Say you have the matrix given by three arrays, being:
x = N-dimensional array.
y = M-dimensional array.
And z is a set of "somewhat random" values from -0.3 to 0.3 in a NxM shape. I need to create a plot in which the x values are in the x-axis, y values are in the y-axis and using z as the source to indicate the intensity of each pixel with a color map.
So far, I have tried using
plt.contourf(x,y,z)
and the resulting plot is very nice for me (attached at the end of this paragraph), but a smoothing is automatically applied to the plot! I need to be able to distinguish the pixels and I cannot find a way to do it.
contourf result
I have also studied the possibility of using
ax.matshow(z)
in order to sucesfully see the pixels... but then I am struggling trying to personalize the x and y axis, since only the index of the pixel is shown (see below).
matshow result
Would you please give me some ideas? Thank you.
Without more information on your x,y data it's hard to know, but I would guess you are looking for pcolormesh.
plt.pcolormesh(x,y,z)
This would take the x and y data as input and hence shows the z data at the appropriate coordinates.
You can use imshow with the keyword interpolation='nearest'.
plt.imshow(z, interpolation='nearest')
Related
So I've produced a 3-d graph on python using trisruf:
ax.plot_trisurf(x,y,z)
and I end up with the following:
3d plot
So now I want to plot contours on the base of this same plot, When I tried using ax.contour(x,y,z) I get an error saying my z should be in 2-dimensions, however my data comes from three 1-d arrays.
How can I go about plotting contours on the base of my graph?
Ok so I managed to find the answer after a bit of searching,
ax.tricontourf(angle_x,angle_y,nlist,zdir='-z', offset = -0.859, cmap='coolwarm') worked, its important to make the offset just slightly lower than the lowest z point (or whatever direction you want the projection) so you can actually see the contour plot!
Here's the result:
updated plot with contour
I am trying to create an image with imshow, but the bins in my matrix are not equal.
For example the following matrix
C = [[1,2,2],[2,3,2],[3,2,3]]
is for X = [1,4,8] and for Y = [2,4,9]
I know I can just do xticks and yticks, but I want the axis to be equal..This means that I will need the squares which build the imshow to be in different sizes.
Is it possible?
This seems like a job for pcolormesh.
From When to use imshow over pcolormesh:
Fundamentally, imshow assumes that all data elements in your array are
to be rendered at the same size, whereas pcolormesh/pcolor associates
elements of the data array with rectangular elements whose size may
vary over the rectangular grid.
pcolormesh plots a matrix as cells, and take as argument the x and y coordinates of the cells, which allows you to draw each cell in a different size.
I assume the X and Y of your example data are meant to be the size of the cells. So I converted them in coordinates with:
xSize=[1,4,9]
ySize=[2,4,8]
x=np.append(0,np.cumsum(xSize)) # gives [ 0 1 5 13]
y=np.append(0,np.cumsum(ySize)) # gives [ 0 2 6 15]
Then if you want a similar behavior as imshow, you need to revert the y axis.
c=np.array([[1,2,2],[2,3,2],[3,2,3]])
plt.pcolormesh(x,-y,c)
Which gives us:
i am using MPAndroid charting toolkit for data visualization. chart plotting smooth in MPAndroid but problem arise when i try plot dual y axis(left& right). As right axis appear data points on spread over the x axis completely. all data points appearing on the left of chart. How can i spread the data points ?
mChart = (LineChart) findViewById(R.id.Chart);
mChart.setGridBackgroundColor(Color.parseColor("#F4F4F4"));
mChart.setDrawGridBackground(false);
mChart.setTouchEnabled(true);
mChart.setHighlightEnabled(false);
mChart.setDragEnabled(false);
mChart.setScaleEnabled(true);
mChart.setPinchZoom(true);
mChart.setDescription("");
mChart.getAxisLeft().setAxisMaxValue(YMaxValue);
mChart.getAxisLeft().setAxisMinValue(YMinValue);
mChart.getAxisLeft().setStartAtZero(false);
mChart.getAxisRight().setEnabled(false);
if(General.InnerClass.Y2AxisValues.size()>0)
{
mChart.getAxisRight().setEnabled(true);
mChart.getAxisRight().setSpaceBottom(12.25f);
mChart.getAxisRight().setAxisMaxValue(Y2MaxValue);
mChart.getAxisRight().setAxisMinValue(Y2MinValue);
mChart.getAxisRight().setStartAtZero(false);
mChart.getXAxis().setAvoidFirstLastClipping(true);
}
mChart.setData(data);
progbar.setVisibility(View.GONE);
mChart.invalidate();
Before
After
The data points are exactly where they should be. The only thing that has changed is the range of values that is displayed on each axis.
--> this range is now significationly lower because only one set of data is represented by each axis, before, one axis had to scale large enough to display both datasets.
I suggest you read the documentation of the YAxis and simply increase the range of values that should be displayed on the axis.
while plotting y2 axis providing the x values is the problem. values for the x axis should be provide for single time. providing twice add the values to x axis exiting values get doubled.
thnx #philipp
I'm trying to display 2D data with axis labels using both contour and pcolormesh. As has been noted on the matplotlib user list, these functions obey different conventions: pcolormesh expects the x and y values to specify the corners of the individual pixels, while contour expects the centers of the pixels.
What is the best way to make these behave consistently?
One option I've considered is to make a "centers-to-edges" function, assuming evenly spaced data:
def centers_to_edges(arr):
dx = arr[1]-arr[0]
newarr = np.linspace(arr.min()-dx/2,arr.max()+dx/2,arr.size+1)
return newarr
Another option is to use imshow with the extent keyword set.
The first approach doesn't play nicely with 2D axes (e.g., as created by meshgrid or indices) and the second discards the axis numbers entirely
Your data is a regular mesh? If it doesn't, you can use griddata() to obtain it. I think that if your data is too big, a sub-sampling or regularization always is possible. If the data is too big, maybe your output image always will be small compared with it and you can exploit this.
If you use imshow() with "extent" and "interpolation='nearest'", you will see that the data is cell-centered, and extent provided the lower edges of cells (corners). On the other hand, contour assumes that the data is cell-centered, and X,Y must be the center of cells. So, you need to be care about the input domain for contour. The trivial example is:
x = np.arange(-10,10,1)
X,Y = np.meshgrid(x,x)
P = X**2+Y**2
imshow(P,extent=[-10,10,-10,10],interpolation='nearest',origin='lower')
contour(X+0.5,Y+0.5,P,20,colors='k')
My tests told me that pcolormesh() is a very slow routine, and I always try to avoid it. griddata and imshow() always is a good choose for me.
I am trying to plot a matrix in Gnuplot as I would using imshow in Matplotlib. That means I just want to plot the actual matrix values, not the interpolation between values. I have been able to do this by trying
splot "file.dat" u 1:2:3 ps 5 pt 5 palette
This way we are telling the program to use columns 1,2 and 3 in the file, use squares of size 5 and space the points with very narrow gaps. However the points in my dataset are not evenly spaced and hence I get discontinuities.
Anyone a method of plotting matrix values in gnuplot regardless of not evenly spaced in Xa and y axes?
Gnuplot doesn't need to have evenly space X and Y axes. ( see another one of my answers: https://stackoverflow.com/a/10690041/748858 ). I frequently deal with grids that look like x[i] = f_x(i) and y[j] = f_y(j). This is quite trivial to plot, the datafile just looks like:
#datafile.dat
x1 y1 z11
x1 y2 z12
...
x1 yN z1N
#<--- blank line (leave these comments out of your datafile ;)
x2 y1 z21
x2 y2 z22
...
x2 yN z2N
#<--- blank line
...
...
#<--- blank line
xN y1 zN1
...
xN yN zNN
(note the blank lines)
A datafile like that can be plotted as:
set view map
splot "datafile.dat" u 1:2:3 w pm3d
the option set pm3d corners2color can be used to fine tune which corner you want to color the rectangle created.
Also note that you could make essentially the same plot doing this:
set view map
plot "datafile.dat" u 1:2:3 w image
Although I don't use this one myself, so it might fail with a non-equally spaced rectangular grid (you'll need to try it).
Response to your comment
Yes, pm3d does generate (M-1)x(N-1) quadrilaterals as you've alluded to in your comment -- It takes the 4 corners and (by default) averages their value to assign a color. You seem to dislike this -- although (in most cases) I doubt you'd be able to tell a difference in the plot for reasonably large M and N (larger than 20). So, before we go on, you may want to ask yourself if it is really necessary to plot EVERY POINT.
That being said, with a little work, gnuplot can still do what you want. The solution is to specify that a particular corner is to be used to assign the color to the entire quadrilateral.
#specify that the first corner should be used for coloring the quadrilateral
set pm3d corners2color c1 #could also be c2,c3, or c4.
Then simply append the last row and last column of your matrix to plot it twice (making up an extra gridpoint to accommodate the larger dataset. You're not quite there yet, you still need to shift your grid values by half a cell so that your quadrilaterals are centered on the point in question -- which way you shift the cells depends on your choice of corner (c1,c2,c3,c4) -- You'll need to play around with it to figure out which one you want.
Note that the problem here isn't gnuplot. It's that there isn't enough information in the datafile to construct an MxN surface given MxN triples. At each point, you need to know it's position (x,y) it's value (z) and also the size of the quadrilateral to be draw there -- which is more information than you've packed into the file. Of course, you can guess the size in the interior points (just meet halfway), but there's no guessing on the exterior points. but why not just use the size of the next interior point?. That's a good question, and it would (typically) work well for rectangular grids, but that is only a special case (although a common one) -- which would (likely) fail miserably for many other grids. The point is that gnuplot decided that averaging the corners is typically "close enough", but then gives you the option to change it.
See the explanation for the input data here. You may have to change your data file's format accordingly.