I have been able to visualize ONE time period (one financial quarter) using Anaconda’s Orange3.
Seen here:
However I would like to be able to visualize this data, a la Hans Rosling, over ~120 time periods (i.e. 30+ years of financial quarters).
have tried:
Gapminder. Their software crashes when I try to visualize 5,000+ bubbles.
Google Sheets: They discontinued the “Motion Chart” feature a couple of years ago.
Orange3. Works great for one time period, Is there a way to visualize data over multiple time periods (e.g. motion bubble charts)?
Other options?
How can one build a motion bubble chart using Orange3?
What software can one use that can handle large data sets and motion bubble charts?
Related
In 2007, when I was young and foolish and before I knew about Open Street Map, I started an urban historical map project. I was working in Illustrator, it was going to be an interactive Flash piece, and my process was to draw the maps first, with the thought that I'd label some, but not all, of the street later on.
As we know Flash was began to die about 2010 and I put the project away for a number of years. I picked it up again a couple years ago and continued my earlier practice of just drawing streets and water features, this time with the intention of making it a conventional web map. Now I'm pretty close to finishing the drawing of a five-layer (1871, 1903, 1932, 1952 and 2016) historical map of a medium-sized city, though it still lacks labels.
My problem now is how to add large numbers of labels, many of them duplicates. There could be as many as 10,000 for all five layers, though as a practical matter I may have to settle for a smallish fraction of that number. Based on web searches I gather my workflow is unusual and that mine is therefore an unusual problem.
I've exported my maps and brought them into QGIS and played with the software a little. The process of adding labels to objects doesn't seem terribly efficient or user-friendly, but that's probably due to my unfamiliarity with the program.
So my question is this: Are there any tricks to speed up the painful process of adding large numbers of duplicate labels in either QGIS or ArcGIS? Since so many of the streets exist in all five layers, functionality like the ability to select multiple objects in different layers and edit their attributes simultaneously in the Attribute Table would be a godsend. (Doesn't seem possible.) So would the ability to copy the attributes from one object and paste them onto other objects. Or the ability to do either of these things in Illustrator via a plugin and then export the data along with the shapes to a GIS program.
Thanks for your help!
If I understand the issue correctly I think are several different solutions. When you say that you
Typically for a spatial layer in ArcGIS or QGIS you define how to label all features in a layer once by defining a label scheme to use across all features, 1 or 1 million. This assumes that each feature in the layer has one or more attributes in the associated table for the layer.
How are you converting the Illustrator vectors to a spatial layer? DXF?
You will likely have better/faster responses to this question by posting it to the GIS Stack exchange. https://gis.stackexchange.com/
I'm participating in small data analysis competition in our school.
We use Fitbit wearable devices, which is loaned to each participants by host of contest.
For 2 months during the contest, they walk and sleep with this small device 24/7,
allow it to gather data about participant's walk count with heart rate(bpm), etc.
and we need to solve some problems based on these participants' data
like, example,
show the relations between rainy days and participants' working out rate using the chart,
i think purpose of problem is,
because of rain, lot of participants are expected to be at home.
can you show some cause and effect numerically?
i'm now studying python library numpy, pandas with ipython notebook.
but still i have no idea about solving these problems..
could you recommend some projects or sites use for references? i really eager to win this competition.:(
and lastly, sorry for my poor English.
Thank you.
that's a fun project. I'm working on something kind of similar.
Here's what you need to do:
Learn the fitbit API and stream the data from the fitbit accelerometer and gyroscope. If you can combine this with heart rate data, great. The more types of data you have, the more effective your algorithm will be. You can store this data in a simple csv file (streaming the accel/gyro data at 50Hz is recommended). Or setup a web server and store it in a database for easy access
Learn how to use pandas and scikit learn
[optional but recommended]: Learn matplotlib so you can graph you data and get a feel for how it looks
Load the data into pandas and create features on the data - notably using 1-2 second sliding window analysis with 50% overlap. Good features include (for all three Accel X, Y, Z): max, min, standard deviation, root mean square, root sum square and tilt. Polynomials will help.
Since this is a supervised classification problem, you will need to create some labelled data - so do this manually (state 1 = rainy day, state 2 = non-rainy day) and then train a classification algorithm. I would recommend a random forest
Test using unlabeled data - don't forget to use cross validation
Voila, you now have a highly accurate model and will win the competition. Plus you've learned about a bunch of really cool Python and machine learning stuff.
For more tutorials on how all this stuff works, I'd highly recommend the Kaggle tutorial projects
BONUS: If you want to take it to a new level, you can start adding smoothers on top of your classifier, for example by using a Hidden Markov Model as explained in this talk
BONUS 2: Go get a PhD in Human Activity Recognition.
Does the Leap Motion device perform any filtering on the measurements it takes before constructing a frame? In other words, are extreme values in the Leap's measurements smoothed out before being presented to the developer?
Yes, the Leap Motion software does quite a bit between taking a snapshot of the sensors' field of view and constructing the eventual Frame object presented by the API. Among other things, this includes a certain amount of filtering. The methods and amount of filtering are an implementation detail that are subject to change as we are always working on improving the software. For example, in the next major version of the software (now in beta), an internal skeletal model of the human hand is fit to the observed data, which is quite a different method than is used in the current version.
I am trying to create a 'TRENDS' chart.
For that, I am trying to create a 3-D bar chart with left axis (Y-axis) being a number scale (like 0-1000), Bottom axis (X-axis) to be 7 days (Mon-Sun), and depth axis (Z-axis) to be weeks. My report data spans back to 11/2/12 and has data (a single number) for each day. So, basically the graph should have a scale of 0-1000 on the left, 7 labels along the horizontal (Mon-Sun), and then approx 36 rows deep (Z-axis) containing the data (bar for EACH day) with a 'row' label for that week (eg. xx/xx/13 to xx/xx/13).
Step by step instructions for TeeCharts are scarce to start AND I have found nothing on the Net describing how to do a Z-axis (3rd axis). Am I trying to use the TeeChart software to create a chart it is NOT capable of delivering??
Example 3d layout on page 12: http://www.nrdc.org/air/pollution/benchmarking/2002/benchmark2002_pt2.pdf
PLEASE, any input would be welcome. Thanks.
Mike
Since you posted the same question at the Embarcadero Forums, I assume you are using TeeChart VCL/FMX. Having this in mind, you can achieve what you request with TeeChart Pro version. You can see the differences between TeeChart versions in this feature matrix.
To plot this kind of charts you should use Tower series. You'll find examples at the "All Features\Welcome !\Chart styles\Extended\Tower" section in the new features demo included with both evaluation and registered versions. Fully functional evaluation version can be downloaded here.
Also, bear in mind that 3D series like Tower series need to be populated as I explained here.
Looking for some help with a Labview data collection program. If I could collect 2ms of data at 8kHz (gives 16 data points) per channel (I am collecting data on 4 analog channels with an National Instruments data acquisition board). The DAQ-MX collection task gives a 1D array of 4 waveforms.
If I don't display the data I can do all my computation time is about 2ms and it is OK if the processing loop lags a little behind the collection loop. Updating the chart in Labview's front panel introduces an unacceptable delay. We don't need to update the display very quickly probably at 5-10Hz would be sufficient. But I don't know how to set this up.
My current Labview VI has three parallel loops
A timed-loop for data collection
A loop for analysis and processing
A low priority loop for caching data to disk as a TDMS file
Data is passed from the collection loop to the other loops using a queue. Labview examples gave me some ideas but I am stuck.
Any suggestions, references, ideas would be appreciated.
Thanks
Azim
Follow Up Question
eaolson suggests that I re-sample the data for display purposes. The data coming from the DAQ-MX read is a one dimensional array of waveforms. So I would need to somehow build or concatenate the waveform data for each channel. And then re-sample the data before updating the front panel chart. I suppose the best approach would be to queue the data and in a display loop dequeue the stack build and re-sample the data based on screen resolution and then update the chart. Would there be any other approach. I will look on
(NI Labview Forum)[http://forums.ni.com/ni/board?board.id=170] for more information as suggetsted by eaolson.
Updates
changed acceptable update rate for graphs to 5-10Hz (thanks Underflow and eaolson)
disk cache loop is a low priority one (thanks eaolson)
Thanks for all the responses.
Your overall architecture description sounds solid, but... getting to 30Hz for any non-trivial graph is going to be challenging. Make sure you really need that rate before trying to make it happen. Optimizing to that level might take some time.
References that should be helpful:
You can defer panel updates. This keeps the front panel from refreshing until you're ready for it to do so, allowing you to buffer data in the background, and only draw it occasionally.
You should know about (a)synchronous display. This option allows some control over display rates.
There is some general advice available about speeding execution.
There is a (somewhat dated) report on execution speed on the LAVA forums. Googling around the LAVA forums is a great idea if you need to optimize your speed.
Television updates at about 30 Hz. Any more than that is faster than the human eye can see. 30 Hz should be at the maximum update rate you should consider for a display, not the starting point. Consider an update rate of 5-10 Hz.
LabVIEW charts append the most recent data to the historical data they store and display all the data at once. At 8 kHz, you're acquiring at least 8000 data points per channel per second. That means the array backing that graph has to continuously be resized to hold the new data. Also, even if your graph is 1000 pixels across, that means you're displaying 8 data points per screen pixel. There's not usually any reason to display any more than one data point per pixel. If you really need fast update rates, plot less data. Create an array to hold the historical data and plot only every Nth data point, where N is chosen so you're plotting, say, only a few hundred points.
Remember that your loops can run at different rates. It may be satisfactory to run the write-to-disk loop at a much lower frequency than the data collection rate, maybe every couple of seconds.
Avoid property nodes if you can. They run in the UI thread, which is slower than most other execution.
Other than that, it's really hard to offer a lot of substantial advice without seeing code or more specifics. Consider also asking your question at the NI LabVIEW forums. There are a lot of helpful people there.