diff --git a/AbinitioDMFT/data/mlwf/lco.h5 b/AbinitioDMFT/data/mlwf/lco.h5 deleted file mode 100644 index ba95cb1..0000000 Binary files a/AbinitioDMFT/data/mlwf/lco.h5 and /dev/null differ diff --git a/AbinitioDMFT/data/mlwf/lco_wannier.h5 b/AbinitioDMFT/data/mlwf/lco_wannier.h5 deleted file mode 100644 index 6f3d013..0000000 Binary files a/AbinitioDMFT/data/mlwf/lco_wannier.h5 and /dev/null differ diff --git a/AbinitioDMFT/solutions/b10-U3.6-hubbardI/lco_wannier.h5 b/AbinitioDMFT/solutions/b10-U3.6-hubbardI/lco_wannier.h5 deleted file mode 100644 index 0327dcd..0000000 Binary files a/AbinitioDMFT/solutions/b10-U3.6-hubbardI/lco_wannier.h5 and /dev/null differ diff --git a/AbinitioDMFT/solutions/b10-U3.6/lco_wannier.h5 b/AbinitioDMFT/solutions/b10-U3.6/lco_wannier.h5 deleted file mode 100644 index 05affa7..0000000 Binary files a/AbinitioDMFT/solutions/b10-U3.6/lco_wannier.h5 and /dev/null differ diff --git a/AbinitioDMFT/solutions/b10-U5.0/lco_wannier.h5 b/AbinitioDMFT/solutions/b10-U5.0/lco_wannier.h5 deleted file mode 100644 index 81a0b21..0000000 Binary files a/AbinitioDMFT/solutions/b10-U5.0/lco_wannier.h5 and /dev/null differ diff --git a/Basics/00a-Introducing_the_ipython_notebook.ipynb b/Basics/00a-Introducing_the_ipython_notebook.ipynb index 38ea61c..4ae2d5d 100644 --- a/Basics/00a-Introducing_the_ipython_notebook.ipynb +++ b/Basics/00a-Introducing_the_ipython_notebook.ipynb @@ -52,9 +52,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.887976Z", + "iopub.status.busy": "2023-08-28T15:03:26.887691Z", + "iopub.status.idle": "2023-08-28T15:03:26.897518Z", + "shell.execute_reply": "2023-08-28T15:03:26.897112Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world!\n" + ] + } + ], "source": [ "print(\"Hello world!\")" ] @@ -94,9 +109,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.925726Z", + "iopub.status.busy": "2023-08-28T15:03:26.925595Z", + "iopub.status.idle": "2023-08-28T15:03:26.927821Z", + "shell.execute_reply": "2023-08-28T15:03:26.927564Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "first result: 2.3095238095238093\n", + "i^2: (-1+0j)\n", + "complex calculation: (16+12j)\n" + ] + } + ], "source": [ "# Comments start with a #\n", "\n", @@ -121,9 +153,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.929314Z", + "iopub.status.busy": "2023-08-28T15:03:26.929209Z", + "iopub.status.idle": "2023-08-28T15:03:26.931138Z", + "shell.execute_reply": "2023-08-28T15:03:26.930887Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "i = 0\n", + "x = 1\n", + "i = 1\n", + "x = 2\n", + "i = 2\n", + "x = 4\n", + "i = 3\n", + "x = 7\n", + "i = 4\n", + "x = 11\n", + "That's it!\n" + ] + } + ], "source": [ "# Look how indentation is used in python to define code blocks\n", "# Also note that range(5) produces numbers from 0 to 4\n", @@ -147,9 +204,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.932536Z", + "iopub.status.busy": "2023-08-28T15:03:26.932436Z", + "iopub.status.idle": "2023-08-28T15:03:26.934427Z", + "shell.execute_reply": "2023-08-28T15:03:26.934188Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "i is different\n", + "i is different\n", + "i is 4\n", + "i is 6\n", + "i is different\n" + ] + } + ], "source": [ "# Comparing symbols are == (equal), != (not equal), >, <, <=, >=, etc.\n", "\n", @@ -172,9 +248,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.935841Z", + "iopub.status.busy": "2023-08-28T15:03:26.935755Z", + "iopub.status.idle": "2023-08-28T15:03:26.937522Z", + "shell.execute_reply": "2023-08-28T15:03:26.937287Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.0\n" + ] + } + ], "source": [ "# Define a new function\n", "def fnct(x):\n", @@ -194,9 +285,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.938860Z", + "iopub.status.busy": "2023-08-28T15:03:26.938769Z", + "iopub.status.idle": "2023-08-28T15:03:26.940627Z", + "shell.execute_reply": "2023-08-28T15:03:26.940422Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cos(pi/2) = 0.000\n", + "cos(pi) = -1.000\n" + ] + } + ], "source": [ "# In order to have access to new functions, you\n", "# import them from a library. Here we import mathematical functions\n", @@ -221,9 +328,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.941971Z", + "iopub.status.busy": "2023-08-28T15:03:26.941880Z", + "iopub.status.idle": "2023-08-28T15:03:26.943643Z", + "shell.execute_reply": "2023-08-28T15:03:26.943425Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The second element of l is 2\n", + "l3 is [1, 2, 3, 4, 5, 6]\n" + ] + } + ], "source": [ "# Lists are defined with []\n", "# Note that indices start at 0 (not 1 like in Fortran)\n", @@ -252,9 +375,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.944886Z", + "iopub.status.busy": "2023-08-28T15:03:26.944814Z", + "iopub.status.idle": "2023-08-28T15:03:26.982548Z", + "shell.execute_reply": "2023-08-28T15:03:26.982320Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding term by term: [2 2 3 4]\n", + "dot product: 7\n", + "A x B = \n", + "[[1 2]\n", + " [3 4]]\n" + ] + } + ], "source": [ "from numpy import array,dot\n", "\n", @@ -284,9 +426,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.983897Z", + "iopub.status.busy": "2023-08-28T15:03:26.983811Z", + "iopub.status.idle": "2023-08-28T15:03:26.985930Z", + "shell.execute_reply": "2023-08-28T15:03:26.985723Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x is 10\n", + "x is 12\n" + ] + } + ], "source": [ "# A new class\n", "# Note that all member functions must have \"self\" as a first argument\n", @@ -322,8 +480,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:26.987090Z", + "iopub.status.busy": "2023-08-28T15:03:26.987024Z", + "iopub.status.idle": "2023-08-28T15:03:26.988786Z", + "shell.execute_reply": "2023-08-28T15:03:26.988579Z" + } + }, "outputs": [], "source": [ "array?" @@ -346,7 +511,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.11.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/Basics/00b-Matplotlib_Examples.ipynb b/Basics/00b-Matplotlib_Examples.ipynb index 9e4382f..2716b54 100644 --- a/Basics/00b-Matplotlib_Examples.ipynb +++ b/Basics/00b-Matplotlib_Examples.ipynb @@ -26,8 +26,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:28.721166Z", + "iopub.status.busy": "2023-08-28T15:03:28.720697Z", + "iopub.status.idle": "2023-08-28T15:03:28.931362Z", + "shell.execute_reply": "2023-08-28T15:03:28.931121Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -40,9 +47,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:28.932901Z", + "iopub.status.busy": "2023-08-28T15:03:28.932800Z", + "iopub.status.idle": "2023-08-28T15:03:28.999771Z", + "shell.execute_reply": "2023-08-28T15:03:28.999534Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# The plot command takes the x coordinates as first argument\n", "# then the y coordinates. The third argument controls the\n", @@ -71,9 +106,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:29.001137Z", + "iopub.status.busy": "2023-08-28T15:03:29.001060Z", + "iopub.status.idle": "2023-08-28T15:03:29.107116Z", + "shell.execute_reply": "2023-08-28T15:03:29.106909Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.0, 2.0, -0.2, 1.1)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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kiWmpBSKSDoMVEZENMhpNX//9/rv5+QkTgIkTLVsTUV3AYEVEZINmzgQ2bTI/16cPsHixRcshqjMYrIiIbMzq1cCHH5qfCwkBtFrA3t6yNRHVFQxWREQ25OhRYMwY83NeXsCPP3JZBaKaxGBFRGQjsrOBQYPM3wHo4ABs2AA0a2bxsojqFAYrIiIbUFQEPPOMqbGyOZ9/DnTvbtmaiOoiBisiIhtw5QqQl2d+btKkir8eJCJpMVgREdmAxo2BQ4dM61b9U69ewEcfyVMTUV3EYEVEZCPc3ExLLMyYYXrcooVpZXXeAUhkOfzXjYjIhiiVwPvvAx06mJZWaNBA7oqI6hYGKyIiGzR0qNwVENVN/CqQiIiISCIMVkREREQSYbAiIqpFDh8G5swxNVkmIuvDa6yIiGqJy5dN105duGAKWP/9L+DhIXdVRPRP/MSKiKgWKC0Fhg83hSoA2LIF6NwZ+OMPeesiorIYrIiIaoGZM4E9e8qOpacDDz8MHD8uT01EVB6DFRGRldu8GZg71/zcI48A7dtbth4iqhiDFRGRFTt3Dhg50vxcYCCwZg1gZ2fZmoioYgxWRERW6tYt08Xq5porOzoC69YBXl6Wr4uIKsZgRURkpV57reLrp5YsAbp2tWg5RFQJDFZERFbo//4PWLHC/NzzzwOvvmrZeoiochisiIiszMmTwCuvmJ8LCQG++AJQKCxbExFVDoMVEZEVKSgAnn0WKCwsP1evHrB+vel/icg6MVgREVmRCROAlBTzcytXAm3aWLYeInowDFZERFbi22+BVavMz40bZ1p5nYisG4MVEZEVSE0F/v1v83MdOwKLFlm2HiKqGgYrIiKZFRZWfF2Vmxuwdi3g5GT5uojowTFYERHJbNIkIDnZ/NyXXwLNm1u0HCKqBgYrIiIZ/f676aJ0c/79b2DYMMvWQ0TVw2BFRCSjDh2ArVvLt6YJCwMWLpSlJCKqBgYrIiKZ9esHJCUBPXqYHru6AnFxvK6KqDayuWC1bNkyBAUFwcnJCeHh4Thy5Mg9t79+/TrGjx8PPz8/qFQqtGrVCtu2bbNQtUREJgEBQHw88M47ppXVW7WSuyIiqgp7uQuQUlxcHGJiYrB8+XKEh4djyZIliIqKQlpaGry9vcttX1xcjF69esHb2xvr1q1DQEAAMjIy4OHhYfniiajOs7MDZs+Wuwoiqg6FIAiC3EVIJTw8HF26dMHSpUsBAEajEYGBgZg4cSKmTp1abvvly5fjo48+wqlTp+Dg4FCpYxQVFaGoqEh8rNfrERgYiLy8PLi5uUnzQoiIiKhG6fV6uLu7S/7322a+CiwuLkZiYiIiIyPFMaVSicjISCQkJJjdZ/PmzYiIiMD48ePh4+MDtVqNDz/8EAaDocLjxMbGwt3dXfwJDAyU/LUQERFR7WQzwSo3NxcGgwE+Pj5lxn18fKDT6czuc+7cOaxbtw4GgwHbtm3DzJkzsXDhQsyZM6fC40ybNg15eXniT1ZWlqSvg4hs182bcldARDXNZoJVVRiNRnh7e2PFihXo1KkToqOj8fbbb2P58uUV7qNSqeDm5lbmh4jofpKSgKAg091+RGS7bCZYeXl5wc7ODjk5OWXGc3Jy4Ovra3YfPz8/tGrVCnZ2duJY27ZtodPpUFxcXKP1ElHdUVgIPPcccPkyoNEAo0cDN27IXRUR1QSbCVaOjo7o1KkT4uPjxTGj0Yj4+HhERESY3eeRRx7B2bNnYTQaxbHTp0/Dz88Pjo6ONV4zEdUNb7xharJ826pVpsbK91kNhohqIZsJVgAQExODlStX4ttvv0VqairGjRuHgoICjB49GgAwcuRITJs2Tdx+3LhxuHbtGl577TWcPn0aW7duxYcffojx48fL9RKIyMZs3gx8/nn58fR0YMMGy9dDRDXLptaxio6OxpUrVzBr1izodDqEhYVh+/bt4gXtmZmZUCrvZMnAwEDs2LEDkydPRvv27REQEIDXXnsNU6ZMkeslEJEN0emAMWPMz3XuDLz3nmXrIaKaZ1PrWMmhptbBIKLazWg0tarZsaP8nIsLcPw4V1cnkhPXsSIiqkWWLjUfqgDgk08YqohsFYMVEZHEUlKAt94yP/fMM8CLL1q2HiKyHMmusSopKYFOp0NhYSEaNWoET09PqZ6aiKjWKC4G/vUv4B+dr0T+/sCKFYBCYfm6iMgyqvWJ1Y0bN/D555+jR48ecHNzQ1BQENq2bYtGjRqhadOmeOmll3D06FGpaiUisnqzZpkWAzVn1SqgYUNLVkNEllblYLVo0SIEBQXhm2++QWRkJDZt2oSkpCScPn0aCQkJmD17NkpLS9G7d2/06dMHZ86ckbJuIiKr8/PPwPz55ucmTwZ69bJsPURkeVW+K3D48OGYMWMGQkND77ldUVERvvnmGzg6OuJFG7ywgHcFEhEA6PVA+/ZARkb5ObUaOHoUcHKyfF1EZF5N/f3mcgvVxGBFRICpTc2qVeXHHR1Noap9e4uXRET3YNXLLXTr1g16vV6KpyIiqnU2bTIfqgDggw8YqojqEkmC1aFDh3Dr1q1y43q9nquYE5FNy8kBXnrJ/FyPHqZrq4io7qhWsBo6dCjmzp0LhUKBy5cvl5svKCjAggULqnMIIiKrJQimUJWbW36ufn3g228BOzvL10VE8qnWOlZNmjTBli1bIAgCOnTogIYNG6JDhw7o0KEDwsLCkJaWBj8/P6lqJSKyKjt2AD/+aH7u00+Bpk0tWw8Rya9awWrRokUAAEdHRxw8eBCXLl3C8ePHkZSUhI0bN8JoNGJ+RfceExHVclFRpgU/Y2KA/Pw744MHAyNHylcXEclHkrsCS0pK4ODgIEU9tQ7vCiSi8+eBF14wrWPl7Q0kJwONGsldFRHdi9XdFZiZmSn+c2VC1cWLF6t6KCIiqxYcDOzdCyxaBHz5JUMVUV1W5WDVpUsXvPLKK/dsWZOXl4eVK1dCrVZj/fr1VT0UEZHVUypNdwAOGCB3JUQkpypfY3Xy5El88MEH6NWrF5ycnNCpUyf4+/vDyckJf/31F06ePImUlBQ89NBDmD9/Pvr16ydl3URERERWp9rXWN28eRNbt27FgQMHkJGRgZs3b8LLywsdO3ZEVFQU1Gq1VLVaJV5jRUREVPuwpY2VYrAiIiKqfazu4vV/unnzJgoLC8XHGRkZWLJkCXbu3CnF0xMRyaqkBBg61HSBOhHRvUgSrAYOHIjvvvsOAHD9+nV07doVCxcuxMCBA/H5559LcQgiItnMnw+sXw888QQwcSJQUCB3RURkrSQJVseOHUP37t0BAOvWrYOvry8yMjLw3Xff4ZNPPpHiEEREsvjjD+Ddd+88XrrU1FT5l1/kq4mIrJckwaqwsBD169cHAOzcuRPPPPMMlEolHn74YWRkZEhxCCIiiyspMS38WVJSdvzcOWDcOMBgkKUsIrJikgSrFi1aYNOmTcjKysKOHTvQu3dvAMDly5d5QTcR1Vpz5wLHj5cfVyqBr79mg2UiKk+SYDVr1iy88cYbCAoKQnh4OCIiIgCYPr3q2LGjFIcgIrKoP/4A3n/f/NxbbwFdu1q2HiKqHSRbbkGn0yE7OxsdOnSAUmnKa0eOHIGbmxvatGkjxSGsEpdbILI9JSVAeLj5T6tCQoBjxwCVyvJ1EZF0aurvd5VXXr+br68vfH19y4x15X/SEVEtNG+e+VBlZwesWsVQRUQVkyxYXb9+HV999RVSU1MBAKGhoXjxxRfh7u4u1SGIiGpccjLw3nvm5956C+jSxbL1EFHtIsk1Vr/99huaN2+OxYsX49q1a7h27RoWLVqE5s2b49ixY1IcgoioxpWWAi++WP4uQMD0FeDs2ZaviYhqF0muserevTtatGiBlStXwt7e9CFYaWkpxo4di3PnzuHnn3+udqHWitdYEdmO+fOBKVPKjyuVQEICL1gnsiVW3SvQ2dkZx48fL3eR+smTJ9G5c+cy7W5sDYMVkW1ISwM6dACKisrPvfmmKXQRke2w6l6Bbm5uyMzMLDeelZUlLhxKRGStjEZgzBjzoapVq7IrrxMR3YskwSo6OhpjxoxBXFwcsrKykJWVBa1Wi7Fjx2L48OFSHIKIqMYsWwYcPFh+XKEAvvkGcHa2fE1EVDtJclfgggULoFAoMHLkSJSWlgIAHBwcMG7cOMydO1eKQxAR1Yjz54GpU83PvfYa0K2bZeshotpNsgVCAVPPwPT0dABA8+bN4eLiItVTWy1eY0VUewkC0Ls3sHt3+blmzUyrr9erZ/m6iKjmWf0CoQDg4uKCdu3aSfmUREQ15ptvzIcqAFi5kqGKiB6cJNdYxcbG4uuvvy43/vXXX2PevHlSHIKISHJXrwL2Zv7z8uWXgSeesHw9RFT7SRKsvvjiC7P9AENDQ7F8+XIpDkFEJLk33wQSE8uuph4QwKUViKjqJAlWOp0Ofn5+5cYbNWqE7OxsKQ5BRFQj2rc3Lf65cKHp7r/PPwfYiYuIqkqSYBUYGIiDZu5VPnjwIPz9/aU4BBFRjbGzA2JiTHcIDhggdzVEVJtJcvH6Sy+9hEmTJqGkpARP/H1hQnx8PN566y28/vrrUhyCiKjG+fjIXQER1XaSBKs333wTV69exb///W8UFxcDAJycnDBlyhRMmzZNikMQERERWT1J17HKz89HamoqnJ2d0bJlS6hUKqme2mpxHSsiIqLap1asY+Xq6oou/7y9hojISly9CjRsKHcVRGTrJLl4vVu3btDr9VI8FRGR5BITgSZNgPffB/6+WoGIqEZIEqwOHTqEW7dulRvX6/WYMmWKFIcgIqqSkhJg7FigsBCYNQt46CHg0CG5qyIiW1WtYDV06FDMnTsXCoUCly9fLjdfUFCABQsWVOcQRETVsngxkJR053FKiqmx8ptvylYSUZXtPrcbIctCsPtcBb2YSHbVClZNmjTBli1bIAgCOnToAG9vb/Tq1QtvvPEG/u///g+fffaZ2YVDa9KyZcsQFBQEJycnhIeH48iRI5XaT6vVQqFQYNCgQTVbIBFZTHo68M475ccFAXB1tXg5RNUiCAKmx09Ham4qpsdPh4T3npGEqnXx+qJFiwAAjo6OOHjwIC5duoTjx48jKSkJGzduhNFoxHwL9oaIi4tDTEwMli9fjvDwcCxZsgRRUVFIS0uDt7d3hfv9+eefeOONN9C9e3eL1UpENUsQgFdfBW7eLD8XEgJMnWr5moiqY2f6Thy9dBQAcPTSUexM34moFlEyV0V3k2S5hZKSEtjb20OhUEhRU5WFh4ejS5cuWLp0KQDAaDQiMDAQEydOxNQKfosaDAY89thjePHFF/HLL7/g+vXr2LRpU4XHKCoqQlFRkfhYr9cjMDCQyy0QWZn//hcYObL8uEIBHDhg+jqQqLYoLi2G+nM1zl47CwEClFCik38nHB57WPa/vbVVTS23UOWvAiMiIvDvf/8bK1asQFJSUpmwIYfi4mIkJiYiMjJSHFMqlYiMjERCQkKF+7333nvw9vbGmDFjKnWc2NhYuLu7iz+BgYHVrp2IpJWbC0yebH5u3DiGKqodDEYD9p7fi1d+fAVeH3nhzLUzEGD6LMQIo/ipFVmXKn8V2L9/f/zxxx9YuHAh0tPToVAo0LJlS4SFhZX5uddXcFLKzc2FwWCAz109KXx8fHDq1Cmz+xw4cABfffUVkv55Zet9TJs2DTExMeLj259YEZH1eP1107pVd/P3Bz780PL1EFWWIAg4dOEQtMlafH/ye+jydRVua6eww8y9M9G7eW9+amVFqhysZsyYIf7zkSNHMGjQIKjVaigUCqxatQqnTp2CQqGAj48PLl26JEmxUrpx4wZGjBiBlStXwsvLq9L7qVSqOrGiPFFttXs38N135ueWLQPc3S1bD9H9CIKA33N+hzZZC22yFhl5GeKch5MHwgPCsSN9R7n9DIKB11pZIUlWXh83bhyWLVuGwYMHi2Pbtm3Dyy+/jFGjRklxiPvy8vKCnZ0dcnJyyozn5OTA19e33Pbp6en4888/MeAfreyNRiMAwN7eHmlpaWjevHnNFk1Ekrp503TBujmDBwO86ZesSeqVVMSlxEGbrEXa1TRx3NXRFQNbD4RGrUGvZr3Q/ZvuUEIJI4zlnkMJJT+1sjKSBKvU1FSEhYWVGevXrx8+++wzLF68WIpD3JejoyM6deqE+Ph4cckEo9GI+Ph4TJgwodz2bdq0wYkTJ8qMzZgxAzdu3MDHH3/Mr/eIaqE5c0xLLNytfn3g008tXw/R3c7/dV4MU7/n/C6Oq+xUeKrVU9CoNejXsh9cHFwAAEWlRcjMyzQbqgDTtVZZ+iwUG4qhsue3KdZAkmDVpUsXfPvtt3jnrgVj2rVrV+l1pKQQExODUaNGoXPnzujatSuWLFmCgoICjB49GgAwcuRIBAQEIDY2Fk5OTlCr1WX29/DwAIBy40Rk/ZKTgYpWd4mNBQICLFsP0W0X9Rex9uRaaJO1OHzxsDhur7RHVPMoaNQaPN36abipyt+ZprJX4ehLR3Gl8EqFz+9dz5uhyopIEqwWLVqEJ554AhkZGZg8eTLUajWKi4uxcOHCB7p+qbqio6Nx5coVzJo1CzqdDmFhYdi+fbt4QXtmZiaUSkm6+BCRFTEagVdeAUpLy8+Fh1f89SBRTblScAXrU9dDm6zFzxk/i3fzKRVKPB70ODRqDQa3GYyGLvfvDB7oHohAd36LUltIso4VAJw6dQoTJkzAnj174ODgAKPRCHt7e3z11Vd47rnnpDiEVaqpdTCIqPK++MJ8eLK3NzVgbt/e8jVR3XP91nVsOrUJ2mQtdp/bDYNgEOceCXwEGrUGQ0OGwte1/HW/ZHk19fdbkk+sANM1S7t370ZmZiaSkpKgVCrRqVMni7e0IaK6RacDKur1/vrrDFVUswqKC/Dj6R+hTdbip7M/odhQLM495PcQNKEaRKuj0cS9iYxVkiVJFqxua9KkCZo04RuIiCwjJgbIyys/HhwMzJpl+XrI9t0qvYXtZ7dDm6zFj6d/RGFJoTjX1qsthquHI1odjVYNW8lYJclF8mBFRGQpO3YAa9aYn/vsM8DFxbL1kO0qMZRgz/k9WJO8BhtPbYS+SC/ONWvQDJpQDTRqDdTeai57UMc9cLASBIFvGiKyCioV0KwZcO5c2XGNBujTR56ayHYYjAYcyDwAbbIW61LXIbcwV5wLqB+A6NBoaNQadPbvzL+LJKp0sDpx4gReffVVhIaGYsWKFTVZExFRpfTsaVpmYc4c01ILpaWmldUttHwe2SBBEHDk4hGxpcylG3c6hzRyaYRhIcOgUWvwSJNHoFTwLnMqr9LB6tlnn4VKpcKQIUNqsh4iogfi7Ax88AHw3HOmOwOffx4w02yBqEKCIOCPnD9MLWVStPjz+p/inLvKHUPaDoFGrcHjwY/DXskraOjeKv0OuXHjBnx9feHg4IDU1FS4urqKPw4ODjVZIxHRfYWGAvv3y10F1SZpuWlimDqVe0ocr+dQDwPbDIQmVIPezXtz8U16IJUOVgsWLMDo0aPRq1evcnMODg5lgtbtn/r164v/3KhRI3Tp0qVMbz4iIilx/V+6nz+v/4m45DhoU7RI0iWJ4yo7Ffq36g9NqAb9W/UXW8oQPagHWiD0woULOHToEC5evIj8/PwyPzdu3Cg3dnv85s2bpoMpFPjXv/6Fb7/9tsZekKVxgVAiIut26cYlrE1ZC22KFocuHBLH7ZX26NWsF4arh2Ngm4FmW8qQ7aqpv9+Srbx+L4Ig4OrVq1i8eDHmzp2LzMxMBNhI4y4GKyIi65NbmIv1J9dDm6LF/j/3iy1lFFCgZ1BPaNQaPNP2GXi5WK7tGlkXq195/V4UCgWOHz+OTZs2wdPTE56enpY4LBHZkCtXAC8vgHe1U0XybuXhh7QfsCZ5DXal7yrTUiaicQQ0ag2GhQyDX312BKGaY7HbG3Q6HVq1aoXVq1fD2dnZUoclIhtQUgI8/jjg42Na+LN1a7krImtRWFKILae3QJusxbYz21BkKBLnOvp2hEatwbOhzyLII0i+IqlOschXgbaMXwUS1bz58+/0A3R0BKZOBaZNA5yc5K2L5FFUWoQd6TugTdZic9pmFJQUiHNtvNqYWsqERqO1FxM4VaxWX2NlyxisiGpWRgYQEgIUFpYdb94c2LnTtPI62b5SYyn2nN8DbbIWG1I3IK/oToPIYI9gaNSmljLtvNtxFXSqlFp9jRURUVW99lr5UAWY+gAGBlq+HrIco2C801Lm5DpcKbwizvnX9xdbynTx78IwRVaDwYqIrNaPPwI//GB+7vPPAa5NbHsEQcDRS0dNLWVSvsfFGxfFOS8XL7GlzKNNHmVLGbJKDFZEZJUKC4GJE83PjRkDPPKIZeuhmiMIAk5cPmFaBT1Zi/PXz4tzbio3PNP2GQxXD8cTwU+wpQxZPb5DicgqzZljur7qbg0bAvPmWb4ekt7pq6cRlxyHNclrkJqbKo67OLjg6dZPQxOqQZ8WfdhShmoVBisisjqpqcCCBebn5s83hSuqnTKuZ+D7lO+hTdHiWPYxcdzRzhH9WvaDJlSDp1o9hXqO9WSskqjqGKyIyKoIAjB+vGntqrs98gjwwgsWL4mqSZevE1vK/Jr1qzhup7BDr+a9oAnVYFCbQXB3cpexSiJpMFgRkVXRaoG9e8uP29mZLlhno+Xa4WrhVWxI3QBtihb7/twHo2AEYGop0yOoBzShGgwJGcKWMmRzGKyIyGrk5QExMebnJk0C2rWzaDn0gPRFevxw6gdoU7TYmb4TpcZSce7hxg9DE6rBsNBh8K/vL2OVRDWLwYqIrMasWYBOV348IAB45x2Ll0OVUFhSiK2nt0KbosXW01vLtJQJ8w2DJtTUUia4QbCMVRJZDoMVEVmFpCRg6VLzc0uWAK6ulqyG7qWotAg703dCm6LFD6d+KNNSpnXD1qaWMupotPFqI2OVRPJgsCIi2RmNwLhxpv+9W1QUMGSI5WuiskqNpdh7fq+ppcypDbh+67o4F+QRBE2oqaVMe5/2XAWd6jQGKyKS3TffAIcOlR93dAQ+/RTg32l5GAUjfs36FdpkLdaeXIvLBZfFOT9XP0SHRiNaHY3wgHCGKaK/MVgRkayuXgWmTDE/N2UK0LKlZeup6wRBQGJ2IrTJWsSlxOGC/oI419C5IYaGDIVGrUH3Jt1hp7STsVIi68RgRUSyevttU7i6W3AwMG2a5eupq5IvJ4stZdL/ShfH3VRuGNxmMDRqDZ4MfhIOdmzQSHQvDFZEJBujEdDrzc998gng7GzZeuqaM1fPIC4lDtpkLVKupIjjzvbOppYyalNLGSd7JxmrJKpdGKyISDZKJbB6tamp8oQJwKlTpvGnnwaeekre2mxVZl6mqaVMshaJ2YniuKOdI/q26AuN2tRSxtWRt2ESVQWDFRHJ7skngd9/BxYvNvUI/PhjuSuyLTn5OVh7ci20yVoczDoojtsp7BDZLBIatamljIeTh3xFEtkIhSAIgtxF1GZ6vR7u7u7Iy8uDm5ub3OUQ1Xo3b/IrQClcu3nN1FImWYu9f+4t01LmsaaPQaPWYEjbIWhUr5HMlRLJo6b+fvMTKyKyKgxVVXej6AZ+SPsB2mRTS5kS451O1uEB4dCoNRgWMgwBbgEyVklk2xisiIhqsZslN7HtzDasSV6DrWe24lbpLXGug08HaNSmljLNGjSTsUqiuoPBioiolik2FGNX+i5oU7TYdGoT8ovzxblWDVuJq6C3bdRWxiqJ6iYGKyKyGKPRdCcgPTiD0YB9f+6DNlmL9anr8detv8S5Ju5NxDAV5hvGVdCJZMRgRUQWceUK0L07MHUqMHIkA1ZlGAUjErISxJYyOQU54pyvqy+eDXkWGrUGDzd+mGGKyEowWBGRRUybBqSlAaNHAytWAMuWAR07yl2V9REEAceyj4ktZbL0WeKcp7MnhrY1tZR5rOljbClDZIUYrIioxh0+DHz11Z3HCQlA587AuHGmNavsmA+QcjnF1FImRYuz186K4/Ud62Nw28HQhGoQ2SySLWWIrByDFRHVKIPBtKr63YxGIDu7boeqs9fOIi45DtoULZIvJ4vjzvbOGNB6ADShGvRt2ZctZYhqEQYrIqpRX30F/PZb+XFnZ9NK63XNBf0FfJ/yPdYkr8Fvl+6cGAelA/q27AtNqAYDWg9gSxmiWorBiohqzNWrpmurzJkxA2jSxLL1yOVywWWsO7kO2mQtfsn8RRy3U9jhyWZPQhNqainTwLmBjFUSkRQYrIioxrz9NnDtWvnxFi2A11+3fD2W9NfNv7Dx1EZok7WIPx8vtpQBgO5NukOj1mBoyFB41/OWsUoikhqDFRHViMRE091/5nz6KaBSWbYeS8gvzsfmtM3QJmux/ez2Mi1luvh3EVdBb+zWWMYqiagmMVgRkeSMRtMF6+ZavA8cCPTpY/maasrNkpv46exP0CZrseX0FtwsvSnOtfNuB41ag+jQaDT3bC5jlURkKTa3RN+yZcsQFBQEJycnhIeH48iRIxVuu3LlSnTv3h0NGjRAgwYNEBkZec/tiahyvvsOOHSo/LiTk21csF5iKMG2M9swcuNI+CzwwZDvh2DtybW4WXoTLTxbYOZjM5E8Lhl/jPsD07tPZ6giqkNs6hOruLg4xMTEYPny5QgPD8eSJUsQFRWFtLQ0eHuXv45h3759GD58OLp16wYnJyfMmzcPvXv3RkpKCgIC2P2dqCquXwemTDE/N20aEBxs0XIkYzAasD9jv9hS5trNOxePBboFQqM2tZTp6NuRq6AT1WEKQTD3YX3tFB4eji5dumDp0qUAAKPRiMDAQEycOBFTp0697/4GgwENGjTA0qVLMXLkyEodU6/Xw93dHXl5eXBzc6tW/US2YNIk06KfdwsOBlJSTMss1BZGwYhDFw6JLWV0+TpxzqeeD54NvdNSRqmwuS8AiGxaTf39tplPrIqLi5GYmIhp/7i3W6lUIjIyEgkJCZV6jsLCQpSUlMDT07PCbYqKilBUVCQ+1uv1VS+ayMacOAH8/d815SxZUjtClSAISNIliaugZ+ZlinMNnBpgaIippUyPpj3YUoaIyrGZYJWbmwuDwQAfH58y4z4+Pjh16lSlnmPKlCnw9/dHZGRkhdvExsbi3XffrVatRLZIEICJE00rrd+tb19gwADL1/QgTl45Ka6CfvrqaXHc1dEVg9sMhkZtainjaOcoY5VEZO1sJlhV19y5c6HVarFv3z44OVXcPmLatGmIiYkRH+v1egQGBlqiRCKrFhcH7N9fftzBwfTVoDVednTur3NimPoj5w9x3MneCU+1egrD1cPRt0VfODvUgo/aiMgq2Eyw8vLygp2dHXJycsqM5+TkwNfX9577LliwAHPnzsXu3bvRvn37e26rUqmgssUFeIiqIT+/4gU/33gDaNnSsvXcy0X9RXyf8j20KVocuXjnLmAHpQOiWkRBE6rB062fRn1VfRmrJKLaymaClaOjIzp16oT4+HgMGjQIgOni9fj4eEww1wH2b/Pnz8cHH3yAHTt2oHPnzhaqlsi2zJkDXLpUfrxxY9Pq63K7UnDF1FImRYtfMn6BANM9O0qFEk8EPwFNqAaD2w6Gp3PF11cSEVWGzQQrAIiJicGoUaPQuXNndO3aFUuWLEFBQQFGjx4NABg5ciQCAgIQGxsLAJg3bx5mzZqF1atXIygoCDqd6Y4fV1dXuLqyASpRZRQWAqtWmZ9bsACoV8+i5Yiu37qOjakboU3RIv5cPAzCnYu/Hm3yKDShppYyPq4+93gWIqIHY1PBKjo6GleuXMGsWbOg0+kQFhaG7du3ixe0Z2ZmQqm8c0v0559/juLiYgwdOrTM88yePRvvvPOOJUsnqrVcXEx3A06fDnz11Z3V1h9/HHj2WcvWkl+cjx/TfoQ2xdRSpthQLM519u8MTaippUygO6+LJKKaYVPrWMmB61gR3XH0qOnOwN9+A5KSALW65o95q/QWfjrzE7QpWvyY9mOZljJqbzU0oRpEq6PRwrNFzRdDRLUG17EiIqvXpQvw66+mgFWToarEUIL48/FYk7wGm05tgr7oznpyzRs0x3D1cESro6H2tkCyIyL6BwYrIpKUUgmEh0v/vAajAb9k/gJtshbrTq7D1ZtXxbnGbo2hCTW1lHnI7yG2lCEi2TBYEZHVEgQBhy8ehjZZi+9Tvkd2frY4513PG8NChmG4ejgiAiPYUoaIrAKDFRFZFUEQ8HvO76aWMslaZORliHMeTh4Y0nYINGoNegb1hL2Sv8KIyLrwtxIRWYVTuafEMJV2NU0cd3V0xcDWA6FRa9C7eW+2lCEiq8ZgRUSVVloKvPsuMGEC4CPB8k/n/zqPuJQ4aJO1+D3nd3FcZafCU62egkatQb+W/eDi4FL9gxERWQCDFRFV2uefm1ZZ/+QTU8AaP97UC/BBXLpxydRSJlmLwxcPi+P2SntENY+CRm1qKeOm4vIlRFT7cB2rauI6VlRXXLkCtGoFXL9+ZywkBPj0U+CJJ+69b25hLtafXI81yWvwc8bPZVrKPB70ODRqDQa3GYyGLg1r7gUQEf0D17EiIlnNmFE2VAHAyZPAf/4D/P47YGdXdi7vVh42ndoEbYoWu9J3lWkp80jgI9CoTS1lfF3v3SSdiKg2YbAiovtKTARWrjQ/98knd0JVQXEBtpzeAm2KFtvObCvTUqaTXydo1KaWMk3cm1igaiIiy2OwIqJ7MhpNbWrMXTQwdCjwyGNF+OHUdmhTtNicthmFJYXifEijENMq6KHRaNmwpQWrJiKSB4MVEd3T//4HJCTcNagsgUPrPcAgLXwWbEReUZ441axBM3EVdLW3mqugE1GdwmBFRBW6cQN4662/HyiMQJNfALUWCFmHknq5WHfWNBVQPwDRodHQqDXo7N+ZYYqI6iwGKyKq0PtzBOjsjgBRWiD0e8DtkjjXyKURhoUMg0atwSNNHmFLGSIiMFgR0V0EQcAfOX/gs1+0WFEUB7x0/s7kLXfg5BDMfEaDWSMeZ0sZIqK78LciEQEA0nLTEJcShzXJa3Aq95RpsAGA4nrAqYFAsgZI740ne6rw7kiA3/YREZXHYEVUh2VczxBbyhzXHRfHHRQqlKT0N4WpM/2BElNLGTs74OOPGaqIiCrCYEVUx2TfyMbak2uhTdYi4cKd2/3slfbo3bw3hrTW4IMRA3EutfxKxOPHA6GhlqyWiKh2YbAiqgOuFl7F+tT10CZrse/PfWJLGQUUeDz4cWhCNXim7TNo6NIQ8+cD51LLP4eXl6k/IBERVYzBishG5d3Kww9pP0CbrMWuc7tQaiwV5yIaR0Cj1mBYyDD41fcTx7OzgfffN/98sbGAh0cNF01EVMsxWBHZkMKSQlNLmWRTS5kiQ5E419G3o9hSJsgjyOz+U6cC+fnlxx96CBg9uoaKJiKyIQxWRLVcUWkRdqTvgDbZ1FKmoKRAnGvj1UZsKdPaq/U9n+fQIeC778zP/bMfIBERVYzBiqgWKjWWYs/5PdAma7EhdUOZljLBHsHQqE0tZdp5t6vUKuhGI/Cf/5ife/554JFHpKqciMi2MVgR1RJGwYiDmQexJnkN1p1chyuFV8Q5//r+YkuZLv5dqtRSZuJEU/sane7OWL16wPz5UlRPRFQ3MFgRWTFBEPDbpd+gTdYiLiUOF29cFOe8XLzEljKPNnm0Wi1llEpgxAhg0CDggw+ARYuAkhJgxgzA31+CF0JEVEcoBEEQ5C6iNtPr9XB3d0deXh7c3Mqv+0P0oARBQPLlZGiTtdCmaHHur3PinLvKHc+0fQYatQZPBD9RYy1lzpwBFiwwXVulUtXIIYiIZFVTf7/5iRWRlTh99TTikuOgTdHi5JWT4riLgwsGth4IjVqDqOZRUNnXfNJp2RL44osaPwwRkc1hsCKSUWZephimjmUfE8cd7RzRr2U/aEI1eKrVU6jnWE/GKomIqLIYrIgsTJevw9qUtdCmaPFr1q/iuJ3CDr2a94ImVINBbQbB3cldxiqJiKgqGKyILOBq4VVsSN0AbYqppYxRMAIwtZTpEdQDmlANhoQMgZeLl8yVEhFRdTBYEdUQfZEem9M2Q5usxY70HWVayjzc+GFoQjUYFjoM/vUtf9vdhQtA48YWPywRkc1jsCKSUGFJIbad2QZtshZbz2zFrdJb4lyYbxg0oaaWMsENgmWrMTsbaNsW6NULWLgQCJavFCIim8NgRVRNxYZi7EzfCW2yFj+k/YD84jvN9lo3bG1qKaOORhuvNjJWecftfoAbNwLbtgFvvAFMm2ZaDJSIiKqH61hVE9exqptKjaXY9+c+aJO1WJ+6HtdvXRfngjyCoAk1tZRp79O+Squg15RDh4CIiPLjjRsDycmAO6+XJ6I6gutYEcnMKBjxa9av0CZrsfbkWlwuuCzO+bn6iS1lugZ0taowddu9+gH27MlQRUQkBQYronsQBAGJ2YliS5kL+gviXEPnhhgaMhQatQbdm3SHndJOxkrv79tvgaNHy4/XqwfMm2f5eoiIbBGDFZEZYkuZZC3S/0oXx91UbhjcZjA0ag2eDH4SDnYOMlZZeXq96Toqc95+m/0AiYikwmBF9Lez186KYSrlSoo47mzvjKdbPw2NWoM+LfrAyd5JxiqrZs4cICen/HizZsDkyZavh4jIVjFYUZ2WlZeF71O+x5rkNUjMThTHHe0c0bdFX2jUppYyro6uMlZZPadPA0uWmJ9btAhwqn05kYjIajFYUZ2Tk5+DdSfXQZuixYHMA+K4ncIOkc0ioVGbWsp4OHnIV6SEYmKAkpLy4716AU8/bfl6iIhsGYMV1QnXbl7DxtSN0KZosef8njItZR5r+hg0ag2GtB2CRvUayVyptH76Cdi6tfy4nZ3pUywrvHmRiKhWY7Aim3Wj6IappUyKFjvO7kCJ8c7HNuEB4dCoNRgWMgwBbgEyVllziouBSZPMz02YAISEWLQcIqI6gcGKbMrNkpumljIpWmw5vaVMS5kOPh2gUZtayjRr0EzGKi3j009N11fdzcsLmD3b8vUQEdUFDFZU6xUbirErfRe0KVpsOrWpTEuZVg1biaugt23UVsYqLSsnB3j3XfNzH3wANGhg2XqIiOoKBiuqlQxGQ5mWMn/d+kuca+LeRAxTYb5hVrkKek2bPh24caP8eMeOwJgxlq+HiKiuYLCiWsMoGJGQlSC2lMkpuLMwk6+rL54NeRYatQYPN364Toap2377DfjmG/NzH39sunCdiIhqhlLuAqS2bNkyBAUFwcnJCeHh4Thy5Mg9t1+7di3atGkDJycntGvXDtu2bbNQpXS33ed2I2RZCHaf2y2OCYKAY9nH8ObONxG0JAiPfvMolh5dipyCHHg6e+Llh17GnpF7cGHyBXzc92NEBEbU6VAlCKZ+gOZaq2s0QPfulq+JiKgusalPrOLi4hATE4Ply5cjPDwcS5YsQVRUFNLS0uDt7V1u+19//RXDhw9HbGwsnnrqKaxevRqDBg3CsWPHoFarZXgFdZcgCJgePx2puamYHj8dfq5+iEuJgzZZizPXzojb1Xesj8FtB0MTqkFks8ha01LGUlavBhISyo87OwPz51u+HiKiukYhCOb+27Z2Cg8PR5cuXbB06VIAgNFoRGBgICZOnIipU6eW2z46OhoFBQXYsmWLOPbwww8jLCwMy5cvr9Qx9Xo93N3dkZeXBzc3N2leSB204+wO9PlfH7NzzvbOGNB6ADShGvRt2bdWtpSxlJdeAr78svz4e+8BM2davh4iImtVU3+/bearwOLiYiQmJiIyMlIcUyqViIyMRIK5/4QHkJCQUGZ7AIiKiqpwewAoKiqCXq8v80PVIwgCxm8bX258QKsBWP3Malx+8zLihsZhcNvBDFX3sXKlaUHQVq3ujDVtCrzxhnw1ERHVJTYTrHJzc2EwGODj41Nm3MfHBzqdzuw+Op3ugbYHgNjYWLi7u4s/gYGB1S++jtuZvhPpf6WXGx/fZTyGtxteq/v0yaFfP+DECeCjj4D69YEFC0xfBRIRUc2zmWBlKdOmTUNeXp74k5WVJXdJtZogCJi5dybsFGVvVbNT2GHm3pmwoW+qLcrR0fQp1blzwJAhcldDRFR32Eyw8vLygp2dHXJycsqM5+TkwNfX1+w+vr6+D7Q9AKhUKri5uZX5oarbmb4TRy8dhUEwlBk3CAYcvXQUO9N3ylSZbfDyYj9AIiJLsplg5ejoiE6dOiE+Pl4cMxqNiI+PR0REhNl9IiIiymwPALt27apwe5LW7U+rlBW8DZVQ8lMrIiKqVWxquYWYmBiMGjUKnTt3RteuXbFkyRIUFBRg9OjRAICRI0ciICAAsbGxAIDXXnsNPXr0wMKFC9G/f39otVr89ttvWLFihZwvo84oNhQjMy8TRhjNzhthRJY+C8WGYqjsVRaujoiI6MHZVLCKjo7GlStXMGvWLOh0OoSFhWH79u3iBeqZmZlQKu98OtKtWzesXr0aM2bMwPTp09GyZUts2rSJa1hZiMpehaMvHcWVwisVbuNdz5uhioiIag2bWsdKDlzHioiIqPbhOlZEREREVo7BioiIiEgiDFZEREREEmGwIiIiIpIIgxURERGRRBisiIiIiCTCYEVEREQkEQYrIiIiIokwWBERERFJhMGKiIiISCIMVkREREQSYbAiIiIikgiDFREREZFEGKyIiIiIJMJgRURERCQRBisiIiIiiTBYEREREUmEwYqIiIhIIgxWRERERBJhsCIiIiKSCIMVERERkUQYrIiIiIgkwmBFREREJBEGKyIiIiKJMFgRERERSYTBioiIiEgiDFZEREREEmGwIiIiIpIIgxURERGRRBisiIiIiCTCYEVEREQkEQYrIiIiIokwWBERERFJhMGKiIiISCIMVkREREQSYbAiIiIikgiDFREREZFEGKyIiIiIJMJgRURERCQRBisiIiIiiTBYEREREUmEwYqIiIhIIgxWRERERBJhsCIiIiKSCIMVERERkUQYrIiIiIgkYjPB6tq1a3j++efh5uYGDw8PjBkzBvn5+ffcfuLEiWjdujWcnZ3RpEkT/Oc//0FeXp4FqyYiIiJbYjPB6vnnn0dKSgp27dqFLVu24Oeff8bLL79c4faXLl3CpUuXsGDBAiQnJ2PVqlXYvn07xowZY8GqiYiIyJYoBEEQ5C6iulJTUxESEoKjR4+ic+fOAIDt27ejX79+uHDhAvz9/Sv1PGvXrsW//vUvFBQUwN7evlL76PV6uLu7Iy8vD25ublV+DURERGQ5NfX3u3LpwcolJCTAw8NDDFUAEBkZCaVSicOHD2Pw4MGVep7bJ/deoaqoqAhFRUVl9gFM/wcRERFR7XD777bUny/ZRLDS6XTw9vYuM2Zvbw9PT0/odLpKPUdubi7ef//9e359CACxsbF49913y40HBgZWvmAiIiKyClevXoW7u7tkz2fVwWrq1KmYN2/ePbdJTU2t9nH0ej369++PkJAQvPPOO/fcdtq0aYiJiREfX79+HU2bNkVmZqak/8fURXq9HoGBgcjKyuLXqtXA8ygdnkvp8FxKg+dROnl5eWjSpAk8PT0lfV6rDlavv/46XnjhhXtu06xZM/j6+uLy5ctlxktLS3Ht2jX4+vrec/8bN26gT58+qF+/PjZu3AgHB4d7bq9SqaBSqcqNu7u7800uETc3N55LCfA8SofnUjo8l9LgeZSOUintfXxWHawaNWqERo0a3Xe7iIgIXL9+HYmJiejUqRMAYM+ePTAajQgPD69wP71ej6ioKKhUKmzevBlOTk6S1U5ERER1j00st9C2bVv06dMHL730Eo4cOYKDBw9iwoQJ0Gg04h2BFy9eRJs2bXDkyBEAplDVu3dvFBQU4KuvvoJer4dOp4NOp4PBYJDz5RAREVEtZdWfWD2I//3vf5gwYQKefPJJKJVKDBkyBJ988ok4X1JSgrS0NBQWFgIAjh07hsOHDwMAWrRoUea5zp8/j6CgoEodV6VSYfbs2Wa/HqQHw3MpDZ5H6fBcSofnUho8j9KpqXNpE+tYEREREVkDm/gqkIiIiMgaMFgRERERSYTBioiIiEgiDFZEREREEmGwqoIPPvgA3bp1g4uLCzw8PCq1jyAImDVrFvz8/ODs7IzIyEicOXOmZgutBa5du4bnn38ebm5u8PDwwJgxY5Cfn3/PfXr27AmFQlHm59VXX7VQxdZh2bJlCAoKgpOTE8LDw8VlRCqydu1atGnTBk5OTmjXrh22bdtmoUqt34Ocy1WrVpV773H9O+Dnn3/GgAED4O/vD4VCgU2bNt13n3379uGhhx6CSqVCixYtsGrVqhqvszZ40HO5b9++cu9JhUJR6XZutio2NhZdunRB/fr14e3tjUGDBiEtLe2++0nxu5LBqgqKi4sxbNgwjBs3rtL7zJ8/H5988gmWL1+Ow4cPo169eoiKisKtW7dqsFLr9/zzzyMlJQW7du3Cli1b8PPPP9+3XyMAvPTSS8jOzhZ/5s+fb4FqrUNcXBxiYmIwe/ZsHDt2DB06dEBUVFS57gO3/frrrxg+fDjGjBmD48ePY9CgQRg0aBCSk5MtXLn1edBzCZhWvP7ney8jI8OCFVungoICdOjQAcuWLavU9ufPn0f//v3x+OOPIykpCZMmTcLYsWOxY8eOGq7U+j3oubwtLS2tzPvy7v65dc3+/fsxfvx4HDp0CLt27UJJSYm4dmVFJPtdKVCVffPNN4K7u/t9tzMajYKvr6/w0UcfiWPXr18XVCqVsGbNmhqs0LqdPHlSACAcPXpUHPvpp58EhUIhXLx4scL9evToIbz22msWqNA6de3aVRg/frz42GAwCP7+/kJsbKzZ7Z999lmhf//+ZcbCw8OFV155pUbrrA0e9FxW9t/5ugyAsHHjxntu89ZbbwmhoaFlxqKjo4WoqKgarKz2qcy53Lt3rwBA+OuvvyxSU211+fJlAYCwf//+CreR6nclP7GygPPnz0On0yEyMlIcc3d3R3h4OBISEmSsTF4JCQnw8PBA586dxbHIyEgolUpx8daK/O9//4OXlxfUajWmTZsmLvxq64qLi5GYmFjmvaRUKhEZGVnheykhIaHM9gAQFRVVp997QNXOJQDk5+ejadOmCAwMxMCBA5GSkmKJcm0K35PSCwsLg5+fH3r16oWDBw/KXY7VycvLA4B7NlyW6n1pMyuvW7Pb33X7+PiUGffx8anT34PrdLpyH1fb29vD09PznuflueeeQ9OmTeHv748//vgDU6ZMQVpaGjZs2FDTJcsuNzcXBoPB7Hvp1KlTZvfR6XR875lRlXPZunVrfP3112jfvj3y8vKwYMECdOvWDSkpKWjcuLElyrYJFb0n9Xo9bt68CWdnZ5kqq338/PywfPlydO7cGUVFRfjyyy/Rs2dPHD58GA899JDc5VkFo9GISZMm4ZFHHoFara5wO6l+VzJY/W3q1KmYN2/ePbdJTU1FmzZtLFRR7VXZc1lV/7wGq127dvDz88OTTz6J9PR0NG/evMrPS3Q/ERERiIiIEB9369YNbdu2xRdffIH3339fxsqormrdujVat24tPu7WrRvS09OxePFi/Pe//5WxMusxfvx4JCcn48CBAxY5HoPV315//XW88MIL99ymWbNmVXpuX19fAEBOTg78/PzE8ZycHISFhVXpOa1ZZc+lr69vuYuES0tLce3aNfGcVUZ4eDgA4OzZszYfrLy8vGBnZ4ecnJwy4zk5ORWeM19f3wfavq6oyrm8m4ODAzp27IizZ8/WRIk2q6L3pJubGz+tkkDXrl0tFiKs3YQJE8Qbo+73qbJUvyt5jdXfGjVqhDZt2tzzx9HRsUrPHRwcDF9fX8THx4tjer0ehw8fLvNfv7aisucyIiIC169fR2Jiorjvnj17YDQaxbBUGUlJSQBQJrTaKkdHR3Tq1KnMe8loNCI+Pr7C91JERESZ7QFg165dNvneexBVOZd3MxgMOHHiRJ1470mJ78malZSUVOffk4IgYMKECdi4cSP27NmD4ODg++4j2fuyKlfX13UZGRnC8ePHhXfffVdwdXUVjh8/Lhw/fly4ceOGuE3r1q2FDRs2iI/nzp0reHh4CD/88IPwxx9/CAMHDhSCg4OFmzdvyvESrEafPn2Ejh07CocPHxYOHDggtGzZUhg+fLg4f+HCBaF169bC4cOHBUEQhLNnzwrvvfee8Ntvvwnnz58XfvjhB6FZs2bCY489JtdLsDitViuoVCph1apVwsmTJ4WXX35Z8PDwEHQ6nSAIgjBixAhh6tSp4vYHDx4U7O3thQULFgipqanC7NmzBQcHB+HEiRNyvQSr8aDn8t133xV27NghpKenC4mJiYJGoxGcnJyElJQUuV6CVbhx44b4exCAsGjRIuH48eNCRkaGIAiCMHXqVGHEiBHi9ufOnRNcXFyEN998U0hNTRWWLVsm2NnZCdu3b5frJViNBz2XixcvFjZt2iScOXNGOHHihPDaa68JSqVS2L17t1wvwSqMGzdOcHd3F/bt2ydkZ2eLP4WFheI2NfW7ksGqCkaNGiUAKPezd+9ecRsAwjfffCM+NhqNwsyZMwUfHx9BpVIJTz75pJCWlmb54q3M1atXheHDhwuurq6Cm5ubMHr06DIB9fz582XObWZmpvDYY48Jnp6egkqlElq0aCG8+eabQl5enkyvQB6ffvqp0KRJE8HR0VHo2rWrcOjQIXGuR48ewqhRo8ps//333wutWrUSHB0dhdDQUGHr1q0Wrth6Pci5nDRpkritj4+P0K9fP+HYsWMyVG1dbt/yf/fP7XM3atQooUePHuX2CQsLExwdHYVmzZqV+X1Zlz3ouZw3b57QvHlzwcnJSfD09BR69uwp7NmzR57irYi5c3j33+Wa+l2p+LsAIiIiIqomXmNFREREJBEGKyIiIiKJMFgRERERSYTBioiIiEgiDFZEREREEmGwIiIiIpIIgxURERGRRBisiIiIiCTCYEVEREQkEQYrIrJ5PXv2xKRJk+Qug4jqALa0ISKb0rNnT4SFhWHJkiXi2LVr1+Dg4ID69etbvJ7JkycjIyMDGzZssPixicjy+IkVEdk8T09PWUIVABw5cgSdO3eW5dhEZHkMVkRkM1544QXs378fH3/8MRQKBRQKBf78889yXwX27NkTEydOxKRJk9CgQQP4+Phg5cqVKCgowOjRo1G/fn20aNECP/30k7iP0WhEbGwsgoOD4ezsjA4dOmDdunUV1lJcXAwHBwf8+uuvePvtt6FQKPDwww/X5MsnIivAYEVENuPjjz9GREQEXnrpJWRnZyM7OxuBgYFmt/3222/h5eWFI0eOYOLEiRg3bhyGDRuGbt264dixY+jduzdGjBiBwsJCAEBsbCy+++47LF++HCkpKZg8eTL+9a9/Yf/+/Waf397eHgcPHgQAJCUlITs7G9u3b6+ZF05EVoPXWBGRTTF3jdXdYz179oTBYMAvv/wCADAYDHB3d8czzzyD7777DgCg0+ng5+eHhIQEdOzYEZ6enti9ezciIiLE5x07diwKCwuxevVqs7Vs2rQJY8eORW5ubs28WCKyOvZyF0BEJIf27duL/2xnZ4eGDRuiXbt24piPjw8A4PLlyzh79iwKCwvRq1evMs9RXFyMjh07VniM48ePo0OHDhJXTkTWjMGKiOokBweHMo8VCkWZMYVCAcB0bVV+fj4AYOvWrQgICCizn0qlqvAYSUlJDFZEdQyDFRHZFEdHRxgMBkmfMyQkBCqVCpmZmejRo0el9ztx4gSGDBkiaS1EZN0YrIjIpgQFBeHw4cP4888/4erqCk9Pz2o/Z/369fHGG29g8uTJMBqNePTRR5GXl4eDBw/Czc0No0aNMruf0WhEWloaLl26hHr16sHd3b3atRCRdeNdgURkU9544w3Y2dkhJCQEjRo1QmZmpiTP+/7772PmzJmIjY1F27Zt0adPH2zduhXBwcEV7jNnzhysWrUKAQEBmDNnjiR1EJF1412BRERERBLhJ1ZEREREEmGwIiIiIpIIgxURERGRRBisiIiIiCTCYEVEREQkEQYrIiIiIokwWBERERFJhMGKiIiISCIMVkREREQSYbAiIiIikgiDFREREZFE/h//uV60ODd+pQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "xr = np.arange(0,1,0.01)\n", "yr = np.sin(xr)\n", @@ -101,9 +164,47 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:29.108489Z", + "iopub.status.busy": "2023-08-28T15:03:29.108412Z", + "iopub.status.idle": "2023-08-28T15:03:29.274949Z", + "shell.execute_reply": "2023-08-28T15:03:29.274711Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'figure 2')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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hIa5cuYKmTZvelQMAPDw8DDnupV27dpVGgjw8PJCYmAgASExMhFarrVSegIpTN87Ozobncrm80ntX97jaVudlJCQkBH/88UelbTt37kRISEhdv3WtsZZL8WR7dzzZ3h2Z+TfxY2wqvjmYggs5RYjYcBxf7buIGf3bItTfReyoRGQCBEGo1qkSMUmlUuzcuROHDh3Cn3/+icWLF+ODDz7AkSNHjPpQuvOD/PYH8r+36XQ6AEBhYSEAYNWqVQgODr4rjzEkEsld82PunE9RVb5/56nPfzDfL0dNjiksLIRUKkVcXNxdP7tGjRoZfm9tbV2pKFX3uNpm9D/nCwsLkZCQYJick5KSgoSEBKSmpgKomO8xbNgww/5vvPEGLl68iPfeew9nzpzBsmXLsGHDBrz99tu18x3UM3d7JSKeaImDUx/De0+2gr21FZKzCjD46yMYs/YYruaViB2RiKhWCIKA0NBQfPTRR/j7778hl8uxadMmqFQqeHp64uDBg5X2P3jwINq2bVvj93Nzc4OnpycuXrwIf3//Sg8/Pz+jXqtx48bIyMgwPFer1UhJSTHqNTp27Ii//vqryhIDVIwqaLXa+75GmzZtEBMTU6kYHTx4EHZ2dmjSpIlReYzRqVMnaLVaZGdn3/WzvN9ppZoe97CMLiPHjh1Dp06d0KlTJwBAREQEOnXqhBkzZgCoOM94u5gAFef7fv/9d+zcuRMBAQGYP38+vv7663te1msq7JRWeLO3P/a92xsjuvtCKhGwKykLfb/Yjx+PpPLqGyIyaUeOHMHs2bNx7NgxpKamYuPGjcjJyUGbNm0AAO+++y7mzJmD9evXIzk5GVOnTkVCQgImTZr0UO/70UcfISoqCosWLcLZs2eRmJiINWvWYMGCBUa9zmOPPYbvvvsOf/31FxITEzF8+HCjR1cmTJgAtVqNgQMH4tixYzh37hy+++47JCcnA6i4YufEiRNITk5Gbm5ulaXlzTffRFpaGt566y2cOXMGv/32GyIjIxEREVGnl9C2bNkSgwcPxrBhw7Bx40akpKQgNjYWUVFR+P3332v9uIdl9Dhh79697/tBW9Xqqr1798bff/9t7FuZBAcbOWY+2w6Dg5tiyq8nEJ+ah/c3JeKPxAwsGBAAV7ua3cGQiEhMKpUK+/fvx8KFC6FWq+Hj44P58+cb1pmaOHEi8vPz8X//93/Izs5G27ZtsWXLloe+2mLMmDGwsbHBvHnz8O6778LW1hYdOnTA5MmTjXqdadOmISUlBc888wzs7e0xa9Yso0dGnJ2dsXv3brz77rvo1asXpFIpAgMDDXNlxo4di71796Jz584oLCzEnj17DJcW3+bl5YU//vgD7777LgICAuDk5ITRo0fjww8/NCpLTaxZswaffPIJ/u///g/p6elwcXFBt27d8Mwzz9TJcQ9D0JvAP+HVajXs7e2Rn58PlUoldpx70ur0+ObQJczbcQY3y3RwaaTAlwMDOZeEyILdvHkTKSkp8PPzq/Ht1Ykasvv9Ga/u5zcvAalFUomA0Y/6YetbPdDa3Q65haUY8t8jWLDzLHS6Bt/5iIiIRMEyUgf8XRth8/hQDOrqDb0eWBR9DuN+iEOxpvzBBxMREVkYlpE6orSSIuqFjljwSgDkUgl2nMrCyytikJHPq22IiIjuxDJSx154pAl+ei0YzrZynLqqxn+WHERSRt3c+I+IiMgUsYzUgyAfJ2weH4pWbnbILijFgK9iEHf5htixiKgemcC1AkQ1Uht/tllG6om3kw02vBGCzj6OUN8sx5Cvj+CvczkPPpCITNrtVTKLi4tFTkJUN27/2X6YZfN5aW89K9aU443v47H/bA6spAKWDQ7CE23dHnwgEZmsjIwM5OXlwdXVFTY2NpWW3yYyVXq9HsXFxcjOzoaDgwM8PDzu2qe6n98sIyLQlOvw9voE/J6YASupgJXDOqNPK1exYxFRHdHr9cjMzEReXp7YUYhqnYODA9zd3ass2SwjDVy5VodJ6yoKiVwmwerhXfBoCy6ORmTOtFrtPe9zQmSKrKys7rvMfnU/vxv2bSPNmEwqwcKBgdBoddh5Ogtjvj2Kb0Z2RbdmdXeLZiISl1QqNfr+KESWgBNYRWQllWDJq53wWGtX3CzTYezaY7zsl4iILA7LiMgUMimWDX4EXX2dUFBajhFrYpGex4XRiIjIcrCMNABKKylWDeuMlm6NkKUuxfDVscgr1ogdi4iIqF6wjDQQ9jZW+GZkV7irlDifXYjRa4/hZplW7FhERER1jmWkAfF0sMbaUV2hUsoQd/kGpv56gqs2EhGR2WMZaWBaudthxZAgSCUCNidcxYp9F8WOREREVKdYRhqg7v4uiOzfFgAwd8cZ7DqdJXIiIiKiusMy0kAN7eaDwcFNodcDk9b9jeTMArEjERER1QmWkQZKEATMfLYdujVzQpFGi9e+O4b8Eq7cSERE5odlpAGzkkqwbHAQvByscflaMd79+TgntBIRkdlhGWngnGzlWDb4EcilEvx5Ogur/uKEViIiMi8sIyYgwNsB029NaJ2zPRmxKddFTkRERFR7WEZMxJDgpvhPoCe0Oj0m/BiPnIJSsSMRERHVCpYREyEIAmY/3wEtXBshu6AU7/x8HDod548QEZHpYxkxIbYKGZYOfgQKmQT7zuZgzaFLYkciIiJ6aCwjJqalmx0+7NcGADBn2xmcvqoWOREREdHDYRkxQUO6+SCsjSs0Wh0mrvsbJRreUI+IiEwXy4gJEgQBc17siMZ2CpzPLsSnf5wWOxIREVGNsYyYKOdGCix4JQAA8P3hVN6/hoiITBbLiAnr0aIxxjzqBwCYujERN4o0IiciIiIyHsuIiXsnvBX8XRsht7AUkVtOiR2HiIjIaCwjJk5pJcX8lwMglQjYcvwqtiVmiB2JiIjIKCwjZiDA2wHjejUHAHyw+SRyC7k6KxERmQ6WETPx1uP+aO1uh+tFGkzffJJ39yUiIpPBMmImFDIpPn85ADKJgG0nM7H1BE/XEBGRaWAZMSPtvewx4TF/AMDMLaeQV8yra4iIqOFjGTEzb/b2RwvXRrhWpMGnvyeJHYeIiOiBWEbMjFwmwWcvdoAgAD/HXcGh87liRyIiIrovlhEzFOTjhCHBPgCA9zcl4mYZ711DREQNF8uImXrvyVZwVylx6VoxFkWfEzsOERHRPbGMmCk7pRU++k87AMDK/ReRlKEWOREREVHVWEbMWHg7dzzZzh3lOj2mbkyEVse1R4iIqOFhGTFzH/2nHewUMhxPy8O6o6lixyEiIroLy4iZc1Mp8X99WwIA5m5PxnXe2ZeIiBoYlhELMKSbD9p4qJBfUoa528+IHYeIiKgSlhELIJNKMOvWZNZ1R9MQn3pD5ERERET/YBmxEJ19nfBSUBMAwIzfTnIyKxERNRgsIxZk6lOtYaeU4WS6Gj/GcjIrERE1DCwjFsSlkQLvhrcCAMzbfgbXCktFTkRERMQyYnEGB/ugrYcK6pvl+GwbJ7MSEZH4WEYsjFQiYNZzFZNZf467guNpeeIGIiIii8cyYoGCfJzwwiNeAICPt56GXs/JrEREJB6WEQv1XnhrWFtJEXf5Bv53IkPsOEREZMFYRiyUu70S43o3BwB89kcSbpZpRU5ERESWimXEgr3Wsxk87ZW4mn8Tq/ZfFDsOERFZqBqVkaVLl8LX1xdKpRLBwcGIjY297/4LFy5Eq1atYG1tDW9vb7z99tu4efNmjQJT7VFaSTH16TYAgGV7LyAzn/9NiIio/hldRtavX4+IiAhERkYiPj4eAQEBCA8PR3Z2dpX7//jjj5g6dSoiIyORlJSE//73v1i/fj3ef//9hw5PD69/Rw8E+TiipEyLuTt4qS8REdU/o8vIggULMHbsWIwcORJt27bFihUrYGNjg9WrV1e5/6FDhxAaGopXX30Vvr6+6Nu3LwYNGvTA0RSqH4IgYMYzbQEAG+PTeakvERHVO6PKiEajQVxcHMLCwv55AYkEYWFhiImJqfKY7t27Iy4uzlA+Ll68iD/++ANPP/30Pd+ntLQUarW60oPqToC3Ay/1JSIi0RhVRnJzc6HVauHm5lZpu5ubGzIzM6s85tVXX8XHH3+MRx99FFZWVmjevDl69+5939M0UVFRsLe3Nzy8vb2NiUk1MOVJXupLRETiqPOrafbu3YvZs2dj2bJliI+Px8aNG/H7779j1qxZ9zxm2rRpyM/PNzzS0tLqOqbFc1P9c6nvvB1nUFrOS32JiKh+yIzZ2cXFBVKpFFlZWZW2Z2Vlwd3dvcpjpk+fjqFDh2LMmDEAgA4dOqCoqAivvfYaPvjgA0gkd/chhUIBhUJhTDSqBWN6+OH7w5eRdr0E3x9OxehH/cSOREREFsCokRG5XI6goCBER0cbtul0OkRHRyMkJKTKY4qLi+8qHFKpFAA4N6GBsZHLEPFESwDA4t3nkF9SJnIiIiKyBEafpomIiMCqVauwdu1aJCUlYdy4cSgqKsLIkSMBAMOGDcO0adMM+/fv3x/Lly/HunXrkJKSgp07d2L69Ono37+/oZRQw/FSUBO0cG2EvOIyrNh3Qew4RERkAYw6TQMAAwYMQE5ODmbMmIHMzEwEBgZi+/bthkmtqamplUZCPvzwQwiCgA8//BDp6elo3Lgx+vfvj08//bT2vguqNTKpBFOfao3Ra49h9YEUDO3mA08Ha7FjERGRGRP0JnCuRK1Ww97eHvn5+VCpVGLHMXt6vR4DVh5GbMp1vBTUBJ+/HCB2JCIiMkHV/fzmvWnoLoIg4P1by8T/Gn8FSRlc54WIiOoOywhVKdDbAf06ekCvBz7bxmXiiYio7rCM0D29F94KVlIB+87m4MC5XLHjEBGRmWIZoXvycbbF4GAfAEDUtiTodA1+ehEREZkglhG6r7ce84edQoZTV9X434mrYschIiIzxDJC9+XcSIHXezUDACzYeRZlWp3IiYiIyNywjNADjQz1g0sjOS5fK8aGY7xPEBER1S6WEXogW4UM4/v4AwAWRZ/DzTLeRI+IiGoPywhVy6vBTeHlYI0sdSm+jbkkdhwiIjIjLCNULQqZFJPDWgAAlu29APVN3kSPiIhqB8sIVdvznbzQvLEt8orL8PX+i2LHISIiM8EyQtUmk0rwTt9WAICvD6Qgt7BU5ERERGQOWEbIKE+2d0cHL3sUa7RYtueC2HGIiMgMsIyQUQRBwLvhFaMj3x++jPS8EpETERGRqWMZIaP1aOGCbs2coNHqsGjXObHjEBGRiWMZIaNVjI60BgD8HJeGCzmFIiciIiJTxjJCNRLk44iwNq7Q6YEFf54VOw4REZkwlhGqsXfCW0EQgN8TM3AyPV/sOEREZKJYRqjGWrur0L+jJwBgIeeOEBFRDbGM0EOZ+HgLSARgV1IWTlzJEzsOERGZIJYReij+ro3wXKAXAOCLnZw7QkRExmMZoYc28fEWkEoE7EnOQdzlG2LHISIiE8MyQg/N18UWLz5SMTqycBdHR4iIyDgsI1Qr3nqsBWQSAX+dy0VsynWx4xARkQlhGaFa4e1kg1e6eAMAFuxMFjkNERGZEpYRqjXj+/hDLpXg8MXrOHQhV+w4RERkIlhGqNZ4OVhjYNeK0ZEvdp6FXq8XOREREZkClhGqVW/29odcJsHRSzdw4DxHR4iI6MFYRqhWudsrMSTYBwAw/0+OjhAR0YOxjFCte6N3MyitJEhIy8Pe5Byx4xARUQPHMkK1ztVOiWEhvgCABZw7QkRED8AyQnXi9Z7NYCOXIjE9HztPZ4kdh4iIGjCWEaoTzo0UGNHdFwDwxa5z0Ok4OkJERFVjGaE6M7ZHMzRSyJCUocafpzPFjkNERA0UywjVGUdbOUaG+gIAvow+z9ERIiKqEssI1anRj/rdMTrCuSNERHQ3lhGqUw42csPckUXR53hlDRER3YVlhOrc6Ef9YCuX4jRHR4iIqAosI1TnHG3lGM7RESIiugeWEaoXY3o0g61cilNX1diVlC12HCIiakBYRqheONnKMezW6MjCXVyVlYiI/sEyQvVmbI+KVVlPXVUjmqMjRER0C8sI1RsnW7nhnjVfcu4IERHdwjJC9WpsDz9YW1Xcs2b3GY6OEBERywjVM+dGCgwL8QHA0REiIqrAMkL1bmzPZrC2kuLElXzsTc4ROw4REYmMZYTqnUsjBYbeGh3hlTVERMQyQqIY26MZlFYSHL+Sj71nOTpCRGTJWEZIFI3tFBja7fboCOeOEBFZMpYREs1rPZtXjI6k5WEfR0eIiCwWywiJprGdAoODeWUNEZGlYxkhUb3eqxkUMgn+Ts3D/nO5YschIiIRsIyQqFztlP+MjvDKGiIii8QyQqJ749boSHxqHg6c5+gIEZGlYRkh0bmqlHg1uCkAXllDRGSJWEaoQXijV3PIZRLEXb6Bg+eviR2HiIjqUY3KyNKlS+Hr6wulUong4GDExsbed/+8vDyMHz8eHh4eUCgUaNmyJf74448aBSbz5KZS4tWut0dHOHeEiMiSGF1G1q9fj4iICERGRiI+Ph4BAQEIDw9HdnbVd2DVaDR44okncOnSJfzyyy9ITk7GqlWr4OXl9dDhybyM610xOnLs8g0cusDRESIiS2F0GVmwYAHGjh2LkSNHom3btlixYgVsbGywevXqKvdfvXo1rl+/js2bNyM0NBS+vr7o1asXAgICHjo8mRc3lRKDungD4OgIEZElMaqMaDQaxMXFISws7J8XkEgQFhaGmJiYKo/ZsmULQkJCMH78eLi5uaF9+/aYPXs2tFrtPd+ntLQUarW60oMsw7je/pBLJTh66QZiODpCRGQRjCojubm50Gq1cHNzq7Tdzc0NmZmZVR5z8eJF/PLLL9Bqtfjjjz8wffp0zJ8/H5988sk93ycqKgr29vaGh7e3tzExyYS52ysxqOvt0RFeWUNEZAnq/GoanU4HV1dXrFy5EkFBQRgwYAA++OADrFix4p7HTJs2Dfn5+YZHWlpaXcekBuSN3s0hl0oQe+k6Yi5ydISIyNwZVUZcXFwglUqRlZVVaXtWVhbc3d2rPMbDwwMtW7aEVCo1bGvTpg0yMzOh0WiqPEahUEClUlV6kOXwsLfGQI6OEBFZDKPKiFwuR1BQEKKjow3bdDodoqOjERISUuUxoaGhOH/+PHQ6nWHb2bNn4eHhAblcXsPYZO7G3R4dSeHoCBGRuTP6NE1ERARWrVqFtWvXIikpCePGjUNRURFGjhwJABg2bBimTZtm2H/cuHG4fv06Jk2ahLNnz+L333/H7NmzMX78+Nr7LsjscHSEiMhyyIw9YMCAAcjJycGMGTOQmZmJwMBAbN++3TCpNTU1FRLJPx3H29sbO3bswNtvv42OHTvCy8sLkyZNwpQpU2rvuyCzNK53c6yLTTOMjnRv7iJ2JCIiqgOC3gT+yalWq2Fvb4/8/HzOH7Ew0zefxHeHLyPYzwnrX6/6VCARETVM1f385r1pqEG7PXfkSMp1rjtCRGSmWEaoQfN0sMaAO1ZlJSIi88MyQg0eR0eIiMwbywg1eHeOjnwZzdERIiJzwzJCJmFc7+awkgo4fJGjI0RE5oZlhEwCR0eIiMwXywiZjDd7+3N0hIjIDLGMkMng6AgRkXliGSGTcufoyGHes4aIyCywjJBJ8XSwxiudb42O7DonchoiIqoNLCNkct7sUzE6EnPxGkdHiIjMAMsImRwvjo4QEZkVlhEySXeOjhzh6AgRkUljGSGTVGl0JJqjI0REpoxlhEzW7dGRQxc4OkJEZMpYRshkeTlY42WOjhARmTyWETJpb966Z82hC9cQm3Jd7DhERFQDLCNk0po42twxOsJVWYmITBHLCJm826MjB89zdISIyBSxjJDJa+Jog5eCODpCRGSqWEbILIzv0xwyCUdHiIhMEcsImYU7544s3MXRESIiU8IyQmZjfJ9/rqyJucB1R4iITAXLCJmNJo42GNilKQBgwc5k6PV6kRMREVF1sIyQWZnwmD8UMgmOXrqB/edyxY5DRETVwDJCZsVNpcSQbj4AgAV/cnSEiMgUsIyQ2RnXuzmsraQ4fiUfu5KyxY5DREQPwDJCZselkQIjQn0BAPP/TIZOx9ERIqKGjGWEzNLrPZvBTiHDmcwCbDuZKXYcIiK6D5YRMksONnKMetQPAPDFrrPQcnSEiKjBYhkhszW6hx/sra1wPrsQW46nix2HiIjugWWEzJZKaYXXejYDACzcdQ5lWp3IiYiIqCosI2TWRnT3hbOtHJevFWNj/BWx4xARURVYRsis2SpkGNe7OQBgUfR5lJZrRU5ERET/xjJCZm9INx+42imQnleCDUfTxI5DRET/wjJCZk9pJcWEx/wBAIt3n8fNMo6OEBE1JCwjZBEGdPGGl4M1sgtK8f3hy2LHISKiO7CMkEVQyKSY+HjF6MjyvRdQVFouciIiIrqNZYQsxguPNIGPsw2uFWmw5mCK2HGIiOgWlhGyGFZSCSKeaAkA+GrfRdwo0oiciIiIAJYRsjD9O3qitbsdCkrLsWLfBbHjEBERWEbIwkgkAqY82RoA8M2hS8jILxE5ERERsYyQxendqjG6+jqhtFyHRdHnxI5DRGTxWEbI4giCgPeebAUA2HDsCi7kFIqciIjIsrGMkEXq7OuEsDau0Or0WPDnWbHjEBFZNJYRsljvhLeCIAC/J2bgxJU8seMQEVkslhGyWK3dVXg+0AsAMG9HsshpiIgsF8sIWbS3n2gJK6mAv87l4uD5XLHjEBFZJJYRsmjeTjYYHOwDAJi7/Qz0er3IiYiILA/LCFm88X38YSOX4viVfOw4lSl2HCIii8MyQhavsZ0CYx71A1Axd6RcqxM5ERGRZWEZIQIwpmczONpY4UJOEX6NvyJ2HCIii8IyQgRApbTC+D7+AIAFO8+iWFMuciIiIsvBMkJ0y9AQHzRxtEaWuhT//StF7DhERBaDZYToFoVMinfDK5aJX7HvAnIKSkVORERkGVhGiO7Qv6MnAprYo0ijxZfRXCaeiKg+1KiMLF26FL6+vlAqlQgODkZsbGy1jlu3bh0EQcBzzz1Xk7clqnMSiYD3n24DAPgpNg3ns3kTPSKiumZ0GVm/fj0iIiIQGRmJ+Ph4BAQEIDw8HNnZ2fc97tKlS3jnnXfQo0ePGoclqg/BzZwR1sYNWp0en207I3YcIiKzZ3QZWbBgAcaOHYuRI0eibdu2WLFiBWxsbLB69ep7HqPVajF48GB89NFHaNas2UMFJqoPU59qDalEwK6kLBy5eE3sOEREZs2oMqLRaBAXF4ewsLB/XkAiQVhYGGJiYu553McffwxXV1eMHj26Wu9TWloKtVpd6UFUn/xdG2FgF28AwOw/kqDTcZl4IqK6YlQZyc3NhVarhZubW6Xtbm5uyMysehntAwcO4L///S9WrVpV7feJioqCvb294eHt7W1MTKJaMTmsJWxvLRO/NTFD7DhERGarTq+mKSgowNChQ7Fq1Sq4uLhU+7hp06YhPz/f8EhLS6vDlERVa2ynwBu9mgOouIleablW5EREROZJZszOLi4ukEqlyMrKqrQ9KysL7u7ud+1/4cIFXLp0Cf379zds0+kq7vshk8mQnJyM5s2b33WcQqGAQqEwJhpRnRjToxm+P3IZV26U4NtDlzG2J+c8ERHVNqNGRuRyOYKCghAdHW3YptPpEB0djZCQkLv2b926NRITE5GQkGB4PPvss+jTpw8SEhJ4+oUaPGu5FP/3RMVCaIt3n0NesUbkRERE5seokREAiIiIwPDhw9G5c2d07doVCxcuRFFREUaOHAkAGDZsGLy8vBAVFQWlUon27dtXOt7BwQEA7tpO1FC9GNQEqw+m4ExmARbuOoeZz7YTOxIRkVkxuowMGDAAOTk5mDFjBjIzMxEYGIjt27cbJrWmpqZCIuHCrmQ+pBIBH/ZriyH/PYLvDl/GkG5N4e9qJ3YsIiKzIej1+gZ/zaJarYa9vT3y8/OhUqnEjkMWaszaY9iVlIVeLRtj7aiuYschImrwqvv5zSEMomr6sF8bWEkF7Dubgz1n7r/iMBERVR/LCFE1+brYYlSoHwBg1tbT0JTrRE5ERGQeWEaIjDDhMX+4NJLjYm4Rvo25JHYcIiKzwDJCZAQ7pRXe6Vtxqe+X0edwrbBU5ERERKaPZYTISC939kZbDxUKbpZjwc6zYschIjJ5LCNERpJKBET2bwsA+Ck2FUkZvJEjEdHDYBkhqoHgZs7o18EDOj3w8f9OwwSukCciarBYRohqaOpTrSGXSRBz8Rp2nKr6rtVERPRgLCNENeTtZIPXelTcOG/W1iSUaHhXXyKimmAZIXoIb/ZpDk97JdLzSrBs73mx4xARmSSWEaKHYCOXYcatyaxf7buIlNwikRMREZkelhGihxTezh09WzaGRqtD5JZTnMxKRGQklhGihyQIAj56th3kUgn2n83BjlNZYkciIjIpLCNEtcDPxRav9bw9mfU0ijXlIiciIjIdLCNEtWR8H394OVgjPa8ES/dwMisRUXWxjBDVEmu51DCZdeX+i7iYUyhyIiIi08AyQlSL+rZ1Q+9WjVGm1XMyKxFRNbGMENUiQRAws3/FZNa/zuVi20muzEpE9CAsI0S1zNfFFm/0qpjMOnPLKahvlomciIioYWMZIaoDb/bxh5+LLbILSjFve7LYcYiIGjSWEaI6oLSS4tPn2gMAvj9yGXGXb4iciIio4WIZIaoj3f1d8FJQE+j1wPsbE1Gm1YkdiYioQWIZIapDHzzdBk62ciRnFWDl/otixyEiapBYRojqkKOtHB/2awMAWBR9Dpd4Iz0ioruwjBDVsec7eeFRfxeUluvwweZErj1CRPQvLCNEdUwQBHz6fHsoZBIcPH8Nm/5OFzsSEVGDwjJCVA98nG0xKawFgIob6eUUlIqciIio4WAZIaonY3s0QxsPFW4UlyFyy0mx4xARNRgsI0T1xEoqwbyXOkImEfBHYiZ+P5EhdiQiogaBZYSoHrX3ssebvZsDAGb8dhLXCnm6hoiIZYSonk14rAVaudnhWpEGM/93Wuw4RESiYxkhqmdymQTzXu4IqUTA/45fxXbe2ZeILBzLCJEIOjZxwOs9K+7s++Hmk7hRpBE5ERGReFhGiEQy8fEW8HdthNzCUny8ladriMhysYwQiURpJcW8lzpCIgCb/k7Hn6d4uoaILBPLCJGIOjV1xNhbp2umbUzkYmhEZJFYRohEFvFES7R2r7i6ZuqvJ3jvGiKyOCwjRCJTyKRYODAQcqkE0Wey8VNsmtiRiIjqFcsIUQPQ2l2F955sBaDi3jUpuUUiJyIiqj8sI0QNxKhQP4Q0c0ZJmRZvr09AuVYndiQionrBMkLUQEgkAj5/JQB2ShkS0vKwdM8FsSMREdULlhGiBsTLwRqz/tMeALBo9zkcT8sTNxARUT1gGSFqYP4T6Il+HT2g1ekxad3fKCwtFzsSEVGdYhkhamAEQcCnz7WHp70Sl64V48NNibzcl4jMGssIUQPkYCPHokGdIJUI2JxwFb/EXRE7EhFRnWEZIWqgOvs64e2wFgCAGb+dwvnsApETERHVDZYRogZsXG9/hPpXXO474ce/cbNMK3YkIqJaxzJC1IBJJQK+eCUQzrZynMkswKe/J4kdiYio1rGMEDVwriol5r8SAAD47vBlbEvMEDkREVHtYhkhMgG9W7ni9V4Vd/d975cTXC6eiMwKywiRiXinbyt08XVEQWk53vguDsUarj9CROaBZYTIRFhJJVj66iNwaaRAclYBpm3k+iNEZB5YRohMiKtKiaWvVqw/8lvCVXwbc1nsSERED41lhMjEBDdzxrSnWgMAPvn9NOIu3xA5ERHRw2EZITJBox/1w9Md3FGm1WP8D/HILSwVOxIRUY2xjBCZIEEQMPelADRvbItM9U28+UM8NOU6sWMREdVIjcrI0qVL4evrC6VSieDgYMTGxt5z31WrVqFHjx5wdHSEo6MjwsLC7rs/EVVPI4UMXw0NQiOFDLEp1xG55RQntBKRSTK6jKxfvx4RERGIjIxEfHw8AgICEB4ejuzs7Cr337t3LwYNGoQ9e/YgJiYG3t7e6Nu3L9LT0x86PJGl83e1w6JBgRAE4KfYVE5oJSKTJOiN/KdUcHAwunTpgiVLlgAAdDodvL298dZbb2Hq1KkPPF6r1cLR0RFLlizBsGHDqvWearUa9vb2yM/Ph0qlMiYukUVYse8CPtt2BlKJgG9HdUWov4vYkYiIqv35bdTIiEajQVxcHMLCwv55AYkEYWFhiImJqdZrFBcXo6ysDE5OTvfcp7S0FGq1utKDiO7t9Z7N8EInL2h1erz5QzxXaCUik2JUGcnNzYVWq4Wbm1ul7W5ubsjMzKzWa0yZMgWenp6VCs2/RUVFwd7e3vDw9vY2JiaRxREEAbNf6IBOTR2QX1KGMWuPQn2zTOxYRETVUq9X03z22WdYt24dNm3aBKVSec/9pk2bhvz8fMMjLS2tHlMSmSallRRfDQmCu0qJCzlFGP9DPMq0vMKGiBo+o8qIi4sLpFIpsrKyKm3PysqCu7v7fY/9/PPP8dlnn+HPP/9Ex44d77uvQqGASqWq9CCiB3NVKfH18M6wtpLir3O5eJ9LxhORCTCqjMjlcgQFBSE6OtqwTafTITo6GiEhIfc8bu7cuZg1axa2b9+Ozp071zwtET1Qey97LHm1EyQC8HPcFSyKPi92JCKi+zL6NE1ERARWrVqFtWvXIikpCePGjUNRURFGjhwJABg2bBimTZtm2H/OnDmYPn06Vq9eDV9fX2RmZiIzMxOFhYW1910QUSWPt3HDrOfaAwC+2HUWPx/jqU4iarhkxh4wYMAA5OTkYMaMGcjMzERgYCC2b99umNSampoKieSfjrN8+XJoNBq89NJLlV4nMjISM2fOfLj0RHRPg4N9cOVGCZbvvYBpGxPhbq9EjxaNxY5FRHQXo9cZEQPXGSGqGZ1Oj7c3JOC3hKtopJBhw+shaOvJ/4eIqH7UyTojRGRaJBIBc1/qiG7NnFBYWo5hq2O5BgkRNTgsI0RmTiGT4quhndHGQ4XcwlIM+foIMvJLxI5FRGTAMkJkAeytrfDtqK7wc7FFel4Jhnx9BNcKS8WORUQEgGWEyGI0tlPgu9Fd4WFfsSja8DWxXKWViBoElhEiC9LE0QbfjQ6Gs60cJ9PVGPPNMZRotGLHIiILxzJCZGH8XRth7aiusFPIEHvpOl777hhulrGQEJF4WEaILFB7L3usHtkFNvKKZePHfstCQkTiYRkhslBdfJ2wZgQLCRGJj2WEyIIFN3PGNyO7GgrJmLUsJERU/1hGiCxcVz8nQyE5cL6ikHBSKxHVJ5YRIrqrkIxYE4sCXvZLRPWEZYSIAFQUkttX2RxJuY5Bqw5zYTQiqhcsI0Rk0MXXCT+91s2wDsnLX8Xgah6XjieiusUyQkSVtPeyx4Y3QuBpr8TFnCK8tPwQLuQUih2LiMwYywgR3aV540b4ZVx3NGtsi6v5N/HKihgkXskXOxYRmSmWESKqkqeDNX5+PQTtvVS4VqTBK1/FYOfpLLFjEZEZYhkhontybqTAT2O7oWfLxigp0+K1745hzcEUsWMRkZlhGSGi+7JTWuG/wztjUFdv6PXAR/87jZlbTkGr04sdjYjMBMsIET2QlVSC2c93wNSnWgMAvjl0Ca9/dwxFpeUiJyMic8AyQkTVIggC3ujVHEtffQRymQS7krLx4vJDuHytSOxoRGTiWEaIyCj9Onrgp7Hd0NhOgTOZBXh2yUHsO5sjdiwiMmEsI0RktCAfR/xvwqMI9HZAfkkZRq6JxfK9F6DXcx4JERmPZYSIasTdXon1r3fDwC7e0OmBOdvPYMKPf3MeCREZjWWEiGpMIZMi6oUO+OS59rCSCvg9MQP9Fx/AqatcII2Iqo9lhIgeiiAIGNLNB+te6wYPeyUu5hbh+WWH8G3MJZ62IaJqYRkholoR5OOEPyb2QFgbV2jKdZjx2ym88X0c8ovLxI5GRA0cywgR1RpHWzlWDeuMGc+0hZVUwI5TWXh60V84cvGa2NGIqAFjGSGiWiUIAkY96oeN40Lh42yD9LwSDFx1GLO2nsbNMq3Y8YioAWIZIaI60aGJPba+9SgGdqlYRv6/B1Lw9KK/kJCWJ3Y0ImpgWEaIqM7YKa3w2YsdsWZEF7jaKXAxpwgvLj+Ez3cko7ScoyREVIFlhIjqXJ/Wrvjz7Z74T6AntDo9luw5j6e+/AsxFziXhIhYRoionjjYyPHlwE5YPvgRNL41SjJo1WG88/NxXC/SiB2PiETEMkJE9eqpDh7YFdELQ7o1hSAAv8RdwePz92LDsTTodFyXhMgSCXoTWJVIrVbD3t4e+fn5UKlUYscholoSn3oD729MxJnMAgBAoLcDZvRvi0eaOoqcjIhqQ3U/v1lGiEhUZVodVh9IwaLocyjSVExq/U+gJ6Y82RqeDtYipyOih8EyQkQmJbvgJubvOIsNcWnQ6wGllQSv9WiG13o1RyOFTOx4RFQDLCNEZJJOpufj462nEZtyHQDgaGOFN3v7Y2iID5RWUpHTEZExWEaIyGTp9XrsOJWJuTuScTGnCADgplJgwmMtMKCzN+Qyzr0nMgUsI0Rk8sq1Omz8Ox1f7jqH9LwSAEATR2u83qs5Xg5qwpESogaOZYSIzEZpuRbrj6Zh8e7zyCkoBQC4NFJg9KN+GNKtKeyUViInJKKqsIwQkdkp0Wix4VgaVu6/aBgpsVPKMLSbD0aE+sLVTilyQiK6E8sIEZmtMq0OWxKuYvm+CzifXQgAsJIKeKq9B4Z398UjTR0gCILIKYmIZYSIzJ5Op8fOpCys3H8RcZdvGLa391JheIgv+gd4cl4JkYhYRojIopxMz8faQ5fw2/Gr0JTrAAAONlb4T4AnXu7sjfZe9iInJLI8LCNEZJGuF2mw/mgavj982TCvBADaeKjwclATPNfJC062chETElkOlhEismhanR4Hzufi52Np+PN0lmG0xEoqoEeLxujXwQNPtHODilfiENUZlhEiolvyijXYcvwqfj52BYnp+YbtcqkEPVs2Rv8ADzzexo3LzhPVMpYRIqIqnM8uwNYTGdh6IsNwJQ4AyGUSdG/ujMdbu+KxNm7w4k36iB4aywgR0X3o9XqczSrE7yeuYuuJDFzMLar09dbudni8jSv6tHJFgLcDrKRcgp7IWCwjRETVpNfrcS67ELvPZCM6KQtxl29Ad8ffjLZyKYKbOaN7c2d0b+6C1u52kEi4jgnRg7CMEBHV0I0iDfadzcGupCwcOJ+LvOKySl93spUjpJkzgnwc8YiPI9p6qHjzPqIqsIwQEdUCnU6P0xlqxFy4hoMXchGbch3FGm2lfRQyCTo2sccjTR3RqakjHmnqAFcVl6YnYhkhIqoDZVodTlzJQ8yFa4hPzUN86o27Rk4AoLGdAu08Vbce9mjnqYK3ow1P75BFYRkhIqoHer0eF3OLEH/5BuJTbyD+ch7OZhegqr9Z7RQytHK3g79rI/i7NkJz10bwb9wIXg7WLClkllhGiIhEUlRajjOZBTh9NR+nrqpxOkONM5kFhoXX/k1pJUEzl4qC4utsA2+nikdTJxu4qZSQsqiQiWIZISJqQMq0OlzIKURyZgEuZBfiQk4RzmcXIiW3CBpt1SUFqFgxtomjDZo4WqOpkw08HazhplLCXaWEu70Cbiol7LiKLDVQ1f385nKDRET1wEoqQWt3FVq7V/4LuVyrQ9qNEpzPLsT57EKkXi/GlRvFSL1ejPQbJSjT6pGSW4SUf62DcidbuRRuKmVFSbFXorGdAo42cjjbyuFkK4ej7a3fN5LDTiGDIHCkhRqWGo2MLF26FPPmzUNmZiYCAgKwePFidO3a9Z77//zzz5g+fTouXbqEFi1aYM6cOXj66aer/X4cGSEiS1Su1SFTfRNp10uQdr0YaTeKcTXvJrLUFY9M9U0U3Cw36jWtpAIcbSpKir21FVTWVrBTyqBSWkGllMFOeev5HdvtlDI0UspgbSWFjVzG00ZUbXU2MrJ+/XpERERgxYoVCA4OxsKFCxEeHo7k5GS4urretf+hQ4cwaNAgREVF4ZlnnsGPP/6I5557DvHx8Wjfvr2xb09EZDFkUsmtUzQ2CGnuXOU+xZpyZObfRJa61FBQrhWW4lqRBteLNLhRpDH8vlijRZlWj+yCUmQXlNY4l1wmgY1cCmsrKazl0jt+L4ONVcVz5a1tcpkEcqmk8q///r1MAsW/nltJK/aRSgTIJMKtXyWQSiueS4Rbv7IYmQWjR0aCg4PRpUsXLFmyBACg0+ng7e2Nt956C1OnTr1r/wEDBqCoqAhbt241bOvWrRsCAwOxYsWKKt+jtLQUpaX//I+iVqvh7e3NkREioodws0yL67eKybUiDdQlZVDfLEPBzXIU3PpVXXL7ebnha+qbZSgsLa/yCiGxCQIqlxXJPyXln+0Vv0qEiocgAIIgQCLg1raKF7r9XADu2A9VHvfPPree37Ef7nid22fEBENeodJzGL4uGL6fSl+vtO1f+/zrRe5+r+q89j/PRj/qB28nmwf9yI1SJyMjGo0GcXFxmDZtmmGbRCJBWFgYYmJiqjwmJiYGERERlbaFh4dj8+bN93yfqKgofPTRR8ZEIyKiB1BaSeHpYA3PGtwEUK/Xo7RchxKNFsVlWpRoylGi0aFYU37rubbS14o1Wtws00FTroNGq634tVwHjfb2r3poyrWVt92xT2m5DuVaPbR6PbS6ikfVuYAyrR5lWj2Ae08Epgf7T6BnrZeR6jKqjOTm5kKr1cLNza3Sdjc3N5w5c6bKYzIzM6vcPzMz857vM23atEoF5vbICBERiUMQBCitpFBaSeEowvvrb5WScl3lX7WG57rKX9fe+ppeD61OhzKtHjq9HtADOj2g01c81996bZ0OlZ/rK4qOYT89oEfV+xm+/q/nun8NJd1+WnH0v7dVvU9Vo1G3T2jc69iqjjd86faxVeRyE3HV4AZ5NY1CoYBCoRA7BhERNRCCIEAmFSCTip2E6oJRd3ZycXGBVCpFVlZWpe1ZWVlwd3ev8hh3d3ej9iciIiLLYlQZkcvlCAoKQnR0tGGbTqdDdHQ0QkJCqjwmJCSk0v4AsHPnznvuT0RERJbF6NM0ERERGD58ODp37oyuXbti4cKFKCoqwsiRIwEAw4YNg5eXF6KiogAAkyZNQq9evTB//nz069cP69atw7Fjx7By5cra/U6IiIjIJBldRgYMGICcnBzMmDEDmZmZCAwMxPbt2w2TVFNTUyGR/DPg0r17d/z444/48MMP8f7776NFixbYvHkz1xghIiIiALw3DREREdWR6n5+GzVnhIiIiKi2sYwQERGRqFhGiIiISFQsI0RERCQqlhEiIiISFcsIERERiYplhIiIiETFMkJERESiapB37f232+uyqdVqkZMQERFRdd3+3H7Q+qomUUYKCgoAAN7e3iInISIiImMVFBTA3t7+nl83ieXgdTodrl69Cjs7OwiCUGuvq1ar4e3tjbS0NC4zX8f4s64f/DnXD/6c6wd/zvWnrn7Wer0eBQUF8PT0rHTfun8ziZERiUSCJk2a1Nnrq1Qq/kGvJ/xZ1w/+nOsHf871gz/n+lMXP+v7jYjcxgmsREREJCqWESIiIhKVRZcRhUKByMhIKBQKsaOYPf6s6wd/zvWDP+f6wZ9z/RH7Z20SE1iJiIjIfFn0yAgRERGJj2WEiIiIRMUyQkRERKJiGSEiIiJRsYwQERGRqCy6jCxduhS+vr5QKpUIDg5GbGys2JHMSlRUFLp06QI7Ozu4urriueeeQ3JystixzN5nn30GQRAwefJksaOYpfT0dAwZMgTOzs6wtrZGhw4dcOzYMbFjmRWtVovp06fDz88P1tbWaN68OWbNmvXAm63R/e3fvx/9+/eHp6cnBEHA5s2bK31dr9djxowZ8PDwgLW1NcLCwnDu3Ll6yWaxZWT9+vWIiIhAZGQk4uPjERAQgPDwcGRnZ4sdzWzs27cP48ePx+HDh7Fz506UlZWhb9++KCoqEjua2Tp69Ci++uordOzYUewoZunGjRsIDQ2FlZUVtm3bhtOnT2P+/PlwdHQUO5pZmTNnDpYvX44lS5YgKSkJc+bMwdy5c7F48WKxo5m0oqIiBAQEYOnSpVV+fe7cuVi0aBFWrFiBI0eOwNbWFuHh4bh582bdh9NbqK5du+rHjx9veK7VavWenp76qKgoEVOZt+zsbD0A/b59+8SOYpYKCgr0LVq00O/cuVPfq1cv/aRJk8SOZHamTJmif/TRR8WOYfb69eunHzVqVKVtL7zwgn7w4MEiJTI/APSbNm0yPNfpdHp3d3f9vHnzDNvy8vL0CoVC/9NPP9V5HoscGdFoNIiLi0NYWJhhm0QiQVhYGGJiYkRMZt7y8/MBAE5OTiInMU/jx49Hv379Kv25ptq1ZcsWdO7cGS+//DJcXV3RqVMnrFq1SuxYZqd79+6Ijo7G2bNnAQDHjx/HgQMH8NRTT4mczHylpKQgMzOz0t8f9vb2CA4OrpfPRZO4a29ty83NhVarhZubW6Xtbm5uOHPmjEipzJtOp8PkyZMRGhqK9u3bix3H7Kxbtw7x8fE4evSo2FHM2sWLF7F8+XJERETg/fffx9GjRzFx4kTI5XIMHz5c7HhmY+rUqVCr1WjdujWkUim0Wi0+/fRTDB48WOxoZiszMxMAqvxcvP21umSRZYTq3/jx43Hy5EkcOHBA7ChmJy0tDZMmTcLOnTuhVCrFjmPWdDodOnfujNmzZwMAOnXqhJMnT2LFihUsI7Vow4YN+OGHH/Djjz+iXbt2SEhIwOTJk+Hp6cmfs5myyNM0Li4ukEqlyMrKqrQ9KysL7u7uIqUyXxMmTMDWrVuxZ88eNGnSROw4ZicuLg7Z2dl45JFHIJPJIJPJsG/fPixatAgymQxarVbsiGbDw8MDbdu2rbStTZs2SE1NFSmReXr33XcxdepUDBw4EB06dMDQoUPx9ttvIyoqSuxoZuv2Z59Yn4sWWUbkcjmCgoIQHR1t2KbT6RAdHY2QkBARk5kXvV6PCRMmYNOmTdi9ezf8/PzEjmSWHn/8cSQmJiIhIcHw6Ny5MwYPHoyEhARIpVKxI5qN0NDQuy5PP3v2LHx8fERKZJ6Ki4shkVT+eJJKpdDpdCIlMn9+fn5wd3ev9LmoVqtx5MiRevlctNjTNBERERg+fDg6d+6Mrl27YuHChSgqKsLIkSPFjmY2xo8fjx9//BG//fYb7OzsDOcd7e3tYW1tLXI682FnZ3fXPBxbW1s4Oztzfk4te/vtt9G9e3fMnj0br7zyCmJjY7Fy5UqsXLlS7GhmpX///vj000/RtGlTtGvXDn///TcWLFiAUaNGiR3NpBUWFuL8+fOG5ykpKUhISICTkxOaNm2KyZMn45NPPkGLFi3g5+eH6dOnw9PTE88991zdh6vz63UasMWLF+ubNm2ql8vl+q5du+oPHz4sdiSzAqDKx5o1a8SOZvZ4aW/d+d///qdv3769XqFQ6Fu3bq1fuXKl2JHMjlqt1k+aNEnftGlTvVKp1Ddr1kz/wQcf6EtLS8WOZtL27NlT5d/Jw4cP1+v1FZf3Tp8+Xe/m5qZXKBT6xx9/XJ+cnFwv2QS9nkvaERERkXgscs4IERERNRwsI0RERCQqlhEiIiISFcsIERERiYplhIiIiETFMkJERESiYhkhIiIiUbGMEBERkahYRoiIiEhULCNEREQkKpYRIiIiEtX/A+yHHZTz/l04AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "xr = np.arange(0,10,0.01)\n", "plt.figure(1)\n", @@ -135,9 +236,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:29.276362Z", + "iopub.status.busy": "2023-08-28T15:03:29.276290Z", + "iopub.status.idle": "2023-08-28T15:03:29.371725Z", + "shell.execute_reply": "2023-08-28T15:03:29.371458Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "mu, sigma = 100, 15\n", "x = mu + sigma * np.random.randn(10000)\n", @@ -178,9 +297,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:29.373142Z", + "iopub.status.busy": "2023-08-28T15:03:29.373067Z", + "iopub.status.idle": "2023-08-28T15:03:29.489379Z", + "shell.execute_reply": "2023-08-28T15:03:29.489167Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "xr = np.arange(0,3,0.2)\n", "yr = np.tanh(xr)\n", @@ -209,9 +356,77 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:29.490711Z", + "iopub.status.busy": "2023-08-28T15:03:29.490640Z", + "iopub.status.idle": "2023-08-28T15:03:29.912923Z", + "shell.execute_reply": "2023-08-28T15:03:29.912702Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "xr = np.arange(0,3,.1)\n", "yr1 = np.exp(xr)*(np.sin(5*xr))\n", @@ -283,7 +498,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.11.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/Basics/02-Archiving_your_data.ipynb b/Basics/02-Archiving_your_data.ipynb index d41b29c..8c46bcd 100644 --- a/Basics/02-Archiving_your_data.ipynb +++ b/Basics/02-Archiving_your_data.ipynb @@ -20,11 +20,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:35.056624Z", + "iopub.status.busy": "2023-08-28T15:03:35.056317Z", + "iopub.status.idle": "2023-08-28T15:03:35.120162Z", + "shell.execute_reply": "2023-08-28T15:03:35.119880Z" + }, "jupyter": { "outputs_hidden": false }, @@ -56,11 +62,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:35.121719Z", + "iopub.status.busy": "2023-08-28T15:03:35.121629Z", + "iopub.status.idle": "2023-08-28T15:03:35.174374Z", + "shell.execute_reply": "2023-08-28T15:03:35.174147Z" + }, "jupyter": { "outputs_hidden": false }, @@ -100,11 +112,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:35.175881Z", + "iopub.status.busy": "2023-08-28T15:03:35.175804Z", + "iopub.status.idle": "2023-08-28T15:03:35.303512Z", + "shell.execute_reply": "2023-08-28T15:03:35.302920Z" + }, "jupyter": { "outputs_hidden": false }, @@ -113,18 +131,32 @@ "read_only": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "my_archive.h5\r\n" + ] + } + ], "source": [ "!ls *.h5" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:35.305752Z", + "iopub.status.busy": "2023-08-28T15:03:35.305595Z", + "iopub.status.idle": "2023-08-28T15:03:35.451931Z", + "shell.execute_reply": "2023-08-28T15:03:35.451177Z" + }, "jupyter": { "outputs_hidden": false }, @@ -133,7 +165,16 @@ "read_only": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G Group\r\n", + "number Dataset {SCALAR}\r\n" + ] + } + ], "source": [ "!h5ls my_archive.h5" ] @@ -172,11 +213,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:35.455554Z", + "iopub.status.busy": "2023-08-28T15:03:35.455295Z", + "iopub.status.idle": "2023-08-28T15:03:35.748030Z", + "shell.execute_reply": "2023-08-28T15:03:35.747769Z" + }, "jupyter": { "outputs_hidden": false }, @@ -185,7 +232,28 @@ "read_only": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HDFArchive (partial view) with the following content:\n", + " G : subgroup\n", + " number : data \n", + "Number = 12\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "with HDFArchive(\"my_archive.h5\",'r') as A:\n", " # show the contents of the archive\n", @@ -219,7 +287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.11.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/Basics/03-Operators.ipynb b/Basics/03-Operators.ipynb index d7deda5..8826271 100644 --- a/Basics/03-Operators.ipynb +++ b/Basics/03-Operators.ipynb @@ -26,11 +26,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:37.497752Z", + "iopub.status.busy": "2023-08-28T15:03:37.497224Z", + "iopub.status.idle": "2023-08-28T15:03:37.598380Z", + "shell.execute_reply": "2023-08-28T15:03:37.598143Z" + }, "jupyter": { "outputs_hidden": false }, @@ -39,7 +45,18 @@ "read_only": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1*c_dag('up',0)\n", + "1*c('up',0)\n", + "1*c_dag('down',0)\n", + "1*c('down',0)\n" + ] + } + ], "source": [ "from triqs.operators import c, c_dag, n, Operator # n and Operator will be needed later\n", "print(c_dag('up',0))\n", @@ -66,11 +83,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:37.614409Z", + "iopub.status.busy": "2023-08-28T15:03:37.614286Z", + "iopub.status.idle": "2023-08-28T15:03:37.616031Z", + "shell.execute_reply": "2023-08-28T15:03:37.615781Z" + }, "jupyter": { "outputs_hidden": false }, @@ -79,7 +102,15 @@ "read_only": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1*c_dag('up',0)*c('up',0)\n" + ] + } + ], "source": [ "print(n('up',0))" ] @@ -103,11 +134,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:37.617300Z", + "iopub.status.busy": "2023-08-28T15:03:37.617231Z", + "iopub.status.idle": "2023-08-28T15:03:37.619226Z", + "shell.execute_reply": "2023-08-28T15:03:37.619005Z" + }, "jupyter": { "outputs_hidden": false }, @@ -116,7 +153,15 @@ "read_only": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "# Should give 0\n", "print(n('up',0) - c_dag('up',0)*c('up',0))" @@ -124,11 +169,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:37.620421Z", + "iopub.status.busy": "2023-08-28T15:03:37.620338Z", + "iopub.status.idle": "2023-08-28T15:03:37.621914Z", + "shell.execute_reply": "2023-08-28T15:03:37.621723Z" + }, "jupyter": { "outputs_hidden": false }, @@ -137,7 +188,15 @@ "read_only": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1*c_dag('up',0)*c('up',0)\n" + ] + } + ], "source": [ "# Some calculation\n", "print(n('up',0) - 2 * c_dag('up',0)*c('up',0))" @@ -145,11 +204,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "execution": { + "iopub.execute_input": "2023-08-28T15:03:37.623092Z", + "iopub.status.busy": "2023-08-28T15:03:37.623016Z", + "iopub.status.idle": "2023-08-28T15:03:37.624885Z", + "shell.execute_reply": "2023-08-28T15:03:37.624690Z" + }, "jupyter": { "outputs_hidden": false }, @@ -158,7 +223,15 @@ "read_only": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n" + ] + } + ], "source": [ "# Define the parameters\n", "U = 4\n", @@ -184,9 +257,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mAtomDiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Lightweight exact diagonalization solver\n", + "\n", + "Use the QR algorithm to diagonalize the Hamiltonian.\n", + "Auto-partitions the Hamiltonian into subspaces (blocks)\n", + "such that all creation and annihilation operators map one\n", + "subspace to exactly one other subspace.\n", + "\n", + "Parameters\n", + "----------\n", + "h: Operator\n", + " Hamiltonian to be diagonalized.\n", + "fops: list of tuple of strings and ints\n", + " List of all annihilation / creation operator flavors (indices).\n", + " Must at least contain all flavors met in `h`.\n", + "qn_vector: list of Operator, optional\n", + " Vector of quantum number operators to be used for the auto-partitioning\n", + "n_min, n_max: integers, optional\n", + " Truncate the Fock-space to states with particle number in [n_min, n_max]\n", + " Cannot be combined with qn_vector\n", + "\u001b[0;31mFile:\u001b[0m ~/opt/triqs/lib/python3.11/site-packages/triqs/atom_diag/__init__.py\n", + "\u001b[0;31mType:\u001b[0m function" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from triqs.atom_diag import AtomDiag\n", "?AtomDiag" @@ -194,9 +299,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:37.654014Z", + "iopub.status.busy": "2023-08-28T15:03:37.653949Z", + "iopub.status.idle": "2023-08-28T15:03:37.656261Z", + "shell.execute_reply": "2023-08-28T15:03:37.656068Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-3.0\n" + ] + } + ], "source": [ "# List of operator flavors\n", "fops = [('up',0), ('down',0)]\n", @@ -216,9 +336,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:37.657447Z", + "iopub.status.busy": "2023-08-28T15:03:37.657374Z", + "iopub.status.idle": "2023-08-28T15:03:37.922401Z", + "shell.execute_reply": "2023-08-28T15:03:37.922143Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from triqs.atom_diag import atomic_g_tau\n", "Gtau = atomic_g_tau(atom=AD, beta=10, gf_struct=[('up',1),('down',1)], n_tau=1001)\n", @@ -245,7 +383,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.11.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/Basics/04-Multivariable_Green_functions.ipynb b/Basics/04-Multivariable_Green_functions.ipynb index 683aac4..626177a 100644 --- a/Basics/04-Multivariable_Green_functions.ipynb +++ b/Basics/04-Multivariable_Green_functions.ipynb @@ -28,8 +28,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:39.785873Z", + "iopub.status.busy": "2023-08-28T15:03:39.785564Z", + "iopub.status.idle": "2023-08-28T15:03:39.890524Z", + "shell.execute_reply": "2023-08-28T15:03:39.890289Z" + } + }, "outputs": [], "source": [ "# Relevant Imports \n", @@ -41,8 +48,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:39.892001Z", + "iopub.status.busy": "2023-08-28T15:03:39.891910Z", + "iopub.status.idle": "2023-08-28T15:03:39.893526Z", + "shell.execute_reply": "2023-08-28T15:03:39.893323Z" + } + }, "outputs": [], "source": [ "# Physical parameters\n", @@ -62,8 +76,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:39.894716Z", + "iopub.status.busy": "2023-08-28T15:03:39.894649Z", + "iopub.status.idle": "2023-08-28T15:03:39.896831Z", + "shell.execute_reply": "2023-08-28T15:03:39.896620Z" + } + }, "outputs": [], "source": [ "BL = BravaisLattice([(1,0,0), (0,1,0)]) # Two unit vectors in R3\n", @@ -89,8 +110,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:39.898087Z", + "iopub.status.busy": "2023-08-28T15:03:39.898021Z", + "iopub.status.idle": "2023-08-28T15:03:39.900298Z", + "shell.execute_reply": "2023-08-28T15:03:39.900104Z" + } + }, "outputs": [], "source": [ "iw_mesh = MeshImFreq(beta=beta, S='Fermion', n_iw=128)\n", @@ -109,8 +137,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:39.901552Z", + "iopub.status.busy": "2023-08-28T15:03:39.901489Z", + "iopub.status.idle": "2023-08-28T15:03:46.977606Z", + "shell.execute_reply": "2023-08-28T15:03:46.977216Z" + } + }, "outputs": [], "source": [ "#%%timeit\n", @@ -133,8 +168,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:46.979204Z", + "iopub.status.busy": "2023-08-28T15:03:46.979124Z", + "iopub.status.idle": "2023-08-28T15:03:46.993450Z", + "shell.execute_reply": "2023-08-28T15:03:46.993148Z" + } + }, "outputs": [], "source": [ "iw_arr = np.array(list(iw_mesh.values()))\n", @@ -144,8 +186,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:46.994672Z", + "iopub.status.busy": "2023-08-28T15:03:46.994606Z", + "iopub.status.idle": "2023-08-28T15:03:47.285171Z", + "shell.execute_reply": "2023-08-28T15:03:47.284925Z" + } + }, "outputs": [], "source": [ "#%%timeit\n", @@ -188,9 +237,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:47.286544Z", + "iopub.status.busy": "2023-08-28T15:03:47.286469Z", + "iopub.status.idle": "2023-08-28T15:03:47.288284Z", + "shell.execute_reply": "2023-08-28T15:03:47.288064Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0j\n" + ] + } + ], "source": [ "G_eval = G((pi,pi,0), 2)\n", "G_exact = 1.0/(1j * (2*2+1)*pi/beta - 4)\n", @@ -208,9 +272,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:47.304810Z", + "iopub.status.busy": "2023-08-28T15:03:47.304700Z", + "iopub.status.idle": "2023-08-28T15:03:47.306512Z", + "shell.execute_reply": "2023-08-28T15:03:47.306311Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Greens Function with mesh Imaginary Freq Mesh with beta = 2, statistic = Fermion, n_iw = 128, positive_only = false and target_shape (): \n", + "\n" + ] + } + ], "source": [ "k0 = (0.02,0.01,0) # a k-point as a tuple of 3 floats\n", "Giw = G(k0, all) # We use the \"built-in\" function all here as equivalent of :, \n", @@ -235,9 +315,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:47.307649Z", + "iopub.status.busy": "2023-08-28T15:03:47.307586Z", + "iopub.status.idle": "2023-08-28T15:03:47.309446Z", + "shell.execute_reply": "2023-08-28T15:03:47.309256Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_k = 0.9996637574643963\n" + ] + } + ], "source": [ "# This is the density n_k at k=(0.02, 0.01)\n", "print(\"n_k =\", Giw.density().real)" @@ -260,9 +355,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mInit signature:\u001b[0m \u001b[0mTightBinding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m/\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m \n", + "A tight-binding Hamiltonian on a Bravais lattice.\n", + "\n", + "Requires the displacements in units of the lattice basis vectors (units)\n", + "and the associated overlap (hopping) matrices.\n", + "The matrix structure is w.r.t. the atoms in the unit cell.\n", + "\n", + "Parameters\n", + "----------\n", + "bl : BravaisLattice\n", + " Underlying bravais lattice\n", + "hoppings : dict(vector->matrix)\n", + " The mapping between displacement vectors and overlap (hopping) matrices\n", + "\u001b[0;31mFile:\u001b[0m ~/opt/triqs/lib/python3.11/site-packages/triqs/lattice/lattice_tools.cpython-311-darwin.so\n", + "\u001b[0;31mType:\u001b[0m type\n", + "\u001b[0;31mSubclasses:\u001b[0m " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from triqs.lattice import TightBinding\n", "?TightBinding" @@ -270,8 +391,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:47.328602Z", + "iopub.status.busy": "2023-08-28T15:03:47.328538Z", + "iopub.status.idle": "2023-08-28T15:03:47.631755Z", + "shell.execute_reply": "2023-08-28T15:03:47.631374Z" + } + }, "outputs": [], "source": [ "# Define mapping between displacement vectors and hopping amplitudes\n", @@ -303,9 +431,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:47.633210Z", + "iopub.status.busy": "2023-08-28T15:03:47.633140Z", + "iopub.status.idle": "2023-08-28T15:03:47.995078Z", + "shell.execute_reply": "2023-08-28T15:03:47.994854Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.92, '$\\\\epsilon(\\\\mathbf{k})$')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Prepare the data\n", "k_grid = k_arr.reshape(n_k,n_k,3)\n", @@ -344,7 +500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/Basics/solutions/01s-Greens_functions.ipynb b/Basics/solutions/01s-Greens_functions.ipynb index 5a646c5..1c7d63d 100644 --- a/Basics/solutions/01s-Greens_functions.ipynb +++ b/Basics/solutions/01s-Greens_functions.ipynb @@ -33,9 +33,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mInit signature:\u001b[0m \u001b[0mMeshImTime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m/\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m \n", + "Mesh of imaginary times\n", + "\n", + "Mesh-points are evenly distributed in the interval [0,beta]\n", + "including points at both edges.\n", + "\n", + "Parameters\n", + "----------\n", + "beta : float\n", + " Inverse temperature\n", + "statistic : str\n", + " Statistic, 'Fermion' or 'Boson'\n", + "n_tau : int\n", + " Number of mesh-points\n", + "\u001b[0;31mFile:\u001b[0m ~/opt/triqs/lib/python3.11/site-packages/triqs/gf/meshes.cpython-311-darwin.so\n", + "\u001b[0;31mType:\u001b[0m type\n", + "\u001b[0;31mSubclasses:\u001b[0m " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Import the Mesh type we want to use\n", "from triqs.gf import MeshImTime\n", @@ -46,9 +73,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.448048Z", + "iopub.status.busy": "2023-08-28T15:03:58.447950Z", + "iopub.status.idle": "2023-08-28T15:03:58.449936Z", + "shell.execute_reply": "2023-08-28T15:03:58.449731Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "0.5\n", + "1.0\n", + "1.5\n", + "2.0\n", + "2.5\n", + "3.0\n", + "3.5\n", + "4.0\n", + "4.5\n", + "5.0\n" + ] + } + ], "source": [ "# Provide the inverse temperature, Statistic, and number of points\n", "tau_mesh = MeshImTime(beta=5, S='Fermion', n_tau=11)\n", @@ -74,9 +126,50 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mInit signature:\u001b[0m \u001b[0mGf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m \n", + "TRIQS Greens function container class\n", + "\n", + "Parameters\n", + "----------\n", + "\n", + "mesh: Types defined in triqs.gf beginning with 'Mesh'\n", + " The mesh on which the Green function is defined.\n", + "\n", + "data: numpy.array, optional\n", + " The data of the Greens function.\n", + " Must be of dimension ``mesh.rank + target_rank``.\n", + "\n", + "target_shape: list of int, optional\n", + " Shape of the target space.\n", + "\n", + "is_real: bool\n", + " Is the Greens function real valued?\n", + " If true, and target_shape is set, the data will be real.\n", + " Mutually exclusive with argument ``data``.\n", + "\n", + "name: str \n", + " The name of the Greens function for plotting.\n", + "\n", + "Notes\n", + "-----\n", + "\n", + "One of ``target_shape`` or ``data`` must be set, and the other must be `None`.\n", + "\u001b[0;31mFile:\u001b[0m ~/opt/triqs/lib/python3.11/site-packages/triqs/gf/gf.py\n", + "\u001b[0;31mType:\u001b[0m AddMethod\n", + "\u001b[0;31mSubclasses:\u001b[0m GfReFreq, GfImFreq, GfImTime, GfReTime, GfLegendre" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from triqs.gf import Gf\n", "?Gf" @@ -84,9 +177,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.469637Z", + "iopub.status.busy": "2023-08-28T15:03:58.469541Z", + "iopub.status.idle": "2023-08-28T15:03:58.471763Z", + "shell.execute_reply": "2023-08-28T15:03:58.471565Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Greens Function with mesh Imaginary Time Mesh with beta = 5, statistic = Fermion, n_tau = 11 and target_shape (): \n", + "\n", + "-0.119\n", + "-0.146\n", + "-0.178\n", + "-0.217\n", + "-0.265\n", + "-0.324\n", + "-0.396\n", + "-0.483\n", + "-0.590\n", + "-0.721\n", + "-0.881\n" + ] + } + ], "source": [ "# Create scalar-valued imaginary-time Green's function\n", "G = Gf(mesh=tau_mesh, target_shape=[], is_real=True)\n", @@ -113,9 +233,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.473018Z", + "iopub.status.busy": "2023-08-28T15:03:58.472934Z", + "iopub.status.idle": "2023-08-28T15:03:58.736480Z", + "shell.execute_reply": "2023-08-28T15:03:58.736227Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from triqs.plot.mpl_interface import oplot,plt\n", "\n", @@ -149,9 +287,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.737885Z", + "iopub.status.busy": "2023-08-28T15:03:58.737792Z", + "iopub.status.idle": "2023-08-28T15:03:58.740099Z", + "shell.execute_reply": "2023-08-28T15:03:58.739889Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Greens Function with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (2, 2): " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# A uniform real-frequency mesh on a given interval\n", "from triqs.gf import MeshReFreq\n", @@ -164,9 +320,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.741226Z", + "iopub.status.busy": "2023-08-28T15:03:58.741159Z", + "iopub.status.idle": "2023-08-28T15:03:58.742729Z", + "shell.execute_reply": "2023-08-28T15:03:58.742529Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.+0.j 0.+0.j]\n", + " [0.+0.j 0.+0.j]]\n" + ] + } + ], "source": [ "# Accessing a specific mesh point gives us a matrix\n", "from triqs.gf import Idx # Use Idx to access Gf at specific Index\n", @@ -175,9 +347,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.743789Z", + "iopub.status.busy": "2023-08-28T15:03:58.743721Z", + "iopub.status.idle": "2023-08-28T15:03:58.745579Z", + "shell.execute_reply": "2023-08-28T15:03:58.745351Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Greens Function with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (): " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# By Fixing the orbital indices we obtain a Green's function that is no longer matrix but complex-valued\n", "G[0,0]" @@ -252,9 +442,44 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mInit signature:\u001b[0m \u001b[0mBlockGf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m Generic Green's Function by Block.\n", + "\u001b[0;31mInit docstring:\u001b[0m\n", + "* There are several possible constructors, which accept only keyword arguments.\n", + "\n", + " * BlockGf(block_list = list of blocks, name_list = list of names, make_copies = False)\n", + "\n", + " * ``block_list``: list of blocks of Green's functions.\n", + " * ``name_list``: list of the names of the blocks, e.g. [\"up\",\"down\"]. Default to [\"0\", \"1\", ...]\n", + " * ``make_copies``: If True, build the Green's function from a copy of the blocks (default: False).\n", + "\n", + " * BlockGf(mesh = mesh, gf_struct = block structure, target_rank = rank of target space)\n", + "\n", + " * ``mesh``: The mesh used to construct each block\n", + " * ``gf_struct``: List of pairs [ (str,int), ... ] providing the block name and its linear size\n", + " * ``target_rank``: The rank of the target space of each block (default: 2)\n", + "\n", + " * BlockGf(name_block_generator, make_copies = False)\n", + "\n", + " * ``name_block_generator``: a generator of (name, block)\n", + " * ``make_copies``: If True, build the Green's function from a copy of the blocks (default: False).\n", + "\n", + " \n", + "\u001b[0;31mFile:\u001b[0m ~/opt/triqs/lib/python3.11/site-packages/triqs/gf/block_gf.py\n", + "\u001b[0;31mType:\u001b[0m type\n", + "\u001b[0;31mSubclasses:\u001b[0m " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from triqs.gf import BlockGf\n", "?BlockGf" @@ -271,9 +496,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.749683Z", + "iopub.status.busy": "2023-08-28T15:03:58.749620Z", + "iopub.status.idle": "2023-08-28T15:03:58.751494Z", + "shell.execute_reply": "2023-08-28T15:03:58.751285Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Green Function G composed of 2 blocks: \n", + " Greens Function G_eg with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (2, 2): \n", + " \n", + " Greens Function G_t2g with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (3, 3): \n", + " \n", + "\n" + ] + } + ], "source": [ "# Construct individual blocks\n", "g_eg = Gf(mesh=w_mesh, target_shape=[2,2])\n", @@ -293,9 +538,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.752651Z", + "iopub.status.busy": "2023-08-28T15:03:58.752587Z", + "iopub.status.idle": "2023-08-28T15:03:58.754315Z", + "shell.execute_reply": "2023-08-28T15:03:58.754118Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Greens Function G_eg with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (2, 2): " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "G['eg']" ] @@ -309,9 +572,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.755483Z", + "iopub.status.busy": "2023-08-28T15:03:58.755408Z", + "iopub.status.idle": "2023-08-28T15:03:58.757105Z", + "shell.execute_reply": "2023-08-28T15:03:58.756889Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Green Function G composed of 2 blocks: \n", + " Greens Function G_eg with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (2, 2): \n", + " \n", + " Greens Function G_t2g with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (3, 3): \n", + " \n", + "\n" + ] + } + ], "source": [ "# List of Block-names and their linear matrix size\n", "gf_struct = [('eg',2), ('t2g',3)]\n", @@ -329,8 +612,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.758429Z", + "iopub.status.busy": "2023-08-28T15:03:58.758362Z", + "iopub.status.idle": "2023-08-28T15:03:58.766758Z", + "shell.execute_reply": "2023-08-28T15:03:58.766560Z" + } + }, "outputs": [], "source": [ "from triqs.gf import Omega\n", @@ -366,9 +656,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.768008Z", + "iopub.status.busy": "2023-08-28T15:03:58.767932Z", + "iopub.status.idle": "2023-08-28T15:03:58.769621Z", + "shell.execute_reply": "2023-08-28T15:03:58.769354Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is the block called eg\n", + "The associated Green's function is Greens Function G_eg with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (2, 2): \n", + "\n", + "This is the block called t2g\n", + "The associated Green's function is Greens Function G_t2g with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (3, 3): \n", + "\n" + ] + } + ], "source": [ "# Loop over the blocks\n", "for name, g in G:\n", @@ -408,9 +718,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.770800Z", + "iopub.status.busy": "2023-08-28T15:03:58.770738Z", + "iopub.status.idle": "2023-08-28T15:03:58.775847Z", + "shell.execute_reply": "2023-08-28T15:03:58.775632Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Greens Function with mesh Real Freq Mesh with w_min = -4, w_max = 4, n_w = 1000 and target_shape (): " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "eps_d = 1.0 # Energy\n", "V = 0.2 # Bath Hybridization\n", @@ -432,9 +760,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.777048Z", + "iopub.status.busy": "2023-08-28T15:03:58.776989Z", + "iopub.status.idle": "2023-08-28T15:03:58.844916Z", + "shell.execute_reply": "2023-08-28T15:03:58.844688Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from math import pi\n", "oplot(-G.imag/pi, linewidth=2, name=r\"$\\rho$\")" @@ -476,9 +840,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.913468Z", + "iopub.status.busy": "2023-08-28T15:03:58.913402Z", + "iopub.status.idle": "2023-08-28T15:03:58.980774Z", + "shell.execute_reply": "2023-08-28T15:03:58.980547Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "D = 1.0 # Half bandwidth\n", "\n", @@ -509,9 +891,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:58.982199Z", + "iopub.status.busy": "2023-08-28T15:03:58.982129Z", + "iopub.status.idle": "2023-08-28T15:03:59.075776Z", + "shell.execute_reply": "2023-08-28T15:03:59.075550Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 10.0)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Define the imaginary-frequency mesh\n", "from triqs.gf import MeshImFreq\n", @@ -538,9 +948,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.077138Z", + "iopub.status.busy": "2023-08-28T15:03:59.077067Z", + "iopub.status.idle": "2023-08-28T15:03:59.159460Z", + "shell.execute_reply": "2023-08-28T15:03:59.159239Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 10.0)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "oplot(G, \"-o\", name='G')\n", "oplot(G+G, \"-o\", name='G+G')\n", @@ -559,9 +997,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.160726Z", + "iopub.status.busy": "2023-08-28T15:03:59.160658Z", + "iopub.status.idle": "2023-08-28T15:03:59.166773Z", + "shell.execute_reply": "2023-08-28T15:03:59.166565Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Density = (0.26894142138025784+1.816357384858482e-15j)\n" + ] + } + ], "source": [ "G = Gf(mesh=iw_mesh, target_shape=[])\n", "G << inverse(iOmega_n - 0.2)\n", @@ -586,9 +1039,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.168089Z", + "iopub.status.busy": "2023-08-28T15:03:59.168024Z", + "iopub.status.idle": "2023-08-28T15:03:59.234020Z", + "shell.execute_reply": "2023-08-28T15:03:59.233784Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# A Green's function in frequency set to semi-circular\n", "Giw = Gf(mesh=iw_mesh, target_shape=[])\n", @@ -609,9 +1080,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.235308Z", + "iopub.status.busy": "2023-08-28T15:03:59.235237Z", + "iopub.status.idle": "2023-08-28T15:03:59.305266Z", + "shell.execute_reply": "2023-08-28T15:03:59.305066Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 5.0)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "Giw_2 = make_gf_from_fourier(Gtau)\n", "oplot(Giw, 'o')\n", @@ -630,9 +1129,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.306527Z", + "iopub.status.busy": "2023-08-28T15:03:59.306454Z", + "iopub.status.idle": "2023-08-28T15:03:59.308760Z", + "shell.execute_reply": "2023-08-28T15:03:59.308568Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Greens Function with mesh Imaginary Time Mesh with beta = 5, statistic = Fermion, n_tau = 6001 and target_shape (): " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from triqs.gf import Fourier\n", "Gtau << Fourier(Giw)" @@ -667,9 +1184,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.309975Z", + "iopub.status.busy": "2023-08-28T15:03:59.309904Z", + "iopub.status.idle": "2023-08-28T15:03:59.316075Z", + "shell.execute_reply": "2023-08-28T15:03:59.315877Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Greens Function with mesh Imaginary Freq Mesh with beta = 100, statistic = Fermion, n_iw = 1500, positive_only = false and target_shape (): " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "iw_mesh = MeshImFreq(beta=100, S='Fermion', n_iw=1500)\n", "Giw = Gf(mesh=iw_mesh, target_shape=[])\n", @@ -685,9 +1220,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.317369Z", + "iopub.status.busy": "2023-08-28T15:03:59.317309Z", + "iopub.status.idle": "2023-08-28T15:03:59.329773Z", + "shell.execute_reply": "2023-08-28T15:03:59.329565Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DLR imfreq mesh of size 43 with beta = 100, statistic = Fermion, w_max = 1.2, eps = 1e-15\n" + ] + } + ], "source": [ "# import DLR mesh\n", "from triqs.gf.meshes import MeshDLRImFreq\n", @@ -709,8 +1259,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 27, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.330987Z", + "iopub.status.busy": "2023-08-28T15:03:59.330922Z", + "iopub.status.idle": "2023-08-28T15:03:59.332474Z", + "shell.execute_reply": "2023-08-28T15:03:59.332265Z" + } + }, "outputs": [], "source": [ "Giw_dlr << SemiCircular(1.0);" @@ -725,9 +1282,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.333685Z", + "iopub.status.busy": "2023-08-28T15:03:59.333624Z", + "iopub.status.idle": "2023-08-28T15:03:59.421940Z", + "shell.execute_reply": "2023-08-28T15:03:59.421724Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(1,figsize=(12,5))\n", "oplot(Giw, \"-\", name='G')\n", @@ -750,9 +1325,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.423235Z", + "iopub.status.busy": "2023-08-28T15:03:59.423169Z", + "iopub.status.idle": "2023-08-28T15:03:59.559066Z", + "shell.execute_reply": "2023-08-28T15:03:59.558854Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# import helper functions\n", "from triqs.gf import make_gf_dlr, make_gf_dlr_imtime, make_gf_imfreq, make_gf_imtime\n", @@ -785,9 +1378,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.560424Z", + "iopub.status.busy": "2023-08-28T15:03:59.560351Z", + "iopub.status.idle": "2023-08-28T15:03:59.562264Z", + "shell.execute_reply": "2023-08-28T15:03:59.562074Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.31556737050747535-9.638049093627782e-17j)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "G_dlr(1.2)" ] @@ -801,9 +1412,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.563495Z", + "iopub.status.busy": "2023-08-28T15:03:59.563435Z", + "iopub.status.idle": "2023-08-28T15:03:59.565227Z", + "shell.execute_reply": "2023-08-28T15:03:59.565032Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(6.83481049534862e-16-1.9381548639656863j)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "G_dlr(iw_mesh(0))" ] @@ -817,9 +1446,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.566414Z", + "iopub.status.busy": "2023-08-28T15:03:59.566345Z", + "iopub.status.idle": "2023-08-28T15:03:59.631892Z", + "shell.execute_reply": "2023-08-28T15:03:59.631687Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "Gtau_dlr = make_gf_dlr_imtime(G_dlr)\n", "Gtau = make_gf_imtime(G_dlr, n_tau=5001)\n", @@ -843,9 +1490,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 33, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.633117Z", + "iopub.status.busy": "2023-08-28T15:03:59.633053Z", + "iopub.status.idle": "2023-08-28T15:03:59.710725Z", + "shell.execute_reply": "2023-08-28T15:03:59.710512Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# The Matsubara Green's function to be continued\n", "iw_mesh = MeshImFreq(beta=50, S='Fermion', n_iw=1000)\n", @@ -896,9 +1561,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 34, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.712097Z", + "iopub.status.busy": "2023-08-28T15:03:59.712026Z", + "iopub.status.idle": "2023-08-28T15:03:59.783014Z", + "shell.execute_reply": "2023-08-28T15:03:59.782770Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Parameters\n", "eps_d, V = 0.3, 0.2\n", @@ -928,9 +1611,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 35, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:03:59.784311Z", + "iopub.status.busy": "2023-08-28T15:03:59.784241Z", + "iopub.status.idle": "2023-08-28T15:04:00.394631Z", + "shell.execute_reply": "2023-08-28T15:04:00.394382Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Consider various temperatures\n", "for beta in [3, 10, 100]:\n", @@ -964,9 +1675,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 36, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:04:00.395928Z", + "iopub.status.busy": "2023-08-28T15:04:00.395861Z", + "iopub.status.idle": "2023-08-28T15:04:00.514514Z", + "shell.execute_reply": "2023-08-28T15:04:00.514281Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Im G[$\\\\beta$]')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Consider various temperatures\n", "for beta in [5, 7, 10]:\n", @@ -1006,9 +1745,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 37, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:04:00.515788Z", + "iopub.status.busy": "2023-08-28T15:04:00.515719Z", + "iopub.status.idle": "2023-08-28T15:04:00.612695Z", + "shell.execute_reply": "2023-08-28T15:04:00.612474Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 10.0)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Parameters\n", "U = 2.0\n", @@ -1054,9 +1821,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 38, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:04:00.613959Z", + "iopub.status.busy": "2023-08-28T15:04:00.613893Z", + "iopub.status.idle": "2023-08-28T15:04:00.695790Z", + "shell.execute_reply": "2023-08-28T15:04:00.695583Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 4.0)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Parameters\n", "U = 2.0\n", @@ -1090,9 +1885,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 39, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:04:00.697087Z", + "iopub.status.busy": "2023-08-28T15:04:00.697010Z", + "iopub.status.idle": "2023-08-28T15:04:00.778542Z", + "shell.execute_reply": "2023-08-28T15:04:00.778327Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$\\\\rho(\\\\omega)$')" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Create Green's functions\n", "w_mesh = MeshReFreq(window=(-4,4), n_w=1000)\n", @@ -1127,7 +1950,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/C++/.clang-format b/C++/.clang-format deleted file mode 100644 index 10b3840..0000000 --- a/C++/.clang-format +++ /dev/null @@ -1,45 +0,0 @@ -BasedOnStyle: LLVM - -AccessModifierOffset: 0 -AlignAfterOpenBracket: Align -AlignConsecutiveAssignments: true -AlignConsecutiveDeclarations: false -AlignEscapedNewlinesLeft: false -AlignOperands: false -AlignTrailingComments: true -AllowAllParametersOfDeclarationOnNextLine: false -AllowShortBlocksOnASingleLine: true -AllowShortCaseLabelsOnASingleLine: true -AllowShortFunctionsOnASingleLine: All -AllowShortIfStatementsOnASingleLine: true -AllowShortLoopsOnASingleLine: true -AlwaysBreakBeforeMultilineStrings: true -AlwaysBreakTemplateDeclarations: false -BinPackArguments: true -BinPackParameters: true -BreakBeforeBinaryOperators: NonAssignment -BreakBeforeBraces: Attach -BreakBeforeTernaryOperators: false -BreakConstructorInitializersBeforeComma: false -BreakStringLiterals: false -ColumnLimit: 150 -ConstructorInitializerAllOnOneLineOrOnePerLine: true -ConstructorInitializerIndentWidth: 3 -ContinuationIndentWidth: 3 -Cpp11BracedListStyle: true -DerivePointerBinding : false -IndentCaseLabels: true -IndentWidth: 2 -Language: Cpp -MaxEmptyLinesToKeep: 1 -NamespaceIndentation : All -PointerAlignment: Right -ReflowComments: false -SortIncludes: false -SpaceAfterControlStatementKeyword: true -SpaceBeforeAssignmentOperators: true -SpaceInEmptyParentheses: false -SpacesInParentheses: false -Standard: Cpp11 -TabWidth: 2 -UseTab: Never diff --git a/C++/CMakeLists.txt b/C++/CMakeLists.txt deleted file mode 100644 index daa7491..0000000 --- a/C++/CMakeLists.txt +++ /dev/null @@ -1,22 +0,0 @@ -# Start configuration -cmake_minimum_required(VERSION 3.12.4) -cmake_policy(VERSION 3.12.4) -project(examples VERSION 1.0 LANGUAGES CXX) - -# Default to Release build type -if(NOT CMAKE_BUILD_TYPE) - set(CMAKE_BUILD_TYPE Release CACHE STRING "Type of build" FORCE) -endif() -message( STATUS "-------- BUILD-TYPE: ${CMAKE_BUILD_TYPE} --------") - -# Enable compiler warnings for the whole project -add_definitions(-Wall) - -# Load TRIQS, including all predefined variables from TRIQS installation -find_package(TRIQS 3.1 REQUIRED) - -set(all_sources array gf gtodelta) - foreach(t ${all_sources}) - add_executable(${t} ${t}.cpp) - target_link_libraries(${t} PRIVATE triqs) -endforeach() diff --git a/C++/array.cpp b/C++/array.cpp deleted file mode 100644 index 64fbe04..0000000 --- a/C++/array.cpp +++ /dev/null @@ -1,38 +0,0 @@ -#include -#include - -using namespace nda; - -int main() { - - auto a = matrix(2, 2); // Declare a 2x2 matrix of double - auto b = array(5, 2, 2); // Declare a 5x2x2 array of double - auto c = array{{1, 2, 3}, {4, 5, 6}}; // 2x3 array, with initialization - - clef::placeholder<0> i_; - clef::placeholder<1> j_; - clef::placeholder<2> k_; - - // Assign values - a(i_, j_) << i_ + j_; - b(i_, j_, k_) << i_ * a(k_, j_); - - std::cout << "a = " << a << std::endl; // Printing - - matrix i = inverse(a); // Inverse using LAPACK - double d = determinant(a); // Determinant using LAPACK - - auto ac = a; // Make a copy (the container is a regular type) - ac = a * a + 2 * ac; // Basic operations (uses BLAS for matrix product) - b(0, range(), range()) = ac; // Assign ac into partial view of b - - // Writing the array into an hdf5 file. - auto f = h5::file("a_file.h5", 'w'); - h5_write(f, "a", a); - - auto m = max_element(abs(b)); // maximum of the absolute value of the array. - - // A more "functional" example: compute the norm sum_{i,j} |A_{ij}| - auto lambda = [](double r, double x) { return r + std::abs(x); }; - auto norm = fold(lambda, a, 0); -} diff --git a/C++/ctint/.clang-format b/C++/ctint/.clang-format deleted file mode 100644 index 10b3840..0000000 --- a/C++/ctint/.clang-format +++ /dev/null @@ -1,45 +0,0 @@ -BasedOnStyle: LLVM - -AccessModifierOffset: 0 -AlignAfterOpenBracket: Align -AlignConsecutiveAssignments: true -AlignConsecutiveDeclarations: false -AlignEscapedNewlinesLeft: false -AlignOperands: false -AlignTrailingComments: true -AllowAllParametersOfDeclarationOnNextLine: false -AllowShortBlocksOnASingleLine: true -AllowShortCaseLabelsOnASingleLine: true -AllowShortFunctionsOnASingleLine: All -AllowShortIfStatementsOnASingleLine: true -AllowShortLoopsOnASingleLine: true -AlwaysBreakBeforeMultilineStrings: true -AlwaysBreakTemplateDeclarations: false -BinPackArguments: true -BinPackParameters: true -BreakBeforeBinaryOperators: NonAssignment -BreakBeforeBraces: Attach -BreakBeforeTernaryOperators: false -BreakConstructorInitializersBeforeComma: false -BreakStringLiterals: false -ColumnLimit: 150 -ConstructorInitializerAllOnOneLineOrOnePerLine: true -ConstructorInitializerIndentWidth: 3 -ContinuationIndentWidth: 3 -Cpp11BracedListStyle: true -DerivePointerBinding : false -IndentCaseLabels: true -IndentWidth: 2 -Language: Cpp -MaxEmptyLinesToKeep: 1 -NamespaceIndentation : All -PointerAlignment: Right -ReflowComments: false -SortIncludes: false -SpaceAfterControlStatementKeyword: true -SpaceBeforeAssignmentOperators: true -SpaceInEmptyParentheses: false -SpacesInParentheses: false -Standard: Cpp11 -TabWidth: 2 -UseTab: Never diff --git a/C++/ctint/CMakeLists.txt b/C++/ctint/CMakeLists.txt deleted file mode 100644 index a55044d..0000000 --- a/C++/ctint/CMakeLists.txt +++ /dev/null @@ -1,59 +0,0 @@ -# CMake Start configuration -cmake_minimum_required(VERSION 3.12.4 FATAL_ERROR) -cmake_policy(VERSION 3.12.4) -if(POLICY CMP0077) - cmake_policy(SET CMP0077 NEW) -endif() - -# ############ -# Define Project -project(ctint_tutorial VERSION 3.1.0 LANGUAGES C CXX) - -# Default to Release build type -if(NOT CMAKE_BUILD_TYPE) - set(CMAKE_BUILD_TYPE Release CACHE STRING "Type of build" FORCE) -endif() -message( STATUS "-------- BUILD-TYPE: ${CMAKE_BUILD_TYPE} -------------") - -# ############ -# Load TRIQS and CPP2PY -find_package(TRIQS 3.1 REQUIRED) - -# Default Install directory to TRIQS_ROOT if not given or invalid. -if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT OR (NOT IS_ABSOLUTE ${CMAKE_INSTALL_PREFIX})) - message(STATUS "No install prefix given (or invalid). Defaulting to TRIQS_ROOT") - set(CMAKE_INSTALL_PREFIX ${TRIQS_ROOT} CACHE PATH "default install path" FORCE) - set(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT FALSE) -endif() -message(STATUS "-------- CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX} --------") - -# Default to Release build type -if(NOT CMAKE_BUILD_TYPE) - set(CMAKE_BUILD_TYPE Release CACHE STRING "Type of build" FORCE) -endif() -if(NOT IS_SUBPROJECT) - message(STATUS "-------- BUILD-TYPE: ${CMAKE_BUILD_TYPE} --------") -endif() - -# Export the list of compile-commands into compile_commands.json -set(CMAKE_EXPORT_COMPILE_COMMANDS ON) - -# Testing -option(Build_Tests "Build tests" ON) -if(Build_Tests) - enable_testing() -endif() - -# ############# -# Build Project -# -# Build and install the library -add_subdirectory(c++) - -# Python interface -add_subdirectory(python) - -# Tests -if(Build_Tests) - add_subdirectory(test) -endif() diff --git a/C++/ctint/c++/CMakeLists.txt b/C++/ctint/c++/CMakeLists.txt deleted file mode 100644 index 27c202c..0000000 --- a/C++/ctint/c++/CMakeLists.txt +++ /dev/null @@ -1,7 +0,0 @@ -add_library(ctint_tutorial_c ctint.cpp) - -target_link_libraries(ctint_tutorial_c PUBLIC triqs) -target_include_directories(ctint_tutorial_c PUBLIC $) -set_target_properties(ctint_tutorial_c PROPERTIES POSITION_INDEPENDENT_CODE ON) - -install(TARGETS ctint_tutorial_c DESTINATION lib) diff --git a/C++/ctint/c++/ctint.cpp b/C++/ctint/c++/ctint.cpp deleted file mode 100644 index 60860f3..0000000 --- a/C++/ctint/c++/ctint.cpp +++ /dev/null @@ -1,214 +0,0 @@ -#include "ctint.hpp" - -#include - -#include -#include - -// --------------- The QMC configuration ---------------- - -// Operator types -struct c_t { - double tau; // Imaginary time - int s; // Auxiliary spin -}; -struct cdag_t { - double tau; - int s; -}; - -// The function that appears in the calculation of the determinant -struct g0bar_tau { - gf const > - double beta, delta; - int s; - - dcomplex operator()(c_t const &c, cdag_t const &cdag) const { - if ((c.tau == cdag.tau)) { // G_\sigma(0^-) - \alpha(\sigma s) - return 1.0 + gt[0] - (0.5 + (s == c.s ? 1 : -1) * delta); - } - auto tau = c.tau - cdag.tau; - if (tau >= 0) - return gt[closest_mesh_pt(tau)]; - else // tau < 0, Account for anti-periodicity - return -gt[closest_mesh_pt(tau + beta)]; - } -}; - -// The Monte Carlo configuration -struct configuration { - // M-matrices for up and down - std::vector> Mmatrices; - - int perturbation_order() const { return Mmatrices[up].size(); } - - configuration(block_gf &G0tilde_tau, double beta, double delta) { - // Initialize the M-matrices. 100 is the initial matrix size - for (auto spin : {up, down}) Mmatrices.emplace_back(g0bar_tau{G0tilde_tau[spin], beta, delta, spin}, 100); - } -}; - -// ------------ QMC move : inserting a vertex ------------------ - -struct move_insert { - configuration *config; - triqs::mc_tools::random_generator &rng; - double beta, U; - - dcomplex attempt() { // Insert an interaction vertex at time tau with aux spin s - double tau = rng(beta); - int s = rng(2); - auto k = config->perturbation_order(); - auto det_ratio = config->Mmatrices[up].try_insert(k, k, {tau, s}, {tau, s}) * config->Mmatrices[down].try_insert(k, k, {tau, s}, {tau, s}); - return -beta * U / (k + 1) * det_ratio; // The Metropolis ratio - } - - dcomplex accept() { - for (auto &d : config->Mmatrices) d.complete_operation(); // Finish insertion - return 1.0; - } - - void reject() { - for (auto &d : config->Mmatrices) d.reject_last_try(); // Finish insertion - } -}; - -// ------------ QMC move : deleting a vertex ------------------ - -struct move_remove { - configuration *config; - triqs::mc_tools::random_generator &rng; - double beta, U; - - dcomplex attempt() { - auto k = config->perturbation_order(); - if (k <= 0) return 0; // Config is empty, trying to remove makes no sense - int p = rng(k); // Choose one of the operators for removal - auto det_ratio = config->Mmatrices[up].try_remove(p, p) * config->Mmatrices[down].try_remove(p, p); - return -k / (beta * U) * det_ratio; // The Metropolis ratio - } - - dcomplex accept() { - for (auto &d : config->Mmatrices) d.complete_operation(); - return 1.0; - } - - void reject() { - for (auto &d : config->Mmatrices) d.reject_last_try(); // Finish insertion - } // Nothing to do -}; - -// -------------- QMC measurement ---------------- - -struct measure_M { - - configuration const *config; // Pointer to the MC configuration - block_gf &M_tau; // reference to M-matrix - nda::array &M_hatree; // Equal-time peak in M-matrix - double beta; - dcomplex Z = 0; - long count = 0; - - measure_M(configuration const *config_, block_gf &M_tau_, nda::array &M_hatree_, double beta_) - : config(config_), M_tau(M_tau_), M_hatree(M_hatree_), beta(beta_) { - M_tau() = 0; - M_hatree() = 0; - } - - void accumulate(dcomplex sign) { - Z += sign; - count++; - - // Loop over blocks - for (auto s : {up, down}) { - // Loop over every index pair (x,y) in the determinant matrix - foreach (config->Mmatrices[s], [&](c_t const &c, cdag_t const &cdag, auto const &Ginv) { - // Check for the equal-time case - if (c.tau == cdag.tau) { - M_hatree[s] += Ginv * sign; - } else { - // Project onto M_tau grid - auto tau = c.tau - cdag.tau; - if (tau >= 0) - M_tau[s][closest_mesh_pt(tau)] += Ginv * sign; - else // tau < 0, Account for anti-periodicity - M_tau[s][closest_mesh_pt(tau + beta)] += -Ginv * sign; - } - }) - ; - } - } - - void collect_results(mpi::communicator const &c) { - Z = mpi::all_reduce(Z, c); - - M_tau = mpi::all_reduce(M_tau, c); - M_tau = M_tau / (-Z * beta); - - M_hatree = mpi::all_reduce(M_hatree, c); - M_hatree = M_hatree / (-Z * beta); - ; - - // Correct normalization for first and last bin - for (auto s : {up, down}) { - M_tau[s][0] *= 2.0; - M_tau[s][M_tau[0].mesh().size() - 1] *= 2.0; - } - - // Print the sign - if (c.rank() == 0) std::cerr << "Average sign " << Z / c.size() / count << std::endl; - } -}; - -// ------------ The main class of the solver ------------------------ - -ctint_solver::ctint_solver(double beta_, int n_iw, int n_tau) : beta(beta_) { - - G0_iw = make_block_gf({"up", "down"}, gf{{beta, Fermion, n_iw}, {}}); - G0tilde_tau = make_block_gf({"up", "down"}, gf{{beta, Fermion, n_tau}, {}}); - G0tilde_iw = G0_iw; - G_iw = G0_iw; - M_iw = G0_iw; - M_tau = G0tilde_tau; -} - -// The method that runs the qmc -void ctint_solver::solve(double U, double delta, int n_cycles, int length_cycle, int n_warmup_cycles, std::string random_name, int max_time) { - - mpi::communicator world; - - // Apply shift to G0_iw and Fourier transform - nda::clef::placeholder<1> iw_; - for (auto spin : {up, down}) { - G0tilde_iw[spin](iw_) << 1.0 / (1.0 / G0_iw[spin](iw_) - U / 2); - array mom{0, 1}; // Fix the moments: 0 + 1/omega - G0tilde_tau()[spin] = triqs::gfs::fourier(G0tilde_iw[spin], make_const_view(mom)); - } - - // Rank-specific variables - int verbosity = (world.rank() == 0 ? 3 : 0); - int random_seed = 34788 + 928374 * world.rank(); - - // Construct a Monte Carlo loop - triqs::mc_tools::mc_generic CTQMC(random_name, random_seed, 1.0, verbosity); - - // Prepare the configuration - auto config = configuration{G0tilde_tau, beta, delta}; - - // Register moves and measurements - CTQMC.add_move(move_insert{&config, CTQMC.get_rng(), beta, U}, "insertion"); - CTQMC.add_move(move_remove{&config, CTQMC.get_rng(), beta, U}, "removal"); - CTQMC.add_measure(measure_M{&config, M_tau, M_hatree, beta}, "M measurement"); - - // Run and collect results - CTQMC.warmup_and_accumulate(n_warmup_cycles, n_cycles, length_cycle, triqs::utility::clock_callback(max_time)); - CTQMC.collect_results(world); - - // Calculate M_iw from M_tau and M_hartree - M_iw = make_gf_from_fourier(block_gf{M_tau}, G0_iw[0].mesh(), make_zero_tail(G0_iw)); - M_iw = make_hermitian(M_iw); - for (auto s: {up, down}) M_iw[s](iw_) << M_iw[s](iw_) + M_hatree[s]; - - // Compute the Green's function from M_iw - G_iw = G0tilde_iw + G0tilde_iw * M_iw * G0tilde_iw; -} diff --git a/C++/ctint/c++/ctint.hpp b/C++/ctint/c++/ctint.hpp deleted file mode 100644 index f941ea4..0000000 --- a/C++/ctint/c++/ctint.hpp +++ /dev/null @@ -1,26 +0,0 @@ -#include -#include - -// ------------ The main class of the solver ----------------------- - -using namespace triqs::gfs; -using namespace nda; - -enum spin { up, down }; - -class ctint_solver { - double beta; - int n_matsubara, n_times_slices; - - public: - block_gf G0_iw, G0tilde_iw, G_iw, M_iw; - block_gf G0tilde_tau, M_tau; - nda::array M_hatree = nda::zeros(2); - - /// Construct a ctint solver - ctint_solver(double beta_, int n_iw = 1024, int n_tau = 100001); - - /// Method that performs the QMC calculation - void solve(double U, double delta, int n_cycles, int length_cycle = 50, int n_warmup_cycles = 5000, std::string random_name = "", - int max_time = -1); -}; diff --git a/C++/ctint/cmake/sitecustomize.py b/C++/ctint/cmake/sitecustomize.py deleted file mode 100644 index b20d3ec..0000000 --- a/C++/ctint/cmake/sitecustomize.py +++ /dev/null @@ -1,7 +0,0 @@ -def application_triqs_import(name,*args,**kwargs): - if name.startswith('@package_name@'): - name = name[len('@package_name@')+1:] - return builtin_import(name,*args,**kwargs) - -import builtins -builtins.__import__, builtin_import = application_triqs_import, builtins.__import__ diff --git a/C++/ctint/examples/dmft_bethe.ipynb b/C++/ctint/examples/dmft_bethe.ipynb deleted file mode 100644 index 0817c0e..0000000 --- a/C++/ctint/examples/dmft_bethe.ipynb +++ /dev/null @@ -1,112 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Starting on 1 Nodes at : 2014-10-22 18:06:12.886058\n" - ] - } - ], - "source": [ - "from triqs.gf import *\n", - "from h5 import *\n", - "import triqs.utility.mpi as mpi\n", - "from triqs.applications.impurity_solvers.ctint_tutorial import CtintSolver\n", - "from triqs.plot.mpl_interface import oplot\n", - "\n", - "# Parameters\n", - "U = 2.5 # Hubbard interaction\n", - "mu = U/2.0 # Chemical potential\n", - "half_bandwidth=1.0 # Half bandwidth (energy unit)\n", - "beta = 40.0 # Inverse temperature\n", - "n_iw = 128 # Number of Matsubara frequencies\n", - "n_cycles = 10000 # Number of MC cycles\n", - "delta = 0.1 # delta parameter\n", - "n_iterations = 21 # Number of DMFT iterations\n", - "\n", - "S = CtintSolver(beta, n_iw) # Initialize the solver\n", - "\n", - "S.G_iw << SemiCircular(half_bandwidth) # Initialize the Green's function\n", - "\n", - "with HDFArchive(\"dmft_bethe.output.h5\",'w') as A:\n", - " A['n_iterations'] = n_iterations # Save a parameter\n", - "\n", - " for it in range(n_iterations): # DMFT loop\n", - " for name, G0 in S.G0_iw:\n", - " G0 << inverse(iOmega_n + mu - (half_bandwidth/2.0)**2 * S.G_iw[name] ) # Set G0\n", - " # Change random number generator on final iteration\n", - " random_name = 'mt19937' if it" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "A = HDFArchive(\"dmft_bethe.output.h5\",'r') # Open file\n", - "n_iterations = A['n_iterations'] # Load a parameter\n", - "for it in range(n_iterations): # Use parameter in the analysis\n", - " if not it%2: oplot(A['G%i'%it].imag, '-o', name='Im G%i'%it) # Plot every second result\n", - "xlim(0,5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/C++/ctint/examples/dmft_bethe.py b/C++/ctint/examples/dmft_bethe.py deleted file mode 100644 index ac149db..0000000 --- a/C++/ctint/examples/dmft_bethe.py +++ /dev/null @@ -1,38 +0,0 @@ -from triqs.gf import * -from h5 import * -import triqs.utility.mpi as mpi -from triqs.applications.impurity_solvers.ctint_tutorial import CtintSolver -from triqs.plot.mpl_interface import oplot - -# Parameters -U = 2.5 # Hubbard interaction -mu = U/2.0 # Chemical potential -half_bandwidth=1.0 # Half bandwidth (energy unit) -beta = 40.0 # Inverse temperature -n_iw = 128 # Number of Matsubara frequencies -n_cycles = 10000 # Number of MC cycles -delta = 0.1 # delta parameter -n_iterations = 21 # Number of DMFT iterations - -S = CtintSolver(beta, n_iw) # Initialize the solver - -S.G_iw << SemiCircular(half_bandwidth) # Initialize the Green's function - -with HDFArchive("dmft_bethe.output.h5",'w') as A: - A['n_iterations'] = n_iterations # Save a parameter - - for it in range(n_iterations): # DMFT loop - for name, G0 in S.G0_iw: - G0 << inverse(iOmega_n + mu - (half_bandwidth/2.0)**2 * S.G_iw[name] ) # Set G0 - # Change random number generator on final iteration - random_name = 'mt19937' if it. -# -################################################################################ - -r""" -DOC - -""" -from ctint_tutorial import CtintSolver - -__all__ = ['CtintSolver'] diff --git a/C++/ctint/python/ctint_tutorial_desc.py b/C++/ctint/python/ctint_tutorial_desc.py deleted file mode 100644 index 84f941a..0000000 --- a/C++/ctint/python/ctint_tutorial_desc.py +++ /dev/null @@ -1,76 +0,0 @@ -# Generated automatically using the command : -# c++2py ../c++/ctint.hpp -p --members_read_only -a ctint_tutorial -m ctint_tutorial -o ctint_tutorial -C triqs --moduledoc="CTInt Tutorial" --includes=../c++ --cxxflags="-std=c++20 $(triqs++ -cxxflags)" --target_file_only -from cpp2py.wrap_generator import * - -# The module -module = module_(full_name = "ctint_tutorial", doc = r"CTInt Tutorial", app_name = "ctint_tutorial") - -# Imports -module.add_imports(*['triqs.gf', 'triqs.gf.meshes']) - -# Add here all includes -module.add_include("ctint.hpp") - -# Add here anything to add in the C++ code at the start, e.g. namespace using -module.add_preamble(""" -#include -#include -#include - -""") - -module.add_enum("spin", ['spin::up', 'spin::down'], "spin", doc = r"""""") - -# The class ctint_solver -c = class_( - py_type = "CtintSolver", # name of the python class - c_type = "ctint_solver", # name of the C++ class - doc = r"""""", # doc of the C++ class - hdf5 = False, -) - -c.add_member(c_name = "G0_iw", - c_type = "block_gf", - read_only= True, - doc = r"""""") - -c.add_member(c_name = "G0tilde_iw", - c_type = "block_gf", - read_only= True, - doc = r"""""") - -c.add_member(c_name = "G_iw", - c_type = "block_gf", - read_only= True, - doc = r"""""") - -c.add_member(c_name = "M_iw", - c_type = "block_gf", - read_only= True, - doc = r"""""") - -c.add_member(c_name = "G0tilde_tau", - c_type = "block_gf", - read_only= True, - doc = r"""""") - -c.add_member(c_name = "M_tau", - c_type = "block_gf", - read_only= True, - doc = r"""""") - -c.add_member(c_name = "M_hatree", - c_type = "nda::array", - read_only= True, - doc = r"""""") - -c.add_constructor("""(double beta_, int n_iw = 1024, int n_tau = 100001)""", doc = r"""Construct a ctint solver""") - -c.add_method("""void solve (double U, double delta, int n_cycles, int length_cycle = 50, int n_warmup_cycles = 5000, std::string random_name = \"\", int max_time = -1)""", - doc = r"""Method that performs the QMC calculation""") - -module.add_class(c) - - - -module.generate_code() \ No newline at end of file diff --git a/C++/ctint/test/CMakeLists.txt b/C++/ctint/test/CMakeLists.txt deleted file mode 100644 index 4332e17..0000000 --- a/C++/ctint/test/CMakeLists.txt +++ /dev/null @@ -1,8 +0,0 @@ -# load triqs helper to set up tests -find_package(TriqsTest) - -add_subdirectory(c++) -if (${TRIQS_WITH_PYTHON_SUPPORT}) - add_subdirectory(python) -endif() - diff --git a/C++/ctint/test/c++/CMakeLists.txt b/C++/ctint/test/c++/CMakeLists.txt deleted file mode 100644 index 8873543..0000000 --- a/C++/ctint/test/c++/CMakeLists.txt +++ /dev/null @@ -1,20 +0,0 @@ -# -- GTest -include(FetchContent) -FetchContent_Declare( - googletest - GIT_REPOSITORY https://github.com/google/googletest - GIT_TAG main -) -FetchContent_MakeAvailable(googletest) - -# -- Copy h5 files to binary dir -file(GLOB all_h5_files *.h5) -file(COPY ${all_h5_files} DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) - -# -- Add Tests -set(all_tests anderson_c) -foreach(t ${all_tests}) - add_executable(${t} ${CMAKE_CURRENT_SOURCE_DIR}/${t}.cpp) - target_link_libraries(${t} ctint_tutorial_c gtest) - add_test(${t} ${CMAKE_CURRENT_BINARY_DIR}/${t}) -endforeach() diff --git a/C++/ctint/test/c++/anderson_c.cpp b/C++/ctint/test/c++/anderson_c.cpp deleted file mode 100644 index 995367f..0000000 --- a/C++/ctint/test/c++/anderson_c.cpp +++ /dev/null @@ -1,43 +0,0 @@ -#include "ctint.hpp" - -#include - -// Anderson model test -TEST(CtInt, Anderson) { - - // Initialize mpi - int rank = mpi::communicator().rank(); - - // set parameters - double beta = 20.0; - double U = 1.0; - double delta = 0.1; - int n_cycles = 10000; - int n_iw = 200; - - // construct the ct-int solver - auto ctqmc = ctint_solver{beta, n_iw}; - - // parameters - double mu = 1.3 - U / 2.0; // mu=0 corresponds to half filling - double epsilon = 0.2; - - // initialize g0(omega) - nda::clef::placeholder<0> om_; - for (auto sig : {0, 1}) ctqmc.G0_iw()[sig](om_) << 1.0 / (om_ + mu - 1.0 / (om_ - epsilon)); - - // launch the Monte Carlo - ctqmc.solve(U, delta, n_cycles); - - std::string filename = "anderson_c"; - gf g_ref; - if (rank == 0) { - h5::file f_out(filename + ".out.h5", 'w'); - h5_write(f_out, "G", ctqmc.G_iw()[0]); - - h5::file f_ref(filename + ".ref.h5", 'r'); - h5_read(f_ref, "G", g_ref); - EXPECT_GF_NEAR(g_ref, ctqmc.G_iw()[0]); - } -} -MAKE_MAIN; diff --git a/C++/ctint/test/c++/anderson_c.ref.h5 b/C++/ctint/test/c++/anderson_c.ref.h5 deleted file mode 100644 index 130c41f..0000000 Binary files a/C++/ctint/test/c++/anderson_c.ref.h5 and /dev/null differ diff --git a/C++/ctint/test/python/CMakeLists.txt b/C++/ctint/test/python/CMakeLists.txt deleted file mode 100644 index b35025c..0000000 --- a/C++/ctint/test/python/CMakeLists.txt +++ /dev/null @@ -1,13 +0,0 @@ -# Copy h5 files to binary dir -file(GLOB all_h5_files *.h5) -file(COPY ${all_h5_files} DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) - -set(all_tests anderson) - -foreach(t ${all_tests}) - add_test(NAME ${t} COMMAND ${TRIQS_PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/${t}.py) -endforeach() - -# Set the PythonPath -set_property(TEST ${all_tests} PROPERTY ENVIRONMENT PYTHONPATH=${CMAKE_BINARY_DIR}/python:$ENV{PYTHONPATH} ) - diff --git a/C++/ctint/test/python/anderson.py b/C++/ctint/test/python/anderson.py deleted file mode 100644 index b3168f3..0000000 --- a/C++/ctint/test/python/anderson.py +++ /dev/null @@ -1,31 +0,0 @@ -from h5 import * -from triqs.gf import * -from triqs.utility.h5diff import h5diff -import triqs.utility.mpi as mpi - -from numpy import zeros - -from ctint_tutorial import CtintSolver - -# Parameters -beta = 20.0 -n_iw = 200; -U = 1.0 -delta = 0.1; -n_cycles = 10000; - -S = CtintSolver(beta, n_iw) - -# init the Green's function -mu = 1.3 - U/2 -eps0 = 0.2 -S.G0_iw << inverse(iOmega_n + mu - 1.0 * inverse(iOmega_n - eps0)) - -S.solve(U, delta, n_cycles) - -if mpi.is_master_node(): - A = HDFArchive("anderson.out.h5",'w') - A['G'] = S.G_iw['up'] - -# -------- Compare --------- -h5diff("anderson.out.h5", "anderson.ref.h5") diff --git a/C++/ctint/test/python/anderson.ref.h5 b/C++/ctint/test/python/anderson.ref.h5 deleted file mode 100644 index 47f0067..0000000 Binary files a/C++/ctint/test/python/anderson.ref.h5 and /dev/null differ diff --git a/C++/gf.cpp b/C++/gf.cpp deleted file mode 100644 index b745cb6..0000000 --- a/C++/gf.cpp +++ /dev/null @@ -1,40 +0,0 @@ -#include -#include -using namespace triqs; -using namespace triqs::gfs; -using namespace triqs::lattice; -int main() { - - double beta = 10; - int n_freq = 1000; - - clef::placeholder<0> iw_; - clef::placeholder<1> k_; - - // Construction of a 1x1 matrix-valued fermionic gf on Matsubara frequencies. - auto g_iw = gf{{beta, Fermion, n_freq}, {1, 1}}; - - // Automatic placeholder evaluation - g_iw(iw_) << 1 / (iw_ + 2); - - // Inverse Fourier transform to imaginary time - auto g_tau = gf{{beta, Fermion, 2 * n_freq + 1}, {1, 1}}; - g_tau() = fourier(g_iw); // Fills a full view of g_tau with FFT result - - // Create a block Green's function composed of three blocks, - // labeled a,b,c and made of copies of the g_iw functions. - auto G_iw = make_block_gf({"a", "b", "c"}, {g_iw, g_iw, g_iw}); - - // A multivariable gf: G(k,omega) - auto bz = brillouin_zone{bravais_lattice{{{1, 0}, {0, 1}}}}; - auto g_k_iw = gf>{{{bz, 100}, {beta, Fermion, n_freq}}, {1, 1}}; - - g_k_iw(k_, iw_) << 1 / (iw_ - 2 * (cos(k_(0)) + cos(k_(1))) - 1 / (iw_ + 2)); - - // Writing the Green's functions into an HDF5 file. - auto f = h5::file("file_g_k_iw.h5", 'w'); - h5_write(f, "g_k_iw", g_k_iw); - h5_write(f, "g_iw", g_iw); - h5_write(f, "g_tau", g_tau); - h5_write(f, "block_gf", G_iw); -} diff --git a/C++/gtodelta.cpp b/C++/gtodelta.cpp deleted file mode 100644 index c2a6310..0000000 --- a/C++/gtodelta.cpp +++ /dev/null @@ -1,34 +0,0 @@ -#include -#include - -using namespace triqs::gfs; -using namespace triqs::lattice; - -int main() { - - double beta = 10, mu = 0; - int n_freq = 100, n_pts = 100; - - // Green's function on Matsubara frequencies, 1x1 matrix-valued. - auto Delta_iw = gf{{beta, Fermion, n_freq}, {1, 1}}; - auto Gloc = gf{{beta, Fermion, n_freq}, {1, 1}}; - - // Green's function in imaginary time, 1x1 matrix-valued. - auto Delta_tau = gf{{beta, Fermion, 2 * n_freq + 1}, {1, 1}}; - - auto bz = brillouin_zone{bravais_lattice{{{1, 0}, {0, 1}}}}; - auto bz_mesh = mesh::brzone{bz, n_pts}; - - nda::clef::placeholder<1> k_; - nda::clef::placeholder<2> iw_; - - // The actual equations - Gloc(iw_) << sum(1 / (iw_ + mu - 2 * (cos(k_[0]) + cos(k_[1]))), k_ = bz_mesh) / bz_mesh.size(); // (3) - Delta_iw(iw_) << iw_ + mu - 1 / Gloc(iw_); // (4) - Delta_tau() = fourier(Delta_iw); // (5) - - // Write the hybridization to an HDF5 archive - auto file = h5::file("Delta.h5", 'w'); - h5_write(file, "Delta_tau", Delta_tau); - h5_write(file, "Delta_iw", Delta_iw); -} diff --git a/ModelDMFT/02-Introduction_to_the_CTHYB_solver.ipynb b/ModelDMFT/02-Introduction_to_the_CTHYB_solver.ipynb index d62a361..1d0322c 100644 --- a/ModelDMFT/02-Introduction_to_the_CTHYB_solver.ipynb +++ b/ModelDMFT/02-Introduction_to_the_CTHYB_solver.ipynb @@ -2,8 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:49:03.216298Z", + "iopub.status.busy": "2023-08-28T15:49:03.216066Z", + "iopub.status.idle": "2023-08-28T15:49:03.427181Z", + "shell.execute_reply": "2023-08-28T15:49:03.426896Z" + } + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -71,9 +78,90 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:49:03.428882Z", + "iopub.status.busy": "2023-08-28T15:49:03.428783Z", + "iopub.status.idle": "2023-08-28T15:49:04.116887Z", + "shell.execute_reply": "2023-08-28T15:49:04.116405Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: could not identify MPI environment!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting serial run at: 2023-08-28 17:49:03.483848\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "0.2*c_dag('down',0)*c('down',0) + 0.2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:49:03 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:49:03 19% ETA 00:00:00 cycle 9760 of 50000\n", + "17:49:04 100% ETA 00:00:00 cycle 49999 of 50000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00566571\n", + "Average order | 0.000908685\n", + "Average sign | 0.000881916\n", + "G_tau measure | 0.0114234 \n", + "Total measure time | 0.0188797 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0512121\n", + " Move Insert Delta_up: 0.0513189\n", + " Move Insert Delta_down: 0.0511046\n", + "Move set Remove two operators: 0.0509981\n", + " Move Remove Delta_up: 0.0513082\n", + " Move Remove Delta_down: 0.0506868\n", + "Move set Insert four operators: 0.00488083\n", + " Move Insert Delta_up_up: 0.00562934\n", + " Move Insert Delta_up_down: 0.0033534\n", + " Move Insert Delta_down_up: 0.00373554\n", + " Move Insert Delta_down_down: 0.00676994\n", + "Move set Remove four operators: 0.00496258\n", + " Move Remove Delta_up_up: 0.00575551\n", + " Move Remove Delta_up_down: 0.0038171\n", + " Move Remove Delta_down_up: 0.00347195\n", + " Move Remove Delta_down_down: 0.00682145\n", + "Move Shift one operator: 0.689225\n", + "[Rank 0] Warmup lasted: 0.0479879 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.507693 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 50000\n", + "Total number of measures: 50000\n", + "Average sign: 1\n", + "Average order: 14.716\n", + "Auto-correlation time: 86.6392\n" + ] + } + ], "source": [ "from triqs.gf import *\n", "from triqs.operators import *\n", @@ -158,9 +246,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:49:04.133867Z", + "iopub.status.busy": "2023-08-28T15:49:04.133740Z", + "iopub.status.idle": "2023-08-28T15:49:04.251105Z", + "shell.execute_reply": "2023-08-28T15:49:04.250836Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from triqs.plot.mpl_interface import *\n", "\n", @@ -185,9 +291,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:49:04.252469Z", + "iopub.status.busy": "2023-08-28T15:49:04.252396Z", + "iopub.status.idle": "2023-08-28T15:49:04.321342Z", + "shell.execute_reply": "2023-08-28T15:49:04.321107Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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b/ModelDMFT/solutions/01s-IPT_and_DMFT.ipynb @@ -37,8 +37,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:34:38.517708Z", + "iopub.status.busy": "2023-08-28T15:34:38.517420Z", + "iopub.status.idle": "2023-08-28T15:34:38.626232Z", + "shell.execute_reply": "2023-08-28T15:34:38.625997Z" + } + }, "outputs": [], "source": [ "from triqs.gf import *\n", @@ -117,9 +124,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:34:38.627795Z", + "iopub.status.busy": "2023-08-28T15:34:38.627699Z", + "iopub.status.idle": "2023-08-28T15:34:39.362884Z", + "shell.execute_reply": "2023-08-28T15:34:39.362626Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from triqs.plot.mpl_interface import *\n", "%matplotlib inline\n", @@ -165,9 +190,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:34:39.379640Z", + "iopub.status.busy": "2023-08-28T15:34:39.379505Z", + "iopub.status.idle": "2023-08-28T15:34:40.518644Z", + "shell.execute_reply": "2023-08-28T15:34:40.518395Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "\n", @@ -233,7 +276,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ModelDMFT/solutions/04s-Two-orbital_Hubbard_with_CTQMC.ipynb b/ModelDMFT/solutions/04s-Two-orbital_Hubbard_with_CTQMC.ipynb index 89d1a1e..67933ad 100644 --- a/ModelDMFT/solutions/04s-Two-orbital_Hubbard_with_CTQMC.ipynb +++ b/ModelDMFT/solutions/04s-Two-orbital_Hubbard_with_CTQMC.ipynb @@ -45,12 +45,6822 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:36:18.445806Z", + "iopub.status.busy": "2023-08-28T15:36:18.445318Z", + "iopub.status.idle": "2023-08-28T15:37:04.063335Z", + "shell.execute_reply": "2023-08-28T15:37:04.063054Z" + }, "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: could not identify MPI environment!\n", + "U = 1.0\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:18 41% ETA 00:00:00 cycle 2083 of 5000\n", + "17:36:18 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:18 20% ETA 00:00:00 cycle 2046 of 10000\n", + "17:36:19 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00121841\n", + "Average order | 0.000180423\n", + "Average sign | 0.000183033\n", + "G_tau measure | 0.0022792 \n", + "Total measure time | 0.00386106\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.134194\n", + " Move Insert Delta_up: 0.135137\n", + " Move Insert Delta_down: 0.133256\n", + "Move set Remove two operators: 0.134078\n", + " Move Remove Delta_up: 0.134495\n", + " Move Remove Delta_down: 0.13366\n", + "Move set Insert four operators: 0.0237206\n", + " Move Insert Delta_up_up: 0.0263818\n", + " Move Insert Delta_up_down: 0.0215067\n", + " Move Insert Delta_down_up: 0.0192815\n", + " Move Ins" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting serial run at: 2023-08-28 17:36:18.568460\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ert Delta_down_down: 0.0276912\n", + "Move set Remove four operators: 0.0240728\n", + " Move Remove Delta_up_up: 0.027297\n", + " Move Remove Delta_up_down: 0.0201105\n", + " Move Remove Delta_down_up: 0.0206622\n", + " Move Remove Delta_down_down: 0.0282551\n", + "Move Shift one operator: 0.83864\n", + "[Rank 0] Warmup lasted: 0.240672 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.491363 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.1807\n", + "Auto-correlation time: 3.07816\n", + "\n", + "\n", + "Iteration = 2 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 3 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:19 41% ETA 00:00:00 cycle 2096 of 5000\n", + "17:36:19 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:19 20% ETA 00:00:00 cycle 2041 of 10000\n", + "17:36:20 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00122602\n", + "Average order | 0.000181799\n", + "Average sign | 0.000187614\n", + "G_tau measure | 0.00154239\n", + "Total measure time | 0.00313782\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.137529\n", + " Move Insert Delta_up: 0.138608\n", + " Move Insert Delta_down: 0.136454\n", + "Move set Remove two operators: 0.135646\n", + " Move Remove Delta_up: 0.137095\n", + " Move Remove Delta_down: 0.134192\n", + "Move set Insert four operators: 0.0248063\n", + " Move Insert Delta_up_up: 0.029023\n", + " Move Insert Delta_up_down: 0.0202982\n", + " Move Insert Delta_down_up: 0.0212102\n", + " Move Insert Delta_down_down: 0.0286456\n", + "Move set Remove four operators: 0.0258106\n", + " Move Remove Delta_up_up: 0.0300133\n", + " Move Remove Delta_up_down: 0.0207927\n", + " Move Remove Delta_down_up: 0.0216027\n", + " Move Remove Delta_down_down: 0.0308751\n", + "Move Shift one operator: 0.82967\n", + "[Rank 0] Warmup lasted: 0.242133 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.496411 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.0726\n", + "Auto-correlation time: 2.95457\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 4 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:20 40% ETA 00:00:00 cycle 2012 of 5000\n", + "17:36:20 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:20 20% ETA 00:00:00 cycle 1999 of 10000\n", + "17:36:20 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00126701\n", + "Average order | 0.000178569\n", + "Average sign | 0.000182007\n", + "G_tau measure | 0.00399153\n", + "Total measure time | 0.00561911\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.136691\n", + " Move Insert Delta_up: 0.136118\n", + " Move Insert Delta_down: 0.137262\n", + "Move set Remove two operators: 0.135948\n", + " Move Remove Delta_up: 0.135115\n", + " Move Remove Delta_down: 0.136781\n", + "Move set Insert four operators: 0.024431\n", + " Move Insert Delta_up_up: 0.0279642\n", + " Move Insert Delta_up_down: 0.0212578\n", + " Move Insert Delta_down_up: 0.021372\n", + " Move Insert Delta_down_down: 0.0270992\n", + "Move set Remove four operators: 0.0251374\n", + " Move Remove Delta_up_up: 0.0298838\n", + " Move Remove Delta_up_down: 0.0205143\n", + " Move Remove Delta_down_up: 0.0209849\n", + " Move Remove Delta_down_down: 0.0291752\n", + "Move Shift one operator: 0.832854\n", + "[Rank 0] Warmup lasted: 0.247163 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.502235 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.1617\n", + "Auto-correlation time: 1.88829\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:21 40% ETA 00:00:00 cycle 2023 of 5000\n", + "17:36:21 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:21 20% ETA 00:00:00 cycle 2072 of 10000\n", + "17:36:21 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00126531\n", + "Average order | 0.00018556\n", + "Average sign | 0.000181974\n", + "G_tau measure | 0.00307852\n", + "Total measure time | 0.00471137\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.137877\n", + " Move Insert Delta_up: 0.13878\n", + " Move Insert Delta_down: 0.136976\n", + "Move set Remove two operators: 0.136859\n", + " Move Remove Delta_up: 0.137844\n", + " Move Remove Delta_down: 0.135872\n", + "Move set Insert four operators: 0.0240401\n", + " Move Insert Delta_up_up: 0.0279466\n", + " Move Insert Delta_up_down: 0.0193165\n", + " Move Insert Delta_down_up: 0.0205293\n", + " Move Inse\n", + "\n", + "Iteration = 5 / 10\n", + "rt Delta_down_down: 0.0283427\n", + "Move set Remove four operators: 0.0247222\n", + " Move Remove Delta_up_up: 0.0272073\n", + " Move Remove Delta_up_down: 0.0219802\n", + " Move Remove Delta_down_up: 0.0218302\n", + " Move Remove Delta_down_down: 0.0278651\n", + "Move Shift one operator: 0.830322\n", + "[Rank 0] Warmup lasted: 0.246525 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.498298 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.1484\n", + "Auto-correlation time: 1.59076\n", + "\n", + "\n", + "Iteration = 6 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:21 40% ETA 00:00:00 cycle 2026 of 5000\n", + "17:36:21 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:22 20% ETA 00:00:00 cycle 2064 of 10000\n", + "17:36:22 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00124302\n", + "Average order | 0.000183717\n", + "Average sign | 0.000188498\n", + "G_tau measure | 0.00344506\n", + "Total measure time | 0.0050603 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.136129\n", + " Move Insert Delta_up: 0.135304\n", + " Move Insert Delta_down: 0.136951\n", + "Move set Remove two operators: 0.137127\n", + " Move Remove Delta_up: 0.1367\n", + " Move Remove Delta_down: 0.137554\n", + "Move set Insert four operators: 0.024601\n", + " Move Insert Delta_up_up: 0.0281926\n", + " Move Insert Delta_up_down: 0.0207839\n", + " Move Insert Delta_down_up: 0.0216895\n", + " Move Insert Delta_down_down: 0.027688\n", + "Move set Remove four operators: 0.0242563\n", + " Move Remove Delta_up_up: 0.0271906\n", + " Move Remove Delta_up_down: 0.0216098\n", + " Move Remove Delta_down_up: 0.0202847\n", + " Move Remove Delta_down_down: 0.0279555\n", + "Move Shift one operator: 0.830414\n", + "[Rank 0] Warmup lasted: 0.242121 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.490822 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.1626\n", + "Auto-correlation time: 2.51126\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 7 / 10Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:22 41% ETA 00:00:00 cycle 2094 of 5000\n", + "17:36:22 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:22 20% ETA 00:00:00 cycle 2046 of 10000\n", + "17:36:23 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00124401\n", + "Average order | 0.000175229\n", + "Average sign | 0.000179357\n", + "G_tau measure | 0.00229849\n", + "Total measure time | 0.00389709\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.136631\n", + " Move Insert Delta_up: 0.137952\n", + " Move Insert Delta_down: 0.135314\n", + "Move set Remove two operators: 0.138106\n", + " Move Remove Delta_up: 0.13836\n", + " Move Remove Delta_down: 0.137851\n", + "Move set Insert four operators: 0.0257115\n", + " Move Insert Delta_up_up: 0.029352\n", + " Move Insert Delta_up_down: 0.0212757\n", + " Move Insert Delta_down_up: 0.0216313\n", + " Move Inse\n", + "rt Delta_down_down: 0.030564\n", + "Move set Remove four operators: 0.0249482\n", + " Move Remove Delta_up_up: 0.0290644\n", + " Move Remove Delta_up_down: 0.0214022\n", + " Move Remove Delta_down_up: 0.0207806\n", + " Move Remove Delta_down_down: 0.0285886\n", + "Move Shift one operator: 0.830217\n", + "[Rank 0] Warmup lasted: 0.237919 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.48821 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.1595\n", + "Auto-correlation time: 3.00369\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:23 41% ETA 00:00:00 cycle 2071 of 5000\n", + "17:36:23 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:23 20% ETA 00:00:00 cycle 2093 of 10000\n", + "17:36:23 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00121949\n", + "Average order | 0.000174776\n", + "Average sign | 0.000177332\n", + "G_tau measure | 0.00220899\n", + "Total measure time | 0.00378058\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.138683\n", + " Move Insert Delta_up: 0.138785\n", + " Move Insert Delta_down: 0.138581\n", + "Move set Remove two operators: 0.135491\n", + " Move Remove Delta_up: 0.134835\n", + " Move Remove Delta_down: 0.136151\n", + "Move set Insert four operators: 0.0238837\n", + " Move Insert Delta_up_up: 0.0282195\n", + " Move Insert Delta_up_down: 0.0187678\n", + " Move Insert Delta_down_up: 0.0213364\n", + " Move Insert Delta_down_down: 0.0271656\n", + "Move set Remove four operators: 0.0256944\n", + " Move Remove Delta_up_up: 0.0296998\n", + " Move Remove Delta_up_down: 0.0223175\n", + " Move Remove Delta_down_up: 0.0230494\n", + " Move Remove Delta_down_down: 0.0277512\n", + "Move Shift one operator: 0.826742\n", + "[Rank 0] Warmup lasted: 0.240293 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.484146 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.0322\n", + "Auto-correlation time: 3.10339\n", + "\n", + "\n", + "Iteration = 8 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:24 41% ETA 00:00:00 cycle 2055 of 5000\n", + "17:36:24 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:24 20% ETA 00:00:00 cycle 2008 of 10000\n", + "17:36:24 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00122868\n", + "Average order | 0.000178445\n", + "Average sign | 0.000179449\n", + "G_tau measure | 0.00223444\n", + "Total measure time | 0.00382102\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.138506\n", + " Move Insert Delta_up: 0.139623\n", + " Move Insert Delta_down: 0.137394\n", + "Move set Remove two operators: 0.138454\n", + " Move Remove Delta_up: 0.136949\n", + " Move Remove Delta_down: 0.139969\n", + "Move set Insert four operators: 0.0253769\n", + " Move Insert Delta_up_up: 0.0293179\n", + " Move Insert Delta_up_down: 0.0211004\n", + " Move Insert Delta_down_up: 0.0205091\n", + " Move In\n", + "\n", + "Iteration = 9 / 10\n", + "sert Delta_down_down: 0.0305689\n", + "Move set Remove four operators: 0.0253767\n", + " Move Remove Delta_up_up: 0.0309133\n", + " Move Remove Delta_up_down: 0.0217365\n", + " Move Remove Delta_down_up: 0.0212245\n", + " Move Remove Delta_down_down: 0.0276795\n", + "Move Shift one operator: 0.827338\n", + "[Rank 0] Warmup lasted: 0.241515 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.487717 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.0508\n", + "Auto-correlation time: 2.62123\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:24 41% ETA 00:00:00 cycle 2074 of 5000\n", + "17:36:24 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:25 20% ETA 00:00:00 cycle 2058 of 10000\n", + "17:36:25 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00120751\n", + "Average order | 0.000174187\n", + "Average sign | 0.000178604\n", + "G_tau measure | 0.00142541\n", + "Total measure time | 0.00298571\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.137111\n", + " Move Insert Delta_up: 0.138103\n", + " Move Insert Delta_down: 0.136124\n", + "Move set Remove two operators: 0.136868\n", + " Move Remove Delta_up: 0.136946\n", + " Move Remove Delta_down: 0.136791\n", + "Move set Insert four operators: 0.0244609\n", + " Move Insert Delta_up_up: 0.0282271\n", + " Move Insert Delta_up_down: 0.0201996\n", + " Move Insert Delta_down_up: 0.0211721\n", + " Move Insert Delta_down_down: 0.0281887\n", + "Move set Remove four operators: 0.0246914\n", + " Move Remove Delta_up_up: 0.0285174\n", + " Move Remove Delta_up_down: 0.0222497\n", + " Move Remove Delta_down_up: 0.0207763\n", + " Move Remove Delta_down_down: 0.0272237\n", + "Move Shift one operator: 0.831942\n", + "[Rank 0] Warmup lasted: 0.239978 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.48282 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.1865\n", + "Auto-correlation time: 1.6264\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "U = 2.0\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:25 41% ETA 00:00:00 cycle 2077 of 5000\n", + "17:36:25 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:25 20% ETA 00:00:00 cycle 2041 of 10000\n", + "17:36:26 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00127013\n", + "Average order | 0.000206261\n", + "Average sign | 0.000180197\n", + "G_tau measure | 0.00201599\n", + "Total measure time | 0.00367258\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.136865\n", + " Move Insert Delta_up: 0.136144\n", + " Move Insert Delta_down: 0.137585\n", + "Move set Remove two operators: 0.136487\n", + " Move Remove Delta_up: 0.136087\n", + " Move Remove Delta_down: 0.136888\n", + "Move set Insert four operators: 0.0247125\n", + " Move Insert Delta_up_up: 0.0291157\n", + " Move Insert Delta_up_down: 0.0200999\n", + " Move Insert Delta_down_up: 0.0207065\n", + " Move Insert Delta_down_down: 0.0288711\n", + "Move set Remove four operators: 0.0250504\n", + " Move Remove Delta_up_up: 0.0279124\n", + " Move Remove Delta_up_down: 0.0211228\n", + " Move Remove Delta_down_up: 0.0220934\n", + " Move Remove Delta_down_down: 0.0291107\n", + "Move Shift one operator: 0.830829\n", + "[Rank 0] Warmup lasted: 0.24054 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.4861 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 8.1232\n", + "Auto-correlation time: 2.62633\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:26 45% ETA 00:00:00 cycle 2284 of 5000\n", + "17:36:26 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:26 22% ETA 00:00:00 cycle 2255 of 10000\n", + "17:36:26 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00123761\n", + "Average order | 0.000177008\n", + "Average sign | 0.000180409\n", + "G_tau measure | 0.00124214\n", + "Total measure time | 0.00283716\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.125473\n", + " Move Insert Delta_up: 0.125155\n", + " Move Insert Delta_down: 0.12579\n", + "Move set Remove two operators: 0.125671\n", + " Move Remove Delta_up: 0.125471\n", + " Move Remove Delta_down: 0.125871\n", + "Move set Insert four operators: 0.0246864\n", + " Move Insert Delta_up_up: 0.0268028\n", + " Move Insert Delta_up_down: 0.0216412\n", + " Move Insert Delta_down_up: 0.0227946\n", + " Move Insert Delta_down_down: 0.0274721\n", + "Move set Remove four operators: 0.0247316\n", + " Move Remove Delta_up_up: 0.0269343\n", + " Move Remove Delta_up_down: 0.0224071\n", + " Move Remove Delta_down_up: 0.0220799\n", + " Move Remove Delta_down_down: 0.0275138\n", + "Move Shift one operator: 0.698464\n", + "[Rank 0] Warmup lasted: 0.218575 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.443867 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.6646\n", + "Auto-correlation time: 3.28392\n", + "\n", + "\n", + "Iteration = 2 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 3 / 10Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:27 46% ETA 00:00:00 cycle 2314 of 5000\n", + "17:36:27 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:27 23% ETA 00:00:00 cycle 2312 of 10000\n", + "17:36:27 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00120478\n", + "Average order | 0.000177364\n", + "Average sign | 0.000178484\n", + "G_tau measure | 0.00126514\n", + "Total measure time | 0.00282577\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.130538\n", + " Move Insert Delta_up: 0.131357\n", + " Move Insert Delta_down: 0.129719\n", + "Move set Remove two operators: 0.130263\n", + " Move Remove Delta_up: 0.131032\n", + " Move Remove Delta_down: 0.129493\n", + "Move set Insert four operators: 0.0270564\n", + " Move Insert Delta_up_up: 0.0299128\n", + " Move Insert Delta_up_down: 0.0238143\n", + " Move Insert Delta_down_up: 0.0254887\n", + " Move Insert Delta_down_down: 0.0289647\n", + "Move set Remove four operators: 0.0271909\n", + " Move Remove Delta_up_up: 0.0297328\n", + " Move Remove Delta_up_down: 0.0259022\n", + " Move Remove Delta_down_up: 0.0248111\n", + " Move Remove Delta_down_down: 0.028303\n", + "Move Shift one operator: 0.678488\n", + "[Rank 0] Warmup lasted: 0.215686 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.437622 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.3414\n", + "Auto-correlation time: 2.59554\n", + "\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 4 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:27 46% ETA 00:00:00 cycle 2314 of 5000\n", + "17:36:27 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:27 22% ETA 00:00:00 cycle 2256 of 10000\n", + "17:36:28 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00120432\n", + "Average order | 0.000177742\n", + "Average sign | 0.000178896\n", + "G_tau measure | 0.00268057\n", + "Total measure time | 0.00424153\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.127771\n", + " Move Insert Delta_up: 0.128914\n", + " Move Insert Delta_down: 0.12663\n", + "Move set Remove two operators: 0.128221\n", + " Move Remove Delta_up: 0.129491\n", + " Move Remove Delta_down: 0.12695\n", + "Move set Insert four operators: 0.0256648\n", + " Move Insert Delta_up_up: 0.0283026\n", + " Move Insert Delta_up_down: 0.0233949\n", + " Move Insert Delta_down_up: 0.0232993\n", + " Move Insert Delta_down_down: 0.0276514\n", + "Move set Remove four operators: 0.0258822\n", + " Move Remove Delta_up_up: 0.0277958\n", + " Move Remove Delta_up_down: 0.0234759\n", + " Move Remove Delta_down_up: 0.0250552\n", + " Move Remove Delta_down_down: 0.0272286\n", + "Move Shift one operator: 0.684272\n", + "[Rank 0] Warmup lasted: 0.217683 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.44214 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.4618\n", + "Auto-correlation time: 2.75014\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 5 / 10Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:28 46% ETA 00:00:00 cycle 2304 of 5000\n", + "17:36:28 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:28 22% ETA 00:00:00 cycle 2294 of 10000\n", + "17:36:28 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00122231\n", + "Average order | 0.000187478\n", + "Average sign | 0.000183211\n", + "G_tau measure | 0.00135477\n", + "Total measure time | 0.00294777\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.131699\n", + " Move Insert Delta_up: 0.130628\n", + " Move Insert Delta_down: 0.13277\n", + "Move set Remove two operators: 0.130431\n", + " Move Remove Delta_up: 0.129478\n", + " Move Remove Delta_down: 0.131386\n", + "Move set Insert four operators: 0.0271569\n", + " Move Insert Delta_up_up: 0.0281887\n", + " Move Insert Delta_up_down: 0.0258805\n", + " Move Insert Delta_down_up: 0.024888\n", + " Move Inse\n", + "rt Delta_down_down: 0.0296566\n", + "Move set Remove four operators: 0.0275519\n", + " Move Remove Delta_up_up: 0.0287468\n", + " Move Remove Delta_up_down: 0.0255961\n", + " Move Remove Delta_down_up: 0.0253231\n", + " Move Remove Delta_down_down: 0.0305499\n", + "Move Shift one operator: 0.672073\n", + "[Rank 0] Warmup lasted: 0.215632 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.436025 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.2379\n", + "Auto-correlation time: 2.34046\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 6 / 10Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:29 46% ETA 00:00:00 cycle 2308 of 5000\n", + "17:36:29 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:29 22% ETA 00:00:00 cycle 2293 of 10000\n", + "17:36:29 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00123141\n", + "Average order | 0.000177542\n", + "Average sign | 0.000178821\n", + "G_tau measure | 0.00140636\n", + "Total measure time | 0.00299413\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.131567\n", + " Move Insert Delta_up: 0.133715\n", + " Move Insert Delta_down: 0.129426\n", + "Move set Remove two operators: 0.130663\n", + " Move Remove Delta_up: 0.13311\n", + " Move Remove Delta_down: 0.128214\n", + "Move set Insert four operators: 0.0276007\n", + " Move Insert Delta_up_up: 0.0306565\n", + " Move Insert Delta_up_down: 0.0254948\n", + " Move Insert Delta_down_up: 0.025725\n", + " Move Inse\n", + "rt Delta_down_down: 0.0285063\n", + "Move set Remove four operators: 0.0284674\n", + " Move Remove Delta_up_up: 0.0316303\n", + " Move Remove Delta_up_down: 0.0252882\n", + " Move Remove Delta_down_up: 0.0267753\n", + " Move Remove Delta_down_down: 0.0301962\n", + "Move Shift one operator: 0.677651\n", + "[Rank 0] Warmup lasted: 0.216824 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.439615 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.2701\n", + "Auto-correlation time: 2.43454\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 7 / 10Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:29 46% ETA 00:00:00 cycle 2299 of 5000\n", + "17:36:29 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:29 22% ETA 00:00:00 cycle 2264 of 10000\n", + "17:36:30 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00118916\n", + "Average order | 0.000176917\n", + "Average sign | 0.000176414\n", + "G_tau measure | 0.00128476\n", + "Total measure time | 0.00282725\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.127818\n", + " Move Insert Delta_up: 0.127863\n", + " Move Insert Delta_down: 0.127773\n", + "Move set Remove two operators: 0.128597\n", + " Move Remove Delta_up: 0.128824\n", + " Move Remove Delta_down: 0.12837\n", + "Move set Insert four operators: 0.0270821\n", + " Move Insert Delta_up_up: 0.0302706\n", + " Move Insert Delta_up_down: 0.0244285\n", + " Move Insert Delta_down_up: 0.0250341\n", + " Move Insert Delta_down_down: 0.0285486\n", + "Move set Remove four operators: 0.0268681\n", + " Move Remove Delta_up_up: 0.029369\n", + " Move Remove Delta_up_down: 0.0251997\n", + " Move Remove Delta_down_up: 0.0249889\n", + " Move Remove Delta_down_down: 0.0279296\n", + "Move Shift one operator: 0.680676\n", + "[Rank 0] Warmup lasted: 0.218647 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.440596 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.4495\n", + "Auto-correlation time: 4.93486\n", + "\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 8 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:30 45% ETA 00:00:00 cycle 2273 of 5000\n", + "17:36:30 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:30 22% ETA 00:00:00 cycle 2245 of 10000\n", + "17:36:30 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00127285\n", + "Average order | 0.000182139\n", + "Average sign | 0.000183024\n", + "G_tau measure | 0.00171741\n", + "Total measure time | 0.00335542\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.128634\n", + " Move Insert Delta_up: 0.128384\n", + " Move Insert Delta_down: 0.128883\n", + "Move set Remove two operators: 0.129022\n", + " Move Remove Delta_up: 0.12886\n", + " Move Remove Delta_down: 0.129184\n", + "Move set Insert four operators: 0.0264743\n", + " Move Insert Delta_up_up: 0.0287922\n", + " Move Insert Delta_up_down: 0.0235365\n", + " Move Insert Delta_down_up: 0.0237667\n", + " Move Insert Delta_down_down: 0.0297869\n", + "Move set Remove four operators: 0.026674\n", + " Move Remove Delta_up_up: 0.0276172\n", + " Move Remove Delta_up_down: 0.0253356\n", + " Move Remove Delta_down_up: 0.0249358\n", + " Move Remove Delta_down_down: 0.0288193\n", + "Move Shift one operator: 0.681544\n", + "[Rank 0] Warmup lasted: 0.218576 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.440496 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.4043\n", + "Auto-correlation time: 2.96938\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:31 45% ETA 00:00:00 cycle 2292 of 5000\n", + "17:36:31 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:31 22% ETA 00:00:00 cycle 2296 of 10000\n", + "17:36:31 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00121937\n", + "Average order | 0.000173054\n", + "Average sign | 0.000177333\n", + "G_tau measure | 0.00127057\n", + "Total measure time | 0.00284032\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.132223\n", + " Move Insert Delta_up: 0.132401\n", + " Move Insert Delta_down: 0.132044\n", + "Move set Remove two operators: 0.129861\n", + " Move Remove Delta_up: 0.12952\n", + " Move Remove Delta_down: 0.130201\n", + "Move set Insert four operators: 0.0269862\n", + " Move Insert Delta_up_up: 0.0305089\n", + " Move Insert Delta_up_down: 0.0243726\n", + " Move Insert Delta_down_up: 0.0237724\n", + " Move Ins\n", + "\n", + "Iteration = 9 / 10\n", + "ert Delta_down_down: 0.0292517\n", + "Move set Remove four operators: 0.0282147\n", + " Move Remove Delta_up_up: 0.0309872\n", + " Move Remove Delta_up_down: 0.0276375\n", + " Move Remove Delta_down_up: 0.0263264\n", + " Move Remove Delta_down_down: 0.0279213\n", + "Move Shift one operator: 0.677678\n", + "[Rank 0] Warmup lasted: 0.216506 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.437101 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.242\n", + "Auto-correlation time: 2.74536\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:31 46% ETA 00:00:00 cycle 2312 of 5000\n", + "17:36:31 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:31 23% ETA 00:00:00 cycle 2325 of 10000\n", + "17:36:32 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00127472\n", + "Average order | 0.000172614\n", + "Average sign | 0.000177763\n", + "G_tau measure | 0.00117997\n", + "Total measure time | 0.00280507\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.130937\n", + " Move Insert Delta_up: 0.132246\n", + " Move Insert Delta_down: 0.12963\n", + "Move set Remove two operators: 0.131236\n", + " Move Remove Delta_up: 0.132046\n", + " Move Remove Delta_down: 0.130424\n", + "Move set Insert four operators: 0.0276569\n", + " Move Insert Delta_up_up: 0.0298347\n", + " Move Insert Delta_up_down: 0.0258974\n", + " Move Insert Delta_down_up: 0.0245561\n", + " Move Insert Delta_down_down: 0.0303272\n", + "Move set Remove four operators: 0.0277909\n", + " Move Remove Delta_up_up: 0.0306308\n", + " Move Remove Delta_up_down: 0.0249253\n", + " Move Remove Delta_down_up: 0.0255935\n", + " Move Remove Delta_down_down: 0.0300287\n", + "Move Shift one operator: 0.675164\n", + "[Rank 0] Warmup lasted: 0.215985 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.432564 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.2563\n", + "Auto-correlation time: 4.01083\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1*c_dag('down',0)*c('down',0) + -1*c_dag('up',0)*c('up',0) + 2*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "U =Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:32 46% ETA 00:00:00 cycle 2300 of 5000\n", + "17:36:32 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:32 23% ETA 00:00:00 cycle 2318 of 10000\n", + "17:36:33 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00120759\n", + "Average order | 0.000175362\n", + "Average sign | 0.000180967\n", + "G_tau measure | 0.00117891\n", + "Total measure time | 0.00274283\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.131003\n", + " Move Insert Delta_up: 0.131509\n", + " Move Insert Delta_down: 0.1305\n", + "Move set Remove two operators: 0.130783\n", + " Move Remove Delta_up: 0.130729\n", + " Move Remove Delta_down: 0.130837\n", + "Move set Insert four operators: 0.0263867\n", + " Move Insert Delta_up_up: 0.0290737\n", + " Move Insert Delta_up_down: 0.0231511\n", + " Move Insert Delta_down_up: 0.0250389\n", + " Move Inse 3.0\n", + "rt Delta_down_down: 0.0282522\n", + "Move set Remove four operators: 0.0267621\n", + " Move Remove Delta_up_up: 0.0297348\n", + " Move Remove Delta_up_down: 0.0242209\n", + " Move Remove Delta_down_up: 0.0247709\n", + " Move Remove Delta_down_down: 0.0283461\n", + "Move Shift one operator: 0.67701\n", + "[Rank 0] Warmup lasted: 0.215519 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.43658 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.3268\n", + "Auto-correlation time: 0.794236\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 2 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:33 49% ETA 00:00:00 cycle 2498 of 5000\n", + "17:36:33 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:33 24% ETA 00:00:00 cycle 2484 of 10000\n", + "17:36:33 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00119638\n", + "Average order | 0.000171907\n", + "Average sign | 0.000178944\n", + "G_tau measure | 0.00109991\n", + "Total measure time | 0.00264714\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.112247\n", + " Move Insert Delta_up: 0.113798\n", + " Move Insert Delta_down: 0.110696\n", + "Move set Remove two operators: 0.111523\n", + " Move Remove Delta_up: 0.11241\n", + " Move Remove Delta_down: 0.110636\n", + "Move set Insert four operators: 0.0226176\n", + " Move Insert Delta_up_up: 0.0237627\n", + " Move Insert Delta_up_down: 0.0205814\n", + " Move Insert Delta_down_up: 0.0218449\n", + " Move Insert Delta_down_down: 0.0242896\n", + "Move set Remove four operators: 0.0232029\n", + " Move Remove Delta_up_up: 0.0245147\n", + " Move Remove Delta_up_down: 0.022101\n", + " Move Remove Delta_down_up: 0.0226071\n", + " Move Remove Delta_down_down: 0.0235938\n", + "Move Shift one operator: 0.568929\n", + "[Rank 0] Warmup lasted: 0.199267 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.399275 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 7.0545\n", + "Auto-correlation time: 2.35299\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 3 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:33 53% ETA 00:00:00 cycle 2696 of 5000\n", + "17:36:33 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:33 26% ETA 00:00:00 cycle 2670 of 10000\n", + "17:36:34 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00120833\n", + "Average order | 0.000195012\n", + "Average sign | 0.000184797\n", + "G_tau measure | 0.00111078\n", + "Total measure time | 0.00269892\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.119575\n", + " Move Insert Delta_up: 0.118774\n", + " Move Insert Delta_down: 0.120375\n", + "Move set Remove two operators: 0.118525\n", + " Move Remove Delta_up: 0.117625\n", + " Move Remove Delta_down: 0.119424\n", + "Move set Insert four operators: 0.0260688\n", + " Move Insert Delta_up_up: 0.0249124\n", + " Move Insert Delta_up_down: 0.0259468\n", + " Move Insert Delta_down_up: 0.028224\n", + " Move Insert Delta_down_down: 0.0251899\n", + "Move set Remove four operators: 0.0267536\n", + " Move Remove Delta_up_up: 0.026036\n", + " Move Remove Delta_up_down: 0.0281528\n", + " Move Remove Delta_down_up: 0.0270466\n", + " Move Remove Delta_down_down: 0.0257593\n", + "Move Shift one operator: 0.509178\n", + "[Rank 0] Warmup lasted: 0.185284 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.376047 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.1244\n", + "Auto-correlation time: 3.95588\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:34 53% ETA 00:00:00 cycle 2684 of 5000\n", + "17:36:34 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:34 26% ETA 00:00:00 cycle 2678 of 10000\n", + "17:36:34 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00121023\n", + "Average order | 0.000178375\n", + "Average sign | 0.000180273\n", + "G_tau measure | 0.00104384\n", + "Total measure time | 0.00261271\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.116787\n", + " Move Insert Delta_up: 0.116187\n", + " Move Insert Delta_down: 0.117384\n", + "Move set Remove two operators: 0.116633\n", + " Move Remove Delta_up: 0.116475\n", + " Move Remove Delta_down: 0.11679\n", + "Move set Insert four operators: 0.0259455\n", + " Move Insert Delta_up_up: 0.026201\n", + " Move Insert Delta_up_down: 0.0258814\n", + " Move Insert Delta_down_up: 0.0264806\n", + " Move Insert Delta_down_down: 0.0252178\n", + "Move set Remove four operators: 0.0264393\n", + " Move Remove Delta_up_up: 0.0269715\n", + " Move Remove Delta_up_down: 0.0248916\n", + " Move Remove Delta_down_up: 0.026979\n", + " Move Remove Delta_down_down: 0.0269331\n", + "Move Shift one operator: 0.520079\n", + "[Rank 0] Warmup lasted: 0.18574 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.377745 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.2308\n", + "Auto-correlation time: 5.61951\n", + "\n", + "\n", + "Iteration = 4 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 5 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:34 53% ETA 00:00:00 cycle 2683 of 5000\n", + "17:36:35 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:35 26% ETA 00:00:00 cycle 2661 of 10000\n", + "17:36:35 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00118682\n", + "Average order | 0.000179021\n", + "Average sign | 0.000177377\n", + "G_tau measure | 0.000954406\n", + "Total measure time | 0.00249762\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.119259\n", + " Move Insert Delta_up: 0.118998\n", + " Move Insert Delta_down: 0.11952\n", + "Move set Remove two operators: 0.11786\n", + " Move Remove Delta_up: 0.11722\n", + " Move Remove Delta_down: 0.118509\n", + "Move set Insert four operators: 0.0262007\n", + " Move Insert Delta_up_up: 0.0247214\n", + " Move Insert Delta_up_down: 0.0280183\n", + " Move Insert Delta_down_up: 0.0277204\n", + " Move Insert Delta_down_down: 0.0243335\n", + "Move set Remove four operators: 0.0270037\n", + " Move Remove Delta_up_up: 0.0264473\n", + " Move Remove Delta_up_down: 0.0269378\n", + " Move Remove Delta_down_up: 0.0272082\n", + " Move Remove Delta_down_down: 0.027421\n", + "Move Shift one operator: 0.514142\n", + "[Rank 0] Warmup lasted: 0.185126 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.375112 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.1344\n", + "Auto-correlation time: 5.42356\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 6 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:35 53% ETA 00:00:00 cycle 2687 of 5000\n", + "17:36:35 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:35 26% ETA 00:00:00 cycle 2634 of 10000\n", + "17:36:35 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00118086\n", + "Average order | 0.000177055\n", + "Average sign | 0.000180533\n", + "G_tau measure | 0.00158726\n", + "Total measure time | 0.00312571\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.12076\n", + " Move Insert Delta_up: 0.121218\n", + " Move Insert Delta_down: 0.120303\n", + "Move set Remove two operators: 0.121201\n", + " Move Remove Delta_up: 0.121831\n", + " Move Remove Delta_down: 0.120573\n", + "Move set Insert four operators: 0.0267368\n", + " Move Insert Delta_up_up: 0.026411\n", + " Move Insert Delta_up_down: 0.0274809\n", + " Move Insert Delta_down_up: 0.0270616\n", + " Move Insert Delta_down_down: 0.0260013\n", + "Move set Remove four operators: 0.0265407\n", + " Move Remove Delta_up_up: 0.025342\n", + " Move Remove Delta_up_down: 0.0281685\n", + " Move Remove Delta_down_up: 0.0271009\n", + " Move Remove Delta_down_down: 0.0255323\n", + "Move Shift one operator: 0.515457\n", + "[Rank 0] Warmup lasted: 0.18502 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.378509 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.1383\n", + "Auto-correlation time: 3.12987\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 7 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:36 52% ETA 00:00:00 cycle 2643 of 5000\n", + "17:36:36 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:36 26% ETA 00:00:00 cycle 2664 of 10000\n", + "17:36:36 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0012663 \n", + "Average order | 0.000184783\n", + "Average sign | 0.000189254\n", + "G_tau measure | 0.00362876\n", + "Total measure time | 0.0052691 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.117368\n", + " Move Insert Delta_up: 0.117308\n", + " Move Insert Delta_down: 0.117427\n", + "Move set Remove two operators: 0.117241\n", + " Move Remove Delta_up: 0.116591\n", + " Move Remove Delta_down: 0.117894\n", + "Move set Insert four operators: 0.0261457\n", + " Move Insert Delta_up_up: 0.0261487\n", + " Move Insert Delta_up_down: 0.0253525\n", + " Move Insert Delta_down_up: 0.0266688\n", + " Move Insert Delta_down_down: 0.0264136\n", + "Move set Remove four operators: 0.0265045\n", + " Move Remove Delta_up_up: 0.0252665\n", + " Move Remove Delta_up_down: 0.0275678\n", + " Move Remove Delta_down_up: 0.0282901\n", + " Move Remove Delta_down_down: 0.024888\n", + "Move Shift one operator: 0.512693\n", + "[Rank 0] Warmup lasted: 0.187877 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.38964 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.1385\n", + "Auto-correlation time: 3.93431\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:36 54% ETA 00:00:00 cycle 2704 of 5000\n", + "17:36:36 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:36 25% ETA 00:00:00 cycle 2590 of 10000\n", + "17:36:37 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00119782\n", + "Average order | 0.000175158\n", + "Average sign | 0.000179523\n", + "G_tau measure | 0.00180878\n", + "Total measure time | 0.00336127\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.117365\n", + " Move Insert Delta_up: 0.117624\n", + " Move Insert Delta_down: 0.117106\n", + "Move set Remove two operators: 0.117283\n", + " Move Remove Delta_up: 0.118533\n", + " Move Remove Delta_down: 0.116037\n", + "Move set Insert four operators: 0.026697\n", + " Move Insert Delta_up_up: 0.0264597\n", + " Move Insert Delta_up_down: 0.0273612\n", + " Move Insert Delta_down_up: 0.0266847\n", + " Move Insert Delta_down_down: 0.0262799\n", + "Move set Remove four operators: 0.0270184\n", + " Move Remove Delta_up_up: 0.0264397\n", + " Move Remove Delta_up_down: 0.0271184\n", + " Move Remove Delta_down_up: 0.0268783\n", + " Move Remove Delta_down_down: 0.0276341\n", + "Move Shift one operator: 0.514539\n", + "[Rank 0] Warmup lasted: 0.185398 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.379588 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.1263\n", + "Auto-correlation time: 1.74038\n", + "\n", + "\n", + "Iteration = 8 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 9 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:37 53% ETA 00:00:00 cycle 2685 of 5000\n", + "17:36:37 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:37 25% ETA 00:00:00 cycle 2596 of 10000\n", + "17:36:37 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00116657\n", + "Average order | 0.000178551\n", + "Average sign | 0.000180033\n", + "G_tau measure | 0.00131102\n", + "Total measure time | 0.00283617\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.116924\n", + " Move Insert Delta_up: 0.117617\n", + " Move Insert Delta_down: 0.116232\n", + "Move set Remove two operators: 0.118121\n", + " Move Remove Delta_up: 0.119441\n", + " Move Remove Delta_down: 0.116798\n", + "Move set Insert four operators: 0.0269091\n", + " Move Insert Delta_up_up: 0.0271326\n", + " Move Insert Delta_up_down: 0.025719\n", + " Move Insert Delta_down_up: 0.029214\n", + " Move Insert Delta_down_down: 0.0255767\n", + "Move set Remove four operators: 0.0263473\n", + " Move Remove Delta_up_up: 0.0264778\n", + " Move Remove Delta_up_down: 0.0261575\n", + " Move Remove Delta_down_up: 0.0262325\n", + " Move Remove Delta_down_down: 0.0265242\n", + "Move Shift one operator: 0.520462\n", + "[Rank 0] Warmup lasted: 0.185252 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.383034 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.2418\n", + "Auto-correlation time: 2.82905\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:37 51% ETA 00:00:00 cycle 2560 of 5000\n", + "17:36:37 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:38 26% ETA 00:00:00 cycle 2609 of 10000\n", + "17:36:38 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00121845\n", + "Average order | 0.000179815\n", + "Average sign | 0.000202924\n", + "G_tau measure | 0.0021689 \n", + "Total measure time | 0.00377008\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.119297\n", + " Move Insert Delta_up: 0.120148\n", + " Move Insert Delta_down: 0.118445\n", + "Move set Remove two operators: 0.118649\n", + " Move Remove Delta_up: 0.119257\n", + " Move Remove Delta_down: 0.11804\n", + "Move set Insert four operators: 0.0254296\n", + " Move Insert Delta_up_up: 0.0260036\n", + " Move Insert Delta_up_down: 0.0235786\n", + " Move Insert Delta_down_up: 0.0265909\n", + " Move Insert Delta_down_down: 0.0255261\n", + "Move set Remove four operators: 0.0261909\n", + " Move Remove Delta_up_up: 0.0270227\n", + " Move Remove Delta_up_down: 0.0257851\n", + " Move Remove Delta_down_up: 0.0259396\n", + " Move Remove Delta_down_down: 0.0260241\n", + "Move Shift one operator: 0.522949\n", + "[Rank 0] Warmup lasted: 0.190266 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.388758 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.3131\n", + "Auto-correlation time: 3.33377\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-1.5*c_dag('down',0)*c('down',0) + -1.5*c_dag('up',0)*c('up',0) + 3*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "U = 4.0\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:38 51% ETA 00:00:00 cycle 2549 of 5000\n", + "17:36:38 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:38 23% ETA 00:00:00 cycle 2363 of 10000\n", + "17:36:38 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00129221\n", + "Average order | 0.000192149\n", + "Average sign | 0.000188825\n", + "G_tau measure | 0.00803591\n", + "Total measure time | 0.0097091 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.119166\n", + " Move Insert Delta_up: 0.117673\n", + " Move Insert Delta_down: 0.120666\n", + "Move set Remove two operators: 0.119688\n", + " Move Remove Delta_up: 0.11855\n", + " Move Remove Delta_down: 0.120824\n", + "Move set Insert four operators: 0.0250876\n", + " Move Insert Delta_up_up: 0.025384\n", + " Move Insert Delta_up_down: 0.024949\n", + " Move Insert Delta_down_up: 0.0252735\n", + " Move Insert Delta_down_down: 0.0247464\n", + "Move set Remove four operators: 0.0252292\n", + " Move Remove Delta_up_up: 0.0248425\n", + " Move Remove Delta_up_down: 0.0247179\n", + " Move Remove Delta_down_up: 0.0271435\n", + " Move Remove Delta_down_down: 0.0242195\n", + "Move Shift one operator: 0.507301\n", + "[Rank 0] Warmup lasted: 0.195011 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.40592 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.1319\n", + "Auto-correlation time: 1.78747\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 2 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:39 54% ETA 00:00:00 cycle 2734 of 5000\n", + "17:36:39 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:39 28% ETA 00:00:00 cycle 2815 of 10000\n", + "17:36:39 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00119336\n", + "Average order | 0.00017397\n", + "Average sign | 0.000173595\n", + "G_tau measure | 0.00108587\n", + "Total measure time | 0.00262679\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.10269\n", + " Move Insert Delta_up: 0.100883\n", + " Move Insert Delta_down: 0.104505\n", + "Move set Remove two operators: 0.102024\n", + " Move Remove Delta_up: 0.101145\n", + " Move Remove Delta_down: 0.102906\n", + "Move set Insert four operators: 0.0199348\n", + " Move Insert Delta_up_up: 0.0203415\n", + " Move Insert Delta_up_down: 0.0208978\n", + " Move Insert Delta_down_up: 0.020635\n", + " Move Insert Delta_down_down: 0.0178728\n", + "Move set Remove four operators: 0.0204535\n", + " Move Remove Delta_up_up: 0.0205254\n", + " Move Remove Delta_up_down: 0.0194903\n", + " Move Remove Delta_down_up: 0.022088\n", + " Move Remove Delta_down_down: 0.0197157\n", + "Move Shift one operator: 0.460326\n", + "[Rank 0] Warmup lasted: 0.180869 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.359313 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 6.2466\n", + "Auto-correlation time: 3.60183\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up .\n", + "\n", + "Iteration = 3 / 10..\n", + "17:36:39 66% ETA 00:00:00 cycle 3327 of 5000\n", + "17:36:39 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:39 32% ETA 00:00:00 cycle 3220 of 10000\n", + "17:36:40 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00117142\n", + "Average order | 0.000177189\n", + "Average sign | 0.000180591\n", + "G_tau measure | 0.00234315\n", + "Total measure time | 0.00387235\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.110834\n", + " Move Insert Delta_up: 0.111457\n", + " Move Insert Delta_down: 0.110211\n", + "Move set Remove two operators: 0.110152\n", + " Move Remove Delta_up: 0.110913\n", + " Move Remove Delta_down: 0.109392\n", + "Move set Insert four operators: 0.0245707\n", + " Move Insert Delta_up_up: 0.0213173\n", + " Move Insert Delta_up_down: 0.0271374\n", + " Move Insert Delta_down_up: 0.0288638\n", + " Move Insert Delta_down_down: 0.0209527\n", + "\n", + "Move set Remove four operators: 0.0252332\n", + " Move Remove Delta_up_up: 0.0219842\n", + " Move Remove Delta_up_down: 0.027412\n", + " Move Remove Delta_down_up: 0.02984\n", + " Move Remove Delta_down_down: 0.021649\n", + "Move Shift one operator: 0.347207\n", + "[Rank 0] Warmup lasted: 0.15062 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.312381 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 4.5515\n", + "Auto-correlation time: 4.66685\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:40 68% ETA 00:00:00 cycle 3418 of 5000\n", + "17:36:40 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:40 33% ETA 00:00:00 cycle 3354 of 10000\n", + "17:36:40 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00119055\n", + "Average order | 0.000173035\n", + "Average sign | 0.000182784\n", + "G_tau measure | 0.00180668\n", + "Total \n", + "\n", + "Iteration = 4 / 10\n", + "measure time | 0.00335305\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.109566\n", + " Move Insert Delta_up: 0.110312\n", + " Move Insert Delta_down: 0.108824\n", + "Move set Remove two operators: 0.109807\n", + " Move Remove Delta_up: 0.109229\n", + " Move Remove Delta_down: 0.110382\n", + "Move set Insert four operators: 0.0245849\n", + " Move Insert Delta_up_up: 0.0203437\n", + " Move Insert Delta_up_down: 0.0290226\n", + " Move Insert Delta_down_up: 0.0286879\n", + " Move Insert Delta_down_down: 0.0202673\n", + "Move set Remove four operators: 0.0245634\n", + " Move Remove Delta_up_up: 0.0216013\n", + " Move Remove Delta_up_down: 0.0291177\n", + " Move Remove Delta_down_up: 0.0286001\n", + " Move Remove Delta_down_down: 0.0188474\n", + "Move Shift one operator: 0.32021\n", + "[Rank 0] Warmup lasted: 0.145208 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.299348 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 4.2622\n", + "Auto-correlation time: 4.56478\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "\n", + "\n", + "Iteration = 5 / 10\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:40 67% ETA 00:00:00 cycle 3379 of 5000\n", + "17:36:40 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:40 34% ETA 00:00:00 cycle 3452 of 10000\n", + "17:36:41 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011805 \n", + "Average order | 0.000181326\n", + "Average sign | 0.000179286\n", + "G_tau measure | 0.0028098 \n", + "Total measure time | 0.00435092\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.10985\n", + " Move Insert Delta_up: 0.110689\n", + " Move Insert Delta_down: 0.109008\n", + "Move set Remove two operators: 0.108916\n", + " Move Remove Delta_up: 0.110303\n", + " Move Remove Delta_down: 0.107539\n", + "Move set Insert four operators: 0.0246851\n", + " Move Insert Delta_up_up: 0.0198646\n", + " Move Insert Delta_up_down: 0.0291511\n", + " Move Insert Delta_down_up: 0.0290647\n", + " Move Insert Delta_down_down: 0.0206731\n", + "Move set Remove four operators: 0.0254268\n", + " Move Remove Delta_up_up: 0.0218722\n", + " Move Remove Delta_up_down: 0.0289571\n", + " Move Remove Delta_down_up: 0.0286299\n", + " Move Remove Delta_down_down: 0.0222124\n", + "Move Shift one operator: 0.311403\n", + "[Rank 0] Warmup lasted: 0.147042 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.294083 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 4.1267\n", + "Auto-correlation time: 2.44771\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 6 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:41 68% ETA 00:00:00 cycle 3416 of 5000\n", + "17:36:41 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:41 34% ETA 00:00:00 cycle 3399 of 10000\n", + "17:36:41 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00117656\n", + "Average order | 0.00018061\n", + "Average sign | 0.000181128\n", + "G_tau measure | 0.00213807\n", + "Total measure time | 0.00367637\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.108138\n", + " Move Insert Delta_up: 0.108557\n", + " Move Insert Delta_down: 0.107721\n", + "Move set Remove two operators: 0.107349\n", + " Move Remove Delta_up: 0.10754\n", + " Move Remove Delta_down: 0.107159\n", + "Move set Insert four operators: 0.0241131\n", + " Move Insert Delta_up_up: 0.0196703\n", + " Move Insert Delta_up_down: 0.0286151\n", + " Move Insert Delta_down_up: 0.0286033\n", + " Move Insert Delta_down_down: 0.0195795\n", + "Move set Remove four operators: 0.0246752\n", + " Move Remove Delta_up_up: 0.0211784\n", + " Move Remove Delta_up_down: 0.0280695\n", + " Move Remove Delta_down_up: 0.0287669\n", + " Move Remove Delta_down_down: 0.0205929\n", + "Move Shift one operator: 0.304464\n", + "[Rank 0] Warmup lasted: 0.147427 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.29027 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 4.0936\n", + "Auto-correlation time: 5.29194\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:41 70% ETA 00:00:00 cycle 3511 of 5000\n", + "17:36:41 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:41 34% ETA 00:00:00 cycle 3491 of 10000\n", + "17:36:41 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114259\n", + "Average order | 0.000175433\n", + "Average sign | 0.000179077\n", + "G_tau measure | 0.00242444\n", + "Total measure time | 0.00392154\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.10962\n", + " Move Insert Delta_up: 0.109395\n", + " Move Insert Delta_down: 0.109845\n", + "Move set Remove two operators: 0.109451\n", + " Move Remove Delta_up: 0.109748\n", + " Move Remove Delta_down: 0.109154\n", + "Move set Insert four operators: 0.0249161\n", + " Move Insert Delta_up_up: 0.0216244\n", + " Move Insert Delta_up_down: 0.0291868\n", + " Move Insert Delta_down_up: 0.0285771\n", + " Move Ins\n", + "\n", + "Iteration = 7 / 10\n", + "ert Delta_down_down: 0.0203135\n", + "Move set Remove four operators: 0.0252576\n", + " Move Remove Delta_up_up: 0.0204928\n", + " Move Remove Delta_up_down: 0.0309103\n", + " Move Remove Delta_down_up: 0.0297791\n", + " Move Remove Delta_down_down: 0.0197803\n", + "Move Shift one operator: 0.302655\n", + "[Rank 0] Warmup lasted: 0.142606 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.287607 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 4.059\n", + "Auto-correlation time: 2.68458\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 8 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:42 69% ETA 00:00:00 cycle 3489 of 5000\n", + "17:36:42 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:42 33% ETA 00:00:00 cycle 3391 of 10000\n", + "17:36:42 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00117954\n", + "Average order | 0.00018076\n", + "Average sign | 0.000176842\n", + "G_tau measure | 0.00274281\n", + "Total measure time | 0.00427996\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.109167\n", + " Move Insert Delta_up: 0.108878\n", + " Move Insert Delta_down: 0.109457\n", + "Move set Remove two operators: 0.108529\n", + " Move Remove Delta_up: 0.108384\n", + " Move Remove Delta_down: 0.108673\n", + "Move set Insert four operators: 0.0251343\n", + " Move Insert Delta_up_up: 0.0216667\n", + " Move Insert Delta_up_down: 0.0298085\n", + " Move Insert Delta_down_up: 0.0295327\n", + " Move Insert Delta_down_down: 0.0194249\n", + "Move set Remove four operators: 0.0255293\n", + " Move Remove Delta_up_up: 0.0224029\n", + " Move Remove Delta_up_down: 0.0305386\n", + " Move Remove Delta_down_up: 0.0293344\n", + " Move Remove Delta_down_down: 0.0197125\n", + "Move Shift one operator: 0.300808\n", + "[Rank 0] Warmup lasted: 0.144613 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.288052 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 3.9724\n", + "Auto-correlation time: 2.14536\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:42 71% ETA 00:00:00 cycle 3551 of 5000\n", + "17:36:42 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:42 35% ETA 00:00:00 cycle 3575 of 10000\n", + "17:36:42 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011946 \n", + "Average order | 0.000173127\n", + "Average sign | 0.000178696\n", + "G_tau measure | 0.000725342\n", + "Total measure time | 0.00227176\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.10916\n", + " Move Insert Delta_up: 0.109252\n", + " Move Insert Delta_down: 0.109067\n", + "Move set Remove two operators: 0.110798\n", + " Move Remove Delta_up: 0.111607\n", + " Move Remove Delta_down: 0.109984\n", + "Move set Insert four operators: 0.0252051\n", + " Move Insert Delta_up_up: 0.0218661\n", + " Move Insert Delta_up_down: 0.0287308\n", + " Move Insert Delta_down_up: 0.0302921\n", + " Move In\n", + "\n", + "Iteration = 9 / 10\n", + "sert Delta_down_down: 0.0199362\n", + "Move set Remove four operators: 0.0245902\n", + " Move Remove Delta_up_up: 0.0197972\n", + " Move Remove Delta_up_down: 0.0288953\n", + " Move Remove Delta_down_up: 0.0308568\n", + " Move Remove Delta_down_down: 0.0187846\n", + "Move Shift one operator: 0.298548\n", + "[Rank 0] Warmup lasted: 0.140103 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.282513 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 3.9563\n", + "Auto-correlation time: 5.49552\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:42 68% ETA 00:00:00 cycle 3442 of 5000\n", + "17:36:43 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:43 34% ETA 00:00:00 cycle 3488 of 10000\n", + "17:36:43 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00116768\n", + "Average order | 0.000183687\n", + "Average sign | 0.000177624\n", + "G_tau measure | 0.00147453\n", + "Total measure time | 0.00300352\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.107424\n", + " Move Insert Delta_up: 0.105809\n", + " Move Insert Delta_down: 0.109051\n", + "Move set Remove two operators: 0.108968\n", + " Move Remove Delta_up: 0.108578\n", + " Move Remove Delta_down: 0.109357\n", + "Move set Insert four operators: 0.024976\n", + " Move Insert Delta_up_up: 0.0210199\n", + " Move Insert Delta_up_down: 0.0283776\n", + " Move Insert Delta_down_up: 0.0301075\n", + " Move Insert Delta_down_down: 0.0203983\n", + "Move set Remove four operators: 0.0244054\n", + " Move Remove Delta_up_up: 0.0201315\n", + " Move Remove Delta_up_down: 0.0293393\n", + " Move Remove Delta_down_up: 0.0275322\n", + " Move Remove Delta_down_down: 0.0205381\n", + "Move Shift one operator: 0.295469\n", + "[Rank 0] Warmup lasted: 0.14504 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.283466 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 3.9659\n", + "Auto-correlation time: 5.57761\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2*c_dag('down',0)*c('down',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "U = 5.0\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:43 71% ETA 00:00:00 cycle 3593 of 5000\n", + "17:36:43 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:43 35% ETA 00:00:00 cycle 3578 of 10000\n", + "17:36:43 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00119755\n", + "Average order | 0.000171298\n", + "Average sign | 0.000178299\n", + "G_tau measure | 0.000705123\n", + "Total measure time | 0.00225227\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.109572\n", + " Move Insert Delta_up: 0.109969\n", + " Move Insert Delta_down: 0.109176\n", + "Move set Remove two operators: 0.108779\n", + " Move Remove Delta_up: 0.110629\n", + " Move Remove Delta_down: 0.106946\n", + "Move set Insert four operators: 0.025333\n", + " Move Insert Delta_up_up: 0.0219449\n", + " Move Insert Delta_up_down: 0.0308511\n", + " Move Insert Delta_down_up: 0.0284571\n", + " Move Insert Delta_down_down: 0.020084\n", + "Move set Remove four operators: 0.0258108\n", + " Move Remove Delta_up_up: 0.0222851\n", + " Move Remove Delta_up_down: 0.0286898\n", + " Move Remove Delta_down_up: 0.0299628\n", + " Move Remove Delta_down_down: 0.0222489\n", + "Move Shift one operator: 0.300119\n", + "[Rank 0] Warmup lasted: 0.139701 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.28036 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 3.9498\n", + "Auto-correlation time: 2.22266\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:43 62% ETA 00:00:00 cycle 3104 of 5000\n", + "17:36:43 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:44 31% ETA 00:00:00 cycle 3133 of 10000\n", + "17:36:44 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00121134\n", + "Average order | 0.000183093\n", + "Average sign | 0.00017673\n", + "G_tau measure | 0.000906627\n", + "Total measure time | 0.0024778 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0934283\n", + " Move Insert Delta_up: 0.0932668\n", + " Move Insert Delta_down: 0.0935907\n", + "Move set Remove two operators: 0.0930836\n", + " Move Remove Delta_up: 0.092358\n", + " Move Remove Delta_down: 0.0938117\n", + "Move set Insert four operators: 0.0172868\n", + " Move Insert Delta_up_up: 0.0159668\n", + " Move Insert Delta_up_down: 0.0180807\n", + " Move Insert Delta_down_up: 0.0190679\n", + " Mov\n", + "\n", + "Iteration = 2 / 10e Insert Delta_down_down: 0.0160378\n", + "Move set Remove four operators: 0.0178545\n", + " Move Remove Delta_up_up: 0.0169909\n", + " Move Remove Delta_up_down: 0.0189613\n", + " Move Remove Delta_down_up: 0.0198869\n", + " Move Remove Delta_down_down: 0.0155637\n", + "Move Shift one operator: 0.37\n", + "[Rank 0] Warmup lasted: 0.158822 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.319168 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 5.5225\n", + "Auto-correlation time: 7.04599\n", + "\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 3 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:44 83% ETA 00:00:00 cycle 4197 of 5000\n", + "17:36:44 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:44 40% ETA 00:00:00 cycle 4072 of 10000\n", + "17:36:44 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.001176 \n", + "Average order | 0.000176303\n", + "Average sign | 0.000176643\n", + "G_tau measure | 0.00180564\n", + "Total measure time | 0.00333458\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0993743\n", + " Move Insert Delta_up: 0.0997124\n", + " Move Insert Delta_down: 0.0990345\n", + "Move set Remove two operators: 0.100148\n", + " Move Remove Delta_up: 0.101078\n", + " Move Remove Delta_down: 0.0992169\n", + "Move set Insert four operators: 0.0218489\n", + " Move Insert Delta_up_up: 0.0174252\n", + " Move Insert Delta_up_down: 0.0266929\n", + " Move Insert Delta_down_up: 0.0274607\n", + " Move\n", + "\n", + "Iteration = 4 / 10 Insert Delta_down_down: 0.0158329\n", + "Move set Remove four operators: 0.0214816\n", + " Move Remove Delta_up_up: 0.0163185\n", + " Move Remove Delta_up_down: 0.0269219\n", + " Move Remove Delta_down_up: 0.0273557\n", + " Move Remove Delta_down_down: 0.0153002\n", + "Move Shift one operator: 0.220527\n", + "[Rank 0] Warmup lasted: 0.119323 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.244041 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 3.2385\n", + "Auto-correlation time: 1.45713\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:44 92% ETA 00:00:00 cycle 4643 of 5000\n", + "17:36:44 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:44 45% ETA 00:00:00 cycle 4501 of 10000\n", + "17:36:44 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00117401\n", + "Average order | 0.000176022\n", + "Average sign | 0.000174079\n", + "G_tau measure | 0.000579364\n", + "Total measure time | 0.00210348\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0982657\n", + " Move Insert Delta_up: 0.0973164\n", + " Move Insert Delta_down: 0.0992208\n", + "Move set Remove two operators: 0.0987098\n", + " Move Remove Delta_up: 0.0983834\n", + " Move Remove Delta_down: 0.0990353\n", + "Move set Insert four operators: 0.0202451\n", + " Move Insert Delta_up_up: 0.015387\n", + " Move Insert Delta_up_down: 0.0256575\n", + " Move Insert Delta_down_up: 0.0253815\n", + " Move Insert Delta_dow\n", + "n_down: 0.0146119\n", + "Move set Remove four operators: 0.0200653\n", + " Move Remove Delta_up_up: 0.0153902\n", + " Move Remove Delta_up_down: 0.0249793\n", + " Move Remove Delta_down_up: 0.025014\n", + " Move Remove Delta_down_down: 0.0147525\n", + "Move Shift one operator: 0.17415\n", + "[Rank 0] Warmup lasted: 0.107687 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.217982 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 2.68\n", + "Auto-correlation time: 3.62833\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:45 98% ETA 00:00:00 cycle 4923 of 5000\n", + "17:36:45 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:45 48% ETA 00:00:00 cycle 4800 of 10000\n", + "17:36:45 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00119368\n", + "Average order | 0.000175437\n", + "Average sign | 0.00017678\n", + "G_tau measure | 0.000532081\n", + "Total measure time | 0.00207798\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0947289\n", + " Move Insert Delta_up: 0.0935515\n", + " Move Insert Delta_down: 0.0959141\n", + "Move set Remove two operators: 0.096231\n", + " Move Remove Delta_up: 0.0961732\n", + " Move Remove Delta_down: 0.0962883\n", + "Move set Insert four operators: 0.017825\n", + " Move Insert Delta_up_up: 0.0147368\n", + " Move Insert Delta_up_down: 0.0225432\n", + " Move Insert Delta_down_up: 0.0209947\n", + " Move Insert Delta_down_down: 0.012999\n", + "Move set Remove four operators: 0.0170501\n", + " Move Remove Delta_up_up: 0.0140059\n", + " Move Remove Delta_up_down: 0.0210924\n", + " Move Remove Delta_down_up: 0.0203299\n", + " Move Remove Delta_down_down: 0.0127166\n", + "Move Shift one operator: 0.15403\n", + "[Rank 0] Warmup lasted: 0.101398 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.205843 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9996\n", + "Average order: 2.4483\n", + "Auto-correlation time: 3.78104\n", + "\n", + "\n", + "Iteration = 5 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 6 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:45 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:45 50% ETA 00:00:00 cycle 5020 of 10000\n", + "17:36:45 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114739\n", + "Average order | 0.00017381\n", + "Average sign | 0.000178013\n", + "G_tau measure | 0.000545071\n", + "Total measure time | 0.00204428\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0927996\n", + " Move Insert Delta_up: 0.0912686\n", + " Move Insert Delta_down: 0.0943404\n", + "Move set Remove two operators: 0.0945209\n", + " Move Remove Delta_up: 0.0935476\n", + " Move Remove Delta_down: 0.0954924\n", + "Move set Insert four operators: 0.0165653\n", + " Move Insert Delta_up_up: 0.0146711\n", + " Move Insert Delta_up_down: 0.0186621\n", + " Move Insert Delta_down_up: 0.0196094\n", + " Move Insert Delta_down_down: 0.0133023\n", + "Move set Remove four operators: 0.0159569\n", + " Move Remove Delta_up_up: 0.0145658\n", + " Move Remove Delta_up_down: 0.0187106\n", + " Move Remove Delta_down_up: 0.0171739\n", + " Move Remove Delta_down_down: 0.0133664\n", + "Move Shift one operator: 0.141392\n", + "[Rank 0] Warmup lasted: 0.0971904 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.198948 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9986\n", + "Average order: 2.3498\n", + "Auto-correlation time: 1.59359\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:45 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:45 51% ETA 00:00:00 cycle 5111 of 10000\n", + "17:36:45 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00118152\n", + "Average order | 0.000174143\n", + "Average sign | 0.000175604\n", + "G_tau measure | 0.00170096\n", + "Total measure time | 0.00323222\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0928359\n", + " Move Insert Delta_up: 0.0932811\n", + " Move Insert Delta_down: 0.0923878\n", + "Move set Remove two operators: 0.0926255\n", + " Move Remove Delta_up: 0.0928757\n", + " Move Remove Delta_down: 0.092377\n", + "Move set Insert four operators: 0.0146763\n", + " Move Insert Delta_up_up: 0.012186\n", + " Move Insert Delta_up_down: 0.0176325\n", + " Move Insert Delta_down_up: 0.016766\n", + " Move Insert Delta_down_down: 0.0121195\n", + "Move set Re\n", + "\n", + "Iteration = 7 / 10\n", + "move four operators: 0.0149209\n", + " Move Remove Delta_up_up: 0.0131733\n", + " Move Remove Delta_up_down: 0.017507\n", + " Move Remove Delta_down_up: 0.017798\n", + " Move Remove Delta_down_down: 0.0111765\n", + "Move Shift one operator: 0.132209\n", + "[Rank 0] Warmup lasted: 0.0976131 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.194463 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9962\n", + "Average order: 2.2003\n", + "Auto-correlation time: 4.38459\n", + "\n", + "\n", + "Iteration = 8 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:46 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:46 52% ETA 00:00:00 cycle 5262 of 10000\n", + "17:36:46 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00116593\n", + "Average order | 0.000180144\n", + "Average sign | 0.000180288\n", + "G_tau measure | 0.00058201\n", + "Total measure time | 0.00210837\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0928416\n", + " Move Insert Delta_up: 0.0906576\n", + " Move Insert Delta_down: 0.0950149\n", + "Move set Remove two operators: 0.0920252\n", + " Move Remove Delta_up: 0.0907245\n", + " Move Remove Delta_down: 0.0933179\n", + "Move set Insert four operators: 0.013903\n", + " Move Insert Delta_up_up: 0.0125541\n", + " Move Insert Delta_up_down: 0.0153914\n", + " Move Insert Delta_down_up: 0.0162744\n", + " Move Insert Delta_down_down: 0.0113805\n", + "Move set Remove four operators: 0.0144144\n", + " Move Remove Delta_up_up: 0.0126455\n", + " Move Remove Delta_up_down: 0.0165371\n", + " Move Remove Delta_down_up: 0.0155525\n", + " Move Remove Delta_down_down: 0.0128903\n", + "Move Shift one operator: 0.125583\n", + "[Rank 0] Warmup lasted: 0.0940647 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.188429 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9966\n", + "Average order: 2.0978\n", + "Auto-correlation time: 2.6759\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:46 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:46 52% ETA 00:00:00 cycle 5211 of 10000\n", + "17:36:46 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00116479\n", + "Average order | 0.000173907\n", + "Average sign | 0.000176399\n", + "G_tau measure | 0.000532991\n", + "Total measure time | 0.00204808\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0919096\n", + " Move Insert Delta_up: 0.0916205\n", + " Move Insert Delta_down: 0.0921987\n", + "Move set Remove two operators: 0.0927888\n", + " Move Remove Delta_up: 0.0930645\n", + " Move Remove Delta_down: 0.0925146\n", + "Move set Insert four operators: 0.0144265\n", + " Move Insert Delta_up_up: 0.0127007\n", + " Move Insert Delta_up_down: 0.0166647\n", + " Move Insert Delta_down_up: 0.0163973\n", + " Move Insert Delta_down_down: 0.0119593\n", + "Move set Remove four operators: 0.0141665\n", + " Move Remove Delta_up_up: 0.0122729\n", + " Move Remove Delta_up_down: 0.0168521\n", + " Move Remove Delta_down_up: 0.0155449\n", + " Move Remove Delta_down_down: 0.0119799\n", + "Move Shift one operator: 0.129702\n", + "[Rank 0] Warmup lasted: 0.094935 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.190139 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9904\n", + "Average order: 2.1729\n", + "Auto-correlation time: 2.37016\n", + "\n", + "\n", + "Iteration = 9 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:46 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:46 52% ETA 00:00:00 cycle 5241 of 10000\n", + "17:36:46 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00117719\n", + "Average order | 0.000175777\n", + "Average sign | 0.000177851\n", + "G_tau measure | 0.000601851\n", + "Total measure time | 0.00213267\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0924057\n", + " Move Insert Delta_up: 0.0915808\n", + " Move Insert Delta_down: 0.0932353\n", + "Move set Remove two operators: 0.0931618\n", + " Move Remove Delta_up: 0.0928729\n", + " Move Remove Delta_down: 0.0934482\n", + "Move set Insert four operators: 0.0143961\n", + " Move Insert Delta_up_up: 0.0125352\n", + " Move Insert Delta_up_down: 0.0161123\n", + " Move Insert Delta_down_up: 0.0168691\n", + " Move Insert Delta_down_down: 0.0120458\n", + "Move set Remove four operators: 0.0142763\n", + " Move Remove Delta_up_up: 0.0131137\n", + " Move Remove Delta_up_down: 0.0156373\n", + " Move Remove Delta_down_up: 0.0161881\n", + " Move Remove Delta_down_down: 0.0121205\n", + "Move Shift one operator: 0.126835\n", + "[Rank 0] Warmup lasted: 0.0933722 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.192358 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9932\n", + "Average order: 2.1737\n", + "Auto-correlation time: 3.14348\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-2.5*c_dag('down',0)*c('down',0) + -2.5*c_dag('up',0)*c('up',0) + 5*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:47 99% ETA 00:00:00 cycle 4994 of 5000\n", + "17:36:47 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:47 48% ETA 00:00:00 cycle 4864 of 10000\n", + "17:36:47 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00118008\n", + "Average order | 0.000178815\n", + "Average sign | 0.000177486\n", + "G_tau measure | 0.00366987\n", + "Total measure time | 0.00520625\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0920425\n", + " Move Insert Delta_up: 0.0909091\n", + " Move Insert Delta_down: 0.0931849\n", + "Move set Remove two operators: 0.0924267\n", + " Move Remove Delta_up: 0.0933546\n", + " Move Remove Delta_down: 0.091505\n", + "Move set Insert four operators: 0.0137666\n", + " Move Insert Delta_up_up: 0.012333\n", + " Move Insert Delta_up_down: 0.0161586\n", + " Move Insert Delta_down_up: 0.0154218\n", + " Move Insert Delta_down_down: 0.0111436\n", + "Move set Remove four operators: 0.0139227\n", + " Move Remove Delta_up_up: 0.0114924\n", + " Move Remove Delta_up_down: 0.0157556\n", + " Move Remove Delta_down_up: 0.0157009\n", + " Move Remove Delta_down_down: 0.0127152\n", + "Move Shift one operator: 0.129222\n", + "[Rank 0] Warmup lasted: 0.100126 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.201704 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9968\n", + "Average order: 2.1866\n", + "Auto-correlation time: 2.15122\n", + "U = 6.0\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 2 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:47 67% ETA 00:00:00 cycle 3398 of 5000\n", + "17:36:47 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:47 34% ETA 00:00:00 cycle 3460 of 10000\n", + "17:36:47 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00124187\n", + "Average order | 0.000178268\n", + "Average sign | 0.000178552\n", + "G_tau measure | 0.00207588\n", + "Total measure time | 0.00367458\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0855862\n", + " Move Insert Delta_up: 0.0862303\n", + " Move Insert Delta_down: 0.0849443\n", + "Move set Remove two operators: 0.0862647\n", + " Move Remove Delta_up: 0.0861289\n", + " Move Remove Delta_down: 0.0864004\n", + "Move set Insert four operators: 0.0161327\n", + " Move Insert Delta_up_up: 0.0135714\n", + " Move Insert Delta_up_down: 0.0190244\n", + " Move Insert Delta_down_up: 0.0183747\n", + " Move Insert Delta_down_down: 0.0135908\n", + "Move set Remove four operators: 0.016286\n", + " Move Remove Delta_up_up: 0.0142857\n", + " Move Remove Delta_up_down: 0.0191166\n", + " Move Remove Delta_down_up: 0.0189973\n", + " Move Remove Delta_down_down: 0.0126796\n", + "Move Shift one operator: 0.300009\n", + "[Rank 0] Warmup lasted: 0.146407 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.293905 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 4.785\n", + "Auto-correlation time: 5.47307\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "\n", + "\n", + "Iteration = 3 / 10-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:47 93% ETA 00:00:00 cycle 4679 of 5000\n", + "17:36:47 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:47 47% ETA 00:00:00 cycle 4715 of 10000\n", + "17:36:48 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0012242 \n", + "Average order | 0.000183404\n", + "Average sign | 0.000184269\n", + "G_tau measure | 0.00114776\n", + "Total measure time | 0.00273964\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0903981\n", + " Move Insert Delta_up: 0.0901523\n", + " Move Insert Delta_down: 0.090646\n", + "Move set Remove two operators: 0.0904723\n", + " Move Remove Delta_up: 0.0912343\n", + " Move Remove Delt\n", + "a_down: 0.0897165\n", + "Move set Insert four operators: 0.0165848\n", + " Move Insert Delta_up_up: 0.0118808\n", + " Move Insert Delta_up_down: 0.0210818\n", + " Move Insert Delta_down_up: 0.0219767\n", + " Move Insert Delta_down_down: 0.0114437\n", + "Move set Remove four operators: 0.016681\n", + " Move Remove Delta_up_up: 0.0124508\n", + " Move Remove Delta_up_down: 0.0208665\n", + " Move Remove Delta_down_up: 0.0211651\n", + " Move Remove Delta_down_down: 0.0121752\n", + "Move Shift one operator: 0.143248\n", + "[Rank 0] Warmup lasted: 0.107062 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.214947 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 2.4458\n", + "Auto-correlation time: 2.54242\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "\n", + "\n", + "Iteration = 4 / 10-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:48 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:48 55% ETA 00:00:00 cycle 5544 of 10000\n", + "17:36:48 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114817\n", + "Average order | 0.000177973\n", + "Average sign | 0.000178907\n", + "G_tau measure | 0.000659669\n", + "Total measure time | 0.00216472\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0857364\n", + " Move Insert Delta_up: 0.0845932\n", + " Move Insert Delta_down: 0.0868874\n", + "Move set Remove two operators: 0.0848534\n", + " Move Remove Delta_up: 0.0837722\n", + " Move Remove Delta_down: 0.0859277\n", + "Move set Insert four opera\n", + "tors: 0.0134187\n", + " Move Insert Delta_up_up: 0.0102856\n", + " Move Insert Delta_up_down: 0.0163494\n", + " Move Insert Delta_down_up: 0.0164149\n", + " Move Insert Delta_down_down: 0.0106362\n", + "Move set Remove four operators: 0.0138114\n", + " Move Remove Delta_up_up: 0.0111514\n", + " Move Remove Delta_up_down: 0.0171747\n", + " Move Remove Delta_down_up: 0.0166038\n", + " Move Remove Delta_down_down: 0.0102768\n", + "Move Shift one operator: 0.105283\n", + "[Rank 0] Warmup lasted: 0.0887984 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.178604 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9994\n", + "Average order: 1.9382\n", + "Auto-correlation time: 2.24137\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 5 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:48 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:48 58% ETA 00:00:00 cycle 5892 of 10000\n", + "17:36:48 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115656\n", + "Average order | 0.000172163\n", + "Average sign | 0.000171354\n", + "G_tau measure | 0.000511025\n", + "Total measure time | 0.0020111 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0813375\n", + " Move Insert Delta_up: 0.0807074\n", + " Move Insert Delta_down: 0.0819679\n", + "Move set Remove two operators: 0.0805692\n", + " Move Remove Delta_up: 0.0807933\n", + " Move Remove Delta_down: 0.0803457\n", + "Move set Insert four operators: 0.0106639\n", + " Move Insert Delta_up_up: 0.00997202\n", + " Move Insert Delta_up_down: 0.0117378\n", + " Move Insert Delta_down_up: 0.0126783\n", + " Move Insert Delta_down_down: 0.00827334\n", + "Move set Remove four operators: 0.010986\n", + " Move Remove Delta_up_up: 0.0102901\n", + " Move Remove Delta_up_down: 0.011223\n", + " Move Remove Delta_down_up: 0.0124831\n", + " Move Remove Delta_down_down: 0.00993179\n", + "Move Shift one operator: 0.0924834\n", + "[Rank 0] Warmup lasted: 0.0846274 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.167454 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9994\n", + "Average order: 1.7817\n", + "Auto-correlation time: 2.85936\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:48 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:48 60% ETA 00:00:00 cycle 6006 of 10000\n", + "17:36:48 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115569\n", + "Average order | 0.000178144\n", + "Average sign | 0.000176416\n", + "G_tau measure | 0.000650654\n", + "Total measure time | 0.0021609 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0807803\n", + " Move Insert Delta_up: 0.0806915\n", + " Move Insert Delta_down: 0.0808702\n", + "Move set Remove two operators: 0.0812727\n", + " Move Remove Delta_up: 0.081801\n", + " Move Remove Delta_down: 0.0807463\n", + "Move set Insert four operators: 0.0099321\n", + " Move Insert Delta_up_up: 0.00904017\n", + " Move Insert Delta_up_down: 0.0110288\n", + " Move Insert Delta_down_up: 0.0113006\n", + " Move Insert Delta_down_down: 0.00836602\n", + "Move set Remove four operators: 0.00970767\n", + " Move Remove Delta_up_up: 0.00951802\n", + " Move Remove Delta_up_down: 0.0101806\n", + " Move Remove Delta_down_up: 0.0106859\n", + " Move Remove Delta_down_down: 0.00843022\n", + "Move Shift one operator: 0.086534\n", + "[Rank 0] Warmup lasted: 0.0813237 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.165674 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9968\n", + "Average order: 1.7405\n", + "Auto-correlation time: 4.74432\n", + "\n", + "\n", + "Iteration = 6 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:48 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:49 60% ETA 00:00:00 cycle 6050 of 10000\n", + "17:36:49 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00117191\n", + "Average order | 0.000175165\n", + "Average sign | 0.00017454\n", + "G_tau measure | 0.00050787\n", + "Total measure time | 0.00202948\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0802191\n", + " Move Insert Delta_up: 0.0791924\n", + " Move Insert Delta_down: 0.0812492\n", + "Move set Remove two operators: 0.0809095\n", + " Move Remove Delta_up: 0.0803057\n", + " Move Remove Delta_down: 0.0815111\n", + "Move set Insert four operators: 0.00969948\n", + " Move Insert Delta_up_up: 0.00925127\n", + " Move Insert Delta_up_down: 0.0103824\n", + " Move Insert Delta_down_up: 0.0101984\n", + " Move Insert Delta_down_down: 0.00897148\n", + "Move s\n", + "\n", + "Iteration = 7 / 10\n", + "et Remove four operators: 0.00927175\n", + " Move Remove Delta_up_up: 0.0082894\n", + " Move Remove Delta_up_down: 0.010285\n", + " Move Remove Delta_down_up: 0.0103947\n", + " Move Remove Delta_down_down: 0.00810573\n", + "Move Shift one operator: 0.0879604\n", + "[Rank 0] Warmup lasted: 0.0832035 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.16585 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9974\n", + "Average order: 1.7589\n", + "Auto-correlation time: 2.9097\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 8 / 10\n", + "\n", + "\n", + "Iteration = 9 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:49 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:49 61% ETA 00:00:00 cycle 6139 of 10000\n", + "17:36:49 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00118529\n", + "Average order | 0.000172073\n", + "Average sign | 0.000172366\n", + "G_tau measure | 0.000496923\n", + "Total measure time | 0.00202665\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0795869\n", + " Move Insert Delta_up: 0.0790479\n", + " Move Insert Delta_down: 0.0801287\n", + "Move set Remove two operators: 0.0793488\n", + " Move Remove Delta_up: 0.0792065\n", + " Move Remove Delta_down: 0.0794908\n", + "Move set Insert four operators: 0.00889574\n", + " Move Insert Delta_up_up: 0.00825277\n", + " Move Insert Delta_up_down: 0.00949215\n", + " Move Insert Delta_down_up: 0.00962801\n", + " Move Insert Delta_down_down: 0.00821994\n", + "Move set Remove four operators: 0.00910054\n", + " Move Remove Delta_up_up: 0.00828074\n", + " Move Remove Delta_up_down: 0.0100862\n", + " Move Remove Delta_down_up: 0.00971145\n", + " Move Remove Delta_down_down: 0.00831268\n", + "Move Shift one operator: 0.0841257\n", + "[Rank 0] Warmup lasted: 0.0818062 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.16097 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9978\n", + "Average order: 1.6771\n", + "Auto-correlation time: 3.1042\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:49 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:49 61% ETA 00:00:00 cycle 6153 of 10000\n", + "17:36:49 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115225\n", + "Average order | 0.000171762\n", + "Average sign | 0.000172688\n", + "G_tau measure | 0.000498098\n", + "Total measure time | 0.00199479\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.080043\n", + " Move Insert Delta_up: 0.0801369\n", + " Move Insert Delta_down: 0.0799484\n", + "Move set Remove two operators: 0.0794563\n", + " Move Remove Delta_up: 0.0801462\n", + " Move Remove Delta_down: 0.0787702\n", + "Move set Insert four operators: 0.00927618\n", + " Move Insert Delta_up_up: 0.00859689\n", + " Move Insert Delta_up_down: 0.00934467\n", + " Move Insert Delta_down_up: 0.0107302\n", + " Move Insert Delta_down_down: 0.00842416\n", + "Move set Remove four operators: 0.00952857\n", + " Move Remove Delta_up_up: 0.00895115\n", + " Move Remove Delta_up_down: 0.00986343\n", + " Move Remove Delta_down_up: 0.0104291\n", + " Move Remove Delta_down_down: 0.00886475\n", + "Move Shift one operator: 0.0843788\n", + "[Rank 0] Warmup lasted: 0.0796422 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.161897 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9968\n", + "Average order: 1.6928\n", + "Auto-correlation time: 6.26924\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "\n", + "Warming up ...\n", + "17:36:49 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:49 59% ETA 00:00:00 cycle 5957 of 10000\n", + "17:36:49 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115623\n", + "Average order | 0.00017426\n", + "Average sign | 0.000175525\n", + "G_tau measure | 0.000538327\n", + "Total measure time | 0.00204434\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0811482\n", + " Move Insert Delta_up: 0.0799815\n", + " Move Insert Delta_down: 0.0823172\n", + "Move set Remove two operators: 0.079918\n", + " Move Remove Delta_up: 0.0798255\n", + " Move Remove Delta_down: 0.0800096\n", + "Move set Insert four operators: 0.00925476\n", + " Move Insert Delta_up_up: 0.00855502\n", + " Move Insert Delta_up_down: 0.00981487\n", + " Move Insert Delta_down_up: 0.0105368\n", + " Move Insert Delta_down_down: 0.0081249\n", + "Move set Remove four operators: 0.00993752\n", + " Move Remove Delta_up_up: 0.00927793\n", + " Move Remove Delta_up_down: 0.00998653\n", + " Move Remove Delta_down_up: 0.0106884\n", + " Move Remove Delta_down_down: 0.00978858\n", + "Move Shift one operator: 0.0876537\n", + "[Rank 0] Warmup lasted: 0.0830675 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.166809 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9932\n", + "Average order: 1.7751\n", + "Auto-correlation time: 2.16191\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3*c_dag('down',0)*c('down',0) + -3*c_dag('up',0)*c('up',0) + 6*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:50 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:50 60% ETA 00:00:00 cycle 6097 of 10000\n", + "17:36:50 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115244\n", + "Average order | 0.00017814\n", + "Average sign | 0.000175008\n", + "G_tau measure | 0.000491659\n", + "Total measure time | 0.00199724\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0796655\n", + " Move Insert Delta_up: 0.0798424\n", + " Move Insert Delta_down: 0.0794869\n", + "Move set Remove two operators: 0.0806294\n", + " Move Remove Delta_up: 0.0818259\n", + " Move Remove Delta_down: 0.0794333\n", + "Move set Insert four operators: 0.00948261\n", + " Move Insert Delta_up_up: 0.00896616\n", + " Move Insert Delta_up_down: 0.0110163\n", + " Move Insert Delta_down_up: 0.00972801\n", + " Move Insert Delta_down_down: 0.00824513\n", + "MoveU = 7.0\n", + " set Remove four operators: 0.00907485\n", + " Move Remove Delta_up_up: 0.00870093\n", + " Move Remove Delta_up_down: 0.00888078\n", + " Move Remove Delta_down_up: 0.00987143\n", + " Move Remove Delta_down_down: 0.00884075\n", + "Move Shift one operator: 0.0868053\n", + "[Rank 0] Warmup lasted: 0.0820692 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.162871 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9966\n", + "Average order: 1.7002\n", + "Auto-correlation time: 3.1179\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:50 78% ETA 00:00:00 cycle 3931 of 5000\n", + "17:36:50 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:50 39% ETA 00:00:00 cycle 3916 of 10000\n", + "17:36:50 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00116999\n", + "Average order | 0.000178971\n", + "Average sign | 0.000177013\n", + "G_tau measure | 0.000661933\n", + "Total measure time | 0.0021879 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0825932\n", + " Move Insert Delta_up: 0.0821367\n", + " Move Insert Delta_down: 0.0830489\n", + "Move set Remove two operators: 0.0829548\n", + " Move Remove Delta_up: 0.0825535\n", + " Move Remove Delta_down: 0.0833533\n", + "Move set Insert four operators: 0.0149825\n", + " Move Insert Delta_up_up: 0.0128456\n", + " Move Insert Delta_up_down: 0.016206\n", + " Move Insert Delta_down_up: 0.018634\n", + " Mov\n", + "\n", + "Iteration = 2 / 10\n", + "e Insert Delta_down_down: 0.0122235\n", + "Move set Remove four operators: 0.0149866\n", + " Move Remove Delta_up_up: 0.0129654\n", + " Move Remove Delta_up_down: 0.0173514\n", + " Move Remove Delta_down_up: 0.0175802\n", + " Move Remove Delta_down_down: 0.0119693\n", + "Move Shift one operator: 0.233272\n", + "[Rank 0] Warmup lasted: 0.128126 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.253855 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 3.9717\n", + "Auto-correlation time: 5.67936\n", + "\n", + "\n", + "Iteration = 3 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:50 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:50 60% ETA 00:00:00 cycle 6032 of 10000\n", + "17:36:50 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115504\n", + "Average order | 0.000170611\n", + "Average sign | 0.000174805\n", + "G_tau measure | 0.00128361\n", + "Total measure time | 0.00278407\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0831347\n", + " Move Insert Delta_up: 0.0826692\n", + " Move Insert Delta_down: 0.0836028\n", + "Move set Remove two operators: 0.083416\n", + " Move Remove Delta_up: 0.0828652\n", + " Move Remove Delta_down: 0.083969\n", + "Move set Insert four operators: 0.0133214\n", + " Move Insert Delta_up_up: 0.00978535\n", + " Move Insert Delta_up_down: 0.0162945\n", + " Move Insert Delta_down_up: 0.0179412\n", + " Move Insert Delta_down_down: 0.00930456\n", + "Move set Remove four operators: 0.0131809\n", + " Move Remove Delta_up_up: 0.0100251\n", + " Move Remove Delta_up_down: 0.0165614\n", + " Move Remove Delta_down_up: 0.0171715\n", + " Move Remove Delta_down_down: 0.00890798\n", + "Move Shift one operator: 0.0923836\n", + "[Rank 0] Warmup lasted: 0.0827155 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.168187 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 1.7472\n", + "Auto-correlation time: 5.10055\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:51 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:51 65% ETA 00:00:00 cycle 6573 of 10000\n", + "17:36:51 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00119869\n", + "Average order | 0.000176954\n", + "Average sign | 0.000178311\n", + "G_tau measure | 0.000494936\n", + "Total measure time | 0.00204889\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.07622\n", + " Move Insert Delta_up: 0.075692\n", + " Move Insert Delta_down: 0.0767499\n", + "Move set Remove two operators: 0.0754528\n", + " Move Remove Delta_up: 0.0759239\n", + " Move Remove Delta_down: 0.074984\n", + "Move set Insert four operators: 0.0088625\n", + " Move Insert Delta_up_up: 0.00809271\n", + " Move Insert Delta_up_down: 0.00996209\n", + " Move Insert Delta_down_up: 0.0113564\n", + " Move Insert Delta_down_down: 0.00605505\n", + "Move set Remove four operators: 0.00928461\n", + " Move Remove Delta_up_up: 0.00818713\n", + " Move Remove Delta_up_down: 0.0108847\n", + " Move Remove Delta_down_up: 0.0106967\n", + " Move Remove Delta_down_down: 0.00735\n", + "Move Shift one operator: 0.0695589\n", + "[Rank 0] Warmup lasted: 0.0759537 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.150655 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9996\n", + "Average order: 1.4869\n", + "Auto-correlation time: 2.47014\n", + "\n", + "\n", + "Iteration = 4 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:51 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:51 67% ETA 00:00:00 cycle 6728 of 10000\n", + "17:36:51 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011518 \n", + "Average order | 0.000170142\n", + "Average sign | 0.000172553\n", + "G_tau measure | 0.000480502\n", + "Total measure time | 0.001975 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0724759\n", + " Move Insert Delta_up: 0.0715636\n", + " Move Insert Delta_down: 0.0733873\n", + "Move set Remove two operators: 0.0720517\n", + " Move Remove Delta_up: 0.0711002\n", + " Move Remove Delta_down: 0.0730012\n", + "Move set Insert four operators: 0.00731203\n", + " Move Insert Delta_up_up: 0.00710724\n", + " Move Insert Delta_up_down: 0.00752697\n", + " Move Insert Delta_down_up: 0.00819281\n", + " Move Insert Delta_down_down: 0.00642832\n", + "Mo\n", + "\n", + "Iteration = 5 / 10\n", + "ve set Remove four operators: 0.00748132\n", + " Move Remove Delta_up_up: 0.00743524\n", + " Move Remove Delta_up_down: 0.00759443\n", + " Move Remove Delta_down_up: 0.00848466\n", + " Move Remove Delta_down_down: 0.00640077\n", + "Move Shift one operator: 0.0638096\n", + "[Rank 0] Warmup lasted: 0.0742757 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.147119 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9986\n", + "Average order: 1.4453\n", + "Auto-correlation time: 2.27009\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:51 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:51 67% ETA 00:00:00 cycle 6765 of 10000\n", + "17:36:51 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011343 \n", + "Average order | 0.000175784\n", + "Average sign | 0.000175253\n", + "G_tau measure | 0.000505452\n", + "Total measure time | 0.00199079\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0713425\n", + " Move Insert Delta_up: 0.0706458\n", + " Move Insert Delta_down: 0.07204\n", + "Move set Remove two operators: 0.0718967\n", + " Move Remove Delta_up: 0.0720733\n", + " Move Remove Delta_down: 0.0717211\n", + "Move set Insert four operators: 0.00752552\n", + " Move Insert Delta_up_up: 0.00686625\n", + " Move Insert Delta_up_down: 0.00816653\n", + " Move Insert Delta_down_up: 0.00877613\n", + " Move Insert Delta_down_down: 0.00630587\n", + "Move\n", + "\n", + "Iteration = 6 / 10\n", + " set Remove four operators: 0.00721517\n", + " Move Remove Delta_up_up: 0.00625928\n", + " Move Remove Delta_up_down: 0.00727505\n", + " Move Remove Delta_down_up: 0.00856445\n", + " Move Remove Delta_down_down: 0.00676213\n", + "Move Shift one operator: 0.063708\n", + "[Rank 0] Warmup lasted: 0.073321 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.146845 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.991\n", + "Average order: 1.442\n", + "Auto-correlation time: 2.81646\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:51 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:51 67% ETA 00:00:00 cycle 6760 of 10000\n", + "17:36:51 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115614\n", + "Average order | 0.000174304\n", + "Average sign | 0.000175429\n", + "G_tau measure | 0.000510379\n", + "Total measure time | 0.00201626\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0722835\n", + " Move Insert Delta_up: 0.071304\n", + " Move Insert Delta_down: 0.0732643\n", + "Move set Remove two operators: 0.0724337\n", + " Move Remove Delta_up: 0.0720262\n", + " Move Remove Delta_down: 0.07284\n", + "Move set Insert four operators: 0.00747649\n", + " Move Insert Delta_up_up: 0.00767686\n", + " Move Insert Delta_up_down: 0.00721154\n", + " Move Insert Delta_down_up: 0.00852148\n", + " Move Insert Delta_down_down: 0.00649195\n", + "Move \n", + "\n", + "Iteration = 7 / 10\n", + "set Remove four operators: 0.00737793\n", + " Move Remove Delta_up_up: 0.00707822\n", + " Move Remove Delta_up_down: 0.00711645\n", + " Move Remove Delta_down_up: 0.00876561\n", + " Move Remove Delta_down_down: 0.00655318\n", + "Move Shift one operator: 0.0641534\n", + "[Rank 0] Warmup lasted: 0.0730972 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.147456 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.996\n", + "Average order: 1.4256\n", + "Auto-correlation time: 1.87925\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:51 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:52 67% ETA 00:00:00 cycle 6797 of 10000\n", + "17:36:52 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115404\n", + "Average order | 0.000171099\n", + "Average sign | 0.000173656\n", + "G_tau measure | 0.00048374\n", + "Total measure time | 0.00198254\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0719672\n", + " Move Insert Delta_up: 0.0706898\n", + " Move Insert Delta_down: 0.0732397\n", + "Move set Remove two operators: 0.0715593\n", + " Move Remove Delta_up: 0.0704335\n", + " Move Remove Delta_down: 0.0726812\n", + "Move set Insert four operators: 0.00708196\n", + " Move Insert Delta_up_up: 0.00686678\n", + " Move Insert Delta_up_down: 0.0078658\n", + " Move Insert Delta_down_up: 0.00743821\n", + " Move Insert Delta_down_down: 0.0061632\n", + "Move \n", + "\n", + "Iteration = 8 / 10\n", + "set Remove four operators: 0.0071656\n", + " Move Remove Delta_up_up: 0.00718669\n", + " Move Remove Delta_up_down: 0.00766542\n", + " Move Remove Delta_down_up: 0.00723975\n", + " Move Remove Delta_down_down: 0.00656952\n", + "Move Shift one operator: 0.0617984\n", + "[Rank 0] Warmup lasted: 0.0741883 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.146003 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9934\n", + "Average order: 1.4186\n", + "Auto-correlation time: 1.97262\n", + "\n", + "\n", + "Iteration = 9 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:52 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:52 67% ETA 00:00:00 cycle 6733 of 10000\n", + "17:36:52 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00117 \n", + "Average order | 0.000176397\n", + "Average sign | 0.000174993\n", + "G_tau measure | 0.00058778\n", + "Total measure time | 0.00210917\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0723829\n", + " Move Insert Delta_up: 0.0719869\n", + " Move Insert Delta_down: 0.072779\n", + "Move set Remove two operators: 0.0718631\n", + " Move Remove Delta_up: 0.0717641\n", + " Move Remove Delta_down: 0.0719614\n", + "Move set Insert four operators: 0.00704912\n", + " Move Insert Delta_up_up: 0.00711842\n", + " Move Insert Delta_up_down: 0.00702896\n", + " Move Insert Delta_down_up: 0.00800765\n", + " Move Insert Delta_down_down: 0.00603686\n", + "Move set Remove four operators: 0.00728255\n", + " Move Remove Delta_up_up: 0.00728605\n", + " Move Remove Delta_up_down: 0.00757032\n", + " Move Remove Delta_down_up: 0.00808733\n", + " Move Remove Delta_down_down: 0.00617878\n", + "Move Shift one operator: 0.0647355\n", + "[Rank 0] Warmup lasted: 0.0728214 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.146776 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9972\n", + "Average order: 1.4233\n", + "Auto-correlation time: 2.35075\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:52 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:52 67% ETA 00:00:00 cycle 6761 of 10000\n", + "17:36:52 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113392\n", + "Average order | 0.000174943\n", + "Average sign | 0.000180306\n", + "G_tau measure | 0.000498955\n", + "Total measure time | 0.00198812\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0720697\n", + " Move Insert Delta_up: 0.0712123\n", + " Move Insert Delta_down: 0.0729294\n", + "Move set Remove two operators: 0.0721389\n", + " Move Remove Delta_up: 0.0714645\n", + " Move Remove Delta_down: 0.0728079\n", + "Move set Insert four operators: 0.0070591\n", + " Move Insert Delta_up_up: 0.00725576\n", + " Move Insert Delta_up_down: 0.00753265\n", + " Move Insert Delta_down_up: 0.00761844\n", + " Move Insert Delta_down_down: 0.00582509\n", + "Move set Remove four operators: 0.00700508\n", + " Move Remove Delta_up_up: 0.00793267\n", + " Move Remove Delta_up_down: 0.0067748\n", + " Move Remove Delta_down_up: 0.00738887\n", + " Move Remove Delta_down_down: 0.00593318\n", + "Move Shift one operator: 0.0639617\n", + "[Rank 0] Warmup lasted: 0.0738343 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.147712 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9972\n", + "Average order: 1.4578\n", + "Auto-correlation time: 2.74578\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-3.5*c_dag('down',0)*c('down',0) + -3.5*c_dag('up',0)*c('up',0) + 7*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:52 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:52 66% ETA 00:00:00 cycle 6668 of 10000\n", + "17:36:52 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011519 \n", + "Average order | 0.000174972\n", + "Average sign | 0.000177178\n", + "G_tau measure | 0.000500343\n", + "Total measure time | 0.00200439\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0700786\n", + " Move Insert Delta_up: 0.0702968\n", + " Move Insert Delta_down: 0.0698603\n", + "Move set Remove two operators: 0.0696558\n", + " Move Remove Delta_up: 0.0699828\n", + " Move Remove Delta_down: 0.0693299\n", + "Move set Insert four oU = 8.0\n", + "perators: 0.00679017\n", + " Move Insert Delta_up_up: 0.00662199\n", + " Move Insert Delta_up_down: 0.00680628\n", + " Move Insert Delta_down_up: 0.00800669\n", + " Move Insert Delta_down_down: 0.00572275\n", + "Move set Remove four operators: 0.00691247\n", + " Move Remove Delta_up_up: 0.00703885\n", + " Move Remove Delta_up_down: 0.00726199\n", + " Move Remove Delta_down_up: 0.00740299\n", + " Move Remove Delta_down_down: 0.00594573\n", + "Move Shift one operator: 0.0655141\n", + "[Rank 0] Warmup lasted: 0.0722333 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.14909 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9974\n", + "Average order: 1.499\n", + "Auto-correlation time: 2.74692\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 2 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:53 84% ETA 00:00:00 cycle 4202 of 5000\n", + "17:36:53 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:53 43% ETA 00:00:00 cycle 4308 of 10000\n", + "17:36:53 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00119195\n", + "Average order | 0.000172087\n", + "Average sign | 0.000176853\n", + "G_tau measure | 0.000651202\n", + "Total measure time | 0.00219209\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0784645\n", + " Move Insert Delta_up: 0.077737\n", + " Move Insert Delta_down: 0.0791939\n", + "Move set Remove two operators: 0.0778967\n", + " Move Remove Delta_up: 0.0777312\n", + " Move Remove Delta_down: 0.0780616\n", + "Move set Insert four operators: 0.0137605\n", + " Move Insert Delta_up_up: 0.0106226\n", + " Move Insert Delta_up_down: 0.0159981\n", + " Move Insert Delta_down_up: 0.0181051\n", + " Move Insert Delta_down_down: 0.0102649\n", + "Move set Remove four operators: 0.014284\n", + " Move Remove Delta_up_up: 0.0107163\n", + " Move Remove Delta_up_down: 0.0180735\n", + " Move Remove Delta_down_up: 0.0173927\n", + " Move Remove Delta_down_down: 0.010941\n", + "Move Shift one operator: 0.194802\n", + "[Rank 0] Warmup lasted: 0.117678 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.232515 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 3.5417\n", + "Auto-correlation time: 7.37617\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:53 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:53 68% ETA 00:00:00 cycle 6826 of 10000\n", + "17:36:53 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011367 \n", + "Average order | 0.000175053\n", + "Average sign | 0.000174838\n", + "G_tau measure | 0.000507683\n", + "Total measure time | 0.00199427\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0766449\n", + " Move Insert Delta_up: 0.0764292\n", + " Move Insert Delta_down: 0.0768612\n", + "Move set Remove two operators: 0.0761625\n", + " Move Remove Delta_up: 0.0763946\n", + " Move Remove Delta_down: 0.0759317\n", + "Move set Insert four operators: 0.0106741\n", + " Move Insert Delta_up_up: 0.00774466\n", + " Move Insert Delta_up_down: 0.0136975\n", + " Move Insert Delta_down_up: 0.0145652\n", + " Move Insert Delta_down_down: 0.0067659\n", + "Move set Remove four operators: 0.0108867\n", + " Move Remove Delta_up_up: 0.00786259\n", + " Move Remove Delta_up_down: 0.0137408\n", + " Move Remove Delta_down_up: 0.0148857\n", + " Move Remove Delta_down_down: 0.00699944\n", + "Move Shift one operator: 0.0664264\n", + "[Rank 0] Warmup lasted: 0.0726063 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.145565 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 1.4098\n", + "Auto-correlation time: 1.66327\n", + "\n", + "\n", + "Iteration = 3 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 4 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:53 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:53 73% ETA 00:00:00 cycle 7364 of 10000\n", + "17:36:53 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113329\n", + "Average order | 0.000175666\n", + "Average sign | 0.00017359\n", + "G_tau measure | 0.000493932\n", + "Total measure time | 0.00197648\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0666085\n", + " Move Insert Delta_up: 0.0665451\n", + " Move Insert Delta_down: 0.066672\n", + "Move set Remove two operators: 0.0667213\n", + " Move Remove Delta_up: 0.0672132\n", + " Move Remove Delta_down: 0.0662322\n", + "Move set Insert four operators: 0.00669763\n", + " Move Insert Delta_up_up: 0.00602932\n", + " Move Insert Delta_up_down: 0.00748431\n", + " Move Insert Delta_down_up: 0.00769875\n", + " Move Insert Delta_down_down: 0.00559172\n", + "Move set Remove four operators: 0.00652602\n", + " Move Remove Delta_up_up: 0.00578662\n", + " Move Remove Delta_up_down: 0.00698408\n", + " Move Remove Delta_down_up: 0.00760865\n", + " Move Remove Delta_down_down: 0.00571634\n", + "Move Shift one operator: 0.0514166\n", + "[Rank 0] Warmup lasted: 0.0668013 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.135057 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9996\n", + "Average order: 1.2625\n", + "Auto-correlation time: 1.92343\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:53 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:53 72% ETA 00:00:00 cycle 7289 of 10000\n", + "17:36:53 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113231\n", + "Average order | 0.000174572\n", + "Average sign | 0.000175548\n", + "G_tau measure | 0.000526218\n", + "Total measure time | 0.00200864\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0648141\n", + " Move Insert Delta_up: 0.0634921\n", + " Move Insert Delta_down: 0.0661443\n", + "Move set Remove two operators: 0.0649517\n", + " Move Remove Delta_up: 0.0649518\n", + " Move Remove Delta_down: 0.0649516\n", + "Move set Insert four operators: 0.0061691\n", + " Move Insert Delta_up_up: 0.00587146\n", + " Move Insert Delta_up_down: 0.00646862\n", + " Move Insert Delta_down_up: 0.00744427\n", + " Move Insert Delta_down_down: 0.00489611\n", + "Move set Remove four operators: 0.00601018\n", + " Move Remove Delta_up_up: 0.00491658\n", + " Move Remove Delta_up_down: 0.00690701\n", + " Move Remove Delta_down_up: 0.00676294\n", + " Move Remove Delta_down_down: 0.00544022\n", + "Move Shift one operator: 0.0503358\n", + "[Rank 0] Warmup lasted: 0.0671483 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.135564 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9972\n", + "Average order: 1.2763\n", + "Auto-correlation time: 2.76404\n", + "\n", + "\n", + "Iteration = 5 / 10\n", + "\n", + "\n", + "Iteration = 6 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:54 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:54 73% ETA 00:00:00 cycle 7343 of 10000\n", + "17:36:54 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00116586\n", + "Average order | 0.000171716\n", + "Average sign | 0.000175847\n", + "G_tau measure | 0.000581802\n", + "Total measure time | 0.00209523\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0649176\n", + " Move Insert Delta_up: 0.0641392\n", + " Move Insert Delta_down: 0.0657014\n", + "Move set Remove two operators: 0.0647029\n", + " Move Remove Delta_up: 0.0646974\n", + " Move Remove Delta_down: 0.0647083\n", + "Move set Insert four operators: 0.00594983\n", + " Move Insert Delta_up_up: 0.00583274\n", + " Move Insert Delta_up_down: 0.00662917\n", + " Move Insert Delta_down_up: 0.00637781\n", + " Move Insert Delta_down_down: 0.00496899\n", + "Move set Remove four operators: 0.00605891\n", + " Move Remove Delta_up_up: 0.00579733\n", + " Move Remove Delta_up_down: 0.00580425\n", + " Move Remove Delta_down_up: 0.00740682\n", + " Move Remove Delta_down_down: 0.00521816\n", + "Move Shift one operator: 0.0500971\n", + "[Rank 0] Warmup lasted: 0.0681187 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.135501 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9928\n", + "Average order: 1.2804\n", + "Auto-correlation time: 1.52751\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 7 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:54 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:54 73% ETA 00:00:00 cycle 7363 of 10000\n", + "17:36:54 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114743\n", + "Average order | 0.000174272\n", + "Average sign | 0.000173083\n", + "G_tau measure | 0.00048318\n", + "Total measure time | 0.00197796\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0648434\n", + " Move Insert Delta_up: 0.0639435\n", + " Move Insert Delta_down: 0.0657487\n", + "Move set Remove two operators: 0.0652002\n", + " Move Remove Delta_up: 0.0650621\n", + " Move Remove Delta_down: 0.0653372\n", + "Move set Insert four operators: 0.00653185\n", + " Move Insert Delta_up_up: 0.00647684\n", + " Move Insert Delta_up_down: 0.00700224\n", + " Move Insert Delta_down_up: 0.00712778\n", + " Move Insert Delta_down_down: 0.00552508\n", + "Move set Remove four operators: 0.00633317\n", + " Move Remove Delta_up_up: 0.00574294\n", + " Move Remove Delta_up_down: 0.00628081\n", + " Move Remove Delta_down_up: 0.00798945\n", + " Move Remove Delta_down_down: 0.00531256\n", + "Move Shift one operator: 0.0519887\n", + "[Rank 0] Warmup lasted: 0.0671887 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.135337 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9896\n", + "Average order: 1.2736\n", + "Auto-correlation time: 2.97512\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:54 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:54 73% ETA 00:00:00 cycle 7350 of 10000\n", + "17:36:54 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114243\n", + "Average order | 0.000176064\n", + "Average sign | 0.00017554\n", + "G_tau measure | 0.000502765\n", + "Total measure time | 0.0019968 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0651064\n", + " Move Insert Delta_up: 0.0640138\n", + " Move Insert Delta_down: 0.0661968\n", + "Move set Remove two operators: 0.064921\n", + " Move Remove Delta_up: 0.0643386\n", + " Move Remove Delta_down: 0.0655007\n", + "Move set Insert four operators: 0.00608775\n", + " Move Insert Delta_up_up: 0.00550639\n", + " Move Insert Delta_up_down: 0.00688303\n", + " Move Insert Delta_down_up: 0.00702353\n", + " Move Insert Delta_down_down: 0.00494438\n", + "Move\n", + "\n", + "Iteration = 8 / 10\n", + " set Remove four operators: 0.00612031\n", + " Move Remove Delta_up_up: 0.00518802\n", + " Move Remove Delta_up_down: 0.006465\n", + " Move Remove Delta_down_up: 0.00749811\n", + " Move Remove Delta_down_down: 0.00532021\n", + "Move Shift one operator: 0.0501661\n", + "[Rank 0] Warmup lasted: 0.0677592 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.135616 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9908\n", + "Average order: 1.2712\n", + "Auto-correlation time: 2.45363\n", + "\n", + "\n", + "Iteration = 9 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:54 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:54 70% ETA 00:00:00 cycle 7086 of 10000\n", + "17:36:54 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00116354\n", + "Average order | 0.000175041\n", + "Average sign | 0.000174947\n", + "G_tau measure | 0.00112879\n", + "Total measure time | 0.00264231\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0650171\n", + " Move Insert Delta_up: 0.0644201\n", + " Move Insert Delta_down: 0.0656194\n", + "Move set Remove two operators: 0.0655976\n", + " Move Remove Delta_up: 0.0658945\n", + " Move Remove Delta_down: 0.0653032\n", + "Move set Insert four operators: 0.00591916\n", + " Move Insert Delta_up_up: 0.00558637\n", + " Move Insert Delta_up_down: 0.00615606\n", + " Move Insert Delta_down_up: 0.00691088\n", + " Move Insert Delta_down_down: 0.00502934\n", + "Move set Remove four operators: 0.0055814\n", + " Move Remove Delta_up_up: 0.00478277\n", + " Move Remove Delta_up_down: 0.00636994\n", + " Move Remove Delta_down_up: 0.00639437\n", + " Move Remove Delta_down_down: 0.00477878\n", + "Move Shift one operator: 0.0504766\n", + "[Rank 0] Warmup lasted: 0.0694536 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.139764 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9974\n", + "Average order: 1.2853\n", + "Auto-correlation time: 2.65569\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:54 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:55 72% ETA 00:00:00 cycle 7286 of 10000\n", + "17:36:55 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114038\n", + "Average order | 0.000176438\n", + "Average sign | 0.000175781\n", + "G_tau measure | 0.000493773\n", + "Total measure time | 0.00198638\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.06511\n", + " Move Insert Delta_up: 0.0645775\n", + " Move Insert Delta_down: 0.0656442\n", + "Move set Remove two operators: 0.065331\n", + " Move Remove Delta_up: 0.0647873\n", + " Move Remove Delta_down: 0.0658719\n", + "Move set Insert four operators: 0.00569673\n", + " Move Insert Delta_up_up: 0.00567891\n", + " Move Insert Delta_up_down: 0.00563549\n", + " Move Insert Delta_down_up: 0.00576908\n", + " Move Insert Delta_down_down: 0.00570335\n", + "Move set Remove four operators: 0.0056422\n", + " Move Remove Delta_up_up: 0.00595789\n", + " Move Remove Delta_up_down: 0.00547803\n", + " Move Remove Delta_down_up: 0.00585611\n", + " Move Remove Delta_down_down: 0.0052781\n", + "Move Shift one operator: 0.0523878\n", + "[Rank 0] Warmup lasted: 0.0673053 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.136122 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9978\n", + "Average order: 1.3104\n", + "Auto-correlation time: 2.14769\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4*c_dag('down',0)*c('down',0) + -4*c_dag('up',0)*c('up',0) + 8*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:55 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:55 70% ETA 00:00:00 cycle 7086 of 10000\n", + "17:36:55 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114125\n", + "Average order | 0.000176996\n", + "Average sign | 0.000171122\n", + "G_tau measure | 0.00150071\n", + "Total measure time | 0.00299008\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0655441\n", + " Move Insert Delta_up: 0.0641989\n", + " Move Insert Delta_down: 0.066902\n", + "Move set Remove two operators: 0.0656663\n", + " Move Remove Delta_up: 0.0655177\n", + " Move Remove Delta_down: 0.065813\n", + "Move set Insert four operators: 0.00574329\n", + " Move Insert Delta_up_up: 0.00615239\n", + " Move Insert Delta_up_down: 0.00584514\n", + " Move Insert Delta_down_up: 0.00617653\n", + " Move Insert Delta_down_down: 0.0047931\n", + "Move sU = 9.0\n", + "et Remove four operators: 0.00557688\n", + " Move Remove Delta_up_up: 0.00536355\n", + " Move Remove Delta_up_down: 0.00589547\n", + " Move Remove Delta_down_up: 0.0062092\n", + " Move Remove Delta_down_down: 0.00483401\n", + "Move Shift one operator: 0.0511499\n", + "[Rank 0] Warmup lasted: 0.0669602 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.138667 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9968\n", + "Average order: 1.2817\n", + "Auto-correlation time: 2.92277\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:55 92% ETA 00:00:00 cycle 4617 of 5000\n", + "17:36:55 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:55 46% ETA 00:00:00 cycle 4624 of 10000\n", + "17:36:55 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00116428\n", + "Average order | 0.000175073\n", + "Average sign | 0.000179122\n", + "G_tau measure | 0.000624143\n", + "Total measure time | 0.00214262\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.075023\n", + " Move Insert Delta_up: 0.0737795\n", + " Move Insert Delta_down: 0.0762717\n", + "Move set Remove two operators: 0.0754981\n", + " Move Remove Delta_up: 0.0750419\n", + " Move Remove Delta_down: 0.0759501\n", + "Move set Insert four operators: 0.0122848\n", + " Move Insert Delta_up_up: 0.010087\n", + " Move Insert Delta_up_down: 0.0149212\n", + " Move Insert Delta_down_up: 0.0147216\n", + " Mov\n", + "\n", + "Iteration = 2 / 10\n", + "e Insert Delta_down_down: 0.00942308\n", + "Move set Remove four operators: 0.0122644\n", + " Move Remove Delta_up_up: 0.00940893\n", + " Move Remove Delta_up_down: 0.0151437\n", + " Move Remove Delta_down_up: 0.0152795\n", + " Move Remove Delta_down_down: 0.00922575\n", + "Move Shift one operator: 0.160022\n", + "[Rank 0] Warmup lasted: 0.108396 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.214303 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 3.0593\n", + "Auto-correlation time: 8.51099\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 3 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:55 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:55 72% ETA 00:00:00 cycle 7205 of 10000\n", + "17:36:55 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114454\n", + "Average order | 0.000174778\n", + "Average sign | 0.000178598\n", + "G_tau measure | 0.000622807\n", + "Total measure time | 0.00212072\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0673988\n", + " Move Insert Delta_up: 0.0660498\n", + " Move Insert Delta_down: 0.0687521\n", + "Move set Remove two operators: 0.0665999\n", + " Move Remove Delta_up: 0.0656989\n", + " Move Remove Delta_down: 0.0674971\n", + "Move set Insert four operators: 0.00775671\n", + " Move Insert Delta_up_up: 0.00641076\n", + " Move Insert Delta_up_down: 0.00925224\n", + " Move Insert Delta_down_up: 0.0098923\n", + " Move Insert Delta_down_down: 0.00549451\n", + "Move set Remove four operators: 0.00814979\n", + " Move Remove Delta_up_up: 0.0064208\n", + " Move Remove Delta_up_down: 0.0094943\n", + " Move Remove Delta_down_up: 0.0109052\n", + " Move Remove Delta_down_down: 0.00577183\n", + "Move Shift one operator: 0.0525718\n", + "[Rank 0] Warmup lasted: 0.0684983 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.13753 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 1.2476\n", + "Auto-correlation time: 2.18844\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:56 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:56 76% ETA 00:00:00 cycle 7605 of 10000\n", + "17:36:56 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00111446\n", + "Average order | 0.000176218\n", + "Average sign | 0.000173181\n", + "G_tau measure | 0.000567391\n", + "Total measure time | 0.00203125\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0599149\n", + " Move Insert Delta_up: 0.0587469\n", + " Move Insert Delta_down: 0.0610859\n", + "Move set Remove two operators: 0.0603628\n", + " Move Remove Delta_up: 0.0599454\n", + " Move Remove Delta_down: 0.0607787\n", + "Move set Insert four operators: 0.00513004\n", + " Move Insert Delta_up_up: 0.00500828\n", + " Move Insert Delta_up_down: 0.00586709\n", + " Move Insert Delta_down_up: 0.00532469\n", + " Move Insert Delta_down_down: 0.00432035\n", + "Move set Remove four operators: 0.00484395\n", + " Move Remove Delta_up_up: 0.00435256\n", + " Move Remove Delta_up_down: 0.0050489\n", + " Move Remove Delta_down_up: 0.00528113\n", + " Move Remove Delta_down_down: 0.00468487\n", + "Move Shift one operator: 0.0424689\n", + "[Rank 0] Warmup lasted: 0.0623846 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.129958 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 1.1771\n", + "Auto-correlation time: 2.90146\n", + "\n", + "\n", + "Iteration = 4 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:56 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:56 76% ETA 00:00:00 cycle 7679 of 10000\n", + "17:36:56 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00111329\n", + "Average order | 0.000174453\n", + "Average sign | 0.000187997\n", + "G_tau measure | 0.00052281\n", + "Total measure time | 0.00199855\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0597628\n", + " Move Insert Delta_up: 0.0594387\n", + " Move Insert Delta_down: 0.0600886\n", + "Move set Remove two operators: 0.0601823\n", + " Move Remove Delta_up: 0.0602364\n", + " Move Remove Delta_down: 0.0601285\n", + "Move set Insert four operators: 0.00498081\n", + " Move Insert Delta_up_up: 0.00520013\n", + " Move Insert Delta_up_down: 0.00499122\n", + " Move Insert Delta_down_up: 0.00506582\n", + " Move Insert Delta_down_down: 0.00466358\n", + "Mov\n", + "\n", + "Iteration = 5 / 10\n", + "e set Remove four operators: 0.00463958\n", + " Move Remove Delta_up_up: 0.00460643\n", + " Move Remove Delta_up_down: 0.00493022\n", + " Move Remove Delta_down_up: 0.00485765\n", + " Move Remove Delta_down_down: 0.00416083\n", + "Move Shift one operator: 0.041741\n", + "[Rank 0] Warmup lasted: 0.0630625 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.128933 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9988\n", + "Average order: 1.1498\n", + "Auto-correlation time: 3.35153\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:56 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:56 73% ETA 00:00:00 cycle 7352 of 10000\n", + "17:36:56 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011521 \n", + "Average order | 0.000179953\n", + "Average sign | 0.000193562\n", + "G_tau measure | 0.000986017\n", + "Total measure time | 0.00251163\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0607112\n", + " Move Insert Delta_up: 0.0597318\n", + " Move Insert Delta_down: 0.0616933\n", + "Move set Remove two operators: 0.0605566\n", + " Move Remove Delta_up: 0.0600225\n", + " Move Remove Delta_down: 0.0610896\n", + "Move set Insert four operators: 0.0048536\n", + " Move Insert Delta_up_up: 0.00534402\n", + " Move Insert Delta_up_down: 0.00512184\n", + " Move Insert Delta_down_up: 0.00419329\n", + " Move Insert Delta_down_down: 0.00474785\n", + "Mov\n", + "\n", + "Iteration = 6 / 10\n", + "e set Remove four operators: 0.00485389\n", + " Move Remove Delta_up_up: 0.00482044\n", + " Move Remove Delta_up_down: 0.00488813\n", + " Move Remove Delta_down_up: 0.00502294\n", + " Move Remove Delta_down_down: 0.00468169\n", + "Move Shift one operator: 0.0414372\n", + "[Rank 0] Warmup lasted: 0.0637003 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.134183 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.996\n", + "Average order: 1.1644\n", + "Auto-correlation time: 3.2926\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 7 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:56 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:56 77% ETA 00:00:00 cycle 7706 of 10000\n", + "17:36:56 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114883\n", + "Average order | 0.000172738\n", + "Average sign | 0.000173912\n", + "G_tau measure | 0.000500922\n", + "Total measure time | 0.0019964 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0597706\n", + " Move Insert Delta_up: 0.058365\n", + " Move Insert Delta_down: 0.0611883\n", + "Move set Remove two operators: 0.0603617\n", + " Move Remove Delta_up: 0.0596815\n", + " Move Remove Delta_down: 0.0610421\n", + "Move set Insert four operators: 0.00507342\n", + " Move Insert Delta_up_up: 0.00552225\n", + " Move Insert Delta_up_down: 0.00502914\n", + " Move Insert Delta_down_up: 0.00519481\n", + " Move Insert Delta_down_down: 0.0045431\n", + "Move set Remove four operators: 0.00475572\n", + " Move Remove Delta_up_up: 0.00474486\n", + " Move Remove Delta_up_down: 0.00519264\n", + " Move Remove Delta_down_up: 0.00452235\n", + " Move Remove Delta_down_down: 0.00455891\n", + "Move Shift one operator: 0.0425655\n", + "[Rank 0] Warmup lasted: 0.0640778 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.1291 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9972\n", + "Average order: 1.1918\n", + "Auto-correlation time: 2.29282\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:56 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:57 76% ETA 00:00:00 cycle 7662 of 10000\n", + "17:36:57 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114987\n", + "Average order | 0.000175873\n", + "Average sign | 0.000174441\n", + "G_tau measure | 0.000509332\n", + "Total measure time | 0.00200951\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.059439\n", + " Move Insert Delta_up: 0.0581668\n", + " Move Insert Delta_down: 0.0607138\n", + "Move set Remove two operators: 0.0597239\n", + " Move Remove Delta_up: 0.0590741\n", + " Move Remove Delta_down: 0.06037\n", + "Move set Insert four operators: 0.00484312\n", + " Move Insert Delta_up_up: 0.0048189\n", + " Move Insert Delta_up_down: 0.00515876\n", + " Move Insert Delta_down_up: 0.00478774\n", + " Move Insert Delta_down_down: 0.00460903\n", + "Move set Remove four operators: 0.00469727\n", + " Move Remove Delta_up_up: 0.0042228\n", + " Move Remove Delta_up_down: 0.0049556\n", + " Move Remove Delta_down_up: 0.00490294\n", + " Move Remove Delta_down_down: 0.00470213\n", + "Move Shift one operator: 0.0419401\n", + "[Rank 0] Warmup lasted: 0.063158 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.129703 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9962\n", + "Average order: 1.1825\n", + "Auto-correlation time: 3.99906\n", + "\n", + "\n", + "Iteration = 8 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:57 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:57 76% ETA 00:00:00 cycle 7668 of 10000\n", + "17:36:57 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113999\n", + "Average order | 0.000175873\n", + "Average sign | 0.000177145\n", + "G_tau measure | 0.000494697\n", + "Total measure time | 0.0019877 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.059728\n", + " Move Insert Delta_up: 0.058413\n", + " Move Insert Delta_down: 0.0610475\n", + "Move set Remove two operators: 0.06057\n", + " Move Remove Delta_up: 0.0601686\n", + " Move Remove Delta_down: 0.0609698\n", + "Move set Insert four operators: 0.00533349\n", + " Move Insert Delta_up_up: 0.00561465\n", + " Move Insert Delta_up_down: 0.00525122\n", + " Move Insert Delta_down_up: 0.00578842\n", + " Move Insert Delta_down_down: 0.00467645\n", + "Move s\n", + "\n", + "Iteration = 9 / 10\n", + "et Remove four operators: 0.00479575\n", + " Move Remove Delta_up_up: 0.00452891\n", + " Move Remove Delta_up_down: 0.00515258\n", + " Move Remove Delta_down_up: 0.00471774\n", + " Move Remove Delta_down_down: 0.0047805\n", + "Move Shift one operator: 0.0424987\n", + "[Rank 0] Warmup lasted: 0.063466 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.130279 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9944\n", + "Average order: 1.1765\n", + "Auto-correlation time: 2.40606\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:57 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:57 75% ETA 00:00:00 cycle 7561 of 10000\n", + "17:36:57 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00118073\n", + "Average order | 0.000176595\n", + "Average sign | 0.000176277\n", + "G_tau measure | 0.00126962\n", + "Total measure time | 0.00280322\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0596867\n", + " Move Insert Delta_up: 0.0584019\n", + " Move Insert Delta_down: 0.060971\n", + "Move set Remove two operators: 0.0597209\n", + " Move Remove Delta_up: 0.0586373\n", + " Move Remove Delta_down: 0.0608016\n", + "Move set Insert four operators: 0.00478203\n", + " Move Insert Delta_up_up: 0.00492844\n", + " Move Insert Delta_up_down: 0.00489903\n", + " Move Insert Delta_down_up: 0.00478374\n", + " Move Insert Delta_down_down: 0.00451458\n", + "Move\n", + "\n", + "Iteration = 10 / 10\n", + " set Remove four operators: 0.00471166\n", + " Move Remove Delta_up_up: 0.00471679\n", + " Move Remove Delta_up_down: 0.00453695\n", + " Move Remove Delta_down_up: 0.00521538\n", + " Move Remove Delta_down_down: 0.00437776\n", + "Move Shift one operator: 0.0399454\n", + "[Rank 0] Warmup lasted: 0.0650666 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.131095 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9982\n", + "Average order: 1.1588\n", + "Auto-correlation time: 3.33519\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-4.5*c_dag('down',0)*c('down',0) + -4.5*c_dag('up',0)*c('up',0) + 9*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:57 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:57 77% ETA 00:00:00 cycle 7707 of 10000\n", + "17:36:57 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00112932\n", + "Average order | 0.000173649\n", + "Average sign | 0.000176619\n", + "G_tau measure | 0.0005733 \n", + "Total measure time | 0.00205289\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0604564\n", + " Move Insert Delta_up: 0.0588577\n", + " Move Insert Delta_down: 0.0620602\n", + "Move set Remove two operators: 0.0606382\n", + " Move Remove Delta_up: 0.0597842\n", + " Move Remove Delta_down: 0.0614869\n", + "Move set Insert four operators: 0.00508035\n", + " Move Insert Delta_up_up: 0.00542338\n", + " Move Insert Delta_up_down: 0.0051973\n", + " Move Insert Delta_down_up: 0.00528804\n", + " Move Insert Delta_down_down: 0.00440811\n", + "MoveU = 10.0\n", + " set Remove four operators: 0.00493871\n", + " Move Remove Delta_up_up: 0.00482393\n", + " Move Remove Delta_up_down: 0.00470077\n", + " Move Remove Delta_down_up: 0.00566008\n", + " Move Remove Delta_down_down: 0.0045722\n", + "Move Shift one operator: 0.0412753\n", + "[Rank 0] Warmup lasted: 0.0632685 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.129126 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9968\n", + "Average order: 1.1691\n", + "Auto-correlation time: 0.90488\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:57 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:57 49% ETA 00:00:00 cycle 4971 of 10000\n", + "17:36:58 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00118772\n", + "Average order | 0.000174399\n", + "Average sign | 0.000175365\n", + "G_tau measure | 0.000724154\n", + "Total measure time | 0.00226164\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0715393\n", + " Move Insert Delta_up: 0.0714001\n", + " Move Insert Delta_down: 0.0716792\n", + "Move set Remove two operators: 0.0708063\n", + " Move Remove Delta_up: 0.0703415\n", + " Move Remove Delta_down: 0.07127\n", + "Move set Insert four operators: 0.0109482\n", + " Move Insert Delta_up_up: 0.00810392\n", + " Move Insert Delta_up_down: 0.013351\n", + " Move Insert Delta_down_up: 0.014284\n", + " Move Insert Delta_down_down: 0.00807161\n", + "Move set \n", + "\n", + "Iteration = 2 / 10Remove four operators: 0.0114495\n", + " Move Remove Delta_up_up: 0.00895825\n", + " Move Remove Delta_up_down: 0.0147422\n", + " Move Remove Delta_down_up: 0.0144898\n", + " Move Remove Delta_down_down: 0.00756817\n", + "Move Shift one operator: 0.136485\n", + "[Rank 0] Warmup lasted: 0.0947748 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.201123 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 2.8101\n", + "Auto-correlation time: 9.33423\n", + "\n", + "\n", + "\n", + "Iteration = 3 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:58 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:58 77% ETA 00:00:00 cycle 7784 of 10000\n", + "17:36:58 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114748\n", + "Average order | 0.000173434\n", + "Average sign | 0.000173643\n", + "G_tau measure | 0.000492445\n", + "Total measure time | 0.001987 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0620445\n", + " Move Insert Delta_up: 0.0613942\n", + " Move Insert Delta_down: 0.0626974\n", + "Move set Remove two operators: 0.0624537\n", + " Move Remove Delta_up: 0.0626078\n", + " Move Remove Delta_down: 0.0623003\n", + "Move set Insert four operators: 0.00688384\n", + " Move Insert Delta_up_up: 0.00590854\n", + " Move Insert Delta_up_down: 0.00871756\n", + " Move Insert Delta_down_up: 0.00824825\n", + " Move Insert Delta_down_down: 0.00467794\n", + "Move set Remove four operators: 0.00658183\n", + " Move Remove Delta_up_up: 0.00538131\n", + " Move Remove Delta_up_down: 0.00726536\n", + " Move Remove Delta_down_up: 0.00855818\n", + " Move Remove Delta_down_down: 0.0050996\n", + "Move Shift one operator: 0.0414626\n", + "[Rank 0] Warmup lasted: 0.0618828 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.127212 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 1.1285\n", + "Auto-correlation time: 2.2533\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:58 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:58 80% ETA 00:00:00 cycle 8085 of 10000\n", + "17:36:58 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114792\n", + "Average order | 0.000171199\n", + "Average sign | 0.000170999\n", + "G_tau measure | 0.000497123\n", + "Total measure time | 0.00198724\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0558467\n", + " Move Insert Delta_up: 0.0548354\n", + " Move Insert Delta_down: 0.0568613\n", + "Move set Remove two operators: 0.0563139\n", + " Move Remove Delta_up: 0.0556514\n", + " Move Remove Delta_down: 0.0569743\n", + "Move set Insert four operators: 0.00437746\n", + " Move Insert Delta_up_up: 0.00445812\n", + " Move Insert Delta_up_down: 0.00470138\n", + " Move Insert Delta_down_up: 0.00439052\n", + " Move Insert Delta_down_down: 0.00395763\n", + "Mo\n", + "\n", + "Iteration = 4 / 10\n", + "ve set Remove four operators: 0.00414735\n", + " Move Remove Delta_up_up: 0.00428681\n", + " Move Remove Delta_up_down: 0.00416849\n", + " Move Remove Delta_down_up: 0.00410326\n", + " Move Remove Delta_down_down: 0.00403258\n", + "Move Shift one operator: 0.0347029\n", + "[Rank 0] Warmup lasted: 0.0598513 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.122534 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9994\n", + "Average order: 1.0673\n", + "Auto-correlation time: 3.48919\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:58 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:58 80% ETA 00:00:00 cycle 8093 of 10000\n", + "17:36:58 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011215 \n", + "Average order | 0.000173062\n", + "Average sign | 0.00017243\n", + "G_tau measure | 0.000481739\n", + "Total measure time | 0.00194873\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0551622\n", + " Move Insert Delta_up: 0.0540573\n", + " Move Insert Delta_down: 0.056271\n", + "Move set Remove two operators: 0.0554291\n", + " Move Remove Delta_up: 0.0548532\n", + " Move Remove Delta_down: 0.0560021\n", + "Move set Insert four operators: 0.00379637\n", + " Move Insert Delta_up_up: 0.00380545\n", + " Move Insert Delta_up_down: 0.00399505\n", + " Move Insert Delta_down_up: 0.00374965\n", + " Move Insert Delta_down_down: 0.00363535\n", + "Move set Remove four operators: 0.00366381\n", + " Move Remove Delta_up_up: 0.00335177\n", + " Move Remove Delta_up_down: 0.00377209\n", + " Move Remove Delta_down_up: 0.00381376\n", + " Move Remove Delta_down_down: 0.00371272\n", + "Move Shift one operator: 0.0336264\n", + "[Rank 0] Warmup lasted: 0.0597385 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.12209 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9988\n", + "Average order: 1.0624\n", + "Auto-correlation time: 3.12545\n", + "\n", + "\n", + "Iteration = 5 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:58 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:58 81% ETA 00:00:00 cycle 8174 of 10000\n", + "17:36:58 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113455\n", + "Average order | 0.000172962\n", + "Average sign | 0.000172111\n", + "G_tau measure | 0.000494535\n", + "Total measure time | 0.00197415\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0555556\n", + " Move Insert Delta_up: 0.0548071\n", + " Move Insert Delta_down: 0.056308\n", + "Move set Remove two operators: 0.0565826\n", + " Move Remove Delta_up: 0.0561847\n", + " Move Remove Delta_down: 0.0569795\n", + "Move set Insert four operators: 0.00401386\n", + " Move Insert Delta_up_up: 0.00431683\n", + " Move Insert Delta_up_down: 0.00367912\n", + " Move Insert Delta_down_up: 0.00402102\n", + " Move Insert Delta_down_down: 0.00403548\n", + "Move set Remove four operators: 0.00343265\n", + " Move Remove Delta_up_up: 0.00319218\n", + " Move Remove Delta_up_down: 0.00357058\n", + " Move Remove Delta_down_up: 0.00384326\n", + " Move Remove Delta_down_down: 0.00311738\n", + "Move Shift one operator: 0.0336175\n", + "[Rank 0] Warmup lasted: 0.0608379 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.120854 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9988\n", + "Average order: 1.0345\n", + "Auto-correlation time: 3.44062\n", + "\n", + "\n", + "Iteration = 6 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "\n", + "\n", + "Iteration = 7 / 10\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:58 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:59 81% ETA 00:00:00 cycle 8150 of 10000\n", + "17:36:59 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011493 \n", + "Average order | 0.000176394\n", + "Average sign | 0.000175145\n", + "G_tau measure | 0.000636652\n", + "Total measure time | 0.00213749\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0561644\n", + " Move Insert Delta_up: 0.0553785\n", + " Move Insert Delta_down: 0.0569541\n", + "Move set Remove two operators: 0.0563292\n", + " Move Remove Delta_up: 0.0563507\n", + " Move Remove Delta_down: 0.0563077\n", + "Move set Insert four operators: 0.0040616\n", + " Move Insert Delta_up_up: 0.00442985\n", + " Move Insert Delta_up_down: 0.00424611\n", + " Move Insert Delta_down_up: 0.00385272\n", + " Move Insert Delta_down_down: 0.00371569\n", + "Move set Remove four operators: 0.00386509\n", + " Move Remove Delta_up_up: 0.003755\n", + " Move Remove Delta_up_down: 0.00376864\n", + " Move Remove Delta_down_up: 0.00392764\n", + " Move Remove Delta_down_down: 0.00400866\n", + "Move Shift one operator: 0.0356184\n", + "[Rank 0] Warmup lasted: 0.0611042 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.121234 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9978\n", + "Average order: 1.0107\n", + "Auto-correlation time: 3.51989\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 8 / 10Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:59 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:59 80% ETA 00:00:00 cycle 8028 of 10000\n", + "17:36:59 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113187\n", + "Average order | 0.000172906\n", + "Average sign | 0.000173854\n", + "G_tau measure | 0.00119973\n", + "Total measure time | 0.00267836\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0569562\n", + " Move Insert Delta_up: 0.0557674\n", + " Move Insert Delta_down: 0.0581538\n", + "Move set Remove two operators: 0.0573977\n", + " Move Remove Delta_up: 0.0568736\n", + " Move Remove Delta_down: 0.057919\n", + "Move set Insert four operators: 0.00397292\n", + " Move Insert Delta_up_up: 0.00431444\n", + " Move Insert Delta_up_down: 0.00403564\n", + " Move Insert Delta_down_up: 0.00342248\n", + " Move Insert Delta_down_down: 0.00411819\n", + "Move\n", + " set Remove four operators: 0.00370689\n", + " Move Remove Delta_up_up: 0.00370983\n", + " Move Remove Delta_up_down: 0.0034267\n", + " Move Remove Delta_down_up: 0.00365311\n", + " Move Remove Delta_down_down: 0.00403935\n", + "Move Shift one operator: 0.0337761\n", + "[Rank 0] Warmup lasted: 0.0612707 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.122749 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9968\n", + "Average order: 1.0148\n", + "Auto-correlation time: 2.53234\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 9 / 10Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:59 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:59 80% ETA 00:00:00 cycle 8010 of 10000\n", + "17:36:59 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115006\n", + "Average order | 0.000171089\n", + "Average sign | 0.000170973\n", + "G_tau measure | 0.000614332\n", + "Total measure time | 0.00210645\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0595088\n", + " Move Insert Delta_up: 0.0578924\n", + " Move Insert Delta_down: 0.0611341\n", + "Move set Remove two operators: 0.0595746\n", + " Move Remove Delta_up: 0.0587787\n", + " Move Remove Delta_down: 0.0603688\n", + "Move set Insert four operators: 0.00426946\n", + " Move Insert Delta_up_up: 0.00422307\n", + " Move Insert Delta_up_down: 0.00518465\n", + " Move Insert Delta_down_up: 0.00388528\n", + " Move Insert Delta_down_down: 0.00378426\n", + "Move set Remove four operators: 0.00421233\n", + " Move Remove Delta_up_up: 0.00335218\n", + " Move Remove Delta_up_down: 0.00464931\n", + " Move Remove Delta_down_up: 0.00480216\n", + " Move Remove Delta_down_down: 0.00403033\n", + "Move Shift one operator: 0.0367278\n", + "[Rank 0] Warmup lasted: 0.0603263 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.123466 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9806\n", + "Average order: 1.0485\n", + "Auto-correlation time: 2.82185\n", + "\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:59 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:59 81% ETA 00:00:00 cycle 8154 of 10000\n", + "17:36:59 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00112582\n", + "Average order | 0.000175281\n", + "Average sign | 0.000174666\n", + "G_tau measure | 0.000548011\n", + "Total measure time | 0.00202378\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.056208\n", + " Move Insert Delta_up: 0.0553238\n", + " Move Insert Delta_down: 0.0570951\n", + "Move set Remove two operators: 0.0564072\n", + " Move Remove Delta_up: 0.0561066\n", + " Move Remove Delta_down: 0.0567066\n", + "Move set Insert four operators: 0.00407237\n", + " Move Insert Delta_up_up: 0.00458353\n", + " Move Insert Delta_up_down: 0.00414805\n", + " Move Insert Delta_down_up: 0.00358938\n", + " Move Insert Delta_down_down: 0.00396333\n", + "Move set Remove four operators: 0.00385295\n", + " Move Remove Delta_up_up: 0.00384102\n", + " Move Remove Delta_up_down: 0.00381134\n", + " Move Remove Delta_down_up: 0.00384082\n", + " Move Remove Delta_down_down: 0.0039189\n", + "Move Shift one operator: 0.0336746\n", + "[Rank 0] Warmup lasted: 0.0591636 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.121264 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9988\n", + "Average order: 1.0118\n", + "Auto-correlation time: 3.32063\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5*c_dag('down',0)*c('down',0) + -5*c_dag('up',0)*c('up',0) + 10*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:36:59 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:36:59 81% ETA 00:00:00 cycle 8130 of 10000\n", + "17:36:59 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00112664\n", + "Average order | 0.000169903\n", + "Average sign | 0.000170967\n", + "G_tau measure | 0.000570233\n", + "Total measure time | 0.00203775\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0565456\n", + " Move Insert Delta_up: 0.0560489\n", + " Move Insert Delta_down: 0.0570416\n", + "Move set Remove two operators: 0.0565554\n", + " Move Remove Delta_up: 0.0566927\n", + " Move Remove Delta_down: 0.0564188\n", + "Move set Insert four operators: 0.00394695\n", + " Move Insert Delta_up_up: 0.00453041\n", + " Move Insert Delta_up_down: 0.003751\n", + " Move Insert Delta_down_up: 0.00377433\n", + " Move Insert Delta_down_down: 0.00372447\n", + "Move set Remove four operators: 0.00393142\n", + " Move Remove Delta_up_up: 0.00393605\n", + " Move Remove Delta_up_down: 0.00351129\n", + " Move Remove Delta_down_up: 0.00412257\n", + " Move Remove Delta_down_down: 0.00415485\n", + "Move Shift one operator: 0.03294\n", + "[Rank 0] Warmup lasted: 0.0595034 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.121777 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9982\n", + "Average order: 1.0399\n", + "Auto-correlation time: 2.72088\n", + "U = 11.0\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 2 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:00 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:00 54% ETA 00:00:00 cycle 5460 of 10000\n", + "17:37:00 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114313\n", + "Average order | 0.000173784\n", + "Average sign | 0.000177948\n", + "G_tau measure | 0.000703365\n", + "Total measure time | 0.00219823\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0696534\n", + " Move Insert Delta_up: 0.0698218\n", + " Move Insert Delta_down: 0.0694839\n", + "Move set Remove two operators: 0.0686236\n", + " Move Remove Delta_up: 0.0693558\n", + " Move Remove Delta_down: 0.0678957\n", + "Move set Insert four operators: 0.0101898\n", + " Move Insert Delta_up_up: 0.00760697\n", + " Move Insert Delta_up_down: 0.0134782\n", + " Move Insert Delta_down_up: 0.0128854\n", + " Move Insert Delta_down_down: 0.00681329\n", + "Move set Remove four operators: 0.010656\n", + " Move Remove Delta_up_up: 0.00783997\n", + " Move Remove Delta_up_down: 0.0139799\n", + " Move Remove Delta_down_up: 0.0135968\n", + " Move Remove Delta_down_down: 0.00715714\n", + "Move Shift one operator: 0.113309\n", + "[Rank 0] Warmup lasted: 0.0920199 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.183328 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 2.3798\n", + "Auto-correlation time: 6.84919\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:00 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:00 82% ETA 00:00:00 cycle 8267 of 10000\n", + "17:37:00 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113257\n", + "Average order | 0.000174729\n", + "Average sign | 0.000171475\n", + "G_tau measure | 0.000655191\n", + "Total measure time | 0.00213397\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0567635\n", + " Move Insert Delta_up: 0.0558338\n", + " Move Insert Delta_down: 0.0576938\n", + "Move set Remove two operators: 0.0570791\n", + " Move Remove Delta_up: 0.0568651\n", + " Move Remove Delta_down: 0.0572924\n", + "Move set Insert four operators: 0.00513888\n", + " Move Insert Delta_up_up: 0.0044657\n", + " Move Insert Delta_up_down: 0.00659921\n", + " Move Insert Delta_down_up: 0.00590559\n", + " Move Insert Delta_down_down: 0.00359382\n", + "Mov\n", + "\n", + "Iteration = 3 / 10\n", + "e set Remove four operators: 0.00494392\n", + " Move Remove Delta_up_up: 0.00375318\n", + " Move Remove Delta_up_down: 0.00599587\n", + " Move Remove Delta_down_up: 0.00600445\n", + " Move Remove Delta_down_down: 0.00399808\n", + "Move Shift one operator: 0.0324819\n", + "[Rank 0] Warmup lasted: 0.0581535 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.119753 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 0.9786\n", + "Auto-correlation time: 2.6009\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:00 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:00 85% ETA 00:00:00 cycle 8522 of 10000\n", + "17:37:00 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113484\n", + "Average order | 0.000174635\n", + "Average sign | 0.00017281\n", + "G_tau measure | 0.000471962\n", + "Total measure time | 0.00195425\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0522979\n", + " Move Insert Delta_up: 0.0514701\n", + " Move Insert Delta_down: 0.0531272\n", + "Move set Remove two operators: 0.0526289\n", + " Move Remove Delta_up: 0.0525332\n", + " Move Remove Delta_down: 0.052724\n", + "Move set Insert four operators: 0.0035412\n", + " Move Insert Delta_up_up: 0.00398847\n", + " Move Insert Delta_up_down: 0.00370725\n", + " Move Insert Delta_down_up: 0.00335209\n", + " Move Insert Delta_down_down: 0.00311216\n", + "Move \n", + "\n", + "Iteration = 4 / 10\n", + "set Remove four operators: 0.00335832\n", + " Move Remove Delta_up_up: 0.00299014\n", + " Move Remove Delta_up_down: 0.00369106\n", + " Move Remove Delta_down_up: 0.0037407\n", + " Move Remove Delta_down_down: 0.00300276\n", + "Move Shift one operator: 0.0287871\n", + "[Rank 0] Warmup lasted: 0.0573234 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.116241 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9996\n", + "Average order: 0.9456\n", + "Auto-correlation time: 3.58899\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:00 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:00 85% ETA 00:00:00 cycle 8532 of 10000\n", + "17:37:00 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00112831\n", + "Average order | 0.000175032\n", + "Average sign | 0.000170599\n", + "G_tau measure | 0.000477615\n", + "Total measure time | 0.00195155\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0510253\n", + " Move Insert Delta_up: 0.0504098\n", + " Move Insert Delta_down: 0.0516432\n", + "Move set Remove two operators: 0.0515426\n", + " Move Remove Delta_up: 0.051475\n", + " Move Remove Delta_down: 0.0516099\n", + "Move set Insert four operators: 0.00340387\n", + " Move Insert Delta_up_up: 0.00378519\n", + " Move Insert Delta_up_down: 0.0032779\n", + " Move Insert Delta_down_up: 0.0033044\n", + " Move Insert Delta_down_down: 0.00324285\n", + "Move set Remove four operators: 0.00317778\n", + " Move Remove Delta_up_up: 0.00283378\n", + " Move Remove Delta_up_down: 0.00321952\n", + " Move Remove Delta_down_up: 0.00370341\n", + " Move Remove Delta_down_down: 0.00294856\n", + "Move Shift one operator: 0.0277202\n", + "[Rank 0] Warmup lasted: 0.0570392 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.116499 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9996\n", + "Average order: 0.9533\n", + "Auto-correlation time: 2.78912\n", + "\n", + "\n", + "Iteration = 5 / 10\n", + "\n", + "\n", + "Iteration = 6 / 10\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:00 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:00 84% ETA 00:00:00 cycle 8424 of 10000\n", + "17:37:01 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113708\n", + "Average order | 0.000174079\n", + "Average sign | 0.000169747\n", + "G_tau measure | 0.000516366\n", + "Total measure time | 0.00199727\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0509114\n", + " Move Insert Delta_up: 0.0494415\n", + " Move Insert Delta_down: 0.0523881\n", + "Move set Remove two operators: 0.0515835\n", + " Move Remove Delta_up: 0.0508587\n", + " Move Remove Delta_down: 0.0523052\n", + "Move set Insert four operators: 0.00328518\n", + " Move Insert Delta_up_up: 0.00378564\n", + " Move Insert Delta_up_down: 0.00328526\n", + " Move Insert Delta_down_up: 0.00299079\n", + " Move Insert Delta_down_down: 0.00307324\n", + "Mo\n", + "ve set Remove four operators: 0.00292678\n", + " Move Remove Delta_up_up: 0.00257701\n", + " Move Remove Delta_up_down: 0.00286487\n", + " Move Remove Delta_down_up: 0.0034609\n", + " Move Remove Delta_down_down: 0.00279944\n", + "Move Shift one operator: 0.0285168\n", + "[Rank 0] Warmup lasted: 0.0573744 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.117503 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9988\n", + "Average order: 0.9713\n", + "Auto-correlation time: 3.77243\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:01 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:01 84% ETA 00:00:00 cycle 8482 of 10000\n", + "17:37:01 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00112907\n", + "Average order | 0.000171733\n", + "Average sign | 0.000172552\n", + "G_tau measure | 0.000497535\n", + "Total measure time | 0.00197089\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0518978\n", + " Move Insert Delta_up: 0.0515069\n", + " Move Insert Delta_down: 0.0522911\n", + "Move set Remove two operators: 0.0524093\n", + " Move Remove Delta_up: 0.0525957\n", + " Move Remove Delta_down: 0.052223\n", + "Move set Insert four operators: 0.00326625\n", + " Move Insert Delta_up_up: 0.00386679\n", + " Move Insert Delta_up_down: 0.00304207\n", + " Move Insert Delta_down_up: 0.00291603\n", + " Move Insert Delta_down_down: 0.00323224\n", + "Move set Remove four operators: 0.00301172\n", + " Move Remove Delta_up_up: 0.00279397\n", + " Move Remove Delta_up_down: 0.00282048\n", + " Move Remove Delta_down_up: 0.0035536\n", + " Move Remove Delta_down_down: 0.00287666\n", + "Move Shift one operator: 0.0282139\n", + "[Rank 0] Warmup lasted: 0.0573663 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.117064 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9988\n", + "Average order: 0.9679\n", + "Auto-correlation time: 2.24404\n", + "\n", + "\n", + "Iteration = 7 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 8 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:01 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:01 83% ETA 00:00:00 cycle 8341 of 10000\n", + "17:37:01 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011421 \n", + "Average order | 0.000175292\n", + "Average sign | 0.00017538\n", + "G_tau measure | 0.00057952\n", + "Total measure time | 0.00207229\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0514406\n", + " Move Insert Delta_up: 0.0510664\n", + " Move Insert Delta_down: 0.0518164\n", + "Move set Remove two operators: 0.051954\n", + " Move Remove Delta_up: 0.0523787\n", + " Move Remove Delta_down: 0.0515301\n", + "Move set Insert four operators: 0.00338554\n", + " Move Insert Delta_up_up: 0.00366893\n", + " Move Insert Delta_up_down: 0.00344041\n", + " Move Insert Delta_down_up: 0.00307385\n", + " Move Insert Delta_down_down: 0.0033557\n", + "Move set Remove four operators: 0.00305853\n", + " Move Remove Delta_up_up: 0.00254999\n", + " Move Remove Delta_up_down: 0.00326173\n", + " Move Remove Delta_down_up: 0.00297855\n", + " Move Remove Delta_down_down: 0.00343698\n", + "Move Shift one operator: 0.0282974\n", + "[Rank 0] Warmup lasted: 0.0568543 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.118557 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9992\n", + "Average order: 0.9675\n", + "Auto-correlation time: 3.34324\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:01 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:01 85% ETA 00:00:00 cycle 8588 of 10000\n", + "17:37:01 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114523\n", + "Average order | 0.000173347\n", + "Average sign | 0.000170297\n", + "G_tau measure | 0.000496129\n", + "Total measure time | 0.001985 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0516234\n", + " Move Insert Delta_up: 0.0507942\n", + " Move Insert Delta_down: 0.0524561\n", + "Move set Remove two operators: 0.0515959\n", + " Move Remove Delta_up: 0.0514668\n", + " Move Remove Delta_down: 0.0517245\n", + "Move set Insert four operators: 0.00321691\n", + " Move Insert Delta_up_up: 0.00343928\n", + " Move Insert Delta_up_down: 0.00323961\n", + " Move Insert Delta_down_up: 0.00322555\n", + " Move Insert Delta_down_down: 0.00296047\n", + "Mo\n", + "\n", + "Iteration = 9 / 10\n", + "ve set Remove four operators: 0.00319616\n", + " Move Remove Delta_up_up: 0.00286625\n", + " Move Remove Delta_up_down: 0.00345553\n", + " Move Remove Delta_down_up: 0.00322388\n", + " Move Remove Delta_down_down: 0.00323392\n", + "Move Shift one operator: 0.0279356\n", + "[Rank 0] Warmup lasted: 0.0577107 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.115961 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.998\n", + "Average order: 0.9502\n", + "Auto-correlation time: 3.90419\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 10 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:01 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:01 85% ETA 00:00:00 cycle 8520 of 10000\n", + "17:37:01 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113566\n", + "Average order | 0.000171149\n", + "Average sign | 0.000171571\n", + "G_tau measure | 0.000505713\n", + "Total measure time | 0.00198409\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0522993\n", + " Move Insert Delta_up: 0.0518042\n", + " Move Insert Delta_down: 0.0527968\n", + "Move set Remove two operators: 0.0523894\n", + " Move Remove Delta_up: 0.0527668\n", + " Move Remove Delta_down: 0.0520146\n", + "Move set Insert four operators: 0.00332163\n", + " Move Insert Delta_up_up: 0.00385812\n", + " Move Insert Delta_up_down: 0.00331033\n", + " Move Insert Delta_down_up: 0.00287276\n", + " Move Insert Delta_down_down: 0.00323793\n", + "Move set Remove four operators: 0.00320791\n", + " Move Remove Delta_up_up: 0.00303251\n", + " Move Remove Delta_up_down: 0.00257947\n", + " Move Remove Delta_down_up: 0.00374368\n", + " Move Remove Delta_down_down: 0.00347652\n", + "Move Shift one operator: 0.0295904\n", + "[Rank 0] Warmup lasted: 0.0569472 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.116426 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.999\n", + "Average order: 0.9579\n", + "Auto-correlation time: 3.5502\n", + "U = 12.0\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-5.5*c_dag('down',0)*c('down',0) + -5.5*c_dag('up',0)*c('up',0) + 11*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:01 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:01 83% ETA 00:00:00 cycle 8308 of 10000\n", + "17:37:02 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113861\n", + "Average order | 0.00017479\n", + "Average sign | 0.000171791\n", + "G_tau measure | 0.000516632\n", + "Total measure time | 0.00200182\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.052027\n", + " Move Insert Delta_up: 0.0514887\n", + " Move Insert Delta_down: 0.052567\n", + "Move set Remove two operators: 0.0525394\n", + " Move Remove Delta_up: 0.0525082\n", + " Move Remove Delta_down: 0.0525705\n", + "Move set Insert four operators: 0.00335517\n", + " Move Insert Delta_up_up: 0.00413695\n", + " Move Insert Delta_up_down: 0.00323186\n", + " Move Insert Delta_down_up: 0.00292926\n", + " Move Insert Delta_down_down: 0.00311042\n", + "Move set Remove four operators: 0.00302516\n", + " Move Remove Delta_up_up: 0.00310847\n", + " Move Remove Delta_up_down: 0.00289752\n", + " Move Remove Delta_down_up: 0.00330243\n", + " Move Remove Delta_down_down: 0.00279307\n", + "Move Shift one operator: 0.0301017\n", + "[Rank 0] Warmup lasted: 0.05751 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.11865 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9998\n", + "Average order: 0.9797\n", + "Auto-correlation time: 4.09128\n", + "\n", + "\n", + "Iteration = 1 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 2 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:02 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:02 57% ETA 00:00:00 cycle 5758 of 10000\n", + "17:37:02 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.0011648 \n", + "Average order | 0.000171131\n", + "Average sign | 0.000177031\n", + "G_tau measure | 0.00052665\n", + "Total measure time | 0.00203961\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0659214\n", + " Move Insert Delta_up: 0.0645032\n", + " Move Insert Delta_down: 0.0673451\n", + "Move set Remove two operators: 0.0664132\n", + " Move Remove Delta_up: 0.0657103\n", + " Move Remove Delta_down: 0.0671124\n", + "Move set Insert four operators: 0.00972141\n", + " Move Insert Delta_up_up: 0.00816553\n", + " Move Insert Delta_up_down: 0.0120463\n", + " Move Insert Delta_down_up: 0.0123374\n", + " Move Insert Delta_down_down: 0.00630649\n", + "Move set Remove four operators: 0.00948341\n", + " Move Remove Delta_up_up: 0.00713825\n", + " Move Remove Delta_up_down: 0.0120053\n", + " Move Remove Delta_down_up: 0.0127712\n", + " Move Remove Delta_down_down: 0.00596812\n", + "Move Shift one operator: 0.0996916\n", + "[Rank 0] Warmup lasted: 0.0850222 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.173681 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 2.1975\n", + "Auto-correlation time: 6.8545\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 3 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:02 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:02 85% ETA 00:00:00 cycle 8541 of 10000\n", + "17:37:02 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00114865\n", + "Average order | 0.000173988\n", + "Average sign | 0.000171794\n", + "G_tau measure | 0.00119219\n", + "Total measure time | 0.00268662\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0532849\n", + " Move Insert Delta_up: 0.0522147\n", + " Move Insert Delta_down: 0.0543555\n", + "Move set Remove two operators: 0.053342\n", + " Move Remove Delta_up: 0.0532922\n", + " Move Remove Delta_down: 0.0533916\n", + "Move set Insert four operators: 0.00446188\n", + " Move Insert Delta_up_up: 0.00427029\n", + " Move Insert Delta_up_down: 0.0056238\n", + " Move Insert Delta_down_up: 0.00476629\n", + " Move Insert Delta_down_down: 0.0031903\n", + "Move set Remove four operators: 0.00436633\n", + " Move Remove Delta_up_up: 0.00327022\n", + " Move Remove Delta_up_down: 0.00473538\n", + " Move Remove Delta_down_up: 0.00557037\n", + " Move Remove Delta_down_down: 0.00387195\n", + "Move Shift one operator: 0.0277647\n", + "[Rank 0] Warmup lasted: 0.05598 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.116407 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 1\n", + "Average order: 0.8927\n", + "Auto-correlation time: 2.53428\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 4 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:02 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:02 88% ETA 00:00:00 cycle 8864 of 10000\n", + "17:37:02 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113772\n", + "Average order | 0.000172391\n", + "Average sign | 0.000173831\n", + "G_tau measure | 0.000667482\n", + "Total measure time | 0.00215142\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0487876\n", + " Move Insert Delta_up: 0.0475343\n", + " Move Insert Delta_down: 0.0500452\n", + "Move set Remove two operators: 0.0492262\n", + " Move Remove Delta_up: 0.0486641\n", + " Move Remove Delta_down: 0.0497856\n", + "Move set Insert four operators: 0.00317577\n", + " Move Insert Delta_up_up: 0.0035957\n", + " Move Insert Delta_up_down: 0.00350054\n", + " Move Insert Delta_down_up: 0.00284136\n", + " Move Insert Delta_down_down: 0.00275857\n", + "Move set Remove four operators: 0.00292205\n", + " Move Remove Delta_up_up: 0.00258701\n", + " Move Remove Delta_up_down: 0.00302885\n", + " Move Remove Delta_down_up: 0.00338107\n", + " Move Remove Delta_down_down: 0.0026844\n", + "Move Shift one operator: 0.0236387\n", + "[Rank 0] Warmup lasted: 0.0545816 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.112377 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9998\n", + "Average order: 0.8792\n", + "Auto-correlation time: 3.32021\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:02 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:02 86% ETA 00:00:00 cycle 8611 of 10000\n", + "17:37:02 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00115159\n", + "Average order | 0.000175224\n", + "Average sign | 0.000172538\n", + "G_tau measure | 0.000641879\n", + "Total measure time | 0.00214123\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.048189\n", + " Move Insert Delta_up: 0.0475752\n", + " Move Insert Delta_down: 0.048804\n", + "Move set Remove two operators: 0.048684\n", + " Move Remove Delta_up: 0.0485661\n", + " Move Remove Delta_down: 0.0488014\n", + "Move set Insert four operators: 0.00280902\n", + " Move Insert Delta_up_up: 0.00324111\n", + " Move Insert Delta_up_down: 0.00320282\n", + " Move Insert Delta_down_up: 0.00247802\n", + " Move Insert Delta_down_down: 0.0023114\n", + "Move s\n", + "\n", + "Iteration = 5 / 10\n", + "et Remove four operators: 0.0025366\n", + " Move Remove Delta_up_up: 0.00258148\n", + " Move Remove Delta_up_down: 0.00246217\n", + " Move Remove Delta_down_up: 0.00286055\n", + " Move Remove Delta_down_down: 0.00224081\n", + "Move Shift one operator: 0.0246712\n", + "[Rank 0] Warmup lasted: 0.0560449 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.114722 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9992\n", + "Average order: 0.9191\n", + "Auto-correlation time: 3.93015\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 6 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:02 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:03 86% ETA 00:00:00 cycle 8682 of 10000\n", + "17:37:03 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113351\n", + "Average order | 0.00017218\n", + "Average sign | 0.000169732\n", + "G_tau measure | 0.00061197\n", + "Total measure time | 0.0020874 \n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0482681\n", + " Move Insert Delta_up: 0.0473526\n", + " Move Insert Delta_down: 0.0491879\n", + "Move set Remove two operators: 0.0482165\n", + " Move Remove Delta_up: 0.0481162\n", + " Move Remove Delta_down: 0.0483167\n", + "Move set Insert four operators: 0.00294627\n", + " Move Insert Delta_up_up: 0.00344064\n", + " Move Insert Delta_up_down: 0.00291673\n", + " Move Insert Delta_down_up: 0.00275251\n", + " Move Insert Delta_down_down: 0.00267103\n", + "Move set Remove four operators: 0.00298406\n", + " Move Remove Delta_up_up: 0.0026251\n", + " Move Remove Delta_up_down: 0.00285476\n", + " Move Remove Delta_down_up: 0.00329326\n", + " Move Remove Delta_down_down: 0.00315823\n", + "Move Shift one operator: 0.0246552\n", + "[Rank 0] Warmup lasted: 0.0552698 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.114393 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9972\n", + "Average order: 0.911\n", + "Auto-correlation time: 2.88386\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 7 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:03 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:03 86% ETA 00:00:00 cycle 8694 of 10000\n", + "17:37:03 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00112254\n", + "Average order | 0.000171682\n", + "Average sign | 0.000169843\n", + "G_tau measure | 0.000609689\n", + "Total measure time | 0.00207376\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.048889\n", + " Move Insert Delta_up: 0.0478555\n", + " Move Insert Delta_down: 0.0499248\n", + "Move set Remove two operators: 0.049152\n", + " Move Remove Delta_up: 0.0484824\n", + " Move Remove Delta_down: 0.0498193\n", + "Move set Insert four operators: 0.003037\n", + " Move Insert Delta_up_up: 0.00320006\n", + " Move Insert Delta_up_down: 0.00327777\n", + " Move Insert Delta_down_up: 0.0029207\n", + " Move Insert Delta_down_down: 0.00274846\n", + "Move set Remove four operators: 0.00285318\n", + " Move Remove Delta_up_up: 0.00254032\n", + " Move Remove Delta_up_down: 0.00293476\n", + " Move Remove Delta_down_up: 0.00342016\n", + " Move Remove Delta_down_down: 0.00251206\n", + "Move Shift one operator: 0.0245681\n", + "[Rank 0] Warmup lasted: 0.0554533 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.113913 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9988\n", + "Average order: 0.8968\n", + "Auto-correlation time: 4.21281\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:03 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:03 87% ETA 00:00:00 cycle 8797 of 10000\n", + "17:37:03 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00112889\n", + "Average order | 0.000174991\n", + "Average sign | 0.000176317\n", + "G_tau measure | 0.000609379\n", + "Total measure time | 0.00208958\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0473094\n", + " Move Insert Delta_up: 0.045965\n", + " Move Insert Delta_down: 0.048656\n", + "Move set Remove two operators: 0.0473527\n", + " Move Remove Delta_up: 0.046369\n", + " Move Remove Delta_down: 0.0483339\n", + "Move set Insert four operators: 0.00272949\n", + " Move Insert Delta_up_up: 0.00316006\n", + " Move Insert Delta_up_down: 0.00287265\n", + " Move Insert Delta_down_up: 0.00244254\n", + " Move Insert Delta_down_down: 0.00243698\n", + "Move set Remove four operators: 0.0026367\n", + " Move Remove Delta_up_up: 0.00258314\n", + " Move Remove Delta_up_down: 0.00241881\n", + " Move Remove Delta_down_up: 0.00319259\n", + " Move Remove Delta_down_down: 0.00235322\n", + "Move Shift one operator: 0.0233294\n", + "[Rank 0] Warmup lasted: 0.0548638 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.113268 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9988\n", + "Average order: 0.8957\n", + "Auto-correlation time: 2.51035\n", + "\n", + "\n", + "Iteration = 8 / 10\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "\n", + "\n", + "Iteration = 9 / 10\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:03 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:03 87% ETA 00:00:00 cycle 8705 of 10000\n", + "17:37:03 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00116392\n", + "Average order | 0.000174849\n", + "Average sign | 0.000171033\n", + "G_tau measure | 0.000573339\n", + "Total measure time | 0.00208315\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0488708\n", + " Move Insert Delta_up: 0.048186\n", + " Move Insert Delta_down: 0.0495586\n", + "Move set Remove two operators: 0.0490466\n", + " Move Remove Delta_up: 0.0489142\n", + " Move Remove Delta_down: 0.049178\n", + "Move set Insert four operators: 0.00282094\n", + " Move Insert Delta_up_up: 0.00316907\n", + " Move Insert Delta_up_down: 0.00295433\n", + " Move Insert Delta_down_up: 0.00271815\n", + " Move Insert Delta_down_down: 0.00243883\n", + "Move set Remove four operators: 0.00273748\n", + " Move Remove Delta_up_up: 0.00262902\n", + " Move Remove Delta_up_down: 0.00272997\n", + " Move Remove Delta_down_up: 0.00314165\n", + " Move Remove Delta_down_down: 0.00244518\n", + "Move Shift one operator: 0.0249233\n", + "[Rank 0] Warmup lasted: 0.0546016 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.11385 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.998\n", + "Average order: 0.9022\n", + "Auto-correlation time: 3.34419\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:03 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:03 87% ETA 00:00:00 cycle 8791 of 10000\n", + "17:37:03 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00113176\n", + "Average order | 0.000173538\n", + "Average sign | 0.000174227\n", + "G_tau measure | 0.000500731\n", + "Total measure time | 0.00198025\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0492124\n", + " Move Insert Delta_up: 0.0484356\n", + " Move Insert Delta_down: 0.049993\n", + "Move set Remove two operators: 0.0489308\n", + " Move Remove Delta_up: 0.048871\n", + " Move Remove Delta_down: 0.0489904\n", + "Move set Insert four operators: 0.00273515\n", + " Move Insert Delta_up_up: 0.00326991\n", + " Move Insert Delta_up_down: 0.00276099\n", + " Move Insert Delta_down_up: 0.00255092\n", + " Move Insert Delta_down_down: 0.00235247\n", + "Move\n", + "\n", + "Iteration = 10 / 10\n", + " set Remove four operators: 0.00280445\n", + " Move Remove Delta_up_up: 0.00258294\n", + " Move Remove Delta_up_down: 0.0028585\n", + " Move Remove Delta_down_up: 0.00317801\n", + " Move Remove Delta_down_down: 0.00259388\n", + "Move Shift one operator: 0.0245304\n", + "[Rank 0] Warmup lasted: 0.0553609 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.112756 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9986\n", + "Average order: 0.8688\n", + "Auto-correlation time: 3.98793\n", + "\n", + "╔╦╗╦═╗╦╔═╗ ╔═╗ ┌─┐┌┬┐┬ ┬┬ ┬┌┐ \n", + " ║ ╠╦╝║║═╬╗╚═╗ │ │ ├─┤└┬┘├┴┐\n", + " ╩ ╩╚═╩╚═╝╚╚═╝ └─┘ ┴ ┴ ┴ ┴ └─┘\n", + "\n", + "The local Hamiltonian of the problem:\n", + "-6*c_dag('down',0)*c('down',0) + -6*c_dag('up',0)*c('up',0) + 12*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n", + "Using autopartition algorithm to partition the local Hilbert space\n", + "Found 4 subspaces.\n", + "\n", + "Warming up ...\n", + "17:37:03 100% ETA 00:00:00 cycle 4999 of 5000\n", + "\n", + "\n", + "\n", + "Accumulating ...\n", + "17:37:04 89% ETA 00:00:00 cycle 8983 of 10000\n", + "17:37:04 100% ETA 00:00:00 cycle 9999 of 10000\n", + "\n", + "\n", + "[Rank 0] Collect results: Waiting for all mpi-threads to finish accumulating...\n", + "[Rank 0] Timings for all measures:\n", + "Measure | seconds \n", + "Auto-correlation time | 0.00112999\n", + "Average order | 0.000172763\n", + "Average sign | 0.000171394\n", + "G_tau measure | 0.000627904\n", + "Total measure time | 0.00210205\n", + "[Rank 0] Acceptance rate for all moves:\n", + "Move set Insert two operators: 0.0486702\n", + " Move Insert Delta_up: 0.0479463\n", + " Move Insert Delta_down: 0.0493956\n", + "Move set Remove two operators: 0.0488597\n", + " Move Remove Delta_up: 0.0489007\n", + " Move Remove Delta_down: 0.0488189\n", + "Move set Insert four operators: 0.00274177\n", + " Move Insert Delta_up_up: 0.00356041\n", + " Move Insert Delta_up_down: 0.00275273\n", + " Move Insert Delta_down_up: 0.00252121\n", + " Move Insert Delta_down_down: 0.00212272\n", + "Move set Remove four operators: 0.00257861\n", + " Move Remove Delta_up_up: 0.00265775\n", + " Move Remove Delta_up_down: 0.00242603\n", + " Move Remove Delta_down_up: 0.00290282\n", + " Move Remove Delta_down_down: 0.00232661\n", + "Move Shift one operator: 0.0231697\n", + "[Rank 0] Warmup lasted: 0.0553643 seconds [00:00:00]\n", + "[Rank 0] Simulation lasted: 0.110655 seconds [00:00:00]\n", + "[Rank 0] Number of measures: 10000\n", + "Total number of measures: 10000\n", + "Average sign: 0.9978\n", + "Average order: 0.8281\n", + "Auto-correlation time: 1.23047\n" + ] + } + ], "source": [ "# TO BE MODIFIED: Find below the previous script scripts/one_band.py\n", "\n", @@ -114,8 +6924,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:04.064823Z", + "iopub.status.busy": "2023-08-28T15:37:04.064709Z", + "iopub.status.idle": "2023-08-28T15:37:04.066724Z", + "shell.execute_reply": "2023-08-28T15:37:04.066501Z" + } + }, "outputs": [], "source": [ "%load scripts/two_band.py" @@ -130,9 +6947,47 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:04.067955Z", + "iopub.status.busy": "2023-08-28T15:37:04.067876Z", + "iopub.status.idle": "2023-08-28T15:37:04.204658Z", + "shell.execute_reply": "2023-08-28T15:37:04.203947Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "half-U1.00-J0.00.h5 half-U6.00-J0.00.h5 quarter-U2.00-J0.00.h5\r\n", + "half-U1.00-J0.10.h5 half-U6.00-J0.60.h5 quarter-U2.00-J0.20.h5\r\n", + "half-U1.00-J0.20.h5 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"shell.execute_reply": "2023-08-28T15:37:04.210697Z" + } + }, "outputs": [], "source": [ "%load scripts/two_band.py" @@ -189,9 +7051,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:04.213513Z", + "iopub.status.busy": "2023-08-28T15:37:04.213374Z", + "iopub.status.idle": "2023-08-28T15:37:04.585949Z", + "shell.execute_reply": "2023-08-28T15:37:04.585690Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "coeff = 0.1\n", "for U in np.arange(1.0, 13.0):\n", @@ -249,9 +7147,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:04.725340Z", + "iopub.status.busy": "2023-08-28T15:37:04.725243Z", + "iopub.status.idle": "2023-08-28T15:37:04.854645Z", + "shell.execute_reply": "2023-08-28T15:37:04.854379Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "coeff = 0.2\n", "for U in np.arange(1.0, 13.0):\n", @@ -306,9 +7222,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:04.856213Z", + "iopub.status.busy": "2023-08-28T15:37:04.856119Z", + "iopub.status.idle": "2023-08-28T15:37:05.019189Z", + "shell.execute_reply": "2023-08-28T15:37:05.018930Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "coeff = 0.1\n", "for U in np.arange(1.0, 13.0):\n", @@ -358,9 +7310,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:05.155822Z", + "iopub.status.busy": "2023-08-28T15:37:05.155734Z", + "iopub.status.idle": "2023-08-28T15:37:05.285428Z", + "shell.execute_reply": "2023-08-28T15:37:05.285182Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "coeff = 0.2\n", "for U in np.arange(1.0, 13.0):\n", @@ -406,7 +7376,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ModelDMFT/solutions/05s-VBDMFT_Hubbard.ipynb b/ModelDMFT/solutions/05s-VBDMFT_Hubbard.ipynb index 8ca92f8..52445ef 100644 --- a/ModelDMFT/solutions/05s-VBDMFT_Hubbard.ipynb +++ b/ModelDMFT/solutions/05s-VBDMFT_Hubbard.ipynb @@ -46,9 +46,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:07.195084Z", + "iopub.status.busy": "2023-08-28T15:37:07.194662Z", + "iopub.status.idle": "2023-08-28T15:37:07.521934Z", + "shell.execute_reply": "2023-08-28T15:37:07.521683Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: could not identify MPI environment!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting serial run at: 2023-08-28 17:37:07.323600\n" + ] + } + ], "source": [ "from triqs.gf import *\n", "from triqs.operators import *\n", @@ -80,9 +102,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:07.538439Z", + "iopub.status.busy": "2023-08-28T15:37:07.538317Z", + "iopub.status.idle": "2023-08-28T15:37:07.902420Z", + "shell.execute_reply": "2023-08-28T15:37:07.902122Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Parameters\n", "t = 0.25\n", @@ -121,9 +161,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:07.903876Z", + "iopub.status.busy": "2023-08-28T15:37:07.903787Z", + "iopub.status.idle": "2023-08-28T15:37:07.971297Z", + "shell.execute_reply": "2023-08-28T15:37:07.971064Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# A mask giving the k points inside the central patch\n", "in_central_patch = (np.abs(kx) < np.pi/np.sqrt(2)) & (np.abs(ky) < np.pi/np.sqrt(2))\n", @@ -168,8 +226,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:07.972708Z", + "iopub.status.busy": "2023-08-28T15:37:07.972641Z", + "iopub.status.idle": "2023-08-28T15:37:07.975206Z", + "shell.execute_reply": "2023-08-28T15:37:07.974965Z" + } + }, "outputs": [], "source": [ "# Calculate the creation/annihilation operators in the site basis by means of the even/odd basis\n", @@ -205,8 +270,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:37:07.976486Z", + "iopub.status.busy": "2023-08-28T15:37:07.976419Z", + "iopub.status.idle": "2023-08-28T15:40:21.420045Z", + "shell.execute_reply": "2023-08-28T15:40:21.419795Z" + } + }, "outputs": [], "source": [ "# Construct the impurity solver\n", @@ -269,9 +341,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-28T15:40:21.421589Z", + "iopub.status.busy": "2023-08-28T15:40:21.421521Z", + "iopub.status.idle": "2023-08-28T15:40:21.782766Z", + "shell.execute_reply": "2023-08-28T15:40:21.782443Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from triqs.plot.mpl_interface import *\n", "\n", @@ -329,7 +419,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/TwoParticleResponse/solutions/01s-Fermi_surface_nesting.ipynb b/TwoParticleResponse/solutions/01s-Fermi_surface_nesting.ipynb index 542db2a..6ee0434 100644 --- a/TwoParticleResponse/solutions/01s-Fermi_surface_nesting.ipynb +++ b/TwoParticleResponse/solutions/01s-Fermi_surface_nesting.ipynb @@ -26,7 +26,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:00.599462Z", + "iopub.status.busy": "2023-08-29T09:09:00.599177Z", + "iopub.status.idle": "2023-08-29T09:09:00.814690Z", + "shell.execute_reply": "2023-08-29T09:09:00.814365Z" + } + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -58,7 +65,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:00.816381Z", + "iopub.status.busy": "2023-08-29T09:09:00.816294Z", + "iopub.status.idle": "2023-08-29T09:09:00.867522Z", + "shell.execute_reply": "2023-08-29T09:09:00.867316Z" + } + }, "outputs": [ { "name": "stdout", @@ -84,7 +98,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Starting serial run at: 2023-08-24 17:36:52.506117\n" + "Starting serial run at: 2023-08-29 11:09:00.866052\n" ] } ], @@ -118,7 +132,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:00.883989Z", + "iopub.status.busy": "2023-08-29T09:09:00.883893Z", + "iopub.status.idle": "2023-08-29T09:09:00.888623Z", + "shell.execute_reply": "2023-08-29T09:09:00.888364Z" + } + }, "outputs": [ { "name": "stdout", @@ -157,6 +178,12 @@ "cell_type": "code", "execution_count": 4, "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:00.889931Z", + "iopub.status.busy": "2023-08-29T09:09:00.889859Z", + "iopub.status.idle": "2023-08-29T09:09:01.067282Z", + "shell.execute_reply": "2023-08-29T09:09:01.067063Z" + }, "scrolled": false }, "outputs": [ @@ -171,7 +198,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:36:52.670946\n", + " 2023-08-29T11:09:01.035179\n", " image/svg+xml\n", " \n", " \n", @@ -203,17 +230,17 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + 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\" 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stroke-dashoffset: 0; stroke: #440154; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#pf7cd554e24)\" style=\"fill: none; stroke: #bf00bf; stroke-width: 4; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -746,18 +773,18 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + 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" \n", " \n", @@ -805,7 +832,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -845,7 +872,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -875,7 +902,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -888,7 +915,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -901,7 +928,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -914,7 +941,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -927,7 +954,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1057,7 +1084,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1117,6 +1144,12 @@ "cell_type": "code", "execution_count": 5, "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:01.068722Z", + "iopub.status.busy": "2023-08-29T09:09:01.068648Z", + "iopub.status.idle": "2023-08-29T09:09:01.126302Z", + "shell.execute_reply": "2023-08-29T09:09:01.126033Z" + }, "scrolled": false }, "outputs": [ @@ -1131,7 +1164,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:36:52.779346\n", + " 2023-08-29T11:09:01.113436\n", " image/svg+xml\n", " \n", " \n", @@ -1167,16 +1200,16 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1201,11 +1234,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1236,11 +1269,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1272,11 +1305,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1292,16 +1325,16 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1344,11 +1377,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1397,11 +1430,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1442,11 +1475,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1477,11 +1510,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1518,11 +1551,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1536,11 +1569,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1554,11 +1587,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1572,11 +1605,11 @@ " \n", " \n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1767,7 +1800,7 @@ "L 374.831694 258.723941 \n", "L 379.16964 260.586657 \n", "L 383.507585 261.216 \n", - "\" clip-path=\"url(#p3813656419)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#pe743d330c7)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1879,6 +1912,12 @@ "cell_type": "code", "execution_count": 6, "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:01.127961Z", + "iopub.status.busy": "2023-08-29T09:09:01.127871Z", + "iopub.status.idle": "2023-08-29T09:09:01.320874Z", + "shell.execute_reply": "2023-08-29T09:09:01.320627Z" + }, "scrolled": false }, "outputs": [ @@ -1886,7 +1925,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Greens Function with mesh Imaginary Freq Mesh with beta = 2.5, statistic = Fermion, n_iw = 64, positive_only = false, Brillouin Zone Mesh with linear dimensions (128 128 1)\n", + "Greens Function with mesh Imaginary Freq Mesh with beta = 2.5, statistic = Fermion, n_iw = 32, positive_only = false, Brillouin Zone Mesh with linear dimensions (128 128 1)\n", " -- units = \n", "[[0.0490874,0,0]\n", " [0,0.0490874,0]\n", @@ -1934,7 +1973,14 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:01.322296Z", + "iopub.status.busy": "2023-08-29T09:09:01.322205Z", + "iopub.status.idle": "2023-08-29T09:09:01.459973Z", + "shell.execute_reply": "2023-08-29T09:09:01.459631Z" + } + }, "outputs": [], "source": [ "# Write your code here\n", @@ -1993,7 +2039,14 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:01.461536Z", + "iopub.status.busy": "2023-08-29T09:09:01.461453Z", + "iopub.status.idle": "2023-08-29T09:09:01.715649Z", + "shell.execute_reply": "2023-08-29T09:09:01.715396Z" + } + }, "outputs": [ { "data": { @@ -2006,7 +2059,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:36:53.323407\n", + " 2023-08-29T11:09:01.673693\n", " image/svg+xml\n", " \n", " \n", @@ -2038,17 +2091,17 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + 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FDNdKdbDRiodQKuVUSU7cOWK4LRJ4RiUKqTT+ri1/LLTSy0kY8jAL8a3jbDnu76Hv4hQ98lAoFJq94kUeCoVCM9d014rV/X8oQrn/u2b1w4fhlJRSaspuG1WtkLPixem2KaVarWV46mDROCWZsVczpTrDNkAr9Xnd/Stj0vIcyTnnMpT+/FxwypdnktjzzUrcC98urtq28urY9hKLRjwzqhmmNIxTUhKksgZaIU65wkoa1zgRV/WFtFeCUa6qtp3ulGucKOKALdEKXBesfnioP5wEUiKBhzVYVoWc7xXQygpY5GnRYoxr4BZWjOQam1wUglUMWT+lMpKGxmAWVpKsgISqdIu4PSd7w8nSxsAsOGc7B7McemcJKQJ+XrnYJI3Ypt83nj2eGme5TU/93aCcLAdry49QRqyTme1NMxG5HLed5mCJHnkoFArNXPEiD4VCoZmrdMs5DiEXp0aLi1C8fVslZr1Ss1NwSkr2QhD4PM6E6zopdnuf2FOtPHeKg1xWdnw4P62lUozAKV8Bp3y7ElfKn5dvj3GPVIhTXmAG/2k+3p2Sknao9EjlXU13igzT3wCh9NikjS+P8Tu0v+swyw1wyhr2Hia07PA5RCgHulYwzO1jLibBuMT4nshlCfxxXsh3f1+0x/qkEKcIkdAzxFxjk4tCqHK0/ec4j+EoN4v63VPMsldJVIh5/kagldsOrXAxiQNiLgThJf4wmUhhlv6SkDKMKFebqzU704mYPNSgrPCDMYtXXwVrgPJ6ZFyAxEMuPQhSi2Q4eNBJcIoeeSgUCs1c8SIPhUKhmatUiGKEMmtFoTEIxStjayUQqVorD8MpKQlGUThlwJGS0j3MsuT2HVpZ2GjFQyh0qlROadrUuVVWSPx5diEOiK/OBZH86Uxwyner347xtwuJv+kSQ75AooKHU7i2JN0QGqfIWPSqGwJeASO8BSp5Uz2V9sMl4ifH+D1cK+/37Qm6BY/aOKVX6a5gEotKAhpYaaXI7eSgRUG0gjK2pXz3sw6zXBZyUW8WEl+jfV2yvKzEXN2nL0fL2ikq2QfXw7tmHmapO8xSI0mIdWi8MsDEVwq/dJjlDriFmIUYJvOSgBx00m+jkIzCKbXZrkqcJEP8bJRRfjBmqe1kJL2Nva6ni1z6fSoji+dasd+10SMPhUKhmavMPKjuyZoEnZquP7SqvbPIg6piyHbPJ973yF0fOTyw7JGzl83e98qY7DTS7FO63wtHjAnOdImKhpfdqveX0gv/+gKTmudXx5i98O9UL1x85C86T/SzXA5qai/8upb4HToIb7oqha+rz6QNve1fD3aP/GovvfB3iNeH9hhv9nKs9IjvDpzgpG+Z6z/ak52W1MQneucFJoNLtC/RI++95k8W0mu+PshNcLPg6vb2SvdcY7PvqbOCIVPu6RFnt2tM77zPFWBvv0ryOX0Kf0r+hCgnkfuRDs/vtuFIiD3s4bR81Vvu9qN67CrmNZVjctaYSOaaHupVU5ubqvVA+Z7q2zmp6fXIRyw4obc/3U82cA/fV/TIQ6FQaOaKF3koFArNXL6PfEhjqhyOwikGOnG2VYtC5B/GKSkJOuHPOXnp4hRuA4xyOKKVhJ9LfDi34+ocQ6pLLF5wgQUdLtrh9FfEKc6k5l8Wb2QblYKPidJuiP0xOOUthpmv4QH/pZvMfH0QtPLLHphlJ5iFOKWf1Ewppfc7oIZDe4xbIJTtHjhFTa4hVj5jTHJbQ2znVkwKsyAu4C9HvFq0128NDHRWYlJ4Ke23QC63i5W5TV9FUS2YoSYnZZKbHvEpmOUZh+4FfeZXEuP8KbSivObtvjmZfIVrsFOp+FgwQ01aJjMWH3lmb6v6nINTnNI6glBwk6a226URrcoI77TzZvTek/02tbO/EYoeeSgUCs1c8SIPhUKhmat0U0E9DblW3MqGTuq+hWichR2SSq/HUJpVDJl236OVMe4UB6dYGEW1EaHQnXIhw6Qa7pTFBRaIuBR3wssOqfzpXHDKX84k5f5vy1+P8XfEKVjI4BmG2BedW2UMTnkHnPIGw2bilJ8Oz4/xz/tn3b+CU34FTvlth7T8rZyUW+CUO6CT7a49bvqQq72NU1TaNmJlux2a8WfVOuy64iIEiPdAK7vO6bEmblnK+bsDcrkBQlmvJL7jQhldzKqEdJOwqiMxSw3M8mwAs1zkLMspYYV7p8Y9Ra95DSyy72LlHMK5fofrsa/ta8DqhpmJVoy29n+Ix2MW9WriD7zKJPDFKzdLvyMPp9TOu65xMIvlR+e7cOJao9EjD4VCoZkrXuShUCg0c013rfRYxHWqOAjFTdHHUKZvN9LsT9pVkg9StUsil26W/SNwyuHstP3g4hSk319ggYhLGQY/uZTh7FeX4lD57qJFKn87F0eKxiniWvkalfde5Kc4JSUZVhOnrGtxybwDWnmL5JufkF5PnPJqL/FP2xatEKe83QpOea9wChwaW+CDPSrs7bqYw9oDhuAHDmElVEkn7pD8VIq8qAJ7djp1VeK6lu0B7AugF3yX7UKuO0sL9M6clFJaI+4rO7LC48ZJnWes0+7hfsKXt/CawixKSEJKcq8RrfSYp/bWRwU2ucZJPrBIoLMmZzLQyj0/idP+YcxSeD91318OOulrB6At81wm3Ae/I3FKZiQHeehlhKJHHgqFQjNXvMhDoVBo5vKrH1pVDlOyKx16iT+Om0XVSSlOkUtjtKV0P/HHrmJoLQox3Z3itHdIhTjlcClDoOqSOEUSf57QnWLglJQEqfxtyWQf1lGRGixDOCUlQSoeTnmNofxPqJnyav/5Mf7H7oVss5VtXm9b/PJ2g2Qf4JT1Ro5pt4MDYwd3EVwpWRdneznvOTALK+K5aMUrFmckBKkf0yjgoJWGVemK3gkl2x7wXSo4qBRyWRKznMas9qjWKKWbBQdCZwnrpzDhpz9X3j3iYZY6yb1WNXIP9p/poRW6WUgJbhRm4YXgiTVqrUC69sgUzCJtuYM/1JuO6IT1WPpcRdwjDZ0qFZxQtYNTeH85C2WYv0c5VRGjRx4KhUIzV7zIQ6FQaOYqU1kOb/XAJKBG1UxxtjEwi0IoRCtcCAL789bY7OuqWKVo21g+2nKnpKQTfnq3CnEK3SkF3CmXwClfXkoix58vr47xX5Hw89fOofKXUtDKdyXX25TrNIRTUhKk8hbJPq9RSvUVHCk/Aqf8uJX4VedOSSmlXzdwqGxah8r1BvVS6EjZyLHWwCnZDsNcYpQupjtFLTDgrf+oFilIpvpt3BwhjnDVfYl2hVb6exT3AO4v3qNELjXxCxKc9od25zuW7cWCGVtnkQe1KIRKILqSGAk/vcZglhdEFyVrvXT7ZelaulqMtVJTSqlBfOMYMw7HkzxcY0S9V4YwS+Y4RYiOlTmFaIXornfrMaPKwTC82YhciFAsh4qzCIVGLwO1ZUKhUCg0T8WLPBQKhWauMpUjaq0YaKXxihg4iT8eZjHRSmGjEu1UsYezxCjHNTaNUrRte7Jjpxxtj1RYOyVnss8TuFOeiDvlz5d0p6B+ykoSfv66aNs9nPIklwMcwikpCVL5GRabH4FT/r7/QtqBU/65EZzyC3DKb3eS8HN91x7LdiM4pd7iPkKc7zI7Blrp10zMPZzioJXkYZahXArmoTimBx+tdB+NezgnzoObpcZ3pEtndyB+aXfYI5aU9FqkO1VG1k4OUm4W4o1j/RS5LykPs/BeU+ruzVrdf7aDhc4WTwqzdP8eVArPMGbhNmail7rWTESE+4SvrD1QCNt77Kca+QUcnKJcLl7dlW5br77KiLor0SMPhUKhmSte5KFQKDRzlWrVnTGrUmSnQ4wxmMVLAmqYKFSeJgTVpYNZiFDQrlwpBloZs7qPl/DTIxXilMsn4gz4QrlTBKf894UglP9evT7G/7WQ9m/LFsVMxSk3tXz+21qSkH7q+NCPB8Em3+++PMbKnXL3/Bj/ciela4lT1ndI8rlrkUpDhLKBI8XFKcfwiFNSSik/GG2eU0WVq7Xjh9Za0XVX7LinG7hMiYvhEBnVKraRS9VhljvgFrpa6GBRcT1cj6VP4FG1U5DsQ3eKd69ZmKUqb07axspzs/RPDi+vh1nGXL/+3cNtC4fa5ETAXCWKNX66xbiVq+WAoyVaUcjFq7uCQ+nQSeO5Wjxhm+iRh0Kh0MxVNp6P3HvFT+iRN16VQ7dH3sa1WjTC8YizF45M40ql4/c9cvx8xKIQnk+874mzF/6lM6n5V0xqDvXCU0rpZd52Fz6mF/7jQXrTr7qeOHvhf99Iyv0rTGr+vJZJzXdrOSnshVd3uE827bEWW/TCN3LeC5lzVb1wt0feF5Yb4x13euSPMtk5okfeXSY1AVpzMpQ99QVi9sI50dv18Gr0/nbo9anFNug/5yQoDpATov2Eo6pgiJR7yyOe0pjeOfzpI3rn9JrXA4t+8IhU7zzDSVap9hI2RuzmBrCnzlEje9OocNn/bq4mMukzR3vFCU6UomDvXN3Hho/c65E7+RLRIw+FQqGZK17koVAoNHOVXO9SaQCtNN7wxk3FP0Uo99t7pOJNZGqcYqMVVelwdfrzMYtCsIphYfjEvUnN/7qQ9PopOCUlGbZ+DE75+0G84d/v2vj7O2n75x084sApV7dyUrZ3wgOIU/I7TGxu23NcAKcohOKhFWOCk7HGKawml8z490Ar/f2q0Ioz8Ul8pGJ8zx6pVJjsJGY5ALPcVmwHLqmJVoBfmn6yE8+E92BPwCx6AnQYs1QjPOW9MlzIG1qzFZFBFU2FeLmj7l9+tKrAmsyYWFAhtb4yKyatiWHU/Xqw2zm5q/BLF7op/FSglVAoFPpjKl7koVAoNHOVzXI4Rd8aGTXeepwOZqkHnCrcXuEUzPyrKoZAKLVyrSDu0QodKUQrFzJO0WtsyjiYVQz7tPsx7pT/s/zlGA/hlJTsNTaJU17XcnyvDrKu5g97caL8AIdKj1R+XAtOea3cKXJStrc4aUAoBb3hdxL3w88Co2oXpzhohXgh74aixCa5g1kUThmDWQyNwSm6nfdxc7Jthvs195DLwYm7U8/FCCpWgaTRAXzhrrKdIBZmUQtBeNUKuVCFQiRykS38NxWzeMq7i5Y7F+8W1+OQ0U0CzEKXXO9a8ZwqYzDLnu3t9sQpTeXgFLhdlMuFOIWv3aa//+3v7i6aAkWPPBQKhWaueJGHQqHQzFVyUQZ3XUMj4cd1rXhDluIUoaSkkyn6aoVj0MoQTklJkIpyp5wDp6g1NuFOcReFaJHKlJT7lIZxSkqCVHycIljkf/dAKFuJf0DCT49UfrmV37u+lROxW+Ok3dqp9uUdkAFGzT1SIU4pPHcKZ/k9tNINOZWzgy4AF6fY20yRxixe8gidCp1rxcEmrpsFWITbV0Z5AsY1EAodLNzfFl/iioima1dVCelgUWtvem6WU8wyLp1/GLMUuJjFAD/Qbha5jw/OO6tfOMJ3H+E4DFdSSvpa9o+wvrf5c8eRotbyxOEZuDAzKiKetFPOOhShUCgUmqHiRR4KhUIzV1mtRrzLLdO9406xkifub8Mhp9WuF4eQbRVOIXIhOlFopR17VOcYg8CRsrggTpGh4MtL1k+5Osb9ohCPjVNSEqQyBqf8z/blMf7+TnDKP26fH+PXHVK5uZXPPqxx0m7lIhRAKEzyoSulwNoEPUbxnCqFi1M+3O4mAVWnw9CU7icBPZCtqHubO8eu1f3duVYwpvecKqyeV8PVwPu7R0y1+r4SV2ohDacGC9p32P5d1+4lDGnXyodroKSUgEhOnSwpPb6bJcdNQDcLqe4N2g/5AnF7ITTqtbGJwizG+qwpyTVWTha+u9S9bWMWfX8b2zBhaOK9HT3yUCgUmrniRR4KhUIzV1mPQCvWzL5ntNfJPtKuEoK8mf2ja0XaKg+tYORWqwUiUIL2rBuawZ2yvBAe8BTulK+AU767QMLP2ekam4+NU1ISpDIGp/zPWtr/cSsJP7/eSt2Vm86hcriVE5jDnVIwwYfYhDHRCjFKnxBEhKJcK2xncoRskxmJEkOz+il9xMz+BEfWSTu+z7HWChNDOAQnTkFiSGbglJRS6klaVdk/zxzkoobmRC5ccKJrv1aLOeCziVYm1EOxnCwpPT5myXHhC7CGMpeT8ksOVxauya77+IoJQ7SneO46JrkZyIUJjNqRZaNjdc+rRC86rrKuTbbVjqxh7BU98lAoFJq54kUeCoVCM1dZLe13+WA9CrcWhbFturfepotW9L8p+ThFla5lks8Zxifn7bhmdSlc4NkFaqdc2Djlb+dSjvavS8Eof120mOWxcUpKglT+7+6rYxtxyg/KnYL6KTcytLxFws+hc6Xka+CUNZN9krRPwCltezvsG4NWMq8eRU3k0pxsq9fjtH+PGlOPwpJHFIZqCY2pI6RqsBCnqEShblvlVLFrrYyJrS+E055unDUzPVWqNotxsj4hZsmTnTBE5OLhl3cdZtlm8qKoYUk5ALPkiImDC7rruvvfSwLy6wihHe+1vDIwi0p84wsW7Q5BjB55KBQKzVzl4cz5q8w/CEbvfKjwfkrjiu+bPfLlaVtK4gtPKaV6hfgcC0Gcy5/A1Xn75/P5pXQ/v0Iv/E/ndi/8b0uJ/1JK/F1XfP9jVrr3fOJ9T5y98P9dy6IQ7IW/4aTmjfTCK0xsFt3EZrGmR/wY6h45e9uqomFjb9O1q0lN9rb3p73tlEYU32/snrw72Tmx+L4cFPbHdm8dWsNTrlZT96p5Vnav3Zqo9CY4K9Xztn3GbhXIzjPOHv4BD/ONd/rUJOgIf3mvR+idF+U1tmWPHPeU8pdLzEnQoouv8HvbAoumoLut3l/Oojf9I69yJ1TOgB03ylMu7bXhKf+YRVOiRx4KhUIzV7zIQ6FQaOZ6fLRS2LGaBDVwCmM1kalwCsYeZ6hcCJxycc6JzZYffA2c8u2Z4JS/0CO+pDf8t2P8HdYy7JHKY+EUyyfu4ZRfOal5I59fE6dgYrPsKvGriUziFKOaYUqCTU5j2abHKBqnYLg7YoJToZO695FzFQUJXZzyCGhFCTglczBLPzzmIgYKrdT20JwTn7VCK3n3Lw7Dib10/TQwJNc+ZLlHOG98o07reJziVi38KMzS78NeR7QgcsEXXhiYpQBueVfIqjJrePwrxiV85yUmRLvSFYXzHmswCcrqljp3gvHphHaglVAoFPoPVrzIQ6FQaOYqWTmQ8nzkQ2jFRSil06484436NyXtC89WSLU/l7EMccrn5+tj/NV5OzT79vzq2PbdSrAJccp3wCnfgEFYDpXHxikpiU98Kk5h2n2JhQ17pFKOwSlbIBQ4TthuuVK0awXXSaEVtHMsr9ws3TZj0u/HoBWq34YulMreVPvFnTjvm+QAFWbx1qat7W2ONEkNtZ3YcapkTtW8Y6yG5nSwALNgExtoiFiJcGhBiJTSJMxiIpaUFGYpHNeKdrC0F7lE2wKc47fi4hivC2G5OzpbytP0/galFvId0/XlUDPHwZJ5yGUArYSPPBQKhf4DFC/yUCgUmrnKg4NWXNdKv2bnRzlVgE6AUZo+BkIpEK/OZPzy9FyGaAqnnCHhp3OoEKf8ZcEEn6tj/DXsHS9QoP4il2FXP/zzcMpbTFe/Ojw9xlOqGDLZZypOURiliz2cUm4dhLK3MYtypez7lHq0qbR8Wi0MhHK/vccf/PnkKocjMIulzHZoKKcKuzt9O0vjcbxLNwmRC7CSqsLXO3awUITlaml/ER/JobmDTpKBVvTQHJglDWOWft3M/KELpCad8ENZ2FKjF2CMJDc6XSt0syy6E7TAiVqCcywLG7NcF7KPbSnPXN1hlmqL8+RiFqAzB6fUhptFoRV1z6M90EooFAr9MRUv8lAoFJq5yko88snNAzAWkRiDVpoSCAU4pWG8xPB82Q2HzmQ8crYCTjkTNvDiTHDKl8Ap364k4adHKt8tBK18C5zyMsf+BnBKSoJUPJzy40GwyA97qVb4/VYQyvcDVQxZO+WhOCUlwSjlpjlpa2MboeQ7G5fk+1NXCnELsQmdKi5aIQrp9+c5UmqnnXpktJIpp8qpg4Xre9KFotGKg1xwrHnduyHwEarWCQ5KOVhs5GJjlDEJPsOY5WZgN7lbobA2Yyvhx0sY4jOpJUg0z+Q572uzEK0wLnMiF2wD5HJdyrFsFu0F2pfCiOsFXC0LvCf2Hmax665YaEXhFMcYxG2iRx4KhUIzV7zIQ6FQaOYqD0Ar7rqGKvmn68+7aMVBKAsMvRGXSwxxli2muDiTBJ/PVnCTnAk7eLmS2e9vVu+P8Z+XUj/l2w6pfFPIz18WgmqeZeNxSkoprev2uDyc8veD1En5YSc45YcNccrzY8w1NvtFIaqB2ikp+TilVAtENN2/p20ppVQQoaCmiodTNFppY4VQ6lP0cr/dxSxGGdtxNVU8zDKQpJI5/ZfceQDqU7Si0Iuqr8LEH34HPjA4D93z1KBNuVoUNmE7MZVhK2v3frKTMes/KsySyT3Y5yzdqtOB+0UlCtlJQ4XneOkwC90p3jPpYZYik5u9SO17YIEsnCViJgetHDfLspD292X7fKrkoYVglgqYpd5joQrE2Z5oxcAsXsli73YOtBIKhUJ/HMWLPBQKhWausrq0++3+mp0GWoE7JSuxPwwtiVAWC4npSrlctuhC4ZQV3ClLcad8A3fKt4sraYcr5ZuixS8vMER6KE5JKaW3dXusP8Hq8+rw+TH+fido5fs7iX9cw51yK/VTbow1NouB2ikpfagc7alDZQxOKYhNiFOY5HOoEHfbEJVUXHRyAKGkZCf/KHeKM5703CyT5OzbW6eT6rFMbqOVzHGqNDnPyekqWp5jR/W0iGTU+pmMa6Od3wUIx02Gwn/UNu3BHoBHbrIzc1Ou1qMcLMQsxpqcTPahRmEWfvUOoRZJ3hNWwlBKKa1QKIWY5QwY9qxs23vEklJKtzv57M1W3iv7PXDUDs4Wrhh1AHLp3SwOWolaK6FQKPQfoLK6GDYpqrmhY48cPTqktRaYyGT7ail/3c4X8lfvcildyuddT/zzpd0L/3rxHrH8pWUv/GUh3tQvul7BM/zVXqFHPqUXnlJKP3elIn9UvXB6xO1e+C+qFy7e1AN94usJa2wy1X7AJz65F25MarbboEfe97groy0lvQJC4/TCrd73v8o77smbCFSe8uZ028yeyOTkpKr8aE18KseA/b3yhverqNBbIa6NNrt3rtq90Xjvoc+xGj1+foN3xi+53PN+j/y0vVDHhAcAGtM7L7rrUyR5fvNM3h+ev/wME6Ln6JH38UUpbVfsncNzfreXSdDtAtUUUYahwiRo3bdzpKbiJHLu0eiRh0Kh0MwVL/JQKBSaucrighXREarhFYZA3dAyBzYpiVCATVYlJg4wJPkMOOWzhQyfni9afvAFcMpXwCkvS4m/KsRHTpzyAsjnaZd2PxWnvMMCfK9rGTL9eHieUtI45e/wiP/zDpOaaxlaXnNSc22n3Rfr9rgmr7E54BP3cApT8dXiDwqtGBOcKR0RiU7F99Lv2T6w/RiEQjzzKaWqGxrrY6qFJzjBf1rZ8KTdmLRUkKNx+lf4GG7hE6Z+K2sCNCU+8K65wYjplacOKHNxTfSK9wcnQRfGohCckNQp97jR8XU8zNI/889wqDkwS5FuEMvx0XfOSdCzLj5H2wUWsn2/kGf8/Q6p/Us5J9sDkAvwy6FDK7VTAZMXQV3r8JGHQqHQH0fxIg+FQqGZqzy/3Jk/yBVOwUxzN2TiEGkJhLKCZ/vJQvZ9UUr8bCHM4DniF12a7pcoPv8F1vt7iVT7L3JhEBw+PcXwrh9eeThlC4SicEol+3jV4ZSUUvr7vnWl/LgV18qrDdwpwCnv1jLU2q3hdb2VIVVxJ8fVu1LGrLHpLQphOVRcnLJ30IqHU9Tam72P3E7Rp5vFxyyGK8XDJv8q14pa17Oy23t56IXHpFL0iV+0zySle16S3G53NTjEHvKZ3/ugbAC5qDbZxwHr2+6w63fELHhvlCo+XQhCpfknKb2R4CYpcIAWQmWbPjk2ZtEp/XSzdK6VXN5jl+UlYsktuUC8Psizf7OXeLvEueqQywGLiFRAKzXa63CthEKh0B9T8SIPhUKhmat8fmGnxBZOim1feJ3F2M9KO631ErO6dKd8VhCn3CBu0coXcKS8KCQ56DmGNc+RIn2RyTDFGl65OAXJPm8r2cdP1WfH+Me9YJQeqby6e35s+xk45epWhlTbW+IUuFPunLT7LsYp+0D6PZ0oQGCGQ8VFKMqpwsQeJ9UeKfrJRCv8uZPMUzsoZsi14qTiN0NVDicqm1IVkceqEoacfXj4pU+uMSoipnTfzcJzaX+M5TxzE4aY7+06VfjdujZVDZWxbFshaWibybNwlTmYpXOteIk6RB467R7VTHEsFlb1MEuBfeeZPJR01Zxl7YN5RtcKFqa5QALUE2Dk93tBrE8WSOkHvt10aGWHRY73FRf3wHmth+FZKBQKhWaoeJGHQqHQzFV+eS7JNLlTJL7MTmeXl7ldk+AC8ROwg2fAKc+IS5DM87xr/yKXnz/F5zzDcO0iO3Wn3FePVIhTroFT3mCY8lP19Bi/MnBKSin9s3Oo/HIns9Xv1sApdziOOznWfCOfU2wctLLV/6aUUrFzEMqINTZ7pJI7NVWUO2UKTklJkApxypAjJSXfzXL8DCAFD5uMqXg45Gbxqv4lHB9xijKw9NUPnc/zPjtz2MWAMOpPDSBJpo7V/l0zHchJZGqAlWiq0UUW+/VK7Z8TrXBxjBo73BbyjLwr5NlZGO8VopWlQitMGhInGxN++i/vJQOeZ/baoHlON4s8jMt06qrpcUtK2s3yrro4xk/wHW8qwSxroJW7Lt7VSBICZjk0tmuFcfTIQ6FQaOaKF3koFArNXOXXZ9f2D+BKYS2CPuFnldnF2J+CF3BW9zlwylNglucKo+y6Nvnsi2waTqF6pEKc8hZG+9eVIJKfkPjzj53UT3m1RcLPpp2Z/u1Ohk7rO5mJru6Q7AOcUtKp4tVJ6UZmynnC2HOqeGtsdu2qFK2X7DMFp6QkSMVLCLIcKfdjC6O4a3B6bpZHrrtCxwnL8jJBpkcaGNYqtwu7Rl5SkedsGZCLWbxFHY9rYDh1YdRxj3CwdJsXRCjO2r1EUwdglqqQZ2SNhKnfivaZ4pqZfAdxjU3P2cLEnmPCD46POIVy3yX43d41k2eCgs+y01osKaV0qdwsEq8L+fxrYJZth1S2DeqywEVXcQ3V+jSZ7N6hhkKhUGiOihd5KBQKzVzl1yuZ9S1YVhI4he19aUdvWMHZ26e5IBQON54i/izj9t0+VIKPnezjiQ6VddMOh95hNOnhlFd7iX/aSkLQrxsx+vdIxcMpOZ0qqKOSE6HQqYLkn/60qYQg/txwpKR0D6eo5J+q23ZE7ZQpOIXtHk5xVgJSThQLo4wpV/vY9VWoofoqKQkWIT2ig8RFLuOdKmOkMQvaB35P9dwchJID/RS7U4xCnKKq8zpxjo3YXgGtrIv2meoRS0o66ZD4luVlNVphbZYWs6hkHy85CPLai+5kFTimZfKSh+ykoetaHCzEy5u6/cwN0UqNlYXUylCZ2R498lAoFJq54kUeCoVCM1f5p+WV+QOa7q2aByzxyOEDhxWf5XSwYFaXv4shbI9UiFMWiD1pnILFUbvh+5taZoh/QeLPz3txpPwEd8rrrWzzdiNDveu7dtZ5x8SfDXDKFu4Ur2YKcxYURmnUvyndxyl2QhAXPVYOlQ6dqEWRgSsyD4UM4RRu/zE4ZQijOAil+RetEJR5zhLr87ktkUvO8+2Vkv14Zc5CvVmV9RugMTv9edL3VAMHS84StLv+5wnb4p5nIhHu+aZkDDRQyi/sukSha6w2toCDhW4WYhYulrwwYtZOYbIPNQaz5B3SyIE2ciYvNXwHYnF5oGO+Gzcot73uViHbwfazx0nbo70aUag4FAqFQjNUyXUwKa9H3qfKLoyi6ymldMlJUNVrb9COSQf4Yfue+Mf0wt+hF3nV/aV7jWqGrw8S/7zHpOZOJjXfbmRS4noj3s/tpv0r2mxRzXDrpN+jR5Ibk5opaT94HytfuBOrnrdT3fDog3a94w/shTN+rF640ft2e96fcrJzxOebPXVu+3v0ztnLRnNj/Ry980y1y+/lB/ynYE+9a0LHlt7xpmBvFbtgj5we9BL+8q53vi2x7mcpz96ysCussjwIJ0F7YsBJSKbce11Yr3c+9E4qSC0aTohyElS+wy0mNnsjCHveu+iRh0Kh0H+W4kUeCoVCM1f5RSFprUWyh616EqEdKiwT0QonQ4lQuJ5egXaJS6CVoeHLvpHj2CJeYyhzjQpib7sqZG8Ogk1+8XDKViY1329lcnSzlSFQ3SEVVjPMN5wwkmPVCCXZ2xix6xc/MMY2zoRjn46vJjtdVDIBpzD2cIq3DwqIxMQYExeZeHQ5XRzrWBVucb8vvNTAEYJZPgKx1LZ7/IhO8Hyq9UDxewqnAAMpP3j3PYlHUJBUxWrik2gRjzifnb5aYo0J0M1Cnr33pTyTXMiGaIX5LD3uZYVCvrP0QhWsIphhm9P3kfeO0vuAtx3ncoF31hneWZsODe8SJztzxPZnVuozQ6FQKDRrxYs8FAqFZq6SizhQXFhCp762sYdQFo4jZZHZcT7wt2QMTrlCRbA3tSCSN51n/NeD+MLfAKf8trNxynojKfj7DYY1PVrZ2X5x5U4hNkE6tVu5sK9WyJ8rj/iH/eL3txHXiuMOYTr61DU2u+1H4ZQxfvCBRSbu/aLd/thy1ka0bld+l0n+8ySYZZSThdaS2rmW3Ka/JnSnqCqHI/zluKea3llFn7m6tyWmX9xFjsQvi3b7Co6wfclKifJMErNclHCwGKVCzpx0flYxZNp97hQ5GMIsBRenQLxQOEfiPe75Hrns8U6rcD2qJCetdhYliR55KBQKzVzxIg+FQqGZq+SamBTf8AXzA7qhBxHKgkOJzBliPApOkfgao8LrWoZdV1gv7+3hUv2bUkpXe0n2uQJOud3JPnY7GTLVOxx3h1SIVoZcKG1sD0WtVHuFU+hOcRaCSEMp+AqnjFjw4aELQXwMTjErIQ4jlOaRHSwZKxS6a4Ya9y5T1j3M4iGrbgjtJgxlE69TdbpvOpsaOlwqOlXse43npEcqDe65XCUH2Sn/OdtVuj4+vnum6gXWpFwgQWYhG/NZvSJmAee8LNtnXpcPsdPlWcUwd96HvTzXin7X2b9LZ8teJVy2MXFLhZ+rR9wryukecSgUCoVmoXiRh0Kh0MxVXjgF9IuMZnO4NIwqYBqbYFsHs3iqu+HEATO9W8RrDD3eN1KH4QpOlSssHPG2SwQiTnm/J06RfTDxpwJOyXb4Dj1a8RDKYTimsySn2aB3rQAX+LFTxbAycIXCGSNcJnRANAZO4b5H1D15DJwyGaEMYZnMvhe9zzGRC/fB3xuDWdSH9tcJTcQsdCnw2nhSzpbstI3ojq6V2vi9ZN+DRH7EJjXvbSQYec9CYyXEcSGLBWqxALPwWb1FPZb3C3m2L8v2mb/IxaXGRW+0m4VJO4JiCrVoRbewhONOoTzMUuFa5nw3Nn1dGKIViWu2O89c9MhDoVBo5ooXeSgUCs1cJeueUJ4xvkcuniNF72M8TklJDPEbVYdAfn6LIhXXtQypritBJ+/gWnnftb8jWgFOudujVORe9t3sMezZZycx10vMXIRixx5msYatZoLP/bixkYtVD8V1rYzBH1bSkPN7k3BK+wvdPyMQymMkBI3ZB4a+1nFlwB8fhVn6dqf8rXKtqJwhO5lHu1a6X2DpWi5C0djIRTlYDBdVVtg/J05BySP3/q+N54jPW61i1B7Bs8pnmM/2RYdWnpSCU/huuHQWw1ELVTRMFKq6f21sMgaz8JVqvTH5LswdhELMoj8nFAqFQrNWvMhDoVBo5iq9BB5PPXKZ6kihLJzCeIuh7xpDwXWN2WqgFSYBvatOHSrrgyQQbA6yj+3Ocao4aOU4/CNOcYaNqo6KWzPldJvMwRnKqdI4mMXCH42DObw4sdlLijlNNnooTmnDhyUE6c0flhykHCljPr+77/l5j4JZeP5QhITXIOOAfFRCV3+dvBot/A42amuQnNTfm6r+ins/y+6UU4VuFmObWiXMAQPhmeSzukWp280S62B2zzxdak8KeTcwUYjxpSp7i9WFumvM2inUZMxi/K52tQzfzwrFDG4dCoVCoX9rlaWJ3bUKw3f7Mb3wCr0Mtvee8Q16FRvk8b6v5a/rdS1/Xdfond9U8IlX7V/lm730yLcH2V9V4TsgXTlTvfDTHkQ2YlJTFZbj9qqXzd899WZnbi/X6Z1bPWH35w+c4PwYPUIv/LHT8kd5x/Uv9BuY+3B755MOiteABzVx4vM4Ke1MZLojKvrIcc26dj1Jj4/mecBzw2Uw3eelH+2yKOfBfiYbPKt8hvls98/8k4U8+3w3rAuYJfAu8Sc+T9cqZso9e9NpRO+cOm7jTIZWznORJ+bvhEKhUGjWihd5KBQKzVylhU3uaypGGVKN9NM9hg199a89him3DSYzEK9R8fCaQ6YK23fxtpIh1xa+0woTKM0BE5zELMZQMHdxipN+7+EUhS76/TnDasZTJromoojBCU58zqgJzjGTlgPbPDZOGSONSwzM0oxAKKqsARe/5CZ1twtn3c8xE5+e+u/Am46LcLrYLZlxf2/qNUd5b9up/Xyeam5fnWLL2nu2uA88q3yG+Wxvl228wfuA7wa+M54Wd8d4k8NQgfdNv8YmKxSygiFT7kdcGVPue9ahfPp3Q6FQKDRrxYs8FAqFZq7ysbEJNeQXT0kPT3q3Cp0qGqegWiE85VvkA98RrXSz2DvMZh8wy10jVrPs7vCu+1fhES+mO4D7OMUpbdyctNFVkLlD3wFnycRFI9zffQRNQST/snR9akRVRNfN8oBtR0ldA8cPPmbxCauttu8pzwl1DBv7HlZulsbGLBq/pJN46NlLST+rfIb5bPfP/OZgvxv4zuC7hO8YpvFvulKNCyxuw6qJefNwf/mQxjlfQqFQKDRrxYs8FAqFZi57AbqP0JjEH+1aYdyOQ3aY991jinyHmMhli3iHIdOubrc/1DZO0cX0kxmbw78RaEUNOb12lfzTnLR5w2R3G8pqr0egiBGYpTFS9B/DqfK74BRv3wOYxV3f84EOllGLUPD8eNYIXuN+PyPuEZUcNHDfaWzIbW1s4j0LU54t18FVO5ilO8f9O6CN4Wpx3HB8x6h3T3fCNSKWL8ZFIfQ78OGJQlMUPfJQKBSaueJFHgqFQjPXo6MVigiFa81xPTodt39X9kha2MHBske8pWsFCT8HDKX2VRtXHH65axM6MUel/YhJuU3sWA8tbQdLsijBxAqFSladlBHD6lFJQA/UoyTzfEqcMuYzH1gz5VEcLGOSg9TwfaDWilORUWnKPajcLvY++DwNPjvO/rzns3Ge7f6Z798BKel3g0oSxLuE7xj97mlPVoXzWxGh4MtwUQhWMXxootAYRY88FAqFZq54kYdCodDM9ShoxXOq+NsTuUh71U3r7tWQxp5FroBfKkwHH9Bed+01XQINh2XSnHlYxNrGycXInGGjO+RUw880XlORy7+5fo9aKh+rR0/8+T30wPtIOUtch4uNU4aeFw89ei4Y9bppiFnybhf2u6FSJWixHqjzvunfSVXa4fdwqCMWglDvxkd2sESPPBQKhWaueJGHQqHQzPVJXSuU52DRZUNOh6hVys1YDZkwG819VP3wijhFDeccd4ona/g3BqdMkZf4MEaP4e74lKjm93Cf/Kv1CG4Xf99O3ZVJ++A1GHF8H3M/dpr0vIy4/TLnGdb5aW17xWTAxnlnELM47xtrHzTjqHfagx/+hyt65KFQKDRzxYs8FAqFZq5PnBD0sLFYNWLYyJlm9ZkGnlGagFBS+ghEMkZT9v0HcKeEZqIp99onvC1dJONpYBvv3eC9S9Q2D0RZfAfmnzAlKHrkoVAoNHN90h659keO750XI/78Fo7xOh/qQo/5w0oL7Ke0CE/ZdzZTr3Jofppyr33C21I9exOfW0veu8F7l6htHjj0+JQL9+jPCYVCodCsFS/yUCgUmrn+ZT7yHOOeAkM3r1LYcVsgmUJNHMi2ZS7F3lW1sbw+aVOjRrSPQiiZsa0TPxjJ5E5sLwmolT0MZel9fMKxMo/vj+opf2zvuNr3I1ybqcfn3Y8TNOl5GfEVG+cZ5unpn/n+HcC2lO69M/Au8d431j54OtQ77VPyJkfRIw+FQqGZK17koVAoNHM9ClpRM7Nct6GxeYDGLNJedJhlkR2ObYusMmPONHNGucxO8UueWyUM740yOcxzhpPW8K/xcIrbjpRixvnJr/niGPIP4GbpqwfOqQribCseUg+8jxr1TNj7GPMsWNuoNn6O86zqZxjYo8eqzruB7wy+S7z3Tf9O0r/HQx0+fwUONtbsDIVCoZBSvMhDoVBo5vrECUG2U4WpsgW3ST1akaHOUmEWiVf5XuJC2jkbvSjauMg55EIVRsSNF6up8O7fMbPwasjHIefAmonecHfM0JfD/dwaq9qfk2HI19Aek+MAqzG2GeNjcEwPRie/h9vlEdwnj4Jfcvs4eM3ce6Nvt+6LD2nKPWiv2aLveQeLmLGzP+/5TM6z3T/z/TsgJf1u4DuD7xK+Y/S7p73vFFpJznvsd3CwRI88FAqFZq54kYdCodDM9ehoxXew0Egv8QLDkH74smzsmeMl4rMMaAXxMsfQqBtKlUwKKDA0zx3M4cyW97HVdj/2MQva1fAzO2nLXEeAsw1lDoNHoBIOvbkoIT+z209T81xiH/UIFGLgklEY5lNilhE4xcQlYzAMt8lPr2uWj0AlY7CIhWJG3COe+8Tapsm935NwzLMw5dlyE5OIVvBs98/8EjiF7wa+M/guWTqulWWHHBcZ3110oRCnSPundKpQ0SMPhUKhmSte5KFQKDRzlWMWf3jokIC/t3BGd0QuZ92wZe8glIt8K+352TFeYch0XmDIVLbtyxKuFgy/9oirErPiqP9uxWPQih5+ct84EQcDXTjoRaMNaVbD7doa9o9wICic4vzuI2iKg2UyZvmEmuI+efREoTGYZYrLxLvuTpKPiU48nFLw99DOazmEVgaevZRSavCsFsQpiPtn/qy03w18Z5zlzjtG4Zeq+xcoWL3fCjN+DJwy7h0dCoVCoVkrXuShUCg0c5XVmJl/Mw/g4X8DOMPLmd9FN4RgQtAlhje3CrPsjvHTYnOM3xfnx/isG0op8/9C4t1CTDt1KZ/ZFEiQ4XCxGzHVGOblHPJhCFkXTFTg0LIxtz/WmsDnZcQcToKRH3e/QGfOmEs9Jjmo7l0m2PYxHCzOzx8lqWiiBhHJIzhV2k3yk7ZRSUBj1H+mlzzkorZkxsd7cxQ24bPgbI/2fhsfraBGEp7VYiExn+3+mT8DTrlAzHcG3yXEKXz39G4V9b5yXCsPlYdQxryjo0ceCoVCM1d5SCyw7q1Mz21O/yqP6Z2P8ZevulWm95gc5MTnZ7n8Fd00i2N8jYnPJ/hLe1m07U8W8hd3vV9KrCY+0VNeIMbESt31SHK4790eBHsn3P7g9M67fWcVfy47ybiuIHvCqrD+QIq+1+trHD89f/cxLNvowWTw9h972SM84l5P+aE99cmTk0avWO3jMSZg3QnJEdfS6mU7v9eM2EdjtKtRqjfBz3te9eqduNu+diY1+Uymwp7sXGFis3/mLwt59p+oXrhMaj7N744x3zFnynTRfrf+HZXS4/nF+544e971iDVCa7XQTigUCoVmrXiRh0Kh0MxV7tUQll11e8jZV/YiEvE84t4QY8hfvsJxVEix3SVMRDQyNHperI/xul4d45tFi1auD9J2VsJLupR4v5ch02Evx1fjAPOy++4YNnIoyPasxGSPjPj05CiGqD224TBdTfKpIS5wCn3ptRGriS4usOEM0xuimoGJT0xwjpv4pG/+FLM0zs+VJiKXB2sAkYzCKUMTnGyfOsE5ZcLbQzXK9+3ca+oe7O5/ZQCwY25T81kYeHaIZGoiTkxqlktUMVwybwQTm2WLVD5bCCp5VghC4TvjEpjlQnnKgX07b/jH+MW9ycx9h67586qx0YqHXKJHHgqFQjNXvMhDoVBo5io3zrqaXnH0flGI3O3i2+m7UzDLWbJnby8xi7zBcGiDIdMzoJXrzrVys4BTZSnxHRws24Xsu1rIkKlZELN0a4Du7dl5DhtzZ3ZebXM4HbY2GIY2QCWDCCXdc7n0w0IO0QraB+hUeaCDxfCWt5/9MMxCJws1CrnoX/jwzyc6SyZVPJyCU1KSc/hYTpXCwCK8Hu7vOciFcXdvai+4jU08d4raxog1TgHmA1pZ4Fk9h3f8s6W8E54t2nfCZ3w3AKc8RftTvEsulVMlR3yKVqbiFM+VImgFi94gVu0OcokeeSgUCs1c8SIPhUKhmatce111tKtRV8dLuCCEdrDkTjv2PYBZSpjuVxxWqEQhMfrvcxkybQpJFFqXLTpZ14JQbuFguQFm2RxknOc6WDqkUgOt1PJxCUv86dl5B6fUKu24G7aqhCA7VsPqnA4WpuP3CUHEH3ZShSssPqEcLD0CGZPyPwWzOAlBk5OAHpiUM8r5Yu17DE5x93eatOOux1mwNgS2IU5RZQH6Mg0ONnGQi3ffWfiv9lxYzv2v4sVpXC+BVpa2U+VsBfcacAodKs87tPKivJG24lZivDM+w7vkAuebyT/9O2kqTtkDXet2xF37vrHRCtd38R656JGHQqHQzBUv8lAoFJq5yuvaXrYzB8YoiFlSXwVMOvlnXMcOnX8OTaghzLLIhpcSrVEYfpdkaLTBzPSm6dGK4JTbhcTrFdGKjPO2S1RFBFppurjeY0YZmEWhFcbAJfWBM/FwBnUjMCZSZMrBAkdKbSCUlBRG6eu4qG0LG11MXXwi646loctEIRy24/uOwSzHfQwnBD16EhA1xpVy3HYETvEcJ93+shGVEkfFxSmW0Qk+RDIOcilxnRRGOa21woQ5jU1s/OjGHVJpgFZy4JTlUh6oy6U8789XpzglpZRelLfq35R0EtBTVk/FKbnAu2eFeOid5OEUxtvEdvmemy7ewxlW4aGoDNfgfUWPPBQKhWaueJGHQqHQzFW+qS+O/ymcJJ8FmEGPVJYcPsBET+TCcrSsn8KEHzpUrOGLN6RRJnlglrqR4dO+m+XflHCyMCEIY7s10AodLFUlf+t2hx6tYAhEtILcqtrBLNXC2b77yAyopHYSghovsYcYw3KtqOSgwm5vHASg1C0sAfzR2BRNYxbIxSzHz/b29y/qe3jYxlgH0032UfuzXSZHpOK6krxEIsfNYmEWN8GH1y83t1F1Vbp7V9dOwbPgYhbE8vilasn27h5c4V1yJg/OxZmgkM+AUz5fyvP+xVIcKl+W121bcS0/h1PlOWo4PRSn7Bs5PpYDZ5LlFihwg+eMGGXTZUPt8C7c47ndN/ZxELlEjzwUCoVmrniRh0Kh0MxVvqmemD8ogD8WYADLDrMsVE0Ce507IheWoyUWWRk4xxvSsN2rS7BXbpZ2KNW7V9oYCAVjvrvKRiv7A5OD2rg6YGisHCmIOYQEWlE4xYiJVoguFH2AEyWnm0WtONQHwC2N83c797jIkOAg8TCLUyLXq81y3NRBMv+yroeDSExXypgaKF7Cj1EPxY1VHZXh5KDm6DKxt6WbReMSbLM43WaMC6VycIra/gzur7P2ehcrO/GHOOXFShDJl8ApXy3eH+MvukSgF8qpAlSDmikPxSlbxgqtyL27rk8RSkop3fI91MV7PDg7bMv2yk2mDIVCodCsFS/yUCgUmrnK14fPzB94aKV3sCzRxoVMb4FWuJCpWt2HyCWznTLyefZQZ5UtzHb+aaq6RKF9IUOuHYYpWw+tIN5Vp5jlDmiFOEUhFyISOlhADCpgmf6UEL1wQZ+ktuXQVzZpFEbpy9iiJC92p8CBU29nmhzMwqQhfh+nBO5xH45jxkUuj6xRiT29HopT+LtTcUrh4JSS7d32nlNlQQzjYZbTmO4U5VRRLhSJqxVi4pQV7pkOqazO5D3x9EzeKy/OJNmHOOVr4JSXJeLumX+OxJ9nOH8XeH+47xKoRyrEKWsVy/e6xTO3xjvmfS2LxBPx9gmLfDftA62EQqHQf5bKf+6eH/9ToAvIhSPYvurWtOME51kuf2ku8BeQf3W4xiYXhWAVwz7tvnaKqnt/Ob32ujuWupGRwR6+0v1C/upxImILE+yuwl/JLlbecmeyszrYPXLV40ZcdT14taymih1/Ofyopr/cmezkvCcnSR9HE3vnfc/RmQylRlUUfAwN+cHdn0/ohTOe2gv3Fotgez/ZOcYvjp51s7B75NUyU/+mdK/nzclO9sLZU1/hGp/BRHHevk+enrMXLhOVL1fy3H6zeneMv15I/JXyjLfP/HOc96m98G2zR9w+xGt4xK9xi75vsKANS4JwoZv6/BizImtvuuD7krSgwsNaYyzN9uiRh0Kh0MwVL/JQKBSaucqft/ZkZwnfN1P3V0U7xFgBrazgz3xaCMa4BnJh5TGusclFIfoqhky5r5yyAWMwS1+RrMrlWKt0i1iGKXtnElRhlq6KPnELMcuBE5KkCNimctHJ0M8dfzlPj7PcpiWCATUJeri/5UcqI9rJzPam991auKXdwN73Y098TprgHIFQ3EqSBgqZilNKbKOqFXIyszjZtlZr0H7YL56SxiJ97KXZV/K4JxCFVJ9jrcpzea+U5/B1n7fP/ufn8IifyaTmNyuZyPx2cSXtpcQvsXDEs+7r6PT7aThlreL2uK+wksY1TsQVSp1cV4JQrippZxXWa5ys/h2zJVqByYLvqUNt53xEjzwUCoVmrniRh0Kh0MxV/rx5evwPF5NgXGKM3yOXJfDHeSFDkPeFDCueALNwWPEMMdfY7BeFUBUM8Tnen51BzKJ+z8Yse+XhRIyhzGEArdzSwYKYyIULQViecSIUhVkchOItLNHjiFwt+JBMZUAXDSqwPRizeGn5WAOU1yTrP9PCLd0WR6mFMpzSAkO+eM9xQjnVD81qhd6+vaqEVoXCqTjFrWKIvIFuP8oL7iEUesNXp06VlFKqj66VhG3t+HB+mn6fUkqFgVNSEqTyFXDKt3Cn/Hn59hh7OOUF3FdP8/bZ/xic8q6mQ6VFHawWS2xyVV3K76H9HTDLDXDKujrNYdkB4xKhHOhaAZ5kHD3yUCgUmrniRR4KhUIzV/nr3aX5gyK3k4MWRY9WUKWslOHSGTDLZSFDiZuFxNdoX5esTNjGewwP+wqGKUnKfUqS7JOSXi+PQ6l+PVA1vFKkQY61RjIB02CtKmQcAin0Asxyh5iYhShGO1G6fx1sohwsRDIKp9Qn7SorPjnivlHF7cGYhfhjjOOk38bCLff3p8wsnmtlBDqxfuuB63QOOlJOtmHcp9FPxCkKoSA2XCmeI6VeclsHsyyJXPS/KWmnSmVUM0wppQSnyupcnrlnF+Je++q8RSR/OhOc8t3qt2P87ULib1Tij3xOj1NSkmeeawLzPaFxitzcV7jXroCA33a45E0lKPrt4RKxVJF9D5zyfi8n6BZMamNUW93hXbIHvlVJQM7CKtEjD4VCoZkrXuShUCg0c5VX63PzB8rBguFL0c0Ml2hbAq30CUMppfRkIfjj+iDDlJuFDDFYc6B3tnCNTS4KwSqGrJ/ChB/+abKGV8Qsz7AtsU2V5HNYj6VHK3S1cEa5qu3Z5W3DoRFxiYFW1AISEjKZxnWwMOGm+870dTTe71GKBsgBqKSh/riJC+ieoTvFQyuWs8VL8HEqZKoEo8eQ52aZUvHQcqSctBsLPbAGCvftJfsQp7gJQW1cEa0sHYSy8mI5lJ4MaJyCGE6VdAmccinP1vNLwSlfX8Chcn6VUtI45TuFU+SZfIF3zLNc3g8WVvVwynVNd4oc9htUKHxdSbLkmw6d/Hqw0crVXt6j7xCvD3J8N3uJmfCz69DKoea7hLWV7PcKFT3yUCgUmrniRR4KhUIzV3l3C3e/M0JMCrN0iSYFkoQQrxYoto6hxFmJWeKltN8CudwuVic/Z2lHVXhduUwkKYAJP/2fKWvIdb/9GYfvBZOGriTuhjUKraiEIRuzXGGYtOPCEnC/HJN5HPyReQhFUQf+Xa6NNnvLMSYPVZulPm3TGxOz1MPtPVrx0IZX3nZMYs9D5aETq83FKU5pWmN7tSBEPgKnLOxt6ETpkYrCKfw5EMrBwykqyUf/m1JKFeqopEt59pcXWNDhQjDoV8QphkPlL4s38nOV+AO3i1OO1nKoeDjlLZ7J10jm+QWuFC6688u+jd/sxJ1CnEJ3yvsdEh4PqJ+CtYC3eyT/dE421mSqVaIf3EoPrK0UCoVCoX9zxYs8FAqFZq6yWju1CFh+lO6Ovp4B6hrsgVZ2cHmsiVyWMqy5A3K5AUZZr9r4DmVkN6qkrMR0k7DMIzFLn/BDd4o3FLvA7DfdIhVWM6q7oR4Thmq4VvaIFWbB0Ogdhkz7+nTIzhK1mYtWnHYFO/r9DKcEqVwV/sCr6tolOCknyxicwu/Lm6pnNQqhOElFRB6PstYoNMa10ifzZM538VYCcvDLcZUeleDD1XpOa6ek5JegtRwqHk7xEIqKiVE6elBdoCwt3CmLC6y3eSk45SVwyp/OBaf85Uzqp/xt+WtKKaXviFPw7BGn8FkdSvh5B5zyBs8WccpPh+fH+Of9M8SCVn7tkMpvO9RX2SLZBzjlDthku8NKPyxnvWfCYBfX9rOi8t7CtRIKhUJ/TJXF2n6Xqxe/KrB3mk5dlUjNLeXPx75Ar32PhRsW8ld8gwmAfmJgfTitDHY/3jirTDPu0+7pEeekpveXXfXOlTbd/sTfyh45Rwa1t84eegXXOMmHpv85/eLOpKbbC2+MdmsCVLcX3hZuD7Wf7WSvlHUD6I93euc0zPffk71wZaiHvF77Y8hbFIKyqh+6KfroVak1OY1Ue6+aIReFKNlT/3AvPCXpiXu98MOZ0270wlNKqbpoz3d9gcUhLqXH++RSetBfXUov/LsL6YX/7VwmM/teeEopfVe2z9TXqJj6Ip/WC1/X8py/63rkb+HX/gkTmeyFv9pL/NNWeuS/YmLz7bbtib9XvXCYMragBXjXVTs8XSjV0Rww8u7X9/UWknFH4KLokYdCodDMFS/yUCgUmrnK4laVnDOlCuwZaKVhRTpO5CxkHHDYcygIFELksmyHQRq3IK448YkJBU6C4mD6SUmm3NMjzqHMFMxSJ0kzrhrBLN4kaK3W3MP5wTDppsMLB8W0eGKdFH1Ip6z3G1kToO1RWe25gz+07zzr/kEKv7JPY4EIlbqPCXQ1sXM6gW4tknEiD624VRGPB2i3j1ksoo+daoYeQlHecKNd+8XtSU1vUYghn7ieyHQmOx2ccriUc1x1E5sl/OJPOKk5CqfQJ84U/PaZegycklJKr7t3xU9Is3+1//wY/2P34hj/hHWLX2+Rgr+BT7xDKuuNHNNuB8MFEEqDd12mYjn3Odf37UmVu0ZvshU+8lAoFPrjKF7koVAoNHOVpVACz6KoMUo3pGxctMLZd7hZUGmNM+5ELnUX02+5P2CRCVYMQ+H1rbPQQ78ohPacX0kMnyo1hFlecExTsjyAiN7xis4WZ829potvHFPGgSd5RE59cxz6W06WlHSZQ8ctQgShDCo9WuHwz3GkcAhJFMObrUMuXDt0nKc82e1T+idTvONob5Qv3HawuE4VYpQerSxs9OLjFKJK4pJTdOK5U4hT6FQhTqFDpegcKpfAKV9eyv3/58urY/xXeMT/CnfKX0pBK9/h2XmRt8/oVJzyFj7x11gI4lXnSvkROOXHrcSv6E7ZwJ2yEZ/49Qap9p0rZb+Rd0kNnJLtgCeJUBAf3Snpnsmri9WjpxaYSaayQCuhUCj0x1G8yEOhUGjmKos75yd28a3jq99HKxLXGCLmHBbCzVJj6NHP9u4ORC9wuACzcE27XW1XI7QWglBoA+nAfbLPfVlDvSf5ytyWmKVWw0LbwUJniyWFWdB+SFMwC86vk+SlrzUTWuA+IWnYdyiEbQcbLygniudg6bZprHU8U7q3wIbnVHlgcpCDVhqvbkHfTheKg1n8JKBTXKKdKl7iT2ZuM1TFcDJOQdp9zoSfJ+0z8vKJuFP+fEl3ClLuV4JT/rqQdgunpCTP1FSc8jMsNj8iyefv+y/aNuCUf24Ep/wCnPLbHXDKnZys7UYcNPW2e+a28uzlu8yOFU5J0m7gFMb+ojJoj4SgUCgU+mMqXuShUCg0c5WlTRQGh+E6SUhikI2EkVPiGgocetQqbk7aKmCWO8R0ttDBouLuQ71aLEzgUfVTkPBDh4o1/PMwS1XemO1DspwsKSW1dMYYzHL8Vfc62silcKhNTpTQLy5CnII1XDM6VQ44WqIVhVz66odMEsJhAJs0nrPF09CiFZSzWISJWca4U5gopBJ+iPfy7l/iFP7ccaQMOFUYe7VTxuCUyyfi7Pqic6gQp/z3hSCU/169Psb/tZD2b/EsWDglJXuNzZtaPvttLYziJ/ChHw+CTr7ffSntHVJ5dff82PbLnVQ8JE5Z3yHJ505wSkOMsmmPz8cpx1DjlIPTTrRirdc7qtop9mc3h0KhUGguihd5KBQKzVxl4aAV17UygFZy18GCdiIXrGvRI5WKs74YptcYyu/Qrgq2M5mo+9CDWvABZUFZD0Ul7QhmsRJ+rCHh/faUkGw0ArP0SUO1m5UlcjFLhpN8rAkiTY0bEwfY2xQcRvZYBGWK1T2gHClMGkI7tmm6+jOq5K0aZjo4xa3BYjfLATrtE9BK45WrddEK7mO1cMQpWlE4BaV+KlVT5cM4JSVxqLg45WIYp3xpOFT+CnfKGJzyEi8F79npkYqHU348CBZ55eCUv2+kfsqrzqHy81rcKe/WgmSIU6o7vJA2cqzFFkk+m/Z8F2KeUTjFRSsDTpWUhtFKuFZCoVDoP0DxIg+FQqGZqyw3Xk1UCaegFTWcJFpxHCyZMavLYQdxCh0sbD8As9xWbO9qt9REK0Avzuo+lff37RNilmogOYjKMNa6YY6NojLtyW8cd4pb3VaVZE1m3A8vFU6hq2XfoJ2laxEfTtvp0lHoRQ0z/zVoReEUPgs9FsmGEQrbNU45xSgap9hopfZwCraxHCr9yj4pSSnalKR2SkqS7JOSuFNS0g6V/7po66Q8Nk5JSZCKh1P+fvjiGH+/Q3wn8T/vkPDTIZWrWzkhWzhSiFPyO7hTtnKOi82pKyX30IrjVFGuFYVTeP/rf1NKgVZCoVDoP01uj9xfs9NqY8/DngDL0OPIvZ76Qf+bUko1ehussFexkhjnyNAtvatOJxC93rk/8Ynv1i9UoXrV0tv+FJOgx/3hz3LumElvcU0O3TYNTraaoLOuabo3wTnQO+caHbwH2AtvKqcXjvuknxxVvXD62Ru7V0+5xfcH5A2EGm9Nzn5EWji9ba93Xpz2wlOSyX7lC0fPu1aTnYjZI+dkJnvk3Wr3eo1NebhYxdBLu7cmNv/P8pdj22P0wlNK6XXdHuOrgyzs8MNeJi9/wKQme+E/rqUX/lpNbLYnZXuLk4aed7HBROadxJzMLPCo9j1xb4LT63nnh9Oed0r3feTdNt6SteEjD4VCoT++4kUeCoVCM1dZ7IbHpBqztP/Rw3FObnFoKdsQl7iToB0W4baVk+LKuIZ3nBOf/f62+AJXxDMKm9gxJyFr6+/eJ8QsBcZXxQh2oCdB26Els+j1xky5T4Nxodq7e4DlGBhzsofDT+AFhVF6tKLWLOSkJtonV0XsN3B+7FQ5dCc7c+P+LxyEwvvcSbu30MpUnKLiczkp9XGNTUxquotCjE+7f2ycklJKrw4tFvnfPRDKVuIf4BEnTvnlVnDK9a2ciN26O3G3p2n2KaVU3gEF4jG0cEpKghH1vY33nodWFFrENpzs7KtIOLkTHk6hokceCoVCM1e8yEOhUGjmKvPtiOl+5a/txwHyY52WjCEzxvWeU4UV9OpuhpfDTOUp51Aa7ZUqyH6aul+hjSTpHbd13CwefjH1GJhlYtXEHOMxuln6S3aDtkO+QCwXQTktnDwAhVmK/uf29VVuFqKGikPRU8yivbU2hiHn0/7aj19YwnVqqfu7/9dzpNi/x3a9QET3LxEKylYobKLQCrzh5/jucKUsLtoL8eRS7sWXl3SnXB3jv01Iu39snJKSIJX/2b48tn1/JzjlH7fPZR/AKTe38vmHNU7cbXvCCyAU+sKJUFimhPeu5RkvXJwy3G55x9U2rnd8+N6OHnkoFArNXPEiD4VCoZmrLIhWJszsqzaY3vUwnQkeGIYTpyAxJOuGnAqnEKFUNnLJHOTSD89VIhHGuHu0X6sFHfD5RCsT0ugfjlmmJQzlGI8VGI+V3UIPv+SY1cf12OGrVEwaoj3FSwjqDjE3cEtKOhFGz+zbqKFPjshUkhdn7e3kLz2zP4C9HFmOrJT8JKmja4VuK+VaQbuq8ulhlq7NcafUiA9AKPUZTgTS7pcXwgOedg6Vr4BTvrtAss+ZvcbmUNr9Y+OUlASp/M9a2v5xK+6UX28lXf8G7pTDreCUHA6VokvyUdiEMdEKEYpyquDePbpW2MakNvk9hVMqB6cYrqwHO7JS9MhDoVBo9ooXeSgUCs1cZb4fkRBkvO6HalGkNK4eharB0g1PdJIQdq2cKvZw24r1cNz+24URU7px1s20VKm6LF7VxPGYZWpdljzZSUM9cvHQyztglm0m4/oajOQAzJIj7rFCQYeGVxVOITVp1/UoujaYDvLKxiy6HkVmtw9M8g/WETppP03yUevUlmPQihN3p96rZlgzwYc45VxO4OpSTv6zC9RPuWjvGeKUv52/OcZ/XQpC+etCMMtQws/H4JT/u/vqGNOh8kPnUCFOeX0jv3ercAoqF66BU9ZM+OnapuKUHZ6XAbSSeXWEaiIXe3u9Juepa4saU0coeuShUCg0c8WLPBQKhWauMttV9k/siqdSa2UETuF6m5lX3rM6xS8qGUQlhuAjMdyoFE45TRhxS0Iy8Qe/d8DY+8YY7Wgny0S3xABmGVOXpSivj7FGKyx1W6t/UxInS0opFYiv8HvbAsX3CyYNyZH010+VrqULhcNWlfxlx33imE7+cpKDPqL4vmyMTUdgFmvhlDGLprhoZXnazgSfeoUYCKU4l5O2Ohd+9fzy7hh/dSH315/OW6RCnPK3pcR/KSX+DoumvMBFs/DfY+GU/11LOdoeqbyhO+VGcEoFd0pBd8qaCT/HUNCKW5YWCMVBK8qh0sXZ3sEmA4umpJS0y8rALB+zaEr0yEOhUGjmihd5KBQKzVxlth9GK0odUsmc9Q05POaKNAqtYKhsOVhqNZTGPgbcKSk5dVdGDMd1QokM3Tgy6jHLkJPlvtwStAZmGVeXhfs4XUc0pZSK7ksX+MKLEZjlXSFLzKyRrFUxLrv1QEu4WljHQiUHIYabhaWKjwlByqE07Er6XdBKof9NSZerbUY4VZQrpUMq9Qpf7Awr+gCnXJzTnSI45WvglG/PxKHyly7h529LJvj8doyn4JSUBKk8NNknJRunpJTSr51D5fZGPrsmToE7pby1cUpxh3ir/03pniPFQSsWTkkppaxz9+UjnCoKmyhEyKXMJDwilUAroVAo9J+reJGHQqHQzFVmB2ZpYGzpEJfjNsq1clqLpW2WcYDCLN5itR1G4c85GklquO3EypXStzk/94bj2P6AMXR/KDbM0GJJ2TGr+whmmVr+lvuQIysM14p2sGD4jvYF2n8rLo7xuhAesOucLT1iSUnXaGlQVyTfse6KHGpmOFgyA7ekNA6tPEZC0Bi0ckwIctwpLlpZNnbcJflkK9RLgSOFOOXz8/Ux/upcrvW351fH+LuVoJMeqXwHnPIN+MMUnJKSIJWpOOUHVY72FKekJEildmqneDilNHAK42JrJ/iwXSMUz7XSXSeFVuTcKHdK5eCUoVoqY9AKhW2iRx4KhUIzV8m/Kq6y08k9NcGZO9uyPB67SbW9Tf9XTVXgU5MI6Ck4E6KqZ9ZPojm97XE9OvTOu0lQnjH2zjPl4x7xF9UQPeLUmN55jmPNU9tV4WRngSNfYGaR8RIG72Vh986vi3Y/21J6TzV659UWoxi3d45RV/eR7IV7pRlUj1z1atD+0MnOgQlOxv4Ep93zbhAn9L6LLl6dSS/86bl0LVUv/AwecUxqshf+lwW94VcppZS+Rhf2BRYXuchllDUm7f5td6FeHZ4e27xeuFfFkD5xa2LT64WrnvdALzyllMqux6163uyR79kLr81tMjgnjj5yuim8njfbVS/8w71zv8rh8LskeuShUCg0c8WLPBQKhWauMh28WU3IQCuZQij2xGfDNSQxganRioFcCPFrTqLhY1SaPA5MTXz2q2Dg5yMQiq92m0M6nQBNKaWbEbvIjQqFbVyrf1NKrkfcwywcKotkWJ1nMgRnOr+HWTghukS86JDLdSnHsVnIxdmXwhrqBSZEFxi+708xS63W8Ux27OEUhxD227jWf3cBCcQGWmlKIBTglIbxEtd6ifN3JvjqbNUiladnwgVenAlO+RI45duVjVO+W0j8bYdTUkrpZd7u8zFwSkop/XhoscgPe5m8/H4rCOV7Z1KTVQyZdm9NbE7FKeWmMdt7pKIqGO5OUUlKKbECrJrMZHuHRRSKHjOp6WEWvXrNaRsVaCUUCoX++IoXeSgUCs1cZaKPnMqcd3xujFFrG60o/KLS8ukp53CjRyEYjnDdT7QrZ4tCJ2zvhjueNUHhFA6BhhgJnCwZ3Br4tVt1Smw3S2F4zQvP7QLMQneKN1S2MEuRoYJikoUEFjBwLxHTU74y3CzLQtrelzJkVp7zhWCWCpil3gOZdXG2J1qxMYtyqnhVEan+dDqXVPvFce5dtNI5qxROwRAccQmcslwi1f5MvOGfrVr09eJM2MHLlTiXvlm9P8Z/XmLxB+CUbwrZ5mUh7pdn3UodY3DKupZjsnBKSin9/dCm1/+wE5zyw4Y45fkx5hqbXBSiGki793BKqRaI+DBOSSmlosMouVfNUDlViFYQE6P0aEWl34/ALE71w0HPuLPIxPGddk/RIw+FQqGZK17koVAoNHOVulwg5ZXZMlL0KSKZ3EYrmeVUSTK05ZCZw1pv1lf9NSKWOY6buQW/1zBmacxkKP6aXTXxAERyk52Zm7MC4XGNTTCCQi0awW0x5oSsIbRCLPzqGIIX6R1iO2lohfz6HrOcYR9npY1Zbnfy+ZstnC17IKldV02Ri4wcgN8OvI8kfPw1Ox20AodKVnYHACRIhLJYSNw7UlJK6XJ5ilNSSunFqnWofLkUd8o3cKd8u7iSdjhSvikEv7wA4nqWnTpUxuEUOdafKqmA+erw+TH+fteile/vpILhj2u4U27hTnHW2CwG0u79ZJ/xOCUlQSoFsQlxCtPy4dxzXSlVddrmIRQ38YfvL+P96m07QtEjD4VCoZkrXuShUCg0c5W6vOAIWY4OlRzU2NsSuShEclprRVsJuG1ht0N5w2Fkq0JvgXgaZjm2e0NzJkPlxCyyzQ3G/b/kMhS10QrdLnY7E34oa1hNzFLg2hRJhtV5Jg4IL1HorHO2nAOtML4oJb4iZkEC0d1ehtvbLpmoQi2dCq6WGu0KxalYNvGzfzoxUY2XXaEVDNkLnPvOlcK21RLnYSGY43IpDOA5cMrnSyT8dEjl64Wc968XglaIU14W4lz6AljuGa7rCmjFWmPTwyk/V3KdflQ4hQk/LVIhTvlF4RS5vgcm+8CdMrjG5oRkn5RsnJKSIJVsjDsFi+soV0pltFceNvGcKmMwi/EuG1P9EIoeeSgUCs1c8SIPhUKhmavk8GGSVIla21WgF5/AD5wytcd203lyD3I0zt8gfEy/hT9KmYZZjiN2pwyqrtVhH98BdS+uOXzvhvt0siycRSHoLNH1UzD+7DbxkkE4BH9Go1Gim+UGMWuztPiATpYzxOeIL7Ag4vuFDN/f71CnZdkey/YA3AL0cqhszFLX9slX19tICFJGIyZr4XrkQCclMUqHTlZw6ZwBJX0GnPLZQtjB84XYMb6AQ+WrDqm8LAWtfAVHCnHKCxzfU9xHFk5JSZAKcco7LJz6upZr8OPh+TEmTvk7En7+edcilddrwSnXdKes7UUhirUc06Q1Nh+IU9r209ooqnZK5ThVgEi0g6VPLmxO21LSCMXDL0O1VKZibih65KFQKDRzxYs8FAqFZq7prpV+XEok4yUHefiFQ4zCmOEttM/kuAvEJCuD1WPtPI97GoFZjmjFwC33D0TFso8D1kncYdfvumFzAbRSqth2kKh6Laif0if80J3iDcHZrk+QjVl6tLJUThbglFyG8pflJWJJNLlAvD60+OdmLxhou8R5AnI5oKxxBbRSo70ecK3kCqcAWQFd8NwvgVFWXfLNk4V8x4tS4mdAKMQpL1Ar50usAvVFt1brS9RL+SIX/kDsNQWnpJTStsMoCqdU8nuvgFP+vkeSz1ZcK682cKh0SOXdWnDKbo2EMyb+3MkxsX7K0BqbpUr8eRhOSUkwikIrHk5Ra286iKRv53vPxSxeuVrnXRuulVAoFAqVzcTJzsyqimhVRExJ/1Xx1vLUOz/9vdz2jquqiNyFs1K1bGAfkus1Z753f3iOd9xd81HFsn2F77bN2p7NFXvY7JFzpXvH380qhn3aPT3i7N1N7Z0X2HeebbrPk2M6y6RXyonPi3yLWCbJnqAX+37f9vCeLJDOj57jBj3yHRbL3Fdc4APntf5w/6QwSiOkJAtmpKQX0mD5gb4swSUmcTmp+RlXqS9vEMMDrtLrW0/5c4xinuMeucjku0/phaeU0rvOJ/62kn38VH12jH/cS89b9cLvnh/jnzGxeXXbjqK2t+yFY1LzzvaIq3iH2FoIwln13uuFqwnMw2msPeJ41zHVnovruD3ybhvP/10bvff723i9bCMdv3GqHHqKHnkoFArNXPEiD4VCoZnLr37oTGA2qRtiEKeoeU9lSsYvjhhiZAa7GCGM+lMDSJIdj9X+PXd60/G/N91341yssrPn9iSoh1a4OEbd7XRbyPD5XSETggu1fiYWfwBaWSq00hXWR8o9PeJpBGY5z+y1QfMOnRRJxsbL5KXzY1IQ+OBddXGMn3Tf8wZp4muglTvEu5oTnyiD0NiTnX2cO4t7EFmVzjlW5Qe6+Al4wTPglGeFpN8/hwf8Odq/yCV+2n3OM2C2i8ye1KQ8nHKNtPs3HWL6qXp6bHvl4JR/clLzTiao363lHtzedcdyJ8eab+gRd9CK5w3fnWKUfMQam27aveETfxScwnbvPeZNglJAKCY6GVPx0CtNMvyboVAoFPp3VrzIQ6FQaOaa7iPvHSesEkZfNTkLhrgucrH86J6rZYQszJJ5CzqqNTCccgLquLvv7KblS1wQoTjrPxLFHDrMUhWCDtbw2P9WCIpYwl2h/OWG11x5zuEFTw5mIU6hzCE+fm+hXC2CFOgvp5vlUrlZ2nhdyGdfA7NsgVO2jRzHFm6Miuuo1nYeQi+eM5YeWGGBhhWOm+uVPu2YAd04xCZPgVmeK4RCV4p8/kW35usYnEJ5OOUtvPWvqxaR/AS/+D92knL/agucshF3ym93cq+t78ShUt2157sATinpVHERisTKfcK4d60QrXhrbBKzVKc4JSU4WB4Dp3D7MY4UD6G463Aa7RPfy9EjD4VCoZkrXuShUCg0c5VTU0GPKGRMWj5HIaOQyzS3ypD60b6xNMQHpf66GRglx3csdg5CUZjFbmecdxuxrQJaWRcyxFWYBcN0IoC+MqFGK0znR2VDYhEvOQiy2gucqILuDyx8oROITpOGrmtxSBBRbGokBxGtoL1SC4pkJ+16zVNWnURlQ1ZzdJBQ77x5msvxERM9RfwZHDtPcV11kk/Z/TsNp6wbuWbv8Jz1OCUlQSqv9s+lbSsJQb9OwCkppZR3bpUc6fc5EYqT+JPv7Ha1TYdLPEeKcqdwjc29gVNSOqbdPwpOYewsIDEZoVjoZOq7GIoeeSgUCs1c8SIPhUKhmatsHljMPPOcJd7+POSCuhfZsUbGwwusW8qctR2zynOqZOY2fbJCwwUIuDgEhopeEpBKJsKwtCn7f4EFStl4h0Shayx0oOqDFKeY5Sxj8pAd97VT2u8DNwtkDf11IlFmx0xewhfmcV12CII4Y4NKf2ssgLCD7WfflIilvRron3BhDq9WDSs70qHSH+NnqFB4QTcOfw/3kYVT2s+U2JLGKVgLFcP3N7U4fH5B8s/P+9aV8hPcKa+38vO3G8Ep13c4x3e41hsk/2zb7+PWS6ERSiEU26mik3xOE4K4fqZyp6iKhjauyCwU8lCcwngMTnlgxcOHvotTih55KBQKzV7xIg+FQqGZa7prpZM3DBiFXAYwS6bKkD4CZiEqQXPjbQPMkqn29t/8wPq3GGrRqYJhJpOAmoLYAdv3aIWJRCUWUQBm2ZbALKUMiZfFablV1gmhK2OpkAIWV0D9FO/PvIVZhhBBSikV/MyGzpb284lbbuFOoSuECGX3yGhlmdk1bIh8Lrv4TKGXBtvC9YO6P4+BU94BB1wBN71GadrXB4l/3rfxrztxp7zdiDPoeiP72G7kfDdblKbdntZSIU7JPXcKEImb5GPECqE4JWpVMqK3WES/n8fAKSkd0clknOK8X833Z7hWQqFQ6D9X8SIPhUKhmascVTrRkvMnYBRycWdyu6QYoIhHwSy1nQaUqfoqMuxS64Hid49IBU4bldSD70hEwkWOGCsHSzcsxZKeKUdZUFXyFphls5Ah8ftS3Av9qjZEK2dewgsSV3Q5WpbFPXWleIjAa9f7QKJSdz4XSHI5A3rZAC/sEl0rOWL7M/saLEWy73Pt5ME6nYloheek6dq4FmqBbSUucaxDOGWP775FvMZ5uEbNmbcoA/zmIOjklz0Sfjqk8nYr277fyj2y2cq9U2/t0rS8B/vbRyOUdPLzD8ZeOdoOi6ifO5hD1VRRmMVymXw8TkkJSMXbBwVE4jpRzPoqgVZCoVDoP1bxIg+FQqGZq0wTF/k8ylvgdgRymZJMNBmzEJf0QxWrVG5KephEdwpcCFaikEoSwtCu4ew8k4ZQWle5WcqBYSvRywKJQhgG70uWvZUaGT1muSjlA8+NmiEpaeRCtwbL0bJ+Sm5UrBmDWQquMoR4kU5L7u4x9CRy2QM1VLhmVZLvUw+sMMUVgnT9GXxmRidKhvb2uFcKm9hxPqKf1CMVD6dcoSTvmxo4BYk/vx4kfgOHym+7dnvilPVG7pH9BteMaAX1g6yaKRqVSDzGnZJxG5XwY7hWvMQfD6dYK/ZMXSzZS/jpP2eqI2XCgssPfhen6JGHQqHQ7FU2EwF71k/ieX89RvTU3d55/9ertnvKdjp/Silz/ur2ceP8BWcvHJ/ZcHKUafxdL1v1FDipiV54g15Drjzlpyn/KaWUd+2c7OT8HXtJ9YIxfNUL+YXbXdvzusIE6AW6V5elVMnTKejoqWNClFUMmXZvyeudq1640WnmZOheeb0lZk+9wjYqU3ugxCXvUFwO9fmLjCOG/KRdf5eH9cJTkp44qxle4xa9rqUHfYUJzreHSzO+2otP/Krriff3Qkop7XZybeqd3QtXsTFpqdvskaeXaq964eypGwtBpKH0+5T8Z9t69j9mIYj+M6f2wt1KiB/ufU99L0ePPBQKhWaueJGHQqHQzOVPdmb2O97q8hMv6PKC9I7j98ZgluMGHKagmZiFk1uc2LSkJkMzu53DP0529tvXRDKNHR9OsUlKKdUcZiLVvx+WcnjaeF5cLmaxQBo/MEvvEb5FCv/7hWCWy1KG4Bf5E8TeJChQR1fFsFCLVmCCzJnUpCzMUuE65rh38ubUx51SShUmJ2u2D6Q6F7jWuTpuu12jk86Xng1/R6rGzXuAR33bxWsc8/tGrtkVJjivKuIUuWbEKe/3co1vd+1+6BevgFOyHb7DAE5Jyb5HGWuEgvaD84wYceYhlMrBFQppDExO8t3QjMApA/fRY+GUSRjF2Uf0yEOhUGjmihd5KBQKzVx+3vCQpxFDS29okOUPwyxuOr9TNVG5VpTVvNvI9Y6rXHyEQBeNgVzgTsm8WfjC3oY4BRnXxwoBHKrWHLYqRwD2oWKkrO/bIfTdXj7k/U6G7BdAK09KwSnv4Iy4NBZUaOP2YFjBkCn3njtlELMoB4ktIorcGfoSs9ifZ9taNHKxXSnWzz3xWPeq5ADjdptbJExco7LhdSXXidfmPduJVnCN+2vf3wsppdTgHuF9xDjz7rsuzohNqtOft+3DyNH0hlc22vDS9dW7xHKtjMEfE6oYfgxOGUQo4SMPhUKh/1zFizwUCoVmrlEJQcqV0muE24X7noJZXCcLhzXIX+esc8ZB+WBSAIdJRmp/StrBkp8O1xokJnEIqVL3jVTkdhv5mH5Y2tDJ4gxbawezcNjcuxO2qI64WWIdzAPS+eF0eFLQzbI1436NzSXQQYHru0i2c2gIs3i4YqHoFp0tw/dujzfGoBDlYJnoSrn/eSlpnMJ4i/tu3WG8dS3X5hZohUlA7yrbncJruTnIfra7NlZOFQetWAglJfseVOn3zr3tJgE5SKNHJ1ljP3su/mgc1GE9+4k/9hIa7f01VkLQx+CUCRhlzDs6euShUCg0c8WLPBQKhWau4YUW04gkIL0xNzL34WKWKXIShUwHi1O7xR0m0ZViDq+cGi1sV8NJfLyawcf2i/73sK1KqsDhob3mPohZuhoxVSXnd3uQy32zl+H4k4XENxWG7AXcE7UM68+O61ZiUQbUClk4iULEImmEm8X8ueNsqdxECc//0u3Duf8eilN4HGzfJjpVGsTtNXlfy3nnuV4Ds/Da3Fa4ZriWvMbHa1/Z90jmYDzlWjHuR3U/c9vauefHOFEMl0nmIVEXswzURhnjVHmoHgGnTK2vQkWPPBQKhWaueJGHQqHQzDUKrVj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id=\"imagecb308b0f69\" transform=\"scale(1 -1) translate(0 -266.4)\" x=\"43.2\" y=\"-9.36\" width=\"266.4\" height=\"266.4\"/>\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2094,7 +2147,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2130,7 +2183,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2183,12 +2236,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2202,7 +2255,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2215,7 +2268,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2763,7 +2816,7 @@ "L 174.293116 272.985451 \n", "L 176.356 275.048335 \n", "z\n", - "\" clip-path=\"url(#p30ce4b8c06)\" style=\"fill: none; stroke: #ffffff; stroke-width: 1.5\"/>\n", + "\" clip-path=\"url(#p1b4d9ce69a)\" style=\"fill: none; stroke: #ffffff; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", " \n", " \n", + "iVBORw0KGgoAAAANSUhEUgAAABMAAAFyCAYAAAAXj+GHAAAB9ElEQVR4nO2dgQ3CQAwD8/0fjRHYfxSeGYJO4iQnA1h1HDvfgtr1Wu9bUD0UUFXVqcXhnfUsDoy8MrZnITSLpcmB0WqCDvDSLCtNdM7g0SDD0UuTVXMc0K0QNXMcMBu9WWo1tTStap4r7hkJ5qWZ4QByztDRCFFzjN4vswM4LLMDbobRL6im2AHk0KbkmZgmqyYHlmN0Dkx8u2MeDRDMTHPU7IKFbPSQPCOPoWaaM7RtMG1qhITj0OxXzI2YVU3xDtD2bGj2a2j+GyykZ9g/JktN0xvbIdtJS3N61i+x0VNiO4Pm9Kxd6JVNnvVrhvYHsJjR4M7bKaPhVTNmaFGwBAd4BVA7gBsNrwBiNb1G9/ZsHNAHA392Mp/PvEPLGr0ierYyRiOGJgiGpgYpgLhnXprwqrPSBFmqabLexLDqPCk989IEwULUfMDDXoiaYgcMzR/AxA4gaYaoaQWDe0beo4tphqiZkWewmh8STNuzFKODam4vTVZNrmdio+/pWRvM7ICI2N4hNNElTBqdvDLzqhMf9oZmF4xMDfN2IsNRbPSQPGPVtBod3uhWNWFvos81tD0L2QEZecbeb2rVhIc24ikVG47iM6308Y3Z6Np/xsF5xtXZ4p6B79kjL43eAShN68uHYDW1NFk1wc++mHuG0pR+wuQL9NSN5oKR0h0AAAAASUVORK5CYII=\" id=\"image891a30232e\" transform=\"scale(1 -1) translate(0 -266.4)\" x=\"336.96\" y=\"-9.36\" width=\"13.68\" height=\"266.4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2857,7 +2910,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2889,7 +2942,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2905,7 +2958,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2947,7 +3000,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3101,7 +3154,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3168,7 +3221,14 @@ { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:01.717211Z", + "iopub.status.busy": "2023-08-29T09:09:01.717131Z", + "iopub.status.idle": "2023-08-29T09:09:01.772482Z", + "shell.execute_reply": "2023-08-29T09:09:01.772191Z" + } + }, "outputs": [ { "data": { @@ -3181,7 +3241,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:36:53.403726\n", + " 2023-08-29T11:09:01.760870\n", " image/svg+xml\n", " \n", " \n", @@ -3217,16 +3277,16 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3251,11 +3311,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3286,11 +3346,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3322,11 +3382,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3342,16 +3402,16 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3397,11 +3457,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3443,11 +3503,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3484,11 +3544,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3536,11 +3596,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3597,11 +3657,11 @@ " \n", " \n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3803,7 +3863,7 @@ "L 376.014506 19.298703 \n", "L 380.352452 19.296635 \n", "L 384.690398 19.296 \n", - "\" clip-path=\"url(#p61e15099a7)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p1e03651e6d)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3867,7 +3927,14 @@ { "cell_type": "code", "execution_count": 10, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:01.773973Z", + "iopub.status.busy": "2023-08-29T09:09:01.773898Z", + "iopub.status.idle": "2023-08-29T09:09:01.825794Z", + "shell.execute_reply": "2023-08-29T09:09:01.825547Z" + } + }, "outputs": [ { "data": { @@ -3880,7 +3947,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:36:53.470833\n", + " 2023-08-29T11:09:01.813545\n", " image/svg+xml\n", " \n", " \n", @@ -3916,16 +3983,16 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3968,11 +4035,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4021,11 +4088,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4066,11 +4133,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4101,11 +4168,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4142,11 +4209,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4160,11 +4227,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4178,11 +4245,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4196,11 +4263,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4256,16 +4323,16 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4290,11 +4357,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4310,11 +4377,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4330,11 +4397,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4382,11 +4449,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4443,11 +4510,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4569,7 +4636,7 @@ "L 371.417317 261.202138 \n", "L 378.04292 261.210464 \n", "L 384.668523 261.216 \n", - "\" clip-path=\"url(#p625e26c789)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p8a2ccdd751)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4637,7 +4704,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/TwoParticleResponse/solutions/02s-Lindhard.ipynb b/TwoParticleResponse/solutions/02s-Lindhard.ipynb index 17865a9..409ae64 100644 --- a/TwoParticleResponse/solutions/02s-Lindhard.ipynb +++ b/TwoParticleResponse/solutions/02s-Lindhard.ipynb @@ -99,7 +99,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:03.695000Z", + "iopub.status.busy": "2023-08-29T09:09:03.694777Z", + "iopub.status.idle": "2023-08-29T09:09:03.962544Z", + "shell.execute_reply": "2023-08-29T09:09:03.962296Z" + } + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -129,7 +136,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:03.964664Z", + "iopub.status.busy": "2023-08-29T09:09:03.964123Z", + "iopub.status.idle": "2023-08-29T09:09:04.805972Z", + "shell.execute_reply": "2023-08-29T09:09:04.805721Z" + } + }, "outputs": [ { "name": "stdout", @@ -158,7 +172,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Starting serial run at: 2023-08-24 17:40:29.549022\n" + "Starting serial run at: 2023-08-29 11:09:04.009086\n" ] }, { @@ -227,7 +241,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:04.807570Z", + "iopub.status.busy": "2023-08-29T09:09:04.807493Z", + "iopub.status.idle": "2023-08-29T09:09:04.982656Z", + "shell.execute_reply": "2023-08-29T09:09:04.982426Z" + } + }, "outputs": [ { "data": { @@ -240,7 +261,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:40:30.606801\n", + " 2023-08-29T11:09:04.951200\n", " image/svg+xml\n", " \n", " \n", @@ -272,17 +293,17 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + 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\" id=\"imagecdb9e978c3\" transform=\"scale(1 -1) translate(0 -266.4)\" x=\"40.32\" y=\"-21.6\" width=\"285.84\" height=\"266.4\"/>\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -318,7 +339,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -356,7 +377,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -452,12 +473,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -470,7 +491,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -483,7 +504,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -979,18 +1000,18 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + "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\" id=\"image43996a5634\" transform=\"scale(1 -1) translate(0 -266.4)\" x=\"344.16\" y=\"-21.6\" width=\"12.96\" height=\"266.4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1034,7 +1055,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1077,7 +1098,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1093,7 +1114,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1109,7 +1130,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1157,7 +1178,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1173,7 +1194,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1265,7 +1286,14 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:04.984043Z", + "iopub.status.busy": "2023-08-29T09:09:04.983975Z", + "iopub.status.idle": "2023-08-29T09:09:05.035486Z", + "shell.execute_reply": "2023-08-29T09:09:05.035258Z" + } + }, "outputs": [ { "data": { @@ -1278,7 +1306,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:40:30.717461\n", + " 2023-08-29T11:09:05.023254\n", " image/svg+xml\n", " \n", " \n", @@ -1314,16 +1342,16 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1348,11 +1376,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1383,11 +1411,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1419,11 +1447,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1439,16 +1467,16 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1514,11 +1542,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1562,11 +1590,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1583,11 +1611,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1604,11 +1632,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1657,11 +1685,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1678,11 +1706,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1936,7 +1964,7 @@ "L 382.377006 250.298653 \n", "L 386.714952 250.819337 \n", "L 391.052898 250.994318 \n", - "\" clip-path=\"url(#pfe91235166)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p4328d52e3c)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2030,7 +2058,14 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:05.036853Z", + "iopub.status.busy": "2023-08-29T09:09:05.036773Z", + "iopub.status.idle": "2023-08-29T09:09:05.132044Z", + "shell.execute_reply": "2023-08-29T09:09:05.131783Z" + } + }, "outputs": [ { "data": { @@ -2043,7 +2078,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:40:30.809379\n", + " 2023-08-29T11:09:05.113166\n", " image/svg+xml\n", " \n", " \n", @@ -2078,12 +2113,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2147,7 +2182,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2196,7 +2231,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2237,7 +2272,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2268,7 +2303,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2281,7 +2316,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2295,7 +2330,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2309,7 +2344,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2323,7 +2358,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2389,12 +2424,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2418,7 +2453,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2433,7 +2468,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2448,7 +2483,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2463,7 +2498,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2478,7 +2513,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2520,7 +2555,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2567,7 +2602,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2737,7 +2772,7 @@ "L 363.046761 261.216 \n", "L 373.86858 261.216 \n", "L 384.690398 261.216 \n", - "\" clip-path=\"url(#pb0db4557e7)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p6d797369cc)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#p6d797369cc)\" style=\"fill: none; stroke: #ff7f0e; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#p6d797369cc)\" style=\"fill: none; stroke: #2ca02c; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3000,13 +3035,20 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:05.133429Z", + "iopub.status.busy": "2023-08-29T09:09:05.133360Z", + "iopub.status.idle": "2023-08-29T09:09:05.199582Z", + "shell.execute_reply": "2023-08-29T09:09:05.199347Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_36337/228374747.py:8: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/64/_1fkps792lsc0txclg5ky4300000gq/T/ipykernel_50793/228374747.py:8: RuntimeWarning: invalid value encountered in divide\n", " chi_Q_ref = np.sum(np.nan_to_num(np.tanh(e_k.data.real * beta/2) / e_k.data.real, nan=0.)) / len(kmesh)\n" ] }, @@ -3021,7 +3063,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:40:30.893785\n", + " 2023-08-29T11:09:05.183396\n", " image/svg+xml\n", " \n", " \n", @@ -3056,12 +3098,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3125,7 +3167,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3174,7 +3216,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3215,7 +3257,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3246,7 +3288,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3259,7 +3301,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3273,7 +3315,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3287,7 +3329,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3301,7 +3343,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3367,12 +3409,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3396,7 +3438,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3411,7 +3453,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3426,7 +3468,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3441,7 +3483,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3456,7 +3498,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3498,7 +3540,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3545,7 +3587,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3715,11 +3757,11 @@ "L 363.046761 260.753989 \n", "L 373.86858 261.008948 \n", "L 384.690398 261.216 \n", - "\" clip-path=\"url(#p571722f274)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p47662088c4)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3802,7 +3844,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3971,7 +4013,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4032,7 +4074,14 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:05.200936Z", + "iopub.status.busy": "2023-08-29T09:09:05.200866Z", + "iopub.status.idle": "2023-08-29T09:09:05.275075Z", + "shell.execute_reply": "2023-08-29T09:09:05.274861Z" + } + }, "outputs": [ { "data": { @@ -4045,7 +4094,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:40:30.976016\n", + " 2023-08-29T11:09:05.257372\n", " image/svg+xml\n", " \n", " \n", @@ -4080,12 +4129,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4153,7 +4202,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4206,7 +4255,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4222,7 +4271,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4264,7 +4313,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4279,7 +4328,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4294,7 +4343,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4309,7 +4358,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4324,7 +4373,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4368,17 +4417,17 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4389,12 +4438,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4405,12 +4454,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4420,12 +4469,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4435,12 +4484,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4547,313 +4596,313 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + "L 297.969259 104.306928 \n", + "L 301.248598 128.755102 \n", + "L 304.527936 135.608449 \n", + "L 307.807275 138.222967 \n", + "L 311.086614 139.47197 \n", + "L 314.365953 140.167682 \n", + "L 317.645292 140.598637 \n", + "L 320.924631 140.88645 \n", + "L 324.20397 141.089489 \n", + "L 327.483308 141.238735 \n", + "L 330.762647 141.351979 \n", + "L 334.041986 141.4401 \n", + "L 337.321325 141.510093 \n", + "L 340.600664 141.566642 \n", + "L 343.880003 141.612993 \n", + "L 347.159341 141.651457 \n", + "L 350.43868 141.683723 \n", + "L 353.718019 141.711047 \n", + "L 356.997358 141.73438 \n", + "L 360.276697 141.754457 \n", + "L 363.556036 141.771848 \n", + "L 366.835374 141.787006 \n", + "L 370.114713 141.800292 \n", + "L 373.394052 141.811996 \n", + "L 376.673391 141.822356 \n", + "L 379.95273 141.831565 \n", + "L 383.232069 141.839785 \n", + "L 386.511408 141.84715 \n", + "L 389.790746 141.85377 \n", + "L 393.070085 141.859742 \n", + "\" clip-path=\"url(#p4d33894d30)\" style=\"fill: none; stroke: #ff7f0e; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5036,7 +5085,14 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:05.276453Z", + "iopub.status.busy": "2023-08-29T09:09:05.276385Z", + "iopub.status.idle": "2023-08-29T09:09:09.175434Z", + "shell.execute_reply": "2023-08-29T09:09:09.175176Z" + } + }, "outputs": [ { "data": { @@ -5049,7 +5105,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:40:35.378019\n", + " 2023-08-29T11:09:09.147308\n", " image/svg+xml\n", " \n", " \n", @@ -5081,22 +5137,22 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + 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\" id=\"imaged759f892e1\" transform=\"scale(1 -1) translate(0 -266.4)\" x=\"58.32\" y=\"-9.36\" width=\"357.12\" height=\"266.4\"/>\n", " \n", " \n", " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5121,11 +5177,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5156,11 +5212,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5192,11 +5248,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5212,16 +5268,16 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5290,11 +5346,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5348,11 +5404,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5369,11 +5425,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5416,11 +5472,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5436,11 +5492,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5456,11 +5512,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5476,11 +5532,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5496,11 +5552,11 @@ " \n", " \n", + "\" clip-path=\"url(#p5eb0b2df3d)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5668,7 +5724,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5728,7 +5784,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/TwoParticleResponse/solutions/03s-RPA.ipynb b/TwoParticleResponse/solutions/03s-RPA.ipynb index 81d5a42..e469692 100644 --- a/TwoParticleResponse/solutions/03s-RPA.ipynb +++ b/TwoParticleResponse/solutions/03s-RPA.ipynb @@ -35,7 +35,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:11.030900Z", + "iopub.status.busy": "2023-08-29T09:09:11.030364Z", + "iopub.status.idle": "2023-08-29T09:09:11.291857Z", + "shell.execute_reply": "2023-08-29T09:09:11.291458Z" + } + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -78,7 +85,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:11.294008Z", + "iopub.status.busy": "2023-08-29T09:09:11.293450Z", + "iopub.status.idle": "2023-08-29T09:09:11.681289Z", + "shell.execute_reply": "2023-08-29T09:09:11.681003Z" + } + }, "outputs": [ { "data": { @@ -91,7 +105,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:40:46.569515\n", + " 2023-08-29T11:09:11.646500\n", " image/svg+xml\n", " \n", " \n", @@ -123,17 +137,17 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + 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id=\"image815068f704\" transform=\"scale(1 -1) translate(0 -266.4)\" x=\"40.32\" y=\"-21.6\" width=\"285.84\" height=\"266.4\"/>\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -169,7 +183,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -207,7 +221,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -303,12 +317,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -321,7 +335,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -334,7 +348,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -966,18 +980,18 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + 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\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1047,7 +1061,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1061,7 +1075,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1146,7 +1160,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:11.697815Z", + "iopub.status.busy": "2023-08-29T09:09:11.697718Z", + "iopub.status.idle": "2023-08-29T09:09:12.256818Z", + "shell.execute_reply": "2023-08-29T09:09:12.256576Z" + } + }, "outputs": [ { "name": "stdout", @@ -1159,7 +1180,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Starting serial run at: 2023-08-24 17:40:46.609747\n" + "Starting serial run at: 2023-08-29 11:09:11.701127\n" ] }, { @@ -1173,7 +1194,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:40:47.209302\n", + " 2023-08-29T11:09:12.235009\n", " image/svg+xml\n", " \n", " \n", @@ -1209,16 +1230,16 @@ " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1243,11 +1264,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1278,11 +1299,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1314,11 +1335,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1334,16 +1355,16 @@ " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1414,11 +1435,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1450,11 +1471,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1470,11 +1491,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1516,11 +1537,11 @@ " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1761,7 +1782,7 @@ "L 376.014506 259.492533 \n", "L 380.352452 259.574731 \n", "L 384.690398 259.602354 \n", - "\" clip-path=\"url(#p547acd25cf)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #ff7f0e; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#p4e3cacf22c)\" style=\"fill: none; stroke: #2ca02c; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2227,7 +2248,14 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:12.258281Z", + "iopub.status.busy": "2023-08-29T09:09:12.258203Z", + "iopub.status.idle": "2023-08-29T09:09:13.831259Z", + "shell.execute_reply": "2023-08-29T09:09:13.831008Z" + } + }, "outputs": [ { "data": { @@ -2240,7 +2268,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:40:49.059218\n", + " 2023-08-29T11:09:13.813198\n", " image/svg+xml\n", " \n", " \n", @@ -2275,12 +2303,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2339,7 +2367,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2381,7 +2409,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2422,7 +2450,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2437,7 +2465,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2486,7 +2514,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2501,7 +2529,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2568,12 +2596,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2628,7 +2656,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2644,7 +2672,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2660,7 +2688,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2675,7 +2703,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2690,7 +2718,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2705,7 +2733,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2720,7 +2748,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2886,9 +2914,9 @@ "L 323.024631 207.456 \n", "L 359.097358 234.336 \n", "L 395.170085 261.216 \n", - "\" clip-path=\"url(#p2af0830f54)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p6c469f00b6)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2937,7 +2965,7 @@ "L 323.024631 162.74665 \n", "L 359.097358 162.74665 \n", "L 395.170085 162.74665 \n", - "\" clip-path=\"url(#p2af0830f54)\" style=\"fill: none; stroke: #000000; stroke-width: 0.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p6c469f00b6)\" style=\"fill: none; stroke: #000000; stroke-width: 0.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3062,7 +3090,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3159,7 +3187,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3221,7 +3249,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/TwoParticleResponse/solutions/04s-TPSC.ipynb b/TwoParticleResponse/solutions/04s-TPSC.ipynb index 8890651..5402a99 100644 --- a/TwoParticleResponse/solutions/04s-TPSC.ipynb +++ b/TwoParticleResponse/solutions/04s-TPSC.ipynb @@ -68,6 +68,12 @@ "cell_type": "code", "execution_count": 1, "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:15.695625Z", + "iopub.status.busy": "2023-08-29T09:09:15.695361Z", + "iopub.status.idle": "2023-08-29T09:09:15.953959Z", + "shell.execute_reply": "2023-08-29T09:09:15.953586Z" + }, "scrolled": true }, "outputs": [], @@ -90,7 +96,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:15.955982Z", + "iopub.status.busy": "2023-08-29T09:09:15.955472Z", + "iopub.status.idle": "2023-08-29T09:09:15.992187Z", + "shell.execute_reply": "2023-08-29T09:09:15.991853Z" + } + }, "outputs": [], "source": [ "from h5 import HDFArchive\n", @@ -139,7 +152,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:15.993724Z", + "iopub.status.busy": "2023-08-29T09:09:15.993647Z", + "iopub.status.idle": "2023-08-29T09:09:15.997003Z", + "shell.execute_reply": "2023-08-29T09:09:15.996770Z" + } + }, "outputs": [], "source": [ "from triqs_tprf.lattice import solve_rpa_PH\n", @@ -182,13 +202,20 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:15.998370Z", + "iopub.status.busy": "2023-08-29T09:09:15.998302Z", + "iopub.status.idle": "2023-08-29T09:09:17.117636Z", + "shell.execute_reply": "2023-08-29T09:09:17.117315Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1.5092181189127825\n" + "1.5092181189127931\n" ] } ], @@ -241,13 +268,20 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:17.134676Z", + "iopub.status.busy": "2023-08-29T09:09:17.134554Z", + "iopub.status.idle": "2023-08-29T09:09:18.634087Z", + "shell.execute_reply": "2023-08-29T09:09:18.633856Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "3.59186772137269\n" + "3.5918677213723913\n" ] } ], @@ -281,13 +315,20 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:18.635577Z", + "iopub.status.busy": "2023-08-29T09:09:18.635502Z", + "iopub.status.idle": "2023-08-29T09:09:21.033984Z", + "shell.execute_reply": "2023-08-29T09:09:21.033731Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(1.5092181189127825, 3.59186772137269, 0.1886522648640978, 2.778901915273311)\n" + "(1.5092181189127931, 3.5918677213723913, 0.18865226486409914, 2.778901915273313)\n" ] } ], @@ -316,7 +357,14 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:21.035381Z", + "iopub.status.busy": "2023-08-29T09:09:21.035305Z", + "iopub.status.idle": "2023-08-29T09:09:40.184226Z", + "shell.execute_reply": "2023-08-29T09:09:40.183937Z" + } + }, "outputs": [ { "name": "stdout", @@ -359,7 +407,14 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:40.185603Z", + "iopub.status.busy": "2023-08-29T09:09:40.185528Z", + "iopub.status.idle": "2023-08-29T09:09:40.253405Z", + "shell.execute_reply": "2023-08-29T09:09:40.253170Z" + } + }, "outputs": [ { "data": { @@ -372,7 +427,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:41:24.061288\n", + " 2023-08-29T11:09:40.228139\n", " image/svg+xml\n", " \n", " \n", @@ -395,9 +450,9 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", - " \n", + " \n", " \n", + 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\" id=\"image31f35a5bed\" transform=\"scale(1 -1) translate(0 -266.4)\" x=\"7.2\" y=\"-6.912\" width=\"357.84\" height=\"266.4\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#p219b2ab0a3)\" style=\"fill: none; stroke: #ff0000; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -519,7 +574,14 @@ { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:40.254866Z", + "iopub.status.busy": "2023-08-29T09:09:40.254776Z", + "iopub.status.idle": "2023-08-29T09:09:43.349367Z", + "shell.execute_reply": "2023-08-29T09:09:43.349107Z" + } + }, "outputs": [ { "name": "stdout", @@ -562,7 +624,14 @@ { "cell_type": "code", "execution_count": 10, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:43.350697Z", + "iopub.status.busy": "2023-08-29T09:09:43.350625Z", + "iopub.status.idle": "2023-08-29T09:09:43.476293Z", + "shell.execute_reply": "2023-08-29T09:09:43.476053Z" + } + }, "outputs": [ { "data": { @@ -575,7 +644,7 @@ " \n", " \n", " \n", - " 2023-08-24T17:41:27.733184\n", + " 2023-08-29T11:09:43.440362\n", " image/svg+xml\n", " \n", " \n", @@ -610,12 +679,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -660,7 +729,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -702,7 +771,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -733,7 +802,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -748,7 +817,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -820,12 +889,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -842,7 +911,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -859,7 +928,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -876,7 +945,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -893,7 +962,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -910,7 +979,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -927,12 +996,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + "\" clip-path=\"url(#p1841b475fa)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1025,7 +1094,7 @@ "L 311.569943 258.38546 \n", "L 347.64267 258.38546 \n", "L 383.715398 258.38546 \n", - "\" clip-path=\"url(#p48691b57a9)\" style=\"fill: none; stroke: #000000; stroke-width: 0.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#p1841b475fa)\" style=\"fill: none; stroke: #000000; stroke-width: 0.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1339,7 +1408,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1381,7 +1450,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/TwoParticleResponse/solutions/05s-TPSC_MerminWagner.ipynb b/TwoParticleResponse/solutions/05s-TPSC_MerminWagner.ipynb index 3e28347..055f642 100644 --- a/TwoParticleResponse/solutions/05s-TPSC_MerminWagner.ipynb +++ b/TwoParticleResponse/solutions/05s-TPSC_MerminWagner.ipynb @@ -58,7 +58,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:45.339540Z", + "iopub.status.busy": "2023-08-29T09:09:45.339071Z", + "iopub.status.idle": "2023-08-29T09:09:45.613081Z", + "shell.execute_reply": "2023-08-29T09:09:45.612838Z" + } + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -81,7 +88,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:45.615131Z", + "iopub.status.busy": "2023-08-29T09:09:45.614607Z", + "iopub.status.idle": "2023-08-29T09:09:45.621718Z", + "shell.execute_reply": "2023-08-29T09:09:45.621523Z" + } + }, "outputs": [ { "name": "stdout", @@ -94,7 +108,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Starting serial run at: 2023-08-25 09:10:01.559848\n" + "Starting serial run at: 2023-08-29 11:09:45.617709\n" ] } ], @@ -139,7 +153,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:45.638123Z", + "iopub.status.busy": "2023-08-29T09:09:45.638026Z", + "iopub.status.idle": "2023-08-29T09:09:45.726470Z", + "shell.execute_reply": "2023-08-29T09:09:45.726133Z" + } + }, "outputs": [], "source": [ "from scipy.optimize import brentq\n", @@ -205,6 +226,12 @@ "cell_type": "code", "execution_count": 4, "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:45.728211Z", + "iopub.status.busy": "2023-08-29T09:09:45.728125Z", + "iopub.status.idle": "2023-08-29T09:09:46.076876Z", + "shell.execute_reply": "2023-08-29T09:09:46.076646Z" + }, "scrolled": false }, "outputs": [ @@ -294,6 +321,12 @@ "cell_type": "code", "execution_count": 5, "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:46.078314Z", + "iopub.status.busy": "2023-08-29T09:09:46.078233Z", + "iopub.status.idle": "2023-08-29T09:09:47.375999Z", + "shell.execute_reply": "2023-08-29T09:09:47.375724Z" + }, "scrolled": false }, "outputs": [ @@ -305,7 +338,7 @@ "--------------------------------------------------------------------------------\n", "| 4.0000E+00 | 2.5000E-01 | 3.1151E+00 | 4.9735E+00 | 1.9469E-01 | 1.6108E-01 |\n", "| 3.0000E+00 | 3.3333E-01 | 2.9065E+00 | 5.3054E+00 | 1.8166E-01 | 2.3189E-01 |\n", - "| 2.5000E+00 | 4.0000E-01 | 2.7717E+00 | 5.5812E+00 | 1.7323E-01 | 2.9463E-01 |\n", + "| 2.5000E+00 | 4.0000E-01 | 2.7718E+00 | 5.5801E+00 | 1.7324E-01 | 2.9464E-01 |\n", "| 2.0000E+00 | 5.0000E-01 | 2.6148E+00 | 6.0225E+00 | 1.6343E-01 | 3.9851E-01 |\n", "| 1.5000E+00 | 6.6667E-01 | 2.4421E+00 | 6.8411E+00 | 1.5263E-01 | 5.9599E-01 |\n", "| 1.2000E+00 | 8.3333E-01 | 2.3406E+00 | 7.7133E+00 | 1.4629E-01 | 8.2155E-01 |\n", @@ -313,8 +346,8 @@ "| 8.0000E-01 | 1.2500E+00 | 2.2301E+00 | 9.6547E+00 | 1.3938E-01 | 1.5004E+00 |\n", "| 6.0000E-01 | 1.6667E+00 | 2.1949E+00 | 1.0900E+01 | 1.3718E-01 | 2.3575E+00 |\n", "| 4.0000E-01 | 2.5000E+00 | 2.1657E+00 | 1.2228E+01 | 1.3535E-01 | 4.8974E+00 |\n", - "| 3.5000E-01 | 2.8571E+00 | 2.1538E+00 | 1.2638E+01 | 1.3461E-01 | 6.6135E+00 |\n", - "| 3.0000E-01 | 3.3333E+00 | 2.1344E+00 | 1.3188E+01 | 1.3340E-01 | 1.0281E+01 |\n", + "| 3.5000E-01 | 2.8571E+00 | 2.1527E+00 | 1.2658E+01 | 1.3454E-01 | 6.6014E+00 |\n", + "| 3.0000E-01 | 3.3333E+00 | 2.1327E+00 | 1.3218E+01 | 1.3330E-01 | 1.0230E+01 |\n", "| 2.5000E-01 | 4.0000E+00 | 2.0869E+00 | 1.4279E+01 | 1.3043E-01 | 2.2999E+01 |\n" ] } @@ -361,7 +394,14 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-29T09:09:47.377434Z", + "iopub.status.busy": "2023-08-29T09:09:47.377361Z", + "iopub.status.idle": "2023-08-29T09:09:47.678024Z", + "shell.execute_reply": "2023-08-29T09:09:47.677790Z" + } + }, "outputs": [ { "data": { @@ -374,7 +414,7 @@ " \n", " \n", " \n", - " 2023-08-25T09:10:03.875676\n", + " 2023-08-29T11:09:47.624035\n", " image/svg+xml\n", " \n", " \n", @@ -409,12 +449,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -459,7 +499,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -501,7 +541,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -532,7 +572,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -547,7 +587,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -588,7 +628,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -603,7 +643,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -652,7 +692,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -667,7 +707,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -724,12 +764,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -742,7 +782,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -755,7 +795,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -769,7 +809,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -783,7 +823,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -809,9 +849,9 @@ "L 117.323749 295.534592 \n", "L 102.786978 282.784169 \n", "L 88.250208 193.344998 \n", - "\" clip-path=\"url(#p02d705280f)\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", + "\" clip-path=\"url(#pbdfb412280)\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + "\" clip-path=\"url(#pbdfb412280)\" style=\"fill: none; stroke: #ff7f0e; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -970,7 +1010,7 @@ "L 292.966761 35.416562 \n", "\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1086,7 +1126,7 @@ "L 292.966761 50.094687 \n", "\" style=\"fill: none; stroke: #ff7f0e; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1142,11 +1182,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1162,11 +1202,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1182,11 +1222,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1202,11 +1242,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1222,11 +1262,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1242,11 +1282,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1262,11 +1302,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1282,11 +1322,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1302,11 +1342,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1330,11 +1370,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1348,11 +1388,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1366,11 +1406,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1384,11 +1424,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1402,11 +1442,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1420,11 +1460,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1438,11 +1478,11 @@ " \n", " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1499,27 +1539,27 @@ "L 482.560113 292.57238 \n", "L 468.023342 306.176989 \n", "L 453.486571 320.046279 \n", - "\" clip-path=\"url(#p111384c75b)\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#p20cb21847a)\" style=\"fill: none; stroke: #ff7f0e; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1587,7 +1627,7 @@ "L 424.339489 36.818125 \n", "\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1616,7 +1656,7 @@ "L 424.339489 53.618125 \n", "\" style=\"fill: none; stroke: #ff7f0e; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1647,11 +1687,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1668,11 +1708,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1689,11 +1729,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1710,11 +1750,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1743,11 +1783,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1764,11 +1804,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1785,11 +1825,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1806,11 +1846,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1827,11 +1867,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1856,11 +1896,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1876,11 +1916,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1896,11 +1936,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1916,11 +1956,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1936,11 +1976,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1956,11 +1996,11 @@ " \n", " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1987,27 +2027,27 @@ "L 212.721307 595.398688 \n", "L 182.284943 629.496675 \n", "L 151.84858 664.258048 \n", - "\" clip-path=\"url(#p83f75c06c2)\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#p263c62b2b5)\" style=\"fill: none; stroke: #ff7f0e; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2075,7 +2115,7 @@ "L 59.103125 399.698125 \n", "\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2095,7 +2135,7 @@ "L 59.103125 416.498125 \n", "\" style=\"fill: none; stroke: #ff7f0e; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2126,11 +2166,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2146,11 +2186,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2166,11 +2206,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2186,11 +2226,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2206,11 +2246,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2226,11 +2266,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2246,11 +2286,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2266,11 +2306,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2286,11 +2326,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2314,11 +2354,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2335,11 +2375,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2356,11 +2396,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2377,11 +2417,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2398,11 +2438,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2419,11 +2459,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2481,11 +2521,11 @@ " \n", " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2534,7 +2574,7 @@ " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#p8d4a902f28)\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2602,7 +2642,7 @@ "L 424.339489 398.296562 \n", "\" style=\"fill: none; stroke: #1f77b4; stroke-opacity: 0.5; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2649,16 +2689,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2765,7 +2805,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.5" } }, "nbformat": 4,