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Merge pull request #1 from dpohanlon/update_readme
Update readme
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‎.github/workflows/python-package.yml‎

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# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
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# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
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name: Limao
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on:
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push:
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branches: [ "main" ]
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tags: '*'
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pull_request:
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branches: [ "main" ]
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.9"]
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v3
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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python -m pip install flake8 nose2
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python -m pip install .
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if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
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- name: Lint with flake8
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run: |
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# stop the build if there are Python syntax errors or undefined names
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flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
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# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
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flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
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- name: Test with nose2
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run: |
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nose2 --with-coverage
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- name: Upload coverage reports to Codecov
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uses: codecov/codecov-action@v3
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build:
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needs: [test]
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if: needs.test.result == 'success'
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.9"]
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v3
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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python -m pip install poetry
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- name: Poetry build
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run: |
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poetry build
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# Save the build package as an artifact
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- uses: actions/upload-artifact@v3
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with:
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path: dist/*
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publish:
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needs: [build]
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runs-on: ubuntu-latest
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steps:
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- uses: actions/download-artifact@v3
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with:
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# unpacks default artifact into dist/
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# if `name: artifact` is omitted, the action will create extra parent dir
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name: artifact
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path: dist
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- name: Publish to test PyPI
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if: ${{!startsWith(github.ref, 'refs/tags')}}
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uses: pypa/gh-action-pypi-publish@release/v1
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with:
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password: ${{ secrets.TEST_PYPI_API_TOKEN }}
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repository-url: https://test.pypi.org/legacy/
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- name: Publish tagged version to PyPI
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if: startsWith(github.ref, 'refs/tags')
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uses: pypa/gh-action-pypi-publish@release/v1
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with:
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password: ${{ secrets.PYPI_API_TOKEN }}

‎README.md‎

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Primarily this depends on the the building orientation, the latitude, and the time of the year, but also on occluding obstacles. Limão incorporates time and location dependent sunlight intensity projections, with digital surface and terrain maps, to determine an estimated overall intensity as a function of time.
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# Usage
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Requires digital surface models (DSM) and digital terrain models (DTM) for the region of interest, in addition to the latitude and longitude. For the UK these are available from [DEFRA](https://www.data.gov.uk/dataset/f0db0249-f17b-4036-9e65-309148c97ce4/national-lidar-programme) ([direct link](https://environment.data.gov.uk/DefraDataDownload/?Mode=survey)), and are derived from satellite LIDAR measurements. Latitude and longitude can be obtained from Google Maps.
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# Applications
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For illumination of a wall (for example, for photovoltaic panel placement), Limão provides predicted sunlight intensities on a user-defined vertical plane in real space:
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<p align="center">
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<img width="600" height="229" src="assets/profile.png">
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</p>
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Here the intensities are averaged over the year, and the variation is mostly due to occluding buildings. The intensities over time for a single point `(lat, lon, height)` can also be queried:
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**Currently this assumes UK DSM/DTMs as the lat/lon is converted into the UK OS map coordinates.**
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<p align="center">
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<img width="400" height="300" src="assets/weekAvg.png">
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</p>
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This is useful for comparing the intensity from different directions, and the produced plots indicate the maximum and minimum intensity during daylight hours.
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At the moment this ignores occlusions within a configurable distance from the indicated location to avoid 'self' intersections, but this will probably be improved later.
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# Installation
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Either from the PyPI release
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```bash
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pip install limao
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```
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usage: limao.py [-h] [-s FILENAMEDSM] [-t FILENAMEDTM] [-lat LAT] [-lon LON] [--size SIZE]
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or the bleeding edge via GitHub
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optional arguments:
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-h, --help show this help message and exit
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-s FILENAMEDSM DSM input file (.tif).
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-t FILENAMEDTM DTM input file (.tif).
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-lat LAT Latitude.
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-lon LON Longitude.
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--size SIZE Region Size around location.
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```bash
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git clone git@github.com:dpohanlon/limao.git
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cd limao
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pip install .
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```
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# Usage
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Limão requires digital surface models (DSM) and digital terrain models (DTM) for the region of interest, in addition to the latitude and longitude. For the UK these are available from [DEFRA](https://www.data.gov.uk/dataset/f0db0249-f17b-4036-9e65-309148c97ce4/national-lidar-programme) ([direct link](https://environment.data.gov.uk/DefraDataDownload/?Mode=survey)), and are derived from satellite LIDAR measurements. Latitude and longitude can be obtained from Google Maps, and must correspond to a point on the DSM and DTMs.
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**Currently this assumes UK DSM/DTMs, as the lat/lon is converted into UK Ordnance Survey map coordinates.**
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Limão ships with a standalone executable and as a library - here we'll just use the command line interface. All use cases require the latitude, longitude, a DSM, and a DTM:
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```bash
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limao --lat 52.198 --lon 0.135 -s TL45ne_LZ_DSM_2m.tif -t TL45ne_DTM_2m.tif
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```
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By default this will just produce the intensity from the north and south on the point defined by the latitude and longitude coordinates. To produce the 2D projection, additionally pass the `--proj` argument, with optional horizontal and vertical extent of the surface in metres:
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```bash
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--proj --horizontal 20 --vertical 8
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```

‎assets/profile.png‎

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‎assets/weekAvg.png‎

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‎limao/run_limao.py‎

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from limao.limao import Limao
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def intensityProjection(fileNameDSM, fileNameDTM, latLon, size):
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def intensityProjection(fileNameDSM, fileNameDTM, latLon, size, horizontal, vertical):
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nx = 20
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ny = 8
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nx = horizontal
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ny = vertical
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intensities = np.zeros((nx, ny))
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table = limao.yearlyIntensityTable()
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plt.plot(table[table["altitude"] > 0]["azimuth"], ".")
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plt.plot(table[table["altitude"] < 0]["azimuth"], ".")
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plt.savefig("az.pdf")
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plt.clf()
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plt.plot(table["altitude"], ".")
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plt.savefig("alt.pdf")
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plt.clf()
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# plt.plot(table[table["altitude"] > 0]["azimuth"], ".")
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# plt.plot(table[table["altitude"] < 0]["azimuth"], ".")
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# plt.savefig("az.pdf")
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# plt.clf()
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#
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# plt.plot(table["altitude"], ".")
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# plt.savefig("alt.pdf")
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# plt.clf()
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isNorth, _, _ = limao.intensityOnElevation(table)
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table["isNorth"] = isNorth
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plt.plot(table[table["isNorth"]]["azimuth"], ".")
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plt.savefig("north_az.pdf")
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plt.clf()
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plt.plot(table[~table["isNorth"]]["intensity_passed"], alpha=0.5, label="South")
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plt.plot(table[table["isNorth"]]["intensity_passed"], alpha=0.5, label="North")
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plt.xlabel("Hours from 1/1", fontsize=14)
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plt.ylabel("Direct sunlight intensity $(W/m^2)$", fontsize=14)
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plt.legend(loc=0, fontsize=14)
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plt.savefig("test.pdf")
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plt.clf()
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# plt.plot(table[table["isNorth"]]["azimuth"], ".")
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# plt.savefig("north_az.pdf")
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# plt.clf()
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#
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# plt.plot(table[~table["isNorth"]]["intensity_passed"], alpha=0.5, label="South")
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# plt.plot(table[table["isNorth"]]["intensity_passed"], alpha=0.5, label="North")
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#
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# plt.xlabel("Hours from 1/1", fontsize=14)
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# plt.ylabel("Direct sunlight intensity $(W/m^2)$", fontsize=14)
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# plt.legend(loc=0, fontsize=14)
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# plt.savefig("test.pdf")
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# plt.clf()
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tableDayAvg = (
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table.groupby(["day", "isNorth"])
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plt.xlabel("Day", fontsize=14)
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plt.ylabel("Direct sunlight intensity $(W/m^2)$", fontsize=14)
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plt.legend(loc=0, fontsize=14)
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plt.savefig("testDayAvg.pdf")
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plt.savefig("dayAvg.pdf")
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plt.clf()
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plt.plot(
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plt.xlabel("Week number", fontsize=14)
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plt.ylabel("Direct sunlight intensity $(W/m^2)$", fontsize=14)
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plt.legend(loc=0, fontsize=14)
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plt.savefig("testWeekAvg.pdf")
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plt.savefig("weekAvg.pdf", dpi = 300)
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plt.savefig("weekAvg.png", dpi = 300)
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plt.clf()
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if __name__ == "__main__":
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def run():
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# I'd like an argument, please
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argParser = argparse.ArgumentParser()
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help="Plot a spatial 2d projection map of intensity.",
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)
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argParser.add_argument(
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"--vertical",
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type=int,
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dest="vertical",
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default=10,
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help="Vertical extent of the projection.",
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)
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argParser.add_argument(
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"--horizontal",
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type=int,
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dest="horizontal",
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default=10,
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help="Horizontal extent of the projection.",
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)
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args = argParser.parse_args()
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if args.proj:
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intensityProjection(
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args.fileNameDSM, args.fileNameDTM, (args.lat, args.lon), args.size
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args.fileNameDSM, args.fileNameDTM, (args.lat, args.lon), args.size, args.horizontal, args.vertical
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)
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else:
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dailyAvgIntensity(
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args.fileNameDSM, args.fileNameDTM, (args.lat, args.lon), args.size
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)
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if __name__ == "__main__":
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run()

‎pyproject.toml‎

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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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[tool.poetry.scripts]
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limao = "limao.run_limao:run"

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