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bph2-co2-1.0.4
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مشاهده بیشترتوضیحات
Educational tool for co2_concentration simulations
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bph2-co2-1.0.4 |
| نام | bph2-co2 |
| نسخه کتابخانه | 1.0.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Max Buehler |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/bph2-co2/ |
| مجوز | MIT |
============================================================
bph2_co2: Educational tool for CO2 concentration simulations
============================================================
Python library for education with tools for CO2 concentration simulations
.. image:: https://raw.githubusercontent.com/bph-tuwien/bph_co2/master/docs/screenshot_1.PNG?sanitize=true
Installation:
^^^^^^^^^^^^^
pip install bph2-co2==1.0.0
Example:
--------
see also main.py
.. code-block:: python
from bph_co2.solver import CO2_Simulation, ppm_to_mg_m3, mg_m3_to_ppm
from bph_co2.timeseries import Timeseries
from bph_co2.window import Window
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 `importlib_resources`.
import importlib_resources as pkg_resources
from bph_co2.resources import Input_Data as case_data
if __name__ == '__main__':
# load .csv files
with pkg_resources.path(case_data, 'persons.csv') as path:
persons_filename = path.__str__()
with pkg_resources.path(case_data, 'internal_co2_source.csv') as path:
internal_co2_source_filename = path.__str__()
with pkg_resources.path(case_data, 'air_change_rate.csv') as path:
air_change_rate_filename = path.__str__()
with pkg_resources.path(case_data, 'window_state.csv') as path:
window_state_filename = path.__str__()
with pkg_resources.path(case_data, 'indoor_temperature.csv') as path:
indoor_temperature_filename = path.__str__()
with pkg_resources.path(case_data, 'outdoor_temperature.csv') as path:
outdoor_temperature_filename = path.__str__()
n_persons = Timeseries.from_csv(persons_filename, interpolation_scheme='previous')
internal_co2_source = Timeseries.from_csv(internal_co2_source_filename, interpolation_scheme='linear')
air_change_rate = Timeseries.from_csv(air_change_rate_filename, interpolation_scheme='linear')
window_state = Timeseries.from_csv(window_state_filename, interpolation_scheme='previous')
indoor_temperature = Timeseries.from_csv(indoor_temperature_filename, interpolation_scheme='linear')
outdoor_temperature = Timeseries.from_csv(outdoor_temperature_filename, interpolation_scheme='linear')
# create a window:
window = Window(hight=1,
area=1,
state=window_state)
sim = CO2_Simulation(name='test_simulation',
volume=51.48,
n_persons=n_persons,
emission_rate=27000,
internal_co2_source=internal_co2_source,
indoor_temperature=indoor_temperature,
outdoor_temperature=outdoor_temperature,
windows=[window],
air_change_rate=air_change_rate,
timestep=60,
t_end=26640)
res = sim.calculate()
res.plot()
Usage
-----
Imports:
^^^^^^^^
.. code-block:: python
from src.bph_co2.solver import CO2_Simulation
from src.bph_co2.timeseries import Timeseries
from src.bph_co2.window import Window
CO2_Simulation:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- create a CO2_Simulation object. This is the base for running a simulation:
.. code-block:: python
sim = CO2_Simulation(name='my_test_simulation')
The CO2_Simulation has the following parameters:
- *name*: the name of the CO2_Simulation; default is 'Unnamed Simulation'
- *volume*: the volume of the simulated zone [m³]; default is 75
- *n_persons*: number of persons in the zone; default is 1 *
- *emission_rate*: CO2 emission_rate of a person in mg/h; default is 27000 mg/h;
- *internal_co2_source*: co2 emission rate of internal sources in mg/h; default is 0 *
- *outdoor_temperature*: outdoor temperature in °C; default is 10 °C *
- *indoor_temperature*: indoor temperature in °C; default is 20 °C *
- *windows*: windows of the zone; list of *window*-objects; default is []
- *air_change_rate*: air change rate in 1/h; default is 0.5 *
- *c0i*: initial CO2-concentration in the room/zone in ppm; default is 400
- *c0e*: initial outdoor CO2-concentration in ppm; default is 400
- *timestep*: simulation timestep [s]; default is 360
- *t_end*: end time of the simulation
All parameters can be set on initialization or afterwards.
* Parameters can be Timeseries objects
- run a simulation:
.. code-block:: python
res = sim.calculate()
- display simulation results:
res.plot()
Timeseries Objects:
^^^^^^^^^^^^^^^^^^^^^^^^^^
- A Timeseries handles data and returns a value / values for a time [s]. A Timeseries can handle static values (int, float, etc..), numpy arrays (first column has to be the time in [s]) or pd.Dataframes (index must be the time).
- Timeseries objects can interpolate Data in different ways. To specify interpolation scheme pass keyword *interpolation_scheme* with:
- 'linear': linear interpolation
- 'previous': closest previous value (for example for persons)
- Create a timeseries object with static value (integer):
.. code-block:: python
n_persons = Timeseries(data=1)
- Create a timeseries object with np.array:
.. code-block:: python
array = array = np.empty((2,100))
array[0,:] = np.arange(array.shape[1])
array[1,:] = np.random.rand(array.shape[1])
n_persons = Timeseries(data=array)
- Create a timeseries object with pd.Dataframe:
.. code-block:: python
array = array = np.empty((2,100))
array[0,:] = np.arange(array.shape[1])
array[1,:] = np.random.rand(array.shape[1])
df = pd.DataFrame({'Time': array[0,:],
'n_persons': array[1,:]})
df.set_index('Time', inplace=True)
n_persons = Timeseries(data=array, interpolation_scheme='linear')
- Create a timeseries object from .csv file:
.. code-block:: python
n_persons = Timeseries.from_csv('test.csv', interpolation_scheme='previous')
Windows:
^^^^^^^^^^^^^^^^^^^^^^^^^^
In the Simulation windows can be added. Windows create additional air change in the zone dependent of the indoor- and outdoor-temperatures, the opening state and the geometry.
The window can have three states:
- 0: closed
- 1: tilted
- 2: opened
The window has the following parameters:
- hight: the hight of the window [m]; default is 1
- area: the area of the window [m²]; default is 1
- state: state of the window; 0: closed, 1: tilted; 2: opened; default is 0 (closed)
- c_ref: Austauschkoeffizient [m^0.5 / h * K^0.5], default is 100
- a_tilted: effective ventilation area for tilted window [m²]; default is calculated from the window geometry
- a_opened: effective ventilation area for opened window [m²]; default is calculated from the window geometry
- Create a window:
.. code-block:: python
from src.bph_co2.window import Window
window_state = Timeseries.from_csv('window_state.csv', interpolation_scheme='previous')
window = Window(hight=1,
area=1,
state=window_state)
- Add window to the simulation:
The windows are specified as a list of window objects:
.. code-block:: python
sim.windows = [window]
نیازمندی
| مقدار | نام |
|---|---|
| - | pandas |
| - | numpy |
| - | pandasgui |
| - | progress |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl bph2-co2-1.0.4:
pip install bph2-co2-1.0.4.whl
نصب پکیج tar.gz bph2-co2-1.0.4:
pip install bph2-co2-1.0.4.tar.gz