`Utils`

utils

pooling_2d

pooling_2d(
    input_array: ndarray,
    kernel_size: int = 4,
    stride: int = 4,
    padding: Union[int, Tuple[int, int]] = 0,
    stat: Callable = nanmedian,
) -> ndarray

Performs 2D pooling on the input array with optional zero padding.

Parameters:

input_array (ndarray) –

A 2D numpy array representing the input data.
kernel_size (int, default: 4 ) –

The size of the pooling kernel (square), by default 4. Must be positive.
stride (int, default: 4 ) –

The stride of the pooling operation, by default 4. Must be positive.
padding (Union[int, Tuple[int, int]], default: 0 ) –

Padding to be added to the input array before pooling. - If an int p: applies symmetric padding of p zeros. p rows of zeros are added to the top and p to the bottom. p columns of zeros are added to the left and p to the right. - If a tuple (p_h, p_w): applies p_h rows of zeros to the top and p_h to the bottom, and p_w columns of zeros to the left and p_w to the right. Padding values must be non-negative. Default is 0 (no padding).
stat (Callable, default: nanmedian ) –

The aggregation function to use for pooling (e.g., np.mean, np.max, np.nanmedian), by default np.nanmedian.

Returns:

ndarray –

A 2D numpy array representing the pooled output.

fill_nans_interp

fill_nans_interp(cube: ndarray, deg: int = 3) -> ndarray

Replace nan values in a data cube using plynomial interpolation

Parameters:

cube (ndarray) –

Data cube with nan values to be interpolated
deg (int, default: 3 ) –

Degree of polynomial, defualt is 3.

Returns:

ndarray –

Interpolated data cube without nan values

plot_img

plot_img(
    img: ndarray,
    scol_2d: Optional[ndarray] = None,
    srow_2d: Optional[ndarray] = None,
    plot_type: str = "img",
    extent: Optional[Tuple] = None,
    cbar: bool = True,
    ax: Optional[Axes] = None,
    title: str = "",
    vmin: Optional[float] = None,
    vmax: Optional[float] = None,
    cnorm: Optional[Normalize] = None,
    bar_label: str = "Flux [e-/s]",
) -> Axes

Plots an image with optional scatter points and colorbar.

Parameters:

img (ndarray) –

The 2D image data to be plotted.
scol_2d (Optional[ndarray], default: None ) –

The column coordinates of scatter points, by default None.
srow_2d (Optional[ndarray], default: None ) –

The row coordinates of scatter points, by default None.
plot_type (str, default: 'img' ) –

The type of plot to create ('img' or 'scatter'), by default "img".
extent (Optional[Tuple], default: None ) –

The extent of the image (left, right, bottom, top), by default None.
cbar (bool, default: True ) –

Whether to display a colorbar, by default True.
ax (Optional[Axes], default: None ) –

The matplotlib Axes object to plot on, by default None. If None, a new figure and axes are created.
title (str, default: '' ) –

The title of the plot, by default "".
vmin (Optional[float], default: None ) –

The minimum value for the colormap, by default None.
vmax (Optional[float], default: None ) –

The maximum value for the colormap, by default None.
cnorm (Optional[Normalize], default: None ) –

Custom color normalization, by default None.
bar_label (str, default: 'Flux [e-/s]' ) –

The label for the colorbar, by default "Flux [e-/s]"."

Returns:

axes –

animate_cube

animate_cube(
    cube: ndarray,
    scol_2d: Optional[ndarray] = None,
    srow_2d: Optional[ndarray] = None,
    cadenceno: Optional[ndarray] = None,
    time: Optional[ndarray] = None,
    plot_type: str = "img",
    extent: Optional[Tuple] = None,
    interval: int = 200,
    repeat_delay: int = 1000,
    step: int = 1,
    suptitle: str = "",
    bar_label: str = "Flux [e-/s]",
) -> FuncAnimation

Animates a 3D data cube (time series of 2D images).

Parameters:

cube (ndarray) –

The 3D data cube to be animated (time, row, column).
scol_2d (Optional[ndarray], default: None ) –

The column coordinates of scatter points, by default None.
srow_2d (Optional[ndarray], default: None ) –

The row coordinates of scatter points, by default None.
cadenceno (Optional[ndarray], default: None ) –

Cadence numbers corresponding to the time axis, by default None.
time (Optional[ndarray], default: None ) –

Time values corresponding to the time axis, by default None.
plot_type (str, default: 'img' ) –

The type of plot to create ('img' or 'scatter'), by default "img".
extent (Optional[Tuple], default: None ) –

The extent of the images (left, right, bottom, top), by default None.
interval (int, default: 200 ) –

Delay between frames in milliseconds, by default 200.
repeat_delay (int, default: 1000 ) –

Delay in milliseconds before repeating the animation, by default 1000.
step (int, default: 1 ) –

Step size for frame selection, by default 1.
suptitle (str, default: '' ) –

Overall title for the animation figure, by default "".
bar_label (str, default: 'Flux [e-/s]' ) –

The label for the colorbar, by default "Flux [e-/s]".

Returns:

FuncAnimation –

The matplotlib FuncAnimation object.

int_to_binary_array

int_to_binary_array(integer: int, num_bits: int) -> ndarray

Converts a non-negative integer to its binary representation as a NumPy array.

Parameters:

integer (int) –

The non-negative integer to convert.
num_bits (int) –

The desired number of bits in the output binary representation. The binary string will be left-padded with zeros if necessary. Must be greater than zero.

Returns:

ndarray –

A NumPy array of dtype uint8 representing the binary digits (0 or 1), with the most significant bit first. The length of the array is num_bits.

has_bit

has_bit(quality_array: ndarray, bit: int) -> ndarray

Checks if a specific bit is set in each element of a quality flag array.

Parameters:

quality_array (ndarray) –

A NumPy array of integers (quality flags).
bit (int) –

The bit position to check (1-based index, where 1 is the LSB). Must be between 1 and 16 (inclusive).

Returns:

ndarray –

A boolean NumPy array of the same shape as quality_array, where True indicates that the specified bit is set (1) in the corresponding quality flag integer.