{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Timelapse of timelapses and z-stacks\n",
    "\n",
    "This notebook shows how to repeatedly acquire a short time series and then a z stack at a set with a set delay in between.\n",
    "\n",
    "In this example we acquire a time series of `duration = 2` seconds and then a z stack parameterized by\n",
    "```z_start = -2.5; z_end = 2.5; z_step = 0.25``` every `subset_interval = 20` seconds for `total_duration = 60` seconds. \n",
    "That is, we acquire data subsets containing a 2 second time series and a 5 micrometer z stack every 20 seconds over the\n",
    "span of 60 seconds.\n",
    "\n",
    "Using `pycro-manager` this set of acquisition events is encoded as follows:\n",
    "\n",
    "```\n",
    "events = []\n",
    "for s in range(num_subsets):\n",
    "    for t in range(num_time_points):\n",
    "        events.append(\n",
    "            {\n",
    "                \"axes\": {\"subset\": s, \"time\": t, \"z\": 0},\n",
    "                \"z\": z_sequence[0],\n",
    "                \"min_start_time\": s * subset_interval,\n",
    "            }\n",
    "        )\n",
    "    for z in range(num_z_slices):\n",
    "        events.append(\n",
    "            {\n",
    "                \"axes\": {\"subset\": s, \"time\": num_time_points, \"z\": z},\n",
    "                \"z\": z_sequence[z],\n",
    "                \"min_start_time\": s * subset_interval,\n",
    "            }\n",
    "        )\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from pycromanager import Acquisition, Core, multi_d_acquisition_events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct java objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "mmc = Core()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set acquisition parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Data set parameters\n",
    "path = r\"C:\\test\"\n",
    "name = \"pycromanager test\"\n",
    "total_duration = 60  # in seconds\n",
    "subset_interval = 20  # in seconds\n",
    "\n",
    "# z stack parameters\n",
    "z_start = -2.5\n",
    "z_end = 2.5\n",
    "z_step = 0.25\n",
    "relative = True\n",
    "sequence = False\n",
    "\n",
    "# time series parameters\n",
    "duration = 2  # in seconds\n",
    "framerate = 10\n",
    "\n",
    "num_subsets = np.ceil(total_duration / subset_interval).astype(np.int)\n",
    "num_time_points = duration * framerate\n",
    "z_sequence = np.arange(z_start, z_end + z_step, z_step)\n",
    "num_z_slices = len(z_sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare for acquisition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# setup cameras -- this property may change depending on the particular camera used\n",
    "mmc.set_property(\"Camera\", \"Framerate\", framerate)\n",
    "\n",
    "# setup z stage\n",
    "z_stage = mmc.get_focus_device()\n",
    "\n",
    "if relative:\n",
    "    z_pos = mmc.get_position(z_stage)\n",
    "\n",
    "    z_sequence += z_pos\n",
    "\n",
    "if sequence:\n",
    "    mmc.set_property(z_stage, \"UseSequence\", \"Yes\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "events = []\n",
    "for s in range(num_subsets):\n",
    "    for t in range(num_time_points):\n",
    "        events.append(\n",
    "            {\n",
    "                \"axes\": {\"subset\": s, \"time\": t, \"z\": 0},\n",
    "                \"z\": z_sequence[0],\n",
    "                \"min_start_time\": s * subset_interval,\n",
    "            }\n",
    "        )\n",
    "    for z in range(num_z_slices):\n",
    "        events.append(\n",
    "            {\n",
    "                \"axes\": {\"subset\": s, \"time\": num_time_points, \"z\": z},\n",
    "                \"z\": z_sequence[z],\n",
    "                \"min_start_time\": s * subset_interval,\n",
    "            }\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acquire data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "with Acquisition(directory=path, name=name) as acq:\n",
    "    acq.acquire(events)"
   ]
  }
 ],
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