init package and add first subpackages
This commit is contained in:
Markus Clauß
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poetry.lock
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poetry.lock
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# This file is automatically @generated by Poetry and should not be changed by hand.
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[[package]]
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name = "asteval"
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version = "0.9.29"
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description = "Safe, minimalistic evaluator of python expression using ast module"
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category = "main"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "asteval-0.9.29-py3-none-any.whl", hash = "sha256:134e42fc4790582f2f926999e59abb444fb491046ba396836962268aad8a68a5"},
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{file = "asteval-0.9.29.tar.gz", hash = "sha256:ab98c61ba9394149c774ae7861497e9c32580301aa693ca19746997216c31fab"},
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]
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[package.extras]
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all = ["Sphinx", "build", "coverage", "pytest", "pytest-cov", "twine"]
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dev = ["build", "twine"]
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doc = ["Sphinx"]
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test = ["coverage", "pytest", "pytest-cov"]
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[[package]]
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name = "dnspython"
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version = "2.3.0"
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description = "DNS toolkit"
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category = "main"
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optional = false
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python-versions = ">=3.7,<4.0"
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files = [
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{file = "dnspython-2.3.0-py3-none-any.whl", hash = "sha256:89141536394f909066cabd112e3e1a37e4e654db00a25308b0f130bc3152eb46"},
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{file = "dnspython-2.3.0.tar.gz", hash = "sha256:224e32b03eb46be70e12ef6d64e0be123a64e621ab4c0822ff6d450d52a540b9"},
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]
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[package.extras]
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curio = ["curio (>=1.2,<2.0)", "sniffio (>=1.1,<2.0)"]
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dnssec = ["cryptography (>=2.6,<40.0)"]
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doh = ["h2 (>=4.1.0)", "httpx (>=0.21.1)", "requests (>=2.23.0,<3.0.0)", "requests-toolbelt (>=0.9.1,<0.11.0)"]
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doq = ["aioquic (>=0.9.20)"]
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idna = ["idna (>=2.1,<4.0)"]
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trio = ["trio (>=0.14,<0.23)"]
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wmi = ["wmi (>=1.5.1,<2.0.0)"]
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[[package]]
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name = "future"
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version = "0.18.3"
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description = "Clean single-source support for Python 3 and 2"
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category = "main"
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optional = false
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python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
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files = [
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{file = "future-0.18.3.tar.gz", hash = "sha256:34a17436ed1e96697a86f9de3d15a3b0be01d8bc8de9c1dffd59fb8234ed5307"},
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]
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[[package]]
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name = "lmfit"
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version = "1.1.0"
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description = "Least-Squares Minimization with Bounds and Constraints"
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category = "main"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "lmfit-1.1.0-py3-none-any.whl", hash = "sha256:29f0540f94b3969a23db2b51abf309f327af8ea3667443ac4cd93d07fdfdb14f"},
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{file = "lmfit-1.1.0.tar.gz", hash = "sha256:a2755b708ad7bad010178da28f082f55cbee7a084a625b452632e2d77b5391fb"},
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]
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[package.dependencies]
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asteval = ">=0.9.28"
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numpy = ">=1.19"
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scipy = ">=1.6"
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uncertainties = ">=3.1.4"
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[package.extras]
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all = ["Pillow", "Sphinx", "build", "cairosvg", "check-wheel-contents", "codecov", "corner", "coverage", "dill", "emcee (>=3.0.0)", "flaky", "jupyter-sphinx (>=0.2.4)", "matplotlib", "numdifftools", "pandas", "pre-commit", "pycairo", "pytest", "pytest-cov", "sphinx-gallery (>=0.10)", "sphinxcontrib-svg2pdfconverter", "sympy", "twine"]
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dev = ["build", "check-wheel-contents", "pre-commit", "twine"]
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doc = ["Pillow", "Sphinx", "cairosvg", "corner", "dill", "emcee (>=3.0.0)", "jupyter-sphinx (>=0.2.4)", "matplotlib", "numdifftools", "pandas", "pycairo", "sphinx-gallery (>=0.10)", "sphinxcontrib-svg2pdfconverter", "sympy"]
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test = ["codecov", "coverage", "flaky", "pytest", "pytest-cov"]
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[[package]]
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name = "mongoengine"
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version = "0.26.0"
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description = "MongoEngine is a Python Object-Document Mapper for working with MongoDB."
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category = "main"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "mongoengine-0.26.0-py3-none-any.whl", hash = "sha256:020a0779d1830affc649f2760d8c408e998981f18898e425eb041915181d3a53"},
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{file = "mongoengine-0.26.0.tar.gz", hash = "sha256:3f284bdcbe8d1a3a9b8ab7d3c3ed672d10b8fd2e545447cd1d75e40d6e978332"},
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]
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[package.dependencies]
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pymongo = ">=3.4,<5.0"
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[[package]]
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name = "numpy"
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version = "1.24.2"
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description = "Fundamental package for array computing in Python"
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category = "main"
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optional = false
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python-versions = ">=3.8"
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files = [
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||||||
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]
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||||||
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[[package]]
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name = "pandas"
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version = "1.5.3"
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description = "Powerful data structures for data analysis, time series, and statistics"
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category = "main"
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optional = false
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python-versions = ">=3.8"
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||||||
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files = [
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{file = "pandas-1.5.3.tar.gz", hash = "sha256:74a3fd7e5a7ec052f183273dc7b0acd3a863edf7520f5d3a1765c04ffdb3b0b1"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
numpy = [
|
||||||
|
{version = ">=1.21.0", markers = "python_version >= \"3.10\""},
|
||||||
|
{version = ">=1.23.2", markers = "python_version >= \"3.11\""},
|
||||||
|
]
|
||||||
|
python-dateutil = ">=2.8.1"
|
||||||
|
pytz = ">=2020.1"
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "pymongo"
|
||||||
|
version = "4.3.3"
|
||||||
|
description = "Python driver for MongoDB <http://www.mongodb.org>"
|
||||||
|
category = "main"
|
||||||
|
optional = false
|
||||||
|
python-versions = ">=3.7"
|
||||||
|
files = [
|
||||||
|
{file = "pymongo-4.3.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:74731c9e423c93cbe791f60c27030b6af6a948cef67deca079da6cd1bb583a8e"},
|
||||||
|
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||||||
|
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|
||||||
|
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||||||
|
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||||||
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||||||
|
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||||||
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||||||
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||||||
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||||||
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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||||||
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|
||||||
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||||||
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||||||
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||||||
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|
||||||
|
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|
||||||
|
{file = "pymongo-4.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:b8a03af1ce79b902a43f5f694c4ca8d92c2a4195db0966f08f266549e2fc49bc"},
|
||||||
|
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||||||
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||||||
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||||||
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{file = "pymongo-4.3.3-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:3b93043b14ba7eb08c57afca19751658ece1cfa2f0b7b1fb5c7a41452fbb8482"},
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||||||
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|
||||||
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|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-manylinux2014_s390x.whl", hash = "sha256:4d00b91c77ceb064c9b0459f0d6ea5bfdbc53ea9e17cf75731e151ef25a830c7"},
|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-manylinux2014_x86_64.whl", hash = "sha256:c6258a3663780ae47ba73d43eb63c79c40ffddfb764e09b56df33be2f9479837"},
|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c29e758f0e734e1e90357ae01ec9c6daf19ff60a051192fe110d8fb25c62600e"},
|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:12f3621a46cdc7a9ba8080422262398a91762a581d27e0647746588d3f995c88"},
|
||||||
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{file = "pymongo-4.3.3-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:47f7aa217b25833cd6f0e72b0d224be55393c2692b4f5e0561cb3beeb10296e9"},
|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2c2fdc855149efe7cdcc2a01ca02bfa24761c640203ea94df467f3baf19078be"},
|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5effd87c7d363890259eac16c56a4e8da307286012c076223997f8cc4a8c435b"},
|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6dd1cf2995fdbd64fc0802313e8323f5fa18994d51af059b5b8862b73b5e53f0"},
|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:bb869707d8e30645ed6766e44098600ca6cdf7989c22a3ea2b7966bb1d98d4b2"},
|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-win32.whl", hash = "sha256:49210feb0be8051a64d71691f0acbfbedc33e149f0a5d6e271fddf6a12493fed"},
|
||||||
|
{file = "pymongo-4.3.3-cp37-cp37m-win_amd64.whl", hash = "sha256:54c377893f2cbbffe39abcff5ff2e917b082c364521fa079305f6f064e1a24a9"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:c184ec5be465c0319440734491e1aa4709b5f3ba75fdfc9dbbc2ae715a7f6829"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux1_i686.whl", hash = "sha256:dca34367a4e77fcab0693e603a959878eaf2351585e7d752cac544bc6b2dee46"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:cd6a4afb20fb3c26a7bfd4611a0bbb24d93cbd746f5eb881f114b5e38fd55501"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:0c466710871d0026c190fc4141e810cf9d9affbf4935e1d273fbdc7d7cda6143"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux2014_i686.whl", hash = "sha256:d07d06dba5b5f7d80f9cc45501456e440f759fe79f9895922ed486237ac378a8"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux2014_ppc64le.whl", hash = "sha256:711bc52cb98e7892c03e9b669bebd89c0a890a90dbc6d5bb2c47f30239bac6e9"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux2014_s390x.whl", hash = "sha256:34b040e095e1671df0c095ec0b04fc4ebb19c4c160f87c2b55c079b16b1a6b00"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:4ed00f96e147f40b565fe7530d1da0b0f3ab803d5dd5b683834500fa5d195ec4"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ef888f48eb9203ee1e04b9fb27429017b290fb916f1e7826c2f7808c88798394"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:316498b642c00401370b2156b5233b256f9b33799e0a8d9d0b8a7da217a20fca"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fa7e202feb683dad74f00dea066690448d0cfa310f8a277db06ec8eb466601b5"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:52896e22115c97f1c829db32aa2760b0d61839cfe08b168c2b1d82f31dbc5f55"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7c051fe37c96b9878f37fa58906cb53ecd13dcb7341d3a85f1e2e2f6b10782d9"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:5134d33286c045393c7beb51be29754647cec5ebc051cf82799c5ce9820a2ca2"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:a9c2885b4a8e6e39db5662d8b02ca6dcec796a45e48c2de12552841f061692ba"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-win32.whl", hash = "sha256:a6cd6f1db75eb07332bd3710f58f5fce4967eadbf751bad653842750a61bda62"},
|
||||||
|
{file = "pymongo-4.3.3-cp38-cp38-win_amd64.whl", hash = "sha256:d5571b6978750601f783cea07fb6b666837010ca57e5cefa389c1d456f6222e2"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:81d1a7303bd02ca1c5be4aacd4db73593f573ba8e0c543c04c6da6275fd7a47e"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux1_i686.whl", hash = "sha256:016c412118e1c23fef3a1eada4f83ae6e8844fd91986b2e066fc1b0013cdd9ae"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:8fd6e191b92a10310f5a6cfe10d6f839d79d192fb02480bda325286bd1c7b385"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:e2961b05f9c04a53da8bfc72f1910b6aec7205fcf3ac9c036d24619979bbee4b"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux2014_i686.whl", hash = "sha256:b38a96b3eed8edc515b38257f03216f382c4389d022a8834667e2bc63c0c0c31"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux2014_ppc64le.whl", hash = "sha256:c1a70c51da9fa95bd75c167edb2eb3f3c4d27bc4ddd29e588f21649d014ec0b7"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux2014_s390x.whl", hash = "sha256:8a06a0c02f5606330e8f2e2f3b7949877ca7e4024fa2bff5a4506bec66c49ec7"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:6c2216d8b6a6d019c6f4b1ad55f890e5e77eb089309ffc05b6911c09349e7474"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eac0a143ef4f28f49670bf89cb15847eb80b375d55eba401ca2f777cd425f338"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:08fc250b5552ee97ceeae0f52d8b04f360291285fc7437f13daa516ce38fdbc6"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704d939656e21b073bfcddd7228b29e0e8a93dd27b54240eaafc0b9a631629a6"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1074f1a6f23e28b983c96142f2d45be03ec55d93035b471c26889a7ad2365db3"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7b16250238de8dafca225647608dddc7bbb5dce3dd53b4d8e63c1cc287394c2f"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:7761cacb8745093062695b11574effea69db636c2fd0a9269a1f0183712927b4"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:fd7bb378d82b88387dc10227cfd964f6273eb083e05299e9b97cbe075da12d11"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-win32.whl", hash = "sha256:dc24d245026a72d9b4953729d31813edd4bd4e5c13622d96e27c284942d33f24"},
|
||||||
|
{file = "pymongo-4.3.3-cp39-cp39-win_amd64.whl", hash = "sha256:fc28e8d85d392a06434e9a934908d97e2cf453d69488d2bcd0bfb881497fd975"},
|
||||||
|
{file = "pymongo-4.3.3.tar.gz", hash = "sha256:34e95ffb0a68bffbc3b437f2d1f25fc916fef3df5cdeed0992da5f42fae9b807"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
dnspython = ">=1.16.0,<3.0.0"
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
aws = ["pymongo-auth-aws (<2.0.0)"]
|
||||||
|
encryption = ["pymongo-auth-aws (<2.0.0)", "pymongocrypt (>=1.3.0,<2.0.0)"]
|
||||||
|
gssapi = ["pykerberos"]
|
||||||
|
ocsp = ["certifi", "pyopenssl (>=17.2.0)", "requests (<3.0.0)", "service-identity (>=18.1.0)"]
|
||||||
|
snappy = ["python-snappy"]
|
||||||
|
zstd = ["zstandard"]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "python-dateutil"
|
||||||
|
version = "2.8.2"
|
||||||
|
description = "Extensions to the standard Python datetime module"
|
||||||
|
category = "main"
|
||||||
|
optional = false
|
||||||
|
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
|
||||||
|
files = [
|
||||||
|
{file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
|
||||||
|
{file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
six = ">=1.5"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "pytz"
|
||||||
|
version = "2022.7.1"
|
||||||
|
description = "World timezone definitions, modern and historical"
|
||||||
|
category = "main"
|
||||||
|
optional = false
|
||||||
|
python-versions = "*"
|
||||||
|
files = [
|
||||||
|
{file = "pytz-2022.7.1-py2.py3-none-any.whl", hash = "sha256:78f4f37d8198e0627c5f1143240bb0206b8691d8d7ac6d78fee88b78733f8c4a"},
|
||||||
|
{file = "pytz-2022.7.1.tar.gz", hash = "sha256:01a0681c4b9684a28304615eba55d1ab31ae00bf68ec157ec3708a8182dbbcd0"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "scipy"
|
||||||
|
version = "1.10.1"
|
||||||
|
description = "Fundamental algorithms for scientific computing in Python"
|
||||||
|
category = "main"
|
||||||
|
optional = false
|
||||||
|
python-versions = "<3.12,>=3.8"
|
||||||
|
files = [
|
||||||
|
{file = "scipy-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e7354fd7527a4b0377ce55f286805b34e8c54b91be865bac273f527e1b839019"},
|
||||||
|
{file = "scipy-1.10.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:4b3f429188c66603a1a5c549fb414e4d3bdc2a24792e061ffbd607d3d75fd84e"},
|
||||||
|
{file = "scipy-1.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1553b5dcddd64ba9a0d95355e63fe6c3fc303a8fd77c7bc91e77d61363f7433f"},
|
||||||
|
{file = "scipy-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c0ff64b06b10e35215abce517252b375e580a6125fd5fdf6421b98efbefb2d2"},
|
||||||
|
{file = "scipy-1.10.1-cp310-cp310-win_amd64.whl", hash = "sha256:fae8a7b898c42dffe3f7361c40d5952b6bf32d10c4569098d276b4c547905ee1"},
|
||||||
|
{file = "scipy-1.10.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0f1564ea217e82c1bbe75ddf7285ba0709ecd503f048cb1236ae9995f64217bd"},
|
||||||
|
{file = "scipy-1.10.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:d925fa1c81b772882aa55bcc10bf88324dadb66ff85d548c71515f6689c6dac5"},
|
||||||
|
{file = "scipy-1.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aaea0a6be54462ec027de54fca511540980d1e9eea68b2d5c1dbfe084797be35"},
|
||||||
|
{file = "scipy-1.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:15a35c4242ec5f292c3dd364a7c71a61be87a3d4ddcc693372813c0b73c9af1d"},
|
||||||
|
{file = "scipy-1.10.1-cp311-cp311-win_amd64.whl", hash = "sha256:43b8e0bcb877faf0abfb613d51026cd5cc78918e9530e375727bf0625c82788f"},
|
||||||
|
{file = "scipy-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5678f88c68ea866ed9ebe3a989091088553ba12c6090244fdae3e467b1139c35"},
|
||||||
|
{file = "scipy-1.10.1-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:39becb03541f9e58243f4197584286e339029e8908c46f7221abeea4b749fa88"},
|
||||||
|
{file = "scipy-1.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bce5869c8d68cf383ce240e44c1d9ae7c06078a9396df68ce88a1230f93a30c1"},
|
||||||
|
{file = "scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:07c3457ce0b3ad5124f98a86533106b643dd811dd61b548e78cf4c8786652f6f"},
|
||||||
|
{file = "scipy-1.10.1-cp38-cp38-win_amd64.whl", hash = "sha256:049a8bbf0ad95277ffba9b3b7d23e5369cc39e66406d60422c8cfef40ccc8415"},
|
||||||
|
{file = "scipy-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:cd9f1027ff30d90618914a64ca9b1a77a431159df0e2a195d8a9e8a04c78abf9"},
|
||||||
|
{file = "scipy-1.10.1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:79c8e5a6c6ffaf3a2262ef1be1e108a035cf4f05c14df56057b64acc5bebffb6"},
|
||||||
|
{file = "scipy-1.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:51af417a000d2dbe1ec6c372dfe688e041a7084da4fdd350aeb139bd3fb55353"},
|
||||||
|
{file = "scipy-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1b4735d6c28aad3cdcf52117e0e91d6b39acd4272f3f5cd9907c24ee931ad601"},
|
||||||
|
{file = "scipy-1.10.1-cp39-cp39-win_amd64.whl", hash = "sha256:7ff7f37b1bf4417baca958d254e8e2875d0cc23aaadbe65b3d5b3077b0eb23ea"},
|
||||||
|
{file = "scipy-1.10.1.tar.gz", hash = "sha256:2cf9dfb80a7b4589ba4c40ce7588986d6d5cebc5457cad2c2880f6bc2d42f3a5"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
numpy = ">=1.19.5,<1.27.0"
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"]
|
||||||
|
doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
|
||||||
|
test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "six"
|
||||||
|
version = "1.16.0"
|
||||||
|
description = "Python 2 and 3 compatibility utilities"
|
||||||
|
category = "main"
|
||||||
|
optional = false
|
||||||
|
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
|
||||||
|
files = [
|
||||||
|
{file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
|
||||||
|
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "uncertainties"
|
||||||
|
version = "3.1.7"
|
||||||
|
description = "Transparent calculations with uncertainties on the quantities involved (aka error propagation); fast calculation of derivatives"
|
||||||
|
category = "main"
|
||||||
|
optional = false
|
||||||
|
python-versions = "*"
|
||||||
|
files = [
|
||||||
|
{file = "uncertainties-3.1.7-py2.py3-none-any.whl", hash = "sha256:4040ec64d298215531922a68fa1506dc6b1cb86cd7cca8eca848fcfe0f987151"},
|
||||||
|
{file = "uncertainties-3.1.7.tar.gz", hash = "sha256:80111e0839f239c5b233cb4772017b483a0b7a1573a581b92ab7746a35e6faab"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
future = "*"
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
all = ["nose", "numpy", "sphinx"]
|
||||||
|
docs = ["sphinx"]
|
||||||
|
optional = ["numpy"]
|
||||||
|
tests = ["nose", "numpy"]
|
||||||
|
|
||||||
|
[metadata]
|
||||||
|
lock-version = "2.0"
|
||||||
|
python-versions = ">3.10,< 3.12"
|
||||||
|
content-hash = "aaad37b7d989f5285689b9e2192360da2b496be23cf41eb128e7e616e07a203e"
|
||||||
33
pyproject._toml
Normal file
33
pyproject._toml
Normal file
@@ -0,0 +1,33 @@
|
|||||||
|
[build-system]
|
||||||
|
requires = ["flit_core>=3.4"]
|
||||||
|
build-backend = "flit_core.buildapi"
|
||||||
|
|
||||||
|
[project]
|
||||||
|
name = "paveit"
|
||||||
|
version = "0.0.1"
|
||||||
|
authors = [
|
||||||
|
{ name="Example Author", email="author@example.com" },
|
||||||
|
]
|
||||||
|
description = "A small example package"
|
||||||
|
#readme = "README.md"
|
||||||
|
requires-python = ">=3.9"
|
||||||
|
classifiers = [
|
||||||
|
"Programming Language :: Python :: 3",
|
||||||
|
"License :: OSI Approved :: MIT License",
|
||||||
|
"Operating System :: OS Independent",
|
||||||
|
]
|
||||||
|
|
||||||
|
#[project.urls]
|
||||||
|
#"Homepage" = "https://github.com/pypa/sampleproject"
|
||||||
|
#"Bug Tracker" = "https://github.com/pypa/sampleproject/issues"
|
||||||
|
|
||||||
|
######
|
||||||
|
|
||||||
|
|
||||||
|
#[tool.poetry.dependencies]
|
||||||
|
#python = ">3.10,< 3.12"
|
||||||
|
#lmfit = "~1.1.0"
|
||||||
|
#pandas = "~1.5.3"
|
||||||
|
#numpy = "~1.24.2"
|
||||||
|
#scipy = "~1.10.0"
|
||||||
|
#mongoengine = "~0.26.0"
|
||||||
30
setup.cfg
Normal file
30
setup.cfg
Normal file
@@ -0,0 +1,30 @@
|
|||||||
|
[metadata]
|
||||||
|
name = paveit
|
||||||
|
description = Analysis Pavment Test Data
|
||||||
|
author = Markus Clauß
|
||||||
|
author_email = markus.clauss@tu-dresden.de
|
||||||
|
|
||||||
|
[options]
|
||||||
|
packages = find:
|
||||||
|
python_requires = >=3.9
|
||||||
|
setup_requires = setuptools_scm
|
||||||
|
install_requires =
|
||||||
|
lmfit
|
||||||
|
pandas
|
||||||
|
numpy
|
||||||
|
scipy
|
||||||
|
matplotlib
|
||||||
|
seaborn
|
||||||
|
mongoengine
|
||||||
|
|
||||||
|
[options.packages.find]
|
||||||
|
where=src
|
||||||
|
|
||||||
|
[rstcheck]
|
||||||
|
report=warning
|
||||||
|
ignore_substitutions=release
|
||||||
|
ignore_roles=scipydoc,numpydoc
|
||||||
|
ignore_directives=autoclass,autodoc,autofunction,automethod,jupyter-execute,math
|
||||||
|
|
||||||
|
[flake8]
|
||||||
|
ignore = E121,E123,E126,E226,W503,W504,E501,E731
|
||||||
6
setup.py
Normal file
6
setup.py
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
#!/usr/bin/env python
|
||||||
|
|
||||||
|
import setuptools
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
setuptools.setup()
|
||||||
4
src/paveit/__init__.py
Normal file
4
src/paveit/__init__.py
Normal file
@@ -0,0 +1,4 @@
|
|||||||
|
# main __init__.py
|
||||||
|
from .analysis import *
|
||||||
|
from .helper import *
|
||||||
|
from .labtest import *
|
||||||
9
src/paveit/analysis/__init__.py
Normal file
9
src/paveit/analysis/__init__.py
Normal file
@@ -0,0 +1,9 @@
|
|||||||
|
from .regression import *
|
||||||
|
|
||||||
|
__all__ = [
|
||||||
|
# regession models
|
||||||
|
"fit_cos_simple",
|
||||||
|
"fit_cos",
|
||||||
|
#helper functions
|
||||||
|
"fit_cos_eval",
|
||||||
|
]
|
||||||
159
src/paveit/analysis/regression.py
Normal file
159
src/paveit/analysis/regression.py
Normal file
@@ -0,0 +1,159 @@
|
|||||||
|
import lmfit as lm
|
||||||
|
import numpy as np
|
||||||
|
import scipy.special as sf
|
||||||
|
from scipy.optimize import curve_fit
|
||||||
|
|
||||||
|
|
||||||
|
def cosfunc(t, A, w, p, c, e):
|
||||||
|
"""
|
||||||
|
definition of the sin function
|
||||||
|
"""
|
||||||
|
|
||||||
|
return A * np.cos(2 * np.pi * w * t + p) + e * t + c
|
||||||
|
|
||||||
|
|
||||||
|
def fit_cos_eval(x, par):
|
||||||
|
"""
|
||||||
|
|
||||||
|
par: dict
|
||||||
|
fitting results
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
ys = cosfunc(x, par['amp'], par['freq'], par['phase'], par['offset'],
|
||||||
|
par['slope'])
|
||||||
|
|
||||||
|
return ys
|
||||||
|
|
||||||
|
|
||||||
|
def regression_sine_fft():
|
||||||
|
"""
|
||||||
|
fast fourier transformation for sine data
|
||||||
|
"""
|
||||||
|
|
||||||
|
return []
|
||||||
|
|
||||||
|
|
||||||
|
def fit_cos_simple(x, y, freq=10.0):
|
||||||
|
"""
|
||||||
|
simple sine regression
|
||||||
|
|
||||||
|
x: vector or list
|
||||||
|
y: vector or list
|
||||||
|
|
||||||
|
freq: float
|
||||||
|
initial frequency of regression analysis
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
tt = np.array(x)
|
||||||
|
yy = np.array(y)
|
||||||
|
|
||||||
|
guess_offset = np.mean(yy)
|
||||||
|
offset_b = 0.4 * abs(guess_offset)
|
||||||
|
|
||||||
|
guess_amp = abs(np.max(yy) - np.min(yy)) / 2.0
|
||||||
|
|
||||||
|
param_bounds = ([
|
||||||
|
0.3 * guess_amp, 0.99 * freq, -np.inf, guess_offset - offset_b, -np.inf
|
||||||
|
], [1.3 * guess_amp, 1.01 * freq, np.inf, guess_offset + offset_b, np.inf])
|
||||||
|
|
||||||
|
popt, pcov = curve_fit(cosfunc, tt, yy, bounds=param_bounds)
|
||||||
|
|
||||||
|
A, w, p, c, e = popt
|
||||||
|
|
||||||
|
return {
|
||||||
|
"amp": A,
|
||||||
|
"freq": w,
|
||||||
|
"phase": p,
|
||||||
|
"offset": c,
|
||||||
|
"slope": e,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def fit_cos(x, y, freq=10.0, constfreq=False):
|
||||||
|
"""
|
||||||
|
sine regression
|
||||||
|
|
||||||
|
x: vector or list
|
||||||
|
y: vector or list
|
||||||
|
|
||||||
|
freq: float
|
||||||
|
initial frequency of regression analysis
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
# step 1
|
||||||
|
|
||||||
|
res_step1 = fit_cos_simple(x, y, freq=freq)
|
||||||
|
|
||||||
|
# step 2: lmfit
|
||||||
|
mod = lm.models.Model(cosfunc)
|
||||||
|
|
||||||
|
mod.set_param_hint(
|
||||||
|
'A',
|
||||||
|
value=res_step1['amp'],
|
||||||
|
#min=res_step1['amp'] - 0.5 * abs(res_step1['amp']),
|
||||||
|
#max=res_step1['amp'] + 0.5 * abs(res_step1['amp'])
|
||||||
|
)
|
||||||
|
|
||||||
|
mod.set_param_hint(
|
||||||
|
'w',
|
||||||
|
value=freq,
|
||||||
|
vary=not constfreq,
|
||||||
|
#min=freq - 0.1 * freq,
|
||||||
|
#max=freq + 0.1 * freq,
|
||||||
|
)
|
||||||
|
|
||||||
|
mod.set_param_hint('p', value=res_step1['phase'], vary=True)
|
||||||
|
|
||||||
|
mod.set_param_hint('c', value=res_step1['offset'],
|
||||||
|
vary=True) #, min = -0.5, max = 0.5)
|
||||||
|
|
||||||
|
mod.set_param_hint('e', value=res_step1['slope'], vary=True)
|
||||||
|
|
||||||
|
parms_fit = [
|
||||||
|
mod.param_hints['A']['value'], mod.param_hints['w']['value'],
|
||||||
|
mod.param_hints['p']['value'], mod.param_hints['c']['value'],
|
||||||
|
mod.param_hints['e']['value']
|
||||||
|
]
|
||||||
|
|
||||||
|
abweichung = []
|
||||||
|
chis = []
|
||||||
|
chis_red = []
|
||||||
|
results = []
|
||||||
|
r2 = []
|
||||||
|
|
||||||
|
methods = ['leastsq', 'powell']
|
||||||
|
dof = len(y) - len(parms_fit)
|
||||||
|
|
||||||
|
for method in methods:
|
||||||
|
result = mod.fit(y, t=x, method=method, verbose=False)
|
||||||
|
r2temp = 1 - result.residual.var() / np.var(y)
|
||||||
|
# r2temp = result.redchi / np.var(yfit, ddof=2)
|
||||||
|
if r2temp < 0.:
|
||||||
|
r2temp = 0
|
||||||
|
r2.append(r2temp)
|
||||||
|
|
||||||
|
chi = result.chisqr
|
||||||
|
chis_red.append(result.redchi)
|
||||||
|
abweichung.append(sf.gammaincc(dof / 2., chi / 2))
|
||||||
|
chis.append(chi)
|
||||||
|
results.append(result)
|
||||||
|
|
||||||
|
res = {}
|
||||||
|
best = np.nanargmax(r2)
|
||||||
|
|
||||||
|
res[f'amp'] = results[best].best_values['A']
|
||||||
|
res[f'freq'] = results[best].best_values['w']
|
||||||
|
res[f'phase'] = results[best].best_values['p']
|
||||||
|
res[f'offset'] = results[best].best_values['c']
|
||||||
|
res[f'slope'] = results[best].best_values['e']
|
||||||
|
|
||||||
|
res[f'r2'] = r2[best]
|
||||||
|
|
||||||
|
return res
|
||||||
6
src/paveit/helper/__init__.py
Normal file
6
src/paveit/helper/__init__.py
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
from .filehasher import calc_hash_of_bytes
|
||||||
|
from .minio import get_minio_client_archive, get_minio_client_processing
|
||||||
|
|
||||||
|
__all__ = ['get_minio_client_archive', 'get_minio_client_processing',
|
||||||
|
'calc_hash_of_bytes'
|
||||||
|
]
|
||||||
22
src/paveit/helper/filehasher.py
Normal file
22
src/paveit/helper/filehasher.py
Normal file
@@ -0,0 +1,22 @@
|
|||||||
|
import hashlib
|
||||||
|
from io import BytesIO
|
||||||
|
|
||||||
|
|
||||||
|
def calc_hash_of_bytes(buf: BytesIO):
|
||||||
|
""" calculate the hash of the file """
|
||||||
|
|
||||||
|
algo = hashlib.sha1()
|
||||||
|
|
||||||
|
buffer_size = 65536
|
||||||
|
buffer_size = buffer_size * 1024 * 1024
|
||||||
|
|
||||||
|
while True:
|
||||||
|
data = buf.read(buffer_size)
|
||||||
|
if not data:
|
||||||
|
break
|
||||||
|
algo.update(data)
|
||||||
|
|
||||||
|
hex = algo.hexdigest()
|
||||||
|
|
||||||
|
return hex
|
||||||
|
|
||||||
40
src/paveit/helper/minio.py
Normal file
40
src/paveit/helper/minio.py
Normal file
@@ -0,0 +1,40 @@
|
|||||||
|
import os
|
||||||
|
|
||||||
|
from minio import Minio
|
||||||
|
|
||||||
|
|
||||||
|
def get_minio_client_processing(bucket_name = 'processing'):
|
||||||
|
|
||||||
|
client = Minio(
|
||||||
|
os.environ["MINIO_URL"],
|
||||||
|
access_key=os.environ["MINIO_ACCESS_KEY"],
|
||||||
|
secret_key=os.environ["MINIO_SECRET_KEY"],
|
||||||
|
secure=False
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
found = client.bucket_exists(bucket_name)
|
||||||
|
if not found:
|
||||||
|
client.make_bucket(bucket_name)
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
return client
|
||||||
|
|
||||||
|
|
||||||
|
def get_minio_client_archive(bucket_name = 'archive'):
|
||||||
|
|
||||||
|
client = Minio(
|
||||||
|
os.environ["MINIO_ARCHIVE_URL"],
|
||||||
|
access_key=os.environ["MINIO_ARCHIVE_ACCESS_KEY"],
|
||||||
|
secret_key=os.environ["MINIO_ARCHIVE_SECRET_KEY"],
|
||||||
|
secure=False
|
||||||
|
)
|
||||||
|
|
||||||
|
found = client.bucket_exists(bucket_name)
|
||||||
|
if not found:
|
||||||
|
client.make_bucket(bucket_name)
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
return client
|
||||||
6
src/paveit/labtest/__init__.py
Normal file
6
src/paveit/labtest/__init__.py
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
from .base import DataSineLoad
|
||||||
|
from .citt import CITTBase
|
||||||
|
|
||||||
|
__all__ = ['DataSineLoad',
|
||||||
|
'CITTBase'
|
||||||
|
]
|
||||||
90
src/paveit/labtest/base.py
Normal file
90
src/paveit/labtest/base.py
Normal file
@@ -0,0 +1,90 @@
|
|||||||
|
# coding: utf-8
|
||||||
|
import io
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
from paveit.helper import calc_hash_of_bytes, get_minio_client_processing
|
||||||
|
|
||||||
|
from worker import app, logger
|
||||||
|
|
||||||
|
|
||||||
|
class DataSineLoad():
|
||||||
|
"""
|
||||||
|
Base class for lab tests with sine load
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, filename:str , metadata: dict):
|
||||||
|
self.filename = filename
|
||||||
|
self.metadata = metadata
|
||||||
|
|
||||||
|
self._logger = logger
|
||||||
|
|
||||||
|
self._logger.info(f'filename s3: {self.filename}, metadata: {self.metadata}')
|
||||||
|
|
||||||
|
|
||||||
|
def _connect_to_s3(self):
|
||||||
|
self._logger.info('connect to db')
|
||||||
|
|
||||||
|
self.__minioClient = get_minio_client_processing()
|
||||||
|
|
||||||
|
def _read_from_s3_to_bytesio(self):
|
||||||
|
self._logger.info('read bytes')
|
||||||
|
|
||||||
|
|
||||||
|
try:
|
||||||
|
self._connect_to_s3()
|
||||||
|
response = self.__minioClient.get_object('processing', self.filename)
|
||||||
|
self.data = response.data
|
||||||
|
finally:
|
||||||
|
response.close()
|
||||||
|
response.release_conn()
|
||||||
|
|
||||||
|
|
||||||
|
self.data = io.BytesIO(self.data)
|
||||||
|
|
||||||
|
def _calc_hash_of_bytesio(self):
|
||||||
|
|
||||||
|
self.filehash = calc_hash_of_bytes(self.data)
|
||||||
|
self.data.seek(0)
|
||||||
|
self._logger.debug(f'Hash of file: {self.filehash}')
|
||||||
|
|
||||||
|
|
||||||
|
def _bytes_to_df(self):
|
||||||
|
self._logger.debug('convert bytes to pandas.DataFrame')
|
||||||
|
|
||||||
|
encoding='utf-8'
|
||||||
|
self.df = pd.read_csv(self.data, encoding=encoding)
|
||||||
|
|
||||||
|
def _calc(self):
|
||||||
|
self._logger.debug('calc data')
|
||||||
|
return self.df.mean().mean()
|
||||||
|
|
||||||
|
def _archive_binary_data(self):
|
||||||
|
|
||||||
|
self._logger.debug('send file to archive')
|
||||||
|
app.send_task('ArchiveFile', args=[self.filename,
|
||||||
|
self.metadata,
|
||||||
|
self.filehash,
|
||||||
|
'org',
|
||||||
|
'citt'
|
||||||
|
],
|
||||||
|
queue='archive'
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def run(self):
|
||||||
|
self._logger.info('run task')
|
||||||
|
self._read_from_s3_to_bytesio()
|
||||||
|
self._calc_hash_of_bytesio()
|
||||||
|
|
||||||
|
self._bytes_to_df()
|
||||||
|
|
||||||
|
res = self._calc()
|
||||||
|
self._logger.debug(f'results: {res}')
|
||||||
|
|
||||||
|
self._archive_binary_data()
|
||||||
|
|
||||||
|
return res
|
||||||
|
|
||||||
192
src/paveit/labtest/citt.py
Normal file
192
src/paveit/labtest/citt.py
Normal file
@@ -0,0 +1,192 @@
|
|||||||
|
import io
|
||||||
|
import os
|
||||||
|
from csv import reader
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
from paveit.labtest import DataSineLoad
|
||||||
|
|
||||||
|
|
||||||
|
class CITTBase(DataSineLoad):
|
||||||
|
def _calc(self):
|
||||||
|
return (self.df.mean().mean(), self.df.max().max())
|
||||||
|
|
||||||
|
class CITT_KIT(DataSineLoad):
|
||||||
|
def _calc(self):
|
||||||
|
return (self.df.mean().mean(), self.df.max().max())
|
||||||
|
|
||||||
|
def _bytes_to_df(self):
|
||||||
|
logger.debug('convert bytes to pandas.DataFrame')
|
||||||
|
|
||||||
|
self.data.seek(0)
|
||||||
|
with io.TextIOWrapper(self.data, encoding='latin-1') as read_obj:
|
||||||
|
csv_reader = reader(read_obj, delimiter=';')
|
||||||
|
|
||||||
|
read = False
|
||||||
|
|
||||||
|
data = []
|
||||||
|
temp = []
|
||||||
|
|
||||||
|
for idx_row, row in enumerate(csv_reader):
|
||||||
|
if row == ['*****']:
|
||||||
|
|
||||||
|
if read == False:
|
||||||
|
read = True
|
||||||
|
else:
|
||||||
|
read = False
|
||||||
|
data.append(temp)
|
||||||
|
|
||||||
|
temp = []
|
||||||
|
|
||||||
|
continue
|
||||||
|
|
||||||
|
if read:
|
||||||
|
|
||||||
|
row = [r.replace(',', '.') for r in row]
|
||||||
|
|
||||||
|
temp.append(row)
|
||||||
|
|
||||||
|
#convert to pandas
|
||||||
|
|
||||||
|
res = []
|
||||||
|
|
||||||
|
freqs = [10.0, 5.0, 1.0, 0.1, 10.0]
|
||||||
|
|
||||||
|
for idx_data, d in enumerate(data):
|
||||||
|
|
||||||
|
t = pd.DataFrame(d[3:])
|
||||||
|
t.columns = d[1]
|
||||||
|
|
||||||
|
freq = freqs[idx_data]
|
||||||
|
t['f'] = freq
|
||||||
|
|
||||||
|
for col in t.columns:
|
||||||
|
t[col] = pd.to_numeric(t[col])
|
||||||
|
|
||||||
|
# add cycle number
|
||||||
|
dt = 1. / freq
|
||||||
|
|
||||||
|
Nmax = int(np.ceil(t['ZEIT'].max() / dt))
|
||||||
|
|
||||||
|
N = np.zeros_like(t['ZEIT'])
|
||||||
|
|
||||||
|
for i in range(Nmax):
|
||||||
|
if i == 0:
|
||||||
|
tmin = 0
|
||||||
|
tmax = dt
|
||||||
|
else:
|
||||||
|
tmax = (i + 1) * dt
|
||||||
|
tmin = (i) * dt
|
||||||
|
|
||||||
|
idx = t[(t['ZEIT'] >= tmin) & (t['ZEIT'] < tmax)].index
|
||||||
|
N[idx] = i
|
||||||
|
|
||||||
|
|
||||||
|
t['N'] = N
|
||||||
|
|
||||||
|
res.append(t)
|
||||||
|
|
||||||
|
#remove second 10 Hz
|
||||||
|
res = pd.concat(res[:-1])
|
||||||
|
|
||||||
|
res['T'] = self.temperature
|
||||||
|
#res = res.sort_values(['f', 'ZEIT'])
|
||||||
|
|
||||||
|
#define in class
|
||||||
|
self.df = res.reset_index()
|
||||||
|
|
||||||
|
class CITT_PTMDortmund(DataSineLoad):
|
||||||
|
def _calc(self):
|
||||||
|
return (self.df.mean().mean(), self.df.max().max())
|
||||||
|
|
||||||
|
def _bytes_to_df(self):
|
||||||
|
res = []
|
||||||
|
|
||||||
|
xl = pd.ExcelFile(self.data)
|
||||||
|
num_sheets = len(xl.sheet_names)
|
||||||
|
|
||||||
|
print(num_sheets)
|
||||||
|
|
||||||
|
diameter = []
|
||||||
|
height = []
|
||||||
|
|
||||||
|
|
||||||
|
for sheetid in range(num_sheets):
|
||||||
|
temp = pd.read_excel(self.data, sheetid, skiprows=97)
|
||||||
|
temp = temp.drop(index=0)
|
||||||
|
|
||||||
|
#convert data to numerical data
|
||||||
|
for col in temp.columns:
|
||||||
|
temp[col] = pd.to_numeric(temp[col])
|
||||||
|
|
||||||
|
|
||||||
|
#read metadata from file
|
||||||
|
|
||||||
|
meta = pd.read_excel(self.data, sheetid,
|
||||||
|
skiprows=1,
|
||||||
|
nrows=90)
|
||||||
|
|
||||||
|
meta = meta[meta.columns[[0, 2]]]
|
||||||
|
meta = meta.set_index(
|
||||||
|
meta.columns[0]).to_dict()[meta.columns[1]]
|
||||||
|
|
||||||
|
temp['sigma'] = float(meta['Max. Spannung'])
|
||||||
|
temp['T'] = float(meta['Versuchstemperatur'])
|
||||||
|
freq = float(meta['Frequenz'])
|
||||||
|
dt = 1 / freq
|
||||||
|
temp['f'] = freq
|
||||||
|
|
||||||
|
Nfrom = int(meta['Erster Aufzeichnungslastwechsel'])
|
||||||
|
Nto = int(meta['Letzer Aufzeichnungslastwechsel'])
|
||||||
|
|
||||||
|
#add cycle number to dataframe
|
||||||
|
time_idx = temp['Zeitfolgen'].values
|
||||||
|
N = np.zeros_like(time_idx)
|
||||||
|
self._logger.debug(len(N))
|
||||||
|
|
||||||
|
self._logger.info(f'cycles from {Nfrom} to {Nto}')
|
||||||
|
|
||||||
|
#BUG: Ist in Messdatei falsch definiert und wird von PTM angepasst. '''
|
||||||
|
#for cycle in range(Nfrom, Nto+1):
|
||||||
|
for cycle in range(10):
|
||||||
|
|
||||||
|
# time window
|
||||||
|
tmin = (cycle) * dt
|
||||||
|
tmax = (cycle + 1) * dt
|
||||||
|
|
||||||
|
#filter data
|
||||||
|
idx = temp[(time_idx >= tmin)
|
||||||
|
& (time_idx < tmax)].index
|
||||||
|
|
||||||
|
#FIX: siehe bug oben
|
||||||
|
if any(idx)>=500:
|
||||||
|
idx = idx[idx<500]
|
||||||
|
|
||||||
|
#set cycle number
|
||||||
|
N[idx] = cycle
|
||||||
|
|
||||||
|
temp['N'] = N
|
||||||
|
|
||||||
|
# add diameter and height to list
|
||||||
|
diameter.append(float(meta['Durchmesser (mm)']))
|
||||||
|
height.append(float(meta['Länge (mm)']))
|
||||||
|
|
||||||
|
#append data to final dataframe
|
||||||
|
res.append(temp)
|
||||||
|
|
||||||
|
#concat all parts to single dataframe
|
||||||
|
res = pd.concat(res)
|
||||||
|
|
||||||
|
# add data from speciment to metadata
|
||||||
|
|
||||||
|
if not 'diameter' in self.metadata:
|
||||||
|
self.metadata['diameter'] = np.mean(diameter)
|
||||||
|
if not 'height' in self.metadata:
|
||||||
|
self.metadata['height'] = np.mean(height)
|
||||||
|
|
||||||
|
#define in class
|
||||||
|
self.df = res.reset_index()
|
||||||
|
|
||||||
|
# log infos
|
||||||
|
logger.debug(self.metadata)
|
||||||
|
logger.debug(self.df.head())
|
||||||
683
src/paveit/labtest/sheartest.py
Normal file
683
src/paveit/labtest/sheartest.py
Normal file
@@ -0,0 +1,683 @@
|
|||||||
|
import os
|
||||||
|
|
||||||
|
import lmfit as lm
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
from paveit_worker.libs.labtests.base import DataSineLoad
|
||||||
|
|
||||||
|
#import scipy.fft as sfft
|
||||||
|
|
||||||
|
|
||||||
|
#from pytestpavement.labtests.base import DataSineLoad
|
||||||
|
#from pytestpavement.models.data import DataSheartest
|
||||||
|
#from pytestpavement.models.sheartest import DynamicShearTestExtension
|
||||||
|
|
||||||
|
|
||||||
|
class ShearTest(DataSineLoad):
|
||||||
|
"""
|
||||||
|
Dynamic Shear Bounding Test
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self,
|
||||||
|
fname: str,
|
||||||
|
debug: bool = False,
|
||||||
|
gap_width: float = 1.0,
|
||||||
|
roundtemperature: bool = True,
|
||||||
|
archive_file=False,
|
||||||
|
s3_params: dict = {}):
|
||||||
|
|
||||||
|
#set parameter
|
||||||
|
self.gap_width = gap_width
|
||||||
|
self.debug = debug
|
||||||
|
self.file = fname
|
||||||
|
self.roundtemperature = roundtemperature
|
||||||
|
self.archive_file = archive_file
|
||||||
|
self.s3_params = s3_params
|
||||||
|
|
||||||
|
# process file
|
||||||
|
self._run()
|
||||||
|
|
||||||
|
def plot_fited_data(self, opath=None, pkname=None, r2min=0.99):
|
||||||
|
|
||||||
|
ylabel_dict = {
|
||||||
|
'F': 'Kraft in N',
|
||||||
|
's_vert_sum': 'norm. mittlerer Scherweg\n $S_{mittel}$ in mm',
|
||||||
|
's_piston': 'norm. Kolbenweg\n in mm',
|
||||||
|
's_vert_1': 'Scherweg\n $S_1$ in mm',
|
||||||
|
's_vert_2': 'Scherweg\n $S_2$ in mm'
|
||||||
|
}
|
||||||
|
|
||||||
|
columns_analyse = [
|
||||||
|
'F',
|
||||||
|
's_vert_sum',
|
||||||
|
's_vert_1',
|
||||||
|
's_vert_2',
|
||||||
|
's_piston',
|
||||||
|
]
|
||||||
|
|
||||||
|
if not (opath is None) & (pkname is None):
|
||||||
|
showplot = False
|
||||||
|
|
||||||
|
opath = os.path.join(opath, pkname, 'raw_data')
|
||||||
|
if not os.path.exists(opath):
|
||||||
|
os.makedirs(opath)
|
||||||
|
|
||||||
|
else:
|
||||||
|
showplot = True
|
||||||
|
|
||||||
|
for i, fit in self.fit.iterrows():
|
||||||
|
|
||||||
|
if not any([fit['r2_F'] < r2min, fit['r2_s_vert_sum'] < r2min]):
|
||||||
|
continue
|
||||||
|
|
||||||
|
data = self.data[int(fit['idx_data'])]
|
||||||
|
|
||||||
|
if data is None:
|
||||||
|
continue
|
||||||
|
|
||||||
|
freq = data['f'].unique()[0]
|
||||||
|
sigma = data['sigma_normal'].unique()[0]
|
||||||
|
s = data['extension'].unique()[0]
|
||||||
|
T = data['T'].unique()[0]
|
||||||
|
|
||||||
|
fig, axs = plt.subplots(len(columns_analyse),
|
||||||
|
1,
|
||||||
|
figsize=(8, len(columns_analyse) * 2),
|
||||||
|
sharex=True)
|
||||||
|
|
||||||
|
for idxcol, col in enumerate(columns_analyse):
|
||||||
|
x, y = data.index, data[col]
|
||||||
|
|
||||||
|
#add fit
|
||||||
|
f = self.fit.iloc[i]
|
||||||
|
parfit = {}
|
||||||
|
for k in ['amp', 'freq', 'phase', 'offset', 'slope']:
|
||||||
|
parfit[k] = f[f'fit_{k}_{col}']
|
||||||
|
|
||||||
|
yreg = fit_cos_eval(x, parfit)
|
||||||
|
|
||||||
|
if col in ['s_piston', 's_vert_sum']:
|
||||||
|
y = y - np.mean(y)
|
||||||
|
yreg = yreg - np.mean(yreg)
|
||||||
|
|
||||||
|
plt.sca(axs[idxcol])
|
||||||
|
plt.plot(x, y, label='Messdaten')
|
||||||
|
|
||||||
|
r2 = np.round(f[f'r2_{col}'], 3)
|
||||||
|
plt.plot(x,
|
||||||
|
yreg,
|
||||||
|
alpha=0.7,
|
||||||
|
label=f'Regression ($R^2 = {r2}$)')
|
||||||
|
|
||||||
|
if not ('F' in col):
|
||||||
|
s = f['extension']
|
||||||
|
parline = dict(lw=0.4,
|
||||||
|
ls='--',
|
||||||
|
color='lightgrey',
|
||||||
|
alpha=0.4,
|
||||||
|
label='Bereich des zul. Scherweges')
|
||||||
|
plt.axhspan(-s, s, **parline)
|
||||||
|
|
||||||
|
if idxcol == len(columns_analyse) - 1:
|
||||||
|
plt.xlabel('Zeit in s')
|
||||||
|
|
||||||
|
plt.ylabel(ylabel_dict[col])
|
||||||
|
plt.legend()
|
||||||
|
|
||||||
|
plt.tight_layout()
|
||||||
|
|
||||||
|
if showplot:
|
||||||
|
plt.show()
|
||||||
|
break
|
||||||
|
|
||||||
|
else:
|
||||||
|
ofile = f'{T}deg_{sigma}MPa_{freq}Hz_{s}mm'.replace('.', 'x')
|
||||||
|
ofile = os.path.join(opath, ofile + '.pdf')
|
||||||
|
|
||||||
|
plt.savefig(ofile)
|
||||||
|
plt.close()
|
||||||
|
|
||||||
|
|
||||||
|
class ShearTestExtension(ShearTest):
|
||||||
|
|
||||||
|
def runfit(self):
|
||||||
|
self._fit_data()
|
||||||
|
|
||||||
|
def file_in_db(self):
|
||||||
|
|
||||||
|
n = DynamicShearTestExtension.objects(filehash=self.filehash).count()
|
||||||
|
|
||||||
|
if n > 0:
|
||||||
|
return True
|
||||||
|
else:
|
||||||
|
return False
|
||||||
|
|
||||||
|
def save(self, material1, material2, bounding, meta: dict):
|
||||||
|
|
||||||
|
for i, fit in self.fit.iterrows():
|
||||||
|
|
||||||
|
data = self.data[int(fit['idx_data'])]
|
||||||
|
|
||||||
|
#check if data in db
|
||||||
|
n = DynamicShearTestExtension.objects(
|
||||||
|
f=fit['f'],
|
||||||
|
sigma_normal=fit['sigma_normal'],
|
||||||
|
T=fit['T'],
|
||||||
|
extension=fit['extension'],
|
||||||
|
material1=material1,
|
||||||
|
material2=material2,
|
||||||
|
bounding=bounding,
|
||||||
|
filehash=self.filehash,
|
||||||
|
).count()
|
||||||
|
if n > 0: continue
|
||||||
|
|
||||||
|
# save fit
|
||||||
|
|
||||||
|
values = {}
|
||||||
|
for col in ['F', 's_vert_1', 's_vert_2', 's_vert_sum']:
|
||||||
|
values[f'fit_amp_{col}'] = fit[f'fit_amp_{col}']
|
||||||
|
values[f'fit_freq_{col}'] = fit[f'fit_freq_{col}']
|
||||||
|
values[f'fit_phase_{col}'] = fit[f'fit_phase_{col}']
|
||||||
|
values[f'fit_offset_{col}'] = fit[f'fit_offset_{col}']
|
||||||
|
values[f'fit_slope_{col}'] = fit[f'fit_slope_{col}']
|
||||||
|
values[f'r2_{col}'] = fit[f'r2_{col}']
|
||||||
|
|
||||||
|
values.update(meta)
|
||||||
|
|
||||||
|
try:
|
||||||
|
r = DynamicShearTestExtension(
|
||||||
|
#metadata
|
||||||
|
f=fit['f'],
|
||||||
|
sigma_normal=fit['sigma_normal'],
|
||||||
|
T=fit['T'],
|
||||||
|
extension=fit['extension'],
|
||||||
|
filehash=self.filehash,
|
||||||
|
material1=material1,
|
||||||
|
material2=material2,
|
||||||
|
bounding=bounding,
|
||||||
|
#results
|
||||||
|
stiffness=fit['G'],
|
||||||
|
#
|
||||||
|
**values).save()
|
||||||
|
|
||||||
|
#save raw data
|
||||||
|
rdata = DataSheartest(
|
||||||
|
result_id=r.id,
|
||||||
|
time=data.index.values,
|
||||||
|
F=data['F'].values,
|
||||||
|
N=data['N'].values,
|
||||||
|
s_vert_1=data['s_vert_1'].values,
|
||||||
|
s_vert_2=data['s_vert_2'].values,
|
||||||
|
s_vert_sum=data['s_vert_sum'].values,
|
||||||
|
s_piston=data['s_piston'].values,
|
||||||
|
).save()
|
||||||
|
|
||||||
|
except:
|
||||||
|
print('error saving data')
|
||||||
|
raise
|
||||||
|
rdata.delete()
|
||||||
|
|
||||||
|
if self.archive_file:
|
||||||
|
mclient = MinioClient(self.s3_params['S3_URL'],
|
||||||
|
self.s3_params['S3_ACCESS_KEY'],
|
||||||
|
self.s3_params['S3_SECRET_KEY'],
|
||||||
|
bucket=str(meta['org_id']))
|
||||||
|
|
||||||
|
extension = os.path.splitext(self.file)[-1]
|
||||||
|
ofilename = self.filehash + extension
|
||||||
|
outpath = 'sheartest'
|
||||||
|
|
||||||
|
metadata_s3 = {
|
||||||
|
'project_id': str(meta['project_id']),
|
||||||
|
'user_id': str(meta['user_id']),
|
||||||
|
'filename': os.path.split(self.file)[-1],
|
||||||
|
'speciment': meta['speciment_name']
|
||||||
|
}
|
||||||
|
|
||||||
|
mclient.compress_and_upload_file(self.file,
|
||||||
|
ofilename,
|
||||||
|
outpath=outpath,
|
||||||
|
content_type="application/raw",
|
||||||
|
metadata=metadata_s3)
|
||||||
|
|
||||||
|
def _set_parameter(self):
|
||||||
|
|
||||||
|
self.split_data_based_on_parameter = [
|
||||||
|
'T', 'sigma_normal', 'f', 'extension'
|
||||||
|
]
|
||||||
|
|
||||||
|
self.col_as_int = ['N']
|
||||||
|
self.col_as_float = ['T', 'F', 'f', 's_vert_sum']
|
||||||
|
|
||||||
|
self.val_col_names = ['time', 'T', 'f', 'N', 'F', 's_vert_sum']
|
||||||
|
# Header names after standardization; check if exists
|
||||||
|
self.val_header_names = ['speciment_diameter']
|
||||||
|
|
||||||
|
self.columns_analyse = [
|
||||||
|
'F', 's_vert_sum', 's_vert_1', 's_vert_2', 's_piston'
|
||||||
|
]
|
||||||
|
|
||||||
|
self.number_of_load_cycles_for_analysis = 5
|
||||||
|
|
||||||
|
def _calc_missiong_values(self):
|
||||||
|
|
||||||
|
cols = self.data.columns
|
||||||
|
|
||||||
|
for c in ['vert']:
|
||||||
|
if not f's_{c}_sum' in cols:
|
||||||
|
self.data[f's_{c}_sum'] = self.data[[f's_{c}_1', f's_{c}_2'
|
||||||
|
]].sum(axis=1).div(2.0)
|
||||||
|
|
||||||
|
def _fit_data(self):
|
||||||
|
|
||||||
|
self.fit = []
|
||||||
|
|
||||||
|
for idx_data, data in enumerate(self.data):
|
||||||
|
|
||||||
|
if data is None: continue
|
||||||
|
|
||||||
|
data.index = data.index - data.index[0]
|
||||||
|
|
||||||
|
res = {}
|
||||||
|
res['idx_data'] = int(idx_data)
|
||||||
|
|
||||||
|
# Fitting
|
||||||
|
freq = float(np.round(data['f'].mean(), 4))
|
||||||
|
if (self.debug):
|
||||||
|
sigma_normal = np.round(data['sigma_normal'].mean(), 3)
|
||||||
|
T = np.round(data['T'].mean(), 3)
|
||||||
|
|
||||||
|
for idxcol, col in enumerate(self.columns_analyse):
|
||||||
|
|
||||||
|
if not col in data.columns: continue
|
||||||
|
|
||||||
|
x = data.index.values
|
||||||
|
y = data[col].values
|
||||||
|
|
||||||
|
# Fourier Transformation
|
||||||
|
"""
|
||||||
|
dt = np.diff(x).mean() #mean sampling rate
|
||||||
|
n = len(x)
|
||||||
|
|
||||||
|
res[f'psd_{col}'] = sfft.rfft(y) #compute the FFT
|
||||||
|
res[f'freq_{col}'] = sfft.rfftfreq(n, dt)
|
||||||
|
"""
|
||||||
|
|
||||||
|
res_fit = fit_cos(x, y, freq=freq, constfreq=True)
|
||||||
|
|
||||||
|
res[f'r2_{col}'] = res_fit['r2']
|
||||||
|
|
||||||
|
res[f'fit_amp_{col}'] = res_fit['amp']
|
||||||
|
res[f'fit_freq_{col}'] = res_fit['freq']
|
||||||
|
res[f'fit_phase_{col}'] = res_fit['phase']
|
||||||
|
res[f'fit_offset_{col}'] = res_fit['offset']
|
||||||
|
res[f'fit_slope_{col}'] = res_fit['slope']
|
||||||
|
|
||||||
|
## Schersteifigkeit berechnen
|
||||||
|
deltaF = res['fit_amp_F']
|
||||||
|
deltaS = res['fit_amp_s_vert_sum']
|
||||||
|
|
||||||
|
A = np.pi * self.meta['speciment_diameter']**2 / 4
|
||||||
|
tau = deltaF / A
|
||||||
|
gamma = deltaS / self.gap_width
|
||||||
|
|
||||||
|
res['G'] = tau / gamma
|
||||||
|
|
||||||
|
#metadaten
|
||||||
|
for c in ['T', 'extension', 'sigma_normal', 'f']:
|
||||||
|
res[c] = data[c][0]
|
||||||
|
|
||||||
|
self.fit.append(res)
|
||||||
|
|
||||||
|
if (self.debug) & (len(self.fit) > 5):
|
||||||
|
break
|
||||||
|
|
||||||
|
self.fit = pd.DataFrame.from_records(self.fit)
|
||||||
|
|
||||||
|
def plot_results(self, opath=None, pkname=None, r2min=0.96):
|
||||||
|
if not (opath is None) & (pkname is None):
|
||||||
|
showplot = False
|
||||||
|
|
||||||
|
opath = os.path.join(opath, pkname)
|
||||||
|
if not os.path.exists(opath):
|
||||||
|
os.makedirs(opath)
|
||||||
|
else:
|
||||||
|
showplot = True
|
||||||
|
|
||||||
|
dfplot = self.fit.copy()
|
||||||
|
for col in ['extension', 'fit_amp_s_vert_sum']:
|
||||||
|
dfplot[col] = dfplot[col].mul(1000)
|
||||||
|
|
||||||
|
fig, ax = plt.subplots()
|
||||||
|
|
||||||
|
xticks = list(dfplot['extension'].unique())
|
||||||
|
|
||||||
|
df = dfplot
|
||||||
|
df = df[(df['r2_F'] >= r2min) & (df['r2_s_vert_sum'] >= r2min)]
|
||||||
|
|
||||||
|
sns.scatterplot(
|
||||||
|
data=df,
|
||||||
|
x='fit_amp_s_vert_sum',
|
||||||
|
y='G',
|
||||||
|
hue='T',
|
||||||
|
ax=ax,
|
||||||
|
alpha=0.7,
|
||||||
|
#size=150,
|
||||||
|
size="G",
|
||||||
|
sizes=(50, 160),
|
||||||
|
edgecolor='k',
|
||||||
|
palette='muted',
|
||||||
|
zorder=10)
|
||||||
|
|
||||||
|
df = dfplot
|
||||||
|
df = df[(df['r2_F'] < r2min) & (df['r2_s_vert_sum'] < r2min)]
|
||||||
|
|
||||||
|
if not df.empty:
|
||||||
|
sns.scatterplot(data=df,
|
||||||
|
x='fit_amp_s_vert_sum',
|
||||||
|
y='G',
|
||||||
|
facecolor='grey',
|
||||||
|
alpha=0.5,
|
||||||
|
legend=False,
|
||||||
|
zorder=1,
|
||||||
|
ax=ax)
|
||||||
|
|
||||||
|
ax.set_xlabel(r'gemessene Scherwegamplitude in $\mu m$')
|
||||||
|
ax.set_ylabel(r'Scherseteifigkeit in MPa/mm')
|
||||||
|
|
||||||
|
ax.set_xticks(xticks)
|
||||||
|
ax.grid()
|
||||||
|
|
||||||
|
if not showplot:
|
||||||
|
ofile = os.path.join(opath, 'shearstiffness.pdf')
|
||||||
|
|
||||||
|
plt.savefig(ofile)
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
def plot_stats(self, opath=None, pkname=None, r2min=0.96):
|
||||||
|
if not (opath is None) & (pkname is None):
|
||||||
|
showplot = False
|
||||||
|
|
||||||
|
opath = os.path.join(opath, pkname)
|
||||||
|
if not os.path.exists(opath):
|
||||||
|
os.makedirs(opath)
|
||||||
|
else:
|
||||||
|
showplot = True
|
||||||
|
|
||||||
|
dfplot = self.fit.copy()
|
||||||
|
for col in ['extension', 'fit_amp_s_vert_sum']:
|
||||||
|
dfplot[col] = dfplot[col].mul(1000)
|
||||||
|
|
||||||
|
#r2
|
||||||
|
|
||||||
|
df = self.fit
|
||||||
|
|
||||||
|
fig, axs = plt.subplots(1, 2, sharey=True, sharex=True)
|
||||||
|
|
||||||
|
parscatter = dict(palette='muted', alpha=0.7, edgecolor='k', lw=0.3)
|
||||||
|
|
||||||
|
# r2
|
||||||
|
ax = axs[0]
|
||||||
|
sns.scatterplot(data=df,
|
||||||
|
x='fit_amp_s_vert_sum',
|
||||||
|
y='r2_F',
|
||||||
|
hue='T',
|
||||||
|
ax=ax,
|
||||||
|
**parscatter)
|
||||||
|
ax.set_ylabel('Bestimmtheitsmaß $R^2$')
|
||||||
|
ax.set_title('Kraft')
|
||||||
|
|
||||||
|
ax = axs[1]
|
||||||
|
sns.scatterplot(data=df,
|
||||||
|
x='fit_amp_s_vert_sum',
|
||||||
|
y='r2_s_vert_sum',
|
||||||
|
hue='T',
|
||||||
|
legend=False,
|
||||||
|
ax=ax,
|
||||||
|
**parscatter)
|
||||||
|
ax.set_ylabel('$R^2$ (S_{mittel})')
|
||||||
|
ax.set_title('mittlerer Scherweg')
|
||||||
|
|
||||||
|
for ax in axs.flatten():
|
||||||
|
ax.grid()
|
||||||
|
ax.set_xlabel(r'gemessene Scherwegamplitude in $\mu m$')
|
||||||
|
|
||||||
|
plt.tight_layout()
|
||||||
|
|
||||||
|
if not showplot:
|
||||||
|
ofile = os.path.join(opath, 'stats_r2.pdf')
|
||||||
|
plt.savefig(ofile)
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
|
||||||
|
class ShearTestExtensionLaborHart(ShearTestExtension):
|
||||||
|
|
||||||
|
def _define_units(self):
|
||||||
|
|
||||||
|
self.unit_F = 1 / 1000.0 #N
|
||||||
|
self.unit_t = 1 / 1000. #s
|
||||||
|
|
||||||
|
def _set_units(self):
|
||||||
|
|
||||||
|
#for col in ['F']:
|
||||||
|
# self.data[col] = self.data[col].mul(self.unit_F)
|
||||||
|
|
||||||
|
for col in ['time']:
|
||||||
|
self.data[col] = self.data[col].mul(self.unit_t)
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
def _read_data(self):
|
||||||
|
"""
|
||||||
|
read data from Labor Hart
|
||||||
|
"""
|
||||||
|
|
||||||
|
# parameter
|
||||||
|
encoding = 'latin-1'
|
||||||
|
skiprows = 14
|
||||||
|
hasunits = True
|
||||||
|
splitsign = ':;'
|
||||||
|
|
||||||
|
# metadata from file
|
||||||
|
meta = {}
|
||||||
|
|
||||||
|
with open(self.file, 'r', encoding=encoding) as f:
|
||||||
|
count = 0
|
||||||
|
|
||||||
|
for line in f:
|
||||||
|
count += 1
|
||||||
|
|
||||||
|
#remove whitespace
|
||||||
|
linesplit = line.strip()
|
||||||
|
linesplit = linesplit.split(splitsign)
|
||||||
|
|
||||||
|
if len(linesplit) == 2:
|
||||||
|
|
||||||
|
meta[linesplit[0]] = linesplit[1]
|
||||||
|
|
||||||
|
if count >= skiprows:
|
||||||
|
break
|
||||||
|
|
||||||
|
# data
|
||||||
|
data = pd.read_csv(self.file,
|
||||||
|
encoding=encoding,
|
||||||
|
skiprows=skiprows,
|
||||||
|
decimal=',',
|
||||||
|
sep=';')
|
||||||
|
|
||||||
|
## add header to df
|
||||||
|
with open(self.file, 'r', encoding=encoding) as f:
|
||||||
|
count = 0
|
||||||
|
|
||||||
|
for line in f:
|
||||||
|
count += 1
|
||||||
|
|
||||||
|
if count >= skiprows:
|
||||||
|
break
|
||||||
|
|
||||||
|
head = line.split(';')
|
||||||
|
data.columns = head
|
||||||
|
|
||||||
|
#clean data
|
||||||
|
data = data.dropna(axis=1)
|
||||||
|
|
||||||
|
#define in class
|
||||||
|
self.meta = meta
|
||||||
|
self.data = data
|
||||||
|
return True
|
||||||
|
|
||||||
|
def _standardize_meta(self):
|
||||||
|
|
||||||
|
keys = list(self.meta.keys())
|
||||||
|
for key in keys:
|
||||||
|
|
||||||
|
if any(map(key.__contains__, ['Probenbezeichnung'])):
|
||||||
|
self.meta['speciment'] = self.meta.pop(key)
|
||||||
|
|
||||||
|
elif any(map(key.__contains__, ['Datum/Uhrzeit'])):
|
||||||
|
self.meta['datetime'] = self.meta.pop(key)
|
||||||
|
try:
|
||||||
|
self.meta['datetime'] = pd.to_datetime(
|
||||||
|
self.meta['datetime'])
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
|
||||||
|
elif any(map(key.__contains__, ['Probenhöhe'])):
|
||||||
|
self.meta['speciment_height'] = float(
|
||||||
|
self.meta.pop(key).replace(',', '.'))
|
||||||
|
elif any(map(key.__contains__, ['Probendurchmesser'])):
|
||||||
|
self.meta['speciment_diameter'] = float(
|
||||||
|
self.meta.pop(key).replace(',', '.'))
|
||||||
|
elif any(map(key.__contains__, ['Solltemperatur'])):
|
||||||
|
self.meta['temperature'] = float(
|
||||||
|
self.meta.pop(key).replace(',', '.'))
|
||||||
|
elif any(map(key.__contains__, ['Prüfbedingungen'])):
|
||||||
|
self.meta['test_version'] = self.meta.pop(key)
|
||||||
|
elif any(map(key.__contains__, ['Name des VersAblf'])):
|
||||||
|
self.meta['test'] = self.meta.pop(key)
|
||||||
|
elif any(map(key.__contains__, ['Prüfer'])):
|
||||||
|
self.meta['examiner'] = self.meta.pop(key)
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
def _standardize_data(self):
|
||||||
|
|
||||||
|
colnames = list(self.data.columns)
|
||||||
|
|
||||||
|
for i, col in enumerate(colnames):
|
||||||
|
if col == 'TIME':
|
||||||
|
colnames[i] = 'time'
|
||||||
|
|
||||||
|
#set values
|
||||||
|
elif col == 'Sollwert Frequenz':
|
||||||
|
colnames[i] = 'f'
|
||||||
|
elif col == 'SollTemperatur':
|
||||||
|
colnames[i] = 'T'
|
||||||
|
elif col == 'Max Scherweg':
|
||||||
|
colnames[i] = 'extension'
|
||||||
|
elif col == 'Sollwert Normalspannung':
|
||||||
|
colnames[i] = 'sigma_normal'
|
||||||
|
elif col == 'Impulsnummer':
|
||||||
|
colnames[i] = 'N'
|
||||||
|
|
||||||
|
# measurements
|
||||||
|
|
||||||
|
elif col == 'Load':
|
||||||
|
colnames[i] = 'F'
|
||||||
|
elif col == 'Position':
|
||||||
|
colnames[i] = 's_piston'
|
||||||
|
|
||||||
|
elif col == 'VERTIKAL Links':
|
||||||
|
colnames[i] = 's_vert_1'
|
||||||
|
elif col == 'VERTIKAL Rechts':
|
||||||
|
colnames[i] = 's_vert_2'
|
||||||
|
|
||||||
|
elif col == 'HORIZONTAL links':
|
||||||
|
colnames[i] = 's_hor_1'
|
||||||
|
|
||||||
|
elif col == 'HOIZONTAL Rechts':
|
||||||
|
colnames[i] = 's_hor_2'
|
||||||
|
|
||||||
|
self.data.columns = colnames
|
||||||
|
|
||||||
|
|
||||||
|
class ShearTestExtensionTUDresdenGeosys(ShearTestExtension):
|
||||||
|
|
||||||
|
def _define_units(self):
|
||||||
|
|
||||||
|
self.unit_S = 1 / 1000.0 #N
|
||||||
|
|
||||||
|
def _set_units(self):
|
||||||
|
|
||||||
|
for col in [
|
||||||
|
's_vert_sum', 's_vert_1', 's_vert_2', 's_piston', 'extension'
|
||||||
|
]:
|
||||||
|
self.data[col] = self.data[col].mul(self.unit_S)
|
||||||
|
|
||||||
|
#convert internal units to global
|
||||||
|
f = np.mean([0.9 / 355, 0.6 / 234.0, 0.3 / 116.0])
|
||||||
|
|
||||||
|
self.data['sigma_normal'] = self.data['sigma_normal'].mul(f).apply(
|
||||||
|
lambda x: np.round(x, 1))
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
def _read_data(self):
|
||||||
|
"""
|
||||||
|
read data from Labor Hart
|
||||||
|
"""
|
||||||
|
|
||||||
|
# parameter
|
||||||
|
encoding = 'latin-1'
|
||||||
|
skiprows = 14
|
||||||
|
hasunits = True
|
||||||
|
splitsign = ':;'
|
||||||
|
|
||||||
|
head, data = read_geosys(self.file, '015')
|
||||||
|
|
||||||
|
#define in class
|
||||||
|
self.meta = head
|
||||||
|
self.data = data
|
||||||
|
return True
|
||||||
|
|
||||||
|
def _standardize_meta(self):
|
||||||
|
|
||||||
|
keys = list(self.meta.keys())
|
||||||
|
for key in keys:
|
||||||
|
|
||||||
|
if key == 'd':
|
||||||
|
self.meta['speciment_diameter'] = self.meta.pop(key)
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
def _standardize_data(self):
|
||||||
|
|
||||||
|
colnames = list(self.data.columns)
|
||||||
|
|
||||||
|
for i, col in enumerate(colnames):
|
||||||
|
|
||||||
|
#set values
|
||||||
|
if col == 'soll temperature':
|
||||||
|
colnames[i] = 'T'
|
||||||
|
elif col == 'soll extension':
|
||||||
|
colnames[i] = 'extension'
|
||||||
|
elif col == 'soll sigma':
|
||||||
|
colnames[i] = 'sigma_normal'
|
||||||
|
elif col == 'soll frequency':
|
||||||
|
colnames[i] = 'f'
|
||||||
|
|
||||||
|
elif col == 'Number of vertical cycles':
|
||||||
|
colnames[i] = 'N'
|
||||||
|
|
||||||
|
# measurements
|
||||||
|
elif col == 'vertical load from hydraulic pressure':
|
||||||
|
colnames[i] = 'F'
|
||||||
|
elif col == 'vertical position from hydraulic pressure':
|
||||||
|
colnames[i] = 's_piston'
|
||||||
|
|
||||||
|
elif col == 'Vertical position from LVDT 1':
|
||||||
|
colnames[i] = 's_vert_1'
|
||||||
|
elif col == 'Vertical position from LVDT 2':
|
||||||
|
colnames[i] = 's_vert_2'
|
||||||
|
|
||||||
|
self.data.columns = colnames
|
||||||
Reference in New Issue
Block a user