`jump` step¶

This step detects jumps in the pixel ramps by looking for outliers. On output, the GROUPDQ array is updated with the DQ flag JUMP_DET to indicate the location of each jump that was found.

Official documentation for jump can be found here:

https://jwst-pipeline.readthedocs.io/en/latest/jwst/jump/index.html

Input data¶

An example of running the jump step is now shown using a simple simulated observation of a galaxy with the MIRI Imager (F1130W filter) produced with MIRISim v2.3, with precending pipeline steps applied, i.e. refpix output.

Python¶

Start by importing what will be used and set the CRDS_CONTEXT

# imports
import os, glob, shutil
import numpy as np
from matplotlib.colors import LogNorm
import matplotlib.pyplot as plt
from jwst import datamodels

# set the CRDS_CONTEXT
os.environ["CRDS_CONTEXT"] = "jwst_0641.pmap"

Import jump and print the docstring and spec to show some information

# import the step
from jwst.jump import jump_step

# print the description and options
print(jump_step.JumpStep.__doc__)
print(jump_step.JumpStep.spec)

    JumpStep: Performs CR/jump detection on each ramp integration within an
    exposure. The 2-point difference method is applied.
    

        rejection_threshold = float(default=4.0,min=0) # CR sigma rejection threshold
        maximum_cores = option('none', 'quarter', 'half', 'all', default='none') # max number of processes to create
        flag_4_neighbors = boolean(default=True) # flag the four perpendicular neighbors of each CR
        max_jump_to_flag_neighbors = float(default=200) # maximum jump sigma that will trigger neighbor flagging
        min_jump_to_flag_neighbors = float(default=10) # minimum jump sigma that will trigger neighbor flagging
    

Set the name of the input file and run the step. This will produce an output file ending with _jumpstep.fits

Parameters used:

output_use_model : boolean, optional, default=False
propagate the input filename to the output

save_results: boolean, optional, default=False
save the results to file

Note that the jump will return the output datamodel so we set this to the dm variable.

# user specified
my_input_file = 'det_image_seq1_MIRIMAGE_F1130Wexp1_refpixstep.fits'

# run the step
dm = jump_step.JumpStep.call(my_input_file, output_use_model=True, save_results=True)

2020-10-29 14:10:10,970 - CRDS - ERROR -  Error determining best reference for 'pars-jumpstep'  =   Unknown reference type 'pars-jumpstep'
2020-10-29 14:10:10,973 - stpipe.JumpStep - INFO - JumpStep instance created.
2020-10-29 14:10:11,061 - stpipe.JumpStep - INFO - Step JumpStep running with args ('det_image_seq1_MIRIMAGE_F1130Wexp1_refpixstep.fits',).
2020-10-29 14:10:11,063 - stpipe.JumpStep - INFO - Step JumpStep parameters are: {'pre_hooks': [], 'post_hooks': [], 'output_file': None, 'output_dir': None, 'output_ext': '.fits', 'output_use_model': True, 'output_use_index': True, 'save_results': True, 'skip': False, 'suffix': None, 'search_output_file': True, 'input_dir': '', 'rejection_threshold': 4.0, 'maximum_cores': 'none', 'flag_4_neighbors': True, 'max_jump_to_flag_neighbors': 200.0, 'min_jump_to_flag_neighbors': 10.0}
2020-10-29 14:10:11,351 - stpipe.JumpStep - INFO - CR rejection threshold = 4 sigma
2020-10-29 14:10:11,357 - stpipe.JumpStep - INFO - Using GAIN reference file: /Users/patrickkavanagh/crds_mirror/references/jwst/miri/jwst_miri_gain_0008.fits
2020-10-29 14:10:11,421 - stpipe.JumpStep - INFO - Using READNOISE reference file: /Users/patrickkavanagh/crds_mirror/references/jwst/miri/jwst_miri_readnoise_0059.fits
2020-10-29 14:10:11,453 - stpipe.JumpStep - INFO - Found 8 possible cores to use for jump detection 
2020-10-29 14:10:11,905 - stpipe.JumpStep - INFO - Executing two-point difference method
2020-10-29 14:10:12,547 - stpipe.JumpStep - INFO -  working on integration 1
2020-10-29 14:10:13,988 - stpipe.JumpStep - INFO - From highest outlier Two point found 35216 pixels with at least one CR
2020-10-29 14:10:15,270 - stpipe.JumpStep - INFO -  working on integration 2
2020-10-29 14:10:16,699 - stpipe.JumpStep - INFO - From highest outlier Two point found 32831 pixels with at least one CR
2020-10-29 14:10:19,175 - stpipe.JumpStep - INFO - Total elapsed time = 7.2693 sec
2020-10-29 14:10:19,193 - stpipe.JumpStep - INFO - The execution time in seconds: 7.841480
2020-10-29 14:10:21,178 - stpipe.JumpStep - INFO - Saved model in det_image_seq1_MIRIMAGE_F1130Wexp1_jumpstep.fits
2020-10-29 14:10:21,179 - stpipe.JumpStep - INFO - Step JumpStep done

We can plot the location of detected jumps

# set the sample pixel (selected knowing there is a jump on the ramp)
pixel = [550, 393]

# define group numbers for integration ramps
group = range(1,dm.data[0,:,pixel[1],pixel[0]].shape[0]+1,1)

# plot--------------------------------------
fig, axs = plt.subplots(2, 1, figsize=(10, 8), sharex=True)

# first integration for input/output ramps
axs[0].plot(group, dm.data[0,:,pixel[1],pixel[0]], c='b', marker='o', linestyle='-', linewidth=2, label='ramp')
axs[0].set_title('jump detection',fontsize=15)
axs[0].set_ylabel('DN',fontsize=15)
axs[0].set_xlim(-1,max(group)+1)
axs[0].set_ylim(min(dm.data[0,:,pixel[1],pixel[0]])-200,max(dm.data[0,:,pixel[1],pixel[0]])+200)
axs[0].legend(prop={'size':12}, loc=2)

# DQ flag values
axs[1].plot(group, dm.groupdq[0,:,pixel[1],pixel[0]], c='b', marker='o', linestyle='-', 
            linewidth=2, label='GROUPDQ')
axs[1].plot([-10,100],[4,4], linestyle='--', linewidth=3, c='g', label='JUMP_DET (GROUPDQ=4)')
axs[1].set_ylabel('GROUPDQ value',fontsize=15)
axs[1].set_xlim(-1,max(group)+1)
axs[1].set_ylim(-0.5,6.5)
axs[1].legend(prop={'size':12}, loc=2)

# draw lines to show the groups which have been flagged as jumps
for n, val in enumerate(group):
    if (dm.groupdq[0,n,pixel[1],pixel[0]] >= 4): 
        axs[0].plot([n+1,n+1],[min(dm.data[0,:,pixel[1],pixel[0]])-200,max(dm.data[0,:,pixel[1],pixel[0]])+200], 
                    linestyle='--', linewidth=0.3, c='k')
        axs[1].plot([n+1,n+1],[-1,6], linestyle='--', linewidth=0.3, c='k')

plt.tight_layout(h_pad=0)
plt.show()

Command line¶

To achieve the same result from the command line there are a couple of options.

Option 1: Run the JumpStep class using the strun command:

strun jwst.jump.JumpStep det_image_seq1_MIRIMAGE_F1130Wexp1_refpixstep.fits

Option 2: If they don’t already exist, collect the pipeline configuration files in your working directory using collect_pipeline_configs and then run the JumpStep using the strun command with the associated jump.cfg file.

collect_pipeline_cfgs cfgs/

strun cfgs/jump.cfg det_image_seq1_MIRIMAGE_F1130Wexp1_refpixstep.fits

This will produce the same output file ending with _jumpstep.fits

A full list of the command line options are given by running the following:

strun jwst.jump.JumpStep -h

or

strun cfgs/jump.cfg -h

Override reference file¶

The jump step uses the readnoise and gain reference files. To override these in Python:

# set the readnoise and gain reference file names
my_ref_rn = 'my_readnoise.fits'
my_ref_gn = 'my_gain.fits'

dm = jump.JumpStep.call(my_input_file, output_use_model=True, save_results=True,
                        override_readnoise=my_ref_rn, override_gain=my_ref_gn)

and using the command line:

strun jwst.jump.JumpStep det_image_seq1_MIRIMAGE_F1130Wexp1_refpixstep.fits  --override_readnoise my_readnoise.fits --override_gain my_gain.fits

or

strun cfgs/jump.cfg det_image_seq1_MIRIMAGE_F1130Wexp1_refpixstep.fits  --override_readnoise my_readnoise.fits --override_gain my_gain.fits

Manually set the `rejection_threshold` parameter¶

By default, the rejection threshold is set at 4 sigma. To manually set this in Python:

dm = jump.JumpStep.call(my_input_file, output_use_model=True, save_results=True,
                        rejection_threshold=3.0)