basic(self): for N in range(0,11): for k in range(0,N+1): - ans = product(grid[N-k+1:N+1]) / product(grid[1:k+1]) + ans = product(grid[N-k+1:N+1],axis=0) / product(grid[1:k+1],axis=0) assert_almost_equal(comb(N,k),ans,9) self.failUnlessRaises(ValueError, comb, -10,1) self.failUnlessRaises(ValueError, comb, 10,-1) Modified: trunk/Lib/tests/test_common.py =================================================================== --- trunk/Lib/tests/test_common.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/tests/test_common.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -49,7 +49,7 @@ class test_factorial(unittest.TestCase): def check_basic(self): for k in range(0,13): - assert_equal(factorial(k),product(mgrid[1:k+1])) + assert_equal(factorial(k),product(mgrid[1:k+1],axis=0)) assert_equal(factorial(-10),0) def check_exact(self): @@ -63,7 +63,7 @@ def check_basic(self): for N in range(0,11): for k in range(0,N+1): - ans = product(mgrid[N-k+1:N+1]) / product(mgrid[1:k+1]) + ans = product(mgrid[N-k+1:N+1],axis=0) / product(mgrid[1:k+1],axis=0) assert_almost_equal(comb(N,k),ans,9) assert_equal(comb(-10,1),0) assert_equal(comb(10,-1),0) Modified: trunk/Lib/weave/size_check.py =================================================================== --- trunk/Lib/weave/size_check.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/weave/size_check.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -127,7 +127,7 @@ return 0 if len(self.shape) == len(other.shape) == 0: return 0 - return not alltrue(equal(self.shape,other.shape)) + return not alltrue(equal(self.shape,other.shape),axis=0) def __add__(self,other): return self.binary_op(other) def __radd__(self,other): return self.binary_op(other) Modified: trunk/Lib/weave/tests/test_blitz_tools.py =================================================================== --- trunk/Lib/weave/tests/test_blitz_tools.py 2006-08-29 07:22:11 UTC (rev 2182) +++ trunk/Lib/weave/tests/test_blitz_tools.py 2006-08-29 10:30:44 UTC (rev 2183) @@ -90,7 +90,7 @@ print diff[:4,-4:] print diff[-4:,:4] print diff[-4:,-4:] - print sum(abs(diff.ravel())) + print sum(abs(diff.ravel()),axis=0) raise AssertionError return standard,compiled From scipy-svn at scipy.org Tue Aug 29 06:46:15 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 29 Aug 2006 05:46:15 -0500 (CDT) Subject: [Scipy-svn] r2184 - trunk/Lib/sandbox/svm Message-ID: <20060829104615.B6E0C39C023@new.scipy.org> Author: fullung Date: 2006-08-29 05:45:55 -0500 (Tue, 29 Aug 2006) New Revision: 2184 Modified: trunk/Lib/sandbox/svm/classification.py trunk/Lib/sandbox/svm/predict.py Log: Undo axis changes to itertools.repeat calls. Modified: trunk/Lib/sandbox/svm/classification.py =================================================================== --- trunk/Lib/sandbox/svm/classification.py 2006-08-29 10:30:44 UTC (rev 2183) +++ trunk/Lib/sandbox/svm/classification.py 2006-08-29 10:45:55 UTC (rev 2184) @@ -55,7 +55,7 @@ d = {} labels = self.labels for v, (li, lj) in \ - izip(vv, chain(*[izip(repeat(x,axis=0), labels[i+1:]) + izip(vv, chain(*[izip(repeat(x), labels[i+1:]) for i, x in enumerate(labels[:-1])])): d[li, lj] = v d[lj, li] = -v Modified: trunk/Lib/sandbox/svm/predict.py =================================================================== --- trunk/Lib/sandbox/svm/predict.py 2006-08-29 10:30:44 UTC (rev 2183) +++ trunk/Lib/sandbox/svm/predict.py 2006-08-29 10:45:55 UTC (rev 2184) @@ -114,7 +114,7 @@ vote = N.zeros((nr_class, dec_values.shape[0]), N.uint32) classidx = range(nr_class) for pos, (i, j) in \ - enumerate(chain(*[izip(repeat(idx,axis=0), classidx[k+1:]) + enumerate(chain(*[izip(repeat(idx), classidx[k+1:]) for k, idx in enumerate(classidx[:-1])])): ji = N.array((j, i)) From scipy-svn at scipy.org Wed Aug 30 11:19:01 2006 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 30 Aug 2006 10:19:01 -0500 (CDT) Subject: [Scipy-svn] r2185 - trunk/Lib/interpolate Message-ID: <20060830151901.BAA3939C12D@new.scipy.org> Author: stefan Date: 2006-08-30 10:18:55 -0500 (Wed, 30 Aug 2006) New Revision: 2185 Modified: trunk/Lib/interpolate/fitpack.py Log: Pass 32-bit integers to Fortran C-wrappers. Modified: trunk/Lib/interpolate/fitpack.py =================================================================== --- trunk/Lib/interpolate/fitpack.py 2006-08-29 10:45:55 UTC (rev 2184) +++ trunk/Lib/interpolate/fitpack.py 2006-08-30 15:18:55 UTC (rev 2185) @@ -33,7 +33,7 @@ __version__ = "$Revision$"[10:-1] import _fitpack from numpy import atleast_1d, array, ones, zeros, sqrt, ravel, transpose, \ - dot, sin, cos, pi, arange, empty + dot, sin, cos, pi, arange, empty, intc myasarray = atleast_1d # Try to replace _fitpack interface with @@ -101,7 +101,7 @@ An error occured""",TypeError]} _parcur_cache = {'t': array([],float), 'wrk': array([],float), - 'iwrk':array([],int), 'u': array([],float),'ub':0,'ue':1} + 'iwrk':array([],intc), 'u': array([],float),'ub':0,'ue':1} def splprep(x,w=None,u=None,ub=None,ue=None,k=3,task=0,s=None,t=None, full_output=0,nest=None,per=0,quiet=1): @@ -169,7 +169,7 @@ """ if task<=0: _parcur_cache = {'t': array([],float), 'wrk': array([],float), - 'iwrk':array([],int),'u': array([],float), + 'iwrk':array([],intc),'u': array([],float), 'ub':0,'ue':1} x=myasarray(x) idim,m=x.shape @@ -245,7 +245,7 @@ return tcku _curfit_cache = {'t': array([],float), 'wrk': array([],float), - 'iwrk':array([],int)} + 'iwrk':array([],intc)} def splrep(x,y,w=None,xb=None,xe=None,k=3,task=0,s=1e-3,t=None, full_output=0,per=0,quiet=1): """Find the B-spline representation of 1-D curve. @@ -353,7 +353,7 @@ _curfit_cache['t'] = t if task <= 0: _curfit_cache['wrk'] = empty((m*(k+1)+nest*(7+3*k),),float) - _curfit_cache['iwrk'] = empty((nest,),int) + _curfit_cache['iwrk'] = empty((nest,),intc) try: t=_curfit_cache['t'] wrk=_curfit_cache['wrk'] @@ -532,7 +532,7 @@ # full_output=0,nest=None,per=0,quiet=1): _surfit_cache = {'tx': array([],float),'ty': array([],float), - 'wrk': array([],float), 'iwrk':array([],int)} + 'wrk': array([],float), 'iwrk':array([],intc)} def bisplrep(x,y,z,w=None,xb=None,xe=None,yb=None,ye=None,kx=3,ky=3,task=0, s=None,eps=1e-16,tx=None,ty=None,full_output=0, nxest=None,nyest=None,quiet=1):