This is a question about the Allen Brain Observatory API.
I am trying to find both the ROI mask and L0 regularization events for each cell in a particular experiment. I know how to get the ROI mask information given a particular cell ID, but I don't know how to get the L0 regularization events. As far as I can tell, the return value of get_ophys_experiment_events() is an [N_cells,N_times]-sized array with no information about particular cell IDs.
I've looked through the API and done some googling and haven't found anything.
The following code gets the ROI mask for the first cell in a particular experiment:
from allensdk.core.brain_observatory_cache import BrainObservatoryCache
boc = BrainObservatoryCache(manifest_file='boc/manifest.json')
data_set = boc.get_ophys_experiment_data(510221121)
cid = data_set.get_cell_specimen_ids()[0]
roi_mask = data_set.get_roi_mask(cell_specimen_ids=[cid])
The following code gets the events associated with the same experiment:
events = boc.get_ophys_experiment_events(ophys_experiment_id=510221121)
However, I don't know which row in the events variable corresponds to the ID stored in cid.
Is there a way to do this?
A similar question has been answered in the Allen Brain Map Community Forum
https://community.brain-map.org/t/how-can-i-get-the-l0-regularization-events-for-a-specific-cell-id/261
Related
I am working on pedestrian step detection (acceleration). I want to calculate statistical features from my filtered signal. I have already calculated some and now I want to calculate gradient.
My data is of 1x37205 double. I calculated features using for loop with moving window size=2samples and 50% overlap of previous window. Below I am attaching the code I tried to calculate the gradient.
I am not sure if it is the right way to calculate or not? In addition, I am also unable to understand that what is the purpose to use gradient, how it can be useful for step detection and how to work with gradient? Could some one guide me or provide any code help in matlab?
%%Here M is mean and V is variance i already calculated from filtered data
G = zeros(length(window:length(M)), 2);
for i = window:length(M)
temp = gradient(M(i+1-window:i),V(i+1-window:i));
G(i, 1) = temp(2, 1); % "c1"
G(i, 2) = temp(2, 1); % "c2"
end
One of the best features of Matlab is its documentation. If you are unfamiliar on how to get specific function documentation, enter the following in the command line:
doc functionName
Alternatively, for 'brief' documentation that displays in the command line, you can enter:
help functionName
Also see the documentation link here.
Your question is worded poorly, so I will summarize what I understand and answer accordingly:
You have a step detection data (1*37205) double, let us call it stepSignal
stepSignal is position data
You want your window to be 2 steps with 50% overlap. This is the default behavior for the gradient function.
You do not need a "for" loop to achieve your goal. According to the documentation, "gradient" can take one input.
See the code below, and if need be add clarifications to the original question.
%% Assume that stepSignal is already imported into the workspace
velocity = gradient(stepSignal);
One last note, when you give "gradient" two inputs, it automatically assumes the second input is a uniform spacing value.
let us suppose that we have following graph of singular value distribution
which was given by following command
stem(SV)
SV_singular values,from visually of course we can find approximate values of singular values,but is there any possibility to get values from graph itself?of course someone may say that if we have SV,we can directly access,but i want just graphicl tool to get it from picture itself,for example like this
b=stem(SV);
but when i type b,i am getting following number
b
b =
174.0051
it is matlab self learning,so please help me to learn how to find values from graphics in matlab
The value stored in your variable b is a handle to the current axes. You can access the properties of this axes using get. To access the values in the plot, you can use
b=stem(SV);
values = get(b, 'ydata');
Given a BW image that contains some connected components.
Then, given a single pixel P in the image. How to find which component that contains the pixel P? It is guaranteed that the pixel P is always on the white area in one of the connected components.
Currently, I use CC = bwconncomp(BW) than I iterate each component using 'for' loop. In the each component, I iterate the index pixel. For each pixels, I check whether the value equal to the (index of) pixel P or not. If I find it, I record the number of connected component.
However, it seems it is not efficient for this simple task. Any suggestion for improvement? Thank you very much in advance.
MATLAB provides multiple functions that implement connected-component in different ways.
In your example, I would suggest bwlabel.
http://www.mathworks.com/help/images/ref/bwlabel.html
[L, num] = bwlabel(imgBW) This will perform a full-image connected-component labeling on a black-and-white image.
After calling this function, the label value that pixel P belongs to can be read off the result matrix L, as in label_to_find = L(row, col) index. Simple as that.
To extract a mask image for that label, use logical(L == label_to_find).
If you use different software packages such as OpenCV you will be able to get better performance (efficiency in terms of cutting unnecessary or redundant computation), but in MATLAB the emphasis is on convenience and prototyping speed.
I made a Delaunay Triangulation using Matlab version 2013. I want to remove some of the triangles, meaning canceling their connectivity, for example triangle number 760. How can I make this change? When I tried to edit the connectivity list:
dt.ConnectivityList(760 , :) = [];
I got the message:
Cannot assign values to the triangulation.
I thought about maybe copying specific fields to a different structure, but:
a. I'm not familiar with structures so I don't know how to do it right.
b. After I copy the structure, how can I get my triangles?
dt contains 3 fields: Points, ConnectivityList and Constraints (empty field).
A brief note on MATLAB objects. When you access a field for reading, you are basically doing get(obj, fieldname);. When you try to set a field as you are doing, you are actually calling set(obj, fieldname, new_value). Objects do not necessarily allow you to do these operations.
The triangulation object is read-only, so you will have to make copies of all the fields. If, as you mentioned, you would like to make a structure with similar fields, you can do as follows:
dts = struct('Points', dt.Points, 'ConnectivityList', dt.ConnectivityList);
Now you can edit the fields.
dts.ConnectivityList(760) = [];
You may be able to plot the new structure, but the methods of the delaunayTriangulation class will not be available to you.
To plot the result, use trisurf:
trisurf(dts.ConnectivityList, dts.Points);
I was facing same problem. I found another solution. Instead of creating a new struct just create an object of its super class i.e. triangulation class with edited connectivity list.
Here is my code
P- list of points
C- Constraints (optional)
dt=delaunayTriangulation(P,C); %created triangulation but dt won't let you change connectivity list
list=dt.ConnectivityList;
%your changes here
x=triangulation(list,dt.Points);
Now you can use x as triangulation object
triplot(x)
first of all, I'd like to describe my issue with the kmeans in Matlab.
I select a point via mouse and use it for cluster initialization. This works fine.
After the segmentation of the data, I reshape the data back into proper style, because I need a matrix.
Now I want to select only the cluster of which the user selected the data via mouse.
Therefore I select the index of the mouse-coordinates to select the label, which I want to segmentate. Because of other extra data which is not connected or nearby the relevant data, but also gots the same label.
I want to select only connected-components in a neighbourhood of 8.
So here is my code snippet so far:
flatimg = double(reshape(croppedimg,size(croppedimg,1)*size(croppedimg,2),size(croppedimg,3)));
% kmeans
[idx, clusters] = kmeans(flatimg,2,'start',[seedpoint1(3);seedpoint2(3)]);
% form it back to a matrix
k=reshape(idx,size(croppedimg,1),size(croppedimg,2));
%convert point, which is part of the label I want to linear index
selectedobjectpoint = sub2ind(size(croppedimg),seedpoint1(2),seedpoint1(1));
hgplabel = k(selectedobjectpoint);
idx_object = find(k, hgplabel);
% also tried: idx_object = find(k == hgplabel);
I added a screenshot, which shows the direct output of kmeans:
So my aim is it here to get only the "white" OR the "black" ones.
Help or advice appreciated. If you've got any questions regarding the snippet or the goal, feel free to ask.
Thank you in advance!
I think the FIND command is throwing you off. You want something like:
logicalImage = k == hgplabel;
bwImg = bwlabel(logicalImage);
imagesc(bwImg)
FIND will output the indices where k == hgplabel. You want the matrix of zeros and ones where k takes that value (I think).
If you just want the connected components of that, the output of bwlabel will contain unique integers for each connected component, so imagesc(bwImg == 1) will show just component 1. And you can specify the connectivity