JMatrixBehaviour.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "03d1882d-dfda-4993-bbe4-9c2aef9a379c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b940c62-696d-4a63-8cba-f4e05df065cc",
   "metadata": {},
   "source": [
    "### Our Calculated J matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a543990-d6d7-46d6-ae52-bc7e09d1c632",
   "metadata": {},
   "outputs": [],
   "source": [
    "J_mat = np.load(\"mouse599975_sweep1_param70_J_2000iters.npy\")\n",
    "#To look at the distributed of values in the interaction matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0a01e0a-24c1-4af1-9679-1e18778ade30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No of values in the bin')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(J_mat.flatten(), bins=30)\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"J Matrix Values bins\")\n",
    "plt.ylabel(\"No of values in the bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e20ce785-b0ff-4821-ad7a-78c4fb168b4d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "btp_lakshika",
   "language": "python",
   "name": "btp_lakshika"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}