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Merge pull request #2 from thor-harsh/thor-harsh-patch-2

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Thor harsh patch 2
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thor-harsh 2023-08-18 18:44:48 +05:30 committed by GitHub
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3
DIRECTORY.md
View File

@ -710,6 +710,7 @@
* [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py)
* Activation Functions
* [Exponential Linear Unit](neural_network/activation_functions/exponential_linear_unit.py)
* [Leaky Rectified Linear Unit](neural_network/activation_functions/leaky_rectified_linear_unit.py)
* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](neural_network/convolution_neural_network.py)
* [Perceptron](neural_network/perceptron.py)
@ -1212,6 +1213,7 @@
* [Daily Horoscope](web_programming/daily_horoscope.py)
* [Download Images From Google Query](web_programming/download_images_from_google_query.py)
* [Emails From Url](web_programming/emails_from_url.py)
* [Fetch Anime And Play](web_programming/fetch_anime_and_play.py)
* [Fetch Bbc News](web_programming/fetch_bbc_news.py)
* [Fetch Github Info](web_programming/fetch_github_info.py)
* [Fetch Jobs](web_programming/fetch_jobs.py)
@ -1220,6 +1222,7 @@
* [Get Amazon Product Data](web_programming/get_amazon_product_data.py)
* [Get Imdb Top 250 Movies Csv](web_programming/get_imdb_top_250_movies_csv.py)
* [Get Imdbtop](web_programming/get_imdbtop.py)
* [Get Top Billionaires](web_programming/get_top_billionaires.py)
* [Get Top Hn Posts](web_programming/get_top_hn_posts.py)
* [Get User Tweets](web_programming/get_user_tweets.py)
* [Giphy](web_programming/giphy.py)

227
linear_programming/simplex.py
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@ -20,40 +20,60 @@ import numpy as np
class Tableau:
"""Operate on simplex tableaus
>>> t = Tableau(np.array([[-1,-1,0,0,-1],[1,3,1,0,4],[3,1,0,1,4.]]), 2)
>>> Tableau(np.array([[-1,-1,0,0,1],[1,3,1,0,4],[3,1,0,1,4]]), 2, 2)
Traceback (most recent call last):
...
TypeError: Tableau must have type float64
>>> Tableau(np.array([[-1,-1,0,0,-1],[1,3,1,0,4],[3,1,0,1,4.]]), 2, 2)
Traceback (most recent call last):
...
ValueError: RHS must be > 0
>>> Tableau(np.array([[-1,-1,0,0,1],[1,3,1,0,4],[3,1,0,1,4.]]), -2, 2)
Traceback (most recent call last):
...
ValueError: number of (artificial) variables must be a natural number
"""
def __init__(self, tableau: np.ndarray, n_vars: int) -> None:
# Max iteration number to prevent cycling
maxiter = 100
def __init__(
self, tableau: np.ndarray, n_vars: int, n_artificial_vars: int
) -> None:
if tableau.dtype != "float64":
raise TypeError("Tableau must have type float64")
# Check if RHS is negative
if np.any(tableau[:, -1], where=tableau[:, -1] < 0):
if not (tableau[:, -1] >= 0).all():
raise ValueError("RHS must be > 0")
if n_vars < 2 or n_artificial_vars < 0:
raise ValueError(
"number of (artificial) variables must be a natural number"
)
self.tableau = tableau
self.n_rows, _ = tableau.shape
self.n_rows, n_cols = tableau.shape
# Number of decision variables x1, x2, x3...
self.n_vars = n_vars
# Number of artificial variables to be minimised
self.n_art_vars = len(np.where(tableau[self.n_vars : -1] == -1)[0])
self.n_vars, self.n_artificial_vars = n_vars, n_artificial_vars
# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
self.n_stages = (self.n_art_vars > 0) + 1
self.n_stages = (self.n_artificial_vars > 0) + 1
# Number of slack variables added to make inequalities into equalities
self.n_slack = self.n_rows - self.n_stages
self.n_slack = n_cols - self.n_vars - self.n_artificial_vars - 1
# Objectives for each stage
self.objectives = ["max"]
# In two stage simplex, first minimise then maximise
if self.n_art_vars:
if self.n_artificial_vars:
self.objectives.append("min")
self.col_titles = [""]
self.col_titles = self.generate_col_titles()
# Index of current pivot row and column
self.row_idx = None
@ -62,48 +82,39 @@ class Tableau:
# Does objective row only contain (non)-negative values?
self.stop_iter = False
@staticmethod
def generate_col_titles(*args: int) -> list[str]:
def generate_col_titles(self) -> list[str]:
"""Generate column titles for tableau of specific dimensions
>>> Tableau.generate_col_titles(2, 3, 1)
['x1', 'x2', 's1', 's2', 's3', 'a1', 'RHS']
>>> Tableau(np.array([[-1,-1,0,0,1],[1,3,1,0,4],[3,1,0,1,4.]]),
... 2, 0).generate_col_titles()
['x1', 'x2', 's1', 's2', 'RHS']
>>> Tableau.generate_col_titles()
Traceback (most recent call last):
...
ValueError: Must provide n_vars, n_slack, and n_art_vars
>>> Tableau.generate_col_titles(-2, 3, 1)
Traceback (most recent call last):
...
ValueError: All arguments must be non-negative integers
>>> Tableau(np.array([[-1,-1,0,0,1],[1,3,1,0,4],[3,1,0,1,4.]]),
... 2, 2).generate_col_titles()
['x1', 'x2', 'RHS']
"""
if len(args) != 3:
raise ValueError("Must provide n_vars, n_slack, and n_art_vars")
args = (self.n_vars, self.n_slack)
if not all(x >= 0 and isinstance(x, int) for x in args):
raise ValueError("All arguments must be non-negative integers")
# decision | slack | artificial
string_starts = ["x", "s", "a"]
# decision | slack
string_starts = ["x", "s"]
titles = []
for i in range(3):
for i in range(2):
for j in range(args[i]):
titles.append(string_starts[i] + str(j + 1))
titles.append("RHS")
return titles
def find_pivot(self, tableau: np.ndarray) -> tuple[Any, Any]:
def find_pivot(self) -> tuple[Any, Any]:
"""Finds the pivot row and column.
>>> t = Tableau(np.array([[-2,1,0,0,0], [3,1,1,0,6], [1,2,0,1,7.]]), 2)
>>> t.find_pivot(t.tableau)
>>> Tableau(np.array([[-2,1,0,0,0], [3,1,1,0,6], [1,2,0,1,7.]]),
... 2, 0).find_pivot()
(1, 0)
"""
objective = self.objectives[-1]
# Find entries of highest magnitude in objective rows
sign = (objective == "min") - (objective == "max")
col_idx = np.argmax(sign * tableau[0, : self.n_vars])
col_idx = np.argmax(sign * self.tableau[0, :-1])
# Choice is only valid if below 0 for maximise, and above for minimise
if sign * self.tableau[0, col_idx] <= 0:
@ -117,15 +128,15 @@ class Tableau:
s = slice(self.n_stages, self.n_rows)
# RHS
dividend = tableau[s, -1]
dividend = self.tableau[s, -1]
# Elements of pivot column within slice
divisor = tableau[s, col_idx]
divisor = self.tableau[s, col_idx]
# Array filled with nans
nans = np.full(self.n_rows - self.n_stages, np.nan)
# If element in pivot column is greater than zeron_stages, return
# If element in pivot column is greater than zero, return
# quotient or nan otherwise
quotients = np.divide(dividend, divisor, out=nans, where=divisor > 0)
@ -134,18 +145,18 @@ class Tableau:
row_idx = np.nanargmin(quotients) + self.n_stages
return row_idx, col_idx
def pivot(self, tableau: np.ndarray, row_idx: int, col_idx: int) -> np.ndarray:
def pivot(self, row_idx: int, col_idx: int) -> np.ndarray:
"""Pivots on value on the intersection of pivot row and column.
>>> t = Tableau(np.array([[-2,-3,0,0,0],[1,3,1,0,4],[3,1,0,1,4.]]), 2)
>>> t.pivot(t.tableau, 1, 0).tolist()
>>> Tableau(np.array([[-2,-3,0,0,0],[1,3,1,0,4],[3,1,0,1,4.]]),
... 2, 2).pivot(1, 0).tolist()
... # doctest: +NORMALIZE_WHITESPACE
[[0.0, 3.0, 2.0, 0.0, 8.0],
[1.0, 3.0, 1.0, 0.0, 4.0],
[0.0, -8.0, -3.0, 1.0, -8.0]]
"""
# Avoid changes to original tableau
piv_row = tableau[row_idx].copy()
piv_row = self.tableau[row_idx].copy()
piv_val = piv_row[col_idx]
@ -153,48 +164,47 @@ class Tableau:
piv_row *= 1 / piv_val
# Variable in pivot column becomes basic, ie the only non-zero entry
for idx, coeff in enumerate(tableau[:, col_idx]):
tableau[idx] += -coeff * piv_row
tableau[row_idx] = piv_row
return tableau
for idx, coeff in enumerate(self.tableau[:, col_idx]):
self.tableau[idx] += -coeff * piv_row
self.tableau[row_idx] = piv_row
return self.tableau
def change_stage(self, tableau: np.ndarray) -> np.ndarray:
def change_stage(self) -> np.ndarray:
"""Exits first phase of the two-stage method by deleting artificial
rows and columns, or completes the algorithm if exiting the standard
case.
>>> t = Tableau(np.array([
>>> Tableau(np.array([
... [3, 3, -1, -1, 0, 0, 4],
... [2, 1, 0, 0, 0, 0, 0.],
... [1, 2, -1, 0, 1, 0, 2],
... [2, 1, 0, -1, 0, 1, 2]
... ]), 2)
>>> t.change_stage(t.tableau).tolist()
... ]), 2, 2).change_stage().tolist()
... # doctest: +NORMALIZE_WHITESPACE
[[2.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 2.0, -1.0, 0.0, 1.0, 2.0],
[2.0, 1.0, 0.0, -1.0, 0.0, 2.0]]
[[2.0, 1.0, 0.0, 0.0, 0.0],
[1.0, 2.0, -1.0, 0.0, 2.0],
[2.0, 1.0, 0.0, -1.0, 2.0]]
"""
# Objective of original objective row remains
self.objectives.pop()
if not self.objectives:
return tableau
return self.tableau
# Slice containing ids for artificial columns
s = slice(-self.n_art_vars - 1, -1)
s = slice(-self.n_artificial_vars - 1, -1)
# Delete the artificial variable columns
tableau = np.delete(tableau, s, axis=1)
self.tableau = np.delete(self.tableau, s, axis=1)
# Delete the objective row of the first stage
tableau = np.delete(tableau, 0, axis=0)
self.tableau = np.delete(self.tableau, 0, axis=0)
self.n_stages = 1
self.n_rows -= 1
self.n_art_vars = 0
self.n_artificial_vars = 0
self.stop_iter = False
return tableau
return self.tableau
def run_simplex(self) -> dict[Any, Any]:
"""Operate on tableau until objective function cannot be
@ -205,15 +215,29 @@ class Tableau:
ST: x1 + 3x2 <= 4
3x1 + x2 <= 4
>>> Tableau(np.array([[-1,-1,0,0,0],[1,3,1,0,4],[3,1,0,1,4.]]),
... 2).run_simplex()
... 2, 0).run_simplex()
{'P': 2.0, 'x1': 1.0, 'x2': 1.0}
# Standard linear program with 3 variables:
Max: 3x1 + x2 + 3x3
ST: 2x1 + x2 + x3 ≤ 2
x1 + 2x2 + 3x3 ≤ 5
2x1 + 2x2 + x3 ≤ 6
>>> Tableau(np.array([
... [-3,-1,-3,0,0,0,0],
... [2,1,1,1,0,0,2],
... [1,2,3,0,1,0,5],
... [2,2,1,0,0,1,6.]
... ]),3,0).run_simplex() # doctest: +ELLIPSIS
{'P': 5.4, 'x1': 0.199..., 'x3': 1.6}
# Optimal tableau input:
>>> Tableau(np.array([
... [0, 0, 0.25, 0.25, 2],
... [0, 1, 0.375, -0.125, 1],
... [1, 0, -0.125, 0.375, 1]
... ]), 2).run_simplex()
... ]), 2, 0).run_simplex()
{'P': 2.0, 'x1': 1.0, 'x2': 1.0}
# Non-standard: >= constraints
@ -227,7 +251,7 @@ class Tableau:
... [1, 1, 1, 1, 0, 0, 0, 0, 40],
... [2, 1, -1, 0, -1, 0, 1, 0, 10],
... [0, -1, 1, 0, 0, -1, 0, 1, 10.]
... ]), 3).run_simplex()
... ]), 3, 2).run_simplex()
{'P': 70.0, 'x1': 10.0, 'x2': 10.0, 'x3': 20.0}
# Non standard: minimisation and equalities
@ -235,73 +259,76 @@ class Tableau:
ST: 2x1 + x2 = 12
6x1 + 5x2 = 40
>>> Tableau(np.array([
... [8, 6, 0, -1, 0, -1, 0, 0, 52],
... [1, 1, 0, 0, 0, 0, 0, 0, 0],
... [2, 1, 1, 0, 0, 0, 0, 0, 12],
... [2, 1, 0, -1, 0, 0, 1, 0, 12],
... [6, 5, 0, 0, 1, 0, 0, 0, 40],
... [6, 5, 0, 0, 0, -1, 0, 1, 40.]
... ]), 2).run_simplex()
... [8, 6, 0, 0, 52],
... [1, 1, 0, 0, 0],
... [2, 1, 1, 0, 12],
... [6, 5, 0, 1, 40.],
... ]), 2, 2).run_simplex()
{'P': 7.0, 'x1': 5.0, 'x2': 2.0}
# Pivot on slack variables
Max: 8x1 + 6x2
ST: x1 + 3x2 <= 33
4x1 + 2x2 <= 48
2x1 + 4x2 <= 48
x1 + x2 >= 10
x1 >= 2
>>> Tableau(np.array([
... [2, 1, 0, 0, 0, -1, -1, 0, 0, 12.0],
... [-8, -6, 0, 0, 0, 0, 0, 0, 0, 0.0],
... [1, 3, 1, 0, 0, 0, 0, 0, 0, 33.0],
... [4, 2, 0, 1, 0, 0, 0, 0, 0, 60.0],
... [2, 4, 0, 0, 1, 0, 0, 0, 0, 48.0],
... [1, 1, 0, 0, 0, -1, 0, 1, 0, 10.0],
... [1, 0, 0, 0, 0, 0, -1, 0, 1, 2.0]
... ]), 2, 2).run_simplex() # doctest: +ELLIPSIS
{'P': 132.0, 'x1': 12.000... 'x2': 5.999...}
"""
# Stop simplex algorithm from cycling.
for _ in range(100):
for _ in range(Tableau.maxiter):
# Completion of each stage removes an objective. If both stages
# are complete, then no objectives are left
if not self.objectives:
self.col_titles = self.generate_col_titles(
self.n_vars, self.n_slack, self.n_art_vars
)
# Find the values of each variable at optimal solution
return self.interpret_tableau(self.tableau, self.col_titles)
return self.interpret_tableau()
row_idx, col_idx = self.find_pivot(self.tableau)
row_idx, col_idx = self.find_pivot()
# If there are no more negative values in objective row
if self.stop_iter:
# Delete artificial variable columns and rows. Update attributes
self.tableau = self.change_stage(self.tableau)
self.tableau = self.change_stage()
else:
self.tableau = self.pivot(self.tableau, row_idx, col_idx)
self.tableau = self.pivot(row_idx, col_idx)
return {}
def interpret_tableau(
self, tableau: np.ndarray, col_titles: list[str]
) -> dict[str, float]:
def interpret_tableau(self) -> dict[str, float]:
"""Given the final tableau, add the corresponding values of the basic
decision variables to the `output_dict`
>>> tableau = np.array([
>>> Tableau(np.array([
... [0,0,0.875,0.375,5],
... [0,1,0.375,-0.125,1],
... [1,0,-0.125,0.375,1]
... ])
>>> t = Tableau(tableau, 2)
>>> t.interpret_tableau(tableau, ["x1", "x2", "s1", "s2", "RHS"])
... ]),2, 0).interpret_tableau()
{'P': 5.0, 'x1': 1.0, 'x2': 1.0}
"""
# P = RHS of final tableau
output_dict = {"P": abs(tableau[0, -1])}
output_dict = {"P": abs(self.tableau[0, -1])}
for i in range(self.n_vars):
# Gives ids of nonzero entries in the ith column
nonzero = np.nonzero(tableau[:, i])
# Gives indices of nonzero entries in the ith column
nonzero = np.nonzero(self.tableau[:, i])
n_nonzero = len(nonzero[0])
# First entry in the nonzero ids
# First entry in the nonzero indices
nonzero_rowidx = nonzero[0][0]
nonzero_val = tableau[nonzero_rowidx, i]
nonzero_val = self.tableau[nonzero_rowidx, i]
# If there is only one nonzero value in column, which is one
if n_nonzero == nonzero_val == 1:
rhs_val = tableau[nonzero_rowidx, -1]
output_dict[col_titles[i]] = rhs_val
# Check for basic variables
for title in col_titles:
# Don't add RHS or slack variables to output dict
if title[0] not in "R-s-a":
output_dict.setdefault(title, 0)
if n_nonzero == 1 and nonzero_val == 1:
rhs_val = self.tableau[nonzero_rowidx, -1]
output_dict[self.col_titles[i]] = rhs_val
return output_dict

27
machine_learning/k_means_clust.py
View File

@ -10,11 +10,11 @@ Inputs:
- k , number of clusters to create.
- initial_centroids , initial centroid values generated by utility function(mentioned
in usage).
- maxiter , maximum number of iterations to process.
- heterogeneity , empty list that will be filled with hetrogeneity values if passed
- maxiter , the maximum number of iterations to process.
- heterogeneity, empty list that will be filled with heterogeneity values if passed
to kmeans func.
Usage:
1. define 'k' value, 'X' features array and 'hetrogeneity' empty list
1. define 'k' value, 'X' features array and 'heterogeneity' empty list
2. create initial_centroids,
initial_centroids = get_initial_centroids(
X,
@ -31,8 +31,8 @@ Usage:
record_heterogeneity=heterogeneity,
verbose=True # whether to print logs in console or not.(default=False)
)
4. Plot the loss function, hetrogeneity values for every iteration saved in
hetrogeneity list.
4. Plot the loss function, heterogeneity values for every iteration saved in
heterogeneity list.
plot_heterogeneity(
heterogeneity,
k
@ -46,6 +46,7 @@ import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances
import doctest
warnings.filterwarnings("ignore")
@ -198,10 +199,10 @@ def report_generator(
df: pd.DataFrame, clustering_variables: np.ndarray, fill_missing_report=None
) -> pd.DataFrame:
"""
Function generates easy-erading clustering report. It takes 2 arguments as an input:
DataFrame - dataframe with predicted cluester column;
FillMissingReport - dictionary of rules how we are going to fill missing
values of for final report generate (not included in modeling);
Function generates an easy-reading clustering report. It takes 3 arguments as input:
DataFrame,predicted cluster column,
FillMissingReport - dictionary of rules on how we are going to fill in missing
values of for final report generate (not included in modelling);
in order to run the function following libraries must be imported:
import pandas as pd
import numpy as np
@ -306,10 +307,10 @@ def report_generator(
a.columns = report.columns # rename columns to match report
report = report.drop(
report[report.Type == "count"].index
) # drop count values except cluster size
) # drop count values except for cluster size
report = pd.concat(
[report, a, clustersize, clusterproportion], axis=0
) # concat report with clustert size and nan values
[report, a, cluster size, clusterproportion], axis=0
) # concat report with cluster size and nan values
report["Mark"] = report["Features"].isin(clustering_variables)
cols = report.columns.tolist()
cols = cols[0:2] + cols[-1:] + cols[2:-1]
@ -343,6 +344,6 @@ def report_generator(
if __name__ == "__main__":
import doctest
doctest.testmod()

18
sorts/bucket_sort.py
View File

@ -30,7 +30,7 @@ Source: https://en.wikipedia.org/wiki/Bucket_sort
from __future__ import annotations
def bucket_sort(my_list: list) -> list:
def bucket_sort(my_list: list, bucket_count: int = 10) -> list:
"""
>>> data = [-1, 2, -5, 0]
>>> bucket_sort(data) == sorted(data)
@ -43,21 +43,27 @@ def bucket_sort(my_list: list) -> list:
True
>>> bucket_sort([]) == sorted([])
True
>>> data = [-1e10, 1e10]
>>> bucket_sort(data) == sorted(data)
True
>>> import random
>>> collection = random.sample(range(-50, 50), 50)
>>> bucket_sort(collection) == sorted(collection)
True
"""
if len(my_list) == 0:
if len(my_list) == 0 or bucket_count <= 0:
return []
min_value, max_value = min(my_list), max(my_list)
bucket_count = int(max_value - min_value) + 1
bucket_size = (max_value - min_value) / bucket_count
buckets: list[list] = [[] for _ in range(bucket_count)]
for i in my_list:
buckets[int(i - min_value)].append(i)
for val in my_list:
index = min(int((val - min_value) / bucket_size), bucket_count - 1)
buckets[index].append(val)
return [v for bucket in buckets for v in sorted(bucket)]
return [val for bucket in buckets for val in sorted(bucket)]
if __name__ == "__main__":

71
web_programming/fetch_anime_and_play.py.BROKEN → web_programming/fetch_anime_and_play.py
View File

@ -1,7 +1,5 @@
from xml.dom import NotFoundErr
import requests
from bs4 import BeautifulSoup, NavigableString
from bs4 import BeautifulSoup, NavigableString, Tag
from fake_useragent import UserAgent
BASE_URL = "https://ww1.gogoanime2.org"
@ -41,25 +39,23 @@ def search_scraper(anime_name: str) -> list:
# get list of anime
anime_ul = soup.find("ul", {"class": "items"})
if anime_ul is None or isinstance(anime_ul, NavigableString):
msg = f"Could not find and anime with name {anime_name}"
raise ValueError(msg)
anime_li = anime_ul.children
# for each anime, insert to list. the name and url.
anime_list = []
for anime in anime_li:
if not isinstance(anime, NavigableString):
try:
anime_url, anime_title = (
anime.find("a")["href"],
anime.find("a")["title"],
)
anime_list.append(
{
"title": anime_title,
"url": anime_url,
}
)
except (NotFoundErr, KeyError):
pass
if isinstance(anime, Tag):
anime_url = anime.find("a")
if anime_url is None or isinstance(anime_url, NavigableString):
continue
anime_title = anime.find("a")
if anime_title is None or isinstance(anime_title, NavigableString):
continue
anime_list.append({"title": anime_title["title"], "url": anime_url["href"]})
return anime_list
@ -93,22 +89,24 @@ def search_anime_episode_list(episode_endpoint: str) -> list:
# With this id. get the episode list.
episode_page_ul = soup.find("ul", {"id": "episode_related"})
if episode_page_ul is None or isinstance(episode_page_ul, NavigableString):
msg = f"Could not find any anime eposiodes with name {anime_name}"
raise ValueError(msg)
episode_page_li = episode_page_ul.children
episode_list = []
for episode in episode_page_li:
try:
if not isinstance(episode, NavigableString):
episode_list.append(
{
"title": episode.find("div", {"class": "name"}).text.replace(
" ", ""
),
"url": episode.find("a")["href"],
}
)
except (KeyError, NotFoundErr):
pass
if isinstance(episode, Tag):
url = episode.find("a")
if url is None or isinstance(url, NavigableString):
continue
title = episode.find("div", {"class": "name"})
if title is None or isinstance(title, NavigableString):
continue
episode_list.append(
{"title": title.text.replace(" ", ""), "url": url["href"]}
)
return episode_list
@ -140,11 +138,16 @@ def get_anime_episode(episode_endpoint: str) -> list:
soup = BeautifulSoup(response.text, "html.parser")
try:
episode_url = soup.find("iframe", {"id": "playerframe"})["src"]
download_url = episode_url.replace("/embed/", "/playlist/") + ".m3u8"
except (KeyError, NotFoundErr) as e:
raise e
url = soup.find("iframe", {"id": "playerframe"})
if url is None or isinstance(url, NavigableString):
msg = f"Could not find url and download url from {episode_endpoint}"
raise RuntimeError(msg)
episode_url = url["src"]
if not isinstance(episode_url, str):
msg = f"Could not find url and download url from {episode_endpoint}"
raise RuntimeError(msg)
download_url = episode_url.replace("/embed/", "/playlist/") + ".m3u8"
return [f"{BASE_URL}{episode_url}", f"{BASE_URL}{download_url}"]

27
web_programming/get_top_billionaires.py.disabled → web_programming/get_top_billionaires.py
View File

@ -3,7 +3,7 @@ CAUTION: You may get a json.decoding error.
This works for some of us but fails for others.
"""
from datetime import datetime
from datetime import UTC, datetime, timedelta
import requests
from rich import box
@ -20,18 +20,31 @@ API_URL = (
)
def calculate_age(unix_date: int) -> str:
def calculate_age(unix_date: float) -> str:
"""Calculates age from given unix time format.
Returns:
Age as string
>>> calculate_age(-657244800000)
'73'
>>> calculate_age(46915200000)
'51'
>>> from datetime import datetime, UTC
>>> years_since_create = datetime.now(tz=UTC).year - 2022
>>> int(calculate_age(-657244800000)) - years_since_create
73
>>> int(calculate_age(46915200000)) - years_since_create
51
"""
birthdate = datetime.fromtimestamp(unix_date / 1000).date()
# Convert date from milliseconds to seconds
unix_date /= 1000
if unix_date < 0:
# Handle timestamp before epoch
epoch = datetime.fromtimestamp(0, tz=UTC)
seconds_since_epoch = (datetime.now(tz=UTC) - epoch).seconds
birthdate = (
epoch - timedelta(seconds=abs(unix_date) - seconds_since_epoch)
).date()
else:
birthdate = datetime.fromtimestamp(unix_date, tz=UTC).date()
return str(
TODAY.year
- birthdate.year