Disable quantum/quantum_random.py (attempt 2) (#8902)

* Disable quantum/quantum_random.py

Temporarily disable quantum/quantum_random.py because it produces an illegal instruction error that causes all builds to fail

* updating DIRECTORY.md

* Disable quantum/quantum_random.py attempt 2

---------

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

.devcontainer/Dockerfile

@@ -3,4 +3,6 @@ ARG VARIANT=3.11-bookworm
 FROM mcr.microsoft.com/vscode/devcontainers/python:${VARIANT}
 COPY requirements.txt /tmp/pip-tmp/
 RUN python3 -m pip install --upgrade pip \
-  && python3 -m pip install --no-cache-dir install ruff -r /tmp/pip-tmp/requirements.txt
+  && python3 -m pip install --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
+  && pipx install pre-commit ruff \
+  && pre-commit install

DIRECTORY.md

@@ -511,7 +511,7 @@
   * Lstm
     * [Lstm Prediction](machine_learning/lstm/lstm_prediction.py)
   * [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
-  * [Polymonial Regression](machine_learning/polymonial_regression.py)
+  * [Polynomial Regression](machine_learning/polynomial_regression.py)
   * [Scoring Functions](machine_learning/scoring_functions.py)
   * [Self Organizing Map](machine_learning/self_organizing_map.py)
   * [Sequential Minimum Optimization](machine_learning/sequential_minimum_optimization.py)
@@ -741,6 +741,7 @@
 
 ## Physics
   * [Archimedes Principle](physics/archimedes_principle.py)
+  * [Basic Orbital Capture](physics/basic_orbital_capture.py)
   * [Casimir Effect](physics/casimir_effect.py)
   * [Centripetal Force](physics/centripetal_force.py)
   * [Grahams Law](physics/grahams_law.py)
@@ -1062,7 +1063,6 @@
   * [Q Fourier Transform](quantum/q_fourier_transform.py)
   * [Q Full Adder](quantum/q_full_adder.py)
   * [Quantum Entanglement](quantum/quantum_entanglement.py)
-  * [Quantum Random](quantum/quantum_random.py)
   * [Quantum Teleportation](quantum/quantum_teleportation.py)
   * [Ripple Adder Classic](quantum/ripple_adder_classic.py)
   * [Single Qubit Measure](quantum/single_qubit_measure.py)

ciphers/diffie_hellman.py

@@ -10,13 +10,13 @@ primes = {
     5: {
         "prime": int(
             "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
-            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
-            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
-            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
-            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
-            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
-            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
-            + "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
+            "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
             base=16,
         ),
         "generator": 2,
@@ -25,16 +25,16 @@ primes = {
     14: {
         "prime": int(
             "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
-            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
-            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
-            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
-            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
-            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
-            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
-            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
-            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+            "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
-            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+            "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
-            + "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
+            "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
             base=16,
         ),
         "generator": 2,
@@ -43,21 +43,21 @@ primes = {
     15: {
         "prime": int(
             "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
-            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
-            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
-            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
-            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
-            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
-            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
-            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
-            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+            "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
-            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+            "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
-            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+            "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
-            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+            "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
-            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+            "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
-            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+            "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
-            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+            "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
-            + "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
+            "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
             base=16,
         ),
         "generator": 2,
@@ -66,27 +66,27 @@ primes = {
     16: {
         "prime": int(
             "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
-            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
-            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
-            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
-            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
-            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
-            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
-            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
-            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+            "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
-            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+            "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
-            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+            "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
-            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+            "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
-            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+            "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
-            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+            "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
-            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+            "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
-            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+            "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
-            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+            "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
-            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+            "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
-            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+            "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
-            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+            "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
-            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+            "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
-            + "FFFFFFFFFFFFFFFF",
+            "FFFFFFFFFFFFFFFF",
             base=16,
         ),
         "generator": 2,
@@ -95,33 +95,33 @@ primes = {
     17: {
         "prime": int(
             "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
-            + "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+            "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
-            + "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+            "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
-            + "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+            "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
-            + "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+            "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
-            + "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+            "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
-            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
-            + "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+            "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
-            + "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+            "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
-            + "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+            "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
-            + "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+            "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
-            + "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+            "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
-            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+            "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
-            + "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+            "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
-            + "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+            "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
-            + "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+            "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
-            + "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+            "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
-            + "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+            "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
-            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+            "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
-            + "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+            "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
-            + "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+            "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
-            + "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+            "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
-            + "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+            "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
-            + "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+            "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
-            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+            "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
-            + "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+            "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
-            + "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+            "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
-            + "6DCC4024FFFFFFFFFFFFFFFF",
+            "6DCC4024FFFFFFFFFFFFFFFF",
             base=16,
         ),
         "generator": 2,
@@ -130,48 +130,48 @@ primes = {
     18: {
         "prime": int(
             "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
-            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+            "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
-            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+            "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
-            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+            "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
-            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+            "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
-            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+            "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
-            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+            "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
-            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+            "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
-            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+            "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
-            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+            "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
-            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+            "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
-            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+            "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
-            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+            "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
-            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+            "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
-            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+            "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
-            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+            "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
-            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+            "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
-            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+            "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
-            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+            "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
-            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+            "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
-            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+            "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
-            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+            "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
-            + "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+            "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
-            + "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+            "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
-            + "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+            "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
-            + "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+            "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
-            + "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+            "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
-            + "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+            "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
-            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+            "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
-            + "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+            "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
-            + "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+            "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
-            + "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+            "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
-            + "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+            "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
-            + "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+            "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
-            + "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+            "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
-            + "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+            "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
-            + "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+            "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
-            + "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+            "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
-            + "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+            "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
-            + "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+            "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
-            + "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+            "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
-            + "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+            "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
-            + "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
+            "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
             base=16,
         ),
         "generator": 2,

compression/burrows_wheeler.py

@@ -150,7 +150,7 @@ def reverse_bwt(bwt_string: str, idx_original_string: int) -> str:
         raise ValueError("The parameter idx_original_string must not be lower than 0.")
     if idx_original_string >= len(bwt_string):
         raise ValueError(
-            "The parameter idx_original_string must be lower than" " len(bwt_string)."
+            "The parameter idx_original_string must be lower than len(bwt_string)."
         )
 
     ordered_rotations = [""] * len(bwt_string)

machine_learning/polymonial_regression.py

@@ -1,44 +0,0 @@
-import pandas as pd
-from matplotlib import pyplot as plt
-from sklearn.linear_model import LinearRegression
-
-# Splitting the dataset into the Training set and Test set
-from sklearn.model_selection import train_test_split
-
-# Fitting Polynomial Regression to the dataset
-from sklearn.preprocessing import PolynomialFeatures
-
-# Importing the dataset
-dataset = pd.read_csv(
-    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
-    "position_salaries.csv"
-)
-X = dataset.iloc[:, 1:2].values
-y = dataset.iloc[:, 2].values
-
-
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
-
-
-poly_reg = PolynomialFeatures(degree=4)
-X_poly = poly_reg.fit_transform(X)
-pol_reg = LinearRegression()
-pol_reg.fit(X_poly, y)
-
-
-# Visualizing the Polymonial Regression results
-def viz_polymonial():
-    plt.scatter(X, y, color="red")
-    plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color="blue")
-    plt.title("Truth or Bluff (Linear Regression)")
-    plt.xlabel("Position level")
-    plt.ylabel("Salary")
-    plt.show()
-
-
-if __name__ == "__main__":
-    viz_polymonial()
-
-    # Predicting a new result with Polymonial Regression
-    pol_reg.predict(poly_reg.fit_transform([[5.5]]))
-    # output should be 132148.43750003

machine_learning/polynomial_regression.py

@@ -0,0 +1,213 @@
+"""
+Polynomial regression is a type of regression analysis that models the relationship
+between a predictor x and the response y as an mth-degree polynomial:
+
+y = β₀ + β₁x + β₂x² + ... + βₘxᵐ + ε
+
+By treating x, x², ..., xᵐ as distinct variables, we see that polynomial regression is a
+special case of multiple linear regression. Therefore, we can use ordinary least squares
+(OLS) estimation to estimate the vector of model parameters β = (β₀, β₁, β₂, ..., βₘ)
+for polynomial regression:
+
+β = (XᵀX)⁻¹Xᵀy = X⁺y
+
+where X is the design matrix, y is the response vector, and X⁺ denotes the Moore–Penrose
+pseudoinverse of X. In the case of polynomial regression, the design matrix is
+
+    |1  x₁  x₁² ⋯ x₁ᵐ|
+X = |1  x₂  x₂² ⋯ x₂ᵐ|
+    |⋮  ⋮   ⋮   ⋱ ⋮  |
+    |1  xₙ  xₙ² ⋯  xₙᵐ|
+
+In OLS estimation, inverting XᵀX to compute X⁺ can be very numerically unstable. This
+implementation sidesteps this need to invert XᵀX by computing X⁺ using singular value
+decomposition (SVD):
+
+β = VΣ⁺Uᵀy
+
+where UΣVᵀ is an SVD of X.
+
+References:
+    - https://en.wikipedia.org/wiki/Polynomial_regression
+    - https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse
+    - https://en.wikipedia.org/wiki/Numerical_methods_for_linear_least_squares
+    - https://en.wikipedia.org/wiki/Singular_value_decomposition
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+class PolynomialRegression:
+    __slots__ = "degree", "params"
+
+    def __init__(self, degree: int) -> None:
+        """
+        @raises ValueError: if the polynomial degree is negative
+        """
+        if degree < 0:
+            raise ValueError("Polynomial degree must be non-negative")
+
+        self.degree = degree
+        self.params = None
+
+    @staticmethod
+    def _design_matrix(data: np.ndarray, degree: int) -> np.ndarray:
+        """
+        Constructs a polynomial regression design matrix for the given input data. For
+        input data x = (x₁, x₂, ..., xₙ) and polynomial degree m, the design matrix is
+        the Vandermonde matrix
+
+            |1  x₁  x₁² ⋯ x₁ᵐ|
+        X = |1  x₂  x₂² ⋯ x₂ᵐ|
+            |⋮  ⋮   ⋮   ⋱ ⋮  |
+            |1  xₙ  xₙ² ⋯  xₙᵐ|
+
+        Reference: https://en.wikipedia.org/wiki/Vandermonde_matrix
+
+        @param data:    the input predictor values x, either for model fitting or for
+                        prediction
+        @param degree:  the polynomial degree m
+        @returns:       the Vandermonde matrix X (see above)
+        @raises ValueError: if input data is not N x 1
+
+        >>> x = np.array([0, 1, 2])
+        >>> PolynomialRegression._design_matrix(x, degree=0)
+        array([[1],
+               [1],
+               [1]])
+        >>> PolynomialRegression._design_matrix(x, degree=1)
+        array([[1, 0],
+               [1, 1],
+               [1, 2]])
+        >>> PolynomialRegression._design_matrix(x, degree=2)
+        array([[1, 0, 0],
+               [1, 1, 1],
+               [1, 2, 4]])
+        >>> PolynomialRegression._design_matrix(x, degree=3)
+        array([[1, 0, 0, 0],
+               [1, 1, 1, 1],
+               [1, 2, 4, 8]])
+        >>> PolynomialRegression._design_matrix(np.array([[0, 0], [0 , 0]]), degree=3)
+        Traceback (most recent call last):
+        ...
+        ValueError: Data must have dimensions N x 1
+        """
+        rows, *remaining = data.shape
+        if remaining:
+            raise ValueError("Data must have dimensions N x 1")
+
+        return np.vander(data, N=degree + 1, increasing=True)
+
+    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
+        """
+        Computes the polynomial regression model parameters using ordinary least squares
+        (OLS) estimation:
+
+        β = (XᵀX)⁻¹Xᵀy = X⁺y
+
+        where X⁺ denotes the Moore–Penrose pseudoinverse of the design matrix X. This
+        function computes X⁺ using singular value decomposition (SVD).
+
+        References:
+            - https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse
+            - https://en.wikipedia.org/wiki/Singular_value_decomposition
+            - https://en.wikipedia.org/wiki/Multicollinearity
+
+        @param x_train: the predictor values x for model fitting
+        @param y_train: the response values y for model fitting
+        @raises ArithmeticError:    if X isn't full rank, then XᵀX is singular and β
+                                    doesn't exist
+
+        >>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        >>> y = x**3 - 2 * x**2 + 3 * x - 5
+        >>> poly_reg = PolynomialRegression(degree=3)
+        >>> poly_reg.fit(x, y)
+        >>> poly_reg.params
+        array([-5.,  3., -2.,  1.])
+        >>> poly_reg = PolynomialRegression(degree=20)
+        >>> poly_reg.fit(x, y)
+        Traceback (most recent call last):
+        ...
+        ArithmeticError: Design matrix is not full rank, can't compute coefficients
+
+        Make sure errors don't grow too large:
+        >>> coefs = np.array([-250, 50, -2, 36, 20, -12, 10, 2, -1, -15, 1])
+        >>> y = PolynomialRegression._design_matrix(x, len(coefs) - 1) @ coefs
+        >>> poly_reg = PolynomialRegression(degree=len(coefs) - 1)
+        >>> poly_reg.fit(x, y)
+        >>> np.allclose(poly_reg.params, coefs, atol=10e-3)
+        True
+        """
+        X = PolynomialRegression._design_matrix(x_train, self.degree)  # noqa: N806
+        _, cols = X.shape
+        if np.linalg.matrix_rank(X) < cols:
+            raise ArithmeticError(
+                "Design matrix is not full rank, can't compute coefficients"
+            )
+
+        # np.linalg.pinv() computes the Moore–Penrose pseudoinverse using SVD
+        self.params = np.linalg.pinv(X) @ y_train
+
+    def predict(self, data: np.ndarray) -> np.ndarray:
+        """
+        Computes the predicted response values y for the given input data by
+        constructing the design matrix X and evaluating y = Xβ.
+
+        @param data:    the predictor values x for prediction
+        @returns:       the predicted response values y = Xβ
+        @raises ArithmeticError:    if this function is called before the model
+                                    parameters are fit
+
+        >>> x = np.array([0, 1, 2, 3, 4])
+        >>> y = x**3 - 2 * x**2 + 3 * x - 5
+        >>> poly_reg = PolynomialRegression(degree=3)
+        >>> poly_reg.fit(x, y)
+        >>> poly_reg.predict(np.array([-1]))
+        array([-11.])
+        >>> poly_reg.predict(np.array([-2]))
+        array([-27.])
+        >>> poly_reg.predict(np.array([6]))
+        array([157.])
+        >>> PolynomialRegression(degree=3).predict(x)
+        Traceback (most recent call last):
+        ...
+        ArithmeticError: Predictor hasn't been fit yet
+        """
+        if self.params is None:
+            raise ArithmeticError("Predictor hasn't been fit yet")
+
+        return PolynomialRegression._design_matrix(data, self.degree) @ self.params
+
+
+def main() -> None:
+    """
+    Fit a polynomial regression model to predict fuel efficiency using seaborn's mpg
+    dataset
+
+    >>> pass    # Placeholder, function is only for demo purposes
+    """
+    import seaborn as sns
+
+    mpg_data = sns.load_dataset("mpg")
+
+    poly_reg = PolynomialRegression(degree=2)
+    poly_reg.fit(mpg_data.weight, mpg_data.mpg)
+
+    weight_sorted = np.sort(mpg_data.weight)
+    predictions = poly_reg.predict(weight_sorted)
+
+    plt.scatter(mpg_data.weight, mpg_data.mpg, color="gray", alpha=0.5)
+    plt.plot(weight_sorted, predictions, color="red", linewidth=3)
+    plt.title("Predicting Fuel Efficiency Using Polynomial Regression")
+    plt.xlabel("Weight (lbs)")
+    plt.ylabel("Fuel Efficiency (mpg)")
+    plt.show()
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
+
+    main()

neural_network/input_data.py

@@ -263,9 +263,7 @@ def _maybe_download(filename, work_directory, source_url):
     return filepath
 
 
-@deprecated(
+@deprecated(None, "Please use alternatives such as: tensorflow_datasets.load('mnist')")
-    None, "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"
-)
 def read_data_sets(
     train_dir,
     fake_data=False,

physics/basic_orbital_capture.py

@@ -0,0 +1,178 @@
+from math import pow, sqrt
+
+from scipy.constants import G, c, pi
+
+"""
+These two functions will return the radii of impact for a target object
+of mass M and radius R as well as it's effective cross sectional area σ(sigma).
+That is to say any projectile with velocity v passing within σ, will impact the
+target object with mass M. The derivation of which is given at the bottom
+of this file.
+
+The derivation shows that a projectile does not need to aim directly at the target
+body in order to hit it, as  R_capture>R_target. Astronomers refer to the effective
+cross section for capture as σ=π*R_capture**2.
+
+This algorithm does not account for an N-body problem.
+
+"""
+
+
+def capture_radii(
+    target_body_radius: float, target_body_mass: float, projectile_velocity: float
+) -> float:
+    """
+    Input Params:
+    -------------
+    target_body_radius: Radius of the central body SI units: meters | m
+    target_body_mass: Mass of the central body SI units: kilograms | kg
+    projectile_velocity: Velocity of object moving toward central body
+        SI units: meters/second | m/s
+    Returns:
+    --------
+    >>> capture_radii(6.957e8, 1.99e30, 25000.0)
+    17209590691.0
+    >>> capture_radii(-6.957e8, 1.99e30, 25000.0)
+    Traceback (most recent call last):
+        ...
+    ValueError: Radius cannot be less than 0
+    >>> capture_radii(6.957e8, -1.99e30, 25000.0)
+    Traceback (most recent call last):
+        ...
+    ValueError: Mass cannot be less than 0
+    >>> capture_radii(6.957e8, 1.99e30, c+1)
+    Traceback (most recent call last):
+        ...
+    ValueError: Cannot go beyond speed of light
+
+    Returned SI units:
+    ------------------
+    meters | m
+    """
+
+    if target_body_mass < 0:
+        raise ValueError("Mass cannot be less than 0")
+    if target_body_radius < 0:
+        raise ValueError("Radius cannot be less than 0")
+    if projectile_velocity > c:
+        raise ValueError("Cannot go beyond speed of light")
+
+    escape_velocity_squared = (2 * G * target_body_mass) / target_body_radius
+    capture_radius = target_body_radius * sqrt(
+        1 + escape_velocity_squared / pow(projectile_velocity, 2)
+    )
+    return round(capture_radius, 0)
+
+
+def capture_area(capture_radius: float) -> float:
+    """
+    Input Param:
+    ------------
+    capture_radius: The radius of orbital capture and impact for a central body of
+    mass M and a projectile moving towards it with velocity v
+        SI units: meters | m
+    Returns:
+    --------
+    >>> capture_area(17209590691)
+    9.304455331329126e+20
+    >>> capture_area(-1)
+    Traceback (most recent call last):
+        ...
+    ValueError: Cannot have a capture radius less than 0
+
+    Returned SI units:
+    ------------------
+    meters*meters | m**2
+    """
+
+    if capture_radius < 0:
+        raise ValueError("Cannot have a capture radius less than 0")
+    sigma = pi * pow(capture_radius, 2)
+    return round(sigma, 0)
+
+
+if __name__ == "__main__":
+    from doctest import testmod
+
+    testmod()
+
+"""
+Derivation:
+
+Let: Mt=target mass, Rt=target radius, v=projectile_velocity,
+     r_0=radius of projectile at instant 0 to CM of target
+     v_p=v at closest approach,
+     r_p=radius from projectile to target CM at closest approach,
+     R_capture= radius of impact for projectile with velocity v
+
+(1)At time=0  the projectile's energy falling from infinity| E=K+U=0.5*m*(v**2)+0
+
+    E_initial=0.5*m*(v**2)
+
+(2)at time=0 the angular momentum of the projectile relative to CM target|
+    L_initial=m*r_0*v*sin(Θ)->m*r_0*v*(R_capture/r_0)->m*v*R_capture
+
+    L_i=m*v*R_capture
+
+(3)The energy of the projectile at closest approach will be its kinetic energy
+   at closest approach plus gravitational potential energy(-(GMm)/R)|
+    E_p=K_p+U_p->E_p=0.5*m*(v_p**2)-(G*Mt*m)/r_p
+
+    E_p=0.0.5*m*(v_p**2)-(G*Mt*m)/r_p
+
+(4)The angular momentum of the projectile relative to the target at closest
+   approach will be L_p=m*r_p*v_p*sin(Θ), however relative to the target Θ=90°
+   sin(90°)=1|
+
+    L_p=m*r_p*v_p
+(5)Using conservation of angular momentum and energy, we can write a quadratic
+   equation that solves for r_p|
+
+   (a)
+    Ei=Ep-> 0.5*m*(v**2)=0.5*m*(v_p**2)-(G*Mt*m)/r_p-> v**2=v_p**2-(2*G*Mt)/r_p
+
+   (b)
+    Li=Lp-> m*v*R_capture=m*r_p*v_p-> v*R_capture=r_p*v_p-> v_p=(v*R_capture)/r_p
+
+   (c) b plugs int a|
+    v**2=((v*R_capture)/r_p)**2-(2*G*Mt)/r_p->
+
+    v**2-(v**2)*(R_c**2)/(r_p**2)+(2*G*Mt)/r_p=0->
+
+    (v**2)*(r_p**2)+2*G*Mt*r_p-(v**2)*(R_c**2)=0
+
+   (d) Using the quadratic formula, we'll solve for r_p then rearrange to solve to
+       R_capture
+
+    r_p=(-2*G*Mt ± sqrt(4*G^2*Mt^2+ 4(v^4*R_c^2)))/(2*v^2)->
+
+    r_p=(-G*Mt ± sqrt(G^2*Mt+v^4*R_c^2))/v^2->
+
+    r_p<0 is something we can ignore, as it has no physical meaning for our purposes.->
+
+    r_p=(-G*Mt)/v^2 + sqrt(G^2*Mt^2/v^4 + R_c^2)
+
+   (e)We are trying to solve for R_c. We are looking for impact, so we want r_p=Rt
+
+    Rt + G*Mt/v^2 = sqrt(G^2*Mt^2/v^4 + R_c^2)->
+
+    (Rt + G*Mt/v^2)^2 = G^2*Mt^2/v^4 + R_c^2->
+
+    Rt^2 + 2*G*Mt*Rt/v^2 + G^2*Mt^2/v^4 = G^2*Mt^2/v^4 + R_c^2->
+
+    Rt**2 + 2*G*Mt*Rt/v**2 = R_c**2->
+
+    Rt**2 * (1 + 2*G*Mt/Rt *1/v**2) = R_c**2->
+
+    escape velocity = sqrt(2GM/R)= v_escape**2=2GM/R->
+
+    Rt**2 * (1 + v_esc**2/v**2) = R_c**2->
+
+(6)
+    R_capture = Rt * sqrt(1 + v_esc**2/v**2)
+
+Source: Problem Set 3 #8 c.Fall_2017|Honors Astronomy|Professor Rachel Bezanson
+
+Source #2: http://www.nssc.ac.cn/wxzygx/weixin/201607/P020160718380095698873.pdf
+           8.8 Planetary Rendezvous: Pg.368
+"""

pyproject.toml

@@ -49,6 +49,7 @@ select = [  # https://beta.ruff.rs/docs/rules
   "ICN",    # flake8-import-conventions
   "INP",    # flake8-no-pep420
   "INT",    # flake8-gettext
+  "ISC",  # flake8-implicit-str-concat
   "N",      # pep8-naming
   "NPY",    # NumPy-specific rules
   "PGH",    # pygrep-hooks
@@ -72,7 +73,6 @@ select = [  # https://beta.ruff.rs/docs/rules
   # "DJ",   # flake8-django
   # "ERA",  # eradicate -- DO NOT FIX
   # "FBT",  # flake8-boolean-trap  # FIX ME
-  # "ISC",  # flake8-implicit-str-concat  # FIX ME
   # "PD",   # pandas-vet
   # "PT",   # flake8-pytest-style
   # "PTH",  # flake8-use-pathlib  # FIX ME

requirements.txt

@@ -9,6 +9,7 @@ pandas
 pillow
 projectq
 qiskit
+qiskit-aer
 requests
 rich
 scikit-fuzzy

strings/is_srilankan_phone_number.py

@@ -22,9 +22,7 @@ def is_sri_lankan_phone_number(phone: str) -> bool:
     False
     """
 
-    pattern = re.compile(
+    pattern = re.compile(r"^(?:0|94|\+94|0{2}94)7(0|1|2|4|5|6|7|8)(-| |)\d{7}$")
-        r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$"
-    )
 
     return bool(re.search(pattern, phone))
 

web_programming/world_covid19_stats.py

@@ -22,6 +22,5 @@ def world_covid19_stats(url: str = "https://www.worldometers.info/coronavirus")
 
 
 if __name__ == "__main__":
-    print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
+    print("\033[1m COVID-19 Status of the World \033[0m\n")
-    for key, value in world_covid19_stats().items():
+    print("\n".join(f"{key}\n{value}" for key, value in world_covid19_stats().items()))
-        print(f"{key}\n{value}\n")