Plotting Tips¶
Masaru Nakanotani
CSPAR, UAH, Huntsville, AL 35805, USA
Feb/01/2021 (Date created)
Feb/26/2025 (Last modified)
In this post, I show some tips to create plots using Matplotlib.
useful sites: https://pythonmatplotlibtips.blogspot.com/, https://sabopy.com/
You can toggle input cells by clicking a prompt (i.e. In [ ]: ) or an output cell.
In [1]:
%config InlineBackend.figure_format='retina'
from spacepy import pycdf
import math as mt
import matplotlib.pyplot as plt
import numpy as np
import scipy.fft as sf
import datetime
import bisect
import matplotlib.patches as patches
import matplotlib.dates as mdates
from IPython.display import display,Math
import matplotlib as mpl
import pandas as pd
from matplotlib.gridspec import GridSpec
import matplotlib.animation as animation
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset
from matplotlib.ticker import ScalarFormatter
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
plot parameters¶
You need to define these different from the cell which called matplotlib.pyplot
In [2]:
### This is optional: if you want to use a not-bulid-in font in your system, you can use this cell to read it.
### for more information: https://scentellegher.github.io/visualization/2018/05/02/custom-fonts-matplotlib.html ###
from matplotlib import font_manager
font_dirs = ['/Library/Fonts/']
font_files = font_manager.findSystemFonts(fontpaths=font_dirs)
for font_file in font_files:
font_manager.fontManager.addfont(font_file)
# set font
mpl.rcParams['font.family'] = 'Avenir Next World'
In [3]:
color='#2c2c2c'
#color='#ffffff'
#mpl.rcParams['font.family' ]='Helvetica Neue'#, 'Times New Roman'
mpl.rcParams['mathtext.default' ]='regular'
mpl.rcParams["mathtext.fallback"]=None
#mpl.rcParams['font.family' ]='Avenir', 'Times'
#mpl.rcParams['mathtext.fontset'] = 'custom'
#mpl.rcParams['mathtext.default' ]='rm'
#mpl.rcParams['mathtext.rm'] = 'Avenir'
mpl.rcParams['font.size' ]=15
mpl.rcParams['figure.figsize' ]=(4,2)
mpl.rcParams['figure.dpi' ]=180
mpl.rcParams['axes.linewidth' ]=1.2
mpl.rcParams['xtick.major.size' ]=5
mpl.rcParams['xtick.minor.size' ]=4
mpl.rcParams['ytick.major.size' ]=5
mpl.rcParams['ytick.minor.size' ]=3
mpl.rcParams['xtick.major.width']=1
mpl.rcParams['ytick.major.width']=1
mpl.rcParams['xtick.direction' ]='out'
mpl.rcParams['ytick.direction' ]='out'
mpl.rcParams['axes.facecolor' ]='None'
mpl.rcParams['figure.facecolor' ]='None'
mpl.rcParams['axes.edgecolor' ]=color
mpl.rcParams['xtick.color' ]=color
mpl.rcParams['ytick.color' ]=color
mpl.rcParams['axes.labelcolor' ]=color
mpl.rcParams['text.color' ]=color
mpl.rcParams['patch.edgecolor' ]=color
mpl.rcParams['savefig.bbox' ]='tight'
mpl.rcParams['animation.html' ]='jshtml'
mpl.rcParams['animation.embed_limit'] = 256 # Set limit to 256MB
###color scheme for lines: https://www.nature.com/articles/nmeth.1618 ###
from cycler import cycler
line_cycler = (cycler(color =[color, '#e69f00', '#56b4e9', '#009e73', '#f0e442', '#0072b2', '#d55e00', '#cc79a7']))
#line_cycler = (cycler(color =[color, color, color, color])+
# cycler(linestyle=["-" , "--" , "-." , ":" ]))
mpl.rcParams['axes.prop_cycle']=line_cycler
#########################################################################
In [4]:
x = np.linspace(0., 1, 200)
plt.rc("axes", prop_cycle=line_cycler)
plt.figure(figsize=(6, 2))
plt.plot(x, np.sin(np.pi*x), label=r"$\sin(\pi x)$")
plt.plot(x, np.cos(np.pi*x), label=r"$\cos(\pi x)$")
plt.plot(x, np.sin(2*np.pi*x), label=r"$\sin(2\pi x)$")
plt.plot(x, np.cos(2*np.pi*x), label=r"$\cos(2\pi x) \alpha a$")
plt.plot(x, np.sin(4*np.pi*x), label=r"$\cos(4\pi x) \alpha a$")
plt.plot(x, np.cos(4*np.pi*x), label=r"$\beta cos(4\pi x) \alpha a \chi$")
plt.legend(bbox_to_anchor=(1.05, 1.05))
plt.xlabel(r"$x$xo0O1lIi $\gamma\kappa\omega\Omega\theta\pi$")
plt.xlim(x[0], x[-1])
plt.ylabel("Function Values")
plt.show()
In [5]:
def plot_cube_faces(arr, ax):
x0 = np.arange(arr.shape[2])
y0 = np.arange(arr.shape[1])
z0 = np.arange(arr.shape[0])
z, y, x = np.meshgrid(z0, y0, x0,indexing='ij')
xmax, ymax, zmax = max(x0), max(y0), max(z0)
xmin, ymin, zmin = min(x0), min(y0), min(z0)
vmin, vmax = np.min(arr), np.max(arr)
ax.contourf(x[0,:,:],y[0,:,:],arr[-1,:,:],100,zorder=-1,
zdir='z',offset=zmax,vmin=vmin,vmax=vmax)
ax.contourf(x[:,0,:],arr[:,0,:],z[:,0,:],100,zorder=-1,
zdir='y',offset=0,vmin=vmin,vmax=vmax)
ax.contourf(arr[:,:,-1],y[:,:,0],z[:,:,0],100,zorder=-1,
zdir='x',offset=xmax,vmin=vmin,vmax=vmax)
edges_kw = dict(color='r', linewidth=1, zorder=1e3)
ax.plot([xmax, xmax], [ymin, ymax], zmax, **edges_kw)
ax.plot([xmin, xmax], [ymin, ymin], zmax, **edges_kw)
ax.plot([xmax, xmax], [ymin, ymin], [zmin, zmax], **edges_kw)
x0 = np.arange(128)
y0 = np.arange(64)
z0 = np.arange(32)
z, y, x = np.meshgrid(z0, y0, x0,indexing='ij')
arr = (x + y + z) // 10
fig = plt.figure(figsize=(2,2))
ax = fig.add_subplot(111, projection='3d')
ax.set_box_aspect(aspect = (4,2,1))
plot_cube_faces(arr, ax)
plt.show()
Your own colormap¶
In [6]:
### you need "LinearSegmentedColormap"
#from matplotlib.colors import LinearSegmentedColormap
colors = ['#1164B4', 'white', 'yellow', 'red','black']
cmap1 = LinearSegmentedColormap.from_list("mycmap", colors)
x=np.linspace(0,2*np.pi,100); y=np.linspace(0,2*np.pi,100)
X,Y=np.meshgrid(x,y)
Z=np.sin(X)*np.cos(Y)
plt.imshow(Z,cmap=cmap1)
plt.colorbar()
plt.show()
Line and marker styles¶
http://scipy-lectures.org/intro/matplotlib/auto_examples/options/plot_linestyles.html
In [7]:
def linestyle(ls, i):
X = i * 1 * np.ones(11)
Y = np.arange(11)
plt.plot(X, Y, ls, color=(.0, .0, 1, 1), lw=3, ms=8,
mfc=(.75, .75, 1, 1), mec=(0, 0, 1, 1))
plt.text(1 * i, 10.5, ls, rotation=0, fontsize=12, va='bottom', ha='center')
linestyles = ['-', '--', ':', '-.', '.', ',', 'o', '^', 'v', '<', '>', 's',
'+', 'x', 'd', '*', '1', '2', '3', '4', 'h', 'p', '|', '_', 'D', 'H', 'P']
n_lines = len(linestyles)
size = 40 * n_lines, 300
dpi = 120
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
plt.axes([0, 0.01, 1, .9], frameon=False)
for i, ls in enumerate(linestyles):
linestyle(ls, i)
#plt.xlim(-.2, .2 + 1*n_lines)
plt.xticks([])
plt.yticks([])
plt.show()
Multiple polar plots¶
In [8]:
fig, ax = plt.subplots(nrows=2,ncols=1,figsize=(10,3),sharex='all')
nx=1600
ny=64
r =np.linspace(5,40,nx)
th=np.linspace(-10,10,ny,endpoint=True)/180*np.pi
R,Th=np.meshgrid(r,th)
X=R*np.cos(Th)
Y=R*np.sin(Th)
im1=ax[0].pcolormesh(X,Y,np.sin(R),shading='auto')
ax[0].set_aspect('auto')
ax[0].spines['right'].set_color('none')
ax[0].spines['top' ].set_color('none')
cb1=fig.colorbar(im1, ax=ax[0])
im2=ax[1].pcolormesh(X,Y,R,shading='auto')
ax[1].set_aspect('auto')
ax[1].spines['right'].set_color('none')
ax[1].spines['top' ].set_color('none')
ax[1].set_xlim(5,38)
cb2=fig.colorbar(im2, ax=ax[1])
plt.show()
Change color according to values¶
In [9]:
from matplotlib import colors
x=np.linspace(0,6*np.pi,1000)
y=2*np.sin(x)+np.random.randn(1000)
# make a color map of fixed colors
cmap = colors.ListedColormap(['orange', 'blue'])
bounds=([-5,1,5])
norm = colors.BoundaryNorm(bounds, cmap.N)
plt.scatter(x,y,s=5,c=y,cmap=cmap,norm=norm,alpha=0.5)
#plt.colorbar()
plt.hlines(1,x.min(),x.max(),linestyles='dashed')
### another way ###
#plt.scatter(x[y< 1],y[y< 1],s=5,c='orange',alpha=0.5)
#plt.scatter(x[y>=1],y[y>=1],s=5,c='blue' ,alpha=0.5)
###################
plt.show()
Step plot¶
Using the 'drawstyle' parameter of plt.plot, we can obtain the same plot.
In [10]:
x = np.arange(14)
y = np.sin(x / 2)
fig=plt.figure(figsize=(6,3))
plt.step(x, y + 2, where='pre', label='pre (default)')
plt.plot(x, y + 2, 'o--', color='grey', alpha=0.3)
plt.step(x, y + 1, where='mid', label='mid')
plt.plot(x, y + 1, 'o--', color='grey', alpha=0.3)
plt.step(x, y, where='post', label='post')
plt.plot(x, y, 'o--', color='grey', alpha=0.3)
plt.grid(axis='x', color='0.95')
plt.legend(title='Parameter where:')
plt.title('plt.step(where=...)')
plt.show()
Markevery plot¶
In [11]:
x = np.linspace(0, 2*np.pi, 200)
y = np.cos(x)
fig,ax=plt.subplots()
ax.plot(x, y, 'o', ls='-', ms=4, markevery=10)
plt.show()
Ticks control¶
In [12]:
import matplotlib.ticker as ticker
x=np.linspace(0,8,100)
y=np.cos(x)
fig,ax=plt.subplots(ncols=2,figsize=(8,2))
ax[0].plot(x,y)
ax[0].set_xticks([0, 2, 4,5, 6, 7,8])
ax[0].set_xticklabels([])
ax[0].xaxis.set_minor_locator(ticker.MultipleLocator(0.2))
ax[0].set_title('without tick lables')
ax[1].plot(x,y)
ax[1].set_xticks([0, 2, 4, 6, 8])
ax[1].set_xticklabels(['A', 'B', 'C', 'D', 'E'])
ax[1].xaxis.set_minor_locator(ticker.MultipleLocator(0.5))
ax[1].set_title('replaced by texts')
plt.show()
In [13]:
import matplotlib.ticker as ticker
t = np.arange(0.0, 120.0, 1)
y = np.sin(0.1 * np.pi * t) * np.exp(-t * 0.03)
fig,ax=plt.subplots(nrows=2,ncols=4,figsize=(16,5))
fig.subplots_adjust(hspace=0.4)
ax[0,0].plot(t,y)
#ax[0,0].xaxis.set_major_locator(ticker.NullLocator())
#ax[0,0].xaxis.set_minor_locator(ticker.NullLocator())
ax[0,0].xaxis.set_major_formatter(ticker.NullFormatter())
ax[0,0].set_title( "NullLocator()", fontsize=14)
ax[0,1].plot(t,y)
ax[0,1].xaxis.set_major_locator(ticker.MultipleLocator(20))
ax[0,1].xaxis.set_minor_locator(ticker.MultipleLocator(5))
ax[0,1].set_title( "MultipleLocator(20, 5)", fontsize=14)
#ax[0,1].xaxis.set_major_formatter(ticker.NullFormatter())
majors = [0, 20, 40, 80]
minors = np.linspace(0, 20, 11)[1:-1] #array([ 2., 4., 6., 8., 10., 12., 14., 16., 18.])
ax[0,2].plot(t,y)
ax[0,2].xaxis.set_major_locator(ticker.FixedLocator(majors))
ax[0,2].xaxis.set_minor_locator(ticker.FixedLocator(minors))
ax[0,2].set_title( "FixedLocator(0, 20, 40, 80)", fontsize=14)
ax[0,3].plot(t,y)
ax[0,3].set_xlim(0,120)
ax[0,3].xaxis.set_major_locator(ticker.LinearLocator(6))
ax[0,3].xaxis.set_minor_locator(ticker.LinearLocator(31))
ax[0,3].set_title( "LinearLocator(6)", fontsize=14)
ax[1,0].plot(t,y)
ax[1,0].xaxis.set_major_locator(ticker.IndexLocator(base=20, offset=10))
ax[1,0].set_title( "IndexLocator(base=20, offset=10)", fontsize=14)
ax[1,1].plot(t,y)
ax[1,1].xaxis.set_major_locator(ticker.AutoLocator())
ax[1,1].xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax[1,1].set_title( "AutoLocator", fontsize=14)
ax[1,2].plot(t,y)
ax[1,2].xaxis.set_major_locator(ticker.MaxNLocator(4))
ax[1,2].xaxis.set_minor_locator(ticker.MaxNLocator(8))
ax[1,2].set_title( "MaxNLocator", fontsize=14)
ax[1,3].plot(t,y)
ax[1,3].set_xlim(10**0, 10**5)
ax[1,3].set_xscale('log')
ax[1,3].xaxis.set_major_locator(ticker.LogLocator(base=10.0,subs=(1,10), numticks=7))
#ax[1,3].xaxis.set_minor_locator(ticker.LogLocator(base=10.0,subs=(1,10), numticks=7))
ax[1,3].xaxis.set_minor_locator(ticker.LogLocator(base=10.0,subs=np.arange(2, 10)*.1,numticks=7))
ax[1,3].xaxis.set_minor_formatter(ticker.NullFormatter())
ax[1,3].set_title( "LogLocator", fontsize=14)
plt.show()
Combine axes¶
In [14]:
fig=plt.figure()
ax1=fig.add_axes([0,0,1,1], label='cartesian', projection=None)
ax1.set_xlim(0,10)
ax1.set_ylim(0,10)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_aspect('equal')
ax2=fig.add_axes([0,0,1,1], label='polar' , projection='polar')
ax2.set_ylim(2,10)
ax2.set_thetamin(0)
ax2.set_thetamax(90)
ax2.set_rorigin(-2)
ax2.set_xticklabels([])
ax2.set_yticklabels([])
#ax2.set_rlabel_position(22.5) # Move radial labels away from plotted line
ax2.grid(True)
Annotate¶
In [15]:
fig,ax = plt.subplots(figsize=(6,2))
x = np.linspace(0,7,100)
y = np.random.randn(100)
ax.plot(x,y)
ax.scatter(3,0.1)
ax.annotate('test', xy=(3,0.1), xytext=(4,2),color='r',
arrowprops=dict(arrowstyle='->',connectionstyle='arc3, rad=-0.1',color='r'),
bbox=dict(boxstyle='round,pad=-0.07', fc='none', ec='none'),)
plt.show()
In [16]:
x = ["a","b","c","d"]
y1 = np.array([3,8,6,4])
y2 = np.array([10,2,4,3])
plt.figure(figsize=(4,3))
plt.bar(x, y1, label='y1')
plt.bar(x, y2 ,bottom=y1,label='y2')
# add text annotation corresponding to the values of each bar.
for xpos, ypos, yval in zip(x, y1/2, y1):
plt.text(xpos, ypos, yval, ha="center", va="center",color='w')
for xpos, ypos, yval in zip(x, y1+y2/2, y2):
plt.text(xpos, ypos, yval, ha="center", va="center")
# add text annotation corresponding to the "total" value of each bar
for xpos, ypos, yval in zip(x, y1+y2, y1+y2):
plt.text(xpos, ypos, "N=%d"%yval, ha="center", va="bottom")
plt.ylim(0,15)
plt.legend()
plt.show()
In [17]:
fig = plt.figure(figsize=(6,2))
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2,sharex=ax1)
ax1.set_ylabel("y Axis 1")
ax2.set_ylabel("y Axis 2")
ax2.set_xlabel("x Axis")
plt.setp(ax1.get_xticklabels(),visible=False)
fig.align_ylabels([ax1,ax2])
plt.show()
fig = plt.figure(figsize=(6,2))
ax1 = plt.subplot(2,1,1)
ax2 = plt.subplot(2,1,2,sharex=ax1)
ax1.set_ylabel("y Axis 1")
ax2.set_ylabel("y Axis 2")
ax2.set_xlabel("x Axis")
ax1.yaxis.set_label_coords(-0.1,0.5)
ax2.yaxis.set_label_coords(-0.1,0.5)
plt.setp(ax1.get_xticklabels(),visible=False)
plt.show()
Streamplot¶
In [18]:
x,y = np.linspace(-3,3,100),np.linspace(-3,3,100)
X,Y = np.meshgrid(x,y)
U = -Y
V = X
Z=np.sqrt(X**2+Y**2)
stream_points = np.array((3*(1-2*np.random.rand(100)), 3*(1-2*np.random.rand(100)))).reshape(-1,2)
stream_points = np.array((np.linspace(-3,3,100), np.linspace(-3,3,100))).reshape(-1,2)
fig=plt.figure(figsize=(4,2))
plt.streamplot(X,Y,U,V,color=Z,start_points=stream_points,density=30,linewidth=0.5,arrowstyle='-')
plt.colorbar()
#plt.streamplot(X,Y,U,V,density=2)
plt.plot(stream_points[:,0],stream_points[:,1],'o',ms=2)
plt.grid()
plt.show()
Scientific notation¶
In [19]:
fig, ax = plt.subplots()
ax.plot(range(2003,2012,1),range(200300,201200,100))
ax.ticklabel_format(useOffset=False)
plt.show()
In [20]:
fig,ax=plt.subplots(figsize=(8,2))
ax.plot(np.random.randn(1000)*1e4)
ax.yaxis.set_major_formatter(ScalarFormatter(useMathText=True))
ax.ticklabel_format(axis='y', style='sci', scilimits=(2,2))
ax.yaxis.offsetText.set_fontsize(12)
plt.show()
fig,ax=plt.subplots(figsize=(2,2))
im1=ax.imshow(np.random.randn(100,100)*1e5)
fmt=ScalarFormatter(useMathText=True)
fmt.set_powerlimits((0,0))
cb=fig.colorbar(im1,ax=ax,format=fmt)
cb.ax.yaxis.set_offset_position('left')
cb.update_ticks()
plt.show()
Sky map¶
You can also use cartopy for more flexible maps.
In [21]:
fig = plt.figure(figsize=(6,2))
ax = fig.add_subplot(111, projection='mollweide')
lon = np.linspace(-np.pi, np.pi,36)
lat = np.linspace(-np.pi/2., np.pi/2.,18)
arr = np.random.randn(18, 36)
im = ax.pcolormesh(lon,lat,arr,shading='nearest',cmap='RdBu')
fig.colorbar(im,ax=ax)
plt.show()
Ticks for both sides¶
In [22]:
fig, ax = plt.subplots(nrows=1,ncols=1,figsize=(6,3),sharex='all',dpi=80)
ax.xaxis.set_tick_params(which='major', top='on')
ax.yaxis.set_tick_params(which='both' ,right='on')
ax.set_ylim(0.1,10)
ax.set_yscale('log')
ax.set_xlabel(r'x$\alpha a$a')
plt.show()
In [23]:
fig, ax = plt.subplots(nrows=3,ncols=1,figsize=(6,3),sharex='all',dpi=80)
plt.subplots_adjust(hspace=0.)
for ic in range(3):
ax[ic].xaxis.set_tick_params(which='major', top='on', direction='in', length=5, width=1)
plt.show()
Heat map with log-scaled bins¶
It is better to use 'pcolormesh' rather than 'imshow'
In [24]:
x=np.linspace(0,2*np.pi,200)
y=np.logspace(-1,1,200)
X,Y=np.meshgrid(x,y)
Z=np.cos(X)*np.sin(Y)
fig,ax=plt.subplots(figsize=(8,2))
im1=ax.pcolormesh(X,Y,Z,cmap='jet',shading='auto')
ax.set_yscale('log')
fig.colorbar(im1,ax=ax,label='test',pad=0.02)
plt.show()
Shaded region over several panels¶
In [25]:
#### for observational data ###
#data = cdas.get_data(
# 'sp_phys',
# 'STA_L1_MAG_RTN',
# datetime.datetime(2010, 1, 1, 0, 0, 0),
# datetime.datetime(2010, 1, 1, 1, 0, 1),
# ['BFIELD'],
# cdf=True # download data in CDF format
#)
#
#### convert to the dataframe of pandas ###
#d = {'BR': data['BFIELD'][:,0], 'BT': data['BFIELD'][:,1], 'BN':data['BFIELD'][:,2]}
#df0 = pd.DataFrame(data=d,index=data['Epoch'][:])
#df0.index.name='time'
###########################################
dates = pd.date_range(start='2010-01-01', periods=3601, freq='S')
d={'R':np.linspace(0,100,3601),'BR': np.random.randn(3601), 'BT': np.random.randn(3601), 'BN': np.random.randn(3601)}
df0=pd.DataFrame(data=d, index=dates)
df0.index.name='time'
#print(df0)
fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(8, 2))
ax[2].set_xlim(0,10)
### add a rectangle patch ###
rect = patches.Rectangle((2, 0), 3, 3+2*fig.subplotpars.hspace, color='grey',alpha=.25, clip_on=False,transform=ax[2].get_xaxis_transform())
ax[2].add_patch(rect)
#############################
plt.show()
### date time format ###
fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(8, 2))
ax[2].plot(df0.index[:], df0['BR'][:])
### add a rectangle patch ###
#start='2010-01-01 00:20:00'
#end ='2010-01-01 00:30:00'
#startplt = mdates.date2num(start)
#endplt = mdates.date2num(end)
#rect = patches.Rectangle((startplt, 0), endplt-startplt, 3+2*fig.subplotpars.hspace, color='grey',alpha=.25, clip_on=False, transform=ax[2].get_xaxis_transform())
start = datetime.datetime(2010, 1, 1, 0,20,0)
end = datetime.datetime(2010, 1, 1, 0,30,0)
rect = patches.Rectangle((start, 0), end-start, 3+2*fig.subplotpars.hspace, color='grey',alpha=.25, clip_on=False, transform=ax[2].get_xaxis_transform())
ax[2].add_patch(rect)
#############################
#ax[2].set_xlim(start,end)
plt.show()
Vertical line over several panels¶
In [26]:
fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(8, 2))
ax[2].set_xlim(0,10)
ax[2].axvline(4, 0, 3+2*fig.subplotpars.hspace, ls='dashed', color='k', clip_on=False)
plt.show()
### for observation ###
fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(8, 2))
start = datetime.datetime(2010, 1, 1, 0,20,0)
ax[2].plot(df0.index[:], df0['BR'][:])
ax[2].axvline(start, 0, 3+2*fig.subplotpars.hspace, ls='dashed', color='k', clip_on=False)
plt.show()
Crossed axes and equal aspect¶
In [27]:
fig,ax=plt.subplots(nrows=1,ncols=1,figsize=(4,2))
# Move left y-axis and bottim x-axis to centre, passing through (0,0)
ax.spines['left'].set_position('zero')
ax.spines['bottom'].set_position('center')
# Eliminate upper and right axes
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
# Show ticks in the left and lower axes only
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set_xlim(-5,20)
ax.set_ylim(-5,5)
ax.set_aspect('equal')
plt.show()
Log scale color map¶
In [28]:
fig,ax=plt.subplots(figsize=(4,2))
im1=ax.imshow(10**np.random.randn(100,100),cmap='jet',aspect='auto',origin='lower', norm=mpl.colors.LogNorm())
fig.colorbar(im1,ax=ax,label='test',pad=0.02)
plt.show()
Colorbar scale of date¶
In [29]:
#### plot figure ###
fig, ax = plt.subplots(ncols=1, figsize=(3,3))
N_TICKS = 12
cmap = plt.cm.get_cmap('jet', N_TICKS) # 11 discrete colors
smap = ax.scatter(df0['BR'][:],df0['BT'][:],c=df0.index,cmap=cmap,marker='o',s=10,alpha=0.8)
smap.set_facecolor("none")
indexes = [df0['2010-01-01 00:00'].index[0], df0['2010-01-01 00:15'].index[0], df0['2010-01-01 00:30'].index[0], df0['2010-01-01 00:45'].index[0], df0['2010-01-01 01:00'].index[0]]
cb = fig.colorbar(smap, ax=ax, orientation='vertical', ticks= df0.loc[indexes].index.astype(int), label='hh:mm')
cb.ax.set_yticklabels([index.strftime('%H:%M') for index in indexes])
ax.set_xlabel(r'$B_R$ [nT]')
ax.set_ylabel(r'$B_T$ [nT]')
plt.show()
X label time format¶
In [30]:
### plot figure ###
fig, ax = plt.subplots(figsize=(8,2))
ax.plot(df0.index, df0['BR'][:],'k')
myfmt=mdates.DateFormatter('%H:%M:%S\n%d %b %Y')
ax.xaxis.set_major_formatter(myfmt)
plt.show()
In [31]:
nt=len(df0)
### date time format ###
fig, ax = plt.subplots(nrows=1, sharex=True, figsize=(8, 2))
ax.plot(df0.index[:], df0['BR'][:])
ax.set_xticks([df0.index[nt//4*i] for i in range(5)])
ax.set_xticklabels(['%s\n%d' %(df0.index[nt//4*i].strftime('%M:%S'),df0['R'][nt//4*i]) for i in range(5)])
plt.show()
Different size panels¶
In [32]:
fig7, f7_axs = plt.subplots(ncols=3, nrows=3,figsize=(6,3))
gs = f7_axs[1, 2].get_gridspec()
# remove the underlying axes
for ax in f7_axs[1:, -1]:
ax.remove()
axbig = fig7.add_subplot(gs[1:, -1])
fig7.tight_layout()
In [33]:
widths = [2, 3, 1]
heights = [1, 3, 2]
#gs_kw = dict(width_ratios=widths, height_ratios=heights)
fig6, f6_axes = plt.subplots(ncols=3, nrows=3, figsize=(6,3), constrained_layout=True,gridspec_kw={'width_ratios':widths, 'height_ratios':heights}) #or gridspec_kw=gs_kw
Mixed cartesian and Polar plots¶
In [34]:
### read the H number distribution ###
r =np.linspace(0, 70,100)
th=np.linspace(0,360,200,endpoint=True)/180*np.pi
R,Th=np.meshgrid(r,th)
### plot figure ###
#fig,ax=plt.subplots(nrows=2,figsize=(10,12))
fig=plt.figure(figsize=(12,3),dpi=120)
#plt.rcParams['font.size'] = 10
gs=GridSpec(1,3) # 2 rows, 3 columns
gs.update(wspace=0.1) # set the spacing between axes.
ax0=fig.add_subplot(gs[0,0:2]) # First row, first column
ax1=fig.add_subplot(gs[0,2], polar=True) # First row, second column
#fig.add_subplot(121)
#ax0.plot(mu,label='monthly')
#ax0.plot(mu.rolling("8760D").mean(),label='24 year')
#ax0.legend()
ax0.grid()
ax0.set_xlabel('time [year]')
ax0.set_ylabel(r'$\mu$')
#ax0.set_xlim(mu.index[154],mu.index[634])
#ax=fig.add_subplot(122, polar=True,facecolor='grey')
#CS=ax.pcolormesh(Th,R,data,cmap=plt.cm.get_cmap('coolwarm', 10))
#fig.colorbar(CS, ax=ax,shrink=0.8,ticks=np.linspace(0,0.1,11),label='$n_H$ [cm$^{-3}$]')
#CS=ax1.pcolormesh(Th,R,data2,cmap='coolwarm')
#fig.colorbar(CS, ax=ax1,shrink=1,label='$n_H$ [cm$^{-3}$]')
#ax1.plot(P10rphi[::2000]['phi']-np.pi, P10rphi[::2000]['R'],'k-')
#ax1.plot(V1rphi[::1000]['phi']-np.pi, V1rphi[::1000]['R'],'k-.')
#ax1.plot(V2rphi[::100]['phi']-np.pi, V2rphi[::100]['R'],'k--')
#ax1.grid(False)
#ax.clabel(CS, inline=1, fontsize=10)
#ax.set_yticklabels([])
#ax1.thetagrids([theta * 15 for theta in range(360//15)])
#ax1.set_rmax(70)
#ax1.set_rlabel_position(135/2)
#import matplotlib.text as txt
#ax1.text(177*np.pi/180,60,'P10')
#ax1.text(-10*np.pi/180,60,'V1')
#ax1.text( 30*np.pi/180,60,'V2')
#ax1.text(135/2*np.pi/180,82,'[au]')
#plt.colorbar()
#ax.grid(True)
#cmap = cm.get_cmap('PiYG', 11) # 11 discrete colors
#ax.set_thetamin(0)
#ax.set_thetamax(180)
#fig.tight_layout()
plt.show()
Scatter points over a line¶
In [35]:
x=np.arange(100)
y=np.random.randn(100)
plt.plot(np.zeros(100), 'red', lw=5)
plt.scatter(x,y, zorder=5)
plt.show()
Subplots including a heatmap and a line plot.¶
Also setting gridspec_kw and width_ratios, you can adjust the size of figures.
In [36]:
xx=np.linspace(0,100*np.pi,1000)
yy=np.linspace(0, 2*np.pi,1000)
yy=np.cos(xx/4)*np.exp(-xx/100)
XX,YY=np.meshgrid(xx,yy)
ZZ=np.cos(XX/4)*np.exp(-XX/100)
fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(16, 4))
plt.subplots_adjust(hspace=0.)
im0=ax[0].imshow(np.random.randn(200,1000),aspect='auto',origin='lower')
im1=ax[1].plot(np.random.randn(1000))
im2=ax[2].imshow(np.random.randn(200,1000)*1e5,aspect='auto',origin='lower',cmap='jet')
fig.colorbar(im0,ax=ax[0],label='test' ,aspect=5, pad=0.01)
fig.colorbar(im2,ax=ax[2],label='test\ntest\ntest',aspect=5, pad=0.01)
cb = fig.colorbar(im0,ax=ax[1],aspect=5, pad=0.01)
cb.ax.set_visible(False)
plt.show()
In [37]:
widths = [2, 2, 2]
heights = [1, 1]
gs_kw = dict(width_ratios=widths, height_ratios=heights)
fig, ax = plt.subplots(ncols=3, nrows=2, constrained_layout=True,gridspec_kw=gs_kw)
Z=np.random.randn(20,20)
im1=ax[1,2].imshow(Z, aspect='auto')
fig.colorbar(im1, ax=ax[1,2])
plt.show()
Multiple axes¶
In [38]:
fig,ax=plt.subplots(figsize=(8,2))
ax.plot(np.random.rand(100))
ax.set_ylim(0,3)
ax.set_ylabel('Y values for exp(-x)')
ax.set_xlabel('Same X for both exp(-x) and ln(x)')
ax2=ax.twinx()
ax2.plot(np.random.rand(100)+1,'r')
ax2.set_ylim([0,3])
ax2.set_ylabel('Y values for ln(x)', color='r')
ax2.tick_params(axis='y', colors='r')
ax2.spines["right"].set_edgecolor('r')
ax3=ax.twinx()
ax3.spines["right"].set_position(("axes", 1.12))# move the spine of the second axes outwards
ax3.plot(np.random.rand(100)+2,'g')
ax3.set_ylim(0,3)
ax3.set_ylabel('Y values for ln(x)', color='g')
ax3.tick_params(axis='y', colors='g')
ax3.spines["right"].set_edgecolor('g')
plt.show()
In [39]:
fig,ax=plt.subplots(nrows=1,ncols=2,figsize=(6,1))
plt.subplots_adjust(wspace=0.4,hspace=0.4)
xx=np.linspace(0,2*np.pi,100)
ax[0].plot(xx,np.sin(xx))
ax[1].plot(xx,np.sin(xx))
ax2=np.copy(ax)
for i in range(2):
ax2[i]=ax[i].twinx()
ax2[0].plot(xx,np.cos(xx),'r--')
ax2[1].plot(xx,np.cos(xx),'r--')
plt.show()
Animation of heatmaps¶
In [40]:
def update_anim(it):
fig.clf() #clear the figure
ax=fig.subplots(2, 2) #add subplots
fig.tight_layout() #reduce spacing around the figure
#for i in range(2):
# for j in range(2):
# ax[i,j].cla()
Z1=np.random.randn(N,N) #random numbers in 2D
Z2=np.random.randn(N,N) #random numbers in 2D
Z3=np.random.randn(N,N) #random numbers in 2D
Z4=np.random.randn(N,N) #random numbers in 2D
im1=ax[0,0].imshow(Z1 ,aspect='equal', origin='lower', cmap='Greys')
im2=ax[1,0].imshow(Z2 ,aspect='equal', origin='lower', cmap='Blues')
im3=ax[0,1].imshow(Z3 ,aspect='equal', origin='lower', cmap='Reds')
im4=ax[1,1].imshow(Z4 ,aspect='equal', origin='lower', cmap='Greens')
#im4=ax[1,1].plot(x,np.sin(it*x))
cb1=fig.colorbar(im1, ax=ax[0,0])
cb2=fig.colorbar(im2, ax=ax[1,0])
cb3=fig.colorbar(im3, ax=ax[0,1])
cb4=fig.colorbar(im4, ax=ax[1,1])
N=10
nt=10
fig=plt.figure(figsize=(6,6))
anim=animation.FuncAnimation(fig,update_anim,frames=nt)
#plt.subplots_adjust(wspace=0.1,hspace=0.01, bottom=0,top=1)
plt.close()
anim
#anim.save('sample.gif', writer='imagemagick') #save the animation as a gif file
Out[40]:
Hatched region¶
In [41]:
x=np.linspace(0,10,1000)
y1=np.zeros(1000)
y2=x**2/(1+x**2)
y3=x**(1/4)
fig, ax = plt.subplots()
ax.plot(x, y1, "k")
ax.plot(x, y2, "k")
ax.plot(x, y3, "k")
ax.fill_between(x, y1, y2, fc='w', hatch= "x") #{'/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
ax.fill_between(x, y2, y3, fc='w', hatch="//")
ax.set_ylim(0,2)
plt.show()
Figure in a figure¶
In [42]:
Z = np.random.randn(100,100)
fig = plt.figure()
ax = plt.axes()
plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower', cmap='jet')
plt.colorbar()
axins = ax.inset_axes([0.25, 0.3, 0.4, 0.4])
axins.imshow(Z, extent=[0, 5, 0, 5], origin="lower",cmap='jet')
# sub region of the original image
x1, x2, y1, y2 = 2, 3, 0.4, 1.4
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
axins.set_xticklabels('')
axins.set_yticklabels('')
#ax.indicate_inset_zoom(axins,ec='red',alpha=1)
mark_inset(ax, axins, loc1=3, loc2=4, fc="none", ec="k",alpha=1)
plt.show()
In [43]:
x = np.arange(-10, 10, 0.1)
plt.plot(x, x**2)
ax=plt.axes([0.5, 0.6, 0.2, 0.2])
ax.plot(x, x)
ax.set_xlim(-5,5)
plt.show()
Connect paths over panels¶
In [44]:
from matplotlib.patches import ConnectionPatch
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(4, 2))
# Draw a simple arrow between two points in axes coordinates
# within a single axes.
xyA = (0.2, 0.2)
xyB = (0.8, 0.8)
coordsA = "data"
coordsB = "data"
con = ConnectionPatch(xyA, xyB, coordsA, coordsB,
arrowstyle="->", shrinkA=5, shrinkB=5,
mutation_scale=20, fc="w")
ax1.plot([xyA[0], xyB[0]], [xyA[1], xyB[1]], "o")
ax1.add_artist(con)
# Draw an arrow between the same point in data coordinates,
# but in different axes.
xy = (0.0, 0.0)
coordsA = "data"
coordsB = "data"
con = ConnectionPatch(xyA=(0.3, 0.2), xyB=(0,0), coordsA=coordsA, coordsB=coordsB,
axesA=ax2, axesB=ax1,
arrowstyle="-", shrinkB=0)
ax2.add_artist(con)
# Draw a line between the different points, defined in different coordinate
# systems.
xyA = (0.6, 1.0) # in axes coordinates
xyB = (0.0, 0.2) # x in axes coordinates, y in data coordinates
coordsA = "axes fraction"
coordsB = ax2.get_yaxis_transform()
con = ConnectionPatch(xyA=xyA, xyB=xyB, coordsA=coordsA, coordsB=coordsB,
arrowstyle="-")
ax2.add_artist(con)
ax1.set_xlim(0, 1)
ax1.set_ylim(0, 1)
ax2.set_xlim(0, .5)
ax2.set_ylim(0, .5)
plt.show()
Contour plot and colorbar controll¶
In [45]:
x=np.linspace(-5,5,200)
y=np.linspace(-5,5,200)
X, Y = np.meshgrid(x,y)
nr=2000
cx=-10+20*np.random.rand(nr)
cy=-10 +20*np.random.rand(nr)
k=np.zeros(nr)
for i in range(nr):
if cx[i]<0: k[i]=1
if cx[i]>0: k[i]=1
Jz=np.zeros((200,200))
for i in range(nr):
R=np.sqrt((X-cx[i])**2+(Y-cy[i])**2)
Jz=Jz+2*k[i]/(1+(k[i]*R)**2)**2
fig,ax = plt.subplots(nrows=1, ncols=1, sharex=True, figsize=(2,2))
im0=ax.contour( X, Y, Jz, levels=20, colors='black',alpha=0.9)
In [46]:
x=np.linspace(-5,5,200)
y=np.linspace(-4,4,200)
X, Y = np.meshgrid(x,y)
nr=1000
cx=-10+20*np.random.rand(nr)
cy=-5 +10*np.random.rand(nr)
k=np.zeros(nr)
for i in range(nr):
if cx[i]<0: k[i]=1
if cx[i]>0: k[i]=2
Jz=np.zeros((200,200))
for i in range(nr):
R=np.sqrt((X-cx[i])**2+(Y-cy[i])**2)
Jz=Jz+2*k[i]/(1+(k[i]*R)**2)**2
fig,ax = plt.subplots(nrows=1, ncols=3, sharex=True, figsize=(12,4))
im0=ax[0].contour( X, Y, Jz, levels=40, colors='grey',alpha=0.4)
im1=ax[1].contour( X, Y, Jz, levels=40, cmap='jet')
im2=ax[2].contourf(X, Y, Jz, levels=20, cmap='jet')
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
divider = make_axes_locatable(ax[2])
cax = divider.append_axes("top", size="5%", pad=0.1)
cb2=fig.colorbar(im2, cax=cax, ax=ax[2], orientation='horizontal')
cax.xaxis.set_ticks_position('top')
divider1 = make_axes_locatable(ax[1])
cax1 = divider1.append_axes("top", size="5%", pad=0.1)
cb1=fig.colorbar(im1, cax=cax1, ax=ax[1], orientation='horizontal', pad=0.05)
cax1.xaxis.set_ticks_position('top')
#cb1.ax.set_visible(False)
divider0 = make_axes_locatable(ax[0])
cax0 = divider0.append_axes("top", size="5%", pad=0.1)
cb2=fig.colorbar(im0, cax=cax0, ax=ax[0], orientation='horizontal', pad=0.05)
cax0.xaxis.set_ticks_position('top')
cb2.ax.set_visible(False)
#fig.colorbar(im2, cax=cax, ax=ax[1], orientation='horizontal', pad=0.05, anchor=(0.5, 1.0))
for i in range(3):
ax[i].set_aspect('equal')
plt.show()
Multicolored lines¶
Actually, there is no easy to plot it. Alternatively, you can use scatter (but points). If you really need multicolored lines, you can check here; [https://matplotlib.org/3.3.3/gallery/lines_bars_and_markers/multicolored_line.html]
In [47]:
x = np.linspace(0, 2*np.pi, 1000)
y = np.sin(10*x)*np.exp(-x)
z = np.arange(1000)
fig, ax = plt.subplots(figsize=(3,2 ))
im1=ax.scatter(x,y, s=10, c=z, edgecolor='none', cmap='jet')
fig.colorbar(im1, ax=ax)
plt.show()
In [48]:
from matplotlib.collections import LineCollection
from matplotlib.colors import ListedColormap, BoundaryNorm
x = np.linspace(0, 3 * np.pi, 100)
y = np.sin(x)
dydx = np.cos(0.5 * (x[:-1] + x[1:])) # first derivative
# Create a set of line segments so that we can color them individually
# This creates the points as a N x 1 x 2 array so that we can stack points
# together easily to get the segments. The segments array for line collection
# needs to be (numlines) x (points per line) x 2 (for x and y)
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
fig, axs = plt.subplots(2, 1, sharex=True, sharey=True)
# Create a continuous norm to map from data points to colors
norm = plt.Normalize(dydx.min(), dydx.max())
lc = LineCollection(segments, cmap='viridis', norm=norm)
# Set the values used for colormapping
lc.set_array(dydx)
lc.set_linewidth(2)
line = axs[0].add_collection(lc)
fig.colorbar(line, ax=axs[0])
# Use a boundary norm instead
cmap = ListedColormap(['r', 'g', 'b'])
norm = BoundaryNorm([-1, -0.5, 0.5, 1], cmap.N)
lc = LineCollection(segments, cmap=cmap, norm=norm)
lc.set_array(dydx)
lc.set_linewidth(2)
line = axs[1].add_collection(lc)
fig.colorbar(line, ax=axs[1])
axs[0].set_xlim(x.min(), x.max())
axs[0].set_ylim(-1.1, 1.1)
plt.show()
Several lines with a colorbar¶
Reference: https://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots
In [49]:
### discrete ###
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
#cmap = mpl.cm.get_cmap('jet', n_lines)
#fig, ax = plt.subplots(dpi=100)
## Make dummie mappable
#dummie_cax = ax.scatter(c, c, c=c, cmap=cmap)
## Clear axis
#ax.cla()
#for i, yi in enumerate(y.T):
# ax.plot(x, yi, c=cmap(i))
#fig.colorbar(dummie_cax, ticks=c)
#plt.show();
cmap = plt.get_cmap("jet", len(c))
norm = mpl.colors.BoundaryNorm(np.arange(len(c)+1)+0.5,len(c))
sm = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([]) # this line may be ommitted for matplotlib >= 3.1
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap(i))
fig.colorbar(sm, ticks=c)
plt.show()
### continuous ###
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
cmap.set_array([])
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap.to_rgba(i + 1))
fig.colorbar(cmap, ticks=c)
plt.show();
Axis setting¶
In [50]:
color='#545454'
fig = plt.figure(figsize=(4,2))
ax = fig.add_subplot(111)
ax.plot(np.random.rand(10), 'k', lw=1.5, alpha=0.8)
ax.plot(np.random.rand(10), 'o', color='none', markersize=10, markeredgewidth=3, markeredgecolor=color, alpha=0.8)
ax.spines['top' ].set_linewidth(0)
ax.spines['right' ].set_linewidth(0)
ax.spines['left' ].set_linewidth(2)
ax.spines['bottom'].set_linewidth(2)
ax.spines['left' ].set_color(color)
ax.spines['bottom'].set_color(color)
#ax.spines['right'].set_visible(False)
#ax.spines['left'].set_visible(False)
#ax.spines['top'].set_visible(False)
ax.tick_params(direction='out', length=6, width=2, colors=color, labelsize=14)
#ax.set_xlim(0,370)
#ax.set_ylim(0,105)
#ax.set_xticks([60*i for i in np.arange(7)])
#ax.set_yticks([0, 25, 50, 75, 100])
#ax.set_xticklabels([x for x in np.arange(7)], fontsize=14, color=color)
#ax.set_yticklabels([0,25,50,75, 100] , fontsize=14, color=color)
#ax.xaxis.label.set_color('red')
#ax.tick_params(axis='x', colors='red')`1
ax.set_xlabel('X', color=color)
ax.set_ylabel('Y', color=color)
#plt.savefig('test.pdf')
plt.show()
Error bar¶
In [51]:
x=np.linspace(0,45,20)
y=x*np.exp(-(x/10.0)**2)*np.sin(x/4)
yerror=np.random.rand(20)
plt.figure(1, figsize = (3,2) )
plt.plot(x, y, 'r-', label="theory")
plt.errorbar(x, y,yerr=yerror,fmt='o', ecolor='green', markeredgecolor = "black", color='w',capsize=4,label="data")
plt.xlabel('x')
plt.ylabel('transverse displacement')
plt.legend(frameon=False,numpoints=1)
#plt.legend(loc='upper right')
plt.show()
Common label¶
You can use fig.text(). Also you can check here;https://pythonmatplotlibtips.blogspot.com/2017/08/one-ylabel-two-subplots-python-matplotlib-pyplot.html.
In [52]:
fig,ax = plt.subplots(nrows=2,ncols=1,sharex=True, sharey=True)
ax[0].loglog(10**np.random.randn(100))
ax[1].loglog(10**np.random.randn(100))
ax[1].set_ylim(1e-2,1e2)
# Set common labels
fig.text(0.50,-0.05, 'common xlabel', ha='center', va='center')
fig.text(0.00, 0.50, 'common ylabel', ha='center', va='center', rotation='vertical')
plt.show()
Panel label¶
In [53]:
label=[['A', 'B'], ['C', 'D']]
### put panel labels outside ###
fig,ax = plt.subplots(nrows=2, ncols=2)
for i in range(2):
for j in range(2):
ax[j,i].text(-0.1, 1.15, label[i][j], transform=ax[j,i].transAxes, fontsize=16, fontweight='bold', va='top', ha='right')
plt.show()
################################
### put panel labels inside with a frame ###
props = dict(boxstyle='square', facecolor='w', alpha=0.8)
fig,ax = plt.subplots(nrows=2, ncols=2)
for i in range(2):
for j in range(2):
ax[j,i].text(0.1, 0.95, label[i][j], transform=ax[j,i].transAxes, fontsize=14, fontweight='bold', va='top', ha='right', bbox=props)
ax[0,0].imshow(np.random.randn(100,100), aspect='auto')
plt.show()
Colorbar over several plots¶
In [54]:
fig, ax = plt.subplots(nrows=4, sharex=True, figsize=(6, 2),dpi=120)
plt.subplots_adjust(hspace=0.4)
im0=ax[0].imshow(np.random.randn(20,200),cmap='jet',origin='lower',aspect='auto')
im1=ax[1].imshow(np.random.randn(20,200),cmap='jet',origin='lower',aspect='auto')
im2=ax[2].imshow(np.random.randn(20,200),cmap='jet',origin='lower',aspect='auto')
im3=ax[3].imshow(np.random.randn(20,200),cmap='jet',origin='lower',aspect='auto')
fig.colorbar(im0,ax=ax[:],pad=0.02)
plt.show()
In [55]:
fig,ax=plt.subplots(figsize=(4,2), nrows=3, ncols=2)
#fig.subplots_adjust(wspace=0.1)
im0=ax[0,0].imshow(np.random.randn(20,100), origin='lower', aspect='equal')
im1=ax[1,0].imshow(np.random.randn(20,100), origin='lower', aspect='equal')
im2=ax[2,0].imshow(np.random.randn(20,100), origin='lower', aspect='equal')
im3=ax[0,1].imshow(np.random.randn(20,100), origin='lower', aspect='equal')
im4=ax[1,1].imshow(np.random.randn(20,100), origin='lower', aspect='equal')
im5=ax[2,1].imshow(np.random.randn(20,100), origin='lower', aspect='equal')
cbar=fig.colorbar(im0, ax=ax[:,:], orientation='horizontal', shrink=0.8)
#cbar=fig.colorbar(im0, ax=ax[:,1], orientation='horizontal', shrink=0.6)
plt.show()
Scatter plot with histograms¶
https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/scatter_hist.html
In [56]:
#Fixing random state for reproducibility
np.random.seed(19680801)
# the random data
x = np.random.randn(1000)
y = np.random.randn(1000)
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
spacing = 0.01
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom + height + spacing, width, 0.2]
rect_histy = [left + width + spacing, bottom, 0.2, height]
# start with a rectangular Figure
plt.figure(figsize=(4, 4))
ax_scatr= plt.axes(rect_scatter)
ax_histx= plt.axes(rect_histx)
ax_histy= plt.axes(rect_histy)
ax_scatr.tick_params(direction='in', top=True, right=True)
ax_histy.tick_params(direction='in', labelleft=False)
ax_histx.tick_params(direction='in', labelbottom=False)
# the scatter plot:
ax_scatr.scatter(x, y)
#ax_scatr.hist2d(x,y)
# now determine nice limits by hand:
binwidth = 0.25
lim = np.ceil(np.abs([x, y]).max() / binwidth) * binwidth
ax_scatr.set_xlim((-lim, lim))
ax_scatr.set_ylim((-lim, lim))
bins = np.arange(-lim, lim + binwidth, binwidth)
ax_histx.hist(x, bins=bins)
ax_histy.hist(y, bins=bins, orientation='horizontal')
ax_histx.set_xlim(ax_scatr.get_xlim())
ax_histy.set_ylim(ax_scatr.get_ylim())
plt.show()
Uneven subplots and ticks¶
In [57]:
fig, ax = plt.subplots(3,2,sharex=True)
ax[2,1].axis('off') #or fig.delaxes(ax[2,1])
ax[1,1].xaxis.set_tick_params(labelbottom=True)
ax[1,1].set_xlabel('X')
plt.show()
Uneven spacing subplots¶
In [58]:
from matplotlib import gridspec
fig = plt.figure(figsize=(12,3))
gs0 = gridspec.GridSpec(2,2, hspace=0.5, wspace=0.4)
gsList = [gridspec.GridSpecFromSubplotSpec(nrows=1,ncols=2, subplot_spec=gs0[i,j], hspace=0, wspace=0) for i in range(2) for j in range(2)]
ax000=fig.add_subplot(gsList[0][0,0])
ax001=fig.add_subplot(gsList[0][0,1])
ax100=fig.add_subplot(gsList[1][0,0])
ax101=fig.add_subplot(gsList[1][0,1])
ax200=fig.add_subplot(gsList[2][0,0])
ax201=fig.add_subplot(gsList[2][0,1])
ax300=fig.add_subplot(gsList[3][0,0])
ax301=fig.add_subplot(gsList[3][0,1])
plt.show()
Bar plot along a trajectory¶
I'm not sure there is an official way to do this using plt.bar. Here, I use rectangle patches. If the trajectory is just a circle, you may wanna use plt.bar in polar coordinates.
In [59]:
nt=100
tt=np.linspace(0,2*np.pi,nt)
x=6*np.cos(tt)
y=2*np.sin(tt)
fig, ax = plt.subplots(nrows=1,ncols=1)
ax.set_xlim(-10,10)
ax.set_ylim( -5, 5)
ax.plot(x,y)
ax.set_aspect('equal')
### calculate vectors vertical to the orbit ###
angle=np.zeros(nt)
for i in range(1,nt-1):
a=[x[i-1],y[i-1]]
b=[x[i+1],y[i+1]]
angle[i]=np.arccos((b[1]-a[1])/np.sqrt((b[0]-a[0])**2+(b[1]-a[1])**2))
if (b[0]-a[0])>0: angle[i]=2*np.pi-angle[i]
angle[i]=angle[i]/np.pi*180-90
### add a rectangle patch ###
height=np.ones(100)
for i in range(1,nt-1):
rect = patches.Rectangle(xy=(x[i], y[i]), width=0.2, height=height[i], angle=angle[i], color='r', alpha=.25, transform=ax.transData)
ax.add_patch(rect)
plt.show()
Line plot with space between markers and a line¶
In [60]:
x = np.arange(11)
y = np.arange(11)**(3/4)
# plot
fig=plt.figure(facecolor='w')
plt.plot(x, y, "m")
plt.plot(x, y, 'wo',ms=12)
plt.plot(x, y, 'go',ms=8)
plt.show()
In [61]:
fig, ax = plt.subplots()
ax.plot(np.random.rand(20), '-o', ms=8, lw=2, alpha=0.7, mfc='orange')
ax.grid()
ax.text(0.5, 0.5, 'created with matplotlib', transform=ax.transAxes,
fontsize=30, color='grey', alpha=0.5,
ha='center', va='center', rotation='-20',zorder=-5)
plt.show()
xkcd-like plot¶
It's better to wrap plt.xkcd() with a 'with' sentence since xkcd modifies the setting of rcParams.
In [62]:
x = np.linspace(0, 2*np.pi, 100)
y1 = np.sin(x)
y2 = np.cos(x)
fig = plt.figure(figsize=(8, 2))
with plt.xkcd():
ax=fig.add_subplot(1,2,1,title='xkcd plot')
ax.plot(x, y1, label="$y=sin(x)$")
ax.plot(x, y2, label="$y=cos(x)$")
ax.set_xlabel(r'abcd $abcd$')
ax.legend(frameon=False)
ax=fig.add_subplot(1,2,2,title='usual plot')
ax.plot(x, y1, label="$y=sin(x)$")
ax.plot(x, y2, label="$y=cos(x)$")
ax.legend()
plt.show()
Old-Style Chart¶
This section is inspired by this post, https://scipython.com/blog/old-style-matplotlib-charts/. Mine is not as authentic as this post... I was looking for a possibility to put noises into 'fig' directory, but I couldn't find a way.
In [63]:
fig=plt.figure(figsize=(4,2),facecolor='w')
ax1=fig.add_axes([0.,0.,1,1], label='cartesian', projection=None)
x=np.linspace(1,9,50)
y=10-x
y_err=10-x+2*np.random.randn(50)
ax1.set_xlim(0,10)
ax1.set_ylim(0,10)
ax1.plot(x,y)
ax1.scatter(x,y_err,ec='k',fc='none')
#ax1.set_xticks([])
#ax1.set_yticks([])
ax1.set_title('THIS IS AN OLD-STYLE PLOT')
ax1.set_aspect('equal')
ax2=fig.add_axes([-0.2,-0.2,1.4,1.4])
x = np.random.rand(3000)
y = np.random.rand(3000)
z = np.random.rand(3000)
for i in np.arange(.1,1.01,.1):
ax2.scatter(x, y, s=(2*i*(z+.1))**2, ec='none',fc='w', alpha=0.5,zorder=10)
ax2.set_xticks([])
ax2.set_yticks([])
ax2.spines['right' ].set_visible(False)
ax2.spines['left' ].set_visible(False)
ax2.spines['top' ].set_visible(False)
ax2.spines['bottom'].set_visible(False)
ax2.set_aspect('equal')
plt.show()
Output this notebook as html file¶
I use a template answered in this post; https://stackoverflow.com/questions/24062013/how-to-convert-an-ipython-notebook-to-html-with-collapsed-output-and-or-input and tweak as follows. nbconvert=6.0.6 does not work for this template. So you need to downgrade nbconvert.
In [69]:
%%writefile tmp.tpl
{%- extends 'full.tpl' -%}
{% block output_group %}
<div class="output_hidden">
{{ super() }}
</div>
{% endblock output_group %}
{% block input_group -%}
<div class="input_hidden">
{{ super() }}
</div>
{% endblock input_group %}
{%- block input -%}
<div class="in_container">
<div class="in_hidden">
{{ super() }}
<div class="gradient">
</div>
</div>
</div>
{%- endblock input -%}
{%- block header -%}
{{ super() }}
<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
<style type="text/css">
div.output_wrapper {
margin-top: 0px;
}
.output_hidden {
display: block;
margin-top: 5px;
}
.in_hidden {
width: 100%;
overflow: hidden;
position: relative;
}
.container{
width: 70% !important;*/
}
.in_container {
width: 100%;
margin-left: 0px;
margin-right: 0px;
overflow: auto;
}
.gradient {
width:100%;
height:3px;
background:#eeeeee;
position:absolute;
bottom:0px;
left:0;
display: none;
opacity: 0.4;
border-bottom: 2px dashed #000;
}
div.input_prompt {
color: #178CE3;
font-weight: bold;
}
div.output_prompt {
color: rgba(249, 33, 33, 1);
font-weight: bold;
}
.rendered_html img {
display: block;
/* float: left;*/
margin-left:unset;
max-height:initial;
text-align:center;
}
</style>
<script>
$(document).ready(function(){
// $(".output_hidden").click(function(){
// $(this).prev('.input_hidden').slideToggle();
// });
// $(".input_hidden").click(function(){
// $(this).next('.output_hidden').slideToggle();
// });
var slideHeight = 25;
$(".in_container").each(function () {
var $this = $(this);
var $in_hidden = $this.children(".in_hidden");
var defHeight = $in_hidden.height();
if (defHeight >= 61) {
var $prompt = $this.prev(".input_prompt");
var $input_hidden =$this.parents(".input_hidden");
var $output_hidden = $input_hidden.next(".output_hidden");
var $gradient = $in_hidden.children(".gradient");
var $anim_control=$output_hidden.find(".anim-controls")
$in_hidden.css("height", slideHeight + "px");
$gradient.css("display", "block");
$prompt.click(function () {
var curHeight = $in_hidden.height();
if (curHeight == slideHeight) {
$in_hidden.animate({
height: defHeight
}, "normal");
$gradient.fadeOut();
}
else {
$in_hidden.animate({
height: slideHeight
}, "normal");
$gradient.fadeIn();
}
return false;
});
$anim_control.click(function (){
return false;
});
$output_hidden.click(function () {
var curHeight = $in_hidden.height();
if (curHeight == slideHeight) {
$in_hidden.animate({
height: defHeight
}, "normal");
$gradient.fadeOut();
}
else {
$in_hidden.animate({
height: slideHeight
}, "normal");
$gradient.fadeIn();
}
return false;
});
}
});
});
</script>
{%- endblock header -%}
In [ ]:
%%javascript //save the notebook before output
$("#save-notbook button").trigger('click')
In [66]:
!jupyter nbconvert --to html --template tmp.tpl plotting_tips.ipynb
In [67]:
!rm tmp.tpl ### erase the temporary template (tmp.tpl)
note: 2022/Oct/05 may need to change the version of jinja2
In [68]:
!pip install jinja2==3.0.3