郑君的学习笔记

self.name = tk.StringVar()
self.nameEntered = ttk.Entry(topFrame, width=15, textvariable=self.name)

self.measureTypeStr = tk.StringVar()
measureType = ttk.Combobox(topFrame, width=15, textvariable=self.measureTypeStr, state='readonly', command=self.on_cmb_select)
# Adding combobox drop down list
measureType['values'] = ('圆弧',
                            '距离',
                            '角度')
measureType.current(1)
measureType.pack(side=tk.LEFT, padx=4)
cmbMeasureType.bind("<<ComboboxSelected>>", self.on_cmb_select)

def on_cmb_select(self,event):
    if not self.name.get():
        self.nameEntered.delete(0,tk.END)
        self.nameEntered.insert(0, self.measureTypeStr.get())
    pass

self.conn = sqlite3.connect('conf.db')
self.cursor = self.conn.cursor()
sql = """
create table if not exists t_measurements(f_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
f_solutionId INTEGER NOT NULL,
f_name CHAR(300) NOT NULL , f_measureType CHAR(100) NOT NULL , f_points CHAR(500) NOT NULL );
"""
self.cursor.execute(sql)
sql = """
create table if not exists t_solution(f_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
f_name CHAR(300) NOT NULL , f_createDT CHAR(100) NOT NULL );
"""
self.cursor.execute(sql)
self.conn.commit()

def onSpanSelect(self, x0, x1):
    """
    记住当前 span 的数值，当新的 span 绘制完毕后，
    根据记录信息，主动绘制之前的 span
    :param x0:
    :param x1:
    :return:
    """
    print("onSpanSelect enter... x0={},x1={}".format(x0, x1))
    self.x1 = x0
    self.x2 = x1
    ylim = plt.gca().get_ylim()
    self.y2 = ylim[1]
    self.y1 = ylim[0]

def draw(self):
    df = load_data_txt('4444_height.csv')
    # 设置坐标轴范围
    plt.xlim((13000, 35000))  # 也可写成plt.xlim(-5, 5)
    plt.ylim((-2000, 4500))  # 也可写成plt.ylim(-2, 2)

    self.fig = plt.figure(num=1)
    self.ax = self.fig.add_subplot(111)
    # self.ax.autoscale(enable=True , tight=False)

    self.ax.plot(df['width'], df['height'], marker='o', mec='b', mfc='w')
    self.ax.set_aspect(1)
    self.ax.grid(True, linestyle='-.')

    # ax.legend(p1, 'orig')

    self.canvas = FigureCanvasTkAgg(self.fig, self)

    toolbar = NavigationToolbar2Tk(self.canvas, self)
    toolbar.update()
    self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.X, expand=True)

    self.canvas.draw()
    self.canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)

    ##关联鼠标点击事件
    # self.fig.canvas.mpl_connect('button_press_event', self.on_ml_click)
    # self.fig.canvas.mpl_connect('button_release_event', self.on_mouse_release)
    # self.fig.canvas.mpl_connect('motion_notify_event', self.on_mouse_motion)

    self.on_draw_points()

    self.span = SpanSelector(self.ax, self.onSpanSelect, 'horizontal', useblit=True)
    self.canvas.mpl_connect('key_press_event', self.span)

    self.fig.canvas.draw()

# 显示中文标签
plt.rcParams['font.sans-serif'] = ['SimHei']
# 显示负号
plt.rcParams['axes.unicode_minus'] = False
# 有中文使用 u'中文内容'
# 隐藏工具栏
plt.rcParams['toolbar'] = 'None'

# 已知三点坐标，求外接圆圆心坐标与半径。

x=((y2-y1)*(y3*y3-y1*y1+x3*x3-x1*x1)-(y3-y1)*(y2*y2-y1*y1+x2*x2-x1*x1))/(2.0*((x3-x1)*(y2-y1)-(x2-x1)*(y3-y1)))
y=((x2-x1)*(x3*x3-x1*x1+y3*y3-y1*y1)-(x3-x1)*(x2*x2-x1*x1+y2*y2-y1*y1))/(2.0*((y3-y1)*(x2-x1)-(y2-y1)*(x3-x1)))
r=(x1-x)*(x1-x+(y1-y)*(y1-y)

def fit_circle(df):
    # Coordinates of the 2D points
    # x = r_[ 20.9564, 20.9593,20.9629]
    # y = r_[  30.1163, 30.0848,30.0259]

    # x = r_[9, 35, -13, 10, 23, 0]
    # y = r_[34, 10, 6, -14, 27, -10]

    x = df['inner']
    y = df['height']
    df_list, name_list = load_data('凸轮轴信号轮，球形测头_10次5101(1)')

    # coordinates of the barycenter
    x_m = mean(x)
    y_m = mean(y)

    method_2 = "leastsq"

    def calc_R(c):
        """ calculate the distance of each 2D points from the center c=(xc, yc) """
        return sqrt((x - c[0]) ** 2 + (y - c[1]) ** 2)

    def calc_ecart(c):
        """ calculate the algebraic distance between the 2D points and the mean circle centered at c=(xc, yc) """
        Ri = calc_R(c)
        return Ri - Ri.mean()

    center_estimate = x_m, y_m
    center_2, ier = optimize.leastsq(calc_ecart, center_estimate)

    xc_2, yc_2 = center_2
    Ri_2 = calc_R(center_2)
    R_2 = Ri_2.mean()
    residu_2 = sum((Ri_2 - R_2) ** 2)

    print('x={},y={},r={}'.format(xc_2,yc_2,R_2))

    from matplotlib import pyplot as p, cm

    f = p.figure()
    p.plot(x, y, 'ro', label='data', ms=9, mec='b', mew=1)

    theta_fit = linspace(-pi, pi, 180)

    x_fit2 = xc_2 + R_2 * cos(theta_fit)
    y_fit2 = yc_2 + R_2 * sin(theta_fit)
    p.plot(x_fit2, y_fit2, 'k--', label=method_2, lw=2)

    p.plot([xc_2], [yc_2], 'gD', mec='r', mew=1)

    p.grid()
    p.title('Leasts Squares Circle')
    p.show()

# 处理 校零 't1+t2+5'
pattern = re.compile(r't\d+')  # 查找数字
idx_list = pattern.findall(it)
idx_list = [x[1:] for x in idx_list]