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Calibration Check
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{ | |
"metadata": { | |
"name": "" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Ignore this stuff. Just importing the numerical array and plotting packages. Set the plots to be in the page rather than pop-out interactive windows." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"%matplotlib inline" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 1 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"I rewrote a lot of the extraction and plotting code from the previous worksheets into a separate file. This way, all that other stuff doesn't clutter things up." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import gcms" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 2 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"First, I'll load in the dataset that Ashley gave me from one of her runs. This first cell is the TIC csv file. The second cell loads the full data set of all the MS scans for the chromatogram. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"times, tic = gcms.csv_extract('AB1711.D/AB1711.CSV')\n", | |
"print times.size" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"9221\n" | |
] | |
} | |
], | |
"prompt_number": 3 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"masses, fulldata = gcms.full_extract('AB1711.D/Testing')\n", | |
"print masses.shape, fulldata.shape" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"(131L,) (9221L, 131L)\n" | |
] | |
} | |
], | |
"prompt_number": 4 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Let's just check that the exported TIC is equivalent to the sum of the mass spec data from the full data set that was loaded above. These look okay so far." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"plt.plot(times, tic)\n", | |
"plt.plot(times, fulldata.sum(axis=1))" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 5, | |
"text": [ | |
"[<matplotlib.lines.Line2D at 0x126bd668>]" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
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bU9ra2jBNk2Aw6JQtKiri0KFDABw+fJiioiJn3/Tp04lEIliWldKy4m36mjwRcZOk7tF+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\nEUkhBa2IiEgKKWhFRERSSEErIiKSQv8fcDrwn4pBfigAAAAASUVORK5CYII=\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x126bd5f8>" | |
] | |
} | |
], | |
"prompt_number": 5 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Here I'll set up an empty dictionary that will contain the calibration information." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cal_data = {}" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 6 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The first calibration sample that we'll load is benzene. This looks really good." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"benzene_files = ['rebenzene10.CSV', 'rebenzene100.CSV', 'rebenzene250.CSV', 'rebenzene500.CSV', 'rebenzene750.CSV']\n", | |
"benzene_concs = np.array([10, 100, 250, 500, 750], dtype=float)\n", | |
"benzene_start, benzene_end = 3.0, 3.3\n", | |
"\n", | |
"cal_data['benzene'] = gcms.Calibration(benzene_files, benzene_start, benzene_end, benzene_concs)\n", | |
"cal_data['benzene'].cal_plot()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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1agRA586dmTNnDgApKSksXboUm81G7969mTp1Kl5eXgCUlZUxa9Ystm/fjp+fH7GxsfTo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\nB6ayb6JOW7fKAAAAAElFTkSuQmCC\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x126d92e8>" | |
] | |
} | |
], | |
"prompt_number": 7 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The second calibration is cyclohexanone. The calibration has one spurious point, but otherwise looks pretty good." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cyclone_files = ['reanone10.CSV', 'reanone100.CSV', 'reanone250.CSV', 'reanone500.CSV', 'reanone750.CSV']\n", | |
"cyclone_concs = np.array([10, 100, 250, 500, 750], dtype=float)\n", | |
"cyclone_start, cyclone_end = 11.03, 11.5\n", | |
"\n", | |
"cal_data['cyclohexanone'] = gcms.Calibration(cyclone_files, cyclone_start, cyclone_end, cyclone_concs)\n", | |
"cal_data['cyclohexanone'].cal_plot()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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dl+4D8PLyIi8vz/Gcqlrr7e3NiRMnrtjTlClTLnp+SEgIISEhFfhuiLOLjo4l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\nKXhFRESq0P8P0h/moKLfARIAAAAASUVORK5CYII=\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x12747ac8>" | |
] | |
} | |
], | |
"prompt_number": 8 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now we'll analyze the catalytic run data set. The benzene value looks good; however, the cylcohexanone is giving a negative concentration. This is what Ashley had seen previously." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"ab1711 = gcms.DataSet(times, tic, cal_data)\n", | |
"for result in ab1711.results:\n", | |
" print result, ab1711.results[result]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"benzene {'conc': 147.13841173577165, 'integration': 1446498.4527027027}\n", | |
"cyclohexanone {'conc': -37.79690594360283, 'integration': 116844.05240174598}\n" | |
] | |
} | |
], | |
"prompt_number": 9 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"To figure out what's happening, let's take a look at the cylcohexanone calibration data. Some of the chromatograms have a weird background (left); however, the background-subtracted regions for integration look fine (right)." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cyclo = cal_data['cyclohexanone']\n", | |
"\n", | |
"plt.figure(figsize=(12,5))\n", | |
"plt.subplot(121)\n", | |
"plt.plot(cyclo.time, cyclo.data.T)\n", | |
"plt.xlim(0, 12)\n", | |
"\n", | |
"plt.subplot(122)\n", | |
"plt.plot(cyclo.selected_time, cyclo.bkg_data.T)\n", | |
"plt.ylim(0, 500000)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 10, | |
"text": [ | |
"(0, 500000)" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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89thjeDwebr75Zu69917sdjsAPT09NDc3s2vXLioqKrj77ruZN29e4nGzNVZS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\nyU9+ws6dO+uPr1y5knnz5nHRRRfxyCOPEAweCSusqKjg17/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Ue717qfCVYwuBNcqDbtWI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UN6FUAxFGLnJoiRYMoX39/vuI7r/RRs3CIUUzb9uV60DgPbhRazgC9pko5K1JeHqGzwqrJ96u7Z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\nbGzE7/dz3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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x126bd470>" | |
] | |
} | |
], | |
"prompt_number": 10 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Here's where the strange behavior starts. Plotted below is the region from the catalytic run chromatogram that corresponds to the integration range from the standards. Clearly, there is some shifting of the data, and this splitting of the peaks is causing problems as well. Because we are doing a linear background subtraction using the first and last data point from this region, we are going to get really strange results. Both first and last point are not zero as they were in the calibration standards." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"plt.plot(ab1711.time, ab1711.data)\n", | |
"plt.xlim(cyclone_start, cyclone_end)\n", | |
"plt.ylim(0, 100000)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 11, | |
"text": [ | |
"(0, 100000)" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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Us2amea9XL/Mhs2GD+faekGBCzJeGcBw8aJoYZ840Z1AKVns1a2bO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\nEYspXEVERCymcBUREbHY/wcu+qJhAE2eJQAAAABJRU5ErkJggg==\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x126eeda0>" | |
] | |
} | |
], | |
"prompt_number": 11 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 12 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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