rizer.io.radiation_data_reader#

Attributes#

emission_radius_mm

Classes#

RadiationDataReader

Load and process the radiation data for a gas, assuming LTE.

Module Contents#

class rizer.io.radiation_data_reader.RadiationDataReader(gas_name: str, pressure_atm: int, emission_radius_mm: int, source: str)#

Load and process the radiation data for a gas, assuming LTE.

LTE (Local Thermodynamic Equilibrium) means that thermal and chemical equilibrium are established. The data is assumed to be given at a fixed pressure.

Methods are provided to interpolate the data and plot it. The interpolations are linear, and out-of-bounds values take the value at the boundary.

Parameters:

  • gas_name (str) – Gas used for the radiation data.

  • pressure_atm (int) – Pressure (in atm) for the radiation data.

  • emission_radius_mm (int) – Emission radius (in mm) for the radiation data. The net emission coefficient depends on the size of the emitting volume.

  • source (str) – Source used for the radiation data.

Examples

Plot radiation data vs. temperature for CH4, H2 and N2.

Plot radiation data vs. temperature for CH4, H2 and N2.
temperature#
nec_data#
load_data() → tuple[numpy.ndarray, numpy.ndarray]#

Load the data from the files.

Files are expected to be in ./data/radiation.

The radiation data file is expected to be a CSV file with the following columns:

  • Temperature [K]

  • Net emission coefficient [W/(m^3.sr)]

It is assumed that the data is sorted by increasing temperature. It is also assumed that the header is 3 lines long.

Returns:

Tuple containing the following arrays:

  • temperature: Temperature [K]

  • nec: Net emission coefficient [W/(m^3.sr)]

Return type:

tuple[np.ndarray, ]

nec(T: float) → float#

Get the net emission coefficient at a given temperature.

Parameters:

T (float) – Temperature [K].

Returns:

Net emission coefficient [W/(m^3.sr)] at the given temperature.

Return type:

float

plot(x: str = 'temperature', y: str = 'net_emission_coefficient', show: bool = True, yscale: str = 'linear', fig_ax: tuple[matplotlib.figure.Figure, matplotlib.axes.Axes] | None = None, **plot_options) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]#

Plot data.

Parameters:

  • x (str) – X axis data. Can only be “temperature” or “T”.

  • y (str) – Y axis data. Could be “nec” or “net_emission_coefficient”. Can also be “temperature” or “T”, but that would be a trivial plot.

  • show (bool, optional) – Whether to display the plot immediately (default is True).

  • yscale (str, optional) – The scale of the y-axis. Options are “linear” or “log” (default is “linear”).

  • fig_ax (tuple[Figure, Axes] | None, optional) – A tuple containing a Matplotlib Figure and Axes to plot on. If None, a new figure and axes are created (default is None).

  • **plot_options – Additional keyword arguments to pass to the plotting function. E.g., label, color, linestyle, marker, etc.

Returns:

The figure and axes objects of the plot.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

Raises:

ValueError – If an invalid variable is specified for the x or y axis.

Examples

>>> from rizer.io.radiation_data_reader import radiation_data_reader
>>> # Define the hydrogen data.
>>> hydrogen_data = radiation_data_reader(
...     gas_name="H2",
...     pressure_atm=1,
...     emission_radius_mm=0,
...     source="Gueye2017",
... )
>>> # Plot the data.
>>> hydrogen_data.plot(x="temperature", y="nec")
rizer.io.radiation_data_reader.emission_radius_mm = 10#