I'm wondering if there's a tool/workaround to show min/max values on scalar plots in TensorBoard.
A pretty common scenario considers zooming in and finding the optimal point manually for each series, as it is not necessarily the last one.
For example, it is epoch 22 with IoU=75.08 (the maximum value) for the grey series below.
I'd like to have these numbers displayed somewhere (for example in the tooltip or in the chart) or at least a marker in the optimal point.
I've found an open ticket relating to this issue, but it seems that it is still not resolved.
Maybe someone is aware of some sort of a frontend script/plugin extracting these values? Preferably for Safari or Chrome.
Related
I'm presenting visualizations of time to complete different tasks. Some the data is heavily skewed by certain tasks which take much longer than the rest, so I thought it would be a good idea to show both the means and the medians, to demonstrate where that skew is present. I have one page of visualizations for the mean, and an identical page where the mean has been replaced by the median. However, when PowerBI Calculates the medians, it seems to be giving me integer values, where I would like it to display the full decimal value.
Here's screenshots of each page (I've had to black out the labels for confidentiality reasons).
And a snippet of the data so you can see it's being read in as decimal numbers.
I've a sprite sheet containing a set of icons as shown here:
I'd like to get the bounding box (at pixel precision) of all icons inside it, some cases like list, grid have to be considered as only one icons. Any ideas are more than welcome.
I think the main issue in your problem is that some icons contain disjoint parts.
If all the icons were in only one part, you could just find the "connected components" (groups of white pixels) in your image and isolate them.
I don't know your level in image processing but to connect the parts of one icons, I would probably use dilation, which is a morphological method to expand (under constraints) the areas of maximum intensity in an image.
If you need any clarification, please let me know !
In general, it is not possible: only the humans have enough context to determine which of the disjoint parts belong together. You can approximate it using various ways, but it's a lost cause - and IMHO completely unnecessary. Imagine writing a test for this functionality - it's impossible, it requires a human in the loop, since the results for any particular icon sheet don't generalize. Knowing that the algorithm works for some sheet tells you nothing about whether it will work for some other sheet that you know nothing about a-priori.
It'd be simpler to manually colorize each sprite to have a color different than that of its neighbors. Then a greedy algorithm could find the bounding boxes easily without having to approximate anything.
What would b the best way to implement a simple shape-matching algorithm to match a plot interpolated from just 8 points (x, y) against a database of similar plots (> 12 000 entries), each plot having >100 nodes. The database has 6 categories of plots (signals measured under 6 different conditions), and the main aim is to find the right category (so for every category there's around 2000 plots to compare against).
The 8-node plot would represent actual data from measurement, but for now I am simulating this by selecting a random plot from the database, then 8 points from it, then smearing it using gaussian random number generator.
What would be the best way to implement non-linear least-squares to compare the shape of the 8-node plot against each plot from the database? Are there any c++ libraries you know of that could help with this?
Is it necessary to find the actual formula (f(x)) of the 8-node plot to use it with least squares, or will it be sufficient to use interpolation in requested points, such as interpolation from the gsl library?
You can certainly use least squares without knowing the actual formula. If all of your plots are measured at the same x value, then this is easy -- you simply compute the sum in the normal way:
where y_i is a point in your 8-node plot, sigma_i is the error on the point and Y(x_i) is the value of the plot from the database at the same x position as y_i. You can see why this is trivial if all your plots are measured at the same x value.
If they're not, you can get Y(x_i) either by fitting the plot from the database with some function (if you know it) or by interpolating between the points (if you don't know it). The simplest interpolation is just to connect the points with straight lines and find the value of the straight lines at the x_i that you want. Other interpolations might do better.
In my field, we use ROOT for these kind of things. However, scipy has a great collections of functions, and it might be easier to get started with -- if you don't mind using Python.
One major problem you could have would be that the two plots are not independent. Wikipedia suggests McNemar's test in this case.
Another problem you could have is that you don't have much information in your test plot, so your results will be affected greatly by statistical fluctuations. In other words, if you only have 8 test points and two plots match, how will you know if the underlying functions are really the same, or if the 8 points simply jumped around (inside their error bars) in such a way that it looks like the plot from the database -- purely by chance! ... I'm afraid you won't really know. So the plots that test well will include false positives (low purity), and some of the plots that don't happen to test well were probably actually good matches (low efficiency).
To solve that, you would need to either use a test plot with more points or else bring in other information. If you can throw away plots from the database that you know can't match for other reasons, that will help a lot.
I am trying to build a graph that will change resolution depending on how far you are zoomed in. Here is what it looks like when you are complete zoomed out.
So this looks good so when I zoom in I get a higher resolution data and my graph looks like this:
The problem is when I zoom out the higher resolution data does not get cleared out of the graph:
The tables below the graphs are table display what is in the DataTable. This is what drawing code looks like.
var g_graph = new google.visualization.AnnotatedTimeLine(document.getElementById('graph_div_json'));
var table = new google.visualization.Table(document.getElementById('table_div_json'));
function handleQueryResponse(response){
log("Drawing graph")
var data = response.getDataTable()
g_graph.draw(data, {allowRedraw:true, thickness:2, fill:50, scaleType:'maximized'})
table.draw(data, {allowRedraw:true})
}
I am try to find a way for it to only displaying the data that is in the DataTable. I have tried removing the allowRedraw flag but then it breaks the zooming operation.
Any help would be greatly appreciated.
Thanks
See also
Annotated TimeLine when zoomed-out, Too Many Datapoints.
you can remove the allow redraw flag.
In that case you have to put the data points manually in your data table
The latest date of the actual whole data
The most outdated date in the actual whole data.
this will retain your zooming operation.
I think you have already seen removing the allowRedraw flag, works but with a small problem, flickering the whole chart.
It seems to me that the best solution would be to draw every nth data point, depending on your level of zoom. On the Google Finance graph(s), the zoom levels are pre-determined at the top: 1m, 5m, 1h, 1 day, 5 days, etc. It seems evident that this is exactly what Google is doing. At the max view level, they're plotting points that fall on the month. If you're polling 1000 times a day (with each poll generating a single point), then you'd be taking every 30,000th point (the fist point being the very first one of the month, and the 30,000th one being the last point).
Each of these zoom levels would implement a different plot of the data points. Each point should have a time stamp with accuracy to the second, so you'll easily be able to scale the plot based on the level of detail.
I am currently working on a data visualization project.My aim is to produce contour lines ,in other words iso-lines, from gridded data.Data can be temperature, weather data or any kind of other environmental parameters but only condition is it must be regularly spaced.
I searched in internet , however i could not find a good algorithm, pseudo-code or source code for producing contour lines from grids.
Does anybody knows a library, source code or an algorithm for producing contour lines from gridded data?
it will be good if your suggestion has a good run time performance, i don't want to wait my users so much :)
Edit: thanks for response but isolines have some constrains like they should not intersects
so just generating bezier curves does not accomplish my goal.
See this question: How to approximate a vector contour from an elevation raster?
It's a near duplicate, but uses quite different terminology. You'll find that cartography and computer graphics solve many of the same problems, but use different terminology for them.
there's some reasonably good contouring available in GNUplot - if you're able to use GPL code that may help.
If your data is placed at regular intervals, this can be done fairly easily (assuming I understand your problem correctly). First you need to determine at what interval you want your contours. Next create the grid you are going to use to store the contour information (i'm assuming just a simple on/off or elevation at this contour level type of data), which should be one interval smaller than the source data.
Now the trick here is to offset the 2 grids by 1/2 an interval (won't actually show up in code like this, but its the concept I'm dealing with here) and compare the 4 coordinates surrounding the current point in the contour data grid you are calculating. If any of the 4 points are in a different interval range, then that 'pixel' in the contour grid should be set to true (or the value of the contour range being crossed).
With this method, there will be a problem when the interval is too fine which will cause several contours to overlap onto each other.
As the link from Paul Tomblin suggests, Bezier curves (which are a subset of B-splines) are a ripe solution for your problem. If runtime performance is an issue, Bezier curves have the added benefit of being constructable via the very fast de Casteljau algorithm, instead of drawing them according to the parametric equations. On the off chance you're working with DirectX, it has a library function for the de Casteljau, but it should not be challenging to brew one yourself using the 1001 web pages that describe it.