Skip to main content

Earth Science PRoblems for the Evaluation of Strategies, Solvers and Optimizers

Project description

Espresso

PyPI version build Documentation Status Slack

Related repositories by InLab

Introduction

Earth Science PRoblems for the Evaluation of Strategies, Solvers and Optimizers (Espresso) is a collection of datasets, and associated simulation codes, spanning a wide range of geoscience problems. Together they form a suite of real-world test problems that can be used to support the development, evaluation and benchmarking of a wide range of tools and algorithms for inference, inversion and optimisation. All problems are designed to share a common interface, so that changing from one test problem to another requires changing one line of code.

The Espresso project is a community effort - if you think it sounds useful, please consider contributing an example or two from your own research. The project is currently being coordinated by InLab, with support from the CoFI development team.

For more information, please visit our documentation.

Installation

$ pip install geo-espresso

Check Espresso documentation - installation page for details on dependencies and setting up with virtual environments.

Basic usage

Once installed, each test problem can be imported using the following command:

from espresso import <testproblem>

Replace <testproblem> with an actual problem class in Espresso, such as SimpleRegression and FmWavefrontTracker. See here for a full list of problems Espresso currently includes.

Once a problem is imported, its main functions can be called using the same structure for each problem. For instance:

from espresso import FmWavefrontTracker

problem = FmWavefrontTracker(example_number=1)
model = problem.good_model
data = problem.data
pred = problem.forward(model)
fig_model = problem.plot_model(model)

You can access related metadata programatically:

print(FmWavefrontTracker.metadata["problem_title"])
print(FmWavefrontTracker.metadata["problem_short_description"])
print(FmWavefrontTracker.metadata["author_names"])

Other problem-specific parameters can be accessed through the problem instance. For instance:

print(problem.extent)
print(problem.model_shape)

Which additional values are set is highly problem-specific and we suggest to consult the Espresso Documentation on the problems.

Contributing

Interested in contributing? Please check out our contributor's guide.

Licence

Espresso is a community driven project to create a large suite of forward simulations to enable researchers to get example data without the need to understand each individual problem in detail.

Licensing is done individually by each contributor. If a contributor wants to freely share their code example we recommend the MIT licence or a 2-clause BSD licence. To determine the licence of an existing Espresso problem, please consult the documentation section of that problem.

All the other core functions of Espresso written by InLab Espresso developer team are distributed under a 2-clause BSD licence. A copy of this licence is provided with distributions of the software.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

geo-espresso-0.3.5.tar.gz (1.5 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

geo_espresso-0.3.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

geo_espresso-0.3.5-cp311-cp311-macosx_10_9_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

geo_espresso-0.3.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

geo_espresso-0.3.5-cp310-cp310-macosx_10_9_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

geo_espresso-0.3.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

geo_espresso-0.3.5-cp39-cp39-macosx_10_9_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

geo_espresso-0.3.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

geo_espresso-0.3.5-cp38-cp38-macosx_10_9_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

geo_espresso-0.3.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

geo_espresso-0.3.5-cp37-cp37m-macosx_10_9_x86_64.whl (1.3 MB view details)

Uploaded CPython 3.7mmacOS 10.9+ x86-64

File details

Details for the file geo-espresso-0.3.5.tar.gz.

File metadata

  • Download URL: geo-espresso-0.3.5.tar.gz
  • Upload date:
  • Size: 1.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for geo-espresso-0.3.5.tar.gz
Algorithm Hash digest
SHA256 517b48e276a671df4756b0fbf15ff2cffd3cbcac567af9f9973204706e4d90f7
MD5 41e1ad07045ad297ba4150992ae52acf
BLAKE2b-256 610ab881526af909429038b331d7f6204006d6340fd33079f902c36f802f6bb2

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 96159aa8c255621746fe6fda03499ddbe647537e5da97c20ed2f8530878d54ab
MD5 ade2ea9c1e66e16ceeaaf4c9cea7930d
BLAKE2b-256 4bff38f69e7cd3551c3d587013aff4cc372e1260e8bef23128f58d573a738fe9

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 71b8407a6b925f746aa950bc28e9f0947698f9ce9b2041298249ce0fb98f432b
MD5 1dec87b8250e7f1a9ecc38686fb6c634
BLAKE2b-256 927212fab10624c75c1c3cb6ba7d58d7efb500b8c288de64c99d64d4d4765c08

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 aeea505757ebfa3fb4404370654a211b4defaf1c0f51aeb176b446be52737da9
MD5 dcdd754dfccc5c07b2b917f72b51745c
BLAKE2b-256 fc570c776f2c6826b9686b168158faef6a5514c280aae1618f06ede9de75f6f4

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0c2d42f5ad284fd138d73bfa6497bcaf0b562213cdd2813da2254dd88dce620d
MD5 35c1a5ec3b02e9b11be4b2915984d9a1
BLAKE2b-256 73c7e47ff9b4849e97fb6ba4daf4e501ae2cd22783968ec47555c70a1833dfe8

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9f2506af9e44ac15f52e250ae343334b4858be2c305ba090bbda346245e57487
MD5 50f15ab113119ebd0d09f68d88efb712
BLAKE2b-256 e58328008b1d63d10413f64b03d1c256945573887283aaefde884aa06c327170

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 da69f40b58b65153b8992df2b8c15f9efb8c314e46481f9f9ee14292d5483ecd
MD5 08d88a1c65c114aebbe2a6a4b21b5e79
BLAKE2b-256 6f4ae807764dca1e8366834252036d374a40f8eadd1101cc9d094158d738087c

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d380571d7dc93838ec57d25636abbb908cced9131c811bbdb720b1c36935ad1e
MD5 6dc7f86e89628030cfec355e758cccd5
BLAKE2b-256 94ef5f0207aa486f043f71e5b2d560a96cb524b43fdec9d8bd9039502243db89

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 59eccaec734df8c81b991cf9a780d38044675fef15f3a0fa21cd5dedf606b47d
MD5 12570db840d4a2c3347b5ed4efc0cd8f
BLAKE2b-256 4faf9bf91b1af29e96221a5d53f52561632b0276fa54e5488678e18ee9ec6081

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 bf38758bf830927197f22a7d08b5093d88dab884a8b1932cab133ff80e978eed
MD5 66ecf7d40b33ece0160c705493210bc3
BLAKE2b-256 bbdf31b7c491ab91aa377cae3b1179103ab605817f45a0d9615868c745ee45be

See more details on using hashes here.

File details

Details for the file geo_espresso-0.3.5-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for geo_espresso-0.3.5-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7be7f99dbc881804eb880da5961dab62223310cbe7b599d463a99c414706b7af
MD5 a752bc5145323b379f2603a29fe805f9
BLAKE2b-256 33e144cab5120e79d01f0820cf2590dd1858c6e918abaefcf1b8fe3485b3f00f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page