{"id":1787,"date":"2018-03-03T18:50:17","date_gmt":"2018-03-03T09:50:17","guid":{"rendered":"http:\/\/research.itplants.com\/?p=1787"},"modified":"2018-03-06T18:04:01","modified_gmt":"2018-03-06T09:04:01","slug":"3%e8%bb%b8%e7%a3%81%e6%b0%97%e3%82%bb%e3%83%b3%e3%82%b5%e3%83%bclis3md%e3%81%ae%e6%a4%9c%e8%a8%8e","status":"publish","type":"post","link":"https:\/\/research.itplants.com\/?p=1787","title":{"rendered":"3\u8ef8\u78c1\u6c17\u30bb\u30f3\u30b5\u30fcLIS3MD\u306e\u691c\u8a0e"},"content":{"rendered":"

3\u8ef8\u3067\u78c1\u6c17\u3092\u6e2c\u5b9a\u3067\u304d\u308bLIS3MD\u306e\u691c\u8a0e\u3092\u3057\u3066\u307f\u307e\u3057\u305f\u3002\u3053\u308c\u306f\u3001\u30ab\u30e1\u30e9\u3092\u52d5\u304b\u3059\u3068\u3001\u3069\u306e\u65b9\u5411\u3092\u898b\u3066\u3044\u308b\u306e\u304b\u5206\u304b\u3089\u306a\u304f\u306a\u308b\u306e\u3067\u3001\u5730\u78c1\u6c17\u3067\u65b9\u4f4d\u3092\u53d6\u5f97\u3057\u3088\u3046\u3068\u8003\u3048\u305f\u304b\u3089\u3067\u3059\u3002\u4f55\u6545\u3001\uff13\u8ef8\u3082\u5fc5\u8981\u306a\u306e\u304b\u3001\u6700\u521d\u306f\u5206\u304b\u3089\u306a\u304b\u3063\u305f\u306e\u3067\u3059\u304c\u3001\uff13\u8ef8\u52a0\u901f\u5ea6\u30bb\u30f3\u30b5\u30fc\u3068\u7d44\u307f\u5408\u308f\u305b\u3066\u4f7f\u3046\u5834\u5408\u306b\uff13\u8ef8\u5fc5\u8981\u306b\u306a\u308b\u8a33\u3067\u3059\u3002\u666e\u901a\u306b\u3001\u6c34\u5e73\u306b\u7f6e\u3044\u3066\u4f7f\u3046\u5206\u306b\u306f\u3001\uff12\u8ef8\u3067\u5341\u5206\u3067\u3057\u3087\u3046\u3002<\/p>\n

LIS3MD\u306f\u3001I2C\u3067Raspberry Pi Model3\u306b\u63a5\u7d9a\u3057\u307e\u3057\u305f\u3002\u30a2\u30c9\u30ec\u30b9\u306f0x1E\u3067\u3057\u305f\u3002<\/p>\n

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Python\u306escipy\u3092\u4f7f\u3048\u3070\u3001\u69d8\u3005\u306a\u95a2\u6570\u3092\u30d5\u30a3\u30c6\u30a3\u30f3\u30b0\u3067\u304d\u307e\u3059\u3002<\/p>\n

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\r\n#!\/usr\/bin\/python3\r\nimport numpy as np\r\nfrom scipy import odr\r\nfrom scipy import optimize\r\nfrom matplotlib.patches import Ellipse\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.stats import norm\r\n\r\ndef f(B, x):\r\n    return ((x[0]\/B[0])**2+(x[1]\/B[1])**2-1.)\r\n\r\n#Least squares method with scipy.optimize\r\ndef fit_func(parameter,xy):\r\n    a = parameter[0]\r\n    b = parameter[1]\r\n    c = parameter[2]\r\n    residual = (a*xy[0]+b*xy[1]+c)\r\n    return residual\r\n\r\ndef f(B, x):\r\n    return ((x[0]\/B[0])**2+(x[1]\/B[1])**2-1.)\r\n\r\nif __name__ == '__main__':\r\n    file_id = 'magdir.csv'\r\n    file_path = '.\/'\r\n\r\n    rfile = file_path + file_id\r\n    data = np.loadtxt(rfile, comments='#' ,delimiter=',')\r\n\r\n    x_csv = data[:,0]\r\n    y_csv = data[:,1]\r\n\r\n    # sub average\r\n    x_ave=np.average(x_csv)\r\n    y_ave=np.average(y_csv)\r\n    x_csv = x_csv-x_ave\r\n    y_csv = y_csv-y_ave\r\n\r\n    xy = np.array([x_csv, y_csv])\r\n\r\n    #mdr = odr.Model(f, implicit=True)\r\n    mdr = odr.Model(g, implicit=True)\r\n    mydata = odr.Data(xy,y=1)\r\n    #myodr = odr.ODR(mydata, mdr, beta0=[1., 2.])\r\n    myodr = odr.ODR(mydata, mdr, beta0=[1., 2.,3.,4.])\r\n    myoutput = myodr.run()\r\n    myoutput.pprint()\r\n\r\n    ax = plt.subplot(111, aspect='equal')\r\n    plt.scatter(x_csv, y_csv) # raw data with randomness\r\n\r\n    ell = Ellipse(xy=(0., 0.), width=2.*myoutput.beta[0], height=2.*myoutput.beta[1], angle=0.0)\r\n    ell.set_facecolor('none')\r\n    ell.set_edgecolor('black')\r\n    ax.add_artist(ell) # fitted curve\r\n    plt.grid()\r\n    plt.show()\r\n    plt.show()\r\n<\/pre>\n
\u305d\u306e\u5f8c\u30018BYJ-48 Stepper Motor\u3068\u3044\u3046\u30b9\u30c6\u30c3\u30d4\u30f3\u30b0\u30e2\u30fc\u30bf\u30fc\u3067\u30bb\u30f3\u30b5\u30fc\u3092\u56de\u8ee2\u3055\u305b\u3066\u518d\u6e2c\u5b9a\u3057\u307e\u3057\u305f\u30028BYJ-48 Stepper Motor\u306f\u3001\u5b89\u4fa1\u306a\u30b9\u30c6\u30c3\u30d4\u30f3\u30b0\u30e2\u30fc\u30bf\u30fc\u3067\u3059\u3002RaspberryPi\u30674\u5c64\u306e\u56de\u8ee2\u30d1\u30eb\u30b9\u3092\u51fa\u3057\u3066\u5236\u5fa1\u3057\u307e\u3059\u3002\u56de\u8ee2\u901f\u5ea6\u306f\u3001\u975e\u5e38\u306b\u3086\u3063\u304f\u308a\u3068\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
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 <\/p>\n","protected":false},"excerpt":{"rendered":"
3\u8ef8\u3067\u78c1\u6c17\u3092\u6e2c\u5b9a\u3067\u304d\u308bLIS3MD\u306e\u691c\u8a0e\u3092\u3057\u3066\u307f\u307e\u3057\u305f\u3002\u3053\u308c\u306f\u3001\u30ab\u30e1\u30e9\u3092\u52d5\u304b\u3059\u3068\u3001\u3069\u306e\u65b9\u5411\u3092\u898b\u3066\u3044\u308b\u306e\u304b\u5206\u304b\u3089\u306a\u304f\u306a\u308b\u306e\u3067\u3001\u5730\u78c1\u6c17\u3067\u65b9\u4f4d\u3092\u53d6\u5f97\u3057\u3088\u3046\u3068\u8003\u3048\u305f\u304b\u3089\u3067\u3059\u3002\u4f55\u6545\u3001\uff13\u8ef8\u3082\u5fc5\u8981\u306a\u306e\u304b\u3001\u6700\u521d\u306f\u5206\u304b\u3089\u306a\u304b\u3063\u305f\u306e\u3067\u3059\u304c\u3001…<\/p>\n
 Read more →<\/a><\/p>\n","protected":false},"author":1,"featured_media":1797,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/research.itplants.com\/index.php?rest_route=\/wp\/v2\/posts\/1787"}],"collection":[{"href":"https:\/\/research.itplants.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.itplants.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.itplants.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/research.itplants.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1787"}],"version-history":[{"count":7,"href":"https:\/\/research.itplants.com\/index.php?rest_route=\/wp\/v2\/posts\/1787\/revisions"}],"predecessor-version":[{"id":1801,"href":"https:\/\/research.itplants.com\/index.php?rest_route=\/wp\/v2\/posts\/1787\/revisions\/1801"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.itplants.com\/index.php?rest_route=\/wp\/v2\/media\/1797"}],"wp:attachment":[{"href":"https:\/\/research.itplants.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1787"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.itplants.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1787"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.itplants.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1787"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}