Consider I have load a dataset as follows:
ds = yt.load('pltxxx')
The dataset includes the following fields
density, mag_vort, tracer, x_velocity, y_velocity
One can simply plot the mag_vort which is the magnitude of vorticity in 2D domain in this case, by means of:
slc = yt.SlicePlot(ds, 'z', 'mag_vort')
If I want to export the x-cooridnates, y-coordinates and vorticity_magnitude in the txt file (or numpy array) or plot it via matplotlib scatter plot
plt.scatter(x_coor, y_coor, c=mag_vort)
Is there an easy way to extract those information from dataset?
You can use a data object (in this case we use the all_data data object) to access the field values for the 'x', 'y', and 'mag_vort' fields:
ad = ds.all_data()
x = ad['x']
y = ad['y']
mag_vort = ad['mag_vort']
The arrays you get back from accessing a data object are YTArray instances. YTArray is a subclass of numpy's ndarray that has units attached.
Before you pass these arrays to matplotlib, convert them to whichever units you want to do the plot in, then cast them to numpy arrays:
x_plot = np.array(x.to('km'))
y_plot = np.array(y.to('km'))
plt.scatter(x_plot, y_plot, c=np.array(mag_vort))
Related
I have a row that contains the names and photos of people in Oracle, how do I make face recognition that can recognize names only by taking pictures from the camera ??
what techniques can I use?
Firstly, do not store the raw images in the blob column. You should store the vector representation of raw images. The following python code block will find the vector representation of a face image.
#!pip install deepface
from deepface.basemodels import VGGFace, Facenet
model = VGGFace.loadModel() #you can use google facenet instead of vgg
target_size = model.layers[0].input_shape
#preprocess detects facial area and aligns it
img = functions.preprocess_face(img="img.jpg", target_size=target_size)
representation = model.predict(img)[0,:]
Here, you can either pass exact image path like img.jpg or the 3D array to img argument of preprocess_face. In this way, you will store the vector representations in the blob column of oracle database.
When you have a new face image, and want to find its identity in the database find its representation again.
#preprocess detects facial area and aligns it
target_img = functions.preprocess_face(img="target.jpg", target_size=target_size)
target_representation = model.predict(target_img )[0,:]
Now, you have the vector representation of the target image and vector representations of the database images. You need to find the similarity score of target image representation and each instance of database representations.
Euclidean distance is the easiest way to compare vectors.
def findEuclideanDistance(source_representation, test_representation):
euclidean_distance = source_representation - test_representation
euclidean_distance = np.sum(np.multiply(euclidean_distance, euclidean_distance))
euclidean_distance = np.sqrt(euclidean_distance)
return euclidean_distance
We will compare each data base instance to target. Suppose that representations of data base instances are stored in representations object.
distances = []
for i in range(0, len(representations)):
source_representation = representations[i]
#find the distance between target_representation and source_representation
distance = findEuclideanDistance(source_representation, target_representation )
distances.append(distance)
Distances list stores the distance of each item in the data base to target. We need to find the lowest distance.
import numpy as np
idx = np.argmax(distances)
Idx is the id of the target image in the database.
I have a working sns.heatmap, showing volatility of currency pairs. What I want is to be retain the color from the volatility, but superimpose the numeric spot rate in the relevant cell.
This way each cell displays two values- spot numeric, and volatility color. Is there a way to use sns.heatmap to do this directly, or failing that, grab the graphical info and superimpose the spot data.
never mind, I've found an updated part of seaborn documentation. annot can now be set to an array of values separate from the color map.
Simple example:
import seaborn as sns
data_number = pd.DataFrame(np.random.randint(0, 3, (11,5)), columns=['A', 'B', 'C', 'D', 'E'], index = range(2000, 2011, 1))
data_string = data_number.replace(0,"").replace(1,"Some").replace(2,"Lots")
sns.heatmap(data_number, annot=data_string,
xticklabels=data_number.columns,
yticklabels=data_number.index,
fmt="s", cmap = sns.cm.rocket_r)
Results in:
I have the following code to import multiple images from one directory into a struct in Matlab, here is an example of the images.
myPath= 'E:\conduit_stl(smooth contour)\Collagen Contour Slices\'; %'
fileNames = dir(fullfile(myPath, '*.tif'));
C = cell(length(fileNames), 1);
for k = 1:length(fileNames)
filename = fileNames(k).name;
C{k} = imread(filename);
se = strel('disk', 2, 0);
C = imclose(C, se);
filled = imfill(C,'holes');
end
Though now I would like to perform a fill on all the images, later finding the centroids. However, when attempting this, an error stating: "Expected input number 1, I1 or BW1, to be one of these types: double, ... etc" I tried converting the images into double precision, though that just resulted in: "Conversion to double from cell is not possible."
This is most likely due to the structure type, the images are 'housed' in, but I have no idea concerning that.
Help on this would be greatly appreciated.
So to elaborate on my previous comments, here are a few things to change with your code:
C is not a structure but a cell array. The content of a cell array is access with {curly brackets}. If all your images are the same size, then it is more efficient to store them into a numeric array instead of a cell array. Since they seem to be logical images, your array would have 3 dimensions:
[height, width, numberofimages]
You could therefore start your code with:
myPath= 'E:\conduit_stl(smooth contour)\Collagen Contour Slices\'; %'
fileNames = dir(fullfile(myPath, '*.tif'));
%// if your images are of type uint8
C(height,width,length(fileNames)) = uint8(0);
C_filled = C; %// initialize new array to stored filled images
Also, since you are using the same structuring elements for your morphological operation on all the images, you can define it once outside the loop.
So your code could look like this:
se = strel('disk', 2, 0);
for k = 1:length(fileNames)
C(:,:,k) = imread(fileNames(k).name);
C_filled(:,:,k) = imfill(imclose(C(:,:,k), se),'holes');
end
I am using sci-kit image to get the "regionprops" of a segmented image. I then wish to replace each of the segment labels with their corresponding statistic (e.g eccentricity).
from skimage import segmentation
from skimage.measure import regionprops
#a segmented image
labels = segmentation.slic(img1, compactness=10, n_segments=200)
propimage = labels
#props loop
for region in regionprops(labels1, properties ='eccentricity') :
eccentricity = region.eccentricity
propimage[propimage==region] = eccentricity
This runs, but the propimage values do not change from their original labels
I have also tried:
for i in range(0,max(labels)):
prop = regions[i].eccentricity #the way to cal a single prop
propimage[i]= prop
This delivers this error
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
I am a recent migrant from matlab where I have implemented this, but the data structures used are completely different.
Can any one help me with this?
Thanks
Use ndimage from scipy : the sum() function can operate using your label array.
from scipy import ndimage as nd
sizes = nd.sum(label_file[0]>0, labels=label_file[0], index=np.arange(0,label_file[1])
You can then evaluate the distribution with numpy.histogram and so on.
I have obtained blood vessels of an eye in an image variable ves. I found the number of connected components(8-connectivity) as blobs. For each blob I need to calculate the Area, Major axis length and Centroid and store these values in a matrix testfv (each row corresponding to each property).For a single blob, Area returns a 1x1 struct, Centroid returns a 1x2 struct, and MajorAxisLength returns a 1x1 struct. So ,I guess depending on the number of blobs the number of cells required to store the values of Areas, Centroids and MajorAxisLength's vary, so using just one testfv to store these values as I have done would be wrong.
Is it possible? This is the code I tried(i assumed that testfv has 25 columns which allows me to store upto 8 blobs info)
[labeledImage numberOfBlobs] = bwlabel(ves, 8);
col=numberOfBlobs*2;
testfv = zeros(3,col);
for i=1:col
blobMeasurements = regionprops(labeledImage, 'Area');
testfv(1,col) = [blobMeasurements.Area];
blobMeasurements = regionprops(labeledImage, 'MajorAxisLength');
testfv(2,col)= [blobMeasurements.MajorAxisLength];
blobMeasurements = regionprops(labeledImage, 'Centroid');
testfv(3,col) = [blobMeasurements.Centroid];
end
I am getting the following error....
??? Subscripted assignment dimension mismatch.
Error in ==> alpha1 at 191 <br/>
testfv(1,col) = [blobMeasurements.Area];
Also, I need to write the data of the testfv matrix to an excel sheet file. How do I that ?
Would really appreciate the help as I am new to Matlab.