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import itertools
import math
import operator
from .utils import aggregate_common_doc
from .utils import aliasing_py as aliasing
from .utils import funcs_no_separate_nan, get_func
# min, max, sum, all, any - builtin
def _last(x):
return x[-1]
def _first(x):
return x[0]
def _array(x):
return x
def _mean(x):
return sum(x) / len(x)
def _var(x, ddof=0):
mean = _mean(x)
return sum((xx - mean) ** 2 for xx in x) / (len(x) - ddof)
def _std(x, ddof=0):
return math.sqrt(_var(x, ddof=ddof))
def _prod(x):
r = x[0]
for xx in x[1:]:
r *= xx
return r
def _anynan(x):
return any(math.isnan(xx) for xx in x)
def _allnan(x):
return all(math.isnan(xx) for xx in x)
def _argmax(x_and_idx):
return max(x_and_idx, key=operator.itemgetter(1))[0]
_argmax.x_and_idx = True # tell aggregate what to use as first arg
def _argmin(x_and_idx):
return min(x_and_idx, key=operator.itemgetter(1))[0]
_argmin.x_and_idx = True # tell aggregate what to use as first arg
def _sort(group_idx, a, reverse=False):
def _argsort(unordered):
return sorted(range(len(unordered)), key=lambda k: unordered[k])
sortidx = _argsort(list((gi, aj) for gi, aj in zip(group_idx, -a if reverse else a)))
revidx = _argsort(_argsort(group_idx))
a_srt = [a[si] for si in sortidx]
return [a_srt[ri] for ri in revidx]
_impl_dict = dict(
min=min,
max=max,
sum=sum,
prod=_prod,
last=_last,
first=_first,
all=all,
any=any,
mean=_mean,
std=_std,
var=_var,
anynan=_anynan,
allnan=_allnan,
sort=_sort,
array=_array,
argmax=_argmax,
argmin=_argmin,
len=len,
)
_impl_dict.update(("nan" + k, v) for k, v in list(_impl_dict.items()) if k not in funcs_no_separate_nan)
def aggregate(group_idx, a, func="sum", size=None, fill_value=0, order=None, dtype=None, axis=None, **kwargs):
if axis is not None:
raise NotImplementedError("axis arg not supported in purepy implementation.")
# Check for 2d group_idx
if size is None:
try:
size = 1 + int(max(group_idx))
except (TypeError, ValueError):
raise NotImplementedError("pure python implementation doesn't accept ndim idx input.")
for i in group_idx:
try:
i = int(i)
except (TypeError, ValueError):
if isinstance(i, (list, tuple)):
raise NotImplementedError("pure python implementation doesn't accept ndim idx input.")
else:
try:
len(i)
except TypeError:
raise ValueError(f"invalid value found in group_idx: {i}")
else:
raise NotImplementedError("pure python implementation doesn't accept ndim indexed input.")
else:
if i < 0:
raise ValueError("group_idx contains negative value")
func = get_func(func, aliasing, _impl_dict)
if isinstance(a, (int, float)):
if func not in ("sum", "prod", "len"):
raise ValueError("scalar inputs are supported only for 'sum', 'prod' and 'len'")
a = [a] * len(group_idx)
elif len(group_idx) != len(a):
raise ValueError("group_idx and a must be of the same length")
if isinstance(func, str):
if func.startswith("nan"):
func = func[3:]
# remove nans
group_idx, a = zip(*((ix, val) for ix, val in zip(group_idx, a) if not math.isnan(val)))
func = _impl_dict[func]
if func is _sort:
return _sort(group_idx, a, reverse=kwargs.get("reverse", False))
# sort data and evaluate function on groups
ret = [fill_value] * size
if not getattr(func, "x_and_idx", False):
data = sorted(zip(group_idx, a), key=operator.itemgetter(0))
for ix, group in itertools.groupby(data, key=operator.itemgetter(0)):
ret[ix] = func(list(val for _, val in group), **kwargs)
else:
data = sorted(zip(range(len(a)), group_idx, a), key=operator.itemgetter(1))
for ix, group in itertools.groupby(data, key=operator.itemgetter(1)):
ret[ix] = func(list((val_idx, val) for val_idx, _, val in group), **kwargs)
return ret
aggregate.__doc__ = (
"""
This is the pure python implementation of aggregate. It is terribly slow.
Using the numpy version is highly recommended.
"""
+ aggregate_common_doc
)
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