In [29]:
import h5py
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import multifile
import os
matplotlib.rcParams["figure.dpi"] = 100

import matplotlib.pyplot as plt

SMALL_SIZE = 8
MEDIUM_SIZE = 10
BIGGER_SIZE = 12

plt.rc('font', size=BIGGER_SIZE)          # controls default text sizes
plt.rc('axes', titlesize=BIGGER_SIZE)     # fontsize of the axes title
plt.rc('axes', labelsize=BIGGER_SIZE)    # fontsize of the x and y labels
plt.rc('xtick', labelsize=BIGGER_SIZE)    # fontsize of the tick labels
plt.rc('ytick', labelsize=BIGGER_SIZE)    # fontsize of the tick labels
plt.rc('legend', fontsize=BIGGER_SIZE)    # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title
plt.rcParams["font.family"] = "serif"
plt.rcParams["text.usetex"] = True
plt.rcParams["font.family"] = "cmr10",  # Computer Modern Roman
In [2]:
def get_GammaXMGamma_path():
    def tup_path_GaXMGa(k_dim, *rpa):                                             ### takes k_dim and returns an array containing the tuples along the path Gamma->X->M->Gamma through the Brillioun zone                                                                                     
        k = int(np.sqrt(k_dim))                                         # make sure k is an integer                                                                                                                                                                                       
        tup_p = []                                                      # initialise empty path tup_p                                                                                                                                                                                     
        xx=0                                                        # Gamma=(k/2-1,k/2-1); centre of Brillioun zone                                                                                                                                                                       
        yy=0
        tup_p.append([xx,yy])                                           # Start at Gamma                                                                                                                                                                                                  
        while xx < k/2:                                                 # Path from Gamma to X                                                                                                                                                                                            
            xx+=1
            tup_p.append([xx,yy])
        while yy < k/2:                                                 # Path from X to M                                                                                                                                                                                                
            yy+=1
            tup_p.append([xx,yy])
        while yy > 0 and xx > 0:                                # Path from M back to Gamma                                                                                                                                                                                               
            xx-=1
            yy-=1
            tup_p.append([xx,yy])
        return tup_p                                                    # Return path as an array of tuples                                                                                                                                                                               
    
    def tup_to_momentry(k_dim,tup):
        k = int(np.sqrt(k_dim))
        return int(tup[1]*k+tup[0])
    
    def momentry_path_GaXMGa(k_dim):
        tup_path=tup_path_GaXMGa(k_dim)                                 # calculate path in tuples                                                                                                                                                                                        
        p_len=len(tup_path)                                             # length of path                                                                                                                                                                                                  
        momentry_p=np.zeros(p_len)                                      # initialize array of zeros for path momentry_p                                                                                                                                                                   
        i=0
        for pp in range(p_len):                                         # loop over path                                                                                                                                                                                                  
            momentry=int(tup_to_momentry(k_dim,tup_path[pp]))       # convert tuples to momentries                                                                                                                                                                                    
            momentry_p[i]=momentry
            i+=1
        return momentry_p
    
    
    
    k_dim = 16*16
    path = momentry_path_GaXMGa(k_dim)
    p_len = len(path)                               # more helper variables                                                                                                                                                                                                           
    p_lenf = float(p_len)
    p = range(p_len)
    pp = np.asarray(p)/(p_lenf-1)
    for pi in p:
        #print(pi)
        if pi>2*(p_len-1)/3:
            pp[pi] = pp[int(2*(p_len-1)/3)] + np.sqrt(2)*(pp[pi]-pp[int(2*(p_len-1)/3)])
    pp = pp/pp[-1]
    return path, pp
In [3]:
dir_doped_norest = "/media/aiman/data/rest test/finite_doping_rest_test_with_no_rest"
dir_doped_rest = "/media/aiman/data/rest test/finite_doping_rest_test_with_rest/"

datnames_doped_norest = [dir_doped_norest + "/" + o for o in os.listdir(dir_doped_norest)]
datnames_doped_rest = [dir_doped_rest + "/" + o for o in os.listdir(dir_doped_rest)]
In [15]:
def load(dat_names, drop=0):
    objs = []

    for dat_dir in dat_names:
        print(dat_dir)
        mf = multifile.MultiFile(dat_dir + '/')
        obj = {}
        
        idx_M = mf.params_file["Model/Special_points"].attrs["idx_M"]
        idx_00 = mf.params_file["Model/Special_points"].attrs["idx_00"]

        obj['dir'] = dat_dir
        obj['Beta'] = mf.params_file["General"].attrs["Beta"]
        FreqCountParam = mf.params_file["General"].attrs["COUNT"]
        obj['U'] = mf.params_file["Model"].attrs["U"]
        obj['g0'] = mf.params_file["Model"].attrs["g0"]
        obj['Omega0'] = mf.params_file["Model"].attrs["OMEGA0"]
        obj['Vh'] = 2*obj['g0']*obj['g0']/obj['Omega0']

        if not mf.is_finished:
            print(dat_dir)
            continue
        #
        if not mf.is_diverged:
            drop = 0
        if drop == 0:
            f = mf.open_final_file()
        else:
            f = mf.open_file_number(mf.scales_count - drop)
        #print(f["Flow_obs/S_Wave_Susc_info"].attrs.keys())
        #print(np.array(f["Flow_obs/S_Wave_Susc_info"].keys)[0, 0])
        #print(np.array(f["lambda_func/RE_SC"]).shape, dat_dir)
    
        obj['leading_susc'] = f["Flow_obs/S_Wave_Susc_info"].attrs['Leading_name']
        obj['max_sc'] = f["Flow_obs/S_Wave_Susc_info"].attrs['RE_Max_sc'] # max(f['Flow_obs/Postprocessing_Susc_info/RE_Susc_sc'][0, :, 0, 0, 0, 0, 0, 0])  # 
        obj['max_d'] = f["Flow_obs/S_Wave_Susc_info"].attrs['RE_Max_d'] #max(f['Flow_obs/Postprocessing_Susc_info/RE_Susc_d'][0, :, 0, 0, 0, 0, 0, 0]) #
        obj['max_m'] = f["Flow_obs/S_Wave_Susc_info"].attrs['RE_Max_m'] #max(f['Flow_obs/Postprocessing_Susc_info/RE_Susc_m'][0, :, 0, 0, 0, 0, 0, 0]) #

        dsc = f['Flow_obs/Postprocessing_Susc_info/RE_Susc_sc'].shape[2] > 1
        if dsc:
            obj['max_dsc'] = max(f['Flow_obs/Postprocessing_Susc_info/RE_Susc_sc'][0, :, 1, 1, 0, 0, 0, 0])

        obj['leading_w'] = f["Flow_obs/w_info"].attrs['Leading_name']
        obj['max_w_sc'] = f["Flow_obs/w_info"].attrs['RE_Max_sc']
        obj['max_w_d'] = f["Flow_obs/w_info"].attrs['RE_Max_d']
        obj['max_w_m'] = f["Flow_obs/w_info"].attrs['RE_Max_m']
        
        obj['lambda_sc'] = np.array(f["lambda_func/RE_SC"])[:, 0, :, 0]
        obj['lambda_d'] = np.array(f["lambda_func/RE_D"])[:, 0, :, 0]
        obj['lambda_m'] = np.array(f["lambda_func/RE_M"])[:, 136, :, 0]
        #print(np.array(f["w_func/RE_SC"]).shape)
        
        mom_path, _ = get_GammaXMGamma_path()
        #print(int((np.array(f["w_func/RE_SC"]).shape[0]-1)/2))
        #print(mom_path)
        obj['w_sc'] = np.array([np.array(f["w_func/RE_SC"])[int((np.array(f["w_func/RE_SC"]).shape[0]-1)/2), int(i)] for i in mom_path])
        obj['w_d'] = np.array([np.array(f["w_func/RE_D"])[int((np.array(f["w_func/RE_SC"]).shape[0]-1)/2), int(i)] for i in mom_path])
        obj['w_m'] = np.array([np.array(f["w_func/RE_M"])[int((np.array(f["w_func/RE_SC"]).shape[0]-1)/2), int(i)] for i in mom_path])
        
        obj['chi_sc'] = np.array([np.array(f["Flow_obs/S_Wave_Susc_info/RE_Susc_sc"])[0, int(i)] for i in mom_path])
        obj['chi_d'] = np.array([np.array(f["Flow_obs/S_Wave_Susc_info/RE_Susc_d"])[0, int(i)] for i in mom_path])
        obj['chi_m'] = np.array([np.array(f["Flow_obs/S_Wave_Susc_info/RE_Susc_m"])[0, int(i)] for i in mom_path])
        
        f.close()
        
        f = mf.open_final_file()
        #print(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_d"].shape)
        chi_split_objs = {}
        for chan in ["sc", "d", "m"]:
            if chan == "sc":
                    special_idx = idx_00
            else:
                    special_idx = idx_M

            contribs_from_minus_2U = -2.0*np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_bare_vertex".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]# + np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_sc_double_counting".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_d_double_counting".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_m_double_counting".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]

            chi_split_objs[("chi_{0}_fl".format(chan))] = np.array(f["Flow_obs/S_Wave_Susc_info/RE_Susc_{0}".format(chan)])[0, special_idx]
            chi_split_objs[("chi_{0}_full".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
            chi_split_objs[("chi_{0}_bubble".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_bubble_contribution".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
            chi_split_objs[("chi_{0}_mbe".format(chan))] = -contribs_from_minus_2U/2.0
            chi_split_objs[("chi_{0}_sbe_from_sc".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_nabla_sc".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + contribs_from_minus_2U/2.0
            chi_split_objs[("chi_{0}_sbe_from_d".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_nabla_d".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + 0.5 * contribs_from_minus_2U/2.0
            chi_split_objs[("chi_{0}_sbe_from_m".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_nabla_m".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + 1.5 * contribs_from_minus_2U/2.0

            if "RE_Susc_{0}_vertex_contribution_from_M_m".format(chan) in f["Flow_obs/Postprocessing_Susc_info/"].keys():
                chi_split_objs[("chi_{0}_sbe_from_Mm".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_M_m".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
                chi_split_objs[("chi_{0}_sbe_from_Md".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_M_d".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
                chi_split_objs[("chi_{0}_sbe_from_Msc".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_M_sc".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
        
        if dsc:
            special_idx = idx_00
            chan = 'sc'    
            contribs_from_minus_2U = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_bare_vertex".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0] + np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_sc_double_counting".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0] + np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_d_double_counting".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0] + np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_m_double_counting".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0]

            chi_split_objs[("chi_d{0}_fl".format(chan))] = 0.0*np.array(f["Flow_obs/S_Wave_Susc_info/RE_Susc_{0}".format(chan)])[0, special_idx]
            chi_split_objs[("chi_d{0}_full".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0]
            chi_split_objs[("chi_d{0}_bubble".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_bubble_contribution".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0]
            chi_split_objs[("chi_d{0}_mbe".format(chan))] = -contribs_from_minus_2U/2.0
            chi_split_objs[("chi_d{0}_sbe_from_sc".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_nabla_sc".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0] + contribs_from_minus_2U/2.0
            chi_split_objs[("chi_d{0}_sbe_from_d".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_nabla_d".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0] + 0.5 * contribs_from_minus_2U/2.0
            chi_split_objs[("chi_d{0}_sbe_from_m".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_nabla_m".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0] + 1.5 * contribs_from_minus_2U/2.0
            if "RE_Susc_{0}_vertex_contribution_from_M_m".format(chan) in f["Flow_obs/Postprocessing_Susc_info/"].keys():
                chi_split_objs[("chi_d{0}_sbe_from_Mm".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_M_m".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0]
                chi_split_objs[("chi_d{0}_sbe_from_Md".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_M_d".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0]
                chi_split_objs[("chi_d{0}_sbe_from_Msc".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_M_sc".format(chan)])[0, special_idx, 1, 1, 0, 0, 0, 0]


        
        f.close()
  
        obj['chi_split'] = chi_split_objs
        
        objs.append(obj)
    return objs
In [16]:
objs_doped_norest
Out[16]:
[{'dir': '/media/aiman/data/rest test/finite_doping_rest_test_with_no_rest/dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA5_C5_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_ALLSYMM_SELFEN_FLOW',
  'Beta': 5.0,
  'U': 0.0,
  'g0': 1.3693063937629153,
  'Omega0': 1.5,
  'Vh': 2.5,
  'leading_susc': 'd',
  'max_sc': 0.48195511921022494,
  'max_d': 1.689648265865558,
  'max_m': 0.1723747007663737,
  'leading_w': 'd',
  'max_w_sc': 217.61370315301315,
  'max_w_d': 515.6000430378865,
  'max_w_m': -98.69516596880338,
  'lambda_sc': array([[0.96202551, 0.96316605, 0.96457389, 0.96550468, 0.96173561,
          0.91031826, 0.91400629, 0.91605396, 0.91754498, 0.91840822,
          0.91840825, 0.91754504, 0.91605408, 0.91400644, 0.91031848,
          0.96173585, 0.965505  , 0.96457425, 0.96316649, 0.962026  ],
         [0.95360581, 0.95439923, 0.95491544, 0.95246263, 0.96568578,
          0.90892638, 0.90519781, 0.90579417, 0.90665734, 0.90713411,
          0.90665739, 0.90579426, 0.90519794, 0.90892658, 0.96568603,
          0.95246291, 0.95491578, 0.95439964, 0.95360627, 0.95299989],
         [0.94236568, 0.94254585, 0.94200069, 0.94693081, 0.9590974 ,
          0.96473173, 0.89833634, 0.89290231, 0.89280115, 0.89324749,
          0.8932475 , 0.89280122, 0.89290241, 0.89833653, 0.96473196,
          0.95909767, 0.94693111, 0.94200106, 0.94254627, 0.94236618],
         [0.92810579, 0.93054762, 0.93438278, 0.94467616, 0.95388481,
          0.95860516, 0.88371841, 0.87682272, 0.8761134 , 0.87608148,
          0.87611344, 0.87682281, 0.88371857, 0.95860538, 0.95388507,
          0.94467646, 0.93438313, 0.93054803, 0.92810625, 0.92854477],
         [0.91273557, 0.91478481, 0.91765404, 0.92663563, 0.93552294,
          0.94482082, 0.95037081, 0.86548232, 0.85689666, 0.85544991,
          0.85544994, 0.85689672, 0.86548246, 0.95037101, 0.94482106,
          0.93552322, 0.92663598, 0.91765442, 0.91478526, 0.91273606],
         [0.89294499, 0.89496975, 0.90324079, 0.91149688, 0.92084774,
          0.93166826, 0.93894501, 0.84173732, 0.83099288, 0.82930113,
          0.83099293, 0.84173743, 0.93894519, 0.93166848, 0.92084801,
          0.91149721, 0.90324117, 0.89497017, 0.89294548, 0.89141007],
         [0.86307252, 0.86452474, 0.87260883, 0.88060309, 0.8896215 ,
          0.90076414, 0.91426664, 0.92458444, 0.8117098 , 0.79875515,
          0.79875517, 0.8117099 , 0.92458459, 0.91426685, 0.90076439,
          0.88962182, 0.88060345, 0.87260925, 0.86452519, 0.86307304],
         [0.82101411, 0.8293638 , 0.83745384, 0.84654112, 0.8576462 ,
          0.87197241, 0.89008847, 0.90565818, 0.77343375, 0.75991422,
          0.77343382, 0.90565832, 0.89008865, 0.87197265, 0.85764649,
          0.84654147, 0.83745424, 0.82936426, 0.82101461, 0.82001782],
         [0.75611439, 0.76554277, 0.77432191, 0.78399609, 0.79571194,
          0.81076043, 0.83099632, 0.85773121, 0.88443612, 0.7297695 ,
          0.72976955, 0.88443622, 0.85773138, 0.83099653, 0.81076071,
          0.79571226, 0.78399648, 0.77432234, 0.76554326, 0.75611491],
         [0.66038929, 0.67074906, 0.6818962 , 0.69521062, 0.71229353,
          0.73537429, 0.76790351, 0.81473275, 0.87127795, 0.70737915,
          0.87127803, 0.81473288, 0.7679037 , 0.73537454, 0.71229384,
          0.69521098, 0.68189661, 0.67074953, 0.66038981, 0.64876303],
         [0.40769967, 0.42268756, 0.43856001, 0.45725872, 0.48130534,
          0.51431903, 0.56206048, 0.63649846, 0.76357434, 1.00695571,
          1.00695571, 0.76357434, 0.63649846, 0.56206048, 0.51431903,
          0.48130534, 0.45725872, 0.43856001, 0.42268756, 0.40769967],
         [0.66038981, 0.67074953, 0.68189661, 0.69521098, 0.71229384,
          0.73537454, 0.7679037 , 0.81473288, 0.87127803, 0.70737915,
          0.87127795, 0.81473275, 0.76790351, 0.73537429, 0.71229353,
          0.69521062, 0.6818962 , 0.67074906, 0.66038929, 0.64876303],
         [0.75611491, 0.76554326, 0.77432234, 0.78399648, 0.79571226,
          0.81076071, 0.83099653, 0.85773138, 0.88443622, 0.72976955,
          0.7297695 , 0.88443612, 0.85773121, 0.83099632, 0.81076043,
          0.79571194, 0.78399609, 0.77432191, 0.76554277, 0.75611439],
         [0.82101461, 0.82936426, 0.83745424, 0.84654147, 0.85764649,
          0.87197265, 0.89008865, 0.90565832, 0.77343382, 0.75991422,
          0.77343375, 0.90565818, 0.89008847, 0.87197241, 0.8576462 ,
          0.84654112, 0.83745384, 0.8293638 , 0.82101411, 0.82001781],
         [0.86307304, 0.86452519, 0.87260925, 0.88060345, 0.88962182,
          0.90076439, 0.91426685, 0.92458459, 0.8117099 , 0.79875517,
          0.79875515, 0.8117098 , 0.92458444, 0.91426664, 0.90076414,
          0.8896215 , 0.88060309, 0.87260883, 0.86452474, 0.86307252],
         [0.89294548, 0.89497017, 0.90324117, 0.91149721, 0.92084801,
          0.93166848, 0.93894519, 0.84173743, 0.83099293, 0.82930113,
          0.83099288, 0.84173732, 0.93894501, 0.93166826, 0.92084774,
          0.91149688, 0.90324079, 0.89496975, 0.89294499, 0.89141007],
         [0.91273606, 0.91478526, 0.91765442, 0.92663598, 0.93552322,
          0.94482106, 0.95037101, 0.86548246, 0.85689672, 0.85544994,
          0.85544991, 0.85689666, 0.86548232, 0.95037081, 0.94482082,
          0.93552294, 0.92663563, 0.91765404, 0.91478481, 0.91273557],
         [0.92810625, 0.93054803, 0.93438313, 0.94467646, 0.95388507,
          0.95860538, 0.88371857, 0.87682281, 0.87611344, 0.87608148,
          0.8761134 , 0.87682272, 0.88371841, 0.95860516, 0.95388481,
          0.94467616, 0.93438278, 0.93054762, 0.92810579, 0.92854478],
         [0.94236618, 0.94254627, 0.94200106, 0.94693111, 0.95909767,
          0.96473196, 0.89833653, 0.89290241, 0.89280122, 0.8932475 ,
          0.89324749, 0.89280115, 0.89290231, 0.89833634, 0.96473173,
          0.9590974 , 0.94693081, 0.94200069, 0.94254585, 0.94236568],
         [0.95360627, 0.95439964, 0.95491578, 0.95246291, 0.96568603,
          0.90892658, 0.90519794, 0.90579426, 0.90665739, 0.90713411,
          0.90665734, 0.90579417, 0.90519781, 0.90892638, 0.96568578,
          0.95246263, 0.95491544, 0.95439923, 0.95360581, 0.95299989],
         [0.962026  , 0.96316649, 0.96457425, 0.965505  , 0.96173585,
          0.91031848, 0.91400644, 0.91605408, 0.91754504, 0.91840825,
          0.91840822, 0.91754498, 0.91605396, 0.91400629, 0.91031826,
          0.96173561, 0.96550468, 0.96457389, 0.96316605, 0.96202551]]),
  'lambda_d': array([[0.99047427, 0.98673212, 0.98262108, 0.97929622, 0.98288212,
          1.04217834, 1.04336972, 1.04113425, 1.03901362, 1.03798718,
          1.03798708, 1.0390134 , 1.04113383, 1.04336917, 1.04217759,
          0.9828813 , 0.97929524, 0.98262001, 0.98673087, 0.99047293],
         [0.98935448, 0.98474204, 0.97900925, 0.97245934, 0.97438806,
          1.05199712, 1.05199824, 1.04884342, 1.04659682, 1.04593426,
          1.04659666, 1.04884309, 1.05199773, 1.05199644, 0.97438729,
          0.97245842, 0.97900819, 0.98474085, 0.98935315, 0.99320819],
         [0.99273073, 0.98794504, 0.98128219, 0.97722984, 0.97217183,
          0.9756705 , 1.06294884, 1.0624535 , 1.05909162, 1.05713425,
          1.05713419, 1.05909134, 1.06245309, 1.06294821, 0.97566981,
          0.97217098, 0.97722887, 0.98128103, 0.98794379, 0.99272929],
         [0.99240281, 0.98825378, 0.98371848, 0.97786356, 0.97255715,
          0.97682993, 1.07656444, 1.07596609, 1.07272874, 1.07136023,
          1.07272856, 1.07596573, 1.0765639 , 0.97682929, 0.97255638,
          0.97786264, 0.9837174 , 0.98825256, 0.99240143, 0.99686296],
         [0.99970455, 0.99613007, 0.99149955, 0.98618789, 0.97969963,
          0.97358895, 0.97897656, 1.09555835, 1.09505591, 1.09221102,
          1.09221091, 1.09505565, 1.09555786, 0.97897601, 0.97358823,
          0.97969881, 0.98618688, 0.99149842, 0.99612875, 0.99970312],
         [1.00546065, 1.00087034, 0.99629723, 0.99028915, 0.98271442,
          0.97543374, 0.98278226, 1.12274144, 1.12235306, 1.12066305,
          1.12235287, 1.12274106, 0.98278177, 0.97543311, 0.98271365,
          0.99028822, 0.99629616, 1.0008691 , 1.00545926, 1.00872364],
         [1.01846317, 1.0140276 , 1.0102528 , 1.0050873 , 0.99812251,
          0.98893248, 0.98027154, 0.99088173, 1.16467983, 1.16475734,
          1.16475726, 1.16467951, 0.99088135, 0.98027097, 0.9889318 ,
          0.99812164, 1.00508632, 1.01025162, 1.0140263 , 1.01846168],
         [1.03372604, 1.03062075, 1.02636183, 1.02054905, 1.01213542,
          1.00088537, 0.99047413, 1.00672903, 1.23279593, 1.23298095,
          1.23279572, 1.0067287 , 0.99047366, 1.00088475, 1.01213464,
          1.02054812, 1.02636074, 1.03061951, 1.03372463, 1.03774593],
         [1.06855722, 1.06613645, 1.06283158, 1.05844349, 1.05170017,
          1.04152234, 1.02752472, 1.01465355, 1.04159115, 1.35408409,
          1.35408396, 1.04159093, 1.01465314, 1.02752421, 1.04152162,
          1.05169934, 1.05844246, 1.06283043, 1.0661351 , 1.06855575],
         [1.14245453, 1.14037449, 1.13781718, 1.13340077, 1.12596326,
          1.11399054, 1.09635078, 1.07961671, 1.12642383, 1.58205653,
          1.12642368, 1.07961641, 1.09635033, 1.11398993, 1.12596249,
          1.13339983, 1.13781608, 1.14037323, 1.14245311, 1.14331062],
         [1.49871811, 1.5042387 , 1.50783755, 1.50982557, 1.51014765,
          1.50770924, 1.49882263, 1.47504828, 1.41155078, 1.24793416,
          1.24793416, 1.41155078, 1.47504828, 1.49882263, 1.50770924,
          1.51014765, 1.50982557, 1.50783755, 1.5042387 , 1.49871811],
         [1.14245311, 1.14037323, 1.13781608, 1.13339983, 1.12596249,
          1.11398993, 1.09635033, 1.07961641, 1.12642368, 1.58205653,
          1.12642383, 1.07961671, 1.09635078, 1.11399054, 1.12596326,
          1.13340077, 1.13781718, 1.14037449, 1.14245453, 1.14331062],
         [1.06855575, 1.0661351 , 1.06283043, 1.05844246, 1.05169934,
          1.04152162, 1.02752421, 1.01465314, 1.04159093, 1.35408396,
          1.35408409, 1.04159115, 1.01465355, 1.02752472, 1.04152234,
          1.05170017, 1.05844349, 1.06283158, 1.06613645, 1.06855722],
         [1.03372463, 1.03061951, 1.02636074, 1.02054812, 1.01213464,
          1.00088475, 0.99047366, 1.0067287 , 1.23279572, 1.23298095,
          1.23279593, 1.00672903, 0.99047413, 1.00088537, 1.01213542,
          1.02054905, 1.02636183, 1.03062075, 1.03372604, 1.03774594],
         [1.01846168, 1.0140263 , 1.01025162, 1.00508632, 0.99812164,
          0.9889318 , 0.98027097, 0.99088135, 1.16467951, 1.16475726,
          1.16475734, 1.16467983, 0.99088173, 0.98027154, 0.98893248,
          0.99812251, 1.0050873 , 1.0102528 , 1.0140276 , 1.01846317],
         [1.00545926, 1.0008691 , 0.99629616, 0.99028822, 0.98271365,
          0.97543311, 0.98278177, 1.12274106, 1.12235287, 1.12066305,
          1.12235306, 1.12274144, 0.98278226, 0.97543374, 0.98271442,
          0.99028915, 0.99629723, 1.00087034, 1.00546065, 1.00872365],
         [0.99970312, 0.99612875, 0.99149842, 0.98618688, 0.97969881,
          0.97358823, 0.97897601, 1.09555786, 1.09505565, 1.09221091,
          1.09221102, 1.09505591, 1.09555835, 0.97897656, 0.97358895,
          0.97969963, 0.98618789, 0.99149955, 0.99613007, 0.99970455],
         [0.99240143, 0.98825256, 0.9837174 , 0.97786264, 0.97255638,
          0.97682929, 1.0765639 , 1.07596573, 1.07272856, 1.07136023,
          1.07272874, 1.07596609, 1.07656444, 0.97682993, 0.97255715,
          0.97786356, 0.98371848, 0.98825378, 0.99240281, 0.99686298],
         [0.99272929, 0.98794379, 0.98128103, 0.97722887, 0.97217098,
          0.97566981, 1.06294821, 1.06245309, 1.05909134, 1.05713419,
          1.05713425, 1.05909162, 1.0624535 , 1.06294884, 0.9756705 ,
          0.97217183, 0.97722984, 0.98128219, 0.98794504, 0.99273073],
         [0.98935315, 0.98474085, 0.97900819, 0.97245842, 0.97438729,
          1.05199644, 1.05199773, 1.04884309, 1.04659666, 1.04593426,
          1.04659682, 1.04884342, 1.05199824, 1.05199712, 0.97438806,
          0.97245934, 0.97900925, 0.98474204, 0.98935448, 0.99320818],
         [0.99047293, 0.98673087, 0.98262001, 0.97929524, 0.9828813 ,
          1.04217759, 1.04336917, 1.04113383, 1.0390134 , 1.03798708,
          1.03798718, 1.03901362, 1.04113425, 1.04336972, 1.04217834,
          0.98288212, 0.97929622, 0.98262108, 0.98673212, 0.99047427]]),
  'lambda_m': array([[1.01795488, 1.0134036 , 1.00796998, 1.00222357, 1.00063782,
          1.06004416, 1.06606271, 1.06651089, 1.0659821 , 1.06573234,
          1.06573236, 1.06598215, 1.06651099, 1.06606282, 1.06004432,
          1.000638  , 1.00222381, 1.00797027, 1.01340395, 1.01795527],
         [1.02159674, 1.0158864 , 1.00874526, 1.00012342, 1.00064302,
          1.07411223, 1.07981474, 1.07954924, 1.07878488, 1.07862409,
          1.07878491, 1.07954931, 1.07981483, 1.07411236, 1.00064319,
          1.00012363, 1.00874552, 1.01588672, 1.02159711, 1.02610225],
         [1.03177302, 1.02593568, 1.01786422, 1.01302051, 1.00890043,
          1.00838866, 1.09025911, 1.09618701, 1.09585636, 1.09522532,
          1.09522533, 1.09585642, 1.09618708, 1.09025923, 1.0083888 ,
          1.00890064, 1.01302074, 1.0178645 , 1.025936  , 1.03177342],
         [1.03800296, 1.03404661, 1.02821252, 1.02460848, 1.0189119 ,
          1.01716848, 1.10970711, 1.11647447, 1.11639998, 1.11594575,
          1.11640001, 1.11647453, 1.1097072 , 1.0171686 , 1.01891209,
          1.02460872, 1.0282128 , 1.03404692, 1.03800331, 1.04441758],
         [1.05748758, 1.05322983, 1.04719115, 1.04494182, 1.0395491 ,
          1.03195191, 1.02865992, 1.13529464, 1.14341776, 1.14368017,
          1.14368019, 1.14341779, 1.13529471, 1.02866002, 1.03195209,
          1.03954932, 1.0449421 , 1.04719145, 1.05323019, 1.05748796],
         [1.07759096, 1.07124957, 1.07064453, 1.06644942, 1.05917583,
          1.04912262, 1.0438847 , 1.16926978, 1.17907245, 1.17990268,
          1.17907248, 1.16926983, 1.04388479, 1.04912278, 1.05917604,
          1.06644968, 1.07064483, 1.07124991, 1.07759135, 1.08223133],
         [1.11114516, 1.10436915, 1.10558884, 1.10285493, 1.096908  ,
          1.08726027, 1.07400785, 1.06583365, 1.21653435, 1.22856882,
          1.22856883, 1.2165344 , 1.06583372, 1.074008  , 1.08726046,
          1.09690825, 1.10285521, 1.10558918, 1.10436952, 1.11114558],
         [1.15131124, 1.15469629, 1.15342642, 1.14902539, 1.14095565,
          1.12822091, 1.11025446, 1.09701859, 1.28259199, 1.29555188,
          1.28259202, 1.09701866, 1.11025459, 1.12822109, 1.14095588,
          1.14902567, 1.15342674, 1.15469666, 1.15131164, 1.15908513],
         [1.22288843, 1.22923168, 1.22960394, 1.22677387, 1.22055685,
          1.20975855, 1.19239409, 1.16639196, 1.14314996, 1.37506644,
          1.37506647, 1.14315002, 1.16639208, 1.19239424, 1.20975877,
          1.2205571 , 1.22677418, 1.22960428, 1.22923208, 1.22288885],
         [1.35130192, 1.35338723, 1.35219251, 1.34783597, 1.33931686,
          1.32431229, 1.29857409, 1.25588179, 1.2051661 , 1.47437632,
          1.20516616, 1.2558819 , 1.29857424, 1.32431248, 1.3393171 ,
          1.34783626, 1.35219284, 1.3533876 , 1.35130235, 1.34127069],
         [1.610471  , 1.61339342, 1.61389672, 1.61154416, 1.60536264,
          1.59286571, 1.56850715, 1.51959537, 1.41399199, 1.16733841,
          1.16733841, 1.41399199, 1.51959537, 1.56850715, 1.59286571,
          1.60536264, 1.61154416, 1.61389672, 1.61339342, 1.610471  ],
         [1.35130235, 1.3533876 , 1.35219284, 1.34783626, 1.3393171 ,
          1.32431248, 1.29857424, 1.2558819 , 1.20516616, 1.47437632,
          1.2051661 , 1.25588179, 1.29857409, 1.32431229, 1.33931686,
          1.34783597, 1.35219251, 1.35338723, 1.35130192, 1.34127069],
         [1.22288885, 1.22923208, 1.22960428, 1.22677418, 1.2205571 ,
          1.20975877, 1.19239424, 1.16639208, 1.14315002, 1.37506647,
          1.37506644, 1.14314996, 1.16639196, 1.19239409, 1.20975855,
          1.22055685, 1.22677387, 1.22960394, 1.22923168, 1.22288843],
         [1.15131164, 1.15469666, 1.15342674, 1.14902567, 1.14095588,
          1.12822109, 1.11025459, 1.09701866, 1.28259202, 1.29555188,
          1.28259199, 1.09701859, 1.11025446, 1.12822091, 1.14095565,
          1.14902539, 1.15342642, 1.15469629, 1.15131124, 1.15908513],
         [1.11114558, 1.10436952, 1.10558918, 1.10285521, 1.09690825,
          1.08726046, 1.074008  , 1.06583372, 1.2165344 , 1.22856883,
          1.22856882, 1.21653435, 1.06583365, 1.07400785, 1.08726027,
          1.096908  , 1.10285493, 1.10558884, 1.10436915, 1.11114516],
         [1.07759135, 1.07124991, 1.07064483, 1.06644968, 1.05917604,
          1.04912278, 1.04388479, 1.16926983, 1.17907248, 1.17990268,
          1.17907245, 1.16926978, 1.0438847 , 1.04912262, 1.05917583,
          1.06644942, 1.07064453, 1.07124957, 1.07759096, 1.08223135],
         [1.05748796, 1.05323019, 1.04719145, 1.0449421 , 1.03954932,
          1.03195209, 1.02866002, 1.13529471, 1.14341779, 1.14368019,
          1.14368017, 1.14341776, 1.13529464, 1.02865992, 1.03195191,
          1.0395491 , 1.04494182, 1.04719115, 1.05322983, 1.05748758],
         [1.03800331, 1.03404692, 1.0282128 , 1.02460872, 1.01891209,
          1.0171686 , 1.1097072 , 1.11647453, 1.11640001, 1.11594575,
          1.11639998, 1.11647447, 1.10970711, 1.01716848, 1.0189119 ,
          1.02460848, 1.02821252, 1.03404661, 1.03800296, 1.04441761],
         [1.03177342, 1.025936  , 1.0178645 , 1.01302074, 1.00890064,
          1.0083888 , 1.09025923, 1.09618708, 1.09585642, 1.09522533,
          1.09522532, 1.09585636, 1.09618701, 1.09025911, 1.00838866,
          1.00890043, 1.01302051, 1.01786422, 1.02593568, 1.03177302],
         [1.02159711, 1.01588672, 1.00874552, 1.00012363, 1.00064319,
          1.07411236, 1.07981483, 1.07954931, 1.07878491, 1.07862409,
          1.07878488, 1.07954924, 1.07981474, 1.07411223, 1.00064302,
          1.00012342, 1.00874526, 1.0158864 , 1.02159674, 1.02610226],
         [1.01795527, 1.01340395, 1.00797027, 1.00222381, 1.000638  ,
          1.06004432, 1.06606282, 1.06651099, 1.06598215, 1.06573236,
          1.06573234, 1.0659821 , 1.06651089, 1.06606271, 1.06004416,
          1.00063782, 1.00222357, 1.00796998, 1.0134036 , 1.01795488]]),
  'w_sc': array([217.61370315, 209.86584284, 193.48603699, 178.33276549,
         167.41210737, 160.16655673, 155.63137047, 153.14377897,
         152.35094895, 152.36723257, 152.34138012, 152.07291212,
         151.36294333, 150.24125825, 149.03030452, 148.12750976,
         147.79790163, 148.47569637, 150.56818116, 154.28377432,
         160.10469628, 169.07140094, 183.18433009, 203.86658315,
         217.61370315]),
  'w_d': array([260.24347819, 261.41531044, 263.79823524, 265.14581147,
         264.22572791, 261.6121369 , 258.72907598, 256.63455937,
         255.87797987, 261.03709956, 277.89617717, 310.93718812,
         366.96173091, 442.23841087, 498.59874165, 514.97604768,
         515.43191251, 509.28543002, 432.84917329, 347.88291558,
         304.64543952, 282.67739246, 270.09426018, 262.76845173,
         260.24347819]),
  'w_m': array([-61.48220638, -61.42475676, -61.31405068, -61.26597761,
         -61.33535219, -61.4857582 , -61.64437091, -61.75843539,
         -61.79960843, -61.53470344, -60.75685281, -59.53848573,
         -58.08363216, -56.82885045, -56.22920736, -56.16429138,
         -56.20880834, -56.18700194, -56.9965893 , -58.52359544,
         -59.71761098, -60.49817456, -61.02056245, -61.35879329,
         -61.48220638]),
  'chi_sc': array([0.48195512, 0.45055422, 0.38416937, 0.32275548, 0.27849572,
         0.2491306 , 0.23075019, 0.22066836, 0.21745514, 0.21752113,
         0.21741636, 0.2163283 , 0.2134509 , 0.20890488, 0.20399707,
         0.20033818, 0.19900233, 0.20174933, 0.21022985, 0.22528859,
         0.24887989, 0.28522058, 0.34241813, 0.42624014, 0.48195512]),
  'chi_d': array([0.65472709, 0.65947635, 0.66913398, 0.6745955 , 0.67086654,
         0.66027405, 0.64858945, 0.64010069, 0.63703439, 0.65794352,
         0.72627078, 0.86018096, 1.08723988, 1.39232477, 1.62074459,
         1.68711931, 1.68896686, 1.6640561 , 1.35427162, 1.00991635,
         0.83468146, 0.74564832, 0.69465081, 0.66496042, 0.65472709]),
  'chi_m': array([0.150822  , 0.15105484, 0.15150351, 0.15169835, 0.15141718,
         0.15080761, 0.15016477, 0.14970249, 0.14953562, 0.15060924,
         0.15376175, 0.15869961, 0.16459591, 0.16968134, 0.17211161,
         0.1723747 , 0.17219428, 0.17228266, 0.16900152, 0.1628128 ,
         0.15797364, 0.15481013, 0.15269298, 0.15132218, 0.150822  ]),
  'chi_split': {'chi_sc_fl': 0.48195511921022494,
   'chi_sc_full': 0.5754313997406323,
   'chi_sc_bubble': 0.2820785917858855,
   'chi_sc_mbe': 0.06620641616088081,
   'chi_sc_sbe_from_sc': 0.24882777577609688,
   'chi_sc_sbe_from_d': -0.00973405732471733,
   'chi_sc_sbe_from_m': 0.2534815001410311,
   'chi_sc_sbe_from_Mm': 0.0,
   'chi_sc_sbe_from_Md': 0.0,
   'chi_sc_sbe_from_Msc': 0.0,
   'chi_d_fl': 1.6889668585170456,
   'chi_d_full': 1.8023238283155445,
   'chi_d_bubble': 0.22386085567590522,
   'chi_d_mbe': 0.22006079528108624,
   'chi_d_sbe_from_sc': -0.09461026345236478,
   'chi_d_sbe_from_d': 1.3586824695060296,
   'chi_d_sbe_from_m': -0.09522320559534281,
   'chi_d_sbe_from_Mm': 0.0,
   'chi_d_sbe_from_Md': 0.0,
   'chi_d_sbe_from_Msc': 0.0,
   'chi_m_fl': 0.1721942803342969,
   'chi_m_full': 0.14950416402986555,
   'chi_m_bubble': 0.22386085567590522,
   'chi_m_mbe': -0.030507618380831127,
   'chi_m_sbe_from_sc': -0.09494291344788883,
   'chi_m_sbe_from_d': 0.025657008295558482,
   'chi_m_sbe_from_m': -0.16411634501313413,
   'chi_m_sbe_from_Mm': 0.0,
   'chi_m_sbe_from_Md': 0.0,
   'chi_m_sbe_from_Msc': 0.0}},
 {'dir': '/media/aiman/data/rest test/finite_doping_rest_test_with_no_rest/dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA7p5_C5_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_ALLSYMM_SELFEN_FLOW',
  'Beta': 7.5,
  'U': 0.0,
  'g0': 1.3693063937629153,
  'Omega0': 1.5,
  'Vh': 2.5,
  'leading_susc': 'd',
  'max_sc': 0.7613841940290486,
  'max_d': 2.7926675798763356,
  'max_m': 0.1808097557778987,
  'leading_w': 'd',
  'max_w_sc': 286.5600638186179,
  'max_w_d': 787.75914993907,
  'max_w_m': -98.82334072020745,
  'lambda_sc': array([[0.93308541, 0.93394401, 0.93447273, 0.93293381, 0.92194629,
          0.86434357, 0.86336957, 0.86298072, 0.86337204, 0.86390929,
          0.86390936, 0.86337218, 0.86298101, 0.86336994, 0.8643441 ,
          0.92194689, 0.93293458, 0.93447361, 0.93394506, 0.93308657],
         [0.92041952, 0.92073218, 0.91984099, 0.91309725, 0.92533835,
          0.86532477, 0.85251145, 0.84962378, 0.84905671, 0.84930574,
          0.84905682, 0.849624  , 0.85251179, 0.8653253 , 0.92533898,
          0.91309794, 0.91984181, 0.92073315, 0.92042062, 0.91992172],
         [0.90447114, 0.90436695, 0.90270715, 0.90884051, 0.92437159,
          0.92514362, 0.85354087, 0.83760532, 0.83351714, 0.83253327,
          0.83253331, 0.83351732, 0.83760559, 0.85354137, 0.92514423,
          0.92437225, 0.90884124, 0.90270805, 0.90436796, 0.90447232],
         [0.88440906, 0.88812872, 0.89332096, 0.90834805, 0.91918209,
          0.91731648, 0.83713611, 0.81866289, 0.81374596, 0.81250557,
          0.81374607, 0.81866313, 0.83713654, 0.91731707, 0.91918273,
          0.90834877, 0.89332179, 0.8881297 , 0.88441015, 0.88514741],
         [0.86449328, 0.86785227, 0.87213161, 0.88656802, 0.8992597 ,
          0.90947026, 0.90767333, 0.81812124, 0.79701696, 0.79133795,
          0.79133803, 0.79701713, 0.81812163, 0.90767385, 0.90947087,
          0.89926038, 0.88656884, 0.87213251, 0.86785335, 0.86449444],
         [0.84029441, 0.84359814, 0.85780256, 0.87095232, 0.88386938,
          0.89576617, 0.89513134, 0.79484943, 0.77081005, 0.76593907,
          0.77081019, 0.79484976, 0.89513184, 0.89576673, 0.88387005,
          0.87095311, 0.85780346, 0.84359915, 0.84029557, 0.83745619],
         [0.80440236, 0.8070981 , 0.82160033, 0.83530635, 0.84938319,
          0.86473809, 0.8798711 , 0.88182862, 0.76883775, 0.74281543,
          0.74281548, 0.76883803, 0.88182904, 0.87987162, 0.8647387 ,
          0.84938396, 0.83530721, 0.82160133, 0.80709918, 0.80440362],
         [0.75792323, 0.77313782, 0.78755407, 0.80282537, 0.81995164,
          0.83959776, 0.86029083, 0.86683757, 0.74061807, 0.71675199,
          0.74061827, 0.86683795, 0.86029129, 0.83959834, 0.81995235,
          0.80282621, 0.78755505, 0.7731389 , 0.75792442, 0.75568075],
         [0.6896261 , 0.70675429, 0.7226191 , 0.73949726, 0.7587603 ,
          0.78152403, 0.80900253, 0.83987335, 0.85719176, 0.72010208,
          0.72010221, 0.85719205, 0.83987376, 0.80900303, 0.78152471,
          0.75876108, 0.7394982 , 0.72262012, 0.70675546, 0.68962735],
         [0.60246343, 0.62109241, 0.64077465, 0.66334275, 0.69057982,
          0.72464415, 0.76819196, 0.82352754, 0.87128403, 0.74850351,
          0.87128424, 0.82352787, 0.76819243, 0.72464475, 0.69058056,
          0.66334362, 0.64077565, 0.62109353, 0.60246466, 0.58168399],
         [0.33100875, 0.35833798, 0.38776115, 0.42158936, 0.46313887,
          0.5170376 , 0.59001717, 0.69642977, 0.86377506, 1.11816657,
          1.11816657, 0.86377506, 0.69642977, 0.59001717, 0.5170376 ,
          0.46313887, 0.42158936, 0.38776115, 0.35833798, 0.33100875],
         [0.60246466, 0.62109353, 0.64077565, 0.66334362, 0.69058056,
          0.72464475, 0.76819243, 0.82352787, 0.87128424, 0.74850351,
          0.87128403, 0.82352754, 0.76819196, 0.72464415, 0.69057982,
          0.66334275, 0.64077465, 0.62109241, 0.60246343, 0.58168396],
         [0.68962735, 0.70675546, 0.72262012, 0.7394982 , 0.75876108,
          0.78152471, 0.80900303, 0.83987376, 0.85719205, 0.72010221,
          0.72010208, 0.85719176, 0.83987335, 0.80900253, 0.78152403,
          0.7587603 , 0.73949726, 0.7226191 , 0.70675429, 0.6896261 ],
         [0.75792442, 0.7731389 , 0.78755505, 0.80282621, 0.81995235,
          0.83959834, 0.86029129, 0.86683795, 0.74061827, 0.71675199,
          0.74061807, 0.86683757, 0.86029083, 0.83959776, 0.81995164,
          0.80282537, 0.78755407, 0.77313782, 0.75792323, 0.75568073],
         [0.80440362, 0.80709918, 0.82160133, 0.83530721, 0.84938396,
          0.8647387 , 0.87987162, 0.88182904, 0.76883803, 0.74281548,
          0.74281543, 0.76883775, 0.88182862, 0.8798711 , 0.86473809,
          0.84938319, 0.83530635, 0.82160033, 0.8070981 , 0.80440236],
         [0.84029557, 0.84359915, 0.85780346, 0.87095311, 0.88387005,
          0.89576673, 0.89513184, 0.79484976, 0.77081019, 0.76593907,
          0.77081005, 0.79484943, 0.89513134, 0.89576617, 0.88386938,
          0.87095232, 0.85780256, 0.84359814, 0.84029441, 0.8374562 ],
         [0.86449444, 0.86785335, 0.87213251, 0.88656884, 0.89926038,
          0.90947087, 0.90767385, 0.81812163, 0.79701713, 0.79133803,
          0.79133795, 0.79701696, 0.81812124, 0.90767333, 0.90947026,
          0.8992597 , 0.88656802, 0.87213161, 0.86785227, 0.86449328],
         [0.88441015, 0.8881297 , 0.89332179, 0.90834877, 0.91918273,
          0.91731707, 0.83713654, 0.81866313, 0.81374607, 0.81250557,
          0.81374596, 0.81866289, 0.83713611, 0.91731648, 0.91918209,
          0.90834805, 0.89332096, 0.88812872, 0.88440906, 0.88514744],
         [0.90447232, 0.90436796, 0.90270805, 0.90884124, 0.92437225,
          0.92514423, 0.85354137, 0.83760559, 0.83351732, 0.83253331,
          0.83253327, 0.83351714, 0.83760532, 0.85354087, 0.92514362,
          0.92437159, 0.90884051, 0.90270715, 0.90436695, 0.90447114],
         [0.92042062, 0.92073315, 0.91984181, 0.91309794, 0.92533898,
          0.8653253 , 0.85251179, 0.849624  , 0.84905682, 0.84930574,
          0.84905671, 0.84962378, 0.85251145, 0.86532477, 0.92533835,
          0.91309725, 0.91984099, 0.92073218, 0.92041952, 0.91992171],
         [0.93308657, 0.93394506, 0.93447361, 0.93293458, 0.92194689,
          0.8643441 , 0.86336994, 0.86298101, 0.86337218, 0.86390936,
          0.86390929, 0.86337204, 0.86298072, 0.86336957, 0.86434357,
          0.92194629, 0.93293381, 0.93447273, 0.93394401, 0.93308541]]),
  'lambda_d': array([[0.98438937, 0.97992243, 0.97639728, 0.9765545 , 0.99061927,
          1.06481442, 1.07372354, 1.07465385, 1.0737401 , 1.07318788,
          1.07318765, 1.07373957, 1.07465283, 1.07372223, 1.06481264,
          0.99061735, 0.97655223, 0.97639482, 0.97991958, 0.98438631],
         [0.98281751, 0.97715265, 0.97158423, 0.96914128, 0.98417627,
          1.0770265 , 1.08782925, 1.08801691, 1.08697874, 1.08670632,
          1.08697834, 1.0880161 , 1.08782802, 1.07702488, 0.98417445,
          0.96913914, 0.97158181, 0.97714993, 0.98281447, 0.98829437],
         [0.98745994, 0.98056982, 0.97192785, 0.96840609, 0.9682023 ,
          0.9884404 , 1.09372779, 1.10501312, 1.10517183, 1.10440908,
          1.10440892, 1.10517115, 1.10501212, 1.09372627, 0.98843878,
          0.96820032, 0.96840385, 0.97192519, 0.98056695, 0.98745667],
         [0.98671396, 0.9806106 , 0.97417823, 0.96945322, 0.97025095,
          0.9944508 , 1.11434317, 1.12663972, 1.12706926, 1.12653533,
          1.12706882, 1.12663885, 1.11434187, 0.99444928, 0.97024916,
          0.96945111, 0.97417574, 0.98060778, 0.98671079, 0.99375309],
         [0.99781434, 0.99179236, 0.98389082, 0.97799918, 0.97289898,
          0.97444892, 1.00324557, 1.14244776, 1.15611355, 1.15673008,
          1.1567298 , 1.15611292, 1.14244659, 1.00324428, 0.97444724,
          0.97289709, 0.97799686, 0.98388821, 0.99178933, 0.99781106],
         [1.00558503, 0.9968691 , 0.99085312, 0.98396142, 0.97840866,
          0.980411  , 1.01581813, 1.1809682 , 1.19588404, 1.19689674,
          1.19588358, 1.18096728, 1.01581696, 0.98040955, 0.97840687,
          0.9839593 , 0.99085065, 0.99686624, 1.00558183, 1.0114465 ],
         [1.0243572 , 1.01523789, 1.00977426, 1.00260839, 0.994902  ,
          0.98827531, 0.99119691, 1.03632488, 1.23713709, 1.25361372,
          1.25361354, 1.23713632, 1.03632396, 0.99119559, 0.98827377,
          0.9949    , 1.00260615, 1.00977157, 1.01523491, 1.02435378],
         [1.04092979, 1.03602497, 1.0292957 , 1.02136144, 1.01211999,
          1.0042175 , 1.0088405 , 1.06893741, 1.32119096, 1.33755143,
          1.32119048, 1.06893665, 1.00883943, 1.00421608, 1.01211822,
          1.02135931, 1.02929321, 1.03602212, 1.04092653, 1.0498036 ],
         [1.0813971 , 1.07723617, 1.0713398 , 1.06433558, 1.05511256,
          1.04390722, 1.03434048, 1.04163342, 1.1266882 , 1.45553583,
          1.45553553, 1.12668769, 1.0416325 , 1.03433931, 1.04390559,
          1.05511067, 1.06433323, 1.07133718, 1.07723308, 1.08139371],
         [1.15368798, 1.14918171, 1.14409959, 1.13672343, 1.12609996,
          1.11241177, 1.09971462, 1.10930166, 1.23263563, 1.64367764,
          1.23263528, 1.10930099, 1.09971359, 1.11241038, 1.1260982 ,
          1.1367213 , 1.14409708, 1.14917883, 1.15368472, 1.15592784],
         [1.51010171, 1.51651333, 1.52117325, 1.52290034, 1.52079809,
          1.51256518, 1.49225201, 1.44961033, 1.36117966, 1.20382073,
          1.20382073, 1.36117966, 1.44961033, 1.49225201, 1.51256518,
          1.52079809, 1.52290034, 1.52117325, 1.51651333, 1.51010171],
         [1.15368472, 1.14917883, 1.14409708, 1.1367213 , 1.1260982 ,
          1.11241038, 1.09971359, 1.10930099, 1.23263528, 1.64367764,
          1.23263563, 1.10930166, 1.09971462, 1.11241177, 1.12609996,
          1.13672343, 1.14409959, 1.14918171, 1.15368798, 1.15592786],
         [1.08139371, 1.07723308, 1.07133718, 1.06433323, 1.05511067,
          1.04390559, 1.03433931, 1.0416325 , 1.12668769, 1.45553553,
          1.45553583, 1.1266882 , 1.04163342, 1.03434048, 1.04390722,
          1.05511256, 1.06433558, 1.0713398 , 1.07723617, 1.0813971 ],
         [1.04092653, 1.03602212, 1.02929321, 1.02135931, 1.01211822,
          1.00421608, 1.00883943, 1.06893665, 1.32119048, 1.33755143,
          1.32119096, 1.06893741, 1.0088405 , 1.0042175 , 1.01211999,
          1.02136144, 1.0292957 , 1.03602497, 1.04092979, 1.04980362],
         [1.02435378, 1.01523491, 1.00977157, 1.00260615, 0.9949    ,
          0.98827377, 0.99119559, 1.03632396, 1.23713632, 1.25361354,
          1.25361372, 1.23713709, 1.03632488, 0.99119691, 0.98827531,
          0.994902  , 1.00260839, 1.00977426, 1.01523789, 1.0243572 ],
         [1.00558183, 0.99686624, 0.99085065, 0.9839593 , 0.97840687,
          0.98040955, 1.01581696, 1.18096728, 1.19588358, 1.19689674,
          1.19588404, 1.1809682 , 1.01581813, 0.980411  , 0.97840866,
          0.98396142, 0.99085312, 0.9968691 , 1.00558503, 1.01144655],
         [0.99781106, 0.99178933, 0.98388821, 0.97799686, 0.97289709,
          0.97444724, 1.00324428, 1.14244659, 1.15611292, 1.1567298 ,
          1.15673008, 1.15611355, 1.14244776, 1.00324557, 0.97444892,
          0.97289898, 0.97799918, 0.98389082, 0.99179236, 0.99781434],
         [0.98671079, 0.98060778, 0.97417574, 0.96945111, 0.97024916,
          0.99444928, 1.11434187, 1.12663885, 1.12706882, 1.12653533,
          1.12706926, 1.12663972, 1.11434317, 0.9944508 , 0.97025095,
          0.96945322, 0.97417823, 0.9806106 , 0.98671396, 0.99375313],
         [0.98745667, 0.98056695, 0.97192519, 0.96840385, 0.96820032,
          0.98843878, 1.09372627, 1.10501212, 1.10517115, 1.10440892,
          1.10440908, 1.10517183, 1.10501312, 1.09372779, 0.9884404 ,
          0.9682023 , 0.96840609, 0.97192785, 0.98056982, 0.98745994],
         [0.98281447, 0.97714993, 0.97158181, 0.96913914, 0.98417445,
          1.07702488, 1.08782802, 1.0880161 , 1.08697834, 1.08670632,
          1.08697874, 1.08801691, 1.08782925, 1.0770265 , 0.98417627,
          0.96914128, 0.97158423, 0.97715265, 0.98281751, 0.98829433],
         [0.98438631, 0.97991958, 0.97639482, 0.97655223, 0.99061735,
          1.06481264, 1.07372223, 1.07465283, 1.07373957, 1.07318765,
          1.07318788, 1.0737401 , 1.07465385, 1.07372354, 1.06481442,
          0.99061927, 0.9765545 , 0.97639728, 0.97992243, 0.98438937]]),
  'lambda_m': array([[1.03681207, 1.03011459, 1.0230813 , 1.01733304, 1.02079545,
          1.09307229, 1.11042809, 1.11638291, 1.11852966, 1.11952071,
          1.11952076, 1.11852974, 1.1163831 , 1.11042831, 1.0930726 ,
          1.0207958 , 1.01733355, 1.02308189, 1.03011534, 1.03681291],
         [1.042614  , 1.03417187, 1.02488201, 1.01631592, 1.02561581,
          1.11198866, 1.13166786, 1.1370917 , 1.13885446, 1.13955141,
          1.13885453, 1.13709183, 1.13166805, 1.11198889, 1.02561614,
          1.01631633, 1.02488255, 1.03417253, 1.04261478, 1.04974599],
         [1.05821114, 1.04905743, 1.03748886, 1.03086282, 1.02934269,
          1.03886013, 1.13529027, 1.15626733, 1.16178094, 1.1634331 ,
          1.16343311, 1.16178104, 1.15626746, 1.13529048, 1.0388604 ,
          1.02934314, 1.0308633 , 1.03748944, 1.04905811, 1.05821198],
         [1.06682602, 1.06071923, 1.05121546, 1.04847293, 1.04494781,
          1.05412356, 1.16194016, 1.18495759, 1.19080726, 1.19184708,
          1.19080731, 1.1849577 , 1.16194031, 1.05412381, 1.0449482 ,
          1.04847345, 1.05121605, 1.06071989, 1.06682675, 1.07722518],
         [1.09542037, 1.08811364, 1.0776003 , 1.07576615, 1.07021895,
          1.0645855 , 1.07271946, 1.19503823, 1.2207585 , 1.22654402,
          1.22654406, 1.22075856, 1.19503837, 1.07271967, 1.06458588,
          1.07021942, 1.07576677, 1.07760094, 1.0881144 , 1.09542117],
         [1.12148069, 1.11000462, 1.11005386, 1.10483812, 1.09676661,
          1.08800174, 1.09479034, 1.23538304, 1.26383111, 1.26894234,
          1.26383116, 1.23538314, 1.09479054, 1.08800208, 1.09676707,
          1.10483869, 1.11005454, 1.11000536, 1.12148151, 1.12965287],
         [1.16467142, 1.152274  , 1.15470228, 1.15084984, 1.14298219,
          1.13144431, 1.11863053, 1.12320992, 1.2864284 , 1.31756708,
          1.31756709, 1.28642849, 1.12321009, 1.11863086, 1.13144472,
          1.14298274, 1.15085046, 1.15470304, 1.15227479, 1.16467233],
         [1.20741311, 1.21271231, 1.21040929, 1.20370727, 1.19232538,
          1.17624507, 1.15754021, 1.15786581, 1.34838663, 1.37761586,
          1.34838668, 1.15786598, 1.1575405 , 1.17624547, 1.19232588,
          1.20370788, 1.21040999, 1.21271312, 1.20741399, 1.22138524],
         [1.28406203, 1.29316994, 1.29277688, 1.28754034, 1.27738041,
          1.26127671, 1.23824427, 1.2093559 , 1.20033496, 1.41808749,
          1.41808754, 1.20033511, 1.20935619, 1.23824461, 1.26127719,
          1.27738096, 1.28754102, 1.29277761, 1.29317081, 1.28406294],
         [1.41166543, 1.41305844, 1.40936302, 1.40054389, 1.38540729,
          1.36148998, 1.32527766, 1.27436101, 1.23563618, 1.4441541 ,
          1.23563633, 1.27436125, 1.325278  , 1.3614904 , 1.38540782,
          1.4005445 , 1.40936374, 1.41305924, 1.41166634, 1.39832523],
         [1.64505943, 1.64667201, 1.64416972, 1.63638828, 1.62157309,
          1.59613476, 1.55292926, 1.47757554, 1.34054652, 1.10240311,
          1.10240311, 1.34054652, 1.47757554, 1.55292926, 1.59613476,
          1.62157309, 1.63638828, 1.64416972, 1.64667201, 1.64505943],
         [1.41166634, 1.41305924, 1.40936374, 1.4005445 , 1.38540782,
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  'chi_d': array([0.92055932, 0.92805689, 0.92647263, 0.88611979, 0.82352136,
         0.76343185, 0.71728489, 0.68942978, 0.67971224, 0.70870226,
         0.80782065, 1.03837751, 1.63772774, 3.92128132, 8.99859985,
         7.15175116, 5.87341649, 7.29912974, 2.48083491, 1.38324173,
         1.10865797, 1.01975522, 0.9773076 , 0.9389699 , 0.92055932]),
  'chi_m': array([0.16239248, 0.16260575, 0.16251857, 0.16123172, 0.15908823,
         0.15677679, 0.15479273, 0.15349348, 0.15302048, 0.15440718,
         0.15853257, 0.16552383, 0.17535267, 0.18625154, 0.19031443,
         0.1882592 , 0.18677325, 0.18876733, 0.18096337, 0.17161266,
         0.16681417, 0.16488687, 0.16391719, 0.16291715, 0.16239248]),
  'chi_split': {'chi_sc_fl': 4.842149794982901,
   'chi_sc_full': 6.276461506690675,
   'chi_sc_bubble': 0.3961092462908137,
   'chi_sc_mbe': 0.17548689283043933,
   'chi_sc_sbe_from_sc': 5.662332054868955,
   'chi_sc_sbe_from_d': 0.06971598316300448,
   'chi_sc_sbe_from_m': 0.4063562356241899,
   'chi_sc_sbe_from_Mm': 0.0,
   'chi_sc_sbe_from_Md': 0.0,
   'chi_sc_sbe_from_Msc': 0.0,
   'chi_d_fl': 5.873416492297454,
   'chi_d_full': 6.022065680826326,
   'chi_d_bubble': 0.262950578930983,
   'chi_d_mbe': 0.2943587777826031,
   'chi_d_sbe_from_sc': -0.11466061837518846,
   'chi_d_sbe_from_d': 5.461840961725728,
   'chi_d_sbe_from_m': -0.12542651358595286,
   'chi_d_sbe_from_Mm': 0.0,
   'chi_d_sbe_from_Md': 0.0,
   'chi_d_sbe_from_Msc': 0.0,
   'chi_m_fl': 0.18677324630912992,
   'chi_m_full': 0.16965881197732965,
   'chi_m_bubble': 0.262950578930983,
   'chi_m_mbe': -0.05135628343461879,
   'chi_m_sbe_from_sc': -0.1283418759727734,
   'chi_m_sbe_from_d': 0.025325308058483438,
   'chi_m_sbe_from_m': -0.1819214099527169,
   'chi_m_sbe_from_Mm': 0.0,
   'chi_m_sbe_from_Md': 0.0,
   'chi_m_sbe_from_Msc': 0.0}},
 {'dir': '/media/aiman/data/rest test/finite_doping_rest_test_with_no_rest/dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA20_C8_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_ALLSYMM_SELFEN_FLOW',
  'Beta': 20.0,
  'U': 0.0,
  'g0': 1.3693063937629153,
  'Omega0': 1.5,
  'Vh': 2.5,
  'leading_susc': 'sc',
  'max_sc': 52.96033342172985,
  'max_d': 11.4959171019114,
  'max_m': 0.19263977123186568,
  'leading_w': 'sc',
  'max_w_sc': 13166.134539567514,
  'max_w_d': 2935.1998946004733,
  'max_w_m': -99.42680401227001,
  'lambda_sc': array([[0.87886825, 0.87841093, 0.87763421, ..., 0.8776374 , 0.87841432,
          0.87887196],
         [0.86789708, 0.8672485 , 0.86615257, ..., 0.8672518 , 0.86790064,
          0.8680971 ],
         [0.85599721, 0.85564953, 0.85475099, ..., 0.85475414, 0.85565299,
          0.85600086],
         ...,
         [0.85600086, 0.85565299, 0.85475414, ..., 0.85475099, 0.85564953,
          0.85599721],
         [0.86790064, 0.8672518 , 0.86615563, ..., 0.8672485 , 0.86789708,
          0.868097  ],
         [0.87887196, 0.87841432, 0.8776374 , ..., 0.87763421, 0.87841093,
          0.87886825]]),
  'lambda_d': array([[0.97850859, 0.9763085 , 0.97529249, ..., 0.97528446, 0.97630012,
          0.97849951],
         [0.97799727, 0.97544067, 0.97403019, ..., 0.97543233, 0.97798839,
          0.98122937],
         [0.98063858, 0.97696941, 0.97391534, ..., 0.97390729, 0.97696066,
          0.98062945],
         ...,
         [0.98062945, 0.97696066, 0.97390729, ..., 0.97391534, 0.97696941,
          0.98063858],
         [0.97798839, 0.97543233, 0.97402237, ..., 0.97544067, 0.97799727,
          0.98122912],
         [0.97849951, 0.97630012, 0.97528446, ..., 0.97529249, 0.9763085 ,
          0.97850859]]),
  'lambda_m': array([[1.07803595, 1.07225631, 1.06652642, ..., 1.06652821, 1.07225824,
          1.07803813],
         [1.08327927, 1.07666342, 1.07001103, ..., 1.07666527, 1.08328131,
          1.08951631],
         [1.09575164, 1.08863349, 1.08105242, ..., 1.08105409, 1.08863542,
          1.09575369],
         ...,
         [1.09575369, 1.08863542, 1.08105409, ..., 1.08105242, 1.08863349,
          1.09575164],
         [1.08328131, 1.07666527, 1.07001268, ..., 1.07666342, 1.08327927,
          1.08951634],
         [1.07803813, 1.07225824, 1.06652821, ..., 1.06652642, 1.07225631,
          1.07803595]]),
  'w_sc': array([13166.13453957,   441.0020795 ,   239.68441292,   194.37989625,
           175.38895594,   165.19820853,   159.58815105,   156.54666992,
           155.68983158,   155.87027492,   156.70046686,   158.01195596,
           160.01257054,   162.02443798,   162.86933488,   162.27261485,
           161.80843401,   162.92577034,   166.69207246,   173.25911431,
           183.45621184,   201.01406112,   238.75895559,   373.61458469,
         13166.13453957]),
  'w_d': array([ 320.1294708 ,  322.88459562,  322.79973783,  310.94038772,
          294.08528723,  278.79249999,  267.66567117,  261.12216169,
          258.63995213,  265.53451474,  288.25485517,  340.66562096,
          477.89393339, 1059.58762517, 2935.1998946 , 2130.39867166,
         1695.27729961, 2184.18327093,  685.25518818,  427.56949044,
          363.65556501,  345.04743711,  337.12203365,  326.44015193,
          320.1294708 ]),
  'w_m': array([-58.63452307, -58.55087156, -58.56320135, -58.95769389,
         -59.5754795 , -60.21358022, -60.73426432, -61.06610992,
         -61.19718018, -60.8369453 , -59.79849135, -58.03165379,
         -55.48336569, -52.44194803, -51.16408566, -51.70484876,
         -52.09288813, -51.58389964, -53.88808123, -56.31052279,
         -57.52995613, -57.96544129, -58.15175413, -58.44404143,
         -58.63452307]),
  'chi_sc': array([52.96033342,  1.38731411,  0.57140434,  0.38779205,  0.31082466,
          0.26952312,  0.24678641,  0.23445976,  0.23098712,  0.23171843,
          0.23508307,  0.24039834,  0.24850652,  0.25666031,  0.26008455,
          0.25766614,  0.25578488,  0.26031328,  0.27557752,  0.30219274,
          0.34352002,  0.4146793 ,  0.5676536 ,  1.11420288, 52.96033342]),
  'chi_d': array([ 0.89743588,  0.90860198,  0.90825806,  0.86019393,  0.79188278,
          0.72990344,  0.6848081 ,  0.65828826,  0.64822824,  0.67617085,
          0.76825292,  0.98066576,  1.53683116,  3.89434689, 11.4959171 ,
          8.2341806 ,  6.4707001 ,  8.45216137,  2.37723467,  1.33287387,
          1.07384049,  0.99842459,  0.96630414,  0.9230121 ,  0.89743588]),
  'chi_m': array([0.16236323, 0.16270226, 0.16265228, 0.16105347, 0.15854968,
         0.15596355, 0.1538533 , 0.15250838, 0.15197717, 0.15343715,
         0.15764584, 0.16480657, 0.17513439, 0.18746079, 0.19263977,
         0.19044814, 0.18887548, 0.19093833, 0.18159983, 0.17178205,
         0.16683987, 0.16507492, 0.16431982, 0.16313522, 0.16236323]),
  'chi_split': {'chi_sc_fl': 52.96033342172985,
   'chi_sc_full': 71.57413481961201,
   'chi_sc_bubble': 0.4179661724637017,
   'chi_sc_mbe': 0.20171702760088905,
   'chi_sc_sbe_from_sc': 70.91347636011679,
   'chi_sc_sbe_from_d': 0.08261112676766869,
   'chi_sc_sbe_from_m': 0.428408740713337,
   'chi_sc_sbe_from_Mm': 0.0,
   'chi_sc_sbe_from_Md': 0.0,
   'chi_sc_sbe_from_Msc': 0.0,
   'chi_d_fl': 6.470700103952391,
   'chi_d_full': 6.356336640331232,
   'chi_d_bubble': 0.26837956410685243,
   'chi_d_mbe': 0.3060277107658522,
   'chi_d_sbe_from_sc': -0.11834462161307863,
   'chi_d_sbe_from_d': 5.786810690641131,
   'chi_d_sbe_from_m': -0.13845408575020351,
   'chi_d_sbe_from_Mm': 0.0,
   'chi_d_sbe_from_Md': 0.0,
   'chi_d_sbe_from_Msc': 0.0,
   'chi_m_fl': 0.1888754765943895,
   'chi_m_full': 0.18261230365425035,
   'chi_m_bubble': 0.26837956410685243,
   'chi_m_mbe': -0.05411032858522715,
   'chi_m_sbe_from_sc': -0.13357276056750914,
   'chi_m_sbe_from_d': 0.027614502027330207,
   'chi_m_sbe_from_m': -0.17761605550782364,
   'chi_m_sbe_from_Mm': 0.0,
   'chi_m_sbe_from_Md': 0.0,
   'chi_m_sbe_from_Msc': 0.0}}]
In [17]:
objs_doped_norest = sorted(load(datnames_doped_norest, drop=10) , key=lambda o1: o1['Beta']) 
objs_doped_rest = sorted(load(datnames_doped_rest, drop=10) , key=lambda o1: o1['Beta'])

/media/aiman/data/rest test/finite_doping_rest_test_with_no_rest/dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA10_C8_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_ALLSYMM_SELFEN_FLOW
/media/aiman/data/rest test/finite_doping_rest_test_with_no_rest/dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA5_C5_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_ALLSYMM_SELFEN_FLOW

/tmp/ipykernel_169275/314559591.py:48: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  obj['lambda_sc'] = np.array(f["lambda_func/RE_SC"])[:, 0, :, 0]
/tmp/ipykernel_169275/314559591.py:49: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  obj['lambda_d'] = np.array(f["lambda_func/RE_D"])[:, 0, :, 0]
/tmp/ipykernel_169275/314559591.py:50: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  obj['lambda_m'] = np.array(f["lambda_func/RE_M"])[:, 136, :, 0]
/tmp/ipykernel_169275/314559591.py:56: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  obj['w_sc'] = np.array([np.array(f["w_func/RE_SC"])[int((np.array(f["w_func/RE_SC"]).shape[0]-1)/2), int(i)] for i in mom_path])
/tmp/ipykernel_169275/314559591.py:57: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  obj['w_d'] = np.array([np.array(f["w_func/RE_D"])[int((np.array(f["w_func/RE_SC"]).shape[0]-1)/2), int(i)] for i in mom_path])
/tmp/ipykernel_169275/314559591.py:58: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  obj['w_m'] = np.array([np.array(f["w_func/RE_M"])[int((np.array(f["w_func/RE_SC"]).shape[0]-1)/2), int(i)] for i in mom_path])
/tmp/ipykernel_169275/314559591.py:60: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  obj['chi_sc'] = np.array([np.array(f["Flow_obs/S_Wave_Susc_info/RE_Susc_sc"])[0, int(i)] for i in mom_path])
/tmp/ipykernel_169275/314559591.py:61: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  obj['chi_d'] = np.array([np.array(f["Flow_obs/S_Wave_Susc_info/RE_Susc_d"])[0, int(i)] for i in mom_path])
/tmp/ipykernel_169275/314559591.py:62: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  obj['chi_m'] = np.array([np.array(f["Flow_obs/S_Wave_Susc_info/RE_Susc_m"])[0, int(i)] for i in mom_path])
/tmp/ipykernel_169275/314559591.py:75: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  contribs_from_minus_2U = -2.0*np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_bare_vertex".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]# + np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_sc_double_counting".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_d_double_counting".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_m_double_counting".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
/tmp/ipykernel_169275/314559591.py:77: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  chi_split_objs[("chi_{0}_fl".format(chan))] = np.array(f["Flow_obs/S_Wave_Susc_info/RE_Susc_{0}".format(chan)])[0, special_idx]
/tmp/ipykernel_169275/314559591.py:78: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  chi_split_objs[("chi_{0}_full".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
/tmp/ipykernel_169275/314559591.py:79: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  chi_split_objs[("chi_{0}_bubble".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_bubble_contribution".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
/tmp/ipykernel_169275/314559591.py:81: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  chi_split_objs[("chi_{0}_sbe_from_sc".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_nabla_sc".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + contribs_from_minus_2U/2.0
/tmp/ipykernel_169275/314559591.py:82: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  chi_split_objs[("chi_{0}_sbe_from_d".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_nabla_d".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + 0.5 * contribs_from_minus_2U/2.0
/tmp/ipykernel_169275/314559591.py:83: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  chi_split_objs[("chi_{0}_sbe_from_m".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_nabla_m".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0] + 1.5 * contribs_from_minus_2U/2.0
/tmp/ipykernel_169275/314559591.py:86: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  chi_split_objs[("chi_{0}_sbe_from_Mm".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_M_m".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
/tmp/ipykernel_169275/314559591.py:87: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  chi_split_objs[("chi_{0}_sbe_from_Md".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_M_d".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]
/tmp/ipykernel_169275/314559591.py:88: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
  chi_split_objs[("chi_{0}_sbe_from_Msc".format(chan))] = np.array(f["Flow_obs/Postprocessing_Susc_info/RE_Susc_{0}_vertex_contribution_from_M_sc".format(chan)])[0, special_idx, 0, 0, 0, 0, 0, 0]

/media/aiman/data/rest test/finite_doping_rest_test_with_no_rest/dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA15_C8_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_ALLSYMM_SELFEN_FLOW
/media/aiman/data/rest test/finite_doping_rest_test_with_no_rest/dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA7p5_C5_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_ALLSYMM_SELFEN_FLOW
/media/aiman/data/rest test/finite_doping_rest_test_with_no_rest/dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA20_C8_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_ALLSYMM_SELFEN_FLOW
/media/aiman/data/rest test/finite_doping_rest_test_with_rest//dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA5_C5_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_RESTFUNC_ALLSYMM_SELFEN_FLOW
/media/aiman/data/rest test/finite_doping_rest_test_with_rest//dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA15_C8_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_RESTFUNC_ALLSYMM_SELFEN_FLOW
/media/aiman/data/rest test/finite_doping_rest_test_with_rest//dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA7p5_C5_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_RESTFUNC_ALLSYMM_SELFEN_FLOW
/media/aiman/data/rest test/finite_doping_rest_test_with_rest//dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA20_C8_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_RESTFUNC_ALLSYMM_SELFEN_FLOW
/media/aiman/data/rest test/finite_doping_rest_test_with_rest//dat__g01p369306_OMEGA01p5_U0_V0_TP-0p25_Mu-1_KDIM16_FineMoms6400_RefdMoms410_FFShellCount1_BETA10_C8_T_START5_Square Hubbard-Holstein_OMFL_FLOWEQN_MULTI1LOOP_1SELOOP_NOMIXEDBUBS_FIXFILLING_RESTFUNC_ALLSYMM_SELFEN_FLOW
In [121]:
def plot_suscs(rest_data, norest_data, index, channel, ax_ = None, with_legend=True, with_title = True):
    ax = None
    if ax_ is None:
        ax = plt.gca()
    else:
        ax = ax_
        
    path, pp = get_GammaXMGamma_path()
    if with_title:
        ax.set_title(r"$\beta="+str(int(rest_data[index]["Beta"]))+r"/t$" )

    markerfacecolor = {'d': "green", 'sc': "red" } 
    marker = {'d': "s", 'sc': "o" } 
    
    #markerfacecolor='green', marker='s', markeredgecolor='black', markersize=8
    #markerfacecolor='red', marker='o', markeredgecolor='black', markersize=8
    #plt.gca().set_aspect('equal')
    ax.plot(pp, (norest_data[index]['chi_'+channel]), marker=marker[channel], linestyle="--", markerfacecolor=markerfacecolor[channel], color = markerfacecolor[channel], markersize=4)
    ax.plot(pp, (rest_data[index]['chi_'+channel]), marker=marker[channel], markerfacecolor=markerfacecolor[channel], color = markerfacecolor[channel], markeredgecolor='black', markersize=4)

    #ax.plot(pp, (norest_data[index]['chi_'+channel]), "x-", label=r"$\dot{M} = 0$", markeredgecolor='black', markersize=8)
    p_len = len(path)
    print(path)
    ax.set_xticks([pp[0],pp[int((p_len-1)/3)],pp[int(2*(p_len-1)/3)],pp[-1]],[r"$\Gamma$",r'$X$',r'$M$',r"$\Gamma$"])
    ax.set_ylabel(r"$\chi^{\mathrm{"+channel.upper()+r"}}(i\Omega = 0, \mathbf{q})t$", fontsize=14)
    if with_legend:
        if channel == 'd':
            ax.legend()
        else:
            ax.legend()
    #ax.show()
In [122]:
from matplotlib.ticker import MaxNLocator

fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(3.7*3*0.9, 2.9*2*0.9))  



plt.subplots_adjust(wspace=0.2, hspace=0.00)

plot_suscs(objs_doped_rest, objs_doped_norest, 0, "d", ax_ = axes[1][0], with_legend=False, with_title = False)
plot_suscs(objs_doped_rest, objs_doped_norest, 2, "d", ax_ = axes[1][1], with_legend=False, with_title = False)
plot_suscs(objs_doped_rest, objs_doped_norest, 4, "d", ax_ = axes[1][2], with_legend = False, with_title = False )
plot_suscs(objs_doped_rest, objs_doped_norest, 0, "sc", ax_ = axes[0][0], with_legend=False)
plot_suscs(objs_doped_rest, objs_doped_norest, 2, "sc", ax_ = axes[0][1], with_legend=False)
plot_suscs(objs_doped_rest, objs_doped_norest, 4, "sc", ax_ = axes[0][2], with_legend=False)
#axes[1].set_title(r"$U_{\mathrm{eff}} < 0$", loc="center", y=1.0, x=0.5, fontsize=15)
#axes[0].set_title(r"$U_{\mathrm{eff}} > 0$", loc="center", y=1.0, x=0.5, fontsize=15)

axes[0][0].plot([], "-", label=r"with $M$-flow", color='black')
axes[0][0].plot([], "--", label=r"SBE approx", color='black')

axes[0][0].legend()

axes[1][1].set_ylabel("")
axes[1][2].set_ylabel("")

axes[0][1].set_ylabel("")
axes[0][2].set_ylabel("")

axes[0][0].yaxis.set_major_locator(MaxNLocator(integer=True, nbins=6))  # Set 6 ticks on y-axis
axes[0][1].yaxis.set_major_locator(MaxNLocator(integer=True, nbins=6))  # Set 6 ticks on y-axis
axes[0][2].yaxis.set_major_locator(MaxNLocator(integer=True, nbins=6))  # Set 6 ticks on y-axis
axes[1][0].yaxis.set_major_locator(MaxNLocator(integer=True, nbins=6))  # Set 6 ticks on y-axis
axes[1][1].yaxis.set_major_locator(MaxNLocator(integer=True, nbins=6))  # Set 6 ticks on y-axis
#axes[1][2].yaxis.set_major_locator(MaxNLocator(integer=True, nbins=4))  # Set 6 ticks on y-axis

plt.savefig('doped_rest_vs_no_rest.svg', dpi=300, bbox_inches='tight', pad_inches=0)  # Save the figure with higher DPI for better resolution

/tmp/ipykernel_169275/3397683445.py:3: UserWarning: cmr10 font should ideally be used with mathtext, set axes.formatter.use_mathtext to True
  fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(3.7*3*0.9, 2.9*2*0.9))

[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
In [24]:
plot_suscs(objs_doped_rest, objs_doped_norest, 0, "d")
plt.show()
plot_suscs(objs_doped_rest, objs_doped_norest, 1, "d")
plt.show()
plot_suscs(objs_doped_rest, objs_doped_norest, 2, "d")
plt.show()
plot_suscs(objs_doped_rest, objs_doped_norest, 3, "d")
plt.show()
plot_suscs(objs_doped_rest, objs_doped_norest, 4, "d")
plt.show()

[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
In [14]:
plot_suscs(objs_doped_rest, objs_doped_norest, 0, "sc")
plot_suscs(objs_doped_rest, objs_doped_norest, 1, "sc")
plot_suscs(objs_doped_rest, objs_doped_norest, 2, "sc")
plot_suscs(objs_doped_rest, objs_doped_norest, 3, "sc")
plot_suscs(objs_doped_rest, objs_doped_norest, 4, "sc")

[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image
In [41]:
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(3.7*1*0.9, 3.0*3*0.9))  



plt.subplots_adjust(wspace=0.0, hspace=0.00)

plot_suscs(objs_doped_rest, objs_doped_norest, 0, "sc", ax_ = axes[0])
plot_suscs(objs_doped_rest, objs_doped_norest, 2, "sc", ax_ = axes[1], with_legend=False)
plot_suscs(objs_doped_rest, objs_doped_norest, 4, "sc", ax_ = axes[2], with_legend=False)
#axes[1].set_title(r"$U_{\mathrm{eff}} < 0$", loc="center", y=1.0, x=0.5, fontsize=15)
#axes[0].set_title(r"$U_{\mathrm{eff}} > 0$", loc="center", y=1.0, x=0.5, fontsize=15)


plt.savefig('sc_doped_mu_m0p95_rest_no_rest.svg', dpi=300, bbox_inches='tight', pad_inches=0)  # Save the figure with higher DPI for better resolution

/tmp/ipykernel_169275/2247581643.py:1: UserWarning: cmr10 font should ideally be used with mathtext, set axes.formatter.use_mathtext to True
  fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(3.7*1*0.9, 3.0*3*0.9))

[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
[  0.   1.   2.   3.   4.   5.   6.   7.   8.  24.  40.  56.  72.  88.
 104. 120. 136. 119. 102.  85.  68.  51.  34.  17.   0.]
No description has been provided for this image