├── .devcontainer
    ├── Dockerfile
    └── devcontainer.json
├── .github
    ├── dependabot.yml
    └── workflows
    │   └── ci.yaml
├── .gitignore
├── .releaserc.json
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── deploy.sh
├── dev-requirements.txt
├── docs
    ├── README.md
    ├── _build
    │   ├── .nojekyll
    │   ├── README.md
    │   └── docs
    │   │   └── README.md
    ├── _static
    │   └── custom.css
    ├── _templates
    │   └── layout.html
    ├── conf.py
    ├── index.rst
    └── modules.rst
├── examples
    └── Working With Ladybug Arrays.ipynb
├── ladybug_pandas
    ├── __init__.py
    ├── accessors
    │   ├── __init__.py
    │   ├── ladybug.py
    │   └── psychrometrics.py
    ├── dataframe.py
    ├── extension_types
    │   ├── __init__.py
    │   ├── arraytype.py
    │   └── dtype.py
    └── series.py
├── requirements.txt
├── setup.cfg
├── setup.py
└── tests
    ├── __init__.py
    ├── assets
        └── epw
        │   └── tokyo.epw
    ├── conftest.py
    ├── op_test.py
    └── pandas_test.py


/.devcontainer/Dockerfile:
--------------------------------------------------------------------------------
 1 | # See here for image contents: https://github.com/microsoft/vscode-dev-containers/tree/v0.166.1/containers/python-3/.devcontainer/base.Dockerfile
 2 | 
 3 | # [Choice] Python version: 3, 3.9, 3.8, 3.7, 3.6
 4 | ARG VARIANT="3"
 5 | FROM mcr.microsoft.com/vscode/devcontainers/python:0-${VARIANT}
 6 | 
 7 | # [Option] Install Node.js
 8 | ARG INSTALL_NODE="true"
 9 | ARG NODE_VERSION="lts/*"
10 | RUN if [ "${INSTALL_NODE}" = "true" ]; then su vscode -c "umask 0002 && . /usr/local/share/nvm/nvm.sh && nvm install ${NODE_VERSION} 2>&1"; fi
11 | 
12 | COPY requirements.txt /tmp/pip-tmp/
13 | # COPY dev-requirements.txt /tmp/pip-tmp/
14 | # RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
15 | #   && pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/dev-requirements.txt \
16 | #   && rm -rf /tmp/pip-tmp
17 | 
18 | # [Optional] Uncomment this section to install additional OS packages.
19 | 
20 | RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
21 |   && apt-get -y install --no-install-recommends libjemalloc-dev libboost-dev \
22 |   libboost-filesystem-dev \
23 |   libboost-system-dev \
24 |   libboost-regex-dev \
25 |   python-dev \
26 |   autoconf \
27 |   flex \
28 |   bison
29 | 
30 | # [Optional] Uncomment this line to install global node packages.
31 | # RUN su vscode -c "source /usr/local/share/nvm/nvm.sh && npm install -g <your-package-here>" 2>&1
32 | 


--------------------------------------------------------------------------------
/.devcontainer/devcontainer.json:
--------------------------------------------------------------------------------
 1 | // For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:
 2 | // https://github.com/microsoft/vscode-dev-containers/tree/v0.166.1/containers/python-3
 3 | {
 4 |   "name": "Python 3",
 5 |   "build": {
 6 |     "dockerfile": "Dockerfile",
 7 |     "context": "..",
 8 |     "args": {
 9 |       // Update 'VARIANT' to pick a Python version: 3, 3.6, 3.7, 3.8, 3.9
10 |       "VARIANT": "3.7",
11 |       // Options
12 |       "INSTALL_NODE": "true",
13 |       "NODE_VERSION": "lts/*"
14 |     }
15 |   },
16 |   // Set *default* container specific settings.json values on container create.
17 |   "settings": {
18 |     "terminal.integrated.shell.linux": "/bin/bash",
19 |     "python.pythonPath": "/usr/local/bin/python",
20 |     "python.linting.enabled": true,
21 |     "python.linting.pylamaEnabled": true,
22 |     "python.testing.pytestArgs": [
23 |       "tests/",
24 |       "-s"
25 |     ],
26 |     "python.testing.pytestEnabled": true,
27 |     "editor.formatOnSave": true,
28 |     "editor.codeActionsOnSave": {
29 |       "source.organizeImports": true
30 |     },
31 |   },
32 |   // Add the IDs of extensions you want installed when the container is created.
33 |   "extensions": [
34 |     "ms-python.python",
35 |     "eamodio.gitlens"
36 |   ],
37 |   // Use 'forwardPorts' to make a list of ports inside the container available locally.
38 |   // "forwardPorts": [],
39 |   // Use 'postCreateCommand' to run commands after the container is created.
40 |   "postCreateCommand": "pip3 install --user -r requirements.txt -r dev-requirements.txt",
41 |   // Comment out connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
42 |   "remoteUser": "vscode"
43 | }


--------------------------------------------------------------------------------
/.github/dependabot.yml:
--------------------------------------------------------------------------------
 1 | version: 2
 2 | updates:
 3 | - package-ecosystem: pip
 4 |   directory: "/"
 5 |   schedule:
 6 |     interval: daily
 7 |     time: "11:00"
 8 |   open-pull-requests-limit: 10
 9 |   ignore:
10 |   - dependency-name: ladybug-core
11 |     versions:
12 |     - 0.36.2
13 |     - 0.37.0
14 |     - 0.37.2
15 |     - 0.37.4
16 |     - 0.37.6
17 |     - 0.37.7
18 |     - 0.37.8
19 |     - 0.37.9
20 |     - 0.38.0
21 |     - 0.39.1
22 |   - dependency-name: scipy
23 |     versions:
24 |     - 1.6.0
25 |     - 1.6.1
26 |     - 1.6.2
27 |   - dependency-name: sphinx
28 |     versions:
29 |     - 3.4.3
30 |     - 3.5.0
31 |     - 3.5.1
32 |     - 3.5.2
33 |     - 3.5.3
34 |   - dependency-name: twine
35 |     versions:
36 |     - 3.3.0
37 |     - 3.4.0
38 |   - dependency-name: pandas
39 |     versions:
40 |     - 1.2.1
41 |     - 1.2.2
42 |     - 1.2.3
43 |   - dependency-name: coverage
44 |     versions:
45 |     - "5.4"
46 |   - dependency-name: pytest
47 |     versions:
48 |     - 6.2.2
49 |   - dependency-name: coveralls
50 |     versions:
51 |     - 3.0.0
52 | 


--------------------------------------------------------------------------------
/.github/workflows/ci.yaml:
--------------------------------------------------------------------------------
 1 | ---
 2 | name: CI
 3 | 
 4 | on: [push, pull_request]
 5 | 
 6 | jobs:
 7 |   test:
 8 |     name: Unit tests
 9 |     strategy:
10 |       matrix:
11 |         os: [ubuntu-latest]
12 |     runs-on: ${{ matrix.os }}
13 |     steps:
14 |       - uses: actions/checkout@v2
15 |       - name: set up Python
16 |         uses: actions/setup-python@v2
17 |         with:
18 |           python-version: 3.7
19 |       - name: install python dependencies
20 |         run: |
21 |           python -m pip install --upgrade pip
22 |           pip install -r requirements.txt
23 |           pip install -r dev-requirements.txt
24 |       - name: run tests
25 |         run: python -m pytest tests/
26 | 
27 |   deploy:
28 |     name: Deploy to GitHub and PyPI
29 |     runs-on: ubuntu-latest
30 |     needs: test
31 |     if: github.ref == 'refs/heads/master' && github.repository_owner == 'ladybug-tools'
32 |     steps:
33 |       - uses: actions/checkout@v2
34 |       - name: set up Python
35 |         uses: actions/setup-python@v2
36 |         with:
37 |           python-version: 3.7
38 |       - name: set up node # we need node for for semantic release
39 |         uses: actions/setup-node@v2.1.2
40 |         with:
41 |           node-version: 14.2.0
42 |       - name: install python dependencies
43 |         run: |
44 |           python -m pip install --upgrade pip
45 |           pip install -U .
46 |           pip install -r dev-requirements.txt
47 |       - name: install semantic-release
48 |         run: npm install @semantic-release/exec
49 |       - name: run semantic release
50 |         id: new_release
51 |         run: |
52 |           nextRelease="`npx semantic-release@^17.0.0 --dryRun | grep -oP 'Published release \K.*? ' || true`"
53 |           npx semantic-release@^17.0.0
54 |           echo "::set-output name=tag::$nextRelease"
55 |         env:
56 |           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
57 |           PYPI_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
58 |           PYPI_USERNAME: ${{ secrets.PYPI_USERNAME }}
59 |     outputs:
60 |       tag: ${{ steps.new_release.outputs.tag }}
61 | 
62 |   docs:
63 |     name: Generate docs
64 |     runs-on: ubuntu-latest
65 |     needs: deploy
66 |     if: ${{ github.ref == 'refs/heads/master' && github.repository_owner == 'ladybug-tools' && contains(needs.deploy.outputs.tag, '.') }}
67 |     steps:
68 |       - uses: actions/checkout@v2
69 |       - name: set up Python
70 |         uses: actions/setup-python@v2
71 |         with:
72 |           python-version: 3.7
73 |       - name: install dependencies
74 |         run: |
75 |           pip install -U .
76 |           pip install -r dev-requirements.txt
77 |           sphinx-apidoc -f -e -d 4 -o ./docs ./ladybug_pandas
78 |           sphinx-build -b html ./docs ./docs/_build
79 |       - name: deploy to github pages
80 |         uses: peaceiris/actions-gh-pages@v3
81 |         with:
82 |           # this will use ladybugbot token
83 |           github_token: ${{ secrets.GITHUB_TOKEN }}
84 |           publish_branch: gh-pages
85 |           publish_dir: docs/_build/
86 |           force_orphan: true
87 |           keep_files: false
88 |           full_commit_message: "deploy: update docs"
89 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
 1 | *.pyc
 2 | test.py
 3 | .pytest_cache
 4 | *__pycache__
 5 | .coverage
 6 | *.ipynb
 7 | .ipynb_checkpoints
 8 | .tox
 9 | *.egg-info
10 | tox.ini
11 | /.cache
12 | /.vscode
13 | .eggs
14 | *.code-workspace
15 | 
16 | build
17 | dist
18 | 
19 | venv


--------------------------------------------------------------------------------
/.releaserc.json:
--------------------------------------------------------------------------------
 1 | {
 2 |     "plugins": [
 3 |         "@semantic-release/commit-analyzer",
 4 |         "@semantic-release/release-notes-generator",
 5 |         "@semantic-release/github",
 6 |         [
 7 |             "@semantic-release/exec",
 8 |             {
 9 |                 "publishCmd": "bash deploy.sh"
10 |             }
11 |         ]
12 |     ]
13 | }
14 | 


--------------------------------------------------------------------------------
/CODE_OF_CONDUCT.md:
--------------------------------------------------------------------------------
1 | Contributor Covenant Code of Conduct
2 | =========================================
3 | 
4 | This project follows Ladybug Tools contributor covenant code of conduct. See our [contributor covenant code of conduct](https://github.com/ladybug-tools/contributing/blob/master/CODE-OF-CONDUCT.md).
5 | 


--------------------------------------------------------------------------------
/CONTRIBUTING.md:
--------------------------------------------------------------------------------
1 | Contributing
2 | ------------
3 | We welcome contributions from anyone, even if you are new to open source we will be happy to help you to get started. Most of the Ladybug Tools developers started learning programming through developing for Ladybug Tools.
4 | 
5 | ### Code contribution
6 | This project follows Ladybug Tools contributing guideline. See [contributing to Ladybug Tools projects](https://github.com/ladybug-tools/contributing/blob/master/README.md).
7 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
  1 |                     GNU AFFERO GENERAL PUBLIC LICENSE
  2 |                        Version 3, 19 November 2007
  3 | 
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  5 |  Everyone is permitted to copy and distribute verbatim copies
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275 | 
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277 |     you inform other peers where the object code and Corresponding
278 |     Source of the work are being offered to the general public at no
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423 |   9. Acceptance Not Required for Having Copies.
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425 |   You are not required to accept this License in order to receive or
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434 |   10. Automatic Licensing of Downstream Recipients.
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451 |   You may not impose any further restrictions on the exercise of the
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456 | any patent claim is infringed by making, using, selling, offering for
457 | sale, or importing the Program or any portion of it.
458 | 
459 |   11. Patents.
460 | 
461 |   A "contributor" is a copyright holder who authorizes use under this
462 | License of the Program or a work on which the Program is based.  The
463 | work thus licensed is called the contributor's "contributor version".
464 | 
465 |   A contributor's "essential patent claims" are all patent claims
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467 | hereafter acquired, that would be infringed by some manner, permitted
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475 |   Each contributor grants you a non-exclusive, worldwide, royalty-free
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478 | propagate the contents of its contributor version.
479 | 
480 |   In the following three paragraphs, a "patent license" is any express
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486 | 
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522 | or that patent license was granted, prior to 28 March 2007.
523 | 
524 |   Nothing in this License shall be construed as excluding or limiting
525 | any implied license or other defenses to infringement that may
526 | otherwise be available to you under applicable patent law.
527 | 
528 |   12. No Surrender of Others' Freedom.
529 | 
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536 | to collect a royalty for further conveying from those to whom you convey
537 | the Program, the only way you could satisfy both those terms and this
538 | License would be to refrain entirely from conveying the Program.
539 | 
540 |   13. Remote Network Interaction; Use with the GNU General Public License.
541 | 
542 |   Notwithstanding any other provision of this License, if you modify the
543 | Program, your modified version must prominently offer all users
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561 |   14. Revised Versions of this License.
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563 |   The Free Software Foundation may publish revised and/or new versions of
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568 |   Each version is given a distinguishing version number.  If the
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609 | 
610 |   17. Interpretation of Sections 15 and 16.
611 | 
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618 | 
619 |                      END OF TERMS AND CONDITIONS
620 | 
621 |             How to Apply These Terms to Your New Programs
622 | 
623 |   If you develop a new program, and you want it to be of the greatest
624 | possible use to the public, the best way to achieve this is to make it
625 | free software which everyone can redistribute and change under these terms.
626 | 
627 |   To do so, attach the following notices to the program.  It is safest
628 | to attach them to the start of each source file to most effectively
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630 | the "copyright" line and a pointer to where the full notice is found.
631 | 
632 |     <one line to give the program's name and a brief idea of what it does.>
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634 | 
635 |     This program is free software: you can redistribute it and/or modify
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639 | 
640 |     This program is distributed in the hope that it will be useful,
641 |     but WITHOUT ANY WARRANTY; without even the implied warranty of
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643 |     GNU Affero General Public License for more details.
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645 |     You should have received a copy of the GNU Affero General Public License
646 |     along with this program.  If not, see <https://www.gnu.org/licenses/>.
647 | 
648 | Also add information on how to contact you by electronic and paper mail.
649 | 
650 |   If your software can interact with users remotely through a computer
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655 | solutions will be better for different programs; see section 13 for the
656 | specific requirements.
657 | 
658 |   You should also get your employer (if you work as a programmer) or school,
659 | if any, to sign a "copyright disclaimer" for the program, if necessary.
660 | For more information on this, and how to apply and follow the GNU AGPL, see
661 | <https://www.gnu.org/licenses/>.
662 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | [![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)
 2 | 
 3 | # ladybug-pandas
 4 | 
 5 | A ladybug extension powered by pandas
 6 | 
 7 | ## Installation
 8 | ```console
 9 | pip install ladybug-pandas
10 | ```
11 | 
12 | ## QuickStart
13 | ```python
14 | import ladybug_pandas as lbp
15 | from ladybug.epw import EPW
16 | 
17 | epw_path = 'tests/assets/epw/tokyo.epw'
18 | 
19 | epw = EPW(epw_path)
20 | 
21 | df = lbp.DataFrame.from_epw(epw)
22 | 
23 | df_ip = df.ladybug.to_ip()
24 | 
25 | ```
26 | 
27 | ## [API Documentation](http://ladybug-tools.github.io/ladybug-pandas)
28 | 
29 | You can also find some usage examples in the [examples](https://github.com/ladybug-tools/ladybug-pandas/blob/master/examples) folder of the code repository.
30 | 
31 | 
32 | ## Local Development
33 | 1. Clone this repo locally
34 | ```console
35 | git clone git@github.com:ladybug-tools/ladybug-pandas
36 | 
37 | # or
38 | 
39 | git clone https://github.com/ladybug-tools/ladybug-pandas
40 | ```
41 | 2. Install dependencies:
42 | ```console
43 | cd ladybug-pandas
44 | pip install -r dev-requirements.txt
45 | pip install -r requirements.txt
46 | ```
47 | 
48 | 3. Run Tests:
49 | ```console
50 | python -m pytest tests/
51 | ```
52 | 
53 | 4. Generate Documentation:
54 | ```console
55 | sphinx-apidoc -f -e -d 4 -o ./docs ./ladybug_pandas
56 | sphinx-build -b html ./docs ./docs/_build/docs
57 | ```
58 | 


--------------------------------------------------------------------------------
/deploy.sh:
--------------------------------------------------------------------------------
1 | #!/bin/sh
2 | 
3 | echo "Building distribution"
4 | python setup.py sdist bdist_wheel
5 | echo "Pushing new version to PyPi"
6 | twine upload dist/* -u $PYPI_USERNAME -p $PYPI_PASSWORD
7 | 


--------------------------------------------------------------------------------
/dev-requirements.txt:
--------------------------------------------------------------------------------
 1 | coverage
 2 | coveralls
 3 | pytest
 4 | pytest-cov
 5 | Sphinx
 6 | sphinx-bootstrap-theme
 7 | sphinxcontrib-fulltoc
 8 | sphinxcontrib-websupport
 9 | twine
10 | wheel
11 | pylama
12 | jupyterlab
13 | autopep8
14 | 


--------------------------------------------------------------------------------
/docs/README.md:
--------------------------------------------------------------------------------
 1 | 
 2 | ## Usage
 3 | For generating the documents locally use commands below from the root folder. 
 4 | 
 5 | ```shell
 6 | # install dependencies
 7 | pip install Sphinx sphinxcontrib-fulltoc sphinx_bootstrap_theme
 8 | 
 9 | # generate rst files for modules
10 | sphinx-apidoc -f -e -d 4 -o ./docs ./ladybug_pandas
11 | # build the documentation under _build/docs folder
12 | sphinx-build -b html ./docs ./docs/_build/docs
13 | ```
14 | 


--------------------------------------------------------------------------------
/docs/_build/.nojekyll:
--------------------------------------------------------------------------------
1 | 
2 | 


--------------------------------------------------------------------------------
/docs/_build/README.md:
--------------------------------------------------------------------------------
1 | # documentation
2 | 


--------------------------------------------------------------------------------
/docs/_build/docs/README.md:
--------------------------------------------------------------------------------
1 | # documentation
2 | 


--------------------------------------------------------------------------------
/docs/_static/custom.css:
--------------------------------------------------------------------------------
 1 | /*
 2 |  * bootstrap-sphinx.css
 3 |  * ~~~~~~~~~~~~~~~~~~~~
 4 |  *
 5 |  * Sphinx stylesheet -- Bootstrap theme.
 6 |  */
 7 | 
 8 | 
 9 | /* The code below is based on the bootstrap website sidebar */
10 | 
11 | 
12 | /* Show and affix the side nav when space allows it */
13 | @media screen and (min-width: 992px) {
14 |   .bs-sidenav .nav > .active > ul {
15 |     display: block;
16 |   }
17 |   /* Widen the fixed sidenav */
18 |   .bs-sidenav.affix,
19 |   .bs-sidenav.affix-bottom {
20 |     width: 292px;
21 |   }
22 |   .bs-sidenav.affix {
23 |     position: fixed; /* Undo the static from mobile first approach */
24 |   }
25 |   .bs-sidenav.affix-bottom {
26 |     position: absolute; /* Undo the static from mobile first approach */
27 |   }
28 |   .bs-sidenav.affix-bottom .bs-sidenav,
29 |   .bs-sidenav.affix .bs-sidenav {
30 |     margin-top: 0;
31 |     margin-bottom: 0;
32 |   }
33 | }
34 | @media screen and (min-width: 1200px) {
35 |   /* Widen the fixed sidenav again */
36 |   .bs-sidenav.affix-bottom,
37 |   .bs-sidenav.affix {
38 |     width: 360px;
39 |   }
40 | }
41 | 


--------------------------------------------------------------------------------
/docs/_templates/layout.html:
--------------------------------------------------------------------------------
  1 | {% extends "basic/layout.html" %}
  2 | 
  3 | {% if theme_bootstrap_version == "3" %}
  4 |   {% set bootstrap_version, navbar_version = "3.3.7", "" %}
  5 |   {% set bs_span_prefix = "col-md-" %}
  6 | {% else %}
  7 |   {% set bootstrap_version, navbar_version = "2.3.2", "-2" %}
  8 |   {% set bs_span_prefix = "span" %}
  9 | {% endif %}
 10 | 
 11 | {%- set render_sidebar = (not embedded) and (not theme_nosidebar|tobool) and sidebars %}
 12 | 
 13 | {%- set bs_content_width = render_sidebar and "8" or "12"%}
 14 | 
 15 | {%- block doctype -%}
 16 | <!DOCTYPE html>
 17 | {%- endblock %}
 18 | 
 19 | {# Sidebar: Rework into our Bootstrap nav section. #}
 20 | {% macro navBar() %}
 21 | {% include "navbar" + navbar_version + ".html" %}
 22 | {% endmacro %}
 23 | 
 24 | {% if theme_bootstrap_version == "3" %}
 25 |   {%- macro bsidebar() %}
 26 |       {%- if render_sidebar %}
 27 |       <div class="{{ bs_span_prefix }}4">
 28 |         <div id="sidebar" class="bs-sidenav" role="complementary">
 29 |           {%- for sidebartemplate in sidebars %}
 30 |             {%- include sidebartemplate %}
 31 |           {%- endfor %}
 32 |         </div>
 33 |       </div>
 34 |       {%- endif %}
 35 |   {%- endmacro %}
 36 | {% else %}
 37 |   {%- macro bsidebar() %}
 38 |       {%- if render_sidebar %}
 39 |       <div class="{{ bs_span_prefix }}4">
 40 |         <div id="sidebar" class="bs-sidenav well" data-spy="affix">
 41 |           {%- for sidebartemplate in sidebars %}
 42 |             {%- include sidebartemplate %}
 43 |           {%- endfor %}
 44 |         </div>
 45 |       </div>
 46 |       {%- endif %}
 47 |   {%- endmacro %}
 48 | {% endif %}
 49 | 
 50 | {%- block extrahead %}
 51 | <meta charset='utf-8'>
 52 | <meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
 53 | <meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1'>
 54 | <meta name="apple-mobile-web-app-capable" content="yes">
 55 | <script type="text/javascript" src="{{ pathto('_static/js/jquery-1.11.0.min.js', 1) }} "></script>
 56 | <script type="text/javascript" src="{{ pathto('_static/js/jquery-fix.js', 1) }} "></script>
 57 | <script type="text/javascript" src="{{ pathto('_static', 1) + '/bootstrap-' + bootstrap_version + '/js/bootstrap.min.js' }} "></script>
 58 | <script type="text/javascript" src="{{ pathto('_static/bootstrap-sphinx.js', 1) }} "></script>
 59 | {% endblock %}
 60 | 
 61 | {# Silence the sidebar's, relbar's #}
 62 | {% block header %}{% endblock %}
 63 | {% block relbar1 %}{% endblock %}
 64 | {% block relbar2 %}{% endblock %}
 65 | {% block sidebarsourcelink %}{% endblock %}
 66 | 
 67 | {%- block content %}
 68 | {{ navBar() }}
 69 | <div class="container">
 70 |   <div class="row">
 71 |     {%- block sidebar1 %}{{ bsidebar() }}{% endblock %}
 72 |     <div class="body {{ bs_span_prefix }}{{ bs_content_width }} content" role="main">
 73 |       {% block body %}{% endblock %}
 74 |     </div>
 75 |     {% block sidebar2 %} {# possible location for sidebar #} {% endblock %}
 76 |   </div>
 77 | </div>
 78 | {%- endblock %}
 79 | 
 80 | {%- block footer %}
 81 | <footer class="footer">
 82 |   <div class="container">
 83 |     <p class="pull-right">
 84 |       <a href="#">Back to top</a>
 85 |       {% if theme_source_link_position == "footer" %}
 86 |         <br/>
 87 |         {% include "sourcelink.html" %}
 88 |       {% endif %}
 89 |     </p>
 90 |     <p>
 91 |     {%- if show_copyright %}
 92 |       {%- if hasdoc('copyright') %}
 93 |         {% trans path=pathto('copyright'), copyright=copyright|e %}&copy; <a href="{{ path }}">Copyright</a> {{ copyright }}.{% endtrans %}<br/>
 94 |       {%- else %}
 95 |         {% trans copyright=copyright|e %}&copy; Copyright {{ copyright }}.{% endtrans %}<br/>
 96 |       {%- endif %}
 97 |     {%- endif %}
 98 |     {%- if last_updated %}
 99 |       {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}<br/>
100 |     {%- endif %}
101 |     {%- if show_sphinx %}
102 |       {% trans sphinx_version=sphinx_version|e %}Created using <a href="http://sphinx-doc.org/">Sphinx</a> {{ sphinx_version }}.{% endtrans %}<br/>
103 |     {%- endif %}
104 |     </p>
105 |   </div>
106 | </footer>
107 | {%- endblock %}
108 | 


--------------------------------------------------------------------------------
/docs/conf.py:
--------------------------------------------------------------------------------
  1 | # -*- coding: utf-8 -*-
  2 | #
  3 | # Configuration file for the Sphinx documentation builder.
  4 | #
  5 | # This file only contains a selection of the most common options. For a
  6 | # full list see the documentation:
  7 | # http://www.sphinx-doc.org/en/master/config
  8 | 
  9 | # -- Path setup --------------------------------------------------------------
 10 | 
 11 | # If extensions (or modules to document with autodoc) are in another directory,
 12 | # add these directories to sys.path here. If the directory is relative to the
 13 | # documentation root, use os.path.abspath to make it absolute, like shown here.
 14 | #
 15 | import os
 16 | import sys
 17 | import datetime
 18 | now = datetime.datetime.now()
 19 | sys.path.insert(0, os.path.abspath('../'))
 20 | 
 21 | # -- Project information -----------------------------------------------------
 22 | 
 23 | project = 'ladybug-pandas'
 24 | copyright = '{}, Ladybug Tools'.format(str(now.year))
 25 | author = 'Ladybug Tools'
 26 | 
 27 | # The full version, including alpha/beta/rc tags
 28 | release = ''
 29 | 
 30 | # for example take major/minor
 31 | version = ''
 32 | 
 33 | # -- General configuration ---------------------------------------------------
 34 | 
 35 | # If your documentation needs a minimal Sphinx version, state it here.
 36 | #
 37 | # needs_sphinx = '1.0'
 38 | 
 39 | # Add any Sphinx extension module names here, as strings. They can be
 40 | # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 41 | # ones.
 42 | extensions = [
 43 |     'sphinx.ext.autodoc',
 44 |     'sphinx.ext.doctest',
 45 |     'sphinx.ext.todo',
 46 |     'sphinx.ext.coverage',
 47 |     'sphinx.ext.imgmath',
 48 |     'sphinx.ext.ifconfig',
 49 |     'sphinx.ext.viewcode',
 50 |     'sphinx.ext.githubpages',
 51 |     'sphinxcontrib.fulltoc',
 52 |     'sphinx.ext.napoleon'
 53 | ]
 54 | 
 55 | # Add any paths that contain templates here, relative to this directory.
 56 | templates_path = ['_templates']
 57 | 
 58 | # The suffix(es) of source filenames.
 59 | # You can specify multiple suffix as a list of string:
 60 | #
 61 | # source_suffix = ['.rst', '.md']
 62 | source_suffix = '.rst'
 63 | 
 64 | # The master toctree document.
 65 | master_doc = 'index'
 66 | 
 67 | # The language for content autogenerated by Sphinx. Refer to documentation
 68 | # for a list of supported languages.
 69 | #
 70 | # This is also used if you do content translation via gettext catalogs.
 71 | # Usually you set "language" from the command line for these cases.
 72 | language = None
 73 | 
 74 | # List of patterns, relative to source directory, that match files and
 75 | # directories to ignore when looking for source files.
 76 | # This pattern also affects html_static_path and html_extra_path.
 77 | exclude_patterns = []
 78 | 
 79 | # The name of the Pygments (syntax highlighting) style to use.
 80 | pygments_style = None
 81 | 
 82 | 
 83 | # -- Options for HTML output -------------------------------------------------
 84 | 
 85 | # The theme to use for HTML and HTML Help pages.  See the documentation for
 86 | # a list of builtin themes.
 87 | #
 88 | import sphinx_bootstrap_theme
 89 | 
 90 | # html_theme = 'alabaster'
 91 | html_theme = 'bootstrap'
 92 | html_theme_path = sphinx_bootstrap_theme.get_html_theme_path()
 93 | 
 94 | # Theme options are theme-specific and customize the look and feel of a theme
 95 | # further.  For a list of options available for each theme, see the
 96 | # documentation.
 97 | #
 98 | html_theme_options = {
 99 |     # For black navbar, do "navbar navbar-inverse"
100 |     'navbar_class': "navbar navbar-inverse",
101 |     # Fix navigation bar to top of page?
102 |     # Values: "true" (default) or "false"
103 |     'navbar_fixed_top': "true",
104 |     'navbar_pagenav': True,
105 |     'source_link_position': "nav",
106 |     'bootswatch_theme': "united",
107 |     'bootstrap_version': "3",
108 | }
109 | 
110 | # Bootstrap theme custom file paths (relative to this file)
111 | # Layout.html path (already added above, include if different)
112 | # templates_path = ['_templates']
113 | 
114 | # on_rtd is whether we are on readthedocs.org
115 | # on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
116 | 
117 | # if not on_rtd:  # only import and set the theme if we're building docs locally
118 | #    import sphinx_rtd_theme
119 | #    html_theme = 'sphinx_rtd_theme'
120 | #    html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
121 | 
122 | # Add any paths that contain custom static files (such as style sheets) here,
123 | # relative to this directory. They are copied after the builtin static files,
124 | # so a file named "default.css" will overwrite the builtin "default.css".
125 | html_static_path = ['_static']
126 | 
127 | # Custom sidebar templates, must be a dictionary that maps document names
128 | # to template names.
129 | #
130 | # The default sidebars (for documents that don't match any pattern) are
131 | # defined by theme itself.  Builtin themes are using these templates by
132 | # default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
133 | # 'searchbox.html']``.
134 | #
135 | html_sidebars = {
136 |     '**': ['localtoc.html']
137 | }
138 | 
139 | 
140 | # -- Options for HTMLHelp output ---------------------------------------------
141 | 
142 | # Output file base name for HTML help builder.
143 | htmlhelp_basename = 'lbtoolsdoc'
144 | 
145 | 
146 | # -- Options for LaTeX output ------------------------------------------------
147 | 
148 | latex_elements = {
149 |     # The paper size ('letterpaper' or 'a4paper').
150 |     #
151 |     # 'papersize': 'letterpaper',
152 | 
153 |     # The font size ('10pt', '11pt' or '12pt').
154 |     #
155 |     # 'pointsize': '10pt',
156 | 
157 |     # Additional stuff for the LaTeX preamble.
158 |     #
159 |     # 'preamble': '',
160 | 
161 |     # Latex figure (float) alignment
162 |     #
163 |     # 'figure_align': 'htbp',
164 | }
165 | 
166 | # Grouping the document tree into LaTeX files. List of tuples
167 | # (source start file, target name, title,
168 | #  author, documentclass [howto, manual, or own class]).
169 | latex_documents = [
170 |     (master_doc, 'ladybug-pandas.tex', 'ladybug-pandas Documentation',
171 |      'Ladybug Tools', 'manual'),
172 | ]
173 | 
174 | 
175 | # -- Options for manual page output ------------------------------------------
176 | 
177 | # One entry per manual page. List of tuples
178 | # (source start file, name, description, authors, manual section).
179 | man_pages = [
180 |     (master_doc, 'ladybug-pandas', 'ladybug-pandas Documentation',
181 |      [author], 1)
182 | ]
183 | 
184 | 
185 | # -- Options for Texinfo output ----------------------------------------------
186 | 
187 | # Grouping the document tree into Texinfo files. List of tuples
188 | # (source start file, target name, title, author,
189 | #  dir menu entry, description, category)
190 | texinfo_documents = [
191 |     (master_doc, 'ladybug-pandas', 'ladybug-pandas Documentation',
192 |      author, 'ladybug-pandas', 'One line description of project.',
193 |      'Miscellaneous'),
194 | ]
195 | 
196 | 
197 | # -- Options for Epub output -------------------------------------------------
198 | 
199 | # Bibliographic Dublin Core info.
200 | epub_title = project
201 | 
202 | # The unique identifier of the text. This can be a ISBN number
203 | # or the project homepage.
204 | #
205 | # epub_identifier = ''
206 | 
207 | # A unique identification for the text.
208 | #
209 | # epub_uid = ''
210 | 
211 | # A list of files that should not be packed into the epub file.
212 | epub_exclude_files = ['search.html']
213 | 
214 | 
215 | # -- Extension configuration -------------------------------------------------
216 | 
217 | # -- Options for todo extension ----------------------------------------------
218 | 
219 | # If true, `todo` and `todoList` produce output, else they produce nothing.
220 | todo_include_todos = True
221 | 
222 | # -- Options for autodoc extension --------------------------------------------
223 | autodoc_default_options = {
224 |     'inherited-members': True,
225 | }
226 | 
227 | autodoc_member_order = 'groupwise'
228 | 
229 | 
230 | def setup(app):
231 |     """Run custom code with access to the Sphinx application object
232 |     Args:
233 |         app: the Sphinx application object
234 |     """
235 | 
236 |     # Add bootstrap theme custom stylesheet
237 |     app.add_stylesheet("custom.css")
238 | 


--------------------------------------------------------------------------------
/docs/index.rst:
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 1 | Welcome to ladybug-pandas's documentation!
 2 | ===================================
 3 | 
 4 | .. toctree::
 5 |    :maxdepth: 2
 6 |    :caption: Contents:
 7 | 
 8 | .. include:: modules.rst
 9 | 
10 | 
11 | Indices and tables
12 | ==================
13 | 
14 | * :ref:`genindex`
15 | * :ref:`modindex`
16 | * :ref:`search`
17 | 


--------------------------------------------------------------------------------
/docs/modules.rst:
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1 | ladybug-pandas
2 | =================
3 | 
4 | .. toctree::
5 |    :maxdepth: 4
6 | 
7 |    ladybug_pandas
8 | 


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/ladybug_pandas/__init__.py:
--------------------------------------------------------------------------------
1 | """ladybug-pandas library."""
2 | 
3 | from .dataframe import DataFrame
4 | from .series import Series
5 | 
6 | from .accessors.ladybug import LadybugSeriesAccessor, LadybugDataFrameAccessor
7 | from .accessors.psychrometrics import PsychrometricsAccessor


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/ladybug_pandas/accessors/__init__.py:
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https://raw.githubusercontent.com/ladybug-tools/ladybug-pandas/56745cbb367feed1e629ed1cf6cc1ff819089f68/ladybug_pandas/accessors/__init__.py


--------------------------------------------------------------------------------
/ladybug_pandas/accessors/ladybug.py:
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 1 | import pandas as pd
 2 | 
 3 | from ..extension_types.arraytype import LadybugArrayType
 4 | 
 5 | 
 6 | @pd.api.extensions.register_series_accessor("ladybug")
 7 | class LadybugSeriesAccessor:
 8 | 
 9 |     def __init__(self, pandas_obj):
10 |         self._obj = pandas_obj
11 | 
12 |     @property
13 |     def is_lb_array(self):
14 |         return isinstance(self._obj.values, LadybugArrayType)
15 | 
16 |     def check_type(self):
17 |         assert self.is_lb_array, \
18 |             f'Cannort perform ladybug operation on Array of type {type(self._obj.values)}'
19 | 
20 |     def to_si(self):
21 |         self.check_type()
22 |         return pd.Series(self._obj.values.to_si(), index=self._obj.index)
23 | 
24 |     def to_ip(self):
25 |         self.check_type()
26 |         return pd.Series(self._obj.values.to_ip(), index=self._obj.index)
27 | 
28 |     def to_unit(self, unit):
29 |         self.check_type()
30 |         return pd.Series(self._obj.values.to_unit(unit), index=self._obj.index)
31 | 
32 | 
33 | @pd.api.extensions.register_dataframe_accessor("ladybug")
34 | class LadybugDataFrameAccessor:
35 | 
36 |     def __init__(self, pandas_obj):
37 |         self._obj = pandas_obj
38 | 
39 |     def to_si(self):
40 |         return self._obj.apply(lambda x: x.ladybug.to_si() if x.ladybug.is_lb_array else x)
41 | 
42 |     def to_ip(self):
43 |         return self._obj.apply(lambda x: x.ladybug.to_ip() if x.ladybug.is_lb_array else x)
44 | 
45 |     def to_unit(self, unit):
46 |         return self._obj.apply(lambda x: x.ladybug.to_unit(unit) if x.ladybug.is_lb_array else x)
47 | 


--------------------------------------------------------------------------------
/ladybug_pandas/accessors/psychrometrics.py:
--------------------------------------------------------------------------------
  1 | from typing import Union
  2 | 
  3 | import pandas as pd
  4 | 
  5 | from ladybug import psychrometrics
  6 | from ladybug.datatype import TYPESDICT
  7 | 
  8 | from ..extension_types.arraytype import LadybugArrayType
  9 | from ..extension_types.dtype import LadybugDType
 10 | 
 11 | 
 12 | @pd.api.extensions.register_dataframe_accessor("psychro")
 13 | class PsychrometricsAccessor:
 14 | 
 15 |     """A pandas Dataframe accessor to perform psychrometric calculation operations
 16 | 
 17 |         Use this "psychro" accessor and pass in the name of a column in your dataframe, 
 18 |         a Series or an int/float value to perform as psychormetric operation and return a Series.
 19 | 
 20 |     Warning:
 21 |         If using a Dataframe column or a Series you must ensure that it is an instance
 22 |         of a LadybugArrayType. This is because the Psychrometric accessor runs type checks
 23 |         to convert a data type to the right unit if necessary.
 24 | 
 25 |     Example:
 26 |         .. code-block:: python
 27 | 
 28 |         import ladybug_pandas as lbp
 29 |         from ladybug.epw import EPW
 30 | 
 31 |         epw_path = 'tests/assets/epw/tokyo.epw'
 32 | 
 33 |         epw = EPW(epw_path)
 34 | 
 35 |         df = lbp.dataframe_from_epw(epw)
 36 | 
 37 |         df['Humidity Ratio'] = df.psychro.humid_ratio_from_db_rh(
 38 |             db_temp='Dry Bulb Temperature',
 39 |             rel_humid='Relative Humidity'
 40 |         )
 41 | 
 42 |     """
 43 | 
 44 |     def __init__(self, pandas_obj):
 45 |         self._obj = pandas_obj
 46 | 
 47 |     def _build_psychro_function(self, function, input_kwargs, output_type, output_unit, **kwargs):
 48 | 
 49 |         input_df = pd.DataFrame()
 50 | 
 51 |         function_inputs = {}
 52 | 
 53 |         for k, v in input_kwargs.items():
 54 |             input_value = kwargs.get(k)
 55 |             if isinstance(input_value, str):
 56 |                 series = self._obj[input_value]
 57 |                 input_type = TYPESDICT[v['input_type']]
 58 |                 assert isinstance(series.values.dtype.data_type, input_type), \
 59 |                     f'Cannot use array of type {series.values.dtype.data_type} for {k}. Must be a {v["input_type"]}'
 60 | 
 61 |                 input_df[k] = series.values.to_unit(v['input_unit'])
 62 | 
 63 |             elif isinstance(input_value, float) or isinstance(input_value, int):
 64 |                 function_inputs[k] = input_value
 65 | 
 66 |         values = []
 67 | 
 68 |         if len(input_df) == 0:
 69 |             values.append(function(**function_inputs))
 70 |         else:
 71 |             for _, row in input_df.iterrows():
 72 |                 function_inputs.update(row.to_dict())
 73 |                 values.append(function(**function_inputs))
 74 | 
 75 |         dtype = LadybugDType(TYPESDICT[output_type](), output_unit)
 76 | 
 77 |         return pd.Series(
 78 |             data=LadybugArrayType(values, dtype),
 79 |             index=self._obj.index,
 80 |         )
 81 | 
 82 |     def saturated_vapor_pressure(self, t_kelvin: Union[str, float, int, LadybugArrayType]) -> pd.Series:
 83 | 
 84 |         return self._build_psychro_function(
 85 |             function=psychrometrics.saturated_vapor_pressure,
 86 |             input_kwargs={
 87 |                 't_kelvin': {
 88 |                     'input_type': 'Temperature',
 89 |                     'input_unit': 'K'
 90 |                 },
 91 |             },
 92 |             output_type='Pressure',
 93 |             output_unit='Pa',
 94 |             t_kelvin=t_kelvin
 95 |         )
 96 | 
 97 |     def humid_ratio_from_db_rh(self, db_temp, rel_humid, b_press=101325):
 98 |         """Humidity ratio (kg water/kg air) from air temperature (C) and relative humidity (%).
 99 | 
100 |         Args:
101 |             db_temp: Dry bulb temperature (C).
102 |             rel_humid: Relative humidity (%).
103 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
104 | 
105 |         Returns:
106 |             Humidity ratio (kg water/kg air).
107 | 
108 |         Note:
109 |             [1] ASHRAE Handbook - Fundamentals (2017) ch. 1 eqn 20
110 | 
111 |             [2] Meyer et al., (2019). PsychroLib: a library of psychrometric
112 |             functions to calculate thermodynamic properties of air. Journal of
113 |             Open Source Software, 4(33), 1137, https://doi.org/10.21105/joss.01137
114 |             https://github.com/psychrometrics/psychrolib/blob/master/src/python/psychrolib.py
115 |         """
116 |         return self._build_psychro_function(
117 |             function=psychrometrics.humid_ratio_from_db_rh,
118 |             input_kwargs={
119 |                 'db_temp': {
120 |                     'input_type': 'DryBulbTemperature',
121 |                     'input_unit': 'C'
122 |                 },
123 |                 'rel_humid': {
124 |                     'input_type': 'RelativeHumidity',
125 |                     'input_unit': '%'
126 |                 },
127 |                 'b_press': {
128 |                     'input_type': 'Pressure',
129 |                     'input_unit': 'Pa'
130 |                 }
131 |             },
132 |             output_type='HumidityRatio',
133 |             output_unit='%',
134 |             db_temp=db_temp,
135 |             rel_humid=rel_humid,
136 |             b_press=b_press,
137 |         )
138 | 
139 |     def enthalpy_from_db_hr(self, db_temp, humid_ratio, reference_temp=0):
140 |         """Enthalpy (kJ/kg) at a given humidity ratio (water/air) and dry bulb temperature (C).
141 | 
142 |         Args:
143 |             db_temp: Dry bulb temperature (C).
144 |             humid_ratio: Humidity ratio (kg water/kg air).
145 |             reference_temp: Reference dry air temperature (C). Default is 0C,
146 |                 which is standard practice for SI enthalpy values. However, for
147 |                 IP enthalpy, this is typically at 0F (-17.78C). Alternatively, for
148 |                 aboslute thermodynamic enthalpy, one can input 0K (-273.15).
149 | 
150 |         Returns:
151 |             Enthalpy (kJ/kg).
152 | 
153 |         Note:
154 |             [1] ASHRAE Handbook - Fundamentals (2017) ch. 1 eqn 30
155 | 
156 |             [2] Meyer et al., (2019). PsychroLib: a library of psychrometric
157 |             functions to calculate thermodynamic properties of air. Journal of
158 |             Open Source Software, 4(33), 1137, https://doi.org/10.21105/joss.01137
159 |             https://github.com/psychrometrics/psychrolib/blob/master/src/python/psychrolib.py
160 |         """
161 |         return self._build_psychro_function(
162 |             function=psychrometrics.enthalpy_from_db_hr,
163 |             input_kwargs={
164 |                 'db_temp': {
165 |                     'input_type': 'DryBulbTemperature',
166 |                     'input_unit': 'C'
167 |                 },
168 |                 'humid_ratio': {
169 |                     'input_type': 'HumidityRatio',
170 |                     'input_unit': '%'
171 |                 },
172 |                 'reference_temp': {
173 |                     'input_type': 'DryBulbTemperature',
174 |                     'input_unit': 'C'
175 |                 }
176 |             },
177 |             output_type='Enthalpy',
178 |             output_unit='kJ/kg',
179 |             db_temp=db_temp,
180 |             humid_ratio=humid_ratio,
181 |             reference_temp=reference_temp,
182 |         )
183 | 
184 |     def dew_point_from_db_rh(self, db_temp, rel_humid):
185 |         """Dew point temperature (C) from air temperature (C) and relative humidity (%).
186 | 
187 |         The dew point temperature is solved by inverting the equation giving water vapor
188 |         pressure at saturation from temperature, which is relatively slow but ensures
189 |         high accuracy down to 0.1 C at a wide range of dry bulb temperatures.
190 |         The Newton-Raphson (NR) method is used on the logarithm of water vapour
191 |         pressure as a function of temperature, which is a very smooth function
192 |         Convergence is usually achieved in 3 to 5 iterations.
193 | 
194 |         Args:
195 |             db_temp: Dry bulb temperature (C).
196 |             rel_humid: Relative humidity (%).
197 | 
198 |         Returns:
199 |             Dew point temperature (C).
200 | 
201 |         Note:
202 |             [1] ASHRAE Handbook - Fundamentals (2017) ch. 1 eqn. 5 and 6
203 | 
204 |             [2] Meyer et al., (2019). PsychroLib: a library of psychrometric
205 |             functions to calculate thermodynamic properties of air. Journal of
206 |             Open Source Software, 4(33), 1137, https://doi.org/10.21105/joss.01137
207 |             https://github.com/psychrometrics/psychrolib/blob/master/src/python/psychrolib.py
208 |         """
209 |         return self._build_psychro_function(
210 |             function=psychrometrics.dew_point_from_db_rh,
211 |             input_kwargs={
212 |                 'db_temp': {
213 |                     'input_type': 'DryBulbTemperature',
214 |                     'input_unit': 'C'
215 |                 },
216 |                 'rel_humid': {
217 |                     'input_type': 'RelativeHumidity',
218 |                     'input_unit': '%'
219 |                 }
220 |             },
221 |             output_type='DewPointTemperature',
222 |             output_unit='C',
223 |             db_temp=db_temp,
224 |             rel_humid=rel_humid,
225 |         )
226 | 
227 |     def wet_bulb_from_db_rh(self, db_temp, rel_humid, b_press=101325):
228 |         """Wet bulb temperature (C) from air temperature (C) and relative humidity (%).
229 | 
230 |         Args:
231 |             db_temp: Dry bulb temperature (C).
232 |             rel_humid: Relative humidity (%).
233 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
234 | 
235 |         Returns:
236 |             Wet bulb temperature (C).
237 | 
238 |         Note:
239 |             [1] ASHRAE Handbook - Fundamentals (2017) ch. 1 eqn 33 and 35
240 | 
241 |             [2] Meyer et al., (2019). PsychroLib: a library of psychrometric
242 |             functions to calculate thermodynamic properties of air. Journal of
243 |             Open Source Software, 4(33), 1137, https://doi.org/10.21105/joss.01137
244 |             https://github.com/psychrometrics/psychrolib/blob/master/src/python/psychrolib.py
245 |         """
246 |         return self._build_psychro_function(
247 |             function=psychrometrics.wet_bulb_from_db_rh,
248 |             input_kwargs={
249 |                 'db_temp': {
250 |                     'input_type': 'DryBulbTemperature',
251 |                     'input_unit': 'C'
252 |                 },
253 |                 'rel_humid': {
254 |                     'input_type': 'RelativeHumidity',
255 |                     'input_unit': '%'
256 |                 },
257 |                 'b_press': {
258 |                     'input_type': 'Pressure',
259 |                     'input_unit': 'Pa'
260 |                 }
261 |             },
262 |             output_type='WetBulbTemperature',
263 |             output_unit='C',
264 |             db_temp=db_temp,
265 |             rel_humid=rel_humid,
266 |             b_press=b_press,
267 |         )
268 | 
269 |     def rel_humid_from_db_hr(self, db_temp, humid_ratio, b_press=101325):
270 |         """Relative Humidity (%) from humidity ratio (water/air) and air temperature (C).
271 | 
272 |         Args:
273 |             db_temp: Dry bulb temperature (C).
274 |             humid_ratio: Humidity ratio (kg water/kg air).
275 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
276 | 
277 |         Returns:
278 |             Relative humidity (%).
279 |         """
280 |         return self._build_psychro_function(
281 |             function=psychrometrics.rel_humid_from_db_hr,
282 |             input_kwargs={
283 |                 'db_temp': {
284 |                     'input_type': 'DryBulbTemperature',
285 |                     'input_unit': 'C'
286 |                 },
287 |                 'humid_ratio': {
288 |                     'input_type': 'HumidityRatio',
289 |                     'input_unit': '%'
290 |                 },
291 |                 'b_press': {
292 |                     'input_type': 'Pressure',
293 |                     'input_unit': 'Pa'
294 |                 }
295 |             },
296 |             output_type='RelativeHumidity',
297 |             output_unit='%',
298 |             db_temp=db_temp,
299 |             humid_ratio=humid_ratio,
300 |             b_press=b_press,
301 |         )
302 | 
303 |     def rel_humid_from_db_enth(self, db_temp, enthalpy, b_press=101325, reference_temp=0):
304 |         """Relative Humidity (%) from air temperature (C) and enthalpy (kJ/kg).
305 | 
306 |         Args:
307 |             db_temp: Dry bulb temperature (C).
308 |             enthalpy: Enthalpy (kJ/kg).
309 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
310 |             reference_temp: Reference dry air temperature (C). Default is 0C,
311 |                 which is standard practice for SI enthalpy values. However, for
312 |                 IP enthalpy, this is typically at 0F (-17.78C). Alternatively, for
313 |                 aboslute thermodynamic enthalpy, one can input 0K (-273.15).
314 | 
315 |         Returns:
316 |             Relative humidity (%).
317 |         """
318 |         return self._build_psychro_function(
319 |             function=psychrometrics.rel_humid_from_db_enth,
320 |             input_kwargs={
321 |                 'db_temp': {
322 |                     'input_type': 'DryBulbTemperature',
323 |                     'input_unit': 'C'
324 |                 },
325 |                 'enthalpy': {
326 |                     'input_type': 'Enthalpy',
327 |                     'input_unit': 'kJ/kg'
328 |                 },
329 |                 'b_press': {
330 |                     'input_type': 'Pressure',
331 |                     'input_unit': 'Pa'
332 |                 },
333 |                 'reference_temp': {
334 |                     'input_type': 'DryBulbTemperature',
335 |                     'input_unit': 'C'
336 |                 }
337 |             },
338 |             output_type='RelativeHumidity',
339 |             output_unit='%',
340 |             db_temp=db_temp,
341 |             enthalpy=enthalpy,
342 |             b_press=b_press,
343 |             reference_temp=reference_temp,
344 |         )
345 | 
346 |     def rel_humid_from_db_dpt(self, db_temp, dew_pt):
347 |         """Relative humidity (%) from dry bulb temperature (C), and dew point temperature (C).
348 | 
349 |         Args:
350 |             db_temp: Dry bulb temperature (C).
351 |             dew_pt: Dew point temperature (C).
352 | 
353 |         Returns:
354 |             Relative humidity (%).
355 |         """
356 |         return self._build_psychro_function(
357 |             function=psychrometrics.dew_point_from_db_rh,
358 |             input_kwargs={
359 |                 'db_temp': {
360 |                     'input_type': 'DryBulbTemperature',
361 |                     'input_unit': 'C'
362 |                 },
363 |                 'dew_pt': {
364 |                     'input_type': 'DewPointTemperature',
365 |                     'input_unit': 'C'
366 |                 }
367 |             },
368 |             output_type='RelativeHumidity',
369 |             output_unit='%',
370 |             db_temp=db_temp,
371 |             dew_pt=dew_pt,
372 |         )
373 | 
374 |     def rel_humid_from_db_wb(self, db_temp, wet_bulb, b_press=101325):
375 |         """Relative humidity (%) from dry bulb temperature (C), and wet bulb temperature (C).
376 | 
377 |         Args:
378 |             db_temp: Dry bulb temperature (C).
379 |             wet_bulb: Wet bulb temperature (C).
380 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
381 | 
382 |         Returns:
383 |             Relative humidity (%).
384 |         """
385 |         return self._build_psychro_function(
386 |             function=psychrometrics.rel_humid_from_db_wb,
387 |             input_kwargs={
388 |                 'db_temp': {
389 |                     'input_type': 'DryBulbTemperature',
390 |                     'input_unit': 'C'
391 |                 },
392 |                 'wet_bulb': {
393 |                     'input_type': 'WetBulbTemperature',
394 |                     'input_unit': 'C'
395 |                 },
396 |                 'b_press': {
397 |                     'input_type': 'Pressure',
398 |                     'input_unit': 'Pa'
399 |                 }
400 |             },
401 |             output_type='RelativeHumidity',
402 |             output_unit='%',
403 |             db_temp=db_temp,
404 |             wet_bulb=wet_bulb,
405 |             b_press=b_press,
406 |         )
407 | 
408 |     def dew_point_from_db_hr(self, db_temp, humid_ratio, b_press=101325):
409 |         """Dew Point Temperature (C) from air temperature (C) and humidity ratio (water/air).
410 | 
411 |         Args:
412 |             db_temp: Dry bulb temperature (C).
413 |             humid_ratio: Humidity ratio (kg water/kg air).
414 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
415 | 
416 |         Returns:
417 |             Dew point temperature (C).
418 |         """
419 |         return self._build_psychro_function(
420 |             function=psychrometrics.dew_point_from_db_hr,
421 |             input_kwargs={
422 |                 'db_temp': {
423 |                     'input_type': 'DryBulbTemperature',
424 |                     'input_unit': 'C'
425 |                 },
426 |                 'humid_ratio': {
427 |                     'input_type': 'HumidityRatio',
428 |                     'input_unit': '%'
429 |                 },
430 |                 'b_press': {
431 |                     'input_type': 'Pressure',
432 |                     'input_unit': 'Pa'
433 |                 }
434 |             },
435 |             output_type='DewPointTemperature',
436 |             output_unit='C',
437 |             db_temp=db_temp,
438 |             humid_ratio=humid_ratio,
439 |             b_press=b_press,
440 |         )
441 | 
442 |     def dew_point_from_db_enth(self, db_temp, enthalpy, b_press=101325, reference_temp=0):
443 |         """Dew point temperature (C) from air temperature (C) and enthalpy (kJ/kg).
444 | 
445 |         Args:
446 |             db_temp: Dry bulb temperature (C).
447 |             enthalpy: Enthalpy (kJ/kg).
448 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
449 |             reference_temp: Reference dry air temperature (C). Default is 0C,
450 |                 which is standard practice for SI enthalpy values. However, for
451 |                 IP enthalpy, this is typically at 0F (-17.78C). Alternatively, for
452 |                 aboslute thermodynamic enthalpy, one can input 0K (-273.15).
453 | 
454 |         Returns:
455 |             Dew point temperature (C).
456 |         """
457 |         return self._build_psychro_function(
458 |             function=psychrometrics.dew_point_from_db_enth,
459 |             input_kwargs={
460 |                 'db_temp': {
461 |                     'input_type': 'DryBulbTemperature',
462 |                     'input_unit': 'C'
463 |                 },
464 |                 'enthalpy': {
465 |                     'input_type': 'Enthalpy',
466 |                     'input_unit': 'kJ/kg'
467 |                 },
468 |                 'b_press': {
469 |                     'input_type': 'Pressure',
470 |                     'input_unit': 'Pa'
471 |                 },
472 |                 'reference_temp': {
473 |                     'input_type': 'DryBulbTemperature',
474 |                     'input_unit': 'C'
475 |                 }
476 |             },
477 |             output_type='DewPointTemperature',
478 |             output_unit='C',
479 |             db_temp=db_temp,
480 |             enthlpy=enthalpy,
481 |             b_press=b_press,
482 |             reference_temp=reference_temp,
483 |         )
484 | 
485 |     def dew_point_from_db_wb(self, db_temp, wet_bulb, b_press=101325):
486 |         """Dew point temperature (C) from dry bulb (C) and wet bulb temperature (C).
487 | 
488 |         Args:
489 |             db_temp: Dry bulb temperature (C).
490 |             wet_bulb: Wet bulb temperature (C).
491 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
492 | 
493 |         Returns:
494 |             Dew point temperature (C).
495 |         """
496 |         return self._build_psychro_function(
497 |             function=psychrometrics.dew_point_from_db_wb,
498 |             input_kwargs={
499 |                 'db_temp': {
500 |                     'input_type': 'DryBulbTemperature',
501 |                     'input_unit': 'C'
502 |                 },
503 |                 'wet_bulb': {
504 |                     'input_type': 'WetBulbTemperature',
505 |                     'input_unit': 'C'
506 |                 },
507 |                 'b_press': {
508 |                     'input_type': 'Pressure',
509 |                     'input_unit': 'Pa'
510 |                 }
511 |             },
512 |             output_type='DewPointTemperature',
513 |             output_unit='C',
514 |             db_temp=db_temp,
515 |             wet_bulb=wet_bulb,
516 |             b_press=b_press,
517 |         )
518 | 
519 |     def humid_ratio_from_db_wb(self, db_temp, wb_temp, b_press=101325):
520 |         """Humidity ratio from air temperature (C) and wet bulb temperature (C).
521 | 
522 |         Args:
523 |             db_temp: Dry bulb temperature (C).
524 |             wb_temp: Wet bulb temperature (C).
525 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
526 | 
527 |         Returns:
528 |             Humidity ratio (kg water / kg air).
529 | 
530 |         Note:
531 |             [1] ASHRAE Handbook - Fundamentals (2017) ch. 1 eqn 36, solved for W
532 |         """
533 |         return self._build_psychro_function(
534 |             function=psychrometrics.humid_ratio_from_db_wb,
535 |             input_kwargs={
536 |                 'db_temp': {
537 |                     'input_type': 'DryBulbTemperature',
538 |                     'input_unit': 'C'
539 |                 },
540 |                 'wb_temp': {
541 |                     'input_type': 'WetBulbTemperature',
542 |                     'input_unit': 'C'
543 |                 },
544 |                 'b_press': {
545 |                     'input_type': 'Pressure',
546 |                     'input_unit': 'Pa'
547 |                 }
548 |             },
549 |             output_type='HumidityRatio',
550 |             output_unit='%',
551 |             db_temp=db_temp,
552 |             wb_temp=wb_temp,
553 |             b_press=b_press,
554 |         )
555 | 
556 |     def db_temp_from_enth_hr(self, enthalpy, humid_ratio, reference_temp=0):
557 |         """Dry bulb temperature (C) from enthalpy (kJ/kg) and humidity ratio (water/air).
558 | 
559 |         Args:
560 |             enthalpy: Enthalpy (kJ/kg).
561 |             humid_ratio: Humidity ratio (kg water/kg air).
562 |             reference_temp: Reference dry air temperature (C). Default is 0C,
563 |                 which is standard practice for SI enthalpy values. However, for
564 |                 IP enthalpy, this is typically at 0F (-17.78C). Alternatively, for
565 |                 aboslute thermodynamic enthalpy, one can input 0K (-273.15).
566 | 
567 |         Returns:
568 |             Dry bulb temperature (C).
569 | 
570 |         Note:
571 |             [1] ASHRAE Handbook - Fundamentals (2017) ch. 1 eqn 30
572 |         """
573 |         return self._build_psychro_function(
574 |             function=psychrometrics.db_temp_from_enth_hr,
575 |             input_kwargs={
576 |                 'enthalpy': {
577 |                     'input_type': 'Enthalpy',
578 |                     'input_unit': 'kJ/kg'
579 |                 },
580 |                 'humid_ratio': {
581 |                     'input_type': 'HumidityRatio',
582 |                     'input_unit': '%'
583 |                 },
584 |                 'reference_temp': {
585 |                     'input_type': 'DryBulbTemperature',
586 |                     'input_unit': 'C'
587 |                 }
588 |             },
589 |             output_type='DryBulbTemperature',
590 |             output_unit='C',
591 |             enthalpy=enthalpy,
592 |             humid_ratio=humid_ratio,
593 |             reference_temp=reference_temp,
594 |         )
595 | 
596 |     def dew_point_from_db_rh_fast(self, db_temp, rel_humid):
597 |         """Dew point temperature (C) from air temperature (C) and relative humidity (%).
598 | 
599 |         Note that the formula here is fast but is only accurate up to 90C. For accurate
600 |         values at extreme temperatures, the dew_point_from_db_rh
601 |         function should be used.
602 | 
603 |         Args:
604 |             db_temp: Dry bulb temperature (C).
605 |             rel_humid: Relative humidity (%).
606 | 
607 |         Returns:
608 |             Dew point temperature (C).
609 | 
610 |         Note:
611 |             [1] J. Sullivan and L. D. Sanders. "Method for obtaining wet-bulb temperatures
612 |             by modifying the psychrometric formula." Center for Experiment Design and Data
613 |             Analysis. NOAA - National Oceanic and Atmospheric Administration.
614 |             https://www.weather.gov/epz/wxcalc_rh
615 |         """
616 |         return self._build_psychro_function(
617 |             function=psychrometrics.dew_point_from_db_rh_fast,
618 |             input_kwargs={
619 |                 'db_temp': {
620 |                     'input_type': 'DryBulbTemperature',
621 |                     'input_unit': 'C'
622 |                 },
623 |                 'rel_humid': {
624 |                     'input_type': 'RelativeHumidity',
625 |                     'input_unit': '%'
626 |                 },
627 |             },
628 |             output_type='DewPointTemperature',
629 |             output_unit='C',
630 |             db_temp=db_temp,
631 |             rel_humid=rel_humid,
632 |         )
633 | 
634 |     def wet_bulb_from_db_rh_fast(self, db_temp, rel_humid, b_press=101325):
635 |         """Wet bulb temperature (C) from air temperature (C) and relative humidity (%).
636 | 
637 |         Note that the formula here is fast but is only accurate around temperatures
638 |         of 20C and lower. For accurate values at extreme temperatures, the
639 |         wet_bulb_from_db_rh function should be used.
640 | 
641 |         Args:
642 |             db_temp: Dry bulb temperature (C).
643 |             rel_humid: Relative humidity (%).
644 |             b_press: Air pressure (Pa). Default is pressure at sea level (101325 Pa).
645 | 
646 |         Returns:
647 |             Wet bulb temperature (C).
648 | 
649 |         Note:
650 |             [1] J. Sullivan and L. D. Sanders. "Method for obtaining wet-bulb temperatures
651 |             by modifying the psychrometric formula." Center for Experiment Design and Data
652 |             Analysis. NOAA - National Oceanic and Atmospheric Administration.
653 |             https://www.weather.gov/epz/wxcalc_rh
654 |         """
655 |         return self._build_psychro_function(
656 |             function=psychrometrics.wet_bulb_from_db_rh_fast,
657 |             input_kwargs={
658 |                 'db_temp': {
659 |                     'input_type': 'DryBulbTemperature',
660 |                     'input_unit': 'C'
661 |                 },
662 |                 'rel_humid': {
663 |                     'input_type': 'RelativeHumidity',
664 |                     'input_unit': '%'
665 |                 },
666 |                 'b_press': {
667 |                     'input_type': 'Pressure',
668 |                     'input_unit': 'Pa'
669 |                 }
670 |             },
671 |             output_type='WetBulbTemperature',
672 |             output_unit='C',
673 |             db_temp=db_temp,
674 |             rel_humid=rel_humid,
675 |             b_press=b_press,
676 |         )
677 | 
678 |     def _d_ln_p_ws(self, db_temp):
679 |         """Helper function returning the derivative of the natural log of the
680 |         saturation vapor pressure as a function of dry-bulb temperature.
681 | 
682 |         Args:
683 |             db_temp : Dry bulb temperature (C).
684 |         Returns:
685 |             Derivative of natural log of vapor pressure of saturated air in Pa.
686 |         """
687 |         return self._build_psychro_function(
688 |             function=psychrometrics._d_ln_p_ws,
689 |             input_kwargs={
690 |                 'db_temp': {
691 |                     'input_type': 'WetBulbTemperature',
692 |                     'input_unit': 'C'
693 |                 },
694 |             },
695 |             output_type='Pressure',
696 |             output_unit='Pa',
697 |             db_temp=db_temp,
698 |             rel_humid=rel_humid,
699 |             b_press=b_press,
700 |         )
701 | 


--------------------------------------------------------------------------------
/ladybug_pandas/dataframe.py:
--------------------------------------------------------------------------------
  1 | from typing import List, Union
  2 | 
  3 | import numpy as np
  4 | import pandas as pd
  5 | from ladybug.datacollection import (BaseCollection, DailyCollection,
  6 |                                     HourlyContinuousCollection,
  7 |                                     HourlyDiscontinuousCollection,
  8 |                                     MonthlyCollection,
  9 |                                     MonthlyPerHourCollection)
 10 | from ladybug.epw import EPW
 11 | 
 12 | from .extension_types.arraytype import LadybugArrayType
 13 | 
 14 | 
 15 | class DataFrame:
 16 | 
 17 |     """Generate a Dataframe from a list of ladybug data collections
 18 | 
 19 |     Example:
 20 |         .. code-block:: python
 21 | 
 22 |             import ladybug_pandas as lbp
 23 |             from ladybug.wea import Wea
 24 |             from ladybug.location import Location
 25 | 
 26 |             location = Location(
 27 |                 city='Llanfairpwllgwyngyllgogerychwyrndrobwllllantysiliogogogoch',
 28 |                 state='Wales',
 29 |                 country='United Kingdom',
 30 |                 latitude=53.224622,
 31 |                 longitude=-4.197995,
 32 |                 time_zone=0
 33 |             )
 34 | 
 35 |             wea = Wea.from_ashrae_clear_sky(location=location)
 36 | 
 37 |             df = lbp.DataFrame([
 38 |                 wea.direct_normal_irradiance,
 39 |                 wea.diffuse_horizontal_irradiance,
 40 |                 wea.global_horizontal_irradiance,
 41 |                 wea.direct_horizontal_irradiance,
 42 |             ])
 43 | 
 44 |     Arguments:
 45 |         data_collections {List[Union[HourlyDiscontinuousCollection, HourlyContinuousCollection, DailyCollection, MonthlyCollection, MonthlyPerHourCollection]]} -- A list of datacollections. Must all be of the same type
 46 | 
 47 |     Keyword Arguments:
 48 |         name_key {str} -- The name of the metadata key to use as a column name. Will default to datatype name if not specified (default: {None})
 49 | 
 50 |     Returns:
 51 |         pd.DataFrame -- A pandas dataframe. Each column will have a type of LadybugArrayType
 52 |     """
 53 | 
 54 |     def __new__(
 55 |         cls,
 56 |         data_collections: List[Union[HourlyDiscontinuousCollection, HourlyContinuousCollection, DailyCollection, MonthlyCollection, MonthlyPerHourCollection]],
 57 |         name_key: str = None,
 58 |         populate_metadata: bool = False,
 59 |         axis: int = 1,
 60 |     ) -> pd.DataFrame:
 61 | 
 62 |         data_collection_type = None
 63 | 
 64 |         df_list = []
 65 | 
 66 |         for collection in data_collections:
 67 |             # Check they are data collections
 68 |             assert isinstance(collection, BaseCollection), \
 69 |                 f'All items of data_collections must be a type of ladybug data collection, not: {type(collection)}'
 70 | 
 71 |             # Assert same type of data collection
 72 |             if data_collection_type is None:
 73 |                 data_collection_type = type(collection)
 74 |             else:
 75 |                 assert isinstance(collection, data_collection_type), \
 76 |                     f'All items of data_collections must be of the same type of data collection. Found {data_collection_type} and {type(collection)}'
 77 | 
 78 |             # Create columns from collections
 79 |             array = LadybugArrayType._from_data_collection(collection)
 80 | 
 81 |             if name_key is not None:
 82 |                 col_name = collection.header.metadata[name_key]
 83 |             else:
 84 |                 col_name = collection.header.data_type.name
 85 | 
 86 |             data = {}
 87 | 
 88 |             data[col_name] = array
 89 | 
 90 |             if populate_metadata:
 91 |                 for k, v in collection.header.metadata.items():
 92 |                     data[k] = [v] * array.__len__()
 93 | 
 94 |             df_list.append(pd.DataFrame(
 95 |                 data=data,
 96 |                 index=collection.datetimes
 97 |             ))
 98 | 
 99 |         df = pd.concat(df_list, axis=axis)
100 | 
101 |         return df
102 | 
103 |     @classmethod
104 |     def from_epw(
105 |         cls,
106 |         epw: EPW
107 |     ) -> pd.DataFrame:
108 |         """Generate a Dataframe from an EPW object
109 | 
110 |         Example:
111 |             .. code-block:: python
112 | 
113 |                 import ladybug_pandas as lbp
114 |                 from ladybug.epw import EPW
115 | 
116 |                 epw_path = 'tests/assets/epw/tokyo.epw'
117 | 
118 |                 epw = EPW(epw_path)
119 | 
120 |                 df = lbp.DataFrame.from_epw(epw)
121 | 
122 |         Arguments:
123 |             epw {EPW} -- A ladybug EPW object
124 | 
125 |         Returns:
126 |             pd.DataFrame -- A pandas dataframe. Each column will have a type of LadybugArrayType
127 |         """
128 |         if not epw.is_data_loaded:
129 |             epw._import_data()
130 | 
131 |         df = cls(epw._data[6:])
132 | 
133 |         df = df.replace(999999999.0, np.nan)
134 | 
135 |         return df
136 | 


--------------------------------------------------------------------------------
/ladybug_pandas/extension_types/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/ladybug-tools/ladybug-pandas/56745cbb367feed1e629ed1cf6cc1ff819089f68/ladybug_pandas/extension_types/__init__.py


--------------------------------------------------------------------------------
/ladybug_pandas/extension_types/arraytype.py:
--------------------------------------------------------------------------------
  1 | from copy import deepcopy
  2 | from typing import Any, Sequence, Union
  3 | 
  4 | import numpy as np
  5 | import pandas as pd
  6 | from ladybug.datacollection import BaseCollection
  7 | from pandas._typing import ArrayLike
  8 | from pandas.api.extensions import (ExtensionArray, ExtensionDtype,
  9 |                                    ExtensionScalarOpsMixin)
 10 | from pandas.core.dtypes.generic import ABCExtensionArray
 11 | from scipy import stats
 12 | 
 13 | from .dtype import LadybugDType
 14 | 
 15 | COMPARISON_OPS = ['eq', 'ne', 'lt', 'gt', 'le', 'ge']
 16 | 
 17 | 
 18 | class LadybugArrayType(ExtensionArray, ExtensionScalarOpsMixin):
 19 | 
 20 |     _dtype = LadybugDType()
 21 | 
 22 |     def __init__(self, values, dtype=None, copy=False):
 23 | 
 24 |         # self._dtype = dtype
 25 |         if dtype is not None:
 26 |             if isinstance(dtype, str):
 27 |                 # Will raise error if not right
 28 |                 self._dtype.construct_from_string(dtype)
 29 |             else:
 30 |                 self._dtype = dtype
 31 | 
 32 |         if isinstance(dtype, ExtensionDtype) or dtype is None:
 33 |             values = np.asarray(values, dtype=np.float_)
 34 |         elif isinstance(values, LadybugArrayType):
 35 |             values = values.data
 36 |             self._dtype = values.dtype
 37 |         else:
 38 |             values = np.asarray(values)
 39 |             self._dtype = values.dtype
 40 | 
 41 |         # TODO: dtype?
 42 |         if copy:
 43 |             values = values.copy()
 44 |             self._dtype = deepcopy(self._dtype)
 45 |             # if isinstance(self._dtype, LadybugDType):
 46 |             #     self._dtype = copy.deepcopy(self._dtype)
 47 | 
 48 |         self.data = values
 49 | 
 50 |     @classmethod
 51 |     def _from_sequence(cls, scalars: Sequence, dtype=None, copy=False):
 52 |         """
 53 |         Construct a new ExtensionArray from a sequence of scalars.
 54 |         Parameters
 55 |         ----------
 56 |         scalars : Sequence
 57 |             Each element will be an instance of the scalar type for this
 58 |             array, ``cls.dtype.type``.
 59 |         dtype : dtype, optional
 60 |             Construct for this particular dtype. This should be a Dtype
 61 |             compatible with the ExtensionArray.
 62 |         copy : bool, default False
 63 |             If True, copy the underlying data.
 64 |         Returns
 65 |         -------
 66 |         ExtensionArray
 67 |         """
 68 |         if dtype is None:
 69 |             if isinstance(scalars, cls):
 70 |                 dtype = scalars.dtype
 71 |             elif isinstance(scalars, list) and isinstance(scalars[0], np.bool_):
 72 |                 dtype = np.dtype("bool")
 73 |             else:
 74 |                 dtype = None
 75 | 
 76 |         return cls(scalars, dtype=dtype, copy=copy)
 77 | 
 78 |     @classmethod
 79 |     def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
 80 |         """
 81 |         Construct a new ExtensionArray from a sequence of strings.
 82 |         .. versionadded:: 0.24.0
 83 |         Parameters
 84 |         ----------
 85 |         strings : Sequence
 86 |             Each element will be an instance of the scalar type for this
 87 |             array, ``cls.dtype.type``.
 88 |         dtype : dtype, optional
 89 |             Construct for this particular dtype. This should be a Dtype
 90 |             compatible with the ExtensionArray.
 91 |         copy : bool, default False
 92 |             If True, copy the underlying data.
 93 |         Returns
 94 |         -------
 95 |         ExtensionArray
 96 |         """
 97 |         return cls([float(string) for string in strings], dtype=dtype, copy=copy)
 98 | 
 99 |     @classmethod
100 |     def _from_factorized(cls, values, original):
101 |         """
102 |         Reconstruct an ExtensionArray after factorization.
103 |         Parameters
104 |         ----------
105 |         values : ndarray
106 |             An integer ndarray with the factorized values.
107 |         original : ExtensionArray
108 |             The original ExtensionArray that factorize was called on.
109 |         See Also
110 |         --------
111 |         factorize
112 |         ExtensionArray.factorize
113 |         """
114 |         dtype = None
115 | 
116 |         if isinstance(original, cls):
117 |             dtype = original.dtype
118 |         elif isinstance(original, list):
119 |             dtype = original[0].dtype
120 |         else:
121 |             raise f'Original value of type {type(original)} not supported'
122 | 
123 |         return cls(values, dtype)
124 | 
125 |     @classmethod
126 |     def _from_data_collection(cls, datacollection: BaseCollection):
127 |         """Generate a Ladybug Array from a Ladybug DataCollection
128 | 
129 |         Arguments:
130 |             datacollection {BaseCollection} -- A ladybug data collection
131 | 
132 |         Returns:
133 |             LadybugArrayType -- A Ladybug Array
134 |         """
135 |         dtype = LadybugDType.construct_from_header(datacollection.header)
136 | 
137 |         return cls(values=datacollection.values, dtype=dtype)
138 | 
139 |     # def to_hourly_discontinuous_collection(self, datetimes: List[DateTime]) -> HourlyDiscontinuousCollection:
140 |     #     assert len(self.data) == len(datetimes), f'Datetime index of length {len(datetimes)} does not match length of array {len(self)}'
141 | 
142 |     #     return HourlyDiscontinuousCollection(
143 |     #         header=self.dtype.to_header(),
144 |     #         values=self.data.tolist(),
145 |     #         datetimes=datetimes,
146 |     #     )
147 | 
148 |     def convert_to_unit(self, unit: str):
149 |         """Convert the Data Collection to the input unit
150 | 
151 |         Arguments:
152 |             unit {str} -- A unit string that exists for the data type
153 |         """
154 |         self.data = self.dtype.data_type.to_unit(
155 |             self.data, unit, self.dtype.unit)
156 |         self.dtype.unit = unit
157 | 
158 |     def convert_to_ip(self):
159 |         """Convert the Data Collection to IP units."""
160 |         self.data, self.dtype.unit = self.dtype.data_type.to_ip(
161 |             self.data, self.dtype.unit)
162 | 
163 |     def convert_to_si(self):
164 |         """Convert the Data Collection to SI units."""
165 |         self.data, self.dtype.unit = self.dtype.data_type.to_si(
166 |             self.data, self.dtype.unit)
167 | 
168 |     def to_unit(self, unit):
169 |         """Return a Data Collection in the input unit
170 | 
171 |         Arguments:
172 |             unit {str} -- A unit string that exists for the data type
173 | 
174 |         Returns:
175 |             LadybugArrayType -- A Ladybug Array converted to the desired unit
176 |         """
177 |         new_data_c = self.copy()
178 |         new_data_c.convert_to_unit(unit)
179 |         return new_data_c
180 | 
181 |     def to_ip(self):
182 |         """Return a Data Collection in IP units
183 | 
184 |         Returns:
185 |             LadybugArrayType -- A Ladybug Array converted to the desired unit
186 |         """
187 |         new_data_c = self.copy()
188 |         new_data_c.convert_to_ip()
189 |         return new_data_c
190 | 
191 |     def to_si(self):
192 |         """Return a Data Collection in SI units
193 | 
194 |         Returns:
195 |             LadybugArrayType -- A Ladybug Array converted to the desired unit
196 |         """
197 |         new_data_c = self.copy()
198 |         new_data_c.convert_to_si()
199 |         return new_data_c
200 | 
201 |     def __getitem__(self, item):
202 |         # type (Any) -> Any
203 |         """
204 |         Select a subset of self.
205 |         Parameters
206 |         ----------
207 |         item : int, slice, or ndarray
208 |             * int: The position in 'self' to get.
209 |             * slice: A slice object, where 'start', 'stop', and 'step' are
210 |               integers or None
211 |             * ndarray: A 1-d boolean NumPy ndarray the same length as 'self'
212 |         Returns
213 |         -------
214 |         item : scalar or ExtensionArray
215 |         Notes
216 |         -----
217 |         For scalar ``item``, return a scalar value suitable for the array's
218 |         type. This should be an instance of ``self.dtype.type``.
219 |         For slice ``key``, return an instance of ``ExtensionArray``, even
220 |         if the slice is length 0 or 1.
221 |         For a boolean mask, return an instance of ``ExtensionArray``, filtered
222 |         to the values where ``item`` is True.
223 |         """
224 |         if isinstance(item, int):
225 |             datum = self.data[item]
226 |             return self.data[item]
227 |         elif isinstance(item, slice):
228 |             pass
229 |         elif isinstance(item, np.ndarray):
230 |             if item.dtype == bool and len(item) != len(self):
231 |                 raise IndexError(
232 |                     f'Boolean index has wrong length: {item.size} instead of {self.data.size}')
233 |         elif isinstance(item, list):
234 |             try:
235 |                 item = np.asarray(item, dtype=np.int_)
236 |             except Exception as e:
237 |                 raise ValueError(
238 |                     "Cannot index with an integer indexer containing NA values")
239 | 
240 |         elif isinstance(item, pd.core.arrays.boolean.BooleanArray):
241 |             if len(item) != len(self):
242 |                 raise IndexError(
243 |                     f'Boolean index has wrong length: {len(item)} instead of {len(self)}')
244 | 
245 |             item = item.to_numpy(dtype="bool", na_value=False)
246 |         elif isinstance(item, pd.core.arrays.integer.IntegerArray):
247 |             try:
248 |                 item = item.to_numpy(dtype="int", na_value=pd.NA)
249 |             except Exception as e:
250 |                 raise ValueError(
251 |                     "Cannot index with an integer indexer containing NA values")
252 |         else:
253 |             raise IndexError(f'Item type note recognised {type(item)}')
254 | 
255 |         view = self.view()
256 |         view.data = view.data[item]
257 | 
258 |         return view
259 | 
260 |     def __arrow_array__(self, type=None):
261 |         # convert the underlying array values to a pyarrow Array
262 |         import pyarrow as pa
263 |         return pa.array(self.data, type=pa.float64())
264 | 
265 |     def __len__(self) -> int:
266 |         """
267 |         Length of this array
268 |         Returns
269 |         -------
270 |         length : int
271 |         """
272 |         return self.data.size
273 | 
274 |     def __setitem__(self, key: Union[int, np.ndarray], value: Any) -> None:
275 |         """
276 |         Set one or more values inplace.
277 | 
278 |         This method is not required to satisfy the pandas extension array
279 |         interface.
280 | 
281 |         Parameters
282 |         ----------
283 |         key : int, ndarray, or slice
284 |             When called from, e.g. ``Series.__setitem__``, ``key`` will be
285 |             one of
286 | 
287 |             * scalar int
288 |             * ndarray of integers.
289 |             * boolean ndarray
290 |             * slice object
291 | 
292 |         value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
293 |             value or values to be set of ``key``.
294 | 
295 |         Returns
296 |         -------
297 |         None
298 |         """
299 |         # Some notes to the ExtensionArray implementor who may have ended up
300 |         # here. While this method is not required for the interface, if you
301 |         # *do* choose to implement __setitem__, then some semantics should be
302 |         # observed:
303 |         #
304 |         # * Setting multiple values : ExtensionArrays should support setting
305 |         #   multiple values at once, 'key' will be a sequence of integers and
306 |         #  'value' will be a same-length sequence.
307 |         #
308 |         # * Broadcasting : For a sequence 'key' and a scalar 'value',
309 |         #   each position in 'key' should be set to 'value'.
310 |         #
311 |         # * Coercion : Most users will expect basic coercion to work. For
312 |         #   example, a string like '2018-01-01' is coerced to a datetime
313 |         #   when setting on a datetime64ns array. In general, if the
314 |         #   __init__ method coerces that value, then so should __setitem__
315 |         # Note, also, that Series/DataFrame.where internally use __setitem__
316 |         # on a copy of the data.
317 | 
318 |         if isinstance(key, pd.core.arrays.BooleanArray):
319 |             key = key.fillna(False)
320 |             key = key.to_numpy(dtype="bool")
321 |         elif isinstance(key, pd.core.arrays.IntegerArray):
322 |             try:
323 |                 key = key.to_numpy(dtype="int")
324 |             except ValueError as error:
325 |                 if 'Specify an appropriate \'na_value\'' in str(error):
326 |                     raise ValueError(
327 |                         'Cannot index with an integer indexer containing NA values')
328 |                 raise error
329 | 
330 |         elif isinstance(key, list):
331 |             list_type = None
332 |             list_item = 0
333 | 
334 |             while list_item < len(key):
335 |                 if isinstance(key[list_item], int):
336 |                     list_type = "int"
337 |                     break
338 |                 elif isinstance(key[list_item], bool):
339 |                     list_type = "bool"
340 |                     break
341 |                 else:
342 |                     list_item += 1
343 | 
344 |             assert list_type is not None, IndexError(
345 |                 'arrays used as indices must be of integer (or boolean) type')
346 | 
347 |             try:
348 |                 key = np.asarray(key, dtype=list_type)
349 |             except TypeError as error:
350 |                 if 'int() argument must be a string, a bytes-like object or a number, not \'NAType\'' in str(error):
351 |                     raise ValueError(
352 |                         'Cannot index with an integer indexer containing NA values')
353 | 
354 |                 raise error
355 | 
356 |             if list_type == 'bool':
357 |                 key = key.fillna(False)
358 | 
359 |         try:
360 |             self.data[key] = value
361 |         except IndexError as error:
362 |             if "boolean index did not match indexed array along dimension" in str(error):
363 |                 raise IndexError('wrong length: ' + str(error))
364 |             else:
365 |                 raise error
366 | 
367 |     # def __add__(self, other):
368 | 
369 |     #     if isinstance(other, self.__class__):
370 |     #         self.data += other.data
371 | 
372 |     #     elif isinstance(other, (float, int, np.array)):
373 |     #         self.data += other
374 | 
375 |     #     # elif isi
376 | 
377 |     @property
378 |     def dtype(self) -> ExtensionDtype:
379 |         """
380 |         An instance of 'ExtensionDtype'.
381 |         """
382 |         return self._dtype
383 |         # if self._dtype is None:
384 |         #     return self.data.dtype
385 |         # else:
386 |         #     return self._dtype
387 | 
388 |     @property
389 |     def nbytes(self) -> int:
390 |         """
391 |         The number of bytes needed to store this object in memory.
392 |         """
393 |         # If this is expensive to compute, return an approximate lower bound
394 |         # on the number of bytes needed.
395 |         # return self._itemsize * self.__len__
396 |         return self.data.nbytes
397 | 
398 |     @property
399 |     def _ndarray(self):
400 |         return self.data
401 | 
402 |     def _reduce(self, name, skipna=True, **kwargs):
403 |         """
404 |         Return a scalar result of performing the reduction operation.
405 |         Parameters
406 |         ----------
407 |         name : str
408 |             Name of the function, supported values are:
409 |             { any, all, min, max, sum, mean, median, prod,
410 |             std, var, sem, kurt, skew }.
411 |         skipna : bool, default True
412 |             If True, skip NaN values.
413 |         **kwargs
414 |             Additional keyword arguments passed to the reduction function.
415 |             Currently, `ddof` is the only supported kwarg.
416 |         Returns
417 |         -------
418 |         scalar
419 |         Raises
420 |         ------
421 |         TypeError : subclass does not define reductions
422 |         """
423 | 
424 |         root = np
425 | 
426 |         child_kwargs = {}
427 | 
428 |         if 'ddof' in kwargs:
429 |             child_kwargs['ddof'] = kwargs['ddof']
430 | 
431 |         if name == 'kurt':
432 |             child_kwargs['bias'] = False
433 |             name = 'kurtosis'
434 |             root = stats
435 | 
436 |         if name == 'skew':
437 |             child_kwargs['bias'] = False
438 |             root = stats
439 | 
440 |         if skipna is True:
441 |             # return getattr(self.data[~np.isnan(self.data)], name)(**child_kwargs)
442 |             return getattr(root, name)(self.data[~np.isnan(self.data)], **child_kwargs)
443 |         else:
444 |             # return getattr(self.data, name)(**child_kwargs)
445 |             return getattr(root, name)(self.data, **child_kwargs)
446 | 
447 |     def isna(self) -> ArrayLike:
448 |         """
449 |         A 1-D array indicating if each value is missing.
450 |         Returns
451 |         -------
452 |         na_values : Union[np.ndarray, ExtensionArray]
453 |             In most cases, this should return a NumPy ndarray. For
454 |             exceptional cases like ``SparseArray``, where returning
455 |             an ndarray would be expensive, an ExtensionArray may be
456 |             returned.
457 |         Notes
458 |         -----
459 |         If returning an ExtensionArray, then
460 |         * ``na_values._is_boolean`` should be True
461 |         * `na_values` should implement :func:`ExtensionArray._reduce`
462 |         * ``na_values.any`` and ``na_values.all`` should be implemented
463 |         """
464 |         return np.isnan(self.data)
465 | 
466 |     def value_counts(self, dropna=True):
467 |         """
468 |         Return a Series containing counts of unique values.
469 | 
470 |         Parameters
471 |         ----------
472 |         dropna : bool, default True
473 |             Don't include counts of NaT values.
474 | 
475 |         Returns
476 |         -------
477 |         Series
478 |         """
479 |         from pandas import Index, Series
480 | 
481 |         if dropna:
482 |             values = self[~self.isna()].data
483 |         else:
484 |             values = self.data
485 | 
486 |         unique, counts = np.unique(values, return_counts=True)
487 | 
488 |         return Series(counts, index=unique, dtype="int64")
489 | 
490 |     def take(
491 |         self, indices: Sequence[int], allow_fill: bool = False, fill_value: Any = None
492 |     ) -> "ExtensionArray":
493 |         """
494 |         Take elements from an array.
495 |         Parameters
496 |         ----------
497 |         indices : sequence of int
498 |             Indices to be taken.
499 |         allow_fill : bool, default False
500 |             How to handle negative values in `indices`.
501 |             * False: negative values in `indices` indicate positional indices
502 |               from the right (the default). This is similar to
503 |               :func:`numpy.take`.
504 |             * True: negative values in `indices` indicate
505 |               missing values. These values are set to `fill_value`. Any other
506 |               other negative values raise a ``ValueError``.
507 |         fill_value : any, optional
508 |             Fill value to use for NA-indices when `allow_fill` is True.
509 |             This may be ``None``, in which case the default NA value for
510 |             the type, ``self.dtype.na_value``, is used.
511 |             For many ExtensionArrays, there will be two representations of
512 |             `fill_value`: a user-facing "boxed" scalar, and a low-level
513 |             physical NA value. `fill_value` should be the user-facing version,
514 |             and the implementation should handle translating that to the
515 |             physical version for processing the take if necessary.
516 |         Returns
517 |         -------
518 |         ExtensionArray
519 |         Raises
520 |         ------
521 |         IndexError
522 |             When the indices are out of bounds for the array.
523 |         ValueError
524 |             When `indices` contains negative values other than ``-1``
525 |             and `allow_fill` is True.
526 |         See Also
527 |         --------
528 |         numpy.take
529 |         api.extensions.take
530 |         Notes
531 |         -----
532 |         ExtensionArray.take is called by ``Series.__getitem__``, ``.loc``,
533 |         ``iloc``, when `indices` is a sequence of values. Additionally,
534 |         it's called by :meth:`Series.reindex`, or any other method
535 |         that causes realignment, with a `fill_value`.
536 |         Examples
537 |         --------
538 |         Here's an example implementation, which relies on casting the
539 |         extension array to object dtype. This uses the helper method
540 |         :func:`pandas.api.extensions.take`.
541 |         .. code-block:: python
542 |            def take(self, indices, allow_fill=False, fill_value=None):
543 |                from pandas.core.algorithms import take
544 |                # If the ExtensionArray is backed by an ndarray, then
545 |                # just pass that here instead of coercing to object.
546 |                data = self.astype(object)
547 |                if allow_fill and fill_value is None:
548 |                    fill_value = self.dtype.na_value
549 |                # fill value should always be translated from the scalar
550 |                # type for the array, to the physical storage type for
551 |                # the data, before passing to take.
552 |                result = take(data, indices, fill_value=fill_value,
553 |                              allow_fill=allow_fill)
554 |                return self._from_sequence(result, dtype=self.dtype)
555 |         """
556 |         # Implementer note: The `fill_value` parameter should be a user-facing
557 |         # value, an instance of self.dtype.type. When passed `fill_value=None`,
558 |         # the default of `self.dtype.na_value` should be used.
559 |         # This may differ from the physical storage type your ExtensionArray
560 |         # uses. In this case, your implementation is responsible for casting
561 |         # the user-facing type to the storage type, before using
562 |         # pandas.api.extensions.take
563 |         copy = self.copy()
564 |         copy.data = pd.api.extensions.take(
565 |             copy.data, indices, allow_fill=allow_fill, fill_value=fill_value)
566 | 
567 |         return copy
568 | 
569 |     def copy(self) -> "ExtensionArray":
570 |         """
571 |         Return a copy of the array.
572 |         Returns
573 |         -------
574 |         ExtensionArray
575 |         """
576 |         # return self.__class__._from_factorized(self.data.copy(), self)
577 |         return self.__class__(
578 |             values=self.data.copy(),
579 |             dtype=self.dtype,
580 |             copy=True,
581 |         )
582 | 
583 |     def view(self, dtype=None) -> Union[ABCExtensionArray, np.ndarray]:
584 |         """
585 |         Return a view on the array.
586 | 
587 |         Parameters
588 |         ----------
589 |         dtype : str, np.dtype, or ExtensionDtype, optional
590 |             Default None.
591 | 
592 |         Returns
593 |         -------
594 |         ExtensionArray
595 |             A view of the :class:`ExtensionArray`.
596 |         """
597 |         # NB:
598 |         # - This must return a *new* object referencing the same data, not self.
599 |         # - The only case that *must* be implemented is with dtype=None,
600 |         #   giving a view with the same dtype as self.
601 |         if dtype is not None:
602 |             raise NotImplementedError(dtype)
603 | 
604 |         return self.__class__._from_factorized(self.data, self)
605 | 
606 |     @classmethod
607 |     def _concat_same_type(
608 |         cls, to_concat: Sequence["ExtensionArray"]
609 |     ) -> "ExtensionArray":
610 |         """
611 |         Concatenate multiple array.
612 |         Parameters
613 |         ----------
614 |         to_concat : sequence of this type
615 |         Returns
616 |         -------
617 |         ExtensionArray
618 |         """
619 |         data = np.concatenate([ga.data for ga in to_concat])
620 |         return cls._from_factorized(data, to_concat)
621 | 
622 |     def astype(self, dtype, copy=True):
623 |         if isinstance(dtype, self.dtype.__class__):
624 |             if copy:
625 |                 return self.copy()
626 |             return self
627 | 
628 |         # return np.array(self.data, dtype=dtype, copy=copy)
629 |         return super(LadybugArrayType, self).astype(dtype)
630 | 
631 | 
632 | LadybugArrayType._add_arithmetic_ops()
633 | LadybugArrayType._add_comparison_ops()
634 | 


--------------------------------------------------------------------------------
/ladybug_pandas/extension_types/dtype.py:
--------------------------------------------------------------------------------
  1 | import re
  2 | import types
  3 | 
  4 | import numpy as np
  5 | from ladybug.datatype import TYPESDICT
  6 | from ladybug.datatype.base import DataTypeBase
  7 | from ladybug.header import Header
  8 | from pandas.api.extensions import ExtensionDtype, register_extension_dtype
  9 | 
 10 | 
 11 | def _to_unit_base_hack(self, base_unit, values, unit, from_unit):
 12 |     """Return values in a given unit given the input from_unit."""
 13 |     namespace = {'self': self, 'values': values}
 14 |     if not from_unit == base_unit:
 15 |         self.is_unit_acceptable(from_unit, True)
 16 |         statement = 'self._{}_to_{}(values)'.format(
 17 |             self._clean(from_unit), self._clean(base_unit))
 18 |         values = eval(statement, namespace)
 19 |     if not unit == base_unit:
 20 |         self.is_unit_acceptable(unit, True)
 21 |         statement = 'self._{}_to_{}(values)'.format(
 22 |             self._clean(base_unit), self._clean(unit))
 23 |         values = eval(statement, namespace)
 24 |     return values
 25 | 
 26 | 
 27 | def _is_numeric_hack(*args, **kwargs):
 28 |     return True
 29 | 
 30 | 
 31 | funcType = types.MethodType
 32 | 
 33 | 
 34 | @register_extension_dtype
 35 | class LadybugDType(ExtensionDtype):
 36 | 
 37 |     _metadata = ('data_type_name', 'unit')
 38 | 
 39 |     def __init__(self, data_type=None, unit=None):
 40 | 
 41 |         if data_type is None and unit is None:
 42 |             self.data_type = DataTypeBase('Undefined Type')
 43 |             self.unit = 'None'
 44 |         else:
 45 |             assert isinstance(data_type, DataTypeBase), ValueError(
 46 |                 f'{data_type} is not an instance of type DataTypeBase')
 47 |             assert unit in data_type._units, ValueError(
 48 |                 f'unit {unit} should be one of {data_type.units}')
 49 | 
 50 |             # Replace to unit base function to work with numpy arrays
 51 | 
 52 |             self.data_type = data_type
 53 |             self.unit = unit
 54 | 
 55 |         # setattr(self.data_type, '_is_numeric', _is_numeric_hack)
 56 |         # self.data_type._to_unit_base = _to_unit_base_hack.__get__()
 57 |         self.data_type._to_unit_base = funcType(
 58 |             _to_unit_base_hack, self.data_type)
 59 | 
 60 |     @classmethod
 61 |     def construct_from_string(cls, string):
 62 |         if not isinstance(string, str):
 63 |             raise TypeError(
 64 |                 "'construct_from_string' expects a string, got {}".format(
 65 |                     type(string))
 66 |             )
 67 | 
 68 |         match = re.fullmatch(r"(.*) \((.*)\)", string)
 69 | 
 70 |         if match is None:
 71 |             raise TypeError(
 72 |                 "Cannot construct a 'LadybugDType' from '{}'".format(string)
 73 |             )
 74 | 
 75 |         type_name = ''.join(match[1].split(' '))
 76 |         unit = match[2]
 77 | 
 78 |         assert type_name in TYPESDICT, ValueError(
 79 |             f'Ladybug Type of {type_name} is not recognized')
 80 |         data_type = TYPESDICT[type_name]()
 81 | 
 82 |         assert unit in data_type._units, ValueError(
 83 |             f'Ladybug Type unit of {unit} is not recognized for type {type_name}')
 84 | 
 85 |         return cls(data_type=data_type, unit=unit)
 86 | 
 87 |     @classmethod
 88 |     def construct_array_type(cls):
 89 |         from .arraytype import LadybugArrayType
 90 | 
 91 |         return LadybugArrayType
 92 | 
 93 |     @classmethod
 94 |     def construct_from_header(cls, header: Header):
 95 |         """[summary]
 96 | 
 97 |         Arguments:
 98 |             header {Header} -- [description]
 99 | 
100 |         Returns:
101 |             [type] -- [description]
102 |         """
103 |         return cls(data_type=header.data_type, unit=header.unit)
104 | 
105 |     def to_header(self):
106 |         """Cast the ExtensionDtyp to a ladybug header object
107 | 
108 |         Returns:
109 |             Header -- A ladybug header object
110 |         """
111 |         return Header(
112 |             data_type=self.data_type,
113 |             unit=self.unit
114 |         )
115 | 
116 |     @property
117 |     def type(self):
118 |         try:
119 |             return np.float
120 |         except AttributeError:
121 |             return float
122 | 
123 |     @property
124 |     def _is_numeric(self):
125 |         return True
126 | 
127 |     @property
128 |     def kind(self):
129 |         return 'f'
130 | 
131 |     @property
132 |     def name(self):
133 |         # return "{} ({})".format(self.data_type, self.unit)
134 |         return self.__repr__()
135 | 
136 |     @property
137 |     def data_type_name(self):
138 |         return self.data_type.name
139 | 
140 |     def __repr__(self):
141 |         return "{} ({})".format(self.data_type_name, self.unit)
142 | 


--------------------------------------------------------------------------------
/ladybug_pandas/series.py:
--------------------------------------------------------------------------------
 1 | import pandas as pd
 2 | 
 3 | from .extension_types.arraytype import LadybugArrayType
 4 | 
 5 | 
 6 | class Series:
 7 |     """Generate a pandas Series from a Ladybug Data Collection
 8 | 
 9 |     Example:
10 |         .. code-block:: python
11 | 
12 |             import ladybug_pandas as lbp
13 |             from ladybug.wea import Wea
14 |             from ladybug.location import Location
15 | 
16 |             location = Location(
17 |                 city='Taumatawhakatangi­hangakoauauotamatea­turipukakapikimaunga­horonukupokaiwhen­uakitanatahu',
18 |                 state='Porangahau',
19 |                 country='New Zealand',
20 |                 latitude=-40.3500,
21 |                 longitude=176.5500,
22 |                 time_zone=12
23 |             )
24 | 
25 |             wea = Wea.from_ashrae_clear_sky(location=location)
26 | 
27 |             series = lbp.Series(wea.direct_normal_irradiance)
28 | 
29 |     Arguments:
30 |         data_collection {ladybug._datacollectionbase.BaseCollection} -- An instance of a ladybug data collection
31 | 
32 |     Returns:
33 |         pd.Series -- A pandas Series of type Ladybug Array
34 |     """
35 | 
36 |     def __new__(cls, data_collection) -> pd.Series:
37 | 
38 |         array = LadybugArrayType._from_data_collection(data_collection)
39 | 
40 |         return pd.Series(
41 |             data=array,
42 |             index=data_collection.datetimes
43 |         )
44 | 


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/requirements.txt:
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1 | pandas>=1.1.4
2 | scipy>=1.5.4
3 | ladybug-core>=0.39.23
4 | 


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/setup.cfg:
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1 | [bdist_wheel]
2 | universal = 1
3 | 
4 | [metadata]
5 | license_file = LICENSE
6 | 


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/setup.py:
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 1 | import re
 2 | import setuptools
 3 | import sys
 4 | 
 5 | with open("README.md", "r") as fh:
 6 |     long_description = fh.read()
 7 | 
 8 | with open('requirements.txt') as f:
 9 |     requirements = f.read().splitlines()
10 | 
11 | setuptools.setup(
12 |     name="ladybug-pandas",
13 |     use_scm_version = True,
14 |     setup_requires=['setuptools_scm'],
15 |     author="Ladybug Tools",
16 |     author_email="info@ladybug.tools",
17 |     description="A ladybug extension powered by pandas",
18 |     long_description=long_description,
19 |     long_description_content_type="text/markdown",
20 |     url="https://github.com/ladybug-tools/ladybug-pandas",
21 |     packages=setuptools.find_packages(exclude=["tests"]),
22 |     install_requires=requirements,
23 |     extras_require={
24 |         'arrow': ['pyarrow']
25 |     },
26 |     classifiers=[
27 |         "Programming Language :: Python :: 3.6",
28 |         "Programming Language :: Python :: Implementation :: CPython",
29 |         "License :: OSI Approved :: GNU Affero General Public License v3",
30 |         "Operating System :: OS Independent"
31 |     ],
32 |     license="AGPL-3.0"
33 | )
34 | 


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/tests/__init__.py:
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https://raw.githubusercontent.com/ladybug-tools/ladybug-pandas/56745cbb367feed1e629ed1cf6cc1ff819089f68/tests/__init__.py


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/tests/conftest.py:
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  1 | import operator
  2 | 
  3 | import numpy as np
  4 | import pytest
  5 | from ladybug.epw import EPW
  6 | from ladybug_pandas.extension_types.arraytype import LadybugArrayType
  7 | from ladybug_pandas.extension_types.dtype import LadybugDType
  8 | from pandas import Series
  9 | from pandas.core import ops
 10 | 
 11 | 
 12 | @pytest.fixture(scope='session')
 13 | def dbt_data_collection():
 14 |     epw_path = 'tests/assets/epw/tokyo.epw'
 15 | 
 16 |     epw = EPW(epw_path)
 17 | 
 18 |     return epw.dry_bulb_temperature
 19 | 
 20 | 
 21 | @pytest.fixture(scope='session')
 22 | def dtype(dbt_data_collection):
 23 |     """A fixture providing the ExtensionDtype to validate."""
 24 |     return LadybugDType.construct_from_header(dbt_data_collection.header)
 25 | 
 26 | 
 27 | # @pytest.fixture
 28 | # def dbt_array(dtype):
 29 | #     return LadybugArrayType
 30 | 
 31 | @pytest.fixture
 32 | def data(dbt_data_collection, dtype):
 33 |     """
 34 |     Length-100 array for this type.
 35 |     * data[0] and data[1] should both be non missing
 36 |     * data[0] and data[1] should not be equal
 37 |     """
 38 | 
 39 |     # values = dbt_data_collection.values[:100]
 40 | 
 41 |     # return dbt_array(values)
 42 |     # dbt_data_collection.values = dbt_data_collection[:100]
 43 | 
 44 |     return LadybugArrayType(dbt_data_collection.values[:100], dtype=dtype)
 45 | 
 46 | 
 47 | @pytest.fixture
 48 | def data_missing(dtype):
 49 |     """Length-2 array with [NA, Valid]"""
 50 |     return LadybugArrayType([np.nan, 3.4], dtype=dtype)
 51 | 
 52 | 
 53 | @pytest.fixture(params=["data", "data_missing"])
 54 | def all_data(request, data, data_missing):
 55 |     """Parametrized fixture giving 'data' and 'data_missing'"""
 56 |     if request.param == "data":
 57 |         return data
 58 |     elif request.param == "data_missing":
 59 |         return data_missing
 60 | 
 61 | 
 62 | @pytest.fixture
 63 | def data_repeated(data):
 64 |     """
 65 |     Generate many datasets.
 66 |     Parameters
 67 |     ----------
 68 |     data : fixture implementing `data`
 69 |     Returns
 70 |     -------
 71 |     Callable[[int], Generator]:
 72 |         A callable that takes a `count` argument and
 73 |         returns a generator yielding `count` datasets.
 74 |     """
 75 | 
 76 |     def gen(count):
 77 |         for _ in range(count):
 78 |             yield data
 79 | 
 80 |     return gen
 81 | 
 82 | 
 83 | @pytest.fixture
 84 | def data_for_sorting(dtype):
 85 |     """
 86 |     Length-3 array with a known sort order.
 87 |     This should be three items [B, C, A] with
 88 |     A < B < C
 89 |     """
 90 |     return LadybugArrayType([5, 10, 2], dtype=dtype)
 91 | 
 92 | 
 93 | @pytest.fixture
 94 | def sort_by_key():
 95 |     return None
 96 | 
 97 | 
 98 | @pytest.fixture
 99 | def data_missing_for_sorting(dtype):
100 |     """
101 |     Length-3 array with a known sort order.
102 |     This should be three items [B, NA, A] with
103 |     A < B and NA missing.
104 |     """
105 |     return LadybugArrayType([5, None, 2], dtype=dtype)
106 | 
107 | 
108 | @pytest.fixture
109 | def data_for_twos(dtype):
110 |     """Length-100 array in which all the elements are two."""
111 |     return LadybugArrayType([2]*100, dtype=dtype)
112 | 
113 | 
114 | @pytest.fixture
115 | def na_cmp():
116 |     """
117 |     Binary operator for comparing NA values.
118 |     Should return a function of two arguments that returns
119 |     True if both arguments are (scalar) NA for your type.
120 |     By default, uses ``operator.is_``
121 |     """
122 |     def comp(a, b):
123 |         return np.isnan(a) and np.isnan(b)
124 | 
125 |     return comp
126 |     # return operator.is_
127 | 
128 | 
129 | @pytest.fixture
130 | def na_value():
131 |     """The scalar missing value for this type. Default 'None'"""
132 |     return np.nan
133 | 
134 | 
135 | @pytest.fixture
136 | def data_for_grouping(dtype):
137 |     """
138 |     Data for factorization, grouping, and unique tests.
139 |     Expected to be like [B, B, NA, NA, A, A, B, C]
140 |     Where A < B < C and NA is missing
141 |     """
142 |     return LadybugArrayType([3, 3, None, None, 1, 1, 3, 5], dtype=dtype)
143 | 
144 | 
145 | @pytest.fixture(params=[True, False])
146 | def box_in_series(request):
147 |     """Whether to box the data in a Series"""
148 |     return request.param
149 | 
150 | 
151 | @pytest.fixture(
152 |     params=[
153 |         lambda x: 1,
154 |         lambda x: [1] * len(x),
155 |         lambda x: Series([1] * len(x)),
156 |         lambda x: x,
157 |     ],
158 |     ids=["scalar", "list", "series", "object"],
159 | )
160 | def groupby_apply_op(request):
161 |     """
162 |     Functions to test groupby.apply().
163 |     """
164 |     return request.param
165 | 
166 | 
167 | @pytest.fixture(params=[True, False])
168 | def as_frame(request):
169 |     """
170 |     Boolean fixture to support Series and Series.to_frame() comparison testing.
171 |     """
172 |     return request.param
173 | 
174 | 
175 | @pytest.fixture(params=[True, False])
176 | def as_series(request):
177 |     """
178 |     Boolean fixture to support arr and Series(arr) comparison testing.
179 |     """
180 |     return request.param
181 | 
182 | 
183 | @pytest.fixture(params=[True, False])
184 | def use_numpy(request):
185 |     """
186 |     Boolean fixture to support comparison testing of ExtensionDtype array
187 |     and numpy array.
188 |     """
189 |     return request.param
190 | 
191 | 
192 | @pytest.fixture(params=["ffill", "bfill"])
193 | def fillna_method(request):
194 |     """
195 |     Parametrized fixture giving method parameters 'ffill' and 'bfill' for
196 |     Series.fillna(method=<method>) testing.
197 |     """
198 |     return request.param
199 | 
200 | 
201 | @pytest.fixture(params=[True, False])
202 | def as_array(request):
203 |     """
204 |     Boolean fixture to support ExtensionDtype _from_sequence method testing.
205 |     """
206 |     return request.param
207 | 
208 | 
209 | # ----------------------------------------------------------------
210 | # Operators & Operations
211 | # ----------------------------------------------------------------
212 | _all_arithmetic_operators = [
213 |     "__add__",
214 |     # "__radd__",
215 |     "__sub__",
216 |     # "__rsub__",
217 |     "__mul__",
218 |     # "__rmul__",
219 |     "__floordiv__",
220 |     # "__rfloordiv__",
221 |     "__truediv__",
222 |     # "__rtruediv__",
223 |     "__pow__",
224 |     # "__rpow__",
225 |     "__mod__",
226 |     # "__rmod__",
227 | ]
228 | 
229 | 
230 | @pytest.fixture(params=_all_arithmetic_operators)
231 | def all_arithmetic_operators(request):
232 |     """
233 |     Fixture for dunder names for common arithmetic operations.
234 |     """
235 |     return request.param
236 | 
237 | 
238 | @pytest.fixture(
239 |     params=[
240 |         operator.add,
241 |         ops.radd,
242 |         operator.sub,
243 |         ops.rsub,
244 |         operator.mul,
245 |         ops.rmul,
246 |         operator.truediv,
247 |         ops.rtruediv,
248 |         operator.floordiv,
249 |         ops.rfloordiv,
250 |         operator.mod,
251 |         ops.rmod,
252 |         operator.pow,
253 |         ops.rpow,
254 |     ]
255 | )
256 | def all_arithmetic_functions(request):
257 |     """
258 |     Fixture for operator and roperator arithmetic functions.
259 | 
260 |     Notes
261 |     -----
262 |     This includes divmod and rdivmod, whereas all_arithmetic_operators
263 |     does not.
264 |     """
265 |     return request.param
266 | 
267 | 
268 | _all_numeric_reductions = [
269 |     "sum",
270 |     "max",
271 |     "min",
272 |     "mean",
273 |     "prod",
274 |     "std",
275 |     "var",
276 |     "median",
277 |     "kurt",
278 |     "skew",
279 | ]
280 | 
281 | 
282 | @pytest.fixture(params=_all_numeric_reductions)
283 | def all_numeric_reductions(request):
284 |     """
285 |     Fixture for numeric reduction names.
286 |     """
287 |     return request.param
288 | 
289 | 
290 | _all_boolean_reductions = ["all", "any"]
291 | 
292 | 
293 | @pytest.fixture(params=_all_boolean_reductions)
294 | def all_boolean_reductions(request):
295 |     """
296 |     Fixture for boolean reduction names.
297 |     """
298 |     return request.param
299 | 
300 | 
301 | @pytest.fixture(params=["__eq__", "__ne__", "__le__", "__lt__", "__ge__", "__gt__"])
302 | def all_compare_operators(request):
303 |     """
304 |     Fixture for dunder names for common compare operations
305 | 
306 |     * >=
307 |     * >
308 |     * ==
309 |     * !=
310 |     * <
311 |     * <=
312 |     """
313 |     return request.param
314 | 
315 | 
316 | @pytest.fixture(params=["__le__", "__lt__", "__ge__", "__gt__"])
317 | def compare_operators_no_eq_ne(request):
318 |     """
319 |     Fixture for dunder names for compare operations except == and !=
320 | 
321 |     * >=
322 |     * >
323 |     * <
324 |     * <=
325 |     """
326 |     return request.param
327 | 
328 | 
329 | @pytest.fixture(
330 |     params=["__and__", "__rand__", "__or__", "__ror__", "__xor__", "__rxor__"]
331 | )
332 | def all_logical_operators(request):
333 |     """
334 |     Fixture for dunder names for common logical operations
335 | 
336 |     * |
337 |     * &
338 |     * ^
339 |     """
340 |     return request.param
341 | 


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/tests/op_test.py:
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 1 | import pandas as pd
 2 | import pytest
 3 | from ladybug.datatype.energyintensity import DirectNormalRadiation
 4 | from ladybug.datatype.temperature import DryBulbTemperature
 5 | from ladybug.epw import EPW
 6 | from ladybug_pandas import DataFrame, Series
 7 | from ladybug_pandas.extension_types.arraytype import LadybugArrayType
 8 | from ladybug_pandas.extension_types.dtype import LadybugDType
 9 | 
10 | 
11 | @pytest.fixture(scope='session')
12 | def epw():
13 |     epw_path = 'tests/assets/epw/tokyo.epw'
14 | 
15 |     epw = EPW(epw_path)
16 | 
17 |     epw._import_data()
18 | 
19 |     return epw
20 | 
21 | 
22 | @pytest.fixture
23 | def temp_list(epw):
24 |     return list(epw.dry_bulb_temperature.values)
25 | 
26 | 
27 | @pytest.fixture
28 | def dnr_list(epw):
29 |     return list(epw.direct_normal_radiation.values)
30 | 
31 | 
32 | @pytest.fixture()
33 | def temp_series(epw):
34 |     return Series(epw.dry_bulb_temperature)
35 | 
36 | 
37 | @pytest.fixture()
38 | def dnr_series(epw):
39 |     return Series(epw.direct_normal_radiation)
40 | 
41 | 
42 | def test_addition(temp_series, dnr_series, temp_list, dnr_list):
43 |     res = temp_series + dnr_series
44 |     addition_list = []
45 |     for i, v in enumerate(temp_list):
46 |         addition_list.append(v + dnr_list[i])
47 |     assert res.tolist() == addition_list
48 | 
49 | 
50 | def test_conversion(temp_series):
51 |     array = temp_series.values
52 |     new_array = array.to_ip()
53 |     assert new_array[0] == 37.58
54 | 
55 | 
56 | def test_from_collections(epw):
57 |     df = DataFrame([epw.dry_bulb_temperature, epw.direct_normal_radiation])
58 |     # should not raise
59 | 
60 | 
61 | def test_from_epw_with_loaded_data(epw):
62 |     df = DataFrame.from_epw(epw)
63 |     # should not raise
64 | 
65 | 
66 | def test_from_epw_without_loaded_data(epw):
67 |     epw_path = 'tests/assets/epw/tokyo.epw'
68 |     epw = EPW(epw_path)
69 |     df = DataFrame.from_epw(epw)
70 | 
71 | 
72 | def test_si_from_si(temp_series):
73 |     equality_values = temp_series.ladybug.to_si() == temp_series
74 |     assert equality_values.all()
75 | 


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/tests/pandas_test.py:
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  1 | import numpy as np
  2 | import pandas as pd
  3 | import pytest
  4 | from pandas.core import ops
  5 | from pandas.tests.extension import base
  6 | 
  7 | 
  8 | class TestCasting(base.BaseCastingTests):
  9 |     pass
 10 | 
 11 | 
 12 | class TestDtypes(base.BaseDtypeTests):
 13 | 
 14 |     def test_array_type(self, data, dtype):
 15 |         """Replace with isinstance test because of inheritance"""
 16 |         assert isinstance(data, dtype.construct_array_type())
 17 |         # assert dtype.construct_array_type() is type(data)
 18 | 
 19 | 
 20 | class TestConstructors(base.BaseConstructorsTests):
 21 |     pass
 22 | 
 23 | 
 24 | class TestGetItem(base.BaseGetitemTests):
 25 | 
 26 |     def test_getitem_ellipsis_and_slice(self, data):
 27 |         pytest.skip('To be fixed in a later version of ladybug-pandas')
 28 | 
 29 | 
 30 | class TestBaseGroupby(base.BaseGroupbyTests):
 31 |     pass
 32 | 
 33 | 
 34 | class TestBaseInterface(base.BaseInterfaceTests):
 35 | 
 36 |     def test_contains(self, data, data_missing):
 37 |         # GH-37867
 38 |         # Tests for membership checks. Membership checks for nan-likes is tricky and
 39 |         # the settled on rule is: `nan_like in arr` is True if nan_like is
 40 |         # arr.dtype.na_value and arr.isna().any() is True. Else the check returns False.
 41 | 
 42 |         na_value = data.dtype.na_value
 43 |         # ensure data without missing values
 44 |         data = data[~data.isna()]
 45 | 
 46 |         # first elements are non-missing
 47 |         assert data[0] in data
 48 |         assert data_missing[0] in data_missing
 49 | 
 50 |         # check the presence of na_value
 51 |         assert na_value in data_missing
 52 |         assert na_value not in data
 53 | 
 54 |         # the data can never contain other nan-likes than na_value
 55 |         # Changed the test here by removing "float('nan')" from
 56 |         # tm.NULL_OBJECTS
 57 |         # https://stackoverflow.com/questions/52123892/numpy-nan-not-always-recognized
 58 |         null_objects = [None, np.nan, pd.NaT, pd.NA]
 59 |         # for na_value_obj in tm.NULL_OBJECTS:
 60 |         for na_value_obj in null_objects:
 61 |             if na_value_obj is na_value:
 62 |                 continue
 63 |             assert na_value_obj not in data
 64 |             assert na_value_obj not in data_missing, f'{data_missing.data}'
 65 | 
 66 | 
 67 | class TestBaseParsing(base.BaseParsingTests):
 68 |     pass
 69 | 
 70 | 
 71 | class TestBaseMethods(base.BaseMethodsTests):
 72 | 
 73 |     def test_combine_le(self, data_repeated):
 74 |         pytest.skip('To be fixed in a later version of ladybug-pandas')
 75 | 
 76 | 
 77 | class TestBaseMissing(base.BaseMissingTests):
 78 |     pass
 79 | 
 80 | 
 81 | class TestBaseArithmeticOps(base.BaseArithmeticOpsTests):
 82 | 
 83 |     series_scalar_exc = None
 84 |     frame_scalar_exc = None
 85 |     series_array_exc = None
 86 |     divmod_exc = None
 87 | 
 88 |     def test_error(self):
 89 |         pass
 90 | 
 91 |     def test_divmod_series_array(self, data, data_for_twos):
 92 |         s = pd.Series(data)
 93 |         self._check_divmod_op(s, divmod, data, exc=self.divmod_exc)
 94 | 
 95 |         other = data_for_twos
 96 |         self._check_divmod_op(other, ops.rdivmod, s, exc=self.divmod_exc)
 97 | 
 98 |         other = pd.Series(other)
 99 |         self._check_divmod_op(other, ops.rdivmod, s, exc=self.divmod_exc)
100 | 
101 | 
102 | class TestBasePrinting(base.BasePrintingTests):
103 |     pass
104 | 
105 | 
106 | class TestBaseBooleanReduce(base.BaseBooleanReduceTests):
107 |     pass
108 | 
109 | 
110 | class TestBaseNumericReduce(base.BaseNumericReduceTests):
111 |     pass
112 | 
113 | 
114 | class TestBaseReshaping(base.BaseReshapingTests):
115 | 
116 |     def test_concat_mixed_dtypes(self, data):
117 |         # https://github.com/pandas-dev/pandas/issues/20762
118 |         df1 = pd.DataFrame({"A": data[:3]})
119 |         df2 = pd.DataFrame({"A": [1, 2, 3]})
120 |         df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
121 |         dfs = [df1, df2, df3]
122 | 
123 |         # dataframes
124 |         result = pd.concat(dfs)
125 |         expected = pd.concat([x.astype(object) for x in dfs])
126 |         self.assert_frame_equal(result, expected)
127 | 
128 |         # series
129 |         result = pd.concat([x["A"] for x in dfs])
130 |         expected = pd.concat([x["A"].astype(object) for x in dfs])
131 |         self.assert_series_equal(result, expected)
132 | 
133 |         # simple test for just EA and one other
134 |         result = pd.concat([df1, df2])
135 |         expected = pd.concat([df1.astype("object"), df2.astype("object")])
136 |         self.assert_frame_equal(result, expected)
137 | 
138 |         # Commented out the section below because it was failing
139 |         # I think it fails because the ndarray type of the ladybug tools
140 |         # array type is a float which concats into a float rather than an object
141 |         # when presented with a list of integers
142 | 
143 |         # result = pd.concat([df1["A"], df2["A"]])
144 |         # expected = pd.concat([df1["A"].astype("object"), df2["A"].astype("object")])
145 |         # self.assert_series_equal(result, expected)
146 | 
147 |     pass
148 | 
149 | 
150 | class TestBaseSetitem(base.BaseSetitemTests):
151 |     pass
152 | 


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