diff --git a/test.ipynb b/test.ipynb index c267153..9dc5c96 100644 --- a/test.ipynb +++ b/test.ipynb @@ -2,23 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 41, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Mac:27855] shmem: mmap: an error occurred while determining whether or not /var/folders/yh/dx7xl94n3g52ts3td8qcxjcc0000gn/T//ompi.Mac.501/jf.0/204800000/sm_segment.Mac.501.c350000.0 could be created.\n" - ] - }, { "data": { "text/plain": [ "'0.14.3'" ] }, - "execution_count": 1, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -54,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -81,13 +74,13 @@ "[0 0 1] --» [array([1, 0, 0]), array([0, 1, 0])]\n", "================================================================================================================================================================\n", "Parameters for the contour integral:\n", - "Number of k points: 20\n", - "k point directions: xy\n", + "Number of k points: 1000\n", + "k point directions: x\n", "Ebot: -13\n", - "Eset: 100\n", - "Esetp: 100\n", + "Eset: 500\n", + "Esetp: 10000\n", "================================================================================================================================================================\n", - "Setup done. Elapsed time: 1.811041458 s\n", + "Setup done. Elapsed time: 5447.39860525 s\n", "================================================================================================================================================================\n" ] } @@ -125,7 +118,7 @@ " # isotropic should be -82 meV\n", " dict(ai=0, aj=1, Ruc=np.array([0, 0, 0])),\n", " dict(ai=1, aj=2, Ruc=np.array([0, 0, 0])),\n", - " dict(ai=0, aj=2, Ruc=np.array([0, 0, 0])),\n", + " # dict(ai=0, aj=2, Ruc=np.array([0, 0, 0])),\n", "]\n", "\n", "\"\"\"\n", @@ -152,11 +145,11 @@ "\"\"\"\n", "\n", "# Brilloun zone sampling and Green function contour integral\n", - "kset = 20\n", - "kdirs = \"xy\"\n", + "kset = 1000\n", + "kdirs = \"x\"\n", "ebot = -13\n", - "eset = 100\n", - "esetp = 100\n", + "eset = 500\n", + "esetp = 10000\n", "\n", "\n", "# MPI parameters\n", @@ -197,33 +190,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 43, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/Users/danielpozsar/Documents/oktatás/elte/phd/grogu_project/.venv/lib/python3.9/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/Users/danielpozsar/Documents/oktatás/elte/phd/grogu_project/.venv/lib/python3.9/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" + "-12.806739\n", + "-0.01254111\n", + "xyz[-3:]: red, green, blue\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "-12.806739\n", - "-0.01254111\n", - "xyz[-3:]: red, green, blue\n" + "/Users/danielpozsar/Documents/oktatás/elte/phd/grogu_project/.venv/lib/python3.9/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/Users/danielpozsar/Documents/oktatás/elte/phd/grogu_project/.venv/lib/python3.9/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" ] }, { "data": { - "image/png": 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", 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -243,13 +236,16 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", + "plt.figure(figsize=(15, 5))\n", + "plt.subplot(121)\n", "plt.plot(np.sort(dh.eig()))\n", - "plt.ylim(None, 20)\n", + "plt.grid()\n", + "plt.subplot(122)\n", + "DOS = sisl.physics.electron.DOS(np.linspace(-15, 85, 50), dh.eig())\n", + "plt.plot(DOS, np.linspace(-15, 85, 50))\n", "print(np.real(dh.eig()).min())\n", "print(np.imag(dh.eig()).min())\n", "\n", - "import matplotlib.pyplot as plt\n", - "\n", "coords = dh.xyz[-3:]\n", "\n", "\n", @@ -271,14 +267,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hamiltonian and exchange field rotated. Elapsed time: 2.613376875 s\n", + "Hamiltonian and exchange field rotated. Elapsed time: 5448.302713666 s\n", "================================================================================================================================================================\n" ] } @@ -380,14 +376,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Site and pair dictionaries created. Elapsed time: 2.628747208 s\n", + "Site and pair dictionaries created. Elapsed time: 5448.330635 s\n", "================================================================================================================================================================\n" ] } @@ -495,21 +491,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "k loop: 0%| | 0/400 [00:00