def myFunc():
    print("hello")

fname = "myFunc"

f = locals()[fname]
f()

hello

f = eval(fname)
f()

hello

from gliner import GLiNER

# https://genius.com/Genius-english-translations-bts-on-english-translation-lyrics

# Initialize GLiNER with a pre-trained model
model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")  # Adjust to a location-specific model if needed

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/home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages/transformers/convert_slow_tokenizer.py …

from gliner import GLiNER

# Initialize GLiNER with a pre-trained model
model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")  # Adjust to a location-specific model if needed

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/home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages/transformers/convert_slow_tokenizer.py:562: UserWarning: The sentencepiece …

import logging

logger = logging.getLogger()
#logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger.setLevel(logging.DEBUG)
logging.debug("test")

DEBUG:root:test

def do_sample():
    for x in range(10):
        logging.info('one …

import logging

logger = logging.getLogger()
#fhandler = logging.FileHandler(filename='mylog.log', mode='a')
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
#fhandler.setFormatter(formatter)
logger.addHandler(fhandler)
logger.setLevel(logging.DEBUG)

def do_sample …

from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
from bokeh.models import ColumnDataSource

# Set Bokeh output to notebook
output_notebook()

from sklearn.linear_model import LogisticRegression
import multiprocessing
import pandas as pd
from gensim.models import Doc2Vec
from gensim.models.doc2vec import LabeledSentence
import multiprocessing
from sklearn import utils
from tqdm import tqdm
tqdm.pandas(desc="progress-bar")

def labelize_tweets_ug …

import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
import statsmodels.api as sm
import scipy.stats as st
from statsmodels.tools import add_constant as add_constant

df = load_breast_cancer()
df_cancer = pd.DataFrame(np …

import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: ml312-2024; pyv: 3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 13:27:36) [GCC 11.2.0]'

import ipaddress

def long_to_ip(ip_long):
    # Convert long integer to IPv4 string
    return str(ipaddress.IPv4Address(ip_long))

# Example usage
ip_long = 3232235777
ip …

d = {'person': 2, 'cat': 4, 'spider': 8}

for animal in d:
    legs = d[animal]
    print ('A %s has %d legs' % (animal, legs))

A person has 2 legs
A cat has 4 legs
A spider has 8 legs