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AndrewILWilliams/xarray_fit_function.ipynb

Created August 13, 2020 16:51
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xarray_fit_function.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### https://github.com/pydata/xarray/issues/4300"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.optimize import curve_fit \n",
"\n",
"from xarray.core.missing import get_clean_interp_index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate 20 timesteps of noisy data and put it into a `DataArray`"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [],
"source": [
"def pulse_shape_with_noise(x, peak_height, peak_location, FWHM):\n",
" return peak_height * np.exp(-((x-peak_location)/FWHM)**2.0) + 0.3*np.random.randn(len(x))"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(20, 50)"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = np.vstack([pulse_shape_with_noise(np.linspace(-5,5), 1, 0, 3) for _ in range(20)])\n",
"\n",
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [
{
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"</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 20</li><li><span class='xr-has-index'>x</span>: 50</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a6c199a3-d368-439a-89e5-75730e4597c4' class='xr-array-in' type='checkbox' ><label for='section-a6c199a3-d368-439a-89e5-75730e4597c4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.1187 0.224 -0.4141 -0.03501 -0.137 ... 0.7064 -0.5369 0.2976 0.8066</span></div><pre class='xr-array-data'>array([[ 1.18719504e-01, 2.24041523e-01, -4.14066640e-01,\n",
" -3.50146250e-02, -1.37028755e-01, 1.73704169e-01,\n",
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" 2.40190562e-01, 4.27753998e-01, 1.24500678e+00,\n",
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" 6.75621367e-01, 6.50716981e-01, 5.88711339e-01,\n",
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" 3.34590340e-01, 5.37991651e-02, 5.16476843e-01,\n",
" 1.04898194e+00, 1.04408310e+00, 6.87425312e-01,\n",
" 1.20077299e+00, 1.17567924e+00, 1.44505413e+00,\n",
" 1.36425676e+00, 9.29543335e-01, 1.30182402e+00,\n",
" 1.19062915e+00, 1.08817163e+00, 7.40324585e-01,\n",
" 1.06651608e+00, 1.06418798e+00, 1.10101976e+00,\n",
" 1.29228324e+00, 1.34075327e+00, 1.25523788e+00,\n",
" 6.69556369e-01, 4.43001850e-01, 3.27347813e-01,\n",
" 2.81065861e-01, 8.54626361e-01, -2.28971891e-02,\n",
" 3.66318198e-01, 8.53634923e-01, 4.31076255e-01,\n",
" 5.28044792e-01, 3.05939742e-02, 6.39995740e-01,\n",
" -3.82046529e-02, 7.06421586e-01, -5.36933494e-01,\n",
" 2.97626891e-01, 8.06564590e-01]])</pre></div></li><li class='xr-section-item'><input id='section-b2eb0011-6677-4091-95bf-5a34d4d1e936' class='xr-section-summary-in' type='checkbox' checked><label for='section-b2eb0011-6677-4091-95bf-5a34d4d1e936' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0 ... 17.0 18.0 19.0</div><input id='attrs-8d1b26d5-e150-4ad3-ac98-a680a3012ca9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d1b26d5-e150-4ad3-ac98-a680a3012ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8065c07e-92bd-4af0-9a38-2ac35d4b5de2' class='xr-var-data-in' type='checkbox'><label for='data-8065c07e-92bd-4af0-9a38-2ac35d4b5de2' 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><pre class='xr-var-data'>array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,\n",
" 14., 15., 16., 17., 18., 19.])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.0 -4.796 -4.592 ... 4.796 5.0</div><input id='attrs-b628945d-267f-46fc-b478-9e9edb6ec918' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b628945d-267f-46fc-b478-9e9edb6ec918' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d242d5d-f5a7-414c-9d5f-f71190ec9186' class='xr-var-data-in' type='checkbox'><label for='data-0d242d5d-f5a7-414c-9d5f-f71190ec9186' 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><pre class='xr-var-data'>array([-5. , -4.795918, -4.591837, -4.387755, -4.183673, -3.979592,\n",
" -3.77551 , -3.571429, -3.367347, -3.163265, -2.959184, -2.755102,\n",
" -2.55102 , -2.346939, -2.142857, -1.938776, -1.734694, -1.530612,\n",
" -1.326531, -1.122449, -0.918367, -0.714286, -0.510204, -0.306122,\n",
" -0.102041, 0.102041, 0.306122, 0.510204, 0.714286, 0.918367,\n",
" 1.122449, 1.326531, 1.530612, 1.734694, 1.938776, 2.142857,\n",
" 2.346939, 2.55102 , 2.755102, 2.959184, 3.163265, 3.367347,\n",
" 3.571429, 3.77551 , 3.979592, 4.183673, 4.387755, 4.591837,\n",
" 4.795918, 5. ])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-d7ded5a2-ba08-46d7-bc4f-0ef48ac969b8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d7ded5a2-ba08-46d7-bc4f-0ef48ac969b8' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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" [ 5.41935283e-01, 1.36775049e-01, -1.69039496e-02,\n",
" 5.89031609e-02, 1.30240084e-01, -4.77104504e-01,\n",
" -5.34421220e-02, -4.95155938e-02, 2.66495147e-01,\n",
" 1.87977947e-01, -1.28108461e-02, 6.84609813e-01,\n",
" 1.36094747e-01, 7.20384757e-01, -1.00619794e-01,\n",
" 9.21741004e-01, 5.45106872e-01, 8.11415714e-01,\n",
" 1.12235530e+00, 5.29518165e-01, 1.25130984e+00,\n",
" 1.40557438e+00, 1.01250746e+00, 9.85098529e-01,\n",
" 1.12080337e+00, 7.21462358e-01, 1.39946410e+00,\n",
" 9.49765464e-01, 1.05832201e+00, 1.14956297e+00,\n",
" 9.75340270e-01, 9.93214117e-01, 9.90964335e-01,\n",
" 2.38325969e-01, 1.02461452e+00, 1.03928569e-01,\n",
" 5.87288938e-01, 5.88532306e-01, 8.31252480e-01,\n",
" 9.78099513e-01, 5.04140821e-01, -1.59253954e-01,\n",
" 2.31780511e-01, 5.67870166e-01, 1.68394949e-01,\n",
" 4.47322219e-01, 3.37742886e-01, -1.49402880e-01,\n",
" 5.29578482e-01, 8.52036607e-02],\n",
" [-2.37694237e-01, 1.72305886e-01, 6.66944977e-02,\n",
" -9.35445302e-02, -1.60010837e-01, -1.76516770e-01,\n",
" 3.14446938e-01, -1.40822508e-02, -8.52410834e-02,\n",
" 9.13158377e-02, 3.84895664e-01, 8.85249278e-01,\n",
" 2.01857612e-01, 7.11758641e-01, 3.94806556e-01,\n",
" 4.33091914e-01, 5.22708219e-01, 4.96104683e-01,\n",
" 9.78011396e-01, 8.13128178e-01, 3.94357732e-01,\n",
" 1.16166410e+00, 6.32930668e-01, 1.72243931e+00,\n",
" 6.65730982e-01, 7.77766684e-01, 6.55545954e-01,\n",
" 1.18529579e+00, 6.44076460e-01, 1.35326269e+00,\n",
" 1.26515714e+00, 5.24916648e-01, -1.35394010e-01,\n",
" 5.44811912e-01, 1.22574297e+00, 1.17269083e+00,\n",
" 7.31968879e-01, 9.61086074e-01, 8.61063571e-02,\n",
" 5.41933297e-01, 3.88283696e-01, -3.90048541e-04,\n",
" 7.39042758e-01, 3.41289381e-01, -1.21027206e-02,\n",
" 2.99949543e-01, 2.88800908e-01, 1.07233200e-01,\n",
" 2.19413604e-01, -5.70910868e-01],\n",
" [-3.54851704e-01, -2.00576762e-01, 8.57062827e-03,\n",
" -1.50117539e-01, 1.36706650e-01, 2.33977407e-01,\n",
" -4.79466784e-03, 3.89889463e-01, -2.36888023e-01,\n",
" -8.70684059e-02, 3.51905479e-01, 6.61514573e-01,\n",
" -1.11327651e-03, 8.31997566e-01, 6.54143991e-01,\n",
" 5.18824066e-01, 9.09408003e-01, 9.28406852e-01,\n",
" 1.10231892e+00, 1.00386553e+00, 9.49747906e-01,\n",
" 1.12741752e+00, 8.98681922e-01, 1.16228641e+00,\n",
" 9.34404421e-01, 9.71844745e-01, 6.28494027e-01,\n",
" 1.52201434e+00, 1.51657531e+00, 5.51461673e-01,\n",
" 1.23484904e+00, 1.13323354e+00, 6.10945073e-01,\n",
" 8.70396865e-01, 1.39247793e+00, 6.33716566e-01,\n",
" 5.65387035e-01, 1.74475974e-01, 3.64073157e-01,\n",
" -1.87105742e-01, 2.17276542e-01, 3.44673580e-01,\n",
" -5.51203407e-01, 5.72214483e-01, 8.14960993e-01,\n",
" -1.11139161e-01, -3.48690198e-01, -8.25824221e-02,\n",
" 2.23045849e-01, 1.89030911e-01],\n",
" [ 3.04400717e-01, 7.26782888e-02, -7.99350524e-02,\n",
" 3.45668751e-01, 1.96695640e-01, 1.06259748e-01,\n",
" 3.80198566e-01, 8.44409049e-02, 1.89244998e-01,\n",
" -6.91368744e-02, 7.69157984e-01, 9.53240177e-01,\n",
" 1.41959507e-01, 3.84280644e-01, 9.38346302e-01,\n",
" 7.99469122e-01, 9.83293915e-01, 9.81825195e-01,\n",
" 2.29909700e-01, 6.08360288e-01, 1.56488305e-01,\n",
" 7.59199137e-01, 1.49268953e+00, 1.29530406e+00,\n",
" 2.57557099e-01, 1.09866445e+00, 8.28232602e-01,\n",
" 1.34685971e+00, 1.30056745e+00, 1.62501711e+00,\n",
" 3.49285078e-01, 6.89180663e-01, 9.70217020e-01,\n",
" 6.48620233e-01, 6.27218220e-01, 3.08564905e-01,\n",
" 4.92367689e-01, 7.97002155e-01, 6.88611587e-01,\n",
" 2.03928759e-01, 1.45463785e-01, 1.24220970e-01,\n",
" 7.17625725e-02, 2.78453795e-01, 1.07740552e-01,\n",
" 6.12834213e-01, -1.64117029e-01, -9.74406484e-02,\n",
" -3.26756170e-01, 3.05252273e-01],\n",
" [-1.02204721e-01, 6.67042739e-01, 4.95708547e-01,\n",
" 1.65570673e-01, 2.12690534e-01, 3.83177248e-01,\n",
" 5.21239937e-01, 8.20487274e-03, 7.15107255e-01,\n",
" -3.95061996e-02, 1.27498524e-01, 7.34697147e-01,\n",
" 3.40463086e-01, 1.74392242e-01, 7.29951498e-01,\n",
" 3.09730240e-01, 4.73430207e-01, 8.88374669e-01,\n",
" 9.43336800e-01, 1.15223784e+00, 5.05061130e-01,\n",
" 1.00866125e+00, 9.63055965e-01, 8.86114781e-01,\n",
" 1.06188599e+00, 3.71017616e-01, 9.93545547e-01,\n",
" 1.06473563e+00, 1.08158484e+00, 5.14425699e-01,\n",
" 3.16984793e-01, 7.29063893e-01, 6.38746424e-01,\n",
" 7.62123640e-01, 6.88661887e-01, 6.38025262e-01,\n",
" 1.08416214e+00, 7.18624368e-01, 1.39688068e-01,\n",
" 4.53762464e-01, 5.23632499e-01, 5.38133123e-01,\n",
" 5.86026982e-03, 2.61972828e-01, 1.89896172e-01,\n",
" 9.60229278e-02, -1.32873756e-02, -3.23674079e-01,\n",
" 3.61578436e-01, 2.79933242e-01],\n",
" [ 4.19119578e-03, -7.23319860e-02, 2.84080737e-01,\n",
" 2.44070523e-01, 7.32948264e-02, 7.46907242e-01,\n",
" 6.41830792e-02, 3.89604322e-01, 7.42398779e-01,\n",
" -2.43091489e-01, 1.01398990e-03, 5.72160096e-01,\n",
" 5.36944439e-01, 9.48662029e-01, 5.32419984e-02,\n",
" 5.92401945e-01, 2.17108964e-01, 5.25082645e-01,\n",
" 8.59521756e-01, 8.51215619e-01, 9.15383381e-01,\n",
" 1.25527442e+00, 7.65657441e-01, 1.02395005e+00,\n",
" 9.48968723e-01, 1.15688696e+00, 1.34913372e+00,\n",
" 1.48350988e+00, 1.07801563e+00, 1.41868182e+00,\n",
" 1.12361168e+00, 8.84395895e-01, 1.10691364e+00,\n",
" 4.22909327e-01, 9.54092725e-01, 3.91136218e-01,\n",
" 5.00076408e-01, 4.81405211e-01, 5.17295919e-02,\n",
" 5.75721175e-01, 5.29119624e-01, 2.08690430e-01,\n",
" -8.70661254e-02, 3.59439980e-02, 1.05353891e-01,\n",
" 1.16328188e-01, 7.65569747e-01, -1.78583632e-01,\n",
" 3.64933673e-01, 2.77987819e-01],\n",
" [ 4.01383144e-01, -1.99211003e-01, 2.14286561e-01,\n",
" -5.05094224e-02, -1.46279768e-01, -2.24781952e-01,\n",
" 2.10912726e-01, -2.30122236e-02, 1.14771432e-01,\n",
" -2.34398775e-01, 3.38045181e-01, 3.22800409e-01,\n",
" 4.14148537e-01, 8.43897813e-01, 1.21250643e+00,\n",
" 2.14274423e-01, 6.48500132e-01, 7.99745331e-01,\n",
" 9.13817909e-01, 9.36570266e-01, 8.85504692e-01,\n",
" 1.08222340e+00, 5.88689634e-01, 6.02140237e-01,\n",
" 7.36165790e-01, 1.23651094e+00, 1.10128755e+00,\n",
" 6.19509977e-01, 3.62296145e-01, 7.98860536e-01,\n",
" 1.16352921e+00, 2.35979911e-01, 8.31680630e-01,\n",
" 7.83218980e-01, 1.68927592e-01, 4.76185986e-01,\n",
" 7.41020393e-01, 5.61704495e-01, 5.89398234e-01,\n",
" 3.51986544e-01, 2.17066531e-01, 4.71679192e-01,\n",
" 3.11419358e-01, -1.69062237e-01, 1.98113853e-01,\n",
" -6.36591994e-01, 1.80295864e-01, 3.59737007e-01,\n",
" -6.59851626e-01, -4.08927610e-01],\n",
" [ 4.62567083e-01, 6.82008905e-01, 4.70321460e-01,\n",
" 2.51011584e-01, 5.87126264e-01, 1.39122402e-01,\n",
" -1.63962079e-02, 2.27156913e-01, 5.89099047e-01,\n",
" 3.36541732e-01, 2.90509726e-01, 6.95224674e-01,\n",
" 8.17196571e-01, 6.32990851e-02, 1.69919456e-01,\n",
" 5.45736591e-01, 1.03512861e+00, 9.64593011e-01,\n",
" 1.11327648e+00, 1.76990726e+00, 7.97706886e-01,\n",
" 9.04758071e-01, 1.15076235e+00, 9.34408704e-01,\n",
" 1.10508811e+00, 2.66921245e-01, 1.35004111e+00,\n",
" 9.23011531e-01, 8.22333195e-01, 6.44342192e-01,\n",
" 1.30973226e+00, 8.59182188e-01, 1.27244417e+00,\n",
" 1.20124417e+00, 8.81506566e-01, 5.37549368e-01,\n",
" 8.53514246e-01, 8.67419613e-01, 4.37738461e-01,\n",
" 6.17717809e-02, 4.95638473e-01, -3.41763164e-01,\n",
" 3.31011503e-01, -3.89526073e-01, 1.49109463e-01,\n",
" 5.49517156e-01, 3.20177672e-01, -1.16629973e-01,\n",
" -2.65468928e-01, 2.09463738e-01],\n",
" [-2.28402315e-01, 4.25592973e-01, 3.68182849e-01,\n",
" 3.89982657e-01, 5.05849379e-01, 1.45804186e-01,\n",
" -1.03848543e-01, 1.53714728e-01, 3.96609421e-01,\n",
" 5.83050550e-01, 4.13723438e-01, 3.19435935e-01,\n",
" 3.34590340e-01, 5.37991651e-02, 5.16476843e-01,\n",
" 1.04898194e+00, 1.04408310e+00, 6.87425312e-01,\n",
" 1.20077299e+00, 1.17567924e+00, 1.44505413e+00,\n",
" 1.36425676e+00, 9.29543335e-01, 1.30182402e+00,\n",
" 1.19062915e+00, 1.08817163e+00, 7.40324585e-01,\n",
" 1.06651608e+00, 1.06418798e+00, 1.10101976e+00,\n",
" 1.29228324e+00, 1.34075327e+00, 1.25523788e+00,\n",
" 6.69556369e-01, 4.43001850e-01, 3.27347813e-01,\n",
" 2.81065861e-01, 8.54626361e-01, -2.28971891e-02,\n",
" 3.66318198e-01, 8.53634923e-01, 4.31076255e-01,\n",
" 5.28044792e-01, 3.05939742e-02, 6.39995740e-01,\n",
" -3.82046529e-02, 7.06421586e-01, -5.36933494e-01,\n",
" 2.97626891e-01, 8.06564590e-01]])\n",
"Coordinates:\n",
" * time (time) float64 0.0 1.0 2.0 3.0 4.0 5.0 ... 15.0 16.0 17.0 18.0 19.0\n",
" * x (x) float64 -5.0 -4.796 -4.592 -4.388 ... 4.388 4.592 4.796 5.0"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Example data\n",
"da = xr.DataArray(data,\n",
" dims=('time', 'x'),\n",
" coords={'time': np.linspace(0,19, 20), \n",
" 'x': np.linspace(-5, 5, 50)})\n",
"da"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x101d9c2e50>]"
]
},
"execution_count": 166,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"da[0,...].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define function to fit to noisy data, test"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {},
"outputs": [],
"source": [
"def pulse_shape(x, peak_height, peak_location, FWHM):\n",
" return peak_height * np.exp(-((x-peak_location)/FWHM)**2.0)"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [],
"source": [
"popt, pcov = curve_fit(f=pulse_shape, xdata=get_clean_interp_index(da, dim='x'), ydata=da.isel(time=0).values)"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.99733061, 0.06619805, 2.88877507])"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"popt"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x101da71950>]"
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"da[0,...].plot()\n",
"plt.plot(da.x, pulse_shape(da.x, *popt))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use `xr.apply_ufunc` to apply this across all other dimensions"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [],
"source": [
"param_names = ['peak_height', 'peak_location', 'FWHM']"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
"<defs>\n",
"<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
"<title>Show/Hide data repr</title>\n",
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"<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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".xr-wrap {\n",
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".xr-header {\n",
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"\n",
".xr-header > div,\n",
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"\n",
".xr-section-summary-in:disabled + label:before {\n",
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"\n",
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".xr-section-summary-in:checked ~ .xr-section-details {\n",
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"\n",
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"\n",
".xr-array-wrap > label {\n",
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"\n",
".xr-preview {\n",
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"\n",
".xr-array-preview,\n",
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".xr-dim-list li {\n",
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"\n",
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".xr-var-item label,\n",
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"\n",
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"\n",
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".xr-attrs dt {\n",
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".xr-var-name:hover,\n",
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"\n",
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"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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"\n",
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"</style><div class='xr-wrap'><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-df7bdfed-9104-4e08-941c-279e461e50cc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-df7bdfed-9104-4e08-941c-279e461e50cc' 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'>time</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-14e2b59d-8481-4f4a-98a8-57551c6af341' class='xr-section-summary-in' type='checkbox' checked><label for='section-14e2b59d-8481-4f4a-98a8-57551c6af341' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0 ... 17.0 18.0 19.0</div><input id='attrs-0de885f4-9d78-41eb-822e-cbe9be911e8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0de885f4-9d78-41eb-822e-cbe9be911e8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca3f47a0-f5df-41c0-b741-748746aabf38' class='xr-var-data-in' type='checkbox'><label for='data-ca3f47a0-f5df-41c0-b741-748746aabf38' 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><pre class='xr-var-data'>array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,\n",
" 14., 15., 16., 17., 18., 19.])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-9ad285f0-47d2-42cf-a9a8-a8d7aef4981b' class='xr-section-summary-in' type='checkbox' checked><label for='section-9ad285f0-47d2-42cf-a9a8-a8d7aef4981b' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>peak_height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-3fa0a8a4-3adf-41f1-9512-9db48cf135f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3fa0a8a4-3adf-41f1-9512-9db48cf135f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-868d3d05-48e8-4722-8425-5a49bf566235' class='xr-var-data-in' type='checkbox'><label for='data-868d3d05-48e8-4722-8425-5a49bf566235' 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><pre class='xr-var-data'>array(None, dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>peak_location</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-c71a055f-bc4d-49a7-bf2d-026150e1718a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c71a055f-bc4d-49a7-bf2d-026150e1718a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f714fd3-3072-46a3-95cb-9bafeddb180a' class='xr-var-data-in' type='checkbox'><label for='data-2f714fd3-3072-46a3-95cb-9bafeddb180a' 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><pre class='xr-var-data'>array(None, dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FWHM</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None</div><input id='attrs-71904b81-f508-4f78-9fef-c554a8d48adf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71904b81-f508-4f78-9fef-c554a8d48adf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4329651-8ca2-4b83-bc90-96d3cc8d4cb6' class='xr-var-data-in' type='checkbox'><label for='data-b4329651-8ca2-4b83-bc90-96d3cc8d4cb6' 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><pre class='xr-var-data'>array(None, dtype=object)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-3b95c6b8-cdbe-45f5-88ee-d21e07d639ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3b95c6b8-cdbe-45f5-88ee-d21e07d639ff' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 20)\n",
"Coordinates:\n",
" * time (time) float64 0.0 1.0 2.0 3.0 4.0 ... 16.0 17.0 18.0 19.0\n",
"Data variables:\n",
" peak_height object None\n",
" peak_location object None\n",
" FWHM object None"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"param_ds = xr.Dataset(data_vars={key:None for key in param_names}, \n",
" coords={'time': np.linspace(0,19, 20)})\n",
"\n",
"param_ds"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [],
"source": [
"def fit_curve(x, y):\n",
" popt, pcov = curve_fit(f=pulse_shape, xdata=x, ydata=y)\n",
" return popt"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
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"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" 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><div class='xr-wrap'><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-f55e49dd-ac4d-41c2-b7c6-f3b05316c095' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f55e49dd-ac4d-41c2-b7c6-f3b05316c095' 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'>time</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2e013a25-da5e-46aa-b76e-83886070021a' class='xr-section-summary-in' type='checkbox' checked><label for='section-2e013a25-da5e-46aa-b76e-83886070021a' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0 ... 17.0 18.0 19.0</div><input id='attrs-e4700992-dcf8-438e-8695-9a2e1e27762d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4700992-dcf8-438e-8695-9a2e1e27762d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4cdcaca-0ff1-4b0e-a5e0-5f5dc2696e89' class='xr-var-data-in' type='checkbox'><label for='data-e4cdcaca-0ff1-4b0e-a5e0-5f5dc2696e89' 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><pre class='xr-var-data'>array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,\n",
" 14., 15., 16., 17., 18., 19.])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-53ae9a8a-9afc-4791-9839-e7cbd0b7e513' class='xr-section-summary-in' type='checkbox' checked><label for='section-53ae9a8a-9afc-4791-9839-e7cbd0b7e513' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>peak_height</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9973 0.9896 1.04 ... 1.069 1.178</div><input id='attrs-0abf7790-5449-4c45-95c0-52d09f2924b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0abf7790-5449-4c45-95c0-52d09f2924b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a183be2-758b-4f56-9ed2-b1d7d5d4da2b' class='xr-var-data-in' type='checkbox'><label for='data-2a183be2-758b-4f56-9ed2-b1d7d5d4da2b' 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><pre class='xr-var-data'>array([0.99733061, 0.98960801, 1.04038468, 0.90657188, 0.92639098,\n",
" 1.0835697 , 1.13454544, 0.96717898, 0.84015212, 1.03930542,\n",
" 1.16408409, 1.10203088, 0.9698329 , 1.17561284, 0.98833836,\n",
" 0.86605152, 1.14725387, 0.92151256, 1.06850401, 1.17806063])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>peak_location</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0662 0.03605 ... -0.09023 0.01197</div><input id='attrs-fe422553-881f-47af-8308-1210bec31fc1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe422553-881f-47af-8308-1210bec31fc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0909e882-e79a-432d-ae9b-439b7b070be9' class='xr-var-data-in' type='checkbox'><label for='data-0909e882-e79a-432d-ae9b-439b7b070be9' 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><pre class='xr-var-data'>array([ 0.06619805, 0.03605198, -0.03113847, 0.19983239, 0.25508462,\n",
" -0.02432185, -0.18255736, 0.0108336 , -0.10965545, -0.06915063,\n",
" -0.11720612, 0.29459154, 0.38177797, 0.06623754, 0.01748489,\n",
" 0.00960531, 0.2200327 , -0.09146497, -0.09022656, 0.01196683])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>FWHM</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.889 2.878 2.954 ... 3.204 3.015</div><input id='attrs-de6143ec-16fa-45c3-b482-e82412c32449' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de6143ec-16fa-45c3-b482-e82412c32449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7b0f0e7-7876-4a9a-bec9-ccdf9b97af71' class='xr-var-data-in' type='checkbox'><label for='data-b7b0f0e7-7876-4a9a-bec9-ccdf9b97af71' 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><pre class='xr-var-data'>array([2.88877507, 2.87792054, 2.9540101 , 3.31300325, 3.15310614,\n",
" 2.71092283, 2.92081555, 3.03016326, 3.89984208, 2.68994271,\n",
" 2.74186424, 2.81935556, 2.94562848, 2.5445283 , 3.00116404,\n",
" 3.49255898, 2.63105391, 2.73751329, 3.20380438, 3.01521956])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-e76d57f6-2315-4a47-9b0a-f928c5df4208' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e76d57f6-2315-4a47-9b0a-f928c5df4208' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 20)\n",
"Coordinates:\n",
" * time (time) float64 0.0 1.0 2.0 3.0 4.0 ... 16.0 17.0 18.0 19.0\n",
"Data variables:\n",
" peak_height (time) float64 0.9973 0.9896 1.04 ... 0.9215 1.069 1.178\n",
" peak_location (time) float64 0.0662 0.03605 -0.03114 ... -0.09023 0.01197\n",
" FWHM (time) float64 2.889 2.878 2.954 3.313 ... 2.738 3.204 3.015"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"popts = xr.apply_ufunc(fit_curve,\n",
" da.x, da,\n",
" vectorize=True,\n",
" input_core_dims=[['x'], ['x']],# reduce along 'x'\n",
" output_core_dims=[['params']]\n",
" )\n",
"\n",
"for idx,name in enumerate(param_names): \n",
" param_ds[name] = popts[:, idx] \n",
" \n",
"param_ds"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x216 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(ncols=3, figsize=(15,3))\n",
"\n",
"param_ds['peak_height'].plot(x='time', ax=axs[0])\n",
"param_ds['peak_location'].plot(x='time', ax=axs[1])\n",
"param_ds['FWHM'].plot(x='time', ax=axs[2])\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Try using `inspect` to get the *default* parameters of the fit function"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [],
"source": [
"import inspect"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['x', 'peak_height', 'peak_location', 'FWHM']"
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inspect.getfullargspec(pulse_shape).args"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['peak_height', 'peak_location', 'FWHM']"
]
},
"execution_count": 209,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"param_names = inspect.getfullargspec(pulse_shape).args[1:]\n",
"param_names"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"def fit(self, f, fit_along=None, skipna=None, full=False, cov=False, param_names=None):\n",
" \"\"\"\n",
" Fits the function f to the DataArray.\n",
"\n",
" Expects the function f to have a signature like\n",
" `result = f(*coords, **params)`\n",
" for example\n",
" `result_da = f(da.xcoord, da.ycoord, da.zcoord, A=5, B=None)`\n",
" The names of the `**params` kwargs will be used to name the output variables.\n",
"\n",
" Returns\n",
" -------\n",
" fit_results - A single dataset which contains the variables (for each parameter in the fitting function):\n",
" `param1`\n",
" The optimised fit coefficients for parameter one.\n",
" `param1_residuals`\n",
" The residuals of the fit for parameter one.\n",
" ...\n",
" \"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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