index
int64 | repo_name
string | branch_name
string | path
string | content
string | import_graph
string |
|---|---|---|---|---|---|
13,351
|
sajetan/contact_book
|
refs/heads/master
|
/project/tables.py
|
from project import *
from passlib.apps import custom_app_context as pwd_context
from itsdangerous import (TimedJSONWebSignatureSerializer as Serializer, BadSignature, SignatureExpired)
#app.config['SECRET_KEY'] = 'the quick brown fox jumps over the lazy dog'
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///contacts_database.db'
db = SQLAlchemy(app)
'''
class User(db.Model):
__tablename__ = "user_info"
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(128), index=True, nullable=False,unique=True)
password = db.Column(db.String(128),nullable=False)
def hash_password(self, password):
self.password = pwd_context.encrypt(password)
def verify_password(self, password):
return pwd_context.verify(password, self.password)
def generate_auth_token(self, expiration=400000):
s = Serializer(db.app.config['SECRET_KEY'], expires_in=expiration)
return s.dumps({'id': self.id})
@staticmethod
def verify_auth_token(token):
s = Serializer('the quick brown fox jumps over the lazy dog')
try:
data = s.loads(token)
except SignatureExpired:
return None # valid token, but expired
except BadSignature:
return None # invalid token
user = User.query.filter_by(id = data['id']).first()
return user
'''
class Contact(db.Model):
__tablename__ = 'contacts'
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(80), nullable=False)
surname = db.Column(db.String(100), nullable=True)
email = db.Column(db.String(200), nullable=True, unique=True)
phone = db.Column(db.String(20), nullable=True)
db.create_all()
|
{"/project/Authentication.py": ["/project/__init__.py"], "/project/tables.py": ["/project/__init__.py"], "/project/contacts.py": ["/project/__init__.py", "/project/tables.py"], "/app.py": ["/project/__init__.py", "/project/authentication.py"], "/project/authentication.py": ["/project/__init__.py"]}
|
13,352
|
sajetan/contact_book
|
refs/heads/master
|
/project/contacts.py
|
from project import *
from project.tables import db,Contact
from Authentication import UserAuth
#from test import *
from validate_form import *
from validate_email import validate_email
class ContactsAppShow(Resource):
def get(self,id=None):
'''
Shows all the contacts in alphabetical order by name.
Shows 10 contacts per page.
'''
if not session.get('logged_in'):
flash("Please Log in")
return redirect(url_for('index'))
contact_data=[]
app.logger.info('GET contacts data query')
try:
contact_data = Contact.query.order_by(Contact.name).paginate(per_page=10, page=id, error_out=True)
except:
contact_data = Contact.query.order_by(Contact.name).paginate(per_page=10, page=1, error_out=True)
headers = {'Content-Type': 'text/html'}
return make_response(render_template('app.html',contact_data=contact_data), 200, headers)
class ContactsAppSearch(Resource):
def get(self):
headers = {'Content-Type': 'text/html'}
return make_response(render_template('search.html'), 200, headers)
def post(self):
'''
Search any contact by name or email address
'''
if not session.get('logged_in'):
flash("Please Log in")
return redirect(url_for('index'))
contact_data=None
if 'name' in request.form.keys():
if (request.form['name']):
app.logger.info('Search for name [ %s ]',request.form['name'])
contact_data = Contact.query.filter_by(name=request.form['name'])
if 'email' in request.form.keys():
if (request.form['email']):
if (validate_email(request.form['email'])):
app.logger.info('Search for email [ %s ]',request.form['email'])
contact_data = Contact.query.filter_by(email=request.form['email'])
else:
flash("Enter valid Email Address")
headers = {'Content-Type': 'text/html'}
return make_response(render_template('search.html',contact_data=contact_data), 200, headers)
class ContactsAppDelete(Resource):
def post(self):
'''
Delete the contact
'''
if not session.get('logged_in'):
flash("Please Log in")
return redirect(url_for('index'))
try:
contact_data = Contact.query.filter_by(id=request.form['id']).first()
app.logger.info('Deleting contact [ %s ]',contact_data.name)
db.session.delete(contact_data)
db.session.commit()
message="Deleted " +contact_data.name + "'s Contact"
flash(message)
except:
db.session.rollback()
flash('Error deleting contact.')
app.logger.info('Error deleting contact ')
return redirect(url_for('contacts',page=request.form['page']))
class ContactsAppEdit(Resource):
def get(self,id):
if not session.get('logged_in'):
flash("Please Log in")
return redirect(url_for('index'))
contact_data= Contact.query.filter_by(id=id).first()
form = ContactForm(obj=contact_data)
headers = {'Content-Type': 'text/html'}
return make_response(render_template('edit.html', form=form), 200, headers)
def post(self,id):
'''
Edit by name/surname/email/phonenumber
Email id is unique for the user, so throws error if email already exists in the database
'''
if not session.get('logged_in'):
flash("Please Log in")
return redirect(url_for('index'))
pageid = request.args.to_dict()
contact_data = Contact.query.filter_by(id=id).first()
form = ContactForm(obj=contact_data)
if form.validate_on_submit():
try:
#update to database
form.populate_obj(contact_data)
db.session.add(contact_data)
db.session.commit()
message = "Changed " + contact_data.name + "'s Contact Details"
app.logger.info('Edited contact [ %s ] success', contact_data.name)
flash(message, 'success')
return redirect(url_for('contacts',page=pageid["page"]))
except:
db.session.rollback()
flash('Error updating contact. Email already exist or User not found')
headers = {'Content-Type': 'text/html'}
return make_response(render_template('edit.html', form=form), 200, headers)
class ContactsAppAdd(Resource):
def get(self):
if not session.get('logged_in'):
flash("Please Log in")
return redirect(url_for('index'))
form = ContactForm()
headers = {'Content-Type': 'text/html'}
return make_response(render_template('add.html',form=form), 200, headers)
def post(self):
'''
Add new contact with name/surname/email/phonenumber
Email id is unique for the user, so throws error if email already exists in the database
'''
if not session.get('logged_in'):
flash("Please Log in")
return redirect(url_for('index'))
form = ContactForm()
if form.validate_on_submit():
contact_data = Contact()
form.populate_obj(contact_data)
db.session.add(contact_data)
try:
# update to database
db.session.commit()
flash('Contact created Success')
app.logger.info('Added a new user successfully')
return redirect(url_for('contacts'))
except:
db.session.rollback()
flash('Error Creating this contact - Email already in use')
headers = {'Content-Type': 'text/html'}
return make_response(render_template('add.html', form=form), 200, headers)
api.add_resource(ContactsAppShow,'/contacts','/contacts/<int:id>',endpoint="contacts")
api.add_resource(ContactsAppDelete,'/contacts/delete',endpoint="contacts_delete")
api.add_resource(ContactsAppEdit,'/edit_contact/<int:id>',endpoint="edit_contact")
api.add_resource(ContactsAppAdd,'/contact_add',endpoint="contact_add")
api.add_resource(ContactsAppSearch,'/contact_search',endpoint="contact_search")
|
{"/project/Authentication.py": ["/project/__init__.py"], "/project/tables.py": ["/project/__init__.py"], "/project/contacts.py": ["/project/__init__.py", "/project/tables.py"], "/app.py": ["/project/__init__.py", "/project/authentication.py"], "/project/authentication.py": ["/project/__init__.py"]}
|
13,353
|
sajetan/contact_book
|
refs/heads/master
|
/app.py
|
from project import *
from project.authentication import *
@app.route('/')
def index():
if not session.get('logged_in'):
return render_template('login.html')
else:
return redirect(url_for('contacts'))
if __name__ == '__main__':
app.run(host='0.0.0.0',debug=True,threaded=True)
|
{"/project/Authentication.py": ["/project/__init__.py"], "/project/tables.py": ["/project/__init__.py"], "/project/contacts.py": ["/project/__init__.py", "/project/tables.py"], "/app.py": ["/project/__init__.py", "/project/authentication.py"], "/project/authentication.py": ["/project/__init__.py"]}
|
13,354
|
sajetan/contact_book
|
refs/heads/master
|
/project/authentication.py
|
#vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
from project import *
#from project.tables import db,User
from Authentication import UserAuth
from contacts import *
class UserAuthentication(Resource):
def get(self):
headers = {'Content-Type': 'text/html'}
return make_response(render_template('login.html'), 200, headers)
def post(self):
userObj = UserAuth(request.form['username'],request.form['password'])
res = userObj.do_authentication()
if res:
flash("Login Success")
app.logger.info('Login successful')
return redirect(url_for('contacts'))
else:
flash("Password entered is Invalid")
app.logger.info('Invalid Password')
return redirect(url_for('index'))
class UserLogout(Resource):
def get(self):
session['logged_in'] = False
app.logger.info('Logout successful')
return redirect(url_for('index'))
'''
#test
@auth.error_handler
def unauthorized():
return make_response(jsonify( { 'error': 'Unauthorized access!!!' } ), 403)
@auth.verify_password
def verify_password(username_or_token, password):
# first try to authenticate by token
print('Authenticating..')
user = User.verify_auth_token(username_or_token)
#return True
if not user:
# try to authenticate with username/password
user = User.query.filter_by(username = username_or_token).first()
g.user = user
print('Authentication successful')
return True
@app.route('/api/token')
@auth.login_required
def get_auth_token():
token = g.user.generate_auth_token(600)
return jsonify({'token': token.decode('ascii'), 'duration': 600})
'''
api.add_resource(UserAuthentication, '/login',endpoint="login")
api.add_resource(UserLogout, '/logout',endpoint="logout")
|
{"/project/Authentication.py": ["/project/__init__.py"], "/project/tables.py": ["/project/__init__.py"], "/project/contacts.py": ["/project/__init__.py", "/project/tables.py"], "/app.py": ["/project/__init__.py", "/project/authentication.py"], "/project/authentication.py": ["/project/__init__.py"]}
|
13,377
|
guilhermeulbriki/Django-CadastroClientes
|
refs/heads/master
|
/clientes/migrations/0002_funcionario.py
|
# Generated by Django 2.2.7 on 2019-11-25 14:18
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('clientes', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Funcionario',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('nome', models.CharField(max_length=255, verbose_name='Nome')),
('apelido', models.CharField(max_length=255, verbose_name='Apelido')),
('snap', models.CharField(max_length=255, verbose_name='Snap')),
('cpf', models.CharField(max_length=255, verbose_name='CPF')),
],
),
]
|
{"/clientes/forms.py": ["/clientes/models.py"], "/clientes/admin.py": ["/clientes/models.py"], "/clientes/views.py": ["/clientes/models.py", "/clientes/forms.py"]}
|
13,378
|
guilhermeulbriki/Django-CadastroClientes
|
refs/heads/master
|
/clientes/forms.py
|
from django import forms
from .models import Cliente, Funcionario
class ClienteForm(forms.ModelForm):
class Meta:
model = Cliente
fields = ('nome', 'endereco', 'telefone', 'cpf')
class FuncionarioForm(forms.ModelForm):
class Meta:
model = Funcionario
fields = ('nome', 'apelido', 'snap', 'cpf')
|
{"/clientes/forms.py": ["/clientes/models.py"], "/clientes/admin.py": ["/clientes/models.py"], "/clientes/views.py": ["/clientes/models.py", "/clientes/forms.py"]}
|
13,379
|
guilhermeulbriki/Django-CadastroClientes
|
refs/heads/master
|
/clientes/admin.py
|
from django.contrib import admin
from.models import Cliente
admin.site.register(Cliente)
|
{"/clientes/forms.py": ["/clientes/models.py"], "/clientes/admin.py": ["/clientes/models.py"], "/clientes/views.py": ["/clientes/models.py", "/clientes/forms.py"]}
|
13,380
|
guilhermeulbriki/Django-CadastroClientes
|
refs/heads/master
|
/clientes/views.py
|
from datetime import datetime
from django.shortcuts import render, redirect
from django.http import HttpResponse
from .models import Cliente, Funcionario
from .forms import ClienteForm, FuncionarioForm
from django.views.generic.edit import CreateView, UpdateView
from django.urls import reverse_lazy
def home(request):
clientes = Cliente.objects.all()
funcionarios = Funcionario.objects.all()
contexto = {
'clientes': clientes,
'funcionarios': funcionarios,
}
resposta = render(request, template_name="clientes/home.html", context=contexto)
return HttpResponse(resposta)
class ClienteCreateView(CreateView):
model = Cliente
form_class = ClienteForm
template_name = "clientes/cliente_form.html"
success_url = reverse_lazy('home')
class ClienteUpdateView(UpdateView):
model = Cliente
form_class = ClienteForm
template_name = "clientes/cliente_form.html"
success_url = reverse_lazy('home')
def detalhes_cliente(request, pk):
cliente = Cliente.objects.get(pk=pk)
contexto = {
'cliente': cliente,
}
resposta = render(request, template_name="clientes/cliente.html", context=contexto)
return HttpResponse(resposta)
def deleta_cliente(request, pk):
cliente = Cliente.objects.get(pk=pk)
cliente.delete()
return redirect('home')
class FuncionarioCreateView(CreateView):
model = Funcionario
form_class = FuncionarioForm
template_name = "funcionarios/funcionario_form.html"
success_url = reverse_lazy('home')
class FuncionarioUpdateView(UpdateView):
model = Funcionario
form_class = FuncionarioForm
template_name = "funcionarios/funcionario_form.html"
success_url = reverse_lazy('home')
def detalhes_funcionario(request, pk):
funcionario = Funcionario.objects.get(pk=pk)
contexto = {
'funcionario': funcionario,
}
resposta = render(request, template_name="funcionarios/funcionario.html", context=contexto)
return HttpResponse(resposta)
def deleta_funcionario(request, pk):
funcionario = Funcionario.objects.get(pk=pk)
funcionario.delete()
return redirect('home')
|
{"/clientes/forms.py": ["/clientes/models.py"], "/clientes/admin.py": ["/clientes/models.py"], "/clientes/views.py": ["/clientes/models.py", "/clientes/forms.py"]}
|
13,381
|
guilhermeulbriki/Django-CadastroClientes
|
refs/heads/master
|
/aulatopicos/urls.py
|
from django.contrib import admin
from django.urls import path
from clientes import views as cliente_views
urlpatterns = [
path('', cliente_views.home, name='home'),
path('cliente/add/', cliente_views.ClienteCreateView.as_view(), name="add_cliente"),
path('funcionario/add/', cliente_views.FuncionarioCreateView.as_view(), name="add_funcionario"),
path('cliente/<int:pk>/', cliente_views.detalhes_cliente, name="detalhes_cliente"),
path('funcionario/<int:pk>/', cliente_views.detalhes_funcionario, name="detalhes_funcionario"),
path('cliente/<int:pk>/update/', cliente_views.ClienteUpdateView.as_view(), name="update_cliente"),
path('cliente/<int:pk>/deleta/', cliente_views.deleta_cliente, name="deleta_cliente"),
path('funcionario/<int:pk>/update/', cliente_views.FuncionarioUpdateView.as_view(), name="update_funcionario"),
path('funcionario/<int:pk>/deleta/', cliente_views.deleta_funcionario, name="deleta_funcionario"),
path('admin/', admin.site.urls),
]
|
{"/clientes/forms.py": ["/clientes/models.py"], "/clientes/admin.py": ["/clientes/models.py"], "/clientes/views.py": ["/clientes/models.py", "/clientes/forms.py"]}
|
13,382
|
guilhermeulbriki/Django-CadastroClientes
|
refs/heads/master
|
/clientes/models.py
|
from django.db import models
class Cliente(models.Model):
nome = models.CharField(max_length=255, verbose_name="Nome")
endereco = models.CharField(max_length=350, verbose_name="Endereço")
telefone = models.CharField(max_length=255, verbose_name="Telefone")
cpf = models.CharField(max_length=255, verbose_name="CPF")
class Funcionario(models.Model):
nome = models.CharField(max_length=255, verbose_name="Nome")
apelido = models.CharField(max_length=255, verbose_name="Apelido")
snap = models.CharField(max_length=255, verbose_name="Snap")
cpf = models.CharField(max_length=255, verbose_name="CPF")
|
{"/clientes/forms.py": ["/clientes/models.py"], "/clientes/admin.py": ["/clientes/models.py"], "/clientes/views.py": ["/clientes/models.py", "/clientes/forms.py"]}
|
13,384
|
cnry/icevision
|
refs/heads/master
|
/tests/data/test_data_splitter.py
|
import pytest
from icevision.all import *
@pytest.fixture
def records():
def create_record_func():
return BaseRecord([])
records = RecordCollection(create_record_func)
for record_id in ["file1", "file2", "file3", "file4"]:
records.get_by_record_id(record_id)
return records
def test_single_split_splitter(records):
data_splitter = SingleSplitSplitter()
splits = data_splitter(records)
assert splits == [["file1", "file2", "file3", "file4"]]
def test_random_splitter(records):
data_splitter = RandomSplitter([0.6, 0.2, 0.2], seed=42)
splits = data_splitter(records)
np.testing.assert_equal(splits, [["file2", "file4"], ["file1"], ["file3"]])
def test_fixed_splitter(records):
presplits = [["file4", "file3"], ["file2"], ["file1"]]
data_splitter = FixedSplitter(presplits)
splits = data_splitter(records)
assert splits == presplits
|
{"/icevision/models/fastai/unet/fastai/__init__.py": ["/icevision/models/fastai/unet/fastai/callbacks.py", "/icevision/models/fastai/unet/fastai/learner.py"], "/icevision/models/fastai/unet/fastai/learner.py": ["/icevision/models/fastai/unet/fastai/callbacks.py"]}
|
13,385
|
cnry/icevision
|
refs/heads/master
|
/icevision/models/mmdet/models/vfnet/backbones/__init__.py
|
from icevision.models.mmdet.models.vfnet.backbones.resnet_fpn import *
|
{"/icevision/models/fastai/unet/fastai/__init__.py": ["/icevision/models/fastai/unet/fastai/callbacks.py", "/icevision/models/fastai/unet/fastai/learner.py"], "/icevision/models/fastai/unet/fastai/learner.py": ["/icevision/models/fastai/unet/fastai/callbacks.py"]}
|
13,386
|
cnry/icevision
|
refs/heads/master
|
/icevision/models/fastai/unet/lightning/__init__.py
|
from icevision.models.fastai.unet.lightning.model_adapter import *
|
{"/icevision/models/fastai/unet/fastai/__init__.py": ["/icevision/models/fastai/unet/fastai/callbacks.py", "/icevision/models/fastai/unet/fastai/learner.py"], "/icevision/models/fastai/unet/fastai/learner.py": ["/icevision/models/fastai/unet/fastai/callbacks.py"]}
|
13,387
|
cnry/icevision
|
refs/heads/master
|
/icevision/models/fastai/unet/fastai/__init__.py
|
from icevision.models.fastai.unet.fastai.callbacks import *
from icevision.models.fastai.unet.fastai.learner import *
|
{"/icevision/models/fastai/unet/fastai/__init__.py": ["/icevision/models/fastai/unet/fastai/callbacks.py", "/icevision/models/fastai/unet/fastai/learner.py"], "/icevision/models/fastai/unet/fastai/learner.py": ["/icevision/models/fastai/unet/fastai/callbacks.py"]}
|
13,388
|
cnry/icevision
|
refs/heads/master
|
/tests/models/torchvision_models/keypoints_rcnn/test_backbones.py
|
import pytest
from icevision.all import *
from icevision.models.torchvision import keypoint_rcnn
@pytest.mark.parametrize(
"model_name,param_groups_len",
(
("mobilenet", 6),
("resnet18", 7),
("resnet18_fpn", 8),
("resnext50_32x4d_fpn", 8),
("wide_resnet50_2_fpn", 8),
),
)
def test_keypoint_rcnn_fpn_backbones(model_name, param_groups_len):
backbone_fn = getattr(models.torchvision.keypoint_rcnn.backbones, model_name)
backbone = backbone_fn(pretrained=False)
model = keypoint_rcnn.model(num_keypoints=2, num_classes=4, backbone=backbone)
assert len(model.param_groups()) == param_groups_len
|
{"/icevision/models/fastai/unet/fastai/__init__.py": ["/icevision/models/fastai/unet/fastai/callbacks.py", "/icevision/models/fastai/unet/fastai/learner.py"], "/icevision/models/fastai/unet/fastai/learner.py": ["/icevision/models/fastai/unet/fastai/callbacks.py"]}
|
13,389
|
cnry/icevision
|
refs/heads/master
|
/icevision/models/fastai/unet/fastai/callbacks.py
|
__all__ = ["UnetCallback"]
from icevision.imports import *
class UnetCallback(fastai.Callback):
def before_batch(self):
assert len(self.xb) == len(self.yb) == 1
self.learn.records = self.yb[0]
self.learn.yb = (self.xb[0][1],)
self.learn.xb = (self.xb[0][0],)
|
{"/icevision/models/fastai/unet/fastai/__init__.py": ["/icevision/models/fastai/unet/fastai/callbacks.py", "/icevision/models/fastai/unet/fastai/learner.py"], "/icevision/models/fastai/unet/fastai/learner.py": ["/icevision/models/fastai/unet/fastai/callbacks.py"]}
|
13,390
|
cnry/icevision
|
refs/heads/master
|
/icevision/models/torchvision/retinanet/prediction.py
|
__all__ = [
"predict",
"predict_from_dl",
"convert_raw_prediction",
"convert_raw_predictions",
"end2end_detect",
]
from icevision.models.torchvision.faster_rcnn.prediction import *
|
{"/icevision/models/fastai/unet/fastai/__init__.py": ["/icevision/models/fastai/unet/fastai/callbacks.py", "/icevision/models/fastai/unet/fastai/learner.py"], "/icevision/models/fastai/unet/fastai/learner.py": ["/icevision/models/fastai/unet/fastai/callbacks.py"]}
|
13,391
|
cnry/icevision
|
refs/heads/master
|
/icevision/models/fastai/unet/fastai/learner.py
|
__all__ = ["learner"]
from icevision.imports import *
from icevision.engines.fastai import *
from icevision.models.fastai.unet.fastai.callbacks import *
def learner(
dls: List[Union[DataLoader, fastai.DataLoader]],
model: nn.Module,
cbs=None,
loss_func=fastai.CrossEntropyLossFlat(axis=1),
**kwargs,
):
cbs = L(UnetCallback()) + L(cbs)
learn = adapted_fastai_learner(
dls=dls,
model=model,
cbs=cbs,
loss_func=loss_func,
**kwargs,
)
return learn
|
{"/icevision/models/fastai/unet/fastai/__init__.py": ["/icevision/models/fastai/unet/fastai/callbacks.py", "/icevision/models/fastai/unet/fastai/learner.py"], "/icevision/models/fastai/unet/fastai/learner.py": ["/icevision/models/fastai/unet/fastai/callbacks.py"]}
|
13,465
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/miri/test_sloperpipeline.py
|
from glob import glob
import os
import pytest
from jwst.pipeline import Detector1Pipeline
from jwst.tests.base_classes import BaseJWSTTest
@pytest.mark.bigdata
class TestMIRISloperPipeline(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_sloperpipeline','truth']
test_dir = 'test_sloperpipeline'
def test_gain_scale_naming(self):
"""
Regression test for gain_scale naming when results are requested to
be saved for the gain_scale step.
"""
expfile = 'jw00001001001_01101_00001_MIRIMAGE'
input_file = self.get_data(self.test_dir, expfile+'_uncal.fits')
input_name = os.path.basename(input_file)
step = Detector1Pipeline()
step.group_scale.skip = True
step.dq_init.skip = True
step.saturation.skip = True
step.ipc.skip = True
step.superbias.skip = True
step.refpix.skip = True
step.rscd.skip = True
step.firstframe.skip = True
step.lastframe.skip = True
step.linearity.skip = True
step.dark_current.skip = True
step.persistence.skip = True
step.jump.skip = True
step.ramp_fit.skip = False
step.gain_scale.skip = False
step.gain_scale.save_results = True
step.run(input_file)
files = glob('*.fits')
if input_name in files:
files.remove(input_name)
output_file = expfile + '_gain_scale.fits'
assert output_file in files
files.remove(output_file)
output_file = expfile + '_gain_scaleints.fits'
assert output_file in files
files.remove(output_file)
assert not len(files)
def test_detector1pipeline1(self):
"""
Regression test of calwebb_detector1 pipeline performed on MIRI data.
"""
input_file = self.get_data(self.test_dir,
'jw00001001001_01101_00001_MIRIMAGE_uncal.fits')
step = Detector1Pipeline()
step.save_calibrated_ramp = True
step.ipc.skip = True
step.refpix.odd_even_columns = True
step.refpix.use_side_ref_pixels = True
step.refpix.side_smoothing_length=11
step.refpix.side_gain=1.0
step.refpix.odd_even_rows = True
step.persistence.skip = True
step.jump.rejection_threshold = 250.0
step.ramp_fit.save_opt = False
step.output_file='jw00001001001_01101_00001_MIRIMAGE'
step.suffix='rate'
step.run(input_file)
outputs = [('jw00001001001_01101_00001_MIRIMAGE_ramp.fits',
'jw00001001001_01101_00001_MIRIMAGE_uncal_jump.fits'),
('jw00001001001_01101_00001_MIRIMAGE_rateints.fits',
'jw00001001001_01101_00001_MIRIMAGE_uncal_integ.fits'),
('jw00001001001_01101_00001_MIRIMAGE_rate.fits',
'jw00001001001_01101_00001_MIRIMAGE_uncal_MiriSloperPipeline.fits')
]
self.compare_outputs(outputs)
def test_detector1pipeline2(self):
"""
Regression test of calwebb_detector1 pipeline performed on MIRI data.
"""
input_file = self.get_data(self.test_dir,
'jw80600012001_02101_00003_mirimage_uncal.fits')
step = Detector1Pipeline()
step.save_calibrated_ramp = True
step.ipc.skip = True
step.refpix.odd_even_columns = True
step.refpix.use_side_ref_pixels = True
step.refpix.side_smoothing_length=11
step.refpix.side_gain=1.0
step.refpix.odd_even_rows = True
step.persistence.skip = True
step.jump.rejection_threshold = 250.0
step.ramp_fit.save_opt = False
step.output_file='jw80600012001_02101_00003_mirimage'
step.suffix='rate'
step.run(input_file)
outputs = [('jw80600012001_02101_00003_mirimage_ramp.fits',
'jw80600012001_02101_00003_mirimage_ramp.fits'),
('jw80600012001_02101_00003_mirimage_rateints.fits',
'jw80600012001_02101_00003_mirimage_rateints.fits'),
('jw80600012001_02101_00003_mirimage_rate.fits',
'jw80600012001_02101_00003_mirimage_rate.fits')
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,466
|
mperrin/jwst
|
refs/heads/master
|
/jwst/lib/exposure_types.py
|
"""
This module contains lists of modes grouped in different ways
"""
from ..associations.lib.dms_base import (ACQ_EXP_TYPES, IMAGE2_SCIENCE_EXP_TYPES,
IMAGE2_NONSCIENCE_EXP_TYPES,
SPEC2_SCIENCE_EXP_TYPES)
IMAGING_TYPES = set(tuple(ACQ_EXP_TYPES) + tuple(IMAGE2_SCIENCE_EXP_TYPES)
+ tuple(IMAGE2_NONSCIENCE_EXP_TYPES) +
('fgs_image', 'fgs_focus'))
SPEC_TYPES = SPEC2_SCIENCE_EXP_TYPES
# FGS guide star exposures
FGS_GUIDE_EXP_TYPES = [
'fgs_acq1',
'fgs_acq2',
'fgs_fineguide',
'fgs_id-image',
'fgs_id-stack',
'fgs_track',
]
def is_moving_target(input_models):
""" Determine if a moving target exposure."""
model = input_models[0]
if hasattr(model.meta.target, 'type') and \
model.meta.target.type is not None and model.meta.target.type.lower() == 'moving':
return True
return False
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,467
|
mperrin/jwst
|
refs/heads/master
|
/jwst/outlier_detection/tests/test_outlier_detection.py
|
import pytest
import numpy as np
from scipy.ndimage.filters import gaussian_filter
from jwst.outlier_detection.outlier_detection import flag_cr
from jwst import datamodels
@pytest.fixture
def sci_blot_image_pair():
"""Provide a science and blotted ImageModel pair."""
shape = (10, 10)
sci = datamodels.ImageModel(shape)
# Populate keywords
sci.meta.exposure.exposure_time = 1
# Add poisson noise to image data
p = np.random.poisson(size=shape, lam=1e3)
sci.data = p / p.mean() - 1
# The blot image is just a smoothed version of the science image
blot = sci.copy()
blot.data = gaussian_filter(blot.data, sigma=3)
return sci, blot
def test_flag_cr(sci_blot_image_pair):
"""Test the flag_cr function. Test logic, not the actual noise model."""
sci, blot = sci_blot_image_pair
assert (sci.dq == 0).all()
# Add some background
sci.data += 3
blot.data += 3
# Drop a CR on the science array
sci.data[5, 5] += 10
flag_cr(sci, blot)
assert sci.dq[5, 5] > 0
def test_flag_cr_with_subtracted_background(sci_blot_image_pair):
"""Test the flag_cr function on background-subtracted data"""
sci, blot = sci_blot_image_pair
sci.meta.background.subtracted = True
sci.meta.background.level = 3
# Drop a CR on the science array
sci.data[5, 5] += 10
flag_cr(sci, blot)
assert sci.dq[5, 5] > 0
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,468
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/associations/test_level3_product_names.py
|
import pytest
import re
from jwst.associations.tests.helpers import (
func_fixture,
generate_params,
registry_level3_only,
t_path,
)
from jwst.associations import (AssociationPool, generate)
from jwst.associations.lib.dms_base import DMSAttrConstraint
LEVEL3_PRODUCT_NAME_REGEX = (
r'jw'
r'(?P<program>\d{5})'
r'-(?P<acid>[a-z]\d{3,4})'
r'_(?P<target>(?:t\d{3})|(?:\{source_id\}))'
r'(?:-(?P<epoch>epoch\d+))?'
r'_(?P<instrument>.+?)'
r'_(?P<opt_elem>.+)'
)
LEVEL3_PRODUCT_NAME_NO_OPTELEM_REGEX = (
r'jw'
r'(?P<program>\d{5})'
r'-(?P<acid>[a-z]\d{3,4})'
r'_(?P<target>(?:t\d{3})|(?:s\d{5}))'
r'(?:-(?P<epoch>epoch\d+))?'
r'_(?P<instrument>.+?)'
)
# Null values
EMPTY = (None, '', 'NULL', 'Null', 'null', 'F', 'f', 'N', 'n')
pool_file = func_fixture(
generate_params,
scope='module',
params=[
t_path('data/mega_pool.csv'),
]
)
global_constraints = func_fixture(
generate_params,
scope='module',
params=[
DMSAttrConstraint(
name='asn_candidate',
value=['.+o002.+'],
sources=['asn_candidate'],
force_unique=True,
is_acid=True,
evaluate=True,
),
]
)
@pytest.mark.slow
def test_level35_names(pool_file):
rules = registry_level3_only()
pool = AssociationPool.read(pool_file)
asns = generate(pool, rules)
for asn in asns:
product_name = asn['products'][0]['name']
if asn['asn_rule'] == 'Asn_IFU':
m = re.match(LEVEL3_PRODUCT_NAME_NO_OPTELEM_REGEX, product_name)
else:
m = re.match(LEVEL3_PRODUCT_NAME_REGEX, product_name)
assert m is not None
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,469
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py
|
import pytest
from jwst.pipeline.calwebb_image2 import Image2Pipeline
from jwst.tests.base_classes import BaseJWSTTest
@pytest.mark.bigdata
class TestImage2Pipeline(BaseJWSTTest):
input_loc = 'nircam'
ref_loc = ['test_image2pipeline', 'truth']
def test_image2pipeline2b(self):
"""
Regression test of calwebb_image2 pipeline performed on NIRCam data,
using a multiple integration rate (rateints) file as input.
"""
input_file = self.get_data('test_image2pipeline',
'jw82500001003_02101_00001_NRCALONG_rateints.fits')
output_file = 'jw82500001003_02101_00001_NRCALONG_calints.fits'
Image2Pipeline.call(input_file,
output_file=output_file)
outputs = [(output_file,
'jw82500001003_02101_00001_NRCALONG_calints_ref.fits',
['primary','sci','err','dq','area']
)
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,470
|
mperrin/jwst
|
refs/heads/master
|
/jwst/ami/nrm_model.py
|
#
# A module for conveniently manipulating an 'NRM object' using the
# Lacour-Greenbaum algorithm. First written by Alexandra Greenbaum in 2014.
#
# This module:
# Defines mask geometry and detector-scale parameters
# Simulates PSF (broadband or monochromatic)
# Builds a fringe model - either by user definition, or automated to data
# Fits model to data by least squares
#
# Algorithm documented in: Greenbaum, A. Z., Pueyo, L. P.,
# Sivaramakrishnan, A., and Lacour, S. ; Astrophysical Journal (submitted) 2014.
# Developed with NASA APRA (AS, AZG), NSF GRFP (AZG), NASA Sagan (LP), and
# French taxpayer (SL) support.
#
# Heritage mathematica nb from Alex Greenbaum & Laurent Pueyo
# Heritage python by Alex Greenbaum & Anand Sivaramakrishnan Jan 2013
# - updated May 2013 to include hexagonal envelope
# - updated (hard refactored) Oct-Nov 2014 Anand S.
import logging
import numpy as np
from . import leastsqnrm as leastsqnrm
from . import analyticnrm2
from . import utils
from . import hexee
from . import nrm_consts
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
class NrmModel:
def __init__(self, mask=None, holeshape="circ", pixscale=hexee.mas2rad(65),
rotate=False, over=1, flip=False, pixweight=None, scallist=None,
rotlist_deg=None, phi="perfect"):
"""
Short Summary
-------------
Set attributes of NrmModel class.
Parameters
----------
mask: string
keyword for built-in values
holeshape: string
shape of apertures
pixscale: float
initial estimate of pixel scale in radians
rotate: float
initial estimate of rotation in radians
over: integer
oversampling factor
flip: Boolean, default=False
change sign of 2nd coordinate of holes
pixweight: 2D float array, default is None
weighting array
scallist: float 1D array
candidate relative pixel scales
rotlist_deg: float 1D array
Search window for rotation fine-tuning, in degrees
phi: float 1D array
distance of fringe from hole center in units of waves
"""
self.holeshape = holeshape
self.pixel = pixscale
self.over = over
self.maskname = mask
self.pixweight = pixweight
if mask.lower() == 'jwst':
self.ctrs = np.array( [[ 0.00000000, -2.640000],
[-2.2863100, 0.0000000],
[ 2.2863100 , -1.3200001],
[-2.2863100 , 1.3200001],
[-1.1431500 , 1.9800000],
[ 2.2863100 , 1.3200001],
[ 1.1431500 , 1.9800000]] )
self.d = 0.80
self.D = 6.5
else:
try:
log.debug('mask.ctrs:%s', mask.ctrs)
except AttributeError:
raise AttributeError("mask must be either 'jwst' \
or NRM_mask_geometry object")
log.debug('NrmModel: ctrs flipped in init for CV1, CV2')
if rotate:
log.info('Providing additional rotation %s degrees',
rotate * 180. / np.pi)
# Rotates vector counterclockwise in coordinates
self.rotation = rotate
self.ctrs = leastsqnrm.rotatevectors(self.ctrs, rotate)
# From now on this 'rotated' set of centers is used as the
# nominal, and rotation searches (using rotlist_rad) are
# performed with this rotated version of the 'as designed'
# mask.. In CV1 and CV2 the mask is "flipped" by
# multiplying ctrs[:1] by -1... which places segment B4
# (the 6 o clock segment) at the top instead of at bottom
# in traditional XY plots
self.N = len(self.ctrs)
if scallist is None:
self.scallist = np.array([0.995, 0.998, 1.0, 1.002, 1.005, ])
else:
self.scallist = scallist
if rotlist_deg is None:
self.rotlist_rad = np.array([-1.0,-0.5,0.0,0.5,1.0]) * np.pi / 180.0
else:
self.rotlist_rad = rotlist_deg * np.pi / 180.0
if phi == "perfect":
self.phi = np.zeros(len(self.ctrs))
elif phi == 'nb':
self.phi = nrm_consts.phi_nb
else:
self.phi = phi
def simulate(self, fov=None, bandpass=None, over=None, pixweight=None,
pixel=None, rotate=False, centering="PIXELCENTERED"):
"""
Short Summary
-------------
Simulate a psf using parameters input from the call and already stored in
the object. It also generates a simulation fits header storing all of the
parameters used to generate that psf. If the input bandpass is one
number it will calculate a monochromatic PSF.
Parameters
----------
fov: integer, default=None
number of detector pixels on a side
bandpass: 2D float array, default=None
array of the form: [(weight1, wavl1), (weight2, wavl2), ...]
over: integer
Oversampling factor
pixweight: 2D float array, default=None
weighting array
pixel: float, default=None
pixel scale
rotate: float, default=False,
rotation angle in radians
centering: string, default=None
type of centerings
Returns
-------
Object's 'psf': float 2D array
simulated psf
"""
# First set up conditions for choosing various parameters
if fov is None:
if not hasattr(self, 'fov'):
log.critical('Field is not specified')
return None
else:
self.fov_sim = self.fov
log.debug('Using predefined FOV size: %s', self.fov)
else:
self.fov_sim = fov
if hasattr(centering, '__iter__'):
if centering == 'PIXELCENTERED':
centering=(0.5, 0.5)
elif centering == 'PIXELCORNER':
centering=(0.0, 0.0)
self.bandpass = bandpass
if not hasattr(self, 'over'):
if over is None:
self.over = 1
else:
self.over = over
if self.over is None:
self.over = over
if pixweight is not None:
self.over = self.pixweight.shape[0]
self.phi = np.zeros(len(self.ctrs))
if rotate: # this is a 'temporary' rotation of self.ctrs
# without altering self.ctrs
self.rotate = rotate
self.rotctrs = leastsqnrm.rotatevectors(self.ctrs, self.rotate)
else:
self.rotctrs = self.ctrs
if pixel is None:
self.pixel_sim = self.pixel
else:
self.pixel_sim = pixel
# The polychromatic case:
if hasattr(self.bandpass, '__iter__'):
log.debug("------Simulating Polychromatic------")
self.psf_over = np.zeros((self.over*self.fov_sim,
self.over*self.fov_sim))
for w,l in self.bandpass: # w: weight, l: lambda (wavelength)
self.psf_over += w*analyticnrm2.PSF(self.pixel_sim,
self.fov_sim, self.over, self.rotctrs, self.d, l,
self.phi, centering = centering, shape=self.holeshape)
log.debug("BINNING UP TO PIXEL SCALE")
# The monochromatic case if bandpass input is a single wavelength
else:
self.lam = bandpass
log.debug("Calculating Oversampled PSF")
self.psf_over = analyticnrm2.PSF(self.pixel_sim, self.fov_sim,
self.over, self.rotctrs, self.d, self.lam,
self.phi, centering=centering,
shape=self.holeshape)
self.psf = utils.rebin(self.psf_over, (self.over, self.over))
return self.psf
def make_model(self, fov=None, bandpass=None, over=False,
centering='PIXELCENTERED', pixweight=None, pixscale=None,
rotate=False, flip=False):
"""
Short Summary
-------------
Generates the fringe model with the attributes of the object
using a bandpass as a list of tuples.
Parameters
----------
fov: integer, default=None
number of detector pixels on a side
bandpass: 2D float array, default=None
array of the form: [(weight1, wavl1), (weight2, wavl2), ...]
over: integer
oversampling factor
centering: string, default=None
type of centering
pixweight: 2D float array, default=None
weighting array
pixscale: float, default=None
pixel scale
rotate: float, default=False
rotation angle in radians
flip: Boolean, default=False
change sign of 2nd coordinate of holes
Returns
-------
Object's 'model': fringe model
Generated fringe model
"""
if fov:
self.fov = fov
if over is False:
self.over = 1
else:
self.over = over
if pixweight is not None:
self.over = self.pixweight.shape[0]
if hasattr(self, 'pixscale_measured'):
if self.pixscale_measured is not None:
self.modelpix = self.pixscale_measured
if pixscale is None:
self.modelpix = self.pixel
else:
self.modelpix = pixscale
if rotate:
if flip is True:
self.modelctrs = leastsqnrm.flip(
leastsqnrm.rotatevectors(self.ctrs, self.rot_measured))
else:
self.modelctrs = leastsqnrm.rotatevectors(
self.ctrs, self.rot_measured)
else:
self.modelctrs = self.ctrs
if not hasattr(bandpass, '__iter__'):
self.lam = bandpass
self.model = np.ones((self.fov, self.fov, self.N*(self.N-1)+2))
self.model_beam, self.fringes = leastsqnrm.model_array(
self.modelctrs, self.lam, self.over, self.modelpix,
self.fov, self.d, shape=self.holeshape, centering=centering)
log.debug("centering: {0}".format(centering))
log.debug("what primary beam has the model created?"+
" {0}".format(self.model_beam))
# this routine multiplies the envelope by each fringe "image"
self.model_over = leastsqnrm.multiplyenv(self.model_beam, self.fringes)
self.model = np.zeros((self.fov,self.fov, self.model_over.shape[2]))
# loop over slices "sl" in the model
for sl in range(self.model_over.shape[2]):
self.model[:,:,sl] = utils.rebin( self.model_over[:,:,sl],
(self.over, self.over))
return self.model
else:
self.bandpass = bandpass
# The model shape is (fov) x (fov) x (# solution coefficients)
# the coefficient refers to the terms in the analytic equation
# There are N(N-1) independent pistons, double-counted by cosine
# and sine, one constant term and a DC offset.
self.model = np.ones((self.fov, self.fov, self.N*(self.N-1)+2))
self.model_beam = np.zeros((self.over*self.fov, self.over*self.fov))
self.fringes = np.zeros((
self.N*(self.N-1)+1, self.over*self.fov, self.over*self.fov))
for w,l in self.bandpass: # w: weight, l: lambda (wavelength)
# model_array returns the envelope and fringe model
pb, ff = leastsqnrm.model_array(
self.modelctrs, l, self.over, self.modelpix, self.fov,
self.d, shape=self.holeshape, centering=centering)
log.debug("centering: {0}".format(centering))
log.debug("what primary beam has the model created? {0}".format(pb))
self.model_beam += pb
self.fringes += ff
# this routine multiplies the envelope by each fringe "image"
self.model_over = leastsqnrm.multiplyenv(pb, ff)
model_binned = np.zeros((
self.fov,self.fov, self.model_over.shape[2]))
# loop over slices "sl" in the model
for sl in range(self.model_over.shape[2]):
model_binned[:,:,sl] = utils.rebin(
self.model_over[:,:,sl], (self.over, self.over))
self.model += w*model_binned
return self.model
def fit_image(self, image, reference=None, pixguess=None, rotguess=0,
modelin=None, weighted=False, centering='PIXELCENTERED',
savepsfs=True):
"""
Short Summary
-------------
Run a least-squares fit on an input image; find the appropriate
wavelength scale and rotation. If a model is not specified then this
method will find the appropriate wavelength scale, rotation (and
hopefully centering as well -- This is not written into the object yet,
but should be soon).
Parameters
----------
image: 2D float array
input image
reference: 2D float array
input reference image
pixguess: float
estimate of pixel scale of the data
rotguess: float
estimate of rotation
modelin: 2D array
optional model image
weighted: boolean
use weighted operations in the least squares routine
centering: string, default=None
type of centering
savepsfs: boolean
save the psfs for writing to file (currently unused)
Returns
-------
None
"""
self.model_in = modelin
self.weighted = weighted
self.saveval = savepsfs
if modelin is None: # No model provided
# Perform a set of automatic routines
# A Cleaned up version of your image to enable Fourier fitting for
# centering crosscorrelation with FindCentering() and
# magnification and rotation via improve_scaling().
if reference is None:
self.reference = image
if np.isnan(image.any()):
raise ValueError("Must have non-NaN image to "+
"crosscorrelate for scale. Reference "+
"image should also be centered. Get to it.")
else:
self.reference = reference
if pixguess is None or rotguess is None:
raise ValueError("MUST SPECIFY GUESSES FOR PIX & ROT")
self.improve_scaling(self.reference, scaleguess=self.pixel,
rotstart=rotguess, centering=centering)
self.pixscale_measured = self.pixscale_factor*self.pixel
self.fov = image.shape[0]
self.fittingmodel = self.make_model(self.fov, bandpass=self.bandpass,
over=self.over, rotate=True, centering=centering,
pixscale=self.pixscale_measured)
else:
self.fittingmodel = modelin
if weighted is not False:
self.soln, self.residual = leastsqnrm.weighted_operations(image,
self.fittingmodel, weights=self.weighted)
else:
self.soln, self.residual, self.cond = leastsqnrm.matrix_operations(
image, self.fittingmodel)
self.rawDC = self.soln[-1]
self.flux = self.soln[0]
self.soln = self.soln/self.soln[0]
self.deltapsin = leastsqnrm.sin2deltapistons(self.soln)
self.deltapcos = leastsqnrm.cos2deltapistons(self.soln)
self.fringeamp, self.fringephase = leastsqnrm.tan2visibilities(self.soln)
self.piston = utils.fringes2pistons(self.fringephase, len(self.ctrs))
self.closurephase = leastsqnrm.closurephase(self.fringephase, N=self.N)
self.redundant_cps = leastsqnrm.redundant_cps(self.fringephase, N=self.N)
self.redundant_cas = leastsqnrm.return_CAs(self.fringeamp, N=self.N)
def create_modelpsf(self):
"""
Short Summary
-------------
Make an image from the object's model and fit solutions, by setting the
NrmModel object's modelpsf attribute
Parameters
----------
None
Returns
-------
None
"""
try:
self.modelpsf = np.zeros((self.fov, self.fov))
except AttributeError:
self.modelpsf = np.zeros((self.fov_sim, self.fov_sim))
for ind, coeff in enumerate(self.soln):
self.modelpsf += self.flux * coeff * self.fittingmodel[:, :, ind]
return None
def improve_scaling(self, img, scaleguess=None, rotstart=0.0,
centering='PIXELCENTERED'):
"""
Short Summary
-------------
Determine the scale and rotation that best fits the data. Correlations
are calculated in the image plane, in anticipation of data with many
bad pixels.
Parameters
----------
img: 2D float array
input image
scaleguess: float
initial estimate of pixel scale in radians
rotstart: float
estimate of rotation
centering: string, default='PIXELCENTERED'
type of centering
Returns
-------
self.pixscale_factor: float
improved estimate of pixel scale in radians
self.rot_measured: float
value of mag at the extreme value of rotation from quadratic fit
self.gof: float
goodness of fit
"""
if not hasattr(self, 'bandpass'):
raise ValueError("This object has no specified bandpass/wavelength")
reffov = img.shape[0]
scal_corrlist = np.zeros((len(self.scallist), reffov, reffov))
pixscl_corrlist = scal_corrlist.copy()
scal_corr = np.zeros(len(self.scallist))
self.pixscl_corr = scal_corr.copy()
# User can specify a reference set of phases (m) at an earlier point so
# that all PSFs are simulated with those phase pistons (e.g. measured
# from data at an earlier iteration
if not hasattr(self, 'refphi'):
self.refphi = np.zeros(len(self.ctrs))
else:
pass
self.pixscales = np.zeros(len(self.scallist))
for q, scal in enumerate(self.scallist):
self.test_pixscale = self.pixel*scal
self.pixscales[q] = self.test_pixscale
psf = self.simulate(bandpass=self.bandpass, fov=reffov,
pixel = self.test_pixscale, centering=centering)
pixscl_corrlist[q,:,:] = run_data_correlate(img,psf)
self.pixscl_corr[q] = np.max(pixscl_corrlist[q])
if True in np.isnan(self.pixscl_corr):
raise ValueError("Correlation produced NaNs, check your work!")
self.pixscale_optimal, scal_maxy = utils.findmax(
mag=self.pixscales, vals=self.pixscl_corr)
self.pixscale_factor = self.pixscale_optimal / self.pixel
radlist = self.rotlist_rad
corrlist = np.zeros((len(radlist), reffov, reffov))
self.corrs = np.zeros(len(radlist))
self.rots = radlist
for q,rad in enumerate(radlist):
psf = self.simulate(bandpass=self.bandpass, fov=reffov,
pixel=self.pixscale_optimal, rotate=rad, centering=centering)
corrlist[q,:,:] = run_data_correlate(psf, img)
self.corrs[q] = np.max(corrlist[q])
self.rot_measured, maxy = utils.findmax(mag=self.rots, vals = self.corrs)
self.refpsf = self.simulate(bandpass=self.bandpass,
pixel=self.pixscale_factor*self.pixel, fov=reffov,
rotate=self.rot_measured, centering=centering)
try:
self.gof = goodness_of_fit(img,self.refpsf)
except Exception:
self.gof = False
return self.pixscale_factor, self.rot_measured, self.gof
def makedisk(N, R, ctr=(0,0)):
"""
Short Summary
-------------
Calculate a 'disk', an array whose values =1 in a circular region near
the center of the array, and =0 elsewhere. (Anand's emailed version)
Parameters
----------
N: integer
size of 1 dimension of the array to be returned
R: integer
radius of disk
ctr: (integer, integer)
center of disk
array: 'ODD' or 'EVEN'
parity of size of edge
Returns
-------
array: 2D integer array
"""
if N%2 == 1: # odd
M = (N-1)/2
xx = np.linspace(-M-ctr[0],M-ctr[0],N)
yy = np.linspace(-M-ctr[1],M-ctr[1],N)
if N%2 == 0: # even
M = N/2
xx = np.linspace(-M-ctr[0],M-ctr[0]-1,N)
yy = np.linspace(-M-ctr[1],M-ctr[1]-1,N)
(x,y) = np.meshgrid(xx, yy.T)
r = np.sqrt((x**2)+(y**2))
array = np.zeros((N,N))
array[r<R] = 1
return array
def goodness_of_fit(data, bestfit, diskR=8):
"""
Short Summary
-------------
Calculate goodness of fit between the data and the fit.
Parameters
----------
data: 2D float array
input image
bestfit: 2D float array
fit to input image
diskR: integer
radius of disk
Returns
-------
gof: float
goodness of fit
"""
mask = np.ones(data.shape) + makedisk(data.shape[0], 2) -\
makedisk(data.shape[0], diskR)
difference = np.ma.masked_invalid(mask * (bestfit - data))
masked_data = np.ma.masked_invalid(mask * data)
gof = abs(difference).sum() / abs(masked_data).sum()
return gof
def run_data_correlate(data, model):
"""
Short Summary
-------------
Calculate correlation between data and model
Parameters
----------
data: 2D float array
reference image
model: 2D float array
simulated psf
Returns
-------
cor: 2D float array
correlation between data and model
"""
sci = data
log.debug('shape sci: %s', np.shape(sci))
cor = utils.rcrosscorrelate(sci, model)
return cor
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,471
|
mperrin/jwst
|
refs/heads/master
|
/jwst/master_background/__init__.py
|
from .master_background_step import MasterBackgroundStep
__all__ = ['MasterBackgroundStep']
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,472
|
mperrin/jwst
|
refs/heads/master
|
/jwst/lib/basic_utils.py
|
"""General utility objects"""
import re
def multiple_replace(string, rep_dict):
"""Single-pass replacement of multiple substrings
Similar to `str.replace`, except that a dictionary of replacements
can be specified.
The replacements are done in a single-pass. This means that a previous
replacement will not be replaced by a subsequent match.
Parameters
----------
string: str
The source string to have replacements done on it.
rep_dict: dict
The replacements were key is the input substring and
value is the replacement
Returns
-------
replaced: str
New string with the replacements done
Examples
--------
Basic example that also demonstrates the single-pass nature.
If the replacements where chained, the result would have been
'lamb lamb'
>>> multiple_replace('button mutton', {'but': 'mut', 'mutton': 'lamb'})
'mutton lamb'
"""
pattern = re.compile(
"|".join([re.escape(k) for k in sorted(rep_dict,key=len,reverse=True)]),
flags=re.DOTALL
)
return pattern.sub(lambda x: rep_dict[x.group(0)], string)
class LoggingContext:
"""Logging context manager
Keep logging configuration within a context
Based on the Python 3 Logging Cookbook example
Parameters
==========
logger: logging.Logger
The logger to modify.
level: int
The log level to set.
handler: logging.Handler
The handler to use.
close: bool
Close the handler when done.
"""
def __init__(self, logger, level=None, handler=None, close=True):
self.logger = logger
self.level = level
self.handler = handler
self.close = close
self.old_level = None
def __enter__(self):
if self.level is not None:
self.old_level = self.logger.level
self.logger.setLevel(self.level)
if self.handler:
self.logger.addHandler(self.handler)
def __exit__(self, et, ev, tb):
if self.level is not None:
self.logger.setLevel(self.old_level)
if self.handler:
self.logger.removeHandler(self.handler)
if self.handler and self.close:
self.handler.close()
# implicit return of None => don't swallow exceptions
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,473
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py
|
import pytest
from jwst.pipeline.calwebb_spec2 import Spec2Pipeline
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
@pytest.mark.bigdata
class TestSpec2Pipeline(BaseJWSTTest):
input_loc = 'niriss'
ref_loc = ['test_spec2pipeline', 'truth']
test_dir = 'test_spec2pipeline'
def test_nis_wfss_spec2(self):
"""
Regression test of calwebb_spec2 pipeline performed on NIRISS WFSS data.
"""
# Collect data
asn_file = self.get_data(self.test_dir,
'jw87600-a3001_20171109T145456_spec2_001_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
# Run the step
collect_pipeline_cfgs('cfgs')
Spec2Pipeline.call(asn_file, config_file='cfgs/calwebb_spec2.cfg', save_bsub=True)
# Test results.
outputs = [('jw87600017001_02101_00002_nis_cal.fits',
'jw87600017001_02101_00002_nis_cal_ref.fits'),
('jw87600017001_02101_00002_nis_x1d.fits',
'jw87600017001_02101_00002_nis_x1d_ref.fits')]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,474
|
mperrin/jwst
|
refs/heads/master
|
/jwst/regtest/test_miri_image_detector1.py
|
import os
import pytest
from astropy.io.fits.diff import FITSDiff
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
@pytest.fixture(scope="module")
def run_pipeline(jail, rtdata_module):
"""Run calwebb_detector1 pipeline on MIRI imaging data."""
rtdata = rtdata_module
rtdata.get_data("miri/image/jw00001001001_01101_00001_MIRIMAGE_uncal.fits")
collect_pipeline_cfgs("config")
args = ["config/calwebb_detector1.cfg", rtdata.input,
"--steps.dq_init.save_results=True",
"--steps.lastframe.save_results=True",
"--steps.firstframe.save_results=True",
"--steps.saturation.save_results=True",
"--steps.rscd.save_results=True",
"--steps.linearity.save_results=True",
"--steps.dark_current.save_results=True",
"--steps.refpix.save_results=True",
"--steps.jump.rejection_threshold=25",
"--steps.jump.save_results=True",
"--steps.ramp_fit.save_opt=True",
"--steps.ramp_fit.save_results=True"]
Step.from_cmdline(args)
return rtdata
@pytest.mark.bigdata
@pytest.mark.parametrize("output", ['rate', 'rateints', 'linearity', 'rscd',
'dq_init', 'firstframe', 'lastframe',
'saturation', 'dark_current', 'refpix',
'jump', 'fitopt'])
def test_miri_image_detector1(run_pipeline, request, fitsdiff_default_kwargs, output):
"""
Regression test of calwebb_detector1 pipeline performed on MIRI data.
"""
rtdata = run_pipeline
rtdata.output = "jw00001001001_01101_00001_MIRIMAGE_" + output + ".fits"
rtdata.get_truth(os.path.join("truth/test_miri_image_detector1",
"jw00001001001_01101_00001_MIRIMAGE_" + output + ".fits"))
diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs)
assert diff.identical, diff.report()
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,475
|
mperrin/jwst
|
refs/heads/master
|
/jwst/flatfield/flat_field_step.py
|
from ..stpipe import Step
from .. import datamodels
from . import flat_field
# For the following types of data, it is OK -- and in some cases
# required -- for the extract_2d step to have been run. For all
# other types of data, the extract_2d step must not have been run.
EXTRACT_2D_IS_OK = [
"NRS_BRIGHTOBJ",
"NRS_FIXEDSLIT",
"NRS_LAMP",
"NRS_MSASPEC",
]
# NIRSpec imaging types (see exp_type2transform in assign_wcs/nirspec.py)
NRS_IMAGING_MODES = [
"NRS_CONFIRM",
"NRS_FOCUS",
"NRS_IMAGE",
"NRS_MIMF",
"NRS_MSATA",
"NRS_TACONFIRM",
"NRS_TACQ",
"NRS_TASLIT",
"NRS_WATA",
]
# Supported NIRSpec spectrographic types. No flat fielding for NRS_AUTOFLAT
NRS_SPEC_MODES = [
"NRS_BRIGHTOBJ",
"NRS_FIXEDSLIT",
"NRS_IFU",
"NRS_MSASPEC",
]
__all__ = ["FlatFieldStep"]
class FlatFieldStep(Step):
"""Flat-field a science image using a flatfield reference image.
"""
spec = """
save_interpolated_flat = boolean(default=False) # Save interpolated NRS flat
"""
reference_file_types = ["flat", "fflat", "sflat", "dflat"]
# Define a suffix for optional saved output of the interpolated flat for NRS
flat_suffix = 'interpolatedflat'
def process(self, input):
input_model = datamodels.open(input)
exposure_type = input_model.meta.exposure.type.upper()
self.log.debug("Input is {} of exposure type {}".format(
input_model.__class__.__name__, exposure_type))
if input_model.meta.instrument.name.upper() == "NIRSPEC":
if (exposure_type not in NRS_SPEC_MODES and
exposure_type not in NRS_IMAGING_MODES):
self.log.warning("Exposure type is %s; flat-fielding will be "
"skipped because it is not currently "
"supported for this mode.", exposure_type)
return self.skip_step(input_model)
# Check whether extract_2d has been run.
if (input_model.meta.cal_step.extract_2d == 'COMPLETE' and
not exposure_type in EXTRACT_2D_IS_OK):
self.log.warning("The extract_2d step has been run, but for "
"%s data it should not have been run, so ...",
exposure_type)
self.log.warning("flat fielding will be skipped.")
return self.skip_step(input_model)
# Get reference file paths
reference_file_names = {}
for reftype in self.reference_file_types:
reffile = self.get_reference_file(input_model, reftype)
reference_file_names[reftype] = reffile if reffile != 'N/A' else None
# Define mapping between reftype and datamodel type
model_type = dict(
flat=datamodels.FlatModel,
fflat=datamodels.NirspecFlatModel,
sflat=datamodels.NirspecFlatModel,
dflat=datamodels.NirspecFlatModel,
)
if exposure_type == "NRS_MSASPEC":
model_type["fflat"] = datamodels.NirspecQuadFlatModel
# Open the relevant reference files as datamodels
reference_file_models = {}
for reftype, reffile in reference_file_names.items():
if reffile is not None:
reference_file_models[reftype] = model_type[reftype](reffile)
self.log.debug('Using %s reference file: %s', reftype.upper(), reffile)
else:
reference_file_models[reftype] = None
# Do the flat-field correction
output_model, interpolated_flats = flat_field.do_correction(
input_model,
**reference_file_models,
)
# Close the input and reference files
input_model.close()
try:
for model in reference_file_models.values():
model.close()
except AttributeError:
pass
if self.save_interpolated_flat and interpolated_flats is not None:
self.log.info("Writing interpolated flat field.")
self.save_model(interpolated_flats, suffix=self.flat_suffix)
interpolated_flats.close()
return output_model
def skip_step(self, input_model):
"""Set the calibration switch to SKIPPED.
This method makes a copy of input_model, sets the calibration
switch for the flat_field step to SKIPPED in the copy, closes
input_model, and returns the copy.
"""
result = input_model.copy()
result.meta.cal_step.flat_field = "SKIPPED"
input_model.close()
return result
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,476
|
mperrin/jwst
|
refs/heads/master
|
/jwst/ami/analyticnrm2.py
|
#! /usr/bin/env python
# Heritage mathematia nb from Alex & Laurent
# Python by Alex Greenbaum & Anand Sivaramakrishnan Jan 2013
# updated May 2013 to include hexagonal envelope
from . import hexee
import logging
import numpy as np
import scipy.special
from . import leastsqnrm
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
def Jinc(x, y):
"""
Short Summary
-------------
Compute 2d Jinc for given coordinates
Parameters
----------
x,y: floats
input coordinates
Returns
-------
jinc_2d: float array
2d Jinc at the given coordinates, with NaNs replaced by pi/4.
"""
R = (Jinc.d / Jinc.lam) * Jinc.pitch * \
np.sqrt((x - Jinc.offx)*(x - Jinc.offx) + \
(y - Jinc.offy)*(y - Jinc.offy))
jinc_2d = leastsqnrm.replacenan(scipy.special.jv(1, np.pi * R)/(2.0 * R))
return jinc_2d
def phasor(kx, ky, hx, hy, lam, phi, pitch):
"""
Short Summary
-------------
Calculate wavefront for a single hole ??
Parameters
----------
kx, ky: float
image plane coords in units of sampling pitch (oversampled, or not)
hx, hy: float
hole centers in meters
lam: float
wavelength
phi: float
distance of fringe from hole center in units of waves
pitch: float
sampling pitch in radians in image plane
Returns
-------
phasor: complex
Calculate wavefront for a single hole
"""
return np.exp(-2 * np.pi * 1j * ((pitch * kx * hx + pitch * ky * hy)
/ lam + (phi / lam)))
def interf(kx, ky):
"""
Short Summary
-------------
Calculate interference for all holes.
Parameters
----------
kx, ky: float, float
x-component and y-component of image plane (spatial frequency) vector
Returns
-------
interference: 2D complex array
interference for all holes
"""
interference = 0j
for hole, ctr in enumerate(interf.ctrs):
interference += phasor((kx - interf.offx), (ky - interf.offy),
ctr[0], ctr[1], interf.lam,
interf.phi[hole], interf.pitch)
return interference
def ASF(pixel, fov, oversample, ctrs, d, lam, phi, centering=(0.5, 0.5)):
"""
Short Summary
-------------
Calculate the Amplitude Spread Function (a.k.a. image plane complex
amplitude) for a circular aperture
Parameters
----------
pixel: float
pixel scale
fov: integer
number of detector pixels on a side
oversample: integer
oversampling factor
ctrs: float, float
coordinates of hole center
d: float
hole diameter
lam: float
wavelength
phi: float
distance of fringe from hole center in units of waves
centering: string
if set to 'PIXELCENTERED' or unspecified, the offsets will be set to
(0.5,0.5); if set to 'PIXELCORNER', the offsets will be set to
(0.0,0.0).
Returns
-------
asf: 2D complex array
Amplitude Spread Function (a.k.a. image plane complex amplitude) for
a circular aperture
"""
if centering == 'PIXELCENTERED':
off_x = 0.5
off_y = 0.5
elif centering == 'PIXELCORNER':
off_x = 0.0
off_y = 0.0
else:
off_x, off_y = centering
log.debug('ASF centering %s:', centering)
log.debug('ASF offsets %s %s:', off_x, off_y)
# Jinc parameters
Jinc.lam = lam
Jinc.offx = oversample * fov / 2.0 - off_x # in pixels
Jinc.offy = oversample * fov / 2.0 - off_y
Jinc.pitch = pixel / float(oversample)
Jinc.d = d
primarybeam = np.fromfunction(Jinc, (int((oversample * fov)),
int((oversample * fov))))
primarybeam = primarybeam.transpose()
# interference terms' parameters
interf.lam = lam
interf.offx = oversample * fov / 2.0 - off_x # in pixels
interf.offy = oversample * fov / 2.0 - off_y
interf.pitch = pixel / float(oversample)
interf.ctrs = ctrs
interf.phi = phi
fringing = np.fromfunction(interf, (int((oversample * fov)),
int((oversample * fov))))
fringing = fringing.transpose()
asf = primarybeam * fringing
return asf
def ASFfringe(pixel, fov, oversample, ctrs, d, lam, phi, centering=(0.5, 0.5)):
"""
Short Summary
-------------
Amplitude Spread Function (a.k.a. image plane complex amplitude)
for a fringe
Parameters
----------
pixel: float
pixel scale
fov: integer
number of detector pixels on a side
oversample: integer
oversampling factor
ctrs: 2D float array
centers of holes
d: float
hole diameter
lam: float
wavelength
phi: float
distance of fringe from hole center in units of waves
centering: string
if set to 'PIXELCENTERED' or unspecified, the offsets will be set to
(0.5,0.5); if set to 'PIXELCORNER', the offsets will be set to
(0.0,0.0).
Returns
-------
fringing: 2D complex array
Amplitude Spread Function (a.k.a. image plane complex amplitude) for
a fringe
"""
if centering == 'PIXELCENTERED':
off_x = 0.5
off_y = 0.5
elif centering == 'PIXELCORNER':
off_x = 0.0
off_y = 0.0
else:
off_x, off_y = centering
log.debug('ASFfringe centering %s:', centering)
log.debug('ASFfringe offsets %s %s:', off_x, off_y)
# Jinc parameters
Jinc.lam = lam
Jinc.offx = oversample * fov / 2.0 - off_x # in pixels
Jinc.offy = oversample * fov / 2.0 - off_y
Jinc.pitch = pixel / float(oversample)
Jinc.d = d
# interference terms' parameters
interf.lam = lam
interf.offx = oversample * fov / 2.0 - off_x # in pixels
interf.offy = oversample * fov / 2.0 - off_y
interf.pitch = pixel / float(oversample)
interf.ctrs = ctrs
interf.phi = phi
fringing = np.fromfunction(interf, (int((oversample * fov)),
int((oversample * fov))))
fringing = fringing.transpose()
return fringing
def ASFhex(pixel, fov, oversample, ctrs, d, lam, phi, centering='PIXELCENTERED'):
"""
Short Summary
-------------
Amplitude Spread Function (a.k.a. image plane complex amplitude)
for a hexagonal aperture
Parameters
----------
pixel: float
pixel scale
fov: integer
number of detector pixels on a side
oversample: integer
oversampling factor
ctrs: 2D float array
centers of holes
d: float
flat-to-flat distance across hexagon
lam: float
wavelength
phi: float
distance of fringe from hole center in units of waves
centering: string
type of centering
Returns
-------
asf: 2D complex array
Amplitude Spread Function (a.k.a. image plane complex amplitude) for
a hexagonal aperture
"""
log.debug('centering: %s', centering)
if centering == 'PIXELCENTERED':
off_x = 0.5
off_y = 0.5
elif centering == 'PIXELCORNER':
off_x = 0.0
off_y = 0.0
else:
off_x, off_y = centering
#Hex kwargs
offx = (float(oversample * fov) / 2.0) - off_x # in pixels
offy = (float(oversample * fov) / 2.0) - off_y
log.debug('ASF offsets for x and y in pixels: %s %s', offx, offy)
log.debug('ASF centering:%s', centering)
pitch = pixel / float(oversample)
# interference terms' parameters
interf.lam = lam
interf.offx = (oversample * fov) / 2.0 - off_x # in pixels
interf.offy = (oversample * fov) / 2.0 - off_y
interf.pitch = pixel / float(oversample)
interf.ctrs = ctrs
interf.phi = phi
primarybeam = hexee.hex_eeAG(s=(oversample * fov, oversample * fov),
c=(offx, offy), d=d, lam=lam, pitch=pitch)
fringing = np.fromfunction(interf, (int((oversample * fov)),
int((oversample * fov))))
fringing = fringing.transpose()
asf = primarybeam * fringing
return asf
def PSF(pixel, fov, oversample, ctrs, d, lam, phi, centering='PIXELCENTERED',
shape='circ'):
"""
Short Summary
-------------
Calculate the PSF for the requested shape
Parameters
----------
pixel: float
pixel scale
fov: integer
number of detector pixels on a side
oversample: integer
oversampling factor
ctrs: 2D float array
centers of holes
d: float
hole diameter for 'circ'; flat-to-flat distance across for 'hex'
lam: float
wavelength
phi: float
distance of fringe from hole center in units of waves
centering: string
type of centering
shape: string
shape of hole; possible values are 'circ', 'hex', and 'fringe'
Returns
-------
PSF - 2D float array
"""
if shape == 'circ':
asf = ASF(pixel, fov, oversample, ctrs, d, lam, phi, centering)
elif shape == 'hex':
asf = ASFhex(pixel, fov, oversample, ctrs, d, lam, phi, centering)
elif shape == 'fringe': # Alex: "not needed,only used for visualization"
asf = ASFfringe(pixel, fov, oversample, ctrs, d, lam, phi, centering)
else:
log.critical('Pupil shape %s not supported', shape)
log.debug('-----------------')
log.debug(' PSF Parameters: ')
log.debug('-----------------')
log.debug('pixel: %s, fov: %s, oversampling: %s', pixel, fov, oversample)
log.debug('d: %s, wavelength: %s, pistons: %s, shape: %s', d, lam, phi,
shape)
PSF_ = asf * asf.conj()
return PSF_.real
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,477
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/tests/test_level1b.py
|
"""Test Level1bModel"""
import pytest
import numpy as np
from .. import Level1bModel
@pytest.mark.xfail
def test_no_zeroframe():
"""Test for default zeroframe"""
nx = 10
ny = 10
ngroups = 5
nints = 2
data = np.zeros((nints, ngroups, ny, nx), np.int16)
model = Level1bModel(data)
assert model.data.shape == (nints, ngroups, ny, nx)
assert model.zeroframe.shape == (nints, ny, nx)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,478
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py
|
import pytest
from jwst.pipeline import Spec2Pipeline
from jwst.tests.base_classes import BaseJWSTTest
from jwst.tests.base_classes import pytest_generate_tests # noqa: F401
@pytest.mark.bigdata
class TestSpec2Pipeline(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_pipelines', 'truth']
test_dir = 'test_pipelines'
# Specification of parameters for Spec2Pipeline tests
params = {'test_spec2':
# test_nrs_fs_multi_spec2_1: NIRSpec fixed-slit data
[dict(input='jw00023001001_01101_00001_NRS1_rate.fits',
outputs=[('jw00023001001_01101_00001_NRS1_cal.fits',
'jw00023001001_01101_00001_NRS1_cal_ref.fits'),
('jw00023001001_01101_00001_NRS1_s2d.fits',
'jw00023001001_01101_00001_NRS1_s2d_ref.fits'),
('jw00023001001_01101_00001_NRS1_x1d.fits',
'jw00023001001_01101_00001_NRS1_x1d_ref.fits')
],
id="nirspec_fs_multi_1"
),
# test_nrs_fs_multi_spec2_2: NIRSpec fixed-slit data
dict(input= 'jwtest1013001_01101_00001_NRS1_rate.fits',
outputs=[('jwtest1013001_01101_00001_NRS1_cal.fits',
'jwtest1013001_01101_00001_NRS1_cal_ref.fits'),
('jwtest1013001_01101_00001_NRS1_s2d.fits',
'jwtest1013001_01101_00001_NRS1_s2d_ref.fits'),
('jwtest1013001_01101_00001_NRS1_x1d.fits',
'jwtest1013001_01101_00001_NRS1_x1d_ref.fits')
],
id="nirspec_fs_multi_2"
),
# test_nrs_fs_multi_spec2_3:
# NIRSpec fixed-slit data using the ALLSLITS subarray and detector NRS2
# NIRSpec fixed-slit data that uses a single-slit subarray (S200B1).
dict(input= 'jw84600002001_02101_00001_nrs2_rate.fits',
outputs=[('jw84600002001_02101_00001_nrs2_cal.fits',
'jw84600002001_02101_00001_nrs2_cal_ref.fits'),
('jw84600002001_02101_00001_nrs2_s2d.fits',
'jw84600002001_02101_00001_nrs2_s2d_ref.fits'),
('jw84600002001_02101_00001_nrs2_x1d.fits',
'jw84600002001_02101_00001_nrs2_x1d_ref.fits')
],
id="nirspec_fs_multi_3"
),
# test_nrs_ifu_spec2: NIRSpec IFU data
dict(input= 'jw95175001001_02104_00001_nrs1_rate.fits',
outputs=[('jw95175001001_02104_00001_nrs1_cal.fits',
'jw95175001001_02104_00001_nrs1_cal_ref.fits'),
('jw95175001001_02104_00001_nrs1_s3d.fits',
'jw95175001001_02104_00001_nrs1_s3d_ref.fits'),
('jw95175001001_02104_00001_nrs1_x1d.fits',
'jw95175001001_02104_00001_nrs1_x1d_ref.fits')
],
id = "nirspec_ifu"
)
]
}
def test_spec2(self, input, outputs):
"""
Regression test of calwebb_spec2 pipeline performed on NIRSpec data.
"""
input_file = self.get_data(self.test_dir, input)
step = Spec2Pipeline()
step.save_bsub = True
step.save_results = True
step.resample_spec.save_results = True
step.cube_build.save_results = True
step.extract_1d.save_results = True
step.run(input_file)
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,479
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/miri/test_miri_steps_single.py
|
import os
import numpy as np
from numpy.testing import assert_allclose
import pytest
from gwcs.wcstools import grid_from_bounding_box
from ci_watson.artifactory_helpers import get_bigdata
from jwst import datamodels
from jwst.datamodels import ImageModel, RegionsModel, CubeModel
from jwst.stpipe import crds_client
from jwst.lib.set_telescope_pointing import add_wcs
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.assign_wcs import AssignWcsStep
from jwst.cube_build import CubeBuildStep
from jwst.linearity import LinearityStep
from jwst.ramp_fitting import RampFitStep
from jwst.master_background import MasterBackgroundStep
@pytest.mark.bigdata
class TestMIRIRampFit(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_ramp_fit', 'truth']
test_dir = 'test_ramp_fit'
def test_ramp_fit_miri1(self):
"""
Regression test of ramp_fit step performed on MIRI data.
"""
input_file = self.get_data(self.test_dir, 'jw00001001001_01101_00001_MIRIMAGE_jump.fits')
result = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit1_opt_out.fits')
output_file = result[0].save(path=result[0].meta.filename.replace('jump','rampfit'))
int_output = result[1].save(path=result[1].meta.filename.replace('jump','rampfit_int'))
result[0].close()
result[1].close()
outputs = [(output_file,
'jw00001001001_01101_00001_MIRIMAGE_ramp_fit.fits'),
(int_output,
'jw00001001001_01101_00001_MIRIMAGE_int.fits'),
('rampfit1_opt_out_fitopt.fits',
'jw00001001001_01101_00001_MIRIMAGE_opt.fits')
]
self.compare_outputs(outputs)
def test_ramp_fit_miri2(self):
"""
Regression test of ramp_fit step performed on MIRI data.
"""
input_file = self.get_data(self.test_dir,
'jw80600012001_02101_00003_mirimage_jump.fits')
result = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit2_opt_out.fits')
output_file = result[0].save(path=result[0].meta.filename.replace('jump','rampfit'))
int_output = result[1].save(path=result[1].meta.filename.replace('jump','rampfit_int'))
result[0].close()
result[1].close()
outputs = [(output_file,
'jw80600012001_02101_00003_mirimage_ramp.fits'),
(int_output,
'jw80600012001_02101_00003_mirimage_int.fits'),
('rampfit2_opt_out_fitopt.fits',
'jw80600012001_02101_00003_mirimage_opt.fits')
]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestMIRICube(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_cube_build', 'truth']
test_dir = 'test_cube_build'
rtol = 0.000001
def test_cubebuild_miri(self):
"""
Regression test of cube_build performed on MIRI MRS data.
"""
input_file = self.get_data(self.test_dir,
'jw10001001001_01101_00001_mirifushort_cal.fits')
input_model = datamodels.IFUImageModel(input_file)
CubeBuildStep.call(input_model, output_type='multi', save_results=True)
outputs = [('jw10001001001_01101_00001_mirifushort_s3d.fits',
'jw10001001001_01101_00001_mirifushort_s3d_ref.fits',
['primary','sci','err','dq','wmap']) ]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestMIRILinearity(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_linearity','truth']
test_dir ='test_linearity'
def test_linearity_miri3(self):
"""
Regression test of linearity step performed on MIRI data.
"""
input_file = self.get_data(self.test_dir,
'jw00001001001_01109_00001_MIRIMAGE_dark_current.fits')
# get supplemental input
override_file = self.get_data(self.test_dir,
"lin_nan_flag_miri.fits")
# run calibration step
result = LinearityStep.call(input_file,
override_linearity=override_file)
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00001001001_01109_00001_MIRIMAGE_linearity.fits') ]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestMIRIWCSFixed(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_wcs','fixed','truth']
test_dir = os.path.join('test_wcs','fixed')
def test_miri_fixed_slit_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
'jw00035001001_01101_00001_mirimage_rate.fits')
result = AssignWcsStep.call(input_file, save_results=True)
cwd = os.path.abspath('.')
os.makedirs('truth', exist_ok=True)
os.chdir('truth')
truth_file = self.get_data(*self.ref_loc,
'jw00035001001_01101_00001_mirimage_assign_wcs.fits')
os.chdir(cwd)
truth = ImageModel(truth_file)
x, y = grid_from_bounding_box(result.meta.wcs.bounding_box)
ra, dec, lam = result.meta.wcs(x, y)
raref, decref, lamref = truth.meta.wcs(x, y)
assert_allclose(ra, raref)
assert_allclose(dec, decref)
assert_allclose(lam, lamref)
@pytest.mark.bigdata
class TestMIRIWCSIFU(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_wcs', 'ifu', 'truth']
test_dir = os.path.join('test_wcs', 'ifu')
def test_miri_ifu_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
'jw00024001001_01101_00001_MIRIFUSHORT_uncal_MiriSloperPipeline.fits')
result = AssignWcsStep.call(input_file, save_results=True)
# Get the region file
region = RegionsModel(crds_client.get_reference_file(result, 'regions'))
# Choose the same plane as in the miri.py file (hardcoded for now).
regions = region.regions[7, :, :]
# inputs
x, y = grid_from_bounding_box(result.meta.wcs.bounding_box)
# Get indices where pixels == 0. These should be NaNs in the output.
ind_zeros = regions == 0
cwd = os.path.abspath('.')
os.makedirs('truth', exist_ok=True)
os.chdir('truth')
truth_file = self.get_data(*self.ref_loc,
'jw00024001001_01101_00001_MIRIFUSHORT_assign_wcs.fits')
os.chdir(cwd)
truth = ImageModel(truth_file)
ra, dec, lam = result.meta.wcs(x, y)
raref, decref, lamref = truth.meta.wcs(x, y)
assert_allclose(ra, raref, equal_nan=True)
assert_allclose(dec, decref, equal_nan=True)
assert_allclose(lam, lamref, equal_nan=True)
# Test that we got NaNs at ind_zero
assert(np.isnan(ra).nonzero()[0] == ind_zeros.nonzero()[0]).all()
assert(np.isnan(ra).nonzero()[1] == ind_zeros.nonzero()[1]).all()
# Test the inverse transform
x1, y1 = result.meta.wcs.backward_transform(ra, dec, lam)
assert(np.isnan(x1).nonzero()[0] == ind_zeros.nonzero()[0]).all()
assert (np.isnan(x1).nonzero()[1] == ind_zeros.nonzero()[1]).all()
# Also run a smoke test with values outside the region.
dec[100][200] = -80
ra[100][200] = 7
lam[100][200] = 15
x2, y2 = result.meta.wcs.backward_transform(ra, dec, lam)
assert np.isnan(x2[100][200])
assert np.isnan(x2[100][200])
@pytest.mark.bigdata
class TestMIRIWCSImage(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_wcs', 'image', 'truth']
test_dir = os.path.join('test_wcs', 'image')
def test_miri_image_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
"jw00001001001_01101_00001_MIRIMAGE_ramp_fit.fits")
result = AssignWcsStep.call(input_file, save_results=True)
cwd = os.path.abspath('.')
os.makedirs('truth', exist_ok=True)
os.chdir('truth')
truth_file = self.get_data(*self.ref_loc,
"jw00001001001_01101_00001_MIRIMAGE_assign_wcs.fits")
os.chdir(cwd)
truth = ImageModel(truth_file)
x, y = grid_from_bounding_box(result.meta.wcs.bounding_box)
ra, dec = result.meta.wcs(x, y)
raref, decref = truth.meta.wcs(x, y)
assert_allclose(ra, raref)
assert_allclose(dec, decref)
@pytest.mark.bigdata
class TestMIRIWCSSlitless(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_wcs', 'slitless', 'truth']
test_dir = os.path.join('test_wcs', 'slitless')
def test_miri_slitless_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(self.test_dir,
"jw80600012001_02101_00003_mirimage_rateints.fits")
result = AssignWcsStep.call(input_file, save_results=True)
cwd = os.path.abspath('.')
os.makedirs('truth', exist_ok=True)
os.chdir('truth')
truth_file = self.get_data(*self.ref_loc,
"jw80600012001_02101_00003_mirimage_assignwcsstep.fits")
os.chdir(cwd)
truth = CubeModel(truth_file)
x, y = grid_from_bounding_box(result.meta.wcs.bounding_box)
ra, dec, lam = result.meta.wcs(x, y)
raref, decref, lamref = truth.meta.wcs(x, y)
assert_allclose(ra, raref)
assert_allclose(dec, decref)
assert_allclose(lam, lamref)
@pytest.mark.bigdata
class TestMIRISetPointing(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_pointing', 'truth']
test_dir = 'test_pointing'
rtol = 0.000001
def test_miri_setpointing(self):
"""
Regression test of the set_telescope_pointing script on a level-1b MIRI file.
"""
# Copy original version of file to test file, which will get overwritten by test
input_file = self.get_data(self.test_dir,
'jw80600010001_02101_00001_mirimage_uncal_orig.fits')
# Get SIAF PRD database file
siaf_prd_loc = ['jwst-pipeline', self.env, 'common', 'prd.db']
siaf_path = get_bigdata(*siaf_prd_loc)
add_wcs(input_file, allow_default=True, siaf_path=siaf_path)
outputs = [(input_file,
'jw80600010001_02101_00001_mirimage_uncal_ref.fits')]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestMIRIMasterBackgroundLRS(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_masterbackground', 'lrs', 'truth']
test_dir = ['test_masterbackground', 'lrs']
rtol = 0.000001
def test_miri_lrs_masterbg_user(self):
"""
Regression test of masterbackgound subtraction with lrs, with user provided 1-D background
"""
# input file has the background added
input_file = self.get_data(*self.test_dir, 'miri_lrs_sci+bkg_cal.fits')
# user provided 1-D background
user_background = self.get_data(*self.test_dir, 'miri_lrs_bkg_x1d.fits')
result = MasterBackgroundStep.call(input_file,
user_background=user_background,
save_results=True)
# Compare result (background subtracted image) to science image with no
# background. Subtract these images, smooth the subtracted image and
# the mean should be close to zero.
input_sci_cal_file = self.get_data(*self.test_dir,
'miri_lrs_sci_cal.fits')
input_sci = datamodels.open(input_sci_cal_file)
# find the LRS region
bb = result.meta.wcs.bounding_box
x, y = grid_from_bounding_box(bb)
result_lrs_region = result.data[y.astype(int), x.astype(int)]
sci_lrs_region = input_sci.data[y.astype(int), x.astype(int)]
# do a 5 sigma clip on the science image
sci_mean = np.nanmean(sci_lrs_region)
sci_std = np.nanstd(sci_lrs_region)
upper = sci_mean + sci_std*5.0
lower = sci_mean - sci_std*5.0
mask_clean = np.logical_and(sci_lrs_region < upper, sci_lrs_region > lower)
sub = result_lrs_region - sci_lrs_region
mean_sub = np.absolute(np.mean(sub[mask_clean]))
atol = 0.1
rtol = 0.001
assert_allclose(mean_sub, 0, atol=atol, rtol=rtol)
# Test 3 Compare background subtracted science data (results)
# to a truth file.
truth_file = self.get_data(*self.ref_loc,
'miri_lrs_sci+bkg_masterbackgroundstep.fits')
result_file = result.meta.filename
outputs = [(result_file, truth_file)]
self.compare_outputs(outputs)
result.close()
input_sci.close()
@pytest.mark.bigdata
class TestMIRIMasterBackgroundMRSDedicated(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_masterbackground', 'mrs', 'dedicated', 'truth']
test_dir = ['test_masterbackground', 'mrs', 'dedicated']
rtol = 0.000001
def test_miri_masterbg_mrs_dedicated(self):
"""Run masterbackground step on MIRI MRS association"""
asn_file = self.get_data(*self.test_dir,
'miri_mrs_mbkg_0304_spec3_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(*self.test_dir, file)
collect_pipeline_cfgs('./config')
result = MasterBackgroundStep.call(
asn_file,
config_file='config/master_background.cfg',
save_background=True,
save_results=True,
)
# test 1
# loop over the background subtracted data and compare to truth files
# check that the cal_step master_background ran to complete
for model in result:
assert model.meta.cal_step.master_background == 'COMPLETE'
truth_file = self.get_data(*self.ref_loc,
model.meta.filename)
outputs = [(model.meta.filename, truth_file)]
self.compare_outputs(outputs)
# test 2
# compare the master background combined file to truth file
master_combined_bkg_file = 'MIRI_MRS_seq1_MIRIFULONG_34LONGexp1_bkg_o002_masterbg.fits'
truth_background = self.get_data(*self.ref_loc,
master_combined_bkg_file)
outputs = [(master_combined_bkg_file, truth_background)]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestMIRIMasterBackgroundMRSNodded(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_masterbackground', 'mrs', 'nodded', 'truth']
test_dir = ['test_masterbackground', 'mrs', 'nodded']
rtol = 0.000001
def test_miri_masterbg_mrs_nodded(self):
"""Run masterbackground step on MIRI MRS association"""
asn_file = self.get_data(*self.test_dir,
'miri_mrs_mbkg_spec3_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(*self.test_dir, file)
collect_pipeline_cfgs('./config')
result = MasterBackgroundStep.call(
asn_file,
config_file='config/master_background.cfg',
save_background=True,
save_results=True,
)
# test 1
# loop over the background subtracted data and compare to truth files
# check that the cal_step master_background ran to complete
for model in result:
assert model.meta.cal_step.master_background == 'COMPLETE'
truth_file = self.get_data(*self.ref_loc,
model.meta.filename)
outputs = [(model.meta.filename, truth_file)]
self.compare_outputs(outputs)
# test 2
# compare the master background combined file to truth file
master_combined_bkg_file = 'MIRI_MRS_nod_seq1_MIRIFUSHORT_12SHORTexp1_o001_masterbg.fits'
truth_background = self.get_data(*self.ref_loc,
master_combined_bkg_file)
outputs = [(master_combined_bkg_file, truth_background)]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestMIRIMasterBackgroundLRSNodded(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_masterbackground', 'lrs', 'nodded', 'truth']
test_dir = ['test_masterbackground', 'lrs', 'nodded']
rtol = 0.000001
def test_miri_masterbg_lrs_nodded(self):
"""Run masterbackground step on MIRI LRS association"""
asn_file = self.get_data(*self.test_dir,
'miri_lrs_mbkg_nodded_spec3_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(*self.test_dir, file)
collect_pipeline_cfgs('./config')
result = MasterBackgroundStep.call(
asn_file,
config_file='config/master_background.cfg',
save_background=True,
save_results=True,
)
# test 1
# loop over the background subtracted data and compare to truth files
for model in result:
assert model.meta.cal_step.master_background == 'COMPLETE'
truth_file = self.get_data(*self.ref_loc,
model.meta.filename)
outputs = [(model.meta.filename, truth_file)]
self.compare_outputs(outputs)
# test 2
# compare the master background combined file to truth file
master_combined_bkg_file = 'MIRI_LRS_nod_seq1_MIRIMAGE_P750Lexp1_o002_masterbg.fits'
truth_background = self.get_data(*self.ref_loc,
master_combined_bkg_file)
outputs = [(master_combined_bkg_file, truth_background)]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestMIRIMasterBackgroundLRSDedicated(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_masterbackground', 'lrs', 'dedicated', 'truth']
test_dir = ['test_masterbackground', 'lrs', 'dedicated']
rtol = 0.000001
def test_miri_masterbg_lrs_dedicated(self):
"""Run masterbackground step on MIRI LRS association"""
asn_file = self.get_data(*self.test_dir,
'miri_lrs_mbkg_dedicated_spec3_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(*self.test_dir, file)
collect_pipeline_cfgs('./config')
result = MasterBackgroundStep.call(
asn_file,
config_file='config/master_background.cfg',
save_background=True,
save_results=True,
)
# test 1
# loop over the background subtracted data and compare to truth files
for model in result:
assert model.meta.cal_step.master_background == 'COMPLETE'
truth_file = self.get_data(*self.ref_loc,
model.meta.filename)
outputs = [(model.meta.filename, truth_file)]
self.compare_outputs(outputs)
# test 2
# compare the master background combined file to truth file
master_combined_bkg_file = 'MIRI_LRS_seq1_MIRIMAGE_P750Lexp1_o001_masterbg.fits'
truth_background = self.get_data(*self.ref_loc,
master_combined_bkg_file)
outputs = [(master_combined_bkg_file, truth_background)]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,480
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nircam/test_wfs_combine.py
|
"""Test wfs_combine"""
from glob import glob
import os.path as op
import pytest
from jwst.tests.base_classes import BaseJWSTTest
from jwst.associations import load_asn
from jwst.associations.lib.rules_level3_base import format_product
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe.step import Step
@pytest.mark.bigdata
class TestWFSImage3Pipeline(BaseJWSTTest):
input_loc = 'nircam'
ref_loc = ['test_wfs_combine', 'truth']
test_dir = 'test_wfs_combine'
def test_asn_naming(self):
"""Test a full run"""
# Get the data
collect_pipeline_cfgs('cfgs')
asn_path = self.get_data(
self.test_dir, 'wfs_3sets_asn.json'
)
with open(asn_path) as fh:
asn = load_asn(fh)
for product in asn['products']:
for member in product['members']:
self.get_data(
self.test_dir, member['expname']
)
input_files = glob('*')
# Run the step.
args = [
op.join('cfgs', 'calwebb_wfs-image3.cfg'),
asn_path
]
Step.from_cmdline(args)
# Test.
output_files = glob('*')
for input_file in input_files:
output_files.remove(input_file)
print('output_files = {}'.format(output_files))
for product in asn['products']:
prod_name = product['name']
prod_name = format_product(prod_name, suffix='wfscmb')
prod_name += '.fits'
assert prod_name in output_files
output_files.remove(prod_name)
# There should be no more files
assert len(output_files) == 0
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,481
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/niriss/test_niriss_steps.py
|
import pytest
from jwst.tests.base_classes import BaseJWSTTestSteps
from jwst.tests.base_classes import pytest_generate_tests # noqa: F401
from jwst.ami import AmiAnalyzeStep
from jwst.refpix import RefPixStep
from jwst.dark_current import DarkCurrentStep
from jwst.dq_init import DQInitStep
from jwst.flatfield import FlatFieldStep
from jwst.jump import JumpStep
from jwst.linearity import LinearityStep
from jwst.saturation import SaturationStep
from jwst.pathloss import PathLossStep
# Parameterized regression tests for NIRISS processing
# All tests in this set run with 1 input file and
# only generate 1 output for comparison.
#
@pytest.mark.bigdata
class TestNIRISSSteps(BaseJWSTTestSteps):
input_loc = 'niriss'
params = {'test_steps':
[dict(input='ami_analyze_input_16.fits',
test_dir='test_ami_analyze',
step_class=AmiAnalyzeStep,
step_pars=dict(oversample=3, rotation=1.49),
output_truth=('ami_analyze_ref_output_16.fits',
dict(rtol = 0.00001)),
output_hdus=['primary','fit','resid','closure_amp',
'closure_pha','fringe_amp','fringe_pha',
'pupil_pha','solns'],
id='ami_analyze_niriss'
),
dict(input='jw00034001001_01101_00001_NIRISS_dq_init.fits',
test_dir='test_bias_drift',
step_class=RefPixStep,
step_pars=dict(odd_even_columns=True,
use_side_ref_pixels=False,
side_smoothing_length=10,
side_gain=1.0),
output_truth='jw00034001001_01101_00001_NIRISS_bias_drift.fits',
output_hdus=[],
id='refpix_niriss'
),
dict(input='jw00034001001_01101_00001_NIRISS_saturation.fits',
test_dir='test_dark_step',
step_class=DarkCurrentStep,
step_pars=dict(),
output_truth='jw00034001001_01101_00001_NIRISS_dark_current.fits',
output_hdus=[],
id='dark_current_niriss'
),
dict(input='jw00034001001_01101_00001_NIRISS_uncal.fits',
test_dir='test_dq_init',
step_class=DQInitStep,
step_pars=dict(),
output_truth='jw00034001001_01101_00001_NIRISS_dq_init.fits',
output_hdus=[],
id='dq_init_niriss'
),
dict(input='jw00034001001_01101_00001_NIRISS_ramp_fit.fits',
test_dir='test_flat_field',
step_class=FlatFieldStep,
step_pars=dict(),
output_truth='jw00034001001_01101_00001_NIRISS_flat_field.fits',
output_hdus=[],
id='flat_field_niriss'
),
dict(input='jw00034001001_01101_00001_NIRISS_linearity.fits',
test_dir='test_jump',
step_class=JumpStep,
step_pars=dict(rejection_threshold=20.0),
output_truth='jw00034001001_01101_00001_NIRISS_jump.fits',
output_hdus=[],
id='jump_niriss'
),
dict(input='jw00034001001_01101_00001_NIRISS_dark_current.fits',
test_dir='test_linearity',
step_class=LinearityStep,
step_pars=dict(),
output_truth='jw00034001001_01101_00001_NIRISS_linearity.fits',
output_hdus=[],
id='linearity_niriss'
),
dict(input='jw00034001001_01101_00001_NIRISS_bias_drift.fits',
test_dir='test_saturation',
step_class=SaturationStep,
step_pars=dict(),
output_truth='jw00034001001_01101_00001_NIRISS_saturation.fits',
output_hdus=[],
id='saturation_niriss'
),
dict(input='soss_2AB_results_int_assign_wcs.fits',
test_dir='test_pathloss',
step_class=PathLossStep,
step_pars=dict(),
output_truth='soss_2AB_results_int_pathloss.fits',
output_hdus=[],
id='pathloss_niriss'
),
]
}
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,482
|
mperrin/jwst
|
refs/heads/master
|
/jwst/saturation/saturation.py
|
#
# Module for 2d saturation
#
import logging
from ..datamodels import dqflags
from ..lib import reffile_utils
from ..lib import pipe_utils
from . import x_irs2
import numpy as np
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
HUGE_NUM = 100000.
def do_correction(input_model, ref_model):
"""
Short Summary
-------------
Execute all tasks for saturation, including using a saturation reference
file.
Parameters
----------
input_model: data model object
The input science data to be corrected
ref_model: data model object
Saturation reference file mode object
Returns
-------
output_model: data model object
object having GROUPDQ array saturation flags set
"""
ramparr = input_model.data
# Was IRS2 readout used?
is_irs2_format = pipe_utils.is_irs2(input_model)
if is_irs2_format:
irs2_mask = x_irs2.make_mask(input_model)
# Create the output model as a copy of the input
output_model = input_model.copy()
groupdq = output_model.groupdq
# Extract subarray from reference file, if necessary
if reffile_utils.ref_matches_sci(input_model, ref_model):
satmask = ref_model.data
dqmask = ref_model.dq
else:
log.info('Extracting reference file subarray to match science data')
ref_sub_model = reffile_utils.get_subarray_model(input_model, ref_model)
satmask = ref_sub_model.data.copy()
dqmask = ref_sub_model.dq.copy()
ref_sub_model.close()
# For pixels flagged in reference file as NO_SAT_CHECK, set the dq mask
# and saturation mask
wh_sat = np.bitwise_and(dqmask, dqflags.pixel['NO_SAT_CHECK'])
dqmask[wh_sat == dqflags.pixel['NO_SAT_CHECK']] = dqflags.pixel['NO_SAT_CHECK']
satmask[wh_sat == dqflags.pixel['NO_SAT_CHECK']] = HUGE_NUM
# Correct saturation values for NaNs in the ref file
correct_for_NaN(satmask, dqmask)
dq_flag = dqflags.group['SATURATED']
nints = ramparr.shape[0]
ngroups = ramparr.shape[1]
detector = input_model.meta.instrument.detector
flagarray = np.zeros(ramparr.shape[-2:], dtype=groupdq.dtype)
for ints in range(nints):
for plane in range(ngroups):
# Update the 4D groupdq array with the saturation flag. The
# flag is set in the current plane and all following planes.
if is_irs2_format:
sci_temp = x_irs2.from_irs2(ramparr[ints, plane, :, :],
irs2_mask, detector)
flag_temp = np.where(sci_temp >= satmask, dq_flag, 0)
# Copy flag_temp into flagarray.
x_irs2.to_irs2(flagarray, flag_temp, irs2_mask, detector)
else:
flagarray[:, :] = np.where(ramparr[ints, plane, :, :] >= satmask,
dq_flag, 0)
np.bitwise_or(groupdq[ints, plane:, :, :], flagarray,
groupdq[ints, plane:, :, :])
output_model.groupdq = groupdq
if is_irs2_format:
pixeldq_temp = x_irs2.from_irs2(output_model.pixeldq, irs2_mask,
detector)
pixeldq_temp = np.bitwise_or(pixeldq_temp, dqmask)
x_irs2.to_irs2(output_model.pixeldq, pixeldq_temp, irs2_mask, detector)
else:
output_model.pixeldq = np.bitwise_or(output_model.pixeldq, dqmask)
return output_model
def correct_for_NaN(satmask, dqmask):
"""
Short Summary
-------------
If there are NaNs in the saturation values in the reference file, reset
them to a very high value such that the comparison never results in a
positive (saturated) result for the associated pixels in the science data.
Also reset the associated dqmask values to indicate that, effectively,
no saturation check will be done for those pixels.
Parameters
----------
satmask: 2-d array
Subarray of saturation thresholds, from the saturation reference
file. This may be modified in-place.
dqmask: ndarray, same shape as `satmask`
The DQ array from the saturation reference file, used to update
the PIXELDQ array in the output. This may be modified in-place.
"""
# If there are NaNs as the saturation values, update those values
# to ensure there will not be saturation.
wh_nan = np.isnan(satmask)
if np.any(wh_nan):
satmask[wh_nan] = HUGE_NUM
dqmask[wh_nan] |= dqflags.pixel['NO_SAT_CHECK']
log.info("Unflagged pixels having saturation values set to NaN were"
" detected in the ref file; for those affected pixels no"
" saturation check will be made.")
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,483
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/associations/conftest.py
|
"""Pytest configurations"""
import pytest
from jwst.tests_nightly.general.associations.sdp_pools_source import SDPPoolsSource
# Add option to specify a single pool name
def pytest_addoption(parser):
parser.addoption(
'--sdp-pool', metavar='sdp_pool', default=None,
help='SDP test pool to run. Specify the name only, not extension or path'
)
parser.addoption(
'--standard-pool', metavar='standard_pool', default=None,
help='Standard test pool to run. Specify the name only, not extension or path'
)
@pytest.fixture
def sdp_pool(request):
"""Retrieve a specific SDP pool to test"""
return request.config.getoption('--sdp-pool')
@pytest.fixture
def standard_pool(request):
"""Retrieve a specific standard pool to test"""
return request.config.getoption('--standard-pool')
def pytest_generate_tests(metafunc):
"""Prefetch and parametrize a set of test pools"""
if 'pool_path' in metafunc.fixturenames:
SDPPoolsSource.inputs_root = metafunc.config.getini('inputs_root')[0]
SDPPoolsSource.results_root = metafunc.config.getini('results_root')[0]
SDPPoolsSource.env = metafunc.config.getoption('env')
pools = SDPPoolsSource()
try:
pool_paths = pools.pool_paths
except Exception:
pool_paths = []
metafunc.parametrize('pool_path', pool_paths)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,484
|
mperrin/jwst
|
refs/heads/master
|
/jwst/master_background/nirspec_corrections.py
|
import logging
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
def correct_nrs_ifu_bkg(input):
"""Apply point source vs. uniform source pathloss adjustments
to a NIRSpec IFU 2D master background array.
Parameters
----------
input : `~jwst.datamodels.IFUImageModel`
The input background data.
Returns
-------
input : `~jwst.datamodels.IFUIMAGEModel`
An updated (in place) version of the input with the data
replaced by the corrected 2D background.
"""
log.info('Applying point source pathloss updates to IFU background')
# Try to load the appropriate pathloss correction arrays
try:
pl_point = input.getarray_noinit('pathloss_point')
except AttributeError:
log.warning('Pathloss_point array not found in input')
log.warning('Skipping pathloss background updates')
return input
try:
pl_uniform = input.getarray_noinit('pathloss_uniform')
except AttributeError:
log.warning('Pathloss_uniform array not found in input')
log.warning('Skipping pathloss background updates')
return input
# Apply the corrections
input.data *= (pl_point / pl_uniform)
return input
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,485
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nircam/test_tso3.py
|
import pytest
from jwst.pipeline import Tso3Pipeline
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
@pytest.mark.bigdata
class TestTso3Pipeline(BaseJWSTTest):
input_loc = 'nircam'
ref_loc = ['test_caltso3', 'truth']
test_dir = 'test_caltso3'
def test_tso3_pipeline_nrc1(self):
"""Regression test of calwebb_tso3 pipeline on NIRCam simulated data.
Default imaging mode outlier_detection will be tested here.
"""
asn_file = self.get_data(self.test_dir,
"jw93065-a3001_20170511t111213_tso3_001_asn.json")
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
step = Tso3Pipeline()
step.scale_detection = False
step.outlier_detection.weight_type = 'exptime'
step.outlier_detection.pixfrac = 1.0
step.outlier_detection.kernel = 'square'
step.outlier_detection.fillval = 'INDEF'
step.outlier_detection.nlow = 0
step.outlier_detection.nhigh = 0
step.outlier_detection.maskpt = 0.7
step.outlier_detection.grow = 1
step.outlier_detection.snr = '4.0 3.0'
step.outlier_detection.scale = '0.5 0.4'
step.outlier_detection.backg = 0.0
step.outlier_detection.save_intermediate_results = False
step.outlier_detection.resample_data = False
step.outlier_detection.good_bits = 4
step.extract_1d.smoothing_length = 0
step.extract_1d.bkg_order = 0
step.run(asn_file)
outputs = [
# Compare level-2c product
('jw93065002001_02101_00001_nrca1_a3001_crfints.fits',
'jw93065002001_02101_00001_nrca1_a3001_crfints_ref.fits',
['primary', 'sci', 'dq', 'err']),
# Compare level-3 product
('jw93065-a3001_t1_nircam_f150w-wlp8_phot.ecsv',
'jw93065-a3001_t1_nircam_f150w-wlp8_phot_ref.ecsv'),
]
self.compare_outputs(outputs)
def test_tso3_pipeline_nrc2(self):
"""Regression test of calwebb_tso3 pipeline on NIRCam simulated data.
Scaled imaging mode outlier_detection will be tested here.
"""
asn_file = self.get_data(self.test_dir,
"jw93065-a3002_20170511t111213_tso3_001_asn.json")
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
step = Tso3Pipeline()
step.scale_detection = True
step.outlier_detection.weight_type = 'exptime'
step.outlier_detection.pixfrac = 1.0
step.outlier_detection.kernel = 'square'
step.outlier_detection.fillval = 'INDEF'
step.outlier_detection.nlow = 0
step.outlier_detection.nhigh = 0
step.outlier_detection.maskpt = 0.7
step.outlier_detection.grow = 1
step.outlier_detection.snr = '4.0 3.0'
step.outlier_detection.scale = '0.5 0.4'
step.outlier_detection.backg = 0.0
step.outlier_detection.save_intermediate_results = False
step.outlier_detection.resample_data = False
step.outlier_detection.good_bits = 4
step.extract_1d.smoothing_length = 0
step.extract_1d.bkg_order = 0
step.run(asn_file)
outputs = [
# Compare level-2c product
('jw93065002002_02101_00001_nrca1_a3002_crfints.fits',
'jw93065002002_02101_00001_nrca1_a3002_crfints_ref.fits',
['primary', 'sci', 'dq', 'err']),
# Compare level-3 product
('jw93065-a3002_t1_nircam_f150w-wlp8_phot.ecsv',
'jw93065-a3002_t1_nircam_f150w-wlp8_phot_ref.ecsv'),
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,486
|
mperrin/jwst
|
refs/heads/master
|
/jwst/coron/median_replace_img.py
|
"""Replace bad pixels with the median of the surrounding pixel and median fill
the input images.
"""
import logging
import numpy as np
from jwst.datamodels import dqflags
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
def median_fill_value(input_array, input_dq_array, bsize, xc, yc):
"""
Arguments:
----------
input_array : ndarray
Input array to filter.
input_dq_array : ndarray
Input data quality array
bsize : scalar
box size of the data to extract
xc: scalar
x position of the data extraction
xc: scalar
y position of the data extraction
"""
# set the half box size
hbox = int(bsize/2)
# Extract the region of interest for the data
try:
data_array = input_array[xc - hbox:xc + hbox, yc - hbox: yc + hbox]
dq_array = input_dq_array[xc - hbox:xc + hbox, yc - hbox: yc + hbox]
except IndexError:
# If the box is outside the data return 0
log.warning('Box for median filter is outside the data.')
return 0.
filtered_array = data_array[dq_array != dqflags.pixel['DO_NOT_USE']]
median_value = np.median(filtered_array)
if np.isnan(median_value):
# If the median fails return 0
log.warning('Median filter returned NaN setting value to 0.')
median_value = 0.
return median_value
def median_replace_img(img_model, box_size):
""" Routine to replace any bad pixels with the median value of the surrounding
pixels.
Arguments:
----------
input_array : image model
Input array to filter.
box_size : scalar
box size for the median filter
"""
n_ints, _, _ = img_model.data.shape
for nimage in range(n_ints):
img_int = img_model.data[nimage]
img_dq = img_model.dq[nimage]
# check to see if any of the pixels are flagged
if np.count_nonzero(img_dq == dqflags.pixel['DO_NOT_USE']) > 0:
bad_locations = np.where(np.equal(img_dq, dqflags.pixel['DO_NOT_USE']))
# fill the bad pixel values with the median of the data in a box region
for i_pos in range(len(bad_locations[0])):
x_box_pos = bad_locations[0][i_pos]
y_box_pos = bad_locations[1][i_pos]
median_fill = median_fill_value(img_int, img_dq, box_size, x_box_pos, y_box_pos)
img_int[x_box_pos, y_box_pos] = median_fill
img_model.data[nimage] = img_int
return img_model
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,487
|
mperrin/jwst
|
refs/heads/master
|
/jwst/regtest/test_nirspec_masterbackground.py
|
import pytest
from astropy.io.fits.diff import FITSDiff
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
pytestmark = pytest.mark.bigdata
def test_nirspec_fs_mbkg_user(rtdata, fitsdiff_default_kwargs):
"""Run a test for NIRSpec FS data with a user-supplied background file."""
# Get user-supplied background
user_background = "v2_nrs_bkg_user_clean_x1d.fits"
rtdata.get_data(f"nirspec/fs/{user_background}")
# Get input data
rtdata.get_data("nirspec/fs/nrs_sci+bkg_cal.fits")
collect_pipeline_cfgs("config")
args = ["config/master_background.cfg", rtdata.input,
"--user_background", user_background]
Step.from_cmdline(args)
output = "nrs_sci+bkg_master_background.fits"
rtdata.output = output
# Get the truth file
rtdata.get_truth(f"truth/test_nirspec_fs_mbkg_user/{output}")
# Compare the results
diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs)
assert diff.identical, diff.report()
def test_nirspec_ifu_mbkg_user(rtdata, fitsdiff_default_kwargs):
"""Test NIRSpec IFU data with a user-supplied background file."""
# Get user-supplied background
user_background = "prism_bkg_x1d.fits"
rtdata.get_data(f"nirspec/ifu/{user_background}")
# Get input data
rtdata.get_data("nirspec/ifu/prism_sci_bkg_cal.fits")
collect_pipeline_cfgs("config")
args = ["config/master_background.cfg", rtdata.input,
"--user_background", user_background]
Step.from_cmdline(args)
output = "prism_sci_bkg_master_background.fits"
rtdata.output = output
# Get the truth file
rtdata.get_truth(f"truth/test_nirspec_ifu_mbkg_user/{output}")
# Compare the results
diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs)
assert diff.identical, diff.report()
def test_nirspec_mos_mbkg_user(rtdata, fitsdiff_default_kwargs):
"""Test NIRSpec MOS data with a user-supplied background file."""
# Get user-supplied background
user_background = "v2_nrs_mos_bkg_x1d.fits"
rtdata.get_data(f"nirspec/mos/{user_background}")
# Get input data
rtdata.get_data("nirspec/mos/nrs_mos_sci+bkg_cal.fits")
collect_pipeline_cfgs("config")
args = ["config/master_background.cfg", rtdata.input,
"--user_background", user_background]
Step.from_cmdline(args)
output = "nrs_mos_sci+bkg_master_background.fits"
rtdata.output = output
# Get the truth file
rtdata.get_truth(f"truth/test_nirspec_mos_mbkg_user/{output}")
# Compare the results
diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs)
assert diff.identical, diff.report()
@pytest.mark.parametrize(
'output_file',
['ifu_prism_source_on_NRS1_master_background.fits',
'ifu_prism_source_off_NRS1_o001_masterbg.fits'],
ids=["on-source", "off-source"]
)
def test_nirspec_ifu_mbkg_nod(rtdata, fitsdiff_default_kwargs, output_file):
"""Test NIRSpec IFU prism nodded data."""
# Get input data
rtdata.get_asn("nirspec/ifu/nirspec_spec3_asn.json")
collect_pipeline_cfgs("config")
args = ["config/master_background.cfg", rtdata.input,
"--save_background=True"]
Step.from_cmdline(args)
rtdata.output = output_file
# Get the truth file
rtdata.get_truth(f"truth/test_nirspec_ifu_mbkg_nod/{output_file}")
# Compare the results
diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs)
assert diff.identical, diff.report()
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,488
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/properties.py
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import numpy as np
from collections.abc import Mapping
from astropy.io import fits
from astropy.utils.compat.misc import override__dir__
from asdf import yamlutil
from asdf.tags.core import ndarray
from . import util
from . import validate
from . import schema as mschema
import logging
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
log.addHandler(logging.NullHandler())
__all__ = ['ObjectNode', 'ListNode']
def _is_struct_array(val):
return (isinstance(val, (np.ndarray, fits.FITS_rec)) and
val.dtype.names is not None and val.dtype.fields is not None)
def _is_struct_array_precursor(val):
return isinstance(val, list) and isinstance(val[0], tuple)
def _is_struct_array_schema(schema):
return (isinstance(schema['datatype'], list) and
any('name' in t for t in schema['datatype']))
def _cast(val, schema):
val = _unmake_node(val)
if val is None:
return None
if 'datatype' in schema:
# Handle lazy array
if isinstance(val, ndarray.NDArrayType):
val = val._make_array()
if (_is_struct_array_schema(schema) and len(val) and
(_is_struct_array_precursor(val) or _is_struct_array(val))):
# we are dealing with a structured array. Because we may
# modify schema (to add shape), we make a deep copy of the
# schema here:
schema = copy.deepcopy(schema)
for t, v in zip(schema['datatype'], val[0]):
if not isinstance(t, Mapping):
continue
aval = np.asanyarray(v)
shape = aval.shape
val_ndim = len(shape)
# make sure that if 'ndim' is specified for a field,
# it matches the dimensionality of val's field:
if 'ndim' in t and val_ndim != t['ndim']:
raise ValueError(
"Array has wrong number of dimensions. "
"Expected {}, got {}".format(t['ndim'], val_ndim)
)
if 'max_ndim' in t and val_ndim > t['max_ndim']:
raise ValueError(
"Array has wrong number of dimensions. "
"Expected <= {}, got {}".format(t['max_ndim'], val_ndim)
)
# if shape of a field's value is not specified in the schema,
# add it to the schema based on the shape of the actual data:
if 'shape' not in t:
t['shape'] = shape
dtype = ndarray.asdf_datatype_to_numpy_dtype(schema['datatype'])
val = util.gentle_asarray(val, dtype)
if dtype.fields is not None:
val = _as_fitsrec(val)
if 'ndim' in schema and len(val.shape) != schema['ndim']:
raise ValueError(
"Array has wrong number of dimensions. Expected {}, got {}"
.format(schema['ndim'], len(val.shape)))
if 'max_ndim' in schema and len(val.shape) > schema['max_ndim']:
raise ValueError(
"Array has wrong number of dimensions. Expected <= {}, got {}"
.format(schema['max_ndim'], len(val.shape)))
if isinstance(val, np.generic) and np.isscalar(val):
val = val.item()
return val
def _as_fitsrec(val):
"""
Convert a numpy record into a fits record if it is not one already
"""
if isinstance(val, fits.FITS_rec):
return val
else:
coldefs = fits.ColDefs(val)
uint = any(c._pseudo_unsigned_ints for c in coldefs)
fits_rec = fits.FITS_rec(val)
fits_rec._coldefs = coldefs
# FITS_rec needs to know if it should be operating in pseudo-unsigned-ints mode,
# otherwise it won't properly convert integer columns with TZEROn before saving.
fits_rec._uint = uint
return fits_rec
def _get_schema_type(schema):
"""
Create a list of types used by a schema and its subschemas when
the subschemas are joined by combiners. Then return a type string
if all the types are the same or 'mixed' if they differ
"""
def callback(subschema, path, combiner, types, recurse):
if 'type' in subschema:
types.append(subschema['type'])
has_combiner = ('anyOf' in subschema.keys() or
'allOf' in subschema.keys())
return not has_combiner
types = []
mschema.walk_schema(schema, callback, types)
schema_type = None
for a_type in types:
if schema_type is None:
schema_type = a_type
elif schema_type != a_type:
schema_type = 'mixed'
break
return schema_type
def _make_default_array(attr, schema, ctx):
dtype = schema.get('datatype')
if dtype is not None:
dtype = ndarray.asdf_datatype_to_numpy_dtype(dtype)
ndim = schema.get('ndim', schema.get('max_ndim'))
default = schema.get('default', None)
primary_array_name = ctx.get_primary_array_name()
if attr == primary_array_name:
if ctx.shape is not None:
shape = ctx.shape
elif ndim is not None:
shape = tuple([0] * ndim)
else:
shape = (0,)
else:
if dtype.names is not None:
if ndim is None:
shape = (0,)
else:
shape = tuple([0] * ndim)
default = None
else:
has_primary_array_shape = False
if primary_array_name is not None:
primary_array = getattr(ctx, primary_array_name, None)
has_primary_array_shape = primary_array is not None
if has_primary_array_shape:
if ndim is None:
shape = primary_array.shape
else:
shape = primary_array.shape[-ndim:]
elif ndim is None:
shape = (0,)
else:
shape = tuple([0] * ndim)
array = np.empty(shape, dtype=dtype)
if default is not None:
array[...] = default
return array
def _make_default(attr, schema, ctx):
if 'max_ndim' in schema or 'ndim' in schema or 'datatype' in schema:
return _make_default_array(attr, schema, ctx)
elif 'default' in schema:
return schema['default']
else:
schema_type = _get_schema_type(schema)
if schema_type == 'object':
return {}
elif schema_type == 'array':
return []
else:
return None
def _make_node(attr, instance, schema, ctx):
if isinstance(instance, dict):
return ObjectNode(attr, instance, schema, ctx)
elif isinstance(instance, list):
return ListNode(attr, instance, schema, ctx)
else:
return instance
def _unmake_node(obj):
if isinstance(obj, Node):
return obj.instance
return obj
def _get_schema_for_property(schema, attr):
subschema = schema.get('properties', {}).get(attr, None)
if subschema is not None:
return subschema
for combiner in ['allOf', 'anyOf']:
for subschema in schema.get(combiner, []):
subsubschema = _get_schema_for_property(subschema, attr)
if subsubschema != {}:
return subsubschema
return {}
def _get_schema_for_index(schema, i):
items = schema.get('items', {})
if isinstance(items, list):
if i >= len(items):
return {}
else:
return items[i]
else:
return items
def _find_property(schema, attr):
subschema = _get_schema_for_property(schema, attr)
if subschema == {}:
find = False
else:
find = 'default' in subschema
return find
class Node():
def __init__(self, attr, instance, schema, ctx):
self._name = attr
self._instance = instance
self._schema = schema
self._ctx = ctx
def _validate(self):
instance = yamlutil.custom_tree_to_tagged_tree(self._instance,
self._ctx._asdf)
return validate.value_change(self._name, instance, self._schema,
False, self._ctx._strict_validation)
@property
def instance(self):
return self._instance
class ObjectNode(Node):
@override__dir__
def __dir__(self):
return list(self._schema.get('properties', {}).keys())
def __eq__(self, other):
if isinstance(other, ObjectNode):
return self._instance == other._instance
else:
return self._instance == other
def __getattr__(self, attr):
from . import ndmodel
if attr.startswith('_'):
raise AttributeError('No attribute {0}'.format(attr))
schema = _get_schema_for_property(self._schema, attr)
try:
val = self._instance[attr]
except KeyError:
if schema == {}:
raise AttributeError("No attribute '{0}'".format(attr))
val = _make_default(attr, schema, self._ctx)
if val is not None:
self._instance[attr] = val
if isinstance(val, dict):
# Meta is special cased to support NDData interface
if attr == 'meta':
node = ndmodel.MetaNode(attr, val, schema, self._ctx)
else:
node = ObjectNode(attr, val, schema, self._ctx)
elif isinstance(val, list):
node = ListNode(attr, val, schema, self._ctx)
else:
node = val
return node
def __setattr__(self, attr, val):
if attr.startswith('_'):
self.__dict__[attr] = val
else:
schema = _get_schema_for_property(self._schema, attr)
if val is None:
val = _make_default(attr, schema, self._ctx)
val = _cast(val, schema)
node = ObjectNode(attr, val, schema, self._ctx)
if node._validate():
self._instance[attr] = val
def __delattr__(self, attr):
if attr.startswith('_'):
del self.__dict__[attr]
else:
schema = _get_schema_for_property(self._schema, attr)
if not validate.value_change(attr, None, schema, False,
self._ctx._strict_validation):
return
try:
del self._instance[attr]
except KeyError:
raise AttributeError(
"Attribute '{0}' missing".format(attr))
def __iter__(self):
return NodeIterator(self)
def hasattr(self, attr):
return attr in self._instance
def items(self):
# Return a (key, value) tuple for the node
for key in self:
val = self
for field in key.split('.'):
val = getattr(val, field)
yield (key, val)
class ListNode(Node):
def __cast(self, other):
if isinstance(other, ListNode):
return other._instance
return other
def __repr__(self):
return repr(self._instance)
def __eq__(self, other):
return self._instance == self.__cast(other)
def __ne__(self, other):
return self._instance != self.__cast(other)
def __contains__(self, item):
return item in self._instance
def __len__(self):
return len(self._instance)
def __getitem__(self, i):
schema = _get_schema_for_index(self._schema, i)
return _make_node(self._name, self._instance[i], schema, self._ctx)
def __setitem__(self, i, val):
schema = _get_schema_for_index(self._schema, i)
val = _cast(val, schema)
node = ObjectNode(self._name, val, schema, self._ctx)
if node._validate():
self._instance[i] = val
def __delitem__(self, i):
del self._instance[i]
self._validate()
def __getslice__(self, i, j):
if isinstance(self._schema['items'], list):
r = range(*(slice(i, j).indices(len(self._instance))))
schema_parts = [
_get_schema_for_index(self._schema, x) for x in r
]
else:
schema_parts = self._schema['items']
schema = {'type': 'array', 'items': schema_parts}
return _make_node(self._name, self._instance[i:j], schema, self._ctx)
def __setslice__(self, i, j, other):
parts = _unmake_node(other)
parts = [_cast(x, _get_schema_for_index(self._schema, k))
for (k, x) in enumerate(parts)]
self._instance[i:j] = _unmake_node(other)
self._validate()
def __delslice__(self, i, j):
del self._instance[i:j]
self._validate()
def append(self, item):
schema = _get_schema_for_index(self._schema, len(self._instance))
item = _cast(item, schema)
node = ObjectNode(self._name, item, schema, self._ctx)
if node._validate():
self._instance.append(item)
def insert(self, i, item):
schema = _get_schema_for_index(self._schema, i)
item = _cast(item, schema)
node = ObjectNode(self._name, item, schema, self._ctx)
if node._validate():
self._instance.insert(i, item)
def pop(self, i=-1):
schema = _get_schema_for_index(self._schema, 0)
x = self._instance.pop(i)
return _make_node(self._name, x, schema, self._ctx)
def remove(self, item):
self._instance.remove(item)
def count(self, item):
return self._instance.count(item)
def index(self, item):
return self._instance.index(item)
def reverse(self):
self._instance.reverse()
def sort(self, *args, **kwargs):
self._instance.sort(*args, **kwargs)
def extend(self, other):
for part in _unmake_node(other):
self.append(part)
def item(self, **kwargs):
assert isinstance(self._schema['items'], dict)
node = ObjectNode(self._name, kwargs, self._schema['items'],
self._ctx)
if not node._validate():
node = None
return node
class NodeIterator:
"""
An iterator for a node which flattens the hierachical structure
"""
def __init__(self, node):
self.key_stack = []
self.iter_stack = [iter(node._instance.items())]
def __iter__(self):
return self
def __next__(self):
while self.iter_stack:
try:
key, val = next(self.iter_stack[-1])
except StopIteration:
self.iter_stack.pop()
if self.iter_stack:
self.key_stack.pop()
continue
if isinstance(val, dict):
self.key_stack.append(key)
self.iter_stack.append(iter(val.items()))
else:
return '.'.join(self.key_stack + [key])
raise StopIteration
def put_value(path, value, tree):
"""
Put a value at the given path into tree, replacing it if it is
already present.
Parameters
----------
path : list of str or int
The path to the element.
value : any
The value to place
tree : JSON object tree
"""
cursor = tree
for i in range(len(path) - 1):
part = path[i]
if isinstance(part, int):
while len(cursor) <= part:
cursor.append({})
cursor = cursor[part]
else:
if isinstance(path[i + 1], int) or path[i + 1] == 'items':
cursor = cursor.setdefault(part, [])
else:
cursor = cursor.setdefault(part, {})
if isinstance(path[-1], int):
while len(cursor) <= path[-1]:
cursor.append({})
cursor[path[-1]] = value
def merge_tree(a, b):
"""
Merge elements from tree `b` into tree `a`.
"""
def recurse(a, b):
if isinstance(b, dict):
if not isinstance(a, dict):
return copy.deepcopy(b)
for key, val in b.items():
a[key] = recurse(a.get(key), val)
return a
return copy.deepcopy(b)
recurse(a, b)
return a
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,489
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/util.py
|
"""
Various utility functions and data types
"""
import sys
import warnings
import os
from os.path import basename
import numpy as np
from astropy.io import fits
from ..lib import s3_utils
import logging
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
log.addHandler(logging.NullHandler())
class NoTypeWarning(Warning):
pass
def open(init=None, memmap=False, **kwargs):
"""
Creates a DataModel from a number of different types
Parameters
----------
init : shape tuple, file path, file object, astropy.io.fits.HDUList,
numpy array, dict, None
- None: A default data model with no shape
- shape tuple: Initialize with empty data of the given shape
- file path: Initialize from the given file (FITS , JSON or ASDF)
- readable file object: Initialize from the given file object
- astropy.io.fits.HDUList: Initialize from the given
`~astropy.io.fits.HDUList`
- A numpy array: A new model with the data array initialized
to what was passed in.
- dict: The object model tree for the data model
memmap : bool
Turn memmap of FITS file on or off. (default: False). Ignored for
ASDF files.
kwargs : dict
Additional keyword arguments passed to lower level functions. These arguments
are generally file format-specific. Arguments of note are:
- FITS
skip_fits_update - bool or None
`True` to skip updating the ASDF tree from the FITS headers, if possible.
If `None`, value will be taken from the environmental SKIP_FITS_UPDATE.
Otherwise, the default value is `True`.
Returns
-------
model : DataModel instance
"""
from . import model_base
from . import filetype
# Initialize variables used to select model class
hdulist = {}
shape = ()
file_name = None
file_to_close = None
# Get special cases for opening a model out of the way
# all special cases return a model if they match
if init is None:
return model_base.DataModel(None)
elif isinstance(init, model_base.DataModel):
# Copy the object so it knows not to close here
return init.__class__(init)
elif isinstance(init, (str, bytes)) or hasattr(init, "read"):
# If given a string, presume its a file path.
# if it has a read method, assume a file descriptor
if isinstance(init, bytes):
init = init.decode(sys.getfilesystemencoding())
file_name = basename(init)
file_type = filetype.check(init)
if file_type == "fits":
if s3_utils.is_s3_uri(init):
hdulist = fits.open(s3_utils.get_object(init))
else:
hdulist = fits.open(init, memmap=memmap)
file_to_close = hdulist
elif file_type == "asn":
# Read the file as an association / model container
from . import container
return container.ModelContainer(init, **kwargs)
elif file_type == "asdf":
# Read the file as asdf, no need for a special class
return model_base.DataModel(init, **kwargs)
elif isinstance(init, tuple):
for item in init:
if not isinstance(item, int):
raise ValueError("shape must be a tuple of ints")
shape = init
elif isinstance(init, np.ndarray):
shape = init.shape
elif isinstance(init, fits.HDUList):
hdulist = init
elif is_association(init) or isinstance(init, list):
from . import container
return container.ModelContainer(init, **kwargs)
# If we have it, determine the shape from the science hdu
if hdulist:
# So we don't need to open the image twice
init = hdulist
info = init.fileinfo(0)
if info is not None:
file_name = info.get('filename')
try:
hdu = hdulist[('SCI', 1)]
except (KeyError, NameError):
shape = ()
else:
if hasattr(hdu, 'shape'):
shape = hdu.shape
else:
shape = ()
# First try to get the class name from the primary header
new_class = _class_from_model_type(hdulist)
has_model_type = new_class is not None
# Special handling for ramp files for backwards compatibility
if new_class is None:
new_class = _class_from_ramp_type(hdulist, shape)
# Or get the class from the reference file type and other header keywords
if new_class is None:
new_class = _class_from_reftype(hdulist, shape)
# Or Get the class from the shape
if new_class is None:
new_class = _class_from_shape(hdulist, shape)
# Throw an error if these attempts were unsuccessful
if new_class is None:
raise TypeError("Can't determine datamodel class from argument to open")
# Log a message about how the model was opened
if file_name:
log.debug(f'Opening {file_name} as {new_class}')
else:
log.debug(f'Opening as {new_class}')
# Actually open the model
model = new_class(init, **kwargs)
# Close the hdulist if we opened it
if file_to_close is not None:
model._files_to_close.append(file_to_close)
if not has_model_type:
class_name = new_class.__name__.split('.')[-1]
if file_name:
warnings.warn(f"model_type not found. Opening {file_name} as a {class_name}",
NoTypeWarning)
try:
delattr(model.meta, 'model_type')
except AttributeError:
pass
return model
def _class_from_model_type(hdulist):
"""
Get the model type from the primary header, lookup to get class
"""
from . import _defined_models as defined_models
if hdulist:
primary = hdulist[0]
model_type = primary.header.get('DATAMODL')
if model_type is None:
new_class = None
else:
new_class = defined_models.get(model_type)
else:
new_class = None
return new_class
def _class_from_ramp_type(hdulist, shape):
"""
Special check to see if file is ramp file
"""
if not hdulist:
new_class = None
else:
if len(shape) == 4:
try:
hdulist['DQ']
except KeyError:
from . import ramp
new_class = ramp.RampModel
else:
new_class = None
else:
new_class = None
return new_class
def _class_from_reftype(hdulist, shape):
"""
Get the class name from the reftype and other header keywords
"""
if not hdulist:
new_class = None
else:
primary = hdulist[0]
reftype = primary.header.get('REFTYPE')
if reftype is None:
new_class = None
else:
from . import reference
if len(shape) == 0:
new_class = reference.ReferenceFileModel
elif len(shape) == 2:
new_class = reference.ReferenceImageModel
elif len(shape) == 3:
new_class = reference.ReferenceCubeModel
elif len(shape) == 4:
new_class = reference.ReferenceQuadModel
else:
new_class = None
return new_class
def _class_from_shape(hdulist, shape):
"""
Get the class name from the shape
"""
if len(shape) == 0:
from . import model_base
new_class = model_base.DataModel
elif len(shape) == 4:
from . import quad
new_class = quad.QuadModel
elif len(shape) == 3:
from . import cube
new_class = cube.CubeModel
elif len(shape) == 2:
try:
hdulist[('SCI', 2)]
except (KeyError, NameError):
# It's an ImageModel
from . import image
new_class = image.ImageModel
else:
# It's a MultiSlitModel
from . import multislit
new_class = multislit.MultiSlitModel
else:
new_class = None
return new_class
def can_broadcast(a, b):
"""
Given two shapes, returns True if they are broadcastable.
"""
for i in range(1, min(len(a), len(b)) + 1):
adim = a[-i]
bdim = b[-i]
if not (adim == 1 or bdim == 1 or adim == bdim):
return False
return True
def to_camelcase(token):
return ''.join(x.capitalize() for x in token.split('_-'))
def is_association(asn_data):
"""
Test if an object is an association by checking for required fields
"""
if isinstance(asn_data, dict):
if 'asn_id' in asn_data and 'asn_pool' in asn_data:
return True
return False
def gentle_asarray(a, dtype):
"""
Performs an asarray that doesn't cause a copy if the byteorder is
different. It also ignores column name differences -- the
resulting array will have the column names from the given dtype.
"""
out_dtype = np.dtype(dtype)
if isinstance(a, np.ndarray):
in_dtype = a.dtype
# Non-table array
if in_dtype.fields is None and out_dtype.fields is None:
if np.can_cast(in_dtype, out_dtype, 'equiv'):
return a
else:
return np.asanyarray(a, dtype=out_dtype)
elif in_dtype.fields is not None and out_dtype.fields is not None:
# When a FITS file includes a pseudo-unsigned-int column, astropy will return
# a FITS_rec with an incorrect table dtype. The following code rebuilds
# in_dtype from the individual fields, which are correctly labeled with an
# unsigned int dtype.
# We can remove this once the issue is resolved in astropy:
# https://github.com/astropy/astropy/issues/8862
if isinstance(a, fits.fitsrec.FITS_rec):
new_in_dtype = []
updated = False
for field_name in in_dtype.fields:
table_dtype = in_dtype[field_name]
field_dtype = a.field(field_name).dtype
if np.issubdtype(table_dtype, np.signedinteger) and np.issubdtype(field_dtype, np.unsignedinteger):
new_in_dtype.append((field_name, field_dtype))
updated = True
else:
new_in_dtype.append((field_name, table_dtype))
if updated:
in_dtype = np.dtype(new_in_dtype)
if in_dtype == out_dtype:
return a
in_names = {n.lower() for n in in_dtype.names}
out_names = {n.lower() for n in out_dtype.names}
if in_names == out_names:
# Change the dtype name to match the fits record names
# as the mismatch causes case insensitive access to fail
out_dtype.names = in_dtype.names
else:
raise ValueError(
"Column names don't match schema. "
"Schema has {0}. Data has {1}".format(
str(out_names.difference(in_names)),
str(in_names.difference(out_names))))
new_dtype = []
for i in range(len(out_dtype.fields)):
in_type = in_dtype[i]
out_type = out_dtype[i]
if in_type.subdtype is None:
type_str = in_type.str
else:
type_str = in_type.subdtype[0].str
if np.can_cast(in_type, out_type, 'equiv'):
new_dtype.append(
(out_dtype.names[i],
type_str,
in_type.shape))
else:
return np.asanyarray(a, dtype=out_dtype)
return a.view(dtype=np.dtype(new_dtype))
else:
return np.asanyarray(a, dtype=out_dtype)
else:
try:
a = np.asarray(a, dtype=out_dtype)
except Exception:
raise ValueError("Can't convert {0!s} to ndarray".format(type(a)))
return a
def get_short_doc(schema):
title = schema.get('title', None)
description = schema.get('description', None)
if description is None:
description = title or ''
else:
if title is not None:
description = title + '\n\n' + description
return description.partition('\n')[0]
def ensure_ascii(s):
if isinstance(s, bytes):
s = s.decode('ascii')
return s
def create_history_entry(description, software=None):
"""
Create a HistoryEntry object.
Parameters
----------
description : str
Description of the change.
software : dict or list of dict
A description of the software used. It should not include
asdf itself, as that is automatically notated in the
`asdf_library` entry.
Each dict must have the following keys:
``name``: The name of the software
``author``: The author or institution that produced the software
``homepage``: A URI to the homepage of the software
``version``: The version of the software
Examples
--------
>>> soft = {'name': 'jwreftools', 'author': 'STSCI', \
'homepage': 'https://github.com/spacetelescope/jwreftools', 'version': "0.7"}
>>> entry = create_history_entry(description="HISTORY of this file", software=soft)
"""
from asdf.tags.core import Software, HistoryEntry
import datetime
if isinstance(software, list):
software = [Software(x) for x in software]
elif software is not None:
software = Software(software)
entry = HistoryEntry({
'description': description,
'time': datetime.datetime.utcnow()
})
if software is not None:
entry['software'] = software
return entry
def get_envar_as_boolean(name, default=False):
"""Interpret an environmental as a boolean flag
Truth is any numeric value that is not 0 or
any of the following case-insensitive strings:
('true', 't', 'yes', 'y')
Parameters
----------
name : str
The name of the environmental variable to retrieve
default : bool
If the environmental variable cannot be accessed, use as the default.
"""
truths = ('true', 't', 'yes', 'y')
falses = ('false', 'f', 'no', 'n')
if name in os.environ:
value = os.environ[name]
try:
value = bool(int(value))
except ValueError:
value_lowcase = value.lower()
if value_lowcase not in truths + falses:
raise ValueError(f'Cannot convert value "{value}" to boolean unambiguously.')
return value_lowcase in truths
return value
log.debug(f'Environmental "{name}" cannot be found. Using default value of "{default}".')
return default
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,490
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests/test_velocity_aberration.py
|
"""
Test script for set_velocity_aberration.py
"""
from numpy import isclose
import os
import sys
sys.path.insert(
0,
os.path.join(os.path.dirname(__file__), '../../scripts')
)
import set_velocity_aberration as sva # noqa: E402
# Testing constants
GOOD_VELOCITY = (100.0, 100.0, 100.0)
GOOD_POS = (0., 0.)
GOOD_SCALE_FACTOR = 1.000333731048419
GOOD_OFFSET_X = 0.00033356409519815205
GOOD_OFFSET_Y = 0.00033356409519815205
ZERO_VELOCITY = 0.
ZERO_SCALE_FACTOR = 1.0
ZERO_OFFSET_X = 0.
ZERO_OFFSET_Y = 0.
def test_scale_factor_valid():
scale_factor = sva.aberration_scale(
GOOD_VELOCITY[0], GOOD_VELOCITY[1], GOOD_VELOCITY[2],
GOOD_POS[0], GOOD_POS[1]
)
assert isclose(scale_factor, GOOD_SCALE_FACTOR)
def test_scale_factor_zero_velocity():
scale_factor = sva.aberration_scale(
ZERO_VELOCITY, ZERO_VELOCITY, ZERO_VELOCITY,
GOOD_POS[0], GOOD_POS[1]
)
assert isclose(scale_factor, ZERO_SCALE_FACTOR)
def test_offset_valid():
delta_x, delta_y = sva.aberration_offset(
GOOD_VELOCITY[0], GOOD_VELOCITY[1], GOOD_VELOCITY[2],
GOOD_POS[0], GOOD_POS[1]
)
assert isclose(delta_x, GOOD_OFFSET_X)
assert isclose(delta_y, GOOD_OFFSET_Y)
def test_offset_zero_velocity():
delta_x, delta_y = sva.aberration_offset(
ZERO_VELOCITY, ZERO_VELOCITY, ZERO_VELOCITY,
GOOD_POS[0], GOOD_POS[1]
)
assert isclose(delta_x, ZERO_OFFSET_X)
assert isclose(delta_y, ZERO_OFFSET_Y)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,491
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/history.py
|
from asdf.tags.core import HistoryEntry
def _iterable(values):
if isinstance(values, str) or not hasattr(values, '__iter__'):
values = (values,)
return values
class HistoryList:
"""
A list that coerces a new value into a HistoryEntry.
Only a subset of the list interface is implemented.
"""
def __init__(self, asdf):
self._context = asdf
if len(self._context.get_history_entries()):
self._entries = self._context.get_history_entries()
else:
self._context.add_history_entry("fake entry")
self._entries = self._context.get_history_entries()
self._entries.clear()
def __len__(self):
return len(self._entries)
def __getitem__(self, key):
return self._entries[key]
def __setitem__(self, key, value):
self.append(value)
value = self._entries.pop()
self._entries[key] = value
def __delitem__(self, key):
del self._entries[key]
def __iter__(self):
return iter(self._entries)
def __repr__(self):
return repr(self._entries)
def __str__(self):
return str(self._entries)
def __eq__(self, other):
if isinstance(other, HistoryList):
other = other._entries
else:
other = _iterable(other)
if len(self) != len(other):
return False
for self_entry, other_entry in zip(self._entries, other):
if isinstance(other_entry, str):
if self_entry.get('description') != other_entry:
return False
elif isinstance(other_entry, dict):
for key in other_entry.keys():
if self_entry.get(key) != other_entry.get(key):
return False
return True
def append(self, value):
if isinstance(value, HistoryEntry):
self._entries.append(value)
else:
self._context.add_history_entry(value)
def clear(self):
self._entries.clear()
def extend(self, values):
values = _iterable(values)
for value in values:
self.append(value)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,492
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/level1b.py
|
from .model_base import DataModel
__all__ = ['Level1bModel']
class Level1bModel(DataModel):
"""
A data model for raw 4D ramps level-1b products.
Parameters
__________
data : numpy uint16 array
The science data
zeroframe : numpy uint16 array
Zeroframe array
refout : numpy uint16 array
Reference Output
group : numpy table
group parameters table
int_times : numpy table
table of times for each integration
"""
schema_url = "http://stsci.edu/schemas/jwst_datamodel/level1b.schema"
def __init__(self, init=None, **kwargs):
super(Level1bModel, self).__init__(init=init, **kwargs)
# zeroframe is a lower dimensional array than
# the science data. However, its dimensions are not
# consecutive with data, so the default model
# creates a wrongly shaped array. If data is given
# use the appropriate dimensions.
#
# TODO: Hacky. Need solution which involves schema
# specification and embedded in DataModel.
#if 'zeroframe' not in self.instance and \
# 'data' in self.instance and \
# len(self.data.shape) == 4:
# nints, ngroups, ny, nx = self.data.shape
# self.zeroframe = np.zeros((nints, ny, nx))
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,493
|
mperrin/jwst
|
refs/heads/master
|
/jwst/resample/tests/test_resample_spec.py
|
import numpy as np
from numpy.testing import assert_allclose
from ...datamodels import ImageModel
from jwst.assign_wcs import AssignWcsStep
from jwst.extract_2d import Extract2dStep
from jwst.resample import ResampleSpecStep
from gwcs.wcstools import grid_from_bounding_box
def test_spatial_transform_nirspec():
wcsinfo = {
'dec_ref': -0.00601415671349804,
'ra_ref': -0.02073605215697509,
'roll_ref': -0.0,
'v2_ref': -453.5134,
'v3_ref': -373.4826,
'v3yangle': 0.0,
'vparity': -1}
instrument = {
'detector': 'NRS1',
'filter': 'CLEAR',
'grating': 'PRISM',
'name': 'NIRSPEC',
'gwa_tilt': 37.0610,
'gwa_xtilt': 0.0001,
'gwa_ytilt': 0.0001}
subarray = {
'fastaxis': 1,
'name': 'SUBS200A1',
'slowaxis': 2,
'xsize': 72,
'xstart': 1,
'ysize': 416,
'ystart': 529}
observation = {
'date': '2016-09-05',
'time': '8:59:37'}
exposure = {
'duration': 11.805952,
'end_time': 58119.85416,
'exposure_time': 11.776,
'frame_time': 0.11776,
'group_time': 0.11776,
'groupgap': 0,
'integration_time': 11.776,
'nframes': 1,
'ngroups': 100,
'nints': 1,
'nresets_between_ints': 0,
'nsamples': 1,
'readpatt': 'NRSRAPID',
'sample_time': 10.0,
'start_time': 58119.8333,
'type': 'NRS_FIXEDSLIT',
'zero_frame': False}
im = ImageModel()
im.data = np.random.rand(2048, 2048)
im.error = np.random.rand(2048, 2048)
im.dq = np.random.rand(2048, 2048)
im.meta.wcsinfo._instance.update(wcsinfo)
im.meta.instrument._instance.update(instrument)
im.meta.observation._instance.update(observation)
im.meta.exposure._instance.update(exposure)
im.meta.subarray._instance.update(subarray)
im.meta.filename = 'test.fits'
im = AssignWcsStep.call(im)
im = Extract2dStep.call(im)
im = ResampleSpecStep.call(im)
for slit in im.slits:
x, y =grid_from_bounding_box(slit.meta.wcs.bounding_box)
ra, dec, lam = slit.meta.wcs(x, y)
ra1 = np.where(ra < 0, 360 + ra, ra)
assert_allclose(slit.meta.wcs.invert(ra, dec, lam), slit.meta.wcs.invert(ra1, dec, lam))
def test_spatial_transform_miri():
wcsinfo = {
'dec_ref': -0.00601415671349804,
'ra_ref': -0.02073605215697509,
'roll_ref': -0.0,
'v2_ref': -453.5134,
'v3_ref': -373.4826,
'v3yangle': 0.0,
'vparity': -1}
instrument = {
'detector': 'MIRIMAGE',
'filter': 'P750L',
'name': 'MIRI'}
observation = {
'date': '2019-01-01',
'time': '17:00:00'}
subarray = {
'fastaxis': 1,
'name': 'SLITLESSPRISM',
'slowaxis': 2,
'xsize': 72,
'xstart': 1,
'ysize': 416,
'ystart': 529}
exposure = {
'duration': 11.805952,
'end_time': 58119.85416,
'exposure_time': 11.776,
'frame_time': 0.11776,
'group_time': 0.11776,
'groupgap': 0,
'integration_time': 11.776,
'nframes': 1,
'ngroups': 100,
'nints': 1,
'nresets_between_ints': 0,
'nsamples': 1,
'readpatt': 'FAST',
'sample_time': 10.0,
'start_time': 58119.8333,
'type': 'MIR_LRS-SLITLESS',
'zero_frame': False}
im = ImageModel()
im.data = np.random.rand(416, 72)
im.error = np.random.rand(416, 72)
im.dq = np.random.rand(416, 72)
im.meta.wcsinfo._instance.update(wcsinfo)
im.meta.instrument._instance.update(instrument)
im.meta.observation._instance.update(observation)
im.meta.exposure._instance.update(exposure)
im.meta.subarray._instance.update(subarray)
out = AssignWcsStep.call(im)
out = ResampleSpecStep.call(out)
x, y =grid_from_bounding_box(out.meta.wcs.bounding_box)
ra, dec, lam = out.meta.wcs(x, y)
ra1 = np.where(ra < 0, 360 + ra, ra)
assert_allclose(out.meta.wcs.invert(ra, dec, lam), out.meta.wcs.invert(ra1, dec, lam))
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,494
|
mperrin/jwst
|
refs/heads/master
|
/jwst/regtest/test_nirspec_image2.py
|
import pytest
from astropy.io.fits.diff import FITSDiff
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
@pytest.mark.bigdata
def test_nirspec_image2(_jail, rtdata, fitsdiff_default_kwargs):
rtdata.get_data("nirspec/imaging/jw84600010001_02102_00001_nrs2_rate.fits")
collect_pipeline_cfgs("config")
args = ["config/calwebb_image2.cfg", rtdata.input]
Step.from_cmdline(args)
rtdata.output = "jw84600010001_02102_00001_nrs2_cal.fits"
rtdata.get_truth("truth/test_nirspec_image2/jw84600010001_02102_00001_nrs2_cal.fits")
diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs)
assert diff.identical, diff.report()
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,495
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py
|
"""Test calwebb_spec2 for NIRSpec MSA"""
import os.path as op
import pytest
from jwst.tests.base_classes import BaseJWSTTest
from jwst.associations import load_asn
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe.step import Step
@pytest.mark.bigdata
class TestSpec2NRSMSA(BaseJWSTTest):
"""Test various aspects of calibrating NIRSpec MSA mode"""
input_loc = 'nirspec'
ref_loc = ['test_datasets', 'msa', 'simulated-3nod', 'truth']
test_dir = ['test_datasets', 'msa', 'simulated-3nod']
def test_msa_missing(self, caplog):
"""Test MSA missing failure"""
input_file = self.get_data(
*self.test_dir, 'level2a_twoslit', 'F170LP-G235M_MOS_observation-6-c0e0_001_DN_NRS1_mod.fits'
)
collect_pipeline_cfgs('cfgs')
args = [
op.join('cfgs', 'calwebb_spec2.cfg'),
input_file
]
with pytest.raises(Exception):
Step.from_cmdline(args)
assert 'Missing MSA meta (MSAMETFL) file' in caplog.text
def test_msa_missing_nofail(self, caplog):
"""Test MSA missing failure"""
input_file = self.get_data(
*self.test_dir, 'level2a_twoslit', 'F170LP-G235M_MOS_observation-6-c0e0_001_DN_NRS1_mod.fits'
)
collect_pipeline_cfgs('cfgs')
args = [
op.join('cfgs', 'calwebb_spec2.cfg'),
input_file,
'--fail_on_exception=false'
]
Step.from_cmdline(args)
assert 'Missing MSA meta (MSAMETFL) file' in caplog.text
def test_msa_missing_skip(self, caplog):
"""Test MSA missing failure"""
input_file = self.get_data(
*self.test_dir, 'level2a_twoslit', 'F170LP-G235M_MOS_observation-6-c0e0_001_DN_NRS1_mod.fits'
)
collect_pipeline_cfgs('cfgs')
args = [
op.join('cfgs', 'calwebb_spec2.cfg'),
input_file,
'--steps.assign_wcs.skip=true'
]
Step.from_cmdline(args)
assert 'Aborting remaining processing for this exposure.' in caplog.text
def test_run_msaflagging(self, caplog):
"""Test msa flagging operation"""
# Retrieve the data.
collect_pipeline_cfgs('cfgs')
self.get_data(
*self.test_dir, 'jw95065006001_0_msa_twoslit.fits'
)
asn_path = self.get_data(
*self.test_dir, 'mos_udf_g235m_twoslit_spec2_asn.json'
)
with open(asn_path) as fp:
asn = load_asn(fp)
for product in asn['products']:
for member in product['members']:
self.get_data(
*self.test_dir, 'level2a_twoslit', member['expname']
)
# Run step.
args = [
op.join('cfgs', 'calwebb_spec2.cfg'),
asn_path,
'--steps.msa_flagging.skip=false'
]
Step.from_cmdline(args)
# Test.
assert 'Step msa_flagging running with args' in caplog.text
assert 'Step msa_flagging done' in caplog.text
for product in asn['products']:
prod_name = product['name'] + '_cal.fits'
assert op.isfile(prod_name)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,496
|
mperrin/jwst
|
refs/heads/master
|
/jwst/jump/tests/test_detect_jumps.py
|
import numpy as np
import pytest
from jwst.datamodels import GainModel, ReadnoiseModel
from jwst.datamodels import RampModel
from jwst.jump.jump import detect_jumps
import multiprocessing
from jwst.datamodels import dqflags
def test_nocrs_noflux(setup_inputs):
""""
All pixel values are zero. So slope should be zero
"""
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=5)
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 4, True)
assert (0 == np.max(out_model.groupdq))
def test_nocrs_noflux_badgain_pixel(setup_inputs):
""""
all pixel values are zero. So slope should be zero, pixel with bad gain should
have pixel dq set to 'NO_GAIN_VALUE' and 'DO_NOT_USE'
"""
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=5)
gain.data[7, 7] = -10 #bad gain
gain.data[17, 17] = np.nan # bad gain
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 4, True)
assert(np.bitwise_and(out_model.pixeldq[7, 7], dqflags.pixel['NO_GAIN_VALUE']))
assert (np.bitwise_and(out_model.pixeldq[7, 7], dqflags.pixel['DO_NOT_USE']))
assert (np.bitwise_and(out_model.pixeldq[17, 17], dqflags.pixel['NO_GAIN_VALUE']))
assert (np.bitwise_and(out_model.pixeldq[17, 17], dqflags.pixel['DO_NOT_USE']))
def test_nocrs_noflux_subarray(setup_inputs):
""""
All pixel values are zero. This shows that the subarray reference files get extracted from the full frame
versions.
"""
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=5, subarray=True)
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 4, True)
assert (0 == np.max(out_model.groupdq))
def test_onecr_10_groups_neighbors_flagged(setup_inputs):
""""
A single CR in a 10 group exposure
"""
grouptime = 3.0
ingain = 200
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
# two segments perfect fit, second segment has twice the slope
model1.data[0, 0, 5, 5] = 15.0
model1.data[0, 1, 5, 5] = 20.0
model1.data[0, 2, 5, 5] = 25.0
model1.data[0, 3, 5, 5] = 30.0
model1.data[0, 4, 5, 5] = 35.0
model1.data[0, 5, 5, 5] = 140.0
model1.data[0, 6, 5, 5] = 150.0
model1.data[0, 7, 5, 5] = 160.0
model1.data[0, 8, 5, 5] = 170.0
model1.data[0, 9, 5, 5] = 180.0
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 4, True)
assert (4 == np.max(out_model.groupdq[0, 5, 5, 5]))
assert (4 == out_model.groupdq[0, 5, 5, 6])
assert (4 == out_model.groupdq[0, 5, 5, 4])
assert (4 == out_model.groupdq[0, 5, 6, 5])
assert (4 == out_model.groupdq[0, 5, 4, 5])
def test_nocr_100_groups_nframes1(setup_inputs):
""""
NO CR in a 100 group exposure to make sure that frames_per_group is passed correctly to
twopoint_difference. This test recreates the problem found in issue #4571.
"""
grouptime = 3.0
ingain = 1 #to make the noise calculation simple
inreadnoise = np.float64(7)
ngroups = 100
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups, nrows=100, ncols=100,
gain=ingain, readnoise=inreadnoise,
deltatime=grouptime)
model1.meta.exposure.nframes = 1
# two segments perfect fit, second segment has twice the slope
model1.data[0, 0, 5, 5] = 14.0
model1.data[0, 1, 5, 5] = 20.0
model1.data[0, 2, 5, 5] = 27.0
model1.data[0, 3, 5, 5] = 30.0
model1.data[0, 4, 5, 5] = 38.0
model1.data[0, 5, 5, 5] = 40.0
model1.data[0, 6, 5, 5] = 50.0
model1.data[0, 7, 5, 5] = 52.0
model1.data[0, 8, 5, 5] = 63.0
model1.data[0, 9, 5, 5] = 68.0
for i in range(10,100):
model1.data[0,i,5,5] = i * 5
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 4, True)
assert (0 == np.max(out_model.groupdq))
def test_twoints_onecr_each_10_groups_neighbors_flagged(setup_inputs):
""""
Two integrations with CRs in different locations. This makes sure we are correctly
dealing with integrations.
"""
grouptime = 3.0
ingain = 200
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups, nints=2,
gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
# two segments perfect fit, second segment has twice the slope
model1.data[0, 0, 5, 5] = 15.0
model1.data[0, 1, 5, 5] = 20.0
model1.data[0, 2, 5, 5] = 25.0
model1.data[0, 3, 5, 5] = 30.0
model1.data[0, 4, 5, 5] = 35.0
model1.data[0, 5, 5, 5] = 140.0
model1.data[0, 6, 5, 5] = 150.0
model1.data[0, 7, 5, 5] = 160.0
model1.data[0, 8, 5, 5] = 170.0
model1.data[0, 9, 5, 5] = 180.0
model1.data[1, 0, 15, 5] = 15.0
model1.data[1, 1, 15, 5] = 20.0
model1.data[1, 2, 15, 5] = 25.0
model1.data[1, 3, 15, 5] = 30.0
model1.data[1, 4, 15, 5] = 35.0
model1.data[1, 5, 15, 5] = 40.0
model1.data[1, 6, 15, 5] = 45.0
model1.data[1, 7, 15, 5] = 160.0
model1.data[1, 8, 15, 5] = 170.0
model1.data[1, 9, 15, 5] = 180.0
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 4, True)
assert (4 == np.max(out_model.groupdq[0, 5, 5, 5]))
assert (4 == out_model.groupdq[0, 5, 5, 6])
assert (4 == out_model.groupdq[0, 5, 5, 4])
assert (4 == out_model.groupdq[0, 5, 6, 5])
assert (4 == out_model.groupdq[0, 5, 4, 5])
assert (4 == out_model.groupdq[1, 7, 15, 5])
assert (4 == out_model.groupdq[1, 7, 15, 6])
assert (4 == out_model.groupdq[1, 7, 15, 4])
assert (4 == out_model.groupdq[1, 7, 16, 5])
assert (4 == out_model.groupdq[1, 7, 14, 5])
def test_flagging_of_CRs_across_slice_boundaries(setup_inputs):
""""
A multiprocessing test that has two CRs on the boundary between two slices.
This makes sure that we are correctly flagging neighbors in different slices.
"""
grouptime = 3.0
ingain = 200
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups, nints=2,
gain=ingain, readnoise=inreadnoise,
deltatime=grouptime)
nrows = model1.data.shape[3]
num_cores = multiprocessing.cpu_count()
max_cores = 'half'
numslices = num_cores // 2
if numslices > 1:
yincrement = int(nrows / numslices)
# two segments perfect fit, second segment has twice the slope
#add a CR on the last row of the first slice
model1.data[0, 0, yincrement-1, 5] = 15.0
model1.data[0, 1, yincrement-1, 5] = 20.0
model1.data[0, 2, yincrement-1, 5] = 25.0
model1.data[0, 3, yincrement-1, 5] = 30.0
model1.data[0, 4, yincrement-1, 5] = 35.0
model1.data[0, 5, yincrement-1, 5] = 140.0
model1.data[0, 6, yincrement-1, 5] = 150.0
model1.data[0, 7, yincrement-1, 5] = 160.0
model1.data[0, 8, yincrement-1, 5] = 170.0
model1.data[0, 9, yincrement-1, 5] = 180.0
#add a CR on the first row of the second slice
model1.data[1, 0, yincrement, 25] = 15.0
model1.data[1, 1, yincrement, 25] = 20.0
model1.data[1, 2, yincrement, 25] = 25.0
model1.data[1, 3, yincrement, 25] = 30.0
model1.data[1, 4, yincrement, 25] = 35.0
model1.data[1, 5, yincrement, 25] = 40.0
model1.data[1, 6, yincrement, 25] = 50.0
model1.data[1, 7, yincrement, 25] = 160.0
model1.data[1, 8, yincrement, 25] = 170.0
model1.data[1, 9, yincrement, 25] = 180.0
out_model = detect_jumps(model1, gain, rnModel, 4.0, max_cores, 200, 4, True)
#check that the neighbors of the CR on the last row were flagged
assert (4 == out_model.groupdq[0, 5, yincrement-1, 5])
assert (4 == out_model.groupdq[0, 5, yincrement-1, 6])
assert (4 == out_model.groupdq[0, 5, yincrement-1, 4])
assert (4 == out_model.groupdq[0, 5, yincrement, 5])
assert (4 == out_model.groupdq[0, 5, yincrement-2, 5])
# check that the neighbors of the CR on the first row were flagged
assert (4 == out_model.groupdq[1, 7, yincrement, 25])
assert (4 == out_model.groupdq[1, 7, yincrement, 26])
assert (4 == out_model.groupdq[1, 7, yincrement, 24])
assert (4 == out_model.groupdq[1, 7, yincrement+1, 25])
assert (4 == out_model.groupdq[1, 7, yincrement-1, 25])
def test_twoints_onecr_10_groups_neighbors_flagged_multi(setup_inputs):
""""
A multiprocessing test that has two CRs on the boundary between two slices
in different integrations. This makes sure that we are correctly flagging
neighbors in different slices and that we are parsing the integrations correctly.
"""
grouptime = 3.0
ingain = 200
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups, nints=2,
gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
# two segments perfect fit, second segment has twice the slope
model1.data[0, 0, 5, 5] = 15.0
model1.data[0, 1, 5, 5] = 20.0
model1.data[0, 2, 5, 5] = 25.0
model1.data[0, 3, 5, 5] = 30.0
model1.data[0, 4, 5, 5] = 35.0
model1.data[0, 5, 5, 5] = 140.0
model1.data[0, 6, 5, 5] = 150.0
model1.data[0, 7, 5, 5] = 160.0
model1.data[0, 8, 5, 5] = 170.0
model1.data[0, 9, 5, 5] = 180.0
model1.data[1, 0, 15, 5] = 15.0
model1.data[1, 1, 15, 5] = 20.0
model1.data[1, 2, 15, 5] = 25.0
model1.data[1, 3, 15, 5] = 30.0
model1.data[1, 4, 15, 5] = 35.0
model1.data[1, 5, 15, 5] = 40.0
model1.data[1, 6, 15, 5] = 45.0
model1.data[1, 7, 15, 5] = 160.0
model1.data[1, 8, 15, 5] = 170.0
model1.data[1, 9, 15, 5] = 180.0
out_model = detect_jumps(model1, gain, rnModel, 4.0, 'half', 200, 4, True)
assert (4 == np.max(out_model.groupdq[0, 5, 5, 5]))
assert (4 == out_model.groupdq[0, 5, 5, 6])
assert (4 == out_model.groupdq[0, 5, 5, 4])
assert (4 == out_model.groupdq[0, 5, 6, 5])
assert (4 == out_model.groupdq[0, 5, 4, 5])
assert (4 == out_model.groupdq[1, 7, 15, 5])
assert (4 == out_model.groupdq[1, 7, 15, 6])
assert (4 == out_model.groupdq[1, 7, 15, 4])
assert (4 == out_model.groupdq[1, 7, 16, 5])
assert (4 == out_model.groupdq[1, 7, 14, 5])
@pytest.mark.skip(reason="Test is only used to test performance issue. No need to run every time.")
def test_every_pixel_CR_neighbors_flagged(setup_inputs):
""""
A multiprocessing test that has a jump in every pixel. This is used
to test the performance gain from multiprocessing.
"""
grouptime = 3.0
ingain = 200
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
# two segments perfect fit, second segment has twice the slope
model1.data[0, 0, :, :] = 15.0
model1.data[0, 1, :, :] = 20.0
model1.data[0, 2, :, :] = 25.0
model1.data[0, 3, :, :] = 30.0
model1.data[0, 4, :, :] = 35.0
model1.data[0, 5, :, :] = 140.0
model1.data[0, 6, :, :] = 150.0
model1.data[0, 7, :, :] = 160.0
model1.data[0, 8, :, :] = 170.0
model1.data[0, 9, :, :] = 180.0
out_model = detect_jumps(model1, gain, rnModel, 4.0, 'half', 200, 4, True)
assert (4 == np.max(out_model.groupdq[0, 5, 5, 5]))
assert (4 == out_model.groupdq[0, 5, 5, 6])
assert (4 == out_model.groupdq[0, 5, 5, 4])
assert (4 == out_model.groupdq[0, 5, 6, 5])
assert (4 == out_model.groupdq[0, 5, 4, 5])
def test_crs_on_edge_with_neighbor_flagging(setup_inputs):
""""
A test to make sure that the neighbors of CRs on the edges of the
array are flagged correctly.
"""
grouptime = 3.0
ingain = 200
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
gain=ingain, readnoise=inreadnoise,
deltatime=grouptime)
# two segments perfect fit, second segment has twice the slope
# CR on 1st row
model1.data[0, 0, 0, 15] = 15.0
model1.data[0, 1, 0, 15] = 20.0
model1.data[0, 2, 0, 15] = 25.0
model1.data[0, 3, 0, 15] = 30.0
model1.data[0, 4, 0, 15] = 35.0
model1.data[0, 5, 0, 15] = 140.0
model1.data[0, 6, 0, 15] = 150.0
model1.data[0, 7, 0, 15] = 160.0
model1.data[0, 8, 0, 15] = 170.0
model1.data[0, 9, 0, 15] = 180.0
# CR on last row
model1.data[0, 0, 1023, 5] = 15.0
model1.data[0, 1, 1023, 5] = 20.0
model1.data[0, 2, 1023, 5] = 25.0
model1.data[0, 3, 1023, 5] = 30.0
model1.data[0, 4, 1023, 5] = 35.0
model1.data[0, 5, 1023, 5] = 140.0
model1.data[0, 6, 1023, 5] = 150.0
model1.data[0, 7, 1023, 5] = 160.0
model1.data[0, 8, 1023, 5] = 170.0
model1.data[0, 9, 1023, 5] = 180.0
# CR on 1st column
model1.data[0, 0, 5, 0] = 15.0
model1.data[0, 1, 5, 0] = 20.0
model1.data[0, 2, 5, 0] = 25.0
model1.data[0, 3, 5, 0] = 30.0
model1.data[0, 4, 5, 0] = 35.0
model1.data[0, 5, 5, 0] = 140.0
model1.data[0, 6, 5, 0] = 150.0
model1.data[0, 7, 5, 0] = 160.0
model1.data[0, 8, 5, 0] = 170.0
model1.data[0, 9, 5, 0] = 180.0
# CR on last column
model1.data[0, 0, 15, 1027] = 15.0
model1.data[0, 1, 15, 1027] = 20.0
model1.data[0, 2, 15, 1027] = 25.0
model1.data[0, 3, 15, 1027] = 30.0
model1.data[0, 4, 15, 1027] = 35.0
model1.data[0, 5, 15, 1027] = 140.0
model1.data[0, 6, 15, 1027] = 150.0
model1.data[0, 7, 15, 1027] = 160.0
model1.data[0, 8, 15, 1027] = 170.0
model1.data[0, 9, 15, 1027] = 180.0
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 10, True)
# flag CR and three neighbors of first row CR
assert (4 == out_model.groupdq[0, 5, 0, 15])
assert (4 == out_model.groupdq[0, 5, 1, 15])
assert (4 == out_model.groupdq[0, 5, 0, 14])
assert (4 == out_model.groupdq[0, 5, 0, 16])
assert (out_model.groupdq[0, 5, -1, 15] == 0) # The one not to flag
# flag CR and three neighbors of last row CR
assert (4 == out_model.groupdq[0, 5, 1023, 5])
assert (4 == out_model.groupdq[0, 5, 1022, 5])
assert (4 == out_model.groupdq[0, 5, 1023, 4])
assert (4 == out_model.groupdq[0, 5, 1023, 6])
# flag CR and three neighbors of first column CR
assert (4 == out_model.groupdq[0, 5, 5, 0])
assert (4 == out_model.groupdq[0, 5, 6, 0])
assert (4 == out_model.groupdq[0, 5, 4, 0])
assert (4 == out_model.groupdq[0, 5, 5, 1])
assert (out_model.groupdq[0, 5, 5, -1] == 0)# The one not to flag
# flag CR and three neighbors of last column CR
assert (4 == out_model.groupdq[0, 5, 15, 1027])
assert (4 == out_model.groupdq[0, 5, 15, 1026])
assert (4 == out_model.groupdq[0, 5, 16, 1027])
assert (4 == out_model.groupdq[0, 5, 14, 1027])
def test_onecr_10_groups(setup_inputs):
""""
A test to make sure that neighbors are not flagged when they are not requested to be flagged.
"""
grouptime = 3.0
ingain = 200
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
# two segments perfect fit, second segment has twice the slope
model1.data[0, 0, 5, 5] = 15.0
model1.data[0, 1, 5, 5] = 20.0
model1.data[0, 2, 5, 5] = 25.0
model1.data[0, 3, 5, 5] = 30.0
model1.data[0, 4, 5, 5] = 35.0
model1.data[0, 5, 5, 5] = 140.0
model1.data[0, 6, 5, 5] = 150.0
model1.data[0, 7, 5, 5] = 160.0
model1.data[0, 8, 5, 5] = 170.0
model1.data[0, 9, 5, 5] = 180.0
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 10, False)
assert (out_model.groupdq[0, 5, 5, 5] == 4)
assert (out_model.groupdq[0, 5, 4, 5] == 0)
assert (out_model.groupdq[0, 5, 6, 5] == 0)
assert (out_model.groupdq[0, 5, 5, 6] == 0)
assert (out_model.groupdq[0, 5, 5, 4] == 0)
def test_onecr_10_groups_fullarray(setup_inputs):
""""
A test that has a cosmic ray in the 5th group for all pixels except column 10. In column
10 the jump is in the 7th group.
"""
grouptime = 3.0
ingain = 5
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
#
model1.data[0, 0, 5, :] = 15.0
model1.data[0, 1, 5, :] = 20.0
model1.data[0, 2, 5, :] = 25.0
model1.data[0, 3, 5, :] = 30.0
model1.data[0, 4, 5, :] = 35.0
model1.data[0, 5, 5, :] = 140.0
model1.data[0, 6, 5, :] = 150.0
model1.data[0, 7, 5, :] = 160.0
model1.data[0, 8, 5, :] = 170.0
model1.data[0, 9, 5, :] = 180.0
# move the CR to group 7 for row 10 and make difference be 300
model1.data[0, 3, 5, 10] = 100
model1.data[0, 4, 5, 10] = 130
model1.data[0, 5, 5, 10] = 160
model1.data[0, 6, 5, 10] = 190
model1.data[0, 7, 5, 10] = 400
model1.data[0, 8, 5, 10] = 410
model1.data[0, 9, 5, 10] = 420
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 10, False)
assert (np.all(out_model.groupdq[0, 5, 5, 0:10] == 4)) # The jump is in group 5 for columns 0-9
assert (out_model.groupdq[0, 7, 5, 10] == 4) # The jump is in group 7 for column 10
assert (np.all(out_model.groupdq[0, 5, 5, 11:] == 4)) # The jump is in group 5 for columns 11+
def test_onecr_50_groups(setup_inputs):
""""
A test with a fifty group integration. There are two jumps in pixel 5,5. One in group 5 and
one in group 30.
"""
grouptime = 3.0
ingain = 5
inreadnoise = np.float64(7)
ngroups = 50
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
model1.data[0, 0, 5, 5] = 15.0
model1.data[0, 1, 5, 5] = 20.0
model1.data[0, 2, 5, 5] = 25.0
model1.data[0, 3, 5, 5] = 30.0
model1.data[0, 4, 5, 5] = 35.0
model1.data[0, 5, 5, 5] = 140.0
model1.data[0, 6, 5, 5] = 150.0
model1.data[0, 7, 5, 5] = 160.0
model1.data[0, 8, 5, 5] = 170.0
model1.data[0, 9, 5, 5] = 180.0
model1.data[0, 10:30, 5, 5] = np.arange(190, 290, 5)
model1.data[0, 30:50, 5, 5] = np.arange(500, 600, 5)
out_model = detect_jumps(model1, gain, rnModel, 4.0, 1, 200, 10, False)
assert (out_model.groupdq[0, 5, 5, 5] == 4) # CR in group 5
assert (out_model.groupdq[0, 30, 5, 5] == 4) # CR in group 30
assert (np.all(out_model.groupdq[0, 6:30, 5, 5] == 0)) # groups in between are not flagged
def test_single_CR_neighbor_flag( setup_inputs):
""""
A single CR in a 10 group exposure. Tests that:
- if neighbor-flagging is set, the 4 neighboring pixels *ARE* flagged, and
- if neighbor-flagging is *NOT* set, the 4 neighboring pixels are *NOT* flagged
"""
grouptime = 3.0
ingain = 5
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = \
setup_inputs( ngroups=ngroups, nrows=5, ncols=6, gain=ingain, readnoise=inreadnoise,
deltatime=grouptime )
# two segments perfect fit, second segment has twice the slope
model1.data[0, 0, 3, 3] = 15.0
model1.data[0, 1, 3, 3] = 20.0
model1.data[0, 2, 3, 3] = 25.0
model1.data[0, 3, 3, 3] = 30.0
model1.data[0, 4, 3, 3] = 35.0
model1.data[0, 5, 3, 3] = 140.0
model1.data[0, 6, 3, 3] = 150.0
model1.data[0, 7, 3, 3] = 160.0
model1.data[0, 8, 3, 3] = 170.0
model1.data[0, 9, 3, 3] = 180.0
# Flag neighbors
out_model = detect_jumps( model1, gain, rnModel, 4.0, 1, 200, 4, True )
assert (4 == np.max(out_model.groupdq[0, 5, 3, 3]))
assert (4 == out_model.groupdq[0, 5, 3, 4])
assert (4 == out_model.groupdq[0, 5, 3, 2])
assert (4 == out_model.groupdq[0, 5, 2, 3])
assert (4 == out_model.groupdq[0, 5, 4, 3])
# Do not flag neighbors
out_model = detect_jumps( model1, gain, rnModel, 4.0, 1, 200, 4, False )
assert (4 == np.max(out_model.groupdq[0, 5, 3, 3]))
assert (0 == out_model.groupdq[0, 5, 3, 4])
assert (0 == out_model.groupdq[0, 5, 3, 2])
assert (0 == out_model.groupdq[0, 5, 2, 3])
assert (0 == out_model.groupdq[0, 5, 4, 3])
def test_proc(setup_inputs):
""""
A single CR in a 10 group exposure. Verify that the pixels flagged using
multiprocessing are identical to the pixels flagged when no
multiprocessing is done.
"""
grouptime = 3.0
ingain = 5
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = \
setup_inputs( ngroups=ngroups, nrows=5, ncols=6, nints=2, gain=ingain, readnoise=inreadnoise,
deltatime=grouptime )
model1.data[0, 0, 2, 3] = 15.0
model1.data[0, 1, 2, 3] = 21.0
model1.data[0, 2, 2, 3] = 25.0
model1.data[0, 3, 2, 3] = 30.2
model1.data[0, 4, 2, 3] = 35.0
model1.data[0, 5, 2, 3] = 140.0
model1.data[0, 6, 2, 3] = 151.0
model1.data[0, 7, 2, 3] = 160.0
model1.data[0, 8, 2, 3] = 170.0
model1.data[0, 9, 2, 3] = 180.0
out_model_a = detect_jumps( model1, gain, rnModel, 4.0, 'half', 200, 4, True )
out_model_b = detect_jumps( model1, gain, rnModel, 4.0, None, 200, 4, True )
assert( out_model_a.groupdq == out_model_b.groupdq ).all()
out_model_c = detect_jumps( model1, gain, rnModel, 4.0, 'All', 200, 4, True )
assert( out_model_a.groupdq == out_model_c.groupdq ).all()
def test_adjacent_CRs( setup_inputs ):
"""
Three CRs in a 10 group exposure; the CRs have overlapping neighboring
pixels. This test makes sure that the correct pixels are flagged.
"""
grouptime = 3.0
ingain = 5
inreadnoise = np.float64(7)
ngroups = 10
model1, gdq, rnModel, pixdq, err, gain = \
setup_inputs( ngroups=ngroups, nrows=5, ncols=6, gain=ingain,
readnoise=inreadnoise, deltatime=grouptime )
# Populate arrays for 1st CR, centered at (x=2, y=3)
x=2; y=3
model1.data[0, 0, y, x] = 15.0
model1.data[0, 1, y, x] = 20.0
model1.data[0, 2, y, x] = 26.0
model1.data[0, 3, y, x] = 30.0
model1.data[0, 4, y, x] = 35.0
model1.data[0, 5, y, x] = 140.0
model1.data[0, 6, y, x] = 150.0
model1.data[0, 7, y, x] = 161.0
model1.data[0, 8, y, x] = 170.0
model1.data[0, 9, y, x] = 180.0
# Populate arrays for 2nd CR, centered at (x=2, y=2)
x=2; y=2
model1.data[0, 0, y, x] = 20.0
model1.data[0, 1, y, x] = 30.0
model1.data[0, 2, y, x] = 41.0
model1.data[0, 3, y, x] = 51.0
model1.data[0, 4, y, x] = 62.0
model1.data[0, 5, y, x] = 170.0
model1.data[0, 6, y, x] = 200.0
model1.data[0, 7, y, x] = 231.0
model1.data[0, 8, y, x] = 260.0
model1.data[0, 9, y, x] = 290.0
# Populate arrays for 3rd CR, centered at (x=3, y=2)
x=3; y=2
model1.data[0, 0, y, x] = 120.0
model1.data[0, 1, y, x] = 140.0
model1.data[0, 2, y, x] = 161.0
model1.data[0, 3, y, x] = 181.0
model1.data[0, 4, y, x] = 202.0
model1.data[0, 5, y, x] = 70.0
model1.data[0, 6, y, x] = 100.0
model1.data[0, 7, y, x] = 131.0
model1.data[0, 8, y, x] = 160.0
model1.data[0, 9, y, x] = 190.0
out_model = detect_jumps(model1, gain, rnModel, 4.0, 'half', 200, 4, True)
# 1st CR (centered at x=2, y=3)
assert (4 == out_model.groupdq[ 0,5,2,2 ])
assert (4 == out_model.groupdq[ 0,5,3,1 ])
assert (4 == out_model.groupdq[ 0,5,3,2 ])
assert (4 == out_model.groupdq[ 0,5,3,3 ])
assert (4 == out_model.groupdq[ 0,5,4,2 ])
# 2nd CR (centered at x=2, y=2)
assert (4 == out_model.groupdq[ 0,5,1,2 ])
assert (4 == out_model.groupdq[ 0,5,2,1 ])
assert (4 == out_model.groupdq[ 0,5,2,3 ])
# 3rd CR (centered at x=3, y=2)
assert (4 == out_model.groupdq[ 0,5,1,3 ])
assert (4 == out_model.groupdq[ 0,5,2,4 ])
# Need test for multi-ints near zero with positive and negative slopes
@pytest.fixture
def setup_inputs():
def _setup(ngroups=10, readnoise=10, nints=1,
nrows=1024, ncols=1032, nframes=1, grouptime=1.0, gain=1, deltatime=1,
gain_subarray = False, readnoise_subarray = False, subarray = False):
times = np.array(list(range(ngroups)), dtype=np.float64) * deltatime
gain = np.ones(shape=(nrows, ncols), dtype=np.float64) * gain
pixdq = np.zeros(shape=(nrows, ncols), dtype=np.float64)
read_noise = np.full((nrows, ncols), readnoise, dtype=np.float64)
gdq = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.uint32)
if subarray:
data = np.zeros(shape=(nints, ngroups, 20, 20), dtype=np.float64)
err = np.ones(shape=(nints, ngroups, 20, 20), dtype=np.float64)
else:
data = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float64)
err = np.ones(shape=(nints, ngroups, nrows, ncols), dtype=np.float64)
model1 = RampModel(data=data, err=err, pixeldq=pixdq, groupdq=gdq, times=times)
model1.meta.instrument.name = 'MIRI'
model1.meta.instrument.detector = 'MIRIMAGE'
model1.meta.instrument.filter = 'F480M'
model1.meta.observation.date = '2015-10-13'
model1.meta.exposure.type = 'MIR_IMAGE'
model1.meta.exposure.group_time = deltatime
model1.meta.subarray.name = 'FULL'
model1.meta.subarray.xstart = 1
model1.meta.subarray.ystart = 1
if subarray:
model1.meta.subarray.xsize = 20
model1.meta.subarray.ysize = 20
else:
model1.meta.subarray.xsize = ncols
model1.meta.subarray.ysize = nrows
model1.meta.exposure.frame_time = deltatime
model1.meta.exposure.ngroups = ngroups
model1.meta.exposure.group_time = deltatime
model1.meta.exposure.nframes = 1
model1.meta.exposure.groupgap = 0
gain = GainModel(data=gain)
gain.meta.instrument.name = 'MIRI'
gain.meta.subarray.xstart = 1
gain.meta.subarray.ystart = 1
gain.meta.subarray.xsize = ncols
gain.meta.subarray.ysize = nrows
rnModel = ReadnoiseModel(data=read_noise)
rnModel.meta.instrument.name = 'MIRI'
rnModel.meta.subarray.xstart = 1
rnModel.meta.subarray.ystart = 1
rnModel.meta.subarray.xsize = ncols
rnModel.meta.subarray.ysize = nrows
return model1, gdq, rnModel, pixdq, err, gain
return _setup
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,497
|
mperrin/jwst
|
refs/heads/master
|
/jwst/assign_wcs/tests/test_nirspec.py
|
"""
Test functions for NIRSPEC WCS - all modes.
"""
import os.path
import pytest
import numpy as np
from numpy.testing import assert_allclose
from astropy.io import fits
from astropy.modeling import models as astmodels
from astropy import wcs as astwcs
import astropy.units as u
import astropy.coordinates as coords
from gwcs import wcs
from jwst import datamodels
from jwst.transforms.models import Slit
from jwst.assign_wcs import nirspec
from jwst.assign_wcs import assign_wcs_step
from . import data
from jwst.assign_wcs.util import MSAFileError
data_path = os.path.split(os.path.abspath(data.__file__))[0]
wcs_kw = {'wcsaxes': 2, 'ra_ref': 165, 'dec_ref': 54,
'v2_ref': -8.3942412, 'v3_ref': -5.3123744, 'roll_ref': 37,
'crpix1': 1024, 'crpix2': 1024,
'cdelt1': .08, 'cdelt2': .08,
'ctype1': 'RA---TAN', 'ctype2': 'DEC--TAN',
'pc1_1': 1, 'pc1_2': 0, 'pc2_1': 0, 'pc2_2': 1
}
slit_fields_num = ["shutter_id", "dither_position", "xcen", "ycen",
"ymin", "ymax", "quadrant", "source_id",
"stellarity", "source_xpos", "source_ypos"]
slit_fields_str = ["name", "shutter_state", "source_name", "source_alias"]
def _compare_slits(s1, s2):
for f in slit_fields_num:
assert_allclose(getattr(s1, f), getattr(s2, f))
for f in slit_fields_str:
assert getattr(s1, f) == getattr(s2, f)
def get_file_path(filename):
"""
Construct an absolute path.
"""
return os.path.join(data_path, filename)
def create_hdul(detector='NRS1'):
"""
Create a fits HDUList instance.
"""
hdul = fits.HDUList()
phdu = fits.PrimaryHDU()
phdu.header['instrume'] = 'NIRSPEC'
phdu.header['detector'] = detector
phdu.header['time-obs'] = '8:59:37'
phdu.header['date-obs'] = '2016-09-05'
scihdu = fits.ImageHDU()
scihdu.header['EXTNAME'] = "SCI"
for item in wcs_kw.items():
scihdu.header[item[0]] = item[1]
hdul.append(phdu)
hdul.append(scihdu)
return hdul
def create_reference_files(datamodel):
"""
Create a dict {reftype: reference_file}.
"""
refs = {}
step = assign_wcs_step.AssignWcsStep()
for reftype in assign_wcs_step.AssignWcsStep.reference_file_types:
refs[reftype] = step.get_reference_file(datamodel, reftype)
return refs
def create_nirspec_imaging_file():
image = create_hdul()
image[0].header['exp_type'] = 'NRS_IMAGE'
image[0].header['filter'] = 'F290LP'
image[0].header['grating'] = 'MIRROR'
return image
def create_nirspec_mos_file():
image = create_hdul()
image[0].header['exp_type'] = 'NRS_MSASPEC'
image[0].header['filter'] = 'F170LP'
image[0].header['grating'] = 'G235M'
image[0].header['PATT_NUM'] = 1
msa_status_file = get_file_path('SPCB-GD-A.msa.fits.gz')
image[0].header['MSACONFG'] = msa_status_file
return image
def create_nirspec_ifu_file(filter, grating, lamp='N/A', detector='NRS1'):
image = create_hdul(detector)
image[0].header['exp_type'] = 'NRS_IFU'
image[0].header['filter'] = filter
image[0].header['grating'] = grating
image[1].header['crval3'] = 0
image[1].header['wcsaxes'] = 3
image[1].header['ctype3'] = 'WAVE'
image[0].header['lamp'] = lamp
image[0].header['GWA_XTIL'] = 0.3318742513656616
image[0].header['GWA_YTIL'] = 0.1258982867002487
return image
def create_nirspec_fs_file(grating, filter, lamp="N/A"):
image = create_hdul()
image[0].header['exp_type'] = 'NRS_FIXEDSLIT'
image[0].header['filter'] = filter
image[0].header['grating'] = grating
image[0].header['lamp'] = lamp
image[1].header['crval3'] = 0
image[1].header['wcsaxes'] = 3
image[1].header['ctype3'] = 'WAVE'
image[0].header['GWA_XTIL'] = 0.3316612243652344
image[0].header['GWA_YTIL'] = 0.1260581910610199
image[0].header['SUBARRAY'] = "FULL"
return image
def test_nirspec_imaging():
"""
Test Nirspec Imaging mode using build 6 reference files.
"""
#Test creating the WCS
f = create_nirspec_imaging_file()
im = datamodels.ImageModel(f)
refs = create_reference_files(im)
pipe = nirspec.create_pipeline(im, refs, slit_y_range=[-.5, .5])
w = wcs.WCS(pipe)
im.meta.wcs = w
# Test evaluating the WCS
im.meta.wcs(1, 2)
def test_nirspec_ifu_against_esa():
"""
Test Nirspec IFU mode using CV3 reference files.
"""
ref = fits.open(get_file_path('Trace_IFU_Slice_00_SMOS-MOD-G1M-17-5344175105_30192_JLAB88.fits'))
# Test NRS1
pyw = astwcs.WCS(ref['SLITY1'].header)
hdul = create_nirspec_ifu_file("OPAQUE", "G140M")
im = datamodels.ImageModel(hdul)
im.meta.filename = "test_ifu.fits"
refs = create_reference_files(im)
pipe = nirspec.create_pipeline(im, refs, slit_y_range=[-.5, .5])
w = wcs.WCS(pipe)
im.meta.wcs = w
# Test evaluating the WCS (slice 0)
w0 = nirspec.nrs_wcs_set_input(im, 0)
# get positions within the slit and the coresponding lambda
slit1 = ref['SLITY1'].data # y offset on the slit
lam = ref['LAMBDA1'].data
# filter out locations outside the slit
cond = np.logical_and(slit1 < .5, slit1 > -.5)
y, x = cond.nonzero() # 0-based
x, y = pyw.wcs_pix2world(x, y, 0)
# The pipeline accepts 0-based cooridnates
x -= 1
y -= 1
sca2world = w0.get_transform('sca', 'msa_frame')
_, slit_y, lp = sca2world(x, y)
lp *= 10**-6
assert_allclose(lp, lam[cond], atol=1e-13)
def test_nirspec_fs_esa():
"""
Test Nirspec FS mode using build 6 reference files.
"""
#Test creating the WCS
filename = create_nirspec_fs_file(grating="G140M", filter="F100LP")
im = datamodels.ImageModel(filename)
im.meta.filename = "test_fs.fits"
refs = create_reference_files(im)
pipe = nirspec.create_pipeline(im, refs, slit_y_range=[-.5, .5])
w = wcs.WCS(pipe)
im.meta.wcs = w
# Test evaluating the WCS
w1 = nirspec.nrs_wcs_set_input(im, "S200A1")
ref = fits.open(get_file_path('Trace_SLIT_A_200_1_V84600010001P0000000002101_39547_JLAB88.fits'))
pyw = astwcs.WCS(ref[1].header)
# get positions within the slit and the coresponding lambda
slit1 = ref[5].data # y offset on the slit
lam = ref[4].data
# filter out locations outside the slit
cond = np.logical_and(slit1 < .5, slit1 > -.5)
y, x = cond.nonzero() # 0-based
x, y = pyw.wcs_pix2world(x, y, 0)
# The pipeline works with 0-based coordinates
x -= 1
y -= 1
sca2world = w1.get_transform('sca', 'v2v3')
ra, dec, lp = sca2world(x, y)
# w1 now outputs in microns hence the 1e6 factor
lp *= 1e-6
lam = lam[cond]
nan_cond = ~np.isnan(lp)
assert_allclose(lp[nan_cond], lam[nan_cond], atol=10**-13)
ref.close()
def test_correct_tilt():
"""
Example provided by Catarina.
"""
disp = datamodels.DisperserModel()
xtilt = 0.35896975
ytilt = 0.1343827
# ztilt = None
corrected_theta_x = 0.02942671219861111
corrected_theta_y = 0.00018649006677464447
# corrected_theta_z = -0.2523269848788889
disp.gwa_tiltx = {'temperatures': [39.58],
'tilt_model': astmodels.Polynomial1D(1, c0=3307.85402614,
c1=-9182.87552123),
'unit': 'arcsec',
'zeroreadings': [0.35972327]}
disp.gwa_tilty = {'temperatures': [39.58],
'tilt_model': astmodels.Polynomial1D(1, c0=0.0, c1=0.0),
'unit': 'arcsec',
'zeroreadings': [0.0]}
disp.meta = {'instrument': {'name': 'NIRSPEC', 'detector': 'NRS1'},
'reftype': 'DISPERSER'}
disp.theta_x = 0.02942671219861111
disp.theta_y = -0.0007745488724972222
# disp.theta_z = -0.2523269848788889
disp.tilt_x = 0.0
disp.tilt_y = -8.8
disp_corrected = nirspec.correct_tilt(disp, xtilt, ytilt)#, ztilt)
assert np.isclose(disp_corrected.theta_x, corrected_theta_x)
# assert(np.isclose(disp_corrected['theta_z'], corrected_theta_z))
assert np.isclose(disp_corrected.theta_y, corrected_theta_y)
def test_msa_configuration_normal():
"""
Test the get_open_msa_slits function.
"""
# Test 1: Reasonably normal as well
msa_meta_id = 12
msaconfl = get_file_path('msa_configuration.fits')
dither_position = 1
slitlet_info = nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position,
slit_y_range=[-.5, .5])
ref_slit = Slit(55, 9376, 1, 251, 26, -5.15, 0.55, 4, 1, '1111x', '95065_1', '2122',
0.13, -0.31716078999999997, -0.18092266)
_compare_slits(slitlet_info[0], ref_slit)
def test_msa_configuration_no_background():
"""
Test the get_open_msa_slits function.
"""
# Test 2: Two main shutters, not allowed and should fail
msa_meta_id = 13
msaconfl = get_file_path('msa_configuration.fits')
dither_position = 1
with pytest.raises(MSAFileError):
nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position,
slit_y_range=[-.5, .5])
def test_msa_configuration_all_background():
"""
Test the get_open_msa_slits function.
"""
# Test 3: No non-background, not acceptable.
msa_meta_id = 14
msaconfl = get_file_path('msa_configuration.fits')
dither_position = 1
slitlet_info = nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position,
slit_y_range=[-.5, .5])
ref_slit = Slit(57, 8646, 1, 251, 24, -2.85, .55, 4, 0, '11x', 'background_57', 'bkg_57',
0, -0.5, -0.5)
_compare_slits(slitlet_info[0], ref_slit)
def test_msa_configuration_row_skipped():
"""
Test the get_open_msa_slits function.
"""
# Test 4: One row is skipped, should be acceptable.
msa_meta_id = 15
msaconfl = get_file_path('msa_configuration.fits')
dither_position = 1
slitlet_info = nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position,
slit_y_range=[-.5, .5])
ref_slit = Slit(58, 8646, 1, 251, 24, -2.85, 5.15, 4, 1, '11x1011', '95065_1', '2122',
0.130, -0.31716078999999997, -0.18092266)
_compare_slits(slitlet_info[0], ref_slit)
def test_msa_configuration_multiple_returns():
"""
Test the get_open_msa_slits function.
"""
# Test 4: One row is skipped, should be acceptable.
msa_meta_id = 16
msaconfl = get_file_path('msa_configuration.fits')
dither_position = 1
slitlet_info = nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position,
slit_y_range=[-.5, .5])
ref_slit1 = Slit(59, 8651, 1, 256, 24, -2.85, 5.15, 4, 1, '11x1011', '95065_1', '2122',
0.13000000000000003, -0.31716078999999997, -0.18092266)
ref_slit2 = Slit(60, 11573, 1, 258, 32, -2.85, 4, 4, 2, '11x111', '95065_2', '172',
0.70000000000000007, -0.31716078999999997, -0.18092266)
_compare_slits(slitlet_info[0], ref_slit1)
_compare_slits(slitlet_info[1], ref_slit2)
open_shutters = [[24], [23, 24], [22, 23, 25, 27], [22, 23, 25, 27, 28]]
main_shutter = [24, 23, 25, 28]
result = ["x", "x1", "110x01", "110101x"]
test_data = list(zip(open_shutters, main_shutter, result))
@pytest.mark.parametrize(('open_shutters', 'main_shutter', 'result'),
test_data)
def test_shutter_state(open_shutters, main_shutter, result):
shutter_state = nirspec._shutter_id_to_str(open_shutters, main_shutter)
assert shutter_state == result
def test_slit_projection_on_detector():
step = assign_wcs_step.AssignWcsStep()
hdul = create_nirspec_fs_file(grating="G395M", filter="OPAQUE", lamp="ARGON")
hdul[0].header['DETECTOR'] = 'NRS2'
im = datamodels.ImageModel(hdul)
refs = {}
for reftype in step.reference_file_types:
refs[reftype] = step.get_reference_file(im, reftype)
open_slits = nirspec.get_open_slits(im, refs)
assert len(open_slits) == 1
assert open_slits[0].name == "S200B1"
hdul[0].header['DETECTOR'] = 'NRS1'
im = datamodels.ImageModel(hdul)
open_slits = nirspec.get_open_slits(im, refs)
assert len(open_slits) == 4
names = [s.name for s in open_slits]
assert "S200A1" in names
assert "S200A2" in names
assert "S400A1" in names
assert "S1600A1" in names
def test_missing_msa_file():
image = create_nirspec_mos_file()
model = datamodels.ImageModel(image)
model.meta.instrument.msa_metadata_file = ""
with pytest.raises(MSAFileError):
assign_wcs_step.AssignWcsStep.call(model)
model.meta.instrument.msa_metadata_file = "missing.fits"
with pytest.raises(MSAFileError):
assign_wcs_step.AssignWcsStep.call(model)
def test_open_slits():
""" Test that get_open_slits works with MSA data.
Issue #2321
"""
image = create_nirspec_mos_file()
model = datamodels.ImageModel(image)
msaconfl = get_file_path('msa_configuration.fits')
model.meta.instrument.msa_metadata_file = msaconfl
model.meta.instrument.msa_metadata_id = 12
slits = nirspec.get_open_slits(model)
assert len(slits) == 1
def test_shutter_size_on_sky():
"""
Test the size of a MOS shutter on sky is ~ .2 x .4 arcsec.
"""
image = create_nirspec_mos_file()
model = datamodels.ImageModel(image)
msaconfl = get_file_path('msa_configuration.fits')
model.meta.instrument.msa_metadata_file = msaconfl
model.meta.instrument.msa_metadata_id = 12
refs = create_reference_files(model)
pipe = nirspec.create_pipeline(model, refs, slit_y_range=(-.5, .5))
w = wcs.WCS(pipe)
model.meta.wcs = w
slit = w.get_transform('slit_frame', 'msa_frame').slits[0]
wslit = nirspec.nrs_wcs_set_input(model, slit.name)
virtual_corners_x = [-.5, -.5, .5, .5, -.5]
virtual_corners_y = [-.5, .5, .5, -.5, -.5]
input_lam = [2e-6] * 5
slit2world = wslit.get_transform('slit_frame', 'world')
ra, dec, lam = slit2world(virtual_corners_x,
virtual_corners_y,
input_lam)
sky = coords.SkyCoord(ra*u.deg, dec*u.deg)
sep_x = sky[0].separation(sky[3]).to(u.arcsec)
sep_y = sky[0].separation(sky[1]).to(u.arcsec)
assert sep_x.value > 0.193
assert sep_x.value < 0.194
assert sep_y.value > 0.45
assert sep_y.value < 0.46
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,498
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/associations/test_generate.py
|
"""Test general asn_generate operations"""
import pytest
from jwst.associations import (
generate,
load_asn
)
@pytest.mark.slow
def test_generate(full_pool_rules):
"""Run a full sized pool using all rules"""
pool, rules, pool_fname = full_pool_rules
asns = generate(pool, rules)
assert len(asns) == 99
for asn in asns:
asn_name, asn_store = asn.dump()
asn_table = load_asn(asn_store)
schemas = rules.validate(asn_table)
assert len(schemas) > 0
@pytest.mark.slow
def test_serialize(full_pool_rules):
"""Test serializing roundtripping"""
pool, rules, pool_fname = full_pool_rules
asns = generate(pool, rules)
for asn in asns:
for format in asn.ioregistry:
fname, serialized = asn.dump(format=format)
assert serialized is not None
recovered = load_asn(serialized)
assert recovered is not None
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,499
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py
|
import pytest
from jwst.tests.base_classes import BaseJWSTTestSteps
from jwst.tests.base_classes import pytest_generate_tests # noqa: F401
from jwst.refpix import RefPixStep
from jwst.dark_current import DarkCurrentStep
from jwst.dq_init import DQInitStep
from jwst.extract_1d import Extract1dStep
from jwst.extract_2d import Extract2dStep
from jwst.flatfield import FlatFieldStep
from jwst.group_scale import GroupScaleStep
from jwst.jump import JumpStep
from jwst.linearity import LinearityStep
from jwst.photom import PhotomStep
from jwst.saturation import SaturationStep
from jwst.superbias import SuperBiasStep
# Parameterized regression tests for NIRISS processing
# All tests in this set run with 1 input file and
# only generate 1 output for comparison.
#
@pytest.mark.bigdata
class TestNIRSpecSteps(BaseJWSTTestSteps):
input_loc = 'nirspec'
params = {'test_steps':
[dict(input='jw00023001001_01101_00001_NRS1_dq_init.fits',
test_dir='test_bias_drift',
step_class=RefPixStep,
step_pars=dict(odd_even_columns=True,
use_side_ref_pixels=False,
side_smoothing_length=10,
side_gain=1.0),
output_truth='jw00023001001_01101_00001_NRS1_bias_drift.fits',
output_hdus=[],
id='refpix_nirspec'
),
dict(input='jw00023001001_01101_00001_NRS1_saturation.fits',
test_dir='test_dark_step',
step_class=DarkCurrentStep,
step_pars=dict(),
output_truth='jw00023001001_01101_00001_NRS1_dark_current.fits',
output_hdus=[],
id='dark_current_nirspec'
),
dict(input='jw00023001001_01101_00001_NRS1_uncal.fits',
test_dir='test_dq_init',
step_class=DQInitStep,
step_pars=dict(),
output_truth='jw00023001001_01101_00001_NRS1_dq_init.fits',
output_hdus=[],
id='dq_init_nirspec'
),
dict(input='jw00023001001_01101_00001_NRS1_cal.fits',
test_dir='test_extract_1d',
step_class=Extract1dStep,
step_pars=dict(),
output_truth='jw00023001001_01101_00001_NRS1_spec.fits',
output_hdus=[],
id='extract1d_nirspec'
),
dict(input='jw00023001001_01101_00001_NRS1_assign_wcs.fits',
test_dir='test_extract_2d',
step_class=Extract2dStep,
step_pars=dict(),
output_truth='jw00023001001_01101_00001_NRS1_extract_2d.fits',
output_hdus=[],
id='extract2d_nirspec'
),
dict(input='jw00023001001_01101_00001_NRS1_extract_2d.fits',
test_dir='test_flat_field',
step_class=FlatFieldStep,
step_pars=dict(save_interpolated_flat=True),
output_truth='jw00023001001_01101_00001_NRS1_flat_field.fits',
output_hdus=[],
id='flat_field_nirspec'
),
dict(input='NRSIRS2_230_491_uncal.fits',
test_dir='test_group_scale',
step_class=GroupScaleStep,
step_pars=dict(),
output_truth='NRSIRS2_230_491_groupscale.fits',
output_hdus=[],
id='group_scale_nirspec'
),
dict(input='jw00023001001_01101_00001_NRS1_linearity.fits',
test_dir='test_jump',
step_class=JumpStep,
step_pars=dict(rejection_threshold=50.0),
output_truth='jw00023001001_01101_00001_NRS1_jump.fits',
output_hdus=[],
id='jump_nirspec'
),
dict(input='jw00023001001_01101_00001_NRS1_dark_current.fits',
test_dir='test_linearity',
step_class=LinearityStep,
step_pars=dict(),
output_truth='jw00023001001_01101_00001_NRS1_linearity.fits',
output_hdus=[],
id='linearity_nirspec'
),
dict(input='jw00023001001_01101_00001_NRS1_flat_field.fits',
test_dir='test_photom',
step_class=PhotomStep,
step_pars=dict(),
output_truth='jw00023001001_01101_00001_NRS1_photom.fits',
output_hdus=[],
id='photom_nirspec'
),
dict(input='jw84600007001_02101_00001_nrs1_superbias.fits',
test_dir='test_bias_drift',
step_class=RefPixStep,
step_pars=dict(),
output_truth='jw84600007001_02101_00001_nrs1_refpix.fits',
output_hdus=[],
id='refpix_nirspec_irs2'
),
dict(input='jw00023001001_01101_00001_NRS1_bias_drift.fits',
test_dir='test_saturation',
step_class=SaturationStep,
step_pars=dict(),
output_truth='jw00023001001_01101_00001_NRS1_saturation.fits',
output_hdus=[],
id='saturation_nirspec'
),
dict(input='jw00011001001_01106_00001_NRS2_saturation.fits',
test_dir='test_superbias',
step_class=SuperBiasStep,
step_pars=dict(),
output_truth='jw00011001001_01106_00001_NRS2_superbias.fits',
output_hdus=[],
id='superbias_nirspec'
)
]
}
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,500
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/associations/test_main.py
|
"""test_associations: Test of general Association functionality."""
import re
import pytest
from jwst.associations.main import Main
@pytest.mark.skip(
reason='Takes too long and is not currently contributing to any actual testing'
)
@pytest.mark.slow
def test_script(full_pool_rules):
"""Test full run of the script code"""
pool, rules, pool_fname = full_pool_rules
ref_rule_set = {
'candidate_Asn_Coron', 'discover_Asn_TSO', 'candidate_Asn_Lv2NRSFSS',
'candidate_Asn_SpectralTarget', 'candidate_Asn_TSO', 'candidate_Asn_WFSCMB',
'candidate_Asn_Lv2SpecSpecial', 'candidate_Asn_Image', 'candidate_Asn_IFU',
'candidate_Asn_Lv2NRSMSA', 'candidate_Asn_Lv2Image', 'candidate_Asn_Lv2Spec',
'discover_Asn_AMI', 'candidate_Asn_AMI', 'candidate_Asn_Lv2ImageSpecial',
'candidate_Asn_Lv2SpecTSO', 'candidate_Asn_SpectralSource',
'candidate_Asn_Lv2WFSS', 'discover_Asn_Coron', 'candidate_Asn_WFSS_NIS',
'discover_Asn_IFU', 'candidate_Asn_Lv2WFSC',
'candidate_Asn_Lv2ImageNonScience', 'discover_Asn_SpectralTarget',
'candidate_Asn_Lv2ImageTSO', 'discover_Asn_SpectralSource',
'discover_Asn_Image', 'candidate_Asn_Lv2FGS', 'candidate_Asn_Lv3SpecAux'
}
generated = Main([pool_fname, '--dry-run'])
asns = generated.associations
assert len(asns) == 938
assert len(generated.orphaned) == 61
found_rules = set(
asn['asn_rule']
for asn in asns
)
assert ref_rule_set == found_rules
@pytest.mark.slow
def test_asn_candidates(full_pool_rules):
"""Test basic candidate selection"""
pool, rules, pool_fname = full_pool_rules
generated = Main([pool_fname, '--dry-run', '-i', 'o001'])
assert len(generated.associations) == 12
generated = Main([pool_fname, '--dry-run', '-i', 'o001', 'o002'])
assert len(generated.associations) == 24
@pytest.mark.slow
def test_version_id(full_pool_rules):
"""Test that version id is properly included"""
pool, rules, pool_fname = full_pool_rules
generated = Main([pool_fname, '--dry-run', '-i', 'o001', '--version-id'])
regex = re.compile(r'\d{3}t\d{6}')
for asn in generated.associations:
assert regex.search(asn.asn_name)
version_id = 'mytestid'
generated = Main([pool_fname, '--dry-run', '-i', 'o001', '--version-id', version_id])
for asn in generated.associations:
assert version_id in asn.asn_name
@pytest.mark.slow
def test_pool_as_parameter(full_pool_rules):
"""Test passing the pool as an object"""
pool, rules, pool_fname = full_pool_rules
full = Main([pool_fname, '--dry-run'])
full_as_param = Main(['--dry-run'], pool=pool)
assert len(full.associations) == len(full_as_param.associations)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,501
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/tests/test_open.py
|
"""
Test datamodel.open
"""
import os
import os.path
import warnings
import pytest
import numpy as np
from astropy.io import fits
from jwst.datamodels import (DataModel, ModelContainer, ImageModel,
ReferenceFileModel, ReferenceImageModel, ReferenceCubeModel,
ReferenceQuadModel, FlatModel, MaskModel, NrcImgPhotomModel, GainModel,
ReadnoiseModel, DistortionModel)
from jwst import datamodels
def test_open_fits():
"""Test opening a model from a FITS file"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "model_type not found")
fits_file = t_path('test.fits')
with datamodels.open(fits_file) as model:
assert isinstance(model, DataModel)
def test_open_fits_s3(s3_root_dir):
"""Test opening a model from a FITS file on S3"""
path = str(s3_root_dir.join("test.fits"))
with DataModel() as dm:
dm.save(path)
with datamodels.open("s3://test-s3-data/test.fits") as m:
assert isinstance(m, DataModel)
def test_open_asdf_s3(s3_root_dir):
"""Test opening a model from an ASDF file on S3"""
path = str(s3_root_dir.join("test.asdf"))
with DataModel() as dm:
dm.save(path)
with datamodels.open("s3://test-s3-data/test.asdf") as m:
assert isinstance(m, DataModel)
def test_open_association():
"""Test for opening an association"""
asn_file = t_path('association.json')
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "model_type not found")
with datamodels.open(asn_file) as c:
assert isinstance(c, ModelContainer)
for model in c:
assert model.meta.asn.table_name == "association.json"
assert model.meta.asn.pool_name == "pool"
def test_container_open_asn_with_sourcecat():
path = t_path("association_w_cat.json")
with datamodels.open(path, asn_exptypes="science") as c:
for model in c:
assert model.meta.asn.table_name == "association_w_cat.json"
def test_open_shape():
init = (200, 200)
with datamodels.open(init) as model:
assert type(model) == ImageModel
def test_open_illegal():
with pytest.raises(ValueError):
init = 5
datamodels.open(init)
def test_open_hdulist():
hdulist = fits.HDUList()
data = np.empty((50, 50), dtype=np.float32)
primary = fits.PrimaryHDU()
hdulist.append(primary)
science = fits.ImageHDU(data=data, name='SCI')
hdulist.append(science)
with datamodels.open(hdulist) as model:
assert type(model) == ImageModel
def test_open_image():
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "model_type not found")
image_name = t_path('jwst_image.fits')
with datamodels.open(image_name) as model:
assert type(model) == ImageModel
def test_open_reference_files():
files = {'nircam_flat.fits' : FlatModel,
'nircam_mask.fits' : MaskModel,
'nircam_photom.fits' : NrcImgPhotomModel,
'nircam_gain.fits' : GainModel,
'nircam_readnoise.fits' : ReadnoiseModel}
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "model_type not found")
for base_name, klass in files.items():
file = t_path(base_name)
model = datamodels.open(file)
if model.shape:
ndim = len(model.shape)
else:
ndim = 0
if ndim == 0:
my_klass = ReferenceFileModel
elif ndim == 2:
my_klass = ReferenceImageModel
elif ndim == 3:
my_klass = ReferenceCubeModel
elif ndim == 4:
my_klass = ReferenceQuadModel
else:
my_klass = None
assert isinstance(model, my_klass)
model.close()
model = klass(file)
assert isinstance(model, klass)
model.close()
def test_open_fits_readonly(tmpdir):
"""Test opening a FITS-format datamodel that is read-only on disk"""
tmpfile = str(tmpdir.join('readonly.fits'))
data = np.arange(100, dtype=np.float).reshape(10, 10)
with ImageModel(data=data) as model:
model.meta.telescope = 'JWST'
model.meta.instrument.name = 'NIRCAM'
model.meta.instrument.detector = 'NRCA4'
model.meta.instrument.channel = 'SHORT'
model.save(tmpfile)
os.chmod(tmpfile, 0o440)
assert os.access(tmpfile, os.W_OK) == False
with datamodels.open(tmpfile) as model:
assert model.meta.telescope == 'JWST'
def test_open_asdf_readonly(tmpdir):
tmpfile = str(tmpdir.join('readonly.asdf'))
with DistortionModel() as model:
model.meta.telescope = 'JWST'
model.meta.instrument.name = 'NIRCAM'
model.meta.instrument.detector = 'NRCA4'
model.meta.instrument.channel = 'SHORT'
model.save(tmpfile)
os.chmod(tmpfile, 0o440)
assert os.access(tmpfile, os.W_OK) == False
with datamodels.open(tmpfile) as model:
assert model.meta.telescope == 'JWST'
# Utilities
def t_path(partial_path):
"""Construction the full path for test files"""
test_dir = os.path.join(os.path.dirname(__file__), 'data')
return os.path.join(test_dir, partial_path)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,502
|
mperrin/jwst
|
refs/heads/master
|
/jwst/pipeline/linear_pipeline.py
|
#
# Simple linear pipeline
from ..stpipe import LinearPipeline
from ..ipc.ipc_step import IPCStep
from ..dq_init.dq_init_step import DQInitStep
from ..refpix.refpix_step import RefPixStep
from ..saturation.saturation_step import SaturationStep
from ..dark_current.dark_current_step import DarkCurrentStep
from ..linearity.linearity_step import LinearityStep
from ..jump.jump_step import JumpStep
from ..ramp_fitting.ramp_fit_step import RampFitStep
from ..assign_wcs.assign_wcs_step import AssignWcsStep
from ..extract_2d.extract_2d_step import Extract2dStep
from ..flatfield.flat_field_step import FlatFieldStep
from ..persistence.persistence_step import PersistenceStep
from ..straylight.straylight_step import StraylightStep
from ..fringe.fringe_step import FringeStep
from ..photom.photom_step import PhotomStep
class TestLinearPipeline(LinearPipeline):
pipeline_steps = [
('ipc', IPCStep),
('dq_init', DQInitStep),
('refpix', RefPixStep),
('saturation', SaturationStep),
('dark_current', DarkCurrentStep),
('linearity', LinearityStep),
('jump', JumpStep),
('ramp_fit', RampFitStep),
('assign_wcs', AssignWcsStep),
('extract_2d', Extract2dStep),
('flat_field', FlatFieldStep),
('persistence', PersistenceStep),
('straylight', StraylightStep),
('fringe', FringeStep),
('photom', PhotomStep)
]
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,503
|
mperrin/jwst
|
refs/heads/master
|
/jwst/stpipe/utilities.py
|
# Copyright (C) 2010 Association of Universities for Research in Astronomy(AURA)
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of AURA and its representatives may not be used to
# endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED
# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
# DAMAGE.
"""
Utilities
"""
import inspect
import os
import sys
def import_class(full_name, subclassof=object, config_file=None):
"""
Import the Python class `full_name` given in full Python package format,
e.g.::
package.another_package.class_name
Return the imported class. Optionally, if `subclassof` is not None
and is a Python class, make sure that the imported class is a
subclass of `subclassof`.
"""
# Understand which class we need to instantiate. The class name is given in
# full Python package notation, e.g.
# package.subPackage.subsubpackage.className
# in the input parameter `full_name`. This means that
# 1. We HAVE to be able to say
# from package.subPackage.subsubpackage import className
# 2. If `subclassof` is defined, the newly imported Python class MUST be a
# subclass of `subclassof`, which HAS to be a Python class.
if config_file is not None:
sys.path.insert(0, os.path.dirname(config_file))
try:
full_name = full_name.strip()
package_name, sep, class_name = full_name.rpartition('.')
if not package_name:
raise ImportError("{0} is not a Python class".format(full_name))
imported = __import__(
package_name, globals(), locals(), [class_name, ], level=0)
step_class = getattr(imported, class_name)
if not isinstance(step_class, type):
raise TypeError(
'Object {0} from package {1} is not a class'.format(
class_name, package_name))
elif not issubclass(step_class, subclassof):
raise TypeError(
'Class {0} from package {1} is not a subclass of {2}'.format(
class_name, package_name, subclassof.__name__))
finally:
if config_file is not None:
del sys.path[0]
return step_class
def get_spec_file_path(step_class):
"""
Given a Step (sub)class, divine and return the full path to the
corresponding spec file. Use the fact that by convention, the spec
file is in the same directory as the `step_class` source file. It
has the name of the Step (sub)class and extension .spec.
"""
try:
step_source_file = inspect.getfile(step_class)
except TypeError:
return None
step_source_file = os.path.abspath(step_source_file)
# Since `step_class` could be defined in a file called whatever,
# we need the source file basedir and the class name.
dir = os.path.dirname(step_source_file)
return os.path.join(dir, step_class.__name__ + '.spec')
def find_spec_file(step_class):
"""
Return the full path of the given Step subclass `step_class`, if
it exists or None if it does not.
"""
spec_file = get_spec_file_path(step_class)
if spec_file is not None and os.path.exists(spec_file):
return spec_file
return None
def islist_tuple(obj):
"""
Return True if `obj` is either a list or a tuple. False otherwise.
"""
return isinstance(obj, tuple) or isinstance(obj, list)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,504
|
mperrin/jwst
|
refs/heads/master
|
/jwst/stpipe/linear_pipeline.py
|
# Copyright (C) 2010 Association of Universities for Research in Astronomy(AURA)
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of AURA and its representatives may not be used to
# endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED
# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
# DAMAGE.
"""
LinearPipeline
"""
import gc
from .pipeline import Pipeline
class _LinearPipelineMetaclass(type):
def __init__(cls, name, bases, dct):
super(_LinearPipelineMetaclass, cls).__init__(name, bases, dct)
pipeline_steps = cls.pipeline_steps
if pipeline_steps is not None and len(pipeline_steps) == 0:
raise ValueError(
"{0!r} LinearPipeline subclass defines no pipeline_steps"
.format(name))
if pipeline_steps is None:
pipeline_steps = []
cls.step_defs = dict(pipeline_steps)
# Since the pipeline_steps member needs to be converted to a step_defs
# at the class level, we need to use a metaclass.
class LinearPipeline(Pipeline, metaclass=_LinearPipelineMetaclass):
"""
A LinearPipeline is a way of combining a number of steps together
in a simple linear order.
"""
spec = """
# start_step and end_step allow only a part of the pipeline to run
start_step = string(default=None) # Start the pipeline at this step
end_step = string(default=None) # End the pipeline right before this step
# [steps] section is implicitly added by the Pipeline class.
"""
# To be overridden by subclasses
pipeline_steps = None
def _check_start_and_end_steps(self):
"""
Given the start_step and end_step members (which are strings
or None), find the actual step objects they correspond to.
"""
start_step = end_step = None
if self.start_step is not None:
if hasattr(self, self.start_step):
start_step = getattr(self, self.start_step)
else:
raise ValueError(
"start_step {0!r} not found".format(
self.start_step))
if self.end_step is not None:
if hasattr(self, self.end_step):
end_step = getattr(self, self.end_step)
else:
raise ValueError(
"end_step {0!r} not found".format(
self.end_step))
return start_step, end_step
def process(self, input_file):
"""
Run the pipeline.
"""
self._check_start_and_end_steps()
do_caching = (
self.end_step is not None and
self.end_step != self.pipeline_steps[-1][0])
if self.start_step is None:
mode = 'RUN'
else:
mode = 'BEFORE'
# It would be easiest to do this in a loop,
# but we use recursion instead to make the "with" statements
# work correctly
def recurse(mode, input_file, pipeline_steps):
gc.collect()
if pipeline_steps == []:
if (hasattr(self, 'output_file') and
self.output_file is not None):
input_file.save(self.output_file)
return input_file
name, cls = pipeline_steps[0]
step = getattr(self, name)
filename = '{0}.fits'.format(self.qualified_name)
if name == self.start_step:
mode = 'RUN'
if mode == 'BEFORE':
from .. import datamodels
try:
with datamodels.open(filename) as dm:
pass
except (ValueError, TypeError, IOError):
return recurse(mode, filename, pipeline_steps[1:])
else:
dm = datamodels.open(filename)
return recurse(mode, dm, pipeline_steps[1:])
elif mode == 'RUN':
dm = step(input_file)
if do_caching:
dm.save(filename)
if name == self.end_step:
return None
return recurse(mode, dm, pipeline_steps[1:])
result = recurse(mode, input_file, self.pipeline_steps)
gc.collect()
return result
def set_input_filename(self, path):
for name, cls in self.pipeline_steps:
getattr(self, name).set_input_filename(path)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,505
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py
|
import os
import glob
import pytest
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
from ci_watson.artifactory_helpers import get_bigdata
from jwst.ramp_fitting import RampFitStep
from jwst.wfs_combine import WfsCombineStep
from jwst.pipeline import Detector1Pipeline
from jwst.lib.set_telescope_pointing import add_wcs
from jwst.lib import engdb_tools
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
@pytest.mark.bigdata
class TestWFSImage2(BaseJWSTTest):
input_loc = 'nircam'
ref_loc = ['test_datasets', 'sdp_jw82600_wfs', 'level2a', 'truth']
test_dir = 'test_datasets'
def test_wfs_image2(self):
"""
Regression test of the WFS&C `calwebb_wfs-image2.cfg` pipeline
"""
data_base = 'jw82600026001_02101_00001_nrca1_rate'
ext = '.fits'
input_name = '{}{}'.format(data_base, ext)
input_file = self.get_data(self.test_dir, 'sdp_jw82600_wfs', 'level2a',
input_name)
collect_pipeline_cfgs('cfgs')
Step.from_cmdline([os.path.join('cfgs', 'calwebb_wfs-image2.cfg'), input_file])
cal_name = input_name.replace('rate', 'cal')
output_name = input_name.replace('rate','cal_ref')
outputs = [(cal_name, output_name)]
self.compare_outputs(outputs)
output_files = glob.glob('*')
output_files.remove('cfgs')
# these would happen when docopy=True
if input_name in output_files:
output_files.remove(input_name)
if output_name in output_files:
output_files.remove(output_name)
if "truth" in output_files:
output_files.remove("truth")
assert cal_name in output_files
output_files.remove(cal_name)
assert not output_files, 'Unexpected output files {}'.format(output_files)
@pytest.mark.bigdata
class TestDetector1Pipeline(BaseJWSTTest):
input_loc = 'nircam'
ref_loc = ['test_detector1pipeline', 'truth']
test_dir = 'test_detector1pipeline'
def test_detector1pipeline3(self):
"""
Regression test of calwebb_detector1 pipeline performed on NIRCam data.
"""
input_file = self.get_data(self.test_dir,
'jw82500001003_02101_00001_NRCALONG_uncal.fits')
step = Detector1Pipeline()
step.save_calibrated_ramp = True
step.ipc.skip = True
step.refpix.odd_even_columns = True
step.refpix.use_side_ref_pixels = False
step.refpix.side_smoothing_length = 10
step.refpix.side_gain = 1.0
step.persistence.skip = True
step.jump.rejection_threshold = 250.0
step.ramp_fit.save_opt = True
step.output_file = 'jw82500001003_02101_00001_NRCALONG_rate.fits'
step.run(input_file)
outputs = [('jw82500001003_02101_00001_NRCALONG_ramp.fits',
'jw82500001003_02101_00001_NRCALONG_ramp_ref.fits'),
('jw82500001003_02101_00001_NRCALONG_rate.fits',
'jw82500001003_02101_00001_NRCALONG_rate_ref.fits'),
('jw82500001003_02101_00001_NRCALONG_rateints.fits',
'jw82500001003_02101_00001_NRCALONG_rateints_ref.fits')
]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestNIRCamRamp(BaseJWSTTest):
input_loc = 'nircam'
ref_loc = ['test_ramp_fit', 'truth']
test_dir = 'test_ramp_fit'
def test_ramp_fit_nircam(self):
"""
Regression test of ramp_fit step performed on NIRCam data.
"""
input_file = self.get_data(self.test_dir,
'jw00017001001_01101_00001_NRCA1_jump.fits')
result, result_int = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit_opt_out.fits'
)
optout_file = 'rampfit_opt_out_fitopt.fits'
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00017001001_01101_00001_NRCA1_ramp_fit.fits'),
(optout_file,
'rampfit_opt_out.fits',
['primary','slope','sigslope','yint','sigyint','pedestal','weights','crmag']) ]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestWFSCombine(BaseJWSTTest):
input_loc = 'nircam'
ref_loc = ['test_wfs_combine', 'truth']
test_dir = 'test_wfs_combine'
def test_wfs_combine(self):
"""
Regression test of wfs_combine using do_refine=False (default)
Association table has 3 (identical) pairs of input files to combine
"""
asn_file = self.get_data(self.test_dir,
'wfs_3sets_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
WfsCombineStep.call(asn_file)
outputs = [('test_wfscom_wfscmb.fits',
'test_wfscom.fits'),
('test_wfscoma_wfscmb.fits',
'test_wfscoma.fits'),
('test_wfscomb_wfscmb.fits',
'test_wfscomb.fits')
]
self.compare_outputs(outputs)
def test_wfs_combine1(self):
"""
Regression test of wfs_combine using do_refine=True
"""
asn_file = self.get_data(self.test_dir,
'wfs_3sets_asn2.json')
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
WfsCombineStep.call(asn_file,
do_refine=True )
outputs = [('test_wfscom2_wfscmb.fits',
'test_wfscom_do_ref.fits'),
('test_wfscom2a_wfscmb.fits',
'test_wfscoma_do_ref.fits'),
('test_wfscom2b_wfscmb.fits',
'test_wfscomb_do_ref.fits')
]
self.compare_outputs(outputs)
def test_wfs_combine2(self):
"""
Regression test of wfs_combine using do_refine=True
"""
asn_file = self.get_data(self.test_dir,
'wfs_3sets_asn3.json')
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
WfsCombineStep.call(asn_file,
do_refine=True)
outputs = [('test_wfscom3_wfscmb.fits',
'test_wfscom_do_ref.fits'),
('test_wfscom3a_wfscmb.fits',
'test_wfscoma_do_ref.fits'),
('test_wfscom3b_wfscmb.fits',
'test_wfscomb_do_ref.fits')
]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestNrcGrismSetPointing(BaseJWSTTest):
input_loc = 'nircam'
ref_loc = ['test_pointing', 'truth']
test_dir = 'test_pointing'
rtol = 0.000001
def test_nircam_setpointing(self):
"""
Regression test of the set_telescope_pointing script on a level-1b NIRCam file.
"""
# Copy original version of file to test file, which will get overwritten by test
input_file = self.get_data(self.test_dir,
'jw00721012001_03103_00001-seg001_nrcalong_uncal_orig.fits')
# Get SIAF PRD database file
siaf_prd_loc = ['jwst-pipeline', self.env, 'common', 'prd.db']
siaf_path = get_bigdata(*siaf_prd_loc)
# Call the WCS routine, using the ENGDB_Service
add_wcs(input_file, siaf_path=siaf_path, engdb_url=engdb_tools.ENGDB_BASE_URL)
outputs = [(input_file,
'jw00721012001_03103_00001-seg001_nrcalong_uncal_ref.fits')]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,506
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py
|
import pytest
from jwst.pipeline.calwebb_detector1 import Detector1Pipeline
from jwst.tests.base_classes import BaseJWSTTest
@pytest.mark.skip
@pytest.mark.bigdata
class TestSloperPipeline(BaseJWSTTest):
input_loc = 'fgs'
ref_loc = ['test_sloperpipeline', 'truth']
def test_fgs_detector1_1(self):
"""
Regression test of calwebb_detector1 pipeline performed on FGS imaging mode data.
"""
input_file = self.get_data('test_sloperpipeline',
'jw86500007001_02101_00001_GUIDER2_uncal.fits')
pipe = Detector1Pipeline()
pipe.ipc.skip = True
pipe.refpix.odd_even_columns = True
pipe.refpix.use_side_ref_pixels = True
pipe.refpix.side_smoothing_length = 11
pipe.refpix.side_gain = 1.0
pipe.refpix.odd_even_rows = True
pipe.jump.rejection_threshold = 250.0
pipe.persistence.skip = True
pipe.ramp_fit.save_opt = False
pipe.save_calibrated_ramp = True
pipe.output_file = 'jw86500007001_02101_00001_GUIDER2_rate.fits'
pipe.run(input_file)
outputs = [('jw86500007001_02101_00001_GUIDER2_ramp.fits',
'jw86500007001_02101_00001_GUIDER2_ramp_ref.fits',
['primary','sci','err','groupdq','pixeldq']),
('jw86500007001_02101_00001_GUIDER2_rateints.fits',
'jw86500007001_02101_00001_GUIDER2_rateints_ref.fits',
['primary','sci','err','dq']),
('jw86500007001_02101_00001_GUIDER2_rate.fits',
'jw86500007001_02101_00001_GUIDER2_rate_ref.fits',
['primary','sci','err','dq'])
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,507
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nirspec/test_nrs_ifu_wcs.py
|
import pytest
from numpy.testing import assert_allclose
from gwcs.wcstools import grid_from_bounding_box
from ci_watson.artifactory_helpers import get_bigdata
from jwst.assign_wcs import AssignWcsStep, nirspec
from jwst.datamodels import ImageModel
testdata = [
('nrs1', 'jw00011001001_01120_00001_NRS1_rate.fits',
'jw00011001001_01120_00001_NRS1_assign_wcs.fits'),
('nrs1_opaque', 'jw00011001001_01120_00001_NRS1_rate_opaque.fits',
'jw00011001001_01120_00001_NRS1_rate_opaque_assign_wcs.fits'),
('nrs2', 'NRSIFU-COMBO-030_NRS2_SloperPipeline.fits',
'NRSIFU-COMBO-030_NRS2_SloperPipeline_assign_wcs.fits')
]
@pytest.mark.bigdata
@pytest.mark.parametrize("test_id, input_file, truth_file", testdata)
def test_nirspec_ifu_wcs(envopt, _jail, test_id, input_file, truth_file):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
del test_id
input_file = get_bigdata('jwst-pipeline', envopt,
'nirspec', 'test_wcs', 'nrs1-ifu', input_file)
truth_file = get_bigdata('jwst-pipeline', envopt,
'nirspec', 'test_wcs', 'nrs1-ifu', 'truth', truth_file)
result = AssignWcsStep.call(input_file, save_results=True, suffix='assign_wcs')
result.close()
im = ImageModel(result.meta.filename)
imref = ImageModel(truth_file)
w = nirspec.nrs_wcs_set_input(im, 0)
grid = grid_from_bounding_box(w.bounding_box)
ra, dec, lam = w(*grid)
wref = nirspec.nrs_wcs_set_input(imref, 0)
raref, decref, lamref = wref(*grid)
# equal_nan is used here as many of the entries are nan.
# The domain is defined but it is only a few entries in there that are valid
# as it is a curved narrow slit.
assert_allclose(ra, raref, equal_nan=True)
assert_allclose(dec, decref, equal_nan=True)
assert_allclose(lam, lamref, equal_nan=True)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,508
|
mperrin/jwst
|
refs/heads/master
|
/jwst/lib/s3_utils.py
|
"""
Experimental support for reading reference files from S3. Use of these functions
requires installing the [aws] extras (but this module can be safely imported without
them).
"""
import atexit
__all__ = ["object_exists", "get_object", "get_client", "is_s3_uri", "split_uri"]
_CLIENT = None
def object_exists(uri):
"""
Determine if an object exists on S3.
Parameters
----------
uri : str
S3 URI (s3://bucket-name/some/key)
Returns
-------
bool
`True` if object exists, `False` if not.
"""
bucket_name, key = split_uri(uri)
return get_client().object_exists(bucket_name, key)
def get_object(uri):
"""
Fetch the content of an object from S3.
Parameters
----------
uri : str
S3 URI (s3://bucket-name/some/key)
Returns
-------
io.BytesIO
The content of the object.
"""
bucket_name, key = split_uri(uri)
return get_client().get_object(bucket_name, key)
def get_client():
"""
Get the shared instance of ConcurrentS3Client.
Returns
-------
stsci_aws_utils.s3.ConcurrentS3Client
"""
global _CLIENT
if _CLIENT is None:
from stsci_aws_utils.s3 import ConcurrentS3Client
_CLIENT = ConcurrentS3Client()
atexit.register(_CLIENT.close)
return _CLIENT
def is_s3_uri(value):
"""
Determine if a value represents an S3 URI.
Parameters
----------
value : str
Value to test.
Returns
-------
bool
`True` if value is an S3 URI, `False` if not.
"""
return value.startswith("s3://")
def split_uri(uri):
"""
Split an S3 URI into bucket name and key components.
Parameters
----------
uri : str
S3 URI (s3://bucket-name/some/key)
Returns
-------
str
Bucket name URI component
str
Key URI component
"""
if not uri.startswith("s3://"):
raise ValueError("Expected S3 URI")
bucket_name, key = uri.replace("s3://", "").split("/", 1)
return bucket_name, key
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,509
|
mperrin/jwst
|
refs/heads/master
|
/jwst/regtest/test_fgs_guider.py
|
"""Regression tests for FGS Guidestar in ID and FINEGUIDE modes"""
import os
import pytest
from astropy.io.fits.diff import FITSDiff
from jwst.lib.suffix import replace_suffix
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
def is_like_truth(rtdata, fitsdiff_default_kwargs, suffix, truth_path='truth/fgs/test_fgs_guider'):
"""Compare step outputs with truth"""
output = replace_suffix(
os.path.splitext(os.path.basename(rtdata.input))[0], suffix
) + '.fits'
rtdata.output = output
rtdata.get_truth(os.path.join(truth_path, output))
diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs)
assert diff.identical, diff.report()
file_roots = ['exptype_fgs_acq1', 'exptype_fgs_fineguide', 'exptype_fgs_id_image', 'exptype_fgs_id_stack']
@pytest.fixture(scope='module', params=file_roots, ids=file_roots)
def run_guider_pipelines(jail, rtdata_module, request):
"""Run pipeline for guider data"""
rtdata = rtdata_module
rtdata.get_data('fgs/level1b/' + request.param + '_uncal.fits')
collect_pipeline_cfgs('config')
args = [
'config/calwebb_guider.cfg',
rtdata.input,
'--steps.dq_init.save_results=true',
'--steps.guider_cds.save_results=true',
]
Step.from_cmdline(args)
return rtdata
guider_suffixes = ['cal', 'dq_init', 'guider_cds']
@pytest.mark.bigdata
@pytest.mark.parametrize('suffix', guider_suffixes, ids=guider_suffixes)
def test_fgs_guider(run_guider_pipelines, fitsdiff_default_kwargs, suffix):
"""Regression for FGS Guider data"""
is_like_truth(run_guider_pipelines, fitsdiff_default_kwargs, suffix)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,510
|
mperrin/jwst
|
refs/heads/master
|
/jwst/regtest/test_miri_mrs.py
|
"""Regression tests for MIRI MRS modes"""
from pathlib import Path
import pytest
from numpy.testing import assert_allclose
from jwst.associations import load_asn
from jwst.lib.suffix import replace_suffix
from jwst import datamodels
from gwcs.wcstools import grid_from_bounding_box
from . import regtestdata as rt
# Define artifactory source and truth
INPUT_PATH = 'miri/mrs'
TRUTH_PATH = 'truth/test_miri_mrs'
@pytest.fixture(scope='module')
def run_spec2(jail, rtdata_module):
"""Run the Spec2Pipeline on a single exposure"""
rtdata = rtdata_module
# Setup the inputs
asn_name = 'ifushort_ch12_rate_asn3.json'
rtdata.get_data(INPUT_PATH + '/' + asn_name)
asn_path = rtdata.input
with open(asn_path, 'r') as asn_fh:
asn = load_asn(asn_fh)
member_path = Path(asn['products'][0]['members'][0]['expname'])
rate_path = member_path.stem
rate_path = replace_suffix(rate_path, 'rate')
rate_path = INPUT_PATH + '/' + rate_path + member_path.suffix
# Run the pipeline
step_params = {
'input_path': rate_path,
'step': 'calwebb_spec2.cfg',
'args': [
'--steps.bkg_subtract.save_results=true',
'--steps.assign_wcs.save_results=true',
'--steps.imprint_subtract.save_results=true',
'--steps.msa_flagging.save_results=true',
'--steps.extract_2d.save_results=true',
'--steps.flat_field.save_results=true',
'--steps.srctype.save_results=true',
'--steps.straylight.save_results=true',
'--steps.fringe.save_results=true',
'--steps.pathloss.save_results=true',
'--steps.barshadow.save_results=true',
'--steps.photom.save_results=true',
'--steps.resample_spec.save_results=true',
'--steps.cube_build.save_results=true',
'--steps.extract_1d.save_results=true',
]
}
rtdata = rt.run_step_from_dict(rtdata, **step_params)
return rtdata, asn_path
@pytest.fixture(scope='module')
def run_spec3(jail, run_spec2):
"""Run the Spec3Pipeline on the results from the Spec2Pipeline run"""
rtdata, asn_path = run_spec2
# The presumption is that `run_spec2` has set the input to the
# original association. To use this default, and not re-download
# the association, simply do not specify `step_params["input_path"]`
rtdata.input = asn_path
step_params = {
'step': 'calwebb_spec3.cfg',
'args': [
'--steps.master_background.save_results=true',
'--steps.mrs_imatch.save_results=true',
'--steps.outlier_detection.save_results=true',
'--steps.resample_spec.save_results=true',
'--steps.cube_build.save_results=true',
'--steps.extract_1d.save_results=true',
'--steps.combine_1d.save_results=true',
]
}
return rt.run_step_from_dict(rtdata, **step_params)
@pytest.fixture(scope='module')
def run_spec3_multi(jail, rtdata_module):
"""Run the Spec3Pipeline on multi channel/multi filter data"""
step_params = {
'input_path': INPUT_PATH + '/' + 'ifushort_set2_asn3.json',
'step': 'calwebb_spec3.cfg',
'args': [
'--steps.master_background.save_results=true',
'--steps.mrs_imatch.save_results=true',
'--steps.outlier_detection.save_results=true',
'--steps.resample_spec.save_results=true',
'--steps.cube_build.save_results=true',
'--steps.extract_1d.save_results=true',
'--steps.combine_1d.save_results=true',
]
}
return rt.run_step_from_dict(rtdata_module, **step_params)
@pytest.mark.bigdata
@pytest.mark.parametrize(
'suffix',
['assign_wcs', 'cal', 'flat_field', 'fringe', 'photom', 's3d', 'srctype', 'straylight', 'x1d']
)
def test_spec2(run_spec2, fitsdiff_default_kwargs, suffix):
"""Test ensuring the callwebb_spec2 is operating appropriately for MIRI MRS data"""
rtdata, asn_path = run_spec2
rt.is_like_truth(rtdata, fitsdiff_default_kwargs, suffix,
truth_path=TRUTH_PATH)
@pytest.mark.bigdata
@pytest.mark.parametrize(
'output',
[
'ifushort_ch12_spec3_mrs_imatch.fits',
'ifushort_ch12_spec3_ch1-medium_s3d.fits',
'ifushort_ch12_spec3_ch2-medium_s3d.fits',
'ifushort_ch12_spec3_ch1-medium_x1d.fits',
'ifushort_ch12_spec3_ch2-medium_x1d.fits',
],
ids=["mrs_imatch", "ch1-s3d", "ch2-s3d", "ch1-x1d", "ch2-x1d"]
)
def test_spec3(run_spec3, fitsdiff_default_kwargs, output):
"""Regression test matching output files"""
rt.is_like_truth(
run_spec3, fitsdiff_default_kwargs, output,
truth_path=TRUTH_PATH,
is_suffix=False
)
@pytest.mark.bigdata
@pytest.mark.parametrize(
'output',
[
'ifushort_set2_0_mrs_imatch.fits',
'ifushort_set2_1_mrs_imatch.fits',
'ifushort_set2_0_a3001_crf.fits',
'ifushort_set2_1_a3001_crf.fits',
'ifushort_set2_ch1-short_s3d.fits',
'ifushort_set2_ch2-short_s3d.fits',
'ifushort_set2_ch1-short_x1d.fits',
'ifushort_set2_ch2-short_x1d.fits',
],
ids=["ch1-mrs_imatch", "ch2-mrs_imatch", "ch1-crf", "ch2-crf",
"ch1-s3d", "ch2-s3d", "ch1-x1d", "ch2-x1d"]
)
def test_spec3_multi(run_spec3_multi, fitsdiff_default_kwargs, output):
"""Regression test matching output files"""
rt.is_like_truth(
run_spec3_multi, fitsdiff_default_kwargs, output,
truth_path=TRUTH_PATH,
is_suffix=False
)
@pytest.mark.bigdata
def test_miri_mrs_wcs(run_spec2, fitsdiff_default_kwargs):
rtdata, asn_path = run_spec2
# get input assign_wcs and truth file
output = "ifushort_ch12_assign_wcs.fits"
rtdata.output = output
rtdata.get_truth(f"truth/test_miri_mrs/{output}")
# Open the output and truth file
with datamodels.open(rtdata.output) as im, datamodels.open(rtdata.truth) as im_truth:
x, y = grid_from_bounding_box(im.meta.wcs.bounding_box)
ra, dec, lam = im.meta.wcs(x, y)
ratruth, dectruth, lamtruth = im_truth.meta.wcs(x, y)
assert_allclose(ra, ratruth)
assert_allclose(dec, dectruth)
assert_allclose(lam, lamtruth)
# Test the inverse transform
xtest, ytest = im.meta.wcs.backward_transform(ra, dec, lam)
xtruth, ytruth = im_truth.meta.wcs.backward_transform(ratruth, dectruth, lamtruth)
assert_allclose(xtest, xtruth)
assert_allclose(ytest, ytruth)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,511
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py
|
import pytest
from jwst.pipeline import Spec2Pipeline
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
@pytest.mark.bigdata
class TestSpec2Pipeline(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_spec2pipeline', 'truth']
test_dir = 'test_spec2pipeline'
def test_miri_lrs_bkgnod(self):
"""
Regression test of calwebb_spec2 pipeline performed on an association
of nodded MIRI LRS fixed-slit exposures.
"""
asn_file = self.get_data(self.test_dir,
'lrs_bkgnod_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
step = Spec2Pipeline()
step.save_bsub=True
step.save_results=True
step.resample_spec.save_results = True
step.cube_build.save_results = True
step.extract_1d.save_results = True
step.run(asn_file)
outputs = [('test_lrs1_bsub.fits', 'test_lrs1_bsub_ref.fits',
['primary','sci','err','dq']),
('test_lrs2_bsub.fits','test_lrs2_bsub_ref.fits',
['primary','sci','err','dq']),
('test_lrs3_bsub.fits','test_lrs3_bsub_ref.fits',
['primary','sci','err','dq']),
('test_lrs4_bsub.fits','test_lrs4_bsub_ref.fits',
['primary','sci','err','dq']),
('test_lrs1_cal.fits', 'test_lrs1_cal_ref.fits',
['primary','sci','err','dq']),
('test_lrs2_cal.fits', 'test_lrs2_cal_ref.fits',
['primary','sci','err','dq']),
('test_lrs3_cal.fits', 'test_lrs3_cal_ref.fits',
['primary','sci','err','dq']),
('test_lrs4_cal.fits', 'test_lrs4_cal_ref.fits',
['primary','sci','err','dq'])
]
self.compare_outputs(outputs)
def test_miri_lrs_slit_1(self):
"""
Regression test of calwebb_spec2 pipeline performed on a single
MIRI LRS fixed-slit exposure.
"""
input_file = self.get_data(self.test_dir,
'jw00035001001_01101_00001_MIRIMAGE_rate.fits')
step = Spec2Pipeline()
step.save_bsub=True
step.save_results=True
step.resample_spec.save_results = True
step.cube_build.save_results = True
step.extract_1d.save_results = True
step.run(input_file)
outputs = [('jw00035001001_01101_00001_MIRIMAGE_cal.fits',
'jw00035001001_01101_00001_MIRIMAGE_cal_ref.fits',
['primary','sci','err','dq']),
('jw00035001001_01101_00001_MIRIMAGE_x1d.fits',
'jw00035001001_01101_00001_MIRIMAGE_x1d_ref.fits',
['primary','extract1d'])
]
self.compare_outputs(outputs)
def test_miri_lrs_slit_1b(self):
"""
Regression test of calwebb_spec2 pipeline performed on a single
MIRI LRS fixed-slit exposure with multiple integrations. Compare _calints.
"""
input_file = self.get_data(self.test_dir,
'jw00035001001_01101_00001_MIRIMAGE_rateints.fits')
step = Spec2Pipeline()
step.save_bsub=True
step.save_results=True
step.extract_1d.save_results = True
step.run(input_file)
outputs = [('jw00035001001_01101_00001_MIRIMAGE_calints.fits',
'jw00035001001_01101_00001_MIRIMAGE_calints_ref.fits',
['primary','sci','err','dq']),
('jw00035001001_01101_00001_MIRIMAGE_x1dints.fits',
'jw00035001001_01101_00001_MIRIMAGE_x1dints_ref.fits',
['primary', ('extract1d', 1), ('extract1d', 2), ('extract1d', 3), ('extract1d', 4)]
)
]
self.compare_outputs(outputs)
def test_mrs2pipeline1(self):
"""
Regression test of calwebb_spec2 pipeline performed on MIRI MRS data.
"""
test_dir = 'test_mrs2pipeline'
self.ref_loc = ['test_mrs2pipeline', 'truth']
input_file = self.get_data(test_dir,
'jw80500018001_02101_00002_MIRIFUSHORT_rate.fits')
step = Spec2Pipeline()
step.save_bsub=True
step.save_results=True
step.resample_spec.save_results = True
step.cube_build.save_results = True
step.extract_1d.save_results = True
step.run(input_file)
outputs = [('jw80500018001_02101_00002_MIRIFUSHORT_cal.fits',
'jw80500018001_02101_00002_MIRIFUSHORT_cal_ref.fits',
['primary','sci','err','dq']),
('jw80500018001_02101_00002_MIRIFUSHORT_s3d.fits',
'jw80500018001_02101_00002_MIRIFUSHORT_s3d_ref.fits',
['primary','sci','err','dq','wmap']),
('jw80500018001_02101_00002_MIRIFUSHORT_x1d.fits',
'jw80500018001_02101_00002_MIRIFUSHORT_x1d_ref.fits',
['primary','extract1d'])
]
self.compare_outputs(outputs)
def test_mrs_spec2(self):
"""
Regression test of calwebb_spec2 pipeline performed on MIRI MRS data.
"""
self.rtol = 0.000001
input_file = self.get_data(self.test_dir,
'jw10001001001_01101_00001_mirifushort_rate.fits')
step = Spec2Pipeline()
step.save_bsub=True
step.save_results=True
step.resample_spec.save_results = True
step.cube_build.save_results = True
step.extract_1d.save_results = True
step.run(input_file)
outputs = [('jw10001001001_01101_00001_mirifushort_cal.fits',
'jw10001001001_01101_00001_mirifushort_cal_ref.fits',
['primary','sci','err','dq']),
('jw10001001001_01101_00001_mirifushort_s3d.fits',
'jw10001001001_01101_00001_mirifushort_s3d_ref.fits',
['primary','sci','err','dq','wmap']),
('jw10001001001_01101_00001_mirifushort_x1d.fits',
'jw10001001001_01101_00001_mirifushort_x1d_ref.fits',
['primary','extract1d'])
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,512
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/fgs/test_guider_pipeline.py
|
import pytest
from jwst.pipeline.calwebb_guider import GuiderPipeline
from jwst.tests.base_classes import BaseJWSTTest
@pytest.mark.bigdata
class TestGuiderPipeline(BaseJWSTTest):
input_loc = 'fgs'
ref_loc = ['test_guiderpipeline', 'truth']
test_dir = 'test_guiderpipeline'
rtol = 0.000001
def test_guider_pipeline1(self):
"""
Regression test of calwebb_guider pipeline performed on ID-image data.
"""
input_file = self.get_data(self.test_dir,
'jw88600073001_gs-id_7_image-uncal.fits')
GuiderPipeline.call(input_file,
output_file='jw88600073001_gs-id_7_image-cal.fits')
# Compare calibrated ramp product
outputs = [('jw88600073001_gs-id_7_image-cal.fits',
'jw88600073001_gs-id_7_image-cal_ref.fits',
['primary','sci','dq'])
]
self.compare_outputs(outputs)
def test_guider_pipeline2(self):
"""
Regression test of calwebb_guider pipeline performed on ACQ-1 data.
"""
input_file = self.get_data(self.test_dir,
'jw88600073001_gs-acq1_2016022183837_uncal.fits')
GuiderPipeline.call(input_file,
output_file='jw88600073001_gs-acq1_2016022183837_cal.fits')
# Compare calibrated ramp product
outputs = [('jw88600073001_gs-acq1_2016022183837_cal.fits',
'jw88600073001_gs-acq1_2016022183837_cal_ref.fits',
['primary','sci','dq'])
]
self.compare_outputs(outputs)
def test_guider_pipeline3(self):
"""
Regression test of calwebb_guider pipeline performed on ID STACKED data.
"""
input_file = self.get_data(self.test_dir,
'jw86600004001_gs-id_1_stacked-uncal.fits')
GuiderPipeline.call(input_file,
output_file='jw86600004001_gs-id_1_stacked-cal.fits')
# Compare calibrated ramp product
outputs = [('jw86600004001_gs-id_1_stacked-cal.fits',
'jw86600004001_gs-id_1_stacked-cal_ref.fits',
['primary','sci','dq'])
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,513
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nircam/test_nircam_steps.py
|
import pytest
from jwst.tests.base_classes import BaseJWSTTestSteps
from jwst.tests.base_classes import pytest_generate_tests # noqa: F401
from jwst.refpix import RefPixStep
from jwst.dark_current import DarkCurrentStep
from jwst.dq_init import DQInitStep
from jwst.flatfield import FlatFieldStep
from jwst.ipc import IPCStep
from jwst.jump import JumpStep
from jwst.linearity import LinearityStep
from jwst.persistence import PersistenceStep
from jwst.photom import PhotomStep
from jwst.saturation import SaturationStep
# Parameterized regression tests for NIRCAM processing
# All tests in this set run with 1 input file and
# only generate 1 output for comparison.
#
@pytest.mark.bigdata
class TestNIRCamSteps(BaseJWSTTestSteps):
input_loc = 'nircam'
params = {'test_steps':
[dict(input='jw00017001001_01101_00001_NRCA1_dq_init.fits',
test_dir='test_bias_drift',
step_class=RefPixStep,
step_pars=dict(odd_even_columns=True,
use_side_ref_pixels=False,
side_smoothing_length=10,
side_gain=1.0),
output_truth='jw00017001001_01101_00001_NRCA1_bias_drift.fits',
output_hdus=[],
id='refpix_nircam'
),
dict(input='jw00017001001_01101_00001_NRCA1_saturation.fits',
test_dir='test_dark_step',
step_class=DarkCurrentStep,
step_pars=dict(),
output_truth='jw00017001001_01101_00001_NRCA1_dark_current.fits',
output_hdus=[],
id='dark_current_nircam'
),
dict(input='jw00017001001_01101_00001_NRCA1_uncal.fits',
test_dir='test_dq_init',
step_class=DQInitStep,
step_pars=dict(),
output_truth='jw00017001001_01101_00001_NRCA1_dq_init.fits',
output_hdus=[],
id='dq_init_nircam'
),
dict(input='jw00017001001_01101_00001_NRCA1_ramp_fit.fits',
test_dir='test_flat_field',
step_class=FlatFieldStep,
step_pars=dict(),
output_truth='jw00017001001_01101_00001_NRCA1_flat_field.fits',
output_hdus=[],
id='flat_field_nircam'
),
dict(input='jw00017001001_01101_00001_NRCA3_uncal.fits',
test_dir='test_ipc_step',
step_class=IPCStep,
step_pars=dict(),
output_truth='jw00017001001_01101_00001_NRCA3_ipc.fits',
output_hdus=['primary', 'sci'],
id='ipc_nircam'
),
dict(input='jw00017001001_01101_00001_NRCA1_linearity.fits',
test_dir='test_jump',
step_class=JumpStep,
step_pars=dict(rejection_threshold=25.0),
output_truth='jw00017001001_01101_00001_NRCA1_jump.fits',
output_hdus=[],
id='jump_nircam'
),
dict(input='jw00017001001_01101_00001_NRCA1_dark_current.fits',
test_dir='test_linearity',
step_class=LinearityStep,
step_pars=dict(),
output_truth='jw00017001001_01101_00001_NRCA1_linearity.fits',
output_hdus=[],
id='linearity_nircam'
),
dict(input='jw00017001001_01101_00001_NRCA1_ramp.fits',
test_dir='test_persistence',
step_class=PersistenceStep,
step_pars=dict(),
output_truth='jw00017001001_01101_00001_NRCA1_persistence.fits',
output_hdus=[],
id='persistence_nircam'
),
dict(input='jw00017001001_01101_00001_NRCA1_emission.fits',
test_dir='test_photom',
step_class=PhotomStep,
step_pars=dict(),
output_truth='jw00017001001_01101_00001_NRCA1_photom.fits',
output_hdus=[],
id='photom_nircam'
),
dict(input='jw00017001001_01101_00001_NRCA1_bias_drift.fits',
test_dir='test_saturation',
step_class=SaturationStep,
step_pars=dict(),
output_truth='jw00017001001_01101_00001_NRCA1_saturation.fits',
output_hdus=[],
id='saturation_nircam'
),
]
}
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,514
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py
|
import pytest
from jwst.pipeline import Image2Pipeline
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.tests.base_classes import BaseJWSTTest
@pytest.mark.bigdata
class TestImage2Pipeline(BaseJWSTTest):
input_loc = 'nircam'
ref_loc = ['test_image2pipeline', 'truth']
def test_image2pipeline2_cal(self):
"""
Regression test of calwebb_image2 pipeline performed on NIRCam data.
"""
input_file = self.get_data('test_image2pipeline',
'jw82500001003_02101_00001_NRCALONG_rate.fits')
output_file = 'jw82500001003_02101_00001_NRCALONG_cal.fits'
collect_pipeline_cfgs('cfgs')
Image2Pipeline.call(input_file,
config_file='cfgs/calwebb_image2.cfg',
output_file=output_file)
outputs = [(output_file,
'jw82500001003_02101_00001_NRCALONG_cal_ref.fits',
['primary','sci','err','dq','area']),
('jw82500001003_02101_00001_NRCALONG_i2d.fits',
'jw82500001003_02101_00001_NRCALONG_i2d_ref.fits',
['primary','sci','con','wht'])
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,515
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests/base_classes.py
|
from glob import glob as _sys_glob
import os
from os import path as op
from pathlib import Path
import sys
import pytest
import requests
from ci_watson.artifactory_helpers import (
BigdataError,
check_url,
get_bigdata,
get_bigdata_root,
)
from .compare_outputs import compare_outputs
from jwst.associations import load_asn
__all__ = [
'BaseJWSTTest',
]
# Define location of default Artifactory API key, for Jenkins use only
ARTIFACTORY_API_KEY_FILE = '/eng/ssb2/keys/svc_rodata.key'
@pytest.mark.usefixtures('_jail')
@pytest.mark.bigdata
class BaseJWSTTest:
'''
Base test class from which to derive JWST regression tests
'''
rtol = 0.00001
atol = 0
input_loc = '' # root directory for 'input' files
ref_loc = [] # root path for 'truth' files: ['test1','truth'] or ['test3']
ignore_table_keywords = []
ignore_fields = []
ignore_hdus = ['ASDF']
ignore_keywords = ['DATE', 'CAL_VER', 'CAL_VCS', 'CRDS_VER', 'CRDS_CTX', 'FILENAME']
@pytest.fixture(autouse=True)
def config_env(self, pytestconfig, envopt):
self.env = pytestconfig.getoption('env')
@pytest.fixture(autouse=True)
def config_access(self, pytestconfig):
self.inputs_root = pytestconfig.getini('inputs_root')[0]
self.results_root = pytestconfig.getini('results_root')[0]
@property
def repo_path(self):
return [self.inputs_root, self.env, self.input_loc]
def get_data(self, *pathargs, docopy=True):
"""
Download `filename` into working directory using
`artifactory_helpers/get_bigdata()`.
This will then return the full path to the local copy of the file.
"""
local_file = get_bigdata(*self.repo_path, *pathargs, docopy=docopy)
return local_file
def compare_outputs(self, outputs, raise_error=True, **kwargs):
# Parse any user-specified kwargs
ignore_keywords = kwargs.get('ignore_keywords', self.ignore_keywords)
ignore_hdus = kwargs.get('ignore_hdus', self.ignore_hdus)
ignore_fields = kwargs.get('ignore_fields', self.ignore_fields)
rtol = kwargs.get('rtol', self.rtol)
atol = kwargs.get('atol', self.atol)
compare_kws = dict(ignore_fields=ignore_fields, ignore_hdus=ignore_hdus,
ignore_keywords=ignore_keywords,
rtol=rtol, atol=atol)
input_path = [self.inputs_root, self.env, self.input_loc, *self.ref_loc]
return compare_outputs(outputs,
input_path=input_path,
docopy=True,
results_root=self.results_root,
**compare_kws)
def data_glob(self, *pathargs, glob='*'):
"""Retrieve file list matching glob
Parameters
----------
pathargs: (str[, ...])
Path components
glob: str
The file name match criterion
Returns
-------
file_paths: [str[, ...]]
File paths that match the glob criterion.
Note that the TEST_BIGDATA and `repo_path`
roots are removed so these results can be fed
back into `get_data()`
"""
# Get full path and proceed depending on whether
# is a local path or URL.
root = get_bigdata_root()
if op.exists(root):
path = op.join(root, *self.repo_path)
root_len = len(path) + 1
path = op.join(path, *pathargs)
file_paths = _data_glob_local(path, glob)
elif check_url(root):
root_len = len(op.join(*self.repo_path[1:])) + 1
path = op.join(*self.repo_path, *pathargs)
file_paths = _data_glob_url(path, glob, root=root)
else:
raise BigdataError('Path cannot be found: {}'.format(path))
# Remove the root from the paths
file_paths = [
file_path[root_len:]
for file_path in file_paths
]
return file_paths
# Pytest function to support the parameterization of BaseJWSTTestSteps
def pytest_generate_tests(metafunc):
# called once per each test function
funcarglist = metafunc.cls.params[metafunc.function.__name__]
argnames = sorted(funcarglist[0])
idlist = [funcargs['id'] for funcargs in funcarglist]
del argnames[argnames.index('id')]
metafunc.parametrize(argnames, [[funcargs[name] for name in argnames]
for funcargs in funcarglist], ids=idlist)
class BaseJWSTTestSteps(BaseJWSTTest):
params = {'test_steps':[dict(input="",
test_dir=None,
step_class=None,
step_pars=dict(),
output_truth="",
output_hdus=[])
]
}
def test_steps(self, input, test_dir, step_class, step_pars,
output_truth, output_hdus):
"""
Template method for parameterizing all the tests of JWST pipeline
processing steps.
"""
if test_dir is None:
return
self.test_dir = test_dir
self.ref_loc = [self.test_dir, 'truth']
# can be removed once all truth files have been updated
self.ignore_keywords += ['FILENAME']
input_file = self.get_data(self.test_dir, input)
result = step_class.call(input_file, save_results=True, **step_pars)
output_file = result.meta.filename
result.close()
output_pars = None
if isinstance(output_truth, tuple):
output_pars = output_truth[1]
output_truth = output_truth[0]
if not output_pars:
if output_hdus:
output_spec = (output_file, output_truth, output_hdus)
else:
output_spec = (output_file, output_truth)
else:
output_spec = {'files':(output_file, output_truth),
'pars':output_pars}
outputs = [output_spec]
self.compare_outputs(outputs)
def raw_from_asn(asn_file):
"""
Return a list of all input files from a given association.
Parameters
----------
asn_file : str
Filename for the ASN file.
Returns
-------
members : list of str
A list of all input files in the association
"""
members = []
with open(asn_file) as f:
asn = load_asn(f)
for product in asn['products']:
for member in product['members']:
members.append(member['expname'])
return members
def _data_glob_local(*glob_parts):
"""Perform a glob on the local path
Parameters
----------
glob_parts: (path-like,[...])
List of components that will be built into a single path
Returns
-------
file_paths: [str[, ...]]
Full file paths that match the glob criterion
"""
full_glob = Path().joinpath(*glob_parts)
return _sys_glob(str(full_glob))
def _data_glob_url(*url_parts, root=None):
"""
Parameters
----------
url: (str[,...])
List of components that will be used to create a URL path
root: str
The root server path to the Artifactory server.
Normally retrieved from `get_bigdata_root`.
Returns
-------
url_paths: [str[, ...]]
Full URLS that match the glob criterion
"""
# Fix root root-ed-ness
if root.endswith('/'):
root = root[:-1]
# Access
try:
envkey = os.environ['API_KEY_FILE']
except KeyError:
envkey = ARTIFACTORY_API_KEY_FILE
try:
with open(envkey) as fp:
headers = {'X-JFrog-Art-Api': fp.readline().strip()}
except (PermissionError, FileNotFoundError):
print("Warning: Anonymous Artifactory search requests are limited to "
"1000 results. Use an API key and define API_KEY_FILE environment "
"variable to get full search results.", file=sys.stderr)
headers = None
search_url = '/'.join([root, 'api/search/pattern'])
# Join and re-split the url so that every component is identified.
url = '/'.join([root] + [idx for idx in url_parts])
all_parts = url.split('/')
# Pick out "jwst-pipeline", the repo name
repo = all_parts[4]
# Format the pattern
pattern = repo + ':' + '/'.join(all_parts[5:])
# Make the query
params = {'pattern': pattern}
with requests.get(search_url, params=params, headers=headers) as r:
url_paths = r.json()['files']
return url_paths
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,516
|
mperrin/jwst
|
refs/heads/master
|
/jwst/master_background/tests/test_nirspec_corrections.py
|
"""
Unit tests for master background NIRSpec corrections
"""
import numpy as np
from jwst import datamodels
from ..nirspec_corrections import correct_nrs_ifu_bkg
def test_ifu_pathloss_existence():
"""Test the case where the input is missing a pathloss array"""
input = datamodels.IFUImageModel((10, 10))
result = correct_nrs_ifu_bkg(input)
assert (result == input)
def test_ifu_correction():
"""Test application of IFU corrections"""
data = np.ones((5, 5))
pl_ps = 2 * data
pl_un = data / 2
input = datamodels.IFUImageModel(data=data,
pathloss_point=pl_ps,
pathloss_uniform=pl_un)
corrected = input.data * pl_ps / pl_un
result = correct_nrs_ifu_bkg(input)
assert np.allclose(corrected, result.data, rtol=1.e-10)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,517
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/tests/test_fits.py
|
import os
import shutil
import tempfile
import pytest
import numpy as np
from numpy.testing import assert_array_equal
from asdf import schema as mschema
from .. import DataModel, ImageModel, RampModel
from ..util import open
ROOT_DIR = None
FITS_FILE = None
TMP_FITS = None
TMP_FITS2 = None
TMP_YAML = None
TMP_JSON = None
TMP_DIR = None
def setup():
global ROOT_DIR, FITS_FILE, TMP_DIR, TMP_FITS, TMP_YAML, TMP_JSON, TMP_FITS2
ROOT_DIR = os.path.join(os.path.dirname(__file__), 'data')
FITS_FILE = os.path.join(ROOT_DIR, 'test.fits')
TMP_DIR = tempfile.mkdtemp()
TMP_FITS = os.path.join(TMP_DIR, 'tmp.fits')
TMP_YAML = os.path.join(TMP_DIR, 'tmp.yaml')
TMP_JSON = os.path.join(TMP_DIR, 'tmp.json')
TMP_FITS2 = os.path.join(TMP_DIR, 'tmp2.fits')
def teardown():
shutil.rmtree(TMP_DIR)
def records_equal(a, b):
a = a.item()
b = b.item()
a_size = len(a)
b_size = len(b)
equal = a_size == b_size
for i in range(a_size):
if not equal: break
equal = a[i] == b[i]
return equal
def test_from_new_hdulist():
with pytest.raises(AttributeError):
from astropy.io import fits
hdulist = fits.HDUList()
with open(hdulist) as dm:
dm.data
def test_from_new_hdulist2():
from astropy.io import fits
hdulist = fits.HDUList()
data = np.empty((50, 50), dtype=np.float32)
primary = fits.PrimaryHDU()
hdulist.append(primary)
science = fits.ImageHDU(data=data, name='SCI')
hdulist.append(science)
with open(hdulist) as dm:
dq = dm.dq
assert dq is not None
def test_setting_arrays_on_fits():
from astropy.io import fits
hdulist = fits.HDUList()
data = np.empty((50, 50), dtype=np.float32)
primary = fits.PrimaryHDU()
hdulist.append(primary)
science = fits.ImageHDU(data=data, name='SCI')
hdulist.append(science)
with open(hdulist) as dm:
dm.data = np.empty((50, 50), dtype=np.float32)
dm.dq = np.empty((10, 50, 50), dtype=np.uint32)
def delete_array():
with pytest.raises(AttributeError):
from astropy.io import fits
hdulist = fits.HDUList()
data = np.empty((50, 50))
science = fits.ImageHDU(data=data, name='SCI')
hdulist.append(science)
hdulist.append(science)
with open(hdulist) as dm:
del dm.data
assert len(hdulist) == 1
def test_from_fits():
with RampModel(FITS_FILE) as dm:
assert dm.meta.instrument.name == 'MIRI'
assert dm.shape == (5, 35, 40, 32)
def test_from_scratch():
with ImageModel((50, 50)) as dm:
data = np.asarray(np.random.rand(50, 50), np.float32)
dm.data[...] = data
dm.meta.instrument.name = 'NIRCAM'
dm.to_fits(TMP_FITS, overwrite=True)
with ImageModel.from_fits(TMP_FITS) as dm2:
assert dm2.shape == (50, 50)
assert dm2.meta.instrument.name == 'NIRCAM'
assert dm2.dq.dtype.name == 'uint32'
assert np.all(dm2.data == data)
def test_delete():
with DataModel(FITS_FILE) as dm:
dm.meta.instrument.name = 'NIRCAM'
assert dm.meta.instrument.name == 'NIRCAM'
del dm.meta.instrument.name
assert dm.meta.instrument.name is None
# def test_section():
# with RampModel((5, 35, 40, 32)) as dm:
# section = dm.get_section('data')[3:4, 1:3]
# assert section.shape == (1, 2, 40, 32)
# def test_date_obs():
# with DataModel(FITS_FILE) as dm:
# assert dm.meta.observation.date.microsecond == 314592
def test_fits_without_sci():
from astropy.io import fits
schema = {
"allOf": [
mschema.load_schema(
os.path.join(os.path.dirname(__file__),
"../schemas/core.schema.yaml"),
resolve_references=True),
{
"type": "object",
"properties": {
"coeffs": {
'max_ndim': 1,
'fits_hdu': 'COEFFS',
'datatype': 'float32'
}
}
}
]
}
fits = fits.HDUList(
[fits.PrimaryHDU(),
fits.ImageHDU(name='COEFFS', data=np.array([0.0], np.float32))])
with DataModel(fits, schema=schema) as dm:
assert_array_equal(dm.coeffs, [0.0])
def _header_to_dict(x):
return dict((a, b) for (a, b, c) in x)
def test_extra_fits():
path = os.path.join(ROOT_DIR, "headers.fits")
assert os.path.exists(path)
with DataModel(path) as dm:
assert 'BITPIX' not in _header_to_dict(dm.extra_fits.PRIMARY.header)
assert _header_to_dict(dm.extra_fits.PRIMARY.header)['SCIYSTRT'] == 705
dm2 = dm.copy()
dm2.to_fits(TMP_FITS, overwrite=True)
with DataModel(TMP_FITS) as dm:
assert 'BITPIX' not in _header_to_dict(dm.extra_fits.PRIMARY.header)
assert _header_to_dict(dm.extra_fits.PRIMARY.header)['SCIYSTRT'] == 705
def test_hdu_order():
from astropy.io import fits
with ImageModel(data=np.array([[0.0]]),
dq=np.array([[0.0]]),
err=np.array([[0.0]])) as dm:
dm.save(TMP_FITS)
with fits.open(TMP_FITS, memmap=False) as hdulist:
assert hdulist[1].header['EXTNAME'] == 'SCI'
assert hdulist[2].header['EXTNAME'] == 'DQ'
assert hdulist[3].header['EXTNAME'] == 'ERR'
def test_casting():
with RampModel(FITS_FILE) as dm:
sum = np.sum(dm.data)
dm.data[:] = dm.data + 2
assert np.sum(dm.data) > sum
# def test_comments():
# with RampModel(FITS_FILE) as dm:
# assert 'COMMENT' in (x[0] for x in dm._extra_fits.PRIMARY)
# dm._extra_fits.PRIMARY.COMMENT = ['foobar']
# assert dm._extra_fits.PRIMARY.COMMENT == ['foobar']
def test_fits_comments():
with ImageModel() as dm:
dm.meta.subarray.xstart = 42
dm.save(TMP_FITS, overwrite=True)
from astropy.io import fits
with fits.open(TMP_FITS, memmap=False) as hdulist:
header = hdulist[0].header
find = ['Subarray parameters']
found = 0
for card in header.cards:
if card[1] in find:
found += 1
assert found == len(find)
def test_metadata_doesnt_override():
with ImageModel() as dm:
dm.save(TMP_FITS, overwrite=True)
from astropy.io import fits
with fits.open(TMP_FITS, mode='update', memmap=False) as hdulist:
hdulist[0].header['FILTER'] = 'F150W2'
with ImageModel(TMP_FITS) as dm:
assert dm.meta.instrument.filter == 'F150W2'
def test_table_with_metadata():
schema = {
"allOf": [
mschema.load_schema(
os.path.join(os.path.dirname(__file__),
"../schemas/core.schema.yaml"),
resolve_references=True),
{"type": "object",
"properties": {
"flux_table": {
"title": "Photometric flux conversion table",
"fits_hdu": "FLUX",
"datatype":
[
{"name": "parameter", "datatype": ['ascii', 7]},
{"name": "factor", "datatype": "float64"},
{"name": "uncertainty", "datatype": "float64"}
]
},
"meta": {
"type": "object",
"properties": {
"fluxinfo": {
"title": "Information about the flux conversion",
"type": "object",
"properties": {
"exposure": {
"title": "Description of exposure analyzed",
"type": "string",
"fits_hdu": "FLUX",
"fits_keyword": "FLUXEXP"
}
}
}
}
}
}
}
]
}
class FluxModel(DataModel):
def __init__(self, init=None, flux_table=None, **kwargs):
super(FluxModel, self).__init__(init=init, schema=schema, **kwargs)
if flux_table is not None:
self.flux_table = flux_table
flux_im = [
('F560W', 1.0e-5, 1.0e-7),
('F770W', 1.1e-5, 1.6e-7),
]
with FluxModel(flux_table=flux_im) as datamodel:
datamodel.meta.fluxinfo.exposure = 'Exposure info'
datamodel.save(TMP_FITS, overwrite=True)
del datamodel
from astropy.io import fits
with fits.open(TMP_FITS, memmap=False) as hdulist:
assert len(hdulist) == 3
assert isinstance(hdulist[1], fits.BinTableHDU)
assert hdulist[1].name == 'FLUX'
assert hdulist[2].name == 'ASDF'
def test_replace_table():
from astropy.io import fits
schema_narrow = {
"allOf": [
mschema.load_schema(
os.path.join(os.path.dirname(__file__),
"../schemas/core.schema.yaml"),
resolve_references=True),
{
"type": "object",
"properties": {
"data": {
"title": "relative sensitivity table",
"fits_hdu": "RELSENS",
"datatype": [
{"name": "TYPE", "datatype": ["ascii", 16]},
{"name": "T_OFFSET", "datatype": "float32"},
{"name": "DECAY_PEAK", "datatype": "float32"},
{"name": "DECAY_FREQ", "datatype": "float32"},
{"name": "TAU", "datatype": "float32"}
]
}
}
}
]
}
schema_wide = {
"allOf": [
mschema.load_schema(
os.path.join(os.path.dirname(__file__),
"../schemas/core.schema.yaml"),
resolve_references=True),
{
"type": "object",
"properties": {
"data": {
"title": "relative sensitivity table",
"fits_hdu": "RELSENS",
"datatype": [
{"name": "TYPE", "datatype": ["ascii", 16]},
{"name": "T_OFFSET", "datatype": "float64"},
{"name": "DECAY_PEAK", "datatype": "float64"},
{"name": "DECAY_FREQ", "datatype": "float64"},
{"name": "TAU", "datatype": "float64"}
]
}
}
}
]
}
x = np.array([("string", 1., 2., 3., 4.)],
dtype=[('TYPE', 'S16'),
('T_OFFSET', np.float32),
('DECAY_PEAK', np.float32),
('DECAY_FREQ', np.float32),
('TAU', np.float32)])
m = DataModel(schema=schema_narrow)
m.data = x
m.to_fits(TMP_FITS, overwrite=True)
with fits.open(TMP_FITS, memmap=False) as hdulist:
assert records_equal(x, np.asarray(hdulist[1].data))
assert hdulist[1].data.dtype[1].str == '>f4'
assert hdulist[1].header['TFORM2'] == 'E'
with DataModel(TMP_FITS, schema=schema_wide) as m:
m.to_fits(TMP_FITS2, overwrite=True)
with fits.open(TMP_FITS2, memmap=False) as hdulist:
assert records_equal(x, np.asarray(hdulist[1].data))
assert hdulist[1].data.dtype[1].str == '>f8'
assert hdulist[1].header['TFORM2'] == 'D'
def test_table_with_unsigned_int():
schema = {
'title': 'Test data model',
'$schema': 'http://stsci.edu/schemas/fits-schema/fits-schema',
'type': 'object',
'properties': {
'meta': {
'type': 'object',
'properties': {}
},
'test_table': {
'title': 'Test table',
'fits_hdu': 'TESTTABL',
'datatype': [
{'name': 'FLOAT64_COL', 'datatype': 'float64'},
{'name': 'UINT32_COL', 'datatype': 'uint32'}
]
}
}
}
with DataModel(schema=schema) as dm:
float64_info = np.finfo(np.float64)
float64_arr = np.random.uniform(size=(10,))
float64_arr[0] = float64_info.min
float64_arr[-1] = float64_info.max
uint32_info = np.iinfo(np.uint32)
uint32_arr = np.random.randint(uint32_info.min, uint32_info.max + 1, size=(10,), dtype=np.uint32)
uint32_arr[0] = uint32_info.min
uint32_arr[-1] = uint32_info.max
test_table = np.array(list(zip(float64_arr, uint32_arr)), dtype=dm.test_table.dtype)
def assert_table_correct(model):
for idx, (col_name, col_data) in enumerate([('float64_col', float64_arr), ('uint32_col', uint32_arr)]):
# The table dtype and field dtype are stored separately, and may not
# necessarily agree.
assert np.can_cast(model.test_table.dtype[idx], col_data.dtype, 'equiv')
assert np.can_cast(model.test_table.field(col_name).dtype, col_data.dtype, 'equiv')
assert np.array_equal(model.test_table.field(col_name), col_data)
# The datamodel casts our array to FITS_rec on assignment, so here we're
# checking that the data survived the casting.
dm.test_table = test_table
assert_table_correct(dm)
# Confirm that saving the table (and converting the uint32 values to signed int w/TZEROn)
# doesn't mangle the data.
dm.save(TMP_FITS)
assert_table_correct(dm)
# Confirm that the data loads from the file intact (converting the signed ints back to
# the appropriate uint32 values).
with DataModel(TMP_FITS, schema=schema) as dm2:
assert_table_correct(dm2)
def test_metadata_from_fits():
from astropy.io import fits
mask = np.array([[0, 1], [2, 3]])
fits.ImageHDU(data=mask, name='DQ').writeto(TMP_FITS, overwrite=True)
with DataModel(init=TMP_FITS) as dm:
dm.save(TMP_FITS2)
with fits.open(TMP_FITS2, memmap=False) as hdulist:
assert hdulist[2].name == 'ASDF'
# def test_float_as_int():
# from astropy.io import fits
# hdulist = fits.HDUList()
# primary = fits.PrimaryHDU()
# hdulist.append(primary)
# hdulist[0].header['SUBSTRT1'] = 42.7
# hdulist.writeto(TMP_FITS, overwrite=True)
# with DataModel(TMP_FITS) as dm:
# assert dm.meta.subarray.xstart == 42.7
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,518
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py
|
import pytest
from jwst.pipeline.calwebb_image2 import Image2Pipeline
from jwst.tests.base_classes import BaseJWSTTest
@pytest.mark.bigdata
class TestImage2Pipeline(BaseJWSTTest):
input_loc = 'fgs'
ref_loc = ['test_image2pipeline', 'truth']
def test_fgs_image2pipeline1(self):
"""
Regression test of calwebb_image2 pipeline performed on FGS imaging mode data.
"""
input_file = self.get_data('test_image2pipeline',
'jw86500007001_02101_00001_GUIDER2_rate.fits')
output_file = 'jw86500007001_02101_00001_GUIDER2_cal.fits'
Image2Pipeline.call(input_file, save_results=True)
outputs = [(output_file, 'jw86500007001_02101_00001_GUIDER2_cal_ref.fits')]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,519
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py
|
import pytest
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
from jwst.tests.base_classes import BaseJWSTTest
@pytest.mark.bigdata
class TestSpec2Pipeline(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['test_spec2pipeline', 'truth']
def test_mirilrs2pipeline1(self):
"""
Regression test of calwebb_spec2 pipeline performed on
MIRI LRS slitless data.
"""
input_file = self.get_data('test_spec2pipeline',
'jw80600012001_02101_00003_mirimage_rateints.fits')
collect_pipeline_cfgs()
args = [ 'calwebb_tso-spec2.cfg',
input_file,
]
Step.from_cmdline(args)
outputs = [('jw80600012001_02101_00003_mirimage_calints.fits',
'jw80600012001_02101_00003_mirimage_calints_ref.fits',
['primary', 'sci', 'err', 'dq']
),
('jw80600012001_02101_00003_mirimage_x1dints.fits',
'jw80600012001_02101_00003_mirimage_x1dints_ref.fits',
['primary', ('extract1d', 1), ('extract1d', 2), ('extract1d', 3), ('extract1d', 4)]
)
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,520
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py
|
import pytest
import numpy as np
from numpy.testing import assert_allclose
from gwcs.wcstools import grid_from_bounding_box
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
from jwst.assign_wcs import AssignWcsStep, nirspec
from jwst.datamodels import ImageModel
from jwst.pipeline import Detector1Pipeline, Spec2Pipeline
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.imprint import ImprintStep
from jwst.ramp_fitting import RampFitStep
from jwst.master_background import MasterBackgroundStep
from jwst import datamodels
@pytest.mark.bigdata
class TestDetector1Pipeline(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_pipelines', 'truth']
test_dir = 'test_pipelines'
def test_detector1pipeline4(self):
"""
Regression test of calwebb_detector1 pipeline performed on NIRSpec data.
"""
input_file = self.get_data(self.test_dir,
'jw84600007001_02101_00001_nrs1_uncal.fits')
step = Detector1Pipeline()
step.save_calibrated_ramp = True
step.ipc.skip = True
step.persistence.skip = True
step.jump.rejection_threshold = 4.0
step.ramp_fit.save_opt = False
step.output_file = 'jw84600007001_02101_00001_nrs1_rate.fits'
step.run(input_file)
outputs = [('jw84600007001_02101_00001_nrs1_ramp.fits',
'jw84600007001_02101_00001_nrs1_ramp_ref.fits'),
('jw84600007001_02101_00001_nrs1_rate.fits',
'jw84600007001_02101_00001_nrs1_rate_ref.fits')
]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestNIRSpecImprint(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_imprint', 'truth']
test_dir = 'test_imprint'
def test_imprint_nirspec(self):
"""
Regression test of imprint step performed on NIRSpec MSA data.
"""
input_file = self.get_data(self.test_dir,
'jw00038001001_01101_00001_NRS1_rate.fits')
model_file = self.get_data(self.test_dir,
'NRSMOS-MODEL-21_NRS1_rate.fits')
result = ImprintStep.call(input_file, model_file, name='imprint')
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00038001001_01101_00001_NRS1_imprint.fits')]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestNIRSpecRampFit(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_ramp_fit', 'truth']
test_dir = 'test_ramp_fit'
def test_ramp_fit_nirspec(self):
"""
Regression test of ramp_fit step performed on NIRSpec data. This is a single
integration dataset.
"""
input_file = self.get_data(self.test_dir,
'jw00023001001_01101_00001_NRS1_jump.fits')
result, result_int = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit_opt_out.fits', name='RampFit'
)
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00023001001_01101_00001_NRS1_ramp_fit.fits'),
('rampfit_opt_out_fitopt.fits',
'jw00023001001_01101_00001_NRS1_opt.fits',
['primary','slope','sigslope','yint','sigyint',
'pedestal','weights','crmag'])
]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestNIRSpecWCS(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_wcs', 'nrs1-fs', 'truth']
test_dir = ['test_wcs', 'nrs1-fs']
def test_nirspec_nrs1_wcs(self):
"""
Regression test of creating a WCS object and doing pixel to sky transformation.
"""
input_file = self.get_data(*self.test_dir,
'jw00023001001_01101_00001_NRS1_ramp_fit.fits')
ref_file = self.get_data(*self.ref_loc,
'jw00023001001_01101_00001_NRS1_ramp_fit_assign_wcs.fits')
result = AssignWcsStep.call(input_file, save_results=True, suffix='assign_wcs')
result.close()
im = ImageModel(result.meta.filename)
imref = ImageModel(ref_file)
for slit in ['S200A1', 'S200A2', 'S400A1', 'S1600A1']:
w = nirspec.nrs_wcs_set_input(im, slit)
grid = grid_from_bounding_box(w.bounding_box)
ra, dec, lam = w(*grid)
wref = nirspec.nrs_wcs_set_input(imref, slit)
raref, decref, lamref = wref(*grid)
assert_allclose(ra, raref, equal_nan=True)
assert_allclose(dec, decref, equal_nan=True)
assert_allclose(lam, lamref, equal_nan=True)
@pytest.mark.bigdata
class TestNRSSpec2(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_pipelines', 'truth']
test_dir = 'test_pipelines'
def test_nrs_fs_single_spec2(self):
"""
Regression test of calwebb_spec2 pipeline performed on NIRSpec fixed-slit data
that uses a single-slit subarray (S200B1).
"""
input_file = self.get_data(self.test_dir,
'jw84600002001_02101_00001_nrs2_rate.fits')
step = Spec2Pipeline()
step.save_bsub = True
step.save_results = True
step.resample_spec.save_results = True
step.cube_build.save_results = True
step.extract_1d.save_results = True
step.run(input_file)
outputs = [('jw84600002001_02101_00001_nrs2_cal.fits',
'jw84600002001_02101_00001_nrs2_cal_ref.fits'),
('jw84600002001_02101_00001_nrs2_s2d.fits',
'jw84600002001_02101_00001_nrs2_s2d_ref.fits'),
('jw84600002001_02101_00001_nrs2_x1d.fits',
'jw84600002001_02101_00001_nrs2_x1d_ref.fits')
]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestNIRSpecMasterBackground_FS(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_masterbackground', 'nrs-fs', 'truth']
test_dir = ['test_masterbackground', 'nrs-fs']
def test_nirspec_fs_masterbg_user(self):
"""
Regression test of master background subtraction for NRS FS when a
user 1-D spectrum is provided.
"""
# input file has 2-D background image added to it
input_file = self.get_data(*self.test_dir, 'nrs_sci+bkg_cal.fits')
# user provided 1-D background was created from the 2-D background image
input_1dbkg_file = self.get_data(*self.test_dir, 'nrs_bkg_user_clean_x1d.fits')
result = MasterBackgroundStep.call(input_file,
user_background=input_1dbkg_file,
save_results=True)
# Compare background-subtracted science data (results)
# to a truth file. These data are MultiSlitModel data
result_file = result.meta.filename
truth_file = self.get_data(*self.ref_loc,
'nrs_sci+bkg_masterbackgroundstep.fits')
outputs = [(result_file, truth_file)]
self.compare_outputs(outputs)
result.close()
@pytest.mark.bigdata
class TestNIRSpecMasterBackground_IFU(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_masterbackground', 'nrs-ifu', 'truth']
test_dir = ['test_masterbackground', 'nrs-ifu']
def test_nirspec_ifu_masterbg_user(self):
"""
Regression test of master background subtraction for NRS IFU when a
user 1-D spectrum is provided.
"""
# input file has 2-D background image added to it
input_file = self.get_data(*self.test_dir, 'prism_sci_bkg_cal.fits')
# user-provided 1-D background was created from the 2-D background image
user_background = self.get_data(*self.test_dir, 'prism_bkg_x1d.fits')
result = MasterBackgroundStep.call(input_file,
user_background=user_background,
save_results=True)
# Test 2 compare the science data with no background
# to the output from the masterBackground Subtraction step
# background subtracted science image.
input_sci_cal_file = self.get_data(*self.test_dir,
'prism_sci_cal.fits')
input_sci_model = datamodels.open(input_sci_cal_file)
# We don't want the slices gaps to impact the statisitic
# loop over the 30 Slices
for i in range(30):
slice_wcs = nirspec.nrs_wcs_set_input(input_sci_model, i)
x, y = grid_from_bounding_box(slice_wcs.bounding_box)
ra, dec, lam = slice_wcs(x, y)
valid = np.isfinite(lam)
result_slice_region = result.data[y.astype(int), x.astype(int)]
sci_slice_region = input_sci_model.data[y.astype(int),
x.astype(int)]
sci_slice = sci_slice_region[valid]
result_slice = result_slice_region[valid]
sub = result_slice - sci_slice
# check for outliers in the science image
sci_mean = np.nanmean(sci_slice)
sci_std = np.nanstd(sci_slice)
upper = sci_mean + sci_std*5.0
lower = sci_mean - sci_std*5.0
mask_clean = np.logical_and(sci_slice < upper, sci_slice > lower)
sub_mean = np.absolute(np.nanmean(sub[mask_clean]))
atol = 2.0
assert_allclose(sub_mean, 0, atol=atol)
# Test 3 Compare background sutracted science data (results)
# to a truth file. This data is MultiSlit data
input_sci_model.close()
result_file = result.meta.filename
truth_file = self.get_data(*self.ref_loc,
'prism_sci_bkg_masterbackgroundstep.fits')
outputs = [(result_file, truth_file)]
self.compare_outputs(outputs)
input_sci_model.close()
result.close()
@pytest.mark.bigdata
class TestNIRSpecMasterBackground_MOS(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_masterbackground', 'nrs-mos', 'truth']
test_dir = ['test_masterbackground', 'nrs-mos']
def test_nirspec_mos_masterbg_user(self):
"""
Regression test of master background subtraction for NRS MOS when
a user 1-D spectrum is provided.
"""
# input file has 2-D background image added to it
input_file = self.get_data(*self.test_dir, 'nrs_mos_sci+bkg_cal.fits')
# user provide 1-D background was created from the 2-D background image
input_1dbkg_file = self.get_data(*self.test_dir, 'nrs_mos_bkg_x1d.fits')
result = MasterBackgroundStep.call(input_file,
user_background=input_1dbkg_file,
save_results=True)
# Compare background subtracted science data (results)
# to a truth file. These data are MultiSlit data.
result_file = result.meta.filename
ref_file = self.get_data(*self.ref_loc, 'nrs_mos_sci+bkg_masterbackgroundstep.fits')
outputs = [(result_file, ref_file)]
self.compare_outputs(outputs)
result.close()
@pytest.mark.bigdata
class TestNIRSpecMasterBackgroundNodded(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_masterbackground', 'nrs-ifu', 'nodded', 'truth']
test_dir = ['test_masterbackground', 'nrs-ifu', 'nodded']
rtol = 0.000001
def test_nirspec_masterbg_nodded(self):
"""Run masterbackground step on NIRSpec association"""
asn_file = self.get_data(*self.test_dir,
'nirspec_spec3_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(*self.test_dir, file)
collect_pipeline_cfgs('./config')
result = MasterBackgroundStep.call(
asn_file,
config_file='config/master_background.cfg',
save_background=True,
save_results=True
)
# test 1
# compare background subtracted data to truth files
# check that the cal_step master_background ran to complete
outputs = []
for model in result:
assert model.meta.cal_step.master_background == 'COMPLETE'
result_file = model.meta.filename.replace('cal', 'master_background')
truth_file = self.get_data(*self.ref_loc, result_file)
outputs.append((result_file, truth_file))
self.compare_outputs(outputs)
# test 2
# compare the master background combined file to truth file
master_combined_bkg_file = 'ifu_prism_source_off_fix_NRS1_o001_masterbg.fits'
truth_background = self.get_data(*self.ref_loc,
master_combined_bkg_file)
outputs = [(master_combined_bkg_file, truth_background)]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,521
|
mperrin/jwst
|
refs/heads/master
|
/jwst/stpipe/tests/test_pipeline.py
|
from os.path import dirname, join, abspath
import sys
import numpy as np
from numpy.testing import assert_allclose
import pytest
from jwst.stpipe import Step, Pipeline, LinearPipeline
from jwst import datamodels
# TODO: Test system call steps
def library_function():
import logging
log = logging.getLogger()
log.info("This is a library function log")
class FlatField(Step):
"""
An example flat-fielding Step.
"""
# Load the spec from a file
def process(self, science, flat):
self.log.info("Removing flat field")
self.log.info("Threshold: {0}".format(self.threshold))
library_function()
output = datamodels.ImageModel(data=science.data - flat.data)
return output
class Combine(Step):
"""
A Step that combines a list of images.
"""
def process(self, images):
combined = np.zeros((50, 50))
for image in images:
combined += image.data
return datamodels.ImageModel(data=combined)
class Display(Step):
"""
A Step to display an image.
"""
def process(self, image):
pass
class MultiplyBy2(Step):
"""
A Step that does the incredibly complex thing of multiplying by 2.
"""
def process(self, image):
with datamodels.ImageModel(image) as dm:
dm2 = datamodels.ImageModel()
dm2.data = dm.data * 2
return dm2
class MyPipeline(Pipeline):
"""
A test pipeline.
"""
step_defs = {
'flat_field': FlatField,
'combine': Combine,
'display': Display
}
spec = """
science_filename = input_file() # The input science filename
flat_filename = input_file(default=None) # The input flat filename
output_filename = output_file() # The output filename
"""
def process(self, *args):
science = datamodels.open(self.science_filename)
if self.flat_filename is None:
self.flat_filename = join(dirname(__file__), "data/flat.fits")
flat = datamodels.open(self.flat_filename)
calibrated = []
calibrated.append(self.flat_field(science, flat))
combined = self.combine(calibrated)
self.display(combined)
dm = datamodels.ImageModel(combined)
dm.save(self.output_filename)
science.close()
flat.close()
return dm
def test_pipeline(_jail):
pipeline_fn = join(dirname(__file__), 'steps', 'python_pipeline.cfg')
pipe = Step.from_config_file(pipeline_fn)
pipe.output_filename = "output.fits"
assert pipe.flat_field.threshold == 42.0
assert pipe.flat_field.multiplier == 2.0
pipe.run()
def test_pipeline_python(_jail):
steps = {
'flat_field': {'threshold': 42.0}
}
pipe = MyPipeline(
"MyPipeline",
config_file=__file__,
steps=steps,
science_filename=abspath(join(dirname(__file__), 'data', 'science.fits')),
flat_filename=abspath(join(dirname(__file__), 'data', 'flat.fits')),
output_filename="output.fits")
assert pipe.flat_field.threshold == 42.0
assert pipe.flat_field.multiplier == 1.0
pipe.run()
class MyLinearPipeline(LinearPipeline):
pipeline_steps = [
('multiply', MultiplyBy2),
('multiply2', MultiplyBy2),
('multiply3', MultiplyBy2)
]
def test_partial_pipeline(_jail):
pipe = MyLinearPipeline()
pipe.end_step = 'multiply2'
result = pipe.run(abspath(join(dirname(__file__), 'data', 'science.fits')))
pipe.start_step = 'multiply3'
pipe.end_step = None
result = pipe.run(abspath(join(dirname(__file__), 'data', 'science.fits')))
assert_allclose(np.sum(result.data), 9969.82514685, rtol=1e-4)
def test_pipeline_commandline(_jail):
args = [
abspath(join(dirname(__file__), 'steps', 'python_pipeline.cfg')),
'--steps.flat_field.threshold=47'
]
pipe = Step.from_cmdline(args)
assert pipe.flat_field.threshold == 47.0
assert pipe.flat_field.multiplier == 2.0
pipe.run()
def test_pipeline_commandline_class(_jail):
args = [
'jwst.stpipe.tests.test_pipeline.MyPipeline',
'--logcfg={0}'.format(
abspath(join(dirname(__file__), 'steps', 'log.cfg'))),
# The file_name parameters are *required*
'--science_filename={0}'.format(
abspath(join(dirname(__file__), 'data', 'science.fits'))),
'--output_filename={0}'.format(
'output.fits'),
'--steps.flat_field.threshold=47'
]
pipe = Step.from_cmdline(args)
assert pipe.flat_field.threshold == 47.0
assert pipe.flat_field.multiplier == 1.0
pipe.run()
def test_pipeline_commandline_invalid_args():
from io import StringIO
args = [
'jwst.stpipe.tests.test_pipeline.MyPipeline',
# The file_name parameters are *required*, and one of them
# is missing, so we should get a message to that effect
# followed by the commandline usage message.
'--flat_filename={0}'.format(
abspath(join(dirname(__file__), 'data', 'flat.fits'))),
'--steps.flat_field.threshold=47'
]
sys.stdout = buffer = StringIO()
with pytest.raises(ValueError):
Step.from_cmdline(args)
help = buffer.getvalue()
assert "Multiply by this number" in help
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,522
|
mperrin/jwst
|
refs/heads/master
|
/jwst/rscd/__init__.py
|
from .rscd_step import RSCD_Step
__all__ = ['RSCD_Step']
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,523
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py
|
import pytest
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
from jwst.pipeline import (
Ami3Pipeline,
Detector1Pipeline,
)
from jwst.ramp_fitting import RampFitStep
from jwst.photom import PhotomStep
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
@pytest.mark.bigdata
class TestAMIPipeline(BaseJWSTTest):
input_loc = 'niriss'
ref_loc = ['test_ami_pipeline', 'truth']
test_dir = 'test_ami_pipeline'
def test_ami_pipeline(self):
"""
Regression test of the AMI pipeline performed on NIRISS AMI data.
"""
asn_file = self.get_data(self.test_dir,
'test_lg1_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
pipe = Ami3Pipeline()
pipe.save_averages = True
pipe.ami_analyze.oversample = 3
pipe.ami_analyze.rotation = 1.49
pipe.run(asn_file)
outputs = [('test_targ_aminorm.fits',
'ami_pipeline_targ_lgnorm.fits'),
]
self.compare_outputs(outputs, rtol=0.00001,
ignore_hdus=['ASDF', 'HDRTAB'])
@pytest.mark.bigdata
class TestDetector1Pipeline(BaseJWSTTest):
input_loc = 'niriss'
ref_loc = ['test_detector1pipeline', 'truth']
test_dir = 'test_detector1pipeline'
def test_niriss_detector1(self):
"""
Regression test of calwebb_detector1 pipeline performed on NIRISS data.
"""
input_file = self.get_data(self.test_dir,
'jw00034001001_01101_00001_NIRISS_uncal.fits')
step = Detector1Pipeline()
step.save_calibrated_ramp = True
step.ipc.skip = True
step.persistence.skip = True
step.refpix.odd_even_columns = True
step.refpix.use_side_ref_pixels = True
step.refpix.side_smoothing_length = 11
step.refpix.side_gain = 1.0
step.refpix.odd_even_rows = True
step.jump.rejection_threshold = 250.0
step.ramp_fit.save_opt = False
step.ramp_fit.suffix = 'ramp'
step.output_file = 'jw00034001001_01101_00001_NIRISS_rate.fits'
step.run(input_file)
outputs = [('jw00034001001_01101_00001_NIRISS_ramp.fits',
'jw00034001001_01101_00001_NIRISS_ramp_ref.fits'),
('jw00034001001_01101_00001_NIRISS_rate.fits',
'jw00034001001_01101_00001_NIRISS_rate_ref.fits')
]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestNIRISSSOSS2Pipeline(BaseJWSTTest):
input_loc = 'niriss'
ref_loc = ['test_spec2pipeline', 'truth']
test_dir = 'test_spec2pipeline'
def test_nirisssoss2pipeline1(self):
"""
Regression test of calwebb_tso_spec2 pipeline performed on NIRISS SOSS data.
"""
input_file = self.get_data(self.test_dir,
'jw10003001002_03101_00001-seg003_nis_rateints.fits')
collect_pipeline_cfgs()
args = [
'calwebb_tso-spec2.cfg',
input_file
]
Step.from_cmdline(args)
outputs = [{'files':('jw10003001002_03101_00001-seg003_nis_calints.fits',
'jw10003001002_03101_00001-seg003_nis_calints_ref.fits'),
'pars':dict(ignore_hdus=['INT_TIMES', 'VAR_POISSON',
'VAR_RNOISE', 'ASDF'])},
{'files':('jw10003001002_03101_00001-seg003_nis_x1dints.fits',
'jw10003001002_03101_00001-seg003_nis_x1dints_ref.fits'),
'pars':dict(ignore_hdus=['INT_TIMES', 'ASDF'])}
]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestNIRISSPhotom(BaseJWSTTest):
input_loc = 'niriss'
ref_loc = ['test_photom', 'truth']
test_dir = 'test_photom'
def test_photom_niriss(self):
"""
Regression test of photom step performed on NIRISS imaging data.
"""
input_file = self.get_data(self.test_dir,
'jw00034001001_01101_00001_NIRISS_flat_field.fits')
result = PhotomStep.call(input_file)
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00034001001_01101_00001_NIRISS_photom.fits')
]
self.compare_outputs(outputs)
@pytest.mark.bigdata
class TestNIRISSRampFit(BaseJWSTTest):
input_loc = 'niriss'
ref_loc = ['test_ramp_fit', 'truth']
test_dir = 'test_ramp_fit'
def test_ramp_fit_niriss(self):
"""
Regression test of ramp_fit step performed on NIRISS data.
"""
input_file = self.get_data(self.test_dir,
'jw00034001001_01101_00001_NIRISS_jump.fits')
result, result_int = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit_opt_out.fits'
)
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00034001001_01101_00001_NIRISS_ramp_fit.fits'),
('rampfit_opt_out_fitopt.fits',
'jw00034001001_01101_00001_NIRISS_uncal_opt.fits',
['primary','slope','sigslope','yint','sigyint',
'pedestal','weights','crmag'])
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,524
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py
|
"""Test calwebb_spec3 against NIRSpec Fixed-slit science (FSS)"""
from glob import glob
from os import path
import pytest
from jwst.associations import load_asn
from jwst.pipeline import Spec3Pipeline
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
@pytest.mark.bigdata
class TestSpec3Pipeline(BaseJWSTTest):
input_loc = 'nirspec'
def test_save_source_only(self):
"""Test saving the source-based files only"""
datapath = ['test_datasets', 'fss', '93045', 'level2b']
asn_file = self.get_data(*datapath,
'jw93045-o010_20180725t035735_spec3_001_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(*datapath, file)
pipe = Spec3Pipeline()
pipe.mrs_imatch.skip = True
pipe.outlier_detection.skip = True
pipe.resample_spec.skip = True
pipe.cube_build.skip = True
pipe.extract_1d.skip = True
pipe.run(asn_file)
# Check resulting product
with open(asn_file) as fh:
asn = load_asn(fh)
base_name = asn['products'][0]['name']
product_name = base_name.format(source_id='s00000') + '_cal.fits'
output_files = glob('*')
if product_name in output_files:
output_files.remove(product_name)
else:
assert False
@pytest.mark.xfail(
reason='See Issue JP-1144',
run=False
)
def test_nrs_fs_spec3(self):
"""
Regression test of calwebb_spec3 pipeline performed on
NIRSpec fixed-slit data.
"""
cfg_dir = './cfgs'
collect_pipeline_cfgs(cfg_dir)
datapath = ['test_datasets', 'fss', '93045', 'level2b']
asn_file = self.get_data(*datapath,
'jw93045-o010_20180725t035735_spec3_001_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(*datapath, file)
args = [
path.join(cfg_dir, 'calwebb_spec3.cfg'),
asn_file
]
Step.from_cmdline(args)
# Compare results
outputs = [('jw00023001001_01101_00001_NRS1_cal.fits',
'jw00023001001_01101_00001_NRS1_cal_ref.fits'),
('jw00023001001_01101_00001_NRS1_s2d.fits',
'jw00023001001_01101_00001_NRS1_s2d_ref.fits'),
('jw00023001001_01101_00001_NRS1_x1d.fits',
'jw00023001001_01101_00001_NRS1_x1d_ref.fits')
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,525
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/ndmodel.py
|
"""
Subclass of NDDataBase to support DataModel compatibility with NDData
"""
import os.path
import numpy as np
import collections
from astropy.units import Quantity
from astropy.nddata import nddata_base
from . import util
from . import filetype
from . import properties
#---------------------------------------
# astropy.io.registry compatibility
#---------------------------------------
def identify(origin, path, fileobj, *args, **kwargs):
"""
Identify if file is a DataModel for astropy.io.registry
"""
if fileobj:
file_type = filetype.check(fileobj)
elif path:
if os.path.isfile(path):
file_type = filetype.check(path)
else:
file_type = path.lower().split(".")[1]
else:
file_type = None
flag = file_type and (file_type == "asdf" or file_type == "fits")
return flag
def read(data, *args, **kwargs):
"""
Astropy.io registry compatibility function to wrap util.open
"""
# Translate keyword arguments to those expected by ImageModel
xargs = {}
if kwargs.get("mask"):
xargs["dq"] = kwargs["mask"]
uncertainty = kwargs.get("uncertainty")
if uncertainty:
if isinstance(uncertainty, Quantity):
uncertainty_type = uncertainty.unit
uncertainty = uncertainty.data
else:
uncertainty_type = None
xargs["err"] = uncertainty
else:
uncertainty_type = None
if hasattr(data, 'mask') and hasattr(data, 'data'):
xargs["dq"] = data.mask
data = data.data
if isinstance(data, Quantity):
unit = data.unit
data = data.value
else:
unit = kwargs.get("unit")
# Create the model using the transformed arguments
model = util.open(data, **xargs)
# Add attributes passed as keyword arguments to model
if unit:
model.meta.bunit_data = unit
wcs = kwargs.get("wcs")
if wcs:
model.set_fits_wcs(wcs)
if uncertainty_type:
model.meta.bunit_err = uncertainty_type
return model
def write(data, path, *args, **kwargs):
"""
Astropy.io registry compatabilty function to wrap datamodel.savw
"""
from .model_base import DataModel
if not isinstance(data, DataModel):
model = DataModel(data)
else:
model = data
if isinstance(path, str):
model.save(path, *args, **kwargs)
else:
raise ValueError("Path to write DataModel was not found")
#---------------------------------------
# Astropy NDData compatibility
#---------------------------------------
class NDModel(nddata_base.NDDataBase):
def my_attribute(self, attr):
"""
Test if attribute is part of the NDData interface
"""
properties = frozenset(("data", "mask", "unit", "wcs", "unceratainty"))
return attr in properties
@property
def data(self):
"""
Read the stored dataset.
"""
primary_array_name = self.get_primary_array_name()
if primary_array_name:
primary_array = self.__getattr__(primary_array_name)
else:
raise AttributeError("No attribute 'data'")
return primary_array
@data.setter
def data(self, value):
"""
Write the stored dataset.
"""
primary_array_name = self.get_primary_array_name()
if not primary_array_name:
primary_array_name = 'data'
properties.ObjectNode.__setattr__(self, primary_array_name, value)
@property
def mask(self):
"""
Read the mask for the dataset.
"""
return self.__getattr__('dq')
@mask.setter
def mask(self, value):
"""
Write the mask for the dataset.
"""
properties.ObjectNode.__setattr__(self, 'dq', value)
@property
def unit(self):
"""
Read the units for the dataset.
"""
try:
val = self.meta.bunit_data
except AttributeError:
val = None
return val
@unit.setter
def unit(self, value):
"""
Write the units for the dataset.
"""
self.meta.bunit_data = value
@property
def wcs(self):
"""
Read the world coordinate system (WCS) for the dataset.
"""
return self.get_fits_wcs()
@wcs.setter
def wcs(self, value):
"""
Write the world coordinate system (WCS) to the dataset.
"""
return self.set_fits_wcs(value)
@property
def meta(self):
"""
Read additional meta information about the dataset.
"""
return self.__getattr__('meta')
@property
def uncertainty(self):
"""
Read the uncertainty in the dataset.
"""
err = self.err
try:
val = self.meta.bunit_err
except AttributeError:
val = None
return Uncertainty(err, uncertainty_type=val)
@uncertainty.setter
def uncertainty(self, value):
"""
Write the uncertainty in the dataset.
"""
properties.ObjectNode.__setattr__(self, 'err', value)
if hasattr(value, 'uncertainty_type'):
self.meta.bunit_err = value.uncertainty_type
#---------------------------------------------
# The following classes provide support
# for the NDData interface to Datamodels
#---------------------------------------------
class MetaNode(properties.ObjectNode, collections.abc.MutableMapping):
"""
NDData compatibility class for meta node
"""
def __init__(self, name, instance, schema, ctx):
properties.ObjectNode.__init__(self, name, instance, schema, ctx)
def _find(self, path):
if not path:
return self
cursor = self._instance
schema = self._schema
for attr in path:
try:
cursor = cursor[attr]
except KeyError:
raise KeyError("'%s'" % '.'.join(path))
schema = properties._get_schema_for_property(schema, attr)
key = '.'.join(path)
return properties._make_node(key, cursor, schema, self._ctx)
def __delitem__(self, key):
path = key.split('.')
parent = self._find(path[:-1])
try:
parent.__delattr__(path[-1])
except KeyError:
raise KeyError("'%s'" % key)
def __getitem__(self, key):
path = key.split('.')
return self._find(path)
def __len__(self):
def recurse(val):
n = 0
for subval in val.values():
if isinstance(subval, dict):
n += recurse(subval)
else:
n += 1
return n
return recurse(self._instance)
def __setitem__(self, key, value):
path = key.split('.')
parent = self._find(path[:-1])
try:
parent.__setattr__(path[-1], value)
except KeyError:
raise KeyError("'%s'" % key)
class Uncertainty(np.ndarray):
"""
Subclass ndarray to include an additional property, uncertainty_type
"""
def __new__(cls, err, uncertainty_type=None):
# info on how to subclass np.ndarray is at
# https://docs.scipy.org/doc/numpy/user/basics.subclassing.html
# this code is taken from there
obj = np.asarray(err).view(cls)
obj.uncertainty_type = uncertainty_type
return obj
def __array_finalize__(self, obj):
if obj is None:
return
self.uncertainty_type = getattr(obj, 'uncertainty_type', None)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,526
|
mperrin/jwst
|
refs/heads/master
|
/jwst/ami/utils.py
|
import logging
from jwst.datamodels import dqflags
import numpy as np
import numpy.fft as fft
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
def quadratic(p, x):
"""
Short Summary
-------------
Calculate value of x at minimum or maximum value of y,
(value of quadratic function at argument)
Parameters
----------
p: numpy array, 3 floats
quadratic function: p[0]*x*x + p[1]*x + p[2]
x: 1D float array
arguments of p()
Returns
-------
maxx: float
value of x at minimum or maximum value of y
maxy: float
max y = -b^2/4a occurs at x = -b^2/2a
fit_val: 1D float array
values of quadratic function at arguments in x array
"""
maxx = -p[1] / (2.0 * p[0])
maxy = -p[1] * p[1] / (4.0 * p[0]) + p[2]
fit_val = p[0] * x * x + p[1] * x + p[2]
return maxx, maxy, fit_val
def makeA(nh):
"""
Long Summary
-------------
Writes the 'NRM matrix' that gets pseudo-inverted to provide
(arbitrarily constrained) zero-mean phases of the holes.
Algorithm is taken verbatim from Anand's pseudoinverse.py
Ax = b where x are the nh hole phases, b the nh(nh-1)/2 fringe phases,
and A the NRM matrix
Solve for the hole phases:
Apinv = np.linalg.pinv(A)
Solution for unknown x's:
x = np.dot(Apinv, b)
Following Noah Gamper's convention of fringe phases,
for holes 'a b c d e f g', rows of A are
(-1 +1 0 0 ...)
(0 -1 +1 0 ...)
which is implemented in makeA() as:
matrixA[row,h2] = -1
matrixA[row,h1] = +1
To change the convention just reverse the signs of the 'ones'.
When tested against Alex'' nrm_model.py 'piston_phase' text output
of fringe phases, these signs appear to be correct -
anand@stsci.edu 12 Nov 2014
Parameters
----------
nh: integer
number of holes in NR mask
Returns
-------
matrixA: 2D float array
nh columns, nh(nh-1)/2 rows (eg 21 for nh=7)
"""
log.debug('-------')
log.debug(' makeA:')
ncols = (nh * (nh - 1)) // 2
nrows = nh
matrixA = np.zeros((ncols, nrows))
row = 0
for h2 in range(nh):
for h1 in range(h2 + 1, nh):
if h1 >= nh:
break
else:
log.debug(' row: %s, h1: %s, h2: %s', row, h1, h2)
matrixA[row, h2] = -1
matrixA[row, h1] = +1
row += 1
log.debug('matrixA:')
log.debug(' %s', matrixA)
return matrixA
def fringes2pistons(fringephases, nholes):
"""
Short Summary
-------------
For nrm_model.py to use to extract pistons out of fringes, given
its hole bookkeeping, which apparently matches that of this module,
and is the same as Noah Gamper's.
Parameters
----------
fringephases: 1D integer array
fringe phases
nholes: integer
number of holes
Returns
-------
np.dot(Apinv, fringephases): 1D integer array
pistons in same units as fringe phases
"""
Anrm = makeA(nholes)
Apinv = np.linalg.pinv(Anrm)
return -np.dot(Apinv, fringephases)
def rebin(a=None, rc=(2, 2)):
"""
Short Summary
-------------
Perform simple-minded flux-conserving binning using specified binning
kernel, clipping trailing size mismatch: eg a 10x3 array binned by
3 results in a 3x1 array
Parameters
----------
a: 2D float array
input array to bin
rc: 2D float array
binning kernel
Returns
-------
binned_arr: float array
binned array
"""
binned_arr = krebin(a, (a.shape[0] // rc[0], a.shape[1] // rc[1]))
return binned_arr
def krebin(a, shape):
"""
Short Summary
-------------
Klaus P's fastrebin from web
Parameters
----------
a: 2D float array
input array to rebin
shape: tuple (integer, integer)
dimensions of array 'a' binned down by dimensions of binning kernel
Returns
-------
reshaped_a: 2D float array
reshaped input array
"""
sh = shape[0], a.shape[0] // shape[0], shape[1], a.shape[1] // shape[1]
reshaped_a = a.reshape(sh).sum(-1).sum(1)
return reshaped_a
def rcrosscorrelate(a=None, b=None):
"""
Short Summary
-------------
Calculate cross correlation of two identically-shaped real arrays
Parameters
----------
a: 2D float array
first input array
b: 2D float array
second input array
Returns
-------
c.real.copy():
real part of array that is the correlation of the two input arrays.
"""
c = crosscorrelate(a=a, b=b)/(np.sqrt((a*a).sum())*np.sqrt((b*b).sum()))
return c.real.copy()
def crosscorrelate(a=None, b=None):
"""
Short Summary
-------------
Calculate cross correlation of two identically-shaped real or complex arrays
Parameters
----------
a: 2D complex float array
first input array
b: 2D complex float array
second input array
Returns
-------
fft.fftshift(c)
complex array that is the correlation of the two input arrays.
"""
if a.shape != b.shape:
log.critical('crosscorrelate: need identical arrays')
return None
fac = np.sqrt(a.shape[0] * a.shape[1])
A = fft.fft2(a) / fac
B = fft.fft2(b) / fac
c = fft.ifft2(A * B.conj()) * fac * fac
log.debug('----------------')
log.debug(' crosscorrelate:')
log.debug(' a: %s:', a)
log.debug(' A: %s:', A)
log.debug(' b: %s:', b)
log.debug(' B: %s:', B)
log.debug(' c: %s:', c)
log.debug(' a.sum(): %s:', a.sum())
log.debug(' b.sum(): %s:', b.sum())
log.debug(' c.sum(): %s:', c.sum())
log.debug(' a.sum()*b.sum(): %s:', a.sum() * b.sum())
log.debug(' c.sum().real: %s:', c.sum().real)
log.debug(' a.sum()*b.sum()/c.sum().real: %s:', a.sum()*b.sum()/c.sum().real)
return fft.fftshift(c)
def findmax(mag, vals, mid=1.0):
"""
Short Summary
-------------
Fit a quadratic to the given input arrays mag and vals, and calculate the
value of mag at the extreme value of vals.
Parameters
----------
mag: 1D float array
array for abscissa
vals: 1D float array
array for ordinate
mid: float
midpoint of range
Returns
-------
maxx: float
value of mag at the extreme value of vals
maxy: float
value of vals corresponding to maxx
"""
p = np.polyfit(mag, vals, 2)
fitr = np.arange(0.95 * mid, 1.05 * mid, .01)
maxx, maxy, fitc = quadratic(p, fitr)
return maxx, maxy
def pix_median_fill_value(input_array, input_dq_array, bsize, xc, yc):
"""
Short Summary
-------------
For the pixel specified by (xc, yc), calculate the median value of the
good values within the box of size bsize neighboring pixels. If any of
the box is outside the data, 0 will be returned.
Parameters
----------
input_array: ndarray
2D input array to filter
input_dq_array: ndarray
2D input data quality array
bsize: scalar
square box size of the data to extract
xc: scalar
x position of the data extraction
yc: scalar
y position of the data extraction
Returns
-------
median_value: float
median value of good values within box of neighboring pixels
"""
# set the half box size
hbox = int(bsize/2)
# Extract the region of interest for the data
try:
data_array = input_array[xc - hbox:xc + hbox, yc - hbox: yc + hbox]
dq_array = input_dq_array[xc - hbox:xc + hbox, yc - hbox: yc + hbox]
except IndexError:
# If the box is outside the data return 0
log.warning('Box for median filter is outside the data.')
return 0.
wh_good = np.where((np.bitwise_and(dq_array, dqflags.pixel['DO_NOT_USE'])
== 0))
filtered_array = data_array[wh_good]
median_value = np.nanmedian(filtered_array)
if np.isnan(median_value):
# If the median fails return 0
log.warning('Median filter returned NaN setting value to 0.')
median_value = 0.
return median_value
def img_median_replace(img_model, box_size):
"""
Short Summary
-------------
Replace bad pixels (either due to a dq value of DO_NOT_USE or having a value
of NaN) with the median value of surrounding good pixels.
Parameters
----------
img_model: image model containing input array to filter.
box_size: scalar
box size for the median filter
Returns
-------
img_model: input image model whose input array has its bad pixels replaced
by the median of the surrounding good-value pixels.
"""
input_data = img_model.data
input_dq = img_model.dq
num_nan = np.count_nonzero(np.isnan(input_data))
num_dq_bad = np.count_nonzero(input_dq == dqflags.pixel['DO_NOT_USE'])
# check to see if any of the pixels are flagged
if (num_nan + num_dq_bad > 0):
bad_locations = np.where(np.isnan(input_data) |
np.equal(input_dq, dqflags.pixel['DO_NOT_USE']))
# fill the bad pixel values with the median of the data in a box region
for i_pos in range(len(bad_locations[0])):
x_box_pos = bad_locations[0][i_pos]
y_box_pos = bad_locations[1][i_pos]
median_fill = pix_median_fill_value(input_data, input_dq,
box_size, x_box_pos, y_box_pos)
input_data[x_box_pos, y_box_pos] = median_fill
img_model.data = input_data
return img_model
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,527
|
mperrin/jwst
|
refs/heads/master
|
/scripts/set_velocity_aberration.py
|
#!/usr/bin/env python
# Copyright (C) 2010-2011 Association of Universities for Research in Astronomy (AURA)
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# 3. The name of AURA and its representatives may not be used to
# endorse or promote products derived from this software without
# specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED
# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
# DAMAGE.
'''
This script adds velocity aberration correction information to the FITS
files provided to it on the command line (one or more).
It assumes the following keywords are present in the file header:
JWST_DX (km/sec)
JWST_DY (km/sec)
JWST_DZ (km/sec)
RA_REF (deg)
DEC_REF (deg)
The keywords added are:
VA_SCALE (dimensionless scale factor)
It does not currently place the new keywords in any particular location
in the header other than what is required by the standard.
'''
import astropy.io.fits as fits
import logging
import math
import sys
# Configure logging
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
SPEED_OF_LIGHT = 299792.458 # km / s
d_to_r = math.pi / 180.
def aberration_scale(velocity_x, velocity_y, velocity_z,
targ_ra, targ_dec):
"""Compute the scale factor due to velocity aberration.
Parameters
----------
velocity_x, velocity_y, velocity_z: float
The components of the velocity of JWST, in km / s with respect to
the Sun. These are celestial coordinates, with x toward the
vernal equinox, y toward right ascension 90 degrees and declination
0, z toward the north celestial pole.
targ_ra, targ_dec: float
The right ascension and declination of the target (or some other
point, such as the center of a detector). The equator and equinox
should be the same as the coordinate system for the velocity.
Returns
-------
scale_factor: float
Multiply the nominal image scale (e.g. in degrees per pixel) by
this value to obtain the image scale corrected for the "aberration
of starlight" due to the velocity of JWST with respect to the Sun.
"""
speed = math.sqrt(velocity_x**2 + velocity_y**2 + velocity_z**2)
if speed == 0.0:
logger.warning('Speed is zero. Forcing scale to 1.0')
return 1.0
beta = speed / SPEED_OF_LIGHT
gamma = 1. / math.sqrt(1. - beta**2)
# [targ_x, targ_y, targ_z] is a unit vector.
r_xy = math.cos(targ_dec * d_to_r) # radial distance in xy-plane
targ_x = r_xy * math.cos(targ_ra * d_to_r)
targ_y = r_xy * math.sin(targ_ra * d_to_r)
targ_z = math.sin(targ_dec * d_to_r)
dot_prod = (velocity_x * targ_x +
velocity_y * targ_y +
velocity_z * targ_z)
cos_theta = dot_prod / speed
# This sin_theta is only valid over the range [0, pi], but so is the
# angle between the velocity vector and the direction toward the target.
sin_theta = math.sqrt(1. - cos_theta**2)
tan_theta_p = sin_theta / (gamma * (cos_theta + beta))
theta_p = math.atan(tan_theta_p)
scale_factor = (gamma * (cos_theta + beta)**2 /
(math.cos(theta_p)**2 * (1. + beta * cos_theta)))
return scale_factor
def aberration_offset(velocity_x, velocity_y, velocity_z,
targ_ra, targ_dec):
"""Compute the RA/Dec offsets due to velocity aberration.
Parameters
----------
velocity_x, velocity_y, velocity_z: float
The components of the velocity of JWST, in km / s with respect to
the Sun. These are celestial coordinates, with x toward the
vernal equinox, y toward right ascension 90 degrees and declination
0, z toward the north celestial pole.
targ_ra, targ_dec: float
The right ascension and declination of the target (or some other
point, such as the center of a detector). The equator and equinox
should be the same as the coordinate system for the velocity.
Returns
-------
delta_ra, delta_dec: float
The offset to be added to the input RA/Dec, in units of radians.
"""
xdot = velocity_x / SPEED_OF_LIGHT
ydot = velocity_y / SPEED_OF_LIGHT
zdot = velocity_z / SPEED_OF_LIGHT
sin_alpha = math.sin(targ_ra * d_to_r)
cos_alpha = math.cos(targ_ra * d_to_r)
sin_delta = math.sin(targ_dec * d_to_r)
cos_delta = math.cos(targ_dec * d_to_r)
delta_ra = (-xdot * sin_alpha + ydot * cos_alpha) / cos_delta
delta_dec = (-xdot * cos_alpha * sin_delta -
ydot * sin_alpha * sin_delta +
zdot * cos_delta)
return delta_ra, delta_dec
def add_dva(filename):
'''
Given the name of a valid partially populated level 1b JWST file,
determine the velocity aberration scale factor.
It presumes all the accessed keywords are present (see first block).
'''
hdulist = fits.open(filename, 'update')
pheader = hdulist[0].header
sheader = hdulist['SCI'].header
jwst_dx = float(pheader['JWST_DX'])
jwst_dy = float(pheader['JWST_DY'])
jwst_dz = float(pheader['JWST_DZ'])
ra_ref = float(sheader['RA_REF'])
dec_ref = float(sheader['DEC_REF'])
# compute the velocity aberration information
scale_factor = aberration_scale(jwst_dx, jwst_dy, jwst_dz,
ra_ref, dec_ref)
ra_off, dec_off = aberration_offset(jwst_dx, jwst_dy, jwst_dz,
ra_ref, dec_ref)
# update header
pheader['DVA_RA'] = ra_off
pheader['DVA_DEC'] = dec_off
sheader['VA_SCALE'] = scale_factor
hdulist.flush()
hdulist.close()
if __name__ == '__main__':
if len(sys.argv) <= 1:
raise ValueError('missing filename argument(s)')
for filename in sys.argv[1:]:
add_dva(filename)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,528
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nirspec/test_spec2.py
|
"""Test aspects of Spec2Pipline"""
import subprocess
import pytest
from ci_watson.artifactory_helpers import get_bigdata
from jwst.assign_wcs.util import NoDataOnDetectorError
from jwst.pipeline import Spec2Pipeline
@pytest.mark.bigdata
def test_nrs2_nodata_api(envopt, _jail):
"""
Regression test of handling NRS2 detector that has no data.\
"""
# Only need to ensure that assing_wcs is run.
# This still will fail and should cause the pipeline to halt.
step = Spec2Pipeline()
step.assign_wcs.skip = False
with pytest.raises(NoDataOnDetectorError):
step.run(get_bigdata('jwst-pipeline', envopt,
'nirspec', 'test_assignwcs',
'jw84700006001_02101_00001_nrs2_rate.fits'
))
@pytest.mark.bigdata
def test_nrs2_nodata_strun(envopt, _jail):
"""Ensure that the appropriate exit status is returned from strun"""
data_file = get_bigdata('jwst-pipeline', envopt,
'nirspec', 'test_assignwcs',
'jw84700006001_02101_00001_nrs2_rate.fits'
)
cmd = [
'strun',
'jwst.pipeline.Spec2Pipeline',
data_file,
'--steps.assign_wcs.skip=false'
]
status = subprocess.run(cmd)
assert status.returncode == 64
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,529
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/tests/test_filetype.py
|
import pytest
from ..filetype import check
SUPPORTED_EXTS = (('fits', 'fits'), ('json', 'asn'), ('asdf', 'asdf')) # (ext, expected filetype)
@pytest.fixture(params=SUPPORTED_EXTS)
def input_file(request):
return f'test_file.{request.param[0]}', request.param[-1]
@pytest.fixture(params=['stpipe.MyPipeline.fits', 'stpipe.MyPipeline.fits.gz'])
def pipeline_file(request):
return request.param
def test_check_on_str_init(input_file):
filename, expected = input_file
filetype = check(filename)
assert filetype == expected
def test_check_fails_on_unsupported_ext():
with pytest.raises(ValueError):
check('test_file')
def test_check_works_for_zipped(input_file):
filename, expected = input_file
filename += '.gz' # extra zip extension
filetype = check(filename)
assert filetype == expected
def test_check_works_for_pipeline_patters(pipeline_file):
assert check(pipeline_file) == 'fits'
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,530
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/tests/test_history.py
|
import datetime
import numpy as np
from astropy.io import fits
from astropy.time import Time
from asdf.tags.core import HistoryEntry
from .. import DataModel
def test_historylist_methods():
m = DataModel()
h1 = m.history
info = "First entry"
h1.append(info)
assert h1 == info, "Append new history entry"
h2 = m.history
assert h2 == info, "Two history lists point to the same object"
assert len(h1) == 1, "Length of a history list"
entry = h1[0]
assert entry["description"] == info, "Get history list item"
info += " for real"
h1[0] = info
assert h1 == info, "Set history list item"
del h1[0]
assert len(h1) == 0, "Delete history list item"
info = ("First entry", "Second_entry", "Third entry")
h1.extend(info)
assert len(h1) == 3, "Length of extended history list"
assert h1 == info, "Contents of extended history list"
for entry, item in zip(h1, info):
assert entry["description"] == item, "Iterate over history list"
h1.clear()
assert len(h1) == 0, "Clear history list"
def test_history_from_model_to_fits(tmpdir):
tmpfits = str(tmpdir.join('tmp.fits'))
m = DataModel()
m.history = [HistoryEntry({
'description': 'First entry',
'time': Time(datetime.datetime.now())})]
m.history.append(HistoryEntry({
'description': 'Second entry',
'time': Time(datetime.datetime.now())
}))
m.save(tmpfits)
with fits.open(tmpfits, memmap=False) as hdulist:
assert list(hdulist[0].header['HISTORY']) == ["First entry",
"Second entry"]
with DataModel(tmpfits) as m2:
m2 = DataModel()
m2.update(m)
m2.history = m.history
assert m2.history == [{'description': "First entry"},
{'description': "Second entry"}]
m2.save(tmpfits)
with fits.open(tmpfits, memmap=False) as hdulist:
assert list(hdulist[0].header['HISTORY']) == ["First entry",
"Second entry"]
def test_history_from_fits(tmpdir):
tmpfits = str(tmpdir.join('tmp.fits'))
header = fits.Header()
header['HISTORY'] = "First entry"
header['HISTORY'] = "Second entry"
fits.writeto(tmpfits, np.array([]), header, overwrite=True)
with DataModel(tmpfits) as m:
assert m.history == [{'description': 'First entry'},
{'description': 'Second entry'}]
del m.history[0]
m.history.append(HistoryEntry({'description': "Third entry"}))
assert m.history == [{'description': "Second entry"},
{'description': "Third entry"}]
m.save(tmpfits)
with DataModel(tmpfits) as m:
assert m.history == [{'description': "Second entry"},
{'description': "Third entry"}]
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,531
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py
|
import pytest
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
@pytest.mark.bigdata
class TestImage3Pipeline1(BaseJWSTTest):
"""Regression test definitions for CALIMAGE3 pipeline.
Regression test of calwebb_image3 pipeline on NIRCam
simulated long-wave data.
"""
input_loc = 'nircam'
ref_loc = ['test_calimage3', 'truth']
test_dir = 'test_calimage3'
def test_image3_pipeline1(self):
asn_name = "mosaic_long_asn.json"
asn_file = self.get_data('test_calimage3', asn_name)
for file in raw_from_asn(asn_file):
self.get_data('test_calimage3', file)
collect_pipeline_cfgs('config')
args = [
'config/calwebb_image3.cfg',
asn_file,
'--steps.tweakreg.skip=True',
]
Step.from_cmdline(args)
self.ignore_keywords += ['NAXIS1', 'TFORM*']
self.ignore_fields = self.ignore_keywords
self.rtol = 0.0001
outputs = [('nrca5_47Tuc_subpix_dither1_newpos_a3001_crf.fits',
'nrca5_47Tuc_subpix_dither1_newpos_cal-a3001_ref.fits'),
('mosaic_long_i2d.fits',
'mosaic_long_i2d_ref.fits'),
('mosaic_long_cat.ecsv',
'mosaic_long_cat_ref.ecsv'),
]
self.compare_outputs(outputs)
def test_image3_pipeline2(self):
"""Regression test definitions for CALIMAGE3 pipeline.
Regression test of calwebb_image3 pipeline on NIRCam
simulated long-wave data with a 6-point dither.
"""
asn_file = self.get_data(self.test_dir,
"jw10002-o001_20171116t191235_image3_002_asn.json")
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
collect_pipeline_cfgs('config')
args = [
'config/calwebb_image3.cfg',
asn_file,
'--steps.tweakreg.kernel_fwhm=2',
'--steps.tweakreg.snr_threshold=5',
'--steps.tweakreg.enforce_user_order=True',
'--steps.tweakreg.searchrad=10',
'--steps.tweakreg.fitgeometry=rscale',
]
Step.from_cmdline(args)
self.ignore_keywords += ['NAXIS1', 'TFORM*']
self.ignore_fields = self.ignore_keywords
self.rtol = 0.0001
outputs = [('jw10002001001_01101_00004_nrcblong_o001_crf.fits',
'jw10002001001_01101_00004_nrcblong_o001_crf_ref.fits'),
('jw10002-o001_t002_nircam_f444w_i2d.fits',
'jw10002-o001_t002_nircam_f444w_i2d_ref.fits'),
('jw10002-o001_t002_nircam_f444w_cat.ecsv',
'jw10002-o001_t002_nircam_f444w_cat_ref.ecsv'),
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,532
|
mperrin/jwst
|
refs/heads/master
|
/jwst/stpipe/__init__.py
|
from .step import Step
from .pipeline import Pipeline
from .linear_pipeline import LinearPipeline
__all__ = ['Step', 'Pipeline', 'LinearPipeline']
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,533
|
mperrin/jwst
|
refs/heads/master
|
/jwst/assign_wcs/tests/test_schemas.py
|
from astropy.modeling import models
from astropy import units as u
from jwst.datamodels import DistortionModel
def test_distortion_schema(tmpdir):
"""Make sure DistortionModel roundtrips"""
m = models.Shift(1) & models.Shift(2)
dist = DistortionModel(model=m, input_units=u.pixel, output_units=u.arcsec)
dist.meta.instrument.name = "NIRCAM"
dist.meta.instrument.detector = "NRCA1"
dist.meta.instrument.p_pupil = "F162M|F164N|CLEAR|"
dist.meta.instrument.pupil = "F162M"
dist.meta.exposure.p_exptype = "NRC_IMAGE|NRC_TSIMAGE|NRC_FLAT|NRC_LED|NRC_WFSC|"
dist.meta.exposure.type = "NRC_IMAGE"
dist.meta.psubarray = "FULL|SUB64P|SUB160)|SUB160P|SUB320|SUB400P|SUB640|"
dist.meta.subarray.name = "FULL"
path = str(tmpdir.join("test_dist.asdf"))
dist.save(path)
with DistortionModel(path) as dist1:
assert dist1.meta.instrument.p_pupil == dist.meta.instrument.p_pupil
assert dist1.meta.instrument.pupil == dist.meta.instrument.pupil
assert dist1.meta.exposure.p_exptype == dist.meta.exposure.p_exptype
assert dist1.meta.exposure.type == dist.meta.exposure.type
assert dist1.meta.psubarray == dist.meta.psubarray
assert dist1.meta.subarray.name == dist.meta.subarray.name
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,534
|
mperrin/jwst
|
refs/heads/master
|
/jwst/lib/tests/test_s3_utils.py
|
import pytest
from jwst.lib import s3_utils
from . import helpers
@pytest.fixture
def s3_text_file(s3_root_dir):
path = str(s3_root_dir.join("test.txt"))
with open(path, "w") as text_file:
print("foo", file=text_file)
return path
def test_object_exists(s3_text_file):
assert s3_utils.object_exists("s3://test-s3-data/test.txt") is True
assert s3_utils.object_exists("s3://test-s3-data/missing.fits") is False
assert s3_utils.object_exists("s3://missing-bucket/test.txt") is False
def test_get_object(s3_text_file):
assert s3_utils.get_object("s3://test-s3-data/test.txt").read() == b"foo\n"
def test_get_client(s3_text_file):
assert isinstance(s3_utils.get_client(), helpers.MockS3Client)
def test_is_s3_uri(s3_text_file):
assert s3_utils.is_s3_uri("s3://test-s3-data/test.fits") is True
assert s3_utils.is_s3_uri("some/filesystem/path") is False
def test_split_uri(s3_text_file):
assert s3_utils.split_uri("s3://test-s3-data/key") == ("test-s3-data", "key")
assert s3_utils.split_uri("s3://test-s3-data/some/longer/key") == ("test-s3-data", "some/longer/key")
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,535
|
mperrin/jwst
|
refs/heads/master
|
/jwst/wfs_combine/wfs_combine_step.py
|
#! /usr/bin/env python
import os.path as op
from ..stpipe import Step
from . import wfs_combine
__all__ = ["WfsCombineStep"]
class WfsCombineStep(Step):
"""
This step combines pairs of dithered PSF images
"""
spec = """
do_refine = boolean(default=False)
"""
def process(self, input_table):
# Load the input ASN table
asn_table = self.load_as_level3_asn(input_table)
num_sets = len(asn_table['products'])
self.log.info('Using input table: %s', input_table)
self.log.info('The number of pairs of input files: %g', num_sets)
# Process each pair of input images listed in the association table
for which_set in asn_table['products']:
# Get the list of science members in this pair
science_members = [
member
for member in which_set['members']
if member['exptype'].lower() == 'science'
]
infile_1 = science_members[0]['expname']
infile_2 = science_members[1]['expname']
outfile = which_set['name']
# Create the step instance
wfs = wfs_combine.DataSet(
infile_1, infile_2, outfile, self.do_refine
)
# Do the processing
output_model = wfs.do_all()
# Update necessary meta info in the output
output_model.meta.cal_step.wfs_combine = 'COMPLETE'
output_model.meta.asn.pool_name = asn_table['asn_pool']
output_model.meta.asn.table_name = op.basename(input_table)
# Save the output file
self.save_model(
output_model, suffix='wfscmb', output_file=outfile, format=False
)
return None
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,536
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/extract1dimage.py
|
from .model_base import DataModel
__all__ = ['Extract1dImageModel']
class Extract1dImageModel(DataModel):
"""
A data model for the extract_1d reference image array.
Parameters
__________
data : numpy float32 array
1-D extraction regions array
"""
schema_url = "http://stsci.edu/schemas/jwst_datamodel/extract1dimage.schema"
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,537
|
mperrin/jwst
|
refs/heads/master
|
/jwst/datamodels/tests/test_storage.py
|
import numpy as np
from .. import util
def test_gentle_asarray():
x = np.array([('abc', 1.0)], dtype=[
('FOO', 'S3'),
('BAR', '>f8')])
new_dtype = [('foo', '|S3'), ('bar', '<f8')]
y = util.gentle_asarray(x, new_dtype)
assert y['BAR'][0] == 1.0
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,538
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/miri/test_miri_steps.py
|
import pytest
from jwst.tests.base_classes import BaseJWSTTestSteps
from jwst.tests.base_classes import pytest_generate_tests # noqa: F401
from jwst.refpix import RefPixStep
from jwst.dark_current import DarkCurrentStep
from jwst.dq_init import DQInitStep
from jwst.extract_1d import Extract1dStep
from jwst.flatfield import FlatFieldStep
from jwst.fringe import FringeStep
from jwst.jump import JumpStep
from jwst.lastframe import LastFrameStep
from jwst.linearity import LinearityStep
from jwst.photom import PhotomStep
from jwst.rscd import RSCD_Step
from jwst.saturation import SaturationStep
from jwst.srctype import SourceTypeStep
from jwst.straylight import StraylightStep
# Parameterized regression tests for MIRI processing
# All tests in this set run with 1 input file and
# only generate 1 output for comparison.
#
@pytest.mark.bigdata
class TestMIRISteps(BaseJWSTTestSteps):
input_loc = 'miri'
params = {'test_steps':
[
# test_refpix_miri: refpix step performed on MIRI data
dict(input='jw00001001001_01101_00001_MIRIMAGE_saturation.fits',
test_dir='test_bias_drift',
step_class=RefPixStep,
step_pars=dict(use_side_ref_pixels=False,
side_smoothing_length=10,
side_gain=1.0),
output_truth='jw00001001001_01101_00001_MIRIMAGE_bias_drift.fits',
output_hdus=[],
id='refpix_miri'
),
# test_refpix_miri2: refpix step performed on MIRI data
dict(input='jw00025001001_01107_00001_MIRIMAGE_saturation.fits',
test_dir='test_bias_drift',
step_class=RefPixStep,
step_pars=dict(use_side_ref_pixels=False,
side_smoothing_length=10,
side_gain=1.0),
output_truth='jw00025001001_01107_00001_MIRIMAGE_bias_drift.fits',
output_hdus=[],
id='refpix_miri2'
),
# test_dark_current_miri: dark current step performed on MIRI data
dict(input='jw00001001001_01101_00001_MIRIMAGE_bias_drift.fits',
test_dir='test_dark_step',
step_class=DarkCurrentStep,
step_pars=dict(),
output_truth='jw00001001001_01101_00001_MIRIMAGE_dark_current.fits',
output_hdus=[],
id='dark_current_miri'
),
# test_dark_current_miri2: dark current step performed on MIRI data
dict(input='jw80600012001_02101_00003_mirimage_lastframe.fits',
test_dir='test_dark_step',
step_class=DarkCurrentStep,
step_pars=dict(),
output_truth='jw80600012001_02101_00003_mirimage_dark.fits',
output_hdus=[],
id='dark_current_miri2'
),
# test_dq_init_miri: dq_init step performed on uncalibrated MIRI data
dict(input='jw00001001001_01101_00001_MIRIMAGE_uncal.fits',
test_dir='test_dq_init',
step_class=DQInitStep,
step_pars=dict(),
output_truth='jw00001001001_01101_00001_MIRIMAGE_dq_init.fits',
output_hdus=[],
id='dq_init_miri'
),
# test_dq_init_miri2: dq_init step performed on uncalibrated MIRI data
dict(input='jw80600012001_02101_00003_mirimage_uncal.fits',
test_dir='test_dq_init',
step_class=DQInitStep,
step_pars=dict(),
output_truth='jw80600012001_02101_00003_mirimage_dqinit.fits',
output_hdus=[],
id='dq_init_miri2'
),
# test_extract1d_miri: extract_1d step performed on MIRI LRS fixed-slit data
dict(input='jw00035001001_01101_00001_mirimage_photom.fits',
test_dir='test_extract1d',
step_class=Extract1dStep,
step_pars=dict(suffix='x1d'),
output_truth='jw00035001001_01101_00001_mirimage_x1d.fits',
output_hdus=[],
id='extract1d_miri'
),
# test_extract1d_miri2: extract_1d step performed on MIRI LRS slitless data
dict(input='jw80600012001_02101_00003_mirimage_photom.fits',
test_dir='test_extract1d',
step_class=Extract1dStep,
step_pars=dict(suffix='x1d'),
output_truth='jw80600012001_02101_00003_mirimage_x1d.fits',
output_hdus=[],
id='extract1d_miri2'
),
# test_flat_field_miri: flat_field step performed on MIRI data.
dict(input='jw00001001001_01101_00001_MIRIMAGE_assign_wcs.fits',
test_dir='test_flat_field',
step_class=FlatFieldStep,
step_pars=dict(),
output_truth='jw00001001001_01101_00001_MIRIMAGE_flat_field.fits',
output_hdus=[],
id='flat_field_miri'
),
# test_flat_field_miri2: flat_field step performed on MIRI data.
dict(input='jw80600012001_02101_00003_mirimage_assign_wcs.fits',
test_dir='test_flat_field',
step_class=FlatFieldStep,
step_pars=dict(),
output_truth='jw80600012001_02101_00003_mirimage_flat_field.fits',
output_hdus=[],
id='flat_field_miri2'
),
# test_fringe_miri: fringe performed on MIRI data.
dict(input='fringe1_input.fits',
test_dir='test_fringe',
step_class=FringeStep,
step_pars=dict(),
output_truth='baseline_fringe1.fits',
output_hdus=['primary','sci','err','dq'],
id='fringe_miri'
),
# test_fringe_miri2: fringe performed on MIRI data.
dict(input='fringe2_input.fits',
test_dir='test_fringe',
step_class=FringeStep,
step_pars=dict(),
output_truth='baseline_fringe2.fits',
output_hdus=['primary','sci','err','dq'],
id='fringe_miri2'
),
# test_fringe_miri3: fringe performed on MIRI data.
dict(input='fringe3_input.fits',
test_dir='test_fringe',
step_class=FringeStep,
step_pars=dict(),
output_truth='baseline_fringe3.fits',
output_hdus=['primary','sci','err','dq'],
id='fringe_miri3'
),
# test_jump_miri: jump step performed on MIRI data.
dict(input='jw00001001001_01101_00001_MIRIMAGE_linearity.fits',
test_dir='test_jump',
step_class=JumpStep,
step_pars=dict(rejection_threshold=200.0),
output_truth='jw00001001001_01101_00001_MIRIMAGE_jump.fits',
output_hdus=[],
id='jump_miri'
),
# test_jump_miri2: jump step performed on MIRI data.
dict(input='jw80600012001_02101_00003_mirimage_dark.fits',
test_dir='test_jump',
step_class=JumpStep,
step_pars=dict(rejection_threshold=25.0),
output_truth='jw80600012001_02101_00003_mirimage_jump.fits',
output_hdus=[],
id='jump_miri2'
),
# test_lastframe_miri2: lastframe step performed on MIRI data
dict(input='jw80600012001_02101_00003_mirimage_rscd.fits',
test_dir='test_lastframe',
step_class=LastFrameStep,
step_pars=dict(),
output_truth='jw80600012001_02101_00003_mirimage_lastframe.fits',
output_hdus=[],
id='lastframe_miri2'
),
# test_linearity_miri: linearity step performed on MIRI data
dict(input='jw00001001001_01101_00001_MIRIMAGE_dark_current.fits',
test_dir='test_linearity',
step_class=LinearityStep,
step_pars=dict(),
output_truth='jw00001001001_01101_00001_MIRIMAGE_linearity.fits',
output_hdus=[],
id='linearity_miri'
),
# test_linearity_miri2: linearity step performed on MIRI data
dict(input='jw80600012001_02101_00003_mirimage_saturation.fits',
test_dir='test_linearity',
step_class=LinearityStep,
step_pars=dict(),
output_truth='jw80600012001_02101_00003_mirimage_linearity.fits',
output_hdus=[],
id='linearity_miri2'
),
# test_photom_miri: photom step performed on MIRI imaging data
dict(input='jw00001001001_01101_00001_MIRIMAGE_emission.fits',
test_dir='test_photom',
step_class=PhotomStep,
step_pars=dict(),
output_truth='jw00001001001_01101_00001_MIRIMAGE_photom.fits',
output_hdus=[],
id='photom_miri'
),
# test_photom_miri2: photom step performed on MIRI LRS slitless data
dict(input='jw80600012001_02101_00003_mirimage_srctype.fits',
test_dir='test_photom',
step_class=PhotomStep,
step_pars=dict(),
output_truth='jw80600012001_02101_00003_mirimage_photom.fits',
output_hdus=[],
id='photom_miri2'
),
# test_rscd_miri2: RSCD step performed on MIRI data
dict(input='jw80600012001_02101_00003_mirimage_linearity.fits',
test_dir='test_rscd',
step_class=RSCD_Step,
step_pars=dict(),
output_truth='jw80600012001_02101_00003_mirimage_rscd.fits',
output_hdus=[],
id='rscd_miri'
),
# test_saturation_miri: saturation step performed on uncalibrated MIRI data
dict(input='jw00001001001_01101_00001_MIRIMAGE_dq_init.fits',
test_dir='test_saturation',
step_class=SaturationStep,
step_pars=dict(),
output_truth='jw00001001001_01101_00001_MIRIMAGE_saturation.fits',
output_hdus=['primary','sci','err','pixeldq','groupdq'],
id='saturation_miri'
),
# test_saturation_miri2: saturation step performed on uncalibrated MIRI data
dict(input='jw80600012001_02101_00003_mirimage_dqinit.fits',
test_dir='test_saturation',
step_class=SaturationStep,
step_pars=dict(),
output_truth='jw80600012001_02101_00003_mirimage_saturation.fits',
output_hdus=[],
id='saturation_miri2'
),
# test_srctype2: srctype step performed on MIRI LRS slitless data
dict(input='jw80600012001_02101_00003_mirimage_flat_field.fits',
test_dir='test_srctype',
step_class=SourceTypeStep,
step_pars=dict(),
output_truth='jw80600012001_02101_00003_mirimage_srctype.fits',
output_hdus=[],
id='srctype_miri'
),
# test_straylight1_miri: straylight performed on MIRI IFUSHORT data
dict(input='jw80500018001_02101_00002_MIRIFUSHORT_flatfield.fits',
test_dir='test_straylight',
step_class=StraylightStep,
step_pars=dict(),
output_truth='jw80500018001_02101_00002_MIRIFUSHORT_straylight.fits',
output_hdus=['primary','sci','err','dq'],
id='straylight_miri'
),
# test_straylight2_miri: straylight performed on MIRI IFULONG data
dict(input='jw80500018001_02101_00002_MIRIFULONG_flatfield.fits',
test_dir='test_straylight',
step_class=StraylightStep,
step_pars=dict(),
output_truth='jw80500018001_02101_00002_MIRIFULONG_straylight.fits',
output_hdus=[],
id='straylight_miri2'
),
]
}
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,539
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/niriss/test_tso3.py
|
import pytest
from jwst.pipeline import Tso3Pipeline
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
@pytest.mark.bigdata
class TestTso3Pipeline(BaseJWSTTest):
input_loc = 'niriss'
ref_loc = ['test_caltso3', 'truth']
test_dir = 'test_caltso3'
def test_tso3_pipeline_nis(self):
"""Regression test of calwebb_tso3 on NIRISS SOSS simulated data.
"""
asn_file = self.get_data(self.test_dir,
"jw87600-a3001_20170527t111213_tso3_001_asn.json")
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
Tso3Pipeline.call(asn_file)
outputs = [
# Compare level-2c product
('jw87600024001_02101_00001_nis_a3001_crfints.fits',
'jw87600-a3001_t1_niriss_clear-gr700xd_crfints_ref.fits',
['primary', 'sci', 'dq', 'err']),
# Compare level-3 product
('jw87600-a3001_t1_niriss_clear-gr700xd_x1dints.fits',
'jw87600-a3001_t1_niriss_clear-gr700xd_x1dints_ref.fits',
['primary', 'extract1d']),
('jw87600-a3001_t1_niriss_clear-gr700xd_whtlt.ecsv',
'jw87600-a3001_t1_niriss_clear-gr700xd_whtlt_ref.ecsv'),
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,540
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/miri/test_mrs_spec3.py
|
import pytest
from jwst.pipeline.calwebb_spec3 import Spec3Pipeline
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
@pytest.mark.bigdata
class TestSpec3Pipeline(BaseJWSTTest):
input_loc = 'miri'
ref_loc = ['mrs_calspec3', 'truth']
test_dir = 'mrs_calspec3'
rtol = 0.000001
def test_spec3_pipeline1(self):
"""
Regression test of calwebb_spec3 pipeline on simulated
MIRI MRS dithered data.
"""
asn_file = self.get_data(self.test_dir, 'test_asn4.json')
for file in raw_from_asn(asn_file):
self.get_data(self.test_dir, file)
step = Spec3Pipeline()
step.save_bsub = False
step.mrs_imatch.suffix = 'mrs_imatch'
step.mrs_imatch.bkg_degree = 1
step.mrs_imatch.subtract = False
step.outlier_detection.skip = True
step.output_use_model = True
step.resample_spec.save_results = True
step.resample_spec.suffix = 's2d'
step.cube_build.save_results = True
step.cube_build.suffix = 's3d'
step.extract_1d.save_results = True
step.extract_1d.suffix = 'x1d'
step.run(asn_file)
outputs = [(# Compare cube product 1
'det_image_ch1-short_s3d.fits',
'det_image_ch1-short_s3d_ref.fits',
['primary', 'sci', 'err', 'dq', 'wmap']),
(# Compare cube product 2
'det_image_ch2-short_s3d.fits',
'det_image_ch2-short_s3d_ref.fits',
['primary', 'sci', 'err', 'dq', 'wmap']),
(# Compare x1d product 1
'det_image_ch1-short_x1d.fits',
'det_image_ch1-short_x1d_ref.fits',
['primary', 'extract1d']),
(# Compare x1d product 2
'det_image_ch2-short_x1d.fits',
'det_image_ch2-short_x1d_ref.fits',
['primary', 'extract1d'])
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,541
|
mperrin/jwst
|
refs/heads/master
|
/jwst/pipeline/tests/test_calwebspec2.py
|
import pytest
from ..calwebb_spec2 import Spec2Pipeline
@pytest.fixture(scope='module')
def fake_pipeline():
return Spec2Pipeline()
def test_filenotfounderror_raised(fake_pipeline, capsys):
with pytest.raises(RuntimeError):
fake_pipeline.run('file_does_not_extis.fits')
captured = capsys.readouterr()
assert 'FileNotFoundError' in captured.err
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,542
|
mperrin/jwst
|
refs/heads/master
|
/jwst/ami/ami_analyze.py
|
#
# Module for applying the LG algorithm to an AMI exposure
#
import logging
import warnings
import numpy as np
from .. import datamodels
from .nrm_model import NrmModel
from . import webb_psf
from . import leastsqnrm
from . import utils
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
def apply_LG(input_model, filter_model, oversample, rotation):
"""
Short Summary
-------------
Applies the LG fringe detection algorithm to an AMI image
Parameters
----------
input_model: data model object
AMI science image to be analyzed
filter_model: filter model object
filter throughput reference data
oversample: integer
Oversampling factor
rotation: float (degrees)
Initial guess at rotation of science image relative to model
Returns
-------
output_model: Fringe model object
Fringe analysis data
"""
# Supress harmless arithmetic warnings for now
warnings.filterwarnings("ignore", ".*invalid value.*", RuntimeWarning)
warnings.filterwarnings("ignore", ".*divide by zero.*", RuntimeWarning)
# Report the FILTER value for this image
log.info('Filter: %s', input_model.meta.instrument.filter)
# Load the filter throughput data from the reference file
bindown = 12
band = webb_psf.get_webbpsf_filter(filter_model, specbin=bindown)
# Set up some params that are needed as input to the LG algorithm:
# Search window for rotation fine-tuning
rots_deg = np.array((-1.00, -0.5, 0.0, 0.5, 1.00))
# Search range for relative pixel scales
relpixscales = np.array((64.2, 64.4, 64.6, 64.8, 65.0, 65.2, 65.4, 65.6, 65.8)) /65.0
# Convert initial rotation guess from degrees to radians
rotation = rotation * np.pi / 180.0
# Instantiate the NRM model object
jwnrm = NrmModel(mask='jwst', holeshape='hex',
pixscale=leastsqnrm.mas2rad(65.),
rotate=rotation, rotlist_deg=rots_deg,
scallist=relpixscales)
# Load the filter bandpass data into the NRM model
jwnrm.bandpass = band
# Set the oversampling factor in the NRM model
jwnrm.over = oversample
# Now fit the data in the science exposure
# (pixguess is a guess at the pixel scale of the data)
# produces a 19x19 image of the fit
input_data = input_model.data.astype(np.float64)
input_dq = input_model.dq
datamodel_img_model = datamodels.ImageModel(data=input_data, dq=input_dq)
box_size = 4
new_img_model = utils.img_median_replace(datamodel_img_model, box_size)
input_data = new_img_model.data.copy()
input_model.data = input_data.astype(np.float64)
del datamodel_img_model, new_img_model
subarray = input_model.meta.subarray.name.upper()
if subarray == 'FULL':
# Instead of using the FULL subarray, extract the same region (size and
# location) as used by SUB80 to make execution time acceptable
xstart = 1045 # / Starting pixel in axis 1 direction
ystart = 1 # / Starting pixel in axis 2 direction
xsize = 80 # / Number of pixels in axis 1 direction
ysize = 80 # / Number of pixels in axis 2 direction
xstop = xstart + xsize - 1
ystop = ystart + ysize - 1
jwnrm.fit_image(input_data[ystart-1:ystop, xstart-1:xstop], pixguess=jwnrm.pixel)
else:
jwnrm.fit_image(input_data, pixguess=jwnrm.pixel)
# Construct model image from fitted PSF
jwnrm.create_modelpsf()
# Reset the warnings filter to its original state
warnings.resetwarnings()
# Store fit results in output model
output_model = datamodels.AmiLgModel(fit_image=jwnrm.modelpsf,
resid_image=jwnrm.residual,
closure_amp_table=np.asarray(jwnrm.redundant_cas),
closure_phase_table=np.asarray(jwnrm.redundant_cps),
fringe_amp_table=np.asarray(jwnrm.fringeamp),
fringe_phase_table=np.asarray(jwnrm.fringephase),
pupil_phase_table=np.asarray(jwnrm.piston),
solns_table=np.asarray(jwnrm.soln))
# Copy header keywords from input to output
output_model.update(input_model)
return output_model
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,543
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py
|
"""Test calwebb_spec3 against NIRSpec MOS science (MSA)"""
from pathlib import Path
import pytest
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
from jwst.tests.base_classes import BaseJWSTTest, raw_from_asn
@pytest.mark.bigdata
class TestSpec3Pipeline(BaseJWSTTest):
"""Tests for Spec3Pipeline"""
input_loc = 'nirspec'
ref_loc = ['test_datasets', 'msa', 'sdp_jw95175', 'truth']
test_dir = ['test_datasets', 'msa', 'sdp_jw95175']
def test_nrs_msa_spec3(self):
"""
Regression test of calwebb_spec3 pipeline performed on
NIRSpec MSA data
"""
cfg_dir = './cfgs'
collect_pipeline_cfgs(cfg_dir)
asn_file = self.get_data(*self.test_dir,
'single_asn.json')
for file in raw_from_asn(asn_file):
self.get_data(*self.test_dir, file)
args = [
str(Path(cfg_dir) / 'calwebb_spec3.cfg'),
asn_file
]
Step.from_cmdline(args)
# Compare results
truths = self.data_glob(*self.ref_loc, glob='*.fits')
outputs = [
(Path(output_file).name, ) * 2
for output_file in truths
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,544
|
mperrin/jwst
|
refs/heads/master
|
/jwst/tests_nightly/general/nirspec/test_pipelines.py
|
import pytest
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
from jwst.pipeline import DarkPipeline
from jwst.pipeline import Spec2Pipeline
from jwst.tests.base_classes import BaseJWSTTest
@pytest.mark.bigdata
class TestNIRSpecPipelines(BaseJWSTTest):
input_loc = 'nirspec'
ref_loc = ['test_pipelines', 'truth']
test_dir = 'test_pipelines'
def test_nirspec_dark_pipeline(self):
"""
Regression test of calwebb_dark pipeline performed on NIRSpec raw data.
"""
input_file = self.get_data(self.test_dir,
'jw84500013001_02103_00003_NRS1_uncal.fits')
pipe = DarkPipeline()
pipe.suffix = 'dark'
pipe.ipc.skip = True
pipe.refpix.odd_even_columns = True
pipe.refpix.use_side_ref_pixels = True
pipe.refpix.side_smoothing_length = 11
pipe.refpix.side_gain = 1.0
pipe.refpix.odd_even_rows = True
pipe.output_file = 'jw84500013001_02103_00003_NRS1_uncal.fits'
pipe.run(input_file)
outputs = [('jw84500013001_02103_00003_NRS1_dark.fits',
'jw84500013001_02103_00003_NRS1_dark_ref.fits',
['primary','sci','err','pixeldq','groupdq'])]
self.compare_outputs(outputs)
def test_nrs_fs_brightobj_spec2(self):
"""
Regression test of calwebb_spec2 pipeline performed on NIRSpec
fixed-slit data that uses the NRS_BRIGHTOBJ mode (S1600A1 slit).
"""
input_file = self.get_data(self.test_dir,
'jw84600042001_02101_00001_nrs2_rateints.fits')
collect_pipeline_cfgs()
args = [
'calwebb_tso-spec2.cfg',
input_file
]
Step.from_cmdline(args)
outputs = [('jw84600042001_02101_00001_nrs2_calints.fits',
'jw84600042001_02101_00001_nrs2_calints_ref.fits'),
('jw84600042001_02101_00001_nrs2_x1dints.fits',
'jw84600042001_02101_00001_nrs2_x1dints_ref.fits')
]
self.compare_outputs(outputs)
def test_nrs_msa_spec2(self):
"""
Regression test of calwebb_spec2 pipeline performed on NIRSpec MSA data.
"""
input = 'f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod.fits'
input_file = self.get_data(self.test_dir, input)
self.get_data(self.test_dir, 'jw95065006001_0_short_msa.fits')
# define step for use in test
step = Spec2Pipeline()
step.save_results = True
step.save_bsub = False
step.output_use_model = True
step.resample_spec.save_results = True
step.extract_1d.save_results = True
step.extract_1d.smoothing_length = 0
step.extract_1d.bkg_order = 0
step.run(input_file)
outputs = [('f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod_cal.fits',
'f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod_cal_ref.fits'),
('f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod_s2d.fits',
'f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod_s2d_ref.fits'),
('f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod_x1d.fits',
'f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod_x1d_ref.fits')
]
self.compare_outputs(outputs)
def test_nrs_msa_spec2b(self):
"""
Regression test of calwebb_spec2 pipeline performed on NIRSpec MSA data,
including barshadow correction.
"""
input = 'jw95065_nrs_msaspec_barshadow.fits'
input_file = self.get_data(self.test_dir, input)
self.get_data(self.test_dir, 'jwst_nirspec_shutters_barshadow.fits')
step = Spec2Pipeline()
step.output_file='jw95065_nrs_msaspec_barshadow_cal.fits'
step.save_bsub = False
step.save_results = True
step.resample_spec.save_results = True
step.extract_1d.save_results = True
step.run(input_file)
outputs = [('jw95065_nrs_msaspec_barshadow_cal.fits',
'jw95065_nrs_msaspec_barshadow_cal_ref.fits'),
('jw95065_nrs_msaspec_barshadow_s2d.fits',
'jw95065_nrs_msaspec_barshadow_s2d_ref.fits'),
('jw95065_nrs_msaspec_barshadow_x1d.fits',
'jw95065_nrs_msaspec_barshadow_x1d_ref.fits')
]
self.compare_outputs(outputs)
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,545
|
mperrin/jwst
|
refs/heads/master
|
/jwst/associations/tests/test_constraints.py
|
"""Constraint Tests"""
import pytest
from ..lib.constraint import (
Constraint,
SimpleConstraint,
SimpleConstraintABC,
)
def test_simpleconstraint_reprocess_match():
"""Test options for reprocessing"""
sc = SimpleConstraint(
value='my_value',
reprocess_on_match=True
)
match, reprocess = sc.check_and_set('my_value')
assert match
assert len(reprocess)
def test_simpleconstraint_reprocess_nomatch():
"""Test options for reprocessing"""
sc = SimpleConstraint(
value='my_value',
reprocess_on_fail=True
)
match, reprocess = sc.check_and_set('bad_value')
assert not match
assert len(reprocess)
def test_constraint_reprocess_match():
"""Test options for reprocessing"""
sc = SimpleConstraint(value='my_value')
c = Constraint([sc], reprocess_on_match=True)
match, reprocess = c.check_and_set('my_value')
assert match
assert len(reprocess)
def test_constraint_reprocess_nomatch():
"""Test options for reprocessing"""
sc = SimpleConstraint(value='my_value')
c = Constraint([sc], reprocess_on_fail=True)
match, reprocess = c.check_and_set('bad_value')
assert not match
assert len(reprocess)
def test_abc():
"""Test ABC istelf"""
with pytest.raises(TypeError):
SimpleConstraintABC()
def test_simpleconstraint():
"""Test initialization"""
# Basic initialization
c = SimpleConstraint()
assert c.value is None
assert c.force_unique
assert c.test == c.eq
# Parameter initialization
c = SimpleConstraint(value='my_value')
assert c.value == 'my_value'
# Dict initialization
c = SimpleConstraint({'value': 'my_value'})
assert c.value == 'my_value'
def test_simpleconstraint_checkset():
"""Test check_and_set"""
# Check and set.
c = SimpleConstraint()
match, reprocess = c.check_and_set('my_value')
assert match
assert c.value == 'my_value'
assert len(reprocess) == 0
# Non-match
c = SimpleConstraint(value='my_value')
match, reprocess = c.check_and_set('bad_value')
assert not match
assert c.value == 'my_value'
assert len(reprocess) == 0
# Don't force unique
c = SimpleConstraint(force_unique=False)
match, reprocess = c.check_and_set('my_value')
assert match
assert c.value is None
assert len(reprocess) == 0
def test_constraint_default():
"""Test constraint operations"""
sc1 = SimpleConstraint()
sc2 = SimpleConstraint()
c = Constraint([sc1, sc2])
match, reprocess = c.check_and_set('my_value')
assert match
for constraint in c.constraints:
assert constraint.value == 'my_value'
def test_invalid_init():
with pytest.raises(TypeError):
Constraint('bad init')
def test_constraint_all():
"""Test the all operation"""
sc1 = SimpleConstraint(value='value_1')
sc2 = SimpleConstraint(value='value_2')
c = Constraint([sc1, sc2])
match, reprocess = c.check_and_set('value_1')
assert not match
def test_constraint_any_basic():
"""Test the all operation"""
sc1 = SimpleConstraint(value='value_1')
sc2 = SimpleConstraint(value='value_2')
c = Constraint([sc1, sc2], reduce=Constraint.any)
match, reprocess = c.check_and_set('value_1')
assert match
match, reprocess = c.check_and_set('value_2')
assert match
match, reprocess = c.check_and_set('value_3')
assert not match
def test_constraint_any_remember():
"""Ensure that any doesn't forget other or propositions"""
sc1 = SimpleConstraint(value='value_1')
sc2 = SimpleConstraint(value='value_2')
c = Constraint([sc1, sc2], reduce=Constraint.any)
match, reprocess = c.check_and_set('value_1')
assert match
match, reprocess = c.check_and_set('value_2')
assert match
match, reprocess = c.check_and_set('value_1')
assert match
match, reprocess = c.check_and_set('value_3')
assert not match
def test_iteration():
"""Test various iterations"""
sc = SimpleConstraint()
for idx in sc:
assert isinstance(idx, SimpleConstraint)
c = Constraint([sc, sc])
count = 0
for idx in c:
assert isinstance(idx, SimpleConstraint)
count += 1
assert count == 2
c = Constraint([
Constraint([sc, sc]),
Constraint([sc, sc])
])
count = 0
for idx in c:
assert isinstance(idx, SimpleConstraint)
count += 1
assert count == 4 # Not 6
def test_name_index():
"""Test for name indexing"""
sc1 = SimpleConstraint(name='sc1', value='value1')
sc2 = SimpleConstraint(name='sc2', value='value2')
c1 = Constraint([sc1, sc2])
assert c1['sc1'].value
assert c1['sc2'].value
sc3 = SimpleConstraint(name='sc3', value='value3')
sc4 = SimpleConstraint(name='sc4', value='value4')
c2 = Constraint([sc3, sc4, c1])
assert c2['sc1'].value
assert c2['sc2'].value
assert c2['sc3'].value
assert c2['sc4'].value
with pytest.raises(KeyError):
c2['nonexistant'].value
with pytest.raises(AttributeError):
c2['sc1'].nonexistant
def test_copy():
sc1 = SimpleConstraint(name='sc1')
sc1_copy = sc1.copy()
assert id(sc1) != id(sc1_copy)
sc1.check_and_set('value1')
assert sc1.value == 'value1'
assert sc1_copy.value is None
sc1_copy.check_and_set('value2')
assert sc1_copy.value == 'value2'
assert sc1.value == 'value1'
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
13,546
|
mperrin/jwst
|
refs/heads/master
|
/jwst/regtest/test_nirspec_mos_spec2.py
|
import os
import pytest
from astropy.io.fits.diff import FITSDiff
from jwst.pipeline.collect_pipeline_cfgs import collect_pipeline_cfgs
from jwst.stpipe import Step
@pytest.fixture(scope="module")
def run_pipeline(jail, rtdata_module):
"""Run the calwebb_spec2 pipeline on a single NIRSpec MOS exposure."""
rtdata = rtdata_module
# Get the cfg files
collect_pipeline_cfgs("config")
# Get the MSA metadata file referenced in the input exposure
rtdata.get_data("nirspec/mos/jw95065006001_0_short_msa.fits")
# Get the input exposure
rtdata.get_data("nirspec/mos/f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod.fits")
# Run the calwebb_spec2 pipeline; save results from intermediate steps
args = ["config/calwebb_spec2.cfg", rtdata.input,
"--steps.assign_wcs.save_results=true",
"--steps.msa_flagging.save_results=true",
"--steps.extract_2d.save_results=true",
"--steps.srctype.save_results=true",
"--steps.wavecorr.save_results=true",
"--steps.flat_field.save_results=true",
"--steps.pathloss.save_results=true",
"--steps.barshadow.save_results=true"]
Step.from_cmdline(args)
return rtdata
@pytest.mark.bigdata
@pytest.mark.parametrize("output",[
"assign_wcs", "msa_flagging", "extract_2d", "wavecorr", "flat_field", "srctype",
"pathloss", "barshadow", "cal", "s2d", "x1d"])
def test_nirspec_mos_spec2(run_pipeline, fitsdiff_default_kwargs, output):
"""Regression test of the calwebb_spec2 pipeline on a
NIRSpec MOS exposure."""
# Run the pipeline and retrieve outputs
rtdata = run_pipeline
rtdata.output = "f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod_" + output + ".fits"
# Get the truth files
rtdata.get_truth(os.path.join("truth/test_nirspec_mos_spec2",
"f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod_" + output + ".fits"))
# Compare the results
diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs)
assert diff.identical, diff.report()
|
{"/jwst/tests_nightly/general/miri/test_sloperpipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2b.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_nis_wfss_spec2.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_miri_image_detector1.py": ["/jwst/stpipe/__init__.py"], "/jwst/flatfield/flat_field_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_spec2pipelines.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_miri_steps_single.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/tests_nightly/general/nircam/test_wfs_combine.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_masterbackground.py": ["/jwst/stpipe/__init__.py"], "/jwst/regtest/test_nirspec_image2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_calwebb_spec2_nrs_msa.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/pipeline/linear_pipeline.py": ["/jwst/stpipe/__init__.py", "/jwst/flatfield/flat_field_step.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/fgs/test_fgs_sloper_1.py": ["/jwst/tests/base_classes.py"], "/jwst/regtest/test_fgs_guider.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_spec2pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/fgs/test_guider_pipeline.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nircam_steps.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_image2pipeline_2.py": ["/jwst/tests/base_classes.py"], "/jwst/tests/base_classes.py": ["/jwst/tests/compare_outputs.py"], "/jwst/master_background/tests/test_nirspec_corrections.py": ["/jwst/master_background/nirspec_corrections.py"], "/jwst/datamodels/tests/test_fits.py": ["/jwst/datamodels/util.py"], "/jwst/tests_nightly/general/fgs/test_fgs_image2_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mirilrs2_slitless.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/master_background/__init__.py"], "/jwst/stpipe/tests/test_pipeline.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/niriss/test_niriss_steps_single.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_fs_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nircam/test_nrc_image3_1.py": ["/jwst/tests/base_classes.py", "/jwst/stpipe/__init__.py"], "/jwst/stpipe/__init__.py": ["/jwst/stpipe/linear_pipeline.py"], "/jwst/wfs_combine/wfs_combine_step.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/miri/test_miri_steps.py": ["/jwst/tests/base_classes.py", "/jwst/rscd/__init__.py"], "/jwst/tests_nightly/general/niriss/test_tso3.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_mrs_spec3.py": ["/jwst/tests/base_classes.py"], "/jwst/ami/ami_analyze.py": ["/jwst/ami/nrm_model.py"], "/jwst/tests_nightly/general/nirspec/test_nirspec_msa_spec3.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/nirspec/test_pipelines.py": ["/jwst/stpipe/__init__.py", "/jwst/tests/base_classes.py"], "/jwst/regtest/test_nirspec_mos_spec2.py": ["/jwst/stpipe/__init__.py"], "/jwst/tests_nightly/general/nircam/test_coron3_1.py": ["/jwst/tests/base_classes.py"], "/jwst/tests_nightly/general/miri/test_image2pipeline_1.py": ["/jwst/tests/base_classes.py"]}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.