.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/Kuramoto_synchronisation/analyse_systematic_inhomogeneous_Kuramoto_6N.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_Kuramoto_synchronisation_analyse_systematic_inhomogeneous_Kuramoto_6N.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_Kuramoto_synchronisation_analyse_systematic_inhomogeneous_Kuramoto_6N.py:


Created on Mon Nov 22 21:19:37 2021

@author: maout

.. GENERATED FROM PYTHON SOURCE LINES 7-81

.. code-block:: default





    import numpy as np


    #from matplotlib import pyplot as plt

    from scipy.spatial.distance import cdist
    import time
    import ot
    import numba
    import math
    import random
    import sys
    import pickle

    dim = 6
    h = 0.001
    t1 = 0
    t2 = 0.5
    T = t2-t1
    timegrid = np.arange(0,T+h/2,h)
    N = 3000#0#0#0#2000
    #g = 1
    k = timegrid.size
    M = 300

    y2 = np.ones(dim)  
    sigmas = np.array([1.0,1.5])
    rep_bridge = 1   #10 different bridge instances for every setting
    reps = 20 ##instanses for stochastic path evaluation of each bridge
    # dy0 = np.array([np.pi/4, np.pi/2, np.pi, 3*np.pi/2])    
    # y0s = np.zeros((dim,rep_bridge, dy0.size))
    random.seed(22)
    # for i in range(rep_bridge):
    #     y0s[0, i, :] = np.random.uniform( low=0, high=2*np.pi,size=1 ) 
    #     y0s[1, i, :] = (y0s[0, i, :] + dy0) %(2* np.pi)



    Ks   = np.linspace(0,6,7) 
    repetition = 0
    Rttcont = np.zeros((Ks.size,  sigmas.size, timegrid.size,reps   ))*np.nan
    Rttnon = np.zeros((  Ks.size,  sigmas.size, timegrid.size,reps   ))*np.nan
    used_us = np.zeros((dim,  Ks.size,  sigmas.size,timegrid.size,  reps   ))*np.nan    
    for gii in range(0,2):
        noise = gii+1
        for ki in range(Ks.size):   
            K = ki#Ks[ki]
            naming = 'Kuramoto_6N\\dim6\\Latest%d_N_systematic_Kuramoto_inhomogeneous_k_%d_gi_%d_N_%d_M_%d_repetition_%d'%(dim, K,noise,N, M,repetition)
          
            try:
                file = open(naming+'.dat','rb')
                to_save = pickle.load(file)   
                # naming2 = 'kuramot\\2N_systematic_Kuramoto_homogeneous_repb_%d_ki_%d_yi_%d__gi_%d_N_%d_M_%d_UNCONTROLLED'%(bi, ki,yi,gii,N, M)
                # file2 = open(naming2+'.dat','rb')
                # to_save2 = pickle.load(file2)
            
                #Fcont = to_save['Fcont'] 
                Rttcont[ki,gii,:,:] = to_save['Rttcont']  
                #Fnon = to_save2['Fnon'] 
                Rttnon[ki, gii,:,:] = to_save['Rttnon']  
                #Kin = to_save['K']                     
                            
                used_us[:,ki, gii,:] = to_save['used_us']   
            
            except FileNotFoundError:
                i=0
                print(naming)
        
        
    

.. GENERATED FROM PYTHON SOURCE LINES 82-118

.. code-block:: default

            
            
    from matplotlib import pyplot as plt                     

    #figure and plotting settings
    import seaborn as sns
    plt.rcParams['figure.dpi'] = 300
    plt.rcParams['savefig.dpi'] = 300
    plt.rcParams['savefig.facecolor'] = (1,1,1,0)
    plt.rcParams['savefig.bbox'] = "tight"
    plt.rcParams['font.size'] = 10*1.2
    # plt.rcParams['font.family'] = 'sans-serif'     # not available in Colab
    # plt.rcParams['font.sans-serif'] = 'Helvetica'  # not available in Colab
    plt.rcParams['pdf.fonttype'] = 42
    plt.rcParams['xtick.labelsize'] = 10*1.2
    plt.rcParams['ytick.labelsize'] = 10*1.2
    plt.rcParams['axes.labelsize'] = 12*1.2
    plt.rcParams['axes.linewidth'] = 2
    plt.rcParams['axes.spines.top'] = False
    plt.rcParams['axes.spines.right'] = False
    #plt.rcParams['axes.Axes.tick_params'] = True
    #plt.rcParams['axes.solid_capstyle'] = 'round'
    plt.rc('axes',edgecolor='#4f4949')
    plt.rcParams['figure.frameon'] = False
    plt.rcParams['figure.subplot.hspace'] = 0.51
    plt.rcParams['figure.subplot.wspace'] = 0.51
    plt.rcParams['figure.subplot.left'] = 0.1
    plt.rcParams['figure.subplot.right'] = 0.9
    plt.rcParams['figure.subplot.top'] = 0.9
    plt.rcParams['figure.subplot.bottom'] = 0.1
    plt.rcParams['lines.solid_capstyle'] = 'round'
    plt.rcParams['lines.solid_joinstyle'] = 'round'
    #plt.rcParams['xtick.major.size'] = 20
    #plt.rcParams['xtick.major.width'] = 4
    plt.rcParams['text.usetex'] = True


.. GENERATED FROM PYTHON SOURCE LINES 119-223

.. code-block:: default





    onset_cont = np.zeros((Ks.size,2,20)) *np.nan
    onset_uncont = np.zeros((Ks.size,2,20)) *np.nan

    cont_synched_durations_m = np.zeros((Ks.size,2,20)) *np.nan
    cont_synched_durations_std  = np.zeros((Ks.size,2,20)) *np.nan
    cont_synched_durations_max = np.zeros((Ks.size,2,20)) *np.nan
    cont_synched_durations_sum  = np.zeros((Ks.size,2,20)) *np.nan

    uncont_synched_durations_m = np.zeros((Ks.size,2,20)) *np.nan
    uncont_synched_durations_std  = np.zeros((Ks.size,2,20)) *np.nan
    uncont_synched_durations_max = np.zeros((Ks.size,2,20)) *np.nan
    uncont_synched_durations_sum  = np.zeros((Ks.size,2,20)) *np.nan

    counter_cont = np.zeros((Ks.size,2))
    counter_uncont =  np.zeros((Ks.size,2))

    threshold = 0.95
    min_duri = 20

    for ki in range(Ks.size):
    
        for gi in range(2):#3                
            for repi in range(20):                    
                positions = np.where( Rttcont[ ki,gi,1:-2, repi] >=threshold)[0]  ###this one detects if there is at all any synchronisation
                synch_or_not = Rttcont[ ki, gi,1:-2, repi] >=threshold ##this is boolean indicating the synchronised positions
            
                if positions.size==0: 
                    onset_cont[ki,gi,repi] = np.nan
                else:
                    diffpos = np.diff(synch_or_not.astype(int)   ) #difference between consecutive steps
                
                    diffpos2 = np.zeros(diffpos.size+1) # extend by one step at the beginning to detect 
                    diffpos2[1:] = diffpos
                    starts = np.argwhere(diffpos2 == 1)
                    stops = np.argwhere(diffpos2 == -1)                        
                    if starts.size == 0:
                        onset_cont[ki,gi,repi] = np.nan
                    elif stops.size == 0:
                        onset_cont[ki,gi,repi] = timegrid[ starts[0] ]
                        counter_cont[ki,gi] += 1
                        dur = timegrid[ -1 ] - timegrid[ starts[0] ]
                        cont_synched_durations_m[ki,gi,repi]  = dur
                        cont_synched_durations_std[ki,gi,repi]  = 0
                        cont_synched_durations_max[ki,gi,repi]  = dur
                        cont_synched_durations_sum[ki,gi,repi]  = np.sum(  synch_or_not )/ (timegrid.size-1 - starts[0] )
                    else:
                        if stops[0,0] < starts[0,0]:
                            stops = stops[1:,:]
                        durations = stops[:,0] - starts[:stops.size, 0]                            
                        synchned_more_than_50 = np.where( durations>=min_duri )[0]
                        if synchned_more_than_50.size == 0:
                            onset_cont[ki,gi,repi] = np.nan
                        else:                                
                            onset_cont[ki,gi,repi] = timegrid[ starts[synchned_more_than_50[0]] ]
                            counter_cont[ki,gi] += 1                                
                            cont_synched_durations_m[ki,gi,repi]  = np.mean(  durations  )
                            cont_synched_durations_std[ki,gi,repi]  = np.std(  durations )
                            cont_synched_durations_max[ki,gi,repi]  = np.max(  durations )
                            cont_synched_durations_sum[ki,gi,repi]  = np.sum(  synch_or_not[ starts[synchned_more_than_50[0]][0] :   ] )/(timegrid.size-1 -starts[synchned_more_than_50[0]] ) ##timesteps in synchronied

               
                
                positions = np.where( Rttnon[ ki,  gi, 1:-2,repi] >=threshold)[0]
                synch_or_not = Rttnon[ ki,  gi,1:-2, repi] >=threshold                     
                if positions.size ==0: 
                    onset_uncont[ki,gi,repi] = np.nan
                else:
                    diffpos = np.diff(synch_or_not.astype(int)   ) #difference between consecutive steps
                
                    diffpos2 = np.zeros(diffpos.size+1) # extend by one step at the beginning to detect 
                    diffpos2[1:] = diffpos
                    starts = np.argwhere(diffpos2 == 1)
                    stops = np.argwhere(diffpos2 == -1)
                    #start_stop =[starts, stops - starts]
                    if starts.size == 0:
                        onset_uncont[ki,gi,repi] = np.nan
                    elif stops.size == 0:
                        onset_uncont[ki,gi,repi] = timegrid[ starts[0] ]
                        counter_uncont[ki,gi] += 1
                        dur = timegrid[ -1 ] - timegrid[ starts[0] ]
                        uncont_synched_durations_m[ki,gi,repi]  = dur
                        uncont_synched_durations_std[ki,gi,repi]  = 0
                        uncont_synched_durations_max[ki,gi,repi]  = dur
                        uncont_synched_durations_sum[ki,gi,repi]  = np.sum(  synch_or_not )/ (timegrid.size-1 - starts[0] )
                    else:
                        if stops[0,0] < starts[0,0]:
                            stops = stops[1:,:]
                        durations = stops[:,0] - starts[:stops.size, 0]                            
                        synchned_more_than_50 = np.where( durations>=min_duri )[0]
                    
                        if synchned_more_than_50.size == 0:
                            onset_uncont[ki,gi,repi] = np.nan
                        else:                                
                            onset_uncont[ki,gi,repi] = timegrid[starts[synchned_more_than_50[0]] ]
                            counter_uncont[ki,gi] += 1
                            uncont_synched_durations_m[ki,gi,repi]  = np.mean(  durations )
                            uncont_synched_durations_std[ki,gi,repi]  = np.std(  durations )
                            uncont_synched_durations_max[ki,gi,repi]  = np.max(  durations  )
                            uncont_synched_durations_sum[ki,gi,repi]  = np.sum(  synch_or_not[ starts[synchned_more_than_50[0]][0] :   ] ) /(timegrid.size-1 -starts[synchned_more_than_50[0]] )


.. GENERATED FROM PYTHON SOURCE LINES 224-242

.. code-block:: default


    import seaborn as sns
    from matplotlib import pyplot as plt


    pali = sns.diverging_palette(145, 300, s=80, as_cmap=True)
    cols = [pali(0.99), pali(0.95), pali(0.85), pali(0.80), pali(0.75), pali(0.70) ]
    cols2 = [pali(0.),pali(0.05),pali(0.1),pali(0.15),pali(0.2),pali(0.25)]

    frecmap = plt.get_cmap( 'plasma')
    # minw = np.abs(np.min(ws))+0.3
    # maxw = np.max(ws)
    # intervalw = maxw +minw +0.4
    # print([ ( (wsi + np.pi/2)/(np.pi)   ) for wsi in ws       ])
    # wscols = [ frecmap( (wsi + minw)/(intervalw)   ) for wsi in ws       ]

    fig9 = plt.figure(constrained_layout=False, figsize=(6,3))
    gs1 = fig9.add_gridspec(nrows=4, ncols=8, wspace=1.5, hspace=1.2)

.. GENERATED FROM PYTHON SOURCE LINES 243-244

ax01 = fig9.add_subplot(gs1[0:2,0:2 ]) 

.. GENERATED FROM PYTHON SOURCE LINES 244-283

.. code-block:: default


    fig9.text(0.7, -0.05, 'time', ha='center',fontsize=16)
    fig9.text(0.45, 0.5, r'order  param. $R$', va='center', rotation='vertical',fontsize=14)

    fig9.text(0.7, 0.95, r'coupling  $J= 3.0$', ha='center',fontsize=10)
    fig9.text(0.95, 0.95, r'noise', ha='center',fontsize=10)
    fig9.text(0.95, 0.73, r'$\sigma= 1.0$', ha='center',fontsize=10)
    fig9.text(0.95, 0.23, r'$\sigma= 1.5$', ha='center',fontsize=10)



    ax02 = fig9.add_subplot(gs1[0:4,0:4 ]) 

    #color = next(ax02._get_lines.prop_cycler)['color']
    plt.plot(Ks[1:], np.nanmean(np.nanmean(Rttcont[1 :, 0,:-2 ], axis=-2), axis=(-1)), c=cols[5],marker='^',label=r'$\sigma=1.0$',zorder=5 ,lw=2,markersize=6,markeredgecolor='#4f4949')  

    #color = next(ax02._get_lines.prop_cycler)['color']
    plt.plot(Ks[1:], np.nanmean(np.nanmean(Rttcont[1 :, 1 ,:-2], axis=-2), axis=(-1)) , c=cols[1],marker='.',label=r'$\sigma=1.5$' ,zorder=5,lw=2,markersize=10,markeredgecolor='#4f4949')    
    plt.plot(Ks[1:], np.nanmean(np.nanmean(Rttnon[1:, 0,:-2 ], axis=-2), axis=(-1)) ,'--', c=cols2[5],marker='^',label=r'$\sigma=1.0$',lw=2 ,markersize=6,markeredgecolor='#4f4949')                                   
    plt.plot(Ks[1:], np.nanmean(np.nanmean(Rttnon[1 :, 1,:-2 ], axis=-2), axis=(-1)) ,'--', c=cols2[1],marker='.' ,label=r'$\sigma=1.5$',lw=2,markersize=10,markeredgecolor='#4f4949')
    plt.ylim(0.5,1)    
    #plt.xlim(0,4.01)

    legend = ax02.legend()        
    legend.get_frame().set_linewidth(1.8)
    legend.get_frame().set_facecolor('white')
    legend.get_frame().set_edgecolor('white')
    handles, labels = ax02.get_legend_handles_labels()
    #handles = [ handles[-2],handles[0] , handles[-1]]
    #labels= [ labels[-2],labels[0] , labels[-1]]
    if True:
        ax02.legend(handles, labels, title=r'controlled $\,$    uncontrolled',
                  handletextpad=0.5, columnspacing=0.5,handlelength=1.3, bbox_to_anchor=[0.10, 0.0],
                  loc=3, ncol=2, frameon=True,fontsize = 10,shadow=None,framealpha =0,edgecolor ='#0a0a0a')    

    plt.setp(plt.gca().get_legend().get_title(), fontsize='10') 
    plt.ylabel(r'order  param. $R$') 
    plt.xlabel(r'coupling $J$')


.. GENERATED FROM PYTHON SOURCE LINES 284-324

.. code-block:: default


    ax1 = fig9.add_subplot(gs1[0:2,4:6 ] ) 

    plt.plot(timegrid[:-2],Rttcont[3,0,:-2,:],c=cols[5],alpha=1),
    plt.plot(timegrid[:-2],np.mean(Rttcont[3,0,:-2,:],axis=1), '--',c='#4f4949')

    ax1.set_ylim(0.0,1.01)
    #plt.yticks([0.5,1.0])
    plt.xticks([0.5,1.5])
    ax1b = fig9.add_subplot(gs1[0:2,6:8 ] ,  sharey = ax1) 

    plt.plot(timegrid[:-2],Rttnon[3,0,:-2,:],c=cols2[5],alpha=0.5)
    ax1b.set_ylim(0.0,1.01)
    plt.plot(timegrid[:-2],np.mean(Rttnon[3,0,:-2,:],axis=1), '--',c='#4f4949')
    plt.plot([0,timegrid[-2]],[1,1], '--',c='silver',zorder=0)
    #plt.ylabel(r'order  param. $R$')   
    plt.xticks([0.5,1.5])
    #plt.yticks([0.5,1.0])
    ax2 = fig9.add_subplot(gs1[ 2:4,4:6], sharex = ax1)
    ax2.set_ylim(0.0,1.01)
    plt.plot(timegrid[:-2],Rttcont[3,1,:-2,:],c=cols[1],alpha=1),
    plt.plot(timegrid[:-2],np.mean(Rttcont[3,1,:-2,:],axis=1), '--',c='#4f4949')
    #plt.xlabel('time')
    #plt.ylabel(r'order  param. $R$')   
    #plt.xticks([0.5,1.5])
    #plt.yticks([0.5,1.0])
    ax2b = fig9.add_subplot(gs1[ 2:4,6:8], sharex = ax1b, sharey = ax2) 
    plt.plot(timegrid[:-2],Rttnon[3,1,:-2,:],c=cols2[1],alpha=0.5)
    plt.plot(timegrid[:-2],np.mean(Rttnon[3,1,:-2,:],axis=1), '--',c='#4f4949')
    plt.plot([0,timegrid[-2]],[1,1], '--',c='silver',zorder=0)
    ax2b.set_ylim(0.0,1.01)
    #plt.xticks([0.5,1.5])



    plt.savefig('systematic_Kuramoto_6N.png', bbox_inches='tight',dpi=300 , pad_inches = 0.)
    plt.savefig('systematic_Kuramoto_6N.pdf', bbox_inches='tight',dpi=300,  pad_inches = 0.)




.. GENERATED FROM PYTHON SOURCE LINES 325-352

.. code-block:: default





    ax6 = fig9.add_subplot(gs1[0:4,4:8 ]) 

    plt.plot(Ks, np.nanmean(onset_cont[:, 0], axis=(-1)) , c=cols[5],marker='^',label=r'$\sigma=0.5$',zorder=5 ,lw=2.8,markersize=6,markeredgecolor='#4f4949') 
    plt.plot(Ks, np.nanmean(onset_uncont[:, 0], axis=(-1)),'--', c=cols2[5],marker='^',label=r'$\sigma=0.5$' ,zorder=5,lw=2.8,markersize=6,markeredgecolor='#4f4949')  

    plt.plot(Ks, np.nanmean(onset_cont[:, 1], axis=(-1)) , c=cols[1],marker='.',label=r'$\sigma=1.0$',lw=2.8 ,markersize=12,markeredgecolor='#4f4949') 
    plt.plot(Ks, np.nanmean(onset_uncont[:, 1], axis=(-1)),'--', c=cols2[0],marker='.' ,label=r'$\sigma=1.0$',lw=2.8,markersize=12,markeredgecolor='#4f4949')   

                   
    plt.ylabel(r'onset of synchrony $t^{syn}$') 
    plt.xlabel(r'coupling $J$')                



    ax6.tick_params(axis='both',which='both',direction='in', length=3, width=1,colors='#4f4949',zorder=3)
    ax6.tick_params(bottom=True, top=True, left=True, right=True)
    ax6.spines['top'].set_visible(True)
    ax6.spines['right'].set_visible(True)
    ax6.minorticks_on()
    ax6.tick_params(axis='both',which='major',direction='in', length=3.5, width=1,colors='#4f4949',zorder=3)
    ax6.tick_params(axis='both',which='minor',direction='in', length=2.5, width=0.5,colors='#4f4949',zorder=3)
    ax6.tick_params(bottom=True, top=True, left=True, right=True)
    ax6.tick_params(axis='both', which='minor', bottom=True, top=True, left=True, right=True)

.. GENERATED FROM PYTHON SOURCE LINES 353-392

.. code-block:: default


    ax7 = fig9.add_subplot(gs1[0:4,8:12 ]) 



    plt.plot(Ks, np.nanmean(cont_synched_durations_sum[:, 0], axis=(-1)) , c=cols[5],marker='^',label=r'$\sigma=1.0$',zorder=2 ,lw=2.8,markersize=6,markeredgecolor='#4f4949') 
    plt.plot(Ks, np.nanmean(uncont_synched_durations_sum[:, 0], axis=(-1)),'--', c=cols2[5],marker='^',label=r'$\sigma=1.0$' ,zorder=2,lw=2.8,markersize=6,markeredgecolor='#4f4949')  


    plt.plot(Ks, np.nanmean(cont_synched_durations_sum[:, 1], axis=(-1)) , c=cols[1],marker='.',label=r'$\sigma=1.5$',lw=2.8 ,markersize=12,markeredgecolor='#4f4949') 
    plt.plot(Ks, np.nanmean(uncont_synched_durations_sum[:, 1], axis=(-1)),'--', c=cols2[0],marker='.' ,label=r'$\sigma=1.5$',lw=2.8,markersize=12,markeredgecolor='#4f4949')   

    plt.ylabel('$\%$ time synchronised\nafter $t^{syn}$', multialignment='center',labelpad=-0.1) # "Mat\nTTp\n123"
    plt.xlabel(r'coupling $J$') 
    plt.yticks([0, 0.5,1])
    ax7 = plt.gca()
    ax7.tick_params(axis='both',which='both',direction='in', length=3, width=1,colors='#4f4949',zorder=3)
    ax7.tick_params(bottom=True, top=True, left=True, right=True)
    ax7.spines['top'].set_visible(True)
    ax7.spines['right'].set_visible(True)
    ax7.minorticks_on()
    ax7.tick_params(axis='both',which='major',direction='in', length=3.5, width=1,colors='#4f4949',zorder=3)
    ax7.tick_params(axis='both',which='minor',direction='in', length=2.5, width=0.5,colors='#4f4949',zorder=3)
    ax7.tick_params(bottom=True, top=True, left=True, right=True)
    ax7.tick_params(axis='both', which='minor', bottom=True, top=True, left=True, right=True)

    legend = ax7.legend()        
    legend.get_frame().set_linewidth(1.8)
    legend.get_frame().set_facecolor('white')
    legend.get_frame().set_edgecolor('white')
    handles, labels = ax7.get_legend_handles_labels()
    handles = [ handles[0],handles[2] , handles[1], handles[3]]
    labels= [ labels[0],labels[2] , labels[1], labels[3]]
    if True:
        ax7.legend(handles, labels, title=r'$\,$controlled $\,\,$ uncontrolled',
                  handletextpad=0.5, columnspacing=1,handlelength=0.45, bbox_to_anchor=[-0.65, 0.99],
                  loc=3, ncol=2, frameon=True,fontsize = 'small',shadow=None,framealpha =0,edgecolor ='#0a0a0a')    
    plt.setp(plt.gca().get_legend().get_title(), fontsize='10') 
    plt.subplots_adjust(wspace=0.325)#, hspace=0)
    

.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_Kuramoto_synchronisation_analyse_systematic_inhomogeneous_Kuramoto_6N.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: analyse_systematic_inhomogeneous_Kuramoto_6N.py <analyse_systematic_inhomogeneous_Kuramoto_6N.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: analyse_systematic_inhomogeneous_Kuramoto_6N.ipynb <analyse_systematic_inhomogeneous_Kuramoto_6N.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_