{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial about analyzing localization properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import locan as lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc.show_versions(system=False, dependencies=False, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A path in which test data can be found:\n",
    "TEST_DIR: Path = Path.cwd().parents[2] / \"tests\"\n",
    "TEST_DIR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load rapidSTORM data file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify some data in the test_data directory and provide a path using `pathlib.Path` (returned by `lc.ROOT_DIR`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = TEST_DIR / 'test_data/rapidSTORM_dstorm_data.txt'\n",
    "print(path, '\\n')\n",
    "\n",
    "dat = lc.load_rapidSTORM_file(path=path, nrows=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print information about the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dat.data.head(), '\\n')\n",
    "print('Summary:')\n",
    "dat.print_summary()\n",
    "print('Properties:')\n",
    "print(dat.properties)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc.render_2d(dat, bin_size=1000, rescale=(0,100));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze a localization property"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a look at a certain localization property in locdata. \n",
    "\n",
    "The analysis class `LocalizationProperty` provides a dataframe with the property as function of another property (index), and a plot or histogram of this property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop = lc.LocalizationProperty(loc_property='intensity', index='frame')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.compute(dat)\n",
    "print(lprop.results.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows results smoothed by a running average according to the specified window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.plot(window=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram shows the probability density function of results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.hist(fit=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Per default the distribution is fitted to an exponential decay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit results (as derived using the lmfit library) are provided in the distribution_statistics attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.distribution_statistics.parameter_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.results.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting different distribution models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per default the 'with_constraints' flag is True to apply standard fit constraints. This can be set to false and other parameters can be passed to the fit function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.fit_distributions(with_constraints=False, floc=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.distribution_statistics.parameter_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.hist(fit=True)\n",
    "print(lprop.distribution_statistics.parameter_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Showing correlations between two properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By setting the index to another localization property correlations can be shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop = lc.LocalizationProperty(loc_property='intensity', index='local_background').compute(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop.plot(marker='o', linestyle=\"\", alpha=0.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation coefficients can be investigated in more detail using the LocalizationPropertyCorrelation class that is just a visualization of `pandas.DataFrame.corr()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lpcorr = lc.LocalizationPropertyCorrelations(loc_properties=['intensity', 'local_background']).compute(dat)\n",
    "lpcorr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lpcorr.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-dimensional distribution of localization properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to investigate a certain localization property in 2D you can just print the image with a color code representing the mean value of the chosen localization property in each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc.render_2d_mpl(dat, other_property='local_background', bin_size=500);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otherwise use a specific class to analyse localization properties in 2d. Per default a bimodal normal distribution is fitted. This can e.g. help to check on even illumination during the recording."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop2d = lc.LocalizationProperty2d(loc_properties=None, other_property='local_background', bin_size=500).compute(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop2d.plot_deviation_from_mean();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop2d.plot(colors=\"r\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lprop2d.report()"
   ]
  }
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