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plots.py
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plots.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Feb 3 12:23:59 2021
@author: Paul
"""
import matplotlib.pyplot as plt
import numpy as np
import Miscfunc as M
def AllUsefulVariablesSeparate(r,UsefulVarDic,j=0):
Nvar = len(UsefulVarDic)
plt.figure('AllvariableSeparated',figsize=(15,Nvar))
for i,key in enumerate(UsefulVarDic.keys()):
infos = UsefulVarDic[key]
plt.subplot(int(Nvar/3)+1,3,i+1)
plt.title(infos['name'])
plt.plot(r['t'],r[key],)
plt.xlabel('time')
if infos['unit']=='no' : plt.ylabel(infos['name'])
if infos['type']=='extensive' :
plt.yscale("log")
plt.ylabel(infos['name']+'('+infos['unit']+')')
plt.show()
def PhilAndInvest(p):
'''
Show behavior functions
'''
plt.figure('Behaviors Function')
ax1=plt.subplot(121)
ax2=plt.subplot(122)
plt.title('Behavior functions')
x = np.arange(0,1,0.01)
ax1.plot(x,M.philips(x,p));ax1.set_xlabel('$\lambda$');ax1.set_ylabel('$\Phi(\lambda)$')
ax2.semilogy(x,M.fk(x,p) );ax2.set_xlabel('$\pi$');ax2.set_ylabel('$\kappa(\pi)$')
plt.show()
def GoodwinKeenTypical(r,p,title=''):
'''
Typical 3-Dimension phase-plot
'''
cm = plt.cm.jet(np.linspace(0,1,p['Nx']))
plt.figure('GraphesTemporels2',figsize=(10,5))
ax1=plt.subplot(131)
ax2=plt.subplot(132)
ax3=plt.subplot(133)
for j in range(p['Nx']):
ax1.plot(r['t'],r['lambda'][:,j],c=cm[j,:])
ax2.plot(r['t'],r['omega'][:,j] ,c=cm[j,:])
ax3.plot(r['t'],r['d'][:,j] ,c=cm[j,:])
ax1.set_ylabel('$\lambda$')
ax2.set_ylabel('$\omega$')
ax3.set_ylabel('d')
#ax2.plot(r['t'],r['d'] [:,j] ,c='k',label='d')
plt.legend()
plt.title(title)
plt.show()
def omegalambdacycles(r,p,title=''):
'''
2-D omega-lambda phase portrait
'''
cm = plt.cm.jet(np.linspace(0,1,p['Nx']))
plt.figure('Cycles Goodwin2')
for j in range(p['Nx']): plt.plot(r['omega'][:,j],r['lambda'][:,j],c=cm[j,:])
plt.plot(r['omega'][0,0],r['lambda'][0,0],'*')
plt.axis('scaled')
plt.title(title)
plt.show()
def GraphesIntensive(r,p):
plt.figure('IntensiveVariable',figsize=(15,10))
plt.subplot(321);plt.plot(r['t'],r['omega'] ,);plt.xlabel('time');plt.ylabel('wage share')
plt.subplot(322);plt.plot(r['t'],r['lambda'],);plt.xlabel('time');plt.ylabel('employement')
plt.subplot(323);plt.plot(r['t'],r['d'] ,);plt.xlabel('time');plt.ylabel('rel debt')
plt.subplot(324);plt.plot(r['t'],r['pi'] ,);plt.xlabel('time');plt.ylabel('rel profit')
plt.subplot(325);plt.plot(r['t'],r['i'] ,);plt.xlabel('time');plt.ylabel('inflation')
plt.subplot(326);plt.plot(r['t'],r['g'] ,);plt.xlabel('time');plt.ylabel('growth')
plt.show()
def GraphesExtensive(r,p,j=0):
plt.figure('IntensiveVariable',figsize=(10,5))
plt.subplot(121)
plt.semilogy(r['t'],r['Y'][:,j] ,label='GDP')
plt.plot( r['t'],r['K'][:,j] ,label='Kapital')
#plt.plot( r['t'],r['p'][:,j] ,label='Price')
plt.plot( r['t'],r['Pi'][:,j] ,label='Profit')
plt.plot( r['t'],r['D'][:,j] ,label='Debt')
plt.plot( r['t'],r['I'][:,j] ,label='Investment')
plt.xlabel('time');plt.ylabel('Money')
plt.legend()
plt.subplot(122)
plt.semilogy(r['t'],r['N'][:,j],label='Population');
plt.plot (r['t'],r['L'][:,j],label='Labor');
plt.xlabel('time');plt.ylabel('Humans')
plt.legend()
plt.show()
def PhasewithG(r,p,op,title='skp'):
'''
Phase diagram with growth
'''
if title=='skp': title='$\eta=$'+str(p['eta'])+'\n, $b =$'+str(p['b'])+'\n r='+str(p['r'])
cm = plt.cm.jet(np.linspace(0,1,p['Nx']))
plt.figure('Osc2')
ax1=plt.subplot(231)
ax2=plt.subplot(232)
ax3=plt.subplot(233)
ax4=plt.subplot(212)
for j in range(p['Nx']):
ax1.plot(r['omega'] [:,j],r['g'] [:,j],c=cm[j,:])
ax2.plot(r['lambda'][:,j],r['g'] [:,j],c=cm[j,:])
ax3.plot(r['omega'] [:,j],r['lambda'][:,j],c=cm[j,:],)
ax4.plot(r['t'] ,r['g'] [:,j],c=cm[j,:],)
ax1.set_xlabel('$\omega$')
ax1.set_ylabel('g')
ax2.set_xlabel('$\lambda$')
ax2.set_ylabel('g')
ax3.set_xlabel('$\omega$')
ax3.set_ylabel('$\lambda$')
ax4.set_xlabel('$t$')
ax4.set_ylabel('$g$')
plt.suptitle(title)
plt.show()
def PeriodPlots(r,p,op,title='skp'):
if title=='skp':title='$b =$'+str(p['b'])+'\n r='+str(p['r'])
cm = plt.cm.jet(np.linspace(0,1,p['Nx']))
plt.figure('PeriodMeasures')
ax1=plt.subplot(231)
ax2=plt.subplot(232)
ax3=plt.subplot(233)
ax4=plt.subplot(234)
ax5=plt.subplot(235)
ax6=plt.subplot(236)
for j in range(p['Nx']):
Tv=[]
gv=[]
tv=[]
lm=[]
deltalamb=[]
for z in range(len(r['PeriodID'][j])-1):
id1 = r['PeriodID'][j][z]
id2 = r['PeriodID'][j][z+1]
t2 = r['t'][id2]
t1 = r['t'][id1]
T =t2-t1
gmoy=(np.sum(r['g'][id1:id2])*p['dt'])/T
Tv.append(T)
gv.append(gmoy)
tv.append((t1+t2)/2)
lm.append(r['lambda'][id1,j])
deltalamb.append(r['lambda'][id1,j]-r['lambda'][id2,j])
#plt.plot((t1+t2)/2,T,'*')
#plt.plot(T,gmoy,'*')
ax1.plot(Tv,gv ,c=cm[j,:])
ax2.plot(tv,Tv ,c=cm[j,:])
ax3.plot(tv,gv ,c=cm[j,:])
ax4.plot(lm,Tv ,c=cm[j,:])
ax5.plot(lm,gv ,c=cm[j,:])
ax6.semilogy(tv,np.array(deltalamb)/np.array(Tv),c=cm[j,:])
# ax6.plot(lm,gv,'-*',c=cm[j,:])
ax1.set_xlabel('Period of a cycle')
ax1.set_ylabel('Mean growth speed')
ax2.set_xlabel('time evolution')
ax2.set_ylabel('Period of a cycle')
ax3.set_xlabel('time')
ax3.set_ylabel('Mean growth speed')
ax4.set_xlabel('maximum $\lambda$ in cycle')
ax4.set_ylabel('Period')
ax5.set_xlabel('maximum $\lambda$ in cycle')
ax5.set_ylabel('Mean growth speed')
ax6.set_xlabel('time')
ax6.set_ylabel('deltalamb/t')
#ax6.legend()
#ax6.set_axis_off()
plt.suptitle(title)
plt.show()
def map2DLambdaT(r,op,title='skp'):
"""
if title='skp':
title='Superposed "synchronisation" with meso Philips ,'+
'$N_x$ ='+str(p['Nx'])+
', $\lambda_s =$' +"{0:.2f}".format(p['lambdamin']) +
', $\Delta \lambda =$'+"{0:.2f}".format(p['lambdamax']-p['lambdamin']))
"""
plt.figure('2DLambdaT',figsize=(20,10))
OO,LL = np.meshgrid(r['t'],r['lambda'][0,:])
plt.pcolormesh(OO,LL,r['lambda'].T,cmap='inferno')
plt.xlabel('time')
plt.ylabel('$\lambda(t=0)$')
plt.colorbar()
plt.title(title)
plt.show()
def MesoMeanSTD(r,p,op,title='skp'):
'''
if title=='skp': title = 'Leontiev r='+str(p['r'])+
', Nx ='+str(p['Nx'])+
' \n $\mu_{\lambda}=$'+str(lmmoy)+
', $\Delta_{\lambda}=$'+str(deltalamb)+
'\n $g_{global}$='+str(p['g1'])
'''
ls='-'
lw=.5
cm = plt.cm.jet(np.linspace(0,1,p['Nx']))
meanlambd= np.mean(r['lambda'],axis=1)
stdlambd = np.std( r['lambda'],axis=1)
meanomega= np.mean(r['omega'] ,axis=1)
stdomega = np.std( r['omega'] ,axis=1)
meand = np.mean(r['d'] ,axis=1)
stdd = np.std( r['d'] ,axis=1)
plt.figure('GraphesTemporels2'+str(int(100*p['g1'])),figsize=(15,20))
ax0=plt.subplot(231)
ax1=plt.subplot(232)
ax2=plt.subplot(233)
ax3=plt.subplot(234)
ax4=plt.subplot(235)
ax5=plt.subplot(236)
for j in range(p['Nx']):
ax1.plot(r['t'],r['lambda'][:,j],c=cm[j,:],ls=ls,lw=lw)
ax2.plot(r['t'],r['omega'][:,j] ,c=cm[j,:],ls=ls,lw=lw)
ax3.plot(r['t'],r['d'][:,j] ,c=cm[j,:],ls=ls,lw=lw)
ax0.plot(r['omega'][:,j],r['lambda'][:,j],c=cm[j,:],lw=0.3)
ax1.plot(r['t'],meanlambd ,c='k' ,lw=1)
ax1.set_xlabel('$t$')
ax1.set_ylabel('$\lambda$')
ax2.plot(r['t'],meanomega ,c='k' ,lw=1)
ax2.set_xlabel('t')
ax2.set_ylabel('$\omega$')
ax3.plot(r['t'],meand ,c='k' ,lw=1)
ax3.set_xlabel('t')
ax3.set_ylabel('d')
ax4.plot(r['t'],stdlambd ,lw=1,c='b',label='$\sigma_{\lambda}$')
ax4.plot(r['t'],stdd ,lw=1,c='r',label='$\sigma_{d}$')
ax4.plot(r['t'],stdomega ,lw=1,c='g',label='$\sigma_{\omega}$')
ax4.legend()
OO,LL = np.meshgrid(r['t'],r['lambda'][0,:])
ax5.pcolormesh(OO,LL,r['lambda'].T,cmap='gist_ncar')
ax5.set_xlabel('time')
ax5.set_ylabel('$\lambda_0$')
ax5.set_title('$\lambda$ map')
#plt.suptitle()
plt.show()