Ramp Integration Format#

Load extracted ramp integration profiles.

Overview#

The Ramp Integration format is used for loading 1D integration profiles extracted from JWST ramp data. These profiles show how the accumulated signal varies across groups (reads) for a specific spatial extraction, useful for analyzing ramp fitting and cosmic ray detection.

Usage#

import jdaviz as jd
import numpy as np
from astropy.nddata import NDDataArray

jd.show()

# Load an integration profile
integration_data = np.array([100, 205, 310, 415])  # counts per group
jd.load(integration_data, format='Ramp Integration',
        data_label='Extracted Integration')

Data Requirements#

The data should be a 1D or 3D array representing accumulated signal as a function of group/read number:

Array Structure#

1D array: Direct sequence of values for each group (n_groups,)
3D array: Collapsed spatial array (1, 1, n_groups) where the first two dimensions are spatial (reduced to 1×1) and the third is the group axis

The data can be provided as:

Plain numpy array (np.ndarray)
Astropy NDDataArray (astropy.nddata.NDDataArray) with optional metadata and units

When loading as a plain array, it will be automatically wrapped in an NDDataArray for compatibility with the viewer.

Supported File Formats#

Ramp Integration data is typically not loaded from files but generated from:

The Ramp Extraction plugin
Programmatic extraction from ramp cubes
Python numpy arrays or NDDataArray objects

The format accepts in-memory Python objects rather than file-based data.

Typical Workflow#

Ramp integration profiles are usually created by spatially collapsing ramp data:

ramp_ext = jd.plugins['Ramp Extraction']
ramp_ext.function = 'median'  # or 'mean', 'sum', etc.

# Extract and export the integration profile
integration = ramp_ext.extract()

The resulting integration profile can then be analyzed to examine:

Signal accumulation linearity
Jump detection artifacts
Cosmic ray impacts
Saturation effects