From ad63ea643939889d27f77e40d62d633e6e9bcb6f Mon Sep 17 00:00:00 2001 From: "Hood, Jonathan D" Date: Fri, 5 Mar 2021 15:20:30 -0500 Subject: [PATCH] routine --- .../FileLoadScript-checkpoint.ipynb | 93 ++++++++------- .../PostProcessing-checkpoint.ipynb | 110 ++++++++++-------- 2 files changed, 113 insertions(+), 90 deletions(-) diff --git a/.ipynb_checkpoints/FileLoadScript-checkpoint.ipynb b/.ipynb_checkpoints/FileLoadScript-checkpoint.ipynb index 54082df..a78b85d 100644 --- a/.ipynb_checkpoints/FileLoadScript-checkpoint.ipynb +++ b/.ipynb_checkpoints/FileLoadScript-checkpoint.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -17,8 +17,7 @@ "output_type": "stream", "text": [ "['index', 'variable', 'transmission', 'amp', 'amperror', 'sigmax', 'sigmaxerror', 'sigmay', 'sigmayerror']\n", - "[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.\n", - " 18. 19.]\n" + "[0.]\n" ] } ], @@ -27,7 +26,7 @@ "import matplotlib.pyplot as plt\n", "\n", "# Put your file name in the np.load\n", - "file = np.load(r'//?/S:/flir_images/binaries/ 02172021175251_H17M52S51MS_F3ImagingPower.npz') #DONT FORGET THE SPACE BEFORE THE FILENAME!\n", + "file = np.load(r'//?/S:/flir_images/binaries/03022021_H14M18S5MS413_ExpansionTime.npz') #DONT FORGET THE SPACE BEFORE THE FILENAME!\n", "\n", "# np.load().files will display all variables within the file\n", "\n", @@ -47,16 +46,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "filename = '02222021_H10M10S29MS224_F4_MOT_power' #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n", + "filename = '03042021_H10M29S29MS143_FormationTime' #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n", "\n", - "path = r'//?/S:/flir_images/binaries/complete_45deg_scan/'\n", + "path = r'//?/S:/flir_images/binaries/'\n", "file = np.load(path+filename+'.npz')\n", "\n", "index = file['index']\n", @@ -72,22 +71,26 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 71, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "24\n", + "[ nan 0. 3.28556674 2.93907685 2.79781204 2.64583981\n", + " 2.76401192 2.75865437 2.87877424 2.70551571 2.67999878 2.80252799\n", + " 2.72037937 2.86090613 2.76348333 2.82233893 2.78208963 2.80139726\n", + " 2.79280084 2.88529704 2.73468322 2.81890705 2.72398383 2.84901335\n", + " 2.7688844 2.80969582 2.89166643 2.86002381 2.80136814 2.79876938\n", + " 2.82271463]\n", + "hello\n" + ] }, { "data": { - "image/png": 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\n", 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" ] @@ -100,29 +103,30 @@ ], "source": [ "#Put plot labels, x and y scaling (i.e. y goes from 0 to 1 for amp), and change scatter points to be smaller\n", - "\n", - "plt.scatter(variable,amp)\n", - "# plt.plot(variable,sigmax)\n", + "print(np.nanargmax(amp))\n", + "print(sigmax)\n", + "#plt.scatter(variable,amp)\n", + "plt.plot(variable,sigmax)\n", "# plt.plot(variable,sigmay)\n", - "# plt.plot(variable,transmission)" + "#plt.plot(variable,transmission)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 65, "metadata": {}, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '//?/S:/flir_images/binaries/complete_45deg_scan/npyfiles/02222021_H10M10S29MS224_F4_MOT_power_img0.npy'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mimageindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;31m#Starts from zero!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'npyfiles/'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'_img'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimageindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.npy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mbcg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'npyfiles/'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'_bcg'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimageindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.npy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\npyio.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[0;32m 420\u001b[0m \u001b[0mown_fid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 421\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 422\u001b[1;33m \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 423\u001b[0m \u001b[0mown_fid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 424\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '//?/S:/flir_images/binaries/complete_45deg_scan/npyfiles/02222021_H10M10S29MS224_F4_MOT_power_img0.npy'" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -130,17 +134,21 @@ "\n", "img = np.load(path+'npyfiles/'+filename+'_img'+str(imageindex)+'.npy')\n", "bcg = np.load(path+'npyfiles/'+filename+'_bcg'+str(imageindex)+'.npy')\n", - "plt.imshow(img)\n", - "plt.show()\n", - "plt.imshow(bcg)\n", - "plt.show()\n", - "plt.imshow(img/bcg)\n", - "plt.show()" + "# plt.imshow(img)\n", + "# plt.show()\n", + "# plt.imshow(bcg)\n", + "# plt.show()\n", + "# plt.imshow(img/bcg)\n", + "# plt.show()\n", + "sub=img/bcg\n", + "plt.imshow(sub)\n", + "plt.colorbar()\n", + "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -151,6 +159,13 @@ "# print(np.argwhere(transmission>0.5))\n", "# print(np.argwhere(variable == 19))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb b/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb index 7933636..9e843c3 100644 --- a/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb +++ b/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -32,13 +32,13 @@ "wavelength = 852 * nm\n", "sigma_0 = 3*wavelength**2/(2*np.pi)\n", "\n", - "formationrun = '03032021_H11M56S14MS475_ExpansionTime' # Make sure units of self.variable are ms !!!\n", - "expansionrun = '03022021_H14M18S5MS413_ExpansionTime' # Make sure units of self.variable are us !!!" + "formationrun = '03042021_H10M29S29MS143_FormationTime' # Make sure units of self.variable are ms !!!\n", + "expansionrun = '03042021_H11M20S19MS093_FormationTime' # Make sure units of self.variable are us !!!\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -97,47 +97,78 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": 78, + "metadata": { + "collapsed": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.25556025] [0.0041291] [0.00352961]\n", - "You have 67520229 atoms in this image\n" + "[ nan 0. 0.05629712 0.08383055 0.11269062 0.15244252\n", + " 0.15429671 0.19016292 0.19347884 0.21207961 0.23446757 0.22321317\n", + " 0.23637298 0.24435703 0.24023957 0.24995799 0.25056974 0.25177797\n", + " 0.24494132 0.26517839 0.25334804 0.25934796 0.26707901 0.26173192\n", + " 0.26779199 0.26239763 0.25603638 0.23503068 0.25668221 0.2647878\n", + " 0.26266358] [ nan 0. 0.00025052 0.0002241 0.00021333 0.00020175\n", + " 0.00021076 0.00021035 0.00021951 0.0002063 0.00020435 0.00021369\n", + " 0.00020743 0.00021814 0.00021072 0.0002152 0.00021213 0.00021361\n", + " 0.00021295 0.00022 0.00020852 0.00021494 0.0002077 0.00021724\n", + " 0.00021113 0.00021424 0.00022049 0.00021808 0.0002136 0.00021341\n", + " 0.00021523]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Given an amp, sigmax, and sigmay, it will output the atom number of the fit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mamp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msigmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"You have\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0matomnumber\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mamp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msigmax\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msigmay\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"atoms in this image\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m 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However, based on\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\mpmath\\libmp\\gammazeta.py\u001b[0m in \u001b[0;36mmpf_zeta_int\u001b[1;34m(s, prec, rnd)\u001b[0m\n\u001b[0;32m 1140\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1141\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mmpf_neg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfhalf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1142\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmpf_div\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmpf_bernoulli\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m 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precisions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\mpmath\\libmp\\gammazeta.py\u001b[0m in \u001b[0;36mmpf_bernoulli_huge\u001b[1;34m(n, prec, rnd)\u001b[0m\n\u001b[0;32m 479\u001b[0m \u001b[0mpiprec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwp\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmpf_gamma_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 481\u001b[1;33m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmpf_mul\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmpf_zeta_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 482\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmpf_mul\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmpf_pow_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmpf_pi\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpiprec\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 483\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m 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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt_fit_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN_fit_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Raw'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# y axis should be 1k-10million, x axis should be 1-100's ms\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcovariance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcurve_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0matomnumbervstime\u001b[0m\u001b[1;33m,\u001b[0m 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**kwargs)\u001b[0m\n\u001b[0;32m 750\u001b[0m \u001b[1;31m# Remove full_output from kwargs, otherwise we're passing it in twice.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 751\u001b[0m \u001b[0mreturn_full\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'full_output'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 752\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mleastsq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDfun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjac\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m 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full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)\u001b[0m\n\u001b[0;32m 385\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 386\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 387\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Improper input: N=%s must not exceed M=%s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 389\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mepsfcn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: Improper input: N=2 must not exceed M=1" + "name": "stdout", + "output_type": "stream", + "text": [ + "Data point 0 failed. Averaging nearest neighbors.\n", + "Loading rate is : 134728.69531147732 \n", + "Error in loading rate fit is : 12851.808687679712\n", + "Time constant is : 1.1972527051434785 \n", + "Error in time constant fit is : 0.14713785809977178\n" ] }, { "data": { - "image/png": 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\n", 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Jxcy26+h5rdwvrrrXFvHd6mu2P6tRq5e5vPoapr4G6WkDIHs4L+9Tza3iq7cNpHa3l9uUW71tILmDbiuVNLqimF/y3bFFUignFzPbbvX6Rk7Z7Z7UIf1Wi+SW9S37EKb2+Q39aPsQ4NQ+vwG+uz32EoPy3sbK4m+5ps+ZXNB0dYsHCzdFH67pc2bLjnAqlzQqpbu1SArl9VzMbLtJez7I5dXXtJiW5PLqa5i054Mtyu1L21ZGvnihD+2N+fhkpsXkFkNpp8Vkxnx8chGuyirByaWba55yv/nnueeeo76+nvPOyzpF77rrLv785z9vLz9//nyefPLJLp9nzz33LFqdrZsqYELFC6pvyDstyQXVN7SIqZ0nx1vHC31ob+LYoRz7D1/h0/3+mwPenM2n+/03x/7DV3rkX+yW8W2xbq55+pdcw4cPp66uDsiSy5577snRR2fj4OfPn89JJ53EQQcd1OZY1jsVNCS4wKG+/drpWG8THzet5SgwgOqaNk+OZx3Sm1sMza2pruK7PaBD2naOWy7FVKZFeO666y5OOukknnvuOX7yk59w5ZVXMmbMGP74xz+yYMECpkyZwpgxY1ixYgUrVqxgwoQJHHbYYRx33HE8/fTTAPz1r3/lqKOO4vDDD+df/uVfSlJPK71CZ+PedNu0txJL0jzUt4UC57LikNPg5BnQfxig7PXkGW2e25g4dijf/cT7GDqgBpE97/HdT7zPSWQX4JZLsRQ4nr+rmmdFBhgxYgTz5s3b/tnw4cP50pe+xJ577sm3vvUtAE455RROOukkPvnJbE7PcePG8ZOf/ISRI0fywAMP8JWvfIU77riD888/ny9/+cucddZZXoCsByt0SHChQ30LbZEABT0ECG6R7KqcXIqlwPH8XZXvtlihNm7cyJ///Gc+9am3Ok/ffPNNAO69915++9vfAvC5z32OCy+8cIfraKVRyO2uQocEFzrUd/v/q509UW/WCSeXYil03YUy2rZtGwMGDGg3OXmW5O6r0IcZCx0S3JWhvoW2SMw64j6XYin0XnWR7bXXXrz++ut5t/fee29GjBjBb37zGyBbYOyxxx4D4JhjjmHOnDlAtjaMdS/Nt7vu6XMez+7xWe7pcx4nbP1jm8XnCh0S7KG+Vm5OLsUyblp2bzpXe/eqi+jkk09m3rx5jBkzhrvvvpvTTz+d6dOnM3bsWFasWMHs2bO59tpref/738/BBx/MzTdna6r96Ec/4qqrruLwww9nw4YNJa2jdV3da4vyPm9S99qiFuUKHRLsob5WbirVIo2SfgacBKyJiNGtPvsWMB0YHBEvK7s/8yPgY8Am4B8j4uFUdhJwcdr1soiYleKHAdcBNcCtwPkREZLeAdwADAeeA06LiFc7q29dXV3U19e3iD311FNdW72xkNlfe5gu/w6sU4X0pbx4yYF5n2x/kcHsd8nytwKtB5JA9kdNnpFbZqUgaXFE1LWOl7LP5Trgv4DrW1VkGHAC8Lec8IlkSxuPBD4A/Bj4QEoU3wHqgAAWS1qQksWPgcnA/WTJZQJwG9nKk7dHxOWSLkrb5emt9r1q60ShfSmF3u5yB7x1VyVLLhHxJ0nD83x0JXABb615D3AqcH1a6/5+SQMk7Q98CFgUEesAJC0CJki6C9g7Iu5L8euBiWTJ5dS0H8As4C7KlVxsl1ZIi6TQocPqX5sNZ28l720w/1Fj3VBZ+1wknQKsiojHWn00FMj9l9SQYh3FG/LEAfaNiBcA0us+HdRnsqR6SfVr1+Zf7rRUtw17gl352ruq0IcZC+1LqVQfnlmxlC25SOoH/F8g37+OvLNy70C8SyJiZkTURUTd4MFt18ju27cvr7zyyi75JRsRvPLKK/Tt27fzwtbhVPW5pvb5Td65u7KhwzkKfALerLsq53MuBwAjgMfS8xW1wMOSjiBreQzLKVsLrE7xD7WK35XitXnKA7wkaf+IeCHdWluzoxWura2loaGB9lo1vV3fvn2prS3tUOreotDlaAvuSwHf7rIerWzJJSKWknOLStJzQF0aLbYA+JqkOWQd+htSclgI/Iekt6fdPgpMjYh1kl6XdCTwAHAW8J+pzAJgEnB5es3t2+mS6upqRowYsaO72y5kyIAaDnttUZt1UBa3mv23S30pZj1YyZKLpF+TtToGSWoAvhMR17ZT/FayYcjLyYYifx4gJZF/Ax5K5S5t7twHvsxbQ5FvSz+QJZW5ks4mG5HWcuEIsx3QWWf9Dw96htGLr9m+/G6tXuZ71dfw+EHDyZ2GpUtzd5n1YCV7zqWnyfecixm0HT4Madr43Nl9rxydt0VC/2Hwjcdbxnrh81C266rEcy5mvUJB68p3ZW4596XYLsDTv5h1oqDO+grNLWfWXTm5mHViyIAaTtntnhaTSJ6y2z0MGZDzHIqfSzFrwbfFzDpRUGe9p2Exa8HJxawTh6/4T2j14GONNmdxvvhW0H0pZtv5tphZZ7rhQnBm3Z1bLrZLK2SySdp58NGd9Wbtc8vFdlnzH1nFPfOu5oZNX2DFHp/lhk1f4J55V7eZbNKd9WZd5+Riu6xHfzeTSzWzxQzFl2omj/5uZsuCnkTSrMt8W8x2Weds/iX9dms7Q/E5m38J/GvLwu6sN+sSt1xslzVkt1e6FDezwjm52C7rjZr9uhQ3s8I5udguq9+Jl7KlquViaFuq+tLvxEsrVCOz3sN9LtYrFTTE+JDTsn8AOU/V7+6n6s2KwsnFepRCksb2IcbMYcgeL7N60yB+OO904Ct5E4yTiVnxley2mKSfSVoj6fGc2HRJT0taImmepAE5n02VtFzSMknjc+ITUmy5pIty4iMkPSDpGUk3SOqT4nuk7eXp8+GlukYrr+Z1VVatbySAVesbmXrT0jbPpRQ8xNjMSqaUfS7XARNaxRYBoyPiEOAvwFQASQcBpwMHp32ullQlqQq4CjgROAj4TCoL8D3gyogYCbwKnJ3iZwOvRsSBwJWpnPUCHa2rkuuczb+kn9obYmxm5VCy5BIRfwLWtYr9ISK2pM37geb5M04F5kTEmxHxV7Lljo9IP8sj4tmI2AzMAU6VJLLpaG9M+88CJuYca1Z6fyMwLpW3Hq6gdVXwEGOz7qCSo8X+ibfWvR8K5E7e1JBi7cUHAutzElVzvMWx0ucbUvk2JE2WVC+pfu3atTt9QVZaLdZP6SDuIcZmlVeR5CLp/wJbgNnNoTzFYgfiHR2rbTBiZkTURUTd4MGDO660VdyU8aOoqa5qEauprmLK+FEtYh5ibFZ5ZR8tJmkScBIwLiKav/QbgGE5xWqB1el9vvjLwABJu6fWSW755mM1SNod6E+r23PWMzWP9PIQY7Pur6zJRdIE4ELggxGxKeejBcCvJP0AGAKMBB4ka4WMlDQCWEXW6f/ZiAhJdwKfJOuHmQTcnHOsScB96fM7cpKY9XATq+5l4h6XQt8G2KMWqqYBeZKGhxibVVTJkoukXwMfAgZJagC+QzY6bA9gUepjvz8ivhQRT0iaCzxJdrvsqxGxNR3na8BCoAr4WUQ8kU5xITBH0mXAI8C1KX4t8AtJy8laLKeX6hqtzJbMhVvOg6bUgb9hZbYNTiRm3Yz8R32mrq4u6uvrK10N68iVo9tZtGsYfOPxtnEzKzlJiyOirnXcT+hbt1DQdC1ebtisx/DElVZxhT553+6ywl5u2KzbcXKxiiv0yXsvN2zWczi5WMUV+uS9lxs26znc52IVN2RADavyJJi8T+R7iLFZj+DkYhU3Zfwo7pl3NV9nDkP0MqtjED/kdI4d/5VKV83MdpCTi1XcxKp7Oan6Gnbf+gYAtXqZy6uuYfeq95P3AUkz6/bc52KVd/ul2xNLs923vpFN32JmPZKTi1Wen18x63WcXKzy/PyKWa/TYXKRtJsk3/S20vLzK2a9TofJJSK2AV8rU11sV+XnV8x6nUJGiy2S9C3gBuB/m4MR4TVSrHj8/IpZr1JIcvmn9PrVnFgA7yp+dczMrDfoNLlExIhyVMTMzHqPTkeLSeon6WJJM9P2SEknFbDfzyStkfR4TuwdkhZJeia9vj3FJWmGpOWSlkg6NGefSan8M2mJ5Ob4YZKWpn1mKK0+1t45rDIeWvBTXrzkQLZ9pz8vXnIgDy34aaWrZGZlUMhQ5J8Dm4Gj03YDcFkB+10HTGgVuwi4PSJGArenbYATyZY2HglMBn4MWaIgW8HyA8ARwHdyksWPU9nm/SZ0cg4rs4cW/JTRiy9mP9aym2A/1jJ68cVOMGa7gEKSywERcQXQBBARjWRr23coIv5EtsxwrlOBWen9LGBiTvz6yNwPDJC0PzAeWBQR6yLiVWARMCF9tndE3BfZUprXtzpWvnNYmQ17eDo12twiVqPNDHt4eoVqZGblUkhy2SyphqwTH0kHAG/u4Pn2jYgXANLrPik+FMhdv7YhxTqKN+SJd3SONiRNllQvqX7t2rU7eEnWnn0i/+90n3i5zDUxs3IrJLl8B/g9MEzSbLJbTRcUuR75WkKxA/EuiYiZEVEXEXWDBw/u6u7WiTXK/ztdo0FlromZlVunySUiFgGfAP4R+DVQFxF37eD5Xkq3tEiva1K8ARiWU64WWN1JvDZPvKNzWJmtPHQKjdGnRawx+rDy0CkVqpGZlUuhc4t9EBgH/D1w3E6cbwHQPOJrEnBzTvysNGrsSGBDuqW1EPiopLenjvyPAgvTZ69LOjKNEjur1bHyncPK7PBTvsjjh13GiwxmW4gXGczjh13G4ad8sdJVM7MSU9Yf3kEB6WrgQLJWC8CngRUR8dX29wJJvwY+BAwCXiK7vTYfmAu8E/gb8KmIWJcSxH+RjfjaBHw+IurTcf4J+HY67L9HxM9TvI5sRFoNcBtwbkSEpIH5ztHZL6Kuri7q6+s7K2ZmZjkkLY6IujbxApLLE8DoNCoLSbsBSyPi4JLUtEKcXMzMuq695FLIbbFlZK2AZsOAJcWqmJmZ9T7tTv8i6RayEVj9gackPZi2PwD8uTzVMzOznqijucW+X7ZaWM+zZG62DPGGhmxRr3HTPKuxmW3XbnKJiD/mbkvau6PytgtZMpctN5/71rr3G1Zm2+AEY2ZAYRNXTpb0Elk/Sz2wOL3aLmrTbdPeSizJ7lvfYNNtXjnSzDKFtESmAAdHeM4Oy/RtfLFLcTPb9RQyWmwF2bMnZgCs3jawS3Ez2/UUklymAn+W9NO0bsoMSTNKXTHrvq7pcyabWk3rsin6cE2fMytUIzPrbgq5LfZT4A5gKbCttNWxnmDMxyczbd4Wvh5zGKJXWB0D+SGnc+zHJ1e6ambWTRSSXLZExDdLXhPrMSaOHQp8hU8vHMfq9Y0MGVDDlPGjUtzMrLDkcqekycAt5KzjUsh8XdZ7TRw71MnEzNpVSHL5bHqdmhML4F3Fr46ZmfUGnSaXiBhRjoqYmVnv0WlykXRWvnhEXF/86piZWW9QyG2xw3Pe9yVbNOxhwMmlN/KcYWZWBIUsc3xuzs8XgLFAn87264ikb0h6QtLjkn4tqa+kEZIekPSMpBsk9Ull90jby9Pnw3OOMzXFl0kanxOfkGLLJV20M3XdpSyZC7ecBxtWApG93nJeFjcz64JClznOtQkYuaMnlDQUOA+oi4jRQBVwOvA94MqIGAm8CpyddjkbeDUiDgSuTOWQdFDa72CyFSyvllQlqQq4CjgROAj4TCprnbn9UmhqbBlrasziZmZdUEifS/O6LpAlo4PIlhHe2fPWSGoC+gEvAB/mrZFps4BLgB8Dp6b3ADcC/5WWRT4VmBMRbwJ/lbQcOCKVWx4Rz6b6z0lln9zJOndo/iOrmL5wWc9+7mNDQ9fiZmbtKKTPJXddly3A8xGxw982EbFK0vfJ1rdvBP5ANtPy+ojYkoo1AM3fzEOBlWnfLZI2AANT/P6cQ+fus7JV/AP56pKe35kM8M53vjNfkYLMf2QVU29aSmPTVgBWrW9k6k1LAXpUgtlUsx/9Gl/IH69Afcys5yqkz+WPOT/37kxiAZD0drKWxAhgCPA2sltYbU7dvEs7n3U13jYYMTMi6iKibvDgwZ1VvV3TFy7bnliaNTZtZfrCZTt8zEq4ounTeecMu6Lp0xWqkZn1VIWs5/KJ1Mm+QdJrkl6X9NoYvegAABKSSURBVNpOnPMjwF8jYm1ENAE3AUcDAyQ1t6RqgdXpfQMwLNVld7Jll9flxlvt0168ZFavb+xSvLuatfEILmo6h4Ztg9gWomHbIC5qOodZG4/ofGczsxyF3Ba7Ajg5Ip4q0jn/BhwpqR/ZbbFxZIuP3Ql8EpgDTAJuTuUXpO370ud3RERIWgD8StIPyFpAI4EHyVouIyWNAFaRdfo39+WUxJABNazKk0iGDKgp5WmLbsiAGhasP5YFm49tER/aw67DzCqvkNFiLxUxsRARD5B1zD9MNtPybsBM4ELgm6ljfiBwbdrlWmBgin8TuCgd5wmygQVPAr8HvhoRW1O/zdeAhcBTwNxUtmSmjB9FTXVVi1hNdRVTxo8q5WmLrrdch5lVniLydke8VUD6EbAfMJ+WE1feVNqqlVddXV3U1+/46s29YrQYvec6zKw8JC2OiLo28QKSy8/zhCMi/qlYlesOdja5mJntitpLLoVMXPn50lTJzMx6qx15Qt/MzKxDTi5mZlZ0Ti5mZlZ0hcwtNgA4CxieWz4izitdtazoPJW+mZVRIQ9R3ko2h9dSYFtpq2Ml0TyVfvOMx81T6YMTjJmVRCHJpW9EfLPkNbHS6WgqfScXMyuBQvpcfiHpC5L2l/SO5p+S18yKx1Ppm1mZFdJy2QxMB/4vb80uHMC7SlUpK7L+tWl1yTxxM7MSKKTl8k3gwIgYHhEj0o8TSw/y0AHn0thqKv3G6MNDB5xboRqZWW9XSHJ5gmxpY+uhvv7kSC5sNZX+hU3n8PUnd3i1ajOzDhVyW2wr8KikO2k5caWHIvcQq9c3soq2U+mrh603Y2Y9RyHJZX76sR6qt6w3Y2Y9RyETV86S1Ad4dwotSytIWg8xZfwopt60tMVSzF6nxcxKqZBljj8EPANcBVwN/EXS8TtzUkkDJN0o6WlJT0k6Kg1xXpSWVF4k6e2prCTNkLRc0hJJh+YcZ1Iq/4ykSTnxwyQtTfvMkKSdqW9PN3HsUL77ifcxdEANIltZ8rufeJ/XaTGzkilkPZfFwGcjYlnafjfw64g4bIdPKs0C7o6Ia1KrqB/wbWBdRFwu6SLg7RFxoaSPAecCHwM+APwoIj6QnrWpB+rIhkYvBg6LiFclPQicTzazwK3AjIi4raM6eT0XM7Oua289l0JGi1U3JxaAiPgLUL0TFdkbOJ60jHFEbI6I9cCpwKxUbBYwMb0/Fbg+MvcDAyTtD4wHFkXEuoh4FVgETEif7R0R90WWOa/POZaZmZVBIR369ZKuBX6Rts8gayXsqHcBa4GfS3p/Otb5wL4R8QJARLwgaZ9UfiiQ+wRgQ4p1FG/IEzczszIppOXyZbJnXc4jSwJPAl/ciXPuDhwK/DgixgL/C1zUQfl8/SWxA/G2B5YmS6qXVL927dqOa21mZgUrJLl8KSJ+EBGfiIh/iIgryRLOjmoAGiLigbR9I1myeSnd0iK9rskpPyxn/1pgdSfx2jzxNiJiZkTURUTd4MGDd+KSzMwsVyHJZVKe2D/u6Akj4kVgpaTmcbDjyFpDC3LONQm4Ob1fAJyVRo0dCWxIt88WAh+V9PY0suyjwML02euSjkyjxM7KOVbvs2QuXDkaLhmQvS6ZW+kamZm13+ci6TPAZ4ERkhbkfLQ38MpOnvdcYHYaKfYs8HmyRDdX0tnA34BPpbK3ko0UW042Dc3nASJinaR/Ax5K5S6NiHXp/ZeB64Aa4Lb00/t4nRYz66baHYos6e+AEcB3adkn8jrwWERszbtjD9UjhyJfObqd2Y6HwTceL399zGyX095Q5HZbLhHxPPA8cFSrAx0DzAC+WuxKWhd5nRYz66YK6XNB0hhJV0h6DrgMeLqktbLCtLcei9dpMbMKaze5SHq3pGmSngL+i+yZEkXE30fEf5athta+cdPYUtW3RWhLVV8YN61CFTIzy3TUcnmabCTXyRFxbEoovaqfpaebv/UYLmq1TstFTecwf+sxla6ame3iOnpC//8ApwN3Svo9MIf8DyhahUxfuIxVm4/mRo5uEb9v4TJPSmlmFdVuyyUi5kXEp4H3AHcB3wD2lfRjSR8tU/2sA6vbWeyrvbiZWbl02qEfEf8bEbMj4iSyp90fpePpWqxM2lvsy4uAmVmlFTRarFmagfinEfHhUlXICjdl/ChqqqtaxLwImJl1B4XMimzdVHO/yvSFy1i9vpEhA2qYMn6U+1vMrOKcXHq4iWOHOpmYWbfTpdtiZmZmhXByMTOzonNyMTOzonNyMTOzonNyMTOzonNyMTOzoqtYcpFUJekRSf+TtkdIekDSM5JuSKtUImmPtL08fT485xhTU3yZpPE58QkptlySZxMwMyuzSrZczgeeytn+HnBlRIwEXgXOTvGzgVcj4kDgylQOSQeRTax5MDABuDolrCrgKuBE4CDgM6lsz7JkbrbS5CUDstclcytdIzOzglUkuUiqBT4OXJO2BXwYuDEVmQVMTO9PTdukz8el8qcCcyLizYj4K7AcOCL9LI+IZyNiM9lszqeW/qqKaMlcuOW8tIRxZK+3nOcEY2Y9RqVaLj8ELgC2pe2BwPqI2JK2G4Dmx86Hki1URvp8Qyq/Pd5qn/bibUiaLKleUv3atWt39pqK5/ZLoanVzMZNjVnczKwHKHtykXQSsCYiFueG8xSNTj7rarxtMGJmRNRFRN3gwYM7qHWZbWjoWtzMrJupxNxixwCnSPoY0BfYm6wlM0DS7ql1UgusTuUbgGFAg6Tdgf7Aupx4s9x92ov3DP1r0y2xPHEzsx6g7C2XiJgaEbURMZysQ/6OiDgDuBP4ZCo2Cbg5vV+Qtkmf3xERkeKnp9FkI4CRwIPAQ8DINPqsTzrHgjJcWvGMm8aWqr4tQluq+sK4aRWqkJlZ13Sn51wuBL4paTlZn8q1KX4tMDDFv0laqCwingDmAk8Cvwe+GhFbU8vna8BCstFoc1PZHmP+1mO4qOkcGrYNYluIhm2DuKjpHOZvPabSVTMzK4iyRoDV1dVFfX19pasBwDGX38GqPEsVDx1Qw70XeZ02M+s+JC2OiLrW8e7UcrFkdZ7E0lHczKy7cXLphoYMqOlS3Mysu3Fy6YamjB9FTXVVi1hNdRVTxo+qUI3MzLrGyxx3Q83LFk9fuIzV6xsZMqCGKeNHeTljM+sxnFy6qYljhzqZmFmP5dtiZmZWdE4uZmZWdE4uZmZWdE4uZmZWdE4uZmZWdE4uZmZWdE4uZmZWdE4uZmZWdE4uZmZWdE4u5bZkLlw5Gi4ZkL0umVvpGpmZFV3Zk4ukYZLulPSUpCcknZ/i75C0SNIz6fXtKS5JMyQtl7RE0qE5x5qUyj8jaVJO/DBJS9M+MySp3NeZ15K5cMt5aQnjyF5vOc8Jxsx6nUq0XLYA/xwR7wWOBL4q6SCyFSZvj4iRwO1pG+BEsiWMRwKTgR9DloyA7wAfAI4AvtOckFKZyTn7TSjDdXXu9kuhqdWaLE2NWdzMrBcpe3KJiBci4uH0/nWypYiHAqcCs1KxWcDE9P5U4PrI3A8MkLQ/MB5YFBHrIuJVYBEwIX22d0TcF9kym9fnHKuyNjR0LW5m1kNVtM9F0nBgLPAAsG9EvABZAgL2ScWGAitzdmtIsY7iDXni+c4/WVK9pPq1a9fu7OV0rn9t1+JmZj1UxZKLpD2B3wJfj4jXOiqaJxY7EG8bjJgZEXURUTd48ODOqrzzxk2D6larSVbXZHEzs16kIslFUjVZYpkdETel8EvplhbpdU2KNwDDcnavBVZ3Eq/NE6+8Q06Dk2dA/2GAsteTZ2RxM7NepOyLhaWRW9cCT0XED3I+WgBMAi5PrzfnxL8maQ5Z5/2GiHhB0kLgP3I68T8KTI2IdZJel3Qk2e22s4D/LPmFFeqQ05xMzKzXq8RKlMcAnwOWSno0xb5NllTmSjob+BvwqfTZrcDHgOXAJuDzACmJ/BvwUCp3aUSsS++/DFwH1AC3pR8zMysTZQOqrK6uLurr6ytdDTOzHkXS4oioax33E/pmZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0Ti5mZlZ0lVgsrCwkTQB+BFQB10TE5SU94ZK5cPulsKEB+tfCuGl5V5yc/8gqpi9cxur1jQwZUMOU8aOYOHZoSatmZlZuvTK5SKoCrgJOABqAhyQtiIgnS3LCJXPhlvOgqTHb3rAy24YWCWb+I6uYetNSGpu2ArBqfSNTb1oK4ARjZr1Kb70tdgSwPCKejYjNwBzg1JKd7fZL30oszZoas3iO6QuXbU8szRqbtjJ94bKSVc3MrBJ6a3IZCqzM2W5IsRYkTZZUL6l+7dq1O362DQ0FxVevb8xbrL24mVlP1VuTi/LEok0gYmZE1EVE3eDBg3f8bP1rC4oPGVCTt1h7cTOznqq3JpcGYFjOdi2wumRnGzcNqlsliOqaLJ5jyvhR1FRXtYjVVFcxZfyoklXNzKwSemWHPvAQMFLSCGAVcDrw2ZKdrbnTvpPRYs2d9h4tZma9nSLa3C3qFSR9DPgh2VDkn0XEv3dUvq6uLurr68tSNzOz3kLS4oioax3vrS0XIuJW4NZK18PMbFfUW/tczMysgpxczMys6JxczMys6JxczMys6JxczMys6JxczMys6JxczMys6HrtQ5RdJWkt8HwRDjUIeLkIx+kpdqXr3ZWuFXy9vV2xrvfvIqLN5IxOLkUmqT7f06q91a50vbvStYKvt7cr9fX6tpiZmRWdk4uZmRWdk0vxzax0BcpsV7reXelawdfb25X0et3nYmZmReeWi5mZFZ2Ti5mZFZ2TSxFJmiBpmaTlki6qdH1KSdLPJK2R9Hil61JqkoZJulPSU5KekHR+petUSpL6SnpQ0mPpev+10nUqNUlVkh6R9D+VrkupSXpO0lJJj0oq2QqJ7nMpEklVwF+AE4AGsqWWPxMRT1a0YiUi6XhgI3B9RIyudH1KSdL+wP4R8bCkvYDFwMRe/N9WwNsiYqOkauAe4PyIuL/CVSsZSd8E6oC9I+KkStenlCQ9B9RFREkfGHXLpXiOAJZHxLMRsRmYA5xa4TqVTET8CVhX6XqUQ0S8EBEPp/evA08BQytbq9KJzMa0WZ1+eu1foZJqgY8D11S6Lr2Jk0vxDAVW5mw30Iu/gHZVkoYDY4EHKluT0kq3iR4F1gCLIqI3X+8PgQuAbZWuSJkE8AdJiyVNLtVJnFyKR3livfavvV2RpD2B3wJfj4jXKl2fUoqIrRExBqgFjpDUK299SjoJWBMRiytdlzI6JiIOBU4EvppucRedk0vxNADDcrZrgdUVqosVWep7+C0wOyJuqnR9yiUi1gN3ARMqXJVSOQY4JfVDzAE+LOmXla1SaUXE6vS6BphHdku/6JxciuchYKSkEZL6AKcDCypcJyuC1MF9LfBURPyg0vUpNUmDJQ1I72uAjwBPV7ZWpRERUyOiNiKGk/2bvSMizqxwtUpG0tvSoBQkvQ34KFCSEZ9OLkUSEVuArwELyTp850bEE5WtVelI+jVwHzBKUoOksytdpxI6Bvgc2V+1j6afj1W6UiW0P3CnpCVkfzQtioheP0R3F7EvcI+kx4AHgd9FxO9LcSIPRTYzs6Jzy8XMzIrOycXMzIrOycXMzIrOycXMzIrOycXMzIrOycWsyCQNzBmy/KKkVTnbfy7ROcdKandurPTsSkmGnJrls3ulK2DW20TEK8AYAEmXABsj4vslPu23gcs6qNNaSS9IOiYi7i1xXczccjErJ0kb0+uHJP1R0lxJf5F0uaQz0joqSyUdkMoNlvRbSQ+ln2PyHHMv4JCIeCxtfzCnpfRI8xPZwHzgjDJdqu3inFzMKuf9wPnA+8hmAHh3RBxBNvX7uanMj4ArI+Jw4P+Qf1r4OlpO4fEt4Ktp4snjgMYUr0/bZiXn22JmlfNQRLwAIGkF8IcUXwr8fXr/EeCgbHozAPaWtFdaV6bZ/sDanO17gR9Img3cFBENKb4GGFL8yzBry8nFrHLezHm/LWd7G2/929wNOCoiGmlfI9C3eSMiLpf0O+BjwP2SPhIRT6cyHR3HrGh8W8yse/sD2YSoAEgak6fMU8CBOWUOiIilEfE9slth70kfvZsSzYBr1pqTi1n3dh5QJ2mJpCeBL7UukFol/XM67r8u6fE0820jcFuK/z3wu3JU2syzIpv1ApK+AbweER096/In4NSIeLV8NbNdlVsuZr3Dj2nZh9OCpMHAD5xYrFzccjEzs6Jzy8XMzIrOycXMzIrOycXMzIrOycXMzIrOycXMzIru/wEfRqk/OTlW0AAAAABJRU5ErkJggg==\n", 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" ] @@ -180,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -210,29 +241,20 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data point 7 failed. Averaging nearest neighbors.\n", - "Data point 9 failed. Averaging nearest neighbors.\n", - "Temperature (uK) is : 4.943155238477439e-05 \n", - "Error in Temperature fit is : 2.4459883493049123e-05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: RuntimeWarning: invalid value encountered in sqrt\n" + "Temperature (uK) is : 87.03000709201798 \n", + "Error in Temperature fit is : 11.127097406062417\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -272,20 +294,6 @@ "plt.legend()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {