evaluation.ipynb

generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test spot detection (Generation)\n",
    "\n",
    "This is a test suite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!pip uninstall -y scopyon\n",
    "!pip install git+https://github.com/ecell/scopyon\n",
    "!pip freeze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scopyon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set physical parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = scopyon.DefaultConfiguration()\n",
    "config.update(\"\"\"\n",
    "default:\n",
    "    magnification: 360\n",
    "    detector:\n",
    "        exposure_time: 0.033\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_length = config.default.detector.pixel_length / config.default.magnification\n",
    "L_2 = config.default.detector.image_size[0] * pixel_length * 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the number of processes to enable `multiprocessing`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "config.environ.processes = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare for generating inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "rng = numpy.random.RandomState(123)\n",
    "N = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collect data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "artifacts = Path(\"./artifacts\")\n",
    "artifacts.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(artifacts / 'config.yaml', 'w') as f:\n",
    "    f.write(repr(config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    inputs = rng.uniform(-L_2, +L_2, size=(N, 2))\n",
    "    numpy.save(artifacts / f'inputs{i:03d}.npy', inputs)\n",
    "    img, infodict = scopyon.form_image(inputs, config=config, rng=rng, full_output=True)\n",
    "    img.save(str(artifacts / f'image{i:03d}.npy'))\n",
    "    data = numpy.array([(row[2], row[3]) for row in infodict['true_data'].values()])\n",
    "    numpy.save(artifacts / f'data{i:03d}.npy', data)"
   ]
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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