fixing transition bugs

cycle_analysis/cycle_tools_coalescence.py

@@ -8,16 +8,17 @@
 import networkx as nx
 import numpy as np
 import sys
-import simple_cycle_tools
+import cycle_tools_simple
 
 class coalescence(cycle_tools_simple.simple,object):
 
     def __init__(self):
-        super(cycle_coalescence,self).__init__()
+        super(coalescence,self).__init__()
 
     def calc_cycle_asymmetry(self,input_graph):
 
-        minimum_basis=self.construct_minimum_basis(input_graph)
+        minimum_basis=self.construct_networkx_basis(input_graph)
+        
         cycle_tree=self.calc_cycle_coalescence(input_graph,minimum_basis)
         tree_asymmetry=self.calc_tree_asymmetry(cycle_tree)
 

cycle_analysis/cycle_tools_simple.py

@@ -186,12 +186,12 @@ class simple(cycle_tools.toolbox,object):
             idx_nz=np.nonzero(E[column:,column])[0]
             idx=idx_nz[0]+column
 
-            if len(idx_nz)==1
+            if len(idx_nz)==1:
                 E[[column,idx_nz[0]+column],:]=E[[idx_nz[0]+column,column],:]
 
             else:
 
-                new_idx=idx_nz[1:]+colum
+                new_idx=idx_nz[1:]+column
                 aux_E=np.add(E[new_idx],E[idx])
                 E[new_idx]=np.mod(aux_E,2)
                 E[[column,idx_nz[0]+column],:]=E[[idx_nz[0]+column,column],:]

notebook/cycle_coalescence_asymmetry.ipynb

@@ -6,23 +6,35 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcuElEQVR4nO3df5RUZ53n8ffHDhnboGEy0V7TMMI4DBnG/MC0oOJqo0aIPwZ04jExxjGa5WRHorMqI4xndOY454jL6Bo1yvYxmHFnxnZHsQcTNm2OWPG3AgJpIWllURMKN/EXxI6tAfzuH1VN3yqquy9N37pN3c/rnD6pe+/zVH37m6K/dZ+n7n0UEZiZWXE9Lu8AzMwsXy4EZmYF50JgZlZwLgRmZgXnQmBmVnDn5B3A6brwwgtj7ty5k+r76KOPct55501tQGcx56OW8zHKuajVCvnYtWvXzyLiyY2OnXWFYO7cuezcuXNSfUulEt3d3VMb0FnM+ajlfIxyLmq1Qj4k/XisYx4aMjMrOBcCM7OCcyEwMys4FwIzs4JzITAzK7jMCoGkzZIelvS9MY5L0oclHZB0r6RnZhXLlR8sMXfdnQyUjzJ33Z1c+cFSVi81rr7dZZZu2M68dXeydMN2+naXc4ljupgu+RiJY6B81P9fpgm/NxrHkVU+sjwjuB1YMc7xq4D51Z/VwMezCOLKD5b4wcOP1uz7wcOPNr0Y9O0us37LAOUjwwRQPjLM+i0Dhf2jM13ykYyDHOOwUX5vjB1HVvnIrBBExFeAX4zTZCXwqaj4FjBL0lOnOo76IjDR/qxs7B9k+NiJmn3Dx06wsX+wqXFMF9MlH9MlDhs1Xf6fFCkOZbkegaS5wB0R8YwGx+4ANkTE16rbXwLeGRGnXC0maTWVswY6Ojqu6O3tTR3DQPnoyccd7fDQ8OixSzrPT/08ZyoZR71mxpE0NDTEzJkzc3nt6ZKP6fL+mG783pg+742pyseyZct2RURXo2N5XlmsBvsaVqWI6AF6ALq6uuJ0rvB7w7o7Tz5++yXH+cDA6K/8o+vSP8+ZeteG7SdPMZM6Z7VzcxPjSMrzasnpko9kHMn3R57/X6YDvzemz3ujGfnI81tDh4A5ie3ZwOGpfpH5T2l8f5Cx9mdl7fIFtM9oq9nXPqONtcsXNDWO6WK65GO6xGGjpsv/kyLFkWch2Aq8vvrtoWcDRyPiJ1P9Ine/rfuUP/rzn3Ied7+te6pfalyrFnXyvlddQuesdkSlmr/vVZewalFnU+OYLqZLPpJxkGMcNsrvjbHjyCofmc0RSPo00A1cCDwEvAeYARARmyQJ+CiVbxb9Grih0fxAva6urvBN56aG81HL+RjlXNRqhXxIav4cQURcO8HxAN6c1eubmVk6vrLYzKzgXAjMzArOhcDMrOBcCMzMCs6FwMys4FwIzMwKzoXAzKzgXAjMzArOhcDMrOBcCMzMCs6FwMys4FwIzMwKzoXAzKzgXAjMzArOhcDMrOBcCMzMCi7TQiBphaRBSQckrWtw/PclfV7SvZK+I+kZWcZjZmanyqwQSGoDbgWuAhYC10paWNfsb4E9EXEp8HrglqziMTOzxrI8I1gMHIiIgxHxGNALrKxrsxD4EkBE3A/MldSRYUxmZlYnszWLgU7gwcT2IWBJXZu9wKuAr0laDDwNmE1lsfuTJK0GVgN0dHRQKpUmFdDQ0NCk+7Yi56OW8zHKuajV6vnIshCowb6o294A3CJpDzAA7AaOn9IpogfoAejq6oru7u5JBVQqlZhs31bkfNRyPkY5F7VaPR9ZFoJDwJzE9mzgcLJBRDwC3AAgScAPqz9mZtYkWc4R7ADmS5on6VzgGmBrsoGkWdVjADcCX6kWBzMza5LMzggi4rikNUA/0AZsjoh9km6qHt8E/CnwKUkngP3Am7KKx8zMGstyaIiI2AZsq9u3KfH4m8D8LGMwM7Px+cpiM7OCcyEwMys4FwIzs4JzITAzKzgXAjOzgnMhMDMrOBcCM7OCcyEwMys4FwIzs4JzITAzKzgXAjOzgnMhMDMrOBcCM7OCcyEwMys4FwIzs4JzITAzK7hMC4GkFZIGJR2QtK7B8fMlfUHSXkn7JN2QZTxmZnaqzAqBpDbgVuAqYCFwraSFdc3eDOyPiMuAbuADiTWMzcysCbI8I1gMHIiIgxHxGNALrKxrE8ATJQmYCfwCOJ5hTGZmVifLNYs7gQcT24eAJXVtPgpsBQ4DTwReExG/q38iSauB1QAdHR2USqVJBTQ0NDTpvq3I+ajlfIxyLmq1ej6yLARqsC/qtpcDe4AXAk8H7pb01Yh4pKZTRA/QA9DV1RXd3d2TCqhUKjHZvq3I+ajlfIxyLmq1ej6yHBo6BMxJbM+m8sk/6QZgS1QcAH4IXJxhTGZmVifLQrADmC9pXnUC+Boqw0BJDwAvApDUASwADmYYk5mZ1clsaCgijktaA/QDbcDmiNgn6abq8U3Ae4HbJQ1QGUp6Z0T8LKuYzMzsVFnOERAR24Btdfs2JR4fBl6SZQxmZjY+X1lsZlZwLgRmZgXnQmBmVnAuBFZ4fbvLLN2wnYHyUZZu2E7f7nLeIZk1VaaTxWbTXd/uMuu3DDB87ATMgfKRYdZvGQBg1aLOnKMzaw6fEVihbewfrBSBhOFjJ9jYP5hTRGbN50JghXb4yPBp7TdrRS4EVmgXzWo/rf1mrciFwApt7fIFtM9oq9nXPqONtcsX5BSRWfN5stgKbWRCuDIn8Cs6Z7WzdvkCTxRbobgQWOGtWtTJqkWdlEolbr6uO+9wzJrOQ0NmZgXnQmBmVnAuBGZmBedCYGZWcJkWAkkrJA1KOiBpXYPjayXtqf58T9IJSRdkGZOZmdXKrBBIagNuBa4CFgLXSlqYbBMRGyPi8oi4HFgP3BMRv8gqJjMzO1WqQjDJT+mLgQMRcTAiHgN6gZXjtL8W+PQkXsfMzM6AImLiRtIPgD3AJ4H/Eyk6SboaWBERN1a3rweWRMSaBm2fABwC/rjRGYGk1cBqgI6Ojit6e3snjLmRoaEhZs6cOam+rcj5qOV8jHIuarVCPpYtW7YrIroaHUt7QdmfAC8G3gh8RNJngNsj4vvj9FGDfWMVkFcAXx9rWCgieoAegK6uruju7k4Zdq1SqcRk+7Yi56OW8zHKuajV6vlINTQUFXdHxLXAjcBfAt+RdI+k54zR7RAwJ7E9Gzg8Rttr8LCQmVkuUp0RSPoD4HXA9cBDwM3AVuBy4N+BeQ267QDmS5oHlKn8sX9tg+c+H3hB9fnNzKzJ0g4NfRP4X8CqiDiU2L9T0qZGHSLiuKQ1QD/QBmyOiH2SbqoeH+n3SuCLEfHopH4DMzM7IxMWgurXQO+IiPc2Oh4R7x+rb0RsA7bV7dtUt307cHuKWM3MLAMTzhFExAngsibEYmZmOUg7NLRH0lYq8wEnh3AiYksmUZmZWdOkLQQXAD8HXpjYF4ALgZnZWS5tIfhERHw9uUPS0gziMTOzJkt7r6GPpNxnZmZnmXHPCKoXiz0XeLKktyUOPYnKV0LNzOwsN9HQ0LnAzGq7Jyb2PwJcnVVQZmbWPOMWgoi4B7hH0u0R8WNJ5/nCLzOz1pJ2juAiSfuB+wAkXSbpY9mFZWZmzZK2EHwIWE7lK6RExF7g+RnFZGZmTZR6hbKIeLBu14kpjsXMzHKQ9jqCByU9FwhJ5wJvoTpMZGZmZ7e0ZwQ3AW8GOqmsM3B5ddvMzM5yqc4IIuJnwHUZx2JmZjlIuzDNPCqL0cxN9omIP88mLDMza5a0cwR9wG3AF4DfZRaNmZk1Xdo5gt9ExIcj4ssRcc/Iz0SdJK2QNCjpgKR1Y7TplrRH0j5JEz6nnbm+3WWWbtjOQPkoSzdsp293Oe+QzCxHac8IbpH0HuCLwG9HdkbEd8fqUF3Z7FbgSioTzDskbY2I/Yk2s4CPASsi4gFJTzn9X8FOR9/uMuu3DDB87ATMgfKRYdZvGQBg1aLOnKMzszykLQSXUFm4/oWMDg0FtesT1FsMHIiIgwCSeoGVwP5Em9cCWyLiAYCIeDh96DYZG/sHK0UgYfjYCTb2D7oQmBWUImLiRtL9wKUR8VjqJ5aupvJJ/8bq9vXAkohYk2jzIWAG8GdUbmp3S0R8qsFzrQZWA3R0dFzR29ubNowaQ0NDzJw5c1J9W8VA+ejJxx3t8NDw6LFLOs/PIaLpw++PUc5FrVbIx7Jly3ZFRFejY2nPCPYCs4DT+cSuBvvqq845wBXAi4B24JuSvhUR36/pFNED9AB0dXVFd3f3aYQxqlQqMdm+reJdG7ZTPlL56//2S47zgYHKW6BzVjs3X9edY2T58/tjlHNRq9XzkXayuAO4X1K/pK0jPxP0OQTMSWzPBg43aHNXRDxavVbhK8BlKWOySVi7fAHtM2qXkmif0cba5QtyisjM8pb2jOA9k3juHcD86jUIZeAaKnMCSf8BfFTSOVTWPlgC/I9JvJalNDIPsLF/EPgVnbPaWbt8gecHzAos7ZXF9wBIetJp9DkuaQ3QT2U1s80RsU/STdXjmyLiPkl3AfdSmYT+RER8bxK/h52GVYs6WbWok1KpVPjhIDNLf2XxauC9wDCVP9iiMt7/R+P1i4htwLa6fZvqtjcCG9OHbGZmUynt0NBa4M+q4/hmZtZC0k4W/1/g11kGYmZm+Uh7RrAe+Iakb1N7ZfFbMonKzMyaJm0h+J/AdmAA33TOzKylpC0ExyPibZlGYmZmuUg7R/BlSaslPVXSBSM/mUZmZmZNkfaMYORCsPWJfRN+fdRsPH27y2zsH+TwkWEu8oVtZrlJe3HYvKwDsWKpuR02vh22WZ5SDQ1J2inpr6rrB5idsfFuh21mzZV2juAaoBPYKalX0nJJje4uapbK4SPDp7XfzLKTqhBExIGIeBfwJ8C/AZuBByT9gyeNbTIumtV+WvvNLDtpzwiQdCnwASr3BfoccDXwCJXrC8xOi2+HbTZ9pL3p3C7gCHAbsC4iRq4u/rakpRnFZi0seTtsf2vILF9pvz766pG1h+tFxKumMB4rkJHbYZtZvtIODb1C0pNUcZuk70p6SaaRmZlZU6QtBG+MiEeAlwBPBm4ANmQWlZmZNU3aQjDyVdGXAp+MiL00Xpy+tpO0QtKgpAOS1jU43i3pqKQ91Z93pw/dzMymQto5gl2SvgjMA9ZLeiIT3IVUUhtwK3AllUXqd0jaGhH765p+NSJefppxm5nZFElbCN4EXA7MALqAC4HbJ+izGDgwMsksqRdYCdQXAjMzy1HaQvBG4K3AbGAP8Gzgm8BHxunTCTyY2D4ELGnQ7jmS9gKHgXdExL76BtU1k1cDdHR0UCqVUoZda2hoaNJ9W5HzUcv5GOVc1Gr1fKQtBG8FngV8KyKWSboY+IcJ+jSaQ4i67e8CT4uIIUkvBfqA+ad0iugBegC6urqiu7s7Zdi1SqUSk+3bipyPWs7HKOeiVqvnI+1k8W8i4jcAkn4vIu4HJroE9BAwJ7E9m8qn/pMi4pGIGKo+3gbMkHRhypjMzGwKpD0jOFS982gfcLekX1L3R72BHcB8SfOAMpUb17022UDSfwIeioiQtJhKYfp5+vDNzOxMpV2P4JXVh38v6cvA+cBdE/Q5LmkN0A+0AZsjYp+km6rHN1G5X9F/lXQcGAauiYj64SMzM8tQ2jOCkyLintNouw3YVrdvU+LxR4GPnm4MZmY2dVLffdTMzFqTC4GZWcG5EJiZFZwLgZlZwbkQmJkVnAuBmVnBuRCYmRWcC4GZWcGd9gVlZpaNvt1lNvYPcvjIMBfNamft8gVe09mawoXAbBro211m/ZYBho+dAKB8ZJj1WwYAXAwscx4aMpsGNvYPniwCI4aPnWBj/2BOEVmRuBCYTQOHjwyf1v6s9O0us3TDdgbKR1m6YTt9u8tNfX3LhwuB2TRw0az209qfhZHhqXK1+IwMT7kYtD4XArNpYO3yBbTPaKvZ1z6jjbXLJ1r/aep4eKq4PFlsNg2MTAjn+a2h6TI8Zc2XaSGQtAK4hcrCNJ+IiA1jtHsW8C3gNRHx2SxjMpuuVi3qzPUbQhfNaj85LFS/31pbZkNDktqAW4GrgIXAtZIWjtHu/VRWMjOznEyH4SnLR5ZnBIuBAxFxEEBSL7AS2F/X7mbgc8CzMozFzCaQHJ6CX9Hpi9oKQ1ktESzpamBFRNxY3b4eWBIRaxJtOoF/A14I3Abc0WhoSNJqYDVAR0fHFb29vZOKaWhoiJkzZ06qbytyPmo5H6Oci1qtkI9ly5btioiuRseyPCNQg331VedDwDsj4oTUqHm1U0QP0APQ1dUV3d3dkwqoVCox2b6tyPmo5XyMci5qtXo+siwEh4A5ie3ZwOG6Nl1Ab7UIXAi8VNLxiOjLMC4zM0vIshDsAOZLmgeUgWuA1yYbRMS8kceSbqcyNNSXYUxmZlYns0IQEcclraHybaA2YHNE7JN0U/X4pqxe28zM0sv0OoKI2AZsq9vXsABExBuyjMXMzBrzLSbMzArOhcDMrOBcCMzMCs6FwMys4FwIzMwKzoXAzKzgXAjMzArOhcDMrOBcCArIC5SbWZKXqiyYkQXKh4+dgDmjC5QDvu+8WUH5jKBgvEC5mdVzISgYL1BuZvVcCApmrIXIvUC5WXG5EBSMFyg3s3qeLC4YL1BuZvVcCApo1aJOVi3qpFQqcfN13XmHY2Y5y3RoSNIKSYOSDkha1+D4Skn3Stojaaek52UZj5mZnSqzMwJJbcCtwJVUFrLfIWlrROxPNPsSsDUiQtKlwP8GLs4qJjMzO1WWZwSLgQMRcTAiHgN6gZXJBhExFBFR3TwPCMzMrKmynCPoBB5MbB8CltQ3kvRK4H3AU4CXNXoiSauB1QAdHR2USqVJBTQ0NDTpvq3I+ajlfIxyLmq1ej6yLARqsO+UT/wR8Xng85KeD7wXeHGDNj1AD0BXV1d0d3dPKqBSqcRk+7Yi56OW8zHKuajV6vnIcmjoEDAnsT0bODxW44j4CvB0SRdmGJOZmdXJshDsAOZLmifpXOAaYGuygaQ/lqTq42cC5wI/zzAmMzOrk9nQUEQcl7QG6AfagM0RsU/STdXjm4C/AF4v6RgwDLwmMXlsZmZNkOkFZRGxDdhWt29T4vH7gfdnGYOZmY3P9xoyMys4FwIzs4JzITAzKzgXAjOzgnMhMDMrOBcCM7OCcyEwMys4FwIzs4JzITAzKzgXAjOzgnMhMDMrOBcCM7OCcyEwMys4FwIzs4JzITAzKzgXAjOzgsu0EEhaIWlQ0gFJ6xocv07SvdWfb0i6LIs4+naXWbphOwPloyzdsJ2+3eUsXiZ1HPPW3ZlrHGZmSZmtUCapDbgVuJLKQvY7JG2NiP2JZj8EXhARv5R0FdADLJnKOPp2l1m/ZYDhYydgDpSPDLN+ywAAqxZ1TuVLpY+D/OIwM6uX5RnBYuBARByMiMeAXmBlskFEfCMiflnd/BYwe6qD2Ng/ePKP74jhYyfY2D841S91VsRhZlZPWa0VL+lqYEVE3Fjdvh5YEhFrxmj/DuDikfZ1x1YDqwE6Ojqu6O3tTR3HQPnoyccd7fDQ8OixSzrPT/08ZyoZR71mxpE0NDTEzJkzc3nt6cj5GOVc1GqFfCxbtmxXRHQ1Opbl4vVqsK9h1ZG0DHgT8LxGxyOih8qwEV1dXdHd3Z06iHdt2E75SOWv/9svOc4HBiq/cuesdm6+Lv3znKlkHEnNjiOpVCpxOrlsdc7HKOeiVqvnI8uhoUPAnMT2bOBwfSNJlwKfAFZGxM+nOoi1yxfQPqOtZl/7jDbWLl8w1S91VsRhZlYvyzOCHcB8SfOAMnAN8NpkA0l/CGwBro+I72cRxMhEbGUs/ld0zmpn7fIFTZ+gTcZx+MgwF+UUh5lZvcwKQUQcl7QG6AfagM0RsU/STdXjm4B3A38AfEwSwPGxxrDOxKpFnaxa1EmpVMptGCYZh5nZdJLlGQERsQ3YVrdvU+LxjcApk8NmZtY8vrLYzKzgXAjMzArOhcDMrOBcCMzMCi6zK4uzIumnwI8n2f1C4GdTGM7Zzvmo5XyMci5qtUI+nhYRT2504KwrBGdC0s4svp56tnI+ajkfo5yLWq2eDw8NmZkVnAuBmVnBFa0Q9OQdwDTjfNRyPkY5F7VaOh+FmiMwM7NTFe2MwMzM6rgQmJkVXGEKgaQVkgYlHZC0Lu948iRpjqQvS7pP0j5Jb807prxJapO0W9IdeceSN0mzJH1W0v3V98hz8o4pL5L+W/XfyPckfVrS4/OOKQuFKASS2oBbgauAhcC1khbmG1WujgNvj4g/BZ4NvLng+QB4K3Bf3kFME7cAd0XExcBlFDQvkjqBtwBdEfEMKrfTvybfqLJRiEIALAYORMTBiHgM6AVW5hxTbiLiJxHx3erjX1H5h17YhRIkzQZeRmWlvEKT9CTg+cBtABHxWEQcyTWofJ0DtEs6B3gCDVZZbAVFKQSdwIOJ7UMU+A9fkqS5wCLg2zmHkqcPAX8D/C7nOKaDPwJ+CnyyOlT2CUnn5R1UHiKiDPwT8ADwE+BoRHwx36iyUZRCoAb7Cv+9WUkzgc8Bfx0Rj+QdTx4kvRx4OCJ25R3LNHEO8Ezg4xGxCHgUKOScmqTfpzJyMA+4CDhP0uvyjSobRSkEh4A5ie3ZtOgpXlqSZlApAv8aEVvyjidHS4E/l/QjKkOGL5T0L/mGlKtDwKGIGDlD/CyVwlBELwZ+GBE/jYhjVNZXf27OMWWiKIVgBzBf0jxJ51KZ8Nmac0y5UWWB6NuA+yLig3nHk6eIWB8RsyNiLpX3xfaIaMlPfWlExP8DHpS0oLrrRcD+HEPK0wPAsyU9ofpv5kW06MR5pmsWTxcRcVzSGqCfysz/5ojYl3NYeVoKXA8MSNpT3fe31TWmzW4G/rX6oekgcEPO8eQiIr4t6bPAd6l80243LXqrCd9iwsys4IoyNGRmZmNwITAzKzgXAjOzgnMhMDMrOBcCM7OCcyGws46kuZK+1+TXLEk6ZfFySV2SPjxFr/H3kt4xFc+V5XNa6ynEdQRWTJLaIuJElq8RETuBnVm+hlnWfEZgZ6tzJP2zpHur985/AoCkH0l6t6SvAa+W9F8k7ZC0V9LnEu1ul/RhSd+QdFDS1SNPLOlvJA1U+2xIvOarJX1H0vcl/edq2+6RNQyqn743V88eDkp6S+I5/656f/+7q/e1H/dTuqSnS7pL0i5JX5V0saTzq7/f46ptniDpQUkzGrWfqkRb63MhsLPVAqAnIi4FHgH+KnHsNxHxvIjoBbZExLMiYuS++m9KtHsq8Dzg5cAGAElXAauAJdU+/z3R/pyIWAz8NfCeMeK6GFhO5dbn76n+ke4C/oLKXV5fBZwyxNRAD3BzRFwBvAP4WEQcBfYCL6i2eQXQX70PzintU7yGGeChITt7PRgRX68+/hcqC4j8U3X7M4l2z5D0j8AsYCaV24yM6IuI3wH7JXVU970Y+GRE/BogIn6RaD9yc75dwNwx4rozIn4L/FbSw0AHlWLzHxExDCDpC+P9YtW7wj4X+PfKLW4A+L3E7/Ya4MtU7o30sQnam03IhcDOVvX3RkluP5p4fDuwKiL2SnoD0J049tvEYyX+O9Z9V0ban2DsfzvJ5xxp1+g26ON5HHAkIi5vcGwr8D5JFwBXANuB88ZpbzYhDw3Z2eoPE2vpXgt8bYx2TwR+Ur3t9nUpnveLwBsTcwkXnHGkldheIenx1U/vLxuvcXVtiB9KenU1Bkm6rHpsCPgOleUk74iIE+O1N0vDhcDOVvcBfynpXuAC4ONjtPs7Kquv3Q3cP9GTRsRdVD5176zemfWMv3oZETuqz7mXyvDSTuDoBN2uA94kaS+wj9qlVT8DvI7aIbDx2puNy3cfNWsCSTMjYqh6pvEVYPXIutFmefMcgVlz9EhaCDwe+GcXAZtOfEZgZlZwniMwMys4FwIzs4JzITAzKzgXAjOzgnMhMDMruP8PJkDUVcEerqMAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[<networkx.classes.graph.Graph object at 0x1114e8f60>, <networkx.classes.graph.Graph object at 0x1114ec438>, <networkx.classes.graph.Graph object at 0xa24a82a58>, <networkx.classes.graph.Graph object at 0xa24a82630>, <networkx.classes.graph.Graph object at 0xa24a827b8>, <networkx.classes.graph.Graph object at 0xa24a825f8>, <networkx.classes.graph.Graph object at 0xa24a82588>, <networkx.classes.graph.Graph object at 0xa24a82780>, <networkx.classes.graph.Graph object at 0xa24a82940>, <networkx.classes.graph.Graph object at 0xa24a82748>, <networkx.classes.graph.Graph object at 0xa24a826d8>, <networkx.classes.graph.Graph object at 0xa24a82668>, <networkx.classes.graph.Graph object at 0xa24a82978>, <networkx.classes.graph.Graph object at 0xa24a829b0>, <networkx.classes.graph.Graph object at 0xa24a82908>, <networkx.classes.graph.Graph object at 0xa24a82898>, <networkx.classes.graph.Graph object at 0xa24a82828>, <networkx.classes.graph.Graph object at 0xa24a827f0>, <networkx.classes.graph.Graph object at 0xa24a828d0>, <networkx.classes.graph.Graph object at 0xa24a829e8>, <networkx.classes.graph.Graph object at 0xa24a82860>, <networkx.classes.graph.Graph object at 0xa24a82a20>, <networkx.classes.graph.Graph object at 0xa24a826a0>, <networkx.classes.graph.Graph object at 0xa24a82a90>, <networkx.classes.graph.Graph object at 0xa24a82ac8>, <networkx.classes.graph.Graph object at 0xa24a82b00>, <networkx.classes.graph.Graph object at 0xa24a82b38>, <networkx.classes.graph.Graph object at 0xa24a82b70>, <networkx.classes.graph.Graph object at 0xa24a82ba8>, <networkx.classes.graph.Graph object at 0xa24a82be0>, <networkx.classes.graph.Graph object at 0xa24a82c18>, <networkx.classes.graph.Graph object at 0xa24a82c50>, <networkx.classes.graph.Graph object at 0xa24a82c88>, <networkx.classes.graph.Graph object at 0xa24a82cc0>, <networkx.classes.graph.Graph object at 0xa24a82cf8>, <networkx.classes.graph.Graph object at 0xa24a82d30>]\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "__call__() got an unexpected keyword argument 'keys'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-ba13ee564711>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mminimum_basis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct_networkx_basis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminimum_basis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mcycle_tree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_cycle_coalescence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mminimum_basis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mdict_asymmetry\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc_tree_asymmetry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcycle_tree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/GitHub/cycle-coalescence-algorithm/cycle_analysis/cycle_tools_coalescence.py\u001b[0m in \u001b[0;36mcalc_cycle_coalescence\u001b[0;34m(self, input_graph, cycle_basis)\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0mcycle_tree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcycle\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcycle_basis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m              \u001b[0mcycle_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcycle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'base'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbranch_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;31m# get the weights of the input graph and sort\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: __call__() got an unexpected keyword argument 'keys'"
+     ]
     }
    ],
    "source": [
+    "import sys\n",
+    "sys.path.insert(0, \"../cycle_analysis\")\n",
+    "import cycle_tools_coalescence as ctc\n",
+    "import test as cat\n",
     "import networkx as nx\n",
     "import matplotlib.pyplot as plt\n",
-    "import cycle_analysis.cycle_coalescence as cc\n",
-    "import cycle_analysis.test as cat\n",
+    "# import cycle_analysis.cycle_tools_coalescence as ctc\n",
+    "# import cycle_analysis.test as cat\n",
+    "\n",
     "# generate a dummy graph for testing\n",
     "# put an edge weight distribution on the system, available are random/gradient/bigradient/nested_square\n",
     "G=nx.grid_graph((7,7,1))\n",
@@ -32,9 +44,11 @@
     "pos=nx.get_node_attributes(G,'pos')\n",
     "\n",
     "# merge all shortest cycles and create merging tree, then calc asymmetry of the tree's branches\n",
-    "minimum_basis=cc.construct_minimum_basis(G)\n",
-    "cycle_tree=cc.calc_cycle_coalescence(G,minimum_basis)\n",
-    "dict_asymmetry=cc.calc_tree_asymmetry(cycle_tree)\n",
+    "T=ctc.coalescence()\n",
+    "minimum_basis=T.construct_networkx_basis(G)\n",
+    "print(minimum_basis)\n",
+    "cycle_tree=T.calc_cycle_coalescence(G,minimum_basis)\n",
+    "dict_asymmetry=T.calc_tree_asymmetry(cycle_tree)\n",
     "\n",
     "# plot branching asymmetry in dependence of branching level\n",
     "x,y=[],[]\n",
@@ -48,6 +62,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,