vista.sensors.sensor.Sensor

class vista.sensors.sensor.Sensor(name, bias_images=None, bias_image_frames=None, uniformity_gain_images=None, uniformity_gain_image_frames=None, bad_pixel_masks=None, bad_pixel_mask_frames=None, _instance_count=0)

Base class for sensor position, line-of-sight, geodetic conversion, and radiometric modeling

The Sensor class provides a framework for representing sensor platforms and their associated characteristics including projection polynomials and radiometric calibration data.

name

Unique name for this sensor. Used as the primary identifier.

Type:

str

bias_images

3D array of bias/dark frames with shape (num_bias_images, height, width).

Type:

NDArray, optional

bias_image_frames

1D array specifying frame ranges for each bias image.

Type:

NDArray, optional

uniformity_gain_images

3D array of flat-field/gain correction images.

Type:

NDArray, optional

uniformity_gain_image_frames

1D array specifying frame ranges for each uniformity gain image.

Type:

NDArray, optional

bad_pixel_masks

3D array of bad pixel masks.

Type:

NDArray, optional

bad_pixel_mask_frames

1D array specifying frame ranges for each bad pixel mask.

Type:

NDArray, optional

Notes

• All positions are in Earth-Centered Earth-Fixed (ECEF) Cartesian coordinates

• Position units are kilometers

• Positions are represented as (3, N) arrays with x, y, z in each column

• PSF modeling is optional and can be used for fitting signal blobs to estimate irradiance

• Sensor names must be unique within a VISTA session

• Class variable _instance_count tracks the total number of Sensor instances created

Examples

>>> # Subclass can implement get_positions
>>> class MySensor(Sensor):
...     def get_positions(self, times):
...         # Return sensor positions for given times
...         return np.array([[x1, x2], [y1, y2], [z1, z2]])

__init__(name, bias_images=None, bias_image_frames=None, uniformity_gain_images=None, uniformity_gain_image_frames=None, bad_pixel_masks=None, bad_pixel_mask_frames=None, _instance_count=0)

Methods

__init__(name[, bias_images, ...])
add_imagery(imagery)	Register imagery with this sensor and update frame/time tracking.
can_correct_bad_pixel()	Check if sensor has radiometric bad pixel masks.
can_correct_bias()	Check if sensor has bias images
can_correct_non_uniformity()	Check if sensor has uniformity gain images
can_geolocate()	Check if sensor can convert pixels to geodetic coordiantes and vice versa.
geodetic_to_pixel(frame, loc)	Convert geodetic coordinates to pixel coordinates using polynomial coefficients.
get_imagery_frames_and_times()	Get all unique imagery frames and corresponding times in increasing order.
get_positions(times)	Return sensor positions in Cartesian ECEF coordinates for given times.
model_psf([sigma, size])	Model the sensor's point spread function (PSF).
pixel_to_geodetic(frame, rows, columns)	Convert pixel coordinates to geodetic coordinates using polynomial coefficients.
to_hdf5(group)	Save sensor radiometric calibration data to an HDF5 group.

Attributes

bad_pixel_mask_frames
bad_pixel_masks
bias_image_frames
bias_images
uniformity_gain_image_frames
uniformity_gain_images
uuid
name

name: str

bias_images: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

bias_image_frames: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

uniformity_gain_images: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

uniformity_gain_image_frames: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

bad_pixel_masks: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

bad_pixel_mask_frames: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None

uuid: str = None

__post_init__()

Initialize sensor instance and increment global counter.

Generates a unique UUID for this sensor instance and initializes internal data structures for tracking associated imagery.

__eq__(other)

Compare Sensors based on UUID

get_imagery_frames_and_times()

Get all unique imagery frames and corresponding times in increasing order.

Returns:

• frames (NDArray[np.int_]) – Sorted array of unique frame numbers

• times (NDArray[np.datetime64]) – Sorted array of corresponding timestamps

Return type:

Tuple[ndarray[tuple[Any, …], dtype[_ScalarT]], ndarray[tuple[Any, …], dtype[_ScalarT]]]

Notes

This method aggregates frames and times from all imagery objects that have been registered with this sensor via add_imagery().

add_imagery(imagery)

Register imagery with this sensor and update frame/time tracking.

Adds the imagery’s frames and times to the sensor’s internal registry for coordinate conversion and time-to-frame mapping. Duplicate imagery (by UUID) or imagery without times is ignored.

Parameters:

imagery (Imagery) – Imagery object to register with this sensor

Notes

• Only imagery with non-None times are tracked

• Duplicate frame/time pairs are automatically removed

• This method is typically called automatically when Imagery is created

get_positions(times)

Return sensor positions in Cartesian ECEF coordinates for given times.

Parameters:

times (NDArray[np.datetime64]) – Array of times for which to retrieve sensor positions

Returns:

Sensor positions as (3, N) array where N is the number of times. Each column contains [x, y, z] coordinates in ECEF frame (km).

Return type:

NDArray[np.float64]

Notes

The default implementation returns None. Subclasses should override this method

model_psf(sigma=None, size=None)

Model the sensor’s point spread function (PSF).

This is an optional method that can be overridden by subclasses to provide PSF modeling capability. The PSF can be used to fit signal pixel blobs in imagery to estimate irradiance.

Parameters:

• sigma (float, optional) – Standard deviation parameter for PSF modeling (e.g., Gaussian width)

• size (int, optional) – Size of the PSF kernel to generate

Returns:

2D array representing the point spread function, or None if not implemented

Return type:

NDArray[np.float64] or None

Notes

The default implementation returns None. Subclasses should override this method to provide specific PSF models (e.g., Gaussian, Airy disk, etc.).

can_geolocate()

Check if sensor can convert pixels to geodetic coordiantes and vice versa.

Notes

The default implementation returns False. Subclasses can override this method.

Returns:

True if sensor has both forward and reverse geolocation polynomials.

Return type:

bool

can_correct_bad_pixel()

Check if sensor has radiometric bad pixel masks.

Returns:

True if sensor has radiometric bad pixel masks.

Return type:

bool

can_correct_bias()

Check if sensor has bias images

Returns:

True if sensor has bias images.

Return type:

bool

can_correct_non_uniformity()

Check if sensor has uniformity gain images

Returns:

True if sensor has uniformity gain images.

Return type:

bool

geodetic_to_pixel(frame, loc)

Convert geodetic coordinates to pixel coordinates using polynomial coefficients.

Parameters:

• frame (int) – Frame number for which to perform the conversion

• loc (EarthLocation) – Astropy EarthLocation object(s) containing geodetic coordinates

Returns:

• rows (np.ndarray) – Array of row pixel coordinates (NaN for base implementation)

• columns (np.ndarray) – Array of column pixel coordinates (NaN for base implementation)

Return type:

Tuple[ndarray, ndarray]

Notes

The default implementation returns NaN arrays. Subclasses should override this method to provide geodetic-to-pixel conversion using their specific projection model (e.g., polynomial coefficients).

pixel_to_geodetic(frame, rows, columns)

Convert pixel coordinates to geodetic coordinates using polynomial coefficients.

Parameters:

• frame (int) – Frame number for which to perform the conversion

• rows (np.ndarray) – Array of row pixel coordinates

• columns (np.ndarray) – Array of column pixel coordinates

Returns:

Astropy EarthLocation object(s) with geodetic coordinates (zeros for base implementation)

Return type:

EarthLocation

Notes

The default implementation returns EarthLocation with zero coordinates. Subclasses should override this method to provide pixel-to-geodetic conversion using their specific projection model.

to_hdf5(group)

Save sensor radiometric calibration data to an HDF5 group.

Parameters:

group (h5py.Group) – HDF5 group to write sensor data to (typically sensors/<sensor_uuid>/)

Notes

This method writes radiometric calibration data to the HDF5 group: - bias_images and bias_image_frames - uniformity_gain_images and uniformity_gain_image_frames - bad_pixel_masks and bad_pixel_mask_frames

Subclasses should call super().to_hdf5(group) and then add their own data.

__init__(name, bias_images=None, bias_image_frames=None, uniformity_gain_images=None, uniformity_gain_image_frames=None, bad_pixel_masks=None, bad_pixel_mask_frames=None, _instance_count=0)