Im currently doing a real time plot where I generate data every second. The problem I'm running into is that after about 15 seconds or so, because of how I have my xRange set, my plot begins to move/scroll automatically, which is an intentional effect, however my y axis seems to be rooted at the origin and quickly falls off screen. How would I either set the position to be rooted at the center of the view constantly, or what property do I need to update in order for the Y axis to also move?
I've looked through them all but I don't see anything obvious, or anything that seems to apply based on the included definitions/help.
I happened to find the answer in an old google groups thread, so I will post it here as a confirmation of it working in Core Plot 1.0, and since this is a bit more organized/searchable place.
When you're initializing the plot, you want to set the axis constraint as follows.
axisSet.yAxis.axisConstraints = [CPTConstraints constraintWithLowerOffset:0.0];
Related
I have a data set “mydatainR”
With that I created a barplot and when I download the barplot, part of the y axis is cut off because it’s “off of the paper”
I want to give myself more room to work with, or make my entire bar plot smaller. Thx
I'm having some issues in setting the ticks in my plots using matplotlib. What I need is to set the ticks inwards, so inside the figure (but the labels must stay outside), and at the same time I need to have ticks on all four boundaries. Do you know a simple way to do this?
Thanks in advance!
Just because it can be useful for other people, I post here the simple solution I found after looking for some time in the documentation, as suggested by cfort:
ax.xaxis.set_ticks_position('both')
ax.xaxis.set_tick_params(direction='in', which='both', labelsize=16)
ax.yaxis.set_ticks_position('both')
ax.yaxis.set_tick_params(direction='in', which='both', labelsize=16)
ax.minorticks_on()
these lines of code set the tick inwards on both sides (e.g. for the x axis they are placed at top and bottom of the figure), set the label size and add the minorticks, which will point in the inward direction as well.
In Scale Domains docs of Vega-Lite it is noted:
An alternate way to construct this technique would be to filter out
the input data to the top (detail) view like so:
{
"vconcat": [{
"transform": [{"filter": {"selection": "brush"}}],
...
}]
}
Which is indeed almost the same (although filter method being much slower, as noted in the docs), except for one difference:
With filter-selection method (demo), the y-axis of the upper chart will be automatically zoomed in to the selected points. This is pretty neat, especially if you have large amount of points.
With scale-domain method (demo), the y-axis remains frozen as you move the selection around.
The question: is it possible to have the y-axis automatically zoom in to the selected points as you move the selection, with "scale domain" method (same as it does with filter-selection method)?
Why is the above difference important? Imagine a stock price that has been increasing on average by a total of $1 every day last year (but within a particular day it may have experinced any kind of volatile behaviour) and we're plotting it with line marks. If you plot the entire year, you see the whole picture. If you zoom in on a particular day without resetting your y-axis zoom, however, your intraday price plot will be just a flat line, or close to that.
// I've checked all scale-domain-related issues on vega-lite, on altair repo and SO and couldn't find anything related; I've also posted this question on vega-lite repo on GH, but was forwarded over to SO.
No. Unless otherwise specified, the y scale is determined from all of the data within the plot.
When you filter the data, the data in the plot changes, which causes the y axis to change. When you change the scale based on an x-selection without filtering the data, it does not change the data in the plot, and so the y scale remains constant.
If you want the y-scale to be determined automatically based on the data within the selection, the only option is to filter on that selection.
I'm trying to create a stacked graph using core plot.i have followed this tutorial.I have a UISlider on sliding i'm updating my graph.The issue i'm facing is my top value for y-axis is touching top border(As in the screen shot). As per my requirement in need to round off the highest value to next 10th value so that my graph doesn't touch the borders.On changing my slider value the x and y axis should also get refresh,i'm doing it by regenerating plot but it takes a lot of memory.Any idea how can i achieve these requirement ?
Don't recreate the whole graph every time the values change. Call -reloadData on the graph to refresh the plot data and set the xRange and yRange of the plot space to change the scale of the axes.
I'm trying to get and set the position of a draggable legend in matplotlib. My application consists of an interactive GUI, which has a redraw/plot function that should perform the follow steps:
save the position of the current legend.
clear the current axes and perform various plotting operations, which may or may add labels to their plots.
build a new draggable legend (ax.legend().draggable()) and restore the old position of the legend.
In between these steps the user is free to drag the legend around, and the goal is to persist the legend position when the plots are redrawn.
My first approach was to use oldpos = legend.get_bbox_to_anchor() and legend.set_bbox_to_anchor(oldpos) in steps 1 and 3. However this causes to move the legend completely off the visible area.
Note that I have to use ax.legend() and cannot use fig.legend(lines, labels), since step 2 is completely decoupled, i.e., I don't know anything about lines and labels in step 3. According to answers to the question How to position and align a matplotlib figure legend? there seems to be a difference between these two possibilities regarding axes or figure coordinates. Obviously my problem calls for figure coordinates, but I haven't fully understood how to convert the bbox to a "bbox in figure coordinates".
The even more severe problem I just realized is that apparently legend.get_bbox_to_anchor() always seems to return the same values irrespective of the drag position. So maybe the anchor can only be (ab-)used to manipulate the position of static legends? Is there another/proper way to save and restore the position of a draggable legend?
By looking at the implementation of Legend I found out that there is an undocumented property _loc, which exactly does what I want. My solution now looks astonishingly simple:
oldLegPos = ax.get_legend()._loc
# perform all plotting operations...
legend = ax.legend().draggable()
legend._loc = oldLegPos
It looks like _loc automatically stores figure coordinates, since I do not have to convert the coordinates in any way (eg. when the plotting operations completely change the axes ranges/coordinates).