I need two axes in one plot with the same grid and fixed values for the ticks.
This works fine for some plots. enter image description here
But sometimes the last tick on the top is not located at the end of the axis and now i have two grids in my plot.
enter image description here
How i can set up the last tick for every plot at the end of the axis?
Related
I'm trying to plot two datasets (called Height and Temperature) on different y axes.
Both datasets have the same length.
Both datasets are linked together by a third dataset, RH.
I have tried to use matplotlib to plot the data using twiny() but I am struggling to align both datasets together on the same plot.
Here is the plot I want to align.
The horizontal black line on the figure is defined as the 0°C degree line that was found from Height and was used to test if both datasets, when plotted, would be aligned. They do not. There is a noticable difference between the black line and the 0°C tick from Temperature.
Rather than the two y axes changing independently from each other I would like to plot each index from Height and Temperature at the same y position on the plot.
Here is the code that I used to create the plot:
#Define number of subplots sharing y axis
f, ax1 = plt.subplots()
ax1.minorticks_on()
ax1.grid(which='major',axis='both',c='grey')
#Set axis parameters
ax1.set_ylabel('Height $(km)$')
ax1.set_ylim([np.nanmin(Height), np.nanmax(Height)])
#Plot RH
ax1.plot(RH, Height, label='Original', lw=0.5)
ax1.set_xlabel('RH $(\%)$')
ax2 = ax1.twinx()
ax2.plot(RH, Temperature, label='Original', lw=0.5, c='black')
ax2.set_ylabel('Temperature ($^\circ$C)')
ax2.set_ylim([np.nanmin(Temperature), np.nanmax(Temperature)])
Any help on this would be amazing. Thanks.
Maybe the atmosphere is wrong. :)
It sounds like you are trying to align the two y axes at particular values. Why are you doing this? The relationship of Height vs. Temperature is non-linear, so I think you are setting the stage for a confusing graph. Any particular line you plot can only be interpreted against one vertical axis.
If needed, I think you will be forced to "do some math" on the limits of the y axes. This link may be helpful:
align scales
Is there a way to anchor the ticks and tick labels of the x-axis so that they cross the y-axis at a different location than where the actual x-axis crosses the y-axis? This can basically be accomplished with:
ax = plt.gca()
ax.get_xaxis().set_tick_params(pad=5)
or
ax.xaxis.set_tick_params(pad=500)
For example:
Except that I am working with audio file inputs and the y-axis is variable (based on the highest/lowest amplitude of the waveform). Therefore, the maximum and minimum y-axis values change depending on the audio file. I am concerned that pad=NUM will be moving around relative to the y-axis.
Therefore, I am looking for a way to accomplish what pad does, but have the ticks and tick labels be anchored at the minimum y-axis value.
As a bonus, flipping this around so that the y-axis is anchored somewhere differently than the y-axis tick labels would surely benefit someone also.
In my particular case, I have the x-axis crossing the y-axis at y=0. The x-axis ticks and tick labels will sometimes be at -1.0, sometimes at -0.5, sometimes at -0.25, etc. I always know what the minimum value of the y-axis is, and therefore want it to be the anchor point for x-axis ticks and tick labels. (In fact, I am happy to do it with only the x-axis tick labels, if it is possible to treat ticks and tick labels separately). An example of this is shown in this image above (which I accomplished with pad=500).
I looked around other threads and in the documentation, but I'm either missing it or don't know the correct terms to find it.
UPDATE: I added gridlines and was getting very unexpected behavior (e.g. linestyle and linewidth didn't work as expected) due to the top x-axis being shifted. I realized yet a better way - keep the axes (turn off the splines) and simply plot a second line at (0, 0) to (max_time, 0).
ax.plot([0,times[-1]], [0,0], color='k') # Creates a 'false' x-axis at y=0
ax.spines['top'].set_color('none') # Position unchanged
ax.spines['bottom'].set_color('none') # Position unchanged
Figured it out! I was thinking about this the wrong way...
Problem: Moving the bottom x-axis to the center and padding the tick labels
Solution: Keep the bottom x-axis where it is (turn off the bottom spine) and move the top x-axis to the center (keep top spine, but turn off ticks and tick labels).
ax.spines['top'].set_position('center')
ax.spines['bottom'].set_color('none') # Position unchanged
ax.xaxis.set_tick_params(top='off')
plt.setp() as in https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html#sphx-glr-gallery-images-contours-and-fields-image-annotated-heatmap-py solved the problem for me.
I've got a simple plot in matplotlib. Every time that I plot a data, the graph render an exact Y axis to my plot. What I want is to add some space or allowance on my Y-axis. My maximum value in plot is 5
I want my graph to show at least up to 6 or 10 on it's Y-axis.
How ?
Thanks
I have 2 subplots in matplotlib in Python. They are stacked on top of each other.
I want to have gridlines on each plot, which I have done successfully. But each plot has a different x axis and, therefore, the vertical grid lines of the top plot are not aligned with those of the bottom plot.
I would like the grid lines of the top plot to be in the same position on the x axis as they are on the bottom plot i.e. the vertical grid lines in both plots should be aligned.
I imaging that I can tell my grid lines exactly where to be, and so I could achieve my goal by adjusting the lines until they match as well as possible.
I just hoped that there might be some easier way that would just allow me to align the gridlines on both plots.
Edit:
I don't think the shared axis stuff is quite what I want.
My top and bottom plot have very different scales, so when I share the axes, it shifts the scaling too. For example, say my top plot has data that runs from 0-100 on the x axis and on the bottom plot the data runs from 0-50. When I share the axis, the top plot only shows data from 0-50, which I don't want it to.
I want it to show from 0-100 as it did before, but just want it to share the axis and gridlines from the other plot.
You could use LinearLocator:
from matplotlib.ticker import LinearLocator
Then on each of your x-axis or only on one of them call:
N = 6 # Set number of gridlines you want to have in each graph
ax1.xaxis.set_major_locator(LinearLocator(N))
ax2.xaxis.set_major_locator(LinearLocator(N))
Or get the number of ticks from your source axis and set it on target axis:
N = source_ax.xaxis.get_major_ticks()
target_ax.xaxis.set_major_locator(LinearLocator(N))
How can I use yaxis and xaxis, which I want and that are not correlated with data in the plot?
For example, I want to plot the world map as an image using the code below:
import matplotlib.pyplot as plt
fig = plt.figure()
plt.imshow(world_map)
As a result, I got xaxis: 0...image_size_x from the left to the rigth and yaxis: 0...image_size_y from top to bottom.
What do I need to to do to change its axis range into latitude and longitude formats? Thus the figure axis should contain degrees (from 90 to -90) on the both fields (x and y) regardless of what its real data plotted in the figure.
Setting
pylab.ylim([90,-90])
will shift the image to the bottom by 90 pixels and reduced the y-dimension of the image into the scale of image_size_y/90. So it'll not work because xlim/ylim works with data, plotted in the figure.
In short: Use the extent keyword with imshow.
In code:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subaxis(111)
ax.imshow(world_map, extent=[-180,180,-90,90], aspect='auto')
If your map is then upside down, add the keyword argument origin='lower' to the imshow. That aspect='auto' is needed to make the map scalable in both dimensions independently. (The rest of the extra rows with add_subaxis are just to make the code more object-oriented, the real beef is in the keyword arguments.)
If imshow is not given the extents of the image, it thinks that you'll want to have each pixel centered at positions (0,0), (0,1), ..., (Nx-1, Ny-1), and then the image extents will start from (-.5, -.5).
Assuming (based on your post) the image is fine but the axis labels are off, try playing around with this, which will manually implement the axis labels:
plt.figure(1)
ax = plt.subplot(111)
#... do your stuff
#need to figure out your image size divided by the number of labels you want
#FOR EXample, if image size was 180, and you wanted every second coordinate labeled:
ax.set_xticks([i for i in range(0,180,2)]) #python3 code to create 90 tick marks
ax.set_xticklabels([-i for i in range(-90,90,2)]) #python3 code to create 90 labels
#DO SAME FOR Y
The trick im using is to figure out how many labels you want (here, its 90: 180/2), add the tickmarks evenly in the range (0,imagesize), then manually do the labels. Here is a general formula:
ax.set_xticks([i for i in range(0,IMAGE_SIZE,_EVERY_XTH_COORD_LABELED)]) #python3 code to create 90 tick marks
ax.set_xticklabels([-i for i in range(-90,90,EVERY_XTH_COORD_LABELED)]) #python3 code to create 90 labels