I'm trying to create a plot with 4 hist2d subplots and one color bar.
The thing is that each subplot can have different ranges of z values, so the color bar is not uniform.
I want to set the color bar to a pre-defined range.
here is the code I'm using:
def multiple_med_plot_test(file):
extent = [-8, 37, 28, 46]
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(26, 11), constrained_layout=True,
subplot_kw={'projection': ccrs.PlateCarree()})
ax0 = axes[0][0]
ax1 = axes[0][1]
ax2 = axes[1][0]
ax3 = axes[1][1]
axes_dict = {'Dec': ax0, 'Aug': ax1, 'Sep': ax2, 'Sum': ax3}
for month in axes_dict.keys():
ax = axes_dict[month]
ax.add_feature(cfeature.LAND, edgecolor='k', zorder=50)
ax.set_extent(extent)
gl = ax.gridlines(draw_labels=True, zorder=100, color='grey', linestyle='--')
gl.top_labels = False
gl.right_labels = False
gl.xlabel_style = {'size': 16}
gl.ylabel_style = {'size': 16}
if ax in [ax1, ax3]:
gl.left_labels = False
ax.set_title(month, fontsize=18, color='darkred')
if month != 'Sum':
hist0 = ax.hist2d(file.Long, file.Lat, range=[(-8, 37), (28, 46)], bins=(500, 200))
elif month == 'Sum':
hist1 = ax.hist2d(file.Long, file.Lat, range=[(-8, 37), (28, 46)], bins=(500, 200))
fig.suptitle('Lightning Density per Month', fontsize=22)
cbar = fig.colorbar(hist1[3], ax=axes, shrink=0.95)
cbar.set_label('# of lightnings', fontsize=20, rotation=-90, labelpad=30)
cbar.ax.tick_params(labelsize=16)
# plt.savefig('D:/Lightning Data/Yearly_Summary', dpi=100)
plt.show()
In previous versions of the code I used plt.clim and that was awesome, but the way my code is right now doesn't let me do it.
I would like to get some help on this!
If you want a linear scale, set vmin and vmax parameters. For log-like scale or similar, use norm. See hist2d documentation.
I am trying to create an axis plot. I was trying to loop over it as I am plotting the same variable for two different categories. Currently, I have written code two times but I am looking for a smarter way with looping, if possible. Any other suggestion will also be helpful.
zone = ['AB','CD']
plt.style.use('default')
fig,(ax0,ax1) = plt.subplots(2,1, figsize = (18,18), sharex = False)
i = 0
while i < len(zone):
if zone[i] == zone[0]:
ax0.plot(df0['datetime'], df0['pnl1'], color='k', linewidth=1, label ='PnL1')
ax0.plot(df0['datetime'], df0['pnl2'], color='m', linewidth=1, label ='PnL2')
ax00 = ax0.twinx()
ax00.bar(df0['datetime'], df0['qty'], width = 1/96, color='g', align = 'edge', alpha = 0.5, label ='Qty')
elif zone[i] == zone[1]:
ax1.plot(df0['datetime'], df0['pnl1'], color='k', linewidth=1, label ='PnL1')
ax1.plot(df0['datetime'], df0['pnl2'], color='m', linewidth=1, label ='PnL2')
ax01 = ax1.twinx()
ax01.bar(df0['datetime'], df0['hedge'], width = 1/96, color='g', align = 'edge', alpha = 0.5, label ='Qty')
i = i + 1
I want to check if something like below can be done with axis plots or not.
zone = ['AB','CD']
plt.style.use('default')
fig,(ax0,ax1) = plt.subplots(2,1, figsize = (18,18), sharex = False)
i = 0
while i < len(zone):
ax{''}.format(i).plot(df0['datetime'], df0['pnl1'], color='k', linewidth=1, label ='PnL1')
ax{''}.format(i).plot(df0['datetime'], df0['pnl2'], color='m', linewidth=1, label ='PnL2')
ax0{''}.format(i) = ax{''}.format(i).twinx()
ax0{''}.format(i).bar(df0['datetime'], df0['qty'], width = 1/96, color='g', align = 'edge', alpha = 0.5, label ='Qty')
It did not work for me. Any leads to execute axis plot with loop will be helpful.
Here are some ways:
Simply loop over the list of axes
import matplotlib.pyplot as plt
import numpy as np
fig,axes = plt.subplots(2,1)
x = np.linspace(0,5,21)
for ax in axes:
ax.plot(x,np.sin(x))
plt.show()
Works also with index:
for i in range(len(axes)):
axes[i].plot(x,np.sin(x))
For a grid of plot you can use a similar approach:
import matplotlib.pyplot as plt
import numpy as np
fig,axes = plt.subplots(2,2)
x = np.linspace(0,5,21)
for i in range(len(axes)):
for j in range(len(axes[0])):
axes[i][j].plot(x,np.sin(x))
plt.show()
If you don't like double-loops, you can flatten the array with np.ravel()
fig,axes = plt.subplots(2,2)
x = np.linspace(0,5,21)
for ax in np.ravel(axes):
ax.plot(x,np.sin(x))
plt.show()
I am making a matplotlib figure with a 2x2 dimension where x- and y-axis are shared, and then loop over the different axes to plot in them. I'm plotting variant data per sample, and it is possible that a sample doesn't have variant data, so then I want the plot to say "NA" in the middle of it.
import matplotlib.pyplot as plt
n_plots_per_fig = 4
nrows = 2
ncols = 2
fig, axs = plt.subplots(nrows, ncols, sharex="all", sharey="all", figsize=(8, 6))
axs = axs.ravel()
for i, ax in enumerate(axs):
x = [1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3] # example values, but this list CAN be empty
bins = 3 # example bins
if x:
ax.hist(x, bins=bins) # plot the hist
ax.set_yscale("log")
ax.set_title(str(i), fontsize="medium")
else:
ax.set_title(str(i), fontsize="medium")
ax.text(0.5, 0.5, 'NA', ha='center', va='center', transform=ax.transAxes)
fig.show()
This works in almost every case; example of wanted output:
However, only if the last plot in the figure doesn't have any data, then this disturbs the log scale. Example code that triggers this:
import matplotlib.pyplot as plt
n_plots_per_fig = 4
nrows = 2
ncols = 2
fig, axs = plt.subplots(nrows, ncols, sharex="all", sharey="all", figsize=(8, 6))
axs = axs.ravel()
for i, ax in enumerate(axs):
x = [1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
bins = 3
if i == n_plots_per_fig-1: # this will distort the log scale
ax.set_title(str(i), fontsize="medium")
ax.text(0.5, 0.5, 'NA', ha='center', va='center', transform=ax.transAxes)
elif x:
ax.hist(x, bins=bins) # plot the hist
ax.set_yscale("log")
ax.set_title(str(i), fontsize="medium")
else:
ax.set_title(str(i), fontsize="medium")
ax.text(0.5, 0.5, 'NA', ha='center', va='center', transform=ax.transAxes)
fig.show()
The log scale is now set to really low values, and this is not what I want. I've tried several things to fix this, like unsharing the y-axes for the plot that doesn't have any data [ax.get_shared_y_axes().remove(axis) for axis in axs] or hiding the plot ax.set_visible(False), but none of this works. The one thing that does work is removing the axes from the plot with ax.remove(), but since this is the bottom most sample, this also removes the values for the x ticks for that column:
And besides that, I would still like the name of the sample that didn't have any data to be visible in the axes (and the "NA" text), and removing the axes doesn't allow this.
Any ideas on a fix?
Edit: I simplified my example.
You can set the limits manually with ax.set_xlim() / ax.set_ylim().
Note, that if you share the axes it does not matter on which subplot you call those functions. For example:
axs[-1][-1].set_ylim(1e0, 1e2)
If you do not know the limits before, you can infer it from the other plots:
x = np.random.random(100)
bins = 10
if bins != 0:
...
yy, xx = np.histogram(x, bins=bins)
ylim = yy.min(), yy.max()
xlim = xx.min(), xx.max()
else:
ax.set_xlim(xlim)
ax.set_ylim(ylim)
I have a plot with two y-axes, using twinx(). I also give labels to the lines, and want to show them with legend(), but I only succeed to get the labels of one axis in the legend:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(time, Swdown, '-', label = 'Swdown')
ax.plot(time, Rn, '-', label = 'Rn')
ax2 = ax.twinx()
ax2.plot(time, temp, '-r', label = 'temp')
ax.legend(loc=0)
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
So I only get the labels of the first axis in the legend, and not the label 'temp' of the second axis. How could I add this third label to the legend?
You can easily add a second legend by adding the line:
ax2.legend(loc=0)
You'll get this:
But if you want all labels on one legend then you should do something like this:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
time = np.arange(10)
temp = np.random.random(10)*30
Swdown = np.random.random(10)*100-10
Rn = np.random.random(10)*100-10
fig = plt.figure()
ax = fig.add_subplot(111)
lns1 = ax.plot(time, Swdown, '-', label = 'Swdown')
lns2 = ax.plot(time, Rn, '-', label = 'Rn')
ax2 = ax.twinx()
lns3 = ax2.plot(time, temp, '-r', label = 'temp')
# added these three lines
lns = lns1+lns2+lns3
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=0)
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
Which will give you this:
I'm not sure if this functionality is new, but you can also use the get_legend_handles_labels() method rather than keeping track of lines and labels yourself:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
pi = np.pi
# fake data
time = np.linspace (0, 25, 50)
temp = 50 / np.sqrt (2 * pi * 3**2) \
* np.exp (-((time - 13)**2 / (3**2))**2) + 15
Swdown = 400 / np.sqrt (2 * pi * 3**2) * np.exp (-((time - 13)**2 / (3**2))**2)
Rn = Swdown - 10
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(time, Swdown, '-', label = 'Swdown')
ax.plot(time, Rn, '-', label = 'Rn')
ax2 = ax.twinx()
ax2.plot(time, temp, '-r', label = 'temp')
# ask matplotlib for the plotted objects and their labels
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc=0)
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
From matplotlib version 2.1 onwards, you may use a figure legend. Instead of ax.legend(), which produces a legend with the handles from the axes ax, one can create a figure legend
fig.legend(loc="upper right")
which will gather all handles from all subplots in the figure. Since it is a figure legend, it will be placed at the corner of the figure, and the loc argument is relative to the figure.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,10)
y = np.linspace(0,10)
z = np.sin(x/3)**2*98
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y, '-', label = 'Quantity 1')
ax2 = ax.twinx()
ax2.plot(x,z, '-r', label = 'Quantity 2')
fig.legend(loc="upper right")
ax.set_xlabel("x [units]")
ax.set_ylabel(r"Quantity 1")
ax2.set_ylabel(r"Quantity 2")
plt.show()
In order to place the legend back into the axes, one would supply a bbox_to_anchor and a bbox_transform. The latter would be the axes transform of the axes the legend should reside in. The former may be the coordinates of the edge defined by loc given in axes coordinates.
fig.legend(loc="upper right", bbox_to_anchor=(1,1), bbox_transform=ax.transAxes)
You can easily get what you want by adding the line in ax:
ax.plot([], [], '-r', label = 'temp')
or
ax.plot(np.nan, '-r', label = 'temp')
This would plot nothing but add a label to legend of ax.
I think this is a much easier way.
It's not necessary to track lines automatically when you have only a few lines in the second axes, as fixing by hand like above would be quite easy. Anyway, it depends on what you need.
The whole code is as below:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
time = np.arange(22.)
temp = 20*np.random.rand(22)
Swdown = 10*np.random.randn(22)+40
Rn = 40*np.random.rand(22)
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twinx()
#---------- look at below -----------
ax.plot(time, Swdown, '-', label = 'Swdown')
ax.plot(time, Rn, '-', label = 'Rn')
ax2.plot(time, temp, '-r') # The true line in ax2
ax.plot(np.nan, '-r', label = 'temp') # Make an agent in ax
ax.legend(loc=0)
#---------------done-----------------
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
The plot is as below:
Update: add a better version:
ax.plot(np.nan, '-r', label = 'temp')
This will do nothing while plot(0, 0) may change the axis range.
One extra example for scatter
ax.scatter([], [], s=100, label = 'temp') # Make an agent in ax
ax2.scatter(time, temp, s=10) # The true scatter in ax2
ax.legend(loc=1, framealpha=1)
Preparation
import numpy as np
from matplotlib import pyplot as plt
fig, ax1 = plt.subplots( figsize=(15,6) )
Y1, Y2 = np.random.random((2,100))
ax2 = ax1.twinx()
Content
I'm surprised it did not show up so far but the simplest way is to either collect them manually into one of the axes objs (that lie on top of each other)
l1 = ax1.plot( range(len(Y1)), Y1, label='Label 1' )
l2 = ax2.plot( range(len(Y2)), Y2, label='Label 2', color='orange' )
ax1.legend( handles=l1+l2 )
or have them collected automatically into the surrounding figure by fig.legend() and fiddle around with the the bbox_to_anchor parameter:
ax1.plot( range(len(Y1)), Y1, label='Label 1' )
ax2.plot( range(len(Y2)), Y2, label='Label 2', color='orange' )
fig.legend( bbox_to_anchor=(.97, .97) )
Finalization
fig.tight_layout()
fig.savefig('stackoverflow.png', bbox_inches='tight')
A quick hack that may suit your needs..
Take off the frame of the box and manually position the two legends next to each other. Something like this..
ax1.legend(loc = (.75,.1), frameon = False)
ax2.legend( loc = (.75, .05), frameon = False)
Where the loc tuple is left-to-right and bottom-to-top percentages that represent the location in the chart.
I found an following official matplotlib example that uses host_subplot to display multiple y-axes and all the different labels in one legend. No workaround necessary. Best solution I found so far.
http://matplotlib.org/examples/axes_grid/demo_parasite_axes2.html
from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
import matplotlib.pyplot as plt
host = host_subplot(111, axes_class=AA.Axes)
plt.subplots_adjust(right=0.75)
par1 = host.twinx()
par2 = host.twinx()
offset = 60
new_fixed_axis = par2.get_grid_helper().new_fixed_axis
par2.axis["right"] = new_fixed_axis(loc="right",
axes=par2,
offset=(offset, 0))
par2.axis["right"].toggle(all=True)
host.set_xlim(0, 2)
host.set_ylim(0, 2)
host.set_xlabel("Distance")
host.set_ylabel("Density")
par1.set_ylabel("Temperature")
par2.set_ylabel("Velocity")
p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature")
p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity")
par1.set_ylim(0, 4)
par2.set_ylim(1, 65)
host.legend()
plt.draw()
plt.show()
If you are using Seaborn you can do this:
g = sns.barplot('arguments blah blah')
g2 = sns.lineplot('arguments blah blah')
h1,l1 = g.get_legend_handles_labels()
h2,l2 = g2.get_legend_handles_labels()
#Merging two legends
g.legend(h1+h2, l1+l2, title_fontsize='10')
#removes the second legend
g2.get_legend().remove()
As provided in the example from matplotlib.org, a clean way to implement a single legend from multiple axes is with plot handles:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
fig.subplots_adjust(right=0.75)
twin1 = ax.twinx()
twin2 = ax.twinx()
# Offset the right spine of twin2. The ticks and label have already been
# placed on the right by twinx above.
twin2.spines.right.set_position(("axes", 1.2))
p1, = ax.plot([0, 1, 2], [0, 1, 2], "b-", label="Density")
p2, = twin1.plot([0, 1, 2], [0, 3, 2], "r-", label="Temperature")
p3, = twin2.plot([0, 1, 2], [50, 30, 15], "g-", label="Velocity")
ax.set_xlim(0, 2)
ax.set_ylim(0, 2)
twin1.set_ylim(0, 4)
twin2.set_ylim(1, 65)
ax.set_xlabel("Distance")
ax.set_ylabel("Density")
twin1.set_ylabel("Temperature")
twin2.set_ylabel("Velocity")
ax.yaxis.label.set_color(p1.get_color())
twin1.yaxis.label.set_color(p2.get_color())
twin2.yaxis.label.set_color(p3.get_color())
tkw = dict(size=4, width=1.5)
ax.tick_params(axis='y', colors=p1.get_color(), **tkw)
twin1.tick_params(axis='y', colors=p2.get_color(), **tkw)
twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)
ax.tick_params(axis='x', **tkw)
ax.legend(handles=[p1, p2, p3])
plt.show()
Here is another way to do this:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
fig = plt.figure()
ax = fig.add_subplot(111)
pl_1, = ax.plot(time, Swdown, '-')
label_1 = 'Swdown'
pl_2, = ax.plot(time, Rn, '-')
label_2 = 'Rn'
ax2 = ax.twinx()
pl_3, = ax2.plot(time, temp, '-r')
label_3 = 'temp'
ax.legend([pl[enter image description here][1]_1, pl_2, pl_3], [label_1, label_2, label_3], loc=0)
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
enter image description here
The solutions proposed so far have one or two inconvenients:
Handles needs to be collected individually when plotting, e.g. lns1 = ax.plot(time, Swdown, '-', label = 'Swdown'). There is a risk of forgetting handles when updating the code.
Legend is drawn for the whole figure, not by subplot, which is likely a no-go if you have multiple subplots.
This new solution takes advantage of Axes.get_legend_handles_labels() to collect existing handles and labels for the main axis and for the twin axis.
Collecting handles and labels automatically
This numpy operation will scan all axes which share the same subplot area than ax, including ax and return merged handles and labels:
hl = np.hstack([axis.get_legend_handles_labels()
for axis in ax.figure.axes
if axis.bbox.bounds == ax.bbox.bounds])
It can be used to feed legend() arguments this way:
import numpy as np
import matplotlib.pyplot as plt
t = np.arange(1, 200)
signals = [np.exp(-t/20) * np.cos(t*k) for k in (1, 2)]
fig, axes = plt.subplots(nrows=2, figsize=(10, 3), layout='constrained')
axes = axes.flatten()
for i, (ax, signal) in enumerate(zip(axes, signals)):
# Plot as usual, no change to the code
ax.plot(t, signal, label=f'plotted on axes[{i}]', c='C0', lw=9, alpha=0.3)
ax2 = ax.twinx()
ax2.plot(t, signal, label=f'plotted on axes[{i}].twinx()', c='C1')
# The only specificity of the code is when plotting the legend
h, l = np.hstack([axis.get_legend_handles_labels()
for axis in ax.figure.axes
if axis.bbox.bounds == ax.bbox.bounds]).tolist()
ax2.legend(handles=h, labels=l, loc='upper right')