Masking a Numpy array multiple times produces wrong result

Masking a Numpy array multiple times produces wrong result

By : Redek Project
Date : November 20 2020, 03:01 PM
This might help you The masking is definitely working correctly in the code from the question. You can visualize the ranges that get masked with fill_between. Also, sharing all axes makes it easier to compare the three plots.
code :
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["axes.xmargin"] = 0
plt.rcParams["axes.ymargin"] = 0

f1 = np.random.randint(51, size=150)
lt_vals = np.arange(0,25,1)
alt_vals = np.arange(0,15,1)
alt = np.tile(alt_vals,10)
lt = np.tile(lt_vals, 6)
x_vals = range(len(f1))

f1m = np.ma.masked_where((lt>5) & (lt<20), f1)
f1am = np.ma.masked_where(alt>5, f1m)

variables = [f1am, alt, lt]
ylabels = ['Function', 'Sim Alt', 'Sim Time']
number_of_subplots= len(variables)

fig, axes = plt.subplots(nrows=3, sharex=True)
for i,j,k in zip(range(number_of_subplots), variables, ylabels):  
    ax1 = axes[i]
    ax1.plot(x_vals,j, marker=".")

axes[1].fill_between(x_vals,alt.max(),0, where=alt>5, alpha=0.2)
axes[2].fill_between(x_vals,lt.max(),0, where=(lt>5) & (lt<20), alpha=0.2)
axes[0].fill_between(x_vals,51,0, where=((lt>5) & (lt<20)) | (alt>5) , alpha=0.2)

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php sort() on array produces wrong result

php sort() on array produces wrong result

By : user3563232
Date : March 29 2020, 07:55 AM
help you fix your problem sort() sorts the array in-place. Don't re-assign it.
code :
$this->pageLinks = sort($this->pageLinks);
Python numpy array: wrong result when mixing int32 and int8

Python numpy array: wrong result when mixing int32 and int8

By : user3105843
Date : March 29 2020, 07:55 AM
This might help you I saw a very strange behavior in numpy array, when I mixed int32 and int8 arrays in a simple operation, the int32 array element ct[4,0] seems to have become 8bit when taking the result of += dleng[4]*4: , dleng[4]*4 is an array:
code :
In [94]: dleng[4]
Out[94]: array([3], dtype=int8)

In [95]: dleng[4]*4
Out[95]: array([12], dtype=int8)
In [98]: ct[4,0]
Out[98]: 117

In [99]: type(_)
Out[99]: numpy.int32
ct[4,0] += dleng[4,0]*4
numpy array multiply 10 got wrong result

numpy array multiply 10 got wrong result

By : Jittam Bhattacharya
Date : March 29 2020, 07:55 AM
To fix this issue The problem is, that your third entry isn't exactly 52.0 but 51.999999999999993 (see Is floating point math broken?). Truncating that value therefore results in 51.
The correct way would be to first round the values. (As pointed out in Safest way to convert float to integer in python? all small enough integer numbers can be exactly expressed as a float.) You therefore have to calculate: times = np.array(np.round(times * 10), dtype=np.int)
Unexpected result when masking alpha values of QImage using NumPy

Unexpected result when masking alpha values of QImage using NumPy

By : user11050
Date : March 29 2020, 07:55 AM
Hope this helps n Python, all numbers are considered "True" except for the 0 that is "False", so when you convert a Boolean to "alpha" if a pixel of 10 is a black color in the gray scale, it becomes 1 that is white in the boolean scale so that it generates the change, so to convert to boolean using astype(np.bool) is not the best option but to establish a more suitable threshold, for example 127:
code :
from PySide2.QtGui import QImage
from PySide2.QtCore import Qt
import numpy as np
import qimage2ndarray as q2np

contour = QImage()
np_cont = q2np.byte_view(contour)
np_cont = np_cont.reshape(np_cont.shape[:-1])
red = QImage(contour.size(), QImage.Format_ARGB32)

alpha = q2np.alpha_view(red)
alpha *= np_cont > 127  # .astype(np.bool)
Smallest Common Multiple Code produces the wrong answer on one array

Smallest Common Multiple Code produces the wrong answer on one array

By : samyslimdude
Date : March 29 2020, 07:55 AM
I wish this help you Your endless loop is becouse of your inner for loop which starts at the value 19 and runs to 22
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