Wayne's Bioinformatics Code Portal fomightez

## README.md

      
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                fomightez
                / README.md
            
            
              Last active
              June 23, 2025 15:17
            
              
                JupyterLab current on Binder with awscli and zstd
              
          
    JupyterLab current on Binder with awscli and zstd

To work with LOGAN Search data, see slide 35 of Rayan Chikhi | Project Logan Assembling all public sequencing data | CGSI 2024 (around 20.32 minute mark), need awscli and zst installed


## Centered plots based on Discourse Post.ipynb

      
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                fomightez
                / Centered plots based on Discourse Post.ipynb
            
            
              Created
              June 20, 2025 17:48
            
              
                Centered MAtplotlib plots based on Discourse Post
              
          
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## useful_python_snippets.py
# These are meant to work in both Python 2 and 3, except where noted.

# See my useful_pandas_snippets.py for those related to dataframes (such as pickling/`df.to_pickle(save_as)`)
# https://gist.github.com/fomightez/ef57387b5d23106fabd4e02dab6819b4
# also see https://gist.github.com/fomightez/324b7446dc08e56c83fa2d7af2b89a33 for examples of my
# frequently used Python functions and slight variations for more expanded, modular structures.


#argparse
# good snippet collection at https://mkaz.tech/code/python-argparse-cookbook/

## useful_notebook_snippets
# Use `%%capture` to hush 'noisy' stdout and stderr streams, but still combine with getting `%%time` after
%%capture out_stream
%%time
---rest of a cell that does something with LOTS of output--
#In cell after, put following to get time of completion from that:
#time it took to run cell above
for x in out_stream.stdout.split("\n")[-3:]:
    print(x)


## useful_pandas_snippets.py
# 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/)

## Converting_Bytes_to_MBytes.ipynb

      
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                fomightez
                / Converting_Bytes_to_MBytes.ipynb
            
            
              Created
              June 17, 2025 18:47
            
              
                Converting Bytes to MBytes in the sense used by Sequence Read Archive tables and metadata and Logan Search Results. Allows inter-relating the various numbers given in exported data.
              
          
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## demo_ipylab_using_documentsearch.ipynb

      
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                fomightez
                / demo_ipylab_using_documentsearch.ipynb
            
            
              Created
              May 16, 2025 16:20
            
              
                Demonstrate using documentsearch in a Jupyter Notebook via ipylab for programmatic text search, in reply to a Jupyter Discourse thread https://discourse.jupyter.org/t/targeted-documentsearch-command/34957?u=fomightez
              
          
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## p4339 natMX plasmid from Boone Lab.fa
> p4339 natMX plasmid from Boone Lab as described at http://sites.utoronto.ca/boonelab/sga_technology/index.shtml 339-1460 are natMX amplicon insert made from primers 5'-ACATGGAGGCCCAGAATACCC-3' and 5'-CAGTATAGCGACCAGCATTCAC-3' according to Boone Lab
AGCGCCCAATACGCAAACCGCCTCTCCCCGCGCGTTGGCCGATTCATTAATGCAGCTGGCACGACAGGTTTCCCGACTGG
AAAGCGGGCAGTGAGCGCAACGCAATTAATGTGAGTTAGCTCACTCATTAGGCACCCCAGGCTTTACACTTTATGCTTCC
GGCTCGTATGTTGTGTGGAATTGTGAGCGGATAACAATTTCACACAGGAAACAGCTATGACCATGATTACGCCAAGCTAT
TTAGGTGACACTATAGAATACTCAAGCTATGCATCAAGCTTGGTACCGAGCTCGGATCCACTAGTAACGGCCGCCAGTGT
GCTGGAATTCGCCCttaaACATGGAGGCCCAGAATACCCtccttgacagtcttgacgtgcgcagctcaggggcatgatgt
gactgtcgcccgtacatttagcccatacatccccatgtataatcatttgcatccatacattttgatggccgcacggcgcg
aagcaaaaattacggctcctcgctgcagacctgcgagcagggaaacgctcccctcacagacgcgttgaattgtccccacg
ccgcgcccctgtagagaaatataaaaggttaggatttgccactgaggttcttctttcatatacttccttttaaaatcttg
ctaggatacagttctcacatcacatccgaacataaacaaccatgggtaccactcttgacgacacggcttaccggtaccgc

## buffons_python2.ipynb

      
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                fomightez
                / buffons_python2.ipynb
            
            
              Created
              April 8, 2025 16:08
            
              
                Python 2 version of `Buffons_Needle_Sim.ipynb` from https://nbviewer.org/github/unpingco/Python-for-Signal-Processing/ , see https://discourse.jupyter.org/t/how-to-convert-nb-v3-into-v4-format/34404/2?u=fomightez
              
          
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## mre_for_highlight_gene_names.py
import pandas as pd
import numpy as np
import matplotlib as mpl
import re

df = pd.DataFrame({
    "Gene_name": ["sdsR", "arrS","gadF"],
    "Genes_in_same_transcription_unit": ['pphA, sdsR', 'arrS','mdtF, mdtE, gadF, gadE'],
})
# Convert Gene_name column to a set for quick lookup
	# These are meant to work in both Python 2 and 3, except where noted.

	# See my useful_pandas_snippets.py for those related to dataframes (such as pickling/`df.to_pickle(save_as)`)
	# https://gist.github.com/fomightez/ef57387b5d23106fabd4e02dab6819b4
	# also see https://gist.github.com/fomightez/324b7446dc08e56c83fa2d7af2b89a33 for examples of my
	# frequently used Python functions and slight variations for more expanded, modular structures.


	#argparse
	# good snippet collection at https://mkaz.tech/code/python-argparse-cookbook/
	# Use `%%capture` to hush 'noisy' stdout and stderr streams, but still combine with getting `%%time` after
	%%capture out_stream
	%%time
	---rest of a cell that does something with LOTS of output--
	#In cell after, put following to get time of completion from that:
	#time it took to run cell above
	for x in out_stream.stdout.split("\n")[-3:]:
	print(x)
	# 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/)
	> p4339 natMX plasmid from Boone Lab as described at http://sites.utoronto.ca/boonelab/sga_technology/index.shtml 339-1460 are natMX amplicon insert made from primers 5'-ACATGGAGGCCCAGAATACCC-3' and 5'-CAGTATAGCGACCAGCATTCAC-3' according to Boone Lab
	AGCGCCCAATACGCAAACCGCCTCTCCCCGCGCGTTGGCCGATTCATTAATGCAGCTGGCACGACAGGTTTCCCGACTGG
	AAAGCGGGCAGTGAGCGCAACGCAATTAATGTGAGTTAGCTCACTCATTAGGCACCCCAGGCTTTACACTTTATGCTTCC
	GGCTCGTATGTTGTGTGGAATTGTGAGCGGATAACAATTTCACACAGGAAACAGCTATGACCATGATTACGCCAAGCTAT
	TTAGGTGACACTATAGAATACTCAAGCTATGCATCAAGCTTGGTACCGAGCTCGGATCCACTAGTAACGGCCGCCAGTGT
	GCTGGAATTCGCCCttaaACATGGAGGCCCAGAATACCCtccttgacagtcttgacgtgcgcagctcaggggcatgatgt
	gactgtcgcccgtacatttagcccatacatccccatgtataatcatttgcatccatacattttgatggccgcacggcgcg
	aagcaaaaattacggctcctcgctgcagacctgcgagcagggaaacgctcccctcacagacgcgttgaattgtccccacg
	ccgcgcccctgtagagaaatataaaaggttaggatttgccactgaggttcttctttcatatacttccttttaaaatcttg
	ctaggatacagttctcacatcacatccgaacataaacaaccatgggtaccactcttgacgacacggcttaccggtaccgc
	import pandas as pd
	import numpy as np
	import matplotlib as mpl
	import re

	df = pd.DataFrame({
	"Gene_name": ["sdsR", "arrS","gadF"],
	"Genes_in_same_transcription_unit": ['pphA, sdsR', 'arrS','mdtF, mdtE, gadF, gadE'],
	})
	# Convert Gene_name column to a set for quick lookup