I'm having a problem trying to plot a series of lines in a 3D plot in MatPlotLib.
When I run the code below all the lines are plotted at the last value of y??? Even though y is correctly incremented in the loop.
Any Help understanding this would be appreciated.
Thanks
David
#========== Code Start=================
import numpy as np
import matplotlib
from matplotlib.figure import Figure
import pylab as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
x=np.arange(5)
y=np.zeros(len(x))
for i in range(1,10):
y.fill(i)
z=plt.randn(len(y))
ax.plot(xs=x, ys=y, zs=z)#, zdir='z', label='ys=0, zdir=z')
plt.draw()
print i,len(y),y,x,z
plt.xlabel('X')
plt.ylabel('Y')
plt.zlabel('Z')
plt.show()
#========== Code End=================
It looks like y might be pointed to by all plots. So you are passing the reference to y when you execute ax.plot. It is the same reference each time, but the values are changed on each pass. When the plt.show() is executed the reference to y is used and it is now set at 9. So, create a different object for y on each pass with the values you want for that pass:
y = np.zeros(len(x))
y.file(i)
There might be a numpy command that fills with the value you want in one go, but you get the picture.
Related
In the answers to how to dynamically update a plot in a loop in ipython notebook (within one cell), an example is given of how to dynamically update a plot inside a Jupyter notebook within a Python loop. However, this works by destroying and re-creating the plot on every iteration, and a comment in one of the threads notes that this situation can be improved by using the new-ish %matplotlib nbagg magic, which provides an interactive figure embedded in the notebook, rather than a static image.
However, this wonderful new nbagg feature seems to be completely undocumented as far as I can tell, and I'm unable to find an example of how to use it to dynamically update a plot. Thus my question is, how does one efficiently update an existing plot in a Jupyter/Python notebook, using the nbagg backend? Since dynamically updating plots in matplotlib is a tricky issue in general, a simple working example would be an enormous help. A pointer to any documentation on the topic would also be extremely helpful.
To be clear what I'm asking for: what I want to do is to run some simulation code for a few iterations, then draw a plot of its current state, then run it for a few more iterations, then update the plot to reflect the current state, and so on. So the idea is to draw a plot and then, without any interaction from the user, update the data in the plot without destroying and re-creating the whole thing.
Here is some slightly modified code from the answer to the linked question above, which achieves this by re-drawing the whole figure every time. I want to achieve the same result, but more efficiently using nbagg.
%matplotlib inline
import time
import pylab as pl
from IPython import display
for i in range(10):
pl.clf()
pl.plot(pl.randn(100))
display.display(pl.gcf())
display.clear_output(wait=True)
time.sleep(1.0)
Here is an example that updates a plot in a loop. It updates the data in the figure and does not redraw the whole figure every time. It does block execution, though if you're interested in running a finite set of simulations and saving the results somewhere, it may not be a problem for you.
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import time
def pltsin(ax, colors=['b']):
x = np.linspace(0,1,100)
if ax.lines:
for line in ax.lines:
line.set_xdata(x)
y = np.random.random(size=(100,1))
line.set_ydata(y)
else:
for color in colors:
y = np.random.random(size=(100,1))
ax.plot(x, y, color)
fig.canvas.draw()
fig,ax = plt.subplots(1,1)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_xlim(0,1)
ax.set_ylim(0,1)
for f in range(5):
pltsin(ax, ['b', 'r'])
time.sleep(1)
I put this up on nbviewer here.
There is an IPython Widget version of nbagg that is currently a work in progress at the Matplotlib repository. When that is available, that will probably be the best way to use nbagg.
EDIT: updated to show multiple plots
I'm using jupyter-lab and this works for me (adapt it to your case):
from IPython.display import clear_output
from matplotlib import pyplot as plt
import numpy as np
import collections
%matplotlib inline
def live_plot(data_dict, figsize=(7,5), title=''):
clear_output(wait=True)
plt.figure(figsize=figsize)
for label,data in data_dict.items():
plt.plot(data, label=label)
plt.title(title)
plt.grid(True)
plt.xlabel('epoch')
plt.legend(loc='center left') # the plot evolves to the right
plt.show();
Then in a loop you populate a dictionary and you pass it to live_plot():
data = collections.defaultdict(list)
for i in range(100):
data['foo'].append(np.random.random())
data['bar'].append(np.random.random())
data['baz'].append(np.random.random())
live_plot(data)
make sure you have a few cells below the plot, otherwise the view snaps in place each time the plot is redrawn.
If you don't want to clear all outputs, you can use display_id=True to obtain a handle and use .update() on it:
import numpy as np
import matplotlib.pyplot as plt
import time
from IPython import display
def pltsin(ax, *,hdisplay, colors=['b']):
x = np.linspace(0,1,100)
if ax.lines:
for line in ax.lines:
line.set_xdata(x)
y = np.random.random(size=(100,1))
line.set_ydata(y)
else:
for color in colors:
y = np.random.random(size=(100,1))
ax.plot(x, y, color)
hdisplay.update(fig)
fig,ax = plt.subplots(1,1)
hdisplay = display.display("", display_id=True)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_xlim(0,1)
ax.set_ylim(0,1)
for f in range(5):
pltsin(ax, colors=['b', 'r'], hdisplay=hdisplay)
time.sleep(1)
plt.close(fig)
(adapted from #pneumatics)
I've adapted #Ziofil answer and modified it to accept x,y as list and output a scatter plot plus a linear trend on the same plot.
from IPython.display import clear_output
from matplotlib import pyplot as plt
%matplotlib inline
def live_plot(x, y, figsize=(7,5), title=''):
clear_output(wait=True)
plt.figure(figsize=figsize)
plt.xlim(0, training_steps)
plt.ylim(0, 100)
x= [float(i) for i in x]
y= [float(i) for i in y]
if len(x) > 1:
plt.scatter(x,y, label='axis y', color='k')
m, b = np.polyfit(x, y, 1)
plt.plot(x, [x * m for x in x] + b)
plt.title(title)
plt.grid(True)
plt.xlabel('axis x')
plt.ylabel('axis y')
plt.show();
you just need to call live_plot(x, y) inside a loop.
here's how it looks:
The canvas.draw method of the figure dynamically updates its graphs, for the current figure:
from matplotlib import pyplot as plt
plt.gcf().canvas.draw()
I am trying to use ax.scatter to plot a 3D scattering plot. I've read the data from a fits file and stored data from three column into x,y,z. And I have made sure x,y,z data are the same size. z has been normolized between 0 and 1.
import numpy as np
import matplotlib
from matplotlib import pylab,mlab,pyplot,cm
plt = pyplot
import pyfits as pf
from mpl_toolkits.mplot3d import Axes3D
import fitsio
data = fitsio.read("xxx.fits")
x=data["x"]
y=data["y"]
z=data["z"]
z = (z-np.nanmin(z)) /(np.nanmax(z) - np.nanmin(z))
Cen3D = plt.figure()
ax = Cen3D.add_subplot(111, projection='3d')
cmap=cm.ScalarMappable(norm=z, cmap=plt.get_cmap('hot'))
ax.scatter(x,y,z,zdir=u'z',cmap=cmap)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
What I am trying to achieve is use color to indicate the of size of z. Like higher value of z will get darker color. But I am keep getting a plot without the colormap I want, they are all the same default blue color. What did I do wrong? Thanks.
You can use the c keyword in the scatter command, to tell it how to color the points.
You don't need to set zdir, as that is for when you are plotting a 2d set
As #Lenford pointed out, you can use cmap='hot' in this case too, since you have already normalized your data.
I've modified your example to use some random data rather than your fits file.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
z = (z-np.nanmin(z)) /(np.nanmax(z) - np.nanmin(z))
Cen3D = plt.figure()
ax = Cen3D.add_subplot(111, projection='3d')
ax.scatter(x,y,z,cmap='hot',c=z)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
As per the pyplot.scatter documentation, the points specified to be plotted must be in the form of an array of floats for cmap to apply, otherwise the default colour (in this case, jet) will continue to apply.
As an aside, simply stating cmap='hot' will work for this code, as the colour map hot is a registered colour map in matplotlib.
I am trying to simply fill the area under the curve of a plot in Python using MatPlotLib.
Here is my SSCCE:
import json
import pprint
import numpy as np
import matplotlib.pyplot as plt
y = [0,0,0,0,0,0,0,0,0,0,0,863,969,978,957,764,767,1009,1895,980,791]
x = np.arange(len(y))
fig2, ax2 = plt.subplots()
ax2.fill(x, y)
plt.savefig('picForWeb.png')
plt.show()
The attached picture shows the output produced.
Does anyone know why Python is not filling the entire area in between the x-axis and the curve?
I've done Google and StackOverflow searches, but could not find a similar example. Intuitively it seems that it should fill the entire area under the curve.
I usually use the fill_between function for these kinds of plots. Try something like this instead:
import numpy as np
import matplotlib.pyplot as plt
y = [0,0,0,0,0,0,0,0,0,0,0,863,969,978,957,764,767,1009,1895,980,791]
x = np.arange(len(y))
fig, (ax1) = plt.subplots(1,1);
ax1.fill_between(x, 0, y)
plt.show()
See more examples here.
If you want to use this on a pd.DataFrame use this:
df.abs().interpolate().plot.area(grid=1, linewidth=0.5)
interpolate() is optional.
plt.fill assumes that you have a closed shape to fill - interestingly if you add a final 0 to your data you get a much more sensible looking plot.
import numpy as np
import matplotlib.pyplot as plt
y = [0,0,0,0,0,0,0,0,0,0,0,863,969,978,957,764,767,1009,1895,980,791,0]
x = np.arange(len(y))
fig2, ax2 = plt.subplots()
ax2.fill(x, y)
plt.savefig('picForWeb.png')
plt.show()
Results in:
Hope this helps to explain your odd plot.
I am trying to plot multiple lines in a 3D plot using matplotlib. I have 6 datasets with x and y values. What I've tried so far was, to give each point in the data sets a z-value. So all points in data set 1 have z=1 all points of data set 2 have z=2 and so on.
Then I exported them into three files. "X.txt" containing all x-values, "Y.txt" containing all y-values, same for "Z.txt".
Here's the code so far:
#!/usr/bin/python
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
import pylab
xdata = '/X.txt'
ydata = '/Y.txt'
zdata = '/Z.txt'
X = np.loadtxt(xdata)
Y = np.loadtxt(ydata)
Z = np.loadtxt(zdata)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(X,Y,Z)
plt.show()
What I get looks pretty close to what I need. But when using wireframe, the first point and the last point of each dataset are connected. How can I change the colour of the line for each data set and how can I remove the connecting lines between the datasets?
Is there a better plotting style then wireframe?
Load the data sets individually, and then plot each one individually.
I don't know what formats you have, but you want something like this
from mpl_toolkits.mplot3d.axes3d import Axes3D
import matplotlib.pyplot as plt
fig, ax = plt.subplots(subplot_kw={'projection': '3d'})
datasets = [{"x":[1,2,3], "y":[1,4,9], "z":[0,0,0], "colour": "red"} for _ in range(6)]
for dataset in datasets:
ax.plot(dataset["x"], dataset["y"], dataset["z"], color=dataset["colour"])
plt.show()
Each time you call plot (or plot_wireframe but i don't know what you need that) on an axes object, it will add the data as a new series. If you leave out the color argument matplotlib will choose them for you, but it's not too smart and after you add too many series' it will loop around and start using the same colours again.
n.b. i haven't tested this - can't remember if color is the correct argument. Pretty sure it is though.
So I am trying to plot a 3d chart using mplot3d with matplotlib. Code as following:
# generate the graph
# vols is a 2D array indexed by [maturity, strike].
def DrawGraph(self, strikes, maturities, vols):
import matplotlib.dates as dates
import matplotlib.pyplot as pyplot
# prepare data
Y = dates.date2num(maturities)
X, Y = numpy.meshgrid(strikes, Y)
# plot
fig = pyplot.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(X, Y, vols)
pyplot.show()
Where maturities and strikes are 1D arrays, and vols is a 2D array with proper size.
Everything runs proper and all data are valid. However, I got a totally blank window with nothing but gray background.
Anyone can give me some hints of what's going on? I suspect the version of matplotlib isn't right but not sure how to check it.
Thanks in advance.
In the version of matplotlib I have (1.2.1) the projection="3d" will raise an error unless you also include:
from mpl_toolkits.mplot3d import Axes3D
The only thing I can't check is the line
Y = dates.date2num(maturities)
As I am unfamiliar with what data type date2num expects, do you have any sample data?
Other than this everything works fine for me
What about
matplotlib.pyplot.ioff()
That solved my "nothing but gray background"-problem with mplot3d.