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enter image description here
import numpy as np
import numpy.linalg as LA
import pandas as pd
import matplotlib.pyplot as plt
path = "./A.csv";
A = pd.read_csv(path).values;
tposeA = np.transpose(A);
normA = LA.norm(A);
X0 = tposeA / ((normA)**2)
print(X0)
I have been working on this piece and it returns strange result in the matrix, do anyone know what this means?
Many thanks!!
[First col and 4th row]
[0.0089 0.0035 0.0017 0.0053]
[0.0035 0.0089 0.0035 0.0089]
[0.0035 0.0178 0.0106 0.0124]
[0.(blank) 0.0017 0.0124 0.0267]
Since you are looking at floating point numbers, 0. means that the number equals exactly zero. It is the way NumPy displays float.
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How to fix these errors that are appearing again and again??????
You should check out matplotlib.pyplot.show() and .plot() documents to know how to use them correctly.
You should use plt.plot(x, y) instead of print(plt.show(x, y))
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 25)
y = x * x + 2
plt.plot(x, y)
plt.show()
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I want to split images like this in a way that every symbols gets splits up vertically kind of like this input image:
![input image][1]
to this:
![here][2]
The problem is each symbol might have different width so I can't really fix the splitting points like we do in array splitting. If all objects had same width then I could segment the image base on width. In this scenario, what logic I should use to extract these connected objects?
First load the img from the url
import numpy as np
import urllib.request
from PIL import Image
from matplotlib import pyplot as plt
urllib.request.urlretrieve(
'https://i.stack.imgur.com/GRHzg.png',
"img.png")
img = Image.open("img.png")
img.show()
Then consider the black part as "filled" and convert in numpy array
arr = (np.array(img)[:,:,:-1].sum(axis=-1)==0)
If we sum the rows values for each column we can have a simple sum of how much pixel are filled in each column:
plt.subplot(211)
plt.imshow(arr, aspect="auto")
plt.subplot(212)
plt.plot(arr.sum(axis=0))
plt.xlim(0,arr.shape[1])
finally if we compute the differential of this sum over the columns we can obtain the following result:
plt.subplot(211)
plt.imshow(arr, aspect="auto")
plt.subplot(212)
plt.plot(np.diff(arr.sum(axis=0)))
plt.xlim(0,arr.shape[1])
At this point you can simply chose a threshold and cut the image:
threshold = 25
cut = np.abs(np.diff(arr.sum(axis=0)))>threshold
x_lines = np.arange(len(cut))[cut]
plt.imshow(arr, aspect="auto")
plt.vlines(x_lines, 0, arr.shape[0], color="r")
This is my solution and it works fine, but it is sensitive to the chosen threshold and to the columns gradient. I hope it is useful.
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I have an image of shape (31278,25794,3). I would like to know how is possible to obtain MxN segment of the picture, using np functions. For example starting from:
I would like to obtain:
In numpy you can split a picture like you slice an array.
Here's an example with your image:
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
img = np.array(Image.open("cat.jpg"))
plt.imshow(img)
xs = img.shape[0]//2 # division lines for the picture
ys = img.shape[1]//2
# now slice up the image (in a shape that works well with subplots)
splits = [[img[0:xs, 0:ys], img[0:xs, ys:]], [img[xs:, 0:ys], img[xs:, ys:]]]
fig, axs = plt.subplots(2, 2)
for i in range(2):
for j in range(2):
axs[i][j].imshow(splits[i][j])
Keep in mind that the splits here are views into the original array, not arrays with new data, so changes you make to the views will change the original data. If you don't want this, you can do something to copy the data after slice up the array.
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I successfully created my function but when I call it I get the error:
not defined function ' elbow'
What's wrong?
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
from sklearn.cluster import KMeans
import numpy as np
from scipy.spatial.distance import cdist, pdist
def eblow(df,n):
kMeansVar = [KMeans(n_clusters=NUM_CLUSTERS, n_jobs=1, random_state=0, n_init=1, verbose=True).fit(df) for k in range(1, n)]
centroids = [X.cluster_centers_ for X in kMeansVar]
k_euclid = [cdist(df, cent) for cent in centroids]
dist = [np.min(ke, axis=1) for ke in k_euclid]
wcss = [sum(d**2) for d in dist]
tss = sum(pdist(df)**2)/df.shape[0]
bss = tss - wcss
plt.plot(bss)
plt.show()
X=np.random.rand(60,45)
el=elbow(X,30)
You defined eblow but I don't see a function elbow.
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I have a binary file from which I have to read data. The file consists of a 128x128x243 matrix (hex-formatted) which I have read with the following code:
with open("zubal_voxel_man.dat", "rb") as fileHandle:
dim_x = 128
dim_y = 128
dim_z = 243
data = np.zeros((dim_x,dim_y,dim_z), dtype=np.int)
for p in range(0, dim_x):
for q in range (0, dim_y):
for r in range(0, dim_z):
data[p][q][r] = ord(fileHandle.read(1))
How do I visualize these data with Python? Each x,y,z position has a value from 0 to 255 (grey scale) which I would like to render.
Any help is greatly appreciated!
Part of your problem is with the code:
datax = data[:,0]
datay = data[:,1]
dataz = data[:,2]
Which is not doing what you are expecting of slicing in a single axis it is taking a slice of the Y=0 then of Y=1, Y=2 and plotting them against each other - your other issue is that you have a 3 dimensional array of values which gives each value 4 dimensions X, Y, Z, Value - and you are trying to plot these into a surface. which only has 3 dimensions.
I think that your first priority is to clarify your what your data represents and how it is structured.