using Python I would like to plot a curve for the function y=cosh(x)*cos(5x) in my Jupyter Notebook.
In other words:
(cosine hyperbolicus of x) times (cosine of 5x)
How do I do this?
What do I need to import?
Thank you very much in advance.
Greetings
Specify the range of values for x that you need.
You can use Seaborn on top of Matplotlib to make it prettier, but this is optional:
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-5,5,0.1) # start,stop,step
y= (np.cosh(x))*(np.cos(5*x) )
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above)
sns.set(style="darkgrid")
plt.plot(x,y)
plt.show()
You will need to import a plotting library and a maths library. The most commonly used plotting library is matplotlib, and for maths it's numpy. For plotting, bokeh is a an alternative to matplotlib, which I think is great because graphs are interactive by default. The disadvantage is that because it's not as widely used as matplotlib, you're less likely to find help on it in terms of StackOverflow answers and tutorials.
Anyway, to the code:
# Import the necessary packages and modules
import matplotlib.pyplot as plt
import numpy as np
# Set your x-range and calculate y
xmin = -2.5
xmax = 2.5
numPoints = 100
x = np.linspace(xmin, xmax, numPoints)
y = np.cosh(x)*np.cos(5*x)
# Plot -- it really can be this simple [1]
plt.plot(x,y)
Both of the graphing libraries above give you flexible options on where to place the axes, legends, titles, and so on. I recommend searching for beginner's tutorials on them to learn this stuff in depth.
[1] There are two ways to plot in matplotlib. What is shown here is the MATLAB-like interface. The other method is to use the object-based interface, which takes a bit more of getting used to, and requires a bit more boilerplate code, but that's what you will end up using once you demand more control over the appearance of your plots.
I recommend starting with the MATLAB-like commands first. The documentation has a good beginner's tutorial: https://matplotlib.org/stable/tutorials/introductory/pyplot.html
Related
I working on a PyQt GUI in order to plot 2D/3D data.
Matplotlib is very interesting concerning all its available features.
However, Matplotlib seems to not be relevant when you have huge amount of data (for instance 10000x10000). I found that maybe GTKAgg backend is really efficient for this issue, but not really.
Maybe you can help me in order to enhance the performance of matplotlib when you want to pan or zoom in the figure.
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
mpl.use('GTK3Agg')
fig, ax = plt.subplots()
n = 10000
Z = np.random.randint(10,size=(n,n))
ax.imshow(Z)
plt.show()
I've been messing around with Mayavi for 3D graphing, and I can graph the scatter plot I want but can't seem to get the Axes to look proper. I found the following previous question that starts to get at what I'm after, but doesn't go into detail. I want a 3D scatter plot like #1 but with nice-looking axes like #2 (I'd embed but I don't have enough reputation).
The regular Mayavi Axes aren't going to cut it. Any tips for getting the planes that Matplotlib has?
This is actually pretty straightforward once we get the "trick", and I have done it many times.
The 'trick' is to generate the mayavi plot first, then transfer it into the matplotlib window where you can use all the familiar matplotlib tools to make axes with numbers, dates, arrows, or the other pieces that matplotlib provides. In this code example I'll just drop the mayavi 'copper spheres' example into a matplotlib set of axes:
import numpy, pylab, mayavi, mayavi.mlab
import matplotlib.pyplot as plt
t = numpy.linspace(0, 4 * numpy.pi, 20)
cos,sin = numpy.cos, numpy.sin
x = sin(2 * t)
y = cos(t)
z = cos(2 * t)
s = 2 + sin(t)
mayavi.mlab.points3d(x, y, z, s, colormap="copper", scale_factor=.25)
arr = mayavi.mlab.screenshot()
fig = plt.figure(figsize=(5, 5))
pylab.imshow(arr)
plt.show()
The result is just the mayavi plot in a matplotlib set of axes:
The only tricky part is that you'll need to build a working knowledge of the scales and dimensions of mayavi and matplotlib to get the two to scale together if you want to produce more than one or two manually built plots. That is non-trivial but fairly well documented for both Mayavi and Matplotlib and from the sounds of things you know both so I won't belabor those points. (Now that things are in Matplotlib you can make them part of generated PDFs too or port them into Microsoft Word with python-docx)
I have data (a spectrum) that I want to plot as a histogram.
I import the data and spectrum.shape shows me (1024,) as the format,
however plt.hist does not plot the data correctly.
If I use plt.bar(...) it works just fine, but for aesthetic reasons (I want to use the "stepfilled" histogram design) and I have to employ plt.hist which offers this option.
I really don't know what to do.
Here is my code:
import matplotlib.pyplot as plt
import numpy as np
spectrum = np.loadtxt('3000.mp', skiprows=53)
y1=spectrum[:]
num_bins = 1024
diagram = plt.hist(y1, num_bins, alpha=0.5)
plt.xlabel("TOF / $\mu$s")
plt.ylabel("# ions")
plt.show()
I hope for your help.
I am interested also in this answer. Would you share how did you get the stepfilled design with bars?
Myself I am looking for something like this:
(this image comes from http://astroplotlib.stsci.edu/page_histograms.htm)
But I do not manage to generate it easily with a spectrum as an input.
I want to plot some (x,y) points on the same graph and I don't need any special features at all short of support for polar coordinates which would be nice but not necessary. It's mostly for visualizing my data. Is there a simple way to do this? Matplotlib seems like way more than I need right now. Are there any more basic modules available? What do You recommend?
Go with matplotlib Chance is that sometime in the future you might need to do more than just "simple" stuff and then you don't need to invest time learning a new plot-tool.
See this link for list of plotting tools for python...
Absolutely. Matplotlib is the way to go.
The pyplot module provides a nice interface to get simple plots up and running fast, especially if you are familiar with MatLab's plotting environment. Here is a simple example using pyplot:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
x_points = xrange(0,9)
y_points = xrange(0,9)
p = ax.plot(x_points, y_points, 'b')
ax.set_xlabel('x-points')
ax.set_ylabel('y-points')
ax.set_title('Simple XY point plot')
fig.show()
import matplotlib.pyplot as plt
x = range(1,10)
y = range(1,10)
plt.plot(x,y,'o')
plt.show()
Here's a simple line with made up x, y. Note: x and y are lists.
Their lengths should be equal or you'll get a error. Cheers!
I suggest the most good looking plotting library for Python: CairoPlot
You can use the Tkinter canvas widget. It uses rectangular coordinates but of course you can translate to polar. The canvas is pretty much just like it sounds -- a blank canvas on which you can draw points, lines, circles, rectangles, etc.
You could always write a plotting function that uses the turtle module from the standard library.
MathGL is GPL plotting library which have Python interface, arbitrary (including polar) curved coordinates, a lot of plot types, export to PNG, EPS, SVG, widgets, and so on. For 1D plot samples see here.
Have you tried to use pillow?
from PIL import Image, ImageDraw
#Set up canvas
img = Image.new (mode, size)
draw = ImageDraw.Draw (img)
#Draw your points
draw.point (xy, colour)
Here is a simple matlab script to read a csv file, and generate a plot (with which I can zoom in with the mouse as I desire). I would like to see an example of how this is done in python and mathplotlib.
data = csvread('foo.csv'); % read csv data into vector 'data'
figure; % create figure
plot (data, 'b'); % plot the data in blue
In general, the examples in mathplotlib tutorials I've seen will create a static graph, but it's not interactively "zoomable". Would any python expert care to share an equivalent?
Thanks
import matplotlib.pyplot as plt
import numpy as np
arr=np.genfromtxt('foo.csv',delimiter=',')
plt.plot(arr[:,0],arr[:,1],'b-')
plt.show()
on this data (foo.csv):
1,2
2,4
3,9
produces
When you setup the matplotlibrc, one of the key parameters you need to set is the backend. Which backend you choose depends on your OS and installation.
For any typical OS there should be a backend that allows you to pan and zoom the plot interactively. (GtkAgg works on Ubuntu). The buttons highlighted in red allow you to pan and zoom, respectively.
Since you're familiar with Matlab, I'd suggest using the pylab interface to matplotlib - it mostly mimics Matlab's plotting. As unutbu says, the zoomability of the plot is determined by the backend you use, a separate issue.
from pylab import *
data = genfromtxt("file.csv")
plot(data, 'b')