如何从头开始创建实时渲染窗口？(How do I create a real-time rendering window from scratch?)
我已经在自己研究3D图形了一段时间了，我希望能够更好地理解一切是如何工作的。 我想做的是在不使用DirectX或OpenGL的情况下创建一个简单的游戏。 我理解我相信的大部分数学，但我遇到的问题是我不知道如何控制窗口中显示的像素。
如何在窗口中指定每个像素的颜色？
我知道我可能会遇到缓冲区和图像剪切以及可能是非常糟糕的效率问题，但我想创建自己的程序，以便从最低级别的高级语言中看到渲染过程的工作原理。 我真的不知道从哪里开始。 我已经想出了如何输出BMP，但我希望有一个正在运行的程序每秒吐出20多帧。 我该如何做到这一点？
I've been studying 3D graphics on my own for a while now and I want to get a greater understanding of just how everything works. What I would like to do is to create a simple game without using DirectX or OpenGL. I understand most of the math I believe, but the problem I am running up against is I do not know how to get control of the pixels being displayed in a window.
How do I specify what color I want each pixel in my window to be?
I understand I will probably run into issues with buffers and image shearing and probably terrible efficiency problems, but I want to create my own program so that I could see from the very lowest level, of the high level language, how the rendering process works. I really have no idea where to start though. I've figured out how to output BMPs, but I would like to have a running program spitting out 20+ frames per second. How do I accomplish this?
原文：https://stackoverflow.com/questions/8977728
更新时间：2023-12-21 22:12
最满意答案
您可以使用：
#convert if dtype not datetime df.Date = pd.to_datetime(df.Date) #create index from column Date df = df.set_index('Date') #convert all times to timedelta df.columns = pd.to_timedelta(df.columns + ':00', unit='h') #reshape - columns to second level of index df = df.stack() #Multindex to index df.index = df.index.get_level_values(0) + df.index.get_level_values(1) #create column from index df = df.reset_index() #get new column names df.columns = ['date','val'] print (df) date val 0 2004-05-01 00:15:00 3.74618 1 2004-05-01 00:30:00 3.58507 2 2004-05-01 00:45:00 3.30998 3 2004-05-01 23:15:00 2.97236 4 2004-05-01 23:30:00 2.92008 5 2004-05-01 23:45:00 2.80101 6 2004-05-02 00:00:00 2.60670 7 2004-05-02 00:15:00 3.09098 8 2004-05-02 00:30:00 3.84625 9 2004-05-02 00:45:00 3.54672 10 2004-05-02 23:15:00 2.83725 11 2004-05-02 23:30:00 2.93876 12 2004-05-02 23:45:00 2.82762 13 2004-05-03 00:00:00 2.62550You can use:
#convert if dtype not datetime df.Date = pd.to_datetime(df.Date) #create index from column Date df = df.set_index('Date') #convert all times to timedelta df.columns = pd.to_timedelta(df.columns + ':00', unit='h') #reshape - columns to second level of index df = df.stack() #Multindex to index df.index = df.index.get_level_values(0) + df.index.get_level_values(1) #create column from index df = df.reset_index() #get new column names df.columns = ['date','val'] print (df) date val 0 2004-05-01 00:15:00 3.74618 1 2004-05-01 00:30:00 3.58507 2 2004-05-01 00:45:00 3.30998 3 2004-05-01 23:15:00 2.97236 4 2004-05-01 23:30:00 2.92008 5 2004-05-01 23:45:00 2.80101 6 2004-05-02 00:00:00 2.60670 7 2004-05-02 00:15:00 3.09098 8 2004-05-02 00:30:00 3.84625 9 2004-05-02 00:45:00 3.54672 10 2004-05-02 23:15:00 2.83725 11 2004-05-02 23:30:00 2.93876 12 2004-05-02 23:45:00 2.82762 13 2004-05-03 00:00:00 2.62550
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