Matplotlib：如何將大量線段着色爲獨立梯度，高效

Question

Python。 matplotlib：如何高效地將大量線段着色爲獨立的梯度？
已經閱讀this和this等東西;他們都不是我們的答案！

我們有許多單獨的行希望繪製每個漸變顏色。

上面第一個鏈接中提到的解決方案，如果您有多個字符串的行不起作用。換句話說，改變顏色週期會影響情節中的所有內容，而不是唯一感興趣的方面。 這完全不符合我們的興趣。

第二個連接到matplotlib網站的鏈接使用了將每一行分割成許多的鏈接。這不是一個好方法，因爲對於大量的行數，比如10000甚至更多;即使你每行只選擇10段，結果也太大了！即使這樣，所產生的線條也不會平滑上色！如果將分割數作爲線段的函數以獲得更好的漸變效果，結果將會非常巨大！很難顯示，難以妥善保存爲文件。

Answer 1

一個（次要）加速將會添加一個單獨的行集合，而不是10000個單獨的行集合。

只要所有線共享相同的顏色映射表，您可以將它們分組爲單個線條集合，並且每個線條仍可以有獨立的漸變。

Matplotlib對於這類事情仍然很慢。它針對質量輸出進行了優化，而不是快速繪製時間。不過，你可以加速一些（〜3x）。 （？）

所以，我怎麼想你可能是一個例子做它現在：

import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib.collections import LineCollection 
# Make random number generation consistent between runs 
np.random.seed(5) 

def main(): 
    numlines, numpoints = 2, 3 
    lines = np.random.random((numlines, numpoints, 2)) 

    fig, ax = plt.subplots() 
    for line in lines: 
     # Add "num" additional segments to the line 
     segments, color_scalar = interp(line, num=20) 
     coll = LineCollection(segments) 
     coll.set_array(color_scalar) 
     ax.add_collection(coll) 
    plt.show() 

def interp(data, num=20): 
    """Add "num" additional points to "data" at evenly spaced intervals and 
    separate into individual segments.""" 
    x, y = data.T 
    dist = np.hypot(np.diff(x - x.min()), np.diff(y - y.min())).cumsum() 
    t = np.r_[0, dist]/dist.max() 

    ti = np.linspace(0, 1, num, endpoint=True) 
    xi = np.interp(ti, t, x) 
    yi = np.interp(ti, t, y) 

    # Insert the original vertices 
    indices = np.searchsorted(ti, t) 
    xi = np.insert(xi, indices, x) 
    yi = np.insert(yi, indices, y) 

    return reshuffle(xi, yi), ti 

def reshuffle(x, y): 
    """Reshape the line represented by "x" and "y" into an array of individual 
    segments.""" 
    points = np.vstack([x, y]).T.reshape(-1,1,2) 
    points = np.concatenate([points[:-1], points[1:]], axis=1) 
    return points 

if __name__ == '__main__': 
    main() 

相反，我會建議做的東西沿着這些路線（唯一的區別是在main功能）：

import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib.collections import LineCollection 
# Make random number generation consistent between runs 
np.random.seed(5) 

def main(): 
    numlines, numpoints = 2, 3 
    points = np.random.random((numlines, numpoints, 2)) 

    # Add "num" additional segments to each line 
    segments, color_scalar = zip(*[interp(item, num=20) for item in points]) 

    segments = np.vstack(segments) 
    color_scalar = np.hstack(color_scalar) 

    fig, ax = plt.subplots() 
    coll = LineCollection(segments) 
    coll.set_array(color_scalar) 
    ax.add_collection(coll) 

    plt.show() 

def interp(data, num=20): 
    """Add "num" additional points to "data" at evenly spaced intervals and 
    separate into individual segments.""" 
    x, y = data.T 
    dist = np.hypot(np.diff(x - x.min()), np.diff(y - y.min())).cumsum() 
    t = np.r_[0, dist]/dist.max() 

    ti = np.linspace(0, 1, num, endpoint=True) 
    xi = np.interp(ti, t, x) 
    yi = np.interp(ti, t, y) 

    # Insert the original vertices 
    indices = np.searchsorted(ti, t) 
    xi = np.insert(xi, indices, x) 
    yi = np.insert(yi, indices, y) 

    return reshuffle(xi, yi), ti 

def reshuffle(x, y): 
    """Reshape the line represented by "x" and "y" into an array of individual 
    segments.""" 
    points = np.vstack([x, y]).T.reshape(-1,1,2) 
    points = np.concatenate([points[:-1], points[1:]], axis=1) 
    return points 

if __name__ == '__main__': 
    main() 

兩個版本都產生相同的情節：

---

如果我們將行數增加到10000，我們將開始看到顯着的性能差異。

使用萬線，每3個點和額外的20個點的色梯度內插（23段中的每一行），並在看它需要一個數字保存到PNG時間：

Took 10.866694212 sec with a single collection 
Took 28.594727993 sec with multiple collections 

因此，在這種特殊情況下，使用單行集合會使速度略低於3倍。這不是一流的，但總比沒有好。

下面是計時代碼和輸出圖，無論它值什麼（由於繪圖順序不同，輸出數字並不完全相同。如果你需要的Z級控制，你就必須堅持獨立的行集合）：

import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib.collections import LineCollection 
import time 
# Make random number generation consistent between runs 
np.random.seed(5) 

def main(): 
    numlines, numpoints = 10000, 3 
    lines = np.random.random((numlines, numpoints, 2)) 

    # Overly simplistic timing, but timeit is overkill for this exmaple 
    tic = time.time() 
    single_collection(lines).savefig('/tmp/test_single.png') 
    toc = time.time() 
    print 'Took {} sec with a single collection'.format(toc-tic) 

    tic = time.time() 
    multiple_collections(lines).savefig('/tmp/test_multiple.png') 
    toc = time.time() 
    print 'Took {} sec with multiple collections'.format(toc-tic) 

def single_collection(lines): 
    # Add "num" additional segments to each line 
    segments, color_scalar = zip(*[interp(item, num=20) for item in lines]) 
    segments = np.vstack(segments) 
    color_scalar = np.hstack(color_scalar) 

    fig, ax = plt.subplots() 
    coll = LineCollection(segments) 
    coll.set_array(color_scalar) 
    ax.add_collection(coll) 
    return fig 

def multiple_collections(lines): 
    fig, ax = plt.subplots() 
    for line in lines: 
     # Add "num" additional segments to the line 
     segments, color_scalar = interp(line, num=20) 
     coll = LineCollection(segments) 
     coll.set_array(color_scalar) 
     ax.add_collection(coll) 
    return fig 

def interp(data, num=20): 
    """Add "num" additional points to "data" at evenly spaced intervals and 
    separate into individual segments.""" 
    x, y = data.T 
    dist = np.hypot(np.diff(x - x.min()), np.diff(y - y.min())).cumsum() 
    t = np.r_[0, dist]/dist.max() 

    ti = np.linspace(0, 1, num, endpoint=True) 
    xi = np.interp(ti, t, x) 
    yi = np.interp(ti, t, y) 

    # Insert the original vertices 
    indices = np.searchsorted(ti, t) 
    xi = np.insert(xi, indices, x) 
    yi = np.insert(yi, indices, y) 

    return reshuffle(xi, yi), ti 

def reshuffle(x, y): 
    """Reshape the line represented by "x" and "y" into an array of individual 
    segments.""" 
    points = np.vstack([x, y]).T.reshape(-1,1,2) 
    points = np.concatenate([points[:-1], points[1:]], axis=1) 
    return points 

if __name__ == '__main__': 
    main()