debug_flex.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# original library\n",
    "from flex_tools import *\n",
    "\n",
    "import numpy as np\n",
    "import numpy.linalg as la\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import axes3d, Axes3D\n",
    "import seaborn as sns\n",
    "\n",
    "import gc\n",
    "import sys\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "import mpi4py\n",
    "\n",
    "from typing import Union, Optional, Callable\n",
    "from dataclasses import dataclass\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FLEX_pipeline は、flextools の中にあるものを使用してもよいが、mu の探索範囲や mix_rate を変化させるなどのカスタマイズをすると効率的な計算が可能。\n",
    "def FLEX_pipeline(ENERGY, ENERGY_DIAG, U_charge, U_spin, mu_init, pm, mix_rate, N_flexiter=1000, tol_flex=1e-3, mask=None):\n",
    "    mu = mu_init\n",
    "    SIGMA_tmp = None\n",
    "    if mask is not None:\n",
    "        U_charge = mask.apply_mask(U_charge)\n",
    "        U_spin = mask.apply_mask(U_spin)\n",
    "    for i in range(N_flexiter):\n",
    "        G = calc_Green(SIGMA_tmp, ENERGY, mu, *pm.calc_Green())\n",
    "        G_tau_r = from_iwn_k_to_tau_r(G)\n",
    "        CHI0_tau_r = calc_chi0_tau_r(G_tau_r)\n",
    "        if mask is not None:\n",
    "            CHI0_tau_r = mask.apply_mask(CHI0_tau_r)\n",
    "        CHI0, CHI_charge, CHI_spin = \\\n",
    "            calc_Susceptibilities(CHI0_tau_r, U_charge, U_spin, *pm.calc_Susceptibilities())\n",
    "        V_Sigma = calc_V_effective(U_charge, U_spin, CHI0, CHI_charge, CHI_spin)\n",
    "        V_Sigma_tau_r = from_iwn_k_to_tau_r(V_Sigma)\n",
    "        if mask is not None:\n",
    "            V_Sigma_tau_r = mask.restore(V_Sigma_tau_r)\n",
    "        SIGMA = calc_Self_Energy(V_Sigma_tau_r, G_tau_r, *pm.calc_Self_Energy())\n",
    "        # 収束の判定\n",
    "        err = eval_Self_Energy_err(SIGMA, SIGMA_tmp)\n",
    "        if err < tol_flex:\n",
    "            SIGMA = mix_Self_Energy(SIGMA, SIGMA_tmp, 0.5)\n",
    "            mu = calc_mu(SIGMA, ENERGY, ENERGY_DIAG, *pm.calc_mu(), mu_init=mu)\n",
    "            G = calc_Green(SIGMA, ENERGY, mu, *pm.calc_Green())\n",
    "            return CHI0, CHI_charge, CHI_spin, SIGMA, mu, G\n",
    "        else:\n",
    "            print('err={:.4g}'.format(err))\n",
    "            SIGMA_tmp = mix_Self_Energy(SIGMA, SIGMA_tmp, mix_rate)\n",
    "            if i < 2/mix_rate:\n",
    "                mu = calc_mu(SIGMA_tmp, ENERGY, ENERGY_DIAG, *pm.calc_mu(), search_min=mu-1, search_max=mu+1, solver='brent')\n",
    "            else:\n",
    "                try:\n",
    "                    mu = calc_mu(SIGMA_tmp, ENERGY, ENERGY_DIAG, *pm.calc_mu(), mu_init=mu)\n",
    "                except:\n",
    "                    mu = calc_mu(SIGMA_tmp, ENERGY, ENERGY_DIAG, *pm.calc_mu(), search_min=mu-1, search_max=mu+1, solver='brent')\n",
    "    print('Waring: Self Energy is not converge!  err={:.4g}'.format(err))\n",
    "    G = calc_Green(SIGMA_tmp, ENERGY, mu, *pm.calc_Green())\n",
    "    return CHI0, CHI_charge, CHI_spin, SIGMA, mu, G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2次元2サイト2軌道  ( 2層系, U=4 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>hop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x    y    z  hop\n",
       "0   0.0  0.0  0.0  0.0\n",
       "1   1.0  0.0  0.0  1.0\n",
       "2  -1.0  0.0  0.0  1.0\n",
       "3   0.0  1.0  0.0  1.0\n",
       "4   0.0 -1.0  0.0  1.0\n",
       "5   0.0  0.0  0.0  2.0\n",
       "6   1.0  0.0  0.0  0.0\n",
       "7  -1.0  0.0  0.0  0.0\n",
       "8   0.0  1.0  0.0  0.0\n",
       "9   0.0 -1.0  0.0  0.0\n",
       "10  0.0  0.0  0.0  2.0\n",
       "11  1.0  0.0  0.0  0.0\n",
       "12 -1.0  0.0  0.0  0.0\n",
       "13  0.0  1.0  0.0  0.0\n",
       "14  0.0 -1.0  0.0  0.0\n",
       "15  0.0  0.0  0.0  0.0\n",
       "16  1.0  0.0  0.0  1.0\n",
       "17 -1.0  0.0  0.0  1.0\n",
       "18  0.0  1.0  0.0  1.0\n",
       "19  0.0 -1.0  0.0  1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Nqx = 16\n",
    "Nqy = 16\n",
    "Nqz = 1\n",
    "Nm = 256\n",
    "Nrvec = 5\n",
    "\n",
    "Nsite = 2\n",
    "Norbit_each_site = (1,1)\n",
    "Norbit = 2\n",
    "\n",
    "hopping_info_file = 'hop.dat'\n",
    "hopping_df = pd.read_csv(hopping_info_file, sep='\\s+', names=['x', 'y', 'z', 'hop', '-'])\n",
    "hopping_df.drop('-', axis=1, inplace=True)\n",
    "display(hopping_df)\n",
    "\n",
    "temperature = 0.05\n",
    "band_filling = 0.9\n",
    "Uinner = 4.     # 軌道内クーロン相互作用\n",
    "Uinter = 0.     # 軌道間クーロン相互作用\n",
    "Jhund = 0.      # フント結合\n",
    "Jpair = 0.      # ペアホッピング\n",
    "\n",
    "pm = parameter_manager(\n",
    "    Nqx, Nqy, Nqz, Nm, Norbit, Norbit_each_site, Nrvec, Nsite, temperature, band_filling, Uinner, Uinter, Jhund, Jpair\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chemical potential = -0.5174\n",
      "err=inf\n",
      "err=267.6\n",
      "err=27.45\n",
      "err=18.02\n",
      "err=97.16\n",
      "err=3.576\n",
      "err=1.869\n",
      "err=4.436\n",
      "err=9.816\n",
      "err=6.238\n",
      "err=4.264\n",
      "err=22.02\n",
      "err=2.708\n",
      "err=4.669\n",
      "err=12.66\n",
      "err=3.88\n",
      "err=1.782\n",
      "err=1.056\n",
      "err=0.7006\n",
      "err=0.495\n",
      "err=0.4021\n",
      "err=0.351\n",
      "err=0.3026\n",
      "err=0.2582\n",
      "err=0.2182\n",
      "err=0.1831\n",
      "err=0.1526\n",
      "err=0.1266\n",
      "err=0.1046\n",
      "err=0.08619\n",
      "err=0.07081\n",
      "err=0.05805\n",
      "err=0.0475\n",
      "err=0.03882\n",
      "err=0.03168\n",
      "err=0.02589\n",
      "err=0.02132\n",
      "err=0.01753\n",
      "err=0.01441\n",
      "err=0.01183\n",
      "err=0.009699\n",
      "err=0.007949\n",
      "err=0.006512\n",
      "err=0.005332\n",
      "err=0.004365\n",
      "err=0.003572\n",
      "err=0.002923\n",
      "err=0.002393\n",
      "err=0.001958\n",
      "err=0.001604\n",
      "err=0.001314\n",
      "err=0.001077\n",
      "chi0= 0.1446501955242917\n",
      "chi_S= 1.2400577706549123\n",
      "chi_C= 0.08485510319373057\n",
      "Sigma= 0.5011196841696874\n",
      "chemical potential = -0.59029\n",
      "G= 1.8174195454461293\n"
     ]
    }
   ],
   "source": [
    "ENERGY, ENERGY_DIAG, UNITARY = get_ENERGY(hopping_df, *pm.get_Energy())\n",
    "U_charge, U_spin = get_INTERACTION(*pm.get_INTERACTION())\n",
    "mask = mkmask(U_charge, U_spin)\n",
    "mu = calc_mu_non_interacting(ENERGY_DIAG, *pm.calc_mu_non_interacting())\n",
    "print('chemical potential = {:.4g}'.format(mu))\n",
    "CHI0, CHI_charge, CHI_spin, SIGMA, mu, G = \\\n",
    "    FLEX_pipeline(ENERGY, ENERGY_DIAG, U_charge, U_spin, mu, pm, mix_rate=0.2, mask=mask)\n",
    "print('chi0=', np.max(np.real(CHI0)))\n",
    "print('chi_S=', np.max(np.real(CHI_spin)))\n",
    "print('chi_C=', np.max(np.real(CHI_charge)))\n",
    "print('Sigma=', np.max(np.absolute(SIGMA)))\n",
    "print('chemical potential = {:.5g}'.format(mu))\n",
    "print('G=', np.max(np.absolute(G)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.7312631367232529\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.2100977320104879\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.18620786114731064\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DELTA_init_gap = get_DELTA_band_init(*pm.get_DELTA_init(), gap_symmetry='random')\n",
    "DELTA_init = from_band_to_orbit(DELTA_init_gap, UNITARY)\n",
    "lambda_list, DELTA_list = \\\n",
    "    Eliashberg_pipeline(G, U_charge, U_spin, CHI_charge, CHI_spin, DELTA_init, pm, mask=mask, k=3)\n",
    "print('lambda=', lambda_list[0])\n",
    "sns.heatmap(np.sum(np.real(from_orbit_to_band(DELTA_list[0], UNITARY)), axis=0)[:,:,0,0,0])\n",
    "plt.show()\n",
    "print('lambda=', lambda_list[1])\n",
    "sns.heatmap(np.sum(np.real(from_orbit_to_band(DELTA_list[1], UNITARY)), axis=0)[:,:,0,0,0])\n",
    "plt.show()\n",
    "print('lambda=', lambda_list[2])\n",
    "sns.heatmap(np.sum(np.imag(from_orbit_to_band(DELTA_list[2], UNITARY)), axis=0)[:,:,0,0,0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2次元2サイト2軌道  ( 2層系, U=6 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>hop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x    y    z  hop\n",
       "0   0.0  0.0  0.0  0.0\n",
       "1   1.0  0.0  0.0  1.0\n",
       "2  -1.0  0.0  0.0  1.0\n",
       "3   0.0  1.0  0.0  1.0\n",
       "4   0.0 -1.0  0.0  1.0\n",
       "5   0.0  0.0  0.0  2.0\n",
       "6   1.0  0.0  0.0  0.0\n",
       "7  -1.0  0.0  0.0  0.0\n",
       "8   0.0  1.0  0.0  0.0\n",
       "9   0.0 -1.0  0.0  0.0\n",
       "10  0.0  0.0  0.0  2.0\n",
       "11  1.0  0.0  0.0  0.0\n",
       "12 -1.0  0.0  0.0  0.0\n",
       "13  0.0  1.0  0.0  0.0\n",
       "14  0.0 -1.0  0.0  0.0\n",
       "15  0.0  0.0  0.0  0.0\n",
       "16  1.0  0.0  0.0  1.0\n",
       "17 -1.0  0.0  0.0  1.0\n",
       "18  0.0  1.0  0.0  1.0\n",
       "19  0.0 -1.0  0.0  1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Nqx = 16\n",
    "Nqy = 16\n",
    "Nqz = 1\n",
    "Nm = 256\n",
    "Nrvec = 5\n",
    "\n",
    "Nsite = 2\n",
    "Norbit_each_site = (1,1)\n",
    "Norbit = 2\n",
    "\n",
    "hopping_info_file = 'hop.dat'\n",
    "hopping_df = pd.read_csv(hopping_info_file, sep='\\s+', names=['x', 'y', 'z', 'hop', '-'])\n",
    "hopping_df.drop('-', axis=1, inplace=True)\n",
    "display(hopping_df)\n",
    "\n",
    "temperature = 0.1\n",
    "band_filling = 0.9\n",
    "Uinner = 6.     # 軌道内クーロン相互作用\n",
    "Uinter = 0.     # 軌道間クーロン相互作用\n",
    "Jhund = 0.      # フント結合\n",
    "Jpair = 0.      # ペアホッピング\n",
    "\n",
    "pm = parameter_manager(\n",
    "    Nqx, Nqy, Nqz, Nm, Norbit, Norbit_each_site, Nrvec, Nsite, temperature, band_filling, Uinner, Uinter, Jhund, Jpair\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chemical potential = -0.4772\n",
      "err=inf\n",
      "err=1.305e+04\n",
      "err=23.78\n",
      "err=12.75\n",
      "err=703.5\n",
      "err=10.78\n",
      "err=5.734\n",
      "err=3.612\n",
      "err=4.425\n",
      "err=5.665\n",
      "err=5.973\n",
      "err=6.527\n",
      "err=6.957\n",
      "err=13.57\n",
      "err=9.555\n",
      "err=4.52\n",
      "err=3.48\n",
      "err=6.955\n",
      "err=30.27\n",
      "err=2.686\n",
      "err=2.746\n",
      "err=2.484\n",
      "err=5.469\n",
      "err=1.653\n",
      "err=1.475\n",
      "err=1.169\n",
      "err=65.14\n",
      "err=6.113\n",
      "err=3.837\n",
      "err=3.501\n",
      "err=4.054\n",
      "err=4.462\n",
      "err=4.144\n",
      "err=3.239\n",
      "err=2.378\n",
      "err=1.743\n",
      "err=1.395\n",
      "err=1.363\n",
      "err=1.314\n",
      "err=1.247\n",
      "err=2.506\n",
      "err=1.186\n",
      "err=4.199\n",
      "err=15.42\n",
      "err=19.52\n",
      "err=47.08\n",
      "err=26.35\n",
      "err=8.761\n",
      "err=20.06\n",
      "err=13.51\n",
      "err=17.23\n",
      "err=10.5\n",
      "err=9.03\n",
      "err=8.419\n",
      "err=5.02\n",
      "err=3.164\n",
      "err=2.335\n",
      "err=1.782\n",
      "err=1.399\n",
      "err=1.125\n",
      "err=0.9223\n",
      "err=0.7797\n",
      "err=0.6723\n",
      "err=0.581\n",
      "err=0.5036\n",
      "err=0.4405\n",
      "err=0.3893\n",
      "err=0.3448\n",
      "err=0.3059\n",
      "err=0.272\n",
      "err=0.2423\n",
      "err=0.2162\n",
      "err=0.1932\n",
      "err=0.1729\n",
      "err=0.155\n",
      "err=0.1391\n",
      "err=0.125\n",
      "err=0.1124\n",
      "err=0.1013\n",
      "err=0.09127\n",
      "err=0.08235\n",
      "err=0.07436\n",
      "err=0.0672\n",
      "err=0.06078\n",
      "err=0.05501\n",
      "err=0.04982\n",
      "err=0.04516\n",
      "err=0.04096\n",
      "err=0.03814\n",
      "err=0.0358\n",
      "err=0.0336\n",
      "err=0.03154\n",
      "err=0.02961\n",
      "err=0.0278\n",
      "err=0.02611\n",
      "err=0.02452\n",
      "err=0.02304\n",
      "err=0.02165\n",
      "err=0.02035\n",
      "err=0.01913\n",
      "err=0.01799\n",
      "err=0.01693\n",
      "err=0.01593\n",
      "err=0.015\n",
      "err=0.01413\n",
      "err=0.01331\n",
      "err=0.01255\n",
      "err=0.01183\n",
      "err=0.01116\n",
      "err=0.01054\n",
      "err=0.009948\n",
      "err=0.009397\n",
      "err=0.008881\n",
      "err=0.008396\n",
      "err=0.007941\n",
      "err=0.007514\n",
      "err=0.007113\n",
      "err=0.006737\n",
      "err=0.006383\n",
      "err=0.006049\n",
      "err=0.005736\n",
      "err=0.005441\n",
      "err=0.005163\n",
      "err=0.004902\n",
      "err=0.004655\n",
      "err=0.004422\n",
      "err=0.004203\n",
      "err=0.003995\n",
      "err=0.0038\n",
      "err=0.003615\n",
      "err=0.00344\n",
      "err=0.003275\n",
      "err=0.003118\n",
      "err=0.00297\n",
      "err=0.00283\n",
      "err=0.002697\n",
      "err=0.002571\n",
      "err=0.002451\n",
      "err=0.002338\n",
      "err=0.002231\n",
      "err=0.002128\n",
      "err=0.002032\n",
      "err=0.001939\n",
      "err=0.001852\n",
      "err=0.001769\n",
      "err=0.001689\n",
      "err=0.001614\n",
      "err=0.001542\n",
      "err=0.001474\n",
      "err=0.001409\n",
      "err=0.001347\n",
      "err=0.001288\n",
      "err=0.001232\n",
      "err=0.001178\n",
      "err=0.001127\n",
      "err=0.001078\n",
      "err=0.001032\n",
      "chi0= 0.10369158058052415\n",
      "chi_S= 1.1530279064871005\n",
      "chi_C= 0.06089059402333649\n",
      "Sigma= 1.1728589209234703\n",
      "chemical potential = -0.82028\n",
      "G= 0.6968546072771399\n"
     ]
    }
   ],
   "source": [
    "ENERGY, ENERGY_DIAG, UNITARY = get_ENERGY(hopping_df, *pm.get_Energy())\n",
    "U_charge, U_spin = get_INTERACTION(*pm.get_INTERACTION())\n",
    "mask = mkmask(U_charge, U_spin)\n",
    "mu = calc_mu_non_interacting(ENERGY_DIAG, *pm.calc_mu_non_interacting())\n",
    "print('chemical potential = {:.4g}'.format(mu))\n",
    "CHI0, CHI_charge, CHI_spin, SIGMA, mu, G = \\\n",
    "    FLEX_pipeline(ENERGY, ENERGY_DIAG, U_charge, U_spin, mu, pm, mix_rate=0.1, mask=mask)\n",
    "print('chi0=', np.max(np.real(CHI0)))\n",
    "print('chi_S=', np.max(np.real(CHI_spin)))\n",
    "print('chi_C=', np.max(np.real(CHI_charge)))\n",
    "print('Sigma=', np.max(np.absolute(SIGMA)))\n",
    "print('chemical potential = {:.5g}'.format(mu))\n",
    "print('G=', np.max(np.absolute(G)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.9330413160109651\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.2210816312155107\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.41719240643271976\n"
     ]
    },
    {
     "data": {
      "image/png": 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4K/0/Y8WSxBCvBU6VdDIwCZgh6asR8futGqUVKTYzq5gqLZeLiA9FxPz8Nn1nAv9W1CmDO2Yzq5mRUNuPFJJOl7QWOBa4StI1+fZ5kpLuRuGpDDOrlW5OZbR+n7gcuHyU7Y8DJ4+y/UbgxnZiFxUxeq+k9HsAmZn1yciw2n5UVdFUxl8At0j6oaT/Iell7QRtLGJ06fOPpGdpZtamOhQxKuqYHwLmk3XQrwLukfQ9SW+X1HRNXkSsiIjFEbH4jOkHdDFdM7PW+jXH3EtFHXNExEhEXBsRZwPzgL8HTiLrtM3MKiVCbT+qqujk30syj4gdwJXAlZKm9CwrM7MO9bFWRs8Udcy/22xHRGxpts/MbHep8hRFu4qqy93Xr0TMzLphpMIn9drldcxmViu1HzF3w4zZW5Nj7NiQfrOUgQMTK5FNmJycw7h906uIxdbnk2MMP/FMUvuBaGvVZEvzmJgc46Ad6Reu7nNs2nW549/8zuQcNCX9vsbae35yjOFbb05qPy692F9XVPmkXrs8YjazWvGI2cysYmqwKMMds5nVy/DI2K/N5o7ZzGqlwje/bps7ZjOrlaDmc8ySJpAVd348Iq6X9FbgNWR3e12RXwloZlYZIzWYZC6ajPkS2d1d3yfpK8BS4Bbg14ALmjVqrC73tafWdS1ZM7MiI6jtR1UVTWX8ckT8iqTxwDpgXkQMS/oqcGezRo03OHzkqCU1+P1lZmNF7acygHH5dMZUYAowE9gETAQGe5ybmVlpw3tAx3whcC8wACwHvinpIeAY4OIe52ZmVlodVmW0nGOOiM8CxwHHRsTfAL8DXAOcHREf60N+ZmaljJR4pJC0VNJqSSOSFrc4bpakSyXdK2mNpGOLYhcul8tvLLjz+bPApe0mbmbWb32cY14FvAX4QsFx5wPfi4gz8qnhwlr2XsdsZrXSr6qfEbEGQGr+hpJmAscDZ+VttgPbi2L3vGPe/Ex6VbYpc9KXS4878MCk9vH048UHFcWYk37D8aHvNF2l2LaN/57Wfs3gcHIOG4o/m4UWKv2zNbDfrKT2I89uSM5Bm59OjsG2n3chRtrPZHBeesXAbiizDE7SMmBZw6YV+aqyblkEbAS+JOlXgduB90VEyx+YR8xmVitlhg2NS3tHI+l6YL9Rdi2PiCvaeIvxwFHAeyLiFknnA+cC/7eokZlZbYy0mFooKyKWJIZYC6yNiFvy15eSdcwtjf0yTGZmDaLEo+e5RGwAHpN0SL7pROCeonbumM2sVvq4XO50SWuBY4GrJF2Tb58n6eqGQ98DXCTpLuAI4BNFsQunMiQdSLYkZAHZ9M19wNciYnPZL8TMrNf6uCrjcuDyUbY/Dpzc8Hol0HSd82hajpglvRf4PDCJrHDRRLIO+mZJJ7Ro94siRpc+/0iZfMzMkgyjth9VVTRifhdwRF646DPA1RFxgqQvAFcAR47WqPFM592L3uwiRmbWN/0aMfdSO6syxpNNYUwEpgFExKOSXMTIzCqnDrUyijrmC4BbJd0CvA44D0DSy8iqzJmZVUod/kRv2TFHxPn5AuvDgE9HxL359o1klxmamVXKHjGVERGrgdV9yMXMLNmeMJVhZjamDO8JI+ZUUvqMz46fp18HM7IqbdA/7tCh9BxmzEmOMXTHT5NjPLZxZlL7uyc+l5zDI9vSC/fMmbwoPY+LtyS1X3Tsfck5sE96cSsmpBd0GnpkY1L7GKrG7K5HzGZmFeOO2cysYqoxbk/jjtnMamWPWJVhZjaW1GEqo6hWxkxJn8xvIrhJ0tP5zQQ/KWlWn3I0M2vbcIlHVRUtd7gEeAY4ISL2iog5wK/n2y7pdXJmZmWNqP1HVRV1zAsj4ry82DOQFX6OiPOAA5o1aqwu983Nj3YrVzOzQv2qx9xLRR3zI5I+KGnfnRsk7SvpHOCxZo0iYkVELI6IxUtnvLxbuZqZFarSHUw6VdQx/y4wB/h+Pse8CbgR2AtY2uPczMxKGyHaflRVURGjZ4Bz8sdLSHoH8KUe5WVm1pEqn9RrV8q1zh/rWhZmZl3Sx3v+LZW0WtKIpKa3jpL0J/lxqyR9XdKkotgtR8z5zQNH3QXs22Sfmdlu08fVFqvI7of6hWYHSNofeC9weERslXQJcCbw5VaBiy4w2Rf4TbLlcS95P+A/CtqamfVdv+aOI2INgFT4m2A8MFnSDmAK8Hg7DVr5DjAtv8vrS0i6sSg4QET6r6+tmyckx5i24dmk9uMWbk3OIR5ZkxzjyX9P/37eNGliUvvVL6RXuFv3fHp1uSVdqC737JbCvypbGrnppuQcNP/h5BixMf2GQpowkNR+463pn81ZyRGqtdoiItZJ+ivgUWArcG1EXFvUruUcc0ScHRE/arLvrR1lambWQ2XmmBuvucgfyxpjSbo+nxve9XFaO7lImg2cBiwC5gFTJf1+UTvXyjCzWhkuMWaOiBXAihb7lySmswR4OL8dH5IuA14DfLVVI3fMZlYrFbui71HgGElTyKYyTgRuK2qUfmsQM7MK6dcFJpJOl7QWOBa4StI1+fZ5kq4GiIhbgEuBO4C7yfrcpiP0nToeMUv6bkS8qdP2Zma90K+TfxFxOXD5KNsfB05ueP0R4CNlYhetYz6q2S7giDJvZGbWDxWbyuhI0Yj5VuD7ZB3xrmY1a5Sf2VwG8OE5v8wZ05sWojMz66oyJ/+qqqhjXgP8cUTcv+sOSS2ry5HPo9y96M1j/7tkZmNGlYsTtauoY/4ozU8Qvqe7qZiZpRv73XJxdblLW+ye3eVczMyS1WHE7OpyZlYrdbiDiavLmVmtRA1GzD2vLnf/1ukdpPVSg1vTv9Gv/PFTSe33nnxvcg6xbUdyjK1b0ws6bdBQUvuI9J/HpPHpX8em2J4c45mRtIJO2+9bn5zDuEfTCxAxPr2A0O1X75XU/ulxg8k5HJQcYc9YlZFcXc7MrJ+qPEXRrqKTf2e32OfqcmZWOSNd+Itud3MRIzOrlbHfLbtjNrOaqf1yOUkzJP0/SV+R9NZd9v19b1MzMysvSvxXVUXrmL9EtgLjW8CZkr4laedp7GN6mpmZWQeGiLYfVVXUMb8iIs6NiH+JiFPJaor+m6Q5rRo13q7l2i0PdC1ZM7MidRgxF80xT5Q0LiJGACLi45LWAT8ApjVr1FjE6LL93lrdr97MaqcOy+WKRszfBt7QuCEivgx8AEhf3W9m1mUR0fajqorWMX+wyfbvSfpEb1IyM+tc7VdlFHARIzOrnGGi7UdVuYiRmdVKv0bMkj4FvJlsWvdB4B0R8ewox50EnA8MABdExCeLYve8iJGZWT/1ce74OuBDETEk6TzgQ8A5jQdIGgD+DngjsBa4VdKVEXFPq8A9L2L03UnpFdVmd+ECxYeeSRvgn/aDJ5JzePaZyckxNrw4JTnGzMkDSe0Pmzo/OYeYmv6P5wBNSo4xlFiUbc2PWq4cbcuLI2k/D4BNXajstmFC2jfjjoEXk3NYmhyhf6syIuLahpc3A2eMctjRwAMR8RCApIuB04DOO2YXMTKzsabM+uTGG0fnVuTLfct6J/CNUbbvDzTeH3Ut8OqiYK6VYWa1UmaOufGai9FIuh7Yb5RdyyPiivyY5cAQcFG5TJtzx2xmtTIc3ZvMiIglrfZLOgs4BTgxRp/cXgcsaHg9P9/WkjtmM6uVfl1qna+2+CDw+ojY0uSwW4GDJS0i65DPBAqngYuqy+0n6R8k/Z2kOZI+KuluSZdImlvy6zAz67mRiLYfiT4HTAeuk7RS0ucBJM2TdDVARAwB7wauAdYAl0TE6qLARSPmLwNXAVOBG8jmUE4Gfhv4PNnZRTOzyujXYrmIGPUWhRHxOFk/ufP11cDVZWIXXfm3b0T8bb4gelZEnBcRj0XE3wIHNGvUWF3u3ucfKpOPmVmSEaLtR1UVdcyN+/95l31NF19GxIqIWBwRiw+dfmDHyZmZlVWHjrloKuMKSdMi4oWI+D87N0o6CPhpb1MzMyuvm6sydpeiC0w+3GT7A5Ku6k1KZmadq3IB/Ha5upyZ1Urt6zG7upyZjTVVnjtul6vLmVmtVHkk3K6eV5f76Y5N5bPqgYMGZye135ZYnQ5gWxd+ky8Yn1gODRhOrL917Li9knMYSf8ymN2FIM8lFnZ7PtIrBr4wITkEDw0MJ8d4MfFzcff2J5Nz6IbUz3cVuLqcmdVKF67o2+1cK8PMaqUOqzLcMZtZrdRhxFx6uZykfXqRiJlZN0SJ/6qqaLncrmd5BPxY0pGAIqIaZ/bMzHJ1GDEXTWU8BTyyy7b9gTvIijiNWgij8XYtB808hLlT909M08ysPXW4JLtoKuN/kdXEODUiFkXEImBt/rxpdaLGIkbulM2sn2o/lRERn5b0DeCzkh4DPkL/yp2amZUWNRgxF67KiIi1wFJJpwLXAVN6npWZWYfqcEl226syIuJK4NeBJQCS3tGrpMzMOlWHIkallstFxNaIWJW/dHU5M6uc2hfKd3U5Mxtrhkf6M8cs6VPAm4HtwIPAOyLi2V2OWUB296d9yc7PrYiI84ti97y63P0/X9/OYS1NG0wvFJP6u3F4cFZyDoN0oejO4GByjBmRUoYbpkb61/GKben/eO6dmJ7HxJG0T8atE9O/jg2xLTnGIzueS4+xNa0I0YDSPlfd0sfVFtcBH4qIIUnnAR8CztnlmCHgAxFxh6TpwO2SrouIe1oF7nl1OTOzfurX3HFEXNvw8mbgjFGOWQ+sz58/L2kN2bUgnXfMri5nZmNNmbnjxovhcisiYkUHb/tO4BsF77UQOBK4pSiYixiZWa2UGTHnnXDTjljS9cB+o+xaHhFX5McsJ5uyuKhFnGnAt4D3R8TmorzcMZtZrXTz5F9ELGm1X9JZwCnAidHkN4KkQbJO+aKIuKyd9205Wy/ppIbnMyVdKOkuSV+T5FUZZlY5/Voul/ePHyQrWbGlyTECLgTWRMRn2o1ddBr1Ew3PP002if1m4FbgC+2+iZlZv/TxApPPAdOB6yStlPR5AEnzJF2dH/Na4A+AN+THrJR0clHgMlMZiyPiiPz5ZyW9vdmBjRPqMybvx5QJaffbMzNrV7/KfkbEQU22Pw6cnD//EZRfJ1vUMe8j6U/zwDMkqWEepelou3FCfe6sw6t7eY2Z1U6Vq8a1q6hj/keyoTrAPwF7Axsl7Qes7GFeZmYdqX2h/IgYtR5GRGyQdENvUjIz69xIDcp+plxD6SJGZlY5dagu5yJGZlYrVe5w29XzIkZmZv009rtlWg/7yRZGH9dk39fK/MlQ8D7Ldmf7OsWoQg7+Ovy96GWMPeGh/Ju1W0m6LSIW7672dYpRhRy6EaMKOVQlRhVyqFKMPUE1CqiamdkvuGM2M6uYqnTMndQ/7Wb7OsWoQg7diFGFHKoSowo5VClG7VVijtnMzP5TVUbMZmaWc8dsZlYxu7VjlnSSpJ9KekDSuR20/6KkJyWtSshhgaQbJN0jabWk95VsP0nSjyXdmbfv+FJ1SQOSfiLpOx22/5mku/Oar7d1GGOWpEsl3StpjaRjS7Q9pKHm7EpJmyW9v4Mc/iT/Xq6S9HVJkzqI8b68/ep2cxjt8yRpL0nXSbo//3/TGrZN2i/NcxiRVLhMrEmMT+U/j7skXS5pVgcx/iJvv1LStZLmlY3RsO8DkkLS3iVz+KikdWXqEu+xdtcCamAAeBA4EJgA3AkcXjLG8cBRwKqEPOYCR+XPpwP3lcmD7CrIafnzQbIbLR7TYS5/CnwN+E6H7X8G7J34c/kn4I/y5xOAWQk/3w3AASXb7Q88DEzOX18CnFUyxiuBVcAUsqtbrwcO6uTzBPwlcG7+/FzgvJLtDwMOAW4kq2neSQ6/AYzPn5/XKocWMWY0PH8v8PmyMfLtC4BrgEdafdaa5PBR4M9SPp97ymN3jpiPBh6IiIciYjtwMXBamQAR8QNgU0oSEbE+Iu7Inz8P7Ly9eLvtIyJeyF8O5o/SZ1QlzQd+C7igbNtukTST7B/UhQARsT0inu0w3InAgxHxSAdtxwOTJY0n61wfL9n+MOCWiNgSEUPA94G3FDVq8nk6jeyXFfn/f7tM+4hYExE/bTfxJjGuzb8OgJuB+R3EaLwB6FQKPqMt/m19lux2Sp22tzbszo55f+CxhtdrKdEh9oJK3F58l3YDklYCTwLXRUSp9rm/JvvAp9QsDOBaSbfnd5EpaxGwEfhSPqVygaSpHeZyJvD1so0iYh3wV8CjZLcyey4iri0ZZhXwOklzJE0hu5vEgrK55PaNiPX58w3s/uJd7wS+20lDSR+X9BjwNuDDHbQ/DVgXEXd28v65d+dTKl9sNS20p/PJv5xK3l68UUQMR3bbrfnA0ZJeWfK9TwGejIjby7QbxXERcRTwJuB/Sjq+ZPvxZH9+/kNEHAn8nOzP91IkTQBOBb7ZQdvZZKPURcA8YKqk3y8TIyLWkP3Jfy3wPbKbOgyXzWWUuMFurJEjaTkwBFzUSfuIWB4RC/L27y753lOA/00HHXqDfwBeARxB9kv30wmxam13dszreOkoZn6+re/Uwe3FR5P/2X8DcFLBobt6LXCqpJ+RTem8QdJXO3j/dfn/nwQuJ5suKmMtsLZhxH8pWUdd1puAOyLiiQ7aLgEejoiNEbEDuAx4TdkgEXFhRLwqIo4nq454Xwe5ADwhaS5A/v8nO4yTRNJZwCnA2/JfECkuAn6nZJtXkP2yvDP/nM4H7lB2N6O2RMQT+SBmhOzuSGU/n3uM3dkx3wocLGlRPsI6E7iy30lInd1evKH9y3aeJZc0GXgjcG+ZGBHxoYiYHxELyb4P/xYRpUaJkqZKmr7zOdkJo1KrVSJiA/CYpEPyTScC95SJkfs9OpjGyD0KHCNpSv6zOZFs3r8USfvk/3852fzy1zrM50pg542H3w5c0WGcjkk6iWya69SI2NJhjIMbXp5G+c/o3RGxT0QszD+na8lOmm8okcPchpenU/LzuUfZnWceyeb+7iNbnbG8g/ZfJ/uTaAfZB+XsDmIcR/bn6V1kf/KuBE4u0f5XgJ/k7VcBH078npxAB6syyFa33Jk/Vnfy/czjHAHcln89/wLMLtl+KvA0MDPhe/Axso5jFfAVYGIHMX5I9kvlTuDETj9PwBzgX4H7yVZ37FWy/en5823AE8A1HeTwANn5mJ2fz6IVFaPF+Fb+/bwL+Dawf9kYu+z/Ga1XZYyWw1eAu/McrgTmpvxbqfPDl2SbmVWMT/6ZmVWMO2Yzs4pxx2xmVjHumM3MKsYds5lZxbhjNjOrGHfMZmYV8/8BzAyC4zjyZyYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.13995596133195212\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.12488265614622686\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.11416630731527146\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DELTA_init_gap = get_DELTA_band_init(*pm.get_DELTA_init(), gap_symmetry='random')\n",
    "DELTA_init = from_band_to_orbit(DELTA_init_gap, UNITARY)\n",
    "lambda_list, DELTA_list = \\\n",
    "    Eliashberg_pipeline(G, U_charge, U_spin, CHI_charge, CHI_spin, DELTA_init, pm, mask=mask, k=6, symmetrize='auto')\n",
    "for i in range(6):\n",
    "    print('lambda=', lambda_list[i])\n",
    "    sns.heatmap(np.sum(np.real(from_orbit_to_band(DELTA_list[i], UNITARY)), axis=0)[:,:,0,0,0])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.12488275636179919\n"
     ]
    },
    {
     "data": {
      "image/png": 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zXa/1/rN14CERM2uUKLEU2AK0Xum9MN825HMkTQbmANvaLFuaG2wza5T+gUltLwXWAYdKWixpKtlJxLWDnrMWWJmvvxX4fkREvv2kfBbJYuBQ4MbU1+YhETNrlKqyq+Zj0qcCVwM9wIURcYeks4D1EbEWuAD4kqSNwHayRp38eZeTnf3qA/4ydYYIuME2s4YJqruCMSKuAq4atO3MlvVfAycOU/Zs4OzKgqGgwW75GvCriPiepLcDrwPuIjuburvKYMzMUg00+ErHoh72RflzZkhaCcwCvg4sI5tTuHKoQq1TZU6fswQngDKzsTJQYQ+72xQ12L8bEa/Iz35uAZ4fEf2S/hW4ZbhCrVNl1i1Y0eDPOzPrNlUOiXSbogZ7Uj4sMhOYQTZlZTswDZgyyrGZmZXWP4Eb7AuAu8nOkJ4BfFXSJuC1ZJdpmpl1lQbfg7cw+dN5ki7L138l6YvAMcDnIyJ5TqGZWdUmbIMNWUPdsv4YWYITM7OuNJHHsM3MaqWGt2psWy0a7Ef6piXXsXVK2kvd3JM+2WWrnk2uY2f6xVLMVtrv4rCBqckxVJFp75DX7UiuY+rL5yeV196zkmOIp9IzMPZveTS5jmc2/Tqp/BNb90qOoQoTeVqfmVmtdEXG3FHiBtvMGmVA7mGbmdVCk6/Uc4NtZo0yoaf1SToY+COyZNz9wC+ASyLiiVGOzcystCbPEhkxg7ek9wKfBfYCXk12SfpBwE8lHT1CuVWS1ktaf+XO+yoL1sysSD9qe6mboh72u4ElecKnfwCuioijJX0O+CbwyqEKOfmTmY2XJvew2xnDnkw2FDKNLL0qEfGAJCd/MrOuM5HHsM8H1km6AXg9cC6ApP3JsvaZmXWVJn+lL0r+9ClJ3wNeCnwyIu7Otz8CHDUG8ZmZldLkIZHC2wZHxB0RccWextrMrJsNlFhSSNpH0rWSNuQ/5w3zvJX5czbkd+7as/16SfdIujlfnld0zMIG28ysTvrV/pJoNXBdRBwKXJc/fg5J+wAfBl5DdlvFDw9q2N8REUvy5eGiA9biwpkdPelhbpqc9nl660D6tPMNux5KrmPbM+lx7DdtTloFMxYlx3BkBQkfUhM3AfQsOz6pvPZ/QXIMsa03uY5Jd/88uQ5NuTOpfN+uXckxVGEMTzouB47O1y8GrgdOG/ScNwHXRsR2AEnXAscBX+nkgO5hm1mjlBkSab1mJF9WlTjUARGxNV9/EDhgiOcsADa3PO7Nt+1xUT4c8r+k4iQotehhm5m1q8wskdZrRoaST7o4cIhdZwyqJySVnaDyjojYImk28DXgT4EvjlTADbaZNUqVs0Qi4pjh9kl6SNL8iNgqaT4w1Bj0Fn47bAKwkGzohIjYkv98UtIlZGPcIzbYHhIxs0YZq1kiwFpgz6yPlWRXfw92NfBGSfPyk41vBK6WNFnSfgD5RYhvBm4vOmBRLpE5ks6RdLek7ZK2Sbor3za3/ddlZjY2+kssic4BjpW0gezm5OcASFoq6XyA/GTjx4B1+XJWvm0aWcN9K3AzWU/880UHLBoSuRz4PnB0RDyYB3Mg2afJ5WSfFmZmXWOsLpyJiG3AsiG2rwdOaXl8IXDhoOfsBA4ve8yiIZFFEXHunsY6P9CDEXEu8MLhCjlbn5mNlzEcEhlzRQ32/ZI+KOk301UkHSDpNJ47VeU5ImJNRCyNiKUrZi6qKFQzs2JRYqmbogb7bcC+wA/zMeztZGc49wFOHOXYzMxKGyDaXuqmKPnTDrIrdwZfvYOkdwEXjVJcZmYdafJd01Om9X20sijMzCrS5DHsEXvY+ZSTIXcx9GWYZmbjqsnpVYum9R1Alrxkx6DtAn4yKhGZmSWo49h0u4oa7G8DsyLi5sE7JF0/GgENZXcFn5hPJX4Berw/PRNZFZn2dux6KrmOVE9O7xvvEADQ3rPS60jMtjdpv4OSY6jiq3nMuz+5Ds2enlR+0pSdyTFUobnNdfFJx5NH2Pf26sMxM0tTx7Hpdjn5k5k1Sn+D+9husM2sUdzDNjOriSafdOx4Hrakf6syEDOzKjT50vSiedivGm4XsKTyaMzMEk3kIZF1wA/JGujB5g5XKL8v2iqA0+cswQmgzGysTOSTjncBfxERGwbvkDRitj7y+6StW7Ciub89M+s6TR7DLmqwP8Lw49x/VW0oZmbpmttcF184c8UIu+dVHIuZWbIm97Cdrc/MGmWssvVJ2kfStZI25D+H7MRK+q6kxyR9e9D2xZJukLRR0mWSphYds+gmvLcOs9yGs/WZWReKEv8SrQaui4hDgevyx0P5BPCnQ2w/FzgvIg4hS7A3bCqQPWqRrS/la8AeU4ac6NK+mZMKP/wKzZ2anqyoCvtO2zup/Ez1VBBFegKpeOrp9Dq29SaVryRx02MPFj+pyFNPptexO/H/JLojr+kYzhJZDhydr19Mdjeu/3Czl4i4TtLRrdskCXgDsCcn08Vk5ww/M9IBa5Gtz8ysXWM4D/uAiNiarz9IuVGHfYHHImLPp2QvsKCokLP1mVmjDET7PezWa0Zya/JpyXv2fw84cIiiZ7Q+iIiQNOpde+cSMbNGKdNqtl4zMsz+Y4bbJ+khSfMjYquk+cDDJQ69DZgraXLey14IbCkqVMXwsJlZ1xjDu6avBVbm6yuBb7ZbMCIC+AHw1jLli2aJ7C3p7yR9SdLbB+37l3aDMzMbK2M4S+Qc4FhJG4Bj8sdIWirp/D1PkvQj4KvAMkm9kt6U7zoNeL+kjWRj2hcUHbBoSOQiYAPwNeDPJf0x8PaIeAZ4bamXZmY2BvrGaJZIRGwDlg2xfT1wSsvj1w9TfhNwRJljFg2JvCgiVkfENyLiBOAm4PuS9h2pkKRVktZLWn/lzvvKxGNmlmQMe9hjrqiHPU3SpIgYAIiIsyVtAf4dGHZSsZM/mdl4aXJ61aIe9rfIJnf/RkR8Afgb4NlRisnMrGMR0fZSN0XzsD84zPbvSvr46IRkZtY5J38ampM/mVnX6SfaXuqm6BZhtw63Cyd/MrMu1OQedi2SP5mZtauOY9PtqkXyp+kD6f8B+0ZahrkFPTOTY2Cv5yVXsXPq3OQ65vRMTyo/P9IzF1aRra9/y6PJdUy6++dJ5WPe/ckxVJFpb2Bz4VXNhfofeSqp/O5d3XHhdJNniTj5k5k1Sh3nV7fLyZ/MrFEm8hi2mVmt9EdzB0XcYJtZozR5SKQoW9+Bkj4j6dOS9pX0EUm3Sbo8z/9qZtZVBiLaXuqm6LTuF4A7gc1kuVt3AccDPwI+O6qRmZl1IEosdVPUYB8QEf8UEecAcyPi3IjYHBH/BLxwuELO1mdm42UMb2Aw5ooa7Nb9Xxy0b9iJzRGxJiKWRsTSFTMXdRqbmVlpTW6wi046flPSrIh4KiI+tGejpEOAe0Y3NDOz8ibsLJGIOHOY7RslfWd0QjIz69yEnSVSwNn6zKzrTNh82M7WZ2Z1M1Zj05L2AS4DFgH3AX8SEYMT5SHpu2T3wP1xRLy5ZfsXgP8MPJ5veudQeZtaOVufmTXKGPacVwPXRcQ5klbnj08b4nmfAGYAfzHEvr+NiCvaPWAtsvXNHOhPruPAvsSLOifvlRzD3J4pyXXs7kl/M85KGgmDg/qUHEMVntn06+Q6NOXOtPKz0zIfArC7gsyFiZn2AJ7pTYvjmZ3pfyNV6B+7fH3LgaPz9YuB6xmiwY6I6yQdPXh7J0b8y42IkyPix8Psc7Y+M+s6Za50bL1mJF9WlTjUARGxNV9/kM6Gic+WdKuk8yRNK3qyc4mYWaOUmSUSEWuANcPtl/Q94MAhdp0xqJ6QVPbr7+lkDf3UPIbTgLNGKuAG28wapcocIRFxzHD7JD0kaX5EbM1zKz1csu49vfNnJF0EfKCoTOnBTEnpt00xMxslUeJforXAynx9JfDNMoX3JNCTJOAtwO1FZYqm9e0zeBNwo6RXAoqI7WUCNDMbbWOYhe8c4HJJJwP3A38CIGkp8J6IOCV//CPgJcAsSb3AyRFxNfBlSfuTtas3A+8pOmDRkMijeSCtFgA3kSW7OnioQvnA/SqA0+cswflEzGysjNWl6RGxDVg2xPb1wCktj18/TPk3lD1m0ZDI35LlDDkhIhZHxGKgN18fsrHOA3HyJzMbF2M4JDLminKJfFLSZcB5kjYDH6aeaWTNbIKIiZr8CSAieoETJZ0AXEt2xY6ZWVeqY9rUdrU9SyQi1gK/DxwDIOldoxWUmVmnmpz8qdS0vojYFRF7pp44W5+ZdZ0JewMDZ+szs7rpH5i4Y9hdka1vltKTP5GYX2fWwLB3RGvbgknpdVTxVpyS2LGY15+erKiKa2yf2JqebKhv166k8pOm7EyOgUhPprV7V1pCL0hP3rTr6fTkZlWo4+yPdtUiW5+ZWbvqODbdrqJpfSePsM/Z+sys69RxbLpdTv5kZo0yYXvYZmZ10+STjiOeqZB0XMv6HEkX5Mm2L5HkWSJm1nWaPK2v6NTyx1vWPwlsBf4LsA743GgFZWbWKV84k1kaER+KiPsj4jyyOwUPqfW2O1fuvC81RjOztpW5RVjdFI1hP0/S+8nmXe8tSfHbj6VhG/vW2+6sW7Cifr8VM6utiTwP+/PA7Hz9YmA/4BFJB5Il3DYz6yp17Dm3q2ge9pD5QiLiQUk/GJ2QzMw6N9Dg9Kop17M6+ZOZdZ0mn3R08icza5Q6NsTtqkXyJzOzdjW3uWbkrw/ABcCRw+y7pMxXj4LjrBrP8k2qoxti8Ovw72I065jIi/Jf4riStD4ilo5X+SbV0Q0xVFFHN8TQLXV0QwzdVMdElp5E18zMxoQbbDOzmuiWBnvNOJdvUh3dEEMVdXRDDN1SRzfE0E11TFhdMYZtZmbFuqWHbWZmBdxgm5nVxLg22JKOk3SPpI2SVndQ/kJJD0u6PSGGgyT9QNKdku6Q9Ncly+8l6UZJt+TlO75kX1KPpJ9L+naH5e+TdJukmyWt77COuZKukHS3pLsk/acSZV+cH3vP8oSk93UQw//If5e3S/qKpNK385b013n5O9qNYaj3k6R9JF0raUP+c17J8ifmMQxIKpzONkwdn8j/P26VdKWkuR3U8bG8/M2SrpH0/LJ1tOz7G0khab+SMXxE0paW98fxI8VgQxivCeBAD3AvcDAwFbgFeFnJOo4CXgXcnhDHfOBV+fps4Bdl4iC76nNWvj4FuAF4bYexvB+4BPh2h+XvA/ZL/H+5GDglX58KzE34/30QeGHJcguAXwLT88eXA+8sWcfvALcDM8iu5v0ecEgn7yfgfwOr8/XVwLkly78UeDFwPVlO+U5ieCMwOV8/d6QYRqhj75b19wKfLVtHvv0g4Grg/pHea8PE8BHgAynvz4m+jGcP+whgY0RsiohngUuB5WUqiIh/B7anBBERWyPipnz9SeAuskaj3fIREU/lD6fkS+kzuZIWAn8InF+2bFUkzSH7Q7sAICKejYjHOqxuGXBvRNzfQdnJwHRJk8ka3V+VLP9S4IaIeDoi+oAfAn9UVGiY99Nysg8x8p9vKVM+Iu6KiHvaDXyYOq7JXwfAT4GFHdTxRMvDmRS8R0f42zoP+GBCeUswng32AmBzy+NeSjSUo0HSIuCVZL3kMuV6JN0MPAxcGxGlyuf+kewPISU3ZADXSPqZpFUdlF8MPAJclA/NnC9pZoexnAR8pWyhiNgC/D3wANkt6R6PiGtKVnM78HpJ+0qaARxP1jPsxAERsTVff5DxT3r258C/dVJQ0tmSNgPvAM7soPxyYEtE3NLJ8XOn5kMzF440vGRD80nHnKRZwNeA9w3qjRSKiP6IWELW8zlC0u+UPPabgYcj4mdlyg3hyIh4FfAHwF9KOqpk+clkX2M/ExGvBHaSDQOUImkqcALw1Q7KziPr1S4Gng/MlPTfytQREXeRDR1cA3yX7GYb/WVjGaLeYBxzC0k6A+gDvtxJ+Yg4IyIOysufWvLYM4D/SQcNfYvPAC8ClpB9GH8yoa4JaTwb7C08t9ezMN825iRNIWusvxwRX++0nnz44AfAcQVPHez3gBMk3Uc2NPQGSf/awfG35D8fBq4kG3YqoxfobfmGcAVZA17WHwA3RcRDHZQ9BvhlRDwSEbuBrwOvK1tJRFwQEYdHxFFk2SZ/0UEsAA9Jmg+Q/3y4w3qSSHon8GbgHfkHR4ovA39cssyLyD5Eb8nfpwuBm5TdfaotEfFQ3rkZILubVdn354Q3ng32OuBQSYvzHtlJwNqxDkKSyMZs74qIf+ig/P57ztpLmg4cC9xdpo6IOD0iFkbEIrLfw/cjolSvUtJMSbP3rJOdqCo1eyYiHgQ2S3pxvmkZcGeZOnL/lQ6GQ3IPAK+VNCP/v1lGdl6hFEnPy3++gGz8+pIO41kLrMzXVwLf7LCejkk6jmy47ISIeLrDOg5tebic8u/R2yLieRGxKH+f9pKdrH+wRAzzWx6uoOT70xi/WSJ5J+F4sp7PvcAZHZT/CtlXq91kb6CTO6jjSLKvubeSfXW+GTi+RPlXAD/Py98OnJn4OzmaDmaJkM22uSVf7ujk95nXswRYn7+ebwDzSpafCWwD5iT8Dj5K1qDcDnwJmNZBHT8i+7C5BVjW6fsJ2Be4DthANttkn5LlV+TrzwAPAVd3EMNGsvM9e96fRTM8hqrja/nv81bgW8CCsnUM2n8fI88SGSqGLwG35TGsBean/K1MxMWXppuZ1YRPOpqZ1YQbbDOzmnCDbWZWE26wzcxqwg22mVlNuME2M6sJN9hmZjXx/wHljn4Kqj5JqAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DELTA_init_gap = get_DELTA_band_init(*pm.get_DELTA_init(), gap_symmetry='random')\n",
    "DELTA_init = from_band_to_orbit(DELTA_init_gap, UNITARY)\n",
    "lambda_list, DELTA_list = \\\n",
    "    Eliashberg_pipeline(G, U_charge, U_spin, CHI_charge, CHI_spin, DELTA_init, pm, mask=mask, k=1, symmetrize='dxy')\n",
    "print('lambda=', lambda_list[0])\n",
    "sns.heatmap(np.sum(np.real(from_orbit_to_band(DELTA_list[0], UNITARY)), axis=0)[:,:,0,0,0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda= 0.1399562024448451\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DELTA_init_gap = get_DELTA_band_init(*pm.get_DELTA_init(), gap_symmetry='random')\n",
    "DELTA_init = from_band_to_orbit(DELTA_init_gap, UNITARY)\n",
    "lambda_list, DELTA_list = \\\n",
    "    Eliashberg_pipeline(G, U_charge, U_spin, CHI_charge, CHI_spin, DELTA_init, pm, mask=mask, k=1, symmetrize='dx2-y2')\n",
    "print('lambda=', lambda_list[0])\n",
    "sns.heatmap(np.sum(np.real(from_orbit_to_band(DELTA_list[0], UNITARY)), axis=0)[:,:,0,0,0])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pade 近似"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 49/49 [00:00<00:00, 60.21it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idelta = 0.05j\n",
    "Neps = 1001\n",
    "epsilon = np.linspace(-13,13,Neps)\n",
    "\n",
    "G_band = from_orbit_to_band(G, UNITARY)\n",
    "G_R = pade_approx(G_band, pm.matsubara_half, epsilon+idelta, Nm_cutoff=50)\n",
    "A_spec = -np.imag(G_R)\n",
    "\n",
    "DOS0 = np.sum(A_spec, axis=(1,2,3))[:,0,0]\n",
    "DOS1 = np.sum(A_spec, axis=(1,2,3))[:,1,1]\n",
    "\n",
    "plt.plot(np.real(epsilon), DOS0)\n",
    "plt.plot(np.real(epsilon), DOS1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "590b91e4b9eb59ee8553e47789ab7b6106a26a4abe1ef7193a8ece9c0232e5ab"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('localization': pyenv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}