{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Combining data from separate but concurrent runs\n", "\n", "Here we will look at XGM (X-ray Gas Monitor) data that was recorded in the same short time interval, but in different parts of EuXFEL.\n", "We will compare an XGM in SASE1 (XTD2) to another one in SASE3 (XTD10). These data were stored in two different runs, belonging to two different proposals.\n", "\n", "Conceptually, this section makes use of the data-object format *xarray.DataArray*." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "\n", "from extra_data import open_run" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SASE1\n", "\n", "Load the SASE1 run:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# of trains: 6296\n", "Duration: 0:10:29.6\n", "First train ID: 38227866\n", "Last train ID: 38234161\n", "\n", "0 detector modules ()\n", "\n", "1 instrument sources (excluding detectors):\n", " - SA1_XTD2_XGM/XGM/DOOCS:output\n", "\n", "0 control sources:\n", "\n" ] } ], "source": [ "sa1_data = open_run(proposal=700000, run=8)\n", "sa1_data.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are interested in fast, i.e. pulse-resolved data from the instrument source `SA1_XTD2_XGM/DOOCS:output`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'data.intensityTD'}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sa1_data['SA1_XTD2_XGM/XGM/DOOCS:output'].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are particularly interested in data for quantity \"intensityTD\". The ***xarray DataArray*** class is suited for work with axis-labeled data, and the extra_data method `get_array()` serves the purpose of shaping a 2D array of that type from pulse-resolved data (which is originally stored \"flat\" in terms of pulses: there is one dimension of N(train) x N(pulse) values in HDF5, and the same number of train and pulse identifiers for reference).\n", "The unique train identifier values are taken as coordinate values (\"labels\")." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "array([[2.0451287e+03, 7.8204414e+01, 1.9644452e+03, ..., 1.0000000e+00,\n", " 1.0000000e+00, 1.0000000e+00],\n", " [2.0914644e+03, 4.2423668e+01, 1.9155820e+03, ..., 1.0000000e+00,\n", " 1.0000000e+00, 1.0000000e+00],\n", " [1.8729647e+03, 4.3682529e+01, 1.9840254e+03, ..., 1.0000000e+00,\n", " 1.0000000e+00, 1.0000000e+00],\n", " ...,\n", " [1.6113424e+03, 5.5693771e+01, 1.8114177e+03, ..., 1.0000000e+00,\n", " 1.0000000e+00, 1.0000000e+00],\n", " [1.5365902e+03, 6.4186798e+01, 1.6430872e+03, ..., 1.0000000e+00,\n", " 1.0000000e+00, 1.0000000e+00],\n", " [1.8715566e+03, 5.9838600e+01, 1.7388638e+03, ..., 1.0000000e+00,\n", " 1.0000000e+00, 1.0000000e+00]], dtype=float32)\n", "Coordinates:\n", " * trainId (trainId) uint64 38227866 38227867 38227868 ... 38234160 38234161\n", "Dimensions without coordinates: dim_0\n" ] } ], "source": [ "sa1_flux = sa1_data['SA1_XTD2_XGM/XGM/DOOCS:output', 'data.intensityTD'].xarray()\n", "print(sa1_flux)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will plot a portion of the data in two dimensions, taking the first 1500 trains for the x-Axis and the first 30 pulses per train for the y-Axis (1500, 30). Because the Matplotlib convention takes the slow axis to be *y*, we have to transpose to (30, 1500):" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10, 6))\n", "\n", "image = ax.imshow(sa1_flux[:1500, :30].transpose(), origin='lower', cmap='inferno')\n", "ax.set_title('SASE1 XTD2 XGM intensity (fast)')\n", "fig.colorbar(image, orientation='horizontal')\n", "ax.set_xlabel('train index')\n", "ax.set_ylabel('pulse index')\n", "ax.set_aspect(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pattern tells us what was done in this experiment: the lasing scheme was set to provide an alternating X-ray pulse delivery within a train, where every \"even\" electron bunch caused lasing in SASE1 and every \"odd\" bunch caused lasing in SASE3. This scheme was applied for the first 20 pulses.\n", "Therefore, we see signal only for data at even pulses here (0,2,...18), throughout all trains, of which 1500 are depicted. The intensity varies somewhat around 2000 units, but for odd pulses it is suppressed and neglegibly small." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A relevant measure to judge the efficiency of pulse suppression is the ratio of mean intensity between the odd and even set. The numpy `mean` method can work with DataArray objects and average over a specified dimension. \n", "We make use of the numpy indexing and slicing syntax with square brackets and comma to seperate axes (dimensions). We specify `[:, :20:2]` to take every element of the slow axis (trains) and every second pulse up to but excluding # 20. That is, start:end:step = 0:20:2 (start index 0 is default, thus not put, and stop means first index beyond range). We specify `axis=1` to explicitly average over that dimension. The result is a DataArray reduced to the \"trainId\" dimension." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "array([1931.4768, 1977.8414, 1873.7828, ..., 1771.5828, 1697.2053,\n", " 1857.7439], dtype=float32)\n", "Coordinates:\n", " * trainId (trainId) uint64 38227866 38227867 38227868 ... 38234160 38234161\n" ] } ], "source": [ "sa1_mean_on = np.mean(sa1_flux[:, :20:2], axis=1)\n", "sa1_stddev_on = np.std(sa1_flux[:, :20:2], axis=1)\n", "print(sa1_mean_on)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Accordingly for the odd \"off\" pulses:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "array([96.10835 , 84.489044, 59.212048, ..., 90.2944 , 84.33766 ,\n", " 85.03202 ], dtype=float32)\n", "Coordinates:\n", " * trainId (trainId) uint64 38227866 38227867 38227868 ... 38234160 38234161\n" ] } ], "source": [ "sa1_mean_off = np.mean(sa1_flux[:, 1:21:2], axis=1)\n", "sa1_stddev_off = np.std(sa1_flux[:, 1:21:2], axis=1)\n", "print(sa1_mean_off)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can calculate the ratio of averages for every train - data types like *numpy ndarray* or *xarray DataArray* may be just divided \"as such\", a shortcut notation for dividing every corresponding element - and plot. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'suppression')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "sa1_suppression = sa1_mean_off / sa1_mean_on\n", "sa1_suppression.plot(ax=ax)\n", "\n", "ax.ticklabel_format(style='plain', useOffset=False)\n", "ax.ticklabel_format()\n", "plt.xticks(rotation=60)\n", "ax.set_ylabel('suppression')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Moreover, the relative error of this ratio can be calculated by multiplicative error propagation as the square root of the sum of squared relative errors (enumerator and denominator), and from it the absolute error. The Numpy functions \"sqrt\" and \"square\" applied to array-like structures perform these operations element-wise, so the entire calculation can be conveniently done using the arrays as arguments, and we obtain individual errors for every train in the end." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sa1_rel_error = np.sqrt(np.square(sa1_stddev_off / sa1_mean_off) + np.square(sa1_stddev_on / sa1_mean_on))\n", "sa1_abs_error = sa1_rel_error * sa1_suppression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can as well plot the suppression ratio values with individual error bars according to the respective absolute error. Here, we restrict ourselves to the first 50 trains for clarity:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'suppression')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "ax.errorbar(sa1_suppression.coords['trainId'].values[:50], sa1_suppression[:50], yerr=sa1_abs_error[:50], fmt='ro')\n", "ax.set_xlabel('train identifier')\n", "ax.ticklabel_format(style='plain', useOffset=False)\n", "plt.xticks(rotation=60)\n", "ax.set_ylabel('suppression')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we draw a histogram of suppression ratio values:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'frequency')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "ax.hist(sa1_suppression, bins=50)\n", "ax.set_xlabel('suppression')\n", "ax.set_ylabel('frequency')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that there is a suppression of signal from odd pulses to approximately 4% of the intensity of even pulses. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## SASE3\n", "\n", "We repeat everything for the second data set from the different run - SASE3:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# of trains: 6236\n", "Duration: 0:10:23.6\n", "First train ID: 38227850\n", "Last train ID: 38234085\n", "\n", "0 detector modules ()\n", "\n", "1 instrument sources (excluding detectors):\n", " - SA3_XTD10_XGM/XGM/DOOCS:output\n", "\n", "0 control sources:\n", "\n" ] } ], "source": [ "sa3_data = open_run(proposal=700000, run=9)\n", "sa3_data.info()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6235, 1000)\n" ] } ], "source": [ "sa3_flux = sa3_data['SA3_XTD10_XGM/XGM/DOOCS:output', 'data.intensityTD'].xarray()\n", "print(sa3_flux.shape)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "image = ax.imshow(sa3_flux[:1500, :30].transpose(), origin='lower', cmap='inferno')\n", "ax.set_title('SASE3 XTD10 XGM intensity (fast)')\n", "fig.colorbar(image, orientation='horizontal')\n", "ax.set_xlabel('train index')\n", "ax.set_ylabel('pulse index')\n", "ax.set_aspect(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference here is that the selection scheme (indexing and slicing) shifts by one with respect to SASE1 data: odd pulses are \"on\", even pulses are \"off\". Moreover, while the alternating scheme is upheld to pulse # 19, pulses beyond that exclusively went to SASE3. There is signal up to pulse # 70, which we could see with a wider plotting range (but not done due to emphasis on the alternation)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "array([ 963.89746, 1073.1758 , 902.22656, ..., 883.9881 , 960.5875 ,\n", " 889.625 ], dtype=float32)\n", "Coordinates:\n", " * trainId (trainId) uint64 38227850 38227851 38227852 ... 38234084 38234085\n" ] } ], "source": [ "sa3_mean_on = np.mean(sa3_flux[:, 1:21:2], axis=1)\n", "sa3_stddev_on = np.std(sa3_flux[:, 1:21:2], axis=1)\n", "print(sa3_mean_on)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "array([5.435107 , 6.6155367, 8.361802 , ..., 2.3786662, 7.1359987,\n", " 4.612433 ], dtype=float32)\n", "Coordinates:\n", " * trainId (trainId) uint64 38227850 38227851 38227852 ... 38234084 38234085\n" ] } ], "source": [ "sa3_mean_off = np.mean(sa3_flux[:, :20:2], axis=1)\n", "sa3_stddev_off = np.std(sa3_flux[:, :20:2], axis=1)\n", "print(sa3_mean_off)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The suppression ratio calculation and its plot: " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'suppression')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "sa3_suppression = sa3_mean_off / sa3_mean_on\n", "ax.plot(sa3_suppression.coords['trainId'].values, sa3_suppression)\n", "ax.set_xlabel('train identifier')\n", "ax.ticklabel_format(style='plain', useOffset=False)\n", "plt.xticks(rotation=60)\n", "ax.set_ylabel('suppression')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The error calculation with (selective) plot" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'suppression')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "sa3_rel_error = np.sqrt(np.square(sa3_stddev_off / sa3_mean_off) + np.square(sa3_stddev_on / sa3_mean_on))\n", "sa3_abs_error = sa3_rel_error * sa3_suppression\n", "\n", "ax.errorbar(sa1_suppression.coords['trainId'].values[:50], sa3_suppression[:50], yerr=sa3_abs_error[:50], fmt='ro')\n", "ax.set_xlabel('train identifier')\n", "ax.ticklabel_format(style='plain', useOffset=False)\n", "plt.xticks(rotation=60)\n", "ax.set_ylabel('suppression')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The histogram:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'frequency')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MA5Ikdc4wIElS5wwDkiR1zjAgSVLnDAOSJHXOMCBJUucMA5Ikdc7piKVVaKlpqXdu2TSlSiT1wJEBSZI6ZxiQJKlzhgFJkjpnGJAkqXOGAUmSOmcYkCSpc4YBSZI6ZxiQJKlzhgFJkjrnDITSDHKGQknLyZEBSZI6ZxiQJKlzhgFJkjpnGJAkqXOGAUmSOmcYkCSpc4YBSZI65zwDU7bU/eGSJE2bIwOSJHXOkQFpDXKGQkm3xGAjA0nemuS6JF8ea9svydlJLm/f797ak+S0JDuSXJzkqKHqkiRJv2jI0wR/BTxhXttm4JyqOgI4p70GeCJwRPs6GXjzgHVJkqQxg4WBqvo08J15zccDZ7blM4ETxtrfXiOfBfZNctBQtUmSpN2mfQHhgVV1TVv+FnBgWz4YuHJsu6ta2y9JcnKS7Um279q1a7hKJUnqxIrdTVBVBdSteN/pVbWxqjauX79+gMokSerLtMPAtXPD/+37da39auDQse0OaW2SJGlg0w4DW4ET2/KJwFlj7c9tdxUcA9wwdjpBkiQNaLB5BpK8G3gMcECSq4D/CGwB3pfkJOAbwNPb5h8BjgN2ADcCzx+qLkmS9IsGCwNV9axFVh27wLYFnDJULZIkaXFORyxJUucMA5Ikdc4wIElS5wwDkiR1zjAgSVLnDAOSJHXOMCBJUucMA5Ikdc4wIElS5wwDkiR1zjAgSVLnDAOSJHVusAcVSVq9Nmzetsf1O7dsmlIlklYDRwYkSeqcIwOSfokjB1JfHBmQJKlzhgFJkjpnGJAkqXOGAUmSOmcYkCSpc4YBSZI6ZxiQJKlzzjMg6RZzHgJpbXFkQJKkzhkGJEnqnGFAkqTOGQYkSeqcYUCSpM4ZBiRJ6pxhQJKkzhkGJEnqnGFAkqTOOQPhMltqZjapB5N8DpylUFo9HBmQJKlzhgFJkjpnGJAkqXOGAUmSOmcYkCSpc4YBSZI6ZxiQJKlzhgFJkjpnGJAkqXPOQChpRSw1S6EzFErTYxiQtCoZFqTp8TSBJEmdc2RA0kxy5EBaPo4MSJLUOUcGbgEfTyxJWosMA5K65GkGaTdPE0iS1DnDgCRJnfM0wRivCZDWDj/P0uQMA5K0AK8pUE88TSBJUuccGZCkFTDJaQxHHzQtq2pkIMkTklyWZEeSzStdjyRJPVg1IwNJ9gL+B/B44Crg80m2VtVXVrYySfplq+GagtVQg9aGVRMGgKOBHVV1BUCS9wDHA4YBSTNnOe5mWOk7Igwb/VhNYeBg4Mqx11cBD1uhWiRpzVsLYeO27mOl379apKpWugYAkjwNeEJVvaC9fg7wsKp68bztTgZObi/vB1w2b1cHAN8euNyVYt9mk32bTWu1b2u1X9BH3+5VVeuXe+eraWTgauDQsdeHtLZfUFWnA6cvtpMk26tq4/KXt/Ls22yyb7NprfZtrfYL7NttsZruJvg8cESSw5PcHngmsHWFa5Ikac1bNSMDVfXTJC8GPgbsBby1qi5d4bIkSVrzVk0YAKiqjwAfuY27WfQUwhpg32aTfZtNa7Vva7VfYN9utVVzAaEkSVoZq+maAUmStAJWZRhYalriJHdI8t62/oIkG8bW/WFrvyzJby61z3bB4gWt/b3t4sWZ6FeSQ5Ocm+QrSS5N8rKx7V+d5OokF7Wv44bq1xB9a+07k1zS6t8+1r5fkrOTXN6+332W+pbkfmPH5aIk30/y8rZuJo5bkv3b794Pk7xp3nse2o7bjiSnJUlrn4njtljfktw5ybYkX2ufty1j656XZNfYcXvBLPWtrTuv7XOuD/fY075mpW9J7jrv8/btJG9o62bluD0+yYXtc3VhkseNvWd5Pm9Vtaq+GF08+HXg3sDtgS8BD5i3zb8D3tKWnwm8ty0/oG1/B+Dwtp+99rRP4H3AM9vyW4AXzVC/DgKOatvcFfjHsX69GnjFrB6ztm4ncMACP++1wOa2vBk4ddb6Nm//32J07/AsHbd9gEcC/xZ407z3fA44Bgjwd8ATZ+y4Ldg34M7AY9vy7YG/H+vb8+b/OcxS39q684CNC/y8Bfc1S32b9/4LgUfP2HF7CHDPtvxrwNVj71mWz9tqHBn4+bTEVfUTYG5a4nHHA2e25Q8Ax7Y0dDzwnqq6qar+CdjR9rfgPtt7Htf2QdvnCbPSr6q6pqq+AFBVPwC+ymgmx2kb4pjtyfi+hjxmMHzfjgW+XlXfGKwHi7vVfauqH1XVZ4Afj2+c5CDgV6rqszX6W+jt7D4+M3HcFutbVd1YVee25Z8AX2A0H8q0LXvflrDY7/cQBu1bkvsC92AU5KbttvTti1X1zdZ+KXCnNoqwbJ+31RgGFpqWeP4/cD/fpqp+CtwA7L+H9y7Wvj/wvbaPxX7WchmiXz/XhpMeAlww1vziJBcneevAQ7JD9a2Aj7dhsZPHtjmwqq5py98CDlyOTixi0OPGKP2/e17bLBy3Pe3zqkX2OSvHbUlJ9gWeDJwz1vzUdtw+kOTQRd66HIbs29vacPl/GPsH/1b/Od0Kgx43dv9ve/zK+Vk7bk8FvlBVN7GMn7fVGAZ0CyW5C/A3wMur6vut+c3ArwJHAtcAf7ZC5d0Wj6yqo4AnAqckefT8DdqHeiZvicno+pSnAO8fa14Lx21JM37c1jEKcKdVe7Aa8LfAhqr6deBsdv+PbJY8u6oeBDyqfT1nhesZwvzwPVPHLckDgVOB37sl75vk87Yaw8Ak0xL/fJv2wbwbcP0e3rtY+/XAvm0fi/2s5TJEv0iyN6Mg8K6q+uDcBlV1bVXdXFU/A/6CpYfeb4tB+lZVc9+vAz7E7j5c24bH5oalr1vGvsw3SN+aJzJK+NfONczQcdvTPseHzsf3OSvHbSmnA5dX1RvmGqrq+vY/NYC/BB56K+uexCB9G/u8/QD4a3b/7t3aP6dbY7DjluTBwLqqunCubZaOW5JDGP09+Nyq+vrY9svyeVuNYWCSaYm3Aie25acBn2zJZyvwzHYu5XDgCEYXVyy4z/aec9s+aPs8a1b61YbxzgC+WlWvG9/R3C9B81vAl5e9R7sN0bd9kty19WUf4DfG+jC+ryGPGQzz+zjnWcw7RTBDx21BbVjy+0mOab+fz2X38ZmV47aoJH/C6C/ol89rHz9uT2F0/c5Qlr1vSdYlOaAt7w08iYU/bxP9Od0Ggxy3ZqnP26o9bu201DZGFwSeP7fxsn7eag9XF67UF3Acoyvjvw68qrX9Z+ApbfmOjIZWdzD6y/XeY+99VXvfZbSrKhfbZ2u/d9vHjrbPO8xKvxhdOVvAxcBF7eu4tu4dwCVt3VbgoFk6Zu24fKl9XTrvmO3P6Fzt5cAngP1mqW+tfR9Gif9u837WLB23ncB3gB8yOlc5dyfLRkb/kHwdeBO7JzebpeP2S31j9L+uYvQPxtzn7QVt+//Wfk+/xOg/GPefsb7tw+gq+4tbP97I7rt6Ft3XLPRtbN0V84/LrBw34I+BH4393l0E3GM5P2/OQChJUudW42kCSZI0RYYBSZI6ZxiQJKlzhgFJkjpnGJAkqXOGAUlTleSeST6w9JaSpsVbCyVNLMleVXXzStchaXk5MiDNuDZb47YkX0ry5STPSLJzbEa5jUnOa8uvTvKOJP+Q0XPOX9jaH5Pk020/lyV5S5LbtXU/TPJnSb4EPDyj56d/qj1A6mNjU56+NMlX2kNf3tPa/lV2Pyv+ixk9W35Dki+39XdM8raMnsf+xSSPbe3PS/LBJB9tdb522n+uUk/WLb2JpFXuCcA3q2oTQJK7MXqYyWJ+ndHzz/cBvphkW2s/mtFsdN8APgr8NqPHqO4DXFBVf9Cmqv0UcHxV7UryDOC/AP+G0TPTD6+qm9r0qQCvAE6pqvMzeqDW/MfLnsLoOSoPSnJ/Rk+pvG9bdySjJ3HeBFyW5M+r6kokLTtHBqTZdwnw+CSnJnlUVd2wxPZnVdX/q6pvM5qCde6BNJ+r0bPWb2Y0h/sjW/vNjB6GBXA/4NeAs5NcxGia1LkHpVwMvCvJ7wJzjwU/H3hdkpcC+9bux4XPeSTwToCq+hqjIDIXBs6pqhuq6sfAV4B7TfSnIekWc2RAmnFV9Y9JjmI07/mfJDmH0T/Gc2H/jvPfssjrxdp/PHadQIBLq+rhC5SyCXg08GTgVUkeVFVb2sjDccD5SX6TXx4dWMxNY8s3499X0mAcGZBmXJJ7AjdW1TuBPwWOYvTAlrnHsT513luOb+fq9wcew+hpagBHtyeq3Q54BvCZBX7cZcD6JA9vP3vvJA9s7zm0qs4FXsnoyX53SfKrVXVJVZ3afs795+3v74Fnt33dFzis/QxJU2TSlmbfg4A/TfIz4J+BFwF3As5I8hrgvHnbX8zo9MABwGuq6pvtH+LPM3rq2X3a+g/N/0FV9ZMkTwNOa9cmrAPewOhJbO9sbQFOq6rvJXlNuyjwZ4yeDvd3wPhjY/8n8OYklzAazXheu+bgNv+hSJqctxZKHUnyauCHVfXf57U/BnhFVT1pJeqStLI8TSBJUuccGZAkqXOODEiS1DnDgCRJnTMMSJLUOcOAJEmdMwxIktQ5w4AkSZ37/0alLG8b4adMAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "ax.hist(sa3_suppression, bins=50)\n", "ax.set_xlabel('suppression')\n", "ax.set_ylabel('frequency')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, suppression of signal for even \"off\" pulses is to approximately 0.5% of intensity from odd \"on\" pulses. The \"suppression factor\" is almost 10 times the value of SASE1. However, the relative error of these values is larger as well, as can be seen in the error-bar plot. For the smaller quantities, it is ~ 100% (!). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overall comparison of suppression ratio (with error)\n", "\n", "We ultimately want a single overall compression ratio with error for both beamlines, to complement the error-bar plots. In order to keep the error calculation simple, we do not average the mean values, but create one mean and standard deviation from a flat array of original values. \n", "Because labeled axes are not required for this purpose, we can afford to move from the xarray.DataArray regime to Numpy array." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(62950,)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sa1_on_all = np.array(sa1_flux[:, :20:2]).flatten()\n", "sa1_on_all.shape" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "sa1_mean_on_overall = np.mean(sa1_on_all)\n", "sa1_stddev_on_overall = np.std(sa1_on_all)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(62950,)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sa1_off_all = np.array(sa1_flux[:, 1:21:2]).flatten()\n", "sa1_off_all.shape" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "sa1_mean_off_overall = np.mean(sa1_off_all)\n", "sa1_stddev_off_overall = np.std(sa1_off_all)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SA1 suppression ratio = 0.04107769 ± 0.016009845\n" ] } ], "source": [ "sa1_suppression_overall = sa1_mean_off_overall / sa1_mean_on_overall\n", "sa1_rel_error_overall = np.sqrt(np.square(sa1_stddev_off_overall / sa1_mean_off_overall) + \\\n", " np.square(sa1_stddev_on_overall / sa1_mean_on_overall))\n", "sa1_abs_error_overall = sa1_rel_error_overall * sa1_suppression_overall\n", "print('SA1 suppression ratio =', sa1_suppression_overall, '\\u00b1', sa1_abs_error_overall)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(62350,)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sa3_on_all = np.array(sa3_flux[:, 1:21:2]).flatten()\n", "sa3_on_all.shape" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "sa3_mean_on_overall = np.mean(sa3_on_all)\n", "sa3_stddev_on_overall = np.std(sa3_on_all)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(62350,)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sa3_off_all = np.array(sa3_flux[:, :20:2]).flatten()\n", "sa3_off_all.shape" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "sa3_mean_off_overall = np.mean(sa3_off_all)\n", "sa3_stddev_off_overall = np.std(sa3_off_all)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SA3 suppression ratio = 0.005213415 ± 0.0040653846\n" ] } ], "source": [ "sa3_suppression_overall = sa3_mean_off_overall / sa3_mean_on_overall\n", "sa3_rel_error_overall = np.sqrt(np.square(sa3_stddev_off_overall / sa3_mean_off_overall) + \\\n", " np.square(sa3_stddev_on_overall / sa3_mean_on_overall))\n", "sa3_abs_error_overall = sa3_rel_error_overall * sa3_suppression_overall\n", "print('SA3 suppression ratio =', sa3_suppression_overall, '\\u00b1', sa3_abs_error_overall)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## References\n", "\n", " 1. K. Tiedtke et al., Gas-detector for X-ray lasers , J. Appl. Phys. 103, 094511 (2008) - [DOI 10.1063/1.2913328](https://dx.doi.org/10.1063/1.2913328) \n", " 2. A. A. Sorokin et al., J. Synchrotron Rad. 26 (4), [DOI 10.1107/S1600577519005174](https://dx.doi.org/10.1107/S1600577519005174) (2019)\n", " 3. Th. Maltezopoulos et al., J. Synchrotron Rad. 26 (4), [DOI 10.1107/S1600577519003795](https://dx.doi.org/10.1107/S1600577519003795) (2019)" ] } ], "metadata": { "kernelspec": { "display_name": "xfel", "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": 2 }