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Verreaux's Eagle Owl, also known as the Milky Eagle Owl or Giant Eagle Owl, is a member of the family Strigidae and is the largest African owl. This owl is the world's fourth heaviest living owl, after Blakiston's Fish Owl, the Eurasian Eagle Owl and the Tawny Fish Owl, and is also the fourth longest living owl, after the Great Grey, Blakiston's Fish and Eurasian Eagle Owls. Verreaux's Eagle Owl is found through most of Sub-Saharan Africa, though it is absent from most of the deep rainforests. They are also found in the Middle East.
Verreaux's Eagle Owl, also known as the Milky Eagle Owl or Giant Eagle Owl, is a member of the family Strigidae and is the largest African owl.
This male called 'Numpy' was born on 7 February 1996 and is the oldest owl at the Small Breeds Farm Park and Owl Centre, Kington, Herefordshire. My thanks to Alice and Diana for their kind assistance.
Thanks to everyone who takes the time to view and comment on my photographs – it is greatly appreciated and encouraging!
© Roger Wasley 2015 all rights reserved. Unauthorized use or reproduction for any reason is prohibited.
One from the archives (uncropped version, bottom right, from yesterdays post) as a result of a request from a Flickr friend.
Thanks for the request BT especially since I had bugger-all to post anyway.
It was taken with my old Kodak PAS in 2005 and seems to have turned the reflection of some people on the beach into a giraffe.
Verreaux's Eagle Owl, also known as the Milky Eagle Owl or Giant Eagle Owl, is a member of the family Strigidae and is the largest African owl. This owl is the world's fourth heaviest living owl, after Blakiston's Fish Owl, the Eurasian Eagle Owl and the Tawny Fish Owl, and is also the fourth longest living owl, after the Great Grey, Blakiston's Fish and Eurasian Eagle Owls. Verreaux's Eagle Owl is found through most of Sub-Saharan Africa, though it is absent from most of the deep rainforests. They are also found in the Middle East.
This male called 'Numpy' was born on 7 February 1996 and is the oldest owl at the Small Breeds Farm Park and Owl Centre, Kington, Herefordshire. My thanks to Alice and Diana for their kind assistance.
Thanks to everyone who takes the time to view and comment on my photographs – it is greatly appreciated and encouraging!
© Roger Wasley 2015 all rights reserved. Unauthorized use or reproduction for any reason is prohibited.
This is Numpy, a Verreaux's eagle owl. He likes having his feathers stroked, and has an impressively deep hooting call.
Another fascinating sticker that you can find in Berlin. The city has millions of them, but only a few of them are worth being photographed. Today's sticker is a character from the card game called Numpy, created by the artist Sketchnate.
A photograph of the Seattle skyline (link), chopped into vertical slices and shuffled.
Python code is rather simple: numpy.split(), sort(), numpy.concatenate(). (With appropriate arguments, etc...)
A mosaic of our satellite on the morning of 2022-09-18 around 4 a.m. 122 tiles acquired with a usually fast camera, attached to a 500mm f/8 scope and equipped here with a red filter.
As said, the camera I used is usually fast, just not this time. For some reason, most likely the icy temperature, the camera's rate was limited to USB 2. I had to adapt the acquisition at last moment. The compromise consisted in pushing the exposure time to 20 milliseconds, working at 8-bit dynamics and recording only 400 frames per video.
Regarding the processing, I converted each video into a tile by selecting the best 80 frames. Each tile was deconvolved with the Wiener algorithm, the PSF being semi-empirical. After assembling the mosaic, some correction and tuning were required: tonecurve adjustment, stitch fixing, halo removal along the limb and assignment of a slight yellowish hue to the originally grayscale image.
If you have a look at full size, you'll probably notice some defects, but this is all I could come to without excessive work :)
A word about hardware and software: ASI290 MM camera, Astrosib 500mm telescope, stacking with AutoStakkert! 3, PSF evaluation, noise estimation and deconvolution with Python (NumPy & SciPy), assembling with Hugin, and final processing with Photoshop.
Finally, a few numbers: the Moon covers 12388 pixels pole to pole, thus a projected resolution of 280m per pixel.
using gridded population data from SEDAC. This was based on the maximum resolution raster, which dates back to 2000. The world's population is over 7 billion now, so the boundaries will have changed.
The raster was recursively subdivided into areas of equal population. Each level in the tree alternates between horizontal and vertical partitions.
This was done using a python script with gdal and numpy. The recursion was stopped at 8 levels, giving 256 cells.
Each cell has an approximately equal population, of just under 24 million people.
Rendered in QGIS Print Composer.
Rendered in Blender, modeled from the Bryant parametrization with Python/Numpy.
More info about the surface here: mathworld.wolfram.com/BoySurface.html
Artist(s): Aaron Javier Juarez
Title: Pi Spiral
Medium: digital art - Python scripting language (Numpy and Matplotlib)
The Artist Says: This work contains 3000 digits of pi, approximated will a simple generator function. The background is 314 stacked circles with different radii and colors, to give the illusion of a color gradient.
Python has become the de facto programming language of data scientists and data analysts. It’s concise, easy to learn and data friendly, making it ideal for data analysis. We will start with a crash course on Python before getting into Python Machine Learning. We will also look at Python Machine Learning libraries like NumPy, Pandas, and SciKit-Learn that are needed to perform Python Machine Learning.
Each lecture has detailed and live explanations from the instructor and assignments to test your level of understanding. Once you finish this course you would have taken a giant leap towards the future of data analysis.
For more details please visit our website:https://www.mcal.in/page/data-analytics-and-machine-learning/
modelling what Scotland would look like if sea levels rose by 90 meters. Each frame represents a rise in sea level of 25cm.
Done using a python/GDAL script, numpy, and ffmpeg to stitch the jpegs into an mp4.
Elevation data from Ordnance Survey, Crown copyright and database rights 2015. Area covered is OS plates NN, NO, NS, NT.
Artist(s): Aaron Javier Juarez
Title: Special Triangles of Pi
Medium: digital art - Python scripting language (Numpy and Matplotlib)
The Artist Says: This work conveys the special right triangles [red (30-60-90 deg), green (45-45-90 deg), and blue (60-30-90 deg) triangles] and their angles found from the unit circle. Along the vertical center of the piece are equilateral triangles whose sides are scaled by a factor of pi from one another. The "fog" effect of the color in the background is actually many encircling circles with varying opacity. In fact, all the gradients you see are illusions, created by many stacked shapes with varying colors.
modelling what the South East and London would look like if sea levels rose by 90 meters. Each frame represents a rise in sea level of 25cm.
Done using a python/GDAL script, numpy, and ffmpeg to stitch the jpegs into an mp4.
Elevation data from Ordnance Survey, Crown copyright and database rights 2015. Area covered is OS plates SP, TL, SU, TQ.
One million pixels (1000x1000), and one million unique colours. Image generated by a python script - I'll add it below. One million photo views seems like a lot, and has taken nearly 12 years for my little collection.
Non jpg'd version : c1.staticflickr.com/5/4737/25370854568_f6b0a88e25_o_d.png
Python script requires pygame and numpy and displays the generated image and saves to a .png
import pygame, random, numpy
pygame.init()
RESX=1000
RESY=1000
size = (RESX,RESY)
COLOURS=256*256*256
screen = pygame.display.set_mode(size)
colourList=random.sample(range(COLOURS),COLOURS)
counter=1
sarray=pygame.surfarray.pixels2d(screen)
for x in range(RESX):
for y in range (RESY):
sarray[x,y]=colourList[counter]
counter+=1
pygame.display.update()
del sarray
pygame.image.save(screen, 'output.png')
Fedora Scientific Spin is a Fedora Linux spin that aims to showcase the open source tools for scientific and numerical computing. He numerical computing package GNU Octave, the computer algebra system Maxima, with its front-end wxMaxima, the Python scientific libraries SciPy, NumPy and Spyder are some of the software included in this category.
Mandelbrot plot created using pyCUDA with Python. Shows a 1000x1000 plot with max iterations set at 1000.
Pure CUDA time on GTX 480 - 0.07 seconds.
Python with numpy time on same card - 43.4 seconds (4,800* slower!).
Code is here: ianozsvald.com/2010/07/14/22937-faster-python-math-using-...
Numpies are little rabbit-like creatures that I've created for the Swiggles stories. The first Swiggles book A Camping Nightmare is completed. Just waiting on a few things to come into place before the digital release.
Sourced from the same data as the previous image. I modified my script to perform the Drizzle algorithm on the raw un-debayered images.
See www.stsci.edu/ftp/science/hdf/combination/drizzle.html for more on drizzling.
The Owl Centre in Kington, Herefordshire, gives unequalled access to these beautiful nocturnal birds of prey.
For the story, please visit: www.ursulasweeklywanders.com/travel/small-breeds-farm-par...
An analogy to the Newton Fractal. Here however the Riemannian Newton Method is used for finding critical points for the Rayleigh quotient on the sphere for the matrix
A =
[[1.,4.,5.],
[4.,2.,6.],
[5.,6.,3.]].
That is, it finds an eigenvector and eigenvalue to A.
Each point on the sphere is an initial condition. The sphere is composed of 512*512 spherical coordinates.
A point is red if the Riemannian Newton Method converged to the eigenvector corresponding to the eigenvalue 12.176...,
a point is white for the eigenvalue -2.507...,
and a point is black for -3.669...
Algorithm implemented in NumPy, and the graphics made using MayaVi.
Different matrices yield different pictures of course, maybe some day I'll see if I can find one that makes the picture fractal.
Breather surface. Based on Sine–Gordon equations (more here: en.wikipedia.org/wiki/Sine–Gordon_equation ). Blender/Python/Numpy
import numpy
import PIL
import os
from PIL import Image
os.chdir('H:\\')
w,h=720,576
img = numpy.zeros((h,w,3), numpy.uint8)
i=0
j=0
count=0
stripe=0
while i<720 and j<720 and count<500:
j=numpy.random.randint(0,10)
img[0:576,i:i+numpy.max(j,720)]=[stripe*255,stripe*255,0]
print i,i+j,stripe
i=i+j
if stripe == 0:
stripe = 1
filename='mysin%04d.jpg'%(count)
pilImage = Image.fromarray(img,'RGB')
pilImage.save(filename)
count+=1
else:
stripe = 0
A language is by what it can do for you, and by what you can do with it. No two languages are the same; in this blog – Python Features Infographic, you will see what makes Python any special.
Since its first appearance in 1990, Python has made quite a name for itself with its simplicity and power. Not only is it easy to read and code in, but it is also often the preference of many professionals when it comes to domains like Data Science and Machine Learning. This makes it a good fit for an introductory programming language in schools.
Why is it called Python?
The most intriguing fact about Python is its name. The name of this language was influenced by the British comedy series “Monty Python’s Flying Circus”. The series was aired on BBC during the 1970s and Guido Van Rossum (the creator of Python) wanted the name of the language to be short and mysterious, one that would capture everyone’s attention.
Python offers many features; this escalates its demand in the IT industry. A large number of programmers and developers across the world express their interest in it. Thanks to its English-like syntax, it is easy to read and understand. It is also easy to code in; this boosts productivity as it lets the developer focus on what to do rather than on how to do it. It has a multitude of powerful libraries like scikit-learn and NumPy. With a very large community at its heart.
See The Latest Career Options in Python Programming Language
What is it that makes Python so powerful and popular? As they say, the world’s best camera is no more than a toy in the hands that have no idea what to do with them. To truly harvest the power of something, you must introduce yourself to the tools it gives you. To aid with the same, we have put together the following infographic, hoping to deliver quick insights into what you’re in for.
Take your machine learning to the next level with these artificial intelligence technologies.
Artificial intelligence (AI) technologies are quickly transforming almost every sphere of our lives. From how we communicate to the means we use for transportation, we seem to be getting increasingly addicted to them.
Because of these rapid advancements, massive amounts of talent and resources are dedicated to accelerating the growth of the technologies.
Here is a list of 8 best open sources AI technologies you can use to take your machine learning projects to the next level.
1. TensorFlow
Initially released in 2015, TensorFlow is an open source machine learning framework that is easy to use and deploy across a variety of platforms. It is one of the most well-maintained and extensively used frameworks for machine learning.
Created by Google for supporting its research and production objectives, TensorFlow is now widely used by several companies, including Dropbox, eBay, Intel, Twitter, and Uber.
TensorFlow is available in Python, C++, Haskell, Java, Go, Rust, and most recently, JavaScript. You can also find third-party packages for other
programming languages.
The framework allows you to develop neural networks (and even other computational models) using flowgraphs.
2. Keras
Initially released in 2015, Keras is an open source software library designed to simplify the creation of deep learning models. It is written in Python
and can be deployed on top of other AI technologies such as TensorFlow, Microsoft Cognitive Toolkit (CNTK), and Theano.
Keras is known for its user-friendliness, modularity, and ease of extensibility. It is suitable if you need a machine learning library that allows for easy and fast prototyping, supports both convolutional and recurrent networks, and runs optimally on both CPUs (central processing units) and GPUs (graphics processing units).
3. Scikit-learn
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Initially released in 2007, sci-kit-learn is an open source library developed for machine learning. This traditional framework is written in Python and features several machine learning models including classification, regression, clustering, and dimensionality reduction.
Scikit-learn is designed on three other open source projects—Matplotlib, NumPy, and SciPy—and it focuses on data mining and data analysis.
4. Microsoft Cognitive Toolkit
Initially released in 2016, the Microsoft Cognitive Toolkit (previously referred to as CNTK), is an AI solution that can empower you to take your
machine learning projects to the next level.
Microsoft says that the open source framework is capable of "training deep learning algorithms to function like the human brain."
Some of the vital features of the Microsoft Cognitive Toolkit include highly optimized components capable of handling data from Python, C++, or BrainScript, ability to provide efficient resource usage, ease of integration with Microsoft Azure, and interoperation with NumPy.
5. Theano
Initially released in 2007, Theano is an open source Python library that allows you to easily fashion various machine learning models. Since it's one of the oldest libraries, it is regarded as an industry standard that has inspired developments in deep learning.
At its core, it enables you to simplify the process of defining, optimizing, and assessing mathematical expressions.
Theano is capable of taking your structures and transforming them into very efficient code that integrates with NumPy, efficient native libraries such as BLAS, and native code (C++).
Furthermore, it is optimized for GPUs, provides efficient symbolic differentiation, and comes with extensive code-testing capabilities.
6. Caffe
Initially released in 2017, Caffe (Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that focuses on
expressiveness, speed, and modularity. The open source framework is written in C++ and comes with a Python interface.
Caffe's main features include an expressive architecture that inspires innovation, extensive code that facilitates active development, a fast performance that accelerates industry deployment, and a vibrant community that stimulates growth.
7. Torch
Initially released in 2002, Torch is a machine learning library that offers a wide array of algorithms for deep learning. The open source framework provides you with optimized flexibility and speed when handling machine learning projects—without causing unnecessary complexities in the process.
It is written using the scripting language Lua and comes with an underlying C implementation. Some of Torch's key features include N-dimensional arrays, linear algebra routines, numeric optimization routines, efficient GPU support, and support for iOS and Android platforms.
8. Accord.NET
Initially released in 2010, Accord. NET is a machine learning framework entirely written in C#.
The open source framework is suitable for production-grade scientific computing. With its extensive range of libraries, you can build
various applications in artificial neural networks, statistical data processing, image processing, and many others.
A portion of the Mandelbrot set enlarged 9 million times. Generated with the help of numpy and matplotlib for Python.
Obwohl PlotDevice nicht mit meinem Anaconda-Python (ein Python 2.7.9) arbeitet, sondern sich aus den Tiefen des Systems ein Python 2.7.5 herholt -- vermutlich das von Apple mitgelieferte Python, kennt dieses Python Numpy und die Matplotlib (habe ich vielleicht da mal installiert). Was ich damit anfangen kann, weiß ich aber auch noch nicht. Numpy ist gut, Matplotlib wohl eher eine Spielerei.
this slitscan is made with vitroids trainscanner
Trainscanner uses opencv movement in the movie is compensated.
www.flickr.com/photos/vitroids/
I had to scale my original movie 50% with:
ffmpeg -i excelsior.mts -vf scale=920:-1 ex.mov
This because of low memory issues with python and the mp4:
python trainscanner.py -w 3 -f 0.1,0.35,0.2,0.8 -t 15 28859816723_1080p.mp4
Gives this error:
Traceback (most recent call last):
File "trainscanner.py", line 234, in
alpha = make_alpha( (dx,dy), (h,w), slitpos, slitwidth )
File "trainscanner.py", line 46, in make_alpha
alpha = np.fromfunction(lambda y, x, v: (dx*(x-centerx)+dy*(y-centery))/(r*width), (ih, iw, 3))
File "C:\Anaconda2\lib\site-packages\numpy\core\numeric.py", line 2061, in fromfunction
args = indices(shape, dtype=dtype)
File "C:\Anaconda2\lib\site-packages\numpy\core\numeric.py", line 2000, in indices
res = empty((N,)+dimensions, dtype=dtype)
MemoryError
Manual stack of six photos taken with very bad seeing (and presumably a somewhat out-of-focus telescope).
I took six photos of Saturn pointing the DSC-H55 into a 7.5 mm eyepiece and processed them like this:
1. Using The Gimp, I cropped, aligned, and exported the six RGB images as PNG.
2. Using a small Numpy program, I loaded the six PNG files, converted them to 64-bit floating point, computed the geometric mean, rescaled pixel values to the interval 0..1 and wrote out monochrome PNG files for R, G, and B.
3. Using The Gimp, I loaded and multiplied the G and B monochrome images together (the R image is terrible) and applied a G'MIC octave sharpening filter.
(Using either geometric and arithmetic means produces practically the same result. The final G*B multiplication has the side effect of applying a gamma of 2 which improves contrast.)
Great Learning's this session, you will be working on an end-to-end project to understand how to face recognition works, and also learn about the OpenCV library and Numpy library to detect a face from the image. It will help you to understand the advanced project on object detection.
Enroll in Face Recognition with Python course with Great Learning Academy and upon the course, completion gets a free certificate.
www.greatlearning.in/academy/learn-for-free/courses/face-...
The Owl Centre in Kington, Herefordshire, gives unequalled access to these beautiful nocturnal birds of prey.
For the story, please visit: www.ursulasweeklywanders.com/travel/small-breeds-farm-par...
"Learn Data Science In 5 Easy Steps" is a concise guide designed to provide a structured approach for individuals seeking to acquire data science skills. The guide outlines a systematic process:
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Data Visualization: Master the art of data visualization using libraries like Matplotlib, creating compelling visual representations.
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The Advance Data Science and Artificial Intelligence Course by 1stepGrow is a perfect solution for those looking to deepen their expertise in this area.