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-on: - push: - branches: [ main ] - -jobs: - build-docs: - - runs-on: ubuntu-latest - - steps: - - name: Checkout repo content - uses: actions/checkout@v4 - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install .[dev] - - name: Update Sphinx docs - run: | - cd docs - make html - - name: Deploy - uses: JamesIves/github-pages-deploy-action@v4 - with: - branch: gh-pages - folder: docs/_build/html diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml deleted file mode 100644 index ac77191..0000000 --- a/.github/workflows/build-test.yml +++ /dev/null @@ -1,30 +0,0 @@ -name: Tests - -on: - push: - branches: [ develop, main ] - pull_request: - branches: [ develop, main ] - -jobs: - build: - - runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: ["3.8", "3.9", "3.10", "3.11"] - - steps: - - uses: actions/checkout@v4 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install .[dev] - - name: Test with pytest - run: | - pytest diff --git a/.github/workflows/publish-pypi.yml b/.github/workflows/publish-pypi.yml deleted file mode 100644 index 1f6a952..0000000 --- a/.github/workflows/publish-pypi.yml +++ /dev/null @@ -1,28 +0,0 @@ -name: Publish - -on: - release: - types: [published] - -jobs: - deploy: - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v3 - with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install build - - name: Build package - run: python -m build - - name: Publish package - uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 - with: - user: __token__ - password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 09f84e0..0000000 --- a/.gitignore +++ /dev/null @@ -1,64 +0,0 @@ -.vscode - -# Version file autocreated in pyproject.toml and snowfake/__init__.py -_version.py - -.ipynb_checkpoints - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] - -# C extensions -*.so - -# Distribution / packaging -.Python -env/ -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -*.egg-info/ -.installed.cfg -*.egg - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*,cover - -# Translations -*.mo -*.pot - -# Django stuff: -*.log - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ diff --git a/tests/__init__.py b/.nojekyll similarity index 100% rename from tests/__init__.py rename to .nojekyll diff --git a/AUTHORS.md b/AUTHORS.md deleted file mode 100644 index 3d4e0bc..0000000 --- a/AUTHORS.md +++ /dev/null @@ -1,10 +0,0 @@ -# Authors - -The following people have contributed to the project (in alphabetical order): - -- [Martin Bentley](https://github.com/mtb-za), Agile Scientific, Canada -- [Evan Bianco](https://github.com/EvanBianco), Agile Scientific, Canada -- [Rob Gooder](https://github.com/ragooder/), United Kingdom -- [Matt Hall](https://github.com/kwinkunks), Equinor, Norway (ORCID: [0000-0002-4054-8295](https://orcid.org/0000-0002-4054-8295)) -- [Matteo Niccoli](https://github.com/mycarta), Canada -- [Steve Purves](https://github.com/stevejpurves), Spain diff --git a/CHANGELOG.md b/CHANGELOG.md deleted file mode 100644 index 8e88266..0000000 --- a/CHANGELOG.md +++ /dev/null @@ -1,46 +0,0 @@ -# CHANGELOG - -## 0.2.0, 10 October 2023 - -- Added `gio.find_closures()` to automatically find closing contours in a grid. Also added short notebook about it to the docs. Many thanks to Rob Gooder for hugely improving this algorithm. -- Development note: switched to PEP621-style build system, using only `pyproject.toml`. -- Now also building on and supporting Python 3.11. -- Moving to more conventional semantic versioning. - - -## 0.1.7, 19 September 2022 - -- Fixed [bug issue #26](https://github.com/agilescientific/gio/issues/26), which garbled string comments in `array_to_zmap()`. -- Implemented [enhancement issue #17](https://github.com/agilescientific/gio/issues/17). Created `gio.unmap_to_dataarray()`, putting the guts of the algorithm into its own library, [`unmap`](https://kwinkunks.github.io/unmap), which is now a dependency of `gio`. The library only produces NumPy arrays, so `gio` adds the `xarray` wrapper and a couple of extra options. - - -## 0.1.6, 11 September 2022 - -The overall plan of `gio` is starting to take shape. In general, the plan is to read specialist subsurace formats of various file types, with either `xarray.Dataset` or `xarray.DataArray` as the target. Then, `gio` will write these same formats via an accessor method on the `xarray` object (and possibly also via ordinary functions). This release implements this pattern for the first time, specifically for the ZMAP format. - -- Added the `zmap` module, with functions for reading ZMAP files (`read_zmap()`) and for writing files starting from an `xarray.DataArray` or from a NumPy array. -- Added `xarray.DataArray` accessor (in `xarray.py`) to save in ZMAP and OpendTect formats. The plan is to add more target formats the the accessor, according to need. -- Added tests for the ZMAP components. Removed `run_tests.py` and put the `pytest` options in `setup.cfg`, which seems cleaner. -- Started trying to maintain this file properly! -- Note that there are two 'work in progress' modules: `esri.py` and `usgs.py`, which will eventually read some common DEM formats. - -In general, everything is a work in progress but if there is a chapter on something in [the User Guide](https://code.agilescientific.com/gio/index.html#user-guide) then it mostly works, at least for the test cases I have. If you have a file that should work but doesn't, please consider [making an issue](https://github.com/agilescientific/gio/issues). - -Feedback is welcome on whether the target format for readers should be `xarray.Dataset` every time, even for singleton files. - - -## 0.1.5, 12 February 2022 - -- Added the `random` module, which generates random surfaces using sums of Perlin noise. The high-level interface is `gio.generate_random_surface()`. Also added a notebook for help on this module. -- Added the `logo` module, which plots gio's logo. Using the logo in the docs. - - -## 0.1.4, 3 February 2022 - -- New module: `surfer`, adapted from Seequent's [`steno3d_surfer`](https://pypi.org/project/steno3d_surfer), for reading Surfer 6 binary and ASCII files, and Surfer 7 binary files. The module does not write files yet. -- Started development on `iesx` module for reading IESX formatted files, eg from Petrel or OpendTect - - -## 0.1.0 to 0.1.3, February 2022 - -- Early development included setting up the package, and adding the `opendtect` and `xy_to_grid` modules. diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md deleted file mode 100644 index 1a668d8..0000000 --- a/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,79 +0,0 @@ -# Contributor Covenant Code of Conduct - -## Our Pledge - -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to make participation in our project and -our community a harassment-free experience for everyone, regardless of age, body -size, disability, ethnicity, sex characteristics, gender identity and expression, -level of experience, education, socio-economic status, nationality, personal -appearance, race, religion, or sexual identity and orientation. - -## Our standards - -Examples of behavior that contributes to creating a positive environment -include: - -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy towards other community members - -Examples of unacceptable behavior by participants include: - -* The use of sexualized language or imagery and unwelcome sexual attention or - advances -* Racist or racially biased remarks, attacks, or content. -* Trolling, insulting/derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or electronic - address, without explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -## Our responsibilities - -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. - -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. - -## Scope - -This Code of Conduct applies within all project spaces, and it also applies when -an individual is representing the project or its community in public spaces. -Examples of representing a project or community include using an official -project e-mail address, posting via an official social media account, or acting -as an appointed representative at an online or offline event. Representation of -a project may be further defined and clarified by project maintainers. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting any of the following people: - -- Matt Hall, [matt@agilescientific.com](mailto:matt@agilescientific.com) -- Evan Bianco, [evan@agilescientific.com](mailto:evan@agilescientific.com) - -All complaints will be reviewed and investigated and will result in a response that -is deemed necessary and appropriate to the circumstances. The project team is -obligated to maintain confidentiality with regard to the reporter of an incident. -Further details of specific enforcement policies may be posted separately. - -Project maintainers who do not follow or enforce the Code of Conduct in good -faith may face temporary or permanent repercussions as determined by other -members of the project's leadership. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/), version 1.4, -available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html - -For answers to common questions about this code of conduct, see -https://www.contributor-covenant.org/faq diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 370e72d..0000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,33 +0,0 @@ -# Contributing - -## :raised_hands: Thank you for considering contributing to the `gio` project! - -There are several important ways you can help; here are some examples: - -- Submitting bug reports and feature requests: see [Issues](https://github.com/agilescientific/gio/issues). -- Proposing code for bug fixes and new features, then [making a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests). -- Contributing data files to test against. See below. -- Fixing typos and generally improving the documentation. -- Writing tutorials, examples, and how-to documents. - - -## Contributing data files - -This is a data-driven project and data files are always helpful, especially if they differ substantially from examples we already have. - -Please ensure that data is open access and provide a licence file with it. please also make them as small as possible, preferably well under 1MB. - - -## Code of conduct - -We're fortunate to be part of a large professional community that conducts itself with mutual respect and consideration for others. Agile's [Code of Conduct](https://github.com/agilescientific/corporate/blob/main/CODE_OF_CONDUCT.md) is part of protecting these features for everyone, everywhere. Please read it. - - -## Authorship - -If you contribute a pull request to the project and you wish to be identified as an author, please add yourself to `AUTHORS.md`. - - -## License - -By making a contribution, you agree that it shall be governed by the terms of [the license](LICENSE) unless another, specific agreement is made with Agile. diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 8f71f43..0000000 --- a/LICENSE +++ /dev/null @@ -1,202 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright {yyyy} {name of copyright owner} - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. - diff --git a/README.md b/README.md deleted file mode 100644 index fbe0f2d..0000000 --- a/README.md +++ /dev/null @@ -1,48 +0,0 @@ -# gio - -[![Tests](https://github.com/agilescientific/gio/actions/workflows/build-test.yml/badge.svg)](https://github.com/agilescientific/gio/actions/workflows/build-test.yml) -[![Build docs](https://github.com/agilescientific/gio/actions/workflows/build-docs.yml/badge.svg)](https://github.com/agilescientific/gio/actions/workflows/build-docs.yml) -[![PyPI version](https://img.shields.io/pypi/v/gio.svg)](https://pypi.org/project/gio/) -[![PyPI versions](https://img.shields.io/pypi/pyversions/gio.svg)](https://pypi.org/project/gio/) -[![PyPI license](https://img.shields.io/pypi/l/gio.svg)](https://pypi.org/project/gio/) - -**Geoscience I/O for grids and horizons.** - -The goal of this project is to load and save various geoscience surface data formats (2D and 3D seismic horizons, grids, etc). The interchange formats are the `xarray.DataArray` (and `xarray.Dataset` where we need a collection of arrays). This format is convenient because it allows us to store a NumPy array with Pandas-like indexing (as opposed to ordinary NumPy positional indexing). - -We've started with: - -- OpendTect horizons -- ZMAP grids -- Surfer grids -- Petrel horizons - -**What formats would you like to see? [Make an issue](https://github.com/agilescientific/gio/issues).** - - -## Installation - -This library is on PyPI, so you can install it with: - - pip install gio - - To get the latest unstable release, you can install it from GitHub: - - python -m pip install --upgrade https://github.com/agilescientific/gio/archive/develop.zip - - -## Basic usage - -In general, there's a reader for each supported file format. The reader produces an `xarray.DataArray`, or `xarray.Dataset` if the format supports multiple surfaces in one file. - -```python -import gio - -da = gio.read_surfer(fname) -da.plot() -``` - - -## Documentation - -See [the documentation](https://code.agilescientific.com/gio) for more examples, and for help developing `gio` or making contributions back to this project. diff --git a/_images/00b781fbecdcee7b5a5fb722738ef0f9599ffd62a231acfabe6094163ffa6a1f.png b/_images/00b781fbecdcee7b5a5fb722738ef0f9599ffd62a231acfabe6094163ffa6a1f.png new file mode 100644 index 0000000..4ef9461 Binary files /dev/null and b/_images/00b781fbecdcee7b5a5fb722738ef0f9599ffd62a231acfabe6094163ffa6a1f.png differ diff --git a/_images/06c58ba264e1bdc8dca9dc561d9d624997772add6692eaa04f6db39bd28d647d.png b/_images/06c58ba264e1bdc8dca9dc561d9d624997772add6692eaa04f6db39bd28d647d.png new file mode 100644 index 0000000..eb637cf Binary files /dev/null and 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b/_images/fcdbd05bdb7478dfe201cc93eec76383425505f8931c64aa378bedc51b150ecf.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs/authors.md b/_sources/authors.md.txt similarity index 100% rename from docs/authors.md rename to _sources/authors.md.txt diff --git a/docs/changelog.md b/_sources/changelog.md.txt similarity index 100% rename from docs/changelog.md rename to _sources/changelog.md.txt diff --git a/docs/contributing.md b/_sources/contributing.md.txt similarity index 100% rename from docs/contributing.md rename to _sources/contributing.md.txt diff --git a/docs/development.md b/_sources/development.md.txt similarity index 100% rename from docs/development.md rename to _sources/development.md.txt diff --git a/docs/gio.rst b/_sources/gio.rst.txt similarity index 92% rename from docs/gio.rst rename to _sources/gio.rst.txt index cd86973..a4ee2ac 100644 --- a/docs/gio.rst +++ b/_sources/gio.rst.txt @@ -68,14 +68,6 @@ gio.unmap module :undoc-members: :show-inheritance: -gio.usgs module ---------------- - -.. automodule:: gio.usgs - :members: - :undoc-members: - :show-inheritance: - gio.xarray module ----------------- diff --git a/docs/index.rst b/_sources/index.rst.txt similarity index 100% rename from docs/index.rst rename to _sources/index.rst.txt diff --git a/docs/installation.md b/_sources/installation.md.txt similarity index 100% rename from docs/installation.md rename to _sources/installation.md.txt diff --git a/docs/license.md b/_sources/license.md.txt similarity index 100% rename from docs/license.md rename to _sources/license.md.txt diff --git a/_sources/userguide/Find_closures.ipynb.txt b/_sources/userguide/Find_closures.ipynb.txt new file mode 100644 index 0000000..944ad46 --- /dev/null +++ b/_sources/userguide/Find_closures.ipynb.txt @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "fb404509", "metadata": {}, "source": ["# Find closures\n", "\n", "`gio` is mostly about data loading, but it can do a few other useful things. One of those things is finding closed contours on a grid."]}, {"cell_type": "markdown", "id": "556ab028", "metadata": {}, "source": ["Let's start by making a random grid,and we'll scale it to make it more like a geological surface:"]}, {"cell_type": "code", "execution_count": 2, "id": "83c470d7", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "import gio\n", "\n", "g = gio.generate_random_surface(200, res=3, octaves=3, random_seed=42)\n", "g = 100 * g - 1000\n", "\n", "g.plot()\n", "plt.contour(g, levels=20, colors='black', linewidths=0.5, linestyles='solid', alpha=0.45)"]}, {"cell_type": "markdown", "id": "b5e27bc1", "metadata": {}, "source": ["Now we can ask `gio` to find the closing contours."]}, {"cell_type": "code", "execution_count": 3, "id": "993ef08f", "metadata": {}, "outputs": [], "source": ["contours = gio.find_closures(g)"]}, {"cell_type": "markdown", "id": "fb0f273e", "metadata": {}, "source": ["The result is a bunch of `shapely.Polygon` objects:"]}, {"cell_type": "code", "execution_count": 4, "id": "9976c55a", "metadata": {}, "outputs": [{"data": {"image/svg+xml": [""], "text/plain": [""]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["contours[10]"]}, {"cell_type": "markdown", "id": "824ba997", "metadata": {}, "source": ["The 'level' (contour value) is stored as the z-value of the `Polygon`.\n", "\n", "Let's plot them:"]}, {"cell_type": "code", "execution_count": 5, "id": "cd31044c", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["g.plot.imshow()\n", "plt.contour(g, levels=10, colors='black', linewidths=0.5, linestyles='solid', alpha=0.45)\n", "\n", "for c in contours:\n", " plt.plot(*c.exterior.xy, 'white')"]}, {"cell_type": "markdown", "id": "c121c3b4", "metadata": {}, "source": ["Cool! But we probably don't care about tiny closures. We can apply minimum area (in whatever units the grid cells represent) or minimum closure height (or both!), e.g."]}, {"cell_type": "code", "execution_count": 6, "id": "0a21b348", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["contours = gio.find_closures(g, min_height=25)\n", "\n", "g.plot.imshow()\n", "plt.contour(g, levels=10, colors='black', linewidths=0.5, linestyles='solid', alpha=0.45)\n", "\n", "for c in contours:\n", " plt.plot(*c.exterior.xy, 'white')"]}, {"cell_type": "markdown", "id": "b60d831c", "metadata": {}, "source": ["Shapely `Polygon` objects are nice because they can do lots of things. They also know their own areas:"]}, {"cell_type": "code", "execution_count": 7, "id": "d3de48f9", "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Polygon 0 area: 833.8 grid cells\n", "Polygon 1 area: 539.2 grid cells\n", "Polygon 2 area: 3236.9 grid cells\n", "Polygon 3 area: 454.2 grid cells\n"]}], "source": ["for i, c in enumerate(contours):\n", " print(f\"Polygon {i} area: {c.area:.1f} grid cells\")"]}], "metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8"}}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/docs/userguide/Random_grids.ipynb b/_sources/userguide/Random_grids.ipynb.txt similarity index 100% rename from docs/userguide/Random_grids.ipynb rename to _sources/userguide/Random_grids.ipynb.txt diff --git a/_sources/userguide/Read_OpendTect_horizons.ipynb.txt b/_sources/userguide/Read_OpendTect_horizons.ipynb.txt new file mode 100644 index 0000000..0d39dc5 --- /dev/null +++ b/_sources/userguide/Read_OpendTect_horizons.ipynb.txt @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "ff4a05e8", "metadata": {}, "source": ["# Read OpendTect horizons\n", "\n", "The best way to export horizons from OpendTect is with these options:\n", "\n", "- x/y and inline/crossline\n", "- with header (single or multi-line, it doesn't matter)\n", "- choose all the attributes you want\n", "\n", "On the last point, if you choose multiple horizons in one file, you can only have one attribute in the file. "]}, {"cell_type": "markdown", "id": "30000231", "metadata": {}, "source": ["## IL/XL only, single-line header, multiple attributes"]}, {"cell_type": "code", "execution_count": 1, "id": "6d53a087", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n", "Dimensions:         (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", "  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", "Data variables:\n", "    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", " * xline (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " amplitude (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " correlation (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " seed_index (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " tracking_order (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat"]}, "execution_count": 1, "metadata": {}, "output_type": "execute_result"}], "source": ["import gio\n", "\n", "ds = gio.read_odt('../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat')\n", "ds"]}, {"cell_type": "code", "execution_count": 2, "id": "a3a9ab32", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "63f655ac", "metadata": {}, "source": ["## IL/XL and XY, multi-line header, multiple attributes\n", "\n", "### Load everything (default)\n", "\n", "X and Y are loaded as `cdp_x` and `cdp_y`, to be consistent with [the **`seisnc`** standard](https://segysak.readthedocs.io/en/latest/seisnc-standard.html) in [`segysak`](https://segysak.readthedocs.io/en/latest/index.html)."]}, {"cell_type": "code", "execution_count": 3, "id": "ab626956", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n", "Dimensions:         (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", "  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", "    cdp_x           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    cdp_y           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", "    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h...
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", " * xline (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", " cdp_x (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " amplitude (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " correlation (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " seed_index (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " tracking_order (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h..."]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["ds = gio.read_odt('../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-header.dat')\n", "ds"]}, {"cell_type": "code", "execution_count": 4, "id": "bb9f6a46", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "\n", "plt.scatter(ds.coords['cdp_x'], ds.coords['cdp_y'], s=5)"]}, {"cell_type": "markdown", "id": "5f52d77e", "metadata": {}, "source": ["### Load only inline, crossline, TWT\n", "\n", "There is only one attribute here: Z, which is the two-way time of the horizon.\n", "\n", "**Note that when loading data from OpendTect, you always get an `xarray.Dataset`, even if there's only a single attribute. This is because the format supports multiple grids and we didn't want you to have to guess what a given file would produce.**"]}, {"cell_type": "code", "execution_count": 5, "id": "5eee4c99", "metadata": {}, "outputs": [{"data": {"text/html": ["
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<xarray.Dataset>\n", "Dimensions:  (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline    (iline) int64 376 378 380 382 384 386 ... 472 474 476 478 480 482\n", "  * xline    (xline) int64 812 814 816 818 820 822 ... 914 916 918 920 922 924\n", "Data variables:\n", "    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h...
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 376 378 380 382 384 386 ... 472 474 476 478 480 482\n", " * xline (xline) int64 812 814 816 818 820 822 ... 914 916 918 920 922 924\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h..."]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-header.dat'\n", "names = ['Inline', 'Crossline', 'Z'] # Must match OdT DAT file.\n", "\n", "ds = gio.read_odt(fname, names=names)\n", "ds"]}, {"cell_type": "markdown", "id": "7cc776a1", "metadata": {}, "source": ["## XY only\n", "\n", "If you have a file with no IL/XL, `gio` can try to load data using only X and Y:\n", "\n", "- If there's a header you can load any number of attributes.\n", "- If there's no header, you can only one attribute (e.g. TWT) automagically...\n", "- OR, if there's no header, you can provide `names` to tell `gio` what everything is.\n", "\n", "`gio` must create fake inline and crossline numbers; you can provide an origin and a step size. For example, notice above that the true inline and crossline numbers are:\n", "\n", "- inline: 376, 378, 380, etc.\n", "- crossline: 812, 814, 816, etc.\n", "\n", "So we can pass an origin of `(376, 812)` and a step of `(2, 2)` to mimic these."]}, {"cell_type": "markdown", "id": "88c18cf8", "metadata": {}, "source": ["### Header present"]}, {"cell_type": "code", "execution_count": 6, "id": "bb396530", "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.\n", " dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)\n"]}, {"data": {"text/html": ["
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<xarray.Dataset>\n", "Dimensions:         (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", "  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", "    cdp_x           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    cdp_y           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", "    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", " * xline (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", " cdp_x (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " amplitude (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " correlation (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " seed_index (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " tracking_order (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat'\n", "\n", "ds = gio.read_odt(fname, origin=(376, 812), step=(2, 2))\n", "ds"]}, {"cell_type": "code", "execution_count": 7, "id": "777b4f3b", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "1cac0f29", "metadata": {}, "source": ["### No header, more than one attribute: raises an error"]}, {"cell_type": "code", "execution_count": 8, "id": "c7a736d5", "metadata": {}, "outputs": [{"ename": "TypeError", "evalue": "First two columns must be integers to be interpreted as inline and crossline.", "output_type": "error", "traceback": ["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn [8], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m fname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../data/OdT/3d_horizon/Segment_XY_No-header.dat\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 3\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mgio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_odt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m ds\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Raises an error:\u001b[39;00m\n", "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:222\u001b[0m, in \u001b[0;36mread_odt\u001b[0;34m(fname, names, usecols, na_values, origin, step, attrs)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_odt\u001b[39m(fname,\n\u001b[1;32m 171\u001b[0m names\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 172\u001b[0m usecols\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 176\u001b[0m attrs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 177\u001b[0m ):\n\u001b[1;32m 178\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;124;03m Read an OdT horizon file. (Native format, not IESX.)\u001b[39;00m\n\u001b[1;32m 180\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;124;03m crossline \u2014 and cdp_x and cdp_y, if present \u2014 will be coordinates.\u001b[39;00m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 222\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mread_odt_as_df\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 223\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 224\u001b[0m \u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[43m \u001b[49m\u001b[43mna_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_values\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 228\u001b[0m attrs \u001b[38;5;241m=\u001b[39m attrs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[1;32m 229\u001b[0m attrs\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124modt_filename\u001b[39m\u001b[38;5;124m'\u001b[39m: fname})\n", "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:96\u001b[0m, in \u001b[0;36mread_odt_as_df\u001b[0;34m(fname, names, usecols, na_values, **kwargs)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 95\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFirst two columns must be integers to be interpreted as inline and crossline.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(message)\n\u001b[1;32m 97\u001b[0m cols \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(data_cols))\n\u001b[1;32m 98\u001b[0m names \u001b[38;5;241m=\u001b[39m ix \u001b[38;5;241m+\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvar_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m cols \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m (usecols \u001b[38;5;129;01mor\u001b[39;00m cols)]\n", "\u001b[0;31mTypeError\u001b[0m: First two columns must be integers to be interpreted as inline and crossline."]}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat'\n", "\n", "ds = gio.read_odt(fname)\n", "ds\n", "\n", "# Raises an error:"]}, {"cell_type": "code", "execution_count": 9, "id": "89db9d81", "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.\n", " dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)\n"]}, {"data": {"text/html": ["
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<xarray.Dataset>\n", "Dimensions:  (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline    (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n", "  * xline    (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n", "    cdp_x    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "    cdp_y    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Data variables:\n", "    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_No-header.dat
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n", " * xline (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n", " cdp_x (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY_No-header.dat"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat'\n", "\n", "ds = gio.read_odt(fname, names=['X', 'Y', 'TWT'])\n", "ds"]}, {"cell_type": "code", "execution_count": 10, "id": "ce2a9318", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "23078f1c", "metadata": {}, "source": ["## Sparse data\n", "\n", "Sometimes a surface only exists at a few points, e.g. a 3D seismic interpretation grid. In general, loading data like this is completely safe if you have inline and xline locations. If you only have (x, y) locations, `gio` will attempt to load it, but you should inspect the result carefullly."]}, {"cell_type": "code", "execution_count": 11, "id": "a2cee953", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n", "Dimensions:    (iline: 62, xline: 185)\n", "Coordinates:\n", "  * iline      (iline) int64 1190 1197 1198 1199 1200 ... 1254 1255 1256 1257\n", "  * xline      (xline) int64 1941 1942 1943 1944 1945 ... 2122 2123 2124 2125\n", "    cdp_x      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "    cdp_y      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", "    twt        (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "    amplitude  (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single...
"], "text/plain": ["\n", "Dimensions: (iline: 62, xline: 185)\n", "Coordinates:\n", " * iline (iline) int64 1190 1197 1198 1199 1200 ... 1254 1255 1256 1257\n", " * xline (xline) int64 1941 1942 1943 1944 1945 ... 2122 2123 2124 2125\n", " cdp_x (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", " amplitude (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single..."]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single-line-header.dat'\n", "\n", "ds = gio.read_odt(fname)\n", "ds"]}, {"cell_type": "code", "execution_count": 12, "id": "1a343f40", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ds['twt'].plot.imshow()"]}, {"cell_type": "markdown", "id": "566a6f5f", "metadata": {}, "source": ["There's some sort of artifact with the default plot style, which uses `pcolormesh` I think."]}, {"cell_type": "code", "execution_count": 13, "id": "f4c3f475", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "80dd49d6", "metadata": {}, "source": ["## Multiple horizons in one file\n", "\n", "You can export multiple horizons from OpendTect. These will be loaded as one `xarray.Dataset` as different **Data variables**. (The actual attribute you exported from OdT is always called Z; this information is not retained in the xarray.)"]}, {"cell_type": "code", "execution_count": 14, "id": "e976b148", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n", "Dimensions:               (xline: 93, iline: 45)\n", "Coordinates:\n", "  * xline                 (xline) int64 964 966 968 970 ... 1142 1144 1146 1148\n", "  * iline                 (iline) int64 110 112 114 116 118 ... 192 194 196 198\n", "Data variables:\n", "    F3_Demo_2_FS6         (iline, xline) float64 nan nan nan ... 825.4 823.9\n", "    F3_Demo_4_Truncation  (iline, xline) float64 820.0 820.7 ... 635.3 635.2
"], "text/plain": ["\n", "Dimensions: (xline: 93, iline: 45)\n", "Coordinates:\n", " * xline (xline) int64 964 966 968 970 ... 1142 1144 1146 1148\n", " * iline (iline) int64 110 112 114 116 118 ... 192 194 196 198\n", "Data variables:\n", " F3_Demo_2_FS6 (iline, xline) float64 nan nan nan ... 825.4 823.9\n", " F3_Demo_4_Truncation (iline, xline) float64 820.0 820.7 ... 635.3 635.2"]}, "execution_count": 14, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/F3_Multi-H2-H4_ILXL_Single-line-header.dat'\n", "\n", "ds = gio.read_odt(fname)\n", "ds"]}, {"cell_type": "code", "execution_count": 15, "id": "5965f669", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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+yEI/VJfwtfqualTnO0JjvaxXR+srRXyt9NpwiR9EdRuScAzhEjbs0rjtQfwcCmUkYb0sfiHIzZez4/Lxt7H4NNY5LTrY2bVrF0aMGOGenzlzJgBg8uTJWLlyJU6ePImZM2fi559/RqdOnXD33Xdj7ty5ijaWLFmCoKAgpKWloaamBiNHjsTKlSt17+shIiIyO1/vu3GKA7s2qkUHO8OHD4fcyAsxY8YMzJgxo9E2wsLCkJWVhaysLH93j4iIqEW5YGHOjh+0int2iIiIiLxl6qexiIiI2jKnLMFp4L7LxuoTBztERESm5fTxBmUnL2MB4GUsIiIiCnA8s0NERGRSLtmiGw3SeH2e2QE42Gl2XTufVM4L67ccuVwx/zsifN6msYwclzAvNzoPAFahjixcG9ZK7tTri0XMBNHKCAlqPKtGzB3RisRQZ6A0fl3byHeNmLujdancKmTvSC4hz0N/M3pd1c3MATTyfHQ2odUvvUwczYgg4Th6mrujtR2xiKpfmhk5Qh0Pc3c02zVwb4TquLrE97tQXqPrskVZSBJ3xpt7NHTybjTzcMTPhF5mjlY7elXEDB1AnW8kzqtyhrQCn3TKqPop9qP57oPhZSz/4GUsIiIiCmg8s0NERGRSLvj2RJVGBnWbxMEOERGRSfkeKsgLOAAvYxEREVGA45kdIiIik/L9t7F4TgPgYIeIiMi0XJA0n2z1pD5xsENERGRaPLPjHzwKREREFNB4ZsdkhnU9oJj/R8mlqjJFtZ0V86ddYX7vR5BF+cBikEv9AKNLCGKrdynHzlYvwqysViHcMEhju6pmxTG7EHbo0hrTC6d2xTaFKlrBhHpcIRoLhc1aa3X6odWETiKqkbhET0MEjbXReDCf5nY8DBnU2o6YvGcomFAMBPQwZPDcQjHcTgw71DhqHmbVGQrEFBoRu2XkIoYq7M/In8Gq7XqxYd0wQ60qjR9EWQgilJxa6Z7CgXW6Gl0vCd9LksuLLwQv+R4qyHMaAAc7REREpuWSJbh8ydnhr54D4GUsIiIiCnA8s0NERGRSLh8vYzFU8BwOdoiIiEzK918952AH4GUsIiIiCnA8s0NERGRSTkhw+hAM6EvdQMLBDhERkUnxMpZ/cLBjcn3ij6uXCfPvHrpGMV9R3063XYuQGxIkBJy4hHkxd0ezTSHvxJtHHoOsTsW8VSNnRySWEHN1NHvhajwTRIzREGNZzm1Ip2Ma651C9o54iKy1Qrc04110jqtYRx1M5HGujnbejV4bGvk2QilPc3c0+yJk4kiuxnN3tPrmae7OuTI6uTpaL56Y36TzV7fW/quyeMTNCvsii+91LeK/h3rvMRjI1dFow9N/dw29/8VcHXF/rRobFXPDxDJ66yUOIFobDnaIiIhMygnfLkU59Yu0CRzsEBERmRQvY/kHBztEREQmxR8C9Q8eBSIiIgpoPLNDRERkUjIkuHy4Z0fvJvi2goMdIiIik+JlLP/gUSAiIqKAxjM7AeCOP+xUzgvr5+8bp6pT4wxWzAdZlA8oiqdNQ4xk5oiZIFrRJDqnVEOEnB1HcL2qjCQpQ3DqhSwOp8PAGF7MIhGzWsQ8FK0mrRrLLmQgI8UZKvZLmK3TaEIneki1XuOQe5qro/Wqqcro5Q4BquwVT3N3APX+qTarE/+iVcfT3B1Anb0j7ksDCU9CCaGOOKvVhBj5otqsUEkzJKrxOroZOkba0PrMqLaj06TWe13I1VFlZlmUG5Y13piSRSc3R7VePB7Nd2nIJUteZZZdWJ9a+MzO1q1bMXbsWNjtdkiShLVr1yrWV1VV4eGHH0bnzp0RHh6O3r1744033lCUqa2txfTp0xETE4OIiAikpqbi2LFjzbgXRERETcP5r18992WiFh7sVFdXo3///li2bJnm+kcffRTr169Hbm4ufvjhBzz66KOYPn06PvzwQ3eZ9PR0rFmzBnl5eSgoKEBVVRXGjBkDp5NRSkRERNTCl7FSUlKQkpLS4Prt27dj8uTJGD58OADggQcewJtvvoldu3Zh3LhxqKioQHZ2NnJycpCUlAQAyM3NRXx8PDZu3IhRo0ZptltbW4va2n/n8ldWVvpvp4iIiPyEl7H8w9Tnt4YMGYJ169bh+PHjkGUZmzZtwoEDB9yDmMLCQjgcDiQnJ7vr2O129OvXD9u2bWuw3czMTNhsNvcUHx/f5PtCRETkKRcsPk9k8sHOa6+9hj59+qBz584ICQnB6NGj8frrr2PIkCEAgNLSUoSEhKBDhw6KerGxsSgtLW2w3YyMDFRUVLinkpKSJt0PIiIiajmmH+x88803WLduHQoLC/HKK6/goYcewsaNGxutJ8sypEbulg8NDUX79u0VExERkdk4ZcnnyVOnT59Geno6EhISEB4ejsGDB2Pnzn8/9SvLMubPnw+73Y7w8HAMHz4c33//vaINsz08ZNrBTk1NDZ588kksXrwYY8eOxRVXXIGHH34Yd9xxB15++WUAQFxcHOrq6lBeXq6oW1ZWhtjY2JboNhERkd+cv2fHl8lT9913H/Lz85GTk4O9e/ciOTkZSUlJOH78OABg0aJFWLx4MZYtW4adO3ciLi4ON954I06fPu1uw2wPD5k2Z8fhcMDhcMAi5B1YrVa4XOfCFwYOHIjg4GDk5+cjLS0NAHDy5Ens27cPixYtavY+m9X8fh+qlq3+5wDF/LaqyxXzv9RepJh3WdRvUDEzxyLkWRj5kIllQoKU26nT+GDIQh2nRQi8ETarlffhEt75krAZWcz3MJD3IpYxEjsj9rVeJ+4DAMSXQuyHRRVNZCBoRxbybsR9M5Sho19FLxNGL3cHUL+e6lwhvfwbI7lCYq6Kug1VHTF3R9Y4AuKGdfJsNE9Qq9oQ+uVFRI5uro6BXBnV50yjjuorQaddWSvLSidXR3ytJDGXR6sNq0Vnvfgdoxey5T+yj796LntYt6amBh988AE+/PBDDB06FAAwf/58rF27Fm+88Qaef/55LF26FE899RRuvfVWAMCqVasQGxuLt99+Gw8++KDXDw81pRYd7FRVVeHQoUPu+eLiYhQVFSEqKgpdunTBsGHDMGfOHISHhyMhIQFbtmzB3/72NyxevBgAYLPZMHXqVMyaNQvR0dGIiorC7NmzkZiY6D7AREREbZ341HFoaChCQ8VUU6C+vh5OpxNhYWGK5eHh4SgoKEBxcTFKS0sVDwaFhoZi2LBh2LZtGx588EHdh4daYrDTopexdu3ahQEDBmDAgHNnGWbOnIkBAwbgmWeeAQDk5eXhmmuuwcSJE9GnTx8sXLgQCxYswLRp09xtLFmyBOPHj0daWhquv/56tGvXDh999BGs4kiciIiolXFC8nkCgPj4eMVTyJmZmZrbi4yMxHXXXYfnn38eJ06cgNPpRG5uLnbs2IGTJ0+6H/4RbxW58MEgbx8eakotemZn+PDh2qd6/yUuLg4rVqxotI2wsDBkZWUhKyvL390jIiJqUS7Zt6wc17/+iS0pKVE8jKN1Vue8nJwcTJkyBZdeeimsViuuuuoqTJgwAbt373aXER8C0nswyGiZpmLaG5SJiIjIP8QnkBsb7Fx22WXYsmULqqqqUFJSgm+//RYOhwPdunVDXFwcAKjO0Fz4YJAZHx7iYIeIiMikXP+6QdmXyVsRERHo1KkTysvLsWHDBowbN8494MnPz3eXq6urw5YtWzB48GAAyoeHzjv/8ND5Ms3NtE9jERERtXUuSKonXz2t76kNGzZAlmX07NkThw4dwpw5c9CzZ0/ce++9kCQJ6enpeOGFF9CjRw/06NEDL7zwAtq1a4cJEyYAMOfDQxzsEBERkVtFRQUyMjJw7NgxREVF4bbbbsOCBQsQHBwMAHjsscdQU1ODhx56COXl5Rg0aBA+//xzREZGuttYsmQJgoKCkJaWhpqaGowcORIrV65ssYeHJLmxO4TbiMrKSthsNlRUVDBN+V+e25eqmD9Y3VFVpk4MqxFo3VQn/pVR51S+8U/XKa8jn60PVrVRXRuimD9To5x31gr9Oqs+jWsRlllqpcbnNXKwJCHPRsx7UeW/+ImYCST2w1qnnLc4DLQpfAtILp3cHUAVNKPafyPfLGIbOvk/2mV0+qHZd2H/dNv0oh9OjTqqvonHWZjXaAOe1vEiI0mVVaR1JUTvZlTNjCAPQ4A0jrvFoTyIklOYrxfmHRofXlUZ4UNUL9SpV66vd9Vi4/HlTfpvxvl/lyZ8OQEhF4XoV2hAXVUd3r7h7Tb/7xvP7BAREZmUr/fd+FI3kPAoEBERUUDjmR0iIiKTcsG737e6sD5xsENERGRaso9PY8kc7ADgYIeIiMi0vP3l8gvrE+/ZISIiogDHMztEREQmxaex/IODHSIiIpPiZSz/4GCHND3Tb51umYd2/0kxX+EI161T71KGCNZBOR8apAzv0roxz+FS/qXiqFe2ITuVdZxBGuGGQoinJMzL4rxWMJveH0xa3zFiO0IZI99LYl/FeSNUgXhC2J0shL9p5cdpBe0pG9FfJjarPjzqDctCKVUb4uui0Q+xXb02tQ6Abh2LRh3xjaQT5qf1HlJ1RW/eSKqgXoig1v7r9V1zO2IbjdfRWi1bhfemSzxm4r6oP6iSRXjDW4Uy4uskboM3/bY6HOwQERGZVEv8NlYg4mCHiIjIpHgZyz945xIREREFNJ7ZISIiMime2fEPDnaIiIhMioMd/+BlLCIiIgpoPLNDRERkUjyz4x8c7JDXXr8qVzGfc/Baxfz2039Q1Smva6eYD7PWq8pcyGVVf1BdwUKOjpC743Qq510OjZyRIOUyuV7I5hBzdpzqJiQxmkPVUXUd8VyqN+Gmqu8u4VOs6rtGDo+l3rN5zagWvegRA/Eu4r4YacLzjByN7XrYptY5cDHfRVXHUEaOeAD0tytuRvV+EOqocmi0mtTJ1dH891InE0gvQweA+vMg5jtpBVypcnQaz9kRP6fntiseJPG1E9q0iB8iL8KtvCTDt8fHDXwM2wQOdoiIiEyKZ3b8g/fsEBERUUDjmR0iIiKT4pkd/+Bgh4iIyKQ42PEPXsYiIiKigMYzO0RERCbFMzv+wcEOERGRScmyBNmHAYsvdQMJBzvkN5N6fKOc1ygzd+8tivnDZ2IU8/VC4IfFpU6JCLYoQ28iQuoa7dcZMUMDQJ0lWDHvEnI0ZKuQ1ROs/sKw1CmXWRzK9ZJWzk5TEC5GqzZroB9iX/Xyb4zQ+o7Va0edf2OkjDcZOT62qdE5sY4quwUAhEwYWcyR0cvh0dquTt6RbDGQtKKXq2OgH6pcHY3jriqj84aQNXK2IHwnSGKbYh2tMCvhtRG3I4l1XOKHiAOI1qZF79nZunUrxo4dC7vdDkmSsHbtWsV6SZI0p5deesldpra2FtOnT0dMTAwiIiKQmpqKY8eONfOeEBER+Z8Lks8TtfBgp7q6Gv3798eyZcs01588eVIxvfXWW5AkCbfddpu7THp6OtasWYO8vDwUFBSgqqoKY8aMgdOpEXlLRETUipy/Z8eXiVr4MlZKSgpSUlIaXB8XF6eY//DDDzFixAh0794dAFBRUYHs7Gzk5OQgKSkJAJCbm4v4+Hhs3LgRo0aN0my3trYWtbW17vnKykpfd4WIiIhMqtU8ev7zzz/jk08+wdSpU93LCgsL4XA4kJyc7F5mt9vRr18/bNu2rcG2MjMzYbPZ3FN8fHyT9p2IiMgb529Q9mWiVjTYWbVqFSIjI3Hrrbe6l5WWliIkJAQdOnRQlI2NjUVpaWmDbWVkZKCiosI9lZSUNFm/iYiIvMXLWP7Rap7GeuuttzBx4kSEhYXplpVlWX2H/gVCQ0MRGhrqz+4RERH5HR89949WcWbnq6++wv79+3HfffcplsfFxaGurg7l5eWK5WVlZYiNjW3OLhIREZFJtYozO9nZ2Rg4cCD69++vWD5w4EAEBwcjPz8faWlpAM49wbVv3z4sWrSoJbpKOp5PXNPo+kk7lAPaM/XBqjIWISPDalFmYIg5PEFB6ifz6oOUuTquIOW4X3YK2SQaUSViRIrqL6h6dR2t6BW97ejypo7IqyAdz9v0tIpWeb0yRvJuxBgVT3N3zrUhlDISxaKbkWMkq0fsiDCrl/+jQTdXR+u11MnVUa3XaEf3pINGG5KYVaR6HfQOMlRZPKpcHfG4W8Sda77zBLKPl6J4ZuecFh3sVFVV4dChQ+754uJiFBUVISoqCl26dAFw7kmp9957D6+88oqqvs1mw9SpUzFr1ixER0cjKioKs2fPRmJiovvpLCIiotZKhvoPK0/rUwsPdnbt2oURI0a452fOnAkAmDx5MlauXAkAyMvLgyzLuOuuuzTbWLJkCYKCgpCWloaamhqMHDkSK1euhNVq1SxPREREbUuLDnaGDx+ujksXPPDAA3jggQcaXB8WFoasrCxkZWX5u3tEREQtygXJ0KXIxupTK7lnh4iIqC3i01j+0SqexiIiIiLyFs/sEBERmZRLliD5cHaGoYLncLBDRERkUrLs49NYfBwLAC9jERERUYDjmR0ylZxBf1XM37vzXlWZI6ejFPNOl3LMLj59oHmDnioQUDmvG/6nQbYqK0liuBkAqPMNfSb2VQzI09oXSeyHOkVPuVrrTLgQ7gaX/kHTO6Gu0w1DZVS90M+28zhk8FwbOgGAmqF6Ylid+F4V29DacOOBf+Jq2UjSik6bmgGBeiGCmkGEOttV9UsjEFIVZigEBIp/wls1tqETPCjrhQ4243mCtn6Dcn19PTZt2oSjR48iISEBI0aM8CpahoMdIiIik2prg50ZM2Zg1KhRuPnmm3Hs2DHceOONOHjwIGJiYvDrr7+iT58++Oyzz3DppZd61C4vYxEREZlUW/vV8/fffx/du3cHAMyaNQudO3dGaWkpSktLUVZWhoSEBKSnp3vcLs/sEBERkSmUl5cjLCwMALBt2zZ88MEHiImJAQBERUUhMzNT8csLRvHMDhERkUmdfxrLl6k1ufzyy/Htt98CACIjI1FZWalYf/r0abhc4q/u6uOZHSIiIpM6N2Dx5Z4dP3amGTz66KOYPXs2YmNjkZGRgRkzZiArKwu9e/fG/v378cgjj+DWW2/1uF0OdoiIiMgU7rnnHvz222+4+eabIcsynE4nkpOT3etTU1OxZMkSj9vlYIeIiMik2trTWAAwc+ZMTJkyBfn5+Th8+DBcLhc6deqE66+/Hj169PCqTQ52yNRWXLNCtWz2d3co5gtPdVbMV9WGKOa1nkbwxxeAOjNEOevSiIKw6GTiiAzl/YhtiHU0tqGXzaOidXefmFUk5p1onD/X2x3dzBwDZdQZOlo5K0ImkqqO0KbG8dHN89HMyGl8XhU7Yyirx9OO6fdLnWWjrqKXq6P5ERPzbPS6prX7Yl+EzBxZ/JBp5V1ZhEbEjCzhDSC+H2RV7k7TkaH/udGr3xpdfPHF+I//+A+/tccblImIiMgUfvvtNxw7dkyx7Pvvv8e9996LtLQ0vP322161y8EOERGRSZ2/jOXL1Jr813/9FxYvXuyeLysrw//7f/8PO3fuRG1tLe655x7k5OR43C4HO0RERGYl+2FqRb755hukpqa65//2t78hKioKRUVF+PDDD/HCCy/gz3/+s8ftcrBDRERkVr6e1WllZ3ZKS0vRrVs39/yXX36JW265BUFB524xTk1NxcGDBz1ul4MdIiIiMoX27dvj999/d89/++23uPbaa93zkiShtrbW43Y52CEiIjKptpag/Mc//hGvvfYaXC4X3n//fZw+fRo33HCDe/2BAwcQHx/vcbt89JyIiMik2lrOznPPPYcbb7wRubm5qK+vx5NPPokOHTq41+fl5WHYsGEet8vBDrU6L/d/t9H1QzY+ppg/UxOqLiT+taMzbyjvxkDOiixm74iRKXqZORrLxAwYS70w71Q3YWh/dOhl0cgaB0DSuVtSLzPHSBkjbajyXoRaerk7gEb2jip3RmNf9coImUCy1p/lelk8qln9F1u2ivk33mTz6PRLs4wwL+6/xmZl8bircoaEvCet6xdWsfON5+pIVkPvKvKDAQMG4IcffsC2bdsQFxeHQYMGKdbfeeed6NOnj8ftcrBDRERkVr7eZNzKzuwcPnwY3bp1w7hx4zTX33zzzV61y3t2iIiITKqt3bPTo0cP/PLLL+75O+64Az///LPP7XKwQ0RERKYgXrr99NNPUV1d7XO7vIxFRERkVm31x7H8jIMdIiIik2prT2NJkgRJuLldnPcGBztERERkCrIs45577kFo6LmnaM+ePYtp06YhIiJCUW716tUetct7doiIiMysGX8Xq76+Hk8//TS6deuG8PBwdO/eHc899xxcrn8/819VVYWHH34YnTt3Rnh4OHr37o033nhD0U5tbS2mT5+OmJgYREREIDU1VfVr5lomT56Mjh07wmazwWaz4U9/+hPsdrt7/vzkKZ7ZoYBTkLRIMX/j5kdVZQ6XXqKYd51tglO9GjkrqvwSIXdHfHLC4tBoVsgZkYQcHam+8fXnNqSx7MLVBjKDdLNoNENSxHwTsU25sdXa2/VwvVYhCWK+S+O5O4CB/dWqpHdcxX5ptqGX1SN0y6X/3la9L/UydDT6YeQ9o8rR0ema1v6rMnDE4y7sr2zReAeIx0S8bKKTw6MZvNREmvsy1osvvojly5dj1apV6Nu3L3bt2oV7770XNpsNjzzyCADg0UcfxaZNm5Cbm4uuXbvi888/x0MPPQS73e5+ZDw9PR0fffQR8vLyEB0djVmzZmHMmDEoLCyE1SoGjv3bihUrPOrvsWPHYLfbYbE0/pq06JmdrVu3YuzYsbDb7ZAkCWvXrlWV+eGHH5CamgqbzYbIyEhce+21OHr0qHu9t6NHIiIi02vmXz3fvn07xo0bh5tvvhldu3bF7bffjuTkZOzatUtRZvLkyRg+fDi6du2KBx54AP3793eXqaioQHZ2Nl555RUkJSVhwIAByM3Nxd69e7Fx40ZfjoZKnz59cOTIEd1yLTrYqa6uRv/+/bFs2TLN9f/85z8xZMgQ9OrVC5s3b8Z3332HuXPnIiwszF0mPT0da9asQV5eHgoKClBVVYUxY8bA6dT6c5aIiKjtqaysVEwN/ZjmkCFD8MUXX+DAgQMAgO+++w4FBQW46aabFGXWrVuH48ePQ5ZlbNq0CQcOHMCoUaMAAIWFhXA4HEhOTnbXsdvt6NevH7Zt2+bX/dJMGdfQopexUlJSkJKS0uD6p556CjfddBMWLfr3ZYnu3bu7///86DEnJwdJSUkAgNzcXMTHx2Pjxo3uAy+qra1VvNCVlZW+7goREVETkODbz1Ocqyv+eOa8efMwf/58VenHH38cFRUV6NWrF6xWK5xOJxYsWIC77rrLXea1117D/fffj86dOyMoKAgWiwV//etfMWTIEABAaWkpQkJCFL9pBQCxsbEoLS31YV+8Z9oblF0uFz755BNcfvnlGDVqFDp27IhBgwYpLnV5O3rMzMxU3OjkzS+oEhERNTk/XcYqKSlBRUWFe8rIyNDc3Lvvvovc3Fy8/fbb2L17N1atWoWXX34Zq1atcpd57bXX8M0332DdunUoLCzEK6+8goceekj3EpUsy355jNwbph3slJWVoaqqCgsXLsTo0aPx+eef45ZbbsGtt96KLVu2APB+9JiRkaF40UtKSpp0X4iIiFpS+/btFdP5R7tFc+bMwRNPPIE777wTiYmJmDRpEh599FFkZmYCAGpqavDkk09i8eLFGDt2LK644go8/PDDuOOOO/Dyyy8DAOLi4lBXV4fy8nJF22VlZYiNjW3aHW2AaQc75x9zGzduHB599FFceeWVeOKJJzBmzBgsX7680bp6o8fQ0FDVC09ERGQ6zXyD8pkzZ1RPNlmtVve/yQ6HAw6Ho9EyAwcORHBwMPLz893rT548iX379mHw4MGedUiH0TNFpn30PCYmBkFBQaqfcu/duzcKCgoAKEePF57dKSsr8/sBJSIianbN/KvnY8eOxYIFC9ClSxf07dsXe/bsweLFizFlyhQA584QDRs2DHPmzEF4eDgSEhKwZcsW/O1vf8PixYsBADabDVOnTsWsWbMQHR2NqKgozJ49G4mJie77a/2lVdyg3JiQkBBcc8012L9/v2L5gQMHkJCQAEA5ekxLSwPw79HjhTc1U9uWP3yJbplu/5upmJdrQ5qmMzpZJEbiPPRydCxi7o6BBxPF70PV16NW3ol+EY/ryMISMXfHWBsG+tV43I9u7o5mu6rXViucSMgRsnjTe7nRWVVmjEbOjMe5Ohr7opuro9o3g1k8Om1AyJlSvXd1jzEgiZ8rVa6OmAfU+HwgycrKwty5c/HQQw+hrKwMdrsdDz74IJ555hl3mby8PGRkZGDixIn47bffkJCQgAULFmDatGnuMkuWLEFQUBDS0tJQU1ODkSNHYuXKlY1m7HjjH//4B+x2u265Fh3sVFVV4dChQ+754uJiFBUVISoqCl26dMGcOXNwxx13YOjQoRgxYgTWr1+Pjz76CJs3bwbQvKNHIiKi5ibL6rBRT+t7IjIyEkuXLsXSpUsbLBMXF6cb/hcWFoasrCxkZWV51gGcO2nxxRdfICoqCklJSQgJ+fcfn9XV1XjllVfcgy+jDxi16GBn165dGDFihHt+5syZAM7FRa9cuRK33HILli9fjszMTMyYMQM9e/bEBx984H68DWi+0SMREVGza2O/er5z504kJyfD5XLB4XCgc+fOWLNmDfr27Qvg3EmSZ599VnGmyYgWHewMHz5c93rblClT3NcKtfgyeiQiIiLzePLJJ3HrrbfiL3/5C6qrq/HEE09g2LBhyM/Px4ABA7xu17T37BAREbV5zXyDcksrLCzEn//8Z1gsFkRGRuLPf/4zEhISMHLkSGzYsAFdunTxql0OdoiIiExKkjV/U9ij+q3N2bNnFfOPPfYYLBYLkpOT8dZbb3nVJgc7REREZtXG7tk5/wsIV1xxhWL57NmzIcuy4mcrPGHaUEEiIiJqW+6++258/fXXmuvmzJmD5557zqtLWRzsEBERmdX5e3Z8mVqR++67Dzk5OQ2uf+yxx1BcXOye//rrrxv8BfcL8TIWEYDiicofxbv8/ecV83W/hKvqWJyNf4lofcd4ev1c1kpQEP9EEdtsgtPWmvuis1nNfdUN8xPXawTCCbU8DRnUKqQf7acRkCeW0ntdAFVInuQSCqnC/jQaEdpQP9AqLHDpv3h6IYKa/17qhAgaqaP777Ch7QptCgGBWodQFoMWxWOkasPTNEQ/amOXsTyVkpKCoqIidO/evdFyPp3Zqaurw/79+1FfX69fmIiIiMiPjP5chFeDnTNnzmDq1Klo164d+vbti6NHjwIAZsyYgYULF3rTJBEREYma+YdAA5VXg52MjAx899132Lx5M8LCwtzLk5KS8O677/qtc0RERG0aBzt+4dU9O2vXrsW7776La6+9VvHz6n369ME///lPv3WOiIiIyFdeDXZ++eUXdOzYUbW8urpaMfghIiIiH7SxBGVPGR1zeHUZ65prrsEnn3yi2thf/vIXXHfddd40SURERILzCcq+TIHM6A3KXp3ZyczMxOjRo/GPf/wD9fX1ePXVV/H9999j+/bt2LJlizdNEhERESn88ssv2L9/PyRJwuWXX45LLrlEsf706dOG2vFqsDN48GB8/fXXePnll3HZZZfh888/x1VXXYXt27cjMTHRmyaJTOXA7XN1y3T98yuKeWutToiMn7iE7B1JmJedwnqts7xivEtT9NXAdj3N3TlXRshA8TB3x5vtakcGNZ6Jo5mRJLw2qg0ZiXMRN6uTkaP14nqcq6PVD71cHY06ettVlRfzcABI1sZfPHEbktb+i3lHqowknYPcnFeG2mjOTnV1NaZPn46cnBw4nec+OFarFXfffTeysrLQrl07j9rzOlQwMTERq1at8rY6ERERkaaZM2diy5YtWLduHa6//noAQEFBAWbMmIFZs2bhjTfe8Kg9rwc7LpcLhw4dQllZGVwul2Ld0KFDvW2WiIiI/kWCj7967reeNK8PPvgA77//PoYPH+5edtNNNyE8PBxpaWnNM9j55ptvMGHCBPz000+qm4MkSXKfciIiIiLy1JkzZxAbG6ta3rFjR5w5c8bj9rx6GmvatGm4+uqrsW/fPvz2228oLy93T7/99ps3TRIREZGojf0Q6HnXXXcd5s2bh7Nnz7qX1dTU4Nlnn/XqqW+vzuwcPHgQ77//Pv7whz94U52IiIiMaKM3KL/66qsYPXo0OnfujP79+0OSJBQVFSEsLAwbNmzwuD2vBjuDBg3CoUOHONghIiIiv+vXrx8OHjyI3Nxc/Pjjj5BlGXfeeScmTpyI8PBwj9vzarAzffp0zJo1C6WlpUhMTERwcLBi/RVXXOFNs0RERHShNnpmBwDCw8Nx//33+6UtrwY7t912GwBgypQp7mWSJEGWZd6gTG2GpUOtckFpmHbBC4lZLGJ+hyrvQ6MNIb/FpfxbA5L48XNBTS+bxJsvSAMxQ57ePWCkDU9zd7Qa0cvVMRIZJObOSC6NLXuaiaORQ6POnmm896r3mBaxiF6GjkYdIxk6urePiIfDpdWGeIyEAsJnSBZzeaA+zKpjKnZUaENuxvtgfE1Bbs0JysePH8fXX3+t+dT3jBkzPGrLq8FOcXGxN9WIiIiIdK1YsQLTpk1DSEgIoqOjFb+BJUlS8wx2EhISvKlGREREnmijl7GeeeYZPPPMM8jIyIDF4tWD4wqGBzvr1q1DSkoKgoODsW7dukbLpqam+twxIiKiNq+NDnbOnDmDO++80y8DHcCDwc748eNRWlqKjh07Yvz48Q2W4z07RERE5IupU6fivffewxNPPOGX9gwPdi68OUi8UYiIiIj8r63eoJyZmYkxY8Zg/fr1mk99L1682KP2vP5tLCIiImpivqYgt9IE5RdeeAEbNmxAz549AUB1g7KnDA92XnvtNcONenqXNBEREWloo/fsLF68GG+99Rbuuecev7RneLCzZMkSQ+W8eSSMqDU6POFJxXzXv7ykmLdWCoE4gP4Xj05miFYbYs6OpV4oLswDGhEoerk7+t1Qny7XqOTp7mvRT5lpPHcHAGTxuApX5vVydwyV0doZvRwlA6+/KntGzG4SMmGMXMbwNP9Gs47wpvIqm8fAdlXvXTFHR+e9DEDjdRAzknQ26sWZBfJMaGgorr/+er+1Z/g25+LiYkPT4cOHDW9869atGDt2LOx2OyRJwtq1axXr77nnHkiSpJiuvfZaRZna2lpMnz4dMTExiIiIQGpqKo4dO2a4D0RERGZ1/p4dX6bW6JFHHkFWVpbf2jN8ZmfmzJl4/vnnERERgZkzZzZYTpIkvPLKK4barK6uRv/+/XHvvfe6U5lFo0ePxooVK9zzISEhivXp6en46KOPkJeXh+joaMyaNQtjxoxBYWEhrFaNv6yJiIhaizZ6Gevbb7/Fl19+iY8//hh9+/ZV3aC8evVqj9ozPNjZs2cPHA6H+/8b4smNQykpKUhJSWm0TGhoKOLi4jTXVVRUIDs7Gzk5OUhKSgIA5ObmIj4+Hhs3bsSoUaM069XW1qK29t9R/5WVlYb7TERERE3r4osvxq233uq39gwPdjZt2qT5/01t8+bN6NixIy6++GIMGzYMCxYsQMeOHQEAhYWFcDgcSE5Odpe32+3o168ftm3b1uBgJzMzE88++2yz9J+IiMhrvl6KaqVndi68ouMP/okmbCIpKSn43//9X3z55Zd45ZVXsHPnTtxwww3uszKlpaUICQlBhw4dFPViY2NRWlraYLsZGRmoqKhwTyUlJU26H0RERF6R/TC1UvX19di4cSPefPNNnD59GgBw4sQJVFVVedyWqXN27rjjDvf/9+vXD1dffTUSEhLwySefNHp66/yvrzckNDQUoaGhfu0rERER+cdPP/2E0aNH4+jRo6itrcWNN96IyMhILFq0CGfPnsXy5cs9as/UZ3ZEnTp1QkJCAg4ePAgAiIuLQ11dHcrLyxXlysrKEBsb2xJdJCIi8p82embnkUcewdVXX43y8nKEh4e7l99yyy344osvPG7P1Gd2RKdOnUJJSQk6deoEABg4cCCCg4ORn5+PtLQ0AMDJkyexb98+LFq0qCW7Sm3QkfvnKOa7Ln9ZVSbotM7fF+IXk9YXlbDM4lDOS0Z+mk7vOQIDWSV6OTOa9xnobNdAVI/HdcTcnXNlZHGBx/3QLaN1dlk8KOJ2LcICl8ZBVGXxiBkxQu6OugXdNvUydLSo6xgpo9OuxptIdYzkxo+pZlaR2KyQXSSLD/IKHVf1oQm11Z+LKCgowNdff616AjshIQHHjx/3uL0WHexUVVXh0KFD7vni4mIUFRUhKioKUVFRmD9/Pm677TZ06tQJR44cwZNPPomYmBjccsstAACbzYapU6di1qxZiI6ORlRUFGbPno3ExET301lERETUurhcLs0fFT927BgiIyM9bq9FL2Pt2rULAwYMwIABAwCcy/IZMGAAnnnmGVitVuzduxfjxo3D5ZdfjsmTJ+Pyyy/H9u3bFTu6ZMkSjB8/Hmlpabj++uvRrl07fPTRR8zYISIiaqVuvPFGLF261D0vSRKqqqowb9483HTTTR6316JndoYPHw5ZPAV5gQ0bNui2ERYWhqysLL8mLRIREZlCGw0VXLJkCUaMGIE+ffrg7NmzmDBhAg4ePIiYmBi88847HrfXqu7ZISIiakva6j07drsdRUVFeOedd7B79264XC5MnToVEydOVNywbBQHO0RERGQ64eHhmDJlCqZMmeJzWxzsEBERmVkrPTvjiy+//BKrV6/GkSNHIEkSunfvjttuuw1Dhw71qr1WlbNDRETUprTBnJ1p06YhKSkJ77zzDk6dOoVffvkFubm5GDFiBKZPn+5VmxzsEBERkSmsWbMGK1aswFtvvYVff/0V27dvxzfffINffvkFf/nLX/A///M/WLduncft8jIWURM5Mm22aln3115RzFvPCIFwBkIFxdBA1bxLrKBuQz8QT78fumF+RpL4PN+sp3mIDQQCCkuFA+9NP1RltDYsLhMSMlSBkBqhe7JF2JLO6625/3oHUdiuZnm9IEJv272QVnif8ASv6hBZdRIiAcjin/nivPiUsNCmbGhn/aOt3aC8YsUKzJw5E/fcc49iucViwZQpU7B//35kZ2cjNTXVo3Z5ZoeIiMis2thlrN27d7uDg7XcdtttKCws9LhdDnaIiIjIFH799VdceumlDa6/9NJLcerUKY/b5WUsIiIik2prl7Hq6upUv4d1oaCgINTV1XncLgc7REREZtUGE5Tnzp2Ldu3aaa47c+aMV21ysENERESmMHToUOzfv1+3jKc42CEiIjKrNnZmZ/PmzU3SLm9QJiIiMqnz9+z4MgWy9u3b4/Dhw7rleGaHqBm5gpTfPNYGyp2n9UUlLvPqy8zTjBQD/RCjSTTreLgZI1E9XnRdnZEj5rm4Gs/dMdQPzWwaIa9FbMXAn5+Sq/GMINW+GHh/qF5/AzEy6joeZuhobEeVfyPmDqk3o7G/Bo6p+BlS5ezoHEON/KMm08bO7HhKVn3xaOOZHSIiIgpoPLNDRERkVjyz4xcc7BAREZlUW8vZaSq8jEVEREStkmTw/ime2SEiIjIrXsZqlNEblDnYISIiMilexmrcZ5991uhvaZ3Hy1hERERkGj/88ANWrFiBH3/8EQDw448/4j//8z8xZcoUfPnll4qyQ4YMQWhoqG6bHOwQNaMjD81WTLJVVkwqssYkFrHoTJLG1EDTPp0xl/Qnzb5cMBkpr1fGm354029P902W1K8NJEmYhDaNvJ4WSTF50y/dOsI2zm1HORk5prK18akp3kOwSOpJ7Lu4f5Iwae1/c9H7sBqZPFBfX4+nn34a3bp1Q3h4OLp3747nnnsOLpdLUe6HH35AamoqbDYbIiMjce211+Lo0aPu9bW1tZg+fTpiYmIQERGB1NRUHDt2THf769evx5VXXonZs2djwIABWL9+PYYOHYpDhw7h6NGjGDVqlGrAYwQHO0RERGbVzIOdF198EcuXL8eyZcvwww8/YNGiRXjppZeQlZXlLvPPf/4TQ4YMQa9evbB582Z89913mDt3LsLCwtxl0tPTsWbNGuTl5aGgoABVVVUYM2YMnE6NpMgLPPfcc5gzZw5OnTqFFStWYMKECbj//vuRn5+PjRs34rHHHsPChQs92ynwnh0iIiL6l+3bt2PcuHG4+eabAQBdu3bFO++8g127drnLPPXUU7jpppuwaNEi97Lu3bu7/7+iogLZ2dnIyclBUlISACA3Nxfx8fHYuHEjRo0a1eD2v//+e/ztb38DAKSlpWHSpEm47bbb3OvvuusuZGdne7xfPLNDRERkUgavzDY6AUBlZaViqq2t1dzekCFD8MUXX+DAgQMAgO+++w4FBQW46aabAAAulwuffPIJLr/8cowaNQodO3bEoEGDsHbtWncbhYWFcDgcSE5Odi+z2+3o168ftm3bZnjfLRYLwsLCcPHFF7uXRUZGoqKiwnAb7rY8rkFERETNw0+XseLj42Gz2dxTZmam5uYef/xx3HXXXejVqxeCg4MxYMAApKen46677gIAlJWVoaqqCgsXLsTo0aPx+eef45ZbbsGtt96KLVu2AABKS0sREhKCDh06KNqOjY1FaWlpo7vbtWtXHDp0yD2/fft2dOnSxT1fUlKCTp066R42ES9jERERmZS/Hj0vKSlB+/bt3csbeoLp3XffRW5uLt5++2307dsXRUVFSE9Ph91ux+TJk903Ko8bNw6PPvooAODKK6/Etm3bsHz5cgwbNqzBvsiyrBsC+J//+Z+K+3r69eunWP/ZZ5/hhhtuaLQNLRzsEBERBbj27dsrBjsNmTNnDp544gnceeedAIDExET89NNPyMzMxOTJkxETE4OgoCD06dNHUa93794oKCgAAMTFxaGurg7l5eWKsztlZWUYPHhwo9tPTk5G165dG1y/YMEC3X3QwstYREREZtXMT2OdOXMGFotyaGC1Wt1ndEJCQnDNNddg//79ijIHDhxAQkICAGDgwIEIDg5Gfn6+e/3Jkyexb98+3cFOjx498Ouvv7rn77jjDvz888+e7YQGntkhakGuYOW89axyXuv0tSz+iSKUcQmfaotGG6p2hXkxgV3rzLPYhKpNL6JIZLGO1he12DdXo6sNdUO1GWGHJY2O6G7HwDFTlRHyWySXxnatwgLxSV6p0dlzbegdFGH/NcvrtKF6n2rU0e2HVhtCJUl4s6oycLR+TkDnMwRr49vw5r3tk2ZMQR47diwWLFiALl26oG/fvtizZw8WL16MKVOmuMvMmTMHd9xxB4YOHYoRI0Zg/fr1+Oijj7B582YAgM1mw9SpUzFr1ixER0cjKioKs2fPRmJiovvprIaIP//w6aefNnh/kSda9MzO1q1bMXbsWNjtdkiSpLibW/Tggw9CkiQsXbpUsdzb4CIiIiJSysrKwu23346HHnoIvXv3xuzZs/Hggw/i+eefd5e55ZZbsHz5cixatAiJiYn461//ig8++ABDhgxxl1myZAnGjx+PtLQ0XH/99WjXrh0++ugjWK3iaL15tOiZnerqavTv3x/33nuv4jl60dq1a7Fjxw7Y7XbVuvT0dHz00UfIy8tDdHQ0Zs2ahTFjxqCwsLDFDioREZE/NPdvY0VGRmLp0qWqEwuiKVOmKM72iMLCwpCVlaUIIzRCkiTVTcxGf9m8MS062ElJSUFKSkqjZY4fP46HH34YGzZscIccnedtcFFtba0iY6CystLHPSEiImoCbexXz2VZxj333ON+Wuzs2bOYNm0aIiIiFOVWr17tUbumvmfH5XJh0qRJmDNnDvr27atarxdc1NBgJzMzE88++2yT9ZuIiIg8N3nyZMX8n/70J7+0a+rBzosvvoigoCDMmDFDc723wUUZGRmYOXOme76yshLx8fH+6TQREZGfNPdlrJa2YsWKJmnXtIOdwsJCvPrqq9i9e7fH1+v0gotCQ0MN/SQ8ERFRi2pjl7Gaimlzdr766iuUlZWhS5cuCAoKQlBQEH766SfMmjXLHTh0YXDRhcrKyhAbG9sCvSYiIiKzMe1gZ9KkSfj73/+OoqIi92S32zFnzhxs2LABgG/BRURERGZ3/jKWLxO18GWsqqoqxQ9+FRcXo6ioCFFRUejSpQuio6MV5YODgxEXF4eePXsC8C24iMgMDqfPVMz3fH6JYl4rmE0VoieUEb/cVCF0gCqITu9CsVYum3il2NB3qhh458UXsaqKuP86IYMa3VBvQzzuLnUNMWhQNzBQa5HeMfSmDVWonroNkSrcz8CdA6pjZKSOajuNV5I1EjHV73+dEEGtz5DQEVVmoBhUaBXLN2OqIC9j+UWLDnZ27dqFESNGuOfP3zQ8efJkrFy50lAbS5YsQVBQENLS0lBTU4ORI0di5cqVzNghIqLWj4Mdv2jRwc7w4cNV0dCNOXLkiGqZt8FFRERE1DaY9mksIiKitq6tPXreVDjYISIiMitexvIL0z6NRUREROQPPLNDRERkUpIsq54O87Q+cbBDRERkXryM5Rcc7BCZiCozx6VRRsyqEWNGdNafWyjUaXy1JlU2iRfRI6q4ErFNI22IC4wcQ2FedzsaBWRhoSp3R+smAbEvehlBYoYMALiE3gspG5KQoeRN/lFzZOho1lFtVl1Alb0j7qAqd0q/DUPH3YP1ZD4c7BAREZkUn8byDw52iIiIzIqXsfyCT2MRERFRQOOZHSIiIpPiZSz/4GCHiIjIrHgZyy842CEiIjIpntnxD96zQ0RERAGNZ3aIWlD3pYsV80FCNolWDonuX2o6uTsA9LNojPw1KG7HD39BqjKD9DeroqqjdQx16qjWG2qj8dwdrUp6fdXcV/E9IrZiJGdI789cI+8hnRwd7TqNb1ZVR6PvuvsnHh8xlwdQH3irzjtCnG3OmB1exvILDnaIiIhMjJeifMfLWERERBTQeGaHiIjIrGTZt2vE/CFQABzsEBERmRafxvIPXsYiIiKigMYzO0RERGbFp7H8goMdIiIik5Jc2vEBntQnDnaIWpTF4UUlTzM+DOSdqHJDjOS96IXTeEF1L6WBNnWLaBTQzdUR13vVhn5IkqqEgZwhdc6MTq6MgWOomxujcQD06xjYjhdtqHJ0rI1n4mi2YREykcQ3nrBeXC3rZAyR+XCwQ0REZFa8jOUXHOwQERGZFJ/G8g8OdoiIiMyKOTt+wUfPiYiIKKDxzA4REZFJ8TKWf3CwQ0REZFa8QdkveBmLiIiIAhrP7BC1IMnZDHkdTbUJk0SN6ObdaPxJJwat6UazGMmqMVJFL0dHlaGjvx11JpKQEWPkT3sxu8bIa6uX1eSPOlp/jusdaKuwWmsjwrUd2aJ61yiLi69DM773eRnLP1r0zM7WrVsxduxY2O12SJKEtWvXKtbPnz8fvXr1QkREBDp06ICkpCTs2LFDUaa2thbTp09HTEwMIiIikJqaimPHjjXjXhARETWR809j+TJRyw52qqur0b9/fyxbtkxz/eWXX45ly5Zh7969KCgoQNeuXZGcnIxffvnFXSY9PR1r1qxBXl4eCgoKUFVVhTFjxsDpdDbXbhAREZGJtehlrJSUFKSkpDS4fsKECYr5xYsXIzs7G3//+98xcuRIVFRUIDs7Gzk5OUhKSgIA5ObmIj4+Hhs3bsSoUaM0262trUVtba17vrKy0g97Q0RE5F+8jOUfreYG5bq6OvzP//wPbDYb+vfvDwAoLCyEw+FAcnKyu5zdbke/fv2wbdu2BtvKzMyEzWZzT/Hx8U3efyIiIo/JfpjI/IOdjz/+GBdddBHCwsKwZMkS5OfnIyYmBgBQWlqKkJAQdOjQQVEnNjYWpaWlDbaZkZGBiooK91RSUtKk+0BEREQtx/RPY40YMQJFRUX49ddf8Ze//AVpaWnYsWMHOnbs2GAdWZYhNfKrtKGhoQgNDW2K7hIREfkNL2P5h+nP7EREROAPf/gDrr32WmRnZyMoKAjZ2dkAgLi4ONTV1aG8vFxRp6ysDLGxsS3RXSIiIv9xyb5PZP7BjkiWZffNxQMHDkRwcDDy8/Pd60+ePIl9+/Zh8ODBLdVFIiIi/+A9O37RopexqqqqcOjQIfd8cXExioqKEBUVhejoaCxYsACpqano1KkTTp06hddffx3Hjh3Df/zHfwAAbDYbpk6dilmzZiE6OhpRUVGYPXs2EhMT3U9nEZlJ91cXK+at3qSTefrl5YcvO0OBcf4gBuRp9F2MDRGvWBsJ9xP3p/FIORgKt9MLKjzXjnJLkvBXt15goNYiVWii0A8xZFCL6vVtjsBAjToirWOoukNBXKAKDDSwYdWbSihuFUIXXSZJ1CTDWnSws2vXLowYMcI9P3PmTADA5MmTsXz5cvz4449YtWoVfv31V0RHR+Oaa67BV199hb59+7rrLFmyBEFBQUhLS0NNTQ1GjhyJlStXwmq1qrZHRETUmkjw8Z4dv/WkdWvRwc7w4cMhN5LuuHr1at02wsLCkJWVhaysLH92jYiIqOX5moLMBGUArfCeHSIiIiJPmP7RcyIioraKj577Bwc7REREZuXrE1Uc7ADgZSwiIiIKcDyzQ0REZFKSLEPy4SZjX+oGEg52iJqRVC8saI7vIW+ePTXJ86pGclY8zd0xUkY3d0ejjJFjps7REXJ3YCAjRpWjI2xDrGPgPaabiaOxvknq6DfRQG7OhQWEeQMZSeoywuuil8PTlFxQveYe1ydexiIiIqLAxjM7REREJsXLWP7BwQ4REZFZ8Wksv+Bgh4iIyKyYoOwXvGeHiIiIAhrP7BAREZkUE5T9g4MdIiIis+JlLL/gYIeoOQn5HKq/usQ4jyb6nhLzToxk07QEzTgTnYwTvdwdQ20YKK46RsJNAZKRfBPVcW88d0e7TqOrjb2WYptGcmT0jqGB4y5rvjgXcOnvv2reon8EVK+N2A9JzDuSGp0n8+Ngh4iIyKQkl8GBcyP1iTcoExERmdf5y1i+TB6or6/H008/jW7duiE8PBzdu3fHc889B5dLe9T04IMPQpIkLF26VLG8trYW06dPR0xMDCIiIpCamopjx455exR8xsEOERERAQBefPFFLF++HMuWLcMPP/yARYsW4aWXXkJWVpaq7Nq1a7Fjxw7Y7XbVuvT0dKxZswZ5eXkoKChAVVUVxowZA6fT2Ry7ocLLWERERGblp1DByspKxeLQ0FCEhoaqim/fvh3jxo3DzTffDADo2rUr3nnnHezatUtR7vjx43j44YexYcMGd9nzKioqkJ2djZycHCQlJQEAcnNzER8fj40bN2LUqFE+7JB3eGaHiIjIpM7/XIQvEwDEx8fDZrO5p8zMTM3tDRkyBF988QUOHDgAAPjuu+9QUFCAm266yV3G5XJh0qRJmDNnDvr27atqo7CwEA6HA8nJye5ldrsd/fr1w7Zt2/x5eAzjmR0iIqIAV1JSgvbt27vntc7qAMDjjz+OiooK9OrVC1arFU6nEwsWLMBdd93lLvPiiy8iKCgIM2bM0GyjtLQUISEh6NChg2J5bGwsSktL/bA3nuNgh4iIyKz8lLPTvn17xWCnIe+++y5yc3Px9ttvo2/fvigqKkJ6ejrsdjsmT56MwsJCvPrqq9i9ezckvegAVVdkj+v4Cwc7RM1JJ1en2QJu9LJKTELzcIiRKHptaDXiaRv6TajLaDSqqqOTzSPm7gAALMotqevodkNFlYljoJJ3dTw88EYyknTn1Y3IVr0PWuOvrvi6NSkZgC+Pj3v4nTJnzhw88cQTuPPOOwEAiYmJ+Omnn5CZmYnJkyfjq6++QllZGbp06eKu43Q6MWvWLCxduhRHjhxBXFwc6urqUF5erji7U1ZWhsGDB/uwM97jPTtEREQm5a97dow6c+YMLBbl0MBqtbofPZ80aRL+/ve/o6ioyD3Z7XbMmTMHGzZsAAAMHDgQwcHByM/Pd7dx8uRJ7Nu3r8UGOzyzQ0RERACAsWPHYsGCBejSpQv69u2LPXv2YPHixZgyZQoAIDo6GtHR0Yo6wcHBiIuLQ8+ePQEANpsNU6dOxaxZsxAdHY2oqCjMnj0biYmJ7qezmhsHO0RERGYlw8d7djwrnpWVhblz5+Khhx5CWVkZ7HY7HnzwQTzzzDMetbNkyRIEBQUhLS0NNTU1GDlyJFauXAmr1epZh/yEgx0iIiKzauYfAo2MjMTSpUtViciNOXLkiGpZWFgYsrKyNMMIWwLv2SEiIqKAxjM7REREZuWCb09L8odAAXCwQ0REZFrePFEl1icOdoialZiJIs6raHxPSR5m86jyUKCRIuJh/EmLEjrnTa6MyKtsGr06Go3o1dHL3TnXhlBLL3fHiwPSLBk6WtsRWbQK6L3hhXkjWT3iDR06bXhzTKllteg9O1u3bsXYsWNht9shSRLWrl3rXudwOPD4448jMTERERERsNvtuPvuu3HixAlFG2b7GXkiIiK/OX+Dsi8Ttexgp7q6Gv3798eyZctU686cOYPdu3dj7ty52L17N1avXo0DBw4gNTVVUc5sPyNPRETkNxzs+EWLXsZKSUlBSkqK5jqbzaZIXwTOPf//xz/+EUePHkWXLl1M+TPyREREZC6t6tHziooKSJKEiy++GID3PyNfW1uLyspKxURERGQ6PLPjF61msHP27Fk88cQTmDBhgvuXW739GfnMzEzYbDb3FB8f36R9JyIi8orLDxO1jsGOw+HAnXfeCZfLhddff123vN7PyGdkZKCiosI9lZSU+LO7REREftHcPwQaqEw/2HE4HEhLS0NxcTHy8/PdZ3UAKH5G/kJlZWWIjY1tsM3Q0FC0b99eMREREVFgMvVg5/xA5+DBg9i4caPql1bN+DPyREREfsN7dvyiRZ/GqqqqwqFDh9zzxcXFKCoqQlRUFOx2O26//Xbs3r0bH3/8MZxOp/s+nKioKISEhJjyZ+SJfCJ8L6kCBDXKeLUZMf9NJ6ivpWhGyulk1xnJlNMN9zPQhl6bWpU83Y6hQEidkEFDL6bODqoCAw3V8cN2NeuIbwChlKE/4fVeicbLy5phh03EJTfwReBBfWrZwc6uXbswYsQI9/zMmTMBAJMnT8b8+fOxbt06AMCVV16pqLdp0yYMHz4cgPl+Rp6IiIjMpUUHO8OHD4fcyCm2xtadZ7afkSciIvIbXy9F8TIWAP42FhERkYn5et8NBzuAyW9QJiIiIvIVz+wQERGZFS9j+QUHO0RERGblkuHTpSg+jQWAl7GIiIgowPHMDlFLEnN1jPyOjV4Wjzd/yDVjbIgntHZFEv5Ek4VjZiRBRVVGPKYGcof0Dpms9aekh30V9xUwsr+SsF7/DaHK0THwftDN0dFa72E2j+bnQWzD03nNMjrZPUbabCqyS/2ie1qfONghIiIyLd6z4xcc7BAREZkV79nxC96zQ0RERAGNZ3aIiIjMipex/IKDHSIiIrOS4eNgx289adV4GYuIiIgCGs/sEBERmRUvY/kFBztEzejQnJmK+V7PLlEWMPC9pMrVaQomzd0BNLJodDJxNHdFp5Be7o7h7Yg87auBgB+9NsTcHUP9MrIzXmTPqNrVy90xdO3BSLKSThG9flmF6s0ZXeNyQRXQ5HF94mUsIiIiCmg8s0NERGRWvIzlFxzsEBERmRUHO37By1hEREQU0Hhmh4iIyKz4cxF+wcEOERGRScmyC7IPj3/5UjeQcLBDRERkVrLs29kZ3rMDgIMdopal8z1kKFPHD99lhnJVWoA32TUizcMjNTqrbkOjEW/yfcTcGEn4o1u1GY1GdPvqaQUtXuTseJPNo1dHc7VeG+ILo/EhUuUmiW1ahAXCYMOsnxdqGAc7REREZiX7eM8Oz+wA4GCHiIjIvFwu9SlAT/CeHQB89JyIiIgCHM/sEBERmRUvY/kFBztEREQmJbtckH24jMVHz8/hZSwiIiIKaDyzQ0REZFa8jOUXHOwQERGZlUs2GLjVAA52ALTwYGfr1q146aWXUFhYiJMnT2LNmjUYP368e/3q1avx5ptvorCwEKdOncKePXtw5ZVXKtqora3F7Nmz8c4776CmpgYjR47E66+/js6dOzfvzhB54cf5jza6vte8Japlqu89MeDMLN9tfghe09oV1e7qBPUZa0SnIxr7Iv4bohcyCKhfOzGcTve11WhXVcQf7wcDr506zE9nvRfbMRQIqdMPzXZ16qiPqd5Gyexa9J6d6upq9O/fH8uWLWtw/fXXX4+FCxc22EZ6ejrWrFmDvLw8FBQUoKqqCmPGjIHT6WyqbhMRETUPWT6XleP1ZJa/flpWi57ZSUlJQUpKSoPrJ02aBAA4cuSI5vqKigpkZ2cjJycHSUlJAIDc3FzEx8dj48aNGDVqlN/7TERE1FxklwzZh8tYMgc7AFr501iFhYVwOBxITk52L7Pb7ejXrx+2bdvWYL3a2lpUVlYqJiIiItPx6ayOiwnK/9KqBzulpaUICQlBhw4dFMtjY2NRWlraYL3MzEzYbDb3FB8f39RdJSIiohbSqgc7DZFlGVIjN5BlZGSgoqLCPZWUlDRj74iIiIyRXbLPE7XyR8/j4uJQV1eH8vJyxdmdsrIyDB48uMF6oaGhCA0NbY4uEhEReU92AeAPgfqqVQ92Bg4ciODgYOTn5yMtLQ0AcPLkSezbtw+LFi0y3M75G7h47w6ZjbP2rGqZ7r2KZvlDzh9P52rsi2r/xUfAxe92je96sQ1Dj6vr9E21u1p91/l3R1yv+VqL222K94NJHj03cgx1553qRnTr6LzHnI5zn8vmuPm3Hg6fPtP1cPivM61Yiw52qqqqcOjQIfd8cXExioqKEBUVhS5duuC3337D0aNHceLECQDA/v37AZw7oxMXFwebzYapU6di1qxZiI6ORlRUFGbPno3ExET301lGnD59GgB47w4RERl2+vRp2Gy2Jmk7JCQEcXFxKCj91Oe24uLiEBIS4odetV6S3ILPpW3evBkjRoxQLZ88eTJWrlyJlStX4t5771WtnzdvHubPnw8AOHv2LObMmYO3335bESroycDF5XLhxIkTiIyMbPReH19VVlYiPj4eJSUlaN++fZNtpzlxn1qPQNwv7lPrEUj7JcsyTp8+DbvdDoul6W59PXv2LOrq6nxuJyQkBGFhYX7oUevVooOdtqayshI2mw0VFRWt/sN+Hvep9QjE/eI+tR6Bul/UOgTk01hERERE53GwQ0RERAGNg51mFBoainnz5gXUY+/cp9YjEPeL+9R6BOp+UevAe3aIiIgooPHMDhEREQU0DnaIiIgooHGwQ0RERAGNgx0iIiIKaBzs+Oj06dNIT09HQkICwsPDMXjwYOzcudO9XpIkzemll15ylxk+fLhq/Z133tls+7B161aMHTsWdrsdkiRh7dq1ivWyLGP+/Pmw2+0IDw/H8OHD8f333yvK1NbWYvr06YiJiUFERARSU1Nx7NgxRZny8nJMmjQJNpsNNpsNkyZNwu+//27Kffrtt98wffp09OzZE+3atUOXLl0wY8YMVFRUKNrp2rWr6rV74oknTLlPgLH3WnO+Tv7YryNHjjT4OXvvvffc5cz0Wq1evRqjRo1CTEwMJElCUVGRqo3W9pnS2yczfqao7eBgx0f33Xcf8vPzkZOTg7179yI5ORlJSUk4fvw4gHM/THrh9NZbb0GSJNx2222Kdu6//35FuTfffLPZ9qG6uhr9+/fHsmXLNNcvWrQIixcvxrJly7Bz507ExcXhxhtvdP+mGACkp6djzZo1yMvLQ0FBAaqqqjBmzBg4nU53mQkTJqCoqAjr16/H+vXrUVRUhEmTJplyn06cOIETJ07g5Zdfxt69e7Fy5UqsX78eU6dOVbX13HPPKV67p59+2pT7dJ7ee605Xyd/7Fd8fLzqc/bss88iIiICKSkpirbM8lpVV1fj+uuvx8KFCxtso7V9pvT2yYyfKWpDZPLamTNnZKvVKn/88ceK5f3795efeuopzTrjxo2Tb7jhBsWyYcOGyY888khTddMjAOQ1a9a4510ulxwXFycvXLjQvezs2bOyzWaTly9fLsuyLP/+++9ycHCwnJeX5y5z/Phx2WKxyOvXr5dlWZb/8Y9/yADkb775xl1m+/btMgD5xx9/NN0+afm///s/OSQkRHY4HO5lCQkJ8pIlS5qi243ydp/03mst+TrJsv9eqyuvvFKeMmWKYplZXqsLFRcXywDkPXv2KJa3ts/UhRraJy1m+kxRYOOZHR/U19fD6XSqfmAtPDwcBQUFqvI///wzPvnkE82/ZP73f/8XMTEx6Nu3L2bPnq36a7ylFBcXo7S0FMnJye5loaGhGDZsGLZt2wYAKCwshMPhUJSx2+3o16+fu8z27dths9kwaNAgd5lrr70WNpvNXaa5GNknLed/0ycoKEix/MUXX0R0dDSuvPJKLFiwwC8/3OcpT/apsfeamV4nwLvXqrCwEEVFRZqfMzO8Vka0ts+Ut8z8maLAEqRfhBoSGRmJ6667Ds8//zx69+6N2NhYvPPOO9ixYwd69OihKr9q1SpERkbi1ltvVSyfOHEiunXrhri4OOzbtw8ZGRn47rvvkJ+f31y70qDS0lIAQGxsrGJ5bGwsfvrpJ3eZkJAQdOjQQVXmfP3S0lJ07NhR1X7Hjh3dZZqLkX0SnTp1Cs8//zwefPBBxfJHHnkEV111FTp06IBvv/0WGRkZKC4uxl//+tem6XwDjO6T3nvNTK/T+f4Anr1W2dnZ6N27NwYPHqxYbpbXyojW9pnyhtk/UxRYONjxUU5ODqZMmYJLL70UVqsVV111FSZMmIDdu3eryr711luYOHGi6kzQ/fff7/7/fv36oUePHrj66quxe/duXHXVVU2+D0ZIkqSYl2VZtUwkltEqb6SdpmJ0nyorK3HzzTejT58+mDdvnmLdo48+6v7/K664Ah06dMDtt9/u/su0uentk5H3mtleJ8D4a1VTU4O3334bc+fOVa0z22vlDbN/poxqTZ8pCgy8jOWjyy67DFu2bEFVVRVKSkrw7bffwuFwoFu3bopyX331Ffbv34/77rtPt82rrroKwcHBOHjwYFN127C4uDgAUP2lWFZW5v5rOy4uDnV1dSgvL2+0zM8//6xq/5dfflH91d7UjOzTeadPn8bo0aNx0UUXYc2aNQgODm607WuvvRYAcOjQIT/2WJ8n+3Qh8b1mptfpfH8A4/v1/vvv48yZM7j77rt1226p18qI1vaZ8kRr+UxRYOFgx08iIiLQqVMnlJeXY8OGDRg3bpxifXZ2NgYOHIj+/fvrtvX999/D4XCgU6dOTdVdw85f8rjwklpdXR22bNnivkwwcOBABAcHK8qcPHkS+/btc5e57rrrUFFRgW+//dZdZseOHaioqFBdbmhqRvYJOPfXZ3JyMkJCQrBu3TrVGTkte/bsAYBmf+2M7pNIfK+Z6XUCPN+v7OxspKam4pJLLtFtu6VeKyNa22fKqNb0maIA02K3RgeI9evXy5999pl8+PBh+fPPP5f79+8v//GPf5Tr6urcZSoqKuR27drJb7zxhqr+oUOH5GeffVbeuXOnXFxcLH/yySdyr1695AEDBsj19fXNsg+nT5+W9+zZI+/Zs0cGIC9evFjes2eP/NNPP8myLMsLFy6UbTabvHr1annv3r3yXXfdJXfq1EmurKx0tzFt2jS5c+fO8saNG+Xdu3fLN9xwg9y/f3/FPowePVq+4oor5O3bt8vbt2+XExMT5TFjxphynyorK+VBgwbJiYmJ8qFDh+STJ0+6p/P7tG3bNne7hw8flt99913ZbrfLqampptwno++15nyd/LFf5x08eFCWJEn+7LPPVNsw22t16tQpec+ePfInn3wiA5Dz8vLkPXv2yCdPnnS30do+U3r7ZMbPFLUdHOz46N1335W7d+8uh4SEyHFxcfJ//dd/yb///ruizJtvvimHh4erlsuyLB89elQeOnSoHBUVJYeEhMiXXXaZPGPGDPnUqVPNtQvypk2bZACqafLkybIsn3v8d968eXJcXJwcGhoqDx06VN67d6+ijZqaGvnhhx+Wo6Ki5PDwcHnMmDHy0aNHFWVOnTolT5w4UY6MjJQjIyPliRMnyuXl5abcp4bqA5CLi4tlWZblwsJCedCgQbLNZpPDwsLknj17yvPmzZOrq6tNuU9G32vN+Tr5Y7/Oy8jIkDt37iw7nU7VOrO9VitWrNBcP2/ePHcbre0zpbdPZvxMUdshybIs++ccEREREZH58J4dIiIiCmgc7BAREVFA42CHiIiIAhoHO0RERBTQONghIiKigMbBDhEREQU0DnaIiIgooHGwQ0RERAGNgx2iNmz+/Pm48sor3fP33HMPxo8f32L9ISJqCkEt3QEiMo9XX30VDFUnokDDwQ4RudlstpbuAhGR3/EyFlEA++WXXxAXF4cXXnjBvWzHjh0ICQnB559/riovXsYaPnw4ZsyYgcceewxRUVGIi4vD/PnzFXUqKirwwAMPoGPHjmjfvj1uuOEGfPfdd021S0REHuNghyiAXXLJJXjrrbcwf/587Nq1C1VVVfjTn/6Ehx56CMnJyYbaWLVqFSIiIrBjxw4sWrQIzz33HPLz8wEAsizj5ptvRmlpKT799FMUFhbiqquuwsiRI/Hbb7815a4RERnGwQ5RgLvppptw//33Y+LEiZg2bRrCwsKwcOFCw/WvuOIKzJs3Dz169MDdd9+Nq6++Gl988QUAYNOmTdi7dy/ee+89XH311ejRowdefvllXHzxxXj//febapeIiDzCe3aI2oCXX34Z/fr1w//93/9h165dCAsLM1z3iiuuUMx36tQJZWVlAIDCwkJUVVUhOjpaUaampgb//Oc/fe84EZEfcLBD1AYcPnwYJ06cgMvlwk8//aQawDQmODhYMS9JElwuFwDA5XKhU6dO2Lx5s6rexRdf7EuXiYj8hoMdogBXV1eHiRMn4o477kCvXr0wdepU7N27F7GxsT63fdVVV6G0tBRBQUHo2rWr750lImoCvGeHKMA99dRTqKiowGuvvYbHHnsMvXv3xtSpU/3SdlJSEq677jqMHz8eGzZswJEjR7Bt2zY8/fTT2LVrl1+2QUTkK57ZIQpgmzdvxtKlS7Fp0ya0b98eAJCTk4MrrrgCb7zxhs/tS5KETz/9FE899RSmTJniftR96NChfjlzRETkD5LMuFQiIiIKYLyMRURERAGNgx0iIiIKaBzsEBERUUDjYIeIiIgCGgc7REREFNA42CEiIqKAxsEOERERBTQOdoiIiCigcbBDREREAY2DHSIiIgpoHOwQERFRQPv/qua51SkCqKQAAAAASUVORK5CYII=\n", "text/plain": ["
"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ds['F3_Demo_2_FS6'].plot()"]}, {"cell_type": "code", "execution_count": 16, "id": "52752e71", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ds['F3_Demo_4_Truncation'].plot()"]}, {"cell_type": "markdown", "id": "d7a73a94", "metadata": {}, "source": ["### Multi-horizon, no header\n", "\n", "Unfortunately, OdT exports (x, y) in the first two columns, meaning you can't assume that columns 3 and 4 are inline, crossline. So if there's no header, and XY as well as inline/xline, you have to give the column names:"]}, {"cell_type": "code", "execution_count": 17, "id": "4862f786", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n", "Dimensions:      (iline: 1, xline: 10)\n", "Coordinates:\n", "  * iline        (iline) int64 990\n", "  * xline        (xline) int64 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024\n", "    cdp_x        (iline, xline) float64 1.703e+06 1.703e+06 ... 1.703e+06\n", "    cdp_y        (iline, xline) float64 5.817e+06 5.817e+06 ... 5.818e+06\n", "Data variables:\n", "    blue event   (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03\n", "    green event  (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03
"], "text/plain": ["\n", "Dimensions: (iline: 1, xline: 10)\n", "Coordinates:\n", " * iline (iline) int64 990\n", " * xline (xline) int64 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024\n", " cdp_x (iline, xline) float64 1.703e+06 1.703e+06 ... 1.703e+06\n", " cdp_y (iline, xline) float64 5.817e+06 5.817e+06 ... 5.818e+06\n", "Data variables:\n", " blue event (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03\n", " green event (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03"]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}], "source": ["import gio\n", "\n", "fname = '../../data/OdT/3d_horizon/Test_Multi_XY-and-ILXL_Z-only.dat'\n", "\n", "ds = gio.read_odt(fname, names=['Horizon', 'X', 'Y', 'Inline', 'Crossline', 'Z'])\n", "ds"]}, {"cell_type": "markdown", "id": "1345e4ef", "metadata": {}, "source": ["## Undefined values\n", "\n", "These are exported as `'1e30'` by default. You can override this (not add to it, which is the default `pandas` behaviour) by passing one or more `na_values`."]}, {"cell_type": "code", "execution_count": 18, "id": "10cf625e", "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.\n", " dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)\n"]}, {"data": {"text/html": ["
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<xarray.Dataset>\n", "Dimensions:  (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline    (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n", "  * xline    (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n", "    cdp_x    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "    cdp_y    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Data variables:\n", "    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n", " * xline (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n", " cdp_x (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat"]}, "execution_count": 18, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat'\n", "\n", "ds = gio.read_odt(fname, names=['X', 'Y', 'TWT'])\n", "ds"]}, {"cell_type": "code", "execution_count": 19, "id": "a8f453cd", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", 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"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "d2dff77d", "metadata": {}, "source": ["## Writing OdT files\n", "\n", "You can write an OdT file using `gio.to_odt()`:"]}, {"cell_type": "code", "execution_count": 20, "id": "512d59a1", "metadata": {}, "outputs": [], "source": ["_ = gio.to_odt('out.dat', ds)"]}, {"cell_type": "code", "execution_count": 21, "id": "06b7a481", "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["# 1: Inline\r\n", "# 2: Crossline\r\n", "# 3: X\r\n", "# 4: Y\r\n", "# 5: Z\r\n", "# - - - - - - - - - -\r\n", "0\t51\t620986.88\t6080882.36\t648.6175060272217\r\n", "0\t52\t621036.86\t6080883.76\t647.8394865989685\r\n", "0\t53\t621086.84\t6080885.16\t647.9663848876953\r\n", "1\t50\t620935.5\t6080930.95\t649.9454379081726\r\n"]}], "source": ["!head out.dat"]}], "metadata": {"interpreter": {"hash": "80d1b482999e70934fa1bff5f55b93cc869ff05fc05093ffa26cad7f8d1d8425"}, "kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13"}}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/docs/userguide/Read_Surfer_grids.ipynb b/_sources/userguide/Read_Surfer_grids.ipynb.txt similarity index 100% rename from docs/userguide/Read_Surfer_grids.ipynb rename to _sources/userguide/Read_Surfer_grids.ipynb.txt diff --git a/_sources/userguide/Read_and_write_ZMAP_files.ipynb.txt b/_sources/userguide/Read_and_write_ZMAP_files.ipynb.txt new file mode 100644 index 0000000..f6e2f72 --- /dev/null +++ b/_sources/userguide/Read_and_write_ZMAP_files.ipynb.txt @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "771a0ec0", "metadata": {}, "source": ["# Read and write ZMAP files\n", "\n", "The venerable ZMAP format looks like this:\n", "\n", "```\n", "! Landmark Zmap grid file name: .DATANSLCU.dat\n", "! Created/converted by Oasis Montaj, Geosoft Inc.\n", "@.DATANSLCU.dat, GRID, 3\n", "20, 1e+30, , 7, 1\n", "208, 435, -630000.0, 672000.0, 2000000.0, 2621000.0\n", "0.0, 0.0, 0.0\n", "@\n", " -16481.9570313 -16283.9033203 -16081.5751953\n", " -15856.7861328 -15583.7167969 -15255.7343750\n", " -14869.3769531 -14426.1513672 -13915.8769531\n", "```\n", "\n", "Files often have a the extension `.DAT`. The first two lines shown are comments. The lines between the `@` signs give the name of the grid, with some metadata such as null value, precision, and so on.\n", "\n", "`gio` wraps the [`zmapio`](https://github.com/abduhbm/zmapio) library. There are several functions:\n", "\n", "- `read_zmap()`: read a ZMAP file.\n", "- `array_to_zmap()`: write a NumPy array as a ZMAP file.\n", "- `dataarray_to_zmap()`: write an Xarray `DataArray` as a ZMAP file.\n", "- `xarray.DataArray.gio.to_zmap()`: an Xarray accessor providing another way to save a DataArray as a ZMAP file.\n", "\n", "Let's read some data from a ZMAP file:"]}, {"cell_type": "code", "execution_count": 1, "id": "e1eab6ef", "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["! Landmark Zmap grid file name: .\\DATA\\NStopo.dat\r\n", "! Created/converted by Oasis Montaj, Geosoft Inc.\r\n", "@.\\DATA\\NStopo.dat ,GRID, 4\r\n", " 20, 1.0E+30, , 7, 1\r\n", " 208, 435, -630000.0000, 672000.0000, 2000000.0000, 2621000.0000,\r\n", " 0.0000, 0.0000, 0.0000,\r\n", "@\r\n", " -67.2144775 -67.5697021 -67.1472168 -69.0812378\r\n", " -73.1813660 -74.3084717 -72.7660828 -72.0339966\r\n", " -70.5137329 -68.5551758 -66.1948547 -62.7756653\r\n"]}], "source": ["!head ../../data/ZMAP/NStopo.dat"]}, {"cell_type": "code", "execution_count": 2, "id": "f9e87870", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import gio\n", "\n", "z = gio.read_zmap('../../data/ZMAP/NStopo.dat')\n", "\n", "z.plot()"]}, {"cell_type": "markdown", "id": "8fccd2b3", "metadata": {}, "source": ["Let's take a look at the `xarray` object too:"]}, {"cell_type": "code", "execution_count": 3, "id": "cd567f1f", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray '.\\\\DATA\\\\NStopo.dat' (Y: 208, X: 435)>\n", "array([[  -50.138916 ,   -43.8048401,   -39.550293 , ...,   702.7687378,\n", "          656.3284302,   581.6201782],\n", "       [  -50.7771301,   -43.4010925,   -40.3817444, ...,   626.8185425,\n", "          587.7715454,   528.4921265],\n", "       [  -49.3357849,   -47.8291626,   -43.6928101, ...,   472.8615723,\n", "          492.1585693,   453.5387573],\n", "       ...,\n", "       [  -67.1472168,   -72.3484497,   -78.2303467, ..., -2397.1206055,\n", "        -2378.3427734, -2362.1647949],\n", "       [  -67.5697021,   -72.6132202,   -81.2595825, ..., -2409.6945801,\n", "        -2391.8217773, -2376.5600586],\n", "       [  -67.2144775,   -74.9806519,   -82.2666016, ..., -2416.8630371,\n", "        -2398.3891602, -2387.4147949]])\n", "Coordinates:\n", "  * X        (X) float64 -6.3e+05 -6.27e+05 -6.24e+05 ... 6.69e+05 6.72e+05\n", "  * Y        (Y) float64 2e+06 2.003e+06 2.006e+06 ... 2.618e+06 2.621e+06\n", "Attributes:\n", "    fname:       ../../data/ZMAP/NStopo.dat\n", "    null_value:  1e+30\n", "    comment:     [' Landmark Zmap grid file name:   .\\\\DATA\\\\NStopo.dat', ' C...
"], "text/plain": ["\n", "array([[ -50.138916 , -43.8048401, -39.550293 , ..., 702.7687378,\n", " 656.3284302, 581.6201782],\n", " [ -50.7771301, -43.4010925, -40.3817444, ..., 626.8185425,\n", " 587.7715454, 528.4921265],\n", " [ -49.3357849, -47.8291626, -43.6928101, ..., 472.8615723,\n", " 492.1585693, 453.5387573],\n", " ...,\n", " [ -67.1472168, -72.3484497, -78.2303467, ..., -2397.1206055,\n", " -2378.3427734, -2362.1647949],\n", " [ -67.5697021, -72.6132202, -81.2595825, ..., -2409.6945801,\n", " -2391.8217773, -2376.5600586],\n", " [ -67.2144775, -74.9806519, -82.2666016, ..., -2416.8630371,\n", " -2398.3891602, -2387.4147949]])\n", "Coordinates:\n", " * X (X) float64 -6.3e+05 -6.27e+05 -6.24e+05 ... 6.69e+05 6.72e+05\n", " * Y (Y) float64 2e+06 2.003e+06 2.006e+06 ... 2.618e+06 2.621e+06\n", "Attributes:\n", " fname: ../../data/ZMAP/NStopo.dat\n", " null_value: 1e+30\n", " comment: [' Landmark Zmap grid file name: .\\\\DATA\\\\NStopo.dat', ' C..."]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["z"]}, {"cell_type": "markdown", "id": "16c8e7b4", "metadata": {}, "source": ["## Transform the surface\n", "\n", "The units are feet; let's transform them to metres and save a new file."]}, {"cell_type": "code", "execution_count": 4, "id": "3d8d3f45", "metadata": {}, "outputs": [], "source": ["z_m = 0.3048 * z"]}, {"cell_type": "markdown", "id": "c02de2a9", "metadata": {}, "source": ["## Use the `xarray` accessory\n", "\n", "If your surface is held in an `xarray.DataArray` then the accessory is the easiest way to save it in ZMAP format.\n", "\n", "To use it, access the `to_zmap()` function in the `gio` accessory. After importing `gio`, the accessory is attached to every instance of a `DataArray`."]}, {"cell_type": "code", "execution_count": 5, "id": "1c7b3948", "metadata": {}, "outputs": [], "source": ["z_m.gio.to_zmap('NStopo_m.dat')"]}, {"cell_type": "markdown", "id": "fefae26f", "metadata": {}, "source": ["We can take a peek at this file using IPython to see if it looks legit:"]}, {"cell_type": "code", "execution_count": 6, "id": "f98120b0", "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["!File created by gio.to_zmap xarray accessory\r\n", "@.\\DATA\\NStopo.dat, GRID, 6\r\n", "12, -999.25, , 3, 1\r\n", "208, 435, -630000.0, 672000.0, 2000000.0, 2621000.0\r\n", "0.0, 0.0, 0.0\r\n", "@\r\n", " -20.487 -20.595 -20.466 -21.056 -22.306 -22.649\r\n", " -22.179 -21.956 -21.493 -20.896 -20.176 -19.134\r\n", " -18.187 -17.888 -17.495 -17.983 -17.928 -17.516\r\n", " -18.176 -21.011 -21.595 -20.317 -19.193 -18.450\r\n"]}], "source": ["!head ./NStopo_m.dat"]}, {"cell_type": "markdown", "id": "99158f93", "metadata": {}, "source": ["## Use the function\n", "\n", "If your surface is a NumPy array, then you can use the `array_to_zmap()` function.\n", "\n", "Note that if you want the ZMAP file to have real-world coordinates, you will need to provide the `extent` as a list or tuple of min X, max X, min Y, max Y. For example:"]}, {"cell_type": "code", "execution_count": 7, "id": "d143f021", "metadata": {}, "outputs": [], "source": ["arr = z_m.data\n", "\n", "gio.array_to_zmap('NStopo_m.dat', arr, extent=[-630000.0, 672000.0, 2000000.0, 2621000.0])"]}, {"cell_type": "markdown", "id": "24a1bc27", "metadata": {}, "source": ["It should look like the file we got before:"]}, {"cell_type": "code", "execution_count": 8, "id": "ce30d943", "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["!File created by gio.array_to_zmap\r\n", "@Z, GRID, 6\r\n", "12, -999.25, , 3, 1\r\n", "208, 435, -630000.0, 672000.0, 2000000.0, 2621000.0\r\n", "0.0, 0.0, 0.0\r\n", "@\r\n", " -20.487 -20.595 -20.466 -21.056 -22.306 -22.649\r\n", " -22.179 -21.956 -21.493 -20.896 -20.176 -19.134\r\n", " -18.187 -17.888 -17.495 -17.983 -17.928 -17.516\r\n", " -18.176 -21.011 -21.595 -20.317 -19.193 -18.450\r\n"]}], "source": ["!head ./NStopo_m.dat"]}, {"cell_type": "code", "execution_count": null, "id": "36cd5eb2", "metadata": {}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7"}}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/_sources/userguide/Unmap_data_from_an_image.ipynb.txt b/_sources/userguide/Unmap_data_from_an_image.ipynb.txt new file mode 100644 index 0000000..f4c0dd7 --- /dev/null +++ b/_sources/userguide/Unmap_data_from_an_image.ipynb.txt @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "0334e1a6", "metadata": {}, "source": ["# Unmap data from an image\n", "\n", "Sometimes we'd like to rip **data** (i.e. the underlying digits) from a [pseudocolour (aka false colour) image](https://en.wikipedia.org/wiki/False_color). For example, maybe you found a nice map or seismic section in a paper, and want to try loading the data into some other software to play with.\n", "\n", "The `gio.unmap_to_dataarray()` function attempts to do this. Keep your hopes in check though: depending on the circumstances, it might be impossible. \n", "\n", "**Note:** This function wraps `unmap()` from the [`unmap`](https://kwinkunks.github.io/unmap/) library. If you only want a NumPy array from the plot image, you could just use that function directly. All `gio` is doing is wrapping its output in an `xarray.DataArray`."]}, {"cell_type": "markdown", "id": "fe983420", "metadata": {}, "source": ["## Get images from the web\n", "\n", "Let's write a little function to load images from the Internet and transform them into NumPy arrays."]}, {"cell_type": "code", "execution_count": 1, "id": "d635332c", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 1, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["from io import BytesIO\n", "\n", "import requests\n", "import numpy as np\n", "from PIL import Image\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "def get_image_from_web(uri):\n", " data = requests.get(uri).content\n", " img = Image.open(BytesIO(data)).convert('RGB')\n", " rgb_im = np.asarray(img)[..., :3] / 255.\n", " return rgb_im\n", "\n", "# An image from Matteo Niccoli's blog:\n", "# https://mycartablog.com/2014/08/24/what-your-brain-does-with-colours-when-you-are-not-looking-part-1/\n", "img = get_image_from_web('https://i0.wp.com/mycartablog.com/wp-content/uploads/2014/03/spectrogram_jet.png')\n", "\n", "plt.imshow(img)"]}, {"cell_type": "markdown", "id": "7367f5c7", "metadata": {}, "source": ["## An easy image\n", "\n", "Let's start with that image, it looks approachable:\n", "\n", "- It's large.\n", "- It's a PNG, so losslessly compressed.\n", "- No hillshading.\n", "- It contains the colourmap, and Matteo tells us that it's `jet`\n", "\n", "And, even better, when Matteo used it on his blog, he provided lots of other plots that we can compare to!"]}, {"cell_type": "code", "execution_count": 2, "id": "daa18b5e", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (i: 1351, j: 2402)>\n", "array([[nan, nan, nan, ..., nan, nan, nan],\n", "       [nan, nan, nan, ..., nan, nan, nan],\n", "       [nan, nan, nan, ..., nan, nan, nan],\n", "       ...,\n", "       [nan, nan, nan, ..., nan, nan, nan],\n", "       [nan, nan, nan, ..., nan, nan, nan],\n", "       [nan, nan, nan, ..., nan, nan, nan]])\n", "Coordinates:\n", "  * j        (j) float64 0.0 1.0 2.001 3.001 ... 2.4e+03 2.401e+03 2.402e+03\n", "  * i        (i) float64 0.0 1.001 2.001 3.002 ... 1.349e+03 1.35e+03 1.351e+03\n", "Attributes:\n", "    source:   \n", "    fname:
"], "text/plain": ["\n", "array([[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]])\n", "Coordinates:\n", " * j (j) float64 0.0 1.0 2.001 3.001 ... 2.4e+03 2.401e+03 2.402e+03\n", " * i (i) float64 0.0 1.001 2.001 3.002 ... 1.349e+03 1.35e+03 1.351e+03\n", "Attributes:\n", " source: \n", " fname: "]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["import gio\n", "\n", "data = gio.unmap_to_dataarray(img, cmap='jet')\n", "\n", "data"]}, {"cell_type": "code", "execution_count": 3, "id": "1d82b4d7", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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fI/4PfIeqnt+qXAv2hmHsCh/69P/Bna/5kf2uxp4QXS937sdNVT8AfOCyZb+w6fVTwF96NmVasDcMY1e4844fnaihmq2Idgnj/U/Ggr1hGLvCdA3jjL9dggX7PUBPnUbm53A3HAURXC+Po2wuiaZmqkhR4joDCKFKKZiupwl0nQFpv8A3U0LNo15QJ+SLDcKRWwlZ/JAlvUDSKfDdAhyEAy2cHkUvXKJcWcGrxsO2O9GkqtlAmzU4cRRUCc7FurR76IVLhKfP4pcOxv3yBr7m0cQTagmSBw783pfJX3SCfCah8/ohSRI4PL+Gl0CpjmHpCaWg7RTtOpq3rpL4QH+YUntVh0wF7wNOlKBCXnj6zYxeMyOknnSVddUMgHolnxFCEk3RQkKUGCjkCeRNyJsOl0e1y9xXhxQtT3fJIyU0LpQ0BoHVW1LKWlS2uDIalrkC6hdLBgccRaM6oIvH1gTKBKhFFY4E8EOorWg0LGvJutmZUilwNJbZPFuS9AJrN6WUlUkbGstunskJidA/mMQUjC6ay62nKyygthqQEoazQlmPSh6ISp7acknvkKeoy7rUoszi+clnkg1FE1FtlK0pjUfOkR9fgNe/Cl0dUP/c4wxffILBQkox4+gfTFA/MowTXK7UlwPqIG85BvMZ3Jitm+5tyXjHvh1np56g3S2m7HIYhrFXfPjj/wx1bstpUthhNc6usGtnW0TeKyJnReTzm5YtishHROSRar6wad27KtOfL4rInZuWv0ZEPlet+5nq8WDDMMac7/7Wf7Hxz+xq0wQxzclLfhm467Jl7wQ+qqq3Ax+t3iMiLyVqSV9W7fOeygwI4OeBe4Hbq+nyMg3DGEPUyzWnSWGUg3Y7036xa8FeVf+QaM6zmTcB76tevw/43k3L71fVgao+BpwEXisix4A5Vf1Y9aTYr2zaxzCMcWaaHqoCCnXbmvaLvT7yEVU9DVDND1fLr2T8c7yaTl1h+RURkXtF5EERefDcuXM7WnHDMJ4dv/sHP4w6uer0u3/ww/tdxR1l3IdxxkWNczXjn+0YAm2siM5x9wHccccdV91ur+l/y4txpeIGJb4T7abdoEDyAF6gVNwgh14/7iAjUxiBxCO9YRziLEuSp7rQHwBQXrhE+pIXkB9sElKHywO+X+CGBZp6ykZCMbuIHF+Ix+oM4Pwy2u4Qul38gQNIuohmSTxGnkOjTliYQRdnAQhlGdMnhkBw0edEE4cEpf/1N1P/0hmGrzyG88rCTJcXzJ3nSG2Nussp1XHhWItOUaNQx/KgwVpe4/Gn5ihmPY3WEKfQG6b01upQCklrSLLYp59mqEtpPr2hUimaUDQhn1fyOchWBDeAYiaetqQbUxrmszBowbnFLJ7GkqjYmfXEBxIrH5l8I41hf0EYzPmozhlCyKLyxhUxVWDRiIqbpB+VMIM5oXdQ1tU1roC0o5RpXCZVGsPBvKNoOJKukggMZ6JCpqjDhZdl6+oaKSFtK6RErx0BakTfmhDr5IYQ0qiUGcwJg7kkevEEkByydqBW+eH0DkXPopFKqCiFfCahffQGkoGS9BR/sEa61CRZG1IDimZtXe00IiRCN/Ub6Rtlo70Stv6KveG7fmzLcfk3fNeP8dHfe9fWX57rhX0eotkOex3sz4jIMVU9XQ3RnK2WX83451T1+vLlhmGMOTpBwzTXYieTl+wWe/2f4gHgnur1PcBvb1p+t4jURORW4o3YT1RDPWsi8rpKhfP9m/YxDGOMmaYbtMD03qAVkV8HPga8SEROichbgB8HvltEHiGm3PpxAFV9CHg/MRPL7wBvU9VR6vq/B/wi8abtl4EP7ladDcPYOf7Lh98xNdLLUfKScQ72uzaMo6rfd5VVb7jK9u8G3n2F5Q8CL9/BqhmGsQd8xxt/YmpSEypCEcb7IbFxuUFrGMaEMeb3K3eccR+zt2C/BxTNmHnIFZ40cWRPXCKcOo3LUmR2Bl2ci5rjNIVOFxp1yuMHKRsxO9Uow5QfBtxsHckDmjrCi08wrFXZqwJRJdHLkU6f4oYDFK2UkG58AN18hjvUwhUBKQJ55bED4GZqEHRDbTHIcedWIEspDs9TNhI0qbYdlJSNhJAIZ+68ET9UEl9y0+wyNzcu8pL6UxzwHVwlnOpqjXPFLF8dHOJkZ4nzR1p0n56hdqBL4gKJD9SyghBi+UXpkDkYCORznnTF4Yesq1wIEOpK9+YCSar0UAj9Qujc4pCh4LuO2jLMPqmUKfQXY0Ypl0fVTv1SwOVK76CPKp9WVMiMvq8SqgxWfUjSqKApazBYiJ4xZQ3KRkBHGb8KwQ2EdA1qK7GeMdgJIY3eNBJg6dMdOica9A45QkJUzLjovzOcFaSs1EPE61C0ogJHk416Je2oOmqeL2nfEDNR4WHgHcPZqMIpa7FcqTJYIRu+PvOPdBkcrFE0HOViSjYaQ68UN6PtGhcDaTuwdiJZL8v3Y3at2mr0YdqSCRuT3xLdOT/73WK8/3cYhnHd8gf/+X9Fqx+Zq02TwlSP2RuGMd1825t+cqJuwl6Lce/ZW7A3DGNX0Cm5OQvxBm1pN2gNw5hGxjyXx44z7jdop+xyGIaxV/zRb/7j6RmzVxuzNwxjSvnWN//riXK2vBY65r9eFuz3AFfqei+mrHnyY/P4mQayvAaNOup9lKlJDUl8NBobFjgnhMRFaV1VgG8PkE6fcmkOCYrvl0iIsj71Qr7YhINNUEg6OVIoEgKIEFKHehfTzQUlObtKuTBDMZuRN+KfPDeMZmpSVEZsgyHJchdNWpQ+weWB9FIP6Q7Iuj3WbryN7hGhKDwHa22Opcss+jZ1yfESyDUhp6AmOYtJh5ubHo7Ck7PzeAkkLtDNM9qDjNVLTZJ6QZqWhDLWRx2UDUVUcDnUVsEPhfYJQVNFJYAK4kC8okHxQ8fs4zCcgYsv3kgZ6IooQwwpFA0X5YiV1BCNskwq2eFoCGIkXZRKDhlSyFtK2QpoLUTpJxAKQavzWzZiXV0ObhBlk0UjphQ8+42t6jxD63SIhmV1KCvvu6QH9UvKcC5KIZN+lHvmDQhpPFbREIbz0DviSdsw82RgMO/oL0RTOHWVMdtalJk2zwV6i46QRSO5s69prX82RaF3MIvn2m+YnQUPnaMxm5SEDbO1KIEV2kcTNNk6fEzXMI4ZoRmGMaVMV7Af/579lF0OwzD2io/9+j/aeFDrKtOkoAplkG1N+4X17A3D2BVe9zd/aqrG7MddjWPB3jCMXWHMRzV2FGX8h3Es2BuGsStM0jDNtbEbtAaQXRoiheLbfaQ7oDgyT7FQRxcbSBlIOjnuK6fR4ZDwdTcS6ilShrizEFUxZVRiaOKQssSfWcHN1JHuANoddJhX2wVkpgW1DPoDwvkLaFkiWUZ6YB6aDbSRRXXOTANN4jdSSo1KmwsdwhdOIrfchM7PIHlJaGYxb6gX8npCPjuHGwbUR5OvwQEozjWofV3BYtKm5QbMuiFBhbOhxiODo7TcgEPJKgCn3TypK6n7gsQF6r4g8wWZL8mDp9vLKC9l1M97iqauP3JfNGBwKBAyhVoAr5ALrucJjRLxitRKigVl+SUeNxCkFNQpUgq+B/OPBVZudfSOgDol6QlJl40kmJX+u6xBqCmDQ4rLhaQTzeZCAmlHmHnc01/yDBZjPXzPUbskHP50wcotSVRQwXpKQYB8JiprQhrr0zvsqF2Mqp2YbhH6R5S1r9NYoTIe0/ejwVraiSdiOFdtn8Zz0l+MyqKQgnoFBQmynnIxbwozpwv6C57hrGyYqpXx2Opiu3BxWbaq+FzpHHHkragmkgC+S1Q+hXjcURuvxife9w/5xh/4qauu/+S//4fb/AZdH+jYJEK9MlP122sYxt7xjX/np7ZMXPKNf+fqPwTXI6qyrWm/sJ69YRi7wpiPauwoUY0z3n1nC/aGYewK02SEBjaMYxjGlPKpf/dDU5ODFmwYxzCMKeXVb/03U9OdVPY3kG8HC/Z7QLLSRxOH1lK0liJBcf1otiKqqAjhlmNR3ZJ5ynpCSAVNhFCldnMSPUmkVGh30eEQWcvQY4cIS3PP/MtcpTGUPODnZmC1ja61oSzj8iJAu4skCeXsApoIKhAyR36ohXzzK9BSkTIgzuGWO2TLHcqlOYqZlJC4ZxwvNAIHblphIeky53occANu8I6aJNyadHhF9jAXg3CubFF3OY8kh3nsMye49VWnmPMDggpOlEaWk5QBJ0p7CQahSbrmyA+UlEtVCrxOQu2cp39Mqc0OSNN4HvPckw8SQt8jQ4fkgu8J9Ysx3d9gKdBZKum+eJTGECgcoeNR52g9GRUu3RuUYq5EshC3U6HIhZDG9IhuGLfrvyjATIEkAQ1CkISi7zn/8oSiGdUtrohql5BCPqOERkCzgPioMCpKoXdCkFxIVzxHPlFy+lscYbaEJEAQZDWhflbwOeTNTSkKNaYvzFbgyCf7nPnGOoMFQKO6qHZpI73hekrB4UhyxLoXUG0l4IdK54iPHj0ZlJngh0JtRSnrwqAJZTPAQejISGEkpO1rBLfxjn07zpiP4liwNwxjdxjzju7OoqD7aIWwHabkT5ZhGHvOtcbrxzs2Pmt2csxeRO4SkS+KyEkReedVtvkOEfmMiDwkIn9wrTL3JdiLyA9VFfy8iPy6iNRFZFFEPiIij1TzhU3bv6tq9BdF5M79qLPx7CirP7WpeFJxpKI4wj7XythLrpW4ZNJ6/qrbm66FiHjg54A3Ai8Fvk9EXnrZNgeA9wB/VVVfBrz5WuXuebAXkePA/wLcoaovBzxwN/BO4KOqejvw0eo9VSPvBl4G3AW8pzoZhmGMM1PUsx954+xQz/61wElVfVRVh8D9wJsu2+Z/AH5LVR8HUNWz1yp0v4ZxEqAhIgnQBJ4iNuZ91fr3Ad9bvX4TcL+qDlT1MeAk8WQYhmGMB8r2/srEYH9IRB7cNN17WWnHgSc2vT9VLdvMC4EFEfl9EfmUiHz/taq45zdoVfVJEflXwONAD/iwqn5YRI6o6ulqm9Micrja5Tjw8U1FXKnhAFQn7V6Am266abea8Oz5ypO4uVmo19BGhjvfRZdX0V4PNz9H6HTRwQC/uIBvtfCLM9EfZ1jin75IefwgwwM1ioYnHJvBLzTwnRwJAXVRGRNqnpBW2abygO8ViCrlTB1pZsihAwQRSFxU/RxqEVJPSKsMWIOYgUq/+Bi87AWEejQ+kRBgMERXVvFlSbjpEMELgtD4/FOs3nwzADO1AYfSNVIpmXWBGWmQSvx4DTQqaULVtziarXLXt/8pK3mD1WGdJ9fmuXSpRVovSHxJCA7vA40b2gBkTkl8Gd1yF4EboVGd2hCEXj8jP19n5rGYuSmkUFtRhrPQuQHyAyW0CpIs4HxAFYpBgpaCH8SsUsP56F0zyga1/uXteernPPVz0dembEAxE3ALA+rNIVmlBgoHhPywp9dPCf0Ehm6T305VZhBc18esT0WlgGqV0Cwp54c8fVvAeyVzcbirDI4iLWk3UpJVT7YsZGuxqLIWfW+KFjzxF+uoh5AoEoSQQT63yZOnJfQOJSNx0XqPuswgb0VfnbIORb3ywSmjkmiwUH02hkRlWKYbTcmU/qGtxyQmbZjmWjyLh6rOq+odW6y/0pm7vPQEeA3wBuLX4WMi8nFV/dLVCt2PYZwFYm/9VuAGoCUif3OrXa6w7IqnVVXvU9U7VPWOpaWl519Z41njx16AZhi7gaBhe9M2OAXcuOn9CeLox+Xb/I6qdlT1PPCHwNdvVeh+DOP8ReAxVT2nqjnwW8C3AGdE5BhANR+NQW2n4YZhjBtuG9Mkoducrs0ngdtF5FYRyYj3LB+4bJvfBv6CiCQi0gS+CfjCVoXux+l+HHidiDRFRIh/Q75AbMw91Tb3EBtDtfxuEamJyK3A7cAn9rjOhmEYV0d37gatqhbA24EPEWPj+1X1IRF5q4i8tdrmC8DvAJ8lxsNfVNXPb1XufozZ/4mI/Abwp0ABfBq4D5gB3i8ibyH+ILy52v4hEXk/8HC1/dtUtdzrehuG8eyYruQl7OgjtKr6AeADly37hcve/yTwk9stc1+eoFXVHwF+5LLFA2Iv/0rbvxt4927XyzAM47kz3nekzS7B2FHKMf/AG3vH1PXsx/yZQQv2e4AsHUQvLlM+9TTJ4UPo0gLMz8SwOCxw83MQAhQFhDIalQWlbCYUtx+NRmWVmZU6KGsONCHp5PgnzoAq4dgSoZXF7UrF9XJkWFDO1SkbGQC+myOPPYnkBf7IEiQeipJiaZaildI/2kKOvBw3DPhOjr+4BkHRhVnCiUOUjYQyi9JNFFZfewIEfMcxLBIGIaVUx1pwNGWI15y+ljxa1HmyOMwHLr6Smit4snuAR84t8YqjpzlUb7NY69JfSDnfb7Lcb1CUDu8UL4FSHe1ejc6pWZjLyRo5ZeEplzNwip/LcT6akrVfAOmlkfxSyFtQtqr0haWj6Dik70nXHM12lYpPiOOtPhp8pSuO5IwjVLLGshEYLgSGB+K1VAc4pewl5Gkg8QHn4v/3MjjKbkLr0ZTaJRjOQqhtpO/zg2hchkbpZNGCAZ5AjBMhr54VHOkbSwdDF+WhA8ENQIoohaxfUBoXS7pLnuGcUDSigRmVURmVzNJVqQddEfdNBkrSU/qLLso3HQQXK+BKKH11XoBkANlafO97Ak5ww7isaED3yNaf+6mSXo6kumOMBXvDMHaH8Y59O864Jy+xYG8Yxq6g02ZqYsHeMIxpZMxHNXaeMW+wBXtjx0llU5IMY3qZshu0Yj17wzCmkalS46jAmCcvsWC/BwxvWoQTC4hCDlVqQMV3h3BxGS0KZOEAemAWTT1oVNO43BFqCVoI6bDEn1mBXo9ww6G4XRFAFe31cWvdaFqminQHAGirDkR1DkDIPPJ1JyDEdIiooolbN1BbRzWWNcxhOETPnMUvHIATh9alGr5f0HykTbjjCCFxdAYZjw8WuSG9RF1y+r4PKH3NWAt1LhYzHEh7nB3Msljr8m03fZlBSBiGhCJ4hiEO8NaTgj5JVLYETwiONCnxN7RRFYIK4gI0C7RwFJ0ESRScQqNkmASKlscNBAkx5Z8vPFIISS+qYZIuoBCyqJQJSaU46bOuzoGonnG5Q338LovGZb4PRdMzyB15K8ElgZA76CUkbUc+E8er0zb4HMoUcFEVIyEec3gA8vlAyDQapK0kZBcdLo9KF4BsFRYeKVm5JZqV+T40zwXylhAS6B30NM8UNJ+G9omUshb3SztK0lf6B+J+UkLWjtc7VMquAycHdI5m5M0qQLl43KIR6xjbHuta1qICyPfh8INrPP0tszEF4jVi21QFe7Axe8MwppTx7ujuPBbsDcOYRqxnP15YsDcMY1cYc3HKzmIPVRmGMbWMd+zbcUyNYxjGVGIPVY0XFuz3AN8rUCdIUGRYIEVA8hLyAppNRAOUAVntIsMh2u2hwyGSZSQzLcjSONUzdKZeeecADsKxJUQVDQHp9KHTRbs9Qrcbj33oIMzOoI0M6Q4Ip88g3sOtJwiNFFRJL/Xis94uDrJKdxC9euo1qGW4xQNoliBFIFkb4Hp5bNjyKtnqIfqLjuEw4VR3gS8mx6g3c/oaDWG6WuPh/g388YUX8NCDt3LoJedpZQMu9Zp0exkhCBocZe6g52k+niB5TKvXfBrKOejclpO0CnxSEoJQ5h66CTNf9dQuQvdYTKs3SsvnB1C/EFUoZRaVKz6vFCbDOJY8Uti4avnsEwXt4wllLapn3DD6ySRdOPTpNc6/epYyi7232rLSPyiUNU8hUKYBKaJ/TdKt0g2m0RsHYqo/iMca+dWka6DiKJsalw0lpiusPGpCAoNFOPMajx9GZY8o5E3BFXF9URPaN6RIUNDYXvUxDWHeiuVBXNZfiPkIJUCuQn+hFs+D21AkjRRDIy8dNPrpODdKYQiP3zUb1Tp9SNpbf+7HfFRjx7GevWEYU4n6MY9+O82Y/7pZsDcMY3cY79i3s2w/5eC+sWWwF5E/UtXXi8gaz2yKAKqqc7taO8MwrltMejlebBnsVfX11Xx2b6pjGMbEMGXDOGLJSwzDmEbGfAh75xnz3zYL9nuAP/kkNOrgBAZDKEqoZVCWlBcu4Rp1ZG4WRGK2qrJE6jWkVoOyRJe7lJdW8DMtpNmI+w8GyOxMLLcsoduDoNBqIK0mPixCiIofvXgpKnCaDeS2G9EsQZ2LqqBBjqx1oYy+OhQFOhjG1yF2VaTZQGZacQi2P6B8+gx+cYHBK28hb3mGc5BfrHNqfp6l+hqH0gUO+jZD9Zwv5vjMyk187uGbOfjiC8zXeyz3GrhKupCv1pAiethopvSOBHzPka1A3owqm/RCiq4k6FAQwEu8+Ze3olrk+O/3uPSSxrrviysqhUkaPV1qy4oocX2y4YWDEH2CAnSXPPNfHtI+kVLUo2qlthKoXSq4+IoZ1MeyXKHrmZz8ALTjCKmsK1uGB6IyJ+kKvohKmDKpFEACbgCuH5UzSZ94zYkKmKQT6+6G0bdHPQQf981n4jHLVBCtsmq1lfqlknQ1J1kbsPaCOYZzgjoh7Sqzj3YpZlIGCwkuV4qGUDQEFaG2EkjbJWs3p1GFU1KpmAL9xeipk60pZUpVZpXtqqyyXw2jB8+WTFGwFzU1jmEYU8r09ezHu8EW7A3D2B2mbMzehnEMw5hOxruju+OM+zDOvoijROSAiPyGiPy5iHxBRL5ZRBZF5CMi8kg1X9i0/btE5KSIfFFE7tyPOhuG8exQr9ecJobqPsp2pv1iv5Sw/xb4HVV9MfD1wBeAdwIfVdXbgY9W7xGRlwJ3Ay8D7gLeIyLT5rphGNcfo7uWW02ThG5z2if2fBhHROaAbwP+NoCqDoGhiLwJ+I5qs/cBvw+8A3gTcL+qDoDHROQk8FrgY3ta8edDFn1icB5azajAUAURfKsZFTgQ1TT1GpIkMBiivT44QcSRLFZ/dFQh8UhjHpJNly/LYrnOx3niYpq0vIAyoHkBvT6stXHNJsw00cRFH5z+AO10kUYdWq2o8gkhqnyKMtZvrR3L9R5/4gYY5qTLPXqHq7YVwqBIWMvrrJQNMimoSU4qBTc3LxBeIZzpzfDVC4vMNAaUQUjTkuax1eh3ExyDQULezkhXHbVlyFYVVyprJxxFlVHJVadKSol+MSWceW0jqldcfD/qPUlRZYbKpPLJiT416gDdyMYkpeJzyGd93F+jkqd72NFdymKGqj7MPjEk1BydI/G8uxzSlai8qV9SVIR8diPj03DTI4cSYrkjczB1MQNUSEe2AhL9ekK1TYC0F+uZz0ZfmuGc4grB92MWK5cLecsRkljHtBfIZzxlBoN5YfjKFn6oUXnTKWmcLQiJ0DtSY3DA0T3i1lVEAEUdOkfdegwezMt63dNOrEvjfCAZBNZOJPQPbj1OI/ZQ1VixH5fjNuAc8O9F5NMi8osi0gKOqOppgGp+uNr+OPDEpv1PVcu+BhG5V0QeFJEHz507t3stMAzj2ji99jRBbOePzH7+mdmPYJ8ArwZ+XlW/AehQDdlchSt1H654ylT1PlW9Q1XvWFpaev41NQzjOfOVe94Rv71bTcaesR/B/hRwSlX/pHr/G8Tgf0ZEjgFU87Obtr9x0/4ngKf2qK7G8yBXE3tNM7f86o+Pbzd3NxjzMfs9D/aq+jTwhIi8qFr0BuBh4AHgnmrZPcBvV68fAO4WkZqI3ArcDnxiD6tsGMZzQJxec5oYrgM1zn51vX4Q+DURyYBHgR8g/vC8X0TeAjwOvBlAVR8SkfcTfxAK4G2qWu5PtQ3D2C4TFcy3w5g3d1+Cvap+BrjjCqvecJXt3w28ezfrZBjGzvLo9/0wL7j//9rvauwJwviPStmg6l6QJtGQbGWVcm0NPzMTZY5VGkDt9ijbHcR73NwMkqaohii7TBIQQQdDQruDn5+DVmPDNC3PoSjR4RAd5kirgaQpOIe2O0A0Mhsda32+2oa1NmW3iyRprE+SgBNCqw5eolFapw9rOdrrI1kGqujqGpoXuFYTZI6kB8zlXDwzx6l6j8WsQ9MNmXV96i6n6YfUfU5nmLE01+Ziu0lvuYFkJbVGjveBvPAUnQzX9eTzgUuLihSV3HKgJL34etgETRQC5DOynkYveKBKn6e+Mu0qwK2NUgFqJXmt/k6Xo7kiIab4KzNH8DGNYUjjfiOpZEjh0ouz+EbjOt+DsgFFE7qpkPQrQ7NqG5dD2oUDJ4dcfElGUavSBs7EuqWrwByUNaFoKkUzpidM+tEUjRClomW1H1KlXkyEol7JSlOHy6G71IjSzaq+UsY6hEToHfT0Fxx+mCIhSi2TnuLyeOwo/6z2q+qdtZXWqT7Z2TbtFy6Qt1xMaTgjDA5sSE+34vb/51/gpumJmDEP9tOmhDUMY48Q0WtOE8M2ZZfbbbKI3FU5BpwUkauqFUXkG0WkFJH/7lplWs/eMIxdwU3bmP0O3XytHAJ+DvhuohrxkyLygKo+fIXt/iXwoe2Uaz17wzB2hT//b//Z9PTs2dGe/WuBk6r6aOUwcD/RSeByfhD4TTZk6ltiwd4wjF3hpf/pR6cq2D8Lnf2h0ZP+1XTvZSVd0zVARI4Dfw34he1Wz4ZxDMPYFfw0DeM8uwemzqvqldSII7bjGvDTwDtUtRTZ3qPIFuz3AF1ZQ2oZ0myQZBmh16M8fxFxgtRqUYXTqEeTs7wA75F6/RllyEwLPzezsaAooT8g9HqgiiQJUq9F07NQpRX0PqZChGjG5n1U41TpB6XZwFVKodDuIL0+7tAi0qxBEGSth15ahhCQ+bmo1gklooqGLtrIUCf4AdBNaH3Fc3GpydONeeaSPmlWUpecY+ky83M9jtVX+HJnic4wI/t0ysprlLJ0lKWjGHpUgVTRJCBpAK9oKRSFoxxUxm5J/C8sA4fvC/UL0DpdggjdJUeofNnUARJVM0WjMkMrRqn8AmVdyBtCWZd1JQ9Ew7S0ozSfLugcS2IaPxfXjxIRSWWiNjJM01pU5ZSNTSZsJQQHgxTOvzwj6cGhR3K6RxLyZkwtGDxoN27v8qgUSnqQrlUpD30sww9GZQpJd8OUzA+V1umCsu7oLXpCtunzUoLPlTKTSmkT21G/FJj94iXk3CWYnaF32yLD+SSmYlTwfSVtlyS9kmRlQGhmuKFSy+OjLX7gyFsOHULS3Tq6TVzP/RrsYHO34xpwB3B/FegPAd8jIoWq/qerFWrDOIZh7Aqf/Sv/J87pltNEsXN2CZ8EbheRW6sHT+8mOglsHEr1VlW9RVVvIVrO/M9bBXqwnr1hGLvEq/6//32/q7Cn7JQVgqoWIvJ2osrGA++tnATeWq3f9jj9ZizYG4axK7hpGsbZYZMzVf0A8IHLll0xyKvq395OmRbsDcPYFaZpzP56cGy2MXvDMHaFT73x3XjRq06feuOE2V2NucWx9ez3AO0PoqIlyyDxuNkZXKuJlmX0zCmKmDZwRB8Y5mieE7rdSq0T/XB0mIMT3EwL6jVcqxG9cbo9QrsDGhDvQRxa5EiSQj1AnkefnTJEH53BINbHR/OSkScPaYr0c7SeQj1DDi3GOqUJmnokLyFNcVlGWUvxA4WWUH/Kk89AyBPOdGfIXMEgJCyk0Z+nW9ZYLeo4lCMza7T/yhCfp3T7Gf3TLWoXHf3jOZoGfNtDmRAyrbxahHK2xDWiGYuWDoaOpA/ZmtJb8qhEP5eFD/w5+StuxfcLekcbDOZd5Y1D9LRxUNaFtB2YPdlj5UUzDGejYkUCsT0K3aMJ6oS0o6RdRUqld8hT1qP/TvSxgdpK3K/MAImeN7OnCvoLnnwm+uzEY8KlF6UbvjwFtM4HuksuKm9c9PNxeVQDBQ9FurG/y8ENwA/j/kk/Ro3+YkLSV+qXSvKWo6xJVA05NpRE635AijoYHJ0lrWf40xeoXeijaRMpBZ8rtQtDki8/BfUa9PoMX3ojRdMxnHXRe8cJUipJXwnZ1n3Z133oXetisKut//idP/bsv1Bjyrj/kbFgbxjGrjBNwzjA2BuhWbA3DGNX8G4fM3XsNbq/iUm2g43ZG4axK/zRX/wJnOiW00RhY/aGYUwj3/l7/wg/7hKVHWTcf7ss2BuGsStMXM/9Wox5cy3Y7wVOoh/NCFV0mBPabTRUyzWsz8X7qN6pVDiaF4ReLyprquxVOsyRoNHzxjmk2cBnaVTblNHHRMo0HqvXQ4Pi6rUqG5VDarWqHkN0OARxuFYzKnaKAllZJaysIlkWt3USj5f49Tb4p87jjzUARz6v+J7Ap+f4yokmF462ODUzz2K9RyKBYfB8dWWBixdmqLeGHJ5f42Czw3y9x3C2w7DwNIJnMEwY1FNC4aB0hACSCVL55KCC5g5RGM7C6s2C74PPoZ8IZ/77l+CKDR8bVyh+CEkvoF4oa0LeFAZzCas3zUa1ygghfmElZniSEFUn83/4KMvfedt6NidXAlWmK608c1zBenaqkDrmvjKgdzhjOBOPOfLpGR0jJNA76Co/mo1k1H4Q1T/DWamuYcxaJVqpeColTuNsAQJl3aFOCKngSvDtQNoJoDCY9+teQX6gNM4X1J5uMzzUYniwjh46jiZCWYueQmVNyFt13LHbkKCojDJZyfp5kqCx/SLPPHdXIJmmMXvGv2dvY/aGYewKH/r2nyaRsOU0MSgxecl2pn3CevaGYewKb/qjt5NMSXfSEo4bhjG12Jj9eGHB3jCMXWHagr3oeLd33/5kiYgXkU+LyP9bvV8UkY+IyCPVfGHTtu+qsqx/UUTu3K86G4axfX7zW94zXWP2Y66z388Rtb8PfGHT+3cCH1XV24GPVu8RkZcSzftfBtwFvKfKqm4YxhjzfR+/Fydhy2mS2MGE47vCvgzjiMgJ4L8B3g38w2rxm4DvqF6/D/h94B3V8vtVdQA8JiInidnXP7aHVX5euCOHN9ID9vroWjumExSHS6WSX3rQKJssO92YLjBJogwTQARx8bc5dLtRXikOl2XIKOVgWa4bpUmthjQbUWY5SkVYlhDiMTTP0cEgSjKzDGk1ohFalbKQoMjIfK0o0DyakEmaIlkaj9/r44aKH0aZXroKmkJ6MWEtn6OzUOPJWkktLXAu0Mxylg6tUqqjPaixqjH1oiqoCkWVolCqlII4jVJLB9J3uH5C0hHSDmSrMcVg3pINb9nqy+RyJe0orlBCGqWDRcPhCiVbC2SXhpSNhMEBT94UQhbNw2KaQVk3DZMQpYcX7nwBEOWLfhCP48qYli+koH7DcMxF1Sv5XILLlZmnSvygJHv8IogwuGWR4VwSy04lpgDsl/hegb/QZnjDPL3DNdK2rktIXRklnr6v+GGI5zwPlJnD5UpRr06AKsELwxmHhCg3dW3w/YCUMcoMD7UQVdwwrA87JD2hTB34mKIxtie2aSQF9b2S2rkubrXH4JZFiqanqG3dV5yonvs2GPfm7lfP/qeBf8IzhUhHVPU0QDU/XC2/Zqb1ESJy7yhj+7lz53a80oZhbJ9rWSVM3Ji+DeM8ExH5y8BZVf3Udne5wrIrnjJVvU9V71DVO5aWlp5zHQ3DeP6877W/RM0VW04TwzaHcKZtGOdbgb8qIt8D1IE5Efm/gTMickxVT4vIMeBstf12Mq0bhjFmvPVTf2tLP/uJY8z/qOx5z15V36WqJ6qs6HcDv6eqf5OYPf2earN7gN+uXj8A3C0iNRG5Fbgd+MQeV9swjGeJE6Xmii3nk8LooSrr2W+PHwfeLyJvAR4H3gxQZVV/P/AwUABvU9Vy/6ppGMZ2SKXc1nxSkDDeP177GuxV9feJqhtU9QLwhqts926icuf6pNeDhXk0S6BVRxp1fKV4QRVCiGkIiyIak+UFIS9gOMTPzMQUhEkCIlCWOJGopClLtMjBu+ivVRTxPaDDITKsIc6hZVmlKpSNY1TLXL0W0xMGRQfDqOgpSygKwsggrVGPdRBBB0PKi5cA8LffRu1cF01aLA8S0m5UjzSf1mjy9dVGNO+KvlycPwxpG9IODOcqpUkOflCpW1z8QErlISLVDa3NKgcJ4IdKthaVKTOnSlwR8N0c9YL/6hn08CKy1kPnmpTNDARcPyc0smj+5oXsQp/sAmga/9wWrRQEkk6BX+0h/Zww3yTUEtygQL2ACCF1SKm4fo5b60OvTzh8IKb8S6OBXailSBHw7T7lfAMpAjhHmGuQnWlTe6RDeXQB/5WnIcuiSgrQdof04iWyZjOmBQRYWUXzAslSwlobNzsT12WxLcXSHPUQkLyMdRABRzwmxPY++gSUAXdgDlpNtFmL6q7zy9FIr1LlhDPnkXoNmZ2J6rGg6IEZpDuICq0kgWFO9uBJ/OoarVtv2vJj/2+/4df5oc/cfdX1/+ZV9z+LL9GYs883X7fDlDhXGIax1/yTP3szqZRXnf7Jn715v6u4o4ycS6817RfjNIxjGMYEMUlj8ttizJtrwd4wjF0hlRIvgVLdVeeTxLj/tk3W2TYMY2z456/4jwD4auziavOJQBk9Cn7taZ+wnr1hGLvCjz38PVzDUWGiGPffLgv2e0GrCXkR5SbOoTMNZFhAfwi9HtofREUOxLSAIkhx2dMo3oOvvjnOEYbDqKaZn4ueNs4hIcRygq578WhexEeQpUpz5z1kGVKW0ZMnaDy+EzQvohKnSpEo3iNJuuGZUy1zMzPRjycvKFszMYVf5eMiCsM5IVuDxoXA3Ef+nN43347Lla/cluAKR9KFpAu1ZSXpw+yjXYYHMhoff4T8VbeRrA1RETTzSBnw59eiGkQceuEi0qjH8xEC5S1H8Y+dXn9PkiCdPtQz5MIK/rE15MgS5WNfJbn5xngeRr2rdgdmWlAUuDPncQfmCGtt5MA81Gu4S23k4jLa6+OPH4V2B1+llJR6DUZeRZ9+eOPciiNZmI/n0Xv8hUvxeq2uwRMKjTrF8jJu6UAsp1GHWgZrbaTZQLs9GA7RtTZyYI6w2sYtHiAsr+CWDkIZKJ88Ha9NrUbS7sS2NBqQV+X1+jF9ZBFVVXL0MHS6hLU2rLVxrWasu/eEcxei31G9hju0GM/zYEB5aQXxnnDqSRCHn2khc7Mw20KylGTpYCx/C/y4D2LvIJa8xDCMqWXSdPRbss9DNNthiv5kGYaxl/zQSz5sFsf2BK1hGJPOz/75dzFViSfGu2Nvwd4wjN1hqoZxsDF7wzCmlImSVl4LBcrxjvYW7PeA8vEnQQOSZTGDlPeoBsiLda8aIKpjNKyrOtAAZblhsFQtF+dIRt4plbfO5kxSZMm6ckfSEgZDQq+HDgaxbL/pz7VGBQtFEV+Pjl8RhsN4YycESBPIC0KvjwtR9ZG06hQNj6h/Rs9GFLLVEmk21rNFXd7zSfqQdAPJqfP0jp4gvPBGVISykZCeXom+Lq065aFZ/ONno0qp2UA7XaRRJ9x4BPfwY4Tbb8I9cSY2Z3EOvvIkcnCR4qmnSY4eRs9fxB87SnjyaSRLYwYvVUgS9Ox5pNVEizx6z7SaURGzuhbXV1nB9NyF2K5mA6nXKC9cwr3kBejJr+IaDUI/ehVBCc0G4dRTuGYztn+tTegP8DMtwtoayZHDcH6Fst3B1zKKs0+SHD+GtjvITAs9OI+cu0Tx+JMkhxajguiFtxK+cBKSBNdsokWBzM2iK6uxnsuruJkWurqGzM9BfxCvc6OBnruANOrr117LEhFBu734eamUYNrpRlVWr49fmCe0O/iZGcpON/onFQX65NPIwjwMhuufuavxd1/4X3nvl15PKgW5JlecTxLj3rO3G7SGYewKv/rI62i5AcBV5xPFmD9UZcHeMIxdoe5ynIQt55PETqpxROQuEfmiiJwUkXdeYf3/KCKfraY/FpGvv1aZNoxjGIbxfNlBi2MR8cDPAd9NzNT3SRF5QFUf3rTZY8C3q+olEXkjcB/wTVuVaz17wzB2hTe/4FNkUm45TQoCSKnbmrbBa4GTqvqoqg6B+4E3bd5AVf9YVS9Vbz9OTNe6JdazNwxjV/jAYy8nnaIctLL98fhDIvLgpvf3qep9m94fB57Y9P4UW/fa3wJ88FoHtWBvGMau4Jky6eX2h3HOq+odW6y/0k/kFUsXke8kBvvXX+ugFuz3gHVzsZHEEtbT/40MrVCN5mRluSG7FLe+v+R5TF9XFISVVUJeIE6QJF0vX4Pi0iTKO7MMvEMriZwO8426BEXSBFdLNpZViPdxXxHEbRxfK4MsqdXwBxeikdb8LGUjjTeequ+1hNhAKaMxmvb7GzelSonZeipRgssVCUo4dwFXHidkUfKpiYup86rXrhNlhNrtRiOwdpukUcctd2DpIHJ2mdDuRKljdxBT9q218a0m4eIybvEAxakno4xwbQ3pdnHNJqQJ0qijnS7+4CKkKfQHUZrpXJQ1ikMyD6qE/gDnXDSr04CcvYgcmEcHA9zoeiYputZel12SJEia4p0j9PpRdtvtbcg9l1ejqVivj2Qp5dlz+GYDzXOSG45GKaUIPPIV3MFFynMXkLlalISurMZUie1OvP7DIZJllE+f3bieTuK5W155xufRNRpR0pkmSJKgnW5MV1nJf8tLBW5uBvICV6+BE4qz50mOHYFODxIfZZtGxY4qbU4BN256fwJ46vKNROSVwC8Cb6zSum6JjdkbhrEr3Hnrwzj0qtOdtz587UKuI3ZQjfNJ4HYRuVVEMuBu4IFnHEvkJuC3gL+lql/aTqEW7A3D2BV+7ysvwku46vR7X3nRfldxZ9khnb2qFsDbgQ8BXwDer6oPichbReSt1Wb/DDgIvEdEPnPZPYArYsM4hmHsGh6lRK46nxiU7Spttlec6geAD1y27Bc2vf67wN99NmVaz94wjF0hpdzWfGLQbU77xJ4HexG5UUT+i4h8QUQeEpG/Xy1fFJGPiMgj1Xxh0z7vqp4k+6KI3LnXdTYM49nzF245iasUOVebTxKiuq1pv9iPYZwC+Eeq+qciMgt8SkQ+Avxt4KOq+uPV48HvBN4hIi8l3qB4GXAD8Lsi8kJVvW66BW5kQrWZJFn/E6tlua7OGZlpPWP7yuyMokQHg3X1jAaFYpPKZjQfDKL6plLorKuBKnUPmqNFXqUcjGkQEQeiUb0hEo/vHIQQ1RpVmj0AHQyjMqjdweeHGM4f3OixjG5EBcXnAe304jkYalTqVNOoKi7XmEoPcHkAEVRim6U7QOerFHq9PjI3B5WSRkeqmf4AahmSJJTLy/jZGcjSaGYmEo3cvCc5vASqeBFCrx/PT1FAo44Oo1GcP3Qwvl5eRssSv7iAdrtRLSWyfo6LCxeimdlMi/Krp+K5HJ2bIoe+Q7ynOHsePzeL1DLC8kpM5VhEIzkPuFo9Kl5UoxJmUPnFtDtorx9TRlbX0h1coDh9Bt9qUl5aiQZpQFhZrY7tKdsdXBo/HyNVFrCuuKEM6+knKaIJmfYHlJVKqPpQRTVWUMLK6sZHsOejkqhS7RA0tnUL/utXvm7L9ROHZap6Jqp6WlX/tHq9RrwBcZz4hNj7qs3eB3xv9fpNwP2qOlDVx4CTxCfMDMMYY7wE6q7Ycj4xKOsdmWtO+8S+jtmLyC3ANwB/AhxR1dMQfxCAw9VmV3qa7PgeVtMwjOdAWgXza80nAWF7QzjTNowDgIjMAL8J/ANVXRW56p35Z/M02b3AvQA33XTTTlTTMIznSDZpN2CvRRjvH6996dmLSEoM9L+mqr9VLT4jIseq9ceA0WOA23qaDEBV71PVO1T1jqWlpd2pvGEY28ZVTxFdbT4x2DDO1yKxC/9LwBdU9ac2rXoAuKd6fQ/w25uW3y0iNRG5Fbgd+MRe1dcwjOdGKgGPbjmfJGwY52v5VuBvAZ8Tkc9Uy34Y+HHg/SLyFuBx4M0A1ZNj7wceJip53nY9KXEAKANahg0Pm5EXjTjECRoqFQysqyLE+6jYGak8hnmVfrBY9y8B0LIqZ3MqwbxYXy/er6s14g4hvq6Ova4CGqU+xCNliOkSN/v3jJQ7lXKEoqh8XxJcrusDa1KyrieWUpF6rfLL2bTN6EHCatLBACkUKQKkDlcSPW4A18sJjRRJPHrhYtw2y6qqxnPEYEjodteXl2fP4Q8voYMBfn6OcPbcum8NTqKnUOWLE1ZWcQsHononBMrKZ8ZlGdrpxf2KArIsLhsOSQ4vUZ6/gHcuKmw2XVNXq+NmZwiraySLC5B4wvLquiqIJMGlKZQlYThE+oPoO1SWhLzAVcokSWL9cAJBIc/xc7OEdhuXJhTnLz7js7P+GRKHyzIkjQoqNzuDdnvoMI/Xs/Jnco3G+rV19VpU6FSfydE2I4+m0efANeqUKyv42dlYZ7HHdJ7BmKtx9jzYq+ofceVxeIA3XGWfdwPv3rVKGYax46TVUE2KkiNXnE8O+5tycDuYXYJhGLvC5mA+en35fGJQYAftEnYDC/aGYewKfoKsb7bDfo7HbwcL9oZh7ArpZaO1Ofo1yyYKC/aGYRhfG/wnCiXeSB9jLNjvASMPkc0qmmoBI13Rur5opNApy6iQqDxtxPtnqi8265E2lbOukBipbdiUiWrzsSnjqOkmRYU4iWUVG9uJ99FfZziEQZ9NG5PMz8XXrkrKMMpYVfnjqAja6yF5pQwKghsJkRRcGTNVxcxPIHkZs0JJ9OdBFYoSt1ZGr5wqg1S5uoZfmN+oi3Px3FYqITc7i/b6MYMT4JcOEi4ugxNCfxDbmRdo2cHNzhJW19DhMOqQRyqnoIjbeE2vHz1wajXKi8tR+eNczNY08pkZeRtVPbzQ68XzN1ICjRQ3qkhexHVFEbNl9QdI5YXD0kHoD9B2h7Ldwc/PgXOEdjuqorIUP9OKnkW9flRIhRDr4ST67FR1Dcurz1CBbai7hhuqLXG4Sr2z3l5Yb7+r1aPPUK+Pn52N3kJFfk01Tl08JYpHrjqfHOwGrWEYU8wooF9tPlFYsDcMYxpxCKl4ci2vOp8YlOgqOsZYsDcMY1dIxW9rPhlcNkQ7hliwNwxjV6hJCkCuBakkV5xPFDaMYxjGNJNKsuV8IjA1jgHw4fz+/a6CYew57uiX9rsKe4v17A3DMKYAC/aGYRgTjmrMyzzGWLA3DMPYCaxnbxiGMQVYsDcMw5h01NQ4hmEYE4+C2kNVhmEYU4DZJRiGYUw4qhAs2BuGYUw+doPWMAxj8lHr2RuGYUw6lrzEMAxj8jEjNMMwjMlH2ZT+c0zZOonkGCEid4nIF0XkpIi8c7/rYxiGsY5WyUu2M+0T10XPXkQ88HPAdwOngE+KyAOq+vD+1swwDCOiYz6Mc7307F8LnFTVR1V1CNwPvGmf62QYhrGB9ex3hOPAE5venwK+6fKNRORe4N7q7UBEPr8HddsPDgHn97sSu4i17/rmemzfzc9n5zUufeh39TcObXPzfTk310uwlyss+5r/TKp6H3AfgIg8qKp37HbF9oNJbhtY+653Jr19V0JV79rvOlyL62UY5xRw46b3J4Cn9qkuhmEY1x3XS7D/JHC7iNwqIhlwN/DAPtfJMAzjuuG6GMZR1UJE3g58CPDAe1X1oWvsdt/u12zfmOS2gbXvemfS23ddIjrmj/gahmEYz5/rZRjHMAzDeB5YsDcMw5gCJi7YT4qtgoh8RUQ+JyKfEZEHq2WLIvIREXmkmi9s2v5dVZu/KCJ37l/Nr4yIvFdEzm5+9uG5tEdEXlOdl5Mi8jMiciVZ7p5ylbb9qIg8WV2/z4jI92xad920DUBEbhSR/yIiXxCRh0Tk71fLJ+L6TQ2qOjET8ebtl4HbgAz4M+Cl+12v59iWrwCHLlv2E8A7q9fvBP5l9fqlVVtrwK3VOfD73YbL6v5twKuBzz+f9gCfAL6Z+OzFB4E3jmnbfhT4x1fY9rpqW1WvY8Crq9ezwJeqdkzE9ZuWadJ69pNuq/Am4H3V6/cB37tp+f2qOlDVx4CTxHMxNqjqHwIXL1v8rNojIseAOVX9mMbI8Sub9tk3rtK2q3FdtQ1AVU+r6p9Wr9eALxCfap+I6zctTFqwv5KtwvF9qsvzRYEPi8inKhsIgCOqehriFxA4XC2/Xtv9bNtzvHp9+fJx5e0i8tlqmGc0xHFdt01EbgG+AfgTJv/6TRSTFuy3ZatwnfCtqvpq4I3A20Tk27bYdpLaDVdvz/XUzp8HXgC8CjgN/Otq+XXbNhGZAX4T+AequrrVpldYdl20cZKZtGA/MbYKqvpUNT8L/EfisMyZ6q8w1fxstfn12u5n255T1evLl48dqnpGVUtVDcC/Y2NY7bpsm4ikxED/a6r6W9Xiib1+k8ikBfuJsFUQkZaIzI5eA38J+DyxLfdUm90D/Hb1+gHgbhGpicitwO3EG2HjzrNqTzVUsCYir6tUHN+/aZ+xYhQEK/4a8frBddi2qj6/BHxBVX9q06qJvX4TyX7fId7pCfgeolrgy8A/3e/6PMc23EZUM/wZ8NCoHcBB4KPAI9V8cdM+/7Rq8xcZQ4UD8OvE4Yyc2MN7y3NpD3AHMXB+GfhZqqfAx7Btvwp8DvgsMfgdux7bVtXr9cThls8Cn6mm75mU6zctk9klGIZhTAGTNoxjGIZhXAEL9oZhGFOABXvDMIwpwIK9YRjGFGDB3jAMYwqwYG9MFSLyx/tdB8PYD0x6aRiGMQVYz96YKkSkvd91MIz9wIK9YRjGFGDB3jAMYwqwYG8YhjEFWLA3DMOYAizYG9OGyc+MqcSCvTE1iMhBtp8r1jAmCgv2xlQgIjcAHwP+1X7XxTD2A3uoyjAMYwqwnr1hGMYUYMHeMAxjCrBgbxiGMQVYsDcMw5gCLNgbhmFMAf8/YGrGZoXkAZwAAAAASUVORK5CYII=\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["data.plot()"]}, {"cell_type": "markdown", "id": "d1fc8f44", "metadata": {}, "source": ["Notice that the data ranges from 0 to 1, whereas in the image the colorbar is labelled from about -160 to -70.\n", "\n", "Also the `i` and `j` coordinates are just the indices from the NumPy array, whereas the original plot had time and frequency coordinates.\n", "\n", "In the next section we'll fix these issues."]}, {"cell_type": "markdown", "id": "0578e250", "metadata": {}, "source": ["## Do more\n", "\n", "Let's do more with this image!\n", "\n", "We happen to know the colourmap was `jet` in this case, but we could give `gio` the pixel area of the colourmap, as a (left, top, right, bottom) tuple. We'll also pass in the `vrange` of the values, which we can estimate from the colourbar labels in the image. While we're at it, let's crop the result down to just the data area, and pass in the `extent` of the data in real-world coordinates (time and frequency in this case)."]}, {"cell_type": "code", "execution_count": 4, "id": "67caf004", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (x: 1096, y: 1642)>\n", "array([[ -69.        ,  -69.        ,  -69.        , ...,  -69.        ,\n", "         -69.        ,  -69.        ],\n", "       [ -69.        ,  -69.        , -155.17411765, ..., -154.83215686,\n", "        -154.83215686, -156.2       ],\n", "       [ -69.        ,  -69.        , -155.17411765, ..., -154.83215686,\n", "        -154.83215686, -156.2       ],\n", "       ...,\n", "       [ -69.        ,  -69.        , -156.2       , ..., -146.96705882,\n", "        -146.96705882, -150.38666667],\n", "       [ -69.        ,  -69.        , -156.2       , ..., -146.96705882,\n", "        -146.96705882, -150.38666667],\n", "       [ -69.        ,  -69.        , -156.2       , ..., -146.96705882,\n", "        -146.96705882, -150.38666667]])\n", "Coordinates:\n", "  * y        (y) float64 -0.01 -0.009963 -0.009927 ... 0.04993 0.04996 0.05\n", "  * x        (x) float64 200.0 208.9 217.9 226.8 ... 9.982e+03 9.991e+03 1e+04\n", "Attributes:\n", "    source:   \n", "    fname:
"], "text/plain": ["\n", "array([[ -69. , -69. , -69. , ..., -69. ,\n", " -69. , -69. ],\n", " [ -69. , -69. , -155.17411765, ..., -154.83215686,\n", " -154.83215686, -156.2 ],\n", " [ -69. , -69. , -155.17411765, ..., -154.83215686,\n", " -154.83215686, -156.2 ],\n", " ...,\n", " [ -69. , -69. , -156.2 , ..., -146.96705882,\n", " -146.96705882, -150.38666667],\n", " [ -69. , -69. , -156.2 , ..., -146.96705882,\n", " -146.96705882, -150.38666667],\n", " [ -69. , -69. , -156.2 , ..., -146.96705882,\n", " -146.96705882, -150.38666667]])\n", "Coordinates:\n", " * y (y) float64 -0.01 -0.009963 -0.009927 ... 0.04993 0.04996 0.05\n", " * x (x) float64 200.0 208.9 217.9 226.8 ... 9.982e+03 9.991e+03 1e+04\n", "Attributes:\n", " source: \n", " fname: "]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["cmap = (2035, 102, 2079, 1198) # (left, top, right, bottom)\n", "crop = (312, 102, 1954, 1198) # (left, top, right, bottom)\n", "extent = (-0.01, 0.05, 200, 10_000) # (left, right, bottom, top)\n", "vrange = (-156.2, -69.0)\n", "data = gio.unmap_to_dataarray(img, cmap=cmap, crop=crop, extent=extent, vrange=vrange)\n", "\n", "data"]}, {"cell_type": "code", "execution_count": 5, "id": "cb852f71", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["data.plot()"]}, {"cell_type": "markdown", "id": "24f430d9", "metadata": {}, "source": ["Nice! We can now go off and do things with this dataset."]}], "metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7"}}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8549469 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,134 @@ +/* + * _sphinx_javascript_frameworks_compat.js + * ~~~~~~~~~~ + * + * Compatability shim for jQuery and underscores.js. + * + * WILL BE REMOVED IN Sphinx 6.0 + * xref RemovedInSphinx60Warning + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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+} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/docs/_static/custom.css b/_static/custom.css similarity index 100% rename from docs/_static/custom.css rename to _static/custom.css diff --git a/_static/debug.css b/_static/debug.css new file mode 100644 index 0000000..74d4aec --- /dev/null +++ b/_static/debug.css @@ -0,0 +1,69 @@ +/* + This CSS file should be overridden by the theme authors. It's + meant for debugging and developing the skeleton that this theme provides. +*/ +body { + font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, + "Apple Color Emoji", "Segoe UI Emoji"; + background: lavender; +} +.sb-announcement { + background: rgb(131, 131, 131); +} +.sb-announcement__inner { + background: black; + color: white; +} +.sb-header { + background: lightskyblue; +} +.sb-header__inner { + background: royalblue; + color: white; +} +.sb-header-secondary { + background: lightcyan; +} +.sb-header-secondary__inner { + background: cornflowerblue; + color: white; +} +.sb-sidebar-primary { + background: lightgreen; +} +.sb-main { + background: blanchedalmond; +} +.sb-main__inner { + background: antiquewhite; +} +.sb-header-article { + background: lightsteelblue; +} +.sb-article-container { + background: snow; +} +.sb-article-main { + background: white; +} +.sb-footer-article { + background: lightpink; +} +.sb-sidebar-secondary { + background: lightgoldenrodyellow; +} +.sb-footer-content { + background: plum; +} +.sb-footer-content__inner { + background: palevioletred; +} +.sb-footer { + background: pink; +} +.sb-footer__inner { + background: salmon; +} +.sb-article { + background: white; +} diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 0000000..527b876 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 0000000..b57ae3b --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,14 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/docs/_static/favicon.ico b/_static/favicon.ico similarity index 100% rename from docs/_static/favicon.ico rename to _static/favicon.ico diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 0000000..a858a41 Binary files /dev/null and b/_static/file.png differ diff --git a/docs/_static/gio_logo.png b/_static/gio_logo.png similarity index 100% rename from docs/_static/gio_logo.png rename to _static/gio_logo.png diff --git a/_static/jquery-3.6.0.js b/_static/jquery-3.6.0.js new file mode 100644 index 0000000..fc6c299 --- /dev/null +++ b/_static/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.6.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
" ], + col: [ 2, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
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+
gio documentation
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+
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+
+ + + + + Back to top + +
+ +
+ +
+ +
+ +
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/changelog.html b/changelog.html new file mode 100644 index 0000000..dab56a8 --- /dev/null +++ b/changelog.html @@ -0,0 +1,360 @@ + + + + + + + + + CHANGELOG - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
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+
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+ +
+ +
+ +
+
+
+

CHANGELOG#

+
+

0.2.0, 10 October 2023#

+
    +

  • Added gio.find_closures() to automatically find closing contours in a grid. Also added short notebook about it to the docs. Many thanks to Rob Gooder for hugely improving this algorithm.

    • +

  • Development note: switched to PEP621-style build system, using only pyproject.toml.

    • +

  • Now also building on and supporting Python 3.11.

    • +

  • Moving to more conventional semantic versioning.

    • +
+
+
+

0.1.7, 19 September 2022#

+
    +

  • Fixed bug issue #26, which garbled string comments in array_to_zmap().

    • +

  • Implemented enhancement issue #17. Created gio.unmap_to_dataarray(), putting the guts of the algorithm into its own library, unmap, which is now a dependency of gio. The library only produces NumPy arrays, so gio adds the xarray wrapper and a couple of extra options.

    • +
+
+
+

0.1.6, 11 September 2022#

+

The overall plan of gio is starting to take shape. In general, the plan is to read specialist subsurace formats of various file types, with either xarray.Dataset or xarray.DataArray as the target. Then, gio will write these same formats via an accessor method on the xarray object (and possibly also via ordinary functions). This release implements this pattern for the first time, specifically for the ZMAP format.

+
    +

  • Added the zmap module, with functions for reading ZMAP files (read_zmap()) and for writing files starting from an xarray.DataArray or from a NumPy array.

    • +

  • Added xarray.DataArray accessor (in xarray.py) to save in ZMAP and OpendTect formats. The plan is to add more target formats the the accessor, according to need.

    • +

  • Added tests for the ZMAP components. Removed run_tests.py and put the pytest options in setup.cfg, which seems cleaner.

    • +

  • Started trying to maintain this file properly!

    • +

  • Note that there are two ‘work in progress’ modules: esri.py and usgs.py, which will eventually read some common DEM formats.

    • +
+

In general, everything is a work in progress but if there is a chapter on something in the User Guide then it mostly works, at least for the test cases I have. If you have a file that should work but doesn’t, please consider making an issue.

+

Feedback is welcome on whether the target format for readers should be xarray.Dataset every time, even for singleton files.

+
+
+

0.1.5, 12 February 2022#

+
    +

  • Added the random module, which generates random surfaces using sums of Perlin noise. The high-level interface is gio.generate_random_surface(). Also added a notebook for help on this module.

    • +

  • Added the logo module, which plots gio’s logo. Using the logo in the docs.

    • +
+
+
+

0.1.4, 3 February 2022#

+
    +

  • New module: surfer, adapted from Seequent’s steno3d_surfer, for reading Surfer 6 binary and ASCII files, and Surfer 7 binary files. The module does not write files yet.

    • +

  • Started development on iesx module for reading IESX formatted files, eg from Petrel or OpendTect

    • +
+
+
+

0.1.0 to 0.1.3, February 2022#

+
    +

  • Early development included setting up the package, and adding the opendtect and xy_to_grid modules.

    • +
+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/contributing.html b/contributing.html new file mode 100644 index 0000000..44f8961 --- /dev/null +++ b/contributing.html @@ -0,0 +1,346 @@ + + + + + + + + + Contributing - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
gio documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

Contributing#

+
+

:raised_hands: Thank you for considering contributing to the gio project!#

+

There are several important ways you can help; here are some examples:

+
    +

  • Submitting bug reports and feature requests: see Issues.

    • +

  • Proposing code for bug fixes and new features, then making a pull request.

    • +

  • Contributing data files to test against. See below.

    • +

  • Fixing typos and generally improving the documentation.

    • +

  • Writing tutorials, examples, and how-to documents.

    • +
+
+
+

Contributing data files#

+

This is a data-driven project and data files are always helpful, especially if they differ substantially from examples we already have.

+

Please ensure that data is open access and provide a licence file with it. please also make them as small as possible, preferably well under 1MB.

+
+
+

Code of conduct#

+

We’re fortunate to be part of a large professional community that conducts itself with mutual respect and consideration for others. Agile’s Code of Conduct is part of protecting these features for everyone, everywhere. Please read it.

+
+
+

Authorship#

+

If you contribute a pull request to the project and you wish to be identified as an author, please add yourself to AUTHORS.md.

+
+
+

License#

+

By making a contribution, you agree that it shall be governed by the terms of the license unless another, specific agreement is made with Agile.

+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/data/OdT/3d_horizon/F3_Multi-H2-H4_ILXL_Single-line-header.dat b/data/OdT/3d_horizon/F3_Multi-H2-H4_ILXL_Single-line-header.dat deleted file mode 100644 index d83967e..0000000 --- a/data/OdT/3d_horizon/F3_Multi-H2-H4_ILXL_Single-line-header.dat +++ /dev/null @@ -1,16521 +0,0 @@ -# "Inline" "Crossline" "Z" -# - - - - - - - - - - -"F3_Demo_2_FS6" 110 1042 865.397810935974121 -"F3_Demo_2_FS6" 110 1044 865.243911743164063 -"F3_Demo_2_FS6" 110 1046 865.083515644073486 -"F3_Demo_2_FS6" 110 1048 864.957332611083984 -"F3_Demo_2_FS6" 110 1050 864.874124526977539 -"F3_Demo_2_FS6" 110 1052 864.863038063049316 -"F3_Demo_2_FS6" 110 1054 864.880919456481934 -"F3_Demo_2_FS6" 110 1056 864.868581295013428 -"F3_Demo_2_FS6" 110 1058 864.834308624267578 -"F3_Demo_2_FS6" 110 1060 864.854216575622559 -"F3_Demo_2_FS6" 110 1062 864.926338195800781 -"F3_Demo_2_FS6" 110 1064 865.018188953399658 -"F3_Demo_2_FS6" 110 1066 865.086793899536133 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5828628.69 1190 1989 525.385499 1e30 -1709843.67 5828641.00 1190 1990 525.749683380126953 1e30 -1709845.79 5828653.32 1190 1991 525.672256946563721 1e30 -1709847.91 5828665.63 1190 1992 525.384426116943359 1e30 -1709850.03 5828677.95 1190 1993 525.475442409515381 1e30 -1709852.16 5828690.26 1190 1994 525.682747364044189 1e30 -1709854.28 5828702.58 1190 1995 525.815844535827637 1e30 -1709856.40 5828714.89 1190 1996 525.739014148712158 1e30 -1709858.53 5828727.21 1190 1997 525.6313681602478 1e30 -1709860.65 5828739.52 1190 1998 525.839328765869141 1e30 -1709862.77 5828751.84 1190 1999 526.21215581893921 1e30 -1709864.89 5828764.15 1190 2000 526.342809200286865 1e30 -1709867.02 5828776.47 1190 2001 527.08578109741211 1e30 -1709869.14 5828788.78 1190 2002 527.909457683563232 1e30 -1709871.26 5828801.10 1190 2003 528.07086706161499 1e30 -1709873.39 5828813.41 1190 2004 528.302252292633057 1e30 -1709875.51 5828825.73 1190 2005 527.5188684463501 1e30 -1709877.63 5828838.05 1190 2006 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1e30 -1709926.46 5829121.30 1190 2029 525.474846363067627 1e30 -1709928.58 5829133.61 1190 2030 525.324225425720215 1e30 -1709930.70 5829145.93 1190 2031 524.969339370727539 1e30 -1709932.83 5829158.24 1190 2032 524.727880954742432 1e30 -1709934.95 5829170.56 1190 2033 524.444222450256348 1e30 -1709937.07 5829182.87 1190 2034 524.374961853027344 1e30 -1709939.19 5829195.19 1190 2035 524.320244789123535 1e30 -1709941.32 5829207.50 1190 2036 524.244189262390137 1e30 -1709943.44 5829219.82 1190 2037 524.31190013885498 1e30 -1709945.56 5829232.13 1190 2038 524.427413940429688 1e30 -1709947.69 5829244.45 1190 2039 524.24162626266479 1e30 -1709949.81 5829256.76 1190 2040 524.049818515777588 1e30 -1709951.93 5829269.08 1190 2041 523.73808622360229 1e30 -1709954.05 5829281.39 1190 2042 523.357152938842773 1e30 -1709956.18 5829293.71 1190 2043 522.880792617797852 1e30 -1709958.30 5829306.02 1190 2044 522.848904132843018 1e30 -1709960.42 5829318.34 1190 2045 523.389935493469238 1e30 -1709962.55 5829330.66 1190 2046 523.384571075439453 1e30 -1709964.67 5829342.97 1190 2047 522.902429103851318 1e30 -1709966.79 5829355.29 1190 2048 522.562503814697266 1e30 -1709973.16 5829392.23 1190 2051 523.863852024078369 1e30 -1709975.28 5829404.55 1190 2052 524.1284966468811 1e30 -1709977.41 5829416.86 1190 2053 524.035632610321045 1e30 -1709979.53 5829429.18 1190 2054 523.523032665252686 1e30 -1709981.65 5829441.49 1190 2055 522.230803966522217 1e30 -1709983.77 5829453.81 1190 2056 521.867692470550537 1e30 -1709985.90 5829466.12 1190 2057 522.01187610626221 1e30 -1709988.02 5829478.44 1190 2058 522.539794445037842 1e30 -1709994.39 5829515.38 1190 2061 521.56829833984375 1e30 -1709996.51 5829527.70 1190 2062 521.382927894592285 1e30 -1709998.63 5829540.01 1190 2063 521.437942981719971 1e30 -1710000.76 5829552.33 1190 2064 522.800624370574951 1e30 -1710002.88 5829564.65 1190 2065 524.210214614868164 1e30 -1710005.00 5829576.96 1190 2066 523.645579814910889 1e30 -1710007.13 5829589.28 1190 2067 523.154258728027344 1e30 -1710009.25 5829601.59 1190 2068 523.225724697113037 1e30 -1710011.37 5829613.91 1190 2069 523.225605487823486 1e30 -1710013.49 5829626.22 1190 2070 523.447871208190918 1e30 -1710015.62 5829638.54 1190 2071 523.998022079467773 1e30 -1710017.74 5829650.85 1190 2072 524.59037303924561 1e30 -1710024.11 5829687.80 1190 2075 523.565411567687988 1e30 -1710026.23 5829700.11 1190 2076 522.898018360137939 1e30 -1710028.36 5829712.43 1190 2077 523.091733455657959 1e30 -1710030.48 5829724.74 1190 2078 523.266911506652832 1e30 -1710032.60 5829737.06 1190 2079 523.0175256729126 1e30 -1710034.72 5829749.37 1190 2080 522.593259811401367 1e30 -1710036.85 5829761.69 1190 2081 521.88766002655029 1e30 -1710038.97 5829774.00 1190 2082 521.381855010986328 1e30 -1709973.63 5828365.00 1197 1970 524.9478816986084 1e30 -1709998.26 5828360.76 1198 1970 524.54108 1e30 -1710001.67 5828233.36 1199 1960 525.07251501083374 1e30 -1710022.89 5828356.51 1199 1970 524.411320686340332 1e30 -1710107.81 5828849.12 1199 2010 524.279952049255371 1e30 -1710005.07 5828105.96 1200 1950 525.76166391372681 1e30 -1710007.19 5828118.28 1200 1951 525.91085433959961 1e30 -1710009.31 5828130.60 1200 1952 526.1390209197998 1e30 -1710011.44 5828142.91 1200 1953 525.976598262786865 1e30 -1710013.56 5828155.23 1200 1954 525.527358055114746 1e30 -1710015.68 5828167.54 1200 1955 525.114715099334717 1e30 -1710017.81 5828179.86 1200 1956 525.191605091094971 1e30 -1710019.93 5828192.17 1200 1957 525.701940059661865 1e30 -1710022.05 5828204.49 1200 1958 525.872468948364258 1e30 -1710024.17 5828216.80 1200 1959 525.4894495010376 1e30 -1710026.30 5828229.12 1200 1960 525.147974491119385 1e30 -1710028.42 5828241.43 1200 1961 525.24071931838989 1e30 -1710030.54 5828253.75 1200 1962 525.575637817382813 1e30 -1710032.67 5828266.06 1200 1963 525.672852993011475 1e30 -1710034.79 5828278.38 1200 1964 525.156497955322266 1e30 -1710036.91 5828290.69 1200 1965 524.83445405960083 1e30 -1710039.03 5828303.01 1200 1966 524.670183658599854 1e30 -1710041.16 5828315.32 1200 1967 524.50078725814819 1e30 -1710043.28 5828327.64 1200 1968 524.466514587402344 1e30 -1710045.40 5828339.95 1200 1969 524.508833885192871 1e30 -1710047.53 5828352.27 1200 1970 524.246573448181152 1e30 -1710049.65 5828364.58 1200 1971 523.787617683410645 1e30 -1710051.77 5828376.90 1200 1972 523.78159761428833 1e30 -1710053.89 5828389.22 1200 1973 524.235963821411133 1e30 -1710056.02 5828401.53 1200 1974 524.60169792175293 1e30 -1710058.14 5828413.85 1200 1975 524.618446826934814 1e30 -1710060.26 5828426.16 1200 1976 524.363279342651367 1e30 -1710062.39 5828438.48 1200 1977 523.948192596435547 1e30 -1710064.51 5828450.79 1200 1978 523.741781711578369 1e30 -1710066.63 5828463.11 1200 1979 523.69958162307739 1e30 -1710068.75 5828475.42 1200 1980 522.947072982788086 1e30 -1710070.88 5828487.74 1200 1981 521.7888355255127 1e30 -1710073.00 5828500.05 1200 1982 522.6016640663147 1e30 -1710075.12 5828512.37 1200 1983 523.576855659484863 1e30 -1710077.25 5828524.68 1200 1984 523.923039436340332 1e30 -1710079.37 5828537.00 1200 1985 523.83267879486084 1e30 -1710081.49 5828549.31 1200 1986 523.593604564666748 1e30 -1710083.61 5828561.63 1200 1987 523.485183715820313 1e30 -1710085.74 5828573.94 1200 1988 523.665010929107666 1e30 -1710087.86 5828586.26 1200 1989 523.77176284790039 1e30 -1710089.98 5828598.57 1200 1990 523.504137992858887 1e30 -1710092.11 5828610.89 1200 1991 523.104429244995117 1e30 -1710094.23 5828623.21 1200 1992 522.864580154418945 1e30 -1710096.35 5828635.52 1200 1993 522.105276584625244 1e30 -1710098.47 5828647.84 1200 1994 521.38829231262207 1e30 -1710100.60 5828660.15 1200 1995 521.637022495269775 1e30 -1710102.72 5828672.47 1200 1996 522.103011608123779 1e30 -1710104.84 5828684.78 1200 1997 522.283792495727539 1e30 -1710106.97 5828697.10 1200 1998 522.523224353790283 1e30 -1710109.09 5828709.41 1200 1999 522.621750831604 1e30 -1710111.21 5828721.73 1200 2000 522.767126560211182 1e30 -1710113.33 5828734.04 1200 2001 523.192286491394043 1e30 -1710115.46 5828746.36 1200 2002 523.82141351699829 1e30 -1710117.58 5828758.67 1200 2003 524.273455142974854 1e30 -1710119.70 5828770.99 1200 2004 524.087846279144287 1e30 -1710121.83 5828783.30 1200 2005 523.941397666931152 1e30 -1710123.95 5828795.62 1200 2006 523.794949054718018 1e30 -1710126.07 5828807.93 1200 2007 523.513615131378174 1e30 -1710128.19 5828820.25 1200 2008 523.367404937744141 1e30 -1710130.32 5828832.56 1200 2009 523.508369922637939 1e30 -1710132.44 5828844.88 1200 2010 523.364901542663574 1e30 -1710134.56 5828857.19 1200 2011 522.793173789978027 1e30 -1710136.69 5828869.51 1200 2012 521.866500377655029 1e30 -1710138.81 5828881.83 1200 2013 521.61175012588501 1e30 -1710140.93 5828894.14 1200 2014 521.555423736572266 1e30 -1710143.05 5828906.46 1200 2015 521.918773651123047 1e30 -1710145.18 5828918.77 1200 2016 522.416472434997559 1e30 -1710147.30 5828931.09 1200 2017 522.714495658874512 1e30 -1710149.42 5828943.40 1200 2018 522.185444831848145 1e30 -1710151.55 5828955.72 1200 2019 521.89791202545166 1e30 -1710153.67 5828968.03 1200 2020 521.904468536376953 1e30 -1710155.79 5828980.35 1200 2021 521.35986089706421 1e30 -1710157.92 5828992.66 1200 2022 520.74515819549561 1e30 -1710160.04 5829004.98 1200 2023 520.466268062591553 1e30 -1710162.16 5829017.29 1200 2024 520.380735397338867 1e30 -1710164.28 5829029.61 1200 2025 520.247817039489746 1e30 -1710166.41 5829041.92 1200 2026 520.068347454071045 1e30 -1710168.53 5829054.24 1200 2027 520.116031169891357 1e30 -1710170.65 5829066.55 1200 2028 520.391225814819336 1e30 -1710172.78 5829078.87 1200 2029 520.65384387969971 1e30 -1710174.90 5829091.18 1200 2030 520.776033401489258 1e30 -1710177.02 5829103.50 1200 2031 520.523488521575928 1e30 -1710179.14 5829115.82 1200 2032 520.240724086761475 1e30 -1710181.27 5829128.13 1200 2033 519.986987113952637 1e30 -1710183.39 5829140.45 1200 2034 519.759595394134521 1e30 -1710185.51 5829152.76 1200 2035 519.7104811668396 1e30 -1710187.64 5829165.08 1200 2036 519.691884517669678 1e30 -1710189.76 5829177.39 1200 2037 519.591689109802246 1e30 -1710191.88 5829189.71 1200 2038 519.502222537994385 1e30 -1710194.00 5829202.02 1200 2039 519.355 1e30 -1710196.13 5829214.34 1200 2040 519.184350967407227 1e30 -1710198.25 5829226.65 1200 2041 518.977642059326172 1e30 -1710200.37 5829238.97 1200 2042 518.7375545501709 1e30 -1710202.50 5829251.28 1200 2043 518.97042989730835 1e30 -1710204.62 5829263.60 1200 2044 519.317686557769775 1e30 -1710206.74 5829275.91 1200 2045 519.557535648345947 1e30 -1710208.86 5829288.23 1200 2046 519.77771520614624 1e30 -1710210.99 5829300.54 1200 2047 519.8742151260376 1e30 -1710213.11 5829312.86 1200 2048 519.824981689453125 1e30 -1710215.23 5829325.17 1200 2049 519.8369026184082 1e30 -1710217.36 5829337.49 1200 2050 520 1e30 -1710219.48 5829349.80 1200 2051 520.2106237411499 1e30 -1710221.60 5829362.12 1200 2052 520.254611968994141 1e30 -1710223.72 5829374.44 1200 2053 519.950568675994873 1e30 -1710225.85 5829386.75 1200 2054 519.742310047149658 1e30 -1710227.97 5829399.07 1200 2055 519.901633262634277 1e30 -1710230.09 5829411.38 1200 2056 520.22856473922729 1e30 -1710232.22 5829423.70 1200 2057 520.234167575836182 1e30 -1710234.34 5829436.01 1200 2058 520.0539231300354 1e30 -1710236.46 5829448.33 1200 2059 519.946932792663574 1e30 -1710238.58 5829460.64 1200 2060 519.863903522491455 1e30 -1710240.71 5829472.96 1200 2061 519.95557546615601 1e30 -1710242.83 5829485.27 1200 2062 520.137131214141846 1e30 -1710244.95 5829497.59 1200 2063 520.119428634643555 1e30 -1710247.08 5829509.90 1200 2064 519.946932792663574 1e30 -1710249.20 5829522.22 1200 2065 519.85627412796021 1e30 -1710251.32 5829534.53 1200 2066 519.571006298065186 1e30 -1710253.44 5829546.85 1200 2067 519.075393676757813 1e30 -1710255.57 5829559.16 1200 2068 518.5692310333252 1e30 -1710257.69 5829571.48 1200 2069 518.88662576675415 1e30 -1710259.81 5829583.79 1200 2070 518.965601921081543 1e30 -1710261.94 5829596.11 1200 2071 519.052326679229736 1e30 -1710264.06 5829608.43 1200 2072 519.433081150054932 1e30 -1710266.18 5829620.74 1200 2073 519.613444805145264 1e30 -1710268.30 5829633.06 1200 2074 519.492685794830322 1e30 -1710270.43 5829645.37 1200 2075 519.355297088623047 1e30 -1710272.55 5829657.69 1200 2076 519.159317016601563 1e30 -1710274.67 5829670.00 1200 2077 518.9329981803894 1e30 -1710276.80 5829682.32 1200 2078 518.937826156616211 1e30 -1710278.92 5829694.63 1200 2079 519.001901149749756 1e30 -1710281.04 5829706.95 1200 2080 518.96035671234131 1e30 -1710283.16 5829719.26 1200 2081 518.629491329193115 1e30 -1710285.29 5829731.58 1200 2082 518.014669418334961 1e30 -1710287.41 5829743.89 1200 2083 517.555177211761475 1e30 -1710029.70 5828101.72 1201 1950 525.292694568634033 1e30 -1710050.93 5828224.87 1201 1960 524.874329566955566 1e30 -1710072.16 5828348.03 1201 1970 523.80663156509399 1e30 -1710093.39 5828471.18 1201 1980 521.693587303161621 1e30 -1710114.61 5828594.33 1201 1990 522.980332374572754 1e30 -1710157.07 5828840.64 1201 2010 522.184193134307861 1e30 -1710178.30 5828963.79 1201 2020 520.993530750274658 1e30 -1710199.53 5829086.94 1201 2030 520.782351493835449 1e30 -1710220.76 5829210.09 1201 2040 519.041359424591064 1e30 -1710263.22 5829456.40 1201 2060 519.442558288574219 1e30 -1710054.33 5828097.48 1202 1950 524.509429931640625 1e30 -1710075.56 5828220.63 1202 1960 524.24740791320801 1e30 -1710096.79 5828343.78 1202 1970 523.493587970733643 1e30 -1710118.02 5828466.94 1202 1980 521.043717861175537 1e30 -1710139.25 5828590.09 1202 1990 521.9387412071228 1e30 -1710160.48 5828713.24 1202 2000 521.176338195800781 1e30 -1710181.70 5828836.39 1202 2010 521.00890874862671 1e30 -1710202.93 5828959.55 1202 2020 520.838022232055664 1e30 -1710224.16 5829082.70 1202 2030 520.389199256896973 1e30 -1710245.39 5829205.85 1202 2040 519.036352634429932 1e30 -1710266.62 5829329.00 1202 2050 519.505023956298828 1e30 -1710287.85 5829452.16 1202 2060 518.81116628646851 1e30 -1710309.08 5829575.31 1202 2070 518.751442432403564 1e30 -1710078.96 5828093.24 1203 1950 524.146497249603271 1e30 -1710100.19 5828216.39 1203 1960 523.730456829071045 1e30 -1710121.42 5828339.54 1203 1970 522.972583770751953 1e30 -1710142.65 5828462.69 1203 1980 521.13032341003418 1e30 -1710163.88 5828585.85 1203 1990 522.041141986846924 1e30 -1710185.11 5828709.00 1203 2000 520.760953426361084 1e30 -1710206.34 5828832.15 1203 2010 520.799458026885986 1e30 -1710227.56 5828955.30 1203 2020 520.608782768249512 1e30 -1710248.79 5829078.46 1203 2030 519.912779331207275 1e30 -1710270.02 5829201.61 1203 2040 518.262505531311035 1e30 -1710291.25 5829324.76 1203 2050 519.045770168304443 1e30 -1710312.48 5829447.91 1203 2060 518.538773059844971 1e30 -1710333.71 5829571.07 1203 2070 518.613576889038086 1e30 -1710103.60 5828088.99 1204 1950 523.753225803375244 1e30 -1710124.82 5828212.15 1204 1960 523.334860801696777 1e30 -1710146.05 5828335.30 1204 1970 522.640347480773926 1e30 -1710167.28 5828458.45 1204 1980 520.79850435256958 1e30 -1710188.51 5828581.60 1204 1990 521.645069122314453 1e30 -1710209.74 5828704.76 1204 2000 520.343303680419922 1e30 -1710230.97 5828827.91 1204 2010 520.522773265838623 1e30 -1710252.20 5828951.06 1204 2020 520.246267318725586 1e30 -1710273.43 5829074.21 1204 2030 519.398689270019531 1e30 -1710294.65 5829197.37 1204 2040 517.059266567230225 1e30 -1710315.88 5829320.52 1204 2050 518.3262825012207 1e30 -1710337.11 5829443.67 1204 2060 518.444359302520752 1e30 -1710358.34 5829566.82 1204 2070 518.327176570892334 1e30 -1710128.23 5828084.75 1205 1950 522.645115852355957 1e30 -1710149.46 5828207.90 1205 1960 522.7210521697998 1e30 -1710170.68 5828331.06 1205 1970 522.277474403381348 1e30 -1710191.91 5828454.21 1205 1980 520.21491527557373 1e30 -1710213.14 5828577.36 1205 1990 520.632147789001465 1e30 -1710234.37 5828700.51 1205 2000 519.92893218994141 1e30 -1710255.60 5828823.67 1205 2010 520.462155342102051 1e30 -1710276.83 5828946.82 1205 2020 520.5613374710083 1e30 -1710298.06 5829069.97 1205 2030 519.238293170928955 1e30 -1710319.29 5829193.12 1205 2040 516.865909099578857 1e30 -1710340.51 5829316.28 1205 2050 518.395423889160156 1e30 -1710361.74 5829439.43 1205 2060 517.951428890228271 1e30 -1710382.97 5829562.58 1205 2070 518.251895904541016 1e30 -1710152.86 5828080.51 1206 1950 522.573113441467285 1e30 -1710174.09 5828203.66 1206 1960 521.65299654006958 1e30 -1710195.32 5828326.81 1206 1970 521.431863307952881 1e30 -1710216.55 5828449.97 1206 1980 519.773125648498535 1e30 -1710237.77 5828573.12 1206 1990 520.601153373718262 1e30 -1710259.00 5828696.27 1206 2000 519.643723964691162 1e30 -1710280.23 5828819.42 1206 2010 520.704388618469238 1e30 -1710301.46 5828942.58 1206 2020 520.353853702545166 1e30 -1710322.69 5829065.73 1206 2030 519.11783218383789 1e30 -1710343.92 5829188.88 1206 2040 517.037689685821533 1e30 -1710365.15 5829312.03 1206 2050 518.079161643981934 1e30 -1710386.38 5829435.19 1206 2060 517.129421234130859 1e30 -1710407.60 5829558.34 1206 2070 518.33087205886841 1e30 -1710177.49 5828076.27 1207 1950 521.928489208221436 1e30 -1710198.72 5828199.42 1207 1960 520.7982063293457 1e30 -1710219.95 5828322.57 1207 1970 520.659148693084717 1e30 -1710241.18 5828445.72 1207 1980 519.677340984344482 1e30 -1710262.41 5828568.88 1207 1990 520.472586154937744 1e30 -1710283.63 5828692.03 1207 2000 519.436180591583252 1e30 -1710326.09 5828938.33 1207 2020 520.148217678070068 1e30 -1710347.32 5829061.49 1207 2030 518.900632858276367 1e30 -1710368.55 5829184.64 1207 2040 516.77399873733521 1e30 -1710389.78 5829307.79 1207 2050 516.9 1e30 -1710432.24 5829554.10 1207 2070 517.433643341064453 1e30 -1710202.12 5828072.02 1208 1950 521.119892597198486 1e30 -1710223.35 5828195.18 1208 1960 520.37554979324341 1e30 -1710244.58 5828318.33 1208 1970 520.316123962402344 1e30 -1710265.81 5828441.48 1208 1980 519.170880317687988 1e30 -1710287.04 5828564.63 1208 1990 519.929647445678711 1e30 -1710308.27 5828687.79 1208 2000 519.034862518310547 1e30 -1710350.72 5828934.09 1208 2020 519.985675811767578 1e30 -1710371.95 5829057.24 1208 2030 518.533766269683838 1e30 -1710393.18 5829180.40 1208 2040 516.254723072052 1e30 -1710414.41 5829303.55 1208 2050 516.41160249710083 1e30 -1710435.64 5829426.70 1208 2060 516.062378883361816 1e30 -1710456.87 5829549.85 1208 2070 516.144394874572754 1e30 -1710226.75 5828067.78 1209 1950 520.46501636505127 1e30 -1710247.98 5828190.93 1209 1960 520.28125524520874 1e30 -1710269.21 5828314.09 1209 1970 520.115971565246582 1e30 -1710290.44 5828437.24 1209 1980 519.056975841522217 1e30 -1710311.67 5828560.39 1209 1990 519.235372543334961 1e30 -1710332.90 5828683.54 1209 2000 518.651843070983887 1e30 -1710354.13 5828806.70 1209 2010 519.655585289001465 1e30 -1710375.36 5828929.85 1209 2020 519.837319850921631 1e30 -1710396.58 5829053.00 1209 2030 518.186092376708984 1e30 -1710417.81 5829176.15 1209 2040 516.142904758453369 1e30 -1710439.04 5829299.31 1209 2050 515.663444995880127 1e30 -1710460.27 5829422.46 1209 2060 516.031026840209961 1e30 -1710481.50 5829545.61 1209 2070 514.854013919830322 1e30 -1710247.14 5828038.91 1210 1948 520.357489585876465 1e30 -1710249.26 5828051.22 1210 1949 520.39206027984619 1e30 -1710251.39 5828063.54 1210 1950 520.210921764373779 1e30 -1710253.51 5828075.85 1210 1951 520.105063915252686 1e30 -1710255.63 5828088.17 1210 1952 520.188391208648682 1e30 -1710257.75 5828100.48 1210 1953 520.212769508361816 1e30 -1710259.88 5828112.80 1210 1954 520.185649394989014 1e30 -1710262.00 5828125.11 1210 1955 520.224571228027344 1e30 -1710264.12 5828137.43 1210 1956 520.177781581878662 1e30 -1710266.25 5828149.74 1210 1957 520.087778568267822 1e30 -1710270.49 5828174.38 1210 1959 520.33704519271851 1e30 -1710272.61 5828186.69 1210 1960 520.207822322845459 1e30 -1710274.74 5828199.01 1210 1961 519.934594631195068 1e30 -1710276.86 5828211.32 1210 1962 519.785046577453613 1e30 -1710278.98 5828223.64 1210 1963 519.793927669525146 1e30 -1710281.11 5828235.95 1210 1964 519.719123840332031 1e30 -1710283.23 5828248.27 1210 1965 519.79571580886841 1e30 -1710285.35 5828260.58 1210 1966 519.886195659637451 1e30 -1710287.47 5828272.90 1210 1967 519.903004169464111 1e30 -1710289.60 5828285.21 1210 1968 519.87767219543457 1e30 -1710291.72 5828297.53 1210 1969 519.898056983947754 1e30 -1710293.84 5828309.84 1210 1970 519.988894462585449 1e30 -1710295.97 5828322.16 1210 1971 519.934117794036865 1e30 -1710298.09 5828334.47 1210 1972 519.782721996307373 1e30 -1710300.21 5828346.79 1210 1973 519.518136978149414 1e30 -1710302.34 5828359.10 1210 1974 519.195914268493652 1e30 -1710304.46 5828371.42 1210 1975 519.004583358764648 1e30 -1710306.58 5828383.73 1210 1976 519.117474555969238 1e30 -1710308.70 5828396.05 1210 1977 519.358456134796143 1e30 -1710315.07 5828433.00 1210 1980 519.1044807434082 1e30 -1710317.20 5828445.31 1210 1981 519.09142732620239 1e30 -1710319.32 5828457.63 1210 1982 519.1349983215332 1e30 -1710321.44 5828469.94 1210 1983 519.236862659454346 1e30 -1710323.56 5828482.26 1210 1984 519.336700439453125 1e30 -1710325.69 5828494.57 1210 1985 519.29706335067749 1e30 -1710327.81 5828506.89 1210 1986 519.227921962738037 1e30 -1710329.93 5828519.20 1210 1987 519.16581392288208 1e30 -1710332.06 5828531.52 1210 1988 519.02925968170166 1e30 -1710334.18 5828543.83 1210 1989 518.85116100311279 1e30 -1710336.30 5828556.15 1210 1990 518.76068115234375 1e30 -1710338.42 5828568.46 1210 1991 518.8523530960083 1e30 -1710340.55 5828580.78 1210 1992 518.735170364379883 1e30 -1710342.67 5828593.09 1210 1993 518.472373485565186 1e30 -1710344.79 5828605.41 1210 1994 518.368780612945557 1e30 -1710346.92 5828617.72 1210 1995 518.732249736785889 1e30 -1710349.04 5828630.04 1210 1996 519.141256809234619 1e30 -1710355.41 5828666.99 1210 1999 518.636822700500488 1e30 -1710357.53 5828679.30 1210 2000 518.685340881347656 1e30 -1710359.65 5828691.62 1210 2001 518.854260444641113 1e30 -1710361.78 5828703.93 1210 2002 519.097268581390381 1e30 -1710363.90 5828716.25 1210 2003 519.432544708251953 1e30 -1710366.02 5828728.56 1210 2004 519.647955894470215 1e30 -1710368.14 5828740.88 1210 2005 519.740700721740723 1e30 -1710370.27 5828753.19 1210 2006 519.809603691101074 1e30 -1710372.39 5828765.51 1210 2007 519.7184681892395 1e30 -1710374.51 5828777.82 1210 2008 519.479036331176758 1e30 -1710376.64 5828790.14 1210 2009 519.352138042449951 1e30 -1710378.76 5828802.45 1210 2010 519.399702548980713 1e30 -1710380.88 5828814.77 1210 2011 519.5125937461853 1e30 -1710383.00 5828827.08 1210 2012 519.507646560668945 1e30 -1710385.13 5828839.40 1210 2013 519.6232795715332 1e30 -1710387.25 5828851.71 1210 2014 519.647181034088135 1e30 -1710389.37 5828864.03 1210 2015 519.395232200622559 1e30 -1710391.50 5828876.34 1210 2016 518.998980522155762 1e30 -1710393.62 5828888.66 1210 2017 518.731951713562012 1e30 -1710399.99 5828925.61 1210 2020 519.884169101715088 1e30 -1710406.36 5828962.55 1210 2023 519.538342952728271 1e30 -1710408.48 5828974.87 1210 2024 519.404411315917969 1e30 -1710410.60 5828987.18 1210 2025 519.075632095336914 1e30 -1710421.22 5829048.76 1210 2030 518.10950040817261 1e30 -1710423.34 5829061.07 1210 2031 517.369985580444336 1e30 -1710425.46 5829073.39 1210 2032 516.473591327667236 1e30 -1710427.58 5829085.70 1210 2033 515.91587066650391 1e30 -1710429.71 5829098.02 1210 2034 515.844345092773438 1e30 -1710431.83 5829110.33 1210 2035 516.067385673522949 1e30 -1710436.08 5829134.96 1210 2037 516.247153282165527 1e30 -1710438.20 5829147.28 1210 2038 516.185641288757324 1e30 -1710440.32 5829159.60 1210 2039 516.031503677368164 1e30 -1710442.44 5829171.91 1210 2040 515.73556661605835 1e30 -1710444.57 5829184.23 1210 2041 515.381932258605957 1e30 -1710446.69 5829196.54 1210 2042 515.047907829284668 1e30 -1710448.81 5829208.86 1210 2043 515.125572681427 1e30 -1710450.94 5829221.17 1210 2044 515.432298183441162 1e30 -1710453.06 5829233.49 1210 2045 515.384137630462646 1e30 -1710455.18 5829245.80 1210 2046 515.3469443321228 1e30 -1710457.30 5829258.12 1210 2047 515.232324600219727 1e30 -1710459.43 5829270.43 1210 2048 515.039682388305664 1e30 -1710461.55 5829282.75 1210 2049 515.08629322052002 1e30 -1710463.67 5829295.06 1210 2050 515.36440849304199 1e30 -1710465.80 5829307.38 1210 2051 515.757322311401367 1e30 -1710467.92 5829319.69 1210 2052 515.843033790588379 1e30 -1710470.04 5829332.01 1210 2053 515.5523419380188 1e30 -1710472.16 5829344.32 1210 2054 515.44332504272461 1e30 -1710474.29 5829356.64 1210 2055 515.75189828872681 1e30 -1710476.41 5829368.95 1210 2056 516.2891149520874 1e30 -1710478.53 5829381.27 1210 2057 516.463398933410645 1e30 -1710480.66 5829393.58 1210 2058 516.472160816192627 1e30 -1710482.78 5829405.90 1210 2059 516.493916511535645 1e30 -1710484.90 5829418.22 1210 2060 516.05832576751709 1e30 -1710487.03 5829430.53 1210 2061 515.672624111175537 1e30 -1710489.15 5829442.85 1210 2062 515.08021354675293 1e30 -1710493.39 5829467.48 1210 2064 514.973819255828857 1e30 -1710495.52 5829479.79 1210 2065 514.982819557189941 1e30 -1710497.64 5829492.11 1210 2066 514.821052551269531 1e30 -1710499.76 5829504.42 1210 2067 515.0071382522583 1e30 -1710501.89 5829516.74 1210 2068 515.073955059051514 1e30 -1710504.01 5829529.05 1210 2069 514.654159545898438 1e30 -1710506.13 5829541.37 1210 2070 514.32490348815918 1e30 -1710508.25 5829553.68 1210 2071 514.26839828491211 1e30 -1710510.38 5829566.00 1210 2072 514.466464519500732 1e30 -1710512.50 5829578.31 1210 2073 514.485061168670654 1e30 -1710514.62 5829590.63 1210 2074 514.370441436767578 1e30 -1710516.75 5829602.94 1210 2075 514.287710189819336 1e30 -1710518.87 5829615.26 1210 2076 514.221787452697754 1e30 -1710520.99 5829627.57 1210 2077 514.425039291381836 1e30 -1710523.11 5829639.89 1210 2078 514.62864875793457 1e30 -1710525.24 5829652.21 1210 2079 514.6712064743042 1e30 -1710527.36 5829664.52 1210 2080 514.5530104637146 1e30 -1710529.48 5829676.84 1210 2081 514.513194561004639 1e30 -1710531.61 5829689.15 1210 2082 514.4612193107605 1e30 -1710276.02 5828059.30 1211 1950 520.23017406463623 1e30 -1710297.25 5828182.45 1211 1960 520.160436630249023 1e30 -1710318.48 5828305.60 1211 1970 519.68991756439209 1e30 -1710339.70 5828428.75 1211 1980 519.187688827514648 1e30 -1710360.93 5828551.91 1211 1990 518.683016300201416 1e30 -1710382.16 5828675.06 1211 2000 518.70584487915039 1e30 -1710403.39 5828798.21 1211 2010 519.23239231109619 1e30 -1710424.62 5828921.36 1211 2020 519.071221351623535 1e30 -1710445.85 5829044.52 1211 2030 517.111718654632568 1e30 -1710467.08 5829167.67 1211 2040 515.36703109741211 1e30 -1710488.31 5829290.82 1211 2050 515.38181304931641 1e30 -1710509.53 5829413.97 1211 2060 515.184760093688965 1e30 -1710530.76 5829537.13 1211 2070 514.1913890838623 1e30 -1710300.65 5828055.05 1212 1950 520.099818706512451 1e30 -1710321.88 5828178.21 1212 1960 519.750654697418213 1e30 -1710343.11 5828301.36 1212 1970 519.137978553771973 1e30 -1710364.34 5828424.51 1212 1980 518.842399120330811 1e30 -1710385.56 5828547.66 1212 1990 518.426239490509033 1e30 -1710406.79 5828670.82 1212 2000 518.476784229278564 1e30 -1710428.02 5828793.97 1212 2010 519.08409595489502 1e30 -1710449.25 5828917.12 1212 2020 518.233895301818848 1e30 -1710470.48 5829040.27 1212 2030 516.671180725097656 1e30 -1710491.71 5829163.42 1212 2040 515.084981918334961 1e30 -1710512.94 5829286.58 1212 2050 515.24055004119873 1e30 -1710534.17 5829409.73 1212 2060 514.6973729133606 1e30 -1710555.39 5829532.88 1212 2070 513.524830341339111 1e30 -1710325.28 5828050.81 1213 1950 519.892096519470215 1e30 -1710346.51 5828173.96 1213 1960 519.286751747131348 1e30 -1710367.74 5828297.11 1213 1970 518.576979637146 1e30 -1710388.97 5828420.27 1213 1980 517.867684364318848 1e30 -1710410.20 5828543.42 1213 1990 518.1739330291748 1e30 -1710431.43 5828666.57 1213 2000 518.04959774017334 1e30 -1710452.65 5828789.72 1213 2010 518.644273281097412 1e30 -1710473.88 5828912.88 1213 2020 517.52579212188721 1e30 -1710495.11 5829036.03 1213 2030 516.116321086883545 1e30 -1710516.34 5829159.18 1213 2040 514.455437660217285 1e30 -1710537.57 5829282.33 1213 2050 514.95629549026489 1e30 -1710558.80 5829405.49 1213 2060 514.195084571838379 1e30 -1710580.03 5829528.64 1213 2070 513.270676136016846 1e30 -1710349.91 5828046.57 1214 1950 519.5649266242981 1e30 -1710371.14 5828169.72 1214 1960 518.778681755065918 1e30 -1710392.37 5828292.87 1214 1970 517.254173755645752 1e30 -1710413.60 5828416.02 1214 1980 517.007768154144287 1e30 -1710434.83 5828539.18 1214 1990 517.561197280883789 1e30 -1710456.06 5828662.33 1214 2000 517.48365163803101 1e30 -1710477.29 5828785.48 1214 2010 517.359316349029541 1e30 -1710498.51 5828908.63 1214 2020 515.664160251617432 1e30 -1710519.74 5829031.79 1214 2030 514.927864074707031 1e30 -1710540.97 5829154.94 1214 2040 513.939142227172852 1e30 -1710562.20 5829278.09 1214 2050 514.619886875152588 1e30 -1710583.43 5829401.24 1214 2060 513.037264347076416 1e30 -1710604.66 5829524.40 1214 2070 512.679398059844971 1e30 -1710374.55 5828042.32 1215 1950 518.882930278778076 1e30 -1710395.77 5828165.48 1215 1960 518.088400363922119 1e30 -1710417.00 5828288.63 1215 1970 516.685962677001953 1e30 -1710438.23 5828411.78 1215 1980 517.103135585784912 1e30 -1710459.46 5828534.93 1215 1990 517.717242240905762 1e30 -1710480.69 5828658.09 1215 2000 517.060399055480957 1e30 -1710501.92 5828781.24 1215 2010 516.411840915679932 1e30 -1710523.15 5828904.39 1215 2020 514.5341157913208 1e30 -1710544.37 5829027.54 1215 2030 513.905704021453857 1e30 -1710565.60 5829150.70 1215 2040 513.812243938446045 1e30 -1710586.83 5829273.85 1215 2050 513.611137866973877 1e30 -1710608.06 5829397.00 1215 2060 512.8 1e30 -1710629.29 5829520.15 1215 2070 511.93118095397949 1e30 -1710399.18 5828038.08 1216 1950 518.424808979034424 1e30 -1710420.41 5828161.23 1216 1960 517.824649810791016 1e30 -1710441.63 5828284.39 1216 1970 517.19290018081665 1e30 -1710462.86 5828407.54 1216 1980 516.5 1e30 -1710484.09 5828530.69 1216 1990 518.153905868530273 1e30 -1710505.32 5828653.84 1216 2000 517.794549465179443 1e30 -1710526.55 5828777.00 1216 2010 516.630411148071289 1e30 -1710547.78 5828900.15 1216 2020 514.376819133758545 1e30 -1710569.01 5829023.30 1216 2030 513.826251029968262 1e30 -1710590.24 5829146.45 1216 2040 513.616263866424561 1e30 -1710611.46 5829269.61 1216 2050 513.6793851852417 1e30 -1710632.69 5829392.76 1216 2060 512.859344482421875 1e30 -1710653.92 5829515.91 1216 2070 511.62236928939819 1e30 -1710423.81 5828033.84 1217 1950 517.30799674987793 1e30 -1710445.04 5828156.99 1217 1960 516.958534717559814 1e30 -1710466.27 5828280.14 1217 1970 516.813158988952637 1e30 -1710487.49 5828403.30 1217 1980 516.114175319671631 1e30 -1710508.72 5828526.45 1217 1990 518.045306205749512 1e30 -1710529.95 5828649.60 1217 2000 517.030775547027588 1e30 -1710551.18 5828772.75 1217 2010 516.507148742675781 1e30 -1710572.41 5828895.91 1217 2020 514.549911022186279 1e30 -1710593.64 5829019.06 1217 2030 514.3621563911438 1e30 -1710614.87 5829142.21 1217 2040 512.876510620117188 1e30 -1710636.10 5829265.36 1217 2050 513.661503791809082 1e30 -1710657.32 5829388.52 1217 2060 512.87311315536499 1e30 -1710678.55 5829511.67 1217 2070 511.690795421600342 1e30 -1710448.44 5828029.60 1218 1950 516.525089740753174 1e30 -1710469.67 5828152.75 1218 1960 515.420019626617432 1e30 -1710490.90 5828275.90 1218 1970 515.847444534301758 1e30 -1710512.13 5828399.05 1218 1980 515.910148620605469 1e30 -1710533.36 5828522.21 1218 1990 516.9404149055481 1e30 -1710554.58 5828645.36 1218 2000 515.9873366355896 1e30 -1710575.81 5828768.51 1218 2010 515.63155651092529 1e30 -1710597.04 5828891.66 1218 2020 514.260530471801758 1e30 -1710618.27 5829014.82 1218 2030 513.948142528533936 1e30 -1710639.50 5829137.97 1218 2040 512.1537446975708 1e30 -1710660.73 5829261.12 1218 2050 513.13126087188721 1e30 -1710681.96 5829384.27 1218 2060 512.376844882965088 1e30 -1710703.19 5829507.43 1218 2070 511.022388935089111 1e30 -1710473.07 5828025.35 1219 1950 516.093134880065918 1e30 -1710494.30 5828148.51 1219 1960 514.638721942901611 1e30 -1710515.53 5828271.66 1219 1970 515.287995338439941 1e30 -1710536.76 5828394.81 1219 1980 515.340685844421387 1e30 -1710557.99 5828517.96 1219 1990 515.97219705581665 1e30 -1710579.22 5828641.12 1219 2000 515.16067981719971 1e30 -1710600.44 5828764.27 1219 2010 514.320313930511475 1e30 -1710621.67 5828887.42 1219 2020 513.917982578277588 1e30 -1710642.90 5829010.57 1219 2030 512.971639633178711 1e30 -1710664.13 5829133.73 1219 2040 512.125551700592041 1e30 -1710685.36 5829256.88 1219 2050 512.54647970199585 1e30 -1710706.59 5829380.03 1219 2060 511.9854211807251 1e30 -1710727.82 5829503.18 1219 2070 510.939478874206543 1e30 -1710497.70 5828021.11 1220 1950 515.847742557525635 1e30 -1710501.95 5828045.74 1220 1952 515.7623291015625 1e30 -1710504.07 5828058.06 1220 1953 515.83021879196167 1e30 -1710506.20 5828070.37 1220 1954 515.932679176330566 1e30 -1710508.32 5828082.69 1220 1955 515.706717967987061 1e30 -1710510.44 5828095.00 1220 1956 515.205204486846924 1e30 -1710512.56 5828107.32 1220 1957 515.054225921630859 1e30 -1710518.93 5828144.26 1220 1960 514.8579478263855 1e30 -1710521.06 5828156.58 1220 1961 514.763414859771729 1e30 -1710523.18 5828168.89 1220 1962 514.88602161407471 1e30 -1710525.30 5828181.21 1220 1963 515.347123146057129 1e30 -1710531.67 5828218.16 1220 1966 515.131235122680664 1e30 -1710533.79 5828230.47 1220 1967 514.613747596740723 1e30 -1710535.92 5828242.79 1220 1968 514.767765998840332 1e30 -1710538.04 5828255.10 1220 1969 515.1907205581665 1e30 -1710540.16 5828267.42 1220 1970 515.099763870239258 1e30 -1710542.28 5828279.73 1220 1971 514.4275426864624 1e30 -1710544.41 5828292.05 1220 1972 514.174520969390869 1e30 -1710546.53 5828304.36 1220 1973 514.791607856750488 1e30 -1710548.65 5828316.68 1220 1974 515.489637851715088 1e30 -1710550.78 5828328.99 1220 1975 515.415668487548828 1e30 -1710552.90 5828341.31 1220 1976 514.938533306121826 1e30 -1710555.02 5828353.62 1220 1977 514.517247676849365 1e30 -1710557.14 5828365.94 1220 1978 514.56761360168457 1e30 -1710559.27 5828378.25 1220 1979 514.657497406005859 1e30 -1710561.39 5828390.57 1220 1980 514.560580253601074 1e30 -1710563.51 5828402.88 1220 1981 514.818906784057617 1e30 -1710565.64 5828415.20 1220 1982 514.8579478263855 1e30 -1710567.76 5828427.51 1220 1983 514.61637020111084 1e30 -1710569.88 5828439.83 1220 1984 514.759659767150879 1e30 -1710572.00 5828452.14 1220 1985 514.86110687255859 1e30 -1710574.13 5828464.46 1220 1986 514.665186405181885 1e30 -1710576.25 5828476.78 1220 1987 514.552891254425049 1e30 -1710578.37 5828489.09 1220 1988 514.606714248657227 1e30 -1710580.50 5828501.41 1220 1989 514.908373355865479 1e30 -1710582.62 5828513.72 1220 1990 515.189886093139648 1e30 -1710584.74 5828526.04 1220 1991 515.281081199646 1e30 -1710586.86 5828538.35 1220 1992 515.358686447143555 1e30 -1710588.99 5828550.67 1220 1993 515.49077033996582 1e30 -1710591.11 5828562.98 1220 1994 515.21784067153931 1e30 -1710593.23 5828575.30 1220 1995 514.73343372344971 1e30 -1710595.36 5828587.61 1220 1996 514.338970184326172 1e30 -1710597.48 5828599.93 1220 1997 514.24860954284668 1e30 -1710599.60 5828612.24 1220 1998 514.442384243011475 1e30 -1710601.72 5828624.56 1220 1999 514.707624912261963 1e30 -1710603.85 5828636.87 1220 2000 514.613866806030273 1e30 -1710605.97 5828649.19 1220 2001 514.310121536254883 1e30 -1710608.09 5828661.50 1220 2002 514.48744535446167 1e30 -1710610.22 5828673.82 1220 2003 514.31131362915039 1e30 -1710612.34 5828686.13 1220 2004 513.933241367340088 1e30 -1710614.46 5828698.45 1220 2005 513.896763324737549 1e30 -1710616.59 5828710.77 1220 2006 513.981342315673828 1e30 -1710618.71 5828723.08 1220 2007 513.811945915222168 1e30 -1710620.83 5828735.40 1220 2008 513.796091079711914 1e30 -1710622.95 5828747.71 1220 2009 513.439655303955078 1e30 -1710625.08 5828760.03 1220 2010 513.1528377532959 1e30 -1710627.20 5828772.34 1220 2011 513.152897357940674 1e30 -1710629.32 5828784.66 1220 2012 513.404428958892822 1e30 -1710631.45 5828796.97 1220 2013 513.432145118713379 1e30 -1710633.57 5828809.29 1220 2014 513.341784477233887 1e30 -1710635.69 5828821.60 1220 2015 513.36526870727539 1e30 -1710637.81 5828833.92 1220 2016 513.349294662475586 1e30 -1710639.94 5828846.23 1220 2017 513.563990592956543 1e30 -1710642.06 5828858.55 1220 2018 513.8930082321167 1e30 -1710644.18 5828870.86 1220 2019 513.6493444442749 1e30 -1710646.31 5828883.18 1220 2020 513.28432559967041 1e30 -1710648.43 5828895.49 1220 2021 513.1828784942627 1e30 -1710650.55 5828907.81 1220 2022 513.250291347503662 1e30 -1710652.67 5828920.12 1220 2023 513.506770133972168 1e30 -1710654.80 5828932.44 1220 2024 513.492941856384277 1e30 -1710656.92 5828944.75 1220 2025 513.308227062225342 1e30 -1710659.04 5828957.07 1220 2026 513.422846794128418 1e30 -1710661.17 5828969.39 1220 2027 513.21101188659668 1e30 -1710663.29 5828981.70 1220 2028 512.78227567672729 1e30 -1710665.41 5828994.02 1220 2029 512.764930725097656 1e30 -1710667.53 5829006.33 1220 2030 512.747466564178467 1e30 -1710669.66 5829018.65 1220 2031 512.29 1e30 -1710671.78 5829030.96 1220 2032 512.096047401428223 1e30 -1710673.90 5829043.28 1220 2033 512.1343731880188 1e30 -1710676.03 5829055.59 1220 2034 512.07429170608521 1e30 -1710678.15 5829067.91 1220 2035 511.876523494720459 1e30 -1710680.27 5829080.22 1220 2036 511.767864227294922 1e30 -1710682.39 5829092.54 1220 2037 511.616110801696777 1e30 -1710684.52 5829104.85 1220 2038 511.66534423828125 1e30 -1710686.64 5829117.17 1220 2039 511.986494064331055 1e30 -1710688.76 5829129.48 1220 2040 512.314498424530029 1e30 -1710690.89 5829141.80 1220 2041 512.43668794631958 1e30 -1710693.01 5829154.11 1220 2042 512.187063694 1e30 -1710695.13 5829166.43 1220 2043 511.74634695053101 1e30 -1710697.25 5829178.74 1220 2044 511.67219877243042 1e30 -1710699.38 5829191.06 1220 2045 511.897444725036621 1e30 -1710701.50 5829203.38 1220 2046 511.810660362243652 1e30 -1710703.62 5829215.69 1220 2047 511.804580688476563 1e30 -1710705.75 5829228.01 1220 2048 512.070059776306152 1e30 -1710707.87 5829240.32 1220 2049 512.200355529785156 1e30 -1710709.99 5829252.64 1220 2050 512.23146915435791 1e30 -1710712.11 5829264.95 1220 2051 512.23379373550415 1e30 -1710714.24 5829277.27 1220 2052 512.067914009094238 1e30 -1710716.36 5829289.58 1220 2053 512.05348968505859 1e30 -1710718.48 5829301.90 1220 2054 511.849462985992432 1e30 -1710720.61 5829314.21 1220 2055 511.681020259857178 1e30 -1710722.73 5829326.53 1220 2056 511.716663837432861 1e30 -1710724.85 5829338.84 1220 2057 511.724472045898438 1e30 -1710726.97 5829351.16 1220 2058 511.578798294067383 1e30 -1710729.10 5829363.47 1220 2059 511.581599712371826 1e30 -1710731.22 5829375.79 1220 2060 511.442065238952637 1e30 -1710733.34 5829388.10 1220 2061 511.100113391876221 1e30 -1710735.47 5829400.42 1220 2062 510.824739933013916 1e30 -1710737.59 5829412.73 1220 2063 510.809659957885742 1e30 -1710739.71 5829425.05 1220 2064 510.418236255645752 1e30 -1710741.83 5829437.36 1220 2065 510.15019416809082 1e30 -1710743.96 5829449.68 1220 2066 510.1199746131897 1e30 -1710746.08 5829462.00 1220 2067 509.8845362663269 1e30 -1710748.20 5829474.31 1220 2068 510.29485464096069 1e30 -1710750.33 5829486.63 1220 2069 510.757148265838623 1e30 -1710752.45 5829498.94 1220 2070 510.303616523742676 1e30 -1710754.57 5829511.26 1220 2071 509.849607944488525 1e30 -1710756.69 5829523.57 1220 2072 509.974837303161621 1e30 -1710758.82 5829535.89 1220 2073 509.9906325340271 1e30 -1710760.94 5829548.20 1220 2074 510.3716254234314 1e30 -1710763.06 5829560.52 1220 2075 510.847389698028564 1e30 -1710522.34 5828016.87 1221 1950 515.839934349060059 1e30 -1710543.56 5828140.02 1221 1960 515.332937240600586 1e30 -1710564.79 5828263.17 1221 1970 514.931380748748779 1e30 -1710586.02 5828386.33 1221 1980 514.571487903594971 1e30 -1710607.25 5828509.48 1221 1990 514.662802219390869 1e30 -1710628.48 5828632.63 1221 2000 514.21874761581421 1e30 -1710649.71 5828755.78 1221 2010 513.227343559265137 1e30 -1710670.94 5828878.94 1221 2020 513.117611408233643 1e30 -1710692.17 5829002.09 1221 2030 512.836575508117676 1e30 -1710713.39 5829125.24 1221 2040 512.313723564147949 1e30 -1710734.62 5829248.39 1221 2050 512.49927282333374 1e30 -1710755.85 5829371.55 1221 2060 511.09236478805542 1e30 -1710777.08 5829494.70 1221 2070 510.187268257141113 1e30 -1710546.97 5828012.63 1222 1950 515.05124568939209 1e30 -1710568.20 5828135.78 1222 1960 514.75989818572998 1e30 -1710589.43 5828258.93 1222 1970 513.81576061248779 1e30 -1710610.65 5828382.08 1222 1980 514.011263847351074 1e30 -1710631.88 5828505.24 1222 1990 513.44591379165649 1e30 -1710653.11 5828628.39 1222 2000 513.268649578094482 1e30 -1710674.34 5828751.54 1222 2010 512.984812259674072 1e30 -1710695.57 5828874.69 1222 2020 512.53056526184082 1e30 -1710716.80 5828997.85 1222 2030 512.38560676574707 1e30 -1710738.03 5829121.00 1222 2040 511.619329452514648 1e30 -1710759.26 5829244.15 1222 2050 512.128233909606934 1e30 -1710780.48 5829367.30 1222 2060 510.08915901184082 1e30 -1710801.71 5829490.46 1222 2070 509.871840476989746 1e30 -1710563.11 5827959.12 1223 1946 514.530539512634277 1e30 -1710565.23 5827971.44 1223 1947 514.405369758605957 1e30 -1710567.35 5827983.75 1223 1948 514.363229274749756 1e30 -1710569.48 5827996.07 1223 1949 514.270186424255371 1e30 -1710571.60 5828008.38 1223 1950 513.945877552032471 1e30 -1710573.72 5828020.70 1223 1951 513.9886736869812 1e30 -1710575.85 5828033.01 1223 1952 514.344632625579834 1e30 -1710577.97 5828045.33 1223 1953 514.340043067932129 1e30 -1710580.09 5828057.64 1223 1954 514 1e30 -1710582.21 5828069.96 1223 1955 513.900637626647949 1e30 -1710584.34 5828082.27 1223 1956 513.772904872894287 1e30 -1710586.46 5828094.59 1223 1957 513.772189617156982 1e30 -1710588.58 5828106.91 1223 1958 513.84049654006958 1e30 -1710590.71 5828119.22 1223 1959 513.683915138244629 1e30 -1710592.83 5828131.54 1223 1960 513.599574565887451 1e30 -1710594.95 5828143.85 1223 1961 513.76098394393921 1e30 -1710597.07 5828156.17 1223 1962 514.148592948913574 1e30 -1710599.20 5828168.48 1223 1963 514.346063137054443 1e30 -1710601.32 5828180.80 1223 1964 514.234304428100586 1e30 -1710603.44 5828193.11 1223 1965 513.978183269500732 1e30 -1710605.57 5828205.43 1223 1966 513.647377490997314 1e30 -1710607.69 5828217.74 1223 1967 513.57400417327881 1e30 -1710609.81 5828230.06 1223 1968 513.543367385864258 1e30 -1710611.93 5828242.37 1223 1969 513.289332389831543 1e30 -1710614.06 5828254.69 1223 1970 513.004481792449951 1e30 -1710616.18 5828267.00 1223 1971 512.922585010528564 1e30 -1710618.30 5828279.32 1223 1972 513.025403022766113 1e30 -1710620.43 5828291.63 1223 1973 513.185560703277588 1e30 -1710622.55 5828303.95 1223 1974 513.229966163635254 1e30 -1710624.67 5828316.26 1223 1975 513.219833374023438 1e30 -1710626.79 5828328.58 1223 1976 513.399600982666016 1e30 -1710628.92 5828340.89 1223 1977 513.442397117614746 1e30 -1710631.04 5828353.21 1223 1978 512.983143329620361 1e30 -1710633.16 5828365.53 1223 1979 513.092637062072754 1e30 -1710635.29 5828377.84 1223 1980 513.322651386260986 1e30 -1710637.41 5828390.16 1223 1981 513.279318809509277 1e30 -1710639.53 5828402.47 1223 1982 513.164401054382324 1e30 -1710641.65 5828414.79 1223 1983 513.2794976234436 1e30 -1710643.78 5828427.10 1223 1984 513.289153575897217 1e30 -1710645.90 5828439.42 1223 1985 513.449132442474365 1e30 -1710648.02 5828451.73 1223 1986 513.819575309753418 1e30 -1710650.15 5828464.05 1223 1987 513.303697109222412 1e30 -1710652.27 5828476.36 1223 1988 512.97479867935181 1e30 -1710654.39 5828488.68 1223 1989 512.811481952667236 1e30 -1710656.51 5828500.99 1223 1990 512.622356414794922 1e30 -1710658.64 5828513.31 1223 1991 512.552142143249512 1e30 -1710660.76 5828525.62 1223 1992 512.70604133605957 1e30 -1710662.88 5828537.94 1223 1993 512.935519218444824 1e30 -1710665.01 5828550.25 1223 1994 513.136744499206543 1e30 -1710667.13 5828562.57 1223 1995 513.167202472686768 1e30 -1710669.25 5828574.88 1223 1996 512.92151212692261 1e30 -1710673.50 5828599.52 1223 1998 512.9583477973938 1e30 -1710675.62 5828611.83 1223 1999 512.94553279876709 1e30 -1710677.74 5828624.15 1223 2000 513.051271438598633 1e30 -1710679.87 5828636.46 1223 2001 513.108551502227783 1e30 -1710681.99 5828648.78 1223 2002 513.09323310852051 1e30 -1710684.11 5828661.09 1223 2003 513.146638870239258 1e30 -1710686.23 5828673.41 1223 2004 513.207733631134033 1e30 -1710688.36 5828685.72 1223 2005 513.205885887146 1e30 -1710690.48 5828698.04 1223 2006 513.253688812255859 1e30 -1710692.60 5828710.35 1223 2007 513.261139392852783 1e30 -1710694.73 5828722.67 1223 2008 512.878835201263428 1e30 -1710696.85 5828734.98 1223 2009 512.537777423858643 1e30 -1710698.97 5828747.30 1223 2010 512.368679046630859 1e30 -1710701.09 5828759.61 1223 2011 512.26729154586792 1e30 -1710703.22 5828771.93 1223 2012 512.15595006942749 1e30 -1710705.34 5828784.24 1223 2013 512.420654296875 1e30 -1710707.46 5828796.56 1223 2014 512.6338005065918 1e30 -1710709.59 5828808.87 1223 2015 512.5269889831543 1e30 -1710711.71 5828821.19 1223 2016 512.49790191650391 1e30 -1710713.83 5828833.50 1223 2017 512.504518032073975 1e30 -1710715.95 5828845.82 1223 2018 512.35693693161011 1e30 -1710718.08 5828858.14 1223 2019 512.346863746643066 1e30 -1710720.20 5828870.45 1223 2020 512.475550174713135 1e30 -1710722.32 5828882.77 1223 2021 512.505948543548584 1e30 -1710724.45 5828895.08 1223 2022 512.601912021636963 1e30 -1710726.57 5828907.40 1223 2023 512.683331966400146 1e30 -1710728.69 5828919.71 1223 2024 512.483894824981689 1e30 -1710730.82 5828932.03 1223 2025 512.487530708312988 1e30 -1710732.94 5828944.34 1223 2026 512.675821781158447 1e30 -1710735.06 5828956.66 1223 2027 512.73578405380249 1e30 -1710737.18 5828968.97 1223 2028 512.527644634246826 1e30 -1710743.55 5829005.92 1223 2031 511.7572546005249 1e30 -1710745.68 5829018.23 1223 2032 511.587202548980713 1e30 -1710747.80 5829030.55 1223 2033 511.523127555847168 1e30 -1710749.92 5829042.86 1223 2034 511.121690273284912 1e30 -1710752.04 5829055.18 1223 2035 511.069357395172119 1e30 -1710754.17 5829067.49 1223 2036 511.688888072967529 1e30 -1710756.29 5829079.81 1223 2037 511.530518531799316 1e30 -1710758.41 5829092.13 1223 2038 510.879933834075928 1e30 -1710760.54 5829104.44 1223 2039 510.836601257324219 1e30 -1710762.66 5829116.76 1223 2040 511.270403862 1e30 -1710764.78 5829129.07 1223 2041 511.32434606552124 1e30 -1710769.03 5829153.70 1223 2043 511.362135410308838 1e30 -1710771.15 5829166.02 1223 2044 511.611580848693848 1e30 -1710773.27 5829178.33 1223 2045 511.504292488098145 1e30 -1710775.40 5829190.65 1223 2046 511.66146993637085 1e30 -1710777.52 5829202.96 1223 2047 511.634886264801025 1e30 -1710779.64 5829215.28 1223 2048 511.220335960388184 1e30 -1710781.76 5829227.59 1223 2049 510.79171895980835 1e30 -1710783.89 5829239.91 1223 2050 510.74874401092529 1e30 -1710786.01 5829252.22 1223 2051 510.984480381011963 1e30 -1710788.13 5829264.54 1223 2052 511.001706123352051 1e30 -1710790.26 5829276.85 1223 2053 510.495901107788086 1e30 -1710792.38 5829289.17 1223 2054 510.21498441696167 1e30 -1710794.50 5829301.48 1223 2055 509.913504123687744 1e30 -1710796.62 5829313.80 1223 2056 509.715139865875244 1e30 -1710798.75 5829326.11 1223 2057 509.773910045623779 1e30 -1710800.87 5829338.43 1223 2058 509.607791900634766 1e30 -1710802.99 5829350.75 1223 2059 509.299516677856445 1e30 -1710805.12 5829363.06 1223 2060 509.330570697784424 1e30 -1710807.24 5829375.38 1223 2061 509.67174768447876 1e30 -1710809.36 5829387.69 1223 2062 510.118424892425537 1e30 -1710811.48 5829400.01 1223 2063 510.62244176864624 1e30 -1710813.61 5829412.32 1223 2064 510.513186454772949 1e30 -1710815.73 5829424.64 1223 2065 510.509848594665527 1e30 -1710817.85 5829436.95 1223 2066 510.291039943695068 1e30 -1710819.98 5829449.27 1223 2067 509.56583023071289 1e30 -1710822.10 5829461.58 1223 2068 509.449601173400879 1e30 -1710824.22 5829473.90 1223 2069 509.789884090423584 1e30 -1710826.34 5829486.21 1223 2070 509.52601432800293 1e30 -1710828.47 5829498.53 1223 2071 509.357690811157227 1e30 -1710830.59 5829510.84 1223 2072 509.4146728515625 1e30 -1710832.71 5829523.16 1223 2073 509.325385093688965 1e30 -1710834.84 5829535.47 1223 2074 509.147405624389648 1e30 -1710836.96 5829547.79 1223 2075 509.037435054779053 1e30 -1710839.08 5829560.10 1223 2076 508.99738073349 1e30 -1710841.20 5829572.42 1223 2077 509.100496768951416 1e30 -1710843.33 5829584.74 1223 2078 508.885383605957031 1e30 -1710845.45 5829597.05 1223 2079 508.607864379882813 1e30 -1710847.57 5829609.37 1223 2080 508.618354797363281 1e30 -1710849.70 5829621.68 1223 2081 508.951365947723389 1e30 -1710851.82 5829634.00 1223 2082 509.278357028961182 1e30 -1710853.94 5829646.31 1223 2083 509.313881397247314 1e30 -1710856.06 5829658.63 1223 2084 509.22524929046631 1e30 -1710858.19 5829670.94 1223 2085 509.306132793426514 1e30 -1710860.31 5829683.26 1223 2086 509.243547916412354 1e30 -1710862.43 5829695.57 1223 2087 508.7929368019104 1e30 -1710864.56 5829707.89 1223 2088 508.655965328216553 1e30 -1710596.23 5828004.14 1224 1950 513.320386409759521 1e30 -1710617.46 5828127.29 1224 1960 512.4131441116333 1e30 -1710638.69 5828250.45 1224 1970 512.580931186676025 1e30 -1710659.92 5828373.60 1224 1980 512.734651565551758 1e30 -1710681.15 5828496.75 1224 1990 512.273013591766357 1e30 -1710702.37 5828619.90 1224 2000 512.470602989196777 1e30 -1710723.60 5828743.06 1224 2010 511.929810047149658 1e30 -1710744.83 5828866.21 1224 2020 512.161612510681152 1e30 -1710766.06 5828989.36 1224 2030 511.23631 1e30 -1710787.29 5829112.51 1224 2040 511.2837553024292 1e30 -1710808.52 5829235.67 1224 2050 509.386777877807617 1e30 -1710829.75 5829358.82 1224 2060 508.99893045425415 1e30 -1710850.98 5829481.97 1224 2070 509.06229019165039 1e30 -1710620.86 5827999.90 1225 1950 513.361752033233643 1e30 -1710642.09 5828123.05 1225 1960 511.827468872070313 1e30 -1710663.32 5828246.20 1225 1970 512.661933898925781 1e30 -1710684.55 5828369.36 1225 1980 512.325763702392578 1e30 -1710705.78 5828492.51 1225 1990 511.951208114624023 1e30 -1710727.01 5828615.66 1225 2000 512.452483177185059 1e30 -1710748.24 5828738.81 1225 2010 511.837124824523926 1e30 -1710769.46 5828861.97 1225 2020 512.224137783050537 1e30 -1710790.69 5828985.12 1225 2030 511.447310447692871 1e30 -1710811.92 5829108.27 1225 2040 510.317325592041016 1e30 -1710833.15 5829231.42 1225 2050 509.363710880279541 1e30 -1710854.38 5829354.58 1225 2060 508.665859699249268 1e30 -1710875.61 5829477.73 1225 2070 508.758425712585449 1e30 -1710634.88 5827934.08 1226 1945 514.36769962310791 1e30 -1710637.00 5827946.39 1226 1946 514.085710048675537 1e30 -1710639.13 5827958.71 1226 1947 513.819277286529541 1e30 -1710641.25 5827971.02 1226 1948 513.730823993682861 1e30 -1710643.37 5827983.34 1226 1949 513.6725902557373 1e30 -1710645.49 5827995.66 1226 1950 513.678133487701416 1e30 -1710647.62 5828007.97 1226 1951 513.533055782318115 1e30 -1710649.74 5828020.29 1226 1952 513.262629508972168 1e30 -1710651.86 5828032.60 1226 1953 513.3819580078125 1e30 -1710653.99 5828044.92 1226 1954 513.27139139175415 1e30 -1710656.11 5828057.23 1226 1955 512.626409530639648 1e30 -1710658.23 5828069.55 1226 1956 512.386143207550049 1e30 -1710660.35 5828081.86 1226 1957 512.525618076324463 1e30 -1710662.48 5828094.18 1226 1958 512.665450572967529 1e30 -1710664.60 5828106.49 1226 1959 512.30853796005249 1e30 -1710666.72 5828118.81 1226 1960 511.777639389038086 1e30 -1710668.85 5828131.12 1226 1961 511.90716028213501 1e30 -1710670.97 5828143.44 1226 1962 512.355148792266846 1e30 -1710673.09 5828155.75 1226 1963 512.663424015045166 1e30 -1710675.22 5828168.07 1226 1964 512.930452823638916 1e30 -1710677.34 5828180.38 1226 1965 513.055682182312012 1e30 -1710679.46 5828192.70 1226 1966 513.14288377761841 1e30 -1710681.58 5828205.01 1226 1967 513.080894947052 1e30 -1710683.71 5828217.33 1226 1968 512.980818748474121 1e30 -1710685.83 5828229.64 1226 1969 513.11337947845459 1e30 -1710687.95 5828241.96 1226 1970 512.81052827835083 1e30 -1710690.08 5828254.28 1226 1971 512.191832065582275 1e30 -1710692.20 5828266.59 1226 1972 511.93141937255859 1e30 -1710694.32 5828278.91 1226 1973 511.986494064331055 1e30 -1710696.44 5828291.22 1226 1974 511.877477169036865 1e30 -1710698.57 5828303.54 1226 1975 511.775612831115723 1e30 -1710700.69 5828315.85 1226 1976 512.1840238571167 1e30 -1710702.81 5828328.17 1226 1977 512.144327163696289 1e30 -1710704.94 5828340.48 1226 1978 511.793255805969238 1e30 -1710707.06 5828352.80 1226 1979 511.899650096893311 1e30 -1710709.18 5828365.11 1226 1980 512.2331976890564 1e30 -1710711.30 5828377.43 1226 1981 512.371063232421875 1e30 -1710713.43 5828389.74 1226 1982 512.518584728240967 1e30 -1710715.55 5828402.06 1226 1983 512.618839740753174 1e30 -1710717.67 5828414.37 1226 1984 512.64423131942749 1e30 -1710719.80 5828426.69 1226 1985 512.800991535186768 1e30 -1710721.92 5828439.00 1226 1986 512.84670829772949 1e30 -1710724.04 5828451.32 1226 1987 512.409567832946777 1e30 -1710726.16 5828463.63 1226 1988 511.880934238433838 1e30 -1710728.29 5828475.95 1226 1989 511.7652416229248 1e30 -1710730.41 5828488.27 1226 1990 511.88582181930542 1e30 -1710732.53 5828500.58 1226 1991 511.915266513824463 1e30 -1710734.66 5828512.90 1226 1992 512.171626091003418 1e30 -1710736.78 5828525.21 1226 1993 512.44819164276123 1e30 -1710738.90 5828537.53 1226 1994 512.45957612991333 1e30 -1710741.02 5828549.84 1226 1995 512.540757656097412 1e30 -1710743.15 5828562.16 1226 1996 512.621581554412842 1e30 -1710745.27 5828574.47 1226 1997 512.674570083618164 1e30 -1710747.39 5828586.79 1226 1998 512.52603530883789 1e30 -1710749.52 5828599.10 1226 1999 512.413978576660156 1e30 -1710751.64 5828611.42 1226 2000 512.380063533782959 1e30 -1710753.76 5828623.73 1226 2001 512.207210063934326 1e30 -1710755.88 5828636.05 1226 2002 511.978507041931152 1e30 -1710758.01 5828648.36 1226 2003 512.0549201965332 1e30 -1710760.13 5828660.68 1226 2004 512.104511260986328 1e30 -1710762.25 5828672.99 1226 2005 512.182414531707764 1e30 -1710764.38 5828685.31 1226 2006 512.464523315429688 1e30 -1710766.50 5828697.62 1226 2007 512.40992546081543 1e30 -1710768.62 5828709.94 1226 2008 512.216329574584961 1e30 -1710770.74 5828722.25 1226 2009 512.094736099243164 1e30 -1710772.87 5828734.57 1226 2010 511.955916881561279 1e30 -1710774.99 5828746.89 1226 2011 512.03024387359619 1e30 -1710777.11 5828759.20 1226 2012 512.074828147888184 1e30 -1710779.24 5828771.52 1226 2013 511.939823627471924 1e30 -1710781.36 5828783.83 1226 2014 511.555016040802 1e30 -1710783.48 5828796.15 1226 2015 511.28757 1e30 -1710785.60 5828808.46 1226 2016 511.43729686737061 1e30 -1710787.73 5828820.78 1226 2017 511.844754219055176 1e30 -1710789.85 5828833.09 1226 2018 512.288331985473633 1e30 -1710791.97 5828845.41 1226 2019 512.445032596588135 1e30 -1710794.10 5828857.72 1226 2020 512.069106101989746 1e30 -1710796.22 5828870.04 1226 2021 511.861443519592285 1e30 -1710798.34 5828882.35 1226 2022 511.54994964599609 1e30 -1710800.46 5828894.67 1226 2023 511.40677928924561 1e30 -1710802.59 5828906.98 1226 2024 511.625230312347412 1e30 -1710804.71 5828919.30 1226 2025 511.743843555450439 1e30 -1710806.83 5828931.61 1226 2026 511.903882026672363 1e30 -1710808.96 5828943.93 1226 2027 511.755406856536865 1e30 -1710811.08 5828956.24 1226 2028 511.516451835632324 1e30 -1710813.20 5828968.56 1226 2029 511.47007942199707 1e30 -1710815.32 5828980.88 1226 2030 511.503279209136963 1e30 -1710817.45 5828993.19 1226 2031 511.295318603515625 1e30 -1710819.57 5829005.51 1226 2032 511.11108064651489 1e30 -1710821.69 5829017.82 1226 2033 510.681807994842529 1e30 -1710823.82 5829030.14 1226 2034 510.173618793487549 1e30 -1710825.94 5829042.45 1226 2035 509.728610515594482 1e30 -1710828.06 5829054.77 1226 2036 509.599804878234863 1e30 -1710830.18 5829067.08 1226 2037 509.754836559295654 1e30 -1710832.31 5829079.40 1226 2038 509.733974933624268 1e30 -1710834.43 5829091.71 1226 2039 509.592056274414063 1e30 -1710836.55 5829104.03 1226 2040 509.299099445343018 1e30 -1710838.68 5829116.34 1226 2041 509.1573596 1e30 -1710840.80 5829128.66 1226 2042 509.421586990356445 1e30 -1710845.05 5829153.29 1226 2044 509.238600730896 1e30 -1710847.17 5829165.60 1226 2045 509.063661098480225 1e30 -1710849.29 5829177.92 1226 2046 508.832633495330811 1e30 -1710851.41 5829190.23 1226 2047 508.90821218490601 1e30 -1710857.78 5829227.18 1226 2050 508.978784084320068 1e30 -1710862.03 5829251.81 1226 2052 509.002327919006348 1e30 -1710864.15 5829264.13 1226 2053 508.977949619293213 1e30 -1710866.27 5829276.44 1226 2054 509.25976037979126 1e30 -1710874.77 5829325.70 1226 2058 508.524298667907715 1e30 -1710876.89 5829338.02 1226 2059 508.383750915527344 1e30 -1710879.01 5829350.33 1226 2060 508.508861064910889 1e30 -1710881.13 5829362.65 1226 2061 508.470714092254639 1e30 -1710883.26 5829374.96 1226 2062 508.445143699646 1e30 -1710885.38 5829387.28 1226 2063 508.83781909942627 1e30 -1710887.50 5829399.59 1226 2064 508.96531343460083 1e30 -1710889.63 5829411.91 1226 2065 508.691787719726563 1e30 -1710891.75 5829424.22 1226 2066 508.579730987548828 1e30 -1710893.87 5829436.54 1226 2067 508.712887763977051 1e30 -1710895.99 5829448.85 1226 2068 508.713185787200928 1e30 -1710898.12 5829461.17 1226 2069 508.585155010223389 1e30 -1710900.24 5829473.48 1226 2070 508.489489555358887 1e30 -1710902.36 5829485.80 1226 2071 508.41987133026123 1e30 -1710904.49 5829498.12 1226 2072 508.59147310256958 1e30 -1710906.61 5829510.43 1226 2073 508.801758289337158 1e30 -1710908.73 5829522.75 1226 2074 508.789360523223877 1e30 -1710910.85 5829535.06 1226 2075 508.817195892333984 1e30 -1710912.98 5829547.38 1226 2076 508.378326892852783 1e30 -1710915.10 5829559.69 1226 2077 508.239686489105225 1e30 -1710917.22 5829572.01 1226 2078 508.378744125366211 1e30 -1710919.35 5829584.32 1226 2079 508.329927921295166 1e30 -1710921.47 5829596.64 1226 2080 508.0493688583374 1e30 -1710670.13 5827991.41 1227 1950 513.720273971557617 1e30 -1710691.36 5828114.56 1227 1960 511.632621288299561 1e30 -1710712.58 5828237.72 1227 1970 512.578845024108887 1e30 -1710733.81 5828360.87 1227 1980 511.633217334747314 1e30 -1710755.04 5828484.02 1227 1990 511.313974857330322 1e30 -1710776.27 5828607.17 1227 2000 512.11792230606079 1e30 -1710797.50 5828730.33 1227 2010 511.32833957672119 1e30 -1710818.73 5828853.48 1227 2020 512.07238435745239 1e30 -1710839.96 5828976.63 1227 2030 510.989129543304443 1e30 -1710861.19 5829099.78 1227 2040 509.024441242218018 1e30 -1710882.41 5829222.94 1227 2050 508.921027183532715 1e30 -1710903.64 5829346.09 1227 2060 508.3579421043396 1e30 -1710924.87 5829469.24 1227 2070 508.168578147888184 1e30 -1710694.76 5827987.17 1228 1950 512.62962818145752 1e30 -1710715.99 5828110.32 1228 1960 511.473238468170166 1e30 -1710737.22 5828233.47 1228 1970 511.760294437408447 1e30 -1710758.44 5828356.63 1228 1980 510.655760765075684 1e30 -1710779.67 5828479.78 1228 1990 510.380923748016357 1e30 -1710800.90 5828602.93 1228 2000 511.010706424713135 1e30 -1710822.13 5828726.08 1228 2010 510.964393615722656 1e30 -1710843.36 5828849.24 1228 2020 511.281490325927734 1e30 -1710864.59 5828972.39 1228 2030 509.124815464019775 1e30 -1710885.82 5829095.54 1228 2040 508.76146554946899 1e30 -1710907.05 5829218.69 1228 2050 508.47709178924561 1e30 -1710928.27 5829341.85 1228 2060 508.042275905609131 1e30 -1710949.50 5829465.00 1228 2070 507.9614520072937 1e30 -1710704.53 5827896.72 1229 1943 512.546718120574951 1e30 -1710706.65 5827909.04 1229 1944 512.881875038146973 1e30 -1710708.78 5827921.35 1229 1945 512.9055380821228 1e30 -1710710.90 5827933.67 1229 1946 512.734055519104 1e30 -1710713.02 5827945.98 1229 1947 512.1578574180603 1e30 -1710715.14 5827958.30 1229 1948 511.392652988433838 1e30 -1710717.27 5827970.61 1229 1949 511.387109756469727 1e30 -1710719.39 5827982.93 1229 1950 511.333227157592773 1e30 -1710721.51 5827995.24 1229 1951 511.064231395721436 1e30 -1710723.64 5828007.56 1229 1952 511.1044049263 1e30 -1710725.76 5828019.87 1229 1953 511.255383491516113 1e30 -1710732.13 5828056.82 1229 1956 510.620713233947754 1e30 -1710734.25 5828069.13 1229 1957 510.71822643280029 1e30 -1710736.37 5828081.45 1229 1958 511.128902435302734 1e30 -1710738.50 5828093.76 1229 1959 511.5959644317627 1e30 -1710740.62 5828106.08 1229 1960 511.595070362091064 1e30 -1710742.74 5828118.39 1229 1961 511.251389980316162 1e30 -1710744.86 5828130.71 1229 1962 511.064291 1e30 -1710746.99 5828143.03 1229 1963 511.122047901153564 1e30 -1710749.11 5828155.34 1229 1964 511.555850505828857 1e30 -1710751.23 5828167.66 1229 1965 511.7003321647644 1e30 -1710753.36 5828179.97 1229 1966 511.758327484130859 1e30 -1710755.48 5828192.29 1229 1967 511.733889579772949 1e30 -1710757.60 5828204.60 1229 1968 511.836588382720947 1e30 -1710759.72 5828216.92 1229 1969 511.839747428894043 1e30 -1710761.85 5828229.23 1229 1970 511.3033652305603 1e30 -1710763.97 5828241.55 1229 1971 510.686933994293213 1e30 -1710766.09 5828253.86 1229 1972 510.37442684173584 1e30 -1710768.22 5828266.18 1229 1973 510.362029075622559 1e30 -1710770.34 5828278.49 1229 1974 510.252594947814941 1e30 -1710772.46 5828290.81 1229 1975 510.213494300842285 1e30 -1710774.58 5828303.12 1229 1976 509.88537073135376 1e30 -1710776.71 5828315.44 1229 1977 509.875476360321045 1e30 -1710778.83 5828327.75 1229 1978 510.105311870574951 1e30 -1710780.95 5828340.07 1229 1979 510.421335697174072 1e30 -1710783.08 5828352.38 1229 1980 510.923147201538086 1e30 -1710785.20 5828364.70 1229 1981 511.140286922454834 1e30 -1710789.45 5828389.33 1229 1983 512.219548225402832 1e30 -1710791.57 5828401.65 1229 1984 512.889444828033447 1e30 -1710793.69 5828413.96 1229 1985 512.982428073883057 1e30 -1710795.81 5828426.28 1229 1986 512.642323970794678 1e30 -1710797.94 5828438.59 1229 1987 512.19421625137329 1e30 -1710800.06 5828450.91 1229 1988 511.560440063476563 1e30 -1710802.18 5828463.22 1229 1989 510.8216404914856 1e30 -1710804.31 5828475.54 1229 1990 510.38283109664917 1e30 -1710806.43 5828487.85 1229 1991 510.27983427047729 1e30 -1710808.55 5828500.17 1229 1992 510.366261005401611 1e30 -1710810.67 5828512.48 1229 1993 510.714948177337646 1e30 -1710812.80 5828524.80 1229 1994 511.003971099853516 1e30 -1710814.92 5828537.11 1229 1995 511.773645877838135 1e30 -1710817.04 5828549.43 1229 1996 512.18283176422119 1e30 -1710819.17 5828561.74 1229 1997 511.968314647674561 1e30 -1710821.29 5828574.06 1229 1998 511.52193546295166 1e30 -1710823.41 5828586.37 1229 1999 511.181354522705078 1e30 -1710825.53 5828598.69 1229 2000 510.711669921875 1e30 -1710827.66 5828611.00 1229 2001 510.507941246032715 1e30 -1710829.78 5828623.32 1229 2002 510.391891002655029 1e30 -1710831.90 5828635.64 1229 2003 509.980380535125732 1e30 -1710834.03 5828647.95 1229 2004 509.742259979248047 1e30 -1710836.15 5828660.27 1229 2005 509.742259979248047 1e30 -1710838.27 5828672.58 1229 2006 509.611070156097412 1e30 -1710840.39 5828684.90 1229 2007 509.697377681732178 1e30 -1710842.52 5828697.21 1229 2008 510.1478099822998 1e30 -1710844.64 5828709.53 1229 2009 509.8501443862915 1e30 -1710846.76 5828721.84 1229 2010 509.337544441223145 1e30 -1710848.89 5828734.16 1229 2011 509.42695140838623 1e30 -1710851.01 5828746.47 1229 2012 509.341657161712646 1e30 -1710853.13 5828758.79 1229 2013 509.030997753143311 1e30 -1710855.25 5828771.10 1229 2014 508.8425874710083 1e30 -1710857.38 5828783.42 1229 2015 508.956253528594971 1e30 -1710859.50 5828795.73 1229 2016 509.477674961090088 1e30 -1710861.62 5828808.05 1229 2017 509.866714477539063 1e30 -1710863.75 5828820.36 1229 2018 509.928226470947266 1e30 -1710865.87 5828832.68 1229 2019 509.7392201423645 1e30 -1710867.99 5828844.99 1229 2020 509.51230525970459 1e30 -1710870.11 5828857.31 1229 2021 509.250044822692871 1e30 -1710872.24 5828869.62 1229 2022 509.061872959136963 1e30 -1710874.36 5828881.94 1229 2023 509.049773216247559 1e30 -1710876.48 5828894.26 1229 2024 509.331703186035156 1e30 -1710878.61 5828906.57 1229 2025 510.093152523040771 1e30 -1710880.73 5828918.89 1229 2026 510.711967945098877 1e30 -1710882.85 5828931.20 1229 2027 510.35028696060181 1e30 -1710884.97 5828943.52 1229 2028 509.229898452758789 1e30 -1710887.10 5828955.83 1229 2029 508.791148662567139 1e30 -1710889.22 5828968.15 1229 2030 508.711874485015869 1e30 -1710891.34 5828980.46 1229 2031 508.777976036071777 1e30 -1710893.47 5828992.78 1229 2032 508.790075778961182 1e30 -1710895.59 5829005.09 1229 2033 508.655726909637451 1e30 -1710897.71 5829017.41 1229 2034 508.553147315979 1e30 -1710899.83 5829029.72 1229 2035 508.539736270904541 1e30 -1710901.96 5829042.04 1229 2036 508.593559265136719 1e30 -1710904.08 5829054.35 1229 2037 508.6703896522522 1e30 -1710906.20 5829066.67 1229 2038 508.6027979850769 1e30 -1710908.33 5829078.98 1229 2039 508.449435234069824 1e30 -1710910.45 5829091.30 1229 2040 508.32676887512207 1e30 -1710912.57 5829103.61 1229 2041 508.325815200805664 1e30 -1710914.69 5829115.93 1229 2042 508.46356153488159 1e30 -1710916.82 5829128.25 1229 2043 508.6672306060791 1e30 -1710918.94 5829140.56 1229 2044 508.725941181182861 1e30 -1710921.06 5829152.88 1229 2045 508.781373500823975 1e30 -1710923.19 5829165.19 1229 2046 508.678674697875977 1e30 -1710925.31 5829177.51 1229 2047 508.349418640136719 1e30 -1710927.43 5829189.82 1229 2048 508.179843425750732 1e30 -1710929.55 5829202.14 1229 2049 508.127152919769287 1e30 -1710931.68 5829214.45 1229 2050 508.0680251121521 1e30 -1710935.92 5829239.08 1229 2052 508.096277713775635 1e30 -1710938.05 5829251.40 1229 2053 508.065879344940186 1e30 -1710940.17 5829263.71 1229 2054 508.272647857666016 1e30 -1710942.29 5829276.03 1229 2055 508.542120456695557 1e30 -1710944.41 5829288.34 1229 2056 508.62240791320801 1e30 -1710946.54 5829300.66 1229 2057 508.40604305267334 1e30 -1710948.66 5829312.97 1229 2058 508.195340633392334 1e30 -1710950.78 5829325.29 1229 2059 508.019745349884033 1e30 -1710952.91 5829337.60 1229 2060 507.797181606292725 1e30 -1710955.03 5829349.92 1229 2061 507.854163646698 1e30 -1710957.15 5829362.23 1229 2062 508.0411434173584 1e30 -1710959.28 5829374.55 1229 2063 508.26567411422729 1e30 -1710961.40 5829386.87 1229 2064 508.311808109283447 1e30 -1710963.52 5829399.18 1229 2065 508.211076259613037 1e30 -1710965.64 5829411.50 1229 2066 508.020222187042236 1e30 -1710967.77 5829423.81 1229 2067 507.910013198852539 1e30 -1710969.89 5829436.13 1229 2068 507.848620414733887 1e30 -1710972.01 5829448.44 1229 2069 507.854998111724854 1e30 -1710974.14 5829460.76 1229 2070 507.810115814208984 1e30 -1710976.26 5829473.07 1229 2071 507.751882076263428 1e30 -1710978.38 5829485.39 1229 2072 507.778704166412354 1e30 -1710980.50 5829497.70 1229 2073 507.6630711555481 1e30 -1710982.63 5829510.02 1229 2074 507.4390172958374 1e30 -1710765.25 5828101.84 1230 1960 510.58304309844971 1e30 -1710786.48 5828224.99 1230 1970 511.11304759979248 1e30 -1710807.71 5828348.14 1230 1980 510.574698448181152 1e30 -1710828.94 5828471.29 1230 1990 509.756624698638916 1e30 -1710850.17 5828594.45 1230 2000 510.106563568115234 1e30 -1710871.39 5828717.60 1230 2010 508.773446083068848 1e30 -1710892.62 5828840.75 1230 2020 508.760035037994385 1e30 -1710913.85 5828963.90 1230 2030 508.213222026824951 1e30 -1710935.08 5829087.06 1230 2040 508.087813854217529 1e30 -1710956.31 5829210.21 1230 2050 507.75390863418579 1e30 -1710977.54 5829333.36 1230 2060 507.637500762939453 1e30 -1710998.77 5829456.51 1230 2070 507.524073123931885 1e30 -1710768.65 5827974.44 1231 1950 508.776068687438965 1e30 -1710789.88 5828097.59 1231 1960 510.17904281616211 1e30 -1710811.11 5828220.75 1231 1970 510.199785232543945 1e30 -1710832.34 5828343.90 1231 1980 510.291397571563721 1e30 -1710853.57 5828467.05 1231 1990 509.130537509918213 1e30 -1710874.80 5828590.20 1231 2000 509.199440479278564 1e30 -1710896.03 5828713.36 1231 2010 508.97526741027832 1e30 -1710917.26 5828836.51 1231 2020 508.515655994415283 1e30 -1710938.48 5828959.66 1231 2030 507.970631122589111 1e30 -1710959.71 5829082.81 1231 2040 507.85708427429199 1e30 -1710980.94 5829205.97 1231 2050 507.3009729385376 1e30 -1711002.17 5829329.12 1231 2060 507.276833057403564 1e30 -1711023.40 5829452.27 1231 2070 507 1e30 -1710793.29 5827970.20 1232 1950 508.295178413391113 1e30 -1710814.51 5828093.35 1232 1960 511.113166809082031 1e30 -1710835.74 5828216.50 1232 1970 510 1e30 -1710856.97 5828339.66 1232 1980 509.404838085174561 1e30 -1710878.20 5828462.81 1232 1990 508.621513843536377 1e30 -1710899.43 5828585.96 1232 2000 509.554088115692139 1e30 -1710920.66 5828709.11 1232 2010 508.825123310089111 1e30 -1710941.89 5828832.27 1232 2020 508.508265018463135 1e30 -1710963.12 5828955.42 1232 2030 508.005917072296143 1e30 -1710984.34 5829078.57 1232 2040 507.829904556274414 1e30 -1711005.57 5829201.72 1232 2050 507.36707448959351 1e30 -1711026.80 5829324.88 1232 2060 507.319927215576172 1e30 -1711048.03 5829448.03 1232 2070 506.423056125640869 1e30 -1710817.92 5827965.96 1233 1950 508.259415626525879 1e30 -1710839.15 5828089.11 1233 1960 510.92821359634399 1e30 -1710860.37 5828212.26 1233 1970 509.321153163909912 1e30 -1710881.60 5828335.41 1233 1980 509.149074554443359 1e30 -1710902.83 5828458.57 1233 1990 508.533358573913574 1e30 -1710924.06 5828581.72 1233 2000 509.512007236480713 1e30 -1710945.29 5828704.87 1233 2010 508.758664131164551 1e30 -1710966.52 5828828.02 1233 2020 508.397042751312256 1e30 -1710987.75 5828951.18 1233 2030 507.9423189163208 1e30 -1711008.98 5829074.33 1233 2040 507.7252984046936 1e30 -1711030.20 5829197.48 1233 2050 507.32344388961792 1e30 -1711051.43 5829320.63 1233 2060 507.093191146850586 1e30 -1711072.66 5829443.79 1233 2070 506.637394428253174 1e30 -1710842.55 5827961.71 1234 1950 508.529961109161377 1e30 -1710863.78 5828084.87 1234 1960 509.07266139984131 1e30 -1710885.01 5828208.02 1234 1970 508.820474147796631 1e30 -1710906.24 5828331.17 1234 1980 508.91411304473877 1e30 -1710927.46 5828454.32 1234 1990 508.3048939704895 1e30 -1710948.69 5828577.48 1234 2000 509.713709354400635 1e30 -1710969.92 5828700.63 1234 2010 508.639752864837646 1e30 -1710991.15 5828823.78 1234 2020 508.28319787979126 1e30 -1711012.38 5828946.93 1234 2030 507.81857967376709 1e30 -1711033.61 5829070.09 1234 2040 507.318973541259766 1e30 -1711054.84 5829193.24 1234 2050 507.147550582885742 1e30 -1711076.07 5829316.39 1234 2060 506.703317165374756 1e30 -1710852.32 5827871.26 1235 1943 508.118927478790283 1e30 -1710854.44 5827883.58 1235 1944 508.26931 1e30 -1710856.57 5827895.89 1235 1945 508.228302001953125 1e30 -1710858.69 5827908.21 1235 1946 508.25381278991699 1e30 -1710860.81 5827920.53 1235 1947 508.2593560218811 1e30 -1710862.94 5827932.84 1235 1948 508.1636905670166 1e30 -1710865.06 5827945.16 1235 1949 508.288979530334473 1e30 -1710867.18 5827957.47 1235 1950 508.503437042236328 1e30 -1710869.30 5827969.79 1235 1951 508.645772933959961 1e30 -1710871.43 5827982.10 1235 1952 508.682191371917725 1e30 -1710873.55 5827994.42 1235 1953 508.88544321060181 1e30 -1710875.67 5828006.73 1235 1954 509.165763854980469 1e30 -1710877.80 5828019.05 1235 1955 509.227573871612549 1e30 -1710879.92 5828031.36 1235 1956 508.977711200714111 1e30 -1710882.04 5828043.68 1235 1957 508.68731737136841 1e30 -1710884.16 5828055.99 1235 1958 508.401155471801758 1e30 -1710886.29 5828068.31 1235 1959 508.318543434143066 1e30 -1710888.41 5828080.62 1235 1960 508.535921573638916 1e30 -1710890.53 5828092.94 1235 1961 509.2170238494873 1e30 -1710892.66 5828105.25 1235 1962 509.0758204460144 1e30 -1710894.78 5828117.57 1235 1963 508.072555065155029 1e30 -1710896.90 5828129.88 1235 1964 507.703661918640137 1e30 -1710899.02 5828142.20 1235 1965 507.55840539932251 1e30 -1710901.15 5828154.51 1235 1966 507.574617862701416 1e30 -1710903.27 5828166.83 1235 1967 507.952630519866943 1e30 -1710905.39 5828179.15 1235 1968 508.32676887512207 1e30 -1710907.52 5828191.46 1235 1969 508.493304252624512 1e30 -1710909.64 5828203.78 1235 1970 508.635342121124268 1e30 -1710911.76 5828216.09 1235 1971 508.598983287811279 1e30 -1710913.88 5828228.41 1235 1972 508.378863334655762 1e30 -1710916.01 5828240.72 1235 1973 508.277416229248047 1e30 -1710918.13 5828253.04 1235 1974 508.194983005523682 1e30 -1710924.50 5828289.98 1235 1977 508.44109058380127 1e30 -1710926.62 5828302.30 1235 1978 508.38315486907959 1e30 -1710928.74 5828314.61 1235 1979 508.54635238647461 1e30 -1710930.87 5828326.93 1235 1980 508.792757987976074 1e30 -1710932.99 5828339.24 1235 1981 508.884549140930176 1e30 -1710935.11 5828351.56 1235 1982 508.840322494506836 1e30 -1710937.24 5828363.87 1235 1983 508.63891839981079 1e30 -1710939.36 5828376.19 1235 1984 508.421361446380615 1e30 -1710941.48 5828388.50 1235 1985 508.519411087036133 1e30 -1710943.60 5828400.82 1235 1986 508.704900741577148 1e30 -1710945.73 5828413.14 1235 1987 508.672595024108887 1e30 -1710947.85 5828425.45 1235 1988 508.536756038665771 1e30 -1710949.97 5828437.77 1235 1989 508.594751358032227 1e30 -1710952.10 5828450.08 1235 1990 508.625984191894531 1e30 -1710954.22 5828462.40 1235 1991 508.531630039215088 1e30 -1710956.34 5828474.71 1235 1992 508.533239364624023 1e30 -1710958.46 5828487.03 1235 1993 508.771896362304688 1e30 -1710960.59 5828499.34 1235 1994 509.583532810211182 1e30 -1710966.96 5828536.29 1235 1997 509.965002536773682 1e30 -1710969.08 5828548.60 1235 1998 510.162711143493652 1e30 -1710971.20 5828560.92 1235 1999 509.976029396057129 1e30 -1710973.32 5828573.23 1235 2000 509.867429733276367 1e30 -1710975.45 5828585.55 1235 2001 509.6583366394043 1e30 -1710977.57 5828597.86 1235 2002 509.71454381942749 1e30 -1710979.69 5828610.18 1235 2003 509.639263153076172 1e30 -1710981.82 5828622.49 1235 2004 509.172320365905762 1e30 -1710983.94 5828634.81 1235 2005 508.630514144897461 1e30 -1710986.06 5828647.12 1235 2006 508.493125438690186 1e30 -1710988.18 5828659.44 1235 2007 508.53806734085083 1e30 -1710990.31 5828671.76 1235 2008 508.66544246673584 1e30 -1710992.43 5828684.07 1235 2009 508.730411529541016 1e30 -1710994.55 5828696.39 1235 2010 508.806943893432617 1e30 -1710996.68 5828708.70 1235 2011 508.735001087188721 1e30 -1710998.80 5828721.02 1235 2012 508.5480809211731 1e30 -1711000.92 5828733.33 1235 2013 508.391797542572021 1e30 -1711003.04 5828745.65 1235 2014 508.293569087982178 1e30 -1711005.17 5828757.96 1235 2015 508.337736129760742 1e30 -1711007.29 5828770.28 1235 2016 508.5180401802063 1e30 -1711009.41 5828782.59 1235 2017 508.492708206176758 1e30 -1711011.54 5828794.91 1235 2018 508.554518222808838 1e30 -1711013.66 5828807.22 1235 2019 508.472740650177 1e30 -1711015.78 5828819.54 1235 2020 508.348762989044189 1e30 -1711017.91 5828831.85 1235 2021 508.30948352813721 1e30 -1711020.03 5828844.17 1235 2022 508.330047130584717 1e30 -1711022.15 5828856.48 1235 2023 508.113622665405273 1e30 -1711024.27 5828868.80 1235 2024 507.896482944488525 1e30 -1711026.40 5828881.11 1235 2025 507.935822010040283 1e30 -1711028.52 5828893.43 1235 2026 508.081972599029541 1e30 -1711030.64 5828905.75 1235 2027 508.05908441543579 1e30 -1711032.77 5828918.06 1235 2028 507.9505443572998 1e30 -1711034.89 5828930.38 1235 2029 507.823646068573 1e30 -1711037.01 5828942.69 1235 2030 507.7173113822937 1e30 -1711039.13 5828955.01 1235 2031 507.6301097869873 1e30 -1711041.26 5828967.32 1235 2032 507.6410174369812 1e30 -1711043.38 5828979.64 1235 2033 507.492661476135254 1e30 -1711045.50 5828991.95 1235 2034 507.147729396820068 1e30 -1711047.63 5829004.27 1235 2035 506.852328777313232 1e30 -1711049.75 5829016.58 1235 2036 507.047951221466064 1e30 -1711051.87 5829028.90 1235 2037 507.130563259124756 1e30 -1711053.99 5829041.21 1235 2038 507.032513618469238 1e30 -1711056.12 5829053.53 1235 2039 507.05409049987793 1e30 -1711058.24 5829065.84 1235 2040 507.0723295211792 1e30 -1711060.36 5829078.16 1235 2041 507.296741008758545 1e30 -1711062.49 5829090.47 1235 2042 507.512688636779785 1e30 -1711064.61 5829102.79 1235 2043 507.492005825042725 1e30 -1711066.73 5829115.10 1235 2044 507.360935211181641 1e30 -1711068.85 5829127.42 1235 2045 507.353007793426514 1e30 -1711070.98 5829139.73 1235 2046 507.226765155792236 1e30 -1711073.10 5829152.05 1235 2047 507.21883773803711 1e30 -1711075.22 5829164.37 1235 2048 507.354795932769775 1e30 -1711077.35 5829176.68 1235 2049 507.296085357666016 1e30 -1711079.47 5829189.00 1235 2050 507.299304008483887 1e30 -1711081.59 5829201.31 1235 2051 507.436454296112061 1e30 -1711083.71 5829213.63 1235 2052 507.38132 1e30 -1711085.84 5829225.94 1235 2053 507.383346557617188 1e30 -1711087.96 5829238.26 1235 2054 507.338583469390869 1e30 -1711090.08 5829250.57 1235 2055 507.230103015899658 1e30 -1711092.21 5829262.89 1235 2056 507.118642330169678 1e30 -1711094.33 5829275.20 1235 2057 507.287800312042236 1e30 -1711096.45 5829287.52 1235 2058 507.3549747467041 1e30 -1711100.70 5829312.15 1235 2060 507.057785987854 1e30 -1711102.82 5829324.46 1235 2061 506.906270980834961 1e30 -1711104.94 5829336.78 1235 2062 506.712198257446289 1e30 -1711107.07 5829349.09 1235 2063 506.606996059417725 1e30 -1711109.19 5829361.41 1235 2064 506.820142269134521 1e30 -1711111.31 5829373.72 1235 2065 506.927311420440674 1e30 -1711113.43 5829386.04 1235 2066 506.786823272705078 1e30 -1711115.56 5829398.36 1235 2067 506.738781929016113 1e30 -1711117.68 5829410.67 1235 2068 506.2900185585022 1e30 -1711119.80 5829422.99 1235 2069 505.83344697952271 1e30 -1711121.93 5829435.30 1235 2070 505.453050136566162 1e30 -1711124.05 5829447.62 1235 2071 505.11014461517334 1e30 -1711126.17 5829459.93 1235 2072 505.196809768676758 1e30 -1711128.29 5829472.25 1235 2073 505.172610282897949 1e30 -1711130.42 5829484.56 1235 2074 504.5393705368042 1e30 -1711132.54 5829496.88 1235 2075 504.42647933959961 1e30 -1711134.66 5829509.19 1235 2076 504.758954048156738 1e30 -1711136.79 5829521.51 1235 2077 505.190789699554443 1e30 -1711138.91 5829533.82 1235 2078 505.197346210479736 1e30 -1711141.03 5829546.14 1235 2079 505.0433874130249 1e30 -1711143.15 5829558.45 1235 2080 504.657924175262451 1e30 -1711145.28 5829570.77 1235 2081 504.6466588974 1e30 -1711147.40 5829583.08 1235 2082 504.631757736206055 1e30 -1711149.52 5829595.40 1235 2083 504.29016351699829 1e30 -1711151.65 5829607.71 1235 2084 504.74041700363159 1e30 -1711153.77 5829620.03 1235 2085 505.298376083374023 1e30 -1711155.89 5829632.34 1235 2086 505.922555923461914 1e30 -1711158.01 5829644.66 1235 2087 504.760622978210449 1e30 -1711160.14 5829656.98 1235 2088 504.419684410095215 1e30 -1711162.26 5829669.29 1235 2089 504.277884960174561 1e30 -1711164.38 5829681.61 1235 2090 504.322052001953125 1e30 -1711166.51 5829693.92 1235 2091 504.74923849105835 1e30 -1711168.63 5829706.24 1235 2092 504.955887794494629 1e30 -1711170.75 5829718.55 1235 2093 504.876196384429932 1e30 -1711172.87 5829730.87 1235 2094 505.04410266876221 1e30 -1711175.00 5829743.18 1235 2095 505.17541170120239 1e30 -1711177.12 5829755.50 1235 2096 506.187200546264648 1e30 -1710891.81 5827953.23 1236 1950 508.28099250793457 1e30 -1710913.04 5828076.38 1236 1960 508.367657661437988 1e30 -1710934.27 5828199.53 1236 1970 508.513808250427246 1e30 -1710955.50 5828322.69 1236 1980 508.518695831298828 1e30 -1710976.73 5828445.84 1236 1990 508.905470371246338 1e30 -1710997.96 5828568.99 1236 2000 510.262012481689453 1e30 -1711019.19 5828692.14 1236 2010 508.53425264358521 1e30 -1711040.41 5828815.30 1236 2020 508.489131927490234 1e30 -1711061.64 5828938.45 1236 2030 507.914543151855469 1e30 -1711082.87 5829061.60 1236 2040 507.436037063598633 1e30 -1711104.10 5829184.75 1236 2050 507.670402526855469 1e30 -1711125.33 5829307.91 1236 2060 507.547199726104736 1e30 -1710916.44 5827948.99 1237 1950 507.699489593505859 1e30 -1710937.67 5828072.14 1237 1960 508.192360401153564 1e30 -1710958.90 5828195.29 1237 1970 507.8742504119873 1e30 -1710980.13 5828318.44 1237 1980 508.31449031829834 1e30 -1711001.36 5828441.60 1237 1990 508.370697498321533 1e30 -1711022.59 5828564.75 1237 2000 509.1550350189209 1e30 -1711043.82 5828687.90 1237 2010 508.1169605255127 1e30 -1711065.05 5828811.05 1237 2020 508.331716060638428 1e30 -1711086.27 5828934.21 1237 2030 507.99262523651123 1e30 -1711107.50 5829057.36 1237 2040 507.424652576446533 1e30 -1711128.73 5829180.51 1237 2050 507.7330470085144 1e30 -1711149.96 5829303.66 1237 2060 506.936550140380859 1e30 -1710941.08 5827944.74 1238 1950 506.413459777832031 1e30 -1710962.31 5828067.90 1238 1960 507.66235589981079 1e30 -1710983.53 5828191.05 1238 1970 507.14719295501709 1e30 -1711004.76 5828314.20 1238 1980 507.842481136322021 1e30 -1711025.99 5828437.35 1238 1990 507.979512214660645 1e30 -1711047.22 5828560.51 1238 2000 508.315145969390869 1e30 -1711068.45 5828683.66 1238 2010 508.083641529083252 1e30 -1711089.68 5828806.81 1238 2020 508.312046527862549 1e30 -1711110.91 5828929.96 1238 2030 507.634818553924561 1e30 -1711132.14 5829053.12 1238 2040 507.231354713439941 1e30 -1711153.36 5829176.27 1238 2050 507.491171360015869 1e30 -1711174.59 5829299.42 1238 2060 505.147933959960938 1e30 -1710965.71 5827940.50 1239 1950 505.567073822021484 1e30 -1710986.94 5828063.65 1239 1960 506.950914859771729 1e30 -1711008.17 5828186.81 1239 1970 505.620598793029785 1e30 -1711029.39 5828309.96 1239 1980 507.25322961807251 1e30 -1711050.62 5828433.11 1239 1990 507.62099027633667 1e30 -1711071.85 5828556.26 1239 2000 507.98344612121582 1e30 -1711093.08 5828679.42 1239 2010 507.642865180969238 1e30 -1711114.31 5828802.57 1239 2020 508.231163024902344 1e30 -1711135.54 5828925.72 1239 2030 507.60573148727417 1e30 -1711156.77 5829048.87 1239 2040 507.163465023040771 1e30 -1711178.00 5829172.03 1239 2050 506.35981559753418 1e30 -1711199.22 5829295.18 1239 2060 504.156112670898438 1e30 -1710990.34 5827936.26 1240 1950 505.410969257354736 1e30 -1711011.57 5828059.41 1240 1960 506.621897220611572 1e30 -1711032.80 5828182.56 1240 1970 505.360782146453857 1e30 -1711054.03 5828305.72 1240 1980 507.119834423065186 1e30 -1711096.48 5828552.02 1240 2000 507.9612135887146 1e30 -1711117.71 5828675.17 1240 2010 507.436811923980713 1e30 -1711138.94 5828798.33 1240 2020 508.041441440582275 1e30 -1711160.17 5828921.48 1240 2030 507.60394334793091 1e30 -1711181.40 5829044.63 1240 2040 507.18396902084351 1e30 -1711202.63 5829167.78 1240 2050 506.164908409118652 1e30 -1711223.86 5829290.93 1240 2060 504.45312261581421 1e30 -1711014.97 5827932.01 1241 1950 504.73785400390625 1e30 -1711036.20 5828055.17 1241 1960 506.557822227478027 1e30 -1711057.43 5828178.32 1241 1970 504.993855953216553 1e30 -1711078.66 5828301.47 1241 1980 507.149636745452881 1e30 -1711099.89 5828424.62 1241 1990 507.451415061950684 1e30 -1711121.12 5828547.78 1241 2000 507.660925388336182 1e30 -1711142.34 5828670.93 1241 2010 507.395505905151367 1e30 -1711163.57 5828794.08 1241 2020 507.7776312828064 1e30 -1711184.80 5828917.23 1241 2030 507.302284240722656 1e30 -1711206.03 5829040.39 1241 2040 507.012128829956055 1e30 -1711227.26 5829163.54 1241 2050 506.453156471252441 1e30 -1711248.49 5829286.69 1241 2060 504.427015781402588 1e30 -1711039.60 5827927.77 1242 1950 504.297971725463867 1e30 -1711060.83 5828050.92 1242 1960 504.776 1e30 -1711082.06 5828174.08 1242 1970 504.395425319671631 1e30 -1711103.29 5828297.23 1242 1980 506.939291954040527 1e30 -1711124.52 5828420.38 1242 1990 507.146775722503662 1e30 -1711145.75 5828543.53 1242 2000 506.975233554840088 1e30 -1711166.98 5828666.69 1242 2010 507.061600685119629 1e30 -1711188.20 5828789.84 1242 2020 507.375538349151611 1e30 -1711209.43 5828912.99 1242 2030 506.97183609008789 1e30 -1711230.66 5829036.14 1242 2040 506.501078605651855 1e30 -1711251.89 5829159.30 1242 2050 504.599809646606445 1e30 -1711273.12 5829282.45 1242 2060 503.813207149505615 1e30 -1711064.24 5827923.53 1243 1950 504.1084885597229 1e30 -1711085.46 5828046.68 1243 1960 504.411697387695313 1e30 -1711127.92 5828292.99 1243 1980 505.352556705474854 1e30 -1711149.15 5828416.14 1243 1990 506.294727325439453 1e30 -1711170.38 5828539.29 1243 2000 506.49130344390869 1e30 -1711191.61 5828662.44 1243 2010 506.186485290527344 1e30 -1711212.84 5828785.60 1243 2020 506.5462589263916 1e30 -1711234.07 5828908.75 1243 2030 506.436824798583984 1e30 -1711255.29 5829031.90 1243 2040 504.429280757904053 1e30 -1711276.52 5829155.05 1243 2050 503.515422344207764 1e30 -1711297.75 5829278.21 1243 2060 503.327667713165283 1e30 -1711088.87 5827919.29 1244 1950 504.211544990539551 1e30 -1711110.10 5828042.44 1244 1960 504.545629024505615 1e30 -1711152.55 5828288.74 1244 1980 504.455268383026123 1e30 -1711173.78 5828411.90 1244 1990 505.574345588684082 1e30 -1711195.01 5828535.05 1244 2000 506.102979183197021 1e30 -1711216.24 5828658.20 1244 2010 504.74041700363159 1e30 -1711237.47 5828781.35 1244 2020 506.318867206573486 1e30 -1711258.70 5828904.51 1244 2030 505.309402942657471 1e30 -1711279.93 5829027.66 1244 2040 504.057526588439941 1e30 -1711322.38 5829273.96 1244 2060 503.572285175323486 1e30 -1711094.39 5827804.21 1245 1941 500.99647045135498 1e30 -1711096.52 5827816.52 1245 1942 502.125203609466553 1e30 -1711098.64 5827828.84 1245 1943 502.94792652130127 1e30 -1711100.76 5827841.15 1245 1944 503.226816654205322 1e30 -1711102.88 5827853.47 1245 1945 503.1055212020874 1e30 -1711105.01 5827865.78 1245 1946 503.078937530517578 1e30 -1711107.13 5827878.10 1245 1947 503.265440464019775 1e30 -1711109.25 5827890.41 1245 1948 503.480672836303711 1e30 -1711111.38 5827902.73 1245 1949 503.7650465965271 1e30 -1711113.50 5827915.04 1245 1950 503.709971904754639 1e30 -1711115.62 5827927.36 1245 1951 503.3491849899292 1e30 -1711117.74 5827939.67 1245 1952 503.154158592224121 1e30 -1711119.87 5827951.99 1245 1953 503.253400325775146 1e30 -1711121.99 5827964.31 1245 1954 503.545582294464111 1e30 -1711124.11 5827976.62 1245 1955 503.815591335296631 1e30 -1711126.24 5827988.94 1245 1956 503.924310207366943 1e30 -1711128.36 5828001.25 1245 1957 504.062414169311523 1e30 -1711130.48 5828013.57 1245 1958 504.134237766265869 1e30 -1711132.60 5828025.88 1245 1959 504.154443740844727 1e30 -1711134.73 5828038.20 1245 1960 504.114389419555664 1e30 -1711136.85 5828050.51 1245 1961 504.236698150634766 1e30 -1711138.97 5828062.83 1245 1962 504.56506013870239 1e30 -1711141.10 5828075.14 1245 1963 504.561781883239746 1e30 -1711143.22 5828087.46 1245 1964 504.246711730957031 1e30 -1711145.34 5828099.77 1245 1965 504.308819770812988 1e30 -1711147.47 5828112.09 1245 1966 504.472136497497559 1e30 -1711149.59 5828124.40 1245 1967 504.18621301651001 1e30 -1711151.71 5828136.72 1245 1968 503.88789176940918 1e30 -1711153.83 5828149.03 1245 1969 503.778457641601563 1e30 -1711155.96 5828161.35 1245 1970 503.813564777374268 1e30 -1711158.08 5828173.66 1245 1971 503.879666328430176 1e30 -1711160.20 5828185.98 1245 1972 503.771781921386719 1e30 -1711162.33 5828198.29 1245 1973 503.671348094940186 1e30 -1711164.45 5828210.61 1245 1974 503.687024116516113 1e30 -1711166.57 5828222.93 1245 1975 503.76206636428833 1e30 -1711168.69 5828235.24 1245 1976 503.969013690948486 1e30 -1711170.82 5828247.56 1245 1977 504 1e30 -1711172.94 5828259.87 1245 1978 503.782570362091064 1e30 -1711175.06 5828272.19 1245 1979 503.73721122741699 1e30 -1711177.19 5828284.50 1245 1980 503.986835479736328 1e30 -1711179.31 5828296.82 1245 1981 504.474818706512451 1e30 -1711181.43 5828309.13 1245 1982 505.00345230102539 1e30 -1711183.55 5828321.45 1245 1983 505.15091419219971 1e30 -1711185.68 5828333.76 1245 1984 504.935681819915771 1e30 -1711187.80 5828346.08 1245 1985 504.930257797241211 1e30 -1711189.92 5828358.39 1245 1986 505.25105 1e30 -1711192.05 5828370.71 1245 1987 505.676686763763428 1e30 -1711194.17 5828383.02 1245 1988 505.7106614112854 1e30 -1711196.29 5828395.34 1245 1989 505.443453788757324 1e30 -1711198.41 5828407.65 1245 1990 505.28436899185181 1e30 -1711200.54 5828419.97 1245 1991 505.457162857055664 1e30 -1711202.66 5828432.28 1245 1992 505.653083324432373 1e30 -1711204.78 5828444.60 1245 1993 505.419731140136719 1e30 -1711206.91 5828456.92 1245 1994 504.8288106918335 1e30 -1711209.03 5828469.23 1245 1995 504.701435565948486 1e30 -1711211.15 5828481.55 1245 1996 505.231678485870361 1e30 -1711213.27 5828493.86 1245 1997 506.146073341369629 1e30 -1711215.40 5828506.18 1245 1998 506.088435649871826 1e30 -1711217.52 5828518.49 1245 1999 505.96112012863159 1e30 -1711219.64 5828530.81 1245 2000 506.383299827575684 1e30 -1711221.77 5828543.12 1245 2001 507.129192352294922 1e30 -1711223.89 5828555.44 1245 2002 507.48747587203979 1e30 -1711226.01 5828567.75 1245 2003 507.265925407409668 1e30 -1711228.13 5828580.07 1245 2004 507.04723596572876 1e30 -1711230.26 5828592.38 1245 2005 506.906390190124512 1e30 -1711232.38 5828604.70 1245 2006 506.7 1e30 -1711234.50 5828617.01 1245 2007 506.385385990142822 1e30 -1711236.63 5828629.33 1245 2008 505.765020847320557 1e30 -1711238.75 5828641.64 1245 2009 505.238711833953857 1e30 -1711240.87 5828653.96 1245 2010 504.735648632049561 1e30 -1711245.12 5828678.59 1245 2012 504.490077495574951 1e30 -1711247.24 5828690.90 1245 2013 505.891740322113037 1e30 -1711249.36 5828703.22 1245 2014 506.671130657196045 1e30 -1711251.49 5828715.54 1245 2015 506.763637065887451 1e30 -1711253.61 5828727.85 1245 2016 506.845772266387939 1e30 -1711255.73 5828740.17 1245 2017 506.840109825134277 1e30 -1711257.85 5828752.48 1245 2018 506.83861970901489 1e30 -1711259.98 5828764.80 1245 2019 506.71094655990601 1e30 -1711262.10 5828777.11 1245 2020 506.0938 1e30 -1711264.22 5828789.43 1245 2021 504.7186017036438 1e30 -1711266.35 5828801.74 1245 2022 504.435122013092041 1e30 -1711268.47 5828814.06 1245 2023 504.91023063659668 1e30 -1711270.59 5828826.37 1245 2024 505.912542343139648 1e30 -1711272.71 5828838.69 1245 2025 505.587518215179443 1e30 -1711274.84 5828851.00 1245 2026 504.99832630157471 1e30 -1711276.96 5828863.32 1245 2027 505.008578300476074 1e30 -1711279.08 5828875.63 1245 2028 505.04219532012939 1e30 -1711281.21 5828887.95 1245 2029 504.821181297302246 1e30 -1711283.33 5828900.26 1245 2030 504.4592022895813 1e30 -1711285.45 5828912.58 1245 2031 504.840731620788574 1e30 -1711287.57 5828924.89 1245 2032 506.11191987991333 1e30 -1711289.70 5828937.21 1245 2033 506.317496299743652 1e30 -1711291.82 5828949.53 1245 2034 506.132781505584717 1e30 -1711293.94 5828961.84 1245 2035 505.551457405090332 1e30 -1711296.07 5828974.16 1245 2036 504.971563816070557 1e30 -1711298.19 5828986.47 1245 2037 504.972159862518311 1e30 -1711300.31 5828998.79 1245 2038 504.96929883956909 1e30 -1711302.43 5829011.10 1245 2039 504.901647567749023 1e30 -1711304.56 5829023.42 1245 2040 505.17541170120239 1e30 -1711306.68 5829035.73 1245 2041 504.873991012573242 1e30 -1711308.80 5829048.05 1245 2042 504.2344331741333 1e30 -1711310.93 5829060.36 1245 2043 503.984570503234863 1e30 -1711313.05 5829072.68 1245 2044 504.26226854324341 1e30 -1711315.17 5829084.99 1245 2045 504.46039438247681 1e30 -1711317.29 5829097.31 1245 2046 504.606842994689941 1e30 -1711319.42 5829109.62 1245 2047 504.46623563766479 1e30 -1711321.54 5829121.94 1245 2048 503.494143486022949 1e30 -1711325.79 5829146.57 1245 2050 503.15737724304199 1e30 -1711327.91 5829158.88 1245 2051 503.427624702453613 1e30 -1711330.03 5829171.20 1245 2052 504.076838493347168 1e30 -1711332.16 5829183.51 1245 2053 504.536569118499756 1e30 -1711334.28 5829195.83 1245 2054 504.576385021209717 1e30 -1711336.40 5829208.15 1245 2055 504.633188247680664 1e30 -1711338.52 5829220.46 1245 2056 504.22513484954834 1e30 -1711340.65 5829232.78 1245 2057 503.66455316543579 1e30 -1711342.77 5829245.09 1245 2058 503.554880619049072 1e30 -1711344.89 5829257.41 1245 2059 503.590762615203857 1e30 -1711347.02 5829269.72 1245 2060 503.573119640350342 1e30 -1711349.14 5829282.04 1245 2061 503.737151622772217 1e30 -1711351.26 5829294.35 1245 2062 503.693461418151855 1e30 -1711353.38 5829306.67 1245 2063 503.2539963722229 1e30 -1711355.51 5829318.98 1245 2064 502.796590328216553 1e30 -1711357.63 5829331.30 1245 2065 502.397418022155762 1e30 -1711359.75 5829343.61 1245 2066 502.453863620758057 1e30 -1711361.88 5829355.93 1245 2067 502.652823925018311 1e30 -1711364.00 5829368.24 1245 2068 502.71701812744141 1e30 -1711366.12 5829380.56 1245 2069 502.7230978012085 1e30 -1711368.24 5829392.87 1245 2070 502.561628818511963 1e30 -1711370.37 5829405.19 1245 2071 502.45010852813721 1e30 -1711372.49 5829417.50 1245 2072 502.536654472351074 1e30 -1711374.61 5829429.82 1245 2073 502.883613109588623 1e30 -1711376.74 5829442.14 1245 2074 502.891957759857178 1e30 -1711378.86 5829454.45 1245 2075 502.792775630950928 1e30 -1711380.98 5829466.77 1245 2076 502.861261367797852 1e30 -1711383.10 5829479.08 1245 2077 502.94613838195801 1e30 -1711385.23 5829491.40 1245 2078 502.882659435272217 1e30 -1711387.35 5829503.71 1245 2079 502.9711127281189 1e30 -1711389.47 5829516.03 1245 2080 503.168106079101563 1e30 -1711391.60 5829528.34 1245 2081 503.358066082 1e30 -1711393.72 5829540.66 1245 2082 503.499209880828857 1e30 -1711395.84 5829552.97 1245 2083 503.37451696395874 1e30 -1711397.96 5829565.29 1245 2084 503.080904483795166 1e30 -1711400.09 5829577.60 1245 2085 502.76029109954834 1e30 -1711402.21 5829589.92 1245 2086 502.73978710174561 1e30 -1711404.33 5829602.23 1245 2087 502.874016761779785 1e30 -1711406.46 5829614.55 1245 2088 502.912282943725586 1e30 -1711408.58 5829626.86 1245 2089 503.090322017669678 1e30 -1711410.70 5829639.18 1245 2090 503.254294395446777 1e30 -1711412.82 5829651.49 1245 2091 503.212273120880127 1e30 -1711414.95 5829663.81 1245 2092 503.31568717956543 1e30 -1711417.07 5829676.12 1245 2093 503.220617771148682 1e30 -1711419.19 5829688.44 1245 2094 503.224074840545654 1e30 -1711421.32 5829700.76 1245 2095 503.42559814453125 1e30 -1711423.44 5829713.07 1245 2096 503.52019071578979 1e30 -1711425.56 5829725.39 1245 2097 503.255367279052734 1e30 -1711427.68 5829737.70 1245 2098 503.0704140663147 1e30 -1711429.81 5829750.02 1245 2099 503.30972671508789 1e30 -1711431.93 5829762.33 1245 2100 503.449618816375732 1e30 -1711434.05 5829774.65 1245 2101 503.427028656005859 1e30 -1711436.18 5829786.96 1245 2102 503.453969955444336 1e30 -1711438.30 5829799.28 1245 2103 503.628849983215332 1e30 -1711440.42 5829811.59 1245 2104 503.737032413482666 1e30 -1711442.54 5829823.91 1245 2105 503.825843334198 1e30 -1711444.67 5829836.22 1245 2106 503.95470857620239 1e30 -1711446.79 5829848.54 1245 2107 503.966987133026123 1e30 -1711448.91 5829860.85 1245 2108 503.979086875915527 1e30 -1711451.04 5829873.17 1245 2109 503.659069538116455 1e30 -1711453.16 5829885.48 1245 2110 503.4219622612 1e30 -1711455.28 5829897.80 1245 2111 503.36736440658569 1e30 -1711457.40 5829910.11 1245 2112 503.248453140258789 1e30 -1711459.53 5829922.43 1245 2113 502.969801425933838 1e30 -1711461.65 5829934.75 1245 2114 502.991318702697754 1e30 -1711463.77 5829947.06 1245 2115 503.236353397369385 1e30 -1711465.90 5829959.38 1245 2116 503.356277942657471 1e30 -1711468.02 5829971.69 1245 2117 503.4148097038269 1e30 -1711470.14 5829984.01 1245 2118 503.416001796722412 1e30 -1711472.26 5829996.32 1245 2119 503.396809101104736 1e30 -1711474.39 5830008.64 1245 2120 503.460466861724854 1e30 -1711476.51 5830020.95 1245 2121 503.359436988830566 1e30 -1711478.63 5830033.27 1245 2122 503.28546762466431 1e30 -1711480.76 5830045.58 1245 2123 503.392159938812256 1e30 -1711482.88 5830057.90 1245 2124 503.416717052459717 1e30 -1711485.00 5830070.21 1245 2125 503.476440906524658 1e30 -1711138.13 5827910.80 1246 1950 502.946615219116211 1e30 -1711159.36 5828033.95 1246 1960 503.655135631561279 1e30 -1711201.82 5828280.26 1246 1980 503.877937793731689 1e30 -1711223.05 5828403.41 1246 1990 505.316495895385742 1e30 -1711244.27 5828526.56 1246 2000 506.907522678375244 1e30 -1711265.50 5828649.72 1246 2010 506.115972995758057 1e30 -1711286.73 5828772.87 1246 2020 505.068063735961914 1e30 -1711307.96 5828896.02 1246 2030 504.56506013870239 1e30 -1711329.19 5829019.17 1246 2040 504.477024078369141 1e30 -1711371.65 5829265.48 1246 2060 503.2799243927002 1e30 -1711183.99 5828029.71 1247 1960 502.967119216918945 1e30 -1711226.45 5828276.02 1247 1980 504.39763069152832 1e30 -1711247.68 5828399.17 1247 1990 504.243791103363037 1e30 -1711268.91 5828522.32 1247 2000 506.368339061737061 1e30 -1711290.14 5828645.47 1247 2010 505.43588399887085 1e30 -1711311.36 5828768.63 1247 2020 504.543781280517578 1e30 -1711332.59 5828891.78 1247 2030 503.84378433227539 1e30 -1711353.82 5829014.93 1247 2040 504.354894161224365 1e30 -1711396.28 5829261.24 1247 2060 503.509402275085449 1e30 -1711208.62 5828025.47 1248 1960 502.33554840087891 1e30 -1711251.08 5828271.77 1248 1980 503.988385200500488 1e30 -1711272.31 5828394.93 1248 1990 503.898561 1e30 -1711293.54 5828518.08 1248 2000 504.8530101776123 1e30 -1711314.77 5828641.23 1248 2010 504.508495330810547 1e30 -1711336.00 5828764.38 1248 2020 503.850638866424561 1e30 -1711357.22 5828887.54 1248 2030 503.66055965423584 1e30 -1711378.45 5829010.69 1248 2040 504.455745220184326 1e30 -1711233.25 5828021.23 1249 1960 502.043068408966064 1e30 -1711275.71 5828267.53 1249 1980 503.200829029083252 1e30 -1711296.94 5828390.68 1249 1990 503.723561763763428 1e30 -1711318.17 5828513.84 1249 2000 504.31138277053833 1e30 -1711339.40 5828636.99 1249 2010 504.047513008117676 1e30 -1711360.63 5828760.14 1249 2020 503.550291061401367 1e30 -1711381.86 5828883.29 1249 2030 503.95059585571289 1e30 -1711403.08 5829006.45 1249 2040 504.617393016815186 1e30 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2030 503.225326538085938 1e30 -1711374.24 5828250.56 1253 1980 501.40911340713501 1e30 -1711395.47 5828373.71 1253 1990 503.174841403961182 1e30 -1711416.70 5828496.87 1253 2000 503.624677658081055 1e30 -1711459.15 5828743.17 1253 2020 502.908825874328613 1e30 -1711480.38 5828866.32 1253 2030 503.198385238647461 1e30 -1711398.87 5828246.32 1254 1980 502.185821533203125 1e30 -1711420.10 5828369.47 1254 1990 503.00288200378418 1e30 -1711441.33 5828492.62 1254 2000 503.2421350479126 1e30 -1711406.52 5828143.55 1255 1972 500.153720378875732 1e30 -1711408.64 5828155.87 1255 1973 500.091433525085449 1e30 -1711410.77 5828168.18 1255 1974 500.518977642059326 1e30 -1711412.89 5828180.50 1255 1975 501.350998878479 1e30 -1711415.01 5828192.81 1255 1976 502.094268798828125 1e30 -1711417.13 5828205.13 1255 1977 502.288997173309326 1e30 -1711419.26 5828217.44 1255 1978 502.182662487030029 1e30 -1711421.38 5828229.76 1255 1979 502.299129962921143 1e30 -1711423.50 5828242.07 1255 1980 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6.19049927E+05 6.08217873E+06 0 14 678.14 838.00 838.00 838 5.1131650E+03 I 430 XX - 678.143978118896484 5113.165039 -EOD 6.19099908E+05 6.08218012E+06 0 14 676.51 840.00 840.00 840 5.5801797E+03 I 430 XX - 676.5102744102478 5580.1796875 -EOD 6.19149889E+05 6.08218152E+06 0 14 675.44 842.00 842.00 842 5.3929756E+03 I 430 XX - 675.4448413848877 5392.9755859 -EOD 6.19199869E+05 6.08218292E+06 0 14 673.79 844.00 844.00 844 4.9904951E+03 I 430 XX - 673.789560794830322 4990.4951172 -EOD 6.19249850E+05 6.08218431E+06 0 14 672.40 846.00 846.00 846 5.0790654E+03 I 430 XX - 672.40059375762939 5079.06543 -EOD 6.19299830E+05 6.08218571E+06 0 14 671.28 848.00 848.00 848 4.1914834E+03 I 430 XX - 671.283185482025146 4191.4833984 -EOD 6.19349811E+05 6.08218711E+06 0 14 669.32 850.00 850.00 850 3.3231130E+03 I 430 XX - 669.32070255279541 3323.1130371 -EOD 6.19399791E+05 6.08218850E+06 0 14 666.11 852.00 852.00 852 3.5359639E+03 I 430 XX - 666.106283664703369 3535.9638672 -EOD 6.19449772E+05 6.08218990E+06 0 14 664.01 854.00 854.00 854 3.6510146E+03 I 430 XX - 664.014875888824463 3651.0146484 -EOD 6.19499752E+05 6.08219130E+06 0 14 662.54 856.00 856.00 856 3.7316296E+03 I 430 XX - 662.540555 3731.6296387 -EOD 6.19549733E+05 6.08219269E+06 0 14 662.01 858.00 858.00 858 4.3568799E+03 I 430 XX - 662.00739145278931 4356.8798828 -EOD 6.19599713E+05 6.08219409E+06 0 14 660.85 860.00 860.00 860 5.4049688E+03 I 430 XX - 660.845398902893066 5404.96875 -EOD 6.19649694E+05 6.08219549E+06 0 14 659.99 862.00 862.00 862 5.6300371E+03 I 430 XX - 659.9859 5630.037109 -EOD 6.19699674E+05 6.08219688E+06 0 14 659.66 864.00 864.00 864 5.9658374E+03 I 430 XX - 659.65902805328369 5965.8374023 -EOD 6.19749655E+05 6.08219828E+06 0 14 659.31 866.00 866.00 866 6.8917144E+03 I 430 XX - 659.310638904571533 6891.7143555 -EOD 6.19799635E+05 6.08219968E+06 0 14 658.79 868.00 868.00 868 7.4083838E+03 I 430 XX - 658.78826379776001 7408.383789 -EOD 6.19849616E+05 6.08220107E+06 0 14 658.14 870.00 870.00 870 7.4576406E+03 I 430 XX - 658.13553333282471 7457.640625 -EOD 6.19899596E+05 6.08220247E+06 0 14 657.83 872.00 872.00 872 6.8198174E+03 I 430 XX - 657.82696008682251 6819.8173828 -EOD 6.19949577E+05 6.08220387E+06 0 14 656.94 874.00 874.00 874 6.4516470E+03 I 430 XX - 656.944930553436279 6451.6469727 -EOD 6.19999557E+05 6.08220527E+06 0 14 656.49 876.00 876.00 876 5.7180732E+03 I 430 XX - 656.487703323364258 5718.0732422 -EOD 6.20049538E+05 6.08220666E+06 0 14 656.14 878.00 878.00 878 5.8636416E+03 I 430 XX - 656.142115592956543 5863.6416016 -EOD 6.20099518E+05 6.08220806E+06 0 14 656.16 880.00 880.00 880 6.0506587E+03 I 430 XX - 656.1618447303772 6050.6586914 -EOD 6.20149499E+05 6.08220946E+06 0 14 655.79 882.00 882.00 882 6.3452827E+03 I 430 XX - 655.790448188781738 6345.2827148 -EOD 6.20199479E+05 6.08221085E+06 0 14 655.31 884.00 884.00 884 6.3684517E+03 I 430 XX - 655.309796333312988 6368.45166 -EOD 6.20249460E+05 6.08221225E+06 0 14 654.88 886.00 886.00 886 6.1753379E+03 I 430 XX - 654.880702495574951 6175.337891 -EOD 6.20299440E+05 6.08221365E+06 0 14 654.39 888.00 888.00 888 6.0457393E+03 I 430 XX - 654.391169548034668 6045.7392578 -EOD 6.20349421E+05 6.08221504E+06 0 14 654.21 890.00 890.00 890 4.8677920E+03 I 430 XX - 654.205918312072754 4867.7919922 -EOD 6.20399401E+05 6.08221644E+06 0 14 654.36 892.00 892.00 892 4.5207241E+03 I 430 XX - 654.363036155700684 4520.7241211 -EOD 6.20449382E+05 6.08221784E+06 0 14 653.90 894.00 894.00 894 3.2787827E+03 I 430 XX - 653.902113437652588 3278.7827148 -EOD 6.20499362E+05 6.08221923E+06 0 14 653.21 896.00 896.00 896 3.0029854E+03 I 430 XX - 653.211832046508789 3002.9853516 -EOD 6.20549343E+05 6.08222063E+06 0 14 653.25 898.00 898.00 898 3.1154600E+03 I 430 XX - 653.253555297851563 3115.459961 -EOD 6.20599323E+05 6.08222203E+06 0 14 653.91 900.00 900.00 900 2.8067856E+03 I 430 XX - 653.908669948577881 2806.7856445 -EOD 6.18748648E+05 6.08222032E+06 0 14 684.94 826.00 826.00 826 3.8544429E+03 I 432 XX - 684.94158983230591 3854.4428711 -EOD 6.18798629E+05 6.08222172E+06 0 14 683.40 828.00 828.00 828 4.5896582E+03 I 432 XX - 683.397889137268066 4589.6582031 -EOD 6.18848609E+05 6.08222312E+06 0 14 680.10 830.00 830.00 830 5.0533584E+03 I 432 XX - 680.10258674621582 5053.3583984 -EOD 6.18898590E+05 6.08222451E+06 0 14 679.72 832.00 832.00 832 5.5765957E+03 I 432 XX - 679.722964763641357 5576.5957031 -EOD 6.18948570E+05 6.08222591E+06 0 14 679.14 834.00 834.00 834 5.3282622E+03 I 432 XX - 679.139256477355957 5328.262207 -EOD 6.18998551E+05 6.08222731E+06 0 14 678.82 836.00 836.00 836 4.8660347E+03 I 432 XX - 678.816080093383789 4866.034668 -EOD 6.19048531E+05 6.08222870E+06 0 14 677.41 838.00 838.00 838 4.8860830E+03 I 432 XX - 677.41239070892334 4886.0830078 -EOD 6.19098512E+05 6.08223010E+06 0 14 676.11 840.00 840.00 840 4.9015322E+03 I 432 XX - 676.11449956893921 4901.5322266 -EOD 6.19148492E+05 6.08223150E+06 0 14 674.72 842.00 842.00 842 4.7986255E+03 I 432 XX - 674.716472625732422 4798.6254883 -EOD 6.19198473E+05 6.08223289E+06 0 14 673.25 844.00 844.00 844 4.7427344E+03 I 432 XX - 673.249185085296631 4742.734375 -EOD 6.19248453E+05 6.08223429E+06 0 14 671.80 846.00 846.00 846 4.7257021E+03 I 432 XX - 671.802759170532227 4725.7021484 -EOD 6.19298434E+05 6.08223569E+06 0 14 669.16 848.00 848.00 848 3.9419875E+03 I 432 XX - 669.159471988677979 3941.9875488 -EOD 6.19348415E+05 6.08223709E+06 0 14 665.04 850.00 850.00 850 4.4889243E+03 I 432 XX - 665.04371166229248 4488.9243164 -EOD 6.19398395E+05 6.08223848E+06 0 14 664.14 852.00 852.00 852 4.2265605E+03 I 432 XX - 664.140164852142334 4226.5605469 -EOD 6.19448376E+05 6.08223988E+06 0 14 663.29 854.00 854.00 854 4.2832461E+03 I 432 XX - 663.291692733764648 4283.2460938 -EOD 6.19498356E+05 6.08224128E+06 0 14 661.42 856.00 856.00 856 4.6774932E+03 I 432 XX - 661.415338516235352 4677.4931641 -EOD 6.19548337E+05 6.08224267E+06 0 14 660.97 858.00 858.00 858 4.7345732E+03 I 432 XX - 660.971581935882568 4734.5732422 -EOD 6.19598317E+05 6.08224407E+06 0 14 660.51 860.00 860.00 860 5.6235562E+03 I 432 XX - 660.513222217559814 5623.5561523 -EOD 6.19648298E+05 6.08224547E+06 0 14 659.94 862.00 862.00 862 6.4759858E+03 I 432 XX - 659.936606884002686 6475.98584 -EOD 6.19698278E+05 6.08224686E+06 0 14 659.50 864.00 864.00 864 7.1132041E+03 I 432 XX - 659.501910209655762 7113.2041016 -EOD 6.19748259E+05 6.08224826E+06 0 14 659.23 866.00 866.00 866 6.8159961E+03 I 432 XX - 659.227192401885986 6815.9960938 -EOD 6.19798239E+05 6.08224966E+06 0 14 658.77 868.00 868.00 868 7.4931362E+03 I 432 XX - 658.77014398574829 7493.13623 -EOD 6.19848220E+05 6.08225105E+06 0 14 658.73 870.00 870.00 870 7.3346343E+03 I 432 XX - 658.72699022293091 7334.6342773 -EOD 6.19898200E+05 6.08225245E+06 0 14 658.34 872.00 872.00 872 6.7922788E+03 I 432 XX - 658.3402156829834 6792.2788086 -EOD 6.19948181E+05 6.08225385E+06 0 14 657.50 874.00 874.00 874 6.1756367E+03 I 432 XX - 657.495558261871338 6175.6367188 -EOD 6.19998161E+05 6.08225524E+06 0 14 656.72 876.00 876.00 876 6.0600923E+03 I 432 XX - 656.721055507659912 6060.0922852 -EOD 6.20048142E+05 6.08225664E+06 0 14 656.52 878.00 878.00 878 5.8145596E+03 I 432 XX - 656.518459320068359 5814.55957 -EOD 6.20098122E+05 6.08225804E+06 0 14 656.43 880.00 880.00 880 5.7746904E+03 I 432 XX - 656.431615352630615 5774.69043 -EOD 6.20148103E+05 6.08225944E+06 0 14 656.07 882.00 882.00 882 6.1871313E+03 I 432 XX - 656.0666561126709 6187.1313477 -EOD 6.20198083E+05 6.08226083E+06 0 14 655.67 884.00 884.00 884 6.2504102E+03 I 432 XX - 655.665218830108643 6250.4101563 -EOD 6.20248064E+05 6.08226223E+06 0 14 655.24 886.00 886.00 886 5.9192349E+03 I 432 XX - 655.235707759857178 5919.2348633 -EOD 6.20298044E+05 6.08226363E+06 0 14 654.82 888.00 888.00 888 5.1212754E+03 I 432 XX - 654.817163944244385 5121.275391 -EOD 6.20348025E+05 6.08226502E+06 0 14 654.71 890.00 890.00 890 4.8858438E+03 I 432 XX - 654.714643955230713 4885.84375 -EOD 6.20398005E+05 6.08226642E+06 0 14 654.70 892.00 892.00 892 4.2122549E+03 I 432 XX - 654.697597026824951 4212.2548828 -EOD 6.20447986E+05 6.08226782E+06 0 14 654.24 894.00 894.00 894 3.8484702E+03 I 432 XX - 654.244840145111084 3848.4702148 -EOD 6.20497966E+05 6.08226921E+06 0 14 654.51 896.00 896.00 896 3.0470859E+03 I 432 XX - 654.51347827911377 3047.0859375 -EOD 6.20547947E+05 6.08227061E+06 0 14 654.62 898.00 898.00 898 2.9586670E+03 I 432 XX - 654.615461826324463 2958.6669922 -EOD 6.20597927E+05 6.08227201E+06 0 14 653.84 900.00 900.00 900 3.0598054E+03 I 432 XX - 653.842449188232422 3059.80542 -EOD 6.18697272E+05 6.08226891E+06 0 14 686.24 824.00 824.00 824 3.8186611E+03 I 434 XX - 686.244487762451172 3818.6611328 -EOD 6.18747252E+05 6.08227030E+06 0 14 683.76 826.00 826.00 826 4.1933530E+03 I 434 XX - 683.75861644744873 4193.3530273 -EOD 6.18797233E+05 6.08227170E+06 0 14 681.74 828.00 828.00 828 4.8787661E+03 I 434 XX - 681.736648082733154 4878.7661133 -EOD 6.18847213E+05 6.08227310E+06 0 14 680.23 830.00 830.00 830 4.7899238E+03 I 434 XX - 680.227220058441162 4789.9238281 -EOD 6.18897194E+05 6.08227449E+06 0 14 679.23 832.00 832.00 832 5.2944712E+03 I 434 XX - 679.234147071838379 5294.4711914 -EOD 6.18947174E+05 6.08227589E+06 0 14 677.56 834.00 834.00 834 5.5068247E+03 I 434 XX - 677.563250064849854 5506.824707 -EOD 6.18997155E+05 6.08227729E+06 0 14 677.08 836.00 836.00 836 5.0945781E+03 I 434 XX - 677.075982093811035 5094.578125 -EOD 6.19047135E+05 6.08227868E+06 0 14 675.88 838.00 838.00 838 5.0545664E+03 I 434 XX - 675.884723663330078 5054.5664063 -EOD 6.19097116E+05 6.08228008E+06 0 14 675.53 840.00 840.00 840 5.6339585E+03 I 434 XX - 675.5293607711792 5633.9584961 -EOD 6.19147096E+05 6.08228148E+06 0 14 673.76 842.00 842.00 842 5.0471230E+03 I 434 XX - 673.756062984466553 5047.1230469 -EOD 6.19197077E+05 6.08228287E+06 0 14 672.10 844.00 844.00 844 4.9982549E+03 I 434 XX - 672.103285789489746 4998.2548828 -EOD 6.19247057E+05 6.08228427E+06 0 14 670.97 846.00 846.00 846 4.5352285E+03 I 434 XX - 670.973479747772217 4535.2285156 -EOD 6.19297038E+05 6.08228567E+06 0 14 665.55 848.00 848.00 848 4.0669761E+03 I 434 XX - 665.547788143157959 4066.9760742 -EOD 6.19347018E+05 6.08228706E+06 0 14 663.52 850.00 850.00 850 4.8065913E+03 I 434 XX - 663.523495197296143 4806.5913086 -EOD 6.19396999E+05 6.08228846E+06 0 14 662.71 852.00 852.00 852 4.9886494E+03 I 434 XX - 662.709474563598633 4988.6494141 -EOD 6.19446979E+05 6.08228986E+06 0 14 661.94 854.00 854.00 854 5.1566025E+03 I 434 XX - 661.935925483703613 5156.602539 -EOD 6.19496960E+05 6.08229126E+06 0 14 661.06 856.00 856.00 856 5.3688423E+03 I 434 XX - 661.059975624084473 5368.8422852 -EOD 6.19546941E+05 6.08229265E+06 0 14 660.46 858.00 858.00 858 5.8871948E+03 I 434 XX - 660.462677478790283 5887.1948242 -EOD 6.19596921E+05 6.08229405E+06 0 14 660.07 860.00 860.00 860 7.4391870E+03 I 434 XX - 660.064995288848877 7439.1870117 -EOD 6.19646902E+05 6.08229545E+06 0 14 659.80 862.00 862.00 862 7.7959668E+03 I 434 XX - 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438 XX - 683.536410331726074 4322.734375 -EOD 6.18694480E+05 6.08236886E+06 0 14 683.20 824.00 824.00 824 4.8125225E+03 I 438 XX - 683.2042932510376 4812.522461 -EOD 6.18744460E+05 6.08237026E+06 0 14 681.91 826.00 826.00 826 4.6907305E+03 I 438 XX - 681.909799575805664 4690.7304688 -EOD 6.18794441E+05 6.08237166E+06 0 14 680.09 828.00 828.00 828 4.3450376E+03 I 438 XX - 680.09418249130249 4345.0375977 -EOD 6.18894402E+05 6.08237445E+06 0 14 677.46 832.00 832.00 832 5.1825596E+03 I 438 XX - 677.460968494415283 5182.55957 -EOD 6.18944382E+05 6.08237585E+06 0 14 678.09 834.00 834.00 834 5.1612393E+03 I 438 XX - 678.08997631072998 5161.2392578 -EOD 6.18994363E+05 6.08237725E+06 0 14 675.92 836.00 836.00 836 5.0047617E+03 I 438 XX - 675.918877124786377 5004.7617188 -EOD 6.19044343E+05 6.08237864E+06 0 14 674.31 838.00 838.00 838 4.9256465E+03 I 438 XX - 674.30961132049561 4925.6464844 -EOD 6.19094324E+05 6.08238004E+06 0 14 673.04 840.00 840.00 840 4.7967944E+03 I 438 XX - 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647.185146808624268 -EOD 6.20625848E+05 6.08127242E+06 0 14 649.50 900.00 900.00 900 3.4028235E+38 I 392 XX - 649.499177932739258 -EOD 6.20675828E+05 6.08127382E+06 0 14 648.82 902.00 902.00 902 3.4028235E+38 I 392 XX - 648.824453353881836 -EOD 6.20725809E+05 6.08127521E+06 0 14 649.02 904.00 904.00 904 3.4028235E+38 I 392 XX - 649.020075798034668 -EOD 6.20775789E+05 6.08127661E+06 0 14 648.99 906.00 906.00 906 3.4028235E+38 I 392 XX - 648.98914098739624 -EOD 6.20825770E+05 6.08127801E+06 0 14 648.10 908.00 908.00 908 3.4028235E+38 I 392 XX - 648.096024990081787 -EOD 6.20875750E+05 6.08127941E+06 0 14 647.64 910.00 910.00 910 3.4028235E+38 I 392 XX - 647.639989852905273 -EOD 6.20925731E+05 6.08128080E+06 0 14 647.40 912.00 912.00 912 3.4028235E+38 I 392 XX - 647.403955459594727 -EOD 6.20975711E+05 6.08128220E+06 0 14 647.47 914.00 914.00 914 3.4028235E+38 I 392 XX - 647.469103336334229 -EOD 6.20474510E+05 6.08131821E+06 0 14 650.67 894.00 894.00 894 3.4028235E+38 I 394 XX - 650.671958923339844 -EOD 6.20524491E+05 6.08131961E+06 0 14 650.00 896.00 896.00 896 3.4028235E+38 I 394 XX - 650.001585483551025 -EOD 6.20574471E+05 6.08132100E+06 0 14 649.78 898.00 898.00 898 3.4028235E+38 I 394 XX - 649.781584739685059 -EOD 6.20624452E+05 6.08132240E+06 0 14 649.09 900.00 900.00 900 3.4028235E+38 I 394 XX - 649.094522 -EOD 6.20674432E+05 6.08132380E+06 0 14 649.15 902.00 902.00 902 3.4028235E+38 I 394 XX - 649.149537086486816 -EOD 6.20724413E+05 6.08132519E+06 0 14 648.71 904.00 904.00 904 3.4028235E+38 I 394 XX - 648.709952831268311 -EOD 6.20774393E+05 6.08132659E+06 0 14 648.24 906.00 906.00 906 3.4028235E+38 I 394 XX - 648.2386589050293 -EOD 6.20824374E+05 6.08132799E+06 0 14 647.96 908.00 908.00 908 3.4028235E+38 I 394 XX - 647.9601264 -EOD 6.20874354E+05 6.08132938E+06 0 14 647.63 910.00 910.00 910 3.4028235E+38 I 394 XX - 647.63021469116211 -EOD 6.20924335E+05 6.08133078E+06 0 14 647.46 912.00 912.00 912 3.4028235E+38 I 394 XX - 647.460818290710449 -EOD 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667.460441589355469 7226.3969727 0.8213466 3 618 -619334.45 6082736.88 666.599869728088379 6383.5024414 0.8123415 3 603 -618583.35 6082765.90 683.505415916442871 5067.8168945 0.821839 6 731 -618633.33 6082767.30 681.921243667602539 3855.5649414 0.8879527 6 721 -618683.31 6082768.70 679.199278354644775 3736.6398926 0.87253 6 714 -618733.29 6082770.10 676.459550857543945 4081.510498 0.8629076 6 702 -618783.27 6082771.49 674.712598323822021 4463.2709961 0.8291589 6 703 -618833.25 6082772.89 674.134135246276855 4116.0576172 0.8186976 6 680 -619083.16 6082779.87 670.8950400352478 6887.3647461 0.8598733 3 673 -619133.14 6082781.27 670.182585716247559 6904.3310547 0.8570022 3 658 -619183.12 6082782.67 669.041335582733154 7019.397949 0.8573377 3 645 -619233.10 6082784.06 668.39599609375 7165.974609 0.8182187 3 631 -618531.97 6082814.49 683.696866035461426 5048.0092773 0.819359 6 747 -618581.95 6082815.88 682.67726898193359 4543.2392578 0.874469 6 738 -618631.93 6082817.28 680.311322212219238 3781.0600586 0.8784372 6 728 -618681.92 6082818.68 677.713990211486816 3614.755127 0.85732 6 737 -618731.90 6082820.07 676.07349157333374 4571.616211 0.8163735 6 715 -618931.82 6082825.66 672.45405912399292 6622.4306641 0.8225241 3 732 -618981.80 6082827.06 671.86582088470459 6597.6518555 0.8448937 3 722 -619031.78 6082828.46 671.438157558441162 7447.375 0.8072152 3 707 -619081.76 6082829.85 671.2002158164978 6815.8803711 0.8442451 3 695 -619131.74 6082831.25 670.957446098327637 6786.2080078 0.810491 3 675 -618480.60 6082863.07 682.875990867614746 5284.0556641 0.819101 6 769 -618530.58 6082864.47 682.65664577484131 4811.0146484 0.8480741 6 760 -618580.56 6082865.86 681.001901626586914 4554.46875 0.8760117 6 749 -618630.54 6082867.26 677.681863307952881 4633.7734375 0.8404365 6 739 -618930.42 6082875.64 672.385215759277344 7021.462891 0.865459 3 744 -618980.40 6082877.04 672.03068733215332 6965.241211 0.8469411 3 732 -619030.38 6082878.44 671.351075172424316 7672.02002 0.8334214 3 718 -619080.36 6082879.83 671.065449714660645 7670.71875 0.82623 3 708 -618429.22 6082911.65 683.254122734069824 4992.0898438 0.8304 6 793 -618479.20 6082913.05 682.10071325302124 4655.8334961 0.8427049 6 788 -618529.18 6082914.45 680.557608604431152 4082.6442871 0.866598 6 770 -618579.16 6082915.84 678.341925144195557 4094.6877441 0.8442568 6 762 -618829.06 6082922.83 673.301994800567627 5771.7744141 0.801882 3 775 -618879.05 6082924.22 672.704339027404785 6914.599609 0.8502927 3 763 -618929.03 6082925.62 671.98479175567627 6870.0634766 0.91158 3 752 -618979.01 6082927.02 671.979963779449463 7049.1088867 0.8855852 3 743 -619028.99 6082928.41 671.513080596923828 7407.0273438 0.8133028 3 734 -618527.79 6082964.43 679.617464542388916 4508.8959961 0.8084373 6 786 -618777.69 6082971.41 673.672795295715332 6774.8984375 0.8157076 3 792 -618827.67 6082972.81 673.0157732963562 7378.730957 0.842069 3 787 -618877.65 6082974.20 672.49965667724609 7504.4140625 0.8803915 3 776 -618927.63 6082975.60 672.073543071746826 7455.587891 0.8808733 3 764 -618977.61 6082977.00 671.933293342590332 7716.3505859 0.8435383 3 753 -618676.33 6083018.60 676.2310266494751 6257.6445313 0.8058448 3 817 -618726.31 6083019.99 675.50802230834961 6887.1430664 0.8255024 3 804 -618776.29 6083021.39 674.73071813583374 7180.7915039 0.8418719 3 799 -618826.27 6083022.79 673.555970191955566 7259.380859 0.8625714 3 796 -618876.25 6083024.18 672.933816909790039 7509.366211 0.8841054 3 788 -618926.23 6083025.58 672.85001277923584 7152.269043 0.8366688 3 774 -618674.94 6083068.57 676.543593406677246 6805.2265625 0.8094879 3 820 -618724.92 6083069.97 676.021814346313477 7120.1401367 0.8402357 3 818 -618774.90 6083071.37 675.45473575592041 7441.76416 0.8529767 3 805 -618824.88 6083072.77 674.5879054069519 7242.9238281 0.882599 3 801 -618874.86 6083074.16 674.335241317749023 7623.931641 0.8426436 3 798 -618924.84 6083075.56 674.0570068359375 7592.6689453 0.802233 3 790 -618623.56 6083117.16 677.431344985961914 7014.6040039 0.8030011 3 833 -618673.54 6083118.55 676.977157592773438 6970.9013672 0.8236315 3 825 -618723.52 6083119.95 676.339626312255859 7581.9052734 0.8423485 3 821 -618773.50 6083121.35 675.81635713577271 7807.7646484 0.8561245 3 811 -618823.48 6083122.74 675.419092178344727 8509.953125 0.845209 3 806 -618873.46 6083124.14 674.90470409393311 8224.94043 0.8138816 3 803 -618572.18 6083165.74 677.229046821594238 7169.7402344 0.807728 3 846 -618622.16 6083167.14 677.068710327148438 7043.8945313 0.8446978 3 852 -618672.14 6083168.53 676.740407943725586 7372.39209 0.8499179 3 834 -618722.12 6083169.93 676.226139068603516 7560.7475586 0.8773138 3 826 -618772.10 6083171.33 675.884246826171875 7838.6083984 0.841625 3 823 -618822.08 6083172.72 675.43321847915649 8684.8242188 0.834269 3 809 -618570.79 6083215.72 677.045822143554688 7640.6274414 0.8135412 3 859 -618620.77 6083217.12 676.879525184631348 7746.7446289 0.8355589 3 847 -618670.75 6083218.51 676.67496204376221 7396.2988281 0.8533487 3 841 -618720.73 6083219.91 676.441669464111328 7604.866699 0.8512965 3 832 -618770.71 6083221.31 676.133990287780762 7561.5942383 0.8425522 3 830 -618820.69 6083222.70 675.39322376251221 7554.4023438 0.8145924 3 824 -618519.41 6083264.30 677.442252635955811 7179.3056641 0.8035876 3 867 -618569.39 6083265.70 677.238225936889648 7520.6264648 0.82673 3 864 -618619.37 6083267.09 677.2153377532959 7281.2832031 0.8441564 3 858 -618669.35 6083268.49 676.993608474731445 7132.26123 0.8443956 3 850 -618719.33 6083269.89 676.629066467285156 7389.3510742 0.8428818 3 842 -618769.31 6083271.29 676.325440406799316 6946.1069336 0.8083115 3 834 -618567.99 6083315.68 678.04849147796631 5986.960449 0.8263472 3 868 -618617.97 6083317.07 677.864551544189453 6136.90332 0.820042 3 865 -618667.96 6083318.47 677.22928524017334 6176.4741211 0.8175343 3 854 -618466.64 6083362.86 679.721832275390625 6189.7797852 0.8040577 3 871 -618516.62 6083364.26 679.491996765136719 6464.790039 0.8139377 3 870 -618566.60 6083365.66 679.105877876281738 6341.8598633 0.8009128 3 869 -618365.28 6083410.05 680.7628870010376 6352.5922852 0.8014454 3 874 -618415.26 6083411.45 680.1837682724 6389.2919922 0.8147874 3 873 -618465.24 6083412.84 679.97086048126221 6196.1948242 0.808239 3 872 -618413.86 6083461.42 680.22686243057251 6570.4682617 0.8076513 3 875 diff --git a/data/OdT/3d_horizon/Test_Multi_XY-and-ILXL_Z-only.dat b/data/OdT/3d_horizon/Test_Multi_XY-and-ILXL_Z-only.dat deleted file mode 100644 index 9dedc40..0000000 --- a/data/OdT/3d_horizon/Test_Multi_XY-and-ILXL_Z-only.dat +++ /dev/null @@ -1,20 +0,0 @@ -"green event" 1702847.50 5817482.17 990 1015 1290.0543212890625 -"green event" 1702849.63 5817494.48 990 1016 1293.149948120117188 -"green event" 1702851.75 5817506.80 990 1017 1296.637773513793945 -"green event" 1702853.87 5817519.11 990 1018 1300.151824951171875 -"green event" 1702855.99 5817531.43 990 1019 1303.32267284393311 -"green event" 1702858.12 5817543.75 990 1020 1304.401159286499023 -"green event" 1702860.24 5817556.06 990 1021 1299.368619918823242 -"green event" 1702862.36 5817568.38 990 1022 1301.177978515625 -"green event" 1702864.49 5817580.69 990 1023 1303.662657737731934 -"green event" 1702866.61 5817593.01 990 1024 1305.018186569213867 -"blue event" 1702847.50 5817482.17 990 1015 1290.0543212890625 -"blue event" 1702849.63 5817494.48 990 1016 1293.149948120117188 -"blue event" 1702851.75 5817506.80 990 1017 1296.637773513793945 -"blue event" 1702853.87 5817519.11 990 1018 1300.151824951171875 -"blue event" 1702855.99 5817531.43 990 1019 1303.32267284393311 -"blue event" 1702858.12 5817543.75 990 1020 1304.401159286499023 -"blue event" 1702860.24 5817556.06 990 1021 1299.368619918823242 -"blue event" 1702862.36 5817568.38 990 1022 1301.177978515625 -"blue event" 1702864.49 5817580.69 990 1023 1303.662657737731934 -"blue event" 1702866.61 5817593.01 990 1024 1305.018186569213867 \ No newline at end of file diff --git a/data/OdT/LICENCE.md b/data/OdT/LICENCE.md deleted file mode 100644 index 36bbefe..0000000 --- a/data/OdT/LICENCE.md +++ /dev/null @@ -1,5 +0,0 @@ -# LICENCE - -These horizon files were tracked by [Matt Hall](matt@agilescientific.com) on the [Demo F3 volume](https://terranubis.com/datainfo/F3-Demo-2020) in [dGB's OpendTect software](https://dgbes.com/). - -While the F3 dataset itself is CC BY-SA 3.0, the data in this folder are licensed under the terms of CC0 and should be considered [Public Domain](https://creativecommons.org/share-your-work/public-domain/cc0/). diff --git a/data/Petrel/IESX2D_horizon_extract.txt b/data/Petrel/IESX2D_horizon_extract.txt deleted file mode 100644 index ecb72e3..0000000 --- a/data/Petrel/IESX2D_horizon_extract.txt +++ /dev/null @@ -1,376 +0,0 @@ -PROFILE Fault to seafloor TYPE 1 5 By Petrel 2014.2 (64-bit) 2d_ci7m_gf.ifdf m ms -SNAPPING PARAMETERS 5 2 2 - 4.19846879E+05 4.66812161E+06 1 14 2742.85 5460.00 16382.00 4262 2 276-109::normTOv - 4.19840089E+05 4.66813210E+06 1 14 2756.25 5461.00 16383.00 4263 2 276-109::normTOv - 4.19833299E+05 4.66814259E+06 1 14 2769.65 5461.00 16384.00 4264 2 276-109::normTOv - 4.19826509E+05 4.66815308E+06 1 14 2783.05 5461.00 16385.00 4265 2 276-109::normTOv - 4.19819720E+05 4.66816357E+06 1 14 2733.93 5462.00 16386.00 4266 2 276-109::normTOv - 4.19812930E+05 4.66817406E+06 1 14 2809.85 5462.00 16387.00 4267 2 276-109::normTOv - 4.19806140E+05 4.66818455E+06 1 14 2823.25 5462.00 16388.00 4268 2 276-109::normTOv - 4.19799350E+05 4.66819504E+06 1 14 2836.66 5463.00 16389.00 4269 2 276-109::normTOv - 4.19792560E+05 4.66820553E+06 1 14 2850.06 5463.00 16390.00 4270 2 276-109::normTOv - 4.19785771E+05 4.66821602E+06 1 14 2863.46 5463.00 16391.00 4271 2 276-109::normTOv - 4.19778981E+05 4.66822651E+06 1 14 2876.86 5464.00 16392.00 4272 2 276-109::normTOv - 4.19772191E+05 4.66823700E+06 1 14 2890.26 5464.00 16393.00 4273 2 276-109::normTOv - 4.19765401E+05 4.66824749E+06 1 14 2903.66 5464.00 16394.00 4274 2 276-109::normTOv - 4.19758611E+05 4.66825798E+06 1 14 2917.06 5465.00 16395.00 4275 2 276-109::normTOv - 4.19751822E+05 4.66826847E+06 1 14 2930.46 5465.00 16396.00 4276 2 276-109::normTOv - 4.19745032E+05 4.66827896E+06 1 14 2943.86 5465.00 16397.00 4277 2 276-109::normTOv - 4.19738242E+05 4.66828945E+06 1 14 2957.27 5466.00 16398.00 4278 2 276-109::normTOv - 4.19731452E+05 4.66829994E+06 1 14 2970.67 5466.00 16399.00 4279 2 276-109::normTOv - 4.19724662E+05 4.66831044E+06 1 14 2984.07 5466.00 16400.00 4280 2 276-109::normTOv - 4.19717873E+05 4.66832093E+06 1 14 2997.47 5467.00 16401.00 4281 2 276-109::normTOv - 4.19711083E+05 4.66833142E+06 1 14 3010.87 5467.00 16402.00 4282 2 276-109::normTOv - 4.19704293E+05 4.66834191E+06 1 14 3024.27 5467.00 16403.00 4283 2 276-109::normTOv - 4.19697503E+05 4.66835240E+06 1 14 3037.67 5468.00 16404.00 4284 2 276-109::normTOv - 4.19690713E+05 4.66836289E+06 1 14 3051.07 5468.00 16405.00 4285 2 276-109::normTOv - 4.19683924E+05 4.66837338E+06 1 14 3064.47 5468.00 16406.00 4286 2 276-109::normTOv - 4.19677134E+05 4.66838387E+06 1 14 3077.87 5469.00 16407.00 4287 2 276-109::normTOv - 4.19670344E+05 4.66839436E+06 1 14 3091.28 5469.00 16408.00 4288 2 276-109::normTOv - 4.19663554E+05 4.66840485E+06 1 14 3104.68 5469.00 16409.00 4289 2 276-109::normTOv - 4.19656764E+05 4.66841534E+06 1 14 3118.08 5470.00 16410.00 4290 2 276-109::normTOv - 4.19649975E+05 4.66842583E+06 1 14 3131.48 5470.00 16411.00 4291 2 276-109::normTOv - 4.19643185E+05 4.66843632E+06 1 14 3144.88 5470.00 16412.00 4292 2 276-109::normTOv - 4.19636395E+05 4.66844681E+06 1 14 3158.28 5471.00 16413.00 4293 2 276-109::normTOv - 4.19629605E+05 4.66845730E+06 1 14 3171.68 5471.00 16414.00 4294 2 276-109::normTOv - 4.19622815E+05 4.66846779E+06 1 14 3185.08 5471.00 16415.00 4295 2 276-109::normTOv - 4.19616026E+05 4.66847828E+06 1 14 3198.48 5472.00 16416.00 4296 2 276-109::normTOv - 4.19609236E+05 4.66848877E+06 1 14 3211.89 5472.00 16417.00 4297 2 276-109::normTOv - 4.19602446E+05 4.66849926E+06 1 14 3225.29 5472.00 16418.00 4298 2 276-109::normTOv - 4.19595656E+05 4.66850975E+06 1 14 3238.69 5473.00 16419.00 4299 2 276-109::normTOv - 4.19588866E+05 4.66852024E+06 1 14 3252.09 5473.00 16420.00 4300 2 276-109::normTOv - 4.19582077E+05 4.66853073E+06 1 14 3265.49 5473.00 16421.00 4301 2 276-109::normTOv - 4.19575287E+05 4.66854122E+06 1 14 3278.89 5474.00 16422.00 4302 2 276-109::normTOv - 4.19568497E+05 4.66855171E+06 1 14 3292.29 5474.00 16423.00 4303 2 276-109::normTOv - 4.19561707E+05 4.66856220E+06 1 14 3305.69 5474.00 16424.00 4304 2 276-109::normTOv - 4.19554917E+05 4.66857269E+06 1 14 3319.09 5475.00 16425.00 4305 2 276-109::normTOv - 4.19548128E+05 4.66858318E+06 1 14 3332.50 5475.00 16426.00 4306 2 276-109::normTOv - 4.19541338E+05 4.66859367E+06 1 14 3345.90 5475.00 16427.00 4307 2 276-109::normTOv - 4.19534548E+05 4.66860416E+06 1 14 3359.30 5476.00 16428.00 4308 2 276-109::normTOv - 4.19527758E+05 4.66861465E+06 1 14 3372.70 5476.00 16429.00 4309 2 276-109::normTOv - 4.19520968E+05 4.66862514E+06 1 14 3386.10 5476.00 16430.00 4310 2 276-109::normTOv - 4.19514179E+05 4.66863563E+06 1 14 3399.50 5477.00 16431.00 4311 2 276-109::normTOv - 4.19507389E+05 4.66864612E+06 1 14 3412.90 5477.00 16432.00 4312 2 276-109::normTOv - 4.19500599E+05 4.66865661E+06 1 14 3426.30 5477.00 16433.00 4313 2 276-109::normTOv - 4.19493809E+05 4.66866711E+06 1 14 3439.70 5478.00 16434.00 4314 2 276-109::normTOv - 4.19487019E+05 4.66867760E+06 1 14 3453.10 5478.00 16435.00 4315 2 276-109::normTOv - 4.19480230E+05 4.66868809E+06 1 14 3466.51 5478.00 16436.00 4316 2 276-109::normTOv - 4.19473440E+05 4.66869858E+06 1 14 3479.91 5479.00 16437.00 4317 2 276-109::normTOv - 4.19466650E+05 4.66870907E+06 1 14 3493.31 5479.00 16438.00 4318 2 276-109::normTOv - 4.19459860E+05 4.66871956E+06 1 14 3506.71 5479.00 16439.00 4319 2 276-109::normTOv - 4.19453070E+05 4.66873005E+06 1 14 3520.11 5480.00 16440.00 4320 2 276-109::normTOv - 4.19446281E+05 4.66874054E+06 1 14 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4.67967142E+06 2 14 2837.27 5827.00 17483.00 5363 2 276-109::normTOv - 4.12364521E+05 4.67968191E+06 2 14 2849.98 5828.00 17484.00 5364 2 276-109::normTOv - 4.12357731E+05 4.67969240E+06 2 14 2862.68 5828.00 17485.00 5365 2 276-109::normTOv - 4.12350941E+05 4.67970289E+06 2 14 2875.39 5828.00 17486.00 5366 2 276-109::normTOv - 4.12344151E+05 4.67971338E+06 2 14 2888.10 5829.00 17487.00 5367 2 276-109::normTOv - 4.12337362E+05 4.67972387E+06 2 14 2900.81 5829.00 17488.00 5368 2 276-109::normTOv - 4.12330572E+05 4.67973436E+06 2 14 2913.51 5829.00 17489.00 5369 2 276-109::normTOv - 4.12323782E+05 4.67974485E+06 2 14 2926.22 5830.00 17490.00 5370 2 276-109::normTOv - 4.12316992E+05 4.67975534E+06 2 14 2938.93 5830.00 17491.00 5371 2 276-109::normTOv - 4.12310202E+05 4.67976583E+06 2 14 2951.63 5830.00 17492.00 5372 2 276-109::normTOv - 4.12303413E+05 4.67977632E+06 2 14 2964.34 5831.00 17493.00 5373 2 276-109::normTOv - 4.12296623E+05 4.67978681E+06 2 14 2977.05 5831.00 17494.00 5374 2 276-109::normTOv - 4.12289833E+05 4.67979730E+06 2 14 2989.76 5831.00 17495.00 5375 2 276-109::normTOv - 4.12283043E+05 4.67980779E+06 2 14 3002.46 5832.00 17496.00 5376 2 276-109::normTOv - 4.12276253E+05 4.67981828E+06 2 14 3015.17 5832.00 17497.00 5377 2 276-109::normTOv - 4.12269464E+05 4.67982877E+06 2 14 3027.88 5832.00 17498.00 5378 2 276-109::normTOv - 4.12262674E+05 4.67983926E+06 2 14 3040.58 5833.00 17499.00 5379 2 276-109::normTOv - 4.12255884E+05 4.67984975E+06 2 14 3053.29 5833.00 17500.00 5380 2 276-109::normTOv - 4.12249094E+05 4.67986024E+06 2 14 3066.00 5833.00 17501.00 5381 2 276-109::normTOv - 4.12242304E+05 4.67987073E+06 2 14 3078.70 5834.00 17502.00 5382 2 276-109::normTOv - 4.12235515E+05 4.67988122E+06 2 14 3091.41 5834.00 17503.00 5383 2 276-109::normTOv - 4.12228725E+05 4.67989171E+06 2 14 3104.12 5834.00 17504.00 5384 2 276-109::normTOv - 4.12221935E+05 4.67990220E+06 2 14 3116.83 5835.00 17505.00 5385 2 276-109::normTOv - 4.12215145E+05 4.67991269E+06 2 14 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4.12133668E+05 4.68003858E+06 2 14 3282.02 5839.00 17518.00 5398 2 276-109::normTOv - 4.12126878E+05 4.68004907E+06 2 14 3294.73 5839.00 17519.00 5399 2 276-109::normTOv - 4.12120088E+05 4.68005956E+06 2 14 3307.43 5840.00 17520.00 5400 2 276-109::normTOv - 4.12113298E+05 4.68007005E+06 2 14 3320.14 5840.00 17521.00 5401 2 276-109::normTOv - 4.12106508E+05 4.68008054E+06 2 14 3332.85 5840.00 17522.00 5402 2 276-109::normTOv - 4.12099719E+05 4.68009103E+06 2 14 3345.55 5841.00 17523.00 5403 2 276-109::normTOv - 4.12092929E+05 4.68010152E+06 2 14 3358.26 5841.00 17524.00 5404 2 276-109::normTOv - 4.12086139E+05 4.68011201E+06 2 14 3370.97 5841.00 17525.00 5405 2 276-109::normTOv - 4.12079349E+05 4.68012250E+06 2 14 3383.67 5842.00 17526.00 5406 2 276-109::normTOv - 4.12072559E+05 4.68013299E+06 2 14 3396.38 5842.00 17527.00 5407 2 276-109::normTOv - 4.12065770E+05 4.68014348E+06 2 14 3409.09 5842.00 17528.00 5408 2 276-109::normTOv - 4.12058980E+05 4.68015397E+06 2 14 3421.80 5843.00 17529.00 5409 2 276-109::normTOv - 4.12052190E+05 4.68016446E+06 2 14 3434.50 5843.00 17530.00 5410 2 276-109::normTOv - 4.12045400E+05 4.68017495E+06 2 14 3447.21 5843.00 17531.00 5411 2 276-109::normTOv - 4.12038610E+05 4.68018544E+06 2 14 3459.92 5844.00 17532.00 5412 2 276-109::normTOv - 4.12031821E+05 4.68019593E+06 2 14 3472.62 5844.00 17533.00 5413 2 276-109::normTOv - 4.12025031E+05 4.68020642E+06 2 14 3485.33 5844.00 17534.00 5414 2 276-109::normTOv - 4.12018241E+05 4.68021691E+06 2 14 3498.04 5845.00 17535.00 5415 2 276-109::normTOv - 4.12011451E+05 4.68022740E+06 2 14 3510.75 5845.00 17536.00 5416 2 276-109::normTOv - 4.12004661E+05 4.68023789E+06 2 14 3523.45 5845.00 17537.00 5417 2 276-109::normTOv - 4.11997872E+05 4.68024838E+06 2 14 3536.16 5846.00 17538.00 5418 2 276-109::normTOv - 4.11991082E+05 4.68025887E+06 2 14 3548.87 5846.00 17539.00 5419 2 276-109::normTOv - 4.11984292E+05 4.68026936E+06 2 14 3561.57 5846.00 17540.00 5420 2 276-109::normTOv - 4.11977502E+05 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276-109::normTOv - 4.11896025E+05 4.68040574E+06 2 14 3726.77 5851.00 17553.00 5433 2 276-109::normTOv - 4.11889235E+05 4.68041623E+06 2 14 3739.47 5851.00 17554.00 5434 2 276-109::normTOv - 4.11882445E+05 4.68042672E+06 2 14 3752.18 5851.00 17555.00 5435 2 276-109::normTOv - 4.11875655E+05 4.68043721E+06 2 14 3764.89 5852.00 17556.00 5436 2 276-109::normTOv - 4.11868865E+05 4.68044770E+06 2 14 3777.59 5852.00 17557.00 5437 2 276-109::normTOv - 4.11862076E+05 4.68045819E+06 2 14 3790.30 5852.00 17558.00 5438 2 276-109::normTOv - 4.11855286E+05 4.68046868E+06 2 14 3803.01 5853.00 17559.00 5439 2 276-109::normTOv - 4.11848496E+05 4.68047917E+06 2 14 3815.71 5853.00 17560.00 5440 2 276-109::normTOv - 4.11841706E+05 4.68048966E+06 2 14 3828.42 5853.00 17561.00 5441 2 276-109::normTOv - 4.11834917E+05 4.68050015E+06 2 14 3841.13 5854.00 17562.00 5442 2 276-109::normTOv - 4.11828127E+05 4.68051064E+06 2 14 3853.84 5854.00 17563.00 5443 2 276-109::normTOv - 4.11821337E+05 4.68052113E+06 2 14 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16574.00 4454 2 272-100::normTOv -EOD diff --git a/data/Surfer/LICENSE b/data/Surfer/LICENSE deleted file mode 100644 index 9f2849d..0000000 --- a/data/Surfer/LICENSE +++ /dev/null @@ -1,29 +0,0 @@ -BSD 3-Clause License - -Copyright (c) 2018, Christopher Bane Sullivan -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/data/Surfer/WDS1_Si_TAP_Quant.grd b/data/Surfer/WDS1_Si_TAP_Quant.grd deleted file mode 100644 index 0309e22..0000000 Binary files a/data/Surfer/WDS1_Si_TAP_Quant.grd and /dev/null differ diff --git a/data/Surfer/surfer-6-ascii-tiny.grd b/data/Surfer/surfer-6-ascii-tiny.grd deleted file mode 100644 index da806e4..0000000 --- a/data/Surfer/surfer-6-ascii-tiny.grd +++ /dev/null @@ -1,15 +0,0 @@ -DSAA -10 10 -0.0 9.0 -0.0 7.0 -25.00 97.19 -91.03 77.21 60.55 46.67 52.73 64.05 41.19 54.99 44.30 25.00 -96.04 81.10 62.38 48.74 57.50 63.27 48.67 60.81 51.78 33.63 -92.10 85.05 65.09 53.01 64.44 65.64 52.53 66.54 59.29 41.33 -94.04 85.63 65.56 55.32 73.18 70.88 55.35 76.27 67.20 45.78 -97.19 82.00 64.21 61.97 82.99 80.34 58.55 86.28 75.02 48.75 -91.36 78.73 64.05 65.60 82.58 81.37 61.16 89.09 81.36 54.87 -86.31 77.58 67.71 68.50 73.37 74.84 65.35 95.55 85.92 55.76 -80.88 75.56 74.35 72.47 66.93 75.49 86.39 92.10 84.41 55.00 -74.77 66.02 70.29 75.16 60.56 65.56 85.07 89.81 74.53 51.69 -70.00 54.19 62.27 74.51 55.95 55.42 71.21 74.63 63.14 44.99 \ No newline at end of file diff --git a/data/Surfer/surfer-6-ascii.grd b/data/Surfer/surfer-6-ascii.grd deleted file mode 100644 index 948b0ac..0000000 --- a/data/Surfer/surfer-6-ascii.grd +++ /dev/null @@ -1,4373 +0,0 @@ -DSAA - 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-4.1250000 -3.3588867 -2.1604004 -2.1879272 - -2.9991455 -4.0645142 5.4544067 25.6660156 - 39.1752319 46.1805420 61.4648438 77.3071289 - 83.6080322 88.1118164 96.2167969 100.5911255 - 112.2545166 130.4490967 151.7692261 192.4075928 - 216.6646118 260.0454102 301.1272583 337.8736572 - 322.6683350 329.1975098 362.2561646 357.7191772 - 372.7709961 392.4869995 428.4848633 476.7628784 - 558.2516479 597.1758423 612.7266235 617.0070190 - 616.8992310 622.7410278 637.8987427 638.7216187 - 626.0193481 602.2581177 611.0028687 626.0316772 - 594.0868530 549.5275269 540.5980835 522.9387817 - 466.9944458 434.6455078 414.8156128 427.6823120 - 451.4617310 457.5739136 450.9449463 444.3672485 - 424.1320801 401.8096924 377.2083740 354.8608398 - 344.1297607 376.1441040 382.8047485 374.7465210 - 378.3817139 414.0144653 469.3903198 508.1160278 - 482.1532593 453.5387573 528.4921265 581.6201782 diff --git a/data/synthetic/test.png b/data/synthetic/test.png deleted file mode 100644 index 733cab3..0000000 Binary files a/data/synthetic/test.png and /dev/null differ diff --git a/data/synthetic/test_cbar.png b/data/synthetic/test_cbar.png deleted file mode 100644 index 2af78cc..0000000 Binary files a/data/synthetic/test_cbar.png and /dev/null differ diff --git a/development.html b/development.html new file mode 100644 index 0000000..c375f88 --- /dev/null +++ b/development.html @@ -0,0 +1,370 @@ + + + + + + + + + Development - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
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gio documentation
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Development#

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If you’d like to develop gio, this page should help you get started.

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Installation#

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You can install this package with pip. The dev option will install the packages you need for testing and building the documentation.

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pip install gio[dev]
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Contributing#

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If you’d like to contribute pull requests back to the main gio project, please see CONTRIBUTING.md.

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Testing#

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You can run the tests (requires pytest and pytest-cov) with

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python run_tests.py
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Most of the tests are doctest tests, which are contained in the docstrings of this package’s functions. It is also possible to add test files to the tests folder in the normal way.

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Building the package#

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This repo uses PEP 518-style packaging. Read more about this and about Python packaging in general.

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Building the project requires build, so first:

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python -m pip install build
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Then to build gio locally:

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python -m build
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This builds both .tar.gz and .whl files, either of which you can install with pip.

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Building the docs#

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You can build the docs with the following commands:

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cd docs
+make html
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Don’t just run sphinx-build manually: there is other stuff happening in the Makefile.

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There is a continuous integration script to update the docs on published releases.

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Continuous integration#

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This repo has GitHub ‘workflows’ or ‘actions’:

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    +

  • Push to main: Run all tests on all version of Python. This is the Build and test workflow.

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  • Publish a new release: Build and upload to PyPI. This is the Publish to PyPI workflow. Publish using the GitHub interface, for example (read more).

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+ + + + + + + + \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 7d87379..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help html - -html: - python pre_process_ipynb.py $(SOURCEDIR)/userguide_src - $(SPHINXBUILD) -E -b html $(SPHINXOPTS) $(SOURCEDIR) $(BUILDDIR)/html - python post_process_html.py $(BUILDDIR)/html - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index a5d75db..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,114 +0,0 @@ -# Configuration file for Sphinx documentation builder. - -# -- Setup function ---------------------------------------------------------- - -# Defines custom steps in the process. - -def autodoc_skip_member(app, what, name, obj, skip, options): - """Exclude all private attributes, methods, and dunder methods from Sphinx.""" - import re - exclude = re.findall(r'\._.*', str(obj)) - return skip or exclude - -def remove_module_docstring(app, what, name, obj, options, lines): - """Remove everything after 'Author: '.""" - if what == "module": - keep = [i for i, line in enumerate(lines) if line.startswith("Author: ")] - if keep: - del lines[keep[0]:] - return - -def setup(app): - app.connect('autodoc-skip-member', autodoc_skip_member) - app.connect("autodoc-process-docstring", remove_module_docstring) - return - - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. - -import os -import sys -sys.path.insert(0, os.path.abspath('..')) - - -# -- Project information ----------------------------------------------------- -project = 'gio' -copyright = '2022, Agile Scientific' -author = 'Agile Scientific' - - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.githubpages', - 'sphinxcontrib.apidoc', - 'sphinx.ext.napoleon', - 'myst_nb', - 'sphinx.ext.coverage', -] - -myst_enable_extensions = ["dollarmath", "amsmath"] - -# Apidoc automation -# https://pypi.org/project/sphinxcontrib-apidoc/ -# The apidoc extension and this code automatically update apidoc. -apidoc_module_dir = '../gio' -apidoc_output_dir = './' -apidoc_excluded_paths = [] -apidoc_toc_file = False -apidoc_separate_modules = False - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'userguide_src'] - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -# https://sphinx-themes.org/sample-sites/furo/ -html_theme = 'furo' - -html_theme_options = { - "sidebar_hide_name": True, - "footer_icons": [ - { - "name": "Agile", - "url": "https://code.agilescientific.com", - "html": """ - - - - - - """, - "class": "", - }, - ], -} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -html_css_files = [ - 'custom.css', -] - -# Branding. -html_favicon = '_static/favicon.ico' -html_logo = '_static/gio_logo.png' diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 153be5e..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/post_process_html.py b/docs/post_process_html.py deleted file mode 100644 index 9ab038f..0000000 --- a/docs/post_process_html.py +++ /dev/null @@ -1,54 +0,0 @@ - -import sys -import glob -import re - - -def simplify_credits(html): - """ - Replace the credit part of the HTML footer. Return the new text. - """ - s = r'@pradyunsg\'s' - pattern = re.compile(s) - html = pattern.sub(r'', html) - - s = r'Copyright © 2022, The Bruges Authors' - pattern = re.compile(s) - new_s = '© 2022, The Bruges Authors | CC BY' - html = pattern.sub(new_s, html) - - return html - - -def add_analytics(html): - """ - Add snippet to head. - """ - s = r'' - pattern = re.compile(s) - new_s = '' - html = pattern.sub(new_s, html) - - return html - - -def main(path): - """ - Process the HTML files in path, save in place (side-effect). - """ - fnames = glob.glob(path.strip('/') + '/*.html') - for fname in fnames: - with open(fname, 'r+') as f: - html = f.read() - - new_html = simplify_credits(html) - new_html = add_analytics(html) - - f.seek(0) - f.write(new_html) - f.truncate() - return - - -if __name__ == '__main__': - _ = main(sys.argv[1]) diff --git a/docs/pre_process_ipynb.py b/docs/pre_process_ipynb.py deleted file mode 100644 index 8841831..0000000 --- a/docs/pre_process_ipynb.py +++ /dev/null @@ -1,47 +0,0 @@ - -import sys -import glob -import json -import pathlib -import shutil - - -def change_kernel(notebook): - """ - Vanillafy the kernelspec. - """ - new_kernelspec = { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3", - } - notebook['metadata']['kernelspec'].update(new_kernelspec) - return notebook - - -def main(path): - """ - Process the IPYNB files in path, save in place (side-effect). - """ - fnames = glob.glob(path.strip('/') + '/[!_]*.ipynb') # Not files with underscore. - outpath = pathlib.Path('userguide') - if outpath.exists(): - shutil.rmtree(outpath) - outpath.mkdir(exist_ok=True) - - for fname in fnames: - with open(fname, encoding='utf-8') as f: - notebook = json.loads(f.read()) - - new_nb = change_kernel(notebook) - filepart = pathlib.Path(fname).name - - with open(outpath / filepart, 'w') as f: - _ = f.write(json.dumps(new_nb)) - - return - - -if __name__ == '__main__': - print(sys.argv[1]) - _ = main(sys.argv[1]) \ No newline at end of file diff --git a/docs/userguide/Read_OpendTect_horizons.ipynb b/docs/userguide/Read_OpendTect_horizons.ipynb deleted file mode 100644 index c902f15..0000000 --- a/docs/userguide/Read_OpendTect_horizons.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells": [{"cell_type": "markdown", "id": "ff4a05e8", "metadata": {}, "source": ["# Read OpendTect horizons\n", "\n", "The best way to export horizons from OpendTect is with these options:\n", "\n", "- x/y and inline/crossline\n", "- with header (single or multi-line, it doesn't matter)\n", "- choose all the attributes you want\n", "\n", "On the last point, if you choose multiple horizons in one file, you can only have one attribute in the file. "]}, {"cell_type": "markdown", "id": "30000231", "metadata": {}, "source": ["## IL/XL only, single-line header, multiple attributes"]}, {"cell_type": "code", "execution_count": 1, "id": "6d53a087", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n", "Dimensions:         (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", "  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", "Data variables:\n", "    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", " * xline (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " amplitude (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " correlation (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " seed_index (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " tracking_order (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat"]}, "execution_count": 1, "metadata": {}, "output_type": "execute_result"}], "source": ["import gio\n", "\n", "ds = gio.read_odt('../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat')\n", "ds"]}, {"cell_type": "code", "execution_count": 2, "id": "a3a9ab32", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "63f655ac", "metadata": {}, "source": ["## IL/XL and XY, multi-line header, multiple attributes\n", "\n", "### Load everything (default)\n", "\n", "X and Y are loaded as `cdp_x` and `cdp_y`, to be consistent with [the **`seisnc`** standard](https://segysak.readthedocs.io/en/latest/seisnc-standard.html) in [`segysak`](https://segysak.readthedocs.io/en/latest/index.html)."]}, {"cell_type": "code", "execution_count": 3, "id": "ab626956", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n", "Dimensions:         (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", "  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", "    cdp_x           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    cdp_y           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", "    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h...
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", " * xline (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", " cdp_x (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " amplitude (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " correlation (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " seed_index (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " tracking_order (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h..."]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["ds = gio.read_odt('../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-header.dat')\n", "ds"]}, {"cell_type": "code", "execution_count": 4, "id": "bb9f6a46", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "\n", "plt.scatter(ds.coords['cdp_x'], ds.coords['cdp_y'], s=5)"]}, {"cell_type": "markdown", "id": "5f52d77e", "metadata": {}, "source": ["### Load only inline, crossline, TWT\n", "\n", "There is only one attribute here: Z, which is the two-way time of the horizon.\n", "\n", "**Note that when loading data from OpendTect, you always get an `xarray.Dataset`, even if there's only a single attribute. This is because the format supports multiple grids and we didn't want you to have to guess what a given file would produce.**"]}, {"cell_type": "code", "execution_count": 5, "id": "5eee4c99", "metadata": {}, "outputs": [{"data": {"text/html": ["
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<xarray.Dataset>\n", "Dimensions:  (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline    (iline) int64 376 378 380 382 384 386 ... 472 474 476 478 480 482\n", "  * xline    (xline) int64 812 814 816 818 820 822 ... 914 916 918 920 922 924\n", "Data variables:\n", "    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h...
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 376 378 380 382 384 386 ... 472 474 476 478 480 482\n", " * xline (xline) int64 812 814 816 818 820 822 ... 914 916 918 920 922 924\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h..."]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-header.dat'\n", "names = ['Inline', 'Crossline', 'Z'] # Must match OdT DAT file.\n", "\n", "ds = gio.read_odt(fname, names=names)\n", "ds"]}, {"cell_type": "markdown", "id": "7cc776a1", "metadata": {}, "source": ["## XY only\n", "\n", "If you have a file with no IL/XL, `gio` can try to load data using only X and Y:\n", "\n", "- If there's a header you can load any number of attributes.\n", "- If there's no header, you can only one attribute (e.g. TWT) automagically...\n", "- OR, if there's no header, you can provide `names` to tell `gio` what everything is.\n", "\n", "`gio` must create fake inline and crossline numbers; you can provide an origin and a step size. For example, notice above that the true inline and crossline numbers are:\n", "\n", "- inline: 376, 378, 380, etc.\n", "- crossline: 812, 814, 816, etc.\n", "\n", "So we can pass an origin of `(376, 812)` and a step of `(2, 2)` to mimic these."]}, {"cell_type": "markdown", "id": "88c18cf8", "metadata": {}, "source": ["### Header present"]}, {"cell_type": "code", "execution_count": 6, "id": "bb396530", "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.\n", " dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)\n"]}, {"data": {"text/html": ["
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<xarray.Dataset>\n", "Dimensions:         (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", "  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", "    cdp_x           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    cdp_y           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", "    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", " * xline (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", " cdp_x (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " amplitude (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " correlation (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " seed_index (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", " tracking_order (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat'\n", "\n", "ds = gio.read_odt(fname, origin=(376, 812), step=(2, 2))\n", "ds"]}, {"cell_type": "code", "execution_count": 9, "id": "777b4f3b", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "1cac0f29", "metadata": {}, "source": ["### No header, more than one attribute: raises an error"]}, {"cell_type": "code", "execution_count": 10, "id": "c7a736d5", "metadata": {}, "outputs": [{"ename": "TypeError", "evalue": "First two columns must be integers to be interpreted as inline and crossline.", "output_type": "error", "traceback": ["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Input \u001b[0;32mIn [10]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m fname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../data/OdT/3d_horizon/Segment_XY_No-header.dat\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 3\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mgio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_odt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m ds\n", "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:222\u001b[0m, in \u001b[0;36mread_odt\u001b[0;34m(fname, names, usecols, na_values, origin, step, attrs)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_odt\u001b[39m(fname,\n\u001b[1;32m 171\u001b[0m names\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 172\u001b[0m usecols\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 176\u001b[0m attrs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 177\u001b[0m ):\n\u001b[1;32m 178\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;124;03m Read an OdT horizon file. (Native format, not IESX.)\u001b[39;00m\n\u001b[1;32m 180\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;124;03m crossline \u2014 and cdp_x and cdp_y, if present \u2014 will be coordinates.\u001b[39;00m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 222\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mread_odt_as_df\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 223\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 224\u001b[0m \u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[43m \u001b[49m\u001b[43mna_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_values\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 228\u001b[0m attrs \u001b[38;5;241m=\u001b[39m attrs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[1;32m 229\u001b[0m attrs\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124modt_filename\u001b[39m\u001b[38;5;124m'\u001b[39m: fname})\n", "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:96\u001b[0m, in \u001b[0;36mread_odt_as_df\u001b[0;34m(fname, names, usecols, na_values, **kwargs)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 95\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFirst two columns must be integers to be interpreted as inline and crossline.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(message)\n\u001b[1;32m 97\u001b[0m cols \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(data_cols))\n\u001b[1;32m 98\u001b[0m names \u001b[38;5;241m=\u001b[39m ix \u001b[38;5;241m+\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvar_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m cols \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m (usecols \u001b[38;5;129;01mor\u001b[39;00m cols)]\n", "\u001b[0;31mTypeError\u001b[0m: First two columns must be integers to be interpreted as inline and crossline."]}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat'\n", "\n", "ds = gio.read_odt(fname)\n", "ds\n", "\n", "# Raises an error:"]}, {"cell_type": "code", "execution_count": 11, "id": "89db9d81", "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.\n", " dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)\n"]}, {"data": {"text/html": ["
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<xarray.Dataset>\n", "Dimensions:  (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline    (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n", "  * xline    (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n", "    cdp_x    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "    cdp_y    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Data variables:\n", "    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_No-header.dat
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n", " * xline (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n", " cdp_x (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY_No-header.dat"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat'\n", "\n", "ds = gio.read_odt(fname, names=['X', 'Y', 'TWT'])\n", "ds"]}, {"cell_type": "code", "execution_count": 12, "id": "ce2a9318", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "23078f1c", "metadata": {}, "source": ["## Sparse data\n", "\n", "Sometimes a surface only exists at a few points, e.g. a 3D seismic interpretation grid. In general, loading data like this is completely safe if you have inline and xline locations. If you only have (x, y) locations, `gio` will attempt to load it, but you should inspect the result carefullly."]}, {"cell_type": "code", "execution_count": 13, "id": "a2cee953", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n", "Dimensions:    (iline: 62, xline: 185)\n", "Coordinates:\n", "  * iline      (iline) int64 1190 1197 1198 1199 1200 ... 1254 1255 1256 1257\n", "  * xline      (xline) int64 1941 1942 1943 1944 1945 ... 2122 2123 2124 2125\n", "    cdp_x      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "    cdp_y      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", "    twt        (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "    amplitude  (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single...
"], "text/plain": ["\n", "Dimensions: (iline: 62, xline: 185)\n", "Coordinates:\n", " * iline (iline) int64 1190 1197 1198 1199 1200 ... 1254 1255 1256 1257\n", " * xline (xline) int64 1941 1942 1943 1944 1945 ... 2122 2123 2124 2125\n", " cdp_x (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", " amplitude (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single..."]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single-line-header.dat'\n", "\n", "ds = gio.read_odt(fname)\n", "ds"]}, {"cell_type": "code", "execution_count": 14, "id": "1a343f40", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 14, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ds['twt'].plot.imshow()"]}, {"cell_type": "markdown", "id": "566a6f5f", "metadata": {}, "source": ["There's some sort of artifact with the default plot style, which uses `pcolormesh` I think."]}, {"cell_type": "code", "execution_count": 15, "id": "f4c3f475", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "80dd49d6", "metadata": {}, "source": ["## Multiple horizons in one file\n", "\n", "You can export multiple horizons from OpendTect. These will be loaded as one `xarray.Dataset` as different **Data variables**. (The actual attribute you exported from OdT is always called Z; this information is not retained in the xarray.)"]}, {"cell_type": "code", "execution_count": 16, "id": "e976b148", "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n", "Dimensions:               (xline: 93, iline: 45)\n", "Coordinates:\n", "  * xline                 (xline) int64 964 966 968 970 ... 1142 1144 1146 1148\n", "  * iline                 (iline) int64 110 112 114 116 118 ... 192 194 196 198\n", "Data variables:\n", "    F3_Demo_2_FS6         (iline, xline) float64 nan nan nan ... 825.4 823.9\n", "    F3_Demo_4_Truncation  (iline, xline) float64 820.0 820.7 ... 635.3 635.2
"], "text/plain": ["\n", "Dimensions: (xline: 93, iline: 45)\n", "Coordinates:\n", " * xline (xline) int64 964 966 968 970 ... 1142 1144 1146 1148\n", " * iline (iline) int64 110 112 114 116 118 ... 192 194 196 198\n", "Data variables:\n", " F3_Demo_2_FS6 (iline, xline) float64 nan nan nan ... 825.4 823.9\n", " F3_Demo_4_Truncation (iline, xline) float64 820.0 820.7 ... 635.3 635.2"]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/F3_Multi-H2-H4_ILXL_Single-line-header.dat'\n", "\n", "ds = gio.read_odt(fname)\n", "ds"]}, {"cell_type": "code", "execution_count": 17, "id": "5965f669", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["
"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ds['F3_Demo_2_FS6'].plot()"]}, {"cell_type": "code", "execution_count": 18, "id": "52752e71", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 18, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ds['F3_Demo_4_Truncation'].plot()"]}, {"cell_type": "markdown", "id": "d7a73a94", "metadata": {}, "source": ["### Multi-horizon, no header\n", "\n", "Unfortunately, OdT exports (x, y) in the first two columns, meaning you can't assume that columns 3 and 4 are inline, crossline. So if there's no header, and XY as well as inline/xline, you have to give the column names:"]}, {"cell_type": "code", "execution_count": 19, "id": "4862f786", "metadata": {}, "outputs": [{"data": {"text/html": ["
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<xarray.Dataset>\n", "Dimensions:      (iline: 1, xline: 10)\n", "Coordinates:\n", "  * iline        (iline) int64 990\n", "  * xline        (xline) int64 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024\n", "    cdp_x        (iline, xline) float64 1.703e+06 1.703e+06 ... 1.703e+06\n", "    cdp_y        (iline, xline) float64 5.817e+06 5.817e+06 ... 5.818e+06\n", "Data variables:\n", "    blue event   (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03\n", "    green event  (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03
"], "text/plain": ["\n", "Dimensions: (iline: 1, xline: 10)\n", "Coordinates:\n", " * iline (iline) int64 990\n", " * xline (xline) int64 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024\n", " cdp_x (iline, xline) float64 1.703e+06 1.703e+06 ... 1.703e+06\n", " cdp_y (iline, xline) float64 5.817e+06 5.817e+06 ... 5.818e+06\n", "Data variables:\n", " blue event (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03\n", " green event (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}], "source": ["import gio\n", "\n", "fname = '../../data/OdT/3d_horizon/Test_Multi_XY-and-ILXL_Z-only.dat'\n", "\n", "ds = gio.read_odt(fname, names=['Horizon', 'X', 'Y', 'Inline', 'Crossline', 'Z'])\n", "ds"]}, {"cell_type": "markdown", "id": "1345e4ef", "metadata": {}, "source": ["## Undefined values\n", "\n", "These are exported as `'1e30'` by default. You can override this (not add to it, which is the default `pandas` behaviour) by passing one or more `na_values`."]}, {"cell_type": "code", "execution_count": 20, "id": "10cf625e", "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.\n", " dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)\n"]}, {"data": {"text/html": ["
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<xarray.Dataset>\n", "Dimensions:  (iline: 54, xline: 57)\n", "Coordinates:\n", "  * iline    (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n", "  * xline    (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n", "    cdp_x    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "    cdp_y    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Data variables:\n", "    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat
"], "text/plain": ["\n", "Dimensions: (iline: 54, xline: 57)\n", "Coordinates:\n", " * iline (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n", " * xline (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n", " cdp_x (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", " cdp_y (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Data variables:\n", " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", "Attributes:\n", " odt_filename: ../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["fname = '../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat'\n", "\n", "ds = gio.read_odt(fname, names=['X', 'Y', 'TWT'])\n", "ds"]}, {"cell_type": "code", "execution_count": 21, "id": "a8f453cd", "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ds['twt'].plot()"]}, {"cell_type": "markdown", "id": "d2dff77d", "metadata": {}, "source": ["## Writing OdT files\n", "\n", "You can write an OdT file using `gio.to_odt()`:"]}, {"cell_type": "code", "execution_count": 23, "id": "512d59a1", "metadata": {}, "outputs": [], "source": ["_ = gio.to_odt('out.dat', ds)"]}, {"cell_type": "code", "execution_count": 24, "id": "06b7a481", "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["# 1: Inline\r\n", "# 2: Crossline\r\n", "# 3: X\r\n", "# 4: Y\r\n", "# 5: Z\r\n", "# - - - - - - - - - -\r\n", "0\t51\t620986.88\t6080882.36\t648.6175060272217\r\n", "0\t52\t621036.86\t6080883.76\t647.8394865989685\r\n", "0\t53\t621086.84\t6080885.16\t647.9663848876953\r\n", "1\t50\t620935.5\t6080930.95\t649.9454379081726\r\n"]}], "source": ["!head out.dat"]}], "metadata": {"interpreter": {"hash": "80d1b482999e70934fa1bff5f55b93cc869ff05fc05093ffa26cad7f8d1d8425"}, "kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.10"}}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/docs/userguide_src/Find_closures.ipynb b/docs/userguide_src/Find_closures.ipynb deleted file mode 100644 index bb5685c..0000000 --- a/docs/userguide_src/Find_closures.ipynb +++ /dev/null @@ -1,231 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb404509", - "metadata": {}, - "source": [ - "# Find closures\n", - "\n", - "`gio` is mostly about data loading, but it can do a few other useful things. One of those things is finding closed contours on a grid." - ] - }, - { - "cell_type": "markdown", - "id": "556ab028", - "metadata": {}, - "source": [ - "Let's start by making a random grid,and we'll scale it to make it more like a geological surface:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "83c470d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import gio\n", - "\n", - "g = gio.generate_random_surface(200, res=3, octaves=3, random_seed=42)\n", - "g = 100 * g - 1000\n", - "\n", - "g.plot()\n", - "plt.contour(g, levels=20, colors='black', linewidths=0.5, linestyles='solid', alpha=0.45)" - ] - }, - { - "cell_type": "markdown", - "id": "b5e27bc1", - "metadata": {}, - "source": [ - "Now we can ask `gio` to find the closing contours." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "993ef08f", - "metadata": {}, - "outputs": [], - "source": [ - "contours = gio.find_closures(g)" - ] - }, - { - "cell_type": "markdown", - "id": "fb0f273e", - "metadata": {}, - "source": [ - "The result is a bunch of `shapely.Polygon` objects:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9976c55a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "contours[10]" - ] - }, - { - "cell_type": "markdown", - "id": "824ba997", - "metadata": {}, - "source": [ - "The 'level' (contour value) is stored as the z-value of the `Polygon`.\n", - "\n", - "Let's plot them:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cd31044c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "g.plot.imshow()\n", - "plt.contour(g, levels=10, colors='black', linewidths=0.5, linestyles='solid', alpha=0.45)\n", - "\n", - "for c in contours:\n", - " plt.plot(*c.exterior.xy, 'white')" - ] - }, - { - "cell_type": "markdown", - "id": "c121c3b4", - "metadata": {}, - "source": [ - "Cool! But we probably don't care about tiny closures. We can apply minimum area (in whatever units the grid cells represent) or minimum closure height (or both!), e.g." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "0a21b348", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "contours = gio.find_closures(g, min_height=25)\n", - "\n", - "g.plot.imshow()\n", - "plt.contour(g, levels=10, colors='black', linewidths=0.5, linestyles='solid', alpha=0.45)\n", - "\n", - "for c in contours:\n", - " plt.plot(*c.exterior.xy, 'white')" - ] - }, - { - "cell_type": "markdown", - "id": "b60d831c", - "metadata": {}, - "source": [ - "Shapely `Polygon` objects are nice because they can do lots of things. They also know their own areas:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d3de48f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polygon 0 area: 833.8 grid cells\n", - "Polygon 1 area: 539.2 grid cells\n", - "Polygon 2 area: 3236.9 grid cells\n", - "Polygon 3 area: 454.2 grid cells\n" - ] - } - ], - "source": [ - "for i, c in enumerate(contours):\n", - " print(f\"Polygon {i} area: {c.area:.1f} grid cells\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "unmap", - "language": "python", - "name": "unmap" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/userguide_src/Random_grids.ipynb b/docs/userguide_src/Random_grids.ipynb deleted file mode 100644 index 32bf923..0000000 --- a/docs/userguide_src/Random_grids.ipynb +++ /dev/null @@ -1,558 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1abd588d", - "metadata": {}, - "source": [ - "# Random grids\n", - "\n", - "Because sometimes you just want some random data.\n", - "\n", - "This module implements 2D fractal noise, combining one or more octaves of [Perlin noise](https://en.wikipedia.org/wiki/Perlin_noise)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b476a72b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray (i: 204, j: 204)>\n",
-       "array([[ 0.        , -0.05736413, -0.11728367, ..., -0.11920205,\n",
-       "        -0.08226758, -0.04223049],\n",
-       "       [ 0.01317427, -0.04415499, -0.10391634, ..., -0.1535234 ,\n",
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-       "       [ 0.0252488 , -0.03198195, -0.09153055, ..., -0.18582673,\n",
-       "        -0.14969093, -0.10998765],\n",
-       "       ...,\n",
-       "       [ 0.05144513,  0.04374787,  0.03244042, ...,  0.06511937,\n",
-       "         0.06545176,  0.06280169],\n",
-       "       [ 0.03661664,  0.02913997,  0.01798883, ...,  0.04790077,\n",
-       "         0.04768927,  0.04426351],\n",
-       "       [ 0.01900629,  0.01164235,  0.00067529, ...,  0.0296427 ,\n",
-       "         0.0290597 ,  0.02524733]])\n",
-       "Coordinates:\n",
-       "  * i        (i) int64 0 1 2 3 4 5 6 7 8 ... 195 196 197 198 199 200 201 202 203\n",
-       "  * j        (j) int64 0 1 2 3 4 5 6 7 8 ... 195 196 197 198 199 200 201 202 203\n",
-       "Attributes:\n",
-       "    source:   gio.random
" - ], - "text/plain": [ - "\n", - "array([[ 0. , -0.05736413, -0.11728367, ..., -0.11920205,\n", - " -0.08226758, -0.04223049],\n", - " [ 0.01317427, -0.04415499, -0.10391634, ..., -0.1535234 ,\n", - " -0.11710962, -0.07728829],\n", - " [ 0.0252488 , -0.03198195, -0.09153055, ..., -0.18582673,\n", - " -0.14969093, -0.10998765],\n", - " ...,\n", - " [ 0.05144513, 0.04374787, 0.03244042, ..., 0.06511937,\n", - " 0.06545176, 0.06280169],\n", - " [ 0.03661664, 0.02913997, 0.01798883, ..., 0.04790077,\n", - " 0.04768927, 0.04426351],\n", - " [ 0.01900629, 0.01164235, 0.00067529, ..., 0.0296427 ,\n", - " 0.0290597 , 0.02524733]])\n", - "Coordinates:\n", - " * i (i) int64 0 1 2 3 4 5 6 7 8 ... 195 196 197 198 199 200 201 202 203\n", - " * j (j) int64 0 1 2 3 4 5 6 7 8 ... 195 196 197 198 199 200 201 202 203\n", - "Attributes:\n", - " source: gio.random" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import gio\n", - "\n", - "g = gio.generate_random_surface(200, res=3, octaves=3)\n", - "\n", - "g" - ] - }, - { - "cell_type": "markdown", - "id": "18f62994", - "metadata": {}, - "source": [ - "We can plot this thing:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e08006e4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "g = gio.generate_random_surface(200, res=(2, 5), octaves=3)\n", - "\n", - "g.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "e4b86c81", - "metadata": {}, - "source": [ - "Note that the `size` and `res` can both be tuples.\n", - "\n", - "The size will be 'at least' the specified size in that dimension: it might have to be larger for the algorithm to work." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39", - "language": "python", - "name": "py39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/userguide_src/Read_OpendTect_horizons.ipynb b/docs/userguide_src/Read_OpendTect_horizons.ipynb deleted file mode 100644 index 896cc88..0000000 --- a/docs/userguide_src/Read_OpendTect_horizons.ipynb +++ /dev/null @@ -1,4533 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ff4a05e8", - "metadata": {}, - "source": [ - "# Read OpendTect horizons\n", - "\n", - "The best way to export horizons from OpendTect is with these options:\n", - "\n", - "- x/y and inline/crossline\n", - "- with header (single or multi-line, it doesn't matter)\n", - "- choose all the attributes you want\n", - "\n", - "On the last point, if you choose multiple horizons in one file, you can only have one attribute in the file. " - ] - }, - { - "cell_type": "markdown", - "id": "30000231", - "metadata": {}, - "source": [ - "## IL/XL only, single-line header, multiple attributes" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6d53a087", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset>\n",
-       "Dimensions:         (iline: 54, xline: 57)\n",
-       "Coordinates:\n",
-       "  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n",
-       "  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n",
-       "Data variables:\n",
-       "    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "Attributes:\n",
-       "    odt_filename:  ../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat
" - ], - "text/plain": [ - "\n", - "Dimensions: (iline: 54, xline: 57)\n", - "Coordinates:\n", - " * iline (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", - " * xline (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", - "Data variables:\n", - " twt (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " amplitude (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " correlation (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " seed_index (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " tracking_order (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - "Attributes:\n", - " odt_filename: ../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import gio\n", - "\n", - "ds = gio.read_odt('../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat')\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a3a9ab32", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds['twt'].plot()" - ] - }, - { - "cell_type": "markdown", - "id": "63f655ac", - "metadata": {}, - "source": [ - "## IL/XL and XY, multi-line header, multiple attributes\n", - "\n", - "### Load everything (default)\n", - "\n", - "X and Y are loaded as `cdp_x` and `cdp_y`, to be consistent with [the **`seisnc`** standard](https://segysak.readthedocs.io/en/latest/seisnc-standard.html) in [`segysak`](https://segysak.readthedocs.io/en/latest/index.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ab626956", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:         (iline: 54, xline: 57)\n",
-       "Coordinates:\n",
-       "  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n",
-       "  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n",
-       "    cdp_x           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "    cdp_y           (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "Data variables:\n",
-       "    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n",
-       "Attributes:\n",
-       "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h...
" - ], - "text/plain": [ - "\n", - "Dimensions: (iline: 54, xline: 57)\n", - "Coordinates:\n", - " * iline (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", - " * xline (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", - " cdp_x (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " cdp_y (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - "Data variables:\n", - " twt (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " amplitude (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " correlation (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " seed_index (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " tracking_order (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - "Attributes:\n", - " odt_filename: ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h..." - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = gio.read_odt('../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-header.dat')\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "bb9f6a46", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.scatter(ds.coords['cdp_x'], ds.coords['cdp_y'], s=5)" - ] - }, - { - "cell_type": "markdown", - "id": "5f52d77e", - "metadata": {}, - "source": [ - "### Load only inline, crossline, TWT\n", - "\n", - "There is only one attribute here: Z, which is the two-way time of the horizon.\n", - "\n", - "**Note that when loading data from OpendTect, you always get an `xarray.Dataset`, even if there's only a single attribute. This is because the format supports multiple grids and we didn't want you to have to guess what a given file would produce.**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5eee4c99", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset>\n",
-       "Dimensions:  (iline: 54, xline: 57)\n",
-       "Coordinates:\n",
-       "  * iline    (iline) int64 376 378 380 382 384 386 ... 472 474 476 478 480 482\n",
-       "  * xline    (xline) int64 812 814 816 818 820 822 ... 914 916 918 920 922 924\n",
-       "Data variables:\n",
-       "    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
-       "Attributes:\n",
-       "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h...
" - ], - "text/plain": [ - "\n", - "Dimensions: (iline: 54, xline: 57)\n", - "Coordinates:\n", - " * iline (iline) int64 376 378 380 382 384 386 ... 472 474 476 478 480 482\n", - " * xline (xline) int64 812 814 816 818 820 822 ... 914 916 918 920 922 924\n", - "Data variables:\n", - " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", - "Attributes:\n", - " odt_filename: ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h..." - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fname = '../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-header.dat'\n", - "names = ['Inline', 'Crossline', 'Z'] # Must match OdT DAT file.\n", - "\n", - "ds = gio.read_odt(fname, names=names)\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "id": "7cc776a1", - "metadata": {}, - "source": [ - "## XY only\n", - "\n", - "If you have a file with no IL/XL, `gio` can try to load data using only X and Y:\n", - "\n", - "- If there's a header you can load any number of attributes.\n", - "- If there's no header, you can only one attribute (e.g. TWT) automagically...\n", - "- OR, if there's no header, you can provide `names` to tell `gio` what everything is.\n", - "\n", - "`gio` must create fake inline and crossline numbers; you can provide an origin and a step size. For example, notice above that the true inline and crossline numbers are:\n", - "\n", - "- inline: 376, 378, 380, etc.\n", - "- crossline: 812, 814, 816, etc.\n", - "\n", - "So we can pass an origin of `(376, 812)` and a step of `(2, 2)` to mimic these." - ] - }, - { - "cell_type": "markdown", - "id": "88c18cf8", - "metadata": {}, - "source": [ - "### Header present" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "bb396530", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.\n", - " dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "Dimensions:         (iline: 54, xline: 57)\n",
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-       "  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n",
-       "  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n",
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-       "Attributes:\n",
-       "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat
" - ], - "text/plain": [ - "\n", - "Dimensions: (iline: 54, xline: 57)\n", - "Coordinates:\n", - " * iline (iline) int64 376 378 380 382 384 ... 474 476 478 480 482\n", - " * xline (xline) int64 812 814 816 818 820 ... 916 918 920 922 924\n", - " cdp_x (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " cdp_y (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - "Data variables:\n", - " twt (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " amplitude (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " correlation (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " seed_index (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - " tracking_order (iline, xline) float64 nan nan nan nan ... nan nan nan nan\n", - "Attributes:\n", - " odt_filename: ../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fname = '../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat'\n", - "\n", - "ds = gio.read_odt(fname, origin=(376, 812), step=(2, 2))\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "777b4f3b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds['twt'].plot()" - ] - }, - { - "cell_type": "markdown", - "id": "1cac0f29", - "metadata": {}, - "source": [ - "### No header, more than one attribute: raises an error" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c7a736d5", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "First two columns must be integers to be interpreted as inline and crossline.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [8], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m fname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../data/OdT/3d_horizon/Segment_XY_No-header.dat\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 3\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mgio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_odt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m ds\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Raises an error:\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:222\u001b[0m, in \u001b[0;36mread_odt\u001b[0;34m(fname, names, usecols, na_values, origin, step, attrs)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_odt\u001b[39m(fname,\n\u001b[1;32m 171\u001b[0m names\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 172\u001b[0m usecols\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 176\u001b[0m attrs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 177\u001b[0m ):\n\u001b[1;32m 178\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;124;03m Read an OdT horizon file. (Native format, not IESX.)\u001b[39;00m\n\u001b[1;32m 180\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;124;03m crossline — and cdp_x and cdp_y, if present — will be coordinates.\u001b[39;00m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 222\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mread_odt_as_df\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 223\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 224\u001b[0m \u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[43m \u001b[49m\u001b[43mna_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_values\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 228\u001b[0m attrs \u001b[38;5;241m=\u001b[39m attrs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[1;32m 229\u001b[0m attrs\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124modt_filename\u001b[39m\u001b[38;5;124m'\u001b[39m: fname})\n", - "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:96\u001b[0m, in \u001b[0;36mread_odt_as_df\u001b[0;34m(fname, names, usecols, na_values, **kwargs)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 95\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFirst two columns must be integers to be interpreted as inline and crossline.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(message)\n\u001b[1;32m 97\u001b[0m cols \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(data_cols))\n\u001b[1;32m 98\u001b[0m names \u001b[38;5;241m=\u001b[39m ix \u001b[38;5;241m+\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvar_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m cols \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m (usecols \u001b[38;5;129;01mor\u001b[39;00m cols)]\n", - "\u001b[0;31mTypeError\u001b[0m: First two columns must be integers to be interpreted as inline and crossline." - ] - } - ], - "source": [ - "fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat'\n", - "\n", - "ds = gio.read_odt(fname)\n", - "ds\n", - "\n", - "# Raises an error:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "89db9d81", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.\n", - " dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "Coordinates:\n",
-       "  * iline    (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n",
-       "  * xline    (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n",
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-       "Data variables:\n",
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2Ypw4RkREv+BCk9rhnSAiIiKXxp4ZIiIiHahQYHVizozqxLntDZMZIiIiHXCYSTu8E0REROTS2DNDRESkA6uqwOrEOnjOnNveuH0y8/n5CGHsB2sHYayjZydh7Jkv/58wtqz3P5rWsGb4LGGZMPZfOxYJYxbJz4GqOtaW7u8tbXR/4fj/duyCRETt1NXVr505n+rxThAREZFLc/ueGSIiIj1wmEk7TGaIiIh0YIUHrE4MkDhzbnvDO0FEREQujT0zREREOrCoCixODBU5c257w2SGiIhIB5wzox23T2YGhp8Txj795hZh7CafEmHMoNQKY7OOJApjr/bbIow56uBdKQ6d1/uf4lJqi1U8OqkKfrgi//6C8JxTE/7S9IYREbUTqpOrZqt8ArAN7wQRERG5NLfvmSEiItKDBQosTiwW6cy57Q2TGSIiIh1YVefmvVgdfFJ7e8RhJiIiInJp7JkhIiLSgdXJCcDOnNveMJkhIiLSgRUKrE7Me3Hm3PamzSQzqampWLRoEZ588kmkpaUBAFRVxXPPPYeMjAyUlpZi4MCBeO2119CzZ89WadPtXU8LY19cMAtjnt7igcyKDn7C2JNHHxLGiq8YhbF3B60VxlqCbEVtRREEJT9zLNsmIiJntIk+qkOHDiEjIwO9e/e2279ixQqsXLkSa9aswaFDh2AymXDnnXeioqJCp5YSERFp4+oTgJ3ZqJ7uyUxlZSUSExOxbt06dOrUybZfVVWkpaVh8eLFGD9+PKKjo7Fx40b8/PPP2Lp1q44tJiIict7VOTPObFRP9zsxc+ZMjBkzBiNHjrTbf+7cORQXFyMhIcG2z2AwID4+Hvv37xder7q6GuXl5XYbERERtV+6zpnJzMzEkSNHcOjQoQax4uJiAEBISIjd/pCQEJw/f154zdTUVDz33HPaNpSIiEhjVji5NhMnANvo1jNTVFSEJ598Eps3b4avr6/wOEWx/8dSVbXBvl9LSkpCWVmZbSsqKtKszURERFpR/1PN5OimMpmx0a1nJj8/HyUlJejfv79tn8Viwd69e7FmzRoUFhYCqO+hCQ0NtR1TUlLSoLfm1wwGAwwGQ8s1nIiISANcNVs7uiUzI0aMwPHjx+32/fGPf8Stt96Kp59+GjfddBNMJhNycnIQExMDAKipqUFeXh6WL1+uR5Pt9LlR3ONz5MKNwpjZ+wdhrNSngzD2fc11wtiduXOFsWqL+J9474i/CmNf3rNUGHPELf8jLr+21ok7CLtuWCG+aJ34B/mbqU81qV1EROT6dEtmAgICEB0dbbevQ4cO6Ny5s23/nDlzkJKSgsjISERGRiIlJQX+/v54+OGH9WgyERGRZvgEYO20mYfmNWbhwoWoqqrCjBkzbA/N27VrFwICAvRuGhERkVM4zKSdNpXM5Obm2n2tKAqSk5ORnJysS3uIiIio7WtTyQwREZG74NpM2mEyQ0REpAMOM2mHs4eIiIjIpbFnxkGfn48QxmpUH2HsZ6v4GTidvC4LY907fCeMybLzH6v9hbERe+YJY6VV4vOO3C0usxax1HoKY6qkNFu1iN+bIol1fe1lYcxDUtJ99knxPSEi0hJ7ZrTDnhkiIiIdXE1mnNma6+LFi5g0aRI6d+4Mf39/9O3bF/n5+ba4oiiNbn/96y/PJRs2bFiD+IMPPqjJPXEUe2aIiIjcQGlpKeLi4jB8+HBkZ2cjODgYZ86cQceOHW3HXLp0ye6c7OxsTJkyBb///e/t9k+dOhVLl/7ycFU/P78Wbfu1MJkhIiLSQWsPMy1fvhxmsxkbNmyw7evatavdMSaTye7rDz74AMOHD8dNN91kt9/f37/BsXriMBMREZEOVMDJhSbrlZeX223V1dWNvt727dsRGxuLCRMmIDg4GDExMVi3bp2wfd999x0+/PBDTJkypUFsy5YtCAoKQs+ePbFgwQJUVFRocEccx2SGiIhIB1rNmTGbzTAajbYtNTW10dc7e/Ys0tPTERkZiZ07d2L69OmYPXs2Nm3a1OjxGzduREBAAMaPH2+3PzExEe+88w5yc3Px7LPPYtu2bQ2OaW0cZiIiInJhRUVFCAwMtH1tMDReNWu1WhEbG4uUlBQAQExMDAoKCpCeno7Jkyc3OP7NN99EYmIifH197fZPnTrV9v/R0dGIjIxEbGwsjhw5gn79+mnxlpqNyYyDBoafE8Y+OhctjHkoVmHME+KYt0edMNbJ52dh7IrFWxgrvSKesKV1yd+5xCRhLHy9eGVsWfk1ZGXb4lsJxSKORaauEsZOJYlXJyciai6t5swEBgbaJTMioaGh6NGjh92+qKgobNu2rcGxn376KQoLC/Huu+9e87r9+vWDt7c3Tp06xWSGiIjInbT2BOC4uDgUFhba7Tt58iTCw8MbHLt+/Xr0798fffr0ueZ1CwoKUFtbi9DQ0Ga1R0tMZoiIiNzA3LlzMXjwYKSkpGDixIk4ePAgMjIykJGRYXdceXk5/v73v+Pllxs+fPTMmTPYsmUL7r77bgQFBeHEiROYP38+YmJiEBcX11pvpQEmM0RERDpo7Z6ZAQMGICsrC0lJSVi6dCkiIiKQlpaGxMREu+MyMzOhqioeeuihBtfw8fHB7t27sXr1alRWVsJsNmPMmDFYsmQJPD3FT3pvaUxmiIiIdKCqClQnkhlHzh07dizGjh0rPWbatGmYNm1aozGz2Yy8vLxmv25LY2k2ERERuTT2zLSAuyO+cui8vxXGC2O+iqSayUtczVTmJa5Y+knxFca8PcQlP1FZzwljX9+/RBgTkVUsKbUOVixJFpP0qHHsmt2fF1c6FT7LSiciap6rD79z5nyqx2SGiIhIB1w1WzscZiIiIiKXxp4ZIiIiHegxAbi9YjJDRESkAw4zaYfJDBERkQ7YM6MdzpkhIiIil8aeGYnjRV2EsX9bOghjP1n8hbFaVXzLvZXGVzoFAIukBM9DUYWxLQPXCWMPfDbdoWvWWMRPeey27flG95/8/bPCc76Z9pQw1vW1ho/TvkqxSkqsJYtJSku6JTGIbwkRUbOpTg4zsWfmF0xmiIiIdKACUJ34I4l/X/2Cw0xERETk0tgzQ0REpAMrFCh8ArAmmMwQERHpgNVM2uEwExEREbk09swQERHpwKoqUPjQPE0wmZHoIKvv9bwsDHlDfF6FKl6pukYVlzz7e9QIY7We4vOe+fL/CWOdDeK2/FznLYzJKIKS7pu2pgjPsVSJvw3Pz1zoUDtuXrlSHKx16JJSty4Rr6gtqXKXloKfeEH7lbhjZkraKStLl8Q8LOI3ePiNeU1oFZF7UlUnq5lYzmTDYSYiIiJyaeyZISIi0gEnAGuHyQwREZEOmMxoh8kMERGRDjgBWDucM0NEREQujT0zREREOmA1k3aYzEiEeopLl7+3XhHGPFEljNVKVpzu4FEtjFklnWjVVnEZ9c+S8vLrPMWv9zv/n4SxKxbxt82/L1/X6H6rRdx+i/h2SVfN/mbmfGHszDxxSXC3FHF5smzlNkdX25ZV+MueRt7zaXE7PSTXPP6SuKTb0fJrGVURv4kBj4lL5A+9ybJtcm/1yYwzc2Y0bIyL4zATERERuTT2zBAREemA1UzaYTJDRESkAxXSke0mnU/1OMxERERELo09M0RERDrgMJN2mMwQERHpgeNMmmEyI+EXek4YM0vO++bbUGEsQFJ+LXNF9RHG5kbtEsYWfPGAMFYtKbH2kCz17O8lXna6g0/jq3tfqRaXj3v61wlj1lrxSGjEq+Ky7XOzxGXbJxeJS5ejnnVsVWlZ+bWHZJVu6YrasmuKbxn6TRe/hyNrHVuJO3aKuMTa0fdwW6L438+zWnzR//3HAvFFiVyJkz0zYM+MDefMEBERkUtjzwwREZEO+ARg7TCZISIi0gEnAGuHw0xERETk0tgzQ0REpAdVcW4SL3tmbJjMEBER6YBzZrSjqGr7vh3l5eUwGo0oKytDYGCg3s3BkQs3CmO1qnhF7RJLgDB2viZIGHvi1k+EsRlHJglj/p7ieuLzl68Xxv5d1aHR/RU1BuE5lVXi2JWfxCuXe5aJc3Gvn8V/sZxc7Fh5cvRCSdm2g2XU0lWsJT+ZslWzFdnryX7cZSXWLbCauEet+KLS+2mRnSeOedQ49qsu76OFDp1Hrqk1PjOuvkb4G8/Cw1/8O+5arD9fwfk/Pd+stl68eBFPP/00srOzUVVVhW7dumH9+vXo378/AEBRGv/duWLFCjz11FMAgOrqaixYsADvvPMOqqqqMGLECLz++uvo0qWLw+/FWZwzQ0REpAdVg60ZSktLERcXB29vb2RnZ+PEiRN4+eWX0bFjR9sxly5dstvefPNNKIqC3//+97Zj5syZg6ysLGRmZmLfvn2orKzE2LFjYbFI/gppYRxmIiIi0oFW1Uzl5eV2+w0GAwyGhj3ey5cvh9lsxoYNG2z7unbtaneMyWSy+/qDDz7A8OHDcdNNNwEAysrKsH79erz99tsYOXIkAGDz5s0wm834+OOPMWrUKIffjzPYM0NEROTCzGYzjEajbUtNTW30uO3btyM2NhYTJkxAcHAwYmJisG7dOuF1v/vuO3z44YeYMmWKbV9+fj5qa2uRkJBg2xcWFobo6Gjs379fuzfVTOyZISIi0osGs1aLiors5sw01isDAGfPnkV6ejrmzZuHRYsW4eDBg5g9ezYMBgMmT57c4PiNGzciICAA48ePt+0rLi6Gj48POnXqZHdsSEgIiouLnX8zDmIyQ0REpAOthpkCAwObNAHYarUiNjYWKSkpAICYmBgUFBQgPT290WTmzTffRGJiInx9rz1JWVVV4eTh1qDrMFN6ejp69+5t+4cYNGgQsrOzbXFVVZGcnIywsDD4+flh2LBhKCgo0LHFREREGmnlCcChoaHo0aOH3b6oqChcuHChwbGffvopCgsL8ac//cluv8lkQk1NDUpLS+32l5SUICQkpHkN0pCuPTNdunTBsmXLcMsttwCo79K67777cPToUfTs2RMrVqzAypUr8dZbb6Fbt2544YUXcOedd6KwsBABAeJS5basr484w/2/2svC2GVVvNq2v0fjK1UDwIoTo4UxT4hLrCvrxKt0e0nqggN8Gm+np4e4Rre2TlySbrlO/N4ki1HD62fxKt09FjtWYu0lboq8jNrBVbOlpdLScmhJ6bKjXdoOtkVGWpotvZ+OlWbLrslnj5E7iIuLQ2Fhod2+kydPIjw8vMGxV8u1+/TpY7e/f//+8Pb2Rk5ODiZOnAigvgLqq6++wooVK1qu8dega8/MPffcg7vvvhvdunVDt27d8OKLL+K6667DgQMHoKoq0tLSsHjxYowfPx7R0dHYuHEjfv75Z2zdulXPZhMREWlA0WBrurlz5+LAgQNISUnB6dOnsXXrVmRkZGDmzJl2x5WXl+Pvf/97g14ZADAajZgyZQrmz5+P3bt34+jRo5g0aRJ69eplq27SQ5upZrJYLMjMzMTly5cxaNAgnDt3DsXFxXYzpg0GA+Lj46Uzpqurq1FeXm63ERERtTmtPMw0YMAAZGVl4Z133kF0dDSef/55pKWlITEx0e64zMxMqKqKhx56qNHrrFq1CuPGjcPEiRMRFxcHf39//POf/4Snp7iXvaXpPgH4+PHjGDRoEK5cuYLrrrsOWVlZ6NGjhy1h+e0YXEhICM6fPy+8XmpqKp577rkWbTMREZErGjt2LMaOHSs9Ztq0aZg2bZow7uvri1dffRWvvvqq1s1zmO49M927d8exY8dw4MAB/PnPf8YjjzyCEydO2OK/nR19rRnTSUlJKCsrs21FRUUt1nYiIiKHtXLPTHume8+Mj4+PbQJwbGwsDh06hNWrV+Ppp58GUF/THhoaajv+WjOmRU8+JCIialO4arZmdE9mfktVVVRXVyMiIgImkwk5OTmIiYkBANTU1CAvLw/Lly/XuZWO8zCdFMa6Fd8ijFkgrnSq8PITxq6o4qoek6FMGCuuNgpjHSRlPXXWxsdMPSSlJFd8xZVaFqu481D2SAPr9+L3LVnPU1rNJHhrAOSLSUrn6Dm40KSji0JKObqwpWxxR0kll6PXlMZkVV6S82R91MMTGv99s2fX0+KTiKhV6ZrMLFq0CKNHj4bZbEZFRQUyMzORm5uLHTt2QFEUzJkzBykpKYiMjERkZCRSUlLg7++Phx9+WM9mExEROU1V6zdnzqd6uiYz3333Hf7whz/g0qVLMBqN6N27N3bs2IE777wTALBw4UJUVVVhxowZKC0txcCBA7Fr1y6XfcYMERGRjbPzXpjM2OiazKxfv14aVxQFycnJSE5Obp0GERERkctpc3NmiIiI3AInAGuGyQwREZEOFNWJCftw7tz2hskMERGRHjhnRjNMZtoQL9NpYazzxTBh7IrXT8JYUY14MUnZApWysu1aSW2zj6BG+Yfq68Tt8JYsJmkQv5bVKu5irQoWX1OxiBfR9JD8RHhKFppUxJXz8pJucQiKrNxbQtbzLCuHll9Uck1JOz3qZKs7imOq5MYokjeoSq4pK+WXLV4pcseIZeLXsshqxCUNkbWxRnzNnH2LxScSuQEmM0RERHrgnBnNMJkhIiLSA4eZNOPU2kw1NTUoLCxEXZ2D/eFERERETnIomfn5558xZcoU+Pv7o2fPnrhw4QIAYPbs2Vi2TDyOTERERP/BhSY141Ayk5SUhC+++AK5ubnw9fW17R85ciTeffddzRpHRETUbjGZ0YxDycz777+PNWvWYMiQIVB+NTO/R48eOHPmjGaNIyIiovbD09MTJSUlDfb/8MMP8PSUlH5eg0MTgP/9738jODi4wf7Lly/bJTeknbDf/UsY+6nod8KY0fNnYexSbSdh7IpVvOq0jL9n48skV0jqd/29xEsr/+wpLqP29hLXGVsCxHXUNZJvUU/JatuykmBJtbd8dWjxguHSCl7pStwOkpVYK1bJytiS8mvZebIVrqWk12y9mLQEXnZPHJxjqHo5/oue2ig3rGYSPT6huroaPj6SX6TX4FAyM2DAAHz44YeYNWsWANgSmHXr1mHQoEEON4aIiMhduNMTgF955RUA9fnCG2+8geuu++XZYxaLBXv37sWtt97q8PUdSmZSU1Nx11134cSJE6irq8Pq1atRUFCAzz77DHl5eQ43hoiIiNqfVatWAajvmVm7dq3dkJKPjw+6du2KtWvXOnx9h5KZwYMH43//93/x0ksv4eabb8auXbvQr18/fPbZZ+jVq5fDjSEiInIbbvScmXPnzgEAhg8fjvfeew+dOomnOTjC4Yfm9erVCxs3btSyLURERNSOZWRkaJ7IAE4kM1arFadPn0ZJSQmsVvuZfEOHDnW6YURERO2ZAifnzGjWktbTvXt3hIaGIj4+HvHx8Rg2bBi6d+/u9HUdSmYOHDiAhx9+GOfPn28wM1lRFFgsjq5mR0RERO3VpUuX8MknnyAvLw+rVq3Cn//8Z4SEhNgSm+nTpzt0XUWVLTMr0LdvX3Tr1g3PPfccQkNDG5RjG41GhxrTEsrLy2E0GlFWVobAwEC9m9PqPv3mFmHsVI1JGDt5RRz7vka8ArafR+Nl1lWSUu+KWl9h7Kcacay4IkAYq6oWl/jVXJaUnVeJ83tFUtPtXSF+ZJNnlfjlvCWrbXteEccUyd8LHhbxj7RXlTgmXRVcck3Z60nLryXl0NJVumWvJymJ9qgTN0b6/qoFpdTN/9VZ/1qSdqjekvJrB19v5+Fkh85zV63xmXH1NcKXvQgPX/HvuGuxXrmC888sdunPt9OnT+OFF17Ali1bYLVaHe4Mcahn5tSpU/jHP/6BW24Rf1ASERGRhBtNAL6qsrIS+/btQ25uLvLy8nDs2DFERUVh1qxZiI+Pd/i6DiUzAwcOxOnTp5nMEBERUZN16tQJ119/Pf7whz/gL3/5C4YMGaLJaI5DycysWbMwf/58FBcXo1evXvD2tu+27927t9MNIyIiatfcsGdmzJgx2LdvH95++20UFRXhwoULGDZsGKKiopy6rkPJzO9//3sAwGOPPWbbpygKVFXlBGAiIqImcKcnAF/1/vvvAwC+/PJL5OXlYffu3UhOToaiKBg2bBgyMzMduq5DyczVh98QERERNVfv3r1hsVhQW1uL6upq7NixA++9957D13MomQkPD3f4BYmIiAhuOcy0atUq5Obm4tNPP0VFRQX69u2L+Ph4PP744049o67Jycz27dsxevRoeHt7Y/v27dJj7733XocbRNq6vetpYeyns+K5TRU+4nLBaqv42+ZynaHxcyyOPZ/R11O8wrBBsmp2bZ04Vicpf7VaxeepVvF5dR0kpb21jq22rUgWV/aUrcQti8l++UlKf2Xl0B412q9ULSMro3aoxPoa50FQSq04WCoNq6RE/Ir4H09a0u0j/vm6q8+zwtiOL54XxqgVuGEy8+KLL2Ly5MmYOnUqhg4daispV1UVRUVFDpeYN/kTZty4cSguLkZwcDDGjRsnPI5zZoiIiKgxP/74I5555hkEBwc32B8REdHyz5n59ZIFv12+gIiIiJrHHScAA2jwoF2g/vkzvk48QNDhtZmIiIjICapSvzlzvouYN28egPpE5tlnn4W/v78tZrFY8Pnnn6Nv374OX7/Jycwrr7zS5IvOnj3bocYQERG5DTeaM3P06FEA9XNjjh8/Dh+fXyYM+vj4oE+fPliwYIHD129yMrNq1aomHacoCpMZIiIistmzZw8A4I9//CNWr16t+VpS4tXxfuPcuXNN2s6ePatpA4mIiNqjq3NmnNma6+LFi5g0aRI6d+4Mf39/9O3bF/n5+XbHfP3117j33nthNBoREBCA2267DRcuXLDFhw0bBkVR7LYHH3ywSa+/YcOGFlkUs8k9M/PmzcPzzz+PDh062Ma+GqMoCl5++WVNGkct656bvhTG3jsTI4yV1nUQxrwFdcFVFvHaG+8OWiuMyQzd/ZQwdqVW/K3t4SmewG71lOT3nuLfHKqHLCYe11YliyTX+YtjMopV8nqNL2oOwPHVrx1ejbpGclFZ2XMjkwdtIUlJvqy0GbL37kgJtqz8WtoOWUzy3mTnSb6nR/dYJIxln0gRX5O00crDTKWlpYiLi8Pw4cORnZ2N4OBgnDlzBh07drQdc+bMGQwZMgRTpkzBc889B6PRiK+//rrB5NypU6di6dKltq/9/PyceCPOa3Iyc/ToUdTW1tr+X6SxWcpERETUMsrLy+2+NhgMMBgaPvNr+fLlMJvN2LBhg21f165d7Y5ZvHgx7r77bqxYscK276abbmpwLX9/f5hMJidbrp0mDzPt2bPHlr3t2bNHuH3yySct1VYiIqL2w9khpv/0zJjNZhiNRtuWmpra6Mtt374dsbGxmDBhAoKDgxETE4N169bZ4larFR9++CG6deuGUaNGITg4GAMHDrStp/RrW7ZsQVBQEHr27IkFCxagoqKiBW5Q07E0m4iISA8aDTP99sm5jfXKAMDZs2eRnp6OefPmYdGiRTh48CBmz54Ng8GAyZMno6SkBJWVlVi2bBleeOEFLF++HDt27MD48eOxZ88exMfHAwASExMREREBk8mEr776CklJSfjiiy+Qk5PjxJtxDpMZIiIiFxYYGNikSbVWqxWxsbFISamfDxUTE4OCggKkp6dj8uTJtgfi3nfffZg7dy4AoG/fvti/fz/Wrl1rS2amTp1qu2Z0dDQiIyMRGxuLI0eOoF+/flq/vSZp8jATERERaUjVYGuG0NBQ9OjRw25fVFSUrVIpKCgIXl5e0mMa069fP3h7e+PUqVPNa5CG2DNDjerp851D542/ufHJ4Yu/HO9Mcxq1d8RfhbHhn8wXxuos4hxeNn+9VlYlVCeOWb3F15QtJvl/L84VxnrPEz/3SZX8VMsWtvT6WRyzeklujLf4fnrJqoskFTjSih8ZWeWRtFrLgcoq2TeLJKZ6Sb7/ZFVJHpK/PSX3WXZPsk+tEMao5bX2cgZxcXEoLCy023fy5EmEh4cDqH943YABA6THNKagoAC1tbUIDQ1tXoM0xGSGiIjIDcydOxeDBw9GSkoKJk6ciIMHDyIjIwMZGRm2Y5566ik88MADGDp0KIYPH44dO3bgn//8J3JzcwHUl25v2bIFd999N4KCgnDixAnMnz8fMTExiIuL0+mdcZiJiIjILQwYMABZWVl45513EB0djeeffx5paWlITEy0HXP//fdj7dq1WLFiBXr16oU33ngD27Ztw5AhQwDU997s3r0bo0aNQvfu3TF79mwkJCTg448/hqen5MFZLYw9M0RERHrQYW2msWPHYuzYsdJjHnvsMTz22GONxsxmM/Ly8pr/wi2MyQwREZEOWnvOTHvGYSYiIiJyaeyZISIi0gt7VzTBZIYa1d38L2HsygWzMPbu6QGN7g/y7uR0m5pjzx2OLXba/b2lwpinQVz+aqmWdHJKfll5SBZ+7CUpvz6+Uly23e/PkrJtSTWxrPzas1a28KM4JKPKFoyUnSgro5aEpOXXjlxTsriotGxbRrbQqaz8WnaepNx79I1zhLHsC2nia5I2dJgz015xmImIiIhcGntmiIiIdMAJwNphMkNERKQHDjNphsNMRERE5NLYM0NERKQDDjNph8kMERGRHjjMpBkmM9RsfW4sEsbOnIlpdH+AZ1VLNUdTheP/26Hzuq4Tr+B9KklcRh313+IyatlfXX1nic/zkC28LFmlW5Usq6JcETfGQ1K2LV0ZW1YOLSFdUdvRsm3patuCEz0cXDVbVrYtK7+WvjdJ+62Sa1ZXi2NELoTJDBERkR7YM6MZJjNEREQ64JwZ7TCZISIi0gN7ZjTD0mwiIiJyaeyZISIi0gN7ZjTDZIaIiEgHnDOjHV2TmdTUVLz33nv4v//7P/j5+WHw4MFYvnw5unfvbjtGVVU899xzyMjIQGlpKQYOHIjXXnsNPXv21LHlJDL+5qN6N0EX30x9SvNrWiU/nYqsqlm2gLckJls1W3aeLKbUScq2JSXWjpZfKzWSMuRaSV26rHxZdp6Ih/imKI6uqC0rBZeVZsvO8/ERhkaHzhTGsi+9Jr4mkQ50nTOTl5eHmTNn4sCBA8jJyUFdXR0SEhJw+fJl2zErVqzAypUrsWbNGhw6dAgmkwl33nknKioqdGw5ERGRk1QNNgKgc8/Mjh077L7esGEDgoODkZ+fj6FDh0JVVaSlpWHx4sUYP348AGDjxo0ICQnB1q1b8fjjjze4ZnV1Nap/9SCo8vLyln0TREREDuAwk3baVDVTWVkZAOD6668HAJw7dw7FxcVISEiwHWMwGBAfH4/9+/c3eo3U1FQYjUbbZjabW77hREREpJs2k8yoqop58+ZhyJAhiI6OBgAUFxcDAEJCQuyODQkJscV+KykpCWVlZbatqEj86H0iIiLdcJhJM22mmumJJ57Al19+iX379jWI/XbCnKqqwkl0BoMBBoOhRdpIRESkGZZma6ZN9MzMmjUL27dvx549e9ClSxfbfpPJBAANemFKSkoa9NYQERGRe9K1Z0ZVVcyaNQtZWVnIzc1FRESEXTwiIgImkwk5OTmIialfjbmmpgZ5eXlYvny5Hk0m0tTXS8UravdaIF4ZW/YXmVWy+rWkYlhaYv3p++LS86H3iVcMtxrEjZGumm2RrNItiUnLqKskK7fL2iLoBVbrxK+leDr4jyDj5eCva1mZu2y1cFmMNKH8Z3PmfKqnazIzc+ZMbN26FR988AECAgJsPTBGoxF+fn5QFAVz5sxBSkoKIiMjERkZiZSUFPj7++Phhx/Ws+lERETO4TCTZnRNZtLT0wEAw4YNs9u/YcMGPProowCAhQsXoqqqCjNmzLA9NG/Xrl0ICAho5dYSERFph6XZ2tF9mOlaFEVBcnIykpOTW75BRERE5HLaTDUTERGRW+Ewk2aYzBAREemFCYkm2kRpNhEREZGj2DND1EYdf0lctt3zGXHZtoekOvnYmnnONKlRez/QfsVwmVExS4QxD4tstW3xCtG4Ui2OCSiKg38LyuYKStsvicnKvWUl3RbxauHZxa+LzyNNcAKwdpjMEBER6YFzZjTDYSYiIiJyaeyZISIi0gGHmbTDnhkiIiI96LBq9sWLFzFp0iR07twZ/v7+6Nu3L/Lz8+2O+frrr3HvvffCaDQiICAAt912Gy5cuGCLV1dXY9asWQgKCkKHDh1w77334ttvv21+YzTEZIaIiMgNlJaWIi4uDt7e3sjOzsaJEyfw8ssvo2PHjrZjzpw5gyFDhuDWW29Fbm4uvvjiCzz77LPw9fW1HTNnzhxkZWUhMzMT+/btQ2VlJcaOHQuLZEJ5S+MwExERkQ5ae5hp+fLlMJvN2LBhg21f165d7Y5ZvHgx7r77bqxYscK276abbrL9f1lZGdavX4+3334bI0eOBABs3rwZZrMZH3/8MUaNGtX8N6IBJjNELqhgmbhsu73befQ5YWx0VJL4RMHq19ckKKVWZX+F1ok/ZRRJqbSqSsqvayQxT3Enu3R1cgeN/t0sYSz74quav167pVE1U3l5ud1ug8EAg8HQ4PDt27dj1KhRmDBhAvLy8vC73/0OM2bMwNSpUwEAVqsVH374IRYuXIhRo0bh6NGjiIiIQFJSEsaNGwcAyM/PR21tLRISEmzXDQsLQ3R0NPbv369bMsNhJiIiIj1oNGfGbDbDaDTattTU1EZf7uzZs0hPT0dkZCR27tyJ6dOnY/bs2di0aRMAoKSkBJWVlVi2bBnuuusu7Nq1C/fffz/Gjx+PvLw8AEBxcTF8fHzQqVMnu2uHhISguLhYu3vTTOyZISIicmFFRUUIDAy0fd1YrwxQ3/MSGxuLlJQUAEBMTAwKCgqQnp6OyZMnw/qfXrz77rsPc+fW9/727dsX+/fvx9q1axEfHy9sg6qqUBzt/dQAe2aIiIh0cHXOjDMbAAQGBtptomQmNDQUPXr0sNsXFRVlq1QKCgqCl5eX9BiTyYSamhqUlpbaHVNSUoKQkBAtbotDmMwQERHpoZVLs+Pi4lBYWGi37+TJkwgPDwcA+Pj4YMCAAdJj+vfvD29vb+Tk5Njily5dwldffYXBgwc3r0Ea4jATERGRG5g7dy4GDx6MlJQUTJw4EQcPHkRGRgYyMjJsxzz11FN44IEHMHToUAwfPhw7duzAP//5T+Tm5gIAjEYjpkyZgvnz56Nz5864/vrrsWDBAvTq1ctW3aQHJjNE1H5YJX+qyhaGlCz+qNbUNh6QVTNJ5g6oNTXi8xwlaYuqSFYelSxQeVfQNGFMVpFFTaeoKhTZwqNNOL85BgwYgKysLCQlJWHp0qWIiIhAWloaEhMTbcfcf//9WLt2LVJTUzF79mx0794d27Ztw5AhQ2zHrFq1Cl5eXpg4cSKqqqowYsQIvPXWW/D09HT4vThLUVUn7qQLKC8vh9FoRFlZmd0EKSJqf0Z3f0YcrJV8qF++LAxpncy0CNmvcVlbZKtty8q9JcmMq6+23RqfGVdfo++kF+Hp43vtEwQsNVdwbPNifr6Bc2aIiIjIxbGvkIiISAdcaFI7TGaIiIj0oNETgInDTEREROTi2DNDRESkAw4zaYfJDBG1G9mFy4Sx0d2eFp8oK+kWVC2pLbCAo5SsYklaWSXugN9Z9bYTDSKncZhJM0xmiIiIdMCeGe1wzgwRERG5NPbMEBER6YHDTJphMkNERKQTDhVpg8NMRERE5NLYM0NERKQHVZVXqTXlfALAZIaI3ET2yeXC2KjrHhGfaBGUYKuS0mzZCt0eji1CqcoWypSfKAzd6fmAMJZjedex16MmYzWTdjjMRERERC6NPTNERER6YDWTZpjMEBER6UCx1m/OnE/1OMxERERELo09M0RERHrgMJNmmMwQERHpgNVM2mEyQ0RuT/EQj7hbaxovbVZlK1VLyrY9/PzE58lW75aRvJ4qu6bkvATvB4WxXbWZTWoWXQOfM6MZzpkhIiIil8aeGSIiIh1wmEk7TGaIiIj0wAnAmuEwExEREbk09swQERHpgMNM2mEyQ0REpAdWM2mGyQwRub0d5RuEsdE3zml0v/XHUuE5slJv1Sp5Br1k4F9RxattW2skZeKyFbwlZKXnsrJt2evtqtnqUFuIroXJDBERkQ44zKQdJjNERER6YDWTZljNRERERC6NPTNEREQ64DCTdpjMEBER6cGqOr4e19XzCQCTGSIiIn1wzoxmmMwQEUmoHa9rdL+Hp2TKYZ24rFm5Ui1+Lckq1rBIVuKWlV/LVtSWlF9LY5IeAcVD8h6IWgiTGSIiIh0ocHLOjGYtcX1MZoiIiPTAJwBrhqXZREREbuLixYuYNGkSOnfuDH9/f/Tt2xf5+fm2+KOPPgpFUey22267ze4aw4YNa3DMgw9KngrdCtgzQ0REpIPWLs0uLS1FXFwchg8fjuzsbAQHB+PMmTPo2LGj3XF33XUXNmz4ZYkPHx+fBteaOnUqli5davvaz8+veY3RGJMZIiIiPbRyNdPy5cthNpvtEpWuXbs2OM5gMMBkMkmv5e/vf81jWpOuw0x79+7FPffcg7CwMCiKgvfff98urqoqkpOTERYWBj8/PwwbNgwFBQX6NJaI3JJSZ210g6eneFMU8eYh3hTFQ7jBahVuireXcJO1U5FtXt7iTXIeFA/xRi2ivLzcbquubrxibvv27YiNjcWECRMQHByMmJgYrFu3rsFxubm5CA4ORrdu3TB16lSUlJQ0OGbLli0ICgpCz549sWDBAlRUVGj+vppD1++uy5cvo0+fPlizZk2j8RUrVmDlypVYs2YNDh06BJPJhDvvvFP3m0ZEROQsRVWd3gDAbDbDaDTattTU1EZf7+zZs0hPT0dkZCR27tyJ6dOnY/bs2di0aZPtmNGjR2PLli345JNP8PLLL+PQoUO444477BKkxMREvPPOO8jNzcWzzz6Lbdu2Yfz48S17s65BUdW2MR1aURRkZWVh3LhxAOp7ZcLCwjBnzhw8/fTTAIDq6mqEhIRg+fLlePzxx5t03fLychiNRpSVlSEwMLClmk9E7dToHosaD9TUik+SxQR/NQOQPtFVrakRnyf5NS57XgwcfJaMo3bVbNX8mlprjc+Mq69x+9Al8PLydfg6dXVX8One51BUVGTXVoPBAIPB0OB4Hx8fxMbGYv/+/bZ9s2fPxqFDh/DZZ581+hqXLl1CeHg4MjMzhQlLfn4+YmNjkZ+fj379+jn8fpzRZvv9zp07h+LiYiQkJNj2GQwGxMfH2/1D/FZ1dXWDLjciIqL2KjAw0G5rLJEBgNDQUPTo0cNuX1RUFC5cuCC8dmhoKMLDw3Hq1CnhMf369YO3t7f0mJbWZpOZ4uJiAEBISIjd/pCQEFusMampqXbdbWazuUXbSURE5AithpmaKi4uDoWFhXb7Tp48ifDwcOE5P/zwA4qKihAaGio8pqCgALW1tdJjWlqbTWauUhT7Zxyqqtpg368lJSWhrKzMthUVFbV0E4mIiJpP1WBrhrlz5+LAgQNISUnB6dOnsXXrVmRkZGDmzJkAgMrKSixYsACfffYZvvnmG+Tm5uKee+5BUFAQ7r//fgDAmTNnsHTpUhw+fBjffPMNPvroI0yYMAExMTGIi4tz9o44rM2WZl8t+SouLrbL9kpKShr01vyaaKyQiIioTWnlJwAPGDAAWVlZSEpKwtKlSxEREYG0tDQkJiYCADw9PXH8+HFs2rQJP/30E0JDQzF8+HC8++67CAgIAFA/72b37t1YvXo1KisrYTabMWbMGCxZsgSenp6OvxcntdlkJiIiAiaTCTk5OYiJiQEA1NTUIC8vD8uXL9e5dUTkLrJPpDT7nNG3PCUOyj6AJDHpOjwekmhtnThWJ4m1oYnDpJ2xY8di7Nixjcb8/Pywc+dO6flmsxl5eXkt0TSn6JrMVFZW4vTp07avz507h2PHjuH666/HjTfeiDlz5iAlJQWRkZGIjIxESkoK/P398fDDD+vYaiIiIue19hOA2zNdk5nDhw9j+PDhtq/nzZsHAHjkkUfw1ltvYeHChaiqqsKMGTNQWlqKgQMHYteuXbbuLiIiIpfFhSY1o2syM2zYMMgec6MoCpKTk5GcnNx6jSIiIiKX0mbnzBAREbVnirV+c+Z8qsdkhoiISA8cZtJMm3/ODBEREZEMe2aIiDSWffqvml9zdOhMYUytFa8Fpcie/SGJqVVVTWpXwxPFYx8JPuJKVFdYt0lzDjz4rsH5BIDJDBERkS4cWZLgt+dTPQ4zERERkUtjzwwREZEeOAFYM0xmiIiI9KACcKa8mrmMDZMZIiIiHXDOjHY4Z4aIiIhcGntmiIhcQPal1xw6b/QN04UxVbL6tayk21pd7dA1uTLib6hwcs6MZi1xeUxmiIiI9MAJwJrhMBMRERG5NPbMEBER6cEKQHHyfALAZIaIiEgXrGbSDoeZiIiIyKWxZ4aIiEgPnACsGSYzREQubnS3p4UxWam0Kiuxrq0Tx6zaf4jKVtSWrcQts6s208HWtBImM5rhMBMRERG5NPbMEBER6YE9M5phMkNERKQHlmZrhskMERGRDliarR3OmSEiIiKXxp4ZIiIiPXDOjGaYzBARubjsk8uFsdGRC8Unfv+jOCZbUdvDsY8OWUm34iGbPCJewbslysRbjVV1biVxV37vGuMwExEREbk09swQERHpgcNMmmEyQ0REpAsnkxkwmbmKw0xERETk0tgzQ0REpAcOM2mGyQwREZEerCqcGipiNZMNkxkionYs+9QKh867K2iaOCj5EJWtxK3IypBlK2Mr4hkRirhqm9wIkxkiIiI9qFZ5EteU8wkAJwATERHp4+qcGWe2Zrp48SImTZqEzp07w9/fH3379kV+fr4t/uijj0JRFLvttttus7tGdXU1Zs2ahaCgIHTo0AH33nsvvv32W6dvhzOYzBAREenBqjq/NUNpaSni4uLg7e2N7OxsnDhxAi+//DI6duxod9xdd92FS5cu2baPPvrILj5nzhxkZWUhMzMT+/btQ2VlJcaOHQuL5KnRLY3DTERERC6svLzc7muDwQCDwdDguOXLl8NsNmPDhg22fV27dm1wnMFggMlkavS1ysrKsH79erz99tsYOXIkAGDz5s0wm834+OOPMWrUKCfeiePYM0NERKQHjYaZzGYzjEajbUtNTW305bZv347Y2FhMmDABwcHBiImJwbp16xocl5ubi+DgYHTr1g1Tp05FSUmJLZafn4/a2lokJCTY9oWFhSE6Ohr79+/X+AY1HXtmiIiogR3fZwhjo0P+LD5RNo9DMgyhWsWTWRWPdvp3twonnzNT/5+ioiIEBgbadjfWKwMAZ8+eRXp6OubNm4dFixbh4MGDmD17NgwGAyZPngwAGD16NCZMmIDw8HCcO3cOzz77LO644w7k5+fDYDCguLgYPj4+6NSpk921Q0JCUFxc7Ph7cRKTGSIiIhcWGBhol8yIWK1WxMbGIiUlBQAQExODgoICpKen25KZBx54wHZ8dHQ0YmNjER4ejg8//BDjx48XXltVVSiKbOXzltVO010iIqI2rpWrmUJDQ9GjRw+7fVFRUbhw4YL0nPDwcJw6dQoAYDKZUFNTg9LSUrvjSkpKEBIS0qz2aInJDBERkR6sVue3ZoiLi0NhYaHdvpMnTyI8PFx4zg8//ICioiKEhoYCAPr37w9vb2/k5OTYjrl06RK++uorDB48uFnt0RKTGSIiIjcwd+5cHDhwACkpKTh9+jS2bt2KjIwMzJw5EwBQWVmJBQsW4LPPPsM333yD3Nxc3HPPPQgKCsL9998PADAajZgyZQrmz5+P3bt34+jRo5g0aRJ69eplq27SA+fMEBER6aGVF5ocMGAAsrKykJSUhKVLlyIiIgJpaWlITEwEAHh6euL48ePYtGkTfvrpJ4SGhmL48OF49913ERAQYLvOqlWr4OXlhYkTJ6KqqgojRozAW2+9BU9P/daWUFS1fS+7WV5eDqPRiLKysiZNkCIiIjlZNZNaUys+sZWrmXaUb7j2Qb/RGp8ZV19jZNBj8PLwcfg6ddYafPz9m/x8A3tmiIioufz9hCFZ4qHWihMdxSKZ/9HMuSFX3dXpT8LYjtI3HLomtU1MZoiIiPRgVWF7WIzD5xPAZIaIiEgXqmqF6sTK186c294wmSEiItKD2vzFIhucTwBYmk1EREQujj0zREREelCdnDPDnhkbJjNERER6sFoBxYl5L5wzY8NkhoiImiX73EphbLT5SfGJ1TXCkLRsW/YwNlnvhKd4JsVdQdMa3V9nFbeR2i4mM0RERHrgMJNmmMwQERHpQLVaoToxzMTS7F+wmomIiIhcGntmiIiI9MBhJs0wmSEiItKDVQUUJjNa4DATERERuTSX6Jl5/fXX8de//hWXLl1Cz549kZaWhttvv13vZhER0W9kF60WxmSrWEOyarYq6YHYeXlTk9rVVOXl5TAaN2p6TSFVBeDMc2bYM3NVm++ZeffddzFnzhwsXrwYR48exe23347Ro0fjwoULejeNiIjIYapVdXqjem0+mVm5ciWmTJmCP/3pT4iKikJaWhrMZjPS09MbPb66uhrl5eV2GxERUZujWp3fCEAbT2ZqamqQn5+PhIQEu/0JCQnYv39/o+ekpqbCaDTaNrPZ3BpNJSIiIp206WTm+++/h8ViQUhIiN3+kJAQFBcXN3pOUlISysrKbFtRUVFrNJWIiKhZOMykHZeYAKwoit3Xqqo22HeVwWCAwWBojWYRERE5TrXCuQnAHGa6qk0nM0FBQfD09GzQC1NSUtKgt0bk6ix4zp0hItJXnSpZaFISg9r4H6+A9r/br15PVkGllTrUOvXMvDqIF+d0N206mfHx8UH//v2Rk5OD+++/37Y/JycH9913X5OuUVFRAQCcO0NE1A4Zje+2yHUrKipgNBpb5No+Pj4wmUzYV/yR09cymUzw8fHRoFWurU0nMwAwb948/OEPf0BsbCwGDRqEjIwMXLhwAdOnT2/S+WFhYSgqKkJAQAAURUF5eTnMZjOKiooQGBjYwq13DbwnjeN9aYj3pHG8L41zxfuiqioqKioQFhbWYq/h6+uLc+fOoaZG0hvVRD4+PvD19dWgVa6tzSczDzzwAH744QcsXboUly5dQnR0ND766COEh4c36XwPDw906dKlwf7AwECX+eFqLbwnjeN9aYj3pHG8L41ztfvSUj0yv+br68skRENtPpkBgBkzZmDGjBl6N4OIiIjaoDZdmk1ERER0LW6XzBgMBixZsoTl27/Ce9I43peGeE8ax/vSON4Xai2K2hr1Z0REREQtxO16ZoiIiKh9YTJDRERELo3JDBEREbk0JjNERETk0twqmXn99dcREREBX19f9O/fH59++qneTWpVe/fuxT333IOwsDAoioL333/fLq6qKpKTkxEWFgY/Pz8MGzYMBQUF+jS2laSmpmLAgAEICAhAcHAwxo0bh8LCQrtj3PG+pKeno3fv3raHnQ0aNAjZ2dm2uDvek99KTU2FoiiYM2eObZ873pfk5GQoimK3mUwmW9wd7wm1PrdJZt59913MmTMHixcvxtGjR3H77bdj9OjRuHDhgt5NazWXL19Gnz59sGbNmkbjK1aswMqVK7FmzRocOnQIJpMJd955p219q/YoLy8PM2fOxIEDB5CTk4O6ujokJCTg8uXLtmPc8b506dIFy5Ytw+HDh3H48GHccccduO+++2wfQu54T37t0KFDyMjIQO/eve32u+t96dmzJy5dumTbjh8/bou56z2hVqa6if/6r/9Sp0+fbrfv1ltvVZ955hmdWqQvAGpWVpbta6vVqppMJnXZsmW2fVeuXFGNRqO6du1aHVqoj5KSEhWAmpeXp6oq78uvderUSX3jjTfc/p5UVFSokZGRak5OjhofH68++eSTqqq67/fKkiVL1D59+jQac9d7Qq3PLXpmampqkJ+fj4SEBLv9CQkJ2L9/v06talvOnTuH4uJiu3tkMBgQHx/vVveorKwMAHD99dcD4H0BAIvFgszMTFy+fBmDBg1y+3syc+ZMjBkzBiNHjrTb78735dSpUwgLC0NERAQefPBBnD17FoB73xNqXS6xNpOzvv/+e1gsFoSEhNjtDwkJQXFxsU6taluu3ofG7tH58+f1aFKrU1UV8+bNw5AhQxAdHQ3Ave/L8ePHMWjQIFy5cgXXXXcdsrKy0KNHD9uHkDvek8zMTBw5cgSHDh1qEHPX75WBAwdi06ZN6NatG7777ju88MILGDx4MAoKCtz2nlDrc4tk5ipFUey+VlW1wT5358736IknnsCXX36Jffv2NYi5433p3r07jh07hp9++gnbtm3DI488gry8PFvc3e5JUVERnnzySezatUu62rG73ZfRo0fb/r9Xr14YNGgQbr75ZmzcuBG33XYbAPe7J9T63GKYKSgoCJ6eng16YUpKShr8xeCurlYfuOs9mjVrFrZv3449e/agS5cutv3ufF98fHxwyy23IDY2FqmpqejTpw9Wr17ttvckPz8fJSUl6N+/P7y8vODl5YW8vDy88sor8PLysr13d7svv9WhQwf06tULp06dctvvFWp9bpHM+Pj4oH///sjJybHbn5OTg8GDB+vUqrYlIiICJpPJ7h7V1NQgLy+vXd8jVVXxxBNP4L333sMnn3yCiIgIu7i73pfGqKqK6upqt70nI0aMwPHjx3Hs2DHbFhsbi8TERBw7dgw33XSTW96X36qursbXX3+N0NBQt/1eIR3oNvW4lWVmZqre3t7q+vXr1RMnTqhz5sxRO3TooH7zzTd6N63VVFRUqEePHlWPHj2qAlBXrlypHj16VD1//ryqqqq6bNky1Wg0qu+99556/Phx9aGHHlJDQ0PV8vJynVvecv785z+rRqNRzc3NVS9dumTbfv75Z9sx7nhfkpKS1L1796rnzp1Tv/zyS3XRokWqh4eHumvXLlVV3fOeNObX1Uyq6p73Zf78+Wpubq569uxZ9cCBA+rYsWPVgIAA2+9Wd7wn1PrcJplRVVV97bXX1PDwcNXHx0ft16+frfzWXezZs0cF0GB75JFHVFWtL6NcsmSJajKZVIPBoA4dOlQ9fvy4vo1uYY3dDwDqhg0bbMe443157LHHbD8rN9xwgzpixAhbIqOq7nlPGvPbZMYd78sDDzyghoaGqt7e3mpYWJg6fvx4taCgwBZ3x3tCrU9RVVXVp0+IiIiIyHluMWeGiIiI2i8mM0REROTSmMwQERGRS2MyQ0RERC6NyQwRERG5NCYzRERE5NKYzBAREZFLYzJDRERELo3JDFE7lZycjL59+9q+fvTRRzFu3Djd2kNE1FK89G4AEbWO1atXgw/8JqL2iMkMkZswGo16N4GIqEVwmInIRf373/+GyWRCSkqKbd/nn38OHx8f7Nq1q8Hxvx1mGjZsGGbPno2FCxfi+uuvh8lkQnJyst05ZWVlmDZtGoKDgxEYGIg77rgDX3zxRUu9JSIihzCZIXJRN9xwA958800kJyfj8OHDqKysxKRJkzBjxgwkJCQ06RobN25Ehw4d8Pnnn2PFihVYunQpcnJyAACqqmLMmDEoLi7GRx99hPz8fPTr1w8jRozAjz/+2JJvjYioWZjMELmwu+++G1OnTkViYiKmT58OX19fLFu2rMnn9+7dG0uWLEFkZCQmT56M2NhY7N69GwCwZ88eHD9+HH//+98RGxuLyMhIvPTSS+jYsSP+8Y9/tNRbIiJqNs6ZIXJxL730EqKjo/E///M/OHz4MHx9fZt8bu/eve2+Dg0NRUlJCQAgPz8flZWV6Ny5s90xVVVVOHPmjPMNJyLSCJMZIhd39uxZ/Otf/4LVasX58+cbJCgy3t7edl8rigKr1QoAsFqtCA0NRW5uboPzOnbs6EyTiYg0xWSGyIXV1NQgMTERDzzwAG699VZMmTIFx48fR0hIiNPX7tevH4qLi+Hl5YWuXbs631giohbCOTNELmzx4sUoKyvDK6+8goULFyIqKgpTpkzR5NojR47EoEGDMG7cOOzcuRPffPMN9u/fj7/85S84fPiwJq9BRKQF9swQuajc3FykpaVhz549CAwMBAC8/fbb6N27N9LT052+vqIo+Oijj7B48WI89thjtlLwoUOHatLzQ0SkFUXlI0GJiIjIhXGYiYiIiFwakxkiIiJyaUxmiIiIyKUxmSEiIiKXxmSGiIiIXBqTGSIiInJpTGaIiIjIpTGZISIiIpfGZIaIiIhcGpMZIiIicmlMZoiIiMil/f9tphlb0jeMCQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds['twt'].plot()" - ] - }, - { - "cell_type": "markdown", - "id": "23078f1c", - "metadata": {}, - "source": [ - "## Sparse data\n", - "\n", - "Sometimes a surface only exists at a few points, e.g. a 3D seismic interpretation grid. In general, loading data like this is completely safe if you have inline and xline locations. If you only have (x, y) locations, `gio` will attempt to load it, but you should inspect the result carefullly." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a2cee953", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:    (iline: 62, xline: 185)\n",
-       "Coordinates:\n",
-       "  * iline      (iline) int64 1190 1197 1198 1199 1200 ... 1254 1255 1256 1257\n",
-       "  * xline      (xline) int64 1941 1942 1943 1944 1945 ... 2122 2123 2124 2125\n",
-       "    cdp_x      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n",
-       "    cdp_y      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n",
-       "Data variables:\n",
-       "    twt        (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n",
-       "    amplitude  (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n",
-       "Attributes:\n",
-       "    odt_filename:  ../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single...
" - ], - "text/plain": [ - "\n", - "Dimensions: (iline: 62, xline: 185)\n", - "Coordinates:\n", - " * iline (iline) int64 1190 1197 1198 1199 1200 ... 1254 1255 1256 1257\n", - " * xline (xline) int64 1941 1942 1943 1944 1945 ... 2122 2123 2124 2125\n", - " cdp_x (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", - " cdp_y (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", - "Data variables:\n", - " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", - " amplitude (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan\n", - "Attributes:\n", - " odt_filename: ../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single..." - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fname = '../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single-line-header.dat'\n", - "\n", - "ds = gio.read_odt(fname)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "1a343f40", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds['twt'].plot.imshow()" - ] - }, - { - "cell_type": "markdown", - "id": "566a6f5f", - "metadata": {}, - "source": [ - "There's some sort of artifact with the default plot style, which uses `pcolormesh` I think." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "f4c3f475", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Ri8czRERERAblRuhdXjcYOddiMCFqpHe+fkGTQWdD7nsOALBpx9yQY9UmD8z99LPfaxpXa3I9AW3rmpXzRwDAZx89oVnM+3L/GwCw/W8zNIsJAPf/eIHqtRb74UcPeusqD9TWyuChLwAAtnw8S7OYP7l3PgBg4xdPaRYTAD7eq+3tcAAgO/0ZAMAnGn9f6dblHfS84MYFQxSOLrPCwkI8/bT6e5aYmAi73Q6Xy4WnnnoKH374IY4dO+a9GGnIECxcuBA2m00pn5WVha1bt6pi/PznP8fatWub3pAQMSEiIiIyqHDd3LVXr17YtGmT8jrCd1Xn1atXUVxcjLlz56Jv374oLy/HtGnTkJubiz179qhiFBQU4JlnnlFet2rVqokt0AYTIiIiImqUyMhIWK3WgPctFgs2btyoem/x4sUYOHAgTpw4geTkZOX91q1bB40RLhxUTUREZFACEjwhPoRvDFJFRYXq4XQ669xuaWkpbDYbunbtiry8PBw7dqzOsg6HA5IkoW3btqr316xZgw4dOqBXr16YOXMmLl++rMk+aSqeISIiIjIoLbvMkpKSVO/PmzcPhYWFAeXT0tKwatUq3HHHHTh79izmz5+PzMxMHDx4EO3bt1eVraqqwuzZszF27FjV+Ntx48aha9eusFqtOHDgAObMmYOvvvoq4OzSzcSEKEykuicHDi2uQeYd90RH6BLXHa39BGMiyMzYWnC10f7r5z9DuFZqWmt/vAJmYNdI9kDfAOhas1eHKujsx0S3mJMnT6qSlujo6KDlcnJylJ9TUlKQkZGBbt26YeXKlZg+fbqyzOVyIS8vDx6PB0uXLlXFKCgoUH7u3bs3unfvjgEDBqC4uBipqalaNalRmBAREREZlEdI8NR1T59GxACAuLi4Jl1FHRsbi5SUFJSWlirvuVwujB49GmVlZdi8efMN46ampiIqKgqlpaVhS4g4hoiIiMig3L673Yf6CIXT6cShQ4fQqVMnANeTodLSUmzatCmgGy2YgwcPwuVyKTHCgWeIiIiIqMFmzpyJkSNHIjk5GefOncP8+fNRUVGBCRMmoKamBqNGjUJxcTHef/99uN1u2O12AEB8fDzMZjOOHj2KNWvW4IEHHkCHDh3w9ddfY8aMGejfvz/uvffesLWLCREREZFBadll1lCnTp3CmDFjcOHCBXTs2BHp6enYuXMnOnfujOPHj2PDhg0AgH79+qnW27JlC7KysmA2m/Hpp5/ilVdeQWVlJZKSkjB8+HDMmzdPmc8oHJgQhYle99LTYwDwkEHeWaU3bdVuRunqOH0+em6z9jvWE6nTQOVo7euqx6ByQJ994Iw3ax4TANyxUZrH9ESF75c0UX08MMETYpdXY9evbzbpLl26QIj6L0JISkoKmKW6OeAYIiIiImrxeIaIiIjIoNxCgjvELrNQ179VMCEiIiIyqHCMIbpVMSEiIiIyKKHB3e6FXoNaDYYJUZjoNUuvs60OA0p1GKis1z8kQodPtFufsb8QOozT1WtWbY8O+1WvwerVOnwHRBT/YBDd6pgQERERGZQbEtwIcQxRiOvfKpgQERERGZRHhD4GyMNb9QHgZfdEREREPENERERkVB4NBlWHuv6tIqx7Ydu2bRg5ciRsNhskScK7776rLHO5XJg1axZSUlIQGxsLm82G8ePH4/Tp00FjCSGQk5MTEAcAysvLkZ+fD4vFAovFgvz8fFy6dEm/hhEREd0EHkiaPCjMCdGVK1fQt29fLFmyJGDZ1atXUVxcjLlz56K4uBjr1q3Dt99+i9zc3KCxXn75ZUhS8IM6duxYlJSUoKioCEVFRSgpKUF+fr6mbWkst9mky20m9OCJkODR6eolre14awZ2vDVD05giQtLn6i3J99CQMOlzWxg94upVV8kjIHFQBBE1Uli7zHJycpCTkxN0mcViwcaNG1XvLV68GAMHDsSJEyeQnJysvP/VV1/hpZdewpdffolOnTqp1jl06BCKioqwc+dOpKWlAQCWL1+OjIwMHD58GD169Ai6fafTCafTqbyuqKhoUhuJiIj0wpmqtWOMUxQ+DocDkiShbdu2yntXr17FmDFjsGTJElit1oB1duzYAYvFoiRDAJCeng6LxYLt27fXua0FCxYoXWwWiwVJSUmatoWIiChU8hiiUB9koISoqqoKs2fPxtixYxEXF6e8//jjjyMzMxM//elPg65nt9uRkJAQ8H5CQgLsdnud25szZw4cDofyOHnyZOiNICIiombJEFeZuVwu5OXlwePxYOnSpcr7GzZswObNm7Fv37561w82tkgIUeeYIwCIjo5GdHR00ytNRESkMw80uJcZB1UDMMAZIpfLhdGjR6OsrAwbN25UnR3avHkzjh49irZt2yIyMhKRkd787uGHH0ZWVhYAwGq14uzZswFxz58/j8TExJvShqAkyfvQmB4DgIVJgjBpXFcJugwqTh/3J6SP+5O2QXUiJO1vYaLXQGUj0WO/Sm4Byc2B2tT8CA2uMBNMiAA08zNEcjJUWlqKLVu2oH379qrls2fPxn/+53+q3ktJScGiRYswcuRIAEBGRgYcDgd2796NgQMHAgB27doFh8OBzMzMm9MQIiIiHfBu99oJa0JUWVmJI0eOKK/LyspQUlKC+Ph42Gw2jBo1CsXFxXj//ffhdruVMT/x8fEwm82wWq1BB1InJyeja9euAICePXti2LBhKCgowLJlywAAkydPxogRI+q8woyIiIhalrAmRHv27MHgwYOV19OnTwcATJgwAYWFhdiwYQMAoF+/fqr1tmzZonSJNcSaNWswdepUZGdnAwByc3ODzn1ERERkJJypWjthTYiysrIgRN398vUta8w68fHxWL16daNjERERNWfsMtNOsx5DdCtzx+iTkbvN2sd03RaheUy3WZ8voB4zSuv1z5Mnkr+E9FDTWvsDVtOavyqJbnX8lhMRERmUFvci42X3XkyIiIiIDIpdZtrhSCoiIiJq8XiGiIiIyKB4hkg7TIjCxBWrz8k5T5T2H2x3tB4zamseEgDg0SFujQ7t14tev9d0Gayu02dA81nVAVRb+KuSmicmRNphlxkRERG1ePy3h4iIyKB4hkg7TIiIiIgMSiD0y+Z522IvJkREREQGxTNE2mFCFCYenfa8Hp9rT5T2MY00+7O7leYhAeg0qFiv32tG+heyCbf8uRE9ZoAnouaFCREREZFB8QyRdpgQERERGRQTIu3wsnsiIiJq8XiGiIiIyKB4hkg7TIiIiIgMSggJIsSEJtT1bxWSEDpcknELqqiogMVigcPhQFxcHABg4ISXvAsb8lmS97KvrHyVVbDPoXzrAeVKLF8ZyeN7dgvVaxVfWbfvFh6Sb7sRTu8PJre8kcDDLt+eQbn9hVxXvzoq9Qjyydn9xnTV63seeSlo/WpfZSYk9QYkj1/7/NeptV25DgF19KubvK7bfL3gvqWPq8r0m7JIVdZ//wbd3/KyGu+zqUYE337t+tXxeVHi+9YN2rYbfdb81q29vnLc/PerFLhOnfH8Pjfy+3L75WMHNPwWGsLv8xZs+8r2/I5BvVcryvHq+A3nv79VdfHZ8b8zAACZo//kXUdur9zO2usq31d5x6o/C/L31uQSqtequvgxVbtVMZTvaGRgwyV5ezXBGyy5vRsRtdb1RHl/9pi9z5FXatR1C/Z7wre+//cWyu807/vuVt43tv2/3wWtz60u2N8MPeLf+/9+g8jY6JBi1Vxx4oufLtGtrkbBM0REREQG5YEU8sSMoa5/q2BCREREZFAcQ6QdXmVGRERELR7PEBERERkUB1VrhwlRCHavnH7jQnW455fewcZ7VjQ9RjB3T/YODC5e9vgNSjbcgP/01fWvja/rl68HX+fugkXKz3uXa1fXgRO9dfUf3N0QJYuD16P/f3nruu//066eADBgkvafgVDaX5+0fG/cXatnaBYzY4x3oLI8cFkr8gDo7X/TLq7JN8j483dmahaTSAvsMtMOEyIiIiKD4hki7XAMEREREbV4PENERERkUEKDLjOeIfLiGSIiIiKDEvDOnxnSo5HbLCwshCRJqofVagUAuFwuzJo1CykpKYiNjYXNZsP48eNx+vRpVQyn04kpU6agQ4cOiI2NRW5uLk6dOqXNTmkiJkRhInnqn/m4qarjvA8teSIleCK1/Q9CmK4/9IirbVA0/jdGQ0ho2CznjSCk4LOfh0yPukZIyszL2sYNnHE6VJ4ICR6N6/rvD72oPIiMplevXjhz5ozy2L9/PwDg6tWrKC4uxty5c1FcXIx169bh22+/RW5urmr9adOmYf369Vi7di0+//xzVFZWYsSIEXC73cE2d1Owy4yIiMigPJAghWGm6sjISOWsUG0WiwUbN25Uvbd48WIMHDgQJ06cQHJyMhwOB1asWIE333wTQ4YMAQCsXr0aSUlJ2LRpE4YOHdq0hoSIZ4iIiIgMSr7KLNQH4L0/Wu2H0+msc7ulpaWw2Wzo2rUr8vLycOzYsTrLOhwOSJKEtm3bAgD27t0Ll8uF7OxspYzNZkPv3r2xfft2bXZMEzAhIiIiIiQlJcFisSiPBQsWBC2XlpaGVatW4eOPP8by5ctht9uRmZmJixcvBpStqqrC7NmzMXbsWOXGsXa7HWazGe3atVOVTUxMhN1u175hDcQuMyIiIoPyCAmSRhMznjx5UnW3++jo6KDlc3JylJ9TUlKQkZGBbt26YeXKlZg+/fqksC6XC3l5efB4PFi6dOkN6yGEgCSF74o3JkS3GD0G1LrN2sfU69xki7961EDtN9Kx0nqQNgBIegzUpxZHvlIs1BgAEBcXp0qIGio2NhYpKSkoLS1V3nO5XBg9ejTKysqwefNmVVyr1Yrq6mqUl5erzhKdO3cOmZmZTW9IiNhlRkRERE3mdDpx6NAhdOrUCcD1ZKi0tBSbNm1C+/btVeXvvvtuREVFqQZfnzlzBgcOHAhrQsQzRERERAYVjlt3zJw5EyNHjkRycjLOnTuH+fPno6KiAhMmTEBNTQ1GjRqF4uJivP/++3C73cq4oPj4eJjNZlgsFkyaNAkzZsxA+/btER8fj5kzZyIlJUW56iwcmBAREREZVDgSolOnTmHMmDG4cOECOnbsiPT0dOzcuROdO3fG8ePHsWHDBgBAv379VOtt2bIFWVlZAIBFixYhMjISo0ePxrVr13D//ffjjTfeQESEDv3TDcSEiIiIyKC0HFTdUGvXrq1zWZcuXSAaMKgpJiYGixcvxuLFixu1bT0xIQoTj057PqJah6B6DNQOfvFCyLSeURsAJJ0mThUmA40qbuH0mFVej88qETUdEyIiIiKD0vIqs5aOCREREZFBeROiUMcQaVQZg+Nl90RERNTi8QwRERGRQYXjKrNbVVjPEG3btg0jR46EzWaDJEl49913lWUulwuzZs1CSkoKYmNjYbPZMH78eJw+fVoV49FHH0W3bt3QqlUrdOzYET/96U/xzTffqMqUl5cjPz9fuT9Lfn4+Ll26dBNaSEREpB+h0YPCnBBduXIFffv2xZIlSwKWXb16FcXFxZg7dy6Ki4uxbt06fPvtt8jNzVWVu/vuu/H666/j0KFD+PjjjyGEQHZ2Ntzu65cGjR07FiUlJSgqKkJRURFKSkqQn5+ve/vqs+ev07Hnr9NvXLCRRIT2txlwm7W/fYcwXX9oyRPlfWjJHa3PVXGSR0DyaPurSI99qldc3eoaIUFEaPsfrydS+ytD5c+q1p9XImqasHaZ5eTkqG4SV5vFYlFN6w0AixcvxsCBA3HixAkkJycDACZPnqws79KlC+bPn4++ffvi+PHj6NatGw4dOoSioiLs3LkTaWlpAIDly5cjIyMDhw8fRo8ePYJu3+l0wul0Kq8rKipCaisREZHW2GWmHUMNqnY4HJAkCW3btg26/MqVK3j99dfRtWtXJCUlAQB27NgBi8WiJEMAkJ6eDovFgu3bt9e5rQULFihdbBaLRYlHRETUbLDPTDOGSYiqqqowe/ZsjB07NuBuvEuXLsVtt92G2267DUVFRdi4cSPMZm8fj91uR0JCQkC8hIQE5f4qwcyZMwcOh0N5nDx5UtsGERERhcp3hiiUB3iGCIBBEiKXy4W8vDx4PB4sXbo0YPm4ceOwb98+bN26Fd27d8fo0aNRVVWlLJekwIMthAj6viw6OhpxcXGqBxEREd2aJNGQm47cBJIkYf369XjwwQdV77tcLowePRrHjh3D5s2b0b59+3rjVFdXo127dvjrX/+KMWPG4H/+538wffr0gKvK2rZti0WLFuGRRx5pUP0qKipgsVjgcDiU5OjOwkUA6hjE7NurJpfvuY5bagS9JYAvTzvwx8dVb/eZvqh2aGW7IshIMOV2E35HVy5b7z8EfsukGu+z0pb6bmPg255/u+TtKXWOCFx24L+97e35B287/QebyjFr30rDf/8qZfza7ZFT/1r/AsgDxeV9oqxbU8drVYN84WrUr5W6+m+/1jGS66+sKwf233dBvpnyPgn4zPmtW3v/BxwL3z6Q2y/XTT6uEdf/l4Cp5ga/HuTtBilW12dMKeu/Tq3yyrr+Meppp7/dK70XLQyc8FLQ7cj7oXY9/T8//tvxr1d9g8LrO44BZf3L+H4ty+/veGvGjYNQsxLsb4Ye8bu+/iRMrWNCiuW5WoWyR57Tra5G0azPEMnJUGlpKTZt2nTDZEgmhFAGRGdkZMDhcGD37t3K8l27dsHhcCAzM1OXehMREd0MoXaXaTEo+1YR1qvMKisrceTIEeV1WVkZSkpKEB8fD5vNhlGjRqG4uBjvv/8+3G63MuYnPj4eZrMZx44dw9tvv43s7Gx07NgR//d//4cXXngBrVq1wgMPPAAA6NmzJ4YNG4aCggIsW7YMgPfKtBEjRtR5hRkRERG1LGFNiPbs2YPBgwcrr6dP957injBhAgoLC7FhwwYAQL9+/VTrbdmyBVlZWYiJicE///lPvPzyyygvL0diYiLuu+8+bN++XTWQes2aNZg6dSqys7MBALm5uUHnPiIiIjIULQZF8wwRgDAnRFlZWahvCNONhjfZbDZ8+OGHN9xOfHw8Vq9e3ej6ERERNWe82712eC+zEHxT+PiNC9Xhzqe9A4e/mdfwGP966cZle832xvUfkB2Ku37vjbn/Be1ipvgGiAPAAb92HXqm6dvp87g37lcva1jXmd6Y/3pRu5i14+7XMG6vWd6YBzU8VgCQ+qg3bvEy7eIO+E/vYGetZ2y/55fqQdTy4OpQpI33xty1Sru6yjEBYOeb2s9aT0SNw4SIiIjIqLSYWJFniAAwISIiIjIs3rpDO836snsiIiKim4FniIiIiIyMXV6aYEIUJu5onQLrcObTo0Nd9YgJXJ99WUs1rbSPCQCeYDOchxpTh/YDgKuN9jGFSZ/T9J4I7ePqcazYS0FaYJeZdpgQERERGRUHVWuGY4iIiIioxeMZIiIiIsOSEPpYCXaZAUyIiIiIjItdZpqRxI3uj0EAgIqKClgsFjgcDsTFxQUt0+1P12eelTy+HzzqzFvy39vCrzxwPVn3LTPVyLH8lvueJXetVWvUyzxyyuvrHBV+naS1x9L5101eJtdNroeqrn7tkJ9FpPq1UkdfTFFrkKrwa4//Pyv+y5U21qqTsn3/deV21zco1v+fI7+2KMVqvZb3QcBYRLl98n6W2187VrD3asWS/PdZkM+G3B7l2aRep/Yxuv5Z9D3L+8R/fwf7LPrzKyNvzyQ/1wSWVermt1+V7cjv1/psFv/FOyN26mOLgpf12bs8cObsuyd719n72uOq10q1fNspfrXuWbeV2a7r+FzJ73sia3+BfE9u/w9O8HWDfSaUz00d/7Arx7fWuv6fQflYaDmrNjVeQ/5maBE/6S+FMLWKCSmW51oVTv6qULe6GgXPEBERERkVzxBphgkRERGRUfFu95rhVWZERETU4vEMERERkUEJ4X2EGoOYEBERERkXxxBphgmRho7OaPhVHXc8773y5dvf132lS1Pc9aQ37tfPaRf3zqe9MQ89o2HMedev/Pnmae3i3vWUr/3zNaxroa/9GtYT0OdY9fyD9jEBoNdsb9yDL2gXt+80b8yvXq47Zn1XgtXF/4pC+Wqzxvjyf9Tf5QGTvFed7Vmh3ZVbA/7z+lWpe/7KK8KIwo0JERERkVFxULVmmBAREREZlCSCzG/XhBjEhIiIiMi4OIZIM7zsnoiIiFo8niEKF50yco9Bjmi9t9IIJa6RUnw9uu11+lzVezuPJqqJ1j6mXkSE9gdLmDhugzTAMUSaMcifTyIiIgrALjPNhPT/dHV1NQ4fPoyampobFyYiIiJqppqUEF29ehWTJk1C69at0atXL5w4cQIAMHXqVCxcuFDTChIREVEdhEYPalpCNGfOHHz11Vf47LPPEBMTo7w/ZMgQvP3225pVjoiIiOrBhEgzkhCNv4tJ586d8fbbbyM9PR1t2rTBV199hR/84Ac4cuQIUlNTUVFRoUddw6qiogIWiwUOhwNxcXE3LP+DRd5ZaJX5Hfz2suT2DWLzFQg2GFgZyCoPeKvjSAWbQ0JZJcK3UB4zJ7/0SOptBKujB4FlgmxDFb+O15Lbb51gY/hEHct8rz2+gdimWj201/eR77Xf/lb2q7w/gmxXXufwXO+Mxj2eXRRYqI516+Tf/lr7UN4XN6yzSf1+8ErVv92g79Wxjty+2nX9plA9y/Ndv/fuG/86K22qxROljmdy+VUjQr3d2rNg95rlOwb++6COz5Vq+3JZUx1lPcGfa9dFqqNdcp3lz6KoZxSm0u5qdUx5XdVnwv+Y+O9fv+W1P4vX26EuJA/abswFDKYa+UPofdJyZu6WprF/M5oaP+nFZ2FqFXPjFerhuVaFkzPn6lZXPURERODMmTNISEhQvX/x4kUkJCTA7Q7yS+kGmjSo+vz58wGVAIArV65AkjhanYiI6KZooVeZ1XUux+l0wmw2NylmkxKie+65Bx988AGmTJkCAEoStHz5cmRkZDSpIkRERNQ4LW2m6j//+c8AvHnHX//6V9x2223KMrfbjW3btuHOO+9sUuwmJUQLFizAsGHD8PXXX6OmpgavvPIKDh48iB07dmDr1q1NqggRERE1f4WFhXj66adV7yUmJsJutwMA1q1bh2XLlmHv3r24ePEi9u3bh379+qnKZ2VlBeQLP//5z7F27dp6t71okbc7XQiBV199FRER1/uEzWYzunTpgldffbVJ7WpSQpSZmYkvvvgCL774Irp164ZPPvkEqamp2LFjB1JSUppUESIiImqkMM1D1KtXL2zatEl5XTsxuXLlCu6991787Gc/Q0FBQZ0xCgoK8MwzzyivW7VqdcPtlpWVAQAGDx6MdevWoV27do2vfB2aPDFjSkoKVq5cqVlFbjXHHq9/MGLXxX8CAJRNmaHpdn/wincw97HfajcY8ocveDPyI7Mev0HJRsT875eUn4/8Tru63vG8t67f/r7pdZUHVysxn/PFfFK79gPXB29/M1e7uHcW+mIWalvXO+f54j6vXdxec7wxDy4IjFl7gHVj9ZzrjXvoWe3q2neqN+ZXf9YuZr8p1wfv71us7fEi0ltkZCSsVmvQZfn5+QCA48eP1xujdevWdca4kddee03TZAgIISHyeDw4cuQIzp07B49HfRnSfffdF3LFiIiIqH4SNBhD5Hv2v0I8Ojoa0dHB77FTWloKm82G6OhopKWl4fnnn8cPfvCDRm13zZo1WL16NRITE5GTk4N58+ahTZs2DVq3R48e6NSpEwYNGoRBgwYhKysLPXr0aNT2/TUpIdq5cyfGjh2L7777LmCktyRJTbrcjYiIiMInKSlJ9XrevHkoLCwMKJeWloZVq1bhjjvuwNmzZzF//nxkZmbi4MGDaN++fYO2NW7cOHTt2hVWqxUHDhxQ5jfcuHFjg9Y/c+YMNm/ejK1bt2LRokX41a9+hcTERCU5euyxxxoUp7YmJUSPPfYYBgwYgA8++ACdOnXipfZEREThoOFl9ydPnlTNQ1TX2aGcnBzl55SUFGRkZKBbt25YuXIlpk9v2BCI2mOLevfuje7du2PAgAEoLi5GamrqDddPTEzEmDFjMGbMGADAkSNHMH/+fKxZswZ///vfb15CVFpain/84x/44Q9/2JTViYiISAsaDqqOi4tr0sSMsbGxSElJQWlpaZOrkJqaiqioKJSWljYoIaqsrMTnn3+Ozz77DFu3bkVJSQl69uyJKVOmYNCgQU2qQ5MSorS0NBw5coQJERERUQvndDpx6NAh/OhHP2pyjIMHD8LlcqFTp04NKt+uXTvEx8cjPz8fTz31FP793/8dFoulydsHmpgQTZkyBTNmzIDdbkdKSgqioqJUy/v06RNSpVoCEaXPTFjKrTo05GlVx707QonZ5OH89atprX373dE6Hasm3Umwfnrt1/puUdFU7qZNJntDjblVRUO5g/cchESP408tUBguu585cyZGjhyJ5ORknDt3DvPnz0dFRQUmTJgAAPj+++9x4sQJnD59GgBw+PBhAIDVaoXVasXRo0exZs0aPPDAA+jQoQO+/vprzJgxA/3798e9997boDoMHz4cn3/+Od58802cPHkSJ06cQFZWFnr27Nm4xtTSpF9zDz/8MADgl7/8pfKeJEkQQnBQNRER0U0SjpmqT506hTFjxuDChQvo2LEj0tPTsXPnTnTu3BkAsGHDBjzyyCNK+by8PADXB2mbzWZ8+umneOWVV1BZWYmkpCQMHz4c8+bNU81nVJ93330XAPCvf/0LW7duxaefforCwkJIkoSsrKwbTvAYTJMSInliJCIiImpZbpRsTJw4ERMnTqxzeVJSkmZ3tejTpw/cbjdcLhecTieKioqwbt26JsVqUkIkZ4FEREQURmGaqTrcFi1ahM8++wz//Oc/cfnyZfTr1w+DBg3Co48+2uS5EBvci71hwwa4XC7l5/oeDbVt2zaMHDkSNpsNkiQpp8AAwOVyYdasWUhJSUFsbCxsNhvGjx+v9EkC3n7KKVOmoEePHmjdujWSk5MxdepUOBwO1XbKy8uRn58Pi8UCi8WC/Px8XLp0qcH1JCIiapaERg+Dee6559CtWzesWrUKFy9exJdffokXX3wRw4cPb/Lfd0n4z6xYB5PJBLvdjoSEBJhMdedRjRlD9NFHH+GLL75AamoqHn74Yaxfvx4PPvggAMDhcGDUqFEoKChA3759UV5ejmnTpqGmpgZ79uwBABw4cADz5s3DxIkTcdddd+G7777DY489hj59+uAf//iHsp2cnBycOnUKr732GgBg8uTJ6NKlC957770G1RPwzuBpsVjgcDiadFmivy6vvggAOP7YTO/rv7x4faHk9+wb0yzVqOeaCHbbD/nWHddjeQ+vPICz7DfqdeRbiKji+8oKk/qjYXJJqvqIWmPpPZF+ZeWPgNtvfgz5Ze3i/h8nTx3vC7/n2nyDyYX/vpNX8b2Wao0Pl3x1k/w/rr6yyqBXEbiufzsk/3b617kWZd/4j1VvxCBbuT3yAGK5bkpbam/Xvz3y2x71cuVzVs8YelNNkPjBYtYq4z8+Qa6zx2+oQO1yUg38FgbfntKm+qZh8du+Usd6fvMFjKlQjrNfjCBx/ePLsZR2++psqv1ZlNvr/7n121f7/8RbfBiF1n8z6orf5dnnYIqJCSmWp6oKx+c+qVtd9VA7J6nt4sWLSEhIaNJY5gZ3mdW+PYf/rTqaKicnRzXBU20WiyVgxsrFixdj4MCBOHHiBJKTk9G7d2+88847yvJu3brhueeewy9+8QvU1NQgMjIShw4dQlFREXbu3Im0tDQAwPLly5GRkYHDhw/XOdW30+mE0+lUXvtPaU5ERBRu4RhU3VwEmxS6srISMU1MEHW6SFcfDocDkiShbdu29ZaJi4tDZKS3aTt27IDFYlGSIQBIT0+HxWLB9u3b60yIFixYgKefflrT+hMREWlKw5mqjUCeCVuSJMydOxetW7dWlrndbuzatQv9+vVrUuwGJ0R//vOfGxx06tSpTapMfaqqqjB79myMHTu2zlN6Fy9exLPPPotHH31UeS/YKTUASEhIgN1ur3N7c+bMUU1BXlFREXCfFyIiorBqYYOq9+3bBwAQQmD//v0wm69PaGY2m9G3b1/MnDmzSbEbnBAtWrSoQeUkSdI8IXK5XMjLy4PH48HSpUuDlqmoqMDw4cNx1113Yd68eQF18ifPmVSX+u7yS0RERDffli1bAACPPPIIXnnlFU3HPDU4IQrX3EMulwujR49GWVkZNm/eHLTxly9fxrBhw3Dbbbdh/fr1qpmzrVYrzp49G7DO+fPnkZiYqGvd6+U3YPn4r5qW0QbwhT02rWE32As2MNtf1yXegddHG3jTvoaQYwKBA71D0WWZb7D6oxrtT1wfeH6sAfuqMbr9yTsA/ugM7fbrD//o/cflyBPaDr7t8aw37qFntIvbc64v5rPa1vXOQm/cbwq1i3vXU96YBxdqFzNl5vV/Mve/zMHS1DQtdQzR66+/rnnMBidE06dPx7PPPovY2Nh672YrSRL+9Kc/1bm8MeRkqLS0FFu2bEH79u0DylRUVGDo0KGIjo7Ghg0bAgZTZWRkwOFwYPfu3Rg4cCAAYNeuXXA4HMjMzNSknkRERGHRwrrM9NTghGjfvn3KPERyH14w9XVD+ausrMSRI0eU12VlZSgpKUF8fDxsNhtGjRqF4uJivP/++3C73cqYn/j4eJjNZly+fBnZ2dm4evUqVq9ejYqKCuVqsI4dOyIiIgI9e/bEsGHDUFBQgGXLlgHwXnY/YsSIOgdUExERUcvS4IRI7rfz/zkUe/bsweDBg5XX8pmnCRMmoLCwUJnk0X/E+JYtW5CVlYW9e/di165dAIAf/vCHqjJlZWXo0qULAGDNmjWYOnUqsrOzAQC5ublYsmSJJm0gIiIKGw26zHiGyCusl91nZWWhvnkhbzRn5I3Wl8XHx2P16tWNrh8REVGzxi4zzRhqHqJbiYjQ5xPoidI+rog0RkwAQHTjZye9EWHWp67uVtpMcFqbXp8rt4EuuPTo8FvNf1ZtLRho6heiFoEJERERkVHxDJFmmBAREREZVEu97F4PjbidJBEREdGtiQkRERERtXjsMgsTKUr7AbUAgNtqtI8ZrUNdTfqco42I0X5QNfSICUDosF+FTv/i6DFY22O+cZmmEDr8VtMjpifqxmWIbohjiDTDhIiIiMigOIZIO+wyIyIiohaPZ4iIiIiMjGd4NMGEiIiIyKg4hkgz7DIjIiKiFo9niMIkqpUOV4MBMEXpcEWUDlcY6XWLCT1GB+qyTwFAh1s36DU4UqrRvrIt/SorDmQlLXBQtXaYEBERERkVu8w0wy4zIiIiavF4hoiIiMig2GWmHSZERERERsUuM80wIQqTiAh9bt0h6dAJKnQYUCtF6fMN9NRovwM8rgjNY3oDa79fhQ4DtQHoMgBct7rqQYePq9DpY0VETcOEiIiIyKh4hkgzTIiIiIgMimOItMOEiIiIyKh4hkgzvOyeiIiIWjyeIQoTc5Q+M1VHmrWPK0XqMABcr/9IdIgr6TUAPlr7GbA9MfrUVZe4Og2qdkfr8SHQPmRLn6mbNMIzRJphQkRERGRQHEOkHXaZERERUYvHM0RERERGxS4zzTAhIiIiMih2mWmHCVGYmEz6fAIjdRgAbG7t0jymyaz9gGIAMEVp335TpD7HSo/9KrXRPiagz6BqodN+RYT2cWtaaR/Tw9++RM0Kv5JERERGxS4zzTAhIiIiMiomRJrhVWZERETU4vEMERERkUFJCH3eUJ3mSDUcJkRhEmHSZ0bhyAjtBytHRmofMypan5m69RisbtJppmo9PgN6DCoH9BkA7YnS5zy90OG3u9DhN6Xg+XnSArvMNMOEiIiIyKB42b12+D8KERERNVhhYSEkSVI9rFarsnzdunUYOnQoOnToAEmSUFJSEhDD6XRiypQp6NChA2JjY5Gbm4tTp07dxFYEYkJERERkVEKjRyP16tULZ86cUR779+9Xll25cgX33nsvFi5cWOf606ZNw/r167F27Vp8/vnnqKysxIgRI+B26zNHXUOwy4yIiMjIwtDlFRkZqTorVFt+fj4A4Pjx40GXOxwOrFixAm+++SaGDBkCAFi9ejWSkpKwadMmDB06VJc63wjPEBEREREqKipUD6fTWWfZ0tJS2Gw2dO3aFXl5eTh27FiDt7N37164XC5kZ2cr79lsNvTu3Rvbt28PqQ2hYEIUJiYImHRI6yMjPJrfviMq0o0oja80izB5lIeWJJOApPGVZnrEBABJEpA0Hs1oNtfAbNb+Cj4pUkDS+kozLa4XvklxRYSA0PiWIJ4ooTyImkoeVB3qAwCSkpJgsViUx4IFC4JuMy0tDatWrcLHH3+M5cuXw263IzMzExcvXmxQne12O8xmM9q1a6d6PzExEXa7PaT9EQp2mRERERmVhpfdnzx5EnFxccrb0dHRQYvn5OQoP6ekpCAjIwPdunXDypUrMX369KZXQwhIUvhmReIZIiIiIkJcXJzqUVdC5C82NhYpKSkoLS1tUHmr1Yrq6mqUl5er3j937hwSExMbXW+tMCEiIiIyKC27zJrK6XTi0KFD6NSpU4PK33333YiKisLGjRuV986cOYMDBw4gMzMztMqEIKwJ0bZt2zBy5EjYbDZIkoR3331XWeZyuTBr1iykpKQgNjYWNpsN48ePx+nTp1UxXnvtNWRlZSEuLg6SJOHSpUsB2ykvL0d+fr7SL5qfnx+0HBERkaGE4bL7mTNnYuvWrSgrK8OuXbswatQoVFRUYMKECQCA77//HiUlJfj6668BAIcPH0ZJSYkyPshisWDSpEmYMWMGPv30U+zbtw+/+MUvkJKSolx1Fg6SECJsI/o++ugjfPHFF0hNTcXDDz+M9evX48EHHwTgvSxv1KhRKCgoQN++fVFeXo5p06ahpqYGe/bsUWK8/PLLqKqqAgDMmTMH5eXlaNu2rWo7OTk5OHXqFF577TUAwOTJk9GlSxe89957Da5rRUUFLBYLHA6Hqo+1oQYW/V71Wt7rHt/oz8YMsJbX2ZvzXEBsOa7bd18Aj999DOTljemmNfn9+yDHdLuv59Nuj/c9j8fki+9dR76VxsGfFqpi9Fz/dJ3bk9f1r2OwT6rw1cW/nf7LhSdwuTxQWr7dydcPqut4xzvPBq0XcH2fyAPYr9dZXXflOHuu7yu5rv77Ndh2arcBAFw1EaoY3z48V1W23wfe1yXD1XUHgD7v/UEVr8Z3/A79x7yg9ait29vez5q72rt9Ue07zi6//6lqDT4/Pvl3AIAuy/9bXUY+Fr7x9JJb3lmB25VvbxGwq+r7usjh5EHQ8u6Tx+/L+1Oua63B4qKOwfNKHX3PUo33uWzKDKXMD/78J786SqrKKy/lGMGuU/D/mNbV7lq7PeAWIH7ryPvuaAhjO6jxQv2b0dD4KZOeR4Q5JqRY7uoq7F/x+wbXNS8vD9u2bcOFCxfQsWNHpKen49lnn8Vdd90FAHjjjTfwyCOPBKw3b948FBYWAgCqqqrwu9/9Dm+99RauXbuG+++/H0uXLkVSUlJIbQlFWAdV5+TkqAZn1WaxWFSn0wBg8eLFGDhwIE6cOIHk5GQA3smdAOCzzz4LGufQoUMoKirCzp07kZaWBgBYvnw5MjIycPjwYfTo0SPoek6nU3XJYUVFRWOaRkREpLtw3Lpj7dq19S6fOHEiJk6cWG+ZmJgYLF68GIsXL27cxnVkqDFEDocDkiQFnAGqz44dO2CxWJRkCADS09NhsVjqne9gwYIFqssPw5m1EhERBRWmmapvRYZJiKqqqjB79myMHTu2Uacf7XY7EhISAt5PSEiod76DOXPmwOFwKI+TJ082qd5ERES6YUKkGUPMQ+RyuZCXlwePx4OlS5c2ev1g8xrcaL6D6OjoBl9ySERERMbW7BMil8uF0aNHo6ysDJs3b2704DSr1YqzZ88GvH/+/PmbOt/B7mHPq16nfzwHAPDl0OeDFW9y7Hs+8g6wLn5gfshxZXd/9CSA64O4tdaQQb0Ndde7hQACB0g3hTxgWR5cffihP4Qcs7a+73vjfzUicAC0Hv418pmQYxzPn934dQp+F/T9Lq95B1uX/WpmSHUKiPuXF73b1TBul2U3jnls6ow6lwXzg1deur7ubzngmZomHGOIblXNustMToZKS0uxadMmtG/fvtExMjIy4HA4sHv3buW9Xbt2weFwhHW+AyIiopCxy0wzYT1DVFlZiSNHjiivy8rKUFJSgvj4eNhsNowaNQrFxcV4//334Xa7lTE/8fHxMJvNALxjhOx2uxJn//79aNOmDZKTkxEfH4+ePXti2LBhKCgowLJlywB4L7sfMWJEnVeYERERUcsS1jNEe/bsQf/+/dG/f38AwPTp09G/f3/84Q9/wKlTp7BhwwacOnUK/fr1Q6dOnZRH7avDXn31VfTv3x8FBQUAgPvuuw/9+/fHhg0blDJr1qxBSkoKsrOzkZ2djT59+uDNN9+8uY0lIiLSmCSEJg8K8xmirKws1DcvZEPmjCwsLFQmeqpLfHw8Vq9e3djqERERNW9adHkxHwJggEHVt6qYSJcucfW4UXCEDiPu/Gdj1kqMuUbzmBEmz40LNYEeg6nrmv06VBEROuwDnW5qLaJ02Ac67FdPq2BTVRNRuDAhIiIiMiheZaYdJkRERERGxS4zzTTry+6JiIiIbgaeISIiIjIodplphwlRmMRGVesSNypC+4GaegwqjorUZ0CpWYf2R+oxoFgneg1WN5l0iBupT12lSO2P1/HJwWfbDgkHVZMW2GWmGSZEREREBsUzRNrhGCIiIiJq8XiGiIiIyKjYZaYZJkREREQGxi4vbbDLjIiIiFo8niEKk9Y63brjNrNT85itdboiTg96XGWl15Vb926cBQD44icvaBZTj9usADrtAz2uXAMQEaP91VtdVnmP0fHxszSLGRmt/W1mqAUSwvsINQYxISIiIjIqXmWmHXaZERERUYvHM0RERERGxavMNMOEiIiIyKAkj/cRagxiQhQ2Jp06bduaqzSPGafDQG09brGhF71u3eH2aN9jrcdtVgCdBlXr9F+pSYfjZYoyzueViJqGCREREZFRsctMM0yIiIiIDIpXmWmHCREREZFRcR4izfCyeyIiImrxeIYoTNqZr+gSt3WkDgOgTdoPKDVH6jNINS5a+0HlkToNVNZDjVuf/3Ei9JhVWtI+JKDPP7sRkcb5DFDLwi4z7TAhIiIiMioOqtYMu8yIiIioxeMZIiIiIoNil5l2mBAREREZFa8y0wwTojCJNtUYJm6kDoOqo3SICegzADxKp1m19ZhVWq9ZtaN0GAQf2cqleUwAiNJhVmk9Zuo26TFQnYiajAkRERGRQbHLTDtMiIiIiIyKV5lphleZERERUYvHM0REREQGxS4z7TAhChO9BlW3itB+oKoeMT06TVPsEdrH1WsAuB6DqvWICegzW7ceg58BfQaWSzoMgNYjJrVAHuF9hBqDmBAREREZFscQaYZjiIiIiKjF4xkiIiIig5KgwRgiTWpifEyIiIiIjIozVWuGXWZERETU4jEhChNL5DVYIq9pHve2CCdui3BqGtNsqoFZ46viWkW6lIeW2kZfQ9tobffrZ/e/iM/uf1HTmADQLvoa2mlcV5MkYNLhGtqoCLdutzDR2v7cp7E/92lNY5oj3TBrfPuSww/9QXkQNZV82X2oj8YoLCyEJEmqh9VqVZYLIVBYWAibzYZWrVohKysLBw8eVMXIysoKiJGXl6fFLmkyJkRERERGJTR6NFKvXr1w5swZ5bF//35l2R//+Ee89NJLWLJkCb788ktYrVb85Cc/weXLl1UxCgoKVDGWLVvW+IpoiGOIiIiIqFEiIyNVZ4VkQgi8/PLLePLJJ/HQQw8BAFauXInExES89dZbePTRR5WyrVu3DhojXHiGiIiIyKAkITR5AEBFRYXq4XTWPfyitLQUNpsNXbt2RV5eHo4dOwYAKCsrg91uR3Z2tlI2OjoagwYNwvbt21Ux1qxZgw4dOqBXr16YOXNmwBmkmy2sCdG2bdswcuRI2Gw2SJKEd999V1nmcrkwa9YspKSkIDY2FjabDePHj8fp06dVMZxOJ6ZMmYIOHTogNjYWubm5OHXqlKpMeXk58vPzYbFYYLFYkJ+fj0uXLt2EFhIREenIo9EDQFJSkvJ30mKxYMGCBUE3mZaWhlWrVuHjjz/G8uXLYbfbkZmZiYsXL8JutwMAEhMTVeskJiYqywBg3Lhx+N///V989tlnmDt3Lt555x3ljFK4SEKE73q7jz76CF988QVSU1Px8MMPY/369XjwwQcBAA6HA6NGjUJBQQH69u2L8vJyTJs2DTU1NdizZ48S41e/+hXee+89vPHGG2jfvj1mzJiB77//Hnv37kVERAQAICcnB6dOncJrr70GAJg8eTK6dOmC9957r8F1raiogMVigcPhQFxcXMhtX3TImz2bJO8nMUq6PmBTvv3EVU80AKDKEwUAiPCVjTa5VOs4fcuDlZXLyNu56vbGdIkI1XPt7fqTyzg96h7W2rcf8b8VSY28jjvSF0Ode1+uiVF+/t7ZGgBwW2Q1AKCd+aoqpts3S0aNxxuz9m0/IiX1QFf/7db4tnu1xgwAqHSZr6/rux1FTIR3O/Jg5EjfvpJfy/vOXOsWHrGRTlUd5X3n8W1PrnO1b5+5a+3bCF9c+b0qt/eYVVTHqOosx6y93ZjIGlUdq+R2etT7V34fAKpqvPHdHnVc+ZvvfxuViFojLOVbgcj7Qo7h9ki+NqhfByOU7cn7yG/7fvWqvb0I32045HpIvveleiZOUdaV1Lfw8K+rHCPYrVnkfeJfZ5Oy/cBfm/5lPb7tuGq8n0m327t9U5Bbdsjx5NuOyK9vtM/qI6/jdvm+NzXe12W/mHPDdSl0Wv/NqCv+j+6bh8jImBuvUI+amir8c9vTOHnypKqu0dHRiI6OvuH6V65cQbdu3fDEE08gPT0d9957L06fPo1OnTopZQoKCnDy5EkUFRUFjbF3714MGDAAe/fuRWpqakjtaaqwjiHKyclBTk5O0GUWiwUbN25Uvbd48WIMHDgQJ06cQHJyMhwOB1asWIE333wTQ4YMAQCsXr0aSUlJ2LRpE4YOHYpDhw6hqKgIO3fuRFpaGgBg+fLlyMjIwOHDh9GjR4+g23c6narThRUVFVo0mYiISDO1u7xCiQEAcXFxTUreYmNjkZKSgtLSUuWkht1uVyVE586dCzhrVFtqaiqioqJQWloatoTIUGOIHA4HJElC27ZtAXgzSpfLpeqrtNls6N27t9JXuWPHDlgsFiUZAoD09HRYLJaA/szaFixYoDp1mJSUpE+jiIiImipMV5nV5nQ6cejQIXTq1Aldu3aF1WpVndCorq7G1q1bkZmZWWeMgwcPwuVyqZKom80wCVFVVRVmz56NsWPHKhms3W6H2WxGu3btVGVr91Xa7XYkJCQExEtISFD1Z/qbM2cOHA6H8jh58qSGrSEiItKAPFN1qI9GmDlzJrZu3YqysjLs2rULo0aNQkVFBSZMmABJkjBt2jQ8//zzWL9+PQ4cOICJEyeidevWGDt2LADg6NGjeOaZZ7Bnzx4cP34cH374IX72s5+hf//+uPfee/XYSw1iiMvuXS4X8vLy4PF4sHTp0huWF0JAqjXIQAoy4MC/jL+G9p0SERG1JKdOncKYMWNw4cIFdOzYEenp6di5cyc6d+4MAHjiiSdw7do1/PrXv0Z5eTnS0tLwySefoE2bNgAAs9mMTz/9FK+88goqKyuRlJSE4cOHY968ecrY33Bo9gmRy+XC6NGjUVZWhs2bN6v6N61WK6qrq1FeXq46S3Tu3Dnl1JzVasXZs2cD4p4/f77e/ky9Pd7zE13iPnMgFwDwZK/3NYs586ufAwCWpq7WLCYBj5d4Z2VddM/rYa4JERlVU2aaDhajMdauXVt/PElCYWEhCgsLgy5PSkrC1q1bG7fRm6BZd5nJyVBpaSk2bdqE9u3bq5bffffdiIqKUvVVnjlzBgcOHFASooyMDDgcDuzevVsps2vXLjgcjnr7M4mIiJq9MHSZ3arCeoaosrISR44cUV6XlZWhpKQE8fHxsNlsGDVqFIqLi/H+++/D7XYrY37i4+NhNpthsVgwadIkzJgxA+3bt0d8fDxmzpyJlJQU5aqznj17YtiwYSgoKFCmBZ88eTJGjBhR5xVmRERE1LKENSHas2cPBg8erLyePn06AGDChAkoLCzEhg0bAAD9+vVTrbdlyxZkZWUBABYtWoTIyEiMHj0a165dw/3334833nhD1Q+5Zs0aTJ06VbkaLTc3F0uWLNGxZURERPqTPN5HqDEozAlRVlYW6psXsiFzRsbExGDx4sVYvHhxnWXi4+OxejXHvxAR0S1Giy4vdpkBMMCgamqcP/TeoHlME/jvgx4W9at/YCIREd08TIiIiIiMSoOJFUNe/xbBhIiIiMigtLx1R0vXrC+7JyIiIroZeIaIiIjIqDioWjNMiOiGWkdUh7sKREQUjABCvu6F+RAAJkRERESGxTFE2uEYIiIiImrxeIaIiIjIqAQ0GEOkSU0MjwkRERGRUXFQtWbYZUZEREQtHs8QERERGZUHgKRBDGJCREREZFS8ykw77DIjIiKiFo9niIiIiIyKg6o1w4SIiIjIqJgQaYZdZkRERNTi8QwRERGRUfEMkWaYEBERERkVL7vXDBMiIiIig+Jl99rhGCIiIiJq8XiGiIiIyKg4hkgzTIiIiIiMyiMAKcSExsOECGCXGRERERHPEBERERkWu8w0w4SIiIjIsDRIiMCECGBCpDuP/Q7vs2+ih6ueagBAlBQBADD5JpCogVtZ54jLW/aSpxUAwO0rE+H70LYxVfmeawAALnF9EopLnmgAQKzkAgB0jPDGPe/2bu+KiAIAXPbE+LZ//YsQa3KqtlMtvOtkxJoBAG+Wpvu25/3YxJhcAe11+daRn6s8Ub42eHtnY3z1qvLVAwCcShl1O1v76hMledtw1dc2OXbtdWQxUo3qfbmsHNMkXZ9wI8Lvl4C8jrw9+TnCd+zaRlxVysZHVHqX+eJF+Y7fJU9rAMDZGgsA4Iqvzh5xvXda3s7Mnh8DABYdylbV1X/fPZuyHv7m7v8PAEDrCO8+skRcAwC099WrdjvlOBdq4ryvhfprL++zx3t+ErCdP36do6qzfKxcHnVdPUF6392+z2WEb3yDybcflXWEHDNS9QwA1b6f5TKxkdWqmPLyarf3+e+ZfwnY/q+LfwHg+nGUXXN7P8/nnbf5Ylz/PJl8dY2JqAka9yefPa6qV20Jrbz73hpT4dtOlGp7/qJM1+vVyvddio30Hs+Fff6hKjt5zwQAwBVfrNrf20hfnBrfManybfdqTZTvtXcffTr4paD1ICIvJkRERERGxS4zzTAhIiIiMiqPQMhdXrzKDACvMiMiIiLiGSIiIiLDEh7vI9QYxISIiIjIsDiGSDNMiIiIiIyKY4g0wzFERERE1OLxDBEREZFRsctMM0yIiIiIjEpAg4RIk5oYHrvMiIiIqMXjGSIiIiKjYpeZZpgQERERGZXHAyDEeYQ8nIcIYJcZEREREc8QERERGRa7zDQT1jNE27Ztw8iRI2Gz2SBJEt59913V8nXr1mHo0KHo0KEDJElCSUlJQIyjR4/iP/7jP9CxY0fExcVh9OjROHv2rKpMeXk58vPzYbFYYLFYkJ+fj0uXLunXMCIioptBTohCfTRCYWEhJElSPaxWa60qCRQWFsJms6FVq1bIysrCwYMHVTGcTiemTJmCDh06IDY2Frm5uTh16pQmu6SpwpoQXblyBX379sWSJUvqXH7vvfdi4cKFdS7Pzs6GJEnYvHkzvvjiC1RXV2PkyJHw1OoTHTt2LEpKSlBUVISioiKUlJQgPz9flzYRERHd6nr16oUzZ84oj/379yvL/vjHP+Kll17CkiVL8OWXX8JqteInP/kJLl++rJSZNm0a1q9fj7Vr1+Lzzz9HZWUlRowYAbfbHY7mAAhzl1lOTg5ycnLqXC4nLcePHw+6/IsvvsDx48exb98+xMXFAQBef/11xMfHY/PmzRgyZAgOHTqEoqIi7Ny5E2lpaQCA5cuXIyMjA4cPH0aPHj20bRQREdHNEqZbd0RGRqrOCsmEEHj55Zfx5JNP4qGHHgIArFy5EomJiXjrrbfw6KOPwuFwYMWKFXjzzTcxZMgQAMDq1auRlJSETZs2YejQoaG1p4kMPaja6XRCkiRER0cr78XExMBkMuHzzz8HAOzYsQMWi0VJhgAgPT0dFosF27dvrzd2RUWF6kFERNScCOHR5AEg4G+e0+msc7ulpaWw2Wzo2rUr8vLycOzYMQBAWVkZ7HY7srOzlbLR0dEYNGiQ8jd37969cLlcqjI2mw29e/eu9++y3gydEKWnpyM2NhazZs3C1atXceXKFfzud7+Dx+PBmTNnAAB2ux0JCQkB6yYkJMBut9cZe8GCBcqYI4vFgqSkJN3aQURE1CRCeM/whPLwjSFKSkpS/d1bsGBB0E2mpaVh1apV+Pjjj7F8+XLY7XZkZmbi4sWLyt/VxMRE1TqJiYnKMrvdDrPZjHbt2tVZJhwMnRB17NgRf//73/Hee+/htttug8VigcPhQGpqKiIiIpRykiQFrCuECPq+bM6cOXA4HMrj5MmTurSBiIioOTh58qTq796cOXOClsvJycHDDz+MlJQUDBkyBB988AEAb9eYzP/v643+5ja0jJ4Mf9l9dnY2jh49igsXLiAyMhJt27aF1WpF165dAQBWqzXgqjMAOH/+fEAGW1t0dLSqK46IiKjZERqMIfKdIYqLi1PG4zZGbGwsUlJSUFpaigcffBCA9yxQp06dlDLnzp1T/uZarVZUV1ejvLxcdZbo3LlzyMzMDKEhoTH0GaLaOnTogLZt22Lz5s04d+4ccnNzAQAZGRlwOBzYvXu3UnbXrl1wOBxh3fFEREQh83i0eYTA6XTi0KFD6NSpE7p27Qqr1YqNGzcqy6urq7F161blb+7dd9+NqKgoVZkzZ87gwIEDYf27HNYzRJWVlThy5IjyuqysDCUlJYiPj0dycjK+//57nDhxAqdPnwYAHD58GIA3u5RHt7/++uvo2bMnOnbsiB07duC3v/0tHn/8ceXqsZ49e2LYsGEoKCjAsmXLAACTJ0/GiBEjeIUZERFRI82cORMjR45EcnIyzp07h/nz56OiogITJkyAJEmYNm0ann/+eXTv3h3du3fH888/j9atW2Ps2LEAAIvFgkmTJmHGjBlo37494uPjMXPmTKULLlzCmhDt2bMHgwcPVl5Pnz4dADBhwgS88cYb2LBhAx555BFleV5eHgBg3rx5KCwsBOBNkubMmYPvv/8eXbp0wZNPPonHH39ctZ01a9Zg6tSpyoj23NzcOuc+IiIiMgwNu8wa6tSpUxgzZgwuXLiAjh07Ij09HTt37kTnzp0BAE888QSuXbuGX//61ygvL0daWho++eQTtGnTRomxaNEiREZGYvTo0bh27Rruv/9+vPHGG6rxvzdbWBOirKwsiHoOxMSJEzFx4sR6YyxcuLDOiRtl8fHxWL16dVOqSERE1GwJjwdCCq3LS77svqHWrl1b73JJklBYWKicuAgmJiYGixcvxuLFixu1bT3dMmOIiIiIiJrK8FeZERERtVhh6DK7VTEhIiIiMiqPACQmRFpglxkRERG1eDxDREREZFRCAAhtUDXPEHkxISIiIjIo4REQIXaZ1Xe1d0vChIiIiMiohAehnyEKcf1bBMcQERERUYvHM0REREQGxS4z7TAhIiIiMip2mWmGCVEDyRl0RUVFo9bzXHZ7n30TZ1313VU4SpIAACZ4n2tqfaArXd6fr3i867p9ZSJ8MSSTR/XsEpKy7hVffHkq95gI73Ol21vmqlDHNtWe0MsXT95OtZDX8Za95q7xbc/XNskd0N4a37ou3/6q8ki+9pt89fLGcNaqs9NXRo4m38lGMnnL1vi2U+WJUMWuvY5CUu8zl5Db5GtirSnuI/wmM5PXcftiyNuN8B0bs+n61qIjfMt88aJ8Za561PtKrrNHXO+dlrcjf5aqKuX9Ku87byynr6rBPnPOSpe3PRHeZ3OEN8ZVXx1rt1Pe11U1vjr5/zfoa2ew7ch1k1te7Qvr8sifPe+zJ0jvu9u33Qjff68m+K3jW17ti1Vd647bAWUiqlUx5eUut6fOulf79pH/57Ta99JVXe2Lcf3eSSZfXSN8+9M/bs0Vp6petbk83njOGpdvO0K1PX+i1ucpwvdZj4x0Bd1udaWvrr5dJNX67MrtqxG+74dvu64a73ONu+7jS/qR97feZ19q4Ap5XsYauLSpjMFJgufKGuTUqVNISkoKdzWIiMhATp48idtvv13zuFVVVejatSvsdrsm8axWK8rKyhATE6NJPCNiQtRAHo8Hp0+fRps2bSBJgf8dNlZFRQWSkpJw8uRJxMXFaVDD5uFWbNet2CaA7TKSW7FNwK3frq+//ho9evSAyaTP9UtVVVWo9p3pDJXZbG7RyRDALrMGM5lMumT5cXFxt9QvAtmt2K5bsU0A22Ukt2KbgFu3Xf/2b/+mWzIEeO8Y39KTGC3xsnsiIiJq8ZgQERERUYvHhChMoqOjMW/ePERHR4e7Kpq6Fdt1K7YJYLuM5FZsE8B2UfPCQdVERETU4vEMEREREbV4TIiIiIioxWNCRERERC0eEyIiIiJq8ZgQhWDbtm0YOXIkbDYbJEnCu+++q1p+9uxZTJw4ETabDa1bt8awYcNQWlqqKpOVlQVJklSPvLw8VZny8nLk5+fDYrHAYrEgPz8fly5darbtOn78eECb5Mff//53pVyXLl0Cls+ePVuXNi1YsAD33HMP2rRpg4SEBDz44IM4fPiwqowQAoWFhbDZbGjVqhWysrJw8OBBVRmn04kpU6agQ4cOiI2NRW5uLk6dOqUqc7OOlxZt+v777zFlyhT06NEDrVu3RnJyMqZOnQqHw6GKY8Rj1dy+W1q0q7l9txrSpnXr1mHo0KHo0KEDJElCSUlJQJzm9L3Sql3N8btFNyCoyT788EPx5JNPinfeeUcAEOvXr1eWeTwekZ6eLn70ox+J3bt3i2+++UZMnjxZJCcni8rKSqXcoEGDREFBgThz5ozyuHTpkmo7w4YNE7179xbbt28X27dvF7179xYjRoxotu2qqalRtefMmTPi6aefFrGxseLy5ctKrM6dO4tnnnlGVa72ci0NHTpUvP766+LAgQOipKREDB8+POBYLFy4ULRp00a88847Yv/+/eLnP/+56NSpk6ioqFDKPPbYY+Lf/u3fxMaNG0VxcbEYPHiw6Nu3r6ipqVHK3KzjpUWb9u/fLx566CGxYcMGceTIEfHpp5+K7t27i4cffli1LSMeq+b23dKiXc3tu9WQNq1atUo8/fTTYvny5QKA2LdvX0Cc5vS90qpdzfG7RfVjQqQR/8Th8OHDAoA4cOCA8l5NTY2Ij48Xy5cvV94bNGiQ+O1vf1tn3K+//loAEDt37lTe27FjhwAgvvnmG03bEExT2+WvX79+4pe//KXqvc6dO4tFixZpXeUGOXfunAAgtm7dKoTwJnpWq1UsXLhQKVNVVSUsFot49dVXhRBCXLp0SURFRYm1a9cqZf7v//5PmEwmUVRUJIQI7/FqSpuC+dvf/ibMZrNwuVzKe0Y7VkI0/++WVserOX23/NtUW1lZWdDEobl/r4RoWruCaW7fLVJjl5lOnE4nAKjuMxMREQGz2YzPP/9cVXbNmjXo0KEDevXqhZkzZ+Ly5cvKsh07dsBisSAtLU15Lz09HRaLBdu3b9e5FYEa0y7Z3r17UVJSgkmTJgUse+GFF9C+fXv069cPzz33nGY3KrwR+bR1fHw8AKCsrAx2ux3Z2dlKmejoaAwaNEjZz3v37oXL5VKVsdls6N27t1ImnMerKW2qK05cXBwiI9W3OjTSsZI15++WFseruX23/NvUEM39ewU0rV11xWlO3y1S481ddXLnnXeic+fOmDNnDpYtW4bY2Fi89NJLsNvtOHPmjFJu3Lhx6Nq1K6xWKw4cOIA5c+bgq6++wsaNGwEAdrsdCQkJAfETEhJgt9tvWntkDW1XbStWrEDPnj2RmZmpev+3v/0tUlNT0a5dO+zevRtz5sxBWVkZ/vrXv+raBiEEpk+fjn//939H7969AUDZl4mJiaqyiYmJ+O6775QyZrMZ7dq1Cygjrx+u49XUNvm7ePEinn32WTz66KOq9412rIDm/d3S6ng1p+9WsDY1RHP+XgFNb5e/5vbdokBMiHQSFRWFd955B5MmTUJ8fDwiIiIwZMgQ5OTkqMoVFBQoP/fu3Rvdu3fHgAEDUFxcjNTUVACAJEkB8YUQQd/XW0PbJbt27RreeustzJ07N2DZ448/rvzcp08ftGvXDqNGjVL+W9LLb37zG/zrX/8KekbLf582ZD/7lwnH8dKiTRUVFRg+fDjuuusuzJs3T7XMiMeqOX+3tDheze27VV+bmqI5fK8AbdrVHL9bFIhdZjq6++67UVJSgkuXLuHMmTMoKirCxYsX0bVr1zrXSU1NRVRUlHLVltVqxdmzZwPKnT9/PuA/yZulMe36xz/+gatXr2L8+PE3jJueng4AOHLkiOZ1lk2ZMgUbNmzAli1bcPvttyvvW61WAAj4b/PcuXPKfrZaraiurkZ5eXm9ZW728QqlTbLLly9j2LBhuO2227B+/XpERUXVu83mfqyCaS7fLa3a1Zy+W3W1qSGa6/cKCK1dsub43aI63PxhS7cm+A0+Dubbb78VJpNJfPzxx3WW2b9/v2rwnjyYcNeuXUqZnTt3hm1QdTD1tWvQoEEBV1XU5b333hMAxHfffdeUqtbL4/GI//qv/xI2m018++23QZdbrVbxwgsvKO85nc6gg6rffvttpczp06eDDv68GcdLizYJIYTD4RDp6eli0KBB4sqVKw3adnM/VsGE+7uldbuaw3frRm2q7UaDqpvL90oIbdolRPP7blH9mBCF4PLly2Lfvn1i3759AoB46aWXxL59+5QP8t/+9jexZcsWcfToUfHuu++Kzp07i4ceekhZ/8iRI+Lpp58WX375pSgrKxMffPCBuPPOO0X//v0DLjft06eP2LFjh9ixY4dISUnR9bL7UNslKy0tFZIkiY8++ihg2fbt25W4x44dE2+//baw2WwiNzdXlzb96le/EhaLRXz22Weqy1uvXr2qlFm4cKGwWCxi3bp1Yv/+/WLMmDFBL7u//fbbxaZNm0RxcbH48Y9/HPTy4JtxvLRoU0VFhUhLSxMpKSniyJEjqjhym4x4rJrjd0urz6AQzee71ZA2Xbx4Uezbt0988MEHAoBYu3at2Ldvnzhz5oxSpjl9r7RqV3P8blH9mBCFYMuWLQJAwGPChAlCCCFeeeUVcfvtt4uoqCiRnJwsnnrqKeF0OpX1T5w4Ie677z4RHx8vzGaz6Natm5g6daq4ePGiajsXL14U48aNE23atBFt2rQR48aNE+Xl5c22XbI5c+aI22+/Xbjd7oBle/fuFWlpacJisYiYmBjRo0cPMW/evAb/F9VYwdoDQLz++utKGY/HI+bNmyesVquIjo4W9913n9i/f78qzrVr18RvfvMbER8fL1q1aiVGjBghTpw4oSpzs46XFm2q61gDEGVlZUIIYx6r5vjd0uozKETz+W41pE2vv/560DLz5s1TyjSn75VW7WqO3y2qnySEEA3tXiMiIiK6FXFQNREREbV4TIiIiIioxWNCRERERC0eEyIiIiJq8ZgQERERUYvHhIiIiIhaPCZERERE1OIxISIiIqIWjwkREdWrsLAQ/fr1U15PnDgRDz74YNjqQ0Skh8hwV4CIjOWVV14BJ7gnolsNEyIiahSLxRLuKhARaY5dZkQt3Pnz52G1WvH8888r7+3atQtmsxmffPJJQHn/LrOsrCxMnToVTzzxBOLj42G1WlFYWKhax+FwYPLkyUhISEBcXBx+/OMf46uvvtKrSUREjcaEiKiF69ixI/7nf/4HhYWF2LNnDyorK/GLX/wCv/71r5Gdnd2gGCtXrkRsbCx27dqFP/7xj3jmmWewceNGAIAQAsOHD4fdbseHH36IvXv3IjU1Fffffz++//57PZtGRNRgTIiICA888AAKCgowbtw4PPbYY4iJicHChQsbvH6fPn0wb948dO/eHePHj8eAAQPw6aefAgC2bNmC/fv34+9//zsGDBiA7t2748UXX0Tbtm3xj3/8Q68mERE1CscQEREA4MUXX0Tv3r3xt7/9DXv27EFMTEyD1+3Tp4/qdadOnXDu3DkAwN69e1FZWYn27duryly7dg1Hjx4NveJERBpgQkREAIBjx47h9OnT8Hg8+O677wKSnPpERUWpXkuSBI/HAwDweDzo1KkTPvvss4D12rZtG0qViYg0w4SIiFBdXY1x48bh5z//Oe68805MmjQJ+/fvR2JiYsixU1NTYbfbERkZiS5duoReWSIiHXAMERHhySefhMPhwJ///Gc88cQT6NmzJyZNmqRJ7CFDhiAjIwMPPvggPv74Yxw/fhzbt2/HU089hT179miyDSKiUPEMEVEL99lnn+Hll1/Gli1bEBcXBwB488030adPH/zlL38JOb4kSfjwww/x5JNP4pe//KVymf99992nyRkoIiItSIJTzhIREVELxy4zIiIiavGYEBEREVGLx4SIiIiIWjwmRERERNTiMSEiIiKiFo8JEREREbV4TIiIiIioxWNCRERERC0eEyIiIiJq8ZgQERERUYvHhIiIiIhavP8femi5d8SH2vYAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds['twt'].plot()" - ] - }, - { - "cell_type": "markdown", - "id": "80dd49d6", - "metadata": {}, - "source": [ - "## Multiple horizons in one file\n", - "\n", - "You can export multiple horizons from OpendTect. These will be loaded as one `xarray.Dataset` as different **Data variables**. (The actual attribute you exported from OdT is always called Z; this information is not retained in the xarray.)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e976b148", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset>\n",
-       "Dimensions:               (xline: 93, iline: 45)\n",
-       "Coordinates:\n",
-       "  * xline                 (xline) int64 964 966 968 970 ... 1142 1144 1146 1148\n",
-       "  * iline                 (iline) int64 110 112 114 116 118 ... 192 194 196 198\n",
-       "Data variables:\n",
-       "    F3_Demo_2_FS6         (iline, xline) float64 nan nan nan ... 825.4 823.9\n",
-       "    F3_Demo_4_Truncation  (iline, xline) float64 820.0 820.7 ... 635.3 635.2
" - ], - "text/plain": [ - "\n", - "Dimensions: (xline: 93, iline: 45)\n", - "Coordinates:\n", - " * xline (xline) int64 964 966 968 970 ... 1142 1144 1146 1148\n", - " * iline (iline) int64 110 112 114 116 118 ... 192 194 196 198\n", - "Data variables:\n", - " F3_Demo_2_FS6 (iline, xline) float64 nan nan nan ... 825.4 823.9\n", - " F3_Demo_4_Truncation (iline, xline) float64 820.0 820.7 ... 635.3 635.2" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fname = '../../data/OdT/3d_horizon/F3_Multi-H2-H4_ILXL_Single-line-header.dat'\n", - "\n", - "ds = gio.read_odt(fname)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5965f669", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds['F3_Demo_4_Truncation'].plot()" - ] - }, - { - "cell_type": "markdown", - "id": "d7a73a94", - "metadata": {}, - "source": [ - "### Multi-horizon, no header\n", - "\n", - "Unfortunately, OdT exports (x, y) in the first two columns, meaning you can't assume that columns 3 and 4 are inline, crossline. So if there's no header, and XY as well as inline/xline, you have to give the column names:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "4862f786", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset>\n",
-       "Dimensions:      (iline: 1, xline: 10)\n",
-       "Coordinates:\n",
-       "  * iline        (iline) int64 990\n",
-       "  * xline        (xline) int64 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024\n",
-       "    cdp_x        (iline, xline) float64 1.703e+06 1.703e+06 ... 1.703e+06\n",
-       "    cdp_y        (iline, xline) float64 5.817e+06 5.817e+06 ... 5.818e+06\n",
-       "Data variables:\n",
-       "    blue event   (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03\n",
-       "    green event  (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03
" - ], - "text/plain": [ - "\n", - "Dimensions: (iline: 1, xline: 10)\n", - "Coordinates:\n", - " * iline (iline) int64 990\n", - " * xline (xline) int64 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024\n", - " cdp_x (iline, xline) float64 1.703e+06 1.703e+06 ... 1.703e+06\n", - " cdp_y (iline, xline) float64 5.817e+06 5.817e+06 ... 5.818e+06\n", - "Data variables:\n", - " blue event (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03\n", - " green event (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import gio\n", - "\n", - "fname = '../../data/OdT/3d_horizon/Test_Multi_XY-and-ILXL_Z-only.dat'\n", - "\n", - "ds = gio.read_odt(fname, names=['Horizon', 'X', 'Y', 'Inline', 'Crossline', 'Z'])\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "id": "1345e4ef", - "metadata": {}, - "source": [ - "## Undefined values\n", - "\n", - "These are exported as `'1e30'` by default. You can override this (not add to it, which is the default `pandas` behaviour) by passing one or more `na_values`." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "10cf625e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.\n", - " dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)\n" - ] - }, - { - "data": { - "text/html": [ - "
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<xarray.Dataset>\n",
-       "Dimensions:  (iline: 54, xline: 57)\n",
-       "Coordinates:\n",
-       "  * iline    (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n",
-       "  * xline    (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n",
-       "    cdp_x    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
-       "    cdp_y    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
-       "Data variables:\n",
-       "    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
-       "Attributes:\n",
-       "    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat
" - ], - "text/plain": [ - "\n", - "Dimensions: (iline: 54, xline: 57)\n", - "Coordinates:\n", - " * iline (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53\n", - " * xline (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56\n", - " cdp_x (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", - " cdp_y (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", - "Data variables:\n", - " twt (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan\n", - "Attributes:\n", - " odt_filename: ../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fname = '../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat'\n", - "\n", - "ds = gio.read_odt(fname, names=['X', 'Y', 'TWT'])\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "a8f453cd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ds['twt'].plot()" - ] - }, - { - "cell_type": "markdown", - "id": "d2dff77d", - "metadata": {}, - "source": [ - "## Writing OdT files\n", - "\n", - "You can write an OdT file using `gio.to_odt()`:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "512d59a1", - "metadata": {}, - "outputs": [], - "source": [ - "_ = gio.to_odt('out.dat', ds)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "06b7a481", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# 1: Inline\r\n", - "# 2: Crossline\r\n", - "# 3: X\r\n", - "# 4: Y\r\n", - "# 5: Z\r\n", - "# - - - - - - - - - -\r\n", - "0\t51\t620986.88\t6080882.36\t648.6175060272217\r\n", - "0\t52\t621036.86\t6080883.76\t647.8394865989685\r\n", - "0\t53\t621086.84\t6080885.16\t647.9663848876953\r\n", - "1\t50\t620935.5\t6080930.95\t649.9454379081726\r\n" - ] - } - ], - "source": [ - "!head out.dat" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "80d1b482999e70934fa1bff5f55b93cc869ff05fc05093ffa26cad7f8d1d8425" - }, - "kernelspec": { - "display_name": "py39", - "language": "python", - "name": "py39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/userguide_src/Read_Surfer_grids.ipynb b/docs/userguide_src/Read_Surfer_grids.ipynb deleted file mode 100644 index f80dae4..0000000 --- a/docs/userguide_src/Read_Surfer_grids.ipynb +++ /dev/null @@ -1,1864 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "47ca9a21", - "metadata": {}, - "source": [ - "# Read surfer grids\n", - "\n", - "There are 3 varieties:\n", - "\n", - "- Surfer 6 binary\n", - "- Surfer 6 ASCII\n", - "- Surfer 7 binary\n", - "\n", - "In theory we can read all of these, but I don't have a Surfer 6 binary file to test on." - ] - }, - { - "cell_type": "markdown", - "id": "d56d5022", - "metadata": {}, - "source": [ - "## A small binary file\n", - "\n", - "[From the docs.](https://grapherhelp.goldensoftware.com/subsys/ascii_grid_file_format.htm)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c1f41fea", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DSAA\r\n", - "10 10 \r\n", - "0.0 9.0\r\n", - "0.0 7.0\r\n", - "25.00 97.19\r\n", - "91.03 77.21 60.55 46.67 52.73 64.05 41.19 54.99 44.30 25.00\r\n", - "96.04 81.10 62.38 48.74 57.50 63.27 48.67 60.81 51.78 33.63\r\n", - "92.10 85.05 65.09 53.01 64.44 65.64 52.53 66.54 59.29 41.33\r\n", - "94.04 85.63 65.56 55.32 73.18 70.88 55.35 76.27 67.20 45.78\r\n", - "97.19 82.00 64.21 61.97 82.99 80.34 58.55 86.28 75.02 48.75\r\n" - ] - } - ], - "source": [ - "!head ../data/Surfer/surfer-6-ascii-tiny.grd" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d4081cb5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray (y: 10, x: 10)>\n",
-       "array([[70.  , 54.19, 62.27, 74.51, 55.95, 55.42, 71.21, 74.63, 63.14,\n",
-       "        44.99],\n",
-       "       [74.77, 66.02, 70.29, 75.16, 60.56, 65.56, 85.07, 89.81, 74.53,\n",
-       "        51.69],\n",
-       "       [80.88, 75.56, 74.35, 72.47, 66.93, 75.49, 86.39, 92.1 , 84.41,\n",
-       "        55.  ],\n",
-       "       [86.31, 77.58, 67.71, 68.5 , 73.37, 74.84, 65.35, 95.55, 85.92,\n",
-       "        55.76],\n",
-       "       [91.36, 78.73, 64.05, 65.6 , 82.58, 81.37, 61.16, 89.09, 81.36,\n",
-       "        54.87],\n",
-       "       [97.19, 82.  , 64.21, 61.97, 82.99, 80.34, 58.55, 86.28, 75.02,\n",
-       "        48.75],\n",
-       "       [94.04, 85.63, 65.56, 55.32, 73.18, 70.88, 55.35, 76.27, 67.2 ,\n",
-       "        45.78],\n",
-       "       [92.1 , 85.05, 65.09, 53.01, 64.44, 65.64, 52.53, 66.54, 59.29,\n",
-       "        41.33],\n",
-       "       [96.04, 81.1 , 62.38, 48.74, 57.5 , 63.27, 48.67, 60.81, 51.78,\n",
-       "        33.63],\n",
-       "       [91.03, 77.21, 60.55, 46.67, 52.73, 64.05, 41.19, 54.99, 44.3 ,\n",
-       "        25.  ]])\n",
-       "Coordinates:\n",
-       "  * x        (x) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0\n",
-       "  * y        (y) float64 0.0 0.7778 1.556 2.333 3.111 ... 4.667 5.444 6.222 7.0\n",
-       "Attributes:\n",
-       "    source:   ../data/Surfer/surfer-6-ascii-tiny.grd
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- "id": "ded77202", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "da.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "bef215e2", - "metadata": {}, - "source": [ - "## A binary file" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6e7c02be", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray (y: 352, x: 629)>\n",
-       "array([[34.34440231, 35.18498611, 35.32251358, ..., 36.05297852,\n",
-       "        35.76726532, 35.35245132],\n",
-       "       [35.43532181, 35.00969315, 35.75266647, ..., 36.27960205,\n",
-       "        35.19588852, 36.22369003],\n",
-       "       [35.50966644, 35.82201385, 35.89884949, ..., 36.19258118,\n",
-       "        36.06084442, 35.46885681],\n",
-       "       ...,\n",
-       "       [22.57001305, 23.0938015 , 22.96144295, ..., 36.87253571,\n",
-       "        35.71622849, 35.71092606],\n",
-       "       [22.57962227, 22.91576004, 22.62903214, ..., 37.23720932,\n",
-       "        36.19213867, 36.02495956],\n",
-       "       [23.00543976, 22.40481567, 22.8556118 , ..., 36.53549194,\n",
-       "        35.47878265, 35.03230667]])\n",
-       "Coordinates:\n",
-       "  * x        (x) float64 2.604 2.605 2.605 2.606 ... 2.917 2.917 2.918 2.918\n",
-       "  * y        (y) float64 23.47 23.47 23.47 23.47 ... 23.64 23.64 23.64 23.64\n",
-       "Attributes:\n",
-       "    source:   ../data/Surfer/WDS1_Si_TAP_Quant.grd
" - ], - "text/plain": [ - "\n", - "array([[34.34440231, 35.18498611, 35.32251358, ..., 36.05297852,\n", - " 35.76726532, 35.35245132],\n", - " [35.43532181, 35.00969315, 35.75266647, ..., 36.27960205,\n", - " 35.19588852, 36.22369003],\n", - " [35.50966644, 35.82201385, 35.89884949, ..., 36.19258118,\n", - " 36.06084442, 35.46885681],\n", - " ...,\n", - " [22.57001305, 23.0938015 , 22.96144295, ..., 36.87253571,\n", - " 35.71622849, 35.71092606],\n", - " [22.57962227, 22.91576004, 22.62903214, ..., 37.23720932,\n", - " 36.19213867, 36.02495956],\n", - " [23.00543976, 22.40481567, 22.8556118 , ..., 36.53549194,\n", - " 35.47878265, 35.03230667]])\n", - "Coordinates:\n", - " * x (x) float64 2.604 2.605 2.605 2.606 ... 2.917 2.917 2.918 2.918\n", - " * y (y) float64 23.47 23.47 23.47 23.47 ... 23.64 23.64 23.64 23.64\n", - "Attributes:\n", - " source: ../data/Surfer/WDS1_Si_TAP_Quant.grd" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import gio\n", - "\n", - "da = gio.read_surfer('../data/Surfer/WDS1_Si_TAP_Quant.grd')\n", - "da" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f522a54d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "da.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "fc934e1b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(352, 629)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da.shape" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39", - "language": "python", - "name": "py39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/userguide_src/Read_and_write_ZMAP_files.ipynb b/docs/userguide_src/Read_and_write_ZMAP_files.ipynb deleted file mode 100644 index 123962a..0000000 --- a/docs/userguide_src/Read_and_write_ZMAP_files.ipynb +++ /dev/null @@ -1,702 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "771a0ec0", - "metadata": {}, - "source": [ - "# Read and write ZMAP files\n", - "\n", - "The venerable ZMAP format looks like this:\n", - "\n", - "```\n", - "! Landmark Zmap grid file name: .DATANSLCU.dat\n", - "! Created/converted by Oasis Montaj, Geosoft Inc.\n", - "@.DATANSLCU.dat, GRID, 3\n", - "20, 1e+30, , 7, 1\n", - "208, 435, -630000.0, 672000.0, 2000000.0, 2621000.0\n", - "0.0, 0.0, 0.0\n", - "@\n", - " -16481.9570313 -16283.9033203 -16081.5751953\n", - " -15856.7861328 -15583.7167969 -15255.7343750\n", - " -14869.3769531 -14426.1513672 -13915.8769531\n", - "```\n", - "\n", - "Files often have a the extension `.DAT`. The first two lines shown are comments. The lines between the `@` signs give the name of the grid, with some metadata such as null value, precision, and so on.\n", - "\n", - "`gio` wraps the [`zmapio`](https://github.com/abduhbm/zmapio) library. There are several functions:\n", - "\n", - "- `read_zmap()`: read a ZMAP file.\n", - "- `array_to_zmap()`: write a NumPy array as a ZMAP file.\n", - "- `dataarray_to_zmap()`: write an Xarray `DataArray` as a ZMAP file.\n", - "- `xarray.DataArray.gio.to_zmap()`: an Xarray accessor providing another way to save a DataArray as a ZMAP file.\n", - "\n", - "Let's read some data from a ZMAP file:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e1eab6ef", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "! Landmark Zmap grid file name: .\\DATA\\NStopo.dat\r\n", - "! Created/converted by Oasis Montaj, Geosoft Inc.\r\n", - "@.\\DATA\\NStopo.dat ,GRID, 4\r\n", - " 20, 1.0E+30, , 7, 1\r\n", - " 208, 435, -630000.0000, 672000.0000, 2000000.0000, 2621000.0000,\r\n", - " 0.0000, 0.0000, 0.0000,\r\n", - "@\r\n", - " -67.2144775 -67.5697021 -67.1472168 -69.0812378\r\n", - " -73.1813660 -74.3084717 -72.7660828 -72.0339966\r\n", - " -70.5137329 -68.5551758 -66.1948547 -62.7756653\r\n" - ] - } - ], - "source": [ - "!head ../../data/ZMAP/NStopo.dat" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f9e87870", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import gio\n", - "\n", - "z = gio.read_zmap('../../data/ZMAP/NStopo.dat')\n", - "\n", - "z.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "8fccd2b3", - "metadata": {}, - "source": [ - "Let's take a look at the `xarray` object too:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cd567f1f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray '.\\\\DATA\\\\NStopo.dat' (Y: 208, X: 435)>\n",
-       "array([[  -50.138916 ,   -43.8048401,   -39.550293 , ...,   702.7687378,\n",
-       "          656.3284302,   581.6201782],\n",
-       "       [  -50.7771301,   -43.4010925,   -40.3817444, ...,   626.8185425,\n",
-       "          587.7715454,   528.4921265],\n",
-       "       [  -49.3357849,   -47.8291626,   -43.6928101, ...,   472.8615723,\n",
-       "          492.1585693,   453.5387573],\n",
-       "       ...,\n",
-       "       [  -67.1472168,   -72.3484497,   -78.2303467, ..., -2397.1206055,\n",
-       "        -2378.3427734, -2362.1647949],\n",
-       "       [  -67.5697021,   -72.6132202,   -81.2595825, ..., -2409.6945801,\n",
-       "        -2391.8217773, -2376.5600586],\n",
-       "       [  -67.2144775,   -74.9806519,   -82.2666016, ..., -2416.8630371,\n",
-       "        -2398.3891602, -2387.4147949]])\n",
-       "Coordinates:\n",
-       "  * X        (X) float64 -6.3e+05 -6.27e+05 -6.24e+05 ... 6.69e+05 6.72e+05\n",
-       "  * Y        (Y) float64 2e+06 2.003e+06 2.006e+06 ... 2.618e+06 2.621e+06\n",
-       "Attributes:\n",
-       "    fname:       ../../data/ZMAP/NStopo.dat\n",
-       "    null_value:  1e+30\n",
-       "    comment:     [' Landmark Zmap grid file name:   .\\\\DATA\\\\NStopo.dat', ' C...
" - ], - "text/plain": [ - "\n", - "array([[ -50.138916 , -43.8048401, -39.550293 , ..., 702.7687378,\n", - " 656.3284302, 581.6201782],\n", - " [ -50.7771301, -43.4010925, -40.3817444, ..., 626.8185425,\n", - " 587.7715454, 528.4921265],\n", - " [ -49.3357849, -47.8291626, -43.6928101, ..., 472.8615723,\n", - " 492.1585693, 453.5387573],\n", - " ...,\n", - " [ -67.1472168, -72.3484497, -78.2303467, ..., -2397.1206055,\n", - " -2378.3427734, -2362.1647949],\n", - " [ -67.5697021, -72.6132202, -81.2595825, ..., -2409.6945801,\n", - " -2391.8217773, -2376.5600586],\n", - " [ -67.2144775, -74.9806519, -82.2666016, ..., -2416.8630371,\n", - " -2398.3891602, -2387.4147949]])\n", - "Coordinates:\n", - " * X (X) float64 -6.3e+05 -6.27e+05 -6.24e+05 ... 6.69e+05 6.72e+05\n", - " * Y (Y) float64 2e+06 2.003e+06 2.006e+06 ... 2.618e+06 2.621e+06\n", - "Attributes:\n", - " fname: ../../data/ZMAP/NStopo.dat\n", - " null_value: 1e+30\n", - " comment: [' Landmark Zmap grid file name: .\\\\DATA\\\\NStopo.dat', ' C..." - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z" - ] - }, - { - "cell_type": "markdown", - "id": "16c8e7b4", - "metadata": {}, - "source": [ - "## Transform the surface\n", - "\n", - "The units are feet; let's transform them to metres and save a new file." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3d8d3f45", - "metadata": {}, - "outputs": [], - "source": [ - "z_m = 0.3048 * z" - ] - }, - { - "cell_type": "markdown", - "id": "c02de2a9", - "metadata": {}, - "source": [ - "## Use the `xarray` accessory\n", - "\n", - "If your surface is held in an `xarray.DataArray` then the accessory is the easiest way to save it in ZMAP format.\n", - "\n", - "To use it, access the `to_zmap()` function in the `gio` accessory. After importing `gio`, the accessory is attached to every instance of a `DataArray`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1c7b3948", - "metadata": {}, - "outputs": [], - "source": [ - "z_m.gio.to_zmap('NStopo_m.dat')" - ] - }, - { - "cell_type": "markdown", - "id": "fefae26f", - "metadata": {}, - "source": [ - "We can take a peek at this file using IPython to see if it looks legit:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f98120b0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "!File created by gio.to_zmap xarray accessory\r\n", - "@.\\DATA\\NStopo.dat, GRID, 6\r\n", - "12, -999.25, , 3, 1\r\n", - "208, 435, -630000.0, 672000.0, 2000000.0, 2621000.0\r\n", - "0.0, 0.0, 0.0\r\n", - "@\r\n", - " -20.487 -20.595 -20.466 -21.056 -22.306 -22.649\r\n", - " -22.179 -21.956 -21.493 -20.896 -20.176 -19.134\r\n", - " -18.187 -17.888 -17.495 -17.983 -17.928 -17.516\r\n", - " -18.176 -21.011 -21.595 -20.317 -19.193 -18.450\r\n" - ] - } - ], - "source": [ - "!head ./NStopo_m.dat" - ] - }, - { - "cell_type": "markdown", - "id": "99158f93", - "metadata": {}, - "source": [ - "## Use the function\n", - "\n", - "If your surface is a NumPy array, then you can use the `array_to_zmap()` function.\n", - "\n", - "Note that if you want the ZMAP file to have real-world coordinates, you will need to provide the `extent` as a list or tuple of min X, max X, min Y, max Y. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d143f021", - "metadata": {}, - "outputs": [], - "source": [ - "arr = z_m.data\n", - "\n", - "gio.array_to_zmap('NStopo_m.dat', arr, extent=[-630000.0, 672000.0, 2000000.0, 2621000.0])" - ] - }, - { - "cell_type": "markdown", - "id": "24a1bc27", - "metadata": {}, - "source": [ - "It should look like the file we got before:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ce30d943", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "!File created by gio.array_to_zmap\r\n", - "@Z, GRID, 6\r\n", - "12, -999.25, , 3, 1\r\n", - "208, 435, -630000.0, 672000.0, 2000000.0, 2621000.0\r\n", - "0.0, 0.0, 0.0\r\n", - "@\r\n", - " -20.487 -20.595 -20.466 -21.056 -22.306 -22.649\r\n", - " -22.179 -21.956 -21.493 -20.896 -20.176 -19.134\r\n", - " -18.187 -17.888 -17.495 -17.983 -17.928 -17.516\r\n", - " -18.176 -21.011 -21.595 -20.317 -19.193 -18.450\r\n" - ] - } - ], - "source": [ - "!head ./NStopo_m.dat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36cd5eb2", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "gio", - "language": "python", - "name": "gio" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/userguide_src/Unmap_data_from_an_image.ipynb b/docs/userguide_src/Unmap_data_from_an_image.ipynb deleted file mode 100644 index 8716079..0000000 --- a/docs/userguide_src/Unmap_data_from_an_image.ipynb +++ /dev/null @@ -1,1060 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0334e1a6", - "metadata": {}, - "source": [ - "# Unmap data from an image\n", - "\n", - "Sometimes we'd like to rip **data** (i.e. the underlying digits) from a [pseudocolour (aka false colour) image](https://en.wikipedia.org/wiki/False_color). For example, maybe you found a nice map or seismic section in a paper, and want to try loading the data into some other software to play with.\n", - "\n", - "The `gio.unmap_to_dataarray()` function attempts to do this. Keep your hopes in check though: depending on the circumstances, it might be impossible. \n", - "\n", - "**Note:** This function wraps `unmap()` from the [`unmap`](https://kwinkunks.github.io/unmap/) library. If you only want a NumPy array from the plot image, you could just use that function directly. All `gio` is doing is wrapping its output in an `xarray.DataArray`." - ] - }, - { - "cell_type": "markdown", - "id": "fe983420", - "metadata": {}, - "source": [ - "## Get images from the web\n", - "\n", - "Let's write a little function to load images from the Internet and transform them into NumPy arrays." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d635332c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from io import BytesIO\n", - "\n", - "import requests\n", - "import numpy as np\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "def get_image_from_web(uri):\n", - " data = requests.get(uri).content\n", - " img = Image.open(BytesIO(data)).convert('RGB')\n", - " rgb_im = np.asarray(img)[..., :3] / 255.\n", - " return rgb_im\n", - "\n", - "# An image from Matteo Niccoli's blog:\n", - "# https://mycartablog.com/2014/08/24/what-your-brain-does-with-colours-when-you-are-not-looking-part-1/\n", - "img = get_image_from_web('https://i0.wp.com/mycartablog.com/wp-content/uploads/2014/03/spectrogram_jet.png')\n", - "\n", - "plt.imshow(img)" - ] - }, - { - "cell_type": "markdown", - "id": "7367f5c7", - "metadata": {}, - "source": [ - "## An easy image\n", - "\n", - "Let's start with that image, it looks approachable:\n", - "\n", - "- It's large.\n", - "- It's a PNG, so losslessly compressed.\n", - "- No hillshading.\n", - "- It contains the colourmap, and Matteo tells us that it's `jet`\n", - "\n", - "And, even better, when Matteo used it on his blog, he provided lots of other plots that we can compare to!" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "daa18b5e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "       ...,\n",
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-       "Coordinates:\n",
-       "  * j        (j) float64 0.0 1.0 2.001 3.001 ... 2.4e+03 2.401e+03 2.402e+03\n",
-       "  * i        (i) float64 0.0 1.001 2.001 3.002 ... 1.349e+03 1.35e+03 1.351e+03\n",
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" - ], - "text/plain": [ - "\n", - "array([[nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " ...,\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan]])\n", - "Coordinates:\n", - " * j (j) float64 0.0 1.0 2.001 3.001 ... 2.4e+03 2.401e+03 2.402e+03\n", - " * i (i) float64 0.0 1.001 2.001 3.002 ... 1.349e+03 1.35e+03 1.351e+03\n", - "Attributes:\n", - " source: \n", - " fname: " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import gio\n", - "\n", - "data = gio.unmap_to_dataarray(img, cmap='jet')\n", - "\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1d82b4d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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fI/4PfIeqnt+qXAv2hmHsCh/69P/Bna/5kf2uxp4QXS937sdNVT8AfOCyZb+w6fVTwF96NmVasDcMY1e4844fnaihmq2Idgnj/U/Ggr1hGLvCdA3jjL9dggX7PUBPnUbm53A3HAURXC+Po2wuiaZmqkhR4joDCKFKKZiupwl0nQFpv8A3U0LNo15QJ+SLDcKRWwlZ/JAlvUDSKfDdAhyEAy2cHkUvXKJcWcGrxsO2O9GkqtlAmzU4cRRUCc7FurR76IVLhKfP4pcOxv3yBr7m0cQTagmSBw783pfJX3SCfCah8/ohSRI4PL+Gl0CpjmHpCaWg7RTtOpq3rpL4QH+YUntVh0wF7wNOlKBCXnj6zYxeMyOknnSVddUMgHolnxFCEk3RQkKUGCjkCeRNyJsOl0e1y9xXhxQtT3fJIyU0LpQ0BoHVW1LKWlS2uDIalrkC6hdLBgccRaM6oIvH1gTKBKhFFY4E8EOorWg0LGvJutmZUilwNJbZPFuS9AJrN6WUlUkbGstunskJidA/mMQUjC6ay62nKyygthqQEoazQlmPSh6ISp7acknvkKeoy7rUoszi+clnkg1FE1FtlK0pjUfOkR9fgNe/Cl0dUP/c4wxffILBQkox4+gfTFA/MowTXK7UlwPqIG85BvMZ3Jitm+5tyXjHvh1np56g3S2m7HIYhrFXfPjj/wx1bstpUthhNc6usGtnW0TeKyJnReTzm5YtishHROSRar6wad27KtOfL4rInZuWv0ZEPlet+5nq8WDDMMac7/7Wf7Hxz+xq0wQxzclLfhm467Jl7wQ+qqq3Ax+t3iMiLyVqSV9W7fOeygwI4OeBe4Hbq+nyMg3DGEPUyzWnSWGUg3Y7036xa8FeVf+QaM6zmTcB76tevw/43k3L71fVgao+BpwEXisix4A5Vf1Y9aTYr2zaxzCMcWaaHqoCCnXbmvaLvT7yEVU9DVDND1fLr2T8c7yaTl1h+RURkXtF5EERefDcuXM7WnHDMJ4dv/sHP4w6uer0u3/ww/tdxR1l3IdxxkWNczXjn+0YAm2siM5x9wHccccdV91ur+l/y4txpeIGJb4T7abdoEDyAF6gVNwgh14/7iAjUxiBxCO9YRziLEuSp7rQHwBQXrhE+pIXkB9sElKHywO+X+CGBZp6ykZCMbuIHF+Ix+oM4Pwy2u4Qul38gQNIuohmSTxGnkOjTliYQRdnAQhlGdMnhkBw0edEE4cEpf/1N1P/0hmGrzyG88rCTJcXzJ3nSG2Nussp1XHhWItOUaNQx/KgwVpe4/Gn5ihmPY3WEKfQG6b01upQCklrSLLYp59mqEtpPr2hUimaUDQhn1fyOchWBDeAYiaetqQbUxrmszBowbnFLJ7GkqjYmfXEBxIrH5l8I41hf0EYzPmozhlCyKLyxhUxVWDRiIqbpB+VMIM5oXdQ1tU1roC0o5RpXCZVGsPBvKNoOJKukggMZ6JCpqjDhZdl6+oaKSFtK6RErx0BakTfmhDr5IYQ0qiUGcwJg7kkevEEkByydqBW+eH0DkXPopFKqCiFfCahffQGkoGS9BR/sEa61CRZG1IDimZtXe00IiRCN/Ub6Rtlo70Stv6KveG7fmzLcfk3fNeP8dHfe9fWX57rhX0eotkOex3sz4jIMVU9XQ3RnK2WX83451T1+vLlhmGMOTpBwzTXYieTl+wWe/2f4gHgnur1PcBvb1p+t4jURORW4o3YT1RDPWsi8rpKhfP9m/YxDGOMmaYbtMD03qAVkV8HPga8SEROichbgB8HvltEHiGm3PpxAFV9CHg/MRPL7wBvU9VR6vq/B/wi8abtl4EP7ladDcPYOf7Lh98xNdLLUfKScQ72uzaMo6rfd5VVb7jK9u8G3n2F5Q8CL9/BqhmGsQd8xxt/YmpSEypCEcb7IbFxuUFrGMaEMeb3K3eccR+zt2C/BxTNmHnIFZ40cWRPXCKcOo3LUmR2Bl2ci5rjNIVOFxp1yuMHKRsxO9Uow5QfBtxsHckDmjrCi08wrFXZqwJRJdHLkU6f4oYDFK2UkG58AN18hjvUwhUBKQJ55bED4GZqEHRDbTHIcedWIEspDs9TNhI0qbYdlJSNhJAIZ+68ET9UEl9y0+wyNzcu8pL6UxzwHVwlnOpqjXPFLF8dHOJkZ4nzR1p0n56hdqBL4gKJD9SyghBi+UXpkDkYCORznnTF4Yesq1wIEOpK9+YCSar0UAj9Qujc4pCh4LuO2jLMPqmUKfQXY0Ypl0fVTv1SwOVK76CPKp9WVMiMvq8SqgxWfUjSqKApazBYiJ4xZQ3KRkBHGb8KwQ2EdA1qK7GeMdgJIY3eNBJg6dMdOica9A45QkJUzLjovzOcFaSs1EPE61C0ogJHk416Je2oOmqeL2nfEDNR4WHgHcPZqMIpa7FcqTJYIRu+PvOPdBkcrFE0HOViSjYaQ68UN6PtGhcDaTuwdiJZL8v3Y3at2mr0YdqSCRuT3xLdOT/73WK8/3cYhnHd8gf/+X9Fqx+Zq02TwlSP2RuGMd1825t+cqJuwl6Lce/ZW7A3DGNX0Cm5OQvxBm1pN2gNw5hGxjyXx44z7jdop+xyGIaxV/zRb/7j6RmzVxuzNwxjSvnWN//riXK2vBY65r9eFuz3AFfqei+mrHnyY/P4mQayvAaNOup9lKlJDUl8NBobFjgnhMRFaV1VgG8PkE6fcmkOCYrvl0iIsj71Qr7YhINNUEg6OVIoEgKIEFKHehfTzQUlObtKuTBDMZuRN+KfPDeMZmpSVEZsgyHJchdNWpQ+weWB9FIP6Q7Iuj3WbryN7hGhKDwHa22Opcss+jZ1yfESyDUhp6AmOYtJh5ubHo7Ck7PzeAkkLtDNM9qDjNVLTZJ6QZqWhDLWRx2UDUVUcDnUVsEPhfYJQVNFJYAK4kC8okHxQ8fs4zCcgYsv3kgZ6IooQwwpFA0X5YiV1BCNskwq2eFoCGIkXZRKDhlSyFtK2QpoLUTpJxAKQavzWzZiXV0ObhBlk0UjphQ8+42t6jxD63SIhmV1KCvvu6QH9UvKcC5KIZN+lHvmDQhpPFbREIbz0DviSdsw82RgMO/oL0RTOHWVMdtalJk2zwV6i46QRSO5s69prX82RaF3MIvn2m+YnQUPnaMxm5SEDbO1KIEV2kcTNNk6fEzXMI4ZoRmGMaVMV7Af/579lF0OwzD2io/9+j/aeFDrKtOkoAplkG1N+4X17A3D2BVe9zd/aqrG7MddjWPB3jCMXWHMRzV2FGX8h3Es2BuGsStM0jDNtbEbtAaQXRoiheLbfaQ7oDgyT7FQRxcbSBlIOjnuK6fR4ZDwdTcS6ilShrizEFUxZVRiaOKQssSfWcHN1JHuANoddJhX2wVkpgW1DPoDwvkLaFkiWUZ6YB6aDbSRRXXOTANN4jdSSo1KmwsdwhdOIrfchM7PIHlJaGYxb6gX8npCPjuHGwbUR5OvwQEozjWofV3BYtKm5QbMuiFBhbOhxiODo7TcgEPJKgCn3TypK6n7gsQF6r4g8wWZL8mDp9vLKC9l1M97iqauP3JfNGBwKBAyhVoAr5ALrucJjRLxitRKigVl+SUeNxCkFNQpUgq+B/OPBVZudfSOgDol6QlJl40kmJX+u6xBqCmDQ4rLhaQTzeZCAmlHmHnc01/yDBZjPXzPUbskHP50wcotSVRQwXpKQYB8JiprQhrr0zvsqF2Mqp2YbhH6R5S1r9NYoTIe0/ejwVraiSdiOFdtn8Zz0l+MyqKQgnoFBQmynnIxbwozpwv6C57hrGyYqpXx2Opiu3BxWbaq+FzpHHHkragmkgC+S1Q+hXjcURuvxife9w/5xh/4qauu/+S//4fb/AZdH+jYJEK9MlP122sYxt7xjX/np7ZMXPKNf+fqPwTXI6qyrWm/sJ69YRi7wpiPauwoUY0z3n1nC/aGYewK02SEBjaMYxjGlPKpf/dDU5ODFmwYxzCMKeXVb/03U9OdVPY3kG8HC/Z7QLLSRxOH1lK0liJBcf1otiKqqAjhlmNR3ZJ5ynpCSAVNhFCldnMSPUmkVGh30eEQWcvQY4cIS3PP/MtcpTGUPODnZmC1ja61oSzj8iJAu4skCeXsApoIKhAyR36ohXzzK9BSkTIgzuGWO2TLHcqlOYqZlJC4ZxwvNAIHblphIeky53occANu8I6aJNyadHhF9jAXg3CubFF3OY8kh3nsMye49VWnmPMDggpOlEaWk5QBJ0p7CQahSbrmyA+UlEtVCrxOQu2cp39Mqc0OSNN4HvPckw8SQt8jQ4fkgu8J9Ysx3d9gKdBZKum+eJTGECgcoeNR52g9GRUu3RuUYq5EshC3U6HIhZDG9IhuGLfrvyjATIEkAQ1CkISi7zn/8oSiGdUtrohql5BCPqOERkCzgPioMCpKoXdCkFxIVzxHPlFy+lscYbaEJEAQZDWhflbwOeTNTSkKNaYvzFbgyCf7nPnGOoMFQKO6qHZpI73hekrB4UhyxLoXUG0l4IdK54iPHj0ZlJngh0JtRSnrwqAJZTPAQejISGEkpO1rBLfxjn07zpiP4liwNwxjdxjzju7OoqD7aIWwHabkT5ZhGHvOtcbrxzs2Pmt2csxeRO4SkS+KyEkReedVtvkOEfmMiDwkIn9wrTL3JdiLyA9VFfy8iPy6iNRFZFFEPiIij1TzhU3bv6tq9BdF5M79qLPx7CirP7WpeFJxpKI4wj7XythLrpW4ZNJ6/qrbm66FiHjg54A3Ai8Fvk9EXnrZNgeA9wB/VVVfBrz5WuXuebAXkePA/wLcoaovBzxwN/BO4KOqejvw0eo9VSPvBl4G3AW8pzoZhmGMM1PUsx954+xQz/61wElVfVRVh8D9wJsu2+Z/AH5LVR8HUNWz1yp0v4ZxEqAhIgnQBJ4iNuZ91fr3Ad9bvX4TcL+qDlT1MeAk8WQYhmGMB8r2/srEYH9IRB7cNN17WWnHgSc2vT9VLdvMC4EFEfl9EfmUiHz/taq45zdoVfVJEflXwONAD/iwqn5YRI6o6ulqm9Micrja5Tjw8U1FXKnhAFQn7V6Am266abea8Oz5ypO4uVmo19BGhjvfRZdX0V4PNz9H6HTRwQC/uIBvtfCLM9EfZ1jin75IefwgwwM1ioYnHJvBLzTwnRwJAXVRGRNqnpBW2abygO8ViCrlTB1pZsihAwQRSFxU/RxqEVJPSKsMWIOYgUq/+Bi87AWEejQ+kRBgMERXVvFlSbjpEMELgtD4/FOs3nwzADO1AYfSNVIpmXWBGWmQSvx4DTQqaULVtziarXLXt/8pK3mD1WGdJ9fmuXSpRVovSHxJCA7vA40b2gBkTkl8Gd1yF4EboVGd2hCEXj8jP19n5rGYuSmkUFtRhrPQuQHyAyW0CpIs4HxAFYpBgpaCH8SsUsP56F0zyga1/uXteernPPVz0dembEAxE3ALA+rNIVmlBgoHhPywp9dPCf0Ehm6T305VZhBc18esT0WlgGqV0Cwp54c8fVvAeyVzcbirDI4iLWk3UpJVT7YsZGuxqLIWfW+KFjzxF+uoh5AoEoSQQT63yZOnJfQOJSNx0XqPuswgb0VfnbIORb3ywSmjkmiwUH02hkRlWKYbTcmU/qGtxyQmbZjmWjyLh6rOq+odW6y/0pm7vPQEeA3wBuLX4WMi8nFV/dLVCt2PYZwFYm/9VuAGoCUif3OrXa6w7IqnVVXvU9U7VPWOpaWl519Z41njx16AZhi7gaBhe9M2OAXcuOn9CeLox+Xb/I6qdlT1PPCHwNdvVeh+DOP8ReAxVT2nqjnwW8C3AGdE5BhANR+NQW2n4YZhjBtuG9Mkoducrs0ngdtF5FYRyYj3LB+4bJvfBv6CiCQi0gS+CfjCVoXux+l+HHidiDRFRIh/Q75AbMw91Tb3EBtDtfxuEamJyK3A7cAn9rjOhmEYV0d37gatqhbA24EPEWPj+1X1IRF5q4i8tdrmC8DvAJ8lxsNfVNXPb1XufozZ/4mI/Abwp0ABfBq4D5gB3i8ibyH+ILy52v4hEXk/8HC1/dtUtdzrehuG8eyYruQl7OgjtKr6AeADly37hcve/yTwk9stc1+eoFXVHwF+5LLFA2Iv/0rbvxt4927XyzAM47kz3nekzS7B2FHKMf/AG3vH1PXsx/yZQQv2e4AsHUQvLlM+9TTJ4UPo0gLMz8SwOCxw83MQAhQFhDIalQWlbCYUtx+NRmWVmZU6KGsONCHp5PgnzoAq4dgSoZXF7UrF9XJkWFDO1SkbGQC+myOPPYnkBf7IEiQeipJiaZaildI/2kKOvBw3DPhOjr+4BkHRhVnCiUOUjYQyi9JNFFZfewIEfMcxLBIGIaVUx1pwNGWI15y+ljxa1HmyOMwHLr6Smit4snuAR84t8YqjpzlUb7NY69JfSDnfb7Lcb1CUDu8UL4FSHe1ejc6pWZjLyRo5ZeEplzNwip/LcT6akrVfAOmlkfxSyFtQtqr0haWj6Dik70nXHM12lYpPiOOtPhp8pSuO5IwjVLLGshEYLgSGB+K1VAc4pewl5Gkg8QHn4v/3MjjKbkLr0ZTaJRjOQqhtpO/zg2hchkbpZNGCAZ5AjBMhr54VHOkbSwdDF+WhA8ENQIoohaxfUBoXS7pLnuGcUDSigRmVURmVzNJVqQddEfdNBkrSU/qLLso3HQQXK+BKKH11XoBkANlafO97Ak5ww7isaED3yNaf+6mSXo6kumOMBXvDMHaH8Y59O864Jy+xYG8Yxq6g02ZqYsHeMIxpZMxHNXaeMW+wBXtjx0llU5IMY3qZshu0Yj17wzCmkalS46jAmCcvsWC/BwxvWoQTC4hCDlVqQMV3h3BxGS0KZOEAemAWTT1oVNO43BFqCVoI6bDEn1mBXo9ww6G4XRFAFe31cWvdaFqminQHAGirDkR1DkDIPPJ1JyDEdIiooolbN1BbRzWWNcxhOETPnMUvHIATh9alGr5f0HykTbjjCCFxdAYZjw8WuSG9RF1y+r4PKH3NWAt1LhYzHEh7nB3Msljr8m03fZlBSBiGhCJ4hiEO8NaTgj5JVLYETwiONCnxN7RRFYIK4gI0C7RwFJ0ESRScQqNkmASKlscNBAkx5Z8vPFIISS+qYZIuoBCyqJQJSaU46bOuzoGonnG5Q338LovGZb4PRdMzyB15K8ElgZA76CUkbUc+E8er0zb4HMoUcFEVIyEec3gA8vlAyDQapK0kZBcdLo9KF4BsFRYeKVm5JZqV+T40zwXylhAS6B30NM8UNJ+G9omUshb3SztK0lf6B+J+UkLWjtc7VMquAycHdI5m5M0qQLl43KIR6xjbHuta1qICyPfh8INrPP0tszEF4jVi21QFe7Axe8MwppTx7ujuPBbsDcOYRqxnP15YsDcMY1cYc3HKzmIPVRmGMbWMd+zbcUyNYxjGVGIPVY0XFuz3AN8rUCdIUGRYIEVA8hLyAppNRAOUAVntIsMh2u2hwyGSZSQzLcjSONUzdKZeeecADsKxJUQVDQHp9KHTRbs9Qrcbj33oIMzOoI0M6Q4Ip88g3sOtJwiNFFRJL/Xis94uDrJKdxC9euo1qGW4xQNoliBFIFkb4Hp5bNjyKtnqIfqLjuEw4VR3gS8mx6g3c/oaDWG6WuPh/g388YUX8NCDt3LoJedpZQMu9Zp0exkhCBocZe6g52k+niB5TKvXfBrKOejclpO0CnxSEoJQ5h66CTNf9dQuQvdYTKs3SsvnB1C/EFUoZRaVKz6vFCbDOJY8Uti4avnsEwXt4wllLapn3DD6ySRdOPTpNc6/epYyi7232rLSPyiUNU8hUKYBKaJ/TdKt0g2m0RsHYqo/iMca+dWka6DiKJsalw0lpiusPGpCAoNFOPMajx9GZY8o5E3BFXF9URPaN6RIUNDYXvUxDWHeiuVBXNZfiPkIJUCuQn+hFs+D21AkjRRDIy8dNPrpODdKYQiP3zUb1Tp9SNpbf+7HfFRjx7GevWEYU4n6MY9+O82Y/7pZsDcMY3cY79i3s2w/5eC+sWWwF5E/UtXXi8gaz2yKAKqqc7taO8MwrltMejlebBnsVfX11Xx2b6pjGMbEMGXDOGLJSwzDmEbGfAh75xnz3zYL9nuAP/kkNOrgBAZDKEqoZVCWlBcu4Rp1ZG4WRGK2qrJE6jWkVoOyRJe7lJdW8DMtpNmI+w8GyOxMLLcsoduDoNBqIK0mPixCiIofvXgpKnCaDeS2G9EsQZ2LqqBBjqx1oYy+OhQFOhjG1yF2VaTZQGZacQi2P6B8+gx+cYHBK28hb3mGc5BfrHNqfp6l+hqH0gUO+jZD9Zwv5vjMyk187uGbOfjiC8zXeyz3GrhKupCv1pAiethopvSOBHzPka1A3owqm/RCiq4k6FAQwEu8+Ze3olrk+O/3uPSSxrrviysqhUkaPV1qy4oocX2y4YWDEH2CAnSXPPNfHtI+kVLUo2qlthKoXSq4+IoZ1MeyXKHrmZz8ALTjCKmsK1uGB6IyJ+kKvohKmDKpFEACbgCuH5UzSZ94zYkKmKQT6+6G0bdHPQQf981n4jHLVBCtsmq1lfqlknQ1J1kbsPaCOYZzgjoh7Sqzj3YpZlIGCwkuV4qGUDQEFaG2EkjbJWs3p1GFU1KpmAL9xeipk60pZUpVZpXtqqyyXw2jB8+WTFGwFzU1jmEYU8r09ezHu8EW7A3D2B2mbMzehnEMw5hOxruju+OM+zDOvoijROSAiPyGiPy5iHxBRL5ZRBZF5CMi8kg1X9i0/btE5KSIfFFE7tyPOhuG8exQr9ecJobqPsp2pv1iv5Sw/xb4HVV9MfD1wBeAdwIfVdXbgY9W7xGRlwJ3Ay8D7gLeIyLT5rphGNcfo7uWW02ThG5z2if2fBhHROaAbwP+NoCqDoGhiLwJ+I5qs/cBvw+8A3gTcL+qDoDHROQk8FrgY3ta8edDFn1icB5azajAUAURfKsZFTgQ1TT1GpIkMBiivT44QcSRLFZ/dFQh8UhjHpJNly/LYrnOx3niYpq0vIAyoHkBvT6stXHNJsw00cRFH5z+AO10kUYdWq2o8gkhqnyKMtZvrR3L9R5/4gYY5qTLPXqHq7YVwqBIWMvrrJQNMimoSU4qBTc3LxBeIZzpzfDVC4vMNAaUQUjTkuax1eh3ExyDQULezkhXHbVlyFYVVyprJxxFlVHJVadKSol+MSWceW0jqldcfD/qPUlRZYbKpPLJiT416gDdyMYkpeJzyGd93F+jkqd72NFdymKGqj7MPjEk1BydI/G8uxzSlai8qV9SVIR8diPj03DTI4cSYrkjczB1MQNUSEe2AhL9ekK1TYC0F+uZz0ZfmuGc4grB92MWK5cLecsRkljHtBfIZzxlBoN5YfjKFn6oUXnTKWmcLQiJ0DtSY3DA0T3i1lVEAEUdOkfdegwezMt63dNOrEvjfCAZBNZOJPQPbj1OI/ZQ1VixH5fjNuAc8O9F5NMi8osi0gKOqOppgGp+uNr+OPDEpv1PVcu+BhG5V0QeFJEHz507t3stMAzj2ji99jRBbOePzH7+mdmPYJ8ArwZ+XlW/AehQDdlchSt1H654ylT1PlW9Q1XvWFpaev41NQzjOfOVe94Rv71bTcaesR/B/hRwSlX/pHr/G8Tgf0ZEjgFU87Obtr9x0/4ngKf2qK7G8yBXE3tNM7f86o+Pbzd3NxjzMfs9D/aq+jTwhIi8qFr0BuBh4AHgnmrZPcBvV68fAO4WkZqI3ArcDnxiD6tsGMZzQJxec5oYrgM1zn51vX4Q+DURyYBHgR8g/vC8X0TeAjwOvBlAVR8SkfcTfxAK4G2qWu5PtQ3D2C4TFcy3w5g3d1+Cvap+BrjjCqvecJXt3w28ezfrZBjGzvLo9/0wL7j//9rvauwJwviPStmg6l6QJtGQbGWVcm0NPzMTZY5VGkDt9ijbHcR73NwMkqaohii7TBIQQQdDQruDn5+DVmPDNC3PoSjR4RAd5kirgaQpOIe2O0A0Mhsda32+2oa1NmW3iyRprE+SgBNCqw5eolFapw9rOdrrI1kGqujqGpoXuFYTZI6kB8zlXDwzx6l6j8WsQ9MNmXV96i6n6YfUfU5nmLE01+Ziu0lvuYFkJbVGjveBvPAUnQzX9eTzgUuLihSV3HKgJL34etgETRQC5DOynkYveKBKn6e+Mu0qwK2NUgFqJXmt/k6Xo7kiIab4KzNH8DGNYUjjfiOpZEjh0ouz+EbjOt+DsgFFE7qpkPQrQ7NqG5dD2oUDJ4dcfElGUavSBs7EuqWrwByUNaFoKkUzpidM+tEUjRClomW1H1KlXkyEol7JSlOHy6G71IjSzaq+UsY6hEToHfT0Fxx+mCIhSi2TnuLyeOwo/6z2q+qdtZXWqT7Z2TbtFy6Qt1xMaTgjDA5sSE+34vb/51/gpumJmDEP9tOmhDUMY48Q0WtOE8M2ZZfbbbKI3FU5BpwUkauqFUXkG0WkFJH/7lplWs/eMIxdwU3bmP0O3XytHAJ+DvhuohrxkyLygKo+fIXt/iXwoe2Uaz17wzB2hT//b//Z9PTs2dGe/WuBk6r6aOUwcD/RSeByfhD4TTZk6ltiwd4wjF3hpf/pR6cq2D8Lnf2h0ZP+1XTvZSVd0zVARI4Dfw34he1Wz4ZxDMPYFfw0DeM8uwemzqvqldSII7bjGvDTwDtUtRTZ3qPIFuz3AF1ZQ2oZ0myQZBmh16M8fxFxgtRqUYXTqEeTs7wA75F6/RllyEwLPzezsaAooT8g9HqgiiQJUq9F07NQpRX0PqZChGjG5n1U41TpB6XZwFVKodDuIL0+7tAi0qxBEGSth15ahhCQ+bmo1gklooqGLtrIUCf4AdBNaH3Fc3GpydONeeaSPmlWUpecY+ky83M9jtVX+HJnic4wI/t0ysprlLJ0lKWjGHpUgVTRJCBpAK9oKRSFoxxUxm5J/C8sA4fvC/UL0DpdggjdJUeofNnUARJVM0WjMkMrRqn8AmVdyBtCWZd1JQ9Ew7S0ozSfLugcS2IaPxfXjxIRSWWiNjJM01pU5ZSNTSZsJQQHgxTOvzwj6cGhR3K6RxLyZkwtGDxoN27v8qgUSnqQrlUpD30sww9GZQpJd8OUzA+V1umCsu7oLXpCtunzUoLPlTKTSmkT21G/FJj94iXk3CWYnaF32yLD+SSmYlTwfSVtlyS9kmRlQGhmuKFSy+OjLX7gyFsOHULS3Tq6TVzP/RrsYHO34xpwB3B/FegPAd8jIoWq/qerFWrDOIZh7Aqf/Sv/J87pltNEsXN2CZ8EbheRW6sHT+8mOglsHEr1VlW9RVVvIVrO/M9bBXqwnr1hGLvEq/6//32/q7Cn7JQVgqoWIvJ2osrGA++tnATeWq3f9jj9ZizYG4axK7hpGsbZYZMzVf0A8IHLll0xyKvq395OmRbsDcPYFaZpzP56cGy2MXvDMHaFT73x3XjRq06feuOE2V2NucWx9ez3AO0PoqIlyyDxuNkZXKuJlmX0zCmKmDZwRB8Y5mieE7rdSq0T/XB0mIMT3EwL6jVcqxG9cbo9QrsDGhDvQRxa5EiSQj1AnkefnTJEH53BINbHR/OSkScPaYr0c7SeQj1DDi3GOqUJmnokLyFNcVlGWUvxA4WWUH/Kk89AyBPOdGfIXMEgJCyk0Z+nW9ZYLeo4lCMza7T/yhCfp3T7Gf3TLWoXHf3jOZoGfNtDmRAyrbxahHK2xDWiGYuWDoaOpA/ZmtJb8qhEP5eFD/w5+StuxfcLekcbDOZd5Y1D9LRxUNaFtB2YPdlj5UUzDGejYkUCsT0K3aMJ6oS0o6RdRUqld8hT1qP/TvSxgdpK3K/MAImeN7OnCvoLnnwm+uzEY8KlF6UbvjwFtM4HuksuKm9c9PNxeVQDBQ9FurG/y8ENwA/j/kk/Ro3+YkLSV+qXSvKWo6xJVA05NpRE635AijoYHJ0lrWf40xeoXeijaRMpBZ8rtQtDki8/BfUa9PoMX3ojRdMxnHXRe8cJUipJXwnZ1n3Z133oXetisKut//idP/bsv1Bjyrj/kbFgbxjGrjBNwzjA2BuhWbA3DGNX8G4fM3XsNbq/iUm2g43ZG4axK/zRX/wJnOiW00RhY/aGYUwj3/l7/wg/7hKVHWTcf7ss2BuGsStMXM/9Wox5cy3Y7wVOoh/NCFV0mBPabTRUyzWsz8X7qN6pVDiaF4ReLyprquxVOsyRoNHzxjmk2cBnaVTblNHHRMo0HqvXQ4Pi6rUqG5VDarWqHkN0OARxuFYzKnaKAllZJaysIlkWt3USj5f49Tb4p87jjzUARz6v+J7Ap+f4yokmF462ODUzz2K9RyKBYfB8dWWBixdmqLeGHJ5f42Czw3y9x3C2w7DwNIJnMEwY1FNC4aB0hACSCVL55KCC5g5RGM7C6s2C74PPoZ8IZ/77l+CKDR8bVyh+CEkvoF4oa0LeFAZzCas3zUa1ygghfmElZniSEFUn83/4KMvfedt6NidXAlWmK608c1zBenaqkDrmvjKgdzhjOBOPOfLpGR0jJNA76Co/mo1k1H4Q1T/DWamuYcxaJVqpeColTuNsAQJl3aFOCKngSvDtQNoJoDCY9+teQX6gNM4X1J5uMzzUYniwjh46jiZCWYueQmVNyFt13LHbkKCojDJZyfp5kqCx/SLPPHdXIJmmMXvGv2dvY/aGYewKH/r2nyaRsOU0MSgxecl2pn3CevaGYewKb/qjt5NMSXfSEo4bhjG12Jj9eGHB3jCMXWHagr3oeLd33/5kiYgXkU+LyP9bvV8UkY+IyCPVfGHTtu+qsqx/UUTu3K86G4axfX7zW94zXWP2Y66z388Rtb8PfGHT+3cCH1XV24GPVu8RkZcSzftfBtwFvKfKqm4YxhjzfR+/Fydhy2mS2MGE47vCvgzjiMgJ4L8B3g38w2rxm4DvqF6/D/h94B3V8vtVdQA8JiInidnXP7aHVX5euCOHN9ID9vroWjumExSHS6WSX3rQKJssO92YLjBJogwTQARx8bc5dLtRXikOl2XIKOVgWa4bpUmthjQbUWY5SkVYlhDiMTTP0cEgSjKzDGk1ohFalbKQoMjIfK0o0DyakEmaIlkaj9/r44aKH0aZXroKmkJ6MWEtn6OzUOPJWkktLXAu0Mxylg6tUqqjPaixqjH1oiqoCkWVolCqlII4jVJLB9J3uH5C0hHSDmSrMcVg3pINb9nqy+RyJe0orlBCGqWDRcPhCiVbC2SXhpSNhMEBT94UQhbNw2KaQVk3DZMQpYcX7nwBEOWLfhCP48qYli+koH7DcMxF1Sv5XILLlZmnSvygJHv8IogwuGWR4VwSy04lpgDsl/hegb/QZnjDPL3DNdK2rktIXRklnr6v+GGI5zwPlJnD5UpRr06AKsELwxmHhCg3dW3w/YCUMcoMD7UQVdwwrA87JD2hTB34mKIxtie2aSQF9b2S2rkubrXH4JZFiqanqG3dV5yonvs2GPfm7lfP/qeBf8IzhUhHVPU0QDU/XC2/Zqb1ESJy7yhj+7lz53a80oZhbJ9rWSVM3Ji+DeM8ExH5y8BZVf3Udne5wrIrnjJVvU9V71DVO5aWlp5zHQ3DeP6877W/RM0VW04TwzaHcKZtGOdbgb8qIt8D1IE5Efm/gTMickxVT4vIMeBstf12Mq0bhjFmvPVTf2tLP/uJY8z/qOx5z15V36WqJ6qs6HcDv6eqf5OYPf2earN7gN+uXj8A3C0iNRG5Fbgd+MQeV9swjGeJE6Xmii3nk8LooSrr2W+PHwfeLyJvAR4H3gxQZVV/P/AwUABvU9Vy/6ppGMZ2SKXc1nxSkDDeP177GuxV9feJqhtU9QLwhqts926icuf6pNeDhXk0S6BVRxp1fKV4QRVCiGkIiyIak+UFIS9gOMTPzMQUhEkCIlCWOJGopClLtMjBu+ivVRTxPaDDITKsIc6hZVmlKpSNY1TLXL0W0xMGRQfDqOgpSygKwsggrVGPdRBBB0PKi5cA8LffRu1cF01aLA8S0m5UjzSf1mjy9dVGNO+KvlycPwxpG9IODOcqpUkOflCpW1z8QErlISLVDa3NKgcJ4IdKthaVKTOnSlwR8N0c9YL/6hn08CKy1kPnmpTNDARcPyc0smj+5oXsQp/sAmga/9wWrRQEkk6BX+0h/Zww3yTUEtygQL2ACCF1SKm4fo5b60OvTzh8IKb8S6OBXailSBHw7T7lfAMpAjhHmGuQnWlTe6RDeXQB/5WnIcuiSgrQdof04iWyZjOmBQRYWUXzAslSwlobNzsT12WxLcXSHPUQkLyMdRABRzwmxPY++gSUAXdgDlpNtFmL6q7zy9FIr1LlhDPnkXoNmZ2J6rGg6IEZpDuICq0kgWFO9uBJ/OoarVtv2vJj/2+/4df5oc/cfdX1/+ZV9z+LL9GYs883X7fDlDhXGIax1/yTP3szqZRXnf7Jn715v6u4o4ycS6817RfjNIxjGMYEMUlj8ttizJtrwd4wjF0hlRIvgVLdVeeTxLj/tk3W2TYMY2z456/4jwD4auziavOJQBk9Cn7taZ+wnr1hGLvCjz38PVzDUWGiGPffLgv2e0GrCXkR5SbOoTMNZFhAfwi9HtofREUOxLSAIkhx2dMo3oOvvjnOEYbDqKaZn4ueNs4hIcRygq578WhexEeQpUpz5z1kGVKW0ZMnaDy+EzQvohKnSpEo3iNJuuGZUy1zMzPRjycvKFszMYVf5eMiCsM5IVuDxoXA3Ef+nN43347Lla/cluAKR9KFpAu1ZSXpw+yjXYYHMhoff4T8VbeRrA1RETTzSBnw59eiGkQceuEi0qjH8xEC5S1H8Y+dXn9PkiCdPtQz5MIK/rE15MgS5WNfJbn5xngeRr2rdgdmWlAUuDPncQfmCGtt5MA81Gu4S23k4jLa6+OPH4V2B1+llJR6DUZeRZ9+eOPciiNZmI/n0Xv8hUvxeq2uwRMKjTrF8jJu6UAsp1GHWgZrbaTZQLs9GA7RtTZyYI6w2sYtHiAsr+CWDkIZKJ88Ha9NrUbS7sS2NBqQV+X1+jF9ZBFVVXL0MHS6hLU2rLVxrWasu/eEcxei31G9hju0GM/zYEB5aQXxnnDqSRCHn2khc7Mw20KylGTpYCx/C/y4D2LvIJa8xDCMqWXSdPRbss9DNNthiv5kGYaxl/zQSz5sFsf2BK1hGJPOz/75dzFViSfGu2Nvwd4wjN1hqoZxsDF7wzCmlImSVl4LBcrxjvYW7PeA8vEnQQOSZTGDlPeoBsiLda8aIKpjNKyrOtAAZblhsFQtF+dIRt4plbfO5kxSZMm6ckfSEgZDQq+HDgaxbL/pz7VGBQtFEV+Pjl8RhsN4YycESBPIC0KvjwtR9ZG06hQNj6h/Rs9GFLLVEmk21rNFXd7zSfqQdAPJqfP0jp4gvPBGVISykZCeXom+Lq065aFZ/ONno0qp2UA7XaRRJ9x4BPfwY4Tbb8I9cSY2Z3EOvvIkcnCR4qmnSY4eRs9fxB87SnjyaSRLYwYvVUgS9Ox5pNVEizx6z7SaURGzuhbXV1nB9NyF2K5mA6nXKC9cwr3kBejJr+IaDUI/ehVBCc0G4dRTuGYztn+tTegP8DMtwtoayZHDcH6Fst3B1zKKs0+SHD+GtjvITAs9OI+cu0Tx+JMkhxajguiFtxK+cBKSBNdsokWBzM2iK6uxnsuruJkWurqGzM9BfxCvc6OBnruANOrr117LEhFBu734eamUYNrpRlVWr49fmCe0O/iZGcpON/onFQX65NPIwjwMhuufuavxd1/4X3nvl15PKgW5JlecTxLj3rO3G7SGYewKv/rI62i5AcBV5xPFmD9UZcHeMIxdoe5ynIQt55PETqpxROQuEfmiiJwUkXdeYf3/KCKfraY/FpGvv1aZNoxjGIbxfNlBi2MR8cDPAd9NzNT3SRF5QFUf3rTZY8C3q+olEXkjcB/wTVuVaz17wzB2hTe/4FNkUm45TQoCSKnbmrbBa4GTqvqoqg6B+4E3bd5AVf9YVS9Vbz9OTNe6JdazNwxjV/jAYy8nnaIctLL98fhDIvLgpvf3qep9m94fB57Y9P4UW/fa3wJ88FoHtWBvGMau4Jky6eX2h3HOq+odW6y/0k/kFUsXke8kBvvXX+ugFuz3gHVzsZHEEtbT/40MrVCN5mRluSG7FLe+v+R5TF9XFISVVUJeIE6QJF0vX4Pi0iTKO7MMvEMriZwO8426BEXSBFdLNpZViPdxXxHEbRxfK4MsqdXwBxeikdb8LGUjjTeequ+1hNhAKaMxmvb7GzelSonZeipRgssVCUo4dwFXHidkUfKpiYup86rXrhNlhNrtRiOwdpukUcctd2DpIHJ2mdDuRKljdxBT9q218a0m4eIybvEAxakno4xwbQ3pdnHNJqQJ0qijnS7+4CKkKfQHUZrpXJQ1ikMyD6qE/gDnXDSr04CcvYgcmEcHA9zoeiYputZel12SJEia4p0j9PpRdtvtbcg9l1ejqVivj2Qp5dlz+GYDzXOSG45GKaUIPPIV3MFFynMXkLlalISurMZUie1OvP7DIZJllE+f3bieTuK5W155xufRNRpR0pkmSJKgnW5MV1nJf8tLBW5uBvICV6+BE4qz50mOHYFODxIfZZtGxY4qbU4BN256fwJ46vKNROSVwC8Cb6zSum6JjdkbhrEr3Hnrwzj0qtOdtz587UKuI3ZQjfNJ4HYRuVVEMuBu4IFnHEvkJuC3gL+lql/aTqEW7A3D2BV+7ysvwku46vR7X3nRfldxZ9khnb2qFsDbgQ8BXwDer6oPichbReSt1Wb/DDgIvEdEPnPZPYArYsM4hmHsGh6lRK46nxiU7Spttlec6geAD1y27Bc2vf67wN99NmVaz94wjF0hpdzWfGLQbU77xJ4HexG5UUT+i4h8QUQeEpG/Xy1fFJGPiMgj1Xxh0z7vqp4k+6KI3LnXdTYM49nzF245iasUOVebTxKiuq1pv9iPYZwC+Eeq+qciMgt8SkQ+Avxt4KOq+uPV48HvBN4hIi8l3qB4GXAD8Lsi8kJVvW66BW5kQrWZJFn/E6tlua7OGZlpPWP7yuyMokQHg3X1jAaFYpPKZjQfDKL6plLorKuBKnUPmqNFXqUcjGkQEQeiUb0hEo/vHIQQ1RpVmj0AHQyjMqjdweeHGM4f3OixjG5EBcXnAe304jkYalTqVNOoKi7XmEoPcHkAEVRim6U7QOerFHq9PjI3B5WSRkeqmf4AahmSJJTLy/jZGcjSaGYmEo3cvCc5vASqeBFCrx/PT1FAo44Oo1GcP3Qwvl5eRssSv7iAdrtRLSWyfo6LCxeimdlMi/Krp+K5HJ2bIoe+Q7ynOHsePzeL1DLC8kpM5VhEIzkPuFo9Kl5UoxJmUPnFtDtorx9TRlbX0h1coDh9Bt9qUl5aiQZpQFhZrY7tKdsdXBo/HyNVFrCuuKEM6+knKaIJmfYHlJVKqPpQRTVWUMLK6sZHsOejkqhS7RA0tnUL/utXvm7L9ROHZap6Jqp6WlX/tHq9RrwBcZz4hNj7qs3eB3xv9fpNwP2qOlDVx4CTxCfMDMMYY7wE6q7Ycj4xKOsdmWtO+8S+jtmLyC3ANwB/AhxR1dMQfxCAw9VmV3qa7PgeVtMwjOdAWgXza80nAWF7QzjTNowDgIjMAL8J/ANVXRW56p35Z/M02b3AvQA33XTTTlTTMIznSDZpN2CvRRjvH6996dmLSEoM9L+mqr9VLT4jIseq9ceA0WOA23qaDEBV71PVO1T1jqWlpd2pvGEY28ZVTxFdbT4x2DDO1yKxC/9LwBdU9ac2rXoAuKd6fQ/w25uW3y0iNRG5Fbgd+MRe1dcwjOdGKgGPbjmfJGwY52v5VuBvAZ8Tkc9Uy34Y+HHg/SLyFuBx4M0A1ZNj7wceJip53nY9KXEAKANahg0Pm5EXjTjECRoqFQysqyLE+6jYGak8hnmVfrBY9y8B0LIqZ3MqwbxYXy/er6s14g4hvq6Ova4CGqU+xCNliOkSN/v3jJQ7lXKEoqh8XxJcrusDa1KyrieWUpF6rfLL2bTN6EHCatLBACkUKQKkDlcSPW4A18sJjRRJPHrhYtw2y6qqxnPEYEjodteXl2fP4Q8voYMBfn6OcPbcum8NTqKnUOWLE1ZWcQsHononBMrKZ8ZlGdrpxf2KArIsLhsOSQ4vUZ6/gHcuKmw2XVNXq+NmZwiraySLC5B4wvLquiqIJMGlKZQlYThE+oPoO1SWhLzAVcokSWL9cAJBIc/xc7OEdhuXJhTnLz7js7P+GRKHyzIkjQoqNzuDdnvoMI/Xs/Jnco3G+rV19VpU6FSfydE2I4+m0efANeqUKyv42dlYZ7HHdJ7BmKtx9jzYq+ofceVxeIA3XGWfdwPv3rVKGYax46TVUE2KkiNXnE8O+5tycDuYXYJhGLvC5mA+en35fGJQYAftEnYDC/aGYewKfoKsb7bDfo7HbwcL9oZh7ArpZaO1Ofo1yyYKC/aGYRhfG/wnCiXeSB9jLNjvASMPkc0qmmoBI13Rur5opNApy6iQqDxtxPtnqi8265E2lbOukBipbdiUiWrzsSnjqOkmRYU4iWUVG9uJ99FfZziEQZ9NG5PMz8XXrkrKMMpYVfnjqAja6yF5pQwKghsJkRRcGTNVxcxPIHkZs0JJ9OdBFYoSt1ZGr5wqg1S5uoZfmN+oi3Px3FYqITc7i/b6MYMT4JcOEi4ugxNCfxDbmRdo2cHNzhJW19DhMOqQRyqnoIjbeE2vHz1wajXKi8tR+eNczNY08pkZeRtVPbzQ68XzN1ICjRQ3qkhexHVFEbNl9QdI5YXD0kHoD9B2h7Ldwc/PgXOEdjuqorIUP9OKnkW9flRIhRDr4ST67FR1Dcurz1CBbai7hhuqLXG4Sr2z3l5Yb7+r1aPPUK+Pn52N3kJFfk01Tl08JYpHrjqfHOwGrWEYU8wooF9tPlFYsDcMYxpxCKl4ci2vOp8YlOgqOsZYsDcMY1dIxW9rPhlcNkQ7hliwNwxjV6hJCkCuBakkV5xPFDaMYxjGNJNKsuV8IjA1jgHw4fz+/a6CYew57uiX9rsKe4v17A3DMKYAC/aGYRgTjmrMyzzGWLA3DMPYCaxnbxiGMQVYsDcMw5h01NQ4hmEYE4+C2kNVhmEYU4DZJRiGYUw4qhAs2BuGYUw+doPWMAxj8lHr2RuGYUw6lrzEMAxj8jEjNMMwjMlH2ZT+c0zZOonkGCEid4nIF0XkpIi8c7/rYxiGsY5WyUu2M+0T10XPXkQ88HPAdwOngE+KyAOq+vD+1swwDCOiYz6Mc7307F8LnFTVR1V1CNwPvGmf62QYhrGB9ex3hOPAE5venwK+6fKNRORe4N7q7UBEPr8HddsPDgHn97sSu4i17/rmemzfzc9n5zUufeh39TcObXPzfTk310uwlyss+5r/TKp6H3AfgIg8qKp37HbF9oNJbhtY+653Jr19V0JV79rvOlyL62UY5xRw46b3J4Cn9qkuhmEY1x3XS7D/JHC7iNwqIhlwN/DAPtfJMAzjuuG6GMZR1UJE3g58CPDAe1X1oWvsdt/u12zfmOS2gbXvemfS23ddIjrmj/gahmEYz5/rZRjHMAzDeB5YsDcMw5gCJi7YT4qtgoh8RUQ+JyKfEZEHq2WLIvIREXmkmi9s2v5dVZu/KCJ37l/Nr4yIvFdEzm5+9uG5tEdEXlOdl5Mi8jMiciVZ7p5ylbb9qIg8WV2/z4jI92xad920DUBEbhSR/yIiXxCRh0Tk71fLJ+L6TQ2qOjET8ebtl4HbgAz4M+Cl+12v59iWrwCHLlv2E8A7q9fvBP5l9fqlVVtrwK3VOfD73YbL6v5twKuBzz+f9gCfAL6Z+OzFB4E3jmnbfhT4x1fY9rpqW1WvY8Crq9ezwJeqdkzE9ZuWadJ69pNuq/Am4H3V6/cB37tp+f2qOlDVx4CTxHMxNqjqHwIXL1v8rNojIseAOVX9mMbI8Sub9tk3rtK2q3FdtQ1AVU+r6p9Wr9eALxCfap+I6zctTFqwv5KtwvF9qsvzRYEPi8inKhsIgCOqehriFxA4XC2/Xtv9bNtzvHp9+fJ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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "data.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "d1fc8f44", - "metadata": {}, - "source": [ - "Notice that the data ranges from 0 to 1, whereas in the image the colorbar is labelled from about -160 to -70.\n", - "\n", - "Also the `i` and `j` coordinates are just the indices from the NumPy array, whereas the original plot had time and frequency coordinates.\n", - "\n", - "In the next section we'll fix these issues." - ] - }, - { - "cell_type": "markdown", - "id": "0578e250", - "metadata": {}, - "source": [ - "## Do more\n", - "\n", - "Let's do more with this image!\n", - "\n", - "We happen to know the colourmap was `jet` in this case, but we could give `gio` the pixel area of the colourmap, as a (left, top, right, bottom) tuple. We'll also pass in the `vrange` of the values, which we can estimate from the colourbar labels in the image. While we're at it, let's crop the result down to just the data area, and pass in the `extent` of the data in real-world coordinates (time and frequency in this case)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "67caf004", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.DataArray (x: 1096, y: 1642)>\n",
-       "array([[ -69.        ,  -69.        ,  -69.        , ...,  -69.        ,\n",
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-       "        -154.83215686, -156.2       ],\n",
-       "       ...,\n",
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-       "       [ -69.        ,  -69.        , -156.2       , ..., -146.96705882,\n",
-       "        -146.96705882, -150.38666667],\n",
-       "       [ -69.        ,  -69.        , -156.2       , ..., -146.96705882,\n",
-       "        -146.96705882, -150.38666667]])\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 -0.01 -0.009963 -0.009927 ... 0.04993 0.04996 0.05\n",
-       "  * x        (x) float64 200.0 208.9 217.9 226.8 ... 9.982e+03 9.991e+03 1e+04\n",
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" - ], - "text/plain": [ - "\n", - "array([[ -69. , -69. , -69. , ..., -69. ,\n", - " -69. , -69. ],\n", - " [ -69. , -69. , -155.17411765, ..., -154.83215686,\n", - " -154.83215686, -156.2 ],\n", - " [ -69. , -69. , -155.17411765, ..., -154.83215686,\n", - " -154.83215686, -156.2 ],\n", - " ...,\n", - " [ -69. , -69. , -156.2 , ..., -146.96705882,\n", - " -146.96705882, -150.38666667],\n", - " [ -69. , -69. , -156.2 , ..., -146.96705882,\n", - " -146.96705882, -150.38666667],\n", - " [ -69. , -69. , -156.2 , ..., -146.96705882,\n", - " -146.96705882, -150.38666667]])\n", - "Coordinates:\n", - " * y (y) float64 -0.01 -0.009963 -0.009927 ... 0.04993 0.04996 0.05\n", - " * x (x) float64 200.0 208.9 217.9 226.8 ... 9.982e+03 9.991e+03 1e+04\n", - "Attributes:\n", - " source: \n", - " fname: " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cmap = (2035, 102, 2079, 1198) # (left, top, right, bottom)\n", - "crop = (312, 102, 1954, 1198) # (left, top, right, bottom)\n", - "extent = (-0.01, 0.05, 200, 10_000) # (left, right, bottom, top)\n", - "vrange = (-156.2, -69.0)\n", - "data = gio.unmap_to_dataarray(img, cmap=cmap, crop=crop, extent=extent, vrange=vrange)\n", - "\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cb852f71", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Xmp/+sGaaiToiTcCNWv1GVUBVQzTtBnWDeNfGohBC6YlFawvfxsOQoPhxIJtE9PNP41/+GOH4Bk3HEwtBnbT2/YoLgjSm6XC7Y7hwiebMOcQJrttFuh3TdCxoLliIfyBNhOEI3R2Ad0inYzE1mgCTCm0apMihKKAs0M1V9PAGoVsQc9ON+HEkGyjZToXESLXVZXwgQ70wOCzkuzDeAvVKmHjynmk0vNOZRqNqPNUoRxuH+IgrArHylKsTxkGoNwRphFgIMY+MjgK5QqbgzE5eG0EqR+g5/FDYftThAoQcXAXRgzhBW4FDLJUG4dIrHdkQ8oEdG0uLx1H3hcF7XoWrTLeR7UKxE1EvVCtimowwt/FHILuohNLiO4QCiHYctBoPxywWha+mWg+1e4rFIFEP401hfMDPtQa2Sjp/PqPd/9ARpAbENBoxMz1RKK98nu2YaewHP2nLNL1+sHybDtQrjuHh/lyHETAtxARcpbig+JEVJh809gyXjrrn7L63GhWAZsOhYvFVgJm+wWKtCJO17LIYG7EQmq4wOlTOdCWuUZqOoM6RjZXyYqATlKbn2mcQmq4wPFriaiUfRFaeq/A7FXz1WYt5c/QQzWaPpp/TrFghsnGk2I5ko4AKaO6o+xmTjiMUMo/f4aHpuPY+R8oLFfn2BKkD46MrxPL2/Kfex4Xwvw38TVX9dRH5vvbzd4vI67AwsK8HHgI+KSKvvtmQr2mkkUgkEntMUFlqu0UUWGvfrwPPt+/fCfycqk5U9RvAM8BbrnL+UjxwI41EIpHYTxSh1qV/ag+KyFMLn59U1SeXPPcvAR8Xkf83NiD499v048CnF4470abdFKnTSCQSiT3kBhfCz6rqE9faKSKfBI5eZdcHgO8B/itV/QUR+dPAzwBv5+pxg2/aR0rqNBKJRGIPUW7L1JNdS/Xt19onIh8Bfqz9+P8DPtS+PwE8snDow8ynrm6YtKaRSCQSe0zELbXdIs8D/0H7/j8Cvtq+/2Xg3SJSisjLgceBz95sJmmkkUgkEnuIKvvle+q/AP6+iGTAGHiv5a9fEpGPAl8GGuBHb9ZyClKnkUgkEnuKLYTvvRsRVf3XwJuvse+DwAdvRz4PXqfRRPjWSTTPkI113KPHLc5FE3GjGlSJZWYai4XYFhIibtwgVQMhzmNpaIROCc7ZdYYVflLjnSN2c4uT4R3qLEYATmj6Bfpdb6TOLHYATpBo8T1co2SDBjdpZkWOKyWSb5H1eujFS4SLF3FVhfRXkBCQAUiMVp5OiRa5xdbYWrNNFXXO/vIAUjXIeIJe2iGcOYvf2kSaFYvRUTW4LIPMEQsPanbuMonkFyd0vvAtmlc+hEqXzrmG5/6DHN9tEAdr/RG5i2Q+oCoMqgJpPBoFrR2yk0EjyMEJ4wsdys2x7VPgIcU3jjwPiCjSCg9CdGgUmtoTK08sPDF3ZCPBj5kv8bVaBfVYbIlSUS/EzLQLi9/Zqa4iZhA6UK3DeKu11x+Dr6FzPtK5UFOtZYw3HOMth6uVbAxbX9hl8GiP0UFP0211G205XNPGzZjYsfkgMFm3IBUxB9pwJgizyeGQW9mb7rQugmuArsUV8WOldzrSdB3Vil1nWh8t5nE8JECTm07ET5Te6UA2CAweKqjWhNDGDZmeZ7E5hN63JpTPnmfwukNMtjJCDurtpyF6AcdlsTemsTFAiblQd8X0MsW8fV1tGpDu+UDMhNGWI3SE6AEPLphrDVHTajSdtkFkem/a70Rj+WQjpfv8wNprtYA3v8bizIxq5LNfpHz9q5kcXiF0HU3p0L5jLKYpUmdaHvVimqum1aeMIjETQinUPQ9bHrBKxIzZ9/92cD8pwh+8TiORSCT2EUVSEKZEIpFILE8aaSQSiURiKRSIKQhTIpFIJJZDUrjXRCKRSCyHwr5YT+0XqdNIJBKJPURV0vTUvUzs5ozf9pq5iWsdcaMKCYqKIJOAbyJu5GYmkdJE2yY1TGpzdS5mKkvA3ovY+aowCeAEFwI+qLlQH09mrsl1ZxfprxCPbtGsdYi5ZeSaiKsibmzu1zUzV+uaZcR+AQdWkIc3kUZhVEPVwHAEu0PiaEQcjXFFgTtyEF3pQSaz8lNNwDk0z4i9AvodOLiOyCOoKtpEMyeOZr4bxcxtaV17xyJDmsj4DzyKayL5MKKZ4ALk3ZpuWXN0ZYdjvW0yCThRcglMYkajnkFTUMWMJjqGTY4T5cKox+lT67CbQb+h6FdEFQQItaeZZGjlkChQRMRHWIuEjieMHX7gKLaF3gtm9hq6YmanPcwsNVOaEpo+SGOu3F0FoTs1wTTz2GwA3bNK0xXqFagzGB1ySGz9kKuZmk7taneOr9pbNXNXV89dk6s3c83JhjABJHgkmBmvnygSzQw4ZiAVZGMz6W66guStuWoD2RCK3cjogKNaEyabfm72Gq08+QB8pYSiNSvNzURX27KNDnpky+MaKHYU3TVX46G0fEJp2+k3lvDGY7M6uMbMcfOBoqW1bdNh5lJcVJis2X1y9dwdu5+YCa46Mx9uusJ4K7PrtpsLc3PcbBQpLzXUfTNrrleEULSmu9PZnCi4IIwOOnYeWcePW5PZseLqAomKf+hNuCpSnh4QegV6pMOk6wilmSfHjPn9imbK25SCrLWhC8Tum7Zm2xLNdbwLN+2e6UXsk7hvX7gjNRGR/0pEviQiXxSRfyYiHRHZEpFPiMhX29fNheOvGkBERN4sIl9o9/2EyG00rE7cFpwodbx/huaJxI1i8TRkqe1eYN87DRE5DvxF4AlVfQPgsQAh7wM+paqPA59qP3NFAJF3AD8lItNfoZ/GpPKPt9s79rEqiSWIKuTupj0WJBL3AfsTuW+/uFOlzIBu6yOlhznaeifw4Xb/h4Hvb99fNYCIiBwD1lT1t1VVgY8snJO4S0gjjcSDjpncylLbvcC+r2mo6nNtkJBvASPgn6vqPxeRI6p6sj3mpIgcbk+5VgCRun1/ZfqLEJH30jrvKjsbt7E2iesxHWlUqeNIPKDsl++p/eJOTE9tYqOHl2PxaldE5M++1ClXSdOXSH9xouqTqvqEqj6RFys3WuTELZBGGonEvrlG3xfuhPXU24FvqOoZABH5RSws4SkROdaOMo4Bp9vjrxVA5ET7/sr0xF1EGmkkHnTMNfq9MfW0DHeia/sW8FYR6bXWTt8DPI0FCvnh9pgfBn6pfX/VACLtVNaOiLy1vc4PLZyTuEtwcvvMFhOJe5W0pnELqOpnROTngX+LBQT5HPAk0Ac+KiLvwTqWd7XHv1QAkR8BfhboAr/ebi+NQOj61ibbI0HJRh4/aMguDNBTZwmXtvHra0ivCys9tLRm0swhjTPNhXfo2gqxV7Tuuj2aCdP7bvb7EQmK1KbzmF5D86PEzKGZEL20tuFTQ3/F7YzNRfuBPqGbEQqHeplpJgAIBS4o0qya2/ZGISpRIPipbqS1O2/i7Ppzd+/RNCk7QwgB7XWJKyWxm1nZvJi78aimCwHoeHNzfbik6ktrl6/0y5pj/W2O9y7xWPccm9mAVTeikLnVVKWendjlUuiy23Q4V69QuMBgo2Aw7MPA41aVIm8QUTQXQtkQo32ZtG1YjUL0SsjMBXboOaoNh5uYO3GJrZtwtWMRNTfpnUizqahTq5e0LsoVJAiDxwQJ7VaDHwvZCDrnoXcqsPOIuUGPRWvHX5smorxkbrtFoekIk3Wx41p9Q8yvePgwjYW5/YamBlfbvdKMma5AD2Iu9TNtXaFr6zZcQaXVaghuIvhJ60J90mo0HDStu30/Mc2BrxUUVk4FVp65wPZrt6jWhHrF3JVrNnfx3rSu0KeahmmZ7Qa0Go9Oq21wc3f00/q5BvJdKLbh8P92mtPfedi0M1nbfiWEQmi6ntFBb3qRK/UZrTZmUf9Cq89Y/caIZjWnWsvMtXkB2URQvzJzqz7VrUw1GhJMG7L570ZUmwWDw57QkZn2xdWQVWpu5ceRYieQb1fX/TlZBvNyu/f/z0XkDwL/I/Zb+izwf1bV7Xbf+4H3YMqyv6iqH7/ZfO6IuE9V/zrw169InmCjjqsdf9UAIqr6FPCG217AxJ6TRiCJBwVzI7IvkzofAv6yqv4rEfnPgf8H8NeukC08BHxSRF59s9H77o2Vl0QikbhnsZHGMtst8hrgt9r3nwD+0/b9VWULN5tJ6jQSiURij9knRfgXgT/Zvn8XcwOi48C3F467pjxhGR4431OJRCKxn9yg9dRBEXlq4fOTqvrk9IOIfBI4epXzPgD858BPiMh/ixkQTRdllpYnLEPqNBKJRGKPuYGpp7Oq+sS1dqrq269z/h8DEJFXA3+8TbuWbOGmSNNTiUQisYdMY4Tvtcnt1IuGiDjg/4lZUsE1ZAs3m08aaSQSicQeokCzP9ZTf0ZEfrR9/4vA/wzXlS3cMA9ep6HgGmXxHsbMQTcD18etlGTDQ1A3kHm0yEzz4MVs0bsCRT6PtTEJuGh298F502G0vvtVBNcE/O4YGU7QlQ7R5RAEkWhxAYK2ggEsn8xRHVubCQkkKPluA1EtVkdQcKDOdB7aajfUW15uWCFNJK6UhG5GzD2xa2ps0wZE3CSYfiQEiJa/7AzwTQDXAzIiDlFFmogbB/zOCHWOYnsX3vgwg6MZo8OCm4CIslGOOFJucyy/yIYfsOIm5K1x/yCWEEsKaehIQ+0aStdwvHeRzEUu9HcZ1gVOFO8iIkrVZIzrjMmkoBnmIODygDjQCESZOZOJ+XR6VuYaiArKoWkghkeF0FOiRGRRmO4AUdQrogINuInQPW3XqPuwexx2Hn1xLAspLC5HvWraDtNQzIox04q4mlksh+m+RSSAH7fPYT6/dr0CsVRCqZApmsXL9CUaLV/1QiyFpmd1FfPDbXErKouDIa2GQiKMDnkuvvKgVb/VmnQuROqeUK3JTIsyK9vE9Bb5UKlWTRPhGtOD1KsWu0QFYkdn34kQoekJ4wOw8+jhVvsAxQ6sf71i+9GC8UEYdU0fMtM2BfAVZLumfykvRSbrjtCxuBihhMERx/DIymUz8nZP3FzT4RY0JG2ZNLf4Hs9/Z29+P3TeTtO8Aeq+Y7zp0ay43OvdLbAfOg1V/fvA37/GvqvKFm6GB6/TSCQSif3kHlJ7L0PqNBKJRGIPmQZhul9InUYikUjsMWmkkUgkEomlmAZhul9InUYikUjsIYrQxPtH3ZA6jUQikdhj0prGPYwEpbhgtnVSBdxogowqtN8l9ArCSkGzVtrBUfGTgIwb3Lltmm8/T/bYI8T1FbT0qHMzl+dgJrYqXG5iCaj3iHPIzgg/nKBFTqYKTYCdXXRSIXnrfr1uwAnSX4GyNPvK8QS9tE0cDtGouKLAr/Wh14VOiTrXmo/agxm7OZq52WcXzHTXTQJ+e0T40u/jjh2FtT7a75gZL0DmzJTXCZoJIXPgMlgHOdDBNZHw2DrjTU/Tg3pVkUYYjgu6vqbnJ2z4AYf8DoUEcgnU6rmoPc6FPierDXp+wqofs+rHnK377DQdtl1J7gO5C4goTpTSN3RyTydvmPQ8TfDUjSfUnjDO8DsZ2Y6Y23M/bWcznQwlNH1lcgBiHiFvXaJj7r5pBGnETFkLRbyCD8Rc0DKys+LMfLU1r110dy+10DkPq98KXHyVp16DULTXDoKrzXRTQmvu6ex8gdYM20xaNZuXW1r36FNT1ml6tiusnDAz2GrdE7oRzbQ15XUz8+Ctp2t2Hs6p1uYmpuZu38xJ84FSr4iZyLYmtYvtsTtx+DHkO2ZiGzqtq/K2bQcPM69A+zIzPW6szvmuuWiHtv1XWhfvmRJLqFeEySbsPGr2vJrpzI07sS2vipnKZmay62tH72xABao1T90TQqd1dw4z0+Jpe8fcyj1r12gmxb3TkZjB6KCjWrPyTe8N0c4PHYiFufuve9bmert+HTVNTyUSiURiSdKaRiKRSCRuiNRpJBKJRGIpFCGkhfBEIpFILEtaCE8kEonEUmhaCE8kEonEjaCp00gkEonEciSHhfc00gT8heFck1DmaJmDCBIjUi0KLNTcoXshHlzHHdogZA71jlg4Yu6IXsBL64ZZZue5ABpkdl2GI3Q4smt6B5sb5ip9a8V0Fi8qKGYPr4o0PTiwjh9XMBihwyEaAlI3kGVQOCQojEbm0v3gBtE7Ymbu02GqIRFi0Ue+8400QVt7+2hu0qsGqRqywRiKnNgraPoFsfDoFfVzjfkkjwWEAzUHV0asZmMOZTsc8jsczYasCuTi6Ijw2vwcNWe4FAM7MWM7lpwJq5Su5ly9wjfDJie/dIQDrzlLv5zgRGcKWu8iZQaZj+RZoM48lY/UeaQqMvzAIQGalYiWEcmsXto43MhRnPfUGxH6Db4TcN5cr6sKoXHExkEQqBwSZLa52tyk5wOz/69XldBRmn5kciyy/QcwcYA1LhpBKoerHDoU8l1Y/7qye1yo1qDuR7Ro3bB7NWHCwnkxCK4S3MTyNq2CUm0pWrT6DKfmdj0I0QM4qjXh9JtzYt7qItyChiLaczQ4LsSOmmbFA67VrbSPax2nLtVNw+JHQj4QVn9f2H4F1Gtt23r7bmgQ/MDjx0J+ybQSoTRX6dq6hZ+GB0Bl5oK9d0pxDeweF+q+6WRoBFdBNoZsaLoSaJunvU4+CITS0XSExd9e17qVLwZKsRNoSsdk3VGttloUb7qUqu/IxlBsq30P2vSYK5pDvdAWMw1Kq525XaSRRiLxEsQ2zkV+RWDIXKAjge07U6xE4o6gCiHeP53G/WMHlrhrcNf4ftQKY/VX35lI3MdEZKntVhCRd4nIl0QkisgTV+x7v4g8IyJfEZHvXUh/s4h8od33EyJy3UKkTiNx24l69fTpSCOReJBQbHpqme0W+SLwg8BvLSaKyOuAdwOvB94B/JTILIblTwPvxeKGP97uf0lSp5G47UxHGjXxsvQ00kg8mNhC+DLbraCqT6vqV66y653Az6nqRFW/ATwDvEVEjgFrqvrbqqrAR4Dvv14+qdNI3HamI41rrWkkEg8aqstte8Rx4NsLn0+0acfb91emvyRpITxx20lrGonE5dzA1NNBEXlq4fOTqvrk9IOIfBI4epXzPqCqv3SNa14tc32J9JckdRqJ2068xuOYrKcSDyJmPbX0pM5ZVX3iWjtV9e03UYQTwCMLnx8Gnm/TH75K+kvy4HUaIcLJ04j3yPoq5BmatTqHqoadXcL5C0hZ4lZ6EJVwaRttavz6Oq6/YjEsugWxV6DegYIb1biLO8SNVZr1ktDxxNJRlQXSz/BbPdwk4OoAqtadi5hWJHemhfCmpTBthsW/kCa22g4hrpTQK5CwZtcQOwdnehHdWiFmYtdzZtMuCm4SyHYr3AvniecuIK9+mZXdmY6EGJA6wKSGwQAdjXHra2S6QbNa2vVao4re0y8Q3vTQTKLgysBGZ8RmNqTnKlZcRc8pPcnpOYudgEDQSK3j2W2o22AFB/IBb9g8yWNvu8CwKaiCZ9jkXBj12NnpEsYeySM+D/Zvrf3HlpUNWgTYFFSU3JmOQUQv76+OQ0fmf56iCjE4msqjg4zskmflhOArqLvW1+U7kI2VwVGo1qFeN40FRQQfcX4eV0KjQBAkWowOV2FxMTxceoUQyjYuxcJvhkZrFKkcvnIUF4XygmkHTD9g54RuhF7AFYEsD7i2fov1qA9NtSYOaivLS/2HlNra0EWZaRLUtfEtckU7gdBXwmFl8kprU++wBwlm2pKYRyZ9odo0bYobm6bFT9prZlYP0/dYvbZfIbN4IbN4HigxFxrseFcz+6872RB2j2dAZpoTuKwdQwl1HyZbApqhnrlexS/EFolQBxgfkKk8BtdY7tFZY10WbwVr/1jevvmiPZx6WoZfBv6piPwd4CFswfuzqhpEZEdE3gp8Bvgh4Cevd7G0ppFIJBJ7zH5YT4nID4jICeBtwK+KyMctb/0S8FHgy8BvAD+qqtPFxR8BPoQtjn8N+PXr5fPgjTQSiURiH1Fuiznt9fNR/RjwsWvs+yDwwaukPwW84UbySZ1GIpFI7DF3dnbq9pI6jUQikdhLpmtf9wmp00gkEok95n5yWHhHFsJFZENEfl5E/p2IPC0ibxORLRH5hIh8tX3dXDj+tvlNSewfQeP1D0okHgDusLjvtnKnRhp/H/gNVf1TIlIAPeC/AT6lqj8uIu8D3gf81Sv8pjwEfFJEXt2u/k/9pnwa+DXMb8pLr/5nHjlyECY1euYcYWeH7PAhWO2j3QJWDuKOHYIY0aCIKv7QlpnqhgBNAyEijW2IoN7RrJew2TF35FM34q00Wp0QSnNTrpXDTRr8hSF65hxZWcDmOrGbE8sczcTMC6PiRjVSNXaNToYWGTG3ft7VETescKcv0Tz3PNmRw7C5jnox82Gwa3YLQtdTHejCgeOgx+3cOuImDW4wgXEF3qHdEt1csXwKTyi91cVbXyxB2XnTQ4w3nJmWjoWmcYybjEnMGMecQSxYdQ1Q0yOCwlADwyg822zwteoIv7f7MGcmqxSuYbvqcHJ3jUvbPR49fI6NckwvqznYGVKte8YhY9TkjKqc3XFJVMGJki24OG+CYzLOibu5mZ32ApLb/hgcjDxu5Im9AEVEsrYzKyP1JlzqObKBM3PRYOah6oXQgdAxd+aIokGQxqNNa147EfxYzA2+LJiDamvyOTN5FvzQjpMAoWhNOgszPZ1sKZMtM/ecmn4imKvyiSMCjYLPI95f3hHHxsHY47c9a9+w+zR1DT51lY4DYutKfAjFrqWHcmHriJn4Roeqmvv06Y9Ya5kN9sMmwVyou6Z1I1+1ruRr8JVtroJsomTDiK+U0QFPtTptU4iZXO6OXNt2c61b92DX8LWZx1qakg8VCTBZd2aeXGBu4hfMayXaZ23LPtWTioIfmBt2BJoOxMJcpU9duOc7UOwqg4fMxfrtYOp76n5h30caIrIGfBfwMwCqWqnqRcw/yofbwz7M3AfKbfWbkth7ruWwMJF4IGnjiiy13QPciempVwBngP9ZRD4nIh8SkRXgiKqeBGhfD7fH37LfFBF5r4g8JSJPVWF4e2uTSCQS1+F+mp66E51GBrwJ+GlV/UPAAJuKuha37DdFVZ9U1SdU9YnC9260vIlEInELCBqX2+4F7kSncQI4oaqfaT//PNaJnGqnnGhfTy8cf9v8piQSicS+o0tu9wD73mmo6gvAt0XkNW3S92Dy9l8GfrhN+2Fg6rHxl4F3i0gpIi9n7jflJLAjIm9traZ+aOGcRCKRuDvQfQvCtC/cKeup/xL4J63l1NeB/wvWgX1URN4DfAt4F5jfFBGZ+k1peLHflJ8FupjV1HX9piQSicS+c4+MIpbhjnQaqvp54Gruf7/nGsffNr8piUQisf/cG6OIZXjgFOGaeaqj5lpcHtkCVep230wfUQdkGJCdAbqzi3TKmY5Dsx54aZ3kK66pURFc5oiFx1VmRy9NwO1MkPMXwXv0wDqae9Q5c0MeAjhBxxNkNEZE8HU7gAqK1I3l4RxatrdJdabBUBFir4DuAdzRLeLU9KJ1u67eo7kze/irERVihLqByQSqGh2NkKLAHdhE1rqg5j5douKqMNeNvPYAvnJIEHZWPduTDqeqNfrZmBU3ocazoSM8SkAYxA5DLXiu3uJCs4ITpXANw6ag8IGXb5wn2zxLVKFRRxU9TXQ06mjaOATeKd2ypgmOJjjqxs/t3xV8FpG1qm0DMUsUxbQcRSR4NbfXE4dWDnWKOBCvaDfQ5JGmEVztZjqB6WyBH1sZJIA0QjaCbAjZCPzYXHvHbKo/aDUa3n4mNNK+aYvm59oNaVo9j7Dgetzy95XpHpqOo1lVYscTi0g91WlEgdohY4erzTX77sOtVmICfgTOmYvyqU7B1aZ7ANNmND3TpISeaVHUKebm3+HHQjYUJMzdjU+vke/C5u83bD/qabpWP9dAsQ2d84FQCqGwNqnWHNlI6Z1qOPivz7LzHYcZbXmanp0nAfwYyu2IOqhWHaFYyGukuFpBMPf/3p7HtW9VxEyYbGamt8gE11i4gHrV6jd1ze6aVvMxad20d0Cn+2rwu9A5r2x8eZeT37nKzqPm0h53G4cH95HO9YHrNBL7Q60eL82dLkYiceeZ6jTuE1KnkdgT8hQLPJGYca9oMJYhdRqJPSGNNBKJBe6jTiNF7kvsCWmkkUgssA9uRETkXSLyJRGJIvLEQvoBEfmXIrIrIv/ginNu2Olr6jQSe0I9XX1NJBKILrfdIl8EfhD4rSvSx8BfA/7yVc6ZOn19vN3ecb1MUqeR2BPSSCORaFExa7dltlvJRvVpVf3KVdIHqvqvsc5jxs06fU2dRmJPWBxpFKkDSTzo3J1uRJZ2+rrIA7cQLiHiRzXqWtv7GJE6mP4hRjNziKazoFMiRW7BBFSRcY1oBVUNVYVOKrSuEe9N39DvQZ5bbIr2+npwwzJWtRgcRFQE7Xeh3zVdReviUupguonRGN0dEEdjtKlBHL6/gltbhdUVNHOz8rC9Q3PmLNmRw+jhLbTIbH9Q3LDGN+1itHOzetBEJEbTamQe1ldBFSdbaDYvu9+pkEWzD1U4fxEXttj83fNMvusAEoTBqOS54TqZBDrSMIglK24yO20QSy6FHl8ZHuErlw7zjW+bA+P1A7v0y4oQHZeGHcbDYq5nWPx3VgsycXRPOfwQ6kM2lM8GFv9geAzqAw2UEfERae3rY3BtTApPNnB0XxBWvx3ZftTR9C2uBU5ncS+Igq/susWO2fmHHGKJ6RfamBHZCFytqDPdQMztWIkWq8HVUF5SuqdrLr2imMWsiHn7DEaQBvIhdM5Hei9U7DxWUq3K7JhsBCsnGy6+KgNMvxIDMI1DEQWp23getf1DVTFdRuNASiyWxMKf15i1dSrn8SrsB0BaPYfMtAvSyOxeSGx1K55ZPIwXNjO7RmP6B9c+Zk3P4RpF4jSehzBZE+oVYXDsaDsNo/iJxbGIvtWL9CwAhsT5NE3MYVwKU2GcTPdHYbxZmB7GteXKsXg1biF+RpzfM9e097AtZ1Rm963pwmRDuPTKVaaxPbKR5XPbWL5DOCgiTy18flJVn5x+EJFPAkevct4HVPVG3Sgt7fR1kQeu00gkEol9Z/lO46yqXs1bhl1G9e23pTzGTTl9TdNTiUQisZfcpUGYbtbpa+o0EolEYo/ZD+spEfkBETkBvA34VRH5+MK+Z4G/A/x5ETnRhtEGc/r6ISwi6tdYwulrmp5KJBKJvWYfFrlV9WPAx66x72XXSL9hp6/XHWks9EiLad99I5kkEonEg8w+6TT2hWWmpz4qIn9VjK6I/CTw3+91wRKJROK+4S5c07hZlpme+sPA3wL+D2AV+CfAH9nLQu0pVY37/W8j3Q4UOYiDurZN1UxmRaBpiDu76GSC39iATmn7YoC6RivzMe26XbuO99AEGI2JuwPicIjrdnG9nu1vGnQ8MdPWlR70VyyfGGFSwbg1US0LKEtkpYefRpuPavk2Ac6cR5xAUUC3gx7cwh87RMxak1pAxg3SNEgTYVKbf+7ZdSw/bZqZCa7WNeIc0usi/RUocrPFq2oz/x2OIARkY53q9Y/SlI4zbz3AZMuKPN4pOdVZpXCBflYxzAp6rqJ0NZOYcyl0OV2t8sULxzjx1cP4zYoDG7sUWcP2qIOqIO3fLA0Oxr41e25NSb257h4dibhayHZlZjo5Pmiv2SWPerO1nJqLutbl+NSktlqDi487iktw7NMjLj7eoe6Z6afKgtvwHKr+3MSz94Li6ta802Gu0Asxc9ypK3TXHt82a9UXYpaz8bUJ482c8aazv2hq5qvldqT3woTR4ZKLr+oQi9Zkd9CaijbKeMvq4yvQkSCNa11668xYMnSU0DG371KDqwUX7RpTF+2aKai5OYfWHXhldZ6a0kYVYsTObV2z+7HVBzevZ8yZm/JK62q8OzdPrvuCBJmZz/oKuucC2URxk0i+PUEmgdGjfSbrnlCAirk7z8ZK53zAjwOh46lWvbk7d1h7t+7WAbKRsnKyIpSe3eMZuiozp4Dmvl0pt5WmI9T91n1+pXTPBcabnmpVCNHMj33bLhKmbW/3IZvcpr/+91Ao12VYptOogREWHa8DfENV7yPv8IlEIrHH3EedxjLTU/8G6zT+T8B3An9GRH5+T0uVSCQS9xESl9vuBZYZabynXWEHeAF4p4j8uT0sUyKRSNxf3Ecjjet2GgsdxmLaP9qb4iQSicT9xb1kGbUMSaeRSCQSe809Yhm1DKnTSCQSib0mjTQSiUQisSxpeupexjnTaOSZaStEIOtAr9sa9rdExa32TdcQp/66ox3f7SKd0nQMTYNOJhAV8R6c4PorpsWAuRYjy5D1EryDLLP0aWTF1p26lc9b2dzC/hChMq2FhgCNWt6Xtu26vS5SFGi3tCxVTdNRmf5DhyOkLKHbsW2tDyLm/j1GpK7nGo7RGHYH1jZFDnlm2g2AGMkujCkzx/BIAUDvOcf4UGBSZwybnN2moBQTPNTqySXgUPp+wivXzrH6+gmDuuDc7gpnL/RZWZlQNx4F8o6JL9yqmml7FEJwxMahjUN3zR13vgvlRbO771wI7B7NGB8UQusOHBYtUUyf4Go7x9XKZE14/ju7xIK5W3TzGD9DItDYayhNHzDVNDTduTZjeqyfQDa067gw13VUqxmaWVlDaa7PtQPVqmP3eHem28jG4CfKygs10ijDoznV6ty40dx7C7ILfiSU21DsKKMtIXQgFubqPebmuvyyX6k4dy0Obbkj4O28ONWbOEXzuT7GVSZjmmlRvGk3fGV/nEPX8ooZhK5SrwJqGho/tk2duT1Xr7hCCJ0uxcWKbDcQCkdTOjSD6ISmK0w2HH6c4WvIdyPZKCJByQY1EpXJZknd99R94cKrO6bzyMzFujq7/9FDKITJuth6QqvDiJlQ9+Y/eW6qRVF737kYKc/XDI8VjA4I9eptmlLSe8cyahkevE4jseekUK+JxBWkkUYicW1SqNdE4gpSp5FIXJs00kgkLud+WtNI8TQSt5000kgk7l/SSCNx20kjjUTiCtJII5G4NmmkkUgsoPvje0pE3iUiXxKRKCJPLKT/xyLyOyLyhfb1P1rY9+Y2/RkR+Yk27OtLkjqNxG0njTQSiSvQJbdb44vADwK/dUX6WeA/UdU/APwwsOgG6qeB9wKPt9s7rpfJgzc9JUBZWnyKwRAdTwiXLoE4/ErP9AyZN50CWOyM0Zg4GqEh4Pt9O8Y7OyYqIg510c5zDmJEmwYdjABwa32Lk5G1zR3iZTE8tG5M7xECiMxjdLRxPXRSmYYiz0xj4pzFx5hqPVqNh+wMoK6JO7vE0RjxHpmeU+SWv3No7lHn0E6GBIWQI1VoY2cMoW4srxBm5Q2XtkEjfq2Pn1h6NoSmBzEIu2dWOJMF+nkFHRtt9HwFQO4aeghdX9HxDYO6oG48mxsDLm73TI8xyAHwvRpx4HwkRjGNRuVhZHUMHWX3MWX3sem/Mw8oUoMEwU1jYnTbuBNTLUUj1KtzDYLEeUyI6EyDAG1cjDiPrZCNIDaQVaa/qMv5HzGJQBt/wQXTYkx1H01XLCbGphXA4kG0eoKsjc3h2+dRoVmx8o8OFaahaLUUKqbRkGh6CnV2bOjCeEvIRqabmP5LncYZcbXQPaMc+pVnOPXOx6nWIJbTeCBQbcyvmw0srkTMBc2UWCixhNBrNQ5RZjE4ZpqH3DQes5gd0/8JUYmZQNG2p8DYCa4WizUSYXA0s9gcV2hdXJhqYYTGQ1N6RB2uAQn5XGciVhZXK9nYdCJNR1q9B/N73raHH0M+jBS7kZXffR5d6TF+ZI3Q9TRdi7WhzmKgTNYLYtbGa7lNU0ptWJc9R1WfBrhysKCqn1v4+CWgIyIlsAWsqepvt+d9BPh+rhMn/MHrNBKJRGK/uXvWNP5T4HOqOhGR48CJhX0ngOPXu0DqNBKJRGIvuTEvtwdFZNGz+JOq+uT0g4h8Ejh6lfM+oKq/9FIXFpHXY1FY/9g06eqlfWlSp5FIJBJ7zfKL3GdV9Ylr7VTVt99M9iLyMPAx4IdU9Wtt8gng4YXDHgaev9617thCuIh4EfmciPxK+3lLRD4hIl9tXzcXjn1/u7r/FRH53oX0G175TyQSif1mGlPjetue5C2yAfwq8H5V/d+n6ap6EtgRkbe2v50/BLzkaAXurPXUjwFPL3x+H/ApVX0c+FT7GRF5HfBu4PXYyv5Pich02e2GV/4TiURi39kH6ykR+QEROQG8DfhVEfl4u+v/BrwK+Gsi8vl2O9zu+xHgQ8AzwNe4ziI43KHpqXao9MeBDwL/dZv8TuC72/cfBn4T+Ktt+s+p6gT4hog8A7xFRJ7lJlb+E4lEYl+5Pea0189G9WPYFNSV6f8d8N9d45yngDfcSD53ak3j7wF/BVhdSDvSDpdQ1ZMLPeFx4NMLx01X+GuWXPkXkfdiIxI6soKeO2+uzVu34llZmInseEI4fwE0Illu5qqFmcpKWeKcM7PYqrJ0EXM1DoiTuQv1PEM6JbK2Zi7Oo5pZbIhm6jup0OHIXKp7by7Vs8xeRSyPSeuS3bUmtd7ZfjDT3qKYu09XnR8LuNU+rttFmwZiJG7vgm7jVleRtT5kHsmAoEgdYHeIXtpG6wa3tQEb62bWC+Ziva7xbp3m7Hk0c0gTzVV3DcOHFDlb0jsnbPe6nMzWcKJEdWzkQ/p+QsfV9FzFI50LHMgHXOj2eKR/kbPjFZrg2N3tsv7ljJ1XRGLmkTwSg0ejQBDzSA/g1ExVy2jVduZCnSjQCNSCBIF238xNehAz462EbGAu0ntnA34UGR7NqFeEkDMbd8+CrImZljYdczsurelpNoZsqBS75ra7XnGoF6pVQcVMUQHUzc17XQXFttK5aOeMDpq5Z8zmLr3VWd6at+anam7gQ2FlEGf7VFp34IWZ3xLn7t2nqIPBQ8Lozz+Oa6C8BP0TNaHjGG85mq7M6qlZa9LbmEtxaex8F0Ca1u37yDZpzDxXHWjdmgRHoJKZi3g/gnw0bXtz+d4905CNA8MjhbV3YSayKnNzYXNhb5WYuqMHmbkuz0dK52xN57lt5OxFe+63NqgO96k2C5qOmLmvTNtccY2SjSJ+1ODHjX33Dqzhq4h6oZgAXsgGDdV6TtNpXewrZKPb90uffE/dAiLyJ4DTqvo7y55ylTR9ifQXJ6o+qapPqOoThXSWzDaRSCRuE/sj7tsX7sRI448Af1JEvg/oAGsi8o+BUyJyrB1lHANOt8efAB5ZOH+6wn9TK/+JRCKx39xPQZj2faShqu9X1YdV9WXYAve/UNU/C/wyJnGnfZ2u4v8y8G4RKUXk5diC92dvduU/kUgk9pVlRxlppHHD/DjwURF5D/At4F0AqvolEfko8GWgAX5UVace8X4E+Fmgiy2Ap0XwRCJxV9F6q7lvuKOdhqr+JmYlhaqeA77nGsd9ELO0ujL9hlf+E4lEYt+5R0YRy3A3jTQSiUTivuR+sp5KnUYikUjsNanTuIdRRScTNAQkz5HWZbhkGdLp4Fb7dkzTQIhoXUNj/qZjVMTJ/P5rBpPKjh+NiJW5AndFMdNxaAh2nabGdbtIrwtFgRzomrajMhfpOp7MriFT7QagIaBR527OyxImE9s/dZEeIlpVc93HVEMiAs7h19dM25HnkOeIKtpEcKDOIf0e0mvLg6VNXXNL5u3cboesLIlljhvWlNuRqu+QWgj9hqabIQ62RyUia4w7GTtFyWo2YSWbkEsg4hiGnCpmRBWcKAdXBnTzhvo/HrAWHFWdUU1ywnZO+UJG01dCN6KdiNSWnz+fmV7BaxvgxtxbxyISewHJ5t9QDQJjh4tCNobOBdMCDA951HtcBb3TkfV/8VXizg7hD7+e/PQO1dE1hscKVEwzAKZLUTf/1xhK04V0zzb4cWB4pKRaFYIKMTdNw1TrkE3spKmrdHWm9XAB/FjJdwP1qme84QgdaLqgJebOPbQ6j4uQD5VQmpt19a0eoYZiRykvRSbrjnpFaMrWBbs3rcl4EyYb+dyde7Ry5UMl31U7rw8+t+OnebrGdCl+oqYp8a0L8vxyt+ZTLYWftK7iKzX34q2r+MlWRl17/ETxldJ0HTGj1amYZkQdND0h+nkbS5y2o4IqsXRUR1fJ+h3cqEYu7pDtFqg3X/PqWl1Ho2Q7NdmlCe7MBeiUxBdOoy9/mGatQ7OSUXcdTU/MXfyxrHUsqK0beCXmt+s35/6ynnrwOo1EIpHYb9JII5FIJBLLktY0EolEIrE8qdNIJBKJxLKkkUYikUgklkO5kSBMdz2p00gkEok9REgjjUQikUjcCKnTuIcRAe9bfUMA8rmmoWnQukYHreZCXzymVDCNRAiIql0rRou30aZrNN2Gxcdw4MT0Fd6jVW3nhWA6C+9MO9HG9nAhmEYEIKr5rFGFGE0PMhyabiPPZnE4LOaGtzxUTVvS5hGrCslyXKdEVvtQ1xAiEgOMJ8SdXbtet4PkmWlWQuvaK8va9nIzPYo7dQG8o7ywQtV3lBeg2Wrb5oUOI18yzPqcOTCm161YKSv6xYTMWVs20VEHz+mdPsOLXRBY2xrQyRs6ZUUsalgZUa17wnFHCI4mtK+VJwZH6IvF2mg1GhrFhv9ZG0MjCqr2KqGNsQHUK9A8Cq4RXANSm46iWXGM/vRrZpoA/fe6Fn4zKOplpi3wlRK9EAuz7a+7FrthvFmg3nQGMWPuaKiV0bRVNy2CmC5Bol0vH0T6v3eK3T941OJr5KatcI1VydULNv4O6p7MtBlSTXUabQyKXFh9dkyzmlP1Ta+hrr1m6wBpGkNjGl607glNd35NX1kbTOvvGtM8+InFDak79iWQBlyrzYC5XsS18TfKbdOe+HGgWs8stoi0Og9nz7RrrG2zccSPI5oJTcdR9wT1lo+vIRtF8p1AcX6E5p7QzajWC9go4VifWAixvW7MpW1/oVotkSMl8spVa8PvOIJ6a4/p8dMYJq62WC2usbrYvtvnMUp073sNEXkX8DeA1wJvad0sISJvAZ6cHgb8jTZgEyLyZub++34N+DHVly7snQz3mkgkEvc/++fl9ovADwK/dZX0J1T1jVhI7P9JRKYDhhsOmZ06jUQikdhjpiO76223gqo+rapfuUr6UFXb6QvasSK0cYvWVPW329HFNGT2S/LgTU8lEonEPnMDbkQOishTC5+fVNUnr3n0svmL/GHgHwKPAX9OVRsROc6SIbMXSZ1GIpFI7DXLjyLOquoT19opIp8Ejl5l1wdU9ZpB6FT1M8DrReS1wIdF5Ne5gZDZi6ROI5FIJPaS2zD1NLuU6ttv8fynRWSAxSG6qZDZaU0jkUgk9po7GO5VRF4+XfgWkceA1wDP3mzI7AdvpJFluIeOzk1JQ4SqgvEEHY6I4wloxOWZmaI6QeOCq+2mNnflIUDd4FozVTBTXBUx8zoRxDnLR5U4nhCHw9l1Zu7TvbdygJVlem1AynJmBjs1CxbvIJqb6JnZcBNQ7DytarSpZ+VxvR6u24UiNxPfqBDqmQmv6/VQjRAVrWoYjc1t/NSct7W+k7JAJxWMxkh/he6zFxgeOYSrBBk78gGIipm7eqhjl0vdgm2vuDwgXvE+Ik7RKHgfWTswIERzh96E+f+XGO191NbleRRUZT6YFmbX0fY9CDSCGzskiJl/TgS/4FJcopqr847MTFCnc80SIBspou1rUGIm+CoQSnPj3XQdrlayYcQFJd9uQJXQ8Uw2M5pua/aZtWVtHxv1EETmrrcbyydmQrXmOf+dD5mLcLG8GZu5p6t15nZ86pockZmZqLQ/NBLtOBeUei0nZkI+iHTOR6IXslGgOHEBMk9zoE/Ty6j7nlAKvlIzLVUz//XjYObAwxqZ1IS1Lk0vo1rPKLcj2VhmruIltuaqjc7K6oLixxGJChE0E7JhJJaOUMhlJr+omUg3XaHpeiRYfYqdiK8VqeP8/qhSb3aQYO7wLT/bme9EYuGImQNp23/m5t/aMvr5T4CfKF51Vtbi/BhixA0rqmNrhNLa5naZ3O6XuE9EfgD4SeAQ8Ksi8nlV/V7gO4H3iUiNWXL/X1X1bHvaDYfMfvA6jUQikdhnJO59r9FqLz52lfR/BPyja5xzwyGzU6eRSCQSe8keTj3dCVKnkUgkEntMityXSCQSieVJI41EIpFILEvycptIJBKJ5Zhait0npE4jkUgk9pi0pnEPo1UFF7dhfRXNMygy6Baw0kPW1/AhQNOYLgJMOxGjpYWIVtXMdblWlblQryoQh1+5QhMhMtND+LJEJxM7PwRi3eDEmVv1aK7aNYSZTkOjIq1bc7wz3cT0GBH7DPa5aawcAOJwrf5jqrVQjUhVmx4jRHOdHiJovPy8bge30kOyrK1zW5bhkHDpEq7XQx572HQn3ZzumYbBkRyJQr4NTQeykWkeVp911KuOULbuxjNLlwDD40qsQQem6ZAAwZlLbVeBdsGPWw1AgOhbrcIYmq6luRrTWbRu2KbD/6l+wdWYPX+j5AOzyS+2G9QJ5akBzVqJH9bWllWDnL1I8/KjEJXs3C5aeGK/g9SBWGS4OkATiSuFZRQVzewZKS5VlGeGxE5OzB2uat1897LWZXjETQJ+Z4Sc30a31tAyR3OPeoc0AX96G/KMsNkDEfz5AbFfQmzdap94ATl8EPViz1YT0W6ONBEVwU1qNHNomePOXCRurqG5x5/fgcEQVvvEXoHfneAmDZ1ndmie/Sb+ta9Gzl+ElR56/iKy0rN/xe3znp0SsrKk4x3a79pzdX7bnvlej3jyFG6tjx40//hy/iJa1/bsHdi0OhYZmpmfeAkKTURitPLmHmki0tj3TCYNTCaQZcQXThOHQ/zqqrn1zzPolHYtsLbZ3kVHI7JuF7odK/vOLuH8RQD8+hqs9u34TmHPdJnZuY1prXS1ixYZjCfkn/862XAI3pue6zaQgjAlEolEYnmmQtz7hNRpJBKJxB6TRhqJRCKRWJ7UaSQSiURiWdJII5FIJBLLoUC4f3qN1GkkEonEHpNGGolEIpFYnmQ9de8iZQEba+AcoorGaHbvuW9tyTMIBTKpYNLG2Wj1E6ianfo0zkRRIG3sixeReYuVAVDVQI3Wcz2FX121eBoLmg5RNX3ENK9ZocX0FSHM4zM6s1WX9r3z3jQeUU3jIYrSlhtm2hD0CpWRuFlMEMlzSwvByp6BOEE21pGqRnpdK9/2Li6sEPPW/l2t2VwD5UXTXeQjZevpEcOjHdaePm96giKjOrzCc6tlG3fCNBePfHLI83+0R74DG1+rGW951j76b4j//neQf+UEbG0wfmSd0aGczd85A02gObKOesF/9mncoQOw0jX9zYrpHMi8fQbTKHz7edzLHyGsdZDhhPzUuVkbxnPnkaOHcZ/7CtLtwOYGcnYH/fJZ5Dv+PdznvjKLoeLXVpEsQw8fmNv5nzuP9FeQ4QgPyOYGevES+cEtGAyJFy9ZUx/YgqpGzm8j/RXY3pnd++bkKVxR4LfXoCwgz3GXhjCeQKeEsiR+/ZtWH+9NbwTI+hq6vQub60i3gwxG4B3xd79M9vBxmhPP2bPmPXz7eeh20N0BTWNaCqkb01WMxnDssN3fST17bgAYjSEz3U987gXk6CHipR3imXO4Tgm9LnhBzlyAsrDYK+cvol//prXZww9B3dg9iWr1U7XvThMsva0X44ldL0bcQ0dxTYDBwNpQ1eqYmV6DSQV5bm05GKGXtu057nbwRw5BnkNdo+fOE0dj3EqPuLs7i1cz/R7qKdNl6WMPQ6+LOJCgpom5TaSRRiKRSCSW4z5zjZ7CvSYSicQeItjIZZntlvIReZeIfElEoog8cZX9j4rIroj85YW0N4vIF0TkGRH5iTbs60uy752GiDwiIv9SRJ5uK/hjbfqWiHxCRL7avm4unPP+tlJfEZHvXUi/4QonEonEfiOqS223yBeBHwR+6xr7/y4vDuf608B7gcfb7R3Xy+ROjDQa4P+uqq8F3gr8qIi8Dngf8ClVfRz4VPuZdt+7gddjFfopEZlG/L3hCicSicS+ojew3Uo2qk+r6leutk9Evh/4OvClhbRjwJqq/raqKvAR4Puvl8++dxqqelJV/237fgd4GjgOvBP4cHvYh5kX/p3Az6nqRFW/ATwDvOVmK5xIJBL7i879T11vg4Mi8tTC9t5bzV1EVoC/CvzNK3YdB04sfD7Rpr0kd3QhXEReBvwh4DPAEVU9CdaxiMjh9rDjwKcXTptWrGbJCrcN/16ATrZ6G2uQSCQS1+cGrKfOquqL1iNm1xH5JHA197sfUNVfusZpfxP4u6q6e8UM/tWm869b0jvWaYhIH/gF4C+p6vZLLEdcq2JLV1hVnwSeBFjvHL2P7BgSicQ9wW3Saajq22/itD8M/CkR+dvABhBFZIz9/j68cNzDwPPXu9gd6TREJMcK/E9U9Rfb5FMicqwdZRwDTrfpJ4BHFk6fVuwEN1FhmkA8cZJYVaZBKAqkLM2O3YnFSZjGtGgatNU2aJwGbFjQOYibxbUAWn1FZNYBTl/LAnwbr6KNr8FUH9LGw5jajov3Zl+eeXDzMhEDEiJUFToaEwdDy2uavyzMNGq0wBL4Wbku02iIQ9y8z53GBBHv5/FAmgYRZzFApjoVJ7C9gxw5RFjvkY0iLliAmcXN1YpEUO8othvk4i66uUp1cIWm52cBadSZpsON61lZ6r4nHyr+kYfx3zzD5PWPku1UqFhcDSY11DWxcLhxwL3sYTh/kfDQFn40trLvDqFx6KSyevRXkE6JnjiJvOZlsL1DPH4Id/qi2f6/8mWoF4tDMhzBzi7x0g7Z8WNw5gIcOmB1b/UyFLlpHtZWTe9z+KA1a3/Fzq8qa6+qIl7aJo4nZEcPo8MhsrEOkwo9dx4dT9rb4SwvQC9t24LopKI5ddriqWjEdbtIt4vWNXE4tPutEclztKlpzp4nO3yQcPEiUpZkRw4Tz1/Ab2wgeYYOhkiRE3cH+K0N00SEgJ6/QNzeRYoJXLxEGI3w6+sWV2N6TKjh4AHk4i7ukYdgewe3vmrtFSN6aQcu7cDGOvH0GQgWp8V1Snv2XjiD5NnsO6FVjXRKpFPONBuImEYjz2Bn12LHTKr5Vy3PL4txo7sDq/twaLoNQMrSvjfTeDeXTKcjvS6+24EQkSzHdbuE3V3b1ymh9kiva89Hf6W9z8F0OLcD5ZYto24pe9U/On0vIn8D2FXVf9B+3hGRt2KzPT8E/OT1rncnrKcE+BngaVX9Owu7fhn44fb9DwO/tJD+bhEpReTl2IL3Z9uprB0ReWt7zR9aOCeRSCTuHvZhIVxEfkBETgBvA35VRD6+xGk/AnwIWyv+Gi+2rnoRd2Kk8UeAPwd8QUQ+36b9N8CPAx8VkfcA3wLeBaCqXxKRjwJfxiyvflRVp38BfgT4WaCLVfa6FU4kEon95jaY014XVf0Y8LHrHPM3rvj8FPCGG8ln3zsNVf3XXH09AuB7rnHOB4EPXiX9hiucSCQS+07yPZVIJBKJpVAgXveoe4bUaSQSicQeItwWtfddQ+o0EolEYq+J989Q48HrNKKZA6IRDZh5ZFTUO5t3nJqnYuavU1NBgZnr8cvMbq90NR51ZvJHaM1r6xodjggLZrJTU8rp9WbumltX5VKW5jrdm+tsYkTrxlxiL5jowtRUF/AeVyy4ZI/xclNetaUkyfK5CWlbN3EOnR6/W4E41DukU+LynpkBA3pkBT11Bj+eUG881OaDmRVOXxVQJZRt2VdXIKiZDKufmeaGYtqGOjtPoiJRiQdWkUmNnwSk/cK5AHr2HLK2au0Mdv2iwA0mxBdO41Zb8Wa/R9wdoE2NL8xcM44nuEmNVjXu/A7N8y/g11YJJ0+RvfoV6KkzaNMQRyPLbzKBjXXCN75l7aIRxOHX14ijEdqabU5Nr6XIicMhvt8nDIZ4wB3cMvfeqkivR3zh9MwFvVY12pi5sc8yaBorY+v6fPqM+COH0cEQHY0gy3C9npmkVhU6nuDKDlLkUBa4fh8pcprTZ8le8Rh64SKsrBDPn5iZ6cZLO2ZqioUK0KY281gRsiOH7fmZVGZyPp6YuXgT7YfvwiVYX0XPXbD7SWtqXOQ0X38WyXI0BFynRKsKf2DLzGxb82IdT6zOtZu5d2f6HYhqZen1Zs9FHE/MNN57pCjQqiKGYPnoAOl2QebPuY5Gs+/HNM2FgNYNrlNaOzmHX1+fPz+t+bmsr7VfKQsNoIMht4U0PZVIJBKJGyFNTyUSiURieVKnkUgkEonl0NRpJBKJRGJJFLiDbkRuN6nTSCQSiT0mrWkkEolEYnlSp5FIJBKJpVDmJuL3AQ9kpzG1SRfn5u6Wp2QZvtudi3HafwgzO/CpvT7MXYxf5pZcTZ9BY7b0dWN249XczbNO9R6zhMt1HxoCTCboZDKzrbddOrPrn19rfk1grgGZ6jBU23JGNLblbe3e8d50IG1ZJJieQBfbJKrZ2A9HaAjIzq5dt98zgUdcCDAzlU5EcI25SAeFSzuICHKgh6vizDW6RHONrt7PdR6A1IoMK6RuYLWDNHGWh6ytzlxpu0k918I4hzt8CDqlfR5PZnoKqhqyzOosMnNR7rod4mBIdvggNMHcY2/v4Dc2CBcvztvFe9xa3/QVeU5z4jlct4vLMuJobG3eKdHRyPQDVUW2tQnrqzCeoNs71pSjEX5rk7g7mGkRXLc704XEkbWxVhVuc8P0Ps6ZzqeuQRxxNMblmbn2z3KkUxK2d/BT1+MxQpaRHdyi+fo3cXmG7g7wGxvE0cjO75TEwdDup/em81jtQ1nCcEjz/Elzez59VicBt71rZXFC841v4fIMytKe0eEQ6XbIDh9Ch6OZ+3N/YItw7jwa1coxfUYz07MsIt7bVnZwW5swGlv7bG6YbqLVKbmyRDWa236NUDeoxpl+KdbNZboOomk9XFGgdTMLiTAty1RT44oR0u0QLl7ElR3Lu78CO9wG0kJ4IpFIJG6E1GkkEolEYimUdvbh/iB1GolEIrGnXOF66B5n3yP3JRKJxAOH6nLbLSAi7xKRL4lIFJEnFtJfJiIjEfl8u/2PC/veLCJfEJFnROQnRK5c5H0xaaSRSCQSe8n+WU99EfhB4H+6yr6vqeobr5L+08B7gU8Dvwa8g+tEQE0jjUQikdhr9mGkoapPq+pXlj1eRI4Ba6r626qqwEeA77/eeanTSCQSib1m+U7joIg8tbC99zaV4OUi8jkR+Vci8kfbtOPAiYVjTrRpL8kDNz2lqjZUdK0Net1crr1YZEEjsZhm9v6u/Simd5BW+0CrmWg1D7R5xLq5dhyONh/x3rQYUdFQ8SLEmQbjWtdoy6MhICpXjdUBHhFFYwNVdXm52lgeiANv9VzUl9A0JsVoGmQ0xtXR4oyoXBZPAxY0F1FhpY2PIPIiTQeA1GF+7jQehwOasHA9+1Lp9g4UOa5uM4gR3dlFD23Czi56/oLZ6B8+SPPc8/h+H9lYt2NCMM1HUVgclRCtvSaVxYSI0fQT3to5jka4YYnrr6BVRXPhEmgkO3LY4kLUNa7bmcWdcBvrZiVTFjQnT+E1IllGnMaQAOKlbWvzqAsxUUAnE9z6GjoY4Vb7xDNnTVPgPf7IYbsXTTOPwQJoU6OTClcUSFkSz5yz9IuXiKMR2eFDxJ1dZKqnUHsewqVL+I0NpCzAOZqTL6Bnzs3qMtUuoBHX7ULdgAjhhVP4Y0fJHnsEdnYJl7Zx3Y7F5uh1iS+csXgY3S4aAs3pM/h+H7LMdC/Tuk7jYTS1aU2mz2xRgPeEky/YfXAO3R3M4l0AxKqa6T6m3ztttTJkGW4al6S253v63dKmbmPKRMBfVhaAOBnDZIxfXbV2jvoiLclN0/4OLMlZVX3iWjtF5JPA0avs+oCq/tI1TjsJPKqq50TkzcD/KiKvpw0TdGVpr1fAB67TSCQSiX3nNuk0VPXtN3HOBJi0739HRL4GvBobWTy8cOjDwPPXu16ankokEom9Zh/WNK6FiBwSEd++fwXwOPB1VT0J7IjIW1urqR8CrjVamZE6jUQikdhT2inxZbZbQER+QEROAG8DflVEPt7u+i7g90Tkd4GfB/6Cqp5v9/0I8CHgGeBrXMdyCtL0VCKRSOwtivnJ2utsVD8GfOwq6b8A/MI1znkKeMON5JM6jUQikdhrkhuRRCKRSCyF6txr9n3Ag9dpiJipYlzCH8zV9ms0d+Sirakgl5ulwswsV1pTQb3eXOXU9fmVZnny4iWnK12jz2ldt0+Pu1ZWrXmxRp2Vb5aPRuKVdbmCbGOd5vQZXJ6bC/VFM9nWTbq5PFfUi0Us294BVdyBPqHTukFvQAo7VjuZna9mWitRkcrKKU20L1xszXiLHLnSFX2e4SY1sa7NlXaMsLOL63YJu7t474m7U5PbYK7YRYiT1lRzMkFas9mwO2jNW2vcWt/MZ597Hld2zLS1yAlnz83MRmMzxPV6SLdEhyPCruXrigLpdGamvpfdU43zZ0ujPS9lCa3rbj13AddfwReFmYUPhoTdwdwse+EZ0MnEzFvPnSN79GHimXPm7rzbnZmBa1XNzIwly81l/HBoJrOdcmb+qiHH9Xo0587NngmtKit/p8QfOUw8c86ekdYcN+zsIKMxXsRMb8dYGxQF/sAB4u4AHY3w6+tmHt2Y+e7Upbw2tZnOAmEwRJzg1tcIFy69qK7i/cydO97PXMovupeftrG5Wi/M1LbsEYdDe+Zbs3bX7doaggguz6z9gbAz94U+dQ9/W0hebhOJRCKxLJpGGolEIpFYjhSEKZFIJBLLksK9JhKJRGJZlKusV97DpE4jkUgk9hK9v4IwpU4jkUgk9pjrWlDeQ6ROI5FIJPaa+2ikIXofreovg4jsAEsHKrlLOAicvdOFuAHutfJCKvN+cK+VF+A1qrp6KxcQkd/A6r4MZ1X1HbeS317zIHYaT72Uv/q7kXutzPdaeSGVeT+418oL92aZ95rk5TaRSCQSS5M6jUQikUgszYPYaTx5pwtwE9xrZb7XygupzPvBvVZeuDfLvKc8cGsaiUQikbh5HsSRRiKRSCRuktRpJBKJRGJp7stOQ0S2ROQTIvLV9nXzGse9Q0S+IiLPiMj7FtLfJSJfEpEoIntmbnet/Bf2i4j8RLv/90TkTcuee5eW+R+KyGkR+eLdXl4ReURE/qWIPN0+Cz92D5S5IyKfFZHfbcv8N+/2Mi/s9yLyORH5lbu9vCLyrIh8QUQ+LyJP7Ud57ypU9b7bgL8NvK99/z7gb13lGI8FUn8FUAC/C7yu3fda4DXAbwJP7FEZr5n/wjHfhwV6F+CtwGeWPfduK3O777uANwFf3Kfn4Fba+Bjwpvb9KvD7d3sbt5/77fsc+Azw1ru5zAv7/2vgnwK/creXF3gWOLgfz/DduN2XIw3gncCH2/cfBr7/Kse8BXhGVb+uqhXwc+15qOrTqrrXqvFr5r/AO4GPqPFpYENEji157t1WZlT1t4Dz+1DOWy6vqp5U1X/blnsHeBo4fpeXWVV1tz0mb7f9sHS5pedCRB4G/jjwoX0o6y2X90Hnfu00jqjqSYD29fBVjjkOfHvh8wn250fhRvK/1jF3quy3UuY7wW0pr4i8DPhD2D/3veaWytxO83weOA18QlXv+jIDfw/4K8B+OWi61fIq8M9F5HdE5L17Vsq7lHvWYaGIfBI4epVdH1j2EldJ20/742Xyv9Yxd6rst1LmO8Etl1dE+sAvAH9JVbdvY9muxS2VWVUD8EYR2QA+JiJvUNW9XkO66TKLyJ8ATqvq74jId9/ugl2DW30u/oiqPi8ih4FPiMi/a0fRDwT3bKehqm+/1j4ROTWdYmiHlKevctgJ4JGFzw8Dz9/mYr4Uy+R/rWOKJc7dC26lzHeCWyqviORYh/FPVPUX97CcS5XnRo5R1Ysi8pvAO4C97jRupcx/CviTIvJ9QAdYE5F/rKp/9i4tL6o6fT0tIh/DprsemE7jji+q7MUG/A9cvhD+t69yTAZ8HXg588Ww119xzG+ydwvhy+T/x7l8Me6zy557t5V5Yf/L2L+F8FtpYwE+Avy9fX52b6XMh4CN9n0X+N+AP3E3l/mKY76b/VkIv5U2XgFWF97/H8A79vMZudPbHS/AHj0UB4BPAV9tX7fa9IeAX1s47vswq5ivAR9YSP8B7J/GBDgFfHyPyvmi/IG/APyF9r0A/992/xdY6MCuVfZ9aNtbKfM/A04Cddu+77lbywt8JzYd8XvA59vt++7mNga+A/hcW+YvAv/tvfBcLFzju9mHTuMW2/gVWCfzu8CX9vO7d7dsyY1IIpFIJJbmfrWeSiQSicQekDqNRCKRSCxN6jQSiUQisTSp00gkEonE0qROI5FIJBJLkzqNRCKRSCxN6jQSiUQisTSp00g8sIjI/2sxToaIfFBE/uKdLFMicbeTxH2JB5bWe+0vquqbRMRhHgTeoqrn7mzJEom7l3vWYWEicauo6rMick5E/hBwBPhc6jASiZcmdRqJB50PAX8ec7P/D+9sURKJu580PZV4oBGRAnNIlwOPq8WjSCQS1yCNNBIPNKpaici/BC6mDiORuD6p00g80LQL4G8F3nWny5JI3Askk9vEA4uIvA54BviUqn71TpcnkbgXSGsaiUQikViaNNJIJBKJxNKkTiORSCQSS5M6jUQikUgsTeo0EolEIrE0qdNIJBKJxNL8/wFjj1+XAtxbMwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "data.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "24f430d9", - "metadata": {}, - "source": [ - "Nice! We can now go off and do things with this dataset." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "gio", - "language": "python", - "name": "gio" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/userguide_src/_Gridding_a_bunch_of_xy_points.ipynb b/docs/userguide_src/_Gridding_a_bunch_of_xy_points.ipynb deleted file mode 100644 index 9c6ad27..0000000 --- a/docs/userguide_src/_Gridding_a_bunch_of_xy_points.ipynb +++ /dev/null @@ -1,238 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "id": "79658288", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('mybucket',)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s = \"https://mybucket.s3.amazonaws.com/\"\n", - "import re\n", - "re.search(r'//(.+?)\\.s3', s).groups(0)" - ] - }, - { - "cell_type": "markdown", - "id": "28f9a8ee", - "metadata": {}, - "source": [ - "# Gridding a bunch of x, y points\n", - "\n", - "Often in earth science we'd like to represent some spatial data with a sampled, discretized, regular grid. You might call this a **raster**, a **surface**, a **horizon**, or just a **grid**. Or something else. Here's a very small one:\n", - "\n", - " 3 4 7 9\n", - " 2 3 7 8\n", - " 1 3 8\n", - " 0 2 5 8\n", - " \n", - "You can think of this as a map of heights (or depths, or temperatures, or anything). It has one missing value; we'll get to that.\n", - "\n", - "Notice that there are no coordinates as such, just implicit 'row' and 'column' coordinates. So the value 4 is at (0, 1) for example, and the 5 is at (3, 2). That might seem a bit backwards: we'd normally give the 'x' coordinate first, then the 'y', but this is how matrices are indexed. People conventionally call these row, column indices i and j respectively. They will look like this:\n", - "\n", - " j 0 1 2 3\n", - "\n", - " i 0 3 4 7 9\n", - " 1 2 3 7 8\n", - " 2 1 3 NaN 8\n", - " 3 0 2 5 8\n", - "\n", - "We can represent a grid like this as a `numpy` array. Notice that we don't need to have every sample — one or more might be missing. In their place, we'd have NaNs, not-a-numbers, which are null values that won't be plotted and will generally be ignored.\n", - "\n", - "## Adding real-world coordinates\n", - "\n", - "Very often, we'd like to refer to positions on the grid with real-world coordinates of some kind. For example, if this grid is a map, I might have UTMx and UTMy coordinates. Or, if this it's a seismic horizon, I might have inline and crossline numbers as well as UTMx and UTMy. In that case, `xarray` is useful because I can label the rows and columns with these real-world coordinates:\n", - "\n", - " 100 101 102 103\n", - " \n", - " 306 3 4 7 9\n", - " 304 2 3 7 8\n", - " 302 1 3 NaN 8\n", - " 300 0 2 5 8\n", - "\n", - "Notice that the numbering of the vertical axis is opposite what we used for j, they don't start at 0 or 1, and they increment in steps of 2. That all just depends on how the real-world coordinates were assigned. They are arbitrary.\n", - "\n", - "The data is the same, but now I can index into this grid using my real-world coordinates, which is much more convenient than having to know the (i, j) indices. But that's far from being the only useful thing about `xarray`. For example, imagine a text representation of this array. It might look something like this:\n", - "\n", - " iline,xline,z\n", - " 306,100,3\n", - " 306,101,4\n", - " 306,102,7\n", - " 306,103,9\n", - " 304,100,2\n", - " 304,101,3\n", - " 304,102,7\n", - " 304,103,8\n", - " 302,100,1\n", - " 302,101,3\n", - " 302,103,8\n", - " 300,100,0\n", - " 300,101,2\n", - " 300,102,5\n", - " 300,103,8\n", - " \n", - "Notice that one position, (302, 102) is missing: that's the NaN. This is a bit of a headache to load into NumPy, because I can't just read the data elements and reshape — plus I have to throw away the coordinates. But with `xarray`, I can do this:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "9cb5ccab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "import xarray as xr\n", - "\n", - "df = pd.read_csv('data.csv')\n", - "df = df.set_index(['iline', 'xline'])\n", - "da = xr.DataArray.from_series(df.z)\n", - "da.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "c876da21", - "metadata": {}, - "source": [ - "## Adding (x, y) coordinates\n", - "\n", - "Often I have UTMx and UTMy coordinates for the map — maybe just for one or more of its corner points, or maybe for every cell. If I have 3 corners, I can compute the (x, y) location of every cell, assuming the cell spacing is regular. If I have fewer than 3 corners, I will need to know the cell spacing in both directions, and the angle of one of the axes with respect to north.\n", - "\n", - "I can attach (x, y) coordinates to the `xarray`, but there's a catch. Unless the grid is exactly aligned with north, I will need an (x, y) pair at _every_ cell, because the rows won't line up (i.e. the x-coordinates of the second row cells will be different from the x-coordinates of the first row cells). No problem, [`xarray` can handle this](https://xarray.pydata.org/en/stable/examples/multidimensional-coords.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "e351c22c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5000. 6000.]\n", - "[4992.27466667 6023.77633333]\n", - "[4984.54933333 6047.55266667]\n", - "[4976.824 6071.329]\n", - "[5047.55266667 6015.451 ]\n", - "[5039.82733333 6039.22733333]\n", - "[5032.102 6063.00366667]\n", - "[5024.37666667 6086.78 ]\n", - "[5095.10533333 6030.902 ]\n", - "[5087.38 6054.67833333]\n", - "[5079.65466667 6078.45466667]\n", - "[5071.92933333 6102.231 ]\n", - "[5142.658 6046.353]\n", - "[5134.93266667 6070.12933333]\n", - "[5127.20733333 6093.90566667]\n", - "[5119.482 6117.682]\n" - ] - } - ], - "source": [ - "from bruges.transform import CoordTransform\n", - "\n", - "corner_ix = [[0, 0], [0, 3], [3, 0]]\n", - "corner_xy = [[5000, 6000],\n", - " [5000-23.176, 6000+71.329],\n", - " [5000+142.658, 6000+46.353]]\n", - "transform = CoordTransform(corner_ix, corner_xy)\n", - "\n", - "for i in range(4):\n", - " for j in range(4):\n", - " print(transform([i, j]))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "9f31f65d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1],\n", - " [0, 1],\n", - " [0, 2],\n", - " [0, 3]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr" - ] - }, - { - "cell_type": "markdown", - "id": "7deecb05", - "metadata": {}, - "source": [ - "## Constructing the grid from data\n", - "\n", - "We often have a bunch of arbitary points with (x, y) coordinates (e.g. UTMx and UTMy, or longitude and latitude) and interpolate them to end up with a" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39", - "language": "python", - "name": "py39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/userguide_src/_Logo.ipynb b/docs/userguide_src/_Logo.ipynb deleted file mode 100644 index 070c79a..0000000 --- a/docs/userguide_src/_Logo.ipynb +++ /dev/null @@ -1,65 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Gio logo\n", - "\n", - "Seems like we should be able to make something with mpl." - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from gio import logo\n", - "\n", - "_ = logo.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39", - "language": "python", - "name": "py39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/userguide_src/_Read_IESX_horizons.ipynb b/docs/userguide_src/_Read_IESX_horizons.ipynb deleted file mode 100644 index 62f819c..0000000 --- a/docs/userguide_src/_Read_IESX_horizons.ipynb +++ /dev/null @@ -1,353 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ce4d3082", - "metadata": {}, - "source": [ - "# Read IESX horizons" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "eb4be46e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometryhorizonlinenamedatasetminmaxmeanpoints
0LINESTRING Z (419840.089 4668132.100 2756.250,...Fault to seafloor276-109normTOv2732.923549.573085.83267968
1LINESTRING Z (412513.896 4679451.120 2570.420,...Fault to seafloor272-100normTOv2732.923549.573085.83267968
2LINESTRING Z (417373.758 4666148.690 3549.570,...Fault to seafloor272-100normTOv2732.923549.573085.83267968
\n", - "
" - ], - "text/plain": [ - " geometry horizon \\\n", - "0 LINESTRING Z (419840.089 4668132.100 2756.250,... Fault to seafloor \n", - "1 LINESTRING Z (412513.896 4679451.120 2570.420,... Fault to seafloor \n", - "2 LINESTRING Z (417373.758 4666148.690 3549.570,... Fault to seafloor \n", - "\n", - " linename dataset min max mean points \n", - "0 276-109 normTOv 2732.92 3549.57 3085.832679 68 \n", - "1 272-100 normTOv 2732.92 3549.57 3085.832679 68 \n", - "2 272-100 normTOv 2732.92 3549.57 3085.832679 68 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import gio\n", - "\n", - "df = gio.read_iesx_from_petrel('../data/Petrel/IESX2D_horizon_extract.txt')\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "702ea3d3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.iloc[0]['geometry']" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "8b4c3a37", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[(412513.896, 4679451.12, 2570.42),\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.plot(x, z)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ac78de0", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py39", - "language": "python", - "name": "py39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/userguide_src/foo.dat b/docs/userguide_src/foo.dat deleted file mode 100644 index 516a841..0000000 --- a/docs/userguide_src/foo.dat +++ /dev/null @@ -1,46 +0,0 @@ -!Generated by gio -@None, GRID, 6 -12, -999.25, , 3, 1 -12, 20, 0, 19, 0, 11 -0.0, 0.0, 0.0 -@ - 0.000 0.150 0.083 0.124 0.253 0.200 - 0.000 -0.007 0.178 0.074 -0.156 -0.119 - -0.247 -0.096 -0.155 -0.117 0.050 0.124 - -0.028 -0.063 0.071 0.026 -0.067 0.130 - -0.444 -0.308 -0.363 -0.338 -0.141 0.066 - -0.025 -0.123 -0.104 -0.069 0.009 0.346 - -0.432 -0.328 -0.403 -0.407 -0.232 -0.013 - -0.076 -0.211 -0.243 -0.102 0.090 0.398 - -0.213 -0.148 -0.271 -0.317 -0.208 -0.101 - -0.177 -0.284 -0.239 -0.011 0.165 0.272 - 0.051 0.080 -0.102 -0.189 -0.134 -0.140 - -0.232 -0.268 -0.096 0.153 0.214 0.088 - 0.259 0.260 0.024 -0.096 -0.081 -0.176 - -0.264 -0.211 0.100 0.347 0.279 -0.065 - 0.309 0.305 0.023 -0.124 -0.138 -0.276 - -0.311 -0.148 0.280 0.528 0.399 -0.088 - 0.208 0.223 -0.077 -0.234 -0.241 -0.327 - -0.262 -0.034 0.359 0.578 0.492 0.042 - 0.072 0.111 -0.174 -0.318 -0.288 -0.274 - -0.119 0.099 0.334 0.509 0.527 0.231 - 0.000 0.051 -0.205 -0.321 -0.253 -0.185 - 0.000 0.177 0.282 0.445 0.550 0.375 - -0.027 0.011 -0.219 -0.294 -0.194 -0.096 - 0.100 0.234 0.224 0.388 0.566 0.487 - -0.042 -0.091 -0.301 -0.308 -0.175 -0.067 - 0.143 0.249 0.132 0.304 0.537 0.535 - -0.139 -0.318 -0.508 -0.431 -0.277 -0.197 - 0.010 0.126 0.015 0.230 0.478 0.466 - -0.303 -0.584 -0.752 -0.607 -0.448 -0.422 - -0.247 -0.108 -0.123 0.158 0.392 0.299 - -0.410 -0.736 -0.881 -0.701 -0.542 -0.567 - -0.447 -0.320 -0.271 0.047 0.270 0.106 - -0.469 -0.794 -0.886 -0.694 -0.545 -0.587 - -0.517 -0.421 -0.337 -0.026 0.165 -0.058 - -0.505 -0.752 -0.723 -0.581 -0.476 -0.445 - -0.396 -0.338 -0.228 0.008 0.131 -0.097 - -0.407 -0.555 -0.446 -0.411 -0.371 -0.228 - -0.180 -0.167 -0.060 0.056 0.124 -0.003 - -0.192 -0.296 -0.212 -0.276 -0.280 -0.073 - -0.027 -0.048 0.038 0.063 0.116 0.138 diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000..1bffb08 --- /dev/null +++ b/genindex.html @@ -0,0 +1,617 @@ + + + + + + + Index - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
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Index

+
A | C | D | F | G | I | M | N | P | R | S | T | U | X | Y
+
+
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A

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+

gio package#

+
+

Submodules#

+
+
+

gio.closure module#

+

Closed-contour-related functions.

+
+
+gio.closure.closure_height(arr: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], c: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → float#
+

Check that the max height is greater than the level.

+

This is rather slow, so do it on the fewest possible candidates.

+
+ +
+
+gio.closure.find_closures(arr: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], interval: float = 1.0, min_area: float = 0.0, min_height: float = 0.0) → List[Polygon]#
+

Find the largest closed contour in a grid.

+
+
Parameters:
+
    +

  • arr (ndarray) – The grid to search.

    • +

  • interval (int) – The interval between contours to consider, default 1 unit.

    • +

  • min_area (int) – The minimum area of a closure to consider, in grid squares.

    • +

  • min_closure (int) – The minimum closure of a closure to consider, in z units.

    • +
+
+
Returns:
+

The list of polygons that meet the criteria.

+
+
Return type:
+

list of shapely.geometry.Polygon

+
+
+
+ +
+
+gio.closure.is_closed(c: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → bool#
+

Determine if the last point is the same as the first.

+
+ +
+
+gio.closure.is_high(arr: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], p: Polygon, step: float = 1.0) → bool#
+

Decide if polygon p encloses a high region on grid z (returns True), or a +low region (False).

+

This is much faster than closure_height and can help reduce the number of +times we run that.

+

Sometimes the erosion buffer can result in more than 1 polygon, which tries +to get exterior on a MultiPolygon, which fails. Could maybe do +Polygon.boundary.xy instead, I think that works for MultiPolygon?

+
+ +
+
+

gio.esri module#

+

Read E00 format DEM (digital elevation model) files.

+

THIS IS A WORK IN PROGRESS. IT DOES NOT WORK YET.

+
+
copyright:
+

2022 Agile Geoscience

+
+
license:
+

Apache 2.0

+
+
+
+
+gio.esri.read_e00_text(fname)#
+

Parse a bunch of E00 formatted text and emit a NumPy array.

+
+ +
+
+

gio.iesx module#

+
+
+gio.iesx.check_dialect(fname)#
+

Decide if IESX file is from Petrel or OpendTect.

+
+ +
+
+gio.iesx.read_iesx_from_petrel(filename, threed=False)#
+

Read a Petrel IESX file and create a GeoPandas DataFrame.

+
+ +
+ +
+

gio.opendtect module#

+

I/O for OpendTect horizon formats.

+
+
+gio.opendtect.df_to_xarray(df, attrs=None, origin=(0, 0), step=(1, 1))#
+

Convert a DataFrame to a DataArray.

+
+ +
+
+gio.opendtect.get_meta(fname, comment='#')#
+

Returns a string containing only the lines starting with +the specified comment character. Does not read entire file.

+
+ +
+
+gio.opendtect.get_names_from_header(header)#
+

Get names from header text.

+
+ +
+
+gio.opendtect.read_odt(fname, names=None, usecols=None, na_values=None, origin=(0, 0), step=(1, 1), attrs=None)#
+

Read an OdT horizon file. (Native format, not IESX.)

+
+
Parameters:
+
    +

  • names (list) –

    The names of the columns in the file. If there’s +a header, this is not required. If there is no header, the +function will attempt to interpret the columns:

    +
      +
    • +
      If the first two columns are integers, they are interpreted

      as inline and crossline numbers.

      +
      +
      +
      • +
    • +
      If the first two columns are floats, and there is only one

      other column, then they are interpreted as (x, y) locations. +Inline and crossline numbers will be generated, using +origin as starting points.

      +
      +
      +
      • +
    • +
      In any other case, the function will throw an error;

      provide names to tell it what the columns are. Use ‘inline’ +or ‘iline’ for inlines, ‘xline’ or ‘crossline’ for crosslines, +‘x’ or ‘cdp_x’ for x locations, and ‘y’ or ‘cdp_y’ for y +locations. The names of your attributes are up to you.

      +
      +
      +
      • +
    +

    If there are 5 columns but you only give 4 names, then only +the first 4 columns will be loaded.

    +

    • +

  • usecols (list of ints) – The indices of the columns that correspond +to the names you’re providing. If you’re loading all the columns, +or the N names correspond to the first N columns, then you don’t +need to provide this argument.

    • +

  • na_values (list of str) – The strings that correspond to missing data. +By default, this is [‘1e30’], the deault NULL value in OpendTect. +Any values you provide will replace this default and will be +used to replace missing data with NaNs.

    • +

  • origin (tuple of ints) – If you only have (x, y) locations, then +gio will try to construct a grid from them. It probably doesn’t +always work. It will assign inline and crossline numbers, which +will just be NumPy-like indices by default. If you provide +an origin, it will start numbering there; if you provide a step, +it will use that to increment the indices.

    • +

  • step (tuple of ints) – The step size of the grid (see origin, above).

    • +

  • attrs (dict) – The attributes to add to the Dataset; the filename +will be added as ‘odt_filename’.

    • +
+
+
Returns:
+

+
Each data column will be a data variable. Inline and

crossline — and cdp_x and cdp_y, if present — will be coordinates.

+
+
+

+
+
Return type:
+

xarray.Dataset

+
+
+
+ +
+
+gio.opendtect.read_odt_as_df(fname, names=None, usecols=None, na_values=None, **kwargs)#
+

Read an OdT file as a Pandas DataFrame.

+
+
Parameters:
+
    +

  • fname (str) – Path to file.

    • +

  • names (list) – List of column names.

    • +

  • usecols (list) – List of column indices to use.

    • +

  • na_values (list) – List of values to treat as NA.

    • +

  • pandas.read_csv. (kwargs are passed to) –

    • +
+
+
Returns:
+

DataFrame containing the data.

+
+
Return type:
+

df (pandas.DataFrame)

+
+
+
+ +
+
+gio.opendtect.regularize_columns(df, mapping=None, snake_case=True)#
+

Adjust the column names to use segyio / SEGY-SAK standard names. +And optionally transform to snake case.

+
+ +
+
+gio.opendtect.sense_types(data)#
+

Guess the type of some numbers.

+
+ +
+
+gio.opendtect.to_odt(fname, dx, multi=False, twt=None, force_twt=True, name=None, dropna=True, header='multiline', **kwargs)#
+

Write an OdT horizon file. (Native format, not IESX.)

+

There is an unhealthy amount of magic in this function.

+
+
Parameters:
+
    +

  • fname (str) – The name of the file to write.

    • +

  • dx (xarray.Dataset) – The data to write.

    • +

  • multi (bool) – Whether you want a multi-horizon export.

    • +

  • twt (str) – The name of the variable containing the two-way time, if +not ‘twt’.

    • +

  • force_twt (bool) – Whether to force the first variable to be two-way +time. If there’s more than one variable, this throws a warning.

    • +

  • dropna (bool) – If True, drop any rows with NaNs from the data. This +is usual in OpendTect, but it may make the file harder to read +by other software.

    • +

  • header (str) – The type of header to use. Either ‘multiline’ or +‘singleline’ is recommended, but ‘none’ will omit it entirely.

    • +

  • **kwargs – Additional arguments to pass to pandas.DataFrame.to_csv.

    • +
+
+
Returns:
+

None

+
+
+
+ +
+
+

gio.random module#

+

Random surfaces.

+

The code in this module was substantially adapted from Pierre Vigier’s code at +https://github.com/pvigier/perlin-numpy

+
+
+gio.random.fractal_noise(shape, res, rng=None, octaves=1, persistence=0.5, lacunarity=2, tileable=(False, False), interpolant=<function interpolant>)#
+

Generate a 2D numpy array of fractal noise.

+

By Pierre Vigier https://github.com/pvigier/perlin-numpy +Licence: MIT

+
+
Parameters:
+
    +

  • shape – The shape of the generated array (tuple of two ints). +This must be a multiple of lacunarity**(octaves-1)*res.

    • +

  • res – The number of periods of noise to generate along each +axis (tuple of two ints). Note shape must be a multiple of +(lacunarity**(octaves-1)*res).

    • +

  • octaves – The number of octaves in the noise. Defaults to 1.

    • +

  • persistence – The scaling factor between two octaves.

    • +

  • lacunarity – The frequency factor between two octaves.

    • +

  • tileable – If the noise should be tileable along each axis +(tuple of two bools). Defaults to (False, False).

    • +

  • interpolant – The, interpolation function, defaults to +t*t*t*(t*(t*6 - 15) + 10).

    • +
+
+
Returns:
+

A numpy array of fractal noise and of shape shape generated by +combining several octaves of perlin noise.

+
+
Raises:
+

ValueError – If shape is not a multiple of lacunarity**(octaves-1)*res.

+
+
+
+ +
+
+gio.random.generate_random_surface(size, res=2, octaves=2, lacunarity=2, persistence=0.5, random_seed=None, name=None, coords='i,j')#
+

Make a square grid of at least the given size (length of side), with +the given parameters. Size must be a multiple of lacunarity**(octaves-1)*res +so the smallest possible size greater than the one provided is used.

+
+
Parameters:
+
    +

  • size (int or tuple) – minimum length of side of the grid.

    • +

  • res (int or tuple) – number of periods of noise to generate along +each axis.

    • +

  • octaves (int) – number of octaves of noise (bandwidth).

    • +

  • lacunarity (float) – lacunarity of the noise (frequency factor between +octaves). Default: 2.

    • +

  • persistence (float) – persistence of the noise (amplitude factor between +octaves). Default: 0.5.

    • +

  • random_seed (int) – random seed.

    • +

  • name (str) – name of the resulting array, optional. Default: None.

    • +

  • coords (str) – names of spatial coordinates, separated by commas. +Default: i and j.

    • +
+
+
Returns:
+

the grid.

+
+
Return type:
+

xarray.DataArray

+
+
+
+ +
+
+gio.random.interpolant(t)#
+

By Pierre Vigier https://github.com/pvigier/perlin-numpy +Licence: MIT

+
+ +
+
+gio.random.perlin_noise(shape, res, rng=None, tileable=(False, False), interpolant=<function interpolant>)#
+

Generate a 2D numpy array of perlin noise.

+

By Pierre Vigier https://github.com/pvigier/perlin-numpy +Licence: MIT

+
+
Parameters:
+
    +

  • shape – The shape of the generated array (tuple of two ints). +This must be a multple of res.

    • +

  • res – The number of periods of noise to generate along each +axis (tuple of two ints). Note shape must be a multiple of +res.

    • +

  • tileable – If the noise should be tileable along each axis +(tuple of two bools). Defaults to (False, False).

    • +

  • interpolant – The interpolation function, defaults to +t*t*t*(t*(t*6 - 15) + 10).

    • +
+
+
Returns:
+

A numpy array of shape shape with the generated noise.

+
+
Raises:
+

ValueError – If shape is not a multiple of res.

+
+
+
+ +
+
+

gio.surfer module#

+

Read 2D ASCII/binary Surfer grid files.

+
+
copyright:
+

2022 Agile Geoscience (this adaptation)

+
+
license:
+

Apache 2.0

+
+
+

Adapted from Seequent’s steno3d_surfer: https://pypi.org/project/steno3d_surfer

+
+
copyright:
+

2018 Seequent

+
+
license:
+

MIT

+
+
+

MIT License +Copyright (c) 2018 Seequent +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the “Software”), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE.

+
+
+gio.surfer.read_grd(fname)#
+
+ +
+
+gio.surfer.read_surfer(fname)#
+

Reader for Surfer .grd files, supporting both +Surfer 6 binary & ASCII files, and Surfer 7 binary files. +Only (x, y)-orthogonal grids are supported.

+
+
Parameters:
+

fname (str) – The filename of the Surfer .grd file.

+
+
Returns:
+

The grid data.

+
+
Return type:
+

xarray.DataArray

+
+
+
+ +
+
+

gio.unmap module#

+
+
+gio.unmap.unmap_to_dataarray(arr, cmap, extent=None, dims=None, **kwargs)#
+

Unmap data, via a colourmap, from an image. Reverse false-colouring.

+
+
Parameters:
+
    +

  • arr – NumPy array of image data.

    • +

  • cmap – Either another array, or pixel extent of colourbar in image, +or name of mpl colourbar or if None, just use lightness (say) +If get pixels and they’re a few wide, then average them along +short axis? Again, depends on scatter.

    • +

  • extent – The extent of the image, as a tuple (left, right, bottom, top), +giving the real-world coordinates.

    • +

  • dims (tuple) – The dimensions of the image, as a tuple e.g. (‘x’, ‘y’) +or (‘Lon’, ‘Lat’)).

    • +

  • **kwargs – Passed to unmap.unmap.

    • +
+
+
Returns:
+

An xarray.DataArray of the same shape as the input image, with the +colourmap removed.

+
+
+
+ +
+
+

gio.xarray module#

+

xarray convenience class and accessor.

+
+
+class gio.xarray.GridInfo(xmin: float, ymin: float, xmax: float, ymax: float, data: numpy.ndarray, dims: tuple = ('i', 'j'), source: str = '', fname: str = '')#
+

Bases: object

+
+
+data: ndarray#
+
+ +
+
+dims: tuple = ('i', 'j')#
+
+ +
+
+fname: str = ''#
+
+ +
+
+source: str = ''#
+
+ +
+
+to_xarray()#
+

Convert to xarray.DataArray. Only (x, y)-orthogonal grids are supported.

+
+ +
+
+xmax: float#
+
+ +
+
+xmin: float#
+
+ +
+
+ymax: float#
+
+ +
+
+ymin: float#
+
+ +
+ +
+
+class gio.xarray.SurfaceAccessor(xarray_obj)#
+

Bases: object

+
+
+to_zmap(fname, coords=None, comment_attr='comment', add_comment='File created by gio.to_zmap xarray accessory', nodes_per_line=6, field_width=12, precision=3, null_value=-999.25)#
+

Write a DataArray to a ZMAP file.

+
+
Parameters:
+
    +

  • fname (str) – path to ZMAP file.

    • +

  • coords (list or None) – list of coordinate names to use for the X and +Y axes. If None, use the first two dimensions of the DataArray.

    • +

  • comment_attr (str or None) – name of the DataArray attribute to use +as a comment in the file. If None, no comment is added.

    • +

  • add_comment (str or list or None) – comment to add to the end of any +comments found in the DataArray. If None, no comment is added.

    • +

  • nodes_per_line (int) – number of values to write per line.

    • +

  • field_width (int) – character width of each value field.

    • +

  • precision (int) – number of decimal places to write.

    • +

  • null_value (float) – value to use for NaN values.

    • +
+
+
Returns:
+

None

+
+
+
+ +
+ +
+
+

gio.xy_to_grid module#

+

Special case: binning data with only (x, y) coords.

+
+
copyright:
+

2022 Agile Geoscience

+
+
license:
+

Apache 2.0

+
+
+
+
+gio.xy_to_grid.get_intervals(x)#
+

From an unsorted and possibly sparse collection +of 1D coordinates, compute the number of bins and +the distance between bin centres.

+

Default is nearest-metre precision, which should be +fine for most seismic surveys.

+
+ +
+
+gio.xy_to_grid.nearest_neighbours(x, y, precision=5)#
+

Vector between 2 closest points. If there’s a tie (there +likely will be), then we get the first point with a minimally +nearest neighbour (and its first such neighbour).

+

The precision cut-off is needed to deal with floating-point +imprecision.

+
+ +
+
+gio.xy_to_grid.parabolic(f, x)#
+

Quadratic interpolation for estimating the true position of an +inter-sample maximum when nearby samples are known.

+

f is a vector and x is an index for that vector.

+

Returns (vx, vy), the coordinates of the vertex of a parabola that goes +through point x and its two neighbors.

+

Example: +Defining a vector f with a local maximum at index 3 (= 6), find local +maximum if points 2, 3, and 4 actually defined a parabola.

+
>>> import numpy as np
+>>> f = [2, 3, 1, 6, 4, 2, 3, 1]
+>>> parabolic(f, np.argmax(f))
+(3.2142857142857144, 6.1607142857142856)
+
+
+
+ +
+
+gio.xy_to_grid.rectify(X, vector)#
+

Rotate the geometry X, which should have columns +(x, y, data). Data can be anything, just ones if +you only have points to transform.

+
+ +
+
+gio.xy_to_grid.xy_to_grid(x, y, data, compute_array=False)#
+

Bin a bunch of unsorted (x, y) datapoints into a regular grid.

+
+
Returns:
+

    +

  • arr (ndarray): The binned data.

    • +

  • (dx, dy): The spacing between bins in the x and y directions.

    • +
  • +
    (addx, addy): The destination of each data point into the grid;

    n.b. this is given in NumPy (row, col) format.

    +
    +
    +
    • +
+

+
+
Return type:
+

tuple

+
+
+
+ +
+
+

gio.zmap module#

+

I/O for ZMAP grid formats.

+
+
+gio.zmap.array_to_zmap(fname, arr, extent=None, comment='File created by gio.array_to_zmap', nodes_per_line=6, field_width=12, precision=3, null_value=-999.25, name='Z')#
+

Write a NumPy array as a ZMAP dat file.

+
+
Parameters:
+
    +

  • fname (str) – path to output file.

    • +

  • arr (np.ndarray) – 2D array to write.

    • +

  • extent (None or list) – if not None, should be a list of 4 numbers +representing the extent of the array in real-world coordinates, in +the order [min_x, max_x, min_y, max_y].

    • +

  • comment (str or list) – comment to add to the file.

    • +

  • nodes_per_line (int) – number of values to write per line.

    • +

  • field_width (int) – character width of each value field.

    • +

  • precision (int) – number of decimal places to write.

    • +

  • null_value (float) – value to use for NaN values.

    • +

  • name (str) – name of the variable in the file.

    • +
+
+
Returns:
+

None

+
+
+
+ +
+
+gio.zmap.dataarray_to_zmap(fname, da, coords=None, comment_attr='comments', add_comment='File created by gio.dataarray_to_zmap', nodes_per_line=6, field_width=12, precision=3, null_value=-999.25)#
+

Write an xarray.DataArray as a ZMAP dat file. For now we assume that dx is +a DataArray.

+
+
Parameters:
+
    +

  • fname (str) – path to output file.

    • +

  • da (xarray.DataArray) – 2D array to write.

    • +

  • coords (None or str) – If coords is None, then the first two coordinates +are assumed to represent x and y respectively. If this is not the +case, pass a single string with a comma separating the names of the +coordinates to use, eg “UTMx,UTMy”. Note that the order of the +coordinates might be different from the order of the dimensions in +the DataArray (which is often y, x).

    • +

  • comment_attr (str or None) – the name of the DataArray attribute to use +as a comment in the file, if any. If None, no comments are passed +from the DataArray to the file.

    • +

  • add_comment (str or list or None) – comment to add to the end of any +comments found in the DataArray. If None, no comment is added.

    • +

  • nodes_per_line (int) – number of values to write per line.

    • +

  • field_width (int) – character width of each value field.

    • +

  • precision (int) – number of decimal places to write.

    • +

  • null_value (float) – value to use for NaN values.

    • +
+
+
Returns:
+

None

+
+
+
+ +
+
+gio.zmap.read_zmap(fname, add_attrs=True, add_comment='File read by gio.read_zmap')#
+

Read a ZMAP file and return an xarray.DataArray.

+
+
Parameters:
+
    +

  • fname (str) – path to ZMAP file.

    • +

  • add_attrs (bool) – if True, add the ZMAP file’s metadata as attributes +on the DataArray.

    • +

  • add_comment (str or None) – if not None, add this comment to the +DataArray’s comment attribute (after any found in the ZMAP file).

    • +
+
+
Returns:
+

xarray.DataArray

+
+
+
+ +
+
+

Module contents#

+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/gio/__init__.py b/gio/__init__.py deleted file mode 100644 index 0ba0aaf..0000000 --- a/gio/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -from .opendtect import * -from .xy_to_grid import * -from .iesx import * -from .surfer import read_surfer -from .zmap import * -from .unmap import unmap_to_dataarray -from .closure import find_closures -from .random import * -from .xarray import GridInfo, SurfaceAccessor -from .logo import plot - - -from pkg_resources import get_distribution, DistributionNotFound -try: - VERSION = get_distribution(__name__).version -except DistributionNotFound: - try: - from ._version import version as VERSION - except ImportError: - raise ImportError( - "Failed to find (autogenerated) _version.py. " - "This might be because you are installing from GitHub's tarballs, " - "use the PyPI ones." - ) -__version__ = VERSION diff --git a/gio/closure.py b/gio/closure.py deleted file mode 100644 index 80f1f2c..0000000 --- a/gio/closure.py +++ /dev/null @@ -1,137 +0,0 @@ -""" -Closed-contour-related functions. - -Author: Rob Gooder, Matt Hall -License: MIT License - -Copyright (c) 2023 Rob Gooder, Matt Hall - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -""" -from typing import List - -import numpy as np -from numpy.typing import ArrayLike -import scipy.ndimage as ndimage -from skimage import measure -from shapely.geometry import Polygon - - -def closure_height(arr: ArrayLike, c: ArrayLike) -> float: - """ - Check that the max height is greater than the level. - - This is rather slow, so do it on the fewest possible candidates. - """ - # Create an empty image to store the masked array - mask = np.zeros_like(arr, dtype='bool') - - # Create a contour image by using the contour coordinates rounded to their nearest integer value - mask[np.round(c[:, 1]).astype('int'),np.round(c[:, 0]).astype('int')] = 1 - - # Fill in the hole created by the contour boundary - mask = ndimage.binary_fill_holes(mask) - - return np.nanmax(arr[mask]) - - -def is_high(arr: ArrayLike, p: Polygon, step:float = 1.0) -> bool: - """ - Decide if polygon p encloses a high region on grid z (returns True), or a - low region (False). - - This is much faster than closure_height and can help reduce the number of - times we run that. - - Sometimes the erosion buffer can result in more than 1 polygon, which tries - to get `exterior` on a MultiPolygon, which fails. Could maybe do - Polygon.boundary.xy instead, I think that works for MultiPolygon? - """ - x, y = p.exterior.xy - sample = arr[np.floor(y).astype('int'), np.floor(x).astype('int')] - - # Step inside the polygon. - x, y = p.buffer(step).exterior.xy - sample_in = arr[np.floor(y).astype('int'), np.floor(x).astype('int')] - - return sample.mean() > sample_in.mean() - - -def is_closed(c: ArrayLike) -> bool: - """ - Determine if the last point is the same as the first. - """ - return all(c[0] == c[-1]) - - -def find_closures(arr: ArrayLike, - interval: float = 1.0, - min_area: float = 0.0, - min_height: float = 0.0 - ) -> List[Polygon]: - """ - Find the largest closed contour in a grid. - - Args: - arr (ndarray): The grid to search. - interval (int): The interval between contours to consider, default 1 unit. - min_area (int): The minimum area of a closure to consider, in grid squares. - min_closure (int): The minimum closure of a closure to consider, in z units. - - Returns: - list of shapely.geometry.Polygon: The list of polygons that meet the criteria. - - TODO: - - Accept an xarray with real-world coordinates and use appropriate units. - - Test what happens if pass an xarray [Seems ok] - - Test what happens if grid is partly or completely negative [Seems ok] - - Test what happens if there are NaNs. [Seems ok] - - Test what happens if there are no closures. [Seems ok] - - Test what happens if there are donut highs. Need to add holes to Polygon. - """ - arr_ = np.asarray(arr) - - levels = np.arange(int(np.nanmin(arr)), np.nanmax(arr)-min_height+interval, interval) - - all_contours = [] - - for level in levels: - # Gives row (y), column (x) coordinates. - contours = measure.find_contours(arr_, level) - - # Keep only closed contours with no NaNs. - contours = [c for c in contours if is_closed(c) and ~np.isnan(c).any()] - - # Capture all contours that are high enough. - for contour in contours: - - y_, x = contour.T # y_ is row *from top* of array - contour = np.stack([x, y_, np.ones_like(x)*level]).T - p = Polygon(contour) - - # Do various tests. - if p.area < min_area: continue - if not is_high(arr_, p): continue - if any([p.within(c) for c in all_contours]): continue - if (closure_height(arr_, contour) - level) < min_height: continue - - # What's left is a good closure. - all_contours.append(p) - - return all_contours diff --git a/gio/esri.py b/gio/esri.py deleted file mode 100644 index 1fb2015..0000000 --- a/gio/esri.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Read E00 format DEM (digital elevation model) files. - -THIS IS A WORK IN PROGRESS. IT DOES NOT WORK YET. - -:copyright: 2022 Agile Geoscience -:license: Apache 2.0 -""" -import numpy as np -import re - - -def read_e00_text(fname): - """ - Parse a bunch of E00 formatted text and emit a NumPy array. - """ - with open(fname, 'rt') as f: - text = f.read() - - p = re.compile(r"^EXP\s+\d+\s+(.+?)\s+$", flags=re.MULTILINE) - exp, = p.search(text).groups() - - p = re.compile(r"GRD\s+\d\s+(\d+)\s+(\d+)\s+[-+.E\d]+\s+(.+?)EOG", flags=re.DOTALL) - nx, ny, data = p.search(text).groups() - - p = re.compile(r"LOG(.+?)EOL", flags=re.DOTALL) - log, = p.search(text).groups() - - p = re.compile(r"PRJ(.+?)EOP", flags=re.DOTALL) - prj, = p.search(text).groups() - - p = re.compile(r"IFO(.+?)EOI", flags=re.DOTALL) - ifo, = p.search(text).groups() - - # Unflatten this thing. - values = [] - for row in data: - for col in row: - values.append(float(col)) - - # Convert to an array and reshape. - arr = np.array(values).reshape(ny, nx) - - return arr diff --git a/gio/iesx.py b/gio/iesx.py deleted file mode 100644 index 10e1e2a..0000000 --- a/gio/iesx.py +++ /dev/null @@ -1,97 +0,0 @@ -import re -import warnings - -import numpy as np -import pandas as pd -from shapely.geometry import Point, LineString - -try: - import geopandas as gp - geopandas_is_available = True -except ImportError: - geopandas_is_available = False - - -def read_iesx_from_petrel(filename, threed=False): - """ - Read a Petrel IESX file and create a GeoPandas DataFrame. - """ - warnings.warn("This function is broken.") - with open(filename, 'rt') as f: - points, linestrings, names, datasets, linenames = [], [], [], [], [] - last_cdp = 0 - skip = False - - for line in f: - line = line.strip() - if not line: - # End of file - break - elif line.startswith('EOD'): - # End of horizon - last_cdp = 0 # Force capture - elif line.startswith('SNAPPING'): - continue - elif line.startswith('PROFILE'): - name = re.search(r'PROFILE (.+?) +TYPE', line).group(1) - - # Some 'label' horizons slipped though, skip 'em. - if name.startswith('---'): - skip = True - else: - skip = False - else: - if skip == True: - continue - - line = line.split() - x, y = float(line[0]), float(line[1]) - twtt = float(line[4]) - linename, dataset = line[-1].split('::') - - if threed: - this_cdp = int(line[5]) + int(line[9]) - else: - this_cdp = int(line[7]) - - if abs(this_cdp - last_cdp) < 2: - # Then it's a regular line, so keep adding. - points.append(Point(x, y, twtt)) - last_cdp = this_cdp - else: - if len(points) < 2: - last_cdp = this_cdp - continue - - # Capture what we have. - linestrings.append(LineString(points)) - names.append(name) - linenames.append(linename) - datasets.append(dataset) - zs = [p.z for p in points] - - # Reset segment and carry on. - last_cdp = this_cdp - points = [Point(x, y, twtt)] - - data_dict = {'geometry': linestrings, - 'horizon': names, - 'linename': linenames, - 'dataset': datasets, - 'min': np.min(zs), - 'max': np.max(zs), - 'mean': np.mean(zs), - 'points': len(points) - } - - if geopandas_is_available: - return gp.GeoDataFrame(data_dict, geometry='geometry') - else: - return pd.DataFrame(data_dict) - - -def check_dialect(fname): - """ - Decide if IESX file is from Petrel or OpendTect. - """ - pass diff --git a/gio/logo.py b/gio/logo.py deleted file mode 100644 index 293af43..0000000 --- a/gio/logo.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Make gio's logo. - -Author: Matt Hall -License: Apache 2.0 -""" -import hashlib - -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.cm import get_cmap - -from .random import generate_random_surface - - -def plot(ax=None, fname=None): - """ - Plots as a side effect. Pass in a 3d Axes if you want. Pass a filename - to save the plot. - """ - seed = int(hashlib.sha1("gio".encode()).hexdigest(), 16) - - if ax is None: - fig = plt.figure(figsize=(12, 12)) - fig.patch.set_facecolor('none') - ax = fig.add_subplot(projection='3d', facecolor='none') - - ax.set_box_aspect(aspect = (1,1,0.75)) - X = np.arange(0, 32) - Y = np.arange(0, 32) - X, Y = np.meshgrid(X, Y) - Z = generate_random_surface(32, res=2, octaves=2, random_seed=seed) - Z = 240 * Z + 240 - ax.axis('off') - - ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='RdBu') - ax.plot_wireframe(X, Y, Z, color='k', lw=0.5, alpha=0.75) - c = get_cmap('gray') - ax.text(-10, 20, 220, r"$\mathbf{gio}$", size=240, zorder=10, color=c(0.15)) - ax.set_zlim(0, 1200) - - if fname is not None: - plt.savefig(fname, dpi=200, bbox_inches='tight') - else: - plt.show() - - return ax diff --git a/gio/opendtect.py b/gio/opendtect.py deleted file mode 100644 index 897afb8..0000000 --- a/gio/opendtect.py +++ /dev/null @@ -1,348 +0,0 @@ -""" -I/O for OpendTect horizon formats. - -Author: Matt Hall -License: Apache 2.0 -""" -import warnings -import re -import csv -from io import StringIO - -import pandas as pd -import xarray as xr -import numpy as np - -from .xy_to_grid import xy_to_grid - - -def sense_types(data): - """ - Guess the type of some numbers. - """ - types = [] - for d in data: - if re.fullmatch(r'[-.0-9]+', d): - if '.' in d: - t = float - else: - t = int - else: - t = str - types.append(t) - return types - - -def get_meta(fname, comment='#'): - """ - Returns a string containing only the lines starting with - the specified comment character. Does not read entire file. - """ - header = '' - with open(fname, 'rt') as f: - for line in f: - if line.startswith(comment): - header += line - else: - types = sense_types(line.split()) - break - return header, types - - -def get_names_from_header(header): - """ - Get names from header text. - """ - pattern = re.compile(r'(?:\"|\d: )(.+?)(?:\"|\n)') - return pattern.findall(header) - - -def read_odt_as_df(fname, names=None, usecols=None, na_values=None, **kwargs): - """ - Read an OdT file as a Pandas DataFrame. - - Args: - fname (str): Path to file. - names (list): List of column names. - usecols (list): List of column indices to use. - na_values (list): List of values to treat as NA. - kwargs are passed to pandas.read_csv. - - Returns: - df (pandas.DataFrame): DataFrame containing the data. - """ - header, types = get_meta(fname) - - if header and (len(types) > len(get_names_from_header(header))): - # Then this is a multi-horizon file. - header = '# "Horizon"\n' + header - - if (names is None) and header: - names = get_names_from_header(header) - elif (names is not None) and header: - columns = get_names_from_header(header) - usecols = [columns.index(n) for n in names] - elif (names is not None) and not header: - usecols = range(len(names)) - else: - # Names is None, and no header. - t0, t1, *data_cols = types - if (t0 == int) and (t1 == int): - ix = ['Inline', 'Crossline'] # Standard OdT names. - elif (t0 == float) and (t1 == float) and (len(data_cols) == 1): - ix = ['X', 'Y'] # Standard OdT names. - else: - message = 'First two columns must be integers to be interpreted as inline and crossline.' - raise TypeError(message) - cols = range(len(data_cols)) - names = ix + [f'var_{n}' for n in cols if n in (usecols or cols)] - - if na_values is None: - na_values = ['1e30'] - else: - na_values = [str(n) for n in na_values] - - df = pd.read_csv(fname, - names=names, - sep="\t", - comment='#', - usecols=usecols, - na_values=na_values, - keep_default_na=False, - **kwargs - ) - - return df - - -def regularize_columns(df, mapping=None, snake_case=True): - """ - Adjust the column names to use segyio / SEGY-SAK standard names. - And optionally transform to snake case. - """ - mapping = mapping or {'Inline': 'iline', 'inline': 'iline', - 'Crossline': 'xline', 'crossline': 'xline', - 'X': 'cdp_x', 'x': 'cdp_x', - 'Y': 'cdp_y', 'y': 'cdp_y', - 'Z': 'twt'} - df = df.rename(columns=mapping) - if snake_case: - df.columns = [n.casefold().replace(' ', '_') for n in df.columns] - return df - - -def df_to_xarray(df, attrs=None, origin=(0, 0), step=(1, 1)): - """ - Convert a DataFrame to a DataArray. - """ - df = regularize_columns(df) - if ('iline' in df.index.names) and ('xline' in df.index.names): - pass # Do nothing to df. - elif ('iline' in df.columns) and ('xline' in df.columns): - df = df.set_index(["iline", "xline"]) - elif ('cdp_x' in df.columns) and ('cdp_y' in df.columns): - message = "Attempting to construct grid from (x, y) locations." - warnings.warn(message, stacklevel=2) - ones = np.ones_like(df['cdp_x']) - _, _, (addy, addx) = xy_to_grid(df['cdp_x'], df['cdp_y'], ones) - addy = np.max(addy) - addy # Adjust for origin. - addx = np.max(addx) - addx # I don't know why this is required.. - df['iline'] = step[0] * addx + origin[0] - df['xline'] = step[1] * addy + origin[1] - df = df.set_index(["iline", "xline"]) - else: - message = "Require columns or indexes 'iline' & 'xline', or 'cdp_x' & 'cdp_y'." - raise TypeError(message) - - # Question: should return a DataArray if only 1 attribute? - # But then have to deal with possibly having cdp_x and cdp_y. - dx = xr.Dataset.from_dataframe(df) - - if ('cdp_x' in df.columns) and ('cdp_y' in df.columns): - dx = dx.assign_coords(cdp_x=(('iline', 'xline'), dx['cdp_x'].data)) - dx = dx.assign_coords(cdp_y=(('iline', 'xline'), dx['cdp_y'].data)) - - dx.attrs.update(attrs or {}) - - return dx - - -def read_odt(fname, - names=None, - usecols=None, - na_values=None, - origin=(0, 0), - step=(1, 1), - attrs=None - ): - """ - Read an OdT horizon file. (Native format, not IESX.) - - Args: - names (list): The names of the columns in the file. If there's - a header, this is not required. If there is no header, the - function will attempt to interpret the columns: - - - If the first two columns are integers, they are interpreted - as inline and crossline numbers. - - If the first two columns are floats, and there is only one - other column, then they are interpreted as (x, y) locations. - Inline and crossline numbers will be generated, using - `origin` as starting points. - - In any other case, the function will throw an error; - provide names to tell it what the columns are. Use 'inline' - or 'iline' for inlines, 'xline' or 'crossline' for crosslines, - 'x' or 'cdp_x' for x locations, and 'y' or 'cdp_y' for y - locations. The names of your attributes are up to you. - - If there are 5 columns but you only give 4 names, then only - the first 4 columns will be loaded. - usecols (list of ints): The indices of the columns that correspond - to the names you're providing. If you're loading all the columns, - or the N names correspond to the first N columns, then you don't - need to provide this argument. - na_values (list of str): The strings that correspond to missing data. - By default, this is ['1e30'], the deault NULL value in OpendTect. - Any values you provide will *replace* this default and will be - used to replace missing data with NaNs. - origin (tuple of ints): If you only have (x, y) locations, then - gio will try to construct a grid from them. It probably doesn't - always work. It will assign inline and crossline numbers, which - will just be NumPy-like indices by default. If you provide - an origin, it will start numbering there; if you provide a step, - it will use that to increment the indices. - step (tuple of ints): The step size of the grid (see `origin`, above). - attrs (dict): The attributes to add to the Dataset; the filename - will be added as 'odt_filename'. - - Returns: - xarray.Dataset: Each data column will be a data variable. Inline and - crossline — and cdp_x and cdp_y, if present — will be coordinates. - """ - df = read_odt_as_df(fname, - names=names, - usecols=usecols, - na_values=na_values - ) - - attrs = attrs or {} - attrs.update({'odt_filename': fname}) - - if 'Horizon' in df.columns: - dx = xr.Dataset() - for name, group in df.groupby('Horizon'): - group = group.drop(columns=['Horizon']) - ds = df_to_xarray(group, attrs=attrs, origin=origin, step=step) - - # Get the one DataArray out (surely there's a better way to do this?). - da = ds[list(ds.data_vars.keys())[0]] - - # Add it to the Dataset. - dx[name] = da - - else: - dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step) - - return dx - -def to_odt(fname, dx, multi=False, twt=None, force_twt=True, name=None, dropna=True, header='multiline', **kwargs): - """ - Write an OdT horizon file. (Native format, not IESX.) - - There is an unhealthy amount of magic in this function. - - Args: - fname (str): The name of the file to write. - dx (xarray.Dataset): The data to write. - multi (bool): Whether you want a multi-horizon export. - twt (str): The name of the variable containing the two-way time, if - not 'twt'. - force_twt (bool): Whether to force the first variable to be two-way - time. If there's more than one variable, this throws a warning. - dropna (bool): If True, drop any rows with NaNs from the data. This - is usual in OpendTect, but it may make the file harder to read - by other software. - header (str): The type of header to use. Either 'multiline' or - 'singleline' is recommended, but 'none' will omit it entirely. - **kwargs: Additional arguments to pass to `pandas.DataFrame.to_csv`. - - Returns: - None - """ - # Deal with multi-horizon case, which can only have Z / twt, but can - # have IL/XL and X/Y, and can have single, multi, or no header. - # Split into DataArrays and call this function on each. - if multi: - strs = [] - for name, da in dx.data_vars.items(): - db = da.rename('twt') - sio = StringIO() - _ = to_odt(sio, db, twt=twt, name=name, dropna=dropna, header=header, **kwargs) - strs.append(sio.getvalue()) - with open(fname, 'wt') as f: - f.write('\n'.join(strs)) - return - - # Continue with single-horizon case. - - # Make the dataframe... - df = dx.to_dataframe() - if (twt is None) and force_twt and ('twt' not in df.columns): - # Use the first column for twt. - if (len(df.columns)>1): - # Warn if there are several columns. - message = 'Assuming first column is TWT; pass force_twt=False to prevent this.' - warnings.warn(message, stacklevel=2) - # Assume twt is first non-special column. - twt = [c for c in df.columns if c not in ['cdp_x', 'cdp_y']][0] - else: - twt = twt or 'twt' - df = df.reset_index() - if dropna: - df = df.dropna() - # ... and regularize the columns. - mapping = {'iline': 'Inline', 'xline': 'Crossline', - 'cdp_x': 'X', 'cdp_y': 'Y', - twt: 'Z' - } - df = df.rename(columns=mapping) - df.columns = [col.replace('_', ' ').title() for col in df.columns] - - # Construct the header text. - if header not in ['multiline', 'singleline', 'none', None]: - raise ValueError("header must be one of 'multiline', 'singleline', 'none'.") - if header == 'multiline': - h = [f"# {i+1}: {col}\n" for i, col in enumerate(df.columns)] - h_text = ''.join(h) + "# - - - - - - - - - -\n" - elif header == 'singleline': - h_text = '# "' + '"\t"'.join(df.columns) + '"\n' + "# - - - - - - - - - -\n" - else: - h_text = '' - - # Write the header text. - try: - fname.write(h_text) # fname is open file handle. - except AttributeError: - with open(fname, 'wt') as f: - f.write(h_text) - - # Add the 'name' column, if required (only for multi-horizon export). - if name is not None: - cols = df.columns - df['_'] = name - df = df[['_'] + list(cols)] - - # Write the data. - na_rep = kwargs.pop('na_rep', '1e30') - df.to_csv(fname, - mode='a', - header=False, - index=False, - sep='\t', - na_rep=na_rep, - quotechar='"', - quoting=csv.QUOTE_NONNUMERIC, - **kwargs - ) - - return None diff --git a/gio/random.py b/gio/random.py deleted file mode 100644 index 02bc2ab..0000000 --- a/gio/random.py +++ /dev/null @@ -1,221 +0,0 @@ -""" -Random surfaces. - -The code in this module was substantially adapted from Pierre Vigier's code at -https://github.com/pvigier/perlin-numpy - -Author: Pierre Vigier -Licence: MIT - -Copyright 2022 Pierre Vigier - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is furnished -to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -""" -import numpy as np -import xarray as xr - - -def interpolant(t): - """ - By Pierre Vigier https://github.com/pvigier/perlin-numpy - Licence: MIT - """ - return t*t*t*(t*(t*6 - 15) + 10) - - -def perlin_noise(shape, res, - rng=None, - tileable=(False, False), - interpolant=interpolant): - """ - Generate a 2D numpy array of perlin noise. - - By Pierre Vigier https://github.com/pvigier/perlin-numpy - Licence: MIT - - Args: - shape: The shape of the generated array (tuple of two ints). - This must be a multple of res. - res: The number of periods of noise to generate along each - axis (tuple of two ints). Note shape must be a multiple of - res. - tileable: If the noise should be tileable along each axis - (tuple of two bools). Defaults to (False, False). - interpolant: The interpolation function, defaults to - t*t*t*(t*(t*6 - 15) + 10). - - Returns: - A numpy array of shape shape with the generated noise. - - Raises: - ValueError: If shape is not a multiple of res. - """ - delta = (res[0] / shape[0], res[1] / shape[1]) - d = (shape[0] // res[0], shape[1] // res[1]) - grid = np.mgrid[0:res[0]:delta[0], 0:res[1]:delta[1]].transpose(1, 2, 0) % 1 - - # Gradients. - angles = 2 * np.pi * rng.random(size=(res[0]+1, res[1]+1)) - gradients = np.dstack((np.cos(angles), np.sin(angles))) - if tileable[0]: - gradients[-1, :] = gradients[0, :] - if tileable[1]: - gradients[:,-1] = gradients[:,0] - gradients = gradients.repeat(d[0], 0).repeat(d[1], 1) - g00 = gradients[ :-d[0], :-d[1]] - g10 = gradients[d[0]: , :-d[1]] - g01 = gradients[ :-d[0],d[1]: ] - g11 = gradients[d[0]: ,d[1]: ] - - # Ramps. - n00 = np.sum(np.dstack((grid[:,:,0] , grid[:,:,1] )) * g00, 2) - n10 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1] )) * g10, 2) - n01 = np.sum(np.dstack((grid[:,:,0] , grid[:,:,1]-1)) * g01, 2) - n11 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1]-1)) * g11, 2) - - # Interpolation. - t = interpolant(grid) - n0 = n00*(1-t[:,:,0]) + t[:,:,0]*n10 - n1 = n01*(1-t[:,:,0]) + t[:,:,0]*n11 - return np.sqrt(2)*((1-t[:,:,1])*n0 + t[:,:,1]*n1) - - -def fractal_noise(shape, - res, - rng=None, - octaves=1, - persistence=0.5, - lacunarity=2, - tileable=(False, False), - interpolant=interpolant, - ): - """ - Generate a 2D numpy array of fractal noise. - - By Pierre Vigier https://github.com/pvigier/perlin-numpy - Licence: MIT - - Args: - shape: The shape of the generated array (tuple of two ints). - This must be a multiple of lacunarity**(octaves-1)*res. - res: The number of periods of noise to generate along each - axis (tuple of two ints). Note shape must be a multiple of - (lacunarity**(octaves-1)*res). - octaves: The number of octaves in the noise. Defaults to 1. - persistence: The scaling factor between two octaves. - lacunarity: The frequency factor between two octaves. - tileable: If the noise should be tileable along each axis - (tuple of two bools). Defaults to (False, False). - interpolant: The, interpolation function, defaults to - t*t*t*(t*(t*6 - 15) + 10). - - Returns: - A numpy array of fractal noise and of shape shape generated by - combining several octaves of perlin noise. - - Raises: - ValueError: If shape is not a multiple of lacunarity**(octaves-1)*res. - """ - noise = np.zeros(shape) - f, a = 1, 1 - for _ in range(octaves): - this_res = (f*res[0], f*res[1]) - noise += a * perlin_noise(shape, - this_res, - tileable=tileable, - interpolant=interpolant, - rng=rng - ) - f *= lacunarity - a *= persistence - - return noise - - -def generate_random_surface(size, - res=2, - octaves=2, - lacunarity=2, - persistence=0.5, - random_seed=None, - name=None, - coords='i,j', - ): - """ - Make a square grid of **at least** the given size (length of side), with - the given parameters. Size must be a multiple of lacunarity**(octaves-1)*res - so the smallest possible size greater than the one provided is used. - - Args: - size (int or tuple): minimum length of side of the grid. - res (int or tuple): number of periods of noise to generate along - each axis. - octaves (int): number of octaves of noise (bandwidth). - lacunarity (float): lacunarity of the noise (frequency factor between - octaves). Default: 2. - persistence (float): persistence of the noise (amplitude factor between - octaves). Default: 0.5. - random_seed (int): random seed. - name (str): name of the resulting array, optional. Default: None. - coords (str): names of spatial coordinates, separated by commas. - Default: i and j. - - Returns: - xarray.DataArray: the grid. - """ - # `res` and `size` can be ints or tuples. - if isinstance(res, int): - res = (res, res) - if isinstance(size, int): - size = (size, size) - - # Note shape must be a multiple of (lacunarity**(octaves-1)*res). - # Not sure what happens if res has different values for each axis. - size0, size1 = size - while size0 % (lacunarity**(octaves - 1) * res[0]) > 0: - size0 += 1 - if size0 > 35_500: - raise RuntimeError("Smallest compatible surface is >10GB.") - while size1 % (lacunarity**(octaves - 1) * res[1]) > 0: - size1 += 1 - if size1 > 35_500: - raise RuntimeError("Smallest compatible surface is >10GB.") - - rng = np.random.default_rng(seed=random_seed) - noise = fractal_noise((size0, size1), - res=res, - octaves=octaves, - lacunarity=lacunarity, - persistence=persistence, - rng=rng - ) - i, j = map(np.arange, noise.shape) - - try: - dims = [n.strip() for n in coords.split(',')] - except: - raise ValueError('Could not find coordinates; give a string with commas to separate the coords representing real-world x and y respectively.') - - return xr.DataArray(noise, - dims=dims, - coords=dict(zip(dims, (i, j))), - name=name, - attrs={'source': 'gio.random', - 'description': 'Fractal Perlin noise', - }) diff --git a/gio/surfer.py b/gio/surfer.py deleted file mode 100644 index aa5bb7a..0000000 --- a/gio/surfer.py +++ /dev/null @@ -1,183 +0,0 @@ -""" -Read 2D ASCII/binary Surfer grid files. - -:copyright: 2022 Agile Geoscience (this adaptation) -:license: Apache 2.0 - -Adapted from Seequent's `steno3d_surfer`: https://pypi.org/project/steno3d_surfer - -:copyright: 2018 Seequent -:license: MIT - -MIT License -Copyright (c) 2018 Seequent -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -""" -from struct import unpack -import warnings - -import numpy as np -import xarray as xr - -from .xarray import GridInfo - - -def read_surfer(fname): - """ - Reader for Surfer .grd files, supporting both - Surfer 6 binary & ASCII files, and Surfer 7 binary files. - Only (x, y)-orthogonal grids are supported. - - Args: - fname (str): The filename of the Surfer .grd file. - - Returns: - xarray.DataArray: The grid data. - """ - grd_info = read_grd(fname) - da = grd_info.to_xarray() - return da - -def read_grd(fname): - with open(fname, 'rb') as f: - file_ident = unpack('4s', f.read(4))[0] - - if file_ident == b'DSRB': - grd_info = _surfer7bin(fname) - elif file_ident == b'DSBB': - grd_info = _surfer6bin(fname) - elif file_ident == b'DSAA': - grd_info = _surfer6ascii(fname) - else: - raise RuntimeError('Invalid file identifier for Surfer .grd file. ' - 'First 4 characters must be DSRB, DSBB, or DSAA') - return grd_info - -def _surfer7bin(fname): - with open(fname, 'rb') as f: - if unpack('4s', f.read(4))[0] != b'DSRB': - raise RuntimeError('Invalid file identifier for Surfer 7 binary' - ' .grd grid. First 4 characters must be DSRB.') - f.read(8) # Size & Version - - section = unpack('4s', f.read(4))[0] - if section != b'GRID': - raise RuntimeError('Unsupported Surfer 7 file structure. GRID keyword ' - 'must follow immediately after header but {} ' - 'encountered.'.format(section)) - size = unpack('= blankval, np.nan, data) - try: - section = unpack('4s', f.read(4))[0] - if section == b'FLTI': - warnings.warn('Unsupported feature: Fault Info', stacklevel=2) - else: - warnings.warn('Unrecognized keyword: {}'.format(section), stacklevel=2) - warnings.warn('Remainder of file ignored', stacklevel=2) - except: - pass - - return GridInfo(xmin=xLL, - ymin=yLL, - xmax=xLL + xSize*nCol, - ymax=yLL + ySize*nRow, - data=np.flipud(data.reshape(nRow, nCol)), - fname=fname, - ) - - -def _surfer6bin(fname): - with open(fname, 'rb') as f: - if unpack('4s', f.read(4))[0] != b'DSBB': - raise RuntimeError('Invalid file identifier for Surfer 6 binary .grd ' - 'file. First 4 characters must be DSBB.') - nx = unpack('= 1.701410009187828e+38: - data[i] = np.nan - else: - data[i] = zdata - - return GridInfo(xmin=xlo, - ymin=ylo, - xmax=xhi, - ymax=yhi, - data=np.flipud(data.reshape(ny, nx)), - fname=fname, - ) - - -def _surfer6ascii(fname): - with open(fname, 'rt') as f: - if f.readline().strip() != 'DSAA': - raise RuntimeError('Invalid file identifier for Surfer 6 ASCII .grd ' - 'file. First line must be DSAA') - [nx, ny] = [int(n) for n in f.readline().split()] - [xlo, xhi] = [float(n) for n in f.readline().split()] - [ylo, yhi] = [float(n) for n in f.readline().split()] - [_, _] = [float(n) for n in f.readline().split()] # zmin zmax - data = np.fromiter(f.read().split(), dtype=float) - - return GridInfo(xmin=xlo, - ymin=ylo, - xmax=xhi, - ymax=yhi, - data=np.flipud(data.reshape(ny, nx)), - fname=fname, - ) diff --git a/gio/unmap.py b/gio/unmap.py deleted file mode 100644 index 40bbe0a..0000000 --- a/gio/unmap.py +++ /dev/null @@ -1,42 +0,0 @@ -import unmap - -from .xarray import GridInfo - -def unmap_to_dataarray(arr, - cmap, - extent=None, - dims=None, - **kwargs - ): - """ - Unmap data, via a colourmap, from an image. Reverse false-colouring. - - Args: - arr: NumPy array of image data. - cmap: Either another array, or pixel extent of colourbar in image, - or name of mpl colourbar or if None, just use lightness (say) - If get pixels and they're a few wide, then average them along - short axis? Again, depends on scatter. - extent: The extent of the image, as a tuple (left, right, bottom, top), - giving the real-world coordinates. - dims (tuple): The dimensions of the image, as a tuple e.g. ('x', 'y') - or ('Lon', 'Lat')). - **kwargs: Passed to unmap.unmap. - - Returns: - An xarray.DataArray of the same shape as the input image, with the - colourmap removed. - """ - da = unmap.unmap(arr, cmap, **kwargs) - - if extent is not None: - xmin, xmax, ymin, ymax = extent - dims = dims or ('x', 'y') - else: - xmin, xmax, ymin, ymax = 0, arr.shape[1], 0, arr.shape[0] - dims = dims or ('i', 'j') - - return GridInfo(xmin=xmin, ymin=ymin, - xmax=xmax, ymax=ymax, - data=da, dims=dims, - ).to_xarray() diff --git a/gio/xarray.py b/gio/xarray.py deleted file mode 100644 index 53217e7..0000000 --- a/gio/xarray.py +++ /dev/null @@ -1,81 +0,0 @@ -""" -xarray convenience class and accessor. - -Author: Matt Hall -Email: matt@agilescientific.com -Licence: Apache 2.0 -""" -from dataclasses import dataclass - -import numpy as np -import xarray as xr - -from .zmap import dataarray_to_zmap - - -@dataclass -class GridInfo: - xmin: float # x-value of lower-left corner - ymin: float # y-value of lower-left corner - xmax: float # x-value of upper-right corner - ymax: float # y-value of upper-right corner - data: np.ndarray # grid of data values, shape=(nrow, ncol) - dims: tuple = ('i', 'j') # names of dimensions - source: str = '' - fname: str = '' - - def to_xarray(self): - """ - Convert to xarray.DataArray. Only (x, y)-orthogonal grids are supported. - """ - nrow, ncol = self.data.shape - return xr.DataArray(self.data, - dims=self.dims, - coords={self.dims[1]: np.linspace(self.xmin, self.xmax, ncol), - self.dims[0]: np.linspace(self.ymin, self.ymax, nrow)}, - attrs={'source': self.source, 'fname': self.fname,}) - - -@xr.register_dataarray_accessor("gio") -class SurfaceAccessor: - def __init__(self, xarray_obj): - self._obj = xarray_obj - - def to_zmap(self, fname, - coords=None, - comment_attr='comment', - add_comment='File created by gio.to_zmap xarray accessory', - nodes_per_line=6, - field_width=12, - precision=3, - null_value=-999.25, - ): - """ - Write a DataArray to a ZMAP file. - - Args: - fname (str): path to ZMAP file. - coords (list or None): list of coordinate names to use for the X and - Y axes. If None, use the first two dimensions of the DataArray. - comment_attr (str or None): name of the DataArray attribute to use - as a comment in the file. If None, no comment is added. - add_comment (str or list or None): comment to add to the end of any - comments found in the DataArray. If None, no comment is added. - nodes_per_line (int): number of values to write per line. - field_width (int): character width of each value field. - precision (int): number of decimal places to write. - null_value (float): value to use for NaN values. - - Returns: - None - """ - return dataarray_to_zmap(fname, - self._obj, - coords=coords, - comment_attr=comment_attr, - add_comment=add_comment, - nodes_per_line=nodes_per_line, - field_width=field_width, - precision=precision, - null_value=null_value, - ) diff --git a/gio/xy_to_grid.py b/gio/xy_to_grid.py deleted file mode 100644 index 1d923bf..0000000 --- a/gio/xy_to_grid.py +++ /dev/null @@ -1,179 +0,0 @@ -""" -Special case: binning data with only (x, y) coords. - -:copyright: 2022 Agile Geoscience -:license: Apache 2.0 -""" -import numpy as np -import scipy.spatial.distance as sd -from scipy.stats import gaussian_kde -from scipy.signal import find_peaks -from scipy.interpolate import interp1d - - -def nearest_neighbours(x, y, precision=5): - """ - Vector between 2 closest points. If there's a tie (there - likely will be), then we get the first point with a minimally - nearest neighbour (and its first such neighbour). - - The precision cut-off is needed to deal with floating-point - imprecision. - """ - X = np.array([x, y]).T - - # Make the pairwise distance matrix, D. - D = sd.squareform(sd.pdist(X)) - D[D==0] = np.inf - D = np.round(D, decimals=precision) - - # Find the minimum distance, considering all pairs. - dmin = np.argmin(D) - - # Get the first point p and its near neighbour. - p = X[dmin // len(x)] - q = X[dmin % len(x)] - - return p - q - - -def rectify(X, vector): - """ - Rotate the geometry X, which should have columns - (x, y, data). Data can be anything, just ones if - you only have points to transform. - """ - # Figure out the angle of the given vector. - θ = np.angle(complex(*vector)) - - # Affine transformation matrix. - A = np.array([[np.cos(θ), -np.sin(θ), 0], - [np.sin(θ), np.cos(θ), 0], - [ 0, 0, 1]]) - - # Apply the transformation and return. - return X @ A - - -def parabolic(f, x): - """ - Quadratic interpolation for estimating the true position of an - inter-sample maximum when nearby samples are known. - - f is a vector and x is an index for that vector. - - Returns (vx, vy), the coordinates of the vertex of a parabola that goes - through point x and its two neighbors. - - Example: - Defining a vector f with a local maximum at index 3 (= 6), find local - maximum if points 2, 3, and 4 actually defined a parabola. - - >>> import numpy as np - >>> f = [2, 3, 1, 6, 4, 2, 3, 1] - >>> parabolic(f, np.argmax(f)) - (3.2142857142857144, 6.1607142857142856) - """ - xv = 1/2 * (f[x-1] - f[x+1]) / (f[x-1] - 2 * f[x] + f[x+1]) + x - yv = f[x] - 1/4 * (f[x-1] - f[x+1]) * (xv - x) - return (xv, yv) - - -def get_intervals(x): - """ - From an unsorted and possibly sparse collection - of 1D coordinates, compute the number of bins and - the distance between bin centres. - - Default is nearest-metre precision, which should be - fine for most seismic surveys. - """ - # Preliminaries. - x = np.asanyarray(x) - assert x.ndim == 1, "Array must be 1D." - xmax, xmin = x.max(), x.min() - est = np.sqrt(x.size).astype(int) - N = max(20 * est, 2500) - pad = (xmax - xmin) / est - - # Construct the KDE to 'cluster' values. - kde = gaussian_kde(x, bw_method=5e-3) - x_eval = np.linspace(xmin - pad, xmax + pad, N) - x_kde = kde.evaluate(x_eval) - - # Find the peaks in the KDE. - peaks, _ = find_peaks(x_kde) - n_peaks = len(peaks) - - # Get true positions as indices. - x_pos, _ = parabolic(x_kde, peaks) - - # Get the coords at the actual peaks. - M = x_eval.size - idx = np.linspace(0, M-1, M) # The indices. - f = interp1d(idx, x_eval, kind='linear') - x_best = f(x_pos) - - # Figure out the likely spacing. - # Should be the smallest gap, but data is noisy and - # the mean (minus any large spacings from gaps) is a - # better estimate. - vals = np.sort(np.diff(x_best)) - trim = np.mean(vals[n_peaks//4:-n_peaks//4]) # Central half of data. - minn = vals[0] - - # If the values are close, take the mean; otherwise - # take the minimum (may be very sparse data.) - dx = trim if trim - minn < 1 else minn - dx = np.round(dx, 2) - - # Compute the number of bins from the spacing. - Nx = 1 + np.round((np.max(x) - np.min(x)) / dx, decimals=0).astype(int) - - return Nx, dx - - -def xy_to_grid(x, y, data, compute_array=False): - """ - Bin a bunch of unsorted (x, y) datapoints into a regular grid. - - Returns: - tuple: - - arr (ndarray): The binned data. - - (dx, dy): The spacing between bins in the x and y directions. - - (addx, addy): The destination of each data point into the grid; - n.b. this is given in NumPy (row, col) format. - """ - # Create shapely points. - X = np.vstack([x, y, data]).T - - # Get nearest neighbour and rectify. - v = nearest_neighbours(x, y) - p = rectify(X, v) - - x_new, y_new, _ = p.T # Data has not changed. - - Nx, dx = get_intervals(x_new) - Ny, dy = get_intervals(y_new) - - xedge = np.linspace(np.min(x_new) - dx / 2, np.max(x_new) + dx / 2, Nx + 1) - yedge = np.linspace(np.min(y_new) - dy / 2, np.max(y_new) + dy / 2, Ny + 1) - - assert np.all(data > 0), "Data are not strictly positive; see docs." - - # Don't strictly need to do this if we don't want the array. - # (Don't need it for the OdT loading workflow.) - if compute_array: - arr, *_ = np.histogram2d(y_new, x_new, bins=[yedge, xedge], weights=data) - arr[arr == 0] = np.nan - else: - arr = None - - # Find destination 'address' of each sample. - addx = np.digitize(x_new, xedge) - 1 # No 'outer' bin. - addy = np.digitize(y_new, yedge) - 1 - - # Adjust addy to account for numbering of rows in array. - addy = Ny - addy - - return arr, (dx, dy), (addy, addx) diff --git a/gio/zmap.py b/gio/zmap.py deleted file mode 100644 index b1a9c87..0000000 --- a/gio/zmap.py +++ /dev/null @@ -1,185 +0,0 @@ -""" -I/O for ZMAP grid formats. - -Author: Matt Hall -License: Apache 2.0 -""" -import zmapio -import xarray as xr - - -def read_zmap(fname, add_attrs=True, add_comment='File read by gio.read_zmap'): - """ - Read a ZMAP file and return an xarray.DataArray. - - Args: - fname (str): path to ZMAP file. - add_attrs (bool): if True, add the ZMAP file's metadata as attributes - on the DataArray. - add_comment (str or None): if not None, add this comment to the - DataArray's comment attribute (after any found in the ZMAP file). - - Returns: - xarray.DataArray - """ - # Read file and transform to xr.DataArray. - z = zmapio.ZMAPGrid(fname) - df = z.to_pandas().set_index(['Y', 'X']) - da = xr.DataArray.from_series(df['Z']) - - # Only way I can find to reorder the coordinates. - da = xr.DataArray(da.data, - dims=['Y', 'X'], - coords={'X': da.coords['X'], 'Y': da.coords['Y']}, - name=z.name - ) - - if add_comment is None: - comments = [] - elif isinstance(add_comment, str): - comments = add_comment.split('\n') - else: - comments = add_comment # list - - if add_attrs: - attrs = { - 'fname': fname, - 'null_value': z.null_value, - } - - if z.comments or comments: - attrs['comment'] = z.comments + comments - else: - attrs = {} - - # Capture metadata and return. - return da.assign_attrs(attrs) - - -def array_to_zmap(fname, - arr, - extent=None, - comment='File created by gio.array_to_zmap', - nodes_per_line=6, - field_width=12, - precision=3, - null_value=-999.25, - name='Z', - ): - """ - Write a NumPy array as a ZMAP dat file. - - Args: - fname (str): path to output file. - arr (np.ndarray): 2D array to write. - extent (None or list): if not None, should be a list of 4 numbers - representing the extent of the array in real-world coordinates, in - the order [min_x, max_x, min_y, max_y]. - comment (str or list): comment to add to the file. - nodes_per_line (int): number of values to write per line. - field_width (int): character width of each value field. - precision (int): number of decimal places to write. - null_value (float): value to use for NaN values. - name (str): name of the variable in the file. - - Returns: - None - """ - if arr.ndim != 2: - raise ValueError('Array arr must be 2D.') - - if extent is None: - rows, cols = arr.shape - min_x, max_x, min_y, max_y = 0, cols, 0, rows - else: - min_x, max_x, min_y, max_y = extent - - zgrid = zmapio.ZMAPGrid(z_values=arr[::-1].T, # Horrible; apparently required by zmapio library. - min_x=min_x, max_x=max_x, - min_y=min_y, max_y=max_y) - - if isinstance(comment, str): - comments = comment.split('\n') - else: - comments = comment - - zgrid.comments = comments - zgrid.nodes_per_line = nodes_per_line - zgrid.field_width = field_width - zgrid.decimal_places = precision - zgrid.null_value = null_value - zgrid.name = name - - zgrid.write(fname) - - return None - - -def dataarray_to_zmap(fname, - da, - coords=None, - comment_attr='comments', - add_comment='File created by gio.dataarray_to_zmap', - nodes_per_line=6, - field_width=12, - precision=3, - null_value=-999.25, - ): - """ - Write an xarray.DataArray as a ZMAP dat file. For now we assume that dx is - a DataArray. - - Args: - fname (str): path to output file. - da (xarray.DataArray): 2D array to write. - coords (None or str): If coords is None, then the first two coordinates - are assumed to represent x and y respectively. If this is not the - case, pass a single string with a comma separating the names of the - coordinates to use, eg `"UTMx,UTMy"`. Note that the order of the - coordinates might be different from the order of the dimensions in - the DataArray (which is often y, x). - comment_attr (str or None): the name of the DataArray attribute to use - as a comment in the file, if any. If None, no comments are passed - from the DataArray to the file. - add_comment (str or list or None): comment to add to the end of any - comments found in the DataArray. If None, no comment is added. - nodes_per_line (int): number of values to write per line. - field_width (int): character width of each value field. - precision (int): number of decimal places to write. - null_value (float): value to use for NaN values. - - Returns: - None - """ - if coords is None: - x_coord, y_coord = list(da.coords.keys())[:2] # Rows, columns. - else: - try: - x_coord, y_coord = [i.strip() for i in coords.split(',')] - except: - raise ValueError('Could not find coordinates; give a string with commas to separate the coords representing real-world x and y respectively.') - - min_x, max_x = [d.item() for d in da.coords[x_coord][[0, -1]]] - min_y, max_y = [d.item() for d in da.coords[y_coord][[0, -1]]] - - # Gather metadata from DataArray. - if comment_attr is None: - comment = [] - else: - comment = da.attrs.get(comment_attr, []) - if isinstance(comment, str): - comment = comment.split('\n') - - if add_comment is not None: - comment.extend(add_comment.split('\n')) - - return array_to_zmap(fname, - da.data, - extent=[min_x, max_x, min_y, max_y], - comment=comment, - nodes_per_line=nodes_per_line, - field_width=field_width, - precision=precision, - null_value=null_value, - name=da.name, - ) diff --git a/index.html b/index.html new file mode 100644 index 0000000..1d51e1a --- /dev/null +++ b/index.html @@ -0,0 +1,401 @@ + + + + + + + + + gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
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+

gio: Geo I/O for Subsurface Surfaces#

+
+
+
gio reads and write subsurface data files
+
+
+
describing surfaces, grids and horizons.
+
+
+
+

Quick start#

+
+
+

Install gio with pip:

+
pip install gio
+
+
+

Read a Surfer grid:

+
import gio
+da = gio.read_surfer('surface.grd')
+
+
+

da is a two-dimensional xarray.DataArray object, which is a kind of +array (NumPy, Dask, Pint, whatever) indexed like a Pandas dataframe. In +general, you can treat it like an array (or use da.data to get the +underlying array, or da.values to get the underlying array as a +numpy.ndarray).

+
+
+

User guide#

+
+

User guide

+ +
+
+
+

API reference#

+
+

API reference

+ +
+
+
+

Other resources#

+
+

Other resources

+ +
+
+
+

Indices and tables#

+ +
+
+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/installation.html b/installation.html new file mode 100644 index 0000000..6ea3804 --- /dev/null +++ b/installation.html @@ -0,0 +1,311 @@ + + + + + + + + + Installation - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
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+

Installation#

+

At the command line:

+
$ pip install gio
+
+
+

Or, if you use Conda environments:

+
$ conda create -n gio python=3.9
+$ conda activate gio
+$ pip install gio
+
+
+

Installing scikit-learn allows you to access some extra options for outlier detection:

+
pip install gio[sklearn]
+
+
+

For developers, there are also options for installing tests, docs and dev dependencies.

+

If you want to help develop gio, please read Development.

+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/license.html b/license.html new file mode 100644 index 0000000..29e0bf8 --- /dev/null +++ b/license.html @@ -0,0 +1,474 @@ + + + + + + + + + License - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
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+

License#

+
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+
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+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index a7dc247..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,74 +0,0 @@ -[build-system] -requires = ["setuptools>=68", "setuptools-scm>=8"] -build-backend = "setuptools.build_meta" - -[tool.setuptools_scm] -write_to = "gio/_version.py" - -[tool.setuptools.packages.find] -include = ["gio"] - -[project] -name = "gio" -dynamic = ["version"] -requires-python = ">=3.8" -authors = [{ name="Agile Scientific", email="hello@agilescientific.com" },] -description = "Create xarray.DataArrays from various subsurface data formats." -license = {file = "LICENSE"} -readme = "README.md" -keywords = ["data loading", "data management", "geoscience", "geospatial"] -classifiers = [ - "Intended Audience :: Science/Research", - "Topic :: Scientific/Engineering :: Information Analysis", - "Development Status :: 4 - Beta", - "Natural Language :: English", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "License :: OSI Approved :: Apache Software License", - "Operating System :: OS Independent", -] - -dependencies = [ - "scipy", - "xarray", - "matplotlib", - "shapely", - "zmapio", - "unmap" -] - -[project.optional-dependencies] -geopandas = [ - "geopandas", -] -dev = [ - "build", - "pytest", - "coverage[toml]", - "pytest-cov", - "sphinx", - "sphinxcontrib-apidoc", - "sphinx_copybutton", - "furo", - "myst_nb", - "jupyter", - "requests", - "geopandas", -] - -[project.urls] -"documentation" = "https://code.agilescientific.com/gio/" -"repository" = "https://github.com/agilescientific/gio" - -[tool.pytest.ini_options] -# pytest configuration: http://doc.pytest.org/en/latest/customize.html -addopts = "--ignore=docs --cov=gio" - -[tool.coverage.run] -# coverage configuration: https://coverage.readthedocs.io/ -omit = [ - "gio/__init__.py", - "gio/_version.py", -] diff --git a/reports/userguide/Read_OpendTect_horizons.err.log b/reports/userguide/Read_OpendTect_horizons.err.log new file mode 100644 index 0000000..ccc299d --- /dev/null +++ b/reports/userguide/Read_OpendTect_horizons.err.log @@ -0,0 +1,64 @@ +Traceback (most recent call last): + File "/home/runner/.local/lib/python3.10/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution + executenb( + File "/home/runner/.local/lib/python3.10/site-packages/nbclient/client.py", line 1305, in execute + return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() + File "/home/runner/.local/lib/python3.10/site-packages/jupyter_core/utils/__init__.py", line 173, in wrapped + return loop.run_until_complete(inner) + File "/usr/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete + return future.result() + File "/home/runner/.local/lib/python3.10/site-packages/nbclient/client.py", line 705, in async_execute + await self.async_execute_cell( + File "/home/runner/.local/lib/python3.10/site-packages/nbclient/client.py", line 1058, in async_execute_cell + await self._check_raise_for_error(cell, cell_index, exec_reply) + File "/home/runner/.local/lib/python3.10/site-packages/nbclient/client.py", line 914, in _check_raise_for_error + raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) +nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: +------------------ +fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat' + +ds = gio.read_odt(fname) +ds + +# Raises an error: +------------------ + + +--------------------------------------------------------------------------- +TypeError Traceback (most recent call last) +Cell In[8], line 3 + 1 fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat' +----> 3 ds = gio.read_odt(fname) + 4 ds + 6 # Raises an error: + +File ~/.local/lib/python3.10/site-packages/gio/opendtect.py:222, in read_odt(fname, names, usecols, na_values, origin, step, attrs) + 170 def read_odt(fname, + 171 names=None, + 172 usecols=None, + (...) + 176 attrs=None + 177 ): + 178  """ + 179  Read an OdT horizon file. (Native format, not IESX.) + 180 + (...) + 220  crossline — and cdp_x and cdp_y, if present — will be coordinates. + 221  """ +--> 222 df = read_odt_as_df(fname, + 223  names=names, + 224  usecols=usecols, + 225  na_values=na_values + 226  ) + 228 attrs = attrs or {} + 229 attrs.update({'odt_filename': fname}) + +File ~/.local/lib/python3.10/site-packages/gio/opendtect.py:96, in read_odt_as_df(fname, names, usecols, na_values, **kwargs) + 94 else: + 95 message = 'First two columns must be integers to be interpreted as inline and crossline.' +---> 96 raise TypeError(message) + 97 cols = range(len(data_cols)) + 98 names = ix + [f'var_{n}' for n in cols if n in (usecols or cols)] + +TypeError: First two columns must be integers to be interpreted as inline and crossline. + diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 945c9b4..0000000 --- a/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -. \ No newline at end of file diff --git a/search.html b/search.html new file mode 100644 index 0000000..fa5f821 --- /dev/null +++ b/search.html @@ -0,0 +1,284 @@ + + + + + + + Search - gio documentation + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
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gio documentation
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+ + + + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 0000000..937aa07 --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"docnames": ["authors", "changelog", "contributing", "development", "gio", "index", "installation", "license", "userguide/Find_closures", "userguide/Random_grids", "userguide/Read_OpendTect_horizons", "userguide/Read_Surfer_grids", "userguide/Read_and_write_ZMAP_files", "userguide/Unmap_data_from_an_image"], "filenames": ["authors.md", "changelog.md", "contributing.md", "development.md", "gio.rst", "index.rst", "installation.md", "license.md", "userguide/Find_closures.ipynb", "userguide/Random_grids.ipynb", "userguide/Read_OpendTect_horizons.ipynb", "userguide/Read_Surfer_grids.ipynb", "userguide/Read_and_write_ZMAP_files.ipynb", "userguide/Unmap_data_from_an_image.ipynb"], "titles": ["Authors", "CHANGELOG", "Contributing", "Development", "gio package", "gio: Geo I/O for Subsurface 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"unmap-data-from-an-image"]], "Get images from the web": [[13, "get-images-from-the-web"]], "An easy image": [[13, "an-easy-image"]], "Do more": [[13, "do-more"]]}, "indexentries": {"gridinfo (class in gio.xarray)": [[4, "gio.xarray.GridInfo"]], "surfaceaccessor (class in gio.xarray)": [[4, "gio.xarray.SurfaceAccessor"]], "array_to_zmap() (in module gio.zmap)": [[4, "gio.zmap.array_to_zmap"]], "check_dialect() (in module gio.iesx)": [[4, "gio.iesx.check_dialect"]], "closure_height() (in module gio.closure)": [[4, "gio.closure.closure_height"]], "data (gio.xarray.gridinfo attribute)": [[4, "gio.xarray.GridInfo.data"]], "dataarray_to_zmap() (in module gio.zmap)": [[4, "gio.zmap.dataarray_to_zmap"]], "df_to_xarray() (in module gio.opendtect)": [[4, "gio.opendtect.df_to_xarray"]], "dims (gio.xarray.gridinfo attribute)": [[4, "gio.xarray.GridInfo.dims"]], "find_closures() (in module gio.closure)": [[4, "gio.closure.find_closures"]], "fname (gio.xarray.gridinfo attribute)": [[4, "gio.xarray.GridInfo.fname"]], "fractal_noise() (in module gio.random)": [[4, "gio.random.fractal_noise"]], "generate_random_surface() (in module gio.random)": [[4, "gio.random.generate_random_surface"]], "get_intervals() (in module gio.xy_to_grid)": [[4, "gio.xy_to_grid.get_intervals"]], "get_meta() (in module gio.opendtect)": [[4, "gio.opendtect.get_meta"]], "get_names_from_header() (in module gio.opendtect)": [[4, "gio.opendtect.get_names_from_header"]], "gio": [[4, "module-gio"]], "gio.closure": [[4, "module-gio.closure"]], "gio.esri": [[4, "module-gio.esri"]], "gio.iesx": [[4, "module-gio.iesx"]], "gio.logo": [[4, "module-gio.logo"]], "gio.opendtect": [[4, "module-gio.opendtect"]], "gio.random": [[4, "module-gio.random"]], "gio.surfer": [[4, "module-gio.surfer"]], "gio.unmap": [[4, "module-gio.unmap"]], "gio.xarray": [[4, "module-gio.xarray"]], "gio.xy_to_grid": [[4, "module-gio.xy_to_grid"]], "gio.zmap": [[4, "module-gio.zmap"]], "interpolant() (in module gio.random)": [[4, 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"gio.iesx.read_iesx_from_petrel"]], "read_odt() (in module gio.opendtect)": [[4, "gio.opendtect.read_odt"]], "read_odt_as_df() (in module gio.opendtect)": [[4, "gio.opendtect.read_odt_as_df"]], "read_surfer() (in module gio.surfer)": [[4, "gio.surfer.read_surfer"]], "read_zmap() (in module gio.zmap)": [[4, "gio.zmap.read_zmap"]], "rectify() (in module gio.xy_to_grid)": [[4, "gio.xy_to_grid.rectify"]], "regularize_columns() (in module gio.opendtect)": [[4, "gio.opendtect.regularize_columns"]], "sense_types() (in module gio.opendtect)": [[4, "gio.opendtect.sense_types"]], "source (gio.xarray.gridinfo attribute)": [[4, "gio.xarray.GridInfo.source"]], "to_odt() (in module gio.opendtect)": [[4, "gio.opendtect.to_odt"]], "to_xarray() (gio.xarray.gridinfo method)": [[4, "gio.xarray.GridInfo.to_xarray"]], "to_zmap() (gio.xarray.surfaceaccessor method)": [[4, "gio.xarray.SurfaceAccessor.to_zmap"]], "unmap_to_dataarray() (in module gio.unmap)": [[4, "gio.unmap.unmap_to_dataarray"]], "xmax (gio.xarray.gridinfo attribute)": [[4, "gio.xarray.GridInfo.xmax"]], "xmin (gio.xarray.gridinfo attribute)": [[4, "gio.xarray.GridInfo.xmin"]], "xy_to_grid() (in module gio.xy_to_grid)": [[4, "gio.xy_to_grid.xy_to_grid"]], "ymax (gio.xarray.gridinfo attribute)": [[4, "gio.xarray.GridInfo.ymax"]], "ymin (gio.xarray.gridinfo attribute)": [[4, "gio.xarray.GridInfo.ymin"]]}}) \ No newline at end of file diff --git a/tests/test_closure.py b/tests/test_closure.py deleted file mode 100644 index 9dd2c99..0000000 --- a/tests/test_closure.py +++ /dev/null @@ -1,10 +0,0 @@ -"""Test closing contour detection.""" -import gio - -def test_closure_detection(): - g = 100 * gio.generate_random_surface(200, res=3, octaves=3, random_seed=99999999) - contours = gio.find_closures(g.data, min_area=100, min_height=10) - assert len(contours) == 9 - assert contours[7].area - 4274.59701221374 < 1e-9 - # I can't figure out how to get the level from Polygon - # assert contours[7].z == 29 diff --git a/tests/test_logo.py b/tests/test_logo.py deleted file mode 100644 index d8a8b19..0000000 --- a/tests/test_logo.py +++ /dev/null @@ -1,9 +0,0 @@ -# Only testing that it functions without error, not that it is correct. -import matplotlib.pyplot as plt -from io import BytesIO - -import gio - -def test_logo(): - gio.plot(fname=BytesIO()) - plt.close('all') \ No newline at end of file diff --git a/tests/test_odt.py b/tests/test_odt.py deleted file mode 100644 index f7b4115..0000000 --- a/tests/test_odt.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Test OdT reading and writing.""" -from io import StringIO - -import pytest - -import gio - - -def test_read_odt(): - """Test the basics. - """ - ds = gio.read_odt('data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat') - assert ds['twt'].shape == (54, 57) - assert ds['twt'].mean() - 661.88136 < 1e-5 - - ds = gio.read_odt('data/OdT/3d_horizon/Segment_ILXL_Multi-line-header.dat') - assert ds['twt'].shape == (54, 57) - assert ds['twt'].mean() - 661.88136 < 1e-5 - - ds = gio.read_odt('data/OdT/3d_horizon/Segment_ILXL_No-header.dat') - assert ds['var_0'].shape == (54, 57) - assert ds['var_0'].mean() - 661.88136 < 1e-5 - - -def test_read_odt_xy(): - """ - Test the automagical XY reader. - """ - fname = 'data/OdT/3d_horizon/Segment_XY_No-header.dat' - - with pytest.warns(UserWarning): - # Warns the users it's computing a grid. - ds = gio.read_odt(fname, names=['X', 'Y', 'TWT']) - - assert ds['twt'].shape == (54, 57) - assert ds['twt'].mean() - 661.88136 < 1e-5 - - -def test_write_odt(): - """Test the basics. - """ - sio = StringIO() - ds = gio.read_odt('data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat') - _ = gio.to_odt(sio, ds, header='none') - assert sio.getvalue()[:7].split() == ['376', '914'] - - sio = StringIO() - _ = gio.to_odt(sio, ds) - assert sio.getvalue()[:11] == '# 1: Inline' diff --git a/tests/test_random.py b/tests/test_random.py deleted file mode 100644 index 4eb278f..0000000 --- a/tests/test_random.py +++ /dev/null @@ -1,10 +0,0 @@ -"""Test Surfer .grd readers.""" -import gio - - -def test_random(): - """Test the basics. - """ - da = gio.generate_random_surface(10, random_seed=42) - assert da.shape == (12, 12) - assert da.mean() - -0.14526361 < 1e-6 diff --git a/tests/test_surfer.py b/tests/test_surfer.py deleted file mode 100644 index 16af362..0000000 --- a/tests/test_surfer.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Test Surfer .grd readers.""" -import gio - - -def test_surfer_6(): - """Test the basics. - """ - da = gio.read_surfer('data/Surfer/surfer-6-ascii-tiny.grd') - assert da.shape == (10, 10) - assert da.max() == 97.19 - -def test_surfer_7(): - """Test the basics. - """ - da = gio.read_surfer('data/Surfer/WDS1_Si_TAP_Quant.grd') - assert da.shape == (352, 629) - assert da.mean() - 23.4545715 < 1e-5 diff --git a/tests/test_unmap.py b/tests/test_unmap.py deleted file mode 100644 index e78c334..0000000 --- a/tests/test_unmap.py +++ /dev/null @@ -1,28 +0,0 @@ -from io import BytesIO - -import requests -import numpy as np -from PIL import Image - -import gio - - -def get_image_from_web(uri): - data = requests.get(uri).content - img = Image.open(BytesIO(data)).convert('RGB') - rgb_im = np.asarray(img)[..., :3] / 255. - return rgb_im - - -def test_unmap(): - # An image from Matteo Niccoli's blog: - # https://mycartablog.com/2014/08/24/what-your-brain-does-with-colours-when-you-are-not-looking-part-1/ - - img = get_image_from_web('https://i0.wp.com/mycartablog.com/wp-content/uploads/2014/03/spectrogram_jet.png') - - cmap = (2035, 102, 2079, 1198) # (left, top, right, bottom) - crop = (312, 102, 1954, 1198) - vrange = (-156.2, -69.0) - data = gio.unmap_to_dataarray(img, cmap=cmap, crop=crop, vrange=vrange) - assert data.shape == (1096, 1642) - assert np.mean(data) + 133.46107021444217 < 1e-6 diff --git a/tests/test_zmap.py b/tests/test_zmap.py deleted file mode 100644 index 4e5d603..0000000 --- a/tests/test_zmap.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Test ZMAP reading and writing.""" -from io import StringIO - -import gio - - -def test_read_zmap(): - """Test the basics. - """ - da = gio.read_zmap('data/ZMAP/NStopo.dat') - assert da.shape == (208, 435) - assert da.mean() + 531.92306539 < 1e-6 - assert 'NStopo.dat' in da.attrs['fname'] - - -def test_write_zmap(): - """Test the basics. - """ - sio = StringIO() - z = gio.generate_random_surface(size=(10, 20), random_seed=42) - z.gio.to_zmap(sio) - assert sio.getvalue()[46:60] == '@None, GRID, 6' - assert len(sio.getvalue()) == 3038 - assert sio.getvalue()[-30:] == '0.198 0.332 0.207\n' diff --git a/userguide/Find_closures.html b/userguide/Find_closures.html new file mode 100644 index 0000000..97b4cb2 --- /dev/null +++ b/userguide/Find_closures.html @@ -0,0 +1,385 @@ + + + + + + + + + Find closures - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + 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gio documentation
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+

Find closures#

+

gio is mostly about data loading, but it can do a few other useful things. One of those things is finding closed contours on a grid.

+

Let’s start by making a random grid,and we’ll scale it to make it more like a geological surface:

+
+
+
import matplotlib.pyplot as plt
+import gio
+
+g = gio.generate_random_surface(200, res=3, octaves=3, random_seed=42)
+g = 100 * g - 1000
+
+g.plot()
+plt.contour(g, levels=20, colors='black', linewidths=0.5, linestyles='solid', alpha=0.45)
+
+
+
+
+
<matplotlib.contour.QuadContourSet at 0x7f5f5d64f790>
+
+
+../_images/b3f21a798663779acef61898a989c3c5318d0ced22cb828fb1eccf317016aa08.png +
+
+

Now we can ask gio to find the closing contours.

+
+
+
contours = gio.find_closures(g)
+
+
+
+
+

The result is a bunch of shapely.Polygon objects:

+
+
+
contours[10]
+
+
+
+
+../_images/fcdbd05bdb7478dfe201cc93eec76383425505f8931c64aa378bedc51b150ecf.svg
+
+

The ‘level’ (contour value) is stored as the z-value of the Polygon.

+

Let’s plot them:

+
+
+
g.plot.imshow()
+plt.contour(g, levels=10, colors='black', linewidths=0.5, linestyles='solid', alpha=0.45)
+
+for c in contours:
+    plt.plot(*c.exterior.xy, 'white')
+
+
+
+
+../_images/71bcca61aa42fa0c1a34f69563f8b004f168a86b921b9d46f04b77fa857e7e93.png +
+
+

Cool! But we probably don’t care about tiny closures. We can apply minimum area (in whatever units the grid cells represent) or minimum closure height (or both!), e.g.

+
+
+
contours = gio.find_closures(g, min_height=25)
+
+g.plot.imshow()
+plt.contour(g, levels=10, colors='black', linewidths=0.5, linestyles='solid', alpha=0.45)
+
+for c in contours:
+    plt.plot(*c.exterior.xy, 'white')
+
+
+
+
+../_images/596fdbe05a3800806e7f0dff6553d181a029cd53ccded3da88835963419d249a.png +
+
+

Shapely Polygon objects are nice because they can do lots of things. They also know their own areas:

+
+
+
for i, c in enumerate(contours):
+    print(f"Polygon {i} area: {c.area:.1f} grid cells")
+
+
+
+
+
Polygon 0 area: 833.8 grid cells
+Polygon 1 area: 539.2 grid cells
+Polygon 2 area: 3236.9 grid cells
+Polygon 3 area: 454.2 grid cells
+
+
+
+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/userguide/Random_grids.html b/userguide/Random_grids.html new file mode 100644 index 0000000..9c9ad3c --- /dev/null +++ b/userguide/Random_grids.html @@ -0,0 +1,723 @@ + + + + + + + + + Random grids - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
gio documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

Random grids#

+

Because sometimes you just want some random data.

+

This module implements 2D fractal noise, combining one or more octaves of Perlin noise.

+
+
+
import gio
+
+g = gio.generate_random_surface(200, res=3, octaves=3)
+
+g
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray (i: 204, j: 204)>
+array([[ 0.        , -0.05736413, -0.11728367, ..., -0.11920205,
+        -0.08226758, -0.04223049],
+       [ 0.01317427, -0.04415499, -0.10391634, ..., -0.1535234 ,
+        -0.11710962, -0.07728829],
+       [ 0.0252488 , -0.03198195, -0.09153055, ..., -0.18582673,
+        -0.14969093, -0.10998765],
+       ...,
+       [ 0.05144513,  0.04374787,  0.03244042, ...,  0.06511937,
+         0.06545176,  0.06280169],
+       [ 0.03661664,  0.02913997,  0.01798883, ...,  0.04790077,
+         0.04768927,  0.04426351],
+       [ 0.01900629,  0.01164235,  0.00067529, ...,  0.0296427 ,
+         0.0290597 ,  0.02524733]])
+Coordinates:
+  * i        (i) int64 0 1 2 3 4 5 6 7 8 ... 195 196 197 198 199 200 201 202 203
+  * j        (j) int64 0 1 2 3 4 5 6 7 8 ... 195 196 197 198 199 200 201 202 203
+Attributes:
+    source:   gio.random
+
+

We can plot this thing:

+
+
+
g.plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f3114fe9700>
+
+
+../_images/206eac4544004a4e4811e13bb6f73ae2e64e9f2c4a35283f19dafe27164bb2c8.png +
+
+

The noise does not have to be isotropic:

+
+
+
g = gio.generate_random_surface(200, res=(2, 5), octaves=3)
+
+g.plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f31104a16d0>
+
+
+../_images/06c58ba264e1bdc8dca9dc561d9d624997772add6692eaa04f6db39bd28d647d.png +
+
+

Note that the size and res can both be tuples.

+

The size will be ‘at least’ the specified size in that dimension: it might have to be larger for the algorithm to work.

+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/userguide/Read_OpendTect_horizons.html b/userguide/Read_OpendTect_horizons.html new file mode 100644 index 0000000..22fbba3 --- /dev/null +++ b/userguide/Read_OpendTect_horizons.html @@ -0,0 +1,4452 @@ + + + + + + + + + Read OpendTect horizons - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
gio documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

Read OpendTect horizons#

+

The best way to export horizons from OpendTect is with these options:

+
    +

  • x/y and inline/crossline

    • +

  • with header (single or multi-line, it doesn’t matter)

    • +

  • choose all the attributes you want

    • +
+

On the last point, if you choose multiple horizons in one file, you can only have one attribute in the file.

+
+

IL/XL only, single-line header, multiple attributes#

+
+
+
import gio
+
+ds = gio.read_odt('../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat')
+ds
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:         (iline: 54, xline: 57)
+Coordinates:
+  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482
+  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924
+Data variables:
+    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+Attributes:
+    odt_filename:  ../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat
+
+
+
+
ds['twt'].plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f6d510be230>
+
+
+../_images/b254876fe1ce8cbffa8611c33ddc9f65693f2928e19e2da53d3f6e20c538a2e7.png +
+
+
+
+

IL/XL and XY, multi-line header, multiple attributes#

+
+

Load everything (default)#

+

X and Y are loaded as cdp_x and cdp_y, to be consistent with the seisnc standard in segysak.

+
+
+
ds = gio.read_odt('../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-header.dat')
+ds
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:         (iline: 54, xline: 57)
+Coordinates:
+  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482
+  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924
+    cdp_x           (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    cdp_y           (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+Data variables:
+    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+Attributes:
+    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h...
+
+
+
+
import matplotlib.pyplot as plt
+
+plt.scatter(ds.coords['cdp_x'], ds.coords['cdp_y'], s=5)
+
+
+
+
+
<matplotlib.collections.PathCollection at 0x7f6d53c86bc0>
+
+
+../_images/e2719454dffafab52be4329534c4a209b7183f801662f837021c2716fe76281d.png +
+
+
+
+

Load only inline, crossline, TWT#

+

There is only one attribute here: Z, which is the two-way time of the horizon.

+

Note that when loading data from OpendTect, you always get an xarray.Dataset, even if there’s only a single attribute. This is because the format supports multiple grids and we didn’t want you to have to guess what a given file would produce.

+
+
+
fname = '../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-header.dat'
+names = ['Inline', 'Crossline', 'Z']  # Must match OdT DAT file.
+
+ds = gio.read_odt(fname, names=names)
+ds
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:  (iline: 54, xline: 57)
+Coordinates:
+  * iline    (iline) int64 376 378 380 382 384 386 ... 472 474 476 478 480 482
+  * xline    (xline) int64 812 814 816 818 820 822 ... 914 916 918 920 922 924
+Data variables:
+    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan
+Attributes:
+    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY-and-ILXL_Multi-line-h...
+
+
+
+
+

XY only#

+

If you have a file with no IL/XL, gio can try to load data using only X and Y:

+
    +

  • If there’s a header you can load any number of attributes.

    • +

  • If there’s no header, you can only one attribute (e.g. TWT) automagically…

    • +

  • OR, if there’s no header, you can provide names to tell gio what everything is.

    • +
+

gio must create fake inline and crossline numbers; you can provide an origin and a step size. For example, notice above that the true inline and crossline numbers are:

+
    +

  • inline: 376, 378, 380, etc.

    • +

  • crossline: 812, 814, 816, etc.

    • +
+

So we can pass an origin of (376, 812) and a step of (2, 2) to mimic these.

+
+

Header present#

+
+
+
fname = '../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat'
+
+ds = gio.read_odt(fname, origin=(376, 812), step=(2, 2))
+ds
+
+
+
+
+
/home/runner/.local/lib/python3.10/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.
+  dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:         (iline: 54, xline: 57)
+Coordinates:
+  * iline           (iline) int64 376 378 380 382 384 ... 474 476 478 480 482
+  * xline           (xline) int64 812 814 816 818 820 ... 916 918 920 922 924
+    cdp_x           (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    cdp_y           (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+Data variables:
+    twt             (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    amplitude       (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    correlation     (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    seed_index      (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+    tracking_order  (iline, xline) float64 nan nan nan nan ... nan nan nan nan
+Attributes:
+    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_Single-line-header.dat
+
+
+
+
ds['twt'].plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f6d4edccb50>
+
+
+../_images/b254876fe1ce8cbffa8611c33ddc9f65693f2928e19e2da53d3f6e20c538a2e7.png +
+
+
+
+

No header, more than one attribute: raises an error#

+
+
+
fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat'
+
+ds = gio.read_odt(fname)
+ds
+
+# Raises an error:
+
+
+
+
+
---------------------------------------------------------------------------
+TypeError                                 Traceback (most recent call last)
+Cell In[8], line 3
+      1 fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat'
+----> 3 ds = gio.read_odt(fname)
+      4 ds
+      6 # Raises an error:
+
+File ~/.local/lib/python3.10/site-packages/gio/opendtect.py:222, in read_odt(fname, names, usecols, na_values, origin, step, attrs)
+    170 def read_odt(fname,
+    171              names=None,
+    172              usecols=None,
+   (...)
+    176              attrs=None
+    177              ):
+    178     """
+    179     Read an OdT horizon file. (Native format, not IESX.)
+    180 
+   (...)
+    220             crossline — and cdp_x and cdp_y, if present — will be coordinates.
+    221     """
+--> 222     df = read_odt_as_df(fname,
+    223                         names=names,
+    224                         usecols=usecols,
+    225                         na_values=na_values
+    226                         )
+    228     attrs = attrs or {}
+    229     attrs.update({'odt_filename': fname})
+
+File ~/.local/lib/python3.10/site-packages/gio/opendtect.py:96, in read_odt_as_df(fname, names, usecols, na_values, **kwargs)
+     94 else:
+     95     message = 'First two columns must be integers to be interpreted as inline and crossline.'
+---> 96     raise TypeError(message)
+     97 cols = range(len(data_cols))
+     98 names = ix + [f'var_{n}' for n in cols if n in (usecols or cols)]
+
+TypeError: First two columns must be integers to be interpreted as inline and crossline.
+
+
+
+
+
+
+
fname = '../../data/OdT/3d_horizon/Segment_XY_No-header.dat'
+
+ds = gio.read_odt(fname, names=['X', 'Y', 'TWT'])
+ds
+
+
+
+
+
/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.
+  dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:  (iline: 54, xline: 57)
+Coordinates:
+  * iline    (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53
+  * xline    (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56
+    cdp_x    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan
+    cdp_y    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan
+Data variables:
+    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan
+Attributes:
+    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_No-header.dat
+
+
+
+
ds['twt'].plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f22a5356a90>
+
+
+../_images/a8b21a3f1c7f6adc750b602cbd5b733245c80a1bc1bd9e838a46869d36279f8b.png +
+
+
+
+
+

Sparse data#

+

Sometimes a surface only exists at a few points, e.g. a 3D seismic interpretation grid. In general, loading data like this is completely safe if you have inline and xline locations. If you only have (x, y) locations, gio will attempt to load it, but you should inspect the result carefullly.

+
+
+
fname = '../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single-line-header.dat'
+
+ds = gio.read_odt(fname)
+ds
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:    (iline: 62, xline: 185)
+Coordinates:
+  * iline      (iline) int64 1190 1197 1198 1199 1200 ... 1254 1255 1256 1257
+  * xline      (xline) int64 1941 1942 1943 1944 1945 ... 2122 2123 2124 2125
+    cdp_x      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan
+    cdp_y      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan
+Data variables:
+    twt        (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan
+    amplitude  (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan
+Attributes:
+    odt_filename:  ../../data/OdT/3d_horizon/Nimitz_Salmon_XY-and-ILXL_Single...
+
+
+
+
ds['twt'].plot.imshow()
+
+
+
+
+
<matplotlib.image.AxesImage at 0x7f22a5289e80>
+
+
+../_images/e8703aa75e823f8c54abdf10319252a49ceaba03f2df48eaf3d1087921e4dc15.png +
+
+

There’s some sort of artifact with the default plot style, which uses pcolormesh I think.

+
+
+
ds['twt'].plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f22a51c4700>
+
+
+../_images/9cd18477ebf4679efcb3713f2932d1d599aba44bb1cc09377ac1178d9e636ae9.png +
+
+
+
+

Multiple horizons in one file#

+

You can export multiple horizons from OpendTect. These will be loaded as one xarray.Dataset as different Data variables. (The actual attribute you exported from OdT is always called Z; this information is not retained in the xarray.)

+
+
+
fname = '../../data/OdT/3d_horizon/F3_Multi-H2-H4_ILXL_Single-line-header.dat'
+
+ds = gio.read_odt(fname)
+ds
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:               (xline: 93, iline: 45)
+Coordinates:
+  * xline                 (xline) int64 964 966 968 970 ... 1142 1144 1146 1148
+  * iline                 (iline) int64 110 112 114 116 118 ... 192 194 196 198
+Data variables:
+    F3_Demo_2_FS6         (iline, xline) float64 nan nan nan ... 825.4 823.9
+    F3_Demo_4_Truncation  (iline, xline) float64 820.0 820.7 ... 635.3 635.2
+
+
+
+
ds['F3_Demo_2_FS6'].plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f22a5121280>
+
+
+../_images/00b781fbecdcee7b5a5fb722738ef0f9599ffd62a231acfabe6094163ffa6a1f.png +
+
+
+
+
ds['F3_Demo_4_Truncation'].plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f22a5054070>
+
+
+../_images/4efc94956f94297900fde5c39d553acc0365fa58d5077617a9f288e2cfec82fa.png +
+
+
+

Multi-horizon, no header#

+

Unfortunately, OdT exports (x, y) in the first two columns, meaning you can’t assume that columns 3 and 4 are inline, crossline. So if there’s no header, and XY as well as inline/xline, you have to give the column names:

+
+
+
import gio
+
+fname = '../../data/OdT/3d_horizon/Test_Multi_XY-and-ILXL_Z-only.dat'
+
+ds = gio.read_odt(fname, names=['Horizon', 'X', 'Y', 'Inline', 'Crossline', 'Z'])
+ds
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:      (iline: 1, xline: 10)
+Coordinates:
+  * iline        (iline) int64 990
+  * xline        (xline) int64 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
+    cdp_x        (iline, xline) float64 1.703e+06 1.703e+06 ... 1.703e+06
+    cdp_y        (iline, xline) float64 5.817e+06 5.817e+06 ... 5.818e+06
+Data variables:
+    blue event   (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03
+    green event  (iline, xline) float64 1.29e+03 1.293e+03 ... 1.305e+03
+
+
+
+
+

Undefined values#

+

These are exported as '1e30' by default. You can override this (not add to it, which is the default pandas behaviour) by passing one or more na_values.

+
+
+
fname = '../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat'
+
+ds = gio.read_odt(fname, names=['X', 'Y', 'TWT'])
+ds
+
+
+
+
+
/home/matt/miniconda3/envs/py39/lib/python3.9/site-packages/gio/opendtect.py:244: UserWarning: Attempting to construct grid from (x, y) locations.
+  dx = df_to_xarray(df, attrs=attrs, origin=origin, step=step)
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:  (iline: 54, xline: 57)
+Coordinates:
+  * iline    (iline) int64 0 1 2 3 4 5 6 7 8 9 ... 44 45 46 47 48 49 50 51 52 53
+  * xline    (xline) int64 0 1 2 3 4 5 6 7 8 9 ... 47 48 49 50 51 52 53 54 55 56
+    cdp_x    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan
+    cdp_y    (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan
+Data variables:
+    twt      (iline, xline) float64 nan nan nan nan nan ... nan nan nan nan nan
+Attributes:
+    odt_filename:  ../../data/OdT/3d_horizon/Segment_XY_No-header_NULLs.dat
+
+
+
+
ds['twt'].plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f22a4faa100>
+
+
+../_images/2187d362be7150f8dfa1340cc630ae0a5b678ebf9c1574cb914760106cc4e33c.png +
+
+
+
+

Writing OdT files#

+

You can write an OdT file using gio.to_odt():

+
+
+
_ = gio.to_odt('out.dat', ds)
+
+
+
+
+
+
+
!head out.dat
+
+
+
+
+
# 1: Inline
+# 2: Crossline
+# 3: X
+# 4: Y
+# 5: Z
+# - - - - - - - - - -
+0	51	620986.88	6080882.36	648.6175060272217
+0	52	621036.86	6080883.76	647.8394865989685
+0	53	621086.84	6080885.16	647.9663848876953
+1	50	620935.5	6080930.95	649.9454379081726
+
+
+
+
+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/userguide/Read_Surfer_grids.html b/userguide/Read_Surfer_grids.html new file mode 100644 index 0000000..90f71f8 --- /dev/null +++ b/userguide/Read_Surfer_grids.html @@ -0,0 +1,1957 @@ + + + + + + + + + Read surfer grids - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
gio documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

Read surfer grids#

+

There are 3 varieties:

+
    +

  • Surfer 6 binary

    • +

  • Surfer 6 ASCII

    • +

  • Surfer 7 binary

    • +
+

In theory we can read all of these, but I don’t have a Surfer 6 binary file to test on.

+
+

A small binary file#

+

From the docs.

+
+
+
!head ../data/Surfer/surfer-6-ascii-tiny.grd
+
+
+
+
+
DSAA
+10 10 
+0.0 9.0
+0.0 7.0
+25.00 97.19
+91.03 77.21 60.55 46.67 52.73 64.05 41.19 54.99 44.30 25.00
+96.04 81.10 62.38 48.74 57.50 63.27 48.67 60.81 51.78 33.63
+92.10 85.05 65.09 53.01 64.44 65.64 52.53 66.54 59.29 41.33
+94.04 85.63 65.56 55.32 73.18 70.88 55.35 76.27 67.20 45.78
+97.19 82.00 64.21 61.97 82.99 80.34 58.55 86.28 75.02 48.75
+
+
+
+
+
+
+
import gio
+
+da = gio.read_surfer('../data/Surfer/surfer-6-ascii-tiny.grd')
+da
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray (y: 10, x: 10)>
+array([[70.  , 54.19, 62.27, 74.51, 55.95, 55.42, 71.21, 74.63, 63.14,
+        44.99],
+       [74.77, 66.02, 70.29, 75.16, 60.56, 65.56, 85.07, 89.81, 74.53,
+        51.69],
+       [80.88, 75.56, 74.35, 72.47, 66.93, 75.49, 86.39, 92.1 , 84.41,
+        55.  ],
+       [86.31, 77.58, 67.71, 68.5 , 73.37, 74.84, 65.35, 95.55, 85.92,
+        55.76],
+       [91.36, 78.73, 64.05, 65.6 , 82.58, 81.37, 61.16, 89.09, 81.36,
+        54.87],
+       [97.19, 82.  , 64.21, 61.97, 82.99, 80.34, 58.55, 86.28, 75.02,
+        48.75],
+       [94.04, 85.63, 65.56, 55.32, 73.18, 70.88, 55.35, 76.27, 67.2 ,
+        45.78],
+       [92.1 , 85.05, 65.09, 53.01, 64.44, 65.64, 52.53, 66.54, 59.29,
+        41.33],
+       [96.04, 81.1 , 62.38, 48.74, 57.5 , 63.27, 48.67, 60.81, 51.78,
+        33.63],
+       [91.03, 77.21, 60.55, 46.67, 52.73, 64.05, 41.19, 54.99, 44.3 ,
+        25.  ]])
+Coordinates:
+  * x        (x) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
+  * y        (y) float64 0.0 0.7778 1.556 2.333 3.111 ... 4.667 5.444 6.222 7.0
+Attributes:
+    source:   ../data/Surfer/surfer-6-ascii-tiny.grd
+
+
+
+
da.max()
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray ()>
+array(97.19)
+
+
+
+

Another ASCII#

+
+
+
import gio
+
+da = gio.read_surfer('../data/Surfer/surfer-6-ascii.grd')
+da
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray (y: 182, x: 222)>
+array([[-58.1195, -55.5528, -52.4692, ..., -41.9582, -47.2193, -51.5736],
+       [-57.7877, -55.2337, -52.1599, ..., -41.5877, -46.8911, -51.2844],
+       [-57.4487, -54.9043, -51.8392, ..., -41.1691, -46.5296, -50.9713],
+       ...,
+       [ 98.8531, 103.599 , 106.683 , ...,  23.8501,  22.8811,  22.0409],
+       [104.529 , 108.384 , 111.854 , ...,  18.179 ,  17.2994,  16.5399],
+       [109.395 , 112.507 , 115.76  , ...,  13.2723,  12.4487,  11.7467]])
+Coordinates:
+  * x        (x) float64 0.0 5e+03 1e+04 1.5e+04 ... 1.095e+06 1.1e+06 1.105e+06
+  * y        (y) float64 0.0 5e+03 1e+04 1.5e+04 ... 8.95e+05 9e+05 9.05e+05
+Attributes:
+    source:   ../data/Surfer/surfer-6-ascii.grd
+
+
+
+
da.plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f0adca95640>
+
+
+../_images/6b788e2fbd8a97276ab6392844304696c39b7c522d28332f94a1843ce7c00756.png +
+
+
+
+

A binary file#

+
+
+
import gio
+
+da = gio.read_surfer('../data/Surfer/WDS1_Si_TAP_Quant.grd')
+da
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray (y: 352, x: 629)>
+array([[34.34440231, 35.18498611, 35.32251358, ..., 36.05297852,
+        35.76726532, 35.35245132],
+       [35.43532181, 35.00969315, 35.75266647, ..., 36.27960205,
+        35.19588852, 36.22369003],
+       [35.50966644, 35.82201385, 35.89884949, ..., 36.19258118,
+        36.06084442, 35.46885681],
+       ...,
+       [22.57001305, 23.0938015 , 22.96144295, ..., 36.87253571,
+        35.71622849, 35.71092606],
+       [22.57962227, 22.91576004, 22.62903214, ..., 37.23720932,
+        36.19213867, 36.02495956],
+       [23.00543976, 22.40481567, 22.8556118 , ..., 36.53549194,
+        35.47878265, 35.03230667]])
+Coordinates:
+  * x        (x) float64 2.604 2.605 2.605 2.606 ... 2.917 2.917 2.918 2.918
+  * y        (y) float64 23.47 23.47 23.47 23.47 ... 23.64 23.64 23.64 23.64
+Attributes:
+    source:   ../data/Surfer/WDS1_Si_TAP_Quant.grd
+
+
+
+
da.plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f0ada789790>
+
+
+../_images/b53a6e61569c1ed5b1ec24186fef8733aa67452e4c9b8b9bd47ea516f7742f82.png +
+
+
+
+
da.shape
+
+
+
+
+
(352, 629)
+
+
+
+
+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/userguide/Read_and_write_ZMAP_files.html b/userguide/Read_and_write_ZMAP_files.html new file mode 100644 index 0000000..2397d5d --- /dev/null +++ b/userguide/Read_and_write_ZMAP_files.html @@ -0,0 +1,872 @@ + + + + + + + + + Read and write ZMAP files - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
gio documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

Read and write ZMAP files#

+

The venerable ZMAP format looks like this:

+
! Landmark Zmap grid file name:   .DATANSLCU.dat
+! Created/converted by Oasis Montaj, Geosoft Inc.
+@.DATANSLCU.dat, GRID, 3
+20, 1e+30, , 7, 1
+208, 435, -630000.0, 672000.0, 2000000.0, 2621000.0
+0.0, 0.0, 0.0
+@
+      -16481.9570313      -16283.9033203      -16081.5751953
+      -15856.7861328      -15583.7167969      -15255.7343750
+      -14869.3769531      -14426.1513672      -13915.8769531
+
+
+

Files often have a the extension .DAT. The first two lines shown are comments. The lines between the @ signs give the name of the grid, with some metadata such as null value, precision, and so on.

+

gio wraps the zmapio library. There are several functions:

+
    +

  • read_zmap(): read a ZMAP file.

    • +

  • array_to_zmap(): write a NumPy array as a ZMAP file.

    • +

  • dataarray_to_zmap(): write an Xarray DataArray as a ZMAP file.

    • +

  • xarray.DataArray.gio.to_zmap(): an Xarray accessor providing another way to save a DataArray as a ZMAP file.

    • +
+

Let’s read some data from a ZMAP file:

+
+
+
!head ../../data/ZMAP/NStopo.dat
+
+
+
+
+
! Landmark Zmap grid file name:   .\DATA\NStopo.dat
+! Created/converted by Oasis Montaj, Geosoft Inc.
+@.\DATA\NStopo.dat  ,GRID,  4
+        20,   1.0E+30,       ,     7,       1
+       208,       435,  -630000.0000,   672000.0000,  2000000.0000,  2621000.0000,
+        0.0000,        0.0000,        0.0000,
+@
+         -67.2144775         -67.5697021         -67.1472168         -69.0812378
+         -73.1813660         -74.3084717         -72.7660828         -72.0339966
+         -70.5137329         -68.5551758         -66.1948547         -62.7756653
+
+
+
+
+
+
+
import gio
+
+z = gio.read_zmap('../../data/ZMAP/NStopo.dat')
+
+z.plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f9f2962c5e0>
+
+
+../_images/75bf8db61e315f6fe1b20ac1e86fdc2979e27f2bddace1039a801def3dfc65ca.png +
+
+

Let’s take a look at the xarray object too:

+
+
+
z
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray '.\\DATA\\NStopo.dat' (Y: 208, X: 435)>
+array([[  -50.138916 ,   -43.8048401,   -39.550293 , ...,   702.7687378,
+          656.3284302,   581.6201782],
+       [  -50.7771301,   -43.4010925,   -40.3817444, ...,   626.8185425,
+          587.7715454,   528.4921265],
+       [  -49.3357849,   -47.8291626,   -43.6928101, ...,   472.8615723,
+          492.1585693,   453.5387573],
+       ...,
+       [  -67.1472168,   -72.3484497,   -78.2303467, ..., -2397.1206055,
+        -2378.3427734, -2362.1647949],
+       [  -67.5697021,   -72.6132202,   -81.2595825, ..., -2409.6945801,
+        -2391.8217773, -2376.5600586],
+       [  -67.2144775,   -74.9806519,   -82.2666016, ..., -2416.8630371,
+        -2398.3891602, -2387.4147949]])
+Coordinates:
+  * X        (X) float64 -6.3e+05 -6.27e+05 -6.24e+05 ... 6.69e+05 6.72e+05
+  * Y        (Y) float64 2e+06 2.003e+06 2.006e+06 ... 2.618e+06 2.621e+06
+Attributes:
+    fname:       ../../data/ZMAP/NStopo.dat
+    null_value:  1e+30
+    comment:     [' Landmark Zmap grid file name:   .\\DATA\\NStopo.dat', ' C...
+
+
+

Transform the surface#

+

The units are feet; let’s transform them to metres and save a new file.

+
+
+
z_m = 0.3048 * z
+
+
+
+
+
+
+

Use the xarray accessory#

+

If your surface is held in an xarray.DataArray then the accessory is the easiest way to save it in ZMAP format.

+

To use it, access the to_zmap() function in the gio accessory. After importing gio, the accessory is attached to every instance of a DataArray.

+
+
+
z_m.gio.to_zmap('NStopo_m.dat')
+
+
+
+
+

We can take a peek at this file using IPython to see if it looks legit:

+
+
+
!head ./NStopo_m.dat
+
+
+
+
+
!File created by gio.to_zmap xarray accessory
+@.\DATA\NStopo.dat, GRID, 6
+12, -999.25, , 3, 1
+208, 435, -630000.0, 672000.0, 2000000.0, 2621000.0
+0.0, 0.0, 0.0
+@
+     -20.487     -20.595     -20.466     -21.056     -22.306     -22.649
+     -22.179     -21.956     -21.493     -20.896     -20.176     -19.134
+     -18.187     -17.888     -17.495     -17.983     -17.928     -17.516
+     -18.176     -21.011     -21.595     -20.317     -19.193     -18.450
+
+
+
+
+
+
+

Use the function#

+

If your surface is a NumPy array, then you can use the array_to_zmap() function.

+

Note that if you want the ZMAP file to have real-world coordinates, you will need to provide the extent as a list or tuple of min X, max X, min Y, max Y. For example:

+
+
+
arr = z_m.data
+
+gio.array_to_zmap('NStopo_m.dat', arr, extent=[-630000.0, 672000.0, 2000000.0, 2621000.0])
+
+
+
+
+

It should look like the file we got before:

+
+
+
!head ./NStopo_m.dat
+
+
+
+
+
!File created by gio.array_to_zmap
+@Z, GRID, 6
+12, -999.25, , 3, 1
+208, 435, -630000.0, 672000.0, 2000000.0, 2621000.0
+0.0, 0.0, 0.0
+@
+     -20.487     -20.595     -20.466     -21.056     -22.306     -22.649
+     -22.179     -21.956     -21.493     -20.896     -20.176     -19.134
+     -18.187     -17.888     -17.495     -17.983     -17.928     -17.516
+     -18.176     -21.011     -21.595     -20.317     -19.193     -18.450
+
+
+
+
+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file diff --git a/userguide/Unmap_data_from_an_image.html b/userguide/Unmap_data_from_an_image.html new file mode 100644 index 0000000..57a1c59 --- /dev/null +++ b/userguide/Unmap_data_from_an_image.html @@ -0,0 +1,1183 @@ + + + + + + + + + Unmap data from an image - gio documentation + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
gio documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+ +
+ +
+ +
+
+
+

Unmap data from an image#

+

Sometimes we’d like to rip data (i.e. the underlying digits) from a pseudocolour (aka false colour) image. For example, maybe you found a nice map or seismic section in a paper, and want to try loading the data into some other software to play with.

+

The gio.unmap_to_dataarray() function attempts to do this. Keep your hopes in check though: depending on the circumstances, it might be impossible.

+

Note: This function wraps unmap() from the unmap library. If you only want a NumPy array from the plot image, you could just use that function directly. All gio is doing is wrapping its output in an xarray.DataArray.

+
+

Get images from the web#

+

Let’s write a little function to load images from the Internet and transform them into NumPy arrays.

+
+
+
from io import BytesIO
+
+import requests
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+
+
+def get_image_from_web(uri):
+    data = requests.get(uri).content
+    img = Image.open(BytesIO(data)).convert('RGB')
+    rgb_im = np.asarray(img)[..., :3] / 255.
+    return rgb_im
+
+# An image from Matteo Niccoli's blog:
+# https://mycartablog.com/2014/08/24/what-your-brain-does-with-colours-when-you-are-not-looking-part-1/
+img = get_image_from_web('https://i0.wp.com/mycartablog.com/wp-content/uploads/2014/03/spectrogram_jet.png')
+
+plt.imshow(img)
+
+
+
+
+
<matplotlib.image.AxesImage at 0x7f0100088d30>
+
+
+../_images/e59a5af379c1ea995f2560cc739acedd0d95a92f2471d976da9c117106cd686a.png +
+
+
+
+

An easy image#

+

Let’s start with that image, it looks approachable:

+
    +

  • It’s large.

    • +

  • It’s a PNG, so losslessly compressed.

    • +

  • No hillshading.

    • +

  • It contains the colourmap, and Matteo tells us that it’s jet

    • +
+

And, even better, when Matteo used it on his blog, he provided lots of other plots that we can compare to!

+
+
+
import gio
+
+data = gio.unmap_to_dataarray(img, cmap='jet')
+
+data
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray (i: 1351, j: 2402)>
+array([[nan, nan, nan, ..., nan, nan, nan],
+       [nan, nan, nan, ..., nan, nan, nan],
+       [nan, nan, nan, ..., nan, nan, nan],
+       ...,
+       [nan, nan, nan, ..., nan, nan, nan],
+       [nan, nan, nan, ..., nan, nan, nan],
+       [nan, nan, nan, ..., nan, nan, nan]])
+Coordinates:
+  * j        (j) float64 0.0 1.0 2.001 3.001 ... 2.4e+03 2.401e+03 2.402e+03
+  * i        (i) float64 0.0 1.001 2.001 3.002 ... 1.349e+03 1.35e+03 1.351e+03
+Attributes:
+    source:   
+    fname:
+
+
+
+
data.plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f00da47ae80>
+
+
+../_images/406324c29ad276a68078afd5bd774f12f5a0ee3c20739c2c793c7572c5e01964.png +
+
+

Notice that the data ranges from 0 to 1, whereas in the image the colorbar is labelled from about -160 to -70.

+

Also the i and j coordinates are just the indices from the NumPy array, whereas the original plot had time and frequency coordinates.

+

In the next section we’ll fix these issues.

+
+
+

Do more#

+

Let’s do more with this image!

+

We happen to know the colourmap was jet in this case, but we could give gio the pixel area of the colourmap, as a (left, top, right, bottom) tuple. We’ll also pass in the vrange of the values, which we can estimate from the colourbar labels in the image. While we’re at it, let’s crop the result down to just the data area, and pass in the extent of the data in real-world coordinates (time and frequency in this case).

+
+
+
cmap = (2035, 102, 2079, 1198)       # (left, top, right, bottom)
+crop = (312, 102, 1954, 1198)        # (left, top, right, bottom)
+extent = (-0.01, 0.05, 200, 10_000)  # (left, right, bottom, top)
+vrange = (-156.2, -69.0)
+data = gio.unmap_to_dataarray(img, cmap=cmap, crop=crop, extent=extent, vrange=vrange)
+
+data
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray (x: 1096, y: 1642)>
+array([[ -69.        ,  -69.        ,  -69.        , ...,  -69.        ,
+         -69.        ,  -69.        ],
+       [ -69.        ,  -69.        , -155.17411765, ..., -154.83215686,
+        -154.83215686, -156.2       ],
+       [ -69.        ,  -69.        , -155.17411765, ..., -154.83215686,
+        -154.83215686, -156.2       ],
+       ...,
+       [ -69.        ,  -69.        , -156.2       , ..., -146.96705882,
+        -146.96705882, -150.38666667],
+       [ -69.        ,  -69.        , -156.2       , ..., -146.96705882,
+        -146.96705882, -150.38666667],
+       [ -69.        ,  -69.        , -156.2       , ..., -146.96705882,
+        -146.96705882, -150.38666667]])
+Coordinates:
+  * y        (y) float64 -0.01 -0.009963 -0.009927 ... 0.04993 0.04996 0.05
+  * x        (x) float64 200.0 208.9 217.9 226.8 ... 9.982e+03 9.991e+03 1e+04
+Attributes:
+    source:   
+    fname:
+
+
+
+
data.plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f00d71c3a60>
+
+
+../_images/572f15c9c671875785ddb9c86e5fd990f9c3965498d94d9b4f3a9c5107a43932.png +
+
+

Nice! We can now go off and do things with this dataset.

+
+
+ +
+
+ +
+ +
+
+ + + + + + + + \ No newline at end of file