shbm

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

shbm

import numpy as np
import pylab as pl
from numpy import fft
    
def fourierExtrapolation(x, n_predict):
    n = x.size
    n_harm = 10                     # number of harmonics in model
    t = np.arange(0, n)
    p = np.polyfit(t, x, 1)         # find linear trend in x
    x_notrend = x - p[0] * t        # detrended x

shbm

-- show running queries (pre 9.2)
SELECT procpid, age(clock_timestamp(), query_start), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(clock_timestamp(), query_start), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' 

shbm

#!/usr/bin/env/python

import psycopg2
import os
from io import StringIO
import pandas as pd

# Get a database connection
dsn = os.environ.get('DB_DSN')  # Use ENV vars: keep it secret, keep it safe
conn = psycopg2.connect(dsn)

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# after appcleaner does his magic, do this

sudo rm -rf "/Library/Application Support/Paragon Software/"
sudo rm /Library/LaunchDaemons/com.paragon-software.installer.plist
sudo rm /Library/LaunchDaemons/com.paragon-software.ntfs.loader.plist
sudo rm /Library/LaunchDaemons/com.paragon-software.ntfsd.plist
sudo rm /Library/LaunchAgents/com.paragon-software.ntfs.notification-agent.plist
sudo rm -rf /Library/Filesystems/ufsd_NTFS.fs/
sudo rm -rf /Library/PrivilegedHelperTools/com.paragon-software.installer
sudo rm -rf /Library/Extensions/ufsd_NTFS.kext/

shbm

import time

import xgboost as xgb
from sklearn.model_selection import RandomizedSearchCV

x_train, y_train, x_valid, y_valid, x_test, y_test =  # load datasets

clf = xgb.XGBClassifier()

param_grid = {

shbm

# List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

# Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

# Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(valuelist)]