matplotlib-study

Usage Guide

Pyplot tutorial

python
import matplotlib.pyplot as plt
import numpy as np



fig = plt.figure()
fig.suptitle("No axes")
fig, ax_lst = plt.subplots(2, 2)


<Figure size 432x288 with 0 Axes>

x = np.linspace(0, 2, 100)

plt.plot(x, x, label='linear')
plt.plot(x, x**2, label='quadratic')
plt.plot(x, x**3, label='cubic')

plt.xlabel('x label')
plt.ylabel('y label')

plt.title("Simple Plot")

plt.legend()

x = np.arange(0, 10, 0.2)
y = np.sin(x)
fig, ax = plt.subplots()
ax.plot(x, y)
plt.show()

def my_plotter(ax, data1, data2, param_dict):
    """
    A helper function to make a graph

    Parameters
    ----------
    ax : Axes
        The axes to draw to

    data1 : array
       The x data

    data2 : array
       The y data

    param_dict : dict
       Dictionary of kwargs to pass to ax.plot

    Returns
    -------
    out : list
        list of artists added
    """
    out = ax.plot(data1, data2, **param_dict)
    return out

# which you would then use as:

data1, data2, data3, data4 = np.random.randn(4, 100)
fig, ax = plt.subplots(1, 1)
my_plotter(ax, data1, data2, {'marker': 'x'})



[<matplotlib.lines.Line2D at 0xc23e4a8>]

fig, (ax1, ax2) = plt.subplots(1, 2)
my_plotter(ax1, data1, data2, {'marker': 'x'})
my_plotter(ax2, data3, data4, {'marker': 'o'})


[<matplotlib.lines.Line2D at 0xc570ba8>]

pyplot tutorial

plt.plot([1, 2, 3, 4]) # y valus， x values are automatically generated
plt.ylabel('some numbers')
plt.show()

plt.plot([1, 2, 3, 4], [1, 4, 9, 16]) # default format is b-, which is solid blue line


[<matplotlib.lines.Line2D at 0xc64c7f0>]

plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro') # red circles, color and line type
plt.axis([0, 6, 0, 20])
plt.show()

# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()

data = {'a': np.arange(50),
        'c': np.random.randint(0, 50, 50),
        'd': np.random.randn(50)}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['d']) * 100

plt.scatter('a', 'b', c='c', s='d', data=data) # plotting with keyword strings, 散點圖
plt.xlabel('entry a')
plt.ylabel('entry b')
plt.show()

names = ['group_a', 'group_b', 'group_c']
values = [1, 10, 100]

plt.figure(1, figsize=(9, 3))

plt.subplot(131)
plt.bar(names, values) #柱狀圖
plt.subplot(132)
plt.scatter(names, values) #散點圖
plt.subplot(133)
plt.plot(names, values) #折線圖
plt.suptitle('Categorical Plotting')
plt.show()

lines = plt.plot([1, 2, 3])
plt.setp(lines)


  agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array 
  alpha: float
  animated: bool
  antialiased: bool
  clip_box: `.Bbox`
  clip_on: bool
  clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None] 
  color: color
  contains: callable
  dash_capstyle: {'butt', 'round', 'projecting'}
  dash_joinstyle: {'miter', 'round', 'bevel'}
  dashes: sequence of floats (on/off ink in points) or (None, None)
  drawstyle: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}
  figure: `.Figure`
  fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}
  gid: str
  in_layout: bool
  label: object
  linestyle: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
  linewidth: float
  marker: unknown
  markeredgecolor: color
  markeredgewidth: float
  markerfacecolor: color
  markerfacecoloralt: color
  markersize: float
  markevery: unknown
  path_effects: `.AbstractPathEffect`
  picker: float or callable[[Artist, Event], Tuple[bool, dict]]
  pickradius: float
  rasterized: bool or None
  sketch_params: (scale: float, length: float, randomness: float) 
  snap: bool or None
  solid_capstyle: {'butt', 'round', 'projecting'}
  solid_joinstyle: {'miter', 'round', 'bevel'}
  transform: matplotlib.transforms.Transform
  url: str
  visible: bool
  xdata: 1D array
  ydata: 1D array
  zorder: float

multple figues and axes

The figure() command here is optional because figure(1) will be created by default, just as a subplot(111) will be created by default if you don’t manually specify any axes. The subplot() command specifies numrows, numcols, plot_number where plot_number ranges from 1 to numrowsnumcols. The commas in the subplot command are optional if numrowsnumcols<10. So subplot(211) is identical to subplot(2, 1, 1).

def f(t):
    return np.exp(-t) * np.cos(2*np.pi*t)

t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)

plt.figure(1)
# The subplot() command specifies numrows, numcols, plot_number where plot_number ranges from 1 to numrows*numcols.
# The commas in the subplot command are optional if numrows*numcols<10. So subplot(211) is identical to subplot(2, 1, 1).
plt.subplot(211) # 兩行一列，編號1作圖區
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')

plt.subplot(212) # 兩行一列，編號2作圖區
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
plt.show()

import matplotlib.pyplot as plt
plt.figure(1)                # the first figure
plt.subplot(211)             # the first subplot in the first figure
plt.plot([1, 2, 3])
plt.subplot(212)             # the second subplot in the first figure
plt.plot([4, 5, 6])


plt.figure(2)                # a second figure
plt.plot([4, 5, 6])          # creates a subplot(111) by default

plt.figure(1)                # figure 1 current; subplot(212) still current
plt.subplot(211)             # make subplot(211) in figure1 current
plt.title('Easy as 1, 2, 3') # subplot 211 title




Text(0.5, 1.0, 'Easy as 1, 2, 3')

Text

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)

# the histogram of the data
n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)


plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()

Annotating text

ax = plt.subplot(111)

t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = plt.plot(t, s, lw=2)

plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
             arrowprops=dict(facecolor='black', shrink=0.05),
             )

plt.ylim(-2, 2)
plt.show()

Nonlinear axes

from matplotlib.ticker import NullFormatter  # useful for `logit` scale

# Fixing random state for reproducibility
np.random.seed(19680801)

# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))

# plot with various axes scales
plt.figure(1)

# linear
plt.subplot(221)
plt.plot(x, y)
plt.yscale('linear')
plt.title('linear')
plt.grid(True)


# log
plt.subplot(222)
plt.plot(x, y)
plt.yscale('log')
plt.title('log')
plt.grid(True)


# symmetric log
plt.subplot(223)
plt.plot(x, y - y.mean())
plt.yscale('symlog', linthreshy=0.01)
plt.title('symlog')
plt.grid(True)

# logit
plt.subplot(224)
plt.plot(x, y)
plt.yscale('logit')
plt.title('logit')
plt.grid(True)
# Format the minor tick labels of the y-axis into empty strings with
# `NullFormatter`, to avoid cumbering the axis with too many labels.
plt.gca().yaxis.set_minor_formatter(NullFormatter())
# Adjust the subplot layout, because the logit one may take more space
# than usual, due to y-tick labels like "1 - 10^{-3}"
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
                    wspace=0.35)

plt.show()