From dd903c0ada7b278e715985504f82430760a1bf4a Mon Sep 17 00:00:00 2001 From: "Peana, David" Date: Thu, 18 Feb 2021 13:21:07 -0500 Subject: [PATCH] streamlined. added notes about units --- .../PostProcessing-checkpoint.ipynb | 74 +++++-------------- PostProcessing.ipynb | 74 +++++-------------- 2 files changed, 38 insertions(+), 110 deletions(-) diff --git a/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb b/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb index 0e9ce57..f7f7522 100644 --- a/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb +++ b/.ipynb_checkpoints/PostProcessing-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 136, + "execution_count": 182, "metadata": {}, "outputs": [], "source": [ @@ -33,21 +33,20 @@ "wavelength = 852 * nm\n", "sigma_0 = 3*wavelength**2/(2*np.pi)\n", "\n", - "formationrun = '02182021_H11M46S31MS785_Formation_Time'\n", - "expansionrun = '02182021_H11M44S44MS263_Expansion_Time'" + "formationrun = '02182021_H11M46S31MS785_Formation_Time' # Make sure units of self.variable are ms !!!\n", + "expansionrun = '02182021_H11M44S44MS263_Expansion_Time' # Make sure units of self.variable are us !!!" ] }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 183, "metadata": {}, "outputs": [], "source": [ "# Import formation run file \n", "filename = formationrun #'02182021_H10M31S8MS224_Picomotor_MOTz_y' #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n", "\n", - "path = r'C:\\Users\\dpean\\Box\\HoodLab\\Quick Transfers/'\n", - "#path = r'//?/S:/flir_images/binaries/'\n", + "path = r'//?/S:/flir_images/binaries/'\n", "file = np.load(path+filename+'.npz')\n", "\n", "index = file['index']\n", @@ -61,7 +60,7 @@ "sigmayerror = file['sigmayerror']\n", "\n", "# Fix units\n", - "variable = variable * ms\n", + "variable = variable * ms # Careful here!\n", "sigmax = sigmax * binpixel\n", "sigmaxerror = sigmaxerror * binpixel\n", "sigmay = sigmay * binpixel\n", @@ -70,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 184, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 185, "metadata": { "scrolled": false }, @@ -106,15 +105,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loading rate is : 48132834580.92892 \n", - "Error in loading rate fit is : 3768782596544480.0\n", - "Time constant is : 375.74641624027197 \n", - "Error in time constant fit is : 29861310.716279566\n" + "Loading rate is : 79887.82713878715 \n", + "Error in loading rate fit is : 1276.626218417588\n", + "Time constant is : 1.8712755012079827 \n", + "Error in time constant fit is : 0.046009651457644817\n" ] }, { "data": { - "image/png": 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hkLUIjDGmmHhTR3f0PsX9iTTWW4vAGGOKSSzhFJsbtZPF5jIVW/G5AROBiFSIyFn5CsYYY4IQT6SI7kKxuUzRIis+N2AiUNUe4LI8xWKMMYGItyVzMj7gKbbbVmbTNfSEiHxDRMaLyCjvy/fIjDEmDza2b9nlYnOZiu1agqostrnQ/X5p2joFIrkPxxhj8svrvtmV8tOZhlZXMnbE0KIZMB40Eajq5HwEYowxQfDO2nM1Y8hTTFNIB+0aEpE6EfmuiNzkLu8lIqf6H5oxxvgvnkjlpNhcpmjYmUJaDMXnshkjuAXoBI50l1uAa7LZuYjMEJElIrJURK7sZ5uzROQNEVksIndmFbUxxuRILJHMSbG5TJFwqGiKz2Vz5FFVvRbYAqCqm4FB51iJSCVwPXAyMBWYKSJTM7bZC/gOcJSq7gt8dcfCN8aYXeNNHc01b8wh1lr44wTZJIJOERmKM0CMiESBbFLcdGCpqsZVtROYD5yRsc3FwPWquhZAVVuzjtwYY3ZRt1tsLpdTRz3eLKRYEVxLkE0i+D7wGDBeRO4A/gp8K4vXjQVWpC23uOvSTQGmiMizIvK8iMzoa0ciMkdEmkWkOZFIZPHWxhgzuJU5LDaXySs+VwwtgmxmDT0hIi8CR+B0CV2hqm1Z7Luv7qPMUZMqYC/gGGAc8HcR2U9V12XEcBNwE8C0adMKf+TFGFMUemcM5aD8dCav+Fwx3LYy29GRo4HjgWOBj2f5mhZgfNryOGBlH9s8qKpbVHUZsAQnMRhjjO+86Z1+tAic/dYXRYsgm+mjNwCXAK8BrwP/KiLXZ7HvhcBeIjJZRKqBs4GHMrb5I05yQUQacbqK4tmHb4wxOy+eSDKiLnfF5jJFw/WsXF/4xeeyubL4aGA/dSfDishtOElhQKraJSKXAY8DlcBcVV0sIlcDzar6kPvciSLyBtANfFNVV+/ksRhjzA6JJ1JEGnNXbC5TJBzqLT63z+7DfHmPXMgmESwBJgDL3eXxwKvZ7FxVHwEeyVh3VdpjBb7ufhljTF7FEkk+MSXs2/57p5AmksWZCETkTziDu8OBN0Vkgbt8OPBcfsIzxhh/bGzfQuvGjpzWGMo0ubE4qpAO1CL4Sd6iMMaYPPM+nHNZdTSTV3yu0KuQ9psIVPXp9GURGTbQ9sYYU0zibV6xOf9aBOAkmkJvEWQza2iOiHyIMy7QDCxyvxtjTNGKtXrF5up8fZ9iKD6XzRn+N4F9s7yIzBhjikK8LclEH4rNZYq6xec+3NDBmOG1vr7XzsrmJxADNvkdiDHG5FM8kfJ1fMDj1TEq5JvUZNMi+A7wnIi8QFqxOVW93LeojDHGR909Srwt5evUUU/6FNIj92z0/f12RjaJ4DfA33AuIuvxNxxjjPHfynWb6ezq8aX8dKbdhtUQqq4s6LuVZZMIulTVLvgyxpSMpW43jR/lpzM5xefqC3oKaTZjBE+6M4d2F5FR3pfvkRljjE+86Zx+Tx31FPoU0mxaBOe437+Ttk6BSO7DMcYY/8V8LjaXKdJYz0OvrGRzZzdDqyvz8p47Ipv7EUzORyDGGJMv8UTSt9LTfYk2bS0+N3WPwqs5NGgiEJFZfa1X1Xm5D8cYY/wXT6Q4Og8zhjyRRncKaVuyOBMBcFja41qcG9S8CFgiMMYUHa/YXD4Gij2TG0OIOFczF6Jsuoa+kr4sIsOB232LyBhjfLR1oDh/XUNDqyvZY/jQ3vpGhWZnrq3ehN1O0hhTpGJ5nDqaLhIOFewU0mzGCLz7EoCTOKYC9/gZlDHG+CWeSFFVIUwc7W+xuUzRcD33Nq9AVX27I9rOymaMIP2+BF3AclVt8SkeY4zxVbwtyYRRdQyp9LfYXKZCLj6XzRjB04NtY4wxxSLWmp9ic5nSaw4VWiLI5n4EnxGRd0RkvYhsEJGNIrIhH8EZY0wudfcoy1an8nZFcbpCrkKaTdfQtcBpqvqm38EYY4yf3l/rFJsLokVQyMXnsukk+9CSgDGmFMTydHvKvhRy8blsWgTNInI38Ee2vR/BA75FZYwxPoi1BjN11BMJh2h+d20g7z2QbBLBMJxrB05MW6eAJQJjTFGJt6XyWmwuUzRcz4MvF17xuWxmDV2Qj0CMMcZv8UQykG4hjzc2UWjF5/I7kdYYYwIUS6TyWnU0U/oU0kJiicAYUxY2tG8hsbGDaFNwLQKv+Fyh3aTGEoExpix4H75Btghqh1QydsTQgmsRZFNraAQwC5iUvr2qXu5fWMYYk1vehVxBtgjAmbFUaFVIs5k19AjwPPAa0ONvOMYY4w+v2NyEUfktNpcp0hii+d01BVV8LptEUKuqX/c9EmOM8VEsEUyxuUzRpno2dXbzwYZ2dh8+NNBYPNn8RG4XkYtFZHcRGeV9+R6ZMcbkUDyRCuxCsnRRd4yikAaMs0kEncCPgX8Ci9yv5mx2LiIzRGSJiCwVkSsH2O6zIqIiMi2b/RpjzI7YWmwuuIFijzdGUUgDxtl0DX0d2FNV23ZkxyJSCVwP/AvQAiwUkYdU9Y2M7RqAy4EXdmT/xhiTLa/YXJAXk3maGpzic8XWIliMU2JiR00HlqpqXFU7gfnAGX1s9184FU7bd+I9jDFmUFtvTxl8i6AQi89l0yLoBl4WkSfZtujcYNNHxwIr0pZbgMPTNxCRg4HxqvqwiHyjvx2JyBxgDsCECROyCNkYY7YK6j7F/YmGQywsoOJz2SSCP7pfO6qveVHa+6RIBfAz4PzBdqSqNwE3AUybNk0H2dwYY7YRb0sxMsBic5ki4Xr+WEDF57IpOnebiFQDU9xVS1R1Sxb7bgHGpy2PA1amLTcA+wFPuXNpxwAPicjpqprVYLQxxmQj1posmNYAbK05FG9Lsu8ewwOOJrtbVR4DvIMz8HsD8LaIfCKLfS8E9hKRyW4iORt4yHtSVderaqOqTlLVSTgXrVkSMMbkXLytMGYMebyxikIZMM6ma+inwImqugRARKYAdwGHDvQiVe0SkcuAx4FKYK6qLhaRq4FmVX1ooNcbY0wueMXmCqlF4BWfK5QB42wSwRAvCQCo6tsiMiSbnavqIzglKtLXXdXPtsdks09jjNkR3ll3IUwd9XjF54qpRdAsIjcDt7vL5+JcVGaMMQUvXkBTR9MVUvG5bK4j+BLOtQSXA1cAbwD/6mdQxhiTK7FEsiCKzWWKhkPEEylUg58ImU2L4BJVvQ64zlshIlcAv/AtKmOMyZF4IsWE0cEXm8sUCRdO8blsEsFstv/QP7+PdQbo6VFWbWgnnkgST6RY1pbi3dUpRgwdwuTGeiaHQ0QaQ0xqDFFfk82P3xizK2KJJJHGwhkf8HizmGKtqcJNBCIyEzgHmCwi6TN8hgGr/Q6s0G1s38KythTxRIp4IknMfbysLUn7lq23bQhVVzJxdIh3Pkzy4CsrSW8F7jashsmNISY31hNpDBEJh5jcGGJ8AZTKNaYUdPco77Zt4ti9m4IOZTvp1xJ8bK/GQGMZ6JT0OWAV0IgzhdSzEXjFz6AKRVd3Dy1rNxNvc87uY+6HfrwtRWJjb7UNKgTGj6oj0hjiyOhoIuEQkcZ6IuEQTQ01vTefaN/SzburUyxLpIi3pdxEkuSx11exdtPWa/Qq3f7MyY1O62GymyAijfXsNqymYG5mYUyha1m7ic7uHqIF2CJoaqihvqaqIGYO9ZsIVHU5sBz4aPp6ETkK+CVwqb+h5c+aVGdvV068beuH/fLVKbZ0bz2FH1k3hEi4nmOmhN0unnqi4RATRtdRUzX4ZeK1QyrZe8ww9h4zbLvn1qY6WdabJJK9rY3nYm3btDDqqivdVsTWJBFprGdSY4jhQ7Oa1WtM2ei9T3GBzRgCr/hcqCCuJciqk1pEDsLpJjoLWAY84GdQfujo6ua91Zucs3r3DN/7wF+XdjY+pFKYONr5kD1hn92IhENE3Q/bkT7WKRkZqmZkqJpDJozcZn1Pj/LBhvbebievJfFqy3oeeW0VPWldTY311VuTRLieyY0hDh4/gqZhtb7FbUwh8z5kC+kagnSRxsIoPjfQGMEUnLIQM3HGBO4GRFWPzVNsOfWbp+Nc98TbvctNDTVEwiFO2X93Io0homGnK2fsiKFUFVD/fEWFsMeIoewxYuh2/YgdXd2sWLOptyWzzB2c/ttbCe5pbgGgvqaK+XOOYL+xwdczMSbfYgmn2JyfJ3G7IuoWn9vU2UVddXCTRwZ657eAvwOnqepSABH5Wl6i8sFJ+45h4ui63jPmhtri70apqapkz6YG9mxq2O65De1beOfDjXzlzpc4/5aFPPClI5kwurDmURvjt3giWbCtAdhaFntZWyrQ4nMDnfqeCXwAPCkivxWR4+m7tHRR+MiYBs44aCwHjBtREklgMMNqh3DoxFHMu2g6XT09zJr7Am3JjsFfaEwJiSVSBTk+4Ik2uVNIAx4w7jcRqOofVPXzwN7AU8DXgN1E5NcicmKe4jO7aM+mBm6efRgfbGjnglsWkuzoCjokY/Ji/eYttCU7CrpFMGm0U3wuHvCA8aCd4aqaUtU7VPVUnHsKvAz0eyN6U3gOnTiSG849hDdWbeCS2xfR2dUz+IuMKXLxArsrWV8KpfjcDo2KquoaVf2Nqh7nV0DGH8ftvRv/+5n9+cfSNr5x7yv09ARf38QYPxXy1NF00QK4f7HVOCgjZ00bT1uyg2sfW0JjfQ3fO3UfuzjNlKx4W2EWm8sUCYdYsGwNPT1KRUUw/4+WCMrMl46O0rqhg7nPLqNpWA2XHB0NOiRjfBFrLcxic5mi4Xo2b3GKz+0xIpiaQ5YIyoyIcNWpU2lLdvDDR98iXF/DmYeOCzosY3Iu3lbYU0c96betDCoRFHaqNL6oqBB+etaBHLXnaL51/6s8+VZr0CEZk1NesblCHx8A2DOt+FxQLBGUqZqqSm4871D2HtPAl+94kZfeC/4yd2NypZCLzWUKu8XnYq2WCEwAGmqHcOsF0wk31HDhrQsDn7lgTK703qe4qfBbBF7xuXhbcFNILRGUuXBDDfMunE5lhTDr5gV8uKE96JCM2WXeSU0h3pCmL9FwvbUITLAmNYa49YLprNvUyey5C1i/ecvgLzKmgMUSKUa5FX2LQaQxxMr17WzqDObKf0sEBoD9xg7nN1+YRiyR5OJ5zbRv6Q46JGN2mnN7ysLvFvJEm9wB44CuMLZEYHp9bK9GfnrWQSxYtoavzn+Zbrv62BSpeCJVFFNHPb1TSAMaJ7BEYLZx+oF7cNWpU3ls8Qd878HXUbVkYIqLV2yuGKaOeoIuPmcXlJntXPixybRu7ODGp2M0NdTw1ROmBB2SMVkrhmJzmWqHVDJu5NDAylFbIjB9+vaMj5DY2MHP/+8dwg01nHv4xKBDMiYrvVNHi6hFAM4MJ2sRmIIiIvzwzP1Zk+rge398ndGhGmbsNybosIwZVCzhFJsbX+DF5jJFw/WBFZ+zMQLTryGVFVx/7iEcMG4El89/iRfiq4MOyZhBxRMpJhZBsblMkXCot/hcvhXXT8rkXV11FbecfxjjRw7li/OaeeuDDUGHZMyAYolkUY0PeLxZTkFMIbVEYAY1MlTNvIsOp666ktlzF9CydlPQIRnTp+4eZfnq4ig2l8kb0wii1IslApOVsSOGctuF09nU2c2suQtYk+oMOiRjttNbbK4IWwRe8bkgBox9TQQiMkNElojIUhHZ7j7HIvJ1EXlDRF4Vkb+KiE1NKWB7jxnGzbMPo2XtZi68dWFgl8Mb0x/vbLrYZgyBM0EjGg4FMoXUt0QgIpXA9cDJwFRgpohMzdjsJWCaqh4A3Adc61c8JjemTx7F/5t5MK+2rOPSO15kS3dP0CEZ06v3PsVFUmwuUyQczBRSP1sE04GlqhpX1U5gPnBG+gaq+qSqeh3OzwN2q6wicNK+Y7jmU/vz5JIEV97/ml19bApGLJEsqmJzmaLhYIrP+ZkIxgIr0pZb3HX9uQh4tK8nRGSOiDSLSHMikchhiGZnnXP4BL52whTuf7GFHz22JOhwjAGcqqPF2C3kiQQ0c8jPRNDXFRF9njqKyHnANODHfT2vqjep6jRVnRYOh3MYotkVlx+/J+cePoEbn44x9x/Lgg7HGOKJZNF2C0HaFNI8F5/z88riFkYAFlgAABF5SURBVGB82vI4YGXmRiJyAvAfwNGq2uFjPCbHRISrz9iP1clOrn74DRobajj9wD2CDsuUKafYXGdRTh31TBxdhwh5v0mN+NW/KyJVwNvA8cD7wELgHFVdnLbNwTiDxDNU9Z1s9jtt2jRtbm72IWKfpNrg7vNg7btBR+IbVVi7qZPO7h5G1lVTU2Wzkk3+dXb3sCbVyYi6IdRWVQYdzk5LJDsYUlnBiKFDtn/y+O/DQTN3ar8iskhVp/X1nG8tAlXtEpHLgMeBSmCuqi4WkauBZlV9CKcrqB64V0QA3lPV0/2KKe+6OuGeWfD+i3DA50BK8wNSgPquHv76ViupTV0ct3cTo4p0sM4Ur5a2FC9sWMMnJ+9ObW3xllF74+0Emzu7mbFXH7W9Rozffl0O+PrTUtVHgEcy1l2V9vgEP98/UKrw6Ddh+bPwmd85iaCEVQOHbGjnMzc8xw9j3dx3yZFMKqI7RJnid99jb/HbWJzPnjkDiqzOULqn//QGdy14jxNPPSlvxeeK96dV6Bb+DhbdCh/7WsknAc9uw2qZd9F0unuUWXMX0Lox/8WzTPmKJZJMGFV8xeYyRZvyX3yuuH9ihSr+NDz6bZgyA477XtDR5FU0XM8tF0wnsbGDC25ZyMb2LUGHZMpEPJEqymJzmbxZT/msOWSJINfWxOHe2TB6T/jMb6GieAetdtZB40dww3mHsOSDjVzy+0V0dHUHHZIpcV3dPSxfvakoawxlija59y/O47UElghyqX0D3DXTGR+YeRfUDgs6osAc+5EmfnTmATy7dDX/ds8r9PTY1cfGPy1rN9PZ3VPUU0c94foaGmqq8toiKN6h9ULT0w0PzIG2d+ALD8DoaNARBe7MQ8fRluzgfx99i8b6Gr5/2lTc2WHG5FS8zSs2V/wtAhEhEg7ltUVgiSBX/nYNvP0onPxjiBwTdDQFY84nIrRu7ODmfyyjaVgNXz5mz6BDMiUo1lqc9ynuTzRczz/zeEdA6xrKhdfug39cB4fMhukXBx1NQRER/uOUfTjjoD249rEl3NO8YvAXGbOD4m1JRoeqGVFXGtevRMIhVuWx+Jwlgl31/ovw4KUw4Ug45SdgXR/bqagQfvzZA/n4Xo1854HX+OubHwYdkikxsdZUSYwPePJ920pLBLti4wcw/1wINcHnb4eq0jgb8UN1VQW/Pu9Qpu4+jEvvfJFFy9cGHZIpIfG24i42l8mbBpuvAWNLBDtrS7uTBNrXwcw7IdQYdEQFr76milsuOIwxw2q56LaFLG3dGHRIpgSs3+QUm/OmXZYCr/ictQgKmSr86Qp4vxk+fSOM2T/oiIpGY30N8y48nKqKCmbdvIBV6zcHHZIpcjF3xlAptQhqh1QyfmSdtQgK2j9/Ba/Oh2O+A1PPGHx7s40Jo+u49YLD2NDexey5C1i/ya4+NjvPO2uONpVOIgDyOoXUEsGOeucJeOIqJwF84ltBR1O09hs7nJu+cCjvtm3ii/MW0r7Frj42OyeWSDKkUhg/cmjQoeRUNFzPsrZUXi7GtESwIxJvw30XQtO+8KlfQ4X9+HbFkXs2ct3nD6R5+Vouv+slurp7gg7JFKF4IsnE0SGqirzYXKZI2Ck+tyoPxedK6yfnp81r4a6zobLaGRyuLp2BqSCdesAe/OC0ffnLGx/yvQdfx68bJZnSFUukiJRgyfOtU0j9HyewRJCN7i649wJY9x58/vcwYkLQEZWU2UdO4tJjo9y1YAU/+7+sblRnDOAVmyuNqqOZvOsi8nHbSisxkY0nroL4k3DaL2HiR4OOpiR948SPkNjYwS//+g7hhhq+cMTEoEMyRaBl7Wa2dGvJlJZI5xWfy8eN7C0RDObF2+H56+HwS+DQ2UFHU7JEhP/59P6sTnZy1YOv0xiq5uT9dw86LFPgvOmVpdgiEBEiTfV5mUJqXUMDee95ePhrThG5E/876GhKXlVlBb865xAOHj+CK+a/zPN5LLplilPv1NESbBEARBvzM4XUEkF/1q2Au8+D4ePgs7dApTWe8mFodSVzzz+MCaPruPi2Zt5ctSHokEwBiyVKq9hcpmhTPavWt5Pq8Lf4nCWCvnRugvnnOGUkZs6HulFBR1RWRtRVM+/C6dTXVjF77gJWrNkUdEimQDm3pyzN1gDQOxtqmc/jBJYIMqnCg1+GD16Dz94MTXsHHVFZ2mPEUG67cDrtW7qZPXcBq5MdQYdkClC8LVkSN6Ppj3e1tN/jBJYIMj3zE1j8BzjhBzDlpKCjKWtTdmtg7vmH8f66zVx4W3PearOb4uAVmyvlFsHE0XVUiHOthJ8sEaR780/w5DWw/1lw1BVBR2OAaZNG8atzDuG1lnV86fcvssWuPjauWAndnrI/NVWVjBtZ5/tFZZYIPB8uhgf+FfY4BE7/pd1gpoD8y9Td+J9P78/Tbyf49n2v5qX2iil83oVWpTh1NF00HPK9RWBTYQBSq53yETUNcPadMKS0ileVgrOnTyCxsYOfPvE24YYavnPKPkGHZAIWb0uVZLG5TBH3/sU9PUpFhT8nqJYIujrhnlmw8UO44FEYZhcxFarLjtuTRLKD3zwTJ9xQwxc/Hgk6JBOgWGtpFpvLFA3X076lh1Ub2hk7wp+kZ4ngsW/D8n/Ap2+CcYcGHY0ZgIjw/dP2pS3ZwTV/fpPG+ho+dfDYoMMyAYm3lWaxuUzpNYf8SgSlnUoHs/B30DzXGRg+8PNBR2OyUFkhXHfWQRwRGcU37n2FZ95OBB2SCYBXbK7UbkbTFy8R+DlgXL6JYNkz8Oi3Ya+T4PjvBx2N2QG1Qyq5adY09tqtgUt+v4hXW9YFHZLJsxVusblyaBGE62toqK3ydcC4PBPBmmVwz2wYFYUzfwcVlUFHZHbQsNoh3HbBYYwKVXPBLQt9v/LSFBbv7LgcWgQiQiRcT7zNWgS507HRKR+hPTDzLqgdFnREZic1Datl3oXTUWDW3Bdo3ej/nZxMYfCutI2W0A3rBxL1+f7FviYCEZkhIktEZKmIXNnH8zUicrf7/AsiMsnPeOjpgQfmQGIJfO5WGB319e2M/yLhem45/zBWJzs5f+5CNrZvCTokkwfxRIrRoWqG1w0JOpS8iIb9LT7nWyIQkUrgeuBkYCowU0SmZmx2EbBWVfcEfgb8yK94AHjyv2HJIzDjfyF6rK9vZfLnwPEj+PV5h/L2hxuZM28RHV3dQYdkfBZPpEr6iuJMXpltv7pA/Zw+Oh1YqqpxABGZD5wBvJG2zRnAD9zH9wG/EhFRP25c+/r98PefwCGzYPqcnO/eBOvoKWF+8rkD+erdL3Psj58iVGMzo0vZu6tTnHnIuKDDyBvv6ulYIsl+Y4fnfP9+/reMBVakLbcAh/e3jap2ich6YDTQlr6RiMwB5gBMmLCT9wsOheEjn4RTfmrlI0rUpw4ei6I88caHQYdifDZlTANnTy+fe4dPHF3H8Xs3+XbfBT8TQV+ftpln+tlsg6reBNwEMG3atJ1rLUz+hPNlStqnDx7Hpw8unzNFUx5qqiq5+fzDfNu/n4PFLcD4tOVxwMr+thGRKmA4sMbHmIwxxmTwMxEsBPYSkckiUg2cDTyUsc1DgHdH+M8Cf/NlfMAYY0y/fOsacvv8LwMeByqBuaq6WESuBppV9SHgZuB2EVmK0xI42694jDHG9M3XqRWq+gjwSMa6q9IetwOf8zMGY4wxAyu/K4uNMcZswxKBMcaUOUsExhhT5iwRGGNMmZNim60pIglg+U6+vJGMq5bLgB1zebBjLg+7cswTVTXc1xNFlwh2hYg0q+q0oOPIJzvm8mDHXB78OmbrGjLGmDJnicAYY8pcuSWCm4IOIAB2zOXBjrk8+HLMZTVGYIwxZnvl1iIwxhiTwRKBMcaUubJJBCIyQ0SWiMhSEbky6Hh2lIjMFZFWEXk9bd0oEXlCRN5xv49014uI/NI91ldF5JC018x2t39HRGanrT9URF5zX/NLkWBv4yYi40XkSRF5U0QWi8gV7vpSPuZaEVkgIq+4x/yf7vrJIvKCG//dbll3RKTGXV7qPj8pbV/fcdcvEZGT0tYX5P+BiFSKyEsi8rC7XNLHLCLvun97L4tIs7suuL9tVS35L5wy2DEgAlQDrwBTg45rB4/hE8AhwOtp664FrnQfXwn8yH18CvAozh3gjgBecNePAuLu95Hu45HucwuAj7qveRQ4OeDj3R04xH3cALwNTC3xYxag3n08BHjBPZZ7gLPd9TcCX3Iffxm40X18NnC3+3iq+zdeA0x2//YrC/n/APg6cCfwsLtc0scMvAs0ZqwL7G+7XFoE04GlqhpX1U5gPnBGwDHtEFV9hu3v3nYGcJv7+DbgU2nr56njeWCEiOwOnAQ8oaprVHUt8AQww31umKr+U52/onlp+wqEqq5S1RfdxxuBN3HucV3Kx6yqmnQXh7hfChwH3Oeuzzxm72dxH3C8e+Z3BjBfVTtUdRmwFOd/oCD/D0RkHPBJ4HfuslDix9yPwP62yyURjAVWpC23uOuK3W6qugqcD06gyV3f3/EOtL6lj/UFwW3+H4xzhlzSx+x2kbwMtOL8Y8eAdara5W6SHmfvsbnPrwdGs+M/i6D9HPgW0OMuj6b0j1mBv4jIIhGZ464L7G/b1xvTFJC++sdKed5sf8e7o+sDJyL1wP3AV1V1wwBdnSVxzKraDRwkIiOAPwD79LWZ+31Hj62vE79Aj1lETgVaVXWRiBzjre5j05I5ZtdRqrpSRJqAJ0TkrQG29f1vu1xaBC3A+LTlccDKgGLJpQ/dZiDu91Z3fX/HO9D6cX2sD5SIDMFJAneo6gPu6pI+Zo+qrgOewukTHiEi3klbepy9x+Y+Pxyn+3BHfxZBOgo4XUTexem2OQ6nhVDKx4yqrnS/t+Ik/OkE+bcd9KBJPr5wWj5xnEEkb8Bo36Dj2onjmMS2g8U/ZtvBpWvdx59k28GlBbp1cGkZzsDSSPfxKPe5he623uDSKQEfq+D0bf48Y30pH3MYGOE+Hgr8HTgVuJdtB06/7D6+lG0HTu9xH+/LtgOncZxB04L+PwCOYetgcckeMxACGtIePwfMCPJvO/Bffh5/+KfgzDyJAf8RdDw7Ef9dwCpgC07Gvwinb/SvwDvud++PQIDr3WN9DZiWtp8LcQbSlgIXpK2fBrzuvuZXuFedB3i8H8Npzr4KvOx+nVLix3wA8JJ7zK8DV7nrIzizQJa6H5A17vpad3mp+3wkbV//4R7XEtJmjBTy/wHbJoKSPWb32F5xvxZ7MQX5t20lJowxpsyVyxiBMcaYflgiMMaYMmeJwBhjypwlAmOMKXOWCIwxpsxZIjBlS0RGu9UfXxaRD0Tk/bTl53x6z4NF5HcDPB8Wkcf8eG9j+lMuJSaM2Y6qrgYOAhCRHwBJVf2Jz2/778A1A8SUEJFVInKUqj7rcyzGANYiMKZPIpJ0vx8jIk+LyD0i8raI/FBEzhXnvgGviUjU3S4sIveLyEL366g+9tkAHKCqr7jLR6e1QF5ynwf4I3Bung7VGEsExmThQOAKYH/gC8AUVZ2OUzb5K+42vwB+pqqHAWe6z2Xyrvb0fAO4VFUPAj4ObHbXN7vLxuSFdQ0ZM7iF6pYHFpEY8Bd3/WvAse7jE4CpadVRh4lIgzr3UvDsDiTSlp8FrhORO4AHVNUrHdwK7JH7wzCmb5YIjBlcR9rjnrTlHrb+D1UAH1XVzfRvM06tHABU9Yci8mecWjjPi8gJqvqWu81A+zEmp6xryJjc+AtwmbcgIgf1sc2bwJ5p20RV9TVV/RFOd9De7lNT2LYLyRhfWSIwJjcuB6a5Nxd/A7gkcwP3bH942qDwV0XkdRF5BacF8Ki7/ljgz/kI2hjAqo8ak08i8jVgo6oOdC3BM8AZ6tyH1hjfWYvAmPz6NduOOWxDRMLAdZYETD5Zi8AYY8qctQiMMabMWSIwxpgyZ4nAGGPKnCUCY4wpc5YIjDGmzP1/Vdc+/dN1tu4AAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -156,15 +155,14 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 186, "metadata": {}, "outputs": [], "source": [ "# Import expansion run file \n", "filename = expansionrun #'02182021_H10M31S8MS224_Picomotor_MOTz_y' #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n", "\n", - "path = r'C:\\Users\\dpean\\Box\\HoodLab\\Quick Transfers/'\n", - "#path = r'//?/S:/flir_images/binaries/'\n", + "path = r'//?/S:/flir_images/binaries/'\n", "file = np.load(path+filename+'.npz')\n", "\n", "index = file['index']\n", @@ -178,7 +176,7 @@ "sigmayerror = file['sigmayerror']\n", "\n", "# Fix units\n", - "variable = variable * us\n", + "variable = variable * us # Careful here!\n", "sigmax = sigmax * binpixel\n", "sigmaxerror = sigmaxerror * binpixel\n", "sigmay = sigmay * binpixel\n", @@ -187,41 +185,7 @@ }, { "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 126.71946357 259.12053485 381.35724204 nan 0.\n", - " 0. nan 0. 0. 2942.84597866\n", - " 0. ]\n", - "[ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(variable,sigmay)\n", - "print(sigmax/um)\n", - "print(variable/ms)" - ] - }, - { - "cell_type": "code", - "execution_count": 181, + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -271,13 +235,13 @@ "plt.xlabel('Time (ms)')\n", "plt.ylabel('MOT size (um)')\n", "plt.title('MOT size vs Time')\n", - "plt.plot(t_fit_data, sigma_magn_fit_data/um, label='Raw') # y axis should be 50-500 um, x axis should be 1-100's ms\n", + "plt.plot(t_fit_data/ms, sigma_magn_fit_data/um, label='Raw') # y axis should be 50-500 um, x axis should be 1-100's ms\n", "\n", "params, covariance = curve_fit(tempfind, t_fit_data, sigma_magn_fit_data, p0=[200 * uk]);\n", "T_fit = params[0]\n", "\n", "print('Temperature (uK) is :', T_fit/uk,'\\nError in Temperature fit is :', np.sqrt(np.diagonal(covariance)[0])/uk)\n", - "plt.scatter(t_fit_data,tempfind(t_fit_data,params[0])/um, label='Fitted')\n", + "plt.scatter(t_fit_data/ms,tempfind(t_fit_data,params[0])/um, label='Fitted')\n", "plt.show()" ] }, diff --git a/PostProcessing.ipynb b/PostProcessing.ipynb index 0e9ce57..f7f7522 100644 --- a/PostProcessing.ipynb +++ b/PostProcessing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 136, + "execution_count": 182, "metadata": {}, "outputs": [], "source": [ @@ -33,21 +33,20 @@ "wavelength = 852 * nm\n", "sigma_0 = 3*wavelength**2/(2*np.pi)\n", "\n", - "formationrun = '02182021_H11M46S31MS785_Formation_Time'\n", - "expansionrun = '02182021_H11M44S44MS263_Expansion_Time'" + "formationrun = '02182021_H11M46S31MS785_Formation_Time' # Make sure units of self.variable are ms !!!\n", + "expansionrun = '02182021_H11M44S44MS263_Expansion_Time' # Make sure units of self.variable are us !!!" ] }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 183, "metadata": {}, "outputs": [], "source": [ "# Import formation run file \n", "filename = formationrun #'02182021_H10M31S8MS224_Picomotor_MOTz_y' #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n", "\n", - "path = r'C:\\Users\\dpean\\Box\\HoodLab\\Quick Transfers/'\n", - "#path = r'//?/S:/flir_images/binaries/'\n", + "path = r'//?/S:/flir_images/binaries/'\n", "file = np.load(path+filename+'.npz')\n", "\n", "index = file['index']\n", @@ -61,7 +60,7 @@ "sigmayerror = file['sigmayerror']\n", "\n", "# Fix units\n", - "variable = variable * ms\n", + "variable = variable * ms # Careful here!\n", "sigmax = sigmax * binpixel\n", "sigmaxerror = sigmaxerror * binpixel\n", "sigmay = sigmay * binpixel\n", @@ -70,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 184, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 185, "metadata": { "scrolled": false }, @@ -106,15 +105,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loading rate is : 48132834580.92892 \n", - "Error in loading rate fit is : 3768782596544480.0\n", - "Time constant is : 375.74641624027197 \n", - "Error in time constant fit is : 29861310.716279566\n" + "Loading rate is : 79887.82713878715 \n", + "Error in loading rate fit is : 1276.626218417588\n", + "Time constant is : 1.8712755012079827 \n", + "Error in time constant fit is : 0.046009651457644817\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -156,15 +155,14 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 186, "metadata": {}, "outputs": [], "source": [ "# Import expansion run file \n", "filename = expansionrun #'02182021_H10M31S8MS224_Picomotor_MOTz_y' #CHANGE ONLY THIS LINE WHEN YOU WANT TO LOAD A NEW .npz FILE\n", "\n", - "path = r'C:\\Users\\dpean\\Box\\HoodLab\\Quick Transfers/'\n", - "#path = r'//?/S:/flir_images/binaries/'\n", + "path = r'//?/S:/flir_images/binaries/'\n", "file = np.load(path+filename+'.npz')\n", "\n", "index = file['index']\n", @@ -178,7 +176,7 @@ "sigmayerror = file['sigmayerror']\n", "\n", "# Fix units\n", - "variable = variable * us\n", + "variable = variable * us # Careful here!\n", "sigmax = sigmax * binpixel\n", "sigmaxerror = sigmaxerror * binpixel\n", "sigmay = sigmay * binpixel\n", @@ -187,41 +185,7 @@ }, { "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 126.71946357 259.12053485 381.35724204 nan 0.\n", - " 0. nan 0. 0. 2942.84597866\n", - " 0. ]\n", - "[ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(variable,sigmay)\n", - "print(sigmax/um)\n", - "print(variable/ms)" - ] - }, - { - "cell_type": "code", - "execution_count": 181, + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -271,13 +235,13 @@ "plt.xlabel('Time (ms)')\n", "plt.ylabel('MOT size (um)')\n", "plt.title('MOT size vs Time')\n", - "plt.plot(t_fit_data, sigma_magn_fit_data/um, label='Raw') # y axis should be 50-500 um, x axis should be 1-100's ms\n", + "plt.plot(t_fit_data/ms, sigma_magn_fit_data/um, label='Raw') # y axis should be 50-500 um, x axis should be 1-100's ms\n", "\n", "params, covariance = curve_fit(tempfind, t_fit_data, sigma_magn_fit_data, p0=[200 * uk]);\n", "T_fit = params[0]\n", "\n", "print('Temperature (uK) is :', T_fit/uk,'\\nError in Temperature fit is :', np.sqrt(np.diagonal(covariance)[0])/uk)\n", - "plt.scatter(t_fit_data,tempfind(t_fit_data,params[0])/um, label='Fitted')\n", + "plt.scatter(t_fit_data/ms,tempfind(t_fit_data,params[0])/um, label='Fitted')\n", "plt.show()" ] },