From e7b6d2824a65985790d0044262f717898ffbeb4d Mon Sep 17 00:00:00 2001 From: Sagar Giri Date: Sun, 16 Oct 2022 16:43:29 +0900 Subject: [PATCH] Change to https. (#7277) * Change to https. * Revert the py_tf file. --- fractals/julia_sets.py | 2 +- fractals/sierpinski_triangle.py | 2 +- machine_learning/lstm/lstm_prediction.py | 2 +- machine_learning/sequential_minimum_optimization.py | 4 ++-- maths/matrix_exponentiation.py | 2 +- maths/test_prime_check.py | 2 +- physics/n_body_simulation.py | 4 ++-- strings/frequency_finder.py | 2 +- web_programming/crawl_google_results.py | 2 +- web_programming/crawl_google_scholar_citation.py | 2 +- web_programming/current_weather.py | 2 +- web_programming/giphy.py | 2 +- 12 files changed, 14 insertions(+), 14 deletions(-) diff --git a/fractals/julia_sets.py b/fractals/julia_sets.py index 28c675c75..35fdc45d0 100644 --- a/fractals/julia_sets.py +++ b/fractals/julia_sets.py @@ -12,7 +12,7 @@ The examples presented here are: https://en.wikipedia.org/wiki/File:Julia_z2%2B0,25.png - Other examples from https://en.wikipedia.org/wiki/Julia_set - An exponential map Julia set, ambiantly homeomorphic to the examples in -http://www.math.univ-toulouse.fr/~cheritat/GalII/galery.html +https://www.math.univ-toulouse.fr/~cheritat/GalII/galery.html and https://ddd.uab.cat/pub/pubmat/02141493v43n1/02141493v43n1p27.pdf diff --git a/fractals/sierpinski_triangle.py b/fractals/sierpinski_triangle.py index 8be2897c1..084f6661f 100644 --- a/fractals/sierpinski_triangle.py +++ b/fractals/sierpinski_triangle.py @@ -24,7 +24,7 @@ Usage: - $python sierpinski_triangle.py Credits: This code was written by editing the code from -http://www.riannetrujillo.com/blog/python-fractal/ +https://www.riannetrujillo.com/blog/python-fractal/ """ import sys diff --git a/machine_learning/lstm/lstm_prediction.py b/machine_learning/lstm/lstm_prediction.py index 6fd3cf291..74197c46a 100644 --- a/machine_learning/lstm/lstm_prediction.py +++ b/machine_learning/lstm/lstm_prediction.py @@ -1,7 +1,7 @@ """ Create a Long Short Term Memory (LSTM) network model An LSTM is a type of Recurrent Neural Network (RNN) as discussed at: - * http://colah.github.io/posts/2015-08-Understanding-LSTMs + * https://colah.github.io/posts/2015-08-Understanding-LSTMs * https://en.wikipedia.org/wiki/Long_short-term_memory """ import numpy as np diff --git a/machine_learning/sequential_minimum_optimization.py b/machine_learning/sequential_minimum_optimization.py index fb4b35f31..40adca7e0 100644 --- a/machine_learning/sequential_minimum_optimization.py +++ b/machine_learning/sequential_minimum_optimization.py @@ -28,7 +28,7 @@ Usage: Reference: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/smo-book.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf - http://web.cs.iastate.edu/~honavar/smo-svm.pdf + https://web.cs.iastate.edu/~honavar/smo-svm.pdf """ @@ -43,7 +43,7 @@ from sklearn.datasets import make_blobs, make_circles from sklearn.preprocessing import StandardScaler CANCER_DATASET_URL = ( - "http://archive.ics.uci.edu/ml/machine-learning-databases/" + "https://archive.ics.uci.edu/ml/machine-learning-databases/" "breast-cancer-wisconsin/wdbc.data" ) diff --git a/maths/matrix_exponentiation.py b/maths/matrix_exponentiation.py index 033ceb3f2..7c37151c8 100644 --- a/maths/matrix_exponentiation.py +++ b/maths/matrix_exponentiation.py @@ -5,7 +5,7 @@ import timeit """ Matrix Exponentiation is a technique to solve linear recurrences in logarithmic time. You read more about it here: -http://zobayer.blogspot.com/2010/11/matrix-exponentiation.html +https://zobayer.blogspot.com/2010/11/matrix-exponentiation.html https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/ """ diff --git a/maths/test_prime_check.py b/maths/test_prime_check.py index b6389684a..3ea3b2f1f 100644 --- a/maths/test_prime_check.py +++ b/maths/test_prime_check.py @@ -1,6 +1,6 @@ """ Minimalist file that allows pytest to find and run the Test unittest. For details, see: -http://doc.pytest.org/en/latest/goodpractices.html#conventions-for-python-test-discovery +https://doc.pytest.org/en/latest/goodpractices.html#conventions-for-python-test-discovery """ from .prime_check import Test diff --git a/physics/n_body_simulation.py b/physics/n_body_simulation.py index 2f8153782..e62e1de62 100644 --- a/physics/n_body_simulation.py +++ b/physics/n_body_simulation.py @@ -8,7 +8,7 @@ velocity and position brought about by these forces. Softening is used to preven numerical divergences when a particle comes too close to another (and the force goes to infinity). (Description adapted from https://en.wikipedia.org/wiki/N-body_simulation ) -(See also http://www.shodor.org/refdesk/Resources/Algorithms/EulersMethod/ ) +(See also https://www.shodor.org/refdesk/Resources/Algorithms/EulersMethod/ ) """ @@ -258,7 +258,7 @@ def example_1() -> BodySystem: Example 1: figure-8 solution to the 3-body-problem This example can be seen as a test of the implementation: given the right initial conditions, the bodies should move in a figure-8. - (initial conditions taken from http://www.artcompsci.org/vol_1/v1_web/node56.html) + (initial conditions taken from https://www.artcompsci.org/vol_1/v1_web/node56.html) >>> body_system = example_1() >>> len(body_system) 3 diff --git a/strings/frequency_finder.py b/strings/frequency_finder.py index 7024be17b..19f97afbb 100644 --- a/strings/frequency_finder.py +++ b/strings/frequency_finder.py @@ -2,7 +2,7 @@ import string -# frequency taken from http://en.wikipedia.org/wiki/Letter_frequency +# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency english_letter_freq = { "E": 12.70, "T": 9.06, diff --git a/web_programming/crawl_google_results.py b/web_programming/crawl_google_results.py index a33a3f3bb..1f5e6d319 100644 --- a/web_programming/crawl_google_results.py +++ b/web_programming/crawl_google_results.py @@ -21,4 +21,4 @@ if __name__ == "__main__": if link.text == "Maps": webbrowser.open(link.get("href")) else: - webbrowser.open(f"http://google.com{link.get('href')}") + webbrowser.open(f"https://google.com{link.get('href')}") diff --git a/web_programming/crawl_google_scholar_citation.py b/web_programming/crawl_google_scholar_citation.py index d023380c0..f92a3d139 100644 --- a/web_programming/crawl_google_scholar_citation.py +++ b/web_programming/crawl_google_scholar_citation.py @@ -29,4 +29,4 @@ if __name__ == "__main__": "year": 2018, "hl": "en", } - print(get_citation("http://scholar.google.com/scholar_lookup", params=params)) + print(get_citation("https://scholar.google.com/scholar_lookup", params=params)) diff --git a/web_programming/current_weather.py b/web_programming/current_weather.py index e043b4384..3ed4c8a95 100644 --- a/web_programming/current_weather.py +++ b/web_programming/current_weather.py @@ -1,7 +1,7 @@ import requests APPID = "" # <-- Put your OpenWeatherMap appid here! -URL_BASE = "http://api.openweathermap.org/data/2.5/" +URL_BASE = "https://api.openweathermap.org/data/2.5/" def current_weather(q: str = "Chicago", appid: str = APPID) -> dict: diff --git a/web_programming/giphy.py b/web_programming/giphy.py index dc8c6be08..a5c3f8f74 100644 --- a/web_programming/giphy.py +++ b/web_programming/giphy.py @@ -10,7 +10,7 @@ def get_gifs(query: str, api_key: str = giphy_api_key) -> list: Get a list of URLs of GIFs based on a given query.. """ formatted_query = "+".join(query.split()) - url = f"http://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}" + url = f"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}" gifs = requests.get(url).json()["data"] return [gif["url"] for gif in gifs]