def makeFigure(name='Z dim', path='./'): fig = plt.figure() fig.suptitle(name) ax = HostAxes(fig, [0.08, 0.08, 0.70, 0.8]) ax.axis["right"].set_visible(False) ax.axis["top"].set_visible(False) ax.plot(Z_dim, FID, color=cname[0], label='FID') ax.set_ylabel('FID') ax.set_xlabel('Z dimension') ax.legend() fig.add_axes(ax) def paraAxis(x, y, name, cnt): ax_para = ParasiteAxes(ax, sharex=ax) ax.parasites.append(ax_para) ax_para.axis['right'].set_visible(True) ax_paraD = ax_para.get_grid_helper().new_fixed_axis ax_para.set_ylabel(name) ax_para.axis['right'] = ax_paraD(loc='right', offset=(40 * cnt, 0), axes=ax_para) ax_para.plot(x, y, label=name, color=cname[cnt + 1]) plt.legend(loc=0) paraAxis(Z_dim, IS, "IS", 0) paraAxis(Z_dim, nll, "nll", 1) plt.savefig(os.path.join(path, 'z_dim.png'), dpi=100)
def show_all(): fig = plt.figure() host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) par1 = ParasiteAxes(host, sharex=host) par2 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.parasites.append(par2) host.set_ylabel("Temperature") host.set_xlabel("Datetime") host.axis["right"].set_visible(False) host.set_ylim(25, 35) par1.set_ylabel("eCO2") par1.axis["right"].set_visible(True) par1.axis["right"].major_ticklabels.set_visible(True) par1.axis["right"].label.set_visible(True) par1.set_ylim(400, 2000) par2.set_ylabel("TVOC") new_axisline = par2.get_grid_helper().new_fixed_axis par2.axis["right2"] = new_axisline(loc="right", axes=par2, offset=(60, 0)) par2.set_ylim(0, 1000) fig.add_axes(host) p1, = host.plot(temperatures, label="Temperature") p2, = par1.plot(eCO2s, label="eCO2") p3, = par2.plot(TVOCs, label="TVOC") host.axis["left"].label.set_color(p1.get_color()) par1.axis["right"].label.set_color(p2.get_color()) par2.axis["right2"].label.set_color(p3.get_color()) plt.show()
def main(): wholeDir = 'E:/work/code/deepSpeech/pytorch_deepspeech/result/07-23-12-47/' mdir = wholeDir + 'm.txt' prefftdir = wholeDir + "fft.txt" #gradfile = os.path.join(wholeDir,folder, 'output_now') dimMeans, frameMeans, norm = getM_(mdir) #drawgradgraph(gradfile) lossdir = wholeDir + "loss.txt" ys = np.loadtxt(lossdir) xs = np.zeros_like(ys) for i in range(xs.shape[0]): xs[i] = i + 1 plt.scatter(xs, ys) plt.savefig(os.path.dirname(wholeDir) + '/loss.png', dpi=150) plt.clf() myloss = np.loadtxt(wholeDir + "loss.txt") m_loss = -np.loadtxt(wholeDir + "loss.txt") fig = plt.figure() host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) par1 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.axis['right'].set_visible(False) par1.axis['right'].set_visible(True) par1.set_ylabel('m_entropy=∑log(m_ij))') par1.axis['right'].major_ticklabels.set_visible(True) par1.axis['right'].label.set_visible(True) fig.add_axes(host) host.set_xlabel('epoch') host.set_ylabel('|preOutput-TrueOutput|²') p1, = host.plot(xs, myloss, label='|preOutput-TrueOutput|²') p2, = par1.plot(xs, m_loss, label='m_entropy=∑log(m_ij)); ') plt.title("two parts of loss") host.legend() # 轴名称,刻度值的颜色 host.axis['left'].label.set_color(p1.get_color()) par1.axis['right'].label.set_color(p2.get_color()) plt.savefig(os.path.join(os.path.dirname(wholeDir), 'loss_two_parts.png'), dpi=150) plt.clf() Fftreader.mcolorDrawer(norm, '/m hot map', os.path.dirname(prefftdir)) #Fftreader.mcolorDrawer(norm, '/m hot map color', os.path.dirname(prefftdir)) #print('m, mean:',np.mean(norm),'max:',np.max(norm),'min:',np.min(norm),'std:',np.std(norm,ddof=1)) #print(np.max(norm)) makeVoice(prefftdir, dimMeans, frameMeans, norm)
def drawCurveDonkey(intxtpath, outimgpath, title, xlabel='epoch', par1label='loss', par2label='accuracy(%)'): xs = [] p1s = [] p2s = [] with open(intxtpath, 'r') as fin: lines = [l.strip() for l in fin.readlines()] for line in lines: x, p1, p2 = line.split('\t') xs.append(int(x)) p1s.append(float(p1)) p2s.append(float(p2)) fig = plt.figure() host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) par1 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.axis['right'].set_visible(False) par1.axis['right'].set_visible(True) par1.set_ylabel(par2label) par1.axis['right'].major_ticklabels.set_visible(True) par1.axis['right'].label.set_visible(True) fig.add_axes(host) host.set_xlabel(xlabel) host.set_ylabel(par1label) p1, = host.plot(np.array(xs), np.array(p1s), label=par1label) p2, = par1.plot(np.array(xs), np.array(p2s), label=par2label) plt.title(title) host.legend() host.axis['left'].label.set_color(p1.get_color()) par1.axis['right'].label.set_color(p2.get_color()) plt.savefig(outimgpath, dpi=150) plt.clf()
""" from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes import matplotlib.pyplot as plt if __name__ == "__main__": fig = plt.figure(1) host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) par1 = ParasiteAxes(host, sharex=host) par2 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.parasites.append(par2) host.set_ylabel("Density") host.set_xlabel("Distance") host.axis["right"].set_visible(False) par1.axis["right"].set_visible(True) par1.set_ylabel("Temperature") par1.axis["right"].major_ticklabels.set_visible(True) par1.axis["right"].label.set_visible(True) par2.set_ylabel("Velocity") offset = (60, 0) new_axisline = par2._grid_helper.new_fixed_axis par2.axis["right2"] = new_axisline(loc="right", axes=par2, offset=offset) fig.add_axes(host)
tmpMaxZ = max(zc) if tmpMinZ < minZ: minZ = tmpMinZ if tmpMaxZ > maxZ: maxZ = tmpMaxZ maxOfMax = max(maxPad) host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) par1 = ParasiteAxes(host, sharey=host) par2 = ParasiteAxes(host, sharey=host) host.parasites.append(par1) host.parasites.append(par2) host.set_xlabel("PAD ($m^2.m^{-3}$)") host.set_ylabel("Height (m)") #par1.set_xlabel("RDI") par1.set_xlabel("Average number of returns per 10cm cube voxel") host.axis["top"].set_visible(False) par1.axis["top"].set_visible(True) par1.axis["top"].major_ticklabels.set_visible(True) par1.axis["top"].label.set_visible(True) #offset = (0, 40) #new_axisline = par2._grid_helper.new_fixed_axis #par2.axis["top2"] = new_axisline(loc="top", axes=par2, offset=offset) fig.add_axes(host)
m_loss = np.loadtxt(os.path.join(wholeDir, folder, 'm_loss.txt')) m_mean = np.loadtxt(os.path.join(wholeDir, folder, 'm_mean.txt')) m_var = np.loadtxt(os.path.join(wholeDir, folder, 'm_var.txt')) fig = plt.figure() host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) par1 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.axis['right'].set_visible(False) par1.axis['right'].set_visible(True) par1.set_ylabel('m_entropy=∑log(m_ij))') par1.axis['right'].major_ticklabels.set_visible(True) par1.axis['right'].label.set_visible(True) fig.add_axes(host) host.set_xlabel('epoch') host.set_ylabel('|preOutput-TrueOutput|²') p1, = host.plot(np.arange(len(myloss)), myloss / (K * T), label='|preOutput-TrueOutput|²') p2, = par1.plot(np.arange(len(m_loss)), m_loss / (K * T), label='m_entropy=∑log(m_ij)); ') plt.title("two parts of loss normalized /KT ") host.legend() # 轴名称,刻度值的颜色 host.axis['left'].label.set_color(p1.get_color()) par1.axis['right'].label.set_color(p2.get_color()) plt.savefig(os.path.join(wholeDir, folder, 'loss_two_parts.png'), dpi=150) plt.clf()
def plot_line_(name, x, y1, y2, y3): #fig = plt.figure(figsize=(7,5)) #figsize默认为4,4(图像尺寸),facecolor="blue" fig = plt.figure(1) #figsize默认为4,4(图像尺寸),facecolor="blue" ax = HostAxes( fig, [0.15, 0.1, 0.7, 0.8 ]) #用[left, bottom, weight, height]的方式定义axes,0 <= l,b,w,h <= 1 ax_ndvi = ParasiteAxes(ax, sharex=ax) ax_t = ParasiteAxes(ax, sharex=ax) ax_p = ParasiteAxes(ax, sharex=ax) ax.parasites.append(ax_ndvi) ax.parasites.append(ax_p) ax.parasites.append(ax_t) ax.set_ylim(0.41, 0.75) ax.set_xlim(2004.5, 2015.5) ax.axis['right'].set_visible(False) ax.axis['top'].set_visible(False) # plt.tick_params(top = 'off', right = 'off') #ax_ndvi.axis['left1'] = new_axisline(loc='left', axes=ax_ndvi, offset=offset1) #ax.axis['left'].set_axisline_style('->',size=2) #轴的形状色 ax.axis['left'].line.set_linewidth(3) #轴宽 ax.axis['bottom'].line.set_linewidth(3) #轴宽 offset1 = (20, 0) offset2 = (60, 0) ax_t.set_ylim(10, 30) ax_p.set_ylim(500, 2500) new_axisline = ax_t._grid_helper.new_fixed_axis # "_grid_helper"与"get_grid_helper()"等价,可以代替 #new_axisline = par2.get_grid_helper().new_fixed_axis # 用"get_grid_helper()"代替,结果一样,区别目前不清楚 new_axisline = ax_p._grid_helper.new_fixed_axis ax_t.axis['right2'] = new_axisline(loc='right', axes=ax_t, offset=offset1) #ax_t.axis['right2'].set_axisline_style('-|>',size=-1) #轴的形状色 ax_t.axis['right2'].line.set_linewidth(3) #轴宽 ax_t.axis['right2'].set_label( 'T') # ax_t.axis['right2'].set_axislabel_direction('-') ax_p.axis['right3'] = new_axisline(loc='right', axes=ax_p, offset=offset2) #ax_p.axis['right3'].set_axisline_style('->',size=-1) #轴的形状色 ax_p.axis['right3'].line.set_linewidth(3) #轴宽 ax_p.axis['right3'].set_label('P') ax_t.yaxis.set_major_locator(MultipleLocator(2)) ax_p.yaxis.set_major_locator(MultipleLocator(500)) ax.xaxis.set_major_locator( xmajorLocator ) #设置主刻度标签的位置,没有标签文本格式ax.xaxis.set_major_formatter(FormatStrFormatter('%5.1f')) ax.yaxis.set_major_locator(ymajorLocator) ax.xaxis.set_minor_locator(xminorLocator) #设置次刻度标签的位置,没有标签文本格式 ax.xaxis.set_minor_locator(xminorLocator) #打开网格 ax_ndvi.grid(which='minor', color='black', linestyle='--') #,lw=0.8, alpha=0.5) # grid setting) #绘制网格线 ax_t.grid(which='minor', color='black', linestyle='--') #,lw=0.8, alpha=0.5) # grid setting) #绘制网格线 ax.tick_params(axis='both', which='major', direction='in', width=2, length=lwidth, pad=tick_pad, labelsize=tick_labelsz, grid_linewidth=3) ax.set_ylabel('NDVI', font_properties=getChineseFont(80)) #ax.set_ylabel('NDVI',fontproperties=getChineseFont(100)) ax_t.tick_params(axis='both', which='both', direction='in', width=3, length=100, pad=18, labelsize=100, grid_linewidth=1) ax_p.tick_params(axis='both', which='both', direction='in', width=3, length=100, pad=18, labelsize=100, grid_linewidth=1) fig.add_axes(ax) ax.set_xlabel('Year') p1, = ax.plot(x, y1, label="NDVI", ls=linestyle, lw=lwidth, color=c1, marker=".", ms=16, mfc=c1) p2, = ax_t.plot(x, y3, label="Temperature", ls=linestyle, lw=lwidth, color=c2, marker="o", ms=marksize, mfc=c2) p3, = ax_p.plot(x, y2, label="Precipitation", ls=linestyle, lw=lwidth, color=c3, marker="o", ms=marksize, mfc=c3) #ax.set_xlabel('YEAR') #ax.set_ylabel('NDVI') plt.legend(loc=1, bbox_to_anchor=(0.9, 1.2), prop=getChineseFont(12), ncol=1, frameon=False, mode='expend') #plt.savefig(r'C:\Users\YeHui\Desktop\GGGGGGGGGGG.jpg')#, dpi=1000) plt.show()
] torque_mutil_gear_max = max(torque_mutil_gear_max, max(torque_multi_gear)) line_torque_multi_gear = ax_torque_multi_gear.plot( time[order], torque_multi_gear, linestyle=line_style[vehicle_number[order]], color=line_color[temperature_target[order]]) gear_max = max(gear_max, max(gear[order])) power_max = max(power_max, max(power)) lines_set = lines_set + line_power legend_set = [line.get_label() for line in lines_set] ax_power.legend(lines_set, legend_set, loc='upper left') ax_power.set_xlabel('Time(s)') ax_power.set_ylabel('Power(kw)') ax_power.set_ylim(-20, 2 * power_max) ax_power.set_yticks([i * 20 for i in range(0, int(power_max / 20 + 1))]) ax_gear.set_ylabel('Gear/Gear ratio') ax_gear.set_ylim(-gear_max, gear_max + 1) ax_gear.set_yticks([i for i in range(0, int(gear_max) + 1)]) if gear_max > 10: # if it is gear ratio, plot torque*gear ax_torque_multi_gear.set_ylabel('Torque*gear ratio(Nm)') ax_torque_multi_gear.set_ylim(-torque_mutil_gear_max - 50, torque_mutil_gear_max) ax_torque_multi_gear.set_yticks( [i * 500 for i in range(0, int(torque_mutil_gear_max / 500) + 2)]) ax_gear.set_ylim(0, 2 * gear_max) ax_power.legend(lines_set, legend_set, loc='upper right')
ax_cof.parasites.append(ax_temp) ax_cof.parasites.append(ax_load) #ax_cof.parasites.append(ax_cp) #ax_cof.parasites.append(ax_wear) #invisible right axis of ax_cof ax_cof.axis['right'].set_visible(False) ax_cof.axis['top'].set_visible(True) ax_temp.axis['right'].set_visible(True) ax_temp.axis['right'].major_ticklabels.set_visible(True) ax_temp.axis['right'].label.set_visible(True) #set label for axis ax_cof.set_ylabel('Compress Strength (Kgf/$\mathregular{m^2}$)', fontsize=20) ax_cof.set_xlabel('Addition of Waste Tire Rubber Powder (g)') ax_temp.set_ylabel('Porosity (%)', fontsize=20) ax_load.set_ylabel('Coef. of Permeability (cm/s)', fontsize=20) #ax_cp.set_ylabel('CP') #ax_wear.set_ylabel('Wear') load_axisline = ax_load.get_grid_helper().new_fixed_axis #cp_axisline = ax_cp.get_grid_helper().new_fixed_axis #wear_axisline = ax_wear.get_grid_helper().new_fixed_axis ax_load.axis['right2'] = load_axisline(loc='right', axes=ax_load, offset=(70, 0)) #ax_cp.axis['right3'] = cp_axisline(loc='right', axes=ax_cp, offset=(80,0)) #ax_wear.axis['right4'] = wear_axisline(loc='right', axes=ax_wear, offset=(120,0))
#append axes ax_data.parasites.append(ax_cpu) ax_data.parasites.append(ax_mem) #ax_data.parasites.append(ax_bitrates) #ax_data.parasites.append(ax_wear) #invisible right axis of ax_data ax_data.axis['right'].set_visible(False) ax_data.axis['top'].set_visible(False) ax_cpu.axis['right'].set_visible(True) ax_cpu.axis['right'].major_ticklabels.set_visible(True) ax_cpu.axis['right'].label.set_visible(True) #set label for axis ax_data.set_ylabel('data') ax_data.set_xlabel('time (s)') ax_cpu.set_ylabel('cpu') ax_mem.set_ylabel('memory') #ax_bitrates.set_ylabel('bitrates') #ax_wear.set_ylabel('Wear') #new axsi line load_axisline = ax_mem.get_grid_helper().new_fixed_axis #cp_axisline = ax_bitrates.get_grid_helper().new_fixed_axis #wear_axisline = ax_wear.get_grid_helper().new_fixed_axis #axsi line padding on the right ax_mem.axis['right2'] = load_axisline(loc='right', axes=ax_mem, offset=(40, 0)) #ax_bitrates.axis['right3'] = cp_axisline(loc='right', axes=ax_bitrates, offset=(80,0)) #ax_wear.axis['right4'] = wear_axisline(loc='right', axes=ax_wear, offset=(120,0))
cursor = conn.cursor() #cursor.execute('SELECT * FROM dbo.DealInfo') #cursor.execute("select SUM(DealPrice)/SUM(OutArea), DealDate from LJDT.dbo.DealInfo group by DealDate order by DealDate") #select COUNT(*), CAST(Community as varchar(max)) from LJDT.dbo.DealInfo group by CAST(Community as varchar(max)) order by COUNT(*) # fig, ax1 = plt.subplots() # ax2 = ax1.twinx() # ax3 = ax1.twinx() fig = plt.figure(1) # main y ax_cof = HostAxes( fig, [0.05, 0.05, 0.8, 0.8 ]) #用[left, bottom, right, height]的方式定义axes,0 <= l,b,w,h <= 1 ax_cof.set_xlabel('month') ax_cof.set_ylabel('unit price') ax_cof.axis['bottom'].major_ticklabels.set_rotation(45) ax_cof.axis['bottom'].major_ticklabels.set_fontsize(5) # parasite addition axes, share x ax_1 = ParasiteAxes(ax_cof, sharex=ax_cof) ax_1.set_ylabel('deal count') # ax_2 = ParasiteAxes(ax_cof, sharex=ax_cof) # ax_2.set_ylabel('average area') ax_cof.axis['right'].set_visible(False) ax_cof.axis['top'].set_visible(False) # ax_cof.set_ylim(2.8, 3.2) # append axes
i = 0 f.write("year_intrval,gdp_amount,edu_att_rate \n") while i < len(gdp_values): f.write( str(names[i]) + "," + str(gdp_values[i]) + "," + str(edu_values[i]) + "\n") i += 1 f.close() fig = plt.figure(1) host = HostAxes(fig, [0.1, 0.1, 0.8, 0.8]) par1 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.set_ylabel("Density") host.set_xlabel("Distance") host.axis["right"].set_visible(True) par1.axis["right"].set_visible(True) par1.set_ylabel("Temperature") par1.axis["right"].major_ticklabels.set_visible(True) par1.axis["right"].label.set_visible(True) fig.add_axes(host) host.set_xlim(1975, 2010) host.set_ylim(0, 100) host.set_xlabel("GDP and Success rate") host.set_ylabel("GDP per five years (*2*10^10")
def draw_city(city): select = "SELECT max, min, date FROM weather WHERE city='" + city + "'" + " AND date Between '2018年02月01日' AND '2018年02月28日'" cur.execute(select) all_data = cur.fetchall() # 防止查不到数据 if len(all_data) != 0: # 准备数据 max = [] min = [] time_point = [] # 准备标签 for data in all_data: max_temperature = str(data[0]) max.append(int(max_temperature[0:len(max_temperature) - 1])) min_temperature = str(data[1]) min.append(int(min_temperature[0:len(min_temperature) - 1])) time_point.append(str(data[2])) # 准备字体 my_font = fm.FontProperties( fname="F:\\Project\\Environment\\web\\php\\font\\simsun.ttc") fontcn = {'family': 'SimSun', 'size': 10} # 绘制气温变化 fig = plt.figure(figsize=(40, 8), dpi=80) ax_max = HostAxes( fig, [0, 0, 0.9, 0.9 ]) # 用[left, bottom, weight, height]的方式定义axes,0 <= l,b,w,h <= 1 # parasite addtional axes, share x ax_min = ParasiteAxes(ax_max, sharex=ax_max) ax_max.parasites.append(ax_min) # invisible right axis of ax_so2 ax_max.axis['right'].set_visible(False) ax_max.axis['top'].set_visible(False) ax_min.axis['right'].set_visible(True) ax_min.axis['right'].major_ticklabels.set_visible(True) ax_min.axis['right'].label.set_visible(True) # set label for axis ax_max.set_ylabel("摄氏度(℃)", fontdict=fontcn) ax_max.set_xlabel("时间", fontdict=fontcn) ax_min.set_ylabel("摄氏度(℃)", fontdict=fontcn) fig.add_axes(ax_max) x1 = time_point x2 = [i for i in range(0, len(time_point))] y1 = max y2 = min ax_max.plot(x1, y1, label='最高温度', color='#009966') ax_min.plot(x2, y2, label='最低温度', color='#FFDE33') ax_max.legend() ax_min.axis['right'].label.set_color('#FFDE33') ax_min.axis['right'].major_ticks.set_color('#FFDE33') ax_min.axis['right'].major_ticklabels.set_color('#FFDE33') ax_min.axis['right'].line.set_color('#FFDE33') # 添加图形标题 plt.title(city + '最高温度和最低温度变化情况', loc='center', fontproperties=my_font) # 保存图片 plt.savefig("./../../article/image/temperature/" + city + ".png", bbox_inches='tight') # 显示图形 # show ann img plt.cla() # clear fig to show ann img saved_img = plt.imread("./../../article/image/temperature/" + city + ".png") # keep the origin image size dpi = 80.0 height, width, depth = saved_img.shape plt.figure(figsize=(width / dpi, height / dpi)) plt.axis('off') plt.imshow(saved_img) plt.show()
def dym_quantile(n): """ 动态分位图 当前投资已5年内历史数据计算百分位,价格合适购入 """ # 这里的计算按一年244个交易日计算 windows = int(n * 244) # 将时间取整数 start_date = dt.datetime(2006, 1, 1) end_date = dt.datetime(2020, 4, 1) df = dp.load_bar_data('000300', 'XSHG', start_date=start_date, end_data=end_date) df_finance = dp.load_finance_data('000300.XSHG', start_date=start_date, end_date=end_date) if len(df) == len(df_finance): print('yes!!!, len:%s' % len(df)) df['pe'] = df_finance['pe'] df['quantile'] = df_finance['pe'].rolling(windows).apply(lambda x: pd.Series(x).rank().iloc[-1] / pd.Series(x).shape[0], raw=True) # df['date'] = pd.to_datetime(df['date']) # 转换时间类型 # df.set_index(['date'], inplace=True) # df.index.name = None # 去掉索引列名 df.dropna(inplace=True) # 画出适中估值区间 # plt.figure() # 创建第一个画板 fig = plt.figure(figsize=(16, 9)) host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) par1 = ParasiteAxes(host, sharex=host) par2 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.parasites.append(par2) host.set_xlabel('Date') host.set_ylabel('Close') host.axis['right'].set_visible(False) par1.axis['right'].set_visible(True) par1.set_ylabel('%sY Rolling quantile' % n) par1.axis['right'].major_ticklabels.set_visible(True) par1.axis['right'].label.set_visible(True) par2.set_ylabel('PE') new_axisline = par2.get_grid_helper().new_fixed_axis # "_grid_helper"与"get_grid_helper()"等价,可以代替 par2.axis['right2'] = new_axisline(loc='right', axes=par2, offset=(45, 0)) fig.add_axes(host) df['date'] = pd.to_datetime(df['date']) df['date'] = df['date'].apply(lambda x: dates.date2num(x)) p1, = host.plot(df['date'], df['close'], label="Close") p2, = par1.plot(df['date'], df['quantile'], label="Quantile") p3, = par2.plot(df['date'], df['pe'], label="PE") host.legend() # 轴名称,刻度值的颜色 host.axis['left'].label.set_color(p1.get_color()) host.xaxis.set_major_locator(ticker.MaxNLocator(math.floor(len(df) / 100))) host.xaxis.set_major_formatter(dates.DateFormatter('%Y-%m')) par1.axis['right'].label.set_color(p2.get_color()) par2.axis['right2'].label.set_color(p3.get_color()) par2.axis['right2'].major_ticklabels.set_color(p3.get_color()) # 刻度值颜色 par2.axis['right2'].set_axisline_style('-|>', size=1.5) # 轴的形状色 par2.axis['right2'].line.set_color(p3.get_color()) # 轴的颜色 # ax.xaxis.set_major_formatter(dates.DateFormatter('%Y-%m-%d')) # df[['quantile', 'close']].plot(secondary_y=['quantile'], figsize=(14, 10), alpha=.8) # plt.fill_between(df.index, y1=0.4, y2=0.6, color='blue', alpha=0.7) # plt.fill_between(df.index, y1=0.8, y2=1, color='red', alpha=0.7) # plt.fill_between(df.index, y1=0.0, y2=0.2, color='green', alpha=0.7) # plt.annotate('reasonable zone', (df.index[-1], 0.5)) # 画出固定PE与收盘价的曲线 plt.show()
host.parasites.append(par1) host.axis["right"].set_visible(False) par1.axis["right"].set_visible(True) par1.axis["right"].major_ticklabels.set_visible(True) par1.axis["right"].label.set_visible(True) fig1.add_axes(host) host.set_xlim(np.pi / 2, np.pi) host.set_ylim(0.29, 0.61) host.set_xticks([1.57, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.1415]) host.set_xticklabels( ['$\\pi/2$', '1.8', '2.0', '2.2', '2.4', '2.6', '2.8', '3.0', '$\\pi$']) host.set_xlabel("$\\theta_c$") host.set_ylabel("$\\theta_b^*$") par1.set_ylabel("min$_{\\theta_b} \\varepsilon$ $(\\times 10^{-3})$") p1, = host.plot(C[0, :], X[Z.argmin(axis=0), 0], label="$\\theta_b^*$", color='#1f77b4') p2, = par1.plot(C[0, :], Z.min(axis=0) * 1000, label="min$_{\\theta_b} \\varepsilon$", color='#e6550d') par1.set_ylim(0, 2.5) host.legend(loc='center left') host.grid(linestyle='--')
f = open('output.csv', 'a+') simplejson.dump(names, f) f.write('\n') simplejson.dump(values1, f) f.write('\n') simplejson.dump(values2, f) f.close() fig = plt.figure(1) host = HostAxes(fig, [0.1, 0.1, 0.8, 0.8]) par1 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.set_ylabel("Density") host.set_xlabel("Distance") host.axis["right"].set_visible(True) par1.axis["right"].set_visible(True) par1.set_ylabel("Temperature") par1.axis["right"].major_ticklabels.set_visible(True) par1.axis["right"].label.set_visible(True) fig.add_axes(host) host.set_xlim(1975, 2010) host.set_ylim(0, 100) host.set_xlabel("Years Intervals") host.set_ylabel("Income")
#append axes ax_cof.parasites.append(ax_temp) ax_cof.parasites.append(ax_load) ax_cof.parasites.append(ax_cp) ax_cof.parasites.append(ax_wear) #invisible right axis of ax_cof ax_cof.axis['right'].set_visible(False) ax_cof.axis['top'].set_visible(False) ax_temp.axis['right'].set_visible(True) ax_temp.axis['right'].major_ticklabels.set_visible(True) ax_temp.axis['right'].label.set_visible(True) #set label for axis ax_cof.set_ylabel('cof') ax_cof.set_xlabel('Distance (m)') ax_temp.set_ylabel('Temperature') ax_load.set_ylabel('load') ax_cp.set_ylabel('CP') ax_wear.set_ylabel('Wear') load_axisline = ax_load.get_grid_helper().new_fixed_axis cp_axisline = ax_cp.get_grid_helper().new_fixed_axis wear_axisline = ax_wear.get_grid_helper().new_fixed_axis ax_load.axis['right2'] = load_axisline(loc='right', axes=ax_load, offset=(40, 0)) ax_cp.axis['right3'] = cp_axisline(loc='right', axes=ax_cp, offset=(80, 0)) ax_wear.axis['right4'] = wear_axisline(loc='right', axes=ax_wear,
# host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) #用[left, bottom, weight, height]的方式定义axes,0 <= l,b,w,h <= 1 axis_cpu = ParasiteAxes(host, sharex=host) axis_response = ParasiteAxes(host, sharex=host) axis_container = ParasiteAxes(host, sharex=host) axis_throughput = ParasiteAxes(host, sharex=host) host.parasites.append(axis_cpu) host.parasites.append(axis_response) host.parasites.append(axis_container) host.parasites.append(axis_throughput) #关闭主图的左右上轴线,打开辅轴的右边线 host.axis['left'].set_visible(False) host.axis['right'].set_visible(False) host.axis['top'].set_visible(False) #设置各个轴线标签 host.set_xlabel('Time (20s)') axis_cpu.set_ylabel('CPU Utilization') axis_response.set_ylabel('Response Time') axis_container.set_ylabel('Number of Instances') axis_throughput.set_ylabel('Throughput') #添加辅轴 cpu_axisline = axis_cpu._grid_helper.new_fixed_axis response_axisline = axis_response._grid_helper.new_fixed_axis container_axisline = axis_container._grid_helper.new_fixed_axis throughput_axisline = axis_throughput._grid_helper.new_fixed_axis axis_cpu.axis['right2'] = cpu_axisline(loc='right', axes=axis_cpu, offset=(0, 0)) axis_response.axis['right3'] = response_axisline(loc='right', axes=axis_response, offset=(40, 0))
def plot_vel_acc_rdis(vel, acc, rdis): title_font_dict = { 'family': 'Times New Roman', 'color': 'black', 'weight': 'normal', 'fontsize': 15 } axis_font_dict = { 'family': 'Times New Roman', 'color': 'black', 'weight': 'normal', 'fontsize': 30 } legend_font = { 'family': 'Times New Roman', 'weight': 'normal', 'size': 10, } vel_x = np.array(vel)[:, 0] / 10. vel_y = np.array(vel)[:, 1] acc_x = np.array(acc)[:, 0] / 10. acc_y = np.array(acc)[:, 1] rdis_x = np.array(rdis)[:, 0] / 10. rdis_y = np.array(rdis)[:, 1] fig = plt.figure(figsize=(10, 3.5), dpi=200) vel_axes = HostAxes(fig, [0.05, 0.1, 0.8, 0.8]) acc_axes = ParasiteAxes(vel_axes, sharex=vel_axes) rdis_axes = ParasiteAxes(vel_axes, sharex=vel_axes) vel_axes.parasites.append(acc_axes) vel_axes.parasites.append(rdis_axes) vel_axes.set_ylabel('Velocity (m/s)') vel_axes.set_xlabel('Time (s)') vel_axes.axis['right'].set_visible(False) acc_axes.axis['right'].set_visible(True) acc_axes.set_ylabel('Acceleration (m/s^2)') acc_axes.axis['right'].major_ticklabels.set_visible(True) acc_axes.axis['right'].label.set_visible(True) rdis_axes.set_ylabel('Relative distance (m)') offset = (60, 0) new_axisline = rdis_axes._grid_helper.new_fixed_axis rdis_axes.axis['right2'] = new_axisline(loc='right', axes=rdis_axes, offset=offset) fig.add_axes(vel_axes) p1, = vel_axes.plot(vel_x, vel_y, label="Vel", linewidth=2) p2, = acc_axes.plot(acc_x, acc_y, label="Acc", linewidth=2) p3, = rdis_axes.plot(rdis_x, rdis_y, label="RD", linewidth=2) vel_axes.legend(prop=legend_font) vel_axes.set_ylim(0, np.max(vel_y) + 1) vel_axes.set_xlim(0, np.max(vel_x)) rdis_axes.set_ylim(0, np.max(rdis_y) + 1) # plt.plot(vel_x, vel_y, color="green", label="Velocity", linewidth=2) # plt.ylabel('Velocity (m/s)', fontdict=axis_font_dict) # # plt.plot(acc_x, acc_y, color="orange", label="Acceleration", linewidth=2, secondary_y=True) # plt.ylabel('Acceleration (m/s^2)', fontdict=axis_font_dict) # plt.title('Kinematic information of host vehicle', fontdict=title_font_dict) # plt.xlabel('Time (s)', fontdict=axis_font_dict) # plt.ylabel('Likelihood', fontdict=axis_font_dict) # plt.tick_params(labelsize=20) plt.legend(prop=legend_font) # plt.ylim(0., 1.) # left, right = plt.xlim() # plt.xlim(0., right) plt.show() pass
#host.set_xlabel("# Filter size") host.axis["right"].set_visible(False) #plt.xticks([]) host.axis["top"].major_ticks.set_color('white') par1.axis["right"].set_visible(True) #par1.set_ylabel("Runtime") par1.axis["right"].major_ticklabels.set_visible(True) par1.axis["right"].label.set_visible(True) #par2.set_ylabel("Velocity") #offset = (60, 0) #new_axisline = par2.get_grid_helper().new_fixed_axis #par2.axis["right2"] = new_axisline(loc="right", axes=par2, offset=offset) fig.add_axes(host) host.set_xlim(0, 80) host.set_ylim(0.6, 0.8) host.set_xlabel("Number of filter size") host.set_ylabel("F1 score") par1.set_ylabel("Runtime") #p1, = host.plot([1, 5, 10, 15, 20, 25, 30, 35, 40,50,60,70,80,90,100], [0.66, 0.736, 0.764, 0.778, 0.786, 0.814, 0.786, 0.783, 0.787,0.791,0.789,0.785,0.790,0.792,0.786], 'o-',label="F1 score", linewidth=2.2, color="#59604C") #p2, = par1.plot([1, 5, 10, 15, 20, 25, 30, 35, 40,50,60,70,80,90,100], [3.52, 3.68, 3.71, 3.72, 3.84, 3.85, 3.87, 3.96, 3.86,3.876,3.83,3.83,3.89,3.97,4.04], '*--', label="Runtime", linewidth=2.2, color="#A35638") p1, = host.plot([1, 5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80], [ 0.691, 0.693, 0.710, 0.715, 0.721, 0.727, 0.703, 0.704, 0.705, 0.695, 0.706, 0.687, 0.679 ], 'o-', label="F1 score", linewidth=2.2, color="#59604C") p2, = par1.plot([1, 5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80], [ 5.13, 5.28, 5.33, 5.76, 5.99, 6.01, 6.54, 6.83, 6.99, 7.38, 7.85, 7.97, 8.42
the :doc:`/gallery/ticks_and_spines/multiple_yaxis_with_spines` example. """ from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes import matplotlib.pyplot as plt fig = plt.figure(1) host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) par1 = ParasiteAxes(host, sharex=host) par2 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.parasites.append(par2) host.set_ylabel("Density") host.set_xlabel("Distance") host.axis["right"].set_visible(False) par1.axis["right"].set_visible(True) par1.set_ylabel("Temperature") par1.axis["right"].major_ticklabels.set_visible(True) par1.axis["right"].label.set_visible(True) par2.set_ylabel("Velocity") offset = (60, 0) new_axisline = par2._grid_helper.new_fixed_axis par2.axis["right2"] = new_axisline(loc="right", axes=par2, offset=offset) fig.add_axes(host)
nm = math.ceil(max(effect)) nmin = math.floor(min(effect)) <<<<<<< HEAD fig = plt.figure(1) ======= fig = plt.figure(figsize=(9, 5)) >>>>>>> f5b55f5735c94ec9573668b5d56b78a8eef2e8f7 host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) par1 = ParasiteAxes(host, sharex=host) par2 = ParasiteAxes(host, sharex=host) host.parasites.append(par1) host.parasites.append(par2) <<<<<<< HEAD host.set_ylabel('扬程') host.set_xlabel('流量') host.axis['right'].set_visible(False) par1.axis['right'].set_visible(True) par1.set_ylabel('功率') ======= host.set_ylabel('效率(E)(%)', color="blue") host.set_xlabel('流量(Q)(m3/h)') host.axis['right'].set_visible(False) par1.axis['right'].set_visible(True) par1.set_ylabel('功率(P)(kW)', color="red") >>>>>>> f5b55f5735c94ec9573668b5d56b78a8eef2e8f7 par1.axis['right'].major_ticklabels.set_visible(True) par1.axis['right'].label.set_visible(True) <<<<<<< HEAD
def line_graph(self): ords = [i.text() for i in self.temp_variables_w if i.checkState()] if not len(ords): return absc = self.ui.lineComboW.currentText() def values(s): return self.simulation.state_fct[self.temporal_variables[s][0]] def label(s): if len(self.temporal_variables[s])>1: return s + " (" + self.temporal_variables[s][1] + ")" return s def dimension(s): if "energy" in s: return "Energy (J)" elif "Temperature" in s: return "Temperature (K)" else: return label(s) dimensions = list(set(dimension(s) for s in ords)) axis = dict() visu.plt.ioff() fig = visu.plt.figure() gr_opts = dict() for o in self._1d_options: gr_opts[o] = self._1d_options[o].checkState() host = HostAxes(fig, [0.15, 0.1, 0.75-(0.04*len(dimensions)), 0.8]) host.set_xlabel(label(absc)) host.set_ylabel(dimensions[0]) if gr_opts["log x"]: host.semilogx() if gr_opts["log y"]: host.semilogy() if gr_opts["grid"]: host.grid(True, which="minor", linestyle="--") host.grid(True, which="major") if len(dimensions)>1: host.axis["right"].set_visible(False) axis[dimensions[0]] = host for i, dim in enumerate(dimensions[1:]): par = ParasiteAxes(host, sharex=host) host.parasites.append(par) par.axis["right"] = par.get_grid_helper().new_fixed_axis(loc="right", axes=par, offset=(55*i, 0)) par.axis["right"].set_visible(True) par.set_ylabel(dim) par.axis["right"].major_ticklabels.set_visible(True) par.axis["right"].label.set_visible(True) axis[dim] = par if gr_opts["log x"]: par.semilogx() if gr_opts["log y"]: par.semilogy() for i in ords: axis[dimension(i)].plot(values(absc), values(i), label=label(i)) fig.add_axes(host) host.legend() visu.plt.show()
def draw_city(city): select = "SELECT so2, no2, co, time_point FROM day_data WHERE cityname='" + city + "'" + "AND time_point Between '2019-02-01' AND '2019-03-01'" cur.execute(select) all_data = cur.fetchall() # 防止查不到数据 if len(all_data) != 0: # 准备数据 so2 = [] no2 = [] co = [] time_point = [] # 准备标签 for data in all_data: so2.append(int(data[0])) no2.append(int(data[1])) co.append(float(data[2])) time_point.append(str(data[3])) # 准备字体 my_font = fm.FontProperties( fname="F:\\Project\\Environment\\web\\php\\font\\simsun.ttc") fontcn = {'family': 'SimSun', 'size': 10} # 绘制气温变化 fig = plt.figure(figsize=(40, 8), dpi=80) ax_so2 = HostAxes( fig, [0, 0, 0.9, 0.9 ]) # 用[left, bottom, weight, height]的方式定义axes,0 <= l,b,w,h <= 1 # parasite addtional axes, share x ax_no2 = ParasiteAxes(ax_so2, sharex=ax_so2) ax_co = ParasiteAxes(ax_so2, sharex=ax_so2) ax_so2.parasites.append(ax_no2) ax_so2.parasites.append(ax_co) # invisible right axis of ax_so2 ax_so2.axis['right'].set_visible(False) ax_so2.axis['top'].set_visible(False) ax_no2.axis['right'].set_visible(True) ax_no2.axis['right'].major_ticklabels.set_visible(True) ax_no2.axis['right'].label.set_visible(True) # set label for axis ax_so2.set_ylabel("so2 μg/m3", fontdict=fontcn) ax_so2.set_xlabel("时间", fontdict=fontcn) ax_no2.set_ylabel("no2 μg/m3", fontdict=fontcn) ax_co.set_ylabel("CO mg/m3", fontdict=fontcn) co_axisline = ax_co.get_grid_helper().new_fixed_axis ax_co.axis['right2'] = co_axisline(loc='right', axes=ax_co, offset=(40, 0)) fig.add_axes(ax_so2) x1 = time_point x2 = [i for i in range(0, len(x1))] y1 = so2 y2 = no2 y3 = co ax_so2.plot(x1, y1, label='so2', color='#009966') ax_no2.plot(x2, y2, label='no2', color='#FFDE33') ax_co.plot(x2, y3, label='co', color='#FF9933') ax_so2.legend() ax_no2.axis['right'].label.set_color('#FFDE33') ax_co.axis['right2'].label.set_color('#FF9933') ax_no2.axis['right'].major_ticks.set_color('#FFDE33') ax_co.axis['right2'].major_ticks.set_color('#FF9933') ax_no2.axis['right'].major_ticklabels.set_color('#FFDE33') ax_co.axis['right2'].major_ticklabels.set_color('#FF9933') ax_no2.axis['right'].line.set_color('#FFDE33') ax_co.axis['right2'].line.set_color('#FF9933') # 添加图形标题 plt.title(city + '日二氧化硫、二氧化氮、一氧化碳浓度变化情况', loc='center', fontproperties=my_font) # 保存图片 plt.savefig("./../../article/image/pollution/" + city + ".png", bbox_inches='tight') # 显示图形 # show ann img plt.cla() # clear fig to show ann img saved_img = plt.imread("./../../article/image/pollution/" + city + ".png") # keep the origin image size dpi = 80.0 height, width, depth = saved_img.shape plt.figure(figsize=(width / dpi, height / dpi)) plt.axis('off') plt.imshow(saved_img) plt.show()