# only plot them using a loop. axes = plt.axes() for k in range(1440000): if isEastBorder(i, nx = nx): axes.plot([x+0.5, x+0.5],[y+0.5, y-0.5],'-',color = color,linewidth = linewidth) border[0] = 1 if isSouthBorder(i, nx = nx): axes.plot([x-0.5, x+0.5],[y...
self.line,=ax.plot([],[],'k-')self.x=np.linspace(0,1,200)self.ax=ax# 设置图形参数self.ax.set_xlim(0,1)self.ax.set_ylim(0,10)self.ax.grid(True)# 这条竖直线代表了理论值,图中的分布应该趋近于这个值self.ax.axvline(prob,linestyle='--',color=...
a=np.random.rand(100000)b=np.random.rand(100000)tic=time.time()foriinrange(100000):c+=a[i]*b[i]toc=time.time()print(c)print("for loop:"+str(1000*(toc-tic))+"ms")c=0tic=time.time()c=np.dot(a,b)toc=time.time()print(c)print("Vectorized:"+str(1000*(toc-tic))+"ms")...
python for-loop plot data-visualization 我正在开发一个for循环来为类分配数字,我在这方面很成功,但是我发现很难根据使用相同for循环的类的数量同时插入不同的颜色,但是我得到了一个错误:'list'对象不能解释为整数 代码如下: n_class = 5 colors = ['r', 'g', 'b', 'y','k', 'y'] # plotting ...
prompt ="the answer to the ultimate question of life, the universe, and everything is"tokens = [128000] + tokenizer.encode (prompt)print (tokens)tokens = torch.tensor (tokens)prompt_split_as_tokens = [tokenizer.decode ([token.ite...
forecast_plot:是否打印预测图(可选,默认为False) print_ind:是否打印每个周期的预计回报和是否采样均方误差(可选,默认为False) 代码 代码语言:javascript 代码运行次数:0 运行 AI代码解释 defcycle_analysis(data,split_date,cycle,mode='additive',forecast_plot=False,print_ind=False):training=data[:split_date...
f.write(content)# Start timestarttime = time.time()print(starttime)# Create a new folderfolder_path ="E:/Python/Ordinary_world_1/"#文件保存目录,可修改!!!os.makedirs(folder_path, exist_ok=True)# Loop through categories and chapterscategories = ['yi','er','san']forcategoryincategories:...
def buildmebarchart(i=int): plt.legend(df1.columns) p = plt.plot(df1[:i].index, df1[:i].values) #note it only returns the dataset, up to the point i for i in range(0,4): p[i].set_color(color[i]) #set the colour of each curveimport matplotlib.animation as anianimator =...
In [1]: data = pd.Series(range(1000000)) In [2]: roll = data.rolling(10) In [3]: def f(x): ...: return np.sum(x) + 5 # 第一次运行Numba时,编译时间会影响性能 In [4]: %timeit -r 1 -n 1 roll.apply(f, engine='numba', raw=True) 1.23 s ± 0 ns per loop (mean ...
用matplotlib绘图并将图片贴到excel上 importmatplotlib.pyplotaspltfig=plt.figure(figsize=(4,4))plt....