利用Python制作可以動(dòng)起來(lái)的圖表！
通常大家做出來(lái)的圖表，絕大部分都是靜態(tài)的，有時(shí)會(huì)顯得不夠吸引人。
今天給大家介紹一下，如何用Python繪制動(dòng)態(tài)圖表。
主要是使用到Matplotlib+imageio，其中Matplotlib就有一個(gè)Animation類，可以生成動(dòng)圖GIF，不過(guò)使用起來(lái)學(xué)習(xí)成本較高，還是有一定難度的。
這里我將先創(chuàng)建靜態(tài)圖表的圖片，然后使用Imageio創(chuàng)建一個(gè)GIF(動(dòng)態(tài)圖表)。
一共給大家介紹三種動(dòng)態(tài)圖表的繪制，折線圖，條形圖，散點(diǎn)圖。
01 折線圖
先來(lái)繪制一個(gè)簡(jiǎn)單的折線圖看看。
import os
import numpy as np
import matplotlib.pyplot as plt
import imageio
# 生成40個(gè)取值在30-40的數(shù)
y = np.random.randint(30, 40, size=(40))
# 繪制折線
plt.plot(y)
# 設(shè)置y軸最小值和最大值
plt.ylim(20, 50)
# 顯示
plt.show()
使用Numpy創(chuàng)建一個(gè)數(shù)值范圍在30到40之間的隨機(jī)整數(shù)列表，結(jié)果如下。
下面將對(duì)整數(shù)列表進(jìn)行切片，生成不同階段的圖表。
# 第一張圖
plt.plot(y[:-3])
plt.ylim(20, 50)
plt.savefig('1.png')
plt.show()
# 第二張圖
plt.plot(y[:-2])
plt.ylim(20, 50)
plt.savefig('2.png')
plt.show()
# 第三張圖
plt.plot(y[:-1])
plt.ylim(20, 50)
plt.savefig('3.png')
plt.show()
# 第四張圖
plt.plot(y)
plt.ylim(20, 50)
plt.savefig('4.png')
plt.show()
得到x軸為0:36、0:37、0:38、0:39四個(gè)折線圖表。
有了這四張圖，我們就可以使用Imageio生成GIF了。
# 生成Gif
with imageio.get_writer('mygif.gif', mode='I') as writer:
for filename in ['1.png', '2.png', '3.png', '4.png']:
image = imageio.imread(filename)
writer.append_data(image)
動(dòng)圖來(lái)了。
一個(gè)會(huì)動(dòng)的折線圖表就制作出來(lái)了，不過(guò)不是從x軸坐標(biāo)為0的時(shí)候開(kāi)始的。
filenames = []
num = 0
for i in y:
num += 1
# 繪制40張折線圖
plt.plot(y[:num])
plt.ylim(20, 50)
# 保存圖片文件
filename = f'{num}.png'
filenames.append(filename)
plt.savefig(filename)
plt.close()
# 生成gif
with imageio.get_writer('mygif.gif', mode='I') as writer:
for filename in filenames:
image = imageio.imread(filename)
writer.append_data(image)
# 刪除40張折線圖
for filename in set(filenames):
os.remove(filename)
02 條形圖
x = [1, 2, 3, 4, 5]
coordinates_lists = [[0, 0, 0, 0, 0],
[10, 30, 60, 30, 10],
[70, 40, 20, 40, 70],
[10, 20, 30, 40, 50],
[50, 40, 30, 20, 10],
[75, 0, 75, 0, 75],
[0, 0, 0, 0, 0]]
filenames = []
for index, y in enumerate(coordinates_lists):
# 條形圖
plt.bar(x, y)
plt.ylim(0, 80)
# 保存圖片文件
filename = f'{index}.png'
filenames.append(filename)
# 重復(fù)最后一張圖形15幀(數(shù)值都為0),15張圖片
if (index == len(coordinates_lists) - 1):
for i in range(15):
filenames.append(filename)
# 保存
plt.savefig(filename)
plt.close()
# 生成gif
with imageio.get_writer('mygif.gif', mode='I') as writer:
for filename in filenames:
image = imageio.imread(filename)
writer.append_data(image)
# 刪除20張柱狀圖
for filename in set(filenames):
os.remove(filename)
n_frames = 10
x = [1, 2, 3, 4, 5]
coordinates_lists = [[0, 0, 0, 0, 0],
[10, 30, 60, 30, 10],
[70, 40, 20, 40, 70],
[10, 20, 30, 40, 50],
[50, 40, 30, 20, 10],
[75, 0, 75, 0, 75],
[0, 0, 0, 0, 0]]
print('生成圖表\n')
filenames = []
for index in np.arange(0, len(coordinates_lists) - 1):
# 獲取當(dāng)前圖像及下一圖像的y軸坐標(biāo)值
y = coordinates_lists[index]
y1 = coordinates_lists[index + 1]
# 計(jì)算當(dāng)前圖像與下一圖像y軸坐標(biāo)差值
y_path = np.array(y1) - np.array(y)
for i in np.arange(0, n_frames + 1):
# 分配每幀的y軸移動(dòng)距離
# 逐幀增加y軸的坐標(biāo)值
y_temp = (y + (y_path / n_frames) * i)
# 繪制條形圖
plt.bar(x, y_temp)
plt.ylim(0, 80)
# 保存每一幀的圖像
filename = f'images/frame_{index}_{i}.png'
filenames.append(filename)
# 最后一幀重復(fù),畫(huà)面停留一會(huì)
if (i == n_frames):
for i in range(5):
filenames.append(filename)
# 保存圖片
plt.savefig(filename)
plt.close()
print('保存圖表\n')
# 生成GIF
print('生成GIF\n')
with imageio.get_writer('mybars.gif', mode='I') as writer:
for filename in filenames:
image = imageio.imread(filename)
writer.append_data(image)
print('保存GIF\n')
print('刪除圖片\n')
# 刪除圖片
for filename in set(filenames):
os.remove(filename)
print('完成')
n_frames = 10
bg_color = '#95A4AD'
bar_color = '#283F4E'
gif_name = 'bars'
x = [1, 2, 3, 4, 5]
coordinates_lists = [[0, 0, 0, 0, 0],
[10, 30, 60, 30, 10],
[70, 40, 20, 40, 70],
[10, 20, 30, 40, 50],
[50, 40, 30, 20, 10],
[75, 0, 75, 0, 75],
[0, 0, 0, 0, 0]]
print('生成圖表\n')
filenames = []
for index in np.arange(0, len(coordinates_lists) - 1):
y = coordinates_lists[index]
y1 = coordinates_lists[index + 1]
y_path = np.array(y1) - np.array(y)
for i in np.arange(0, n_frames + 1):
y_temp = (y + (y_path / n_frames) * i)
# 繪制條形圖
fig, ax = plt.subplots(figsize=(8, 4))
ax.set_facecolor(bg_color)
plt.bar(x, y_temp, width=0.4, color=bar_color)
plt.ylim(0, 80)
# 移除圖表的上邊框和右邊框
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# 設(shè)置虛線網(wǎng)格線
ax.set_axisbelow(True)
ax.yaxis.grid(color='gray', linestyle='dashed', alpha=0.7)
# 保存每一幀的圖像
filename = f'images/frame_{index}_{i}.png'
filenames.append(filename)
# 最后一幀重復(fù),畫(huà)面停留一會(huì)
if (i == n_frames):
for i in range(5):
filenames.append(filename)
# 保存圖片
plt.savefig(filename, dpi=96, facecolor=bg_color)
plt.close()
print('保存圖表\n')
# 生成GIF
print('生成GIF\n')
with imageio.get_writer(f'{gif_name}.gif', mode='I') as writer:
for filename in filenames:
image = imageio.imread(filename)
writer.append_data(image)
print('保存GIF\n')
print('刪除圖片\n')
# 刪除圖片
for filename in set(filenames):
os.remove(filename)
print('完成')
coordinates_lists = [[[0], [0]],
[[100, 200, 300], [100, 200, 300]],
[[400, 500, 600], [400, 500, 600]],
[[400, 500, 600, 400, 500, 600], [400, 500, 600, 600, 500, 400]],
[[500], [500]],
[[0], [0]]]
gif_name = 'movie'
n_frames = 10
bg_color = '#95A4AD'
marker_color = '#283F4E'
marker_size = 25
print('生成圖表\n')
filenames = []
for index in np.arange(0, len(coordinates_lists) - 1):
# 獲取當(dāng)前圖像及下一圖像的x與y軸坐標(biāo)值
x = coordinates_lists[index][0]
y = coordinates_lists[index][1]
x1 = coordinates_lists[index + 1][0]
y1 = coordinates_lists[index + 1][1]
# 查看兩點(diǎn)差值
while len(x) < len(x1):
diff = len(x1) - len(x)
x = x + x[:diff]
y = y + y[:diff]
while len(x1) < len(x):
diff = len(x) - len(x1)
x1 = x1 + x1[:diff]
y1 = y1 + y1[:diff]
# 計(jì)算路徑
x_path = np.array(x1) - np.array(x)
y_path = np.array(y1) - np.array(y)
for i in np.arange(0, n_frames + 1):
# 計(jì)算當(dāng)前位置
x_temp = (x + (x_path / n_frames) * i)
y_temp = (y + (y_path / n_frames) * i)
# 繪制圖表
fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect="equal"))
ax.set_facecolor(bg_color)
plt.scatter(x_temp, y_temp, c=marker_color, s=marker_size)
plt.xlim(0, 1000)
plt.ylim(0, 1000)
# 移除邊框線
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# 網(wǎng)格線
ax.set_axisbelow(True)
ax.yaxis.grid(color='gray', linestyle='dashed', alpha=0.7)
ax.xaxis.grid(color='gray', linestyle='dashed', alpha=0.7)
# 保存圖片
filename = f'images/frame_{index}_{i}.png'
filenames.append(filename)
if (i == n_frames):
for i in range(5):
filenames.append(filename)
# 保存
plt.savefig(filename, dpi=96, facecolor=bg_color)
plt.close()
print('保存圖表\n')
# 生成GIF
print('生成GIF\n')
with imageio.get_writer(f'{gif_name}.gif', mode='I') as writer:
for filename in filenames:
image = imageio.imread(filename)
writer.append_data(image)
print('保存GIF\n')
print('刪除圖片\n')
# 刪除圖片
for filename in set(filenames):
os.remove(filename)
print('完成')
import os
import numpy as np
import matplotlib.pyplot as plt
import imageio
import random
import cv2
# 根據(jù)字母的形狀, 將字母轉(zhuǎn)化為多個(gè)隨機(jī)點(diǎn)
def get_masked_data(letter, intensity=2):
# 多個(gè)隨機(jī)點(diǎn)填充字母
random.seed(420)
x = []
y = []
for i in range(intensity):
x = x + random.sample(range(0, 1000), 500)
y = y + random.sample(range(0, 1000), 500)
if letter == ' ':
return x, y
# 獲取圖片的mask
mask = cv2.imread(f'images/letters/{letter.upper()}.png', 0)
mask = cv2.flip(mask, 0)
# 檢測(cè)點(diǎn)是否在mask中
result_x = []
result_y = []
for i in range(len(x)):
if (mask[y[i]][x[i]]) == 0:
result_x.append(x[i])
result_y.append(y[i])
# 返回x,y
return result_x, result_y
# 將文字切割成一個(gè)個(gè)字母
def text_to_data(txt, repeat=True, intensity=2):
print('將文本轉(zhuǎn)換為數(shù)據(jù)\n')
letters = []
for i in txt.upper():
letters.append(get_masked_data(i, intensity=intensity))
# 如果repeat為1時(shí),重復(fù)第一個(gè)字母
if repeat:
letters.append(get_masked_data(txt[0], intensity=intensity))
return letters
def build_gif(coordinates_lists, gif_name='movie', n_frames=10, bg_color='#95A4AD',
marker_color='#283F4E', marker_size=25):
print('生成圖表\n')
filenames = []
for index in np.arange(0, len(coordinates_lists) - 1):
# 獲取當(dāng)前圖像及下一圖像的x與y軸坐標(biāo)值
x = coordinates_lists[index][0]
y = coordinates_lists[index][1]
x1 = coordinates_lists[index + 1][0]
y1 = coordinates_lists[index + 1][1]
# 查看兩點(diǎn)差值
while len(x) < len(x1):
diff = len(x1) - len(x)
x = x + x[:diff]
y = y + y[:diff]
while len(x1) < len(x):
diff = len(x) - len(x1)
x1 = x1 + x1[:diff]
y1 = y1 + y1[:diff]
# 計(jì)算路徑
x_path = np.array(x1) - np.array(x)
y_path = np.array(y1) - np.array(y)
for i in np.arange(0, n_frames + 1):
# 計(jì)算當(dāng)前位置
x_temp = (x + (x_path / n_frames) * i)
y_temp = (y + (y_path / n_frames) * i)
# 繪制圖表
fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect="equal"))
ax.set_facecolor(bg_color)
plt.xticks([]) # 去掉x軸
plt.yticks([]) # 去掉y軸
plt.axis('off') # 去掉坐標(biāo)軸
plt.scatter(x_temp, y_temp, c=marker_color, s=marker_size)
plt.xlim(0, 1000)
plt.ylim(0, 1000)
# 移除框線
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# 網(wǎng)格線
ax.set_axisbelow(True)
ax.yaxis.grid(color='gray', linestyle='dashed', alpha=0.7)
ax.xaxis.grid(color='gray', linestyle='dashed', alpha=0.7)
# 保存圖片
filename = f'images/frame_{index}_{i}.png'
if (i == n_frames):
for i in range(5):
filenames.append(filename)
filenames.append(filename)
# 保存
plt.savefig(filename, dpi=96, facecolor=bg_color)
plt.close()
print('保存圖表\n')
# 生成GIF
print('生成GIF\n')
with imageio.get_writer(f'{gif_name}.gif', mode='I') as writer:
for filename in filenames:
image = imageio.imread(filename)
writer.append_data(image)
print('保存GIF\n')
print('刪除圖片\n')
# 刪除圖片
for filename in set(filenames):
os.remove(filename)
print('完成')
coordinates_lists = text_to_data('Python', repeat=True, intensity=50)
build_gif(coordinates_lists,
gif_name='Python',
n_frames=7,
bg_color='#52A9F0',
marker_color='#000000',
marker_size=0.2)
# 創(chuàng)建一個(gè)隨機(jī)的x/y坐標(biāo)列表，并使用mask對(duì)其進(jìn)行過(guò)濾。
get_masked_data()
# 將文本轉(zhuǎn)化為數(shù)據(jù)
text_to_data()
# 使用坐標(biāo)點(diǎn)生成散點(diǎn)圖, 保存GIF
build_gif()
coordinates_lists = text_to_data('mac_', repeat=True, intensity=50)
build_gif(coordinates_lists,
gif_name='mac',
n_frames=7,
bg_color='#F5B63F',
marker_color='#000000',
marker_size=0.2)
最后在公眾號(hào)回復(fù)「動(dòng)畫(huà)」，獲取到本次使用到的代碼和數(shù)據(jù)。
