The raw data files used in this analysis are currently published on Research Gate here and here. The methods used for generating these data can be found in this preprint.
The Python code was executed using version 3.7.1. To run the code, you will need the following packages installed in your environment:
- pandas 1.2.3
- scipy 1.6.2
- numpy 1.19.2
- matplotlib 3.3.4
- seaborn 0.11.1
- sklearn 0.24.1
In addition, the code uses the following native libraries:
- math
- os
R code was executing using version 3.6.1. R libraries used include the following:
- WaveletComp 1.1
| code file | input file | output file | figure produced (if any) | Notes |
|---|---|---|---|---|
| map_of_catchments.py | dayOfMeanFlowThroughTime.csv | 2 | ||
| map_of_catchments.py | alldata.csv | 2 | ||
| correlateSpectralWithSelf.py | universallyAligned_powers.csv | 4 | ||
| visFreqPlace.R | long_matrix_temp_order_seas_prec.csv | 6 | ||
| meanFrequencyDecomp.py | alldata.csv | long_matrix_temp_order_seas_prec.csv | ||
| meanFrequencyDecomp.py | FullDatabase.csv | long_matrix_temp_order_seas_prec.csv | ||
| meanFrequencyDecomp.py | universallyAligned_powersTranpose.csv | long_matrix_temp_order_seas_prec.csv | ||
| fourier_full.py | universallyAlignedGlobalFlow_DailyQ2_column.csv | glob_flow_fourier_decomposition_blackman.csv | ||
| bin_frequencies.py | glob_flow_fourier_decomposition_blackman.csv | glob_flow_fourier_decomposition_blackman_mini.csv | ||
| correlate_fourier.py | glob_flow_fourier_decomposition_blackman_mini.csv | fourierCorrelations_blackman_mini.csv | ||
| heatmap.py | fourierCorrelations_blackman_mini.csv | S8 | ||
| dams.py | universallyAligned_powersTranpose.csv, alldata.csv | S18 | ||
| correlateFourierWithCatChars.py | glob_flow_fourier_decomposition_blackman_mini.csv, alldata_hemisphereCorrected.csv | correlations_between_fourier_and_cat_chars.csv | ||
| correlateWaveletWithCatChars.py | universallyAligned_powers.csv, alldata_hemisphereCorrected.csv | correlations_between_wavelet_and_cat_chars.csv | ||
| analyzeCorrelationsWavelet.py | correlations_between_wavelet_and_cat_chars.csv | 9, S2, S3, S6, S15 | ||
| analyzeCorrelations.py | correlations_between_fourier_and_cat_chars.csv | S16, S17 | ||
| plotHuman.py | ml_importances_with_categories.csv | 10 | ||
| ml_visualize_predict_flow_metrics.py | ml_predict_flow_metrics_feature_importances_wide.csv | S4, S5 | ||
| humanPlotPrepPVals.py | ml_r_squared.csv | S19 | ||
| ml_predict_flow_metrics.py | universallyAligned_powers.csv, alldata.csv | ml_predict_flow_metrics_feature_importances_wide.csv | ||
| humanPlotPrep.py | ml_feature_importances_short.csv | |||
| machineLearning.py | universallyAligned_powers.csv, alldata_hemisphereCorrected.csv | ml_feature_importances_short.csv, ml_r_squared.csv | ||
| dayOfMeanFlowThroughTime.py | alldata.csv | dayOfMeanFlowThroughTime.csv | ||
| spectralThroughTime.py | alldata.csv | spectralPowersThroughTime.csv | ||
| splitIntoNonGlobalWaterYears.py | alldata.csv | localWaterYear | ||
| plotSpectralNumberThroughTime.py | day_of_mean_flow_vs_size.csv | spectralNumber_acrossTime.csv | ||
| combineThroughTime.py | dayOfMeanFlowThroughTime.csv | throughTimeCombined.csv | ||
| testDifferenceInDayOfMeanFlow.py | FullDatabase.csv | day_of_mean_flow_vs_size.csv | ||
| testDifferencesInMean.py | FullDatabase.csv | specific_discharge_vs_size.csv | Convert discharge to specific disharge data | |
| convertToSpecificDischarge.py | localWaterYear | localWaterYear | converts to specific discharge | |
| dayOfMeanFlowThroughTime.py | localWaterYear | dayOfMeanFlowThroughTime.csv | ||
| example_spectral_properties_hydrographs.py | localWaterYear | |||
| specificDischargeThroughTime.py | localWaterYear | specificDischargeThroughTime.csv | ||
| splitIntoNonGlobalWaterYears.py | localWaterYear | universallyAlignedGlobalFlow_DailyQ2_column.csv | ||
| WaveletAnalysisLocalWaterYear.R | localWaterYear | localWaterYearSpectralDecomposition | ||
| compressTemporalPatternsPreprocess.py | localWaterYearSpectralDecomposition | ml_all_years_data_separate.csv | create dataset for dimensionality compression | |
| example_spectral_properties_hydrographs.py | localWaterYearSpectralDecomposition | |||
| spectralThroughTime.py | localWaterYearSpectralDecomposition | spectralPowersThroughTime.csv | ||
| testDifferenceInDayOfMeanFlow.py | localWaterYearSpectralDecomposition | day_of_mean_flow_vs_size.csv | ||
| testDifferencesInMean.py | localWaterYearSpectralDecomposition | specific_discharge_vs_size.csv | ||
| compressTemporalPaternsSlope.py | ml_all_years_data_separate.csv | ml_slope_encodings1.csv | ||
| combineThroughTime.py | ml_slope_encodings1.csv | throughTimeCombined.csv | ||
| example_spectral_properties_hydrographs.py | ml_slope_encodings1.csv | |||
| plotSpectralNumberThroughTime.py | ml_slope_encodings1.csv | spectralNumber_acrossTime.csv | ||
| plotSpectralNumberThroughTime.py | specific_discharge_vs_size.csv | spectralNumber_acrossTime.csv | ||
| combineThroughTime.py | specificDischargeThroughTime.csv | throughTimeCombined.csv | ||
| dimensionalityCompression.py | universallyAligned_powers.csv | ml_encodings1.csv | ||
| WaveletAnalysisGlobal.R | universallyAlignedGlobalFlow_DailyQ2_column.csv | universallyAligned_powers.csv | ||
| WaveletAnalysisGlobal.R | universallyAlignedGlobalFlow_DailyQ2_column.csv | universallyAligned_powersTranspose.csv |