Commit 1bb22dea authored by Sam Calisch's avatar Sam Calisch
Browse files

add coil coil plots

parent 9ec47d36
{
"cells": [
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: pylab import has clobbered these variables: ['cm']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
}
],
"source": [
"from __future__ import division\n",
"from numpy import *\n",
"%pylab inline\n",
"rcParams.update({'font.size': 18})"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = {}\n",
"with open('coil-coil-force.csv','r') as datafile:\n",
" for i in range(5):\n",
" datafile.readline() #strip headers\n",
" #magnet_os = [item.split(',')[0] for item in datafile.readline().split('magnet_os=') if item is not ''][1:]\n",
" #magnet_os = 1e6*asarray(map(float,magnet_os))\n",
" #Ns = [10+5*i for i in range(8)]\n",
" #gaps = [300 + 100*i for i in range(6)]\n",
" raw_data = []\n",
" for line in datafile.readlines():\n",
" raw_data.append([float(item) for item in line.strip('\\n').split(',') if item is not ''])\n",
" raw_data = asarray(raw_data).reshape(9,6,-1)\n",
" #raw_data = asarray(raw_data)[:,2:].reshape(9,6,-1)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 0.15818192466432918)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wN3RhxhXg3wmMAbO0eRlZWMhgoi2WfQFTPTOmawycbHuKGAkTEUckTJrEcRzz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\nlF5bApbVO8yiQ1iv2KGQJ3Nw0rt8Zd12BAuXgV3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id8K9LWDJyTfLHMfHeo+w9IiD1lGS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M6dOykvL6dDhw7cfvvtTJ8+nXHjxvm8tlmzZrz33nukpqaydu1a3nzzTZo1a8Ztt93G448/Tr9+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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e776650>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cm = plt.get_cmap('cool')\n",
"figure(figsize=(10,8))\n",
"for i,series in enumerate(raw_data):\n",
" plot(series[:,1],-series[:,2],label='N=%d'%(series[0,0]),c=cm(i/len(raw_data)))\n",
"legend(loc=(1.05,0))\n",
"xlabel('gap (um)')\n",
"ylabel('Force (N)')\n",
"grid(True)\n",
"ylim([0,-1.1*amin(raw_data[:,:,2])])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'magnet_os' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-0f8b8e9ee4a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cool'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstroke\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mamin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagnet_os\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagnet_os\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mcurrent_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mgap_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'magnet_os' is not defined"
]
}
],
"source": [
"cm = plt.get_cmap('cool')\n",
"stroke = linspace( amin(magnet_os), amax(magnet_os), 100)\n",
"figure(figsize=(12,10),dpi=600)\n",
"current_ind = 2\n",
"gap_ind = 3\n",
"matplotlib.rcParams.update({'font.size': 18})\n",
"for i,series in enumerate(raw_data[gap_ind,::-1]):\n",
" f = interp1d(magnet_os, series, kind=3)\n",
" plot(magnet_os,series,ls='',marker='+',c=cm((11-i)/11))\n",
" plot(stroke,f(stroke),label='%.1f A'%(currents[::-1][i]),c=cm((11-i)/11))\n",
"#legend(loc=(1.01,.01))\n",
"xlabel('stroke (um)')\n",
"ylabel('Force (N)')\n",
"#title('Linear Motor, %d um gap'%(1e6*gaps[gap_ind]))\n",
"ylim([-.15,.3])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'magnet_os' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-d31a02cbe3fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cool'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstroke\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mamin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagnet_os\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagnet_os\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mseries\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoly1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagnet_os\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'magnet_os' is not defined"
]
}
],
"source": [
"cm = plt.get_cmap('cool')\n",
"stroke = linspace( amin(magnet_os), amax(magnet_os), 100)\n",
"figure(figsize=(12,10))\n",
"for i,series in enumerate(raw_data[:,0]):\n",
" z = poly1d(polyfit(magnet_os, series,11))\n",
" plot(magnet_os,series,label='gap=%d um'%(1e6*gaps[i]),ls='',marker='+',c=cm(i/5))\n",
" plot(stroke, z(stroke),c=cm(i/5))\n",
"legend(loc='lower right')\n",
"xlabel('stroke (um)')\n",
"ylabel('X Force (N)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
%% Cell type:code id: tags:
``` python
from __future__ import division
from numpy import *
%pylab inline
rcParams.update({'font.size': 18})
```
%%%% Output: stream
Populating the interactive namespace from numpy and matplotlib
%%%% Output: stream
WARNING: pylab import has clobbered these variables: ['cm']
`%matplotlib` prevents importing * from pylab and numpy
%% Cell type:code id: tags:
``` python
data = {}
with open('coil-coil-force.csv','r') as datafile:
for i in range(5):
datafile.readline() #strip headers
#magnet_os = [item.split(',')[0] for item in datafile.readline().split('magnet_os=') if item is not ''][1:]
#magnet_os = 1e6*asarray(map(float,magnet_os))
#Ns = [10+5*i for i in range(8)]
#gaps = [300 + 100*i for i in range(6)]
raw_data = []
for line in datafile.readlines():
raw_data.append([float(item) for item in line.strip('\n').split(',') if item is not ''])
raw_data = asarray(raw_data).reshape(9,6,-1)
#raw_data = asarray(raw_data)[:,2:].reshape(9,6,-1)
```
%% Cell type:code id: tags:
``` python
cm = plt.get_cmap('cool')
figure(figsize=(10,8))
for i,series in enumerate(raw_data):
plot(series[:,1],-series[:,2],label='N=%d'%(series[0,0]),c=cm(i/len(raw_data)))
legend(loc=(1.05,0))
xlabel('gap (um)')
ylabel('Force (N)')
grid(True)
ylim([0,-1.1*amin(raw_data[:,:,2])])
```
%%%% Output: execute_result
(0, 0.15818192466432918)
%%%% Output: display_data
%% Cell type:code id: tags:
``` python
cm = plt.get_cmap('cool')
stroke = linspace( amin(magnet_os), amax(magnet_os), 100)
figure(figsize=(12,10),dpi=600)
current_ind = 2
gap_ind = 3
matplotlib.rcParams.update({'font.size': 18})
for i,series in enumerate(raw_data[gap_ind,::-1]):
f = interp1d(magnet_os, series, kind=3)
plot(magnet_os,series,ls='',marker='+',c=cm((11-i)/11))
plot(stroke,f(stroke),label='%.1f A'%(currents[::-1][i]),c=cm((11-i)/11))
#legend(loc=(1.01,.01))
xlabel('stroke (um)')
ylabel('Force (N)')
#title('Linear Motor, %d um gap'%(1e6*gaps[gap_ind]))
ylim([-.15,.3])
```
%%%% Output: error
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-4-0f8b8e9ee4a1> in <module>()
1 cm = plt.get_cmap('cool')
----> 2 stroke = linspace( amin(magnet_os), amax(magnet_os), 100)
3 figure(figsize=(12,10),dpi=600)
4 current_ind = 2
5 gap_ind = 3
NameError: name 'magnet_os' is not defined
%% Cell type:code id: tags:
``` python
cm = plt.get_cmap('cool')
stroke = linspace( amin(magnet_os), amax(magnet_os), 100)
figure(figsize=(12,10))
for i,series in enumerate(raw_data[:,0]):
z = poly1d(polyfit(magnet_os, series,11))
plot(magnet_os,series,label='gap=%d um'%(1e6*gaps[i]),ls='',marker='+',c=cm(i/5))
plot(stroke, z(stroke),c=cm(i/5))
legend(loc='lower right')
xlabel('stroke (um)')
ylabel('X Force (N)')
```
%%%% Output: error
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-5-d31a02cbe3fa> in <module>()
1 cm = plt.get_cmap('cool')
----> 2 stroke = linspace( amin(magnet_os), amax(magnet_os), 100)
3 figure(figsize=(12,10))
4 for i,series in enumerate(raw_data[:,0]):
5 z = poly1d(polyfit(magnet_os, series,11))
NameError: name 'magnet_os' is not defined
%% Cell type:code id: tags:
``` python
```
% Model,coil-coil-speaker.mph
% Version,COMSOL 5.3.1.275
% Date,"Dec 6 2018, 18:31"
% Table,"Table 1 - Global Evaluation 1 (mf.Forcex_0, mf.Forcey_0)"
% n_base,gap (um),"Electromagnetic force, y component (N)","Electromagnetic force, y component (N)"
10,300,-0.08207832378554145,-0.08207832378554127
10,399.99999999999994,-0.07207995122423291,-0.07207995122423318
10,500,-0.06373598062465724,-0.06373598062465777
10,600,-0.0566827914620302,-0.0566827914620302
10,700,-0.0506489792651917,-0.050648979265191414
10,799.9999999999999,-0.04545350371480208,-0.045453503714802175
15,300,-0.10360535584513815,-0.10360535584513784
15,399.99999999999994,-0.0933795780919582,-0.09337957809195868
15,500,-0.08452823010679891,-0.08452823010679804
15,600,-0.07677324661102114,-0.07677324661102099
15,700,-0.06992593745049328,-0.06992593745049319
15,799.9999999999999,-0.06385954116102241,-0.06385954116102234
20,300,-0.11733593022414841,-0.1173359302241507
20,399.99999999999994,-0.10815248353435279,-0.10815248353435303
20,500,-0.09987214162066027,-0.0998721416206589
20,600,-0.0923865951283783,-0.09238659512837842
20,700,-0.08555875581043242,-0.08555875581043178
20,799.9999999999999,-0.07932920066366841,-0.07932920066366683
25,300,-0.12632287888983637,-0.12632287888983676
25,399.99999999999994,-0.11833786175619836,-0.11833786175619584
25,500,-0.11100748269901356,-0.11100748269901378
25,600,-0.1041951104803409,-0.10419511048034096
25,700,-0.09782299733157954,-0.0978229973315815
25,799.9999999999999,-0.09185694713120071,-0.0918569471312006
30,300,-0.13243670824432408,-0.1324367082443262
30,399.99999999999994,-0.1255622679167983,-0.12556226791679798
30,500,-0.11915997994561496,-0.11915997994561418
30,600,-0.11308691704329457,-0.11308691704329465
30,700,-0.1073357944779216,-0.10733579447792117
30,799.9999999999999,-0.10184917943757642,-0.10184917943757658
35,300,-0.13677056452536177,-0.13677056452536293
35,399.99999999999994,-0.13080203713586655,-0.13080203713586697
35,500,-0.12520532188410496,-0.125205321884104
35,600,-0.11987380567515953,-0.11987380567516014
35,700,-0.11473430806567451,-0.1147343080656759
35,799.9999999999999,-0.10976340358680196,-0.10976340358680212
40,300,-0.13990717323235577,-0.13990717323235657
40,399.99999999999994,-0.13464194505199106,-0.1346419450519912
40,500,-0.12971940000903395,-0.1297194000090345
40,600,-0.1250385352876598,-0.12503853528766032
40,700,-0.12048766714948608,-0.12048766714948725
40,799.9999999999999,-0.11605950233797482,-0.11605950233797617
45,300,-0.14211371951863244,-0.14211371951863422
45,399.99999999999994,-0.13748934375145,-0.1374893437514511
45,500,-0.1331845458640706,-0.13318454586407008
45,600,-0.12904000501347995,-0.1290400050134822
45,700,-0.12500747238217932,-0.12500747238217835
45,799.9999999999999,-0.12106189037455688,-0.12106189037455767
50,300,-0.1438017496948447,-0.1438017496948458
50,399.99999999999994,-0.13962160946955932,-0.1396216094695604
50,500,-0.1357819565413621,-0.13578195654136546
50,600,-0.13211486751869694,-0.13211486751869667
50,700,-0.12856833135755893,-0.12856833135755996
50,799.9999999999999,-0.1250506633749004,-0.12505066337490012
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