diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..9016f7f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,141 @@
+.idea/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
diff --git a/Example.ipynb b/Example.ipynb
index a66d81e..d9f77cf 100644
--- a/Example.ipynb
+++ b/Example.ipynb
@@ -3,33 +3,23 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/Matias/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
-      "  \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "import somsphere\n",
     "import healpy as hp\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import time\n",
+    "\n",
+    "data = \"resources/SDSS_MGS.train\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -44,16 +34,14 @@
    ],
    "source": [
     "#just read magnitudes and colors\n",
-    "Data_X=np.loadtxt('SDSS_MGS.train', usecols=(1,2,3,4,5,6,7,8,9), unpack=True).T\n",
+    "Data_X=np.loadtxt(data, usecols=(1, 2,3,4,5,6,7,8,9), unpack=True).T\n",
     "np.shape(Data_X)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -68,28 +56,24 @@
    ],
    "source": [
     "#read zspec (or any other extra column)\n",
-    "Data_Y=np.loadtxt('SDSS_MGS.train', usecols=(0,), unpack=True).T\n",
+    "Data_Y=np.loadtxt(data, usecols=(0,), unpack=True).T\n",
     "np.shape(Data_Y)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#create an instance\n",
-    "M=somsphere.SelfMap(Data_X, Data_Y,topology='grid', Ntop=15, iterations=100, periodic='no')"
+    "M=somsphere.SOMap(Data_X, Data_Y, topology='grid', n_top=15, n_iter=100, periodic=False, som_type=\"batch\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "M.create_map() #This actually creates the map using only Data_X"
@@ -97,10 +81,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "M.evaluate_map() # Will evaluate Data_Y, basically will take mean for every value of Y per cell"
@@ -108,74 +90,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/Matias/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/cbook.py:122: MatplotlibDeprecationWarning: The matplotlib.mpl module was deprecated in version 1.3. Use `import matplotlib as mpl` instead.\n",
-      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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VSa6dJm5Bh2OTKJAADEFPXxQwnmRlkg1pZqWuSTML9YbB9tVJzkgzQ3VhkoNJViU5N8nz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\nUDYAAAAAAAAAAAAAvAz9f4DEPx2A/p/qAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x103740f50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "M.plot_map()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "#We can evaluate other column, for example column 0 from X\n",
-    "M.evaluate_map(inputY=Data_X[:,0])"
+    "M.evaluate_map(input_y=Data_X[:,0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x107020390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "M.plot_map()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "#Every time the map is created it will be different given the random weights\n",
@@ -184,22 +128,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106225e10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "M.evaluate_map()\n",
     "M.plot_map()"
@@ -207,45 +138,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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MrX9unx9af+T/bZ3X54bVrjrvzs8OrX//D39haP2Oji3fnQe8/pGBtW8eWLvqjsH1bx1c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\nAAAAANwL/X+gtx+p8+h6ogAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106da3f50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "M.evaluate_map(inputY=Data_X[:,0])\n",
+    "M.evaluate_map(input_y=Data_X[:,0])\n",
     "M.plot_map()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "#Spherical coordinates\n",
-    "M_sph=somsphere.SelfMap(Data_X, Data_Y,topology='sphere', Ntop=8, iterations=100)"
+    "M_sph=somsphere.SOMap(Data_X, Data_Y, topology='sphere', n_top=8, n_iter=100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "M_sph.create_map()"
@@ -253,22 +167,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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cjUr1QCWCVC48pWxUqoSXs1G5ABUzUrnwlDNSqVJeykjlAlTqwJsr5aWsVCpE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/Zoex7w9XI7wLHlz/cCn61su73yOpcBf1V5vK78eSrwfWB1+Xi931sTQvQRn3PLq5j/f72J+XOL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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x107129610>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "M_sph.evaluate_map()\n",
     "M_sph.plot_map()\n"
@@ -276,24 +177,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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6tO1ZkALyMKUBKWA+YEoDUkEpUGlgCtgPVJYcqxxQsTAlBalYHapc1UHqsMkavgsPMyZ0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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x103772810>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "M_sph.evaluate_map(inputY=Data_X[:,0])\n",
+    "M_sph.evaluate_map(input_y=Data_X[:,0])\n",
     "M_sph.plot_map()"
    ]
   },
@@ -306,79 +194,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "9 dimensions\n",
-      "225 cells\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# To test with fixed weights, so answer is always the same (to be able to compare)\n",
     "# need more iterations to make sure it converges\n",
-    "M=somsphere.SelfMap(Data_X, Data_Y,topology='grid', Ntop=15, iterations=300, periodic='no')\n",
-    "print M.nDim, 'dimensions' #dimensions\n",
-    "print M.npix, 'cells' #cells"
+    "M=somsphere.SOMap(Data_X, Data_Y,topology='grid', n_top=15, n_iter=300, periodic=False)\n",
+    "print(M.n_col, 'dimensions') #dimensions\n",
+    "print(M.n_pix, 'cells') #cells"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dw =  0.0098814229249\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(9, 225)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "#weights are supposed to be random, but we can make them linear for testing\n",
-    "W = np.linspace(0,20,M.nDim*M.npix).reshape(M.nDim,M.npix)\n",
+    "W = np.linspace(0,20,M.n_col*M.n_pix).reshape(M.n_col,M.n_pix)\n",
     "#look carefully onhow this was created, elements go from 0 to 20 with a difference of \n",
     "#W[0][1]-W[0][0] = 0.009881422924901186\n",
-    "print 'dw = ', W[0][1]-W[0][0]\n",
+    "print('dw = ', W[0][1]-W[0][0])\n",
     "np.shape(W)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "M.create_map(inputs_weights=W, random_order=False)"
+    "M.create_map(input_weights=W, random_order=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "M.evaluate_map()"
@@ -386,70 +239,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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pYGj+7mlnh+a/l4+G5sOjYck1PebRD6wA5gODwAZgDbClbcw+4BrglX/AucdxilWSlKym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\nJEl6Evp/wRd2HVn5kqgAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106ddd750>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "M.plot_map()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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pYGj+7mlnh+a/l4+G5sOjYck1PebRD6wA5gODwAZgDbClbcw+4BrglX/AucdxilWSlKym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\nJEl6Evp/wRd2HVn5kqgAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1070aced0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# If I create the map again, results should be the same\n",
-    "M.create_map(inputs_weights=W, random_order=False)\n",
+    "M.create_map(input_weights=W, random_order=False)\n",
     "M.evaluate_map()\n",
     "M.plot_map()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 16.1580654 ,  14.33966559,  13.50589005,  13.13014191,\n",
-       "        12.84625795,   1.81839981,   0.83377553,   0.37574814,   0.28388396])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# To compare, we can use the resulting weights for cell 0\n",
     "M.weights[:,0]"
@@ -457,84 +270,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# in spherical\n",
-    "M_sph=somsphere.SelfMap(Data_X, Data_Y,topology='sphere', Ntop=8, iterations=300)"
+    "M_sph=somsphere.SOMap(Data_X, Data_Y,topology='sphere', n_top=8, n_iter=300)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dw =  0.00289393720156\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(9, 768)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "#weights, the number of pixels is different\n",
     "#weights are supposed to be random, but we can make them linear for testing\n",
-    "W = np.linspace(0,20,M_sph.nDim*M_sph.npix).reshape(M_sph.nDim,M_sph.npix)\n",
+    "W = np.linspace(0,20,M_sph.n_col*M_sph.n_pix).reshape(M_sph.n_col,M_sph.n_pix)\n",
     "#look carefully onhow this was created, elements go from 0 to 20 with a difference of \n",
     "#W[0][1]-W[0][0] = 0.009881422924901186\n",
-    "print 'dw = ', W[0][1]-W[0][0]\n",
+    "print('dw = ', W[0][1]-W[0][0])\n",
     "np.shape(W)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-28-3d4db97b10fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM_sph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/Users/Matias/Dropbox/GitHub/somsphere/somsphere.py\u001b[0m in \u001b[0;36mcreate_map\u001b[0;34m(self, evol, inputs_weights, random_order)\u001b[0m\n\u001b[1;32m    327\u001b[0m                     \u001b[0mtt\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m                     \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex_random\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 329\u001b[0;31m                     \u001b[0mbest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msom_best_cell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    330\u001b[0m                     self.weights += alpha * h(best, self.distLib, sigma) * numpy.transpose(\n\u001b[1;32m    331\u001b[0m                         (inputs - numpy.transpose(self.weights)))\n",
-      "\u001b[0;32m/Users/Matias/Dropbox/GitHub/somsphere/somsphere.py\u001b[0m in \u001b[0;36msom_best_cell\u001b[0;34m(self, inputs, return_vals)\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0mIt\u001b[0m \u001b[0mcan\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmore\u001b[0m \u001b[0mthan\u001b[0m \u001b[0mone\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mneeded\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m         \"\"\"\n\u001b[0;32m--> 273\u001b[0;31m         activations = numpy.sum(numpy.transpose([self.importance]) * (\n\u001b[0m\u001b[1;32m    274\u001b[0m             numpy.transpose(numpy.tile(inputs, (self.npix, 1))) - self.weights) ** 2, axis=0)\n\u001b[1;32m    275\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreturn_vals\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/Matias/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36mtranspose\u001b[0;34m(a, axes)\u001b[0m\n\u001b[1;32m    534\u001b[0m         \u001b[0mtranspose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 536\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'transpose'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    537\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/Matias/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0mwrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "M_sph.create_map(inputs_weights=W, random_order=False)"
+    "M_sph.create_map(input_weights=W, random_order=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "M_sph.evaluate_map()\n",
@@ -544,32 +315,26 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "M_sph.evaluate_map(inputY=Data_X[:,0])\n",
+    "M_sph.evaluate_map(input_y=Data_X[:,0])\n",
     "M_sph.plot_map()\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "M_sph.create_map(inputs_weights=W, random_order=False)"
+    "M_sph.create_map(input_weights=W, random_order=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "M_sph.evaluate_map()\n",
@@ -579,9 +344,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#Weights for cell 0\n",
@@ -591,27 +354,23 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# hexagonal topology with periodic conditions\n",
-    "M_hex=somsphere.SelfMap(Data_X, Data_Y,topology='hex', Ntop=15, iterations=300, periodic='yes')\n",
-    "print M_hex.nDim, 'dimensions' #dimensions\n",
-    "print M_hex.npix, 'cells' #cells"
+    "M_hex=somsphere.SOMap(Data_X, Data_Y,topology='hex', n_top=15, n_iter=300, periodic=True)\n",
+    "print(M_hex.n_col, 'dimensions') #dimensions\n",
+    "print(M_hex.n_pix, 'cells') #cells"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "W=np.linspace(0,20,M_hex.nDim*M_hex.npix).reshape(M_hex.nDim,M_hex.npix)\n",
-    "M_hex.create_map(inputs_weights=W, random_order=False)\n",
+    "W=np.linspace(0,20,M_hex.n_col*M_hex.n_pix).reshape(M_hex.n_col,M_hex.n_pix)\n",
+    "M_hex.create_map(input_weights=W, random_order=False)\n",
     "M_hex.evaluate_map()\n",
     "M_hex.plot_map()"
    ]
@@ -619,12 +378,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "M_hex.evaluate_map(inputY=Data_X[:,0])\n",
+    "M_hex.evaluate_map(input_y=Data_X[:,0])\n",
     "M_hex.plot_map()"
    ]
   },
@@ -639,10 +396,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_R=np.random.rand(45000).reshape(5000,9)"
@@ -650,43 +405,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5000, 9)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "np.shape(X_R)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "M_sph_R=somsphere.SelfMap(X_R, X_R[:,0],topology='sphere', Ntop=8, iterations=100)"
+    "M_sph_R=somsphere.SOMap(X_R, X_R[:,0],topology='sphere', n_top=8, n_iter=100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "M_sph_R.create_map()"
@@ -694,10 +432,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "M_sph_R.evaluate_map()"
@@ -705,334 +441,124 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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0Bai9P0Mb6wtQ2xH+LXwBaidi74ivXPlrYnnPNVHULUQ8FmrkWlqflM9E3fOd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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10294a0d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "M_sph_R.plot_map()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "M_sph_R.evaluate_map(inputY=X_R[:,0])"
+    "M_sph_R.evaluate_map(input_y=X_R[:,0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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0Bai9P0Mb6wtQ2xH+LXwBaidi74ivXPlrYnnPNVHULUQ8FmrkWlqflM9E3fOd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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106c65bd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "M_sph_R.plot_map()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1078b9910>]"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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F4DY3N2cYTzc9FuE6OjoqL168KMfHxzO5AS3EwkjebyqGeZ48eTI9hD///PMOvGFZFbqR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\nMNGK9PYXr23LPQI1WFTnHvtVU9iQt1G44vNYME7XoLW4nSdLMtnyVs0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fROR/FJHPjWdc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\n6NChQ4cOHTp06HA5+H+/I8HWSprDBwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x107385c90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.plot(X_R[:,0],'k.')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[525, 1796, 2251, 3143, 3165, 3185]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "M_sph_R.ivals[0]\n"
+    "M_sph_R.i_vals[0]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "HR=np.zeros(M_sph_R.npix)\n",
-    "MR=np.zeros(M_sph_R.npix)\n",
-    "for i in xrange(M_sph_R.npix):\n",
+    "HR=np.zeros(M_sph_R.n_pix)\n",
+    "MR=np.zeros(M_sph_R.n_pix)\n",
+    "for i in xrange(M_sph_R.n_pix):\n",
     "    try:\n",
-    "        HR[i]=len(M_sph_R.ivals[i])\n",
-    "        MR[i]=np.mean(M_sph_R.yvals[i])\n",
+    "        HR[i]=len(M_sph_R.i_vals[i])\n",
+    "        MR[i]=np.mean(M_sph_R.y_vals[i])\n",
     "    except:\n",
     "        pass"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1091458d0>]"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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pNsr5o1No8pytW6cyQHJyffRRlUFw0SLguefM8ZjilsTcaqm8HkD7cfO64W1U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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1078f24d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.plot(H)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x109d4bf90>]"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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dsl54/uPjxewDDusOhRP8fvulFfxmmwF///dBwf/4x/FB1pRFw+vBqnv6fcWK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\nqFGjRo0aNfLx/wBH8aWv5j2pzwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10855da10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.plot(MM)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(6.510416666666667, 0.49416192027908679)"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "np.mean(H), np.mean(MM)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11.0"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "H[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.7324050443499202"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "np.std(H)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([  13.,   39.,   47.,   90.,   94.,  127.,  100.,   76.,   66.,\n",
-       "          50.,   35.,   20.,    5.,    4.,    2.]),\n",
-       " array([  0.        ,   1.06666667,   2.13333333,   3.2       ,\n",
-       "          4.26666667,   5.33333333,   6.4       ,   7.46666667,\n",
-       "          8.53333333,   9.6       ,  10.66666667,  11.73333333,\n",
-       "         12.8       ,  13.86666667,  14.93333333,  16.        ]),\n",
-       " <a list of 15 Patch objects>)"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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3W7W5+vw64W/+HPAgsLyCvtrN1ee0LxHOEQ6X2tHc5uvzrwi/0xc5/lxmpZYQhmpGgKXU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\nVTUnSZIkSZIkSZIkSZIkSZIkSZIkSfw/UKQPPKNUKNUAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10916ef10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.hist(H, bins=15)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([  5.,  14.,  46.,  60.,  64.,  61.,  30.,  33.,  38.,  25.,  31.,\n",
-       "         31.,  35.,  34.,  42.,  43.,  63.,  61.,  40.,  12.]),\n",
-       " array([ 0.        ,  0.04798922,  0.09597844,  0.14396766,  0.19195688,\n",
-       "         0.2399461 ,  0.28793533,  0.33592455,  0.38391377,  0.43190299,\n",
-       "         0.47989221,  0.52788143,  0.57587065,  0.62385987,  0.67184909,\n",
-       "         0.71983831,  0.76782753,  0.81581675,  0.86380598,  0.9117952 ,\n",
-       "         0.95978442]),\n",
-       " <a list of 20 Patch objects>)"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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FHgE+Wk9ptSvSF/cAbwb+m2x44bZ6Sps4pXOz6seXojvg4PXsUXfcMt/XDcAngHeNqZYm\nFemHu4G9+bodqt3zMMmK9MUm4G3ALwOXkn16+wbZeGkkRfriU2TDLwnZPSqHgbcCi+Mra2KVys2q\nYf5fwLZl89tY+qi42jpX58siKtIfkJ30vIdszHytj1ptVaQffp7sYzZkY6M3k330Pjj26upVpC+e\nJBtaeSF/fY0swKKFeZG++EXgj/PpU8B/AG8g+8QyTWrPzSI3Di0fyL+emCf8zivSH9eQjRteX2tl\n9Sp7Q9n9xL2apUhf7AS+QnaC8FKyk2Jvqq/E2hTpiz8D9uXTs2RhP1NTfXXbTrEToLXl5ko3Dv12\n/jrvs/nXv0v2cTKy9frjXrKTOsfy1zfrLrAmRX4uzosc5lCsLz5JdkXLceDWWqur13p9sQV4kCwr\njpOdHI7oAbLzAi+SfTL7BNOdm5IkSZIkSZIkSZIkSZIkSZIkSZIG/T8EVWDRHn6CHQAAAABJRU5E\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ae85250>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.hist(MM, bins=20)"
@@ -1040,79 +566,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([  27.,   92.,   99.,   83.,   83.,   85.,   71.,  101.,  101.,   26.]),\n",
-       " array([ 0.05620335,  0.14474298,  0.23328261,  0.32182224,  0.41036187,\n",
-       "         0.4989015 ,  0.58744113,  0.67598075,  0.76452038,  0.85306001,\n",
-       "         0.94159964]),\n",
-       " <a list of 10 Patch objects>)"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Negv/+Iy3rlll+uIIaYjmreL2L6RgixbyZfri54A/LqZHgf8Efob0DmcumTW5WeakqN4D\nCOuIe7CxTF9cQRqTXNdoy5o33ZPlHiHup2vK9MXVwHdIByYvIR2MW9tcExtTpi/+HNhSTC8lvQgs\nbqh9TVtFuQOvrefmuU6K+o3idso3ivufI70tjWqqvniIdCBpb3H7XtMNbFCZv4tTIoc8lOuLL5E+\nYbMP+GKjrWvWVH2xBNhOyop9pIPSEW0lHXd4h/RO7h7mbm5KkiRJkiRJkiRJkiRJkiRJkiRJmkv+\nH6HQEKFWLu7eAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e208cd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.hist(M_sph_R.weights[4])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "768"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "len(M_sph_R.weights[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "M_sph=somsphere.SelfMap(Data_X, Data_Y,topology='sphere', Ntop=8, iterations=100)"
+    "M_sph=somsphere.SOMap(Data_X, Data_Y,topology='sphere', n_top=8, n_iter=100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "M_sph.create_map()"
@@ -1120,10 +602,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "M_sph.evaluate_map()"
@@ -1131,147 +611,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/Matias/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/cbook.py:122: MatplotlibDeprecationWarning: The matplotlib.mpl module was deprecated in version 1.3. Use `import matplotlib as mpl` instead.\n",
-      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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9Os0HUFSocgGK+pw+gKLCmA+gKCVTH0CdRnxOX2mNKjVdE9VFG+azUJ3DL6EN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\nQgghhBCiB/l/ZlJ6/4eK+K8AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10686ffd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "M_sph.plot_map()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "H=np.zeros(M_sph.npix)\n",
-    "MM=np.zeros(M_sph.npix)\n",
-    "for i in xrange(M_sph.npix):\n",
+    "H=np.zeros(M_sph.n_pix)\n",
+    "MM=np.zeros(M_sph.n_pix)\n",
+    "for i in xrange(M_sph.n_pix):\n",
     "    try:\n",
-    "        H[i]=len(M_sph.ivals[i])\n",
-    "        MM[i]=np.mean(M_sph.yvals[i])\n",
+    "        H[i]=len(M_sph.i_vals[i])\n",
+    "        MM[i]=np.mean(M_sph.y_vals[i])\n",
     "    except:\n",
     "        pass"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1068f0d10>]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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xeeqmx1pa3xV1YEIepe7KQW+6ecUSkO7G5oqU4CGNY2PuAXdTGb7jSrpBep8n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\nm1F6yCHArrtmStLkvdIjsA4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PwCoNrefL6IOyZGYD6Ee9fvvOAN6A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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x103891f50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.plot(H)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([  33.,   94.,  182.,  200.,   71.,   97.,   62.,   17.,    8.,    4.]),\n",
-       " array([  0. ,   1.8,   3.6,   5.4,   7.2,   9. ,  10.8,  12.6,  14.4,\n",
-       "         16.2,  18. ]),\n",
-       " <a list of 10 Patch objects>)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x108f48a90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.hist(H)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10a609390>]"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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w+/ffZnGzypXNQdhdu8xJVLfemo6hRXzMm4I/Cmhw1b4PMAW/ArDQ8zNAINDG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\nGP4fhLWUMhG25iUAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a1baad0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "MM1=(MM-np.min(MM))\n",
     "MM1/=np.max(MM1)\n",
@@ -1283,67 +668,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1093e8890>]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10755f890>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.plot(np.sort(MR))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 0.22135417,  1.25      ,  1.6015625 ,  0.92447917,  0.83333333,\n",
-       "         0.87239583,  1.015625  ,  1.22395833,  1.51041667,  0.546875  ]),\n",
-       " array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ]),\n",
-       " <a list of 10 Patch objects>)"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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6iOxcwrtkn9o+xfDmpiRJkiRJkiRJkiRJkiRJkiRJkqSUfgsVxHrKZO2VpQAAAABJRU5E\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a1e0d50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.hist(MM1, normed=True)\n",
     "plt.hist(MR1, normed=True, alpha=0.5)"
@@ -1352,32 +688,51 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "env38",
    "language": "python",
-   "name": "python2"
+   "name": "env38"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
-}
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/pyptoject.toml b/pyptoject.toml
new file mode 100644
index 0000000..eb56957
--- /dev/null
+++ b/pyptoject.toml
@@ -0,0 +1,2 @@
+[build-system]
+requires = ["setuptools", "wheel", "Cython"]
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..dce3e83
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+matplotlib==3.2.2
+numpy==1.19.1
+scipy==1.5.2
+healpy==1.14.0
diff --git a/SDSS_MGS.train b/resources/SDSS_MGS.train
similarity index 100%
rename from SDSS_MGS.train
rename to resources/SDSS_MGS.train
diff --git a/som.f90 b/resources/som.f90
similarity index 100%
rename from som.f90
rename to resources/som.f90
diff --git a/setup.py b/setup.py
index f0005a7..cc62785 100644
--- a/setup.py
+++ b/setup.py
@@ -1,22 +1,18 @@
-import sys
-files_f90 = ['som.f90', ]
-from numpy.distutils.core import setup, Extension
+from Cython.Build import cythonize
+from numpy.distutils.core import setup
 
-extra_link_args = []
-libraries = []
-library_dirs = []
 exec(open('version.py').read())
 setup(
-    name = 'somsphere',
-    version = __version__,
-    author = 'Matias Carrasco Kind',
-    author_email = 'mcarras2@illinois.edu',
-    ext_modules = [Extension('somF', files_f90, ), ],
-    packages = [],
-    py_modules = ['somsphere'],
-    license = 'License.txt',
-    description = 'somsphere : Self Organizing Maps in spherical coordinates and other topologies',
-    long_description = open('README.md').read(),
+    name='somsphere',
+    version=__version__,
+    author='Matias Carrasco Kind',
+    author_email='mcarras2@illinois.edu',
+    packages=[],
+    ext_modules=cythonize("somsphere/cython/core.pyx"),
+    py_modules=['somsphere'],
+    license='License.txt',
+    description='somsphere : Self Organizing Maps in spherical coordinates and other topologies',
+    long_description=open('README.md').read(),
     url='https://github.com/mgckind/somsphere',
     install_requires=['numpy', 'matplotlib', 'scipy'],
 )
diff --git a/somsphere.py b/somsphere.py
deleted file mode 100644
index d35790f..0000000
--- a/somsphere.py
+++ /dev/null
@@ -1,543 +0,0 @@
-"""
-.. module:: SOMZ
-.. moduleauthor:: Matias Carrasco Kind
-
-"""
-from __future__ import print_function
-from builtins import zip
-from builtins import range
-from builtins import object
-__author__ = 'Matias Carrasco Kind'
-import numpy
-import copy
-import sys, os, random
-import warnings
-
-warnings.simplefilter("ignore", RuntimeWarning)
-try:
-    import somF
-    SF90 = True
-except:
-    SF90 = False
-
-
-def get_index(ix, iy, nx, ny):
-    return iy * nx + ix
-
-
-def get_pair(ii, nx, ny):
-    iy = int(numpy.floor(ii / nx))
-    ix = ii % nx
-    return ix, iy
-
-
-def get_ns(ix, iy, nx, ny, index=False):
-    """
-    Get neighbors for rectangular grid given its
-    coordinates and size of grid
-
-    :param int ix: Coordinate in the x-axis
-    :param int iy: Coordinate in the y-axis
-    :param int nx: Number fo cells along the x-axis
-    :param int ny: Number fo cells along the y-axis
-    :param bool index: Return indexes in the map format
-    :return: Array of indexes for direct neighbors
-    """
-    ns = []
-    if ix - 1 >= 0: ns.append((ix - 1, iy))
-    if iy - 1 >= 0: ns.append((ix, iy - 1))
-    if ix + 1 < nx: ns.append((ix + 1, iy))
-    if iy + 1 < ny: ns.append((ix, iy + 1))
-
-    if ix - 1 >= 0 and iy - 1 >= 0: ns.append((ix - 1, iy - 1))
-    if ix - 1 >= 0 and iy + 1 < ny: ns.append((ix - 1, iy + 1))
-    if ix + 1 < nx and iy + 1 < ny: ns.append((ix + 1, iy + 1))
-    if ix + 1 < nx and iy - 1 >= 0: ns.append((ix + 1, iy - 1))
-
-    ns = numpy.array(ns)
-    if not index:
-        return ns
-    if index:
-        ins = []
-        for i in range(len(ns)):
-            ins.append(get_index(ns[i, 0], ns[i, 1], nx, ny))
-        return numpy.array(ins)
-
-
-def get_ns_hex(ix, iy, nx, ny, index=False):
-    """
-    Get neighbors for hexagonal grid given its coordinates
-    and size of grid
-    Same parameters as :func:`get_ns`
-    """
-    ns = []
-    even = False
-    if iy % 2 == 0: even = True
-    if ix - 1 >= 0: ns.append((ix - 1, iy))
-    if ix + 1 < nx: ns.append((ix + 1, iy))
-    if iy - 1 >= 0: ns.append((ix, iy - 1))
-    if iy + 1 < ny: ns.append((ix, iy + 1))
-    if even and ix - 1 >= 0 and iy - 1 >= 0: ns.append((ix - 1, iy - 1))
-    if even and ix - 1 >= 0 and iy + 1 < ny: ns.append((ix - 1, iy + 1))
-    if not even and ix + 1 < nx and iy - 1 >= 0: ns.append((ix + 1, iy - 1))
-    if not even and ix + 1 < nx and iy + 1 < ny: ns.append((ix + 1, iy + 1))
-    ns = numpy.array(ns)
-    if not index:
-        return ns
-    if index:
-        ins = []
-        for i in range(len(ns)):
-            ins.append(get_index(ns[i, 0], ns[i, 1], nx, ny))
-        return numpy.array(ins)
-
-
-def geometry(top, Ntop, periodic='no'):
-    """
-    Pre-compute distances between cells in a given topology
-    and store it on a distLib array
-
-    :param str top: Topology ('grid','hex','sphere')
-    :param int Ntop: Size of map,  for grid Size=Ntop*Ntop,
-        for hex Size=Ntop*(Ntop+1[2]) if Ntop is even[odd] and for sphere
-        Size=12*Ntop*Ntop and top must be power of 2
-    :param str periodic: Use periodic boundary conditions ('yes'/'no'), valid for 'hex' and 'grid' only
-    :return: 2D array with distances pre computed between cells and total number of units
-    :rtype: 2D float array, int
-    """
-    if top == 'sphere':
-        try:
-            import healpy as hpx
-        except:
-            print('Error: healpy module not found, use grid or hex topologies')
-            sys.exit(0)
-    if top == 'sphere':
-        nside = Ntop
-        npix = 12 * nside ** 2
-        distLib = numpy.zeros((npix, npix))
-        for i in range(npix):
-            ai = hpx.pix2ang(nside, i)
-            for j in range(i + 1, npix):
-                aj = hpx.pix2ang(nside, j)
-                distLib[i, j] = hpx.rotator.angdist(ai, aj)
-                distLib[j, i] = distLib[i, j]
-        distLib[numpy.where(numpy.isnan(distLib))] = numpy.pi
-    if top == 'grid':
-        nx = Ntop
-        ny = Ntop
-        npix = nx * ny
-        mapxy = numpy.mgrid[0:1:complex(0, nx), 0:1:complex(0, ny)]
-        mapxy = numpy.reshape(mapxy, (2, npix))
-        bX = mapxy[1]
-        bY = mapxy[0]
-        dx = 1. / (nx - 1)
-        dy = 1. / (ny - 1)
-        distLib = numpy.zeros((npix, npix))
-        if periodic == 'no':
-            for i in range(npix):
-                for j in range(i + 1, npix):
-                    distLib[i, j] = numpy.sqrt((bX[i] - bX[j]) ** 2 + (bY[i] - bY[j]) ** 2)
-                    distLib[j, i] = distLib[i, j]
-        if periodic == 'yes':
-            for i in range(npix):
-                for j in range(i + 1, npix):
-                    s0 = numpy.sqrt((bX[i] - bX[j]) ** 2 + (bY[i] - bY[j]) ** 2)
-                    s1 = numpy.sqrt((bX[i] - (bX[j] + 1. + dx)) ** 2 + (bY[i] - bY[j]) ** 2)
-                    s2 = numpy.sqrt((bX[i] - (bX[j] + 1. + dx)) ** 2 + (bY[i] - (bY[j] + 1. + dy)) ** 2)
-                    s3 = numpy.sqrt((bX[i] - (bX[j] + 0.)) ** 2 + (bY[i] - (bY[j] + 1. + dy)) ** 2)
-                    s4 = numpy.sqrt((bX[i] - (bX[j] - 1. - dx)) ** 2 + (bY[i] - (bY[j] + 1. + dy)) ** 2)
-                    s5 = numpy.sqrt((bX[i] - (bX[j] - 1. - dx)) ** 2 + (bY[i] - (bY[j] + 0.)) ** 2)
-                    s6 = numpy.sqrt((bX[i] - (bX[j] - 1. - dx)) ** 2 + (bY[i] - (bY[j] - 1. - dy)) ** 2)
-                    s7 = numpy.sqrt((bX[i] - (bX[j] + 0.)) ** 2 + (bY[i] - (bY[j] - 1. - dy)) ** 2)
-                    s8 = numpy.sqrt((bX[i] - (bX[j] + 1. + dx)) ** 2 + (bY[i] - (bY[j] - 1. - dy)) ** 2)
-                    distLib[i, j] = numpy.min((s0, s1, s2, s3, s4, s5, s6, s7, s8))
-                    distLib[j, i] = distLib[i, j]
-    if top == 'hex':
-        nx = Ntop
-        ny = Ntop
-        xL = numpy.arange(0, nx, 1.)
-        dy = 0.8660254
-        yL = numpy.arange(0, ny, dy)
-        ny = len(yL)
-        nx = len(xL)
-        npix = nx * ny
-        bX = numpy.zeros(nx * ny)
-        bY = numpy.zeros(nx * ny)
-        kk = 0
-        last = ny * dy
-        for jj in range(ny):
-            for ii in range(nx):
-                if jj % 2 == 0: off = 0.
-                if jj % 2 == 1: off = 0.5
-                bX[kk] = xL[ii] + off
-                bY[kk] = yL[jj]
-                kk += 1
-        distLib = numpy.zeros((npix, npix))
-        if periodic == 'no':
-            for i in range(npix):
-                for j in range(i + 1, npix):
-                    distLib[i, j] = numpy.sqrt((bX[i] - bX[j]) ** 2 + (bY[i] - bY[j]) ** 2)
-                    distLib[j, i] = distLib[i, j]
-        if periodic == 'yes':
-            for i in range(npix):
-                for j in range(i + 1, npix):
-                    s0 = numpy.sqrt((bX[i] - bX[j]) ** 2 + (bY[i] - bY[j]) ** 2)
-                    s1 = numpy.sqrt((bX[i] - (bX[j] + nx)) ** 2 + (bY[i] - bY[j]) ** 2)
-                    s2 = numpy.sqrt((bX[i] - (bX[j] + nx)) ** 2 + (bY[i] - (bY[j] + last)) ** 2)
-                    s3 = numpy.sqrt((bX[i] - (bX[j] + 0)) ** 2 + (bY[i] - (bY[j] + last)) ** 2)
-                    s4 = numpy.sqrt((bX[i] - (bX[j] - nx)) ** 2 + (bY[i] - (bY[j] + last)) ** 2)
-                    s5 = numpy.sqrt((bX[i] - (bX[j] - nx)) ** 2 + (bY[i] - (bY[j] + 0)) ** 2)
-                    s6 = numpy.sqrt((bX[i] - (bX[j] - nx)) ** 2 + (bY[i] - (bY[j] - last)) ** 2)
-                    s7 = numpy.sqrt((bX[i] - (bX[j] + 0)) ** 2 + (bY[i] - (bY[j] - last)) ** 2)
-                    s8 = numpy.sqrt((bX[i] - (bX[j] + nx)) ** 2 + (bY[i] - (bY[j] - last)) ** 2)
-                    distLib[i, j] = numpy.min((s0, s1, s2, s3, s4, s5, s6, s7, s8))
-                    distLib[j, i] = distLib[i, j]
-    return distLib, npix
-
-
-def is_power_2(value):
-    """
-    Check if passed value is a power of 2
-    """
-    return value!=0 and ((value & (value- 1)) == 0)
-
-
-def get_alpha(t, alphas, alphae, NT):
-    """
-    Get value of alpha at a given time
-    """
-    return alphas * numpy.power(alphae / alphas, float(t) / float(NT))
-
-
-def get_sigma(t, sigma0, sigmaf, NT):
-    """
-    Get value of sigma at a given time
-    """
-    return sigma0 * numpy.power(sigmaf / sigma0, float(t) / float(NT))
-
-
-def h(bmu, mapD, sigma):
-    """
-    Neighborhood function which quantifies how much cells around the best matching one are modified
-
-    :param int bmu: best matching unit
-    :param float mapD: array of distances computed with :func:`geometry`
-    """
-    return numpy.exp(-(mapD[bmu] ** 2) / sigma ** 2)
-
-
-class SelfMap(object):
-    """
-    Create a som class instance
-
-    :param float X: Attributes array (all columns used)
-    :param float Y: Attribute to be predicted (not really needed, can be zeros)
-    :param str topology: Which 2D topology, 'grid', 'hex' or 'sphere'
-    :param str som_type: Which updating scheme to use 'online' or 'batch'
-    :param int Ntop: Size of map,  for grid Size=Ntop*Ntop,
-        for hex Size=Ntop*(Ntop+1[2]) if Ntop is even[odd] and for sphere
-        Size=12*Ntop*Ntop and top must be power of 2
-    :param  int iterations: Number of iteration the entire sample is processed
-    :param str periodic: Use periodic boundary conditions ('yes'/'no'), valid for 'hex' and 'grid' only
-    :param dict dict_dim: dictionary with attributes names
-    :param float astar: Initial value of alpha
-    :param float aend: End value of alpha
-    :param str importance: Path to the file with importance ranking for attributes, default is none
-    """
-
-    def __init__(self, X, Y, topology='grid', som_type='online', Ntop=28, iterations=30, periodic='no', dict_dim='',
-                 astart=0.8, aend=0.5, importance=None):
-        self.np, self.nDim = numpy.shape(X)
-        self.dict_dim = dict_dim
-        self.X = X
-        self.SF90 = SF90
-        self.Y = Y
-        self.aps = astart
-        self.ape = aend
-        self.top = topology
-        if topology=='sphere' and not is_power_2(Ntop):
-            print('Error, Ntop must be power of 2')
-            sys.exit(0)
-        self.stype = som_type
-        self.Ntop = Ntop
-        self.nIter = iterations
-        self.per = periodic
-        self.distLib, self.npix = geometry(self.top, self.Ntop, periodic=self.per)
-        if importance == None: importance = numpy.ones(self.nDim)
-        self.importance = importance / numpy.sum(importance)
-
-    def som_best_cell(self, inputs, return_vals=1):
-        """
-        Return the closest cell to the input object
-        It can return more than one value if needed
-        """
-        activations = numpy.sum(numpy.transpose([self.importance]) * (
-            numpy.transpose(numpy.tile(inputs, (self.npix, 1))) - self.weights) ** 2, axis=0)
-        if return_vals == 1:
-            best = numpy.argmin(activations)
-            return best, activations
-        else:
-            best_few = numpy.argsort(activations)
-            return best_few[0:return_vals], activations
-
-    def create_mapF(self, evol='no', inputs_weights=''):
-        """
-        This functions actually create the maps, it uses
-        random values to initialize the weights
-        It uses a Fortran subroutine compiled with f2py
-        """
-        if not self.SF90:
-            print()
-            print('Fortran module somF not found, use create_map instead or try' \
-                  ' f2py -c -m somF som.f90')
-            sys.exit(0)
-        if inputs_weights == '':
-            self.weights = (numpy.random.rand(self.nDim, self.npix)) + self.X[0][0]
-        else:
-            self.weights = inputs_weights
-        if self.stype == 'online':
-            self.weightsT = somF.map(self.X, self.nDim, self.nIter, self.distLib, self.np, self.weights,
-                                     self.importance, self.npix, self.aps, self.ape)
-        if self.stype == 'batch':
-            self.weightsT = somF.map_b(self.X, self.nDim, self.nIter, self.distLib, self.np, self.weights,
-                                       self.importance, self.npix)
-        self.weights = copy.deepcopy(self.weightsT)
-
-    def create_map(self, evol='no', inputs_weights='', random_order=True):
-        """
-        This is same as above but uses python routines instead
-        """
-        if inputs_weights == '':
-            self.weights = (numpy.random.rand(self.nDim, self.npix)) + self.X[0][0]
-        else:
-            self.weights = inputs_weights
-        self.NT = self.nIter * self.np
-        if self.stype == 'online':
-            tt = 0
-            sigma0 = self.distLib.max()
-            sigma_single = numpy.min(self.distLib[numpy.where(self.distLib > 0.)])
-            for it in range(self.nIter):
-                #get alpha, sigma
-                alpha = get_alpha(tt, self.aps, self.ape, self.NT)
-                sigma = get_sigma(tt, sigma0, sigma_single, self.NT)
-                if random_order:
-                    index_random = random.sample(range(self.np), self.np)
-                else:
-                    index_random = numpy.arange(self.np)
-                for i in range(self.np):
-                    tt += 1
-                    inputs = self.X[index_random[i]]
-                    best, activation = self.som_best_cell(inputs)
-                    self.weights += alpha * h(best, self.distLib, sigma) * numpy.transpose(
-                        (inputs - numpy.transpose(self.weights)))
-                if evol == 'yes':
-                    self.evaluate_map()
-                    self.save_map(itn=it)
-        if self.stype == 'batch':
-            tt = 0
-            sigma0 = self.distLib.max()
-            sigma_single = numpy.min(self.distLib[numpy.where(self.distLib > 0.)])
-            for it in range(self.nIter):
-                #get alpha, sigma
-                sigma = get_sigma(tt, sigma0, sigma_single, self.NT)
-                accum_w = numpy.zeros((self.nDim, self.npix))
-                accum_n = numpy.zeros(self.npix)
-                for i in range(self.np):
-                    tt += 1
-                    inputs = self.X[i]
-                    best, activation = self.som_best_cell(inputs)
-                    for kk in range(self.nDim):
-                        accum_w[kk, :] += h(best, self.distLib, sigma) * inputs[kk]
-                    accum_n += h(best, self.distLib, sigma)
-                for kk in range(self.nDim):
-                    self.weights[kk] = accum_w[kk] / accum_n
-
-                if evol == 'yes':
-                    self.evaluate_map()
-                    self.save_map(itn=it)
-
-    def evaluate_map(self, inputX='', inputY=''):
-        """
-        This functions evaluates the map created using the input Y or a new Y (array of labeled attributes)
-        It uses the X array passed or new data X as well, the map doesn't change
-
-        :param float inputX: Use this if another set of values for X is wanted using
-            the weigths already computed
-        :param float inputY: One  dimensional array of the values to be assigned to each cell in the map
-            based on the in-memory X passed
-        """
-        self.yvals = {}
-        self.ivals = {}
-        if inputX == '':
-            inX = self.X
-        else:
-            inX = inputX
-        if inputY == '':
-            inY = self.Y
-        else:
-            inY = inputY
-        for i in range(len(inX)):
-            inputs = inX[i]
-            best, activation = self.som_best_cell(inputs)
-            if best not in self.yvals: self.yvals[best] = []
-            self.yvals[best].append(inY[i])
-            if best not in self.ivals: self.ivals[best] = []
-            self.ivals[best].append(i)
-
-    def get_vals(self, line):
-        """
-        Get the predictions  given a line search, where the line
-        is a vector of attributes per individual object fot the
-        10 closest cells.
-
-        :param float line: input data to look in the tree
-        :return: array with the cell content
-        """
-        best, act = self.som_best_cell(line, return_vals=10)
-        for ib in range(10):
-            if best[ib] in self.yvals: return self.yvals[best[ib]]
-        return numpy.array([-1.])
-
-    def get_best(self, line):
-        """
-        Get the predictions  given a line search, where the line
-        is a vector of attributes per individual object for THE best cell
-
-        :param float line: input data to look in the tree
-        :return: array with the cell content
-        """
-        best, act = self.som_best_cell(line, return_vals=10)
-        return best[0]
-
-    def save_map(self, itn=-1, fileout='SOM', path=''):
-        """
-        Saves the map
-
-        :param int itn: Number of map to be included on path, use -1 to ignore this number
-        :param str fileout: Name of output file
-        :param str path: path for the output file
-        """
-        if path == '':
-            path = os.getcwd() + '/'
-        if not os.path.exists(path): os.system('mkdir -p ' + path)
-        if itn >= 0:
-            ff = '_%04d' % itn
-            fileout += ff
-        numpy.save(path + fileout, self)
-
-    def save_map_dict(self, path='', fileout='SOM', itn=-1):
-        """
-        Saves the map in dictionary format
-
-        :param int itn: Number of map to be included on path, use -1 to ignore this number
-        :param str fileout: Name of output file
-        :param str path: path for the output file
-        """
-        SOM = {}
-        SOM['W'] = self.weights
-        SOM['yvals'] = self.yvals
-        SOM['ivals'] = self.ivals
-        SOM['topology'] = self.top
-        SOM['Ntop'] = self.Ntop
-        SOM['npix'] = self.npix
-        if path == '':
-            path = os.getcwd() + '/'
-        if not os.path.exists(path): os.system('mkdir -p ' + path)
-        if itn > 0:
-            ff = '_%04d' % itn
-            fileout += ff
-        numpy.save(path + fileout, SOM)
-
-    def plot_map(self, min_m=-100, max_m=-100, colbar='yes'):
-        """
-        Plots the map after evaluating, the cells are colored with the mean value inside each
-        one of them
-
-        :param float min_m: Lower limit for coloring the cells, -100 uses min value
-        :param float max_m: Upper limit for coloring the cells, -100 uses max value
-        :param str colbar: Include a colorbar ('yes','no')
-        """
-
-        import matplotlib.pyplot as plt
-        import matplotlib as mpl
-        import matplotlib.cm as cm
-        from matplotlib import collections, transforms
-        from matplotlib.colors import colorConverter
-        
-        if self.top == 'sphere': import healpy as H
-
-        if self.top == 'grid':
-            M = numpy.zeros(self.npix) - 20.
-            for i in range(self.npix):
-                if i in self.yvals:
-                    M[i] = numpy.mean(self.yvals[i])
-            M2 = numpy.reshape(M, (self.Ntop, self.Ntop))
-            plt.figure(figsize=(8, 8), dpi=100)
-            if min_m == -100: min_m = M2[numpy.where(M2 > -10)].min()
-            if max_m == -100: max_m = M2.max()
-            SM2 = plt.imshow(M2, origin='center', interpolation='nearest', cmap=cm.jet, vmin=min_m, vmax=max_m)
-            SM2.cmap.set_under("grey")
-            if colbar == 'yes': plt.colorbar()
-            plt.axis('off')
-        if self.top == 'hex':
-            nx = self.Ntop
-            ny = self.Ntop
-            xL = numpy.arange(0, nx, 1.)
-            dy = 0.8660254
-            yL = numpy.arange(0, ny, dy)
-            ny = len(yL)
-            nx = len(xL)
-            npix = nx * ny
-            bX = numpy.zeros(nx * ny)
-            bY = numpy.zeros(nx * ny)
-            kk = 0
-            for jj in range(ny):
-                for ii in range(nx):
-                    if jj % 2 == 0: off = 0.
-                    if jj % 2 == 1: off = 0.5
-                    bX[kk] = xL[ii] + off
-                    bY[kk] = yL[jj]
-                    kk += 1
-            xyo = list(zip(bX, bY))
-            sizes_2 = numpy.zeros(nx * ny) + ((8. * 0.78 / (self.Ntop + 0.5)) / 2. * 72.) ** 2 * 4. * numpy.pi / 3.
-            M = numpy.zeros(npix) - 20.
-            fcolors = [plt.cm.Spectral_r(x) for x in numpy.random.rand(nx * ny)]
-            for i in range(npix):
-                if i in self.yvals:
-                    M[i] = numpy.mean(self.yvals[i])
-            if max_m == -100: max_m = M.max()
-            if min_m == -100: min_m = M[numpy.where(M > -10)].min()
-            M = M - min_m
-            M = M / (max_m - min_m)
-            for i in range(npix):
-                if M[i] <= 0:
-                    fcolors[i] = plt.cm.Greys(.5)
-                else:
-                    fcolors[i] = plt.cm.jet(M[i])
-            figy = ((8. * 0.78 / (self.Ntop + 0.5) / 2.) * (3. * ny + 1) / numpy.sqrt(3)) / 0.78
-            fig3 = plt.figure(figsize=(8, figy), dpi=100)
-            #fig3.subplots_adjust(left=0,right=1.,top=1.,bottom=0.)
-            a = fig3.add_subplot(1, 1, 1)
-            col = collections.RegularPolyCollection(6, sizes=sizes_2, offsets=xyo, transOffset=a.transData)
-            col.set_color(fcolors)
-            a.add_collection(col, autolim=True)
-            a.set_xlim(-0.5, nx)
-            a.set_ylim(-1, nx + 0.5)
-            plt.axis('off')
-            if colbar == 'yes':
-                figbar = plt.figure(figsize=(8, 1.), dpi=100)
-                ax1 = figbar.add_axes([0.05, 0.8, 0.9, 0.15])
-                cmap = cm.jet
-                norm = mpl.colors.Normalize(vmin=min_m, vmax=max_m)
-                cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm, orientation='horizontal')
-                cb1.set_label('')
-        if self.top == 'sphere':
-            M = numpy.zeros(self.npix) + H.UNSEEN
-            for i in range(self.npix):
-                if i in self.yvals:
-                    M[i] = numpy.mean(self.yvals[i])
-            plt.figure(10, figsize=(8, 8), dpi=100)
-            if min_m == -100: min_m = M[numpy.where(M > -10)].min()
-            if max_m == -100: max_m = M.max()
-            if colbar == 'yes': H.mollview(M, fig=10, title="", min=min_m, max=max_m, cbar=True)
-            if colbar == 'no': H.mollview(M, fig=10, title="", min=min_m, max=max_m, cbar=False)
-        plt.show()
diff --git a/somsphere/__init__.py b/somsphere/__init__.py
new file mode 100644
index 0000000..e17b983
--- /dev/null
+++ b/somsphere/__init__.py
@@ -0,0 +1,280 @@
+"""
+.. module:: SOMZ
+.. moduleauthor:: Matias Carrasco Kind
+
+"""
+__author__ = 'Matias Carrasco Kind'
+
+import os
+import random
+
+import core
+import healpy as hp
+import matplotlib as mpl
+import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+import numpy
+from matplotlib import collections
+from matplotlib.colors import colorConverter
+
+from somsphere.models import Topology, DY, SomType
+from somsphere.utils import compute_distance, get_sigma, get_alpha, is_power_2, timeit, count_modified_cells, \
+    get_best_cell
+
+
+class SOMap(object):
+    """
+    Create a som class instance
+
+    :param float X: Attributes array (all columns used)
+    :param float Y: Attribute to be predicted (not really needed, can be zeros)
+    :param str topology: Which 2D topology, 'grid', 'hex' or 'sphere'
+    :param str som_type: Which updating scheme to use 'online' or 'batch'
+    :param int n_top: Size of map,  for grid Size=n_top*n_top,
+        for hex Size=n_top*(n_top+1[2]) if n_top is even[odd] and for sphere
+        Size=12*n_top*n_top and top must be power of 2
+    :param  int n_iter: Number of iteration the entire sample is processed
+    :param bool periodic: Use periodic boundary conditions, valid for 'hex' and 'grid' only
+    :param dict dict_dim: dictionary with attributes names
+    :param float alpha_start: Initial value of alpha
+    :param float alpha_end: End value of alpha
+    :param str importance: Path to the file with importance ranking for attributes, default is none
+    """
+
+    def __init__(self, X, Y, topology='grid', som_type='online', n_top=28, n_iter=30, alpha_start=0.8,
+                 alpha_end=0.5, periodic=False, dict_dim=None, importance=None):
+        if topology == 'sphere' and not is_power_2(n_top):
+            raise Exception("n_top must be power of 2")
+
+        self.X, self.Y = X, Y
+        self.topology: Topology = Topology(topology)
+        self.som_type: SomType = SomType(som_type)
+        self.n_top = n_top
+        self.n_iter = n_iter
+        self.alpha_start = alpha_start
+        self.alpha_end = alpha_end
+        self.periodic = periodic
+        self.dict_dim = dict_dim
+        self.dist_lib, self.n_pix = compute_distance(self.topology, self.n_top, periodic=self.periodic)
+        self.n_row, self.n_col = numpy.shape(X)
+        self.weights = (numpy.random.rand(self.n_col, self.n_pix)) + self.X[0][0]
+        importance = numpy.ones(self.n_col) if importance is None else importance
+        self.importance = importance / numpy.sum(importance)
+
+    def __update_weights(self, input_weights):
+        self.weights = input_weights if input_weights is not None else self.weights
+
+    @timeit
+    def __create_map_online(self, random_order=True, eval_map=False):
+        t, total_t = 0, self.n_iter * self.n_row
+        sigma_0 = self.dist_lib.max()
+        sigma_f = numpy.min(self.dist_lib[numpy.where(self.dist_lib > 0.)])
+
+        for it in range(self.n_iter):
+            alpha = get_alpha(self.alpha_end, self.alpha_start, t, total_t)
+            sigma = get_sigma(sigma_f, sigma_0, t, total_t)
+            random_indices = random.sample(range(self.n_row), self.n_row) if random_order else numpy.arange(
+                self.n_row)
+            self.weights = core.create_map_online(self.X, self.dist_lib, self.weights, random_indices, self.importance,
+                                                  alpha, sigma, self.n_row, self.n_col, self.n_pix, 1)
+            t += self.n_row
+
+            if eval_map:
+                self.evaluate_map()
+                self.save_map(itn=it)
+
+    @timeit
+    def __create_map_batch(self, eval_map=False):
+        t, total_t = 0, self.n_iter * self.n_row
+        sigma_0 = self.dist_lib.max()
+        sigma_f = numpy.min(self.dist_lib[numpy.where(self.dist_lib > 0.)])
+
+        for it in range(self.n_iter):
+            sigma = get_sigma(sigma_f, sigma_0, t, total_t)
+            accum_w = numpy.zeros((self.n_col, self.n_pix))
+            accum_n = numpy.zeros(self.n_pix)
+            self.weights = core.create_map_batch(self.X, self.dist_lib, self.weights, self.importance, accum_w,
+                                                 accum_n, sigma, self.n_row, self.n_col, self.n_pix, 1)
+            t += self.n_row
+
+            if eval_map:
+                self.evaluate_map()
+                self.save_map(itn=it)
+
+    def create_map(self, input_weights=None, random_order=True, eval_map=False):
+        """
+        This functions actually create the maps, it uses
+        random values to initialize the weights
+        """
+        self.__update_weights(input_weights)
+
+        if self.som_type == SomType.ONLINE:
+            self.__create_map_online(random_order=random_order, eval_map=eval_map)
+        elif self.som_type == SomType.BATCH:
+            self.__create_map_batch(eval_map=eval_map)
+        else:
+            raise Exception(f"Unknown type: {self.som_type}")
+
+    def evaluate_map(self, input_x=None, input_y=None):
+        """
+        This functions evaluates the map created using the input Y or a new Y (array of labeled attributes)
+        It uses the X array passed or new data X as well, the map doesn't change
+
+        :param float input_x: Use this if another set of values for X is wanted using
+            the weights already computed
+        :param float input_y: One  dimensional array of the values to be assigned to each cell in the map
+            based on the in-memory X passed
+        """
+        self.y_vals, self.i_vals = {}, {}
+        in_x = self.X if input_x is None else input_x
+        in_y = self.Y if input_y is None else input_y
+        for i in range(len(in_x)):
+            inputs = in_x[i]
+            best, _ = core.get_best_cell(inputs, self.importance, self.weights, len(inputs), self.n_pix, 1)
+            best = best[0]
+            if best not in self.y_vals:
+                self.y_vals[best] = []
+            self.y_vals[best].append(in_y[i])
+            if best not in self.i_vals:
+                self.i_vals[best] = []
+            self.i_vals[best].append(i)
+
+    def predict(self, line, best=True):
+        """
+        Get the predictions  given a line search, where the line
+        is a vector of attributes per individual object fot the
+        10 closest cells if best set to False; otherwise return the
+        BEST cell.
+
+        :param float line: input data to look in the tree
+        :param bool best: Set to True to get only the best cell; otherwise the 10 closest cells will be returned
+        :return: array with the cell content
+        """
+        bests, _ = core.get_best_cell(line, self.importance, self.weights, len(line), self.n_pix, 10)
+        if best:
+            return bests[0]
+        for ib in range(10):
+            if bests[ib] in self.y_vals:
+                return self.y_vals[bests[ib]]
+        return numpy.array([-1.])
+
+    def save_map(self, filename='SOM', path=None, itn=-1):
+        """
+        Saves the map and its dictionary format
+
+        :param int itn: Number of map to be included on path, use -1 to ignore this number
+        :param str filename: Name of output file
+        :param str path: path for the output file
+        """
+        som = {'weights': self.weights, 'y_vals': self.y_vals, 'i_vals': self.i_vals, 'topology': self.topology,
+               'n_top': self.n_top, 'n_pix': self.n_pix}
+
+        path = os.getcwd() + '/' if path is None else path
+        if not os.path.exists(path):
+            os.system('mkdir -p ' + path)
+        if itn >= 0:
+            ff = '_%04d' % itn
+            filename += ff
+
+        numpy.save(path + filename, self)
+        numpy.save(path + filename + ".txt", som)
+
+    def plot_map(self, min_m=-100, max_m=-100, cbar=True):
+        """
+        Plots the map after evaluating, the cells are colored with the mean value inside each
+        one of them
+
+        :param float min_m: Lower limit for coloring the cells, -100 uses min value
+        :param float max_m: Upper limit for coloring the cells, -100 uses max value
+        :param bool cbar: Include a colorbar True/False
+        """
+
+        if self.topology == Topology.SPHERE:
+            self.__plot_map_sphere(min_m, max_m, cbar)
+
+        elif self.topology == Topology.GRID:
+            self.__plot_map_grid(min_m, max_m, cbar)
+
+        elif self.topology == Topology.HEX:
+            self.__plot_map_hex(min_m, max_m, cbar)
+        else:
+            raise Exception(f"Unknown topology: {self.topology}")
+
+    def __plot_map_sphere(self, min_m, max_m, cbar):
+        M = numpy.zeros(self.n_pix) + hp.UNSEEN
+        for i in range(self.n_pix):
+            if i in self.y_vals:
+                M[i] = numpy.mean(self.y_vals[i])
+        plt.figure(10, figsize=(8, 8), dpi=100)
+        min_m = M[numpy.where(M > -10)].min() if min_m == -100 else min_m
+        max_m = M.max() if max_m == -100 else max_m
+        hp.mollview(M, fig=10, title="", min=min_m, max=max_m, cbar=cbar)
+        plt.show()
+
+    def __plot_map_grid(self, min_m, max_m, cbar):
+        M = numpy.zeros(self.n_pix) - 20.
+        for i in range(self.n_pix):
+            if i in self.y_vals:
+                M[i] = numpy.mean(self.y_vals[i])
+        M_new = numpy.reshape(M, (self.n_top, self.n_top))
+        plt.figure(figsize=(8, 8), dpi=100)
+        min_m = M_new[numpy.where(M_new > -10)].min() if min_m == -100 else min_m
+        max_m = M_new.max() if max_m == -100 else max_m
+
+        M_plot = plt.imshow(M_new, origin='center', interpolation='nearest', cmap=cm.jet, vmin=min_m, vmax=max_m)
+        M_plot.cmap.set_under("grey")
+        if cbar:
+            plt.colorbar()
+        plt.axis('off')
+
+        plt.show()
+
+    def __plot_map_hex(self, min_m, max_m, cbar):
+        ptr = 0
+        x_l, y_l = numpy.arange(0, self.n_top, 1.), numpy.arange(0, self.n_top, DY)
+        nx, ny = len(x_l), len(y_l)
+        n_pix = nx * ny
+        b_x, b_y = numpy.zeros(n_pix), numpy.zeros(n_pix)
+        for y_idx in range(ny):
+            for x_idx in range(nx):
+                b_x[ptr] = x_l[x_idx] + 0. if y_idx % 2 == 0 else 0.5
+                b_y[ptr] = y_l[y_idx]
+                ptr += 1
+
+        fcolors = [plt.cm.Spectral_r(x) for x in numpy.random.rand(nx * ny)]
+        M = numpy.zeros(n_pix) - 20.
+        for i in range(n_pix):
+            if i in self.y_vals:
+                M[i] = numpy.mean(self.y_vals[i])
+
+        min_m = M[numpy.where(M > -10)].min() if min_m == -100 else min_m
+        max_m = M.max() if max_m == -100 else max_m
+
+        M = M - min_m
+        M = M / (max_m - min_m)
+        for i in range(n_pix):
+            if M[i] <= 0:
+                fcolors[i] = plt.cm.Greys(.5)
+            else:
+                fcolors[i] = plt.cm.jet(M[i])
+        fig_y = ((8. * 0.78 / (self.n_top + 0.5) / 2.) * (3. * ny + 1) / numpy.sqrt(3)) / 0.78
+        fig = plt.figure(figsize=(8, fig_y), dpi=100)
+        ax = fig.add_subplot(1, 1, 1)
+        col = collections.RegularPolyCollection(6,
+                                                sizes=numpy.zeros(nx * ny) + ((8. * 0.78 / (
+                                                        self.n_top + 0.5)) / 2. * 72.) ** 2 * 4. * numpy.pi / 3.,
+                                                offsets=list(zip(b_x, b_y)),
+                                                transOffset=ax.transData)
+        col.set_color(fcolors)
+        ax.add_collection(col, autolim=True)
+        ax.set_xlim(-0.5, nx)
+        ax.set_ylim(-1, nx + 0.5)
+        plt.axis('off')
+        if cbar:
+            figbar = plt.figure(figsize=(8, 1.), dpi=100)
+            ax1 = figbar.add_axes([0.05, 0.8, 0.9, 0.15])
+            cmap = cm.jet
+            norm = mpl.colors.Normalize(vmin=min_m, vmax=max_m)
+            cb = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm, orientation='horizontal')
+            cb.set_label('')
+        plt.show()
diff --git a/somsphere/models/__init__.py b/somsphere/models/__init__.py
new file mode 100644
index 0000000..671805f
--- /dev/null
+++ b/somsphere/models/__init__.py
@@ -0,0 +1,14 @@
+from enum import Enum
+
+DY = 0.8660254
+
+
+class Topology(Enum):
+    GRID = "grid"
+    SPHERE = "sphere"
+    HEX = "hex"
+
+
+class SomType(Enum):
+    ONLINE = "online"
+    BATCH = "batch"
diff --git a/somsphere/utils/__init__.py b/somsphere/utils/__init__.py
new file mode 100644
index 0000000..0bf163e
--- /dev/null
+++ b/somsphere/utils/__init__.py
@@ -0,0 +1,209 @@
+import time
+from functools import wraps
+from timeit import timeit
+
+import healpy as hp
+import numpy
+
+from somsphere.models import DY, Topology
+
+
+def timeit(my_func):
+    @wraps(my_func)
+    def timed(*args, **kw):
+        tstart = time.time()
+        output = my_func(*args, **kw)
+        tend = time.time()
+
+        print('"{}" took {:.3f} ms to execute\n'.format(my_func.__name__, (tend - tstart) * 1000))
+        return output
+
+    return timed
+
+
+def get_best_cell(inputs, importance, n_pix, weights, return_vals=1):
+    """
+    Return the closest cell to the input object
+    It can return more than one value if needed
+    """
+    activations = numpy.sum(numpy.transpose([importance]) * (
+            numpy.transpose(numpy.tile(inputs, (n_pix, 1))) - weights) ** 2, axis=0)
+
+    return numpy.argmin(activations) if return_vals == 1 else numpy.argsort(activations)[0:return_vals], activations
+
+
+def count_modified_cells(bmu, map_d, sigma):
+    """
+    Neighborhood function which quantifies how much cells around the best matching one are modified
+
+    :param int bmu: best matching unit
+    :param ndarray map_d: array of distances computed with :func:`geometry`
+    """
+    return numpy.exp(-(map_d[bmu] ** 2) / sigma ** 2)
+
+
+def get_index(ix, iy, nx, ny):
+    return iy * nx + ix
+
+
+def get_pair(ii, nx, ny):
+    iy = int(numpy.floor(ii / nx))
+    ix = ii % nx
+    return ix, iy
+
+
+def get_neighbors(ix, iy, nx, ny, index=False, hex=False):
+    """
+    Get neighbors for rectangular/hexagonal grid given its
+    coordinates and size of grid
+
+    :param int ix: Coordinate in the x-axis
+    :param int iy: Coordinate in the y-axis
+    :param int nx: Number fo cells along the x-axis
+    :param int ny: Number fo cells along the y-axis
+    :param bool index: Return indexes in the map format
+    :param bool hex: Set the grid to hexagonal
+    :return: Array of indexes for direct neighbors
+    """
+    ns = []
+
+    if ix - 1 >= 0:
+        ns.append((ix - 1, iy))
+    if iy - 1 >= 0:
+        ns.append((ix, iy - 1))
+    if ix + 1 < nx:
+        ns.append((ix + 1, iy))
+    if iy + 1 < ny:
+        ns.append((ix, iy + 1))
+
+    even = iy % 2 == 0 or (not hex)
+    if even and ix - 1 >= 0 and iy - 1 >= 0:
+        ns.append((ix - 1, iy - 1))
+    if even and ix - 1 >= 0 and iy + 1 < ny:
+        ns.append((ix - 1, iy + 1))
+    if not even and ix + 1 < nx and iy - 1 >= 0:
+        ns.append((ix + 1, iy - 1))
+    if not even and ix + 1 < nx and iy + 1 < ny:
+        ns.append((ix + 1, iy + 1))
+
+    ns = numpy.array(ns)
+    if not index:
+        return ns
+    ins = []
+    for i in range(len(ns)):
+        ins.append(get_index(ns[i, 0], ns[i, 1], nx, ny))
+    return numpy.array(ins)
+
+
+def calc_distance(a, b, c, d):
+    return numpy.sqrt((a - b) ** 2 + (c - d) ** 2)
+
+
+def is_power_2(value):
+    """
+    Check if passed value is a power of 2
+    """
+    return value != 0 and ((value & (value - 1)) == 0)
+
+
+def get_alpha(alpha_end, alpha_start, curr_t, total_t):
+    """
+    Get value of alpha at a given time
+    """
+    return alpha_start * numpy.power(alpha_end / alpha_start, float(curr_t) / float(total_t))
+
+
+def get_sigma(sigma_f, sigma_0, curr_t, total_t):
+    """
+    Get value of sigma at a given time
+    """
+    return sigma_0 * numpy.power(sigma_f / sigma_0, float(curr_t) / float(total_t))
+
+
+def get_map_size(n_top, topology: Topology):
+    if topology == Topology.SPHERE:
+        return 12 * n_top ** 2
+    elif topology == Topology.GRID:
+        return n_top * n_top
+    elif topology == Topology.HEX:
+        x_l, y_l = numpy.arange(0, n_top, 1.), numpy.arange(0, n_top, DY)
+        return len(x_l) * len(y_l)
+
+
+def compute_distance(topology: Topology, n_top, periodic=False):
+    """
+    Pre-compute distances between cells in a given topology
+    and store it on a dist_lib array
+
+    :param Enum topology: Topology ('grid','hex','sphere')
+    :param int n_top: Size of map,  for grid Size=n_top*n_top,
+        for hex Size=n_top*(n_top+1[2]) if Ntop is even[odd] and for sphere
+        Size=12*n_top*n_top and top must be power of 2
+    :param bool periodic: Use periodic boundary conditions ('yes'/'no'), valid for 'hex' and 'grid' only
+    :return: 2D array with distances pre computed between cells and total number of units
+    :rtype: 2D float array, int
+    """
+    n_pix = get_map_size(n_top, topology=topology)
+    dist_lib = numpy.zeros((n_pix, n_pix))
+
+    if topology == Topology.SPHERE:
+        for i in range(n_pix):
+            ai = hp.pix2ang(n_top, i)
+            for j in range(i + 1, n_pix):
+                aj = hp.pix2ang(n_top, j)
+                dist_lib[i, j] = hp.rotator.angdist(ai, aj)
+                dist_lib[j, i] = dist_lib[i, j]
+        dist_lib[numpy.where(numpy.isnan(dist_lib))] = numpy.pi
+    elif topology == Topology.GRID:
+        map_x_y = numpy.mgrid[0:1:complex(0, n_top), 0:1:complex(0, n_top)]
+        map_x_y = numpy.reshape(map_x_y, (2, n_pix))
+        b_x, b_y = map_x_y[1], map_x_y[0]
+        dx, dy = 1. / (n_top - 1), 1. / (n_top - 1)
+        for i in range(n_pix):
+            for j in range(i + 1, n_pix):
+                if not periodic:
+                    dist_lib[i, j] = calc_distance(b_x[i], b_x[j], b_y[i], b_y[j])
+                    dist_lib[j, i] = dist_lib[i, j]
+                else:
+                    s0 = calc_distance(b_x[i], b_x[j], b_y[i], b_y[j])
+                    s1 = calc_distance(b_x[i], b_x[j] + 1. + dx, b_y[i], b_y[j])
+                    s2 = calc_distance(b_x[i], b_x[j] - 1. - dx, b_y[i], b_y[j] + 0.)
+                    s3 = calc_distance(b_x[i], b_x[j] + 0., b_y[i], b_y[j] + 1. + dy)
+                    s4 = calc_distance(b_x[i], b_x[j] + 0., b_y[i], b_y[j] - 1. - dy)
+                    s5 = calc_distance(b_x[i], b_x[j] + 1. + dx, b_y[i], b_y[j] + 1. + dy)
+                    s6 = calc_distance(b_x[i], b_x[j] - 1. - dx, b_y[i], b_y[j] + 1. + dy)
+                    s7 = calc_distance(b_x[i], b_x[j] - 1. - dx, b_y[i], b_y[j] - 1. - dy)
+                    s8 = calc_distance(b_x[i], b_x[j] + 1. + dx, b_y[i], b_y[j] - 1. - dy)
+                    dist_lib[i, j] = numpy.min((s0, s1, s2, s3, s4, s5, s6, s7, s8))
+                    dist_lib[j, i] = dist_lib[i, j]
+    elif topology == Topology.HEX:
+        ptr = 0
+        x_l, y_l = numpy.arange(0, n_top, 1.), numpy.arange(0, n_top, DY)
+        nx, ny = len(x_l), len(y_l)
+        b_x, b_y = numpy.zeros(n_pix), numpy.zeros(n_pix)
+        for y_idx in range(ny):
+            for x_idx in range(nx):
+                b_x[ptr] = x_l[x_idx] + 0. if y_idx % 2 == 0 else 0.5
+                b_y[ptr] = y_l[y_idx]
+                ptr += 1
+
+        last = ny * DY
+        for i in range(n_pix):
+            for j in range(i + 1, n_pix):
+                if not periodic:
+                    dist_lib[i, j] = calc_distance(b_x[i], b_x[j], b_y[i], b_y[j])
+                    dist_lib[j, i] = dist_lib[i, j]
+                else:
+                    s0 = calc_distance(b_x[i], b_x[j], b_y[i], b_y[j])
+                    s1 = calc_distance(b_x[i], b_x[j] + nx, b_y[i], b_y[j])
+                    s2 = calc_distance(b_x[i], b_x[j] - nx, b_y[i], b_y[j] + 0)
+                    s3 = calc_distance(b_x[i], b_x[j] + 0, b_y[i], b_y[j] + last)
+                    s4 = calc_distance(b_x[i], b_x[j] + 0, b_y[i], b_y[j] - last)
+                    s5 = calc_distance(b_x[i], b_x[j] + nx, b_y[i], b_y[j] + last)
+                    s6 = calc_distance(b_x[i], b_x[j] - nx, b_y[i], b_y[j] + last)
+                    s7 = calc_distance(b_x[i], b_x[j] - nx, b_y[i], b_y[j] - last)
+                    s8 = calc_distance(b_x[i], b_x[j] + nx, b_y[i], b_y[j] - last)
+                    dist_lib[i, j] = numpy.min((s0, s1, s2, s3, s4, s5, s6, s7, s8))
+                    dist_lib[j, i] = dist_lib[i, j]
+
+    return dist_lib, n_pix
diff --git a/test/test_models.py b/test/test_models.py
new file mode 100644
index 0000000..ba293b2
--- /dev/null
+++ b/test/test_models.py
@@ -0,0 +1,11 @@
+import unittest
+
+from somsphere import Topology
+
+
+class TestModels(unittest.TestCase):
+    def test_get_enum(self):
+        self.assertEqual(Topology.GRID, Topology("grid"))
+        self.assertEqual(Topology.SPHERE, Topology("sphere"))
+        self.assertEqual(Topology.HEX, Topology("hex"))
+
diff --git a/test/test_somap.py b/test/test_somap.py
new file mode 100644
index 0000000..522f997
--- /dev/null
+++ b/test/test_somap.py
@@ -0,0 +1,60 @@
+import unittest
+
+import core
+import numpy as np
+
+import somsphere
+from somsphere import SOMap, get_best_cell, count_modified_cells
+
+
+class TestSOMap(unittest.TestCase):
+    def setUp(self) -> None:
+        data = "../resources/SDSS_MGS.train"
+        self.dx = np.loadtxt(data, usecols=(1, 2, 3, 4, 5, 6, 7, 8, 9), unpack=True).T
+        self.dy = np.loadtxt(data, usecols=(0,), unpack=True).T
+
+    def test_create_map_online(self):
+        map = somsphere.SOMap(self.dx, self.dy, topology='grid', n_top=15, n_iter=100, periodic=False)
+        map.create_map()
+
+        map = somsphere.SOMap(self.dx, self.dy, topology='sphere', n_top=8, n_iter=100, periodic=False)
+        map.create_map()
+
+        map = somsphere.SOMap(self.dx, self.dy, topology='hex', n_top=15, n_iter=100, periodic=False)
+        map.create_map()
+
+    def test_create_map_batch(self):
+        map = somsphere.SOMap(self.dx, self.dy, topology='grid', n_top=15, n_iter=100, periodic=False, som_type="batch")
+        map.create_map()
+
+        map = somsphere.SOMap(self.dx, self.dy, topology='sphere', n_top=8, n_iter=100, periodic=False,
+                              som_type="batch")
+        map.create_map()
+
+        map = somsphere.SOMap(self.dx, self.dy, topology='hex', n_top=15, n_iter=100, periodic=False, som_type="batch")
+        map.create_map()
+
+    def test_get_best_cell(self):
+        n_pix = 225
+        n_col = 9
+        weights = np.random.rand(n_col, n_pix)
+        random_indices = np.random.randint(5000, size=5000)
+        importance = np.random.rand(n_col)
+        inputs = self.dx[random_indices[0]]
+        old_best, old_activations = get_best_cell(inputs=inputs, importance=importance, weights=weights,
+                                                  n_pix=n_pix, return_vals=1)
+        new_best, new_activations = core.get_best_cell(inputs, importance, weights, n_col, n_pix, 1)
+
+        self.assertEqual(0, sum(old_activations - new_activations))
+        self.assertEqual(old_best, new_best[0])
+
+    def test_count_modified_cells(self):
+        bmu = [1]
+        n_pix = 225
+        sigma = 0.7
+        dist_lib = np.random.rand(n_pix, n_pix)
+
+        old_cmc = count_modified_cells(1, dist_lib, sigma)
+        new_cmc = core.count_modified_cells(bmu, dist_lib, sigma)
+
+        np.allclose(old_cmc, new_cmc)