def testwrite(self):
y, mapobj, _ = maparray('tmp.foo', 'd')
y[1] = 37
mapobj.flush()
z, _, _ = maparray('tmp.foo', 'd')
self.failUnless(
Numeric.alltrue(
z == Numeric.array([0, 37, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5])))
self.failUnless(Numeric.alltrue(z == y))
def testLogical (self):
"Test logical_and, logical_or, sometrue, alltrue"
x = Numeric.array([1,1,0,0])
y = Numeric.array([1,0,1,0])
assert_eq(Numeric.logical_and (x,y), [1,0,0,0])
assert_eq(Numeric.logical_or (x,y), [1,1,1,0])
assert Numeric.sometrue(x)
assert not Numeric.sometrue(Numeric.zeros((3,)))
assert Numeric.alltrue(Numeric.ones((3,)))
assert not Numeric.alltrue(x)
def to_R(self, ds, dates_as_factor, suppress_all_value=True, **kwargs):
if not self.colname:
return
col = ds[self.colname]
if col.is_datetimetype():
rdata = rconv.Missing_Date_to_R(col)
if not dates_as_factor:
return rdata
elif MA.isMaskedArray(col.data):
if self.missing_as_none:
rdata = MA.filled(col.data, sys.maxint)
else:
if Numeric.alltrue(col.data.mask()):
raise PlotError('%r: all data-points masked' % col.name)
rdata = rconv.MA_to_R(col.data)
else:
rdata = col.data
if col.is_discrete():
if 0 and suppress_all_value:
row_vecs = [
vec for value, vec in col.inverted.items()
if value != col.all_value
]
row_vec = union(*row_vecs)
rdata = MA.take(rdata, row_vec)
return self.discrete_col_to_R(col, rdata, suppress_all_value)
else:
return self.continuous_col_to_R(col, rdata)
def testnewlines(self):
# If we forgot to open the file in binary mode, some bytes would
# get mangled on some systems.
x = Numeric.arange(258).astype('b') # bytes
x[-2:, ] = (13, 10) # CRLF
open('tmp.foo', 'wb').write(x.tostring())
y, _, _ = maparray('tmp.foo', 'b')
self.failUnless(Numeric.alltrue(x == y))
def testimplicitflush(self):
y, _, _ = maparray('tmp.foo', 'd')
y[1] = 37
del y, _ # trigger implicit flush as refcounts go to 0
z, _, _ = maparray('tmp.foo', 'd')
self.failUnless(
Numeric.alltrue(
z == Numeric.array([0, 37, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5])))
def to_R(self, ds, dates_as_factor, **kwargs):
col = ds[self.colname]
if MA.isMaskedArray(col.data):
if Numeric.alltrue(col.data.mask()):
raise PlotError('%r: all data-points masked' % col.name)
return rconv.MA_to_R(col.data)
elif col.is_datetimetype():
return rconv.Missing_Date_to_R(col.data)
else:
return col.data
def testPickle (self):
"Test pickling of Numeric arrays."
import pickle
x = Numeric.arange(10)
fpik = open('foo.pik', 'wb')
pickle.dump(x, fpik, 0)
fpik.close()
fpik = open('foo.pik', 'rb')
y = pickle.load(open('foo.pik', 'rb'))
fpik.close()
assert_eq(y,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
assert Numeric.alltrue(Numeric.equal(x,y))
assert Numeric.sometrue(Numeric.equal(x,3))
assert y.shape == (10,)
os.remove('foo.pik')
def to_frame(self, frame, ds, **kwargs):
limit_cols = self.limit_cols(ds)
if limit_cols:
self.max = 0
self.min = 0
for colname in (self.colname, ) + limit_cols:
col = ds[colname]
if MA.isMaskedArray(col.data):
if Numeric.alltrue(col.data.mask()):
raise PlotError('%r: all data-points masked' %
col.name)
data = rconv.MA_to_R(col.data)
self.max = max(MA.maximum(col.data), self.max)
self.min = min(MA.minimum(col.data), self.min)
else:
data = col.data
self.max = max(max(col.data), self.max)
self.min = min(min(col.data), self.min)
frame[rconv.rname(colname)] = data
else:
super(RMeasureCondCol, self).to_frame(frame, ds, **kwargs)
def unique(inarray):
"""Returns unique items in the FIRST dimension of the passed array. Only
works on arrays NOT including string items (e.g., type 'O' or 'c').
"""
inarray = asarray(inarray)
uniques = N.array([inarray[0]])
if len(uniques.shape) == 1: # IF IT'S A 1D ARRAY
for item in inarray[1:]:
if N.add.reduce(N.equal(uniques, item).flat) == 0:
try:
uniques = N.concatenate([uniques, N.array[N.NewAxis, :]])
except TypeError:
uniques = N.concatenate([uniques, N.array([item])])
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)):
try:
uniques = N.concatenate([uniques, item[N.NewAxis, :]])
except TypeError: # the item to add isn't a list
uniques = N.concatenate([uniques, N.array([item])])
else:
pass # this item is already in the uniques array
else: # must be an Object array, alltrue/equal functions don't work
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))))
if test == 0: # if item identical to any 1 row in uniques
newflag = 0 # then not a novel item to add
break
if newflag == 1:
try:
uniques = N.concatenate([uniques, item[N.NewAxis, :]])
except TypeError: # the item to add isn't a list
uniques = N.concatenate([uniques, N.array([item])])
return uniques
def _is_sorted(self, x):
"return true if x is sorted"
if len(x)<2: return 1
return numpy.alltrue(x[1:]-x[0:-1]>=0)
def _is_sorted(self, x):
"return true if x is sorted"
if len(x) < 2: return 1
return numpy.alltrue(x[1:] - x[0:-1] >= 0)
def testread(self):
y, _, _ = maparray('tmp.foo', 'd')
self.failUnless(Numeric.alltrue(self.x == y))
def testx(self):
self.failUnless(
Numeric.alltrue(self.x == Numeric.array(
[0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5])))
