The texts on the right on this pyplot graph are clipped, how can I expand the plot area without changing the x-axis?
Minimal example code (similar to but not identical to example image)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mp
n40=146-1.07*40
n90=146-1.07*90
ageAxis =np.array([10, 40, 90])
Normal=np.array([n40, n40, n90])
plt.plot(ageAxis,Normal)
plt.text(90.2,50,'long text here that will be clipped')
ax = plt.gca()
ax.set_ylim([0,165])
ax.set_xlim([0,90])
fig= plt.gcf()
# set size fig.set_size_inches(20, 10.5)
plt.show()
It seems that it can be done with a combination of set_size_inches and subplots_adjust
Not elegant, I think, but it works:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mp
n40=146-1.07*40
n90=146-1.07*90
ageAxis =np.array([10, 40, 90])
Normal=np.array([n40, n40, n90])
plt.plot(ageAxis,Normal)
plt.text(90.2,50,'long text here that will be clipped')
ax = plt.gca()
ax.set_ylim([0,165])
ax.set_xlim([0,90])
fig= plt.gcf()
fig.set_size_inches(10, 5.5) # set a suitable size
plt.subplots_adjust(right=0.75) # adjust plot area
plt.show()
Related
I am trying to make map lines always on top of data, but cannot find the right command/options to do so in cartopy. In the plot below, I want the thick blue line "under" the black state lines but on top of the beige states.
Code:
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import os,sys
conus_proj = ccrs.LambertConformal(central_longitude=-96,central_latitude=39.0)
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(1,1,1,projection=conus_proj)
ax.set_extent([-120,-70,22,50])
#ax.add_feature(cfeature.BORDERS)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.OCEAN, facecolor='#CCFEFF')
ax.add_feature(cfeature.LAKES, facecolor='#CCFEFF')
ax.add_feature(cfeature.RIVERS, facecolor='#CCFEFF')
ax.add_feature(cfeature.LAND, facecolor='#FFE9B5')
state_borders = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lakes', scale='50m', facecolor='#FFE9B5')
ax.add_feature(state_borders, edgecolor='black')
plt.plot([-120,-70],[35,45],linewidth=8, transform=ccrs.PlateCarree())
plt.show()
I have tried changing the zorder of ax.add_feature(state_borders...) and plt.plot(...) but have received weird results. The state borders are on top by default with pcolormesh, but don't appear to be so with plt.plot
Here is the output of the above code:
The reason things turn out differently for pcolormesh vs. plot is that those have different default zorders. If I set the zorder for the state borders (note below that I use Cartopy's built-in support for states) to 10, I get them to appear on top of the plot:
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import os,sys
conus_proj = ccrs.LambertConformal(central_longitude=-96,central_latitude=39.0)
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(1,1,1,projection=conus_proj)
ax.set_extent([-120,-70,22,50])
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.OCEAN, facecolor='#CCFEFF')
ax.add_feature(cfeature.LAKES, facecolor='#CCFEFF')
ax.add_feature(cfeature.RIVERS, edgecolor='#CCFEFF')
ax.add_feature(cfeature.LAND, facecolor='#FFE9B5')
ax.add_feature(cfeature.STATES, edgecolor='black', zorder=10)
plt.plot([-120,-70],[35,45],linewidth=8, transform=ccrs.PlateCarree())
plt.show()
I also had to remove the face colors on some of the features.
Suppose I need to control line colors myself for some reason, for example:
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
for i in np.linspace(0, 1, 100):
plt.plot([i,i+1,i+2], color=mpl.cm.viridis(i))
How to generate a colorbar for such a plot？
You would need to create a colorbar without any reference axes. This can be done with the matplotlib.colorbar.ColorbarBase class. See also this example from the gallery.
To use this, you need to create a new axis in the plot, where the colorbar should sit in; one way of doing this is to use make_axes_locatable.
Here is a complete example.
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
for i in np.linspace(0, 1, 9):
plt.plot([i,i+1,i+2], color=mpl.cm.viridis(i))
divider = make_axes_locatable(plt.gca())
ax_cb = divider.new_horizontal(size="5%", pad=0.05)
cb1 = mpl.colorbar.ColorbarBase(ax_cb, cmap=mpl.cm.viridis, orientation='vertical')
plt.gcf().add_axes(ax_cb)
plt.show()
everyone,
I have problem when I tried to adjust colorbar to the same height with figure. I know little about the intrinsic mechanism of data visualization, or axis, fig or something like that. my code is following, sorry for unloading images,
For Figure(1), notice that input data is square, i.e., 51 by 51. The figure is satisfying.
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
plt.cla()
plt.clf()
fig1 = plt.figure(1)
ax0 = plt.subplot()
im0 = ax0.imshow(np.arange(51*51).reshape((51,51)), cmap="hsv")
divider0 = make_axes_locatable(ax0)
ax_cb0 = divider0.append_axes("right", size="2%", pad=0.05)
fig1.add_axes(ax_cb0)
plt.colorbar(im0, cax=ax_cb0)
plt.savefig("tmp0.png", bbox_inches="tight")
For figure (2), the code is following, notice that now the input data is 51 by 501, the output is not satisfying,
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
plt.cla()
plt.clf()
fig2 = plt.figure(2)
ax1 = plt.subplot()
im1 = ax1.imshow(np.arange(51*501).reshape((51,501)), cmap="hsv")
divider1 = make_axes_locatable(ax1)
ax_cb1 = divider1.append_axes("right", size="2%", pad=-4)
fig2.add_axes(ax_cb1)
plt.colorbar(im1, cax=ax_cb1)
ax1.set_aspect(4)
plt.savefig("tmp1.png", bbox_inches="tight")
but still, we can make it better by manually adjusting pad parameter in this line
ax_cb0 = divider0.append_axes("right", size="2%", pad=0.05)
but which is absolutely not the recommended way, COULD anyone know the smart way of doing it or smart way of estimating the value of pad parameter? Thanks in advance.
I am making a plot and I want the text to crop at the edge. Right now it hangs over the edge, which is great for readability, but not what I actually want.
Here's a toy version of what I'm doing:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(np.random.random(10), np.random.random(10))
ax.text(0.8, 0.5, "a rather long string")
plt.show()
Just to be clear, I want to crop my text element, but not anything else — e.g. I want to leave the 0.9 in the x-axis alone.
You should set a clipbox for the text as described in the documentation:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(np.random.random(10), np.random.random(10))
ax.text(0.8, 0.5, "a rather long string",
clip_box=ax.clipbox, clip_on=True)
ax.set_xlim(0, 1)
plt.show()
This results in
I have a matplotlib plot with a colorbar attached. I want to position the colorbar so that it is horizontal, and underneath my plot.
I have almost done this via the following:
plt.colorbar(orientation="horizontal",fraction=0.07,anchor=(1.0,0.0))
But the colorbar is still overlapping with the plot slightly (and the labels of the x axis). I want to move the colorbar further down, but I can't figure out how to do it.
using padding pad
In order to move the colorbar relative to the subplot, one may use the pad argument to fig.colorbar.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, ax = plt.subplots(figsize=(4,4))
im = ax.imshow(np.random.rand(11,16))
ax.set_xlabel("x label")
fig.colorbar(im, orientation="horizontal", pad=0.2)
plt.show()
using an axes divider
One can use an instance of make_axes_locatable to divide the axes and create a new axes which is perfectly aligned to the image plot. Again, the pad argument would allow to set the space between the two axes.
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np; np.random.seed(1)
fig, ax = plt.subplots(figsize=(4,4))
im = ax.imshow(np.random.rand(11,16))
ax.set_xlabel("x label")
divider = make_axes_locatable(ax)
cax = divider.new_vertical(size="5%", pad=0.7, pack_start=True)
fig.add_axes(cax)
fig.colorbar(im, cax=cax, orientation="horizontal")
plt.show()
using subplots
One can directly create two rows of subplots, one for the image and one for the colorbar. Then, setting the height_ratios as gridspec_kw={"height_ratios":[1, 0.05]} in the figure creation, makes one of the subplots much smaller in height than the other and this small subplot can host the colorbar.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, (ax, cax) = plt.subplots(nrows=2,figsize=(4,4),
gridspec_kw={"height_ratios":[1, 0.05]})
im = ax.imshow(np.random.rand(11,16))
ax.set_xlabel("x label")
fig.colorbar(im, cax=cax, orientation="horizontal")
plt.show()
Edit: Updated for matplotlib version >= 3.
Three great ways to do this have already been shared in this answer.
The matplotlib documentation advises to use inset_locator. This would work as follows:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np
rng = np.random.default_rng(1)
fig, ax = plt.subplots(figsize=(4,4))
im = ax.imshow(rng.random((11, 16)))
ax.set_xlabel("x label")
axins = inset_axes(ax,
width="100%",
height="5%",
loc='lower center',
borderpad=-5
)
fig.colorbar(im, cax=axins, orientation="horizontal")