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Repository: charlesll/rampy
Branch: master
Commit: a2218b177990
Files: 543
Total size: 89.6 MB
Directory structure:
gitextract_1cpthhgz/
├── .github/
│ └── workflows/
│ └── publish.yml
├── .gitignore
├── .readthedocs.yaml
├── .travis.yml
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── NEWS.md
├── README.md
├── docs/
│ ├── .nojekyll
│ ├── API.rst
│ ├── Makefile
│ ├── _static/
│ │ ├── custom.js
│ │ └── require.js
│ ├── baseline.rst
│ ├── conf.py
│ ├── dac.rst
│ ├── examples.rst
│ ├── firststeps.rst
│ ├── index.rst
│ ├── installation.rst
│ ├── machinelearning.rst
│ ├── make.bat
│ ├── maps.rst
│ ├── modules.rst
│ ├── news.rst
│ ├── notebooks/
│ │ ├── Baseline.ipynb
│ │ ├── Baseline_and_Centroid.ipynb
│ │ ├── ML_Classification.ipynb
│ │ ├── ML_Exploration.ipynb
│ │ ├── ML_Regression.ipynb
│ │ ├── Maps.ipynb
│ │ ├── Mixing.ipynb
│ │ ├── Normalisation.ipynb
│ │ ├── Raman_fitting.ipynb
│ │ ├── Resample_and_flip.ipynb
│ │ ├── Smoothing.ipynb
│ │ └── data/
│ │ ├── 4338-NDC1-(31)_4X4.txt
│ │ ├── LS4.txt
│ │ └── NS4.txt
│ ├── peakfitting.rst
│ ├── philosophy.rst
│ ├── plotting.rst
│ ├── preprocessing.rst
│ ├── requirements.txt
│ ├── setup.rst
│ ├── signalparams.rst
│ ├── smoothing.rst
│ └── utils.rst
├── environment.yml
├── examples/
│ ├── Iron_AmMin_paper/
│ │ ├── .gitignore
│ │ ├── HOWTO.txt
│ │ ├── Iron_MORB_code.ipynb
│ │ ├── MORB_datalist.xlsx
│ │ ├── iron.py
│ │ └── raw/
│ │ ├── BD1469.txt
│ │ ├── BD1470.txt
│ │ ├── BD1471.txt
│ │ ├── BD1472.txt
│ │ ├── BD1473.txt
│ │ ├── BD1474.txt
│ │ ├── BD1475.txt
│ │ ├── BD1476.txt
│ │ ├── BD1477.txt
│ │ ├── BD1478.txt
│ │ ├── VG175_.txt
│ │ ├── VG175_1.txt
│ │ ├── VG175_2.txt
│ │ ├── VG175_3.txt
│ │ ├── VG175_4.txt
│ │ ├── VG183_.txt
│ │ ├── VG183_1.txt
│ │ ├── VG183_2.txt
│ │ ├── VG183_3.txt
│ │ ├── VG183_4.txt
│ │ ├── VG186_0__X_19996.5__Y_-20032__Time_0.txt
│ │ ├── VG186_2__X_20013__Y_-20055__Time_288.txt
│ │ ├── VG186_3__X_20030.8__Y_-20026.4__Time_433.txt
│ │ ├── VG186_4__X_20028.5__Y_-20016.4__Time_577.txt
│ │ ├── VG186_5__X_19960.5__Y_-20013.9__Time_722.txt
│ │ ├── VG187_0__X_24099.5__Y_-26618.8__Time_0.txt
│ │ ├── VG187_1__X_24133.8__Y_-26769.8__Time_144.txt
│ │ ├── VG187_2__X_23341.8__Y_-27123.3__Time_288.txt
│ │ ├── VG187_3__X_23212.5__Y_-27027.5__Time_433.txt
│ │ ├── VG187_4__X_23396__Y_-27469.3__Time_577.txt
│ │ ├── VG195_0__X_12141.4__Y_-19545.2__Time_0.txt
│ │ ├── VG195_1__X_12123.9__Y_-19557.5__Time_144.txt
│ │ ├── VG195_2__X_12157.9__Y_-19558.1__Time_288.txt
│ │ ├── VG195_3__X_12155.3__Y_-19533__Time_433.txt
│ │ ├── VG195_4__X_12127.4__Y_-19533.3__Time_578.txt
│ │ ├── VG202_0__X_7648.44__Y_-27430__Time_0.txt
│ │ ├── VG202_2__X_7634.03__Y_-27444.3__Time_288.txt
│ │ ├── VG202_3__X_7637.69__Y_-27420.2__Time_433.txt
│ │ ├── VG202_4__X_7660.95__Y_-27420.3__Time_577.txt
│ │ ├── VG202_oct_1_0__Time_0.txt
│ │ ├── VG203_0__X_7115.86__Y_-23841.7__Time_0.txt
│ │ ├── VG203_1__X_7125.78__Y_-23853.3__Time_144.txt
│ │ ├── VG203_2__X_7104.88__Y_-23853.2__Time_289.txt
│ │ ├── VG203_3__X_7106.54__Y_-23831.1__Time_433.txt
│ │ ├── VG203_4__X_7128.14__Y_-23832.2__Time_578.txt
│ │ ├── VG285_.txt
│ │ ├── VG285_1.txt
│ │ ├── VG285_2.txt
│ │ ├── VG285_3.txt
│ │ ├── VG285_4.txt
│ │ ├── VG290_.txt
│ │ ├── VG290_1.txt
│ │ ├── VG290_2.txt
│ │ ├── VG290_3.txt
│ │ ├── VG290_4.txt
│ │ ├── VG3073_0__X_24960.6__Y_-31533.1__Time_0.txt
│ │ ├── VG3073_1__X_24992.5__Y_-31555.7__Time_144.txt
│ │ ├── VG3073_2__X_25001__Y_-31550.5__Time_288.txt
│ │ ├── VG3073_3__X_24857.7__Y_-31338__Time_433.txt
│ │ ├── VG3073_4__X_24824.2__Y_-31298.1__Time_577.txt
│ │ ├── VG3079_1_0__X_11675.1__Y_-22752.8__Time_0.txt
│ │ ├── VG3079_1_1__X_11646.1__Y_-22772.1__Time_144.txt
│ │ ├── VG3079_1_2__X_11683.6__Y_-22746.7__Time_288.txt
│ │ ├── VG3079_2__X_12172.8__Y_-22892.5__Time_288.txt
│ │ ├── VG3079_3__X_12190.1__Y_-22882.1__Time_433.txt
│ │ ├── VG322_.txt
│ │ ├── VG322_1_0__X_16100.5__Y_-19847__Time_0.txt
│ │ ├── VG322_1_1__X_16116.9__Y_-19849__Time_144.txt
│ │ ├── VG322_1_2__X_16121.1__Y_-19831.4__Time_288.txt
│ │ ├── VG322_1_3__X_16117.9__Y_-19809.1__Time_433.txt
│ │ ├── VG334__0__X_20679.2__Y_-18282.5__Time_0.txt
│ │ ├── VG334__1__X_20664.6__Y_-18296.1__Time_144.txt
│ │ ├── VG334__2__X_20686.6__Y_-18297.2__Time_288.txt
│ │ ├── VG334__3__X_20688.3__Y_-18269.7__Time_433.txt
│ │ ├── VG334__4__X_20668.5__Y_-18272.8__Time_577.txt
│ │ ├── VG3450_0__X_14838.7__Y_-20142.8__Time_0.txt
│ │ ├── VG3450_1__X_14855.5__Y_-20151.9__Time_144.txt
│ │ ├── VG3450_2__X_14824.5__Y_-20153.1__Time_288.txt
│ │ ├── VG3450_3__X_14828.2__Y_-20132.5__Time_433.txt
│ │ ├── VG3450_4__X_14854.7__Y_-20133.6__Time_577.txt
│ │ ├── VG3831_0__X_22339.7__Y_-19634.3__Time_0.txt
│ │ ├── VG3831_1__X_22347.2__Y_-19640.8__Time_144.txt
│ │ ├── VG3831_2__X_22353.7__Y_-19646.9__Time_289.txt
│ │ ├── VG3831_3__X_22353.7__Y_-19624.4__Time_433.txt
│ │ ├── VG3831_4__X_22307.7__Y_-19611.5__Time_577.txt
│ │ ├── VG4088_0__X_16619.1__Y_-23684.9__Time_0.txt
│ │ ├── VG4088_1__X_16607.2__Y_-23693.9__Time_144.txt
│ │ ├── VG4088_2__X_16637.3__Y_-23693.8__Time_288.txt
│ │ ├── VG4088_3__X_16638.9__Y_-23680.7__Time_433.txt
│ │ ├── VG4088_4__X_16601.1__Y_-23676__Time_577.txt
│ │ ├── VG4522_.txt
│ │ ├── VG4522_1.txt
│ │ ├── VG4522_2.txt
│ │ ├── VG4522_3.txt
│ │ ├── VG4522_4.txt
│ │ ├── VG4700_0__X_18217.1__Y_-19810.8__Time_0.txt
│ │ ├── VG4700_1__X_18227.6__Y_-19821.2__Time_144.txt
│ │ ├── VG4700_2__X_18192.8__Y_-19824__Time_288.txt
│ │ ├── VG4700_3__X_18200.5__Y_-19800.4__Time_433.txt
│ │ ├── VG4700_4__X_18243.3__Y_-19813.8__Time_577.txt
│ │ ├── VG4701_0__X_25688.9__Y_-19810.7__Time_0.txt
│ │ ├── VG4701_1__X_25704.9__Y_-19815.2__Time_144.txt
│ │ ├── VG4701_2__X_25683.3__Y_-19817.4__Time_288.txt
│ │ ├── VG4701_3__X_25682.4__Y_-19803__Time_433.txt
│ │ ├── VG4701_4__X_25702.6__Y_-19801.7__Time_577.txt
│ │ ├── VG4709_0__X_21085.2__Y_-21479.9__Time_0.txt
│ │ ├── VG4709_1__X_21089.9__Y_-21483.4__Time_144.txt
│ │ ├── VG4709_2__X_21078.7__Y_-21485__Time_288.txt
│ │ ├── VG4709_3__X_21076.9__Y_-21474.6__Time_433.txt
│ │ ├── VG4709_4__X_21091.3__Y_-21472.8__Time_577.txt
│ │ ├── VG4971_.txt
│ │ ├── VG4971_1.txt
│ │ ├── VG4971_2.txt
│ │ ├── VG4971_3.txt
│ │ ├── VG4971_4.txt
│ │ ├── VG4974_.txt
│ │ ├── VG4974_1.txt
│ │ ├── VG4974_2.txt
│ │ ├── VG4974_3.txt
│ │ ├── VG4974_4.txt
│ │ ├── VG5374_0__X_18181.5__Y_-18038.6__Time_0.txt
│ │ ├── VG5374_1__X_18194.5__Y_-18046.3__Time_144.txt
│ │ ├── VG5374_2__X_18171__Y_-18046.7__Time_289.txt
│ │ ├── VG5374_3__X_18171.4__Y_-18026.8__Time_433.txt
│ │ ├── VG5374_4__X_18196__Y_-18028.4__Time_578.txt
│ │ ├── VG5697_.txt
│ │ ├── VG5697_1.txt
│ │ ├── VG5697_2.txt
│ │ ├── VG5697_3.txt
│ │ ├── VG5697_4.txt
│ │ ├── VG5781_.txt
│ │ ├── VG5781_1.txt
│ │ ├── VG5781_2.txt
│ │ ├── VG5781_3.txt
│ │ ├── VG5781_4.txt
│ │ ├── VG612_0__X_19514__Y_-19276.6__Time_0.txt
│ │ ├── VG612_1__X_19551.8__Y_-19295.8__Time_144.txt
│ │ ├── VG612_2__X_19501__Y_-19341__Time_288.txt
│ │ ├── VG612_3__X_19376.6__Y_-19350.4__Time_433.txt
│ │ ├── VG612_4__X_19287.5__Y_-19296.3__Time_577.txt
│ │ ├── VG662_0__X_13277.4__Y_-18492.1__Time_0.txt
│ │ ├── VG662_1__X_13303.4__Y_-18492.5__Time_144.txt
│ │ ├── VG662_2__X_13292.4__Y_-18480.4__Time_288.txt
│ │ ├── VG662_3__X_13277.7__Y_-18471.3__Time_433.txt
│ │ ├── VG662_4__X_13305.5__Y_-18472.1__Time_577.txt
│ │ ├── VG663_0__X_19823.3__Y_-22023.6__Time_0.txt
│ │ ├── VG663_1__X_19837.8__Y_-22035.6__Time_144.txt
│ │ ├── VG663_2__X_19805.4__Y_-22037.8__Time_288.txt
│ │ ├── VG663_3__X_19806.7__Y_-22010.3__Time_433.txt
│ │ ├── VG663_4__X_19837.2__Y_-22009.6__Time_577.txt
│ │ ├── VG664_.txt
│ │ ├── VG664_1_0__X_22184.8__Y_-25677.4__Time_0.txt
│ │ ├── VG664_1_1__X_22206.1__Y_-25676.3__Time_144.txt
│ │ ├── VG664_1_2__X_22208__Y_-25658.1__Time_288.txt
│ │ ├── VG664_1_3__X_22187.4__Y_-25657.9__Time_433.txt
│ │ ├── VG665_0__X_20243__Y_-29172.7__Time_0.txt
│ │ ├── VG665_1__X_20224.6__Y_-29185.8__Time_144.txt
│ │ ├── VG665_2__X_20259.3__Y_-29185.8__Time_288.txt
│ │ ├── VG665_3__X_20256__Y_-29162.3__Time_433.txt
│ │ ├── VG665_4__X_20228.7__Y_-29164.4__Time_577.txt
│ │ ├── VG666_0__X_17346.8__Y_-34522__Time_0.txt
│ │ ├── VG666_1__X_17421.8__Y_-34598.7__Time_144.txt
│ │ ├── VG666_2__X_17405.1__Y_-34315.9__Time_288.txt
│ │ ├── VG666_3__X_17494__Y_-34130.6__Time_433.txt
│ │ ├── VG666_4__X_17737.3__Y_-34226.4__Time_577.txt
│ │ ├── VG667_0__X_11954.1__Y_-36113.4__Time_0.txt
│ │ ├── VG667_1__X_11990.4__Y_-36038.7__Time_121.txt
│ │ ├── VG667_2__X_12073__Y_-36190.4__Time_242.txt
│ │ ├── VG667_3__X_12104.7__Y_-36244.6__Time_363.txt
│ │ ├── VG667_4__X_11944.8__Y_-36273.5__Time_484.txt
│ │ ├── VG6820_1__X_18106.3__Y_-19219.9__Time_144.txt
│ │ ├── VG6820_2__X_18084.6__Y_-19220.3__Time_288.txt
│ │ ├── VG6820_3__X_18087__Y_-19205.7__Time_433.txt
│ │ ├── VG6820_4__X_18107.9__Y_-19208.1__Time_577.txt
│ │ ├── VG6821_0__X_20030__Y_-21785.8__Time_0.txt
│ │ ├── VG6821_1__X_20045.4__Y_-21791.9__Time_144.txt
│ │ ├── VG6821_2__X_20057.6__Y_-21798.9__Time_288.txt
│ │ ├── VG6821_3__X_20070.5__Y_-21804.6__Time_433.txt
│ │ ├── VG6821_4__X_20083.2__Y_-21811.1__Time_577.txt
│ │ ├── VG6822_0__X_24458__Y_-27315.2__Time_0.txt
│ │ ├── VG6822_1__X_24458__Y_-27320.2__Time_144.txt
│ │ ├── VG6822_2__X_24458__Y_-27325.9__Time_288.txt
│ │ ├── VG6822_3__X_24458.5__Y_-27311.2__Time_433.txt
│ │ ├── VG6822_4__X_24462.7__Y_-27318.5__Time_577.txt
│ │ ├── VG6823_0__X_22497.6__Y_-30686__Time_0.txt
│ │ ├── VG6823_1__X_22481.9__Y_-30677.8__Time_144.txt
│ │ ├── VG6823_2__X_22494.1__Y_-30681__Time_288.txt
│ │ ├── VG6823_3__X_22498.1__Y_-30698.8__Time_433.txt
│ │ ├── VG6823_4__X_22534.1__Y_-30695.7__Time_577.txt
│ │ ├── VG6825_0__X_20612.2__Y_-36086.8__Time_0.txt
│ │ ├── VG6825_1__X_20606.3__Y_-36082.9__Time_144.txt
│ │ ├── VG6825_2__X_20644.3__Y_-36091.5__Time_288.txt
│ │ ├── VG6825_3__X_20631__Y_-36083.9__Time_433.txt
│ │ ├── VG6825_4__X_20627.8__Y_-36107.1__Time_577.txt
│ │ ├── VG682_.txt
│ │ ├── VG682_1.txt
│ │ ├── VG682_2.txt
│ │ ├── VG682_3.txt
│ │ ├── VG682_4.txt
│ │ ├── VG684_.txt
│ │ ├── VG684_1.txt
│ │ ├── VG684_2.txt
│ │ ├── VG684_3.txt
│ │ ├── VG684_4.txt
│ │ ├── VG758_0__X_19316.1__Y_-19291.2__Time_0.txt
│ │ ├── VG758_1__X_19323.2__Y_-19296__Time_144.txt
│ │ ├── VG758_2__X_19306.6__Y_-19297.7__Time_288.txt
│ │ ├── VG758_3__X_19307.1__Y_-19283.1__Time_433.txt
│ │ ├── VG758_4__X_19324.5__Y_-19284.8__Time_577.txt
│ │ ├── VG884_0__X_17275.9__Y_-20515.2__Time_0.txt
│ │ ├── VG884_1__X_17251.2__Y_-20417.2__Time_144.txt
│ │ ├── VG884_2__X_17127.6__Y_-20407.9__Time_288.txt
│ │ ├── VG884_3__X_17100.7__Y_-20385.9__Time_433.txt
│ │ ├── VG884_4__X_16946.2__Y_-20511.5__Time_577.txt
│ │ ├── r145.txt
│ │ ├── r147.txt
│ │ ├── r148.txt
│ │ ├── r149.txt
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│ │ ├── r239.txt
│ │ ├── r240.txt
│ │ ├── r241.txt
│ │ ├── r268_0.txt
│ │ ├── r268_1.txt
│ │ ├── r268_2.txt
│ │ ├── r269_0.txt
│ │ ├── r269_1.txt
│ │ ├── r269_2.txt
│ │ ├── r270_0.txt
│ │ ├── r270_1.txt
│ │ ├── r270_2.txt
│ │ ├── r271_0.txt
│ │ ├── r271_1.txt
│ │ ├── r271_2.txt
│ │ ├── r272_0.txt
│ │ ├── r272_1.txt
│ │ ├── r272_2.txt
│ │ ├── r273_0.txt
│ │ ├── r273_1.txt
│ │ ├── r273_2.txt
│ │ ├── r275_0.txt
│ │ ├── r275_1.txt
│ │ ├── r275_2.txt
│ │ ├── r276_0.txt
│ │ ├── r276_1.txt
│ │ ├── r276_2.txt
│ │ ├── r277_0.txt
│ │ ├── r277_1.txt
│ │ ├── r277_2.txt
│ │ ├── r278_0.txt
│ │ ├── r278_1.txt
│ │ ├── r278_2.txt
│ │ ├── r362.txt
│ │ ├── r363.txt
│ │ ├── r364.txt
│ │ ├── r365.txt
│ │ ├── r520.txt
│ │ ├── r521.txt
│ │ ├── r522.txt
│ │ ├── r523.txt
│ │ ├── r524.txt
│ │ ├── r711.txt
│ │ ├── r712.txt
│ │ ├── r714.txt
│ │ ├── r715.txt
│ │ ├── r716.txt
│ │ ├── r717.txt
│ │ ├── r718.txt
│ │ ├── r719.txt
│ │ ├── r720.txt
│ │ ├── r721.txt
│ │ ├── r722.txt
│ │ ├── r723.txt
│ │ ├── r724.txt
│ │ ├── r725.txt
│ │ ├── r726.txt
│ │ ├── r727.txt
│ │ ├── r728.txt
│ │ ├── r729.txt
│ │ ├── r730.txt
│ │ ├── r731.txt
│ │ ├── r732.txt
│ │ ├── r733.txt
│ │ ├── r738.txt
│ │ ├── r739.txt
│ │ └── r740.txt
│ ├── README.md
│ └── rameau_example/
│ ├── Python_treatment.ipynb
│ ├── Python_treatment_ext.ipynb
│ ├── README.md
│ ├── dataset.xlsx
│ ├── rameau.py
│ └── raw/
│ ├── Som-13-17.txt
│ ├── Som-13-18.txt
│ ├── Som-13-19.txt
│ ├── Som-14-18.txt
│ ├── Som-14-19.txt
│ ├── Som-14-20.txt
│ ├── Som-15-11.txt
│ ├── Som-15-12.txt
│ ├── Som-15-13.txt
│ ├── Som-16-15.txt
│ ├── Som-16-27.txt
│ ├── Som-16-28.txt
│ ├── ac8014.txt
│ ├── ac8015.txt
│ ├── ac8028.txt
│ ├── ac8037.txt
│ ├── ac8041.txt
│ ├── ac9025.txt
│ ├── ac9026.txt
│ ├── ac9027.txt
│ ├── ac9028.txt
│ ├── ac9029.txt
│ ├── ac9030.txt
│ ├── ad0085.txt
│ ├── ad0086.txt
│ ├── ad0087.txt
│ ├── ad0090.txt
│ ├── ad0091.txt
│ ├── ad0098.txt
│ ├── ad0099.txt
│ ├── ad0100.txt
│ ├── ad0101.txt
│ ├── ad0102.txt
│ ├── ad0104.txt
│ ├── ad0105.txt
│ ├── ad0107.txt
│ ├── ad0108.txt
│ ├── ad0110.txt
│ ├── ad0111.txt
│ ├── ad0112.txt
│ ├── ad0114.txt
│ ├── ad0116.txt
│ ├── ad0117.txt
│ ├── ad0118.txt
│ ├── ad0119.txt
│ ├── ad0120.txt
│ ├── ad0122.txt
│ ├── ad0123.txt
│ ├── ae0187.txt
│ ├── ae0188.txt
│ ├── ae0190.txt
│ ├── ae0191.txt
│ ├── ae0192.txt
│ ├── ae0199.txt
│ ├── ae0202.txt
│ ├── ae0203.txt
│ ├── ae0204.txt
│ ├── ae0405.txt
│ ├── ae0406.txt
│ ├── ae0409.txt
│ ├── ae0441.txt
│ ├── ae0442.txt
│ ├── ae0443.txt
│ ├── ae0660.txt
│ ├── ae0664.txt
│ ├── ae0674.txt
│ ├── ae0675.txt
│ ├── ae0676.txt
│ ├── ae0682.txt
│ ├── ae0684.txt
│ ├── ae0685.txt
│ ├── ae0980.txt
│ ├── r010.txt
│ ├── r015.txt
│ ├── r016.txt
│ ├── r024.txt
│ ├── r025.txt
│ ├── r026.txt
│ ├── r027.txt
│ ├── r030.txt
│ ├── r031.txt
│ ├── r032.txt
│ ├── r034.txt
│ ├── r039.txt
│ ├── r040.txt
│ ├── r041.txt
│ ├── r042.txt
│ ├── r044_0.txt
│ ├── r044_1.txt
│ ├── r044_2.txt
│ ├── r044_3.txt
│ ├── r044_4.txt
│ ├── r044_5.txt
│ ├── r049.txt
│ ├── r051.txt
│ ├── r107.txt
│ ├── r108.txt
│ ├── r109.txt
│ ├── r110.txt
│ ├── r111.txt
│ ├── r112.txt
│ ├── r113.txt
│ ├── r114.txt
│ ├── r116_0.txt
│ ├── r116_1.txt
│ ├── r116_2.txt
│ ├── r116_3.txt
│ ├── r116_4.txt
│ ├── r116_5.txt
│ ├── r118_0.txt
│ ├── r118_1.txt
│ ├── r118_2.txt
│ ├── r386.txt
│ ├── r387.txt
│ ├── r388.txt
│ ├── r389.txt
│ ├── r393.txt
│ ├── r397.txt
│ ├── r487_0.txt
│ ├── r487_1.txt
│ ├── r487_3.txt
│ ├── r487_4.txt
│ ├── r703.txt
│ ├── r704.txt
│ ├── r705.txt
│ ├── r706.txt
│ ├── r707.txt
│ └── r708.txt
├── legacy_code/
│ ├── Diamond_Pressure.py
│ ├── HF_Treatment_Auto.py
│ ├── HT_dec.py
│ ├── IR_dec_comb.py
│ ├── I_Io_calc.py
│ ├── LF_Treatment.py
│ ├── LF_dec.py
│ ├── README.md
│ ├── Remove_Ctx_MI.py
│ ├── cleaning.py
│ └── rameau_construction
├── pyproject.toml
├── roadmap.md
├── src/
│ ├── rampy/
│ │ ├── .gitignore
│ │ ├── __init__.py
│ │ ├── baseline.py
│ │ ├── dac.py
│ │ ├── filters.py
│ │ ├── functions.py
│ │ ├── maps.py
│ │ ├── mixing.py
│ │ ├── ml_classification.py
│ │ ├── ml_exploration.py
│ │ ├── ml_regressor.py
│ │ ├── notused/
│ │ │ └── IRTABS.py
│ │ ├── peak_area.py
│ │ ├── peak_shapes.py
│ │ ├── plotting.py
│ │ ├── rameau.py
│ │ ├── spectranization.py
│ │ └── tlcorrection.py
│ └── rampy.egg-info/
│ ├── PKG-INFO
│ ├── SOURCES.txt
│ ├── dependency_links.txt
│ ├── requires.txt
│ └── top_level.txt
└── tests/
├── __init__.py
├── map_test.txt
├── test_baseline.py
├── test_dac.py
├── test_mixing.py
├── test_mlclassificator.py
├── test_mlexplorer.py
├── test_mlregressor.py
├── test_peakarea.py
├── test_plotting.py
├── test_rameau.py
├── test_smooth.py
├── test_spectranization.py
└── test_tlcorrection.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/publish.yml
================================================
name: Upload Python Package to PyPI when a Release is Created
on:
release:
types: [created]
jobs:
pypi-publish:
name: Publish release to PyPI
runs-on: ubuntu-latest
environment:
name: pypi
url: https://pypi.org/p/rampy
permissions:
id-token: write
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: "3.x"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install --upgrade build
- name: Build package
run: |
python -m build # Could also be python setup.py sdist bdist_wheel
- name: Publish package distributions to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
================================================
FILE: .gitignore
================================================
.DS_STORE
/dist/
/dev/
/docs/doctrees/
/docs/build/
/docs/html/
/build/
.ipynb_checkpoints/
/rampy.egg-info/
/.pytest_cache/
/.cache/
__pycache__
.pyc
================================================
FILE: .readthedocs.yaml
================================================
# Read the Docs configuration file for Sphinx projects
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
# Required
version: 2
# Set the OS, Python version and other tools you might need
build:
os: ubuntu-22.04
tools:
python: "3.12"
# You can also specify other tool versions:
# nodejs: "20"
# rust: "1.70"
# golang: "1.20"
# Build documentation in the "docs/" directory with Sphinx
sphinx:
configuration: docs/conf.py
# You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs
# builder: "dirhtml"
# Fail on all warnings to avoid broken references
# fail_on_warning: true
# Optionally build your docs in additional formats such as PDF and ePub
# formats:
# - pdf
# - epub
# Optional but recommended, declare the Python requirements required
# to build your documentation
# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
python:
install:
- requirements: docs/requirements.txt
- method: pip
path: .
================================================
FILE: .travis.yml
================================================
language: python
python:
- "3.11"
os:
- linux
install:
- pip install cvxpy
- pip install .
# command to run tests
script: pytest
================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as
contributors and maintainers pledge to making participation in our project and
our community a harassment-free experience for everyone, regardless of age, body
size, disability, ethnicity, gender identity and expression, level of experience,
nationality, personal appearance, race, religion, or sexual identity and
orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment
include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or
advances
* Trolling, insulting/derogatory/aggresive comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic
address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable
behavior and are expected to take appropriate and fair corrective action in
response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or
reject comments, commits, code, wiki edits, issues, and other contributions
that are not aligned to this Code of Conduct, or to ban temporarily or
permanently any contributor for other behaviors that they deem inappropriate,
threatening, offensive, or harmful.
## Scope
This Code of Conduct applies both within project spaces and in public spaces
when an individual is representing the project or its community. Examples of
representing a project or community include using an official project e-mail
address, posting via an official social media account, or acting as an appointed
representative at an online or offline event. Representation of a project may be
further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported by contacting the project team at charles.lelosq@anu.edu.au. All
complaints will be reviewed and investigated and will result in a response that
is deemed necessary and appropriate to the circumstances. The project team is
obligated to maintain confidentiality with regard to the reporter of an incident.
Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good
faith may face temporary or permanent repercussions as determined by other
members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
available at [http://contributor-covenant.org/version/1/4][version]
[homepage]: http://contributor-covenant.org
[version]: http://contributor-covenant.org/version/1/4/
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing
Any contribution is more than welcome!
When contributing to this repository, please first discuss the change you wish to make via issue,
email, or any other method with the owners of this repository before making a change.
Please note we have a code of conduct, please follow it in all your interactions with the project.
## Pull Request Process
1. Ensure any install or build dependencies are removed before the end of the layer when doing a
build.
2. Update the README.md and NEWS.md with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.
3. Increase the version numbers in any examples files and the NEWS.md to the new version that this
Pull Request would represent. The versioning scheme we use is [SemVer](http://semver.org/).
================================================
FILE: LICENSE
================================================
GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The licenses for most software are designed to take away your
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software--to make sure the software is free for all its users. This
General Public License applies to most of the Free Software
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using it. (Some other Free Software Foundation software is covered by
the GNU Lesser General Public License instead.) You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
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To protect your rights, we need to make restrictions that forbid
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For example, if you distribute copies of such a program, whether
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you have. You must make sure that they, too, receive or can get the
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We protect your rights with two steps: (1) copyright the software, and
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Finally, any free program is threatened constantly by software
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The precise terms and conditions for copying, distribution and
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GNU GENERAL PUBLIC LICENSE
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
0. This License applies to any program or other work which contains
a notice placed by the copyright holder saying it may be distributed
under the terms of this General Public License. The "Program", below,
refers to any such program or work, and a "work based on the Program"
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Activities other than copying, distribution and modification are not
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is covered only if its contents constitute a work based on the
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Whether that is true depends on what the Program does.
1. You may copy and distribute verbatim copies of the Program's
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and give any other recipients of the Program a copy of this License
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You may charge a fee for the physical act of transferring a copy, and
you may at your option offer warranty protection in exchange for a fee.
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c) If the modified program normally reads commands interactively
when run, you must cause it, when started running for such
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These requirements apply to the modified work as a whole. If
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In addition, mere aggregation of another work not based on the Program
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except as expressly provided under this License. Any attempt
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such claims; this section has the sole purpose of protecting the
integrity of the free software distribution system, which is
implemented by public license practices. Many people have made
generous contributions to the wide range of software distributed
through that system in reliance on consistent application of that
system; it is up to the author/donor to decide if he or she is willing
to distribute software through any other system and a licensee cannot
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This section is intended to make thoroughly clear what is believed to
be a consequence of the rest of this License.
8. If the distribution and/or use of the Program is restricted in
certain countries either by patents or by copyrighted interfaces, the
original copyright holder who places the Program under this License
may add an explicit geographical distribution limitation excluding
those countries, so that distribution is permitted only in or among
countries not thus excluded. In such case, this License incorporates
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of the General Public License from time to time. Such new versions will
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NO WARRANTY
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
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WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
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OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
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POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
{description}
Copyright (C) {year} {fullname}
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) year name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, the commands you use may
be called something other than `show w' and `show c'; they could even be
mouse-clicks or menu items--whatever suits your program.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the program, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
{signature of Ty Coon}, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.
================================================
FILE: NEWS.md
================================================
# Rampy News
Copyright (c) 2014-2025 Dr. Charles Le Losq et al.
email: lelosq@ipgp.fr
Licence: see LICENCE.md
# Wanted features (planned)
- Addition of more unsupervised machine learning techniques
- Classification via 1D CNN and other algorithms using Keras or Pytorch
- peak fitting (in particular for maps): working on a Scikit-Learn API
# 0.6.4 (stable)
- fix usage of tlcorrection with the area normalisation (issue #40)
- fix docstrings (issue #39)
- fix QDA classification example and test (use solver='eigen' and shrinkage='auto').
# 0.6.3
- fix 'roi' and 'method' arguments in baseline() function (issue #38)
# 0.6.2
- Fix the documentation.
# 0.6.0
## Additions
- area_peak(): function to calculate analytically the area of peaks in a spectrum.
## Modifications
- Minimal Python version is now 3.10
- normalise(): "area" normalisation now uses the simpson() function from scipy.integrate, instead of the trapezoidal rule. This should be more precise.
# Improvements
- maps(): documentation of the class has been improved.
## Fix
- baseline(): a problem with the roi has been raised (issue #34). This is now fixed, roi argument is completely optional.
- resample(): fill_value was not working properly (not the good default option). It is now fixed. resample() will extrapolate automatically.
# 0.6.0
## Improvements:
- baseline(): we use the make_smoothing_spline function from Scipy for the GCVSmoothedNSpline smoothing mode (smooth function) and the gcvspline baseline, in replaced of the call to the gcvspline library. make_smoothing_spline is a reimplenetation of the Woltring Fortran code in gcvspline library, so modulo numerical errors, it returns the same results. This offers now the gcvspline baseline to all, without problem of Fortran compilation.
- baseline(): bir are now an optional argument in rampy.baseline (because they do not appear in als, arPLS and drPLS algorithms)
- peakarea(): function is improved and a test is added.
- mlclassificator(): this class was badly handling model parameters. This is now fixed. The code has been improved.
- various improvements of the code formatting
- tests & examples updated
- docstrings re-written in Google Style
- docs updated
## Additions:
- plot_spectrum(): makes an interactive plot of a signal, and also possibly of added baselines and smoothed signals.
- whittaker(): it is now possible to pass weights
- baseline(): Whittaker and Gaussian process baselines going through regions of interest is also available in the baseline() function
- ruby_T_corr(): Computes the temperature correction for the Ruby pressure scale.
- borate_T_corr(): Computes the temperature correction for the SrB4O7:Sm2+ pressure scale.
- pressure_sensor(): Converts the wavelength of fluorescence lines into pressure in GPa.
## Deprecation:
- get_portion_interest() becomes extract_signal(). get_portion_interest() still works but it will be removed in a future release
# 0.5.3
- Correction in despiking function (thanks Kevin Yuan)
# 0.5.2
- fix installation : something went bad as we switched to pyproject.toml in 0.5.1
# 0.5.1
- fix installation dependencies
# 0.5.0
- map() function is now maps() to avoid any conflict with built-in Python map() function
- add the possiblity of importing lines of spectra for Horiba spectrometers
- add a despiking() function
- add external calibration in rameau
- add the posibility to calibrate K coefficient(s) on another dataset of spectra
- correction of a typo in mlclassificator function
# 0.4.9
- quick fix of a bug in read_horiba function (from rampy.maps)
# 0.4.8
- area and area ratio calculations available for maps
- read_horiba function (from rampy.maps) corrected (Github issue #25)
- rubberband baseline was fixed, test added, fit_baseline example update (Github issue #1, thanks @sjfraser05)
# 0.4.7
- urgent correction of a bug in rp.pseudovoigt() > float entries were resulting in an error message...
- map treatment available for HORIBA and RENISHAW spectrometer (see example folder). Please report any bug!
# 0.4.6
- doc improvements
- cvxpy 1.1 or higher is an optional dependency.
- switch to setup.cfg
- add shiftsp() function
# 0.4.5
- update docs
- enhancing the pseudovoigt section for its use with arrays
- adding an experimental "peakarea" function
- drPLS algorithm added in baselines (thanks @Snijderfrey)
# 0.4.4
- Cleaning the code
- Correction of a bug in tlcorrection
- add lorentzian() and pearson7() peak shape functions
- add mlexplorator function for easy PCA/NMF on spectroscopic data
- update docs
# 0.4.3
- Improvements in documentation of mlregressor.
- Improvements of rampy.normalise and rampy.centroid. Those functions can treat arrays of spectra now.
- Correction of a bug in rampy.normalize that caused the "area" method to not work when entering x.
- Better tests
# 0.4.2
- Removing dependency to cvxpy that does not build well in Windows... It affects the use of the rampy.mixing function
# 0.4.1
- Correction of an error in `mlregressor` which made impossible to import X_test datasets.
# 0.4.0
- BREAKING CHANGE: `mlregressor` is now a class and not a function anymore. You can provide directly sklearn arguments to the algorithms through dictionaries.
The use of the class simplifies the use of `mlregressor`, as the created objects saves everything!
It also makes it very easy to change the algorithm and try something else. See the example in the example folder!
- addition of the `centroid()` function, that calculates the centroid of a signal.
- addition of tests and examples for the `mlregressor()` class, the `resample()` and `flipsp()` functions.
- `chemical_splitting()` allows one to select the random seed.
- Correction of the `mixing_sp()` function, rampy is now compatible with cvxpy v1.0.
- arguments can be provided to `resample()` to use different techniques of interpolation in `scipy.interpolate.interp1d`.
- Various documentation improvements
# 0.3.6
- Correction of the `tlcorrection()` function: the 'hehlen' correction was missing a frequency term to be complete (eq. 2 and 3 in Hehlen 2010 J. Phys. Condes. Matter 22: 025401).
# 0.3.5
- Addition of the `rampy.mixing_sp()` function. See `help(rampy.mixing_sp())`, as well as the example folder.
# 0.3.4
- gcvspline is not a requirement anymore. Error messages will outputs when trying to use it, inviting to install it manually. This is implemented to avoid problems with FORTRAN compilation for people not interested in using gcvspline.
- Add early stopping in` mlregressor` neural networks.
# 0.3.3
- Minor dependency correction
# 0.3.2
- Adding the names in the `rameau` object
- Improvements of the `mlregressor` function, with addition of neural nets and bagging neural nets algorithms
# 0.3.1
- Rameau is now an object-oriented interface
- `smooth()` function updated; 10 algorithms are available.
- updated example of peak fitting
# 0.3.0
- Documentation improvements
- Python 3 compatible
- Addition of the Rameau function
# 0.2.9
- addition of gaussian baseline
- addition of `flipsp()` to flip spectra along the axis 0
- addition of `resample()` to resample spectra with scipy.interpolate
- improvement of `tlcorrection`
# 0.2.8
- addition of the `tlcorrection()` function to replace the Long function
# 0.2.7
- Minor correction of the `baseline()` documentation and removing a `print()` command.
# 0.2.6
- standardizatin in the `baseline()` function is now included (improve polynomial fits);
- addition of the arPLS algorithm from Baek et al. (2015) to automatic fit the baseline;
- addition of the whittaker smoother to fit the baseline (Eiler 2003);
- addition of the ALS algorithm (Eilers and Boelens 2005).
================================================
FILE: README.md
================================================
# RamPy
=======
[](https://travis-ci.org/charlesll/rampy)
[](https://doi.org/10.5281/zenodo.1168729) [](https://mybinder.org/v2/gh/charlesll/rampy.git/master)
Rampy is a Python library that aims at helping processing spectroscopic data, such as Raman, Infrared or XAS spectra. It offers various functions, for instance to subtract baselines, resample and smooth spectra, perform machine learning classification, etc. It aims at facilitating the use of Python in processing spectroscopic data. It integrates within a workflow that uses Numpy/Scipy/Matplotlib as well as optimisation libraries such as lmfit, emcee or PyMC, for instance.
Check the documentation for more information!
- **Documentation:** https://rampy.readthedocs.io
- **Examples** see docs and https://github.com/charlesll/rampy/tree/master/examples
- **Source code:** https://github.com/charlesll/rampy/tree/master/rampy
- **Contributing:** https://github.com/charlesll/rampy/blob/master/CONTRIBUTING.md
- **Bug reports:** https://github.com/charlesll/rampy/issues
- **Contact** lelosq@ipgp.fr
================================================
FILE: docs/.nojekyll
================================================
================================================
FILE: docs/API.rst
================================================
rampy API
=============
Subpackages
-----------
.. toctree::
:maxdepth: 4
Submodules
----------
rampy.baseline module
---------------------
.. automodule:: rampy.baseline
:members:
:undoc-members:
:show-inheritance:
rampy.filters module
--------------------
.. automodule:: rampy.filters
:members:
:undoc-members:
:show-inheritance:
rampy.plotting module
---------------------
.. automodule:: rampy.plotting
:members:
:undoc-members:
:show-inheritance:
rampy.functions module
----------------------
.. automodule:: rampy.functions
:members:
:undoc-members:
:show-inheritance:
rampy.maps module
-----------------
.. automodule:: rampy.maps
:members:
:undoc-members:
:show-inheritance:
rampy.mixing module
-------------------
.. automodule:: rampy.mixing
:members:
:undoc-members:
:show-inheritance:
rampy.ml\_classification module
-------------------------------
.. automodule:: rampy.ml_classification
:members:
:undoc-members:
:show-inheritance:
rampy.ml\_exploration module
----------------------------
.. automodule:: rampy.ml_exploration
:members:
:undoc-members:
:show-inheritance:
rampy.ml\_regressor module
--------------------------
.. automodule:: rampy.ml_regressor
:members:
:undoc-members:
:show-inheritance:
rampy.peak\_area module
-----------------------
.. automodule:: rampy.peak_area
:members:
:undoc-members:
:show-inheritance:
rampy.peak\_shapes module
-------------------------
.. automodule:: rampy.peak_shapes
:members:
:undoc-members:
:show-inheritance:
rampy.rameau module
-------------------
.. automodule:: rampy.rameau
:members:
:undoc-members:
:show-inheritance:
rampy.spectranization module
----------------------------
.. automodule:: rampy.spectranization
:members:
:undoc-members:
:show-inheritance:
rampy.tlcorrection module
-------------------------
.. automodule:: rampy.tlcorrection
:members:
:undoc-members:
:show-inheritance:
================================================
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================================================
FILE: docs/baseline.rst
================================================
Baseline
========
Rampy allows you to fit polynomial, spline, generalized cross-validated spline, logarithms, exponential, ALS, arPLS, drPLS, rubberband and whittaker baselines to your spectra, in order to remove the background.
.. image:: ./images/baseline.png
:width: 500
Below you will find the documentation of the relevant functions, and have a look at the example notebooks too: :ref:`RST Notebooks`
.. autofunction:: rampy.baseline
For some baseline types, individual baseline functions also can be used, if you are interested:
.. autofunction:: rampy.rubberband_baseline
.. autofunction:: rampy.als_baseline
.. autofunction:: rampy.arPLS_baseline
.. autofunction:: rampy.drPLS_baseline
================================================
FILE: docs/conf.py
================================================
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
project = 'RamPy'
copyright = '2025, Charles Le Losq'
author = 'Charles Le Losq'
# The full version, including alpha/beta/rc tags
release = '0.6.0'
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['sphinx.ext.autodoc','sphinx.ext.napoleon', 'nbsphinx', 'nbconvert'
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = []
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_rtd_theme'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
html_logo = 'images/Rampy.png'
tml_js_files = [
"require.min.js", # Add to your _static
"custom.js",
]
# Here is for the plotly graphs
import plotly.io as pio
pio.renderers.default = 'sphinx_gallery'
================================================
FILE: docs/dac.rst
================================================
Diamond Anvil Cell Utilities
============================
Rampy can be useful if you are performing Diamond Anvil Cell experiments, to process florescence spectra of pressure sensors for instance, or remove the diamond background.
Here is some helper functions for DAC experiments!
Pressure calculations
---------------------
.. automodule:: rampy.dac
:members:
:undoc-members:
:show-inheritance:
================================================
FILE: docs/examples.rst
================================================
.. _RST Notebooks:
Example notebooks
=================
Processing
----------
.. nbgallery::
notebooks/Resample_and_Flip
notebooks/Normalisation
notebooks/Smoothing
notebooks/Baseline
notebooks/Baseline_and_Centroid
Peak fitting
----------------
.. nbgallery::
notebooks/Raman_fitting
Machine learning
----------------
.. nbgallery::
notebooks/Mixing
notebooks/ML_Classification
notebooks/ML_Exploration
notebooks/ML_Regression
Maps
----------------
.. nbgallery::
notebooks/Maps
================================================
FILE: docs/firststeps.rst
================================================
.. _RST First:
First Steps
===========
You can consult the help of each function/class/method on this website.
Quick examples of the use of the functions/classes are shown to highlight some of the possibilities offered by Rampy.
Example Jupyter notebooks are also shown, and `can be dowloaded <https://github.com/charlesll/rampy/tree/master/docs/notebooks>`_
Library importation
-------------------
Python does not come with a lot of possibilities. This is why you usually need to import a few libraries. You can assign a shortname to a library, as usually people import/use functions with libraries' prefix, to avoid confusion (two libraries can have a function with the same name...). A useful shortname for rampy is ``rp``:
.. code-block:: python
# importing rampy
import rampy as rp
# and for numpy we will respect the usual name:
import numpy as np
# for matplotlib
import matplotlib.pyplot as plt
Data importation
----------------
This is done using directly Pandas or numpy, except for:
- importing multiple spectra can be done with the ``rampy.spectrarray()`` function;
- maps for which rampy provides a function to import the CSV files generated by
the Renishaw or Horiba Raman spectrometers.
To import a spectrum saved in a 2 column text file "data.txt" with space separator, we can use the numpy genfromtxt function:
.. code-block:: python
import numpy as np
spectrum = np.genfromtxt("data.txt")
If our file has a 10 line header, we can skip it like
.. code-block:: python
spectrum = np.genfromtxt("data.txt", skip_header=10)
See the documentation of numpy for further details, as well as that of Pandas.
Plot a spectrum
---------------
This can be done with Matplotlib directly. For instance, to plot our spectrum we can do:
.. code-block:: python
plt.figure(figsize=(5,5))
plt.plot(spectrum[:,0],spectrum[:,1],'k.',markersize=1)
plt.xlabel("Raman shift, cm$^{-1}$", fontsize = 12)
plt.ylabel("Normalized intensity, a. u.", fontsize = 12)
plt.title("Fig. 1: the raw data",fontsize = 12,fontweight="bold")
This will give the image:
.. image:: ./images/spectrum.png
:width: 400
Rampy also provides a function to plot a spectrum, allowing an interactive view (leveraging Plotly) of the data and possibly of baselines and smoothed signals:
.. code-block:: python
import rampy as rp
rp.plot_spectrum(spectrum[:,0],
spectrum[:,1],
xaxis_title="Raman shift, cm$^{-1}$",
yaxis_title="Normalized intensity, a. u.",
title="Fig. 1: the raw data")
See the :ref:`RST Plotting` page for details on the function.
Now you are ready for the next steps: :ref:`RST Preprocessing`
================================================
FILE: docs/index.rst
================================================
.. RamPy documentation master file, created by
sphinx-quickstart on Fri Oct 8 15:22:27 2021.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
Welcome to RamPy's documentation!
=================================
Copyright (2015-2026) C. Le Losq and co.
Charles Le Losq, Institut de physique du globe de Paris, Université Paris Cité
lelosq@ipgp.fr
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1168729.svg
:target: https://doi.org/10.5281/zenodo.1168729
The Rampy project
-----------------
Rampy is a Python library that aims at helping processing spectroscopic data, such as Raman, Infrared or XAS spectra.
It offers, for instance, functions to subtract baselines as well as to stack, resample or smooth spectra.
It aims at facilitating the use of Python in processing spectroscopic data.
The project is hosted on Github (`here! <https://github.com/charlesll/rampy>`_), please do not hesitate to propose new features or simply ideas!
Integration with other packages
-------------------------------
Rampy integrates within a workflow that uses Numpy/Scipy/Matplotlib as well as optimisation libraries such as lmfit, emcee, PyMC3 for instance.
Rampy can be used also to analyse the output of the `RADIS <https://radis.readthedocs.io/en/latest/>`_ package. See the discussion about this `here <https://github.com/charlesll/rampy/issues/13>`_
For more advanced baseline algorithms, please also check the `Pybaselines package <https://pybaselines.readthedocs.io/>`_.
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
.. toctree::
:maxdepth: 1
:caption: Contents:
philosophy
news
installation
firststeps
preprocessing
baseline
smoothing
plotting
signalparams
peakfitting
machinelearning
maps
dac
examples
utils
API
================================================
FILE: docs/installation.rst
================================================
Installation
============
General preparation
-------------------
Rampy runs with a traditional Python stack.
If you are not familiar with Python, you can first have a look at the `scipy lecture notes <https://scipy-lectures.org/>`_,
a set of tutorials for the beginner.
You can install `Anaconda Python <https://www.anaconda.com/products/individual>`_ to get a running Python distribution. See the documentation of Anaconda for those steps.
Rampy installation
------------------
Install with pip in the command line:
``pip install rampy``
Optional dependencies
---------------------
If you want to use the "MSESmoothedNSpline" or "DOFSmoothedNSpline" options from the ``smooth()`` function, please install the following library:
``pip install gcvspline``
Please note that gcvspline installation requires gfortran, see `gcvspline documentation <https://charlesll.github.io/gcvspline/>`_.
Installation is done? Have a look here if you are new to Python: :ref:`RST First`
================================================
FILE: docs/machinelearning.rst
================================================
Machine learning
================
Rampy offers three classes for performing classification, regression or unsupervised ML exploration of a set of spectra. Those are helpful ways to automatically perform usual scalling, test-train splitting and ML training of popular ML algorithms, and use `scikit-learn <https://scikit-learn.org/stable/>`_ in the background.
Those functions work (``rp.regressor`` was used in `this publication <https://doi.org/10.2138/am-2019-6887>`_) but still may evolve in the futur. For advanced, ML, I suggest using directly scikit-learn or other ML libraries.
Below you will find the documentation of the relevant functions, and have a look at the example notebooks too: :ref:`RST Notebooks`
Do not hesitate to ask for new features depending on your needs !
Machine learning classification
-------------------------------
Based on a set of spectra and their labels, the ``rampy.mlclassificator`` class allows you to perform a classification of the spectra using a supervised ML algorithm. The class will take care of splitting the data into training and test sets, scaling the data, and training the model. You can then use the trained model to predict the labels of new spectra.
.. autoclass:: rampy.mlclassificator
:members:
:undoc-members:
:show-inheritance:
Machine learning exploration
----------------------------
The ``rampy.mlexplorer`` class allows you to perform unsupervised ML exploration of a set of spectra. The class will take care of scaling the data and training the model. You can then use the trained model to explore the data and find patterns.
.. autoclass:: rampy.mlexplorer
:members:
:undoc-members:
:show-inheritance:
Machine learning regression
---------------------------
Based on a set of spectra and their labels, the ``rampy.mlregressor`` class allows you to perform a regression using the spectra and a supervised ML algorithm. The class will take care of splitting the data into training and test sets, scaling the data, and training the model. You can then use the trained model to predict the new values of your target from new spectra.
.. autoclass:: rampy.mlregressor
:members:
:undoc-members:
:show-inheritance:
Linear mixture
--------------
This function helps you solve a simple problem: you have spectra that are obtained by a linear combination of two endmember spectra.
If you have the two endmember spectra, you can use the ``rampy.mixing()`` function to know the fraction of each endmember in the mixture.
If you do not know the endmember spectra, then you may be interested in using directly the PyMCR library, see the documentation `here <https://pages.nist.gov/pyMCR/>`_ and an example notebook `here <https://github.com/usnistgov/pyMCR/blob/master/Examples/Demo.ipynb>`_. We used it in `this publication <https://doi.org/10.2138/am-2019-6887>`_, see the code `here <https://github.com/charlesll/rampy/blob/master/examples/Iron_AmMin_paper/Iron_MORB_code.ipynb>`_.
.. autofunction:: rampy.mixing_sp
Helper functions
-------------------------------
The ``rampy.chemical_splitting`` function allows you to split datasets depending on a target (e.g. sample composition).
.. autofunction:: rampy.chemical_splitting
================================================
FILE: docs/make.bat
================================================
@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=.
set BUILDDIR=.
if "%1" == "" goto help
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.https://www.sphinx-doc.org/
exit /b 1
)
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
:end
popd
================================================
FILE: docs/maps.rst
================================================
Maps
====
Rampy offers the possiblity to analyse maps from Raman spectrometers using the ``rampy.maps`` module.
Line and 2D Maps saved in CSV or TXT format from Horiba and Renishaw spectrometers can be loaded, and various data treatments can be performed afterwards.
Below you will find the documentation of the ``rampy.maps`` module, and have a look at the example notebooks too for further examples of use: :ref:`RST Notebooks`
.. autoclass:: rampy.maps
:members:
:undoc-members:
:show-inheritance:
The following functions are also available and used in the ``rampy.maps`` class to load spectra from different spectrometers.
.. autofunction:: rampy.read_renishaw
.. autofunction:: rampy.read_horiba
================================================
FILE: docs/modules.rst
================================================
rampy
=====
.. toctree::
:maxdepth: 2
rampy
================================================
FILE: docs/news.rst
================================================
Rampy News
==========
Copyright (c) 2014-2025 Dr. Charles Le Losq et al.
email: lelosq@ipgp.fr
Licence: see LICENCE.md
Wanted features (planned)
-------------------------
- Addition of more unsupervised machine learning techniques
- Classification via 1D CNN and other algorithms using Keras or Pytorch
- peak fitting (in particular for maps): working on a Scikit-Learn API
0.6.4 (stable)
----------------
- fix usage of tlcorrection with the area normalisation (issue #40)
- fix docstrings (issue #39)
- fix QDA classification example and test (use solver='eigen' and shrinkage='auto').
0.6.3
----------------
- fix 'roi' and 'method' arguments in baseline() function (issue #38)
0.6.2
----------------
- Fix the documentation.
0.6.1
----------------
Additions:
- area_peak(): function to calculate analytically the area of peaks in a spectrum.
Modifications:
- Minimal Python version is now 3.10
- normalise(): "area" normalisation now uses the simpson() function from scipy.integrate, instead of the trapezoidal rule. This should be more precise.
Improvements:
- maps(): documentation of the class has been improved.
Fix:
- baseline(): a problem with the roi has been raised (issue #34). This is now fixed, roi argument is completely optional.
- resample(): fill_value was not working properly (not the good default option). It is now fixed. resample() will extrapolate automatically.
0.6.0
----------------
Improvements:
- baseline(): we use the make_smoothing_spline function from Scipy for the GCVSmoothedNSpline smoothing mode (smooth function) and the gcvspline baseline, in replaced of the call to the gcvspline library. make_smoothing_spline is a reimplenetation of the Woltring Fortran code in gcvspline library, so modulo numerical errors, it returns the same results. This offers now the gcvspline baseline to all, without problem of Fortran compilation.
- baseline(): bir are now an optional argument in rampy.baseline (because they do not appear in als, arPLS and drPLS algorithms)
- peakarea(): function is improved and a test is added.
- mlclassificator(): this class was badly handling model parameters. This is now fixed. The code has been improved.
- various improvements of the code formatting
- tests & examples updated
- docstrings re-written in Google Style
- docs updated
Additions:
- plot_spectrum(): makes an interactive plot of a signal, and also possibly of added baselines and smoothed signals.
- whittaker(): it is now possible to pass weights
- baseline(): Whittaker and Gaussian process baselines going through regions of interest is also available in the baseline() function
- ruby_T_corr(): Computes the temperature correction for the Ruby pressure scale.
- borate_T_corr(): Computes the temperature correction for the SrB4O7:Sm2+ pressure scale.
- pressure_sensor(): Converts the wavelength of fluorescence lines into pressure in GPa.
Deprecation:
- get_portion_interest() becomes extract_signal(). get_portion_interest() still works but it will be removed in a future release
0.5.3
-----
- Correction in despiking function (thanks Kevin Yuan)
0.5.2
-----
- fix installation : something went bad as we switched to pyproject.toml in 0.5.1
0.5.1
-----
- fix installation dependencies
0.5.0
-----
- map() function is now maps() to avoid any conflict with built-in Python map() function
- add the possiblity of importing lines of spectra for Horiba spectrometers
- add a despiking() function
- add external calibration in rameau
- add the posibility to calibrate K coefficient(s) on another dataset of spectra
- correction of a typo in mlclassificator function
0.4.9
-----
- quick fix of a bug in read_horiba function (from rampy.maps)
0.4.8
-----
- area and area ratio calculations available for maps
- read_horiba function (from rampy.maps) corrected (Github issue #25)
- rubberband baseline was fixed, test added, fit_baseline example update (Github issue #1, thanks @sjfraser05)
0.4.7
-----
- urgent correction of a bug in rp.pseudovoigt() > float entries were resulting in an error message...
- map treatment available for HORIBA and RENISHAW spectrometer (see example folder). Please report any bug!
0.4.6
-----
- doc improvements
- cvxpy 1.1 or higher is an optional dependency.
- switch to setup.cfg
- add shiftsp() function
0.4.5
-----
- update docs
- enhancing the pseudovoigt section for its use with arrays
- adding an experimental "peakarea" function
- drPLS algorithm added in baselines (thanks @Snijderfrey)
0.4.4
-----
- Cleaning the code
- Correction of a bug in tlcorrection
- add lorentzian() and pearson7() peak shape functions
- add mlexplorator function for easy PCA/NMF on spectroscopic data
- update docs
0.4.3
-----
- Improvements in documentation of mlregressor.
- Improvements of rampy.normalise and rampy.centroid. Those functions can treat arrays of spectra now.
- Correction of a bug in rampy.normalize that caused the "area" method to not work when entering x.
- Better tests
0.4.2
-----
- Removing dependency to cvxpy that does not build well in Windows... It affects the use of the rampy.mixing function
0.4.1
-----
- Correction of an error in `mlregressor` which made impossible to import X_test datasets.
0.4.0
-----
- BREAKING CHANGE: `mlregressor` is now a class and not a function anymore. You can provide directly sklearn arguments to the algorithms through dictionaries.
The use of the class simplifies the use of `mlregressor`, as the created objects saves everything!
It also makes it very easy to change the algorithm and try something else. See the example in the example folder!
- addition of the `centroid()` function, that calculates the centroid of a signal.
- addition of tests and examples for the `mlregressor()` class, the `resample()` and `flipsp()` functions.
- `chemical_splitting()` allows one to select the random seed.
- Correction of the `mixing_sp()` function, rampy is now compatible with cvxpy v1.0.
- arguments can be provided to `resample()` to use different techniques of interpolation in `scipy.interpolate.interp1d`.
- Various documentation improvements
0.3.6
-----
- Correction of the `tlcorrection()` function: the 'hehlen' correction was missing a frequency term to be complete (eq. 2 and 3 in Hehlen 2010 J. Phys. Condes. Matter 22: 025401).
0.3.5
-----
- Addition of the `rampy.mixing_sp()` function. See `help(rampy.mixing_sp())`, as well as the example folder.
0.3.4
-----
- gcvspline is not a requirement anymore. Error messages will outputs when trying to use it, inviting to install it manually. This is implemented to avoid problems with FORTRAN compilation for people not interested in using gcvspline.
- Add early stopping in` mlregressor` neural networks.
0.3.3
-----
- Minor dependency correction
0.3.2
-----
- Adding the names in the `rameau` object
- Improvements of the `mlregressor` function, with addition of neural nets and bagging neural nets algorithms
0.3.1
-----
- Rameau is now an object-oriented interface
- `smooth()` function updated; 10 algorithms are available.
- updated example of peak fitting
0.3.0
-----
- Documentation improvements
- Python 3 compatible
- Addition of the Rameau function
0.2.9
-----
- addition of gaussian baseline
- addition of `flipsp()` to flip spectra along the axis 0
- addition of `resample()` to resample spectra with scipy.interpolate
- improvement of `tlcorrection`
0.2.8
-----
- addition of the `tlcorrection()` function to replace the Long function
0.2.7
-----
- Minor correction of the `baseline()` documentation and removing a `print()` command.
0.2.6
-----
- standardizatin in the `baseline()` function is now included (improve polynomial fits);
- addition of the arPLS algorithm from Baek et al. (2015) to automatic fit the baseline;
- addition of the whittaker smoother to fit the baseline (Eiler 2003);
- addition of the ALS algorithm (Eilers and Boelens 2005).
================================================
FILE: docs/notebooks/Baseline.ipynb
================================================
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"# Example: fit a baseline\n",
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"## Load the libraries and create a signal\n"
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gitextract_1cpthhgz/
├── .github/
│ └── workflows/
│ └── publish.yml
├── .gitignore
├── .readthedocs.yaml
├── .travis.yml
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── NEWS.md
├── README.md
├── docs/
│ ├── .nojekyll
│ ├── API.rst
│ ├── Makefile
│ ├── _static/
│ │ ├── custom.js
│ │ └── require.js
│ ├── baseline.rst
│ ├── conf.py
│ ├── dac.rst
│ ├── examples.rst
│ ├── firststeps.rst
│ ├── index.rst
│ ├── installation.rst
│ ├── machinelearning.rst
│ ├── make.bat
│ ├── maps.rst
│ ├── modules.rst
│ ├── news.rst
│ ├── notebooks/
│ │ ├── Baseline.ipynb
│ │ ├── Baseline_and_Centroid.ipynb
│ │ ├── ML_Classification.ipynb
│ │ ├── ML_Exploration.ipynb
│ │ ├── ML_Regression.ipynb
│ │ ├── Maps.ipynb
│ │ ├── Mixing.ipynb
│ │ ├── Normalisation.ipynb
│ │ ├── Raman_fitting.ipynb
│ │ ├── Resample_and_flip.ipynb
│ │ ├── Smoothing.ipynb
│ │ └── data/
│ │ ├── 4338-NDC1-(31)_4X4.txt
│ │ ├── LS4.txt
│ │ └── NS4.txt
│ ├── peakfitting.rst
│ ├── philosophy.rst
│ ├── plotting.rst
│ ├── preprocessing.rst
│ ├── requirements.txt
│ ├── setup.rst
│ ├── signalparams.rst
│ ├── smoothing.rst
│ └── utils.rst
├── environment.yml
├── examples/
│ ├── Iron_AmMin_paper/
│ │ ├── .gitignore
│ │ ├── HOWTO.txt
│ │ ├── Iron_MORB_code.ipynb
│ │ ├── MORB_datalist.xlsx
│ │ ├── iron.py
│ │ └── raw/
│ │ ├── BD1469.txt
│ │ ├── BD1470.txt
│ │ ├── BD1471.txt
│ │ ├── BD1472.txt
│ │ ├── BD1473.txt
│ │ ├── BD1474.txt
│ │ ├── BD1475.txt
│ │ ├── BD1476.txt
│ │ ├── BD1477.txt
│ │ ├── BD1478.txt
│ │ ├── VG175_.txt
│ │ ├── VG175_1.txt
│ │ ├── VG175_2.txt
│ │ ├── VG175_3.txt
│ │ ├── VG175_4.txt
│ │ ├── VG183_.txt
│ │ ├── VG183_1.txt
│ │ ├── VG183_2.txt
│ │ ├── VG183_3.txt
│ │ ├── VG183_4.txt
│ │ ├── VG186_0__X_19996.5__Y_-20032__Time_0.txt
│ │ ├── VG186_2__X_20013__Y_-20055__Time_288.txt
│ │ ├── VG186_3__X_20030.8__Y_-20026.4__Time_433.txt
│ │ ├── VG186_4__X_20028.5__Y_-20016.4__Time_577.txt
│ │ ├── VG186_5__X_19960.5__Y_-20013.9__Time_722.txt
│ │ ├── VG187_0__X_24099.5__Y_-26618.8__Time_0.txt
│ │ ├── VG187_1__X_24133.8__Y_-26769.8__Time_144.txt
│ │ ├── VG187_2__X_23341.8__Y_-27123.3__Time_288.txt
│ │ ├── VG187_3__X_23212.5__Y_-27027.5__Time_433.txt
│ │ ├── VG187_4__X_23396__Y_-27469.3__Time_577.txt
│ │ ├── VG195_0__X_12141.4__Y_-19545.2__Time_0.txt
│ │ ├── VG195_1__X_12123.9__Y_-19557.5__Time_144.txt
│ │ ├── VG195_2__X_12157.9__Y_-19558.1__Time_288.txt
│ │ ├── VG195_3__X_12155.3__Y_-19533__Time_433.txt
│ │ ├── VG195_4__X_12127.4__Y_-19533.3__Time_578.txt
│ │ ├── VG202_0__X_7648.44__Y_-27430__Time_0.txt
│ │ ├── VG202_2__X_7634.03__Y_-27444.3__Time_288.txt
│ │ ├── VG202_3__X_7637.69__Y_-27420.2__Time_433.txt
│ │ ├── VG202_4__X_7660.95__Y_-27420.3__Time_577.txt
│ │ ├── VG202_oct_1_0__Time_0.txt
│ │ ├── VG203_0__X_7115.86__Y_-23841.7__Time_0.txt
│ │ ├── VG203_1__X_7125.78__Y_-23853.3__Time_144.txt
│ │ ├── VG203_2__X_7104.88__Y_-23853.2__Time_289.txt
│ │ ├── VG203_3__X_7106.54__Y_-23831.1__Time_433.txt
│ │ ├── VG203_4__X_7128.14__Y_-23832.2__Time_578.txt
│ │ ├── VG285_.txt
│ │ ├── VG285_1.txt
│ │ ├── VG285_2.txt
│ │ ├── VG285_3.txt
│ │ ├── VG285_4.txt
│ │ ├── VG290_.txt
│ │ ├── VG290_1.txt
│ │ ├── VG290_2.txt
│ │ ├── VG290_3.txt
│ │ ├── VG290_4.txt
│ │ ├── VG3073_0__X_24960.6__Y_-31533.1__Time_0.txt
│ │ ├── VG3073_1__X_24992.5__Y_-31555.7__Time_144.txt
│ │ ├── VG3073_2__X_25001__Y_-31550.5__Time_288.txt
│ │ ├── VG3073_3__X_24857.7__Y_-31338__Time_433.txt
│ │ ├── VG3073_4__X_24824.2__Y_-31298.1__Time_577.txt
│ │ ├── VG3079_1_0__X_11675.1__Y_-22752.8__Time_0.txt
│ │ ├── VG3079_1_1__X_11646.1__Y_-22772.1__Time_144.txt
│ │ ├── VG3079_1_2__X_11683.6__Y_-22746.7__Time_288.txt
│ │ ├── VG3079_2__X_12172.8__Y_-22892.5__Time_288.txt
│ │ ├── VG3079_3__X_12190.1__Y_-22882.1__Time_433.txt
│ │ ├── VG322_.txt
│ │ ├── VG322_1_0__X_16100.5__Y_-19847__Time_0.txt
│ │ ├── VG322_1_1__X_16116.9__Y_-19849__Time_144.txt
│ │ ├── VG322_1_2__X_16121.1__Y_-19831.4__Time_288.txt
│ │ ├── VG322_1_3__X_16117.9__Y_-19809.1__Time_433.txt
│ │ ├── VG334__0__X_20679.2__Y_-18282.5__Time_0.txt
│ │ ├── VG334__1__X_20664.6__Y_-18296.1__Time_144.txt
│ │ ├── VG334__2__X_20686.6__Y_-18297.2__Time_288.txt
│ │ ├── VG334__3__X_20688.3__Y_-18269.7__Time_433.txt
│ │ ├── VG334__4__X_20668.5__Y_-18272.8__Time_577.txt
│ │ ├── VG3450_0__X_14838.7__Y_-20142.8__Time_0.txt
│ │ ├── VG3450_1__X_14855.5__Y_-20151.9__Time_144.txt
│ │ ├── VG3450_2__X_14824.5__Y_-20153.1__Time_288.txt
│ │ ├── VG3450_3__X_14828.2__Y_-20132.5__Time_433.txt
│ │ ├── VG3450_4__X_14854.7__Y_-20133.6__Time_577.txt
│ │ ├── VG3831_0__X_22339.7__Y_-19634.3__Time_0.txt
│ │ ├── VG3831_1__X_22347.2__Y_-19640.8__Time_144.txt
│ │ ├── VG3831_2__X_22353.7__Y_-19646.9__Time_289.txt
│ │ ├── VG3831_3__X_22353.7__Y_-19624.4__Time_433.txt
│ │ ├── VG3831_4__X_22307.7__Y_-19611.5__Time_577.txt
│ │ ├── VG4088_0__X_16619.1__Y_-23684.9__Time_0.txt
│ │ ├── VG4088_1__X_16607.2__Y_-23693.9__Time_144.txt
│ │ ├── VG4088_2__X_16637.3__Y_-23693.8__Time_288.txt
│ │ ├── VG4088_3__X_16638.9__Y_-23680.7__Time_433.txt
│ │ ├── VG4088_4__X_16601.1__Y_-23676__Time_577.txt
│ │ ├── VG4522_.txt
│ │ ├── VG4522_1.txt
│ │ ├── VG4522_2.txt
│ │ ├── VG4522_3.txt
│ │ ├── VG4522_4.txt
│ │ ├── VG4700_0__X_18217.1__Y_-19810.8__Time_0.txt
│ │ ├── VG4700_1__X_18227.6__Y_-19821.2__Time_144.txt
│ │ ├── VG4700_2__X_18192.8__Y_-19824__Time_288.txt
│ │ ├── VG4700_3__X_18200.5__Y_-19800.4__Time_433.txt
│ │ ├── VG4700_4__X_18243.3__Y_-19813.8__Time_577.txt
│ │ ├── VG4701_0__X_25688.9__Y_-19810.7__Time_0.txt
│ │ ├── VG4701_1__X_25704.9__Y_-19815.2__Time_144.txt
│ │ ├── VG4701_2__X_25683.3__Y_-19817.4__Time_288.txt
│ │ ├── VG4701_3__X_25682.4__Y_-19803__Time_433.txt
│ │ ├── VG4701_4__X_25702.6__Y_-19801.7__Time_577.txt
│ │ ├── VG4709_0__X_21085.2__Y_-21479.9__Time_0.txt
│ │ ├── VG4709_1__X_21089.9__Y_-21483.4__Time_144.txt
│ │ ├── VG4709_2__X_21078.7__Y_-21485__Time_288.txt
│ │ ├── VG4709_3__X_21076.9__Y_-21474.6__Time_433.txt
│ │ ├── VG4709_4__X_21091.3__Y_-21472.8__Time_577.txt
│ │ ├── VG4971_.txt
│ │ ├── VG4971_1.txt
│ │ ├── VG4971_2.txt
│ │ ├── VG4971_3.txt
│ │ ├── VG4971_4.txt
│ │ ├── VG4974_.txt
│ │ ├── VG4974_1.txt
│ │ ├── VG4974_2.txt
│ │ ├── VG4974_3.txt
│ │ ├── VG4974_4.txt
│ │ ├── VG5374_0__X_18181.5__Y_-18038.6__Time_0.txt
│ │ ├── VG5374_1__X_18194.5__Y_-18046.3__Time_144.txt
│ │ ├── VG5374_2__X_18171__Y_-18046.7__Time_289.txt
│ │ ├── VG5374_3__X_18171.4__Y_-18026.8__Time_433.txt
│ │ ├── VG5374_4__X_18196__Y_-18028.4__Time_578.txt
│ │ ├── VG5697_.txt
│ │ ├── VG5697_1.txt
│ │ ├── VG5697_2.txt
│ │ ├── VG5697_3.txt
│ │ ├── VG5697_4.txt
│ │ ├── VG5781_.txt
│ │ ├── VG5781_1.txt
│ │ ├── VG5781_2.txt
│ │ ├── VG5781_3.txt
│ │ ├── VG5781_4.txt
│ │ ├── VG612_0__X_19514__Y_-19276.6__Time_0.txt
│ │ ├── VG612_1__X_19551.8__Y_-19295.8__Time_144.txt
│ │ ├── VG612_2__X_19501__Y_-19341__Time_288.txt
│ │ ├── VG612_3__X_19376.6__Y_-19350.4__Time_433.txt
│ │ ├── VG612_4__X_19287.5__Y_-19296.3__Time_577.txt
│ │ ├── VG662_0__X_13277.4__Y_-18492.1__Time_0.txt
│ │ ├── VG662_1__X_13303.4__Y_-18492.5__Time_144.txt
│ │ ├── VG662_2__X_13292.4__Y_-18480.4__Time_288.txt
│ │ ├── VG662_3__X_13277.7__Y_-18471.3__Time_433.txt
│ │ ├── VG662_4__X_13305.5__Y_-18472.1__Time_577.txt
│ │ ├── VG663_0__X_19823.3__Y_-22023.6__Time_0.txt
│ │ ├── VG663_1__X_19837.8__Y_-22035.6__Time_144.txt
│ │ ├── VG663_2__X_19805.4__Y_-22037.8__Time_288.txt
│ │ ├── VG663_3__X_19806.7__Y_-22010.3__Time_433.txt
│ │ ├── VG663_4__X_19837.2__Y_-22009.6__Time_577.txt
│ │ ├── VG664_.txt
│ │ ├── VG664_1_0__X_22184.8__Y_-25677.4__Time_0.txt
│ │ ├── VG664_1_1__X_22206.1__Y_-25676.3__Time_144.txt
│ │ ├── VG664_1_2__X_22208__Y_-25658.1__Time_288.txt
│ │ ├── VG664_1_3__X_22187.4__Y_-25657.9__Time_433.txt
│ │ ├── VG665_0__X_20243__Y_-29172.7__Time_0.txt
│ │ ├── VG665_1__X_20224.6__Y_-29185.8__Time_144.txt
│ │ ├── VG665_2__X_20259.3__Y_-29185.8__Time_288.txt
│ │ ├── VG665_3__X_20256__Y_-29162.3__Time_433.txt
│ │ ├── VG665_4__X_20228.7__Y_-29164.4__Time_577.txt
│ │ ├── VG666_0__X_17346.8__Y_-34522__Time_0.txt
│ │ ├── VG666_1__X_17421.8__Y_-34598.7__Time_144.txt
│ │ ├── VG666_2__X_17405.1__Y_-34315.9__Time_288.txt
│ │ ├── VG666_3__X_17494__Y_-34130.6__Time_433.txt
│ │ ├── VG666_4__X_17737.3__Y_-34226.4__Time_577.txt
│ │ ├── VG667_0__X_11954.1__Y_-36113.4__Time_0.txt
│ │ ├── VG667_1__X_11990.4__Y_-36038.7__Time_121.txt
│ │ ├── VG667_2__X_12073__Y_-36190.4__Time_242.txt
│ │ ├── VG667_3__X_12104.7__Y_-36244.6__Time_363.txt
│ │ ├── VG667_4__X_11944.8__Y_-36273.5__Time_484.txt
│ │ ├── VG6820_1__X_18106.3__Y_-19219.9__Time_144.txt
│ │ ├── VG6820_2__X_18084.6__Y_-19220.3__Time_288.txt
│ │ ├── VG6820_3__X_18087__Y_-19205.7__Time_433.txt
│ │ ├── VG6820_4__X_18107.9__Y_-19208.1__Time_577.txt
│ │ ├── VG6821_0__X_20030__Y_-21785.8__Time_0.txt
│ │ ├── VG6821_1__X_20045.4__Y_-21791.9__Time_144.txt
│ │ ├── VG6821_2__X_20057.6__Y_-21798.9__Time_288.txt
│ │ ├── VG6821_3__X_20070.5__Y_-21804.6__Time_433.txt
│ │ ├── VG6821_4__X_20083.2__Y_-21811.1__Time_577.txt
│ │ ├── VG6822_0__X_24458__Y_-27315.2__Time_0.txt
│ │ ├── VG6822_1__X_24458__Y_-27320.2__Time_144.txt
│ │ ├── VG6822_2__X_24458__Y_-27325.9__Time_288.txt
│ │ ├── VG6822_3__X_24458.5__Y_-27311.2__Time_433.txt
│ │ ├── VG6822_4__X_24462.7__Y_-27318.5__Time_577.txt
│ │ ├── VG6823_0__X_22497.6__Y_-30686__Time_0.txt
│ │ ├── VG6823_1__X_22481.9__Y_-30677.8__Time_144.txt
│ │ ├── VG6823_2__X_22494.1__Y_-30681__Time_288.txt
│ │ ├── VG6823_3__X_22498.1__Y_-30698.8__Time_433.txt
│ │ ├── VG6823_4__X_22534.1__Y_-30695.7__Time_577.txt
│ │ ├── VG6825_0__X_20612.2__Y_-36086.8__Time_0.txt
│ │ ├── VG6825_1__X_20606.3__Y_-36082.9__Time_144.txt
│ │ ├── VG6825_2__X_20644.3__Y_-36091.5__Time_288.txt
│ │ ├── VG6825_3__X_20631__Y_-36083.9__Time_433.txt
│ │ ├── VG6825_4__X_20627.8__Y_-36107.1__Time_577.txt
│ │ ├── VG682_.txt
│ │ ├── VG682_1.txt
│ │ ├── VG682_2.txt
│ │ ├── VG682_3.txt
│ │ ├── VG682_4.txt
│ │ ├── VG684_.txt
│ │ ├── VG684_1.txt
│ │ ├── VG684_2.txt
│ │ ├── VG684_3.txt
│ │ ├── VG684_4.txt
│ │ ├── VG758_0__X_19316.1__Y_-19291.2__Time_0.txt
│ │ ├── VG758_1__X_19323.2__Y_-19296__Time_144.txt
│ │ ├── VG758_2__X_19306.6__Y_-19297.7__Time_288.txt
│ │ ├── VG758_3__X_19307.1__Y_-19283.1__Time_433.txt
│ │ ├── VG758_4__X_19324.5__Y_-19284.8__Time_577.txt
│ │ ├── VG884_0__X_17275.9__Y_-20515.2__Time_0.txt
│ │ ├── VG884_1__X_17251.2__Y_-20417.2__Time_144.txt
│ │ ├── VG884_2__X_17127.6__Y_-20407.9__Time_288.txt
│ │ ├── VG884_3__X_17100.7__Y_-20385.9__Time_433.txt
│ │ ├── VG884_4__X_16946.2__Y_-20511.5__Time_577.txt
│ │ ├── r145.txt
│ │ ├── r147.txt
│ │ ├── r148.txt
│ │ ├── r149.txt
│ │ ├── r150.txt
│ │ ├── r151.txt
│ │ ├── r152.txt
│ │ ├── r153.txt
│ │ ├── r154.txt
│ │ ├── r155.txt
│ │ ├── r156.txt
│ │ ├── r157.txt
│ │ ├── r158.txt
│ │ ├── r159.txt
│ │ ├── r160.txt
│ │ ├── r161.txt
│ │ ├── r162.txt
│ │ ├── r163.txt
│ │ ├── r164.txt
│ │ ├── r165.txt
│ │ ├── r236.txt
│ │ ├── r237.txt
│ │ ├── r238.txt
│ │ ├── r239.txt
│ │ ├── r240.txt
│ │ ├── r241.txt
│ │ ├── r268_0.txt
│ │ ├── r268_1.txt
│ │ ├── r268_2.txt
│ │ ├── r269_0.txt
│ │ ├── r269_1.txt
│ │ ├── r269_2.txt
│ │ ├── r270_0.txt
│ │ ├── r270_1.txt
│ │ ├── r270_2.txt
│ │ ├── r271_0.txt
│ │ ├── r271_1.txt
│ │ ├── r271_2.txt
│ │ ├── r272_0.txt
│ │ ├── r272_1.txt
│ │ ├── r272_2.txt
│ │ ├── r273_0.txt
│ │ ├── r273_1.txt
│ │ ├── r273_2.txt
│ │ ├── r275_0.txt
│ │ ├── r275_1.txt
│ │ ├── r275_2.txt
│ │ ├── r276_0.txt
│ │ ├── r276_1.txt
│ │ ├── r276_2.txt
│ │ ├── r277_0.txt
│ │ ├── r277_1.txt
│ │ ├── r277_2.txt
│ │ ├── r278_0.txt
│ │ ├── r278_1.txt
│ │ ├── r278_2.txt
│ │ ├── r362.txt
│ │ ├── r363.txt
│ │ ├── r364.txt
│ │ ├── r365.txt
│ │ ├── r520.txt
│ │ ├── r521.txt
│ │ ├── r522.txt
│ │ ├── r523.txt
│ │ ├── r524.txt
│ │ ├── r711.txt
│ │ ├── r712.txt
│ │ ├── r714.txt
│ │ ├── r715.txt
│ │ ├── r716.txt
│ │ ├── r717.txt
│ │ ├── r718.txt
│ │ ├── r719.txt
│ │ ├── r720.txt
│ │ ├── r721.txt
│ │ ├── r722.txt
│ │ ├── r723.txt
│ │ ├── r724.txt
│ │ ├── r725.txt
│ │ ├── r726.txt
│ │ ├── r727.txt
│ │ ├── r728.txt
│ │ ├── r729.txt
│ │ ├── r730.txt
│ │ ├── r731.txt
│ │ ├── r732.txt
│ │ ├── r733.txt
│ │ ├── r738.txt
│ │ ├── r739.txt
│ │ └── r740.txt
│ ├── README.md
│ └── rameau_example/
│ ├── Python_treatment.ipynb
│ ├── Python_treatment_ext.ipynb
│ ├── README.md
│ ├── dataset.xlsx
│ ├── rameau.py
│ └── raw/
│ ├── Som-13-17.txt
│ ├── Som-13-18.txt
│ ├── Som-13-19.txt
│ ├── Som-14-18.txt
│ ├── Som-14-19.txt
│ ├── Som-14-20.txt
│ ├── Som-15-11.txt
│ ├── Som-15-12.txt
│ ├── Som-15-13.txt
│ ├── Som-16-15.txt
│ ├── Som-16-27.txt
│ ├── Som-16-28.txt
│ ├── ac8014.txt
│ ├── ac8015.txt
│ ├── ac8028.txt
│ ├── ac8037.txt
│ ├── ac8041.txt
│ ├── ac9025.txt
│ ├── ac9026.txt
│ ├── ac9027.txt
│ ├── ac9028.txt
│ ├── ac9029.txt
│ ├── ac9030.txt
│ ├── ad0085.txt
│ ├── ad0086.txt
│ ├── ad0087.txt
│ ├── ad0090.txt
│ ├── ad0091.txt
│ ├── ad0098.txt
│ ├── ad0099.txt
│ ├── ad0100.txt
│ ├── ad0101.txt
│ ├── ad0102.txt
│ ├── ad0104.txt
│ ├── ad0105.txt
│ ├── ad0107.txt
│ ├── ad0108.txt
│ ├── ad0110.txt
│ ├── ad0111.txt
│ ├── ad0112.txt
│ ├── ad0114.txt
│ ├── ad0116.txt
│ ├── ad0117.txt
│ ├── ad0118.txt
│ ├── ad0119.txt
│ ├── ad0120.txt
│ ├── ad0122.txt
│ ├── ad0123.txt
│ ├── ae0187.txt
│ ├── ae0188.txt
│ ├── ae0190.txt
│ ├── ae0191.txt
│ ├── ae0192.txt
│ ├── ae0199.txt
│ ├── ae0202.txt
│ ├── ae0203.txt
│ ├── ae0204.txt
│ ├── ae0405.txt
│ ├── ae0406.txt
│ ├── ae0409.txt
│ ├── ae0441.txt
│ ├── ae0442.txt
│ ├── ae0443.txt
│ ├── ae0660.txt
│ ├── ae0664.txt
│ ├── ae0674.txt
│ ├── ae0675.txt
│ ├── ae0676.txt
│ ├── ae0682.txt
│ ├── ae0684.txt
│ ├── ae0685.txt
│ ├── ae0980.txt
│ ├── r010.txt
│ ├── r015.txt
│ ├── r016.txt
│ ├── r024.txt
│ ├── r025.txt
│ ├── r026.txt
│ ├── r027.txt
│ ├── r030.txt
│ ├── r031.txt
│ ├── r032.txt
│ ├── r034.txt
│ ├── r039.txt
│ ├── r040.txt
│ ├── r041.txt
│ ├── r042.txt
│ ├── r044_0.txt
│ ├── r044_1.txt
│ ├── r044_2.txt
│ ├── r044_3.txt
│ ├── r044_4.txt
│ ├── r044_5.txt
│ ├── r049.txt
│ ├── r051.txt
│ ├── r107.txt
│ ├── r108.txt
│ ├── r109.txt
│ ├── r110.txt
│ ├── r111.txt
│ ├── r112.txt
│ ├── r113.txt
│ ├── r114.txt
│ ├── r116_0.txt
│ ├── r116_1.txt
│ ├── r116_2.txt
│ ├── r116_3.txt
│ ├── r116_4.txt
│ ├── r116_5.txt
│ ├── r118_0.txt
│ ├── r118_1.txt
│ ├── r118_2.txt
│ ├── r386.txt
│ ├── r387.txt
│ ├── r388.txt
│ ├── r389.txt
│ ├── r393.txt
│ ├── r397.txt
│ ├── r487_0.txt
│ ├── r487_1.txt
│ ├── r487_3.txt
│ ├── r487_4.txt
│ ├── r703.txt
│ ├── r704.txt
│ ├── r705.txt
│ ├── r706.txt
│ ├── r707.txt
│ └── r708.txt
├── legacy_code/
│ ├── Diamond_Pressure.py
│ ├── HF_Treatment_Auto.py
│ ├── HT_dec.py
│ ├── IR_dec_comb.py
│ ├── I_Io_calc.py
│ ├── LF_Treatment.py
│ ├── LF_dec.py
│ ├── README.md
│ ├── Remove_Ctx_MI.py
│ ├── cleaning.py
│ └── rameau_construction
├── pyproject.toml
├── roadmap.md
├── src/
│ ├── rampy/
│ │ ├── .gitignore
│ │ ├── __init__.py
│ │ ├── baseline.py
│ │ ├── dac.py
│ │ ├── filters.py
│ │ ├── functions.py
│ │ ├── maps.py
│ │ ├── mixing.py
│ │ ├── ml_classification.py
│ │ ├── ml_exploration.py
│ │ ├── ml_regressor.py
│ │ ├── notused/
│ │ │ └── IRTABS.py
│ │ ├── peak_area.py
│ │ ├── peak_shapes.py
│ │ ├── plotting.py
│ │ ├── rameau.py
│ │ ├── spectranization.py
│ │ └── tlcorrection.py
│ └── rampy.egg-info/
│ ├── PKG-INFO
│ ├── SOURCES.txt
│ ├── dependency_links.txt
│ ├── requires.txt
│ └── top_level.txt
└── tests/
├── __init__.py
├── map_test.txt
├── test_baseline.py
├── test_dac.py
├── test_mixing.py
├── test_mlclassificator.py
├── test_mlexplorer.py
├── test_mlregressor.py
├── test_peakarea.py
├── test_plotting.py
├── test_rameau.py
├── test_smooth.py
├── test_spectranization.py
└── test_tlcorrection.py
SYMBOL INDEX (167 symbols across 36 files)
FILE: docs/_static/require.js
function commentReplace (line 5) | function commentReplace(e,t){return t||""}
function isFunction (line 5) | function isFunction(e){return"[object Function]"===ostring.call(e)}
function isArray (line 5) | function isArray(e){return"[object Array]"===ostring.call(e)}
function each (line 5) | function each(e,t){if(e)for(var i=0;i<e.length&&(!e[i]||!t(e[i],i,e));i+...
function eachReverse (line 5) | function eachReverse(e,t){if(e)for(var i=e.length-1;-1<i&&(!e[i]||!t(e[i...
function hasProp (line 5) | function hasProp(e,t){return hasOwn.call(e,t)}
function getOwn (line 5) | function getOwn(e,t){return hasProp(e,t)&&e[t]}
function eachProp (line 5) | function eachProp(e,t){for(var i in e)if(hasProp(e,i)&&-1==disallowedPro...
function mixin (line 5) | function mixin(i,e,r,n){e&&eachProp(e,function(e,t){!r&&hasProp(i,t)||(!...
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function defaultOnError (line 5) | function defaultOnError(e){throw e}
function getGlobal (line 5) | function getGlobal(e){var t;return e&&(t=global,each(e.split("."),functi...
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function newContext (line 5) | function newContext(u){var t,e,f,c,i,b={waitSeconds:7,baseUrl:"./",paths...
function getInteractiveScript (line 5) | function getInteractiveScript(){return interactiveScript&&"interactive"=...
FILE: examples/Iron_AmMin_paper/iron.py
function preparing_data (line 11) | def preparing_data(dataliste,**kwargs):
function intensity (line 121) | def intensity(x,y,idx1=929.,idx2=931.):
function chimie_control (line 147) | def chimie_control(datalist):
function molarweights (line 189) | def molarweights():
function wt_mol (line 241) | def wt_mol(chemtable):
FILE: examples/rameau_example/rameau.py
function main (line 13) | def main(argv):
function plot_spectra (line 135) | def plot_spectra(rameau_object,**kwargs):
FILE: legacy_code/Diamond_Pressure.py
function residual (line 38) | def residual(pars, x, data=None, eps=None):
FILE: legacy_code/HF_Treatment_Auto.py
function residual (line 182) | def residual(pars, spforcorr, sptarget=None):
FILE: legacy_code/IR_dec_comb.py
function residual (line 39) | def residual(pars, x, data=None, eps=None):
FILE: legacy_code/LF_Treatment.py
function removebas_melt (line 32) | def removebas_melt(name):
function removebas_fluid (line 73) | def removebas_fluid(name):
FILE: legacy_code/LF_dec.py
function residual_melt (line 40) | def residual_melt(pars, x, data=None, eps=None):
function residual_fluid (line 77) | def residual_fluid(pars, x, data=None, eps=None):
FILE: legacy_code/Remove_Ctx_MI.py
function residual (line 133) | def residual(pars, spforcorr, sptarget=None):
function residual2 (line 185) | def residual2(pars, spforcorr, spctx, sptarget=None):
FILE: src/rampy/baseline.py
function get_portion_interest (line 21) | def get_portion_interest(x: np.ndarray, y: np.ndarray, roi) -> np.ndarray:
function extract_signal (line 25) | def extract_signal(x: np.ndarray, y: np.ndarray, roi) -> np.ndarray:
function validate_input (line 80) | def validate_input(x, y):
function validate_roi (line 88) | def validate_roi(roi):
function standardize_data (line 99) | def standardize_data(x: np.ndarray, y: np.ndarray):
function baseline (line 107) | def baseline(x_input: np.ndarray,
function fit_baseline (line 223) | def fit_baseline(x: np.ndarray, y: np.ndarray, roi_: np.ndarray, yafit: ...
function rubberband_baseline (line 310) | def rubberband_baseline(x: np.ndarray, y: np.ndarray) -> np.ndarray:
function als_baseline (line 355) | def als_baseline(y: np.ndarray, lam: float, p: float, niter: int) -> np....
function arPLS_baseline (line 367) | def arPLS_baseline(y: np.ndarray, lam: float, ratio: float) -> np.ndarray:
function drPLS_baseline (line 387) | def drPLS_baseline(y: np.ndarray, niter: int, lam: float, eta: float, ra...
FILE: src/rampy/dac.py
function ruby_T_corr (line 3) | def ruby_T_corr(T_K: float | np.ndarray) -> float | np.ndarray:
function borate_T_corr (line 23) | def borate_T_corr(T_K: float | np.ndarray) -> float | np.ndarray:
function pressure_sensor (line 43) | def pressure_sensor(
FILE: src/rampy/filters.py
function smooth (line 15) | def smooth(x: np.ndarray, y: np.ndarray, method: str = "whittaker", **kw...
function whittaker (line 135) | def whittaker(y: np.ndarray, weights: np.ndarray = None, **kwargs) -> np...
function spectrafilter (line 189) | def spectrafilter(spectre : np.ndarray, filtertype: str, fq: int, numtap...
FILE: src/rampy/functions.py
function funlog (line 14) | def funlog(x, a, b, c, d):
function funexp (line 21) | def funexp(x, a, b, c):
function poly2 (line 28) | def poly2(x, a, b, c):
function linear (line 32) | def linear(x, a, b):
function linear0 (line 36) | def linear0(x, a):
function constant (line 40) | def constant(x, a):
function multigaussian (line 56) | def multigaussian(x,params):
function gauss_lsq (line 68) | def gauss_lsq(params,x):
function gauss_lsq_lfix (line 99) | def gauss_lsq_lfix(params,x):
function diffshort (line 137) | def diffshort(x: np.ndarray, t: float, C0: float, C1: float, D: float) -...
function difffull (line 163) | def difffull(x1: float, x2: float, t: float, C0: float, C1: float, D: fl...
FILE: src/rampy/maps.py
class maps (line 17) | class maps():
method __init__ (line 36) | def __init__(self, file,
method background (line 70) | def background(self, bir, method = "poly", **kwargs):
method normalise (line 115) | def normalise(self, y, method="intensity"):
method smooth (line 130) | def smooth(self, y, method="whittaker",**kwargs):
method centroid (line 154) | def centroid(self, y, region_to_investigate):
method intensity (line 169) | def intensity(self, y, region_to_investigate):
method area (line 186) | def area(self, y, region_to_investigate):
method intensity_ratio (line 203) | def intensity_ratio(self, y, region_to_investigate):
method area_ratio (line 226) | def area_ratio(self, y, region_to_investigate):
function read_renishaw (line 248) | def read_renishaw(file):
function read_horiba (line 281) | def read_horiba(file, map_type="2D"):
function peak (line 319) | def peak(X, Y, lambdas, intensities, function, Xrange, amp, Xmean, sigma...
FILE: src/rampy/mixing.py
function mixing_sp (line 12) | def mixing_sp(y_fit: np.ndarray, ref1: np.ndarray, ref2: np.ndarray) -> ...
FILE: src/rampy/ml_classification.py
class mlclassificator (line 13) | class mlclassificator:
method __init__ (line 37) | def __init__(self,x,y,**kwargs):
method scale_data (line 112) | def scale_data(self):
method fit (line 121) | def fit(self, params_: dict = None):
method refit (line 163) | def refit(self):
method predict (line 176) | def predict(self,X):
FILE: src/rampy/ml_exploration.py
class mlexplorer (line 12) | class mlexplorer:
method __init__ (line 62) | def __init__(self,x,**kwargs):
method fit (line 105) | def fit(self):
method refit (line 121) | def refit(self):
method predict (line 132) | def predict(self,X):
FILE: src/rampy/ml_regressor.py
function chemical_splitting (line 19) | def chemical_splitting(Pandas_DataFrame, target, split_fraction=0.30, ra...
class mlregressor (line 78) | class mlregressor:
method __init__ (line 151) | def __init__(self,x,y,**kwargs):
method fit (line 223) | def fit(self):
method refit (line 283) | def refit(self):
method predict (line 298) | def predict(self,X):
FILE: src/rampy/notused/IRTABS.py
function FTIRout (line 14) | def FTIRout(nameR,nameB,nameout,thick,**options): # FUNCTION TO CALCULAT...
function FTIRcomp (line 65) | def FTIRcomp(names, listelg, plotdisplay, nameout): # FUNCTION TO PLOT T...
FILE: src/rampy/peak_area.py
function peakarea (line 16) | def peakarea(shape: str, amp: float, HWHM: float, pos: float = None,
function gaussianarea (line 137) | def gaussianarea(amp,HWHM,**options):
function area_peak (line 161) | def area_peak(
FILE: src/rampy/peak_shapes.py
function gaussian (line 14) | def gaussian(x: np.ndarray, amp, freq, HWHM) -> np.ndarray:
function lorentzian (line 51) | def lorentzian(x: np.ndarray, amp, freq, HWHM) -> np.ndarray:
function pseudovoigt (line 88) | def pseudovoigt(x: np.ndarray, amp, freq, HWHM, L_ratio) -> np.ndarray:
function pearson7 (line 132) | def pearson7(x, a0, a1, a2, a3):
FILE: src/rampy/plotting.py
function plot_spectrum (line 12) | def plot_spectrum(
FILE: src/rampy/rameau.py
class rameau (line 16) | class rameau:
method __init__ (line 84) | def __init__(self, data_liste, path_spectra='./raw/'):
method data_reduction (line 90) | def data_reduction(self,method="LL2012",delim='\t',laser=514.532,splin...
method calibrate (line 127) | def calibrate(self,data_calib=None,method="LL2012", delim='\t',path_ca...
method predict (line 196) | def predict(self,method="LL2012"):
method external_calibration (line 236) | def external_calibration(self, path_ref='./raw/Standards/', roi = np.a...
function fit_spectra (line 321) | def fit_spectra(data_liste,method="LL2012",delim='\t',path_in='./raw/',l...
function DG2017_predict (line 438) | def DG2017_predict(dictio,a=0.096,b=0.663):
function DG2017_calibrate (line 456) | def DG2017_calibrate(dictio):
function LL2012_predict (line 472) | def LL2012_predict(dictio,A=0.007609):
function LL2012_calibrate (line 487) | def LL2012_calibrate(dictio):
FILE: src/rampy/spectranization.py
function spectrarray (line 15) | def spectrarray(name: np.ndarray, sh: int, sf: int, x: np.ndarray) -> np...
function spectrataux (line 64) | def spectrataux(spectres: np.ndarray) -> np.ndarray:
function spectraoffset (line 110) | def spectraoffset(spectre, oft):
function shiftsp (line 144) | def shiftsp(sp: np.ndarray, shift: float) -> np.ndarray:
function flipsp (line 166) | def flipsp(sp: np.ndarray) -> np.ndarray:
function resample (line 188) | def resample(x: np.ndarray, y: np.ndarray, x_new: np.ndarray, fill_value...
function normalise (line 217) | def normalise(y: np.ndarray, x : np.ndarray = None, method: str = "inten...
function centroid (line 262) | def centroid(x: np.ndarray, y: np.ndarray, smoothing: bool = False, **kw...
function despiking (line 294) | def despiking(x: np.ndarray, y: np.ndarray, neigh:int = 4, threshold:int...
function invcm_to_nm (line 347) | def invcm_to_nm(x_inv_cm: np.ndarray, laser_nm: float = 532.0) -> np.nda...
function nm_to_invcm (line 367) | def nm_to_invcm(x: np.ndarray, laser_nm: float = 532.0) -> np.ndarray:
function convert_x_units (line 387) | def convert_x_units(x: np.ndarray, laser_nm: float = 532.0, way: str = "...
FILE: src/rampy/tlcorrection.py
function tlcorrection (line 13) | def tlcorrection(x: np.ndarray, y: np.ndarray, temperature: float, wavel...
FILE: tests/test_baseline.py
class TestBaseline (line 11) | class TestBaseline(unittest.TestCase):
method test_baseline (line 13) | def test_baseline(self):
FILE: tests/test_dac.py
class TestPressureFunctions (line 5) | class TestPressureFunctions(unittest.TestCase):
method test_ruby_T_corr (line 7) | def test_ruby_T_corr(self):
method test_borate_T_corr (line 16) | def test_borate_T_corr(self):
method test_pressure_sensor_ruby (line 25) | def test_pressure_sensor_ruby(self):
method test_pressure_sensor_borate_datchi (line 39) | def test_pressure_sensor_borate_datchi(self):
method test_pressure_sensor_invalid_scale (line 55) | def test_pressure_sensor_invalid_scale(self):
FILE: tests/test_mixing.py
class TestSmooth (line 8) | class TestSmooth(unittest.TestCase):
method test_mixing (line 10) | def test_mixing(self):
FILE: tests/test_mlclassificator.py
class TestMLC (line 13) | class TestMLC(unittest.TestCase):
method test_mlclassificator (line 15) | def test_mlclassificator(self):
FILE: tests/test_mlexplorer.py
class TestML2 (line 11) | class TestML2(unittest.TestCase):
method test_mlexplorer (line 13) | def test_mlexplorer(self):
FILE: tests/test_mlregressor.py
class TestML (line 11) | class TestML(unittest.TestCase):
method test_mlregressor (line 13) | def test_mlregressor(self):
FILE: tests/test_peakarea.py
class TestPeakArea (line 6) | class TestPeakArea(unittest.TestCase):
method test_gaussian_peak (line 7) | def test_gaussian_peak(self):
method test_gaussian_with_uncertainties (line 17) | def test_gaussian_with_uncertainties(self):
method test_lorentzian_peak (line 32) | def test_lorentzian_peak(self):
method test_pseudovoigt_peak (line 44) | def test_pseudovoigt_peak(self):
method test_pearson7_peak (line 57) | def test_pearson7_peak(self):
method test_invalid_shape (line 70) | def test_invalid_shape(self):
method test_missing_parameters (line 75) | def test_missing_parameters(self):
method test_invalid_L_ratio (line 82) | def test_invalid_L_ratio(self):
method test_array_inputs (line 89) | def test_array_inputs(self):
method test_area_peak (line 102) | def test_area_peak(self):
FILE: tests/test_plotting.py
class TestPlotSpectra (line 6) | class TestPlotSpectra(unittest.TestCase):
method setUp (line 7) | def setUp(self):
method test_plot_spectra_no_baselines_or_smoothed_signals (line 18) | def test_plot_spectra_no_baselines_or_smoothed_signals(self):
method test_plot_spectra_with_baselines_and_smoothed_signals (line 32) | def test_plot_spectra_with_baselines_and_smoothed_signals(self):
method test_plot_spectra_with_missing_labels (line 53) | def test_plot_spectra_with_missing_labels(self):
FILE: tests/test_smooth.py
class TestSmooth (line 10) | class TestSmooth(unittest.TestCase):
method test_smooth (line 12) | def test_smooth(self):
FILE: tests/test_spectranization.py
class TestSpectranization (line 12) | class TestSpectranization(unittest.TestCase):
method test_flipsp (line 14) | def test_flipsp(self):
method test_resample (line 25) | def test_resample(self):
method test_centroid (line 38) | def test_centroid(self):
method test_normalise (line 54) | def test_normalise(self):
method test_despiking (line 68) | def test_despiking(self):
FILE: tests/test_tlcorrection.py
class Testtlcorrection (line 10) | class Testtlcorrection(unittest.TestCase):
method test_tlcorrection (line 12) | def test_tlcorrection(self):
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Condensed preview — 543 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (51,248K chars).
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"path": ".github/workflows/publish.yml",
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"preview": "name: Upload Python Package to PyPI when a Release is Created\n\non:\n release:\n types: [created]\n\njobs:\n pypi-publish"
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"preview": ".DS_STORE\n/dist/\n/dev/\n/docs/doctrees/\n/docs/build/\n/docs/html/\n/build/\n.ipynb_checkpoints/\n/rampy.egg-info/\n/.pytest_ca"
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"preview": "# Read the Docs configuration file for Sphinx projects\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html f"
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"chars": 139,
"preview": "language: python\npython:\n - \"3.11\"\n\nos:\n - linux\n\ninstall:\n - pip install cvxpy\n - pip install .\n# command to run te"
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"chars": 3222,
"preview": "# Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and"
},
{
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"path": "LICENSE",
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"preview": "GNU GENERAL PUBLIC LICENSE\n Version 2, June 1991\n\n Copyright (C) 1989, 1991 Free Software Foundati"
},
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"path": "NEWS.md",
"chars": 7816,
"preview": "# Rampy News\n\nCopyright (c) 2014-2025 Dr. Charles Le Losq et al.\n\nemail: lelosq@ipgp.fr\n\nLicence: see LICENCE.md\n\n# Want"
},
{
"path": "README.md",
"chars": 1311,
"preview": "# RamPy\n=======\n\n[](https://travis-ci.org/charle"
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"preview": "Baseline\n========\n\nRampy allows you to fit polynomial, spline, generalized cross-validated spline, logarithms, exponenti"
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"chars": 2178,
"preview": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common op"
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"path": "docs/dac.rst",
"chars": 409,
"preview": "Diamond Anvil Cell Utilities\n============================\n\nRampy can be useful if you are performing Diamond Anvil Cell "
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"path": "docs/examples.rst",
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"preview": ".. _RST Notebooks:\n\nExample notebooks\n=================\n\nProcessing\n----------\n\n.. nbgallery::\n notebooks/Resample_an"
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"path": "docs/installation.rst",
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"path": "docs/machinelearning.rst",
"chars": 3229,
"preview": "Machine learning\n================\n\nRampy offers three classes for performing classification, regression or unsupervised "
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"path": "docs/make.bat",
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"path": "docs/maps.rst",
"chars": 717,
"preview": "Maps\n====\n\nRampy offers the possiblity to analyse maps from Raman spectrometers using the ``rampy.maps`` module.\n\nLine a"
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"path": "docs/modules.rst",
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"preview": "rampy\n=====\n\n.. toctree::\n :maxdepth: 2\n\n rampy\n"
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"path": "docs/news.rst",
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"preview": "Rampy News\n==========\n\nCopyright (c) 2014-2025 Dr. Charles Le Losq et al.\n\nemail: lelosq@ipgp.fr\n\nLicence: see LICENCE.m"
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"path": "docs/notebooks/Baseline.ipynb",
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"preview": "TITLE\t\r\nDATA TYPE\tRAMAN SPECTRUM\r\nORIGIN\tJASCO\r\nOWNER\t\r\nDATE\t14/01/10\r\nTIME\t17:38:47\r\nSPECTROMETER/DATA SYSTEM\t\r\nLOCALE\t"
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"path": "docs/peakfitting.rst",
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"preview": ".. _RST Fitting:\n\nPeak fitting\n=============\n\nRampy will soon offer a dedicated function for peak fitting: ``rampy.fit_p"
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"path": "docs/philosophy.rst",
"chars": 435,
"preview": "Philosophy\n============\n\nRamPy is created from a set of functions I use everyday to treat various kind of spectroscopic "
},
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"path": "docs/plotting.rst",
"chars": 421,
"preview": ".. _RST Plotting:\n\nPlotting\n========\n\nStarting from v0.6.0, there is now a function ``rampy.plot_spectrum`` to plot a sp"
},
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"path": "docs/preprocessing.rst",
"chars": 2939,
"preview": ".. _RST Preprocessing:\n\nPreprocessing\n=============\n\nRampy offers handful functions to preprocess your spectra. Do not h"
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"path": "docs/requirements.txt",
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"preview": "sphinx>=5.0.0 # Compatible with furo theme\nnbsphinx\nsphinx_gallery\nsphinx_rtd_theme\nplotly # Include other dependencie"
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"path": "docs/setup.rst",
"chars": 103,
"preview": "setup module\n============\n\n.. automodule:: setup\n :members:\n :undoc-members:\n :show-inheritance:\n"
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{
"path": "docs/signalparams.rst",
"chars": 378,
"preview": "Signal parameters\n========================\n\nI will enrich rampy with new functions to calculate the characteristics/para"
},
{
"path": "docs/smoothing.rst",
"chars": 1289,
"preview": "Smoothing and filtering\n========================\n\nFunctions are provided to smooth and filter spectra. Smoothing is usef"
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{
"path": "docs/utils.rst",
"chars": 206,
"preview": "Utilities\n===========\n\nSome functions are included in rampy and have a minor role. They are listed below for reference.\n"
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{
"path": "environment.yml",
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"preview": "name: rampy\ndependencies:\n - numpy\n - scipy\n - matplotlib\n - pandas\n - pip\n - pip:\n - cvxpy\n - rampy\n - l"
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"path": "examples/Iron_AmMin_paper/HOWTO.txt",
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"path": "examples/Iron_AmMin_paper/Iron_MORB_code.ipynb",
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"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"(c) Copyright 2019 Le Losq et al.\\n"
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"path": "examples/Iron_AmMin_paper/iron.py",
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]
// ... and 343 more files (download for full content)
About this extraction
This page contains the full source code of the charlesll/rampy GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 543 files (89.6 MB), approximately 11.5M tokens, and a symbol index with 167 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.