fomightez

REGEX remove blank lines:

FROM: http://www.ultraedit.com/support/tutorials_power_tips/ultraedit/remove_blank_lines.html

---

FIND:

^(?:[\t ]*(?:\r?\n|\r))+

fomightez

#Useful snippets and examples for use of JupyterLite with commands typically related to the shell available in standard Jupyter
# (Also see 'Useful snippets and examples for when converting command line commands from Jupyter/IPython back to Pure Python' at
# https://gist.github.com/fomightez/ed79e33e97601d839dd550fd224d583c because a lot of these overlap, yet JupyterLite has some magics that obviously don't work with Pure Python
# MAGIC COMMANDS THAT SHOULD WORK IN JUPYTERLITE
%pwd
%store s >test_store.txt # if `s` previously defined.

#`%ls` fails at present, but you can do
import os
os.listdir()

fomightez

fomightez

fomightez

fomightez

# Compromises quality for speed/efficiency
# REPLACE CONTENT OF THE CELL WITH THIS CODE, or use a varation of it if you just prefer cartoon represenation for your use:
text_2_save_templ = '''#!/usr/bin/python

import sys, os

# pymol environment
moddir='/opt/pymol-svn/modules'
sys.path.insert(0, moddir)
os.environ['PYMOL_PATH'] = os.path.join(moddir, 'pymol/pymol_path')

fomightez

# meant to be run with `uv run https://gist.githubusercontent.com/fomightez/eee9a448be7287a024d260ec80751120/raw/6fcaeed31d62b0f138e265684ceebfcc49c0f6e5/streamlined_timestamp_start_end_miner_checker.py out.txt`, or similar
# This handles evaluating date timestamp info in start and end timestamps of a pipeline.
#####*****------------------------------------------------------------*****#####
# This is meant to use with `uv` to run. 
# First install `uv` with `pip install uv` then run `!uv run {script_url} {input_text_filepath}` where defined those variables prior
#-------------------------------------------------------------#
# Times printed for now. (Make a dataframe?)
#-------------------------------------------------------------#
# /// script
# requires-python = ">=3.12"

fomightez

# meant to be run with `uv run https://gist.githubusercontent.com/fomightez/f036794b91d10761466341644b3c1cac/raw/15da0209f7b09c1ba0130cf66646635c80a58bae/evaluate_date_timestamps_in_pipeline_stdout.py out.txt`, or similar
# This handles evaluating date timestamp info in typical long and short read pipeline.
#####*****------------------------------------------------------------*****#####
# This is meant to use with `uv` to run. 
# First install `uv` with `pip install uv` then run `!uv run {script_url} {input_text_filepath}` where defined those variables prior
#-------------------------------------------------------------#
# Times printed for now. (Make a dataframe?)
#-------------------------------------------------------------#
# /// script
# requires-python = ">=3.12"

fomightez

#Useful examples for when converting command line commands from Jupyter/IPython back to Pure Python
#(Also see 'Useful snippets and examples for use of JupyterLite with commands typically related to the shell available in standard Jupyter' at 
# https://gist.github.com/fomightez/53a8e6153095402fe2f168789224099c because it has a few special options that will work in addition to these.)
# This is partly for when need to speed up a  `.ipy` script running. It will run much faster as `.py` than as `.ipy` if there 
# are a lot of calls to command line / shell commands because saves time by not spawning new shell instance for 
# each. (`.ipy` version great for quicker development and proto-typing but `.py` MUCH FASTER for running.)
# The Python versions also have the advantage that you can use them inside functions (I think) because don't have problem like 
# with `!cp fn unsanitized_{fn}`or `%store` where actually run in global namespace which cannot see Python variable `fn` 
# local to the function.
# RELATED NOTE

fomightez

# List unique values in a DataFrame column
df['Column Name'].unique() # Note, `NaN` is included as a unique value. If you just want the number, use `nunique()` which stands
# for 'number of unique values';  By default, it excludes `NaN`. `.nunique(dropna=False)` will include `NaN` in the count of unique values.

# To extract a specific column (subset the dataframe), you can use [ ] (brackets) or attribute notation.
df.height
df['height']
# are same thing!!! (from http://www.stephaniehicks.com/learnPython/pages/pandas.html 
# -or-
# http://www.datacarpentry.org/python-ecology-lesson/02-index-slice-subset/)