import numpy as np

import matplotlib

import matplotlib.pyplot as plt

plt.ion()

# TODO axvline axhline

# TODO axhspan axvspan

# TODO demo_xxx

# TODO set_xticks set_xticklabels

def demo_basic00():

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

y = np.sin(x)

fig = plt.figure()

ax = fig.add_axes([0.1,0.1,0.8,0.8])

ax.plot(x, y, label='line0')

ax.plot(x, y+1, label='line1')

# useful keywords: linestyle linewidth markerstyle markersize markevery

ax.set_xlabel('x')

ax.set_ylabel('y')

ax.grid()

ax.set_title('this is axes title')

ax.legend()

fig.suptitle('this is figure title')

# close windows is not enough, TODO add link

plt.close(fig)

# plt.close(plt.gcf())

# plt.close('all')

def demo_basic_plot_type():

# hist

fig,ax = plt.subplots()

ax = fig.add_axes([0.1,0.1,0.8,0.8])

ax.hist(np.random.randn(500), bins=50, density=True)

ax.set_title('.hist()')

plt.show()

# bar

N0 = 5

mean1 = np.random.randint(50, 100, [N0])

std1 = np.random.randint(5, 10, [N0])

mean2 = np.random.randint(60, 80, [N0])

std2 = np.random.randint(2, 5, [N0])

fig,ax = plt.subplots()

bar_width = 0.35

tmp0 = dict(width=bar_width, alpha=0.4, error_kw={'ecolor':'0.3'})

ax.bar(np.arange(N0), mean1, color='b', yerr=std1, label='bar0', **tmp0)

ax.bar(np.arange(N0)+bar_width, mean2, color='r', yerr=std2, label='bar1', **tmp0)

ax.set_xticks(np.arange(N0) + bar_width/2)

ax.set_xticklabels(['x'+str(ind1) for ind1 in range(N0)])

ax.legend()

ax.set_title('.bar()')

def demo_plot_manage_spline():

x = np.linspace(-np.pi, np.pi, 300)

y1 = np.cos(x)

y2 = np.sin(x)

fig,ax = plt.subplots(figsize=(8,6))

ax.plot(x, y1, linewidth=1, label='cosine')

ax.plot(x, y2, linewidth=1, label='sine')

ax.set_xlim(-4, 4)

ax.set_ylim(-1.1, 1.1)

ax.set_xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi])

ax.set_xticklabels([r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

ax.set_yticks([-1, 0, 1])

ax.set_yticklabels([r'$-1$', r'$0$', r'$+1$'])

ax.legend(loc='upper left', frameon=False)

# moving spines

ax.spines['right'].set_color('none')

ax.spines['top'].set_color('none')

ax.xaxis.set_ticks_position('bottom')

ax.spines['bottom'].set_position(('data',0))

ax.yaxis.set_ticks_position('left')

ax.spines['left'].set_position(('data',0))

# plt.annotate

for label in ax.get_xticklabels() + ax.get_yticklabels():

label.set_fontsize(16)

label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65))

def demo_axis_formatter():

fig,ax = plt.subplots()

ax.plot([0,1], [0,1])

hf0 = lambda x,pos: 'a{:1.1f}bb'.format(x)

ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(hf0))

ax.yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('b%1.2fcc'))

def demo_streamplot():

x,y = np.meshgrid(np.linspace(-3,3,100), np.linspace(-3,3,100))

u = -1 - x**2 + y

v = 1 + x - y**2

speed = np.sqrt(u**2 + v**2)

fig = plt.figure()

(ax0,ax1),(ax2,ax3) = fig.subplots(2,2)

ax0.streamplot(x, y, u, v, density=[0.5,1])

ax0.set_title('varying density')

strm = ax1.streamplot(x, y, u, v, color=u, linewidth=2, cmap='autumn')

fig.colorbar(strm.lines, ax=ax1)

ax1.set_title('varying color')

ax2.streamplot(x, y, u, v, density=0.6, color='k', linewidth=5*speed/speed.max())

ax2.set_title('varying line width')

seed_points = np.array([[-2, -1, 0, 1, 2, -1], [-2, -1, 0, 1, 2, 2]])

strm = ax3.streamplot(x, y, u, v, color=u, linewidth=2, cmap='autumn', start_points=seed_points.T)

fig.colorbar(strm.lines, ax=ax3)

ax3.set_title('controlling starting points')

def demo_style():

x = np.linspace(0, 2*np.pi)

y = np.sin(x)

with plt.style.context('dark_background'):

fig,ax = plt.subplots()

ax.plot(x, y, 'r-o')

def demo_contourf():

# see https://matplotlib.org/gallery/images_contours_and_fields/pcolormesh_levels.html

x,y = np.meshgrid(np.linspace(1,5,100), np.linspace(1,5,90))

z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)

fig,ax = plt.subplots()

level = matplotlib.ticker.MaxNLocator(nbins=15).tick_values(z.min(), z.max())

hContourf = ax.contourf(x, y, z, levels=level, cmap=plt.get_cmap('PiYG'))

fig.colorbar(hContourf, ax=ax)

def demo_colorbar():

fig,ax = plt.subplots()

image = ax.imshow(np.arange(100).reshape((10, 10)))

fig.colorbar(image)

def demo_colorbar_multiple_image():

fig, tmp0 = plt.subplots(2, 2)

ax_list = [tmp0[0,0], tmp0[0,1], tmp0[1,0], tmp0[1,1]]

image_list = [ax.imshow(np.random.uniform(x,x+1, size=(10,20)), cmap='cool') for x,ax in enumerate(ax_list)]

tmp0 = min(x.get_array().min() for x in image_list)

tmp1 = max(x.get_array().max() for x in image_list)

norm = matplotlib.colors.Normalize(vmin=tmp0, vmax=tmp1)

for x in image_list:

x.set_norm(norm)

fig.colorbar(image_list[0], ax=ax_list, orientation='horizontal', fraction=0.1)

for x in ax_list:

x.xaxis.set_visible(False)

x.yaxis.set_visible(False)

def demo_xkcd():

xdata = np.linspace(0, 2*np.pi, 50)

ydata = np.sin(5*xdata) - 0.5*xdata

with plt.xkcd():

fig,ax = plt.subplots()

ax.plot(xdata, ydata)

ax.set_xlabel('time')

ax.set_title('personal emotion state')

fig.tight_layout()

def demo_multicolored_line():

xdata = np.linspace(0, 3*np.pi, 500)

ydata = np.sin(xdata)

cdata = np.cos((xdata[1:]+xdata[:-1])/2) #use first derivative as color

fig,ax = plt.subplots()

tmp0 = np.stack([xdata,ydata],axis=1)

segments = np.stack([tmp0[:-1],tmp0[1:]], axis=1)

norm = plt.Normalize(cdata.min(), cdata.max())

lc = matplotlib.collections.LineCollection(segments, cmap='viridis', norm=norm)

lc.set_array(cdata)

lc.set_linewidth(2)

line = ax.add_collection(lc)

ax.set_xlim(xdata.min(), xdata.max())

ax.set_ylim(ydata.min()-0.1, ydata.max()+0.1)