[Scipy-svn] r3774 - in branches/testing_cleanup/scipy/sandbox/dhuard: . tests

scipy-svn@scip... scipy-svn@scip...
Thu Jan 3 15:51:16 CST 2008


Author: matthew.brett@gmail.com
Date: 2008-01-03 15:51:10 -0600 (Thu, 03 Jan 2008)
New Revision: 3774

Added:
   branches/testing_cleanup/scipy/sandbox/dhuard/__init__.py
   branches/testing_cleanup/scipy/sandbox/dhuard/tests/
   branches/testing_cleanup/scipy/sandbox/dhuard/tests/test_histogram.py
   branches/testing_cleanup/scipy/sandbox/dhuard/tests/test_stats.py
Removed:
   branches/testing_cleanup/scipy/sandbox/dhuard/test_histogram.py
   branches/testing_cleanup/scipy/sandbox/dhuard/test_stats.py
Log:
Rearranged tests, added init file

Added: branches/testing_cleanup/scipy/sandbox/dhuard/__init__.py
===================================================================

Deleted: branches/testing_cleanup/scipy/sandbox/dhuard/test_histogram.py
===================================================================
--- branches/testing_cleanup/scipy/sandbox/dhuard/test_histogram.py	2008-01-03 19:36:34 UTC (rev 3773)
+++ branches/testing_cleanup/scipy/sandbox/dhuard/test_histogram.py	2008-01-03 21:51:10 UTC (rev 3774)
@@ -1,99 +0,0 @@
-from numpy.testing import *
-from histogram import _histogram_fixed_binsize, _histogram_digitize,\
-    _histogram_searchsort, histogram,_optimize_binning
-import numpy as np
-from numpy.random import rand
-
-class TestHistogram1DFunctions(NumpyTestCase):
-    def check_consistency(self):
-        n = 100
-        r = rand(n)*12-1
-        bins = range(11)
-        a = _histogram_fixed_binsize(r, bins[0], bins[1]-bins[0], len(bins)-1)
-        b = _histogram_digitize(r, None, np.array(bins), False)
-        c = _histogram_searchsort(r,bins)
-        assert_array_equal(a,b)
-        assert_array_equal(c,b)
-
-class TestHistogram(NumpyTestCase):
-    def check_simple(self):
-        n=100
-        v=rand(n)
-        (a,b)=histogram(v)
-        #check if the sum of the bins equals the number of samples
-        assert_equal(np.sum(a,axis=0),n)
-        #check that the bin counts are evenly spaced when the data is from a linear function
-        (a,b)=histogram(np.linspace(0,10,100))
-        assert_array_equal(a,10)
-        #Check the construction of the bin array
-        a, b = histogram(v, bins=4, range=[.2,.8])
-        assert_array_almost_equal(b['edges'],np.linspace(.2, .8, 5),8)
-        #Check the number of outliers
-        assert_equal((v<.2).sum(), b['lower'])
-        assert_equal((v>.8).sum(),b['upper'])
-        #Check the normalization
-        bins = [0,.5,.75,1]
-        a,b = histogram(v, bins, normed=True)
-        assert_almost_equal((a*np.diff(bins)).sum(), 1)
-
-    def check_axis(self):
-        n,m = 100,20
-        v = rand(n,m)
-        a,b = histogram(v, bins=5)
-        # Check dimension is reduced (axis=None).
-        assert_equal(a.ndim, 1)
-        #Check total number of count is equal to the number of samples.
-        assert_equal(a.sum(), n*m)
-        a,b = histogram(v, bins = 7, axis=0)
-        # Check shape of new array is ok.
-        assert(a.ndim == 2)
-        assert_array_equal(a.shape,[7, m])
-        # Check normalization is consistent
-        a,b = histogram(v, bins = 7, axis=0, normed=True)
-        assert_array_almost_equal((a.T*np.diff(b['edges'])).sum(1), np.ones((m)),5)
-        a,b = histogram(v, bins = 7, axis=1, normed=True)
-        assert_array_equal(a.shape, [n,7])
-        assert_array_almost_equal((a*np.diff(b['edges'])).sum(1), np.ones((n)))
-        # Check results are consistent with 1d estimate
-        a1, b1 = histogram(v[0,:], bins=b['edges'], normed=True)
-        assert_array_almost_equal(a1, a[0,:],7)
-
-    def check_weights(self):
-        # Check weights = constant gives the same answer as no weights.
-        v = rand(100)
-        w = np.ones(100)*5
-        a,b = histogram(v)
-        na,nb = histogram(v, normed=True)
-        wa,wb = histogram(v, weights=w)
-        nwa,nwb = histogram(v, weights=w, normed=True)
-        assert_array_equal(a*5, wa)
-        assert_array_almost_equal(na, nwa,8)
-        # Check weights are properly applied.
-        v = np.linspace(0,10,10)
-        w = np.concatenate((np.zeros(5), np.ones(5)))
-        wa,wb = histogram(v, bins=np.linspace(0,10.01, 11),weights=w)
-        assert_array_almost_equal(wa, w)
-
-    def check_strategies(self):
-        v = rand(100)
-        ae,be = histogram(v, strategy='binsize')
-        ab,bb = histogram(v, strategy='digitize')
-        as,bs = histogram(v, strategy='searchsort')
-        assert_array_equal(ae, ab)
-        assert_array_equal(ae, as)
-
-        w = rand(100)
-        ae,be = histogram(v, weights=w, strategy='binsize')
-        ab,bb = histogram(v, weights=w, strategy='digitize')
-        as,bs = histogram(v, weights=w, strategy='searchsort')
-        assert_array_almost_equal(ae, ab,8)
-        assert_array_almost_equal(ae, as,8)
-
-    def check_automatic_binning(self):
-        v = rand(100)
-        h,b = histogram(v, 'Scott')
-        h,b = histogram(v, 'Freedman')
-
-
-if __name__ == "__main__":
-    NumpyTest().run()

Deleted: branches/testing_cleanup/scipy/sandbox/dhuard/test_stats.py
===================================================================
--- branches/testing_cleanup/scipy/sandbox/dhuard/test_stats.py	2008-01-03 19:36:34 UTC (rev 3773)
+++ branches/testing_cleanup/scipy/sandbox/dhuard/test_stats.py	2008-01-03 21:51:10 UTC (rev 3774)
@@ -1,45 +0,0 @@
-"""
-Test statistical functions.
-"""
-
-
-from numpy.testing import *
-import stats
-import numpy as np
-
-N = 100
-np.random.seed(2)
-r = np.random.randn(N)
-
-class TestEmpiricalCDF(NumpyTestCase):
-    def check_hazen(self):
-
-        f = stats.empiricalcdf(r)
-        assert_equal(len(f), len(r))
-        assert_array_equal(np.argsort(r), np.argsort(f))
-        assert_array_equal(np.sort(f), (np.arange(N)+.5)/N)
-
-    def check_weibull(self):
-        f = stats.empiricalcdf(r, 'weibull')
-        assert_array_equal(np.sort(f), (np.arange(N)+1.)/(N+1.))
-
-    def check_california(self):
-        f = stats.empiricalcdf(r, 'california')
-        assert_array_equal(np.sort(f), (np.arange(N))/float(N))
-
-class TestScoreAtPercentile(NumpyTestCase):
-    def check_simple(self):
-        r = np.random.randn(1000)
-        s = stats.scoreatpercentile(r, [15.9,50,84.1])
-        assert_array_almost_equal(s, [-1,0,1], 1)
-
-class TestPercentileOfScore(NumpyTestCase):
-    def check_simple(self):
-        r = np.random.randn(3000)
-        p = stats.percentileofscore(r, [-1,0,1])
-        assert_array_almost_equal(p, [15.9, 50, 84.1], 0)
-
-
-
-if __name__ == '__main__':
-    NumpyTest().run()

Copied: branches/testing_cleanup/scipy/sandbox/dhuard/tests/test_histogram.py (from rev 3726, branches/testing_cleanup/scipy/sandbox/dhuard/test_histogram.py)
===================================================================
--- branches/testing_cleanup/scipy/sandbox/dhuard/test_histogram.py	2007-12-27 07:49:22 UTC (rev 3726)
+++ branches/testing_cleanup/scipy/sandbox/dhuard/tests/test_histogram.py	2008-01-03 21:51:10 UTC (rev 3774)
@@ -0,0 +1,99 @@
+from scipy.testing import *
+from scipy.sandbox.dhuard.histogram import _histogram_fixed_binsize, _histogram_digitize,\
+    _histogram_searchsort, histogram,_optimize_binning
+import numpy as np
+from numpy.random import rand
+
+class TestHistogram1DFunctions(TestCase):
+    def test_consistency(self):
+        n = 100
+        r = rand(n)*12-1
+        bins = range(11)
+        a = _histogram_fixed_binsize(r, bins[0], bins[1]-bins[0], len(bins)-1)
+        b = _histogram_digitize(r, None, np.array(bins), False)
+        c = _histogram_searchsort(r,bins)
+        assert_array_equal(a,b)
+        assert_array_equal(c,b)
+
+class TestHistogram(TestCase):
+    def test_simple(self):
+        n=100
+        v=rand(n)
+        (a,b)=histogram(v)
+        #check if the sum of the bins equals the number of samples
+        assert_equal(np.sum(a,axis=0),n)
+        #check that the bin counts are evenly spaced when the data is from a linear function
+        (a,b)=histogram(np.linspace(0,10,100))
+        assert_array_equal(a,10)
+        #Check the construction of the bin array
+        a, b = histogram(v, bins=4, range=[.2,.8])
+        assert_array_almost_equal(b['edges'],np.linspace(.2, .8, 5),8)
+        #Check the number of outliers
+        assert_equal((v<.2).sum(), b['lower'])
+        assert_equal((v>.8).sum(),b['upper'])
+        #Check the normalization
+        bins = [0,.5,.75,1]
+        a,b = histogram(v, bins, normed=True)
+        assert_almost_equal((a*np.diff(bins)).sum(), 1)
+
+    def test_axis(self):
+        n,m = 100,20
+        v = rand(n,m)
+        a,b = histogram(v, bins=5)
+        # Check dimension is reduced (axis=None).
+        assert_equal(a.ndim, 1)
+        #Check total number of count is equal to the number of samples.
+        assert_equal(a.sum(), n*m)
+        a,b = histogram(v, bins = 7, axis=0)
+        # Check shape of new array is ok.
+        assert(a.ndim == 2)
+        assert_array_equal(a.shape,[7, m])
+        # Check normalization is consistent
+        a,b = histogram(v, bins = 7, axis=0, normed=True)
+        assert_array_almost_equal((a.T*np.diff(b['edges'])).sum(1), np.ones((m)),5)
+        a,b = histogram(v, bins = 7, axis=1, normed=True)
+        assert_array_equal(a.shape, [n,7])
+        assert_array_almost_equal((a*np.diff(b['edges'])).sum(1), np.ones((n)))
+        # Check results are consistent with 1d estimate
+        a1, b1 = histogram(v[0,:], bins=b['edges'], normed=True)
+        assert_array_almost_equal(a1, a[0,:],7)
+
+    def test_weights(self):
+        # Check weights = constant gives the same answer as no weights.
+        v = rand(100)
+        w = np.ones(100)*5
+        a,b = histogram(v)
+        na,nb = histogram(v, normed=True)
+        wa,wb = histogram(v, weights=w)
+        nwa,nwb = histogram(v, weights=w, normed=True)
+        assert_array_equal(a*5, wa)
+        assert_array_almost_equal(na, nwa,8)
+        # Check weights are properly applied.
+        v = np.linspace(0,10,10)
+        w = np.concatenate((np.zeros(5), np.ones(5)))
+        wa,wb = histogram(v, bins=np.linspace(0,10.01, 11),weights=w)
+        assert_array_almost_equal(wa, w)
+
+    def test_strategies(self):
+        v = rand(100)
+        ae,be = histogram(v, strategy='binsize')
+        ab,bb = histogram(v, strategy='digitize')
+        as,bs = histogram(v, strategy='searchsort')
+        assert_array_equal(ae, ab)
+        assert_array_equal(ae, as)
+
+        w = rand(100)
+        ae,be = histogram(v, weights=w, strategy='binsize')
+        ab,bb = histogram(v, weights=w, strategy='digitize')
+        as,bs = histogram(v, weights=w, strategy='searchsort')
+        assert_array_almost_equal(ae, ab,8)
+        assert_array_almost_equal(ae, as,8)
+
+    def test_automatic_binning(self):
+        v = rand(100)
+        h,b = histogram(v, 'Scott')
+        h,b = histogram(v, 'Freedman')
+
+
+if __name__ == "__main__":
+    unittest.main()

Copied: branches/testing_cleanup/scipy/sandbox/dhuard/tests/test_stats.py (from rev 3726, branches/testing_cleanup/scipy/sandbox/dhuard/test_stats.py)
===================================================================
--- branches/testing_cleanup/scipy/sandbox/dhuard/test_stats.py	2007-12-27 07:49:22 UTC (rev 3726)
+++ branches/testing_cleanup/scipy/sandbox/dhuard/tests/test_stats.py	2008-01-03 21:51:10 UTC (rev 3774)
@@ -0,0 +1,44 @@
+"""
+Test statistical functions.
+"""
+
+from scipy.testing import *
+from scipy.sandbox.dhuard import stats
+import numpy as np
+
+N = 100
+np.random.seed(2)
+r = np.random.randn(N)
+
+class TestEmpiricalCDF(TestCase):
+    def test_hazen(self):
+
+        f = stats.empiricalcdf(r)
+        assert_equal(len(f), len(r))
+        assert_array_equal(np.argsort(r), np.argsort(f))
+        assert_array_equal(np.sort(f), (np.arange(N)+.5)/N)
+
+    def test_weibull(self):
+        f = stats.empiricalcdf(r, 'weibull')
+        assert_array_equal(np.sort(f), (np.arange(N)+1.)/(N+1.))
+
+    def test_california(self):
+        f = stats.empiricalcdf(r, 'california')
+        assert_array_equal(np.sort(f), (np.arange(N))/float(N))
+
+class TestScoreAtPercentile(TestCase):
+    def test_simple(self):
+        r = np.random.randn(1000)
+        s = stats.scoreatpercentile(r, [15.9,50,84.1])
+        assert_array_almost_equal(s, [-1,0,1], 1)
+
+class TestPercentileOfScore(TestCase):
+    def test_simple(self):
+        r = np.random.randn(3000)
+        p = stats.percentileofscore(r, [-1,0,1])
+        assert_array_almost_equal(p, [15.9, 50, 84.1], 0)
+
+
+
+if __name__ == '__main__':
+    unittest.main()



More information about the Scipy-svn mailing list