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2244 lines
126 KiB
2244 lines
126 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pole positions\n",
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"[[[ 0.01414214]\n",
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" [ 0.01414214]\n",
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" [ 0. ]]\n",
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"\n",
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" [[-0.01414214]\n",
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" [ 0.01414214]\n",
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" [ 0. ]]\n",
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"\n",
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" [[-0.01414214]\n",
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" [-0.01414214]\n",
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" [ 0. ]]\n",
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"\n",
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" [[ 0.01414214]\n",
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" [-0.01414214]\n",
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" [ 0. ]]]\n",
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"pseudo charges\n",
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"[0. 0. 0. 0.]\n",
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"simulation time = 0.00010000000000013679\n",
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"[[0.e+00]\n",
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" [0.e+00]\n",
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" [1.e-06]]\n",
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"done!\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import scipy.constants as sc\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"class Ion:\n",
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" def __init__(self,position, charge, mass, v_z):\n",
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" self.position = position\n",
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" self.charge = charge\n",
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" self.m = mass\n",
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" self.v_z = v_z\n",
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" self.F = np.array([[0],[0],[0]])\n",
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"\n",
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"class infiniteLengthQuadrupole:\n",
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" def __init__(self,R,r_0):\n",
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" # rod radius\n",
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" self.R = R\n",
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" # rod placement radius\n",
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" self.r_0 = r_0\n",
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" # rod positions\n",
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" self.pPole = np.array([[[ (R + r_0)/np.sqrt(2)],\n",
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" [(R + r_0)/np.sqrt(2)],\n",
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" [0]],\n",
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" [[-(R + r_0)/np.sqrt(2)],\n",
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" [(R + r_0)/np.sqrt(2)],\n",
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" [0]],\n",
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" [[-(R + r_0)/np.sqrt(2)],\n",
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" [-(R + r_0)/np.sqrt(2)],\n",
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" [0]],\n",
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" [[(R + r_0)/np.sqrt(2)],\n",
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" [-(R + r_0)/np.sqrt(2)],\n",
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" [0]]])\n",
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" # rod pseudo charges\n",
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" self.rodsPseudoQ = np.zeros(4)\n",
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" # K factor for simplification ofcalculating the equations\n",
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" self.K = 1 / (4 * np.pi * sc.epsilon_0)\n",
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" \n",
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" def check(self):\n",
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" print('pole positions')\n",
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" print(self.pPole)\n",
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" print('pseudo charges')\n",
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" print(self.rodsPseudoQ)\n",
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" \n",
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" def phi_0(self,U, V, f, t):\n",
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" return U + V * np.sin(2 * np.pi * f * t)\n",
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" \n",
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" def coulombForceOnQ1(self,Q1,Q2,r1,r2):\n",
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" a = self.K * Q1 * Q2\n",
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" r12 = r1 - r2\n",
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" mag_r12 = np.linalg.norm(r12)\n",
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" # calculate force\n",
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" return a * r12 / (mag_r12**3)\n",
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" \n",
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" \n",
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" def calcNewIonPos(self,U,V,f,ion,t_s,t):\n",
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" \n",
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" # check if ion has left the r_0 boundary\n",
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" if np.linalg.norm(ion.position) >= np.linalg.norm(self.r_0):\n",
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" return None\n",
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" \n",
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" signLUT = [1,-1,1,-1]\n",
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" index = 0;\n",
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" \n",
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" # reset force of ion\n",
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" ion.F = np.array([[0],[0],[0]])\n",
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" \n",
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" for Q in self.rodsPseudoQ:\n",
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"\n",
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" \n",
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" # calculate the pseudo charge points of the quadrupole\n",
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" eg = signLUT[index] * self.phi_0(U,V,f,t)/2 * self.K * self.R\n",
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" \n",
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" # match pseudo charge with ion in z direction\n",
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" self.pPole[index][2] = ion.position[2]\n",
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" \n",
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" # calculate force on ion\n",
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" ion.F = ion.F + self.coulombForceOnQ1(ion.charge,\n",
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" eg,\n",
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" self.pPole[index],\n",
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" ion.position)\n",
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" \n",
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" # update index\n",
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" index = index + 1\n",
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"\n",
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" newPosition = t_s**2 * 1 / ion.m * ion.F + ion.position\n",
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" # velocity component in z\n",
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" newPosition[2] = newPosition[2] + ion.v_z * t_s\n",
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"\n",
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" return newPosition\n",
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" \n",
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"t_step = 1e-9\n",
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"t_sim = 0\n",
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"N = 100000\n",
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"\n",
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"# coffein atom 524.50002164 dalton (8.709527e-25 kg)\n",
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"ion = Ion(np.array([[0],[0],[0]]), 1000 * sc.e, 8.709527e-25,0.01)\n",
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"\n",
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"# quadrupole\n",
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"quad = infiniteLengthQuadrupole(5e-3,15e-3)\n",
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"\n",
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"quad.check()\n",
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"\n",
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"positions = []\n",
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"\n",
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"positions.append(ion.position)\n",
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"\n",
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"for n in range(0,N,1):\n",
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" \n",
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" newPos = quad.calcNewIonPos(1000,5000,1.1e6,ion,t_step,t_sim)\n",
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" \n",
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" if(newPos is None):\n",
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" print('ion has left the boundary')\n",
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" exit\n",
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" \n",
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" \n",
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" ion.position = newPos\n",
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" if n % 100 is 0:\n",
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" positions.append(newPos)\n",
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" \n",
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" t_sim += t_step\n",
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"\n",
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"\n",
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"print('simulation time = ', t_sim)\n",
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"print(ion.position)\n",
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"\n",
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"if 0:\n",
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" ax = plt.axes(projection='3d')\n",
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" #ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], c=positions[:][2], cmap='Greens')\n",
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" ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], cmap='Greens')\n",
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" plt.show()\n",
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"\n",
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"print('done!')\n",
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"# dummy check\n",
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"if 0:\n",
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" ion_1 = Ion(np.array([[0.0000005],[0],[0]]), 1000 * sc.e, 1,0)\n",
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" ion_2 = Ion(np.array([[-0.0000005],[0],[0]]), 1000 * sc.e, 1,0)\n",
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" print(quad.coulombForceOnQ1(ion_1.charge,ion_2.charge,ion_1.position,ion_2.position))\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
|
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"outputs": [
|
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{
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"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[array([[0],\n",
|
|
" [0],\n",
|
|
" [0]]), array([[0.e+00],\n",
|
|
" [0.e+00],\n",
|
|
" [1.e-11]]), array([[0.00e+00],\n",
|
|
" [0.00e+00],\n",
|
|
" [1.01e-09]]), array([[0.00e+00],\n",
|
|
" [0.00e+00],\n",
|
|
" [2.01e-09]]), array([[0.00e+00],\n",
|
|
" [0.00e+00],\n",
|
|
" [3.01e-09]]), array([[0.00e+00],\n",
|
|
" [0.00e+00],\n",
|
|
" [4.01e-09]]), array([[0.00e+00],\n",
|
|
" [0.00e+00],\n",
|
|
" [5.01e-09]]), array([[0.00e+00],\n",
|
|
" [0.00e+00],\n",
|
|
" [6.01e-09]]), array([[0.00e+00],\n",
|
|
" [0.00e+00],\n",
|
|
" [7.01e-09]]), array([[0.00e+00],\n",
|
|
" [0.00e+00],\n",
|
|
" [8.01e-09]]), array([[0.00e+00],\n",
|
|
" [0.00e+00],\n",
|
|
" [9.01e-09]]), array([[0.000e+00],\n",
|
|
" [0.000e+00],\n",
|
|
" [1.001e-08]]), array([[0.000e+00],\n",
|
|
" [0.000e+00],\n",
|
|
" [1.101e-08]]), array([[0.000e+00],\n",
|
|
" [0.000e+00],\n",
|
|
" [1.201e-08]]), array([[0.000e+00],\n",
|
|
" [0.000e+00],\n",
|
|
" [1.301e-08]]), array([[0.000e+00],\n",
|
|
" [0.000e+00],\n",
|
|
" [1.401e-08]]), array([[0.000e+00],\n",
|
|
" [0.000e+00],\n",
|
|
" [1.501e-08]]), array([[0.000e+00],\n",
|
|
" [0.000e+00],\n",
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" [9.9901e-07]])]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# https://jakevdp.github.io/PythonDataScienceHandbook/04.12-three-dimensional-plotting.html\n",
|
|
"ax = plt.axes(projection='3d')\n",
|
|
"#ax.scatter3D(positions[:][0], positions[:][1], positions[:][2], c=positions[:][2], cmap='Greens')\n",
|
|
"ax.scatter3D(positions[:][0], positions[:][2], positions[:][1], cmap='Greens')\n",
|
|
"ax.set_xlabel('x')\n",
|
|
"ax.set_ylabel('z')\n",
|
|
"ax.set_zlabel('y')\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"print(positions)"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.7.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|