Don't run !pip install ...

Author: terasakisatoshi

src/orbital_magnetic_susceptibility_py.ipynb

@@ -78,7 +78,6 @@
    },
    "outputs": [],
    "source": [
-    "!pip install sparse_ir\n",
     "import numpy as np\n",
     "import scipy\n",
     "import itertools\n",
@@ -150,7 +149,7 @@
     "        gx = vd @ gx @ v\n",
     "        gy = vd @ gy @ v\n",
     "        gxy = vd @ gxy @ v\n",
-    "        chi_iw += np.einsum(\"ab, bmn, bc, cmn, cd, dmn, da, amn->mn\", \n",
+    "        chi_iw += np.einsum(\"ab, bmn, bc, cmn, cd, dmn, da, amn->mn\",\n",
     "                            gx, giw, gy, giw, gx, giw, gy, giw, optimize=True)\n",
     "        chi_iw += (1/2) * np.einsum(\"ab, bmn, bc, cmn, ca, amn->mn\",\n",
     "                                    gx, giw, gy, giw, gxy, giw, optimize=True)\n",
@@ -314,12 +313,12 @@
     "    kx, ky = 2*np.pi*k[0]/a, 2*np.pi*(k[0] + 2*k[1])/(a*sq3)\n",
     "    h = -t * (np.exp(1j*ky*a/sq3) + 2 * np.cos(kx*a/2) * np.exp(-1j*ky*a/(2*sq3)))\n",
     "    hk = np.array([ [ 0, h ], [ np.conj(h), 0 ] ])\n",
-    "    \n",
+    "\n",
     "    hx =  t * a * np.sin(kx/2) * np.exp(-1j*ky/(2*sq3))\n",
     "    hy = -t * a * (1j/sq3 * (np.exp(1j*ky/sq3) - np.cos(kx/2) * np.exp(-1j*ky/(2*sq3))))\n",
     "    gx = np.array([ [ 0, hx ], [ np.conj(hx), 0 ] ])\n",
     "    gy = np.array([ [ 0, hy ], [ np.conj(hy), 0 ] ])\n",
-    "    \n",
+    "\n",
     "    hxy = -t * a * a * 1j/(2*sq3) * np.sin(kx/2) * np.exp(-1j*ky/(2*sq3))\n",
     "    gxy = np.array([ [ 0, hxy ], [ np.conj(hxy), 0 ] ])\n",
     "\n",
@@ -379,7 +378,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,

src/spm_py.ipynb

@@ -17,24 +17,7 @@
     "$$ (lehmann-spm)\n",
     "\n",
     "We evaluate $\\rho(\\omega)$ for a given $G(\\tau)$.\n",
-    "<!-- Because of ill-conditioned nature of the kernel function $K$, the inversion of this equation is sensitive to noise in $G(\\tau)$. SpM method extract relevant information in $G(\\tau)$ using $L_1$-norm regularization. -->\n",
-    "\n",
-    "\n",
-    "## Preparation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7d93f9e5",
-   "metadata": {
-    "tags": [
-     "remove-output"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "!pip install admmsolver"
+    "<!-- Because of ill-conditioned nature of the kernel function $K$, the inversion of this equation is sensitive to noise in $G(\\tau)$. SpM method extract relevant information in $G(\\tau)$ using $L_1$-norm regularization. -->"
    ]
   },
   {