diff --git a/env.yml b/env.yml
index a6d14d0..1ca7a3c 100644
--- a/env.yml
+++ b/env.yml
@@ -1,72 +1,70 @@
 name: weightmatrices
 channels:
   - pytorch
   - conda-forge
   - brian-team
   - defaults
 dependencies:
   - blas=1.0=mkl
   - ca-certificates=2020.6.20=hecda079_0
   - certifi=2020.6.20=py37hc8dfbb8_0
   - cloudpickle=1.4.1=py_0
   - cycler=0.10.0=py_2
   - cytoolz=0.10.1=py37h0b31af3_0
   - dask-core=2.19.0=py_0
   - decorator=4.4.2=py_0
   - freetype=2.10.2=ha233b18_0
   - imagecodecs-lite=2019.12.3=py37h10e2902_1
   - imageio=2.8.0=py_0
   - intel-openmp=2019.4=233
   - joblib=0.15.1=py_0
   - jpeg=9b=he5867d9_2
   - kiwisolver=1.2.0=py37ha1cc60f_0
   - libcxx=10.0.0=1
   - libedit=3.1.20191231=haf1e3a3_0
   - libffi=3.3=h0a44026_1
   - libgfortran=3.0.1=h93005f0_2
   - libpng=1.6.37=ha441bb4_0
   - libtiff=4.1.0=hcb84e12_1
   - llvm-openmp=10.0.0=h28b9765_0
   - lz4-c=1.9.2=h0a44026_0
   - matplotlib=3.2.1=0
   - matplotlib-base=3.2.1=py37hddda452_0
   - mkl=2019.4=233
   - mkl-service=2.3.0=py37hfbe908c_0
   - mkl_fft=1.1.0=py37hc64f4ea_0
   - mkl_random=1.1.1=py37h959d312_0
   - ncurses=6.2=h0a44026_1
   - networkx=2.4=py_1
   - ninja=1.9.0=py37h04f5b5a_0
   - numpy=1.18.1=py37h7241aed_0
   - numpy-base=1.18.1=py37h3304bdc_1
   - olefile=0.46=py37_0
   - openssl=1.1.1g=h0b31af3_0
   - pillow=7.1.2=py37h4655f20_0
   - pip=20.1.1=py37_1
   - pyparsing=2.4.7=pyh9f0ad1d_0
   - python=3.7.7=hf48f09d_4
   - python-dateutil=2.8.1=py_0
   - python_abi=3.7=1_cp37m
   - pytorch=1.5.1=py3.7_0
   - pywavelets=1.1.1=py37h10e2902_1
   - pyyaml=5.3.1=py37h9bfed18_0
   - readline=8.0=h1de35cc_0
   - scikit-image=0.17.2=py37h94625e5_1
   - scikit-learn=0.22.1=py37h27c97d8_0
   - scipy=1.4.1=py37h9fa6033_0
   - setuptools=47.3.0=py37_0
   - six=1.15.0=py_0
   - sqlite=3.32.2=hffcf06c_0
   - tifffile=2020.6.3=py_0
   - tk=8.6.10=hb0a8c7a_0
   - toolz=0.10.0=py_0
   - torchvision=0.6.1=py37_cpu
   - tornado=6.0.4=py37h9bfed18_1
   - tqdm=4.46.1=py_0
   - wheel=0.34.2=py37_0
   - xz=5.2.5=h1de35cc_0
   - yaml=0.2.5=h0b31af3_0
   - zlib=1.2.11=h1de35cc_3
   - zstd=1.4.4=h1990bb4_3
-prefix: /Users/Bernd/anaconda3/envs/weightmatrices
-
diff --git a/main.py b/main.py
index 2103d74..bb739e8 100644
--- a/main.py
+++ b/main.py
@@ -1,79 +1,82 @@
 
 import torch
 import numpy as np
 import random
-import matplotlib
-matplotlib.use('TkAgg')
-import matplotlib.pyplot as plt
 import argparse
 # own package
 from weightmatrices.utils import utils
 
 
 # read command line args and kwargs
 parser = argparse.ArgumentParser()
 parser.add_argument("--nhidden", nargs="*", type=int, help="number of hidden neurons", default=100)
 parser.add_argument("--methods", nargs="*", help="methods to be applied to create weight matrix", default='all')
 parser.add_argument("--save", type=bool, help="whether to save results or not", default=True)
 args = parser.parse_args()
 
 
 # data import
 print("loading data")
 data_loader = utils.load_data()
 data_matrix = utils.getbigdatamatrix(data_loader)
 n_in_features = data_matrix.shape[1]
 
 
-# weight matrix creation
-
-if 'pca' in args.methods or args.methods == 'all':
+# weight matrix creation with different methods
+if 'pca' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import pca
     print("creating weight matrix using PCA")
     for n_h in args.nhidden:
         if n_h <= n_in_features: # Number of requested components <= input dimensionality
             W_pca = pca.get_weightmatrices_pca(data_loader, n_h)
             if args.save:
                 utils.saveweightmatrix('pca'+str(n_h), W_pca)
 
-if 'ica' in args.methods or args.methods == 'all':
+if 'ica' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import ica
     print("creating weight matrix using ICA")
     for n_h in args.nhidden:
         if n_h <= n_in_features: # Number of requested components <= input dimensionality
             W_ica = ica.get_weightmatrices_ica(data_matrix, n_h)
             if args.save:
                 utils.saveweightmatrix('ica'+str(n_h), W_ica)
 
-if 'sc' in args.methods or args.methods == 'all':
+if 'sc' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import sc
     print("creating weight matrix using SC")
     for n_h in args.nhidden:
         W_sc = sc.get_weightmatrices_sc(data_matrix, n_h, getsparsity=True)
         if args.save:
             utils.saveweightmatrix('sc'+str(n_h), W_sc)
 
-if 'rg' in args.methods or args.methods == 'all':
+if 'rg' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import rg
     print("creating weight matrix using RG")
     for n_h in args.nhidden:
         W_rg = rg.get_weightmatrices_rg(data_matrix, n_h)
         if args.save:
             utils.saveweightmatrix('rg'+str(n_h), W_rg)
 
-if 'rp' in args.methods or args.methods == 'all':
+if 'rp' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import rp
     print("creating weight matrix using RP")
     for n_h in args.nhidden:
         W_rp = rp.get_weightmatrices_rp(data_matrix, n_h)
         if args.save:
             utils.saveweightmatrix('rp'+str(n_h), W_rp)
 
-# plotting
-#plt.ion()
-#plt.imshow(W_sc[random.sample(range(0, args.nhidden), 1)[0], :].reshape(28, 28), cmap = 'gray')
-#plt.show()
 
 # jump to interactive mode
 import code
 code.interact(local=locals())
+
+
+# sample plotting
+#import matplotlib
+#matplotlib.use('TkAgg')
+#import matplotlib.pyplot as plt
+
+#W = W_pca
+#plt.ion()
+#plt.imshow(W[random.sample(range(0, args.nhidden[-1]), 1)[0], :].reshape(28, 28), cmap = 'gray')
+#plt.show()