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Merge pull request #21 from BuildingEnergySimulationTools/module-rework
Module rework
2 parents d97e949 + ffe4e3f commit 5a33bb6

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‎.github/workflows/build.yaml‎

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name: build
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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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jobs:
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lint:
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name: lint
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/setup-python@v4
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with:
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python-version: 3.11
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- run: python -m pip install --upgrade pip wheel
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- run: pip install tox
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- run: tox -elint
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tests:
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name: ${{ matrix.name }}
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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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include:
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- {name: '3.10', python: '3.10', tox: py310}
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- {name: '3.12', python: '3.12', tox: py312}
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- {name: 'min', python: '3.10', tox: min}
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steps:
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- uses: actions/checkout@v3
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- uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python }}
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- name: Run test
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run: |
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python -m pip install --upgrade pip wheel
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pip install tox
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tox -e${{ matrix.tox }}
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- name: Upload coverage
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uses: codecov/codecov-action@v3
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with:
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token: ${{ secrets.CODECOV_TOKEN }}
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files: ./coverage.xml
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release:
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needs: [lint, tests]
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name: PyPI release
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if: startsWith(github.ref, 'refs/tags')
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/setup-python@v4
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- run: python -m pip install --upgrade pip wheel
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- run: pip install twine
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- run: python setup.py sdist bdist_wheel
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- run: twine upload -u __token__ -p ${{ secrets.PYPI_API_TOKEN }} dist/*

‎.pre-commit-config.yaml‎

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ci:
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skip: [pip-compile]
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repos:
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- repo: https://github.com/asottile/pyupgrade
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rev: v3.3.1
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hooks:
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- id: pyupgrade
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args: [--py310-plus]
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- repo: https://github.com/psf/black
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rev: 23.3.0
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hooks:
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- id: black
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language_version: python3
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- repo: https://github.com/pycqa/flake8
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rev: 6.0.0
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hooks:
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- id: flake8
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additional_dependencies: [flake8-bugbear==23.1.14]

‎LICENCE.md‎

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**Modelitool Copyright (c) 2023, Nobatek-INEF4**
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All rights reserved.
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Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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1- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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2- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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3- Neither the name of Nobatek-INEF4 nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS AND CONTRIBUTORS BE LIABLE FOR ANYDIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

‎README.md‎

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# Modelitool
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Python tools for modelica.
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Use only UTC in datetime index.
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## modelitool.measures.MeasuredDats
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<span style="background-color: #ffe600">***class modelitool.measures.***<span style="color: #ff0040; font-size:1em">**MeasuredDats**</span>***(data=None, data_type=\{\}, corr_dict=\{\})***</span>
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Handle time series of measured data with missing or incorrect values.
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Modelica CombiTimeTable input file can be generated from raw data or corrected data.
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**Parameters:**
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>**data :** *DataFrame* index must be a UTC timezone aware datetime index.
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>**data_type :** *Dictionary* keys are data_type name (eg. temperatures, power), item is a list of columns
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>**corr_dict :** *Dictionary* keys are data_type name. Item is a dictionary holding correction methods properties
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>**Available corrections :**
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- <code>minmax</code> : Remove values beyond or below specific threshold. Arguments : <code>upper, lower</code>
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- <code>derivative</code> : Remove values when absolute variation between to timstep is beyond defined threshold. Arguments : <code>lower_rate, upper_rate</code>
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- <code>fill_nan</code> : Fill <code>nan</code> values. Argument is a list of method :
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- <code>bfill</code> : Back propagates the next not nan value
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- <code>ffill</code> : Front propagates the last not nan value
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- <code>linear_interpolation</code>: Interpolate value with <code>linear</code> method.
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**Attributes**
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- **data :** copy of raw data
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- **data_type_dict :** dictionary of data_type
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- **corrected_data :** corrected data. Copy of data if no correction is applied
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- **corr_dict :** dictionary of corrections
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- **correction_journal :** History of measures properties. Gaps and missing values
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**Methods**
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- **remove_anomalies**_()_ : apply minmax and derivative corrections described in <code>corr_dict</code>. Update correction journal with dict of missing values and measure gaps resume
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- **fill_nan**_()_ : apply <code>fill_nan</code> corrections described in <code>corr_dict</code>. Update correction journal with dict of missing values and measure gaps resume
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- **resample**_(timestep)_ : resample <code>corrected_data</code> to specify timestep. If no timestep is specified, the function uses the DataFrame mean difference of its time index.
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- **generate_combitimetable_input**_(file_path, corrected_data=True)_ : generate a modelica CombiTimeTable input file based on <code>data</code> or <code>corrected_data</code>
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- **auto_correct**_()_ : apply <code>remove_anomalies(), fill_nan(), resample()</code> in that order, using default arguments
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**Example**
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<pre><code>
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my_measures = MeasuredDats(
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data = my_dirty_measure_df,
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data_type_dict = {
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"power": ["Power1", "Power2"],
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"temperatures": ["Temperature1", "Temperature2"]
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},
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corr_dict = {
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"power": {
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"minmax": {
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"upper": 40000,
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"lower": 0
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},
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"derivative": {
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"upper_rate": 5000,
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"lower_rate": 0,
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},
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"fill_nan": [
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"linear_interpolation",
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"bfill",
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"ffill"
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],
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"resample": np.mean,
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},
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"temperatures": {
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"minmax": {
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"upper": 50,
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"lower": -2
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},
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"derivative": {
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"upper_rate": 5,
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"lower_rate": 0,
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},
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"fill_nan": [
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"linear_interpolation",
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"bfill",
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"ffill"
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],
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"resample": np.sum,
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}
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}
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)
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</code></pre>
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## modelitool.weather.OikolabWeatherData
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<span style="background-color: #ffe600">***class modelitool.weather.***<span style="color: #ff0040; font-size:1em">**OikolabWeatherData**</span>***(location_name=None, location_lat=None, location_long=None, start=None, end=None, api_key=None)***</span>
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Get meteorological data from oikolab API. Transforms it to epw and TMY5 file
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Available data are:
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- Dry bulb temperatures [°C]
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- Dew point temperatures [°C]
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- Relative humidity [%]
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- Atmospheric pressure [Pa]
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- Global horizontal radiation [W/m²]
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- Direct normal radiation [W/m²]
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- Diffuse solar radiation [W/m²]
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- Wind direction [deg]
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- Wind speed [m/s]
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- Total cloud cover [0-1]
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- Snowfall [mm of water equivalent]
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- Total precipitation [mm of water]
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**Parameters:**
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>**location_name :** *String* Name of the meteo station.
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>**location_lat :** *Float* Meteo site location latitude
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>**location_long :** *Float* Meteo site location longitude
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>**start :** *String* starting point date time format : <code>"YYYY-MM-DDThh:mm:ss"</code> provite UTC
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>**end :** *String* ending point date time format : <code>"YYYY-MM-DDThh:mm:ss"</code> provite UTC
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>**api_key :** *String* Oikolab API key
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**Attributes**
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- **data :** DataFrame holding meteo data
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**Methods**
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- **get_data**_()_ : Fetch meteorological data from Oikolab API
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- **generate_tmy5**_(file_path)_ : generate a TMY5 *.mos file compatible with Modelica Building library reader
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- **generate_epw**_(file_path)_ : generate a EPW *.epw file compatible with energyPlus
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**Example**
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<pre><code>
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my_weather = OikolabWeatherData(
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location_name="Anglet",
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location_lat=43.47414153237001,
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location_long=-1.5095114151032965,
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start='2017-01-01T00:00:00',
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end='2017-12-31T23:00:00',
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api_key="d414d453fb0f496fb6dfa1c3xxxxxxxx"
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)
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</code></pre>
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Python tools for modelica.

‎modelitool/combitabconvert.py‎

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def datetime_to_seconds(index_datetime):
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time_start = dt.datetime(index_datetime[0].year, 1, 1, tzinfo=dt.timezone.utc)
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new_index = index_datetime.to_frame().diff().squeeze()
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new_index[0] = dt.timedelta(
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new_index.iloc[0] = dt.timedelta(
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seconds=index_datetime[0].timestamp() - time_start.timestamp()
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)
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sec_dt = [elmt.total_seconds() for elmt in new_index]
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"""
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Write a text file compatible with modelica Combitimetables object from a
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Pandas DataFrame with a DatetimeIndex. DataFrames with non monotonically increasing
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datetime index will raise a ValueError to prevent bugs when file is used in Modelica.
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datetime index will raise a ValueError to prevent bugs when file is used in
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Modelica.
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@param df: DataFrame with DatetimeIndex
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@param filename: string or Path to the output file
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@return: None

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