def testOnes(self): # make new datasets with ones
print("test ones")
dds = np.ones(3, dtype=np.float)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(1, dds.ndim)
self.assertEqual(3, dds.shape[0])
self.assertEqual(1, dds[0])
dds = np.ones((2, 3), np.float)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(2, dds.ndim)
self.assertEqual(2, dds.shape[0])
self.assertEqual(3, dds.shape[1])
self.assertEqual(1, dds[0, 0])
dds = np.ones_like(dds)
if isjava:
self.assertEqual(1, dds.dtype.elements)
self.assertEqual(2, dds.ndim)
self.assertEqual(2, dds.shape[0])
self.assertEqual(3, dds.shape[1])
self.assertEqual(1, dds[0, 0])
dds = np.ones(np.array([1, 2]), dtype=np.float)
self.assertEqual((1, 2), dds.shape)
self.assertEqual(1, dds[0, 0])
dds = np.ones_like(np.array([1, 2]), dtype=np.float)
self.assertEqual((2, ), dds.shape)
self.assertEqual(1, dds[0])
def testOnes(self): # make new datasets with ones
print("test ones")
dds = np.ones(3, dtype=np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(1, dds.ndim)
self.assertEquals(3, dds.shape[0])
self.assertEquals(1, dds[0])
dds = np.ones((2,3), np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(1, dds[0,0])
dds = np.ones_like(dds)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(1, dds[0,0])
dds = np.ones(np.array([1,2]), dtype=np.float)
self.assertEqual((1,2), dds.shape)
self.assertEquals(1, dds[0,0])
dds = np.ones_like(np.array([1,2]), dtype=np.float)
self.assertEquals((2,), dds.shape)
self.assertEquals(1, dds[0])
def testOnes(self): # make new datasets with ones
print "test ones"
dds = np.ones(3, np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(1, dds.ndim)
self.assertEquals(3, dds.shape[0])
self.assertEquals(1, dds[0])
dds = np.ones((2,3), np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(1, dds[0,0])
def testScisoft(self):
a = np.ones([3,4])
print(a.shape)
a.shape = [2,6]
print(a.shape)
a.shape = 12
print(a.shape)
a.shape = (2,6)
print(a.shape)
print(a)
print(a*2)
b = np.arange(12)
print(b)
print(b[0])
b[2] = 2.3
print(b[1:8:3])
b[6:2:-1] = -2.1
b.shape = [2,6]
print(b + 2)
print(2 + b)
b += 2
print(b[1,3])
b[0,5] = -2.3
print(b[0,2:5])
b[:,1] = -7.1
print(b)
try:
c = np.add(a, b)
print(c)
except:
print("Failed with an IAE as expected")
def testScisoft(self):
a = np.ones([3,4])
print a.shape
a.shape = [2,6]
print a.shape
a.shape = 12
print a.shape
a.shape = (2,6)
print a.shape
print a
print a*2
b = np.arange(12)
print b
print b[0]
b[2] = 2.3
print b[1:8:3]
b[6:2:-1] = -2.1
b.shape = [2,6]
print b + 2
print 2 + b
b += 2.
print b[1,3]
b[0,5] = -2.3
print b[0,2:5]
b[:,1] = -7.1
print b
try:
c = np.add(a, b)
print c
except:
print "Failed with an IAE as expected"
def testStack(self):
print 'Stack testing'
self.checkitems([1,1,1], np.hstack(np.ones(3)))
self.checkitems([[1],[1],[1]], np.vstack(np.ones(3)))
self.checkitems([[[1,1,1]]], np.dstack(np.ones(3)))
a = np.array([1,2,3])
b = np.array([2,3,4])
self.checkitems([1,2,3,2,3,4], np.hstack((a, b)))
self.checkitems([[1,2], [2,3], [3,4]], np.hstack((a.reshape(3,1), b.reshape(3,1))))
self.checkitems([[1,2,3],[2,3,4]], np.vstack((a, b)))
self.checkitems([[1], [2], [3], [2], [3], [4]], np.vstack((a.reshape(3,1), b.reshape(3,1))))
self.checkitems([[[1,2], [2,3], [3,4]]], np.dstack((a, b)))
self.checkitems([[[1,2]], [[2,3]], [[3,4]]], np.dstack((a.reshape(3,1), b.reshape(3,1))))
def testStack(self):
print 'Stack testing'
self.checkitems([1,1,1], np.hstack(np.ones(3)))
self.checkitems([[1],[1],[1]], np.vstack(np.ones(3)))
self.checkitems([[[1,1,1]]], np.dstack(np.ones(3)))
a = np.array([1,2,3])
b = np.array([2,3,4])
self.checkitems([1,2,3,2,3,4], np.hstack((a, b)))
self.checkitems([[1,2], [2,3], [3,4]], np.hstack((a.reshape(3,1), b.reshape(3,1))))
self.checkitems([[1,2,3],[2,3,4]], np.vstack((a, b)))
self.checkitems([[1], [2], [3], [2], [3], [4]], np.vstack((a.reshape(3,1), b.reshape(3,1))))
self.checkitems([[[1,2], [2,3], [3,4]]], np.dstack((a, b)))
self.checkitems([[[1,2]], [[2,3]], [[3,4]]], np.dstack((a.reshape(3,1), b.reshape(3,1))))
def testScisoft(self):
a = np.ones([3, 4])
print a.shape
a.shape = [2, 6]
print a.shape
a.shape = 12
print a.shape
a.shape = (2, 6)
print a.shape
print a
print a * 2
b = np.arange(12)
print b
print b[0]
b[2] = 2.3
print b[1:8:3]
b[6:2:-1] = -2.1
b.shape = [2, 6]
print b + 2
print 2 + b
b += 2.
print b[1, 3]
b[0, 5] = -2.3
print b[0, 2:5]
b[:, 1] = -7.1
print b
try:
c = np.add(a, b)
print c
except:
print "Failed with an IAE as expected"
def testScisoft(self):
a = np.ones([3, 4])
print(a.shape)
a.shape = [2, 6]
print(a.shape)
a.shape = 12
print(a.shape)
a.shape = (2, 6)
print(a.shape)
print(a)
print(a * 2)
b = np.arange(12)
print(b)
print(b[0])
b[2] = 2.3
print(b[1:8:3])
b[6:2:-1] = -2.1
b.shape = [2, 6]
print(b + 2)
print(2 + b)
b += 2
print(b[1, 3])
b[0, 5] = -2.3
print(b[0, 2:5])
b[:, 1] = -7.1
print(b)
try:
c = np.add(a, b)
print(c)
except:
print("Failed with an IAE as expected")
def testSaveArgs(self):
self.checkArgs(None)
self.checkArgs(([None, None], [None,]))
self.checkArgs(([None, 1, 1.5], [None,]))
self.checkArgs(([None, 1, 1.5], (None,)))
self.checkArgs(([None, 1, 1.5], dnp.arange(12)))
self.checkArgs(([None, 1, 1.5], {'blah':dnp.arange(12), 'foo': dnp.ones((3,4)), 'boo': -2.345}))
def testStack(self):
print('Stack testing')
self.checkitems([1,1,1], np.hstack(np.ones(3)))
self.checkitems([[1],[1],[1]], np.vstack(np.ones(3)))
self.checkitems([[[1,1,1]]], np.dstack(np.ones(3)))
a = np.array([1,2,3])
b = np.array([2,3,4])
self.checkitems([1,2,3,2,3,4], np.hstack((a, b)))
self.checkitems([[1,2], [2,3], [3,4]], np.hstack((a.reshape(3,1), b.reshape(3,1))))
self.checkitems([[1,2,3],[2,3,4]], np.vstack((a, b)))
self.checkitems([[1], [2], [3], [2], [3], [4]], np.vstack((a.reshape(3,1), b.reshape(3,1))))
self.checkitems([[[1,2], [2,3], [3,4]]], np.dstack((a, b)))
self.checkitems([[[1,2]], [[2,3]], [[3,4]]], np.dstack((a.reshape(3,1), b.reshape(3,1))))
self.checkitems([[1,2], [2,3], [3,4]], np.column_stack((a, b)))
self.checkitems([[1,2, 0, 1, 2], [2,3, 3, 4, 5], [3,4, 6, 7, 8]], np.column_stack((a, b, np.arange(9).reshape(3,3))))
def testStack(self):
print('Stack testing')
self.checkitems([1, 1, 1], np.hstack(np.ones(3)))
self.checkitems([[1], [1], [1]], np.vstack(np.ones(3)))
self.checkitems([[[1, 1, 1]]], np.dstack(np.ones(3)))
a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
self.checkitems([1, 2, 3, 2, 3, 4], np.hstack((a, b)))
self.checkitems([[1, 2], [2, 3], [3, 4]],
np.hstack((a.reshape(3, 1), b.reshape(3, 1))))
self.checkitems([[1, 2, 3], [2, 3, 4]], np.vstack((a, b)))
self.checkitems([[1], [2], [3], [2], [3], [4]],
np.vstack((a.reshape(3, 1), b.reshape(3, 1))))
self.checkitems([[[1, 2], [2, 3], [3, 4]]], np.dstack((a, b)))
self.checkitems([[[1, 2]], [[2, 3]], [[3, 4]]],
np.dstack((a.reshape(3, 1), b.reshape(3, 1))))
self.checkitems([[1, 2], [2, 3], [3, 4]], np.column_stack((a, b)))
self.checkitems([[1, 2, 0, 1, 2], [2, 3, 3, 4, 5], [3, 4, 6, 7, 8]],
np.column_stack((a, b, np.arange(9).reshape(3, 3))))
def testOnes(self): # make new datasets with ones
print "test ones"
dds = np.ones(3, dtype=np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(1, dds.ndim)
self.assertEquals(3, dds.shape[0])
self.assertEquals(1, dds[0])
dds = np.ones((2, 3), np.float)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(1, dds[0, 0])
dds = np.ones_like(dds)
if isjava:
self.assertEquals(1, dds.dtype.elements)
self.assertEquals(2, dds.ndim)
self.assertEquals(2, dds.shape[0])
self.assertEquals(3, dds.shape[1])
self.assertEquals(1, dds[0, 0])
def baseline(self,xdataset, ydataset, smoothness): '''find the baseline y value for a peak in y dataset''' ymaxindex=ydataset.argMax() if smoothness > 1: wnd = dnp.ones(smoothness, dtype=dnp.float64)/smoothness ydataset = dnp.convolve(ydataset, wnd, 'same') result=dnp.gradient(ydataset, xdataset) leftresult=result[:ymaxindex] rightresult=result[ymaxindex+1:] leftminderivativeindex=dnp.abs(leftresult).argmin() rightminderivativeindex=dnp.abs(rightresult).argmin() leftbasey=ydataset.getElementDoubleAbs(leftminderivativeindex) rightbasey=ydataset.getElementDoubleAbs(rightminderivativeindex+1+leftresult.shape[0]) basey=(leftbasey+rightbasey)/2 return basey
def baseline(self, xdataset, ydataset, smoothness):
'''find the baseline y value for a peak in y dataset'''
ymaxindex = ydataset.argMax()
if smoothness > 1:
wnd = dnp.ones(smoothness, dtype=dnp.float64) / smoothness
ydataset = dnp.convolve(ydataset, wnd, 'same')
result = dnp.gradient(ydataset, xdataset)
leftresult = result[:ymaxindex]
rightresult = result[ymaxindex + 1:]
leftminderivativeindex = dnp.abs(leftresult).argmin()
rightminderivativeindex = dnp.abs(rightresult).argmin()
leftbasey = ydataset.getElementDoubleAbs(leftminderivativeindex)
rightbasey = ydataset.getElementDoubleAbs(rightminderivativeindex + 1 +
leftresult.shape[0])
basey = (leftbasey + rightbasey) / 2
return basey
def findBases(self, xdataset, ydataset, delta, smoothness): bases=[] peaks=self.findPeaksAndTroughs(ydataset, delta)[0] yslices=[] xslices=[] startindex=0 for index,value in peaks: #@UnusedVariable yslices.append(ydataset[startindex:index]) xslices.append(xdataset[startindex:index]) startindex=index+1 if smoothness > 1: wnd = dnp.ones(smoothness, dtype=dnp.float64)/smoothness for xset, yset in xslices, yslices: if smoothness > 1: yset = dnp.convolve(yset, wnd, 'same') result=dnp.gradient(yset, xset) minimumderivativeindex=dnp.abs(result).argmin() bases.append((xset[minimumderivativeindex],yset[minimumderivativeindex])) return bases
def findBases(self, xdataset, ydataset, delta, smoothness):
bases = []
peaks = self.findPeaksAndTroughs(ydataset, delta)[0]
yslices = []
xslices = []
startindex = 0
for index, value in peaks: #@UnusedVariable
yslices.append(ydataset[startindex:index])
xslices.append(xdataset[startindex:index])
startindex = index + 1
if smoothness > 1:
wnd = dnp.ones(smoothness, dtype=dnp.float64) / smoothness
for xset, yset in xslices, yslices:
if smoothness > 1:
yset = dnp.convolve(yset, wnd, 'same')
result = dnp.gradient(yset, xset)
minimumderivativeindex = dnp.abs(result).argmin()
bases.append(
(xset[minimumderivativeindex], yset[minimumderivativeindex]))
return bases
def testRandom(self):
import os
if os.name == 'java':
import jarray
ja = jarray.array([1, 2], 'i')
else:
ja = np.array([1, 2])
print np.asIterable(ja)
print rnd.rand()
print rnd.rand(1)
print rnd.rand(2, 4)
print rnd.randn()
print rnd.randn(1)
print rnd.randn(2, 4)
for i in range(10):
print i, rnd.randint(1)
print rnd.randint(2)
print rnd.randint(5, size=[2, 4])
print rnd.random_integers(1)
print rnd.random_integers(5, size=[2, 4])
print rnd.exponential(1.1)
print rnd.exponential(1.1, [2, 4])
print rnd.poisson(1.1)
print rnd.poisson(1.1, [2, 4])
a = np.ones([2, 3])
print rnd.poisson(1.2, a.shape)
rnd.seed()
print rnd.rand(2, 3)
rnd.seed()
print rnd.rand(2, 3)
rnd.seed(12343)
print rnd.rand(2, 3)
rnd.seed(12343)
print rnd.rand(2, 3)
a = rnd.rand(200, 300)
print a.mean(), a.std()
def testRandom(self):
import os
if os.name == 'java':
import jarray
ja = jarray.array([1,2], 'i')
else:
ja = np.array([1,2])
print np.asIterable(ja)
print rnd.rand()
print rnd.rand(1)
print rnd.rand(2,4)
print rnd.randn()
print rnd.randn(1)
print rnd.randn(2,4)
for i in range(10):
print i, rnd.randint(1)
print rnd.randint(2)
print rnd.randint(5, size=[2,4])
print rnd.random_integers(1)
print rnd.random_integers(5, size=[2,4])
print rnd.exponential(1.1)
print rnd.exponential(1.1, [2,4])
print rnd.poisson(1.1)
print rnd.poisson(1.1, [2,4])
a = np.ones([2,3])
print rnd.poisson(1.2, a.shape)
rnd.seed()
print rnd.rand(2,3)
rnd.seed()
print rnd.rand(2,3)
rnd.seed(12343)
print rnd.rand(2,3)
rnd.seed(12343)
print rnd.rand(2,3)
a = rnd.rand(200,300)
print a.mean(), a.std()
