chapmanjacobd

def mpv_enrich(args, media) -> List[Dict]:
    for m in media:
        md5 = path_to_mpv_watchlater_md5(m["path"])
        metadata_path = Path(args.watch_later_directory, md5)
        if metadata_path.exists():
            m["time_partial_first"] = int(metadata_path.stat().st_ctime)
            m["time_partial_last"] = int(metadata_path.stat().st_mtime)
        else:
            m["time_partial_first"] = 0

chapmanjacobd

def calculate_sparseness(stat) -> int:
    if stat.st_size == 0:
        sparseness = 0
    else:
        blocks_allocated = stat.st_blocks * 512
        sparseness = blocks_allocated / stat.st_size
    return sparseness

chapmanjacobd

class DictHandler(logging.Handler):
    def __init__(self, log_dict):
        super().__init__()
        self.log_dict = log_dict

    def emit(self, record):
        log_entry = self.format(record)
        self.log_dict.setdefault(record.levelname, []).append(log_entry)

chapmanjacobd

def load_spacy_model(model=None):
    try:
        import spacy
    except ModuleNotFoundError:
        log.error("Install spaCy and sklearn to use:")
        log.error("pip install spacy sklearn")
        log.error("python -m spacy download en_core_web_sm")
        sys.exit(1)

    if model:

chapmanjacobd

How I freed up one TiB of space

I didn't realize just how much space those 24 Hour YouTube tracks take up. After hearing the same song on repeat I decided to check:

$ pip install xklb
$ lb fsadd --audio ~/lb/audio.db /mnt/d/81_New_Music/ /mnt/d/82_Audiobooks/
$ lb listen ~/lb/audio.db /81_New_Music/ --duration +3hr -p a
╒═══════════╤═════════╤═══════════════════════════════════════════╤═══════════════════════════╤══════════════════════════════════════════╤════════╤════════════╕
│ path      │   count │ duration                                  │ avg_duration              │ cadence_adj_duration                     │ size   │ avg_size   │

╞═══════════╪═════════╪═══════════════════════════════════════════╪═══════════════════════════╪══════════════════════════════════════════╪════════╪════════════╡

chapmanjacobd

-- v3
from db import con, fetchall_dict, singleColumnToList


def run_query(sql):
    cur = con.cursor()
    cur.execute(sql)
    con.commit()

chapmanjacobd

CREATE TABLE transport_intercity_guess_bus AS 
WITH bus AS (
    SELECT
        cf.id city_from,
        ct.id city_to,
        0 AS transfers,
        avg(price_avg) as price,
        avg(distance_avg) as distance,
        null transfer_city
    from

chapmanjacobd

[tool.pylint.main]
fail-under = 8.0
ignore = ["input"]
ignored-modules = ["pandas", "numpy"]
limit-inference-results = 100
persistent = true
suggestion-mode = true

[tool.pylint.basic]
argument-naming-style = "snake_case"

chapmanjacobd

sqlite3 artist_similarity.db 'alter table artists add column artist_name text'

---
attach 'artist_similarity.db' AS sim;
attach 'track_metadata.db' AS tr;
UPDATE
    sim.artists SET artist_name = (
        SELECT
            artist_name
        FROM

chapmanjacobd

import math
import osgeo.gdal as gdal
import timeit

file_path = "1677269839.tif"

ds = gdal.Open(file_path)
file_block_size = ds.GetRasterBand(1).GetBlockSize()

xoff = 0