everyone. I want to generate a x-axis like the picture showing below.
Except make several different-sized subplots then merged to a single one.
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
axes=[]
ax1 = plt.subplot2grid((1,10),(0,0),colspan=4,rowspan=1)
ax1.plot([0,1],[2,3])
ax2 = plt.subplot2grid((1,10),(0,4),colspan=1,rowspan=1)
ax2.plot([1,2],[3,4])
ax3 = plt.subplot2grid((1,10),(0,5),colspan=3,rowspan=1)
ax3.plot([2,3],[4,5])
ax4 = plt.subplot2grid((1,10),(0,8),colspan=2,rowspan=1)
ax4.plot([3,4],[5,6])
axes=[ax1,ax2,ax3,ax4]
ax1.spines['right'].set_visible(False)
ax1.set_xticks([0,1])
ax1.set_xticklabels(['0','1'])
ax2.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax2.yaxis.set_major_locator(ticker.NullLocator())
ax2.set_xticks([2])
ax2.set_xticklabels(['2'])
ax3.spines['right'].set_visible(False)
ax3.spines['left'].set_visible(False)
ax3.yaxis.set_major_locator(ticker.NullLocator())
ax3.set_xticks([3])
ax3.set_xticklabels(['3'])
ax4.spines['left'].set_visible(False)
ax4.yaxis.set_major_locator(ticker.NullLocator())
ax4.set_xticks([4])
ax4.set_xticklabels(['4'])
[plt.setp(axes[i],xlim=[i+0,i+1]) for i in range(4)]
[plt.setp(axes[i],ylim=[2,6]) for i in range(4)]
plt.subplots_adjust(wspace=0,)
plt.savefig('xx.png',format='png',dpi=300)
I wonder is there other way to do this?
Related
I am trying to plot 5 charts one under the other with mplfinance.
This works:
for coin in coins:
mpf.plot(df_coins[coin], title=coin, type='line', volume=True, show_nontrading=True)
However each plot is a separate image in my Python Notebook cell output. And the x-axis labelling is repeated for each image.
I try to make a single figure containing multiple subplot/axis, and plot one chart into each axis:
from matplotlib import pyplot as plt
N = len(df_coins)
fig, axes = plt.subplots(N, figsize=(20, 5*N), sharex=True)
for i, ((coin, df), ax) in zip(enumerate(df_coins.items()), axes):
mpf.plot(df, ax=ax, title=coin, type='line', volume=True, show_nontrading=True)
This displays subfigures of the correct dimensions, however they are not getting populated with data. Axes are labelled from 0.0 to 1.0 and the title is not appearing.
What am I missing?
There are two ways to subplot. One is to set up a figure with mplfinance objects. The other way is to use your adopted matplotlib subplot to place it.
yfinace data
import matplotlib.pyplot as plt
import mplfinance as mpf
import yfinance as yf
tickers = ['AAPL','GOOG','TSLA']
data = yf.download(tickers, start="2021-01-01", end="2021-03-01", group_by='ticker')
aapl = data[('AAPL',)]
goog = data[('GOOG',)]
tsla = data[('TSLA',)]
mplfinance
fig = mpf.figure(style='yahoo', figsize=(12,9))
#fig.subplots_adjust(hspace=0.3)
ax1 = fig.add_subplot(3,1,1, sharex=ax3)
ax2 = fig.add_subplot(3,1,2, sharex=ax3)
ax3 = fig.add_subplot(3,1,3)
mpf.plot(aapl, type='line', ax=ax1, axtitle='AAPL', xrotation=0)
mpf.plot(goog, type='line', ax=ax2, axtitle='GOOG', xrotation=0)
mpf.plot(tsla, type='line', ax=ax3, axtitle='TSLA', xrotation=0)
ax1.set_xticklabels([])
ax2.set_xticklabels([])
matplotlib
N = len(tickers)
fig, axes = plt.subplots(N, figsize=(20, 5*N), sharex=True)
for df,t,ax in zip([aapl,goog,tsla], tickers, axes):
mpf.plot(df, ax=ax, axtitle=t, type='line', show_nontrading=True)# volume=True
In addition to the techniques mentioned by #r-beginners there is another technique that may work for you in the case where all plots share the same x-axis. That is to use mpf.make_addplot().
aps = []
for coin in coins[1:]:
aps.append(mpf.make_addplot(df_coins[coin]['Close'], title=coin, type='line'))
coin = coins[0]
mpf.plot(df_coins[coin],axtitle=coin,type='line',volume=True,show_nontrading=True,addplot=aps)
If you choose to do type='candle' instead of 'line', then change
df_coins[coin]['Close']
to simply
df_coins[coin]
I need to plot a set of 9 or more data sets with a common x-axis. I was able to do it for 2 of them but the rest of them just don't appear. They have to be stacked one above the other. with a common x axis. I have attached the image of what I have been able to do so far.
stack of plot
I have used the following code
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib.gridspec as gridspec
from matplotlib.lines import Line2D
import matplotlib.lines as mlines
file1 = '1.dat'
file2 = '10.dat'
data1 = pd.read_csv(file1, delimiter='\s+', header=None, engine='python')
data1.columns = ['M','B','C']
data2 = pd.read_csv(file2, delimiter='\s+', header=None, engine='python')
data2.columns = ['N','A','D']
def fit_data():
fig = plt.figure(1,figsize=(12,11))
ax1= fig.add_subplot(211,)
ax1.plot(data1['M'], data1['B'], color='cornflowerblue', linestyle= '-', lw=0.5)
ax1.scatter(data1['M'], data1['B'], marker='o', color='red', s=25)
ax1.errorbar(data1['M'], data1['B'], data1['C'], fmt='.', ecolor='red',color='red', elinewidth=1,capsize=3)
ax2 = fig.add_subplot(211, sharex=ax1 )
ax2.plot(data2['N'], data2['A'], color='cornflowerblue', linestyle= '-', lw=0.5)
ax2.scatter(data2['N'], data2['A'], marker='o', color='blue', s=25)
ax2.errorbar(data2['N'], data2['A'], data2['D'], fmt='.', ecolor='blue',color='blue', elinewidth=1,capsize=3)
plt.setp(ax1.get_xticklabels(), visible=False) # hide labels
fig.subplots_adjust(hspace=0)
ax1.tick_params(axis='both',which='minor',length=5,width=2,labelsize=18)
ax1.tick_params(axis='both',which='major',length=8,width=2,labelsize=18)
plt.savefig("1.pdf")
#fig.set_size_inches(w=13,h=10)
plt.show()
plt.close()
fit_data()
I read through stacking of plots but wasn't able to apply the same here.
I modified the code to this but this is what I get. modified code.
I need the stacking to be done to do a comparative study. Something like this image. comparative study
This is the part of the code I have modified and used.
plt.setp(ax1.get_xticklabels(), visible=False) # hide labels
fig.subplots_adjust(hspace=0.0) # remove vertical space between subplots
Should it be done seperately for ax1, ax2 and so on?
plt.subplots_adjust(hspace=0.0) removes the space between them.
You can have as many plots as you want:
from matplotlib import pyplot as plt
import numpy as np
numer_of_plots = 9
X = np.random.random((numer_of_plots, 50))
fig, axs = plt.subplots(nrows=numer_of_plots, ncols=1)
for ax, x in zip(axs, X):
ax.plot(range(50), x)
plt.subplots_adjust(hspace=0.0)
plt.show()
I have a pandas series containing numbers ranging between 0 and 100. I want to visualise it in a horizontal bar consisting of 3 main colours.
I have tried using seaborn but all I can get is a heatmap matrix. I have also tried the below code, which is producing what I need but not in the way I need it.
x = my_column.values
y = x
t = x
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(x, y, c=t, cmap='brg')
ax2.scatter(x, y, c=t, cmap='brg')
plt.show()
What I'm looking for is something similar to the below figure, how can I achieve that using matplotlib or seaborn?
The purpose of this is not quite clear, however, the following would produce an image like the one shown in the question:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
x = np.linspace(100,0,101)
fig, ax = plt.subplots(figsize=(6,1), constrained_layout=True)
cmap = LinearSegmentedColormap.from_list("", ["limegreen", "gold", "crimson"])
ax.imshow([x], cmap=cmap, aspect="auto",
extent=[x[0]-np.diff(x)[0]/2, x[-1]+np.diff(x)[0]/2,0,1])
ax.tick_params(axis="y", left=False, labelleft=False)
plt.show()
I am trying to shorten a colorbar by half. Does anyone know how to do this? I tried cax.get_position() and then cax.set_position(), but this method did not work.
Besides, it seems that axes created by axes_grid1 has the same bbox positions as the original axes. Is this a bug?
PS. I have to use axes_grid1 to create colorbar axes, because I need to use tight_layout() afterwards, and tight_layout() only applies to axes created by axes_grid1 but not ones created by add_axes().
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
plt.figure()
ax = plt.gca()
im = ax.imshow(np.arange(100).reshape((10,10)))
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
bbox1 = ax.get_position()
print(bbox1)
bbox1 = cax.get_position()
print(bbox1)
plt.colorbar(im, cax=cax)
plt.show()
The whole point of the axes_divider is to divide the axes to make space for a new axes. This ensures that all axes have the same surrounding box. And that is the box you see being printed.
Some of the usual ways to create a colorbar, at a certain location in the figue are shown in this question. Here the problem seems to be to be able to call tight_layout. This is achievable with the following two options. (There might be others still.)
A. using gridspec
I'm not too sure about the exact requirements here, but it seems that using a normal grid layout would be more in the direction of what you need here.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
fig = plt.figure()
gs = gridspec.GridSpec(2, 2, width_ratios=[95,5],)
ax = fig.add_subplot(gs[:, 0])
im = ax.imshow(np.arange(100).reshape((10,10)))
cax = fig.add_subplot(gs[1, 1])
fig.colorbar(im, cax=cax, ax=ax)
plt.tight_layout()
plt.show()
B. Using axes_grid1
If you really need to use axes_grid1, it might become a little bit more complicated.
import matplotlib.pyplot as plt
import matplotlib.axes
from mpl_toolkits.axes_grid1 import make_axes_locatable, Size
import numpy as np
fig, ax = plt.subplots()
im = ax.imshow(np.arange(100).reshape((10,10)))
divider = make_axes_locatable(ax)
pad = 0.03
pad_size = Size.Fraction(pad, Size.AxesY(ax))
xsize = Size.Fraction(0.05, Size.AxesX(ax))
ysize = Size.Fraction(0.5-pad/2., Size.AxesY(ax))
divider.set_horizontal([Size.AxesX(ax), pad_size, xsize])
divider.set_vertical([ysize, pad_size, ysize])
ax.set_axes_locator(divider.new_locator(0, 0, ny1=-1))
cax = matplotlib.axes.Axes(ax.get_figure(),
ax.get_position(original=True))
locator = divider.new_locator(nx=2, ny=0)
cax.set_axes_locator(locator)
fig.add_axes(cax)
fig.colorbar(im, cax=cax)
plt.tight_layout()
plt.show()
starting from this code:
import numpy as np
import matplotlib.pyplot as pl
import matplotlib
from matplotlib.gridspec import GridSpec
x=np.linspace(0.0,1.0,100)
y=np.linspace(0.0,1.0,100)
xv,yv=np.meshgrid(x,y)
gs = GridSpec(2, 2,hspace=0.00,wspace=0.1,width_ratios=[25,1])
ax1 = pl.subplot(gs[0,0])
im=ax1.imshow(xv.T, origin='lower', cmap=matplotlib.cm.jet,extent=(0,100,0,1.0),aspect='auto')
xax1=ax1.get_xaxis()
xax1.set_ticks([])
ax3 = pl.subplot(gs[0,1])
#cbar=pl.colorbar(im,cax=ax3,shrink=0.5)
cbar=pl.colorbar(im,cax=ax3)
ax2 = pl.subplot(gs[1,0])
ax2.plot(np.sin(x))
pl.savefig('test.pdf')
I would like to keep the two plots sharing the same x-axis but I would like to
shrink the colorbar as well. If I use the commented line it does not work. What is the
better, most elegant, way to do that? I think I should use make_axes_locatable at some point, but I do not know how to use it in the proper way without changing the imshow
x-axis length.
Thank you.
You can do it with a lot of control about positioning, using the inset_axes.
import numpy as np
import matplotlib.pyplot as pl
import matplotlib
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
x=np.linspace(0.0,1.0,100)
y=np.linspace(0.0,1.0,100)
xv,yv=np.meshgrid(x,y)
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212, sharex = ax1)
im = ax1.imshow(xv.T, origin='lower',
cmap=matplotlib.cm.jet,extent=(0,100,0,1.0),aspect='auto')
ax2.plot(np.sin(x))
cax = inset_axes(ax1,
width="5%",
height="70%",
bbox_transform=ax1.transAxes,
bbox_to_anchor=(0.025, 0.1, 1.05, 0.95),
loc= 1)
norm = mpl.colors.Normalize(vmin=xv.min(), vmax=xv.max())
cb1 = mpl.colorbar.ColorbarBase(cax,
cmap=matplotlib.cm.jet, norm=norm,
orientation='vertical')
cb1.set_label(u'some cbar')
This is what I get then. Does that help your question?