bcbio-nextgen
NumPy
bcbio-nextgen | NumPy | |
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2 | 272 | |
973 | 26,360 | |
0.2% | 0.9% | |
6.2 | 10.0 | |
3 days ago | 6 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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bcbio-nextgen
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Deep Sleep May Be the Best Defense Against Alzheimer’s
Re WGS there are a lot of well established tool chains that are FLOSS (eg https://github.com/bcbio/bcbio-nextgen). You could run alignment and variant calling on a beefy workstation. A laptop would potentially work. Easy to test this with publicly available raw data. Another option: The sequencing provider often will run alignment and some default variant calling for you. Annotating and analysing these variants can be done on pretty much any computer, all with open source software. A SNP chip is even easier to deal with as the computational requirements are less.
Interpreting the results is a more manual process. Really depends on what you are interested in.
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Advices on how to advance in the field to a beginner
There are plenty of workshops and free documentation online, a few from the top of my head : https://hbctraining.github.io/main/, https://software-carpentry.org/lessons/, https://github.com/bcbio/bcbio-nextgen.
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
SymPy - A computer algebra system written in pure Python
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Numba - NumPy aware dynamic Python compiler using LLVM
blaze - NumPy and Pandas interface to Big Data
Biopython - Official git repository for Biopython (originally converted from CVS)
SciPy - SciPy library main repository
Dask - Parallel computing with task scheduling
PyDy - Multibody dynamics tool kit.
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).