carefree-toolkit
carefree-toolkit implemented some commonly used functions and modules
Installation
carefree-toolkit requires Python 3.8 or higher.
pip install carefree-toolkit
or
git clone https://github.com/carefree0910/carefree-toolkit.git
cd carefree-toolkit
pip install -e .
Usages
timeit
class timeit(context_error_handler):
def __init__(self, msg)
Timing context manager.
Parameters
msg: str, name of the context which we want to timeit.
Example
import time
from cftool.misc import timeit
# ~~~ [ info ] timing for sleep 1s : 1.0002
with timeit("sleep 1s"):
time.sleep(1)
timestamp
def timestamp(simplify=False, ensure_different=False) -> str
Return current timestamp.
Parameters
simplify: bool. IfTrue, format will be simplified to 'year-month-day'.ensure_different: bool. IfTrue, format will include millisecond.
Example
from cftool.misc import timestamp
# 2019-09-30_21-49-56
print(timestamp())
# 2019-09-30
print(timestamp(simplify=True))
# 2019-09-30_21-49-56-279768
print(timestamp(ensure_different=True))
prod
def prod(iterable) -> float
Return cumulative production of an iterable.
Parameters
iterable: iterable.
Example
from cftool.misc import prod
# 120.0
print(prod(range(1, 6)))
hash_code
def hash_code(code) -> str
Return hash code for string code.
Parameters
code: str.
Example
from cftool.misc import hash_code
# True
hash_code("a") != hash_code("b")
prefix_dict
def prefix_dict(d, prefix) -> dict
Prefix every key in dict d with prefix, connected with '_'.
Parameters
d: dict.prefix: str.
Example
from cftool.misc import prefix_dict
# {"foo_a": 1, "foo_b": 2}
print(prefix_dict({"a": 1, "b": 2}, "foo"))
shallow_copy_dict
def shallow_copy_dict(d) -> dict
Shallow copy dict d, nested dict is also supported.
Parameters
d: dict.
Example
from cftool.misc import shallow_copy_dict
d = {"a": 1, "b": {"c": 2, "d": 3}}
sd = shallow_copy_dict(d)
d_copy = d.copy()
d["b"].pop("c")
# {'a': 1, 'b': {'d': 3}}
print(d)
# {'a': 1, 'b': {'c': 2, 'd': 3}}
print(sd)
# {'a': 1, 'b': {'d': 3}}
print(d_copy)
update_dict
def update_dict(src_dict, tgt_dict) -> dict
Update tgt_dict with src_dict.
Changes will happen only on keys which
src_dictholds, and the update procedure will be recursive.
Changed will happen inplace.
Parameters
src_dict: dict.tgt_dict: str.
Example
from cftool.misc import update_dict
src_dict = {"a": {"b": 1}, "c": 2}
tgt_dict = {"a": {"b": 0, "b1": 1}, "c": 0, "d": 1}
# {"a": {"b": 1, "b1": 1}, "c": 2, "d": 1}
print(update_dict(src_dict, tgt_dict))
fix_float_to_length
def fix_float_to_length(num, length) -> str
Change a float number to string format with fixed length.
Parameters
num: float.length: int.
Example
import math
from cftool.misc import fix_float_to_length
# 1.000000
print(fix_float_to_length(1, 8))
# 1.000000
print(fix_float_to_length(1., 8))
# 1.000000
print(fix_float_to_length(1.0, 8))
# -1.00000
print(fix_float_to_length(-1, 8))
# -1.00000
print(fix_float_to_length(-1., 8))
# -1.00000
print(fix_float_to_length(-1.0, 8))
# 1234567.
print(fix_float_to_length(1234567, 8))
# 12345678
print(fix_float_to_length(12345678, 8))
# 123456789
print(fix_float_to_length(123456789, 8))
# + nan +
print("+" + fix_float_to_length(math.nan, 8) + "+")
truncate_string_to_length
def truncate_string_to_length(string, length) -> str
Truncate a string to make sure its length not exceeding a given length.
Parameters
string: str.length: int.
Example
from cftool.misc import truncate_string_to_length
# 123456
print(truncate_string_to_length("123456", 6))
# 12..67
print(truncate_string_to_length("1234567", 6))
# 12..78
print(truncate_string_to_length("12345678", 6))
# 12...78
print(truncate_string_to_length("12345678", 7))
grouped
def grouped(iterable, n, *, keep_tail) -> list
Group an iterable every n elements.
Parameters
iterable: iterable.n: int.keep_tail: bool, whether keep the 'tail' (see example below).
Example
from cftool.misc import grouped
# [(0, 1), (2, 3), (4, 5)]
print(grouped(range(6), 2))
# [(0, 1, 2), (3, 4, 5)]
print(grouped(range(6), 3))
# [(0, 1, 2, 3)]
print(grouped(range(6), 4))
# [(0, 1, 2, 3), (4, 5)]
print(grouped(range(6), 4, keep_tail=True))
is_number
def is_numeric(s) -> bool
Check whether string s is numeric.
Parameters
s: str.
Example
from cftool.misc import is_numeric
# True
print(is_numeric(0x1))
# True
print(is_numeric(1e0))
# True
print(is_numeric("1"))
# True
print(is_numeric("1."))
# True
print(is_numeric("1.0"))
# True
print(is_numeric("1.00"))
# False
print(is_numeric("1.0.0"))
# True
print(is_numeric("nan"))
get_one_hot
def get_one_hot(feature, dim) -> np.ndarray
Get one-hot representation.
Parameters
feature: array-like, source data of one-hot representation.dim: int, dimension of the one-hot representation.
Example
import numpy as np
from cftool.array import get_one_hot
feature = np.array([0, 1, 0])
# [[1 0], [0 1], [1 0]]
print(get_one_hot(feature, 2))
# [[1 0 0] [0 1 0] [1 0 0]]
print(get_one_hot(feature, 3))
# [[1 0 0] [0 1 0] [1 0 0]]
print(get_one_hot(feature.tolist(), 3))
get_indices_from_another
def get_indices_from_another(base, segment) -> np.ndarray
Get segment elements' indices in base. This function will return positions where elements in segment appear in base.
All elements in segment should appear in base to ensure validity.
Parameters
base: np.ndarray, base array.segment: np.ndarray, segment array.
Example
import numpy as np
from cftool.array import get_indices_from_another
base, segment = np.array([1, 2, 3, 5, 7, 8, 9]), np.array([1, 3, 5, 7, 9])
# [0 2 3 4 6]
print(get_indices_from_another(base, segment))
# [0 1 2 3 4]
print(get_indices_from_another(segment, segment))
# [4 3 2 1 0]
print(get_indices_from_another(segment[::-1], segment))
get_unique_indices
def get_unique_indices(arr) -> UniqueIndices
Get indices for unique values of an array.
Parameters
arr: np.ndarray, target array which we wish to find indices of each unique value.return_raw: bool, whether returning raw information.
Example
import numpy as np
from cftool.array import get_unique_indices
arr = np.array([1, 2, 3, 2, 4, 1, 0, 1], np.int64)
unique_indices = get_unique_indices(arr)
# UniqueIndices(
# unique = array([0, 1, 2, 3, 4], dtype=int64),
# unique_cnt = array([1, 3, 2, 1, 1], dtype=int64),
# sorting_indices = array([6, 0, 5, 7, 1, 3, 2, 4], dtype=int64),
# split_arr = array([1, 4, 6, 7], dtype=int64))
# split_indices = [array([6], dtype=int64), array([0, 5, 7], dtype=int64), array([1, 3], dtype=int64),
# array([2], dtype=int64), array([4], dtype=int64)]
print(get_unique_indices(arr))
And more...
carefree-toolkit is well documented, feel free to dive into the codes and explore something you may need!
License
carefree-toolkit is MIT licensed, as found in the LICENSE file.
1 maintainer
Metadata
1 maintainer
Release files for carefree-toolkit 0.3.12
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