I am plotting using the contourf function from matplotlib and would like to add a colorbar, I've noticed that sometimes the ticks don't go the max/min values.
Is there a clean way to force it to set ticks at these values?
Note: Checking the max and min of z shows that the colorbar represents values from approx -1 to 1, therefor I would expect this ot be reflected such that one can see the range from the colobar, in addition to some ticks in between.
Plot and code demonstrating what I am talking about:
import matplotlib.pyplot as plt
import numpy as np
# Data to plot.
x, y = np.meshgrid(np.arange(7), np.arange(10))
z = np.sin(0.5 * x) * np.cos(0.52 * y)
fig, ax = plt.subplots()
cs = ax.contourf(x, y, z, levels=25)
ax.grid(c="k", ls="-", alpha=0.3)
fig.colorbar(cs, ax=ax)
fig.savefig("example.png", bbox_inches="tight")
The cleanest way seems to be to give explicit levels to contourf. If no explicit levels are given, contourf seems to choose its own, depending on the minimum and maximum value in the data, and also tries to find "nice looking" numbers. After that, ticks get set to a subset of these numbers, such that a tick always coincides with a real level. (If you use colorbar(..., ticks=...) those ticks will not necessarily coincide with the levels.)
As the sine and cosine don't reach -1 and 1 exact in the given example, they are not part of the range.
The following code shows how the ticks depend on the chosen levels. With np.linspace(-1, 1, 24) the levels aren't nice round numbers, but matplotlib still chooses a subset to show.
import matplotlib.pyplot as plt
import numpy as np
x, y = np.meshgrid(np.arange(7), np.arange(10))
z = np.sin(0.5 * x) * np.cos(0.52 * y)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 3))
for ax in (ax1, ax2):
numcontours = 25 if ax == ax1 else 24
cs = ax.contourf(x, y, z, levels=np.linspace(-1, 1, numcontours))
ax.grid(c="k", ls="-", alpha=0.3)
fig.colorbar(cs, ax=ax)
ax.set_title(f'{numcontours} levels from -1 to 1')
plt.show()
Related
I would like to show datetimes as ticklabels on the x-axis of a plot via ax.imshow(). I first tried putting the limits (as datetime objects) into extent, but it appears that extent only accepts arguments of type <float/int>. So instead, I would like to create the original plot via ax.imshow(...), then make the x-axis invisible, then add in the correct xticks and xlim.
I found a similar problem solved using a different approach in this example, but I think my use-case is slightly different; I don't need to convert any time-stamps, but I do know the xlim of the data (in terms of datetime objects). Also, I do not think the suggested use of matplotlib.dates.date2num fits my use-case since some of the data is spaced less than one day apart, but date2num uses days as a base-unit.
I am stuck trying to make this work using my alternate approach; a simple mini-example is below.
import numpy as np
import datetime
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
def f(x, y):
return np.sqrt(np.square(x) + np.square(y))
## SAMPLE DATA
x = np.arange(10) ## elapsed minutes
y = np.square(x) ## arbitrary y-values
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
## DATETIMES FOR ALTERNATE AXIS
lower_dt = datetime.datetime(1999, 1, 1, 0, 0, 0)
# upper_dt = datetime.datetime(2001, 10, 31, 0, 0, 0)
upper_dt = datetime.datetime(1999, 1, 1, x.size-1, 0, 0)
## DO PLOT
fig, ax = plt.subplots()
ax.xaxis.set_visible(False)
# ax.xaxis.tick_top()
ax.imshow(
Z,
origin='lower',
cmap='Oranges',
norm=Normalize(vmin=np.nanmin(Z), vmax=np.nanmax(X)),
extent=(x[0], x[-1], y[0], y[-1]))
## CONVERT XTICKLABELS OF X-AXIS TO DATETIME
mirror_ax = ax.twiny()
# mirror_ax = ax.figure.add_subplot(ax.get_subplotspec(), frameon=False)
mirror_ax.set_xlim([lower_dt, upper_dt])
plt.show()
plt.close(fig)
The obtained plot can be seen here:
I notice that the xticks are shown at the top instead of the bottom of the plot - this is unwanted behavior; using ax.tick_top (commented out above) does not change this. Even worse, the x-axis limits are not retained. I realize I could manually change the xticklabels via ax.get_xticks() and ax.set_xticklabels(...), but I would prefer to leave that for date-formatters and date-locators via matplotlib.
How can I use the approach outlined above to create a "mirror/alternate" x-axis of datetime units such that this x-axis is the same size/orientation of the "original/invisible" x-axis of float/integer units?
I hope one of you may be able to help. I have a plot with one y-axis value and one x-axis corresponding to these y values. I want to add a second y-axis on the right hand side of the plot. The values that will appear on the second y-axis are determined through the first y-axis values by some relation: for example, y2 might be y2 = y1**2 - 100. How do I make a second y-axis which has its values determined by the y1 values, so that the y2 values correctly align with their y1 values on the y-axis?
twin axis
Adding a second y axis can be done by creating a twin axes, ax2 = ax.twinx().
The scale of this axes can be set using its limits, ax2.set_ylim(y2min, y2max). The values of y2min, y2max can be calculated using some known relationship (e.g. implemented as a function) from the limits of the left axis.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
x = np.linspace(0,50,101)
y = np.cumsum(np.random.normal(size=len(x)))+20.
fig, ax = plt.subplots()
ax2 = ax.twinx()
ax.plot(x,y, color="#dd0011")
ax.set_ylabel("Temperature [Celsius]")
ax2.set_ylabel("Temperature [Fahrenheit]")
# set twin scale (convert degree celsius to fahrenheit)
T_f = lambda T_c: T_c*1.8 + 32.
# get left axis limits
ymin, ymax = ax.get_ylim()
# apply function and set transformed values to right axis limits
ax2.set_ylim((T_f(ymin),T_f(ymax)))
# set an invisible artist to twin axes
# to prevent falling back to initial values on rescale events
ax2.plot([],[])
plt.show()
secondary axis
From matplotlib 3.1 onwards one can use a secondary_yaxis. This takes care of synchronizing the limits automatically. As input one needs the conversion function and its inverse.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
x = np.linspace(0,50,101)
y = np.cumsum(np.random.normal(size=len(x)))+20.
# Convert celsius to Fahrenheit
T_f = lambda T_c: T_c*1.8 + 32.
# Convert Fahrenheit to Celsius
T_c = lambda T_f: (T_f - 32.)/1.8
fig, ax = plt.subplots()
ax2 = ax.secondary_yaxis("right", functions=(T_f, T_c))
ax.plot(x,y, color="#dd0011")
ax.set_ylabel("Temperature [Celsius]")
ax2.set_ylabel("Temperature [Fahrenheit]")
plt.show()
The output is the same as above, but as you can see one does not need to set any limits.
I have a subplot with a single legend entry. I am placing the legend at the bottom of the figure and using mode='expand'; however, the single legend entry is placed to the very left of the legend box. To my understanding, changing kwargs such as bbox_to_anchor changes the legend box parameters but not the parameters of the symbols/text within. Below is an example to reproduce my issue.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-10, 10, 21)
y = np.exp(x)
z = x **2
fig, axes = plt.subplots(nrows=1, ncols=2)
axes[0].plot(x, y, color='r', label='exponential')
axes[1].plot(x, z, color='b')
# handles, labels = axes[0].get_legend_handles_labels()
plt.subplots_adjust(bottom=0.125)
fig.legend(mode='expand', loc='lower center')
plt.show()
plt.close(fig)
This code produces . How can I change the position of the symbol and text such that they are centered in the legend box?
PS: I am aware that exponential is a bad label for this subplot since it only describes the first subfigure. But, this is just for examples-sake so that I can apply it to my actual use-case.
The legend entries are placed using a HPacker object. This does not allow to be centered. The behaviour is rather that those HPackers are "justified" (similar to the "justify" option in common word processing software).
A workaround would be to create three (or any odd number of) legend entries, such that the desired entry is in the middle. This would be accomplished via the ncol argument and the use of "dummy" entries (which might be transparent and have no associated label).
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-10, 10, 21)
y = np.exp(x)
z = x **2
fig, axes = plt.subplots(nrows=1, ncols=2)
fig.subplots_adjust(bottom=0.125)
l1, = axes[0].plot(x, y, color='r', label='exponential')
axes[1].plot(x, z, color='b')
dummy = plt.Line2D([],[], alpha=0)
fig.legend(handles=[dummy, l1, dummy],
mode='expand', loc='lower center', ncol=3)
plt.show()
I hope one of you may be able to help. I have a plot with one y-axis value and one x-axis corresponding to these y values. I want to add a second y-axis on the right hand side of the plot. The values that will appear on the second y-axis are determined through the first y-axis values by some relation: for example, y2 might be y2 = y1**2 - 100. How do I make a second y-axis which has its values determined by the y1 values, so that the y2 values correctly align with their y1 values on the y-axis?
twin axis
Adding a second y axis can be done by creating a twin axes, ax2 = ax.twinx().
The scale of this axes can be set using its limits, ax2.set_ylim(y2min, y2max). The values of y2min, y2max can be calculated using some known relationship (e.g. implemented as a function) from the limits of the left axis.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
x = np.linspace(0,50,101)
y = np.cumsum(np.random.normal(size=len(x)))+20.
fig, ax = plt.subplots()
ax2 = ax.twinx()
ax.plot(x,y, color="#dd0011")
ax.set_ylabel("Temperature [Celsius]")
ax2.set_ylabel("Temperature [Fahrenheit]")
# set twin scale (convert degree celsius to fahrenheit)
T_f = lambda T_c: T_c*1.8 + 32.
# get left axis limits
ymin, ymax = ax.get_ylim()
# apply function and set transformed values to right axis limits
ax2.set_ylim((T_f(ymin),T_f(ymax)))
# set an invisible artist to twin axes
# to prevent falling back to initial values on rescale events
ax2.plot([],[])
plt.show()
secondary axis
From matplotlib 3.1 onwards one can use a secondary_yaxis. This takes care of synchronizing the limits automatically. As input one needs the conversion function and its inverse.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
x = np.linspace(0,50,101)
y = np.cumsum(np.random.normal(size=len(x)))+20.
# Convert celsius to Fahrenheit
T_f = lambda T_c: T_c*1.8 + 32.
# Convert Fahrenheit to Celsius
T_c = lambda T_f: (T_f - 32.)/1.8
fig, ax = plt.subplots()
ax2 = ax.secondary_yaxis("right", functions=(T_f, T_c))
ax.plot(x,y, color="#dd0011")
ax.set_ylabel("Temperature [Celsius]")
ax2.set_ylabel("Temperature [Fahrenheit]")
plt.show()
The output is the same as above, but as you can see one does not need to set any limits.
I'm currently trying to get an impression of continuous change in my contour plot. I have to use a logscale for the values, because some of them are some orders of magnitude bigger than the others.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
K = np.linspace(-0.99, 5, 100)
x = np.linspace(1, 5, 100)
K, x = np.meshgrid(K, x)
static_diff = 1 / (1 + K)
fig = plt.figure()
plot = plt.contourf(K, x, static_diff, locator=ticker.LogLocator(numticks=300))
plt.grid(True)
plt.xlabel('K')
plt.ylabel('x')
plt.xlim([-0.99, 5])
plt.ylim([1, 5])
fig.colorbar(plot)
plt.show()
Despite the number of ticks given to be 300 it returns a plot like:
Is there a way to get more of these lines? I also tried adding the number of parameters as the fourth parameter of the plt.contourf function.
To specify the levels of a contourf plot you may
use the levels argument and supply a list of values for the levels. E.g for 20 levels,
plot = plt.contourf(K, x, static_diff, levels=np.logspace(-2, 3, 20))
use the locator argument to which you would supply a matplotlib ticker
plt.contourf(K, x, static_diff, locator=ticker.LogLocator(subs=range(1,10)))
Note however that the LogLocator does not use a numticks argument but instead a base and a subs argument to determine the locations of the ticks. See documentation.
Complete example for the latter case, which also uses a LogNormto distribute the colors better in logspace:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
import matplotlib.colors
K = np.linspace(-0.99, 5, 100)
x = np.linspace(1, 5, 100)
K, x = np.meshgrid(K, x)
static_diff = 1 / (1 + K)
fig = plt.figure()
norm= matplotlib.colors.LogNorm(vmin=static_diff.min(), vmax=static_diff.max())
plot = plt.contourf(K, x, static_diff, locator=ticker.LogLocator(subs=range(1,10)), norm=norm)
#plot = plt.contourf(K, x, static_diff, levels=np.logspace(-2, 3, 20), norm=norm)
plt.grid(True)
plt.xlabel('K')
plt.ylabel('x')
plt.xlim([-0.99, 5])
plt.ylim([1, 5])
fig.colorbar(plot)
plt.show()