I have four sets of data which have been created in dictionaries. After plotting them in one graph it is hard to clearly differentiate the point.
I put all of the data frames in one graph. My intention is to zoom in data points without changing x values that one can see the difference.
Please, help me in this regard. Thank you!
Graph Describes 4 sets of data
Related
I tried to express two displacement values in one waveform chart.
I have two displacement sensors, one is a digital input sensor and the other is analog input sensor.
I have to see those values in one waveform chart simultaneously.
In my attempts, two VIs of each instruments were combined into one VI. I found the error that when I ran the VI, only one VI would report values and not both simultaneously.
By any chance, is there a way to run it at the same time and see the values on one graph?
Let me share a few scenarios that might help solve your issue.
Plot multiple doubles on a chart: bundle them together and put the resulting cluster into a chart.
bundled doubles on a chart
Two VIs measuring a double and plotting those results.
bundled subvi outputs to a chart
You could similarly plot boolean values (your digital input sensor) by using the boolean to 0,1 VI first (converts the T/F to 1/0 respectively) and that can be bundled as above.
(These are the most direct/easiest ways of doing this; you have a number generator and you bundle the numbers to a graph).
Of course, I can imagine that your question might actually be about how to share values from parallel-running subVIs. If that's the case, say so and this answer can be edited to point you in the right direction.
I have some Kinect data of somebody standing (reasonably) still and performing sets of punches. I am given it in the format of an x,y,z co-ordinate for each joint of which they are 20, so I have 60 data points per frame.
I'm trying to perform a classification task on the punches however I'm having some problems normalising my data. As you can see from the graph there are sections with much higher 'amplitude' than the others, my belief is that this is due to how close that person was to the kinect sensor when the readings were taken. (The graph is actually the first principal coefficient obtained by PCA for each frame, multiple sequences of the same punch are strung together in this graph)
Looking back at the data files it looks like those that are 'out' have a z co-ordinate (depth from sensor) of ~2.7 where as the others tent to hover around 3.3-3.6.
How can I perform a normalization with the depth values to make them closer to each other for each sequence? I've already tried differentiation to get the velocity, although it helps to normalise the output actually ends up too similar and makes it very hard to classify.
Edit: I should mention I am already using a normalization method by subtracting the hip position from each joint in an attempt to make the co-ordinates relative.
The Kinect can output some strange values when the person that is tracked is standing near the edges of the view of the Kinect. I would either completly ignore these data or just replace the data with an average of the previous 2 and next 2.
For example:
1,2,1,12,1,2,3
Replace 12 with (2 + 1 + 1 + 2) / 4 = 1.5
You can basically do this with the whole array of values you have, this way you have a more normalised line/graph.
You can also use the clippedEdges value to determine if one or more joints is outside the view.
I want to layer (superimpose) one column graph on another in Excel. So it would be like a stacked column graph, except that each column for a given category on the x-axis would have its origin at 0 on the y-axis. My data are before and after scores. By layering the columns instead of putting them side-by-side, it would be easier to visualize the magnitude and direction of the difference between the two scores. I've seen this done with R, but can't find examples in Excel. Anyone ever attempted this?
I tried the 3D suggestion and it worked. But the other answer I discovered was to choose a Clustered Column graph and click 'Format Data Series' and change the 'overlap' percentage to 100%. I'm using a Mac so it's slightly different, but the person who helped me with this was on a PC and I've used PC's mainly. What I ended up discovering is that using 90% looked quite nice, but 100% will achieve what you're looking for.
I did the same thing for my thesis presentation. It's a little tricky and I made it by myself. To do it, you have to create a 3D bar graph (not a stacked one), in which your columns are put in front of each other. You have to make sure that all the taller columns in each X cell are behind the shorter columns in that cell on the X axis.
Once you created that graph, you can rotate the 3D graph in a way that it looks like a 2D graph (by resetting the axes values to zero). Now you have a bar graph, in which every bar has different columns and all of the columns start at zero. ;)
Short answer: Change the post score to (post - pre), then you can proceed with making the stacked bar chart.
Long and correct answer: DO NOT DO THIS. Clustered bar chart is much better because:
The visual line for comparison is the same line anyway, you're not facilitating the understanding in any means.
Any kind of overlapping of the bars conceals the area of the post-score, which induces visual distortion. A pre-score of 10 and a post score of 20 should have a column area ratio of 1:2. But if you completely overlap them, it'd be reduced to 1:1. Partial overlapping is equally problematic.
I'm stuck with the following problem and I hope I can explain it coherent.
So, I have a number (about 10) of descrete positions on a coordinate system.
Now, I want to analyse data from a program where user could label each point as somethingA and somethingB.
I extracted the data points for each class. So I have about 60 points for the somethingA class and a little bit less for the other class. One class stands for good points and one for bad points. I want to find the positions which have the most good/bad labels. I do that with machine learning algorithms, I just want to visualize this with plots.
I now want to plot those points. So I make one plot per class. But since in every class every point occurs at least once, the two plots would look exactly the same.
But, the amount of occurences has a different distribution thoughout the positions.
Maybe point A has 20 occurences in class A and 1 in class B, both plots would look the same.
So, my question is: How can I take the number of occurences for points into account when plotting scatters in Matplotlib?
Either with different colors (like a heatmap?) maybe with a cool legend.
Or with different sizes (e.g. higher amount = bigger cirlce).
Any help would be appreciated!
I don't know if this helps you but I have had a problem where I wanted a scatterplot to reflect both positions as well as two variables that were attributed to the data points.
Since size and color in the scatter function do not allow variables themselves, meaning one has to specify color code and size in the usual way, meaning sth like
ax.scatter(..., c=whatEverFunction, s=numberOfOccurences, ...)
did not work for me.
what I did was to bin the values of the two variables I wanted to visualize. In my case the variable nodeMass and another variable.
for i in range(Number):
mask[i] = False
if(lowerBound1<variableOne[i]<upperBound1):
mask[i] = True & pmask[i]
if len(positionX[mask])>0:
ax.scatter(positionX[mask], positionY[mask], positionZ[mask],C='#424242',s=10, edgecolors='none')
for i in range(Number):
mask[i] = False
if(lowerBound2<variableOne[i]<upperBound2):
mask[i] = True & pmask[i]
if len(positionX[mask])>0:
ax.scatter(positionX[mask], positionY[mask], positionZ[mask],c='#9E0050',s=25,edgecolors='none')
I know it is not very elegant but it worked for me. I had to make as many for loops as I had bins in my variables. With if-querys and the masks I could at least avoid redundant or 'unreadable' plots.
I am using the code in this website http://blog.chrislowis.co.uk/2008/11/24/ruby-gsl-pearson.html to implement a Pearson Correlation given two time series data like so:
require 'gsl'
pearson_correlation = GSL::Stats::correlation(
GSL::Vector.alloc(first_metrics),GSL::Vector.alloc(second_metrics)
)
This returns a number such as -0.2352461593569471.
I'm currently using the highcharts library and am feeding it two sets of timeseries data. Given that I have a finite time series for both sets, can I do something with this number (-0.2352461593569471) to create a third time series showing the slope of this curve? If anyone can point me in the right direction I'd really appreciate it!
No, correlation doesn't tell you anything about the slope of the line of best fit. It just tells you approximately how much of the variability in one variable (or one time series, in this case) can be explained by the other. There is a reasonably good description here: http://www.graphpad.com/support/faqid/1141/.
How you deal with the data in your specific case is highly dependent on what you're trying to achieve. Are you trying to show that variable X causes variable Y? If so, you could start by dropping the time-series-ness, and just treat the data as paired values, and use linear regression. If you're trying to find a model of how X and Y vary together over time, you could look at multivariate linear regression (I'm not very familiar with this, though).