danibene · October 18, 2022 18:55
diff --git a/nk_pr_734.ipynb b/nk_pr_734.ipynb
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOuupwxb79Q8NI/2A2kzpeW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/danibene/e3ed1200a75ea72a3c52bfb0ac248a2c/nk_pr_734.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hUpofv0TBqN",
        "outputId": "61e9fd1f-aeec-4179-af17-b7626d7e7940"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/neuropsychology/NeuroKit.git@dev\n",
            "  Cloning https://github.com/neuropsychology/NeuroKit.git (to revision dev) to /tmp/pip-req-build-k9e322x9\n",
            "  Running command git clone -q https://github.com/neuropsychology/NeuroKit.git /tmp/pip-req-build-k9e322x9\n",
            "  Running command git checkout -b dev --track origin/dev\n",
            "  Switched to a new branch 'dev'\n",
            "  Branch 'dev' set up to track remote branch 'dev' from 'origin'.\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.2.2) (1.0.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.2.2) (1.21.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.2.2) (1.3.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.2.2) (3.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from neurokit2==0.2.2) (1.7.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0.0->neurokit2==0.2.2) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0.0->neurokit2==0.2.2) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2==0.2.2) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2==0.2.2) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2==0.2.2) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->neurokit2==0.2.2) (1.4.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->neurokit2==0.2.2) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->neurokit2==0.2.2) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->neurokit2==0.2.2) (2022.4)\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/neuropsychology/NeuroKit.git@dev"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/sappelhoff/pyprep.git@master"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ijBibW_7Xa6H",
        "outputId": "f327fe7a-7792-4dd8-8f51-93259091b7aa"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/sappelhoff/pyprep.git@master\n",
            "  Cloning https://github.com/sappelhoff/pyprep.git (to revision master) to /tmp/pip-req-build-b8h8hgqh\n",
            "  Running command git clone -q https://github.com/sappelhoff/pyprep.git /tmp/pip-req-build-b8h8hgqh\n",
            "  Running command git submodule update --init --recursive -q\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.14.1 in /usr/local/lib/python3.7/dist-packages (from pyprep==0.4.2+21.gc6b268b) (1.21.6)\n",
            "Requirement already satisfied: mne>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from pyprep==0.4.2+21.gc6b268b) (1.2.0)\n",
            "Requirement already satisfied: psutil>=5.4.3 in /usr/local/lib/python3.7/dist-packages (from pyprep==0.4.2+21.gc6b268b) (5.4.8)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from pyprep==0.4.2+21.gc6b268b) (1.7.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (21.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (3.2.2)\n",
            "Requirement already satisfied: pooch>=1.5 in /usr/local/lib/python3.7/dist-packages (from mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (1.6.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (4.64.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (2.11.3)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (4.4.2)\n",
            "Requirement already satisfied: appdirs>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.5->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (1.4.4)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.5->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (2.23.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (3.0.9)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.5->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.5->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.5->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.5->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (2022.9.24)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (2.0.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->mne>=0.20.0->pyprep==0.4.2+21.gc6b268b) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import neurokit2 as nk\n",
        "from pyprep import removeTrend"
      ],
      "metadata": {
        "id": "1iwn3-3HTNHp"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean = 10\n",
        "std = 1 \n",
        "duration = 120\n",
        "frequency = [0.2, 0.4, 0.8, 1.6, 3.2]\n",
        "amplitude = [1, 2, 3, 2, 1]\n",
        "sampling_rate = 200\n",
        "num_samples = sampling_rate * duration\n",
        "frequency_band = [(f - 0.1, f + 0.1) for f in frequency]\n",
        "lowcut = 0.5"
      ],
      "metadata": {
        "id": "Uqm5efRiTFp3"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# PSD of original signal\n",
        "signal = nk.signal_simulate(duration=duration, sampling_rate=sampling_rate, frequency=frequency,\n",
        "                            amplitude = amplitude, noise = 2)\n",
        "power_plot = nk.signal_power(signal, frequency_band=frequency_band, sampling_rate=sampling_rate, method=\"welch\", show=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "sItMG2TsT0Mc",
        "outputId": "3fe301bb-32ef-401c-a6e5-2f5060ba9758"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pyprep: window=1.5/lowcut"
      ],
      "metadata": {
        "id": "ihq0eiemYOwD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# pyprep implementation: https://github.com/sappelhoff/pyprep/blob/master/pyprep/removeTrend.py#L92\n",
        "\n",
        "detrended_signal_1 = removeTrend.removeTrend(\n",
        "    signal,\n",
        "    sample_rate=sampling_rate,\n",
        "    detrendType=\"local detrend\",\n",
        "    detrendCutoff=lowcut,\n",
        "    detrendChannels=None,\n",
        "    matlab_strict=False,\n",
        ").flatten()"
      ],
      "metadata": {
        "id": "QK4_hRgvX0oy"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# since window is later multiplied by sampling_rate\n",
        "power_plot = nk.signal_power(detrended_signal_1, frequency_band=frequency_band, sampling_rate=sampling_rate, method=\"welch\", show=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "F2Y0V8LZYD_L",
        "outputId": "f3416189-35e4-46de-cd4e-b19945c8ce45"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neurokit: window = 1.5/lowcut"
      ],
      "metadata": {
        "id": "bii6jEzhYRU9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# similar implementation as https://github.com/sappelhoff/pyprep/blob/master/pyprep/removeTrend.py#L92\n",
        "window=1.5/lowcut\n",
        "detrended_signal_2 = nk.signal_detrend(signal, method=\"locreg\", \n",
        "                                       window=1.5/lowcut,\n",
        "                                       stepsize=0.02,\n",
        "                                       sampling_rate=sampling_rate\n",
        "                                       )"
      ],
      "metadata": {
        "id": "imFYRqDhTgN8"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "power_plot = nk.signal_power(detrended_signal_2, frequency_band=frequency_band, sampling_rate=sampling_rate, method=\"welch\", show=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "iiGaDS7TUH2G",
        "outputId": "f53ffde8-dfe9-4cd7-84ae-cba922cc770b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neurokit: window = (sampling_rate/lowcut)/sampling_rate\n"
      ],
      "metadata": {
        "id": "YBtjOnisZbdl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dividing by sampling_rate \n",
        "# since window is later multiplied by sampling_rate\n",
        "window = (sampling_rate/lowcut)/sampling_rate \n",
        "detrended_signal_3 = nk.signal_detrend(signal, method=\"locreg\", \n",
        "                                       window=window, \n",
        "                                       stepsize=0.02,\n",
        "                                       sampling_rate=sampling_rate\n",
        "                                       )"
      ],
      "metadata": {
        "id": "hXT0XQfeVSNs"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "power_plot = nk.signal_power(detrended_signal_3, frequency_band=frequency_band, sampling_rate=sampling_rate, method=\"welch\", show=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "lLJtOIvJV3lH",
        "outputId": "6b50f2e5-cc71-4975-99d0-a001976ebc14"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This seems to be the most ideal outcome?"
      ],
      "metadata": {
        "id": "UQhaukthZuPz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neurokit: window = lowcut"
      ],
      "metadata": {
        "id": "9CfsgZLPZpLH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "window=lowcut\n",
        "detrended_signal_4 = nk.signal_detrend(signal, method=\"locreg\", \n",
        "                                       window=window, # since window is later multiplied by sampling rate\n",
        "                                       stepsize=0.02,\n",
        "                                       sampling_rate=sampling_rate\n",
        "                                       )"
      ],
      "metadata": {
        "id": "7vGyJHJHV8Ux"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "power_plot = nk.signal_power(detrended_signal_4, frequency_band=frequency_band, sampling_rate=sampling_rate, method=\"welch\", show=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "cKcUShqPWJSV",
        "outputId": "89c59d8d-5aca-4a02-edb9-a014a9c781ef"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Frequencies above 0.5 are attenuated...definitely not ideal"
      ],
      "metadata": {
        "id": "qEV8X1BzZPvJ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I52k10CLbNw-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
 }