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Lukas Schrangl
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Fix and document Tracking notebook
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‎notebooks/01. Tracking.ipynb‎

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"outputs": [],
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"source": [
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"%matplotlib widget\n",
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"from pathlib import Path\n",
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"import sys\n",
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"import os\n",
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"import warnings\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"plt.ioff()\n",
@@ -26,25 +22,15 @@
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"smFRET analysis software version 2.1\n",
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"(git revision 2.1-56-g70f9081)\n",
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"(git revision 2.1-66-gb0db9c8)\n",
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"Output version 11\n",
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"Using sdt-python version 14.1\n"
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"Using sdt-python version 14.3.2\n"
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]
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}
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],
@@ -54,51 +40,60 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"metadata": {
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"nbpresent": {
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"id": "0b4b7b88-8234-4a09-9ec6-7d6a88a2e944"
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}
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},
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"outputs": [],
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"source": [
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"# Create a Tracker instance\n",
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"\n",
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"# If starting fresh, set load = False. If there is already saved data from a previous run,\n",
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"# load = True can be used to load it.\n",
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"load = False\n",
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"\n",
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"if not load:\n",
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" # Start fresh\n",
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" data_dir = \"/media/lukas/LS-ext-blue/Data/Force sensor FRET/2018-11-21\"\n",
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" # donor origin, acceptor origin, size\n",
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" # Directory containing raw data\n",
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" data_dir = \"sm-data\"\n",
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" \n",
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" # Specify\n",
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" # - origin of donor channel\n",
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" # - origin of acceptor channel\n",
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" # - ROI size\n",
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" # - excitation sequence, where \"c\" means cell contours, \"a\" means acceptor excitation,\n",
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" # and \"d\" means donor excitation\n",
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" # - data directory\n",
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" tr = Tracker((201, 0), (0, 0), (190, 111), \"ce\" + \"da\" * 300 + \"c\", data_dir=data_dir)\n",
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" tr.add_dataset(\"DPPC sensor cells\",\n",
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" Path(\"^DPPC_sensor-J4\", r\"cells-\\d{2}_(\\d{3})_.SPE\"), cells=True)\n",
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" tr.add_dataset(\"DPPC sensor no-cells\",\n",
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" Path(\"^DPPC_sensor-J4\", r\"no-cells_(\\d{3})_.SPE\"), cells=False)\n",
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" tr.add_dataset(\"POPC sensor cells\",\n",
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" Path(\"^POPC_sensor-J4\", r\"cells-\\d{2}_(\\d{3})_.SPE\"), cells=True)\n",
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" tr.add_dataset(\"POPC sensor no-cells\",\n",
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" Path(\"^POPC_sensor-J4\", r\"no-cells_(\\d{3})_.SPE\"), cells=False)\n",
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" \n",
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" # Add datasets by giving\n",
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" # - an identifier\n",
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" # - a regular expression describing the file names relative to data_dir.\n",
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" # Use forward slashes as path separators even on Windows.\n",
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" # - cells = True or False depending on whether the sample contained cells\n",
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" tr.add_dataset(\"DPPC control cells\",\n",
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" Path(\"^DPPC_ctrl-J4\", r\"cells-\\d{2}_(\\d{3})_.SPE\"), cells=True)\n",
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" r\"^DPPC_ctrl-J4/cells-\\d{2}_\\d{3}_.tif\", cells=True)\n",
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" tr.add_dataset(\"DPPC control no-cells\",\n",
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" Path(\"^DPPC_ctrl-J4\", r\"no-cells_(\\d{3})_.SPE\"), cells=False)\n",
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" tr.add_dataset(\"POPC control cells\",\n",
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" Path(\"^POPC_ctrl-J4\", r\"cells-\\d{2}_(\\d{3})_.SPE\"), cells=True)\n",
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" tr.add_dataset(\"POPC control no-cells\",\n",
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" Path(\"^POPC_ctrl-J4\", r\"no-cells_(\\d{3})_.SPE\"), cells=False)\n",
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" r\"^DPPC_ctrl-J4/no-cells_\\d{3}_.tif\", cells=False)\n",
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" #tr.add_dataset(\"DPPC sensor cells\",\n",
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" # r\"^DPPC_sensor-J4/cells-\\d{2}_\\d{3}_.SPE\", cells=True)\n",
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" #tr.add_dataset(\"DPPC sensor no-cells\",\n",
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" # r\"^DPPC_sensor-J4/no-cells_\\d{3}_.SPE\", cells=False)\n",
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"else:\n",
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" # load\n",
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" # Load\n",
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" tr = Tracker.load()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2865ab06928d41f498c19348169b04cd",
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"model_id": "b0b6f1c308a245e49cc5081162dc31ac",
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"version_major": 2,
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"version_minor": 0
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},
@@ -111,18 +106,22 @@
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}
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],
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"source": [
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"tr.set_bead_loc_opts(Path(\"beads\", \"beads_\\d+_.SPE\"))"
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"# Set localization options for beads, which are used below to calculate the\n",
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"# coordinate transform between donor and acceptor channels\n",
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"# The argument is a regular expression matching the bead files' names with\n",
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"# forward slashes as path separators\n",
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"tr.set_bead_loc_opts(r\"beads/beads_\\d+_.tif\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a05e9b720fc74c8cb62576d5c54cc1b4",
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"model_id": "29dd3bf000f74d78b8b59d7a4b07b2e9",
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"version_major": 2,
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"version_minor": 0
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "57edc3f24e7248e9ace42a3da08a9da1",
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"model_id": "4151286db6d3482aa96a10c1e5200281",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"FigureCanvasNbAgg()"
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Calculate the coordinate transform from bead localizations\n",
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"tr.make_chromatic()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "be9a527331b641ce8879d22c7a8c1173",
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"model_id": "47fa8ca530b546838cd83fc4404cafff",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(Dropdown(description='dataset', options=('DPPC sensor cells', 'DPPC sensor no-cells', 'POPC sen…"
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"VBox(children=(Dropdown(description='dataset', options=('DPPC control cells', 'DPPC control no-cells'), value=…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Set localization options upon donor excitation\n",
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"tr.set_loc_opts(\"donor\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "582c24aa4b164f92bd0a08546e64df56",
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"model_id": "1c6d128b336a4f5fb9016bf0f9e74d52",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(Dropdown(description='dataset', options=('DPPC sensor cells', 'DPPC sensor no-cells', 'POPC sen…"
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"VBox(children=(Dropdown(description='dataset', options=('DPPC control cells', 'DPPC control no-cells'), value=…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Set localization options upon acceptor excitation\n",
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"tr.set_loc_opts(\"acceptor\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 8,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1e0f12d6598f42b59905d836fef4b4db",
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"model_id": "d06fa01d30c04e83a51f0524d6461309",
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"version_major": 2,
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"version_minor": 0
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},
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}
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],
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"source": [
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"# Run localization algorithm. This can take a while.\n",
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"tr.locate()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "25ef68687c954867993e4052dd0e3ecc",
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"model_id": "c407017e2a2548e4af48cbdcf9d545cf",
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"version_major": 2,
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"version_minor": 0
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},
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}
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],
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"source": [
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"# Track the localizations. \n",
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"tr.track(feat_radius=4, bg_frame=3, link_radius=5, link_mem=3, min_length=4, bg_estimator=\"mean\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the images of the cells\n",
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"tr.extract_cell_images()\n",
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"tr.make_flatfield(\"donor\", Path(\"profile\", r\"green_.*\"), bg=200, smooth_sigma=10)\n",
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"tr.make_flatfield(\"acceptor\", Path(\"profile\", r\"red_.*\"), bg=200, smooth_sigma=10)"
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"\n",
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"# Get the laser profiles from additional recordings of bulk samples\n",
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"tr.make_flatfield(\"donor\", \"profile/green_.*\", bg=200, smooth_sigma=10)\n",
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"tr.make_flatfield(\"acceptor\", \"profile/red_.*\", bg=200, smooth_sigma=10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Save data\n",
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"tr.save()"
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]
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},

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