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import numpy as np
import pandas as pd
import pytz
from datetime import datetime
from typing import List
class DateLogs:
def __init__(
self,
zone: str = "America/Argentina/Cordoba"
) -> None:
self.time_zone = pytz.timezone(zone)
def full(
self
) -> str:
now = datetime.now(self.time_zone)
return now.strftime("%H:%M:%S %d-%m-%Y")
def day(
self
) -> str:
now = datetime.now(self.time_zone)
return now.strftime("%d-%m-%Y")
def take_two_sides_extreme_sorted(
df: pd.DataFrame,
n_extreme: int,
part_column: str=None,
head_value: str='',
tail_value: str=''
) -> pd.DataFrame:
head_df = df.head(n_extreme)[:]
tail_df = df.tail(n_extreme)[:]
if part_column is not None:
head_df[part_column] = head_value
tail_df[part_column] = tail_value
return (pd.concat([head_df, tail_df])
.drop_duplicates()
.reset_index(drop=True))
def normalize(
v: np.ndarray
) -> np.ndarray:
"""Normalize a 1-D vector."""
if v.ndim != 1:
raise ValueError('v should be 1-D, {}-D was given'.format(
v.ndim))
norm = np.linalg.norm(v)
if norm == 0:
return v
return v / norm
def project_params(
u: np.ndarray,
v: np.ndarray
) -> np.ndarray:
"""Projecting and rejecting the vector v onto direction u with scalar."""
normalize_u = normalize(u)
projection = (v @ normalize_u)
projected_vector = projection * normalize_u
rejected_vector = v - projected_vector
return projection, projected_vector, rejected_vector
def cosine_similarity(
v: np.ndarray,
u: np.ndarray
) -> np.ndarray:
"""Calculate the cosine similarity between two vectors."""
v_norm = np.linalg.norm(v)
u_norm = np.linalg.norm(u)
similarity = v @ u / (v_norm * u_norm)
return similarity
def axes_labels_format(
left: str,
right: str,
sep: str,
word_wrap: int = 4
) -> str:
def sparse(
word: str,
max_len: int
) -> str:
diff = max_len-len(word)
rest = diff if diff > 0 else 0
return word+" "*rest
def gen_block(
list_: List[str],
n_rows:int,
n_cols:int
) -> List[str]:
block = []
block_row = []
for r in range(n_rows):
for c in range(n_cols):
i = r * n_cols + c
w = list_[i] if i <= len(list_) - 1 else ""
block_row.append(w)
if (i+1) % n_cols == 0:
block.append(block_row)
block_row = []
return block
# Transform 'string' to list of string
l_list = [word.strip() for word in left.split(",") if word.strip() != ""]
r_list = [word.strip() for word in right.split(",") if word.strip() != ""]
# Get longest word, and longest_list
longest_list = max(len(l_list), len(r_list))
longest_word = len(max( max(l_list, key=len), max(r_list, key=len)))
# Creation of word blocks for each list
n_rows = (longest_list // word_wrap) if longest_list % word_wrap == 0 else (longest_list // word_wrap) + 1
n_cols = word_wrap
l_block = gen_block(l_list, n_rows, n_cols)
r_block = gen_block(r_list, n_rows, n_cols)
# Transform list of list to sparse string
labels = ""
for i,(l,r) in enumerate(zip(l_block, r_block)):
line = ' '.join([sparse(w, longest_word) for w in l]) + sep + \
' '.join([sparse(w, longest_word) for w in r])
labels += f"← {line} →\n" if i==0 else f" {line} \n"
return labels |