How can I plot a graph for big data (csv file) in matlab?

I have a large file (csv file) and has more than 1 million data. it contains time stamp and sensor value. I want to plot the data using matlab. Can you please anyone guide me for that.

Thanks kishan

[Muhammad Maaz Qaiser]

I also have the same problem. Kishan do you get a good solution let me know Please.

Thanks

Maaz

Answer 1

[Benjamin Kraus]

1 million data points shouldn't be too hard for MATLAB to handle. Have you tried just csvread and plot?

If that is too slow, you can start looking into some of our datastore options (introduced in R2014b):

Read and Analyze Large Tabular Text File

[kishan prajapati]

Thank you for the response. I will sure try the method you recommended and give you feedback.

I have got another question as well. I have got lot of csv file for different sensor readings. And, all of them contain almost a million data, but the readings are in different time-stamp. Now, I want to synchronize the all the sensor reading (all sensor readings in same time-stamp by interpolation). Can you give me some ideas for this issue. thank you.

[Benjamin Kraus]

It depends on whether your data can fit in memory on the computer or not.

The first thing that comes to mind is the timetable object, which has two methods: retime and synchronize. If you can load all your data into timetable objects at once (they fit in memory), then you can use synchronize so that they all synchronize to common time vector. If you cannot load them all into memory at once, you can consider loading them one at a time and using retime to set them to common time vectors, then perhaps write back out to new CSV files with the new timing.

I'm not as familiar with the datastore options, but I believe there is a way to create a datastore that references several different CSV files and treats them all as a single data source (without loading them into memory). Once you've created the datastore, you can perform operations on the entire set, and it will coalesce the calculations to perform them more efficiently on the data set.

[kishan prajapati]

Thank you so much. Appreciated a lot. And, surely I will come back after I try your suggestion. Thank :)