femtocode | Quick manipulation of structured data for data analysis
kandi X-RAY | femtocode Summary
kandi X-RAY | femtocode Summary
femtocode is a Python library. femtocode has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However femtocode build file is not available. You can download it from GitHub.
Femtocode is a language and a system to support fast queries on structured data, such as high-energy physics data. The goal of this project is to replace the practice of copying and reducing a centrally produced dataset with direct queries on that dataset. Currently, high-energy physicists write programs to extract the attributes and data records of interest— personally managing the storage and versioning of that private copy— just to make plots from the subset in real time. Femtocode will allow users to make plots from the original dataset in real time, which may be as large as petabytes. Femtocode makes this possible by introducing a novel translation of query semantics into pure array operations, which strips away all unnecessary work at runtime. By dramatically reducing the computation time, the only bottleneck left is the data transfer, so caching is also heavily used to minimize the impact of repeated and similar queries. This project is at an early stage of its development, though it is past the feasibility studies and basic implementation. End-to-end demonstrations will be possible by the end of April and usable prototypes will be available sometime in the summer of 2017.
Femtocode is a language and a system to support fast queries on structured data, such as high-energy physics data. The goal of this project is to replace the practice of copying and reducing a centrally produced dataset with direct queries on that dataset. Currently, high-energy physicists write programs to extract the attributes and data records of interest— personally managing the storage and versioning of that private copy— just to make plots from the subset in real time. Femtocode will allow users to make plots from the original dataset in real time, which may be as large as petabytes. Femtocode makes this possible by introducing a novel translation of query semantics into pure array operations, which strips away all unnecessary work at runtime. By dramatically reducing the computation time, the only bottleneck left is the data transfer, so caching is also heavily used to minimize the impact of repeated and similar queries. This project is at an early stage of its development, though it is past the feasibility studies and basic implementation. End-to-end demonstrations will be possible by the end of April and usable prototypes will be available sometime in the summer of 2017.
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Quality
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Support
femtocode has a low active ecosystem.
It has 23 star(s) with 0 fork(s). There are 8 watchers for this library.
It had no major release in the last 12 months.
femtocode has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of femtocode is 0.0.1-lpc-hats-2017
Quality
femtocode has 0 bugs and 0 code smells.
Security
femtocode has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
femtocode code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
femtocode is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
femtocode releases are available to install and integrate.
femtocode has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions, examples and code snippets are available.
femtocode saves you 10725 person hours of effort in developing the same functionality from scratch.
It has 21767 lines of code, 2048 functions and 109 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed femtocode and discovered the below as its top functions. This is intended to give you an instant insight into femtocode implemented functionality, and help decide if they suit your requirements.
- Parse the grammar .
- Wrapper for yacc .
- Compute inequality .
- Build a Parse from a parsed tree .
- Return the union of two types .
- Lex the module .
- Generate code for code .
- Build the parser table .
- Return the intersection between two types .
- Combine two lists .
Get all kandi verified functions for this library.
femtocode Key Features
No Key Features are available at this moment for femtocode.
femtocode Examples and Code Snippets
Copy
session = RemoteSession("http://testserver:8080")
pending = session.source("xy-dataset")
.define(z = "x + y")
.toPython("Result", a = "z - 3", b = "z - 0.5")
.submit()
result = result.await()
for x
Copy
filter("goodmuons.size >= 2")
mu1, mu2 = goodmuons.maxby($1.pt, 2)
source = session.source("Test", x=real, y=real)
source.type("x / y")
FemtocodeError: Function "/" does not accept arguments with the given types:
/(integer,
real)
Copy
((((((a + b) – (c + d)) + (e + f)) – ((c + d) – ((a + b) – (c + d)))) +
(((a + b) – (c + d)) + (e + f))) + ((c + d) + (e + f)))
Community Discussions
No Community Discussions are available at this moment for femtocode.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install femtocode
But... if you want to try it anyway, note that this repository contains five Python packages with setup.py files:. Installing all of them (python setup.py install --user) from top to bottom in this list would satisfy the dependency order. (There might be local file paths in the unit tests, making them inoperable on your computer.).
lang: the base package and client (no dependencies),
numpyio: back-end for reading Numpy files as a data source (depends on base femtocode, ruamel.yaml, and Numpy),
rootio: back-end for reading ROOT files as a data source (depends on base femtocode, ruamel.yaml, Numpy, and ROOT),
run: compiles Femtocode to native bytecode and manages computation; the Standalone Engine (described below) is contained in this package (depends on base femtocode, femtocode-numpyio, Numba, and Numpy),
server: performs distributed calculations as a production query server (depends on base femtocode, femtocode-run, and pymongo).
lang: the base package and client (no dependencies),
numpyio: back-end for reading Numpy files as a data source (depends on base femtocode, ruamel.yaml, and Numpy),
rootio: back-end for reading ROOT files as a data source (depends on base femtocode, ruamel.yaml, Numpy, and ROOT),
run: compiles Femtocode to native bytecode and manages computation; the Standalone Engine (described below) is contained in this package (depends on base femtocode, femtocode-numpyio, Numba, and Numpy),
server: performs distributed calculations as a production query server (depends on base femtocode, femtocode-run, and pymongo).
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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