Many of Matlab's Mapping toolbox examples require "topo60c" world map data. Here's an example
load topo60c
axesm hatano
meshm(topo60c,topo60cR)
zlimits = [min(topo60c(:)) max(topo60c(:))];
demcmap(zlimits)
colorbar
However, when I run the above script, Matlab displays a file not found error for "topo60c". Does anyone know why I'm getting this error? I have the Mapping toolbox installed, and it works with other Mapping sample code that doesn't reference that file.
In the acknowledgements section of the mapping toolbox docs there is a note about example data sources:
https://uk.mathworks.com/help/map/dedication-and-acknowledgment.html
Except where noted, the information contained in example and sample data files (found in matlabroot/examples/map/data and matlabroot/toolbox/map/mapdata) is derived from publicly available digital data sets. These data files are provided as a convenience to Mapping Toolbox™ users. MathWorks® makes no claims that any of this data is free of defects or errors, or that the representations of geographic features or names are up to date or authoritative.
You can open these folders from MATLAB (on Windows) using
winopen( fullfile( matlabroot, 'examples/map/data' ) )
winopen( fullfile( matlabroot, 'toolbox/map/mapdata' ) )
Or simply use the fullfile commands above to identify the paths and navigate there yourself.
I can see (MATLAB R2020b) the topo60c file within the first of these folders, which isn't on your path by default because it's within "examples" and not a toolbox directory:
So you could either:
Add this folder to your path so that MATLAB can see the file: addpath(fullfile(matlabroot,'examples/map/data'));
Reference the full file path to the data when running examples: load(fullfile(matlabroot,'examples/map/data/topo60c.mat'));
I would prefer option 2 to avoid changing the path.
Additionally, there is another note in the Raster Geodata section of the docs which details what that dataset should contain
https://uk.mathworks.com/help/map/raster-geodata.html
When raster geodata consists of surface elevations, the map can also be referred to as a digital elevation model/matrix (DEM), and its display is a topographical map. The DEM is one of the most common forms of digital terrain model (DTM), which can also be represented as contour lines, triangulated elevation points, quadtrees, octree, or otherwise.
The topo60c MAT-file, which contains global terrain data, is an example of a DEM. In this 180-by-360 matrix, each row represents one degree of latitude, and each column represents one degree of longitude. Each element of this matrix is the average elevation, in meters, for the one-degree-by-one-degree region of the Earth to which its row and column correspond.
Given that it's generated from publically available data anyway (ref the first docs quote) and you now know what data it represents (ref the 2nd docs quote), you could replicate some replacement data if really needed.
Related
Unfortunately I am not too tech proficient and only have a basic MATLAB/programming background...
I have several csv data files in a folder, and would like to make a histogram plot of all of them simultaneously in order to compare them. I am not sure how to go about doing this. Some digging online gave a script:
d=dir('*.csv'); % return the list of csv files
for i=1:length(d)
m{i}=csvread(d(i).name); % put into cell array
end
The problem is I cannot now simply write histogram(m(i)) command, because m(i) is a cell type not a csv file type (I'm not sure I'm using this terminology correctly, but MATLAB definitely isn't accepting the former).
I am not quite sure how to proceed. In fact, I am not sure what exactly is the nature of the elements m(i) and what I can/cannot do with them. The histogram command wants a matrix input, so presumably I would need a 'vector of matrices' and a command which plots each of the vector elements (i.e. matrices) on a separate plot. I would have about 14 altogether, which is quite a lot and would take a long time to load, but I am not sure how to proceed more efficiently.
Generalizing the question:
I will later be writing a script to reduce the noise and smooth out the data in the csv file, and binarise it (the csv files are for noisy images with vague shapes, and I want to distinguish these shapes by setting a cut off for the pixel intensity/value in the csv matrix, such as to create a binary image showing these shapes). Ideally, I would like to apply this to all of the images in my folder at once so I can shift out which images are best for analysis. So my question is, how can I run a script with all of the csv files in my folder so that I can compare them all at once? I presume whatever technique I use for the histogram plots can apply to this too, but I am not sure.
It should probably be better to write a script which:
-makes a histogram plot and/or runs the binarising script for each csv file in the folder
-and puts all of the images into a new, designated folder, so I can sift through these.
I would greatly appreciate pointers on how to do this. As I mentioned, I am quite new to programming and am getting overwhelmed when looking at suggestions, seeing various different commands used to apparently achieve the same thing- reading several files at once.
The function csvread returns natively a matrix. I am not sure but it is possible that if some elements inside the csv file are not numbers, Matlab automatically makes a cell array out of the output. Since I don't know the structure of your csv-files I will recommend you trying out some similar functions(readtable, xlsread):
M = readtable(d(i).name) % Reads table like data, most recommended
M = xlsread(d(i).name) % Excel like structures, but works also on similar data
Try them out and let me know if it worked. If not please upload a file sample.
The function csvread(filename)
always return the matrix M that is numerical matrix and will never give the cell as return.
If you have textual data inside the .csv file, it will give you an error for not having the numerical data only. The only reason I can see for using the cell array when reading the files is if the dimensions of individual matrices read from each file are different, for example first .csv file contains data organised as 3xA, and second .csv file contains data organised as 2xB, so you can place them all into a single structure.
However, it is still possible to use histogram on cell array, by extracting the element as an array instead of extracting it as cell element.
If M is a cell matrix, there are two options for extracting the data:
M(i) and M{i}. M(i) will give you the cell element, and cannot be used for histogram, however M{i} returns element in its initial form which is numerical matrix.
TL;DR use histogram(M{i}) instead of histogram(M(i)).
I'm trying the EEGLAB and FASTER plugins for MATLAB in order to do some processing for my EEG data, When trying to load the data file, I'm asked to choose the "channel location file", but I don't have that with my data, I was wondering if I can create it myself? And if so, How? I know that each channel in my data corresponds to a specific electrode, how can I write that in the location file? Thank you
You can do it by file or by code.
By file (I did not test it so it may not work):
create a text file with electrode names - one electrode per line, the order should be the same as in your file. Load the file through edit -> channel locations --> read locations (left bottom corner of the gui). Choose your text file and then use "look up locs" button to get corresponding locations on BESA or MNI head model.
By code and gui (should work well):
Create a variable with electrode names (have to be correct names in correct order):
elec_names = {'Cz', 'O1', 'O2', 'Fp1', 'Fp2'};
[EEG.chanlocs.labels] = deal(elec_names{:});
eeglab redraw;
Then use the edit -> channel locations --> look up locs option. Later you can type eegh in command window to get the command that would work on your computer.
This sounds like you are not really aware of how EEGLAB works.
From the EEGLAB wiki page on the topic of "Channel Location"
To plot EEG scalp maps in either 2-D or 3-D format, or to estimate
source locations for data components, an EEGLAB dataset must contain
information about the locations of the recording electrodes.
KEY STEP 5: Load the channel locations.
To load or edit channel location information contained in a dataset, select Edit > Channel locations.
I am running kmeans in Mahout and as an output I get folders clusters-x, clusters-x-final and clusteredPoints.
If I understood well, clusters-x are centroid locations in each of iterations, clusters-x-final are final centroid locations, and clusteredPoints should be the points being clustered with cluster id and weight which represents probability of belonging to cluster (depending on the distance between point and its centroid). On the other hand, clusters-x and clusters-x-final contain clusters centroids, number of elements, features values of centroid and the radius of the cluster (distance between centroid and its farthest point.
How do I examine this outputs?
I used cluster dumper successfully for clusters-x and clusters-x-final from terminal, but when I used it clusteredPoints, I got an empty file? What seems to be the problem?
And how can I get this values from code? I mean, the centroid values and points belonging to clusters?
FOr clusteredPoint I used IntWritable as key, and WeightedPropertyVectorWritable for value, in a while loop, but it passes the loop like there are no elements in clusteredPoints?
This is even more strange because the file that I get with clusterDumper is empty?
What could be the problem?
Any help would be greatly appreciated!
I believe your interpretation of the data is correct (I've only been working with Mahout for ~3 weeks, so someone more seasoned should probably weigh in on this).
As far as linking points back to the input that created them I've used NamedVector, where the name is the key for the vector. When you read one of the generated points files (clusteredPoints) you can convert each row (point vector) back into a NamedVector and retrieve the name using .getName().
Update in response to comment
When you initially read your data into Mahout, you convert it into a collection of vectors with which you then write to a file (points) for use in the clustering algorithms later. Mahout gives you several Vector types which you can use, but they also give you access to a Vector wrapper class called NamedVector which will allow you to identify each vector.
For example, you could create each NamedVector as follows:
NamedVector nVec = new NamedVector(
new SequentialAccessSparseVector(vectorDimensions),
vectorName
);
Then you write your collection of NamedVectors to file with something like:
SequenceFile.Writer writer = new SequenceFile.Writer(...);
VectorWritable writable = new VectorWritable();
// the next two lines will be in a loop, but I'm omitting it for clarity
writable.set(nVec);
writer.append(new Text(nVec.getName()), nVec);
You can now use this file as input to one of the clustering algorithms.
After having run one of the clustering algorithms with your points file, it will have generated yet another points file, but it will be in a directory named clusteredPoints.
You can then read in this points file and extract the name you associated to each vector. It'll look something like this:
IntWritable clusterId = new IntWritable();
WeightedPropertyVectorWritable vector = new WeightedPropertyVectorWritable();
while (reader.next(clusterId, vector))
{
NamedVector nVec = (NamedVector)vector.getVector();
// you now have access to the original name using nVec.getName()
}
check the parameter named "clusterClassificationThreshold".
clusterClassificationThreshold should be 0.
You can check this http://mail-archives.apache.org/mod_mbox/mahout-user/201211.mbox/%3C50B62629.5020700#windwardsolutions.com%3E
There are several example data sets in Matlab, for example wind and mri. If you execute the command load wind you will load the data in the data set wind. Some are included in toolboxes and some appear to be included in standard Matlab. These example data sets are valuable as test data when developing algorithms.
Where can one find a list of all such data sets included in Matlab?
You can enter demo in matlab to get a list. The wind table is part of Example — Stream Line Plots of Vector Data, etc.
For the tables on your computer, have a look at:
C:\Program Files\MATLAB\R2007b\toolbox\matlab\demos
The example data is located in .mat files in ../toolbox/matlab/demos.
The following data is available in MATLAB 2014a:
% in matlab run:
> H=what('demos')
> display(H.mat)
You can also use your favorite Linux console:
/usr/local/MATLAB/R2014a/toolbox/matlab/demos$ ls *.mat -1 | sed -e "s/.mat//g"
This is my list for readers who can not try it on their machine while reading this answer:
accidents
airfoil
cape
census
clown
detail
dmbanner
durer
earth
flujet
gatlin
gatlin2
integersignal
logo
mandrill
membrane
mri
patients
penny
quake
seamount
spine
stocks
tetmesh
topo
topography
trimesh2d
trimesh3d
truss
usapolygon
usborder
vibesdat
west0479
wind
xpmndrll
While the command demo in MATLAB 2018b will start a help browser with some demos:
You can find a list of all available dataset and their description in the following link :
https://www.mathworks.com/help/stats/sample-data-sets.html
I am trying to load feature vectors into classifiers such as a k-nearest neighbors classifier.
I have my code for GLCM, so I get contrast, correlation, energy, homogeneity in numbers (feature vectors).
My question is, how can I save every set of feature vectors from all the training images? I have seen somewhere that people had a .set file to load into classifiers (may be it is a special case for the particular classifier toolbox).
load 'mydata.set';
for example.
I suppose it does not have to be a .set file.
I'd just need a way to store all the feature vectors from all the training images in a separate file that can be loaded.
I've google,
and I found this that may be useful
but I am not entirely sure.
Thanks for your time and help in advance.
Regards.
If you arrange your feature vectors as the columns of an array called X, then just issue the command
save('some_description.mat','X');
Alternatively, if you want the save file to be readable, say in ASCII, then just use this instead:
save('some_description.txt', 'X', '-ASCII');
Later, when you want to re-use the data, just say
var = {'X'}; % <-- You can modify this if you want to load multiple variables.
load('some_description.mat', var{:});
load('some_description.txt', var{:}); % <-- Use this if you saved to .txt file.
Then the variable named 'X' will be loaded into the workspace and its columns will be the same feature vectors you computed before.
You will want to replace the some_description part of each file name above and instead use something that allows you to easily identify which data set's feature vectors are saved in the file (if you have multiple data sets). Your array of feature vectors may also be called something besides X, so you can change the name accordingly.