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)
