aces.sefd.plotting

Tooling to support the plotting of SEFD figures

Module Contents

Functions

fitfunc2(p, x)

linfunc(p, x)

fit_gauss(→ tuple[tuple[float, Ellipsis], float, ...)

Given the bin centers and counts for data that has been binned, an

find_mode(→ float)

Estimates and returns the mode of those input data that lie within the limits set by the parameters.

make_sefd_cmap(...)

Construct a colour map that is appropriate (targeted) for SEFD plotting

Attributes

logger

aces.sefd.plotting.logger[source]
aces.sefd.plotting.fitfunc2(p, x)[source]
aces.sefd.plotting.linfunc(p, x)[source]
aces.sefd.plotting.fit_gauss(x: numpy.ndarray, y: numpy.ndarray, p0: list[float], fkey: str = 'gau') → tuple[tuple[float, Ellipsis], float, float, float][source]

Given the bin centers and counts for data that has been binned, an attempt to fit a gaussian to said binned data will be performed.

Args:

x (np.ndarray): The bin centers of the binned data y (np.ndarray): The bin counts per bins of the binned data p0 (list[float]): Initial guess parameters of the desired model to fit fkey (str, optional): Model to fit to the data. Defaults to ‘gau’.

Raises:

ValueError: Raised when an unkown key / fit function is suppliede

Returns:

tuple[tuple[float,…], float, float, float]: Contains the best fit parameters, the x and y of the model evaluated with best fit parameters, and the reduced chi2

aces.sefd.plotting.find_mode(data: numpy.ma.MaskedArray, parameters: dict[Any, Any]) → float[source]

Estimates and returns the mode of those input data that lie within the limits set by the parameters. The mode is determined by fitting a guassian function to the peak of the histogram of the logarithms of the values.

Parameters:

  • data – np.array of real values

  • parameters – dictionary giving the data value bounds

Returns:

aces.sefd.plotting.make_sefd_cmap(ssl: float, ssu: float) → tuple[matplotlib.colors.LinearSegmentedColormap, matplotlib.colors.Normalize][source]

Construct a colour map that is appropriate (targeted) for SEFD plotting

Args:

ssl (float): Lower limit of the SEFD values to plot (Tsys) ssu (float): Upper limit of the SEFD values to plot (Tsys)

Returns:

tuple[mpl.colors.LinearSegmentedColormap, mpl.colors.Normalize]: The colours and normalise matplotlib instances to use