def main(runIndex=None): print("Starting Main.main()") # if the required directory structure doesn't exist, create it makeDirectoryStructure(address) # now start the GMM process Load.main(address, filename_raw_data, runIndex, subsample_uniform,\ subsample_random, subsample_inTime, grid, conc, \ fraction_train, inTime_start, inTime_finish,\ fraction_nan_samples, fraction_nan_depths, cov_type,\ run_bic=False) # loads data, selects train, cleans, centres/standardises, prints PCA.create(address, runIndex, n_dimen, use_fPCA) GMM.create(address, runIndex, n_comp, cov_type) PCA.apply(address, runIndex) GMM.apply(address, runIndex, n_comp) # reconstruction (back into depth space) Reconstruct.gmm_reconstruct(address, runIndex, n_comp) Reconstruct.full_reconstruct(address, runIndex) Reconstruct.train_reconstruct(address, runIndex) # calculate properties mainProperties(address, runIndex, n_comp)
def main(run=None): print("Starting Main.main()") # Now start the GMM process Load.main(address, dir_raw_data, run, subsample_uniform, subsample_random,\ subsample_inTime, grid, conc, fraction_train, inTime_start,\ inTime_finish, fraction_nan_samples, fraction_nan_depths, dtype) #Load.main(address, filename_raw_data, run, subsample_uniform, subsample_random,\ # Loads data, selects Train, cleans, centres/standardises, prints PCA.create(address, run, n_dimen) # Uses Train to create PCA, prints results, stores object GMM.create(address, run, n_comp) # Uses Train to create GMM, prints results, stores object PCA.apply(address, run) # Applies PCA to test dataset GMM.apply(address, run, n_comp) # Applies GMM to test dataset # Reconstruction Reconstruct.gmm_reconstruct(address, run, n_comp) # Reconstructs the results in original space Reconstruct.full_reconstruct(address, run) Reconstruct.train_reconstruct(address, run) # new stuff DD 27/08/18, after seeing updates on DJ github #mainProperties(address, runIndex, n_comp) # Plotting -- first commented out DD #Plot.plotMapCircular(address, address_fronts, run, n_comp) #Plot.plotPosterior(address, address_fronts, run, n_comp, plotFronts=True) Plot.plotPostZonal(address, run, n_comp, dtype, plotFronts=False) ## zonal frequencies #Plot.plotPosterior(address, run, n_comp, dtype, plotFronts=False) ## works but data overlaps spatially... Plot.plotProfileClass(address, run, n_comp, dtype, 'uncentred') Plot.plotProfileClass(address, run, n_comp, dtype, 'depth') Plot.plotGaussiansIndividual(address, run, n_comp, dtype, 'reduced')#uncentred')#'depth')#reduced') # Plot.plotGaussiansIndividual(address, run, n_comp, 'depth') # ERROR NOT WOKRING PROPERLY # Plot.plotGaussiansIndividual(address, run, n_comp, 'uncentred') # ERROR NOT WOKRING PROPERLY #Plot.plotProfile(address, run, dtype, 'original') # these run just fine but are huge and unhelpful Plot.plotProfile(address, run, dtype, 'uncentred') Plot.plotWeights(address, run, dtype)