2024-09-10 17:12:04.049356 new snippets

seeker/report.txt

@@ -1,3 +1,20 @@
+--------------------------------------------------------------------------------
+ 2024-09-10 17:12:04.049356
+--------------------------------------------------------------------------------
+  On branch main
+Your branch is up to date with 'origin/main'.
+
+Untracked files:
+  (use "git add <file>..." to include in what will be committed)
+	snippet/CuteBot.py
+	snippet/Generic 4-Quadrant Regression.py
+	snippet/a.sh
+	snippet/circular_heatexchange.py
+	snippet/dumb-random-number-generator.go
+	snippet/resize.py
+
+nothing added to commit but untracked files present (use "git add" to track)
+
 --------------------------------------------------------------------------------
  2024-09-09 17:12:26.410196
 --------------------------------------------------------------------------------

seeker/snippet/CuteBot.py

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+#date: 2024-09-10T16:45:57Z
+#url: https://api.github.com/gists/0150766320516a0cfcfda5d94c2d18fe
+#owner: https://api.github.com/users/MattieOF
+
+from telethon import *
+import asyncio
+import json
+import os
+import time
+import dataclasses
+import sys
+import datetime
+from dataclasses import dataclass
+
+# Thanks to https://stackoverflow.com/a/54769644
+def dataclass_from_dict(klass, d):
+    try:
+        fieldtypes = {f.name:f.type for f in dataclasses.fields(klass)}
+        return klass(**{f:dataclass_from_dict(fieldtypes[f],d[f]) for f in d})
+    except:
+        return d # Not a dataclass field
+
+api_id = 69420 # get your own at: https://my.telegram.org/apps
+api_hash = "nuh uh, you're not getting this one :3"
+
+client = TelegramClient('CuteBot', api_id, api_hash)
+
+@dataclass
+class ScheduledMessage:
+    username: str
+    message: str
+    interval: int
+    last_sent: float
+
+scheduledMessages = []
+if (os.path.exists("scheduledMessages.json")):
+    with open("scheduledMessages.json", "r") as file:
+        loadedMessages = json.load(file)
+        scheduledMessages = [dataclass_from_dict(ScheduledMessage, msg) for msg in loadedMessages]
+
+def save_messages():
+    with open("scheduledMessages.json", "w") as file:
+        scheduledMessagesAsDict = list(map(lambda msg: dataclasses.asdict(msg), scheduledMessages))
+        json.dump(scheduledMessagesAsDict, file)
+
+async def loop():
+    logOut = False
+
+    async def check_messages():
+        didEdit = False
+        for msg in scheduledMessages:
+            if time.time() - msg.last_sent >= msg.interval:
+                try:
+                    await client.send_message(msg.username, msg.message)
+                    msg.last_sent = time.time()
+                    print(f"Sent message to {msg.username}! Next message in {msg.interval} seconds (at {datetime.datetime.fromtimestamp(time.time() + msg.interval).strftime('%Y-%m-%d %H:%M:%S')})")
+                    didEdit = True
+                except Exception as error:
+                    print(f"Failed to send message to {msg.username}! Due to {error}")
+        if didEdit:
+            save_messages()
+
+    if "autorun" in sys.argv:
+        print("Running!")
+        while True:
+            await check_messages()
+            await asyncio.sleep(1)
+
+    while True:
+        cmd = input("Enter command: ")
+        if cmd == "exit":
+            break
+        elif cmd == "logout":
+            logOut = True
+            break
+
+        cmd = cmd.split(" ")
+        if cmd[0] == "list":
+            # List all scheduled messages
+            # Format: list
+            if len(scheduledMessages) == 0:
+                print("No scheduled messages yet :( Use the 'add' command to add one!")
+                continue
+
+            for i, msg in enumerate(scheduledMessages):
+                print(f"{i}: To {msg.username}, \"{msg.message}\" (every {msg.interval} seconds)")
+        elif cmd[0] == "add":
+            # Add a scheduled message
+            # Format: add <username> <interval in seconds> <message>
+            if len(cmd) < 4:
+                print("Not enough parameters! Format: add <username> <interval in seconds> <message>")
+                continue
+
+            try:
+                interval = float(cmd[2])
+                if interval < 1:
+                    raise ValueError
+            except ValueError:
+                print("Invalid interval! Must be an integer above 1.")
+                continue
+
+            try:
+                await client.send_message(cmd[1], "new scheduled message added :3")
+            except:
+                print("Invalid username! Make sure you have the correct username and that you've sent a message to the user at least once.")
+                continue
+
+            scheduledMessages.append(ScheduledMessage(username=cmd[1], interval=interval, message=" ".join(cmd[3:]), last_sent=time.time()))
+            save_messages()
+        elif cmd[0] == "remove":
+            # Remove a scheduled message by index from the list
+            # Format: remove <index>
+            if len(cmd) < 2:
+                print("Not enough parameters! Format: remove <index>")
+                continue
+
+            try:
+                index = int(cmd[1])
+                if index < 0 or index >= len(scheduledMessages):
+                    raise ValueError
+            except ValueError:
+                print("Invalid index! Must be an integer within the range of the list.")
+                continue
+
+            scheduledMessages.pop(index)
+            save_messages()
+        elif cmd[0] == "run":
+            while True:
+                await check_messages()
+                await asyncio.sleep(1)
+
+    if logOut:
+        await client.log_out()
+        print("Logged out!")
+
+with client:
+    client.loop.run_until_complete(loop())
+
+print("Bye!")

seeker/snippet/Generic 4-Quadrant Regression.py

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+#date: 2024-09-10T16:44:44Z
+#url: https://api.github.com/gists/01e1f2f2fd5a413db7dbd68df088489a
+#owner: https://api.github.com/users/srkim
+
+import pandas as pd
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import mutual_info_score
+import scipy.stats as stats
+from scipy.stats import linregress
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+import numpy as np
+
+# Set the aesthetic style of the plots
+sns.set(style="whitegrid")
+
+# Read data from an Excel file and store it in a Pandas DataFrame
+df = pd.read_excel('CorrelationsHedge2.xlsx')
+
+# Define the response and explanatory variables
+y = df['Y']
+x = df['X']
+
+# Calculate medians and IQRs for x and y
+median_x = np.median(x)
+median_y = np.median(y)
+q1_x, q3_x = np.percentile(x, [25, 75])
+q1_y, q3_y = np.percentile(y, [25, 75])
+iqr_x = q3_x - q1_x
+iqr_y = q3_y - q1_y
+
+# Identify outliers
+outliers = (x < q1_x - 1.5 * iqr_x) | (x > q3_x + 1.5 * iqr_x) | (y < q1_y - 1.5 * iqr_y) | (y > q3_y + 1.5 * iqr_y)
+
+# Calculate the slope, intercept, and R-squared value of the regression line
+slope, intercept, r_value, _, _ = linregress(x, y)
+r_squared = r_value**2
+
+# Generate the regression line
+regression_line = slope * x + intercept
+
+# Divide data into quadrants counterclockwise from the top right
+q1 = (x >= 0) & (y >= 0)  # Top right
+q2 = (x <= 0) & (y >= 0)  # Top left
+q3 = (x <= 0) & (y <= 0)  # Bottom left
+q4 = (x >= 0) & (y <= 0)  # Bottom right
+
+# Calculate correlations for each quadrant
+corr_q1 = np.corrcoef(x[q1], y[q1])[0, 1]
+corr_q2 = np.corrcoef(x[q2], y[q2])[0, 1]
+corr_q3 = np.corrcoef(x[q3], y[q3])[0, 1]
+corr_q4 = np.corrcoef(x[q4], y[q4])[0, 1]
+
+# Calculate overall correlation
+overall_corr = np.corrcoef(x, y)[0, 1]
+
+# Calculate the number of points in each quadrant and the percentage of total points
+total_points = len(x)
+q1_count = np.sum(q1)
+q2_count = np.sum(q2)
+q3_count = np.sum(q3)
+q4_count = np.sum(q4)
+
+q1_percentage = (q1_count / total_points) * 100
+q2_percentage = (q2_count / total_points) * 100
+q3_percentage = (q3_count / total_points) * 100
+q4_percentage = (q4_count / total_points) * 100
+
+# Calculate the number of points where x is greater than y in each quadrant
+q1_x_greater_y = np.sum((q1) & (x > y))
+q2_x_greater_y = np.sum((q2) & (x > y))
+q3_x_greater_y = np.sum((q3) & (x > y))
+q4_x_greater_y = np.sum((q4) & (x > y))
+
+# Calculate the percentage of points where x is greater than y in each quadrant
+q1_x_greater_y_percentage = (q1_x_greater_y / q1_count) * 100
+q2_x_greater_y_percentage = (q2_x_greater_y / q2_count) * 100
+q3_x_greater_y_percentage = (q3_x_greater_y / q3_count) * 100
+q4_x_greater_y_percentage = (q4_x_greater_y / q4_count) * 100
+
+# Print summary statistics
+print(f"Sum of correlations in quadrants: {corr_q1 + corr_q2 + corr_q3 + corr_q4:.4f}")
+print(f"Overall correlation: {overall_corr:.4f}")
+print('________________________________________________')
+
+# Print the number of points in each quadrant and the percentage of points from the total points
+print(f"Number of points in Q1: {q1_count} ({q1_percentage:.2f}%)")
+print(f"Number of points in Q2: {q2_count} ({q2_percentage:.2f}%)")
+print(f"Number of points in Q3: {q3_count} ({q3_percentage:.2f}%)")
+print(f"Number of points in Q4: {q4_count} ({q4_percentage:.2f}%)")
+print('________________________________________________')
+
+# Print the percentage of points where x is greater than y in each quadrant
+print(f"Percentage of points where x > y in Q1: {q1_x_greater_y_percentage:.2f}%")
+print(f"Percentage of points where x > y in Q2: {q2_x_greater_y_percentage:.2f}%")
+print(f"Percentage of points where x > y in Q3: {q3_x_greater_y_percentage:.2f}%")
+print(f"Percentage of points where x > y in Q4: {q4_x_greater_y_percentage:.2f}%")
+
+# Create scatter plot with colored quadrants and 'x' marker for outliers
+plt.figure(figsize=(12, 8))
+plt.scatter(x[q1 & ~outliers], y[q1 & ~outliers], alpha=.6, s=50, label=f'Q1: {corr_q1:.4f}')
+plt.scatter(x[q2 & ~outliers], y[q2 & ~outliers], alpha=.6, s=50, label=f'Q2: {corr_q2:.4f}')
+plt.scatter(x[q3 & ~outliers], y[q3 & ~outliers], alpha=.6, s=50, label=f'Q3: {corr_q3:.4f}')
+plt.scatter(x[q4 & ~outliers], y[q4 & ~outliers], alpha=.6, s=50, label=f'Q4: {corr_q4:.4f}')
+
+# Plot outliers with 'x' marker with the same color as their respective quadrants
+plt.scatter(x[q1 & outliers], y[q1 & outliers], s=40, color='C0', marker='x')
+plt.scatter(x[q2 & outliers], y[q2 & outliers], s=40, color='C1', marker='x')
+plt.scatter(x[q3 & outliers], y[q3 & outliers], s=40, color='C2', marker='x')
+plt.scatter(x[q4 & outliers], y[q4 & outliers], s=40, color='C3', marker='x')
+
+# Plot regression line and axis lines
+plt.plot(x, regression_line, color="gray", label="Regression Line")
+plt.axvline(x=0, color='gray', linestyle='--')
+plt.axhline(y=0, color='gray', linestyle='--')
+
+plt.title(f'Y vs. X\nOverall Correlation: {overall_corr:.4f}\nR-squared: {r_squared:.4f}\nSlope: {slope:.4f}: x are Outliers', fontsize=14)
+plt.xlabel('X')
+plt.ylabel('Y')
+plt.legend()
+
+# Save the plot as an SVG file
+plt.savefig('Correlation.svg')
+
+plt.show()

seeker/snippet/a.sh

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+#date: 2024-09-10T16:56:58Z
+#url: https://api.github.com/gists/b5462dcec2d47ef4e4bdcdf05d3e4303
+#owner: https://api.github.com/users/justin2004
+
+# first download https://dlcdn.apache.org/jena/binaries/apache-jena-5.1.0.zip
+# then unzip it
+
+% mkdir /tmp/db1
+% ls ~/Downloads/labels.ttl
+/Users/justin/Downloads/labels.ttl
+% ~/Downloads/apache-jena-5.1.0/bin/tdb2.tdbloader --loader=parallel --loc /tmp/db1 ~/Downloads/labels.ttl
+11:52:38 INFO  loader          :: Loader = LoaderParallel
+11:52:38 INFO  loader          :: Start: /Users/justin/Downloads/labels.ttl
+11:52:38 INFO  loader          :: Finished: /Users/justin/Downloads/labels.ttl: 5 tuples in 0.11s (Avg: 46)
+11:52:38 INFO  loader          :: Finish - index OSP
+11:52:38 INFO  loader          :: Finish - index POS
+11:52:38 INFO  loader          :: Finish - index SPO
+% cat /tmp/some.rq
+select * where {
+?s ?p ?o
+} limit 2
+% ~/Downloads/apache-jena-5.1.0/bin/tdb2.tdbquery --loc /tmp/db1 --query /tmp/some.rq
+----------------------------------------------------------------------------------------------------------------------
+| s                                           | p                                            | o                     |
+======================================================================================================================
+| <http://www.wikidata.org/entity/Q6553274>   | <http://www.w3.org/2000/01/rdf-schema#label> | "line number"@en      |
+| <http://www.wikidata.org/entity/Q113515824> | <http://www.w3.org/2000/01/rdf-schema#label> | "contiguous lines"@en |
+----------------------------------------------------------------------------------------------------------------------
+% ~/Downloads/apache-jena-5.1.0/bin/tdb2.tdbquery --results=csv --loc /tmp/db1 --query /tmp/some.rq
+s,p,o
+http://www.wikidata.org/entity/Q6553274,http://www.w3.org/2000/01/rdf-schema#label,line number
+http://www.wikidata.org/entity/Q113515824,http://www.w3.org/2000/01/rdf-schema#label,contiguous lines