def prepare(params):
d = datasets.source
caption = options.caption % dict(caption=d.caption,
hashlabel=options.hashlabel)
prev_p = job_params(datasets.previous, default_empty=True)
prev_source = prev_p.datasets.source
if len(d.chain(stop_jobid=prev_source, length=options.length)) == 1:
filename = d.filename
else:
filename = None
dws = []
previous = datasets.previous
for sliceno in range(params.slices):
if options.as_chain and sliceno == params.slices - 1:
name = "default"
else:
name = str(sliceno)
dw = DatasetWriter(
caption="%s (slice %d)" % (caption, sliceno),
hashlabel=options.hashlabel,
filename=filename,
previous=previous,
name=name,
for_single_slice=sliceno,
)
previous = (params.jobid, name)
dws.append(dw)
names = []
for n, c in d.columns.items():
# names has to be in the same order as the add calls
# so the iterator returns the same order the writer expects.
names.append(n)
for dw in dws:
dw.add(n, c.type)
return dws, names, prev_source, caption, filename
def prepare():
from dataset import DatasetWriter
# previous allows chaining this method, should you wish to do so
dw = DatasetWriter(previous=datasets.previous)
dw.add('a string', 'ascii') # ascii is not "any string", use 'unicode' for that
dw.add('large number', 'number') # number is any (real) number, a float or int of any size
dw.add('small number', 'number')
dw.add('small integer', 'int32') # int32 is a signed 32 bit number
dw.add('gauss number', 'number')
dw.add('gauss float', 'float64') # float64 is what many other languages call double
return dw
def prepare(params):
columns = dict(
bytes="bytes",
float="float64",
int="int64",
json="json",
unicode="unicode",
)
a = DatasetWriter(name="a", columns=columns)
b = DatasetWriter(name="b", columns=columns, previous=(params.jobid, "a"))
c = DatasetWriter(name="c", columns=columns)
return a, b, c
def prepare():
d = datasets.source
columns = {}
for colname, coltype in options.column2type.iteritems():
assert d.columns[colname].type in (
'bytes',
'ascii',
), colname
coltype = coltype.split(':', 1)[0]
columns[options.rename.get(colname,
colname)] = dataset_typing.typerename.get(
coltype, coltype)
if options.filter_bad or options.discard_untyped:
assert options.discard_untyped is not False, "Can't keep untyped when filtering bad"
parent = None
else:
parent = datasets.source
return DatasetWriter(
columns=columns,
caption=options.caption,
hashlabel=options.rename.get(d.hashlabel, d.hashlabel),
hashlabel_override=True,
parent=parent,
previous=datasets.previous,
meta_only=True,
)
def prepare(params):
assert params.slices >= test_data.value_cnt
dw_default = DatasetWriter()
dw_default.add("a", "number")
dw_default.add("b", "ascii")
DatasetWriter(name="named", columns={"c": "bool", "d": "date"})
dw_passed = DatasetWriter(name="passed",
columns={t: t
for t in test_data.data})
return dw_passed, 42
def synthesis(prepare_res, params):
if not options.as_chain:
# If we don't want a chain we abuse our knowledge of dataset internals
# to avoid recompressing. Don't do this stuff yourself.
dws, names, prev_source, caption, filename = prepare_res
merged_dw = DatasetWriter(
caption=caption,
hashlabel=options.hashlabel,
filename=filename,
previous=datasets.previous,
meta_only=True,
columns=datasets.source.columns,
)
for sliceno in range(params.slices):
merged_dw.set_lines(sliceno, sum(dw._lens[sliceno] for dw in dws))
for dwno, dw in enumerate(dws):
merged_dw.set_minmax((sliceno, dwno), dw._minmax[sliceno])
for n in names:
fn = merged_dw.column_filename(n, sliceno=sliceno)
with open(fn, "wb") as out_fh:
for dw in dws:
fn = dw.column_filename(n, sliceno=sliceno)
with open(fn, "rb") as in_fh:
copyfileobj(in_fh, out_fh)
for dw in dws:
dw.discard()
def synthesis(jobid):
manual_chain = [Dataset(jobids.selfchain, name) for name in "abcdefgh"]
manual_abf = [manual_chain[0], manual_chain[1], manual_chain[5]]
# build a local abf chain
prev = None
for ix, ds in enumerate(manual_abf):
name = "abf%d" % (ix, )
ds.link_to_here(name, override_previous=prev)
prev = (
jobid,
name,
)
manual_abf_data = list(Dataset.iterate_list(None, None, manual_abf))
local_abf_data = list(Dataset(jobid, "abf2").iterate_chain(None, None))
assert manual_abf_data == local_abf_data
# disconnect h, verify there is no chain
manual_chain[-1].link_to_here("alone", override_previous=None)
assert len(Dataset(jobid, "alone").chain()) == 1
# check that the original chain is unhurt
assert manual_chain == manual_chain[-1].chain()
# So far so good, now make a chain long enough to have a cache.
prev = None
ix = 0
going = True
while going:
if prev and "cache" in Dataset(prev)._data:
going = False
name = "longchain%d" % (ix, )
dw = DatasetWriter(name=name, previous=prev)
dw.add("ix", "number")
dw.get_split_write()(ix)
dw.finish()
prev = (
jobid,
name,
)
ix += 1
# we now have a chain that goes one past the first cache point
full_chain = Dataset(prev).chain()
assert "cache" in full_chain[
-2]._data # just to check the above logic is correct
assert "cache" not in full_chain[-1]._data # just to be sure..
full_chain[-2].link_to_here("nocache", override_previous=None)
full_chain[-1].link_to_here("withcache", override_previous=full_chain[-3])
assert "cache" not in Dataset(jobid, "nocache")._data
assert "cache" in Dataset(jobid, "withcache")._data
# And make sure they both get the right data too.
assert list(Dataset(prev).iterate_chain(None, "ix")) == list(range(ix))
assert list(Dataset(jobid, "nocache").iterate_chain(None,
"ix")) == [ix - 2]
assert list(Dataset(jobid, "withcache").iterate_chain(
None, "ix")) == list(range(ix - 2)) + [ix - 1]
def real_prepare(d, previous, options):
column_types = {n: c.type for n, c in d.columns.items()}
column_sizes = []
column_names = list(column_types)
column_names.remove(options.date_column)
column_names.insert(0, options.date_column)
minmax_typeidx = []
for colname in column_names:
typ = column_types[colname]
column_sizes.append(dataset_typing.typesizes[typ])
minmax_typeidx.append(minmax_type2idx.get(typ, -1))
minmax_typeidx = ffi.new('int []', minmax_typeidx)
kw = dict(
columns=column_types,
hashlabel=d.hashlabel,
caption=options.caption,
previous=previous,
meta_only=True,
)
dw = DatasetWriter(**kw)
dw_spill = DatasetWriter(name='SPILL', **kw)
return dw, dw_spill, column_names, column_sizes, column_types, minmax_typeidx
def prepare(params):
d = datasets.source
ds_list = d.chain(stop_ds={datasets.previous: 'source'})
if options.sort_across_slices:
columniter = partial(Dataset.iterate_list, None, datasets=ds_list)
sort_idx = sort(columniter)
total = len(sort_idx)
per_slice = [total // params.slices] * params.slices
extra = total % params.slices
if extra:
# spread the left over length over pseudo-randomly selected slices
# (using the start of sort_idx to select slices).
# this will always select the first slices if data is already sorted
# but at least it's deterministic.
selector = sorted(range(min(params.slices, total)),
key=sort_idx.__getitem__)
for sliceno in selector[:extra]:
per_slice[sliceno] += 1
# change per_slice to be the actual sort indexes
start = 0
for ix, num in enumerate(per_slice):
end = start + num
per_slice[ix] = sort_idx[start:end]
start = end
assert sum(len(part) for part in per_slice) == total # all rows used
assert len(set(
len(part)
for part in per_slice)) < 3 # only 1 or 2 lengths possible
sort_idx = per_slice
else:
sort_idx = None
if options.sort_across_slices:
hashlabel = None
else:
hashlabel = d.hashlabel
if len(ds_list) == 1:
filename = d.filename
else:
filename = None
dw = DatasetWriter(
columns=d.columns,
caption=params.caption,
hashlabel=hashlabel,
filename=filename,
)
return dw, ds_list, sort_idx
def prepare(params):
dws = {}
prev = None
for name in "abcdefgh":
dw = DatasetWriter(name=name, previous=prev)
dw.add("ds", "ascii")
dw.add("num", "number")
dws[name] = dw
prev = "%s/%s" % (
params.jobid,
name,
)
return dws
def prepare(params):
d = datasets.source
jobs = d.chain(stop_jobid={datasets.previous: 'source'})
if options.sort_across_slices:
columniter = partial(Dataset.iterate_list, None, jobids=jobs)
sort_idx = sort(columniter)
else:
sort_idx = None
if options.sort_across_slices:
hashlabel = None
else:
hashlabel = d.hashlabel
if len(jobs) == 1:
filename = d.filename
else:
filename = None
dw = DatasetWriter(
columns=d.columns,
caption=params.caption,
hashlabel=hashlabel,
filename=filename,
)
return dw, jobs, sort_idx
def prepare(params):
assert params.slices >= 2, "Hashing won't do anything with just one slice"
dws = DotDict()
for name, hashlabel in (
("unhashed_manual", None), # manually interlaved
("unhashed_split", None), # split_write interlaved
("up_checked", "up"), # hashed on up using dw.hashcheck
("up_split", "up"), # hashed on up using split_write
("down_checked", "down"), # hashed on down using dw.hashcheck
("down_discarded", "down"), # hashed on down using discarding writes
("down_discarded_list",
"down"), # hashed on down using discarding list writes
("down_discarded_dict",
"down"), # hashed on down using discarding dict writes
):
dw = DatasetWriter(name=name, hashlabel=hashlabel)
dw.add("up", "int32")
dw.add("down", "int32")
dws[name] = dw
return dws
def synthesis(params):
dw = DatasetWriter(name="parent")
in_parent = [ # list because order matters
"-", # becomes _ because everything must be a valid python identifier.
"a b", # becomes a_b because everything must be a valid python identifier.
"42", # becomes _42 because everything must be a valid python identifier.
"print", # becomes print_ because print is a keyword (in py2).
"print@", # becomes print__ because print_ is taken.
"None", # becomes None_ because None is a keyword (in py3).
]
for colname in in_parent:
dw.add(colname, "unicode")
w = dw.get_split_write()
w(_="- 1", a_b="a b 1", _42="42 1", print_="print 1", None_="None 1", print__="Will be overwritten 1")
w(_="- 2", a_b="a b 2", _42="42 2", print_="print 2", None_="None 2", print__="Will be overwritten 2")
parent = dw.finish()
dw = DatasetWriter(name="child", parent=parent)
in_child = [ # order still matters
"print_*", # becomes print___ because print__ is taken.
"print_", # becomes print____ because all shorter are taken.
"normal", # no collision.
"Normal", # no collision.
"print@", # re-uses print__ from the parent dataset.
]
for colname in in_child:
dw.add(colname, "unicode")
w = dw.get_split_write()
w(print__="print@ 1", print___="print_* 1", print____="print_ 1", normal="normal 1", Normal="Normal 1")
w(print__="print@ 2", print___="print_* 2", print____="print_ 2", normal="normal 2", Normal="Normal 2")
child = dw.finish()
for colname in in_parent + in_child:
data = set(child.iterate(None, colname))
assert data == {colname + " 1", colname + " 2"}, "Bad data for %s: %r" % (colname, data)
def prepare():
dw = DatasetWriter()
dw.add("str", "ascii")
dw.add("num", "number")
return dw
def prepare():
dw = DatasetWriter(parent=datasets.source)
dw.add('prod', 'number') # works for float as well as int
return dw
def prepare(SOURCE_DIRECTORY):
separator = options.separator
assert len(separator) == 1
filename = os.path.join(SOURCE_DIRECTORY, options.filename)
orig_filename = filename
if filename.lower().endswith('.zip'):
from zipfile import ZipFile
filename = 'extracted'
with ZipFile(orig_filename, 'r') as z:
infos = z.infolist()
assert len(
infos
) == 1, 'There is only support for ZIP files with exactly one member.'
# Wouldn't it be nice if ZipFile.extract let me choose the filename?
with open(filename, 'wb') as ofh:
zfh = z.open(infos[0])
while True:
data = zfh.read(1024 * 1024)
if not data:
break
ofh.write(data)
if options.labelsonfirstline:
with gzutil.GzBytesLines(filename, strip_bom=True) as fh:
labels_str = next(fh).decode('ascii', 'replace').encode(
'ascii', 'replace') # garbage -> '?'
if options.quote_support:
labels = []
sep = options.separator
while labels_str is not None:
if labels_str.startswith((
'"',
"'",
)):
q = labels_str[0]
pos = 1
while pos + 1 < len(labels_str):
pos = labels_str.find(q, pos)
if pos == -1: # all is lost
pos = len(labels_str) - 1
if pos + 1 == len(labels_str): # eol
break
if labels_str[pos + 1] == sep:
break
# we'll just assume it was a quote, because what else to do?
labels_str = labels_str[:pos] + labels_str[pos + 1:]
pos += 1
labels.append(labels_str[1:pos])
if len(labels_str) > pos + 1:
labels_str = labels_str[pos + 2:]
else:
labels_str = None
else:
if sep in labels_str:
field, labels_str = labels_str.split(sep, 1)
else:
field, labels_str = labels_str, None
labels.append(field)
else:
labels = labels_str.split(options.separator)
labels = options.labels or labels # only from file if not specified in options
assert labels, "No labels"
labels = [options.rename.get(x, x) for x in labels]
assert '' not in labels, "Empty label for column %d" % (labels.index(''), )
assert len(labels) == len(set(labels)), "Duplicate labels: %r" % (labels, )
dw = DatasetWriter(
columns={n: 'bytes'
for n in labels},
filename=orig_filename,
hashlabel=options.hashlabel,
caption='csvimport of ' + orig_filename,
previous=datasets.previous,
meta_only=True,
)
return separator, filename, orig_filename, labels, dw,
def analysis_lap(sliceno, badmap_fh, first_lap):
known_line_count = 0
badmap_size = 0
badmap_fd = -1
res_bad_count = {}
res_default_count = {}
res_minmax = {}
link_candidates = []
if first_lap:
record_bad = options.filter_bad
skip_bad = 0
else:
record_bad = 0
skip_bad = options.filter_bad
minmax_fn = 'minmax%d' % (sliceno, )
dw = DatasetWriter()
for colname, coltype in iteritems(options.column2type):
out_fn = dw.column_filename(options.rename.get(colname, colname))
fmt = fmt_b = None
if coltype in dataset_typing.convfuncs:
shorttype = coltype
_, cfunc, pyfunc = dataset_typing.convfuncs[coltype]
else:
shorttype, fmt = coltype.split(':', 1)
_, cfunc, pyfunc = dataset_typing.convfuncs[shorttype + ':*']
if cfunc:
cfunc = shorttype.replace(':', '_')
if pyfunc:
tmp = pyfunc(coltype)
if callable(tmp):
pyfunc = tmp
cfunc = None
else:
pyfunc = None
cfunc, fmt, fmt_b = tmp
if coltype == 'number':
cfunc = 'number'
elif coltype == 'number:int':
coltype = 'number'
cfunc = 'number'
fmt = "int"
assert cfunc or pyfunc, coltype + " didn't have cfunc or pyfunc"
coltype = shorttype
d = datasets.source
assert d.columns[colname].type in byteslike_types, colname
if options.filter_bad:
line_count = d.lines[sliceno]
if known_line_count:
assert line_count == known_line_count, (colname, line_count,
known_line_count)
else:
known_line_count = line_count
pagesize = getpagesize()
badmap_size = (line_count // 8 // pagesize + 1) * pagesize
badmap_fh.truncate(badmap_size)
badmap_fd = badmap_fh.fileno()
if d.columns[colname].backing_type.startswith('_v2_'):
backing_format = 2
else:
backing_format = 3
in_fn = d.column_filename(colname, sliceno)
if d.columns[colname].offsets:
offset = d.columns[colname].offsets[sliceno]
max_count = d.lines[sliceno]
else:
offset = 0
max_count = -1
if cfunc:
default_value = options.defaults.get(colname, ffi.NULL)
default_len = 0
if default_value is None:
default_value = ffi.NULL
default_value_is_None = True
else:
default_value_is_None = False
if default_value != ffi.NULL:
if isinstance(default_value, unicode):
default_value = default_value.encode("utf-8")
default_len = len(default_value)
bad_count = ffi.new('uint64_t [1]', [0])
default_count = ffi.new('uint64_t [1]', [0])
c = getattr(backend, 'convert_column_' + cfunc)
res = c(*bytesargs(in_fn, out_fn, minmax_fn, default_value,
default_len, default_value_is_None, fmt, fmt_b,
record_bad, skip_bad, badmap_fd, badmap_size,
bad_count, default_count, offset, max_count,
backing_format))
assert not res, 'Failed to convert ' + colname
res_bad_count[colname] = bad_count[0]
res_default_count[colname] = default_count[0]
coltype = coltype.split(':', 1)[0]
with type2iter[dataset_typing.typerename.get(
coltype, coltype)](minmax_fn) as it:
res_minmax[colname] = list(it)
unlink(minmax_fn)
else:
# python func
nodefault = object()
if colname in options.defaults:
default_value = options.defaults[colname]
if default_value is not None:
if isinstance(default_value, unicode):
default_value = default_value.encode('utf-8')
default_value = pyfunc(default_value)
else:
default_value = nodefault
if options.filter_bad:
badmap = mmap(badmap_fd, badmap_size)
if PY2:
badmap = IntegerBytesWrapper(badmap)
bad_count = 0
default_count = 0
dont_minmax_types = {'bytes', 'ascii', 'unicode', 'json'}
real_coltype = dataset_typing.typerename.get(coltype, coltype)
do_minmax = real_coltype not in dont_minmax_types
with typed_writer(real_coltype)(out_fn) as fh:
col_min = col_max = None
for ix, v in enumerate(
d._column_iterator(sliceno,
colname,
_type='bytes' if backing_format == 3
else '_v2_bytes')):
if skip_bad:
if badmap[ix // 8] & (1 << (ix % 8)):
bad_count += 1
continue
try:
v = pyfunc(v)
except ValueError:
if default_value is not nodefault:
v = default_value
default_count += 1
elif record_bad:
bad_count += 1
bv = badmap[ix // 8]
badmap[ix // 8] = bv | (1 << (ix % 8))
continue
else:
raise Exception(
"Invalid value %r with no default in %s" % (
v,
colname,
))
if do_minmax and not isinstance(v, NoneType):
if col_min is None:
col_min = col_max = v
if v < col_min: col_min = v
if v > col_max: col_max = v
fh.write(v)
if options.filter_bad:
badmap.close()
res_bad_count[colname] = bad_count
res_default_count[colname] = default_count
res_minmax[colname] = [col_min, col_max]
return res_bad_count, res_default_count, res_minmax, link_candidates
def write(data, **kw):
dw = DatasetWriter(columns=columns, **kw)
w = dw.get_split_write_dict()
for values in data:
w(values)
return dw.finish()
def analysis(sliceno, prepare_res):
dw_default = DatasetWriter()
dw_named = DatasetWriter(name="named")
dw_passed, num = prepare_res
dw_default.write(a=sliceno, b="a")
dw_default.write_list([num, str(sliceno)])
dw_named.write(True, date(1536, 12, min(sliceno + 1, 31)))
dw_named.write_dict({"c": False, "d": date(2236, 5, min(sliceno + 1, 31))})
# slice 0 is written in synthesis
if 0 < sliceno < test_data.value_cnt:
dw_passed.write_dict(
{k: v[sliceno]
for k, v in test_data.data.items()})
def synthesis(prepare_res, params):
dw_passed, _ = prepare_res
# Using set_slice on a dataset that was written in analysis is not
# actually supported, but since it currently works (as long as that
# particular slice wasn't written in analysis) let's test it.
dw_passed.set_slice(0)
dw_passed.write(**{k: v[0] for k, v in test_data.data.items()})
dw_synthesis_split = DatasetWriter(name="synthesis_split", hashlabel="a")
dw_synthesis_split.add("a", "int32")
dw_synthesis_split.add("b", "unicode")
dw_synthesis_split.get_split_write()(1, "a")
dw_synthesis_split.get_split_write_list()([2, "b"])
dw_synthesis_split.get_split_write_dict()({"a": 3, "b": "c"})
dw_synthesis_manual = DatasetWriter(name="synthesis_manual",
columns={"sliceno": "int32"})
dw_nonetest = DatasetWriter(name="nonetest",
columns={t: t
for t in test_data.data})
for sliceno in range(params.slices):
dw_synthesis_manual.set_slice(sliceno)
dw_synthesis_manual.write(sliceno)
dw_nonetest.set_slice(sliceno)
dw_nonetest.write(
**{
k: v[0] if k in test_data.not_none_capable else None
for k, v in test_data.data.items()
})
def _verify(name, types, data, coltype, want, default, want_fail, kw):
if callable(want):
check = want
else:
def check(got, fromstr, filtered=False):
want1 = want if isinstance(want, list) else want[typ]
if filtered:
want1 = want1[::2]
assert got == want1, 'Expected %r, got %r from %s.' % (
want1,
got,
fromstr,
)
dw = DatasetWriter(name=name, columns={'data': coltype, 'extra': 'bytes'})
dw.set_slice(0)
for ix, v in enumerate(data):
dw.write(v, b'1' if ix % 2 == 0 else b'skip')
for sliceno in range(1, g.SLICES):
dw.set_slice(sliceno)
bytes_ds = dw.finish()
for typ in types:
opts = dict(column2type=dict(data=typ))
opts.update(kw)
if default is not no_default:
opts['defaults'] = {'data': default}
try:
jid = subjobs.build('dataset_type',
datasets=dict(source=bytes_ds),
options=opts)
except JobError:
if want_fail:
continue
raise Exception('Typing %r as %s failed.' % (
bytes_ds,
typ,
))
assert not want_fail, "Typing %r as %s should have failed, but didn't (%s)." % (
bytes_ds, typ, jid)
typed_ds = Dataset(jid)
got = list(typed_ds.iterate(0, 'data'))
check(got, '%s (typed as %s from %r)' % (
typed_ds,
typ,
bytes_ds,
))
if 'filter_bad' not in opts and not callable(want):
opts['filter_bad'] = True
opts['column2type']['extra'] = 'int32_10'
jid = subjobs.build('dataset_type',
datasets=dict(source=bytes_ds),
options=opts)
typed_ds = Dataset(jid)
got = list(typed_ds.iterate(0, 'data'))
check(
got,
'%s (typed as %s from %r with every other line skipped from filter_bad)'
% (
typed_ds,
typ,
bytes_ds,
), True)
used_type(typ)
def test_filter_bad_across_types():
columns = {
'bytes': 'bytes',
'float64': 'bytes',
'int32_10': 'ascii',
'json': 'unicode',
'number:int': 'unicode',
'unicode:utf-8': 'bytes',
}
# all_good, *values
# Make sure all those types (except bytes) can filter other lines,
# and be filtered by other lines. And that several filtering values
# is not a problem (line 11).
data = [
(
True,
b'first',
b'1.1',
'1',
'"a"',
'001',
b'ett',
),
(
True,
b'second',
b'2.2',
'2',
'"b"',
'02',
b'tv\xc3\xa5',
),
(
True,
b'third',
b'3.3',
'3',
'["c"]',
'3.0',
b'tre',
),
(
False,
b'fourth',
b'4.4',
'4',
'"d"',
'4.4',
b'fyra',
), # number:int bad
(
False,
b'fifth',
b'5.5',
'-',
'"e"',
'5',
b'fem',
), # int32_10 bad
(
False,
b'sixth',
b'6.b',
'6',
'"f"',
'6',
b'sex',
), # float64 bad
[
False,
b'seventh',
b'7.7',
'7',
'{"g"}',
'7',
b'sju',
], # json bad
(
False,
b'eigth',
b'8.8',
'8',
'"h"',
'8',
b'\xa5\xc3tta',
), # unicode:utf-8 bad
(
True,
b'ninth',
b'9.9',
'9',
'"i"',
'9',
b'nio',
),
(
True,
b'tenth',
b'10',
'10',
'"j"',
'10',
b'tio',
),
(
False,
b'eleventh',
b'11a',
'1-',
'"k",',
'1,',
b'elva',
), # float64, int32_10 and number:int bad
(
True,
b'twelfth',
b'12',
'12',
'"l"',
'12',
b'tolv',
),
]
dw = DatasetWriter(name="filter bad across types", columns=columns)
dw.set_slice(0)
want = []
def add_want(v):
want.append((
int(v[3]),
v[1],
json.loads(v[4]),
v[6].decode('utf-8'),
))
for v in data:
if v[0]:
add_want(v)
dw.write(*v[1:])
for sliceno in range(1, g.SLICES):
dw.set_slice(sliceno)
source_ds = dw.finish()
# Once with just filter_bad, once with some defaults too.
defaults = {}
for _ in range(2):
jid = subjobs.build(
'dataset_type',
datasets=dict(source=source_ds),
options=dict(column2type={t: t
for t in columns},
filter_bad=True,
defaults=defaults),
)
typed_ds = Dataset(jid)
got = list(
typed_ds.iterate(0,
['int32_10', 'bytes', 'json', 'unicode:utf-8']))
assert got == want, "Exptected %r, got %r from %s (from %r%s)" % (
want, got, typed_ds, source_ds,
' with defaults' if defaults else '')
# make more lines "ok" for the second lap
defaults = {'number:int': '0', 'float64': '0', 'json': '"replacement"'}
add_want(data[3])
add_want(data[5])
data[6][4] = '"replacement"'
add_want(data[6])
want.sort() # adding them out of order, int32_10 sorts correctly.
def analysis_lap(sliceno, badmap_fh, first_lap):
known_line_count = 0
badmap_size = 0
badmap_fd = -1
res_bad_count = {}
res_default_count = {}
res_minmax = {}
link_candidates = []
if first_lap:
record_bad = options.filter_bad
skip_bad = 0
else:
record_bad = 0
skip_bad = options.filter_bad
minmax_fn = 'minmax%d' % (sliceno, )
dw = DatasetWriter()
for colname, coltype in options.column2type.iteritems():
out_fn = dw.column_filename(options.rename.get(
colname, colname)).encode('ascii')
if ':' in coltype and not coltype.startswith('number:'):
coltype, fmt = coltype.split(':', 1)
_, cfunc, pyfunc = dataset_typing.convfuncs[coltype + ':*']
if '%f' in fmt:
# needs to fall back to python version
cfunc = None
if not cfunc:
pyfunc = pyfunc(coltype, fmt)
else:
_, cfunc, pyfunc = dataset_typing.convfuncs[coltype]
fmt = ffi.NULL
d = datasets.source
assert d.columns[colname].type in (
'bytes',
'ascii',
), colname
if options.filter_bad:
line_count = d.lines[sliceno]
if known_line_count:
assert line_count == known_line_count, (colname, line_count,
known_line_count)
else:
known_line_count = line_count
pagesize = getpagesize()
badmap_size = (line_count // 8 // pagesize + 1) * pagesize
badmap_fh.truncate(badmap_size)
badmap_fd = badmap_fh.fileno()
if d.columns[colname].backing_type.startswith('_v2_'):
backing_format = 2
else:
backing_format = 3
in_fn = d.column_filename(colname, sliceno).encode('ascii')
if d.columns[colname].offsets:
offset = d.columns[colname].offsets[sliceno]
max_count = d.lines[sliceno]
else:
offset = 0
max_count = -1
if coltype == 'number':
cfunc = True
if coltype == 'number:int':
coltype = 'number'
cfunc = True
fmt = "int"
if cfunc:
default_value = options.defaults.get(colname, ffi.NULL)
if default_value is None:
default_value = ffi.NULL
default_value_is_None = True
else:
default_value_is_None = False
bad_count = ffi.new('uint64_t [1]', [0])
default_count = ffi.new('uint64_t [1]', [0])
c = getattr(backend, 'convert_column_' + coltype)
res = c(in_fn, out_fn, minmax_fn, default_value,
default_value_is_None, fmt, record_bad, skip_bad,
badmap_fd, badmap_size, bad_count, default_count, offset,
max_count, backing_format)
assert not res, 'Failed to convert ' + colname
res_bad_count[colname] = bad_count[0]
res_default_count[colname] = default_count[0]
with type2iter[dataset_typing.typerename.get(
coltype, coltype)](minmax_fn) as it:
res_minmax[colname] = list(it)
unlink(minmax_fn)
elif pyfunc is str:
# We skip it the first time around, and link it from
# the source dataset if there were no bad lines.
# (That happens at the end of analysis.)
# We can't do that if the file is not slice-specific though.
# And we also can't do it if the column is in the wrong (old) format.
if skip_bad or '%s' not in d.column_filename(
colname, '%s') or backing_format != 3:
res = backend.filter_strings(in_fn, out_fn, badmap_fd,
badmap_size, offset, max_count,
backing_format)
assert not res, 'Failed to convert ' + colname
else:
link_candidates.append((
in_fn,
out_fn,
))
res_bad_count[colname] = 0
res_default_count[colname] = 0
elif pyfunc is str.strip:
res = backend.filter_stringstrip(in_fn, out_fn, badmap_fd,
badmap_size, offset, max_count,
backing_format)
assert not res, 'Failed to convert ' + colname
res_bad_count[colname] = 0
res_default_count[colname] = 0
else:
# python func
nodefault = object()
if colname in options.defaults:
if options.defaults[colname] is None:
default_value = None
else:
default_value = pyfunc(options.defaults[colname])
else:
default_value = nodefault
if options.filter_bad:
badmap = mmap(badmap_fd, badmap_size)
bad_count = 0
default_count = 0
with typed_writer(dataset_typing.typerename.get(
coltype, coltype))(out_fn) as fh:
col_min = col_max = None
for ix, v in enumerate(d.iterate(sliceno, colname)):
if skip_bad:
if ord(badmap[ix // 8]) & (1 << (ix % 8)):
bad_count += 1
continue
try:
v = pyfunc(v)
except ValueError:
if default_value is not nodefault:
v = default_value
default_count += 1
elif record_bad:
bad_count += 1
bv = ord(badmap[ix // 8])
badmap[ix // 8] = chr(bv | (1 << (ix % 8)))
continue
else:
raise Exception(
"Invalid value %r with no default in %s" % (
v,
colname,
))
if not isinstance(v, (
NoneType,
str,
unicode,
)):
if col_min is None:
col_min = col_max = v
if v < col_min: col_min = v
if v > col_max: col_max = v
fh.write(v)
if options.filter_bad:
badmap.close()
res_bad_count[colname] = bad_count
res_default_count[colname] = default_count
res_minmax[colname] = [col_min, col_max]
return res_bad_count, res_default_count, res_minmax, link_candidates
def prepare():
dw = DatasetWriter(previous=datasets.previous)
dw.add('rflt', 'float64')
dw.add('rint', 'int64')
return dw
def prepare():
return DatasetWriter(columns={t: t for t in test_data.data})
def do_one(params, name, data):
dw = DatasetWriter(name=name, columns=columns)
dw.set_slice(0)
for v in data:
if v is None:
d = dict(
ascii_new=None,
ascii_old=None,
bytes_new=None,
bytes_old=None,
unicode_new=None,
unicode_old=None,
)
else:
d = dict(
ascii_new=v,
ascii_old=v,
bytes_new=uni(v).encode("ascii"),
bytes_old=uni(v).encode("ascii"),
unicode_new=uni(v),
unicode_old=uni(v),
)
dw.write_dict(d)
# We don't really want the other slices, but write one thing to
# each, to make sure it doesn't show up in slice 0.
# (Small slice merging will put it in the same file, so this is
# a real risk.)
for sliceno in range(1, params.slices):
dw.set_slice(sliceno)
dw.write_dict(d)
dw.finish()
# verify we got what we asked for
me_ds = Dataset(params.jobid, name)
for colname, coltype in columns.items():
col = me_ds.columns[colname]
assert col.type == coltype.split("_")[-1], colname
assert col.backing_type == coltype, colname
for want, got in zip(data, me_ds.iterate(0, colname)):
if want is not None:
if PY2 and "unicode" in coltype:
want = uni(want)
if PY3 and "bytes" in coltype:
want = want.encode("ascii")
assert want == got, "%s in %s did not contain the expected value. Wanted %r but got %r." % (colname, me_ds, want, got)
# check that both types of bytes filter correctly through typing
jid = subjobs.build("dataset_type", datasets=dict(source=me_ds), options=dict(
column2type=dict(
ascii_new="bytes",
ascii_old="number", # fails on the string, so that gets filtered out everywhere
bytes_new="bytes",
bytes_old="bytes",
),
filter_bad=True,
))
ds = Dataset(jid)
# verify the number first
data_it = iter(raw_data)
next(data_it) # skip the filtered out string
for got in ds.iterate(0, "ascii_old"):
want = next(data_it)
if want is None:
# Becomes 0 because the typer (unfortunately) sees it as an empty string
want = 0
assert want == got, "ascii_old in %s did not type correctly as number. Wanted %r but got %r." % (ds, want, got)
# now verify all the bytes ones are ok, no longer containing the string.
for colname in ("ascii_new", "bytes_new", "bytes_old",):
data_it = iter(data)
next(data_it) # skip the filtered out string
for got in ds.iterate(0, colname):
want = next(data_it)
if want is not None:
want = want.encode("ascii")
assert want == got, "%s in %s did not roundtrip correctly as bytes. Wanted %r but got %r." % (colname, ds, want, got)
# and now check that the Nones are ok after making bytes from ascii and unicode from bytes.
jid = subjobs.build("dataset_type", datasets=dict(source=me_ds), options=dict(
column2type=dict(
ascii_new="bytes",
ascii_old="bytes",
bytes_new="unicode:ascii",
bytes_old="unicode:ascii",
),
))
ds = Dataset(jid)
for colname in ("ascii_new", "ascii_old", "bytes_new", "bytes_old",):
for want, got in ds.iterate(0, ["unicode_new", colname]):
assert uni(want) == uni(got), "%s in %s did not roundtrip correctly as bytes. Wanted %r but got %r." % (colname, ds, want, got)