diff --git a/data/AMID-04_summary_motility_metrics.csv b/data/AMID-04_summary_motility_metrics.csv
new file mode 100644
index 0000000..88ba747
--- /dev/null
+++ b/data/AMID-04_summary_motility_metrics.csv
@@ -0,0 +1,246 @@
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diff --git a/data/AMID-05_summary_motility_metrics.csv b/data/AMID-05_summary_motility_metrics.csv
new file mode 100644
index 0000000..7012745
--- /dev/null
+++ b/data/AMID-05_summary_motility_metrics.csv
@@ -0,0 +1,309 @@
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diff --git a/notebooks/1_Compute-Summary-Motility-Metrics.ipynb b/notebooks/1_Compute-Summary-Motility-Metrics.ipynb
new file mode 100644
index 0000000..528c124
--- /dev/null
+++ b/notebooks/1_Compute-Summary-Motility-Metrics.ipynb
@@ -0,0 +1,829 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d974e27e-93d8-4c05-b532-314091ff7063",
+ "metadata": {},
+ "source": [
+ "# Compute Summary Motility Metrics\n",
+ "---\n",
+ "\n",
+ "#### Overview\n",
+ "This notebook computes summary motility metrics on cell trajectories.\n",
+ "\n",
+ "all of the cell trajectories output by the script `src/chlamytracker/scripts/track_cells.py`.\n",
+ "\n",
+ "Distributions of select motility metrics are shown.\n",
+ "\n",
+ "Summary motility metrics are filtered by trajectory duration.\n",
+ "\n",
+ "#### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3edafffa-cea3-4716-aeac-f7b51425059c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "from chlamytracker.timelapse import Timelapse\n",
+ "from chlamytracker.tracking_metrics import TrajectoryCSVParser\n",
+ "from natsort import natsorted\n",
+ "from tqdm.notebook import tqdm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab0e1a4b-6729-446f-bcd2-dc521672e01e",
+ "metadata": {},
+ "source": [
+ "## Collect csvs of cell trajectories\n",
+ "---\n",
+ "\n",
+ "#### Datasets\n",
+ "| Experiment ID | Date | Substrate |\n",
+ "|---------------|---------------|-------------------------|\n",
+ "| AMID-04 | 01 March 2024 | Agar microchamber pools |\n",
+ "| AMID-05 | 01 March 2024 | 384-well plate |\n",
+ "\n",
+ "#### Collect csv files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "13ccce58-0fb8-429c-a3e3-0cdee9500c44",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AMID-04 csv files :: 644\n",
+ "AMID-05 csv files :: 53\n",
+ "----------------------\n",
+ "Total :: 697\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# TODO: remove references to local paths\n",
+ "input_directories = {\n",
+ " \"AMID-04\": Path(\"/Volumes/Microscopy/Babu_frik/Tara/2024-03-01/\"),\n",
+ " \"AMID-05\": Path(\"/Volumes/Microscopy/Hina/Tara/2024-03-01/20240301_143720_957\"),\n",
+ "}\n",
+ "\n",
+ "csv_files = {\n",
+ " \"AMID-04\": natsorted(input_directories[\"AMID-04\"].glob(\"*/processed/*/*_tracks.csv\")),\n",
+ " \"AMID-05\": natsorted(input_directories[\"AMID-05\"].glob(\"processed/*_tracks.csv\")),\n",
+ "}\n",
+ "\n",
+ "out = f\"\"\"\\\n",
+ "AMID-04 csv files :: {len(csv_files[\"AMID-04\"])}\n",
+ "AMID-05 csv files :: {len(csv_files[\"AMID-05\"])}\n",
+ "----------------------\n",
+ "Total :: {sum([len(csvs) for csvs in csv_files.values()])}\n",
+ "\"\"\"\n",
+ "print(out)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4b527a33-e3e1-478b-964c-3f2a27528be2",
+ "metadata": {},
+ "source": [
+ "## Experimental parameters\n",
+ "---\n",
+ "\n",
+ "#### Table of experimental parameters\n",
+ "| strain | substrate | position_in_tube | time_in_water |\n",
+ "|--------|----------------|------------------|---------------|\n",
+ "| cc124 | pools | top | 4 |\n",
+ "| | 384-well plate | middle | 21 |\n",
+ "\n",
+ "#### Create mappings from directory structure to experimental parameters\n",
+ "\n",
+ "Each subdirectory within `AMID-04` and `AMID-05` contains timelapse microscopy data of _C. smithii_ cells prepared under different conditions.\n",
+ "\n",
+ "Cells prepared with different conditions for `AMID-04` were deposited onto\n",
+ "\n",
+ "For `AMID-05` each well ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4ee5d5d6-a457-409c-ab90-f96342bec791",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "experimental_parameter_sets = {\n",
+ " \"AMID-04\": {\n",
+ " \"S1-Cs3-T\": {\"strain\": \"cc1373\", \"substrate\": \"pools\", \"position_in_tube\": \"top\", \"time_in_water\": 21}, # noqa: E501\n",
+ " \"S2-Cs3-M\": {\"strain\": \"cc1373\", \"substrate\": \"pools\", \"position_in_tube\": \"middle\", \"time_in_water\": 21}, # noqa: E501\n",
+ " \"S3-Cs4-T\": {\"strain\": \"cc1373\", \"substrate\": \"pools\", \"position_in_tube\": \"top\", \"time_in_water\": 4}, # noqa: E501\n",
+ " \"S4-Cs4-M\": {\"strain\": \"cc1373\", \"substrate\": \"pools\", \"position_in_tube\": \"middle\", \"time_in_water\": 4}, # noqa: E501\n",
+ " },\n",
+ " \"AMID-05\": {\n",
+ " \"I02\": {\"strain\": \"cc1373\", \"substrate\": \"wells\", \"position_in_tube\": \"top\", \"time_in_water\": 21}, # noqa: E501\n",
+ " \"J02\": {\"strain\": \"cc1373\", \"substrate\": \"wells\", \"position_in_tube\": \"top\", \"time_in_water\": 21}, # noqa: E501\n",
+ " \"K02\": {\"strain\": \"cc1373\", \"substrate\": \"wells\", \"position_in_tube\": \"top\", \"time_in_water\": 4}, # noqa: E501\n",
+ " \"L02\": {\"strain\": \"cc1373\", \"substrate\": \"wells\", \"position_in_tube\": \"top\", \"time_in_water\": 4}, # noqa: E501\n",
+ " \"I04\": {\"strain\": \"cc1373\", \"substrate\": \"wells\", \"position_in_tube\": \"middle\", \"time_in_water\": 21}, # noqa: E501\n",
+ " \"J04\": {\"strain\": \"cc1373\", \"substrate\": \"wells\", \"position_in_tube\": \"middle\", \"time_in_water\": 21}, # noqa: E501\n",
+ " \"K04\": {\"strain\": \"cc1373\", \"substrate\": \"wells\", \"position_in_tube\": \"middle\", \"time_in_water\": 4}, # noqa: E501\n",
+ " \"L04\": {\"strain\": \"cc1373\", \"substrate\": \"wells\", \"position_in_tube\": \"middle\", \"time_in_water\": 4}, # noqa: E501\n",
+ " },\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "611d828e-f0dd-471c-8080-53c12d5e2f66",
+ "metadata": {},
+ "source": [
+ "#### Get frame rates and pixel sizes of raw image data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "aa642396-e74c-48b4-b72c-0f950b204847",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pixelsizes\n",
+ "----------\n",
+ "AMID-04 pixel size :: 0.642 µm/px\n",
+ "AMID-05 pixel size :: 0.433 µm/px\n",
+ "\n",
+ "Framerates\n",
+ "----------\n",
+ "AMID-04 frame rate :: 20.0 fps\n",
+ "AMID-05 frame rate :: 20.0 fps\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "nd2_files = {\n",
+ " \"AMID-04\": next(input_directories[\"AMID-04\"].glob(\"*/*.nd2\")),\n",
+ " \"AMID-05\": next(input_directories[\"AMID-05\"].glob(\"*.nd2\")),\n",
+ "}\n",
+ "\n",
+ "pixelsizes = {\n",
+ " \"AMID-04\": Timelapse(nd2_files[\"AMID-04\"], load=False).pixelsize_um,\n",
+ " \"AMID-05\": Timelapse(nd2_files[\"AMID-05\"], load=False).pixelsize_um,\n",
+ "}\n",
+ "\n",
+ "framerates = {\n",
+ " \"AMID-04\": Timelapse(nd2_files[\"AMID-04\"], load=False).framerate,\n",
+ " \"AMID-05\": Timelapse(nd2_files[\"AMID-05\"], load=False).framerate,\n",
+ "}\n",
+ "\n",
+ "out = f\"\"\"\\\n",
+ "Pixelsizes\n",
+ "----------\n",
+ "AMID-04 pixel size :: {pixelsizes[\"AMID-04\"]:.3f} µm/px\n",
+ "AMID-05 pixel size :: {pixelsizes[\"AMID-05\"]:.3f} µm/px\n",
+ "\n",
+ "Framerates\n",
+ "----------\n",
+ "AMID-04 frame rate :: {framerates[\"AMID-04\"]:.1f} fps\n",
+ "AMID-05 frame rate :: {framerates[\"AMID-05\"]:.1f} fps\n",
+ "\"\"\"\n",
+ "print(out)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f468a1b5-938e-4fe1-9f6d-35920cabfa34",
+ "metadata": {},
+ "source": [
+ "## Compute summary motility metrics\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "fcba2449-f532-4418-ac54-8c146374cffd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d62ce7b33adf4273bd59bd7b64e394d3",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/644 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "92d30d2390d04c808d3a86a11f9b7eb8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/53 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# initialize dataframe to collect the summary motility metrics\n",
+ "# for each cell trajectory\n",
+ "motility_metrics_dataframe = pd.DataFrame()\n",
+ "\n",
+ "# loop through all the csv files of cell trajectories\n",
+ "for experiment_ID, csvs in csv_files.items():\n",
+ " for csv in tqdm(csvs):\n",
+ "\n",
+ " # ID tags relevant agar microchamber pools\n",
+ " if experiment_ID == \"AMID-04\":\n",
+ " well_ID = \"NA\"\n",
+ " slide_ID = csv.parents[2].name\n",
+ " timelapse_ID = int(re.findall(r\"\\d+\", csv.parent.name)[-1])\n",
+ " pool_ID = \"x\".join(csv.stem.split(\"_\")[1:3])\n",
+ " # get experimental parameters from slide ID\n",
+ " experimental_parameters = experimental_parameter_sets[experiment_ID].get(slide_ID)\n",
+ "\n",
+ " # ID tags relevant for 384-well plate\n",
+ " elif experiment_ID == \"AMID-05\":\n",
+ " well_ID = csv.name[4:7]\n",
+ " slide_ID = \"NA\"\n",
+ " timelapse_ID = \"NA\"\n",
+ " pool_ID = \"NA\"\n",
+ " # get experimental parameters from well ID\n",
+ " experimental_parameters = experimental_parameter_sets[experiment_ID].get(well_ID)\n",
+ "\n",
+ " else:\n",
+ " raise ValueError(f\"Unknown experiment ID {experiment_ID}\")\n",
+ "\n",
+ " ####\n",
+ " # TODO: remove when becomes unnecessary\n",
+ " if experimental_parameters is None:\n",
+ " continue\n",
+ " ####\n",
+ "\n",
+ " # parse motility data from csv\n",
+ " framerate = framerates[experiment_ID]\n",
+ " pixelsize = pixelsizes[experiment_ID]\n",
+ " cell_trajectories = TrajectoryCSVParser(\n",
+ " csv, framerate, pixelsize\n",
+ " )\n",
+ "\n",
+ " # estimate cell count and compute motility measurements\n",
+ " # for a batch of cell trajectories\n",
+ " cell_count = cell_trajectories.estimate_cell_count()\n",
+ " motility_metrics = cell_trajectories.compute_summary_statistics()\n",
+ " dataframe = pd.DataFrame(motility_metrics)\n",
+ "\n",
+ " # build up dataframe\n",
+ " dataframe[\"cell_count\"] = cell_count\n",
+ " dataframe[\"experiment_ID\"] = experiment_ID\n",
+ " dataframe[\"strain\"] = experimental_parameters[\"strain\"]\n",
+ " dataframe[\"substrate\"] = experimental_parameters[\"substrate\"]\n",
+ " dataframe[\"position_in_tube\"] = experimental_parameters[\"position_in_tube\"]\n",
+ " dataframe[\"time_in_water\"] = experimental_parameters[\"time_in_water\"]\n",
+ " dataframe[\"well_ID\"] = well_ID\n",
+ " dataframe[\"slide_ID\"] = slide_ID\n",
+ " dataframe[\"timelapse_ID\"] = timelapse_ID\n",
+ " dataframe[\"pool_ID\"] = pool_ID\n",
+ "\n",
+ " # concatenate batch of motility metrics\n",
+ " motility_metrics_dataframe = pd.concat([motility_metrics_dataframe, dataframe])\n",
+ "\n",
+ "# clean up dataframe by removing superfluous `cell_id` column and resetting the index\n",
+ "motility_metrics_dataframe = motility_metrics_dataframe\\\n",
+ " .drop(\"cell_id\", axis=1)\\\n",
+ " .reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "21d34f76-39ce-4833-8a42-0f16fb0703e6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " total_time | \n",
+ " total_distance | \n",
+ " net_distance | \n",
+ " max_sprint_length | \n",
+ " confinement_ratio | \n",
+ " mean_curvilinear_speed | \n",
+ " mean_linear_speed | \n",
+ " mean_angular_speed | \n",
+ " num_rotations | \n",
+ " num_direction_changes | \n",
+ " pivot_rate | \n",
+ " cell_count | \n",
+ " experiment_ID | \n",
+ " strain | \n",
+ " substrate | \n",
+ " position_in_tube | \n",
+ " time_in_water | \n",
+ " well_ID | \n",
+ " timelapse_ID | \n",
+ " pool_ID | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 3.900780 | \n",
+ " 229.179349 | \n",
+ " 30.311407 | \n",
+ " 16.290295 | \n",
+ " 0.132261 | \n",
+ " 58.752185 | \n",
+ " 7.770602 | \n",
+ " 2.392120 | \n",
+ " 0.0 | \n",
+ " 3 | \n",
+ " 0.013090 | \n",
+ " 3 | \n",
+ " AMID-04 | \n",
+ " cc1373 | \n",
+ " pools | \n",
+ " top | \n",
+ " 21 | \n",
+ " NA | \n",
+ " 1 | \n",
+ " 2x2 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 0.850170 | \n",
+ " 46.590193 | \n",
+ " 36.584222 | \n",
+ " 16.127942 | \n",
+ " 0.785234 | \n",
+ " 54.801032 | \n",
+ " 43.031655 | \n",
+ " 4.136839 | \n",
+ " 0.0 | \n",
+ " 4 | \n",
+ " 0.085855 | \n",
+ " 3 | \n",
+ " AMID-04 | \n",
+ " cc1373 | \n",
+ " pools | \n",
+ " top | \n",
+ " 21 | \n",
+ " NA | \n",
+ " 1 | \n",
+ " 2x2 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 0.650130 | \n",
+ " 22.472471 | \n",
+ " 19.085556 | \n",
+ " 10.022290 | \n",
+ " 0.849286 | \n",
+ " 34.566119 | \n",
+ " 29.356523 | \n",
+ " 2.799169 | \n",
+ " 0.0 | \n",
+ " 1 | \n",
+ " 0.044499 | \n",
+ " 3 | \n",
+ " AMID-04 | \n",
+ " cc1373 | \n",
+ " pools | \n",
+ " top | \n",
+ " 21 | \n",
+ " NA | \n",
+ " 1 | \n",
+ " 2x2 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 3.000600 | \n",
+ " 168.063715 | \n",
+ " 63.766316 | \n",
+ " 15.426156 | \n",
+ " 0.379418 | \n",
+ " 56.010036 | \n",
+ " 21.251188 | \n",
+ " 1.939173 | \n",
+ " 0.0 | \n",
+ " 2 | \n",
+ " 0.011900 | \n",
+ " 3 | \n",
+ " AMID-04 | \n",
+ " cc1373 | \n",
+ " pools | \n",
+ " top | \n",
+ " 21 | \n",
+ " NA | \n",
+ " 1 | \n",
+ " 2x2 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 2.850570 | \n",
+ " 112.221645 | \n",
+ " 8.051692 | \n",
+ " 15.485106 | \n",
+ " 0.071748 | \n",
+ " 39.368142 | \n",
+ " 2.824590 | \n",
+ " 4.061436 | \n",
+ " 1.0 | \n",
+ " 9 | \n",
+ " 0.080198 | \n",
+ " 3 | \n",
+ " AMID-04 | \n",
+ " cc1373 | \n",
+ " pools | \n",
+ " top | \n",
+ " 21 | \n",
+ " NA | \n",
+ " 1 | \n",
+ " 2x2 | \n",
+ "
\n",
+ " \n",
+ " 1994 | \n",
+ " 19.954072 | \n",
+ " 47.628602 | \n",
+ " 31.877719 | \n",
+ " 0.824480 | \n",
+ " 0.669298 | \n",
+ " 2.386911 | \n",
+ " 1.597555 | \n",
+ " 4.640156 | \n",
+ " 0.0 | \n",
+ " 84 | \n",
+ " 1.763646 | \n",
+ " 19 | \n",
+ " AMID-05 | \n",
+ " cc1373 | \n",
+ " wells | \n",
+ " top | \n",
+ " 21 | \n",
+ " I02 | \n",
+ " NA | \n",
+ " NA | \n",
+ "
\n",
+ " \n",
+ " 1995 | \n",
+ " 19.954072 | \n",
+ " 90.879026 | \n",
+ " 32.830895 | \n",
+ " 1.592052 | \n",
+ " 0.361259 | \n",
+ " 4.554410 | \n",
+ " 1.645323 | \n",
+ " 10.093640 | \n",
+ " 1.0 | \n",
+ " 133 | \n",
+ " 1.463484 | \n",
+ " 19 | \n",
+ " AMID-05 | \n",
+ " cc1373 | \n",
+ " wells | \n",
+ " top | \n",
+ " 21 | \n",
+ " I02 | \n",
+ " NA | \n",
+ " NA | \n",
+ "
\n",
+ " \n",
+ " 1996 | \n",
+ " 19.954072 | \n",
+ " 169.036297 | \n",
+ " 38.048613 | \n",
+ " 3.997798 | \n",
+ " 0.225091 | \n",
+ " 8.471268 | \n",
+ " 1.906809 | \n",
+ " 3.406205 | \n",
+ " 4.0 | \n",
+ " 44 | \n",
+ " 0.260299 | \n",
+ " 19 | \n",
+ " AMID-05 | \n",
+ " cc1373 | \n",
+ " wells | \n",
+ " top | \n",
+ " 21 | \n",
+ " I02 | \n",
+ " NA | \n",
+ " NA | \n",
+ "
\n",
+ " \n",
+ " 1997 | \n",
+ " 19.954072 | \n",
+ " 74.651114 | \n",
+ " 43.324461 | \n",
+ " 1.406703 | \n",
+ " 0.580359 | \n",
+ " 3.741147 | \n",
+ " 2.171209 | \n",
+ " 6.367813 | \n",
+ " 0.0 | \n",
+ " 130 | \n",
+ " 1.741434 | \n",
+ " 19 | \n",
+ " AMID-05 | \n",
+ " cc1373 | \n",
+ " wells | \n",
+ " top | \n",
+ " 21 | \n",
+ " I02 | \n",
+ " NA | \n",
+ " NA | \n",
+ "
\n",
+ " \n",
+ " 1998 | \n",
+ " 19.954072 | \n",
+ " 78.577172 | \n",
+ " 45.183941 | \n",
+ " 1.564895 | \n",
+ " 0.575026 | \n",
+ " 3.937902 | \n",
+ " 2.264397 | \n",
+ " 6.307145 | \n",
+ " 0.0 | \n",
+ " 131 | \n",
+ " 1.667151 | \n",
+ " 19 | \n",
+ " AMID-05 | \n",
+ " cc1373 | \n",
+ " wells | \n",
+ " top | \n",
+ " 21 | \n",
+ " I02 | \n",
+ " NA | \n",
+ " NA | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " total_time total_distance net_distance max_sprint_length \\\n",
+ "0 3.900780 229.179349 30.311407 16.290295 \n",
+ "1 0.850170 46.590193 36.584222 16.127942 \n",
+ "2 0.650130 22.472471 19.085556 10.022290 \n",
+ "3 3.000600 168.063715 63.766316 15.426156 \n",
+ "4 2.850570 112.221645 8.051692 15.485106 \n",
+ "1994 19.954072 47.628602 31.877719 0.824480 \n",
+ "1995 19.954072 90.879026 32.830895 1.592052 \n",
+ "1996 19.954072 169.036297 38.048613 3.997798 \n",
+ "1997 19.954072 74.651114 43.324461 1.406703 \n",
+ "1998 19.954072 78.577172 45.183941 1.564895 \n",
+ "\n",
+ " confinement_ratio mean_curvilinear_speed mean_linear_speed \\\n",
+ "0 0.132261 58.752185 7.770602 \n",
+ "1 0.785234 54.801032 43.031655 \n",
+ "2 0.849286 34.566119 29.356523 \n",
+ "3 0.379418 56.010036 21.251188 \n",
+ "4 0.071748 39.368142 2.824590 \n",
+ "1994 0.669298 2.386911 1.597555 \n",
+ "1995 0.361259 4.554410 1.645323 \n",
+ "1996 0.225091 8.471268 1.906809 \n",
+ "1997 0.580359 3.741147 2.171209 \n",
+ "1998 0.575026 3.937902 2.264397 \n",
+ "\n",
+ " mean_angular_speed num_rotations num_direction_changes pivot_rate \\\n",
+ "0 2.392120 0.0 3 0.013090 \n",
+ "1 4.136839 0.0 4 0.085855 \n",
+ "2 2.799169 0.0 1 0.044499 \n",
+ "3 1.939173 0.0 2 0.011900 \n",
+ "4 4.061436 1.0 9 0.080198 \n",
+ "1994 4.640156 0.0 84 1.763646 \n",
+ "1995 10.093640 1.0 133 1.463484 \n",
+ "1996 3.406205 4.0 44 0.260299 \n",
+ "1997 6.367813 0.0 130 1.741434 \n",
+ "1998 6.307145 0.0 131 1.667151 \n",
+ "\n",
+ " cell_count experiment_ID strain substrate position_in_tube \\\n",
+ "0 3 AMID-04 cc1373 pools top \n",
+ "1 3 AMID-04 cc1373 pools top \n",
+ "2 3 AMID-04 cc1373 pools top \n",
+ "3 3 AMID-04 cc1373 pools top \n",
+ "4 3 AMID-04 cc1373 pools top \n",
+ "1994 19 AMID-05 cc1373 wells top \n",
+ "1995 19 AMID-05 cc1373 wells top \n",
+ "1996 19 AMID-05 cc1373 wells top \n",
+ "1997 19 AMID-05 cc1373 wells top \n",
+ "1998 19 AMID-05 cc1373 wells top \n",
+ "\n",
+ " time_in_water well_ID timelapse_ID pool_ID \n",
+ "0 21 NA 1 2x2 \n",
+ "1 21 NA 1 2x2 \n",
+ "2 21 NA 1 2x2 \n",
+ "3 21 NA 1 2x2 \n",
+ "4 21 NA 1 2x2 \n",
+ "1994 21 I02 NA NA \n",
+ "1995 21 I02 NA NA \n",
+ "1996 21 I02 NA NA \n",
+ "1997 21 I02 NA NA \n",
+ "1998 21 I02 NA NA "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "motility_metrics_dataframe\\\n",
+ " .drop(\"slide_ID\", axis=1)\\\n",
+ " .groupby(\"experiment_ID\")\\\n",
+ " .head(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "75270014-7c09-4024-8652-0bcabb685b1c",
+ "metadata": {},
+ "source": [
+ "## Filter based on trajectory distance and duration\n",
+ "---\n",
+ "\n",
+ "Look at distributions of various metrics to determine threshold criteria."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "3db891e8-5a03-46b6-9a1c-da2c0119a35c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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