New user of Iris here. I have loaded a GRIB file that has created a list of cubes, many of them have "unknown" name and units. In this list of cubes there should be the temperature at different model levels (perhaps in not all the cubes). Example of the first cube in the list:
unknown / (unknown) (--: 14; projection_y_coordinate: 469; projection_x_coordinate: 565)
Dimension coordinates:
projection_y_coordinate - x -
projection_x_coordinate - - x
Auxiliary coordinates:
forecast_period x - -
height x - -
time x - -
Scalar coordinates:
originating_centre: unknown centre lemm
So I would need to create a subset, or sub-cube. I have thought in iterating through all the cubes, looking for the temperature and adding it to a new cube.
So, how could I do it? And what could I use for the temperature if the typical Iris name, air_ temperature is not recognized? I think I should use WMO standard key for the temperature, indicatorOfParameter = 11, but how could I do it in Iris?
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Fitting largest circle in free area in image with distributed particle
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I deal with plots of data on the order of half a million points in Octave. I am trying to find the center of empty spaces that are in the data (on purpose).
I know how many points to look for and I was thinking of feeding in starter locations and then try to expand a circle in one direction until you hit valid data point locations and keep doing that in a few directions until you have a circle that is filled with no data but touches valid data points. The center of that circle would be the center of the void space. I'm not entirely sure how to write that since I'm very green in coding.
Obviously a graphical solution probably isn't the best method, but I don't know how to find big x and y gaps in a huge matrix of x y locations.
A section of the data I deal with. Trying to write a program to automatically find the center of that hole.
A sample of the data I'm working with. Each data point is an x and y location with a z height that isn't really valuable to what I'm trying to solve here. The values do not line up in consistent intervals
Here is a large sample of what I'm working with
I know you said your data does not line-up in x or y, but it still seems suspiciously grid-like.
In this case, you can probably express each gridpoint as a 'pixel' in an image; this gives you access to excellent functions you can use from the image package, such as the imregionalmin function. This will give you connected components of 'holes', in your case. For each component you can find their centres of mass easily by finding the 'average coordinate' over the pixels within that component. You can then perform a distance transform (e.g. using bwdist) to find the exact radius for the circle you describe, as the distance from that centre of mass to the nearest pixel. Alternatively, you can start with bwdist and then use immaximas to detect the centres of mass directly. If you have multiple such regions, you can use bwconncomp to find connected components first (or over the output of imregionalmin).
If your data is not specifically grid-like, then you could probably interpolate your data to make them fit such a grid.
Example:
pkg load image
t = 0 : 0.1 : 2 * pi; % for use when plotting circles later
[X0, Y0] = ndgrid( 1:100, 1:100 ); % Create 'index' grid
X = X0 - 0.25 * Y0; Y = 0.25 * X0 + Y0; % Create transformed grid
Z = 0.5 * (X0 - 50) .^ 2 + (Y0 - 50) .^ 2 > 250; % Assign a logical value to each 'index' point on grid
M = imregionalmin ( Z ); % Find 'hole' as mask
C = { round(mean(X0(M))), round(mean(Y0(M))) }; % Find centre of mass (as index)
R = bwdist( ~M )(C{:}); % Find distance from centre of mass to nearest pixel
R = min( abs( X(C{1}+R, C{2}) - X(C{:}) ), abs( Y(C{1}, C{2}+R) - Y(C{:}) ) ); % Adjust for transformed grid
figure(1); hold on
plot( X(Z), Y(Z), '.', 'markerfacecolor', 'b' ) % Draw original transformed grid data
plot( X(C{:}), Y(C{:}), 'o', 'markerfacecolor', 'r' ); % Draw centre of mass in transformed grid
plot( X(C{:}) + R * cos(t), Y(C{:}) + R * sin(t), 'r-' ) % Draw optimal circle on top
axis equal; hold off
Is there a straightforward way to extract a region from an Iris cube which is described by 2D latitude and longitude variables, for example using NEMO ocean model data?
I found this workaround but was wondering if there was a way to do this in 'pure' Iris, without having to resort to defining a new function?
For example, if I have this cube...
In [30]: print(cube)
mole_concentration_of_dimethyl_sulfide_in_sea_water / (mol m-3) (time: 780; cell index along second dimension: 330; cell index along first dimension: 360)
Dimension coordinates:
time x - -
cell index along second dimension - x -
cell index along first dimension - - x
Auxiliary coordinates:
latitude - x x
longitude - x x
... and then try to extract a region using intersection, I get this...
>>> subset = cube.intersection(longitude=(-10, 10))
CoordinateMultiDimError: Multi-dimensional coordinate not supported: 'longitude'
Thanks!
As you can see from the error messsage, iris does not currently support subsetting by multi-dimensional coordinates, so you have to write a function similar to bbox_extract_2Dcoords() in that blog post. All it does is creates a boolean mask with values set to True within your region of interest and False outside. Then the boundaries of this region are used as indices to subset the cube.
An alternative would be to regrid the data to a regular grid defined by 1D longitude and latitude and then subset the data using the standard Constraint() method.
I have three sections (top, mid, bot) of grayscale images (3D). In each section, I have a point with coordinates (x,y) and intensity values [0-255]. The distance between each section is 20 pixels.
I created an illustration to show how those images were generated using a microscope:
Illustration
Illustration (side view): red line is the object of interest. Blue stars represents the dots which are visible in top, mid, bot section. The (x,y) coordinates of these dots are known. The length of the object remains the same but it can rotate in space - 'out of focus' (illustration shows a rotating line at time point 5). At time point 1, the red line is resting (in 2D image: 2 dots with a distance equal to the length of the object).
I want to estimate the x,y,z-coordinate of the end points (represents as stars) by using the changes in intensity, the knowledge about the length of the object and the information in the sections I have. Any help would be appreciated.
Here is an example of images:
Bot section
Mid section
Top section
My 3D PSF data:
https://drive.google.com/file/d/1qoyhWtLDD2fUy2zThYUgkYM3vMXxNh64/view?usp=sharing
Attempt so far:
enter image description here
I guess the correct approach would be to record three images with slightly different z-coordinates for your bot and your top frame, then do a 3D-deconvolution (using Richardson-Lucy or whatever algorithm).
However, a more simple approach would be as I have outlined in my comment. If you use the data for a publication, I strongly recommend to emphasize that this is just an estimation and to include the steps how you have done it.
I'd suggest the following procedure:
Since I do not have your PSF-data, I fake some by estimating the PSF as a 3D-Gaussiamn. Of course, this is a strong simplification, but you should be able to get the idea behind it.
First, fit a Gaussian to the PSF along z:
[xg, yg, zg] = meshgrid(-32:32, -32:32, -32:32);
rg = sqrt(xg.^2+yg.^2);
psf = exp(-(rg/8).^2) .* exp(-(zg/16).^2);
% add some noise to make it a bit more realistic
psf = psf + randn(size(psf)) * 0.05;
% view psf:
%
subplot(1,3,1);
s = slice(xg,yg,zg, psf, 0,0,[]);
title('faked PSF');
for i=1:2
s(i).EdgeColor = 'none';
end
% data along z through PSF's center
z = reshape(psf(33,33,:),[65,1]);
subplot(1,3,2);
plot(-32:32, z);
title('PSF along z');
% Fit the data
% Generate a function for a gaussian distibution plus some background
gauss_d = #(x0, sigma, bg, x)exp(-1*((x-x0)/(sigma)).^2)+bg;
ft = fit ((-32:32)', z, gauss_d, ...
'Start', [0 16 0] ... % You may find proper start points by looking at your data
);
subplot(1,3,3);
plot(-32:32, z, '.');
hold on;
plot(-32:.1:32, feval(ft, -32:.1:32), 'r-');
title('fit to z-profile');
The function that relates the intensity I to the z-coordinate is
gauss_d = #(x0, sigma, bg, x)exp(-1*((x-x0)/(sigma)).^2)+bg;
You can re-arrange this formula for x. Due to the square root, there are two possibilities:
% now make a function that returns the z-coordinate from the intensity
% value:
zfromI = #(I)ft.sigma * sqrt(-1*log(I-ft.bg))+ft.x0;
zfromI2= #(I)ft.sigma * -sqrt(-1*log(I-ft.bg))+ft.x0;
Note that the PSF I have faked is normalized to have one as its maximum value. If your PSF data is not normalized, you can divide the data by its maximum.
Now, you can use zfromI or zfromI2 to get the z-coordinate for your intensity. Again, I should be normalized, that is the fraction of the intensity to the intensity of your reference spot:
zfromI(.7)
ans =
9.5469
>> zfromI2(.7)
ans =
-9.4644
Note that due to the random noise I have added, your results might look slightly different.
I have loaded some data on a hybrid-p grid with iris which looks like this:
specific_humidity / (1) (atmosphere_hybrid_sigma_pressure_coordinate: 48; latitude: 160; longitude: 320)
Dimension coordinates:
atmosphere_hybrid_sigma_pressure_coordinate x - -
latitude - x -
longitude - - x
Auxiliary coordinates:
vertical coordinate formula term: a(k) x - -
vertical coordinate formula term: b(k) x - -
vertical pressure x - -
surface_air_pressure - x x
Derived coordinates:
air_pressure x x x
Scalar coordinates:
time: 2005-11-01 00:00:00
vertical coordinate formula term: reference pressure: 101325.0 Pa
Attributes:
Conventions: CF-1.4
What I want is to turn the pressure coordinate into a cube for use in calculations. I can do this with the following code:
p_cube=humid.copy(humid.coord('air_pressure').points)
p_cube.rename('air_pressure')
p_cube.units=humd_1t.coord('air_pressure').units
But is there a neater way?
Good question, to which I don't think there is a better solution to the one you've already provided.
From a design perspective, Iris' coordinates aren't quite cubes (with things like their own coordinates, cell measures & methods, etc.). It seems that really in order to do such a thing properly Iris would need to gain the concept of a Dataset (where multiple phenomena share a single set of coordinates).
The only slightly neater approach (untested) might be to do:
p_cube=humid.copy(humid.coord('air_pressure').points)
p_cube.metadata = humid.coord('air_pressure').metadata
Though I'm not 100% sure if it is valid to provide a CoordMetadata object when defining the cube's metadata.
HTH
I have a list of points moving in two dimensions (x- and y-axis) represented as rows in an array. I might have N points - i.e., N rows:
1 t1 x1 y1
2 t2 x2 y2
.
.
.
N tN xN yN
where ti, xi, and yi, is the time-index, x-coordinate, and the y-coordinate for point i. The time index-index ti is an integer from 1 to T. The number of points at each such possible time index can vary from 0 to N (still with only N points in total).
My goal is the filter out all the points that do not move in a certain way; or to keep only those that do. A point must move in a parabolic trajectory - with decreasing x- and y-coordinate (i.e., moving to the left and downwards only). Points with other dynamic behaviour must be removed.
Can I use a simple sorting mechanism on this array - and then analyse the order of the time-index? I have also considered the fact each point having the same time-index ti are physically distinct points, and so should be paired up with other points. The complexity of the problem grew - and now I turn to you.
NOTE: You can assume that the points are confined to a sub-region of the (x,y)-plane between two parabolic curves. These curves intersect only at only at one point: A point close to the origin of motion for any point.
More Information:
I have made some datafiles available:
MATLAB datafile (1.17 kB)
same data as CSV with semicolon as column separator (2.77 kB)
Necessary context:
The datafile hold one uint32 array with 176 rows and 5 columns. The columns are:
pixel x-coordinate in 175-by-175 lattice
pixel y-coordinate in 175-by-175 lattice
discrete theta angle-index
time index (from 1 to T = 10)
row index for this original sorting
The points "live" in a 175-by-175 pixel-lattice - and again inside the upper quadrant of a circle with radius 175. The points travel on the circle circumference in a counterclockwise rotation to a certain angle theta with horizontal, where they are thrown off into something close to a parabolic orbit. Column 3 holds a discrete index into a list with indices 1 to 45 from 0 to 90 degress (one index thus spans 2 degrees). The theta-angle was originally deduces solely from the points by setting up the trivial equations of motions and solving for the angle. This gives rise to a quasi-symmetric quartic which can be solved in close-form. The actual metric radius of the circle is 0.2 m and the pixel coordinate were converted from pixel-coordinate to metric using simple linear interpolation (but what we see here are the points in original pixel-space).
My problem is that some points are not behaving properly and since I need to statistics on the theta angle, I need to remove the points that certainly do NOT move in a parabolic trajoctory. These error are expected and fully natural, but still need to be filtered out.
MATLAB plot code:
% load data and setup variables:
load mat_points.mat;
num_r = 175;
num_T = 10;
num_gridN = 20;
% begin plotting:
figure(1000);
clf;
plot( ...
num_r * cos(0:0.1:pi/2), ...
num_r * sin(0:0.1:pi/2), ...
'Color', 'k', ...
'LineWidth', 2 ...
);
axis equal;
xlim([0 num_r]);
ylim([0 num_r]);
hold all;
% setup grid (yea... went crazy with one):
vec_tickValues = linspace(0, num_r, num_gridN);
cell_tickLabels = repmat({''}, size(vec_tickValues));
cell_tickLabels{1} = sprintf('%u', vec_tickValues(1));
cell_tickLabels{end} = sprintf('%u', vec_tickValues(end));
set(gca, 'XTick', vec_tickValues);
set(gca, 'XTickLabel', cell_tickLabels);
set(gca, 'YTick', vec_tickValues);
set(gca, 'YTickLabel', cell_tickLabels);
set(gca, 'GridLineStyle', '-');
grid on;
% plot points per timeindex (with increasing brightness):
vec_grayIndex = linspace(0,0.9,num_T);
for num_kt = 1:num_T
vec_xCoords = mat_points((mat_points(:,4) == num_kt), 1);
vec_yCoords = mat_points((mat_points(:,4) == num_kt), 2);
plot(vec_xCoords, vec_yCoords, 'o', ...
'MarkerEdgeColor', 'k', ...
'MarkerFaceColor', vec_grayIndex(num_kt) * ones(1,3) ...
);
end
Thanks :)
Why, it looks almost as if you're simulating a radar tracking debris from the collision of two missiles...
Anyway, let's coin a new term: object. Objects are moving along parabolae and at certain times they may emit flashes that appear as points. There are also other points which we are trying to filter out.
We will need some more information:
Can we assume that the objects obey the physics of things falling under gravity?
Must every object emit a point at every timestep during its lifetime?
Speaking of lifetime, do all objects begin at the same time? Can some expire before others?
How precise is the data? Is it exact? Is there a measure of error? To put it another way, do we understand how poorly the points from an object might fit a perfect parabola?
Sort the data with (index,time) as keys and for all locations of a point i see if they follow parabolic trajectory?
Which part are you facing problem? Sorting should be very easy. IMHO, it is the second part (testing if a set of points follow parabolic trajectory) that is difficult.