Initial commit: GasFlux project with core processing pipelines
This commit is contained in:
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# repo stuff
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survey
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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lib/
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parts/
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share/python-wheels/
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*.egg-info/
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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# Unit test / coverage reports
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htmlcov/
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.pytest_cache/
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||||
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||||
# Translations
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*.mo
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*.pot
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||||
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||||
# Django stuff:
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||||
*.log
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||||
local_settings.py
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||||
db.sqlite3
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||||
db.sqlite3-journal
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||||
# Flask stuff:
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instance/
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.webassets-cache
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||||
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||||
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||||
.scrapy
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||||
# Sphinx documentation
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||||
docs/_build/
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||||
# PyBuilder
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||||
target/
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||||
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# Jupyter Notebook
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.ipynb_checkpoints
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||||
# IPython
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profile_default/
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||||
ipython_config.py
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||||
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||||
# pyenv
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.python-version
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||||
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||||
# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
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||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
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||||
# install all needed dependencies.
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||||
#Pipfile.lock
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||||
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||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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||||
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||||
# Celery stuff
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||||
celerybeat-schedule
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||||
celerybeat.pid
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||||
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||||
# SageMath parsed files
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*.sage.py
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||||
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||||
# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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||||
/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# VSCode
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.vscode/
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# Node modules for this repo
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package.json
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package-lock.json
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node_modules/
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/shelf/
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/workspace.xml
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# 基于编辑器的 HTTP 客户端请求
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/httpRequests/
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/dataSources/
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/dataSources.local.xml
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="jdk" jdkName="gasflux2" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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<option name="format" value="PLAIN" />
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<option name="myDocStringFormat" value="Plain" />
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<component name="TemplatesService">
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<option name="TEMPLATE_CONFIGURATION" value="Jinja2" />
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<option name="TEMPLATE_FOLDERS">
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<list>
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<option value="$MODULE_DIR$/src/gasflux/templates" />
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</list>
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</option>
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</component>
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<component name="TestRunnerService">
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<option name="PROJECT_TEST_RUNNER" value="py.test" />
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<item index="0" class="java.lang.String" itemvalue="catboost" />
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<item index="4" class="java.lang.String" itemvalue="zstandard" />
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<item index="10" class="java.lang.String" itemvalue="numpy" />
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<item index="11" class="java.lang.String" itemvalue="opencv_python" />
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<item index="12" class="java.lang.String" itemvalue="Pillow" />
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<item index="13" class="java.lang.String" itemvalue="tensorflow" />
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<item index="14" class="java.lang.String" itemvalue="jupyter" />
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<item index="15" class="java.lang.String" itemvalue="ipykernel" />
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<item index="16" class="java.lang.String" itemvalue="pandas" />
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<item index="17" class="java.lang.String" itemvalue="Werkzeug" />
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<item index="18" class="java.lang.String" itemvalue="cellpose" />
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<item index="19" class="java.lang.String" itemvalue="torchvision" />
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<item index="20" class="java.lang.String" itemvalue="Flask" />
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<item index="21" class="java.lang.String" itemvalue="fiona" />
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</list>
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</value>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="gasflux_env" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="gasflux2" project-jdk-type="Python SDK" />
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<module fileurl="file://$PROJECT_DIR$/.idea/gasflux-develop.iml" filepath="$PROJECT_DIR$/.idea/gasflux-develop.iml" />
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</modules>
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.5.0
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hooks:
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||||
- id: check-added-large-files # prevents giant files from being committed
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args: ["--maxkb=1024"]
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||||
- id: check-ast # simply checks whether the files parse as valid python
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- id: check-builtin-literals # requires literal syntax when initializing empty or zero python builtin types
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||||
- id: check-case-conflict # checks for files that would conflict in case-insensitive filesystems
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- id: check-docstring-first # checks a common error of defining a docstring after code
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- id: check-executables-have-shebangs # ensures that (non-binary) executables have a shebang
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- id: pretty-format-json # pretty formats json files
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||||
- id: check-json # checks json files for syntax errors
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||||
- id: check-merge-conflict # prevents git merge conflicts from being committed (finds conflict markers)
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||||
- id: check-shebang-scripts-are-executable # ensures that (non-binary) files with a shebang are executable
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- id: check-toml # checks toml files for syntax errors
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||||
- id: check-vcs-permalinks # ensures that links to vcs websites are permalinks
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||||
- id: check-yaml # checks yaml files for parseable syntax
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||||
- id: debug-statements # checks for debugger imports and py37+ `breakpoint()` calls in python source
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# - id: detect-aws-credentials # detects *your* aws credentials from the aws cli credentials file
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- id: detect-private-key # detects the presence of private keys
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||||
- id: end-of-file-fixer # ensures that a file is either empty, or ends with one newline
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files: ^.*py$
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- id: mixed-line-ending # replaces or checks mixed line ending
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files: ^.*py$
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- id: no-commit-to-branch
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args: ["--branch", "main", "--branch", "develop"]
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- id: requirements-txt-fixer # sorts entries in requirements.txt
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- id: trailing-whitespace # trims trailing whitespace
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files: ^.*py$
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- repo: local
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hooks:
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- id: nbdev
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name: nbdev clean
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entry: nbdev_clean --clear_all
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pass_filenames: false
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language: python
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files: .ipynb
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- id: mypy
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name: mypy
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entry: mypy
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language: python
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files: ^.*py$
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- id: vulture
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name: vulture
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entry: vulture
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args: ["--min-confidence", "61"]
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language: python
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files: ^.*py$
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||||
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.3.5
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||||
hooks:
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- id: ruff
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types_or: [python, pyi, jupyter]
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args: [--fix]
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- id: ruff-format
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types_or: [python, pyi, jupyter]
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# ci:
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# autofix_commit_msg: |
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# [pre-commit.ci] auto fixes from pre-commit.com hooks
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# for more information, see https://pre-commit.ci
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# autofix_prs: true
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# autoupdate_branch: ""
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# autoupdate_commit_msg: "[pre-commit.ci] pre-commit autoupdate"
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# autoupdate_schedule: monthly
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# skip: []
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# submodules: false
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3DVIEW_README.md
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# 3D轨迹可视化工具
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||||
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||||
一个用于将GPS轨迹数据转换为可交互3D可视化的Python工具。
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||||
|
||||
## 功能特性
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||||
|
||||
- 🎯 **多格式支持**: 支持CSV和Excel文件 (.csv, .xlsx, .xls)
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||||
- 📊 **3D交互式图表**: 基于Plotly的完全可交互3D可视化
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||||
- 🎨 **智能着色**: 按高程自动着色，颜色映射到海拔高度
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||||
- 🔧 **灵活配置**: 可自定义列索引、工作表等参数
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||||
- 📱 **响应式设计**: 生成的HTML可在任何现代浏览器中查看
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||||
|
||||
## 数据格式要求
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||||
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||||
### 文件格式
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||||
- **CSV文件**: 逗号分隔值文件
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||||
- **Excel文件**: .xlsx 或 .xls 格式
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||||
|
||||
### 数据列要求
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||||
文件至少需要包含4列数据，按以下顺序排列：
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||||
|
||||
| 列索引 | 列名 | 数据类型 | 说明 |
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||||
|--------|------|----------|------|
|
||||
| 0 | 经度 | 数值 | GPS经度坐标（原始格式） |
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||||
| 1 | 纬度 | 数值 | GPS纬度坐标（原始格式） |
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||||
| 2 | 高程 | 数值 | 飞行器高程（可选） |
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||||
| 3 | 融合高程 | 数值 | 融合后的高程数据 |
|
||||
|
||||
### 数据示例
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||||
|
||||
**CSV格式**:
|
||||
```csv
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||||
1157855289,403491370,500110,435.789
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||||
1157855290,403491371,500111,436.123
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||||
1157855291,403491372,500112,437.456
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||||
```
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||||
|
||||
**Excel格式**:
|
||||
- 第一行（如果有标题）将被忽略
|
||||
- 数据从第二行开始读取
|
||||
|
||||
### 坐标转换
|
||||
- 经纬度数据会自动除以 10^7 进行单位转换
|
||||
- 这是因为原始GPS数据通常以放大整数形式存储
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||||
|
||||
## 安装依赖
|
||||
|
||||
```bash
|
||||
pip install pandas plotly openpyxl
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||||
```
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||||
|
||||
## 使用方法
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||||
|
||||
### 1. 命令行使用
|
||||
|
||||
#### 基本用法
|
||||
```bash
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||||
# 处理CSV文件
|
||||
python 3Dview.py data.csv
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||||
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||||
# 处理Excel文件
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||||
python 3Dview.py trajectory.xlsx
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||||
|
||||
# 指定输出文件
|
||||
python 3Dview.py data.csv -o my_visualization.html
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||||
```
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||||
|
||||
#### 高级参数
|
||||
```bash
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||||
# 自定义列索引
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||||
python 3Dview.py data.xlsx --lon-col 1 --lat-col 2 --alt-col 4
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||||
|
||||
# 指定Excel工作表
|
||||
python 3Dview.py data.xlsx --sheet-name "Flight_Data"
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||||
|
||||
# 自定义标题
|
||||
python 3Dview.py data.csv --title "无人机飞行轨迹"
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||||
|
||||
# 组合使用
|
||||
python 3Dview.py trajectory.xlsx \
|
||||
--lon-col 0 \
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||||
--lat-col 1 \
|
||||
--alt-col 3 \
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--sheet-name 0 \
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||||
--output flight_path.html \
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||||
--title "2024年飞行数据"
|
||||
```
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||||
|
||||
### 2. Python代码调用
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||||
|
||||
#### 基本调用
|
||||
```python
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||||
from 3Dview import main
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||||
|
||||
# 处理CSV文件
|
||||
success = main('trajectory.csv')
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||||
|
||||
# 处理Excel文件
|
||||
success = main('flight_data.xlsx', sheet_name='Sheet1')
|
||||
```
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||||
|
||||
#### 完整参数调用
|
||||
```python
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||||
from 3Dview import main
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||||
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||||
success = main(
|
||||
input_file='drone_trajectory.xlsx',
|
||||
output_file='visualization.html',
|
||||
lon_col=0, # 经度列索引
|
||||
lat_col=1, # 纬度列索引
|
||||
alt_col=3, # 融合高程列索引
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||||
sheet_name=0, # Excel工作表索引
|
||||
title='无人机轨迹3D可视化'
|
||||
)
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||||
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||||
if success:
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print("可视化生成成功！")
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```
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## 输出结果
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||||
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||||
### HTML文件内容
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||||
- **3D散点图**: 每个数据点在三维空间中的位置
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- **颜色编码**: 点的颜色根据高程自动映射
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||||
- **交互功能**: 支持旋转、缩放、平移等操作
|
||||
|
||||
### 控制说明
|
||||
- **鼠标左键拖拽**: 旋转视角
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||||
- **鼠标滚轮**: 缩放视图
|
||||
- **鼠标右键拖拽**: 平移视图
|
||||
- **双击**: 重置到初始视角
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||||
- **悬停**: 显示数据点的详细信息
|
||||
|
||||
## 参数说明
|
||||
|
||||
### 命令行参数
|
||||
|
||||
| 参数 | 简称 | 默认值 | 说明 |
|
||||
|------|------|--------|------|
|
||||
| `input_file` | - | 必需 | 输入文件路径 |
|
||||
| `--output` | `-o` | `3d_view.html` | 输出HTML文件路径 |
|
||||
| `--lon-col` | - | `0` | 经度列索引 |
|
||||
| `--lat-col` | - | `1` | 纬度列索引 |
|
||||
| `--alt-col` | - | `3` | 融合高程列索引 |
|
||||
| `--sheet-name` | - | `0` | Excel工作表名称或索引 |
|
||||
| `--delimiter` | - | `None` | CSV分隔符（自动检测） |
|
||||
| `--title` | - | `3D轨迹可视化` | 图表标题 |
|
||||
|
||||
### 函数参数
|
||||
|
||||
```python
|
||||
def main(
|
||||
input_file, # 输入文件路径
|
||||
output_file='3d_view.html', # 输出文件路径
|
||||
lon_col=0, # 经度列索引
|
||||
lat_col=1, # 纬度列索引
|
||||
alt_col=3, # 高程列索引
|
||||
delimiter=None, # CSV分隔符
|
||||
title='3D轨迹可视化', # 图表标题
|
||||
sheet_name=0 # Excel工作表
|
||||
):
|
||||
```
|
||||
|
||||
## 故障排除
|
||||
|
||||
### 常见问题
|
||||
|
||||
#### 1. 文件读取错误
|
||||
```
|
||||
错误：输入文件不存在: xxx
|
||||
```
|
||||
**解决**: 检查文件路径是否正确，文件是否存在
|
||||
|
||||
#### 2. 列数不足
|
||||
```
|
||||
数据文件至少需要4列，经度、纬度、高程、融合高程。当前只有X列。
|
||||
```
|
||||
**解决**: 检查数据文件格式，确保包含所需列
|
||||
|
||||
#### 3. Excel工作表不存在
|
||||
```
|
||||
Worksheet 'Sheet1' not found
|
||||
```
|
||||
**解决**: 检查Excel文件中的工作表名称，或使用索引（如0）
|
||||
|
||||
#### 4. 坐标转换问题
|
||||
如果坐标显示不正确，检查：
|
||||
- 经纬度数据格式是否正确
|
||||
- 单位转换是否合适（除以10^7）
|
||||
|
||||
### 数据验证
|
||||
|
||||
运行脚本时会显示以下统计信息：
|
||||
```
|
||||
成功加载数据：XXX 个数据点
|
||||
经度范围: XXX.XXXXXX - XXX.XXXXXX
|
||||
纬度范围: XXX.XXXXXX - XXX.XXXXXX
|
||||
高程范围: XXX.XX - XXX.XX 米
|
||||
```
|
||||
|
||||
## 示例应用
|
||||
|
||||
### 无人机飞行轨迹
|
||||
```python
|
||||
from 3Dview import main
|
||||
|
||||
# 可视化无人机飞行路径
|
||||
main(
|
||||
input_file='drone_flight.xlsx',
|
||||
output_file='drone_path_3d.html',
|
||||
title='无人机飞行轨迹3D可视化',
|
||||
sheet_name='GPS_Data'
|
||||
)
|
||||
```
|
||||
|
||||
### 地面车辆轨迹
|
||||
```python
|
||||
from 3Dview import main
|
||||
|
||||
# 可视化车辆行驶轨迹
|
||||
main(
|
||||
input_file='vehicle_gps.csv',
|
||||
output_file='vehicle_path.html',
|
||||
title='车辆轨迹可视化',
|
||||
alt_col=2 # 如果没有高程数据，使用其他列
|
||||
)
|
||||
```
|
||||
|
||||
## 技术细节
|
||||
|
||||
### 依赖库
|
||||
- **pandas**: 数据处理
|
||||
- **plotly**: 3D可视化
|
||||
- **openpyxl**: Excel文件读取
|
||||
|
||||
### 坐标系统
|
||||
- 输入: 原始GPS坐标（通常为度×10^-7）
|
||||
- 处理: 转换为标准经纬度坐标
|
||||
- 显示: WGS84地理坐标系
|
||||
|
||||
### 性能考虑
|
||||
- 大文件处理: 建议分批处理超大数据集
|
||||
- 内存使用: 每个数据点占用约50-100字节内存
|
||||
- 渲染性能: 浏览器对大量数据点的渲染有限制
|
||||
|
||||
## 许可证
|
||||
|
||||
本工具遵循与主项目相同的许可证。
|
||||
|
||||
---
|
||||
|
||||
**版本**: 1.0.0
|
||||
**最后更新**: 2024年12月
|
||||
370
3Dview.py
Normal file
370
3Dview.py
Normal file
@ -0,0 +1,370 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
3D可视化脚本 - 显示经纬度高程数据
|
||||
|
||||
读取CSV文件中的经度、纬度、融合高程数据，
|
||||
生成可交互的三维点图，输出为HTML格式。
|
||||
|
||||
数据格式要求：
|
||||
经度 纬度 高程 融合高程
|
||||
|
||||
经纬度坐标需要除以10^-7进行单位转换。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
import plotly.io as pio
|
||||
import argparse
|
||||
import sys
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
||||
def load_and_process_data(file_path, lon_col=0, lat_col=1, alt_col=3, delimiter=None, sheet_name=0):
|
||||
"""
|
||||
加载并处理CSV或Excel数据
|
||||
|
||||
Args:
|
||||
file_path: 文件路径 (支持.csv, .xlsx, .xls)
|
||||
lon_col: 经度列索引 (默认0)
|
||||
lat_col: 纬度列索引 (默认1)
|
||||
alt_col: 高程列索引 (默认3，为融合高程)
|
||||
delimiter: CSV分隔符，None表示自动检测
|
||||
sheet_name: Excel工作表名称或索引 (默认0)
|
||||
|
||||
Returns:
|
||||
pandas.DataFrame: 处理后的数据
|
||||
"""
|
||||
try:
|
||||
file_path_obj = Path(file_path)
|
||||
|
||||
# 根据文件扩展名选择读取方式
|
||||
if file_path_obj.suffix.lower() in ['.xlsx', '.xls']:
|
||||
# 读取Excel文件
|
||||
print(f"检测到Excel文件: {file_path}")
|
||||
df = pd.read_excel(file_path, sheet_name=sheet_name, header=None)
|
||||
else:
|
||||
# 读取CSV文件
|
||||
print(f"检测到CSV文件: {file_path}")
|
||||
df = pd.read_csv(file_path, delimiter=delimiter, header=None)
|
||||
|
||||
# 验证数据列数
|
||||
if df.shape[1] < 4:
|
||||
raise ValueError(f"数据文件至少需要4列，经度、纬度、高程、融合高程。当前只有{df.shape[1]}列。")
|
||||
|
||||
# 显示前几行数据以便调试
|
||||
print(f"数据预览 (前3行):")
|
||||
print(df.head(3))
|
||||
print()
|
||||
|
||||
# 提取所需列
|
||||
lon_raw = df.iloc[:, lon_col] # 经度（原始格式）
|
||||
lat_raw = df.iloc[:, lat_col] # 纬度（原始格式）
|
||||
altitude = df.iloc[:, alt_col] # 融合高程
|
||||
|
||||
# 数据类型转换和清理
|
||||
try:
|
||||
lon_raw = pd.to_numeric(lon_raw, errors='coerce')
|
||||
lat_raw = pd.to_numeric(lat_raw, errors='coerce')
|
||||
altitude = pd.to_numeric(altitude, errors='coerce')
|
||||
except Exception as e:
|
||||
print(f"数据类型转换失败: {e}")
|
||||
print("尝试手动转换...")
|
||||
|
||||
# 手动转换函数
|
||||
def safe_convert(value):
|
||||
try:
|
||||
return float(value)
|
||||
except (ValueError, TypeError):
|
||||
return np.nan
|
||||
|
||||
lon_raw = lon_raw.apply(safe_convert)
|
||||
lat_raw = lat_raw.apply(safe_convert)
|
||||
altitude = altitude.apply(safe_convert)
|
||||
|
||||
# 检查转换结果
|
||||
nan_count_lon = lon_raw.isna().sum()
|
||||
nan_count_lat = lat_raw.isna().sum()
|
||||
nan_count_alt = altitude.isna().sum()
|
||||
|
||||
if nan_count_lon > 0 or nan_count_lat > 0 or nan_count_alt > 0:
|
||||
print(f"警告：发现无效数据点 - 经度: {nan_count_lon}, 纬度: {nan_count_lat}, 高程: {nan_count_alt}")
|
||||
print("无效数据点将被移除")
|
||||
|
||||
# 移除NaN值
|
||||
valid_mask = ~(lon_raw.isna() | lat_raw.isna() | altitude.isna())
|
||||
lon_raw = lon_raw[valid_mask]
|
||||
lat_raw = lat_raw[valid_mask]
|
||||
altitude = altitude[valid_mask]
|
||||
|
||||
if len(lon_raw) == 0:
|
||||
raise ValueError("没有有效的坐标数据")
|
||||
|
||||
# 经纬度单位转换 (除以10^7)
|
||||
longitude = lon_raw / 1e7
|
||||
latitude = lat_raw / 1e7
|
||||
|
||||
# 创建处理后的DataFrame
|
||||
processed_df = pd.DataFrame({
|
||||
'longitude': longitude,
|
||||
'latitude': latitude,
|
||||
'altitude': altitude
|
||||
})
|
||||
|
||||
print(f"成功加载数据：{len(processed_df)} 个数据点")
|
||||
print(f"经度范围: {longitude.min():.6f} - {longitude.max():.6f}")
|
||||
print(f"纬度范围: {latitude.min():.6f} - {latitude.max():.6f}")
|
||||
print(f"高程范围: {altitude.min():.2f} - {altitude.max():.2f} 米")
|
||||
|
||||
return processed_df
|
||||
|
||||
except Exception as e:
|
||||
print(f"数据加载失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
def create_3d_plot(df, title="3D轨迹可视化", output_file="3d_view.html"):
|
||||
"""
|
||||
创建三维散点图
|
||||
|
||||
Args:
|
||||
df: 包含longitude, latitude, altitude列的DataFrame
|
||||
title: 图表标题
|
||||
output_file: 输出HTML文件路径
|
||||
"""
|
||||
|
||||
# 创建3D散点图
|
||||
fig = go.Figure(data=[go.Scatter3d(
|
||||
x=df['longitude'],
|
||||
y=df['latitude'],
|
||||
z=df['altitude'],
|
||||
mode='markers',
|
||||
marker=dict(
|
||||
size=3,
|
||||
color=df['altitude'], # 颜色映射到高程
|
||||
colorscale='Viridis', # 颜色方案
|
||||
colorbar=dict(title="高程 (m)"),
|
||||
opacity=0.8
|
||||
),
|
||||
hovertemplate=(
|
||||
'经度: %{x:.6f}<br>' +
|
||||
'纬度: %{y:.6f}<br>' +
|
||||
'高程: %{z:.2f} m<br>' +
|
||||
'<extra></extra>'
|
||||
)
|
||||
)])
|
||||
|
||||
# 设置布局
|
||||
fig.update_layout(
|
||||
title=title,
|
||||
scene=dict(
|
||||
xaxis_title='经度',
|
||||
yaxis_title='纬度',
|
||||
zaxis_title='高程 (m)',
|
||||
xaxis=dict(showgrid=True, gridcolor='lightgray'),
|
||||
yaxis=dict(showgrid=True, gridcolor='lightgray'),
|
||||
zaxis=dict(showgrid=True, gridcolor='lightgray'),
|
||||
# 设置视角
|
||||
camera=dict(
|
||||
eye=dict(x=1.5, y=1.5, z=1.5) # 初始视角位置
|
||||
)
|
||||
),
|
||||
margin=dict(l=0, r=0, b=0, t=40),
|
||||
paper_bgcolor='white',
|
||||
plot_bgcolor='white'
|
||||
)
|
||||
|
||||
# 添加交互说明
|
||||
fig.add_annotation(
|
||||
text="鼠标拖拽旋转视角，滚轮缩放，双击重置",
|
||||
xref="paper", yref="paper",
|
||||
x=0.02, y=0.98,
|
||||
showarrow=False,
|
||||
bgcolor="rgba(255,255,255,0.8)",
|
||||
bordercolor="black",
|
||||
borderwidth=1
|
||||
)
|
||||
|
||||
# 保存为HTML文件
|
||||
try:
|
||||
pio.write_html(fig, file=output_file, auto_open=False)
|
||||
print(f"3D可视化已保存到: {output_file}")
|
||||
print("在浏览器中打开该文件即可查看可交互的3D图表")
|
||||
except Exception as e:
|
||||
print(f"保存HTML文件失败: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
return fig
|
||||
|
||||
def main(input_file, output_file='3d_view.html', lon_col=2, lat_col=3, alt_col=5,
|
||||
delimiter=None, title='3D轨迹可视化', sheet_name=0):
|
||||
"""
|
||||
直接调用函数生成3D可视化
|
||||
|
||||
Args:
|
||||
input_file: 输入文件路径 (支持.csv, .xlsx, .xls)
|
||||
output_file: 输出HTML文件路径
|
||||
lon_col: 经度列索引 (默认: 0)
|
||||
lat_col: 纬度列索引 (默认: 1)
|
||||
alt_col: 融合高程列索引 (默认: 3)
|
||||
delimiter: CSV分隔符 (默认: 自动检测)
|
||||
title: 图表标题 (默认: '3D轨迹可视化')
|
||||
sheet_name: Excel工作表名称或索引 (默认: 0)
|
||||
"""
|
||||
|
||||
# 检查输入文件
|
||||
input_path = Path(input_file)
|
||||
if not input_path.exists():
|
||||
print(f"错误：输入文件不存在: {input_path}")
|
||||
return False
|
||||
|
||||
print(f"正在处理文件: {input_path}")
|
||||
print(f"输出文件: {output_file}")
|
||||
print("-" * 50)
|
||||
|
||||
try:
|
||||
# 加载和处理数据
|
||||
df = load_and_process_data(
|
||||
file_path=input_path,
|
||||
lon_col=lon_col,
|
||||
lat_col=lat_col,
|
||||
alt_col=alt_col,
|
||||
delimiter=delimiter,
|
||||
sheet_name=sheet_name
|
||||
)
|
||||
|
||||
# 创建3D图表
|
||||
fig = create_3d_plot(
|
||||
df=df,
|
||||
title=title,
|
||||
output_file=output_file
|
||||
)
|
||||
|
||||
print("-" * 50)
|
||||
print("处理完成！")
|
||||
print(f"\n✅ 3D可视化已保存到: {output_file}")
|
||||
print("\n使用说明:")
|
||||
print("- 在浏览器中打开生成的HTML文件")
|
||||
print("- 鼠标左键拖拽：旋转视角")
|
||||
print("- 鼠标滚轮：缩放视图")
|
||||
print("- 双击：重置到初始视角")
|
||||
print("- 右键拖拽：平移视图")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 处理失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
# 命令行接口（保持兼容性）
|
||||
def cli_main():
|
||||
"""命令行接口"""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="3D轨迹可视化工具 - 将CSV数据转换为可交互的3D点图",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
使用示例:
|
||||
# CSV文件
|
||||
python 3Dview.py data.csv
|
||||
python 3Dview.py data.csv -o custom_output.html
|
||||
python 3Dview.py data.csv --lon-col 1 --lat-col 2 --alt-col 4
|
||||
|
||||
# Excel文件
|
||||
python 3Dview.py trajectory.xlsx
|
||||
python 3Dview.py data.xlsx --sheet-name "Sheet1"
|
||||
|
||||
数据格式:
|
||||
文件应包含至少4列：经度 纬度 高程 融合高程
|
||||
支持格式: CSV (.csv), Excel (.xlsx, .xls)
|
||||
经纬度将自动除以10^7进行单位转换
|
||||
|
||||
直接调用示例:
|
||||
from 3Dview import main
|
||||
main('data.xlsx', sheet_name='Sheet1') # Excel文件
|
||||
main('data.csv', lon_col=0, lat_col=1, alt_col=3) # CSV文件
|
||||
"""
|
||||
)
|
||||
|
||||
parser.add_argument('input_file', help='输入CSV文件路径')
|
||||
parser.add_argument('-o', '--output', default='3d_view.html',
|
||||
help='输出HTML文件路径 (默认: 3d_view.html)')
|
||||
parser.add_argument('--lon-col', type=int, default=0,
|
||||
help='经度列索引 (默认: 0)')
|
||||
parser.add_argument('--lat-col', type=int, default=1,
|
||||
help='纬度列索引 (默认: 1)')
|
||||
parser.add_argument('--alt-col', type=int, default=3,
|
||||
help='融合高程列索引 (默认: 3)')
|
||||
parser.add_argument('--delimiter', default=None,
|
||||
help='CSV分隔符 (默认: 自动检测)')
|
||||
parser.add_argument('--title', default='3D轨迹可视化',
|
||||
help='图表标题 (默认: 3D轨迹可视化)')
|
||||
parser.add_argument('--sheet-name', default=0,
|
||||
help='Excel工作表名称或索引 (默认: 0)')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
success = main(
|
||||
input_file=args.input_file,
|
||||
output_file=args.output,
|
||||
lon_col=args.lon_col,
|
||||
lat_col=args.lat_col,
|
||||
alt_col=args.alt_col,
|
||||
delimiter=args.delimiter,
|
||||
title=args.title,
|
||||
sheet_name=args.sheet_name
|
||||
)
|
||||
|
||||
if not success:
|
||||
sys.exit(1)
|
||||
|
||||
# 示例用法
|
||||
if __name__ == "__main__":
|
||||
# 检查是否有命令行参数
|
||||
if len(sys.argv) > 1:
|
||||
# 使用命令行接口
|
||||
cli_main()
|
||||
else:
|
||||
# 直接调用示例
|
||||
print("3D可视化工具使用示例")
|
||||
print("=" * 50)
|
||||
|
||||
# 示例1: 处理Excel文件
|
||||
print("\n示例1: 处理Excel数据文件")
|
||||
excel_file = r"C:\Users\HL\Documents\xwechat_files\wxid_rdrswaol1qsr21_737e\msg\file\2025-12\08_51_15_间隔高度10m.xlsx"
|
||||
|
||||
if Path(excel_file).exists():
|
||||
success = main(
|
||||
input_file=excel_file,
|
||||
output_file='./10m_3d_view.html',
|
||||
title='无人机轨迹3D可视化示例'
|
||||
)
|
||||
else:
|
||||
print(f"示例文件不存在: {excel_file}")
|
||||
print("请提供正确的Excel文件路径")
|
||||
success = False
|
||||
|
||||
if success:
|
||||
print("\n示例代码:")
|
||||
print("""
|
||||
# 直接调用示例
|
||||
from 3Dview import main
|
||||
|
||||
# 处理Excel文件
|
||||
main('data.xlsx', sheet_name=0) # 指定工作表
|
||||
|
||||
# 处理CSV文件
|
||||
main('data.csv')
|
||||
|
||||
# 自定义参数
|
||||
main(
|
||||
input_file='trajectory.xlsx',
|
||||
output_file='flight_path.html',
|
||||
lon_col=0, # 经度列索引
|
||||
lat_col=1, # 纬度列索引
|
||||
alt_col=3, # 融合高程列索引
|
||||
sheet_name=0, # Excel工作表
|
||||
title='飞行轨迹可视化'
|
||||
)
|
||||
""")
|
||||
295
DATA_PROCESSOR_README.md
Normal file
295
DATA_PROCESSOR_README.md
Normal file
@ -0,0 +1,295 @@
|
||||
# 无人机数据处理工具
|
||||
|
||||
将Excel格式的无人机测量数据转换为GasFlux标准输入格式的完整解决方案。
|
||||
|
||||
## 📋 处理流程
|
||||
|
||||
### 输入数据格式
|
||||
Excel文件必须包含以下列（顺序可以不同）：
|
||||
|
||||
| 列名 | 数据类型 | 说明 |
|
||||
|------|----------|------|
|
||||
| `日期` | 字符串 | 日期 (YYYY-MM-DD 或 YYYY/MM/DD) |
|
||||
| `时间` | 字符串 | 时间 (HH:MM:SS 或 H:MM:SS) |
|
||||
| `经度` | 数值 | GPS经度坐标 (原始大数值) |
|
||||
| `纬度` | 数值 | GPS纬度坐标 (原始大数值) |
|
||||
| `高程` | 数值 | 飞行器高程 |
|
||||
| `融合高程` | 数值 | 融合后的海拔高度 |
|
||||
| `速度x` | 数值 | 东向速度分量 |
|
||||
| `速度y` | 数值 | 北向速度分量 |
|
||||
| `速度z` | 数值 | 垂直速度分量 |
|
||||
| `四元数_q0-q3` | 数值 | 姿态四元数 |
|
||||
| `pitch` | 数值 | 俯仰角 |
|
||||
| `roll` | 数值 | 横滚角 |
|
||||
| `yaw` | 数值 | 偏航角 |
|
||||
| `CH4` | 数值 | 甲烷浓度 |
|
||||
| `H2O` | 数值 | 水蒸气浓度 |
|
||||
| `修正风向` | 数值 | 修正后的风向 |
|
||||
| `修正风速` | 数值 | 修正后的风速 |
|
||||
| `原始风向` | 数值 | 原始风向 |
|
||||
| `原始风速` | 数值 | 原始风速 |
|
||||
| `风温` | 数值 | 风温 (温度) |
|
||||
|
||||
## 🔄 处理步骤详解
|
||||
|
||||
### 1. 列删除
|
||||
自动删除以下不需要的列：
|
||||
- `高程`, `速度x`, `速度y`, `速度z`
|
||||
- `四元数_q0`, `四元数_q1`, `四元数_q2`, `四元数_q3`
|
||||
- `pitch`, `roll`, `yaw`
|
||||
- `H2O`, `原始风向`, `原始风速`
|
||||
|
||||
### 2. 时间修正
|
||||
根据文件名提取小时信息并修正时间列：
|
||||
|
||||
**文件名示例**: `08_34_01_间隔高度5m.xlsx`
|
||||
- 提取: `08` (小时)
|
||||
- 时间转换: `0:34:01` → `08:34:01`
|
||||
|
||||
### 3. 坐标转换
|
||||
经纬度坐标除以 `10^7` 进行单位转换：
|
||||
```
|
||||
原始经度: 1157855289 → 转换后: 115.7855289
|
||||
原始纬度: 403491370 → 转换后: 40.3491370
|
||||
```
|
||||
|
||||
### 4. 气压计算（智能优化）
|
||||
使用 `qiya.py` 模块智能计算地面气压，支持两种计算模式：
|
||||
|
||||
#### 🎯 三级智能计算策略
|
||||
- **高度变化小**（默认容差≤10米）：只计算一次平均气压，应用到所有行
|
||||
- **中等变化**：使用高度分档策略，每个档位计算一次气压
|
||||
- **高度变化大**：逐行计算气压，支持进度条显示
|
||||
|
||||
#### 📏 高度分档优化
|
||||
- **分档间隔**：默认2米一个档位（可自定义）
|
||||
- **档位计算**：每个档位使用中间高度和平均位置计算一次气压
|
||||
- **减少API调用**：大幅降低重复计算，提高效率
|
||||
|
||||
#### 📊 计算参数
|
||||
- **日期格式**: 转换为 `YYYY-MM-DD`（自动提取日期部分）
|
||||
- **时间格式**: 提取小时部分 `HH`
|
||||
- **海拔高度**: 使用 `融合高程` 列或档位中心高度
|
||||
- **API调用**: Open-Meteo 历史天气数据
|
||||
- **进度条**: 自动显示计算进度（需要安装tqdm）
|
||||
|
||||
#### ⚙️ 自定义参数
|
||||
```python
|
||||
# 自定义参数
|
||||
df = calculate_pressure(
|
||||
df,
|
||||
max_samples=None,
|
||||
height_tolerance=10.0, # 高度变化容差
|
||||
height_bin_size=2.0 # 高度分档间隔
|
||||
)
|
||||
```
|
||||
|
||||
### 5. 高度调整
|
||||
对 `融合高程` 进行基准调整：
|
||||
```
|
||||
调整后高度 = 原始高度 - 最小高度
|
||||
```
|
||||
|
||||
### 6. 时间戳融合
|
||||
将 `日期` 和 `时间` 列合并：
|
||||
```
|
||||
输入: 日期="2024-01-15", 时间="08:34:01"
|
||||
输出: timestamp="2024-01-15 08:34:01"
|
||||
```
|
||||
|
||||
### 7. 字段重命名
|
||||
转换为GasFlux标准字段名：
|
||||
|
||||
| 原始字段 | GasFlux字段 | 数据类型 |
|
||||
|----------|-------------|----------|
|
||||
| `日期+时间` | `timestamp` | datetime |
|
||||
| `经度` | `latitude` | float |
|
||||
| `纬度` | `longitude` | float |
|
||||
| `融合高程` | `height_ato` | float |
|
||||
| `修正风向` | `windir` | float |
|
||||
| `修正风速` | `windspeed` | float |
|
||||
| `风温` | `temperature` | float |
|
||||
| `气压计算结果` | `pressure` | float |
|
||||
| `CH4` | `ch4` | float |
|
||||
|
||||
## 🚀 使用方法
|
||||
|
||||
### 1. 交互式输入模式（推荐新用户）
|
||||
```bash
|
||||
# 直接运行脚本，会自动进入交互式模式
|
||||
python data_processor.py
|
||||
|
||||
# 或者明确指定交互式模式
|
||||
python data_processor.py --interactive
|
||||
```
|
||||
|
||||
在交互式模式下，程序会提示您：
|
||||
- 输入Excel文件路径
|
||||
- 指定输出CSV文件路径（可选）
|
||||
- 确认处理参数
|
||||
|
||||
### 2. 命令行参数模式
|
||||
```bash
|
||||
# 基本用法
|
||||
python data_processor.py your_data.xlsx
|
||||
|
||||
# 指定输出路径
|
||||
python data_processor.py input.xlsx -o output.csv
|
||||
|
||||
# 查看帮助
|
||||
python data_processor.py --help
|
||||
```
|
||||
|
||||
### 3. Python脚本直接调用（推荐）
|
||||
|
||||
```python
|
||||
# 最简单的方式
|
||||
from src.gasflux.data_processor import process_file
|
||||
|
||||
# 处理文件并返回DataFrame
|
||||
df = process_file('your_data.xlsx')
|
||||
|
||||
# 或者指定输出文件名
|
||||
df = process_file('input.xlsx', 'output.csv')
|
||||
|
||||
# 使用返回的DataFrame进行进一步分析
|
||||
print(df.head())
|
||||
print(df['ch4'].describe())
|
||||
```
|
||||
|
||||
### 4. 完整使用脚本
|
||||
复制以下代码到新文件（如 `process_my_data.py`）：
|
||||
|
||||
```python
|
||||
from src.gasflux.data_processor import process_file
|
||||
|
||||
# 修改这里的输入文件名
|
||||
input_file = "08_34_01_间隔高度5m.xlsx" # 您的Excel文件名
|
||||
output_file = "processed_data.csv" # 输出文件名（可选）
|
||||
|
||||
try:
|
||||
df = process_file(input_file, output_file)
|
||||
print("✅ 处理成功！")
|
||||
print(f"数据形状: {df.shape}")
|
||||
print(f"列名: {list(df.columns)}")
|
||||
print(df.head())
|
||||
except Exception as e:
|
||||
print(f"❌ 处理失败: {e}")
|
||||
```
|
||||
|
||||
### Python代码调用
|
||||
|
||||
```python
|
||||
from src.gasflux.data_processor import process_excel_file
|
||||
|
||||
# 处理单个文件
|
||||
df = process_excel_file('path/to/data.xlsx')
|
||||
|
||||
# 查看结果
|
||||
print(df.head())
|
||||
print(df.columns)
|
||||
```
|
||||
|
||||
## 📊 输出格式
|
||||
|
||||
处理后的数据完全符合GasFlux输入要求：
|
||||
|
||||
```csv
|
||||
timestamp,latitude,longitude,height_ato,windspeed,windir,temperature,pressure,ch4
|
||||
2024-01-15 08:34:01,115.785529,40.349137,10.5,3.2,180.5,22.3,1013.25,2.15
|
||||
2024-01-15 08:34:02,115.785530,40.349138,10.8,3.1,181.2,22.4,1013.26,2.18
|
||||
...
|
||||
```
|
||||
|
||||
## ⚙️ 配置选项
|
||||
|
||||
### 文件名格式
|
||||
脚本根据文件名自动提取时间信息：
|
||||
- 格式: `{HH}_{MM}_{SS}_{其他信息}.xlsx`
|
||||
- 示例: `08_34_01_间隔高度5m.xlsx` → 小时=08
|
||||
|
||||
### 气压计算
|
||||
- 默认只计算前20行数据（避免过多API调用）
|
||||
- 可以修改 `max_samples` 参数调整计算数量
|
||||
|
||||
## 🔧 依赖要求
|
||||
|
||||
```bash
|
||||
pip install pandas openpyxl requests
|
||||
```
|
||||
|
||||
确保GasFlux的`qiya.py`模块可用。
|
||||
|
||||
## ⚠️ 注意事项
|
||||
|
||||
### 数据质量
|
||||
- 确保Excel文件格式正确
|
||||
- 检查日期时间格式
|
||||
- 验证经纬度坐标范围
|
||||
|
||||
### API限制
|
||||
- Open-Meteo有每日请求限制
|
||||
- 批量处理时注意请求频率
|
||||
- 网络连接稳定很重要
|
||||
|
||||
### 内存使用
|
||||
- 大文件处理时注意内存占用
|
||||
- 可以分批处理超大数据集
|
||||
|
||||
## 🎯 应用场景
|
||||
|
||||
### 无人机通量测量
|
||||
1. **数据采集**: 无人机飞行收集原始数据
|
||||
2. **数据处理**: 使用本脚本转换为标准格式
|
||||
3. **通量计算**: 输入GasFlux进行气体通量分析
|
||||
|
||||
### 数据预处理工作流
|
||||
```
|
||||
原始Excel → 数据处理脚本 → GasFlux格式 → 通量计算 → 结果分析
|
||||
```
|
||||
|
||||
## 📈 处理结果验证
|
||||
|
||||
处理完成后检查：
|
||||
- ✅ 时间戳格式正确 (YYYY-MM-DD HH:MM:SS)
|
||||
- ✅ 经纬度坐标合理范围
|
||||
- ✅ 高度值从0开始 (已减去最小值)
|
||||
- ✅ 气压值在合理范围 (900-1100 hPa)
|
||||
- ✅ 所有必需字段都存在
|
||||
|
||||
## 🛠️ 故障排除
|
||||
|
||||
### 常见问题
|
||||
|
||||
#### 1. 文件读取错误
|
||||
```
|
||||
确保Excel文件未损坏，列名与预期一致
|
||||
```
|
||||
|
||||
#### 2. 时间格式错误
|
||||
```
|
||||
检查文件名格式，确包含小时信息 (HH_MM_SS...)
|
||||
```
|
||||
|
||||
#### 3. 气压计算失败
|
||||
```
|
||||
检查网络连接，确认日期格式正确
|
||||
```
|
||||
|
||||
#### 4. 坐标转换异常
|
||||
```
|
||||
验证经纬度数据是数值类型，不是字符串
|
||||
```
|
||||
|
||||
## 📞 支持
|
||||
|
||||
如遇问题，请检查：
|
||||
1. 输入数据格式是否正确
|
||||
2. 网络连接是否正常
|
||||
3. GasFlux依赖是否完整安装
|
||||
4. Python版本兼容性
|
||||
|
||||
---
|
||||
|
||||
**版本**: 1.0.0
|
||||
**最后更新**: 2024年12月
|
||||
661
LICENSE
Normal file
661
LICENSE
Normal file
@ -0,0 +1,661 @@
|
||||
GNU AFFERO GENERAL PUBLIC LICENSE
|
||||
Version 3, 19 November 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU Affero General Public License is a free, copyleft license for
|
||||
software and other kinds of works, specifically designed to ensure
|
||||
cooperation with the community in the case of network server software.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
our General Public Licenses are intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
them if you wish), that you receive source code or can get it if you
|
||||
want it, that you can change the software or use pieces of it in new
|
||||
free programs, and that you know you can do these things.
|
||||
|
||||
Developers that use our General Public Licenses protect your rights
|
||||
with two steps: (1) assert copyright on the software, and (2) offer
|
||||
you this License which gives you legal permission to copy, distribute
|
||||
and/or modify the software.
|
||||
|
||||
A secondary benefit of defending all users' freedom is that
|
||||
improvements made in alternate versions of the program, if they
|
||||
receive widespread use, become available for other developers to
|
||||
incorporate. Many developers of free software are heartened and
|
||||
encouraged by the resulting cooperation. However, in the case of
|
||||
software used on network servers, this result may fail to come about.
|
||||
The GNU General Public License permits making a modified version and
|
||||
letting the public access it on a server without ever releasing its
|
||||
source code to the public.
|
||||
|
||||
The GNU Affero General Public License is designed specifically to
|
||||
ensure that, in such cases, the modified source code becomes available
|
||||
to the community. It requires the operator of a network server to
|
||||
provide the source code of the modified version running there to the
|
||||
users of that server. Therefore, public use of a modified version, on
|
||||
a publicly accessible server, gives the public access to the source
|
||||
code of the modified version.
|
||||
|
||||
An older license, called the Affero General Public License and
|
||||
published by Affero, was designed to accomplish similar goals. This is
|
||||
a different license, not a version of the Affero GPL, but Affero has
|
||||
released a new version of the Affero GPL which permits relicensing under
|
||||
this license.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
|
||||
|
||||
"This License" refers to version 3 of the GNU Affero General Public License.
|
||||
|
||||
"Copyright" also means copyright-like laws that apply to other kinds of
|
||||
works, such as semiconductor masks.
|
||||
|
||||
"The Program" refers to any copyrightable work licensed under this
|
||||
License. Each licensee is addressed as "you". "Licensees" and
|
||||
"recipients" may be individuals or organizations.
|
||||
|
||||
To "modify" a work means to copy from or adapt all or part of the work
|
||||
in a fashion requiring copyright permission, other than the making of an
|
||||
exact copy. The resulting work is called a "modified version" of the
|
||||
earlier work or a work "based on" the earlier work.
|
||||
|
||||
A "covered work" means either the unmodified Program or a work based
|
||||
on the Program.
|
||||
|
||||
To "propagate" a work means to do anything with it that, without
|
||||
permission, would make you directly or secondarily liable for
|
||||
infringement under applicable copyright law, except executing it on a
|
||||
computer or modifying a private copy. Propagation includes copying,
|
||||
distribution (with or without modification), making available to the
|
||||
public, and in some countries other activities as well.
|
||||
|
||||
To "convey" a work means any kind of propagation that enables other
|
||||
parties to make or receive copies. Mere interaction with a user through
|
||||
a computer network, with no transfer of a copy, is not conveying.
|
||||
|
||||
An interactive user interface displays "Appropriate Legal Notices"
|
||||
to the extent that it includes a convenient and prominently visible
|
||||
feature that (1) displays an appropriate copyright notice, and (2)
|
||||
tells the user that there is no warranty for the work (except to the
|
||||
extent that warranties are provided), that licensees may convey the
|
||||
work under this License, and how to view a copy of this License. If
|
||||
the interface presents a list of user commands or options, such as a
|
||||
menu, a prominent item in the list meets this criterion.
|
||||
|
||||
1. Source Code.
|
||||
|
||||
The "source code" for a work means the preferred form of the work
|
||||
for making modifications to it. "Object code" means any non-source
|
||||
form of a work.
|
||||
|
||||
A "Standard Interface" means an interface that either is an official
|
||||
standard defined by a recognized standards body, or, in the case of
|
||||
interfaces specified for a particular programming language, one that
|
||||
is widely used among developers working in that language.
|
||||
|
||||
The "System Libraries" of an executable work include anything, other
|
||||
than the work as a whole, that (a) is included in the normal form of
|
||||
packaging a Major Component, but which is not part of that Major
|
||||
Component, and (b) serves only to enable use of the work with that
|
||||
Major Component, or to implement a Standard Interface for which an
|
||||
implementation is available to the public in source code form. A
|
||||
"Major Component", in this context, means a major essential component
|
||||
(kernel, window system, and so on) of the specific operating system
|
||||
(if any) on which the executable work runs, or a compiler used to
|
||||
produce the work, or an object code interpreter used to run it.
|
||||
|
||||
The "Corresponding Source" for a work in object code form means all
|
||||
the source code needed to generate, install, and (for an executable
|
||||
work) run the object code and to modify the work, including scripts to
|
||||
control those activities. However, it does not include the work's
|
||||
System Libraries, or general-purpose tools or generally available free
|
||||
programs which are used unmodified in performing those activities but
|
||||
which are not part of the work. For example, Corresponding Source
|
||||
includes interface definition files associated with source files for
|
||||
the work, and the source code for shared libraries and dynamically
|
||||
linked subprograms that the work is specifically designed to require,
|
||||
such as by intimate data communication or control flow between those
|
||||
subprograms and other parts of the work.
|
||||
|
||||
The Corresponding Source need not include anything that users
|
||||
can regenerate automatically from other parts of the Corresponding
|
||||
Source.
|
||||
|
||||
The Corresponding Source for a work in source code form is that
|
||||
same work.
|
||||
|
||||
2. Basic Permissions.
|
||||
|
||||
All rights granted under this License are granted for the term of
|
||||
copyright on the Program, and are irrevocable provided the stated
|
||||
conditions are met. This License explicitly affirms your unlimited
|
||||
permission to run the unmodified Program. The output from running a
|
||||
covered work is covered by this License only if the output, given its
|
||||
content, constitutes a covered work. This License acknowledges your
|
||||
rights of fair use or other equivalent, as provided by copyright law.
|
||||
|
||||
You may make, run and propagate covered works that you do not
|
||||
convey, without conditions so long as your license otherwise remains
|
||||
in force. You may convey covered works to others for the sole purpose
|
||||
of having them make modifications exclusively for you, or provide you
|
||||
with facilities for running those works, provided that you comply with
|
||||
the terms of this License in conveying all material for which you do
|
||||
not control copyright. Those thus making or running the covered works
|
||||
for you must do so exclusively on your behalf, under your direction
|
||||
and control, on terms that prohibit them from making any copies of
|
||||
your copyrighted material outside their relationship with you.
|
||||
|
||||
Conveying under any other circumstances is permitted solely under
|
||||
the conditions stated below. Sublicensing is not allowed; section 10
|
||||
makes it unnecessary.
|
||||
|
||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
||||
|
||||
No covered work shall be deemed part of an effective technological
|
||||
measure under any applicable law fulfilling obligations under article
|
||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||
similar laws prohibiting or restricting circumvention of such
|
||||
measures.
|
||||
|
||||
When you convey a covered work, you waive any legal power to forbid
|
||||
circumvention of technological measures to the extent such circumvention
|
||||
is effected by exercising rights under this License with respect to
|
||||
the covered work, and you disclaim any intention to limit operation or
|
||||
modification of the work as a means of enforcing, against the work's
|
||||
users, your or third parties' legal rights to forbid circumvention of
|
||||
technological measures.
|
||||
|
||||
4. Conveying Verbatim Copies.
|
||||
|
||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
|
||||
appropriately publish on each copy an appropriate copyright notice;
|
||||
keep intact all notices stating that this License and any
|
||||
non-permissive terms added in accord with section 7 apply to the code;
|
||||
keep intact all notices of the absence of any warranty; and give all
|
||||
recipients a copy of this License along with the Program.
|
||||
|
||||
You may charge any price or no price for each copy that you convey,
|
||||
and you may offer support or warranty protection for a fee.
|
||||
|
||||
5. Conveying Modified Source Versions.
|
||||
|
||||
You may convey a work based on the Program, or the modifications to
|
||||
produce it from the Program, in the form of source code under the
|
||||
terms of section 4, provided that you also meet all of these conditions:
|
||||
|
||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
|
||||
|
||||
b) The work must carry prominent notices stating that it is
|
||||
released under this License and any conditions added under section
|
||||
7. This requirement modifies the requirement in section 4 to
|
||||
"keep intact all notices".
|
||||
|
||||
c) You must license the entire work, as a whole, under this
|
||||
License to anyone who comes into possession of a copy. This
|
||||
License will therefore apply, along with any applicable section 7
|
||||
additional terms, to the whole of the work, and all its parts,
|
||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
|
||||
invalidate such permission if you have separately received it.
|
||||
|
||||
d) If the work has interactive user interfaces, each must display
|
||||
Appropriate Legal Notices; however, if the Program has interactive
|
||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
|
||||
|
||||
A compilation of a covered work with other separate and independent
|
||||
works, which are not by their nature extensions of the covered work,
|
||||
and which are not combined with it such as to form a larger program,
|
||||
in or on a volume of a storage or distribution medium, is called an
|
||||
"aggregate" if the compilation and its resulting copyright are not
|
||||
used to limit the access or legal rights of the compilation's users
|
||||
beyond what the individual works permit. Inclusion of a covered work
|
||||
in an aggregate does not cause this License to apply to the other
|
||||
parts of the aggregate.
|
||||
|
||||
6. Conveying Non-Source Forms.
|
||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
of sections 4 and 5, provided that you also convey the
|
||||
machine-readable Corresponding Source under the terms of this License,
|
||||
in one of these ways:
|
||||
|
||||
a) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
|
||||
customarily used for software interchange.
|
||||
|
||||
b) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by a
|
||||
written offer, valid for at least three years and valid for as
|
||||
long as you offer spare parts or customer support for that product
|
||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
product that is covered by this License, on a durable physical
|
||||
medium customarily used for software interchange, for a price no
|
||||
more than your reasonable cost of physically performing this
|
||||
conveying of source, or (2) access to copy the
|
||||
Corresponding Source from a network server at no charge.
|
||||
|
||||
c) Convey individual copies of the object code with a copy of the
|
||||
written offer to provide the Corresponding Source. This
|
||||
alternative is allowed only occasionally and noncommercially, and
|
||||
only if you received the object code with such an offer, in accord
|
||||
with subsection 6b.
|
||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
place (gratis or for a charge), and offer equivalent access to the
|
||||
Corresponding Source in the same way through the same place at no
|
||||
further charge. You need not require recipients to copy the
|
||||
Corresponding Source along with the object code. If the place to
|
||||
copy the object code is a network server, the Corresponding Source
|
||||
may be on a different server (operated by you or a third party)
|
||||
that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
|
||||
Corresponding Source. Regardless of what server hosts the
|
||||
Corresponding Source, you remain obligated to ensure that it is
|
||||
available for as long as needed to satisfy these requirements.
|
||||
|
||||
e) Convey the object code using peer-to-peer transmission, provided
|
||||
you inform other peers where the object code and Corresponding
|
||||
Source of the work are being offered to the general public at no
|
||||
charge under subsection 6d.
|
||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
|
||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
tangible personal property which is normally used for personal, family,
|
||||
or household purposes, or (2) anything designed or sold for incorporation
|
||||
into a dwelling. In determining whether a product is a consumer product,
|
||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||
product received by a particular user, "normally used" refers to a
|
||||
typical or common use of that class of product, regardless of the status
|
||||
of the particular user or of the way in which the particular user
|
||||
actually uses, or expects or is expected to use, the product. A product
|
||||
is a consumer product regardless of whether the product has substantial
|
||||
commercial, industrial or non-consumer uses, unless such uses represent
|
||||
the only significant mode of use of the product.
|
||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
procedures, authorization keys, or other information required to install
|
||||
and execute modified versions of a covered work in that User Product from
|
||||
a modified version of its Corresponding Source. The information must
|
||||
suffice to ensure that the continued functioning of the modified object
|
||||
code is in no case prevented or interfered with solely because
|
||||
modification has been made.
|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
specifically for use in, a User Product, and the conveying occurs as
|
||||
part of a transaction in which the right of possession and use of the
|
||||
User Product is transferred to the recipient in perpetuity or for a
|
||||
fixed term (regardless of how the transaction is characterized), the
|
||||
Corresponding Source conveyed under this section must be accompanied
|
||||
by the Installation Information. But this requirement does not apply
|
||||
if neither you nor any third party retains the ability to install
|
||||
modified object code on the User Product (for example, the work has
|
||||
been installed in ROM).
|
||||
|
||||
The requirement to provide Installation Information does not include a
|
||||
requirement to continue to provide support service, warranty, or updates
|
||||
for a work that has been modified or installed by the recipient, or for
|
||||
the User Product in which it has been modified or installed. Access to a
|
||||
network may be denied when the modification itself materially and
|
||||
adversely affects the operation of the network or violates the rules and
|
||||
protocols for communication across the network.
|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
in accord with this section must be in a format that is publicly
|
||||
documented (and with an implementation available to the public in
|
||||
source code form), and must require no special password or key for
|
||||
unpacking, reading or copying.
|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
License by making exceptions from one or more of its conditions.
|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
be treated as though they were included in this License, to the extent
|
||||
that they are valid under applicable law. If additional permissions
|
||||
apply only to part of the Program, that part may be used separately
|
||||
under those permissions, but the entire Program remains governed by
|
||||
this License without regard to the additional permissions.
|
||||
|
||||
When you convey a copy of a covered work, you may at your option
|
||||
remove any additional permissions from that copy, or from any part of
|
||||
it. (Additional permissions may be written to require their own
|
||||
removal in certain cases when you modify the work.) You may place
|
||||
additional permissions on material, added by you to a covered work,
|
||||
for which you have or can give appropriate copyright permission.
|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
add to a covered work, you may (if authorized by the copyright holders of
|
||||
that material) supplement the terms of this License with terms:
|
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the
|
||||
terms of sections 15 and 16 of this License; or
|
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or
|
||||
author attributions in that material or in the Appropriate Legal
|
||||
Notices displayed by works containing it; or
|
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or
|
||||
requiring that modified versions of such material be marked in
|
||||
reasonable ways as different from the original version; or
|
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or
|
||||
authors of the material; or
|
||||
|
||||
e) Declining to grant rights under trademark law for use of some
|
||||
trade names, trademarks, or service marks; or
|
||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
||||
material by anyone who conveys the material (or modified versions of
|
||||
it) with contractual assumptions of liability to the recipient, for
|
||||
any liability that these contractual assumptions directly impose on
|
||||
those licensors and authors.
|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
restrictions" within the meaning of section 10. If the Program as you
|
||||
received it, or any part of it, contains a notice stating that it is
|
||||
governed by this License along with a term that is a further
|
||||
restriction, you may remove that term. If a license document contains
|
||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
||||
of that license document, provided that the further restriction does
|
||||
not survive such relicensing or conveying.
|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
must place, in the relevant source files, a statement of the
|
||||
additional terms that apply to those files, or a notice indicating
|
||||
where to find the applicable terms.
|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
form of a separately written license, or stated as exceptions;
|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
provided under this License. Any attempt otherwise to propagate or
|
||||
modify it is void, and will automatically terminate your rights under
|
||||
this License (including any patent licenses granted under the third
|
||||
paragraph of section 11).
|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
license from a particular copyright holder is reinstated (a)
|
||||
provisionally, unless and until the copyright holder explicitly and
|
||||
finally terminates your license, and (b) permanently, if the copyright
|
||||
holder fails to notify you of the violation by some reasonable means
|
||||
prior to 60 days after the cessation.
|
||||
|
||||
Moreover, your license from a particular copyright holder is
|
||||
reinstated permanently if the copyright holder notifies you of the
|
||||
violation by some reasonable means, this is the first time you have
|
||||
received notice of violation of this License (for any work) from that
|
||||
copyright holder, and you cure the violation prior to 30 days after
|
||||
your receipt of the notice.
|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
licenses of parties who have received copies or rights from you under
|
||||
this License. If your rights have been terminated and not permanently
|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
material under section 10.
|
||||
|
||||
9. Acceptance Not Required for Having Copies.
|
||||
|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
for enforcing compliance by third parties with this License.
|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Remote Network Interaction; Use with the GNU General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, if you modify the
|
||||
Program, your modified version must prominently offer all users
|
||||
interacting with it remotely through a computer network (if your version
|
||||
supports such interaction) an opportunity to receive the Corresponding
|
||||
Source of your version by providing access to the Corresponding Source
|
||||
from a network server at no charge, through some standard or customary
|
||||
means of facilitating copying of software. This Corresponding Source
|
||||
shall include the Corresponding Source for any work covered by version 3
|
||||
of the GNU General Public License that is incorporated pursuant to the
|
||||
following paragraph.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the work with which it is combined will remain governed by version
|
||||
3 of the GNU General Public License.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU Affero General Public License from time to time. Such new versions
|
||||
will be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU Affero General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU Affero General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU Affero General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||
SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU Affero General Public License as published
|
||||
by the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU Affero General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU Affero General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If your software can interact with users remotely through a computer
|
||||
network, you should also make sure that it provides a way for users to
|
||||
get its source. For example, if your program is a web application, its
|
||||
interface could display a "Source" link that leads users to an archive
|
||||
of the code. There are many ways you could offer source, and different
|
||||
solutions will be better for different programs; see section 13 for the
|
||||
specific requirements.
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU AGPL, see
|
||||
<https://www.gnu.org/licenses/>.
|
||||
75
README.md
Normal file
75
README.md
Normal file
@ -0,0 +1,75 @@
|
||||
|
||||
[](https://www.gnu.org/licenses/agpl-3.0)
|
||||
[](https://github.com/gasflux/gasflux/actions?query=workflow%3A%22CI%22)
|
||||
[](https://github.com/charliermarsh/ruff)
|
||||
[](https://github.com/psf/black)
|
||||
[](https://github.com/python/mypy)
|
||||
|
||||
# GasFlux
|
||||
|
||||
`pip install gasflux` \
|
||||
`gasflux --help`
|
||||
|
||||
📖 **[详细用户指南](USER_GUIDE.md)** - 完整的功能说明和使用教程
|
||||
|
||||
GasFlux is a tool for processing atmospheric gas concentration data and windspeeds into mass emissions fluxes, with principle applications to greenhouse gas measurement and vulcanology. Currently it works with in situ ("sniffing") data from UAVs and other aircraft, using mass balance as a paradigm and kriging as an interpolation strategy, but the intention is to expand this to other kinds of sampling and processing strategies, such as open-path and tracer methods.
|
||||
|
||||
It is released under the AGPLv3 license as a free and open-source project - comments, pull requests, issues and co-development are warmly welcomed. Currently development is co-ordinated by Jamie McQuilkin ([@pipari](https://github.com/pipari)) at the UAV Greenhouse Gas group at the University of Manchester.
|
||||
|
||||
## User Installation
|
||||
|
||||
The package is available on PyPi and can be installed using `pip install gasflux`.
|
||||
|
||||
## Usage
|
||||
|
||||
The package interface is in active development. Currently it ingests a data csv file (or folder containing only data csv files) and a config file that dictates the parameters of the analysis.
|
||||
|
||||
This is done through the syntax `gasflux process <input_file> --config <config_file>`.
|
||||
|
||||
### The config file
|
||||
|
||||
The default gasflux_config.yaml is located in the package source. It can be generated in a supplied directory using `gasflux generate-config <path>`. If a directory is supplied to `gasflux process` and a config is not also explicitly supplied, the package will look for one config file in that directory or its subdirectories and attempt to process all csv files in that directory and subdirectories. If multiple or no config is found, it will raise an error. If supplying a single csv file for processing (rather than a directory), only the parent directory will be searched for config files (not its subdirectories).
|
||||
|
||||
Through it, variables can be passed to the [scikit-gstat](https://scikit-gstat.readthedocs.io/en/latest/) package used for kriging and the [pybaselines](https://pybaselines.readthedocs.io/en/latest/) package used for background correction.
|
||||
|
||||
### The data file
|
||||
|
||||
Input data files must be csv-type (i.e. readable by `pandas`) and have the following columns (all lower case):
|
||||
|
||||
- `timestamp` (datetime)
|
||||
- `latitude` (float)
|
||||
- `longitude` (float)
|
||||
- `height_ato` (float) - height above takeoff
|
||||
- `windspeed` (float) - in m/s, as measured or inferred at each measurement point
|
||||
- `winddir` (float) - in standard 0-360 degree format, relative to the earth
|
||||
- `temperature` (float) in degrees celsius
|
||||
- `pressure` (float) - in hPa/mBar
|
||||
|
||||
At least one gas concentration in ppm is also required. The column name should be the gas name, e.g. `co2`, `ch4`, `n2o`, etc.
|
||||
|
||||
The gas should be entered in the gasflux_config.yaml file along with a range of concentrations in ppmv, e.g.:
|
||||
|
||||
```
|
||||
gases:
|
||||
ch4: [1.5, 500]
|
||||
co2: [300, 5000]
|
||||
c2h6: [-0.5, 10]
|
||||
```
|
||||
|
||||
Ensuring input data are sufficient and correctly formatted is non-trivial and important, but is left to the user. Data sources vary enormously so it is difficult to generalise this part of the analysis - in many cases these will be a mix of flight logs, GPS, one or more anemometers, one or more gas sensors, a thermometer, hygrometer, barometer.
|
||||
|
||||
Synchronisation and fusion of these data sources is important and should be given great attention - there are several ways to do this, including GPS logging on each sensor, recording everything on a single device, or NTP server synchronisation. Care should also be taken to avoid loss of data through resampling or interpolation.
|
||||
|
||||
One way to convert encoded DJI logs is to use `djiparsetext`, a C++ library available on github [here](https://github.com/uav4geo/djiparsetxt) and documented [here](http://djilogs.live555.com/).
|
||||
|
||||
## Development
|
||||
|
||||
### Installation
|
||||
|
||||
To install, clone the repository using e.g. `git clone` and use `pip install -e .` to install it as an editable python package.
|
||||
|
||||
It's highly recommended to use a virtual environment to manage dependencies. If you're not currently using one, [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/install.html) is a good option.
|
||||
|
||||
Then run `pip install -r requirements.txt` and `pip install -r dev-requirements.txt` to install the required dependencies.
|
||||
|
||||
User requirements.txt is generated using `pigar generate` rather than `pip freeze`.
|
||||
48
build_gasflux_exe.bat
Normal file
48
build_gasflux_exe.bat
Normal file
@ -0,0 +1,48 @@
|
||||
@echo off
|
||||
echo ========================================
|
||||
echo GasFlux RunExample EXE打包工具 (Windows)
|
||||
echo ========================================
|
||||
echo.
|
||||
|
||||
cd /d "%~dp0"
|
||||
|
||||
echo [1/4] 检查Python环境...
|
||||
python --version
|
||||
if errorlevel 1 (
|
||||
echo 错误：未找到Python
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
echo [2/4] 升级pip...
|
||||
python -m pip install --upgrade pip
|
||||
|
||||
echo [3/4] 安装PyInstaller...
|
||||
python -m pip install pyinstaller
|
||||
if errorlevel 1 (
|
||||
echo 错误：PyInstaller安装失败
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
echo [4/4] 开始打包...
|
||||
python -m PyInstaller --clean gasflux_run_example.spec
|
||||
if errorlevel 1 (
|
||||
echo 错误：打包失败
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
echo.
|
||||
echo ========================================
|
||||
echo 打包完成！
|
||||
echo ========================================
|
||||
echo.
|
||||
echo 可执行文件位置：dist\GasFlux_RunExample.exe
|
||||
echo.
|
||||
echo 使用方法：
|
||||
echo GasFlux_RunExample.exe input.xlsx
|
||||
echo GasFlux_RunExample.exe input.xlsx --output result.csv
|
||||
echo GasFlux_RunExample.exe input.xlsx --no-gasflux
|
||||
echo.
|
||||
pause
|
||||
48
build_gasflux_exe.sh
Normal file
48
build_gasflux_exe.sh
Normal file
@ -0,0 +1,48 @@
|
||||
#!/bin/bash
|
||||
|
||||
echo "========================================"
|
||||
echo "GasFlux RunExample EXE打包工具 (Linux/Mac)"
|
||||
echo "========================================"
|
||||
echo
|
||||
|
||||
cd "$(dirname "$0")"
|
||||
|
||||
echo "[1/4] 检查Python环境..."
|
||||
if ! command -v python3 &> /dev/null; then
|
||||
echo "错误：未找到Python3"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
python3 --version
|
||||
|
||||
echo "[2/4] 升级pip..."
|
||||
python3 -m pip install --upgrade pip
|
||||
|
||||
echo "[3/4] 安装PyInstaller..."
|
||||
python3 -m pip install pyinstaller
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "错误：PyInstaller安装失败"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "[4/4] 开始打包..."
|
||||
python3 -m PyInstaller --clean gasflux_run_example.spec
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "错误：打包失败"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo
|
||||
echo "========================================"
|
||||
echo "打包完成！"
|
||||
echo "========================================"
|
||||
echo
|
||||
echo "可执行文件位置：dist/GasFlux_RunExample"
|
||||
echo
|
||||
echo "使用方法："
|
||||
echo " ./GasFlux_RunExample input.xlsx"
|
||||
echo " ./GasFlux_RunExample input.xlsx --output result.csv"
|
||||
echo " ./GasFlux_RunExample input.xlsx --no-gasflux"
|
||||
echo
|
||||
52
examples/basic_usage/advanced_config.yaml
Normal file
52
examples/basic_usage/advanced_config.yaml
Normal file
@ -0,0 +1,52 @@
|
||||
output_dir: ./advanced_output
|
||||
|
||||
required_cols:
|
||||
latitude: [-90, 90]
|
||||
longitude: [-180, 180]
|
||||
height_ato: [-100, 500]
|
||||
windspeed: [1, 15]
|
||||
winddir: [0, 360]
|
||||
temperature: [0, 30]
|
||||
pressure: [980, 1030]
|
||||
|
||||
gases:
|
||||
ch4: [1.8, 5.0]
|
||||
co2: [400, 500]
|
||||
|
||||
|
||||
strategies:
|
||||
background: "algorithm"
|
||||
sensor: "insitu"
|
||||
spatial: "curtain"
|
||||
interpolation: "kriging"
|
||||
|
||||
algorithmic_baseline_settings:
|
||||
algorithm: dietrich
|
||||
dietrich:
|
||||
poly_order: 4
|
||||
smooth_half_window: 4
|
||||
|
||||
semivariogram_settings:
|
||||
model: gaussian
|
||||
estimator: cressie
|
||||
n_lags: 15
|
||||
bin_func: even
|
||||
fit_method: lm
|
||||
maxlag: 80
|
||||
tolerance: 15
|
||||
azimuth: 0
|
||||
bandwidth: 25
|
||||
|
||||
ordinary_kriging_settings:
|
||||
min_points: 5
|
||||
max_points: 80
|
||||
grid_resolution: 200
|
||||
min_nodes: 10
|
||||
y_min: 20
|
||||
cut_ground: True
|
||||
|
||||
filters:
|
||||
course_filter:
|
||||
azimuth_filter: 15
|
||||
azimuth_window: 8
|
||||
elevation_filter: 8
|
||||
BIN
examples/basic_usage/data/08_34_01_间隔高度5m.xlsx
Normal file
BIN
examples/basic_usage/data/08_34_01_间隔高度5m.xlsx
Normal file
Binary file not shown.
BIN
examples/basic_usage/data/08_51_15_间隔高度10m.xlsx
Normal file
BIN
examples/basic_usage/data/08_51_15_间隔高度10m.xlsx
Normal file
Binary file not shown.
69
examples/basic_usage/data/gasflux_config.yaml
Normal file
69
examples/basic_usage/data/gasflux_config.yaml
Normal file
@ -0,0 +1,69 @@
|
||||
# GasFlux configuration file for basic usage example
|
||||
|
||||
output_dir: ./10m
|
||||
|
||||
# Required columns and their maximum valid ranges
|
||||
required_cols:
|
||||
latitude: [-90, 90]
|
||||
longitude: [-180, 180]
|
||||
height_ato: [0, 50] # meters above takeoff
|
||||
windspeed: [0, 50] # m/s
|
||||
winddir: [0, 360] # degrees
|
||||
temperature: [-50, 60] # degrees Celsius
|
||||
pressure: [900, 1100] # hPa/mb
|
||||
|
||||
# Optional gas columns and their maximum ppm concentration ranges.
|
||||
# Relative concentrations are used, so absolute offset can be incorrect as long as gain and linearity are correct.
|
||||
gases:
|
||||
ch4: [2, 2.5]
|
||||
|
||||
|
||||
strategies:
|
||||
background: "algorithm" # Currently only algorithmic baseline correction (via pybaselines) is supported
|
||||
sensor: "insitu" # Currently only in-situ sensor data is supported
|
||||
spatial: "spiral" # Spatial processing mode: "curtain" and "spiral" are supported
|
||||
interpolation: "kriging" # Currently only kriging interpolation is supported
|
||||
|
||||
# Baseline correction algorithm settings
|
||||
algorithmic_baseline_settings:
|
||||
algorithm: fastchrom
|
||||
fastchrom: {
|
||||
"half_window": 6,
|
||||
"threshold": "custom", #
|
||||
"min_fwhm": ~,
|
||||
"interp_half_window": 3,
|
||||
"smooth_half_window": 3,
|
||||
"weights": ~,
|
||||
"max_iter": 100,
|
||||
"min_length": 2}
|
||||
fabc : {
|
||||
"lam": 10000, # The smoothing parameter. Larger values will create smoother baselines. Default is 1e6.
|
||||
"scale": 10, # The scale at which to calculate the continuous wavelet transform. Should be approximately equal to the index-based full-width-at-half-maximum of the peaks or features in the data. Default is None, which will use half of the value from optimize_window(), which is not always a good value, but at least scales with the number of data points and gives a starting point for tuning the parameter.
|
||||
"diff_order": 2} # The order of the differential matrix. Must be greater than 0. Default is 2 (second order differential matrix). Typical values are 2 or 1.
|
||||
dietrich : {
|
||||
"poly_order": 5,
|
||||
"smooth_half_window": 5,}
|
||||
golotvin : {
|
||||
"half_window": 2,
|
||||
"sections": 10}
|
||||
|
||||
# Kriging settings - aggressively optimized for Spiral mode
|
||||
semivariogram_settings:
|
||||
model: exponential # Changed to exponential model for better circular data fitting
|
||||
estimator: cressie # Robust estimator for variogram calculation
|
||||
n_lags: 50 # Further increased to 50 for better variogram resolution
|
||||
bin_func: even # Even binning function
|
||||
fit_method: lm # Least squares fitting method
|
||||
### Aggressively increased search ranges for circular/spiral data distribution
|
||||
maxlag: 5000 # Dramatically increased to 5000m for comprehensive coverage
|
||||
tolerance: 180 # Increased to 180° to allow full circular search
|
||||
azimuth: 0 # Horizontal direction maintained
|
||||
bandwidth: 300 # Further increased to 300m for maximum search bandwidth
|
||||
# fit_sigma: linear # this should allow for a spatial uncertainty but currently producing bugs
|
||||
ordinary_kriging_settings:
|
||||
min_points: 1 # Reduced to 1 to allow interpolation even with sparse data
|
||||
max_points: 20 # Further reduced to 20 to minimize computational load
|
||||
grid_resolution: 100 # Increased density to 100 for finer interpolation grid
|
||||
min_nodes: 20 # Increased to 20 to ensure sufficient grid nodes
|
||||
y_min: ~ # Automatically determine minimum y value
|
||||
cut_ground: False # Keep ground cutting disabled
|
||||
1001
examples/basic_usage/data/testdata.csv
Normal file
1001
examples/basic_usage/data/testdata.csv
Normal file
File diff suppressed because it is too large
Load Diff
61
examples/basic_usage/gasflux_config.yaml
Normal file
61
examples/basic_usage/gasflux_config.yaml
Normal file
@ -0,0 +1,61 @@
|
||||
# GasFlux 基本使用示例配置文件
|
||||
# 这个配置文件演示了最基本的设置，用于处理简单的气体通量数据
|
||||
|
||||
# 输出目录
|
||||
output_dir: ./output
|
||||
|
||||
# 数据验证参数
|
||||
required_cols:
|
||||
latitude: [-90, 90] # 纬度范围
|
||||
longitude: [-180, 180] # 经度范围
|
||||
height_ato: [0, 200] # 相对起飞高度（米）
|
||||
windspeed: [0, 20] # 风速（m/s）
|
||||
winddir: [0, 360] # 风向（度）
|
||||
temperature: [-50, 60] # 温度（°C）
|
||||
pressure: [900, 1100] # 气压（hPa）
|
||||
|
||||
# 气体配置：气体名称和浓度范围（ppmv）
|
||||
gases:
|
||||
ch4: [1.5, 10.0] # 甲烷
|
||||
co2: [300, 500] # 二氧化碳
|
||||
|
||||
# 处理策略
|
||||
strategies:
|
||||
background: "algorithm" # 背景校正方法
|
||||
sensor: "insitu" # 传感器类型
|
||||
spatial: "curtain" # 空间处理模式
|
||||
interpolation: "kriging" # 插值方法
|
||||
|
||||
# 背景校正算法设置
|
||||
algorithmic_baseline_settings:
|
||||
algorithm: fastchrom # 使用FastChrom算法
|
||||
fastchrom:
|
||||
half_window: 3 # 半窗口大小
|
||||
threshold: "custom" # 阈值方法
|
||||
min_fwhm: ~
|
||||
interp_half_window: 2
|
||||
smooth_half_window: 2
|
||||
weights: ~
|
||||
max_iter: 50
|
||||
min_length: 2
|
||||
|
||||
# 克里金插值设置
|
||||
semivariogram_settings:
|
||||
model: spherical # 半变异函数模型
|
||||
estimator: cressie # 估计器
|
||||
n_lags: 10 # 滞后期数
|
||||
bin_func: even # 分箱函数
|
||||
fit_method: lm # 拟合方法
|
||||
maxlag: 50 # 最大滞后距离
|
||||
tolerance: 10 # 方向容差
|
||||
azimuth: 0 # 方位角
|
||||
bandwidth: 15 # 带宽
|
||||
|
||||
# 普通克里金设置
|
||||
ordinary_kriging_settings:
|
||||
min_points: 3 # 最小邻点数
|
||||
max_points: 50 # 最大邻点数
|
||||
grid_resolution: 100 # 网格分辨率
|
||||
min_nodes: 5 # 最小网格节点数
|
||||
y_min: ~ # 最小y值
|
||||
cut_ground: False # 是否切割地面
|
||||
820
examples/basic_usage/result.csv
Normal file
820
examples/basic_usage/result.csv
Normal file
@ -0,0 +1,820 @@
|
||||
timestamp,latitude,longitude,height_ato,windspeed,winddir,temperature,pressure,ch4,course_elevation,course_azimuth
|
||||
2016-02-12 08:34:01,40.349137,115.7855289,11.865000000000009,4.85383,126.121,18.1,957.6,2.28753,8.50882,74.9353
|
||||
2016-02-12 08:34:02,40.349137,115.7855289,11.865000000000009,3.5045,142.744,18.1,957.6,2.27667,8.50882,74.9353
|
||||
2016-02-12 08:34:02,40.349137,115.7855296,9.830000000000041,3.30667,145.626,18.1,957.4,2.28621,7.26866,75.2618
|
||||
2016-02-12 08:34:02,40.3491369,115.7855295,9.830000000000041,3.19669,147.2,18.1,957.4,2.29012,6.6954,75.3488
|
||||
2016-02-12 08:34:02,40.3491366,115.7855289,9.830000000000041,3.1958,146.33,18.1,957.4,2.28968,5.15029,75.4473
|
||||
2016-02-12 08:34:02,40.3491365,115.7855279,9.830000000000041,2.24203,142.349,18.1,957.4,2.27675,3.60147,75.8082
|
||||
2016-02-12 08:34:02,40.3491365,115.7855279,9.830000000000041,2.24203,142.349,18.1,957.4,2.28111,3.60147,75.8082
|
||||
2016-02-12 08:34:02,40.3491362,115.7855267,9.830000000000041,2.65173,132.045,18.1,957.4,2.27628,2.90435,75.7617
|
||||
2016-02-12 08:34:03,40.3491359,115.7855254,9.830000000000041,3.12274,124.934,18.1,957.4,2.26905,1.51899,75.2541
|
||||
2016-02-12 08:34:04,40.3491342,115.7855197,9.482000000000028,3.62267,96.3253,18.1,957.4,2.26865,2.08601,74.3647
|
||||
2016-02-12 08:34:05,40.3491317,115.7855115,6.3040000000000305,3.48429,103.687,18.1,957.4,2.25985,-1.48779,74.6518
|
||||
2016-02-12 08:34:06,40.349131,115.7855068,3.1860000000000355,3.08523,110.241,18.1,957.4,2.25816,-5.50997,78.0309
|
||||
2016-02-12 08:34:07,40.3491309,115.7855052,0.45100000000002183,2.57202,106.603,18.1,957.4,2.26128,-2.11963,61.7648
|
||||
2016-02-12 08:34:08,40.3491307,115.7855046,0.06300000000004502,1.67764,109.782,18.0,957.4,2.26535,-6.78676,47.9709
|
||||
2016-02-12 08:34:09,40.3491332,115.7855083,0.0,1.87728,168.504,18.0,957.4,2.26713,-11.0121,47.0035
|
||||
2016-02-12 08:34:10,40.3491444,115.7855253,0.813000000000045,2.70038,170.113,18.0,957.4,2.27136,-6.98884,46.7738
|
||||
2016-02-12 08:34:11,40.3491573,115.7855439,1.1070000000000277,4.09542,158.538,17.9,957.4,2.26672,-3.50721,45.9275
|
||||
2016-02-12 08:34:12,40.3491708,115.7855627,0.8600000000000136,4.92415,152.325,17.9,957.4,2.26796,-3.67191,47.1102
|
||||
2016-02-12 08:34:13,40.3491838,115.7855814,1.4639999999999986,5.05104,149.272,17.9,957.4,2.26253,-2.6092,47.3419
|
||||
2016-02-12 08:34:14,40.3491963,115.7855993,1.2660000000000196,4.7566,146.774,17.8,957.4,2.25574,-3.68899,47.5405
|
||||
2016-02-12 08:34:15,40.3492086,115.7856167,0.9460000000000264,4.4861,146.945,17.8,957.4,2.27209,-4.73718,47.8124
|
||||
2016-02-12 08:34:16,40.3492212,115.7856339,1.1129999999999995,4.49042,147.234,17.8,957.4,2.2685,-3.71094,47.8571
|
||||
2016-02-12 08:34:17,40.3492333,115.785651,1.330000000000041,4.67508,147.701,17.8,957.4,2.26577,-4.10178,47.8265
|
||||
2016-02-12 08:34:18,40.3492453,115.7856683,0.3660000000000423,4.80069,142.768,17.8,957.4,2.26449,-4.00834,51.3819
|
||||
2016-02-12 08:34:19,40.3492558,115.7856869,0.42900000000003047,4.41849,139.131,17.8,957.4,2.2629,-3.5961,55.2403
|
||||
2016-02-12 08:34:20,40.3492652,115.7857063,0.5040000000000191,4.17348,135.791,17.8,957.4,2.27134,-3.66223,59.0575
|
||||
2016-02-12 08:34:21,40.3492739,115.7857263,0.6690000000000396,4.24922,133.776,17.7,957.4,2.26504,-4.04384,62.0997
|
||||
2016-02-12 08:34:22,40.3492823,115.7857472,0.6270000000000095,4.12397,132.906,17.7,957.4,2.26494,-3.93005,65.1934
|
||||
2016-02-12 08:34:23,40.3492897,115.7857691,0.38800000000003365,3.87817,129.092,17.7,957.4,2.26202,-3.17112,69.0847
|
||||
2016-02-12 08:34:24,40.3492958,115.7857913,0.535000000000025,3.56279,124.29,17.7,957.4,2.2705,-2.8296,73.4975
|
||||
2016-02-12 08:34:25,40.3493007,115.7858139,0.41500000000002046,3.20321,120.437,17.7,957.4,2.26211,-2.58427,74.5562
|
||||
2016-02-12 08:34:26,40.3493053,115.7858363,0.32600000000002183,2.84368,126.241,17.7,957.4,2.27064,-3.00187,74.5266
|
||||
2016-02-12 08:34:27,40.3493102,115.7858587,0.39800000000002456,2.66869,123.302,17.6,957.4,2.26641,-2.85474,74.2713
|
||||
2016-02-12 08:34:28,40.3493153,115.7858814,0.1560000000000059,2.33673,125.416,17.6,957.4,2.2613,-2.58352,74.0617
|
||||
2016-02-12 08:34:29,40.3493205,115.7859044,0.015000000000043201,2.1419,112.039,17.6,957.4,2.27285,-2.4898,74.4684
|
||||
2016-02-12 08:34:30,40.349325,115.7859273,0.023000000000024556,2.12291,112.287,17.6,957.4,2.26546,-2.37166,74.68
|
||||
2016-02-12 08:34:31,40.3493296,115.7859502,0.09500000000002728,2.11867,112.449,17.5,957.4,2.26334,-2.52349,74.727
|
||||
2016-02-12 08:34:32,40.3493339,115.7859728,0.21500000000003183,2.03503,104.255,17.5,957.4,2.25624,-2.35695,77.5604
|
||||
2016-02-12 08:34:33,40.3493375,115.7859955,0.06800000000004047,2.08166,105.808,17.5,957.4,2.26756,-2.18838,81.7064
|
||||
2016-02-12 08:34:34,40.3493398,115.7860185,0.09100000000000819,2.10121,101.349,17.5,957.4,2.27006,-2.16129,86.1149
|
||||
2016-02-12 08:34:35,40.3493408,115.7860419,0.09800000000001319,2.0652,92.3755,17.5,957.4,2.26534,-1.97706,89.1031
|
||||
2016-02-12 08:34:36,40.3493407,115.7860652,0.28400000000004866,2.03562,89.3814,17.5,957.4,2.26928,-1.69654,91.8991
|
||||
2016-02-12 08:34:37,40.3493397,115.7860884,0.16800000000000637,2.01381,86.7262,17.4,957.4,2.26397,-1.49829,95.0727
|
||||
2016-02-12 08:34:38,40.3493378,115.7861116,0.10599999999999454,1.94986,82.7376,17.4,957.4,2.26526,-1.28459,98.4028
|
||||
2016-02-12 08:34:39,40.3493345,115.7861345,0.2300000000000182,1.98893,75.9616,17.4,957.4,2.27142,-1.40811,102.871
|
||||
2016-02-12 08:34:40,40.3493301,115.786157,0.22400000000004638,1.97218,72.7694,17.4,957.4,2.26352,-1.32349,106.444
|
||||
2016-02-12 08:34:41,40.3493248,115.7861794,0.26300000000003365,1.9978,72.1563,17.4,957.4,2.26984,-1.26987,107.053
|
||||
2016-02-12 08:34:42,40.3493192,115.7862018,0.15399999999999636,1.99455,72.3657,17.4,957.4,2.27778,-1.2788,106.81
|
||||
2016-02-12 08:34:43,40.3493138,115.7862243,0.16800000000000637,2.00894,73.2219,17.4,957.4,2.27545,-1.35789,106.736
|
||||
2016-02-12 08:34:44,40.3493084,115.786247,0.23200000000002774,2.01704,73.3629,17.4,957.4,2.26695,-1.14838,107.083
|
||||
2016-02-12 08:34:45,40.3493031,115.7862696,0.14400000000000546,1.99527,72.7411,17.4,957.4,2.26658,-0.891773,107.534
|
||||
2016-02-12 08:34:46,40.3492978,115.7862919,0.14500000000003865,1.9958,72.9977,17.4,957.4,2.26718,-1.23237,107.164
|
||||
2016-02-12 08:34:47,40.3492925,115.7863145,0.20400000000000773,2.00589,73.2645,17.3,957.4,2.27372,-0.841243,107.309
|
||||
2016-02-12 08:34:48,40.3492871,115.7863372,0.11000000000001364,2.00215,73.3395,17.3,957.4,2.26653,-0.756934,106.873
|
||||
2016-02-12 08:34:49,40.3492815,115.7863599,0.16599999999999682,2.00296,69.5246,17.3,957.4,2.27131,-0.454938,110.252
|
||||
2016-02-12 08:34:50,40.3492749,115.7863818,0.27899999999999636,1.97805,64.6886,17.3,957.4,2.25977,-0.706977,114.756
|
||||
2016-02-12 08:34:51,40.349267,115.7864029,0.23900000000003274,1.9369,61.1256,17.3,957.4,2.27473,-0.520325,118.334
|
||||
2016-02-12 08:34:52,40.3492562,115.7864271,0.37700000000000955,1.9232,56.7933,17.3,957.4,2.27288,-0.892102,122.079
|
||||
2016-02-12 08:34:53,40.3492466,115.7864464,0.3160000000000309,1.9402,53.0758,17.3,957.4,2.26006,-0.668285,125.41
|
||||
2016-02-12 08:34:54,40.3492383,115.7864611,0.4070000000000391,1.94647,49.7541,17.3,957.4,2.26102,-0.793088,129.068
|
||||
2016-02-12 08:34:55,40.3492244,115.7864817,0.46200000000004593,1.96366,44.9617,17.3,957.4,2.26623,-0.715602,133.755
|
||||
2016-02-12 08:34:56,40.3492118,115.7864981,0.43200000000001637,1.96034,44.2948,17.3,957.4,2.26446,-0.690077,134.303
|
||||
2016-02-12 08:34:57,40.3491991,115.7865145,0.34700000000003683,1.97744,45.1225,17.3,957.4,2.26972,-0.643342,134.308
|
||||
2016-02-12 08:34:58,40.3491864,115.7865314,0.37700000000000955,1.76954,51.6213,17.3,957.4,2.26913,-0.884507,134.019
|
||||
2016-02-12 08:34:59,40.3491734,115.7865484,0.3170000000000073,1.64919,64.6824,17.3,957.4,2.26522,-0.561871,134.093
|
||||
2016-02-12 08:35:00,40.3491603,115.786565,0.38900000000001,1.53019,22.4667,17.3,957.4,2.27348,-0.283173,139.001
|
||||
2016-02-12 08:35:01,40.3491468,115.7865794,0.3860000000000241,1.66544,16.1733,17.3,957.4,2.25965,-0.622678,146.88
|
||||
2016-02-12 08:35:02,40.349132,115.7865915,0.49600000000003774,1.90088,25.2598,17.3,957.4,2.27091,-0.675699,152.432
|
||||
2016-02-12 08:35:03,40.3491161,115.7866014,0.660000000000025,1.92716,21.0727,17.3,957.4,2.27679,-0.638564,157.896
|
||||
2016-02-12 08:35:04,40.3490997,115.786609,0.6190000000000282,1.94499,15.5196,17.3,957.4,2.2683,-1.18828,163.563
|
||||
2016-02-12 08:35:05,40.3490826,115.7866145,0.6270000000000095,2.06029,5.61009,17.3,957.4,2.26783,-0.849828,170.567
|
||||
2016-02-12 08:35:06,40.349065,115.7866175,0.7410000000000423,2.25531,18.8411,17.3,957.4,2.27565,-1.02481,175.647
|
||||
2016-02-12 08:35:07,40.3490471,115.7866191,0.5990000000000464,2.6897,27.0446,17.3,957.4,2.26251,-0.938632,175.901
|
||||
2016-02-12 08:35:08,40.3490295,115.7866204,0.8120000000000118,3.5706,22.8119,17.3,957.4,2.26682,-1.4553,175.625
|
||||
2016-02-12 08:35:09,40.3490118,115.7866221,0.5160000000000196,3.58223,23.2989,17.3,957.4,2.26834,-1.51818,175.649
|
||||
2016-02-12 08:35:10,40.3489939,115.7866236,0.6400000000000432,3.47975,23.2924,17.3,957.4,2.26655,-1.19849,175.598
|
||||
2016-02-12 08:35:11,40.3489761,115.7866252,0.5400000000000205,3.19407,20.9252,17.3,957.4,2.26637,-0.986002,176.011
|
||||
2016-02-12 08:35:12,40.3489581,115.7866269,0.5540000000000305,2.9534,17.5397,17.3,957.4,2.2573,-1.64013,176.158
|
||||
2016-02-12 08:35:13,40.3489399,115.7866285,0.7060000000000173,3.15458,18.2456,17.3,957.4,2.26939,-0.792553,175.629
|
||||
2016-02-12 08:35:14,40.3489218,115.7866301,0.6890000000000214,3.20536,19.0764,17.3,957.4,2.2633,-1.16851,175.798
|
||||
2016-02-12 08:35:15,40.3489038,115.7866319,0.8970000000000482,3.5823,22.3026,17.3,957.4,2.25549,-0.91357,175.775
|
||||
2016-02-12 08:35:16,40.3488859,115.7866335,0.7669999999999959,3.65156,26.1432,17.3,957.4,2.27439,-1.38495,178.614
|
||||
2016-02-12 08:35:17,40.3488681,115.7866336,0.7250000000000227,3.69689,28.3517,17.2,957.4,2.2692,-1.36653,-178.318
|
||||
2016-02-12 08:35:18,40.3488503,115.7866322,0.8000000000000114,3.65114,28.7382,17.2,957.4,2.26636,-1.90382,-175.758
|
||||
2016-02-12 08:35:19,40.3488325,115.7866297,0.7480000000000473,3.6946,29.076,17.2,957.4,2.27162,-1.64705,-173.079
|
||||
2016-02-12 08:35:20,40.3488148,115.7866265,0.7590000000000146,3.86282,32.9325,17.2,957.4,2.26822,-1.50341,-170.331
|
||||
2016-02-12 08:35:21,40.3487974,115.7866223,0.57000000000005,3.78317,34.2873,17.2,957.4,2.259,-1.77715,-168.664
|
||||
2016-02-12 08:35:22,40.3487801,115.7866172,0.8000000000000114,3.73202,35.6475,17.2,957.4,2.26842,-1.80386,-166.087
|
||||
2016-02-12 08:35:23,40.348763,115.7866112,0.5540000000000305,3.64181,38.9923,17.2,957.4,2.26291,-1.70703,-162.255
|
||||
2016-02-12 08:35:24,40.348746,115.7866039,0.7320000000000277,3.81269,41.8128,17.2,957.4,2.26664,-1.71048,-159.878
|
||||
2016-02-12 08:35:25,40.3487291,115.7865957,0.7630000000000337,3.48729,42.0299,17.2,957.4,2.26374,-1.33685,-159.608
|
||||
2016-02-12 08:35:26,40.3487121,115.7865872,1.0080000000000382,3.75868,43.3489,17.2,957.4,2.26646,-1.55744,-159.91
|
||||
2016-02-12 08:35:27,40.3486957,115.7865788,0.7060000000000173,3.79234,42.8656,17.2,957.4,2.26328,-2.0048,-159.441
|
||||
2016-02-12 08:35:28,40.3486789,115.7865705,0.7350000000000136,4.22793,42.0491,17.2,957.4,2.26176,-1.98951,-159.699
|
||||
2016-02-12 08:35:29,40.3486623,115.7865625,0.7860000000000014,4.27342,41.0633,17.2,957.4,2.2614,-2.18286,-159.799
|
||||
2016-02-12 08:35:30,40.3486421,115.7865527,0.5580000000000496,4.18449,42.0908,17.2,957.4,2.2712,-1.9235,-159.6
|
||||
2016-02-12 08:35:31,40.3486252,115.7865443,0.4430000000000405,4.02941,43.9017,17.2,957.4,2.26236,-1.78458,-159.343
|
||||
2016-02-12 08:35:32,40.3486085,115.7865356,0.4370000000000118,4.02369,47.8521,17.2,957.4,2.26458,-1.82948,-155.262
|
||||
2016-02-12 08:35:33,40.3485927,115.7865251,0.6200000000000045,4.1616,52.7125,17.2,957.4,2.26249,-2.21001,-149.328
|
||||
2016-02-12 08:35:34,40.3485776,115.7865124,0.5920000000000414,4.1949,56.7896,17.2,957.4,2.26734,-1.6575,-143.271
|
||||
2016-02-12 08:35:35,40.3485635,115.7864976,0.6930000000000405,4.27443,63.2883,17.2,957.4,2.27079,-1.35784,-137.443
|
||||
2016-02-12 08:35:36,40.3485507,115.7864814,1.0370000000000346,4.18031,70.4075,17.2,957.4,2.27161,-2.25059,-133.151
|
||||
2016-02-12 08:35:37,40.3485392,115.7864642,1.143000000000029,4.11209,74.1468,17.1,957.4,2.26222,-2.27961,-128.075
|
||||
2016-02-12 08:35:38,40.3485291,115.7864455,1.0510000000000446,4.69329,81.6319,17.1,957.4,2.26785,-2.59902,-121.299
|
||||
2016-02-12 08:35:39,40.3485208,115.7864255,1.2220000000000368,5.02418,87.2004,17.1,957.4,2.26188,-3.57525,-114.178
|
||||
2016-02-12 08:35:40,40.3485143,115.7864042,1.0570000000000164,5.29014,89.51,17.1,957.4,2.25589,-3.38018,-109.557
|
||||
2016-02-12 08:35:41,40.3485083,115.7863817,1.0950000000000273,5.35193,86.5112,17.1,957.4,2.26569,-3.63266,-110.057
|
||||
2016-02-12 08:35:42,40.3485019,115.7863589,0.9279999999999973,5.48287,85.2166,17.1,957.4,2.25651,-3.05251,-109.671
|
||||
2016-02-12 08:35:43,40.3484959,115.7863364,1.3120000000000118,5.33864,85.4933,17.1,957.4,2.2527,-3.46027,-109.986
|
||||
2016-02-12 08:35:44,40.3484896,115.7863142,0.9790000000000418,5.04344,87.0444,17.1,957.4,2.25495,-2.7903,-109.977
|
||||
2016-02-12 08:35:45,40.3484838,115.7862919,0.8730000000000473,5.04691,88.3346,17.1,957.4,2.25385,-2.69067,-109.498
|
||||
2016-02-12 08:35:46,40.3484784,115.7862698,0.7189999999999941,4.85317,86.7704,17.1,957.4,2.25565,-3.02553,-109.5
|
||||
2016-02-12 08:35:47,40.3484727,115.7862479,0.4759999999999991,4.84407,85.5268,17.1,957.4,2.26485,-2.80897,-109.877
|
||||
2016-02-12 08:35:48,40.3484667,115.786226,0.7650000000000432,4.92592,82.6714,17.1,957.4,2.26122,-3.19976,-110.092
|
||||
2016-02-12 08:35:49,40.3484607,115.7862036,0.9590000000000032,4.84169,80.0226,17.1,957.4,2.26519,-3.37566,-109.883
|
||||
2016-02-12 08:35:50,40.3484548,115.7861811,0.9000000000000341,4.92234,79.5242,17.0,957.4,2.25928,-3.44132,-109.953
|
||||
2016-02-12 08:35:51,40.3484488,115.7861583,0.7050000000000409,4.87384,79.8274,17.0,957.4,2.26367,-2.44092,-108.984
|
||||
2016-02-12 08:35:52,40.3484431,115.7861355,1.3520000000000323,4.80479,85.2199,17.0,957.4,2.26016,-2.95146,-104.259
|
||||
2016-02-12 08:35:53,40.3484387,115.7861128,1.2720000000000482,4.68873,88.9,17.0,957.4,2.26904,-3.00915,-99.8611
|
||||
2016-02-12 08:35:54,40.3484358,115.7860899,1.1779999999999973,4.69722,95.5627,17.0,957.4,2.26347,-3.11661,-95.6539
|
||||
2016-02-12 08:35:55,40.3484345,115.7860661,1.05600000000004,4.66468,100.449,17.0,957.4,2.26009,-2.20477,-92.2146
|
||||
2016-02-12 08:35:56,40.3484343,115.7860428,1.3660000000000423,4.72368,105.041,17.0,957.4,2.25921,-2.67096,-88.8258
|
||||
2016-02-12 08:35:57,40.3484347,115.7860198,1.8100000000000023,4.68402,107.835,17.0,957.4,2.25358,-3.15406,-84.749
|
||||
2016-02-12 08:35:58,40.3484367,115.7859975,2.1230000000000473,4.85389,110.385,17.0,957.4,2.26588,-4.38063,-81.1136
|
||||
2016-02-12 08:35:59,40.3484402,115.7859746,1.6659999999999968,5.12074,113.453,17.0,957.4,2.25073,-3.78266,-75.9072
|
||||
2016-02-12 08:36:00,40.3484451,115.7859519,1.5710000000000264,5.51489,114.162,16.9,957.4,2.25645,-3.45966,-73.0747
|
||||
2016-02-12 08:36:01,40.3484506,115.7859298,1.41700000000003,5.60618,114.705,16.9,957.4,2.26324,-3.852,-72.6888
|
||||
2016-02-12 08:36:02,40.3484562,115.7859079,1.4260000000000446,5.9885,113.608,16.9,957.4,2.25939,-4.08462,-72.4005
|
||||
2016-02-12 08:36:03,40.3484618,115.7858859,1.2960000000000491,6.06681,114.146,16.9,957.4,2.26444,-4.33265,-72.7478
|
||||
2016-02-12 08:36:04,40.3484672,115.7858634,1.875,6.16502,113.964,16.9,957.4,2.25122,-4.29353,-72.6671
|
||||
2016-02-12 08:36:05,40.3484723,115.7858407,1.288000000000011,5.83346,114.53,16.9,957.4,2.25211,-4.46077,-72.5697
|
||||
2016-02-12 08:36:06,40.3484779,115.785818,1.2210000000000036,5.89907,113.53,16.9,957.4,2.25987,-4.01207,-72.7203
|
||||
2016-02-12 08:36:07,40.3484833,115.7857951,1.188000000000045,5.87513,112.884,16.9,957.4,2.25998,-3.94424,-72.7445
|
||||
2016-02-12 08:36:08,40.3484887,115.7857725,1.0230000000000246,5.86465,112.761,16.9,957.4,2.25653,-3.75708,-72.6379
|
||||
2016-02-12 08:36:09,40.3484941,115.78575,1.3140000000000214,5.86195,113.211,16.9,957.4,2.26881,-3.84116,-72.7711
|
||||
2016-02-12 08:36:10,40.3484998,115.7857277,1.1030000000000086,5.76417,116.283,16.8,957.4,2.26094,-3.59034,-70.8237
|
||||
2016-02-12 08:36:11,40.3485062,115.785706,1.30600000000004,5.63247,121.963,16.8,957.4,2.26475,-4.2956,-65.0574
|
||||
2016-02-12 08:36:12,40.348514,115.7856854,1.295000000000016,5.60765,127.444,16.8,957.4,2.27279,-4.16794,-59.1853
|
||||
2016-02-12 08:36:13,40.3485233,115.7856661,1.3950000000000387,5.85706,131.455,16.8,957.4,2.24993,-4.41538,-54.0778
|
||||
2016-02-12 08:36:14,40.3485344,115.7856481,1.2980000000000018,5.88649,137.158,16.8,957.4,2.25646,-4.22068,-48.5817
|
||||
2016-02-12 08:36:15,40.3485493,115.7856278,1.413000000000011,5.83127,140.639,16.8,957.4,2.263,-3.64977,-43.7228
|
||||
2016-02-12 08:36:16,40.3485596,115.7856156,1.6090000000000373,5.7416,144.321,16.8,957.4,2.25908,-4.5659,-39.0127
|
||||
2016-02-12 08:36:17,40.3485769,115.7855999,1.2909999999999968,5.95436,149.405,16.7,957.4,2.25161,-3.4582,-31.6394
|
||||
2016-02-12 08:36:18,40.3485927,115.7855881,1.032000000000039,5.88089,152.516,16.7,957.4,2.26682,-3.81466,-26.593
|
||||
2016-02-12 08:36:19,40.3486084,115.7855776,1.410000000000025,5.92927,151.424,16.7,957.4,2.26897,-4.88384,-25.4304
|
||||
2016-02-12 08:36:20,40.3486242,115.7855675,1.4120000000000346,6.06628,150.826,16.7,957.4,2.25404,-4.74525,-25.5442
|
||||
2016-02-12 08:36:21,40.3486401,115.7855574,1.1860000000000355,6.09935,150.336,16.7,957.4,2.25539,-4.647,-25.6555
|
||||
2016-02-12 08:36:22,40.3486565,115.7855475,0.9850000000000136,6.11633,148.496,16.7,957.4,2.26913,-4.63177,-25.7067
|
||||
2016-02-12 08:36:23,40.3486729,115.7855371,0.7600000000000477,5.92629,148.812,16.6,957.4,2.26475,-4.46693,-26.0157
|
||||
2016-02-12 08:36:24,40.3486891,115.7855269,0.7820000000000391,6.07324,148.379,16.6,957.4,2.25667,-4.99766,-25.7595
|
||||
2016-02-12 08:36:25,40.3487052,115.7855169,1.1040000000000418,5.99085,149.5,16.6,957.4,2.25532,-4.78653,-25.4645
|
||||
2016-02-12 08:36:26,40.3487213,115.7855067,0.8840000000000146,6.04219,151.354,16.6,957.4,2.26913,-4.636,-25.7042
|
||||
2016-02-12 08:36:27,40.3487372,115.7854966,0.8730000000000473,5.95603,152.786,16.6,957.4,2.26901,-4.5439,-24.9907
|
||||
2016-02-12 08:36:28,40.348753,115.7854872,0.6620000000000346,6.0444,155.92,16.6,957.4,2.25882,-5.09607,-21.8508
|
||||
2016-02-12 08:36:29,40.3487698,115.7854789,0.7740000000000009,6.23573,157.69,16.6,957.4,2.27421,-4.48643,-17.9076
|
||||
2016-02-12 08:36:30,40.3487874,115.7854718,0.9569999999999936,6.31058,159.216,16.5,957.4,2.25334,-4.42131,-14.4395
|
||||
2016-02-12 08:36:31,40.3488052,115.7854663,0.8700000000000045,6.16818,160.728,16.5,957.4,2.25037,-4.45109,-11.6332
|
||||
2016-02-12 08:36:32,40.3488228,115.785462,0.5060000000000286,6.10734,161.71,16.5,957.4,2.25493,-4.35846,-8.40024
|
||||
2016-02-12 08:36:33,40.3488399,115.7854588,0.799000000000035,6.11898,164.494,16.4,957.4,2.24844,-5.41057,-6.17409
|
||||
2016-02-12 08:36:34,40.3488573,115.7854564,0.742999999999995,6.07141,167.215,16.4,957.4,2.26561,-5.26034,-2.7436
|
||||
2016-02-12 08:36:35,40.3488754,115.7854551,0.6370000000000005,6.0496,171.946,16.4,957.4,2.25455,-4.44777,1.35836
|
||||
2016-02-12 08:36:36,40.3488935,115.785456,0.7320000000000277,5.83808,173.643,16.4,957.4,2.26214,-4.58309,4.52072
|
||||
2016-02-12 08:36:37,40.3489117,115.7854579,0.8480000000000132,5.70385,172.17,16.4,957.4,2.26151,-3.90147,5.07055
|
||||
2016-02-12 08:36:38,40.3489293,115.78546,0.9800000000000182,5.51454,171.791,16.4,957.4,2.25964,-4.5457,5.24711
|
||||
2016-02-12 08:36:39,40.348947,115.7854622,1.1539999999999964,5.33505,173.485,16.4,957.4,2.26292,-4.35318,5.33673
|
||||
2016-02-12 08:36:40,40.3489653,115.7854648,1.0,5.29204,174.852,16.3,957.4,2.25233,-3.87292,4.70492
|
||||
2016-02-12 08:36:41,40.3489836,115.7854674,1.3559999999999945,5.29741,176.258,16.3,957.4,2.25662,-3.21208,5.21414
|
||||
2016-02-12 08:36:42,40.3490015,115.7854698,1.3730000000000473,5.14176,174.023,16.3,957.4,2.25812,-3.51608,5.02582
|
||||
2016-02-12 08:36:43,40.3490189,115.7854713,1.516999999999996,5.12388,174.751,16.2,957.4,2.25788,-4.56814,5.03848
|
||||
2016-02-12 08:36:44,40.3490365,115.7854732,1.4870000000000232,5.10234,176.725,16.2,957.4,2.26351,-3.96444,5.46284
|
||||
2016-02-12 08:36:45,40.3490545,115.7854759,1.3980000000000246,5.09201,176.966,16.2,957.4,2.2583,-3.79534,5.24914
|
||||
2016-02-12 08:36:46,40.3490724,115.7854788,1.6370000000000005,5.11211,176.889,16.2,957.4,2.2544,-3.72235,5.29693
|
||||
2016-02-12 08:36:47,40.3490906,115.785481,1.1280000000000427,4.8991,175.319,16.2,957.4,2.26159,-3.32421,5.01752
|
||||
2016-02-12 08:36:48,40.3491085,115.7854831,0.8170000000000073,4.10668,170.376,16.2,957.4,2.25495,10.1467,5.26258
|
||||
2016-02-12 08:36:49,40.3491158,115.7854839,0.9000000000000341,2.6314,163.343,16.2,957.4,2.26207,0.106344,5.66132
|
||||
2016-02-12 08:36:50,40.349117,115.785484,0.38100000000002865,1.58396,131.609,16.1,957.4,2.26455,-0.625125,5.82946
|
||||
2016-02-12 08:36:51,40.3491165,115.7854841,0.313000000000045,0.960517,119.397,16.1,957.4,2.25937,-1.82616,5.43775
|
||||
2016-02-12 08:36:52,40.3491162,115.7854839,0.5020000000000095,0.136019,56.7013,16.1,957.4,2.26542,-1.73079,5.51902
|
||||
2016-02-12 08:36:53,40.3491157,115.7854835,0.3410000000000082,0.0603939,21.4057,16.1,957.4,2.25628,-1.86747,5.8482
|
||||
2016-02-12 08:36:54,40.3491154,115.7854833,0.19600000000002638,0.0369274,47.6323,16.1,957.4,2.25712,-1.53655,6.2355
|
||||
2016-02-12 08:36:55,40.3491151,115.7854834,0.2470000000000141,0.0871401,79.15,16.1,957.4,2.26202,-1.14965,6.09218
|
||||
2016-02-12 08:36:56,40.3491149,115.7854834,0.08300000000002683,0.393658,71.1975,16.1,957.4,2.25841,-1.21857,5.48189
|
||||
2016-02-12 08:36:57,40.3491148,115.7854834,0.32900000000000773,0.586761,66.23,16.1,957.4,2.27044,-1.31146,5.43551
|
||||
2016-02-12 08:36:58,40.3491145,115.7854834,0.28600000000000136,0.596112,53.5956,16.1,957.4,2.25902,-1.45305,5.54127
|
||||
2016-02-12 08:36:59,40.3491142,115.7854835,0.21200000000004593,0.25565,52.3429,16.1,957.4,2.25571,-1.29331,5.77698
|
||||
2016-02-12 08:37:00,40.3491142,115.7854836,0.25200000000000955,0.0781401,60.0922,16.1,957.4,2.26602,-1.42537,5.60733
|
||||
2016-02-12 08:37:01,40.3491142,115.7854835,0.16100000000000136,0.0230935,155.25,16.1,957.4,2.25023,-1.26584,5.76965
|
||||
2016-02-12 08:37:02,40.349114,115.7854833,0.10099999999999909,0.0296167,138.082,16.1,957.4,2.26859,-0.900714,6.01267
|
||||
2016-02-12 08:37:03,40.349114,115.7854834,0.15500000000002956,0.430488,50.8187,16.1,957.4,2.25236,-1.35083,5.15359
|
||||
2016-02-12 08:37:04,40.3491135,115.7854833,0.48500000000001364,0.980068,84.1409,16.1,957.4,2.26295,-2.14462,6.25571
|
||||
2016-02-12 08:37:05,40.3491133,115.7854835,0.3830000000000382,1.27921,103.371,16.1,957.4,2.25558,-2.13589,6.19036
|
||||
2016-02-12 08:37:06,40.3491131,115.7854836,0.4159999999999968,1.00135,137.031,16.1,957.4,2.26097,-2.52534,5.96649
|
||||
2016-02-12 08:37:07,40.3491134,115.7854836,0.2950000000000159,0.717434,155.678,16.1,957.4,2.26059,-1.63997,5.78432
|
||||
2016-02-12 08:37:08,40.3491138,115.7854835,0.21200000000004593,0.303557,137.275,16.1,957.4,2.25044,-0.967678,5.61883
|
||||
2016-02-12 08:37:09,40.3491137,115.7854835,0.21100000000001273,0.285436,130.174,16.1,957.4,2.25547,-0.834362,5.65676
|
||||
2016-02-12 08:37:10,40.3491134,115.7854835,0.1500000000000341,0.083626,87.1777,16.1,957.4,2.26308,-0.800933,5.2812
|
||||
2016-02-12 08:37:11,40.3491131,115.7854835,0.16700000000003,0.10367,42.9916,16.1,957.4,2.25831,-1.52589,4.76376
|
||||
2016-02-12 08:37:12,40.3491127,115.7854833,0.5340000000000487,0.113468,112.581,16.1,957.4,2.26024,-1.83201,5.26747
|
||||
2016-02-12 08:37:13,40.3491126,115.7854835,0.5110000000000241,0.0976797,81.7381,16.1,957.4,2.2573,-1.50989,5.33285
|
||||
2016-02-12 08:37:14,40.3491124,115.7854837,0.5590000000000259,1.8663,90.5845,16.1,957.4,2.26294,-1.44776,5.84654
|
||||
2016-02-12 08:37:15,40.3491125,115.7854838,1.865000000000009,2.56762,89.8643,16.1,957.4,2.26043,-0.567501,6.1396
|
||||
2016-02-12 08:37:16,40.3491122,115.7854834,4.349000000000046,0.917837,91.5823,16.1,957.6,2.26137,-1.97875,5.82784
|
||||
2016-02-12 08:37:17,40.3491111,115.7854803,5.638000000000034,0.716475,48.2852,16.1,957.6,2.26394,-2.59172,42.491
|
||||
2016-02-12 08:37:18,40.349111,115.7854795,5.432000000000016,0.760453,50.6434,16.1,957.6,2.25914,-6.46643,44.7529
|
||||
2016-02-12 08:37:19,40.3491155,115.785485,5.338999999999999,0.642827,152.992,16.2,957.6,2.25408,-8.15347,44.0491
|
||||
2016-02-12 08:37:20,40.3491267,115.7854981,5.7379999999999995,1.76091,82.2608,16.2,957.6,2.25425,2.78277,45.1
|
||||
2016-02-12 08:37:21,40.3491335,115.7855064,5.6650000000000205,0.881555,11.1025,16.2,957.6,2.26243,3.05661,47.7378
|
||||
2016-02-12 08:37:23,40.3491349,115.7855084,5.471000000000004,0.876476,69.6464,16.2,957.6,2.25292,-11.4077,48.1101
|
||||
2016-02-12 08:37:23,40.3491427,115.7855204,5.588999999999999,1.84099,115.054,16.2,957.6,2.25092,-3.99469,47.4194
|
||||
2016-02-12 08:37:25,40.3491553,115.7855389,5.801000000000045,2.39834,107.803,16.2,957.6,2.25989,-2.57408,47.3755
|
||||
2016-02-12 08:37:26,40.3491683,115.7855575,6.093000000000018,3.37656,124.034,16.2,957.4,2.26015,-2.74762,47.3582
|
||||
2016-02-12 08:37:27,40.3491811,115.785576,6.409000000000049,3.88895,134.223,16.2,957.4,2.25817,-3.03106,47.53
|
||||
2016-02-12 08:37:28,40.3491935,115.7855944,6.5540000000000305,3.92564,147.649,16.2,957.4,2.25108,-3.07153,47.3604
|
||||
2016-02-12 08:37:29,40.3492054,115.7856118,6.573000000000036,4.2193,150.06,16.2,957.4,2.2539,-3.572,47.243
|
||||
2016-02-12 08:37:30,40.3492176,115.7856288,6.5160000000000196,4.64874,149.884,16.2,957.4,2.24965,-3.67273,47.4047
|
||||
2016-02-12 08:37:31,40.3492301,115.7856461,6.145000000000039,4.72077,150.734,16.2,957.4,2.2622,-3.50582,47.5138
|
||||
2016-02-12 08:37:32,40.3492422,115.7856636,6.133000000000038,4.53941,145.099,16.2,957.4,2.25039,-3.6977,50.4106
|
||||
2016-02-12 08:37:33,40.3492531,115.7856817,6.093999999999994,4.65501,139.49,16.2,957.4,2.25039,-4.10876,54.9473
|
||||
2016-02-12 08:37:34,40.3492629,115.785701,6.29400000000004,4.75953,137.043,16.2,957.4,2.26519,-3.8511,58.421
|
||||
2016-02-12 08:37:35,40.349272,115.7857212,6.16700000000003,4.68933,133.723,16.2,957.4,2.25982,-2.84437,61.008
|
||||
2016-02-12 08:37:36,40.3492803,115.7857422,5.970000000000027,3.71239,129.472,16.2,957.6,2.25764,-2.38204,64.3574
|
||||
2016-02-12 08:37:37,40.3492877,115.7857639,5.673000000000002,3.24695,125.042,16.2,957.6,2.25472,-2.49819,68.7895
|
||||
2016-02-12 08:37:38,40.3492935,115.7857858,6.0020000000000095,3.11102,121.256,16.2,957.4,2.26319,-2.82953,72.6732
|
||||
2016-02-12 08:37:39,40.3492985,115.7858079,5.843000000000018,3.55585,127.703,16.2,957.6,2.26381,-3.01438,73.9861
|
||||
2016-02-12 08:37:40,40.3493033,115.78583,5.6860000000000355,3.66656,127.519,16.2,957.6,2.26546,-3.47219,74.2353
|
||||
2016-02-12 08:37:41,40.3493081,115.7858522,5.900000000000034,3.7344,126.386,16.2,957.6,2.26321,-3.65925,74.02
|
||||
2016-02-12 08:37:42,40.3493131,115.7858748,5.757000000000005,3.59181,124.73,16.2,957.6,2.2622,-3.15687,74.24
|
||||
2016-02-12 08:37:43,40.3493182,115.7858977,5.725999999999999,3.52774,122.094,16.2,957.6,2.25546,-3.02432,74.6855
|
||||
2016-02-12 08:37:44,40.349323,115.7859204,5.878000000000043,3.64108,121.595,16.2,957.6,2.25546,-3.44193,74.1486
|
||||
2016-02-12 08:37:45,40.3493279,115.7859431,5.875,4.10979,123.849,16.2,957.6,2.25546,-3.16246,74.2353
|
||||
2016-02-12 08:37:46,40.3493334,115.7859706,5.775000000000034,4.2231,121.878,16.2,957.6,2.25546,-2.85934,77.3826
|
||||
2016-02-12 08:37:47,40.3493371,115.7859941,5.854000000000042,3.38294,116.494,16.2,957.6,2.25546,-1.69435,81.6517
|
||||
2016-02-12 08:37:48,40.349339,115.7860126,5.703000000000031,3.05009,112.988,16.2,957.6,2.25773,-2.59874,84.8266
|
||||
2016-02-12 08:37:49,40.3493403,115.7860404,5.862000000000023,2.45333,104.114,16.2,957.6,2.25121,-2.6631,88.8626
|
||||
2016-02-12 08:37:50,40.3493403,115.7860637,5.515000000000043,2.71353,107.91,16.2,957.6,2.26119,-2.36504,91.8881
|
||||
2016-02-12 08:37:51,40.3493394,115.7860872,5.501000000000033,2.80362,106.534,16.2,957.6,2.272,-2.08923,94.6365
|
||||
2016-02-12 08:37:52,40.3493377,115.7861101,5.75,2.76796,106.874,16.2,957.6,2.26665,-1.91928,98.7676
|
||||
2016-02-12 08:37:53,40.3493346,115.7861332,5.528999999999996,2.30693,90.5027,16.1,957.6,2.27118,-1.50633,102.875
|
||||
2016-02-12 08:37:54,40.3493303,115.7861556,5.57000000000005,2.15987,80.27,16.1,957.6,2.26014,-2.41663,106.747
|
||||
2016-02-12 08:37:55,40.3493251,115.7861782,5.718000000000018,1.98563,72.6664,16.1,957.6,2.26723,-2.04302,107.311
|
||||
2016-02-12 08:37:56,40.3493196,115.7862007,5.552999999999997,2.00151,72.8042,16.1,957.6,2.26513,-2.02087,106.976
|
||||
2016-02-12 08:37:57,40.3493142,115.7862233,5.675000000000011,2.00659,73.3209,16.1,957.6,2.27117,-2.0311,106.897
|
||||
2016-02-12 08:37:58,40.349309,115.786246,5.585000000000036,1.99442,73.2617,16.1,957.6,2.26669,-1.85794,107.051
|
||||
2016-02-12 08:37:59,40.3493037,115.7862686,5.536000000000001,2.00573,73.0795,16.1,957.6,2.26195,-1.67994,106.978
|
||||
2016-02-12 08:38:00,40.3492984,115.7862912,5.532000000000039,2.00435,72.5278,16.1,957.6,2.26446,-1.44107,107.109
|
||||
2016-02-12 08:38:01,40.3492929,115.7863138,5.579000000000008,2.01129,72.6281,16.1,957.6,2.26836,-1.59664,107.211
|
||||
2016-02-12 08:38:02,40.3492874,115.7863364,5.55800000000005,1.98886,72.6863,16.1,957.6,2.2662,-1.67881,106.822
|
||||
2016-02-12 08:38:03,40.3492821,115.7863585,5.632000000000005,1.97927,70.5809,16.1,957.6,2.26912,-1.75376,109.576
|
||||
2016-02-12 08:38:04,40.349276,115.7863801,5.837000000000046,1.97986,64.8357,16.1,957.6,2.27006,-1.97127,114.942
|
||||
2016-02-12 08:38:05,40.3492681,115.7864007,5.823000000000036,1.97842,61.166,16.1,957.6,2.26009,-1.6458,118.698
|
||||
2016-02-12 08:38:06,40.3492592,115.7864208,5.7900000000000205,1.99769,57.8575,16.1,957.6,2.26133,-1.79207,121.582
|
||||
2016-02-12 08:38:07,40.3492495,115.7864403,5.853000000000009,2.00282,54.7015,16.1,957.6,2.26969,-1.57073,124.545
|
||||
2016-02-12 08:38:08,40.349239,115.786459,5.771000000000015,1.9989,50.599,16.1,957.6,2.25815,-1.77694,129.001
|
||||
2016-02-12 08:38:09,40.3492274,115.7864766,5.78000000000003,1.98813,46.3829,16.1,957.6,2.26562,-1.72888,133.366
|
||||
2016-02-12 08:38:10,40.349215,115.7864933,5.866000000000042,1.99488,45.6054,16.1,957.6,2.26581,-2.17574,134.303
|
||||
2016-02-12 08:38:11,40.3492022,115.7865101,5.807000000000016,1.98864,45.893,16.1,957.6,2.2618,-1.81056,134.52
|
||||
2016-02-12 08:38:12,40.3491897,115.7865271,5.6410000000000196,1.99804,46.0968,16.1,957.6,2.26713,-1.72293,134.343
|
||||
2016-02-12 08:38:13,40.3491772,115.7865441,5.715000000000032,2.01774,45.7766,16.2,957.6,2.26881,-1.66066,134.316
|
||||
2016-02-12 08:38:14,40.3491645,115.7865609,5.787000000000035,2.01669,41.1983,16.2,957.6,2.26173,-1.78608,137.866
|
||||
2016-02-12 08:38:15,40.3491509,115.7865759,5.875,1.98778,34.7049,16.2,957.6,2.25985,-1.48482,144.993
|
||||
2016-02-12 08:38:16,40.3491362,115.7865886,5.813000000000045,1.97874,28.2621,16.2,957.6,2.26267,-1.65466,150.873
|
||||
2016-02-12 08:38:17,40.3491205,115.7865992,6.025000000000034,2.26713,25.0355,16.2,957.4,2.2593,-1.70951,156.254
|
||||
2016-02-12 08:38:18,40.3491039,115.7866075,6.006000000000029,2.47573,18.7115,16.2,957.4,2.2718,-1.63082,161.557
|
||||
2016-02-12 08:38:19,40.349087,115.7866139,5.956999999999994,2.56727,8.6849,16.2,957.6,2.26504,-1.88711,168.928
|
||||
2016-02-12 08:38:20,40.3490696,115.7866172,6.229000000000042,2.75719,0.527441,16.2,957.4,2.26636,-1.99268,174.757
|
||||
2016-02-12 08:38:21,40.3490516,115.7866186,6.1370000000000005,3.24292,2.39415,16.2,957.4,2.26015,-2.09729,175.563
|
||||
2016-02-12 08:38:22,40.3490337,115.7866199,6.097000000000037,3.42724,3.36974,16.2,957.4,2.26113,-2.11005,175.792
|
||||
2016-02-12 08:38:23,40.3490159,115.7866215,6.021000000000015,3.56703,2.19504,16.2,957.4,2.26318,-2.58626,176.337
|
||||
2016-02-12 08:38:24,40.3489978,115.7866231,6.031000000000006,2.98014,0.730891,16.2,957.4,2.2592,-2.18878,176.036
|
||||
2016-02-12 08:38:25,40.3489799,115.7866245,6.050000000000011,2.8769,1.18416,16.2,957.4,2.25769,-2.45616,175.722
|
||||
2016-02-12 08:38:26,40.348962,115.7866262,6.004000000000019,2.97136,0.474847,16.2,957.4,2.25814,-2.30747,176.164
|
||||
2016-02-12 08:38:27,40.348944,115.7866277,6.004000000000019,3.28611,0.877931,16.2,957.4,2.269,-2.20202,176.154
|
||||
2016-02-12 08:38:28,40.3489259,115.7866295,5.921000000000049,3.28373,1.71368,16.2,957.6,2.26907,-2.02838,175.744
|
||||
2016-02-12 08:38:29,40.3489042,115.786632,5.921000000000049,3.18593,0.556809,16.2,957.6,2.26987,-1.73959,176.318
|
||||
2016-02-12 08:38:30,40.3488863,115.7866332,5.9430000000000405,3.17403,2.17954,16.2,957.6,2.26099,-1.88535,178.499
|
||||
2016-02-12 08:38:31,40.3488686,115.7866333,6.1370000000000005,3.17068,5.38026,16.2,957.4,2.25922,-2.30753,-178.254
|
||||
2016-02-12 08:38:32,40.3488508,115.786632,6.119000000000028,2.97947,7.49582,16.2,957.4,2.26828,-1.80747,-175.54
|
||||
2016-02-12 08:38:33,40.348833,115.7866299,6.079000000000008,2.76366,9.47064,16.2,957.4,2.2712,-2.05905,-172.77
|
||||
2016-02-12 08:38:34,40.3488154,115.7866269,6.048000000000002,2.80114,12.3371,16.2,957.4,2.26121,-2.1765,-171.028
|
||||
2016-02-12 08:38:35,40.3487978,115.7866228,5.961000000000013,2.38589,13.145,16.2,957.6,2.25807,-1.81427,-168.985
|
||||
2016-02-12 08:38:36,40.3487802,115.7866181,5.861000000000047,2.69336,16.9089,16.2,957.6,2.26228,-1.41431,-166.704
|
||||
2016-02-12 08:38:37,40.3487628,115.7866124,6.129000000000019,3.06392,23.064,16.2,957.4,2.26631,-1.84058,-163.542
|
||||
2016-02-12 08:38:38,40.3487457,115.7866051,6.288000000000011,3.4603,28.1621,16.2,957.4,2.26631,-1.43673,-160.633
|
||||
2016-02-12 08:38:39,40.3487288,115.7865968,6.198000000000036,3.43651,28.6348,16.2,957.4,2.26631,-1.62867,-159.419
|
||||
2016-02-12 08:38:40,40.348712,115.7865883,5.991000000000042,2.75678,26.6074,16.2,957.6,2.26457,-0.846973,-159.113
|
||||
2016-02-12 08:38:41,40.3486952,115.7865797,6.177000000000021,2.54112,26.0398,16.2,957.4,2.26195,-1.4228,-160.025
|
||||
2016-02-12 08:38:42,40.3486785,115.7865715,6.125,2.77843,30.673,16.2,957.4,2.27229,-1.28339,-158.953
|
||||
2016-02-12 08:38:43,40.3486617,115.7865631,5.989000000000033,2.89037,31.4589,16.2,957.6,2.26166,-1.72081,-159.983
|
||||
2016-02-12 08:38:44,40.3486446,115.7865549,6.1059999999999945,3.4807,38.7283,16.2,957.4,2.2613,-0.817808,-159.645
|
||||
2016-02-12 08:38:45,40.3486278,115.7865468,6.263000000000034,3.21584,39.218,16.2,957.4,2.25894,-1.0708,-159.434
|
||||
2016-02-12 08:38:46,40.3486114,115.7865384,6.3799999999999955,3.20978,42.7982,16.3,957.4,2.24972,-1.54731,-156.522
|
||||
2016-02-12 08:38:47,40.3485958,115.7865289,6.196000000000026,3.13252,47.861,16.3,957.4,2.25409,-2.09613,-150.524
|
||||
2016-02-12 08:38:48,40.3485808,115.7865168,6.437000000000012,3.67395,54.5351,16.3,957.4,2.2518,-2.04851,-144.217
|
||||
2016-02-12 08:38:49,40.348567,115.7865025,6.314999999999998,3.8837,60.8641,16.3,957.4,2.2588,-2.03042,-138.723
|
||||
2016-02-12 08:38:50,40.3485541,115.7864867,6.55600000000004,4.24575,66.9797,16.3,957.4,2.25364,-2.63416,-133.698
|
||||
2016-02-12 08:38:51,40.3485426,115.7864697,6.711000000000013,4.50037,69.3373,16.3,957.4,2.26174,-3.24088,-128.675
|
||||
2016-02-12 08:38:52,40.3485322,115.7864511,6.847000000000037,4.77316,73.4454,16.3,957.4,2.26407,-3.37251,-123.113
|
||||
2016-02-12 08:38:53,40.3485233,115.786431,6.723000000000013,5.25047,74.79,16.3,957.4,2.2571,-3.46872,-116.851
|
||||
2016-02-12 08:38:54,40.3485158,115.7864097,6.77800000000002,5.24579,78.8962,16.3,957.4,2.26562,-2.75706,-110.608
|
||||
2016-02-12 08:38:55,40.3485097,115.7863879,6.963000000000022,5.36328,78.6499,16.3,957.4,2.263,-3.80411,-109.608
|
||||
2016-02-12 08:38:56,40.3485038,115.7863656,6.785000000000025,5.18284,77.9227,16.3,957.4,2.24412,-3.57529,-109.918
|
||||
2016-02-12 08:38:57,40.3484977,115.7863432,6.955000000000041,5.49547,77.0321,16.3,957.4,2.25077,-3.53406,-110.109
|
||||
2016-02-12 08:38:58,40.3484917,115.7863208,6.76400000000001,5.50554,76.9921,16.3,957.4,2.25282,-3.11968,-109.696
|
||||
2016-02-12 08:38:59,40.3484855,115.7862986,7.1860000000000355,5.67789,76.452,16.3,957.4,2.25288,-3.84746,-109.962
|
||||
2016-02-12 08:39:00,40.3484793,115.7862769,7.2760000000000105,5.58245,78.3723,16.3,957.4,2.25248,-4.04632,-109.703
|
||||
2016-02-12 08:39:01,40.3484733,115.7862548,7.010000000000048,5.50281,81.2427,16.3,957.4,2.25681,-3.7117,-109.605
|
||||
2016-02-12 08:39:02,40.3484675,115.7862318,6.583000000000027,5.4681,80.5694,16.3,957.4,2.25373,-2.64838,-110.026
|
||||
2016-02-12 08:39:03,40.3484615,115.7862087,6.435000000000002,5.24186,80.0162,16.3,957.4,2.26402,-2.47181,-110.066
|
||||
2016-02-12 08:39:04,40.3484553,115.7861861,6.744000000000028,4.78997,78.1177,16.3,957.4,2.25976,-3.00221,-110.003
|
||||
2016-02-12 08:39:05,40.3484491,115.7861637,6.635000000000048,4.66952,79.3902,16.3,957.4,2.25965,-2.87573,-109.383
|
||||
2016-02-12 08:39:06,40.3484432,115.7861416,6.846000000000004,4.6271,83.315,16.2,957.4,2.25455,-2.74948,-106.217
|
||||
2016-02-12 08:39:07,40.3484387,115.7861197,7.13900000000001,4.8648,90.499,16.2,957.4,2.25685,-3.80011,-100.807
|
||||
2016-02-12 08:39:08,40.348436,115.7860971,7.078000000000031,5.13107,93.7453,16.2,957.4,2.25624,-3.61118,-96.6337
|
||||
2016-02-12 08:39:09,40.3484346,115.7860739,6.874000000000024,5.30274,96.2317,16.2,957.4,2.25331,-3.42438,-93.2439
|
||||
2016-02-12 08:39:10,40.3484343,115.7860502,6.77800000000002,5.30424,98.1641,16.2,957.4,2.2591,-3.43879,-89.3696
|
||||
2016-02-12 08:39:11,40.3484346,115.7860267,6.948000000000036,5.36689,101.168,16.2,957.4,2.26334,-3.13154,-85.9754
|
||||
2016-02-12 08:39:12,40.3484361,115.7860039,6.992999999999995,5.36225,105.602,16.2,957.4,2.26374,-3.71979,-81.9432
|
||||
2016-02-12 08:39:13,40.3484392,115.7859812,6.986000000000047,5.58163,111.056,16.2,957.4,2.27043,-3.86594,-76.8084
|
||||
2016-02-12 08:39:14,40.3484453,115.7859542,7.071000000000026,5.59064,113.182,16.2,957.4,2.26144,-3.68614,-72.9019
|
||||
2016-02-12 08:39:15,40.3484508,115.7859315,6.8180000000000405,5.49402,114.441,16.2,957.4,2.26241,-3.7118,-72.5991
|
||||
2016-02-12 08:39:16,40.3484563,115.7859087,6.723000000000013,5.288,114.232,16.2,957.4,2.2694,-3.66929,-72.6022
|
||||
2016-02-12 08:39:17,40.3484619,115.785886,6.861000000000047,5.0759,114.328,16.2,957.4,2.27796,-3.37821,-72.5735
|
||||
2016-02-12 08:39:18,40.3484673,115.7858638,6.777000000000044,4.85226,114.202,16.2,957.4,2.2586,-3.65391,-72.617
|
||||
2016-02-12 08:39:19,40.3484724,115.7858417,6.831000000000017,4.95735,114.591,16.2,957.4,2.26003,-3.9336,-72.5228
|
||||
2016-02-12 08:39:20,40.3484777,115.7858195,6.79400000000004,5.19502,114.403,16.2,957.4,2.26938,-3.7664,-72.49
|
||||
2016-02-12 08:39:21,40.348483,115.7857968,6.8910000000000196,5.40131,114.221,16.2,957.4,2.26292,-3.57338,-72.637
|
||||
2016-02-12 08:39:22,40.3484882,115.785774,6.80800000000005,5.49249,113.991,16.2,957.4,2.25832,-3.62734,-72.9702
|
||||
2016-02-12 08:39:23,40.3484933,115.7857514,6.855000000000018,5.30347,113.598,16.2,957.4,2.26829,-3.95096,-72.8731
|
||||
2016-02-12 08:39:24,40.3484986,115.7857289,7.079000000000008,5.39845,115.741,16.2,957.4,2.26638,-3.72339,-70.6984
|
||||
2016-02-12 08:39:25,40.3485052,115.7857071,6.997000000000014,5.59344,120.748,16.2,957.4,2.25883,-3.71214,-65.1554
|
||||
2016-02-12 08:39:26,40.3485135,115.7856866,6.882000000000005,5.57928,126.78,16.2,957.4,2.27045,-3.56952,-59.0728
|
||||
2016-02-12 08:39:27,40.3485232,115.7856676,7.5540000000000305,5.53672,130.762,16.2,957.4,2.26442,-4.75008,-53.7121
|
||||
2016-02-12 08:39:28,40.3485341,115.7856494,7.011000000000024,5.59819,132.478,16.2,957.4,2.27287,-4.20102,-49.5037
|
||||
2016-02-12 08:39:29,40.3485461,115.7856316,6.975999999999999,5.6987,136.872,16.1,957.4,2.27001,-3.76073,-44.3638
|
||||
2016-02-12 08:39:30,40.3485592,115.7856157,6.80600000000004,5.66211,141.975,16.1,957.4,2.25656,-4.27646,-39.2167
|
||||
2016-02-12 08:39:31,40.3485731,115.7856017,6.959000000000003,5.84401,147.888,16.1,957.4,2.26741,-4.22084,-33.5571
|
||||
2016-02-12 08:39:32,40.348588,115.7855893,6.881000000000029,5.84942,153.684,16.1,957.4,2.26479,-4.14474,-27.6361
|
||||
2016-02-12 08:39:33,40.348604,115.7855792,7.069000000000017,5.77161,153.734,16.1,957.4,2.25661,-4.20067,-25.4868
|
||||
2016-02-12 08:39:34,40.3486205,115.7855695,6.948000000000036,5.68293,151.175,16.1,957.4,2.26653,-4.28749,-25.5627
|
||||
2016-02-12 08:39:35,40.3486369,115.7855593,6.831999999999994,5.59499,149.507,16.1,957.4,2.26059,-3.60083,-25.7786
|
||||
2016-02-12 08:39:36,40.3486532,115.785549,6.910000000000025,5.34526,148.245,16.1,957.4,2.25904,-3.79517,-25.4626
|
||||
2016-02-12 08:39:37,40.3486694,115.7855391,6.90300000000002,5.2722,148.018,16.1,957.4,2.24588,-4.47698,-26.0671
|
||||
2016-02-12 08:39:38,40.3486856,115.7855286,6.662000000000035,5.58787,149.531,16.1,957.4,2.26247,-3.74773,-25.3043
|
||||
2016-02-12 08:39:39,40.348702,115.7855183,6.588000000000022,5.58677,149.414,16.1,957.4,2.2546,-3.41345,-25.3933
|
||||
2016-02-12 08:39:40,40.3487186,115.7855084,6.819000000000017,5.39394,150.467,16.1,957.4,2.26018,-3.45871,-25.8609
|
||||
2016-02-12 08:39:41,40.3487348,115.785498,6.869000000000028,5.13558,149.501,16.1,957.4,2.25899,-3.54752,-25.3197
|
||||
2016-02-12 08:39:42,40.3487508,115.7854881,7.076999999999998,5.12994,151.902,16.0,957.4,2.2604,-3.27375,-23.0354
|
||||
2016-02-12 08:39:43,40.348767,115.7854795,7.107000000000028,5.24712,156.111,16.0,957.4,2.25978,-4.17538,-19.0296
|
||||
2016-02-12 08:39:44,40.3487838,115.7854726,7.342000000000041,5.14911,158.067,16.0,957.4,2.26895,-3.91688,-14.9458
|
||||
2016-02-12 08:39:45,40.3488012,115.7854673,7.123000000000047,5.12797,160.258,16.0,957.4,2.25685,-3.97569,-12.4655
|
||||
2016-02-12 08:39:46,40.3488187,115.7854626,7.0470000000000255,5.25077,161.458,16.0,957.4,2.2575,-3.93482,-9.47375
|
||||
2016-02-12 08:39:47,40.3488361,115.7854589,6.831000000000017,5.34247,163.638,16.0,957.4,2.2747,-3.97845,-6.77187
|
||||
2016-02-12 08:39:48,40.3488535,115.7854563,6.91700000000003,5.35046,168.762,16.0,957.4,2.2638,-4.43388,-3.3938
|
||||
2016-02-12 08:39:49,40.3488712,115.7854556,7.05800000000005,5.36638,172.552,16.0,957.4,2.24989,-4.01909,0.499612
|
||||
2016-02-12 08:39:50,40.3488894,115.7854567,6.927999999999997,5.18338,176.719,16.0,957.4,2.25543,-2.9186,4.27917
|
||||
2016-02-12 08:39:51,40.3489075,115.7854591,6.824000000000012,4.74102,174.68,16.0,957.4,2.26294,-3.10757,5.12577
|
||||
2016-02-12 08:39:52,40.3489253,115.7854614,6.846000000000004,4.61108,173.089,16.0,957.4,2.26023,-3.50273,5.24859
|
||||
2016-02-12 08:39:53,40.3489432,115.7854633,6.754000000000019,4.60728,171.857,16.0,957.4,2.24849,-3.4541,5.02424
|
||||
2016-02-12 08:39:54,40.348961,115.7854649,6.802000000000021,4.80725,173.53,16.0,957.4,2.24994,-3.31187,5.23906
|
||||
2016-02-12 08:39:55,40.3489786,115.7854669,6.7690000000000055,4.84929,175.141,16.0,957.4,2.26774,-3.80584,4.93247
|
||||
2016-02-12 08:39:56,40.3489961,115.7854684,6.984000000000037,4.82154,177.285,16.0,957.4,2.25052,-4.3044,6.02525
|
||||
2016-02-12 08:39:57,40.3490136,115.7854712,7.0750000000000455,4.97235,176.681,16.0,957.4,2.26103,-4.44092,4.73105
|
||||
2016-02-12 08:39:58,40.3490352,115.7854737,7.221000000000004,5.17525,176.871,16.0,957.4,2.24897,-4.50248,5.03347
|
||||
2016-02-12 08:39:59,40.3490496,115.7854757,7.2830000000000155,5.31328,175.68,15.9,957.4,2.2604,-4.74577,5.08683
|
||||
2016-02-12 08:40:00,40.3490715,115.7854787,6.9150000000000205,5.36721,173.39,15.9,957.4,2.25116,-3.63533,5.45957
|
||||
2016-02-12 08:40:01,40.3490892,115.7854806,7.02800000000002,5.49745,171.838,15.9,957.4,2.24886,-5.20438,5.31366
|
||||
2016-02-12 08:40:02,40.3491065,115.7854825,7.034000000000049,4.96294,169.028,15.9,957.4,2.25694,8.55605,4.86752
|
||||
2016-02-12 08:40:03,40.3491136,115.7854833,7.170000000000016,3.77454,166.109,15.9,957.4,2.2609,-2.98443,5.3243
|
||||
2016-02-12 08:40:04,40.349114,115.7854833,6.729000000000042,2.51774,161.1,15.9,957.4,2.25686,-2.8505,5.30476
|
||||
2016-02-12 08:40:05,40.3491134,115.7854828,6.692000000000007,2.18392,157.804,15.9,957.4,2.255,-3.45238,5.31065
|
||||
2016-02-12 08:40:06,40.3491132,115.7854827,6.604000000000042,1.60109,157.059,15.9,957.4,2.25357,-2.80026,5.5161
|
||||
2016-02-12 08:40:07,40.3491134,115.7854827,6.537000000000035,2.12836,156.218,15.9,957.4,2.25833,-2.45733,5.39147
|
||||
2016-02-12 08:40:08,40.3491137,115.7854829,6.403999999999996,2.12331,158.872,15.9,957.4,2.25188,-2.14385,5.2223
|
||||
2016-02-12 08:40:09,40.3491142,115.7854831,6.421000000000049,1.8301,160.043,15.9,957.4,2.25406,-2.07424,4.97781
|
||||
2016-02-12 08:40:10,40.3491147,115.785483,6.369000000000028,1.81565,164.795,15.9,957.4,2.25066,-2.04528,5.5712
|
||||
2016-02-12 08:40:11,40.349115,115.785483,6.395000000000039,1.20916,168.073,15.9,957.4,2.25142,-2.12568,5.6004
|
||||
2016-02-12 08:40:12,40.3491152,115.785483,6.479000000000042,0.923121,169.145,15.9,957.4,2.25772,-2.39996,5.49499
|
||||
2016-02-12 08:40:13,40.3491156,115.7854834,6.295000000000016,0.112622,176.925,15.9,957.4,2.2419,-0.992479,5.52595
|
||||
2016-02-12 08:40:14,40.3491156,115.7854835,5.983000000000004,0.104385,137.479,15.9,957.6,2.24902,-2.09931,4.83266
|
||||
2016-02-12 08:40:15,40.3491156,115.7854831,6.322000000000003,0.0972996,135.981,15.9,957.4,2.25507,-2.44217,5.66033
|
||||
2016-02-12 08:40:16,40.3491157,115.785483,6.293000000000006,0.399473,144.672,15.9,957.4,2.24642,-1.95636,5.97701
|
||||
2016-02-12 08:40:17,40.3491159,115.7854832,6.54200000000003,0.419162,134.864,15.9,957.4,2.25464,-2.12978,4.8956
|
||||
2016-02-12 08:40:18,40.3491159,115.7854832,6.412000000000035,0.0974758,162.941,15.9,957.4,2.25094,-2.48262,5.51442
|
||||
2016-02-12 08:40:19,40.349116,115.7854833,6.161000000000001,0.109608,160.361,15.9,957.4,2.25091,-1.41836,5.54454
|
||||
2016-02-12 08:40:20,40.3491161,115.7854831,6.199000000000012,0.0171219,103.985,15.9,957.4,2.24909,-1.11168,5.4494
|
||||
2016-02-12 08:40:21,40.349116,115.7854829,6.218000000000018,0.0288269,128.137,15.9,957.4,2.25383,-1.09086,5.29294
|
||||
2016-02-12 08:40:22,40.349116,115.7854829,6.281000000000006,0.0365605,136.308,15.9,957.4,2.25341,-1.30006,5.35584
|
||||
2016-02-12 08:40:23,40.349116,115.7854829,6.138000000000034,0.300536,142.642,15.9,957.4,2.24777,-1.31523,5.10688
|
||||
2016-02-12 08:40:24,40.3491159,115.7854831,6.272000000000048,0.362359,104.119,15.9,957.4,2.2515,-1.26287,5.34579
|
||||
2016-02-12 08:40:25,40.3491158,115.7854836,6.330000000000041,0.57932,67.1366,15.9,957.4,2.25599,-1.2537,5.10477
|
||||
2016-02-12 08:40:26,40.349116,115.7854837,6.2590000000000146,0.293271,61.2633,15.9,957.4,2.25032,-1.35474,4.62019
|
||||
2016-02-12 08:40:27,40.3491159,115.7854839,6.537000000000035,1.43408,87.8411,15.9,957.4,2.24641,-1.43189,4.81176
|
||||
2016-02-12 08:40:28,40.3491158,115.7854845,7.9360000000000355,2.6621,90.2237,15.9,957.4,2.25034,-1.61754,4.72443
|
||||
2016-02-12 08:40:29,40.349116,115.7854846,10.175000000000011,1.31573,89.1393,16.0,957.6,2.24564,-1.20717,4.9345
|
||||
2016-02-12 08:40:30,40.3491154,115.7854833,11.519000000000005,0.487783,84.9451,16.0,957.6,2.23793,1.226,32.3446
|
||||
2016-02-12 08:40:31,40.3491146,115.7854801,11.199000000000012,0.888404,113.077,16.0,957.6,2.24013,-0.910583,49.0509
|
||||
2016-02-12 08:40:32,40.3491176,115.7854848,11.338000000000022,1.21106,161.686,16.0,957.6,2.24855,-8.71139,49.124
|
||||
2016-02-12 08:40:33,40.349127,115.7854984,11.410000000000025,1.15788,160.732,16.0,957.6,2.24702,2.53409,49.1037
|
||||
2016-02-12 08:40:34,40.3491327,115.785507,11.30600000000004,1.57306,101.28,16.0,957.6,2.23904,3.0513,47.7375
|
||||
2016-02-12 08:40:35,40.3491341,115.7855092,11.441000000000031,1.65924,120.583,16.0,957.6,2.23278,-11.256,47.7951
|
||||
2016-02-12 08:40:36,40.3491421,115.7855205,11.745000000000005,2.09369,165.994,16.0,957.6,2.25014,-5.38932,47.3602
|
||||
2016-02-12 08:40:37,40.3491545,115.7855384,11.939999999999998,2.19563,170.682,16.0,957.6,2.25111,-3.70094,47.3836
|
||||
2016-02-12 08:40:38,40.3491676,115.7855572,12.343999999999994,2.72588,175.629,16.0,957.4,2.24778,-3.39111,47.5684
|
||||
2016-02-12 08:40:39,40.3491805,115.7855757,12.349000000000046,3.77058,170.431,16.0,957.4,2.23919,-2.92421,47.3679
|
||||
2016-02-12 08:40:40,40.3491932,115.7855932,12.30400000000003,4.32723,164.713,16.0,957.4,2.24849,-3.69776,47.2196
|
||||
2016-02-12 08:40:41,40.3492058,115.7856107,12.257000000000005,4.97482,149.719,16.0,957.4,2.23899,-4.2628,46.9518
|
||||
2016-02-12 08:40:42,40.3492184,115.7856285,12.064999999999998,5.01454,148.244,16.0,957.4,2.24654,-3.29362,47.9102
|
||||
2016-02-12 08:40:43,40.3492309,115.7856463,11.847000000000037,4.75528,148.534,16.0,957.6,2.2461,-2.87623,47.2497
|
||||
2016-02-12 08:40:44,40.3492429,115.7856634,11.926000000000045,4.45324,145.688,16.0,957.6,2.24807,-3.24547,50.6289
|
||||
2016-02-12 08:40:45,40.3492538,115.7856819,11.890000000000043,4.37085,139.85,16.0,957.6,2.24435,-2.29374,55.1856
|
||||
2016-02-12 08:40:46,40.3492636,115.7857015,11.858000000000004,4.45991,136.808,16.0,957.6,2.23915,-2.55544,58.6457
|
||||
2016-02-12 08:40:47,40.3492726,115.7857216,11.834000000000003,4.44533,135.258,16.0,957.6,2.25354,-2.42264,61.5678
|
||||
2016-02-12 08:40:48,40.3492809,115.7857425,11.573000000000036,4.17545,135.351,16.0,957.6,2.2454,-2.13607,64.4989
|
||||
2016-02-12 08:40:49,40.3492883,115.7857639,11.456000000000017,3.50478,129.539,16.0,957.6,2.23091,-1.86106,68.0806
|
||||
2016-02-12 08:40:50,40.3492945,115.7857856,11.674000000000035,3.04936,123.522,16.0,957.6,2.24878,-2.12556,72.5683
|
||||
2016-02-12 08:40:51,40.3492995,115.7858078,11.607000000000028,2.59356,118.042,16.0,957.6,2.24837,-2.43165,74.4098
|
||||
2016-02-12 08:40:52,40.349304,115.7858305,11.578000000000031,2.53347,125.879,16.0,957.6,2.24868,-2.31417,74.0187
|
||||
2016-02-12 08:40:54,40.3493085,115.7858534,11.64100000000002,2.66835,133.005,16.0,957.6,2.25345,-2.44876,73.9301
|
||||
2016-02-12 08:40:55,40.3493133,115.7858761,11.571000000000026,2.67835,132.957,16.0,957.6,2.24898,-2.21838,74.0699
|
||||
2016-02-12 08:40:56,40.3493182,115.7858987,11.422000000000025,2.77779,137.606,16.0,957.6,2.24687,-2.05583,74.0956
|
||||
2016-02-12 08:40:57,40.3493232,115.7859214,11.383000000000038,2.65526,125.47,16.0,957.6,2.25382,-2.1098,73.9022
|
||||
2016-02-12 08:40:58,40.3493284,115.7859437,11.393000000000029,2.57385,121.79,16.0,957.6,2.25263,-2.48862,74.428
|
||||
2016-02-12 08:40:59,40.3493328,115.7859659,11.778999999999996,2.60091,118.797,16.0,957.6,2.24775,-2.6614,76.9316
|
||||
2016-02-12 08:41:00,40.3493363,115.7859886,11.846000000000004,2.80531,113.214,16.0,957.6,2.25006,-2.35789,81.2004
|
||||
2016-02-12 08:41:01,40.3493386,115.7860116,11.75200000000001,3.46673,114.085,16.0,957.6,2.2521,-2.83111,84.5953
|
||||
2016-02-12 08:41:02,40.3493399,115.7860352,11.573000000000036,3.45173,111.082,16.0,957.6,2.24462,-2.19087,88.0719
|
||||
2016-02-12 08:41:03,40.3493402,115.7860585,11.668000000000006,3.17838,106.4,16.0,957.6,2.23597,-2.32365,91.6851
|
||||
2016-02-12 08:41:04,40.3493394,115.7860821,11.456999999999994,2.92245,104.091,16.0,957.6,2.25143,-1.39402,94.5082
|
||||
2016-02-12 08:41:05,40.3493376,115.7861056,11.509000000000015,2.69757,107.508,16.0,957.6,2.24626,-1.14534,97.7257
|
||||
2016-02-12 08:41:06,40.349335,115.786129,11.551000000000045,2.14221,105.163,16.0,957.6,2.24878,-1.12488,101.724
|
||||
2016-02-12 08:41:07,40.3493308,115.7861515,11.583000000000027,1.70315,97.0246,16.0,957.6,2.24965,-1.2808,105.324
|
||||
2016-02-12 08:41:08,40.3493256,115.7861737,11.629999999999995,1.73263,82.1027,16.0,957.6,2.24099,-1.10128,106.318
|
||||
2016-02-12 08:41:09,40.3493203,115.786196,11.79000000000002,1.88123,77.929,16.0,957.6,2.25065,-0.890223,106.882
|
||||
2016-02-12 08:41:10,40.3493151,115.7862185,11.629999999999995,2.02174,72.4556,16.0,957.6,2.24402,-1.06764,106.757
|
||||
2016-02-12 08:41:11,40.3493095,115.7862408,11.56800000000004,1.92404,75.0366,16.0,957.6,2.25262,-1.81658,106.7
|
||||
2016-02-12 08:41:12,40.3493042,115.7862632,11.723000000000013,1.96386,76.6269,16.1,957.6,2.24628,-2.08718,106.681
|
||||
2016-02-12 08:41:13,40.3492993,115.7862859,11.694000000000017,1.97875,76.6054,16.1,957.6,2.24301,-1.62918,106.355
|
||||
2016-02-12 08:41:14,40.3492944,115.7863083,11.80800000000005,2.00308,72.8553,16.1,957.6,2.24278,-1.89877,107.29
|
||||
2016-02-12 08:41:15,40.3492889,115.7863306,11.740000000000009,2.16399,82.2897,16.1,957.6,2.24469,-1.95941,107.744
|
||||
2016-02-12 08:41:16,40.349282,115.7863579,11.583000000000027,2.06573,79.7034,16.1,957.6,2.24813,-1.54695,110.369
|
||||
2016-02-12 08:41:17,40.3492752,115.7863801,11.729000000000042,1.96264,82.2375,16.1,957.6,2.24149,-1.13449,115.571
|
||||
2016-02-12 08:41:18,40.349269,115.7863971,11.831999999999994,1.75547,77.7442,16.1,957.6,2.2488,-0.483331,119.086
|
||||
2016-02-12 08:41:19,40.3492581,115.7864217,11.521000000000015,1.54649,80.1118,16.1,957.6,2.24992,-0.430019,121.889
|
||||
2016-02-12 08:41:20,40.3492484,115.7864414,11.724000000000046,1.33618,90.5358,16.1,957.6,2.24614,-0.160765,124.514
|
||||
2016-02-12 08:41:21,40.3492379,115.7864599,11.656000000000006,1.45789,75.7241,16.1,957.6,2.24723,-0.584383,129.253
|
||||
2016-02-12 08:41:22,40.3492265,115.7864776,11.586000000000013,1.52183,64.4082,16.1,957.6,2.2461,-0.1055,133.895
|
||||
2016-02-12 08:41:23,40.3492142,115.7864946,11.551000000000045,1.41725,53.5575,16.1,957.6,2.24917,-0.06657,133.811
|
||||
2016-02-12 08:41:24,40.3492018,115.786511,11.67900000000003,1.96325,45.1024,16.1,957.6,2.24306,-0.546846,133.629
|
||||
2016-02-12 08:41:25,40.3491892,115.7865273,11.583000000000027,1.89267,44.8856,16.1,957.6,2.25231,-0.522077,133.854
|
||||
2016-02-12 08:41:26,40.3491766,115.786544,11.583000000000027,1.88507,45.3735,16.1,957.6,2.25312,-0.6115120000000001,134.018
|
||||
2016-02-12 08:41:27,40.349164,115.7865603,11.56800000000004,1.90691,44.2069,16.1,957.6,2.24901,-0.704764,137.995
|
||||
2016-02-12 08:41:28,40.3491509,115.7865759,11.733000000000004,1.89895,34.7881,16.1,957.6,2.2386,0.216962,144.763
|
||||
2016-02-12 08:41:29,40.3491363,115.7865883,12.136000000000024,1.57389,21.6126,16.1,957.4,2.25495,-0.546539,150.518
|
||||
2016-02-12 08:41:30,40.3491207,115.7865983,12.105999999999995,1.35377,6.14142,16.1,957.4,2.25067,-0.483964,155.45
|
||||
2016-02-12 08:41:31,40.3491041,115.7866067,11.953000000000031,1.48538,7.6301,16.1,957.6,2.25743,0.186806,161.625
|
||||
2016-02-12 08:41:32,40.349087,115.7866129,12.30800000000005,1.43146,25.3402,16.1,957.4,2.25932,-1.17416,169.539
|
||||
2016-02-12 08:41:33,40.3490693,115.7866169,12.262,1.67297,11.2128,16.1,957.4,2.24673,-0.856737,175.334
|
||||
2016-02-12 08:41:34,40.3490516,115.7866188,12.27600000000001,1.88287,5.05985,16.1,957.4,2.23945,-1.40952,175.648
|
||||
2016-02-12 08:41:35,40.349034,115.7866202,12.26600000000002,2.07013,7.49265,16.2,957.4,2.25005,-1.36203,175.821
|
||||
2016-02-12 08:41:36,40.3490162,115.7866215,12.102000000000032,2.16284,5.99243,16.2,957.4,2.24761,-1.28881,175.884
|
||||
2016-02-12 08:41:37,40.3489984,115.786623,12.342000000000041,2.62051,11.9578,16.2,957.4,2.2545,-1.72705,176.135
|
||||
2016-02-12 08:41:38,40.3489803,115.7866248,12.266999999999996,2.80948,9.05194,16.2,957.4,2.24654,-1.73863,175.918
|
||||
2016-02-12 08:41:39,40.3489624,115.7866263,12.15500000000003,2.79103,5.93186,16.2,957.4,2.24162,-1.93178,175.373
|
||||
2016-02-12 08:41:40,40.3489442,115.7866283,12.194000000000017,2.98124,7.96713,16.2,957.4,2.23972,-1.38345,175.634
|
||||
2016-02-12 08:41:41,40.3489261,115.7866302,12.290999999999997,2.76977,6.80938,16.2,957.4,2.23927,-1.48399,175.852
|
||||
2016-02-12 08:41:42,40.348908,115.7866319,12.145000000000039,2.88923,11.1758,16.2,957.4,2.24577,-1.11357,175.647
|
||||
2016-02-12 08:41:43,40.34889,115.7866334,12.115000000000009,2.73138,15.2008,16.2,957.4,2.2535,-1.25101,178.042
|
||||
2016-02-12 08:41:44,40.3488722,115.7866338,12.066000000000031,2.96751,22.2279,16.2,957.4,2.24615,-1.35349,-178.621
|
||||
2016-02-12 08:41:45,40.3488544,115.7866327,12.17100000000005,3.10965,27.6051,16.2,957.4,2.25523,-1.46058,-175.496
|
||||
2016-02-12 08:41:46,40.3488367,115.7866304,12.064000000000021,2.92633,28.5539,16.2,957.4,2.24891,-1.73632,-173.742
|
||||
2016-02-12 08:41:47,40.3488192,115.7866273,12.215000000000032,2.91376,27.4537,16.2,957.4,2.25825,-2.3877,-171.862
|
||||
2016-02-12 08:41:48,40.3488017,115.7866234,12.088999999999999,3.08075,27.0256,16.2,957.4,2.24402,-1.70543,-170.067
|
||||
2016-02-12 08:41:49,40.3487842,115.7866186,12.199000000000012,3.22416,29.298,16.3,957.4,2.26024,-1.40647,-166.772
|
||||
2016-02-12 08:41:50,40.3487669,115.7866126,12.105999999999995,3.04744,30.399,16.3,957.4,2.26284,-1.90689,-163.401
|
||||
2016-02-12 08:41:51,40.34875,115.7866053,12.230999999999995,3.03816,32.1537,16.3,957.4,2.25274,-2.12911,-160.487
|
||||
2016-02-12 08:41:52,40.3487332,115.7865977,12.277000000000044,3.16828,36.9141,16.3,957.4,2.24902,-2.42806,-158.691
|
||||
2016-02-12 08:41:53,40.3487164,115.7865891,12.119000000000028,3.51183,36.1496,16.3,957.4,2.25103,-2.7569,-159.977
|
||||
2016-02-12 08:41:54,40.3486995,115.7865807,12.200000000000045,3.67923,34.0202,16.3,957.4,2.25266,-2.03718,-159.794
|
||||
2016-02-12 08:41:55,40.3486826,115.7865724,12.234000000000037,3.99466,32.2461,16.3,957.4,2.25307,-2.50656,-159.647
|
||||
2016-02-12 08:41:56,40.3486656,115.7865644,12.124000000000024,3.87245,32.552,16.3,957.4,2.25371,-2.16503,-159.25
|
||||
2016-02-12 08:41:57,40.3486488,115.7865561,12.090000000000032,4.08144,33.5234,16.3,957.4,2.24347,-2.05622,-159.351
|
||||
2016-02-12 08:41:58,40.3486319,115.7865478,12.209000000000003,3.86491,36.1923,16.3,957.4,2.24522,-1.63832,-159.568
|
||||
2016-02-12 08:41:59,40.348615,115.7865392,12.052000000000021,3.72907,37.497,16.3,957.4,2.24749,-1.51081,-158.014
|
||||
2016-02-12 08:42:00,40.3485989,115.7865303,12.175000000000011,3.44141,42.7996,16.3,957.4,2.24907,-1.9916,-152.302
|
||||
2016-02-12 08:42:01,40.3485838,115.7865188,12.503000000000043,3.81648,51.5505,16.3,957.4,2.24449,-2.30392,-145.352
|
||||
2016-02-12 08:42:02,40.3485697,115.7865051,12.378000000000043,4.09751,56.1889,16.3,957.4,2.24475,-3.35498,-140.287
|
||||
2016-02-12 08:42:03,40.348554,115.7864864,12.354000000000042,4.45431,63.4819,16.3,957.4,2.25055,-2.78677,-133.264
|
||||
2016-02-12 08:42:04,40.3485421,115.7864686,12.239000000000033,4.57333,67.1847,16.3,957.4,2.25146,-2.48321,-128.375
|
||||
2016-02-12 08:42:05,40.3485336,115.7864536,12.379999999999995,4.48019,71.2003,16.3,957.4,2.24778,-2.34836,-123.58
|
||||
2016-02-12 08:42:06,40.348523,115.7864295,12.398000000000025,4.48081,77.9232,16.3,957.4,2.24845,-2.83183,-116.547
|
||||
2016-02-12 08:42:07,40.3485159,115.7864082,12.41500000000002,4.49352,80.9574,16.3,957.4,2.25643,-2.8192,-110.823
|
||||
2016-02-12 08:42:08,40.3485099,115.7863862,12.507000000000005,4.71163,79.3181,16.3,957.4,2.25778,-3.08629,-109.918
|
||||
2016-02-12 08:42:09,40.348504,115.7863639,12.674000000000035,4.83949,77.9107,16.3,957.4,2.24253,-2.90956,-109.746
|
||||
2016-02-12 08:42:10,40.348498,115.7863416,12.966000000000008,5.05952,75.906,16.3,957.4,2.24613,-2.97764,-110.009
|
||||
2016-02-12 08:42:11,40.3484919,115.7863189,12.289000000000044,4.99548,74.3886,16.3,957.4,2.24257,-2.69509,-109.879
|
||||
2016-02-12 08:42:12,40.3484856,115.7862963,12.305000000000007,4.86027,75.585,16.3,957.4,2.24794,-2.39785,-109.656
|
||||
2016-02-12 08:42:13,40.3484795,115.786274,12.30600000000004,4.63504,75.6022,16.3,957.4,2.25423,-2.3631,-109.497
|
||||
2016-02-12 08:42:14,40.3484733,115.7862519,12.319000000000017,4.62832,77.8567,16.3,957.4,2.24955,-2.26553,-109.463
|
||||
2016-02-12 08:42:15,40.3484674,115.7862297,12.467000000000041,4.51333,78.3431,16.3,957.4,2.24946,-2.37776,-110.25
|
||||
2016-02-12 08:42:16,40.3484612,115.7862076,12.367000000000019,4.41789,79.0752,16.3,957.4,2.25625,-2.54596,-110.091
|
||||
2016-02-12 08:42:17,40.3484548,115.7861855,12.536000000000001,4.47514,81.1023,16.3,957.4,2.24774,-2.51819,-109.937
|
||||
2016-02-12 08:42:18,40.3484486,115.7861638,12.861000000000047,4.55631,82.4619,16.3,957.4,2.24383,-2.94212,-109.487
|
||||
2016-02-12 08:42:19,40.3484429,115.7861421,12.637,4.85356,87.9828,16.3,957.4,2.25176,-3.28014,-106.279
|
||||
2016-02-12 08:42:20,40.3484385,115.78612,12.87700000000001,5.05794,92.5173,16.3,957.4,2.25419,-3.86801,-101.332
|
||||
2016-02-12 08:42:21,40.3484355,115.7860973,12.540999999999997,5.22229,96.0485,16.3,957.4,2.26133,-3.48247,-96.9957
|
||||
2016-02-12 08:42:22,40.3484339,115.786074,12.859000000000037,5.29359,99.1907,16.3,957.4,2.25554,-3.74325,-93.293
|
||||
2016-02-12 08:42:23,40.3484335,115.7860508,12.79400000000004,5.51611,102.248,16.3,957.4,2.25186,-3.60425,-89.6605
|
||||
2016-02-12 08:42:24,40.3484341,115.7860275,12.837000000000046,5.91545,104.712,16.3,957.4,2.26173,-4.32424,-86.4569
|
||||
2016-02-12 08:42:25,40.3484362,115.7860043,12.738,5.97849,108.205,16.3,957.4,2.25334,-4.14827,-81.8178
|
||||
2016-02-12 08:42:26,40.3484397,115.7859813,12.995000000000005,5.95724,109.774,16.3,957.4,2.26212,-3.98325,-77.084
|
||||
2016-02-12 08:42:27,40.3484442,115.7859587,13.02600000000001,5.88203,112.074,16.3,957.4,2.26216,-3.93422,-73.0804
|
||||
2016-02-12 08:42:28,40.3484496,115.7859361,12.66700000000003,5.89762,112.488,16.3,957.4,2.25082,-3.98341,-72.3455
|
||||
2016-02-12 08:42:29,40.3484555,115.7859136,13.284000000000049,5.88128,111.104,16.3,957.4,2.25202,-4.08997,-73.1645
|
||||
2016-02-12 08:42:30,40.3484603,115.7858911,13.001000000000033,5.88179,112.502,16.3,957.4,2.25494,-4.32926,-72.1189
|
||||
2016-02-12 08:42:31,40.3484659,115.785869,12.974000000000046,5.89474,113.961,16.3,957.4,2.2582,-4.21522,-72.1881
|
||||
2016-02-12 08:42:32,40.3484716,115.7858467,13.950000000000045,5.89713,112.242,16.3,957.4,2.25086,-4.48302,-72.4782
|
||||
2016-02-12 08:42:33,40.348477,115.785824,12.950000000000045,5.83897,111.227,16.3,957.4,2.25219,-3.79417,-72.9491
|
||||
2016-02-12 08:42:34,40.3484819,115.7858011,13.550000000000011,5.70104,110.514,16.3,957.4,2.25342,-4.27485,-72.8564
|
||||
2016-02-12 08:42:35,40.3484869,115.7857784,13.302999999999997,5.65056,112.288,16.2,957.4,2.25351,-4.61801,-72.341
|
||||
2016-02-12 08:42:36,40.3484922,115.7857551,12.936000000000035,5.64385,114.009,16.2,957.4,2.25632,-3.38854,-72.9091
|
||||
2016-02-12 08:42:37,40.348498,115.7857323,12.79000000000002,5.63199,115.023,16.2,957.4,2.25322,-3.08252,-71.8235
|
||||
2016-02-12 08:42:38,40.3485042,115.7857104,12.858000000000004,5.36999,121.775,16.2,957.4,2.25358,-3.24984,-65.9687
|
||||
2016-02-12 08:42:39,40.3485119,115.78569,12.900000000000034,5.15898,126.711,16.2,957.4,2.26002,-3.20228,-60.2979
|
||||
2016-02-12 08:42:40,40.348521,115.7856707,13.067000000000007,5.14989,132.227,16.2,957.4,2.25982,-3.62738,-54.9838
|
||||
2016-02-12 08:42:41,40.3485316,115.7856526,13.647000000000048,5.22281,136.78,16.2,957.4,2.26323,-4.14084,-49.9837
|
||||
2016-02-12 08:42:42,40.3485433,115.7856361,13.871000000000038,5.67075,139.828,16.2,957.4,2.25176,-4.97773,-45.5356
|
||||
2016-02-12 08:42:43,40.3485561,115.7856201,12.992000000000019,5.82711,142.777,16.2,957.4,2.26061,-4.46511,-40.6979
|
||||
2016-02-12 08:42:44,40.3485702,115.7856055,12.66700000000003,5.82758,147.255,16.2,957.4,2.26035,-3.8026,-34.6946
|
||||
2016-02-12 08:42:45,40.3485853,115.785593,12.718999999999994,5.62707,149.964,16.2,957.4,2.24634,-4.16698,-28.7379
|
||||
2016-02-12 08:42:46,40.3486012,115.7855819,12.712000000000046,5.40462,152.98,16.2,957.4,2.25537,-3.65881,-25.8209
|
||||
2016-02-12 08:42:47,40.3486175,115.7855715,12.614000000000033,5.40776,154.124,16.2,957.4,2.24852,-3.58218,-25.511
|
||||
2016-02-12 08:42:48,40.348634,115.7855611,12.459000000000003,5.20386,155.024,16.2,957.4,2.25714,-3.00576,-25.8377
|
||||
2016-02-12 08:42:49,40.3486501,115.7855508,12.467000000000041,5.17858,156.915,16.2,957.4,2.26037,-3.62848,-25.7774
|
||||
2016-02-12 08:42:50,40.3486696,115.7855384,12.532000000000039,5.27778,157.696,16.2,957.4,2.25554,-3.50805,-25.7918
|
||||
2016-02-12 08:42:51,40.3486825,115.7855303,12.616000000000042,5.37135,158.794,16.2,957.4,2.25686,-3.56731,-25.5099
|
||||
2016-02-12 08:42:52,40.3487016,115.7855185,12.533000000000015,5.46752,156.941,16.2,957.4,2.25935,-3.98915,-25.6481
|
||||
2016-02-12 08:42:53,40.3487176,115.7855085,12.420000000000016,5.58741,155.836,16.2,957.4,2.26681,-4.31272,-25.4288
|
||||
2016-02-12 08:42:54,40.3487336,115.7854986,12.732000000000028,5.78335,155.823,16.2,957.4,2.25226,-4.67656,-25.4368
|
||||
2016-02-12 08:42:55,40.3487496,115.785489,12.701999999999998,5.92013,158.796,16.1,957.4,2.25212,-4.80537,-23.1046
|
||||
2016-02-12 08:42:56,40.348766,115.78548,12.518000000000029,5.9415,162.588,16.1,957.4,2.24934,-4.43397,-19.1058
|
||||
2016-02-12 08:42:57,40.348783,115.7854727,12.524000000000001,5.72876,166.81,16.1,957.4,2.25649,-4.15858,-15.3635
|
||||
2016-02-12 08:42:58,40.3488007,115.7854667,12.446000000000026,5.43129,168.769,16.1,957.4,2.25655,-3.56822,-12.2917
|
||||
2016-02-12 08:42:59,40.3488186,115.7854623,12.591000000000008,5.38271,169.969,16.1,957.4,2.25312,-3.63613,-9.66924
|
||||
2016-02-12 08:43:00,40.3488359,115.7854588,12.819000000000017,5.39655,172.218,16.1,957.4,2.25178,-4.54191,-6.79331
|
||||
2016-02-12 08:43:01,40.3488535,115.7854562,12.608000000000004,5.58193,174.925,16.1,957.4,2.25727,-4.15431,-3.67836
|
||||
2016-02-12 08:43:02,40.3488709,115.7854557,12.687000000000012,5.78932,176.998,16.1,957.4,2.24647,-4.26279,0.864772
|
||||
2016-02-12 08:43:03,40.3488887,115.7854572,12.700000000000045,5.80957,176.641,16.1,957.4,2.24594,-4.19075,4.37661
|
||||
2016-02-12 08:43:04,40.3489065,115.7854597,12.713999999999999,5.79471,176.923,16.1,957.4,2.25494,-4.33135,5.51263
|
||||
2016-02-12 08:43:05,40.3489243,115.7854622,12.805000000000007,5.76599,174.367,16.1,957.4,2.25948,-4.54469,5.39902
|
||||
2016-02-12 08:43:06,40.3489424,115.7854643,12.515000000000043,5.66438,170.978,16.1,957.4,2.24338,-3.95422,5.18129
|
||||
2016-02-12 08:43:07,40.3489605,115.7854662,12.734000000000037,5.51594,168.338,16.1,957.4,2.25627,-3.94131,5.09212
|
||||
2016-02-12 08:43:08,40.3489785,115.7854682,12.634000000000015,5.26331,167.49,16.0,957.4,2.25091,-3.82072,5.21668
|
||||
2016-02-12 08:43:09,40.3489964,115.7854699,11.968000000000018,5.3484,169.105,16.0,957.6,2.253,-3.64647,5.05816
|
||||
2016-02-12 08:43:10,40.3490145,115.7854713,12.449000000000012,5.24774,170.138,16.0,957.4,2.25451,-3.90744,4.97669
|
||||
2016-02-12 08:43:11,40.3490323,115.7854731,12.962000000000046,5.00866,172.165,16.0,957.4,2.25349,-4.42495,5.03938
|
||||
2016-02-12 08:43:12,40.3490499,115.7854749,12.913000000000011,5.17607,173.761,16.0,957.4,2.25456,-3.96802,4.88758
|
||||
2016-02-12 08:43:13,40.3490678,115.7854774,12.772000000000048,5.17531,173.632,16.0,957.4,2.24864,-4.1331,5.31392
|
||||
2016-02-12 08:43:14,40.3490859,115.7854799,12.660000000000025,5.33766,172.301,16.0,957.4,2.24415,-3.63245,5.13994
|
||||
2016-02-12 08:43:15,40.3491037,115.7854819,12.688000000000045,5.06418,172.85,16.0,957.4,2.25334,3.77341,5.43588
|
||||
2016-02-12 08:43:16,40.3491139,115.7854834,12.534000000000049,3.21808,163.848,16.0,957.4,2.25053,-3.0238,4.27478
|
||||
2016-02-12 08:43:17,40.3491151,115.7854831,12.472000000000037,2.30783,161.518,16.0,957.4,2.25659,-2.5161,4.4831
|
||||
2016-02-12 08:43:18,40.3491145,115.7854826,12.115000000000009,1.10665,156.651,16.0,957.4,2.25019,-3.33712,3.89
|
||||
2016-02-12 08:43:19,40.3491143,115.7854821,12.076000000000022,0.729867,160.824,16.0,957.4,2.25651,-2.90574,4.24684
|
||||
2016-02-12 08:43:20,40.3491143,115.785482,12.163000000000011,0.916749,168.514,16.0,957.4,2.24599,-2.40328,5.06117
|
||||
2016-02-12 08:43:21,40.3491144,115.7854825,12.129999999999995,0.736194,174.551,16.0,957.4,2.24683,-2.259,4.69336
|
||||
2016-02-12 08:43:22,40.3491147,115.7854827,12.256000000000029,0.721629,173.06,16.0,957.4,2.24472,-2.30074,3.95699
|
||||
2016-02-12 08:43:23,40.3491147,115.7854826,12.312000000000012,1.02892,170.476,16.0,957.4,2.25346,-2.14544,4.63103
|
||||
2016-02-12 08:43:24,40.3491148,115.7854826,12.391999999999996,1.71353,162.162,16.0,957.4,2.24199,-2.70262,4.38941
|
||||
2016-02-12 08:43:25,40.349115,115.7854826,12.213999999999999,1.90056,157.922,15.9,957.4,2.25142,-2.56983,4.16209
|
||||
2016-02-12 08:43:26,40.3491151,115.785483,12.249000000000024,1.89172,162.111,15.9,957.4,2.24987,-2.37081,4.33851
|
||||
2016-02-12 08:43:27,40.3491153,115.7854835,12.331000000000017,2.02974,161.345,15.9,957.4,2.25097,-2.36842,4.13939
|
||||
2016-02-12 08:43:28,40.3491156,115.7854835,12.210000000000036,2.11439,163.381,15.9,957.4,2.24901,-2.72557,4.44417
|
||||
2016-02-12 08:43:29,40.3491158,115.7854836,12.192000000000007,1.61638,157.271,15.9,957.4,2.2473,-2.35678,4.22427
|
||||
2016-02-12 08:43:30,40.3491159,115.7854837,12.297000000000025,1.69106,156.577,15.9,957.4,2.24907,-2.72985,4.52237
|
||||
2016-02-12 08:43:31,40.3491159,115.785484,12.105999999999995,1.27958,154.251,16.0,957.4,2.24912,-2.4493,4.27238
|
||||
2016-02-12 08:43:32,40.3491158,115.7854842,12.254000000000019,1.37727,157.977,16.0,957.4,2.25123,-3.27265,4.57478
|
||||
2016-02-12 08:43:33,40.3491159,115.7854845,12.354000000000042,1.21603,157.19,16.0,957.4,2.25817,-3.16157,4.23416
|
||||
2016-02-12 08:43:34,40.3491161,115.7854845,12.486000000000047,1.52134,161.142,16.0,957.4,2.24876,-2.97854,4.09014
|
||||
2016-02-12 08:43:35,40.3491163,115.7854843,12.413000000000011,1.62379,159.631,16.0,957.4,2.24613,-2.711,4.14956
|
||||
2016-02-12 08:43:36,40.3491166,115.7854841,12.363,1.91354,161.307,16.0,957.4,2.24829,-2.66831,4.3683
|
||||
2016-02-12 08:43:37,40.3491167,115.7854843,12.518000000000029,2.54485,128.325,16.0,957.4,2.24011,-2.99271,4.54588
|
||||
2016-02-12 08:43:38,40.3491171,115.7854846,13.846000000000004,3.16679,122.058,16.0,957.4,2.24456,-2.10601,4.30452
|
||||
2016-02-12 08:43:39,40.3491173,115.7854846,16.337000000000046,1.4036,139.974,16.0,957.4,2.25051,-2.11919,4.49659
|
||||
2016-02-12 08:43:40,40.3491161,115.7854819,17.247000000000014,0.665258,149.372,16.0,957.4,2.25517,-2.29389,44.7974
|
||||
2016-02-12 08:43:41,40.3491153,115.7854797,17.309000000000026,0.15585,172.788,16.0,957.4,2.25108,-6.16028,50.6785
|
||||
2016-02-12 08:43:42,40.3491182,115.7854836,17.16300000000001,1.79734,144.454,16.0,957.4,2.24024,-9.94753,51.2855
|
||||
2016-02-12 08:43:43,40.3491268,115.7854975,17.49200000000002,2.21825,152.062,16.0,957.4,2.26809,1.95625,50.9678
|
||||
2016-02-12 08:43:44,40.3491325,115.7855068,17.25600000000003,2.33955,175.315,16.0,957.4,2.26068,1.88367,47.4681
|
||||
2016-02-12 08:43:45,40.3491338,115.7855087,17.298000000000002,2.15619,172.161,16.0,957.4,2.23978,-10.4233,47.4627
|
||||
2016-02-12 08:43:46,40.3491408,115.7855185,17.400000000000034,3.19316,157.115,16.0,957.4,2.24856,-5.19856,47.9142
|
||||
2016-02-12 08:43:47,40.349153,115.7855364,18.277000000000044,3.51508,159.235,16.0,957.4,2.25081,-4.18958,47.1094
|
||||
2016-02-12 08:43:48,40.349166,115.7855553,18.075000000000045,4.85972,155.95,16.0,957.4,2.2575,-3.81284,46.5688
|
||||
2016-02-12 08:43:49,40.349179,115.7855737,18.112000000000023,5.30648,153.064,16.0,957.4,2.25406,-4.46158,47.5726
|
||||
2016-02-12 08:43:50,40.3491919,115.7855918,18.206000000000017,5.51106,149.706,16.0,957.4,2.25075,-3.58214,47.8376
|
||||
2016-02-12 08:43:51,40.3492042,115.7856099,18.246000000000038,5.69668,149.791,16.0,957.4,2.25386,-4.1528,47.7509
|
||||
2016-02-12 08:43:52,40.3492167,115.7856276,17.972000000000037,5.65832,151.933,16.0,957.4,2.25417,-3.96174,47.7473
|
||||
2016-02-12 08:43:53,40.349229,115.7856451,17.373000000000047,5.52845,152.475,16.0,957.4,2.25498,-3.35287,47.6634
|
||||
2016-02-12 08:43:54,40.3492411,115.7856621,17.248000000000047,5.19567,150.868,16.0,957.4,2.25428,-3.57325,50.2049
|
||||
2016-02-12 08:43:55,40.3492522,115.7856796,17.732000000000028,4.85903,145.241,16.0,957.4,2.25249,-4.60038,54.3005
|
||||
2016-02-12 08:43:56,40.3492618,115.7856982,17.80200000000002,4.89722,141.182,16.0,957.4,2.25698,-4.268,57.7334
|
||||
2016-02-12 08:43:57,40.3492709,115.7857178,17.648000000000025,5.00458,138.678,16.0,957.4,2.25503,-4.03971,60.8126
|
||||
2016-02-12 08:43:58,40.3492792,115.7857383,17.670000000000016,5.06119,134.395,16.0,957.4,2.25882,-4.43467,64.1499
|
||||
2016-02-12 08:43:59,40.3492867,115.7857602,17.617999999999995,4.97336,132.843,16.0,957.4,2.25522,-3.4857,67.6561
|
||||
2016-02-12 08:44:00,40.3492929,115.7857822,17.749000000000024,4.76678,129.392,16.0,957.4,2.25682,-3.13844,72.5023
|
||||
2016-02-12 08:44:01,40.3492981,115.7858047,17.458000000000027,4.70141,129.949,16.0,957.4,2.25787,-3.00207,73.9701
|
||||
2016-02-12 08:44:02,40.3493031,115.7858268,17.75600000000003,4.64818,127.629,16.0,957.4,2.25249,-4.01299,74.3217
|
||||
2016-02-12 08:44:03,40.3493081,115.7858497,17.591000000000008,4.78015,127.062,16.0,957.4,2.26045,-3.45797,73.9458
|
||||
2016-02-12 08:44:04,40.349313,115.7858725,17.576000000000022,4.80095,126.743,16.0,957.4,2.25445,-3.48772,74.0784
|
||||
2016-02-12 08:44:05,40.3493179,115.7858957,17.460000000000036,4.46846,127.093,16.0,957.4,2.25907,-2.84138,74.6726
|
||||
2016-02-12 08:44:06,40.3493226,115.7859187,17.524,4.40493,128.632,16.0,957.4,2.2653,-2.7391,74.8234
|
||||
2016-02-12 08:44:07,40.3493273,115.7859409,17.883000000000038,4.20996,130.254,16.0,957.4,2.25721,-3.75628,74.3471
|
||||
2016-02-12 08:44:08,40.3493319,115.7859629,18.008000000000038,4.54446,126.276,16.0,957.4,2.24421,-4.13393,75.9746
|
||||
2016-02-12 08:44:09,40.349336,115.7859856,17.601,4.60809,119.994,16.0,957.4,2.25283,-3.65654,80.2085
|
||||
2016-02-12 08:44:10,40.3493389,115.7860089,17.54600000000005,4.59821,116.256,16.0,957.4,2.25826,-3.52848,84.5632
|
||||
2016-02-12 08:44:11,40.3493404,115.7860326,17.343999999999994,4.60662,111.882,16.0,957.4,2.26272,-2.83632,87.8923
|
||||
2016-02-12 08:44:12,40.3493407,115.786056,17.586000000000013,4.26527,108.906,16.0,957.4,2.26141,-2.64772,91.0007
|
||||
2016-02-12 08:44:13,40.34934,115.7860794,17.64300000000003,4.15369,106.719,16.0,957.4,2.25931,-2.97149,93.9182
|
||||
2016-02-12 08:44:14,40.3493383,115.7861028,17.40500000000003,4.06511,102.898,15.9,957.4,2.25609,-2.43398,97.4505
|
||||
2016-02-12 08:44:15,40.3493351,115.7861305,17.194000000000017,4.10584,100.818,15.9,957.4,2.25337,-2.45874,102.19
|
||||
2016-02-12 08:44:16,40.3493312,115.7861533,17.29600000000005,3.5283,94.6166,15.9,957.4,2.25653,-2.18443,106.176
|
||||
2016-02-12 08:44:17,40.349326,115.7861758,17.158000000000015,3.14686,91.3884,15.9,957.4,2.26545,-1.54058,107.424
|
||||
2016-02-12 08:44:18,40.3493205,115.7861983,17.150000000000034,2.79263,94.4733,15.9,957.4,2.25802,-2.1108,106.841
|
||||
2016-02-12 08:44:19,40.349315,115.7862206,17.38900000000001,2.88462,94.7514,15.9,957.4,2.25069,-2.22205,107.513
|
||||
2016-02-12 08:44:20,40.3493094,115.7862432,17.067000000000007,2.76044,95.8841,15.9,957.4,2.2528,-1.19721,107.134
|
||||
2016-02-12 08:44:21,40.3493041,115.7862659,17.110000000000014,2.41459,96.6922,15.9,957.4,2.26201,-1.52747,106.883
|
||||
2016-02-12 08:44:22,40.3492987,115.7862882,17.20500000000004,2.22398,91.4351,15.9,957.4,2.25557,-2.13694,106.758
|
||||
2016-02-12 08:44:23,40.3492936,115.7863106,17.402000000000044,2.22706,93.5224,15.9,957.4,2.25374,-2.07331,107.193
|
||||
2016-02-12 08:44:24,40.3492882,115.786333,17.234000000000037,2.23447,93.8195,16.0,957.4,2.25663,-1.73543,107.136
|
||||
2016-02-12 08:44:25,40.3492828,115.7863553,17.341000000000008,2.369,89.0479,16.0,957.4,2.25711,-2.02998,109.231
|
||||
2016-02-12 08:44:26,40.3492764,115.786377,17.462000000000046,2.58026,83.2347,16.0,957.4,2.26416,-1.803,114.054
|
||||
2016-02-12 08:44:27,40.3492688,115.7863981,17.28200000000004,2.71047,83.2986,16.0,957.4,2.25945,-2.07605,117.681
|
||||
2016-02-12 08:44:28,40.3492603,115.7864185,17.536,2.62323,77.0265,16.0,957.4,2.25908,-1.97262,120.92
|
||||
2016-02-12 08:44:29,40.3492507,115.786438,17.591000000000008,2.65832,73.4184,16.0,957.4,2.25308,-1.5131,124.759
|
||||
2016-02-12 08:44:30,40.3492401,115.7864565,17.560000000000002,2.7659,69.8955,16.0,957.4,2.26277,-2.36132,129.088
|
||||
2016-02-12 08:44:31,40.3492287,115.7864741,17.32800000000003,2.67548,62.8153,16.0,957.4,2.25068,-2.22984,133.597
|
||||
2016-02-12 08:44:32,40.3492162,115.7864915,17.398000000000025,2.61916,62.5652,16.0,957.4,2.2546,-1.56645,134.004
|
||||
2016-02-12 08:44:33,40.3492038,115.7865082,17.36500000000001,2.38325,63.9515,16.0,957.4,2.25572,-2.22344,134.419
|
||||
2016-02-12 08:44:34,40.3491913,115.7865247,17.396000000000015,2.4148,60.3102,16.0,957.4,2.26123,-1.89722,134.63
|
||||
2016-02-12 08:44:35,40.3491787,115.7865415,17.450000000000045,2.71965,65.9189,16.0,957.4,2.24881,-1.83215,134.581
|
||||
2016-02-12 08:44:36,40.3491659,115.7865581,17.43300000000005,2.8837,66.0174,16.0,957.4,2.26288,-1.92149,136.984
|
||||
2016-02-12 08:44:37,40.3491527,115.7865736,17.39300000000003,2.99332,47.2055,16.0,957.4,2.25737,-1.88269,144.475
|
||||
2016-02-12 08:44:39,40.3491381,115.7865865,17.437000000000012,2.60973,38.2454,16.0,957.4,2.26084,-1.88556,150.27
|
||||
2016-02-12 08:44:39,40.3491223,115.7865974,17.41500000000002,2.0888,24.9333,16.0,957.4,2.26337,-1.17669,155.997
|
||||
2016-02-12 08:44:40,40.3491058,115.7866061,17.382000000000005,2.04768,20.821,16.0,957.4,2.26543,-0.978078,161.237
|
||||
2016-02-12 08:44:42,40.3490888,115.7866126,17.50400000000002,2.02731,13.2953,16.0,957.4,2.25556,-1.22699,167.844
|
||||
2016-02-12 08:44:43,40.3490714,115.7866164,17.65500000000003,1.96942,5.29049,16.0,957.4,2.25431,-0.927087,174.693
|
||||
2016-02-12 08:44:44,40.3490538,115.7866182,17.715000000000032,1.96387,3.9822,16.0,957.4,2.25309,-1.37721,176.111
|
||||
2016-02-12 08:44:45,40.349036,115.7866197,17.62900000000002,1.97297,4.3481,16.0,957.4,2.26046,-1.17786,176.094
|
||||
2016-02-12 08:44:46,40.3490182,115.7866212,17.64500000000004,1.97891,4.29482,16.0,957.4,2.25826,-1.22428,175.974
|
||||
2016-02-12 08:44:47,40.3490003,115.7866228,17.56400000000002,1.92987,5.27529,16.0,957.4,2.25967,-1.38526,175.432
|
||||
2016-02-12 08:44:48,40.3489822,115.7866244,17.507000000000005,2.11482,2.04059,16.1,957.4,2.25986,-1.25446,175.941
|
||||
2016-02-12 08:44:49,40.348964,115.786626,17.494000000000028,2.2084,4.66539,16.1,957.4,2.25505,-1.25606,175.786
|
||||
2016-02-12 08:44:50,40.3489461,115.7866279,17.533000000000015,1.98396,12.7157,16.1,957.4,2.25133,-1.10287,176.088
|
||||
2016-02-12 08:44:51,40.3489282,115.7866296,17.510000000000048,2.00829,16.0908,16.1,957.4,2.26272,-1.05535,176.051
|
||||
2016-02-12 08:44:52,40.3489102,115.7866312,17.38900000000001,2.00029,6.62347,16.1,957.4,2.26306,-1.15381,175.658
|
||||
2016-02-12 08:44:53,40.3488923,115.7866329,17.53200000000004,2.00055,5.94855,16.1,957.4,2.26199,-1.15091,177.283
|
||||
2016-02-12 08:44:54,40.3488743,115.7866336,17.375,1.84412,12.2126,16.1,957.4,2.25823,-0.905536,-179.37
|
||||
2016-02-12 08:44:55,40.3488565,115.7866328,17.537000000000035,1.81085,14.9492,16.1,957.4,2.25636,-0.861661,-176.408
|
||||
2016-02-12 08:44:56,40.3488387,115.7866309,17.624000000000024,1.84405,14.0908,16.1,957.4,2.25948,-1.10296,-174.525
|
||||
2016-02-12 08:44:57,40.348821,115.7866281,17.559000000000026,1.93804,11.6952,16.1,957.4,2.26045,-0.947733,-172.353
|
||||
2016-02-12 08:44:58,40.3488035,115.7866242,17.694000000000017,2.00184,14.1004,16.1,957.4,2.25602,-1.26374,-169.775
|
||||
2016-02-12 08:44:59,40.348786,115.7866195,17.708000000000027,2.03629,21.9379,16.1,957.4,2.2498,-1.05616,-166.958
|
||||
2016-02-12 08:45:00,40.3487688,115.7866138,17.646000000000015,2.05078,24.9459,16.1,957.4,2.25291,-1.05454,-163.484
|
||||
2016-02-12 08:45:01,40.3487517,115.7866069,17.65900000000005,1.95402,30.7938,16.2,957.4,2.26536,-0.651226,-160.678
|
||||
2016-02-12 08:45:02,40.3487347,115.786599,17.588000000000022,1.7691,34.7243,16.2,957.4,2.24918,-0.857132,-159.513
|
||||
2016-02-12 08:45:03,40.348718,115.7865908,17.598000000000013,1.67192,47.1372,16.2,957.4,2.26345,-1.21446,-159.669
|
||||
2016-02-12 08:45:04,40.3487011,115.7865824,17.69500000000005,1.68668,54.3084,16.2,957.4,2.25893,-0.823606,-159.862
|
||||
2016-02-12 08:45:05,40.3486843,115.7865742,17.79200000000003,1.74508,47.7868,16.2,957.4,2.25838,-0.884091,-159.588
|
||||
2016-02-12 08:45:06,40.3486642,115.7865643,17.739000000000033,1.86477,34.9555,16.2,957.4,2.25685,-1.2888,-159.629
|
||||
2016-02-12 08:45:07,40.3486473,115.7865559,17.80200000000002,1.9374,38.5253,16.2,957.4,2.26043,-0.978609,-159.995
|
||||
2016-02-12 08:45:08,40.3486305,115.7865474,17.758000000000038,1.92136,38.8821,16.2,957.4,2.26082,-0.655105,-160.113
|
||||
2016-02-12 08:45:09,40.3486139,115.7865389,17.77000000000004,1.86096,61.4759,16.2,957.4,2.26501,-0.96432,-157.731
|
||||
2016-02-12 08:45:10,40.3485975,115.7865297,17.74000000000001,1.84646,65.4398,16.2,957.4,2.26133,-0.770468,-151.285
|
||||
2016-02-12 08:45:11,40.3485824,115.7865178,17.840000000000032,1.77807,66.8571,16.3,957.4,2.25876,-0.859863,-144.656
|
||||
2016-02-12 08:45:12,40.3485682,115.7865036,17.966000000000008,1.88744,61.4756,16.3,957.4,2.26721,-0.782214,-139.476
|
||||
2016-02-12 08:45:13,40.3485553,115.7864884,17.864000000000033,2.1576,77.0466,16.3,957.4,2.26486,-1.47746,-133.976
|
||||
2016-02-12 08:45:14,40.3485435,115.7864713,18.11700000000002,2.70015,81.7995,16.3,957.4,2.26416,-1.10274,-128.533
|
||||
2016-02-12 08:45:15,40.3485332,115.786453,18.287000000000035,2.69214,88.1674,16.3,957.4,2.26025,-1.94088,-122.849
|
||||
2016-02-12 08:45:16,40.3485245,115.7864337,18.45500000000004,3.78728,89.9081,16.3,957.4,2.25168,-3.04325,-117.301
|
||||
2016-02-12 08:45:17,40.3485173,115.7864131,18.396000000000015,4.16305,89.0334,16.3,957.4,2.25147,-3.31608,-111.595
|
||||
2016-02-12 08:45:18,40.3485111,115.786391,18.156000000000006,4.32816,88.5418,16.4,957.4,2.25652,-3.47936,-110.1
|
||||
2016-02-12 08:45:19,40.3485051,115.7863681,18.30000000000001,4.57069,83.1046,16.4,957.4,2.24823,-2.257,-110.004
|
||||
2016-02-12 08:45:20,40.348499,115.7863454,18.563000000000045,4.63882,82.4551,16.4,957.4,2.25442,-2.3749,-109.707
|
||||
2016-02-12 08:45:21,40.3484927,115.786323,18.28800000000001,4.55507,82.4027,16.4,957.4,2.26189,-2.34621,-109.863
|
||||
2016-02-12 08:45:22,40.3484865,115.7863004,18.225000000000023,4.52532,83.4422,16.4,957.4,2.26022,-2.46761,-109.93
|
||||
2016-02-12 08:45:23,40.3484803,115.7862778,18.161,4.26556,86.1035,16.4,957.4,2.25502,-2.08226,-109.794
|
||||
2016-02-12 08:45:24,40.3484741,115.7862556,18.208000000000027,4.23751,87.8882,16.4,957.4,2.25048,-2.05877,-109.916
|
||||
2016-02-12 08:45:25,40.348468,115.7862333,18.089,4.12006,88.2306,16.4,957.4,2.24768,-2.21128,-109.852
|
||||
2016-02-12 08:45:26,40.3484618,115.7862112,18.319000000000017,3.95742,89.733,16.4,957.4,2.25324,-1.93889,-109.628
|
||||
2016-02-12 08:45:27,40.3484557,115.7861892,18.310000000000002,3.98109,89.0515,16.4,957.4,2.25249,-2.38076,-109.893
|
||||
2016-02-12 08:45:28,40.3484497,115.7861672,18.201999999999998,3.97322,90.7361,16.4,957.4,2.25567,-2.45486,-109.649
|
||||
2016-02-12 08:45:29,40.3484439,115.7861451,18.471000000000004,3.97965,94.0065,16.4,957.4,2.26075,-2.21297,-106.317
|
||||
2016-02-12 08:45:30,40.3484394,115.7861226,18.227000000000032,3.89264,97.9598,16.4,957.4,2.25305,-2.42729,-101.534
|
||||
2016-02-12 08:45:31,40.3484363,115.7860996,18.062000000000012,3.88647,103.014,16.4,957.4,2.26572,-2.24522,-97.1811
|
||||
2016-02-12 08:45:32,40.3484347,115.7860764,18.20500000000004,3.96167,107.487,16.4,957.4,2.25969,-2.48252,-93.2813
|
||||
2016-02-12 08:45:33,40.3484342,115.7860533,18.545000000000016,4.19445,109.054,16.4,957.4,2.26069,-2.93529,-89.8609
|
||||
2016-02-12 08:45:34,40.3484348,115.7860307,18.602000000000032,4.59572,106.289,16.4,957.4,2.25846,-3.74323,-87.0064
|
||||
2016-02-12 08:45:35,40.3484361,115.7860075,18.62700000000001,4.55856,108.097,16.5,957.4,2.25328,-3.77968,-82.5635
|
||||
2016-02-12 08:45:36,40.3484388,115.7859842,18.871000000000038,4.94187,112.091,16.5,957.4,2.25766,-3.47161,-77.7257
|
||||
2016-02-12 08:45:37,40.3484431,115.7859614,18.620000000000005,4.99916,114.421,16.5,957.4,2.24696,-4.25361,-73.8859
|
||||
2016-02-12 08:45:38,40.3484484,115.7859379,18.662000000000035,5.041,118.194,16.5,957.4,2.24988,-2.40826,-72.456
|
||||
2016-02-12 08:45:39,40.3484539,115.7859151,18.590000000000032,4.86597,121.342,16.5,957.4,2.23913,-2.65323,-72.0437
|
||||
2016-02-12 08:45:40,40.3484598,115.7858929,18.458000000000027,4.57349,122.896,16.5,957.4,2.24802,-2.92134,-72.8244
|
||||
2016-02-12 08:45:41,40.3484654,115.7858701,18.50400000000002,4.62556,125.251,16.5,957.4,2.25008,-2.46242,-72.5329
|
||||
2016-02-12 08:45:42,40.3484709,115.7858478,18.313000000000045,4.34028,123.989,16.5,957.4,2.2451,-3.08264,-72.7796
|
||||
2016-02-12 08:45:43,40.3484763,115.7858252,17.950000000000045,4.58716,121.914,16.5,957.4,2.24825,-2.61072,-72.9401
|
||||
2016-02-12 08:45:44,40.3484817,115.7858026,17.908000000000015,4.45187,123.576,16.5,957.4,2.2571,-2.53661,-72.1734
|
||||
2016-02-12 08:45:45,40.3484873,115.7857801,18.42700000000002,4.56513,122.201,16.5,957.4,2.24877,-2.19526,-72.9211
|
||||
2016-02-12 08:45:46,40.3484926,115.7857577,18.236000000000047,4.27496,122.441,16.5,957.4,2.25316,-2.56898,-72.6472
|
||||
2016-02-12 08:45:47,40.3484981,115.7857353,18.04000000000002,4.05575,123.342,16.5,957.4,2.24489,-2.28949,-71.977
|
||||
2016-02-12 08:45:48,40.3485039,115.7857134,18.49200000000002,4.03684,127.936,16.5,957.4,2.25751,-2.46188,-67.2151
|
||||
2016-02-12 08:45:49,40.348511,115.785693,18.735000000000014,4.11837,133.691,16.5,957.4,2.25974,-3.48746,-61.2099
|
||||
2016-02-12 08:45:50,40.3485197,115.7856737,18.715000000000032,4.69469,136.928,16.5,957.4,2.24865,-3.85436,-55.4787
|
||||
2016-02-12 08:45:51,40.3485302,115.785655,18.584000000000003,4.95181,138.959,16.5,957.4,2.25789,-3.25282,-50.9018
|
||||
2016-02-12 08:45:52,40.3485445,115.7856342,19.019000000000005,5.16532,142.967,16.5,957.4,2.25162,-3.51994,-44.9958
|
||||
2016-02-12 08:45:53,40.3485551,115.7856217,18.930000000000007,5.17427,145.326,16.5,957.4,2.25415,-3.20807,-40.945
|
||||
2016-02-12 08:45:54,40.3485718,115.7856049,19.383000000000038,5.22752,149.735,16.5,957.4,2.23934,-4.49393,-34.1759
|
||||
2016-02-12 08:45:55,40.3485865,115.7855928,19.444000000000017,5.46417,153.647,16.5,957.4,2.24285,-3.81437,-28.297
|
||||
2016-02-12 08:45:56,40.3486022,115.7855819,19.15900000000005,5.61763,151.635,16.5,957.4,2.24025,-3.88984,-25.5592
|
||||
2016-02-12 08:45:57,40.3486185,115.7855714,19.13100000000003,5.22492,151.447,16.5,957.4,2.24517,-4.60596,-25.7129
|
||||
2016-02-12 08:45:58,40.348635,115.7855608,18.66500000000002,5.22477,152.331,16.5,957.4,2.25713,-3.54813,-26.0158
|
||||
2016-02-12 08:45:59,40.3486512,115.78555,18.549000000000035,5.00249,156.044,16.5,957.4,2.2507,-3.84303,-26.0043
|
||||
2016-02-12 08:46:00,40.3486673,115.7855394,18.598000000000013,4.90608,159.322,16.5,957.4,2.24678,-3.58029,-25.9375
|
||||
2016-02-12 08:46:01,40.3486837,115.7855294,18.55400000000003,4.89749,161.021,16.5,957.4,2.24366,-3.33764,-25.6277
|
||||
2016-02-12 08:46:02,40.3486999,115.7855194,18.826999999999998,4.74312,160.992,16.5,957.4,2.24832,-3.63823,-25.437
|
||||
2016-02-12 08:46:03,40.3487159,115.7855095,18.734000000000037,5.08126,160.834,16.5,957.4,2.24889,-3.83332,-25.7044
|
||||
2016-02-12 08:46:04,40.348732,115.7854993,18.697000000000003,5.29667,161.546,16.5,957.4,2.24539,-3.81592,-25.3915
|
||||
2016-02-12 08:46:05,40.3487486,115.7854894,18.629999999999995,5.33145,161.929,16.5,957.4,2.24517,-3.09275,-23.3054
|
||||
2016-02-12 08:46:06,40.3487655,115.7854806,18.716000000000008,4.78943,163.905,16.5,957.4,2.25106,-2.56043,-19.1261
|
||||
2016-02-12 08:46:07,40.348782,115.7854734,18.864000000000033,4.65857,165.761,16.5,957.4,2.24854,-3.60127,-15.6055
|
||||
2016-02-12 08:46:08,40.3487988,115.7854675,19.091000000000008,4.77459,166.962,16.5,957.4,2.24936,-4.10476,-12.4039
|
||||
2016-02-12 08:46:09,40.3488161,115.785463,19.161,5.17038,169.08,16.5,957.4,2.26161,-3.88614,-10.0499
|
||||
2016-02-12 08:46:10,40.3488335,115.7854591,19.347000000000037,5.30009,171.894,16.5,957.4,2.25124,-4.02029,-7.24302
|
||||
2016-02-12 08:46:11,40.3488513,115.7854566,19.013000000000034,5.39223,174.83,16.5,957.4,2.24748,-4.18501,-4.02827
|
||||
2016-02-12 08:46:12,40.3488692,115.7854554,18.531000000000006,5.22289,177.792,16.5,957.4,2.24002,-3.68792,-0.138169
|
||||
2016-02-12 08:46:13,40.348887,115.7854559,18.712000000000046,5.29908,176.119,16.5,957.4,2.24991,-4.16261,3.40361
|
||||
2016-02-12 08:46:14,40.3489049,115.7854579,18.876000000000033,5.18513,175.604,16.5,957.4,2.25581,-3.8008,4.82798
|
||||
2016-02-12 08:46:15,40.3489226,115.7854601,18.70300000000003,5.28377,174.7,16.5,957.4,2.25573,-4.42704,5.10422
|
||||
2016-02-12 08:46:16,40.3489402,115.7854625,18.723000000000013,5.07867,173.898,16.5,957.4,2.25527,-4.51496,5.49623
|
||||
2016-02-12 08:46:17,40.3489577,115.7854654,19.19500000000005,5.09618,175.518,16.5,957.4,2.25583,-4.36611,5.46923
|
||||
2016-02-12 08:46:18,40.3489756,115.7854678,18.958000000000027,5.11867,176.609,16.4,957.4,2.25081,-4.54822,5.33563
|
||||
2016-02-12 08:46:19,40.3489936,115.7854701,18.870000000000005,5.4073,176.796,16.4,957.4,2.25643,-4.75882,5.40797
|
||||
2016-02-12 08:46:20,40.3490114,115.7854722,18.610000000000014,5.58391,175.575,16.4,957.4,2.24946,-5.50701,5.03366
|
||||
2016-02-12 08:46:21,40.3490293,115.7854738,18.632000000000005,5.9049,176.505,16.4,957.4,2.25577,-4.83407,5.36974
|
||||
2016-02-12 08:46:22,40.3490473,115.7854761,18.746000000000038,5.91032,176.712,16.4,957.4,2.26172,-4.47638,5.38053
|
||||
2016-02-12 08:46:23,40.3490654,115.7854787,18.749000000000024,5.8128,175.791,16.4,957.4,2.24897,-4.6601,5.14888
|
||||
2016-02-12 08:46:24,40.3490833,115.7854808,18.635000000000048,5.96479,174.72,16.4,957.4,2.26363,-5.30749,5.15921
|
||||
2016-02-12 08:46:25,40.3491012,115.7854828,18.662000000000035,5.69564,172.094,16.4,957.4,2.2571,-0.473702,4.87027
|
||||
2016-02-12 08:46:26,40.3491131,115.7854838,18.741000000000042,3.78275,164.729,16.4,957.4,2.25241,-0.887125,4.75248
|
||||
2016-02-12 08:46:27,40.3491144,115.7854838,18.359000000000037,3.20324,161.213,16.4,957.4,2.25493,-3.26957,5.54402
|
||||
2016-02-12 08:46:28,40.3491142,115.7854837,18.223000000000013,2.40983,155.756,16.3,957.4,2.25721,-3.09028,5.36807
|
||||
2016-02-12 08:46:29,40.3491141,115.7854836,18.116000000000042,2.11352,157.024,16.3,957.4,2.25414,-2.34086,5.54559
|
||||
2016-02-12 08:46:30,40.349114,115.7854839,18.098000000000013,1.80536,155.986,16.3,957.4,2.24952,-2.62845,5.28901
|
||||
2016-02-12 08:46:31,40.3491139,115.7854841,18.104000000000042,1.53764,153.71,16.3,957.4,2.25496,-2.72331,4.91931
|
||||
2016-02-12 08:46:32,40.3491141,115.785484,18.013000000000034,1.25194,135.14,16.3,957.4,2.25252,-2.51398,5.07391
|
||||
2016-02-12 08:46:33,40.3491145,115.7854839,18.096000000000004,0.909581,119.528,16.3,957.4,2.26502,-2.23522,4.9321
|
||||
2016-02-12 08:46:34,40.3491148,115.7854839,18.11500000000001,1.00363,116.368,16.3,957.4,2.25633,-1.86102,4.94461
|
||||
2016-02-12 08:46:35,40.3491147,115.7854839,18.156000000000006,1.29567,116.279,16.3,957.4,2.24816,-3.27238,4.93432
|
||||
2016-02-12 08:46:36,40.3491148,115.7854839,18.29200000000003,1.80343,117.547,16.3,957.4,2.25323,-3.03301,5.0516
|
||||
2016-02-12 08:46:37,40.3491153,115.7854844,18.262,2.20721,129.988,16.3,957.4,2.23802,-2.33693,-3.56446
|
||||
2016-02-12 08:46:38,40.349117,115.7854858,18.539000000000044,2.41458,130.441,16.3,957.4,2.25003,-3.03663,-32.2437
|
||||
2016-02-12 08:46:39,40.3491192,115.7854854,18.525000000000034,2.51248,120.631,16.3,957.4,2.2485,-2.14594,-62.1644
|
||||
2016-02-12 08:46:40,40.3491202,115.7854843,18.311000000000035,2.39774,119.713,16.3,957.4,2.26122,-1.66167,-82.6586
|
||||
2016-02-12 08:46:41,40.3491201,115.7854826,18.40100000000001,2.6669,112.549,16.3,957.4,2.24148,-9.69869,-94.4252
|
||||
2016-02-12 08:46:42,40.349119,115.785474,18.451999999999998,3.58092,114.107,16.3,957.4,2.24515,-8.18198,-94.3401
|
||||
2016-02-12 08:46:43,40.3491177,115.7854533,18.446000000000026,4.36065,111.498,16.3,957.4,2.24984,-6.39408,-94.1511
|
||||
2016-02-12 08:46:44,40.3491158,115.7854241,18.548000000000002,4.83268,107.56,16.2,957.4,2.25608,-5.52217,-93.985
|
||||
2016-02-12 08:46:45,40.3491136,115.7853904,18.562000000000012,5.63733,104.996,16.2,957.4,2.25285,-3.73582,-93.9406
|
||||
2016-02-12 08:46:46,40.3491117,115.7853556,18.718000000000018,5.98308,99.2656,16.2,957.4,2.25147,-1.55215,-94.2177
|
||||
2016-02-12 08:46:47,40.3491101,115.7853231,18.66900000000004,5.99918,99.0378,16.2,957.4,2.25063,-1.4597,-93.9427
|
||||
2016-02-12 08:46:48,40.3491088,115.7852936,18.864000000000033,5.58521,98.8318,16.2,957.4,2.25688,-1.74784,-94.2138
|
||||
2016-02-12 08:46:49,40.3491076,115.7852665,18.647000000000048,5.06914,102.902,16.2,957.4,2.25241,-1.92998,-93.8656
|
||||
2016-02-12 08:46:50,40.3491069,115.7852413,18.53800000000001,4.57836,107.973,16.2,957.4,2.25181,-1.92374,-92.0432
|
||||
2016-02-12 08:46:51,40.3491072,115.785218,18.536,4.43143,115.332,16.2,957.4,2.25353,-2.10617,-85.8288
|
||||
2016-02-12 08:46:52,40.3491093,115.7851963,18.79000000000002,4.43013,123.816,16.2,957.4,2.25323,-2.65061,-78.6565
|
||||
2016-02-12 08:46:53,40.3491127,115.7851749,18.78800000000001,4.58193,121.516,16.2,957.4,2.24977,-2.85184,-78.4132
|
||||
2016-02-12 08:46:54,40.3491161,115.7851532,18.649,4.63984,120.281,16.2,957.4,2.2551,-3.52654,-78.4899
|
||||
2016-02-12 08:46:55,40.3491197,115.7851304,18.391999999999996,4.82902,119.083,16.2,957.4,2.25016,-3.82069,-78.4761
|
||||
2016-02-12 08:46:56,40.3491238,115.7851057,18.468999999999994,4.82599,118.463,16.2,957.4,2.25212,-4.37022,-78.4759
|
||||
2016-02-12 08:46:57,40.3491283,115.7850782,18.476,5.24285,115.506,16.2,957.4,2.2514,-5.29125,-78.4982
|
||||
2016-02-12 08:46:58,40.3491332,115.7850471,18.605999999999995,5.63871,114.027,16.2,957.4,2.25888,-4.89839,-78.6514
|
||||
2016-02-12 08:46:59,40.3491386,115.7850129,18.64300000000003,6.21575,110.912,16.2,957.4,2.23991,-4.85329,-78.5702
|
||||
2016-02-12 08:47:00,40.3491446,115.7849764,18.474000000000046,6.52411,109.303,16.2,957.4,2.25149,-4.43201,-78.5637
|
||||
2016-02-12 08:47:01,40.349151,115.784938,18.80800000000005,6.77679,107.561,16.2,957.4,2.24558,-4.74183,-78.8267
|
||||
2016-02-12 08:47:02,40.3491576,115.7848981,18.956999999999994,7.09753,106.696,16.2,957.4,2.25637,-4.52781,-78.8386
|
||||
2016-02-12 08:47:03,40.3491644,115.7848569,18.587000000000046,7.33927,106.749,16.2,957.4,2.26023,-4.96421,-78.5891
|
||||
2016-02-12 08:47:04,40.3491715,115.784814,18.772000000000048,7.50092,106.624,16.2,957.4,2.25672,-5.29089,-78.658
|
||||
2016-02-12 08:47:05,40.3491788,115.7847687,18.714,7.58947,106.181,16.2,957.4,2.24472,-4.61858,-78.6736
|
||||
2016-02-12 08:47:06,40.3491862,115.7847216,18.861000000000047,7.58499,106.907,16.2,957.4,2.26091,-2.96341,-78.4489
|
||||
2016-02-12 08:47:07,40.3491932,115.7846768,19.076000000000022,7.63955,107.249,16.1,957.4,2.24757,-2.96918,-78.5038
|
||||
2016-02-12 08:47:08,40.3492001,115.7846343,18.950000000000045,7.56674,107.19,16.1,957.4,2.25598,-1.65859,-78.5213
|
||||
2016-02-12 08:47:09,40.3492063,115.7845963,18.875,6.51291,106.081,16.1,957.4,2.25284,0.87737,-78.6438
|
||||
2016-02-12 08:47:10,40.3492115,115.7845658,18.902000000000044,5.94177,109.224,16.1,957.4,2.25329,-0.890602,-75.947
|
||||
2016-02-12 08:47:11,40.3492163,115.7845434,18.91700000000003,3.39562,124.224,16.1,957.4,2.25078,5.36068,-61.0265
|
||||
2016-02-12 08:47:12,40.3492168,115.7845391,18.373000000000047,2.35105,144.149,16.1,957.4,2.25335,0.560695,-45.7276
|
||||
2016-02-12 08:47:13,40.3492155,115.784539,18.483000000000004,0.893619,174.724,16.1,957.4,2.25703,-1.3111,-23.1751
|
||||
2016-02-12 08:47:14,40.3492136,115.7845378,18.56800000000004,0.103818,119.339,16.1,957.4,2.25902,-1.38589,10.1569
|
||||
2016-02-12 08:47:15,40.3492129,115.7845354,18.382000000000005,0.0326814,130.99,16.1,957.4,2.25777,-1.22882,35.8235
|
||||
2016-02-12 08:47:16,40.3492121,115.7845341,18.218999999999994,0.387784,31.1506,16.1,957.4,2.25355,-1.60541,40.0295
|
||||
2016-02-12 08:47:17,40.3492103,115.7845329,18.116000000000042,0.509455,87.3243,16.1,957.4,2.23954,-2.1407,39.683
|
||||
2016-02-12 08:47:18,40.3492105,115.7845333,17.775000000000034,0.381268,100.864,16.1,957.4,2.25254,-1.36341,39.8437
|
||||
2016-02-12 08:47:19,40.3492113,115.7845336,17.50200000000001,0.370224,45.6902,16.1,957.4,2.25597,-1.52545,39.8108
|
||||
2016-02-12 08:47:20,40.3492121,115.7845342,17.311000000000035,0.349136,85.5964,16.1,957.4,2.24971,-0.617308,39.8674
|
||||
2016-02-12 08:47:21,40.349212,115.7845348,16.980000000000018,0.418138,75.5565,16.1,957.4,2.25604,-1.80859,39.3206
|
||||
2016-02-12 08:47:22,40.3492118,115.7845347,16.562000000000012,0.729231,92.0266,16.1,957.4,2.24718,-1.91448,39.8363
|
||||
2016-02-12 08:47:23,40.3492116,115.784534,16.108000000000004,0.752719,77.3196,16.1,957.4,2.2566,-2.7967,40.4346
|
||||
2016-02-12 08:47:24,40.3492117,115.7845338,15.56800000000004,0.876472,112.876,16.2,957.6,2.24441,-2.74975,40.4632
|
||||
2016-02-12 08:47:25,40.3492114,115.7845344,14.550000000000011,0.910692,111.859,16.2,957.6,2.24345,-3.14775,40.1516
|
||||
2016-02-12 08:47:26,40.3492114,115.7845344,13.948000000000036,1.52511,98.9897,16.2,957.4,2.25048,-2.48058,39.9685
|
||||
2016-02-12 08:47:27,40.3492112,115.7845344,13.17100000000005,1.46838,143.535,16.2,957.4,2.25029,1.99029,39.8544
|
||||
2016-02-12 08:47:28,40.3492081,115.7845311,12.235000000000014,1.59666,132.338,16.2,957.4,2.24882,0.489737,39.9297
|
||||
2016-02-12 08:47:29,40.3492023,115.7845249,11.288000000000011,2.02849,101.22,16.2,957.6,2.24682,0.807989,40.035
|
||||
2016-02-12 08:47:30,40.3491942,115.7845164,10.459000000000003,2.41615,81.7842,16.2,957.6,2.24377,-1.64567,39.8959
|
||||
2016-02-12 08:47:31,40.3491866,115.7845083,9.474000000000046,2.36492,77.0593,16.2,957.4,2.25103,-9.16326,40.0477
|
||||
2016-02-12 08:47:32,40.3491841,115.7845057,8.593000000000018,2.04153,85.8787,16.2,957.4,2.25579,-3.23226,40.0162
|
||||
2016-02-12 08:47:33,40.3491831,115.7845048,7.691000000000031,2.01561,84.4265,16.2,957.4,2.24309,2.06283,40.2772
|
||||
2016-02-12 08:47:34,40.3491783,115.7844987,6.6720000000000255,2.23031,79.3183,16.2,957.4,2.24866,3.54644,46.1237
|
||||
2016-02-12 08:47:35,40.3491704,115.7844869,5.802000000000021,2.77827,78.7524,16.2,957.6,2.24481,0.782934,49.3694
|
||||
2016-02-12 08:47:36,40.3491614,115.7844734,5.021000000000015,2.6489,80.7009,16.2,957.6,2.24837,-3.47997,49.2569
|
||||
|
95
examples/basic_usage/testconfig.yaml
Normal file
95
examples/basic_usage/testconfig.yaml
Normal file
@ -0,0 +1,95 @@
|
||||
# testconfig.yaml
|
||||
|
||||
horizontal_pixels: 500 # Width of the concentration map in pixels
|
||||
vertical_pixels: 100 # Height of the concentration map in pixels
|
||||
num_plumes: 10 # Number of Gaussian plumes
|
||||
groupiness: 0.5 # Groupiness of the plumes (0.0 to 1.0)
|
||||
spread: 0.1 # Spread of the plumes (0.0 to 1.0)
|
||||
wind_reference_height: 10 # Reference height for wind speed calculation (m)
|
||||
windspeed_avg: 5 # Average wind speed at 10m height (m/s)
|
||||
windspeed_rel_std: 0.2 # Relative standard deviation of wind speed (0.0 to inf), recommend 0.2-0.4
|
||||
surface_roughness: 0.1 # Surface roughness length (m)
|
||||
seed: 42 # Random seed for reproducibility
|
||||
simplex_octaves: 4 # Number of octaves for simplex noise (1 to inf, def 1)
|
||||
simplex_persistence: 0.7 # Persistence of simplex noise (0.0 to 1.0, def 0.5) - specifies the amplitude of each octave relative to the one below it
|
||||
simplex_lacunarity: 2.0 # Lacunarity of simplex noise (1.0 to inf, def 2.0) - specifies the frequency of each octave relative to the one below it
|
||||
winddir_avg: 0.0 # Average wind direction in degrees rel to plane (0 is CW)
|
||||
winddir_std: 10 # Standard deviation of wind direction in degrees
|
||||
timestamp: "2022-09-26 02:03:00"
|
||||
flight_time_seconds: 1000
|
||||
sample_frequency: 1
|
||||
start_coords:
|
||||
- 54.87667
|
||||
- 15.41
|
||||
transect_azimuth: 260 # the wind will start off 90 degrees CW to this azimuth, and is modified relative to that by the winddir_avg. 260 is a good value to test N problems
|
||||
sampling_altitude_ato_range:
|
||||
- -10 # negative values should be fine
|
||||
- 100
|
||||
sampling_horizontal_range:
|
||||
- 50
|
||||
- 950
|
||||
scene_altitude_range:
|
||||
- -20
|
||||
- 120
|
||||
scene_horizontal_range:
|
||||
- 0
|
||||
- 1000
|
||||
number_of_transects: 10
|
||||
gases:
|
||||
ch4:
|
||||
- 1.95
|
||||
- 10.0
|
||||
co2:
|
||||
- 380.0
|
||||
- 500.0
|
||||
c2h6:
|
||||
- 0.0
|
||||
- 1.0
|
||||
temperature: 10.0
|
||||
pressure: 1000.0
|
||||
|
||||
output_dir: ./gasflux_reports
|
||||
|
||||
algorithmic_baseline_settings:
|
||||
algorithm: fastchrom
|
||||
|
||||
semivariogram_settings:
|
||||
model: spherical
|
||||
estimator: cressie
|
||||
n_lags: 20
|
||||
bin_func: even
|
||||
fit_method: lm
|
||||
maxlag: 100
|
||||
#fit_sigma: linear
|
||||
tolerance: 10
|
||||
azimuth: 0
|
||||
bandwidth: 20
|
||||
|
||||
ordinary_kriging_settings:
|
||||
min_points: 3
|
||||
max_points: 100
|
||||
grid_resolution: 500
|
||||
min_nodes: 10
|
||||
cut_ground: False
|
||||
y_min: ~
|
||||
|
||||
required_cols:
|
||||
latitude: [-90, 90]
|
||||
longitude: [-180, 180]
|
||||
height_ato: [-100, 500]
|
||||
windspeed: [0, 30]
|
||||
winddir: [0, 360]
|
||||
temperature: [-50, 60]
|
||||
pressure: [900, 1100]
|
||||
|
||||
filters:
|
||||
course_filter:
|
||||
azimuth_filter: 10
|
||||
azimuth_window: 5
|
||||
elevation_filter: 5
|
||||
|
||||
strategies:
|
||||
background: "algorithm"
|
||||
sensor: "insitu"
|
||||
spatial: "curtain"
|
||||
interpolation: "kriging"
|
||||
120
pyproject.toml
Normal file
120
pyproject.toml
Normal file
@ -0,0 +1,120 @@
|
||||
[project]
|
||||
name = "gasflux"
|
||||
dynamic = ["version", "dependencies", "optional-dependencies"]
|
||||
description = "A package to calculate gas emissions fluxes from natural and anthropogenic sources"
|
||||
authors = [{ name = "Jamie McQuilkin", email = "jamie.mcquilkin@gmail.com" }]
|
||||
license = { text = "AGPL-3.0" }
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
|
||||
[project.scripts]
|
||||
gasflux = "gasflux.cli:main_cli"
|
||||
|
||||
[project.urls]
|
||||
"Homepage" = "https://gasflux.github.io"
|
||||
"Bug Tracker" = "https://github.com/gasflux/gasflux/issues"
|
||||
|
||||
[build-system]
|
||||
requires = ["setuptools>=69.0", "wheel", "setuptools-scm>=8.0"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[tool.setuptools.dynamic]
|
||||
version = {attr = "gasflux.__version__"}
|
||||
dependencies = { file = ["requirements.txt"] }
|
||||
optional-dependencies = { dev = { file = ["dev-requirements.txt"] } }
|
||||
|
||||
[tool.mypy]
|
||||
ignore_missing_imports = true
|
||||
check_untyped_defs = true
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 120
|
||||
target-version = "py311"
|
||||
|
||||
[tool.ruff.lint]
|
||||
ignore = [
|
||||
"G004", # G004: Logging statement uses f-string
|
||||
"PD901", # PD901: Using "df" as a variable name is not recommended
|
||||
"ISC001", # ISC001: Use of implicit string concatenation
|
||||
]
|
||||
|
||||
|
||||
select = [
|
||||
"A", # prevent using keywords that clobber python builtins
|
||||
"B", # bugbear: security warnings
|
||||
"E", # pycodestyle
|
||||
"F", # pyflakes
|
||||
"ISC", # implicit string concatenation
|
||||
"UP", # alert you when better syntax is available in your python version
|
||||
"RUF", # the ruff developer's own rules
|
||||
]
|
||||
|
||||
[tool.ruff.lint.pydocstyle]
|
||||
convention = "google"
|
||||
|
||||
[tool.semantic_release]
|
||||
assets = []
|
||||
commit_message = "{version}\n\nAutomatically generated by python-semantic-release"
|
||||
commit_parser = "angular"
|
||||
logging_use_named_masks = false
|
||||
major_on_zero = true
|
||||
allow_zero_version = true
|
||||
tag_format = "v{version}"
|
||||
version_variables = ["src/gasflux/__init__.py:__version__"]
|
||||
|
||||
[tool.semantic_release.branches.main]
|
||||
match = "(main|master)"
|
||||
prerelease_token = "rc"
|
||||
prerelease = false
|
||||
|
||||
[tool.semantic_release.changelog]
|
||||
# template_dir = "templates"
|
||||
changelog_file = "CHANGELOG.md"
|
||||
exclude_commit_patterns = ["ci", "test", "chore", "docs", "style", "refactor"]
|
||||
|
||||
[tool.semantic_release.changelog.environment]
|
||||
block_start_string = "{%"
|
||||
block_end_string = "%}"
|
||||
variable_start_string = "{{"
|
||||
variable_end_string = "}}"
|
||||
comment_start_string = "{#"
|
||||
comment_end_string = "#}"
|
||||
trim_blocks = false
|
||||
lstrip_blocks = false
|
||||
newline_sequence = "\n"
|
||||
keep_trailing_newline = false
|
||||
extensions = []
|
||||
autoescape = true
|
||||
|
||||
[tool.semantic_release.commit_author]
|
||||
env = "GIT_COMMIT_AUTHOR"
|
||||
default = "semantic-release <semantic-release>"
|
||||
|
||||
[tool.semantic_release.commit_parser_options]
|
||||
allowed_tags = [
|
||||
"build",
|
||||
"chore",
|
||||
"ci",
|
||||
"docs",
|
||||
"feat",
|
||||
"fix",
|
||||
"perf",
|
||||
"style",
|
||||
"refactor",
|
||||
"test",
|
||||
]
|
||||
minor_tags = ["feat"]
|
||||
patch_tags = ["fix", "perf"]
|
||||
default_bump_level = 0
|
||||
|
||||
[tool.semantic_release.remote]
|
||||
name = "origin"
|
||||
type = "github"
|
||||
ignore_token_for_push = false
|
||||
|
||||
[tool.semantic_release.remote.token]
|
||||
env = "GH_TOKEN"
|
||||
|
||||
[tool.semantic_release.publish]
|
||||
dist_glob_patterns = ["dist/*"]
|
||||
upload_to_vcs_release = true
|
||||
20
requirements.txt
Normal file
20
requirements.txt
Normal file
@ -0,0 +1,20 @@
|
||||
# Automatically generated by https://github.com/damnever/pigar.
|
||||
|
||||
colorama==0.4.6
|
||||
geopandas==0.14.3
|
||||
geopy==2.4.1
|
||||
Jinja2==3.1.6
|
||||
joblib==1.3.2
|
||||
matplotlib==3.10.0
|
||||
molmass==2023.8.30
|
||||
noise==1.2.2
|
||||
numpy==2.1.3
|
||||
pandas==2.2.3
|
||||
plotly==5.20.0
|
||||
pybaselines==1.1.0
|
||||
pytest==8.1.1
|
||||
PyYAML==6.0.1
|
||||
scikit-gstat==1.0.19
|
||||
scikit-image==0.24.0
|
||||
scipy==1.15.1
|
||||
simplekml==1.3.6
|
||||
0
src/__init__.py
Normal file
0
src/__init__.py
Normal file
29
src/gasflux/__init__.py
Normal file
29
src/gasflux/__init__.py
Normal file
@ -0,0 +1,29 @@
|
||||
"""Init file for gasflux package."""
|
||||
|
||||
__version__ = "0.2.1" # managed by semantic versioning
|
||||
|
||||
from . import (
|
||||
background,
|
||||
cli,
|
||||
gas,
|
||||
interpolation,
|
||||
ml,
|
||||
plotting,
|
||||
pre_processing,
|
||||
processing,
|
||||
processing_pipelines,
|
||||
reporting,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"background",
|
||||
"cli",
|
||||
"gas",
|
||||
"interpolation",
|
||||
"ml",
|
||||
"plotting",
|
||||
"pre_processing",
|
||||
"processing",
|
||||
"processing_pipelines",
|
||||
"reporting",
|
||||
]
|
||||
64
src/gasflux/background.py
Normal file
64
src/gasflux/background.py
Normal file
@ -0,0 +1,64 @@
|
||||
"""Baselining functions."""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pybaselines as pybs
|
||||
from . import plotting
|
||||
|
||||
# 自定义阈值函数，避免依赖scikit-image
|
||||
def custom_threshold(data):
|
||||
"""
|
||||
简单的三角阈值算法实现，避免依赖scikit-image
|
||||
使用直方图的三角法来确定阈值
|
||||
"""
|
||||
if len(data) == 0:
|
||||
return 0
|
||||
|
||||
# 计算直方图
|
||||
hist, bin_edges = np.histogram(data, bins=256)
|
||||
bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
|
||||
|
||||
# 找到最大峰值
|
||||
max_idx = np.argmax(hist)
|
||||
|
||||
# 如果最大峰在边缘，使用中位数作为阈值
|
||||
if max_idx == 0 or max_idx == len(hist) - 1:
|
||||
return np.median(data)
|
||||
|
||||
# 使用简单的阈值策略：最大峰右侧的第一个局部最小值
|
||||
# 这里简化为使用均值作为阈值
|
||||
return np.mean(data)
|
||||
|
||||
|
||||
def algorithmic_baseline(
|
||||
df: pd.DataFrame,
|
||||
gas: str,
|
||||
algorithmic_baseline_settings: dict,
|
||||
):
|
||||
df = df.copy()
|
||||
algorithm = algorithmic_baseline_settings["algorithm"]
|
||||
settings = algorithmic_baseline_settings.get(algorithm, {}).copy()
|
||||
if settings.get("threshold") == "custom":
|
||||
settings["threshold"] = custom_threshold
|
||||
if len(df) < 20:
|
||||
raise ValueError("Dataframe must contain at least 20 rows for background correction.")
|
||||
index = np.arange(len(df))
|
||||
baseline_fitter = pybs.Baseline(index, check_finite=False)
|
||||
fit = getattr(baseline_fitter, algorithm)
|
||||
bkg, params = fit(df[gas], **settings)
|
||||
bkg_points = params["mask"]
|
||||
df[f"{gas}_normalised"] = df[gas] - bkg
|
||||
df[f"{gas}_fit"] = bkg
|
||||
background = (df[gas] - bkg)[bkg_points]
|
||||
signal = (df[gas] - bkg)[~bkg_points]
|
||||
df[f"{gas}_signal"] = np.invert(bkg_points)
|
||||
fig = plotting.background_plotting(df, gas)
|
||||
output_text = (
|
||||
f"Baseline algorithm: {algorithm}\n"
|
||||
f"Positive and negative 95% percentile of baseline: {np.percentile(background, 2.5):.2f} ppm, \
|
||||
{np.percentile(background, 97.5):.2f} ppm\n"
|
||||
f"Mean of baseline: {np.mean(background):.2f} ppm\n"
|
||||
f"Minimum and maximum of baseline: {np.min(background):.2f} ppm, {np.max(background):.2f} ppm\n"
|
||||
f"Signal points: {len(signal)}; background points: {len(background)}\n"
|
||||
)
|
||||
return df, fig, output_text
|
||||
279
src/gasflux/cli.py
Normal file
279
src/gasflux/cli.py
Normal file
@ -0,0 +1,279 @@
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
|
||||
from colorama import init, Fore, Style
|
||||
from src.gasflux.processing_pipelines import process_main
|
||||
|
||||
init()
|
||||
|
||||
|
||||
def find_config_file(path: Path, recursive: bool = True):
|
||||
if recursive:
|
||||
config_files = [file for file in path.rglob("*.yaml") if "gasflux_config" in file.read_text()]
|
||||
else:
|
||||
config_files = [file for file in path.glob("*.yaml") if "gasflux_config" in file.read_text()]
|
||||
if len(config_files) == 1:
|
||||
return config_files[0]
|
||||
elif len(config_files) > 1:
|
||||
raise ValueError(
|
||||
"Multiple candidate config files found: {}".format(", ".join(str(file) for file in config_files))
|
||||
)
|
||||
else:
|
||||
raise FileNotFoundError("No config file found in the supplied path or its child folders")
|
||||
|
||||
|
||||
def process_command(data_path: str, config_path: str, test: bool):
|
||||
if test:
|
||||
data_file = Path(__file__).parent / "testdata" / "testdata.csv"
|
||||
config_file = Path(__file__).parent / "testdata" / "testconfig.yaml"
|
||||
process_main(data_file, config_file)
|
||||
return
|
||||
|
||||
dpath_obj = Path(data_path)
|
||||
|
||||
if dpath_obj.is_dir():
|
||||
data_files = list(dpath_obj.rglob("*.csv"))
|
||||
if not data_files:
|
||||
raise FileNotFoundError(f"No CSV files found in directory: {data_path}")
|
||||
elif dpath_obj.is_file():
|
||||
data_files = [dpath_obj]
|
||||
else:
|
||||
raise FileNotFoundError(f"Invalid data path: {data_path}")
|
||||
|
||||
if config_path is None:
|
||||
try:
|
||||
config_file = find_config_file(dpath_obj if dpath_obj.is_dir() else dpath_obj.parent)
|
||||
except FileNotFoundError:
|
||||
response = (
|
||||
input(
|
||||
"No configuration file found. Generate config file? \n"
|
||||
"You may have to modify it before processing. [y/n]: "
|
||||
)
|
||||
.strip()
|
||||
.lower()
|
||||
)
|
||||
|
||||
if response == "y":
|
||||
config_file = (dpath_obj if dpath_obj.is_dir() else dpath_obj.parent) / "gasflux_config.yaml"
|
||||
shutil.copy(Path(__file__).parent / "gasflux_config.yaml", config_file)
|
||||
print(f"Config file copied to: {config_file}")
|
||||
else:
|
||||
print("No config file generated. Exiting.")
|
||||
return
|
||||
else:
|
||||
config_file = Path(config_path)
|
||||
|
||||
for data_file in data_files:
|
||||
process_main(data_file, config_file)
|
||||
|
||||
|
||||
def generate_config_command(config_destination: str, recursive: bool = False, template_path: str | None = None):
|
||||
if template_path:
|
||||
template = Path(template_path)
|
||||
print(f"Using custom template: {template}")
|
||||
else:
|
||||
template = Path(__file__).parent / "gasflux_config.yaml"
|
||||
|
||||
if not template.exists():
|
||||
raise FileNotFoundError(f"Template file not found: {template}")
|
||||
|
||||
destination_path = Path(config_destination)
|
||||
|
||||
if not destination_path.is_dir():
|
||||
raise NotADirectoryError(f"Destination path is not a directory: {config_destination}")
|
||||
|
||||
overwrite_all = False # Flag to determine if we should overwrite all files without prompting
|
||||
|
||||
if recursive:
|
||||
for subdir in destination_path.rglob("*"):
|
||||
if subdir.is_dir():
|
||||
config_file = subdir / "gasflux_config.yaml"
|
||||
if config_file.exists() and not overwrite_all:
|
||||
response = (
|
||||
input(f"Config file already exists at {config_file}. Overwrite? [y/n/a/c]: ").strip().lower()
|
||||
)
|
||||
if response == "a":
|
||||
overwrite_all = True
|
||||
elif response == "c":
|
||||
print("Operation canceled.")
|
||||
return
|
||||
elif response == "n":
|
||||
print(f"Skipping {config_file}")
|
||||
continue
|
||||
shutil.copy(template, config_file)
|
||||
print(f"Config file copied to: {config_file}")
|
||||
else:
|
||||
config_file = destination_path / "gasflux_config.yaml"
|
||||
if config_file.exists() and not overwrite_all:
|
||||
response = input(f"Config file already exists at {config_file}. Overwrite? [y/n/a/c]: ").strip().lower()
|
||||
if response == "a":
|
||||
overwrite_all = True
|
||||
elif response == "c":
|
||||
print("Operation canceled.")
|
||||
return
|
||||
elif response == "n":
|
||||
print(f"Skipping {config_file}")
|
||||
return
|
||||
shutil.copy(template, config_file)
|
||||
print(f"Config file copied to: {config_file}")
|
||||
|
||||
|
||||
def main_cli():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="GasFlux Processing Pipeline",
|
||||
formatter_class=argparse.RawTextHelpFormatter,
|
||||
add_help=False, # Disable the default help to use the custom help screen
|
||||
)
|
||||
subparsers = parser.add_subparsers(dest="command")
|
||||
|
||||
process_parser = subparsers.add_parser(
|
||||
"process",
|
||||
help="Process CSV data files",
|
||||
description=(
|
||||
"Process CSV data files located in a specified directory or a single file.\n"
|
||||
"The program will search for a configuration file (gasflux_config.yaml) in the same directory as the data file(s).\n" # noqa
|
||||
),
|
||||
formatter_class=argparse.RawTextHelpFormatter,
|
||||
)
|
||||
process_parser.add_argument(
|
||||
"data_path",
|
||||
nargs="?",
|
||||
default=None,
|
||||
help=(
|
||||
"Path to the data file or directory containing CSV files.\n"
|
||||
"If a directory is provided, all CSV files within the directory will be processed."
|
||||
),
|
||||
)
|
||||
process_parser.add_argument(
|
||||
"--config-path",
|
||||
"-c",
|
||||
default=None,
|
||||
help=(
|
||||
"Path to the configuration file (gasflux_config.yaml).\n"
|
||||
"If not specified, the program will search for the configuration file in the same directory as the data file(s).\n" # noqa
|
||||
"If no configuration file is found, the default configuration will be used."
|
||||
),
|
||||
)
|
||||
process_parser.add_argument(
|
||||
"--test",
|
||||
action="store_true",
|
||||
help="Use test data instead of the specified data file(s).",
|
||||
)
|
||||
|
||||
config_parser = subparsers.add_parser(
|
||||
"generate-config",
|
||||
help="Generate a configuration file",
|
||||
description=(
|
||||
"Generate a configuration file (gasflux_config.yaml) in the specified directory.\n"
|
||||
"If no directory is specified, the configuration file will be generated in the current directory."
|
||||
"Use flag --recursive to generate config files in all subdirectories recursively. YOu might want to"
|
||||
"do this if you want to have a separate config for each data file"
|
||||
),
|
||||
formatter_class=argparse.RawTextHelpFormatter,
|
||||
)
|
||||
config_parser.add_argument(
|
||||
"--recursive",
|
||||
action="store_true",
|
||||
help="Generate config files in all subdirectories recursively.",
|
||||
)
|
||||
|
||||
config_parser.add_argument(
|
||||
"--template",
|
||||
default=None,
|
||||
help="Path to a custom template configuration file.",
|
||||
)
|
||||
|
||||
config_parser.add_argument(
|
||||
"config_destination",
|
||||
nargs="?",
|
||||
default=None,
|
||||
help="Destination directory for the generated configuration file.",
|
||||
)
|
||||
|
||||
# Custom help option
|
||||
parser.add_argument(
|
||||
"-h",
|
||||
"--help",
|
||||
action="store_true",
|
||||
help="Show this help message and exit",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.command == "process":
|
||||
process_command(args.data_path, args.config_path, args.test)
|
||||
elif args.command == "generate-config":
|
||||
generate_config_command(args.config_destination, args.recursive, args.template)
|
||||
elif args.help:
|
||||
display_help()
|
||||
else:
|
||||
display_help()
|
||||
|
||||
|
||||
def display_help():
|
||||
help_text = f"""
|
||||
{Fore.CYAN}GasFlux Processing Pipeline{Style.RESET_ALL}
|
||||
{Fore.CYAN}==========================={Style.RESET_ALL}
|
||||
|
||||
{Fore.YELLOW}Description:{Style.RESET_ALL}
|
||||
The GasFlux Processing Pipeline is a command-line tool for processing CSV data files and generating configuration files.
|
||||
|
||||
{Fore.YELLOW}Usage:{Style.RESET_ALL}
|
||||
gasflux [OPTIONS] COMMAND [ARGS]...
|
||||
|
||||
{Fore.YELLOW}Commands:{Style.RESET_ALL}
|
||||
{Fore.GREEN}process{Style.RESET_ALL} Process CSV data files
|
||||
{Fore.GREEN}generate-config{Style.RESET_ALL} Generate a default configuration file
|
||||
|
||||
{Fore.YELLOW}Options:{Style.RESET_ALL}
|
||||
{Fore.BLUE}-h, --help{Style.RESET_ALL} Show this help message and exit
|
||||
{Fore.BLUE}-v, --version{Style.RESET_ALL} Show the version and exit
|
||||
|
||||
{Fore.YELLOW}Commands Help:{Style.RESET_ALL}
|
||||
{Fore.GREEN}process{Style.RESET_ALL} Process CSV data files located in a specified directory or a single file.
|
||||
The program will search for a singular configuration file (gasflux_config.yaml) in the same directory
|
||||
- if a directory is supplied to [DATA_PATH] then child directories are searched too.
|
||||
|
||||
{Fore.YELLOW}Usage:{Style.RESET_ALL} gasflux process [OPTIONS] [DATA_PATH]
|
||||
|
||||
{Fore.YELLOW}Options:{Style.RESET_ALL}
|
||||
{Fore.BLUE}-c, --config-path PATH{Style.RESET_ALL} Path to the configuration file (gasflux_config.yaml).
|
||||
If not specified, the program will search for the configuration file
|
||||
in the same directory as the data file(s).
|
||||
If no configuration file is found, the default configuration will be used.
|
||||
{Fore.BLUE}--test{Style.RESET_ALL} Use test data instead of the specified data file(s).
|
||||
{Fore.BLUE}-h, --help{Style.RESET_ALL} Show this help message and exit
|
||||
|
||||
{Fore.GREEN}generate-config{Style.RESET_ALL} Generate a default configuration file (gasflux_config.yaml) in the specified directory.
|
||||
If no directory is specified, the configuration file will be generated in the current directory.
|
||||
|
||||
{Fore.YELLOW}Usage:{Style.RESET_ALL} gasflux generate-config [OPTIONS] [CONFIG_DESTINATION]
|
||||
|
||||
{Fore.YELLOW}Options:{Style.RESET_ALL}
|
||||
{Fore.BLUE}-h, --help{Style.RESET_ALL} Show this help message and exit
|
||||
|
||||
{Fore.YELLOW}Examples:{Style.RESET_ALL}
|
||||
Process a single CSV file:
|
||||
$ gasflux process {Fore.MAGENTA}"/path/to/data.csv"{Style.RESET_ALL}
|
||||
|
||||
Process all CSV files in a directory:
|
||||
$ gasflux process {Fore.MAGENTA}"/path/to/data/directory"{Style.RESET_ALL}
|
||||
|
||||
Process a single CSV file with a specific configuration file:
|
||||
$ gasflux process {Fore.MAGENTA}"/path/to/data.csv"{Style.RESET_ALL} --config-path {Fore.MAGENTA}/path/to/config.yaml{Style.RESET_ALL}
|
||||
|
||||
Process test data:
|
||||
$ gasflux process {Fore.BLUE}--test{Style.RESET_ALL}
|
||||
|
||||
Generate a default configuration file in the current directory:
|
||||
$ gasflux generate-config {Fore.MAGENTA}.{Style.RESET_ALL}
|
||||
|
||||
Generate a default configuration file in a specific directory recursively using a custom template:
|
||||
$ gasflux generate-config {Fore.MAGENTA}"/path/to/directory"{Style.RESET_ALL} {Fore.BLUE}--recursive{Style.RESET_ALL} {Fore.BLUE}--template{Style.RESET_ALL} {Fore.MAGENTA}"/path/to/template.yaml"{Style.RESET_ALL}
|
||||
""" # noqa
|
||||
print(help_text)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main_cli()
|
||||
934
src/gasflux/data_processor.py
Normal file
934
src/gasflux/data_processor.py
Normal file
@ -0,0 +1,934 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
无人机数据处理脚本
|
||||
|
||||
处理Excel格式的无人机测量数据，转换为GasFlux标准输入格式。
|
||||
|
||||
支持两种使用方式：
|
||||
1. 命令行调用：python data_processor.py input.xlsx
|
||||
2. 直接调用：from data_processor import process_file; df = process_file('input.xlsx')
|
||||
|
||||
处理步骤：
|
||||
1. 读取Excel文件
|
||||
2. 删除不需要的列
|
||||
3. 根据文件名修正时间格式
|
||||
4. 坐标转换（经纬度除以10^7）
|
||||
5. 计算气压（使用qiya.py）
|
||||
6. 高度调整（减去最小高度）
|
||||
7. 时间戳融合
|
||||
8. 字段重命名
|
||||
|
||||
作者：GasFlux开发团队
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
import re
|
||||
from datetime import datetime
|
||||
import sys
|
||||
import os
|
||||
from collections import Counter
|
||||
|
||||
try:
|
||||
from tqdm import tqdm
|
||||
HAS_TQDM = True
|
||||
except ImportError:
|
||||
HAS_TQDM = False
|
||||
print("⚠️ 未安装tqdm库，将不显示进度条。如需进度条，请运行: pip install tqdm")
|
||||
|
||||
|
||||
def create_height_bins(heights, bin_size=2.0):
|
||||
"""
|
||||
将高度数据按指定间隔分档
|
||||
|
||||
Args:
|
||||
heights: 高度数据Series
|
||||
bin_size: 每个档位的间隔（米）
|
||||
|
||||
Returns:
|
||||
list: [(bin_min, bin_max, bin_center, count), ...] 每个档位的信息
|
||||
"""
|
||||
if len(heights) == 0:
|
||||
return []
|
||||
|
||||
height_min = heights.min()
|
||||
height_max = heights.max()
|
||||
|
||||
# 计算需要的档位数量
|
||||
range_size = height_max - height_min
|
||||
if range_size == 0:
|
||||
# 所有高度相同
|
||||
return [(height_min, height_max, height_min, len(heights))]
|
||||
|
||||
num_bins = max(1, int(np.ceil(range_size / bin_size)))
|
||||
|
||||
bins = []
|
||||
for i in range(num_bins):
|
||||
bin_min = height_min + i * bin_size
|
||||
bin_max = min(height_min + (i + 1) * bin_size, height_max)
|
||||
bin_center = (bin_min + bin_max) / 2
|
||||
|
||||
# 统计这个档位有多少数据点
|
||||
count = ((heights >= bin_min) & (heights < bin_max)).sum()
|
||||
if i == num_bins - 1: # 最后一个档位包含上限
|
||||
count = ((heights >= bin_min) & (heights <= bin_max)).sum()
|
||||
|
||||
if count > 0: # 只保留有数据的档位
|
||||
bins.append((bin_min, bin_max, bin_center, count))
|
||||
|
||||
return bins
|
||||
|
||||
# 导入qiya模块
|
||||
try:
|
||||
from .qiya import get_pressure_at_location
|
||||
print("✅ 成功导入qiya模块")
|
||||
except ImportError as e:
|
||||
print(f"❌ 导入qiya模块失败: {e}")
|
||||
print("请确保GasFlux包结构完整")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def load_excel_data(file_path):
|
||||
"""
|
||||
读取Excel文件并进行初步处理
|
||||
|
||||
Args:
|
||||
file_path: Excel文件路径
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 读取的数据
|
||||
"""
|
||||
try:
|
||||
print(f"正在读取文件: {file_path}")
|
||||
|
||||
# 读取Excel文件
|
||||
df = pd.read_excel(file_path)
|
||||
print(f"✅ 成功读取数据：{len(df)} 行，{len(df.columns)} 列")
|
||||
print(f"列名：{list(df.columns)}")
|
||||
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 读取文件失败: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def remove_columns(df, columns_to_remove):
|
||||
"""
|
||||
删除指定的列
|
||||
|
||||
Args:
|
||||
df: 输入DataFrame
|
||||
columns_to_remove: 要删除的列名列表
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 删除列后的DataFrame
|
||||
"""
|
||||
print(f"\n删除列: {columns_to_remove}")
|
||||
|
||||
# 检查要删除的列是否存在
|
||||
existing_columns = [col for col in columns_to_remove if col in df.columns]
|
||||
missing_columns = [col for col in columns_to_remove if col not in df.columns]
|
||||
|
||||
if missing_columns:
|
||||
print(f"⚠️ 以下列不存在（跳过）: {missing_columns}")
|
||||
|
||||
if existing_columns:
|
||||
df = df.drop(columns=existing_columns)
|
||||
print(f"✅ 已删除 {len(existing_columns)} 列")
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def extract_hour_from_filename(filename):
|
||||
"""
|
||||
从文件名中提取小时信息
|
||||
|
||||
例如: "08_34_01_间隔高度5m.xlsx" -> "08"
|
||||
|
||||
Args:
|
||||
filename: 文件名
|
||||
|
||||
Returns:
|
||||
str: 小时字符串（两位数）
|
||||
"""
|
||||
# 使用正则表达式匹配小时部分
|
||||
match = re.match(r'(\d{2})_', filename)
|
||||
if match:
|
||||
return match.group(1)
|
||||
else:
|
||||
print(f"⚠️ 无法从文件名 '{filename}' 中提取小时信息，使用默认值 '00'")
|
||||
return "00"
|
||||
|
||||
|
||||
def fix_time_column(df, filename):
|
||||
"""
|
||||
根据文件名修正时间列
|
||||
|
||||
Args:
|
||||
df: 输入DataFrame
|
||||
filename: 文件名
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 修正后的DataFrame
|
||||
"""
|
||||
print("修正时间格式...")
|
||||
|
||||
# 提取小时信息
|
||||
hour_prefix = extract_hour_from_filename(filename)
|
||||
print(f"从文件名提取的小时: {hour_prefix}")
|
||||
|
||||
# 检查时间列是否存在
|
||||
if '时间' not in df.columns:
|
||||
print("❌ 未找到 '时间' 列")
|
||||
return df
|
||||
|
||||
# 修正时间格式
|
||||
def fix_single_time(time_str):
|
||||
if pd.isna(time_str):
|
||||
return time_str
|
||||
|
||||
time_str = str(time_str).strip()
|
||||
|
||||
# 如果时间格式类似 "0:34:01" 或 "00:34:01"
|
||||
if re.match(r'^\d{1,2}:\d{2}:\d{2}$', time_str):
|
||||
parts = time_str.split(':')
|
||||
original_hour = int(parts[0])
|
||||
filename_hour = int(hour_prefix)
|
||||
|
||||
# 小时相加
|
||||
new_hour = (filename_hour + original_hour) % 24 # 防止超过24小时
|
||||
|
||||
# 保持分钟和秒不变
|
||||
new_time = f"{new_hour:02d}:{parts[1]}:{parts[2]}"
|
||||
return new_time
|
||||
else:
|
||||
# 其他格式，使用默认时间
|
||||
return f"{hour_prefix}:00:00"
|
||||
|
||||
# 应用时间修正
|
||||
original_times = df['时间'].head(3).tolist()
|
||||
df['时间'] = df['时间'].apply(fix_single_time)
|
||||
corrected_times = df['时间'].head(3).tolist()
|
||||
|
||||
print(f"时间修正示例:")
|
||||
for orig, corr in zip(original_times, corrected_times):
|
||||
print(f" {orig} → {corr}")
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def convert_coordinates(df):
|
||||
"""
|
||||
转换经纬度坐标（除以10^7）
|
||||
|
||||
Args:
|
||||
df: 输入DataFrame
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 转换后的DataFrame
|
||||
"""
|
||||
print("转换经纬度坐标...")
|
||||
|
||||
if '经度' in df.columns:
|
||||
original_lon = df['经度'].head(3).tolist()
|
||||
df['经度'] = df['经度'] / 1e7
|
||||
converted_lon = df['经度'].head(3).tolist()
|
||||
print("经度转换示例:")
|
||||
for orig, conv in zip(original_lon, converted_lon):
|
||||
print(".6f")
|
||||
|
||||
if '纬度' in df.columns:
|
||||
original_lat = df['纬度'].head(3).tolist()
|
||||
df['纬度'] = df['纬度'] / 1e7
|
||||
converted_lat = df['纬度'].head(3).tolist()
|
||||
print("纬度转换示例:")
|
||||
for orig, conv in zip(original_lat, converted_lat):
|
||||
print(".6f")
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def calculate_pressure(df, max_samples=None, height_tolerance=10.0, height_bin_size=2.0):
|
||||
"""
|
||||
计算气压数据（高度分档优化版）
|
||||
|
||||
Args:
|
||||
df: 输入DataFrame
|
||||
max_samples: 最大采样数量（None表示计算所有行）
|
||||
height_tolerance: 高度变化容差（米），如果所有高度都在此范围内，只计算一次
|
||||
height_bin_size: 高度分档间隔（米），每个档位使用中间高度计算气压
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 添加气压列的DataFrame
|
||||
"""
|
||||
print("计算气压数据...")
|
||||
|
||||
# 检查必要列是否存在
|
||||
required_cols = ['日期', '时间', '经度', '纬度', '融合高程']
|
||||
missing_cols = [col for col in required_cols if col not in df.columns]
|
||||
|
||||
if missing_cols:
|
||||
print(f"❌ 缺少必要列: {missing_cols}")
|
||||
return df
|
||||
|
||||
# 检查高度变化范围
|
||||
height_min = df['融合高程'].min()
|
||||
height_max = df['融合高程'].max()
|
||||
height_range = height_max - height_min
|
||||
|
||||
print(f"🏔️ 高度范围: {height_min:.1f} - {height_max:.1f} 米 (变化: {height_range:.1f} 米)")
|
||||
# 创建高度分档
|
||||
height_bins = create_height_bins(df['融合高程'], height_bin_size)
|
||||
print(f"📏 高度分档: {len(height_bins)} 个档位 (间隔: {height_bin_size:.1f} 米)")
|
||||
|
||||
for i, (bin_min, bin_max, bin_center, count) in enumerate(height_bins):
|
||||
print(f" 档位{i+1}: {bin_min:.1f}-{bin_max:.1f}m (中心: {bin_center:.1f}m, 数据: {count}行)")
|
||||
# 决定计算策略
|
||||
if height_range <= height_tolerance:
|
||||
# 高度变化小，只计算一次气压
|
||||
print("🎯 高度变化小，将使用平均高度计算一次气压")
|
||||
use_single_calculation = True
|
||||
mean_height = df['融合高程'].mean()
|
||||
print(f"📍 使用平均高度: {mean_height:.1f} 米")
|
||||
elif len(height_bins) == 1:
|
||||
# 只有一个高度档位，使用档位中心高度
|
||||
print("📦 只有一个高度档位，使用档位中心高度")
|
||||
use_single_calculation = True
|
||||
mean_height = height_bins[0][2] # bin_center
|
||||
print(f"📍 使用档位中心高度: {mean_height:.1f} 米")
|
||||
else:
|
||||
# 高度变化大，使用分档计算
|
||||
print("🏗️ 使用高度分档策略，减少API调用")
|
||||
use_single_calculation = False
|
||||
|
||||
# 确定要处理的行数
|
||||
if max_samples is None or max_samples >= len(df):
|
||||
# 计算所有行
|
||||
sample_df = df.copy()
|
||||
actual_samples = len(df)
|
||||
if not use_single_calculation:
|
||||
print(f"📊 将计算所有 {len(df)} 行的气压数据")
|
||||
else:
|
||||
# 限制采样数量
|
||||
print(f"⚠️ 数据量较大 ({len(df)} 行)，只对前 {max_samples} 行计算气压")
|
||||
sample_df = df.head(max_samples).copy()
|
||||
actual_samples = max_samples
|
||||
|
||||
pressures = []
|
||||
|
||||
if use_single_calculation:
|
||||
# 只计算一次气压
|
||||
try:
|
||||
# 使用第一行的日期和时间作为代表
|
||||
first_row = sample_df.iloc[0]
|
||||
|
||||
# 转换日期格式 - 只提取日期部分，移除任何时间信息
|
||||
date_str = str(first_row['日期'])
|
||||
if ' ' in date_str:
|
||||
date_str = date_str.split(' ')[0]
|
||||
elif 'T' in date_str:
|
||||
date_str = date_str.split('T')[0]
|
||||
|
||||
if '/' in date_str:
|
||||
date_str = date_str.replace('/', '-')
|
||||
|
||||
date_parts = date_str.split('-')
|
||||
if len(date_parts) == 3 and len(date_parts[0]) == 4:
|
||||
year, month, day = date_parts
|
||||
formatted_date = f"{year}-{month.zfill(2)}-{day.zfill(2)}"
|
||||
else:
|
||||
raise ValueError(f"日期格式异常: {date_str}")
|
||||
|
||||
# 使用数据的代表性时间（整点小时，众数）
|
||||
time_strings = []
|
||||
for time_val in sample_df['时间']:
|
||||
time_str = str(time_val).strip()
|
||||
if ':' in time_str:
|
||||
# 确保是有效的 HH:MM 格式，然后取整点小时
|
||||
parts = time_str.split(':')
|
||||
if len(parts) >= 2:
|
||||
try:
|
||||
hour = int(parts[0])
|
||||
# 确保小时在有效范围内 (0-23)
|
||||
if 0 <= hour <= 23:
|
||||
time_strings.append(f"{hour:02d}:00")
|
||||
except ValueError:
|
||||
continue
|
||||
|
||||
if time_strings:
|
||||
time_counts = Counter(time_strings)
|
||||
formatted_time = time_counts.most_common(1)[0][0]
|
||||
occurrence_count = time_counts.most_common(1)[0][1]
|
||||
print(f"数据时间选择: {formatted_time} ({occurrence_count}/{len(time_strings)} 次，{occurrence_count/len(time_strings)*100:.1f}%)")
|
||||
else:
|
||||
formatted_time = "12:00"
|
||||
print("无有效时间数据，使用默认中午12:00")
|
||||
|
||||
print("正在计算平均气压...")
|
||||
pressure = get_pressure_at_location(
|
||||
lat=sample_df['纬度'].mean(),
|
||||
lon=sample_df['经度'].mean(),
|
||||
altitude=mean_height,
|
||||
date=formatted_date,
|
||||
time=formatted_time
|
||||
)
|
||||
|
||||
if pressure is not None:
|
||||
pressures = [pressure] * len(sample_df)
|
||||
print("✅ 平均气压计算成功，将应用到所有行")
|
||||
else:
|
||||
print("❌ 平均气压计算失败")
|
||||
pressures = [None] * len(sample_df)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 平均气压计算失败: {e}")
|
||||
pressures = [None] * len(sample_df)
|
||||
|
||||
else:
|
||||
# 使用高度分档策略
|
||||
print("🏗️ 开始分档计算气压...")
|
||||
|
||||
# 为每个高度档位计算气压
|
||||
bin_pressures = {} # bin_center -> pressure
|
||||
|
||||
# 设置进度条
|
||||
iterator = height_bins
|
||||
if HAS_TQDM:
|
||||
iterator = tqdm(iterator, total=len(height_bins), desc="计算气压档位", unit="档")
|
||||
|
||||
for bin_min, bin_max, bin_center, count in iterator:
|
||||
try:
|
||||
# 使用第一行数据作为代表来获取日期和时间
|
||||
# 找到这个档位中的一行数据
|
||||
bin_rows = sample_df[(sample_df['融合高程'] >= bin_min) &
|
||||
(sample_df['融合高程'] <= bin_max)]
|
||||
if len(bin_rows) == 0:
|
||||
continue
|
||||
|
||||
first_row = bin_rows.iloc[0]
|
||||
|
||||
# 转换日期格式 - 只提取日期部分，移除任何时间信息
|
||||
date_str = str(first_row['日期'])
|
||||
if ' ' in date_str:
|
||||
date_str = date_str.split(' ')[0]
|
||||
elif 'T' in date_str:
|
||||
date_str = date_str.split('T')[0]
|
||||
|
||||
if '/' in date_str:
|
||||
date_str = date_str.replace('/', '-')
|
||||
|
||||
date_parts = date_str.split('-')
|
||||
if len(date_parts) == 3 and len(date_parts[0]) == 4:
|
||||
year, month, day = date_parts
|
||||
formatted_date = f"{year}-{month.zfill(2)}-{day.zfill(2)}"
|
||||
else:
|
||||
print(f"⚠️ 档位高度 {bin_center:.1f}m 日期格式异常: {date_str}")
|
||||
bin_pressures[bin_center] = None
|
||||
continue
|
||||
|
||||
# 使用该档位数据的代表性时间（整点小时）
|
||||
time_strings = []
|
||||
for time_val in bin_rows['时间']:
|
||||
time_str = str(time_val).strip()
|
||||
if ':' in time_str:
|
||||
# 确保是有效的 HH:MM 格式，然后取整点小时
|
||||
parts = time_str.split(':')
|
||||
if len(parts) >= 2:
|
||||
try:
|
||||
hour = int(parts[0])
|
||||
# 确保小时在有效范围内 (0-23)
|
||||
if 0 <= hour <= 23:
|
||||
time_strings.append(f"{hour:02d}:00")
|
||||
except ValueError:
|
||||
continue
|
||||
|
||||
if time_strings:
|
||||
# 使用最常见的时间（众数）
|
||||
time_counts = Counter(time_strings)
|
||||
formatted_time = time_counts.most_common(1)[0][0]
|
||||
occurrence_count = time_counts.most_common(1)[0][1]
|
||||
print(f" 档位时间选择: {formatted_time} ({occurrence_count}/{len(time_strings)} 次，{occurrence_count/len(time_strings)*100:.1f}%)")
|
||||
else:
|
||||
# 如果没有有效时间，使用中午12点
|
||||
formatted_time = "12:00"
|
||||
print(" 无有效时间数据，使用默认中午12:00")
|
||||
|
||||
# 计算这个档位的气压（使用平均位置和档位中心高度）
|
||||
avg_lat = bin_rows['纬度'].mean()
|
||||
avg_lon = bin_rows['经度'].mean()
|
||||
|
||||
pressure = get_pressure_at_location(
|
||||
lat=avg_lat,
|
||||
lon=avg_lon,
|
||||
altitude=bin_center,
|
||||
date=formatted_date,
|
||||
time=formatted_time
|
||||
)
|
||||
|
||||
bin_pressures[bin_center] = pressure
|
||||
|
||||
# 更新进度条
|
||||
if HAS_TQDM:
|
||||
success_count = sum(1 for p in bin_pressures.values() if p is not None)
|
||||
iterator.set_description(f"计算档位 (成功: {success_count}/{len(bin_pressures)})")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 计算高度档位 {bin_center:.1f}m 气压失败: {e}")
|
||||
bin_pressures[bin_center] = None
|
||||
|
||||
# 为每一行分配对应档位的气压
|
||||
pressures = []
|
||||
for idx, row in sample_df.iterrows():
|
||||
# 找到这个高度对应的档位
|
||||
height = row['融合高程']
|
||||
assigned_pressure = None
|
||||
|
||||
for bin_min, bin_max, bin_center, count in height_bins:
|
||||
if bin_min <= height <= bin_max:
|
||||
assigned_pressure = bin_pressures.get(bin_center)
|
||||
break
|
||||
|
||||
pressures.append(assigned_pressure)
|
||||
|
||||
print(f"✅ 完成分档气压计算，共 {len(bin_pressures)} 个档位，{len([p for p in bin_pressures.values() if p is not None])} 个成功")
|
||||
|
||||
# 添加气压列
|
||||
df['pressure'] = None # 初始化
|
||||
df.loc[sample_df.index, 'pressure'] = pressures
|
||||
|
||||
# 对于未计算的行，使用插值或平均值填充
|
||||
if max_samples is not None and len(df) > max_samples:
|
||||
# 只计算了部分行，用平均值填充其余行
|
||||
valid_pressures_for_mean = [p for p in pressures if p is not None]
|
||||
if valid_pressures_for_mean:
|
||||
mean_pressure = sum(valid_pressures_for_mean) / len(valid_pressures_for_mean)
|
||||
df['pressure'] = df['pressure'].fillna(mean_pressure)
|
||||
print(f"使用平均气压填充其余 {len(df) - max_samples} 行: {mean_pressure:.1f} hPa")
|
||||
|
||||
# 统计信息
|
||||
valid_pressures = [p for p in pressures if p is not None]
|
||||
if valid_pressures:
|
||||
avg_pressure = sum(valid_pressures) / len(valid_pressures)
|
||||
print(f"成功计算 {len(valid_pressures)}/{actual_samples} 个气压值，平均值: {avg_pressure:.1f} hPa")
|
||||
else:
|
||||
print("⚠️ 未能计算出任何气压值")
|
||||
return df
|
||||
|
||||
|
||||
def adjust_altitude(df):
|
||||
"""
|
||||
调整融合高程（减去最小值）
|
||||
|
||||
Args:
|
||||
df: 输入DataFrame
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 调整后的DataFrame
|
||||
"""
|
||||
print("调整融合高程...")
|
||||
|
||||
if '融合高程' in df.columns:
|
||||
min_altitude = df['融合高程'].min()
|
||||
print(".2f")
|
||||
|
||||
original_alt = df['融合高程'].head(3).tolist()
|
||||
df['融合高程'] = df['融合高程'] - min_altitude
|
||||
adjusted_alt = df['融合高程'].head(3).tolist()
|
||||
|
||||
print("高度调整示例:")
|
||||
for orig, adj in zip(original_alt, adjusted_alt):
|
||||
print(".2f")
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def merge_timestamp(df):
|
||||
"""
|
||||
融合日期和时间列为时间戳
|
||||
|
||||
Args:
|
||||
df: 输入DataFrame
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 融合后的DataFrame
|
||||
"""
|
||||
print("融合日期和时间...")
|
||||
|
||||
if '日期' in df.columns and '时间' in df.columns:
|
||||
timestamps = []
|
||||
|
||||
for idx, row in df.iterrows():
|
||||
try:
|
||||
date_str = str(row['日期'])
|
||||
time_str = str(row['时间'])
|
||||
|
||||
# 清理日期字符串 - 移除任何时间部分
|
||||
date_str = date_str.strip()
|
||||
if ' ' in date_str:
|
||||
date_str = date_str.split(' ')[0] # 只取日期部分
|
||||
if 'T' in date_str:
|
||||
date_str = date_str.split('T')[0] # 处理ISO格式
|
||||
|
||||
# 标准化日期格式
|
||||
if '/' in date_str:
|
||||
date_str = date_str.replace('/', '-')
|
||||
|
||||
# 确保日期格式正确
|
||||
date_parts = date_str.split('-')
|
||||
if len(date_parts) == 3:
|
||||
year, month, day = date_parts
|
||||
date_formatted = f"{year.zfill(4)}-{month.zfill(2)}-{day.zfill(2)}"
|
||||
else:
|
||||
print(f"⚠️ 日期格式异常: '{date_str}'，使用当前日期")
|
||||
date_formatted = datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
# 时间字符串已经是修正后的格式（如 "08:34:01"），直接使用
|
||||
time_str = time_str.strip()
|
||||
if ':' in time_str and len(time_str.split(':')) >= 2:
|
||||
time_formatted = time_str
|
||||
else:
|
||||
print(f"⚠️ 时间格式异常: '{time_str}'，使用默认时间")
|
||||
time_formatted = "12:00:00"
|
||||
|
||||
# 组合时间戳 - 直接连接日期和时间
|
||||
timestamp = f"{date_formatted} {time_formatted}"
|
||||
timestamps.append(timestamp)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 处理第 {idx+1} 行时间戳失败: {e}")
|
||||
timestamps.append(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
|
||||
df['timestamp'] = timestamps
|
||||
|
||||
print("时间戳融合示例:")
|
||||
for i in range(min(3, len(timestamps))):
|
||||
print(f" {df.loc[i, '日期']} + {df.loc[i, '时间']} → {timestamps[i]}")
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def rename_columns(df):
|
||||
"""
|
||||
重命名字段为GasFlux标准格式
|
||||
|
||||
Args:
|
||||
df: 输入DataFrame
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 重命名后的DataFrame
|
||||
"""
|
||||
print("重命名字段...")
|
||||
|
||||
# 定义字段映射
|
||||
column_mapping = {
|
||||
'timestamp': 'timestamp', # 时间戳（已创建）
|
||||
'经度': 'longitude', # 经度 → latitude
|
||||
'纬度': 'latitude', # 纬度 → longitude
|
||||
'融合高程': 'height_ato', # 融合高程 → height_ato
|
||||
'修正风向': 'winddir', # 修正风向 → winddir
|
||||
'修正风速': 'windspeed', # 修正风速 → windspeed
|
||||
'风温': 'temperature', # 风温 → temperature
|
||||
'pressure': 'pressure', # 气压（已计算）
|
||||
'CH4': 'ch4', # CH4保持不变
|
||||
'pitch': 'course_elevation', # pitch → course_elevation
|
||||
'yaw': 'course_azimuth' # yaw → course_azimuth
|
||||
}
|
||||
|
||||
# 重命名存在的列
|
||||
columns_to_rename = {}
|
||||
for old_name, new_name in column_mapping.items():
|
||||
if old_name in df.columns:
|
||||
columns_to_rename[old_name] = new_name
|
||||
|
||||
if columns_to_rename:
|
||||
df = df.rename(columns=columns_to_rename)
|
||||
print("字段重命名:")
|
||||
for old, new in columns_to_rename.items():
|
||||
print(f" {old} → {new}")
|
||||
|
||||
# 只保留GasFlux需要的列
|
||||
required_columns = ['timestamp', 'latitude', 'longitude', 'height_ato', 'windspeed', 'winddir', 'temperature', 'pressure', 'ch4', 'course_elevation', 'course_azimuth']
|
||||
existing_required_columns = [col for col in required_columns if col in df.columns]
|
||||
|
||||
if len(existing_required_columns) != len(required_columns):
|
||||
missing = [col for col in required_columns if col not in df.columns]
|
||||
print(f"⚠️ 缺少必需列: {missing}")
|
||||
|
||||
# 移除不需要的列，只保留必需的列
|
||||
df = df[existing_required_columns]
|
||||
print(f"最终保留列: {existing_required_columns}")
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def process_excel_file(file_path):
|
||||
"""
|
||||
处理单个Excel文件的主函数
|
||||
|
||||
Args:
|
||||
file_path: Excel文件路径
|
||||
"""
|
||||
print(f"=== 开始处理文件: {file_path} ===\n")
|
||||
|
||||
# 获取文件名（用于时间修正）
|
||||
filename = Path(file_path).name
|
||||
|
||||
# 1. 读取数据
|
||||
df = load_excel_data(file_path)
|
||||
|
||||
# 2. 删除不需要的列
|
||||
columns_to_remove = [
|
||||
'高程', '速度x', '速度y', '速度z',
|
||||
'四元数_q0', '四元数_q1', '四元数_q2', '四元数_q3',
|
||||
'roll', 'H2O', # 保留pitch和yaw，将重命名为course_elevation和course_azimuth
|
||||
'原始风向', '原始风速'
|
||||
]
|
||||
df = remove_columns(df, columns_to_remove)
|
||||
|
||||
# 3. 修正时间格式
|
||||
df = fix_time_column(df, filename)
|
||||
|
||||
# 4. 坐标转换
|
||||
df = convert_coordinates(df)
|
||||
|
||||
# 5. 计算气压
|
||||
df = calculate_pressure(df, max_samples=None, height_tolerance=10.0, height_bin_size=2.0) # 计算所有行，高度容差10米，分档2米
|
||||
|
||||
# 6. 高度调整
|
||||
df = adjust_altitude(df)
|
||||
|
||||
# 7. 时间戳融合
|
||||
df = merge_timestamp(df)
|
||||
|
||||
# 调试：检查当前列
|
||||
print(f"时间戳融合后列名: {list(df.columns)}")
|
||||
if 'timestamp' in df.columns:
|
||||
print(f"timestamp列示例: {df['timestamp'].head(3).tolist()}")
|
||||
|
||||
# 8. 字段重命名
|
||||
df = rename_columns(df)
|
||||
|
||||
# 保存处理结果
|
||||
output_path = Path(file_path).with_suffix('.processed.csv')
|
||||
df.to_csv(output_path, index=False)
|
||||
|
||||
print(f"\n✅ 处理完成！")
|
||||
print(f"📁 输出文件: {output_path}")
|
||||
print(f"📊 最终数据形状: {df.shape[0]} 行 × {df.shape[1]} 列")
|
||||
print(f"📋 最终列名: {list(df.columns)}")
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def process_file(input_file, output_file=None):
|
||||
"""
|
||||
直接处理Excel文件的函数（不使用命令行参数）
|
||||
|
||||
Args:
|
||||
input_file: 输入Excel文件路径（字符串或Path对象）
|
||||
output_file: 输出CSV文件路径（可选，字符串或Path对象）
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: 处理后的DataFrame
|
||||
"""
|
||||
# 转换为Path对象
|
||||
input_path = Path(input_file)
|
||||
|
||||
# 检查输入文件
|
||||
if not input_path.exists():
|
||||
raise FileNotFoundError(f"输入文件不存在: {input_path}")
|
||||
|
||||
if input_path.suffix.lower() not in ['.xlsx', '.xls']:
|
||||
raise ValueError(f"输入文件必须是Excel格式 (.xlsx 或 .xls)，当前文件: {input_path}")
|
||||
|
||||
# 处理文件
|
||||
df = process_excel_file(str(input_path))
|
||||
|
||||
# 如果指定了输出路径，额外保存一份
|
||||
if output_file:
|
||||
output_path = Path(output_file)
|
||||
df.to_csv(output_path, index=False)
|
||||
print(f"📁 额外保存到: {output_path}")
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def interactive_input():
|
||||
"""
|
||||
交互式输入模式 - 手动输入参数
|
||||
|
||||
Returns:
|
||||
tuple: (input_file, output_file) 文件路径元组
|
||||
"""
|
||||
print("=== 手动输入模式 ===")
|
||||
print("请按照提示输入参数...\n")
|
||||
|
||||
# 输入文件路径
|
||||
while True:
|
||||
input_file = input("请输入Excel文件路径 (例如: data.xlsx): ").strip()
|
||||
if not input_file:
|
||||
print("❌ 文件路径不能为空，请重新输入")
|
||||
continue
|
||||
|
||||
input_path = Path(input_file)
|
||||
if not input_path.exists():
|
||||
print(f"❌ 文件不存在: {input_path}")
|
||||
print("提示: 请确保文件路径正确，或者将文件放在当前目录下")
|
||||
continue
|
||||
|
||||
if input_path.suffix.lower() not in ['.xlsx', '.xls']:
|
||||
print(f"❌ 文件格式错误: {input_path.suffix}")
|
||||
print("只支持 .xlsx 和 .xls 格式的Excel文件")
|
||||
continue
|
||||
|
||||
break
|
||||
|
||||
# 输出文件路径（可选）
|
||||
output_file = input("请输入输出CSV文件路径 (可选，直接回车使用默认): ").strip()
|
||||
if not output_file:
|
||||
output_file = None
|
||||
print("使用默认输出文件名")
|
||||
|
||||
print(f"\n✅ 输入确认:")
|
||||
print(f" 输入文件: {input_file}")
|
||||
print(f" 输出文件: {output_file or '自动生成'}")
|
||||
|
||||
confirm = input("\n确认开始处理? (y/N): ").strip().lower()
|
||||
if confirm not in ['y', 'yes', '是', '确认']:
|
||||
print("❌ 用户取消操作")
|
||||
return None, None
|
||||
|
||||
return input_file, output_file
|
||||
|
||||
|
||||
def main(input_file=None, output_file=None, interactive=False):
|
||||
"""
|
||||
主函数 - 支持多种输入方式
|
||||
|
||||
Args:
|
||||
input_file: 输入文件路径（直接调用时使用）
|
||||
output_file: 输出文件路径（直接调用时使用）
|
||||
interactive: 是否启用交互式输入模式
|
||||
"""
|
||||
# 如果启用交互式输入
|
||||
if interactive:
|
||||
input_file, output_file = interactive_input()
|
||||
if input_file is None:
|
||||
return None
|
||||
|
||||
# 如果提供了直接参数，使用直接参数
|
||||
if input_file is not None:
|
||||
try:
|
||||
return process_file(input_file, output_file)
|
||||
except Exception as e:
|
||||
print(f"❌ 处理失败: {e}")
|
||||
raise
|
||||
|
||||
# 否则使用命令行参数
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="无人机数据处理工具 - 将Excel数据转换为GasFlux格式",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
使用方式:
|
||||
|
||||
1. 命令行模式:
|
||||
python data_processor.py data.xlsx
|
||||
python data_processor.py data.xlsx -o output.csv
|
||||
|
||||
2. 交互式模式:
|
||||
python data_processor.py --interactive
|
||||
|
||||
3. 直接调用:
|
||||
from data_processor import process_file
|
||||
df = process_file('data.xlsx', 'output.csv')
|
||||
|
||||
4. Python脚本调用:
|
||||
from data_processor import main
|
||||
df = main(input_file='data.xlsx', output_file='output.csv')
|
||||
|
||||
处理步骤:
|
||||
1. 读取Excel文件
|
||||
2. 删除不需要的列
|
||||
3. 根据文件名修正时间格式
|
||||
4. 经纬度坐标转换 (除以10^7)
|
||||
5. 计算气压数据
|
||||
6. 高度调整 (减去最小值)
|
||||
7. 时间戳融合
|
||||
8. 字段重命名为GasFlux格式
|
||||
"""
|
||||
)
|
||||
|
||||
parser.add_argument('input_file', nargs='?', help='输入的Excel文件路径')
|
||||
parser.add_argument('-o', '--output', help='输出CSV文件路径（可选，默认自动生成）')
|
||||
parser.add_argument('-i', '--interactive', action='store_true', help='启用交互式输入模式')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 如果启用交互式模式
|
||||
if args.interactive:
|
||||
input_file, output_file = interactive_input()
|
||||
if input_file is None:
|
||||
return None
|
||||
else:
|
||||
input_file = args.input_file
|
||||
output_file = args.output
|
||||
|
||||
# 如果没有提供输入文件，显示帮助和使用示例
|
||||
if not input_file:
|
||||
parser.print_help()
|
||||
print("\n" + "="*60)
|
||||
print("📖 使用示例:")
|
||||
print("="*60)
|
||||
print("1. 命令行模式:")
|
||||
print(" python data_processor.py your_file.xlsx")
|
||||
print(" python data_processor.py data.xlsx -o output.csv")
|
||||
print("")
|
||||
print("2. Python脚本中直接调用:")
|
||||
print(" from data_processor import process_file")
|
||||
print(" df = process_file('input.xlsx')")
|
||||
print(" df = process_file('input.xlsx', 'output.csv')")
|
||||
print("")
|
||||
print("3. 交互式模式:")
|
||||
print(" python data_processor.py --interactive")
|
||||
print("="*60)
|
||||
return None
|
||||
|
||||
# 检查输入文件
|
||||
input_path = Path(input_file)
|
||||
if not input_path.exists():
|
||||
print(f"❌ 错误：输入文件不存在: {input_path}")
|
||||
sys.exit(1)
|
||||
|
||||
if input_path.suffix.lower() not in ['.xlsx', '.xls']:
|
||||
print(f"❌ 错误：输入文件必须是Excel格式 (.xlsx 或 .xls)")
|
||||
sys.exit(1)
|
||||
|
||||
# 处理文件
|
||||
try:
|
||||
df = process_excel_file(str(input_path))
|
||||
|
||||
# 如果指定了输出路径，额外保存一份
|
||||
if output_file:
|
||||
output_path = Path(output_file)
|
||||
df.to_csv(output_path, index=False)
|
||||
print(f"📁 额外保存到: {output_path}")
|
||||
|
||||
return df
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n⚠️ 用户中断处理")
|
||||
sys.exit(1)
|
||||
except Exception as e:
|
||||
print(f"\n❌ 处理失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 当直接运行脚本时，使用命令行参数模式
|
||||
main()
|
||||
58
src/gasflux/gas.py
Normal file
58
src/gasflux/gas.py
Normal file
@ -0,0 +1,58 @@
|
||||
"""functions related to gas transformations and calculations, e.g. density, point flux etc."""
|
||||
|
||||
import molmass
|
||||
import pandas as pd
|
||||
|
||||
gas_variables = {
|
||||
"standard_pressure": 1013.25, # hPa/mbar
|
||||
"standard_temperature": 273.15, # degrees K
|
||||
"standard_molar_volume": 0.022413969545014, # m3⋅mol-1
|
||||
}
|
||||
|
||||
|
||||
def mass(formula: str) -> float:
|
||||
"""Return the molar mass of a gas in g/mol."""
|
||||
return molmass.Formula(formula.upper()).mass # only accepts capital letters
|
||||
|
||||
|
||||
def gas_density(local_pressure: float, local_temperature_celsius: float, gas: str) -> float: # millibars and celsius
|
||||
"""
|
||||
Calculate the density of a gas in kg/m3 based on local pressure and temperature.
|
||||
|
||||
Parameters:
|
||||
- local_pressure: The local pressure in hPa/mbar.
|
||||
- local_temperature: The local temperature in degrees Celsius.
|
||||
- gas: The chemical formula of the gas.
|
||||
|
||||
Returns:
|
||||
- The density of the gas in kg/m3.
|
||||
|
||||
Assumes ideal gas behavior.
|
||||
"""
|
||||
local_temperature_kelvin = local_temperature_celsius + gas_variables["standard_temperature"]
|
||||
local_volume = (
|
||||
gas_variables["standard_molar_volume"]
|
||||
* (gas_variables["standard_pressure"] / local_pressure)
|
||||
* ((local_temperature_kelvin + gas_variables["standard_temperature"]) / gas_variables["standard_temperature"])
|
||||
) # m3⋅mol-1
|
||||
return mass(gas) / 1000 / local_volume
|
||||
|
||||
|
||||
def gas_flux_column(df: pd.DataFrame, gas: str, wind: str = "windspeed") -> pd.DataFrame:
|
||||
"""
|
||||
Add columns to the DataFrame for the gas density, mass, and flux.
|
||||
|
||||
Parameters:
|
||||
- df: The DataFrame.
|
||||
- gas: The chemical formula of the gas.
|
||||
- wind: The column name for the wind speed (NB - must be perpendicular to the plane)
|
||||
|
||||
Returns:
|
||||
- The DataFrame with the added columns.
|
||||
"""
|
||||
average_temp = df["temperature"].mean() # celsius
|
||||
average_pressure = df["pressure"].mean() # hPa
|
||||
gd = gas_density(local_pressure=average_pressure, local_temperature_celsius=average_temp, gas=gas) # kg/m3
|
||||
df[f"{gas}_kg_m3"] = gd * (df[f"{gas}_normalised"] * 1e-6) # kg/m3
|
||||
df[f"{gas}_kg_h_m2"] = df[f"{gas}_kg_m3"] * df[wind] * 60 * 60 # kg/h/m2
|
||||
return df
|
||||
69
src/gasflux/gasflux_config.yaml
Normal file
69
src/gasflux/gasflux_config.yaml
Normal file
@ -0,0 +1,69 @@
|
||||
# gasflux_config.yaml
|
||||
|
||||
output_dir: ~/gasflux_reports
|
||||
|
||||
# required columns and maximum ranges
|
||||
required_cols:
|
||||
latitude: [-90, 90]
|
||||
longitude: [-180, 180]
|
||||
height_ato: [-200, 500] # meters above takeoff
|
||||
windspeed: [0, 50] # m/s
|
||||
winddir: [0, 360] # degrees
|
||||
temperature: [-50, 60] # degrees Celsius
|
||||
pressure: [900, 1100] # hPa/mb
|
||||
|
||||
# optional gas columns and maximum ppm ranges. Relative concentrations are used so offset can be wrong as long as gain and linearity are correct.
|
||||
gases:
|
||||
ch4: [1.5, 500]
|
||||
co2: [300, 5000]
|
||||
c2h6: [-0.5, 10]
|
||||
|
||||
strategies:
|
||||
background: "algorithm" # currently only algorithm (via pybaselines) is supported
|
||||
sensor: "insitu" # currently only insitu is supported
|
||||
spatial: "curtain" # currently "curtain" and "spiral" are supported
|
||||
interpolation: "kriging" # currently only kriging is supported
|
||||
|
||||
# baseline settings.
|
||||
algorithmic_baseline_settings:
|
||||
algorithm: fastchrom
|
||||
fastchrom: {
|
||||
"half_window": 6,
|
||||
"threshold": "custom", #
|
||||
"min_fwhm": ~,
|
||||
"interp_half_window": 3,
|
||||
"smooth_half_window": 3,
|
||||
"weights": ~,
|
||||
"max_iter": 100,
|
||||
"min_length": 2}
|
||||
fabc : {
|
||||
"lam": 10000, # The smoothing parameter. Larger values will create smoother baselines. Default is 1e6.
|
||||
"scale": 10, # The scale at which to calculate the continuous wavelet transform. Should be approximately equal to the index-based full-width-at-half-maximum of the peaks or features in the data. Default is None, which will use half of the value from optimize_window(), which is not always a good value, but at least scales with the number of data points and gives a starting point for tuning the parameter.
|
||||
"diff_order": 2} # The order of the differential matrix. Must be greater than 0. Default is 2 (second order differential matrix). Typical values are 2 or 1.
|
||||
dietrich : {
|
||||
"poly_order": 5,
|
||||
"smooth_half_window": 5,}
|
||||
golotvin : {
|
||||
"half_window": 2,
|
||||
"sections": 10}
|
||||
|
||||
# kriging settings
|
||||
semivariogram_settings:
|
||||
model: spherical
|
||||
estimator: cressie
|
||||
n_lags: 20
|
||||
bin_func: even
|
||||
fit_method: lm
|
||||
### Size of the search window; if the algorithm complains about not having enough neighbours, consider increasing these
|
||||
maxlag: 100 # in meters.
|
||||
tolerance: 10 # in degrees
|
||||
azimuth: 0 # in degrees (0 is right/horizontal)
|
||||
bandwidth: 20 # in meters
|
||||
# fit_sigma: linear # this should allow for a spatial uncertainty but currently producing bugs
|
||||
ordinary_kriging_settings:
|
||||
min_points: 3
|
||||
max_points: 100
|
||||
grid_resolution: 500
|
||||
min_nodes: 10
|
||||
y_min: ~ # manual override for the minimum y value. Leave blank or ~ to use ymin.
|
||||
cut_ground: True # cuts everything below the ground level in the krig (need height_agl to work at the moment)
|
||||
171
src/gasflux/interpolation.py
Normal file
171
src/gasflux/interpolation.py
Normal file
@ -0,0 +1,171 @@
|
||||
"""Functions related to kriging and other kinds of interpolation"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import skgstat as skg
|
||||
from scipy import integrate
|
||||
|
||||
from . import plotting
|
||||
|
||||
|
||||
def simpsonintegrate(array: np.ndarray, x_cell_size: float, y_cell_size: float) -> float:
|
||||
"""Function to obtain the volume of the krig in kgh⁻¹, i.e. the cut-fill volume
|
||||
(negative volumes from background noise are subtracted)."""
|
||||
grid = np.nan_to_num(array.copy(), copy=False, nan=0)
|
||||
vol_rows = integrate.simpson(np.transpose(grid)) # this integrates along each row of the grid
|
||||
vol_grid = integrate.simpson(vol_rows) # this integrates the rows together
|
||||
return vol_grid * x_cell_size * y_cell_size # type: ignore
|
||||
|
||||
|
||||
def directional_gas_semivariogram(
|
||||
df: pd.DataFrame, x: str, z: str, gas: str, semivariogram_filter: float | None = None, **semivariogram_settings
|
||||
):
|
||||
"""Function to calculate the directional semivariogram - typically horizontally - of a gas in a dataframe."""
|
||||
if semivariogram_filter:
|
||||
df = df[df[gas] > semivariogram_filter]
|
||||
v = skg.DirectionalVariogram(
|
||||
df[[x, z]].to_numpy(),
|
||||
df[gas].to_numpy(),
|
||||
**semivariogram_settings,
|
||||
)
|
||||
return v
|
||||
|
||||
|
||||
def ordinary_kriging(
|
||||
df: pd.DataFrame,
|
||||
x: str,
|
||||
y: str,
|
||||
gas: str,
|
||||
ordinary_kriging_settings: dict,
|
||||
semivariogram_filter: float | None = None,
|
||||
**semivariogram_settings,
|
||||
):
|
||||
"""Function to calculate the ordinary kriging of a gas in a dataframe, after calculating a semivariogram."""
|
||||
gasflux = f"{gas}_kg_h_m2"
|
||||
skg.plotting.backend("plotly") # type: ignore
|
||||
cut_ground = ordinary_kriging_settings["cut_ground"]
|
||||
semivariogram = directional_gas_semivariogram(df, x, y, gasflux, semivariogram_filter, **semivariogram_settings)
|
||||
ok = skg.OrdinaryKriging(
|
||||
semivariogram,
|
||||
coordinates=df[[x, y]].to_numpy(),
|
||||
values=df[gasflux].to_numpy(),
|
||||
min_points=ordinary_kriging_settings["min_points"],
|
||||
max_points=ordinary_kriging_settings["max_points"],
|
||||
)
|
||||
x_max = df[x].max()
|
||||
x_min = df[x].min()
|
||||
y_max = df[y].max()
|
||||
y_min = df[y].min() if ordinary_kriging_settings["y_min"] is None else ordinary_kriging_settings["y_min"]
|
||||
if cut_ground is True:
|
||||
df["ground_elevation_ato"] = df.loc[:, "height_ato"] - df.loc[:, "height_agl"]
|
||||
y_min = min(df["ground_elevation_ato"].min(), y_min)
|
||||
x_range, y_range = x_max - x_min, y_max - y_min
|
||||
cell_rough_size = np.sqrt((x_range * y_range) / ordinary_kriging_settings["grid_resolution"])
|
||||
x_nodes, y_nodes = (
|
||||
max(int(r / cell_rough_size), ordinary_kriging_settings["min_nodes"]) for r in [x_range, y_range]
|
||||
)
|
||||
x_cell_size = (x_max - x_min) / x_nodes
|
||||
y_cell_size = (y_max - y_min) / y_nodes
|
||||
xx, yy = np.mgrid[
|
||||
x_min : x_max : x_nodes * 1j,
|
||||
y_min : y_max : y_nodes * 1j, # type: ignore
|
||||
] # type: ignore
|
||||
field = ok.transform(xx.flatten(), yy.flatten()).reshape(xx.shape)
|
||||
if cut_ground:
|
||||
field = remove_values_below_ground(df, field, xx, yy)
|
||||
volume = simpsonintegrate(field, x_cell_size, y_cell_size)
|
||||
|
||||
fieldpos = np.copy(field)
|
||||
fieldpos[fieldpos < 0] = 0
|
||||
volumepos = simpsonintegrate(fieldpos, x_cell_size, y_cell_size)
|
||||
|
||||
fieldneg = np.copy(field)
|
||||
fieldneg[fieldneg > 0] = 0
|
||||
volumeneg = simpsonintegrate(fieldneg, x_cell_size, y_cell_size)
|
||||
|
||||
error_1s = ok.sigma.reshape(xx.shape)
|
||||
# np.nan_to_num(error_1s, copy=False, nan=0)
|
||||
volume_error = simpsonintegrate(error_1s, x_cell_size, y_cell_size)
|
||||
|
||||
contour_plot = plotting.contour_krig(df=df, gas=gas, xx=xx, yy=yy, field=field, x=x, y=y, cut_ground=cut_ground)
|
||||
grid_plot = plotting.heatmap_krig(xx, yy, field)
|
||||
output_text = (
|
||||
f"The emissions flux of {gas.upper()} is {volume:.3f}kgh⁻¹; "
|
||||
f"the cut and fill volumes of the grid are {volumepos:.3f} and {volumeneg:.3f}kgh⁻¹. "
|
||||
f"The grid itself is {x_nodes}x{y_nodes} nodes, with nodes measuring {x_cell_size:.2f}m x {y_cell_size:.2f}m."
|
||||
)
|
||||
krig_variables = {
|
||||
"gas": gas,
|
||||
"field": field,
|
||||
"fieldpos": fieldpos,
|
||||
"fieldneg": fieldneg,
|
||||
"xx": xx,
|
||||
"yy": yy,
|
||||
"volume": volume,
|
||||
"volumepos": volumepos,
|
||||
"volumeneg": volumeneg,
|
||||
"error field (1 sigma)": error_1s,
|
||||
"volume_error": volume_error,
|
||||
}
|
||||
semivariogram_plot = semivariogram.plot(show=False)
|
||||
|
||||
return krig_variables, output_text, contour_plot, grid_plot, semivariogram_plot
|
||||
|
||||
|
||||
def remove_values_below_ground(
|
||||
df: pd.DataFrame, field: np.ndarray, xx: np.ndarray, yy: np.ndarray, x: str = "x", alt: str = "height_ato"
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Adjust field values based on elevation data, setting values below ground to NaN.
|
||||
"""
|
||||
max_x = df[x].max()
|
||||
max_y = df[alt].max()
|
||||
|
||||
x_right = np.empty_like(xx)
|
||||
x_right[:-1, :] = xx[1:, :]
|
||||
x_right[-1, :] = max_x
|
||||
x_points = (xx + x_right) / 2
|
||||
|
||||
y_top = np.empty_like(yy)
|
||||
y_top[:, :-1] = yy[:, 1:]
|
||||
y_top[:, -1] = max_y
|
||||
y_points = (yy + y_top) / 2
|
||||
|
||||
x_points_flat = x_points.ravel()
|
||||
y_points_flat = y_points.ravel()
|
||||
|
||||
ground_levels = compute_relative_ground_levels(df, x_points_flat)
|
||||
|
||||
below_ground = y_points_flat < ground_levels
|
||||
field_flat = field.ravel()
|
||||
field_flat[below_ground] = np.nan
|
||||
|
||||
return field_flat.reshape(field.shape)
|
||||
|
||||
|
||||
def compute_relative_ground_levels(
|
||||
df: pd.DataFrame, x_points: np.ndarray, y1: str = "height_agl", y2: str = "height_ato"
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Calculate ground levels at given x coordinates, considering elevation above ground and takeoff altitude. A
|
||||
sort of janky averaged DEM, basically. Will accept either ground elevation and altitude, or height above ground
|
||||
level and height above takeoff as inputs for y1 and y2.
|
||||
"""
|
||||
df_clean = df.dropna(subset=[y1, y2])
|
||||
df_sorted = df_clean.sort_values(by="x").drop_duplicates(subset="x")
|
||||
y1_at_x = np.interp(x_points, df_sorted["x"], df_sorted[y1])
|
||||
y2_at_x = np.interp(x_points, df_sorted["x"], df_sorted[y2])
|
||||
|
||||
return y2_at_x - y1_at_x
|
||||
|
||||
|
||||
# def additive_row_integration(df: pd.DataFrame, rowlabel: str = "slice"):
|
||||
# """2D integration of a dataframe along the x-axis, with the altitude as the y-axis."""
|
||||
# integrals = {}
|
||||
# for i in range(df[rowlabel].max() + 1):
|
||||
# df_slice = df[df[rowlabel] == i]
|
||||
# df_slice = df_slice.sort_values(by="x")
|
||||
# line_integral = integrate.simpson(y=df_slice["ch4_kg_h_m2"], x=df_slice["x"])
|
||||
# area_integral = line_integral * (df_slice["altitude"].max() - df_slice["altitude"].min())
|
||||
# integrals[i] = area_integral
|
||||
# return integrals
|
||||
61
src/gasflux/ml.py
Normal file
61
src/gasflux/ml.py
Normal file
@ -0,0 +1,61 @@
|
||||
"""Experimental module for machine learning flight filtering"""
|
||||
|
||||
import os
|
||||
|
||||
import joblib
|
||||
import pandas as pd
|
||||
|
||||
from . import plotting
|
||||
import plotly.graph_objects as go
|
||||
|
||||
model = None # Lazy loading: Load the model only if it hasn't been loaded yet
|
||||
|
||||
|
||||
def load_model():
|
||||
"""Load the model from the model file path. If the model has already been loaded, return it."""
|
||||
global model
|
||||
if model is None:
|
||||
default_model_path = os.path.join(os.path.dirname(__file__), "resources/model.pkl")
|
||||
model_file_path = os.getenv("GASFLUX_MODEL_PATH", default_model_path)
|
||||
try:
|
||||
model = joblib.load(model_file_path)
|
||||
except FileNotFoundError as e:
|
||||
raise FileNotFoundError(f"Model file not found at {model_file_path}. Please check the file path.") from e
|
||||
except Exception as e:
|
||||
raise Exception("An error occurred while loading the model.") from e
|
||||
|
||||
return model
|
||||
|
||||
|
||||
def make_prediction(
|
||||
df: pd.DataFrame,
|
||||
course_elevation="course_elevation",
|
||||
height_ato="height_ato",
|
||||
horiz_spd="horiz_spd",
|
||||
z_spd="z_spd",
|
||||
) -> tuple[pd.DataFrame, go.Figure]:
|
||||
"""Make predictions based on the input DataFrame and add them to the DataFrame.
|
||||
:param df: DataFrame containing the required features
|
||||
:param course_azimuth: Name of the column containing the course azimuth
|
||||
:param course_elevation: Name of the column containing the course elevation
|
||||
:param height_ato: Name of the column containing the height above take-off
|
||||
:param idx: Name of the column containing the index
|
||||
|
||||
return: Tuple of the DataFrame with the predictions and a Plotly 3D scatter plot of the predictions
|
||||
"""
|
||||
model = load_model()
|
||||
# Ensure the DataFrame contains all the required features
|
||||
required_features = [course_elevation, height_ato, horiz_spd, z_spd]
|
||||
if not all(feature in df.columns for feature in required_features):
|
||||
missing_features = [feature for feature in required_features if feature not in df.columns]
|
||||
raise ValueError(f"DataFrame is missing (or mislabelled) the following required features: {missing_features}")
|
||||
# make idx col if not present
|
||||
if "idx" not in df.columns:
|
||||
df["idx"] = df.index
|
||||
cols_for_model = ["course_elevation", "height_ato", "idx", "horiz_spd", "z_spd"]
|
||||
predictions = model.predict(df[cols_for_model])
|
||||
|
||||
df["predictions"] = predictions
|
||||
fig = plotting.scatter_3d(df)
|
||||
|
||||
return df, fig
|
||||
609
src/gasflux/plotting.py
Normal file
609
src/gasflux/plotting.py
Normal file
@ -0,0 +1,609 @@
|
||||
"""Various plotting functions mainly based around plotly."""
|
||||
|
||||
import matplotlib.colors as mcolors
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
import plotly.io as pio
|
||||
import simplekml
|
||||
from plotly.subplots import make_subplots
|
||||
|
||||
from . import processing
|
||||
|
||||
pio.templates["default"] = go.layout.Template(
|
||||
layout=go.Layout(
|
||||
margin=go.layout.Margin(l=0, r=0, b=0, t=0, pad=0),
|
||||
),
|
||||
)
|
||||
|
||||
pio.templates.default = "simple_white+default"
|
||||
|
||||
|
||||
styling = {
|
||||
"colorscale": "geyser",
|
||||
}
|
||||
|
||||
|
||||
def blank_figure():
|
||||
fig = go.Figure()
|
||||
return fig
|
||||
|
||||
|
||||
def scatter_3d(
|
||||
df: pd.DataFrame,
|
||||
color: str = "",
|
||||
colorbar_title: str = "",
|
||||
timestamp: str = "timestamp",
|
||||
x: str = "utm_easting",
|
||||
y: str = "utm_northing",
|
||||
z: str = "height_ato",
|
||||
courses: bool = False,
|
||||
):
|
||||
fig = px.scatter_3d(df, x=x, y=y, z=z)
|
||||
|
||||
if color:
|
||||
custom_data = [df[timestamp]]
|
||||
if courses:
|
||||
custom_data.extend([df["course_elevation"], df["course_azimuth"]])
|
||||
custom_data = np.stack(custom_data, axis=-1)
|
||||
hover_template = [
|
||||
f"{x}: %{{x:.2f}}",
|
||||
f"{y}: %{{y:.2f}}",
|
||||
f"{z}: %{{z:.2f}}",
|
||||
f"{color}: %{{marker.color:.2f}}",
|
||||
f"{timestamp}: %{{customdata[0]|%Y-%m-%d %H:%M:%S}}",
|
||||
"Index: %{pointNumber}",
|
||||
]
|
||||
|
||||
if courses:
|
||||
hover_template.extend(
|
||||
[
|
||||
"Course Elevation: %{customdata[1]:.2f}",
|
||||
"Course Azimuth: %{customdata[2]:.2f}",
|
||||
]
|
||||
)
|
||||
hover_template_str = "<br>".join(hover_template)
|
||||
fig.update_traces(
|
||||
marker=dict(
|
||||
color=df[color],
|
||||
size=4,
|
||||
opacity=0.5,
|
||||
colorscale=styling["colorscale"],
|
||||
colorbar=dict(title=colorbar_title),
|
||||
),
|
||||
customdata=custom_data,
|
||||
hovertemplate=hover_template_str,
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def scatter_2d(
|
||||
df: pd.DataFrame,
|
||||
x: str,
|
||||
color: str,
|
||||
y: str = "height_ato",
|
||||
**kwargs,
|
||||
):
|
||||
fig = px.scatter(
|
||||
df,
|
||||
x=x,
|
||||
y=y,
|
||||
color=color,
|
||||
color_continuous_scale=styling["colorscale"],
|
||||
opacity=0.8,
|
||||
**kwargs,
|
||||
)
|
||||
fig.update_traces(
|
||||
customdata=df.index,
|
||||
hovertemplate="<br>".join(
|
||||
[
|
||||
"x: %{x:.2f}",
|
||||
"height_ato: %{y:.2f}",
|
||||
f"{color}: %{{marker.color:.2f}}",
|
||||
"Time: %{customdata}",
|
||||
],
|
||||
),
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def time_series(
|
||||
df: pd.DataFrame,
|
||||
ys: str | list[str],
|
||||
x: str = "timestamp",
|
||||
color: str | None = None,
|
||||
split=None,
|
||||
y_mins: float | list[float | int] | None = None,
|
||||
rolling_average: bool = True,
|
||||
scatter: bool = True,
|
||||
rolling_window: int = 5,
|
||||
y_titles: str | list[str] | None = None,
|
||||
legend: bool = True,
|
||||
) -> go.Figure:
|
||||
colors = px.colors.qualitative.Plotly
|
||||
|
||||
if isinstance(ys, str):
|
||||
ys = [ys]
|
||||
if y_titles is None:
|
||||
y_titles = ys
|
||||
single_title = False
|
||||
elif isinstance(y_titles, str):
|
||||
y_titles = [y_titles]
|
||||
single_title = True
|
||||
elif isinstance(y_titles, list):
|
||||
if len(y_titles) != len(ys):
|
||||
raise ValueError("Length of y_titles must be equal to length of ys")
|
||||
single_title = False
|
||||
else:
|
||||
raise ValueError("Invalid y_titles value")
|
||||
if isinstance(y_mins, (float | int)):
|
||||
y_mins = [y_mins]
|
||||
if isinstance(y_mins, list):
|
||||
if len(y_mins) != len(ys):
|
||||
raise ValueError("Length of y_mins must be equal to length of ys")
|
||||
|
||||
fig = go.Figure()
|
||||
|
||||
axis_space = 0.05
|
||||
domain_start = axis_space * (len(ys)) if len(ys) > 1 else 0
|
||||
fig.update_layout(
|
||||
xaxis=dict(
|
||||
domain=[domain_start, 1],
|
||||
),
|
||||
)
|
||||
|
||||
for i, y in enumerate(ys):
|
||||
yaxis_name = f"yaxis{i+1}"
|
||||
yaxis_ref = f"y{i+1}"
|
||||
|
||||
trace_color = "black" if single_title and i == 0 else colors[i % len(colors)]
|
||||
|
||||
marker_i = dict(size=8, opacity=0.3 if rolling_average else 0.5, color=trace_color)
|
||||
if color is not None:
|
||||
marker_i["color"] = df[color] # type: ignore
|
||||
marker_i["colorscale"] = styling["colorscale"]
|
||||
|
||||
hover_template = f"{x}: %{{x}}<br>{y}: %{{y:.2f}}<br>"
|
||||
if color:
|
||||
hover_template += f"{color}: %{{marker.color:.2f}}<br>"
|
||||
|
||||
if scatter:
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=df[x],
|
||||
y=df[y],
|
||||
name=y,
|
||||
mode="markers",
|
||||
marker=marker_i,
|
||||
yaxis=yaxis_ref,
|
||||
hovertemplate=hover_template,
|
||||
showlegend=legend,
|
||||
)
|
||||
)
|
||||
|
||||
if rolling_average:
|
||||
df[f"rolling_avg_{i}"] = df[y].rolling(window=rolling_window, min_periods=1).mean()
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=df[x],
|
||||
y=df[f"rolling_avg_{i}"],
|
||||
name=f"{y} {rolling_window}-point avg",
|
||||
mode="lines",
|
||||
line=dict(color=trace_color, width=2),
|
||||
yaxis=yaxis_ref,
|
||||
showlegend=legend,
|
||||
)
|
||||
)
|
||||
|
||||
y_data = df[y]
|
||||
y_min_var = y_data.min()
|
||||
y_max_var = y_data.max()
|
||||
y_range = y_max_var - y_min_var or y_max_var * 0.05
|
||||
|
||||
y_axis_min = y_mins[i] if y_mins is not None and y_mins[i] is not None else y_min_var - y_range * 0.05
|
||||
y_axis_max = y_max_var + y_range * 0.05
|
||||
|
||||
if single_title and i == 0:
|
||||
axis_title = dict(text=y_titles[0], font=dict(color="black"))
|
||||
elif not single_title:
|
||||
axis_title = dict(text=y_titles[i], font=dict(color=trace_color))
|
||||
else:
|
||||
axis_title = None
|
||||
|
||||
axis_config = dict(
|
||||
title=axis_title,
|
||||
tickfont=dict(color=trace_color),
|
||||
range=[y_axis_min, y_axis_max],
|
||||
side="left",
|
||||
position=axis_space * i if i > 0 else None,
|
||||
anchor="free" if i > 0 else None,
|
||||
overlaying="y" if i > 0 else None,
|
||||
showgrid=(i == 0),
|
||||
)
|
||||
|
||||
fig.layout[yaxis_name] = axis_config
|
||||
|
||||
if split is not None:
|
||||
fig.add_shape(
|
||||
type="line",
|
||||
xref="x",
|
||||
yref="paper",
|
||||
x0=split,
|
||||
y0=0,
|
||||
x1=split,
|
||||
y1=1,
|
||||
line=dict(color="red", width=2),
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def background_plotting(df: pd.DataFrame, gas: str):
|
||||
fig = make_subplots(specs=[[{"secondary_y": True}]])
|
||||
ymin = df[gas].min()
|
||||
ymax = df[gas].max()
|
||||
ylim = [ymin * 0.95, ymax * 1.05]
|
||||
y2min = df[f"{gas}_normalised"].min()
|
||||
y2lim = (y2min, y2min + (ylim[1] - ylim[0]))
|
||||
fig.update_yaxes(range=ylim, secondary_y=False, title_text=f"Sensor {gas} (ppm)")
|
||||
fig.update_yaxes(range=y2lim, secondary_y=True, title_text=f"Normalised {gas} (ppm)")
|
||||
fig.add_scatter(x=df["timestamp"], y=df[gas], opacity=0.3, name="Raw Data")
|
||||
fig.add_scatter(
|
||||
x=df["timestamp"], y=df[f"{gas}_fit"], mode="lines", name="Fitted Background", line=dict(dash="dash")
|
||||
)
|
||||
fig.add_scatter(
|
||||
x=df["timestamp"], y=df[f"{gas}_normalised"], yaxis="y2", name="Normalised Data", mode="lines", opacity=0.5
|
||||
)
|
||||
fig.add_scatter(
|
||||
x=df["timestamp"],
|
||||
y=np.where(df[f"{gas}_signal"], df[f"{gas}_normalised"], np.nan),
|
||||
yaxis="y2",
|
||||
name="Classed as signal",
|
||||
mode="lines",
|
||||
opacity=0.5,
|
||||
# color
|
||||
# mode="markers",
|
||||
# marker=dict(size=3),
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def windrose_process(df: pd.DataFrame):
|
||||
beaufort = {
|
||||
"0": [0, 1],
|
||||
"1": [1, 2],
|
||||
"2": [2, 4],
|
||||
"3": [4, 6],
|
||||
"4": [6, 9],
|
||||
"5": [9, 11],
|
||||
"6": [11, 14],
|
||||
"7": [14, 17],
|
||||
"8": [17, 21],
|
||||
"9": [21, 25],
|
||||
"10": [25, 29],
|
||||
"11": [29, 33],
|
||||
"12": [33, 200],
|
||||
}
|
||||
|
||||
beaufort_ms = {
|
||||
"0": "0-1",
|
||||
"1": "1-2",
|
||||
"2": "2-4",
|
||||
"3": "4-6",
|
||||
"4": "6-9",
|
||||
"5": "9-11",
|
||||
"6": "11-14",
|
||||
"7": "14-17",
|
||||
"8": "17-21",
|
||||
"9": "21-25",
|
||||
"10": "25-29",
|
||||
"11": "29-33",
|
||||
"12": "33+",
|
||||
}
|
||||
|
||||
cardinals = {
|
||||
"N1": [0, 11.25],
|
||||
"NNE": [11.25, 33.75],
|
||||
"NE": [33.75, 56.25],
|
||||
"ENE": [56.25, 78.75],
|
||||
"E": [78.75, 101.25],
|
||||
"ESE": [101.25, 123.75],
|
||||
"SE": [123.75, 146.25],
|
||||
"SSE": [146.25, 168.75],
|
||||
"S": [168.75, 191.25],
|
||||
"SSW": [191.25, 213.75],
|
||||
"SW": [213.75, 236.25],
|
||||
"WSW": [236.25, 258.75],
|
||||
"W": [258.75, 281.25],
|
||||
"WNW": [281.25, 303.75],
|
||||
"NW": [303.75, 326.25],
|
||||
"NNW": [326.25, 348.75],
|
||||
"N2": [348.75, 360],
|
||||
}
|
||||
df["wind_direction_bin"] = pd.cut(
|
||||
df["winddir"],
|
||||
bins=[lower for lower, upper in cardinals.values()] + [list(cardinals.values())[-1][1]],
|
||||
labels=[key for key in cardinals],
|
||||
right=False,
|
||||
)
|
||||
|
||||
df["wind_direction_bin"] = (
|
||||
df["wind_direction_bin"].map(lambda x: "N" if x in ["N1", "N2"] else x).astype("category")
|
||||
)
|
||||
df["beaufort"] = pd.cut(
|
||||
df["windspeed"],
|
||||
bins=[lower for lower, upper in beaufort.values()] + [list(beaufort.values())[-1][1]],
|
||||
labels=[key for key in beaufort],
|
||||
right=False,
|
||||
)
|
||||
df["beaufort_ms"] = df["beaufort"].map(beaufort_ms)
|
||||
df_windrose = df.groupby(["wind_direction_bin", "beaufort"], observed=False).size().reset_index(name="count") # type: ignore
|
||||
df_windrose["frequency"] = df_windrose["count"] / df_windrose["count"].sum() * 100
|
||||
df_windrose["wind_direction_bin_degs"] = df_windrose["wind_direction_bin"].cat.rename_categories(
|
||||
{
|
||||
"N": 0,
|
||||
"NNE": 22.5,
|
||||
"NE": 45,
|
||||
"ENE": 67.5,
|
||||
"E": 90,
|
||||
"ESE": 112.5,
|
||||
"SE": 135,
|
||||
"SSE": 157.5,
|
||||
"S": 180,
|
||||
"SSW": 202.5,
|
||||
"SW": 225,
|
||||
"WSW": 247.5,
|
||||
"W": 270,
|
||||
"WNW": 292.5,
|
||||
"NW": 315,
|
||||
"NNW": 337.5,
|
||||
},
|
||||
)
|
||||
df_windrose["beaufort"] = df_windrose["beaufort"].astype(int)
|
||||
return df_windrose
|
||||
|
||||
|
||||
def windrose_graph(df, plot_transect=False, theta1=None, theta2=None):
|
||||
n_colors = 13
|
||||
colors = px.colors.sample_colorscale("turbo", [n / (n_colors - 1) for n in range(n_colors)])
|
||||
fig = px.bar_polar(
|
||||
df,
|
||||
r="frequency",
|
||||
theta="wind_direction_bin_degs",
|
||||
color="beaufort",
|
||||
labels={
|
||||
"frequency": "Frequency (%)",
|
||||
"wind_direction_bin": "Direction",
|
||||
"beaufort": "Beaufort Scale",
|
||||
},
|
||||
color_discrete_map=colors,
|
||||
)
|
||||
fig.update_layout(polar=dict(radialaxis={"visible": False, "showticklabels": False}))
|
||||
fig.update_layout(
|
||||
polar=dict(
|
||||
angularaxis={
|
||||
"showgrid": False,
|
||||
},
|
||||
),
|
||||
)
|
||||
|
||||
fig.update_layout(polar_bargap=0)
|
||||
if plot_transect:
|
||||
max_freq = df.groupby("wind_direction_bin", observed=False)["frequency"].sum().max()
|
||||
fig.add_trace(
|
||||
go.Scatterpolar(
|
||||
r=[max_freq, max_freq],
|
||||
theta=[theta1, theta2],
|
||||
mode="lines",
|
||||
line=dict(color="black", width=2, dash="dash"),
|
||||
showlegend=False,
|
||||
),
|
||||
)
|
||||
return fig
|
||||
|
||||
|
||||
def windrose(df: pd.DataFrame, plot_transect=False):
|
||||
df_windrose = windrose_process(df)
|
||||
if plot_transect:
|
||||
theta1, theta2 = processing.bimodal_azimuth(df)
|
||||
fig = windrose_graph(df_windrose, plot_transect=plot_transect, theta1=theta1, theta2=theta2)
|
||||
else:
|
||||
fig = windrose_graph(df_windrose, plot_transect=plot_transect)
|
||||
return fig
|
||||
|
||||
|
||||
def outliers(original_data: pd.Series, fence_high: float, fence_low: float):
|
||||
outliers = np.array(original_data > fence_high) | (original_data < fence_low)
|
||||
|
||||
fig = make_subplots(rows=1, cols=2, shared_yaxes=True)
|
||||
fig.add_trace(px.strip(original_data, color=outliers).data[0], row=1, col=1)
|
||||
if sum(outliers) > 0:
|
||||
fig.add_trace(px.strip(original_data, color=outliers).data[1], row=1, col=1)
|
||||
fig.add_shape(
|
||||
go.layout.Shape(
|
||||
type="line",
|
||||
x0=-0.5,
|
||||
y0=fence_high,
|
||||
x1=0.5,
|
||||
y1=fence_high,
|
||||
line=dict(color="red", width=2),
|
||||
),
|
||||
row=1,
|
||||
col=1,
|
||||
)
|
||||
fig.add_shape(
|
||||
go.layout.Shape(
|
||||
type="line",
|
||||
x0=-0.5,
|
||||
y0=fence_low,
|
||||
x1=0.5,
|
||||
y1=fence_low,
|
||||
line=dict(color="red", width=2),
|
||||
),
|
||||
row=1,
|
||||
col=1,
|
||||
)
|
||||
fig.update_traces(offsetgroup=0)
|
||||
|
||||
fig.add_trace(px.scatter(original_data, color=outliers).data[0], row=1, col=2)
|
||||
if sum(outliers) > 0:
|
||||
fig.add_trace(px.scatter(original_data, color=outliers).data[1], row=1, col=2)
|
||||
fig.update_layout(showlegend=False, yaxis_title="Windspeed (ms⁻¹)")
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def contour_krig(
|
||||
df: pd.DataFrame,
|
||||
gas: str,
|
||||
# array of float 64
|
||||
xx: np.ndarray,
|
||||
yy: np.ndarray,
|
||||
field: np.ndarray,
|
||||
cut_ground: bool = False,
|
||||
x: str = "x",
|
||||
y: str = "height_ato",
|
||||
) -> go.Figure:
|
||||
if np.isnan(field).all():
|
||||
return blank_figure()
|
||||
fig = go.Figure()
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=df[x],
|
||||
y=df[y],
|
||||
mode="markers",
|
||||
marker={
|
||||
"color": df[f"{gas}_normalised"],
|
||||
"colorscale": styling["colorscale"],
|
||||
"showscale": True,
|
||||
"colorbar": {
|
||||
"title": f"{gas} (ppm)",
|
||||
},
|
||||
},
|
||||
showlegend=False,
|
||||
)
|
||||
)
|
||||
fig.add_trace(
|
||||
go.Contour(
|
||||
z=field.T,
|
||||
x=xx[:, 0],
|
||||
y=yy[0, :],
|
||||
contours={
|
||||
"start": field.min(),
|
||||
"end": field.max(),
|
||||
"size": (field[~np.isnan(field)].max() - field[~np.isnan(field)].min()) / 21,
|
||||
},
|
||||
colorscale=styling["colorscale"],
|
||||
opacity=0.5,
|
||||
showlegend=False,
|
||||
showscale=False,
|
||||
)
|
||||
)
|
||||
fig.update_xaxes(
|
||||
showline=True,
|
||||
linewidth=1,
|
||||
linecolor="black",
|
||||
title_text="horizontal distance on projected flux plane (m)",
|
||||
range=[np.min(xx), np.max(xx)],
|
||||
ticks="outside",
|
||||
tickwidth=1,
|
||||
tickcolor="black",
|
||||
ticklen=5,
|
||||
nticks=20,
|
||||
)
|
||||
fig.update_yaxes(
|
||||
showline=True,
|
||||
linewidth=1,
|
||||
linecolor="black",
|
||||
title_text="height above takeoff (m)",
|
||||
range=[np.min(yy), np.max(yy)],
|
||||
ticks="outside",
|
||||
tickwidth=1,
|
||||
tickcolor="black",
|
||||
ticklen=5,
|
||||
nticks=10,
|
||||
)
|
||||
if cut_ground:
|
||||
resolution = 200 # how many points to interpolate over
|
||||
df["ground_elevation_ato"] = df.loc[:, "height_ato"] - df.loc[:, "height_agl"]
|
||||
df_sorted = df.dropna(subset=[x, "ground_elevation_ato"]).sort_values(x)
|
||||
x_min, x_max = df_sorted[x].min(), df_sorted[x].max()
|
||||
x_interp = np.linspace(x_min, x_max, resolution)
|
||||
ground_ato_interp = np.interp(x_interp, df_sorted[x], df_sorted["ground_elevation_ato"])
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=x_interp,
|
||||
y=ground_ato_interp,
|
||||
mode="lines",
|
||||
line=dict(color="black", width=2, dash="dash"),
|
||||
name="Interpolated Ground Level",
|
||||
)
|
||||
)
|
||||
fig.layout.coloraxis.colorbar.title = "Emissions flux (kg⋅m⁻²⋅h⁻¹)"
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def heatmap_krig(xx: np.ndarray, yy: np.ndarray, field: np.ndarray):
|
||||
fig = px.imshow(field.T, x=xx[:, 0], y=yy[0, :], color_continuous_scale=styling["colorscale"], origin="lower")
|
||||
fig.layout.coloraxis.colorbar.title = "Emissions flux (kg⋅m⁻²⋅h⁻¹)"
|
||||
fig.update_xaxes(
|
||||
showline=True,
|
||||
linewidth=1,
|
||||
linecolor="black",
|
||||
title_text="horizontal distance on cylindrical projected flux plane (m)",
|
||||
range=[xx.min(), xx.max()],
|
||||
ticks="outside",
|
||||
tickwidth=1,
|
||||
tickcolor="black",
|
||||
ticklen=5,
|
||||
nticks=20,
|
||||
)
|
||||
fig.update_yaxes(
|
||||
showline=True,
|
||||
linewidth=1,
|
||||
linecolor="black",
|
||||
title_text="height above ground level (m)",
|
||||
range=[yy.min(), yy.max()],
|
||||
ticks="outside",
|
||||
tickwidth=1,
|
||||
tickcolor="black",
|
||||
ticklen=5,
|
||||
nticks=10,
|
||||
)
|
||||
fig.update_layout(coloraxis_colorbar=dict(len=0.25))
|
||||
return fig
|
||||
|
||||
|
||||
def create_kml_file(data: pd.DataFrame, output_file: str, column: str, altitudemode: str):
|
||||
kml = simplekml.Kml()
|
||||
|
||||
min_value = data[column].min()
|
||||
max_value = data[column].max()
|
||||
|
||||
custom_colors = [
|
||||
"#008080",
|
||||
"#70a494",
|
||||
"#b4c8a8",
|
||||
"#f6edbd",
|
||||
"#edbb8a",
|
||||
"#de8a5a",
|
||||
"#ca562c",
|
||||
] # based on plotly geyser
|
||||
cmap = mcolors.LinearSegmentedColormap.from_list("custom_cmap", custom_colors)
|
||||
|
||||
for _index, row in data.iterrows():
|
||||
col_normalized = (row[column] - min) / (max_value - min_value)
|
||||
color = mcolors.rgb2hex(cmap(col_normalized))
|
||||
|
||||
pnt = kml.newpoint(coords=[(row["longitude"], row["latitude"], row["height_ato"])], altitudemode=altitudemode)
|
||||
pnt.iconstyle.icon.href = "http://maps.google.com/mapfiles/kml/shapes/placemark_circle.png"
|
||||
pnt.iconstyle.color = simplekml.Color.rgb(int(color[1:3], 16), int(color[3:5], 16), int(color[5:], 16))
|
||||
pnt.iconstyle.scale = 0.6
|
||||
pnt.description = f"Concentration: {row[column]} ppm"
|
||||
|
||||
kml.save(output_file)
|
||||
109
src/gasflux/pre_processing.py
Normal file
109
src/gasflux/pre_processing.py
Normal file
@ -0,0 +1,109 @@
|
||||
"""Functions that organise the data into standard columns in pandas dataframes. Conversion functions (e.g. WGS84 to UTM)
|
||||
are here but transformations take place in processing.py"""
|
||||
|
||||
import geopandas as gpd
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from . import plotting
|
||||
from .processing import circ_median
|
||||
|
||||
|
||||
def data_tests(df: pd.DataFrame):
|
||||
assert df["ch4"].min() > 1.6, "ch4 values are too low"
|
||||
assert df.index.is_monotonic_increasing, "data is not sorted by time"
|
||||
assert df.index.is_unique, "data has duplicate timestamps"
|
||||
assert df["ch4"].isna().sum() == 0, "ch4 has missing values"
|
||||
assert df["windspeed"].min() >= 0, "windspeed values are negative"
|
||||
assert df["windspeed"].max() < 20, "windspeed values are too high"
|
||||
if df["windspeed"].max() > 15:
|
||||
print("Warning: windspeed is greater than 15 m/s, perhaps due to errors in the data.")
|
||||
|
||||
|
||||
# make timestamp column from UTCs, Month, Day, Year
|
||||
def timestamp_from_four_columns(df):
|
||||
df["Year"] = df["Year"] + 2000
|
||||
df["time"] = pd.to_datetime(df["UTCs"], unit="s")
|
||||
df["date"] = pd.to_datetime(df[["Year", "Month", "Day"]])
|
||||
df["timestamp"] = pd.to_datetime(df["date"].dt.date.astype(str) + " " + df["time"].dt.time.astype(str))
|
||||
df.index = df["timestamp"]
|
||||
df.drop(
|
||||
["Year", "Month", "Day", "time", "date", "timestamp", "UTCs"],
|
||||
axis=1,
|
||||
inplace=True,
|
||||
)
|
||||
return df
|
||||
|
||||
|
||||
# add UTM from latitudes and longitudes
|
||||
def add_utm(df: pd.DataFrame) -> pd.DataFrame:
|
||||
gdf = gpd.GeoDataFrame( # type: ignore
|
||||
df,
|
||||
geometry=gpd.points_from_xy(df["longitude"], df["latitude"], crs="EPSG:4326"),
|
||||
)
|
||||
utm = gdf.estimate_utm_crs()
|
||||
gdf = gdf.to_crs(utm)
|
||||
if not isinstance(gdf, gpd.GeoDataFrame):
|
||||
raise TypeError("Failed to reproject to a GeoDataFrame")
|
||||
gdf["utm_easting"] = gdf.geometry.x
|
||||
gdf["utm_northing"] = gdf.geometry.y
|
||||
output_df = pd.DataFrame(gdf.drop(columns="geometry"))
|
||||
|
||||
return output_df
|
||||
|
||||
|
||||
# add columns for drone course azimuth and elevation
|
||||
def add_course(df, rolling_window=1):
|
||||
df["hor_distance"] = np.sqrt((df["utm_northing"].diff()) ** 2 + (df["utm_easting"].diff()) ** 2)
|
||||
df["vert_distance"] = df["height_ato"].diff()
|
||||
df["vert_distance"] = pd.to_numeric(df["vert_distance"], errors="coerce")
|
||||
df["hor_distance"] = pd.to_numeric(df["hor_distance"], errors="coerce")
|
||||
df["course_azimuth"] = (
|
||||
(np.degrees(np.arctan2(df["utm_easting"].diff(), df["utm_northing"].diff())) % 360)
|
||||
.rolling(rolling_window)
|
||||
.apply(lambda x: circ_median(x), raw=True)
|
||||
)
|
||||
df["course_elevation"] = (
|
||||
np.degrees(np.arctan2(df["vert_distance"], df["hor_distance"]))
|
||||
.rolling(rolling_window)
|
||||
.apply(lambda x: circ_median(x), raw=True)
|
||||
)
|
||||
return df
|
||||
|
||||
|
||||
def manual_filtering(dict_dfs: dict, split_times: dict, mask_spans: dict) -> dict:
|
||||
filtered_dfs = {}
|
||||
for name, df in dict_dfs.items():
|
||||
if name in mask_spans:
|
||||
for i in range(len(mask_spans[name])):
|
||||
df = df.drop(
|
||||
df.between_time(mask_spans[name][i].split(" - ")[0], mask_spans[name][i].split(" - ")[1]).index,
|
||||
).copy()
|
||||
filtered_dfs[name] = df.copy()
|
||||
if name in split_times:
|
||||
split_times[name].append("23:59:59")
|
||||
split_times[name].insert(0, "00:00:00")
|
||||
for i in range(len(split_times[name]) - 1):
|
||||
df2 = df.between_time(split_times[name][i], split_times[name][i + 1]).copy()
|
||||
filtered_dfs[name + "_" + str(i)] = df2.copy()
|
||||
elif name not in split_times:
|
||||
filtered_dfs[name] = df.copy()
|
||||
return filtered_dfs
|
||||
|
||||
|
||||
def remove_outliers(df: pd.DataFrame, column: str, name: str):
|
||||
q1 = df[column].quantile(0.25)
|
||||
q3 = df[column].quantile(0.75)
|
||||
iqr = q3 - q1
|
||||
fence_low = q1 - 3 * iqr
|
||||
fence_high = q3 + 3 * iqr
|
||||
fig = plotting.outliers(df[column], fence_high, fence_low)
|
||||
outliers = df.loc[(df[column] < fence_low) | (df[column] > fence_high)]
|
||||
if len(outliers) > 0:
|
||||
print(f"{len(outliers)} outliers removed from {name} {column} data")
|
||||
# nan for outliers, not row removal
|
||||
df.loc[(df[column] < fence_low) | (df[column] > fence_high), column] = float("nan")
|
||||
elif len(outliers) == 0:
|
||||
print(f"No outliers found in {name} {column} data")
|
||||
|
||||
return df, fig
|
||||
650
src/gasflux/processing.py
Normal file
650
src/gasflux/processing.py
Normal file
@ -0,0 +1,650 @@
|
||||
"""Processing function, usually implying some kind of filtering or data transformation."""
|
||||
|
||||
from matplotlib.figure import Figure
|
||||
from itertools import groupby
|
||||
from typing import Any
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from scipy import odr
|
||||
from scipy.optimize import least_squares
|
||||
from scipy.signal import find_peaks
|
||||
from scipy.stats import circmean
|
||||
import warnings
|
||||
import logging
|
||||
|
||||
|
||||
def circ_median(x: np.ndarray | pd.Series) -> float:
|
||||
"""
|
||||
Finds the largest "empty" arc in the circle, splits it there and returns the median of the straight line.
|
||||
Probably there are more elegant ways... see https://github.com/scipy/scipy/issues/6644
|
||||
|
||||
Parameters:
|
||||
x (np.ndarray | pd.Series): The input array of angles.
|
||||
|
||||
Returns:
|
||||
float: The circular median of the input angles.
|
||||
"""
|
||||
angles = sorted(x)
|
||||
n = len(angles)
|
||||
if n == 1:
|
||||
return angles[0]
|
||||
gaps = [(angles[(i + 1) % n] - angles[i]) % 360 for i in range(n)]
|
||||
largest_gap_index = gaps.index(max(gaps))
|
||||
rearranged_angles = angles[largest_gap_index + 1 :] + angles[: largest_gap_index + 1]
|
||||
if n % 2 == 1:
|
||||
return rearranged_angles[n // 2]
|
||||
else:
|
||||
return (rearranged_angles[n // 2 - 1] + rearranged_angles[n // 2]) % 360 / 2
|
||||
|
||||
|
||||
def min_angular_displacement(x: float | np.ndarray, y: float | np.ndarray) -> float | np.ndarray:
|
||||
"""
|
||||
Calculates the minimum circular difference between two angles (in 360 degree space)
|
||||
"""
|
||||
return np.minimum(np.abs(x - y) % 360, (360 - np.abs(x - y)) % 360)
|
||||
|
||||
|
||||
def wind_offset_correction(df: pd.DataFrame, plane_angle: float) -> pd.DataFrame:
|
||||
"""
|
||||
Corrects wind direction data for a given plane angle, assuming the plane is the primary orientation of the dataset.
|
||||
This is useful for aligning wind data with the plane's orientation, facilitating analysis of wind effects.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe containing wind direction data.
|
||||
wind_dir_col (str): Column name for wind direction data.
|
||||
plane_angle (float): Angle of the plane in degrees.
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: The modified dataframe with corrected wind direction data.
|
||||
"""
|
||||
df = df.copy()
|
||||
df["winddir_rel"] = df.apply(lambda row: abs(90 - min_angular_displacement(row["winddir"], plane_angle)), axis=1) # type: ignore
|
||||
df["windspeed_measured"] = df["windspeed"]
|
||||
df["windspeed"] = df["windspeed"] * np.cos(np.radians(df["winddir_rel"]))
|
||||
return df
|
||||
|
||||
|
||||
def bimodal_azimuth(
|
||||
df: pd.DataFrame, course_col: str = "course_azimuth", min_height: int = 5, min_diff: int = 160
|
||||
) -> tuple[float, float]:
|
||||
"""
|
||||
Identifies the two most frequent course azimuths in the dataset, ensuring they are sufficiently
|
||||
distinct. Filters data by height and removes NaNs before analysis.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe.
|
||||
course_col (str): Column name for course azimuth data. Default is "course_azimuth".
|
||||
min_altitude (int): Minimum height (_ato typically) for data to be included. Default is 5.
|
||||
min_diff (int): Minimum difference between the two modes. Default is 160.
|
||||
|
||||
Returns:
|
||||
tuple: Two modes of the course azimuth.
|
||||
"""
|
||||
df = df[df["height_ato"] >= min_height]
|
||||
data = df[course_col].dropna().to_numpy()
|
||||
hist, edges = np.histogram(data, bins=50)
|
||||
edgedist = edges[1] - edges[0]
|
||||
bin_centers = edges[:-1] + edgedist / 2
|
||||
max_freq_idx = np.argsort(hist)[-2:] # top 2 frequencies
|
||||
mode1, mode2 = bin_centers[max_freq_idx]
|
||||
while min_angular_displacement(mode1, mode2) < min_diff:
|
||||
if hist[max_freq_idx[0]] < hist[max_freq_idx[1]]:
|
||||
hist[max_freq_idx[0]] = 0
|
||||
max_freq_idx = np.argsort(hist)[-2:]
|
||||
else:
|
||||
hist[max_freq_idx[1]] = 0
|
||||
max_freq_idx = np.argsort(hist)[-2:]
|
||||
mode1, mode2 = bin_centers[max_freq_idx]
|
||||
if min_angular_displacement(mode1, mode2) < 160:
|
||||
warnings.warn(
|
||||
f"Two modes are close together - this probably should never happen: {mode1:.2f} and {mode2:.2f}",
|
||||
UserWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
return (mode1, mode2)
|
||||
|
||||
|
||||
# this returns modes of slope from -90 to 90 degrees.
|
||||
def bimodal_elevation(
|
||||
df: pd.DataFrame, course_col: str = "course_elevation", min_height: float = 5, max_slope: float = 70
|
||||
) -> tuple[float, float]:
|
||||
"""
|
||||
Identifies the most frequent course elevation in the dataset, adjusted for vertical movements.
|
||||
Filters data by height and removes NaNs before analysis.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe.
|
||||
course_col (str): Column name for course elevation data. Default is "course_elevation".
|
||||
min_height (int): Minimum height for data to be included. Default is 5.
|
||||
max_slope (int): Maximum slope to consider, avoTupleiding vertical movements. Default is 70.
|
||||
|
||||
Returns:
|
||||
tuple: Mode of course elevation and its negative, representing possible ascent/descent angles.
|
||||
"""
|
||||
df = df[df["height_ato"] >= df["height_ato"].min() + min_height]
|
||||
data = df[course_col].to_numpy()
|
||||
data = np.abs(data[~np.isnan(data)])
|
||||
# to get around the edge case where vertical movements are modal
|
||||
data = data[data < max_slope]
|
||||
hist, edges = np.histogram(data, bins=50)
|
||||
max_freq_idx = np.argsort(hist)[::-1][:2]
|
||||
mode = edges[max_freq_idx][0]
|
||||
return (mode, -mode)
|
||||
|
||||
|
||||
def height_transect_splitter(df: pd.DataFrame, height_col: str = "height_ato") -> tuple[pd.DataFrame, Figure]:
|
||||
"""
|
||||
Splits the dataset into height-based transects and plots histogram peaks to identify prominent
|
||||
height ranges. Only works if the flights are flat.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe containing height data.
|
||||
|
||||
Returns:
|
||||
tuple: Modified dataframe with transect labels and a figure showing the histogram with peaks.
|
||||
"""
|
||||
df = df.copy()
|
||||
heights = df[height_col].to_numpy()
|
||||
counts, bin_edges = np.histogram(heights, bins=40)
|
||||
bin_width = bin_edges[1] - bin_edges[0]
|
||||
bin_edges = np.append(heights.min() - bin_width, bin_edges) # avoid literal edge effects
|
||||
bin_edges = np.append(bin_edges, heights.max() + bin_width)
|
||||
counts = np.append(0, counts)
|
||||
counts = np.append(counts, 0)
|
||||
bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
|
||||
peaks, properties = find_peaks(counts)
|
||||
transect_edges = (bin_centers[peaks][:-1] + bin_centers[peaks][1:]) / 2
|
||||
transect_edges = np.append(heights.min(), transect_edges)
|
||||
transect_edges = np.append(transect_edges, heights.max())
|
||||
fig, ax = plt.subplots()
|
||||
ax.stairs(edges=bin_edges, values=counts, fill=True)
|
||||
ax.plot(bin_centers[peaks], counts[peaks], "x", color="red")
|
||||
ax.vlines(transect_edges, ymin=0, ymax=max(counts), color="red")
|
||||
df["transect_num"] = pd.cut(df[height_col], bins=list(transect_edges), labels=False, include_lowest=True) # type: ignore
|
||||
return df, fig
|
||||
|
||||
|
||||
def add_transect_azimuth_switches(df: pd.DataFrame, threshold=150, shift=3) -> pd.DataFrame:
|
||||
"""
|
||||
Identifies transects based on significant changes in course azimuth, incrementing a transect
|
||||
counter to distinguish different flight paths. This is a really crude function and should probably
|
||||
be only used with data that's already been filtered in some way.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe with course azimuths.
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: The modified dataframe with a new 'transect' column indicating transect IDs.
|
||||
"""
|
||||
df = df.copy()
|
||||
df["transect_num"] = 0
|
||||
df["prev_course_azimuth"] = df["course_azimuth"].shift(shift) # this gives better behaviour for very neat transects
|
||||
df["deg_displace"] = df.apply(
|
||||
lambda row: min_angular_displacement(row["course_azimuth"], row["prev_course_azimuth"]) # type: ignore
|
||||
if not pd.isnull(row["prev_course_azimuth"])
|
||||
else np.nan,
|
||||
axis=1,
|
||||
)
|
||||
df.loc[df["deg_displace"] > threshold, "transect_num"] = 1
|
||||
# remove ones that are next to each other
|
||||
for i in range(1, len(df)):
|
||||
if (df.loc[i, "transect_num"] == 1 and df.loc[i - 1, "transect_num"] == 1) or (
|
||||
df.loc[i, "transect_num"] == 1 and df.loc[i - 2, "transect_num"] == 1
|
||||
):
|
||||
df.loc[i, "transect_num"] = 0
|
||||
df["transect_num"] = df["transect_num"].shift(-shift) # recorrect for shift
|
||||
df["transect_num"] = df["transect_num"].cumsum() + 1 # 1 indexed
|
||||
df["transect_num"] = df["transect_num"].ffill()
|
||||
df = df.drop(columns=["prev_course_azimuth", "deg_displace"])
|
||||
return df
|
||||
|
||||
|
||||
def course_filter(
|
||||
df: pd.DataFrame, azimuth_filter: float, azimuth_window: int, elevation_filter: float
|
||||
) -> tuple[pd.DataFrame, pd.DataFrame]:
|
||||
"""
|
||||
Filters data based on specified course azimuth and elevation, aiming to isolate transects that
|
||||
align with main flight directions. First elevation is filtered to remove significant climbs or descents
|
||||
(beware terrain-following flights). Bimodal course azimuths are calculated and used to filter the
|
||||
data based on the main flight directions, with a rolling median applied (the window) to smooth the data.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe.
|
||||
azimuth_filter (float): The tolerance for deviation from the main course azimuths.
|
||||
azimuth_window (int): The window size for rolling median calculation of course azimuths.
|
||||
elevation_filter (float): The tolerance for deviation from horizontal flight.
|
||||
|
||||
Returns:
|
||||
tuple: The filtered dataframe and the original unfiltered dataframe for comparison.
|
||||
"""
|
||||
df_filtered = df.copy()
|
||||
df_filtered = df_filtered[abs(df["course_elevation"]) < elevation_filter]
|
||||
|
||||
azi1, azi2 = bimodal_azimuth(df_filtered)
|
||||
logging.info(f"Drone appears to be flying mainly on the courses {azi1:.2f} degrees and {azi2:.2f} degrees")
|
||||
|
||||
df_filtered["rolling_course_azimuth"] = (
|
||||
df_filtered["course_azimuth"].rolling(azimuth_window, center=True).apply(lambda x: circ_median(x), raw=True)
|
||||
)
|
||||
|
||||
df_filtered = df_filtered[
|
||||
(df_filtered["rolling_course_azimuth"] < azi1 + azimuth_filter)
|
||||
& (df_filtered["rolling_course_azimuth"] > azi1 - azimuth_filter)
|
||||
| (df_filtered["rolling_course_azimuth"] < azi2 + azimuth_filter)
|
||||
& (df_filtered["rolling_course_azimuth"] > azi2 - azimuth_filter)
|
||||
]
|
||||
|
||||
return df_filtered, df
|
||||
|
||||
|
||||
def mCount_max(data_dict: dict[int, float]) -> tuple[int, int]:
|
||||
"""
|
||||
Finds the start and end of the longest monotonic sequence in a dictionary of floats, typically used
|
||||
to identify a series of continuous altitude measurements. The first and last are retained.
|
||||
|
||||
Parameters:
|
||||
data_dict (Dict[int, float]): Dictionary with sequential numeric keys and numeric values representing measures
|
||||
such as altitude.
|
||||
|
||||
Returns:
|
||||
tuple: Start and end indices of the longest monotonic sequence in the dictionary.
|
||||
"""
|
||||
if len(data_dict) < 2:
|
||||
raise ValueError("Dictionary must contain at least two values")
|
||||
if list(data_dict.keys()) != list(range(1, len(data_dict) + 1)):
|
||||
raise ValueError("Keys must be sequential integers starting from 1")
|
||||
poscount = 0
|
||||
negcount = 0
|
||||
max_pos_count = 0
|
||||
max_pos_transect = 0
|
||||
max_neg_count = 0
|
||||
max_neg_transect = 0
|
||||
pos_start = 0
|
||||
neg_start = 0
|
||||
|
||||
for i in range(2, len(data_dict) + 1):
|
||||
if data_dict[i] >= data_dict[i - 1]:
|
||||
poscount += 1
|
||||
negcount = 0
|
||||
elif data_dict[i] < data_dict[i - 1]:
|
||||
negcount += 1
|
||||
poscount = 0
|
||||
|
||||
if max_pos_count < poscount:
|
||||
max_pos_count = poscount
|
||||
max_pos_transect = i
|
||||
pos_start = i - poscount
|
||||
elif max_neg_count < negcount:
|
||||
max_neg_count = negcount
|
||||
max_neg_transect = i
|
||||
neg_start = i - negcount
|
||||
|
||||
if max_pos_count > 0 or max_neg_count > 0:
|
||||
if max_pos_count >= max_neg_count:
|
||||
return pos_start, max_pos_transect
|
||||
else:
|
||||
return neg_start, max_neg_transect
|
||||
else:
|
||||
return 0, 0
|
||||
|
||||
|
||||
def largest_monotonic_transect_series(
|
||||
df: pd.DataFrame, transect_col: str = "transect_num", alt_col: str = "height_ato"
|
||||
) -> tuple[pd.DataFrame, int, int]:
|
||||
"""
|
||||
Filters the input dataframe to include only the largest continuous series of transects based on
|
||||
monotonic altitude changes.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe with transect and altitude information.
|
||||
transect_col (str): Column name for transect numbers. Default is "transect_num".
|
||||
alt_col (str): Column name for altitude data. Default is "height_ato".
|
||||
|
||||
Returns:
|
||||
tuple: The filtered dataframe, start transect, and end transect of the largest monotonic series.
|
||||
"""
|
||||
df = add_transect_azimuth_switches(df) # course switches
|
||||
alt_dict = dict(df.groupby(transect_col)[alt_col].mean())
|
||||
starttransect, endtransect = mCount_max(alt_dict) # type: ignore
|
||||
# filter to the biggest monotonic series of values
|
||||
df = df[(df[transect_col] >= starttransect) & (df[transect_col] <= endtransect)]
|
||||
logging.info(
|
||||
f"Parsed a flight of {len(np.unique(df[transect_col]))} transects from {alt_dict[starttransect]:.0f}m"
|
||||
f" to {alt_dict[endtransect]:.0f}m between {df['timestamp'].iloc[0]} and {df['timestamp'].iloc[-1]}",
|
||||
)
|
||||
return df, starttransect, endtransect
|
||||
|
||||
|
||||
def monotonic_transect_groups(
|
||||
df: pd.DataFrame, transect_col: str = "transect_num", alt_col: str = "height_ato"
|
||||
) -> tuple[pd.DataFrame, dict[int, str]]:
|
||||
"""
|
||||
Groups transects into a dict of monotonic transect sequences based on altitude, facilitating analysis of continuous
|
||||
flight patterns. Current behaviour is to reuse end transects of previous sequences as the start of the next.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe with transect and altitude information.
|
||||
transect_col (str): Column name for transect numbers. Default is "transect_num".
|
||||
alt_col (str): Column name for altitude data. Default is "height_ato".
|
||||
|
||||
Returns:
|
||||
tuple: The dataframe with a new 'group' column indicating the monotonic group ID, and a
|
||||
dictionary mapping transects to group IDs.
|
||||
"""
|
||||
|
||||
df = add_transect_azimuth_switches(df)
|
||||
alt_dict = dict(df.groupby(transect_col)[alt_col].mean())
|
||||
|
||||
group_dict = {}
|
||||
previous_altitude = None
|
||||
current_group = 1
|
||||
previous_trend = None
|
||||
first_transect_in_series = True
|
||||
|
||||
for transect, altitude in alt_dict.items():
|
||||
if previous_altitude is None:
|
||||
group_dict[transect] = f"Group_{current_group}"
|
||||
else:
|
||||
if altitude == previous_altitude:
|
||||
raise ValueError("Error: altitude is the same as the previous transect")
|
||||
elif altitude > previous_altitude:
|
||||
current_trend = "ascending"
|
||||
else:
|
||||
current_trend = "descending"
|
||||
|
||||
if current_trend != previous_trend and previous_trend is not None and not first_transect_in_series:
|
||||
current_group += 1 # Increment group counter
|
||||
first_transect_in_series = True
|
||||
if current_trend == previous_trend or previous_trend is None:
|
||||
# handles edge case where someone flies up, flies down one transect and then up again (yes really)
|
||||
first_transect_in_series = False
|
||||
group_dict[transect] = f"Group_{current_group}"
|
||||
previous_trend = current_trend
|
||||
previous_altitude = altitude
|
||||
df["group"] = df["transect_num"].map(group_dict)
|
||||
|
||||
return df, group_dict
|
||||
|
||||
|
||||
def remove_non_transects(
|
||||
df: pd.DataFrame,
|
||||
chain_length: int = 70,
|
||||
azimuth_tolerance: int = 10,
|
||||
elevation_tolerance: int = 40,
|
||||
smoothing_window: int = 5,
|
||||
) -> tuple[pd.DataFrame, pd.DataFrame]:
|
||||
"""
|
||||
Filters the dataframe to remove segments not matching the criteria for being considered as transects,
|
||||
based on course azimuth and elevation and segment length.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe.
|
||||
chain_length (int): Minimum number of consecutive points to be considered a transect. Default is 70.
|
||||
azimuth_tolerance (int): Tolerance for deviation from modal course azimuths. Default is 10.
|
||||
elevation_tolerance (int): Tolerance for deviation from modal course elevation. Default is 40.
|
||||
smoothing_window (int): Window size for rolling median smoothing of courses. Default is 5. Rolling median
|
||||
avoids single point errors.
|
||||
|
||||
Returns:
|
||||
tuple: Dataframes of removed segments and retained segments that fit transect criteria.
|
||||
"""
|
||||
|
||||
def get_true_runs(mask):
|
||||
enumerated_mask = list(enumerate(mask)) # add index numbers to mask
|
||||
# group consecutive true/false values
|
||||
groups = groupby(enumerated_mask, key=lambda x: x[1])
|
||||
# retain groups of True values
|
||||
true_runs = [list(group) for key, group in groups if key]
|
||||
return true_runs
|
||||
|
||||
def split_runs_on_azimuth_inversion(df, runs, azimuth_inversion_threshold=120):
|
||||
split_runs = []
|
||||
for run in runs:
|
||||
last_azimuth = df.iloc[run[0][0]]["smoothed_course_azimuth"]
|
||||
current_run = [run[0]]
|
||||
for point in run[1:]:
|
||||
current_azimuth = df.iloc[point[0]]["smoothed_course_azimuth"]
|
||||
if min_angular_displacement(current_azimuth, last_azimuth) > azimuth_inversion_threshold:
|
||||
split_runs.append(current_run)
|
||||
current_run = [point]
|
||||
else:
|
||||
current_run.append(point)
|
||||
last_azimuth = current_azimuth
|
||||
split_runs.append(current_run)
|
||||
return split_runs
|
||||
|
||||
# Create new columns for filtering reasons, initialized with False
|
||||
df_removed = df.copy()
|
||||
df_removed["filtered_by_azimuth"] = False
|
||||
df_removed["filtered_by_elevation"] = False
|
||||
df_removed["filtered_by_chain"] = False
|
||||
|
||||
# Apply azimuth filter
|
||||
modal_course_azimuths = bimodal_azimuth(df, course_col="course_azimuth")
|
||||
modal_elevation_azimuths = bimodal_elevation(df, course_col="course_elevation")
|
||||
|
||||
# Apply rolling median to course azimuth and elevation and store them in new columns
|
||||
df_removed["smoothed_course_azimuth"] = (
|
||||
df_removed["course_azimuth"]
|
||||
.rolling(smoothing_window, center=True)
|
||||
.apply(lambda x: circmean(x, 360, 0), raw=True)
|
||||
.fillna(df_removed["course_azimuth"], inplace=False)
|
||||
)
|
||||
|
||||
df_removed["smoothed_course_elevation"] = (
|
||||
df_removed["course_elevation"]
|
||||
.rolling(smoothing_window, center=True)
|
||||
.apply(lambda x: circmean(x, 360, 0), raw=True)
|
||||
.fillna(df_removed["course_elevation"], inplace=False)
|
||||
)
|
||||
|
||||
azimuth_mask = df_removed["smoothed_course_azimuth"].apply(
|
||||
lambda x: any([min_angular_displacement(x, mode) <= azimuth_tolerance for mode in modal_course_azimuths])
|
||||
)
|
||||
|
||||
df_removed.loc[~azimuth_mask, "filtered_by_azimuth"] = True
|
||||
|
||||
# Apply elevation filter
|
||||
elevation_mask = df_removed["smoothed_course_elevation"].apply(
|
||||
lambda x: any([min_angular_displacement(x, mode) <= elevation_tolerance for mode in modal_elevation_azimuths])
|
||||
)
|
||||
df_removed.loc[~elevation_mask, "filtered_by_elevation"] = True
|
||||
|
||||
# Combined mask for azimuth and elevation
|
||||
mask = azimuth_mask & elevation_mask
|
||||
|
||||
true_runs = get_true_runs(mask)
|
||||
split_runs = split_runs_on_azimuth_inversion(df_removed, true_runs)
|
||||
chain_mask = [len(run) >= chain_length for run in split_runs]
|
||||
chain_filtered_runs = [run for run, filter_by_chain in zip(split_runs, chain_mask, strict=False) if filter_by_chain]
|
||||
chain_filtered_indices = [point[0] for run in chain_filtered_runs for point in run]
|
||||
df_removed.loc[df_removed.index.difference(chain_filtered_indices), "filtered_by_chain"] = True
|
||||
|
||||
filtered_segments = [df_removed.iloc[run[0][0] : run[-1][0] + 1] for run in chain_filtered_runs]
|
||||
|
||||
df_retained = pd.concat(filtered_segments)
|
||||
df_removed = df_removed[~df_removed.index.isin(df_retained.index)]
|
||||
|
||||
return df_removed, df_retained
|
||||
|
||||
|
||||
def flatten_linear_plane(
|
||||
df: pd.DataFrame, alt_col: str = "height_ato", distance_filter: float = 10000
|
||||
) -> tuple[pd.DataFrame, float]:
|
||||
"""
|
||||
Transforms a 3D dataset into a linear plane, focusing on the largest contiguous dataset aligned with
|
||||
the plane's primary orientation. The new x coordinate is the distance along the plane, y is the distance
|
||||
perpendicular to the plane (useful only for deviation), and z is the altitude.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): Input dataframe with "utm_easting", "utm_northing", and "height_ato" columns.
|
||||
alt_col (str): Column name for altitude data. Default is "height_ato".
|
||||
distance_filter (float): Threshold for filtering points based on their distance from the regression line.
|
||||
|
||||
Returns:
|
||||
tuple: Modified dataframe with new 'x', 'y', and 'z' columns representing transformed coordinates,
|
||||
and one of the plane's angles of rotation (from N, 0-360) in degrees.
|
||||
"""
|
||||
|
||||
def orthogonal_distance_regression(df: pd.DataFrame) -> tuple[pd.DataFrame, np.ndarray[Any, Any]]:
|
||||
"""
|
||||
Perform orthogonal distance regression on the given DataFrame.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): DataFrame containing "utm_easting" and "utm_northing" columns.
|
||||
|
||||
Returns:
|
||||
tuple[pd.DataFrame, np.ndarra[Any,Any]]]: Updated DataFrame with "distance_from_fit" column
|
||||
and the fitted parameters (slope, intercept).
|
||||
"""
|
||||
|
||||
def linear_reg_equation(B, x):
|
||||
return B[0] * x + B[1] # y = mx + c
|
||||
|
||||
required_columns = ["utm_easting", "utm_northing"]
|
||||
if not all(col in df.columns for col in required_columns):
|
||||
raise ValueError(f"DataFrame must contain columns: {required_columns}")
|
||||
model = odr.Model(linear_reg_equation)
|
||||
data = odr.Data(df["utm_easting"], df["utm_northing"])
|
||||
|
||||
INITIAL_BETA = [1, 0] # Initial guess of slope=1, intercept=0
|
||||
odr_instance = odr.ODR(data, model, beta0=INITIAL_BETA)
|
||||
fit = odr_instance.run()
|
||||
|
||||
if fit.stopreason[0] == "Iteration limit reached":
|
||||
raise RuntimeError("ODR fitting failed to converge")
|
||||
|
||||
slope, intercept = fit.beta
|
||||
df = df.assign(
|
||||
distance_from_fit=abs((slope * df["utm_easting"] - df["utm_northing"] + intercept) / np.sqrt(slope**2 + 1))
|
||||
)
|
||||
|
||||
return df, fit.beta
|
||||
|
||||
df, coefs2D = orthogonal_distance_regression(df)
|
||||
df = df.loc[df["distance_from_fit"] < distance_filter, :]
|
||||
df, coefs2D = orthogonal_distance_regression(df) # this is intentionally done twice
|
||||
df = df.loc[df["distance_from_fit"] < distance_filter, :]
|
||||
rotation = np.arctan(coefs2D[0])
|
||||
df.loc[:, "x"] = (df["utm_easting"] - df["utm_easting"].min()) * np.cos(-rotation) - (
|
||||
df["utm_northing"] - df["utm_northing"].min()
|
||||
) * np.sin(-rotation)
|
||||
df.loc[:, "y"] = (df["utm_easting"] - df["utm_easting"].min()) * np.sin(-rotation) + (
|
||||
df["utm_northing"] - df["utm_northing"].min()
|
||||
) * np.cos(-rotation)
|
||||
df.loc[:, "z"] = df[alt_col]
|
||||
|
||||
plane_angle = (np.pi / 2) - np.arctan(coefs2D[0])
|
||||
plane_angle = np.degrees(plane_angle)
|
||||
return df, plane_angle
|
||||
|
||||
|
||||
## Functions for circular/spiral flights ##
|
||||
|
||||
|
||||
def circle_deviation(df: pd.DataFrame, x_col: str, y_col: str) -> tuple[pd.DataFrame, float, float, float]:
|
||||
"""
|
||||
Calculates the deviation of points from a fitted circle and their azimuth angles.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe containing the data.
|
||||
x (str): Column name for the x-coordinate.
|
||||
y (str): Column name for the y-coordinate.
|
||||
|
||||
Returns:
|
||||
Tuple[pd.Dataframe, float, float, float]: A tuple containing the modified dataframe with azimuth angles
|
||||
and deviations,the radius of the fitted circle, and the coordinates of the circle's center.
|
||||
"""
|
||||
df = df.copy()
|
||||
required_columns = [x_col, y_col]
|
||||
if not all(col in df.columns for col in required_columns):
|
||||
raise ValueError(f"DataFrame must contain columns: {required_columns}")
|
||||
|
||||
x = df[x_col]
|
||||
y = df[y_col]
|
||||
|
||||
def midhalf(x): # select middle half of data to avoid edge effects
|
||||
return x.iloc[int(len(x) * 1 / 4) : int(len(x) * 3 / 4)]
|
||||
|
||||
x_filter, y_filter = midhalf(x), midhalf(y)
|
||||
|
||||
def func(params):
|
||||
xc, yc, r = params
|
||||
return np.sqrt((x - xc) ** 2 + (y - yc) ** 2) - r
|
||||
|
||||
x_m = np.mean(np.array(x_filter)) # initial guess for parameters
|
||||
y_m = np.mean(np.array(y_filter))
|
||||
r_m = np.mean(np.sqrt((np.array(x_filter) - x_m) ** 2 + (np.array(y_filter) - y_m) ** 2))
|
||||
|
||||
params0 = np.array([x_m, y_m, r_m])
|
||||
result = least_squares(func, params0)
|
||||
xc, yc, r = result.x
|
||||
|
||||
deviation = np.sqrt((x - xc) ** 2 + (y - yc) ** 2) - r
|
||||
|
||||
# output azimuth in radians with 0 at north and increasing clockwise thanks to modulos
|
||||
azimuth = np.degrees(np.arctan2(x - xc, y - yc) % (2 * np.pi))
|
||||
|
||||
df["circ_azimuth"] = azimuth
|
||||
df["circ_deviation"] = deviation
|
||||
|
||||
return df, r, xc, yc
|
||||
|
||||
|
||||
def recentre_azimuth(df: pd.DataFrame, r: float, x: str = "circ_azimuth", y: str = "ch4_normalised") -> pd.DataFrame:
|
||||
"""
|
||||
Recentres the azimuth angles based on the angle of maximum value and computes the distance along the
|
||||
circumference of the circle.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): The input dataframe.
|
||||
r (float): The radius of the circle.
|
||||
x (str): Column name for azimuth angles. Default is 'circ_azimuth'.
|
||||
y (str): Column name for the values used to find the maximum. Default is 'ch4_normalised'.
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: The modified dataframe with centered azimuth angles and distances along the circumference.
|
||||
"""
|
||||
df = df.copy()
|
||||
|
||||
def azimuth_of_max(df: pd.DataFrame, x: str = "circ_azimuth", y: str = "ch4_normalised") -> float:
|
||||
return df.loc[df[y].idxmax()][x] # type: ignore
|
||||
|
||||
centre_azimuth = azimuth_of_max(df, x, y)
|
||||
df["centred_azimuth"] = df[x] - centre_azimuth
|
||||
df.loc[df["centred_azimuth"] > 180, "centred_azimuth"] -= 360
|
||||
df.loc[df["centred_azimuth"] < -180, "centred_azimuth"] += 360
|
||||
df["circumference_distance"] = r * np.radians(df["centred_azimuth"])
|
||||
df["circumference_distance"] = df["circumference_distance"] - df["circumference_distance"].min()
|
||||
return df
|
||||
|
||||
|
||||
def drone_anemo_to_point_wind(
|
||||
df: pd.DataFrame, yaw_col: str, anemo_u_col: str, anemo_v_col: str, easting_col: str, northing_col: str
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Convert anemometer wind data from drone's coordinate system to Earth's coordinate system
|
||||
and calculate true wind speed and direction.
|
||||
|
||||
Parameters:
|
||||
df (pd.DataFrame): Input DataFrame containing drone yaw, anemometer data, and drone speed.
|
||||
yaw_col (str): Column name for drone's yaw (in degrees, range [-180, 180]).
|
||||
anemo_u_col (str): Column name for anemometer U (wind speed in drone's X direction, from port to starboard).
|
||||
anemo_v_col (str): Column name for anemometer V (wind speed in drone's Y direction, from aft to nose).
|
||||
easting_col (str): Column name for drone's speed from west to east
|
||||
northing_col (str): Column name for drone's speed from south to north
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: DataFrame with calculated true wind speed ("windspeed") and true wind direction ("winddir").
|
||||
"""
|
||||
yaw_rad = np.deg2rad(df[yaw_col] % 360)
|
||||
rotated_U = df[anemo_u_col] * np.cos(yaw_rad) + df[anemo_v_col] * np.sin(yaw_rad)
|
||||
rotated_V = -df[anemo_u_col] * np.sin(yaw_rad) + df[anemo_v_col] * np.cos(yaw_rad)
|
||||
true_U = -rotated_U - df[easting_col]
|
||||
true_V = -rotated_V - df[northing_col]
|
||||
df["windspeed"] = np.sqrt(true_U**2 + true_V**2)
|
||||
df["winddir"] = np.degrees(np.arctan2(true_U, true_V)) % 360
|
||||
|
||||
return df
|
||||
317
src/gasflux/processing_pipelines.py
Normal file
317
src/gasflux/processing_pipelines.py
Normal file
@ -0,0 +1,317 @@
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from scipy import stats
|
||||
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
import yaml
|
||||
|
||||
from src.gasflux import background,plotting,processing,reporting,interpolation,pre_processing,gas
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def read_csv(file_path: Path) -> pd.DataFrame:
|
||||
"""Read a CSV file and return a DataFrame."""
|
||||
try:
|
||||
return pd.read_csv(file_path)
|
||||
except FileNotFoundError:
|
||||
logger.exception(f"File not found: {file_path}")
|
||||
raise
|
||||
|
||||
|
||||
def load_config(config_path: Path) -> dict:
|
||||
"""Load a YAML config file and return a dictionary."""
|
||||
try:
|
||||
with open(config_path) as file:
|
||||
return yaml.safe_load(file)
|
||||
except FileNotFoundError:
|
||||
logger.exception(f"Config file not found: {config_path}")
|
||||
raise
|
||||
except yaml.YAMLError as e:
|
||||
logger.exception(f"Error parsing YAML config: {e}")
|
||||
raise
|
||||
|
||||
|
||||
class DataValidator: # TODO(me): decide whether to move this to preprocessing
|
||||
"""Validate the data before processing."""
|
||||
|
||||
def __init__(self, df: pd.DataFrame, config: dict) -> None:
|
||||
"""Initialise the validator with the DataFrame and config."""
|
||||
self.df = df
|
||||
self.required_cols = config["required_cols"].copy()
|
||||
self.required_cols.update(config["gases"])
|
||||
|
||||
def validate(self) -> None:
|
||||
"""Validate the data."""
|
||||
self._check_is_df()
|
||||
self._check_cols()
|
||||
self._check_dtypes()
|
||||
self._check_ranges()
|
||||
logger.info("Data validation passed")
|
||||
|
||||
def _check_is_df(self) -> None:
|
||||
"""Check that the input is a DataFrame."""
|
||||
if not isinstance(self.df, pd.DataFrame):
|
||||
logging.error("Input data is not a DataFrame.")
|
||||
raise ValueError("Input data is not a DataFrame.")
|
||||
|
||||
def _check_cols(self) -> None:
|
||||
"""Check that the required columns are present."""
|
||||
missing_cols = set(self.required_cols) - set(self.df.columns)
|
||||
if missing_cols:
|
||||
logging.error(f"Missing or mislabelled columns: {missing_cols}")
|
||||
raise ValueError(f"Missing or mislabelled columns: {missing_cols}")
|
||||
|
||||
def _check_dtypes(self) -> None:
|
||||
"""Check that the required columns are of the correct type and do not contain NaN values."""
|
||||
for col in self.required_cols:
|
||||
if col in self.df:
|
||||
if self.df[col].isna().any():
|
||||
logging.error(f"Column '{col}' contains NaN values.")
|
||||
raise ValueError(f"Column '{col}' contains NaN values.")
|
||||
if self.df[col].dtype != "float64":
|
||||
logging.error(f"Column '{col}' is not of type 'float64'.")
|
||||
raise ValueError(f"Column '{col}' is not of type 'float64'.")
|
||||
|
||||
def _check_ranges(self) -> None:
|
||||
"""Check that the required columns are within the specified ranges."""
|
||||
for col, (min_val, max_val) in self.required_cols.items():
|
||||
if col in self.df.columns and not self.df[col].between(min_val, max_val, inclusive="both").all():
|
||||
logging.error(f"Column '{col}' contains values out of range: {min_val} to {max_val}.")
|
||||
raise ValueError(f"Column '{col}' contains values out of range: {min_val} to {max_val}.")
|
||||
|
||||
|
||||
class BackgroundStrategy(ABC):
|
||||
def __init__(self, data_processor):
|
||||
self.data_processor = data_processor
|
||||
|
||||
@abstractmethod
|
||||
def process(self):
|
||||
pass
|
||||
|
||||
|
||||
class AlgorithmicBaselineStrategy(BackgroundStrategy):
|
||||
def process(self):
|
||||
logger.info("Applying algorithmic background correction")
|
||||
for gas in self.data_processor.gases:
|
||||
(
|
||||
self.data_processor.df,
|
||||
self.data_processor.figs["background"][gas],
|
||||
self.data_processor.text[f"background_{gas}"],
|
||||
) = background.algorithmic_baseline(
|
||||
df=self.data_processor.df,
|
||||
gas=gas,
|
||||
algorithmic_baseline_settings=self.data_processor.config["algorithmic_baseline_settings"],
|
||||
)
|
||||
self.data_processor.df_std = self.data_processor.df.copy()
|
||||
|
||||
|
||||
class SensorStrategy(ABC):
|
||||
def __init__(self, data_processor):
|
||||
self.data_processor = data_processor
|
||||
|
||||
@abstractmethod
|
||||
def process(self):
|
||||
pass
|
||||
|
||||
|
||||
class InSituSensorStrategy(SensorStrategy):
|
||||
def process(self):
|
||||
logger.info("Processing in-situ (point) data")
|
||||
for gas in self.data_processor.gases:
|
||||
self.data_processor.figs["scatter_3d"][gas] = plotting.scatter_3d(
|
||||
df=self.data_processor.df, color=gas, colorbar_title=f"{gas.upper()} flux (kg/m²/h)"
|
||||
)
|
||||
if SpatialProcessingStrategy == CurtainSpatialProcessingStrategy:
|
||||
self.data_processor.figs["windrose"] = plotting.windrose(self.data_processor.df, plot_transect=True)
|
||||
else:
|
||||
self.data_processor.figs["windrose"] = plotting.windrose(self.data_processor.df)
|
||||
self.data_processor.figs["wind_timeseries"] = plotting.time_series(
|
||||
self.data_processor.df, ys=["windspeed", "winddir"]
|
||||
)
|
||||
|
||||
|
||||
class SpatialProcessingStrategy(ABC):
|
||||
def __init__(self, data_processor):
|
||||
self.data_processor = data_processor
|
||||
|
||||
@abstractmethod
|
||||
def process(self):
|
||||
pass
|
||||
|
||||
|
||||
class CurtainSpatialProcessingStrategy(SpatialProcessingStrategy):
|
||||
def process(self):
|
||||
logger.info("Applying curtain spatial processing")
|
||||
self.data_processor.dfs["original"] = self.data_processor.df.copy()
|
||||
self.data_processor.df, self.data_processor.start_transect, self.data_processor.end_transect = (
|
||||
processing.largest_monotonic_transect_series(self.data_processor.df)
|
||||
)
|
||||
self.data_processor.dfs["removed"] = self.data_processor.dfs["original"].loc[
|
||||
self.data_processor.dfs["original"].index.difference(self.data_processor.df.index)
|
||||
]
|
||||
self.data_processor.df, self.data_processor.plane_angle = processing.flatten_linear_plane(
|
||||
self.data_processor.df
|
||||
)
|
||||
self.data_processor.df = processing.wind_offset_correction(
|
||||
self.data_processor.df, self.data_processor.plane_angle
|
||||
)
|
||||
for gas_name in self.data_processor.gases:
|
||||
#计算通量
|
||||
self.data_processor.df = gas.gas_flux_column(self.data_processor.df, gas_name)
|
||||
self.data_processor.figs["scatter_3d"][gas_name].add_trace(
|
||||
go.Scatter3d(
|
||||
x=self.data_processor.dfs["removed"]["utm_easting"],
|
||||
y=self.data_processor.dfs["removed"]["utm_northing"],
|
||||
z=self.data_processor.dfs["removed"]["height_ato"],
|
||||
mode="markers",
|
||||
marker={"size": 2, "color": "black", "symbol": "circle", "opacity": 0.5},
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
class SpiralSpatialProcessingStrategy(SpatialProcessingStrategy):
|
||||
def process(self):
|
||||
logger.info("Applying spiral spatial processing")
|
||||
self.data_processor.dfs["original"] = self.data_processor.df.copy()
|
||||
# self.data_processor.df, self.data_processor.start_transect, self.data_processor.end_transect = (
|
||||
# gasflux.processing.largest_monotonic_transect_series(self.data_processor.df)
|
||||
|
||||
# no wind offset correction - assume wind is perpendicular to the spiral
|
||||
self.data_processor.dfs["removed"] = self.data_processor.dfs["original"].loc[
|
||||
self.data_processor.dfs["original"].index.difference(self.data_processor.df.index)
|
||||
]
|
||||
(
|
||||
self.data_processor.df,
|
||||
self.data_processor.circle_radius,
|
||||
self.data_processor.circle_center_x,
|
||||
self.data_processor.circle_center_y,
|
||||
) = processing.circle_deviation(self.data_processor.df, x_col="utm_easting", y_col="utm_northing")
|
||||
self.data_processor.df = processing.recentre_azimuth(
|
||||
self.data_processor.df, r=self.data_processor.circle_radius
|
||||
)
|
||||
self.data_processor.df["x"] = self.data_processor.df["circumference_distance"]
|
||||
for gas_name in self.data_processor.gases:
|
||||
self.data_processor.df = gas.gas_flux_column(self.data_processor.df, gas_name)
|
||||
self.data_processor.figs["scatter_3d"][gas_name].add_trace(
|
||||
go.Scatter3d(
|
||||
x=self.data_processor.dfs["removed"]["utm_easting"],
|
||||
y=self.data_processor.dfs["removed"]["utm_northing"],
|
||||
z=self.data_processor.dfs["removed"]["height_ato"],
|
||||
mode="markers",
|
||||
marker={"size": 2, "color": "black", "symbol": "circle", "opacity": 0.5},
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
class InterpolationStrategy(ABC):
|
||||
def __init__(self, data_processor):
|
||||
self.data_processor = data_processor
|
||||
|
||||
@abstractmethod
|
||||
def process(self):
|
||||
pass
|
||||
|
||||
|
||||
class KrigingInterpolationStrategy(InterpolationStrategy):
|
||||
def process(self):
|
||||
logger.info("Applying kriging interpolation")
|
||||
for gas in self.data_processor.gases:
|
||||
(
|
||||
self.data_processor.output_vars["krig_parameters"][gas],
|
||||
self.data_processor.text[f"krig_output_{gas}"],
|
||||
self.data_processor.figs["contour"][gas],
|
||||
self.data_processor.figs["krig_grid"][gas],
|
||||
self.data_processor.figs["semivariogram"][gas],
|
||||
) = interpolation.ordinary_kriging(
|
||||
df=self.data_processor.df,
|
||||
x="x",
|
||||
y="height_ato",
|
||||
gas=gas,
|
||||
ordinary_kriging_settings=self.data_processor.config["ordinary_kriging_settings"],
|
||||
**self.data_processor.config["semivariogram_settings"],
|
||||
)
|
||||
logger.info(f"Kriged {gas}")
|
||||
|
||||
|
||||
class DataProcessor:
|
||||
def __init__(self, config: dict, df: pd.DataFrame):
|
||||
self.config: dict = config
|
||||
self.df: pd.DataFrame = df
|
||||
self.gases: list[str] = list(config["gases"].keys())
|
||||
self.processing_time = datetime.now()
|
||||
self.figs: dict = {
|
||||
"scatter_3d": {},
|
||||
"windrose": None,
|
||||
"wind_timeseries": None,
|
||||
"background": {},
|
||||
"contour": {},
|
||||
"krig_grid": {},
|
||||
"semivariogram": {},
|
||||
}
|
||||
self.text: dict = {}
|
||||
self.output_vars: dict = {"krig_parameters": {}, "std": {}}
|
||||
self.dfs: dict = {}
|
||||
self.reports: dict = {}
|
||||
|
||||
def strategy_selection(self):
|
||||
self.background_strategy: BackgroundStrategy
|
||||
if self.config["strategies"]["background"] == "algorithm":
|
||||
self.background_strategy = AlgorithmicBaselineStrategy(self)
|
||||
self.sensor_strategy: SensorStrategy
|
||||
if self.config["strategies"]["sensor"] == "insitu":
|
||||
self.sensor_strategy = InSituSensorStrategy(self)
|
||||
self.spatial_processing_strategy: SpatialProcessingStrategy
|
||||
if self.config["strategies"]["spatial"] == "curtain":
|
||||
self.spatial_processing_strategy = CurtainSpatialProcessingStrategy(self)
|
||||
if self.config["strategies"]["spatial"] == "spiral":
|
||||
self.spatial_processing_strategy = SpiralSpatialProcessingStrategy(self)
|
||||
self.interpolation_strategy: InterpolationStrategy
|
||||
if self.config["strategies"]["interpolation"] == "kriging":
|
||||
self.interpolation_strategy = KrigingInterpolationStrategy(self)
|
||||
|
||||
def process(self):
|
||||
self.df = pre_processing.add_utm(self.df)
|
||||
self.df = pre_processing.add_course(self.df)
|
||||
DataValidator(self.df, self.config).validate()
|
||||
self.background_strategy.process()
|
||||
self.sensor_strategy.process()
|
||||
self.spatial_processing_strategy.process()
|
||||
self.interpolation_strategy.process()
|
||||
|
||||
# Reporting
|
||||
for gas in self.gases:
|
||||
self.reports[gas] = reporting.mass_balance_report(
|
||||
krig_params=self.output_vars["krig_parameters"][gas],
|
||||
wind_fig=self.figs["wind_timeseries"],
|
||||
background_fig=self.figs["background"][gas],
|
||||
threed_fig=self.figs["scatter_3d"][gas],
|
||||
krig_fig=self.figs["contour"][gas],
|
||||
windrose_fig=self.figs["windrose"],
|
||||
)
|
||||
|
||||
# Collecting descriptive variables
|
||||
self.output_vars["std"]["windspeed"] = self.df["windspeed"].std()
|
||||
self.output_vars["std"]["windddir"] = stats.circstd(self.df["winddir"], high=360)
|
||||
for gas in self.gases:
|
||||
self.output_vars["std"][f"{gas}_background"] = self.df.loc[
|
||||
~self.df[f"{gas}_signal"], f"{gas}_normalised"
|
||||
].std()
|
||||
|
||||
|
||||
def process_main(data_file: Path, config_file: Path) -> None:
|
||||
"""Main function to run the pipeline."""
|
||||
config = load_config(config_file)
|
||||
name = data_file.stem
|
||||
df = read_csv(data_file)
|
||||
|
||||
processor = DataProcessor(config, df)
|
||||
processor.strategy_selection()
|
||||
processor.process()
|
||||
reporting.generate_reports(name, processor, config)
|
||||
logger.info("Processing complete")
|
||||
190
src/gasflux/qiya.py
Normal file
190
src/gasflux/qiya.py
Normal file
@ -0,0 +1,190 @@
|
||||
import requests
|
||||
import time
|
||||
from requests.adapters import HTTPAdapter
|
||||
from urllib3.util.retry import Retry
|
||||
|
||||
def get_pressure_at_location(lat, lon, altitude, date, time, max_retries=3, timeout=30):
|
||||
"""
|
||||
获取指定位置、时间、高度的气压
|
||||
|
||||
Args:
|
||||
lat: 纬度
|
||||
lon: 经度
|
||||
altitude: 海拔高度 (米)
|
||||
date: 日期 (格式: YYYY-MM-DD 或 YYYY/MM/DD)
|
||||
time: 时间 (格式: HH:MM 或 HH:MM:SS)
|
||||
max_retries: 最大重试次数
|
||||
timeout: 请求超时时间(秒)
|
||||
|
||||
Returns:
|
||||
float: 气压值 (hPa)，获取失败返回 None
|
||||
"""
|
||||
|
||||
# 标准化日期格式为 YYYY-MM-DD
|
||||
def normalize_date(d):
|
||||
"""将各种日期格式标准化为 YYYY-MM-DD"""
|
||||
if not d:
|
||||
from datetime import datetime
|
||||
return datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
# 处理斜杠分隔符
|
||||
if "/" in d:
|
||||
d = d.replace("/", "-")
|
||||
|
||||
parts = d.split("-")
|
||||
if len(parts) == 3:
|
||||
year = parts[0]
|
||||
month = parts[1].zfill(2) # 确保月份是两位数
|
||||
day = parts[2].zfill(2) # 确保日期是两位数
|
||||
return f"{year}-{month}-{day}"
|
||||
else:
|
||||
# 如果格式不正确，返回今天的日期
|
||||
from datetime import datetime
|
||||
return datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
date = normalize_date(date)
|
||||
|
||||
# 标准化时间格式为 HH:MM
|
||||
def normalize_time(t):
|
||||
"""将各种时间格式标准化为 HH:MM"""
|
||||
if not t or ":" not in t:
|
||||
return "12:00" # 默认中午12点
|
||||
|
||||
parts = t.split(":")
|
||||
if len(parts) >= 2:
|
||||
hour = parts[0].zfill(2) # 确保小时是两位数
|
||||
minute = parts[1].zfill(2) # 确保分钟是两位数
|
||||
return f"{hour}:{minute}"
|
||||
elif len(parts) == 1:
|
||||
hour = parts[0].zfill(2)
|
||||
return f"{hour}:00"
|
||||
else:
|
||||
return "12:00"
|
||||
|
||||
time = normalize_time(time)
|
||||
|
||||
url = "https://archive-api.open-meteo.com/v1/archive"
|
||||
|
||||
params = {
|
||||
"latitude": lat,
|
||||
"longitude": lon,
|
||||
"start_date": date, # 格式: YYYY-MM-DD
|
||||
"end_date": date,
|
||||
"hourly": ["pressure_msl", "surface_pressure"],
|
||||
"timezone": "auto"
|
||||
}
|
||||
|
||||
# 创建带有重试机制的会话
|
||||
session = requests.Session()
|
||||
retry_strategy = Retry(
|
||||
total=max_retries,
|
||||
status_forcelist=[429, 500, 502, 503, 504],
|
||||
backoff_factor=1
|
||||
)
|
||||
adapter = HTTPAdapter(max_retries=retry_strategy)
|
||||
session.mount("http://", adapter)
|
||||
session.mount("https://", adapter)
|
||||
|
||||
try:
|
||||
print(f"正在获取位置 ({lat:.6f}, {lon:.6f}) 在 {date} {time} 的气压数据...")
|
||||
response = session.get(url, params=params, timeout=timeout)
|
||||
|
||||
# 检查响应状态
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
|
||||
# 检查API错误
|
||||
if "error" in data:
|
||||
print(f"API错误: {data['error']}")
|
||||
return None
|
||||
|
||||
# 解析气压数据
|
||||
if data and "hourly" in data:
|
||||
times = data["hourly"]["time"]
|
||||
pressures = data["hourly"]["surface_pressure"] # 地表气压
|
||||
|
||||
if not times or not pressures:
|
||||
print("未找到气压数据")
|
||||
return None
|
||||
|
||||
# 根据时间找到对应气压
|
||||
target_time = f"{date}T{time}"
|
||||
if target_time in times:
|
||||
idx = times.index(target_time)
|
||||
pressure = pressures[idx]
|
||||
print(f"成功获取气压: {pressure} hPa")
|
||||
return pressure
|
||||
else:
|
||||
print(f"在数据中未找到时间: {target_time}")
|
||||
print(f"可用时间范围: {times[0]} 到 {times[-1]}")
|
||||
return None
|
||||
else:
|
||||
print("API响应中没有hourly数据")
|
||||
return None
|
||||
|
||||
except requests.exceptions.ConnectionError as e:
|
||||
print(f"网络连接错误: {e}")
|
||||
print("请检查网络连接或稍后重试")
|
||||
return None
|
||||
except requests.exceptions.Timeout as e:
|
||||
print(f"请求超时: {e}")
|
||||
print(f"已重试 {max_retries} 次，请检查网络连接")
|
||||
return None
|
||||
except requests.exceptions.HTTPError as e:
|
||||
print(f"HTTP错误: {e}")
|
||||
return None
|
||||
except ValueError as e:
|
||||
print(f"数据解析错误: {e}")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"未知错误: {e}")
|
||||
return None
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
def batch_get_pressure(data_list):
|
||||
"""
|
||||
批量获取多个位置的气压数据
|
||||
|
||||
Args:
|
||||
data_list: 包含 (lat, lon, altitude, date, time) 元组的列表
|
||||
|
||||
Returns:
|
||||
list: 气压值列表
|
||||
"""
|
||||
results = []
|
||||
for i, (lat, lon, alt, date, time) in enumerate(data_list):
|
||||
print(f"\n处理第 {i+1} 个位置...")
|
||||
pressure = get_pressure_at_location(lat, lon, alt, date, time)
|
||||
results.append(pressure)
|
||||
if pressure is not None:
|
||||
print(f"位置 {i+1}: {pressure} hPa")
|
||||
else:
|
||||
print(f"位置 {i+1}: 获取失败")
|
||||
|
||||
# 添加短暂延迟，避免请求过于频繁
|
||||
if i < len(data_list) - 1:
|
||||
time.sleep(0.5)
|
||||
|
||||
return results
|
||||
|
||||
# 使用示例
|
||||
if __name__ == "__main__":
|
||||
print("=== 气压数据获取工具 ===\n")
|
||||
|
||||
# 单个位置示例
|
||||
print("1. 单个位置查询:")
|
||||
pressure = get_pressure_at_location(
|
||||
lat=40.3491370, # 纽约纬度
|
||||
lon=115.7855289, # 纽约经度 (西经)
|
||||
altitude=435.789, # 海拔10米
|
||||
date="2016-02-12",
|
||||
time="08:00" # HH:MM格式
|
||||
)
|
||||
|
||||
if pressure is not None:
|
||||
print(f"纽约当前气压: {pressure} hPa")
|
||||
else:
|
||||
print("获取纽约气压数据失败")
|
||||
|
||||
130
src/gasflux/reporting.py
Normal file
130
src/gasflux/reporting.py
Normal file
@ -0,0 +1,130 @@
|
||||
"""This module provides functions for generating mass balance reports."""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import plotly.graph_objects as go
|
||||
from jinja2 import Template
|
||||
from plotly.io import to_html
|
||||
from datetime import datetime
|
||||
|
||||
import yaml
|
||||
|
||||
import logging
|
||||
from . import plotting
|
||||
|
||||
|
||||
import json
|
||||
import numpy as np
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def mass_balance_report(
|
||||
krig_params: dict,
|
||||
wind_fig: go.Figure,
|
||||
background_fig: go.Figure,
|
||||
threed_fig: go.Figure,
|
||||
krig_fig: go.Figure,
|
||||
windrose_fig: go.Figure,
|
||||
) -> str:
|
||||
"""Generate a mass balance report."""
|
||||
template_path = Path(__file__).parent / "templates" / "mass_balance_template.html"
|
||||
|
||||
# Convert the figures to HTML
|
||||
plot_htmls = {}
|
||||
for name, fig in zip(
|
||||
["3D", "krig", "windrose", "wind", "background"],
|
||||
[threed_fig, krig_fig, windrose_fig, wind_fig, background_fig],
|
||||
strict=False,
|
||||
):
|
||||
if fig:
|
||||
plot_htmls[name] = to_html(fig, full_html=False)
|
||||
else:
|
||||
plot_htmls[name] = plotting.blank_figure()
|
||||
|
||||
summary_data = {
|
||||
"Estimated flux": f"{krig_params.get('volume', 0):.3f} kgh⁻¹",
|
||||
}
|
||||
|
||||
with Path.open(template_path) as f:
|
||||
template_content = f.read()
|
||||
|
||||
template = Template(template_content)
|
||||
return template.render(
|
||||
title="Mass Balance Report",
|
||||
summary_data=summary_data,
|
||||
threeD=plot_htmls["3D"],
|
||||
krig=plot_htmls["krig"],
|
||||
windrose=plot_htmls["windrose"],
|
||||
wind=plot_htmls["wind"],
|
||||
background=plot_htmls["background"],
|
||||
)
|
||||
|
||||
|
||||
def generate_reports(name: str, processor, config: dict):
|
||||
"""
|
||||
Generates reports, configuration files, and processed output variables for gasflux processing runs.
|
||||
|
||||
Parameters:
|
||||
name (str): The name identifier for the current processing run.
|
||||
processor (object): The processing object containing report data and output variables.
|
||||
config (dict): Configuration dictionary used for processing.
|
||||
"""
|
||||
output_dir = Path(config["output_dir"]).expanduser()
|
||||
processing_time = datetime.now()
|
||||
output_path = output_dir / name / processing_time.strftime("%Y-%m-%d_%H-%M-%S-%f_processing_run")
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save reports
|
||||
for gas, report in processor.reports.items():
|
||||
report_path = output_path / f"{name}_{gas}_report.html"
|
||||
with open(report_path, "w", encoding="utf-8") as file:
|
||||
file.write(report)
|
||||
|
||||
# Save config
|
||||
header = f"# Gasflux output config for file {name} from processing run at {processing_time}\n"
|
||||
config_path = output_path / f"{name}_config.yaml"
|
||||
with open(config_path, "w") as file:
|
||||
file.write(header)
|
||||
yaml.safe_dump(config, file)
|
||||
|
||||
# Save DataFrame to CSV
|
||||
if hasattr(processor, 'df') and processor.df is not None:
|
||||
csv_path = output_path / f"{name}_data.csv"
|
||||
processor.df.to_csv(csv_path, index=False)
|
||||
logger.info(f"DataFrame saved to {csv_path}")
|
||||
|
||||
# Save output variables
|
||||
output_vars = processor.output_vars
|
||||
# output_vars = delete_large_arrays(output_vars, threshold_size=50)
|
||||
header = (
|
||||
f"# Gasflux output variables for file {name} from processing run at {processing_time}\n"
|
||||
)
|
||||
filename = output_path / f"{name}_output_vars.json"
|
||||
with open(filename, "w") as file:
|
||||
file.write(header)
|
||||
json.dump(
|
||||
output_vars, file, default=lambda item: item.tolist() if isinstance(item, np.ndarray) else item, indent=4
|
||||
)
|
||||
logger.info(f"Processing run saved to {output_path}")
|
||||
|
||||
|
||||
def delete_large_arrays(output_vars: dict, threshold_size: int) -> dict:
|
||||
"""
|
||||
Iterate through the output_vars dictionary and replace large numpy arrays
|
||||
with their metadata (e.g., shape and data type).
|
||||
|
||||
Parameters:
|
||||
output_vars (dict): The dictionary containing output data including potential numpy arrays.
|
||||
threshold_size (int): The number of elements above which an array is considered large.
|
||||
"""
|
||||
del_keys = []
|
||||
for key, value in output_vars.items():
|
||||
if isinstance(value, dict):
|
||||
output_vars[key] = delete_large_arrays(value, threshold_size) # recursive
|
||||
elif isinstance(value, np.ndarray):
|
||||
if value.size > threshold_size:
|
||||
del_keys.append(key)
|
||||
for key in del_keys:
|
||||
del output_vars[key]
|
||||
return output_vars
|
||||
BIN
src/gasflux/resources/model.pkl
Normal file
BIN
src/gasflux/resources/model.pkl
Normal file
Binary file not shown.
142
src/gasflux/run_example.py
Normal file
142
src/gasflux/run_example.py
Normal file
@ -0,0 +1,142 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
GasFlux 完整处理示例脚本
|
||||
|
||||
这个脚本演示了完整的GasFlux处理流程：
|
||||
1. 使用data_processor.py处理原始Excel数据
|
||||
2. GasFlux数据验证和处理：
|
||||
- 背景校正
|
||||
- 空间处理
|
||||
- 克里金插值
|
||||
- 生成报告
|
||||
|
||||
运行方法：
|
||||
python run_example.py input.xlsx
|
||||
python run_example.py input.xlsx --output processed_data.csv
|
||||
"""
|
||||
|
||||
import sys
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
# 导入同一包内的模块
|
||||
# 优先尝试相对导入（当作为包的一部分运行时）
|
||||
# 如果失败，则尝试绝对导入（当直接运行脚本时）
|
||||
try:
|
||||
# 尝试相对导入
|
||||
from .processing_pipelines import process_main
|
||||
from .data_processor import process_file
|
||||
print("✅ 成功导入GasFlux模块（相对导入）")
|
||||
except ImportError:
|
||||
try:
|
||||
# 相对导入失败，尝试绝对导入
|
||||
from src.gasflux.processing_pipelines import process_main
|
||||
from src.gasflux.data_processor import process_file
|
||||
print("✅ 成功导入GasFlux模块（绝对导入）")
|
||||
except ImportError as e:
|
||||
print(f"❌ 导入GasFlux模块失败: {e}")
|
||||
print("请确保GasFlux包结构完整，或从项目根目录运行")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数：运行完整的数据处理和GasFlux分析流程"""
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="GasFlux完整处理流程：Excel数据预处理 + 通量分析",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
使用示例:
|
||||
python run_example.py data.xlsx # 基本使用
|
||||
python run_example.py data.xlsx --output result.csv # 指定输出文件名
|
||||
python run_example.py data.xlsx --no-gasflux # 仅预处理，不进行通量分析
|
||||
|
||||
处理步骤:
|
||||
1. 使用data_processor.py处理Excel文件
|
||||
2. 对处理后的数据进行GasFlux通量分析
|
||||
"""
|
||||
)
|
||||
|
||||
parser.add_argument('input_file', help='输入的Excel文件路径 (.xlsx 或 .xls)')
|
||||
parser.add_argument('-o', '--output', help='预处理后的CSV输出文件名（可选）')
|
||||
parser.add_argument('--no-gasflux', action='store_true',
|
||||
help='仅执行数据预处理，跳过GasFlux通量分析')
|
||||
parser.add_argument('-c', '--config', help='GasFlux配置文件路径（可选，默认使用内置配置）')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
print("=== GasFlux 完整处理流程 ===\n")
|
||||
|
||||
# 检查输入文件
|
||||
input_path = Path(args.input_file)
|
||||
if not input_path.exists():
|
||||
print(f"❌ 错误：输入文件不存在: {input_path}")
|
||||
return
|
||||
|
||||
if input_path.suffix.lower() not in ['.xlsx', '.xls']:
|
||||
print(f"❌ 错误：输入文件必须是Excel格式: {input_path}")
|
||||
return
|
||||
|
||||
# 确定输出文件名
|
||||
if args.output:
|
||||
processed_csv = Path(args.output)
|
||||
else:
|
||||
processed_csv = input_path.with_suffix('.processed.csv')
|
||||
|
||||
# 确定配置文件
|
||||
if args.config:
|
||||
config_file = Path(args.config)
|
||||
else:
|
||||
config_file = Path(__file__).parent / "data" / "gasflux_config.yaml"
|
||||
|
||||
print(f"输入Excel文件: {input_path}")
|
||||
print(f"预处理输出CSV: {processed_csv}")
|
||||
if not args.no_gasflux:
|
||||
print(f"GasFlux配置文件: {config_file}")
|
||||
print()
|
||||
|
||||
try:
|
||||
# 第一步：数据预处理
|
||||
print("🔄 第一步：数据预处理...")
|
||||
processed_df = process_file(str(input_path), str(processed_csv))
|
||||
print(f"✅ 数据预处理完成，输出文件: {processed_csv}")
|
||||
print()
|
||||
|
||||
# 如果不需要进行GasFlux分析，直接返回
|
||||
if args.no_gasflux:
|
||||
print("⏭️ 跳过GasFlux通量分析")
|
||||
return
|
||||
|
||||
# 第二步：GasFlux通量分析
|
||||
print("🔄 第二步：GasFlux通量分析...")
|
||||
|
||||
# 检查配置文件
|
||||
if not config_file.exists():
|
||||
print(f"❌ 错误：GasFlux配置文件不存在: {config_file}")
|
||||
return
|
||||
|
||||
print(f"使用数据文件: {processed_csv}")
|
||||
print(f"使用配置文件: {config_file}")
|
||||
print()
|
||||
|
||||
# 运行GasFlux处理流程
|
||||
process_main(processed_csv, config_file)
|
||||
print("\n✅ GasFlux通量分析完成！")
|
||||
|
||||
# 显示输出信息
|
||||
output_dir = Path("../../examples/basic_usage/5m") / processed_csv.stem / "processing_run"
|
||||
if output_dir.exists():
|
||||
print(f"\n📁 GasFlux输出目录: {output_dir}")
|
||||
print("📊 生成的文件:")
|
||||
for file in output_dir.rglob("*"):
|
||||
if file.is_file():
|
||||
print(f" - {file.name}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ 处理失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
67
src/gasflux/templates/mass_balance_template.html
Normal file
67
src/gasflux/templates/mass_balance_template.html
Normal file
@ -0,0 +1,67 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<style>
|
||||
.grid-container {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(4, 1fr);
|
||||
grid-gap: 10px;
|
||||
height: 800px;
|
||||
}
|
||||
|
||||
.grid-item.double {
|
||||
grid-column: span 2;
|
||||
}
|
||||
|
||||
.grid-item {
|
||||
width: 100%;
|
||||
min-width: 300px;
|
||||
}
|
||||
|
||||
.grid-item .plot-container {
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>{{title}}</h1>
|
||||
<table>
|
||||
<tbody>
|
||||
{% for key, value in summary_data.items() %}
|
||||
<tr>
|
||||
<td>{{key}}</td>
|
||||
<td>{{value}}</td>
|
||||
</tr>
|
||||
{% endfor %}
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="grid-container">
|
||||
<div class="grid-item">
|
||||
<div class="plot-container">
|
||||
{{ threeD|safe }}
|
||||
</div>
|
||||
</div>
|
||||
<div class="grid-item double">
|
||||
<div class="plot-container">
|
||||
{{ krig|safe }}
|
||||
</div>
|
||||
</div>
|
||||
<div class="grid-item">
|
||||
<div class="plot-container">
|
||||
{{ windrose|safe }}
|
||||
</div>
|
||||
</div>
|
||||
<div class="grid-item double">
|
||||
<div class="plot-container">
|
||||
{{ wind|safe }}
|
||||
</div>
|
||||
</div>
|
||||
<div class="grid-item double">
|
||||
<div class="plot-container">
|
||||
{{ background|safe }}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
1001
src/gasflux/testdata/exampledata.csv
vendored
Normal file
1001
src/gasflux/testdata/exampledata.csv
vendored
Normal file
File diff suppressed because it is too large
Load Diff
95
src/gasflux/testdata/testconfig.yaml
vendored
Normal file
95
src/gasflux/testdata/testconfig.yaml
vendored
Normal file
@ -0,0 +1,95 @@
|
||||
# testconfig.yaml
|
||||
|
||||
horizontal_pixels: 500 # Width of the concentration map in pixels
|
||||
vertical_pixels: 100 # Height of the concentration map in pixels
|
||||
num_plumes: 10 # Number of Gaussian plumes
|
||||
groupiness: 0.5 # Groupiness of the plumes (0.0 to 1.0)
|
||||
spread: 0.1 # Spread of the plumes (0.0 to 1.0)
|
||||
wind_reference_height: 10 # Reference height for wind speed calculation (m)
|
||||
windspeed_avg: 5 # Average wind speed at 10m height (m/s)
|
||||
windspeed_rel_std: 0.2 # Relative standard deviation of wind speed (0.0 to inf), recommend 0.2-0.4
|
||||
surface_roughness: 0.1 # Surface roughness length (m)
|
||||
seed: 42 # Random seed for reproducibility
|
||||
simplex_octaves: 4 # Number of octaves for simplex noise (1 to inf, def 1)
|
||||
simplex_persistence: 0.7 # Persistence of simplex noise (0.0 to 1.0, def 0.5) - specifies the amplitude of each octave relative to the one below it
|
||||
simplex_lacunarity: 2.0 # Lacunarity of simplex noise (1.0 to inf, def 2.0) - specifies the frequency of each octave relative to the one below it
|
||||
winddir_avg: 0.0 # Average wind direction in degrees rel to plane (0 is CW)
|
||||
winddir_std: 10 # Standard deviation of wind direction in degrees
|
||||
timestamp: "2022-09-26 02:03:00"
|
||||
flight_time_seconds: 1000
|
||||
sample_frequency: 1
|
||||
start_coords:
|
||||
- 54.87667
|
||||
- 15.41
|
||||
transect_azimuth: 260 # the wind will start off 90 degrees CW to this azimuth, and is modified relative to that by the winddir_avg. 260 is a good value to test N problems
|
||||
sampling_altitude_ato_range:
|
||||
- -10 # negative values should be fine
|
||||
- 100
|
||||
sampling_horizontal_range:
|
||||
- 50
|
||||
- 950
|
||||
scene_altitude_range:
|
||||
- -20
|
||||
- 120
|
||||
scene_horizontal_range:
|
||||
- 0
|
||||
- 1000
|
||||
number_of_transects: 10
|
||||
gases:
|
||||
ch4:
|
||||
- 1.95
|
||||
- 10.0
|
||||
co2:
|
||||
- 380.0
|
||||
- 500.0
|
||||
c2h6:
|
||||
- 0.0
|
||||
- 1.0
|
||||
temperature: 10.0
|
||||
pressure: 1000.0
|
||||
|
||||
output_dir: ~/gasflux_reports
|
||||
|
||||
algorithmic_baseline_settings:
|
||||
algorithm: fastchrom
|
||||
|
||||
semivariogram_settings:
|
||||
model: spherical
|
||||
estimator: cressie
|
||||
n_lags: 20
|
||||
bin_func: even
|
||||
fit_method: lm
|
||||
maxlag: 100
|
||||
#fit_sigma: linear
|
||||
tolerance: 10
|
||||
azimuth: 0
|
||||
bandwidth: 20
|
||||
|
||||
ordinary_kriging_settings:
|
||||
min_points: 3
|
||||
max_points: 100
|
||||
grid_resolution: 500
|
||||
min_nodes: 10
|
||||
cut_ground: False
|
||||
y_min: ~
|
||||
|
||||
required_cols:
|
||||
latitude: [-90, 90]
|
||||
longitude: [-180, 180]
|
||||
height_ato: [-100, 500]
|
||||
windspeed: [0, 30]
|
||||
winddir: [0, 360]
|
||||
temperature: [-50, 60]
|
||||
pressure: [900, 1100]
|
||||
|
||||
filters:
|
||||
course_filter:
|
||||
azimuth_filter: 10
|
||||
azimuth_window: 5
|
||||
elevation_filter: 5
|
||||
|
||||
strategies:
|
||||
background: "algorithm"
|
||||
sensor: "insitu"
|
||||
spatial: "curtain"
|
||||
interpolation: "kriging"
|
||||
1001
src/gasflux/testdata/testdata.csv
vendored
Normal file
1001
src/gasflux/testdata/testdata.csv
vendored
Normal file
File diff suppressed because it is too large
Load Diff
0
tests/__init__.py
Normal file
0
tests/__init__.py
Normal file
205
tests/test_processing.py
Normal file
205
tests/test_processing.py
Normal file
@ -0,0 +1,205 @@
|
||||
import pandas as pd
|
||||
from src.gasflux import processing
|
||||
import yaml
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
from src.gasflux.processing import min_angular_displacement
|
||||
import pytest
|
||||
|
||||
|
||||
testdf = pd.read_csv(Path(__file__).parents[1] / "src" / "gasflux" / "testdata" / "testdata.csv")
|
||||
testconfig = yaml.safe_load(open(Path(__file__).parents[1] / "src" / "gasflux" / "testdata" / "testconfig.yaml"))
|
||||
|
||||
|
||||
def load_cols(cols):
|
||||
return testdf[cols]
|
||||
|
||||
|
||||
def test_min_angular_diff_def():
|
||||
def test_min_angular_displacement():
|
||||
assert min_angular_displacement(10, 350) == 20
|
||||
assert min_angular_displacement(0, 180) == 180
|
||||
x = np.array([10, 0])
|
||||
y = np.array([350, 180])
|
||||
expected = np.array([20, 180])
|
||||
result = min_angular_displacement(x, y)
|
||||
assert np.all(result == expected), "Vectorized function failed"
|
||||
|
||||
|
||||
def test_circ_median():
|
||||
x = np.array([0, 1, 2, 359, 4, 3])
|
||||
median = processing.circ_median(x)
|
||||
assert median == 1.5, "Circular median not calculated correctly"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"plane_angle,expected_winddir_rel,expected_windspeed_normal",
|
||||
[
|
||||
(
|
||||
90,
|
||||
[0, 90, 0, 90, 0],
|
||||
[5, 0, 5, 0, 5],
|
||||
),
|
||||
(
|
||||
30,
|
||||
[60, 30, 60, 30, 60],
|
||||
np.array([1 / 2, np.sqrt(3) / 2, 1 / 2, np.sqrt(3) / 2, 1 / 2]) * 5,
|
||||
),
|
||||
(
|
||||
60,
|
||||
[30, 60, 30, 60, 30],
|
||||
np.array([np.sqrt(3) / 2, 1 / 2, np.sqrt(3) / 2, 1 / 2, np.sqrt(3) / 2]) * 5,
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_wind_offset_correction_parametrized(plane_angle, expected_winddir_rel, expected_windspeed_normal):
|
||||
data = {"winddir": [0, 90, 180, 270, 360], "windspeed": [5, 5, 5, 5, 5]}
|
||||
df = pd.DataFrame(data)
|
||||
corrected_df = processing.wind_offset_correction(df, plane_angle)
|
||||
assert "winddir_rel" in corrected_df.columns, f"Relative wind direction column not added for angle {plane_angle}"
|
||||
assert "windspeed" in corrected_df.columns, f"Normalised wind speed column not added for angle {plane_angle}"
|
||||
assert np.allclose(corrected_df["winddir_rel"], expected_winddir_rel, rtol=1e-5, atol=1e-10), (
|
||||
f"Relative wind directions not calculated correctly for angle {plane_angle}"
|
||||
)
|
||||
assert np.allclose(corrected_df["windspeed"], expected_windspeed_normal, rtol=1e-5, atol=1e-10), (
|
||||
f"Normalised wind speeds not calculated correctly for angle {plane_angle}"
|
||||
)
|
||||
|
||||
|
||||
def test_bimodal_azimuth():
|
||||
input_mode = testconfig["transect_azimuth"]
|
||||
input_reciprocal_mode = (input_mode + 180) % 360
|
||||
df = load_cols(["course_azimuth", "height_ato"])
|
||||
mode1, mode2 = processing.bimodal_azimuth(df)
|
||||
assert (
|
||||
min_angular_displacement(mode1, input_mode) < 3 or min_angular_displacement(mode1, input_reciprocal_mode) < 3
|
||||
), "Mode1 does not match expected azimuth or its reciprocal within 3 degrees"
|
||||
|
||||
if min_angular_displacement(mode1, input_mode) < 3:
|
||||
assert min_angular_displacement(mode2, input_reciprocal_mode) < 3, (
|
||||
"Mode2 does not match expected reciprocal azimuth within 3 degrees"
|
||||
)
|
||||
else:
|
||||
assert min_angular_displacement(mode2, input_mode) < 3, "Mode2 does not match expected azimuth within 3 degrees"
|
||||
|
||||
|
||||
def test_bimodal_elevation():
|
||||
df = load_cols(["course_elevation", "height_ato"])
|
||||
input_mode = 0
|
||||
input_reciprocal_mode = 0 - input_mode
|
||||
mode1, mode2 = processing.bimodal_elevation(df)
|
||||
assert (
|
||||
min_angular_displacement(mode1, input_mode) < 3 or min_angular_displacement(mode1, input_reciprocal_mode) < 3
|
||||
), "Mode1 does not match expected elevation or its reciprocal within 3 degrees"
|
||||
if min_angular_displacement(mode1, input_mode) < 3:
|
||||
assert min_angular_displacement(mode2, input_reciprocal_mode) < 3, (
|
||||
"Mode2 does not match expected reciprocal elevation within 3 degrees"
|
||||
)
|
||||
else:
|
||||
assert min_angular_displacement(mode2, input_mode) < 3, (
|
||||
"Mode2 does not match expected elevation within 3 degrees"
|
||||
)
|
||||
|
||||
|
||||
def test_height_transect_splitter():
|
||||
df = load_cols(["height_ato"])
|
||||
df, fig = processing.height_transect_splitter(df)
|
||||
assert "transect_num" in df.columns, "Transect number column not added to dataframe"
|
||||
assert df["transect_num"].nunique() == testconfig["number_of_transects"], (
|
||||
"Dataframe was not split into the right number of transects"
|
||||
)
|
||||
|
||||
|
||||
def test_add_transect_azimuth_switches():
|
||||
df = load_cols(["course_azimuth"])
|
||||
df = processing.add_transect_azimuth_switches(df)
|
||||
assert df["transect_num"].nunique() == testconfig["number_of_transects"], (
|
||||
"Transect azimuth switches not added to dataframe"
|
||||
)
|
||||
|
||||
|
||||
def test_course_filter():
|
||||
df = load_cols(["course_azimuth", "course_elevation", "height_ato"])
|
||||
azimuth_filter = testconfig["filters"]["course_filter"]["azimuth_filter"]
|
||||
azimuth_window = testconfig["filters"]["course_filter"]["azimuth_window"]
|
||||
elevation_filter = testconfig["filters"]["course_filter"]["elevation_filter"]
|
||||
df_filtered, df_unfiltered = processing.course_filter(
|
||||
df, azimuth_filter=azimuth_filter, azimuth_window=azimuth_window, elevation_filter=elevation_filter
|
||||
)
|
||||
input_mode = testconfig["transect_azimuth"]
|
||||
input_reciprocal_mode = (input_mode + 180) % 360
|
||||
# assert that the filtered dataframe contains the expected azimuth or its reciprocal within the window
|
||||
df_filtered["near_mode1"] = df_filtered["rolling_course_azimuth"].apply(
|
||||
lambda x: min_angular_displacement(x, input_mode) < azimuth_window
|
||||
)
|
||||
df_filtered["near_mode2"] = df_filtered["rolling_course_azimuth"].apply(
|
||||
lambda x: min_angular_displacement(x, input_reciprocal_mode) < azimuth_window
|
||||
)
|
||||
assert df_filtered["near_mode1"].any() or df_filtered["near_mode2"].any(), (
|
||||
"Filtered dataframe does not contain expected azimuth or its reciprocal within the window"
|
||||
)
|
||||
|
||||
|
||||
def test_mCount_max():
|
||||
data_dict = {1: -5.4, 2: 0.6, 3: 5.6, 4: 3.2, 5: 10.4, 6: 18.4, 7: 20.8, 8: 19.4}
|
||||
start, end = processing.mCount_max(data_dict)
|
||||
assert start == 4, "Start index of max count not calculated correctly"
|
||||
assert end == 7, "End index of max count not calculated correctly"
|
||||
|
||||
|
||||
def test_largest_monotonic_transect_series():
|
||||
df = load_cols(
|
||||
["timestamp", "height_ato", "course_azimuth", "longitude", "latitude", "utm_easting", "utm_northing"]
|
||||
)
|
||||
df, starttransect, endtransect = processing.largest_monotonic_transect_series(df)
|
||||
starttransect = 1
|
||||
endtransect = testconfig["number_of_transects"]
|
||||
assert starttransect == starttransect, "Start index of largest monotonic transect not calculated correctly"
|
||||
assert endtransect == endtransect, "End index of largest monotonic transect not calculated correctly"
|
||||
|
||||
|
||||
def test_remove_non_transects():
|
||||
df = load_cols(
|
||||
["height_ato", "course_azimuth", "course_elevation", "longitude", "latitude", "utm_easting", "utm_northing"]
|
||||
)
|
||||
retained_df, removed_df = processing.remove_non_transects(df)
|
||||
assert retained_df is not None, "Retained dataframe is None"
|
||||
assert removed_df is not None, "Removed dataframe is None"
|
||||
|
||||
|
||||
def test_flatten_linear_plane():
|
||||
df = load_cols(["height_ato", "utm_easting", "utm_northing"])
|
||||
df, plane_angle = processing.flatten_linear_plane(df)
|
||||
input_plane_angle = testconfig["transect_azimuth"]
|
||||
reciprocal_plane_angle = (input_plane_angle + 180) % 360
|
||||
assert (
|
||||
min_angular_displacement(plane_angle, input_plane_angle) < 3
|
||||
or min_angular_displacement(plane_angle, reciprocal_plane_angle) < 3
|
||||
), "Plane angle not calculated correctly"
|
||||
|
||||
|
||||
def test_drone_anemo_to_point_wind():
|
||||
data = {
|
||||
"yaw": [0, 90, 0, -90, 180],
|
||||
"anemo_u": [0, 0, 10, 10, 10],
|
||||
"anemo_v": [0, 0, 0, 0, 0],
|
||||
"easting": [0, 10, 0, 10, 0],
|
||||
"northing": [0, 0, 0, 0, 10],
|
||||
}
|
||||
df_test = pd.DataFrame(data)
|
||||
yaw_col = "yaw"
|
||||
anemo_u_col = "anemo_u"
|
||||
anemo_v_col = "anemo_v"
|
||||
easting_col = "easting"
|
||||
northing_col = "northing"
|
||||
result_df = processing.drone_anemo_to_point_wind(
|
||||
df_test, yaw_col, anemo_u_col, anemo_v_col, easting_col, northing_col
|
||||
)
|
||||
expected_windspeed = np.array([0, 10, 10, np.sqrt(200), np.sqrt(200)])
|
||||
expected_winddir = np.array(
|
||||
[180, 270, 270, 225, 135]
|
||||
) # 180 not zero because of the way IEEE 754 handles floating point numbers
|
||||
windspeed_diff = np.abs(result_df["windspeed"].values - expected_windspeed)
|
||||
winddir_diff = processing.min_angular_displacement(result_df["winddir"].to_numpy(), expected_winddir)
|
||||
assert np.all(windspeed_diff < 1e-10), "Wind speed not calculated correctly"
|
||||
assert np.all(np.array(winddir_diff) < 3), "Wind direction not calculated correctly"
|
||||
58
tests/test_processing_pipelines.py
Normal file
58
tests/test_processing_pipelines.py
Normal file
@ -0,0 +1,58 @@
|
||||
from src.gasflux import processing_pipelines
|
||||
import yaml
|
||||
from pathlib import Path
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_test_environment(tmp_path):
|
||||
"""Prepare actual test data and configuration with a modified output directory."""
|
||||
df_path = Path(__file__).parents[1] / "src" / "gasflux" / "testdata" / "testdata.csv"
|
||||
config_path = Path(__file__).parents[1] / "src" / "gasflux" / "testdata" / "testconfig.yaml"
|
||||
|
||||
with open(config_path) as f:
|
||||
config = yaml.safe_load(f)
|
||||
config["output_dir"] = str(tmp_path)
|
||||
|
||||
temp_config_path = tmp_path / "temp_testconfig.yaml"
|
||||
with open(temp_config_path, "w") as f:
|
||||
yaml.safe_dump(config, f)
|
||||
|
||||
return df_path, temp_config_path
|
||||
|
||||
|
||||
def test_process_main_config_output(setup_test_environment):
|
||||
df_path, temp_config_path = setup_test_environment
|
||||
processing_pipelines.process_main(df_path, temp_config_path)
|
||||
with open(temp_config_path) as f:
|
||||
temp_config = yaml.safe_load(f)
|
||||
output_dir = Path(temp_config["output_dir"]) / df_path.stem
|
||||
assert output_dir.exists(), "Output directory does not exist."
|
||||
processing_run_dirs = [d for d in output_dir.iterdir() if d.is_dir()]
|
||||
assert len(processing_run_dirs) > 0, "No processing run directory found."
|
||||
processing_run_dir = processing_run_dirs[0]
|
||||
|
||||
with open(temp_config_path) as f:
|
||||
original_config = yaml.safe_load(f)
|
||||
|
||||
for gas in original_config.get("gases", []):
|
||||
report_path = processing_run_dir / f"{df_path.stem}_{gas}_report.html"
|
||||
assert report_path.exists(), f"Report for {gas} does not exist."
|
||||
|
||||
config_dump_path = processing_run_dir / f"{df_path.stem}_config.yaml"
|
||||
assert config_dump_path.exists(), "Config dump file does not exist."
|
||||
|
||||
def load_and_redump(yaml_file):
|
||||
with open(yaml_file) as file:
|
||||
data = yaml.safe_load(file)
|
||||
redumped_data = yaml.dump(data, sort_keys=True, default_flow_style=False)
|
||||
return redumped_data
|
||||
|
||||
assert load_and_redump(temp_config_path) == load_and_redump(
|
||||
config_dump_path
|
||||
), "The dumped config does not match the input config."
|
||||
|
||||
|
||||
def test_process_main_deterministic_output(setup_test_environment):
|
||||
df_path, temp_config_path = setup_test_environment
|
||||
processing_pipelines.process_main(df_path, temp_config_path)
|
||||
108
tools/mono_flight_splitter.py
Normal file
108
tools/mono_flight_splitter.py
Normal file
@ -0,0 +1,108 @@
|
||||
"""a script to split a folder of flights into multiple flights based on the monotonic changes in altitude.
|
||||
Takes a csv file as an input and will split it out into several csvs.
|
||||
Needs course azimuths to work properly.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
import colorama
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from src.gasflux import processing
|
||||
|
||||
colorama.init()
|
||||
|
||||
|
||||
def main(target_dir, search_string, filter_mask, output_dir=None):
|
||||
df_list = {}
|
||||
for file_path in Path(target_dir).rglob(search_string):
|
||||
df = pd.read_csv(file_path)
|
||||
df_list[file_path] = df
|
||||
|
||||
for file_path, df in df_list.items():
|
||||
print(colorama.Fore.WHITE + "----------------------------------------")
|
||||
if filter_mask in df.columns: # check for boolean mask columns
|
||||
df = df[~df[filter_mask]].reset_index(drop=True)
|
||||
df, groupdict = processing.monotonic_transect_groups(df)
|
||||
last_transect = None
|
||||
last_group_trend = None
|
||||
for group in df["group"].unique():
|
||||
# leaving this out for now as the group folder is a good identifier
|
||||
# if len(df['group'].unique()) == 1:
|
||||
# print(colorama.Fore.RED + f'Skipping{file_path.name} - only one group')
|
||||
# continue
|
||||
group_df = df[df["group"] == group]
|
||||
avg_altitudes = group_df.groupby("transect_num")["height_ato"].mean().values
|
||||
avg_change = (
|
||||
sum([avg_altitudes[i + 1] - avg_altitudes[i] for i in range(len(avg_altitudes) - 1)])
|
||||
/ len(avg_altitudes)
|
||||
if len(avg_altitudes) > 1
|
||||
else 0
|
||||
)
|
||||
# what's the trend
|
||||
current_group_trend = "ascending" if avg_change > 0 else "descending"
|
||||
# add last transect from previous group if there's a change in trend
|
||||
if last_group_trend and last_group_trend != current_group_trend and last_transect is not None:
|
||||
group_df = pd.concat([last_transect, group_df])
|
||||
avg_altitudes = group_df.groupby("transect_num")["height_ato"].mean().values
|
||||
avg_altitudes = np.array(avg_altitudes)
|
||||
# check if the group is monotonic
|
||||
is_monotonic = np.all(np.diff(avg_altitudes) > 0) or np.all(np.diff(avg_altitudes) < 0)
|
||||
if not is_monotonic: # exception
|
||||
print(f"group {group} is not monotonic - check the code!")
|
||||
formatted_avg_altitudes = ", ".join([f"{alt:.1f}" for alt in avg_altitudes])
|
||||
# do it where transect is the maximum transect number
|
||||
last_transect = group_df[group_df["transect_num"] == group_df["transect_num"].max()].copy()
|
||||
last_transect.loc[:, "transect_num"] = 0
|
||||
last_group_trend = current_group_trend
|
||||
unique_transects = len(group_df["transect_num"].unique())
|
||||
if unique_transects < 3:
|
||||
print(
|
||||
colorama.Fore.RED + f"Not saving {group} from {file_path} - too few transects"
|
||||
f"({unique_transects} transects at {formatted_avg_altitudes}m)"
|
||||
)
|
||||
else:
|
||||
file_path = Path(file_path)
|
||||
if output_dir:
|
||||
output_path = Path(
|
||||
Path(output_dir)
|
||||
/ f"{file_path.parents[1].name}"
|
||||
/ f"{file_path.parent.name}_{group}"
|
||||
/ f"{file_path.stem}_{group}.csv"
|
||||
)
|
||||
else:
|
||||
# assuming in date and time folder
|
||||
output_path = Path(
|
||||
file_path.parent.parent.parent # time -> date -> analysis
|
||||
/ "splits"
|
||||
/ file_path.parent.parent.name # date
|
||||
/ f"{file_path.parent.name}_{group}" # time + group
|
||||
/ f"{file_path.stem}_{group}.csv"
|
||||
)
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
group_df.to_csv(output_path, index=False)
|
||||
print(
|
||||
colorama.Fore.GREEN
|
||||
+ f'wrote {unique_transects} monotonic transects at {formatted_avg_altitudes}m to \n'
|
||||
f'{"/".join(output_path.parts[-5:])}'
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--target-dir", help="the directory to search for csvs", default="survey")
|
||||
parser.add_argument("--search-string", help="the string to search for in the directory", default="*filtered.csv")
|
||||
parser.add_argument(
|
||||
"--filter-mask", help="name of a column with a boolean filter; TRUE means discard", default="filtered"
|
||||
)
|
||||
parser.add_argument("--output-dir", help="the metadirectory to save the date/time/csvs to", required=False)
|
||||
|
||||
args = parser.parse_args()
|
||||
main(
|
||||
args.target_dir,
|
||||
args.search_string,
|
||||
args.filter_mask,
|
||||
args.output_dir,
|
||||
)
|
||||
Reference in New Issue
Block a user