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+{
+ "cells": [
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+ "# Transient optical laser reflectivity measurement: finding FEL and OL time overlap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6013ff8",
+ "metadata": {},
+ "source": [
+ "Transient optical laser reflectivity is a technique to determine the temporal overlap between the FEL and the optical laser (OL). The FEL is pumping a large band gap material (usually a 1 micrometer thick Si$_3$N$_4$ membrane) and the OL, spatially overlaped with the FEL, is reflected off the sample. The incoming ($I_0$) and reflected ($I_r$) laser beams are monitored by photodiodes. The FEL pump alters the electronic properties of the material, which in turn modifies the reflectivity. By varying the delay between OL and FEL through the scanning of the optical delay line, the transient response of the material is measured and the exact time overlap between the two beams can be extracted.\n",
+ "\n",
+ "To increase the signal to noise ratio, pumped and unpumped signals acquired closely in time are compared. The reflectivity is then defined as:\n",
+ "\n",
+ "$\\Delta R [\\%] = 100\\times(\\frac{R(pumped)}{R(unpumped)} - 1)$, with $R = I_r / I_0$\n",
+ "\n",
+ "In the `toolbox_scs`, there is a convenience function `reflectivity` that allows the quick calculation of $\\Delta R$. It performs binning along the motor position axis and sorts the data according to the bunch pattern and the sequence of pumped, unpumped.\n",
+ "\n",
+ "Below is an example, where $I_0$ ($I_r$) is measured a by photodiode on the Fast ADC channel 5 (channel 3), respectively. The pump FEL is set at half the repetition rate of the OL, to have alternating pumped/unpumped/pumped/unpumped/... pulses within each train."
+ ]
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+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
+ "Dimensions: (delay: 133)\n",
+ "Coordinates:\n",
+ " * delay (delay) float64 228.2 228.2 228.2 ... 229.5 229.5\n",
+ "Data variables:\n",
+ " FastADC5peaks (delay) float64 2.009e+05 1.958e+05 ... 1.937e+05\n",
+ " FastADC3peaks (delay) float64 7.286e+04 6.932e+04 ... 7.36e+04\n",
+ " FastADC5peaks_unpumped (delay) float64 2.007e+05 1.955e+05 ... 1.935e+05\n",
+ " FastADC3peaks_unpumped (delay) float64 7.226e+04 6.872e+04 ... 7.293e+04\n",
+ " PP800_DelayLine_binned (delay) float64 228.2 228.2 228.2 ... 229.5 229.5\n",
+ " deltaR (delay) float64 0.7039 0.7426 ... 0.8043 0.8176\n",
+ " deltaR_std (delay) float64 0.8555 0.9384 ... 0.9011 0.9902\n",
+ " deltaR_stderr (delay) float64 0.07418 0.06808 ... 0.09245 0.08032\n",
+ " counts (delay) int64 133 190 114 266 304 ... 114 380 95 152\n",
+ "Attributes:\n",
+ " runFolder: /gpfs/exfel/exp/SCS/202201/p002769/raw/r0425</pre><div class='xr-wrap' hidden><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-f0700d5d-1d0d-456f-bf7b-d3b4d2e3140a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f0700d5d-1d0d-456f-bf7b-d3b4d2e3140a' 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'>delay</span>: 133</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c1ec0c89-ecc8-49f1-892b-51c0dc41028e' class='xr-section-summary-in' type='checkbox' checked><label for='section-c1ec0c89-ecc8-49f1-892b-51c0dc41028e' class='xr-section-summary' >Coordinates: <span>(1)</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'>delay</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>228.2 228.2 228.2 ... 229.5 229.5</div><input id='attrs-010ed992-ac4a-4958-8c9b-f5a1ac142f26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-010ed992-ac4a-4958-8c9b-f5a1ac142f26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5142a3f-cd30-42b9-a3ef-3d3ee7d33f16' class='xr-var-data-in' type='checkbox'><label for='data-a5142a3f-cd30-42b9-a3ef-3d3ee7d33f16' 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([228.19, 228.2 , 228.21, 228.22, 228.23, 228.24, 228.25, 228.26, 228.27,\n",
+ " 228.28, 228.29, 228.3 , 228.31, 228.32, 228.33, 228.34, 228.35, 228.36,\n",
+ " 228.37, 228.38, 228.39, 228.4 , 228.41, 228.42, 228.43, 228.44, 228.45,\n",
+ " 228.46, 228.47, 228.48, 228.49, 228.5 , 228.51, 228.52, 228.53, 228.54,\n",
+ " 228.55, 228.56, 228.57, 228.58, 228.59, 228.6 , 228.61, 228.62, 228.63,\n",
+ " 228.64, 228.65, 228.66, 228.67, 228.68, 228.69, 228.7 , 228.71, 228.72,\n",
+ " 228.73, 228.74, 228.75, 228.76, 228.77, 228.78, 228.79, 228.8 , 228.81,\n",
+ " 228.82, 228.83, 228.84, 228.85, 228.86, 228.87, 228.88, 228.89, 228.9 ,\n",
+ " 228.91, 228.92, 228.93, 228.94, 228.95, 228.96, 228.97, 228.98, 228.99,\n",
+ " 229. , 229.01, 229.02, 229.03, 229.04, 229.05, 229.06, 229.07, 229.08,\n",
+ " 229.09, 229.1 , 229.11, 229.12, 229.13, 229.14, 229.15, 229.16, 229.17,\n",
+ " 229.18, 229.19, 229.2 , 229.21, 229.22, 229.23, 229.24, 229.25, 229.26,\n",
+ " 229.27, 229.28, 229.29, 229.3 , 229.31, 229.32, 229.33, 229.34, 229.35,\n",
+ " 229.36, 229.37, 229.38, 229.39, 229.4 , 229.41, 229.42, 229.43, 229.44,\n",
+ " 229.45, 229.46, 229.47, 229.48, 229.49, 229.5 , 229.51])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-03428c8b-6273-4745-8143-d4cc437aaa98' class='xr-section-summary-in' type='checkbox' checked><label for='section-03428c8b-6273-4745-8143-d4cc437aaa98' class='xr-section-summary' >Data variables: <span>(9)</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>FastADC5peaks</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.009e+05 1.958e+05 ... 1.937e+05</div><input id='attrs-db1d8f43-7906-4d2d-b9a4-37ce9ff688aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db1d8f43-7906-4d2d-b9a4-37ce9ff688aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6731be6c-a11a-4932-af21-7e9696f0db1a' class='xr-var-data-in' type='checkbox'><label for='data-6731be6c-a11a-4932-af21-7e9696f0db1a' 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([200920.80827068, 195825.84736842, 189510.48684211, 188501.2406015 ,\n",
+ " 188378.09210526, 178347.77631579, 172510.37559809, 180592.78229665,\n",
+ " 177619.125 , 188322.56725146, 188552.62128146, 182600.14327485,\n",
+ " 179260.08133971, 173384.92105263, 181677.9122807 , 173940.15311005,\n",
+ " 189150.84398496, 197525. , 192838.81578947, 192767.60588972,\n",
+ " 200930.50292398, 192317.79605263, 189984.51476252, 187612.27368421,\n",
+ " 187279.29554656, 182917.86984353, 188577.37559809, 183137.38815789,\n",
+ " 187506.92748538, 187692.29605263, 197161.31359649, 186850.98079659,\n",
+ " 191139.19298246, 183116.69736842, 185739.84586466, 181913.284689 ,\n",
+ " 190850.95614035, 189357.39314195, 180145.93117409, 187107.35087719,\n",
+ " 182932.89927405, 178612.57894737, 197816.58421053, 181232.96710526,\n",
+ " 186243.5877193 , 185072.10651629, 183689.51674641, 189223.09649123,\n",
+ " 184499.76461988, 189261.23916409, 189302.44078947, 201999.93660287,\n",
+ " 188284.12440191, 193327.71929825, 194833.64035088, 190444.93233083,\n",
+ " 188375.27192982, 191503.24285714, 191733.3377193 , 193085.35964912,\n",
+ " 188390.34210526, 200583.54489164, 191159.43859649, 191823.12753036,\n",
+ " 188113.84375 , 192676.14473684, 196094.08133971, 194363.0430622 ,\n",
+ " 179547.44736842, 191910.14819945, 192854.11403509, 184552.23976608,\n",
+ " 199775.16412742, 193359.28947368, 179800.69605263, 200066.39633174,\n",
+ " 195319.82894737, 182674.09758772, 195655.54276316, 186347. ,\n",
+ " 188683.27339181, 194058.23421053, 193583.84210526, 196854.35387812,\n",
+ " 201428.38815789, 194367.72368421, 194212.37055477, 206466.89164087,\n",
+ " 202257.77894737, 191893.76973684, 192610.90191388, 183270.17293233,\n",
+ " 195644.79949875, 192862.02631579, 188519.87763158, 191419.30959752,\n",
+ " 192627.44736842, 186425.55509868, 182790.13157895, 190021.81578947,\n",
+ " 193938.71052632, 197611.14035088, 185728.43421053, 203261.91689751,\n",
+ " 198541.83947368, 206242.96929825, 184223.10087719, 186485.88947368,\n",
+ " 196665.40789474, 191610.07578947, 186898.57142857, 190856.82748538,\n",
+ " 190029.75858124, 193314.24736842, 205812.34210526, 196283.98574561,\n",
+ " 204917.97368421, 204331.94736842, 184858.6622807 , 196732.83223684,\n",
+ " 202482.06390977, 195409.64210526, 189902.76315789, 186521.84210526,\n",
+ " 183957.52105263, 195779.66917293, 200891.96240602, 191778.62828947,\n",
+ " 195179.25 , 195449.13596491, 184506.06052632, 190612.88421053,\n",
+ " 193726.34210526])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC3peaks</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.286e+04 6.932e+04 ... 7.36e+04</div><input id='attrs-4d55edce-3045-44c1-8498-cec339c36332' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d55edce-3045-44c1-8498-cec339c36332' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dd1167d-6d74-4452-81af-51346d36f2e7' class='xr-var-data-in' type='checkbox'><label for='data-7dd1167d-6d74-4452-81af-51346d36f2e7' 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([72856.94736842, 69320.45789474, 65163.81578947, 65712.34962406,\n",
+ " 65466.56578947, 59836.97368421, 56723.22488038, 60682.44617225,\n",
+ " 63105.80592105, 68624.78947368, 65546.69908467, 62159.0877193 ,\n",
+ " 60646.50239234, 56972.43355263, 61569.13450292, 57006.35645933,\n",
+ " 64891.44799499, 74590.20300752, 70388.3708134 , 69410.52506266,\n",
+ " 74529.08479532, 69272.17434211, 67872.14762516, 67641.01578947,\n",
+ " 65823.5951417 , 62037.55405405, 65903.47607656, 61076.13815789,\n",
+ " 65717.6497076 , 64752.94736842, 70336.92982456, 64022.44736842,\n",
+ " 65919.71929825, 62369.47532895, 63710.83383459, 61651.38755981,\n",
+ " 65728.10526316, 65805.31259968, 59630.70242915, 65816.33991228,\n",
+ " 63220.61433757, 59650.04035088, 75057.16710526, 62443.51785714,\n",
+ " 65161.93859649, 64979.57769424, 63049.64114833, 67905.80701754,\n",
+ " 63343.08625731, 67658.85758514, 66645.46710526, 75850.80502392,\n",
+ " 65682.94417863, 70237.10526316, 70576.00584795, 68002.45394737,\n",
+ " 65730.43859649, 68008.1593985 , 68256.3245614 , 69237.14035088,\n",
+ " 66743.81983806, 74070.8250774 , 65874.04385965, 66345.39203779,\n",
+ " 63851.84375 , 67526.69078947, 68314.92643541, 68100.3277512 ,\n",
+ " 57057.98684211, 64602.88642659, 66557.24561404, 62893.44005848,\n",
+ " 72320.7098338 , 68318.89473684, 61111.61710526, 74585.01594896,\n",
+ " 70878.75 , 63732.32017544, 70684.27549342, 66751.86842105,\n",
+ " 68464.04605263, 70226.39078947, 69473.26315789, 73670.37811634,\n",
+ " 74887.55526316, 75554.13157895, 71704.23897582, 76393.81733746,\n",
+ " 72372.25789474, 67687.82090643, 68657.34210526, 63098.15037594,\n",
+ " 72445.66165414, 71142.93421053, 67072.68157895, 69393.92260062,\n",
+ " 66224.47368421, 64661.50575658, 59763.68421053, 66291.94078947,\n",
+ " 69328.44482173, 74323.99122807, 66339.72180451, 75884.17313019,\n",
+ " 75811.38684211, 80378. , 65301.96820175, 66485.62368421,\n",
+ " 73480.75 , 69350.80526316, 68246.83458647, 69184.29239766,\n",
+ " 70283.79405034, 71150.48421053, 79372.39473684, 70509.36184211,\n",
+ " 80602.79934211, 78262.32894737, 63466.9627193 , 73199.79605263,\n",
+ " 75885.94736842, 70899.70526316, 70328.36466165, 65183.76973684,\n",
+ " 64047.02421053, 70102.63157895, 76442.21428571, 69687.48684211,\n",
+ " 73643.92982456, 73165.25 , 66983.13289474, 70555.86315789,\n",
+ " 73600.03289474])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC5peaks_unpumped</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.007e+05 1.955e+05 ... 1.935e+05</div><input id='attrs-5ffb3a56-0d2b-4fb1-81d3-4b96696d98c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ffb3a56-0d2b-4fb1-81d3-4b96696d98c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcb4fb5e-ecf9-48ce-84b7-345fbf467d21' class='xr-var-data-in' type='checkbox'><label for='data-bcb4fb5e-ecf9-48ce-84b7-345fbf467d21' 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([200694.83458647, 195514.69473684, 189227.92982456, 187790.19736842,\n",
+ " 187953.26809211, 177604.73245614, 172034.61244019, 180130.63157895,\n",
+ " 177259.54276316, 187929.04385965, 188007.41533181, 181952.4619883 ,\n",
+ " 178861.65550239, 172836.59473684, 181117.24853801, 173360.22488038,\n",
+ " 188746.0858396 , 197224.45112782, 192317.35167464, 192330.67293233,\n",
+ " 200344.16959064, 191837.80263158, 189574.24775353, 187415.01052632,\n",
+ " 186859.79149798, 182356.79871977, 188098.89712919, 182419.07894737,\n",
+ " 187010.6502924 , 186778.33552632, 196525.86842105, 186379.80369844,\n",
+ " 190684.72807018, 182624.29111842, 185207.22857143, 181195.3062201 ,\n",
+ " 190059.79824561, 189051.35326954, 179828.0465587 , 186593.45175439,\n",
+ " 182213.34482759, 178333.35438596, 197498.12368421, 180725.71616541,\n",
+ " 185644.26315789, 184536.4235589 , 183362.29585327, 188552.94736842,\n",
+ " 183860.20467836, 188857.51083591, 189332.57236842, 201740.62200957,\n",
+ " 187716.30143541, 193076.52631579, 194376.00584795, 189818.75469925,\n",
+ " 187266.53508772, 191055.23383459, 190971.43859649, 192383.68421053,\n",
+ " 188091.2145749 , 200226.3993808 , 190363.00877193, 191284.71592443,\n",
+ " 187636.53782895, 192679.59210526, 195681.7888756 , 194135.10047847,\n",
+ " 179222.42434211, 191471.56648199, 191703.79824561, 183994.07017544,\n",
+ " 199336.78878116, 192943.54385965, 179239.58552632, 199585.28628389,\n",
+ " 194555.97368421, 182331.26425439, 195267.46052632, 185590.5877193 ,\n",
+ " 188152.98538012, 193465.84736842, 192769.02631579, 196444.60872576,\n",
+ " 201143.46973684, 193225.85526316, 193947.40327169, 206039.19504644,\n",
+ " 201794.16315789, 191361.20102339, 192294.14354067, 182721.53947368,\n",
+ " 195173.45864662, 192232.57894737, 188264.06710526, 190931.79411765,\n",
+ " 192739. , 186124.78865132, 182381.03508772, 189245.05263158,\n",
+ " 193306.60950764, 197997.85964912, 185259.27255639, 202629.70498615,\n",
+ " 197956.64473684, 205554.84210526, 183575.36513158, 185874.69736842,\n",
+ " 196401.53947368, 191149.12842105, 185993.93609023, 190067.2748538 ,\n",
+ " 189583.86498856, 192791.53421053, 205283.07142857, 195779.65679825,\n",
+ " 204519.40460526, 203922.07565789, 184360.11951754, 196346.75657895,\n",
+ " 202086. , 194966.75368421, 189266.0075188 , 186279.40789474,\n",
+ " 183627.37473684, 195389.94736842, 200592.60526316, 191638.30372807,\n",
+ " 194755.84210526, 194759.74122807, 184065.14605263, 190230.07894737,\n",
+ " 193532.18421053])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC3peaks_unpumped</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.226e+04 6.872e+04 ... 7.293e+04</div><input id='attrs-e1862777-9f21-4393-ae12-8173a2949739' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1862777-9f21-4393-ae12-8173a2949739' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03c899f4-e8cb-47a8-894f-cf9581a71074' class='xr-var-data-in' type='checkbox'><label for='data-03c899f4-e8cb-47a8-894f-cf9581a71074' 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([72262.08646617, 68715.23947368, 64584.20175439, 64910.38157895,\n",
+ " 64833.49342105, 59106.03947368, 56142.70574163, 60049.11363636,\n",
+ " 62547.86513158, 67968.01461988, 64835.23340961, 61410.92397661,\n",
+ " 60035.79425837, 56307.29210526, 60912.39181287, 56334.40669856,\n",
+ " 64290.58145363, 73954.81954887, 69602.3062201 , 68747.07330827,\n",
+ " 73756.76315789, 68501.36513158, 67171.10590501, 67085.43684211,\n",
+ " 65139.84412955, 61356.56899004, 65240.95454545, 60270.40789474,\n",
+ " 65060.14795322, 63931.81578947, 69509.25438596, 63370.50924609,\n",
+ " 65276.53508772, 61682.54111842, 63032.59398496, 60885.36602871,\n",
+ " 64811.21052632, 65174.12200957, 59047.29959514, 65140.74122807,\n",
+ " 62407.18602541, 59100.0877193 , 74451.58157895, 61769.08082707,\n",
+ " 64408.19298246, 64258.52005013, 62484.62679426, 67085.78947368,\n",
+ " 62560.12573099, 66987.68343653, 66205.17763158, 75253.58732057,\n",
+ " 64950.70414673, 69664.80701754, 69905.63840156, 67168.31109023,\n",
+ " 64805.73684211, 67350.6481203 , 67421.03654971, 68223.87719298,\n",
+ " 66152.13495277, 73418.97213622, 65013.28070175, 65595.4925776 ,\n",
+ " 63168.81085526, 67104.56578947, 68583.23624402, 68974.40909091,\n",
+ " 57950.04605263, 65383.96952909, 66946.88596491, 63264.74561404,\n",
+ " 72609.42243767, 68591.45614035, 61185.63552632, 74705.59888357,\n",
+ " 70767.28947368, 63788.25328947, 70662.82401316, 66433.33333333,\n",
+ " 68244.16008772, 69994.31710526, 69007.31578947, 73454.33448753,\n",
+ " 74729.07894737, 74920.73684211, 71491.28307255, 76140.02167183,\n",
+ " 71930.71578947, 67287.52997076, 68302.85167464, 62664.63909774,\n",
+ " 72003.86716792, 70552.96052632, 66759.21842105, 68895.63931889,\n",
+ " 66216.18421053, 64270.41036184, 59248.26315789, 65624.38815789,\n",
+ " 68693.34974533, 74345.26315789, 65766.31390977, 75203.19806094,\n",
+ " 75142.10789474, 79718.27631579, 64620.84758772, 65860.70789474,\n",
+ " 72925.60087719, 68719.71052632, 67388.46992481, 68395.48538012,\n",
+ " 69628.96453089, 70454.28947368, 78628.03007519, 69799.50109649,\n",
+ " 79918.22368421, 77523.79605263, 62848.68969298, 72544.64802632,\n",
+ " 75206.12030075, 70235.92421053, 69488.56390977, 64681.32236842,\n",
+ " 63473.79578947, 69390.87218045, 75703.09398496, 69123.03508772,\n",
+ " 72850.02631579, 72288.59649123, 66261.175 , 69864.12105263,\n",
+ " 72929.83552632])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PP800_DelayLine_binned</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>228.2 228.2 228.2 ... 229.5 229.5</div><input id='attrs-5c0aaa1a-1cbf-4a4a-983d-b0b3eb6a4e11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c0aaa1a-1cbf-4a4a-983d-b0b3eb6a4e11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc39071a-bd78-4394-8c73-116d30b3035b' class='xr-var-data-in' type='checkbox'><label for='data-dc39071a-bd78-4394-8c73-116d30b3035b' 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([228.19, 228.2 , 228.21, 228.22, 228.23, 228.24, 228.25, 228.26,\n",
+ " 228.27, 228.28, 228.29, 228.3 , 228.31, 228.32, 228.33, 228.34,\n",
+ " 228.35, 228.36, 228.37, 228.38, 228.39, 228.4 , 228.41, 228.42,\n",
+ " 228.43, 228.44, 228.45, 228.46, 228.47, 228.48, 228.49, 228.5 ,\n",
+ " 228.51, 228.52, 228.53, 228.54, 228.55, 228.56, 228.57, 228.58,\n",
+ " 228.59, 228.6 , 228.61, 228.62, 228.63, 228.64, 228.65, 228.66,\n",
+ " 228.67, 228.68, 228.69, 228.7 , 228.71, 228.72, 228.73, 228.74,\n",
+ " 228.75, 228.76, 228.77, 228.78, 228.79, 228.8 , 228.81, 228.82,\n",
+ " 228.83, 228.84, 228.85, 228.86, 228.87, 228.88, 228.89, 228.9 ,\n",
+ " 228.91, 228.92, 228.93, 228.94, 228.95, 228.96, 228.97, 228.98,\n",
+ " 228.99, 229. , 229.01, 229.02, 229.03, 229.04, 229.05, 229.06,\n",
+ " 229.07, 229.08, 229.09, 229.1 , 229.11, 229.12, 229.13, 229.14,\n",
+ " 229.15, 229.16, 229.17, 229.18, 229.19, 229.2 , 229.21, 229.22,\n",
+ " 229.23, 229.24, 229.25, 229.26, 229.27, 229.28, 229.29, 229.3 ,\n",
+ " 229.31, 229.32, 229.33, 229.34, 229.35, 229.36, 229.37, 229.38,\n",
+ " 229.39, 229.4 , 229.41, 229.42, 229.43, 229.44, 229.45, 229.46,\n",
+ " 229.47, 229.48, 229.49, 229.5 , 229.51])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deltaR</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7039 0.7426 ... 0.8043 0.8176</div><input id='attrs-020cecb2-d363-4208-bd3d-b4bc823e8fa4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-020cecb2-d363-4208-bd3d-b4bc823e8fa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ab89866-9425-485b-bc77-346213c688fe' class='xr-var-data-in' type='checkbox'><label for='data-0ab89866-9425-485b-bc77-346213c688fe' 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([ 0.70385124, 0.74255221, 0.77766998, 0.87017641, 0.74800608,\n",
+ " 0.86592472, 0.77128721, 0.80402254, 0.72950695, 0.75897156,\n",
+ " 0.83430226, 0.91101336, 0.78813673, 0.90790016, 0.79596252,\n",
+ " 0.87665395, 0.71683061, 0.71309442, 0.87295462, 0.75857596,\n",
+ " 0.77615089, 0.89693514, 0.83722401, 0.7661077 , 0.83756665,\n",
+ " 0.80458832, 0.78111814, 0.90833003, 0.76409443, 0.84349968,\n",
+ " 0.887969 , 0.79121062, 0.76239562, 0.83904305, 0.80538514,\n",
+ " 0.89965652, 1.00601237, 0.81416877, 0.80146468, 0.78085243,\n",
+ " 0.93731413, 0.76565696, 0.66771189, 0.82960754, 0.80956486,\n",
+ " 0.81472271, 0.7364535 , 0.86384325, 0.9037318 , 0.79830365,\n",
+ " 0.6533153 , 0.66037838, 0.84896043, 0.71070147, 0.73876342,\n",
+ " 0.92704938, 0.84830079, 0.76066275, 0.84390484, 1.09900499,\n",
+ " 0.75314225, 0.7053432 , 0.91964663, 0.88049906, 0.8337061 ,\n",
+ " 0.61332073, -0.4908297 , -1.38092418, -1.71694534, -1.41272649,\n",
+ " -1.12163788, -0.88495334, -0.62656642, -0.57221739, -0.43261207,\n",
+ " -0.4023655 , -0.21184841, -0.25310234, -0.17742998, 0.0979598 ,\n",
+ " 0.0878335 , 0.04662353, 0.29446724, 0.08987701, 0.06824385,\n",
+ " 0.31115536, 0.17084691, 0.12668076, 0.38790246, 0.34540416,\n",
+ " 0.38015566, 0.39309014, 0.39800717, 0.47889755, 0.32988133,\n",
+ " 0.48823219, 0.08986072, 0.45616356, 0.64689386, 0.6260554 ,\n",
+ " 0.63195432, 0.16682678, 0.62454736, 0.60145391, 0.61497957,\n",
+ " 0.52936518, 0.71623744, 0.65510835, 0.62349348, 0.68527502,\n",
+ " 0.79377491, 0.77646893, 0.72685078, 0.75086669, 0.71044156,\n",
+ " 0.78726658, 0.73096593, 0.75446749, 0.77079654, 0.75317538,\n",
+ " 0.71045167, 0.75575987, 0.8700996 , 0.68470576, 0.74954458,\n",
+ " 0.81708488, 0.82238396, 0.77003303, 0.90494369, 0.87380964,\n",
+ " 0.84730445, 0.80425014, 0.81763735])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deltaR_std</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8555 0.9384 ... 0.9011 0.9902</div><input id='attrs-0d757cdd-02c7-4cea-9e66-3032d2046e67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d757cdd-02c7-4cea-9e66-3032d2046e67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2787a4d0-e153-47e3-a4ac-116af0584acc' class='xr-var-data-in' type='checkbox'><label for='data-2787a4d0-e153-47e3-a4ac-116af0584acc' 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([0.85545177, 0.93841195, 1.06069793, 0.97009039, 1.00235237,\n",
+ " 1.02465659, 1.08881174, 1.18354284, 1.10105758, 0.94419448,\n",
+ " 1.10333812, 1.25044795, 1.24834482, 1.38789353, 1.20968082,\n",
+ " 1.34141326, 1.32929632, 0.96091553, 1.13292954, 1.25282489,\n",
+ " 0.84890623, 1.14230059, 1.22441956, 1.11944006, 1.14878082,\n",
+ " 1.25239186, 1.10905762, 1.2463384 , 1.09378785, 0.86490763,\n",
+ " 0.9842779 , 0.97680361, 0.95354097, 1.11267323, 1.15434373,\n",
+ " 1.31644897, 1.22174905, 1.13620792, 1.34936305, 1.12999776,\n",
+ " 1.1443804 , 1.22406337, 1.10262927, 1.15136924, 1.22047518,\n",
+ " 1.04570819, 1.03606003, 1.11332781, 1.25878664, 1.06410925,\n",
+ " 1.01493852, 0.90763132, 1.18090303, 1.06900719, 1.08604532,\n",
+ " 1.05785876, 1.21670241, 1.11325976, 0.92150781, 1.02190008,\n",
+ " 1.17968835, 0.90456589, 1.19403934, 1.1603307 , 1.23112739,\n",
+ " 1.16134584, 1.70470913, 1.56421405, 1.38444676, 1.28389825,\n",
+ " 1.22478851, 1.2201193 , 0.99338145, 0.93029468, 1.12040987,\n",
+ " 0.92498353, 0.85140904, 1.10356919, 0.91579844, 1.09458168,\n",
+ " 1.02056374, 0.96768949, 0.88250351, 1.01015428, 0.95041354,\n",
+ " 0.83544882, 1.11615574, 1.03195493, 1.09973941, 1.03368069,\n",
+ " 1.06273812, 1.28234581, 0.96711403, 1.02144782, 1.17379835,\n",
+ " 1.06847914, 1.42512259, 1.29904952, 1.47429674, 1.18975112,\n",
+ " 1.18065232, 0.95923865, 0.94490389, 1.0050778 , 0.91978474,\n",
+ " 0.82042184, 1.0577044 , 1.03664646, 0.9958693 , 1.01540754,\n",
+ " 0.95103977, 0.97075627, 1.11194096, 0.99553242, 0.7820403 ,\n",
+ " 1.01667655, 0.84189865, 0.94093226, 1.12411352, 0.95974614,\n",
+ " 1.02574266, 1.06979631, 1.15320017, 1.10202532, 1.14919617,\n",
+ " 1.14551393, 0.91429609, 0.97152907, 0.93846568, 0.88445555,\n",
+ " 1.02541101, 0.90108751, 0.99023653])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deltaR_stderr</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.07418 0.06808 ... 0.09245 0.08032</div><input id='attrs-749c4320-c7fb-4668-9889-d2e86313a5b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-749c4320-c7fb-4668-9889-d2e86313a5b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73e083a2-9245-464d-972d-a4cd11d4d575' class='xr-var-data-in' type='checkbox'><label for='data-73e083a2-9245-464d-972d-a4cd11d4d575' 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([0.07417708, 0.06807956, 0.09934346, 0.05948006, 0.05748885,\n",
+ " 0.09596788, 0.07531468, 0.05788897, 0.08930756, 0.07220436,\n",
+ " 0.05277982, 0.09562414, 0.08634982, 0.05034419, 0.0925066 ,\n",
+ " 0.0927875 , 0.04705658, 0.08332194, 0.07836638, 0.04434952,\n",
+ " 0.06491748, 0.09265281, 0.04386938, 0.11485213, 0.07309517,\n",
+ " 0.04723485, 0.07671512, 0.14296482, 0.03740676, 0.09921171,\n",
+ " 0.0651854 , 0.03684085, 0.12629959, 0.06381619, 0.04476353,\n",
+ " 0.09106068, 0.11442728, 0.04537577, 0.08585792, 0.07483593,\n",
+ " 0.04875224, 0.07250726, 0.05656369, 0.04991819, 0.16165589,\n",
+ " 0.05235089, 0.04137625, 0.14746387, 0.06806739, 0.04186682,\n",
+ " 0.11642143, 0.0443937 , 0.04716072, 0.14159346, 0.04795007,\n",
+ " 0.04586399, 0.16115617, 0.04317036, 0.04982944, 0.13535397,\n",
+ " 0.04333693, 0.05033139, 0.15815437, 0.04262581, 0.07061 ,\n",
+ " 0.1332155 , 0.0589586 , 0.10819895, 0.07940347, 0.04778175,\n",
+ " 0.1622272 , 0.0659765 , 0.03696983, 0.12322054, 0.05747581,\n",
+ " 0.03694028, 0.09766331, 0.05167936, 0.03714053, 0.14498089,\n",
+ " 0.03902223, 0.04964142, 0.20246019, 0.03759405, 0.04875519,\n",
+ " 0.13552769, 0.04209661, 0.05741951, 0.11283088, 0.03952377,\n",
+ " 0.07351113, 0.07862567, 0.04841626, 0.16570072, 0.06021459,\n",
+ " 0.05945177, 0.32694554, 0.05268341, 0.19527537, 0.13647381,\n",
+ " 0.04864793, 0.12705426, 0.05793577, 0.05289883, 0.0667282 ,\n",
+ " 0.07683955, 0.04953155, 0.07520624, 0.09327171, 0.04659009,\n",
+ " 0.08246561, 0.07423559, 0.05319135, 0.07222351, 0.06781149,\n",
+ " 0.04761024, 0.068287 , 0.07631968, 0.05264144, 0.07784569,\n",
+ " 0.08894317, 0.04908562, 0.09999514, 0.08938606, 0.05272874,\n",
+ " 0.09932866, 0.07927953, 0.04549602, 0.08789536, 0.08283685,\n",
+ " 0.05260248, 0.09244963, 0.08031879])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>counts</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>133 190 114 266 ... 114 380 95 152</div><input id='attrs-87eac02b-3517-4db1-99b2-6d96dc6cd99b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87eac02b-3517-4db1-99b2-6d96dc6cd99b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25b62ad5-a01b-4523-9ada-093e0c708146' class='xr-var-data-in' type='checkbox'><label for='data-25b62ad5-a01b-4523-9ada-093e0c708146' 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([133, 190, 114, 266, 304, 114, 209, 418, 152, 171, 437, 171, 209,\n",
+ " 760, 171, 209, 798, 133, 209, 798, 171, 152, 779, 95, 247, 703,\n",
+ " 209, 76, 855, 76, 228, 703, 57, 304, 665, 209, 114, 627, 247,\n",
+ " 228, 551, 285, 380, 532, 57, 399, 627, 57, 342, 646, 76, 418,\n",
+ " 627, 57, 513, 532, 57, 665, 342, 57, 741, 323, 57, 741, 304,\n",
+ " 76, 836, 209, 304, 722, 57, 342, 722, 57, 380, 627, 76, 456,\n",
+ " 608, 57, 684, 380, 19, 722, 380, 38, 703, 323, 95, 684, 209,\n",
+ " 266, 399, 38, 380, 323, 19, 608, 57, 76, 589, 57, 266, 361,\n",
+ " 190, 114, 456, 190, 114, 475, 133, 171, 437, 190, 133, 456, 152,\n",
+ " 152, 456, 152, 133, 475, 133, 152, 475, 133, 133, 456, 114, 114,\n",
+ " 380, 95, 152])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d57cf439-b71e-4558-a766-eb4f7917e471' class='xr-section-summary-in' type='checkbox' checked><label for='section-d57cf439-b71e-4558-a766-eb4f7917e471' class='xr-section-summary' >Attributes: <span>(1)</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/202201/p002769/raw/r0425</dd></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (delay: 133)\n",
+ "Coordinates:\n",
+ " * delay (delay) float64 228.2 228.2 228.2 ... 229.5 229.5\n",
+ "Data variables:\n",
+ " FastADC5peaks (delay) float64 2.009e+05 1.958e+05 ... 1.937e+05\n",
+ " FastADC3peaks (delay) float64 7.286e+04 6.932e+04 ... 7.36e+04\n",
+ " FastADC5peaks_unpumped (delay) float64 2.007e+05 1.955e+05 ... 1.935e+05\n",
+ " FastADC3peaks_unpumped (delay) float64 7.226e+04 6.872e+04 ... 7.293e+04\n",
+ " PP800_DelayLine_binned (delay) float64 228.2 228.2 228.2 ... 229.5 229.5\n",
+ " deltaR (delay) float64 0.7039 0.7426 ... 0.8043 0.8176\n",
+ " deltaR_std (delay) float64 0.8555 0.9384 ... 0.9011 0.9902\n",
+ " deltaR_stderr (delay) float64 0.07418 0.06808 ... 0.09245 0.08032\n",
+ " counts (delay) int64 133 190 114 266 304 ... 114 380 95 152\n",
+ "Attributes:\n",
+ " runFolder: /gpfs/exfel/exp/SCS/202201/p002769/raw/r0425"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import toolbox_scs as tb\n",
+ "proposal, runNB = 2769, 425\n",
+ "fields = ['FastADC5raw', 'FastADC3raw', 'PP800_DelayLine', 'BAM1932S']\n",
+ "run, ds = tb.load(proposal, runNB, fields)\n",
+ "\n",
+ "refl = tb.reflectivity(ds, Iokey='FastADC5peaks', Irkey='FastADC3peaks',\n",
+ " delaykey='PP800_DelayLine',\n",
+ " binWidth=0.01, plot=True)\n",
+ "refl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6258228f",
+ "metadata": {},
+ "source": [
+ "The output is an `xarray.Dataset` that contains the binned `deltaR`, its standard deviation and standard error, as well as the delay line position bins and the counts per bin."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4c26f2df",
+ "metadata": {},
+ "source": [
+ "One can also convert the motor position axis in mm into temporal axis in ps. For this, the argument `positionToDelay` can be used, in combination with `origin` which gives the motor position for time zero and `invert`, which gives the sign of time axis relative to the motor axis. In this case, `binWidth` is the width in picosecond, and the output has a new coordinate `delay_ps`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "162c731e",
+ "metadata": {},
+ "outputs": [
+ {
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+ "}\n",
+ "\n",
+ ".xr-array-wrap {\n",
+ " grid-column: 1 / -1;\n",
+ " display: grid;\n",
+ " grid-template-columns: 20px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap > label {\n",
+ " grid-column: 1;\n",
+ " vertical-align: top;\n",
+ "}\n",
+ "\n",
+ ".xr-preview {\n",
+ " color: var(--xr-font-color3);\n",
+ "}\n",
+ "\n",
+ ".xr-array-preview,\n",
+ ".xr-array-data {\n",
+ " padding: 0 5px !important;\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-array-data,\n",
+ ".xr-array-in:checked ~ .xr-array-preview {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
+ " padding: 0 !important;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li {\n",
+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name {\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt, dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2 {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
+ "Dimensions: (delay: 133)\n",
+ "Coordinates:\n",
+ " * delay (delay) float64 228.2 228.2 228.2 ... 229.5 229.5\n",
+ "Data variables:\n",
+ " FastADC5peaks (delay) float64 2.009e+05 1.958e+05 ... 1.937e+05\n",
+ " FastADC3peaks (delay) float64 7.286e+04 6.932e+04 ... 7.36e+04\n",
+ " FastADC5peaks_unpumped (delay) float64 2.007e+05 1.955e+05 ... 1.935e+05\n",
+ " FastADC3peaks_unpumped (delay) float64 7.226e+04 6.872e+04 ... 7.293e+04\n",
+ " PP800_DelayLine_binned (delay) float64 228.2 228.2 228.2 ... 229.5 229.5\n",
+ " deltaR (delay) float64 0.7039 0.7426 ... 0.8043 0.8176\n",
+ " deltaR_std (delay) float64 0.8555 0.9384 ... 0.9011 0.9902\n",
+ " deltaR_stderr (delay) float64 0.07418 0.06808 ... 0.09245 0.08032\n",
+ " counts (delay) int64 133 190 114 266 304 ... 114 380 95 152\n",
+ "Attributes:\n",
+ " runFolder: /gpfs/exfel/exp/SCS/202201/p002769/raw/r0425</pre><div class='xr-wrap' hidden><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-bbcbed29-bcd0-4297-8b02-cb1293afa20d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bbcbed29-bcd0-4297-8b02-cb1293afa20d' 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'>delay</span>: 133</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ae4747ae-1e19-48cd-906e-4c2dfd9f1a89' class='xr-section-summary-in' type='checkbox' checked><label for='section-ae4747ae-1e19-48cd-906e-4c2dfd9f1a89' class='xr-section-summary' >Coordinates: <span>(1)</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'>delay</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>228.2 228.2 228.2 ... 229.5 229.5</div><input id='attrs-b3d5669a-e82b-468a-9a17-12c34016a42b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3d5669a-e82b-468a-9a17-12c34016a42b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee1a5c54-be59-479a-807d-cfb37ea8a6e5' class='xr-var-data-in' type='checkbox'><label for='data-ee1a5c54-be59-479a-807d-cfb37ea8a6e5' 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([228.19, 228.2 , 228.21, 228.22, 228.23, 228.24, 228.25, 228.26, 228.27,\n",
+ " 228.28, 228.29, 228.3 , 228.31, 228.32, 228.33, 228.34, 228.35, 228.36,\n",
+ " 228.37, 228.38, 228.39, 228.4 , 228.41, 228.42, 228.43, 228.44, 228.45,\n",
+ " 228.46, 228.47, 228.48, 228.49, 228.5 , 228.51, 228.52, 228.53, 228.54,\n",
+ " 228.55, 228.56, 228.57, 228.58, 228.59, 228.6 , 228.61, 228.62, 228.63,\n",
+ " 228.64, 228.65, 228.66, 228.67, 228.68, 228.69, 228.7 , 228.71, 228.72,\n",
+ " 228.73, 228.74, 228.75, 228.76, 228.77, 228.78, 228.79, 228.8 , 228.81,\n",
+ " 228.82, 228.83, 228.84, 228.85, 228.86, 228.87, 228.88, 228.89, 228.9 ,\n",
+ " 228.91, 228.92, 228.93, 228.94, 228.95, 228.96, 228.97, 228.98, 228.99,\n",
+ " 229. , 229.01, 229.02, 229.03, 229.04, 229.05, 229.06, 229.07, 229.08,\n",
+ " 229.09, 229.1 , 229.11, 229.12, 229.13, 229.14, 229.15, 229.16, 229.17,\n",
+ " 229.18, 229.19, 229.2 , 229.21, 229.22, 229.23, 229.24, 229.25, 229.26,\n",
+ " 229.27, 229.28, 229.29, 229.3 , 229.31, 229.32, 229.33, 229.34, 229.35,\n",
+ " 229.36, 229.37, 229.38, 229.39, 229.4 , 229.41, 229.42, 229.43, 229.44,\n",
+ " 229.45, 229.46, 229.47, 229.48, 229.49, 229.5 , 229.51])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-421259cb-0097-494b-8018-c278cc38ec5e' class='xr-section-summary-in' type='checkbox' checked><label for='section-421259cb-0097-494b-8018-c278cc38ec5e' class='xr-section-summary' >Data variables: <span>(9)</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>FastADC5peaks</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.009e+05 1.958e+05 ... 1.937e+05</div><input id='attrs-336355a5-6397-4ff1-8dc7-9febdc5eac72' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-336355a5-6397-4ff1-8dc7-9febdc5eac72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f411006-5c3f-4af9-bb82-878f9193fce9' class='xr-var-data-in' type='checkbox'><label for='data-1f411006-5c3f-4af9-bb82-878f9193fce9' 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([200920.80827068, 195825.84736842, 189510.48684211, 188501.2406015 ,\n",
+ " 188378.09210526, 178347.77631579, 172510.37559809, 180592.78229665,\n",
+ " 177619.125 , 188322.56725146, 188552.62128146, 182600.14327485,\n",
+ " 179260.08133971, 173384.92105263, 181677.9122807 , 173940.15311005,\n",
+ " 189150.84398496, 197525. , 192838.81578947, 192767.60588972,\n",
+ " 200930.50292398, 192317.79605263, 189984.51476252, 187612.27368421,\n",
+ " 187279.29554656, 182917.86984353, 188577.37559809, 183137.38815789,\n",
+ " 187506.92748538, 187692.29605263, 197161.31359649, 186850.98079659,\n",
+ " 191139.19298246, 183116.69736842, 185739.84586466, 181913.284689 ,\n",
+ " 190850.95614035, 189357.39314195, 180145.93117409, 187107.35087719,\n",
+ " 182932.89927405, 178612.57894737, 197816.58421053, 181232.96710526,\n",
+ " 186243.5877193 , 185072.10651629, 183689.51674641, 189223.09649123,\n",
+ " 184499.76461988, 189261.23916409, 189302.44078947, 201999.93660287,\n",
+ " 188284.12440191, 193327.71929825, 194833.64035088, 190444.93233083,\n",
+ " 188375.27192982, 191503.24285714, 191733.3377193 , 193085.35964912,\n",
+ " 188390.34210526, 200583.54489164, 191159.43859649, 191823.12753036,\n",
+ " 188113.84375 , 192676.14473684, 196094.08133971, 194363.0430622 ,\n",
+ " 179547.44736842, 191910.14819945, 192854.11403509, 184552.23976608,\n",
+ " 199775.16412742, 193359.28947368, 179800.69605263, 200066.39633174,\n",
+ " 195319.82894737, 182674.09758772, 195655.54276316, 186347. ,\n",
+ " 188683.27339181, 194058.23421053, 193583.84210526, 196854.35387812,\n",
+ " 201428.38815789, 194367.72368421, 194212.37055477, 206466.89164087,\n",
+ " 202257.77894737, 191893.76973684, 192610.90191388, 183270.17293233,\n",
+ " 195644.79949875, 192862.02631579, 188519.87763158, 191419.30959752,\n",
+ " 192627.44736842, 186425.55509868, 182790.13157895, 190021.81578947,\n",
+ " 193938.71052632, 197611.14035088, 185728.43421053, 203261.91689751,\n",
+ " 198541.83947368, 206242.96929825, 184223.10087719, 186485.88947368,\n",
+ " 196665.40789474, 191610.07578947, 186898.57142857, 190856.82748538,\n",
+ " 190029.75858124, 193314.24736842, 205812.34210526, 196283.98574561,\n",
+ " 204917.97368421, 204331.94736842, 184858.6622807 , 196732.83223684,\n",
+ " 202482.06390977, 195409.64210526, 189902.76315789, 186521.84210526,\n",
+ " 183957.52105263, 195779.66917293, 200891.96240602, 191778.62828947,\n",
+ " 195179.25 , 195449.13596491, 184506.06052632, 190612.88421053,\n",
+ " 193726.34210526])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC3peaks</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.286e+04 6.932e+04 ... 7.36e+04</div><input id='attrs-9142076a-f88b-432c-ab70-eaeb83ca457f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9142076a-f88b-432c-ab70-eaeb83ca457f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd5ea06b-d269-4d6c-adc3-75a54fe4b16a' class='xr-var-data-in' type='checkbox'><label for='data-cd5ea06b-d269-4d6c-adc3-75a54fe4b16a' 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([72856.94736842, 69320.45789474, 65163.81578947, 65712.34962406,\n",
+ " 65466.56578947, 59836.97368421, 56723.22488038, 60682.44617225,\n",
+ " 63105.80592105, 68624.78947368, 65546.69908467, 62159.0877193 ,\n",
+ " 60646.50239234, 56972.43355263, 61569.13450292, 57006.35645933,\n",
+ " 64891.44799499, 74590.20300752, 70388.3708134 , 69410.52506266,\n",
+ " 74529.08479532, 69272.17434211, 67872.14762516, 67641.01578947,\n",
+ " 65823.5951417 , 62037.55405405, 65903.47607656, 61076.13815789,\n",
+ " 65717.6497076 , 64752.94736842, 70336.92982456, 64022.44736842,\n",
+ " 65919.71929825, 62369.47532895, 63710.83383459, 61651.38755981,\n",
+ " 65728.10526316, 65805.31259968, 59630.70242915, 65816.33991228,\n",
+ " 63220.61433757, 59650.04035088, 75057.16710526, 62443.51785714,\n",
+ " 65161.93859649, 64979.57769424, 63049.64114833, 67905.80701754,\n",
+ " 63343.08625731, 67658.85758514, 66645.46710526, 75850.80502392,\n",
+ " 65682.94417863, 70237.10526316, 70576.00584795, 68002.45394737,\n",
+ " 65730.43859649, 68008.1593985 , 68256.3245614 , 69237.14035088,\n",
+ " 66743.81983806, 74070.8250774 , 65874.04385965, 66345.39203779,\n",
+ " 63851.84375 , 67526.69078947, 68314.92643541, 68100.3277512 ,\n",
+ " 57057.98684211, 64602.88642659, 66557.24561404, 62893.44005848,\n",
+ " 72320.7098338 , 68318.89473684, 61111.61710526, 74585.01594896,\n",
+ " 70878.75 , 63732.32017544, 70684.27549342, 66751.86842105,\n",
+ " 68464.04605263, 70226.39078947, 69473.26315789, 73670.37811634,\n",
+ " 74887.55526316, 75554.13157895, 71704.23897582, 76393.81733746,\n",
+ " 72372.25789474, 67687.82090643, 68657.34210526, 63098.15037594,\n",
+ " 72445.66165414, 71142.93421053, 67072.68157895, 69393.92260062,\n",
+ " 66224.47368421, 64661.50575658, 59763.68421053, 66291.94078947,\n",
+ " 69328.44482173, 74323.99122807, 66339.72180451, 75884.17313019,\n",
+ " 75811.38684211, 80378. , 65301.96820175, 66485.62368421,\n",
+ " 73480.75 , 69350.80526316, 68246.83458647, 69184.29239766,\n",
+ " 70283.79405034, 71150.48421053, 79372.39473684, 70509.36184211,\n",
+ " 80602.79934211, 78262.32894737, 63466.9627193 , 73199.79605263,\n",
+ " 75885.94736842, 70899.70526316, 70328.36466165, 65183.76973684,\n",
+ " 64047.02421053, 70102.63157895, 76442.21428571, 69687.48684211,\n",
+ " 73643.92982456, 73165.25 , 66983.13289474, 70555.86315789,\n",
+ " 73600.03289474])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC5peaks_unpumped</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.007e+05 1.955e+05 ... 1.935e+05</div><input id='attrs-912836b3-0034-497e-8ab6-f9b4329460f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-912836b3-0034-497e-8ab6-f9b4329460f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f6d7289-a8eb-4f12-b503-26a52beec6e9' class='xr-var-data-in' type='checkbox'><label for='data-8f6d7289-a8eb-4f12-b503-26a52beec6e9' 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([200694.83458647, 195514.69473684, 189227.92982456, 187790.19736842,\n",
+ " 187953.26809211, 177604.73245614, 172034.61244019, 180130.63157895,\n",
+ " 177259.54276316, 187929.04385965, 188007.41533181, 181952.4619883 ,\n",
+ " 178861.65550239, 172836.59473684, 181117.24853801, 173360.22488038,\n",
+ " 188746.0858396 , 197224.45112782, 192317.35167464, 192330.67293233,\n",
+ " 200344.16959064, 191837.80263158, 189574.24775353, 187415.01052632,\n",
+ " 186859.79149798, 182356.79871977, 188098.89712919, 182419.07894737,\n",
+ " 187010.6502924 , 186778.33552632, 196525.86842105, 186379.80369844,\n",
+ " 190684.72807018, 182624.29111842, 185207.22857143, 181195.3062201 ,\n",
+ " 190059.79824561, 189051.35326954, 179828.0465587 , 186593.45175439,\n",
+ " 182213.34482759, 178333.35438596, 197498.12368421, 180725.71616541,\n",
+ " 185644.26315789, 184536.4235589 , 183362.29585327, 188552.94736842,\n",
+ " 183860.20467836, 188857.51083591, 189332.57236842, 201740.62200957,\n",
+ " 187716.30143541, 193076.52631579, 194376.00584795, 189818.75469925,\n",
+ " 187266.53508772, 191055.23383459, 190971.43859649, 192383.68421053,\n",
+ " 188091.2145749 , 200226.3993808 , 190363.00877193, 191284.71592443,\n",
+ " 187636.53782895, 192679.59210526, 195681.7888756 , 194135.10047847,\n",
+ " 179222.42434211, 191471.56648199, 191703.79824561, 183994.07017544,\n",
+ " 199336.78878116, 192943.54385965, 179239.58552632, 199585.28628389,\n",
+ " 194555.97368421, 182331.26425439, 195267.46052632, 185590.5877193 ,\n",
+ " 188152.98538012, 193465.84736842, 192769.02631579, 196444.60872576,\n",
+ " 201143.46973684, 193225.85526316, 193947.40327169, 206039.19504644,\n",
+ " 201794.16315789, 191361.20102339, 192294.14354067, 182721.53947368,\n",
+ " 195173.45864662, 192232.57894737, 188264.06710526, 190931.79411765,\n",
+ " 192739. , 186124.78865132, 182381.03508772, 189245.05263158,\n",
+ " 193306.60950764, 197997.85964912, 185259.27255639, 202629.70498615,\n",
+ " 197956.64473684, 205554.84210526, 183575.36513158, 185874.69736842,\n",
+ " 196401.53947368, 191149.12842105, 185993.93609023, 190067.2748538 ,\n",
+ " 189583.86498856, 192791.53421053, 205283.07142857, 195779.65679825,\n",
+ " 204519.40460526, 203922.07565789, 184360.11951754, 196346.75657895,\n",
+ " 202086. , 194966.75368421, 189266.0075188 , 186279.40789474,\n",
+ " 183627.37473684, 195389.94736842, 200592.60526316, 191638.30372807,\n",
+ " 194755.84210526, 194759.74122807, 184065.14605263, 190230.07894737,\n",
+ " 193532.18421053])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC3peaks_unpumped</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.226e+04 6.872e+04 ... 7.293e+04</div><input id='attrs-12f07414-9070-4428-8b9d-4e662507c86a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12f07414-9070-4428-8b9d-4e662507c86a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4df09ae4-03c1-4637-8a58-5cfd98b3880a' class='xr-var-data-in' type='checkbox'><label for='data-4df09ae4-03c1-4637-8a58-5cfd98b3880a' 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([72262.08646617, 68715.23947368, 64584.20175439, 64910.38157895,\n",
+ " 64833.49342105, 59106.03947368, 56142.70574163, 60049.11363636,\n",
+ " 62547.86513158, 67968.01461988, 64835.23340961, 61410.92397661,\n",
+ " 60035.79425837, 56307.29210526, 60912.39181287, 56334.40669856,\n",
+ " 64290.58145363, 73954.81954887, 69602.3062201 , 68747.07330827,\n",
+ " 73756.76315789, 68501.36513158, 67171.10590501, 67085.43684211,\n",
+ " 65139.84412955, 61356.56899004, 65240.95454545, 60270.40789474,\n",
+ " 65060.14795322, 63931.81578947, 69509.25438596, 63370.50924609,\n",
+ " 65276.53508772, 61682.54111842, 63032.59398496, 60885.36602871,\n",
+ " 64811.21052632, 65174.12200957, 59047.29959514, 65140.74122807,\n",
+ " 62407.18602541, 59100.0877193 , 74451.58157895, 61769.08082707,\n",
+ " 64408.19298246, 64258.52005013, 62484.62679426, 67085.78947368,\n",
+ " 62560.12573099, 66987.68343653, 66205.17763158, 75253.58732057,\n",
+ " 64950.70414673, 69664.80701754, 69905.63840156, 67168.31109023,\n",
+ " 64805.73684211, 67350.6481203 , 67421.03654971, 68223.87719298,\n",
+ " 66152.13495277, 73418.97213622, 65013.28070175, 65595.4925776 ,\n",
+ " 63168.81085526, 67104.56578947, 68583.23624402, 68974.40909091,\n",
+ " 57950.04605263, 65383.96952909, 66946.88596491, 63264.74561404,\n",
+ " 72609.42243767, 68591.45614035, 61185.63552632, 74705.59888357,\n",
+ " 70767.28947368, 63788.25328947, 70662.82401316, 66433.33333333,\n",
+ " 68244.16008772, 69994.31710526, 69007.31578947, 73454.33448753,\n",
+ " 74729.07894737, 74920.73684211, 71491.28307255, 76140.02167183,\n",
+ " 71930.71578947, 67287.52997076, 68302.85167464, 62664.63909774,\n",
+ " 72003.86716792, 70552.96052632, 66759.21842105, 68895.63931889,\n",
+ " 66216.18421053, 64270.41036184, 59248.26315789, 65624.38815789,\n",
+ " 68693.34974533, 74345.26315789, 65766.31390977, 75203.19806094,\n",
+ " 75142.10789474, 79718.27631579, 64620.84758772, 65860.70789474,\n",
+ " 72925.60087719, 68719.71052632, 67388.46992481, 68395.48538012,\n",
+ " 69628.96453089, 70454.28947368, 78628.03007519, 69799.50109649,\n",
+ " 79918.22368421, 77523.79605263, 62848.68969298, 72544.64802632,\n",
+ " 75206.12030075, 70235.92421053, 69488.56390977, 64681.32236842,\n",
+ " 63473.79578947, 69390.87218045, 75703.09398496, 69123.03508772,\n",
+ " 72850.02631579, 72288.59649123, 66261.175 , 69864.12105263,\n",
+ " 72929.83552632])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PP800_DelayLine_binned</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>228.2 228.2 228.2 ... 229.5 229.5</div><input id='attrs-b7d0666c-3891-4429-8c57-5fa9ec15b260' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7d0666c-3891-4429-8c57-5fa9ec15b260' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2cab933-3819-499a-b553-7fe01900ccd7' class='xr-var-data-in' type='checkbox'><label for='data-e2cab933-3819-499a-b553-7fe01900ccd7' 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([228.19, 228.2 , 228.21, 228.22, 228.23, 228.24, 228.25, 228.26,\n",
+ " 228.27, 228.28, 228.29, 228.3 , 228.31, 228.32, 228.33, 228.34,\n",
+ " 228.35, 228.36, 228.37, 228.38, 228.39, 228.4 , 228.41, 228.42,\n",
+ " 228.43, 228.44, 228.45, 228.46, 228.47, 228.48, 228.49, 228.5 ,\n",
+ " 228.51, 228.52, 228.53, 228.54, 228.55, 228.56, 228.57, 228.58,\n",
+ " 228.59, 228.6 , 228.61, 228.62, 228.63, 228.64, 228.65, 228.66,\n",
+ " 228.67, 228.68, 228.69, 228.7 , 228.71, 228.72, 228.73, 228.74,\n",
+ " 228.75, 228.76, 228.77, 228.78, 228.79, 228.8 , 228.81, 228.82,\n",
+ " 228.83, 228.84, 228.85, 228.86, 228.87, 228.88, 228.89, 228.9 ,\n",
+ " 228.91, 228.92, 228.93, 228.94, 228.95, 228.96, 228.97, 228.98,\n",
+ " 228.99, 229. , 229.01, 229.02, 229.03, 229.04, 229.05, 229.06,\n",
+ " 229.07, 229.08, 229.09, 229.1 , 229.11, 229.12, 229.13, 229.14,\n",
+ " 229.15, 229.16, 229.17, 229.18, 229.19, 229.2 , 229.21, 229.22,\n",
+ " 229.23, 229.24, 229.25, 229.26, 229.27, 229.28, 229.29, 229.3 ,\n",
+ " 229.31, 229.32, 229.33, 229.34, 229.35, 229.36, 229.37, 229.38,\n",
+ " 229.39, 229.4 , 229.41, 229.42, 229.43, 229.44, 229.45, 229.46,\n",
+ " 229.47, 229.48, 229.49, 229.5 , 229.51])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deltaR</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7039 0.7426 ... 0.8043 0.8176</div><input id='attrs-9de14aef-08b5-4fc2-b8b2-c607dbcf807b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9de14aef-08b5-4fc2-b8b2-c607dbcf807b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-254b9031-6daa-47ed-9ace-52b5b957e31e' class='xr-var-data-in' type='checkbox'><label for='data-254b9031-6daa-47ed-9ace-52b5b957e31e' 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([ 0.70385124, 0.74255221, 0.77766998, 0.87017641, 0.74800608,\n",
+ " 0.86592472, 0.77128721, 0.80402254, 0.72950695, 0.75897156,\n",
+ " 0.83430226, 0.91101336, 0.78813673, 0.90790016, 0.79596252,\n",
+ " 0.87665395, 0.71683061, 0.71309442, 0.87295462, 0.75857596,\n",
+ " 0.77615089, 0.89693514, 0.83722401, 0.7661077 , 0.83756665,\n",
+ " 0.80458832, 0.78111814, 0.90833003, 0.76409443, 0.84349968,\n",
+ " 0.887969 , 0.79121062, 0.76239562, 0.83904305, 0.80538514,\n",
+ " 0.89965652, 1.00601237, 0.81416877, 0.80146468, 0.78085243,\n",
+ " 0.93731413, 0.76565696, 0.66771189, 0.82960754, 0.80956486,\n",
+ " 0.81472271, 0.7364535 , 0.86384325, 0.9037318 , 0.79830365,\n",
+ " 0.6533153 , 0.66037838, 0.84896043, 0.71070147, 0.73876342,\n",
+ " 0.92704938, 0.84830079, 0.76066275, 0.84390484, 1.09900499,\n",
+ " 0.75314225, 0.7053432 , 0.91964663, 0.88049906, 0.8337061 ,\n",
+ " 0.61332073, -0.4908297 , -1.38092418, -1.71694534, -1.41272649,\n",
+ " -1.12163788, -0.88495334, -0.62656642, -0.57221739, -0.43261207,\n",
+ " -0.4023655 , -0.21184841, -0.25310234, -0.17742998, 0.0979598 ,\n",
+ " 0.0878335 , 0.04662353, 0.29446724, 0.08987701, 0.06824385,\n",
+ " 0.31115536, 0.17084691, 0.12668076, 0.38790246, 0.34540416,\n",
+ " 0.38015566, 0.39309014, 0.39800717, 0.47889755, 0.32988133,\n",
+ " 0.48823219, 0.08986072, 0.45616356, 0.64689386, 0.6260554 ,\n",
+ " 0.63195432, 0.16682678, 0.62454736, 0.60145391, 0.61497957,\n",
+ " 0.52936518, 0.71623744, 0.65510835, 0.62349348, 0.68527502,\n",
+ " 0.79377491, 0.77646893, 0.72685078, 0.75086669, 0.71044156,\n",
+ " 0.78726658, 0.73096593, 0.75446749, 0.77079654, 0.75317538,\n",
+ " 0.71045167, 0.75575987, 0.8700996 , 0.68470576, 0.74954458,\n",
+ " 0.81708488, 0.82238396, 0.77003303, 0.90494369, 0.87380964,\n",
+ " 0.84730445, 0.80425014, 0.81763735])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deltaR_std</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8555 0.9384 ... 0.9011 0.9902</div><input id='attrs-dcdaac0a-6e49-4672-a502-0f0013d0d8b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcdaac0a-6e49-4672-a502-0f0013d0d8b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1139164-64f1-4624-a117-980505cad8d4' class='xr-var-data-in' type='checkbox'><label for='data-c1139164-64f1-4624-a117-980505cad8d4' 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([0.85545177, 0.93841195, 1.06069793, 0.97009039, 1.00235237,\n",
+ " 1.02465659, 1.08881174, 1.18354284, 1.10105758, 0.94419448,\n",
+ " 1.10333812, 1.25044795, 1.24834482, 1.38789353, 1.20968082,\n",
+ " 1.34141326, 1.32929632, 0.96091553, 1.13292954, 1.25282489,\n",
+ " 0.84890623, 1.14230059, 1.22441956, 1.11944006, 1.14878082,\n",
+ " 1.25239186, 1.10905762, 1.2463384 , 1.09378785, 0.86490763,\n",
+ " 0.9842779 , 0.97680361, 0.95354097, 1.11267323, 1.15434373,\n",
+ " 1.31644897, 1.22174905, 1.13620792, 1.34936305, 1.12999776,\n",
+ " 1.1443804 , 1.22406337, 1.10262927, 1.15136924, 1.22047518,\n",
+ " 1.04570819, 1.03606003, 1.11332781, 1.25878664, 1.06410925,\n",
+ " 1.01493852, 0.90763132, 1.18090303, 1.06900719, 1.08604532,\n",
+ " 1.05785876, 1.21670241, 1.11325976, 0.92150781, 1.02190008,\n",
+ " 1.17968835, 0.90456589, 1.19403934, 1.1603307 , 1.23112739,\n",
+ " 1.16134584, 1.70470913, 1.56421405, 1.38444676, 1.28389825,\n",
+ " 1.22478851, 1.2201193 , 0.99338145, 0.93029468, 1.12040987,\n",
+ " 0.92498353, 0.85140904, 1.10356919, 0.91579844, 1.09458168,\n",
+ " 1.02056374, 0.96768949, 0.88250351, 1.01015428, 0.95041354,\n",
+ " 0.83544882, 1.11615574, 1.03195493, 1.09973941, 1.03368069,\n",
+ " 1.06273812, 1.28234581, 0.96711403, 1.02144782, 1.17379835,\n",
+ " 1.06847914, 1.42512259, 1.29904952, 1.47429674, 1.18975112,\n",
+ " 1.18065232, 0.95923865, 0.94490389, 1.0050778 , 0.91978474,\n",
+ " 0.82042184, 1.0577044 , 1.03664646, 0.9958693 , 1.01540754,\n",
+ " 0.95103977, 0.97075627, 1.11194096, 0.99553242, 0.7820403 ,\n",
+ " 1.01667655, 0.84189865, 0.94093226, 1.12411352, 0.95974614,\n",
+ " 1.02574266, 1.06979631, 1.15320017, 1.10202532, 1.14919617,\n",
+ " 1.14551393, 0.91429609, 0.97152907, 0.93846568, 0.88445555,\n",
+ " 1.02541101, 0.90108751, 0.99023653])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deltaR_stderr</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.07418 0.06808 ... 0.09245 0.08032</div><input id='attrs-7a2f82bc-4fad-4d0d-ba41-e8be61c58c03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a2f82bc-4fad-4d0d-ba41-e8be61c58c03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83278f27-9d16-4f22-9bc3-45de7991bc1d' class='xr-var-data-in' type='checkbox'><label for='data-83278f27-9d16-4f22-9bc3-45de7991bc1d' 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([0.07417708, 0.06807956, 0.09934346, 0.05948006, 0.05748885,\n",
+ " 0.09596788, 0.07531468, 0.05788897, 0.08930756, 0.07220436,\n",
+ " 0.05277982, 0.09562414, 0.08634982, 0.05034419, 0.0925066 ,\n",
+ " 0.0927875 , 0.04705658, 0.08332194, 0.07836638, 0.04434952,\n",
+ " 0.06491748, 0.09265281, 0.04386938, 0.11485213, 0.07309517,\n",
+ " 0.04723485, 0.07671512, 0.14296482, 0.03740676, 0.09921171,\n",
+ " 0.0651854 , 0.03684085, 0.12629959, 0.06381619, 0.04476353,\n",
+ " 0.09106068, 0.11442728, 0.04537577, 0.08585792, 0.07483593,\n",
+ " 0.04875224, 0.07250726, 0.05656369, 0.04991819, 0.16165589,\n",
+ " 0.05235089, 0.04137625, 0.14746387, 0.06806739, 0.04186682,\n",
+ " 0.11642143, 0.0443937 , 0.04716072, 0.14159346, 0.04795007,\n",
+ " 0.04586399, 0.16115617, 0.04317036, 0.04982944, 0.13535397,\n",
+ " 0.04333693, 0.05033139, 0.15815437, 0.04262581, 0.07061 ,\n",
+ " 0.1332155 , 0.0589586 , 0.10819895, 0.07940347, 0.04778175,\n",
+ " 0.1622272 , 0.0659765 , 0.03696983, 0.12322054, 0.05747581,\n",
+ " 0.03694028, 0.09766331, 0.05167936, 0.03714053, 0.14498089,\n",
+ " 0.03902223, 0.04964142, 0.20246019, 0.03759405, 0.04875519,\n",
+ " 0.13552769, 0.04209661, 0.05741951, 0.11283088, 0.03952377,\n",
+ " 0.07351113, 0.07862567, 0.04841626, 0.16570072, 0.06021459,\n",
+ " 0.05945177, 0.32694554, 0.05268341, 0.19527537, 0.13647381,\n",
+ " 0.04864793, 0.12705426, 0.05793577, 0.05289883, 0.0667282 ,\n",
+ " 0.07683955, 0.04953155, 0.07520624, 0.09327171, 0.04659009,\n",
+ " 0.08246561, 0.07423559, 0.05319135, 0.07222351, 0.06781149,\n",
+ " 0.04761024, 0.068287 , 0.07631968, 0.05264144, 0.07784569,\n",
+ " 0.08894317, 0.04908562, 0.09999514, 0.08938606, 0.05272874,\n",
+ " 0.09932866, 0.07927953, 0.04549602, 0.08789536, 0.08283685,\n",
+ " 0.05260248, 0.09244963, 0.08031879])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>counts</span></div><div class='xr-var-dims'>(delay)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>133 190 114 266 ... 114 380 95 152</div><input id='attrs-cd25ff3c-57c7-425e-b722-aa2a9402d4d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd25ff3c-57c7-425e-b722-aa2a9402d4d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6a1d74f-b45c-419f-80ac-3a2be50aaa59' class='xr-var-data-in' type='checkbox'><label for='data-a6a1d74f-b45c-419f-80ac-3a2be50aaa59' 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([133, 190, 114, 266, 304, 114, 209, 418, 152, 171, 437, 171, 209,\n",
+ " 760, 171, 209, 798, 133, 209, 798, 171, 152, 779, 95, 247, 703,\n",
+ " 209, 76, 855, 76, 228, 703, 57, 304, 665, 209, 114, 627, 247,\n",
+ " 228, 551, 285, 380, 532, 57, 399, 627, 57, 342, 646, 76, 418,\n",
+ " 627, 57, 513, 532, 57, 665, 342, 57, 741, 323, 57, 741, 304,\n",
+ " 76, 836, 209, 304, 722, 57, 342, 722, 57, 380, 627, 76, 456,\n",
+ " 608, 57, 684, 380, 19, 722, 380, 38, 703, 323, 95, 684, 209,\n",
+ " 266, 399, 38, 380, 323, 19, 608, 57, 76, 589, 57, 266, 361,\n",
+ " 190, 114, 456, 190, 114, 475, 133, 171, 437, 190, 133, 456, 152,\n",
+ " 152, 456, 152, 133, 475, 133, 152, 475, 133, 133, 456, 114, 114,\n",
+ " 380, 95, 152])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b4744e6-e835-4976-acaf-91208bdda9e9' class='xr-section-summary-in' type='checkbox' checked><label for='section-4b4744e6-e835-4976-acaf-91208bdda9e9' class='xr-section-summary' >Attributes: <span>(1)</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/202201/p002769/raw/r0425</dd></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (delay: 133)\n",
+ "Coordinates:\n",
+ " * delay (delay) float64 228.2 228.2 228.2 ... 229.5 229.5\n",
+ "Data variables:\n",
+ " FastADC5peaks (delay) float64 2.009e+05 1.958e+05 ... 1.937e+05\n",
+ " FastADC3peaks (delay) float64 7.286e+04 6.932e+04 ... 7.36e+04\n",
+ " FastADC5peaks_unpumped (delay) float64 2.007e+05 1.955e+05 ... 1.935e+05\n",
+ " FastADC3peaks_unpumped (delay) float64 7.226e+04 6.872e+04 ... 7.293e+04\n",
+ " PP800_DelayLine_binned (delay) float64 228.2 228.2 228.2 ... 229.5 229.5\n",
+ " deltaR (delay) float64 0.7039 0.7426 ... 0.8043 0.8176\n",
+ " deltaR_std (delay) float64 0.8555 0.9384 ... 0.9011 0.9902\n",
+ " deltaR_stderr (delay) float64 0.07418 0.06808 ... 0.09245 0.08032\n",
+ " counts (delay) int64 133 190 114 266 304 ... 114 380 95 152\n",
+ "Attributes:\n",
+ " runFolder: /gpfs/exfel/exp/SCS/202201/p002769/raw/r0425"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 432x288 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "refl = tb.reflectivity(ds, Iokey='FastADC5peaks', Irkey='FastADC3peaks',\n",
+ " delaykey='PP800_DelayLine',\n",
+ " positionToDelay=True, origin=228.845, invert=False,\n",
+ " binWidth=0.01, plot=True)\n",
+ "refl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf8fa1d7",
+ "metadata": {},
+ "source": [
+ "## Correction by the BAM"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2e84a09e",
+ "metadata": {},
+ "source": [
+ "The BAM data can be taken into account by calculating the delay from the motor position and adding the BAM values to it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1600492a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds['delay'] = ds['BAM1932S'] + tb.positionToDelay(ds['PP800_DelayLine'], origin=228.845, invert=False)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "289344cd",
+ "metadata": {},
+ "source": [
+ "Once this is done, the new `delay` variable has two dimensions `trainId` and `sa3_pId`, while the OL photodiodes (`FastADC3peaks` and `FastADC5peaks`) have dimensions `trainId` and `ol_pId`. The aligment of the OL pulses to the FEL pulses can be done by shifting the `ol_pId` coordinates:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "6d159714",
+ "metadata": {},
+ "outputs": [
+ {
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+ " grid-column: 2 / -1;\n",
+ "}\n",
+ "\n",
+ ".xr-section-details {\n",
+ " display: none;\n",
+ " grid-column: 1 / -1;\n",
+ " margin-bottom: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap {\n",
+ " grid-column: 1 / -1;\n",
+ " display: grid;\n",
+ " grid-template-columns: 20px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap > label {\n",
+ " grid-column: 1;\n",
+ " vertical-align: top;\n",
+ "}\n",
+ "\n",
+ ".xr-preview {\n",
+ " color: var(--xr-font-color3);\n",
+ "}\n",
+ "\n",
+ ".xr-array-preview,\n",
+ ".xr-array-data {\n",
+ " padding: 0 5px !important;\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-array-data,\n",
+ ".xr-array-in:checked ~ .xr-array-preview {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
+ " padding: 0 !important;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li {\n",
+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name {\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt, dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2 {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
+ "Dimensions: (pulse_slot: 2700, sa3_pId: 38, trainId: 2222)\n",
+ "Coordinates:\n",
+ " * sa3_pId (sa3_pId) int64 542 582 622 662 ... 1902 1942 1982 2022\n",
+ " * trainId (trainId) uint64 1298104001 1298104002 ... 1298106265\n",
+ "Dimensions without coordinates: pulse_slot\n",
+ "Data variables:\n",
+ " bunchPatternTable (trainId, pulse_slot) uint32 2178857 0 2097193 ... 0 0 0\n",
+ " PP800_DelayLine (trainId) float64 229.1 229.1 229.1 ... 228.3 228.3 228.3\n",
+ " BAM1932S (trainId, sa3_pId) float32 -0.1843975 nan ... nan\n",
+ " delay (trainId, sa3_pId) float64 1.444 nan 1.468 ... -3.665 nan\n",
+ " FastADC3peaks (trainId, sa3_pId) float64 8.711e+04 ... 5.205e+04\n",
+ " FastADC5peaks (trainId, sa3_pId) float64 2.083e+05 ... 1.712e+05\n",
+ "Attributes:\n",
+ " runFolder: /gpfs/exfel/exp/SCS/202201/p002769/raw/r0425</pre><div class='xr-wrap' hidden><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-33f32d8b-cbee-481d-9a3b-bbdf662bd3b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-33f32d8b-cbee-481d-9a3b-bbdf662bd3b1' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>pulse_slot</span>: 2700</li><li><span class='xr-has-index'>sa3_pId</span>: 38</li><li><span class='xr-has-index'>trainId</span>: 2222</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-577a621b-34d3-4514-9749-867367420f85' class='xr-section-summary-in' type='checkbox' checked><label for='section-577a621b-34d3-4514-9749-867367420f85' class='xr-section-summary' >Coordinates: <span>(2)</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'>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'>542 582 622 662 ... 1942 1982 2022</div><input id='attrs-1a48d097-2294-4fb8-82e8-8c18ea596bff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a48d097-2294-4fb8-82e8-8c18ea596bff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1268988-e219-4181-8d14-d919fb1961b1' class='xr-var-data-in' type='checkbox'><label for='data-b1268988-e219-4181-8d14-d919fb1961b1' 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([ 542, 582, 622, 662, 702, 742, 782, 822, 862, 902, 942, 982,\n",
+ " 1022, 1062, 1102, 1142, 1182, 1222, 1262, 1302, 1342, 1382, 1422, 1462,\n",
+ " 1502, 1542, 1582, 1622, 1662, 1702, 1742, 1782, 1822, 1862, 1902, 1942,\n",
+ " 1982, 2022])</pre></div></li><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'>1298104001 ... 1298106265</div><input id='attrs-20e80d83-f44d-47a2-b266-368fa60330e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20e80d83-f44d-47a2-b266-368fa60330e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eb819d7-a964-40e3-a63e-3ac83495a39c' class='xr-var-data-in' type='checkbox'><label for='data-1eb819d7-a964-40e3-a63e-3ac83495a39c' 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([1298104001, 1298104002, 1298104003, ..., 1298106263, 1298106264,\n",
+ " 1298106265], dtype=uint64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e51a52d-6409-426e-a4b6-c427c777c7d8' class='xr-section-summary-in' type='checkbox' checked><label for='section-4e51a52d-6409-426e-a4b6-c427c777c7d8' class='xr-section-summary' >Data variables: <span>(6)</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>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'>2178857 0 2097193 0 ... 0 0 0 0</div><input id='attrs-e4a4043e-4036-4317-acf9-ae00a3f682d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4a4043e-4036-4317-acf9-ae00a3f682d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20526b74-51c6-43c7-aa14-15292a43c57f' class='xr-var-data-in' type='checkbox'><label for='data-20526b74-51c6-43c7-aa14-15292a43c57f' 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([[2178857, 0, 2097193, ..., 0, 0, 0],\n",
+ " [2113321, 0, 2097193, ..., 0, 0, 0],\n",
+ " [2113321, 0, 2097193, ..., 0, 0, 0],\n",
+ " ...,\n",
+ " [2113321, 0, 2097193, ..., 0, 0, 0],\n",
+ " [2113321, 0, 2097193, ..., 0, 0, 0],\n",
+ " [2113321, 0, 2097193, ..., 0, 0, 0]],\n",
+ " dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PP800_DelayLine</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>229.1 229.1 229.1 ... 228.3 228.3</div><input id='attrs-0c9e6c2f-c256-4d69-a1cf-bc615582c988' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c9e6c2f-c256-4d69-a1cf-bc615582c988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4d5641c-ffc4-4eec-bd8b-b717206b4dfc' class='xr-var-data-in' type='checkbox'><label for='data-c4d5641c-ffc4-4eec-bd8b-b717206b4dfc' 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([229.08908844, 229.08906555, 229.088974 , ..., 228.31462097,\n",
+ " 228.31329346, 228.31098175])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BAM1932S</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'>-0.1843975 nan ... -0.1027241 nan</div><input id='attrs-b26d5271-aef2-4363-af7e-e139e0d18413' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b26d5271-aef2-4363-af7e-e139e0d18413' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c87b8217-393a-4d5c-8b9d-d81cf1b2e802' class='xr-var-data-in' type='checkbox'><label for='data-c87b8217-393a-4d5c-8b9d-d81cf1b2e802' 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([[-0.1843975 , nan, -0.16020365, ..., nan,\n",
+ " -0.11839612, nan],\n",
+ " [-0.21036915, nan, -0.18972209, ..., nan,\n",
+ " -0.12564458, nan],\n",
+ " [-0.16471547, nan, -0.16057302, ..., nan,\n",
+ " -0.12399466, nan],\n",
+ " ...,\n",
+ " [-0.14423086, nan, -0.12761527, ..., nan,\n",
+ " -0.13162898, nan],\n",
+ " [-0.15879111, nan, -0.157679 , ..., nan,\n",
+ " -0.12788105, nan],\n",
+ " [-0.11632565, nan, -0.13366464, ..., nan,\n",
+ " -0.1027241 , nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delay</span></div><div class='xr-var-dims'>(trainId, sa3_pId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.444 nan 1.468 ... nan -3.665 nan</div><input id='attrs-f1d858d4-78ae-4e2f-8880-e2cf8260d0b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1d858d4-78ae-4e2f-8880-e2cf8260d0b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d503968-0284-4afd-89d8-ca1903c77e07' class='xr-var-data-in' type='checkbox'><label for='data-7d503968-0284-4afd-89d8-ca1903c77e07' 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([[ 1.44398529, nan, 1.46817914, ..., nan,\n",
+ " 1.50998667, nan],\n",
+ " [ 1.41786094, nan, 1.43850801, ..., nan,\n",
+ " 1.50258552, nan],\n",
+ " [ 1.46290386, nan, 1.4670463 , ..., nan,\n",
+ " 1.50362467, nan],\n",
+ " ...,\n",
+ " [-3.68253887, nan, -3.66592329, ..., nan,\n",
+ " -3.66993699, nan],\n",
+ " [-3.70595535, nan, -3.70484324, ..., nan,\n",
+ " -3.67504529, nan],\n",
+ " [-3.67891193, nan, -3.69625092, ..., nan,\n",
+ " -3.66531038, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC3peaks</span></div><div class='xr-var-dims'>(trainId, sa3_pId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.711e+04 8.624e+04 ... 5.205e+04</div><input id='attrs-fab2f535-bf9e-419a-84d8-293310c99aa0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fab2f535-bf9e-419a-84d8-293310c99aa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1bb3282-eba6-4643-aef2-4abcd5f7c588' class='xr-var-data-in' type='checkbox'><label for='data-e1bb3282-eba6-4643-aef2-4abcd5f7c588' 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([[87111.5, 86240. , 84647. , ..., 63387.5, 60995. , 61653. ],\n",
+ " [84728. , 86681.5, 86086. , ..., 61118. , 60375. , 60461. ],\n",
+ " [88389. , 86537.5, 82353. , ..., 57068.5, 59046.5, 58784. ],\n",
+ " ...,\n",
+ " [73646. , 75065.5, 73397.5, ..., 51347.5, 49307.5, 48422.5],\n",
+ " [69838.5, 70282. , 71442.5, ..., 48335. , 46444. , 48895.5],\n",
+ " [76128. , 75253. , 76561. , ..., 51300. , 50681.5, 52051. ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC5peaks</span></div><div class='xr-var-dims'>(trainId, sa3_pId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.083e+05 2.065e+05 ... 1.712e+05</div><input id='attrs-2c8ebdbc-9a9f-4ea3-a71d-498561b6f504' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c8ebdbc-9a9f-4ea3-a71d-498561b6f504' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b58706a6-477b-4c5c-9880-dd9950ed73fc' class='xr-var-data-in' type='checkbox'><label for='data-b58706a6-477b-4c5c-9880-dd9950ed73fc' 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([[208281.5, 206494.5, 204945. , ..., 195133. , 191177. , 193192. ],\n",
+ " [203924. , 205979.5, 205714.5, ..., 191344. , 190208.5, 190552.5],\n",
+ " [210528. , 208069. , 203214.5, ..., 185302. , 190319.5, 189757. ],\n",
+ " ...,\n",
+ " [179038.5, 181458. , 181309.5, ..., 168465. , 165446.5, 163352.5],\n",
+ " [177458.5, 177457. , 180836. , ..., 164743.5, 161151.5, 167133.5],\n",
+ " [185155. , 184248.5, 186669. , ..., 168975. , 168502.5, 171166. ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a15388b-c93e-4154-b2b8-8d216c268f68' class='xr-section-summary-in' type='checkbox' checked><label for='section-2a15388b-c93e-4154-b2b8-8d216c268f68' class='xr-section-summary' >Attributes: <span>(1)</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/202201/p002769/raw/r0425</dd></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset>\n",
+ "Dimensions: (pulse_slot: 2700, sa3_pId: 38, trainId: 2222)\n",
+ "Coordinates:\n",
+ " * sa3_pId (sa3_pId) int64 542 582 622 662 ... 1902 1942 1982 2022\n",
+ " * trainId (trainId) uint64 1298104001 1298104002 ... 1298106265\n",
+ "Dimensions without coordinates: pulse_slot\n",
+ "Data variables:\n",
+ " bunchPatternTable (trainId, pulse_slot) uint32 2178857 0 2097193 ... 0 0 0\n",
+ " PP800_DelayLine (trainId) float64 229.1 229.1 229.1 ... 228.3 228.3 228.3\n",
+ " BAM1932S (trainId, sa3_pId) float32 -0.1843975 nan ... nan\n",
+ " delay (trainId, sa3_pId) float64 1.444 nan 1.468 ... -3.665 nan\n",
+ " FastADC3peaks (trainId, sa3_pId) float64 8.711e+04 ... 5.205e+04\n",
+ " FastADC5peaks (trainId, sa3_pId) float64 2.083e+05 ... 1.712e+05\n",
+ "Attributes:\n",
+ " runFolder: /gpfs/exfel/exp/SCS/202201/p002769/raw/r0425"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "newds = tb.align_ol_to_fel_pId(ds)\n",
+ "newds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd3828e0",
+ "metadata": {},
+ "source": [
+ "When plotting the reflectivity, one can use `units='ps'` to specify the correct units of the bottom axis (by default `units='mm'`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a8623467",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "refl = tb.reflectivity(newds, delaykey='delay', units='ps')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "156d25c6",
+ "metadata": {},
+ "source": [
+ "The shift in time corresponds to the BAM values, whose average in this run is `newds['BAM1932S'].mean().values = -121.54 fs`."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "xfel (Python 3.7)",
+ "language": "python",
+ "name": "xfel"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/howtos.rst b/doc/howtos.rst
index f6841ff7415f4d6b4ded405c06b443890d75b430..e285ab9cfebe134db4e8a0a575a02c6f4a782cf1 100644
--- a/doc/howtos.rst
+++ b/doc/howtos.rst
@@ -18,6 +18,16 @@ Extracting peaks from digitizers
--------------------------------
* :doc:`How to extract peaks from digitizer traces <How to extract peaks from digitizer traces>`.
+
+Finding time overlap by transient reflectivity
+-----------------------------------------------------------------
+
+Transient reflectivity of the optical laser measured on a large bandgap material pumped by the FEL is often used at SCS to find the time overlap between the two beams. The example notebook
+
+* :doc:`Transient reflectivity measurement <Transient reflectivity measurement>`
+
+shows how to analyze such data, including correcting the delay by the bunch arrival monitor (BAM).
+
DSSC
----
diff --git a/src/toolbox_scs/misc/laser_utils.py b/src/toolbox_scs/misc/laser_utils.py
index 0f052966b995f89e013779378436940bd4c79c59..9395f046fec085b6230430b485934aafa23c7110 100644
--- a/src/toolbox_scs/misc/laser_utils.py
+++ b/src/toolbox_scs/misc/laser_utils.py
@@ -1,7 +1,9 @@
__all__ = [
'degToRelPower',
'positionToDelay',
+ 'delayToPosition',
'fluenceCalibration',
+ 'align_ol_to_fel_pId'
]
import numpy as np
@@ -24,6 +26,22 @@ def positionToDelay(pos, origin=0, invert=True, reflections=1):
return 2*reflections*(pos-origin)*x/c_
+def delayToPosition(delay, origin=0, invert=True, reflections=1):
+ ''' converts an optical delay in picosecond into a motor position in mm
+ Inputs:
+ delay: array-like delay in ps
+ origin: motor position of time zero in mm
+ invert: bool, inverts the sign of delay if True
+ reflections: number of bounces in the delay stage
+
+ Output:
+ delay in picosecond
+ '''
+ c_ = 299792458 * 1e-9 # speed of light in mm/ps
+ x = -1 if invert else 1
+ return origin + 0.5 * x * delay * c_ / reflections
+
+
def degToRelPower(x, theta0=0):
''' converts a half-wave plate position in degrees into relative power
between 0 and 1.
@@ -112,3 +130,46 @@ def fluenceCalibration(hwp, power_mW, npulses, w0x, w0y=None,
ax2.set_ylabel(r'Pulse energy [$\mu$J]')
return F*1e-1, fit_F, E*1e6, fit_E
+
+
+def align_ol_to_fel_pId(ds, ol_dim='ol_pId', fel_dim='sa3_pId',
+ offset=0, fill_value=np.nan):
+ '''
+ Aligns the optical laser (OL) pulse Ids to the FEL pulse Ids.
+ The new OL coordinates are calculated as ds[ol_dim] +
+ ds[fel_dim][0] + offset. The ol_dim is then removed, and if the number
+ of OL and FEL pulses are different, the missing values are replaced by
+ fill_value (NaN by default).
+
+ Parameters
+ ----------
+ ds: xarray.Dataset
+ Dataset containing both OL and FEL dimensions
+ ol_dim: str
+ name of the OL dimension
+ fel_dim: str
+ name of the FEL dimension
+ offset: int
+ offset added to the OL pulse Ids.
+ fill_value: (scalar or dict-like, optional)
+ Value to use for newly missing values. If a dict-like, maps variable
+ names to fill values. Use a data array’s name to refer to its values.
+
+ Output
+ ------
+ ds: xarray.Dataset
+ The newly aligned dataset
+ '''
+ fel_vars = [v for v in ds if fel_dim in ds[v].dims]
+ ol_vars = [v for v in ds if ol_dim in ds[v].dims] + [ol_dim]
+ if len(set.intersection(set(fel_vars), set(ol_vars))) > 0:
+ raise ValueError('Variables share ol and fel dimensions: no alignment'
+ ' possible.')
+ ds_fel = ds.drop(ol_vars)
+ ds_ol = ds[ol_vars]
+ ds_ol = ds_ol.assign_coords({ol_dim: ds[ol_dim] +
+ ds[fel_dim][0].values + offset})
+ ds_ol = ds_ol.rename({ol_dim: fel_dim})
+ ds = ds_fel.merge(ds_ol, join='outer', fill_value=fill_value)
+ return ds
+
diff --git a/src/toolbox_scs/routines/Reflectivity.py b/src/toolbox_scs/routines/Reflectivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..eef1cb375826fcd3f05f439bfb6c430c9db486fb
--- /dev/null
+++ b/src/toolbox_scs/routines/Reflectivity.py
@@ -0,0 +1,220 @@
+""" Toolbox for SCS.
+
+ Various utilities function to quickly process data measured
+ at the SCS instrument.
+
+ Copyright (2019-) SCS Team.
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+import re
+from toolbox_scs.misc.laser_utils import positionToDelay as pTd
+from toolbox_scs.routines.XAS import xas
+
+__all__ = [
+ 'reflectivity'
+]
+
+
+def prepare_reflectivity_ds(ds, Iokey, Irkey, alternateTrains,
+ pumpOnEven, pumpedOnly):
+ """
+ Sorts the dataset according to the bunch pattern:
+ Alternating pumped/unpumped pulses, alternating pumped/unpumped
+ trains, or pumped only.
+ """
+ assert ds[Iokey].dims == ds[Irkey].dims, \
+ f"{Iokey} and {Irkey} do not share dimensions."
+ if alternateTrains:
+ p = 0 if pumpOnEven else 1
+ pumped_tid = ds['trainId'].where(ds.trainId % 2 == p, drop=True)
+ unpumped_tid = ds['trainId'].where(ds.trainId % 2 == int(not p),
+ drop=True)
+ max_size = min(pumped_tid.size, unpumped_tid.size)
+ pumped = ds.sel(trainId=pumped_tid[:max_size])
+ unpumped = ds.sel(trainId=unpumped_tid[:max_size]
+ ).assign_coords(trainId=pumped.trainId)
+ for v in [Iokey, Irkey]:
+ pumped[v+'_unpumped'] = unpumped[v].rename(v+'_unpumped')
+ ds = pumped
+ elif pumpedOnly is False:
+ # check that number of pulses is even with pumped / unpumped pattern
+ dim_name = [dim for dim in ds[Iokey].dims if dim != 'trainId'][0]
+ if ds[dim_name].size % 2 == 1:
+ ds = ds.isel({dim_name: slice(0, -1)})
+ print('The dataset contains an odd number of pulses '
+ 'per train. Ignoring the last pulse.')
+ pumped = ds.isel({dim_name: slice(0, None, 2)})
+ unpumped = ds.isel({dim_name: slice(1, None, 2)}).assign_coords(
+ {dim_name: pumped[dim_name]})
+ for v in [Iokey, Irkey]:
+ pumped[v+'_unpumped'] = unpumped[v].rename(v+'_unpumped')
+ ds = pumped
+ return ds
+
+
+def reflectivity(data, Iokey='FastADC5peaks', Irkey='FastADC3peaks',
+ delaykey='PP800_DelayLine', binWidth=0.05,
+ positionToDelay=True, origin=None, invert=False,
+ pumpedOnly=False, alternateTrains=False, pumpOnEven=True,
+ Ioweights=False, plot=True, plotErrors=True, units='mm'
+ ):
+ """
+ Computes the reflectivity R = 100*(Ir/Io[pumped] / Ir/Io[unpumped] - 1)
+ as a function of delay. Delay can be a motor position in mm or an
+ optical delay in ps, with possibility to convert from position to delay.
+ The default scheme is alternating pulses pumped/unpumped/... in each
+ train, also possible are alternating trains and pumped only.
+ If fitting is enabled, attempts a double exponential (default) or step
+ function fit.
+
+ Parameters
+ ----------
+ data: xarray Dataset
+ Dataset containing the Io, Ir and delay data
+ Iokey: str
+ Name of the Io variable
+ Irkey: str
+ Name of the Ir variable
+ delaykey: str
+ Name of the delay variable (motor position in mm or
+ optical delay in ps)
+ binWidth: float
+ width of bin in units of delay variable
+ positionToDelay: bool
+ If True, adds a time axis converted from position axis according
+ to origin and invert parameters. Ignored if origin is None.
+ origin: float
+ Position of time overlap, shown as a vertical line.
+ Used if positionToDelay is True to convert position to time axis.
+ invert: bool
+ Used if positionToDelay is True to convert position to time axis.
+ pumpedOnly: bool
+ Assumes that all trains and pulses are pumped. In this case,
+ Delta R is defined as Ir/Io.
+ alternateTrains: bool
+ If True, assumes that trains alternate between pumped and
+ unpumped data.
+ pumpOnEven: bool
+ Only used if alternateTrains=True. If True, even trains are pumped,
+ if False, odd trains are pumped.
+ Ioweights: bool
+ If True, computes the ratio of the means instead of the mean of
+ the ratios Irkey/Iokey. Useful when dealing with large intensity
+ variations.
+ plot: bool
+ If True, plots the results.
+ plotErrors: bool
+ If True, plots the 95% confidence interval.
+
+ Output
+ ------
+ xarray Dataset containing the binned Delta R, standard deviation,
+ standard error, counts and delays, and the fitting results if full
+ is True.
+ """
+ # select relevant variables from dataset
+ variables = [Iokey, Irkey, delaykey]
+ ds = data[variables]
+ # prepare dataset according to pulse pattern
+ ds = prepare_reflectivity_ds(ds, Iokey, Irkey, alternateTrains,
+ pumpOnEven, pumpedOnly)
+
+ if (len(ds[delaykey].dims) > 1) and (ds[delaykey].dims !=
+ ds[Iokey].dims):
+ raise ValueError("Dimensions mismatch: delay variable has dims "
+ f"{ds[delaykey].dims} but (It, Io) variables have "
+ f"dims {ds[Iokey].dims}.")
+
+ bin_delays = binWidth * np.round(ds[delaykey] / binWidth)
+ ds[delaykey+'_binned'] = bin_delays
+ counts = xr.ones_like(ds[Iokey]).groupby(bin_delays).sum(...)
+ if Ioweights is False:
+ ds['deltaR'] = ds[Irkey]/ds[Iokey]
+ if pumpedOnly is False:
+ ds['deltaR'] = 100*(ds['deltaR'] /
+ (ds[Irkey+'_unpumped']/ds[Iokey+'_unpumped']) - 1)
+ groupBy = ds.groupby(bin_delays)
+ binned = groupBy.mean(...)
+ std = groupBy.std(...)
+ binned['deltaR_std'] = std['deltaR']
+ binned['deltaR_stderr'] = std['deltaR'] / np.sqrt(counts)
+ binned['counts'] = counts.astype(int)
+ else:
+ xas_pumped = xas(ds, Iokey=Iokey, Itkey=Irkey, nrjkey=delaykey,
+ fluorescence=True, bins=binWidth)
+ if pumpedOnly:
+ deltaR = xas_pumped['muA']
+ stddev = xas_pumped['sigmaA']
+ else:
+ xas_unpumped = xas(ds, Iokey=Iokey+'_unpumped',
+ Itkey=Irkey+'_unpumped', nrjkey=delaykey,
+ fluorescence=True, bins=binWidth)
+ deltaR = 100*(xas_pumped['muA'] / xas_unpumped['muA'])
+ stddev = np.abs(deltaR) * np.sqrt(
+ (xas_pumped['sigmaA']/xas_pumped['muA'])**2 +
+ (xas_unpumped['sigmaA']/xas_unpumped['muA'])**2)
+ deltaR -= 100
+ deltaR = xr.DataArray(deltaR, dims=delaykey, name='deltaR',
+ coords={delaykey: xas_pumped['nrj']})
+ stddev = xr.DataArray(stddev, dims=delaykey, name='deltaR_std',
+ coords={delaykey: xas_pumped['nrj']})
+ stderr = xr.DataArray(stddev / np.sqrt(xas_pumped['counts']),
+ dims=delaykey, name='deltaR_stderr',
+ coords={delaykey: xas_pumped['nrj']})
+ counts = xr.DataArray(xas_pumped['counts'], dims=delaykey,
+ name='counts',
+ coords={delaykey: xas_pumped['nrj']})
+ binned = xr.merge([deltaR, stddev, stderr, counts])
+
+ # copy attributes
+ for key, val in data.attrs.items():
+ binned.attrs[key] = val
+
+ binned = binned.rename({delaykey: 'delay'})
+
+ if plot:
+ plot_reflectivity(binned, delaykey, positionToDelay,
+ origin, invert, plotErrors, units)
+
+ return binned
+
+
+def plot_reflectivity(data, delaykey, positionToDelay, origin,
+ invert, plotErrors, units):
+ fig, ax = plt.subplots(figsize=(6, 4), constrained_layout=True)
+ ax.plot(data['delay'], data['deltaR'], 'o-', color='C0')
+ xlabel = delaykey + f' [{units}]'
+ if plotErrors:
+ ax.fill_between(data['delay'],
+ data['deltaR'] - 1.96*data['deltaR_stderr'],
+ data['deltaR'] + 1.96*data['deltaR_stderr'],
+ color='C0', alpha=0.2)
+ ax2 = ax.twinx()
+ ax2.bar(data['delay'], data['counts'],
+ width=0.80*(data['delay'][1]-data['delay'][0]),
+ color='C1', alpha=0.2)
+ ax2.set_ylabel('counts', color='C1', fontsize=13)
+ ax2.set_ylim(0, data['counts'].max()*3)
+ if origin is not None:
+ ax.axvline(origin, color='grey', ls='--')
+ if positionToDelay:
+ ax3 = ax.twiny()
+ xmin, xmax = ax.get_xlim()
+ ax3.set_xlim(pTd(xmin, origin, invert),
+ pTd(xmax, origin, invert),)
+ ax3.set_xlabel('delay [ps]', fontsize=13)
+ try:
+ proposalNB = int(re.findall(r'p(\d{6})',
+ data.attrs['runFolder'])[0])
+ runNB = int(re.findall(r'r(\d{4})', data.attrs['runFolder'])[0])
+ ax.set_title(f'run {runNB} p{proposalNB}', fontsize=14)
+ except Exception:
+ if 'plot_title' in data.attrs:
+ ax.set_title(data.attrs['plot_title'])
+ ax.set_xlabel(xlabel, fontsize=13)
+ ax.set_ylabel(r'$\Delta R$ [%]', color='C0', fontsize=13)
+ ax.grid()
+
+ return fig, ax
diff --git a/src/toolbox_scs/routines/XAS.py b/src/toolbox_scs/routines/XAS.py
index 63c34c206188fc770cd9fa1f635186d62d41ca72..696bda9190a6de5b28afb650d8bdbadd22250030 100644
--- a/src/toolbox_scs/routines/XAS.py
+++ b/src/toolbox_scs/routines/XAS.py
@@ -9,6 +9,7 @@
Copyright (2017-2019) Loïc Le Guyader <loic.le.guyader@xfel.eu>
"""
+from toolbox_scs.base.knife_edge import arrays_to1d
import numpy as np
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
@@ -153,18 +154,8 @@ def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3peaks', nrjkey='nrj',
muIo: the mean of the Io
counts: the number of events in each bin
"""
- if 'pulseId' in nrun:
- pId = 'pulseId'
- else:
- pId = 'sa3_pId'
-
Io = nrun[Iokey].values.flatten() + Iooffset
- It = nrun[Itkey].values.flatten()
-
- if pId in nrun[nrjkey].dims:
- nrj = nrun[nrjkey].values.flatten()
- else:
- nrj = np.repeat(nrun[nrjkey].values.flatten(), nrun.dims[pId])
+ nrj, It = arrays_to1d(nrun[nrjkey].values, nrun[Itkey].values)
names_list = ['nrj', 'Io', 'It']
rundata = np.vstack((nrj, Io, It))
diff --git a/src/toolbox_scs/routines/__init__.py b/src/toolbox_scs/routines/__init__.py
index 1452cf1e2531fb13dd4b6658dc04f01c000c3299..286975a0a1b279adfc553845673801c4376aa589 100644
--- a/src/toolbox_scs/routines/__init__.py
+++ b/src/toolbox_scs/routines/__init__.py
@@ -1,5 +1,6 @@
from .XAS import *
from .boz import *
+from .Reflectivity import *
# Module name is the same as a child function, we use alias to avoid conflict
import toolbox_scs.routines.knife_edge as knife_edge_module
@@ -9,4 +10,5 @@ __all__ = (
knife_edge_module.__all__
+ XAS.__all__
+ boz.__all__
+ + Reflectivity.__all__
)