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README.md

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 # Dripp
-Drop-In Property Prediction
+DRop-In Property Prediction
+
+Property prediction tool for Drop-in fuels using FTIR spectra as input predictors.
+
+## Scripts and purpose
+
+### compile_data.ipynb: 
+
+Takes neat property csv file and blend volume composition file to export compiled dataframe csv files with the spectra, properties, and identification information.
+
+### blend_property_correlations.py
+Contains a python class for calculating properties for blends based on volume fraction. 
+
+### make_artificial_spectra.ipynb
+
+Creates and export artificial FTIR spectra for binary blends based on polyfits of absorbances at each wavenumber. Uses real spectra and volume fractions to interpolate absorbances for a requested additive volume fraction.
+
+### data_viz.ipynb
+Creates data visualizations of property and spectra patterns with whole dataset.
+
+## Folders and organization
+
+### data
+
+#### spectra
+raw spectra csv files for real experimental FTIR spectra for neat and blend fuels.
+
+#### interpolated_spectra
+artificial spectra created from make_artificial_spectra.ipynb
+
+#### interpolated_spectra_analysis
+
+contains csv files of R2 correlation fits of polyfits at each wavenumber and percent differences in absorbance agreement for artificial spectra made at compositions there is real spectra for. This is to analyze the performance of the polyfit method to synthesize FTIR spectra. Plots are stored in data/interpolated_spectra_analysis.
+
+### plots
+
+#### spectra_plots
+contains plots of all spectra real and artificial.
+
+#### interpolated_spectra_analysis
+
+contains csv files of R2 correlation fits of polyfits at each wavenumber and percent differences in absorbance agreement for artificial spectra made at compositions there is real spectra for. This is to analyze the performance of the polyfit method to synthesize FTIR spectra. Plots of this data is stored in data/interpolated_spectra_analysis.
+
+### input_files
+
+Contains input files in yaml format for model comparison runs.
+
+### hyperparameter_searches
+
+Contains hyperparameter search space arrays in yaml format for model comparison runs.
+
+## Workflow
+
+### Make data files from raw data
+
+Run compile_data.ipynb to create csv files with properties, spectra, and ID columns organized for neat fuels and fuel blends. It will use neat_properties.csv and blend_composition.csv and include all fuels from both files. It uses blend_property_correlations.py to calculate properties for fuel blends.
+
+
+### Make artificial spectra via polynomial fit interpolation at each wavenumber
+
+Run make_artificial_spectra.ipynb to create artificial spectra and export them to data/interpolated_spectra. Composition data is appended to blend_composition.csv file.
+
+Also can make artificial spectra for blends there already exists real experimental spectra for. Agreement of real and artificial spectra is analyzed via $R^2$ fits, residuals, and percent differences at each wavenumber. These performance metrics are exported as csv files to data/interpolated_spectra_analysis and plotted and saved to plots/interpolated_spectra_analysis. Also stored here are plots for absorbance vs. additive volume fraction for requested wavenumbers.
+
+### Recompile dataframe files to include artificial spectra
+
+If artificial spectra needs to be added to compiled dataframes, re-run compile_data.ipynb.
+