mirror of
https://github.com/msberends/AMR.git
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202 lines
7.1 KiB
Bash
202 lines
7.1 KiB
Bash
#!/bin/bash
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# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
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# AMR: An R Package for Working with Antimicrobial Resistance Data. #
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# Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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# Output Python file
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output_file="python_wrapper/amr_python_wrapper.py"
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# Write header to the output Python file, including the convert_to_python function
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cat <<EOL > "$output_file"
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import rpy2.robjects as robjects
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from rpy2.robjects.packages import importr
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from rpy2.robjects.vectors import StrVector, FactorVector, IntVector, FloatVector
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from rpy2.robjects import pandas2ri
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import pandas as pd
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# Activate automatic conversion between R data frames and pandas data frames
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pandas2ri.activate()
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# Import the AMR R package
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amr_r = importr('AMR')
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def convert_to_python(r_output):
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# Check if it's a StrVector (R character vector)
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if isinstance(r_output, StrVector):
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return list(r_output) # Convert to a Python list of strings
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# Check if it's a FactorVector (R factor)
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elif isinstance(r_output, FactorVector):
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return list(r_output) # Convert to a list of integers (factor levels)
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# Check if it's an IntVector or FloatVector (numeric R vectors)
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elif isinstance(r_output, (IntVector, FloatVector)):
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return list(r_output) # Convert to a Python list of integers or floats
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# Check if it's a pandas-compatible R data frame
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elif isinstance(r_output, pd.DataFrame):
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return r_output # Return as pandas DataFrame (already converted by pandas2ri)
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# Fallback: return the raw rpy2 object if we don't know how to convert it
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return r_output
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EOL
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# Directory where the .Rd files are stored (update path as needed)
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rd_dir="../man"
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# Iterate through each .Rd file in the man directory
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for rd_file in "$rd_dir"/*.Rd; do
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# Extract function names and their arguments from the .Rd files
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awk '
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BEGIN {
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usage_started = 0
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}
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# Detect the start of the \usage block
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/^\\usage\{/ {
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usage_started = 1
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}
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# Detect the end of the \usage block
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usage_started && /^\}/ {
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usage_started = 0
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}
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# Process lines within the \usage block that look like function calls
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usage_started && /^[a-zA-Z_]+/ {
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func_line = $0
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func_line_py = $0
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# Extract the function name (up to the first parenthesis)
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sub(/\(.*/, "", func_line)
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func_name = func_line
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func_name_py = func_name
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# Replace dots with underscores in Python function names
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gsub(/\./, "_", func_name_py)
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# Extract the arguments (inside the parentheses)
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sub(/^[^(]+\(/, "", $0)
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sub(/\).*/, "", $0)
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func_args = $0
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# Count the number of arguments
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arg_count = split(func_args, arg_array, ",")
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# Handle "..." arguments (convert them to *args in Python)
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gsub("\\.\\.\\.", "*args", func_args)
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# Remove default values from arguments
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gsub(/ = [^,]+/, "", func_args)
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# If no arguments, skip the function (dont print it)
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if (arg_count == 0) {
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next
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}
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# If more than 1 argument, replace the 2nd to nth arguments with *args
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if (arg_count > 1) {
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first_arg = arg_array[1]
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func_args = first_arg ", *args"
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}
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# Skip functions where func_name_py is identical to func_args
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if (func_name_py == func_args) {
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next
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}
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# Skip functions matching the regex pattern ^(x |facet|scale|set|get|NA_)
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if (func_name_py ~ /^(x |facet|scale|set|get|NA_)/) {
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next
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}
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# Write the Python function definition to the output file
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print "def " func_name_py "(" func_args "):" >> "'"$output_file"'"
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print " \"\"\"See our website of the R package for the manual: https://msberends.github.io/AMR/index.html\"\"\"" >> "'"$output_file"'"
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print " return convert_to_python(amr_r." func_name_py "(" func_args "))" >> "'"$output_file"'"
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}
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' "$rd_file"
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done
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# Output completion message
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echo "Python wrapper functions generated in $output_file."
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cp ../README.md python_wrapper/
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echo "README copied"
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# Path to your DESCRIPTION file
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description_file="../DESCRIPTION"
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# Output setup.py file
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output_file="python_wrapper/setup.py"
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# Extract the relevant fields from DESCRIPTION
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version=$(grep "^Version:" "$description_file" | awk '{print $2}')
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license=$(grep "^License:" "$description_file" | awk '{print $2}')
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# Write the setup.py file
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cat <<EOL > "$output_file"
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from setuptools import setup, find_packages
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setup(
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name='AMR',
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#version='$version',
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version='2.1.1.1',
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packages=find_packages(),
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install_requires=[
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'rpy2',
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'pandas',
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],
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author='Matthijs Berends',
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author_email='m.s.berends@umcg.nl',
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description='A Python wrapper for the AMR R package',
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long_description=open('README.md').read(),
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long_description_content_type='text/markdown',
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url='https://github.com/msberends/AMR',
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project_urls={
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'Bug Tracker': 'https://github.com/msberends/AMR/issues',
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},
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license='GPL 2',
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classifiers=[
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'Programming Language :: Python :: 3',
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'Operating System :: OS Independent',
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],
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python_requires='>=3.6',
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)
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EOL
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# Output completion message
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echo "setup.py has been generated in $output_file."
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cd python_wrapper
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python3 setup.py sdist bdist_wheel
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