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"cell_type": "markdown",
"id": "6386344d-b7ac-440d-9926-f03af4ff9d6f",
"metadata": {},
"source": [
"# Training the Virtual Spectrometer with Viking and PES data"
]
},
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"cell_type": "markdown",
"id": "1711c3b9-5065-4a44-8b1b-a3e861b92bc5",
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"The objective here is to use the Viking detector to train the Virtual Spectrometer. This means that we will fit (\"train\") a model, which maps the PES measurements with the Viking measurements and use their correlation to interpolate in cases where the Viking is not available.\n",
"\n",
"The following conditions must be satisfied for this to be possible:\n",
"* The PES settings are the same in the \"training\" run and interesting run.\n",
"* The photon energies of the beam in \"training\" and in the interesting run are similar.\n",
"* The beam intensities are similar.\n",
"* The sample between PES and Viking is transparent.\n",
"* 1 pulse trains in \"training\".\n",
"\n",
"The following software implements:\n",
"1. retrieve data and calibrate Viking using the SCS toolbox;\n",
"2. the Virtual Spectrometer training excluding the last 10 trains avalable so that we can use them for validation;\n",
"3. the Virtual Spectrometer resolution function plotting;\n",
"4. comparison of the Virtual spectrometer results in a selected set in which the Viking data was available.\n",
"\n",
"Finally, the model is applied in data without the grating. This last part may be applied independently from the rest if the modal has been written in a `joblib` file."
]
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{
"cell_type": "code",
"id": "4a627555-522a-4c9d-b6b2-6ff77148eaab",
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"source": [
"import sys\n",
"# replace this \n",
"sys.path.append('/home/danilo/scratch/karabo/devices/pes_to_spec')"
]
},
{
"cell_type": "code",
"id": "78bbc433-ac5e-44c3-8740-3e93800c4532",
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"Cupy is not installed in this environment, no access to the GPU\n"
]
}
],
"source": [
"import numpy as np\n",
"import dask.array as da\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from pes_to_spec.model import Model\n",
"\n",
"import toolbox_scs as tb\n",
"from euxfel_bunch_pattern import indices_at_sase\n",
"\n",
"from scipy.signal import fftconvolve"
]
},
{
"cell_type": "markdown",
"id": "c7609899-5bc0-4211-ae97-010b3edcf676",
"metadata": {},
"source": [
"## Get data and calibrate Viking"
]
},
{
"cell_type": "code",
"id": "95da5231-e454-4f7f-a1ce-eef7e52fe457",
"metadata": {},
"outputs": [],
"source": [
"# pes channel names to be used for reference later\n",
"pes_map = dict(channel_1_A=\"PES_S_raw\",\n",
" channel_1_B=\"PES_SSW_raw\",\n",
" channel_1_C=\"PES_SW_raw\",\n",
" channel_1_D=\"PES_WSW_raw\",\n",
" channel_2_A=\"PES_W_raw\",\n",
" channel_2_B=\"PES_WNW_raw\",\n",
" channel_2_C=\"PES_NW_raw\",\n",
" channel_2_D=\"PES_NNW_raw\",\n",
" channel_3_A=\"PES_E_raw\",\n",
" channel_3_B=\"PES_ESE_raw\",\n",
" channel_3_C=\"PES_SE_raw\",\n",
" channel_3_D=\"PES_SSE_raw\",\n",
" channel_4_A=\"PES_N_raw\",\n",
" channel_4_B=\"PES_NNE_raw\",\n",
" channel_4_C=\"PES_NE_raw\",\n",
" channel_4_D=\"PES_ENE_raw\",\n",
" )"
]
},
{
"cell_type": "code",
"id": "48bb4c8c-04ad-44d5-b123-643ce3253ceb",
"metadata": {},
"outputs": [],
"source": [
"proposal = 2953\n",
"runTrain = 322 # run containing the data without sample\n",
"darkNB = 375 # dark run"
]
},
{
"cell_type": "code",
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"id": "0a467b2f-5f99-4ed8-bb1d-cb429454d3ce",
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"newton: only 50.0% of trains (629 out of 1259) contain data.\n"
]
}
],
"source": [
"v = tb.Viking(proposal)\n",
"fields = ['XTD10_SA3',\n",
" *list(pes_map.values()) # add PES\n",
" ]\n",
"v.FIELDS += fields\n",
"v.X_RANGE = slice(0, 1500) # define the dispersive axis range of interest (in pixels)\n",
"v.Y_RANGE = slice(29, 82) # define the non-dispersive axis range of interest (in pixels)\n",
"v.ENERGY_CALIB = [1.47802667e-06, 2.30600328e-02, 5.15884589e+02] # energy calibration, see further below\n",
"v.BL_POLY_DEG = 1 # define the polynomial degree for baseline subtraction\n",
"v.BL_SIGNAL_RANGE = [500, 545] # define the range containing the signal, to be excluded for baseline subtraction\n",
"\n",
"v.load_dark(darkNB) # load a dark image (averaged over the dark run number)"
]
},
{
"cell_type": "code",
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"id": "4f6124d9-8c1b-44f8-a078-07475a9674fc",
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"newton: only 50.0% of trains (661 out of 1323) contain data.\n"
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
"Dimensions: (trainId: 660, newt_y: 53, newt_x: 1500,\n",
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" * newt_x (newt_x) float64 515.9 515.9 515.9 ... 553.7 553.7 553.8\n",
"Dimensions without coordinates: newt_y, PESsampleId, pulse_slot\n",
"Data variables: (12/21)\n",
" newton (trainId, newt_y, newt_x) float64 943.0 800.0 ... 758.0\n",
" PES_S_raw (trainId, PESsampleId) int16 -2 1 1 2 -1 ... 2 -1 3 -3 1\n",
" PES_SSW_raw (trainId, PESsampleId) int16 -3 0 -3 -3 ... -3 -4 -4 -3\n",
" PES_SW_raw (trainId, PESsampleId) int16 -5 -8 -7 -4 ... -9 -7 -6 -9\n",
" PES_WSW_raw (trainId, PESsampleId) int16 -5 -6 -5 -5 ... 0 -3 -2 -7\n",
" PES_W_raw (trainId, PESsampleId) int16 3 1 3 1 3 1 ... 4 2 3 0 3 1\n",
" ... ...\n",
" PES_NE_raw (trainId, PESsampleId) int16 -4 -5 -1 -5 ... -2 -2 -2 -1\n",
" PES_ENE_raw (trainId, PESsampleId) int16 -5 -2 -5 -2 ... -7 0 -4 -1\n",
" bunchPatternTable (trainId, pulse_slot) uint32 2146089 2048 ... 16777216\n",
" XTD10_SA3 (trainId, sa3_pId) float32 1.674e+03 ... 1.465e+03\n",
" spectrum (trainId, newt_x) float64 941.8 960.7 ... 1.319e+03\n",
" spectrum_nobl (trainId, newt_x) float64 -25.84 -7.057 ... -41.9 -25.1\n",
"Attributes:\n",
" runFolder: /gpfs/exfel/exp/SCS/202202/p002953/raw/r0322\n",
" vbin:: 4\n",
" hbin: 1\n",
" startX: 1\n",
" endX: 2048\n",
" startY: 1\n",
" endY: 512\n",
" temperature: -50.04199981689453\n",
" high_capacity: 0\n",
" exposure_s: 0.0004\n",
" gain: 2\n",
" photoelectrons_per_count: 2.05</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5c25c23a-638a-4d61-8679-07e9bad05d01' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5c25c23a-638a-4d61-8679-07e9bad05d01' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>trainId</span>: 660</li><li><span>newt_y</span>: 53</li><li><span class='xr-has-index'>newt_x</span>: 1500</li><li><span>PESsampleId</span>: 700000</li><li><span>pulse_slot</span>: 2700</li><li><span class='xr-has-index'>sa3_pId</span>: 43</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c7bb192-4de6-4d39-9e05-db9bdf19c8cb' class='xr-section-summary-in' type='checkbox' checked><label for='section-8c7bb192-4de6-4d39-9e05-db9bdf19c8cb' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trainId</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>1473952798 ... 1473954118</div><input id='attrs-44d63e58-96be-47df-bd46-729870bef781' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44d63e58-96be-47df-bd46-729870bef781' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-956868a6-7982-4dd4-9f15-62de50bce54f' class='xr-var-data-in' type='checkbox'><label for='data-956868a6-7982-4dd4-9f15-62de50bce54f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1473952798, 1473952800, 1473952802, ..., 1473954114, 1473954116,\n",
" 1473954118], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sa3_pId</span></div><div class='xr-var-dims'>(sa3_pId)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1056 1088 1120 ... 2336 2368 2400</div><input id='attrs-2013517d-11c6-4a0f-bacf-fc75be67340f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2013517d-11c6-4a0f-bacf-fc75be67340f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5982e0ca-a510-4eab-8cf1-24f8ed3f5bf7' class='xr-var-data-in' type='checkbox'><label for='data-5982e0ca-a510-4eab-8cf1-24f8ed3f5bf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408,\n",
" 1440, 1472, 1504, 1536, 1568, 1600, 1632, 1664, 1696, 1728, 1760, 1792,\n",
" 1824, 1856, 1888, 1920, 1952, 1984, 2016, 2048, 2080, 2112, 2144, 2176,\n",
" 2208, 2240, 2272, 2304, 2336, 2368, 2400])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>newt_x</span></div><div class='xr-var-dims'>(newt_x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>515.9 515.9 515.9 ... 553.7 553.8</div><input id='attrs-a1c4d87f-6ac1-4f0d-b02c-daecb9cebd58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1c4d87f-6ac1-4f0d-b02c-daecb9cebd58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68dda5fd-bbaf-49b3-a914-8709bc1c122f' class='xr-var-data-in' type='checkbox'><label for='data-68dda5fd-bbaf-49b3-a914-8709bc1c122f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([515.884589, 515.907651, 515.930715, ..., 553.717729, 553.745216,\n",
" 553.772706])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5fbdd184-b4c6-4a11-84e9-1cde45f68601' class='xr-section-summary-in' type='checkbox' ><label for='section-5fbdd184-b4c6-4a11-84e9-1cde45f68601' class='xr-section-summary' >Data variables: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>newton</span></div><div class='xr-var-dims'>(trainId, newt_y, newt_x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>943.0 800.0 697.0 ... 805.0 758.0</div><input id='attrs-38bd3d1a-cab9-4b8e-94b4-b00810c2b3fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38bd3d1a-cab9-4b8e-94b4-b00810c2b3fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f7281ec-9692-41e9-90e8-0f1b568813b1' class='xr-var-data-in' type='checkbox'><label for='data-7f7281ec-9692-41e9-90e8-0f1b568813b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 943., 800., 697., ..., 985., 1057., 1038.],\n",
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" [ 842., 921., 957., ..., 1037., 1041., 978.],\n",
" [ 744., 587., 558., ..., 1094., 925., 1030.],\n",
" ...,\n",
" [ 600., 688., 836., ..., 970., 1061., 1204.],\n",
" [ 681., 625., 675., ..., 921., 938., 887.],\n",
" [ 695., 593., 822., ..., 666., 582., 829.]],\n",
"\n",
" [[ 918., 949., 901., ..., 892., 976., 905.],\n",
" [ 857., 912., 1083., ..., 731., 757., 758.],\n",
" [ 630., 575., 599., ..., 1058., 967., 914.],\n",
" ...,\n",
" [ 741., 776., 874., ..., 784., 961., 1391.],\n",
" [ 684., 971., 878., ..., 954., 1218., 1041.],\n",
" [ 831., 647., 744., ..., 643., 690., 733.]],\n",
"\n",
" [[ 634., 709., 727., ..., 985., 963., 836.],\n",
" [ 553., 612., 787., ..., 1169., 788., 903.],\n",
" [ 668., 618., 621., ..., 785., 863., 835.],\n",
" ...,\n",
"...\n",
" ...,\n",
" [ 920., 815., 759., ..., 844., 1050., 839.],\n",
" [1080., 956., 661., ..., 968., 1001., 915.],\n",
" [ 811., 918., 652., ..., 873., 823., 1034.]],\n",
"\n",
" [[ 733., 606., 582., ..., 880., 1039., 1139.],\n",
" [ 784., 806., 787., ..., 1075., 1125., 827.],\n",
" [ 889., 848., 957., ..., 962., 1071., 811.],\n",
" ...,\n",
" [ 860., 649., 578., ..., 962., 1151., 985.],\n",
" [ 845., 663., 688., ..., 836., 978., 1340.],\n",
" [ 732., 784., 586., ..., 734., 872., 829.]],\n",
"\n",
" [[ 697., 934., 742., ..., 873., 753., 931.],\n",
" [ 694., 730., 774., ..., 802., 1020., 1206.],\n",
" [ 697., 956., 694., ..., 700., 785., 899.],\n",
" ...,\n",
" [ 799., 717., 918., ..., 898., 951., 1050.],\n",
" [ 870., 949., 918., ..., 911., 1283., 1080.],\n",
" [ 894., 627., 652., ..., 1032., 805., 758.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_S_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-2 1 1 2 -1 1 0 ... -2 2 -1 3 -3 1</div><input id='attrs-c923b58a-8a04-43df-af4d-df322852e792' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c923b58a-8a04-43df-af4d-df322852e792' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-592aae53-4746-4663-b2c9-6d60457d6efd' class='xr-var-data-in' type='checkbox'><label for='data-592aae53-4746-4663-b2c9-6d60457d6efd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-2, 1, 1, ..., 4, -3, -2],\n",
" [ 1, 0, -1, ..., 3, -2, 0],\n",
" [-1, 6, 0, ..., 1, -4, 1],\n",
" ...,\n",
" [-2, 1, -1, ..., -1, 3, 0],\n",
" [-1, 4, 0, ..., 0, 2, 1],\n",
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" [-2, 1, 0, ..., 3, -3, 1]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_SSW_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-3 0 -3 -3 -1 0 ... 1 -3 -4 -4 -3</div><input id='attrs-4b9318e0-75df-431d-ba44-63b082d17d05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b9318e0-75df-431d-ba44-63b082d17d05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb55e6a3-96fc-475e-955a-6326b4a9f825' class='xr-var-data-in' type='checkbox'><label for='data-bb55e6a3-96fc-475e-955a-6326b4a9f825' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3, 0, -3, ..., -3, -1, -2],\n",
" [-1, 1, -1, ..., 1, 1, 1],\n",
" [-3, -2, 0, ..., -3, -1, 0],\n",
" ...,\n",
" [-3, -1, -1, ..., -7, 2, -2],\n",
" [ 1, -3, -3, ..., -2, -4, -1],\n",
" [-4, -2, -3, ..., -4, -4, -3]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_SW_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-5 -8 -7 -4 -7 ... -9 -9 -7 -6 -9</div><input id='attrs-e81e9f33-58cc-4738-b5e0-e5944ed30608' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e81e9f33-58cc-4738-b5e0-e5944ed30608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9925ddb-fd09-4a0e-852e-d448820fcaad' class='xr-var-data-in' type='checkbox'><label for='data-c9925ddb-fd09-4a0e-852e-d448820fcaad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -5, -8, -7, ..., -7, -8, -5],\n",
" [ -5, -6, -9, ..., -8, -5, -9],\n",
" [-10, -8, -10, ..., -8, -6, -8],\n",
" ...,\n",
" [ -7, -6, -7, ..., -9, -8, -9],\n",
" [ -8, -6, -8, ..., -7, -9, -9],\n",
" [ -8, -7, -8, ..., -7, -6, -9]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_WSW_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-5 -6 -5 -5 -5 -8 ... -7 0 -3 -2 -7</div><input id='attrs-d4307e12-0054-4826-8f67-f9a9a0512c73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4307e12-0054-4826-8f67-f9a9a0512c73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14cc428e-3848-43ef-a376-2509dd5610dd' class='xr-var-data-in' type='checkbox'><label for='data-14cc428e-3848-43ef-a376-2509dd5610dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-5, -6, -5, ..., -4, -5, -6],\n",
" [-5, -5, -4, ..., -6, -6, -4],\n",
" [-2, -6, -4, ..., -3, -3, -5],\n",
" ...,\n",
" [-2, -5, -4, ..., -2, -5, -5],\n",
" [-2, -4, -5, ..., -4, -4, -3],\n",
" [-1, -4, -4, ..., -3, -2, -7]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_W_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>3 1 3 1 3 1 3 2 ... 2 3 4 2 3 0 3 1</div><input id='attrs-56badc9c-94f3-4881-837c-d5f2a7773d66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56badc9c-94f3-4881-837c-d5f2a7773d66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc35f2d0-23b5-46aa-9bea-4d53b46b0061' class='xr-var-data-in' type='checkbox'><label for='data-bc35f2d0-23b5-46aa-9bea-4d53b46b0061' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 3, 1, 3, ..., 1, 3, 3],\n",
" [ 3, 5, 1, ..., 4, 4, 3],\n",
" [-1, 2, -1, ..., 1, 1, 5],\n",
" ...,\n",
" [ 3, 3, 2, ..., 3, 2, 3],\n",
" [ 2, 4, 3, ..., 1, 1, 0],\n",
" [ 2, 3, 2, ..., 0, 3, 1]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_WNW_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-6 -3 -5 -6 -5 ... -2 -4 -5 -4 -7</div><input id='attrs-5ae2779a-4fd3-4d32-8cd1-299494cfa506' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ae2779a-4fd3-4d32-8cd1-299494cfa506' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3daf0a40-8a50-4de7-a01c-064999adc1f2' class='xr-var-data-in' type='checkbox'><label for='data-3daf0a40-8a50-4de7-a01c-064999adc1f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-6, -3, -5, ..., -2, -1, -4],\n",
" [-7, -2, -2, ..., -2, -2, -5],\n",
" [-5, -4, -1, ..., -5, -3, -5],\n",
" ...,\n",
" [-1, -7, -6, ..., -6, -5, -7],\n",
" [-5, -4, -4, ..., -6, -3, -6],\n",
" [-6, -5, -7, ..., -5, -4, -7]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_NW_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-9 -11 -11 -13 ... -10 -10 -10 -9</div><input id='attrs-607da0a0-d09d-4b2c-ac82-14ec87becfc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-607da0a0-d09d-4b2c-ac82-14ec87becfc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6d0c51a-1651-4357-a0f4-ad4ad7a6ce11' class='xr-var-data-in' type='checkbox'><label for='data-d6d0c51a-1651-4357-a0f4-ad4ad7a6ce11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -9, -11, -11, ..., -11, -7, -11],\n",
" [-10, -10, -9, ..., -8, -12, -9],\n",
" [-11, -11, -13, ..., -10, -10, -12],\n",
" ...,\n",
" [ -7, -11, -8, ..., -11, -11, -9],\n",
" [-12, -10, -10, ..., -12, -8, -11],\n",
" [-11, -10, -8, ..., -10, -10, -9]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_NNW_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-2 -5 -5 -4 -5 ... -5 -7 -7 -6 -7</div><input id='attrs-c69dec9d-70bc-42aa-99fc-0b91982ae181' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c69dec9d-70bc-42aa-99fc-0b91982ae181' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58939b35-465c-4c46-a31d-d41a5f867c45' class='xr-var-data-in' type='checkbox'><label for='data-58939b35-465c-4c46-a31d-d41a5f867c45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-2, -5, -5, ..., -3, -6, -5],\n",
" [-4, -5, -4, ..., -1, -6, -5],\n",
" [-4, -5, -6, ..., -3, -6, -7],\n",
" ...,\n",
" [-4, -7, -3, ..., -7, -5, -7],\n",
" [-4, -4, -5, ..., -6, -7, -8],\n",
" [-4, -4, -5, ..., -7, -6, -7]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_E_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-7 -1 -6 -4 -5 ... -1 -4 -2 -4 -2</div><input id='attrs-8d4a4770-07a1-47de-9477-f09b0e740c2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d4a4770-07a1-47de-9477-f09b0e740c2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4723261-f956-44b3-8f6c-13b4465b143e' class='xr-var-data-in' type='checkbox'><label for='data-e4723261-f956-44b3-8f6c-13b4465b143e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-7, -1, -6, ..., -6, -8, -4],\n",
" [-5, -4, -6, ..., -4, -5, -4],\n",
" [-6, -4, -3, ..., -5, -6, -2],\n",
" ...,\n",
" [-5, -4, -6, ..., -1, -5, -5],\n",
" [-3, -3, -7, ..., -6, -8, -6],\n",
" [-8, -1, -4, ..., -2, -4, -2]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_ESE_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-11 -10 -12 -12 ... -11 -11 -13 -10</div><input id='attrs-6bbb9738-b814-4001-88e8-f6b61d814819' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6bbb9738-b814-4001-88e8-f6b61d814819' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c714a94-e83b-45a9-9137-c96b4a05e12d' class='xr-var-data-in' type='checkbox'><label for='data-1c714a94-e83b-45a9-9137-c96b4a05e12d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-11, -10, -12, ..., -8, -9, -11],\n",
" [ -8, -13, -10, ..., -10, -9, -12],\n",
" [ -9, -11, -11, ..., -9, -10, -10],\n",
" ...,\n",
" [-11, -7, -12, ..., -9, -13, -10],\n",
" [ -9, -11, -11, ..., -10, -11, -9],\n",
" [-12, -9, -10, ..., -11, -13, -10]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_SE_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>0 -7 -4 -3 -3 -4 ... -5 -2 -2 -4 -3</div><input id='attrs-909808f0-cc7d-4d9b-b650-9655154357da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-909808f0-cc7d-4d9b-b650-9655154357da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-962407ab-5a91-499e-abda-62f716a7c575' class='xr-var-data-in' type='checkbox'><label for='data-962407ab-5a91-499e-abda-62f716a7c575' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0, -7, -4, ..., -1, -2, -5],\n",
" [-4, -3, -4, ..., -3, 0, -4],\n",
" [-2, -2, -7, ..., -2, -1, -4],\n",
" ...,\n",
" [-4, -4, -3, ..., -4, -3, -3],\n",
" [-3, -5, -3, ..., -4, -4, -5],\n",
" [-4, -3, -6, ..., -2, -4, -3]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_SSE_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-13 -13 -15 -12 ... -12 -12 -13 -14</div><input id='attrs-a432445a-c371-4de6-b733-fe1f6c1e600d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a432445a-c371-4de6-b733-fe1f6c1e600d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29f92f24-7dbf-49fa-b8b3-435173d7e1a6' class='xr-var-data-in' type='checkbox'><label for='data-29f92f24-7dbf-49fa-b8b3-435173d7e1a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-13, -13, -15, ..., -14, -12, -14],\n",
" [-14, -15, -13, ..., -13, -9, -15],\n",
" [-13, -14, -11, ..., -14, -11, -11],\n",
" ...,\n",
" [-13, -14, -13, ..., -13, -12, -14],\n",
" [-13, -14, -11, ..., -11, -13, -14],\n",
" [-16, -13, -15, ..., -12, -13, -14]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_N_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-10 -12 -10 -9 -8 ... -8 -8 -8 -10</div><input id='attrs-a715828f-1f78-430b-add5-f509446a1813' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a715828f-1f78-430b-add5-f509446a1813' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73a7b7c2-cbfd-4521-add4-b991d51953ad' class='xr-var-data-in' type='checkbox'><label for='data-73a7b7c2-cbfd-4521-add4-b991d51953ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-10, -12, -10, ..., -10, -10, -11],\n",
" [ -8, -11, -9, ..., -9, -7, -10],\n",
" [-11, -8, -11, ..., -12, -11, -12],\n",
" ...,\n",
" [ -8, -10, -10, ..., -7, -9, -12],\n",
" [-11, -9, -8, ..., -11, -10, -9],\n",
" [ -8, -9, -10, ..., -8, -8, -10]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_NNE_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-6 -8 -7 -9 -9 ... -9 -7 -8 -10 -9</div><input id='attrs-f1972a0e-05f9-4144-b637-bee2f304c5cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1972a0e-05f9-4144-b637-bee2f304c5cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1b9f190-7131-4308-a5f7-dc0b4704e7df' class='xr-var-data-in' type='checkbox'><label for='data-d1b9f190-7131-4308-a5f7-dc0b4704e7df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -6, -8, -7, ..., -9, -5, -10],\n",
" [ -5, -7, -9, ..., -9, -6, -10],\n",
" [-10, -8, -9, ..., -8, -8, -10],\n",
" ...,\n",
" [ -7, -8, -10, ..., -8, -7, -10],\n",
" [ -5, -7, -5, ..., -8, -9, -11],\n",
" [ -7, -7, -7, ..., -8, -10, -9]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_NE_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-4 -5 -1 -5 -2 ... -5 -2 -2 -2 -1</div><input id='attrs-493315df-dfff-4f85-9edb-edca2ce3ec44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-493315df-dfff-4f85-9edb-edca2ce3ec44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-673dc8a1-009b-4458-8a8e-743dbccf9d20' class='xr-var-data-in' type='checkbox'><label for='data-673dc8a1-009b-4458-8a8e-743dbccf9d20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-4, -5, -1, ..., -5, 0, -5],\n",
" [-3, -4, -4, ..., -5, -1, -1],\n",
" [-2, -1, -1, ..., -3, -2, -5],\n",
" ...,\n",
" [-4, -3, -3, ..., -3, -1, -4],\n",
" [ 0, -3, -3, ..., 1, -2, -5],\n",
" [-1, -5, -6, ..., -2, -2, -1]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PES_ENE_raw</span></div><div class='xr-var-dims'>(trainId, PESsampleId)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-5 -2 -5 -2 -5 -1 ... -3 -7 0 -4 -1</div><input id='attrs-05020386-2993-4cfb-b615-bed6052bc600' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05020386-2993-4cfb-b615-bed6052bc600' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c608bc72-5f42-470e-a290-b2521b7efc27' class='xr-var-data-in' type='checkbox'><label for='data-c608bc72-5f42-470e-a290-b2521b7efc27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-5, -2, -5, ..., -4, -6, -1],\n",
" [-4, 2, -4, ..., -2, -6, -2],\n",
" [-2, 0, -5, ..., -1, -2, -3],\n",
" ...,\n",
" [-1, -4, -3, ..., 2, -4, -1],\n",
" [-9, -2, -5, ..., -2, -2, -3],\n",
" [-5, 0, 0, ..., 0, -4, -1]], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bunchPatternTable</span></div><div class='xr-var-dims'>(trainId, pulse_slot)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>2146089 2048 ... 16777216 16777216</div><input id='attrs-a62a3040-56f4-4b0e-9330-026309882ce5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a62a3040-56f4-4b0e-9330-026309882ce5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-048aad8c-b895-40ce-ae86-50e70e4d7da9' class='xr-var-data-in' type='checkbox'><label for='data-048aad8c-b895-40ce-ae86-50e70e4d7da9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 2146089, 2048, 2099241, ..., 16777216, 16777216, 16777216],\n",
" [ 2146089, 2048, 2099241, ..., 16777216, 16777216, 16777216],\n",
" [ 2211625, 2048, 2099241, ..., 16777216, 16777216, 16777216],\n",
" ...,\n",
" [ 2146089, 2048, 2099241, ..., 16777216, 16777216, 16777216],\n",
" [ 2146089, 2048, 2099241, ..., 16777216, 16777216, 16777216],\n",
" [ 2146089, 2048, 2099241, ..., 16777216, 16777216, 16777216]],\n",
" dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XTD10_SA3</span></div><div class='xr-var-dims'>(trainId, sa3_pId)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.674e+03 1.781e+03 ... 1.465e+03</div><input id='attrs-6ebe0c60-1349-48e9-be94-e828558f3453' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ebe0c60-1349-48e9-be94-e828558f3453' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7e7f684-e844-4f5e-adc7-162202f80191' class='xr-var-data-in' type='checkbox'><label for='data-a7e7f684-e844-4f5e-adc7-162202f80191' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1673.9749, 1780.605 , 1452.1677, ..., 1836.0759, 1695.688 ,\n",
" 1458.0745],\n",
" [2012.4326, 1767.7134, 1716.7632, ..., 1651.4255, 1813.9778,\n",
" 1431.3564],\n",
" [1630.8784, 1645.9148, 1469.2832, ..., 1508.0568, 1385.6311,\n",
" 1416.7161],\n",
" ...,\n",
" [1507.3145, 1752.1653, 1686.9208, ..., 1737.3125, 1577.063 ,\n",
" 1616.5239],\n",
" [2101.6008, 1569.2412, 1855.7173, ..., 1483.9696, 1664.9822,\n",
" 1348.7126],\n",
" [1564.1768, 1731.567 , 1535.6467, ..., 1721.9434, 1681.0325,\n",
" 1465.4915]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectrum</span></div><div class='xr-var-dims'>(trainId, newt_x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>941.8 960.7 ... 1.302e+03 1.319e+03</div><input id='attrs-48baa7bb-053f-4b03-b3ae-9436b60a23e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48baa7bb-053f-4b03-b3ae-9436b60a23e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6497bc4-e3ab-46cf-8192-e82d08659f44' class='xr-var-data-in' type='checkbox'><label for='data-b6497bc4-e3ab-46cf-8192-e82d08659f44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 941.7556739 , 960.7466906 , 985.17017035, ..., 1429.04684533,\n",
" 1345.94695049, 1329.10718964],\n",
" [1078.21605126, 1053.65423777, 1074.17111375, ..., 1328.01665665,\n",
" 1424.27242218, 1363.57039719],\n",
" [ 935.14152295, 949.06555853, 981.37960431, ..., 1409.16571326,\n",
" 1329.469592 , 1194.42605757],\n",
" ...,\n",
" [1025.26416446, 1002.32687928, 985.82771752, ..., 1286.78458118,\n",
" 1334.07242218, 1294.76001983],\n",
" [1083.24435314, 1097.98065287, 1044.15601941, ..., 1231.7053359 ,\n",
" 1391.47242218, 1360.74681228],\n",
" [1022.09246635, 1066.147634 , 1049.91922696, ..., 1362.59590193,\n",
" 1302.00732785, 1319.01190662]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectrum_nobl</span></div><div class='xr-var-dims'>(trainId, newt_x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-25.84 -7.057 17.15 ... -41.9 -25.1</div><input id='attrs-dc5974f5-52db-46c1-97f6-88458417fb16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc5974f5-52db-46c1-97f6-88458417fb16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46a92e7e-8342-4a63-bf31-e73134d55547' class='xr-var-data-in' type='checkbox'><label for='data-46a92e7e-8342-4a63-bf31-e73134d55547' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -25.83624416, -7.0570592 , 17.15456166, ..., 113.93823559,\n",
" 30.58586118, 13.49359361],\n",
" [ 132.95311623, 108.13436457, 128.39426944, ..., -38.76150904,\n",
" 57.18801513, -3.82028416],\n",
" [ -4.23122332, 9.49350667, 41.60822133, ..., 142.82583547,\n",
" 62.89216441, -72.38894537],\n",
" ...,\n",
" [ 22.22432542, -0.8607938 , -17.50780854, ..., 41.21831883,\n",
" 88.32995832, 48.8413355 ],\n",
" [ 80.40705362, 94.91392577, 40.85983533, ..., -147.51518149,\n",
" 11.97845297, -19.02063816],\n",
" [ -34.8527069 , 9.02766964, -7.37555092, ..., 18.90036362,\n",
" -41.89654178, -25.10031672]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d49f9f59-de72-4395-b51c-20ba00881c51' class='xr-section-summary-in' type='checkbox' ><label for='section-d49f9f59-de72-4395-b51c-20ba00881c51' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>trainId</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b4d1f00c-1a1b-4d7e-940f-e13ba4c79909' class='xr-index-data-in' type='checkbox'/><label for='index-b4d1f00c-1a1b-4d7e-940f-e13ba4c79909' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1473952798, 1473952800, 1473952802, 1473952804, 1473952806, 1473952808,\n",
" 1473952810, 1473952812, 1473952814, 1473952816,\n",
" ...\n",
" 1473954100, 1473954102, 1473954104, 1473954106, 1473954108, 1473954110,\n",
" 1473954112, 1473954114, 1473954116, 1473954118],\n",
" dtype='uint64', name='trainId', length=660))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sa3_pId</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d1499bd9-c822-43c5-ad5d-2e37d90a46ae' class='xr-index-data-in' type='checkbox'/><label for='index-d1499bd9-c822-43c5-ad5d-2e37d90a46ae' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408,\n",
" 1440, 1472, 1504, 1536, 1568, 1600, 1632, 1664, 1696, 1728, 1760, 1792,\n",
" 1824, 1856, 1888, 1920, 1952, 1984, 2016, 2048, 2080, 2112, 2144, 2176,\n",
" 2208, 2240, 2272, 2304, 2336, 2368, 2400],\n",
" dtype='int64', name='sa3_pId'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>newt_x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5111143b-1c38-4e7a-90b1-d7acf156d259' class='xr-index-data-in' type='checkbox'/><label for='index-5111143b-1c38-4e7a-90b1-d7acf156d259' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 515.884589, 515.9076505108267, 515.9307149777067,\n",
" 515.9537824006401, 515.9768527796267, 515.9999261146668,\n",
" 516.0230024057602, 516.0460816529069, 516.0691638561069,\n",
" 516.0922490153603,\n",
" ...\n",
" 553.525404882067, 553.5528709123703, 553.5803398987268,\n",
" 553.6078118411368, 553.6352867396001, 553.6627645941168,\n",
" 553.6902454046867, 553.71772917131, 553.7452158939867,\n",
" 553.7727055727166],\n",
" dtype='float64', name='newt_x', length=1500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-027fcb0a-bc9c-4f33-9fe9-411021548132' class='xr-section-summary-in' type='checkbox' ><label for='section-027fcb0a-bc9c-4f33-9fe9-411021548132' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>runFolder :</span></dt><dd>/gpfs/exfel/exp/SCS/202202/p002953/raw/r0322</dd><dt><span>vbin: :</span></dt><dd>4</dd><dt><span>hbin :</span></dt><dd>1</dd><dt><span>startX :</span></dt><dd>1</dd><dt><span>endX :</span></dt><dd>2048</dd><dt><span>startY :</span></dt><dd>1</dd><dt><span>endY :</span></dt><dd>512</dd><dt><span>temperature :</span></dt><dd>-50.04199981689453</dd><dt><span>high_capacity :</span></dt><dd>0</dd><dt><span>exposure_s :</span></dt><dd>0.0004</dd><dt><span>gain :</span></dt><dd>2</dd><dt><span>photoelectrons_per_count :</span></dt><dd>2.05</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (trainId: 660, newt_y: 53, newt_x: 1500,\n",
" PESsampleId: 700000, pulse_slot: 2700, sa3_pId: 43)\n",
"Coordinates:\n",
" * trainId (trainId) uint64 1473952798 1473952800 ... 1473954118\n",
" * sa3_pId (sa3_pId) int64 1056 1088 1120 1152 ... 2336 2368 2400\n",
" * newt_x (newt_x) float64 515.9 515.9 515.9 ... 553.7 553.7 553.8\n",
"Dimensions without coordinates: newt_y, PESsampleId, pulse_slot\n",
"Data variables: (12/21)\n",
" newton (trainId, newt_y, newt_x) float64 943.0 800.0 ... 758.0\n",
" PES_S_raw (trainId, PESsampleId) int16 -2 1 1 2 -1 ... 2 -1 3 -3 1\n",
" PES_SSW_raw (trainId, PESsampleId) int16 -3 0 -3 -3 ... -3 -4 -4 -3\n",
" PES_SW_raw (trainId, PESsampleId) int16 -5 -8 -7 -4 ... -9 -7 -6 -9\n",
" PES_WSW_raw (trainId, PESsampleId) int16 -5 -6 -5 -5 ... 0 -3 -2 -7\n",
" PES_W_raw (trainId, PESsampleId) int16 3 1 3 1 3 1 ... 4 2 3 0 3 1\n",
" ... ...\n",
" PES_NE_raw (trainId, PESsampleId) int16 -4 -5 -1 -5 ... -2 -2 -2 -1\n",
" PES_ENE_raw (trainId, PESsampleId) int16 -5 -2 -5 -2 ... -7 0 -4 -1\n",
" bunchPatternTable (trainId, pulse_slot) uint32 2146089 2048 ... 16777216\n",
" XTD10_SA3 (trainId, sa3_pId) float32 1.674e+03 ... 1.465e+03\n",
" spectrum (trainId, newt_x) float64 941.8 960.7 ... 1.319e+03\n",
" spectrum_nobl (trainId, newt_x) float64 -25.84 -7.057 ... -41.9 -25.1\n",
"Attributes:\n",
" runFolder: /gpfs/exfel/exp/SCS/202202/p002953/raw/r0322\n",
" vbin:: 4\n",
" hbin: 1\n",
" startX: 1\n",
" endX: 2048\n",
" startY: 1\n",
" endY: 512\n",
" temperature: -50.04199981689453\n",
" high_capacity: 0\n",
" exposure_s: 0.0004\n",
" gain: 2\n",
" photoelectrons_per_count: 2.05"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_train = v.from_run(runTrain) # load refNB. The `newton` variable contains the CCD images.\n",
"v.integrate(data_train) # integrate over the non-dispersive dimension \n",
"v.removePolyBaseline(data_train) # remove baseline\n",
"data_train"
]
},
{
"cell_type": "code",
"id": "294b5f3a-1d59-444e-80ab-4834d26d62dc",
"metadata": {},
"outputs": [],
"source": [
"# transform PES data into the format expected\n",
"pes_data = {k: da.from_array(data_train[item].to_numpy())\n",
" for k, item in pes_map.items() if item in data_train}\n",
"xgm = data_train.XTD10_SA3.isel(sa3_pId=0).to_numpy()[:, np.newaxis]"
]
},
{
"cell_type": "code",
"id": "b477bf49-f5ca-4df0-b6ed-a270ee35cd28",
"metadata": {},
"outputs": [],
"source": [
"channels = tuple(pes_data.keys())"
]
},
{
"cell_type": "code",
"id": "8f154e38-d208-477e-9d9c-ef2a632514c8",
"metadata": {},
"outputs": [],
"source": [
"energy = data_train.newt_x.to_numpy()"
]
},
{
"cell_type": "code",
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"id": "0c5ff2a0-0737-417d-9f57-158d4fbd8090",
"metadata": {},
"outputs": [],
"source": [
"vik = data_train.spectrum.to_numpy()"
]
},
{
"cell_type": "markdown",
"id": "995e2ac0-1898-46dd-b95f-f65a24496871",
"metadata": {},
"source": [
"## Train Virtual Spectrometer"
]
},
{
"cell_type": "markdown",
"id": "9cbf75c8-fbe0-42ec-af85-6194aede91f5",
"metadata": {},
"source": [
"So far we have only done pre-processing due to experimental problems with some data not being available in certain train IDs.\n",
"\n",
"Let's finally take a look at the data before training the model."
]
},
{
"cell_type": "code",
"id": "63b35dac-ad50-4124-b6f8-e1ceea667b4d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2b563aa392d0>]"
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(energy, vik[2])"
]
},
{
"cell_type": "code",
"id": "d0b70fef-5e27-4cb1-90e7-2653989cf48a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2b563ab51330>]"
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(-pes_data[\"channel_1_A\"][0,31400:31700])"
]
},
{
"cell_type": "markdown",
"id": "a6606c28-28c8-4d27-9f38-4a7ca88ee397",
"metadata": {},
"source": [
"Now, let's fit the model:"
]
},
{
"cell_type": "code",
"id": "5690cf09-4fed-497d-a09d-0f3cdceea04d",
"metadata": {},
"outputs": [],
"source": [
"n_test = 10 # exclude some trains to validate the training"
]
},
{
"cell_type": "code",
"id": "cb86aa32-dc1d-4684-bd62-25aa77a97245",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checking data quality in high-resolution data.\n",
"Finding region-of-interest\n",
"Excluding outliers\n",
"Selected 585 of 650 samples.\n",
"Fitting PCA on low-resolution data.\n",
"Using 585 comp. for PES PCA.\n",
"Fitting PCA on high-resolution data.\n",
"Using 20 comp. for grating spec. PCA.\n",
"Fitting outlier detection\n",
"Fitting model.\n",
"Calculate PCA unc. on high-resolution data.\n",
"Calculate transfer function\n",
"Resolution = 0.21 eV, S/R = 31.65\n",
"Calculate PCA on channel_1_A\n",
"Calculate PCA on channel_1_B\n",
"Calculate PCA on channel_1_C\n",
"Calculate PCA on channel_1_D\n",
"Calculate PCA on channel_2_A\n",
"Calculate PCA on channel_2_B\n",
"Calculate PCA on channel_2_C\n",
"Calculate PCA on channel_2_D\n",
"Calculate PCA on channel_3_A\n",
"Calculate PCA on channel_3_B\n",
"Calculate PCA on channel_3_C\n",
"Calculate PCA on channel_3_D\n",
"Calculate PCA on channel_4_A\n",
"Calculate PCA on channel_4_B\n",
"Calculate PCA on channel_4_C\n",