My question is not about matplotlib in detail, but a general programming and question, and i'm looking for an answer on the mechanisms making this possible in python or matplotlib core.
Let's say I have a scatter plot using the code:
import matplotlib.pyplot as plt
plt.scatter(a,b)
plt.show()
I'm wondering how is this statement handled?
How does python (or matplotlib?) know what to plot and where to get the data?
How are these statement handled by interpreter?
Maybe I finally see the point of this question. Of course we cannot explain pyplot here, because that is much too complicated and would require a complete tutorial (which btw do exist). But we can have a look at how pyplot would work as a module in a very simplified manner.
So let's create myplot, the ultimative console plotting library. ;-)
The module myplot could look as follows. It has two functions, scatter and show and two variables, figures and plot. plot would store our coordinate system to plot to. figures would store the figures we create.
plot = """
^
|
|
|
|
|
+----------->"""
figures = []
def scatter(X,Y):
thisplot = list(plot[:])
for x,y in zip(X,Y):
thisplot[1+14*(6-y)+x] = "*"
thisplot = "".join(thisplot)
figures.append(thisplot)
def show():
for fig in figures:
print(fig)
Calling scatter creates a new figure from plot and stores it in the figures list. Calling show takes all figures from that list, and shows them (prints them in the console).
So using myplot would look exactly like the example above.
import myplot as mlt
mlt.scatter([2,3,4,5,6,8],[2,5,4,4,3,2])
mlt.show()
Creating the output:
^
| *
| **
| *
| * *
|
+----------->
Related
I have a complicated method called plotter() which processes some data and produces a matplotlib plot with several components. Due to its complexity I simply want to test that the plot appears. This will confirm that all of the data is processed reasonably and that something gets shown without any errors being thrown. I am not looking to run an image comparison as that's not currently possible for this project.
My function is too complicated to show here, so the following example could be considered instead.
import matplotlib.pyplot as plt
import numpy as np
def plotter():
x = np.arange(0,10)
y = 2*x
fig = plt.plot(x, y)
plotter()
plt.show()
Is there a way to use PyTest to simply assert that a figure appears? If not then solutions using other test frameworks would also be greatly appreciated.
(For context I am using Python 3.)
I often plot a point on a matplotlib plot with:
x = 10
y = 100
plot(x, y, "k*", label="Global Optimum")
legend()
However, this causes the legend to put a star in the legend twice, such that it looks like:
* * Global Optimum
when I really want it to look like:
* Global Optimum
How do I do this?
This should work:
legend(numpoints=1)
BTW, if you add the line
legend.numpoints : 1 # the number of points in the legend line
to your matplotlibrc file, then this will be the new default.
[See also scatterpoints, depending on your plot.]
API: Link to API docs
I like to change my matplotlib rc parameters dynamically in every python script. To achieve this goal I simply use somthing like that at the beginning of my python files.
from pylab import *
rcParams['legend.numpoints'] = 1
This will apply to all plots generated from my python file.
EDIT: For those who do not like to import pylab, the long answer is
import matplotlib as mpl
mpl.rcParams['legend.numpoints'] = 1
I use matplotlib for my plots, I find it great, but sometimes too much complicated. Here an example:
import matplotlib.pyplot as plt
import numpy as np
idx1 = -3
idx2 = 3
x = np.arange(-3, 3, 0.01)
y = np.sin(np.pi*x*7)/(np.pi*x*7)
major_ticks = np.arange(idx1, idx2, 1)
minor_ticks = np.arange(idx1, idx2, 0.1)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_ylim(-0.3, 1.2)
ax.set_xlim(idx1, idx2)
ax.set_xticks(major_ticks)
ax.set_xticks(minor_ticks, minor = True)
ax.grid(True, which = 'both')
ax.tick_params(axis = 'x', labelsize = 18)
ax.tick_params(axis = 'y', labelsize = 18)
ax.plot(x, y)
plt.show()
Is there anything implemented on matplotlib and/or seaborn in which I can provide all these plot settings just as argument of a function only? It may considerably reduce the number of code lines and make the script easier both to write and understand.
Matplotlib provides an object oriented API. This means that all the elements of the figure are acutally objects for which one can get and set properties and which can be easily manipulated. This makes matplotlib really flexible such that it can produce almost any plot you'd imagine.
Since a plot may consist of a hundred or more elements, a function that would allow the same flexibility would need that amount of possible arguments. It is not necessarily easier to remember all possible arguments of a function than all possible attributes of a class.
Having a single function call that does all of this, does not necessarily mean that you have to type in less characters. The commands would just be ordered differently.
Furthermore the object oriented approach allows to keep things seperate. Some properties of the axes, like the grid or the axis labels are completely independend on what you plot to the axes. So you wouldn't want to set the xticks in the call to plot, because they are simply not related and it may be very confusing to set twice the same ticklabels when plotting two lines in the same axes.
On the other hand, matplotlib is really easy. In order to produce a plot you need two lines
import matplotlib.pyplot as plt
plt.plot([1,2,3],[2,1,3])
which sets most of the parameters exactly as they are needed. The more you want to customize this plot, the more settings you have to apply. Which is fine as it allows the user himself to determine how much in depth he wants to control the appearance of the plot.
Most matplotlib codes can be separated into three parts.
Setting the style
Creating the plot
Customizing the plot
Setting the style in the case of the code from the question involves e.g. the ticklabel size and the use of a grid. Those properties can set as it's done in the code but it may indeed be that one always wants to use the same properities here and finds it annoying to type the same parameters in every time one creates a plot. Therefore matplotlib provides general style settings, called rcParams. They can be set at the beginning of a script, e.g.
plt.rcParams['lines.linewidth'] = 2
plt.rcParams['axes.grid '] = True
plt.rcParams['axes.labelsize'] = 18
and will be applied to all plots within the script. It is also possible to define a complete stylesheet using those parameters. For more information see the Customizing matplotlib article.
It is equally possible to use predefined stylesheets for certain applications.
Simply importing import seaborn is also a possible way to change the style.
Creating the plot can not be simplified much more. It's clear that one needs as many plotting commands as items to plot. Creating the figure and axes like
fig, ax = plt.subplots()
saves one line though.
Equally no simplification is possible if customizing ticks or tickmarks are required. One may however consider to use Tickers and Formatters for this purpose.
At the end one may of course consider to write a custom function which performs much of those tasks, but everyone can decide if that is useful for himself.
Browsing around I saw this wabe page.
This line of code can summarise many settings
import matplotlib as mpl
mpl.rc('lines', linewidth=2, color='r')
ax.set is very useful for this:
ax.set(xlim=(idx1, idx2), ylim=(-0.3, 1.2),
xticks=major_ticks, ...)
You can only set simple single-argument properties (e.g. those which don't need further keywords), but it's a nice timesaver.
Is there an equivalent plotting function and/or a simple way to make this plot.ly plot in python in "pure" python using e.g. matplotlib?
from here.
Just wondering if there is an equivalent function or similar. Cannot find any, or am not searching for the right thing. "heatmap python" only comes up with square plots, and changing their shape seems cumbersome.
To give you a simple example, the following will generate the attached plot.
from pylab import *
Z = rand(6, 100) # tried to make it look similar to your plot
c = pcolor(Z)
show()
Building on Hun answer, if you don't want your eyes to hurt too much, you can use an alternate color map. Here viridis
import matplotlib.pyplot as plt
import numpy as np
Z = np.random.rand(6, 100)
c = plt.pcolor(Z, cmap='viridis')
plt.show()
and remember: pyplot & numpy will keep your namespace tidy...
I will not be able to put the code here because it is my assignment.
My program is printing multiple graphs on one plot. Please look at the example figure on the following link: Python: Plot multiple graphs on the same figure
The link above is just an example. That is not my code nor do I have the same program. My topic is completely different. That figure is just for reference.
The line of code I am using to achieve this is: plot(a,b, label=str(meters))
What I want to do is get any one of those graph from those three curves and also plot it separately as if it is the main graph. I am doing all this inside a function, and I have created an array of numbers to loop through these different values to get three different graphs.
Do you mean something like this?
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
a = np.arange(5)
line1, = plt.plot(a, a**2) # a new figure instance is opened automatically
line2, = plt.plot(a, a**3-a)
line3, = plt.plot(a, 4*a-a**2/2.)
fig_handle = plt.figure() # Force a new figure instance to open
plt.plot(a, a**2) # This will replot 'line1', but in this new figure instance.
If not, please update your question, perhaps showing the code you already have.
Note that this is information you could find on the matplotlib pyplot tutorial.