def gen_range_query2d_data(draw):
global MAX_LEN
max_key = 2147483647 #2**31
ixys = draw(st.lists(
st.tuples(
st.integers(min_value=1, max_value=MAX_LEN),
st.integers(min_value=1, max_value=max_key),
st.integers(min_value=1, max_value=max_key),
),
min_size=2, max_size=MAX_LEN,
unique_by=lambda ixy:ixy[0]
))
nt_ixys = F.lmap(tup(Ixy), ixys)
# min/max x
max_x_ = max(map(prop('x'),nt_ixys))
max_xv = min(max_x_ + max_x_ // 2, max_key)
min_x_ = min(map(prop('x'),nt_ixys))
min_xv = max(min_x_ - min_x_ // 2, 1)
min_x, max_x = sorted([
draw(st.integers(
min_value=min_xv, max_value=max_xv)),
draw(st.integers(
min_value=min_xv, max_value=max_xv))
])
# min/max y
max_y_ = max(map(prop('y'),nt_ixys))
max_yv = min(max_y_ + max_y_ // 2, max_key)
min_y_ = min(map(prop('y'),nt_ixys))
min_yv = max(min_y_ - min_y_ // 2, 1)
min_y, max_y = sorted([
draw(st.integers(
min_value=min_yv, max_value=max_yv)),
draw(st.integers(
min_value=min_yv, max_value=max_yv))
])
def included(ixy):
return(min_x <= ixy.x <= max_x
and min_y <= ixy.y <= max_y)
#def excluded(ixy): return key(ixy) < min_key or max_key < key(ixy)
includeds = go(
filter(included,nt_ixys),
F.partial(sorted, key=prop('x')),
F.curry(F.lmap)(tuple))
#excludeds = [tuple(ixy) for ixy in filter(excluded,nt_ixys)]
ixy_map = F.zipdict(
map(F.first, ixys), map(tup(Ixy), ixys))
return dict(
ixys=ixys, ixy_map=ixy_map,
min_x=min_x, max_x=max_x,
min_y=min_y, max_y=max_y,
includeds=includeds
)
def compile_template(func):
spec = inspect.getargspec(func)
assert len(spec.args) == len(
spec.defaults or []), "All template args should have AST classes"
compiler = TemplateCompiler(zipdict(spec.args, spec.defaults or []))
template = map(compiler.visit, get_body_ast(func))
# Strip Expr node wrapping single expression to let it match inside statement
if len(template) == 1 and isinstance(template[0], ast.Expr):
return template[0].value
return template
def gen_ixys_mode_map(draw):
ixys = draw(st.lists(
st.tuples(
st.integers(min_value=1, max_value=MAX_LEN),
st.integers(min_value=1, max_value=2147483647),#2**31
st.integers(min_value=1, max_value=2147483647),#2**31
),
min_size=2, max_size=MAX_LEN,
unique_by=lambda ixy:ixy[0]
))
mode = random.choice('xy')
ixy_map = F.zipdict(
map(F.first, ixys), map(tup(Ixy), ixys))
return dict(ixys=ixys, mode=mode, ixy_map=ixy_map)
def gen_range_search_data(draw):
global MAX_LEN
max_key = 2147483647 #2**31
mode = random.choice('xy')
key = prop(mode)
ixys = draw(st.lists(
st.tuples(
st.integers(min_value=1, max_value=MAX_LEN),
st.integers(min_value=1, max_value=max_key),
st.integers(min_value=1, max_value=max_key),
),
min_size=2, max_size=MAX_LEN,
unique_by=lambda ixy:ixy[0]
))
nt_ixys = F.lmap(tup(Ixy), ixys)
max_val_ = key(max(nt_ixys, key=key))
max_val = min(max_val_ + max_val_ // 2, max_key)
min_val_ = key(min(nt_ixys, key=key))
min_val = max(min_val_ - min_val_ // 2, 1)
min_key, max_key = sorted([
draw(st.integers(
min_value=min_val, max_value=max_val)),
draw(st.integers(
min_value=min_val, max_value=max_val))
])
def included(ixy):
return min_key <= key(ixy) <= max_key
def excluded(ixy):
return key(ixy) < min_key or max_key < key(ixy)
includeds = [tuple(ixy) for ixy in
sorted(filter(included,nt_ixys), key=key)]
#key=lambda ixy:(key(ixy), ixy[0]))]
excludeds = [tuple(ixy) for ixy in filter(excluded,nt_ixys)]
ixy_map = F.zipdict(
map(F.first, ixys), map(tup(Ixy), ixys))
return dict(
ixys=ixys, mode=mode, ixy_map=ixy_map,
min_key=min_key, max_key=max_key,
includeds=includeds, excludeds=excludeds
)
def ast_eval(node):
"""
Faster ast.literal_eval() with better error messages.
Works only with nodes not strings.
"""
if isinstance(node, ast.Num):
return node.n
elif isinstance(node, ast.Str):
return node.s
elif isinstance(node, ast.Name) and node.id in NAMED_CONSTS:
return NAMED_CONSTS[node.id]
elif isinstance(node, ast.Tuple):
return tuple(ast_eval(n) for n in node.elts)
elif isinstance(node, ast.List):
return [ast_eval(n) for n in node.elts]
elif isinstance(node, ast.Dict):
return zipdict(ast_eval(node.keys), ast_eval(node.values))
else:
raise ValueError("Don't know how to eval %s" % node.__class__.__name__)
def gen_bst_delete_data(draw):
ixys = draw(st.lists(
st.tuples(
st.integers(min_value=1, max_value=MAX_LEN),
st.integers(min_value=1, max_value=2**31),
st.integers(min_value=1, max_value=2**31),
),
min_size=1, max_size=MAX_LEN,
unique_by=lambda ixy:ixy[0]
))
ixy_idxes = F.lmap(F.first, ixys)
ixy_map = F.zipdict(
ixy_idxes, map(tup(Ixy), ixys))
# if tuple, ixy. if int, delete index.
# if +: ins, -: del
cmd_idxes = ixy_idxes + [-i for i in ixy_idxes]
random.shuffle(cmd_idxes)
return dict(ixys=ixys, del_idxes=ixy_idxes,
ixy_map=ixy_map, cmd_idxes=cmd_idxes)
def csvdictstream(filename, encoding="utf-8", fields=None, stripchars="\r\n"):
"""Stream every line in the given file interpreting each line as a dictionary of fields to items.
:param filename: A ``str`` filename, A ``py._path.local.LocalPath`` instance or open ``file`` instnace.
:type filename: ``str``, ``py._path.local.LocalPath`` or ``file``.
:param encoding: A ``str`` indicating the charset/encoding to use.
:type encoding: ``str``
:param stripchars: An iterable of characters to strip from the surrounding line. ``line.strip(...)`` is used.
:type stripchars: ``list``, ``tuple`` or ``str``
This is a wrapper around ``csvstream`` where the stream is treated as dict of field(s) to item(s).
"""
stream = csvstream(filename, encoding=encoding, stripchars=stripchars)
if fields is None:
fields = map(strip, next(stream))
for values in stream:
yield compact(zipdict(fields, values))
def get_defaults(func):
func = unwrap(func)
return zipdict(get_kwargnames(func), func.__defaults__ or ())
def accumulate(data):
dates, counts = zip(*sorted(data.items()))
return zipdict(dates, sums(counts))
def dashboard(request):
keys = ('core.stats.tags', 'core.stats.serie_annotations', 'core.stats.sample_annotations',
'core.graph')
return {
'stats': zipdict(keys, redis_client.mget(*keys))
}
def zip_to_series(a, b):
assert len(a) == len(b)
return pd.Series(funcy.zipdict(a, b))
