{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Data processing of spectroscopy data (analytical chemistry)\n", "\n", "The data consists of Raman spectra of macrophages.\n", "Data treatment is mainly based on the library specio.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from specio import specread # pip install git+https://github.com/paris-saclay-cds/specio.git\n", "from os import path\n", "import glob\n", "from SPARQLWrapper import SPARQLWrapper, JSON\n", "import urllib.request\n", "import os\n", "import errno\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Query data to select spc files in your campaign" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410LDL_L01_cell01_n1.spc',\n", " 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'/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410LDL_L01_cell02_c1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413EPA_L03_cell01_n1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413EPA_L03_cell01_n2.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413EPA_L03_cell01_c1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413EPA_L03_cell01_c2.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413EPA_L03_cell01_c3.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410LDL_L01_cell03_n3.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410EDL_L01_cell04_n1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410EDL_L01_cell04_n3.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410EDL_L01_cell04_c1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410EDL_L01_cell04_c2.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413EPA_L03_cell02_n1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413EPA_L03_cell06_n2.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413EPA_L03_cell06_n3.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170411EPA_L01_cell03_c1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170411EPA_L01_cell03_c2.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170411EPA_L01_cell03_c3.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170411STD_L01_cell06_n2.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170411STD_L01_cell06_c1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170411STD_L01_cell06_c2.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413LDL_L03_cell07_n1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413LDL_L03_cell07_n3.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413LDL_L03_cell07_c1.spc']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sparql = SPARQLWrapper(\"https://opendata1.opendata.u-psud.fr/sparql/\")\n", "sparql.setQuery(\"\"\"\n", " prefix daapp: \n", "prefix project: \n", "prefix rdfs: \n", "select ?spc\n", "where {\n", " daapp:hasCampaign ?campaign .\n", " ?campaign project:hasSlide ?slide .\n", "\n", " \n", " ?slide project:hasCell ?cell .\n", " ?cell project:hasPosition ?position.\n", " ?position project:hasMeasure ?spc .\n", " \n", "} \n", "\"\"\")\n", "sparql.setReturnFormat(JSON)\n", "results = sparql.query().convert()\n", "\n", "path = \"/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02\"\n", "if not os.path.exists(path):\n", " os.makedirs(path, exist_ok=False)\n", "\n", "filenames = []\n", "for result in results[\"results\"][\"bindings\"]:\n", " filename = result[\"spc\"][\"value\"].replace(\"http://134.158.74.40/\", \"/home/jupyter/Notebooks/htdocs/\")\n", " if not os.path.exists(filename):\n", " print('Beginning data file:'+filename)\n", " urllib.request.urlretrieve(result[\"spc\"][\"value\"],filename)\n", " \n", " filenames.append(filename)\n", " \n", "filenames" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## It is also possible to directly indicate directory and file extension to upload data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# DATA_PATH = (\"/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02\")\n", "# EXT_FILE = \"*.spc\"\n", "# filenames = glob.glob(path.join(DATA_PATH, EXT_FILE))\n", "# filenames" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410LDL_L01_cell01_n1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410LDL_L01_cell01_n2.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170410LDL_L01_cell01_c1.spc',\n", " '/home/jupyter/Notebooks/htdocs/Macrophages_imagerie/Methode_01/Campagne_02/20170413EPA_L03_cell07_n2.spc',\n", " 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"x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n" ] } ], "source": [ "# def convert(f):\n", "# #print(f) \n", "# specread(f)\n", "\n", "list_raman = [specread(f)\n", " for f in filenames]\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(list_raman[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the different groups of data (J774 macrophages are prepared in four different growth media)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cell_medium = [\"EDL\", \"LDL\", \"STD\", \"EPA\"]\n", "dict_raman = {k: [] for k in cell_medium}" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", 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"x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n", "x-y(1)\n" ] } ], "source": [ "for key in dict_raman.keys():\n", " for f in filenames:\n", " if key in f:\n", " dict_raman[key].append(specread(f))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the size of the matrix; create the dataframe and import it to a readable format" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(504, 1732)\n" ] } ], "source": [ "spectrum = np.array([sp.amplitudes for sp in list_raman])\n", "print(spectrum.shape)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "metadata = []" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "for f in filenames:\n", " name = os.path.basename(f)\n", " date, plate, cell, comp = name.split('_')\n", " date, medium = date[:8], date[8:]\n", " comp = comp.split('.')[0]\n", " \n", " metadata.append({'filename': name,\n", " 'date': date,\n", " 'medium': medium,\n", " 'plate': plate,\n", " 'cell': cell,\n", " 'comp': comp[0]})" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " cell comp date filename medium plate\n", "0 cell01 n 20170410 20170410LDL_L01_cell01_n1.spc LDL L01\n", "1 cell01 n 20170410 20170410LDL_L01_cell01_n2.spc LDL L01\n", "2 cell01 c 20170410 20170410LDL_L01_cell01_c1.spc LDL L01\n", "3 cell07 n 20170413 20170413EPA_L03_cell07_n2.spc EPA L03\n", "4 cell07 n 20170413 20170413EPA_L03_cell07_n3.spc EPA L03\n" ] } ], "source": [ "import pandas as pd\n", "metadata = pd.DataFrame(metadata)\n", "print(metadata.head())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "df_spectrum = pd.DataFrame(spectrum, columns=list_raman[0].wavelength)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "df_spectrum = pd.concat([metadata[['filename', 'medium', 'plate', 'comp']],\n", " df_spectrum], axis='columns')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from openpyxl import Workbook\n", "df_spectrum.to_excel(\"/home/jupyter/Notebooks/htdocs/data_RAMAN.xlsx\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "df = df_spectrum.set_index(['filename', 'medium', 'plate', 'comp'])\n", "print(df.head)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate the mean spectrum by group according to cell culture medium" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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XMKYLwimbdGMF4YmzmPi9L/S1CR1xUt/tQCCAEzZpSCgWNS3a6Tmtwp4lRiwG\nkVqMoEvFMIM+baWpHHAed3qTBH256h3RK5PC9pXF89VUESj8QW9O7lyKFHuHBP0e9GLTi5z/j/O5\ne/Xd3PH6HTyy4REAmpJNPLzhYdoybZz0l/6gfaXlFToHhKavUMVJWSne7PBOoP7q1F8Rt32E73mc\nOqt/dLUluXPQL92+FICZW71ftM7phwAQ/dAlBCstKiZl8B16zg6PsSIhqpIOfsPPTctu2uG+XMr7\nRTVicQhEMIOa2DBLNxk7s0PQz9kspZtylXfyBCyNMoceF766WgxbEcpptqeG/v9EjA5ZGTsKsnaW\nt7reYm7d3B2Or+laA8B9a+/rK4Ws7lhNpHBCa27tXN5of6Ov/Tee+QbVZpwbCl/3Bv3Gno1sT23n\nwzM+zIkTTmTb1dfQfd99fPi9NfzmKO8dQFeua6d+vdX1FgBHbY9h+LpwjzgDABUIM/WX34ZMOzTs\n2OdcPE4w18GCiWdx38YHueaoa4gH4qzrXoed9l7DV+EtgzeCJvEMrB1G0CesBJVpjWuAP66Y0aRZ\nlZXpleUoY+eocUAFhhH0Y8cBUJ2E9rTU6EtFRvSj4IYlN3DBgxewNbkV8GaP3Lr8Vla0rwDYod59\ny/Jb+PnLP2de/TzuPPNOTp10Kl87/Gt8/9jvk7ASbO/Z2te2N+jXdq0lYSU4a+pZZFaupPu++wCY\nurn/ROyuRtVru9ZyysRTOGKrj1CNxdiDTuy7Tx1xIerEr+30GKt+DAALAkeQdbI8uuFRAF7e/rK3\nKhbwV3lB74YCVKTZ7W6X76Qj001VCpyICdVhGro07Zmd/1iJ0kvkkvhtMIczop/s7Y5ak9R0ZOXf\nt1Qk6EfBytYVxNO6b2HSfWvv4+cv/5wH3npgh3YLz1/IIfWHoFB89uDPEjSD/OyUn3HRQRexYPoC\nwr5wX7iDF/QXzb2I8bHxXHjghRwx9ghW3XUvrlL4JzhM3pLnW0dcg0LtFLY5J8emnk3MqJyO2t6F\nXWkyeXwR+8lM9ubHV65oojHayPNbnwdgU2IT0bz336V3V0InEiaWgdbU0Desakt3UZECJxqA2grq\nuqEzIyO+ctSTSxK0QPmHVp8H8E2bB0BVErrzEvSlIkE/Ct77bBe3/dzhrXXelgKvtb7Wd9/smtkA\nTK+aztjoWP5w5h949mPPcsL4E2jp6d+61VAGd55xJ9ccclXfsavMs5htn8MDCx7iyiOuBCD3+KOE\nx+Spa0xgZvKcFz6GymDlDqWbtkwb67rW4WiHOblqlO2SrKvDMNSg30totrdtcfL6n3Bex1RebHoR\n27XZ1LOJhox3TiA0xtv+wIk+njacAAAgAElEQVTG8LnQMow50h3pLm9v+2gY3dBAwAK7Q07WlaNE\nPuWdjPUNvdLrm+JNGhjTo0jIOZiSkRr9KJiyzJs21vXaK3AyLGtdhqEMAkaAH9RdRMshESZUeguM\nlFJUhaq4ceFqfvHEGj582AQMBYvXd3DnxUdyXKVD74XXkq+uZeodH+Tej32eD378/Ww491wqgOr5\nKTKNdYBN9wuLaDQr+ko3T216ii8/9WVqQjXM2qwZ/+MfAWBNn13U9zJh9iGYx3Ww9V81HHvXG/zq\nwh5eb3udjT0bmZnyARpfnbcroVtTjUkTuc2bcbWLoYofN3Qmm5maBtVYgTF+Ii6vEt4uJ+vKUU8+\nQcACAsVdQnAgIxpF+RUNCU3KlnMwpSIj+lHQGfS2LUi8/hoXP3oxLekWrjriKp6Y8Sv0RV9n2r3L\nOaCy/xJsa1uS3PTEKt6/YTFbH3yEJ55/g/bmdk654Wk2vdW/J0jdG2sBaHjqQV67+fa+475DZ9N5\nwmUoU9P+g+/zve+uo6swo+HF5hcBr2Z/9Or+Puq5Rxf1vVRPPhhrUpj40Tb+pnYOaFY8u+VZNvVs\nYkyXiy/somJe6UaP80b2Y7bnhjzzpjOxnYoU+Ktq8U+eDkC0Y/j75og9J5lP4XeAQGDIj1VK4asI\nUZ90yThSuimV/WJEn8wneaXlFU6YcMIef61lLct4tfVVPn3gp1FKYbkWvowX9FNaNPc2v0RN2uSo\nx7eytnkVYWDdX+/hpeYsk154lPih83iq8T384dFfUpVJAuCiyEXjfPLkb7D8j49yJKB8LsG897wN\nLZvg0U34qxwmHtNO8ORv0lA9j57KG8l2eL98xjavTt470wegvts7eTrp5Da65h5V3Ddo+nls5vc4\nI3MNCVXJB5sb+dnyW4hmNNPW5QnVWBD1Ttj6D5iJNh5n6nZNe6Z9h+vPDibZ2UTAAaOuntDMuWSA\nym7bm8pnDj1QxJ6TyPbgc0ANI+gB/PU11HdtxtZyDqZU9vkRfXOqmS8++UUuf+Lyvlkue9IPFv2A\nG5bc0DevvS3dRl3hHelBbQH++aF/cvuqE7B+dTvhv90NQF17C7Pu/R3h7VuxH3mQE26/lqpMguqZ\nXtAbaMKpHv6w8Icc+ap38rNyys5b9k48to3gAVPg4I9RO/kgQuP6N0BTLR38+tVfs7hpMadMPIWa\nUA1TuwNEx2ZpG1PFuGkHF/09vv+DH+d3kY8Sqspz9GYfNaEaTlsTJpS1iR0C+EMAhCbOQdfbzN2o\nacvseg/73cm3eeWuwNixVE2dix3Q1Hdr2cGyDKXS3rs1IxQc1uP9EydS1a1wlNToS2WfDvo32t/g\n9HtO71sY1DtDZE+wXAtXu6zu9Oohn1v4OS745wVc+vAlVHt5TbwtR2OHJvdcfz+MQP/OjnUH9TD5\ntFZC1XnGH9vJ2Ot/zcSTOhgzr5vqmUkiee/k7IST2tHv8aalhWryVM9IMuHEdpwjz4XL/uUtQzd9\nJOeNpeFQ75fH7E7wq2W/AuCTcz7J0x99mppuCydikr7kWZRR/D91TTTAYWdfQmRMHuPNjfzz1D9z\nbvd7MEMu1rz+BV7Vkw4iWJ9nynZo72oa0s/T6PTexocnTSUUr8aOaep7oEum4JWdbMYLejMUGtbj\nAwfMIJRR+KzMqF41TRRvny7d9O5xHTJDjI2O5cXmF7n04Evf8THJfJKMnaE+Ul/062itOe3u05hV\nPYuqpGbWFs3i2Z105jqp69YYGsxJWZxNId4640wAAnGbfMJHcJZBZV0YU/VQceLJEK1nSsP/oU66\nCmafTew7jxJrWw0V46j82fnkOiB23meJTTiSqp7PEKq20GNnYtbNgA//qm80DRCZdy6V6evZ/kpl\n3yrTm4/+CTNf2EL++CmYGYd8XT1zJjcO7QcLnDBvLmunVsEqG+vBhQTWrCRUmafi7O/2tamqG0tr\nNIipIbF5Pczd/fO9XaDL63B8pjfLx46a1HVr2rPtTGf6kPsr9gzLtXCy3r5IRnho+9z0Cszw/mM0\ndMO2RCuTK8eNWv9EcfbpoO+dO/7whx/mtuW3cffqu9me2k7QDFIVquKhdQ+xrHUZV86/kqZUE7cu\nv5X71t5HzB9j4fkL0Wjigfigr5OyUnRkO/h307/5wd8dZm2FJdddQHelj5X//CMAlVMsHNMl3V1F\ntKaTMQf3kNgaou3ED1F9yc07PJ868zrwFd4Gj5vnfQDhrz9AeNXDcNI3QSki518BE4+C6afusl/j\nz7qa3OSD4L6rqEx79fgDFq6i6Zc34cw/ChMwpk8dzo8Wn2mw+sAPc9CiP7L9xz/GD9gHBgnVTen/\nPpSipXoatWwku23nLRjAWz7/yIZHOH3y6YR94b5joYS32Cs82TtJ7cTD1LekaR5iCUjsWR2ZDu8a\nBEAgPvjvyq74p3p/uBu6NGvaN0nQl8A+HfRVVoAf3h3mhX9dy6b3HUnOyXHaPacxpWIK9y24jx8s\n+gFJK8ldb961w+OSVpJj7jqG6mA1T3zkCfzmO08b68z1n0SaVVi4esqTrSQeXUhvBD8QOY4Lj1xI\nQHWxwp3Mv0/8IydknmLae7+48xP6dlPrnHik99HrlG+98w/AMAjOOg1f0OWonnrmHnclTd/4HRHA\nXLIY0NQcXdxsm12ZfdYXCL50B7nnCvvFHzhjpzad4w6mlo04W3d9BaH71nr/Dqs7VvetBdic2ExN\nQmMFwegtB1RWE8ml6WzbAgfs8qlECbRn2wnnvNuBeGxYzxGY5E0tbuiE1R3rGV9ZyZee/BI3nnwj\nB9cXf+5IDN+ghVul1ESl1FNKqRVKqTeUUl8pHK9RSi1USq0pfK4uHFdKqV8opdYqpV5TSh22pzo/\n7k0fM9YmmPHsg0z43WPMih4LeHup//jFH5O0kkypmAJATaiG60+8nmWfWsbE+ETAC/CBV2naltxG\nc6qZTz/8af644o90ZbvY2LOxb1fF2p7+XSgTjy7s/xmZmo0HnM75+e+ywPoRN077Haceewz153wH\nFa3dU9++JxDBiEFdW4Yj/+81IitXE4jboDS1ByapGbDtwVDNnFDPo4d8hsmntjH++A7SR31opzbm\n1EK9prl1p/sA1q54ge//3ubl5/9G2kqzuWczS5qXUJcAJzpgnFHvjfKSG9bs8nlEabRn2gnnvf/3\nZsXwRvRmPI4Km0zu0Dy7dSH3rL6H7ent/HHFH0ezq+IdFDOit4Gva61fVkrFgaVKqYXAfwJPaK2v\nVUpdDVwNfBM4E5hR+DgKuLnwedRZq14kAAQqLM5d9y/8D53Pn295ha88/RX+suov1HdpfvRMJZMv\n/CXk82T+vJSW0xq54chfEq/w86mHP8WXnvwSc2rm8LNTfsb7//Z+FAqN5pWWV7h1+a20Z9v59tHf\nBvp3gQxWWeS6/Ew4oR07axKssDn++FP4xNh6Zo2NYxaxAnU0OVVBAmtSdP3f/wEwZl430TF5spFq\n1NiRjZg+fuk1PH9LK/Rs4fjT/2On+2unzCYd0fjbk7t8vG/RMmZvhfMf7OKCqRf0XbnquoSGeP/V\nivSEA4CXyG7cMKL+itHVlmkjVCjdGBXDH7T46io4qK2NX3a9xBtdLwGwsmPlaHRRFGHQoNdaNwFN\nhdsJpdRKYDywADi50OxO4Gm8oF8A/F57m7AvUkpVKaUaC88zqpzt3nUoJ5+Voum5IGe/cA8vz3uQ\n06bO4Zj//ir+G/+O7+WlbH1haf+D7rwTVxmocz7A1edezDfW/D9Wdqzk/X97f+Ebdvn6vS6vTVF0\nxFuZ3w0/4AcAzN6i0D5N5akZKvId/Cq4AAX06AgfaqjjwMJFt/c2a2wNxhpvHn3jUZ10f/J64no7\nkQNO7ruoyHCFAyanf+Hnu71/zIRpbItpYt07X+nKdm1iG7yR/oxtsKljHZgKtKahC5wD+0+I+w44\nEADdPPR9c8Se055tJ1Io3RjVxa+TeLvg+Abql7fiw4+NxcT4RLYktmC7Nj5jn64g7xOG9BNWSk0B\nDgUWAw0DwrsZaCjcHg8MLNhuKRzbIeiVUpcClwJMKtTwhmrCUROIpzowvrGS6rrPkPzlSuJWhtmr\nX8a6ZAX+XJbYuCzZLh8omHRiB51vRejaFKX7H/9g3GOP8dTPfsbd+iVu2vQnzn7Rmwp51CrNUav6\nyzR1Cc3vjxnPSW+1EqzJ8uZHH2f9ypeZPOskvvbXV6mPBblm7PDe1o6G8HtPQL1xF5H6PO419zBh\n9vF77bUb66rZEFVU9bjknBxBs//8w+bEZiY3ez9TnwsnL9fM2eQyd95xRHLP0zH9oL62FTMPJm9q\ngrt5ZyBKoz3TTixrAi5G9ZhhP09w8mSSi1dyRvLjHBTqYtq1D3H9AoumZBMTKyaOXofFLhUd9Eqp\nGPA34Kta6x6l+ssTWmutlNr1tex2Q2v9W+C3APPnzx/SY3uNP/0CmDUZItXELv0j00Mfh9YVpN5M\n0LS4CsOvib63nlqnFeJjCJ7wTeoTrZj/+h2V3S1sfr6W1su+yMnAxCvPY9oTf9/l6yxYpAk0NxDp\n3ELq4CqOO2QOxx0yB4DjptWh8WaplMrUc75McuPf6JxwBg17MeQBQn6TXMTHmK0OrelWJsT7d8hc\n17qaiW2g6yxUm5/PPVxYU/CGt84gcvzpfW3Hjp/Cqrgm2pXbq/0X76w51UpjwgClMeuHP1smOGsu\n6EepXbGGGW+tJN6T5YyXFRsTGyXo94Kigl4p5ccL+T9prXvTcHtvSUYp1Qj0vufeCgz8l5tQODb6\nGub2XzgjEMH/mfsBqFzzOKHbP00uOoaKLz+Aio7pK2GYQP17r0G3v4X/Vx8n+FwLic1hpv7sgZ2e\nPlybZ/yxHax9sIEzN3jln7ZDjtyhzZiK4S0iGU0qNob41SuJ7+a6sXtaLhYjnO+mtWX9DkG/4bXn\nGeeAO96H2sWsyTnv7V98VRkNkY0pqrtdMnambyqmKK1tiRbmJsEXclGRmmE/T+S9Z8P/3MgHXnoW\n3eMNEqdv02zs2cjx40c+ONnQvYGFGxcS8oU454BzqApVjfg59yfFzLpRwG3ASq31jQPuegC4sHD7\nQuD+Acc/XZh9czTQvSfq8+9EzTiN0H+9TuU3X0TFx+6yTq1qpzH+m0/h//SpjD+uA8Puvyi3GXIY\nO78L69gAx5q/pO7oJLVzEkw8r5tZn75qp+cqG2rvngTu5a/0qnbr31zcd6w7183rL3h/PBc3vKfv\neNURaR46+Ghe+NQXCfl3HGdYUT/1Pd62FqI8tGXaqE25mCEXosUvMnw7/9hxNH7mLChU5oJjbBo7\nYd3WVSPu483Lbuac+87hF6/8gutfup4T/nICn3/882zq2TTi595fFDOiPw74FLBcKdV7hepvAdcC\nf1VKXQJsBD5auO8h4CxgLZAGLhrVHheriNGHGYzS8KnbyMz9C43WV0i3BPFFHKoOSPPT4Ed4reZ0\nHv7cOdzxQCUHhVo440MXEStRmJYz35gDgNUsXf4IHzzzq/gMH3e9eRdnLvJO0HbO+xDVGxbRsyHC\nTfUL+Oz/dy0Ta3YesTuVMapWddHcuZmplcNb6CVGj+M6tOeaaex08NcFRnxiv+qKG4jOaSS1bDGt\nxji4/TE6X18Gpw3/Oe9ZfQ83vXoTE+MTue3UW2jraeLuTQ/wj3X/YMH9C/j64V/nkwd+ckT93h8U\nM+vmeWB36bbTks3CbJsvjLBfe1X4sI+RuLiTLa8vZkNnjnUN7+ejH/owX64MEw6YXPmJD5S6i2VN\nT54NPEL8zW38YcUf+OSBn+T5JX/npDYI1ljMO+IErl/zcT7ynmeZf/ZnmVQb2eXzqOoxQBft61fA\nlOHP/xejY1tqG75cnsoel9DBo1AKUQr/mVdSdSbo5++n+fbHYNOGYc+8WdayjB8u+iHn5d/DBTdv\npvu/T8VnKz49uYGPnnI6n6l6iOteuo5XWl7hh8f/kJCv9GXWUpF5TQVjTr6MMSdfxuGu3uvz4Pd1\nwWkH44/anLXUz1ef+Ak3Lr2Ryx71ronYdHg9h09r4Pczzufjq0/ixffsvLq273nGeu8MOte8Dqfs\npc6/i/111V+54407uOyQy/jgtA/udP/67vVMLKyDC00Z3W0Lqg47ma0Bl+mbbV5sfpFjxx07pMev\n6VzD5Y9fztFbQnzs968AEGnMEaq0SLfmMe/Yzu3AGx+Yzff0o2xJbuGGE29415743ad3r9wTJOSH\nbuqMAwme6ODXLhc9CQevczn5dU3l9DR3Tv0SVZEAv/zEoSz5r9MYE9/9qCo03avl59Zv2Es9f/dK\nW2mue/E6Nic28z+L/ofW9M4rm5/f+jyTW73fh+DBR4zq66tIJWajySEbNL9Y+vOidrW0XIvHNz7O\n5Y9fznkPnEdVc5Iv3tWN6XNZee4c1v3yOa674AEevPhbbD3Jmwo6959v8oc/xTHfXM/Z956903Yo\n7xYS9GLEJtdX8r/jvk7NzCTzVzn8119cTKV5duY8Pn++t5tnPOSnLvbO+5nH5hxG3q8xt8qiqT3t\n39v+Td7N851jvoOrXb7/7++jB8zacrXLwo2PM3NrCMPn4j90BIX03bDmzKEmAe6yN7j4kYu5dfmt\nu7ymhOM6XPvitRz2h8O44ukreG7Ls5z/nMv1t9v4lc3Kj5zAuT/6GyfPncBPLjiML1xyCdXfupdf\nXfgl3Hkmoa2dfPvWBB/6t+ZHi37IB+/7IGkrPerfTzmT0o0YFd+47DPcm1jKufoxUs1BOMIgf8Y1\nHDqpuujnqJ80i1VVmmibXFJwT3t6y9PEfTHmbqjlKzM/x/Urf8H8P87n8nmX859z/5OnNz9NW7qF\nwzYogrUOatwho96H4PmfQT31Fa6+38e14ZX8vHUZP3/ZW4V98UEXM71qOmu61nD767cTymnOfE2z\nYFstNSu8dx++mM2WT57Pgq/8CPW2SRKHT67h8Gsu59lVH6bpfy9l/uI3uOBpOPU1g+vPXcdZubP4\n0fE/4tjxQysZ7auULtHc64Hmz5+vlyxZUupuiBFq6ujmuV9/GTPTxiNjP8+vP3/2kEphWmue+MBB\nBFtcZj79NA3RhsEfJIbMcR3m/WEen1g6iXMfW0d7VQPrb/wYP33rlh1KKEdtqufrf2qi4QON1Nzw\n5Oh3RGuWXvU+4gs34mRNrGiAVePh7iMc3pwIWimCec0xG4N86kmbeEf/NhuBCpvm713LcWd+eNCX\n6UzlueLHN3HtqzfSuSaKVpqHjg/yf0c5fGDOeXzv2O/t9IdiX6GUWqq1nj9oOwl6MZq01qTyDiGf\nMazVwo9+5gQmPd/Gpluu4v0nlGZm7v7uN6/+htsW/y+/+V8IWTbaUPRUj6Hq/I/wyuFw7YZbiBhh\n/vu3IaZ1bWP6b6/GOPriPdKXdCrBthveR+XyTaSaQmTavevS2n7wDSjb+8IO+iA/Gw+ejznhEA5f\n8AUqI0O7tOHKbV28dOf1HL/wr+S2+UmE4YbzTHyHHcx/H/PfzK6ZPZrf2l4hQS/2SU//9AoafvMI\nT394Kp//4UOl7s5+Q2vNc1uf476197Fw40LOewEueMZm0gcsrEyOln9HcNLeH+aML0DY9kbPdcdA\n/W9f2eHKZnvCli1beGzRS9Su/Dvz1r6AscXByZoYYZfOSdW0nf1FTlpwIT6fOaLX0Vrz95fWM+Wn\nn6DizQ7sjMmmerjl/SbvOfUjfPnQL1MdKr7cWGoS9GKf9MKLLxL48qfYFja4/tIavnPsdzl98umD\nP1DskuVY3Lr8Vm57/TZyjreP0H9E3sfZ33uEqoYUE2/9PUTr2fTXbxN+ahG5Hh+Jzd5itvp5Ceq+\nfxvMfP9e7XMik2fFay+hlY9DDjmcUMAc9dKK7bjc9c9nmH37lcRWpUArXj5A8af3Gvx/H/8Nx40/\nblRfb0+RoBf7pKzlsPqiQ/EvsfjbeWN46lAfD5/3MAEzUOqu7TO6sl281vYa96y+h2e2PIOrXY4a\neyRzNoQ45J9bGb9mFWbApeaig6i74m/9D0y1QfcW9Mt/wu3ainnSV3e84tl+qDttce8dt3H8n36C\n1e7NTXn2IEXisvP53InfKOpSo6UkQS/2WU8/8EemXP8dMm0BHjtUEbzyci47fBeXZBSANxVya2Ir\nz2x5hkc2PMKrra8SzWgOag5w4VMm0R4bXE04178z6OT3thH58TKoHF/CnpePjlSee3/yI47++18w\nst6x350T5H2fv5bTDzijtJ17BxL0Yp/lupold1zDxPvvomtVhMUzFRu+fA4XHHYRM6tnYqh9d/nH\n3U//kmikirOOHP7+K1prtiS3sKxlGU9sfJyVy55kfLvL2E6Y2WQwtdXHmLbsDo+JjcsSacgRnx3C\nf/zFqOO+BKHKkX47+52XN3bQ9IOvMPX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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.plot(df.groupby('medium').mean().T)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Principle Component Analysis, illustration of loading plots, of screeplot and score plots" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n", " svd_solver='auto', tol=0.0, whiten=False)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca = PCA()\n", "pca.fit(spectrum)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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vUUt/HNpjRnvuKL1XbEEY5OzOhOLLMcLQnGBhsCf3ub2w6HMm9HAw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\neaGQ31tfCTwAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wavelength=list_raman[0].wavelength\n", "plt.plot(wavelength,pca.components_[:2].T)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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PAgFj9bI63mk9y3tt53zHEZEsonL37Kt3VVMUDuq0SBFJKZW7Z1MK8vjynVVs2v0xZ7oH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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(pca.explained_variance_ratio_[0:5])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "from sklearn.pipeline import make_pipeline" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "pca_norm = make_pipeline(StandardScaler(), PCA(n_components=6, whiten=True))\n", "pca = PCA(n_components=6, whiten=True)\n", "X_r = pca.fit_transform(spectrum)\n", "X_r = pca_norm.fit_transform(spectrum)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(504, 6)\n" ] } ], "source": [ "print(X_r.shape)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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GO8EYExGRW7AC3A08bIzZKCJ3AbXGmJXAQ8BjIlIHtGC9MGCMaRWRH2G9cBhglTHmxWH6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\nMURDXymlxhANfaWUGkP+H5Iny6iBOPezAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wavelength,pca.components_[0].T)\n", "plt.axvspan(1000, 1200, alpha=0.2, color='grey')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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eJlXplp5+5W5KmlRVQkckTnvE+sLh7BOuho9G8xBzLwvhxTo/fWeQ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\n0WBQSimV5v8DCMvF0yOm45wAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wavelength,pca.components_[1].T)\n", "plt.axvspan(1000, 1200, alpha=0.2, color='grey')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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3cyKw1sQa378rK77dz/UvrPLNyn5lxQ5uePFLAIbkZwWstvvkTwr48webcTqE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\nOGACcLdfArkD2GuMGQ6MAv4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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wavelength,pca.components_[2].T)\n", "plt.axvspan(1000, 1200, alpha=0.2, color='grey')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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43W3+C6hfz+H0mOz0O+dMjtm+Ym97nMlDrUSZ3QVsWB5LGMJBSWVm+gmG1mi8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\nKIqieFDBoCiKonhQwaAoiqJ4+P+WTHjM6UpnlgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wavelength,pca.components_[3].T)\n", "plt.axvspan(1000, 1200, alpha=0.2, color='grey')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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o53+Xbxp0/qNvm8XZh0/i0tue42t/Ws/X/rSej75tFtfZoY700lZgv+YaPPJy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\nSgoqDIqiKEoKKgyKoihKCv8/MBKJW+sm9AgAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wavelength,pca.components_[4].T)\n", "plt.axvspan(1000, 1200, alpha=0.2, color='grey')\n", "plt.axvspan(1350, 1400, alpha=0.2, color='grey')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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sFkKMBHAvgF/rnx0N4CIAhwKYAuDP+vlcQ1WpHzefPRolPnOzrT4GeTO7whFD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\nng9ioAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wavelength,pca.components_[5].T)\n", "plt.axvspan(1000, 1200, alpha=0.2, color='grey')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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51asfICOxKzplrYgWe2CgR2+EKprxC2M4z9eZzWTMno2nrg59djaunbt8BUWd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\nPZZVqgpFTwxoYe8613MpjZ1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CHCsSEm10XW/1AMpSiYxEe3/jRVTCLoT4Z+BHwIVAkZRy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\nAPvju6qekVI+IqUskFKOxfc3/d5AEHUhRKYQwuz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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for medium in metadata['medium'].unique() :\n", " mask = metadata['medium'] == medium\n", " plt.scatter(X_r[mask, 0], X_r[mask, 1],\n", " label=medium, s=5, alpha=0.7)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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5JO/myii1HLuI7AWwFwC2bi1WbxY/XotxQTNDrwXBse/MGp3F24EqbgfFKJor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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X_r[:, 0], X_r[:, 1])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from specio.core.util import Spectrum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a matrix with the spectra of a specific cell culture medium (one group) and plot the spectra, than calculate the second derivative and plot it" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spectrum: \n", "wavelength:\n", " [ 400.20645142 402.73706055 405.26681519 ... 3795.69213867 3797.21679688\n", " 3798.74194336] \n", "amplitudes: \n", " [[210.1111145 216.55555725 222. ... 107.77482605 103.10241699\n", " 103.65222931]\n", " [175.00001526 179.1111145 178.66667175 ... 102.47871399 105.25252533\n", " 105.91522217]\n", " [175.00001526 179.1111145 178.66667175 ... 102.47871399 105.25252533\n", " 105.91522217]\n", " ...\n", " [187.66667175 204.777771 209.8888855 ... 95.94671631 97.15467072\n", " 92.02928925]\n", " [163.99998474 180.99998474 177.44444275 ... 100.88535309 102.9912796\n", " 98.875 ]\n", " [189.1111145 197.55557251 198.8888855 ... 99.46099854 103.56742096\n", " 104.78495026]] \n", "metadata: \n", " {} \n", "\n" ] } ], "source": [ "wavelength = dict_raman[\"EDL\"][0].wavelength\n", "spectrum = np.array([sp.amplitudes for sp in dict_raman[\"EDL\"]])\n", "inst_spect = Spectrum(amplitudes=spectrum, wavelength=wavelength, meta=None)\n", "print(inst_spect)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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FPJTOkbFqowHAszxaah5uSrF/+/AzvYWYRBIAnoOiOpb/WmuerLmcvmNsDf8b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\n5MlzmvL/AQlz2VseMU40AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(inst_spect.wavelength, inst_spect.amplitudes.T)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "from scipy.misc import derivative" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "f=lambda x : np.exp(-x)*np.sin(x)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "second=derivative(f,spectrum[0,:],n=2)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(wavelength,second)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }