{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is set up to be used with Python 3(.6+). Also to properly run this notebook the following libraries need to be installed:\n",
    "- `pip install matplotlib`\n",
    "- `pip install numpy`\n",
    "- `pip install pandas`\n",
    "- `pip install nixio==1.5.0b4`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First lets check which files are available to us in the general and in the excercise folder\n",
    "import os\n",
    "\n",
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.listdir(\"./excercise\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the pandas library to read in columns from CSV files\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The raw data files do not provide column headers so we'll write them first.\n",
    "#   Check the \"README.md\" files for details.\n",
    "col_names = [\"current_frame\", \"time_elapsed\", \"obj_substracted\", \"substracted_value\", \"obj_value\", \"obj_size\", \"background_value\", \"x_old\", \"y_old\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the content of the first file.\n",
    "content = pd.read_csv(\"./excercise/20121202Pflp178GCaMP5kN2shift_210421W1bag.log\", header=None, names=col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Briefly check the content\n",
    "content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot raw data to get a general idea how it looks like\n",
    "import matplotlib.pyplot as plot\n",
    "\n",
    "x = content[\"current_frame\"]\n",
    "y = content[\"obj_substracted\"]\n",
    "plot.plot(x,y)\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are set\n",
    "- create a NIX file and a Block in it.\n",
    "- create a DataArray for each raw data file and transfer the data of one of the columns.\n",
    "- properly add Dimensions, labels and units.\n",
    "- plot the data from the data stored in the NIX file.\n",
    "- add meaningful metadata to the general NIX file and the individual DataArrays from information found in the README.md.\n",
    "- connect all three DataArrays via a MultiTag. You can use the shift paradim as a reference or come up with your own points of interest.\n",
    "- Plot the data using the MultiTag.\n",
    "- If you work with Binder, make sure you regularly download the notebook to your local machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new, writeable NIX file. Don't forget to close it when you are done.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Block to store Data in, use an appropriate naming and type scheme.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataArray on the block with the already loaded data above.\n",
    "#  Again use appropriate naming and type, label and unit.\n",
    "#  Make sure you can identify the corresponding data later on.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the appropriate two dimensions to the DataArray.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot data from the DataArray and compare to the plot above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add DataArrays for the other two data files as well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create metadata Sections and Properties in the file to document general metadata e.g. Species and Hardware\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create metadata Sections specific to the DataArrays, document metadata specific to the raw data \n",
    "#  and connect them to the appropriate DataArrays.\n",
    "#  Where appropriate try to add units to the Property values via the \"property.unit\" method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try to access the saved metadata from the DataArrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a MultiTag to connect all three DataArrays via a common region of interest\n",
    "#  e.g. using the experiments' stimulus protocol oxygen upshift from t1 110s to t2 470s\n",
    "#  or define your own region of interest. You will need an additional DataArray providing \n",
    "#  this region of interest. Connect the three DataArrays to the MultiTag after it has been\n",
    "#  created.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}