add archive as Paramter to Materials, optimize MaterialProperties

.drone.yml

@@ -0,0 +1,16 @@
+kind: pipeline
+type: docker
+name: lib-paveit
+
+environment:
+  MONGO_USER: ''
+  MONGO_PASSWD: ''
+  MONGO_URI: ''
+  MONGO_USER: ''
+
+steps:
+- name: test
+  image: python:3.11-buster
+  commands:
+  - pip install --no-cache-dir .
+  - pip install --no-cache-dir pytest

.gitignore

@@ -0,0 +1,180 @@
+temp
+.DS_Store
+
+
+# ---> Python
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# ---> VisualStudioCode
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+

Makefile

@@ -1,2 +1,5 @@
 link:
 	pip install -e ./
+
+test:
+	pytest -v -log_cli=True --log-cli-level=INFO tests

debug.csv

@@ -0,0 +1,4 @@
+,fit_F_amp,fit_F_freq,fit_F_phase,fit_F_offset,fit_F_slope,fit_F_r2,fit_F_max,fit_F_min,f,sigma,fit_s_hor_sum_amp,fit_s_hor_sum_freq,fit_s_hor_sum_phase,fit_s_hor_sum_offset,fit_s_hor_sum_slope,fit_s_hor_sum_r2,fit_s_hor_sum_max,fit_s_hor_sum_min,fit_s_hor_1_amp,fit_s_hor_1_freq,fit_s_hor_1_phase,fit_s_hor_1_offset,fit_s_hor_1_slope,fit_s_hor_1_r2,fit_s_hor_1_max,fit_s_hor_1_min,fit_s_hor_2_amp,fit_s_hor_2_freq,fit_s_hor_2_phase,fit_s_hor_2_offset,fit_s_hor_2_slope,fit_s_hor_2_r2,fit_s_hor_2_max,fit_s_hor_2_min,nu,E
+0,1162.037522728264,0.09999816445250176,3.2731742438169205,1657.4959341169797,0.022890975975805593,0.9999709812370754,2822.8786686693848,498.4860405788809,0.1,0.2,0.004904662057765795,0.09994473426198426,3.274570732678786,0.004472897149678457,3.4796345898322193e-06,0.9995438125784065,0.009632119781608398,-0.00042915385165576136,0.0022048443407161134,0.0999473113711256,3.2789165848392394,0.002036487114427019,1.317283541472095e-06,0.9992245191638016,0.0043773692868893654,-0.00022888205421645047,0.0026998634649033275,0.0999425971739857,3.271026693390654,0.00243640933189622,2.1623427295265008e-06,0.9993713553565571,0.005254750494719032,-0.0002479555587344695,0.2983926664681502,2260.236445571626
+1,1163.9861551163267,0.29999672326752724,3.271466866301432,1657.5773060905333,0.023592068619978698,0.999977491807627,2827.1702071859427,492.85935674606014,0.30003,0.2,0.004904630239776472,0.30002953724325576,3.261420279897325,0.004476978416102744,2.2128929628375675e-05,0.9997651921759285,0.009765634313234614,-0.0004482273561737665,0.0021960586065051407,0.300085988714776,3.2617587973425652,0.0020390391186955238,8.035203621628222e-06,0.9992996273163816,0.004420284672054908,-0.0002098085496983204,0.0027085993503841803,0.29998369085814713,3.2611491963027257,0.002437939646841411,1.4093566880537998e-05,0.9995179610005985,0.005354886393438715,-0.0002384188064754461,0.2983926664681502,2264.0413462626584
+2,1173.2940951101361,3.0019781539143713,3.1127799064755783,1652.6775323274487,2.2793532011736803,0.9997118511163391,2828.2192499344346,494.76670719786375,3.003,0.2,0.004927618845400971,3.0012837674744888,3.1051127487990566,0.004715737141843021,-1.2305236334063097e-05,0.998488708969846,0.009899148844860886,-0.0004005435948787328,0.0022065238872148044,3.0014146858816817,3.110359353742398,0.0021183309358349563,-8.842607057128579e-06,0.9965020191798836,0.004558567579810768,-0.00018119829292129186,0.002721172122260612,3.0011630113467382,3.100932209486545,0.00259739494570079,-3.4648940648246214e-06,0.9979287207765057,0.0054359487876403795,-0.000257492310993479,0.2983926664681502,2271.499199111919

poetry.lock

@@ -1,375 +0,0 @@
-# This file is automatically @generated by Poetry and should not be changed by hand.
-
-[[package]]
-name = "asteval"
-version = "0.9.29"
-description = "Safe, minimalistic evaluator of python expression using ast module"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "asteval-0.9.29-py3-none-any.whl", hash = "sha256:134e42fc4790582f2f926999e59abb444fb491046ba396836962268aad8a68a5"},
-    {file = "asteval-0.9.29.tar.gz", hash = "sha256:ab98c61ba9394149c774ae7861497e9c32580301aa693ca19746997216c31fab"},
-]
-
-[package.extras]
-all = ["Sphinx", "build", "coverage", "pytest", "pytest-cov", "twine"]
-dev = ["build", "twine"]
-doc = ["Sphinx"]
-test = ["coverage", "pytest", "pytest-cov"]
-
-[[package]]
-name = "dnspython"
-version = "2.3.0"
-description = "DNS toolkit"
-category = "main"
-optional = false
-python-versions = ">=3.7,<4.0"
-files = [
-    {file = "dnspython-2.3.0-py3-none-any.whl", hash = "sha256:89141536394f909066cabd112e3e1a37e4e654db00a25308b0f130bc3152eb46"},
-    {file = "dnspython-2.3.0.tar.gz", hash = "sha256:224e32b03eb46be70e12ef6d64e0be123a64e621ab4c0822ff6d450d52a540b9"},
-]
-
-[package.extras]
-curio = ["curio (>=1.2,<2.0)", "sniffio (>=1.1,<2.0)"]
-dnssec = ["cryptography (>=2.6,<40.0)"]
-doh = ["h2 (>=4.1.0)", "httpx (>=0.21.1)", "requests (>=2.23.0,<3.0.0)", "requests-toolbelt (>=0.9.1,<0.11.0)"]
-doq = ["aioquic (>=0.9.20)"]
-idna = ["idna (>=2.1,<4.0)"]
-trio = ["trio (>=0.14,<0.23)"]
-wmi = ["wmi (>=1.5.1,<2.0.0)"]
-
-[[package]]
-name = "future"
-version = "0.18.3"
-description = "Clean single-source support for Python 3 and 2"
-category = "main"
-optional = false
-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-files = [
-    {file = "future-0.18.3.tar.gz", hash = "sha256:34a17436ed1e96697a86f9de3d15a3b0be01d8bc8de9c1dffd59fb8234ed5307"},
-]
-
-[[package]]
-name = "lmfit"
-version = "1.1.0"
-description = "Least-Squares Minimization with Bounds and Constraints"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "lmfit-1.1.0-py3-none-any.whl", hash = "sha256:29f0540f94b3969a23db2b51abf309f327af8ea3667443ac4cd93d07fdfdb14f"},
-    {file = "lmfit-1.1.0.tar.gz", hash = "sha256:a2755b708ad7bad010178da28f082f55cbee7a084a625b452632e2d77b5391fb"},
-]
-
-[package.dependencies]
-asteval = ">=0.9.28"
-numpy = ">=1.19"
-scipy = ">=1.6"
-uncertainties = ">=3.1.4"
-
-[package.extras]
-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"]
-dev = ["build", "check-wheel-contents", "pre-commit", "twine"]
-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"]
-test = ["codecov", "coverage", "flaky", "pytest", "pytest-cov"]
-
-[[package]]
-name = "mongoengine"
-version = "0.26.0"
-description = "MongoEngine is a Python Object-Document Mapper for working with MongoDB."
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "mongoengine-0.26.0-py3-none-any.whl", hash = "sha256:020a0779d1830affc649f2760d8c408e998981f18898e425eb041915181d3a53"},
-    {file = "mongoengine-0.26.0.tar.gz", hash = "sha256:3f284bdcbe8d1a3a9b8ab7d3c3ed672d10b8fd2e545447cd1d75e40d6e978332"},
-]
-
-[package.dependencies]
-pymongo = ">=3.4,<5.0"
-
-[[package]]
-name = "numpy"
-version = "1.24.2"
-description = "Fundamental package for array computing in Python"
-category = "main"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "numpy-1.24.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:eef70b4fc1e872ebddc38cddacc87c19a3709c0e3e5d20bf3954c147b1dd941d"},
-    {file = "numpy-1.24.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e8d2859428712785e8a8b7d2b3ef0a1d1565892367b32f915c4a4df44d0e64f5"},
-    {file = "numpy-1.24.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6524630f71631be2dabe0c541e7675db82651eb998496bbe16bc4f77f0772253"},
-    {file = "numpy-1.24.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a51725a815a6188c662fb66fb32077709a9ca38053f0274640293a14fdd22978"},
-    {file = "numpy-1.24.2-cp310-cp310-win32.whl", hash = "sha256:2620e8592136e073bd12ee4536149380695fbe9ebeae845b81237f986479ffc9"},
-    {file = "numpy-1.24.2-cp310-cp310-win_amd64.whl", hash = "sha256:97cf27e51fa078078c649a51d7ade3c92d9e709ba2bfb97493007103c741f1d0"},
-    {file = "numpy-1.24.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:7de8fdde0003f4294655aa5d5f0a89c26b9f22c0a58790c38fae1ed392d44a5a"},
-    {file = "numpy-1.24.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4173bde9fa2a005c2c6e2ea8ac1618e2ed2c1c6ec8a7657237854d42094123a0"},
-    {file = "numpy-1.24.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4cecaed30dc14123020f77b03601559fff3e6cd0c048f8b5289f4eeabb0eb281"},
-    {file = "numpy-1.24.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9a23f8440561a633204a67fb44617ce2a299beecf3295f0d13c495518908e910"},
-    {file = "numpy-1.24.2-cp311-cp311-win32.whl", hash = "sha256:e428c4fbfa085f947b536706a2fc349245d7baa8334f0c5723c56a10595f9b95"},
-    {file = "numpy-1.24.2-cp311-cp311-win_amd64.whl", hash = "sha256:557d42778a6869c2162deb40ad82612645e21d79e11c1dc62c6e82a2220ffb04"},
-    {file = "numpy-1.24.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:d0a2db9d20117bf523dde15858398e7c0858aadca7c0f088ac0d6edd360e9ad2"},
-    {file = "numpy-1.24.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:c72a6b2f4af1adfe193f7beb91ddf708ff867a3f977ef2ec53c0ffb8283ab9f5"},
-    {file = "numpy-1.24.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c29e6bd0ec49a44d7690ecb623a8eac5ab8a923bce0bea6293953992edf3a76a"},
-    {file = "numpy-1.24.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2eabd64ddb96a1239791da78fa5f4e1693ae2dadc82a76bc76a14cbb2b966e96"},
-    {file = "numpy-1.24.2-cp38-cp38-win32.whl", hash = "sha256:e3ab5d32784e843fc0dd3ab6dcafc67ef806e6b6828dc6af2f689be0eb4d781d"},
-    {file = "numpy-1.24.2-cp38-cp38-win_amd64.whl", hash = "sha256:76807b4063f0002c8532cfeac47a3068a69561e9c8715efdad3c642eb27c0756"},
-    {file = "numpy-1.24.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4199e7cfc307a778f72d293372736223e39ec9ac096ff0a2e64853b866a8e18a"},
-    {file = "numpy-1.24.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:adbdce121896fd3a17a77ab0b0b5eedf05a9834a18699db6829a64e1dfccca7f"},
-    {file = "numpy-1.24.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:889b2cc88b837d86eda1b17008ebeb679d82875022200c6e8e4ce6cf549b7acb"},
-    {file = "numpy-1.24.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f64bb98ac59b3ea3bf74b02f13836eb2e24e48e0ab0145bbda646295769bd780"},
-    {file = "numpy-1.24.2-cp39-cp39-win32.whl", hash = "sha256:63e45511ee4d9d976637d11e6c9864eae50e12dc9598f531c035265991910468"},
-    {file = "numpy-1.24.2-cp39-cp39-win_amd64.whl", hash = "sha256:a77d3e1163a7770164404607b7ba3967fb49b24782a6ef85d9b5f54126cc39e5"},
-    {file = "numpy-1.24.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:92011118955724465fb6853def593cf397b4a1367495e0b59a7e69d40c4eb71d"},
-    {file = "numpy-1.24.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f9006288bcf4895917d02583cf3411f98631275bc67cce355a7f39f8c14338fa"},
-    {file = "numpy-1.24.2-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:150947adbdfeceec4e5926d956a06865c1c690f2fd902efede4ca6fe2e657c3f"},
-    {file = "numpy-1.24.2.tar.gz", hash = "sha256:003a9f530e880cb2cd177cba1af7220b9aa42def9c4afc2a2fc3ee6be7eb2b22"},
-]
-
-[[package]]
-name = "pandas"
-version = "1.5.3"
-description = "Powerful data structures for data analysis, time series, and statistics"
-category = "main"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "pandas-1.5.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3749077d86e3a2f0ed51367f30bf5b82e131cc0f14260c4d3e499186fccc4406"},
-    {file = "pandas-1.5.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:972d8a45395f2a2d26733eb8d0f629b2f90bebe8e8eddbb8829b180c09639572"},
-    {file = "pandas-1.5.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:50869a35cbb0f2e0cd5ec04b191e7b12ed688874bd05dd777c19b28cbea90996"},
-    {file = "pandas-1.5.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3ac844a0fe00bfaeb2c9b51ab1424e5c8744f89860b138434a363b1f620f354"},
-    {file = "pandas-1.5.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a0a56cef15fd1586726dace5616db75ebcfec9179a3a55e78f72c5639fa2a23"},
-    {file = "pandas-1.5.3-cp310-cp310-win_amd64.whl", hash = "sha256:478ff646ca42b20376e4ed3fa2e8d7341e8a63105586efe54fa2508ee087f328"},
-    {file = "pandas-1.5.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6973549c01ca91ec96199e940495219c887ea815b2083722821f1d7abfa2b4dc"},
-    {file = "pandas-1.5.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c39a8da13cede5adcd3be1182883aea1c925476f4e84b2807a46e2775306305d"},
-    {file = "pandas-1.5.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f76d097d12c82a535fda9dfe5e8dd4127952b45fea9b0276cb30cca5ea313fbc"},
-    {file = "pandas-1.5.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e474390e60ed609cec869b0da796ad94f420bb057d86784191eefc62b65819ae"},
-    {file = "pandas-1.5.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5f2b952406a1588ad4cad5b3f55f520e82e902388a6d5a4a91baa8d38d23c7f6"},
-    {file = "pandas-1.5.3-cp311-cp311-win_amd64.whl", hash = "sha256:bc4c368f42b551bf72fac35c5128963a171b40dce866fb066540eeaf46faa003"},
-    {file = "pandas-1.5.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:14e45300521902689a81f3f41386dc86f19b8ba8dd5ac5a3c7010ef8d2932813"},
-    {file = "pandas-1.5.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:9842b6f4b8479e41968eced654487258ed81df7d1c9b7b870ceea24ed9459b31"},
-    {file = "pandas-1.5.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:26d9c71772c7afb9d5046e6e9cf42d83dd147b5cf5bcb9d97252077118543792"},
-    {file = "pandas-1.5.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5fbcb19d6fceb9e946b3e23258757c7b225ba450990d9ed63ccceeb8cae609f7"},
-    {file = "pandas-1.5.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:565fa34a5434d38e9d250af3c12ff931abaf88050551d9fbcdfafca50d62babf"},
-    {file = "pandas-1.5.3-cp38-cp38-win32.whl", hash = "sha256:87bd9c03da1ac870a6d2c8902a0e1fd4267ca00f13bc494c9e5a9020920e1d51"},
-    {file = "pandas-1.5.3-cp38-cp38-win_amd64.whl", hash = "sha256:41179ce559943d83a9b4bbacb736b04c928b095b5f25dd2b7389eda08f46f373"},
-    {file = "pandas-1.5.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c74a62747864ed568f5a82a49a23a8d7fe171d0c69038b38cedf0976831296fa"},
-    {file = "pandas-1.5.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c4c00e0b0597c8e4f59e8d461f797e5d70b4d025880516a8261b2817c47759ee"},
-    {file = "pandas-1.5.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a50d9a4336a9621cab7b8eb3fb11adb82de58f9b91d84c2cd526576b881a0c5a"},
-    {file = "pandas-1.5.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dd05f7783b3274aa206a1af06f0ceed3f9b412cf665b7247eacd83be41cf7bf0"},
-    {file = "pandas-1.5.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9f69c4029613de47816b1bb30ff5ac778686688751a5e9c99ad8c7031f6508e5"},
-    {file = "pandas-1.5.3-cp39-cp39-win32.whl", hash = "sha256:7cec0bee9f294e5de5bbfc14d0573f65526071029d036b753ee6507d2a21480a"},
-    {file = "pandas-1.5.3-cp39-cp39-win_amd64.whl", hash = "sha256:dfd681c5dc216037e0b0a2c821f5ed99ba9f03ebcf119c7dac0e9a7b960b9ec9"},
-    {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"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux1_i686.whl", hash = "sha256:66413c50d510e5bcb0afc79880d1693a2185bcea003600ed898ada31338c004e"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:9b87b23570565a6ddaa9244d87811c2ee9cffb02a753c8a2da9c077283d85845"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux2014_i686.whl", hash = "sha256:695939036a320f4329ccf1627edefbbb67cc7892b8222d297b0dd2313742bfee"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux2014_ppc64le.whl", hash = "sha256:ffcc8394123ea8d43fff8e5d000095fe7741ce3f8988366c5c919c4f5eb179d3"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux2014_s390x.whl", hash = "sha256:943f208840777f34312c103a2d1caab02d780c4e9be26b3714acf6c4715ba7e1"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:01f7cbe88d22440b6594c955e37312d932fd632ffed1a86d0c361503ca82cc9d"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cdb87309de97c63cb9a69132e1cb16be470e58cffdfbad68fdd1dc292b22a840"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d86c35d94b5499689354ccbc48438a79f449481ee6300f3e905748edceed78e7"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a966d5304b7d90c45c404914e06bbf02c5bf7e99685c6c12f0047ef2aa837142"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:be1d2ce7e269215c3ee9a215e296b7a744aff4f39233486d2c4d77f5f0c561a6"},
-    {file = "pymongo-4.3.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:55b6163dac53ef1e5d834297810c178050bd0548a4136cd4e0f56402185916ca"},
-    {file = "pymongo-4.3.3-cp310-cp310-win32.whl", hash = "sha256:dc0cff74cd36d7e1edba91baa09622c35a8a57025f2f2b7a41e3f83b1db73186"},
-    {file = "pymongo-4.3.3-cp310-cp310-win_amd64.whl", hash = "sha256:cafa52873ae12baa512a8721afc20de67a36886baae6a5f394ddef0ce9391f91"},
-    {file = "pymongo-4.3.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:599d3f6fbef31933b96e2d906b0f169b3371ff79ea6aaf6ecd76c947a3508a3d"},
-    {file = "pymongo-4.3.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c0640b4e9d008e13956b004d1971a23377b3d45491f87082161c92efb1e6c0d6"},
-    {file = "pymongo-4.3.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:341221e2f2866a5960e6f8610f4cbac0bb13097f3b1a289aa55aba984fc0d969"},
-    {file = "pymongo-4.3.3-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e7fac06a539daef4fcf5d8288d0d21b412f9b750454cd5a3cf90484665db442a"},
-    {file = "pymongo-4.3.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d3a51901066696c4af38c6c63a1f0aeffd5e282367ff475de8c191ec9609b56d"},
-    {file = "pymongo-4.3.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f3055510fdfdb1775bc8baa359783022f70bb553f2d46e153c094dfcb08578ff"},
-    {file = "pymongo-4.3.3-cp311-cp311-win32.whl", hash = "sha256:524d78673518dcd352a91541ecd2839c65af92dc883321c2109ef6e5cd22ef23"},
-    {file = "pymongo-4.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:b8a03af1ce79b902a43f5f694c4ca8d92c2a4195db0966f08f266549e2fc49bc"},
-    {file = "pymongo-4.3.3-cp37-cp37m-macosx_10_6_intel.whl", hash = "sha256:39b03045c71f761aee96a12ebfbc2f4be89e724ff6f5e31c2574c1a0e2add8bd"},
-    {file = "pymongo-4.3.3-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:6fcfbf435eebf8a1765c6d1f46821740ebe9f54f815a05c8fc30d789ef43cb12"},
-    {file = "pymongo-4.3.3-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:7d43ac9c7eeda5100fb0a7152fab7099c9cf9e5abd3bb36928eb98c7d7a339c6"},
-    {file = "pymongo-4.3.3-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:3b93043b14ba7eb08c57afca19751658ece1cfa2f0b7b1fb5c7a41452fbb8482"},
-    {file = "pymongo-4.3.3-cp37-cp37m-manylinux2014_i686.whl", hash = "sha256:c09956606c08c4a7c6178a04ba2dd9388fcc5db32002ade9c9bc865ab156ab6d"},
-    {file = "pymongo-4.3.3-cp37-cp37m-manylinux2014_ppc64le.whl", hash = "sha256:b0cfe925610f2fd59555bb7fc37bd739e4b197d33f2a8b2fae7b9c0c6640318c"},
-    {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"},
-    {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"

pyproject._toml

@@ -1,33 +0,0 @@
-[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"

pyproject.toml

@@ -0,0 +1,6 @@
+[build-system]
+requires = [
+    "setuptools >= 40.8.0",
+    "wheel"
+]
+build-backend = "setuptools.build_meta"

setup.cfg

@@ -16,6 +16,9 @@ install_requires =
     matplotlib
     seaborn
     mongoengine
+    statsmodels
+    toml
+    minio
 
 [options.packages.find]
 where=src

setup.py

@@ -1,6 +0,0 @@
-#!/usr/bin/env python
-
-import setuptools
-
-if __name__ == "__main__":
-    setuptools.setup()

src/paveit/__init__.py

@@ -1,4 +1,5 @@
 # main __init__.py
 from .analysis import *
+from .functions import *
 from .helper import *
 from .labtest import *

src/paveit/datamodels/__init__.py

@@ -0,0 +1,19 @@
+from .calibration import *
+from .citt import *
+from .components import *
+from .data import *
+from .enumeration import *
+from .labworks import *
+from .machines import *
+from .material import *
+from .material_properties import *
+from .messages import *
+from .metrics import *
+from .norm_documents import *
+from .norm_specification import *
+from .project import *
+from .regression import *
+from .sheartest import *
+from .taskmanager import *
+from .usermanagement import *
+from .workpackage import *

src/paveit/datamodels/calibration.py

@@ -0,0 +1,91 @@
+import datetime
+
+from mongoengine import *
+
+from paveit.datamodels.components import Components
+from paveit.helper import fetch_recursive, mongo_to_dict
+
+from .usermanagement import Organisation
+
+
+class Calibrarion(Document):
+    """ Durchführung von Kalibrierungen der Prüfmittel, externer Dienst """
+        
+    company = StringField(required=True)
+    name = StringField(required=True)
+    
+    department = StringField(required=False)
+    room = StringField(required=False)
+    serialnumber = StringField(required=False)
+    
+    extrainfo = StringField(required=False)
+    
+    component = LazyReferenceField(Components, required=True)
+    
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    org_id = LazyReferenceField(Organisation,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+    tags = ListField(StringField())
+    
+    def to_dict(self):
+        # convert data to dict
+        data = fetch_recursive(self)
+        data = mongo_to_dict(data)
+       
+        return data
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'calibration',
+        "db_alias": 'dblabtests',
+        'indexes': [
+            [("name", 1)],
+        ]
+    }
+    
+#####################
+class Monitoring(Document):
+    """ Eigenüberwachung der Prüfmittel, interne Durchführung """
+    
+    date = DateTimeField(default=datetime.datetime.now,
+                        wtf_options={"render_kw": {
+                            "step": "60"
+                        }})
+    
+    component = LazyReferenceField(Components, required=True)
+    
+    org_id = LazyReferenceField(Organisation,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+    tags = ListField(StringField())
+    
+    def to_dict(self):
+        # convert data to dict
+        data = fetch_recursive(self)
+        data = mongo_to_dict(data)
+       
+        return data
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'calibration',
+        "db_alias": 'dblabtests',
+        'indexes': [
+            [("name", 1)],
+        ]
+    }

src/paveit/datamodels/citt.py

@@ -0,0 +1,181 @@
+import datetime
+
+from mongoengine import *
+
+from .taskmanager import TaskManagerBase
+
+
+class CyclicIndirectTensileTest(Document):
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    standard = StringField(default='TP Asphalt Teil 24')
+
+    #org_id = LazyReferenceField(Organisation, required=True)
+
+    #user_id = LazyReferenceField(User,
+    #                             required=True,
+    #                             reverse_delete_rule=DO_NOTHING)
+
+    task_id = LazyReferenceField(TaskManagerBase, required=True)
+
+    tags = ListField(StringField())
+
+
+    filehash = StringField(required=True)
+    
+    speciment_name = StringField(required=True, default=None)
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'lab_citt',
+        "db_alias": 'dblabtests',
+    }
+
+
+class CITTSiffnessResults(CyclicIndirectTensileTest):
+
+    #metadata
+    f_set = FloatField()
+    sigma_set = FloatField()
+    T_set = FloatField()
+    speciment_diameter = FloatField(required=True)    
+    speciment_height = FloatField(required=True)
+
+
+    N_from = IntField()
+    N_to = IntField()
+    N_tot = IntField()
+    n_samples_per_cycle = IntField()
+
+    #results
+    stiffness = FloatField()
+    nu = FloatField()
+    phase = FloatField()
+    el_strains = FloatField()
+    #required parameter
+    ## F
+    F_amp = FloatField()
+    F_freq = FloatField()
+    F_phase = FloatField()
+    F_offset = FloatField()
+    F_slope = FloatField()
+    F_r2 = FloatField()
+    F_cycle_min = ListField(FloatField())
+    F_min = FloatField()
+    F_min_std = FloatField()
+    F_min_diff_rel = FloatField()
+    F_cycle_max = ListField(FloatField())
+    F_max = FloatField()
+    F_max_std = FloatField()
+    F_max_diff_rel = FloatField()
+    F_cycle_mean = ListField(FloatField())
+    F_mean = FloatField()
+    F_mean_std = FloatField()
+    F_mean_diff_rel = FloatField()
+    F_cycle_diff = ListField(FloatField())
+    F_diff = FloatField()
+    F_diff_std = FloatField()
+    F_diff_diff_rel= FloatField()
+    
+    ## S1
+    s_hor_1_amp = FloatField()
+    s_hor_1_freq = FloatField()
+    s_hor_1_phase = FloatField()
+    s_hor_1_offset = FloatField()
+    s_hor_1_slope = FloatField()
+    s_hor_1_r2 = FloatField()
+    s_hor_1_cycle_min = ListField(FloatField())
+    s_hor_1_min = FloatField()
+    s_hor_1_min_std = FloatField()
+    s_hor_1_min_diff_rel = FloatField()
+    s_hor_1_cycle_max = ListField(FloatField())
+    s_hor_1_max = FloatField()
+    s_hor_1_max_std = FloatField()
+    s_hor_1_max_diff_rel = FloatField()
+    s_hor_1_cycle_mean = ListField(FloatField())
+    s_hor_1_mean = FloatField()
+    s_hor_1_mean_std = FloatField()
+    s_hor_1_mean_diff_rel = FloatField()
+    s_hor_1_cycle_diff = ListField(FloatField())
+    s_hor_1_diff = FloatField()
+    s_hor_1_diff_std = FloatField()
+    s_hor_1_diff_diff_rel = FloatField()
+    ## S2
+    s_hor_2_amp = FloatField()
+    s_hor_2_freq = FloatField()
+    s_hor_2_phase = FloatField()
+    s_hor_2_offset = FloatField()
+    s_hor_2_slope = FloatField()
+    s_hor_2_r2 = FloatField()
+    s_hor_2_cycle_min = ListField(FloatField())
+    s_hor_2_min = FloatField()
+    s_hor_2_min_std = FloatField()
+    s_hor_2_min_diff_rel = FloatField()
+    s_hor_2_cycle_max = ListField(FloatField())
+    s_hor_2_max = FloatField()
+    s_hor_2_max_std = FloatField()
+    s_hor_2_max_diff_rel = FloatField()
+    s_hor_2_cycle_mean = ListField(FloatField())
+    s_hor_2_mean = FloatField()
+    s_hor_2_mean_std = FloatField()
+    s_hor_2_mean_diff_rel = FloatField()
+    s_hor_2_cycle_diff = ListField(FloatField())
+    s_hor_2_diff = FloatField()
+    s_hor_2_diff_std = FloatField()
+    s_hor_2_diff_diff_rel = FloatField()
+    ## S-Sum
+    s_hor_sum_amp = FloatField()
+    s_hor_sum_freq = FloatField()
+    s_hor_sum_phase = FloatField()
+    s_hor_sum_offset = FloatField()
+    s_hor_sum_slope = FloatField()
+    s_hor_sum_r2 = FloatField()
+    s_hor_sum_cycle_min = ListField(FloatField())
+    s_hor_sum_min = FloatField()
+    s_hor_sum_min_std = FloatField()
+    s_hor_sum_min_diff_rel = FloatField()
+    s_hor_sum_cycle_max = ListField(FloatField())
+    s_hor_sum_max = FloatField()
+    s_hor_sum_max_std = FloatField()
+    s_hor_sum_max_diff_rel = FloatField()
+    s_hor_sum_cycle_mean = ListField(FloatField())
+    s_hor_sum_mean = FloatField()
+    s_hor_sum_mean_std = FloatField()
+    s_hor_sum_mean_diff_rel = FloatField()
+    s_hor_sum_cycle_diff = ListField(FloatField())
+    s_hor_sum_diff = FloatField()
+    s_hor_sum_diff_std = FloatField()
+    s_hor_sum_diff_diff_rel = FloatField()
+
+    #optional parameter
+    ## Piston
+    s_piston_amp = FloatField(required=False)
+    s_piston_freq = FloatField(required=False)
+    s_piston_phase = FloatField(required=False)
+    s_piston_offset = FloatField(required=False)
+    s_piston_slope = FloatField(required=False)
+    s_piston_r2 = FloatField(required=False)
+    s_piston_cycle_min = ListField(FloatField(),required=False)
+    s_piston_min = FloatField(required=False)
+    s_piston_min_std = FloatField(required=False)
+    s_piston_min_diff_rel = FloatField(required=False)
+    s_piston_cycle_max = ListField(FloatField(),required=False)
+    s_piston_max = FloatField(required=False)
+    s_piston_max_std = FloatField(required=False)
+    s_piston_max_diff_rel = FloatField(required=False)
+    s_piston_cycle_mean = ListField(FloatField(),required=False)
+    s_piston_mean = FloatField(required=False)
+    s_piston_mean_std = FloatField(required=False)
+    s_piston_mean_diff_rel = FloatField(required=False)
+    s_piston_cycle_diff = ListField(FloatField(),required=False)
+    s_piston_diff = FloatField(required=False)
+    s_piston_diff_std = FloatField(required=False)
+    s_piston_diff_diff_rel = FloatField(required=False)

src/paveit/datamodels/client.py

@@ -0,0 +1,48 @@
+from mongoengine import *
+
+import datetime
+
+from .usermanagement import Organisation, User
+
+class Client(Document):
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+    
+    org_id = LazyReferenceField(Organisation, required=True)
+    user_id = LazyReferenceField(User,
+                                 required=True,
+                                 reverse_delete_rule=DO_NOTHING)
+    
+    name = StringField(max_length=100)
+    name_short = StringField(max_length=100)
+    
+    customer_id = StringField(max_length=100)
+    
+    address_country = StringField(max_length=100, default='Germany')
+    address_road = StringField(max_length=100)
+    address_plz = StringField(max_length=5)
+    address_city = StringField(max_length=100)
+    
+    vat_id = StringField(max_length=100) #Umsatzsteuer
+    
+    billing_country = StringField(max_length=100, default='Germany')
+    billing_road = StringField(max_length=100)
+    billing_plz = StringField(max_length=5)
+    billing_city = StringField(max_length=100)
+    billing_addition = StringField(max_length=100)
+    
+    
+
+    
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'clients',
+        "db_alias": 'dblabtests',
+    }

src/paveit/datamodels/components.py

@@ -0,0 +1,67 @@
+import datetime
+
+from mongoengine import *
+
+from paveit.helper import fetch_recursive, mongo_to_dict
+
+from .usermanagement import Organisation
+
+
+class Components(Document):
+    
+    company = StringField(required=True)
+    name = StringField(required=True)
+    
+    department = StringField(required=False)
+    room = StringField(required=False)
+    
+    serialnumber = StringField(required=True) # Seriennummer
+    internalnumber = StringField(required=False) # Interne Bezeichnung
+    
+    extrainfo = StringField(required=False)
+    year_manufacture = IntField(min=1900, max=2100, required=False)
+    
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    org_id = LazyReferenceField(Organisation,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+    tags = ListField(StringField())
+    
+    def to_dict(self):
+        # convert data to dict
+        data = fetch_recursive(self)
+        data = mongo_to_dict(data)
+       
+        return data
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'components',
+        "db_alias": 'dblabtests',
+        'indexes': [
+            [("name", 1)],
+        ]
+    }
+
+# Servohydraulik
+class ComponentsServoHydraulicMachineTemperatureControl(Components):
+    """ Kraftmessdosen """
+    pass
+
+class ComponentsServoHydraulicMachineKMD(Components):
+    """ Kraftmessdosen """
+    pass
+
+class ComponentsServoHydraulicMachineLVDT(Components):
+    """ Wegaufnehmer """
+    pass
+

src/paveit/datamodels/data.py

@@ -0,0 +1,87 @@
+import datetime
+
+from mongoengine import *
+
+from .citt import CyclicIndirectTensileTest
+from .sheartest import DynamicShearTest
+
+
+class RawSinData(Document):
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    time = ListField(FloatField())
+    F = ListField(FloatField())
+    N = ListField(IntField())
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'data_rawsine',
+        "db_alias": 'dblabtests',
+    }
+    
+class RawData(Document):
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'data_raw',
+        "db_alias": 'dblabtests',
+    }
+
+
+class DataSheartest(RawSinData):
+
+    #results
+    result = LazyReferenceField(DynamicShearTest,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+    # data
+    s_vert_1 = ListField(FloatField())
+    s_vert_2 = ListField(FloatField())
+    s_piston = ListField(FloatField(), required=False)
+    s_hor_1 = ListField(FloatField(), required=False)
+    s_hor_2 = ListField(FloatField(), required=False)
+
+
+class CITTSiffness(RawSinData):
+
+    result = LazyReferenceField(CyclicIndirectTensileTest,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+    # data
+    s_hor_1 = ListField(FloatField())
+    s_hor_2 = ListField(FloatField())
+    s_hor_sum = ListField(FloatField())
+    s_piston = ListField(FloatField(), required=False)
+
+# Single Data Points
+
+
+class BitumenParameterStrassenbaubitumen(RawData):
+    
+    penetration = FloatField(min_value=0, max_value=1000)
+    softening_point = FloatField(min_value=0, max_value =500)
+    flash_point = FloatField(min_value=0, max_value=500)
+    solubility = FloatField(default=99.0, min_value=0, max_value=100)
+    fraass_breaking_point = FloatField(min_value=-100, max_value=100)
+    hardening_resistance_penetration = FloatField(min_value=0, max_value=100)
+    hardening_resistance_softening_point= FloatField(min_value=0, max_value=100)
+    hardening_resistance_masschange = FloatField(min_value=0, max_value=100)

src/paveit/datamodels/enumeration.py

@@ -0,0 +1,113 @@
+import datetime
+from enum import Enum
+
+from mongoengine import *
+
+from paveit.helper import fetch_recursive, mongo_to_dict
+
+class ModelSelection(Enum):
+    """
+    Welche Module sind in der App verfügbar
+    """
+    
+    BASE = 'base'
+    ADVANCED = 'advanced'
+    
+class ProcessStatusEnum(Enum):
+    """Status eines Prozesses wie Projekt, Task
+
+    Ongoing: Ein fortlaufender Prozess, der noch nicht abgeschlossen ist.
+    In progress: Der Prozess ist aktiv und befindet sich in Arbeit.
+    Stalled: Der Prozess ist ins Stocken geraten oder vorübergehend gestoppt.
+    Completed: Der Prozess ist abgeschlossen oder beendet.
+    Pending: Der Prozess steht noch aus oder wurde noch nicht gestartet.
+    Suspended: Der Prozess wurde vorübergehend oder dauerhaft ausgesetzt.
+    Initiated: Der Prozess wurde gestartet oder eingeleitet.
+    Advanced: Der Prozess hat einen hohen Grad an Fortschritt oder Entwicklung erreicht.
+    Delayed: Der Prozess wurde verzögert und läuft hinter dem Zeitplan zurück.
+    Finalized: Der Prozess wurde abgeschlossen, und alle Details sind geklärt.
+    """
+    INITIATED='initiated'
+    ONGOING = 'ongoing'
+    COMPLETED = 'completed'
+    ARCHIVE='archive'
+ 
+class RelationalOperatorsEnum(Enum):
+    between = 'between'
+    lt = 'less than or equal to'
+    gt = 'greater than or equal to'
+    
+class BitumenCategoryEnum(Enum):
+    Strassenbau = "Straßenbaubitumen"
+    PmbA = "Elastomermodifizierte Bitumen"
+    PmbC = "Plastomermodifizierte Bitumen"
+    
+class AsphaltCategoryEnum(Enum):
+    ATS = "Asphalttragschichtmischgut"
+    ABS = "Asphaltbindermischgut"
+    ADS = "Asphaltdeckschichtmischgut"
+    SMA = "Splittmastixasphalt"
+    MA = "Gussasphalt"
+    PA = "Offenporiger Asphalt"
+    ACTD = 'Asphalttragdeckschichtmischgut'
+
+class TaskType(Enum):
+    
+    SINGLE = "single"
+    FLOW = "flow"
+
+class LabtestsEnum(Enum):
+    # Performance
+    CITTStiffness = 'CITTStiffness'
+    SHEARStiffness = 'SheartestStiffness'
+
+class Config(Document):
+    
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    tags = ListField(StringField())
+    
+    def to_dict(self):
+        # convert data to dict
+        data = fetch_recursive(self)
+        data = mongo_to_dict(data)
+        
+        return data
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'enumeration',
+        "db_alias": 'dblabtests',
+        'indexes': [
+            [("material", 1)],
+            [("name", 1)],
+        ]
+    }
+    
+    
+class Labtest(Config):
+    """ Speicherung der Zuordnung zwischen Firma und Labortest. Die hier
+    definierten Tests können die Kunden verwenden. Somit muss erstmal jeder
+    Einzeltest für jeden Kunden neu angelegt werden, jedoch ermöglicht dies auch
+    eine Steuerung der verfügbaren Module. Die Versuche werden nachfolgend in
+    Klasse/Kategorien eingeteil, um eine Trennung in Module zu ermöglichen."""
+    
+    test = EnumField(LabtestsEnum, required=True)    
+    modul = ListField(EnumField(ModelSelection, required=True, default=ModelSelection.BASE))
+    typ = EnumField(TaskType, required=True, default=TaskType.SINGLE)
+    
+    
+class LabtestPerformAsphalt(Labtest):
+    """ Performanceprüfung Asphalt """
+    pass
+
+class LabtestPerformBitumen(Labtest):
+    """ Performanceprüfung Bitumen """
+    pass

src/paveit/datamodels/labworks.py

@@ -0,0 +1,28 @@
+from mongoengine import *
+
+from .taskmanager import TaskManagerBase
+
+
+# Vorbereitungen Performance Untersuchungen
+class LabworksDrillRoad(TaskManagerBase):
+    pass
+
+class LabworksMakingAsphaltSlabs(TaskManagerBase):
+    pass
+
+class LabworksDrillAsphaltSlabs(TaskManagerBase):
+    pass
+
+class LabworksSawDrillCores(TaskManagerBase):
+    pass
+
+class LabworksGrindingAsphaltSamples(TaskManagerBase):
+    pass
+
+class LabworksDetermineDensity(TaskManagerBase):
+    pass
+
+class LabworksDetermineGeometry(TaskManagerBase):
+    pass
+
+

src/paveit/datamodels/machines.py

@@ -0,0 +1,93 @@
+import datetime
+
+from bson import ObjectId
+from mongoengine import *
+
+from paveit.helper import fetch_recursive, mongo_to_dict
+
+from .components import (
+    ComponentsServoHydraulicMachineKMD,
+    ComponentsServoHydraulicMachineLVDT,
+    ComponentsServoHydraulicMachineTemperatureControl,
+)
+from .enumeration import Labtest, LabtestsEnum
+from .usermanagement import Organisation
+
+
+# ??? Labtest: Ist das richtig hier?
+class Experiment(EmbeddedDocument):
+    test = LazyReferenceField(Labtest, required=True)
+    config = ListField(StringField(), required=True)
+
+
+class MachineBase(Document):
+    
+    company = StringField(required=True)
+    name = StringField(required=True)
+    
+    department = StringField(required=False)
+    room = StringField(required=False)
+    
+    serialnumber = StringField(required=True)
+    
+    extrainfo = StringField(required=False)
+    
+    year_manufacture = IntField(min=1900, max=2100, required=False)
+
+    tests = ListField(EmbeddedDocumentField(Experiment), required=True)
+    
+    # Standartkomponenten festlegen: wenn ortsveränderlich, dann leer lassen
+    component_temperature = LazyReferenceField(ComponentsServoHydraulicMachineTemperatureControl, required=True)
+    component_kmd = LazyReferenceField(ComponentsServoHydraulicMachineKMD, required=False)
+    component_lvdt = ListField(LazyReferenceField(ComponentsServoHydraulicMachineLVDT), required=False)
+    
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    org_id = LazyReferenceField(Organisation,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+    tags = ListField(StringField())
+    
+    def to_dict(self):
+        # convert data to dict
+        data = fetch_recursive(self)
+        data = mongo_to_dict(data)
+       
+        return data
+    
+    def get_config(self, testname: LabtestsEnum):
+        test_id = Labtest.objects(org_id=self.org_id, test=testname).first()
+        
+        if not test_id:
+            return [] 
+        
+        test_id = test_id.id
+        
+        config_array = []
+        for test in self.tests:
+            if test.test.id == test_id:
+                config_array = test.config
+                
+        return config_array    
+
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'machines',
+        "db_alias": 'dblabtests',
+        'indexes': [
+            [("name", 1)],
+        ]
+    }
+
+class ServoHydraulicMachine(MachineBase):
+    
+    pass

src/paveit/datamodels/material.py

@@ -0,0 +1,116 @@
+import datetime
+from re import T
+
+from bson.json_util import loads
+from mongoengine import *
+
+from paveit.helper import fetch_recursive, mongo_to_dict
+
+from .enumeration import AsphaltCategoryEnum, BitumenCategoryEnum
+from .norm_documents import (
+    NormDocumentAggregate,
+    NormDocumentAsphalt,
+    NormDocumentBitumen,
+)
+from .norm_specification import DeliveryGrain, EnumerateBase, AdditiveEnum
+from .project import Project
+from .usermanagement import Organisation, User
+
+
+class Material(Document):
+    
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    org_id = LazyReferenceField(Organisation,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+
+    user_id = LazyReferenceField(User,
+                                 required=True,
+                                 reverse_delete_rule=DO_NOTHING)
+
+    project_ids = ListField(LazyReferenceField(Project,
+                                              reverse_delete_rule=CASCADE),
+                                              required=False)
+    
+    archived = BooleanField(default=False)  
+
+    def to_dict(self):
+        # convert data to dict
+        data = fetch_recursive(self)
+        data = mongo_to_dict(data)
+       
+        return data
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'materials',
+        "db_alias": 'dblabtests',
+        'indexes': [
+            [("material_id", 1)],
+            [("name", 1)],
+        ]
+    }
+
+
+class Asphalt(Material):
+    
+    pass
+    #limits = LazyReferenceField(EnumerateBase)
+
+# Bitumen 
+class Bitumen(Material):
+    
+    pass
+    
+    #norm =  LazyReferenceField(NormDocumentBitumen, required=True)
+    #limits = LazyReferenceField(EnumerateBase)
+    #ce_marking = StringField(required=False) #CE Kennzeichen
+
+    
+class Bitumenemulsion(Material):
+
+    norm = StringField(required=False, default='TP Asphalt Teil 24')
+    limits = LazyReferenceField(EnumerateBase)
+    ce_marking = StringField(required=False) #CE Kennzeichen
+
+
+class Epoxy(Material):
+
+    norm = StringField(required=False, default='TP Asphalt Teil 24')
+    limits = LazyReferenceField(EnumerateBase)
+    ce_marking = StringField(required=False) #CE Kennzeichen
+    
+
+
+class Kompaktasphalt(Material):
+    norm = StringField(required=False, default='TP Asphalt Teil 24')
+    name = StringField()
+    ce_marking = StringField(required=False) #CE Kennzeichen
+    
+
+class Aggregate(Material):
+    
+    pass
+    
+
+class Additive(Material):
+    
+    category = LazyReferenceField(AdditiveEnum, required=True)
+    
+            
+
+class Dummy(Material):
+
+    name = StringField()
+    material = StringField()
+
+    young_modulus = DictField()

src/paveit/datamodels/material_properties.py

@@ -0,0 +1,275 @@
+import datetime
+
+from mongoengine import *
+
+from paveit.helper import fetch_recursive, mongo_to_dict
+
+from enum import Enum
+
+from .material import Material, Additive, Bitumen, Aggregate
+from .usermanagement import Organisation, User
+
+from .enumeration import AsphaltCategoryEnum, BitumenCategoryEnum
+from .norm_documents import (
+    NormDocumentAggregate,
+    NormDocumentAsphalt,
+    NormDocumentBitumen,
+)
+from .norm_specification import (
+    AsphaltParameterLimitsBaseEnum,
+    BitumenParameterLimitsBaseEnum,
+    AdditiveEnum,
+    AdditiveSubEnum,
+    DeliveryGrain
+)
+
+class ProcessParameters(Enum):
+    """
+    Typ der Wert:
+    NOMINAL: Istwer
+    SET: Sollwert
+    """
+    
+    NOMINAL = 'nominal'
+    SET = 'set'
+    
+
+class Address(EmbeddedDocument):
+    company = StringField()
+    
+    street = StringField()
+    number = IntField()
+    city = StringField()
+    postal_code = IntField()
+    country = StringField()
+
+
+
+class Propertie(Document):
+    
+    material_id = LazyReferenceField(Material,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+    
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+    
+    
+    def to_dict(self):
+        # convert data to dict
+        data = fetch_recursive(self)
+        data = mongo_to_dict(data)
+       
+        return data
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'materialproperties',
+        "db_alias": 'dblabtests',
+        'indexes': [
+            [("material_id", 1)],
+            [("name", 1)],
+        ]
+    }
+
+# Additives
+
+class PropertieAdditive(Propertie):
+    pass
+    
+class PropertieAdditiveMeta(PropertieAdditive):
+    
+    name = StringField()
+    productnumber = StringField()
+    
+    certificate_number = StringField()
+    certificate_date = IntField()
+    
+    address = EmbeddedDocumentField(Address)
+    
+    subcategory = LazyReferenceField(AdditiveSubEnum, required=True)
+
+
+# Aggregates
+
+class PropertieAggregate(Propertie):
+    pass
+    
+class PropertieAggregateMeta(PropertieAggregate):
+    
+    name = StringField()
+    productnumber = StringField()
+    
+    certificate_number = StringField()
+    certificate_date = IntField()
+    
+    mine = StringField()
+    address = EmbeddedDocumentField(Address)
+    
+    category = LazyReferenceField(DeliveryGrain, required=False)
+
+
+# Bitumen
+class PropertieBitumen(Propertie):
+    pass
+    
+class PropertieBitumenMeta(PropertieBitumen):
+    
+    name = StringField()
+    productnumber = StringField()
+    
+    certificate_number = StringField()
+    certificate_date = IntField()
+    
+    address = EmbeddedDocumentField(Address)
+    
+    category = LazyReferenceField(BitumenParameterLimitsBaseEnum, required=True)
+
+
+# Asphate
+class PropertieAsphalt(Propertie):
+    pass
+    
+class PropertieAsphaltMeta(PropertieAsphalt):
+    
+    name = StringField()
+    productnumber = StringField()
+    
+    recipenumber = StringField()
+    certificate_number = StringField()
+    certificate_date = IntField()
+    
+    mixingplant = StringField()
+    address = EmbeddedDocumentField(Address)
+    
+    category = LazyReferenceField(AsphaltParameterLimitsBaseEnum, required=True)
+
+class Additive(EmbeddedDocument):
+    
+    additive_id = LazyReferenceField(Additive, required=True)
+    
+    A = FloatField()
+    B = FloatField()
+    C = FloatField()
+    min = FloatField()
+    max = FloatField()
+    
+class BitumenInfo(EmbeddedDocument):
+    added_binder_A = FloatField(required=False)
+    added_binder_B = FloatField(required=False)
+    added_binder_C = FloatField(required=False)
+    added_binder_min = FloatField(required=False)
+    added_binder_max = FloatField(required=False)
+    
+    binder_from_additives_A = FloatField(required=False)
+    binder_from_additives_B = FloatField(required=False)
+    binder_from_additives_C = FloatField(required=False)
+    binder_from_additives_min = FloatField(required=False)
+    binder_from_additives_max = FloatField(required=False)
+    
+    total_binder_A = FloatField(required=False)
+    total_binder_B = FloatField(required=False)
+    total_binder_C = FloatField(required=False)
+    total_binder_min = FloatField(required=False)
+    total_binder_max = FloatField(required=False)
+    
+    total_binder_vol_A = FloatField(required=False)
+    total_binder_vol_B = FloatField(required=False)
+    total_binder_vol_C = FloatField(required=False)
+    total_binder_vol_min = FloatField(required=False)
+    total_binder_vol_max = FloatField(required=False)
+    
+    elastic_recovery_A = FloatField(required=False)
+    elastic_recovery_B = FloatField(required=False)
+    elastic_recovery_C = FloatField(required=False)
+    elastic_recovery_min = FloatField(required=False)
+    elastic_recovery_max = FloatField(required=False)
+    
+    equi_stiffness_A = FloatField(required=False)
+    equi_stiffness_B = FloatField(required=False)
+    equi_stiffness_C = FloatField(required=False)
+    equi_stiffness_min = FloatField(required=False)
+    equi_stiffness_max = FloatField(required=False)
+    
+    phase_angle_A = FloatField(required=False)
+    phase_angle_B = FloatField(required=False)
+    phase_angle_C = FloatField(required=False)
+    phase_angle_min = FloatField(required=False)
+    phase_angle_max = FloatField(required=False)    
+    
+class PropertieAsphaltBitumenParameters(PropertieAsphalt):
+    
+    bitumen_id = LazyReferenceField(Bitumen, required=True)
+    
+    bitumen = EmbeddedDocumentField(BitumenInfo)
+    additives = ListField(EmbeddedDocumentField(Additive))
+    
+class AggregateInfo(EmbeddedDocument):
+    
+    id = LazyReferenceField(Aggregate, required=False)
+    size_45x0 = FloatField(required=False)
+    size_31x5 = FloatField(required=False)
+    size_22x4 = FloatField(required=False)
+    size_16x0 = FloatField(required=False)
+    size_11x2 = FloatField(required=False)
+    size_8x0 = FloatField(required=False)
+    size_5x6 = FloatField(required=False)
+    size_2x0 = FloatField(required=False)
+    size_1x0 = FloatField(required=False)
+    size_0x25 = FloatField(required=False)
+    size_0x125 = FloatField(required=False)
+    size_0x063 = FloatField(required=False)
+    size_less_0x063 = FloatField(required=False)
+    oversize = FloatField(required=False)
+    normal_grain = FloatField(required=False)
+    bulk_density = FloatField(required=False)
+    flow_coefficient = FloatField(required=False)
+    aggregates_bulk_density = FloatField(required=False)
+    
+class PropertieAsphaltAggregates(PropertieAsphalt):
+    aggregates = ListField(EmbeddedDocumentField(AggregateInfo), required=True)
+
+class MineralMixtureInfo(EmbeddedDocument):
+    
+    size_45x0 = FloatField(required=False)
+    size_31x5 = FloatField(required=False)
+    size_22x4 = FloatField(required=False)
+    size_16x0 = FloatField(required=False)
+    size_11x2 = FloatField(required=False)
+    size_8x0 = FloatField(required=False)
+    size_5x6 = FloatField(required=False)
+    size_2x0 = FloatField(required=False)
+    size_1x0 = FloatField(required=False)
+    size_0x25 = FloatField(required=False)
+    size_0x125 = FloatField(required=False)
+    size_0x063 = FloatField(required=False)
+    size_less_0x063 = FloatField(required=False)
+    
+class PropertieAsphaltMineralMixture(PropertieAsphalt):
+    trailing = EmbeddedDocumentField(MineralMixtureInfo, required=False)
+    passage = EmbeddedDocumentField(MineralMixtureInfo, required=False)
+ 
+class PropertyMetrics(EmbeddedDocument):
+    A = FloatField(required=False)
+    B = FloatField(required=False)
+    C = FloatField(required=False)
+    min = FloatField(required=False)
+    max = FloatField(required=False)
+
+class PropertiesAsphaltMixingParameters(PropertieAsphalt):
+    bulk_density = EmbeddedDocumentField(PropertyMetrics)
+    asphalt_density = EmbeddedDocumentField(PropertyMetrics)
+    marshall_density = EmbeddedDocumentField(PropertyMetrics)
+    cavity_content = EmbeddedDocumentField(PropertyMetrics)
+    cavity_filling_level = EmbeddedDocumentField(PropertyMetrics)
+    marshall_compression_temperature = EmbeddedDocumentField(PropertyMetrics)
+    expired_binder_quantity = EmbeddedDocumentField(PropertyMetrics)
+    water_sensitivity_ITSR = EmbeddedDocumentField(PropertyMetrics)
+    swelling_DIN1996 = EmbeddedDocumentField(PropertyMetrics)

src/paveit/datamodels/messages.py

@@ -0,0 +1,32 @@
+import datetime
+
+from mongoengine import *
+
+from .usermanagement import Organisation, User
+
+
+class MessageBase(Document):
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+    
+    org_id = LazyReferenceField(Organisation, required=True)
+    user_id = LazyReferenceField(User, required=True)
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'messages',
+        "db_alias": 'dblabtests',
+    }
+    
+    
+class Bugreport(MessageBase):
+    
+    message = StringField(max_length=300, required=True)
+    currentPage = StringField(required=True)

src/paveit/datamodels/metrics.py

@@ -0,0 +1,50 @@
+import datetime
+
+from mongoengine import *
+
+from .project import Project
+from .usermanagement import User, Organisation
+from .workpackage import Workpackage
+
+class MetricsBase(Document):
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+    
+    app = StringField(required=True, default='backend')
+    
+    method = StringField(required=True)
+    url = StringField(required=False)
+    client = StringField(required=False)
+    
+    status_code = IntField(required=False)
+    
+
+    
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'metrics',
+        "db_alias": 'dblabtests',
+    }
+
+    
+    
+class MetricsBackend(MetricsBase):
+    
+    project_id = LazyReferenceField(Project, required=False)
+    user_id = LazyReferenceField(User,required=False)
+    
+    workpackage_id = LazyReferenceField(Workpackage, required=False)
+    org_id = LazyReferenceField(Organisation, required=False)
+    
+    runtime = FloatField() # in s
+    task = StringField(max_length=30)
+    
+
+    

src/paveit/datamodels/norm_documents.py

@@ -0,0 +1,36 @@
+from mongoengine import *
+from enum import Enum
+
+
+class NormPublisherEnum(Enum):
+    FGSV = 'FGSV'
+
+class NormDocument(Document):
+    
+    name = StringField(required=True)    
+    name_short = StringField(required=True)    
+    
+    publisher = EnumField(NormPublisherEnum, required=True, default=NormPublisherEnum.FGSV)
+    number = StringField(required=True)    
+    
+    year =IntField(min_value=1900, max_value=2099)
+    month = IntField(min_value=1, max_value=12, required=False) 
+    
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'norm_documents',
+        "db_alias": 'dblabtests',
+    }
+    
+class NormDocumentAggregate(NormDocument):
+    pass
+
+class NormDocumentBitumen(NormDocument):
+    pass
+
+class NormDocumentAsphalt(NormDocument):
+    pass

src/paveit/datamodels/norm_specification.py

@@ -0,0 +1,225 @@
+from mongoengine import *
+
+from .enumeration import RelationalOperatorsEnum, BitumenCategoryEnum, AsphaltCategoryEnum
+from .norm_documents import NormDocumentAggregate, NormDocumentBitumen, NormDocumentAsphalt
+
+from paveit.helper import fetch_recursive, mongo_to_dict
+
+class EnumerateBase(Document):
+    
+    def to_dict(self):
+            # convert data to dict
+            data = fetch_recursive(self)
+            data = mongo_to_dict(data)
+            print(data)
+            return data    
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'norm_specification',
+        "db_alias": 'dblabtests',
+    }
+     
+
+# Addidive
+class AdditiveEnum(EnumerateBase):
+    name = StringField()
+
+class AdditiveSubEnum(EnumerateBase):
+    categoryId =  LazyReferenceField(AdditiveEnum, required=True)
+    name = StringField()
+
+# Gesteine    
+class DeliveryGrain(EnumerateBase):
+    name = StringField()
+    category = StringField()
+    norm =  LazyReferenceField(NormDocumentAggregate, required=True)
+    
+# Bitumen
+class BitumenParameterLimitsBaseEnum(EnumerateBase):
+    pass
+
+class BitumenParameterLimitsStrassenbaubitumen(BitumenParameterLimitsBaseEnum):
+    
+    name = StringField()
+    category = EnumField(BitumenCategoryEnum, required=True)
+    norm =  LazyReferenceField(NormDocumentBitumen, required=True)
+    
+    
+    penetration_unit = StringField('0.1 mm')
+    penetration_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.between)
+    penetration_max = FloatField(min_value=0, max_value=1000)
+    penetration_min = FloatField(min_value=0, max_value=1000)
+    
+    # Erweichungspunkt Ring und Kugel
+    softening_point_unit = StringField('°C')
+    softening_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.between)
+    softening_point_min = FloatField(min_value=0, max_value=500)
+    softening_point_max = FloatField(min_value=0, max_value=500)
+    
+    # Flammpunk
+    flash_point_unit = StringField('°C')
+    flash_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    flash_point_min = FloatField(min_value=0, max_value=500)
+    
+    # Löslichkeit
+    solubility_unit = StringField('%')
+    solubility_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    solubility_min = FloatField(default=99.0, min_value=0, max_value=100)
+    
+    # Brechpunkt nach Fraaß
+    fraass_breaking_point_unit = StringField('°C')
+    fraass_breaking_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.lt)
+    fraass_breaking_point_max = FloatField(min_value=-100, max_value=100)
+    
+    # Beständigkeit gegen Verhärtung unter Einfluss von Wärme und Luft
+    ## verbleibende Penetration
+    hardening_resistance_penetration_unit = StringField('%')
+    hardening_resistance_penetration_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    hardening_resistance_penetration_min = FloatField(min_value=0, max_value=100)
+    
+    ## Zunahme des Erweichungspunktes Ring und Kugel
+    hardening_resistance_softening_point_unit = StringField('°C')
+    hardening_resistance_softening_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.lt)
+    hardening_resistance_softening_point_max = FloatField(min_value=0, max_value=100)
+    
+    ## Massenänderung
+    hardening_resistance_masschange_unit = StringField('%')
+    hardening_resistance_masschange_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.lt)
+    hardening_resistance_masschange_max = FloatField(min_value=0, max_value=100)
+    
+class BitumenParameterLimitsPmB(BitumenParameterLimitsBaseEnum):
+    
+    name = StringField()
+    category = EnumField(BitumenCategoryEnum, required=True)
+    norm =  LazyReferenceField(NormDocumentBitumen, required=True)
+    
+    
+    penetration_unit = StringField('0.1 mm')
+    penetration_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.between)
+    penetration_max = FloatField(min_value=0, max_value=1000)
+    penetration_min = FloatField(min_value=0, max_value=1000)
+    
+    # Erweichungspunkt Ring und Kugel
+    softening_point_unit = StringField('°C')
+    softening_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    softening_point_min = FloatField(min_value=0, max_value=500)
+    
+    # Kraftduktilität
+    force_ductility_unit = StringField('J/cm²')
+    force_ductility_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    force_ductility_min = FloatField(min_value=0, max_value=10)
+    
+    # Flammpunk
+    flash_point_unit = StringField('°C')
+    flash_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    flash_point_min = FloatField(min_value=0, max_value=500)
+        
+    # Brechpunkt nach Fraaß
+    fraass_breaking_point_unit = StringField('°C')
+    fraass_breaking_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.lt)
+    fraass_breaking_point_max = FloatField(min_value=-100, max_value=100)
+    
+    # Elastische Rückstellung 25 °C
+    elastic_recovery_25deg_unit = StringField('%')
+    elastic_recovery_25deg_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    elastic_recovery_25deg_min = FloatField(min_value=0, max_value=100, default=None)
+    
+    # Elastische Rückstellung 10 °C
+    elastic_recovery_10deg_unit = StringField('%', default='%')
+    elastic_recovery_10deg_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    elastic_recovery_10deg_min = FloatField(min_value=0, max_value=100, default=None)
+    
+    #Plastizitätsbereich
+    plasticity_range_unit = StringField('°C', default='°C')
+    plasticity_range_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt) 
+    plasticity_range_min = FloatField(min_value=0, max_value=100, required=False, default=None)
+    
+    # Lagerbeständigkeit Differenz Erweichungspunkt
+    storage_stability_softening_point_unit = StringField('°C')
+    storage_stability_softening_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.lt)
+    storage_stability_softening_point_max = FloatField(min_value=-100, max_value=100)
+    
+        # Lagerbeständigkeit Penetration
+    storage_stability_penetration_unit = StringField('mm')
+    storage_stability_penetration_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.lt)
+    storage_stability_penetration_max = FloatField(min_value=-100, max_value=100)
+    
+    # Beständigkeit gegen Verhärtung unter Einfluss von Wärme und Luft
+    ## Massenänderung
+    hardening_resistance_masschange_unit = StringField('%')
+    hardening_resistance_masschange_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.lt)
+    hardening_resistance_masschange_max = FloatField(min_value=0, max_value=100)
+
+    ## verbleibende Penetration
+    hardening_resistance_penetration_unit = StringField('%')
+    hardening_resistance_penetration_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    hardening_resistance_penetration_min = FloatField(min_value=0, max_value=100)
+    
+    ## Zunahme des Erweichungspunktes Ring und Kugel
+    hardening_resistance_softening_point_unit = StringField('°C')
+    hardening_resistance_softening_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.lt)
+    hardening_resistance_softening_point_max = FloatField(min_value=0, max_value=100)
+    
+    ## Abfall des Erweichungspunktes Ring und Kugel
+    hardening_resistance_decrease_softening_point_unit = StringField('°C')
+    hardening_resistance_decrease_softening_point_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.lt)
+    hardening_resistance__decreasesoftening_point_max = FloatField(min_value=0, max_value=100)
+    
+        # Elastische Rückstellung 25 °C
+    hardening_resistance_elastic_recovery_25deg_unit = StringField('%')
+    hardening_resistance_elastic_recovery_25deg_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    hardening_resistance_elastic_recovery_25deg_min = FloatField(min_value=0, max_value=100, default=None)
+    
+    # Elastische Rückstellung 10 °C
+    hardening_resistance_elastic_recovery_10deg_unit = StringField('%', default='%')
+    hardening_resistance_elastic_recovery_10deg_operator = EnumField(RelationalOperatorsEnum, default=RelationalOperatorsEnum.gt)
+    hardening_resistance_elastic_recovery_10deg_min = FloatField(min_value=0, max_value=100, required=False, default=None)
+    
+    
+    
+    
+     	 
+# Asphalt
+class SievePassage(EmbeddedDocument):
+    wide = FloatField(required=True)
+    passage_min = FloatField(required=True)
+    passage_max = FloatField(required=True)
+
+class AsphaltParameterLimitsBaseEnum(EnumerateBase):
+    pass
+
+class AsphaltParameterLimitsAsphalttragschicht(AsphaltParameterLimitsBaseEnum):
+    
+    name = StringField()
+    norm =  LazyReferenceField(NormDocumentBitumen, required=True)
+    
+    
+    #Gestein
+    sieve_passage = ListField(EmbeddedDocumentField(SievePassage))
+    
+    # Bitumen
+    bitumen_type = ListField(LazyReferenceField(BitumenParameterLimitsBaseEnum), required=True)
+    min_bitumen_content = FloatField(min=0, max=100)
+    
+    min_voids_content = FloatField(min=0, max=100)
+    max_voids_content = FloatField(min=0, max=100)
+  
+class AsphaltParameterLimitsAsphaltbinderschicht(AsphaltParameterLimitsAsphalttragschicht):
+    pass
+      
+class AsphaltParameterLimitsAsphaltdeckschicht(AsphaltParameterLimitsAsphalttragschicht):
+    pass
+
+class AsphaltParameterLimitsGussasphalt(AsphaltParameterLimitsAsphalttragschicht):
+    pass
+class AsphaltParameterLimitsSMA(AsphaltParameterLimitsAsphalttragschicht):
+    pass
+class AsphaltParameterLimitsPA(AsphaltParameterLimitsAsphalttragschicht):
+    pass
+class AsphaltParameterLimitsACTD(AsphaltParameterLimitsAsphalttragschicht):
+    pass

src/paveit/datamodels/project.py

@@ -0,0 +1,53 @@
+import datetime
+
+from mongoengine import *
+
+from .client import Client
+from .usermanagement import Organisation, User
+from .enumeration import ProcessStatusEnum
+
+
+class Project(Document):
+
+    project_number = StringField(required=False)
+    
+    client_id = LazyReferenceField(Client,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+    
+    status = EnumField(ProcessStatusEnum, default=ProcessStatusEnum.ONGOING)
+
+    org_id = LazyReferenceField(Organisation,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+    user_id = LazyReferenceField(User,
+                                 required=True,
+                                 reverse_delete_rule=DO_NOTHING)
+    
+    name = StringField(required=True)
+    name_short = StringField(required=False)
+    
+    tags = ListField(StringField())
+
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'projects',
+        "db_alias": 'dblabtests',
+        'indexes': [
+            [("name_short", 1)],
+            [("client_id", 1)],
+            [("name", 1)],
+            [("project_number", 1)],
+        ]
+    }

src/paveit/datamodels/regression.py

@@ -0,0 +1,44 @@
+import datetime
+
+from mongoengine import *
+
+from .taskmanager import TaskManagerBase
+
+class RegressionBase(Document):
+
+    date = DateTimeField(default=datetime.datetime.now(),
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+    
+    task_id = LazyReferenceField(TaskManagerBase, required=True)
+    
+    #statistische Werte
+    stat_r2 = FloatField(required=False)
+    
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'regression',
+        "db_alias": 'dblabtests',
+    }
+    
+class RegCITT(RegressionBase):
+    
+    nsamples = IntField()
+
+    Emax = FloatField(min_value=0, max_value=150000)
+    Emin = FloatField()
+    
+    T0 = FloatField(min_value=-100, max_value=100)
+    
+    phi = FloatField()
+    z0 = FloatField()
+    z1 = FloatField()
+    
+    
+    

src/paveit/datamodels/sheartest.py

@@ -0,0 +1,237 @@
+import datetime
+
+from mongoengine import *
+
+from .taskmanager import TaskManagerBase
+from .usermanagement import User
+
+
+class DynamicShearTest(Document):
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+    
+    task_id = LazyReferenceField(TaskManagerBase, required=True)
+    user_id = LazyReferenceField(User, required=True)
+
+    gap_width = FloatField(default=1.0)
+
+    tags = ListField(StringField())
+
+    standard = StringField(default='TP Asphalt Teil 48 C')
+
+    filehash = StringField(required=True)
+    speciment_name = StringField()
+
+    meta = {
+        'allow_inheritance':
+        True,
+        'index_opts': {},
+        'index_background':
+        True,
+        'index_cls':
+        False,
+        'auto_create_index':
+        True,
+        "db_alias":
+        'dblabtests',
+        'collection':
+        'lab_sheartest',
+        'indexes': [
+            [("lab", 1)],
+            [("speciment_name", 1)],
+            [("project", 1)],
+            [("bruch", 1)],
+            [("lab", 1), ("project", 1)],
+            [("lab", 1), ("project", 1), ("workpackage", 1)],
+            [("lab", 1), ("project", 1), ("bounding", 1)],
+        ]
+    }
+
+
+class DynamicShearTestExtension(DynamicShearTest):
+
+    #metadata
+    f_set = FloatField(required=True)
+    sigma_normal = FloatField(required=True)
+    T_set = FloatField(required=True)
+    extension = FloatField(required=True)
+
+    N_from = IntField()
+    N_to = IntField()
+    N_tot = IntField()
+    n_samples_per_cycle = IntField()
+
+    G = FloatField(required=True)
+    broken = BooleanField(required=True)
+    phase = FloatField()
+    
+    #fit parameter
+    ## required parameters
+
+    ## F
+    F_amp = FloatField(required=True)
+    F_freq = FloatField(required=True)
+    F_phase = FloatField(required=True)
+    F_offset = FloatField(required=True)
+    F_slope = FloatField(required=True)
+    F_r2 = FloatField(required=True)
+    F_cycle_min = ListField(FloatField())
+    F_min = FloatField()
+    F_min_std = FloatField()
+    F_min_diff_rel = FloatField()
+    F_cycle_max = ListField(FloatField())
+    F_max = FloatField()
+    F_max_std = FloatField()
+    F_max_diff_rel = FloatField()
+    F_cycle_mean = ListField(FloatField())
+    F_mean = FloatField()
+    F_mean_std = FloatField()
+    F_mean_diff_rel = FloatField()
+    F_cycle_diff = ListField(FloatField())
+    F_diff = FloatField()
+    F_diff_std = FloatField()
+    F_diff_diff_rel= FloatField()
+
+    ## S1
+    s_vert_1_amp = FloatField()
+    s_vert_1_freq = FloatField()
+    s_vert_1_phase = FloatField()
+    s_vert_1_offset = FloatField()
+    s_vert_1_slope = FloatField()
+    s_vert_1_r2 = FloatField()
+    s_vert_1_cycle_min = ListField(FloatField())
+    s_vert_1_min = FloatField()
+    s_vert_1_min_std = FloatField()
+    s_vert_1_min_diff_rel = FloatField()
+    s_vert_1_cycle_max = ListField(FloatField())
+    s_vert_1_max = FloatField()
+    s_vert_1_max_std = FloatField()
+    s_vert_1_max_diff_rel = FloatField()
+    s_vert_1_cycle_mean = ListField(FloatField())
+    s_vert_1_mean = FloatField()
+    s_vert_1_mean_std = FloatField()
+    s_vert_1_mean_diff_rel = FloatField()
+    s_vert_1_cycle_diff = ListField(FloatField())
+    s_vert_1_diff = FloatField()
+    s_vert_1_diff_std = FloatField()
+    s_vert_1_diff_diff_rel = FloatField()
+    ## S2
+    s_vert_2_amp = FloatField()
+    s_vert_2_freq = FloatField()
+    s_vert_2_phase = FloatField()
+    s_vert_2_offset = FloatField()
+    s_vert_2_slope = FloatField()
+    s_vert_2_r2 = FloatField()
+    s_vert_2_cycle_min = ListField(FloatField())
+    s_vert_2_min = FloatField()
+    s_vert_2_min_std = FloatField()
+    s_vert_2_min_diff_rel = FloatField()
+    s_vert_2_cycle_max = ListField(FloatField())
+    s_vert_2_max = FloatField()
+    s_vert_2_max_std = FloatField()
+    s_vert_2_max_diff_rel = FloatField()
+    s_vert_2_cycle_mean = ListField(FloatField())
+    s_vert_2_mean = FloatField()
+    s_vert_2_mean_std = FloatField()
+    s_vert_2_mean_diff_rel = FloatField()
+    s_vert_2_cycle_diff = ListField(FloatField())
+    s_vert_2_diff = FloatField()
+    s_vert_2_diff_std = FloatField()
+    s_vert_2_diff_diff_rel = FloatField()
+    ## S-Sum
+    s_vert_mean_amp = FloatField()
+    s_vert_mean_freq = FloatField()
+    s_vert_mean_phase = FloatField()
+    s_vert_mean_offset = FloatField()
+    s_vert_mean_slope = FloatField()
+    s_vert_mean_r2 = FloatField()
+    s_vert_mean_cycle_min = ListField(FloatField())
+    s_vert_mean_min = FloatField()
+    s_vert_mean_min_std = FloatField()
+    s_vert_mean_min_diff_rel = FloatField()
+    s_vert_mean_cycle_max = ListField(FloatField())
+    s_vert_mean_max = FloatField()
+    s_vert_mean_max_std = FloatField()
+    s_vert_mean_max_diff_rel = FloatField()
+    s_vert_mean_cycle_mean = ListField(FloatField())
+    s_vert_mean_mean = FloatField()
+    s_vert_mean_mean_std = FloatField()
+    s_vert_mean_mean_diff_rel = FloatField()
+    s_vert_mean_cycle_diff = ListField(FloatField())
+    s_vert_mean_diff = FloatField()
+    s_vert_mean_diff_std = FloatField()
+    s_vert_mean_diff_diff_rel = FloatField()
+    
+    ## optional parameters
+    ## S1
+    s_hor_1_amp = FloatField(required=False)
+    s_hor_1_freq = FloatField(required=False)
+    s_hor_1_phase = FloatField(required=False)
+    s_hor_1_offset = FloatField(required=False)
+    s_hor_1_slope = FloatField(required=False)
+    s_hor_1_r2 = FloatField(required=False)
+    s_hor_1_cycle_min = ListField(FloatField(),required=False)
+    s_hor_1_min = FloatField(required=False)
+    s_hor_1_min_std = FloatField(required=False)
+    s_hor_1_min_diff_rel = FloatField(required=False)
+    s_hor_1_cycle_max = ListField(FloatField(),required=False)
+    s_hor_1_max = FloatField(required=False)
+    s_hor_1_max_std = FloatField(required=False)
+    s_hor_1_max_diff_rel = FloatField(required=False)
+    s_hor_1_cycle_mean = ListField(FloatField(),required=False)
+    s_hor_1_mean = FloatField(required=False)
+    s_hor_1_mean_std = FloatField(required=False)
+    s_hor_1_mean_diff_rel = FloatField(required=False)
+    s_hor_1_cycle_diff = ListField(FloatField(),required=False)
+    s_hor_1_diff = FloatField(required=False)
+    s_hor_1_diff_std = FloatField(required=False)
+    s_hor_1_diff_diff_rel = FloatField(required=False)
+    ## S2
+    s_hor_2_amp = FloatField(required=False)
+    s_hor_2_freq = FloatField(required=False)
+    s_hor_2_phase = FloatField(required=False)
+    s_hor_2_offset = FloatField(required=False)
+    s_hor_2_slope = FloatField(required=False)
+    s_hor_2_r2 = FloatField(required=False)
+    s_hor_2_cycle_min = ListField(FloatField(),required=False)
+    s_hor_2_min = FloatField(required=False)
+    s_hor_2_min_std = FloatField(required=False)
+    s_hor_2_min_diff_rel = FloatField(required=False)
+    s_hor_2_cycle_max = ListField(FloatField(),required=False)
+    s_hor_2_max = FloatField(required=False)
+    s_hor_2_max_std = FloatField(required=False)
+    s_hor_2_max_diff_rel = FloatField(required=False)
+    s_hor_2_cycle_mean = ListField(FloatField(), required=False)
+    s_hor_2_mean = FloatField(required=False)
+    s_hor_2_mean_std = FloatField(required=False)
+    s_hor_2_mean_diff_rel = FloatField(required=False)
+    s_hor_2_cycle_diff = ListField(FloatField(), required=False)
+    s_hor_2_diff = FloatField(required=False)
+    s_hor_2_diff_std = FloatField(required=False)
+    s_hor_2_diff_diff_rel = FloatField(required=False)
+    ## Piston
+    s_piston_amp = FloatField(required=False)
+    s_piston_freq = FloatField(required=False)
+    s_piston_phase = FloatField(required=False)
+    s_piston_offset = FloatField(required=False)
+    s_piston_slope = FloatField(required=False)
+    s_piston_r2 = FloatField(required=False)
+    s_piston_cycle_min = ListField(FloatField(),required=False)
+    s_piston_min = FloatField(required=False)
+    s_piston_min_std = FloatField(required=False)
+    s_piston_min_diff_rel = FloatField(required=False)
+    s_piston_cycle_max = ListField(FloatField(),required=False)
+    s_piston_max = FloatField(required=False)
+    s_piston_max_std = FloatField(required=False)
+    s_piston_max_dif_rel = FloatField(required=False)
+    s_piston_cycle_mean = ListField(FloatField(),required=False)
+    s_piston_mean = FloatField(required=False)
+    s_piston_mean_std = FloatField(required=False)
+    s_piston_mean_diff_rel = FloatField(required=False)
+    s_piston_cycle_diff = ListField(FloatField(),required=False)
+    s_piston_diff = FloatField(required=False)
+    s_piston_diff_std = FloatField(required=False)
+    s_piston_diff_diff_rel = FloatField(required=False)

src/paveit/datamodels/taskmanager.py

@@ -0,0 +1,64 @@
+
+import datetime
+
+from mongoengine import *
+
+from .client import Client
+from .enumeration import ProcessStatusEnum
+from .machines import MachineBase
+from .material import Material
+from .project import Project
+from .usermanagement import Organisation, User
+from .workpackage import Workpackage
+
+
+class TaskManagerBase(Document):
+    
+    org_id = LazyReferenceField(Organisation, required=True)
+    user_id = LazyReferenceField(User, required=True)
+    client_id = LazyReferenceField(Client, required=True)
+    project_id = LazyReferenceField(Project, required=True)
+    wp_id = LazyReferenceField(Workpackage, required=False)
+        
+    status = EnumField(ProcessStatusEnum, default=ProcessStatusEnum.ONGOING)
+    
+    task_added = DateTimeField(default=datetime.datetime.now,
+                               wtf_options={"r  ender_kw": {
+                                 "step": "60"
+                                 }})
+    
+    task_finished = DateTimeField(required=False)
+    
+    assign_users = ListField(LazyReferenceField(User), required=False)
+    assign_machine = LazyReferenceField(MachineBase, required=False)
+    
+    series = StringField(default='Serie 01')
+    
+    step_before = LazyReferenceField('self', required=False)
+    step_after = LazyReferenceField('self', required=False)
+    
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'taskmanager',
+        "db_alias": 'dblabtests',
+    }
+    
+
+class TaskCITTStiffness(TaskManagerBase):
+    
+    material = LazyReferenceField(Material, required=True)
+    
+class TaskCITTFatigue(TaskManagerBase):
+    
+    material = LazyReferenceField(Material, required=True)
+    
+    
+class TaskDynShearStiffness(TaskManagerBase):
+    
+    material = LazyReferenceField(Material, required=True)
+    material2 = LazyReferenceField(Material, required=True)
+    bounding = LazyReferenceField(Material, required=True)

src/paveit/datamodels/usermanagement.py

@@ -0,0 +1,65 @@
+import datetime
+
+from mongoengine import *
+from .enumeration import ModelSelection
+
+class Organisation(Document):
+
+    name = StringField(required=True)
+    name_short = StringField(required=True)
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    labtest_citt = StringField(required=False)
+    labtest_shear_extension = StringField(required=False)
+    
+    domain = StringField(required=True)
+    
+    modul = EnumField(ModelSelection, required=True, default=ModelSelection.BASE)
+    
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'organisation',
+        'db_alias': 'dblabtests',
+    }
+
+
+class User(Document):
+
+    _id = UUIDField(binary=True, primary_key=True)
+    
+    org_id = LazyReferenceField(Organisation,
+                                required=True,
+                                reverse_delete_rule=CASCADE)
+
+    date_added = DateTimeField(default=datetime.datetime.now,
+                               wtf_options={"render_kw": {
+                                   "step": "60"
+                               }})
+    
+    email = EmailField(required=True)
+    
+    last_login = DateTimeField(default=datetime.datetime.now,
+                               required=False,
+                               wtf_options={"render_kw": {
+                                   "step": "60"
+                               }})
+
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'user',
+        'db_alias': 'dblabtests',
+    }

src/paveit/datamodels/workpackage.py

@@ -0,0 +1,34 @@
+import datetime
+
+from mongoengine import *
+
+from .project import Project
+from .usermanagement import User
+
+
+class Workpackage(Document):
+
+    name = StringField(required=True)
+    name_short = StringField(required=False)
+    wp_id = StringField(required=True)
+
+    project_id = LazyReferenceField(Project, required=True)
+
+    user_id = LazyReferenceField(User,
+                                 required=False,
+                                 reverse_delete_rule=DO_NOTHING)
+
+    date = DateTimeField(default=datetime.datetime.now,
+                         wtf_options={"render_kw": {
+                             "step": "60"
+                         }})
+
+    meta = {
+        'allow_inheritance': True,
+        'index_opts': {},
+        'index_background': True,
+        'index_cls': False,
+        'auto_create_index': True,
+        'collection': 'workpackages',
+        "db_alias": 'dblabtests',
+    }

src/paveit/functions/__init__.py

@@ -0,0 +1 @@
+from .citt import *

src/paveit/functions/citt.py

@@ -0,0 +1,103 @@
+import numpy as np
+
+
+def stiffness_tp26(T, f, Emax, Emin, phi, z0, z1, T0=20.0):
+
+    alphaT = np.exp(phi * ((1 / (T + 273.15)) - (1 / (T0 + 273.15))))
+    x = np.log(f * alphaT) / np.log(10)
+    E = Emin + (Emax - Emin) / (1 + np.exp(z0 * x + z1))
+
+    return E
+
+
+def calc_nu(T):
+    #TODO: Prüfen ob Formel stimmt!
+    nu = 0.15 + (0.35) / (1 + np.exp(3.1849 - 0.04233 * (9 / 5 * T + 32)))
+    return nu
+
+def calc_E(data, metadata, columns_analyse):
+
+    data.index = data.index - data.index[0]
+    
+    res_temp = {}
+
+    x = data.index.values
+
+    freq = np.round(float(data['f'].unique()), 2)
+    sigma = float(data['sigma'].unique())
+    temperature = float(data['T'].unique())
+    
+    for idxcol, col in enumerate(columns_analyse):
+
+        if not col in data.columns: continue
+
+        y = data[col].values
+        res = fit_cos(x, y, freq=freq)
+
+        for key, value in res.items():
+            res_temp[f'fit_{col}_{key}'] = value
+            
+
+        # analyse cycle data
+        
+        cycle_min = []
+        cycle_max = []
+        cycle_mean = []
+        cycle_diff = []
+        
+        for N, data_cycle in data.groupby('N'):
+            y = data_cycle[col].values
+            
+            cycle_min.append(y.min())
+            cycle_max.append(y.max())
+            cycle_mean.append(y.mean())
+            cycle_diff.append(cycle_max[-1] - cycle_min[-1])
+            
+        res_temp[f'fit_{col}_cycle_min'] = cycle_min
+        res_temp[f'fit_{col}_min'] = np.mean(cycle_min)
+        res_temp[f'fit_{col}_min_std'] = np.std(cycle_min)
+        res_temp[f'fit_{col}_min_diff_rel'] = (np.max(cycle_min) - np.min(cycle_min))/np.mean(cycle_min)
+        res_temp[f'fit_{col}_cycle_max'] = cycle_max
+        res_temp[f'fit_{col}_max'] = np.mean(cycle_max)
+        res_temp[f'fit_{col}_max_std'] = np.std(cycle_max)
+        res_temp[f'fit_{col}_max_diff_rel'] = (np.max(cycle_max) - np.min(cycle_max))/np.mean(cycle_max)
+        res_temp[f'fit_{col}_cycle_mean'] = cycle_mean
+        res_temp[f'fit_{col}_mean'] = np.mean(cycle_mean)
+        res_temp[f'fit_{col}_mean_std'] = np.std(cycle_mean)
+        res_temp[f'fit_{col}_mean_diff_rel'] = (np.max(cycle_mean) - np.min(cycle_mean))/np.mean(cycle_mean)
+        res_temp[f'fit_{col}_cycle_diff'] = cycle_diff
+        res_temp[f'fit_{col}_diff'] = np.mean(cycle_diff)
+        res_temp[f'fit_{col}_diff_std'] = np.std(cycle_diff)
+        res_temp[f'fit_{col}_diff_diff_rel'] = (np.max(cycle_diff) - np.min(cycle_diff))/np.mean(cycle_diff)
+
+    # add more metadata
+    res_temp['f_set'] = freq
+    res_temp['sigma_set'] = sigma
+    res_temp['T_set'] = temperature
+
+    res_temp['N_from'] = data['N'].min()
+    res_temp['N_to'] = data['N'].max()
+
+    res_temp['n_samples_per_cycle'] = int(
+        len(data) / (res_temp['N_to'] - res_temp['N_from'] + 1))
+
+    ## Stiffness
+    deltaF = res_temp['fit_F_amp']
+    deltaU = res_temp['fit_s_hor_sum_amp']
+    
+    h = float(metadata['speciment_height'])
+    d = float(metadata['speciment_diameter'])
+    
+    nu = calc_nu(temperature)
+    res_temp['nu'] = nu
+    
+    #nach TP Asphalt 26
+    res_temp['stiffness'] = deltaF /(h * deltaU) * (4.0/np.pi -1 + nu)
+    
+    ## Elastische hori. Dehnung                
+    res_temp['el_strains'] = 2*2*deltaU/d * (1+3*nu)/(4 + np.pi*nu - np.pi) * 1000.0 # 2*2 daher, da deltaU nur Ampl. nicht Gesamtkraft ist
+    
+    # TODO: Überarbeiten und erweitern (ISSUE #2)
+    res_temp['phase'] = res_temp['fit_F_phase'] - res_temp['fit_s_hor_sum_phase']
+    
+    return res_temp

src/paveit/helper/__init__.py

@@ -1,6 +1,10 @@
+from .filehandling import read_file_to_bytesio
 from .filehasher import calc_hash_of_bytes
 from .minio import get_minio_client_archive, get_minio_client_processing
+from .mongo import connect_mongo_db, fetch_recursive, mongo_get_results, mongo_to_dict
 
-__all__ = ['get_minio_client_archive', 'get_minio_client_processing',
-                'calc_hash_of_bytes'
-           ]
+__all__ = [
+    'read_file_to_bytesio', 'connect_mongo_db', 'mongo_get_results', 'fetch_recursive', 'mongo_to_dict',
+    'get_minio_client_processing', 'get_minio_client_archive',
+    'calc_hash_of_bytes'
+]

src/paveit/helper/filehandling.py

@@ -0,0 +1,12 @@
+import logging
+from io import BytesIO
+
+logger = logging.getLogger(__name__)
+
+
+def read_file_to_bytesio(filename: str):
+    
+    with open(filename, "rb") as fh:
+        buf = BytesIO(fh.read())
+    
+    return buf

src/paveit/helper/minio.py

@@ -9,7 +9,7 @@ def get_minio_client_processing(bucket_name = 'processing'):
             os.environ["MINIO_URL"],
             access_key=os.environ["MINIO_ACCESS_KEY"],
             secret_key=os.environ["MINIO_SECRET_KEY"],
-            secure=False
+            secure=True
         )
     
     
@@ -28,7 +28,7 @@ def get_minio_client_archive(bucket_name = 'archive'):
             os.environ["MINIO_ARCHIVE_URL"],
             access_key=os.environ["MINIO_ARCHIVE_ACCESS_KEY"],
             secret_key=os.environ["MINIO_ARCHIVE_SECRET_KEY"],
-            secure=False
+            secure=True
     )
     
     found = client.bucket_exists(bucket_name)

src/paveit/helper/mongo.py

@@ -0,0 +1,119 @@
+import os
+
+import mongoengine
+from bson import ObjectId
+from mongoengine import connect as mongo_connect
+from pandas import DataFrame
+
+
+def connect_mongo_db(username=os.environ['MONGO_USER'] ,
+                      password=os.environ['MONGO_PASSWD'] ,
+                      host=os.environ['MONGO_URI'],
+                      dbname=os.environ['MONGO_DB'] ):
+    
+    c = mongo_connect(dbname,
+                  username=username,
+                  password=password,
+                  host=host,
+                  authentication_source='admin',
+                  alias='dblabtests')
+
+
+def mongo_upload_results(resultsmodel, results: DataFrame, datamodel,
+                         data: DataFrame, filehash: str, org_id: ObjectId,
+                         project_id: ObjectId, material_id: ObjectId,
+                         user_id: ObjectId):
+
+    for idx, res in results.iterrows():
+
+        #upload results
+        meta['filehash'] = filehash
+        meta['org_id'] = org_id
+        meta['project_id'] = project_id
+        meta['material'] = material_id
+        meta['user_id'] = user_id
+
+        #check if result in db
+        #n = CITTSiffness.objects(**meta).count()
+
+        # write data
+        data_dict = res.to_dict()
+        data_dict.update(meta)
+
+        f = resultsmodel(**data_dict).save()
+
+        # upload data
+        data_sel = data[idx_fit]
+
+        # required data
+        data_out = dict(
+            time=data_sel.index,
+            F=list(data_sel['F']),
+            N=list(data_sel['N']),
+            s_hor_1=list(data_sel['s_hor_1']),
+            s_hor_2=list(data_sel['s_hor_2']),
+            s_hor_sum=list(data_sel['s_hor_sum']),
+        )
+        #optional data
+        for col in ['S_piston']:
+            if col in data_sel.columns:
+                data_out[col] = data_sel[col]
+
+        g = datamodel(result=f.id, **data_out).save()
+
+
+def mongo_get_results(resultsmodel, results: DataFrame, datamodel,
+                      data: DataFrame, filehash: str, org_id: ObjectId,
+                      project_id: ObjectId, material_id: ObjectId,
+                      user_id: ObjectId):
+
+    return True
+
+def fetch_recursive(data, fetch_parameter=['norm', 'limits', 'assign_machine']):
+    
+    fields = data._fields
+    
+    data_out = data.to_mongo().to_dict()
+    
+    for par in fetch_parameter:
+        if par in fields.keys():
+            
+            try:
+                # if is LazyReferenceField
+                if isinstance(fields[par], mongoengine.fields.LazyReferenceField):
+                        d = data[par].fetch()
+                else:
+                        d = data[par]
+            
+            except:
+                continue
+            
+            if d  is None: 
+                continue
+            
+            data_out[par] = d.to_mongo().to_dict()
+    
+    return data_out
+
+def mongo_to_dict(data, drop_parameters=['_cls','user_id', 'org_id', 'project_id']):
+    '''
+    data: dict
+    
+    '''    
+    for key in list(data.keys()):
+        if key in drop_parameters:
+            del data[key]  # Remove the unwanted key
+        elif isinstance(data[key], dict):
+            mongo_to_dict(data[key])  # Recurse into nested dictionaries
+        elif isinstance(data[key], list):  # Add this line to process lists
+            for i, item in enumerate(data[key]):
+                if isinstance(item, ObjectId):
+                    data[key][i] = str(item)
+                elif isinstance(item, dict):
+                    mongo_to_dict(item, drop_parameters)  # Recurse into nested dictionaries in list
+        else:
+            # process data
+            if isinstance(data[key], ObjectId):
+                data[key] = str(data[key])
+    
+    return data

src/paveit/io/__init__.py

@@ -0,0 +1,3 @@
+from .geosys import read_geosys
+
+__all__ = ["read_geosys"]

src/paveit/io/geosys.py

@@ -0,0 +1,249 @@
+import csv
+import os
+from io import BytesIO
+from sys import getsizeof
+
+from numpy import array
+from pandas import DataFrame
+
+
+def detect_tabnum(filename, tabstr, encoding='utf-8'):
+    filename = os.path.normpath(filename)
+
+    tabstr = tabstr.lower()
+
+    #Einlesen
+    with open(filename, 'r', encoding=encoding) as inFile:
+        reader = csv.reader(inFile, delimiter='\t')
+        counter = 0
+        for row in reader:
+
+            row = [r.lower() for r in row]
+            if any(tabstr in mystring for mystring in row):
+                if 'plain' in row:
+                    return row[1]
+
+            counter += 1
+
+            if counter > 100:
+                return False
+
+
+def str2float(str):
+    try:
+        str = str.replace(',', '.')
+        return float(str)
+    except:
+        return None
+
+
+def read_geosys(buffer: BytesIO,
+                table,
+                pkdata='001',
+                metadata_ids=['003', '015'],
+                encoding='utf-8',
+                to_si=False,
+                debug=False):
+    '''
+
+    :param buffer: Bytes IO Object
+    :param table: Table-Number
+    :param pkdata: Table-Number of speciment definitions, default: 1
+    :param encoding: Encoding, default: utf-8
+    :param debug: debug-mode
+    :return:
+
+    '''
+
+    try:
+        dictOut = {}
+        dictOut['durch'] = 0
+        dictOut['hoehe'] = 0
+
+        #---------------------------------------------------------------------
+        #Daten einlesen und umwandeln
+        #---------------------------------------------------------------------
+
+
+        #Einlesen
+        buffer.seek(0)
+
+        lines = buffer.readlines()
+        
+        data = []
+        
+        for line in lines:
+            try:
+                line = line.decode(encoding)
+                line = line.split('\t')
+
+                if len(line) > 2:   
+                    v = line[0][0:3]
+                    if len(v) == 3:
+                        if (table == v) or (pkdata == v) or (v in metadata_ids):
+                            data.append(line)
+            except:
+                pass
+        
+        if debug:
+            print('Anz. Datensätze: ', str(len(data)), getsizeof(data))
+            
+        #aufräumen
+        ##Datenstruktur anlegen
+
+        data_processed = {}
+        data_processed['head'] = []
+        data_processed['metadata'] = {}
+        data_processed['data'] = []
+
+        for i in metadata_ids:
+            data_processed['metadata'][i] = []
+
+        for idx, d in enumerate(data):
+            try:
+                v = d[0][0:3]
+                if v in pkdata: data_processed['head'].append(d)
+                if v in metadata_ids: data_processed['metadata'][v].append(d)
+                if v in table: data_processed['data'].append(d)
+
+            except:
+                pass
+
+        # replace object
+        data = data_processed
+        
+        assert len(data['data']) != 0
+
+        if debug:
+            print('data_clean fin')
+            
+        ## Header aufbereiten
+
+        for idx, row in enumerate(data['head']):
+            if idx == 0:
+                id_durchmesser = None
+                id_hoehe = None
+                id_name = None
+
+                for idx_name, name in enumerate(row):
+                    name_lower = name.lower()
+
+                    if any(map(name_lower.__contains__, ['durchmesser'])):
+                        id_durchmesser = idx_name
+
+                    elif any(map(name_lower.__contains__, ['bezeichnung'])):
+                        id_name = idx_name
+
+                    elif any(map(name_lower.__contains__, ['höhe'])):
+                        id_hoehe = idx_name
+
+                if debug:
+                    print(id_durchmesser, id_hoehe, id_name)
+            
+            elif idx == 1:
+                unit_durch = None
+                unit_hoehe = None
+
+                try:
+                    unit_durch = row[id_durchmesser]
+                    unit_hoehe = row[id_hoehe]
+                except:
+                    pass
+
+            elif idx == 2:
+                durchmesser = None
+                hoehe = None
+                name = None
+                try:
+                    durchmesser = str2float(row[id_durchmesser])
+                    hoehe = str2float(row[id_hoehe])
+                    name = row[id_name]
+
+                except:
+                    pass
+
+        header = {
+            'speciment_diameter': durchmesser,
+            'speciment_height': hoehe,
+            'name': name,
+            'unit_h': unit_hoehe,
+            'unit_d': unit_durch
+        }
+
+        meta = data['metadata']
+        
+        for key in meta.keys():
+            sel = meta[key]
+
+            assert len(sel[0]) == len(sel[2])
+            
+            if len(sel) <= 3:
+                d = { sel[0][i]: sel[2][i].strip() for i in range(len(sel[0])) }
+            # Fix: In Geosys gibt es den Parameter Oberspannung zweimal. Erster entfernen
+            else:    
+                d = { sel[0][i]: sel[3][i].strip() for i in range(len(sel[0])) }
+                
+            header_append = d
+
+            header.update(header_append)
+            
+        #Fix Frequenz: Ich muss dies in den Eingangsdaten der TUD anpassen
+        try:
+            l = 'Versuchsart\r\n'
+            
+            header['Frequenz'] = float(header[l].split('Hz')[0].split('Steifigkeit')[1].strip().replace(',','.'))
+            
+        except:
+            pass
+
+        if debug:
+            print('header\n', header)
+
+        # add metadata to header
+
+        ## Daten in Pandas DataFrame umwandeln
+        if debug:
+            print('daten umwandel')
+
+        temp = []
+        
+        for idx, row in enumerate(data['data']):
+            
+            if idx == 0:
+                if debug:
+                    print('convert head')
+                data_head = []
+                for idx_name, name in enumerate(row):
+                    if idx_name <= 1: continue
+                    data_head.append(name)
+            elif idx == 1:
+                data_units = []
+                for idx_name, name in enumerate(row):
+                    if idx_name <= 1: continue
+                    data_units.append(name)
+            else:
+                t = []
+                for idx_col, value in enumerate(row):
+                    if idx_col <= 1:
+                        continue
+                    else:
+                        t.append(str2float(value))
+
+                temp.append(t)
+
+        data = array(temp)
+
+        if debug:
+            print(data_head, data_units)
+
+        ## Bezeichnungen der Daten normalisieren
+        # Pandas DataFrame erstellen
+        data = DataFrame(data=data, columns=data_head)
+        if debug:
+            print(data.head())
+
+        return header, data
+
+    except:
+        print('Fehler beim lesen')
+        raise

src/paveit/labtest/__init__.py

@@ -1,5 +1,8 @@
 from .base import DataSineLoad
+from .citt import *
 from .citt import CITTBase
+from .citt_fatigue import *
+from .dsv import *
 
 __all__ = ['DataSineLoad',
             'CITTBase'

src/paveit/labtest/base.py

@@ -1,10 +1,13 @@
 # coding: utf-8
 import io
+import logging
 
+import numpy as np
 import pandas as pd
-from paveit.helper import calc_hash_of_bytes, get_minio_client_processing
 
-from worker import app, logger
+from paveit.analysis import fit_cos
+from paveit.functions import calc_nu
+from paveit.helper import calc_hash_of_bytes, get_minio_client_processing
 
 
 class DataSineLoad():
@@ -13,78 +16,411 @@ class DataSineLoad():
     
     """
 
-    def __init__(self, filename:str , metadata: dict):
+    def __init__(self,
+                 filename: str,
+                 metadata: dict,
+                 logger=None,
+                 debug: bool = False,
+                 data: None | io.BytesIO = None):
+
         self.filename = filename
         self.metadata = metadata
 
-        self._logger = logger
+        if isinstance(data, io.BytesIO):
+            self.data = data
 
-        self._logger.info(f'filename s3: {self.filename}, metadata: {self.metadata}')
+        self.debug = debug
 
+        if logger == None:
+            self._logger = logging.getLogger(__name__)
+        else:
+            self._logger = logger
+
+        self._logger.info(
+            f'filename s3: {self.filename}, metadata: {self.metadata}')
+
+        self._pre_run()
+
+    def _set_parameter(self):
+        self._logger.debug('run _set_parameter')
+
+        self.split_data_based_on_parameter = ['T', 'sigma', 'f']
+
+        self.col_as_int = ['N']
+        self.col_as_float = [
+            'T', 'F', 's_piston', 's_hor_1', 'f', 's_hor_1', 's_hor_2'
+        ]
+
+        self.val_col_names = [
+            'time', 'T', 'f', 'sigma', 'N', 'F', 's_hor_1', 's_hor_2', 's_piston'
+        ]
+
+        self.columns_analyse = [
+            'F', 's_hor_sum', 's_hor_1', 's_hor_2', 's_piston'
+        ]
+
+        self.round_values = [('T', 3)]
+
+        # Header names after standardization; check if exists
+        self.val_header_names = ['speciment_height', 'speciment_diameter']
+
+        self.number_of_load_cycles_for_analysis = 5
+
+        self.meta_names_of_parameter = {
+            'sigma': ['Max. Spannung']
+        }  #list of names
+
+        self.data_column_names = {
+            'time': ['Time Series'],
+            'F': ['Load Series'],
+            's_hor_1': ['LVDT1 Series'],
+            's_hor_2': ['LVDT2 Series'],
+        }
+
+    def update_parameter():
+        """ update standard prameter from function self._set_parameter()"""
+        pass
+
+    def _define_units(self):
+
+        self.unit_s = 1  #mm
+        self.unit_F = 1  #N
+        self.unit_t = 1 / 1000.  #s
 
     def _connect_to_s3(self):
-        self._logger.info('connect to db')
+        self._logger.debug('run _connect to db')
 
         self.__minioClient = get_minio_client_processing()
 
     def _read_from_s3_to_bytesio(self):
-        self._logger.info('read bytes')
-        
+        self._logger.debug('run _read bytes')
 
         try:
             self._connect_to_s3()
-            response = self.__minioClient.get_object('processing', self.filename)
+            response = self.__minioClient.get_object('processing',
+                                                     self.filename)
             self.data = response.data
         finally:
             response.close()
             response.release_conn()
 
-        
         self.data = io.BytesIO(self.data)
+        self._logger.debug('read data from s3')
 
     def _calc_hash_of_bytesio(self):
+        self._logger.debug('run _calc_hash_of_bytesio')
 
         self.filehash = calc_hash_of_bytes(self.data)
         self.data.seek(0)
         self._logger.debug(f'Hash of file: {self.filehash}')
         
+    def _define_data_models(self):
+        pass
+        
+    def _data_in_db(self):
+        
+        nsamples = self._datamodel.objects(filehash = self.filehash).count()
+        
+        if nsamples>0:
+            self.file_in_db = True
+        else:
+            self.file_in_db = False
+        
+
+    def _process_data(self):
+        """ convert self.data (BytesIO) to pandas.DataFrame, update
+        self.metadata with informations from file """
 
-    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)
+        encoding = 'utf-8'
+        self.data = pd.read_csv(self.data, encoding=encoding)
+
+    def _meta_to_float(self):
+
+        for key, d in self.metadata.items():
+            try:
+                #remove units
+                for unit in ["°C", 'Hz']:
+                    if unit in d:
+                        d = d.split(unit)[0].strip()
+
+                f = float(d.replace(',', '.'))
+                self.metadata[key] = f
+            except:
+                pass
+
+    def _standardize_data(self):
+        self._logger.debug('run _standardize_data')
+
+        colnames = list(self.data.columns)
+
+        for par, names in self.data_column_names.items():
+            for name in names:
+                colnames = [sub.replace(name, par) for sub in colnames]
+
+        self.data.columns = colnames
+        
+        self._logger.debug(f'columns: {colnames}')
+        
+        print(self.data.head())
+        
+        self._logger.debug(f'standardize_data: {self.data.columns}')
+        
+
+    def _standardize_meta(self):
+        self._logger.debug('run _standardize_meta')
+        
+        # remove "\r\n" ending from Windows and whitespace
+        for col in list(self.metadata.keys()):
+            
+            col_mod = col.replace('\r\n', '')
+            col_mod = col_mod.strip()
+            
+            if col != col_mod:
+                self.metadata[col_mod] = self.metadata[col]
+                self.metadata.pop(col)
+            
+
+        for par, names in self.meta_names_of_parameter.items():
+            for name in names:
+                if name in self.metadata:
+
+                    self.metadata[par] = self.metadata[name]
+                    self.metadata.pop(name)
+
+                    break
+                
+        # stip data
+        for key in self.metadata.keys():
+            try:                
+                self.metadata[key] = self.metadata[key].strip()
+            except:
+                pass
+        
+        self._logger.debug(f'meta (stand.): {self.metadata}')
+
+    def _modify_meta(self):
+        pass
+
+    def _validate_data(self):
+        self._logger.debug('run _validate_data')
+
+        for name in self.val_col_names:
+            if not name in self.data.columns:
+
+                # check if value in metadata:
+                if name in self.metadata.keys():
+                    self._logger.error(f'add {name} from metadata to data')
+                    self.data[name] = self.metadata[name]
+
+                else:
+                    self._logger.error(f'{name} not in data')
+                    raise
+        
+        self._logger.debug(f'validate_data: {self.data.columns}')
+
+    def _validate_meta(self):
+        self._logger.debug('run _validate_meta')
+
+        for name in self.val_header_names:
+            if not name in self.metadata:
+                self._logger.error(f'{name} not found')
+                raise
+
+    def _post_string_to_float(self):
+
+        sel = self.data.select_dtypes(include=['object'])
+
+        if sel.empty:
+            return
+
+        for col in sel.columns:
+            try:
+                self.data[col] = pd.to_numeric(self.data[col].str.replace(
+                ',', '.'))
+            except:
+                pass
+
+    def _post_apply_units(self):
+
+        for col in [
+                's_hor_sum', 's_hor_1', 's_hor_2', 's_vert_sum', 's_vert_1',
+                's_vert_2', 's_piston', 'extension',
+        ]:
+            if col in self.data.columns:
+                self.data[col] = self.data[col].mul(self.unit_s)
+
+        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)
+            
+        try:
+            
+            self.data['f'] = self.data['f'].mul(self.unit_freq)
+            
+        except:
+            pass
+
+        return True
+
+    def _post_round_values(self):
+
+        for par, digits in self.round_values:
+
+            if par in self.data.columns:
+                self.data[par] = self.data[par].round(digits)
+
+    def _post_select_importent_columns(self):
+
+        # TODO: add more columns, check datamodel
+
+        self.data = self.data[self.val_col_names]
+
+    def _post_calc_missiong_values(self):
+
+        cols = self.data.columns
+
+        if not 's_hor_sum' in cols:
+            if ('s_hor_1' in self.data.columns) & ('s_hor_2'
+                                                   in self.data.columns):
+                self.data['s_hor_sum'] = self.data[['s_hor_1',
+                                                    's_hor_2']].sum(axis=1)
+
+        if not 's_vert_sum' in cols:
+            if ('s_vert_1' in self.data.columns) & ('s_vert_2'
+                                                    in self.data.columns):
+                self.data['s_vert_sum'] = self.data[['s_vert_1',
+                                                     's_vert_2']].sum(axis=1)
+
+    def _post_opt_data(self):
+        #set dtypes:
+        for col in self.col_as_int:
+            self.data[col] = self.data[col].astype('int')
+        for col in self.col_as_float:
+            try:
+                self.data[col] = self.data[col].astype('float')
+            except:
+                pass
+
+        #set index
+        self.data = self.data.set_index('time')
+
+        return True
+
+    def _fit_split_data(self):
+        self._logger.debug('run _fit_split_data')
+
+        data_gp = self.data.groupby(self.split_data_based_on_parameter)
+
+        data_list = []
+
+        for idx, d in data_gp:
+
+            if d.empty: continue
+
+            if any(d['f'] <= 0.0): continue
+
+            #reset N
+            d['N'] = d['N'] - d['N'].iloc[0] + 1
+
+            idx_diff = np.diff(d.index)
+            dt_mean = idx_diff.mean()
+
+            gaps = idx_diff > (4 * dt_mean)
+            has_gaps = any(gaps)
+            
+            if has_gaps == False:
+                data_list.append(d)
+
+            else:
+
+                #FIX: GAP FINDING
+                data_list.append(d)
+                """
+                print('has gaps')
+                print(gaps)
+                idx_gaps = (np.where(gaps)[0] - 1)[0]
+                print(idx_gaps)
+                data_list.append(d.iloc[0:idx_gaps])
+                """
+
+        #add self.
+        if len(data_list) == 0:
+            self.num_tests = 0
+            self.data = data_list[0]
+
+        else:
+            self.num_tests = len(data_list)
+            self.data = data_list
+            #break
+
+        nchunks = len(self.data)
+        self._logger.debug(f'data splited in {nchunks} chunks')
+
+    def _fit_select_data(self):
+        """ 
+        select N load cycles from original data 
+        (a): Based on window of TP-Asphalt
+        (b) last N cycles
+        
+        DUMMY FUNCTION
+        """
+        pass
 
     def _calc(self):
-        self._logger.debug('calc data')
-        return self.df.mean().mean()
+        """
+        Calculate Results        
+        DUMMY FUNCTION
+        """
 
-    def _archive_binary_data(self):
+        self._logger.info('run _calc base')
+        print('run BASE')
 
-        self._logger.debug('send file to archive')
-        app.send_task('ArchiveFile', args=[self.filename, 
-                                           self.metadata,
-                                           self.filehash,
-                                           'org',
-                                           'citt'
-                                           ],
-                      queue='archive'
-                      )
+    def save(self):
+        '''
+        save results to database
         
+        DUMMY FUNCTION
+        '''
 
+        pass
 
+    def _pre_run(self):
+
+        if not hasattr(self, 'data'):
+            self._read_from_s3_to_bytesio()
+
+        self._calc_hash_of_bytesio()
+        self._define_data_models()
+        #self._data_in_db()
+        self._set_parameter()
+        self.update_parameter()
+        self._define_units()
 
     def run(self):
         self._logger.info('run task')
-        self._read_from_s3_to_bytesio()
-        self._calc_hash_of_bytesio()
 
-        self._bytes_to_df()
+        self._process_data()
+        self._meta_to_float()
 
-        res = self._calc()
-        self._logger.debug(f'results: {res}')
+        self._standardize_meta()
+        self._standardize_data()
+        self._modify_meta()
+        self._validate_meta()
+        self._validate_data()
 
-        self._archive_binary_data()
+        self._post_string_to_float()
+        self._post_select_importent_columns()
+        self._post_apply_units()
+        self._post_round_values()
+        self._post_calc_missiong_values()
+        self._post_opt_data()
 
-        return res
+        self._fit_split_data()
+        self._fit_select_data()
 
+        self._calc()
+        #self._logger.info(f'results: {self.fit['E']}')

src/paveit/labtest/citt_fatigue.py

@@ -0,0 +1,91 @@
+import numpy as np
+import pandas as pd
+
+from paveit.functions.citt import calc_E
+
+
+class CittAnalyseFatigue():
+    
+    def _fit_split_data(self):
+        
+        data_exp = []
+        
+        N = self.data['N'].unique()
+        N = np.array(N)
+        gaps = N[1:][np.diff(N)>1]
+
+        for i,gap in enumerate(gaps):
+            print(i, gap)
+            if i == 0:
+                f = self.data['N']<gap
+            elif i == len(gaps):
+                f = self.data['N']>=gap
+            else:
+                f = (self.data['N']>=gaps[i-1]) & (self.data['N']<gap)
+        
+            # filter data by geps
+            d = self.data[f]
+            
+            # get 5 cycles
+            if i == 0:
+                f = (d['N']>=98) & (d['N']<=102)
+            else:
+                Nsel = d['N'].unique()
+                f = (d['N']>=Nsel[-5]) & (d['N']<=Nsel[-1])
+            
+            d = d[f]
+            
+            
+            
+            data_exp.append(d)
+        
+        
+        self.data = data_exp
+        
+    
+    def _fit_select_data(self):
+        
+        ''' analyse data
+
+        '''
+        pass 
+        
+    def _calc(self):
+        print('calc fatigue')
+        
+        print(self.metadata)
+        
+        fit = []
+        
+        # Je Aufzeichnungsintervall
+        
+        
+        for i, d in enumerate(self.data):
+            
+            try:
+                res = calc_E(d, metadata=self.metadata, columns_analyse=['F', 's_hor_sum'])
+            
+                res['idx'] = i
+                
+                res['energy_ratio'] = res['stiffness']*np.round(res['N_from'] + (res['N_to'] - res['N_from'])/2, 0)
+            
+                fit.append(res)
+            except:
+                raise
+            
+        self.fit_single_results = pd.DataFrame.from_records(fit)
+        
+        EN_max = self.fit_single_results['energy_ratio'].max()
+        
+        sel_f = self.fit_single_results[(self.fit_single_results['energy_ratio']>=0.8*EN_max) & (self.fit_single_results['energy_ratio']<=1.2*EN_max)]
+        par = np.polyfit(sel_f['N_from'], sel_f['energy_ratio'], 4)
+        
+        x = np.arange(sel_f['N_from'].min(),sel_f['N_from'].max(), 1)
+        y = np.polyval(par, x)
+
+        Nmakro = x[y.argmax()]
+
+        self.fit = {'Nmakro': Nmakro, 
+                        'energy_ratio_max': y.max(),
+                        'par_fit': par,
+                        'epislon_elast_98': self.fit_single_results.iloc[0]['el_strains']}

src/paveit/labtest/dsv.py

@@ -0,0 +1,393 @@
+import io
+import os
+from csv import reader
+
+import numpy as np
+import pandas as pd
+from bson import ObjectId
+
+from paveit import calc_nu, fit_cos
+from paveit.io import read_geosys
+from paveit.labtest import DataSineLoad
+
+
+class TP25A1base(DataSineLoad):
+    
+    def _set_parameter(self):
+        self._logger.debug('run _set_parameter')
+
+        self.split_data_based_on_parameter = ['T', 'sigma', 'f']
+
+        self.col_as_int = ['N']
+        
+        self.col_as_float = [
+            'T', 'F', 's_piston', 's_hor_1', 'f', 's_hor_1', 's_hor_2'
+        ]
+
+        self.val_col_names = [
+            'time', 'T', 'f', 'sigma', 'N', 'F', 's_hor_1', 's_hor_2', 's_piston'
+        ]
+
+        self.columns_analyse = [
+            'F', 's_hor_sum', 's_hor_1', 's_hor_2', 's_piston'
+        ]
+
+        self.round_values = [('T', 3), ('sigma', 3)]
+
+        # Header names after standardization; check if exists
+        self.val_header_names = ['speciment_height', 'speciment_diameter']
+
+        self.number_of_load_cycles_for_analysis = 5
+
+        #Dummy Data, replace in Machine Config
+        self.meta_names_of_parameter = {
+            'sigma': ['Max. Spannung']
+        }  #list of names
+
+        #Dummy Data, replace in Machine Config
+        self.data_column_names = {
+            'time': ['Time Series'],
+            'F': ['Load Series'],
+            's_hor_1': ['LVDT1 Series'],
+            's_hor_2': ['LVDT2 Series'],
+        }
+
+    def _sel_df(self, df, num=5, shift=-1):
+        
+        print(df.head())
+
+        N = df['N'].unique()
+        n_N = len(N)
+        max_N = max(N)
+        min_N = min(N)
+
+        freq = float(df['f'].unique()[0])
+        
+        # define cycles to select
+        if freq == 10.0:
+            Nfrom = 98
+            Nto = 103
+        elif freq == 5.0:
+            Nfrom = 93
+            Nto = 97
+        elif freq == 3.0:
+            Nfrom = 43
+            Nto = 47
+        elif freq == 1.0:
+            Nfrom = 13
+            Nto = 17
+        elif freq == 0.3:
+            Nfrom = 8
+            Nto = 12
+        elif freq == 0.1:
+            Nfrom = 3
+            Nto = 7
+        else:
+            Nfrom = None
+            Nto = None
+            
+            
+        self._logger.debug(f'{min_N}, {max_N}, {n_N}, {num}, {shift}')
+        self._logger.debug(f'Frequenz: {freq}, Nfrom: {Nfrom}, Nto: {Nto}')
+
+        # Fall 1: nur num Lastwechsel
+        if n_N < num:
+            df_sel = None
+        elif n_N == num:
+            df_sel = df
+        # Fall 2: nicht alle LW in Datei
+        elif (max_N < Nto) & (n_N > num):
+
+            df_sel = df[(df['N'] >= N[-num + shift])
+                        & (df['N'] <= N[-1 + shift])]
+
+        # Fall 3: Auswahl wie oben definiert
+        elif (Nfrom >= min_N) & (Nto < max_N):
+            df_sel = df[(df['N'] >= Nfrom) & (df['N'] <= Nto)]
+        # Fall 4: Auswahl unbekannt
+        else:
+            df_sel = None
+
+        return df_sel
+
+    def _fit_select_data(self):
+        """ 
+        select N load cycles from original data 
+        (a): Based on window of TP-Asphalt
+        (b) last N cycles
+        
+        """
+
+        self._logger.debug('run _fit_select_data')
+
+        self.max_N_in_data = []
+
+        if not isinstance(self.data, list):
+            if self.number_of_load_cycles_for_analysis > 1:
+
+                self.max_N_in_data.append(self.data['N'].max())
+
+                df_sel = [
+                    self._sel_df(self.data,
+                                 num=self.number_of_load_cycles_for_analysis)
+                ]
+            else:
+                df_sel = [self.data]
+
+        else:
+            df_sel = []
+            for d in self.data:
+
+                self.max_N_in_data.append(d['N'].max())
+
+                if self.number_of_load_cycles_for_analysis > 1:
+                    d_sel = self._sel_df(
+                        d, num=self.number_of_load_cycles_for_analysis)
+                else:
+                    d_sel = d
+
+                df_sel.append(d_sel)
+
+        # replace data
+        self.data = df_sel
+
+    def _calc(self):
+
+        self._logger.info('run _calc CITT')
+        print('run CITT')
+
+        self.fit = []
+        for idx_data, data in enumerate(self.data):
+
+            if data is None: continue
+            if len(data) < 10: continue
+            
+            try:
+                
+                self._logger.debug(f'run fit on subset {idx_data}')
+
+                data.index = data.index - data.index[0]
+
+                res_temp = {}
+                res_temp['idx'] = idx_data
+
+                x = data.index.values
+
+                freq = np.round(float(data['f'].unique()), 2)
+                sigma = float(data['sigma'].unique())
+                temperature = float(data['T'].unique())
+
+                for idxcol, col in enumerate(self.columns_analyse):
+
+                    if not col in data.columns: continue
+
+                    y = data[col].values
+                    res = fit_cos(x, y, freq=freq)
+
+                    for key, value in res.items():
+                        res_temp[f'fit_{col}_{key}'] = value
+                        
+
+                    # analyse cycle data
+                    
+                    cycle_min = []
+                    cycle_max = []
+                    cycle_mean = []
+                    cycle_diff = []
+                    
+                    for N, data_cycle in data.groupby('N'):
+                        y = data_cycle[col].values
+                        
+                        cycle_min.append(y.min())
+                        cycle_max.append(y.max())
+                        cycle_mean.append(y.mean())
+                        cycle_diff.append(cycle_max[-1] - cycle_min[-1])
+                        
+                    res_temp[f'fit_{col}_cycle_min'] = cycle_min
+                    res_temp[f'fit_{col}_min'] = np.mean(cycle_min)
+                    res_temp[f'fit_{col}_min_std'] = np.std(cycle_min)
+                    res_temp[f'fit_{col}_min_diff_rel'] = (np.max(cycle_min) - np.min(cycle_min))/np.mean(cycle_min)
+                    res_temp[f'fit_{col}_cycle_max'] = cycle_max
+                    res_temp[f'fit_{col}_max'] = np.mean(cycle_max)
+                    res_temp[f'fit_{col}_max_std'] = np.std(cycle_max)
+                    res_temp[f'fit_{col}_max_diff_rel'] = (np.max(cycle_max) - np.min(cycle_max))/np.mean(cycle_max)
+                    res_temp[f'fit_{col}_cycle_mean'] = cycle_mean
+                    res_temp[f'fit_{col}_mean'] = np.mean(cycle_mean)
+                    res_temp[f'fit_{col}_mean_std'] = np.std(cycle_mean)
+                    res_temp[f'fit_{col}_mean_diff_rel'] = (np.max(cycle_mean) - np.min(cycle_mean))/np.mean(cycle_mean)
+                    res_temp[f'fit_{col}_cycle_diff'] = cycle_diff
+                    res_temp[f'fit_{col}_diff'] = np.mean(cycle_diff)
+                    res_temp[f'fit_{col}_diff_std'] = np.std(cycle_diff)
+                    res_temp[f'fit_{col}_diff_diff_rel'] = (np.max(cycle_diff) - np.min(cycle_diff))/np.mean(cycle_diff)
+
+                # add more metadata
+                res_temp['f_set'] = freq
+                res_temp['sigma_set'] = sigma
+                res_temp['T_set'] = temperature
+
+                res_temp['N_from'] = data['N'].min()
+                res_temp['N_to'] = data['N'].max()
+                res_temp['N_tot'] = self.max_N_in_data[idx_data]
+
+                res_temp['n_samples_per_cycle'] = int(
+                    len(data) / (res_temp['N_to'] - res_temp['N_from'] + 1))
+
+                ## Stiffness
+                deltaF = res_temp['fit_F_amp']
+                deltaU = res_temp['fit_s_hor_sum_amp']
+                
+                h = float(self.metadata['speciment_height'])
+                d = float(self.metadata['speciment_diameter'])
+                
+                nu = calc_nu(temperature)
+                res_temp['nu'] = nu
+                
+                print(deltaF, deltaU, h, d, nu, np.pi)
+
+                #nach TP Asphalt 26
+                res_temp['stiffness'] = deltaF /(h * deltaU) * (4.0/np.pi -1 + nu)
+                
+                ## Elastische hori. Dehnung                
+                res_temp['el_strains'] = 2*2*deltaU/d * (1+3*nu)/(4 + np.pi*nu - np.pi) * 1000.0 # 2*2 daher, da deltaU nur Ampl. nicht Gesamtkraft ist
+                
+                # TODO: Überarbeiten und erweitern (ISSUE #2)
+                res_temp['phase'] = res_temp['fit_F_phase'] - res_temp['fit_s_hor_sum_phase']
+                
+            except Exception as e:
+                self._logger.exception(e)
+                res_temp = None
+                
+            self._logger.debug(res_temp)
+
+            self.fit.append(res_temp)
+
+        self.fit = pd.DataFrame.from_records(self.fit)
+
+        self.fit = self.fit.reset_index(drop=True).set_index('idx')
+
+        #self.fit = self.fit.set_index(['T', 'f', 'sigma'])
+
+        nsamples = len(self.fit)
+        self._logger.info(f'fitting finished, add {nsamples} samples')
+        self._logger.debug(self.fit['stiffness'])
+
+    def save(self,
+             task_id: ObjectId,
+             meta: dict = {}
+             ):
+        """
+        save results to mongodb
+        """
+
+        if not hasattr(self, 'fit'):
+            raise
+
+        # precheck data and results
+        #assert len(self.data) == len(self.fit)
+
+        for idx_fit, fit in self.fit.iterrows():
+            data = self.data[idx_fit]
+
+            meta['filehash'] = self.filehash
+            meta['task_id'] = task_id
+            
+            if not self.metadata['speciment_name'] == None:
+                meta['speciment_name'] = self.metadata['speciment_name']
+            else:
+                meta['speciment_name'] = self.filename
+            
+            meta['speciment_diameter'] = self.metadata['speciment_diameter']
+            meta['speciment_height'] = self.metadata['speciment_height']
+            
+
+            #check if result in db
+            #n = CITTSiffness.objects(**meta).count()
+            #print(n)
+
+            # write data
+            data_dict = fit.to_dict()
+            data_dict.update(meta)
+
+            # remove 'fit_' from keys:
+            for key in list(data_dict.keys()):
+                if key.startswith('fit_'):
+                    data_dict[key[4:]] = data_dict[key]
+                    data_dict.pop(key)
+
+            # rename fields
+
+            def rename_field(d, old, new):
+                d[new] = d[old]
+                d.pop(old)
+
+            f = CITTSiffnessResults(**data_dict).save()
+
+            # required data
+            data_out = dict(
+                time=data.index,
+                F=list(data['F']),
+                N=list(data['N']),
+                s_hor_1=list(data['s_hor_1']),
+                s_hor_2=list(data['s_hor_2']),
+                s_hor_sum=list(data['s_hor_sum']),
+            )
+            
+            self._logger.debug(f'columns data,  {data.columns}')
+
+            # add optional datas
+            for col in ['s_piston']:
+                if col in data.columns:
+                    self._logger.debug(f'add {col} to output data')
+                    data_out[col] = list(data[col])
+
+            outkeys = list(data_out.keys())
+            self._logger.debug(f'write raw data to db,  {outkeys}')
+            g = CITTSiffness(result=f.id, **data_out).save()
+
+class TP25A1_TUDresdenWille(TP25A1base):
+
+    def _define_units(self):
+
+        self.unit_s = 1 / 1000.  #mm
+        self.unit_F = 1.0  #N
+        self.unit_t = 1.  #s
+
+    def update_parameter(self):
+
+        self.meta_names_of_parameter = {
+            'sigma': ['Oberspannung'],
+            'sigma_min': ['Unterspannung'],
+            'f': ['Frequenz'],
+            'T': ['Versuchstemperatur'],
+            't': ['Zeit'],
+            't_pulse': ['Impulsdauer'],
+            't_break': ['Lastpauseit'],
+            'speciment_diameter': ['PK-Durchmesser'],
+            'speciment_height': ['PK-Höhe'],
+            'punch_diameter': ['Stempeldurchmesser'],
+            'speciment_name': ['Probekörperbezeichnung'],
+        }  #list of names
+
+        self.data_column_names = {
+            'time': ['Zeit'],
+            'F': ['Kraft'],
+            'N': ['Zyklenzähler'],
+            's_vert_1': ['Vertikalweg 1'],
+            's_vert_2': ['Vertikalweg 2'],
+            's_vert_3': ['Vertikalweg 3'],
+            's_piston': ['Kolbenweg'],
+        }
+
+    def _process_data(self):
+
+        meta, data = read_geosys(self.data, '015', metadata_ids=['001','003', '005'])
+
+        #define in class
+        self.data = data.reset_index()
+        self.metadata.update(meta)
+
+        # log infos
+        self._logger.debug(f'metadata: {self.metadata}')
+        self._logger.debug(f'data: {self.data.head()}')
+        
+        print(data.head())

src/paveit/postprocessing/__init__.py

@@ -0,0 +1,4 @@
+from .citt import citt
+
+__all__ = ['citt',
+           ]

src/paveit/postprocessing/citt.py

@@ -0,0 +1,135 @@
+import datetime
+
+import lmfit as lm
+import numpy as np
+import pandas as pd
+import scipy.special as sf
+from bson import ObjectId
+from scipy.optimize import curve_fit
+
+from paveit.datamodels import CITTSiffnessResults, RegCITT
+
+
+def temp_freq_equivalence(T, f, phi, T0=20.0):
+    
+    alphaT = np.exp(phi * ((1 / (T + 273.15)) - (1 / (T0 + 273.15))))
+    x = np.log(f * alphaT) / np.log(10)
+    
+    return x
+
+def stiffness_tp26(T, f, phi, Emax, Emin, z0, z1, T0=20.0):
+
+    x = temp_freq_equivalence(T, f, phi, T0)    
+    
+    E = Emin + (Emax - Emin) / (1 + np.exp(z1 * x + z0))
+
+    return E
+
+
+def calc_nu(T):
+    #TODO: Prüfen ob Formel stimmt!
+    nu = 0.15 + (0.35) / (1 + np.exp(3.1849 - 0.04233 * (9 / 5 * T + 32)))
+    return nu
+
+
+def citt(task_id: str):
+    """
+    Postprocessing task
+    """
+    
+    print('postprocessing')
+    
+    task_id = ObjectId(task_id)
+    
+    # read all data
+    data = []
+    parlist = ['f_set', 'T_set', 'stiffness', 'phase']
+    
+    for obj in CITTSiffnessResults.objects(task_id=task_id).only(*parlist):
+        data.append(dict((k, obj[k]) for k in parlist ))
+        
+    data = pd.DataFrame.from_records(data)
+    
+    #Emax/Emin
+    line_mod = lm.models.LinearModel()
+    out = line_mod.fit(data.stiffness, x=data.phase)
+    
+    Emax = line_mod.eval(out.params, x=0.0)
+    
+    Emin = 0
+    
+    assert Emin < Emax
+    
+    # Fit data        
+    mod = lm.models.Model(stiffness_tp26, independent_vars=['f','T'])
+    
+    mod.set_param_hint(
+        'Emin',
+        value=Emin,
+        min=0,
+        max=0.9*Emax,
+        vary=True,
+    )
+    
+    mod.set_param_hint(
+        'Emax',
+        value=Emax,
+        min=0.9*Emax,
+        max=1.1*Emax,
+        vary=True,
+    )
+    
+    mod.set_param_hint(
+        'T0',
+        value=20.0,
+        vary=False,
+    )
+    
+    mod.set_param_hint('phi', value=25000, min=15000, max=35000, vary=True)
+    mod.set_param_hint('z0', value=1,min=1e-10, max=1000., vary=True)
+    mod.set_param_hint('z1', value=-1, min=-1000., max=-1e-10, vary=True)
+    
+    parms_fit = [
+        mod.param_hints['Emin']['value'], mod.param_hints['Emax']['value'],
+        mod.param_hints['phi']['value'], mod.param_hints['z0']['value'],
+        mod.param_hints['z1']['value']
+    ]
+    
+    
+    ## run fit
+    results = []
+    r2 = []
+    
+    
+    try:
+        methods = ['leastsq', 'powell']
+
+        for method in methods:
+            result = mod.fit(data.stiffness, T=data.T_set, f=data.f_set, method=method, verbose=False)
+            
+            r2temp =  1.0 - result.redchi / np.var(data.stiffness.values, ddof=2)
+            r2.append(r2temp)
+
+            results.append(result)
+
+        best = np.nanargmax(r2)
+        
+        res = results[best].best_values
+        res['stat_r2'] = r2[best]
+        
+    except:
+        print('error regression, send default values')
+        
+        res = mod.valuesdict()
+        
+    #add metadata
+    res['nsamples'] = len(data)
+    res['task_id'] = task_id        
+    res['date'] = datetime.datetime.now()
+
+    print(res)
+    
+    # save results to db
+    doc = RegCITT.objects(task_id=task_id).modify(upsert=True, **res)
+    
+    return True

tests/analysis/citt_test.py

@@ -0,0 +1,52 @@
+import logging
+import os
+
+import toml
+from src.paveit.helper import read_file_to_bytesio
+from src.paveit.labtest.citt import CITT_PTMDortmund
+
+logger = logging.getLogger(__name__)
+
+
+def test_base_class():
+    pass
+
+
+def test_citt_ptmdortmund():
+
+    data_path = 'tests/data/citt/PTM_Dortmund'
+
+    res_dict = toml.load(os.path.join(data_path, 'meta.toml'))
+    logger.info(res_dict)
+
+    for filename, meta in res_dict.items():
+
+        logger.info(f'run test on: {filename}, {meta}')
+
+        file = os.path.join(data_path, filename)
+
+        buf = read_file_to_bytesio(file)
+
+        metadata = {'org': 'pytest_ptm_dortmund'}
+
+        res = CITT_PTMDortmund(filename, metadata, archive=False,
+                               data=buf)
+        res.run()
+
+        fit = res.fit.reset_index()
+        logger.info(fit.head())
+        assert len(fit) == 5
+        
+        m = res_dict[filename]
+        
+        for col in ['F', 's_hor_sum', 's_hor_1', 's_hor_2']:
+            assert all(fit[f'fit_{col}_r2'] >= m['min_r2'])
+        
+        
+        
+        sel = fit[(fit['f']==10.0) & (fit['T']==20.0)].iloc[0]
+        
+        Emin = (1-m['max_diff'])*m['stiffness_10Hz']
+        Emax = (1+m['max_diff'])*m['stiffness_10Hz']
+        
+        assert Emin <= sel['E'] <= Emax

tests/analysis/sine_test.py

@@ -0,0 +1,116 @@
+from random import uniform
+
+import numpy as np
+from paveit.analysis.regression import fit_cos, fit_cos_eval
+
+
+def fit(freq: float = 10,
+        ampl: float = 100.0,
+        offset: float = 20.0,
+        slope: float = 0.1,
+        phase: float = 0.05,
+        error: float = 0.001) -> None:
+
+    N: int = 5
+    num_samples_per_cycle: int = 50
+
+    t = np.linspace(0, N / freq, N * num_samples_per_cycle)
+    y = ampl * np.cos(2 * np.pi * freq * t + phase) + slope * t + offset
+
+    r = fit_cos(t, y)
+
+    error_min = (1 - error)
+    error_max = (1 + error)
+
+    # ampltude
+    rel_error = (r['amp'] / ampl)
+    assert error_min <= rel_error <= error_max
+
+    # offset
+    rel_error = (r['offset'] / offset)
+    assert error_min <= rel_error <= error_max
+
+    # slope
+    rel_error = (r['slope'] / slope)
+    assert error_min <= rel_error <= error_max
+
+    # phase
+    rel_error = (r['phase'] / phase)
+    assert error_min <= rel_error <= error_max
+
+    # freq
+    rel_error = (r['freq'] / freq)
+    assert error_min <= rel_error <= error_max
+
+
+def test_fit_simple_sine(ntest: int = 50) -> None:
+    """
+    fit a simple sine signal and evaluate amplitude
+    
+    error: percentage error of ampl, Error max 0.1 %
+    """
+
+    fit()
+
+    #run multiple tests with random parameters
+    for i in range(ntest):
+
+        fit(
+            ampl=uniform(1e-3, 1000),
+            offset=uniform(1e-3, 1),
+            slope=uniform(1e-5, 1),
+            phase=uniform(1e-5, 1),
+        )
+
+
+def fit_noise(freq: float = 10,
+              ampl: float = 100.0,
+              offset: float = 20.0,
+              slope: float = 0.1,
+              phase: float = 0.05,
+              noise_level: float = 0.01,
+              error: float = 0.01) -> None:
+
+    N: int = 5
+    num_samples_per_cycle: int = 50
+
+    t = np.linspace(0, N / freq, N * num_samples_per_cycle)
+    y = ampl * np.cos(2 * np.pi * freq * t + phase) + slope * t + offset
+    y_noise = np.random.normal(0, noise_level * ampl, len(t))
+
+    y = y + y_noise
+
+    r = fit_cos(t, y)
+
+    error_min = (1 - error)
+    error_max = (1 + error)
+
+    # ampltude
+    rel_error = (r['amp'] / ampl)
+    assert error_min <= rel_error <= error_max
+    
+    # freq
+    rel_error = (r['freq'] / freq)
+    assert error_min <= rel_error <= error_max
+
+
+def test_fit_simple_sine_with_noise(ntest: int = 50) -> None:
+    """
+    fit a simple sine signal and evaluate amplitude
+    
+    error: percentage error of ampl, Error max 0.1 %
+    """
+
+    fit_noise()
+
+    #run multiple tests with random parameters
+    for i in range(ntest):
+
+        fit_noise(
+            ampl=uniform(1e-3, 1000),
+            offset=uniform(1e-3, 1),
+            slope=uniform(1e-5, 1),
+            phase=uniform(1e-5, 1),
+            noise_level=uniform(0.01, 0.1),
+            error=0.02,
+        )

tests/data/citt/PTM_Dortmund/meta.toml

@@ -0,0 +1,14 @@
+["sample_01.xlsm"]
+min_r2 = 0.993
+max_diff = 0.005 #%
+stiffness_10Hz = 2269.0 #MPa
+
+["sample_02.xlsm"]
+min_r2 = 0.993
+max_diff = 0.005 #%
+stiffness_10Hz = 2250.0 #MPa
+
+["sample_03.xlsm"]
+min_r2 = 0.993
+max_diff = 0.005 #%
+stiffness_10Hz = 2231.0 #MPa

tests/helper/filehandling_test.py

@@ -0,0 +1,24 @@
+import glob
+import logging
+import os
+
+from src.paveit.helper import read_file_to_bytesio
+
+logger = logging.getLogger(__name__)
+
+data_path = 'tests/data/citt/PTM_Dortmund'
+
+
+def test_read_file_compare_filesize():
+
+    files = glob.glob(os.path.join(data_path, '*.xlsm'))
+
+    for file in files:
+
+        file_stat = os.stat(file)
+        file_size = file_stat.st_size
+
+        buf = read_file_to_bytesio(file)
+        buf_size = buf.getbuffer().nbytes
+
+        assert file_size == buf_size