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polar_freq.py
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mat
import sys
import glob, os, re
mat.rcParams.update({'font.size': 16})
mat.rcParams["font.family"] = "Times New Roman"
size=12
rs=1.0
Lambda=1.0
Beta=20
############## 3D ##################################
# kF=(9.0*np.pi/4.0)**(1.0/3.0)/rs #3D
###### Bare Green's function #########################
# Bubble=0.08871 # 3D, Beta=0.5, rs=1
# Bubble=0.0971916 #3D, Beta=10, rs=1
# Bubble=0.0971613 #3D, T=0.04Ef, rs=1
# Bubble= 0.097226 # 3D, zero temperature, rs=1
###### Fock dressed Green's function ###################
# Bubble, Density=0.088883, 0.2387 #3D, Beta=0.1, rs=1, Lambda=1.0
############## 2D ##################################
###### Bare Green's function #########################
kF=np.sqrt(2.0)/rs #2D
# Bubble=0.11635 #2D, Beta=0.5, rs=1
Bubble=0.15916 #2D, Beta=10, rs=1
ScanOrder=[1,2,3]
# ScanOrder=[3]
Index={}
Index[1]=[1,]
Index[2]=[1,]
Index[3]=[1,2,3]
# Index[4]=[1,]
# Index[5]=[1,]
DataAll={}
Data={}
DataOrderByOrder={}
DataAtOrder={}
Normalization=1
folder="./Beta{0}_rs{1}_lambda{2}_freq/".format(Beta, rs, Lambda)
files=os.listdir(folder)
for order in ScanOrder:
Num=0
data0=None
for f in files:
if re.match("group"+str(order)+"_pid[0-9]+.dat", f):
print f
Num+=1
d=np.loadtxt(folder+f)
if data0 is None:
data0=d
else:
data0[:,1:]+=d[:,1:]
print "Found {0} files.".format(Num)
data0[:,1:]/=Num
# data0[:,1]*=(-1)**(order-1)
# print data0
DataAll[order]=np.array(data0)
# Data[order]=[]
# for i in Index[order]:
# Num=0
# data=None
# # for f in glob.glob("Diag"+str(order)+"_*_"+str(i)+".dat"):
# for f in files:
# if re.match("GROUP"+str(order-1)+"DIAG"+str(i)+"_PID[0-9]+.dat", f):
# # print f
# Num+=1
# d=np.loadtxt(folder+f)
# # print f, d[0,1]
# if data is None:
# data=d
# else:
# data[:,1:]+=d[:,1:]
# print "Found {0} files.".format(Num)
# data[:,1:]/=Num
# # data[:,1]*=(-1)**(order-1)
# # print data
# Data[order].append(np.array(data))
Normalization=DataAll[1][0,1]/Bubble
for key in DataAll.keys():
DataAll[key][:,1]/=Normalization
# for i in range(len(Data[key])):
# Data[key][i][:,1]/=Normalization
for i in ScanOrder:
DataOrderByOrder[i]=np.copy(DataAll[i])
# DataOrderByOrder[2]=np.copy(DataAll[2])
# DataOrderByOrder[3]=np.copy(DataAll[3])
# DataOrderByOrder[4]=np.copy(DataAll[4])
# DataOrderByOrder[5]=np.copy(DataAll[5])
DataOrderByOrder[2][:,1]*=-1.0
# DataOrderByOrder[4][:,1]*=-1.0
DataAtOrder[1]=np.copy(DataOrderByOrder[1])
DataAtOrder[2]=np.copy(DataOrderByOrder[1])
DataAtOrder[2][:,1]+=DataOrderByOrder[2][:,1]
DataAtOrder[3]=np.copy(DataOrderByOrder[1])
DataAtOrder[3][:,1]+=DataOrderByOrder[2][:,1]
DataAtOrder[3][:,1]+=DataOrderByOrder[3][:,1]
def ErrorPlot(p, d, color, marker, label=None, size=4, shift=False):
data=np.array(d)
data[:,0]/=kF
if shift:
data[:,1]-=data[0,1]
p.plot(data[:,0],data[:,1],marker=marker,c=color, label=label,lw=1, markeredgecolor="None", linestyle="--", markersize=size)
# p.errorbar(data[:,0],data[:,1], yerr=data[:,2], c=color, ecolor=color, capsize=0, linestyle="None")
# p.fill_between(data[:,0], data[:,1]-data[:,2], data[:,1]+data[:,2], alpha=0.5, facecolor=color, edgecolor=color)
w=1-0.429
fig, ax = plt.subplots()
# ax=fig.add_axes()
# ax = fig.add_subplot(122)
# plt.subplot(1,2,2)
ColorList=['k','r', 'b', 'g', 'm', 'c']
for i in range(0, len(ScanOrder)):
o=ScanOrder[i]
ErrorPlot(ax, DataOrderByOrder[o], ColorList[i], 's', "Order {0}".format(o))
# ErrorPlot(ax, Data[1][0], 'k', 's', "Diag 1")
# ErrorPlot(ax, tmp, 'm', 's', "Diag 3+c 1")
# ErrorPlot(ax, DataAll[3], 'k', 'o', "Order 3")
# ErrorPlot(ax, Data[2][1], 'g', 'o', "Order 3 counterbubble 1")
# ErrorPlot(ax, Data[2][2], 'g', '*', "Order 3 counterbubble 2")
# ErrorPlot(ax, Data[2][3], 'g', '>', "Order 3 counterbubble 3")
# ErrorPlot(ax, Data[1][1], 'olive', 'o', "Order 3 shift 1")
# ErrorPlot(ax, Data[1][2], 'olive', '*', "Order 3 shift 2")
# ErrorPlot(ax, Data[3][0], 'k', 's', "Diag 1", shift=True)
# ErrorPlot(ax, Data[3][1], 'g', 's', "Diag 2", shift=True)
# ErrorPlot(ax, Data[3][2], 'r', '*', "Diag 3", shift=True)
# ErrorPlot(ax, Data[3][3], 'b', 's', "Diag 4", shift=True)
# ErrorPlot(ax, Data[3][4], 'olive', '*', "Diag 5", shift=True)
# ErrorPlot(ax, Data[3][5], 'm', 's', "Diag 6", shift=True)
# ErrorPlot(ax, Data[3][6], 'c', '*', "Diag 7", shift=True)
# ErrorPlot(ax, Data[5], 'g', 's', "Diag 6")
x=np.arange(0,0.2,0.001)
y=0.5*x**w
# ax.plot(x,y,'k-', lw=2)
ax.set_xlim([0.0, DataAll[1][-1,0]/kF])
# ax.set_xticks([0.0,0.04,0.08,0.12])
# ax.set_yticks([0.35,0.4,0.45,0.5])
# ax.set_ylim([-0.02, 0.125])
# ax.set_ylim([0.07, 0.125])
ax.set_xlabel("$q/k_F$", size=size)
# ax.xaxis.set_label_coords(0.97, -0.01)
# # ax.yaxis.set_label_coords(0.97, -0.01)
# ax.text(-0.012,0.52, "$-I$", fontsize=size)
ax.set_ylabel("$-P(\omega=0, q)$", size=size)
# ax.text(0.02,0.47, "$\\sim {\\frac{1}{2}-}\\frac{1}{2} {\\left( \\frac{r}{L} \\right)} ^{2-s}$", fontsize=28)
plt.legend(loc=1, frameon=False, fontsize=size)
# plt.title("2D density integral")
plt.tight_layout()
# plt.savefig("spin_rs1_lambda1.pdf")
plt.show()