各种绘图实例

简单绘图

plot 函数：

In [1]:

%matplotlib inline
 
import numpy as np
import matplotlib.pyplot as plt
 
t = np.arange(0.0, 2.0, 0.01)
s = np.sin(2*np.pi*t)
plt.plot(t, s)
 
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.title('About as simple as it gets, folks')
plt.grid(True)
plt.show()

子图

subplot 函数：

In [2]:

import numpy as np
import matplotlib.mlab as mlab
 
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, 'yo-')
plt.title('A tale of 2 subplots')
plt.ylabel('Damped oscillation')
 
plt.subplot(2, 1, 2)
plt.plot(x2, y2, 'r.-')
plt.xlabel('time (s)')
plt.ylabel('Undamped')
 
plt.show()

直方图

hist 函数：

In [3]:

import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
 
# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * np.random.randn(10000)
 
num_bins = 50
# the histogram of the data
n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)
# add a 'best fit' line
y = mlab.normpdf(bins, mu, sigma)
plt.plot(bins, y, 'r--')
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
 
# Tweak spacing to prevent clipping of ylabel
plt.subplots_adjust(left=0.15)
plt.show()

路径图

matplotlib.path 包：

In [4]:

import matplotlib.path as mpath
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
 
fig, ax = plt.subplots()
 
Path = mpath.Path
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)
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)
line, = ax.plot(x, y, 'go-')
 
ax.grid()
ax.axis('equal')
plt.show()

三维绘图

导入 Axex3D：

In [5]:

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
import numpy as np
 
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
        linewidth=0, antialiased=False)
ax.set_zlim(-1.01, 1.01)
 
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
 
fig.colorbar(surf, shrink=0.5, aspect=5)
 
plt.show()

流向图

主要函数：plt.streamplot

In [6]:

import numpy as np
import matplotlib.pyplot as plt
 
Y, X = np.mgrid[-3:3:100j, -3:3:100j]
U = -1 - X**2 + Y
V = 1 + X - Y**2
speed = np.sqrt(U*U + V*V)
 
plt.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)
plt.colorbar()
 
f, (ax1, ax2) = plt.subplots(ncols=2)
ax1.streamplot(X, Y, U, V, density=[0.5, 1])
 
lw = 5*speed/speed.max()
ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)
 
plt.show()

椭圆

Ellipse 对象：

In [7]:

from pylab import figure, show, rand
from matplotlib.patches import Ellipse
 
NUM = 250
 
ells = [Ellipse(xy=rand(2)*10, width=rand(), height=rand(), angle=rand()*360)
        for i in range(NUM)]
 
fig = figure()
ax = fig.add_subplot(111, aspect='equal')
for e in ells:
    ax.add_artist(e)
    e.set_clip_box(ax.bbox)
    e.set_alpha(rand())
    e.set_facecolor(rand(3))
 
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)
 
show()

条状图

bar 函数：

In [8]:

import numpy as np
import matplotlib.pyplot as plt
 
 
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
 
opacity = 0.4
error_config = {'ecolor': '0.3'}
 
rects1 = plt.bar(index, means_men, bar_width,
                 alpha=opacity,
                 color='b',
                 yerr=std_men,
                 error_kw=error_config,
                 label='Men')
 
rects2 = plt.bar(index + bar_width, means_women, bar_width,
                 alpha=opacity,
                 color='r',
                 yerr=std_women,
                 error_kw=error_config,
                 label='Women')
 
plt.xlabel('Group')
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(index + bar_width, ('A', 'B', 'C', 'D', 'E'))
plt.legend()
 
plt.tight_layout()
plt.show()

饼状图

pie 函数：

In [9]:

import matplotlib.pyplot as plt
 
 
# The slices will be ordered and plotted counter-clockwise.
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
 
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
        autopct='%1.1f%%', shadow=True, startangle=90)
# Set aspect ratio to be equal so that pie is drawn as a circle.
plt.axis('equal')
 
plt.show()

图像中的表格

table 函数：

In [10]:

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(columns)))
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.array([0.0] * 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])
    y_offset = y_offset + data[row]
    cell_text.append(['%1.1f' % (x/1000.0) for x in y_offset])
# 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,
                      rowLabels=rows,
                      rowColours=colors,
                      colLabels=columns,
                      loc='bottom')
 
# 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()

散点图

scatter 函数：

In [11]:

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 python datetime.date as an object array in
# the date column
datafile = cbook.get_sample_data('goog.npy')
price_data = np.load(datafile).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=20)
ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=20)
ax.set_title('Volume and percent change')
 
ax.grid(True)
fig.tight_layout()
 
plt.show()

设置按钮

matplotlib.widgets 模块：

In [12]:

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)
t = np.arange(0.0, 1.0, 0.001)
a0 = 5
f0 = 3
s = a0*np.sin(2*np.pi*f0*t)
l, = plt.plot(t,s, lw=2, color='red')
plt.axis([0, 1, -10, 10])
 
axcolor = 'lightgoldenrodyellow'
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
axamp  = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
 
sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)
samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
 
def update(val):
    amp = samp.val
    freq = sfreq.val
    l.set_ydata(amp*np.sin(2*np.pi*freq*t))
    fig.canvas.draw_idle()
sfreq.on_changed(update)
samp.on_changed(update)
 
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
    sfreq.reset()
    samp.reset()
button.on_clicked(reset)
 
rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)
radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
def colorfunc(label):
    l.set_color(label)
    fig.canvas.draw_idle()
radio.on_clicked(colorfunc)
 
plt.show()

填充曲线

fill 函数：

In [13]:

import numpy as np
import matplotlib.pyplot as plt
 
 
x = np.linspace(0, 1)
y = np.sin(4 * np.pi * x) * np.exp(-5 * x)
 
plt.fill(x, y, 'r')
plt.grid(True)
plt.show()

时间刻度

In [14]:

"""
Show how to make date plots in matplotlib using date tick locators and
formatters.  See major_minor_demo1.py for more information on
controlling major and minor ticks
 
All matplotlib date plotting is done by converting date instances into
days since the 0001-01-01 UTC.  The conversion, tick locating and
formatting is done behind the scenes so this is most transparent to
you.  The dates module provides several converter functions date2num
and num2date
 
"""
import datetime
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 python datetime.date as an object array in
# the date column
datafile = cbook.get_sample_data('goog.npy')
r = np.load(datafile).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)
 
datemin = datetime.date(r.date.min().year, 1, 1)
datemax = datetime.date(r.date.max().year+1, 1, 1)
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()

金融数据

In [15]:

import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
 
 
startdate = datetime.date(2006,1,1)
today = enddate = datetime.date.today()
ticker = 'SPY'
 
 
fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)
# a numpy record array with fields: date, open, high, low, close, volume, adj_close)
 
r = mlab.csv2rec(fh); fh.close()
r.sort()
 
 
def moving_average(x, n, type='simple'):
    """
    compute an n period moving average.
 
    type is 'simple' | 'exponential'
 
    """
    x = np.asarray(x)
    if type=='simple':
        weights = np.ones(n)
    else:
        weights = np.exp(np.linspace(-1., 0., n))
 
    weights /= weights.sum()
 
 
    a =  np.convolve(x, weights, mode='full')[:len(x)]
    a[:n] = a[n]
    return a
 
def relative_strength(prices, n=14):
    """
    compute the n period relative strength indicator
    http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex
    http://www.investopedia.com/terms/r/rsi.asp
    """
 
    deltas = np.diff(prices)
    seed = deltas[:n+1]
    up = seed[seed>=0].sum()/n
    down = -seed[seed<0].sum()/n
    rs = up/down
    rsi = np.zeros_like(prices)
    rsi[:n] = 100. - 100./(1.+rs)
 
    for i in range(n, len(prices)):
        delta = deltas[i-1] # cause the diff is 1 shorter
 
        if delta>0:
            upval = delta
            downval = 0.
        else:
            upval = 0.
            downval = -delta
 
        up = (up*(n-1) + upval)/n
        down = (down*(n-1) + downval)/n
 
        rs = up/down
        rsi[i] = 100. - 100./(1.+rs)
 
    return rsi
 
def moving_average_convergence(x, nslow=26, nfast=12):
    """
    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
    return value is emaslow, emafast, macd which are len(x) arrays
    """
    emaslow = moving_average(x, nslow, type='exponential')
    emafast = moving_average(x, nfast, type='exponential')
    return emaslow, emafast, emafast - emaslow
 
 
plt.rc('axes', grid=True)
plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)
 
textsize = 9
left, width = 0.1, 0.8
rect1 = [left, 0.7, width, 0.2]
rect2 = [left, 0.3, width, 0.4]
rect3 = [left, 0.1, width, 0.2]
 
 
fig = plt.figure(facecolor='white')
axescolor  = '#f6f6f6'  # the axes background color
 
ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
ax2t = ax2.twinx()
ax3  = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)
 
 
 
### plot the relative strength indicator
prices = r.adj_close
rsi = relative_strength(prices)
fillcolor = 'darkgoldenrod'
 
ax1.plot(r.date, rsi, color=fillcolor)
ax1.axhline(70, color=fillcolor)
ax1.axhline(30, color=fillcolor)
ax1.fill_between(r.date, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor)
ax1.fill_between(r.date, rsi, 30, where=(rsi<=30), facecolor=fillcolor, edgecolor=fillcolor)
ax1.text(0.6, 0.9, '>70 = overbought', va='top', transform=ax1.transAxes, fontsize=textsize)
ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes, fontsize=textsize)
ax1.set_ylim(0, 100)
ax1.set_yticks([30,70])
ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes, fontsize=textsize)
ax1.set_title('%s daily'%ticker)
 
### plot the price and volume data
dx = r.adj_close - r.close
low = r.low + dx
high = r.high + dx
 
deltas = np.zeros_like(prices)
deltas[1:] = np.diff(prices)
up = deltas>0
ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_')
ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_')
ma20 = moving_average(prices, 20, type='simple')
ma200 = moving_average(prices, 200, type='simple')
 
linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')
linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')
 
 
last = r[-1]
s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (
    today.strftime('%d-%b-%Y'),
    last.open, last.high,
    last.low, last.close,
    last.volume*1e-6,
    last.close-last.open )
t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)
 
props = font_manager.FontProperties(size=10)
leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props)
leg.get_frame().set_alpha(0.5)
 
 
volume = (r.close*r.volume)/1e6  # dollar volume in millions
vmax = volume.max()
poly = ax2t.fill_between(r.date, volume, 0, label='Volume', facecolor=fillcolor, edgecolor=fillcolor)
ax2t.set_ylim(0, 5*vmax)
ax2t.set_yticks([])
 
 
### compute the MACD indicator
fillcolor = 'darkslategrey'
nslow = 26
nfast = 12
nema = 9
emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)
ema9 = moving_average(macd, nema, type='exponential')
ax3.plot(r.date, macd, color='black', lw=2)
ax3.plot(r.date, ema9, color='blue', lw=1)
ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)
 
 
ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema), va='top',
         transform=ax3.transAxes, fontsize=textsize)
 
#ax3.set_yticks([])
# turn off upper axis tick labels, rotate the lower ones, etc
for ax in ax1, ax2, ax2t, ax3:
    if ax!=ax3:
        for label in ax.get_xticklabels():
            label.set_visible(False)
    else:
        for label in ax.get_xticklabels():
            label.set_rotation(30)
            label.set_horizontalalignment('right')
 
    ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
 
 
 
class MyLocator(mticker.MaxNLocator):
    def __init__(self, *args, **kwargs):
        mticker.MaxNLocator.__init__(self, *args, **kwargs)
 
    def __call__(self, *args, **kwargs):
        return mticker.MaxNLocator.__call__(self, *args, **kwargs)
 
# at most 5 ticks, pruning the upper and lower so they don't overlap
# with other ticks
#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
 
ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))
ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))
 
plt.show()