diff --git a/analysis.py b/analysis.py
index cca9bf1..03a35de 100644
--- a/analysis.py
+++ b/analysis.py
@@ -34,15 +34,17 @@ def autocorr(x):
     return r, lag
 
 # sort the particles into regions
-def density_map(x, dimx ,div):
+def density_map(x, dimx ,div,ini,fim):
     # input: x: array (1D) of positions in one diraction
     #        dimx: dimension of the region in the diraction of the divison
     #        div: number of divisions
+    #        initial position: ini
+    #        final position: fin
     # outut: y: list of arrays that contains the indices of the particles that are in a region that is the position in the array
     div = int(dimx/div)
     xp = x // div # This gives the location of each particle
     dmap = [[] for _ in range(div)]
-    for i in range(len(x)):
+    for i in range(ini,fim):
         dmap[int(xp[i])].append(i)
     return dmap
 
@@ -52,7 +54,7 @@ def density_map(x, dimx ,div):
 print("Choose a folder there the results are contained:\nNo | Folder")
 for a in range(len(dirlist)):
     print("{} | {}\n".format(a,dirlist[a]))
-a = int(input("Enter the number of the folder\n"))
+a = 0 #int(input("Enter the number of the folder\n"))
 res_dir = dirlist[a]
 
 # Read the configuration file
@@ -60,6 +62,17 @@ def density_map(x, dimx ,div):
 config.read(res_dir + '/settings.txt')
 dimx = float(config['global']['dimX'].split()[0])
 dimy = float(config['global']['dimY'].split()[0])
+n_files = int(config['out_files']['out_files'].split()[0])
+ntype = int(config['global']['Ntype'].split()[0])
+t_fim = float(config['global']['t_fim'].split()[0])
+dt = float(config['global']['dt'].split()[0])
+quant = []
+m = []
+for i in range(ntype):
+    quant.append(int(config['par_'+str(i)]['quantidade'].split()[0]))
+    m.append(float(config['par_'+str(i)]['m'].split()[0]))
+
+
 Vol = dimx*dimy
 
 # Open the zipped files
@@ -69,51 +82,70 @@ def density_map(x, dimx ,div):
 
 len_list_files =  len(zip_positions.namelist()) # number of files (steps)
 
-div = input("Enter in how many regions the region of calculus will be divided in the x diraction [1]: ")
+div = 8 #input("Enter in how many regions the region of calculus will be divided in the x diraction [1]: ")
 if div == '':
     div = 1
 else:
     div = int(div)
 
+
+nesb = 500 #int(input("Enter the number of assembles in which this simulation will be divided:\n"))
+
+periodic = True #('y' == input("Were the boundaries periodic? [y/n]\n"))
+if periodic:
+    len_list_files =  len(zip_rfup.namelist())
+mic = 0
+ini = 0
+fim = quant[0]
+if ntype > 0:
+    vpg = np.zeros((len_list_files,quant[1],2)) #velocidade das particulas grandes
+
+# len_list_files = 2000
+# nesb = 100
+
+esb_len = int(len_list_files/nesb) # length of each ensamble
 KE  = np.zeros((len_list_files,div))
 tauk  = np.zeros((len_list_files,div))
 tauxyp  = np.zeros((len_list_files,div))
 msq  = np.zeros((len_list_files,div))
 tauyxp  = np.zeros((len_list_files,div))
 
-nesb = int(input("Enter the number of assembles in which this simulation will be divided:\n"))
-periodic = ('y' == input("Were the boundaries periodic? [y/n]\n"))
-esb_len = int(len_list_files/nesb) # length of each ensamble
-mic = 0
 print("Reading files...")
 if pbar:
     bar = progressbar.ProgressBar(max_value=(len_list_files))
 for step in range(1,len_list_files):
     esb = step//esb_len
     if periodic:
-        rfup = pd.read_csv(zip_rfup.open('rF_u_P.csv.'+str(step)), header=None, names = ["micx","micy","RxFy","RyFx","u","m","K"])
+        rfup = pd.read_csv(zip_rfup.open('rF_u_P.csv.'+str(step)), header=None, names = ["micx","micy","RxFy","RyFx","u","K"])
     else:
-        rfup = pd.read_csv(zip_rfup.open('rF_u_P.csv.'+str(step)), header=None, names = ["RxFy","RyFx","u","m","K"])
+        rfup = pd.read_csv(zip_rfup.open('rF_u_P.csv.'+str(step)), header=None, names = ["RxFy","RyFx","u","K"])
     pos = pd.read_csv(zip_positions.open("position.csv."+str(step)), header=None, names = ["x","y"])
     vel = pd.read_csv(zip_velocities.open("velocity.csv."+str(step)), header=None, names = ["v_x", "v_y"])
+    dmap = density_map(pos['x'],dimx,div,ini,fim)
 
-    dmap = density_map(pos['x'],dimx,div)
-
+    if ntype > 0:
+        vpg[step,:,:] = vel.loc[quant[0]:quant[1]+quant[0]-1,['v_x','v_y']]
     # Depende da região, terá um for
     if step%esb_len == 1: # se step == inicio do ensamble
-        r0 = pos[['x','y']] # um r0 por ensamble
-        lp = dmap # salva as partículas numa daterminada região
-    # if periodic: 
-        # mic = rfup[["micx", "micy"]].to_numpy()*np.array([dimx, dimy])
+        if periodic:
+            r0 = pos[['x','y']] + rfup[["micx", "micy"]].to_numpy()*np.array([dimx, dimy]) # um r0 por ensamble
+            lp = dmap # salva as partículas numa daterminada região
+        else:
+            r0 = pos[['x','y']] # um r0 por ensamble
+            lp = dmap # salva as partículas numa daterminada região
+    if periodic: 
+        mic = rfup[["micx", "micy"]].to_numpy()*np.array([dimx, dimy])
     
     # Separar aqui por região
 
     for i in range(div):
+
+
         #mesmas particulas
-        # msq[step,i] = np.mean(np.sum(np.square(pos.loc[lp[i],['x','y']] + mic[lp[i],:] - r0.loc[lp[i]]),axis=1))
+        msq[step,i] = np.mean(np.sum(np.square(pos.loc[lp[i],['x','y']] + mic[lp[i],:] - r0.loc[lp[i]]),axis=1))
         # diferentes particulas
         KE[step,i] = np.mean(rfup.loc[dmap[i],'K'])
-        tauk[step,i] = np.sum(vel.loc[dmap[i],'v_x'] * vel.loc[dmap[i],'v_y'] * rfup.loc[dmap[i],'m'])/Vol
+        tauk[step,i] = np.sum(vel.loc[dmap[i],'v_x'] * vel.loc[dmap[i],'v_y'] * m[0])/Vol
         tauxyp[step,i] = np.sum(rfup.loc[dmap[i],'RxFy'])/Vol
         tauyxp[step,i] = np.sum(rfup.loc[dmap[i],'RyFx'])/Vol
 
@@ -125,14 +157,20 @@ def density_map(x, dimx ,div):
 # Calculamos t * viscosidade*2*kT/Vol. kT = KE
 
 
-etat_kk = np.zeros(esb_len)
-etat_kp1 = np.zeros(esb_len)
-etat_pp1 = np.zeros(esb_len)
-etat_kp2 = np.zeros(esb_len) 
-etat_pp2 = np.zeros(esb_len)
+etat_kk = np.zeros((esb_len,1))
+etat_kp1 = np.zeros((esb_len,1))  
+etat_pp1 = np.zeros((esb_len,1))  
+etat_kp2 = np.zeros((esb_len,1))  
+etat_pp2 = np.zeros((esb_len,1))  
+etat_fit1 = np.zeros((esb_len,1))  
+etat_fit2 = np.zeros((esb_len,1))  
+msq_fit = np.zeros((esb_len,1))  
+msq2 = np.zeros((esb_len,1))  
+
+
 
 if (div > 1):
-    overdiv = np.zeros((div,2))
+    overdiv = np.zeros((div,3))
 
 for i in range(div):
     etat_kk = 0*etat_kk
@@ -140,15 +178,16 @@ def density_map(x, dimx ,div):
     etat_pp1 = 0*etat_pp1
     etat_kp2 = 0*etat_kp2
     etat_pp2 = 0*etat_pp2
+    msq2 = 0*msq2
     for ii in range(1,esb_len): # ao longo de cada ensemble  
         for j in range(nesb): # ao longo dos ensambles
             start = j*esb_len
             fin =  j*esb_len + ii
-            etat_kk[ii]  += np.trapz(tauk[start:fin,i])**2
-            etat_kp1[ii] += np.trapz(tauk[start:fin,i])*np.trapz(tauxyp[start:fin,i])
-            etat_pp1[ii] += np.trapz(tauxyp[start:fin,i])**2 
-            etat_kp2[ii] += np.trapz(tauk[start:fin,i])*np.trapz(tauyxp[start:fin,i])
-            etat_pp2[ii] += np.trapz(tauyxp[start:fin,i])**2 
+            etat_kk[ii,0]  += np.trapz(tauk[start:fin,i])**2
+            etat_kp1[ii,0] += np.trapz(tauk[start:fin,i])*np.trapz(tauxyp[start:fin,i])
+            etat_pp1[ii,0] += np.trapz(tauxyp[start:fin,i])**2 
+            etat_kp2[ii,0] += np.trapz(tauk[start:fin,i])*np.trapz(tauyxp[start:fin,i])
+            etat_pp2[ii,0] += np.trapz(tauyxp[start:fin,i])**2 
 
     # Viscosidade * t
 
@@ -159,29 +198,35 @@ def density_map(x, dimx ,div):
     etat_pp2 = (etat_pp2 * Vol / (2*kT[i]))/nesb
 
     # Para descobrir  fazemos um curve fit linear no último terço de pontos
-    t = np.linspace(0,esb_len-1, esb_len)
-    eta_kk = np.polyfit(t[int(esb_len/2):esb_len],etat_kk[int(esb_len/2):esb_len],1)
-    eta_kp1 = np.polyfit(t[int(esb_len/2):esb_len],etat_kp1[int(esb_len/2):esb_len],1)
-    eta_pp1 = np.polyfit(t[int(esb_len/2):esb_len],etat_pp1[int(esb_len/2):esb_len],1)
-    eta_kp2 = np.polyfit(t[int(esb_len/2):esb_len],etat_kp2[int(esb_len/2):esb_len],1)
-    eta_pp2 = np.polyfit(t[int(esb_len/2):esb_len],etat_pp2[int(esb_len/2):esb_len],1)
+    t = np.linspace(0,esb_len-1, esb_len)*dt
+    eta_kk  =  np.polyfit(t[int(esb_len/2):esb_len],etat_kk[int(esb_len/2):esb_len,0],1)
+    eta_kp1 = np.polyfit(t[int(esb_len/2):esb_len],etat_kp1[int(esb_len/2):esb_len,0],1)
+    eta_pp1 = np.polyfit(t[int(esb_len/2):esb_len],etat_pp1[int(esb_len/2):esb_len,0],1)
+    eta_kp2 = np.polyfit(t[int(esb_len/2):esb_len],etat_kp2[int(esb_len/2):esb_len,0],1)
+    eta_pp2 = np.polyfit(t[int(esb_len/2):esb_len],etat_pp2[int(esb_len/2):esb_len,0],1)
 
     # # Calcula a Difusividade
-    # D = np.polyfit(t,msq,1)
+    for j in range(nesb):
+        start = j*esb_len
+        fin = (j+1)*esb_len
+        msq2[:,0] += msq[start:fin,i]
+    msq2 = msq2/nesb
+
+    D = np.polyfit(np.arange(0, len(msq2[int(esb_len/2):esb_len,0]),1)*dt,msq2[int(esb_len/2):esb_len,0],1)
+    overdiv[i,2] = D[0]/4
 
     print("Region: {}\n".format(i))
     fig, axs = plt.subplots(1,3)
-    # axs[1,0].figure()
-
-    # # Plota a difusividade
-    # axs[1,0].scatter(t,msq[:,i])
-    # axs[1,0].plot(t,t*D[0] + D[1],'k')
-    # axs[1,0].title('Mean square displacement')
-    # props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-    # string = "D = {}".format(D[0]/4)
-    # print(string + "\n")
-    # axs[1,0].text(0.95,0.05,string, transform=axs[1,0].transAxes, fontsize=10, verticalalignment='bottom', bbox=props)
 
+    # Plota a difusividade
+    msq_fit[:,0] = np.arange(0, len(msq2),1)*dt*D[0] 
+    axs[0].scatter(np.arange(0, len(msq2),1) ,msq2)
+    axs[0].plot(np.arange(0, len(msq2),1) ,msq_fit,'k')
+    axs[0].set_title('Mean square displacement')
+    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+    string = "D = {}".format(D[0]/4)
+    print(string + "\n")
+    axs[0].text(0.95,0.05,string, transform=axs[0].transAxes, fontsize=10, verticalalignment='bottom', bbox=props)
 
     # Plota as viscosidades usando taupxy
 
@@ -194,10 +239,12 @@ def density_map(x, dimx ,div):
     axs[1].plot(t, t*eta_kp1[0] + etat_kp1[1],'b')
     axs[1].plot(t, t*eta_pp1[0] + etat_pp1[1],'r')
 
-    axs[1].plot(t, t*(eta_kk[0] + eta_kp1[0] + eta_pp1[0] ) + etat_kk[1] + etat_kp1[1] + etat_pp1[1],'k',linewidth=2, label='t*eta')
+    etat_fit1[:,0] = t*(eta_kk[0] + eta_kp1[0] + eta_pp1[0] ) + etat_kk[1] + etat_kp1[1] + etat_pp1[1]
+    axs[1].plot(t,etat_fit1[:,0],'k',linewidth=2, label='t*eta')
     axs[1].legend()
     axs[1].set_title("Viscosity using tau_yx")
 
+
     props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
     string = "etaxy = {:.3}".format(eta_kk[0]+eta_kp1[0]+eta_pp1[0])
     overdiv[i,0] = eta_kk[0]+eta_kp1[0]+eta_pp1[0]
@@ -213,6 +260,7 @@ def density_map(x, dimx ,div):
     axs[2].plot(t, t*eta_kp2[0] + etat_kp2[1],'b')
     axs[2].plot(t, t*eta_pp2[0] + etat_pp2[1],'r')
 
+    etat_fit2[:,0] = t*(eta_kk[0] + eta_kp2[0] + eta_pp2[0] ) + etat_kk[1] + etat_kp2[1] + etat_pp2[1]
     axs[2].plot(t, t*(eta_kk[0] + eta_kp2[0] + eta_pp2[0] ) + etat_kk[1] + etat_kp2[1] + etat_pp2[1],'k',linewidth=2, label='t*eta')
     axs[2].legend()
     axs[2].set_title("Viscosity using tau_xy.")
@@ -225,10 +273,20 @@ def density_map(x, dimx ,div):
     print("\nCom tau_x]yx: \neta_kk = {}\neta_kp = {}\neta_pp = {}\neta = {}\n".format(eta_kk[0],eta_kp2[0],eta_pp2[0], eta_kk[0]+eta_kp2[0]+eta_pp2[0]))
 
     fig.suptitle("Region {}".format(i))
+    t = t.reshape((len(t),1))
+    csv_out = pd.DataFrame(data=np.concatenate((etat_kk,etat_kp1,etat_pp1,etat_fit1,etat_kp2,etat_pp2, etat_fit2, msq2, msq_fit,t),axis=1), columns=["eta_kk","eta_kp_xy","eta_pp_xy","fit_eta_xy","eta_kp_yx","eta_pp_yx","fit_eta_yx","msq","msq_fit","t"])
 
+    csv_out.to_csv("Region_{}.csv".format(i),index=False,sep='\t')
 plt.figure()
 plt.plot(overdiv[:,0],label='eta_xy')
 plt.plot(overdiv[:,1],label='eta_yx')
-plt.title('Over the positions')
+plt.title('eta Over the positions')
+
+plt.figure()
+plt.plot(overdiv[:,2],label='D')
+plt.title('Dif Over the positions')
+
+
+plt.show()
+
 
-plt.show()
\ No newline at end of file
diff --git a/tecplot_lines.py b/tecplot_lines.py
index ce3f813..ce4902f 100644
--- a/tecplot_lines.py
+++ b/tecplot_lines.py
@@ -1,7 +1,7 @@
 import numpy as np
 import tecplot as tp
 import pandas as pd
-from tecplot.constant import PlotType, Color, LinePattern, AxisTitleMode
+from tecplot.constant import PlotType, Color,Units, LinePattern, AxisTitleMode
 import sys
 # if '-c' in sys.argv:
 #     tp.session.connect()
@@ -12,8 +12,29 @@
 csv_input = pd.read_csv('Distributions.csv')
 hl = list(csv_input) #headers list
 
+## NOME DAS ZONAS ##
+zonas = \
+[['A','neon_3sep_stat'], \
+['B','carlos_2sep_stat'], \
+['C','neon4sep_stat'], \
+['D','kubuntu_3sep_stat'], \
+['E','kubuntu_5sep_stat'], \
+['F','suse_6sep_stat'], \
+['G','rafa_3sep_stat'], \
+['H','carlos_4sep_stat'], \
+['I','rafa_4sep_stat'], \
+['J','kubuntu_4sep_stat'], \
+['K','rafa_6sep_stat'], \
+['L','suse_3sep_stat'], \
+['M','rafa_5sep_stat'], \
+['N','carlos_3sep_stat'], \
+['O','kubuntu_7_sep_stat'], \
+['P','suse_7sep_STAT'], \
+['Q','rafa_7sep_stat']]
+
+
 for i in range(len(hl)):
-    print("{} | {}\n".format(i,hl[i]))
+    print("{} | {} | {}\n".format(i,[zonas[j][0] for j in range(len(zonas)) if zonas[j][1] == hl[i]][0],hl[i]))
 linhas = input("Entre os numeros dos dados que quer plotar separados por espaço:\n").split()
 linhas = [int(x) for x in linhas]
 
@@ -23,8 +44,8 @@
 frame.width = 11
 dataset = frame.create_dataset("Data", ['x', 'y'])
 for dado in linhas:
-    col = hl[dado]
-    x = csv_input[col].to_numpy()
+    col = [zonas[j][0] for j in range(len(zonas)) if zonas[j][1] == hl[dado]][0]
+    x = csv_input[hl[dado]].to_numpy()
     zone = dataset.add_ordered_zone(col, len(x))
     zone.values('x')[:] = np.linspace(0,1,len(x))   
     zone.values('y')[:] = x