I am working with big data sets where I need to graph multiple stacked trends but have come across an issue relating the the number of y axes allowed in a plotly graph. I have tried multiple methods to resolve this but to no avail.
Anytime I attempt to graph data where I use over 100 y-axes trends the trends begin to eclipse the previous ones. Here is an image of my attempt to graph 106 trends:
106 trends
If downsize the number of trends to 99 trends then all of these show in the graph:
99 trends
I am building these graphs in ipython notebook using plotly's python API.
Does plotly have a limit on the number of y axes that are permitted in one graph?
Related
How can I plot NDVI time series graph (Using Sentinel-2) of multiple shapefiles in a single graph plot. I run code for one shapefile and its works well but unable to plot the ndvi curve for other shapefile area for comparison in the same graph plot due to my minimum command on GEE. Sharing code link below
https://code.earthengine.google.com/d003711d7f18dd242b834426703e1a01
I created a dataset in Google Vertex AI that contains numerous features and after clicking on "Generate Statistics" inside the dataset, I can see some basic stats about each feature, and when I click on each feature a pie chart of % distribution of each value and a histogram show up.
Here is my question. For one of my numerical features, I have lots of zeros, specifically 652 zeros and my whole dataset contains 81K distinct values of that feature. The pie chart shows me that my dataset contains %83 percent zero values.
How is it possible? When I calculate the percentages I get that the dataset has only %0.80 percent zeros. (652/81K)*100=0.80%
Is it a reporting problem, a formatting problem? Has anyone had any issues with the stats in Vertex AI datasets?
Note: I don't have such problems with my other numerical features, I have the problem with only one feature containing a large number of zeros.
I would like to know if anyone has had success labeling images with a continuous variables in the AutoML Vision platform.
Specifically, I would like to predict the height of a sand castle from a birds eye view photograph. I would train the model on birds eye view photographs of sandcastles labeled with the height in inches. I have 10,000 images in my data set. The range of heights in my data set is 1cm to 110cm, so the variable is continuous but not infinite.
Is this achievable through Google AutoML Vision?
Thank you!
As for now, there is no such feature on AutoML Vision that allows you to annotate images using continuous variables. As far as I understand, the labels are treated individually and the model will require "1000 training images per label. The minimum per label is 10, or 50 for advanced models".
A feasible approach with this would be to use discrete heights or ranges (1cm-20cm, 21cm-50cm, etc).
I am working with Longitude-Latitude values using the default powerBi map visualization. I want to be able to specify a certain distance and also be able to specify how many map points will be displayed within that radius. For example I would like maps to plot 500 or less lat-long points within any 1 mile radius in the US. Just to be clear, maps should plot all map points within the US that are quantity 500 or less in any 1 mile radius.
Can anyone help me to solve this in PowerBi or is there any other visualization tool in powerBi that can help me to do this?
I have data for sin curves of differing magnitudes over time and I would like to combine all the curves into a single curve on a chart.
Is it possible to do this with the Google Chart API or can alternative approaches be recommended?
Google's Chart API isn't a graphic calculator. It displays pre-calculated data, not equations. So if you have the results pre-calculated for a series of x-values, then yes, the Chart API can plot it.
(If the number of points is < ~5k, a Google Spreadsheet is probably your fastest route to try it out.)
If you don't have the results already calculated - if you just have the equations - then your problem's more of a maths question than a Chart API question. (But again, a spreadsheet might be a fast way to get going.)