def test_chunks():
c = convert(chunks(np.ndarray), a, chunksize=2)
assert isinstance(c, chunks(np.ndarray))
assert len(list(c)) == 2
assert eq(list(c)[1], [3, 4])
assert eq(convert(np.ndarray, c), a[:])
def test_chunks_numpy_pandas():
x = np.array([('Alice', 100), ('Bob', 200)],
dtype=[('name', 'S7'), ('amount', 'i4')])
n = chunks(np.ndarray)([x, x])
pan = convert(chunks(pd.DataFrame), n)
num = convert(chunks(np.ndarray), pan)
assert isinstance(pan, chunks(pd.DataFrame))
assert all(isinstance(chunk, pd.DataFrame) for chunk in pan)
assert isinstance(num, chunks(np.ndarray))
assert all(isinstance(chunk, np.ndarray) for chunk in num)
def test_chunks_numpy_pandas():
x = np.array([('Alice', 100), ('Bob', 200)],
dtype=[('name', 'S7'), ('amount', 'i4')])
n = chunks(np.ndarray)([x, x])
pan = convert(chunks(pd.DataFrame), n)
num = convert(chunks(np.ndarray), pan)
assert isinstance(pan, chunks(pd.DataFrame))
assert all(isinstance(chunk, pd.DataFrame) for chunk in pan)
assert isinstance(num, chunks(np.ndarray))
assert all(isinstance(chunk, np.ndarray) for chunk in num)
def pre_compute(expr,
data,
comfortable_memory=None,
chunksize=2**18,
**kwargs):
comfortable_memory = comfortable_memory or min(1e9, available_memory() / 4)
kwargs = dict()
# Chunk if the file is large
if os.path.getsize(data.path) > comfortable_memory:
kwargs['chunksize'] = chunksize
else:
chunksize = None
# Insert projection into read_csv
oexpr = optimize(expr, data)
leaf = oexpr._leaves()[0]
pth = list(path(oexpr, leaf))
if len(pth) >= 2 and isinstance(pth[-2], (Projection, Field)):
kwargs['usecols'] = pth[-2].fields
if chunksize:
return into(chunks(pd.DataFrame), data, dshape=leaf.dshape, **kwargs)
else:
return into(pd.DataFrame, data, dshape=leaf.dshape, **kwargs)
def test_iterator_and_numpy_chunks():
c = iterator_to_numpy_chunks([1, 2, 3], chunksize=2)
assert isinstance(c, chunks(np.ndarray))
assert all(isinstance(chunk, np.ndarray) for chunk in c)
c = iterator_to_numpy_chunks([1, 2, 3], chunksize=2)
L = convert(list, c)
assert L == [1, 2, 3]
def test_iterator_and_numpy_chunks():
c = iterator_to_numpy_chunks([1, 2, 3], chunksize=2)
assert isinstance(c, chunks(np.ndarray))
assert all(isinstance(chunk, np.ndarray) for chunk in c)
c = iterator_to_numpy_chunks([1, 2, 3], chunksize=2)
L = convert(list, c)
assert L == [1, 2, 3]
def test_pandas_and_chunks_pandas():
df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [1., 2., 3., 4.]})
c = dataframe_to_chunks_dataframe(df, chunksize=2)
assert isinstance(c, chunks(pd.DataFrame))
assert len(list(c)) == 2
df2 = chunks_dataframe_to_dataframe(c)
assert str(df2) == str(df)
def pre_compute(expr, data, comfortable_memory=None, chunksize=2**18, **kwargs):
comfortable_memory = comfortable_memory or min(1e9, available_memory() / 4)
kwargs = dict()
# Chunk if the file is large
if os.path.getsize(data.path) > comfortable_memory:
kwargs['chunksize'] = chunksize
else:
chunksize = None
# Insert projection into read_csv
oexpr = optimize(expr, data)
leaf = oexpr._leaves()[0]
pth = list(path(oexpr, leaf))
if len(pth) >= 2 and isinstance(pth[-2], (Projection, Field)):
kwargs['usecols'] = pth[-2].fields
if chunksize:
return into(chunks(pd.DataFrame), data, dshape=leaf.dshape, **kwargs)
else:
return into(pd.DataFrame, data, dshape=leaf.dshape, **kwargs)
def test_append_chunks():
with file(df) as (fn, f, dset):
append(dset, chunks(pd.DataFrame)([df, df]))
assert discover(dset).shape[0] == len(df) * 3
def test_chunks_of_lists_and_iterators():
L = [1, 2], [3, 4]
cl = chunks(list)(L)
assert convert(list, cl) == [1, 2, 3, 4]
assert list(convert(Iterator, cl)) == [1, 2, 3, 4]
assert len(list(convert(chunks(Iterator), cl))) == 2
def test_numpy_to_chunks_numpy():
x = np.arange(100)
c = numpy_to_chunks_numpy(x, chunksize=10)
assert isinstance(c, chunks(np.ndarray))
assert len(list(c)) == 10
assert eq(list(c)[0], x[:10])
def test_convert_chunks():
with file(df) as (fn, f, dset):
c = convert(chunks(pd.DataFrame), dset, chunksize=len(df) / 2)
assert len(list(c)) == 2
assert eq(convert(pd.DataFrame, c), df)
def test_pre_compute_on_large_csv_gives_chunked_reader():
csv = CSV(example('iris.csv'))
s = symbol('s', discover(csv))
assert isinstance(pre_compute(s.species, csv, comfortable_memory=10),
(chunks(pd.DataFrame), pd.io.parsers.TextFileReader))
def test_append_chunks():
with file(x) as (fn, f, dset):
append(dset, chunks(np.ndarray)([x, x]))
assert len(dset) == len(x) * 3
def test_append_chunks():
b = carray(x)
append(b, chunks(np.ndarray)([x, x]))
assert len(b) == len(x) * 3
def test_append_chunks():
with file(df) as (fn, f, dset):
append(dset, chunks(pd.DataFrame)([df, df]))
assert discover(dset).shape[0] == len(df) * 3
def test_chunks():
with file(df) as (fn, f, dset):
c = convert(chunks(pd.DataFrame), dset)
assert eq(convert(np.ndarray, c), df)
def test_append_chunks():
with file(x) as (fn, f, dset):
append(dset, chunks(np.ndarray)([x, x]))
assert len(dset) == len(x) * 3
def test_chunks():
with file(x) as (fn, f, dset):
c = convert(chunks(np.ndarray), dset)
assert eq(convert(np.ndarray, c), x)
def test_chunks():
with file(df) as (fn, f, dset):
c = convert(chunks(pd.DataFrame), dset)
assert eq(convert(np.ndarray, c), df)
def test_convert_chunks():
with file(df) as (fn, f, dset):
c = convert(chunks(pd.DataFrame), dset, chunksize=len(df) / 2)
assert len(list(c)) == 2
assert eq(convert(pd.DataFrame, c), df)
def test_pre_compute_on_large_csv_gives_chunked_reader():
csv = CSV(example('iris.csv'))
s = symbol('s', discover(csv))
assert isinstance(pre_compute(s.species, csv, comfortable_memory=10),
(chunks(pd.DataFrame), pd.io.parsers.TextFileReader))
def test_chunks():
with file(x) as (fn, f, dset):
c = convert(chunks(np.ndarray), dset)
assert eq(convert(np.ndarray, c), x)
