Issue with _init_.py files fixed.

boxsers/__init__.py

@@ -1,9 +1,9 @@
-from ramanbox.machine_learning import SpectroGmixture, SpectroKmeans, SpectroRF, SpectroSVM, \
+from boxsers.machine_learning import SpectroGmixture, SpectroKmeans, SpectroRF, SpectroSVM, \
     SpectroCNN, SpectroLDA
-from ramanbox.data_augmentation import aug_mixup, aug_xshift, aug_noise, aug_multiplier, aug_linslope, aug_offset
-from ramanbox.dimension_reduction import SpectroPCA, SpectroFA, SpectroICA
-from ramanbox.preprocessing import spectral_normalization, spline_interpolation, baseline_subtraction,\
+from boxsers.data_augmentation import aug_mixup, aug_xshift, aug_noise, aug_multiplier, aug_linslope, aug_offset
+from boxsers.dimension_reduction import SpectroPCA, SpectroFA, SpectroICA
+from boxsers.preprocessing import spectral_normalization, spline_interpolation, baseline_subtraction,\
     spectral_cut, savgol_smoothing, median_filter
-from ramanbox.useful_features import data_split, database_creator, distribution_plot, load_rruff, \
+from boxsers.useful_features import data_split, database_creator, distribution_plot, load_rruff, \
     ramanshift_converter, wavelength_converter, import_sp
-from ramanbox.visualization import random_plot, spectro_plot, class_plot
+from boxsers.visualization import random_plot, spectro_plot, class_plot

boxsers/machine_learning/__init__.py

@@ -1,3 +1,3 @@
-from ramanbox.machine_learning.classifier import SpectroRF, SpectroSVM, SpectroLDA
-from ramanbox.machine_learning.neural_networks import SpectroCNN
-from ramanbox.machine_learning.unsupervised_models import SpectroGmixture, SpectroKmeans
+from boxsers.machine_learning.classifier import SpectroRF, SpectroSVM, SpectroLDA
+from boxsers.machine_learning.neural_networks import SpectroCNN
+from boxsers.machine_learning.unsupervised_models import SpectroGmixture, SpectroKmeans

setup.py

@@ -10,7 +10,7 @@
                       'tensorflow'],
     python_requires='>=3',
     # *strongly* suggested for sharing
-    version='0.1.0',
+    version='0.1.1',
     # The license can be anything you like
     license='MIT',
     description='Provides a full range of functionality to process and analyze vibrational spectra.',