I have recorded WiFi CSI sensor data 5000 packets in 5 seconds(5000 packets x 57 subcarriers). But due to dynamic hardware configuration sometimes I only receive 4998 x 57. I want to add and estimate 2 rows so that my original design has consistent 5000 rows x 57 columns.
As you can see some data are 5000x57, and some are 4998x57.
You can achieve your desired output using mean()-function combined with the concatenation operator [] and the repmat() like this:
A=randi(100,4998,57);
A=[A;repmat(mean(A),2,1)];
Most of the functions in Matlab that take arrays as an input will calculate for each column except if the input array hast just 1 row. So does the mean function and you can just append means output to your arrays.
If you show me the code that you used to import the data, I might be able to help you create a cleaner data structure and thus be able to automatically process all of your arrays. The way the data is currently designed it's only possible to do this with dynamic variable names which is considered bad programming practice.
Related
I have a large table in MATLAB which contains over 1000 rows of data, in two columns. Column 1 is the ID of the sensor which gathered the data, and column two is the data itself (in this case a voltage).
I have been able to sort my table to gather all the data for sensors together. So, all the data from Sensor 1 is in rows 1 to 100, the data for Sensor 2 is in rows 101 to 179, the data for Sensor 3 is in rows 180 to 310, and so on. In other words, the number of rows which contain data for a given sensor is never the same.
Now, I want to split this main table into separate tables for each sensor ID, and I am having trouble figuring out a way to do it. I imagine I could do it with a loop, where my I cycle through the various IDs, but that doesn't seem like a very, MATLAB way of doing it.
What would be an efficient way to complete this task? Or would a loop really be the only way?
I have attached a small screenshot of some of my data.
The screenshot you shared shows a 1244x1 structure array with 2 fields but the question describes a table. You could convert the structure array to a table using,
T = struct2table(S); % Assuming S is the name of your structure
Whether the variable is a structure or table, it's better to not separate the variable and to use indexing instead. For example, assuming the variable is a table, you can compute the mean voltage for sensor1 using,
mean(T.reported_voltage(strcmp(T.sensor_id,'Sensor1')))
and you could report the mean of all groups using,
groupsummary(T,'sensor_id', 'mean')
or
splitapply(#mean,T.reported_voltage,findgroups(T.sensor_id))
But if you absolutely must break apart and tidy, well-organized table, you can do so by splitting the table into sub-tables stored within a cell array using,
unqSensorID = unique(T.sensor_id);
C = arrayfun(#(id){T(strcmp(T.sensor_id, id),:)},unqSensorID)
In this case the for loop is fine because (I guess) there aren't that many different sensors and your code will likely spend most of its time processing the data anyway - the loop won't give you a significant overhead.
Assuming your table is called t, the following should do what you want.
unique_sensors = unique(t.sensor_id)
for i = 1:length(unique_sensors)
sensor_data = t(t.sensor_id == unique_sensors(i), :);
% save or do some processing on this data
end
So, I am working on a Wikipedia dump to compute the pageranks of around 5,700,000 pages give or take.
The files are preprocessed and hence are not in XML.
They are taken from http://haselgrove.id.au/wikipedia.htm
and the format is:
from_page(1): to(12) to(13) to(14)..
from_page(2): to(21) to(22)..
.
.
.
from_page(5,700,000): to(xy) to(xz)
so on. So. basically it's a construction of a [5,700,000*5,700,000] matrix, which would just break my 4 gigs of RAM. Since, it is very-very Sparse, that makes it easier to store using scipy.lil.sparse or scipy.dok.sparse, now my issue is:
How on earth do I go about converting the .txt file with the link information to a sparse matrix? Read it and compute it as a normal N*N matrix then convert it or what? I have no idea.
Also, the links sometimes span across lines so what would be the correct way to handle that?
eg: a random line is like..
[
1: 2 3 5 64636 867
2:355 776 2342 676 232
3: 545 64646 234242 55455 141414 454545 43
4234 5545345 2423424545
4:454 6776
]
exactly like this: no commas & no delimiters.
Any information on sparse matrix construction and data handling across lines would be helpful.
Scipy offers several implementations of sparse matrices. Each of them has its own advantages and disadvantages. You can find information about the matrix formats here:
There are several ways to get to your desired sparse matrix. Computing the full NxN matrix and then converting is probably not possible, due high memory requirements (about 10^12 entries!).
In your case I would prepare your data to construct a coo_matrix.
coo_matrix((data, (i, j)), [shape=(M, N)])
data[:] the entries of the matrix, in any order
i[:] the row indices of the matrix entries
j[:] the column indices of the matrix entries
You might also want to have a look at lil_matrix, which can be used to incrementally build your matrix.
Once you created the matrix you can then convert it to a better suited format for calculation, depending on your use case.
I do not recognize the data format, there might be parsers for it, there might not. Writing your own parser should not be very difficult, though. Each line containing a colon starts a new row, all indices after the colon and in consecutive lines without colons are the column entries for said row.
I am fairly new to matlab and I am trying to figure out when it is best to use cells, tables, or matrixes to store sets of data and then work with the data.
What I want is to store data that has multiple lines that include strings and numbers and then want to work with the numbers.
For example a line would look like
'string 1' , time, number1, number 2
. I know a matrix works best if al elements are numbers, but when I use a cell I keep having to convert the numbers or strings to a matrix in order to work with them. I am running matlab 2012 so maybe that is a part of the problem. Any help is appreciated. Thanks!
Use a matrix when :
the tabular data has a uniform type (all are floating points like double, or integers like int32);
& either the amount of data is small, or is big and has static (predefined) size;
& you care about the speed of accessing data, or you need matrix operations performed on data, or some function requires the data organized as such.
Use a cell array when:
the tabular data has heterogeneous type (mixed element types, "jagged" arrays etc.);
| there's a lot of data and has dynamic size;
| you need only indexing the data numerically (no algebraic operations);
| a function requires the data as such.
Same argument for structs, only the indexing is by name, not by number.
Not sure about tables, I don't think is offered by the language itself; might be an UDT that I don't know of...
Later edit
These three types may be combined, in the sense that cell arrays and structs may have matrices and cell arrays and structs as elements (because thy're heterogeneous containers). In your case, you might have 2 approaches, depending on how you need to access the data:
if you access the data mostly by row, then an array of N structs (one struct per row) with 4 fields (one field per column) would be the most effective in terms of performance;
if you access the data mostly by column, then a single struct with 4 fields (one field per column) would do; first field would be a cell array of strings for the first column, second field would be a cell array of strings or a 1D matrix of doubles depending on how you want to store you dates, the rest of the fields are 1D matrices of doubles.
Concerning tables: I always used matrices or cell arrays until I
had to do database related things such as joining datasets by a unique key; the only way I found to do this in was by using tables. It takes a while to get used to them and it's a bit annoying that some functions that work on cell arrays don't work on tables vice versa. MATLAB could have done a better job explaining when to use one or the other because it's not super clear from the documentation.
The situation that you describe, seems to be as follows:
You have several columns. Entire columns consist of 1 datatype each, and all columns have an equal number of rows.
This seems to match exactly with the recommended situation for using a [table][1]
T = table(var1,...,varN) creates a table from the input variables,
var1,...,varN . Variables can be of different sizes and data types,
but all variables must have the same number of rows.
Actually I don't have much experience with tables, but if you can't figure it out you can always switch to using 1 cell array for the first column, and a matrix for all others (in your example).
I'm reading in a csv file that is about 80MB - data_O3. It's about 250,000 x 5 in size. I created E, which is a little bit larger because it has all the days (data_O3 is missing some days). I want to compare the two so that if the date (saved in variable d3) and siteID (d4) are the same, the data point (column 5) is placed in E.
for j = 1:size(data_O3,1)
E(strcmp(d3,data_O3{j,3})&d4 == data_O3{j,4},5) = data_O3(j,5);
end
This script works fine, but for some reason, running it takes longer than expected. I've run the same code for other data that were only slightly smaller with no problem. Is this an issue with the strcmp code or something else?
The script and files used can be found here: https://www.dropbox.com/sh/7bzq3m1ixfeuhu6/i4oOvxHPkn
There are certainly see a number of ways to speed this up significantly.
First of all, read in all numeric data in as numbers. Matlab is not optimized to work with strings, and even cells should generally be avoided as much as possible. If you want to keep everything as strings, use another language (python or perl)
Once you have the state, county and site read in as numbers, then create a number instead of a string for the siteID. One approach would be to use the formula:
siteID = siteNum + 1e4*countyCode + 1e7*stateCode
That would generate unique siteIDs for all sites.
Use datenum to convert the date field into a number.
You are now in a position where the data_O3 defined on line 79 can be a purely numeric array (no cells!), as can your E matrix. That alone will make the process many times faster.
You also might want to define the E as something other than NaN. Maybe give it values of -1.
There may be more optimizations you can do in the comparison, but do the above first and I expect you will see a huge improvement.
I'm trying to create a dataset from a double matrix and cell array of labels.
I don't have access to the mat2dataset function so I'm trying to write something similar.
>> whos data feature_labels
Name Size Bytes Class Attributes
data 2x208 3328 double
feature_labels 1x208 50776 cell
In actual use the data will have ~2million rows and always be double format. The number of columns will range from 20 up to 2000, so doing something like;
>> D = dataset([],[],[],[],[],...[], 'VarNames', feature_labels);
isn't really feasible.
Any suggestions?
edit:
Currently using a for loop and horzcat to concatenate new dataset columns on each loop. I don't see a way to pre-allocate the dataset size is this way so I imagine performance will chug with the larger datasets though..
Have you considered using a struct? I use these all the time in MATLAB for database things. I know it works absolutely fantastic for up to 20,000 elements with about 15 fields each, so I think it would still work just as well as anything else for 2 million items with 2 fields.
Alternatively, can't you just put it in a cell array?
DataBase{rowNum,1}=dataVector(rowNum,:);
DataBase{rowNum,2}=label{rowNum};
To preallocate a struct or cell, its relatively easy, with a struct, once you make your first one to initialize the fields, just say Struct(2000000).fieldName =[]
TO preallocate your cell array, just do
DataBase={[]}
DataBase{2000000,2}=[]
This preallocates all of it and fills it with empty values.