matplotlib教程學習筆記
這部分給出一些簡單的示例。
Line Plot
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2 * np.pi * t)
fig, ax = plt.subplots()
ax.plot(t, s)
ax.set(xlabel='time (s)', ylabel='voltage (mV)',
title='About as simple as it gets, folks') #設置一些屬性
ax.grid() #顯示網格
#fig.savefig("test.png") 保存圖片在當前目錄下
plt.show()
matplotlib.axes.Axes.plot
matplotlib.pyplot.plot
matplotlib.pyplot.subplots
matplotlib.figure.Figure.savefig
#First create some toy data:
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
#Creates just a figure and only one subplot
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')
#Creates two subplots and unpacks the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)
#Creates four polar axes, and accesses them through the returned array
fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))
axes[0, 0].plot(x, y)
axes[1, 1].scatter(x, y)
#Share a X axis with each column of subplots
plt.subplots(2, 2, sharex='col')
#Share a Y axis with each row of subplots
plt.subplots(2, 2, sharey='row')
#Share both X and Y axes with all subplots
plt.subplots(2, 2, sharex='all', sharey='all')
#Note that this is the same as
plt.subplots(2, 2, sharex=True, sharey=True)
#Creates figure number 10 with a single subplot
#and clears it if it already exists.
fig, ax=plt.subplots(num=10, clear=True)
One figure, a set of subplots
x1 = np.linspace(0.0, 5.0)
x2 = np.linspace(0.0, 2.0)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
y2 = np.cos(2 * np.pi * x2)
plt.subplot(2, 1, 1)
plt.plot(x1, y1, 'o-')
plt.title('A tale of 2 subplots')
plt.ylabel('Damped oscillation')
plt.subplot(2, 1, 2)
plt.plot(x2, y2, '.-')
plt.xlabel('time (s)')
plt.ylabel('Undamped')
plt.show()
這樣寫也可以達到同樣的目的：
fig, (ax1, ax2) = plt.subplots(2, 1) # one figure, 2 * 1 axes
ax1.plot(x1, y1, 'o-')
ax1.set(title='A tale of 2 subplots',
ylabel='Damped oscillation')
ax2.plot(x2, y2, '.-')
ax2.set(**{'xlabel':'times(s)',
'ylabel':'Undamped'})
均為下圖：
Image 展示圖片
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
from matplotlib.path import Path
from matplotlib.patches import PathPatch
展示二元正態分布
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
fig, ax = plt.subplots()
im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
origin='lower', extent=[-3, 3, -3, 3],
vmax=abs(Z).max(), vmin=-abs(Z).max())
plt.show()
A sample image
# A sample image
with cbook.get_sample_data('Lenna.png') as image_file:
image = plt.imread(image_file)
fig, ax = plt.subplots()
ax.imshow(image)
ax.axis('off') # clear x-axis and y-axis
Interpolating images 插值圖像？
A = np.random.rand(5, 5)
fig, axs = plt.subplots(1, 3, figsize=(10, 3))
for ax, interp in zip(axs, ['nearest', 'bilinear', 'bicubic']):
ax.imshow(A, interpolation=interp)
ax.set_title(interp.capitalize())
ax.grid(True)
plt.show()
x = np.arange(120).reshape((10, 12))
interp = 'bilinear'
fig, axs = plt.subplots(nrows=2, sharex=True, figsize=(3, 5))
axs[0].set_title('blue should be up')
axs[0].imshow(x, origin='upper', interpolation=interp)
axs[1].set_title('blue should be down')
axs[1].imshow(x, origin='lower', interpolation=interp)
plt.show()
Clip path
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
path = Path([[0, 1], [1, 0], [0, -1], [-1, 0], [0, 1]])
patch = PathPatch(path, facecolor='none')
fig, ax = plt.subplots()
ax.add_patch(patch)
im = ax.imshow(Z, interpolation='bilinear', cmap=cm.gray,
origin='lower', extent=[-3, 3, -3, 3],
clip_path=patch, clip_on=True)
im.set_clip_path(patch)
plt.show()
matplotlib.axes.Axes.imshow
matplotlib.pyplot.imshow
matplotlib.artist.Artist.set_clip_path
matplotlib.patches.PathPatch
Contouring and Pseudocolor 輪廓與偽彩色
# make these smaller to increase the resolution
dx, dy = 0.05, 0.05
# generate 2 2d grids for the x & y bounds
y, x = np.mgrid[slice(1, 5 + dy, dy),
slice(1, 5 + dx, dx)]
z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)
# x and y are bounds, so z should be the value *inside* those bounds.
# Therefore, remove the last value from the z array.
z = z[:-1, :-1]
levels = MaxNLocator(nbins=15).tick_values(z.min(), z.max())
# pick the desired colormap, sensible levels, and define a normalization
# instance which takes data values and translates those into levels.
cmap = plt.get_cmap('PiYG')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) #壓縮到[0-1]，因為顏色的要求？
fig, (ax0, ax1) = plt.subplots(nrows=2)
im = ax0.pcolormesh(x, y, z, cmap=cmap, norm=norm)
fig.colorbar(im, ax=ax0) # 添加右邊的顏色的標度
ax0.set_title('pcolormesh with levels')
# contours are *point* based plots, so convert our bound into point
# centers
cf = ax1.contourf(x[:-1, :-1] + dx/2., #在輪廓里面填充顏色？
y[:-1, :-1] + dy/2., z, levels=levels,
cmap=cmap)
fig.colorbar(cf, ax=ax1)
ax1.set_title('contourf with levels')
# adjust spacing between subplots so `ax1` title and `ax0` tick labels
# don't overlap
fig.tight_layout()
plt.show()
numpy.mgrid j表示末端數字也包括
matplotlib.axes.Axes.pcolormesh
matplotlib.pyplot.pcolormesh
matplotlib.axes.Axes.contourf
matplotlib.pyplot.contourf
matplotlib.figure.Figure.colorbar
matplotlib.pyplot.colorbar
matplotlib.colors.BoundaryNorm
matplotlib.ticker.MaxNLocator
Histograms hist()
np.random.seed(19680801)
# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * np.random.randn(437)#x ~ N(mu, sigma^2)
num_bins = 50 #柱狀圖數目
fig, ax = plt.subplots()
# the histogram of the data
n, bins, patches = ax.hist(x, num_bins, density=1)
#n: 每個bin在圖上對應的y軸，這里就是概率密度
#bins:如果沒猜錯，是bin所對應的x軸（中間的那個值）
#patches：應該是畫bin所用的一些信息？
#density:一個可選參數，如果為True，那么bins下的面積和會被調整(scale)為1,這樣#就類似一個密度函數了
# add a 'best fit' line
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
ax.plot(bins, y, '--')
ax.set_xlabel('Smarts')
ax.set_ylabel('Probability density')
ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
# Tweak spacing to prevent clipping of ylabel
fig.tight_layout()
plt.show()
matplotlib.axes.Axes.hist
matplotlib.axes.Axes.set_title
matplotlib.axes.Axes.set_xlabel
matplotlib.axes.Axes.set_ylabel
Paths
你可以使用matplotlib.path模塊添加任意路徑。
fig, ax = plt.subplots()
Path = mpath.Path #import matplotlib.path as mpath
path_data = [
(Path.MOVETO, (1.58, -2.57)),
(Path.CURVE4, (0.35, -1.1)),
(Path.CURVE4, (-1.75, 2.0)),
(Path.CURVE4, (0.375, 2.0)),
(Path.LINETO, (0.85, 1.15)),
(Path.CURVE4, (2.2, 3.2)),
(Path.CURVE4, (3, 0.05)),
(Path.CURVE4, (2.0, -0.5)),
(Path.CLOSEPOLY, (1.58, -2.57)),
]
codes, verts = zip(*path_data)
#codes:(1, 4, 4, 4, 2, 4, 4, 4, 79) 大概是對應的path的方法，曲線啊，直線啊啥的
#verts:((1.58, -2.57), (0.35, -1.1), (-1.75, 2.0), (0.375, 2.0), (0.85, 1.15),
#(2.2, 3.2), (3, 0.05), (2.0, -0.5), (1.58, -2.57))
path = mpath.Path(verts, codes)
patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)
ax.add_patch(patch)
# plot control points and connecting lines
x, y = zip(*path.vertices)
#x:(1.58, 0.35, -1.75, 0.375, 0.85, 2.2, 3.0, 2.0, 1.58)
#y:(-2.57, -1.1, 2.0, 2.0, 1.15, 3.2, 0.05, -0.5, -2.57)
line, = ax.plot(x, y, 'go-')
ax.grid()
ax.axis('equal')
for i, (item1, item2) in enumerate(path_data, 1):
plt.text(item2[0], item2[1], str(i), size=20)
plt.show()
zip() ，傳入迭代子，講不同迭代子相同位置的元素“捏”在一起返回一個新的迭代子。注意，視情況，zip不僅可以解壓，而且可以反解。
matplotlib.path
matplotlib.path.Path
matplotlib.patches
matplotlib.patches.PathPatch
matplotlib.axes.Axes.add_patch
Three-dimensional plotting
mplot3d 工具包支持3d作圖，比如曲面，線框，散點等。
# This import registers the 3D projection, but is otherwise unused.
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d') #.gca() 獲取當前的axes，如果有必要（大概是沒有？），就創建一個
#projection 設置工程為3d？
# Make data.
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y) #生成二維坐標點
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
# Plot the surface.
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
# Customize the z axis.
ax.set_zlim(-1.01, 1.01) #設置z軸標度的表示范圍
ax.zaxis.set_major_locator(LinearLocator(10)) #大概就是將z軸分成10格
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))#2位小數
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5) #添加顏色標度
plt.show()
Streamplot 流線圖？
streamplot()函數，可以畫向量場的流線，還可以。。。
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
#並沒有采用Demo里面的模型，而是選擇了:
#F=(-yi+xj)/(x^2+y^2)^(1/2)
#缺點是，線寬啥的都體現不出來了（因為速度大小均為1）
w = 3
Y, X = np.mgrid[-w:w:100j, -w:w:100j] # 生成10000個[-3, 3] * [-3, 3]的均勻網格坐標
U = -Y / np.sqrt(X ** 2 + Y ** 2)
V = X / np.sqrt(X ** 2 + Y ** 2)
speed = np.sqrt(U*U + V*V)
print(np.where(speed == np.sqrt(2)))
fig = plt.figure(figsize=(7, 9)) #7行9列
#應該是把分好的塊再重新分了, height_ratios, 從下面的圖片中可以看到
#3行圖片的高度比例是1：1：2
#不出意外，len(height_ratios) == nrows
gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])
# Varying density along a streamline
ax0 = fig.add_subplot(gs[0, 0])
ax0.streamplot(X, Y, U, V, density=[0.5, 1])
ax0.set_title('Varying Density')
#x,y: 位置坐標
#U, V： x and y-velocities(x軸分速度，y-軸分速度)
# Varying color along a streamline
ax1 = fig.add_subplot(gs[0, 1])
strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn') #顏色的區別
fig.colorbar(strm.lines)
ax1.set_title('Varying Color')
# Varying line width along a streamline
ax2 = fig.add_subplot(gs[1, 0])
lw = 5*speed / speed.max()
ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw) #不同密度線寬不同
ax2.set_title('Varying Line Width')
# Controlling the starting points of the streamlines
seed_points = np.array([[-2, -1, 0, 1, 2, -1], [-2, -1, 0, 1, 2, 2]])
#與圖中藍色點對應，好像是只有經過上述某點的流線才被保留
ax3 = fig.add_subplot(gs[1, 1])
strm = ax3.streamplot(X, Y, U, V, color=U, linewidth=2,
cmap='autumn', start_points=seed_points.T)
fig.colorbar(strm.lines)
ax3.set_title('Controlling Starting Points')
# Displaying the starting points with blue symbols.
ax3.plot(seed_points[0], seed_points[1], 'bo')
ax3.axis((-w, w, -w, w))
# Create a mask
mask = np.zeros(U.shape, dtype=bool)
mask[40:60, 40:60] = True
U[:20, :20] = np.nan
U = np.ma.array(U, mask=mask) #掩碼數組
ax4 = fig.add_subplot(gs[2:, :])
ax4.streamplot(X, Y, U, V, color='r')
ax4.set_title('Streamplot with Masking')
ax4.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
interpolation='nearest', cmap='gray', aspect='auto')
ax4.set_aspect('equal')
plt.tight_layout()
plt.show()
matplotlib.axes.Axes.streamplot
matplotlib.pyplot.streamplot
matplotlib.gridspec
matplotlib.gridspec.GridSpec
Ellipses 橢圓
#這個例子畫了很多橢圓
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Ellipse
NUM = 250 #250個
ells = [Ellipse(xy=np.random.rand(2) * 10, #（x, y） [0,10)
width=np.random.rand(), height=np.random.rand(),
angle=np.random.rand() * 360) #看來angle的表示是用度
for i in range(NUM)]
fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'})
for e in ells:
ax.add_artist(e)
e.set_clip_box(ax.bbox)#
e.set_alpha(np.random.rand())
e.set_facecolor(np.random.rand(3)) #rgb?
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Ellipse
delta = 45.0 # degrees 每次偏轉的角度
angles = np.arange(0, 360 + delta, delta)
ells = [Ellipse((1, 1), 4, 2, a) for a in angles]
a = plt.subplot(111, aspect='equal')
for e in ells:
e.set_clip_box(a.bbox)
e.set_alpha(0.1)
a.add_artist(e) #和上面的例子進行比較，可以發現，這行代碼是綁定
plt.xlim(-2, 4)
plt.ylim(-1, 3)
plt.show()
matplotlib.patches
matplotlib.patches.Ellipse
matplotlib.axes.Axes.add_artist
matplotlib.artist.Artist.set_clip_box
matplotlib.artist.Artist.set_alpha
matplotlib.patches.Patch.set_facecolor
Bar charts 條形圖
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
n_groups = 5
means_men = (20, 35, 30, 35, 27)
std_men = (2, 3, 4, 1, 2)
means_women = (25, 32, 34, 20, 25)
std_women = (3, 5, 2, 3, 3)
fig, ax = plt.subplots()
index = np.arange(n_groups)
bar_width = 0.35 #條寬
opacity = 0.4 #不透明度
error_config = {'ecolor': '#0000FF'} #設置誤差的一些屬性
error_config2 = {'ecolor': '#00FF00'}
rects1 = ax.bar(index, means_men, bar_width,
alpha=opacity, color='b',
yerr=std_men, error_kw=error_config, #有yerr才會有那個一豎
label='Men')
rects2 = ax.bar(index + bar_width, means_women, bar_width,
alpha=opacity, color='r',
yerr=std_women, error_kw=error_config2,
label='Women')
ax.set_xlabel('Group')
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(('A', 'B', 'C', 'D', 'E')) #設置x軸標簽
ax.legend()
fig.tight_layout()
plt.show()
# Credit: Josh Hemann
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
Student = namedtuple('Student', ['name', 'grade', 'gender']) #利用name來存取的tuple
Score = namedtuple('Score', ['score', 'percentile'])
# GLOBAL CONSTANTS
testNames = ['Pacer Test', 'Flexed Arm\n Hang', 'Mile Run', 'Agility',
'Push Ups'] #測試項目的名稱
testMeta = dict(zip(testNames, ['laps', 'sec', 'min:sec', 'sec', ''])) #項目與單位匹配
def attach_ordinal(num):
"""helper function to add ordinal string to integers
1 -> 1st
56 -> 56th
"""
suffixes = {str(i): v
for i, v in enumerate(['th', 'st', 'nd', 'rd', 'th',
'th', 'th', 'th', 'th', 'th'])} #名次縮寫的匹配
v = str(num)
# special case early teens
if v in {'11', '12', '13'}:
return v + 'th' #特殊情況
return v + suffixes[v[-1]] #普通情況依照最后一位數字分配縮寫
def format_score(scr, test):
"""
Build up the score labels for the right Y-axis by first
appending a carriage return to each string and then tacking on
the appropriate meta information (i.e., 'laps' vs 'seconds'). We
want the labels centered on the ticks, so if there is no meta
info (like for pushups) then don't add the carriage return to
the string
"""
md = testMeta[test] #獲取對應的單位
if md:
return '{0}\n{1}'.format(scr, md)
else:
return scr
def format_ycursor(y):
y = int(y)
if y < 0 or y >= len(testNames):
return ''
else:
return testNames[y]
def plot_student_results(student, scores, cohort_size):
# create the figure
fig, ax1 = plt.subplots(figsize=(9, 7))
fig.subplots_adjust(left=0.115, right=0.88)
fig.canvas.set_window_title('Eldorado K-8 Fitness Chart')
pos = np.arange(len(testNames))
rects = ax1.barh(pos, [scores[k].percentile for k in testNames],
align='center',
height=0.5, color='m', #height 就是bar的高（用寬可能更加貼切）占的百分比
tick_label=testNames)
ax1.set_title(student.name)
ax1.set_xlim([0, 100]) #設置x軸的范圍
ax1.xaxis.set_major_locator(MaxNLocator(11)) #MaxNLocator：就是x軸最多的標度數目為11
ax1.xaxis.grid(True, linestyle='--', which='major', #which???
color='grey', alpha=.25)
# Plot a solid vertical gridline to highlight the median position
ax1.axvline(50, color='grey', alpha=0.25) #畫一條實線，在50坐標處
# set X-axis tick marks at the deciles
cohort_label = ax1.text(.5, -.07, 'Cohort Size: {0}'.format(cohort_size),
horizontalalignment='center', size='small',
transform=ax1.transAxes)
# Set the right-hand Y-axis ticks and labels
ax2 = ax1.twinx() #twinx, 擁有同一個x？
scoreLabels = [format_score(scores[k].score, k) for k in testNames]
# set the tick locations
ax2.set_yticks(pos)
# make sure that the limits are set equally on both yaxis so the
# ticks line up
ax2.set_ylim(ax1.get_ylim()) #ax2設置為同ax1一樣的y的范圍
# set the tick labels
ax2.set_yticklabels(scoreLabels)
ax2.set_ylabel('Test Scores')
ax2.set_xlabel(('Percentile Ranking Across '
'{grade} Grade {gender}s').format(
grade=attach_ordinal(student.grade),
gender=student.gender.title()))
rect_labels = []
# Lastly, write in the ranking inside each bar to aid in interpretation
for rect in rects: #rect 條 每個bar
# Rectangle widths are already integer-valued but are floating
# type, so it helps to remove the trailing decimal point and 0 by
# converting width to int type
width = int(rect.get_width())
rankStr = attach_ordinal(width)
# The bars aren't wide enough to print the ranking inside
if width < 5: #如果bar的寬度(可能叫長度更為貼切)不夠容納排名，那么就寫在右邊
# Shift the text to the right side of the right edge
xloc = width + 1
# Black against white background
clr = 'black'
align = 'left'#應該是指，xloc是文本的左貼邊
else:
# Shift the text to the left side of the right edge
xloc = 0.98*width
# White on magenta
clr = 'white'
align = 'right'
# Center the text vertically in the bar
yloc = rect.get_y() + rect.get_height()/2.0 #居中顯示
label = ax1.text(xloc, yloc, rankStr, horizontalalignment=align,
verticalalignment='center', color=clr, weight='bold',
clip_on=True)
rect_labels.append(label)
# make the interactive mouse over give the bar title
ax2.fmt_ydata = format_ycursor #這個交互信息，從圖片沒法直接體現
# return all of the artists created
return {'fig': fig,
'ax': ax1,
'ax_right': ax2,
'bars': rects,
'perc_labels': rect_labels,
'cohort_label': cohort_label}
student = Student('Johnny Doe', 2, 'boy')
scores = dict(zip(testNames,
(Score(v, p) for v, p in
zip(['7', '48', '12:52', '17', '14'],
np.round(np.random.uniform(0, 1,
len(testNames))*100, 0)))))
cohort_size = 62 # The number of other 2nd grade boys
arts = plot_student_results(student, scores, cohort_size)
plt.show()
Pie charts 餅圖
本節的例子同時對下面的功能進行了展式：
- 切片標簽
- 自動標記百分比
- 用“爆炸”來偏移切片
- 下拉陰影
- 自定義起始角度
import matplotlib.pyplot as plt
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 43, 10] #sum == 98 not 100
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs') hogs被孤立了
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90) #startangle :起始角度
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
Tables
import numpy as np
import matplotlib.pyplot as plt
data = [[ 66386, 174296, 75131, 577908, 32015],
[ 58230, 381139, 78045, 99308, 160454],
[ 89135, 80552, 152558, 497981, 603535],
[ 78415, 81858, 150656, 193263, 69638],
[139361, 331509, 343164, 781380, 52269]]
columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]
values = np.arange(0, 2500, 500)
value_increment = 1000
# Get some pastel shades for the colors
colors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))#colormap?
n_rows = len(data)
index = np.arange(len(columns)) + 0.3
bar_width = 0.4
# Initialize the vertical-offset for the stacked bar chart.
y_offset = np.zeros(len(columns))
# Plot bars and create text labels for the table
cell_text = []
for row in range(n_rows):
plt.bar(index, data[row], bar_width, bottom=y_offset, color=colors[row]) #bottom!!!!!!!!!!!!!
y_offset = y_offset + data[row]
cell_text.append(['%1.1f' % (x / 1000.0) for x in y_offset]) #記錄下每層的數據
# Reverse colors and text labels to display the last value at the top.
colors = colors[::-1]
cell_text.reverse()
# Add a table at the bottom of the axes
the_table = plt.table(cellText=cell_text, #在圖片下方加一個表格
rowLabels=rows,
rowColours=colors,
colLabels=columns,
loc='bottom')#loc location 位置
# Adjust layout to make room for the table:
plt.subplots_adjust(left=0.2, bottom=0.2)
plt.ylabel("Loss in ${0}'s".format(value_increment))
plt.yticks(values * value_increment, ['%d' % val for val in values])
plt.xticks([])
plt.title('Loss by Disaster')
plt.show()
Scatter plots 散點圖
下面代碼我並沒有執行，圖片是直接照搬的。
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
# Load a numpy record array from yahoo csv data with fields date, open, close,
# volume, adj_close from the mpl-data/example directory. The record array
# stores the date as an np.datetime64 with a day unit ('D') in the date column.
with cbook.get_sample_data('goog.npz') as datafile:
price_data = np.load(datafile)['price_data'].view(np.recarray)
price_data = price_data[-250:] # get the most recent 250 trading days
delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]
# Marker size in units of points^2
volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]
fig, ax = plt.subplots()
ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)
ax.set_xlabel(r'$\Delta_i$', fontsize=15)
ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=15)
ax.set_title('Volume and percent change')
ax.grid(True)
fig.tight_layout()
plt.show()
GUI widgets
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25) #左邊右邊都留點空
t = np.arange(0.0, 1.0, 0.001)
a0 = 5 #幅度
f0 = 3 #頻率相關
delta_f = 5.0 #頻率的幅度吧
s = a0*np.sin(2*np.pi*f0*t)
l, = plt.plot(t, s, lw=2, color='red')
plt.axis([0, 1, -10, 10]) # 設置axis的范圍 x:[0, 1], y:[-10, 10]
axcolor = 'lightgoldenrodyellow' #調整框的顏色
axfreq = plt.axes([0.25, 0.3, 0.65, 0.03], facecolor=axcolor) #[left, bottom, length, height]
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0, valstep=delta_f) #valstep?
samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
def update(val):
amp = samp.val #獲取調節后的amp 幅度
freq = sfreq.val #獲取調節后的 freq 頻率
l.set_ydata(amp*np.sin(2*np.pi*freq*t)) #畫新的圖
fig.canvas.draw_idle() #？？？
sfreq.on_changed(update) #綁定更新函數
samp.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
#設定button reset的一些屬性
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
sfreq.reset()
samp.reset()
button.on_clicked(reset) #button reset點擊動作綁定reset
rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)
radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0) # 倒是有點像網頁 選擇按鈕
def colorfunc(label): #改變曲線顏色
l.set_color(label)
fig.canvas.draw_idle()
radio.on_clicked(colorfunc) #綁定colorfunc到選擇按鈕
plt.show()
Filled curves 填充曲線？
import numpy as np
import matplotlib.pyplot as plt
x = [0, 1, 2, 1]
y = [1, 2, 1, 0]
fig, ax = plt.subplots()
ax.fill(x, y)
plt.show()
下面的例子要稍微復雜一些，涉及多條曲線，設置填充顏色，設置透明度等。
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1.5 * np.pi, 500)
y1 = np.sin(x)
y2 = np.sin(3 * x)
fig, ax = plt.subplots()
ax.fill(x, y1, 'b', x, y2, 'r', alpha=0.3)
# Outline of the region we've filled in
ax.plot(x, y1, c='b', alpha=0.8)
ax.plot(x, y2, c='r', alpha=0.8)
ax.plot([x[0], x[-1]], [y1[0], y1[-1]], c='b', alpha=0.8)
ax.plot([x[0], x[-1]], [y2[0], y2[-1]], c='r', alpha=0.8)
plt.show()
Date handling 處理時間（序列?）
下面代碼並沒有跑，只是照搬
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.cbook as cbook
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter('%Y')
# Load a numpy record array from yahoo csv data with fields date, open, close,
# volume, adj_close from the mpl-data/example directory. The record array
# stores the date as an np.datetime64 with a day unit ('D') in the date column.
with cbook.get_sample_data('goog.npz') as datafile:
r = np.load(datafile)['price_data'].view(np.recarray)
fig, ax = plt.subplots()
ax.plot(r.date, r.adj_close)
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
# round to nearest years...
datemin = np.datetime64(r.date[0], 'Y')
datemax = np.datetime64(r.date[-1], 'Y') + np.timedelta64(1, 'Y')
ax.set_xlim(datemin, datemax)
# format the coords message box
def price(x):
return '$%1.2f' % x
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
ax.format_ydata = price
ax.grid(True)
# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
fig.autofmt_xdate()
plt.show()
Log plots
import numpy as np
import matplotlib.pyplot as plt
# Data for plotting
t = np.arange(0.01, 20.0, 0.01)
# Create figure
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
# log y axis
ax1.semilogy(t, np.exp(-t / 5.0))
ax1.set(title='semilogy')
ax1.grid()
# log x axis
ax2.semilogx(t, np.sin(2 * np.pi * t))
ax2.set(title='semilogx')
ax2.grid()
# log x and y axis
ax3.loglog(t, 20 * np.exp(-t / 10.0), basex=2)
ax3.set(title='loglog base 2 on x')
ax3.grid()
# With errorbars: clip non-positive values
# Use new data for plotting
x = 10.0**np.linspace(0.0, 2.0, 20)
y = x**2.0
ax4.set_xscale("log", nonposx='clip')
ax4.set_yscale("log", nonposy='clip')
ax4.set(title='Errorbars go negative')
ax4.errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y)
# ylim must be set after errorbar to allow errorbar to autoscale limits
ax4.set_ylim(bottom=0.1)
fig.tight_layout()
plt.show()
matplotlib.axes. Axes.semilogx
matplotlib.axes. Axes.semilogy
Polar plots 極坐標
import numpy as np
import matplotlib.pyplot as plt
r = np.arange(0, 2, 0.01)
theta = 2 * np.pi * r #看來采用的是弧度制
ax = plt.subplot(111, projection='polar')
ax.plot(theta, r)
ax.set_rmax(2) #數字大了 整體會縮小？明白了，rmax就是極坐標最大半徑，最大半徑設置得越大，相應得原本的內容就顯得越小。
ax.set_rticks([0.5, 1, 1.5, 2]) # Less radial ticks
ax.set_rlabel_position(-12.5) # 0.5 1 1.5 2位於-12.5度處
ax.grid(True)
ax.set_title("A line plot on a polar axis", va='bottom')
plt.show()
matplotlib.axes.Axes.plot
matplotlib.projections.polar
matplotlib.projections.polar.PolarAxes
matplotlib.projections.polar.PolarAxes.set_rticks
matplotlib.projections.polar.PolarAxes.set_rmax
matplotlib.projections.polar.PolarAxes.set_rlabel_position
Legends 圖例
import numpy as np
import matplotlib.pyplot as plt
# Make some fake data.
a = b = np.arange(0, 3, .02)
c = np.exp(a)
d = c[::-1]
# Create plots with pre-defined labels.
fig, ax = plt.subplots()
ax.plot(a, c, 'k--', label='Model length')
ax.plot(a, d, 'k:', label='Data length')
ax.plot(a, c + d, 'k', label='Total message length')
legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')
#loc: location best:0, upper right:1 upper left:2 ...
#shadow 是否添加陰影
#fontsize 字體大小
# xx-small, x-small, small, medium, large, x-large, xx-large, larger, smaller, None
#或者，數字 fontsize=15
# Put a nicer background color on the legend.
legend.get_frame().set_facecolor('C0')
plt.show()
matplotlib.axes.Axes.plot
matplotlib.pyplot.plot
matplotlib.axes.Axes.legend
matplotlib.pyplot.legend
TeX-notation for text objects
import matplotlib.pyplot as plt
import subprocess
import sys
import re
# Selection of features following "Writing mathematical expressions" tutorial
mathtext_titles = {
0: "Header demo",
1: "Subscripts and superscripts",
2: "Fractions, binomials and stacked numbers",
3: "Radicals",
4: "Fonts",
5: "Accents",
6: "Greek, Hebrew",
7: "Delimiters, functions and Symbols"}
n_lines = len(mathtext_titles)
# Randomly picked examples
mathext_demos = {
0: r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = "
r"U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} "
r"\int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ "
r"U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_"
r"{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$",
1: r"$\alpha_i > \beta_i,\ "
r"\alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ "
r"\ldots$",
2: r"$\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ "
r"\left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$",
3: r"$\sqrt{2},\ \sqrt[3]{x},\ \ldots$",
4: r"$\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ "
r"\mathrm{or}\ \mathcal{CALLIGRAPHY}$",
5: r"$\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ "
r"\hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ "
r"\ldots$",
6: r"$\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ "
r"\Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ "
r"\aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$",
7: r"$\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ "
r"\log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ "
r"\infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$"}
def doall():
# Colors used in mpl online documentation.
mpl_blue_rvb = (191. / 255., 209. / 256., 212. / 255.)
mpl_orange_rvb = (202. / 255., 121. / 256., 0. / 255.)
mpl_grey_rvb = (51. / 255., 51. / 255., 51. / 255.)
# Creating figure and axis.
plt.figure(figsize=(6, 7))
plt.axes([0.01, 0.01, 0.98, 0.90], facecolor="white", frameon=True)
plt.gca().set_xlim(0., 1.)
plt.gca().set_ylim(0., 1.)
plt.gca().set_title("Matplotlib's math rendering engine",
color=mpl_grey_rvb, fontsize=14, weight='bold')
plt.gca().set_xticklabels("", visible=False)
plt.gca().set_yticklabels("", visible=False)
# Gap between lines in axes coords
line_axesfrac = (1. / (n_lines))
# Plotting header demonstration formula
full_demo = mathext_demos[0]
plt.annotate(full_demo,
xy=(0.5, 1. - 0.59 * line_axesfrac),
xycoords='data', color=mpl_orange_rvb, ha='center',
fontsize=20)
# Plotting features demonstration formulae
for i_line in range(1, n_lines):
baseline = 1 - (i_line) * line_axesfrac
baseline_next = baseline - line_axesfrac
title = mathtext_titles[i_line] + ":"
fill_color = ['white', mpl_blue_rvb][i_line % 2]
plt.fill_between([0., 1.], [baseline, baseline],
[baseline_next, baseline_next],
color=fill_color, alpha=0.5)
plt.annotate(title,
xy=(0.07, baseline - 0.3 * line_axesfrac),
xycoords='data', color=mpl_grey_rvb, weight='bold')
demo = mathext_demos[i_line]
plt.annotate(demo,
xy=(0.05, baseline - 0.75 * line_axesfrac),
xycoords='data', color=mpl_grey_rvb,
fontsize=16)
for i in range(n_lines):
s = mathext_demos[i]
print(i, s)
plt.show()
if '--latex' in sys.argv:
# Run: python mathtext_examples.py --latex
# Need amsmath and amssymb packages.
fd = open("mathtext_examples.ltx", "w")
fd.write("\\documentclass{article}\n")
fd.write("\\usepackage{amsmath, amssymb}\n")
fd.write("\\begin{document}\n")
fd.write("\\begin{enumerate}\n")
for i in range(n_lines):
s = mathext_demos[i]
s = re.sub(r"(?<!\\)\$", "$$", s)
fd.write("\\item %s\n" % s)
fd.write("\\end{enumerate}\n")
fd.write("\\end{document}\n")
fd.close()
subprocess.call(["pdflatex", "mathtext_examples.ltx"])
else:
doall()
Native TeX rendering
不曉得，為啥使用原始的TeX,我的程序會掛，不過，matplotlib提供的已經足夠了吧。下面的直接照搬了。
import numpy as np
import matplotlib
matplotlib.rcParams['text.usetex'] = True
import matplotlib.pyplot as plt
t = np.linspace(0.0, 1.0, 100)
s = np.cos(4 * np.pi * t) + 2
fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True)
ax.plot(t, s)
ax.set_xlabel(r'\textbf{time (s)}')
ax.set_ylabel('\\textit{Velocity (\N{DEGREE SIGN}/sec)}', fontsize=16)
ax.set_title(r'\TeX\ is Number $\displaystyle\sum_{n=1}^\infty'
r'\frac{-e^{i\pi}}{2^n}$!', fontsize=16, color='r')
plt.show()
EEG GUI
你可以把matplotlib嵌入到pygtk,wx,Tk或者Qt應用中。
這部分姑且先跳過吧。
import tkinter
from matplotlib.backends.backend_tkagg import (
FigureCanvasTkAgg, NavigationToolbar2Tk)
# Implement the default Matplotlib key bindings.
from matplotlib.backend_bases import key_press_handler
from matplotlib.figure import Figure
import numpy as np
root = tkinter.Tk()
root.wm_title("Embedding in Tk")
fig = Figure(figsize=(5, 4), dpi=100)
t = np.arange(0, 3, .01)
fig.add_subplot(111).plot(t, 2 * np.sin(2 * np.pi * t))
canvas = FigureCanvasTkAgg(fig, master=root) # A tk.DrawingArea.
canvas.draw()
canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
toolbar = NavigationToolbar2Tk(canvas, root)
toolbar.update()
canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
def on_key_press(event):
print("you pressed {}".format(event.key))
key_press_handler(event, canvas, toolbar)
canvas.mpl_connect("key_press_event", on_key_press)
def _quit():
root.quit() # stops mainloop
root.destroy() # this is necessary on Windows to prevent
# Fatal Python Error: PyEval_RestoreThread: NULL tstate
button = tkinter.Button(master=root, text="Quit", command=_quit)
button.pack(side=tkinter.BOTTOM)
tkinter.mainloop()
# If you put root.destroy() here, it will cause an error if the window is
# closed with the window manager.
XKCD-style sketch plots
單純為了好玩，matplotlib支持xkcd-style.
import matplotlib.pyplot as plt
import numpy as np
with plt.xkcd():
# Based on "Stove Ownership" from XKCD by Randall Munroe
# http://xkcd.com/418/
fig = plt.figure()
ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.xticks([])
plt.yticks([])
ax.set_ylim([-30, 10])
data = np.ones(100)
data[70:] -= np.arange(30)
plt.annotate(
'THE DAY I REALIZED\nI COULD COOK BACON\nWHENEVER I WANTED',
xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))
plt.plot(data)
plt.xlabel('time')
plt.ylabel('my overall health')
fig.text(
0.5, 0.05,
'"Stove Ownership" from xkcd by Randall Munroe',
ha='center')
with plt.xkcd():
# Based on "The Data So Far" from XKCD by Randall Munroe
# http://xkcd.com/373/
fig = plt.figure()
ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))
ax.bar([0, 1], [0, 100], 0.25)
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.set_xticks([0, 1])
ax.set_xlim([-0.5, 1.5])
ax.set_ylim([0, 110])
ax.set_xticklabels(['CONFIRMED BY\nEXPERIMENT', 'REFUTED BY\nEXPERIMENT'])
plt.yticks([])
plt.title("CLAIMS OF SUPERNATURAL POWERS")
fig.text(
0.5, 0.05,
'"The Data So Far" from xkcd by Randall Munroe',
ha='center')
plt.show()
Subplot examples
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(19680801)
data = np.random.randn(2, 100)
fig, axs = plt.subplots(2, 2, figsize=(5, 5))
axs[0, 0].hist(data[0])
axs[1, 0].scatter(data[0], data[1])
axs[0, 1].plot(data[0], data[1])
axs[1, 1].hist2d(data[0], data[1])
plt.show()
