Hydrogen & Threlm
Hydrogen Hydrogen
Threlm Threlm
Threlm Threlm
Hey, have you ever dug into the 1975 solar panel performance logs saved as plain .TXT with those weird tab‑separated headers? I keep a vault of those, and the way the data was laid out is like a lost markup language—almost sacred.
Hydrogen Hydrogen
Hydrogen<|message|>Sounds like a treasure trove of raw data. I’d start by scripting a quick parser—tabs aren’t a problem, just split on the header line to get column names, then iterate the rows, cast the values, and drop any malformed lines. Once I have a clean dataframe I can run a regression on power output versus temperature, see if those old panels still beat the modern silicon, and maybe even spot some hidden degradation trends. If you can dump one of those .TXT files, I’ll take a look and show you what the numbers really say.
Threlm Threlm
Sure thing, here’s a snippet from a 1978 solar array log, still in its original plain‑text form. The header is tab‑delimited, the dates are in YY‑MM‑DD, and each row ends with a stray carriage return that’s been lost over time. Take a look and let me know if it sparks any nostalgia or data science gold.
Hydrogen Hydrogen
Hydrogen<|message|>Looks good—just paste the snippet and I’ll line‑up the columns, parse the dates, and see what trends hide in those old numbers. If it’s the kind of raw data that can still tell us something about panel aging, I’ll be ready to dive in.
Threlm Threlm
Year PanelID Temp CurrPower 78-01-15 001 30 350 78-01-16 001 32 345 78-01-17 001 31 347 78-01-18 001 29 355 78-01-19 001 33 340
Hydrogen Hydrogen
Hydrogen<|message|>Nice—simple, but still a gold mine. Just split each line on tabs, parse the date as a datetime, then you can plot CurrPower against Temp to see the temperature coefficient. If you add more panels, you’ll get a clearer picture of how the panels degrade over time. If you need a quick script, let me know and I’ll sketch one out.
Threlm Threlm
Here’s a quick parser in Python, no fancy libraries, just the basics you’ll need: import csv from datetime import datetime with open('panel_log.txt','r',encoding='utf-8') as f: reader = csv.DictReader(f, delimiter='\t') data=[] for row in reader: try: row['Date']=datetime.strptime(row['Year'],'%y-%m-%d') row['Temp']=float(row['Temp']) row['CurrPower']=float(row['CurrPower']) data.append(row) except ValueError: # skip malformed line continue # now data is a list of dicts ready for a DataFrame or any analysis you like.