peak_detection_ben.ipynb

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    "colab": {
      "name": "peak_detection_ben.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPrseKdBgvma"
      },
      "source": [
        "#!pip install plotly --upgrade  # make sure your plotly is up to date\r\n",
        "\r\n",
        "import plotly.express as px\r\n",
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "from scipy.io import loadmat\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from scipy.signal import find_peaks\r\n",
        "from scipy.signal import savgol_filter"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEqpj-E3g2hA",
        "outputId": "72e32f2e-b109-44dc-fdc1-20cf71e49000"
      },
      "source": [
        "spectra = loadmat('/content/drive/MyDrive/Spectra_3s_25c.mat')\r\n",
        "#spectra = loadmat('Spectra_1s_5c.mat')\r\n",
        "spectra.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['__header__', '__version__', '__globals__', 'AmpSAmp', 'Dm_total_save', 'GammaAmp', 'GammaSAmp', 'GammaS_total_save', 'Gamma_total_save', 'M', 'NmaxS', 'dG', 'dGS', 'frequencies', 'n_int', 'noiseAmpScaled', 'omegaS_total_save', 'omega_total_save'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAB2stosg48N"
      },
      "source": [
        "pd.options.plotting.backend = \"plotly\" # set default pandas plot to plotly"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-bOH0wSxiLem"
      },
      "source": [
        "##Peak Detection on the Channels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJAH8d6thTyP"
      },
      "source": [
        "# set parameters for the analysis\r\n",
        "all_channel_prominence = 0.09\r\n",
        "density_window_size = 20\r\n",
        "density_smoothness = 11\r\n",
        "density_prominence = 0.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "MaFRwPR9g_tB",
        "outputId": "74ef620e-9e94-4b48-8003-0f138ba994f0"
      },
      "source": [
        "# select a spectra (0,1, or 2)\r\n",
        "spectra_number=0\r\n",
        "modes = [x[spectra_number] for x in spectra['omega_total_save']]\r\n",
        "all_channels = pd.DataFrame(np.transpose(np.transpose(spectra['Dm_total_save'])[spectra_number]))\r\n",
        "all_channels.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>0.107335</td>\n",
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              "      <td>0.027296</td>\n",
              "      <td>0.063752</td>\n",
              "      <td>0.027163</td>\n",
              "      <td>0.004901</td>\n",
              "      <td>0.019518</td>\n",
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              "      <td>0.051305</td>\n",
              "      <td>0.019380</td>\n",
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              "         0         1         2   ...        22        23        24\n",
              "0  0.157020  0.022974  0.060893  ...  0.008303  0.001976  0.068524\n",
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              "2  0.147236  0.022970  0.061008  ...  0.006499  0.008722  0.061215\n",
              "3  0.149816  0.023015  0.063253  ...  0.009341  0.001978  0.073772\n",
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              "\n",
              "[5 rows x 25 columns]"
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          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Km6iglZfhH7Y",
        "outputId": "5e4d98a0-5f03-4cd9-9876-c85ff0820a4a"
      },
      "source": [
        "# detect peaks across all channels\r\n",
        "all_peaks=[]\r\n",
        "for c in all_channels:\r\n",
        "    peaks, properties = find_peaks(all_channels[c], prominence=all_channel_prominence)\r\n",
        "    all_peaks.extend(peaks)\r\n",
        "all_peaks=np.sort(all_peaks)\r\n",
        "len(all_peaks)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "81491"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrJJ0cpHhaoA",
        "outputId": "f0be4e9f-1ced-43cf-dd32-f2de8a3d7bcc"
      },
      "source": [
        "# create a density map of all peaks using window\r\n",
        "half_size=int(density_window_size/2)\r\n",
        "density = [0]*half_size\r\n",
        "seeker=0\r\n",
        "for i in range(half_size,all_channels.shape[0]-half_size):\r\n",
        "  count = 0\r\n",
        "  # count peaks\r\n",
        "  while seeker<len(all_peaks) and all_peaks[seeker] < i+half_size:\r\n",
        "    count += 1\r\n",
        "    seeker += 1\r\n",
        "  # return seeker to value half_size back\r\n",
        "  if count > 0:\r\n",
        "    while seeker>=0 and seeker < len(all_peaks) and all_peaks[seeker] > i-half_size:\r\n",
        "      seeker-=1\r\n",
        "    seeker = max(0, seeker)\r\n",
        "  if count < np.shape(all_channels)[1]/10:\r\n",
        "    count=0\r\n",
        "  density.append(count)\r\n",
        "len(density)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "999990"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNTdUJCxhy-d"
      },
      "source": [
        "# a smoothing algorithm can be applied to better aggregate the peaks\r\n",
        "smooth_density=savgol_filter(density, density_smoothness, 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8MXwSmNh2VI",
        "outputId": "8a87e115-fb8c-49bb-f83e-980aac223490"
      },
      "source": [
        "peaks_detected_count = []\r\n",
        "peaks_detected = []\r\n",
        "for s in range(10):\r\n",
        "  window_peaks, _ = find_peaks(smooth_density[s*100000 : (s+1)*100000], prominence=density_prominence)\r\n",
        "  peaks_detected_count.append(len(window_peaks))\r\n",
        "  peaks_detected.extend(window_peaks+s*100000)\r\n",
        "print(\"number of peaks detected:\", str(np.sum(peaks_detected_count)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of peaks detected: 11076\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNlNlmjUiKSg",
        "outputId": "4bfb8062-263a-4721-d12e-0dcd62cea089"
      },
      "source": [
        "# distribution of detected peaks across each 10th\r\n",
        "peaks_detected_count"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[183, 496, 774, 991, 1088, 1239, 1455, 1524, 1633, 1693]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdXTNFy8irM7",
        "outputId": "404ee87b-0ad8-4d81-a3a3-b325ea6e0781"
      },
      "source": [
        "actual_mode_count = []\r\n",
        "for s in range(10):\r\n",
        "  actual_mode_count.append(len(pd.Series(modes)[pd.Series(modes)<(s+1)/10][pd.Series(modes)>s/10]))\r\n",
        "actual_mode_count # actual mode distribution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[100, 300, 500, 700, 900, 1100, 1300, 1500, 1700, 1900]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-CDFF7IiwkI"
      },
      "source": [
        "Compared to the actual mode distribution, we are overcounting on the lower end and over counting on the upper end of the spectra."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YI0Lved9ipGU"
      },
      "source": [
        "##Plotting the Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6jpuWm5iDUg"
      },
      "source": [
        "def show_spectra(view_range):\r\n",
        "  # take subset of plot\r\n",
        "  t_modes = np.sort([x*all_channels.shape[0] for x in modes])\r\n",
        "  temp_modes = pd.Series(t_modes)[(pd.Series(t_modes) < view_range[1])]\r\n",
        "  temp_modes = pd.Series(temp_modes)[(pd.Series(temp_modes) > view_range[0])]\r\n",
        "  temp_peaks = pd.Series(peaks_detected)[(pd.Series(peaks_detected) < view_range[1])]\r\n",
        "  temp_peaks = pd.Series(temp_peaks)[(pd.Series(temp_peaks) > view_range[0])]\r\n",
        "  # plot the predicted peaks, modes, and density\r\n",
        "  fig = pd.Series(smooth_density).iloc[range(view_range[0],view_range[1])].plot(template='plotly_dark', kind='line')\r\n",
        "  for mode in temp_modes:\r\n",
        "      fig.add_vline(x=mode, line_width=1, line_color=\"green\") #requires plotly 4.12 and above\r\n",
        "  for peak in temp_peaks:\r\n",
        "      fig.add_vline(x=peak, line_width=1, line_dash=\"dash\", line_color=\"red\") #requires plotly 4.12 and above\r\n",
        "  fig.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "X99NVoSWgm2y",
        "outputId": "b99c491d-01b5-462c-a33c-e667308da96a"
      },
      "source": [
        "# red is predicted\r\n",
        "# green is actual modes\r\n",
        "# don't make the range too wide or it will take a while to run\r\n",
        "show_spectra((500000, 510000))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "scaled_modes = []\r\n",
        "for m in modes:\r\n",
        "  scaled_modes.append(m*1000000)\r\n",
        "  found_modes=[]"
      ],
      "execution_count": null,
      "outputs": []
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        "for i in scaled_modes:\r\n",
        "    if (np.min(abs(peaks_detected - i)) < 50):\r\n",
        "        found_modes.append(i)\r\n",
        "len(found_modes)/10000"
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      "execution_count": null,
      "outputs": [
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      "source": [
        "97% of the modes are within 50 hz of a predicted mode"
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      "source": [
        "pd.Series(peaks_detected).plot.hist()"
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      "source": [
        "pd.Series(scaled_modes).plot.hist()"
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