{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65e78297",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90917d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_dir = \"../results/260812-Si/Si2V1_2\"\n",
    "train_x = np.load(os.path.join(result_dir, 'train_x.npy')).T\n",
    "train_y = np.load(os.path.join(result_dir, 'train_y.npy')).T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05c0f656",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47b5dd69",
   "metadata": {},
   "outputs": [],
   "source": [
    "xparams = [\n",
    "    'defocus [A]',\n",
    "    'layers',\n",
    "    'thickness [A]'\n",
    "]\n",
    "\n",
    "sort_index = np.argsort(train_y)\n",
    "\n",
    "x = train_x[:,sort_index]\n",
    "y = train_y[sort_index]\n",
    "\n",
    "EPSILON = 1e-3 # choose best according to plot\n",
    "\n",
    "train_y_scaled = np.log(-train_y + y[-1] + EPSILON)\n",
    "y_scaled = np.log(-y + y[-1] + EPSILON)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(7,3))\n",
    "ax[0].plot(y, 'k')\n",
    "ax[1].plot(y_scaled, 'k')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(7,3))\n",
    "ax[0].plot(train_y, 'k')\n",
    "ax[1].plot(train_y_scaled, 'k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4990770c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cut = 0 # set to -1 to get all points\n",
    "\n",
    "fig, axs = plt.subplots(1, len(x), figsize=(3*len(x)+0.5, 3.5), sharey=True)\n",
    "\n",
    "for i, axi in enumerate(axs):\n",
    "    axi.scatter(x[i][cut:], y_scaled[cut:], c=y_scaled[cut:], cmap='coolwarm')\n",
    "    axi.set_xlabel(xparams[i])\n",
    "\n",
    "axs[0].set_ylabel('$- \\\\log (\\ \\\\mathtt{fourier\\_error}\\ )$')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2694f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fig, axs = plt.subplots(len(x), len(x), figsize=(3*len(x)+0.5, 3*len(x)+0.5))\n",
    "\n",
    "for i, axi in enumerate(axs):\n",
    "    for j, axij in enumerate(axi):\n",
    "        axij.scatter(\n",
    "            x[j], x[i],\n",
    "            c = y_scaled, # color\n",
    "            cmap = 'coolwarm',\n",
    "            s = 20, # size\n",
    "            alpha = 0 if i == j else 1 # make diagonal transparent\n",
    "            )\n",
    "        if i == len(x)-1:\n",
    "            axij.set_xlabel(xparams[j])\n",
    "        else:\n",
    "            axij.set_xticks([])\n",
    "        if j == 0:\n",
    "            axij.set_ylabel(xparams[i])\n",
    "        else:\n",
    "            axij.set_yticks([])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "295ca65c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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