def _extend_data(filters, data, batch_idx, balance_idx):
get_batches = compose(
reduceby("name", lambda x: x[0]),
lambda batches: frappe.db.sql(
"""
SELECT
name,
expiry_date,
IF(expiry_date, DATEDIFF(expiry_date, %(today)s), '') AS expiry_in_days
FROM `tabBatch` WHERE name IN %(batches)s
""",
values={
"today": frappe.utils.today(),
"batches": batches
},
as_dict=1,
) if batches else [],
set,
filter(lambda x: x),
map(lambda x: x[batch_idx]),
)
batches = get_batches(data)
idx = batch_idx + 1
def get_cells(row):
batch = batches.get(row[batch_idx])
if batch:
return [batch.get("expiry_date"), batch.get("expiry_in_days")]
return [None, None]
def will_show(row):
if not filters.hide_zero_stock:
return True
return row[balance_idx] != 0
return [(x[:idx] + get_cells(x) + x[idx:]) for x in data if will_show(x)]
if __name__ == "__main__":
if len(sys.argv) != 1:
print("USAGE: python3 generate_dd_txt.py ")
sys.exit(1)
fname, output_dir = sys.argv[0], "zrm_phone_xhe_shape"
if not Path(output_dir).exists():
os.makedirs(output_dir)
char_to_shape = pipe(CharShapeTable.select(),
map(lambda e: (e.char, e.shapes)),
reduceby(lambda e: e[0], lambda e1, e2: e1),
valmap(lambda e: e[1]), dict)
print(f"total {len(char_to_shape)} char shapes")
char_to_phones = pipe(CharPhoneTable.select(),
map(lambda e: (e.char, e.zrm)),
groupby(lambda e: e[0]),
valmap(lambda phones: [e[1] for e in phones]), dict)
print(f"total {len(char_to_phones)} char phones")
one_hit_char_items = generate_one_hit_char(60000)
top_single_chars_items = generate_topest_char(char_to_phones, 60000)
sys_top_chars_data = f"{output_dir}/sys_top_chars_data.txt"
with open(sys_top_chars_data, 'w', encoding='utf8') as fout:
fout.write("---config@码表分类=主码-1\n")
fout.write("---config@允许编辑=否\n")
if __name__ == "__main__":
if len(sys.argv) != 2:
print(f"USAGE: python3 {sys.argv[0]} words.txt", file=sys.stderr)
sys.exit(1)
_, words_path = sys.argv
exist_wordphones = pipe(EngWordTable.select(),
map(lambda e: e.word),
set
)
with open(words_path, "r", encoding='utf8') as fin:
to_add_words = pipe(fin,
map(lambda e: e.strip()),
filter(lambda e: e != ''),
filter(lambda e: e not in exist_wordphones),
reduceby(lambda e: e, lambda e1, e2: e1),
map(lambda e: EngWordTable(word=e, priority=1, updatedt=datetime.now())),
)
with db.atomic():
EngWordTable.bulk_create(to_add_words, batch_size=100)
# for w in to_add_words:
# print(f"add {w}")
# # w.save()
print('done')
chained(dict,
curried.itemfilter(lambda i: i[0] % 2 == 0 and i[1] < 4)),
dict.items({
1: 2,
2: 3,
3: 4,
4: 5
}),
),
# example taken from toolz docs
"mapcat": (
chained(curried.mapcat(lambda s: [c.upper() for c in s]), list),
[["a", "b"], ["c", "d", "e"]],
),
"reduce": (curried.reduce(op.add), range(20)),
"reduceby": (curried.reduceby(lambda x: x % 2 == 0, op.add), range(20)),
"topk": (chained(curried.topk(5), list), range(20)),
"curried.unique": (chained(curried.unique, sorted), [1, 1, 2, 3, 4, 4]),
"unique": (chained(toolz.unique, sorted), [1, 1, 2, 3, 4, 4]),
}
def params(spec, m):
return pytest.mark.parametrize(spec, [v for (_, v) in sorted(m.items())],
ids=sorted(m.keys()))
@params("func, arg", examples)
def test_input_outputs(func, arg):
actual_no_dask = func(arg)
actual_dask = apply(db.from_sequence(arg, npartitions=3), func).compute()