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 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)
def display(dates, cases, covid, vaccine): 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("Cases") host.axis["right"].set_visible(False) par1.axis["right"].set_visible(True) par1.set_ylabel("COVID - 19 Clicks") par1.axis["right"].major_ticklabels.set_visible(True) par1.axis["right"].label.set_visible(True) par2.set_ylabel("Vaccine Clicks") 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) plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d')) plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=7)) p1, = host.plot(dates, cases, label="USA 7 Day Average") p2, = par1.plot(dates, covid, label="COVID Keyword Clicks") p3, = par2.plot(dates, vaccine, label="Vaccine Keyword Clicks") host.legend() 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.setp(host.axis["bottom"].major_ticklabels, rotation=45, ha="right") plt.gcf().canvas.set_window_title('COVID-19 Cases vs. BI Search Queries') 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()
def plot_multi_y(line_series_list, line_label_list, scatter_series_list, scatter_label_list): """ 第一条line做主轴,散点与主轴共用Y轴 :param line_series_list: :param line_label_list: :param scatter_series_list: :param scatter_label_list: :return: """ color_list = ['red', 'green', 'blue', 'yellow', 'pink', 'black', 'orange'] fig = plt.figure(1) host_axes = HostAxes(fig, [0.1, 0.1, 0.6, 0.8]) fig.add_axes(host_axes) host_axes.set_ylabel(line_label_list[0]) host_axes.axis['right'].set_visible(False) host_axes.set_ylim( min(line_series_list[0][1]) * 0.9, max(line_series_list[0][1]) * 1.1) host_axes.plot(line_series_list[0][0], line_series_list[0][1], label=line_label_list[0], color=color_list[0]) label_offset = 0 # line_axes = [] for i in range(len(line_series_list) - 1): axes = ParasiteAxes(host_axes, sharex=host_axes) axes.set_ylabel(line_label_list[i + 1]) axis_line = axes.get_grid_helper().new_fixed_axis axes.axis['right' + str(label_offset)] = axis_line( loc='right', axes=axes, offset=(label_offset, 0)) axes.axis['right' + str(label_offset)].label.set_color(color_list[i + 1]) axes.axis['right' + str(label_offset)].major_ticks.set_color( color_list[i + 1]) axes.axis['right' + str(label_offset)].major_ticklabels.set_color( color_list[i + 1]) axes.axis['right' + str(label_offset)].line.set_color(color_list[i + 1]) label_offset += 40 axes.set_ylim( min(line_series_list[i + 1][1]) * 0.9, max(line_series_list[i + 1][1]) * 1.1) axes.plot(line_series_list[i + 1][0], line_series_list[i + 1][1], label=line_label_list[i + 1], color=color_list[i + 1]) # line_axes.append(axes) host_axes.parasites.append(axes) # scatter_axes = [] for i in range(len(scatter_series_list)): # 与主轴共用Y轴 # axes = ParasiteAxes(host_axes, sharex=host_axes) # axes.set_ylabel(scatter_label_list[i]) # axis_line = axes.get_grid_helper().new_fixed_axis # axes.axis['right' + str(label_offset)] = axis_line(loc='right', axes=axes, offset=(label_offset, 0)) color_item = color_list[len(line_label_list) + i + 1] # axes.axis['right' + str(label_offset)].label.set_color(color_item) # axes.axis['right' + str(label_offset)].major_ticks.set_color(color_item) # axes.axis['right' + str(label_offset)].major_ticklabels.set_color(color_item) # axes.axis['right' + str(label_offset)].line.set_color(color_item) # label_offset += 40 # axes.set_ylim(min(scatter_series_list[i][1]), max(scatter_series_list[i][1])) host_axes.scatter(scatter_series_list[i][0], scatter_series_list[i][1], label=scatter_label_list[i], color=color_item) # scatter_axes.append(axes) # host_axes.parasites.append(axes) host_axes.legend() plt.show()
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") par1.set_ylabel("Pass Rate") p1, = host.plot(names, values1, label="Income") p2, = par1.plot(names, values2, label="Pass Rate") par1.set_ylim(56, 65) host.legend() host.axis["left"].label.set_color(p1.get_color()) par1.axis["right"].label.set_color(p2.get_color()) plt.show()
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) host.set_xlim(0, 2) host.set_ylim(0, 2) host.set_xlabel("Distance") host.set_ylabel("Density") par1.set_ylabel("Temperature") p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density") p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature") p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity") par1.set_ylim(0, 4) par2.set_ylim(1, 65) host.legend() 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()
#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)) #add host axes to fig fig.add_axes(ax_data) #plot fig#################################### #curve_data, = ax_data.plot(times, data, label="data", color='green') width = 0.9 curve_data = ax_data.bar(range(len(times)), list(map(float, data)), width, label="data", alpha=.5, color='g') curve_cpu, = ax_cpu.plot(times, cpu, label="cpu", color='red') curve_mem, = ax_mem.plot(times, mem, label="memory", color='blue') #curve_bitrates, = ax_bitrates.plot(times, mem, label="bitrates", color='yellow') #curve_wear, = ax_wear.plot([0, 1, 2], [25, 18, 9], label="Wear", color='blue') ax_data.legend() #axes name and color############################ #ax_data.axis['left'].label.set_color(ax_data.get_color()) ax_cpu.axis['right'].label.set_color('red') ax_mem.axis['right2'].label.set_color('blue') #ax_bitrates.axis['right3'].label.set_color('yellow') #ax_wear.axis['right4'].label.set_color('blue') ax_cpu.axis['right'].major_ticks.set_color('red') ax_mem.axis['right2'].major_ticks.set_color('blue') #ax_bitrates.axis['right3'].major_ticks.set_color('yellow')
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") par1.set_ylabel("Success Rate (%)") p1, = host.plot(names, gdp_values, label="GDP") p2, = par1.plot(names, edu_values, label="Success Rate") par1.set_ylim(0, 100) host.legend() host.axis["left"].label.set_color(p1.get_color()) par1.axis["right"].label.set_color(p2.get_color()) plt.show()
host.plot(values[i], zcs[i], label="PAD - all scans", linewidth=0.8, color=palettes["PAD"]) elif i == 1: host.plot(values[i], zcs[i], linestyle='--', label="PAD - center scan", linewidth=0.8, color=palettes["PAD"]) elif i == 2: p1, = par1.plot(values[i], zcs[i], label="number of returns", linewidth=0.8, color=palettes["Point"]) #host.plot(values[i], zcs[i], label= "number of returns") #elif i == 2 : #elif i < len(values) - 1 : #p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0]) # p1, = par1.plot(values[i], zcs[i], label= "RDI") elif i == 3: host.plot(values[i], zcs[i], label="L-Architect PAD", linewidth=0.8, color=palettes["L-Architect"])
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()
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()
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()
for i in range(0, len(values)) : #ratio = maxOfMax / maxPad[i] #for j in range(0, len(values[i])) : # values[i][j] = values[i][j] * ratio if i == 0 : host.plot(values[i], zcs[i], label= "PAD") #elif i == 1 : # host.plot(values[i], zcs[i], label= "L-Architect PAD") #host.plot(values[i], zcs[i], label= "number of returns") #elif i == 2 : #elif i < len(values) - 1 : #p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0]) # p1, = par1.plot(values[i], zcs[i], label= "RDI") else : p1, = par1.plot(values[i], zcs[i], label= "number of returns") #par1.set_ylim(0, 4) #par2.set_ylim(1, 65) host.legend() par1.axis["top"].label.set_color(p1.get_color()) #par2.axis["top2"].label.set_color(p2.get_color()) #host.axis["bottom"].label.set_color(p1.get_color()) #title = filenames[1] host.set_ylim(minZ, maxZ)
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) for i in range(0, len(values)) : ratio = maxOfMax / maxPad[i] #for j in range(0, len(values[i])) : # values[i][j] = values[i][j] * ratio if i < len(values) - 2 : host.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0]) elif i < len(values) - 1 : #p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0]) p1, = par1.plot(values[i], zcs[i], label= "RDI") else : p2, = par2.plot(values[i], zcs[i], label= "Nb Points") #par1.set_ylim(0, 4) #par2.set_ylim(1, 65) host.legend() par1.axis["top"].label.set_color(p1.get_color()) par2.axis["top2"].label.set_color(p2.get_color()) #host.axis["bottom"].label.set_color(p1.get_color()) title = filenames[1]
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='--') host.axis["left"].label.set_color(p1.get_color()) par1.axis["right"].label.set_color(p2.get_color()) plt.show() path_fig1 = join(dirname(__file__), 'result//' + 'approximation2_2.pdf') fig1.savefig(path_fig1, dpi=300, transparent=True, bbox_inches='tight')
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()
fig.add_axes(host) for i in range(0, len(values)): #for j in range(0, len(values[i])) : # values[i][j] = values[i][j] * ratio if i < len(values) - 2: h, = host.plot(values[i], zcs[i], label="PAD", linewidth=0.8, color=palettes["PAD"]) elif i < len(values) - 1: #p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0]) p1, = par1.plot(values[i], zcs[i], label="Nb of returns", linewidth=0.8, color=palettes["Point"]) else: p2, = par2.plot(values[i] * 100, zcs[i], label="Occlusion", linewidth=0.8, color=palettes["Occlusion"]) #par1.set_ylim(0, 4) #par2.set_ylim(1, 65) host.legend() par1.axis["top"].major_ticklabels.set_color(p1.get_color())
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() 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)
offset=(0, 0)) axis_response.axis['right3'] = response_axisline(loc='right', axes=axis_response, offset=(40, 0)) axis_container.axis['right4'] = container_axisline(loc='right', axes=axis_container, offset=(80, 0)) axis_throughput.axis['right5'] = throughput_axisline(loc='right', axes=axis_throughput, offset=(120, 0)) #将主轴加至原图 fig.add_axes(host) #添加数据 plot_cpu, = axis_cpu.plot(anscpuList[botton], label='utilization', color='#3366FF') plot_response, = axis_response.plot([250 for i in range(1000)], color='black', linestyle='--') # SLA violation定为250ms plot_response, = axis_response.plot(ansresponseList[botton], label='response time', color='#CC3333') plot_container, = axis_container.plot(anssupplypodList[botton], label='container', color='green') plot_throughput, = axis_throughput.plot(ansthroughputList[botton], label='throughput', color='orange') #通过范围限制调整数据在图中的位置(一个是调整上界,可以抬升;一个是调整占比比例,可以让其出现在想出现的区域) axis_cpu.set_ylim(0, 100)
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) host.set_xlim(0, 2) host.set_ylim(0, 2) host.set_xlabel("Distance") host.set_ylabel("Density") par1.set_ylabel("Temperature") p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density") p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature") p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity") par1.set_ylim(0, 4) par2.set_ylim(1, 65) host.legend() 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.draw() plt.show()
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 ], '*--', label="Runtime(second)", linewidth=2.2, color="#A35638") #p1, = host.plot([1, 5, 10, 15, 20, 25, 30, 40,50,60,70,80], [0.589, 0.604, 0.614, 0.647, 0.634, 0.621, 0.612, 0.608, 0.531,0.546,0.448,0.486], 'o-',label="F1 score", linewidth=2.2, color="#59604C") #p2, = par1.plot([1, 5, 10, 15, 20, 25, 30, 40,50,60,70,80], [19.39, 17.82, 18.902, 18.904, 19.04, 19.509, 18.68, 19.53, 19.795,20.596,21.228,21.562], '*--', label="Runtime(second)", linewidth=2.2, color="#A35638") #p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity") par1.set_ylim(0, 30) #par1.set_ylim(0, 1) #par2.set_ylim(3.5, 4.2) host.legend(loc="upper left", fontsize=12) host.axis["left"].label.set_color(p1.get_color()) host.axis["left"].label.set_fontsize(14) host.axis["left"].label.fontweight = "bold" host.axis["bottom"].label.set_fontsize(14) par1.axis["right"].label.set_color(p2.get_color())
#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) for i in range(0, len(values)): ratio = maxOfMax / maxPad[i] #for j in range(0, len(values[i])) : # values[i][j] = values[i][j] * ratio if i == 0: host.plot(values[i], zcs[i], label="PAD", linewidth=0.8, color="black") elif i == 1: p1, = par1.plot(values[i], zcs[i], label="number of returns", linewidth=0.8) #host.plot(values[i], zcs[i], label= "number of returns") #elif i == 2 : #elif i < len(values) - 1 : #p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0]) # p1, = par1.plot(values[i], zcs[i], label= "RDI") else: host.plot(values[i], zcs[i], label="L-Architect PAD", linewidth=0.8) #par1.set_ylim(0, 4) #par2.set_ylim(1, 65) host.legend()
int(outside_at_end[order])) + 'C' if same_vehicle_flag: # lines for fig2 power = [ torque[order][i] * speed[order][i] / 9550 for i in range(0, len(torque[order])) ] line_power = ax_power.plot( time[order], power, linestyle=line_style[speed_at_start[order] > pre_speed_thre], label=vehicle_temperautre, color=line_color[temperature_target[order]]) line_gear = ax_gear.plot( time[order], gear[order], linestyle=line_style[speed_at_start[order] > pre_speed_thre], color=line_color[temperature_target[order]]) if max(gear[order]) > 10: # if it is gear ratio, plot torque*gear torque_multi_gear = [ torque[order][i] * gear[order][i] for i in range(0, len(torque[order])) ] 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[ speed_at_start[order] > pre_speed_thre], color=line_color[temperature_target[order]])
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, offset=(120, 0)) fig.add_axes(ax_cof) ''' #set limit of x, y ax_cof.set_xlim(0,2) ax_cof.set_ylim(0,3) ''' curve_cof, = ax_cof.plot([0, 1, 2], [0, 1, 2], label="CoF", color='black') curve_temp, = ax_temp.plot([0, 1, 2], [0, 3, 2], label="Temp", color='red') curve_load, = ax_load.plot([0, 1, 2], [1, 2, 3], label="Load", color='green') curve_cp, = ax_cp.plot([0, 1, 2], [0, 40, 25], label="CP", color='pink') curve_wear, = ax_wear.plot([0, 1, 2], [25, 18, 9], label="Wear", color='blue') ax_temp.set_ylim(0, 4) ax_load.set_ylim(0, 4) ax_cp.set_ylim(0, 50) ax_wear.set_ylim(0, 30) ax_cof.legend() #轴名称,刻度值的颜色 #ax_cof.axis['left'].label.set_color(ax_cof.get_color()) ax_temp.axis['right'].label.set_color('red') ax_load.axis['right2'].label.set_color('green')
par2.axis["top2"] = new_axisline(loc="top", axes=par2, offset=offset) par2.axis["top2"].set_visible(True) par2.axis["top2"].major_ticklabels.set_visible(True) par2.set_xlim(0,100) fig.add_axes(host) for i in range(0, len(values)) : #for j in range(0, len(values[i])) : # values[i][j] = values[i][j] * ratio if i < len(values) - 2 : h, = host.plot(values[i], zcs[i], label= "PAD", linewidth=0.8) elif i < len(values) - 1 : #p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0]) p1, = par1.plot(values[i], zcs[i], label= "Nb of returns", linewidth=0.8) else : p2, = par2.plot(values[i]*100, zcs[i], label= "Occlusion)", linewidth=0.8) #par1.set_ylim(0, 4) #par2.set_ylim(1, 65) host.legend() par1.axis["top"].major_ticklabels.set_color(p1.get_color()) par2.axis["top2"].major_ticklabels.set_color(p2.get_color()) host.axis["bottom"].major_ticklabels.set_color(h.get_color()) #host.axis["bottom"].label.set_color(p1.get_color())
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
par2.axis['right2'] = new_axisline(loc='right', axes=par2, offset=offset) fig.add_axes(host) host.set_xlim(0, fm + 1) <<<<<<< HEAD host.set_ylim(0, hm + 5) host.set_xlabel('流量') host.set_ylabel('功率') host.set_ylabel('效率') x = np.linspace(0, fm, 500) y = headparameters[0] * x ** 2 + headparameters[1] * x + headparameters[2] p1, = host.plot(x, y, label="HQ拟合曲线", color="black") host.scatter(flow1, head1, label="HQ离散数据") x1 = np.linspace(0, fm, 500) y1 = powerparameters[0] * x ** 2 + powerparameters[1] * x + powerparameters[2] p2, = par1.plot(x, y1, label="PQ拟合曲线", color="red") par1.scatter(flow1, power1, label="PQ离散数据") x2 = np.linspace(0, fm, 500) y2 = effectparameters[0] * x ** 2 + effectparameters[1] * x + effectparameters[2] p3, = par2.plot(x, y2, label="EQ拟合曲线", color="blue") par2.scatter(flow1, effect, label="EQ离散数据") par1.set_ylim(0, pm * 2) par2.set_ylim(0, nm + 5) host.legend() 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 ======= # plt.xticks(range(0, fm + 1, 1)) host.set_ylim(0, hm + 5)
new_axisline = par2._grid_helper.new_fixed_axis par2.axis["top"] = new_axisline(loc="top", axes=par2) fig.add_axes(host) for i in range(0, len(values)): ratio = maxOfMax / maxPad[i] #for j in range(0, len(values[i])) : # values[i][j] = values[i][j] * ratio if i < len(values) - 1: host.plot(values[i], zcs[i], label=os.path.splitext(os.path.basename(filenames[i + 1]))[0]) else: par2.plot(values[i], zcs[i], label=os.path.splitext(os.path.basename(filenames[i + 1]))[0]) #par1.set_ylim(0, 4) #par2.set_ylim(1, 65) host.legend() #host.axis["bottom"].label.set_color(p1.get_color()) #par1.axis["top"].label.set_color(p1.get_color()) #par2.axis["top"].label.set_color(p2.get_color()) title = filenames[1] plt.show()
h, = host.plot(values[i], zcs[i], label="PAD - all scans", linewidth=0.8, color=palettes["PAD"]) elif i == 1: host.plot(values[i], zcs[i], linestyle='--', label="PAD - center scan", linewidth=0.8, color=palettes["PAD"]) elif i == 2: p1, = par1.plot(values[i], zcs[i], label="number of returns", linewidth=0.8, color=palettes["Point"]) #p1, = par1.plot(values[i], zcs[i], label= "Nb of returns", linewidth=0.8, color=palettes["Point"]) else: p2, = par2.plot(values[i] * 100, zcs[i], label="Pulses attenuation", linewidth=0.8, color=palettes["Occlusion"]) #par1.set_ylim(0, 4) #par2.set_ylim(1, 65) host.legend()