def test_createDataset(self):
"histogram.__init__: createDataset"
name = "intensity"
unit = "1"
shape = [100,200]
types = [ 'double', 'float', 'int', 'unsigned' ]
for datatype in types:
try:
ds = createDataset( name, unit, shape=shape, data_type = datatype )
except:
raise RuntimeError, datatype
self.assertEqual( name, ds.name() )
self.assertEqual( 1, ds.unit() )
self.assertVectorEqual( shape, ds.shape() )
self.assertEqual( ds.typecodeAsC(), datatype )
continue
from ndarray.NumpyNdArray import NdArray
storage = NdArray( "float", 20000, 0 )
storage.setShape( shape )
ds2 = createDataset( name, unit, storage = storage )
self.assertEqual( name, ds2.name() )
self.assertEqual( 1, ds2.unit() )
self.assertVectorEqual( shape, ds2.shape() )
self.assertEqual( ds2.typecodeAsC(), "float" )
return
def testSum(self):
"Dataset: method 'sum'"
storage = NdArray( 'double', range(6) )
storage.setShape( (2,3) )
ds = self.Dataset(name = "distance", unit = "meter",
attributes = { }, shape = [2,3], storage = storage)
self.assertAlmostEqual( ds.sum()/meter, 15. )
ds1 = ds.sum(0)
expected = NdArray( 'double', [3,5,7] )
self.assert_( ds1.storage().compare( expected ) )
return
def test_0( **kwds):
from histogram.Axis import Axis
from histogram.NdArrayDataset import Dataset
name = 'test'
unit = '1'
attributes = {'plottable':True, 'nifty':False, 'pi':3.14159, 3.14159:'pi'}
lengths = [2,3,4]
dtype = 6 # double
stor1 = NdArray( dtype, lengths[0]+1, 1.0)
ax1 = Axis( name+'ax1', unit, attributes, lengths[0], stor1)
stor2 = NdArray( dtype, lengths[1]+1, 1.0)
ax2 = Axis( name+'ax2', unit, attributes, lengths[1], stor2)
stor3 = NdArray( dtype, lengths[2]+1, 1.0)
ax3 = Axis( name+'ax3', unit, attributes, lengths[2], stor3)
size = lengths[0]*lengths[1]*lengths[2]
dataStore = NdArray( dtype, size, 1.0); dataStore.setShape( (2, 3,4) )
data = Dataset( name+'data', unit, attributes, lengths, dataStore)
errorStore = NdArray( dtype, size, 1.0); errorStore.setShape( (2, 3,4) )
error = Dataset( name+'error', unit, attributes, lengths, errorStore)
from histogram.Histogram import Histogram
histogram = Histogram( 'testHist', data, error, [ax1, ax2, ax3],
attributes)
return True
def test_2( **kwds):
from histogram.Axis import Axis
from histogram.NdArrayDataset import Dataset
name = 'test'
unit = '1'
attributes = {'plottable':True, 'nifty':False, 'pi':3.14159, 3.14159:'pi'}
lengths = [2,3,4]
dtype = 6 # double
stor1 = NdArray( dtype, lengths[0]+1, 1.0)
ax1 = Axis( name+'ax1', unit, attributes, lengths[0], stor1)
stor2 = NdArray( dtype, lengths[1]+1, 1.0)
ax2 = Axis( name+'ax2', unit, attributes, lengths[1], stor2)
stor3 = NdArray( dtype, lengths[2]+1, 1.0)
ax3 = Axis( name+'ax3', unit, attributes, lengths[2], stor3)
size = lengths[0]*lengths[1]*lengths[2]
dataStore = NdArray( dtype, size, 1.0); dataStore.setShape( (2, 3,4) )
data = Dataset( name+'data', unit, attributes, lengths, dataStore)
errorStore = NdArray( dtype, size, 1.0); errorStore.setShape( (2, 3,4) )
error = Dataset( name+'error', unit, attributes, lengths, errorStore)
from histogram.Histogram import Histogram
hist = Histogram( 'testHist', data, error, [ax1, ax2, ax3],
attributes)
passed = True
hax1 = hist.axisFromName( 'testax1')
if hax1 is not ax1:
passed = False
log("hax1 was %s instead of %s" % (hax1, ax1))
return passed
def test__getitem__(self):
"Dataset: operator 'a[3:5], a[3]'"
#1D dataset
ds = self.Dataset(name = "distance", unit = "meter",
attributes = { },
shape = [12], storage = NdArray( 'double', range(12) ) )
#slicing
self.assert_( ds[3:5].storage().compare( NdArray('double', [3,4] ) ) )
#indexing
self.assertAlmostEqual( ds[3]/meter, 3 )
#2D dataset
stor = NdArray( 'double', range(12) ); stor.setShape( (3,4) )
ds = self.Dataset(name = "distance", unit = "meter",
attributes = { },
shape = [3,4], storage = stor)
#slicing
subarr = ds[1:2, 1:2].storage()
expected = NdArray('double', [5] ) ; expected.setShape( (1,1) )
self.assert_( subarr.compare( expected ) )
#indexing
self.assertAlmostEqual( ds[1,1]/meter, 5 )
return
def test__setitem__(self):
"Dataset: operator 'a[3]=4'"
#1D dataset
ds = self.Dataset(name = "distance", unit = "meter",
attributes = { },
shape = [12], storage = NdArray( 'double', range(12) ) )
#set slice
from numpy import array
ds[3:5] = array([1,2])*meter
self.assert_( ds[3:5].storage().compare( NdArray('double', [1,2] ) ) )
#set item
ds[10] = 11*meter
self.assertAlmostEqual( ds[10]/meter, 11 )
#2D dataset
stor = NdArray( 'double', range(12) ); stor.setShape( (3,4) )
ds = self.Dataset(name = "distance", unit = "meter",
attributes = { },
shape = [3,4], storage = stor)
#slicing
ds[1:2, 1:2] = array([[999]])*meter
subarr = ds[1:2, 1:2].storage()
expected = NdArray('double', [999] ) ; expected.setShape( (1,1) )
self.assert_( subarr.compare( expected ) )
#indexing
self.assertAlmostEqual( ds[1,1]/meter, 999 )
ds[1,1] = 333 * meter
self.assertAlmostEqual( ds[1,1]/meter, 333 )
#set slice with datasets
ds1v = NdArray( 'double', range(12) ); ds1v.setShape( (3,4) )
ds1 = self.Dataset(
name = "distance", unit = "meter", shape = [3,4], storage = ds1v )
ds2v = NdArray( 'double', range(4) ); ds2v.setShape( (2,2) )
ds2 = self.Dataset(
name = "distance", unit = "meter", shape = [2,2], storage = ds2v )
ds1[1:3, 1:3] = ds2
#set slice with one number
ds1v = NdArray( 'double', range(12) ); ds1v.setShape( (3,4) )
ds1 = self.Dataset(
name = "distance", unit = "meter", shape = [3,4], storage = ds1v )
ds1[1:3,1:3] = 1*meter
return