289_tracking_particles_in_3D.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1sGB4509dtXobh8durkSbUU1uB7kp1wdH",
      "authorship_tag": "ABX9TyMEq5hoSTcoMzG8aW1nbwEM",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bnsreenu/python_for_microscopists/blob/master/289_tracking_particles_in_3D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Tracking particles in 3D**\n",
        "\n",
        "https://youtu.be/vqmavdOcjrU\n",
        "\n"
      ],
      "metadata": {
        "id": "Ltl-3uZuktqq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trackpy installation: http://soft-matter.github.io/trackpy/dev/installation.html\n",
        "\n",
        "Trackpy strongly recommends using Conda and not pip. Therefore, we need to set up conda environment for our colab notebook. \n",
        "\n",
        "This specific topic has been covered in a separate tutorial: https://youtu.be/v4qskw8EHXQ\n",
        "\n",
        "For more examples of trackpy and to download sample data sets: https://github.com/soft-matter/trackpy-examples"
      ],
      "metadata": {
        "id": "9O9WZqwgWRGX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us start by understanding the current python version in our colab and installing the corresponding conda environment. "
      ],
      "metadata": {
        "id": "-oKxTS9sZGOb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RDHRimB7Vjs8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0481fa2-0036-4105-fdd5-af7b7b161307"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/bin/python\n",
            "Python 3.7.13\n"
          ]
        }
      ],
      "source": [
        "!which python\n",
        "!python --version"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It gives python 3.7.13.\n",
        "https://docs.conda.io/en/latest/miniconda.html#system-requirements\n",
        "The documentation recommends miniconda 4.12.0 for python 3.7 Linux 64 bit\n",
        "\n",
        "Let us reset the default python path for colab. Just to make sure we do not run into any issues later when miniconda installs its own version of python. "
      ],
      "metadata": {
        "id": "NIZ3OZpvZjPL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!echo $PYTHONPATH"
      ],
      "metadata": {
        "id": "EUxNfBrSWJih",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c14113ac-4031-42ad-88ef-bd0427f98cee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/env/python\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%env PYTHONPATH="
      ],
      "metadata": {
        "id": "NQKuXwpNWPkM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84a249bc-dec7-405c-ae64-3837f646a24c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: PYTHONPATH=\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "MINICONDA_INSTALLER_SCRIPT=Miniconda3-py37_4.12.0-Linux-x86_64.sh\n",
        "MINICONDA_PREFIX=/usr/local\n",
        "wget https://repo.continuum.io/miniconda/$MINICONDA_INSTALLER_SCRIPT\n",
        "chmod +x $MINICONDA_INSTALLER_SCRIPT\n",
        "./$MINICONDA_INSTALLER_SCRIPT -b -f -p $MINICONDA_PREFIX"
      ],
      "metadata": {
        "id": "cQfgqkXzWggd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verify to see if the conda executable is available"
      ],
      "metadata": {
        "id": "0mb7VEk5Zq5b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!which conda # should return /usr/local/bin/conda"
      ],
      "metadata": {
        "id": "ElMwj5xUWrG1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9033c572-913a-4a2e-d0bc-7594e7238b20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/bin/conda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confirm the conda version"
      ],
      "metadata": {
        "id": "aGdK_a47Zxbm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!conda --version # should return 4.12.0"
      ],
      "metadata": {
        "id": "Lr1325GLWtss",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7415a0e0-e112-4582-b4f7-44da95697ba7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conda 4.12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Update conda"
      ],
      "metadata": {
        "id": "rqg4jJvmZ0Vj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "conda install --channel defaults conda python=3.7 --yes\n",
        "conda update --channel defaults --all --yes"
      ],
      "metadata": {
        "id": "wnGYfHvFW0-I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b62a650-e603-4833-ecd9-5f34e54e94d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): ...working... done\n",
            "Solving environment: ...working... done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - conda\n",
            "    - python=3.7\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    ca-certificates-2022.4.26  |       h06a4308_0         124 KB\n",
            "    certifi-2022.6.15          |   py37h06a4308_0         153 KB\n",
            "    conda-4.13.0               |   py37h06a4308_0         890 KB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         1.1 MB\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                      2022.3.29-h06a4308_1 --> 2022.4.26-h06a4308_0\n",
            "  certifi                          2021.10.8-py37h06a4308_2 --> 2022.6.15-py37h06a4308_0\n",
            "  conda                               4.12.0-py37h06a4308_0 --> 4.13.0-py37h06a4308_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\rconda-4.13.0         | 890 KB    |            |   0% \rconda-4.13.0         | 890 KB    | ##8        |  29% \rconda-4.13.0         | 890 KB    | ########## | 100% \n",
            "\rcertifi-2022.6.15    | 153 KB    |            |   0% \rcertifi-2022.6.15    | 153 KB    | ########## | 100% \n",
            "\rca-certificates-2022 | 124 KB    |            |   0% \rca-certificates-2022 | 124 KB    | ########## | 100% \n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "Collecting package metadata (current_repodata.json): ...working... done\n",
            "Solving environment: ...working... done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _openmp_mutex-5.1          |            1_gnu          21 KB\n",
            "    cryptography-37.0.1        |   py37h9ce1e76_0         1.3 MB\n",
            "    ld_impl_linux-64-2.38      |       h1181459_1         654 KB\n",
            "    libgcc-ng-11.2.0           |       h1234567_1         5.3 MB\n",
            "    libgomp-11.2.0             |       h1234567_1         474 KB\n",
            "    libstdcxx-ng-11.2.0        |       h1234567_1         4.7 MB\n",
            "    ncurses-6.3                |       h5eee18b_3         781 KB\n",
            "    openssl-1.1.1p             |       h5eee18b_0         2.5 MB\n",
            "    requests-2.28.0            |   py37h06a4308_0          93 KB\n",
            "    sqlite-3.38.5              |       hc218d9a_0         1.0 MB\n",
            "    tk-8.6.12                  |       h1ccaba5_0         3.0 MB\n",
            "    tqdm-4.64.0                |   py37h06a4308_0         126 KB\n",
            "    urllib3-1.26.9             |   py37h06a4308_0         180 KB\n",
            "    xz-5.2.5                   |       h7f8727e_1         339 KB\n",
            "    zlib-1.2.12                |       h7f8727e_2         106 KB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        20.6 MB\n",
            "\n",
            "The following packages will be REMOVED:\n",
            "\n",
            "  colorama-0.4.4-pyhd3eb1b0_0\n",
            "  conda-content-trust-0.1.1-pyhd3eb1b0_0\n",
            "  six-1.16.0-pyhd3eb1b0_1\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  _openmp_mutex                                   4.5-1_gnu --> 5.1-1_gnu\n",
            "  cryptography                        36.0.0-py37h9ce1e76_0 --> 37.0.1-py37h9ce1e76_0\n",
            "  ld_impl_linux-64                        2.35.1-h7274673_9 --> 2.38-h1181459_1\n",
            "  libgcc-ng                               9.3.0-h5101ec6_17 --> 11.2.0-h1234567_1\n",
            "  libgomp                                 9.3.0-h5101ec6_17 --> 11.2.0-h1234567_1\n",
            "  libstdcxx-ng                            9.3.0-hd4cf53a_17 --> 11.2.0-h1234567_1\n",
            "  ncurses                                    6.3-h7f8727e_2 --> 6.3-h5eee18b_3\n",
            "  openssl                                 1.1.1n-h7f8727e_0 --> 1.1.1p-h5eee18b_0\n",
            "  requests           pkgs/main/noarch::requests-2.27.1-pyh~ --> pkgs/main/linux-64::requests-2.28.0-py37h06a4308_0\n",
            "  sqlite                                  3.38.2-hc218d9a_0 --> 3.38.5-hc218d9a_0\n",
            "  tk                                      8.6.11-h1ccaba5_0 --> 8.6.12-h1ccaba5_0\n",
            "  tqdm               pkgs/main/noarch::tqdm-4.63.0-pyhd3eb~ --> pkgs/main/linux-64::tqdm-4.64.0-py37h06a4308_0\n",
            "  urllib3            pkgs/main/noarch::urllib3-1.26.8-pyhd~ --> pkgs/main/linux-64::urllib3-1.26.9-py37h06a4308_0\n",
            "  xz                                       5.2.5-h7b6447c_0 --> 5.2.5-h7f8727e_1\n",
            "  zlib                                    1.2.12-h7f8727e_1 --> 1.2.12-h7f8727e_2\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\rxz-5.2.5             | 339 KB    |            |   0% \rxz-5.2.5             | 339 KB    | ########## | 100% \rxz-5.2.5             | 339 KB    | ########## | 100% \n",
            "\rzlib-1.2.12          | 106 KB    |            |   0% \rzlib-1.2.12          | 106 KB    | ########## | 100% \n",
            "\rlibgcc-ng-11.2.0     | 5.3 MB    |            |   0% \rlibgcc-ng-11.2.0     | 5.3 MB    | ########## | 100% \rlibgcc-ng-11.2.0     | 5.3 MB    | ########## | 100% \n",
            "\rld_impl_linux-64-2.3 | 654 KB    |            |   0% \rld_impl_linux-64-2.3 | 654 KB    | ########## | 100% \n",
            "\rtk-8.6.12            | 3.0 MB    |            |   0% \rtk-8.6.12            | 3.0 MB    | ########## | 100% \rtk-8.6.12            | 3.0 MB    | ########## | 100% \n",
            "\rsqlite-3.38.5        | 1.0 MB    |            |   0% \rsqlite-3.38.5        | 1.0 MB    | ########## | 100% \n",
            "\ropenssl-1.1.1p       | 2.5 MB    |            |   0% \ropenssl-1.1.1p       | 2.5 MB    | ########## | 100% \ropenssl-1.1.1p       | 2.5 MB    | ########## | 100% \n",
            "\rtqdm-4.64.0          | 126 KB    |            |   0% \rtqdm-4.64.0          | 126 KB    | ########## | 100% \n",
            "\rurllib3-1.26.9       | 180 KB    |            |   0% \rurllib3-1.26.9       | 180 KB    | ########## | 100% \n",
            "\rcryptography-37.0.1  | 1.3 MB    |            |   0% \rcryptography-37.0.1  | 1.3 MB    | ########## | 100% \rcryptography-37.0.1  | 1.3 MB    | ########## | 100% \n",
            "\r_openmp_mutex-5.1    | 21 KB     |            |   0% \r_openmp_mutex-5.1    | 21 KB     | ########## | 100% \n",
            "\rlibstdcxx-ng-11.2.0  | 4.7 MB    |            |   0% \rlibstdcxx-ng-11.2.0  | 4.7 MB    | ########## | 100% \rlibstdcxx-ng-11.2.0  | 4.7 MB    | ########## | 100% \n",
            "\rlibgomp-11.2.0       | 474 KB    |            |   0% \rlibgomp-11.2.0       | 474 KB    | ########## | 100% \n",
            "\rrequests-2.28.0      | 93 KB     |            |   0% \rrequests-2.28.0      | 93 KB     | ########## | 100% \n",
            "\rncurses-6.3          | 781 KB    |            |   0% \rncurses-6.3          | 781 KB    | ########## | 100% \rncurses-6.3          | 781 KB    | ########## | 100% \n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda --version "
      ],
      "metadata": {
        "id": "PY9tivXBW4YE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f62ba33-b684-4ae3-9ed9-99ad0fefee69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conda 4.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python --version"
      ],
      "metadata": {
        "id": "AEQv2ILlW6ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c71fb382-e6d5-445e-aa56-287461774ec3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.7.13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Provide directory where site packages are installed within miniconda\n",
        "\n",
        "First, check the current paths."
      ],
      "metadata": {
        "id": "itfwlH7FZ3FE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path"
      ],
      "metadata": {
        "id": "k15koYw6XAXp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f96ed262-74b5-4416-e008-26b32a9254d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['',\n",
              " '/content',\n",
              " '/env/python',\n",
              " '/usr/lib/python37.zip',\n",
              " '/usr/lib/python3.7',\n",
              " '/usr/lib/python3.7/lib-dynload',\n",
              " '/usr/local/lib/python3.7/dist-packages',\n",
              " '/usr/lib/python3/dist-packages',\n",
              " '/usr/local/lib/python3.7/dist-packages/IPython/extensions',\n",
              " '/root/.ipython']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now add the path for our site-packages"
      ],
      "metadata": {
        "id": "0a1TRiTXZ7NG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "_ = (sys.path\n",
        "        .append(\"/usr/local/lib/python3.7/site-packages\"))"
      ],
      "metadata": {
        "id": "EzAI-e03XHrN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we are all set to install trackpy library using conda.\n",
        "\n",
        "Please note that you will be prompted to answer Yes/No a couple of times."
      ],
      "metadata": {
        "id": "FJKO0JPEZ9mO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!conda install -c conda-forge trackpy\n",
        "#!conda install -c conda-forge pims"
      ],
      "metadata": {
        "id": "VzW99yxyXcJK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd38cab8-0835-40fe-96ea-a9dacdcf9fcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - trackpy\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    bottleneck-1.3.5           |   py37h7deecbd_0         115 KB\n",
            "    ca-certificates-2022.6.15  |       ha878542_0         149 KB  conda-forge\n",
            "    certifi-2022.6.15          |   py37h89c1867_0         155 KB  conda-forge\n",
            "    conda-4.13.0               |   py37h89c1867_1         989 KB  conda-forge\n",
            "    cycler-0.11.0              |     pyhd8ed1ab_0          10 KB  conda-forge\n",
            "    freetype-2.10.4            |       h0708190_1         890 KB  conda-forge\n",
            "    icu-67.1                   |       he1b5a44_0        12.9 MB  conda-forge\n",
            "    kiwisolver-1.4.2           |   py37h295c915_0          83 KB\n",
            "    libblas-3.9.0              |15_linux64_openblas          12 KB  conda-forge\n",
            "    libcblas-3.9.0             |15_linux64_openblas          12 KB  conda-forge\n",
            "    libgfortran-ng-12.1.0      |      h69a702a_16          23 KB  conda-forge\n",
            "    libgfortran5-12.1.0        |      hdcd56e2_16         1.8 MB  conda-forge\n",
            "    liblapack-3.9.0            |15_linux64_openblas          12 KB  conda-forge\n",
            "    libopenblas-0.3.20         |pthreads_h78a6416_0        10.1 MB  conda-forge\n",
            "    libpng-1.6.37              |       h21135ba_2         306 KB  conda-forge\n",
            "    matplotlib-base-3.2.2      |   py37h1d35a4c_1         7.0 MB  conda-forge\n",
            "    nomkl-1.0                  |       h5ca1d4c_0           4 KB  conda-forge\n",
            "    numexpr-2.8.0              | py37h85a3170_102         133 KB  conda-forge\n",
            "    numpy-1.21.6               |   py37h976b520_0         6.1 MB  conda-forge\n",
            "    pandas-1.3.5               |   py37h8c16a72_0         9.3 MB\n",
            "    pyparsing-3.0.9            |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    python-dateutil-2.8.2      |     pyhd8ed1ab_0         240 KB  conda-forge\n",
            "    python_abi-3.7             |          2_cp37m           4 KB  conda-forge\n",
            "    pytz-2022.1                |     pyhd8ed1ab_0         242 KB  conda-forge\n",
            "    pyyaml-6.0                 |   py37h540881e_4         178 KB  conda-forge\n",
            "    scipy-1.7.3                |   py37hf2a6cf1_0        21.8 MB  conda-forge\n",
            "    six-1.16.0                 |     pyh6c4a22f_0          14 KB  conda-forge\n",
            "    tornado-5.1.1              |py37h14c3975_1000         662 KB  conda-forge\n",
            "    trackpy-0.5.0              |     pyhd8ed1ab_0          98 KB  conda-forge\n",
            "    typing_extensions-4.3.0    |     pyha770c72_0          28 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        73.2 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  bottleneck         pkgs/main/linux-64::bottleneck-1.3.5-py37h7deecbd_0\n",
            "  cycler             conda-forge/noarch::cycler-0.11.0-pyhd8ed1ab_0\n",
            "  freetype           conda-forge/linux-64::freetype-2.10.4-h0708190_1\n",
            "  icu                conda-forge/linux-64::icu-67.1-he1b5a44_0\n",
            "  kiwisolver         pkgs/main/linux-64::kiwisolver-1.4.2-py37h295c915_0\n",
            "  libblas            conda-forge/linux-64::libblas-3.9.0-15_linux64_openblas\n",
            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-15_linux64_openblas\n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-12.1.0-h69a702a_16\n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-12.1.0-hdcd56e2_16\n",
            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-15_linux64_openblas\n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.20-pthreads_h78a6416_0\n",
            "  libpng             conda-forge/linux-64::libpng-1.6.37-h21135ba_2\n",
            "  matplotlib-base    conda-forge/linux-64::matplotlib-base-3.2.2-py37h1d35a4c_1\n",
            "  nomkl              conda-forge/noarch::nomkl-1.0-h5ca1d4c_0\n",
            "  numexpr            conda-forge/linux-64::numexpr-2.8.0-py37h85a3170_102\n",
            "  numpy              conda-forge/linux-64::numpy-1.21.6-py37h976b520_0\n",
            "  pandas             pkgs/main/linux-64::pandas-1.3.5-py37h8c16a72_0\n",
            "  pyparsing          conda-forge/noarch::pyparsing-3.0.9-pyhd8ed1ab_0\n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0\n",
            "  python_abi         conda-forge/linux-64::python_abi-3.7-2_cp37m\n",
            "  pytz               conda-forge/noarch::pytz-2022.1-pyhd8ed1ab_0\n",
            "  pyyaml             conda-forge/linux-64::pyyaml-6.0-py37h540881e_4\n",
            "  scipy              conda-forge/linux-64::scipy-1.7.3-py37hf2a6cf1_0\n",
            "  six                conda-forge/noarch::six-1.16.0-pyh6c4a22f_0\n",
            "  tornado            conda-forge/linux-64::tornado-5.1.1-py37h14c3975_1000\n",
            "  trackpy            conda-forge/noarch::trackpy-0.5.0-pyhd8ed1ab_0\n",
            "  typing_extensions  conda-forge/noarch::typing_extensions-4.3.0-pyha770c72_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates    pkgs/main::ca-certificates-2022.4.26-~ --> conda-forge::ca-certificates-2022.6.15-ha878542_0\n",
            "  conda              pkgs/main::conda-4.13.0-py37h06a4308_0 --> conda-forge::conda-4.13.0-py37h89c1867_1\n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  certifi            pkgs/main::certifi-2022.6.15-py37h06a~ --> conda-forge::certifi-2022.6.15-py37h89c1867_0\n",
            "\n",
            "\n",
            "Proceed ([y]/n)? y\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "pytz-2022.1          | 242 KB    | : 100% 1.0/1 [00:00<00:00,  5.88it/s]\n",
            "bottleneck-1.3.5     | 115 KB    | : 100% 1.0/1 [00:00<00:00,  9.93it/s]\n",
            "numpy-1.21.6         | 6.1 MB    | : 100% 1.0/1 [00:01<00:00,  1.41s/it]              \n",
            "libopenblas-0.3.20   | 10.1 MB   | : 100% 1.0/1 [00:01<00:00,  1.83s/it]               \n",
            "freetype-2.10.4      | 890 KB    | : 100% 1.0/1 [00:00<00:00,  5.38it/s]\n",
            "scipy-1.7.3          | 21.8 MB   | : 100% 1.0/1 [00:03<00:00,  3.94s/it]\n",
            "liblapack-3.9.0      | 12 KB     | : 100% 1.0/1 [00:00<00:00, 32.57it/s]\n",
            "certifi-2022.6.15    | 155 KB    | : 100% 1.0/1 [00:00<00:00, 18.49it/s]\n",
            "icu-67.1             | 12.9 MB   | : 100% 1.0/1 [00:02<00:00,  2.20s/it]\n",
            "libgfortran-ng-12.1. | 23 KB     | : 100% 1.0/1 [00:00<00:00, 20.75it/s]\n",
            "python-dateutil-2.8. | 240 KB    | : 100% 1.0/1 [00:00<00:00, 15.81it/s]\n",
            "pyparsing-3.0.9      | 79 KB     | : 100% 1.0/1 [00:00<00:00, 18.04it/s]\n",
            "matplotlib-base-3.2. | 7.0 MB    | : 100% 1.0/1 [00:01<00:00,  1.36s/it]               \n",
            "kiwisolver-1.4.2     | 83 KB     | : 100% 1.0/1 [00:00<00:00, 15.75it/s]\n",
            "pandas-1.3.5         | 9.3 MB    | : 100% 1.0/1 [00:00<00:00,  1.41it/s]               \n",
            "tornado-5.1.1        | 662 KB    | : 100% 1.0/1 [00:00<00:00,  4.89it/s]\n",
            "libcblas-3.9.0       | 12 KB     | : 100% 1.0/1 [00:00<00:00, 22.67it/s]\n",
            "pyyaml-6.0           | 178 KB    | : 100% 1.0/1 [00:00<00:00, 13.08it/s]\n",
            "conda-4.13.0         | 989 KB    | : 100% 1.0/1 [00:00<00:00,  3.61it/s]\n",
            "six-1.16.0           | 14 KB     | : 100% 1.0/1 [00:00<00:00, 21.40it/s]\n",
            "python_abi-3.7       | 4 KB      | : 100% 1.0/1 [00:00<00:00, 27.00it/s]\n",
            "cycler-0.11.0        | 10 KB     | : 100% 1.0/1 [00:00<00:00, 27.63it/s]\n",
            "trackpy-0.5.0        | 98 KB     | : 100% 1.0/1 [00:00<00:00, 19.88it/s]\n",
            "numexpr-2.8.0        | 133 KB    | : 100% 1.0/1 [00:00<00:00, 15.71it/s]\n",
            "libblas-3.9.0        | 12 KB     | : 100% 1.0/1 [00:00<00:00, 18.05it/s]\n",
            "typing_extensions-4. | 28 KB     | : 100% 1.0/1 [00:00<00:00, 27.50it/s]\n",
            "nomkl-1.0            | 4 KB      | : 100% 1.0/1 [00:00<00:00, 26.31it/s]\n",
            "libgfortran5-12.1.0  | 1.8 MB    | : 100% 1.0/1 [00:00<00:00,  2.73it/s]\n",
            "libpng-1.6.37        | 306 KB    | : 100% 1.0/1 [00:00<00:00,  9.95it/s]\n",
            "ca-certificates-2022 | 149 KB    | : 100% 1.0/1 [00:00<00:00, 17.38it/s]\n",
            "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda install -c conda-forge pims"
      ],
      "metadata": {
        "id": "zlriaFsQmG8-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b581bdc-aeb6-4650-cdd0-7d6f583ff582"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - pims\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    enum34-1.1.10              |   py37hc8dfbb8_2           4 KB  conda-forge\n",
            "    imagecodecs-lite-2019.12.3 |   py37hda87dfa_5         176 KB  conda-forge\n",
            "    imageio-2.6.1              |           py37_0         3.3 MB  conda-forge\n",
            "    jinja2-3.1.2               |     pyhd8ed1ab_1          99 KB  conda-forge\n",
            "    jpeg-9e                    |       h166bdaf_1         268 KB  conda-forge\n",
            "    libtiff-4.0.10             |    hc3755c2_1005         602 KB  conda-forge\n",
            "    lz4-c-1.9.3                |       h9c3ff4c_1         179 KB  conda-forge\n",
            "    markupsafe-2.1.1           |   py37h540881e_1          22 KB  conda-forge\n",
            "    olefile-0.46               |     pyh9f0ad1d_1          32 KB  conda-forge\n",
            "    pathlib-1.0.1              |   py37h89c1867_6           4 KB  conda-forge\n",
            "    pillow-6.2.1               |   py37h6b7be26_0         637 KB  conda-forge\n",
            "    pims-0.6.1                 |     pyhd8ed1ab_0          66 KB  conda-forge\n",
            "    slicerator-1.1.0           |     pyhd8ed1ab_0          13 KB  conda-forge\n",
            "    tifffile-2019.7.26.2       |           py37_0         218 KB  conda-forge\n",
            "    zstd-1.4.9                 |       ha95c52a_0         431 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         6.0 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  enum34             conda-forge/linux-64::enum34-1.1.10-py37hc8dfbb8_2\n",
            "  imagecodecs-lite   conda-forge/linux-64::imagecodecs-lite-2019.12.3-py37hda87dfa_5\n",
            "  imageio            conda-forge/linux-64::imageio-2.6.1-py37_0\n",
            "  jinja2             conda-forge/noarch::jinja2-3.1.2-pyhd8ed1ab_1\n",
            "  jpeg               conda-forge/linux-64::jpeg-9e-h166bdaf_1\n",
            "  libtiff            conda-forge/linux-64::libtiff-4.0.10-hc3755c2_1005\n",
            "  lz4-c              conda-forge/linux-64::lz4-c-1.9.3-h9c3ff4c_1\n",
            "  markupsafe         conda-forge/linux-64::markupsafe-2.1.1-py37h540881e_1\n",
            "  olefile            conda-forge/noarch::olefile-0.46-pyh9f0ad1d_1\n",
            "  pathlib            conda-forge/linux-64::pathlib-1.0.1-py37h89c1867_6\n",
            "  pillow             conda-forge/linux-64::pillow-6.2.1-py37h6b7be26_0\n",
            "  pims               conda-forge/noarch::pims-0.6.1-pyhd8ed1ab_0\n",
            "  slicerator         conda-forge/noarch::slicerator-1.1.0-pyhd8ed1ab_0\n",
            "  tifffile           conda-forge/linux-64::tifffile-2019.7.26.2-py37_0\n",
            "  zstd               conda-forge/linux-64::zstd-1.4.9-ha95c52a_0\n",
            "\n",
            "\n",
            "Proceed ([y]/n)? y\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "jpeg-9e              | 268 KB    | : 100% 1.0/1 [00:00<00:00,  7.65it/s]\n",
            "pims-0.6.1           | 66 KB     | : 100% 1.0/1 [00:00<00:00, 17.84it/s]\n",
            "jinja2-3.1.2         | 99 KB     | : 100% 1.0/1 [00:00<00:00, 17.60it/s]\n",
            "tifffile-2019.7.26.2 | 218 KB    | : 100% 1.0/1 [00:00<00:00,  9.93it/s]\n",
            "imageio-2.6.1        | 3.3 MB    | : 100% 1.0/1 [00:00<00:00,  2.15it/s]\n",
            "olefile-0.46         | 32 KB     | : 100% 1.0/1 [00:00<00:00, 20.88it/s]\n",
            "zstd-1.4.9           | 431 KB    | : 100% 1.0/1 [00:00<00:00,  8.69it/s]\n",
            "pathlib-1.0.1        | 4 KB      | : 100% 1.0/1 [00:00<00:00, 17.51it/s]\n",
            "pillow-6.2.1         | 637 KB    | : 100% 1.0/1 [00:00<00:00,  5.69it/s]\n",
            "enum34-1.1.10        | 4 KB      | : 100% 1.0/1 [00:00<00:00, 27.16it/s]\n",
            "slicerator-1.1.0     | 13 KB     | : 100% 1.0/1 [00:00<00:00, 18.65it/s]\n",
            "lz4-c-1.9.3          | 179 KB    | : 100% 1.0/1 [00:00<00:00, 13.79it/s]\n",
            "imagecodecs-lite-201 | 176 KB    | : 100% 1.0/1 [00:00<00:00, 11.37it/s]\n",
            "libtiff-4.0.10       | 602 KB    | : 100% 1.0/1 [00:00<00:00,  6.87it/s]\n",
            "markupsafe-2.1.1     | 22 KB     | : 100% 1.0/1 [00:00<00:00, 22.61it/s]\n",
            "Preparing transaction: \\ \b\bdone\n",
            "Verifying transaction: / \b\bdone\n",
            "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we are ready to focus on our goal of tracking particles in 3D.\n",
        "\n",
        "Download the test data set from here: "
      ],
      "metadata": {
        "id": "VPFKGI1JcmED"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the required libraries"
      ],
      "metadata": {
        "id": "BzcEtYZzaBBz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('figure',  figsize=(10, 6))\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pandas import DataFrame, Series  # for convenience\n",
        "\n",
        "import pims\n",
        "import trackpy as tp\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", module=\"matplotlib\")"
      ],
      "metadata": {
        "id": "0b2-4HuUYFSL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43d0aa8a-1146-4b76-eff9-13b51654b545"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Imageio: 'ffmpeg-linux64-v3.3.1' was not found on your computer; downloading it now.\n",
            "Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-linux64-v3.3.1 (43.8 MB)\n",
            "Downloading: 8192/45929032 bytes (0.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b3563520/45929032 bytes (7.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b7307264/45929032 bytes (15.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b11042816/45929032 bytes (24.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b14483456/45929032 bytes (31.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b18169856/45929032 bytes (39.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b21889024/45929032 bytes (47.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b25526272/45929032 bytes (55.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b29065216/45929032 bytes (63.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b32825344/45929032 bytes (71.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b36569088/45929032 bytes (79.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b40009728/45929032 bytes (87.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b43622400/45929032 bytes (95.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45929032/45929032 bytes (100.0%)\n",
            "  Done\n",
            "File saved as /root/.imageio/ffmpeg/ffmpeg-linux64-v3.3.1.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make sure to upload your data to Google Drive and mount the drive to make it accessible via your colab notebook.\n",
        "\n",
        "Load the 3D time series image from zip drive. We make use of ImageSequenceND. This reader class searches for files matching a certain search string. The filename should contain indentifiers for each axis (dimension). For example \"demo_t001_z001.png\" contains identifiers t and z. We set the returned image axes to zyx and the iterating axis to t. In this way we get 3D images indexed by t.\n",
        "\n",
        "We will be using this data set:\n",
        "https://github.com/soft-matter/trackpy-examples/blob/master/sample_data/pmma_colloids.zip"
      ],
      "metadata": {
        "id": "yFE4wLpiaG-u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frames = pims.ImageSequenceND(r'/content/drive/MyDrive/ColabNotebooks/data/tracking_data/pmma_colloids.zip', axes_identifiers = ['z', 't'])\n",
        "frames.bundle_axes = ['z', 'y', 'x']\n",
        "frames.iter_axes = 't'\n",
        "frames"
      ],
      "metadata": {
        "id": "yuoIb1GSYwpI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca574f2c-df78-4bf9-9a6d-4d28a8538375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ImageSequenceND>\n",
              "Source: /content/drive/MyDrive/ColabNotebooks/data/tracking_data/pmma_colloids.zip\n",
              "Axes: 4\n",
              "Axis 'z' size: 25\n",
              "Axis 't' size: 33\n",
              "Axis 'y' size: 128\n",
              "Axis 'x' size: 128\n",
              "Pixel Datatype: uint8"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_slice=frames[0]  #specific t slice (3D stack)\n",
        "t_slice_for_specific_z=t_slice[0] #specific t slice for a given z stack(2D)"
      ],
      "metadata": {
        "id": "bcHOXK9Aap7T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(t_slice_for_specific_z)"
      ],
      "metadata": {
        "id": "_iR79t7bcG58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "bfe8e606-3186-4ddd-cddc-c975e0b2b15a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7374eaedd0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Locating features**\n",
        "\n",
        "This step performs segmentation using Gaussian blob detection. We can use our own segmentation, e.g, Deep Learning based. This will be covered in the next tutorial. \n",
        "\n",
        "For blob detection, estimate the size of features. locate only requires a feature diameter. This needs to be an odd integer and can be specified for each dimension seperately. The order of dimensions is the same as the order of the image array, typically (z, y, x).\n",
        "\n"
      ],
      "metadata": {
        "id": "K2fQluLFgaN3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = tp.locate(frames[0], diameter=(5, 9, 9))\n",
        "features.head()  # displays first 5 rows\n"
      ],
      "metadata": {
        "id": "ERXWHkbPcQF0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3422b040-8b6c-4efe-fb5f-71544d150907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          z           y          x         mass    size_z    size_y    size_x  \\\n",
              "0  2.150307    9.127058  92.698579  4078.904707  1.397100  2.346068  2.775242   \n",
              "1  1.966345   12.324782  54.751706  2798.073313  1.299836  2.373661  2.861476   \n",
              "2  2.003977   37.991313   4.868746  3145.774586  1.272156  2.673398  2.848487   \n",
              "3  2.008338   80.036620  58.814761  2922.205302  1.339245  2.223283  2.692462   \n",
              "4  1.671356  115.246732  28.295780  2644.966218  1.288565  2.809659  2.506161   \n",
              "\n",
              "   ecc     signal  raw_mass      ep_z      ep_y      ep_x  frame  \n",
              "0  NaN  83.961955   11471.0  0.006037  0.013466  0.013466      0  \n",
              "1  NaN  60.255050    7476.0  0.009536  0.021271  0.021271      0  \n",
              "2  NaN  60.913575    8632.0  0.008166  0.018216  0.018216      0  \n",
              "3  NaN  76.388916    7986.0  0.008879  0.019806  0.019806      0  \n",
              "4  NaN  55.316112    7970.0  0.008898  0.019848  0.019848      0  "
            ],
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              "      <td>1.339245</td>\n",
              "      <td>2.223283</td>\n",
              "      <td>2.692462</td>\n",
              "      <td>NaN</td>\n",
              "      <td>76.388916</td>\n",
              "      <td>7986.0</td>\n",
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              "      <td>0.019806</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "locate returns a spreadsheet-like object called a DataFrame. It lists each feature's position,\n",
        "various characterizations of its appearance, which we will use to filter out spurious features,\n",
        "the \"signal\" strength and an estimate of uncertainty, both derived from this paper: https://www.cell.com/biophysj/fulltext/S0006-3495(05)73136-2"
      ],
      "metadata": {
        "id": "IiF6rDvYgtbd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can use tp.annotate3d to display the objects that we will be tracking."
      ],
      "metadata": {
        "id": "Bu3Cyd5EhZee"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#tp.annotate3d(features, frames[0])\n"
      ],
      "metadata": {
        "id": "6d2yNk0WcZCo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remember that we can filter objects based on a few criteria. We can also force minimum separation between objects."
      ],
      "metadata": {
        "id": "b5KHS_Lmh3Fw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Locating (segmenting) features in all 3D frames**\n",
        "\n",
        "We use tp.batch"
      ],
      "metadata": {
        "id": "msGTzV8PigPF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f = tp.batch(frames, diameter=(7, 11, 11), separation=(3, 11, 11))"
      ],
      "metadata": {
        "id": "c1yQVFapc2OA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "159ca38c-3ff4-4e83-b2ab-ed293e7d0cba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frame 32: 165 features\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Link features into particle trajectories**\n",
        "\n",
        "We have the locations of the particles in each frame. Next we'll track particles from frame to frame, giving each one a number for identification.\n",
        "\n",
        "The linking features of trackpy support an arbitrary number of dimensions. The argument pos_columns will determine which and how many dimensions will be used. The standard set of columns is ['x', 'y']. For 3d we have to set this to ['x', 'y', 'z']. \n",
        "\n",
        "Linking treats all dimensions equally. \n",
        "\n",
        "For this example we are using a file with unequal pixel sizes: 0.21 microns in XY and 0.75 microns in Z, so we first define new columns in units of microns."
      ],
      "metadata": {
        "id": "DojeS8Bfi73-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f['xum'] = f['x'] * 0.21\n",
        "f['yum'] = f['y'] * 0.21\n",
        "f['zum'] = f['z'] * 0.75"
      ],
      "metadata": {
        "id": "KJ7NBoJcc6X9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Experimentally, there is a tradeoff between framerate and z pixel size. If you want to have higher z resolution, you scan slower and your framerate goes down. In this specific experiment the z resolution is low, so that we are able to link particles between subsequent frames. We try several linking distances here."
      ],
      "metadata": {
        "id": "QSR77lhpFaI6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for search_range in [1.0, 1.5, 2.0, 2.5]:\n",
        "    linked = tp.link_df(f, search_range, pos_columns=['xum', 'yum', 'zum'])\n",
        "    hist, bins = np.histogram(np.bincount(linked.particle.astype(int)),\n",
        "                              bins=np.arange(30), normed=True)\n",
        "    plt.step(bins[1:], hist, label='range = {} microns'.format(search_range))\n",
        "plt.ylabel('relative frequency')\n",
        "plt.xlabel('track length (frames)')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "ZzP0DlNqc698",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "9c7d91ab-b648-4f7b-8299-d26b20c972e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frame 32: 165 trajectories present.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: VisibleDeprecationWarning: Passing `normed=True` on non-uniform bins has always been broken, and computes neither the probability density function nor the probability mass function. The result is only correct if the bins are uniform, when density=True will produce the same result anyway. The argument will be removed in a future version of numpy.\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f737343c8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use a search range of 2.0 microns, because there is no significant improvement between 2.0 and 2.5 microns."
      ],
      "metadata": {
        "id": "LibHo9lRF129"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linked = tp.link_df(f, 2.0, pos_columns=['xum', 'yum', 'zum'])"
      ],
      "metadata": {
        "id": "tk9C6t-SFzwi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2a80e56-fe20-45e9-8055-8163b991b376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frame 32: 165 trajectories present.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mean squared displacement\n",
        "\n",
        "With the trajectories we can calculate the mean squared displacement. To calculate the mean squared displacement from all three dimensions, we need to tell trackpy which position columns to use. It defaults to ['x', 'y'].\n",
        "\n",
        "We specify mpp=1 because we have already transformed our data to microns."
      ],
      "metadata": {
        "id": "EVJ3Rr68GAIa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "msd3D = tp.emsd(linked, mpp=1, fps=1/0.8582, max_lagtime=20,\n",
        "                pos_columns=['xum', 'yum', 'zum'])\n",
        "ax = msd3D.plot(style='o', label='MSD in 3D')\n",
        "ax.set_ylabel(r'$\\langle \\Delta r^2 \\rangle$ [$\\mu$m$^2$]')\n",
        "ax.set_xlabel('lag time $t$')\n",
        "ax.set_xlim(0, 16)\n",
        "ax.set_ylim(0, 20)\n",
        "ax.legend(loc='upper left')"
      ],
      "metadata": {
        "id": "H6o_jZDiGHgE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "fad48968-2c6f-4c6f-a42a-063fe4d5741c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/site-packages/trackpy/motion.py:235: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
            "  results = msds.mul(msds['N'], axis=0).mean(level=1)  # weighted average\n",
            "/usr/local/lib/python3.7/site-packages/trackpy/motion.py:236: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
            "  results = results.div(msds['N'].mean(level=1), axis=0)  # weights normalized\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7373439e50>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The mean squared displacement in three dimensional Brownian motion is described by the following relation: ⟨$x^{2}$⟩=6Dt\n",
        "\n",
        "Let's do a fit and calculate the diffusion constant!"
      ],
      "metadata": {
        "id": "CPxNgQJyGP-S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "slope = np.linalg.lstsq(msd3D.index[:, np.newaxis], msd3D)[0][0]\n",
        "\n",
        "ax = msd3D.plot(style='o', label='MSD in 3D')\n",
        "ax.plot(np.arange(20), slope * np.arange(20), label='linear fit')\n",
        "ax.set(ylabel=r'$\\langle \\Delta r^2 \\rangle$ [$\\mu$m$^2$]', xlabel='lag time $t$')\n",
        "ax.set(xlim=(0, 16), ylim=(0, 20))\n",
        "ax.legend(loc='upper left')\n",
        "print(r'The diffusion constant is {0:.2f} μm²/s'.format(slope / 6))"
      ],
      "metadata": {
        "id": "v5Y-WLVnG0ii",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "7d65d868-6262-43e0-b4bd-3113a3852387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
            "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The diffusion constant is 0.18 μm²/s\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}