From 74b02980b5d400b82eebb250f568ffeffa299d8e Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Wed, 20 Mar 2024 22:36:34 -0700
Subject: [PATCH 01/27] Initial framework for data mocking.

---
 src/kbmod/mocking/__init__.py                 |   3 +
 .../mocking/data/headers_archive.tar.bz2      | Bin 0 -> 312243 bytes
 src/kbmod/mocking/mock_fits.py                | 227 ++++++++++++++++++
 src/kbmod/mocking/utils.py                    |  30 +++
 4 files changed, 260 insertions(+)
 create mode 100644 src/kbmod/mocking/__init__.py
 create mode 100644 src/kbmod/mocking/data/headers_archive.tar.bz2
 create mode 100644 src/kbmod/mocking/mock_fits.py
 create mode 100644 src/kbmod/mocking/utils.py

diff --git a/src/kbmod/mocking/__init__.py b/src/kbmod/mocking/__init__.py
new file mode 100644
index 000000000..a7cb6539c
--- /dev/null
+++ b/src/kbmod/mocking/__init__.py
@@ -0,0 +1,3 @@
+from . import utils
+from .mock_fits import *
+
diff --git a/src/kbmod/mocking/data/headers_archive.tar.bz2 b/src/kbmod/mocking/data/headers_archive.tar.bz2
new file mode 100644
index 0000000000000000000000000000000000000000..cdaa2fe9b205bff15f322495e08192b55c085fbd
GIT binary patch
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HcmV?d00001

diff --git a/src/kbmod/mocking/mock_fits.py b/src/kbmod/mocking/mock_fits.py
new file mode 100644
index 000000000..5d2c43677
--- /dev/null
+++ b/src/kbmod/mocking/mock_fits.py
@@ -0,0 +1,227 @@
+import abc
+import tarfile
+import warnings
+
+import numpy as np
+from astropy.table import Table, MaskedColumn
+from astropy.utils.exceptions import AstropyUserWarning
+from astropy.wcs import WCS
+from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, ImageHDU, BinTableHDU, TableHDU, Header
+
+from .utils import header_archive_to_table
+
+
+__all__ = [
+    "SimpleHeaderFactory",
+    "ArchivedHeadersFactory",
+    "ZeroedData",
+    "HDUFactory",
+    "DECamImdiffs"
+]
+
+
+class HeaderFactory(abc.ABC):
+    @abc.abstractmethod
+    def mock(self, hdu=None, **kwargs):
+        raise NotImplementedError()
+
+
+class SimpleHeaderFactory(HeaderFactory):
+    def __init__(self, metadata=None):
+        self.metadata = metadata
+
+    def mock(self, hdu=None, **kwargs):
+        # I have no idea why cards are an empty list by default
+        cards = [] if self.metadata is None else self.metadata
+        return Header(cards=cards)
+
+
+# HeadersFromSerializedHeadersFactory - how to name this?
+class ArchivedHeadersFactory(HeaderFactory):
+    # will almost never be anything else. Rather, it would be a miracle if it
+    # were something else, since FITS standard shouldn't allow it. Further
+    # casting by some packages will always be casting implemented in terms of
+    # parsing these types.
+    lexical_type_map = {
+        "int": int,
+        "str": str,
+        "float": float,
+        "bool": bool,
+    }
+    """Map between type names and types themselves."""
+
+    def __init__(self, archive_name, fname, compression="bz2", format="ascii.ecsv"):
+        self.table = header_archive_to_table(archive_name, fname, compression, format)
+
+        # Create HDU groups for easier iteration
+        self.table = self.table.group_by(["filename", "hdu"])
+        self.n_hdus = len(self.table)
+
+        # Internal counter for the current fits index,
+        # so that we may auto-increment it and avoid returning
+        # the same data all the time.
+        self._current = 0
+
+    def lexical_cast(self, value, format):
+        """Cast str literal of a type to the type itself. Supports just
+        the builtin Python types.
+        """
+        if format in self.lexical_type_map:
+            return self.lexical_type_map[format](value)
+        return value
+
+    def mock(self, hdu=None):
+        header = Header()
+        warnings.filterwarnings("ignore", category=AstropyUserWarning)
+        for k, v, f in self.table.groups[self._current]["keyword", "value", "format"]:
+            header[k] = self.lexical_cast(v, f)
+        warnings.resetwarnings()
+        self._current += 1
+        return header
+
+
+class DataFactory(abc.ABC):
+    # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
+    bitpix_type_map = {
+        # or char
+        8: int,
+        # actually no idea what dtype, or C type for that matter,
+        # is used to represent these two values. But default Headers
+        16: np.float16,
+        32: np.float32,
+        64: np.float64,
+        # classic IEEE float and double
+        -32: np.float32,
+        -64: np.float64
+    }
+
+    @abc.abstractmethod
+    def mock(self, *args, **kwargs):
+        pass
+
+
+class ZeroedData(DataFactory):
+    def __init__(self):
+        super().__init__()
+
+    def mock_image_data(self, hdu):
+        cols = hdu.header.get("NAXIS1", False)
+        rows = hdu.header.get("NAXIS2", False)
+
+        cols = 5 if not cols else cols
+        rows = 5 if not rows else rows
+
+        data = np.zeros(
+            (cols, rows),
+            dtype=self.bitpix_type_map[hdu.header["BITPIX"]]
+        )
+        return data
+
+    def mock_table_data(self, hdu):
+        # interestingly, table HDUs create their own empty
+        # tables from headers, but image HDUs do not, this
+        # is why hdu.data exists and has a valid dtype
+        nrows = hdu.header["TFIELDS"]
+        return np.zeros((nrows,), dtype=hdu.data.dtype)
+
+    def mock(self, hdu=None, **kwargs):
+        if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
+            return self.mock_image_data(hdu)
+        elif isinstance(hdu, (TableHDU, BinTableHDU)):
+            return self.mock_table_data(hdu)
+        else:
+            raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
+
+
+class HDUFactory():
+    def __init__(self, hdu_cls, header_factory=None, data_factory=None):
+        self.hdu_cls = hdu_cls
+
+        self.update_header = False if header_factory is None else True
+        self.header_factory = header_factory
+
+        self.update_data = False if data_factory is None else True
+        self.data_factory = data_factory
+
+    def mock(self, **kwargs):
+        hdu = self.hdu_cls()
+
+        if self.update_header:
+            header = self.header_factory.mock(hdu=hdu, **kwargs)
+            hdu.header.update(header)
+
+        if self.update_data:
+            data = self.data_factory.mock(hdu=hdu, **kwargs)
+            hdu.data = data
+            # not sure if we want this tbh
+            #hdu.update_header()
+
+        return hdu
+
+
+class HDUListFactory():
+    def __init__(self, layout):
+        self.layout = layout
+
+    def mock(self, **kwargs):
+        hdul = HDUList()
+        for layer in self.layout:
+            hdul.append(layer.mock(**kwargs))
+        return hdul
+
+
+class SimpleFits(HDUListfactory):
+    def __init__(self):
+        primary = SimpleHeaderFactory({
+            "EXTNAME": "PRIMARY",
+        })
+
+        wcs = WCS(naxis=2)
+        wcs.wcs.crpix = [1024.0, 2048.0]
+        wcs.wcs.cd = np.array([
+            [-1.44e-07, 7.32e-05],
+            [7.32e-05, 1.44e-05]
+        ])
+        wcs.wcs.crval = [351, -5]
+        wcs.wcs.ctype = ["RA---TAN", "DEC--TAN"]
+        wcs.wcs.cunit = ['deg', 'deg']
+        wcs.wcs.radesys = "ICRS"
+
+        header = wcs.to_header()
+        header["NAXIS"] = 2
+        # eader["EXTNAME"]
+        header["NAXIS1"] = 2048
+        header["NAXIS2"] = 4096
+
+        image_hdr = SimpleHeaderFactory(header)
+        data = ZeroedData()
+
+        layout = [
+            HDUFactory(primary)
+            HDUFactory(CompImageHDU, image_hdr, data)
+            HDUFactory(CompImageHDU, image_hdr, data)
+            HDUFactory(CompImageHDU, image_hdr, data)
+        ]
+
+
+class DECamImdiffs(HDUListFactory):
+    def __init__(self):
+        headers = ArchivedHeadersFactory("headers_archive.tar.bz2", "decam_imdiff_headers.ecsv")
+        data = ZeroedData()
+        image = HDUFactory(CompImageHDU, headers, data)
+
+        # don't let Black have its way with these lines because it's a massacre
+        # fmt: off
+        # primary, science variance and mask
+        layout = [
+            HDUFactory(PrimaryHDU, headers),
+            image,
+            image,
+            image,
+        ]
+
+        # PSF, SkyWCS, catalog meta, higher order corrections etc.
+        layout.extend([HDUFactory(BinTableHDU, headers) ] * 12)
+        # fmt: on
+
+        super().__init__(layout)
diff --git a/src/kbmod/mocking/utils.py b/src/kbmod/mocking/utils.py
new file mode 100644
index 000000000..d79a48bcb
--- /dev/null
+++ b/src/kbmod/mocking/utils.py
@@ -0,0 +1,30 @@
+import tarfile
+from os import path
+
+from astropy.table import Table, MaskedColumn
+
+
+def get_absolute_data_path(file_or_directory):
+    test_dir = path.abspath(path.dirname(__file__))
+    data_dir = path.join(test_dir, "data")
+    return path.join(data_dir, file_or_directory)
+
+
+def get_absolute_demo_data_path(file_or_directory):
+    project_root_dir = path.abspath(path.dirname(path.dirname(__file__)))
+    data_dir = path.join(project_root_dir, "data")
+    return path.join(data_dir, file_or_directory)
+
+
+def header_archive_to_table(archive_path, fname, compression, format):
+    archive_path = get_absolute_data_path(archive_path)
+    with tarfile.open(archive_path, f"r:{compression}") as archive:
+        tblfile = archive.extractfile(fname)
+        table = Table.read(tblfile.read().decode(), format=format)
+        # sometimes empty strings get serialized as masked, to cover that
+        # eventuality we'll just substitute an empty string
+        if isinstance(table["value"], MaskedColumn):
+            table["value"].fill_value = ""
+            table["value"] = table["value"].filled()
+
+    return table

From 3d6ee6e1764493a20295ba33119d4faf290c9697 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Thu, 21 Mar 2024 17:46:50 -0700
Subject: [PATCH 02/27] Add generic mutable and static header factories.

---
 src/kbmod/mocking/mock_fits.py | 167 ++++++++++++++++++++++++++-------
 1 file changed, 133 insertions(+), 34 deletions(-)

diff --git a/src/kbmod/mocking/mock_fits.py b/src/kbmod/mocking/mock_fits.py
index 5d2c43677..4e4832f3d 100644
--- a/src/kbmod/mocking/mock_fits.py
+++ b/src/kbmod/mocking/mock_fits.py
@@ -1,9 +1,8 @@
 import abc
-import tarfile
 import warnings
+import functools
 
 import numpy as np
-from astropy.table import Table, MaskedColumn
 from astropy.utils.exceptions import AstropyUserWarning
 from astropy.wcs import WCS
 from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, ImageHDU, BinTableHDU, TableHDU, Header
@@ -12,10 +11,11 @@
 
 
 __all__ = [
-    "SimpleHeaderFactory",
-    "ArchivedHeadersFactory",
+    "StaticHeader",
+    "ArchivedHeader",
     "ZeroedData",
     "HDUFactory",
+    "SimpleFits",
     "DECamImdiffs"
 ]
 
@@ -26,7 +26,7 @@ def mock(self, hdu=None, **kwargs):
         raise NotImplementedError()
 
 
-class SimpleHeaderFactory(HeaderFactory):
+class StaticHeader(HeaderFactory):
     def __init__(self, metadata=None):
         self.metadata = metadata
 
@@ -36,8 +36,42 @@ def mock(self, hdu=None, **kwargs):
         return Header(cards=cards)
 
 
+class MutableHeader(HeaderFactory):
+    def __init__(self, metadata=None, mutables=None, callbacks=None):
+        for k in mutables:
+            if k not in metadata:
+                raise ValueError(f"Registered mutable key {k} does not exists "
+                                 "in given metadata: f{metadata}.")
+
+        self.metadata = metadata
+        self.mutables = mutables
+        self.callbacks = callbacks
+
+    def mock(self, hdu=None, **kwargs):
+        for i, mutable in enumerate(self.mutables):
+            self.metadata[mutable] = self.callbacks[i](self.metadata[mutable])
+
+        return Header(self.metadata)
+
+
+class IncrementalObstimeHeader(StaticHeader):
+    def __init__(self, obstime_key, dt, metadata=None):
+        self.metadata = metadata
+        self.obstime_key = obstime_key
+        self.dt = dt
+
+    def mock(self, hdu=None, **kwargs):
+        # I have no idea why cards are an empty list by default
+        cards = [] if self.metadata is None else self.metadata
+
+        # has to be a header or dict now
+        cards[self.obstime_key] += self.dt
+
+        return Header(cards=cards)
+
+
 # HeadersFromSerializedHeadersFactory - how to name this?
-class ArchivedHeadersFactory(HeaderFactory):
+class ArchivedHeader(HeaderFactory):
     # will almost never be anything else. Rather, it would be a miracle if it
     # were something else, since FITS standard shouldn't allow it. Further
     # casting by some packages will always be casting implemented in terms of
@@ -160,8 +194,31 @@ def mock(self, **kwargs):
 
 
 class HDUListFactory():
-    def __init__(self, layout):
+    def __init__(self, layout, base_primary={}, base_ext={}, base_wcs={}):
         self.layout = layout
+        self.base_primary = base_primary
+        self.base_ext = base_ext
+        self.base_wcs = base_wcs
+
+    def gen_wcs(self, metadata=None):
+        metadata = self.base_wcs if metadata is None else metadata
+        wcs = WCS(naxis=2)
+        for k, v in metadata.items():
+            setattr(wcs.wcs, k, v)
+        return wcs.to_header()
+
+    def gen_header(self, base, metadata, extend, add_wcs):
+        header = Header(base) if extend else Header()
+        header.update(metadata)
+        if add_wcs:
+            header.update(self.gen_wcs())
+        return header
+
+    def gen_primary(self, metadata=None, extend_base=True, add_wcs=False):
+        return self.gen_header(self.base_primary, metadata, extend_base, add_wcs)
+
+    def gen_ext(self, metadata=None, extend_base=True, add_wcs=True):
+        return self.gen_header(self.base_ext, metadata, extend_base, add_wcs)
 
     def mock(self, **kwargs):
         hdul = HDUList()
@@ -170,43 +227,84 @@ def mock(self, **kwargs):
         return hdul
 
 
-class SimpleFits(HDUListfactory):
-    def __init__(self):
-        primary = SimpleHeaderFactory({
-            "EXTNAME": "PRIMARY",
-        })
+# I am sure a decorator like this must exist somewhere in functools, but can't
+# find it and I'm doing something wrong with functools.partial because that's
+# strictly right-side binding?
+def callback(func):
+    def wrapper(*args, **kwargs):
+        @functools.wraps(func)
+        def f(*fargs, **fkwargs):
+            kwargs.update(fkwargs)
+            return func(*(args+fargs), **kwargs)
+        return f
+    return wrapper
+
+class SimpleFits(HDUListFactory):
+    base_primary = {
+        "EXTNAME": "PRIMARY",
+        "NAXIS": 0,
+        "BITPIX": 8,
+        "OBS-MJD": 58914.0,
+        "NEXTEND": 3,
+        "OBS-LAT": -30.166,
+        "OBS-LONG": -70.814,
+        "STD": "SimpleFits"
+    }
 
-        wcs = WCS(naxis=2)
-        wcs.wcs.crpix = [1024.0, 2048.0]
-        wcs.wcs.cd = np.array([
+    base_ext = {
+        "NAXIS": 2,
+        "NAXIS1": 2048,
+        "NAXIS2": 4096,
+        "BITPIX": 32
+    }
+
+    base_wcs = {
+        "crpix": [1024.0, 2048.0],
+        "crval" : [351, -5],
+        "ctype": ["RA---TAN", "DEC--TAN"],
+        "cunit": ["deg", "deg"],
+        "radesys": "ICRS",
+        "cd": [
             [-1.44e-07, 7.32e-05],
             [7.32e-05, 1.44e-05]
-        ])
-        wcs.wcs.crval = [351, -5]
-        wcs.wcs.ctype = ["RA---TAN", "DEC--TAN"]
-        wcs.wcs.cunit = ['deg', 'deg']
-        wcs.wcs.radesys = "ICRS"
-
-        header = wcs.to_header()
-        header["NAXIS"] = 2
-        # eader["EXTNAME"]
-        header["NAXIS1"] = 2048
-        header["NAXIS2"] = 4096
-
-        image_hdr = SimpleHeaderFactory(header)
+        ]
+    }
+
+    @callback
+    def increment_obstime(self, old, dt):
+        return old+dt
+
+    def __init__(self):
+        primary_hdr = self.gen_primary()
+
+        # multiple options on how to handle callbacks as class members
+        #callbacks=[lambda old: old+0.01, ]
+        #callbacks=[functools.partial(self.increment_obstime, dt=0.001), ]
+        primary_header_factory = MutableHeader(
+            metadata=primary_hdr,
+            mutables=["OBS-MJD", ],
+            callbacks=[self.increment_obstime(dt=0.001), ]
+        )
+
+        image_hdr = self.gen_ext({"EXTNAME": "IMAGE"}, add_wcs=True)
+        variance_hdr = self.gen_ext({"EXTNAME": "VARIANCE"}, add_wcs=True)
+        mask_hdr = self.gen_ext({"EXTNAME": "MASK", "BITPIX": 8}, add_wcs=True)
+
         data = ZeroedData()
 
         layout = [
-            HDUFactory(primary)
-            HDUFactory(CompImageHDU, image_hdr, data)
-            HDUFactory(CompImageHDU, image_hdr, data)
-            HDUFactory(CompImageHDU, image_hdr, data)
+            HDUFactory(PrimaryHDU, primary_header_factory),
+            HDUFactory(CompImageHDU, StaticHeader(image_hdr), data),
+            HDUFactory(CompImageHDU, StaticHeader(variance_hdr), data),
+            HDUFactory(CompImageHDU, StaticHeader(mask_hdr), data)
         ]
 
+        super().__init__(layout)
+
 
 class DECamImdiffs(HDUListFactory):
     def __init__(self):
-        headers = ArchivedHeadersFactory("headers_archive.tar.bz2", "decam_imdiff_headers.ecsv")
+        headers = ArchivedHeader("headers_archive.tar.bz2", "decam_imdiff_headers.ecsv")
         data = ZeroedData()
         image = HDUFactory(CompImageHDU, headers, data)
 
@@ -220,7 +318,8 @@ def __init__(self):
             image,
         ]
 
-        # PSF, SkyWCS, catalog meta, higher order corrections etc.
+        # PSF, SkyWCS, catalog meta, chebyshev higher order corrections etc.
+        # we don't use these so it's fine to leave them empty
         layout.extend([HDUFactory(BinTableHDU, headers) ] * 12)
         # fmt: on
 

From 6e91c9dc2780c6ee257bf5adea3fb620cfa8724e Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Thu, 21 Mar 2024 20:28:08 -0700
Subject: [PATCH 03/27] Make decorator take static and bound methods.

---
 src/kbmod/mocking/mock_fits.py | 27 ++++++++++++++++++++-------
 1 file changed, 20 insertions(+), 7 deletions(-)

diff --git a/src/kbmod/mocking/mock_fits.py b/src/kbmod/mocking/mock_fits.py
index 4e4832f3d..a660c25ae 100644
--- a/src/kbmod/mocking/mock_fits.py
+++ b/src/kbmod/mocking/mock_fits.py
@@ -1,4 +1,5 @@
 import abc
+import types
 import warnings
 import functools
 
@@ -231,14 +232,25 @@ def mock(self, **kwargs):
 # find it and I'm doing something wrong with functools.partial because that's
 # strictly right-side binding?
 def callback(func):
-    def wrapper(*args, **kwargs):
-        @functools.wraps(func)
-        def f(*fargs, **fkwargs):
-            kwargs.update(fkwargs)
-            return func(*(args+fargs), **kwargs)
-        return f
+    if isinstance(func, types.FunctionType):
+        # bound methods
+        def wrapper(*args, **kwargs):
+            @functools.wraps(func)
+            def f(*fargs, **fkwargs):
+                kwargs.update(fkwargs)
+                return func(*(args+fargs), **kwargs)
+            return f
+    else:
+        # functions, static methods
+        def wrapper(*args, **kwargs):
+            @functools.wraps(func)
+            def f(*fargs, **fkwargs):
+                kwargs.update(fkwargs)
+                return func(*fargs, **kwargs)
+            return f
     return wrapper
 
+
 class SimpleFits(HDUListFactory):
     base_primary = {
         "EXTNAME": "PRIMARY",
@@ -271,7 +283,8 @@ class SimpleFits(HDUListFactory):
     }
 
     @callback
-    def increment_obstime(self, old, dt):
+    @staticmethod
+    def increment_obstime(old, dt):
         return old+dt
 
     def __init__(self):

From ad18529ced2df114994df68087c9329b701cecc9 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Thu, 21 Mar 2024 21:40:43 -0700
Subject: [PATCH 04/27] Add a simple masking data factory, break apart into
 modules.

---
 src/kbmod/mocking/__init__.py               |   4 +-
 src/kbmod/mocking/{mock_fits.py => fits.py} | 167 ++------------------
 src/kbmod/mocking/fits_data.py              | 105 ++++++++++++
 src/kbmod/mocking/headers.py                |  96 +++++++++++
 4 files changed, 215 insertions(+), 157 deletions(-)
 rename src/kbmod/mocking/{mock_fits.py => fits.py} (51%)
 create mode 100644 src/kbmod/mocking/fits_data.py
 create mode 100644 src/kbmod/mocking/headers.py

diff --git a/src/kbmod/mocking/__init__.py b/src/kbmod/mocking/__init__.py
index a7cb6539c..740d2139c 100644
--- a/src/kbmod/mocking/__init__.py
+++ b/src/kbmod/mocking/__init__.py
@@ -1,3 +1,5 @@
 from . import utils
-from .mock_fits import *
+from .headers import *
+from .fits_data import *
+from .fits import *
 
diff --git a/src/kbmod/mocking/mock_fits.py b/src/kbmod/mocking/fits.py
similarity index 51%
rename from src/kbmod/mocking/mock_fits.py
rename to src/kbmod/mocking/fits.py
index a660c25ae..876e858d7 100644
--- a/src/kbmod/mocking/mock_fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -1,173 +1,28 @@
-import abc
 import types
-import warnings
 import functools
 
-import numpy as np
-from astropy.utils.exceptions import AstropyUserWarning
 from astropy.wcs import WCS
-from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, ImageHDU, BinTableHDU, TableHDU, Header
+from astropy.io.fits import (
+    HDUList,
+    PrimaryHDU,
+    CompImageHDU,
+    BinTableHDU,
+    Header
+)
 
-from .utils import header_archive_to_table
+from .headers import StaticHeader, MutableHeader
+from .fits_data import ZeroedData, SimpleMask
 
 
 __all__ = [
-    "StaticHeader",
-    "ArchivedHeader",
-    "ZeroedData",
     "HDUFactory",
+    "HDUListFactory",
+    "callback",
     "SimpleFits",
     "DECamImdiffs"
 ]
 
 
-class HeaderFactory(abc.ABC):
-    @abc.abstractmethod
-    def mock(self, hdu=None, **kwargs):
-        raise NotImplementedError()
-
-
-class StaticHeader(HeaderFactory):
-    def __init__(self, metadata=None):
-        self.metadata = metadata
-
-    def mock(self, hdu=None, **kwargs):
-        # I have no idea why cards are an empty list by default
-        cards = [] if self.metadata is None else self.metadata
-        return Header(cards=cards)
-
-
-class MutableHeader(HeaderFactory):
-    def __init__(self, metadata=None, mutables=None, callbacks=None):
-        for k in mutables:
-            if k not in metadata:
-                raise ValueError(f"Registered mutable key {k} does not exists "
-                                 "in given metadata: f{metadata}.")
-
-        self.metadata = metadata
-        self.mutables = mutables
-        self.callbacks = callbacks
-
-    def mock(self, hdu=None, **kwargs):
-        for i, mutable in enumerate(self.mutables):
-            self.metadata[mutable] = self.callbacks[i](self.metadata[mutable])
-
-        return Header(self.metadata)
-
-
-class IncrementalObstimeHeader(StaticHeader):
-    def __init__(self, obstime_key, dt, metadata=None):
-        self.metadata = metadata
-        self.obstime_key = obstime_key
-        self.dt = dt
-
-    def mock(self, hdu=None, **kwargs):
-        # I have no idea why cards are an empty list by default
-        cards = [] if self.metadata is None else self.metadata
-
-        # has to be a header or dict now
-        cards[self.obstime_key] += self.dt
-
-        return Header(cards=cards)
-
-
-# HeadersFromSerializedHeadersFactory - how to name this?
-class ArchivedHeader(HeaderFactory):
-    # will almost never be anything else. Rather, it would be a miracle if it
-    # were something else, since FITS standard shouldn't allow it. Further
-    # casting by some packages will always be casting implemented in terms of
-    # parsing these types.
-    lexical_type_map = {
-        "int": int,
-        "str": str,
-        "float": float,
-        "bool": bool,
-    }
-    """Map between type names and types themselves."""
-
-    def __init__(self, archive_name, fname, compression="bz2", format="ascii.ecsv"):
-        self.table = header_archive_to_table(archive_name, fname, compression, format)
-
-        # Create HDU groups for easier iteration
-        self.table = self.table.group_by(["filename", "hdu"])
-        self.n_hdus = len(self.table)
-
-        # Internal counter for the current fits index,
-        # so that we may auto-increment it and avoid returning
-        # the same data all the time.
-        self._current = 0
-
-    def lexical_cast(self, value, format):
-        """Cast str literal of a type to the type itself. Supports just
-        the builtin Python types.
-        """
-        if format in self.lexical_type_map:
-            return self.lexical_type_map[format](value)
-        return value
-
-    def mock(self, hdu=None):
-        header = Header()
-        warnings.filterwarnings("ignore", category=AstropyUserWarning)
-        for k, v, f in self.table.groups[self._current]["keyword", "value", "format"]:
-            header[k] = self.lexical_cast(v, f)
-        warnings.resetwarnings()
-        self._current += 1
-        return header
-
-
-class DataFactory(abc.ABC):
-    # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
-    bitpix_type_map = {
-        # or char
-        8: int,
-        # actually no idea what dtype, or C type for that matter,
-        # is used to represent these two values. But default Headers
-        16: np.float16,
-        32: np.float32,
-        64: np.float64,
-        # classic IEEE float and double
-        -32: np.float32,
-        -64: np.float64
-    }
-
-    @abc.abstractmethod
-    def mock(self, *args, **kwargs):
-        pass
-
-
-class ZeroedData(DataFactory):
-    def __init__(self):
-        super().__init__()
-
-    def mock_image_data(self, hdu):
-        cols = hdu.header.get("NAXIS1", False)
-        rows = hdu.header.get("NAXIS2", False)
-
-        cols = 5 if not cols else cols
-        rows = 5 if not rows else rows
-
-        data = np.zeros(
-            (cols, rows),
-            dtype=self.bitpix_type_map[hdu.header["BITPIX"]]
-        )
-        return data
-
-    def mock_table_data(self, hdu):
-        # interestingly, table HDUs create their own empty
-        # tables from headers, but image HDUs do not, this
-        # is why hdu.data exists and has a valid dtype
-        nrows = hdu.header["TFIELDS"]
-        return np.zeros((nrows,), dtype=hdu.data.dtype)
-
-    def mock(self, hdu=None, **kwargs):
-        if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
-            return self.mock_image_data(hdu)
-        elif isinstance(hdu, (TableHDU, BinTableHDU)):
-            return self.mock_table_data(hdu)
-        else:
-            raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
-
-
 class HDUFactory():
     def __init__(self, hdu_cls, header_factory=None, data_factory=None):
         self.hdu_cls = hdu_cls
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
new file mode 100644
index 000000000..3c3857741
--- /dev/null
+++ b/src/kbmod/mocking/fits_data.py
@@ -0,0 +1,105 @@
+import abc
+
+import numpy as np
+from astropy.io.fits import (
+    PrimaryHDU,
+    CompImageHDU,
+    ImageHDU,
+    BinTableHDU,
+    TableHDU
+)
+
+
+__all__ = [
+    "DataFactory",
+    "ZeroedData",
+    "SimpleMask"
+]
+
+
+class DataFactory(abc.ABC):
+    # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
+    bitpix_type_map = {
+        # or char
+        8: int,
+        # actually no idea what dtype, or C type for that matter,
+        # is used to represent these two values. But default Headers
+        16: np.float16,
+        32: np.float32,
+        64: np.float64,
+        # classic IEEE float and double
+        -32: np.float32,
+        -64: np.float64
+    }
+
+    @abc.abstractmethod
+    def mock(self, hdu=None,  **kwargs):
+        pass
+
+
+class ZeroedData(DataFactory):
+    def __init__(self):
+        super().__init__()
+
+    def mock_image_data(self, hdu):
+        cols = hdu.header.get("NAXIS1", False)
+        rows = hdu.header.get("NAXIS2", False)
+
+        cols = 5 if not cols else cols
+        rows = 5 if not rows else rows
+
+        data = np.zeros(
+            (cols, rows),
+            dtype=self.bitpix_type_map[hdu.header["BITPIX"]]
+        )
+        return data
+
+    def mock_table_data(self, hdu):
+        # interestingly, table HDUs create their own empty
+        # tables from headers, but image HDUs do not, this
+        # is why hdu.data exists and has a valid dtype
+        nrows = hdu.header["TFIELDS"]
+        return np.zeros((nrows,), dtype=hdu.data.dtype)
+
+    def mock(self, hdu=None, **kwargs):
+        if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
+            return self.mock_image_data(hdu)
+        elif isinstance(hdu, (TableHDU, BinTableHDU)):
+            return self.mock_table_data(hdu)
+        else:
+            raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
+
+
+class SimpleMask(DataFactory):
+    def __init__(self, mask):
+        super().__init__()
+
+        # not sure if this is "best" "best" way, but
+        # it does safe a lot of array copies if we don't
+        # have to write to the array
+        self.mask = mask
+        self.mask.writeable = False
+
+    @classmethod
+    def from_params(cls, shape, padding=0):
+        mask = np.zeros(shape)
+        mask[:padding] = 1
+        mask[shape[0]-padding:] = 1
+        mask[:, :padding] = 1
+        mask[: shape[1]-padding:] = 1
+        return cls(mask)
+
+    # bro I don't know antymore
+    @classmethod
+    def from_patches(cls, shape, patches):
+        mask = np.zeros(shape)
+        for patch, value in patches:
+            if isinstance(patch, tuple):
+                mask[*patch] = value
+            elif isinstance(slice):
+                mask[slice] = value
+            else:
+                raise ValueError("Expected a tuple (x, y), (slice, slice) or "
+                                 f"slice, got {patch} instead.")
+
+        return cls(mask)
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
new file mode 100644
index 000000000..3812adfa2
--- /dev/null
+++ b/src/kbmod/mocking/headers.py
@@ -0,0 +1,96 @@
+import abc
+import types
+import warnings
+import functools
+
+import numpy as np
+from astropy.utils.exceptions import AstropyUserWarning
+from astropy.wcs import WCS
+from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, ImageHDU, BinTableHDU, TableHDU, Header
+
+from .utils import header_archive_to_table
+
+
+__all__ = [
+    "HeaderFactory",
+    "StaticHeader",
+    "MutableHeader",
+    "ArchivedHeader",
+]
+
+
+class HeaderFactory(abc.ABC):
+    @abc.abstractmethod
+    def mock(self, hdu=None, **kwargs):
+        raise NotImplementedError()
+
+
+class StaticHeader(HeaderFactory):
+    def __init__(self, metadata=None):
+        self.metadata = metadata
+
+    def mock(self, hdu=None, **kwargs):
+        # I have no idea why cards are an empty list by default
+        cards = [] if self.metadata is None else self.metadata
+        return Header(cards=cards)
+
+
+class MutableHeader(HeaderFactory):
+    def __init__(self, metadata=None, mutables=None, callbacks=None):
+        for k in mutables:
+            if k not in metadata:
+                raise ValueError(f"Registered mutable key {k} does not exists "
+                                 "in given metadata: f{metadata}.")
+
+        self.metadata = metadata
+        self.mutables = mutables
+        self.callbacks = callbacks
+
+    def mock(self, hdu=None, **kwargs):
+        for i, mutable in enumerate(self.mutables):
+            self.metadata[mutable] = self.callbacks[i](self.metadata[mutable])
+
+        return Header(self.metadata)
+
+
+class ArchivedHeader(HeaderFactory):
+    # will almost never be anything else. Rather, it would be a miracle if it
+    # were something else, since FITS standard shouldn't allow it. Further
+    # casting by some packages will always be casting implemented in terms of
+    # parsing these types.
+    lexical_type_map = {
+        "int": int,
+        "str": str,
+        "float": float,
+        "bool": bool,
+    }
+    """Map between type names and types themselves."""
+
+    def __init__(self, archive_name, fname, compression="bz2", format="ascii.ecsv"):
+        self.table = header_archive_to_table(archive_name, fname, compression, format)
+
+        # Create HDU groups for easier iteration
+        self.table = self.table.group_by(["filename", "hdu"])
+        self.n_hdus = len(self.table)
+
+        # Internal counter for the current fits index,
+        # so that we may auto-increment it and avoid returning
+        # the same data all the time.
+        self._current = 0
+
+    def lexical_cast(self, value, format):
+        """Cast str literal of a type to the type itself. Supports just
+        the builtin Python types.
+        """
+        if format in self.lexical_type_map:
+            return self.lexical_type_map[format](value)
+        return value
+
+    def mock(self, hdu=None):
+        header = Header()
+        warnings.filterwarnings("ignore", category=AstropyUserWarning)
+        for k, v, f in self.table.groups[self._current]["keyword", "value", "format"]:
+            header[k] = self.lexical_cast(v, f)
+        warnings.resetwarnings()
+        self._current += 1
+        return header

From c60249d211be679b84c9bb2d7cce4826dcf1dde4 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Fri, 22 Mar 2024 21:11:23 -0700
Subject: [PATCH 05/27] Add better data mocking, realistic and simplicit data
 factories, add object injection.

---
 src/kbmod/mocking/__init__.py     |   2 +-
 src/kbmod/mocking/fits.py         | 221 ++++++++++++++++++++++++------
 src/kbmod/mocking/fits_data.py    | 190 ++++++++++++++++++++++++-
 src/kbmod/mocking/headers.py      |   5 +-
 src/kbmod/mocking/src_catalogs.py |  47 +++++++
 5 files changed, 418 insertions(+), 47 deletions(-)
 create mode 100644 src/kbmod/mocking/src_catalogs.py

diff --git a/src/kbmod/mocking/__init__.py b/src/kbmod/mocking/__init__.py
index 740d2139c..a300eb65a 100644
--- a/src/kbmod/mocking/__init__.py
+++ b/src/kbmod/mocking/__init__.py
@@ -1,5 +1,5 @@
 from . import utils
+from .src_catalogs import *
 from .headers import *
 from .fits_data import *
 from .fits import *
-
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index 876e858d7..220102c28 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -1,29 +1,29 @@
 import types
 import functools
 
+import numpy as np
 from astropy.wcs import WCS
-from astropy.io.fits import (
-    HDUList,
-    PrimaryHDU,
-    CompImageHDU,
-    BinTableHDU,
-    Header
-)
+from astropy.modeling import models
+from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, BinTableHDU, Header
 
 from .headers import StaticHeader, MutableHeader
-from .fits_data import ZeroedData, SimpleMask
+from .src_catalogs import (
+    gen_random_static_source_catalog,
+    gen_random_moving_object_catalog
+)
+from .fits_data import (
+    ZeroedData,
+    SimpleImage,
+    SimpleVariance,
+    SimpleMask,
+    add_model_objects
+)
 
 
-__all__ = [
-    "HDUFactory",
-    "HDUListFactory",
-    "callback",
-    "SimpleFits",
-    "DECamImdiffs"
-]
+__all__ = ["HDUFactory", "HDUListFactory", "callback", "SimpleFits", "DECamImdiffs"]
 
 
-class HDUFactory():
+class HDUFactory:
     def __init__(self, hdu_cls, header_factory=None, data_factory=None):
         self.hdu_cls = hdu_cls
 
@@ -44,12 +44,12 @@ def mock(self, **kwargs):
             data = self.data_factory.mock(hdu=hdu, **kwargs)
             hdu.data = data
             # not sure if we want this tbh
-            #hdu.update_header()
+            # hdu.update_header()
 
         return hdu
 
 
-class HDUListFactory():
+class HDUListFactory:
     def __init__(self, layout, base_primary={}, base_ext={}, base_wcs={}):
         self.layout = layout
         self.base_primary = base_primary
@@ -70,10 +70,10 @@ def gen_header(self, base, metadata, extend, add_wcs):
             header.update(self.gen_wcs())
         return header
 
-    def gen_primary(self, metadata=None, extend_base=True, add_wcs=False):
+    def gen_primary_hdr(self, metadata=None, extend_base=True, add_wcs=False):
         return self.gen_header(self.base_primary, metadata, extend_base, add_wcs)
 
-    def gen_ext(self, metadata=None, extend_base=True, add_wcs=True):
+    def gen_ext_hdr(self, metadata=None, extend_base=True, add_wcs=True):
         return self.gen_header(self.base_ext, metadata, extend_base, add_wcs)
 
     def mock(self, **kwargs):
@@ -93,7 +93,7 @@ def wrapper(*args, **kwargs):
             @functools.wraps(func)
             def f(*fargs, **fkwargs):
                 kwargs.update(fkwargs)
-                return func(*(args+fargs), **kwargs)
+                return func(*(args + fargs), **kwargs)
             return f
     else:
         # functions, static methods
@@ -106,7 +106,7 @@ def f(*fargs, **fkwargs):
     return wrapper
 
 
-class SimpleFits(HDUListFactory):
+class EmptyFits(HDUListFactory):
     base_primary = {
         "EXTNAME": "PRIMARY",
         "NAXIS": 0,
@@ -115,43 +115,39 @@ class SimpleFits(HDUListFactory):
         "NEXTEND": 3,
         "OBS-LAT": -30.166,
         "OBS-LONG": -70.814,
-        "STD": "SimpleFits"
+        "STD": "SimpleFits",
     }
 
-    base_ext = {
-        "NAXIS": 2,
-        "NAXIS1": 2048,
-        "NAXIS2": 4096,
-        "BITPIX": 32
-    }
+    base_ext = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "BITPIX": 32}
 
     base_wcs = {
         "crpix": [1024.0, 2048.0],
-        "crval" : [351, -5],
+        "crval": [351, -5],
         "ctype": ["RA---TAN", "DEC--TAN"],
         "cunit": ["deg", "deg"],
         "radesys": "ICRS",
-        "cd": [
-            [-1.44e-07, 7.32e-05],
-            [7.32e-05, 1.44e-05]
-        ]
+        "cd": [[-1.44e-07, 7.32e-05], [7.32e-05, 1.44e-05]],
     }
 
     @callback
     @staticmethod
     def increment_obstime(old, dt):
-        return old+dt
+        return old + dt
 
     def __init__(self):
         primary_hdr = self.gen_primary()
 
         # multiple options on how to handle callbacks as class members
-        #callbacks=[lambda old: old+0.01, ]
-        #callbacks=[functools.partial(self.increment_obstime, dt=0.001), ]
+        # callbacks=[lambda old: old+0.01, ]
+        # callbacks=[functools.partial(self.increment_obstime, dt=0.001), ]
         primary_header_factory = MutableHeader(
             metadata=primary_hdr,
-            mutables=["OBS-MJD", ],
-            callbacks=[self.increment_obstime(dt=0.001), ]
+            mutables=[
+                "OBS-MJD",
+            ],
+            callbacks=[
+                self.increment_obstime(dt=0.001),
+            ],
         )
 
         image_hdr = self.gen_ext({"EXTNAME": "IMAGE"}, add_wcs=True)
@@ -164,11 +160,156 @@ def __init__(self):
             HDUFactory(PrimaryHDU, primary_header_factory),
             HDUFactory(CompImageHDU, StaticHeader(image_hdr), data),
             HDUFactory(CompImageHDU, StaticHeader(variance_hdr), data),
-            HDUFactory(CompImageHDU, StaticHeader(mask_hdr), data)
+            HDUFactory(CompImageHDU, StaticHeader(mask_hdr), data),
+        ]
+
+        super().__init__(layout)
+
+
+class SimpleFits(HDUListFactory):
+    base_primary = {
+        "EXTNAME": "PRIMARY",
+        "NAXIS": 0,
+        "BITPIX": 8,
+        "OBS-MJD": 58914.0,
+        "NEXTEND": 3,
+        "OBS-LAT": -30.166,
+        "OBS-LONG": -70.814,
+        "STD": "SimpleFits",
+    }
+
+    base_ext = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "BITPIX": 32}
+
+    base_wcs = {
+        "crpix": [1024.0, 2048.0],
+        "crval": [351, -5],
+        "ctype": ["RA---TAN", "DEC--TAN"],
+        "cunit": ["deg", "deg"],
+        "radesys": "ICRS",
+        "cd": [[-1.44e-07, 7.32e-05], [7.32e-05, 1.44e-05]],
+    }
+
+    n_static_sources = 100
+    base_src_cat_params = {
+        "amplitude": [500, 2000],
+        "x_mean": [0, 4096],
+        "y_mean": [0, 2048],
+        "x_stddev": [1, 2],
+        "y_stddev": [1, 2],
+        "theta": [0, np.pi]
+    }
+
+    n_moving_objects = 10
+    base_obj_cat_params = {
+        "amplitude": [500, 1000],
+        "x_mean": [0, 4096],
+        "y_mean": [0, 2048],
+        "vx": [300, 1000],
+        "vy": [300, 1000],
+        "stddev": [1, 10],
+        "theta": [0, np.pi]
+    }
+
+    # multiple options on how to handle callbacks as class members
+    # callbacks=[lambda old: old+0.01, ]
+    # callbacks=[functools.partial(self.increment_obstime, dt=0.001), ]
+    @callback
+    @staticmethod
+    def increment_obstime(old, dt):
+        return old + dt
+
+    def __init__(self, shape=(2048, 4096), noise=100, dt=0.001,
+                 add_static_sources=False, src_cat="random", src_cat_params=None,
+                 add_moving_objects=False, obj_cat="random", obj_cat_params=None,
+                 **kwargs):
+        # let's just make sure the base headers align with the basic params
+        self.base_ext["NAXIS1"] = shape[0]
+        self.base_ext["NAXIS2"] = shape[1]
+        self.base_wcs["crpix"] = (int(shape[0]/2), int(shape[1]/2))
+        self.base_src_cat_params["y_mean"][1] = shape[0]
+        self.base_src_cat_params["x_mean"][1] = shape[1]
+        self.base_obj_cat_params["y_mean"][1] = shape[0]
+        self.base_obj_cat_params["x_mean"][1] = shape[1]
+        self.dt = dt
+
+        # internal counter of n images created so far
+        self.idx = 0
+
+        # First we gen the catalogs we need to generate data
+        if add_static_sources and src_cat == "random":
+            src_params = self.base_src_cat_params if src_cat_params is None else src_cat_params
+            n = src_params.pop("n", self.n_static_sources)
+            src_cat = gen_random_static_source_catalog(n, src_params)
+        if not add_static_sources:
+            src_cat = None
+
+        if add_moving_objects and obj_cat == "random":
+            obj_params = self.base_obj_cat_params if obj_cat_params is None else obj_cat_params
+            n = obj_params.pop("n", self.n_moving_objects)
+            obj_cat = gen_random_moving_object_catalog(n, obj_params, dt)
+        if not add_moving_objects:
+            obj_cat = None
+        self.obj_cat = obj_cat
+
+        # Then we can generate The header and data factories that build our
+        # HDUList.  Primary header contains no data, but does contain
+        # update-able metadata fields (f.e. timestamps)
+        primary_hdr = self.gen_primary_hdr()
+        primary_header_factory = MutableHeader(
+            metadata=primary_hdr,
+            mutables=[
+                "OBS-MJD",
+            ],
+            callbacks=[
+                self.increment_obstime(dt=dt),
+            ],
+        )
+
+        # The remaining HDUs contain static headers, but data that changes
+        # (f.e. updating positions of linearly moving objects in each image)
+        img_hdr = self.gen_ext_hdr({"EXTNAME": "IMAGE"}, add_wcs=True)
+        img_data = SimpleImage.from_simplistic_sim(
+            shape=shape,
+            noise=noise,
+            catalog=src_cat,
+            return_copy = True if add_moving_objects else False
+        )
+        var_hdr = self.gen_ext_hdr({"EXTNAME": "VARIANCE"}, add_wcs=True)
+        var_data = SimpleVariance(
+            img_data.base_image,
+            read_noise=noise,
+            gain=1.0
+        )
+        mask_hdr = self.gen_ext_hdr({"EXTNAME": "MASK", "BITPIX": 8}, add_wcs=True)
+        mask_data = ZeroedData(np.zeros(shape))
+
+        # Now we can build an HDU layout and let the HDUList factory
+        # generate self-updateable HDULists.
+        layout = [
+            HDUFactory(PrimaryHDU, primary_header_factory),
+            HDUFactory(CompImageHDU, StaticHeader(img_hdr), img_data),
+            HDUFactory(CompImageHDU, StaticHeader(var_hdr), var_data),
+            HDUFactory(CompImageHDU, StaticHeader(mask_hdr), mask_data),
         ]
 
         super().__init__(layout)
 
+    def mock(self, **kwargs):
+        hdul = super().mock(**kwargs)
+
+        if self.obj_cat is not None:
+            # advance the catalog:
+            self.obj_cat["x_mean"] += self.obj_cat["vx"]*self.dt
+            self.obj_cat["y_mean"] += self.obj_cat["vy"]*self.dt
+        new_scene = add_model_objects(
+            hdul["IMAGE"].data,
+            self.obj_cat,
+            models.Gaussian2D(x_stddev=1, y_stddev=1),
+            oversample=1
+        )
+        hdul["IMAGE"].data = new_scene
+        return hdul
+
 
 class DECamImdiffs(HDUListFactory):
     def __init__(self):
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index 3c3857741..a3fb67d2c 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -8,11 +8,15 @@
     BinTableHDU,
     TableHDU
 )
+from astropy.modeling import models
+from astropy.convolution import discretize_model
 
 
 __all__ = [
+    "add_model_objects",
     "DataFactory",
     "ZeroedData",
+    "SimpleImage",
     "SimpleMask"
 ]
 
@@ -38,8 +42,11 @@ def mock(self, hdu=None,  **kwargs):
 
 
 class ZeroedData(DataFactory):
-    def __init__(self):
+    def __init__(self, base_image=None, return_copy=False):
         super().__init__()
+        self.base_image = base_image
+        self.base_image.flags.writeable = False
+        self.return_copy = return_copy
 
     def mock_image_data(self, hdu):
         cols = hdu.header.get("NAXIS1", False)
@@ -61,7 +68,14 @@ def mock_table_data(self, hdu):
         nrows = hdu.header["TFIELDS"]
         return np.zeros((nrows,), dtype=hdu.data.dtype)
 
+    def mock_static(self, hdu=None):
+        if self.return_copy:
+            return self.base_image.copy()
+        return self.base_image
+
     def mock(self, hdu=None, **kwargs):
+        if self.base_image is not None:
+            return self.mock_static(hdu)
         if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
             return self.mock_image_data(hdu)
         elif isinstance(hdu, (TableHDU, BinTableHDU)):
@@ -70,23 +84,100 @@ def mock(self, hdu=None, **kwargs):
             raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
 
 
+def add_model_objects(img, catalog, model, oversample=1):
+    shape = img.shape
+    yidx, xidx = np.indices(shape)
+
+    # find catalog columns that exist for the model
+    params_to_set = []
+    for param in catalog.colnames:
+        if param in model.param_names:
+            params_to_set.append(param)
+
+    # Save the initial model parameters so we can set them back
+    init_params = {param: getattr(model, param) for param in params_to_set}
+
+    try:
+        for i, source in enumerate(catalog):
+            for param in params_to_set:
+                setattr(model, param, source[param])
+            model.render(img)
+    finally:
+        for param, value in init_params.items():
+            setattr(model, param, value)
+
+    return img
+
+
+class SimpleImage(DataFactory):
+    rng = np.random.default_rng(12345)
+    read_noise_gen = rng.normal
+    dark_current_gen = rng.poisson
+    sky_count_gen = rng.poisson
+
+    def __init__(self, image, return_copy=False):
+        self.base_image = image
+        self.base_image.flags.writeable = False
+        self.return_copy = return_copy
+
+    def mock(self, hdu=None, **kwargs):
+        if self.return_copy:
+            return self.base_image.copy()
+        return self.base_image
+
+    @classmethod
+    def from_simplistic_sim(cls, shape, noise, catalog=None,
+                            src_model=models.Gaussian2D(x_stddev=1, y_stddev=1),
+                            oversample=1, **kwargs):
+        # create poisson background
+        img = cls.sky_count_gen(noise, size=shape).astype(np.float32)
+
+        # add catalog objects
+        if catalog is None:
+            return cls(img, **kwargs)
+
+        img = add_model_objects(img, catalog, src_model, oversample)
+        return cls(img, **kwargs)
+
+
+class SimpleVariance(DataFactory):
+    def __init__(self, image, read_noise, gain, return_copy=False):
+        self.base_image = image/gain + read_noise**2
+        self.return_copy = return_copy
+
+    def mock(self, hdu=None, **kwargs):
+        if self.return_copy:
+            return self.base_image.copy()
+        else:
+            return self.base_image
+
+
 class SimpleMask(DataFactory):
-    def __init__(self, mask):
+    def __init__(self, mask, return_copy=False):
         super().__init__()
 
         # not sure if this is "best" "best" way, but
         # it does safe a lot of array copies if we don't
-        # have to write to the array
+        # have to write to the mocked array (which we shouldn't?)
         self.mask = mask
         self.mask.writeable = False
+        self.return_copy = return_copy
 
     @classmethod
-    def from_params(cls, shape, padding=0):
+    def from_params(cls, shape, padding=0, bad_columns=[]):
         mask = np.zeros(shape)
+
+        # padding
         mask[:padding] = 1
         mask[shape[0]-padding:] = 1
         mask[:, :padding] = 1
         mask[: shape[1]-padding:] = 1
+
+
+        # bad columns
+        for col in bad_columns:
+            mask[:, col] = 1
+
         return cls(mask)
 
     # bro I don't know antymore
@@ -103,3 +194,94 @@ def from_patches(cls, shape, patches):
                                  f"slice, got {patch} instead.")
 
         return cls(mask)
+
+    def mock(self, hdu=None, **kwargs):
+        if self.return_copy:
+            return self.mask.copy()
+        return self.mask
+
+
+class SimulatedImage(DataFactory):
+    rng = np.random.default_rng(12345)
+    read_noise_gen = rng.normal
+    dark_current_gen = rng.poisson
+    sky_count_gen = rng.poisson
+
+    def __add_bad_cols(self, image, cols, bias, n, seed, pattern_offset):
+        if not cols:
+            # most of the time I imagine we don't need bad cols
+            return image
+
+        if bad_columns == "random":
+            rng = np.random.RandomState(seed=self.bad_cols_seed)
+            bad_cols = rng.randint(0, shape[1], size=n)
+        else:
+            bad_cols = bad_columns
+
+        self.col_pattern = rng.randint(0, int(pattern_offset * bias), size=shape[0])
+        for col in columns:
+            image[:, col] = bias + col_pattern
+
+        return image
+
+    def __add_hot_pixels(self, image, pixels, percent, offset):
+        if not pixels:
+            # most of the time I imagine we don't need hot pixels
+            return image
+
+        if pixels == "random":
+            rng = np.random.RandomState(seed=self.hot_pixel_seed)
+            n_pixels = image.shape[0] * image.shape[1]
+            n = int(percent * n_pixels)
+            x = rng.randint(0, shape[1], size=n)
+            y = rng.randint(0, shape[0], size=n)
+            hot_pixels = np.column_stack(x, y)
+        else:
+            hot_pixels = pixels
+
+        for pix in hot_pixels:
+            image[*pix] += image[*pix]*offset
+
+        return image
+
+
+    def __init__(
+            self,
+            shape,
+            read_noise, gain,
+            bias,
+            exposure_time, dark_current,
+            sky_level,
+            bad_columns=False, bad_cols_seed=321, n_bad_cols=5, bad_col_pattern_offset=0.1,
+            hot_pixels=False, hot_pix_seed=543, hot_percent=0.00001, hot_offset=1000,
+    ):
+        super().__init__()
+
+        # starting empty image
+        self.base_img = np.zeros(shape)
+
+        # add read noise
+        self.base_img += self.read_noise_gen(scale=read_noise/gain, size=shape)
+
+        # add bias
+        self.base_img += bias
+
+        # add bad columns
+        self.bad_cols_seed = bad_cols_seed
+        self.__add_bad_cols(self.base_image, bad_columns, bias, n_bad_cols, bad_cols_seed)
+
+        # add dark current
+        current = dark_current * exposure_time/gain
+        self.base_img = self.dark_current_gen(current, size=shape)
+
+        # add hot pixels
+        self.hot_pixel_seed = hot_pix_seed
+        self.base_img = self.__add_hot_pixels(self.base_img, hot_pixels, hot_percent, hot_offset)
+
+        # add sky counts
+        self.base_img += self.sky_count_gen(sky_level*gain, shape)/gain
+
+    def mock(self, hdu=None, **kwargs):
+        # we do always have to return a new copy here, since sci images
+        # are expected to be written on
+        return self.base_img.copy()
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index 3812adfa2..2d29d6ee2 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -39,8 +39,9 @@ class MutableHeader(HeaderFactory):
     def __init__(self, metadata=None, mutables=None, callbacks=None):
         for k in mutables:
             if k not in metadata:
-                raise ValueError(f"Registered mutable key {k} does not exists "
-                                 "in given metadata: f{metadata}.")
+                raise ValueError(
+                    f"Registered mutable key {k} does not exists " "in given metadata: f{metadata}."
+                )
 
         self.metadata = metadata
         self.mutables = mutables
diff --git a/src/kbmod/mocking/src_catalogs.py b/src/kbmod/mocking/src_catalogs.py
new file mode 100644
index 000000000..f94b7896e
--- /dev/null
+++ b/src/kbmod/mocking/src_catalogs.py
@@ -0,0 +1,47 @@
+import numpy as np
+from astropy.time import Time
+from astropy.table import QTable, vstack
+
+
+__all__ = [
+    "gen_random_static_source_catalog",
+    "gen_random_moving_object_catalog"
+]
+
+
+def gen_random_static_source_catalog(n, param_ranges, seed=None):
+    cat = QTable()
+    rng = np.random.default_rng(seed)
+
+    for param_name, (lower, upper) in param_ranges.items():
+        cat[param_name] = rng.uniform(lower, upper, n)
+
+    if "stddev" in param_ranges:
+        cat["x_stddev"] = cat["stddev"]
+        cat["y_stddev"] = cat["stddev"]
+
+    # conversion assumes a gaussian
+    if "flux" in param_ranges and "amplitude" not in param_ranges:
+        xstd = cat["x_stddev"] if "x_stddev" in cat.colnames else 1
+        ystd = cat["y_stddev"] if "y_stddev" in cat.colnames else 1
+
+        cat["amplitude"] = cat["flux"] / (2.0 * np.pi * xstd * ystd)
+
+    return cat
+
+
+def gen_random_moving_object_catalog(n, param_ranges, dt, seed=None):
+    obj_cat = gen_random_static_source_catalog(n, param_ranges, seed)
+    return obj_cat
+#    cat = None
+#    for i, t in enumerate(obstimes):
+#        if i == 0:
+#            cat = obj_cat.copy()
+#            continue
+#
+#        obj_cat["x_mean"] += obj_cat["vx"]*dt
+#        vstack(cat, obj_cat)
+#
+#    return cat
+
+

From 4c21279dedf2ebf4bee359d93c4ce3bf7069c680 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Sun, 24 Mar 2024 00:56:23 -0700
Subject: [PATCH 06/27] Add catalog factories, refactor fits and fits_data a
 bit.

---
 src/kbmod/mocking/__init__.py     |   2 +-
 src/kbmod/mocking/catalogs.py     | 103 +++++++++++++
 src/kbmod/mocking/fits.py         |  96 ++++---------
 src/kbmod/mocking/fits_data.py    | 230 +++++++++++++++---------------
 src/kbmod/mocking/src_catalogs.py |  47 ------
 5 files changed, 249 insertions(+), 229 deletions(-)
 create mode 100644 src/kbmod/mocking/catalogs.py
 delete mode 100644 src/kbmod/mocking/src_catalogs.py

diff --git a/src/kbmod/mocking/__init__.py b/src/kbmod/mocking/__init__.py
index a300eb65a..b79086419 100644
--- a/src/kbmod/mocking/__init__.py
+++ b/src/kbmod/mocking/__init__.py
@@ -1,5 +1,5 @@
 from . import utils
-from .src_catalogs import *
+from .catalogs import *
 from .headers import *
 from .fits_data import *
 from .fits import *
diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
new file mode 100644
index 000000000..246fa3ded
--- /dev/null
+++ b/src/kbmod/mocking/catalogs.py
@@ -0,0 +1,103 @@
+import abc
+
+import numpy as np
+from astropy.time import Time
+from astropy.table import QTable, vstack
+
+
+__all__ = [
+    "gen_catalog",
+    "CatalogFactory",
+    "SimpleSourceCatalog",
+    "SimpleObjectCatalog",
+]
+
+
+def gen_catalog(n, param_ranges, seed=None):
+    cat = QTable()
+    rng = np.random.default_rng(seed)
+
+    for param_name, (lower, upper) in param_ranges.items():
+        cat[param_name] = rng.uniform(lower, upper, n)
+
+    if "stddev" in param_ranges:
+        cat["x_stddev"] = cat["stddev"]
+        cat["y_stddev"] = cat["stddev"]
+
+    # conversion assumes a gaussian
+    if "flux" in param_ranges and "amplitude" not in param_ranges:
+        xstd = cat["x_stddev"] if "x_stddev" in cat.colnames else 1
+        ystd = cat["y_stddev"] if "y_stddev" in cat.colnames else 1
+
+        cat["amplitude"] = cat["flux"] / (2.0 * np.pi * xstd * ystd)
+
+    return cat
+
+
+class CatalogFactory(abc.ABC):
+    @abc.abstractmethod
+    def gen_realization(self, *args, t=None, dt=None, **kwargs):
+        raise NotImplementedError()
+
+    def mock(self, *args, **kwargs):
+        return self.gen_realization(self, *args, **kwargs)
+
+
+class SimpleSourceCatalog(CatalogFactory):
+    base_param_ranges = {
+        "amplitude": [500, 2000],
+        "x_mean": [0, 4096],
+        "y_mean": [0, 2048],
+        "x_stddev": [1, 2],
+        "y_stddev": [1, 2],
+        "theta": [0, np.pi],
+    }
+
+    def __init__(self, table, return_copy=False):
+        self.table = table
+        self.return_copy = return_copy
+
+    @classmethod
+    def from_params(cls, n=100, param_ranges=None):
+        param_ranges = {} if param_ranges is None else param_ranges
+        tmp = cls.base_param_ranges.copy()
+        tmp.update(param_ranges)
+        return cls(gen_catalog(n, tmp))
+
+    def gen_realization(self, *args, t=None, dt=None,  **kwargs):
+        if self.return_copy:
+            return self.table.copy()
+        return self.table
+
+
+class SimpleObjectCatalog(CatalogFactory):
+    base_param_ranges = {
+        "amplitude": [100, 500],
+        "x_mean": [0, 4096],
+        "y_mean": [0, 2048],
+        "vx": [500, 5000],
+        "vy": [500, 5000],
+        "stddev": [1, 5],
+        "theta": [0, np.pi]
+    }
+
+    def __init__(self, table, obstime=None):
+        self.table = table
+        self._realization = table.copy()
+        self.obstime = 0 if obstime is None else obstime
+
+    @classmethod
+    def from_params(cls, n=10, param_ranges=None):
+        param_ranges = {} if param_ranges is None else param_ranges
+        tmp = cls.base_param_ranges.copy()
+        tmp.update(param_ranges)
+        return cls(gen_catalog(n, tmp))
+
+    def gen_realization(self, t=None, dt=None, **kwargs):
+        if t is None and dt is None:
+            return self._realization
+
+        dt = dt if t is None else t - self.obstime
+        self._realization["x_mean"] += self._realization["vx"]*dt
+        self._realization["y_mean"] += self._realization["vy"]*dt
+        return self._realization
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index 220102c28..83a72a8d7 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -6,11 +6,8 @@
 from astropy.modeling import models
 from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, BinTableHDU, Header
 
-from .headers import StaticHeader, MutableHeader
-from .src_catalogs import (
-    gen_random_static_source_catalog,
-    gen_random_moving_object_catalog
-)
+from .headers import StaticHeader, MutableHeader, ArchivedHeader
+from .catalogs import gen_catalog, SimpleSourceCatalog, SimpleObjectCatalog
 from .fits_data import (
     ZeroedData,
     SimpleImage,
@@ -189,27 +186,6 @@ class SimpleFits(HDUListFactory):
         "cd": [[-1.44e-07, 7.32e-05], [7.32e-05, 1.44e-05]],
     }
 
-    n_static_sources = 100
-    base_src_cat_params = {
-        "amplitude": [500, 2000],
-        "x_mean": [0, 4096],
-        "y_mean": [0, 2048],
-        "x_stddev": [1, 2],
-        "y_stddev": [1, 2],
-        "theta": [0, np.pi]
-    }
-
-    n_moving_objects = 10
-    base_obj_cat_params = {
-        "amplitude": [500, 1000],
-        "x_mean": [0, 4096],
-        "y_mean": [0, 2048],
-        "vx": [300, 1000],
-        "vy": [300, 1000],
-        "stddev": [1, 10],
-        "theta": [0, np.pi]
-    }
-
     # multiple options on how to handle callbacks as class members
     # callbacks=[lambda old: old+0.01, ]
     # callbacks=[functools.partial(self.increment_obstime, dt=0.001), ]
@@ -219,37 +195,17 @@ def increment_obstime(old, dt):
         return old + dt
 
     def __init__(self, shape=(2048, 4096), noise=100, dt=0.001,
-                 add_static_sources=False, src_cat="random", src_cat_params=None,
-                 add_moving_objects=False, obj_cat="random", obj_cat_params=None,
-                 **kwargs):
+                 source_catalog=None, object_catalog=None, **kwargs):
         # let's just make sure the base headers align with the basic params
         self.base_ext["NAXIS1"] = shape[0]
         self.base_ext["NAXIS2"] = shape[1]
         self.base_wcs["crpix"] = (int(shape[0]/2), int(shape[1]/2))
-        self.base_src_cat_params["y_mean"][1] = shape[0]
-        self.base_src_cat_params["x_mean"][1] = shape[1]
-        self.base_obj_cat_params["y_mean"][1] = shape[0]
-        self.base_obj_cat_params["x_mean"][1] = shape[1]
-        self.dt = dt
 
         # internal counter of n images created so far
-        self.idx = 0
-
-        # First we gen the catalogs we need to generate data
-        if add_static_sources and src_cat == "random":
-            src_params = self.base_src_cat_params if src_cat_params is None else src_cat_params
-            n = src_params.pop("n", self.n_static_sources)
-            src_cat = gen_random_static_source_catalog(n, src_params)
-        if not add_static_sources:
-            src_cat = None
-
-        if add_moving_objects and obj_cat == "random":
-            obj_params = self.base_obj_cat_params if obj_cat_params is None else obj_cat_params
-            n = obj_params.pop("n", self.n_moving_objects)
-            obj_cat = gen_random_moving_object_catalog(n, obj_params, dt)
-        if not add_moving_objects:
-            obj_cat = None
-        self.obj_cat = obj_cat
+        self._idx = 0
+        self.dt = dt
+        self.src_cat = source_catalog
+        self.obj_cat = object_catalog
 
         # Then we can generate The header and data factories that build our
         # HDUList.  Primary header contains no data, but does contain
@@ -271,15 +227,17 @@ def __init__(self, shape=(2048, 4096), noise=100, dt=0.001,
         img_data = SimpleImage.from_simplistic_sim(
             shape=shape,
             noise=noise,
-            catalog=src_cat,
-            return_copy = True if add_moving_objects else False
+            catalog=self.src_cat,
+            return_copy=False if self.obj_cat is None else True
         )
+
         var_hdr = self.gen_ext_hdr({"EXTNAME": "VARIANCE"}, add_wcs=True)
         var_data = SimpleVariance(
-            img_data.base_image,
+            img_data.base_data,
             read_noise=noise,
             gain=1.0
         )
+
         mask_hdr = self.gen_ext_hdr({"EXTNAME": "MASK", "BITPIX": 8}, add_wcs=True)
         mask_data = ZeroedData(np.zeros(shape))
 
@@ -294,21 +252,23 @@ def __init__(self, shape=(2048, 4096), noise=100, dt=0.001,
 
         super().__init__(layout)
 
+    @classmethod
+    def from_defaults(cls, add_static_sources=False, add_moving_objects=True):
+        source_catalog, object_catalog = None, None
+        if add_static_sources:
+            source_catalog = SimpleSourceCatalog.from_params()
+        if add_moving_objects:
+            object_catalog = SimpleObjectCatalog.from_params()
+
+        return cls(source_catalog=source_catalog, object_catalog=object_catalog)
+
     def mock(self, **kwargs):
-        hdul = super().mock(**kwargs)
-
-        if self.obj_cat is not None:
-            # advance the catalog:
-            self.obj_cat["x_mean"] += self.obj_cat["vx"]*self.dt
-            self.obj_cat["y_mean"] += self.obj_cat["vy"]*self.dt
-        new_scene = add_model_objects(
-            hdul["IMAGE"].data,
-            self.obj_cat,
-            models.Gaussian2D(x_stddev=1, y_stddev=1),
-            oversample=1
-        )
-        hdul["IMAGE"].data = new_scene
-        return hdul
+        if self.obj_cat is None:
+            new_cat = None
+        else:
+            new_cat = self.obj_cat.gen_realization(dt=self.dt)
+
+        return super().mock(catalog=new_cat)
 
 
 class DECamImdiffs(HDUListFactory):
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index a3fb67d2c..4519980a5 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -1,5 +1,3 @@
-import abc
-
 import numpy as np
 from astropy.io.fits import (
     PrimaryHDU,
@@ -21,70 +19,7 @@
 ]
 
 
-class DataFactory(abc.ABC):
-    # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
-    bitpix_type_map = {
-        # or char
-        8: int,
-        # actually no idea what dtype, or C type for that matter,
-        # is used to represent these two values. But default Headers
-        16: np.float16,
-        32: np.float32,
-        64: np.float64,
-        # classic IEEE float and double
-        -32: np.float32,
-        -64: np.float64
-    }
-
-    @abc.abstractmethod
-    def mock(self, hdu=None,  **kwargs):
-        pass
-
-
-class ZeroedData(DataFactory):
-    def __init__(self, base_image=None, return_copy=False):
-        super().__init__()
-        self.base_image = base_image
-        self.base_image.flags.writeable = False
-        self.return_copy = return_copy
-
-    def mock_image_data(self, hdu):
-        cols = hdu.header.get("NAXIS1", False)
-        rows = hdu.header.get("NAXIS2", False)
-
-        cols = 5 if not cols else cols
-        rows = 5 if not rows else rows
-
-        data = np.zeros(
-            (cols, rows),
-            dtype=self.bitpix_type_map[hdu.header["BITPIX"]]
-        )
-        return data
-
-    def mock_table_data(self, hdu):
-        # interestingly, table HDUs create their own empty
-        # tables from headers, but image HDUs do not, this
-        # is why hdu.data exists and has a valid dtype
-        nrows = hdu.header["TFIELDS"]
-        return np.zeros((nrows,), dtype=hdu.data.dtype)
-
-    def mock_static(self, hdu=None):
-        if self.return_copy:
-            return self.base_image.copy()
-        return self.base_image
-
-    def mock(self, hdu=None, **kwargs):
-        if self.base_image is not None:
-            return self.mock_static(hdu)
-        if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
-            return self.mock_image_data(hdu)
-        elif isinstance(hdu, (TableHDU, BinTableHDU)):
-            return self.mock_table_data(hdu)
-        else:
-            raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
-
-
-def add_model_objects(img, catalog, model, oversample=1):
+def add_model_objects(img, catalog, model):
     shape = img.shape
     yidx, xidx = np.indices(shape)
 
@@ -109,59 +44,49 @@ def add_model_objects(img, catalog, model, oversample=1):
     return img
 
 
-class SimpleImage(DataFactory):
-    rng = np.random.default_rng(12345)
-    read_noise_gen = rng.normal
-    dark_current_gen = rng.poisson
-    sky_count_gen = rng.poisson
+class DataFactory():
+    # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
+    bitpix_type_map = {
+        # or char
+        8: int,
+        # actually no idea what dtype, or C type for that matter,
+        # are used to represent these values. But default Headers return them
+        16: np.float16,
+        32: np.float32,
+        64: np.float64,
+        # classic IEEE float and double
+        -32: np.float32,
+        -64: np.float64
+    }
 
-    def __init__(self, image, return_copy=False):
-        self.base_image = image
-        self.base_image.flags.writeable = False
+    def __init__(self, base_data=None, return_copy=False, mutable=False, **kwargs):
+        # not sure if this is "best" "best" way, but it does safe a lot of
+        # array copies if we don't have to write to the mocked array
+        # (which we shouldn't?). To be safe we set the writable flag to False
+        # by default
+        self.base_data = base_data
+        self.base_data.flags.writeable = mutable
         self.return_copy = return_copy
+        self.mutable = mutable
 
-    def mock(self, hdu=None, **kwargs):
+    def mock(self, hdu=None,  **kwargs):
         if self.return_copy:
-            return self.base_image.copy()
-        return self.base_image
-
-    @classmethod
-    def from_simplistic_sim(cls, shape, noise, catalog=None,
-                            src_model=models.Gaussian2D(x_stddev=1, y_stddev=1),
-                            oversample=1, **kwargs):
-        # create poisson background
-        img = cls.sky_count_gen(noise, size=shape).astype(np.float32)
-
-        # add catalog objects
-        if catalog is None:
-            return cls(img, **kwargs)
-
-        img = add_model_objects(img, catalog, src_model, oversample)
-        return cls(img, **kwargs)
+            return self.base_data.copy()
+        return self.base_data
 
 
 class SimpleVariance(DataFactory):
-    def __init__(self, image, read_noise, gain, return_copy=False):
-        self.base_image = image/gain + read_noise**2
-        self.return_copy = return_copy
-
-    def mock(self, hdu=None, **kwargs):
-        if self.return_copy:
-            return self.base_image.copy()
-        else:
-            return self.base_image
+    def __init__(self, image, read_noise, gain, return_copy=False, mutable=False):
+        super().__init__(
+            base_data=image/gain + read_noise**2,
+            return_copy=return_copy,
+            mutable=mutable
+        )
 
 
 class SimpleMask(DataFactory):
-    def __init__(self, mask, return_copy=False):
-        super().__init__()
-
-        # not sure if this is "best" "best" way, but
-        # it does safe a lot of array copies if we don't
-        # have to write to the mocked array (which we shouldn't?)
-        self.mask = mask
-        self.mask.writeable = False
-        self.return_copy = return_copy
+    def __init__(self, mask, return_copy=False, mutable=False):
+        super().__init__(mask, return_copy, mutable)
 
     @classmethod
     def from_params(cls, shape, padding=0, bad_columns=[]):
@@ -173,7 +98,6 @@ def from_params(cls, shape, padding=0, bad_columns=[]):
         mask[:, :padding] = 1
         mask[: shape[1]-padding:] = 1
 
-
         # bad columns
         for col in bad_columns:
             mask[:, col] = 1
@@ -195,10 +119,91 @@ def from_patches(cls, shape, patches):
 
         return cls(mask)
 
+
+class ZeroedData(DataFactory):
+    def __init__(self, base_data=None, return_copy=False, mutable=False):
+        super().__init__(base_data, return_copy, mutable)
+
+    def mock_image_data(self, hdu):
+        cols = hdu.header.get("NAXIS1", False)
+        rows = hdu.header.get("NAXIS2", False)
+
+        cols = 5 if not cols else cols
+        rows = 5 if not rows else rows
+
+        data = np.zeros(
+            (cols, rows),
+            dtype=self.bitpix_type_map[hdu.header["BITPIX"]]
+        )
+        return data
+
+    def mock_table_data(self, hdu):
+        # interestingly, table HDUs create their own empty
+        # tables from headers, but image HDUs do not, this
+        # is why hdu.data exists and has a valid dtype
+        nrows = hdu.header["TFIELDS"]
+        return np.zeros((nrows,), dtype=hdu.data.dtype)
+
     def mock(self, hdu=None, **kwargs):
+        # two cases: a) static data shared by all instances created by the
+        # factory set at init time or b) dynamic data generated from a header
+        # specification at call-time.
+        if self.base_data is not None:
+            return super().mock(hdu, **kwargs)
+        if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
+            return self.mock_image_data(hdu)
+        elif isinstance(hdu, (TableHDU, BinTableHDU)):
+            return self.mock_table_data(hdu)
+        else:
+            raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
+
+
+class SimpleImage(DataFactory):
+    rng = np.random.default_rng(12345)
+    read_noise_gen = rng.normal
+    dark_current_gen = rng.poisson
+    sky_count_gen = rng.poisson
+
+    def __init__(self, image, return_copy=False, mutable=False):
+        super().__init__(image, return_copy, mutable)
+
+    def get_base(self):
         if self.return_copy:
-            return self.mask.copy()
-        return self.mask
+            return self.base_data.copy()
+        return self.base_data
+
+    def draw_new(self, catalog):
+        if not self.return_copy and not self.mutable:
+            raise RuntimeError(
+                "Can not get_realization an image that is neither mutable nor "
+                "copyable. Setting the array to mutable will update it in-place, "
+                "while setting copyable returns a copy of the array."
+            )
+
+        model = models.Gaussian2D(x_stddev=1, y_stddev=1)
+        if self.mutable:
+            return add_model_objects(self.base_data, catalog, model)
+        else:
+            return add_model_objects(self.base_data.copy(), catalog, model)
+
+    def mock(self, catalog=None, hdu=None, **kwargs):
+        if catalog is None:
+            return self.get_base()
+        else:
+            return self.draw_new(catalog)
+
+    @classmethod
+    def from_simplistic_sim(cls, shape, noise, catalog=None,
+                            src_model=models.Gaussian2D(x_stddev=1, y_stddev=1), **kwargs):
+        # create poisson background
+        img = cls.sky_count_gen(noise, size=shape).astype(np.float32)
+
+        # add catalog objects
+        if catalog is None:
+            return cls(img, **kwargs)
+
+        img = add_model_objects(img, catalog, src_model)
+        return cls(img, **kwargs)
 
 
 class SimulatedImage(DataFactory):
@@ -244,7 +249,6 @@ def __add_hot_pixels(self, image, pixels, percent, offset):
 
         return image
 
-
     def __init__(
             self,
             shape,
diff --git a/src/kbmod/mocking/src_catalogs.py b/src/kbmod/mocking/src_catalogs.py
deleted file mode 100644
index f94b7896e..000000000
--- a/src/kbmod/mocking/src_catalogs.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import numpy as np
-from astropy.time import Time
-from astropy.table import QTable, vstack
-
-
-__all__ = [
-    "gen_random_static_source_catalog",
-    "gen_random_moving_object_catalog"
-]
-
-
-def gen_random_static_source_catalog(n, param_ranges, seed=None):
-    cat = QTable()
-    rng = np.random.default_rng(seed)
-
-    for param_name, (lower, upper) in param_ranges.items():
-        cat[param_name] = rng.uniform(lower, upper, n)
-
-    if "stddev" in param_ranges:
-        cat["x_stddev"] = cat["stddev"]
-        cat["y_stddev"] = cat["stddev"]
-
-    # conversion assumes a gaussian
-    if "flux" in param_ranges and "amplitude" not in param_ranges:
-        xstd = cat["x_stddev"] if "x_stddev" in cat.colnames else 1
-        ystd = cat["y_stddev"] if "y_stddev" in cat.colnames else 1
-
-        cat["amplitude"] = cat["flux"] / (2.0 * np.pi * xstd * ystd)
-
-    return cat
-
-
-def gen_random_moving_object_catalog(n, param_ranges, dt, seed=None):
-    obj_cat = gen_random_static_source_catalog(n, param_ranges, seed)
-    return obj_cat
-#    cat = None
-#    for i, t in enumerate(obstimes):
-#        if i == 0:
-#            cat = obj_cat.copy()
-#            continue
-#
-#        obj_cat["x_mean"] += obj_cat["vx"]*dt
-#        vstack(cat, obj_cat)
-#
-#    return cat
-
-

From ad107c1706465ffa5eca4ebe690cb1a8cf70141e Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Sun, 24 Mar 2024 03:49:03 -0700
Subject: [PATCH 07/27] Refactor, fix bugs with noise, tidy up.

Join the Static and Mutable headers together. This halves the code
length, but then the validation method triples it...
Clean up and replace Static and Mutable with HeaderFactory.

Move Header base skeleton generators to HeaderFactory from HDUList.
Clean up their use in fits.py.

Compute image size from NAXIS keywords when creating base Header
skeletons.

Move image rendering logic from HDUList to DataFactory. Clean up
fits.py after the change.

Fix EmptyFits generation.

Fix SimpleImage noise issue.

Clean up imports.

Blacken.
---
 src/kbmod/mocking/catalogs.py  |   8 +-
 src/kbmod/mocking/fits.py      | 211 ++++++++++-----------------------
 src/kbmod/mocking/fits_data.py | 129 ++++++++++----------
 src/kbmod/mocking/headers.py   | 108 ++++++++++++-----
 4 files changed, 208 insertions(+), 248 deletions(-)

diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 246fa3ded..03fc1b16e 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -64,7 +64,7 @@ def from_params(cls, n=100, param_ranges=None):
         tmp.update(param_ranges)
         return cls(gen_catalog(n, tmp))
 
-    def gen_realization(self, *args, t=None, dt=None,  **kwargs):
+    def gen_realization(self, *args, t=None, dt=None, **kwargs):
         if self.return_copy:
             return self.table.copy()
         return self.table
@@ -78,7 +78,7 @@ class SimpleObjectCatalog(CatalogFactory):
         "vx": [500, 5000],
         "vy": [500, 5000],
         "stddev": [1, 5],
-        "theta": [0, np.pi]
+        "theta": [0, np.pi],
     }
 
     def __init__(self, table, obstime=None):
@@ -98,6 +98,6 @@ def gen_realization(self, t=None, dt=None, **kwargs):
             return self._realization
 
         dt = dt if t is None else t - self.obstime
-        self._realization["x_mean"] += self._realization["vx"]*dt
-        self._realization["y_mean"] += self._realization["vy"]*dt
+        self._realization["x_mean"] += self._realization["vx"] * dt
+        self._realization["y_mean"] += self._realization["vy"] * dt
         return self._realization
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index 83a72a8d7..56526d62e 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -3,39 +3,35 @@
 
 import numpy as np
 from astropy.wcs import WCS
-from astropy.modeling import models
 from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, BinTableHDU, Header
 
-from .headers import StaticHeader, MutableHeader, ArchivedHeader
+from .headers import HeaderFactory, ArchivedHeader
 from .catalogs import gen_catalog, SimpleSourceCatalog, SimpleObjectCatalog
-from .fits_data import (
-    ZeroedData,
-    SimpleImage,
-    SimpleVariance,
-    SimpleMask,
-    add_model_objects
-)
+from .fits_data import ZeroedData, SimpleImage, SimpleVariance, SimpleMask, add_model_objects
 
 
-__all__ = ["HDUFactory", "HDUListFactory", "callback", "SimpleFits", "DECamImdiffs"]
+__all__ = [
+    "HDUFactory",
+    "HDUListFactory",
+    "callback",
+    "EmptyFits",
+    "SimpleFits",
+    "DECamImdiffs",
+]
 
 
 class HDUFactory:
-    def __init__(self, hdu_cls, header_factory=None, data_factory=None):
+    def __init__(self, hdu_cls, header_factory, data_factory=None):
         self.hdu_cls = hdu_cls
-
-        self.update_header = False if header_factory is None else True
         self.header_factory = header_factory
-
-        self.update_data = False if data_factory is None else True
         self.data_factory = data_factory
+        self.update_data = False if data_factory is None else True
 
     def mock(self, **kwargs):
         hdu = self.hdu_cls()
 
-        if self.update_header:
-            header = self.header_factory.mock(hdu=hdu, **kwargs)
-            hdu.header.update(header)
+        header = self.header_factory.mock(hdu=hdu, **kwargs)
+        hdu.header.update(header)
 
         if self.update_data:
             data = self.data_factory.mock(hdu=hdu, **kwargs)
@@ -53,26 +49,6 @@ def __init__(self, layout, base_primary={}, base_ext={}, base_wcs={}):
         self.base_ext = base_ext
         self.base_wcs = base_wcs
 
-    def gen_wcs(self, metadata=None):
-        metadata = self.base_wcs if metadata is None else metadata
-        wcs = WCS(naxis=2)
-        for k, v in metadata.items():
-            setattr(wcs.wcs, k, v)
-        return wcs.to_header()
-
-    def gen_header(self, base, metadata, extend, add_wcs):
-        header = Header(base) if extend else Header()
-        header.update(metadata)
-        if add_wcs:
-            header.update(self.gen_wcs())
-        return header
-
-    def gen_primary_hdr(self, metadata=None, extend_base=True, add_wcs=False):
-        return self.gen_header(self.base_primary, metadata, extend_base, add_wcs)
-
-    def gen_ext_hdr(self, metadata=None, extend_base=True, add_wcs=True):
-        return self.gen_header(self.base_ext, metadata, extend_base, add_wcs)
-
     def mock(self, **kwargs):
         hdul = HDUList()
         for layer in self.layout:
@@ -83,6 +59,10 @@ def mock(self, **kwargs):
 # I am sure a decorator like this must exist somewhere in functools, but can't
 # find it and I'm doing something wrong with functools.partial because that's
 # strictly right-side binding?
+# multiple options on how to handle callbacks as class members
+# callbacks=[lambda old: old+0.01, ]
+# callbacks=[functools.partial(self.increment_obstime, dt=0.001), ]
+# but they look a bit ugly...
 def callback(func):
     if isinstance(func, types.FunctionType):
         # bound methods
@@ -91,7 +71,9 @@ def wrapper(*args, **kwargs):
             def f(*fargs, **fkwargs):
                 kwargs.update(fkwargs)
                 return func(*(args + fargs), **kwargs)
+
             return f
+
     else:
         # functions, static methods
         def wrapper(*args, **kwargs):
@@ -99,155 +81,81 @@ def wrapper(*args, **kwargs):
             def f(*fargs, **fkwargs):
                 kwargs.update(fkwargs)
                 return func(*fargs, **kwargs)
+
             return f
+
     return wrapper
 
 
 class EmptyFits(HDUListFactory):
-    base_primary = {
-        "EXTNAME": "PRIMARY",
-        "NAXIS": 0,
-        "BITPIX": 8,
-        "OBS-MJD": 58914.0,
-        "NEXTEND": 3,
-        "OBS-LAT": -30.166,
-        "OBS-LONG": -70.814,
-        "STD": "SimpleFits",
-    }
-
-    base_ext = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "BITPIX": 32}
-
-    base_wcs = {
-        "crpix": [1024.0, 2048.0],
-        "crval": [351, -5],
-        "ctype": ["RA---TAN", "DEC--TAN"],
-        "cunit": ["deg", "deg"],
-        "radesys": "ICRS",
-        "cd": [[-1.44e-07, 7.32e-05], [7.32e-05, 1.44e-05]],
-    }
-
     @callback
     @staticmethod
     def increment_obstime(old, dt):
         return old + dt
 
-    def __init__(self):
-        primary_hdr = self.gen_primary()
-
-        # multiple options on how to handle callbacks as class members
-        # callbacks=[lambda old: old+0.01, ]
-        # callbacks=[functools.partial(self.increment_obstime, dt=0.001), ]
-        primary_header_factory = MutableHeader(
+    def __init__(self, primary_hdr=None, dt=0.001):
+        # header and data factories that go into creating HDUs
+        primary = HeaderFactory.from_base_primary(
             metadata=primary_hdr,
-            mutables=[
-                "OBS-MJD",
-            ],
-            callbacks=[
-                self.increment_obstime(dt=0.001),
-            ],
+            mutables=["OBS-MJD"],
+            callbacks=[self.increment_obstime(dt=dt)],
         )
-
-        image_hdr = self.gen_ext({"EXTNAME": "IMAGE"}, add_wcs=True)
-        variance_hdr = self.gen_ext({"EXTNAME": "VARIANCE"}, add_wcs=True)
-        mask_hdr = self.gen_ext({"EXTNAME": "MASK", "BITPIX": 8}, add_wcs=True)
-
+        image = HeaderFactory.from_base_ext({"EXTNAME": "IMAGE"})
+        variance = HeaderFactory.from_base_ext({"EXTNAME": "VARIANCE"})
+        mask = HeaderFactory.from_base_ext({"EXTNAME": "MASK"})
         data = ZeroedData()
 
+        # a map of HDU class and their respective header and data generators
         layout = [
-            HDUFactory(PrimaryHDU, primary_header_factory),
-            HDUFactory(CompImageHDU, StaticHeader(image_hdr), data),
-            HDUFactory(CompImageHDU, StaticHeader(variance_hdr), data),
-            HDUFactory(CompImageHDU, StaticHeader(mask_hdr), data),
+            HDUFactory(PrimaryHDU, primary),
+            HDUFactory(CompImageHDU, image, data),
+            HDUFactory(CompImageHDU, variance, data),
+            HDUFactory(CompImageHDU, mask, data),
         ]
 
         super().__init__(layout)
 
 
 class SimpleFits(HDUListFactory):
-    base_primary = {
-        "EXTNAME": "PRIMARY",
-        "NAXIS": 0,
-        "BITPIX": 8,
-        "OBS-MJD": 58914.0,
-        "NEXTEND": 3,
-        "OBS-LAT": -30.166,
-        "OBS-LONG": -70.814,
-        "STD": "SimpleFits",
-    }
-
-    base_ext = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "BITPIX": 32}
-
-    base_wcs = {
-        "crpix": [1024.0, 2048.0],
-        "crval": [351, -5],
-        "ctype": ["RA---TAN", "DEC--TAN"],
-        "cunit": ["deg", "deg"],
-        "radesys": "ICRS",
-        "cd": [[-1.44e-07, 7.32e-05], [7.32e-05, 1.44e-05]],
-    }
-
-    # multiple options on how to handle callbacks as class members
-    # callbacks=[lambda old: old+0.01, ]
-    # callbacks=[functools.partial(self.increment_obstime, dt=0.001), ]
     @callback
     @staticmethod
     def increment_obstime(old, dt):
         return old + dt
 
-    def __init__(self, shape=(2048, 4096), noise=100, dt=0.001,
-                 source_catalog=None, object_catalog=None, **kwargs):
-        # let's just make sure the base headers align with the basic params
-        self.base_ext["NAXIS1"] = shape[0]
-        self.base_ext["NAXIS2"] = shape[1]
-        self.base_wcs["crpix"] = (int(shape[0]/2), int(shape[1]/2))
-
+    def __init__(self, shape=(2048, 4096), noise=100, dt=0.001, source_catalog=None, object_catalog=None):
         # internal counter of n images created so far
         self._idx = 0
         self.dt = dt
         self.src_cat = source_catalog
         self.obj_cat = object_catalog
-
-        # Then we can generate The header and data factories that build our
-        # HDUList.  Primary header contains no data, but does contain
-        # update-able metadata fields (f.e. timestamps)
-        primary_hdr = self.gen_primary_hdr()
-        primary_header_factory = MutableHeader(
-            metadata=primary_hdr,
-            mutables=[
-                "OBS-MJD",
-            ],
-            callbacks=[
-                self.increment_obstime(dt=dt),
-            ],
+        dims = {"NAXIS1": shape[0], "NAXIS2": shape[1]}
+
+        # Then we can generate the header factories primary header contains no
+        # data, but does contain update-able metadata fields (f.e. timestamps),
+        # others contain static headers, but dynamic, mutually connected data
+        primary = HeaderFactory.from_base_primary(
+            mutables=["OBS-MJD"],
+            callbacks=[self.increment_obstime(dt=0.001)],
         )
+        image = HeaderFactory.from_base_ext({"EXTNAME": "IMAGE", **dims})
+        variance = HeaderFactory.from_base_ext({"EXTNAME": "VARIANCE", **dims})
+        mask = HeaderFactory.from_base_ext({"EXTNAME": "MASK", **dims})
 
-        # The remaining HDUs contain static headers, but data that changes
-        # (f.e. updating positions of linearly moving objects in each image)
-        img_hdr = self.gen_ext_hdr({"EXTNAME": "IMAGE"}, add_wcs=True)
-        img_data = SimpleImage.from_simplistic_sim(
+        img_data = SimpleImage.simulate(
             shape=shape,
             noise=noise,
             catalog=self.src_cat,
-            return_copy=False if self.obj_cat is None else True
-        )
-
-        var_hdr = self.gen_ext_hdr({"EXTNAME": "VARIANCE"}, add_wcs=True)
-        var_data = SimpleVariance(
-            img_data.base_data,
-            read_noise=noise,
-            gain=1.0
+            return_copy=False if self.obj_cat is None else True,
         )
-
-        mask_hdr = self.gen_ext_hdr({"EXTNAME": "MASK", "BITPIX": 8}, add_wcs=True)
+        var_data = SimpleVariance(img_data.base_data, read_noise=noise, gain=1.0)
         mask_data = ZeroedData(np.zeros(shape))
 
-        # Now we can build an HDU layout and let the HDUList factory
-        # generate self-updateable HDULists.
+        # Now we can build the HDU map and the HDUList layout
         layout = [
-            HDUFactory(PrimaryHDU, primary_header_factory),
-            HDUFactory(CompImageHDU, StaticHeader(img_hdr), img_data),
-            HDUFactory(CompImageHDU, StaticHeader(var_hdr), var_data),
-            HDUFactory(CompImageHDU, StaticHeader(mask_hdr), mask_data),
+            HDUFactory(PrimaryHDU, primary),
+            HDUFactory(CompImageHDU, image, img_data),
+            HDUFactory(CompImageHDU, variance, var_data),
+            HDUFactory(CompImageHDU, mask, mask_data),
         ]
 
         super().__init__(layout)
@@ -277,18 +185,19 @@ def __init__(self):
         data = ZeroedData()
         image = HDUFactory(CompImageHDU, headers, data)
 
+        # DECam imdiff data products consist of 16 headers, first 4 are:
+        # primary, science variance and mask and the rest are PSF, SkyWCS,
+        # catalog metadata, chebyshev higher order corrections etc. We don't
+        # use these, so leave them with no data generators. The first 4 we fill
+        # with all-zeros.
         # don't let Black have its way with these lines because it's a massacre
         # fmt: off
-        # primary, science variance and mask
         layout = [
             HDUFactory(PrimaryHDU, headers),
             image,
             image,
             image,
         ]
-
-        # PSF, SkyWCS, catalog meta, chebyshev higher order corrections etc.
-        # we don't use these so it's fine to leave them empty
         layout.extend([HDUFactory(BinTableHDU, headers) ] * 12)
         # fmt: on
 
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index 4519980a5..df05b5fd9 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -1,22 +1,10 @@
 import numpy as np
-from astropy.io.fits import (
-    PrimaryHDU,
-    CompImageHDU,
-    ImageHDU,
-    BinTableHDU,
-    TableHDU
-)
+from astropy.io.fits import PrimaryHDU, CompImageHDU, ImageHDU, BinTableHDU, TableHDU
 from astropy.modeling import models
 from astropy.convolution import discretize_model
 
 
-__all__ = [
-    "add_model_objects",
-    "DataFactory",
-    "ZeroedData",
-    "SimpleImage",
-    "SimpleMask"
-]
+__all__ = ["add_model_objects", "DataFactory", "ZeroedData", "SimpleImage", "SimpleMask"]
 
 
 def add_model_objects(img, catalog, model):
@@ -44,7 +32,7 @@ def add_model_objects(img, catalog, model):
     return img
 
 
-class DataFactory():
+class DataFactory:
     # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
     bitpix_type_map = {
         # or char
@@ -56,7 +44,7 @@ class DataFactory():
         64: np.float64,
         # classic IEEE float and double
         -32: np.float32,
-        -64: np.float64
+        -64: np.float64,
     }
 
     def __init__(self, base_data=None, return_copy=False, mutable=False, **kwargs):
@@ -64,12 +52,13 @@ def __init__(self, base_data=None, return_copy=False, mutable=False, **kwargs):
         # array copies if we don't have to write to the mocked array
         # (which we shouldn't?). To be safe we set the writable flag to False
         # by default
-        self.base_data = base_data
-        self.base_data.flags.writeable = mutable
         self.return_copy = return_copy
         self.mutable = mutable
+        self.base_data = base_data
+        if base_data is not None:
+            self.base_data.flags.writeable = mutable
 
-    def mock(self, hdu=None,  **kwargs):
+    def mock(self, hdu=None, **kwargs):
         if self.return_copy:
             return self.base_data.copy()
         return self.base_data
@@ -77,11 +66,7 @@ def mock(self, hdu=None,  **kwargs):
 
 class SimpleVariance(DataFactory):
     def __init__(self, image, read_noise, gain, return_copy=False, mutable=False):
-        super().__init__(
-            base_data=image/gain + read_noise**2,
-            return_copy=return_copy,
-            mutable=mutable
-        )
+        super().__init__(base_data=image / gain + read_noise**2, return_copy=return_copy, mutable=mutable)
 
 
 class SimpleMask(DataFactory):
@@ -94,9 +79,9 @@ def from_params(cls, shape, padding=0, bad_columns=[]):
 
         # padding
         mask[:padding] = 1
-        mask[shape[0]-padding:] = 1
+        mask[shape[0] - padding :] = 1
         mask[:, :padding] = 1
-        mask[: shape[1]-padding:] = 1
+        mask[: shape[1] - padding :] = 1
 
         # bad columns
         for col in bad_columns:
@@ -114,8 +99,7 @@ def from_patches(cls, shape, patches):
             elif isinstance(slice):
                 mask[slice] = value
             else:
-                raise ValueError("Expected a tuple (x, y), (slice, slice) or "
-                                 f"slice, got {patch} instead.")
+                raise ValueError(f"Expected a tuple (x, y), (slice, slice) or slice, got {patch} instead.")
 
         return cls(mask)
 
@@ -131,10 +115,7 @@ def mock_image_data(self, hdu):
         cols = 5 if not cols else cols
         rows = 5 if not rows else rows
 
-        data = np.zeros(
-            (cols, rows),
-            dtype=self.bitpix_type_map[hdu.header["BITPIX"]]
-        )
+        data = np.zeros((cols, rows), dtype=self.bitpix_type_map[hdu.header["BITPIX"]])
         return data
 
     def mock_table_data(self, hdu):
@@ -159,13 +140,40 @@ def mock(self, hdu=None, **kwargs):
 
 
 class SimpleImage(DataFactory):
-    rng = np.random.default_rng(12345)
+    rng = np.random.default_rng()
     read_noise_gen = rng.normal
     dark_current_gen = rng.poisson
     sky_count_gen = rng.poisson
 
-    def __init__(self, image, return_copy=False, mutable=False):
+    @classmethod
+    def simulate(
+        cls, shape, noise=0, catalog=None, src_model=models.Gaussian2D(x_stddev=1, y_stddev=1), **kwargs
+    ):
+        # make a blank image
+        img = np.zeros(shape, dtype=np.float32)
+
+        # add static sources to it
+        if catalog is not None:
+            img = add_model_objects(img, catalog, src_model)
+
+        return cls(img, noise=noise, **kwargs)
+
+    def __init__(self, image, noise=0, return_copy=False, mutable=False):
         super().__init__(image, return_copy, mutable)
+        self.noise = noise
+
+    def mock(self, catalog=None, hdu=None, **kwargs):
+        # if no catalog of moving obj were given just get
+        # the base image, otherwise add the new sources
+        if catalog is None:
+            base = self.get_base()
+        else:
+            base = self.draw_new(catalog)
+
+        # finally, create poisson background every time and add it to
+        # the base of the image
+        bckg = self.sky_count_gen(self.noise, size=base.shape)
+        return base + bckg
 
     def get_base(self):
         if self.return_copy:
@@ -186,28 +194,9 @@ def draw_new(self, catalog):
         else:
             return add_model_objects(self.base_data.copy(), catalog, model)
 
-    def mock(self, catalog=None, hdu=None, **kwargs):
-        if catalog is None:
-            return self.get_base()
-        else:
-            return self.draw_new(catalog)
-
-    @classmethod
-    def from_simplistic_sim(cls, shape, noise, catalog=None,
-                            src_model=models.Gaussian2D(x_stddev=1, y_stddev=1), **kwargs):
-        # create poisson background
-        img = cls.sky_count_gen(noise, size=shape).astype(np.float32)
-
-        # add catalog objects
-        if catalog is None:
-            return cls(img, **kwargs)
-
-        img = add_model_objects(img, catalog, src_model)
-        return cls(img, **kwargs)
-
 
 class SimulatedImage(DataFactory):
-    rng = np.random.default_rng(12345)
+    rng = np.random.default_rng()
     read_noise_gen = rng.normal
     dark_current_gen = rng.poisson
     sky_count_gen = rng.poisson
@@ -245,19 +234,27 @@ def __add_hot_pixels(self, image, pixels, percent, offset):
             hot_pixels = pixels
 
         for pix in hot_pixels:
-            image[*pix] += image[*pix]*offset
+            image[*pix] += image[*pix] * offset
 
         return image
 
     def __init__(
-            self,
-            shape,
-            read_noise, gain,
-            bias,
-            exposure_time, dark_current,
-            sky_level,
-            bad_columns=False, bad_cols_seed=321, n_bad_cols=5, bad_col_pattern_offset=0.1,
-            hot_pixels=False, hot_pix_seed=543, hot_percent=0.00001, hot_offset=1000,
+        self,
+        shape,
+        read_noise,
+        gain,
+        bias,
+        exposure_time,
+        dark_current,
+        sky_level,
+        bad_columns=False,
+        bad_cols_seed=321,
+        n_bad_cols=5,
+        bad_col_pattern_offset=0.1,
+        hot_pixels=False,
+        hot_pix_seed=543,
+        hot_percent=0.00001,
+        hot_offset=1000,
     ):
         super().__init__()
 
@@ -265,7 +262,7 @@ def __init__(
         self.base_img = np.zeros(shape)
 
         # add read noise
-        self.base_img += self.read_noise_gen(scale=read_noise/gain, size=shape)
+        self.base_img += self.read_noise_gen(scale=read_noise / gain, size=shape)
 
         # add bias
         self.base_img += bias
@@ -275,7 +272,7 @@ def __init__(
         self.__add_bad_cols(self.base_image, bad_columns, bias, n_bad_cols, bad_cols_seed)
 
         # add dark current
-        current = dark_current * exposure_time/gain
+        current = dark_current * exposure_time / gain
         self.base_img = self.dark_current_gen(current, size=shape)
 
         # add hot pixels
@@ -283,7 +280,7 @@ def __init__(
         self.base_img = self.__add_hot_pixels(self.base_img, hot_pixels, hot_percent, hot_offset)
 
         # add sky counts
-        self.base_img += self.sky_count_gen(sky_level*gain, shape)/gain
+        self.base_img += self.sky_count_gen(sky_level * gain, shape) / gain
 
     def mock(self, hdu=None, **kwargs):
         # we do always have to return a new copy here, since sci images
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index 2d29d6ee2..50c5e6978 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -1,57 +1,111 @@
-import abc
-import types
 import warnings
-import functools
 
-import numpy as np
 from astropy.utils.exceptions import AstropyUserWarning
 from astropy.wcs import WCS
-from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, ImageHDU, BinTableHDU, TableHDU, Header
+from astropy.io.fits import Header
 
 from .utils import header_archive_to_table
 
 
 __all__ = [
     "HeaderFactory",
-    "StaticHeader",
-    "MutableHeader",
     "ArchivedHeader",
 ]
 
 
-class HeaderFactory(abc.ABC):
-    @abc.abstractmethod
-    def mock(self, hdu=None, **kwargs):
-        raise NotImplementedError()
+class HeaderFactory:
+    base_primary = {
+        "EXTNAME": "PRIMARY",
+        "NAXIS": 0,
+        "BITPIX": 8,
+        "OBS-MJD": 58914.0,
+        "NEXTEND": 3,
+        "OBS-LAT": -30.166,
+        "OBS-LONG": -70.814,
+    }
 
+    base_ext = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "BITPIX": 32}
 
-class StaticHeader(HeaderFactory):
-    def __init__(self, metadata=None):
-        self.metadata = metadata
+    base_wcs = {
+        "ctype": ["RA---TAN", "DEC--TAN"],
+        "crval": [351, -5],
+        "cunit": ["deg", "deg"],
+        "radesys": "ICRS",
+        "cd": [[-1.44e-07, 7.32e-05], [7.32e-05, 1.44e-05]],
+    }
 
-    def mock(self, hdu=None, **kwargs):
-        # I have no idea why cards are an empty list by default
-        cards = [] if self.metadata is None else self.metadata
-        return Header(cards=cards)
+    def __validate_mutables(self):
+        # !xor
+        if bool(self.mutables) != bool(self.callbacks):
+            raise ValueError(
+                "When providing a list of mutable cards, you must provide associated callback methods."
+            )
 
+        if self.mutables is None:
+            return
 
-class MutableHeader(HeaderFactory):
-    def __init__(self, metadata=None, mutables=None, callbacks=None):
-        for k in mutables:
-            if k not in metadata:
+        if len(self.mutables) != len(self.callbacks):
+            raise ValueError(
+                "The number of mutable cards does not correspond to the number of given callbacks."
+            )
+
+        for k in self.mutables:
+            if k not in self.header:
                 raise ValueError(
-                    f"Registered mutable key {k} does not exists " "in given metadata: f{metadata}."
+                    f"Mutable key {k} does not exists "
+                    "in the header. Please "
+                    "provide the required metadata keys."
                 )
 
-        self.metadata = metadata
+    def __init__(self, metadata=None, mutables=None, callbacks=None):
+        cards = [] if metadata is None else metadata
+        self.header = Header(cards=cards)
+
         self.mutables = mutables
         self.callbacks = callbacks
+        self.__validate_mutables()
 
     def mock(self, hdu=None, **kwargs):
-        for i, mutable in enumerate(self.mutables):
-            self.metadata[mutable] = self.callbacks[i](self.metadata[mutable])
+        if self.mutables is not None:
+            for i, mutable in enumerate(self.mutables):
+                self.header[mutable] = self.callbacks[i](self.header[mutable])
+
+        return self.header
+
+    @classmethod
+    def gen_wcs(cls, crval, metadata=None):
+        metadata = cls.base_wcs if metadata is None else metadata
+        wcs = WCS(naxis=2)
+        for k, v in metadata.items():
+            setattr(wcs.wcs, k, v)
+        return wcs.to_header()
+
+    @classmethod
+    def gen_header(cls, base, metadata, extend, add_wcs):
+        header = Header(base) if extend else Header()
+        header.update(metadata)
+
+        if add_wcs:
+            naxis1 = header.get("NAXIS1", False)
+            naxis2 = header.get("NAXIS2", False)
+            if not all((naxis1, naxis2)):
+                raise ValueError("Adding a WCS to the header requires NAXIS1 and NAXIS2 keys.")
+            crpix = [naxis1 / 2.0, naxis2 / 2.0]
+            header.update(cls.gen_wcs(crpix))
+
+        return header
 
-        return Header(self.metadata)
+    @classmethod
+    def from_base_primary(cls, metadata=None, mutables=None, callbacks=None, extend_base=True, add_wcs=False):
+        hdr = cls.gen_header(cls.base_primary, metadata, extend_base, add_wcs)
+        return cls(hdr, mutables, callbacks)
+
+    @classmethod
+    def from_base_ext(
+        cls, metadata=None, mutables=None, callbacks=None, extend_base=True, add_wcs=True, dims=None
+    ):
+        hdr = cls.gen_header(cls.base_ext, metadata, extend_base, add_wcs)
+        return cls(hdr, mutables, callbacks)
 
 
 class ArchivedHeader(HeaderFactory):

From cf851658dc3107a3e8971abc455821c218170d90 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 25 Mar 2024 13:11:57 -0700
Subject: [PATCH 08/27] Add Standardizer for data produced by SimpleFits.

---
 .../fits_standardizers/__init__.py            |  5 +-
 .../fits_standardizers/test_data_std.py       | 96 +++++++++++++++++++
 2 files changed, 99 insertions(+), 2 deletions(-)
 create mode 100644 src/kbmod/standardizers/fits_standardizers/test_data_std.py

diff --git a/src/kbmod/standardizers/fits_standardizers/__init__.py b/src/kbmod/standardizers/fits_standardizers/__init__.py
index 8cbbbae12..1195f2a82 100644
--- a/src/kbmod/standardizers/fits_standardizers/__init__.py
+++ b/src/kbmod/standardizers/fits_standardizers/__init__.py
@@ -1,5 +1,6 @@
 from .fits_standardizer import *
+from .single_extension_fits import *
+from .multi_extension_fits import *
 from .kbmodv05 import *
 from .kbmodv1 import *
-from .multi_extension_fits import *
-from .single_extension_fits import *
+from .test_data_std import *
diff --git a/src/kbmod/standardizers/fits_standardizers/test_data_std.py b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
new file mode 100644
index 000000000..7b10af26c
--- /dev/null
+++ b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
@@ -0,0 +1,96 @@
+"""Class for standardizing FITS files produced by the mocking module."""
+
+from astropy.time import Time
+from .multi_extension_fits import MultiExtensionFits, FitsStandardizerConfig
+
+
+__all__ = [
+    "TestDataStdConfig",
+    "TestDataStd",
+]
+
+
+class TestDataStdConfig(FitsStandardizerConfig):
+    pass
+
+class TestDataStd(MultiExtensionFits):
+    """Standardizer for test-data produced by the mocking module
+
+    The standardizer will never volunteer to process any data, it must be
+    explicitly forced.
+
+    Parameters
+    ----------
+    location : `str` or `None`, optional
+        Location of the file (if any) that is standardized. Required if
+        ``hdulist`` is not provided.
+    hdulist : `astropy.io.fits.HDUList` or `None`, optional
+        HDUList to standardize. Required if ``location`` is not provided.
+        If provided, and ``location`` is not given, defaults to ``:memory:``.
+    config : `StandardizerConfig`, `dict` or `None`, optional
+        Configuration key-values used when standardizing the file.
+
+    Attributes
+    ----------
+    hdulist : `~astropy.io.fits.HDUList`
+        All HDUs found in the FITS file
+    primary : `~astropy.io.fits.PrimaryHDU`
+        The primary HDU.
+    processable : `list`
+        Any additional extensions marked by the standardizer for further
+        processing. Does not include the  primary HDU if it doesn't contain any
+        image data. Contains at least 1 entry.
+    wcs : `list`
+        WCSs associated with the processable image data. Will contain
+        at least 1 WCS.
+    bbox : `list`
+        Bounding boxes associated with each WCS.
+    """
+
+    name = "TestDataStd"
+    priority = 0
+    configClass = TestDataStdConfig
+
+    @classmethod
+    def resolveTarget(cls, tgt):
+        return False
+
+    def __init__(self, location=None, hdulist=None, config=None, **kwargs):
+        super().__init__(location=location, hdulist=hdulist, config=config, **kwargs)
+
+        # this is the only science-image header for Rubin
+        self.processable = [
+            self.hdulist["IMAGE"],
+        ]
+
+
+    def translateHeader(self):
+        """Returns the following metadata, read from the primary header, as a
+        dictionary:
+
+        ======== ========== ===================================================
+        Key      Header Key Description
+        ======== ========== ===================================================
+        mjd      DATE-AVG   Decimal MJD timestamp of the middle of the exposure
+        observat OBSERVAT   Observatory name
+        obs_lat  OBS-LAT    Observatory Latitude
+        obs_lon  OBS-LONG   Observatory Longitude
+        obs_elev OBS-ELEV   Observatory elevation.
+        ======== ========== ===================================================
+        """
+        # required
+        standardizedHeader = {}
+        obs_datetime = Time(self.primary["OBS-MJD"], format="mjd")
+        standardizedHeader["mjd"] = obs_datetime.mjd
+        # optional
+        standardizedHeader["observat"] = self.primary["OBSERVAT"]
+        standardizedHeader["obs_lat"] = self.primary["OBS-LAT"]
+        standardizedHeader["obs_lon"] = self.primary["OBS-LONG"]
+        standardizedHeader["obs_elev"] = self.primary["OBS-ELEV"]
+        return standardizedHeader
+
+    def standardizeMaskImage(self):
+        return (self.hdulist["MASK"] for i in self.processable)
+
+    def standardizeVarianceImage(self):
+        return (self.hdulist["VARIANCE"] for i in self.processable)

From 9d5e60e31d3688a6738b20c6c0f84688c7d262bc Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 25 Mar 2024 21:42:59 -0700
Subject: [PATCH 09/27] Roadmark.

---
 src/kbmod/mocking/__init__.py     |   1 +
 src/kbmod/mocking/catalogs.py     |  32 ++++++-
 src/kbmod/mocking/fits.py         | 149 ++++++++++++++++++++++--------
 src/kbmod/mocking/fits_data.py    | 126 +++++++++++++++----------
 src/kbmod/mocking/headers.py      |   2 +
 src/kbmod/mocking/test_mocking.py |  40 ++++++++
 6 files changed, 261 insertions(+), 89 deletions(-)
 create mode 100644 src/kbmod/mocking/test_mocking.py

diff --git a/src/kbmod/mocking/__init__.py b/src/kbmod/mocking/__init__.py
index b79086419..853896433 100644
--- a/src/kbmod/mocking/__init__.py
+++ b/src/kbmod/mocking/__init__.py
@@ -3,3 +3,4 @@
 from .headers import *
 from .fits_data import *
 from .fits import *
+from . import test_mocking
diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 03fc1b16e..460853145 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -34,6 +34,9 @@ def gen_catalog(n, param_ranges, seed=None):
     return cat
 
 
+def gen_source_catalog(n, param_ranges, seed=None):
+
+
 class CatalogFactory(abc.ABC):
     @abc.abstractmethod
     def gen_realization(self, *args, t=None, dt=None, **kwargs):
@@ -48,8 +51,8 @@ class SimpleSourceCatalog(CatalogFactory):
         "amplitude": [500, 2000],
         "x_mean": [0, 4096],
         "y_mean": [0, 2048],
-        "x_stddev": [1, 2],
-        "y_stddev": [1, 2],
+        "x_stddev": [1, 7],
+        "y_stddev": [1, 7],
         "theta": [0, np.pi],
     }
 
@@ -69,18 +72,34 @@ def gen_realization(self, *args, t=None, dt=None, **kwargs):
             return self.table.copy()
         return self.table
 
+    def mock(self, n=1):
+        if n == 1:
+            return self.table
+        return [self.table for i in range(n)]
+
 
 class SimpleObjectCatalog(CatalogFactory):
     base_param_ranges = {
-        "amplitude": [100, 500],
+        "amplitude": [50, 500],
         "x_mean": [0, 4096],
         "y_mean": [0, 2048],
         "vx": [500, 5000],
         "vy": [500, 5000],
-        "stddev": [1, 5],
+        "stddev": [1, 2],
         "theta": [0, np.pi],
     }
 
+    dtype = np.dtype([
+        ("amplitude", np.float32),
+        ("x_mean", np.float32),
+        ("y_mean", np.float32),
+        ("vx", np.float32),
+        ("vy", np.float32),
+        ("x_stddev", np.float32),
+        ("y_stddev", np.float32),
+        ("thetae", np.float32)
+    ]
+
     def __init__(self, table, obstime=None):
         self.table = table
         self._realization = table.copy()
@@ -101,3 +120,8 @@ def gen_realization(self, t=None, dt=None, **kwargs):
         self._realization["x_mean"] += self._realization["vx"] * dt
         self._realization["y_mean"] += self._realization["vy"] * dt
         return self._realization
+
+    def mock(self, n=1, dt=0.001):
+        if n == 1:
+            return self.gen_realization()
+
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index 56526d62e..043a44d95 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -1,9 +1,11 @@
+import abc
 import types
 import functools
 
 import numpy as np
 from astropy.wcs import WCS
 from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, BinTableHDU, Header
+from astropy.modeling import models
 
 from .headers import HeaderFactory, ArchivedHeader
 from .catalogs import gen_catalog, SimpleSourceCatalog, SimpleObjectCatalog
@@ -31,7 +33,8 @@ def mock(self, **kwargs):
         hdu = self.hdu_cls()
 
         header = self.header_factory.mock(hdu=hdu, **kwargs)
-        hdu.header.update(header)
+        # hdu.header.update(header) costs more but maybe better?
+        hdu.header = header
 
         if self.update_data:
             data = self.data_factory.mock(hdu=hdu, **kwargs)
@@ -42,19 +45,22 @@ def mock(self, **kwargs):
         return hdu
 
 
-class HDUListFactory:
+class HDUListFactory(abc.ABC):
     def __init__(self, layout, base_primary={}, base_ext={}, base_wcs={}):
         self.layout = layout
         self.base_primary = base_primary
         self.base_ext = base_ext
         self.base_wcs = base_wcs
 
-    def mock(self, **kwargs):
+    def generate(self, **kwargs):
         hdul = HDUList()
         for layer in self.layout:
             hdul.append(layer.mock(**kwargs))
         return hdul
 
+    @abc.abstractmethod
+    def mock(self, n=1):
+        raise NotImplementedError()
 
 # I am sure a decorator like this must exist somewhere in functools, but can't
 # find it and I'm doing something wrong with functools.partial because that's
@@ -95,26 +101,47 @@ def increment_obstime(old, dt):
 
     def __init__(self, primary_hdr=None, dt=0.001):
         # header and data factories that go into creating HDUs
-        primary = HeaderFactory.from_base_primary(
+        self.primary = HeaderFactory.from_base_primary(
             metadata=primary_hdr,
             mutables=["OBS-MJD"],
             callbacks=[self.increment_obstime(dt=dt)],
         )
-        image = HeaderFactory.from_base_ext({"EXTNAME": "IMAGE"})
-        variance = HeaderFactory.from_base_ext({"EXTNAME": "VARIANCE"})
-        mask = HeaderFactory.from_base_ext({"EXTNAME": "MASK"})
+        self.image = HeaderFactory.from_base_ext({"EXTNAME": "IMAGE"})
+        self.variance = HeaderFactory.from_base_ext({"EXTNAME": "VARIANCE"})
+        self.mask = HeaderFactory.from_base_ext({"EXTNAME": "MASK"})
         data = ZeroedData()
 
         # a map of HDU class and their respective header and data generators
         layout = [
-            HDUFactory(PrimaryHDU, primary),
-            HDUFactory(CompImageHDU, image, data),
-            HDUFactory(CompImageHDU, variance, data),
-            HDUFactory(CompImageHDU, mask, data),
+            HDUFactory(PrimaryHDU, self.primary),
+            HDUFactory(CompImageHDU, self.image, data),
+            HDUFactory(CompImageHDU, self.variance, data),
+            HDUFactory(CompImageHDU, self.mask, data),
         ]
 
         super().__init__(layout)
 
+    def mock(self, n=1):
+        if n == 1:
+            return self.step()
+
+        shape = (n*3, self.image.header["NAXIS1"], self.image.header["NAXIS1"])
+        images = np.zeros(shape, dtype=np.float32)
+        imghdr, varhdr, maskhdr = self.image.mock(), self.variance.mock(), self.mask.mock()
+        hduls = []
+        for i in range(n):
+            hduls.append(HDUList(
+                hdus=[
+                    PrimaryHDU(header=self.primary.mock()),
+                    CompImageHDU(header=imghdr, data=images[i]),
+                    CompImageHDU(header=varhdr, data=images[n+i]),
+                    CompImageHDU(header=maskhdr, data=images[2*n+i])
+                ]
+            ))
+
+        return hduls
+
+
 
 class SimpleFits(HDUListFactory):
     @callback
@@ -122,61 +149,105 @@ class SimpleFits(HDUListFactory):
     def increment_obstime(old, dt):
         return old + dt
 
-    def __init__(self, shape=(2048, 4096), noise=100, dt=0.001, source_catalog=None, object_catalog=None):
+    def __init__(self, shape, noise=0, noise_std=1, dt=0.001, source_catalog=None, object_catalog=None):
         # internal counter of n images created so far
         self._idx = 0
         self.dt = dt
         self.src_cat = source_catalog
         self.obj_cat = object_catalog
+        self.shape = shape
+        self.noise = noise
+        self.noise_std = noise_std
         dims = {"NAXIS1": shape[0], "NAXIS2": shape[1]}
 
         # Then we can generate the header factories primary header contains no
         # data, but does contain update-able metadata fields (f.e. timestamps),
         # others contain static headers, but dynamic, mutually connected data
-        primary = HeaderFactory.from_base_primary(
+        self.primary = HeaderFactory.from_base_primary(
             mutables=["OBS-MJD"],
             callbacks=[self.increment_obstime(dt=0.001)],
         )
-        image = HeaderFactory.from_base_ext({"EXTNAME": "IMAGE", **dims})
-        variance = HeaderFactory.from_base_ext({"EXTNAME": "VARIANCE", **dims})
-        mask = HeaderFactory.from_base_ext({"EXTNAME": "MASK", **dims})
+        self.image = HeaderFactory.from_base_ext({"EXTNAME": "IMAGE", **dims})
+        self.variance = HeaderFactory.from_base_ext({"EXTNAME": "VARIANCE", **dims})
+        self.mask = HeaderFactory.from_base_ext({"EXTNAME": "MASK", **dims})
 
-        img_data = SimpleImage.simulate(
+        self.img_data = SimpleImage.simulate(
             shape=shape,
             noise=noise,
             catalog=self.src_cat,
             return_copy=False if self.obj_cat is None else True,
         )
-        var_data = SimpleVariance(img_data.base_data, read_noise=noise, gain=1.0)
-        mask_data = ZeroedData(np.zeros(shape))
+        self.var_data = SimpleVariance(self.img_data.base_data, read_noise=noise, gain=1.0)
+        self.mask_data = ZeroedData(np.zeros(shape))
 
         # Now we can build the HDU map and the HDUList layout
         layout = [
-            HDUFactory(PrimaryHDU, primary),
-            HDUFactory(CompImageHDU, image, img_data),
-            HDUFactory(CompImageHDU, variance, var_data),
-            HDUFactory(CompImageHDU, mask, mask_data),
+            HDUFactory(PrimaryHDU, self.primary),
+            HDUFactory(CompImageHDU, self.image, self.img_data),
+            HDUFactory(CompImageHDU, self.variance, self.var_data),
+            HDUFactory(CompImageHDU, self.mask, self.mask_data),
         ]
 
         super().__init__(layout)
 
     @classmethod
-    def from_defaults(cls, add_static_sources=False, add_moving_objects=True):
+    def from_defaults(cls, shape=(2048, 4096), add_static_sources=False, add_moving_objects=True, **kwargs):
         source_catalog, object_catalog = None, None
+        cat_lims = {"x_mean": [0, shape[1]], "y_mean": [0, shape[0]]}
         if add_static_sources:
-            source_catalog = SimpleSourceCatalog.from_params()
+            source_catalog = SimpleSourceCatalog.from_params(param_ranges=cat_lims)
         if add_moving_objects:
-            object_catalog = SimpleObjectCatalog.from_params()
-
-        return cls(source_catalog=source_catalog, object_catalog=object_catalog)
-
-    def mock(self, **kwargs):
-        if self.obj_cat is None:
-            new_cat = None
-        else:
-            new_cat = self.obj_cat.gen_realization(dt=self.dt)
-
-        return super().mock(catalog=new_cat)
+            object_catalog = SimpleObjectCatalog.from_params(param_ranges=cat_lims)
+
+        return cls(shape=shape, source_catalog=source_catalog, object_catalog=object_catalog, **kwargs)
+
+    def mock(self, n=1, catalog=None):
+        if n == 1:
+            # a valiant effort, but too slow...
+            return self.generate()
+
+        shape = (n*2, *self.shape)
+        images = np.zeros(shape, dtype=np.float32)
+        mask_data = self.mask_data.mock()
+
+        if self.noise != 0:
+            rng = np.random.default_rng()
+            rng.standard_normal(size=shape, dtype=np.float32, out=images)
+            images *= self.noise_std
+            images += self.noise
+
+        # update the base image (static objects, bad columns etc.)
+        if self.src_cat is not None:
+            images += self.img_data.base_data
+
+        #  update the variance images if needed only
+        if self.var_data.gain != 1.0:
+            images[n:] /= self.var_data.gain
+        if self.var_data.read_noise != 0.0:
+            images[n:] += self.var_data.read_noise**2
+
+        # shared headers
+        imghdr, varhdr, maskhdr = self.image.mock(), self.variance.mock(), self.mask.mock()
+
+        hduls = []
+        for i in range(n):
+            if self.obj_cat is not None:
+                new_cat = self.obj_cat.gen_realization(dt=self.dt)
+                new_img = add_model_objects(np.zeros(images[0].shape), new_cat, models.Gaussian2D(x_stddev=1, y_stddev=1))
+                images[i] += new_img
+                images[n+1] += new_img
+
+            hduls.append(HDUList(
+                hdus=[
+                    PrimaryHDU(header=self.primary.mock()),
+                    CompImageHDU(header=imghdr, data=images[i]),
+                    CompImageHDU(header=varhdr, data=images[n+i]),
+                    CompImageHDU(header=maskhdr, data=mask_data),
+                ]
+            ))
+
+        self._idx += n
+        return hduls
 
 
 class DECamImdiffs(HDUListFactory):
@@ -202,3 +273,9 @@ def __init__(self):
         # fmt: on
 
         super().__init__(layout)
+
+    def mock(self, n=1):
+        if n==1:
+            return self.step()
+        hduls = [self.step() for i in range(n)]
+        return hduls
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index df05b5fd9..d42f3e42c 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -24,7 +24,15 @@ def add_model_objects(img, catalog, model):
         for i, source in enumerate(catalog):
             for param in params_to_set:
                 setattr(model, param, source[param])
-            model.render(img)
+
+            if all([
+                    model.x_mean > 0,
+                    model.x_mean < img.shape[1],
+                    model.y_mean > 0,
+                    model.y_mean < img.shape[0]
+            ]):
+                model.render(img)
+
     finally:
         for param, value in init_params.items():
             setattr(model, param, value)
@@ -43,30 +51,32 @@ class DataFactory:
         32: np.float32,
         64: np.float64,
         # classic IEEE float and double
-        -32: np.float32,
+        -32: np.float32
         -64: np.float64,
     }
 
-    def __init__(self, base_data=None, return_copy=False, mutable=False, **kwargs):
+    def __init__(self, base=None, return_copy=False, mutable=False, **kwargs):
         # not sure if this is "best" "best" way, but it does safe a lot of
         # array copies if we don't have to write to the mocked array
         # (which we shouldn't?). To be safe we set the writable flag to False
         # by default
         self.return_copy = return_copy
         self.mutable = mutable
-        self.base_data = base_data
-        if base_data is not None:
-            self.base_data.flags.writeable = mutable
+        self.base = base
+        if base is not None:
+            self.base.flags.writeable = mutable
 
     def mock(self, hdu=None, **kwargs):
         if self.return_copy:
-            return self.base_data.copy()
-        return self.base_data
+            return self.base.copy()
+        return self.base
 
 
 class SimpleVariance(DataFactory):
     def __init__(self, image, read_noise, gain, return_copy=False, mutable=False):
-        super().__init__(base_data=image / gain + read_noise**2, return_copy=return_copy, mutable=mutable)
+        self.read_noise = read_noise
+        self.gain = gain
+        super().__init__(base=image / gain + read_noise**2, return_copy=return_copy, mutable=mutable)
 
 
 class SimpleMask(DataFactory):
@@ -105,8 +115,8 @@ def from_patches(cls, shape, patches):
 
 
 class ZeroedData(DataFactory):
-    def __init__(self, base_data=None, return_copy=False, mutable=False):
-        super().__init__(base_data, return_copy, mutable)
+    def __init__(self, base=None, return_copy=False, mutable=False):
+        super().__init__(base, return_copy, mutable)
 
     def mock_image_data(self, hdu):
         cols = hdu.header.get("NAXIS1", False)
@@ -129,7 +139,7 @@ def mock(self, hdu=None, **kwargs):
         # two cases: a) static data shared by all instances created by the
         # factory set at init time or b) dynamic data generated from a header
         # specification at call-time.
-        if self.base_data is not None:
+        if self.base is not None:
             return super().mock(hdu, **kwargs)
         if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
             return self.mock_image_data(hdu)
@@ -141,58 +151,76 @@ def mock(self, hdu=None, **kwargs):
 
 class SimpleImage(DataFactory):
     rng = np.random.default_rng()
-    read_noise_gen = rng.normal
-    dark_current_gen = rng.poisson
-    sky_count_gen = rng.poisson
+    noise_gen = rng.standard_normal
+    model = models.Gaussian2D
 
     @classmethod
-    def simulate(
-        cls, shape, noise=0, catalog=None, src_model=models.Gaussian2D(x_stddev=1, y_stddev=1), **kwargs
-    ):
+    def simulate(cls, shape, noise=0, catalog=None, **kwargs):
         # make a blank image
         img = np.zeros(shape, dtype=np.float32)
 
         # add static sources to it
         if catalog is not None:
-            img = add_model_objects(img, catalog, src_model)
+            img = add_model_objects(img, catalog.table, src_model)
 
         return cls(img, noise=noise, **kwargs)
 
-    def __init__(self, image, noise=0, return_copy=False, mutable=False):
+    def __init__(self, image=None, shape=(1000, 1000), noise=0, noise_std=1.0, dtype=np.float32, return_copy=False, mutable=False):
+        if image is None:
+            image = np.zeros(self.shape, dtype=dtype)
         super().__init__(image, return_copy, mutable)
+        self._base_contains_data = image.sum() != 0
         self.noise = noise
 
-    def mock(self, catalog=None, hdu=None, **kwargs):
-        # if no catalog of moving obj were given just get
-        # the base image, otherwise add the new sources
-        if catalog is None:
-            base = self.get_base()
-        else:
-            base = self.draw_new(catalog)
+    def mock(self, n=1, catalog=None, hdu=None, **kwargs):
+        shape = (n, *self.shape)
+        images = np.zeros(shape, dtype=np.float32)
 
-        # finally, create poisson background every time and add it to
-        # the base of the image
-        bckg = self.sky_count_gen(self.noise, size=base.shape)
-        return base + bckg
+        if self._base_contains_data:
+            images += self.base
 
-    def get_base(self):
-        if self.return_copy:
-            return self.base_data.copy()
-        return self.base_data
-
-    def draw_new(self, catalog):
-        if not self.return_copy and not self.mutable:
-            raise RuntimeError(
-                "Can not get_realization an image that is neither mutable nor "
-                "copyable. Setting the array to mutable will update it in-place, "
-                "while setting copyable returns a copy of the array."
-            )
-
-        model = models.Gaussian2D(x_stddev=1, y_stddev=1)
-        if self.mutable:
-            return add_model_objects(self.base_data, catalog, model)
-        else:
-            return add_model_objects(self.base_data.copy(), catalog, model)
+        if self.noise != 0:
+            rng = np.random.default_rng()
+            rng.standard_normal(size=shape, dtype=np.float32, out=images)
+            if self.noise_std != 1.0:
+                images *= self.noise_std
+            images += self.noise
+
+        if catalog is not None:
+            for img in images:
+                add_model_objects(img, catalog, self.model(x_stddev=1, y_stddev=1))
+
+        return images
+#        # if no catalog of moving obj were given just get
+#        # the base image, otherwise add the new sources
+#        if catalog is None:
+#            base = self.get_base()
+#        else:
+#            base = self.draw_new(catalog)
+#
+#        # finally, create poisson background every time and add it to
+#        # the base of the image
+#        bckg = self.noise_gen(size=base.shape)
+#        return base + bckg
+#
+#    def get_base(self):
+#        if self.return_copy:
+#            return self.base.copy()
+#        return self.base
+#
+#    def draw_new(self, catalog):
+#        if not self.return_copy and not self.mutable:
+#            raise RuntimeError(
+#                "Can not get_realization an image that is neither mutable nor "
+#                "copyable. Setting the array to mutable will update it in-place, "
+#                "while setting copyable returns a copy of the array."
+#            )
+#
+#        model = models.Gaussian2D(x_stddev=1, y_stddev=1)
+#        if self.mutable:
+#            return add_model_objects(self.base, catalog, model)
+#        else:
+#            return add_model_objects(self.base.copy(), catalog, model)
 
 
 class SimulatedImage(DataFactory):
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index 50c5e6978..033ecee1f 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -22,6 +22,8 @@ class HeaderFactory:
         "NEXTEND": 3,
         "OBS-LAT": -30.166,
         "OBS-LONG": -70.814,
+        "OBS-ELEV": 2200,
+        "OBSERVAT": "CTIO"
     }
 
     base_ext = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "BITPIX": 32}
diff --git a/src/kbmod/mocking/test_mocking.py b/src/kbmod/mocking/test_mocking.py
new file mode 100644
index 000000000..d5c8882fc
--- /dev/null
+++ b/src/kbmod/mocking/test_mocking.py
@@ -0,0 +1,40 @@
+import timeit
+
+from .fits import EmptyFits, SimpleFits, DECamImdiffs
+from kbmod import ImageCollection
+import matplotlib.pyplot as plt
+
+start = timeit.default_timer()
+#fits_files = DECamImdiffs().mock(50)
+print(f"DECam Imdiff Fits: {timeit.default_timer() - start} seconds")
+#del fits_files
+
+start = timeit.default_timer()
+#fits_files = EmptyFits().mock(50)
+print(f"EmptyFits: {timeit.default_timer() - start} seconds")
+#del fits_files
+
+start = timeit.default_timer()
+fits_files = SimpleFits.from_defaults(
+    shape=(500, 500),
+    add_static_sources=False,
+    add_moving_objects=True,
+    noise=1000,
+).mock(50)
+print(f"SimpleFits: {timeit.default_timer() - start} seconds")
+
+#fits_factory = SimpleFits.from_defaults(add_moving_objects=False)
+#print(f"Fits factory took: {timeit.default_timer() - start} seconds")
+
+#fits_files = [fits_factory.mock() for i in range(50)]
+#print(f"Generated 100 images in: {timeit.default_timer() - start} seconds")
+
+start = timeit.default_timer()
+ic = ImageCollection.fromTargets(fits_files, force="TestDataStd")
+print(f"Image collection took: {timeit.default_timer() - start} seconds")
+print(ic)
+
+_ = [plt.imsave(f"/home/dino/Downloads/tmp_fits/{i}.png", h[1].data) for i, h in enumerate(fits_files)]
+
+
+breakpoint()

From b4b68470cae41ac04f1adfc8355c85326619bee0 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Tue, 26 Mar 2024 09:39:10 -0700
Subject: [PATCH 10/27] Add vectorized image creation.

---
 src/kbmod/mocking/catalogs.py     | 34 ++++----------
 src/kbmod/mocking/fits.py         | 55 +++++++----------------
 src/kbmod/mocking/fits_data.py    | 74 ++++++++++---------------------
 src/kbmod/mocking/test_mocking.py | 20 ++++-----
 4 files changed, 55 insertions(+), 128 deletions(-)

diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 460853145..06fa5dadd 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -34,8 +34,6 @@ def gen_catalog(n, param_ranges, seed=None):
     return cat
 
 
-def gen_source_catalog(n, param_ranges, seed=None):
-
 
 class CatalogFactory(abc.ABC):
     @abc.abstractmethod
@@ -72,41 +70,25 @@ def gen_realization(self, *args, t=None, dt=None, **kwargs):
             return self.table.copy()
         return self.table
 
-    def mock(self, n=1):
-        if n == 1:
-            return self.table
-        return [self.table for i in range(n)]
-
 
 class SimpleObjectCatalog(CatalogFactory):
     base_param_ranges = {
-        "amplitude": [50, 500],
+        "amplitude": [1, 100],
         "x_mean": [0, 4096],
         "y_mean": [0, 2048],
-        "vx": [500, 5000],
-        "vy": [500, 5000],
-        "stddev": [1, 2],
+        "vx": [500, 1000],
+        "vy": [500, 1000],
+        "stddev": [1, 1.8],
         "theta": [0, np.pi],
     }
 
-    dtype = np.dtype([
-        ("amplitude", np.float32),
-        ("x_mean", np.float32),
-        ("y_mean", np.float32),
-        ("vx", np.float32),
-        ("vy", np.float32),
-        ("x_stddev", np.float32),
-        ("y_stddev", np.float32),
-        ("thetae", np.float32)
-    ]
-
     def __init__(self, table, obstime=None):
         self.table = table
         self._realization = table.copy()
         self.obstime = 0 if obstime is None else obstime
 
     @classmethod
-    def from_params(cls, n=10, param_ranges=None):
+    def from_params(cls, n=100, param_ranges=None):
         param_ranges = {} if param_ranges is None else param_ranges
         tmp = cls.base_param_ranges.copy()
         tmp.update(param_ranges)
@@ -121,7 +103,7 @@ def gen_realization(self, t=None, dt=None, **kwargs):
         self._realization["y_mean"] += self._realization["vy"] * dt
         return self._realization
 
-    def mock(self, n=1, dt=0.001):
+    def mock(self, n=1, **kwargs):
         if n == 1:
-            return self.gen_realization()
-
+            return self.gen_realization(**kwargs)
+        return [self.gen_realization(**kwargs).copy() for i in range(n)]
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index 043a44d95..9a2dbc37e 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -171,13 +171,13 @@ def __init__(self, shape, noise=0, noise_std=1, dt=0.001, source_catalog=None, o
         self.variance = HeaderFactory.from_base_ext({"EXTNAME": "VARIANCE", **dims})
         self.mask = HeaderFactory.from_base_ext({"EXTNAME": "MASK", **dims})
 
-        self.img_data = SimpleImage.simulate(
+        self.img_data = SimpleImage(
             shape=shape,
             noise=noise,
-            catalog=self.src_cat,
-            return_copy=False if self.obj_cat is None else True,
+            noise_std=noise_std,
+            catalog=self.src_cat
         )
-        self.var_data = SimpleVariance(self.img_data.base_data, read_noise=noise, gain=1.0)
+        self.var_data = SimpleVariance(self.img_data.base, read_noise=noise, gain=1.0)
         self.mask_data = ZeroedData(np.zeros(shape))
 
         # Now we can build the HDU map and the HDUList layout
@@ -201,52 +201,27 @@ def from_defaults(cls, shape=(2048, 4096), add_static_sources=False, add_moving_
 
         return cls(shape=shape, source_catalog=source_catalog, object_catalog=object_catalog, **kwargs)
 
-    def mock(self, n=1, catalog=None):
-        if n == 1:
-            # a valiant effort, but too slow...
-            return self.generate()
-
-        shape = (n*2, *self.shape)
-        images = np.zeros(shape, dtype=np.float32)
-        mask_data = self.mask_data.mock()
-
-        if self.noise != 0:
-            rng = np.random.default_rng()
-            rng.standard_normal(size=shape, dtype=np.float32, out=images)
-            images *= self.noise_std
-            images += self.noise
-
-        # update the base image (static objects, bad columns etc.)
-        if self.src_cat is not None:
-            images += self.img_data.base_data
+    def mock(self, n=1):
+        obj_cats = None
 
-        #  update the variance images if needed only
-        if self.var_data.gain != 1.0:
-            images[n:] /= self.var_data.gain
-        if self.var_data.read_noise != 0.0:
-            images[n:] += self.var_data.read_noise**2
+        if self.obj_cat is not None:
+            obj_cats = self.obj_cat.mock(n, dt=self.dt)
 
-        # shared headers
+        images = self.img_data.mock(n, obj_cats)
+        variances = self.var_data.mock(images=images)
+        mask = self.mask_data.mock()
         imghdr, varhdr, maskhdr = self.image.mock(), self.variance.mock(), self.mask.mock()
 
         hduls = []
         for i in range(n):
-            if self.obj_cat is not None:
-                new_cat = self.obj_cat.gen_realization(dt=self.dt)
-                new_img = add_model_objects(np.zeros(images[0].shape), new_cat, models.Gaussian2D(x_stddev=1, y_stddev=1))
-                images[i] += new_img
-                images[n+1] += new_img
-
             hduls.append(HDUList(
                 hdus=[
                     PrimaryHDU(header=self.primary.mock()),
                     CompImageHDU(header=imghdr, data=images[i]),
-                    CompImageHDU(header=varhdr, data=images[n+i]),
-                    CompImageHDU(header=maskhdr, data=mask_data),
+                    CompImageHDU(header=varhdr, data=variances[i]),
+                    CompImageHDU(header=maskhdr, data=mask),
                 ]
             ))
-
-        self._idx += n
         return hduls
 
 
@@ -276,6 +251,6 @@ def __init__(self):
 
     def mock(self, n=1):
         if n==1:
-            return self.step()
-        hduls = [self.step() for i in range(n)]
+            return self.generate()
+        hduls = [self.generate() for i in range(n)]
         return hduls
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index d42f3e42c..5519ed930 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -51,7 +51,7 @@ class DataFactory:
         32: np.float32,
         64: np.float64,
         # classic IEEE float and double
-        -32: np.float32
+        -32: np.float32,
         -64: np.float64,
     }
 
@@ -78,6 +78,11 @@ def __init__(self, image, read_noise, gain, return_copy=False, mutable=False):
         self.gain = gain
         super().__init__(base=image / gain + read_noise**2, return_copy=return_copy, mutable=mutable)
 
+    def mock(self, images=None):
+        if images is None:
+            return self.base
+        return images/self.gain + self.read_noise**2
+
 
 class SimpleMask(DataFactory):
     def __init__(self, mask, return_copy=False, mutable=False):
@@ -154,31 +159,27 @@ class SimpleImage(DataFactory):
     noise_gen = rng.standard_normal
     model = models.Gaussian2D
 
-    @classmethod
-    def simulate(cls, shape, noise=0, catalog=None, **kwargs):
-        # make a blank image
-        img = np.zeros(shape, dtype=np.float32)
+    def __init__(self, image=None, shape=(1000, 1000), noise=0, noise_std=1.0, src_cat=None, **kwargs):
+        if image is None:
+            image = np.zeros(shape, dtype=np.float32)
+            self.shape = shape
+        else:
+            image = image
+            self.shape = image.shape
 
-        # add static sources to it
-        if catalog is not None:
-            img = add_model_objects(img, catalog.table, src_model)
+        if src_cat is not None:
+            add_model_objects(image, src_cat.table, self.model)
 
-        return cls(img, noise=noise, **kwargs)
+        super().__init__(image, False, False)
 
-    def __init__(self, image=None, shape=(1000, 1000), noise=0, noise_std=1.0, dtype=np.float32, return_copy=False, mutable=False):
-        if image is None:
-            image = np.zeros(self.shape, dtype=dtype)
-        super().__init__(image, return_copy, mutable)
         self._base_contains_data = image.sum() != 0
         self.noise = noise
+        self.noise_std = noise_std
 
-    def mock(self, n=1, catalog=None, hdu=None, **kwargs):
+    def mock(self, n=1, obj_cats=None, **kwargs):
         shape = (n, *self.shape)
         images = np.zeros(shape, dtype=np.float32)
 
-        if self._base_contains_data:
-            images += self.base
-
         if self.noise != 0:
             rng = np.random.default_rng()
             rng.standard_normal(size=shape, dtype=np.float32, out=images)
@@ -186,41 +187,14 @@ def mock(self, n=1, catalog=None, hdu=None, **kwargs):
                 images *= self.noise_std
             images += self.noise
 
-        if catalog is not None:
-            for img in images:
-                add_model_objects(img, catalog, self.model(x_stddev=1, y_stddev=1))
+        if self._base_contains_data:
+            images += self.base
+
+        if obj_cats is not None:
+            for i, (img, cat) in enumerate(zip(images, obj_cats)):
+                add_model_objects(img, cat, self.model(x_stddev=1, y_stddev=1))
 
         return images
-#        # if no catalog of moving obj were given just get
-#        # the base image, otherwise add the new sources
-#        if catalog is None:
-#            base = self.get_base()
-#        else:
-#            base = self.draw_new(catalog)
-#
-#        # finally, create poisson background every time and add it to
-#        # the base of the image
-#        bckg = self.noise_gen(size=base.shape)
-#        return base + bckg
-#
-#    def get_base(self):
-#        if self.return_copy:
-#            return self.base.copy()
-#        return self.base
-#
-#    def draw_new(self, catalog):
-#        if not self.return_copy and not self.mutable:
-#            raise RuntimeError(
-#                "Can not get_realization an image that is neither mutable nor "
-#                "copyable. Setting the array to mutable will update it in-place, "
-#                "while setting copyable returns a copy of the array."
-#            )
-#
-#        model = models.Gaussian2D(x_stddev=1, y_stddev=1)
-#        if self.mutable:
-#            return add_model_objects(self.base, catalog, model)
-#        else:
-#            return add_model_objects(self.base.copy(), catalog, model)
 
 
 class SimulatedImage(DataFactory):
diff --git a/src/kbmod/mocking/test_mocking.py b/src/kbmod/mocking/test_mocking.py
index d5c8882fc..43e13557d 100644
--- a/src/kbmod/mocking/test_mocking.py
+++ b/src/kbmod/mocking/test_mocking.py
@@ -5,36 +5,32 @@
 import matplotlib.pyplot as plt
 
 start = timeit.default_timer()
-#fits_files = DECamImdiffs().mock(50)
+fits_files = DECamImdiffs().mock(50)
 print(f"DECam Imdiff Fits: {timeit.default_timer() - start} seconds")
-#del fits_files
+del fits_files
 
 start = timeit.default_timer()
-#fits_files = EmptyFits().mock(50)
+fits_files = EmptyFits().mock(50)
 print(f"EmptyFits: {timeit.default_timer() - start} seconds")
-#del fits_files
+del fits_files
 
 start = timeit.default_timer()
 fits_files = SimpleFits.from_defaults(
-    shape=(500, 500),
+    shape=(1000, 1000),
     add_static_sources=False,
     add_moving_objects=True,
-    noise=1000,
+    noise=100,
+    noise_std=20
 ).mock(50)
 print(f"SimpleFits: {timeit.default_timer() - start} seconds")
 
-#fits_factory = SimpleFits.from_defaults(add_moving_objects=False)
-#print(f"Fits factory took: {timeit.default_timer() - start} seconds")
-
-#fits_files = [fits_factory.mock() for i in range(50)]
-#print(f"Generated 100 images in: {timeit.default_timer() - start} seconds")
 
 start = timeit.default_timer()
 ic = ImageCollection.fromTargets(fits_files, force="TestDataStd")
 print(f"Image collection took: {timeit.default_timer() - start} seconds")
 print(ic)
 
-_ = [plt.imsave(f"/home/dino/Downloads/tmp_fits/{i}.png", h[1].data) for i, h in enumerate(fits_files)]
+_ = [plt.imsave(f"/home/dino/Downloads/tmp_fits/{i}.png", h[1].data, vmin=50, vmax=500) for i, h in enumerate(fits_files)]
 
 
 breakpoint()

From a018bdfb40dd144695669f03370ff42a459a28ce Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Tue, 9 Apr 2024 13:38:36 -0700
Subject: [PATCH 11/27] Add a config class.

---
 src/kbmod/mocking/config.py | 116 ++++++++++++++++++++++++++++++++++++
 1 file changed, 116 insertions(+)
 create mode 100644 src/kbmod/mocking/config.py

diff --git a/src/kbmod/mocking/config.py b/src/kbmod/mocking/config.py
new file mode 100644
index 000000000..0bb94a8ac
--- /dev/null
+++ b/src/kbmod/mocking/config.py
@@ -0,0 +1,116 @@
+__all__ = ["Config", "ConfigurationError"]
+
+
+class ConfigurationError(Exception):
+    """Error that is raised when configuration parameters contain a logical error."""
+
+
+class Config:
+    """Base configuration class.
+
+    Config classes that inherit from this class define configuration as their
+    class attributes. Particular attributes can be overriden on an per-instance
+    basis by providing a config overrides at initialization time.
+
+    Configs inheriting from this config support basic dictionary operations.
+
+    Parameters
+    ----------
+    config : `dict`, `Config` or `None`, optional
+        Collection of configuration key-value pairs.
+    kwargs : optional
+        Keyword arguments, assigned as configuration key-values.
+    """
+
+    def __init__(self, config=None, **kwargs):
+        # This is a bit hacky, but it makes life a lot easier because it
+        # enables automatic loading of the default configuration and separation
+        # of default config from instance bound config
+        keys = list(set(dir(self.__class__)) - set(dir(Config)))
+
+        # First fill out all the defaults by copying cls attrs
+        self._conf = {k: getattr(self, k) for k in keys}
+
+        # Then override with any user-specified values
+        conf = config
+        if isinstance(config, Config):
+            conf = config._conf
+
+        if conf is not None:
+            self._conf.update(config)
+        self._conf.update(kwargs)
+
+    # now just shortcut the most common dict operations
+    def __getitem__(self, key):
+        return self._conf[key]
+
+    def __setitem__(self, key, value):
+        self._conf[key] = value
+
+    def __str__(self):
+        res = f"{self.__class__.__name__}("
+        for k, v in self.items():
+            res += f"{k}: {v}, "
+        return res[:-2] + ")"
+
+    def __len__(self):
+        return len(self._conf)
+
+    def __contains__(self, key):
+        return key in self._conf
+
+    def __eq__(self, other):
+        if isinstance(other, type(self)):
+            return self._conf == other._conf
+        elif isinstance(other, dict):
+            return self._conf == other
+        else:
+            return super().__eq__(other)
+
+    def __iter__(self):
+        return iter(self._conf)
+
+    def __or__(self, other):
+        if isinstance(other, type(self)):
+            return self.__class__(config=other._conf | self._conf)
+        elif isinstance(other, dict):
+            return self.__class__(config=self._conf | other)
+        else:
+            raise TypeError("unsupported operand type(s) for |: {type(self)} " "and {type(other)}")
+
+    def keys(self):
+        """A set-like object providing a view on config's keys."""
+        return self._conf.keys()
+
+    def values(self):
+        """A set-like object providing a view on config's values."""
+        return self._conf.values()
+
+    def items(self):
+        """A set-like object providing a view on config's items."""
+        return self._conf.items()
+
+    def update(self, conf=None, **kwargs):
+        """Update this config from dict/other config/iterable and
+        apply any explicit keyword overrides.
+
+        A dict-like update. If ``conf`` is given and has a ``.keys()``
+        method, performs:
+
+            for k in conf: this[k] = conf[k]
+
+        If ``conf`` is given but lacks a ``.keys()`` method, performs:
+
+            for k, v in conf: this[k] = v
+
+        In both cases, explicit overrides are applied at the end:
+
+            for k in kwargs:  this[k] = kwargs[k]
+        """
+        if conf is not None:
+            self._conf.update(conf)
+        self._conf.update(kwargs)
+
+    def toDict(self):
+        """Return this config as a dict."""
+        return self._conf

From 0a275874eeb9a84dc34be6f654ee52aa6f475720 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Tue, 9 Apr 2024 13:39:00 -0700
Subject: [PATCH 12/27] Refactor data factories to use the config class.

---
 src/kbmod/mocking/fits_data.py | 361 ++++++++++++++++++++++-----------
 1 file changed, 247 insertions(+), 114 deletions(-)

diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index 5519ed930..fde6fa686 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -1,10 +1,23 @@
 import numpy as np
-from astropy.io.fits import PrimaryHDU, CompImageHDU, ImageHDU, BinTableHDU, TableHDU
+from astropy.io.fits import (
+    PrimaryHDU,
+    CompImageHDU,
+    ImageHDU,
+    BinTableHDU,
+    TableHDU
+)
 from astropy.modeling import models
-from astropy.convolution import discretize_model
 
+from .config import Config, ConfigurationError
 
-__all__ = ["add_model_objects", "DataFactory", "ZeroedData", "SimpleImage", "SimpleMask"]
+
+__all__ = [
+    "add_model_objects",
+    "DataFactory",
+    "ZeroedData",
+    "SimpleImage",
+    "SimpleMask"
+]
 
 
 def add_model_objects(img, catalog, model):
@@ -40,53 +53,89 @@ def add_model_objects(img, catalog, model):
     return img
 
 
+
+
+class DataFactoryConfig(Config):
+    writeable = False
+    """Sets the base array writeable flag."""
+
+    copy_base = False
+    """
+    When `True`, a copy of the base data object is passed into the generate
+    method, otherwise, the original (possibly mutable!) base data object is
+    given.
+    """
+
+    copy_mocked = False
+    """
+    When `True`, the `DataFactory.mock` returns a copy of the final object,
+    otherwise the original (possibly mutable!) object is returned.
+    """
+
+    return_copy = False
+
+
+    isStatic = False
+    """
+    When `False` the `DataFactory.mock` will generate new data every time
+    it is called. Otherwise it will memoize the result of the first time
+    it's called and return that result directly or as a copy.
+    """
+
+
 class DataFactory:
-    # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
-    bitpix_type_map = {
-        # or char
-        8: int,
-        # actually no idea what dtype, or C type for that matter,
-        # are used to represent these values. But default Headers return them
-        16: np.float16,
-        32: np.float32,
-        64: np.float64,
-        # classic IEEE float and double
-        -32: np.float32,
-        -64: np.float64,
-    }
+    default_config = DataFactoryConfig
 
-    def __init__(self, base=None, return_copy=False, mutable=False, **kwargs):
+    def __init__(self, base=None, config=None, **kwargs):
         # not sure if this is "best" "best" way, but it does safe a lot of
         # array copies if we don't have to write to the mocked array
         # (which we shouldn't?). To be safe we set the writable flag to False
         # by default
-        self.return_copy = return_copy
-        self.mutable = mutable
+        self.config = self.default_config()
+        self.config.update(config, **kwargs)
+
         self.base = base
         if base is not None:
-            self.base.flags.writeable = mutable
+            self.base.flags.writeable = self.config.writeable
+        self.counter = 0
 
     def mock(self, hdu=None, **kwargs):
-        if self.return_copy:
+        self.counter += 1
+        if self.config.return_copy:
             return self.base.copy()
         return self.base
 
 
+
+class SimpleVarianceConfig(DataFactoryConfig):
+    read_noise = 0.0
+    gain = 1.0
+    calculate_base = True
+
 class SimpleVariance(DataFactory):
-    def __init__(self, image, read_noise, gain, return_copy=False, mutable=False):
-        self.read_noise = read_noise
-        self.gain = gain
-        super().__init__(base=image / gain + read_noise**2, return_copy=return_copy, mutable=mutable)
+    default_config = SimpleVarianceConfig
+
+    def __init__(self, image=None, config=None, **kwargs):
+        # skip setting the base here since the real base is
+        # derived from given image we just set it manually
+        super().__init__(base=None, config=config, **kwargs)
+
+        if image is not None:
+            self.base = image/self.config.gain + self.config.read_noise**2
 
     def mock(self, images=None):
         if images is None:
             return self.base
-        return images/self.gain + self.read_noise**2
+        return images/self.config.gain + self.config.read_noise**2
 
 
+class SimpleMaskConfig(DataFactoryConfig):
+    pass
+
 class SimpleMask(DataFactory):
-    def __init__(self, mask, return_copy=False, mutable=False):
-        super().__init__(mask, return_copy, mutable)
+    default_config = SimpleMaskConfig
+    def __init__(self, mask, config=None, **kwargs):
+        super().__init__(base=mask, config=config, **kwargs)
 
     @classmethod
     def from_params(cls, shape, padding=0, bad_columns=[]):
@@ -119,18 +168,40 @@ def from_patches(cls, shape, patches):
         return cls(mask)
 
 
+class ZeroedDataConfig(DataFactoryConfig):
+    shape = (5, 5)
+    """Default image size."""
+
+    # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
+    bitpix_type_map = {
+        # or char
+        8: int,
+        # actually no idea what dtype, or C type for that matter,
+        # are used to represent these values. But default Headers return them
+        16: np.float16,
+        32: np.float32,
+        64: np.float64,
+        # classic IEEE float and double
+        -32: np.float32,
+        -64: np.float64,
+    }
+    """Map between FITS header BITPIX keyword value and NumPy return type."""
+
 class ZeroedData(DataFactory):
-    def __init__(self, base=None, return_copy=False, mutable=False):
-        super().__init__(base, return_copy, mutable)
+    default_config = ZeroedDataConfig
+
+    def __init__(self, base=None, config=None, **kwargs):
+        super().__init__(base, config, **kwargs)
 
     def mock_image_data(self, hdu):
         cols = hdu.header.get("NAXIS1", False)
         rows = hdu.header.get("NAXIS2", False)
+        shape = (cols, rows) if all((cols, rows)) else self.config.shape
 
-        cols = 5 if not cols else cols
-        rows = 5 if not rows else rows
-
-        data = np.zeros((cols, rows), dtype=self.bitpix_type_map[hdu.header["BITPIX"]])
+        data = np.zeros(
+            shape,
+            dtype=self.config.bitpix_type_map[hdu.header["BITPIX"]]
+        )
         return data
 
     def mock_table_data(self, hdu):
@@ -154,137 +225,199 @@ def mock(self, hdu=None, **kwargs):
             raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
 
 
-class SimpleImage(DataFactory):
-    rng = np.random.default_rng()
-    noise_gen = rng.standard_normal
+
+class SimpleImageConfig(DataFactoryConfig):
+    shape = (100, 100)
+    seed = None
+    noise = 0
+    noise_std = 1.0
     model = models.Gaussian2D
 
-    def __init__(self, image=None, shape=(1000, 1000), noise=0, noise_std=1.0, src_cat=None, **kwargs):
+
+class SimpleImage(DataFactory):
+    default_config = SimpleImageConfig
+
+    def __init__(self, image=None, config=None, src_cat=None, **kwargs):
+        super().__init__(image, config, **kwargs)
+
         if image is None:
-            image = np.zeros(shape, dtype=np.float32)
-            self.shape = shape
+            image = np.zeros(self.config.shape, dtype=np.float32)
         else:
             image = image
-            self.shape = image.shape
+            self.config.shape = image.shape
 
         if src_cat is not None:
-            add_model_objects(image, src_cat.table, self.model)
-
-        super().__init__(image, False, False)
+            add_model_objects(image, src_cat.table, self.config.model)
 
+        self.base = image
         self._base_contains_data = image.sum() != 0
-        self.noise = noise
-        self.noise_std = noise_std
+
+    @classmethod
+    def add_noise(cls, n, images, config):
+        rng = np.random.default_rng(seed=config.seed)
+        shape = images.shape
+
+        # noise has to be resampled for every image
+        rng.standard_normal(size=shape, dtype=np.float32, out=images)
+
+        # There's a lot of multiplications that happen, skip if possible
+        if self.config.noise_std != 1.0:
+            images *= config.noise_std
+        images += config.noise
+
+        return images
 
     def mock(self, n=1, obj_cats=None, **kwargs):
-        shape = (n, *self.shape)
+        shape = (n, *self.config.shape)
         images = np.zeros(shape, dtype=np.float32)
 
-        if self.noise != 0:
-            rng = np.random.default_rng()
-            rng.standard_normal(size=shape, dtype=np.float32, out=images)
-            if self.noise_std != 1.0:
-                images *= self.noise_std
-            images += self.noise
+        if self.config.noise != 0:
+            images = self.gen_noise(n, images, self.config)
 
+        # but if base has no data (no sources, bad cols etc) skip
         if self._base_contains_data:
             images += self.base
 
+        # same with moving objects
         if obj_cats is not None:
             for i, (img, cat) in enumerate(zip(images, obj_cats)):
-                add_model_objects(img, cat, self.model(x_stddev=1, y_stddev=1))
+                add_model_objects(
+                    img,
+                    cat,
+                    self.config.model(x_stddev=1, y_stddev=1)
+                )
 
         return images
 
 
-class SimulatedImage(DataFactory):
+
+
+class SimulatedImageConfig(DataFactoryConfig):
+    # not sure this is a smart idea to put here
     rng = np.random.default_rng()
+
+    # image properties
+    shape = (100, 100)
+
+    # detector properties
     read_noise_gen = rng.normal
+    read_noise = 0
+    gain = 1.0
+    bias = 0.0
+
+    add_bad_cols = False
+    bad_cols_method = "random"
+    bad_col_locs = []  # for manual setting of cols
+    n_bad_cols = 5
+    bad_cols_seed = 123
+    bad_col_pattern_offset = 0.1
+
     dark_current_gen = rng.poisson
+    dark_current = 0
+
+    add_hot_pixels = False
+    hot_pix_method = "random"
+    hot_pix_locs = []
+    hot_pix_seed = 321
+    n_hot_pix = 10
+    hot_pix_offset = 1000
+
+    # Observation properties
+    exposure_time = 120.0
     sky_count_gen = rng.poisson
+    sky_level = 0
+
+    # Object and Source properties
+    model = models.Gaussian2D
 
-    def __add_bad_cols(self, image, cols, bias, n, seed, pattern_offset):
-        if not cols:
-            # most of the time I imagine we don't need bad cols
+
+class SimulatedImage(SimpleImage):
+    default_config = SimulatedImageConfig
+
+    @classmethod
+    def add_bad_cols(cls, image, config):
+        if not config.add_bad_cols:
             return image
 
-        if bad_columns == "random":
+        if config.bad_cols_method == "random":
             rng = np.random.RandomState(seed=self.bad_cols_seed)
-            bad_cols = rng.randint(0, shape[1], size=n)
+            bad_cols = rng.randint(0, shape[1], size=config.n_bad_cols)
+        elif config.bad_col_locs:
+            bad_cols = config.bad_col_locs
         else:
-            bad_cols = bad_columns
+            raise ConfigurationError("Bad columns method is not 'random', but `bad_col_locs` contains no bad column indices.")
+
+        self.col_pattern = rng.randint(
+            low=0,
+            high=int(config.bad_col_pattern_offset * config.bias),
+            size=shape[0]
+        )
 
-        self.col_pattern = rng.randint(0, int(pattern_offset * bias), size=shape[0])
         for col in columns:
-            image[:, col] = bias + col_pattern
+            image[:, col] = config.bias + col_pattern
 
         return image
 
-    def __add_hot_pixels(self, image, pixels, percent, offset):
-        if not pixels:
-            # most of the time I imagine we don't need hot pixels
+    @classmethod
+    def add_hot_pixels(cls, image, config):
+        if not config.add_hot_pixels:
             return image
 
-        if pixels == "random":
-            rng = np.random.RandomState(seed=self.hot_pixel_seed)
-            n_pixels = image.shape[0] * image.shape[1]
-            n = int(percent * n_pixels)
-            x = rng.randint(0, shape[1], size=n)
-            y = rng.randint(0, shape[0], size=n)
+        if config.hot_pix_method == "random":
+            rng = np.random.RandomState(seed=config.hot_pix_seed)
+            x = rng.randint(0, shape[1], size=config.n_hot_pix)
+            y = rng.randint(0, shape[0], size=config.n_hot_pix)
             hot_pixels = np.column_stack(x, y)
-        else:
+        elif config.hot_pix_locs:
             hot_pixels = pixels
+        else:
+            raise ConfigurationError("Hot pixels method is not 'random', but `hot_pix_locs` contains no (col, row) location indices of hot pixels.")
 
         for pix in hot_pixels:
             image[*pix] += image[*pix] * offset
 
         return image
 
-    def __init__(
-        self,
-        shape,
-        read_noise,
-        gain,
-        bias,
-        exposure_time,
-        dark_current,
-        sky_level,
-        bad_columns=False,
-        bad_cols_seed=321,
-        n_bad_cols=5,
-        bad_col_pattern_offset=0.1,
-        hot_pixels=False,
-        hot_pix_seed=543,
-        hot_percent=0.00001,
-        hot_offset=1000,
-    ):
-        super().__init__()
-
-        # starting empty image
-        self.base_img = np.zeros(shape)
+    @classmethod
+    def add_noise(cls, n, images, config):
+        shape = images.shape
 
         # add read noise
-        self.base_img += self.read_noise_gen(scale=read_noise / gain, size=shape)
-
-        # add bias
-        self.base_img += bias
-
-        # add bad columns
-        self.bad_cols_seed = bad_cols_seed
-        self.__add_bad_cols(self.base_image, bad_columns, bias, n_bad_cols, bad_cols_seed)
+        images += self.read_noise_gen(
+            scale=config.read_noise / config.gain,
+            size=shape
+        )
 
         # add dark current
-        current = dark_current * exposure_time / gain
-        self.base_img = self.dark_current_gen(current, size=shape)
-
-        # add hot pixels
-        self.hot_pixel_seed = hot_pix_seed
-        self.base_img = self.__add_hot_pixels(self.base_img, hot_pixels, hot_percent, hot_offset)
+        current = config.dark_current * config.exposure_time / config.gain
+        images += config.dark_current_gen(current, size=shape)
 
         # add sky counts
-        self.base_img += self.sky_count_gen(sky_level * gain, shape) / gain
+        images += self.sky_count_gen(
+            lam=config.sky_level * config.gain,
+            size=shape
+        ) / config.gain
 
-    def mock(self, hdu=None, **kwargs):
-        # we do always have to return a new copy here, since sci images
-        # are expected to be written on
-        return self.base_img.copy()
+        return images
+
+    @classmethod
+    def gen_base_image(cls, config=None):
+        config = cls.default_config(config)
+
+        # empty image
+        base = np.zeros(config.shape, dtype=np.float32)
+        base += config.bias
+        base = cls.add_bad_cols(base, config)
+        base = cls.add_hot_pixels(base, config)
+
+        return base
+
+    def __init__(self, image=None, config=None, src_cat=None):
+        conf = self.default_config(config)
+        base = self.gen_base_image(conf)
+        super().__init__(base, conf, src_cat)
+
+#    def mock(self, n=1, obj_cats=None, **kwargs):
+#        # we do always have to return a new copy here, since sci images
+#        # are expected to be written on
+#        return self.base_img.copy()

From 9ca7f8194006076199ea1cc1147067c43500d865 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Wed, 10 Apr 2024 10:33:30 -0700
Subject: [PATCH 13/27] Cleanup, add docs.

---
 src/kbmod/mocking/fits.py         |  23 +-
 src/kbmod/mocking/fits_data.py    | 473 ++++++++++++++++++++++++++----
 src/kbmod/mocking/test_mocking.py |  36 ---
 3 files changed, 430 insertions(+), 102 deletions(-)
 delete mode 100644 src/kbmod/mocking/test_mocking.py

diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index 9a2dbc37e..f47579461 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -4,12 +4,25 @@
 
 import numpy as np
 from astropy.wcs import WCS
-from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, BinTableHDU, Header
 from astropy.modeling import models
+from astropy.io.fits import (
+    HDUList,
+    PrimaryHDU,
+    CompImageHDU,
+    BinTableHDU,
+    Header
+)
 
 from .headers import HeaderFactory, ArchivedHeader
 from .catalogs import gen_catalog, SimpleSourceCatalog, SimpleObjectCatalog
-from .fits_data import ZeroedData, SimpleImage, SimpleVariance, SimpleMask, add_model_objects
+from .fits_data import (
+    DataFactory,
+    ZeroedData,
+    SimpleImage,
+    SimpleVariance,
+    SimpleMask,
+    add_model_objects
+)
 
 
 __all__ = [
@@ -175,10 +188,10 @@ def __init__(self, shape, noise=0, noise_std=1, dt=0.001, source_catalog=None, o
             shape=shape,
             noise=noise,
             noise_std=noise_std,
-            catalog=self.src_cat
+            src_cat=self.src_cat,
         )
         self.var_data = SimpleVariance(self.img_data.base, read_noise=noise, gain=1.0)
-        self.mask_data = ZeroedData(np.zeros(shape))
+        self.mask_data = DataFactory(np.zeros(shape))
 
         # Now we can build the HDU map and the HDUList layout
         layout = [
@@ -207,7 +220,7 @@ def mock(self, n=1):
         if self.obj_cat is not None:
             obj_cats = self.obj_cat.mock(n, dt=self.dt)
 
-        images = self.img_data.mock(n, obj_cats)
+        images = self.img_data.mock(n, obj_cats=obj_cats)
         variances = self.var_data.mock(images=images)
         mask = self.mask_data.mock()
         imghdr, varhdr, maskhdr = self.image.mock(), self.variance.mock(), self.mask.mock()
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index fde6fa686..ffd8d79fb 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -21,6 +21,23 @@
 
 
 def add_model_objects(img, catalog, model):
+    """Adds a catalog of model objects to the image.
+
+    Parameters
+    ----------
+    img : `np.array`
+        Image.
+    catalog : `astropy.table.QTable`
+        Table of objects, a catalog
+    model : `astropy.modelling.Model`
+        Astropy's model of the surface brightness of an source. Must contain
+        at least ``x_mean`` and ``y_mean``.
+
+    Returns
+    -------
+    img: `np.array`
+        Image including the rendenred models.
+    """
     shape = img.shape
     yidx, xidx = np.indices(shape)
 
@@ -53,44 +70,69 @@ def add_model_objects(img, catalog, model):
     return img
 
 
-
-
 class DataFactoryConfig(Config):
-    writeable = False
-    """Sets the base array writeable flag."""
-
-    copy_base = False
-    """
-    When `True`, a copy of the base data object is passed into the generate
-    method, otherwise, the original (possibly mutable!) base data object is
-    given.
+    """Data factory configuration primarily controls mutability of the given
+    and returned mocked datasets.
     """
 
-    copy_mocked = False
-    """
-    When `True`, the `DataFactory.mock` returns a copy of the final object,
-    otherwise the original (possibly mutable!) object is returned.
-    """
+    writeable = False
+    """Sets the base array ``writeable`` flag. Default `False`."""
 
     return_copy = False
-
-
-    isStatic = False
     """
-    When `False` the `DataFactory.mock` will generate new data every time
-    it is called. Otherwise it will memoize the result of the first time
-    it's called and return that result directly or as a copy.
+    When `True`, the `DataFactory.mock` returns a copy of the final object,
+    otherwise the original (possibly mutable!) object is returned. Default `False`.
     """
 
 
 class DataFactory:
+    """Generic data factory.
+
+    Data can consists of tabular or array-like data of a single or compound
+    types. Generally the process of creation of a mocked data is relatively
+    simple:
+    1) pre-generate whatever shared common base data is shared by all mocked
+       data
+    2) mock one or many instances of dynamic portions of the data and paste
+       them onto the base static data.
+
+    The base data can be writable or non-writable to prevent accidentally
+    mutating its value. Some data factories consist of only the simple base
+    data. In the case it is desired that the mocked data is further modified
+    it is possible to return a copy of that base data, so that the base data is
+    not mutated. By default it's assumed that all mocked data are allowed to
+    share a single, non-mutable, base data.
+
+    This saves memory and improves performance as, for example, there is no
+    need to mock and allocate N (dimx, dimy) arrays for a shared static masking
+    array shared by all associated images. Therefore the default implementation
+    of mocking is to just continuously return the given base data.
+
+    For dynamically created data, the data may depend on the type of the
+    Header Data Unit (HDU), or dynamically changing header data. Data generated
+    in these scenarios behaves as ``return_copy`` is always `True` regardless
+    of the configuration value.
+
+    Attributes
+    ----------
+    counter : `int`
+        Data factory tracks an internal counter of generated objects that can
+        be used as a ticker for generating new data.
+
+    Parameters
+    ----------
+    base : `np.array`
+        Static data shared by all mocked instances.
+    config : `DataFactoryConfig`
+        Configuration of the data factory.
+    **kwargs :
+        Additional keyword arguments are applied as configuration
+        overrides.
+    """
     default_config = DataFactoryConfig
+    """Default configuration."""
 
     def __init__(self, base=None, config=None, **kwargs):
-        # not sure if this is "best" "best" way, but it does safe a lot of
-        # array copies if we don't have to write to the mocked array
-        # (which we shouldn't?). To be safe we set the writable flag to False
-        # by default
         self.config = self.default_config()
         self.config.update(config, **kwargs)
 
@@ -99,20 +141,54 @@ def __init__(self, base=None, config=None, **kwargs):
             self.base.flags.writeable = self.config.writeable
         self.counter = 0
 
-    def mock(self, hdu=None, **kwargs):
+    def mock(self, **kwargs):
+        """Return a mocked fits data and increase counter.
+
+        Parameters
+        ----------
+        **kwargs
+           Any additional keyword arguments are ignored.
+
+        Returns
+        -------
+        data : `no.array`
+            Mocked data array
+        """
         self.counter += 1
         if self.config.return_copy:
             return self.base.copy()
         return self.base
 
 
-
 class SimpleVarianceConfig(DataFactoryConfig):
+    """Configure noise and gain of a simple variance factory."""
+
     read_noise = 0.0
+    """Read noise"""
+
     gain = 1.0
-    calculate_base = True
+    "Gain."
+
 
 class SimpleVariance(DataFactory):
+    """Simple variance factory.
+
+    Variance is calculated as the
+
+        variance = image/gain + read_noise^2
+
+    thus variance has to be calculated for each individual mocked image.
+
+    Parameters
+    ----------
+    image : `np.array`
+        Science image from which the variance will be derived from.
+    config : `DataFactoryConfig`
+        Configuration of the data factory.
+    **kwargs :
+        Additional keyword arguments are applied as config
+        overrides.
+    """
     default_config = SimpleVarianceConfig
 
     def __init__(self, image=None, config=None, **kwargs):
@@ -130,15 +206,73 @@ def mock(self, images=None):
 
 
 class SimpleMaskConfig(DataFactoryConfig):
+    """Simple mask configuration."""
     pass
 
 class SimpleMask(DataFactory):
+    """Simple mask factory.
+
+    Masks are assumed to be shared, static data. To create an instance of this
+    factory use one of the provided class methods. Created mask will correspond
+    to a bitmask already appropriate for use with KBMOD.
+
+    Parameters
+    ----------
+    mask : `np.array`
+        Bitmask array.
+    """
     default_config = SimpleMaskConfig
-    def __init__(self, mask, config=None, **kwargs):
-        super().__init__(base=mask, config=config, **kwargs)
+
+    def __init__(self, mask):
+        super().__init__(base=mask)
 
     @classmethod
-    def from_params(cls, shape, padding=0, bad_columns=[]):
+    def from_params(cls, shape, padding=0, bad_columns=[], patches=[]):
+        """Create a mask by adding a padding around the edges of the array with
+        the given dimensions and mask out bad columns.
+
+        Parameters
+        ----------
+        shape : `tuple`
+            Tuple of (width, height)/(cols, rows) dimensions of the mask.
+        padding : `int`
+            Number of pixels near the edge that will be masked.
+        bad_columns : `list[int]`
+            Indices of bad columns to mask
+        patches : `list[tuple]`
+            Patches to mask. This is a list of tuples. Each tuple consists of
+            a patch and a value. The patch can be any combination of array
+            coordinates such as ``(int, int)`` for individual pixels,
+           ``(slice, int)`` or ``(int, slice)`` for columns and rows
+           respectively  or ``(slice, slice)`` for areas.
+
+        Returns
+        -------
+        mask_factory : `SimpleMask`
+            Mask factory.
+
+        Examples
+        --------
+        >>> SimpleMask.from_params(
+                shape=(10, 10),
+                padding=1,
+                bad_columns=[2, 3],
+                patches=[
+                    ((5, 5), 2),
+                    ((slice(6, 8), slice(6, 8)), 3)
+                ]
+            )
+        array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
+               [1., 0., 1., 1., 0., 0., 0., 0., 0., 1.],
+               [1., 0., 1., 1., 0., 0., 0., 0., 0., 1.],
+               [1., 0., 1., 1., 0., 0., 0., 0., 0., 1.],
+               [1., 0., 1., 1., 0., 0., 0., 0., 0., 1.],
+               [1., 0., 1., 1., 0., 1., 0., 0., 0., 1.],
+               [1., 0., 1., 1., 0., 0., 1., 1., 0., 1.],
+               [1., 0., 1., 1., 0., 0., 1., 1., 0., 1.],
+               [1., 0., 1., 1., 0., 0., 0., 0., 0., 1.],
+               [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])
+        """
         mask = np.zeros(shape)
 
         # padding
@@ -151,17 +285,11 @@ def from_params(cls, shape, padding=0, bad_columns=[]):
         for col in bad_columns:
             mask[:, col] = 1
 
-        return cls(mask)
-
-    # bro I don't know antymore
-    @classmethod
-    def from_patches(cls, shape, patches):
-        mask = np.zeros(shape)
         for patch, value in patches:
             if isinstance(patch, tuple):
-                mask[*patch] = value
+                mask[*patch] = 1
             elif isinstance(slice):
-                mask[slice] = value
+                mask[slice] = 1
             else:
                 raise ValueError(f"Expected a tuple (x, y), (slice, slice) or slice, got {patch} instead.")
 
@@ -169,6 +297,9 @@ def from_patches(cls, shape, patches):
 
 
 class ZeroedDataConfig(DataFactoryConfig):
+    """Zeroed data config."""
+    return_copy = True
+
     shape = (5, 5)
     """Default image size."""
 
@@ -187,7 +318,23 @@ class ZeroedDataConfig(DataFactoryConfig):
     }
     """Map between FITS header BITPIX keyword value and NumPy return type."""
 
+
 class ZeroedData(DataFactory):
+    """Data factory that creates zeroed data arrays from header definitions.
+
+    A convenience factory able to generate the correct size image and bintable
+    data from a header.
+
+    Parameters
+    ----------
+    base : `np.array`
+        Static data shared by all mocked instances.
+    config : `DataFactoryConfig`
+        Configuration of the data factory.
+    **kwargs :
+        Additional keyword arguments are applied as config
+        overrides.
+    """
     default_config = ZeroedDataConfig
 
     def __init__(self, base=None, config=None, **kwargs):
@@ -216,7 +363,7 @@ def mock(self, hdu=None, **kwargs):
         # factory set at init time or b) dynamic data generated from a header
         # specification at call-time.
         if self.base is not None:
-            return super().mock(hdu, **kwargs)
+            return super().mock(hdu=hdu, **kwargs)
         if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
             return self.mock_image_data(hdu)
         elif isinstance(hdu, (TableHDU, BinTableHDU)):
@@ -225,19 +372,57 @@ def mock(self, hdu=None, **kwargs):
             raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
 
 
-
 class SimpleImageConfig(DataFactoryConfig):
+    """Simple image configuration."""
+
+    return_copy = True
+
     shape = (100, 100)
+    """Dimensions of the generated images."""
+
+    add_noise = False
+    """Add noise to the base image."""
+
     seed = None
+    """Seed of the random number generator used to generate noise."""
+
     noise = 0
+    """Mean of the standard Gaussian distribution of the noise."""
+
     noise_std = 1.0
+    """Standard deviation of the Gaussian distribution of the noise."""
+
     model = models.Gaussian2D
+    """Source and object model used to render them on the image."""
 
 
 class SimpleImage(DataFactory):
+    """Simple image data factory.
+
+    Simple image consists of an blank empty base, onto which noise, sources
+    and objects can be added. All returned images act as a copy of the base
+    image.
+
+    Noise realization is drawn from a Gaussian distribution with the given
+    standard deviation and centered on the given mean.
+
+    Parameters
+    ----------
+    image : `np.array`
+        Science image that will be used as a base onto which to render details.
+    config : `SimpleImageConfig`
+        Configuration.
+    src_cat : `CatalogFactory`
+        Static source catalog.
+    obj_cat : `CatalogFactory`
+        Moving object catalog factory.
+    **kwargs :
+        Additional keyword arguments are applied as config
+        overrides.
+    """
     default_config = SimpleImageConfig
 
-    def __init__(self, image=None, config=None, src_cat=None, **kwargs):
+    def __init__(self, image=None, config=None, src_cat=None, obj_cat=None, **kwargs):
         super().__init__(image, config, **kwargs)
 
         if image is None:
@@ -246,19 +431,29 @@ def __init__(self, image=None, config=None, src_cat=None, **kwargs):
             image = image
             self.config.shape = image.shape
 
-        if src_cat is not None:
+        self.src_cat = src_cat
+        if self.src_cat is not None:
             add_model_objects(image, src_cat.table, self.config.model)
 
         self.base = image
         self._base_contains_data = image.sum() != 0
 
     @classmethod
-    def add_noise(cls, n, images, config):
+    def add_noise(cls, images, config):
+        """Adds gaussian noise to the images.
+
+        Parameters
+        ----------
+        images : `np.array`
+           A ``(n_images, image_width, image_height)`` shaped array of images.
+        config : `SimpleImageConfig`
+           Configuration.
+        """
         rng = np.random.default_rng(seed=config.seed)
         shape = images.shape
 
         # noise has to be resampled for every image
-        rng.standard_normal(size=shape, dtype=np.float32, out=images)
+        rng.standard_normal(size=shape, dtype=images.dtype, out=images)
 
         # There's a lot of multiplications that happen, skip if possible
         if self.config.noise_std != 1.0:
@@ -268,11 +463,21 @@ def add_noise(cls, n, images, config):
         return images
 
     def mock(self, n=1, obj_cats=None, **kwargs):
+        """Creates a set of images.
+
+        Parameters
+        ----------
+        n : `int`, optional
+            Number of images to create, default: 1.
+        obj_cats : `list[Catalog]`
+            A list of catalogs as long as the number of requested images of
+            moving objects that will be inserted into the image.
+        """
         shape = (n, *self.config.shape)
         images = np.zeros(shape, dtype=np.float32)
 
         if self.config.noise != 0:
-            images = self.gen_noise(n, images, self.config)
+            images = self.add_noise(n, images, self.config)
 
         # but if base has no data (no sources, bad cols etc) skip
         if self._base_contains_data:
@@ -290,52 +495,155 @@ def mock(self, n=1, obj_cats=None, **kwargs):
         return images
 
 
+class SimulatedImageConfig(DataFactoryConfig):
+    """Simulated image configuration.
 
+    Simulated image add several sources of noise into the image, bad columns,
+    hot pixels, including the possibility of adding static and moving sources.
+    On a higher level it is possible to modify this behavior by setting:
+    - add_noise
+    - add_bad_cols
+    - add_hot_pix
+    parameters to `False`. Optionally, for a more fine-grained control set the
+    distribution parameters, f.e. mean and standard deviation, such that they
+    do not produce measurable values in the image.
+
+    The quantities are expressed in physical units and the defaults were
+    selected to sort of make sense. Sky levels are not modeled very
+    realistically, being expressed as an additional level of noise, in counts,
+    additive to the detector noise.
+
+    Generally expect the mean value of pixel counts to be:
+
+        bias + mean(dark_current)*exposure + mean(sky_level)
+
+    The deviation of the pixel counts around the expected value can be
+    estimated by:
+
+        sqrt( std(read_noise)^2 + sqrt(sky_level)^2 )
+    """
 
-class SimulatedImageConfig(DataFactoryConfig):
     # not sure this is a smart idea to put here
     rng = np.random.default_rng()
 
     # image properties
     shape = (100, 100)
+    """Dimensions of the created images."""
 
     # detector properties
+    add_noise = True
+    """Add noise (includes read noise, dark current and sky) to the image."""
+
     read_noise_gen = rng.normal
-    read_noise = 0
+    """Read noise follows a Gaussian distribution."""
+
+    read_noise = 5
+    """Standard deviation of read noise distribution, in electrons."""
+
     gain = 1.0
+    """Gain in electrons/count."""
+
     bias = 0.0
+    """Bias in counts."""
 
     add_bad_cols = False
+    """Add bad columns to the image."""
+
     bad_cols_method = "random"
-    bad_col_locs = []  # for manual setting of cols
+    """Method by which bad columns are picked. If not 'random', 'bad col_locs'
+    must be provided."""
+
+    bad_col_locs = []
+    """Location, column indices, of bad columns."""
+
     n_bad_cols = 5
+    """When bad columns method is random, sets the number of bad columns."""
+
     bad_cols_seed = 123
+    """Seed for the bad columns random number generator."""
+
     bad_col_pattern_offset = 0.1
+    """Random-looking noise variation along the length of the bad columns is
+    offset from the base column counts by this amount multiplied by bias."""
 
     dark_current_gen = rng.poisson
-    dark_current = 0
+    """Dark current follows a Poisson distribution."""
+
+    dark_current = 0.1
+    """Dark current mean in electrons/pixel/sec. Typically ~0.1-0.2."""
 
     add_hot_pixels = False
+    """Simulate hot pixels."""
+
     hot_pix_method = "random"
+    """When `random` the hop pixels are selected randomly, otherwise their
+    indices must be provided."""
+
     hot_pix_locs = []
+    """A `list[tuple]` of hot pixel indices."""
+
     hot_pix_seed = 321
+    """Seed for hot pixel random number generator."""
+
     n_hot_pix = 10
-    hot_pix_offset = 1000
+    """Number of hot pixels to insert into the image."""
+
+    hot_pix_offset = 10000
+    """Offset of hot pixel counts from the baseline. Usally a very large number."""
 
     # Observation properties
     exposure_time = 120.0
+    """Exposure time of the simulated image, affects noise properties."""
+
     sky_count_gen = rng.poisson
-    sky_level = 0
+    """Sky background random number generator."""
+
+    sky_level = 20
+    """Sky level, in counts."""
 
     # Object and Source properties
     model = models.Gaussian2D
 
 
 class SimulatedImage(SimpleImage):
+    """Simulated image includes multiple sources of noise, bad columns, hot
+    pixels, static sources and moving objects.
+
+    Parameters
+    ----------
+    image : `np.array`
+        Science image that will be used as a base onto which to render details.
+    config : `SimpleImageConfig`
+        Configuration.
+    src_cat : `CatalogFactory`
+        Static source catalog.
+    obj_cat : `CatalogFactory`
+        Moving object catalog factory.
+    **kwargs :
+        Additional keyword arguments are applied as config
+        overrides.
+    """
     default_config = SimulatedImageConfig
 
     @classmethod
     def add_bad_cols(cls, image, config):
+        """Adds bad columns to the image based on the configuration.
+
+        Columns can be sampled randomly, or a list of bad column indices can
+        be provided.
+
+        Parameters
+        ----------
+        image : `np.array`
+            Image.
+        config : `SimpleImageConfig`
+            Configuration.
+
+        Returns
+        -------
+        image : `np.array`
+            Image.
+        """
         if not config.add_bad_cols:
             return image
 
@@ -360,6 +668,23 @@ def add_bad_cols(cls, image, config):
 
     @classmethod
     def add_hot_pixels(cls, image, config):
+        """Adds hot pixels to the image based on the configuration.
+
+        Indices of hot pixels can be sampled randomly, or a list of hot pixel
+        indices can be provided.
+
+        Parameters
+        ----------
+        image : `np.array`
+            Image.
+        config : `SimpleImageConfig`
+            Configuration.
+
+        Returns
+        -------
+        image : `np.array`
+            Image.
+        """
         if not config.add_hot_pixels:
             return image
 
@@ -374,12 +699,27 @@ def add_hot_pixels(cls, image, config):
             raise ConfigurationError("Hot pixels method is not 'random', but `hot_pix_locs` contains no (col, row) location indices of hot pixels.")
 
         for pix in hot_pixels:
-            image[*pix] += image[*pix] * offset
+            image[*pix] += offset
 
         return image
 
     @classmethod
     def add_noise(cls, n, images, config):
+        """Adds read noise (gaussian), dark noise (poissonian) and sky
+        background (poissonian) noise to the image.
+
+        Parameters
+        ----------
+        image : `np.array`
+            Image.
+        config : `SimpleImageConfig`
+            Configuration.
+
+        Returns
+        -------
+        image : `np.array`
+            Image.
+        """
         shape = images.shape
 
         # add read noise
@@ -401,7 +741,21 @@ def add_noise(cls, n, images, config):
         return images
 
     @classmethod
-    def gen_base_image(cls, config=None):
+    def gen_base_image(cls, config=None, src_cat=None):
+        """Generate base image from configuration.
+
+        Parameters
+        ----------
+        config : `SimpleImageConfig`
+            Configuration.
+        src_cat : `CatalogFactory`
+            Static source catalog.
+
+        Returns
+        -------
+        image : `np.array`
+            Image.
+        """
         config = cls.default_config(config)
 
         # empty image
@@ -409,15 +763,12 @@ def gen_base_image(cls, config=None):
         base += config.bias
         base = cls.add_bad_cols(base, config)
         base = cls.add_hot_pixels(base, config)
+        if src_cat is not None:
+            add_model_objects(base, src_cat.table, config.model)
 
         return base
 
-    def __init__(self, image=None, config=None, src_cat=None):
+    def __init__(self, image=None, config=None, src_cat=None, obj_cat=None):
         conf = self.default_config(config)
         base = self.gen_base_image(conf)
-        super().__init__(base, conf, src_cat)
-
-#    def mock(self, n=1, obj_cats=None, **kwargs):
-#        # we do always have to return a new copy here, since sci images
-#        # are expected to be written on
-#        return self.base_img.copy()
+        super().__init__(image=base, config=conf, src_cat=src_cat, obj_cat=obj_cat)
diff --git a/src/kbmod/mocking/test_mocking.py b/src/kbmod/mocking/test_mocking.py
deleted file mode 100644
index 43e13557d..000000000
--- a/src/kbmod/mocking/test_mocking.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import timeit
-
-from .fits import EmptyFits, SimpleFits, DECamImdiffs
-from kbmod import ImageCollection
-import matplotlib.pyplot as plt
-
-start = timeit.default_timer()
-fits_files = DECamImdiffs().mock(50)
-print(f"DECam Imdiff Fits: {timeit.default_timer() - start} seconds")
-del fits_files
-
-start = timeit.default_timer()
-fits_files = EmptyFits().mock(50)
-print(f"EmptyFits: {timeit.default_timer() - start} seconds")
-del fits_files
-
-start = timeit.default_timer()
-fits_files = SimpleFits.from_defaults(
-    shape=(1000, 1000),
-    add_static_sources=False,
-    add_moving_objects=True,
-    noise=100,
-    noise_std=20
-).mock(50)
-print(f"SimpleFits: {timeit.default_timer() - start} seconds")
-
-
-start = timeit.default_timer()
-ic = ImageCollection.fromTargets(fits_files, force="TestDataStd")
-print(f"Image collection took: {timeit.default_timer() - start} seconds")
-print(ic)
-
-_ = [plt.imsave(f"/home/dino/Downloads/tmp_fits/{i}.png", h[1].data, vmin=50, vmax=500) for i, h in enumerate(fits_files)]
-
-
-breakpoint()

From baf5ada075233f3d64639b2d913f4178247dedf7 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Thu, 11 Apr 2024 12:00:36 -0700
Subject: [PATCH 14/27] Remove test_mocking script.

---
 src/kbmod/mocking/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/kbmod/mocking/__init__.py b/src/kbmod/mocking/__init__.py
index 853896433..d678d95d4 100644
--- a/src/kbmod/mocking/__init__.py
+++ b/src/kbmod/mocking/__init__.py
@@ -3,4 +3,4 @@
 from .headers import *
 from .fits_data import *
 from .fits import *
-from . import test_mocking
+#from . import test_mocking

From a2fd87b9fca37130ab5004e615fe85f58247df76 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Tue, 23 Apr 2024 16:01:30 -0700
Subject: [PATCH 15/27] Fixup the mocking code.

---
 src/kbmod/mocking/__init__.py  |   1 -
 src/kbmod/mocking/catalogs.py  | 184 ++++++++----
 src/kbmod/mocking/config.py    |  99 ++++++-
 src/kbmod/mocking/fits.py      | 514 +++++++++++++++++++++++----------
 src/kbmod/mocking/fits_data.py | 361 ++++++++++++++---------
 src/kbmod/mocking/headers.py   | 148 +++++++---
 6 files changed, 905 insertions(+), 402 deletions(-)

diff --git a/src/kbmod/mocking/__init__.py b/src/kbmod/mocking/__init__.py
index d678d95d4..b79086419 100644
--- a/src/kbmod/mocking/__init__.py
+++ b/src/kbmod/mocking/__init__.py
@@ -3,4 +3,3 @@
 from .headers import *
 from .fits_data import *
 from .fits import *
-#from . import test_mocking
diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 06fa5dadd..6e4fc853f 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -1,15 +1,18 @@
 import abc
 
 import numpy as np
-from astropy.time import Time
-from astropy.table import QTable, vstack
+from astropy.table import QTable
+from .config import Config
 
 
 __all__ = [
     "gen_catalog",
     "CatalogFactory",
-    "SimpleSourceCatalog",
-    "SimpleObjectCatalog",
+    "SimpleCatalog",
+    "SourceCatalogConfig",
+    "SourceCatalog",
+    "ObjectCatalogConfig",
+    "ObjectCatalog",
 ]
 
 
@@ -26,84 +29,161 @@ def gen_catalog(n, param_ranges, seed=None):
 
     # conversion assumes a gaussian
     if "flux" in param_ranges and "amplitude" not in param_ranges:
-        xstd = cat["x_stddev"] if "x_stddev" in cat.colnames else 1
-        ystd = cat["y_stddev"] if "y_stddev" in cat.colnames else 1
+        xstd = cat["x_stddev"] if "x_stddev" in cat.colnames else 1.0
+        ystd = cat["y_stddev"] if "y_stddev" in cat.colnames else 1.0
 
         cat["amplitude"] = cat["flux"] / (2.0 * np.pi * xstd * ystd)
 
     return cat
 
 
-
 class CatalogFactory(abc.ABC):
     @abc.abstractmethod
-    def gen_realization(self, *args, t=None, dt=None, **kwargs):
+    def mock(self, *args, **kwargs):
         raise NotImplementedError()
 
-    def mock(self, *args, **kwargs):
-        return self.gen_realization(self, *args, **kwargs)
 
+class SimpleCatalogConfig(Config):
+    return_copy = False
+    seed = None
+    n = 100
+    param_ranges = {}
+
+
+class SimpleCatalog(CatalogFactory):
+    default_config = SimpleCatalogConfig
+
+    def __init_from_table(self, table, config=None, **kwargs):
+        config = self.default_config(config=config, **kwargs)
+        config.n = len(table)
+        params = {}
+        for col in table.keys():
+            params[col] = (table[col].min(), table[col].max())
+        config.param_ranges.update(params)
+        return config, table
+
+    def __init_from_config(self, config, **kwargs):
+        config = self.default_config(config=config, method="subset", **kwargs)
+        table = gen_catalog(config.n, config.param_ranges, config.seed)
+        return config, table
+
+    def __init_from_ranges(self, **kwargs):
+        param_ranges = kwargs.pop("param_ranges", None)
+        if param_ranges is None:
+            param_ranges = {k: v for k, v in kwargs.items() if k in self.default_config.param_ranges}
+            kwargs = {k: v for k, v in kwargs.items() if k not in self.default_config.param_ranges}
+
+        config = self.default_config(**kwargs, method="subset")
+        config.param_ranges.update(param_ranges)
+        return self.__init_from_config(config=config)
+
+    def __init__(self, table=None, config=None, **kwargs):
+        if table is not None:
+            config, table = self.__init_from_table(table, config=config, **kwargs)
+        elif isinstance(config, Config):
+            config, table = self.__init_from_config(config=config, **kwargs)
+        elif isinstance(config, dict) or kwargs:
+            config = {} if config is None else config
+            config, table = self.__init_from_ranges(**{**config, **kwargs})
+        else:
+            raise ValueError(
+                "Expected table or config, or keyword arguments of expected "
+                f"catalog value ranges, got:\n table={table}\n config={config} "
+                f"\n kwargs={kwargs}"
+            )
+
+        self.config = config
+        self.table = table
+        self.current = 0
 
-class SimpleSourceCatalog(CatalogFactory):
-    base_param_ranges = {
-        "amplitude": [500, 2000],
-        "x_mean": [0, 4096],
-        "y_mean": [0, 2048],
-        "x_stddev": [1, 7],
-        "y_stddev": [1, 7],
-        "theta": [0, np.pi],
-    }
+    @classmethod
+    def from_config(cls, config, **kwargs):
+        config = cls.default_config(config=config, method="subset", **kwargs)
+        return cls(gen_catalog(config.n, config.param_ranges, config.seed), config=config)
 
-    def __init__(self, table, return_copy=False):
-        self.table = table
-        self.return_copy = return_copy
+    @classmethod
+    def from_ranges(cls, n=None, config=None, **kwargs):
+        config = cls.default_config(n=n, config=config, method="subset")
+        config.param_ranges.update(**kwargs)
+        return cls.from_config(config)
 
     @classmethod
-    def from_params(cls, n=100, param_ranges=None):
-        param_ranges = {} if param_ranges is None else param_ranges
-        tmp = cls.base_param_ranges.copy()
-        tmp.update(param_ranges)
-        return cls(gen_catalog(n, tmp))
-
-    def gen_realization(self, *args, t=None, dt=None, **kwargs):
-        if self.return_copy:
+    def from_table(cls, table):
+        config = cls.default_config()
+        config.n = len(table)
+        params = {}
+        for col in table.keys():
+            params[col] = (table[col].min(), table[col].max())
+        config["param_ranges"] = params
+        return cls(table, config=config)
+
+    def mock(self):
+        self.current += 1
+        if self.config.return_copy:
             return self.table.copy()
         return self.table
 
 
-class SimpleObjectCatalog(CatalogFactory):
-    base_param_ranges = {
-        "amplitude": [1, 100],
-        "x_mean": [0, 4096],
-        "y_mean": [0, 2048],
-        "vx": [500, 1000],
-        "vy": [500, 1000],
-        "stddev": [1, 1.8],
-        "theta": [0, np.pi],
+class SourceCatalogConfig(SimpleCatalogConfig):
+    param_ranges = {
+        "amplitude": [1., 10.],
+        "x_mean": [0., 4096.],
+        "y_mean": [0., 2048.],
+        "x_stddev": [1., 3.],
+        "y_stddev": [1., 3.],
+        "theta": [0., np.pi],
     }
 
-    def __init__(self, table, obstime=None):
-        self.table = table
-        self._realization = table.copy()
+
+class SourceCatalog(SimpleCatalog):
+    default_config = SourceCatalogConfig
+
+
+class ObjectCatalogConfig(SimpleCatalogConfig):
+    param_ranges = {
+        "amplitude": [0.1, 3.0],
+        "x_mean": [0., 4096.],
+        "y_mean": [0., 2048.],
+        "vx": [500., 1000.],
+        "vy": [500., 1000.],
+        "stddev": [0.25, 1.5],
+        "theta": [0., np.pi],
+    }
+
+
+class ObjectCatalog(SimpleCatalog):
+    default_config = ObjectCatalogConfig
+
+    def __init__(self, table=None, obstime=None, config=None, **kwargs):
+        # put return_copy into kwargs to override whatever user might have
+        # supplied, and to guarantee the default is overriden
+        kwargs["return_copy"] = True
+        super().__init__(table=table, config=config, **kwargs)
+        self._realization = self.table.copy()
         self.obstime = 0 if obstime is None else obstime
 
-    @classmethod
-    def from_params(cls, n=100, param_ranges=None):
-        param_ranges = {} if param_ranges is None else param_ranges
-        tmp = cls.base_param_ranges.copy()
-        tmp.update(param_ranges)
-        return cls(gen_catalog(n, tmp))
+    def reset(self):
+        self.current = 0
+        self._realization = self.table.copy()
 
     def gen_realization(self, t=None, dt=None, **kwargs):
         if t is None and dt is None:
             return self._realization
 
         dt = dt if t is None else t - self.obstime
-        self._realization["x_mean"] += self._realization["vx"] * dt
-        self._realization["y_mean"] += self._realization["vy"] * dt
+        self._realization["x_mean"] += self.table["vx"] * dt
+        self._realization["y_mean"] += self.table["vy"] * dt
         return self._realization
 
     def mock(self, n=1, **kwargs):
+        breakpoint()
         if n == 1:
-            return self.gen_realization(**kwargs)
-        return [self.gen_realization(**kwargs).copy() for i in range(n)]
+            data = self.gen_realization(**kwargs)
+            self.current += 1
+        else:
+            data = []
+            for i in range(n):
+                data.append(self.gen_realization(**kwargs).copy())
+                self.current += 1
+
+        return data
diff --git a/src/kbmod/mocking/config.py b/src/kbmod/mocking/config.py
index 0bb94a8ac..ed34aa649 100644
--- a/src/kbmod/mocking/config.py
+++ b/src/kbmod/mocking/config.py
@@ -1,3 +1,5 @@
+import copy
+
 __all__ = ["Config", "ConfigurationError"]
 
 
@@ -22,37 +24,66 @@ class attributes. Particular attributes can be overriden on an per-instance
         Keyword arguments, assigned as configuration key-values.
     """
 
-    def __init__(self, config=None, **kwargs):
+    def __init__(self, config=None, method="default", **kwargs):
         # This is a bit hacky, but it makes life a lot easier because it
         # enables automatic loading of the default configuration and separation
         # of default config from instance bound config
         keys = list(set(dir(self.__class__)) - set(dir(Config)))
 
         # First fill out all the defaults by copying cls attrs
-        self._conf = {k: getattr(self, k) for k in keys}
+        self._conf = {k: copy.copy(getattr(self, k)) for k in keys}
 
         # Then override with any user-specified values
-        conf = config
-        if isinstance(config, Config):
-            conf = config._conf
+        self.update(config=config, method=method, **kwargs)
 
-        if conf is not None:
-            self._conf.update(config)
-        self._conf.update(kwargs)
+    @classmethod
+    def from_configs(cls, *args):
+        config = cls()
+        for conf in args:
+            config.update(config=conf, method="extend")
+        return config
 
-    # now just shortcut the most common dict operations
     def __getitem__(self, key):
         return self._conf[key]
 
+    # now just shortcut the most common dict operations
+    def __getattribute__(self, key):
+        hasconf = "_conf" in object.__getattribute__(self, "__dict__")
+        if hasconf:
+            conf = object.__getattribute__(self, "_conf")
+            if key in conf:
+                return conf[key]
+        return object.__getattribute__(self, key)
+
     def __setitem__(self, key, value):
         self._conf[key] = value
 
+    def __repr__(self):
+        res = f"{self.__class__.__name__}("
+        for k, v in self.items():
+            res += f"{k}: {v}, "
+        return res[:-2] + ")"
+
     def __str__(self):
         res = f"{self.__class__.__name__}("
         for k, v in self.items():
             res += f"{k}: {v}, "
         return res[:-2] + ")"
 
+    def _repr_html_(self):
+        repr = f"""
+        <table style='tr:nth-child(even){{background-color: #dddddd;}};'>
+        <caption>{self.__class__.__name__}</caption>
+          <tr>
+            <th>Key</th>
+            <th>Value</th>
+          </tr>
+        """
+        for k, v in self.items():
+            repr += f"<tr><td>{k}</td><td>{v}\n"
+        repr += "</table>"
+        return repr
+
     def __len__(self):
         return len(self._conf)
 
@@ -76,7 +107,7 @@ def __or__(self, other):
         elif isinstance(other, dict):
             return self.__class__(config=self._conf | other)
         else:
-            raise TypeError("unsupported operand type(s) for |: {type(self)} " "and {type(other)}")
+            raise TypeError("unsupported operand type(s) for |: {type(self)}and {type(other)}")
 
     def keys(self):
         """A set-like object providing a view on config's keys."""
@@ -90,7 +121,10 @@ def items(self):
         """A set-like object providing a view on config's items."""
         return self._conf.items()
 
-    def update(self, conf=None, **kwargs):
+    def copy(self):
+        return self.__class__(config=self._conf.copy())
+
+    def update(self, config=None, method="default", **kwargs):
         """Update this config from dict/other config/iterable and
         apply any explicit keyword overrides.
 
@@ -107,9 +141,46 @@ def update(self, conf=None, **kwargs):
 
             for k in kwargs:  this[k] = kwargs[k]
         """
-        if conf is not None:
-            self._conf.update(conf)
-        self._conf.update(kwargs)
+        # Python < 3.9 does not support set operations for dicts
+        # [fixme]: Update this to: other = conf | kwargs
+        # and remove current implementation when 3.9 gets too old. Order of
+        # conf and kwargs matter to correctly apply explicit overrides
+
+        # Check if both conf and kwargs are given, just conf or just
+        # kwargs. If none are given do nothing to comply with default
+        # dict behavior
+        if config is not None and kwargs:
+            other = {**config, **kwargs}
+        elif config is not None:
+            other = config
+        elif kwargs is not None:
+            other = kwargs
+        else:
+            return
+
+        # then, see if we the given config and overrides are a subset of this
+        # config or it's superset. Depending on the selected method then raise
+        # errors, ignore or extend the current config if the given config is a
+        # superset (or disjoint) from the current one.
+        subset = {k: v for k, v in other.items() if k in self._conf}
+        superset = {k: v for k, v in other.items() if k not in subset}
+
+        if method.lower() == "default":
+            if superset:
+                raise ConfigurationError(
+                    "Tried setting the following fields, not a part of "
+                    f"this configuration options: {superset}"
+                )
+            conf = other  # == subset
+        elif method.lower() == "subset":
+            conf = subset
+        elif method.lower() == "extend":
+            conf = other
+        else:
+            raise ValueError("Method expected to be one of 'default', "
+                             f"'subset'  or 'extend'. Got {method} instead.")
+
+        self._conf.update(conf)
 
     def toDict(self):
         """Return this config as a dict."""
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index f47579461..93ef68c7b 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -4,6 +4,7 @@
 
 import numpy as np
 from astropy.wcs import WCS
+from astropy.table import Table
 from astropy.modeling import models
 from astropy.io.fits import (
     HDUList,
@@ -13,68 +14,73 @@
     Header
 )
 
-from .headers import HeaderFactory, ArchivedHeader
-from .catalogs import gen_catalog, SimpleSourceCatalog, SimpleObjectCatalog
+from .config import Config
+from .headers import HeaderFactory, ArchivedHeader, HeaderFactoryConfig
+from .catalogs import gen_catalog, SimpleCatalog, SourceCatalog, ObjectCatalog
 from .fits_data import (
+    DataFactoryConfig,
     DataFactory,
-    ZeroedData,
+    SimpleImageConfig,
     SimpleImage,
+    SimulatedImageConfig,
+    SimulatedImage,
+    SimpleVarianceConfig,
     SimpleVariance,
+    SimpleMaskConfig,
     SimpleMask,
     add_model_objects
 )
 
 
 __all__ = [
-    "HDUFactory",
-    "HDUListFactory",
     "callback",
+    "EmptyFitsConfig",
     "EmptyFits",
+    "SimpleFitsConfig",
     "SimpleFits",
-    "DECamImdiffs",
+    "DECamImdiffConfig",
+    "DECamImdiff",
 ]
 
 
-class HDUFactory:
-    def __init__(self, hdu_cls, header_factory, data_factory=None):
-        self.hdu_cls = hdu_cls
-        self.header_factory = header_factory
-        self.data_factory = data_factory
-        self.update_data = False if data_factory is None else True
+class HDUListFactoryConfig(Config):
+    validate_header = False
+    """Call ``Header.update`` instead of assigning header values. This enforces
+    FITS standards and may strip non-standard header keywords."""
 
-    def mock(self, **kwargs):
-        hdu = self.hdu_cls()
+    update_header = False
+    """After mocking and assigning data call ``Header.update_header``. This
+    enforces header to be consistent to the data type and shape. May alter or
+    remove keywords from the mocked header."""
 
-        header = self.header_factory.mock(hdu=hdu, **kwargs)
-        # hdu.header.update(header) costs more but maybe better?
-        hdu.header = header
 
-        if self.update_data:
-            data = self.data_factory.mock(hdu=hdu, **kwargs)
-            hdu.data = data
-            # not sure if we want this tbh
-            # hdu.update_header()
+class HDUListFactory(abc.ABC):
+    default_config = HDUListFactoryConfig
 
-        return hdu
+    def __init__(self, config=None, **kwargs):
+        self.config = self.default_config(config=config, **kwargs)
+        self.current = 0
 
+    def hdu_cast(self, hdu_cls, hdr, data=None, config=None, **kwargs):
+        hdu = hdu_cls()
 
-class HDUListFactory(abc.ABC):
-    def __init__(self, layout, base_primary={}, base_ext={}, base_wcs={}):
-        self.layout = layout
-        self.base_primary = base_primary
-        self.base_ext = base_ext
-        self.base_wcs = base_wcs
-
-    def generate(self, **kwargs):
-        hdul = HDUList()
-        for layer in self.layout:
-            hdul.append(layer.mock(**kwargs))
-        return hdul
+        if self.config.validate_header:
+            hdu.header.update(hdr)
+        else:
+            hdu.header = hdr
+
+        if data is not None:
+            hdu.data = data
+            if self.config.update_header:
+                hdu.update_header()
+
+        return hdu
 
     @abc.abstractmethod
     def mock(self, n=1):
         raise NotImplementedError()
 
+
 # I am sure a decorator like this must exist somewhere in functools, but can't
 # find it and I'm doing something wrong with functools.partial because that's
 # strictly right-side binding?
@@ -90,9 +96,7 @@ def wrapper(*args, **kwargs):
             def f(*fargs, **fkwargs):
                 kwargs.update(fkwargs)
                 return func(*(args + fargs), **kwargs)
-
             return f
-
     else:
         # functions, static methods
         def wrapper(*args, **kwargs):
@@ -100,170 +104,374 @@ def wrapper(*args, **kwargs):
             def f(*fargs, **fkwargs):
                 kwargs.update(fkwargs)
                 return func(*fargs, **kwargs)
-
             return f
-
     return wrapper
 
 
+class EmptyFitsConfig(HDUListFactoryConfig):
+    editable_images = False
+    separate_masks = False
+    writeable_mask = False
+    dt = 0.001
+
+    shape = (100, 100)
+    """Dimensions of the generated images."""
+
+
 class EmptyFits(HDUListFactory):
+    default_config = EmptyFitsConfig
+
     @callback
     @staticmethod
     def increment_obstime(old, dt):
         return old + dt
 
-    def __init__(self, primary_hdr=None, dt=0.001):
-        # header and data factories that go into creating HDUs
-        self.primary = HeaderFactory.from_base_primary(
-            metadata=primary_hdr,
+    def __init__(self, metadata=None, config=None, **kwargs):
+        super().__init__(config=config, method="extend", **kwargs)
+
+        # 1. Update all the default configs. Use class configs that simplify
+        #    and unite the many smaller config settings of each underlying factory.
+        hdr_conf = HeaderFactoryConfig(config=self.config, method="subset", shape=self.config.shape)
+
+        # 2. Set up Header and Data factories that go into creating HDUs
+        #    2.1) First headers, since that metadata specified data formats
+        self.primary_hdr = HeaderFactory.from_primary_template(
+            overrides=metadata,
             mutables=["OBS-MJD"],
-            callbacks=[self.increment_obstime(dt=dt)],
+            callbacks=[self.increment_obstime(dt=self.config.dt)],
+            config=hdr_conf
         )
-        self.image = HeaderFactory.from_base_ext({"EXTNAME": "IMAGE"})
-        self.variance = HeaderFactory.from_base_ext({"EXTNAME": "VARIANCE"})
-        self.mask = HeaderFactory.from_base_ext({"EXTNAME": "MASK"})
-        data = ZeroedData()
-
-        # a map of HDU class and their respective header and data generators
-        layout = [
-            HDUFactory(PrimaryHDU, self.primary),
-            HDUFactory(CompImageHDU, self.image, data),
-            HDUFactory(CompImageHDU, self.variance, data),
-            HDUFactory(CompImageHDU, self.mask, data),
-        ]
 
-        super().__init__(layout)
+        self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, config=hdr_conf)
+        self.var_hdr = HeaderFactory.from_ext_template({"EXTNAME": "VARIANCE"}, config=hdr_conf)
+        self.mask_hdr = HeaderFactory.from_ext_template({"EXTNAME": "MASK"}, config=hdr_conf)
+
+        #   2.2) Then data factories, attempt to save performance and memory
+        #        where possible by really only allocating 1 array whenever the data
+        #        is read-only and content-static between created HDUs.
+        writeable, return_copy = False, False
+        if self.config.editable_images:
+            writeable, return_copy = True, True
+
+        self.data = DataFactory.from_header(
+            kind="image",
+            header=self.img_hdr.header,
+            writeable=writeable,
+            return_copy=return_copy
+        )
+        self.shared_data = DataFactory.from_header("image", self.mask_hdr.header,
+                                                   writeable=self.config.writeable_mask)
 
     def mock(self, n=1):
-        if n == 1:
-            return self.step()
+        # 3) Finally when mocking, vectorize as many operations as possible.
+        #    The amount of data scales fast with number of images generated so
+        #    that even modest iterative generation of data costs significantly.
+        #    (F.e. for each HDU - 3 images are allocated, if we use DECam, for
+        #    each HDU, 62 to 70 images can be generated). Many times generated values
+        #    are not trivial zeros, but randomly drawn, modified, and then
+        #    calculated.
+        var_hdr = self.var_hdr.mock()
+        mask_hdr = self.mask_hdr.mock()
+
+        imgs = self.data.mock(n)
+        variances = self.data.mock(n)
+        masks = self.shared_data.mock(n)
 
-        shape = (n*3, self.image.header["NAXIS1"], self.image.header["NAXIS1"])
-        images = np.zeros(shape, dtype=np.float32)
-        imghdr, varhdr, maskhdr = self.image.mock(), self.variance.mock(), self.mask.mock()
         hduls = []
         for i in range(n):
-            hduls.append(HDUList(
-                hdus=[
-                    PrimaryHDU(header=self.primary.mock()),
-                    CompImageHDU(header=imghdr, data=images[i]),
-                    CompImageHDU(header=varhdr, data=images[n+i]),
-                    CompImageHDU(header=maskhdr, data=images[2*n+i])
-                ]
-            ))
-
+            hduls.append(HDUList(hdus=[
+                self.hdu_cast(PrimaryHDU, self.primary_hdr.mock()),
+                self.hdu_cast(CompImageHDU, self.img_hdr.mock(), imgs[i]),
+                self.hdu_cast(CompImageHDU, var_hdr, variances[i]),
+                self.hdu_cast(CompImageHDU, mask_hdr, masks[i])
+            ]))
+
+        self.current += n
         return hduls
 
 
+class SimpleFitsConfig(HDUListFactoryConfig):
+    editable_images = False
+    separate_masks = False
+    writeable_mask = False
+    noise_generation = "simplistic"
+    dt = 0.001
+
+    shape = (100, 100)
+    """Dimensions of the generated images."""
+
 
 class SimpleFits(HDUListFactory):
+    default_config = SimpleFitsConfig
+
     @callback
     @staticmethod
     def increment_obstime(old, dt):
         return old + dt
 
-    def __init__(self, shape, noise=0, noise_std=1, dt=0.001, source_catalog=None, object_catalog=None):
-        # internal counter of n images created so far
-        self._idx = 0
-        self.dt = dt
+    def __init__(self, metadata=None, source_catalog=None, object_catalog=None,
+                 config=None,  **kwargs):
+        super().__init__(config=config, method="extend", **kwargs)
+
         self.src_cat = source_catalog
         self.obj_cat = object_catalog
-        self.shape = shape
-        self.noise = noise
-        self.noise_std = noise_std
-        dims = {"NAXIS1": shape[0], "NAXIS2": shape[1]}
-
-        # Then we can generate the header factories primary header contains no
-        # data, but does contain update-able metadata fields (f.e. timestamps),
-        # others contain static headers, but dynamic, mutually connected data
-        self.primary = HeaderFactory.from_base_primary(
+
+        # 1. Update all the default configs using more user-friendly kwargs
+        hdr_conf = HeaderFactoryConfig(config=self.config, shape=self.config.shape, method="subset")
+
+        if self.config.noise_generation == "realistic":
+            img_cfg = SimulatedImageConfig(config=self.config, method="subset")
+        else:
+            img_cfg = SimpleImageConfig(config=self.config, method="subset")
+        var_cfg = SimpleVarianceConfig(config=self.config, method="subset")
+        mask_cfg = SimpleMaskConfig(config=self.config, method="subset")
+
+        # 2. Set up Header and Data factories that go into creating HDUs
+        #    2.1) First headers, since that metadata specified data formats
+        self.primary_hdr = HeaderFactory.from_primary_template(
+            overrides=metadata,
             mutables=["OBS-MJD"],
-            callbacks=[self.increment_obstime(dt=0.001)],
-        )
-        self.image = HeaderFactory.from_base_ext({"EXTNAME": "IMAGE", **dims})
-        self.variance = HeaderFactory.from_base_ext({"EXTNAME": "VARIANCE", **dims})
-        self.mask = HeaderFactory.from_base_ext({"EXTNAME": "MASK", **dims})
-
-        self.img_data = SimpleImage(
-            shape=shape,
-            noise=noise,
-            noise_std=noise_std,
-            src_cat=self.src_cat,
+            callbacks=[self.increment_obstime(dt=self.config.dt)],
+            config=hdr_conf
         )
-        self.var_data = SimpleVariance(self.img_data.base, read_noise=noise, gain=1.0)
-        self.mask_data = DataFactory(np.zeros(shape))
-
-        # Now we can build the HDU map and the HDUList layout
-        layout = [
-            HDUFactory(PrimaryHDU, self.primary),
-            HDUFactory(CompImageHDU, self.image, self.img_data),
-            HDUFactory(CompImageHDU, self.variance, self.var_data),
-            HDUFactory(CompImageHDU, self.mask, self.mask_data),
-        ]
-
-        super().__init__(layout)
+        self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, config=hdr_conf)
+        self.var_hdr = HeaderFactory.from_ext_template({"EXTNAME": "VARIANCE"}, config=hdr_conf)
+        self.mask_hdr = HeaderFactory.from_ext_template({"EXTNAME": "MASK"}, config=hdr_conf)
+
+        #   2.2) Then data factories
+        if self.config.noise_generation == "realistic":
+            self.img_data = SimulatedImage(src_cat=self.src_cat, config=img_cfg)
+        else:
+            self.img_data = SimpleImage(src_cat=self.src_cat, config=img_cfg)
+        self.var_data = SimpleVariance(self.img_data.base, config=var_cfg)
+        self.mask_data = SimpleMask.from_image(self.img_data.base, config=mask_cfg)
 
     @classmethod
-    def from_defaults(cls, shape=(2048, 4096), add_static_sources=False, add_moving_objects=True, **kwargs):
-        source_catalog, object_catalog = None, None
-        cat_lims = {"x_mean": [0, shape[1]], "y_mean": [0, shape[0]]}
-        if add_static_sources:
-            source_catalog = SimpleSourceCatalog.from_params(param_ranges=cat_lims)
-        if add_moving_objects:
-            object_catalog = SimpleObjectCatalog.from_params(param_ranges=cat_lims)
-
-        return cls(shape=shape, source_catalog=source_catalog, object_catalog=object_catalog, **kwargs)
+    def from_defaults(cls, config=None, sources=None, objects=None, **kwargs):
+        config = cls.default_config(config, **kwargs, method="extend")
+        cat_lims = {"x_mean": [0, config.shape[1]], "y_mean": [0, config.shape[0]]}
+        src_cat, obj_cat = None, None
+        if sources is not None:
+            if isinstance(sources, SimpleCatalog):
+                src_cat = sources
+            elif isinstance(sources, Table):
+                src_cat = SourceCatalog(table=sources)
+            elif isinstance(sources, Config) or sources == True:
+                sources.param_ranges.update(cat_lims)
+                src_cat = SourceCatalog(**sources)
+            elif isinstance(sources, dict):
+                sources.update(cat_lims)
+                src_cat = SourceCatalog(**sources)
+            elif isinstance(sources, int):
+                src_cat = SourceCatalog(n=sources, **cat_lims)
+            elif sources is True:
+                obj_cat = SourceCatalog(**cat_lims)
+            else:
+                raise ValueError("Sources are expected to be a catalog, table, config or config overrides.")
+        if objects is not None:
+            if isinstance(objects, SimpleCatalog):
+                obj_cat = objects
+            elif isinstance(objects, Table):
+                obj_cat = ObjectCatalog(table=objects)
+            elif isinstance(objects, Config):
+                objects.param_ranges.update(cat_lims)
+                obj_cat = ObjectCatalog(**objects)
+            elif isinstance(objects, dict):
+                objects.update(cat_lims)
+                obj_cat = ObjectCatalog(**objects)
+            elif isinstance(objects, int):
+                obj_cat = ObjectCatalog(n=objects, **cat_lims)
+            elif objects is True:
+                obj_cat = ObjectCatalog(**cat_lims)
+            else:
+                raise ValueError("Objects are expected to be a catalog, table, config or config overrides.")
+        return cls(source_catalog=src_cat, object_catalog=obj_cat, config=config, **kwargs)
 
     def mock(self, n=1):
         obj_cats = None
-
         if self.obj_cat is not None:
-            obj_cats = self.obj_cat.mock(n, dt=self.dt)
+            obj_cats = self.obj_cat.mock(n, dt=self.config.dt)
+
+        var_hdr = self.var_hdr.mock()
+        mask_hdr = self.mask_hdr.mock()
 
         images = self.img_data.mock(n, obj_cats=obj_cats)
         variances = self.var_data.mock(images=images)
-        mask = self.mask_data.mock()
-        imghdr, varhdr, maskhdr = self.image.mock(), self.variance.mock(), self.mask.mock()
+        masks = self.mask_data.mock(n)
 
         hduls = []
         for i in range(n):
-            hduls.append(HDUList(
-                hdus=[
-                    PrimaryHDU(header=self.primary.mock()),
-                    CompImageHDU(header=imghdr, data=images[i]),
-                    CompImageHDU(header=varhdr, data=variances[i]),
-                    CompImageHDU(header=maskhdr, data=mask),
-                ]
-            ))
+            hduls.append(HDUList(hdus=[
+                self.hdu_cast(PrimaryHDU, self.primary_hdr.mock()),
+                self.hdu_cast(CompImageHDU, self.img_hdr.mock(), images[i]),
+                self.hdu_cast(CompImageHDU, var_hdr, variances[i]),
+                self.hdu_cast(CompImageHDU, mask_hdr, masks[i])
+            ]))
+
+        self.current += n
         return hduls
 
 
-class DECamImdiffs(HDUListFactory):
-    def __init__(self):
-        headers = ArchivedHeader("headers_archive.tar.bz2", "decam_imdiff_headers.ecsv")
-        data = ZeroedData()
-        image = HDUFactory(CompImageHDU, headers, data)
-
-        # DECam imdiff data products consist of 16 headers, first 4 are:
-        # primary, science variance and mask and the rest are PSF, SkyWCS,
-        # catalog metadata, chebyshev higher order corrections etc. We don't
-        # use these, so leave them with no data generators. The first 4 we fill
-        # with all-zeros.
-        # don't let Black have its way with these lines because it's a massacre
-        # fmt: off
-        layout = [
-            HDUFactory(PrimaryHDU, headers),
-            image,
-            image,
-            image,
-        ]
-        layout.extend([HDUFactory(BinTableHDU, headers) ] * 12)
-        # fmt: on
+class DECamImdiffConfig2(HDUListFactoryConfig):
+    archive_name = "headers_archive.tar.bz2"
+    file_name = "decam_imdiff_headers.ecsv"
+    n_hdrs_per_hdu = 16
+
+
+class DECamImdiff2(HDUListFactory):
+    default_config = DECamImdiffConfig2
+
+    def __init__(self, config=None, **kwargs):
+        super().__init__(config=config, **kwargs)
+        self.hdr_factory = ArchivedHeader(self.config.archive_name, self.config.file_name)
+
+    def mock(self, n=1):
+        hdrs = self.hdr_factory.mock(n)
+
+        hduls = []
+        for hdul_idx in range(n):
+            hduls.append(HDUList(hdus=[
+                self.hdu_cast(PrimaryHDU,   hdrs[hdul_idx][0]),
+                self.hdu_cast(CompImageHDU, hdrs[hdul_idx][1]),
+                self.hdu_cast(CompImageHDU, hdrs[hdul_idx][2]),
+                self.hdu_cast(CompImageHDU, hdrs[hdul_idx][3]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][4]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][5]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][6]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][7]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][8]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][9]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][10]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][11]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][12]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][13]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][14]),
+                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][15]),
+            ]))
+
+        self.current += 1
+        return hduls
+
+
+
+
+class DECamImdiffConfig(HDUListFactoryConfig):
+    archive_name = "headers_archive.tar.bz2"
+    file_name = "decam_imdiff_headers.ecsv"
+
+    with_data=False
+
+    editable_images = False
+    separate_masks = False
+    writeable_mask = False
+    noise_generation = "simplistic"
+    dt = 0.001
+
+    shape = (100, 100)
+    """Dimensions of the generated images."""
 
-        super().__init__(layout)
+
+class NoneFactory:
+    "Kinda makes some code later prettier. Kinda"
+    def mock(self, n):
+        return [None, ]*n
+
+
+class DECamImdiff(HDUListFactory):
+    default_config = DECamImdiffConfig
+
+    def __init__(self, source_catalog=None, object_catalog=None, config=None, **kwargs):
+        super().__init__(config=config, **kwargs)
+
+        # 1. Get the header factory - this is different than before. In a
+        # header preserving factory, it's the headers that dictate the shape
+        # and format of data. Since these are also optional for this class, we
+        # just create empty placeholders for data and only fill it with
+        # factories if we need to.
+        self.hdr_factory = ArchivedHeader(self.config.archive_name,
+                                          self.config.file_name)
+
+        self.hdu_types = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
+        self.hdu_types.extend([BinTableHDU, ]*12)
+        self.data = [NoneFactory(), ]*16
+
+        self.src_cat = source_catalog
+        self.obj_cat = object_catalog
+
+        if self.config.with_data:
+            self.__init_data_factories()
+
+    def __init_data_factories(self):
+        # 2. To fill in data placehodlers, we get an example set of headers in
+        # a file. Since they dictate data, we must derive whatever configs we
+        # need to and use them as overrides to trump defaults and user-given
+        # values. This is the point at which we rely on user telling this
+        # factory what the important header keys are since these could be
+        # non-standard. And since these change for each HDU, it is rather
+        # difficult to generalize without resorting to iterative and fully
+        # dynamical data generation - which will be slow. The only mercy is
+        # that we only have to do it for the HDUs we care about and use factory
+        # methods for the rest.
+        headers = self.hdr_factory.get(0)
+
+        img_shape = (headers[1]["NAXIS1"], headers[1]["NAXIS2"])
+        img_overrides = {
+            "shape": img_shape,
+            "dtype": DataFactoryConfig.bitpix_type_map[headers[1]["BITPIX"]],
+        }
+
+        if self.config.noise_generation == "realistic":
+            img_cfg = SimulatedImageConfig(config=self.config, **img_overrides,  method="subset")
+        else:
+            img_cfg = SimpleImageConfig(config=self.config,  **img_overrides, method="subset")
+        var_cfg = SimpleVarianceConfig(config=self.config, **img_overrides, method="subset")
+        mask_cfg = SimpleMaskConfig(config=self.config, **img_overrides, method="subset")
+
+        #   2.1) Now we can instantiate data factories with correct configs
+        #        and fill in the data placeholder
+        if self.config.noise_generation == "realistic":
+            self.img_data = SimulatedImage(src_cat=self.src_cat, config=img_cfg)
+        else:
+            self.img_data = SimpleImage(src_cat=self.src_cat, config=img_cfg)
+            self.var_data = SimpleVariance(self.img_data.base, config=var_cfg)
+            self.mask_data = SimpleMask.from_image(self.img_data.base, config=mask_cfg)
+
+        self.hdu_types = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
+        self.hdu_types.extend([BinTableHDU, ]*12)
+
+        self.data = [
+            NoneFactory(),
+            self.img_data,
+            self.var_data,
+            self.mask_data
+        ]
+        self.data.extend([
+            DataFactory.from_header("table", h) for h in headers[4:]
+        ])
 
     def mock(self, n=1):
-        if n==1:
-            return self.generate()
-        hduls = [self.generate() for i in range(n)]
+        obj_cats = None
+        if self.obj_cat is not None:
+            obj_cats = self.obj_cat.mock(n, dt=self.config.dt)
+
+        hdrs = self.hdr_factory.mock(n)
+
+        if self.config.with_data:
+            images = self.img_data.mock(n, obj_cats=obj_cats)
+            variances = self.var_data.mock(images=images)
+            data = [self.data[0].mock(n), images, variances]
+            for factory in self.data[3:]:
+                data.append(factory.mock(n=n))
+        else:
+            data = [f.mock(n=n) for f in self.data]
+
+        hduls = []
+        for hdul_idx in range(n):
+            hdus = []
+            for hdu_idx, hdu_cls in enumerate(self.hdu_types):
+                hdus.append(self.hdu_cast(
+                    hdu_cls, hdrs[hdul_idx][hdu_idx], data[hdu_idx][hdul_idx]
+                ))
+            hduls.append(HDUList(hdus=hdus))
+        self.current += n
         return hduls
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index ffd8d79fb..79b6da1e3 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -9,17 +9,25 @@
 from astropy.modeling import models
 
 from .config import Config, ConfigurationError
+from kbmod import Logging
 
 
 __all__ = [
     "add_model_objects",
+    "DataFactoryConfig",
     "DataFactory",
-    "ZeroedData",
+    "SimpleImageConfig",
     "SimpleImage",
-    "SimpleMask"
+    "SimpleMaskConfig",
+    "SimpleMask",
+    "SimulatedImageConfig",
+    "SimulatedImage"
 ]
 
 
+logger = Logging.getLogger(__name__)
+
+
 def add_model_objects(img, catalog, model):
     """Adds a catalog of model objects to the image.
 
@@ -50,6 +58,9 @@ def add_model_objects(img, catalog, model):
     # Save the initial model parameters so we can set them back
     init_params = {param: getattr(model, param) for param in params_to_set}
 
+    # model could throw a value error if drawn amplitude was too large, we must
+    # restore the model back to its starting values to cover for a general
+    # use-case because Astropy modelling is a bit awkward.
     try:
         for i, source in enumerate(catalog):
             for param in params_to_set:
@@ -62,7 +73,6 @@ def add_model_objects(img, catalog, model):
                     model.y_mean < img.shape[0]
             ]):
                 model.render(img)
-
     finally:
         for param, value in init_params.items():
             setattr(model, param, value)
@@ -74,6 +84,13 @@ class DataFactoryConfig(Config):
     """Data factory configuration primarily controls mutability of the given
     and returned mocked datasets.
     """
+    default_img_shape = (5, 5)
+
+    default_img_bit_width = 32
+
+    default_tbl_length = 5
+
+    default_tbl_dtype = np.dtype([("a", int), ("b", int)])
 
     writeable = False
     """Sets the base array ``writeable`` flag. Default `False`."""
@@ -84,6 +101,21 @@ class DataFactoryConfig(Config):
     otherwise the original (possibly mutable!) object is returned. Default `False`.
     """
 
+    # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
+    bitpix_type_map = {
+        # or char
+        8: int,
+        # actually no idea what dtype, or C type for that matter,
+        # are used to represent these values. But default Headers return them
+        16: np.float16,
+        32: np.float32,
+        64: np.float64,
+        # classic IEEE float and double
+        -32: np.float32,
+        -64: np.float64,
+    }
+    """Map between FITS header BITPIX keyword value and NumPy return type."""
+
 
 class DataFactory:
     """Generic data factory.
@@ -132,32 +164,103 @@ class DataFactory:
     default_config = DataFactoryConfig
     """Default configuration."""
 
-    def __init__(self, base=None, config=None, **kwargs):
-        self.config = self.default_config()
-        self.config.update(config, **kwargs)
+    def __init__(self, base, config=None, **kwargs):
+        self.config = self.default_config(config, **kwargs)
 
         self.base = base
-        if base is not None:
+        if base is None:
+            self.shape = None
+            self.dtype = None
+        else:
+            self.shape = base.shape
+            self.dtype = base.dtype
             self.base.flags.writeable = self.config.writeable
         self.counter = 0
 
-    def mock(self, **kwargs):
-        """Return a mocked fits data and increase counter.
+    @classmethod
+    def gen_image(cls, metadata=None, config=None, **kwargs):
+        conf = cls.default_config(config, method="subset", **kwargs)
+        cols = metadata.get("NAXIS1", conf.default_img_shape[0])
+        rows = metadata.get("NAXIS2", conf.default_img_shape[1])
+        bitwidth = metadata.get("BITPIX", conf.default_img_bit_width)
+        dtype = conf.bitpix_type_map[bitwidth]
+        shape = (cols, rows)
 
-        Parameters
-        ----------
-        **kwargs
-           Any additional keyword arguments are ignored.
+        return np.zeros(shape, dtype)
+
+    @classmethod
+    def gen_table(cls, metadata=None, config=None, **kwargs):
+        conf = cls.default_config(config, **kwargs, method="subset")
+
+        # FITS format standards prescribe FORTRAN-77-like input format strings
+        # for different types, but the base set has been extended significantly
+        # since and Rubin uses completely non-standard keys with support for
+        # their own internal abstractions like 'Angle' objects:
+        # https://archive.stsci.edu/fits/fits_standard/node58.html
+        # https://docs.astropy.org/en/stable/io/fits/usage/table.html#column-creation
+        # https://github.com/lsst/afw/blob/main/src/fits.cc#L207
+        # So we really don't have much of a choice but to force a default
+        # AstroPy HDU and then call the update. This might not preserve the
+        # header or the data formats exactly and if metadata isn't given
+        # could even assume a wrong class all together. The TableHDU is
+        # almost never used however - so hopefully this keeps on working.
+        table_cls = BinTableHDU
+        data = None
+        if metadata is not None:
+            if metadata["XTENSION"] == "BINTABLE":
+                table_cls = BinTableHDU
+            elif metadata["XTENSION"] == "TABLE":
+                table_cls = TableHDU
+
+            hdu = table_cls()
+            hdu.header.update(metadata)
+
+            rows = metadata.get("NAXIS2", conf.default_tbl_length)
+            shape = (rows, )
+            data = np.zeros(shape, dtype=hdu.data.dtype)
+        else:
+            hdu = table_cls()
+            shape = (conf.default_tbl_length, )
+            data = np.zeros(shape, dtype=conf.default_tbl_dtype)
+
+        return data
+
+    @classmethod
+    def from_hdu(cls, hdu, config=None, **kwargs):
+        if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
+            return cls(base=cls.gen_image(hdu), config=config, **kwargs)
+        elif isinstance(hdu, (TableHDU, BinTableHDU)):
+            return cls(base=cls.gen_table(hdu), config=config, **kwargs)
+        else:
+            raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
+
+    @classmethod
+    def from_header(cls, kind, header, config=None, **kwargs):
+        if kind.lower() == "image":
+            return cls(base=cls.gen_image(header), config=config, **kwargs)
+        elif kind.lower() == "table":
+            return cls(base=cls.gen_table(header), config=config, **kwargs)
+        else:
+            raise TypeError(f"Expected an 'image' or 'table', got {kind} instead.")
+
+    @classmethod
+    def zeros(cls, shape, dtype, config=None, **kwargs):
+        return cls(np.zeros(shape, dtype), config, **kwargs)
+
+    def mock(self, n=1, **kwargs):
+        if self.base is None:
+            raise ValueError(
+                "Expected a DataFactory that has a base, but none was set. "
+                "Use `zeros` or `from_hdu` to construct this object correctly."
+            )
 
-        Returns
-        -------
-        data : `no.array`
-            Mocked data array
-        """
-        self.counter += 1
         if self.config.return_copy:
-            return self.base.copy()
-        return self.base
+            base = np.repeat(self.base[np.newaxis, ], (n,), axis=0)
+        else:
+            base = np.broadcast_to(self.base, (n, *self.shape))
+        base.flags.writeable = self.config.writeable
+
+        return base
 
 
 class SimpleVarianceConfig(DataFactoryConfig):
@@ -207,7 +310,15 @@ def mock(self, images=None):
 
 class SimpleMaskConfig(DataFactoryConfig):
     """Simple mask configuration."""
-    pass
+    dtype = np.float32
+
+    threshold = 1e-05
+
+    shape = (5, 5)
+    padding = 0
+    bad_columns = []
+    patches = []
+
 
 class SimpleMask(DataFactory):
     """Simple mask factory.
@@ -223,11 +334,19 @@ class SimpleMask(DataFactory):
     """
     default_config = SimpleMaskConfig
 
-    def __init__(self, mask):
-        super().__init__(base=mask)
+
+    def __init__(self, mask, config=None, **kwargs):
+        super().__init__(base=mask, config=config, **kwargs)
 
     @classmethod
-    def from_params(cls, shape, padding=0, bad_columns=[], patches=[]):
+    def from_image(cls, image, config=None, **kwargs):
+        config = cls.default_config(config=config, **kwargs, method="subset")
+        mask = image.copy()
+        mask[image > config.threshold] = 1
+        return cls(mask)
+
+    @classmethod
+    def from_params(cls, config=None, **kwargs):
         """Create a mask by adding a padding around the edges of the array with
         the given dimensions and mask out bad columns.
 
@@ -273,19 +392,22 @@ def from_params(cls, shape, padding=0, bad_columns=[], patches=[]):
                [1., 0., 1., 1., 0., 0., 0., 0., 0., 1.],
                [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])
         """
-        mask = np.zeros(shape)
+        config = cls.default_config(config=config, **kwargs, method="subset")
+        mask = np.zeros(config.shape, dtype=config.dtype)
+
+        shape, padding = config.shape, config.padding
 
         # padding
         mask[:padding] = 1
-        mask[shape[0] - padding :] = 1
+        mask[shape[0] - padding:] = 1
         mask[:, :padding] = 1
-        mask[: shape[1] - padding :] = 1
+        mask[: shape[1] - padding:] = 1
 
         # bad columns
-        for col in bad_columns:
+        for col in config.bad_columns:
             mask[:, col] = 1
 
-        for patch, value in patches:
+        for patch, value in config.patches:
             if isinstance(patch, tuple):
                 mask[*patch] = 1
             elif isinstance(slice):
@@ -293,83 +415,7 @@ def from_params(cls, shape, padding=0, bad_columns=[], patches=[]):
             else:
                 raise ValueError(f"Expected a tuple (x, y), (slice, slice) or slice, got {patch} instead.")
 
-        return cls(mask)
-
-
-class ZeroedDataConfig(DataFactoryConfig):
-    """Zeroed data config."""
-    return_copy = True
-
-    shape = (5, 5)
-    """Default image size."""
-
-    # https://archive.stsci.edu/fits/fits_standard/node39.html#s:man
-    bitpix_type_map = {
-        # or char
-        8: int,
-        # actually no idea what dtype, or C type for that matter,
-        # are used to represent these values. But default Headers return them
-        16: np.float16,
-        32: np.float32,
-        64: np.float64,
-        # classic IEEE float and double
-        -32: np.float32,
-        -64: np.float64,
-    }
-    """Map between FITS header BITPIX keyword value and NumPy return type."""
-
-
-class ZeroedData(DataFactory):
-    """Data factory that creates zeroed data arrays from header definitions.
-
-    A convenience factory able to generate the correct size image and bintable
-    data from a header.
-
-    Parameters
-    ----------
-    base : `np.array`
-        Static data shared by all mocked instances.
-    config : `DataFactoryConfig`
-        Configuration of the data factory.
-    **kwargs :
-        Additional keyword arguments are applied as config
-        overrides.
-    """
-    default_config = ZeroedDataConfig
-
-    def __init__(self, base=None, config=None, **kwargs):
-        super().__init__(base, config, **kwargs)
-
-    def mock_image_data(self, hdu):
-        cols = hdu.header.get("NAXIS1", False)
-        rows = hdu.header.get("NAXIS2", False)
-        shape = (cols, rows) if all((cols, rows)) else self.config.shape
-
-        data = np.zeros(
-            shape,
-            dtype=self.config.bitpix_type_map[hdu.header["BITPIX"]]
-        )
-        return data
-
-    def mock_table_data(self, hdu):
-        # interestingly, table HDUs create their own empty
-        # tables from headers, but image HDUs do not, this
-        # is why hdu.data exists and has a valid dtype
-        nrows = hdu.header["TFIELDS"]
-        return np.zeros((nrows,), dtype=hdu.data.dtype)
-
-    def mock(self, hdu=None, **kwargs):
-        # two cases: a) static data shared by all instances created by the
-        # factory set at init time or b) dynamic data generated from a header
-        # specification at call-time.
-        if self.base is not None:
-            return super().mock(hdu=hdu, **kwargs)
-        if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
-            return self.mock_image_data(hdu)
-        elif isinstance(hdu, (TableHDU, BinTableHDU)):
-            return self.mock_table_data(hdu)
-        else:
-            raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
+        return cls(mask, config=config)
 
 
 class SimpleImageConfig(DataFactoryConfig):
@@ -386,7 +432,7 @@ class SimpleImageConfig(DataFactoryConfig):
     seed = None
     """Seed of the random number generator used to generate noise."""
 
-    noise = 0
+    noise = 10
     """Mean of the standard Gaussian distribution of the noise."""
 
     noise_std = 1.0
@@ -422,8 +468,10 @@ class SimpleImage(DataFactory):
     """
     default_config = SimpleImageConfig
 
-    def __init__(self, image=None, config=None, src_cat=None, obj_cat=None, **kwargs):
-        super().__init__(image, config, **kwargs)
+    def __init__(self, image=None, src_cat=None, obj_cat=None,
+                 config=None, **kwargs):
+        self.config = self.default_config(config=config, **kwargs)
+        super().__init__(image, self.config, **kwargs)
 
         if image is None:
             image = np.zeros(self.config.shape, dtype=np.float32)
@@ -431,9 +479,15 @@ def __init__(self, image=None, config=None, src_cat=None, obj_cat=None, **kwargs
             image = image
             self.config.shape = image.shape
 
+        # Astropy throws a strange ValueError instead of reporting a non-writeable
+        # array, This must be a bug TODO: report. It's not safe to edit a
+        # non-writeable array and then revert writeability so make a copy.
         self.src_cat = src_cat
         if self.src_cat is not None:
-            add_model_objects(image, src_cat.table, self.config.model)
+            image = image if image.flags.writeable else image.copy()
+            add_model_objects(image, src_cat.table,
+                              self.config.model(x_stddev=1, y_stddev=1))
+            image.flags.writeable = self.config.writeable
 
         self.base = image
         self._base_contains_data = image.sum() != 0
@@ -456,7 +510,7 @@ def add_noise(cls, images, config):
         rng.standard_normal(size=shape, dtype=images.dtype, out=images)
 
         # There's a lot of multiplications that happen, skip if possible
-        if self.config.noise_std != 1.0:
+        if config.noise_std != 1.0:
             images *= config.noise_std
         images += config.noise
 
@@ -476,16 +530,21 @@ def mock(self, n=1, obj_cats=None, **kwargs):
         shape = (n, *self.config.shape)
         images = np.zeros(shape, dtype=np.float32)
 
-        if self.config.noise != 0:
-            images = self.add_noise(n, images, self.config)
+        if self.config.add_noise:
+            images = self.add_noise(images=images, config=self.config)
 
-        # but if base has no data (no sources, bad cols etc) skip
+        # if base has no data (no sources, bad cols etc) skip
         if self._base_contains_data:
             images += self.base
 
-        # same with moving objects
+        # same with moving objects, each image has to have a new realization of
+        # a catalog in which moving objects have different coordinate. This way
+        # any trajectory can be mocked. When we have only 1 mocked image though
+        # zip will attempt to iterate over the next available dimension, and
+        # that's rows of the image and the table - we don't want that.
         if obj_cats is not None:
-            for i, (img, cat) in enumerate(zip(images, obj_cats)):
+            pairs = [(images[0], obj_cats),] if n == 1 else zip(images, obj_cats)
+            for i, (img, cat) in enumerate(pairs):
                 add_model_objects(
                     img,
                     cat,
@@ -526,6 +585,9 @@ class SimulatedImageConfig(DataFactoryConfig):
     # not sure this is a smart idea to put here
     rng = np.random.default_rng()
 
+    seed = None
+    """Random number generator seed shared by all number generators."""
+
     # image properties
     shape = (100, 100)
     """Dimensions of the created images."""
@@ -546,7 +608,7 @@ class SimulatedImageConfig(DataFactoryConfig):
     bias = 0.0
     """Bias in counts."""
 
-    add_bad_cols = False
+    add_bad_columns = False
     """Add bad columns to the image."""
 
     bad_cols_method = "random"
@@ -559,12 +621,15 @@ class SimulatedImageConfig(DataFactoryConfig):
     n_bad_cols = 5
     """When bad columns method is random, sets the number of bad columns."""
 
-    bad_cols_seed = 123
+    bad_cols_seed = seed
     """Seed for the bad columns random number generator."""
 
-    bad_col_pattern_offset = 0.1
+    bad_col_offset = 100
+    """Bad column signal offset (in counts) with regards to the baseline noise."""
+
+    bad_col_pattern_offset = 10
     """Random-looking noise variation along the length of the bad columns is
-    offset from the base column counts by this amount multiplied by bias."""
+    offset from the mean bad column counts by this amount."""
 
     dark_current_gen = rng.poisson
     """Dark current follows a Poisson distribution."""
@@ -582,7 +647,7 @@ class SimulatedImageConfig(DataFactoryConfig):
     hot_pix_locs = []
     """A `list[tuple]` of hot pixel indices."""
 
-    hot_pix_seed = 321
+    hot_pix_seed = seed
     """Seed for hot pixel random number generator."""
 
     n_hot_pix = 10
@@ -644,25 +709,29 @@ def add_bad_cols(cls, image, config):
         image : `np.array`
             Image.
         """
-        if not config.add_bad_cols:
+        if not config.add_bad_columns:
             return image
 
+        shape = image.shape
+        rng = np.random.RandomState(seed=config.bad_cols_seed)
         if config.bad_cols_method == "random":
-            rng = np.random.RandomState(seed=self.bad_cols_seed)
             bad_cols = rng.randint(0, shape[1], size=config.n_bad_cols)
         elif config.bad_col_locs:
             bad_cols = config.bad_col_locs
         else:
-            raise ConfigurationError("Bad columns method is not 'random', but `bad_col_locs` contains no bad column indices.")
+            raise ConfigurationError(
+                "Bad columns method is not 'random', but `bad_col_locs` "
+                "contains no column indices."
+            )
 
-        self.col_pattern = rng.randint(
+        col_pattern = rng.randint(
             low=0,
-            high=int(config.bad_col_pattern_offset * config.bias),
+            high=int(config.bad_col_pattern_offset),
             size=shape[0]
         )
 
-        for col in columns:
-            image[:, col] = config.bias + col_pattern
+        for col in bad_cols:
+            image[:, col] += col_pattern + config.bad_col_offset
 
         return image
 
@@ -688,23 +757,27 @@ def add_hot_pixels(cls, image, config):
         if not config.add_hot_pixels:
             return image
 
+        shape = image.shape
         if config.hot_pix_method == "random":
             rng = np.random.RandomState(seed=config.hot_pix_seed)
             x = rng.randint(0, shape[1], size=config.n_hot_pix)
             y = rng.randint(0, shape[0], size=config.n_hot_pix)
-            hot_pixels = np.column_stack(x, y)
+            hot_pixels = np.column_stack([x, y])
         elif config.hot_pix_locs:
             hot_pixels = pixels
         else:
-            raise ConfigurationError("Hot pixels method is not 'random', but `hot_pix_locs` contains no (col, row) location indices of hot pixels.")
+            raise ConfigurationError(
+                "Hot pixels method is not 'random', but `hot_pix_locs` contains "
+                "no (col, row) location indices of hot pixels."
+            )
 
         for pix in hot_pixels:
-            image[*pix] += offset
+            image[*pix] += config.hot_pix_offset
 
         return image
 
     @classmethod
-    def add_noise(cls, n, images, config):
+    def add_noise(cls, images, config):
         """Adds read noise (gaussian), dark noise (poissonian) and sky
         background (poissonian) noise to the image.
 
@@ -723,7 +796,7 @@ def add_noise(cls, n, images, config):
         shape = images.shape
 
         # add read noise
-        images += self.read_noise_gen(
+        images += config.read_noise_gen(
             scale=config.read_noise / config.gain,
             size=shape
         )
@@ -733,7 +806,7 @@ def add_noise(cls, n, images, config):
         images += config.dark_current_gen(current, size=shape)
 
         # add sky counts
-        images += self.sky_count_gen(
+        images += config.sky_count_gen(
             lam=config.sky_level * config.gain,
             size=shape
         ) / config.gain
@@ -761,14 +834,20 @@ def gen_base_image(cls, config=None, src_cat=None):
         # empty image
         base = np.zeros(config.shape, dtype=np.float32)
         base += config.bias
-        base = cls.add_bad_cols(base, config)
         base = cls.add_hot_pixels(base, config)
+        base = cls.add_bad_cols(base, config)
         if src_cat is not None:
-            add_model_objects(base, src_cat.table, config.model)
+            add_model_objects(base, src_cat.table,
+                              config.model(x_stddev=1, y_stddev=1))
 
         return base
 
-    def __init__(self, image=None, config=None, src_cat=None, obj_cat=None):
-        conf = self.default_config(config)
-        base = self.gen_base_image(conf)
-        super().__init__(image=base, config=conf, src_cat=src_cat, obj_cat=obj_cat)
+    def __init__(self, image=None, config=None, src_cat=None, obj_cat=None, **kwargs):
+        conf = self.default_config(config=config, **kwargs)
+        # static objects are added in SimpleImage init
+        super().__init__(
+            image=self.gen_base_image(conf),
+            config=conf,
+            src_cat=src_cat,
+            obj_cat=obj_cat
+        )
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index 033ecee1f..3947d978c 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -5,16 +5,18 @@
 from astropy.io.fits import Header
 
 from .utils import header_archive_to_table
+from .config import Config
 
 
 __all__ = [
     "HeaderFactory",
+    "HeaderFactoryConfig",
     "ArchivedHeader",
 ]
 
 
-class HeaderFactory:
-    base_primary = {
+class HeaderFactoryConfig(Config):
+    primary_template = {
         "EXTNAME": "PRIMARY",
         "NAXIS": 0,
         "BITPIX": 8,
@@ -26,9 +28,16 @@ class HeaderFactory:
         "OBSERVAT": "CTIO"
     }
 
-    base_ext = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "BITPIX": 32}
+    ext_template = {
+        "NAXIS": 2,
+        "NAXIS1": 2048,
+        "NAXIS2": 4096,
+        "CRPIX1": 1024,
+        "CPRIX2": 2048,
+        "BITPIX": 32
+    }
 
-    base_wcs = {
+    wcs_template = {
         "ctype": ["RA---TAN", "DEC--TAN"],
         "crval": [351, -5],
         "cunit": ["deg", "deg"],
@@ -36,6 +45,19 @@ class HeaderFactory:
         "cd": [[-1.44e-07, 7.32e-05], [7.32e-05, 1.44e-05]],
     }
 
+    def __init__(self, config=None, method="default", shape=None, **kwargs):
+        super().__init__(config=config, method=method, **kwargs)
+
+        if shape is not None:
+            self.ext_template["NAXIS1"] = shape[1]
+            self.ext_template["NAXIS2"] = shape[0]
+            self.ext_template["CRPIX1"] = shape[1]//2
+            self.ext_template["CRPIX2"] = shape[0]//2
+
+
+class HeaderFactory:
+    default_config = HeaderFactoryConfig
+
     def __validate_mutables(self):
         # !xor
         if bool(self.mutables) != bool(self.callbacks):
@@ -59,7 +81,10 @@ def __validate_mutables(self):
                     "provide the required metadata keys."
                 )
 
-    def __init__(self, metadata=None, mutables=None, callbacks=None):
+    def __init__(self, metadata=None, mutables=None, callbacks=None,
+                 config=None, **kwargs):
+        self.config = self.default_config(config=config, **kwargs)
+
         cards = [] if metadata is None else metadata
         self.header = Header(cards=cards)
 
@@ -67,50 +92,61 @@ def __init__(self, metadata=None, mutables=None, callbacks=None):
         self.callbacks = callbacks
         self.__validate_mutables()
 
-    def mock(self, hdu=None, **kwargs):
+        self.is_dynamic = self.mutables is None
+        self.counter = 0
+
+    def get(self):
         if self.mutables is not None:
             for i, mutable in enumerate(self.mutables):
                 self.header[mutable] = self.callbacks[i](self.header[mutable])
-
         return self.header
 
+    def mock(self, **kwargs):
+        self.counter += 1
+        return self.get(**kwargs)
+
     @classmethod
-    def gen_wcs(cls, crval, metadata=None):
-        metadata = cls.base_wcs if metadata is None else metadata
+    def gen_wcs(cls, metadata):
         wcs = WCS(naxis=2)
         for k, v in metadata.items():
             setattr(wcs.wcs, k, v)
         return wcs.to_header()
 
     @classmethod
-    def gen_header(cls, base, metadata, extend, add_wcs):
-        header = Header(base) if extend else Header()
-        header.update(metadata)
+    def gen_header(cls, base, overrides, wcs_base=None):
+        header = Header() if base is None else Header(base)
+        header.update(overrides)
 
-        if add_wcs:
+        if wcs_base is not None:
             naxis1 = header.get("NAXIS1", False)
             naxis2 = header.get("NAXIS2", False)
             if not all((naxis1, naxis2)):
-                raise ValueError("Adding a WCS to the header requires NAXIS1 and NAXIS2 keys.")
-            crpix = [naxis1 / 2.0, naxis2 / 2.0]
-            header.update(cls.gen_wcs(crpix))
+                raise ValueError("Adding a WCS to the header requires "
+                                 "NAXIS1 and NAXIS2 keys.")
+            header.update(cls.gen_wcs(wcs_base))
 
         return header
 
     @classmethod
-    def from_base_primary(cls, metadata=None, mutables=None, callbacks=None, extend_base=True, add_wcs=False):
-        hdr = cls.gen_header(cls.base_primary, metadata, extend_base, add_wcs)
-        return cls(hdr, mutables, callbacks)
+    def from_primary_template(cls, overrides=None, mutables=None, callbacks=None,
+                              config=None):
+        config = cls.default_config(config=config)
+        hdr = cls.gen_header(base=config.primary_template, overrides=overrides)
+        return cls(hdr, mutables, callbacks, config=config)
 
     @classmethod
-    def from_base_ext(
-        cls, metadata=None, mutables=None, callbacks=None, extend_base=True, add_wcs=True, dims=None
-    ):
-        hdr = cls.gen_header(cls.base_ext, metadata, extend_base, add_wcs)
-        return cls(hdr, mutables, callbacks)
-
-
-class ArchivedHeader(HeaderFactory):
+    def from_ext_template(cls, overrides=None, mutables=None, callbacks=None,
+                          wcs=None, config=None):
+        config = cls.default_config(config=config)
+        hdr = cls.gen_header(
+            base=config.ext_template,
+            overrides=overrides,
+            wcs_base=config.wcs_template if wcs is None else wcs
+        )
+        return cls(hdr, mutables, callbacks, config=config)
+
+
+class ArchivedHeaderConfig(HeaderFactoryConfig):
     # will almost never be anything else. Rather, it would be a miracle if it
     # were something else, since FITS standard shouldn't allow it. Further
     # casting by some packages will always be casting implemented in terms of
@@ -123,31 +159,61 @@ class ArchivedHeader(HeaderFactory):
     }
     """Map between type names and types themselves."""
 
-    def __init__(self, archive_name, fname, compression="bz2", format="ascii.ecsv"):
-        self.table = header_archive_to_table(archive_name, fname, compression, format)
+    compression = "bz2"
+
+    format = "ascii.ecsv"
+
+    n_hdrs_per_hdu = 1
+
+class ArchivedHeader(HeaderFactory):
+    default_config = ArchivedHeaderConfig
+
+    def __init__(self, archive_name, fname, config=None, **kwargs):
+        super().__init__(config, **kwargs)
+        self.table = header_archive_to_table(
+            archive_name, fname, self.config.compression, self.config.format
+        )
 
         # Create HDU groups for easier iteration
-        self.table = self.table.group_by(["filename", "hdu"])
+        self.table = self.table.group_by("filename")
         self.n_hdus = len(self.table)
 
-        # Internal counter for the current fits index,
-        # so that we may auto-increment it and avoid returning
-        # the same data all the time.
-        self._current = 0
-
     def lexical_cast(self, value, format):
         """Cast str literal of a type to the type itself. Supports just
         the builtin Python types.
         """
-        if format in self.lexical_type_map:
-            return self.lexical_type_map[format](value)
+        if format in self.config.lexical_type_map:
+            return self.config.lexical_type_map[format](value)
         return value
 
-    def mock(self, hdu=None):
+    def get_item(self, group_idx, hdr_idx):
         header = Header()
-        warnings.filterwarnings("ignore", category=AstropyUserWarning)
-        for k, v, f in self.table.groups[self._current]["keyword", "value", "format"]:
+        # this is equivalent to one hdulist worth of headers
+        group = self.table.groups[group_idx]
+        # this is equivalent to one HDU's header
+        subgroup = group.group_by("hdu")
+        for k, v, f in subgroup.groups[hdr_idx]["keyword", "value", "format"]:
             header[k] = self.lexical_cast(v, f)
         warnings.resetwarnings()
-        self._current += 1
         return header
+
+    def get(self, group_idx):
+        headers = []
+        # this is a bit repetitive but it saves recreating
+        # groups for one HDUL-equivalent many times
+        group = self.table.groups[group_idx]
+        subgroup = group.group_by("hdu")
+        headers = []
+        for subgroup in subgroup.groups:
+            header = Header()
+            for k, v, f in subgroup["keyword", "value", "format"]:
+                header[k] = self.lexical_cast(v, f)
+            headers.append(header)
+        return headers
+
+    def mock(self, n=1):
+        res = []
+        for _ in range(n):
+            res.append(self.get(self.counter))
+            self.counter += 1
+        return res

From 3e41cddcfa2e105cd66f30d2ad6bbefd19450ee4 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 5 Aug 2024 09:29:09 -0700
Subject: [PATCH 16/27] Fixup, merge into subsequent pr with a better message.

---
 src/kbmod/mocking/__init__.py  |   1 +
 src/kbmod/mocking/callbacks.py |  23 ++
 src/kbmod/mocking/catalogs.py  | 126 ++++----
 src/kbmod/mocking/fits.py      | 521 ++++++++++-----------------------
 src/kbmod/mocking/fits_data.py |   1 -
 src/kbmod/mocking/headers.py   |  84 +++---
 tests/test_mocking.py          | 225 ++++++++++++++
 7 files changed, 494 insertions(+), 487 deletions(-)
 create mode 100644 src/kbmod/mocking/callbacks.py
 create mode 100644 tests/test_mocking.py

diff --git a/src/kbmod/mocking/__init__.py b/src/kbmod/mocking/__init__.py
index b79086419..2d479f17b 100644
--- a/src/kbmod/mocking/__init__.py
+++ b/src/kbmod/mocking/__init__.py
@@ -3,3 +3,4 @@
 from .headers import *
 from .fits_data import *
 from .fits import *
+from .callbacks import *
diff --git a/src/kbmod/mocking/callbacks.py b/src/kbmod/mocking/callbacks.py
new file mode 100644
index 000000000..0a3aabbbe
--- /dev/null
+++ b/src/kbmod/mocking/callbacks.py
@@ -0,0 +1,23 @@
+__all__ = [
+    "IncrementObstime",
+    "ObstimeIterator",
+]
+
+class IncrementObstime:
+    def __init__(self, start, dt):
+        self.start = start
+        self.dt = dt
+
+    def __call__(self, mut_val):
+        curr = self.start
+        self.start += self.dt
+        return curr
+
+class ObstimeIterator:
+    def __init__(self, obstimes):
+        self.obstimes = obstimes
+        self.generator  = (t for t in obstimes)
+
+    def __call__(self, mut_val):
+        return next(self.generator)
+
diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 6e4fc853f..0ef2a4e64 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -23,8 +23,9 @@ def gen_catalog(n, param_ranges, seed=None):
     for param_name, (lower, upper) in param_ranges.items():
         cat[param_name] = rng.uniform(lower, upper, n)
 
-    if "stddev" in param_ranges:
+    if "x_stddev" not in param_ranges:
         cat["x_stddev"] = cat["stddev"]
+    if "y_stddev" not in param_ranges:
         cat["y_stddev"] = cat["stddev"]
 
     # conversion assumes a gaussian
@@ -44,6 +45,7 @@ def mock(self, *args, **kwargs):
 
 
 class SimpleCatalogConfig(Config):
+    mode = "static"
     return_copy = False
     seed = None
     n = 100
@@ -53,69 +55,34 @@ class SimpleCatalogConfig(Config):
 class SimpleCatalog(CatalogFactory):
     default_config = SimpleCatalogConfig
 
-    def __init_from_table(self, table, config=None, **kwargs):
-        config = self.default_config(config=config, **kwargs)
-        config.n = len(table)
-        params = {}
-        for col in table.keys():
-            params[col] = (table[col].min(), table[col].max())
-        config.param_ranges.update(params)
-        return config, table
-
-    def __init_from_config(self, config, **kwargs):
-        config = self.default_config(config=config, method="subset", **kwargs)
-        table = gen_catalog(config.n, config.param_ranges, config.seed)
-        return config, table
-
-    def __init_from_ranges(self, **kwargs):
-        param_ranges = kwargs.pop("param_ranges", None)
-        if param_ranges is None:
-            param_ranges = {k: v for k, v in kwargs.items() if k in self.default_config.param_ranges}
-            kwargs = {k: v for k, v in kwargs.items() if k not in self.default_config.param_ranges}
-
-        config = self.default_config(**kwargs, method="subset")
-        config.param_ranges.update(param_ranges)
-        return self.__init_from_config(config=config)
-
-    def __init__(self, table=None, config=None, **kwargs):
-        if table is not None:
-            config, table = self.__init_from_table(table, config=config, **kwargs)
-        elif isinstance(config, Config):
-            config, table = self.__init_from_config(config=config, **kwargs)
-        elif isinstance(config, dict) or kwargs:
-            config = {} if config is None else config
-            config, table = self.__init_from_ranges(**{**config, **kwargs})
-        else:
-            raise ValueError(
-                "Expected table or config, or keyword arguments of expected "
-                f"catalog value ranges, got:\n table={table}\n config={config} "
-                f"\n kwargs={kwargs}"
-            )
-
+    def __init__(self, config, table, **kwargs):
+        config = self.default_config(**kwargs)
         self.config = config
         self.table = table
         self.current = 0
 
     @classmethod
     def from_config(cls, config, **kwargs):
-        config = cls.default_config(config=config, method="subset", **kwargs)
-        return cls(gen_catalog(config.n, config.param_ranges, config.seed), config=config)
-
+        config = cls.default_config(config=config, **kwargs)
+        table = gen_catalog(config.n, config.param_ranges, config.seed)
+        return cls(config, table)
+#
     @classmethod
-    def from_ranges(cls, n=None, config=None, **kwargs):
-        config = cls.default_config(n=n, config=config, method="subset")
-        config.param_ranges.update(**kwargs)
+    def from_defaults(cls, param_ranges=None, **kwargs):
+        config = cls.default_config(**kwargs)
+        if param_ranges is not None:
+            config.param_ranges.update(param_ranges)
         return cls.from_config(config)
 
     @classmethod
-    def from_table(cls, table):
-        config = cls.default_config()
+    def from_table(cls, table, **kwargs):
+        config = cls.default_config(**kwargs)
         config.n = len(table)
         params = {}
         for col in table.keys():
             params[col] = (table[col].min(), table[col].max())
         config["param_ranges"] = params
-        return cls(table, config=config)
+        return cls(config, table)
 
     def mock(self):
         self.current += 1
@@ -140,6 +107,7 @@ class SourceCatalog(SimpleCatalog):
 
 
 class ObjectCatalogConfig(SimpleCatalogConfig):
+    mode = "progressive" # folding
     param_ranges = {
         "amplitude": [0.1, 3.0],
         "x_mean": [0., 4096.],
@@ -154,36 +122,58 @@ class ObjectCatalogConfig(SimpleCatalogConfig):
 class ObjectCatalog(SimpleCatalog):
     default_config = ObjectCatalogConfig
 
-    def __init__(self, table=None, obstime=None, config=None, **kwargs):
-        # put return_copy into kwargs to override whatever user might have
-        # supplied, and to guarantee the default is overriden
+    def __init__(self, config, table, **kwargs):
+        # Obj cat always has to return a copy
         kwargs["return_copy"] = True
-        super().__init__(table=table, config=config, **kwargs)
+        super().__init__(config, table, **kwargs)
         self._realization = self.table.copy()
-        self.obstime = 0 if obstime is None else obstime
+        self.mode = self.config.mode
+
+    @property
+    def mode(self):
+        return self._mode
+
+    @mode.setter
+    def mode(self, val):
+        if val == "folding":
+            self._gen_realization = self.fold
+        elif val == "progressive":
+            self._gen_realization = self.next
+        elif val == "static":
+            self._gen_realization = self.static
+        else:
+            raise ValueError(
+                "Unrecognized object catalog mode. Expected 'static', "
+                f"'progressive', or 'folding', got {val} instead."
+            )
+        self._mode = val
 
     def reset(self):
         self.current = 0
         self._realization = self.table.copy()
 
-    def gen_realization(self, t=None, dt=None, **kwargs):
-        if t is None and dt is None:
-            return self._realization
+    def static(self, **kwargs):
+        return self.table.copy()
 
-        dt = dt if t is None else t - self.obstime
-        self._realization["x_mean"] += self.table["vx"] * dt
-        self._realization["y_mean"] += self.table["vy"] * dt
-        return self._realization
+    def next(self, dt, **kwargs):
+        self._realization["x_mean"] += self._realization["vx"] * dt
+        self._realization["y_mean"] += self._realization["vy"] * dt
+        self.current += 1
+        return self._realization.copy()
+
+    def fold(self, t, **kwargs):
+        self._realization = self.table[self.table["obstime"] == t]
+        self.current += 1
+        return self._realization.copy()
 
     def mock(self, n=1, **kwargs):
-        breakpoint()
-        if n == 1:
-            data = self.gen_realization(**kwargs)
-            self.current += 1
+        data = []
+
+        if self.mode == "folding":
+            for t in kwargs["t"]:
+                data.append(self.fold(t=t))
         else:
-            data = []
             for i in range(n):
-                data.append(self.gen_realization(**kwargs).copy())
-                self.current += 1
+                data.append(self._gen_realization(**kwargs))
 
         return data
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index 93ef68c7b..e52edaeeb 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -1,22 +1,12 @@
-import abc
-import types
-import functools
-
-import numpy as np
-from astropy.wcs import WCS
-from astropy.table import Table
-from astropy.modeling import models
 from astropy.io.fits import (
     HDUList,
     PrimaryHDU,
     CompImageHDU,
-    BinTableHDU,
-    Header
+    BinTableHDU
 )
 
-from .config import Config
-from .headers import HeaderFactory, ArchivedHeader, HeaderFactoryConfig
-from .catalogs import gen_catalog, SimpleCatalog, SourceCatalog, ObjectCatalog
+from .callbacks import IncrementObstime
+from .headers import HeaderFactory, ArchivedHeader
 from .fits_data import (
     DataFactoryConfig,
     DataFactory,
@@ -27,427 +17,218 @@
     SimpleVarianceConfig,
     SimpleVariance,
     SimpleMaskConfig,
-    SimpleMask,
-    add_model_objects
+    SimpleMask
 )
 
 
 __all__ = [
-    "callback",
-    "EmptyFitsConfig",
     "EmptyFits",
-    "SimpleFitsConfig",
     "SimpleFits",
-    "DECamImdiffConfig",
     "DECamImdiff",
 ]
 
 
-class HDUListFactoryConfig(Config):
-    validate_header = False
-    """Call ``Header.update`` instead of assigning header values. This enforces
-    FITS standards and may strip non-standard header keywords."""
-
-    update_header = False
-    """After mocking and assigning data call ``Header.update_header``. This
-    enforces header to be consistent to the data type and shape. May alter or
-    remove keywords from the mocked header."""
-
-
-class HDUListFactory(abc.ABC):
-    default_config = HDUListFactoryConfig
-
-    def __init__(self, config=None, **kwargs):
-        self.config = self.default_config(config=config, **kwargs)
-        self.current = 0
-
-    def hdu_cast(self, hdu_cls, hdr, data=None, config=None, **kwargs):
-        hdu = hdu_cls()
-
-        if self.config.validate_header:
-            hdu.header.update(hdr)
-        else:
-            hdu.header = hdr
+class NoneFactory:
+    "Kinda makes some code later prettier. Kinda"
+    def mock(self, n):
+        return [None, ]*n
 
-        if data is not None:
-            hdu.data = data
-            if self.config.update_header:
-                hdu.update_header()
 
-        return hdu
+def hdu_cast(hdu_cls, hdr, data=None,
+             validate_header=False, update_header=False):
+    hdu = hdu_cls()
 
-    @abc.abstractmethod
-    def mock(self, n=1):
-        raise NotImplementedError()
-
-
-# I am sure a decorator like this must exist somewhere in functools, but can't
-# find it and I'm doing something wrong with functools.partial because that's
-# strictly right-side binding?
-# multiple options on how to handle callbacks as class members
-# callbacks=[lambda old: old+0.01, ]
-# callbacks=[functools.partial(self.increment_obstime, dt=0.001), ]
-# but they look a bit ugly...
-def callback(func):
-    if isinstance(func, types.FunctionType):
-        # bound methods
-        def wrapper(*args, **kwargs):
-            @functools.wraps(func)
-            def f(*fargs, **fkwargs):
-                kwargs.update(fkwargs)
-                return func(*(args + fargs), **kwargs)
-            return f
+    if validate_header:
+        hdu.header.update(hdr)
     else:
-        # functions, static methods
-        def wrapper(*args, **kwargs):
-            @functools.wraps(func)
-            def f(*fargs, **fkwargs):
-                kwargs.update(fkwargs)
-                return func(*fargs, **kwargs)
-            return f
-    return wrapper
-
-
-class EmptyFitsConfig(HDUListFactoryConfig):
-    editable_images = False
-    separate_masks = False
-    writeable_mask = False
-    dt = 0.001
+        hdu.header = hdr
 
-    shape = (100, 100)
-    """Dimensions of the generated images."""
+    if data is not None:
+        hdu.data = data
+        if update_header:
+            hdu.update_header()
 
+    return hdu
 
-class EmptyFits(HDUListFactory):
-    default_config = EmptyFitsConfig
 
-    @callback
-    @staticmethod
-    def increment_obstime(old, dt):
-        return old + dt
+def hdu_cast_array(hdu_cls, hdr, data,
+                   validate_header=False, update_header=False):
+    hdus = []
+    for hdr, dat in zip(hdr, data):
+        hdus.append(hdu_cast(hdu_cls, hdr, dat))
+    return hdus
 
-    def __init__(self, metadata=None, config=None, **kwargs):
-        super().__init__(config=config, method="extend", **kwargs)
 
-        # 1. Update all the default configs. Use class configs that simplify
-        #    and unite the many smaller config settings of each underlying factory.
-        hdr_conf = HeaderFactoryConfig(config=self.config, method="subset", shape=self.config.shape)
-
-        # 2. Set up Header and Data factories that go into creating HDUs
-        #    2.1) First headers, since that metadata specified data formats
-        self.primary_hdr = HeaderFactory.from_primary_template(
-            overrides=metadata,
+class EmptyFits:
+    def __init__(self, header=None, shape=(100, 100), start_mjd=60310,
+                 step_mjd=0.001, editable_images=False, editable_masks=False):
+        self.prim_hdr = HeaderFactory.from_primary_template(
+            overrides=header,
             mutables=["OBS-MJD"],
-            callbacks=[self.increment_obstime(dt=self.config.dt)],
-            config=hdr_conf
+            callbacks=[IncrementObstime(start=start_mjd, dt=step_mjd)]
         )
 
-        self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, config=hdr_conf)
-        self.var_hdr = HeaderFactory.from_ext_template({"EXTNAME": "VARIANCE"}, config=hdr_conf)
-        self.mask_hdr = HeaderFactory.from_ext_template({"EXTNAME": "MASK"}, config=hdr_conf)
+        self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, shape=shape)
+        self.var_hdr = HeaderFactory.from_ext_template({"EXTNAME": "VARIANCE"}, shape=shape)
+        self.mask_hdr = HeaderFactory.from_ext_template({"EXTNAME": "MASK"}, shape=shape)
 
         #   2.2) Then data factories, attempt to save performance and memory
-        #        where possible by really only allocating 1 array whenever the data
-        #        is read-only and content-static between created HDUs.
-        writeable, return_copy = False, False
-        if self.config.editable_images:
-            writeable, return_copy = True, True
-
-        self.data = DataFactory.from_header(
+        #        where possible by really only allocating 1 array whenever the
+        #        data is read-only and content-static between created HDUs.
+        self.img_data = DataFactory.from_header(
             kind="image",
             header=self.img_hdr.header,
-            writeable=writeable,
-            return_copy=return_copy
+            writeable=editable_images,
+            return_copy=editable_images
+        )
+        self.mask_data = DataFactory.from_header(
+            kind="image",
+            header=self.mask_hdr.header,
+            return_copy=editable_masks,
+            writeable=editable_masks
         )
-        self.shared_data = DataFactory.from_header("image", self.mask_hdr.header,
-                                                   writeable=self.config.writeable_mask)
+
+        self.current = 0
 
     def mock(self, n=1):
-        # 3) Finally when mocking, vectorize as many operations as possible.
-        #    The amount of data scales fast with number of images generated so
-        #    that even modest iterative generation of data costs significantly.
-        #    (F.e. for each HDU - 3 images are allocated, if we use DECam, for
-        #    each HDU, 62 to 70 images can be generated). Many times generated values
-        #    are not trivial zeros, but randomly drawn, modified, and then
-        #    calculated.
-        var_hdr = self.var_hdr.mock()
-        mask_hdr = self.mask_hdr.mock()
-
-        imgs = self.data.mock(n)
-        variances = self.data.mock(n)
-        masks = self.shared_data.mock(n)
+        img_hdr = self.img_hdr.mock()[0]
+        var_hdr = self.var_hdr.mock()[0]
+        mask_hdr  = self.mask_hdr.mock()[0]
+        images = self.img_data.mock(n=n)
+        variances = self.img_data.mock(n=n)
+        masks = self.mask_data.mock(n=n)
 
         hduls = []
         for i in range(n):
             hduls.append(HDUList(hdus=[
-                self.hdu_cast(PrimaryHDU, self.primary_hdr.mock()),
-                self.hdu_cast(CompImageHDU, self.img_hdr.mock(), imgs[i]),
-                self.hdu_cast(CompImageHDU, var_hdr, variances[i]),
-                self.hdu_cast(CompImageHDU, mask_hdr, masks[i])
+                PrimaryHDU(header=self.prim_hdr.mock()[0]),
+                CompImageHDU(header=img_hdr, data=images[i]),
+                CompImageHDU(header=var_hdr, data=variances[i]),
+                CompImageHDU(header=mask_hdr, data=masks[i])
             ]))
 
         self.current += n
         return hduls
 
 
-class SimpleFitsConfig(HDUListFactoryConfig):
-    editable_images = False
-    separate_masks = False
-    writeable_mask = False
-    noise_generation = "simplistic"
-    dt = 0.001
-
-    shape = (100, 100)
-    """Dimensions of the generated images."""
-
-
-class SimpleFits(HDUListFactory):
-    default_config = SimpleFitsConfig
-
-    @callback
-    @staticmethod
-    def increment_obstime(old, dt):
-        return old + dt
-
-    def __init__(self, metadata=None, source_catalog=None, object_catalog=None,
-                 config=None,  **kwargs):
-        super().__init__(config=config, method="extend", **kwargs)
-
-        self.src_cat = source_catalog
-        self.obj_cat = object_catalog
-
-        # 1. Update all the default configs using more user-friendly kwargs
-        hdr_conf = HeaderFactoryConfig(config=self.config, shape=self.config.shape, method="subset")
-
-        if self.config.noise_generation == "realistic":
-            img_cfg = SimulatedImageConfig(config=self.config, method="subset")
-        else:
-            img_cfg = SimpleImageConfig(config=self.config, method="subset")
-        var_cfg = SimpleVarianceConfig(config=self.config, method="subset")
-        mask_cfg = SimpleMaskConfig(config=self.config, method="subset")
-
+class SimpleFits:
+    def __init__(self, header=None, shape=(100, 100), start_mjd=60310, step_mjd=0.001,
+                 with_noise=False, noise="simplistic", src_cat=None, obj_cat=None):
         # 2. Set up Header and Data factories that go into creating HDUs
         #    2.1) First headers, since that metadata specified data formats
-        self.primary_hdr = HeaderFactory.from_primary_template(
-            overrides=metadata,
+        self.prim_hdr = HeaderFactory.from_primary_template(
+            overrides=header,
             mutables=["OBS-MJD"],
-            callbacks=[self.increment_obstime(dt=self.config.dt)],
-            config=hdr_conf
+            callbacks=[IncrementObstime(start=start_mjd, dt=step_mjd)]
         )
-        self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, config=hdr_conf)
-        self.var_hdr = HeaderFactory.from_ext_template({"EXTNAME": "VARIANCE"}, config=hdr_conf)
-        self.mask_hdr = HeaderFactory.from_ext_template({"EXTNAME": "MASK"}, config=hdr_conf)
+
+        self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, shape=shape)
+        self.var_hdr = HeaderFactory.from_ext_template({"EXTNAME": "VARIANCE"}, shape=shape)
+        self.mask_hdr = HeaderFactory.from_ext_template({"EXTNAME": "MASK"}, shape=shape)
 
         #   2.2) Then data factories
-        if self.config.noise_generation == "realistic":
-            self.img_data = SimulatedImage(src_cat=self.src_cat, config=img_cfg)
+        if noise == "realistic":
+            self.img_data = SimulatedImage(shape=shape, src_cat=src_cat, add_noise=with_noise)
         else:
-            self.img_data = SimpleImage(src_cat=self.src_cat, config=img_cfg)
-        self.var_data = SimpleVariance(self.img_data.base, config=var_cfg)
-        self.mask_data = SimpleMask.from_image(self.img_data.base, config=mask_cfg)
+            self.img_data = SimpleImage(shape=shape, src_cat=src_cat, add_noise=with_noise)
+        self.var_data = SimpleVariance(self.img_data.base)
+        self.mask_data = SimpleMask.from_image(self.img_data.base)
 
-    @classmethod
-    def from_defaults(cls, config=None, sources=None, objects=None, **kwargs):
-        config = cls.default_config(config, **kwargs, method="extend")
-        cat_lims = {"x_mean": [0, config.shape[1]], "y_mean": [0, config.shape[0]]}
-        src_cat, obj_cat = None, None
-        if sources is not None:
-            if isinstance(sources, SimpleCatalog):
-                src_cat = sources
-            elif isinstance(sources, Table):
-                src_cat = SourceCatalog(table=sources)
-            elif isinstance(sources, Config) or sources == True:
-                sources.param_ranges.update(cat_lims)
-                src_cat = SourceCatalog(**sources)
-            elif isinstance(sources, dict):
-                sources.update(cat_lims)
-                src_cat = SourceCatalog(**sources)
-            elif isinstance(sources, int):
-                src_cat = SourceCatalog(n=sources, **cat_lims)
-            elif sources is True:
-                obj_cat = SourceCatalog(**cat_lims)
-            else:
-                raise ValueError("Sources are expected to be a catalog, table, config or config overrides.")
-        if objects is not None:
-            if isinstance(objects, SimpleCatalog):
-                obj_cat = objects
-            elif isinstance(objects, Table):
-                obj_cat = ObjectCatalog(table=objects)
-            elif isinstance(objects, Config):
-                objects.param_ranges.update(cat_lims)
-                obj_cat = ObjectCatalog(**objects)
-            elif isinstance(objects, dict):
-                objects.update(cat_lims)
-                obj_cat = ObjectCatalog(**objects)
-            elif isinstance(objects, int):
-                obj_cat = ObjectCatalog(n=objects, **cat_lims)
-            elif objects is True:
-                obj_cat = ObjectCatalog(**cat_lims)
-            else:
-                raise ValueError("Objects are expected to be a catalog, table, config or config overrides.")
-        return cls(source_catalog=src_cat, object_catalog=obj_cat, config=config, **kwargs)
+        self.start_mjd = start_mjd
+        self.step_mjd = step_mjd
+        self.obj_cat = obj_cat
+        self.current = 0
 
     def mock(self, n=1):
+        prim_hdrs = self.prim_hdr.mock(n=n)
+        img_hdrs = self.img_hdr.mock(n=n)
+        var_hdrs = self.var_hdr.mock(n=n)
+        mask_hdrs = self.mask_hdr.mock(n=n)
+
         obj_cats = None
         if self.obj_cat is not None:
-            obj_cats = self.obj_cat.mock(n, dt=self.config.dt)
-
-        var_hdr = self.var_hdr.mock()
-        mask_hdr = self.mask_hdr.mock()
+            kwargs = {"dt": self.step_mjd, "t": [hdr["OBS-MJD"] for hdr in prim_hdrs]}
+            obj_cats = self.obj_cat.mock(n=n, **kwargs)
 
         images = self.img_data.mock(n, obj_cats=obj_cats)
         variances = self.var_data.mock(images=images)
         masks = self.mask_data.mock(n)
 
         hduls = []
-        for i in range(n):
+        for ph, ih, vh, mh, imd, vd, md in zip(prim_hdrs, img_hdrs, var_hdrs,
+                                               mask_hdrs, images, variances, masks):
             hduls.append(HDUList(hdus=[
-                self.hdu_cast(PrimaryHDU, self.primary_hdr.mock()),
-                self.hdu_cast(CompImageHDU, self.img_hdr.mock(), images[i]),
-                self.hdu_cast(CompImageHDU, var_hdr, variances[i]),
-                self.hdu_cast(CompImageHDU, mask_hdr, masks[i])
+                PrimaryHDU(header=ph),
+                CompImageHDU(header=ih, data=imd),
+                CompImageHDU(header=vh, data=vd),
+                CompImageHDU(header=mh, data=md)
             ]))
 
         self.current += n
         return hduls
 
 
-class DECamImdiffConfig2(HDUListFactoryConfig):
-    archive_name = "headers_archive.tar.bz2"
-    file_name = "decam_imdiff_headers.ecsv"
-    n_hdrs_per_hdu = 16
-
-
-class DECamImdiff2(HDUListFactory):
-    default_config = DECamImdiffConfig2
-
-    def __init__(self, config=None, **kwargs):
-        super().__init__(config=config, **kwargs)
-        self.hdr_factory = ArchivedHeader(self.config.archive_name, self.config.file_name)
-
-    def mock(self, n=1):
-        hdrs = self.hdr_factory.mock(n)
-
-        hduls = []
-        for hdul_idx in range(n):
-            hduls.append(HDUList(hdus=[
-                self.hdu_cast(PrimaryHDU,   hdrs[hdul_idx][0]),
-                self.hdu_cast(CompImageHDU, hdrs[hdul_idx][1]),
-                self.hdu_cast(CompImageHDU, hdrs[hdul_idx][2]),
-                self.hdu_cast(CompImageHDU, hdrs[hdul_idx][3]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][4]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][5]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][6]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][7]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][8]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][9]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][10]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][11]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][12]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][13]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][14]),
-                self.hdu_cast(BinTableHDU, hdrs[hdul_idx][15]),
-            ]))
-
-        self.current += 1
-        return hduls
-
-
-
-
-class DECamImdiffConfig(HDUListFactoryConfig):
-    archive_name = "headers_archive.tar.bz2"
-    file_name = "decam_imdiff_headers.ecsv"
-
-    with_data=False
-
-    editable_images = False
-    separate_masks = False
-    writeable_mask = False
-    noise_generation = "simplistic"
-    dt = 0.001
-
-    shape = (100, 100)
-    """Dimensions of the generated images."""
-
-
-class NoneFactory:
-    "Kinda makes some code later prettier. Kinda"
-    def mock(self, n):
-        return [None, ]*n
-
-
-class DECamImdiff(HDUListFactory):
-    default_config = DECamImdiffConfig
-
-    def __init__(self, source_catalog=None, object_catalog=None, config=None, **kwargs):
-        super().__init__(config=config, **kwargs)
-
-        # 1. Get the header factory - this is different than before. In a
-        # header preserving factory, it's the headers that dictate the shape
-        # and format of data. Since these are also optional for this class, we
-        # just create empty placeholders for data and only fill it with
-        # factories if we need to.
-        self.hdr_factory = ArchivedHeader(self.config.archive_name,
-                                          self.config.file_name)
-
-        self.hdu_types = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
-        self.hdu_types.extend([BinTableHDU, ]*12)
-        self.data = [NoneFactory(), ]*16
-
-        self.src_cat = source_catalog
-        self.obj_cat = object_catalog
-
-        if self.config.with_data:
-            self.__init_data_factories()
-
-    def __init_data_factories(self):
-        # 2. To fill in data placehodlers, we get an example set of headers in
-        # a file. Since they dictate data, we must derive whatever configs we
-        # need to and use them as overrides to trump defaults and user-given
-        # values. This is the point at which we rely on user telling this
-        # factory what the important header keys are since these could be
-        # non-standard. And since these change for each HDU, it is rather
-        # difficult to generalize without resorting to iterative and fully
-        # dynamical data generation - which will be slow. The only mercy is
-        # that we only have to do it for the HDUs we care about and use factory
-        # methods for the rest.
-        headers = self.hdr_factory.get(0)
-
-        img_shape = (headers[1]["NAXIS1"], headers[1]["NAXIS2"])
-        img_overrides = {
-            "shape": img_shape,
-            "dtype": DataFactoryConfig.bitpix_type_map[headers[1]["BITPIX"]],
-        }
-
-        if self.config.noise_generation == "realistic":
-            img_cfg = SimulatedImageConfig(config=self.config, **img_overrides,  method="subset")
-        else:
-            img_cfg = SimpleImageConfig(config=self.config,  **img_overrides, method="subset")
-        var_cfg = SimpleVarianceConfig(config=self.config, **img_overrides, method="subset")
-        mask_cfg = SimpleMaskConfig(config=self.config, **img_overrides, method="subset")
-
-        #   2.1) Now we can instantiate data factories with correct configs
-        #        and fill in the data placeholder
-        if self.config.noise_generation == "realistic":
-            self.img_data = SimulatedImage(src_cat=self.src_cat, config=img_cfg)
-        else:
-            self.img_data = SimpleImage(src_cat=self.src_cat, config=img_cfg)
-            self.var_data = SimpleVariance(self.img_data.base, config=var_cfg)
-            self.mask_data = SimpleMask.from_image(self.img_data.base, config=mask_cfg)
-
-        self.hdu_types = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
-        self.hdu_types.extend([BinTableHDU, ]*12)
-
-        self.data = [
-            NoneFactory(),
-            self.img_data,
-            self.var_data,
-            self.mask_data
-        ]
-        self.data.extend([
-            DataFactory.from_header("table", h) for h in headers[4:]
-        ])
+class DECamImdiff:
+    @classmethod
+    def from_defaults(
+            cls,
+            with_data=False,
+            noise="simplistic",
+            src_cat=None,
+            obj_cat=None,
+            editable_images=False,
+            separate_masks=False,
+            writeable_masks=False,
+            editable_masks=False
+    ):
+        if obj_cat.config.type == "progressive":
+            raise ValueError(
+                "Only folding or static object catalogs can be used with"
+                "default archived DECam headers since header timestamps are not "
+                "required to be equally spaced."
+            )
+
+        hdr_factory = ArchivedHeader(
+                "headers_archive.tar.bz2",
+                "decam_imdiff_headers.ecsv"
+            )
+
+        hdu_types = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
+        hdu_types.extend([BinTableHDU, ]*12)
+        data = [NoneFactory(), ]*16
+
+        if with_data:
+            headers = hdr_factory.get(0)
+
+            shape = (headers[1]["NAXIS1"], headers[1]["NAXIS2"])
+            dtype = DataFactoryConfig.bitpix_type_map[headers[1]["BITPIX"]]
+
+            #   2.1) Now we can instantiate data factories with correct configs
+            #        and fill in the data placeholder
+            if noise == "realistic":
+                img_data = SimulatedImage(src_cat=src_cat, shape=shape, dtype=dtype)
+            else:
+                img_data = SimpleImage(src_cat=src_cat, shape=shape, dtype=dtype)
+                var_data = SimpleVariance(img_data.base)
+                mask_data = SimpleMask.from_image(img_data.base)
+
+                data = [
+                    NoneFactory(),
+                    img_data,
+                    var_data,
+                    mask_data
+                ]
+                data.extend([
+                    DataFactory.from_header("table", h) for h in headers[4:]
+                ])
+
+        return cls(hdr_factory, data_factories=data, obj_cat=obj_cat)
+
+    def __init__(self, header_factory, data_factories=None, obj_cat=None):
+        self.hdr_factory = header_factory
+        self.data_factories = data_factories
+        self.hdu_layout = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
+        self.hdu_layout.extend([BinTableHDU, ]*12)
 
     def mock(self, n=1):
         obj_cats = None
@@ -456,7 +237,7 @@ def mock(self, n=1):
 
         hdrs = self.hdr_factory.mock(n)
 
-        if self.config.with_data:
+        if self.data_factories is not None:
             images = self.img_data.mock(n, obj_cats=obj_cats)
             variances = self.var_data.mock(images=images)
             data = [self.data[0].mock(n), images, variances]
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index 79b6da1e3..61b0f05ec 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -334,7 +334,6 @@ class SimpleMask(DataFactory):
     """
     default_config = SimpleMaskConfig
 
-
     def __init__(self, mask, config=None, **kwargs):
         super().__init__(base=mask, config=config, **kwargs)
 
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index 3947d978c..7a6d60ba1 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -10,12 +10,11 @@
 
 __all__ = [
     "HeaderFactory",
-    "HeaderFactoryConfig",
     "ArchivedHeader",
 ]
 
 
-class HeaderFactoryConfig(Config):
+class HeaderFactory:
     primary_template = {
         "EXTNAME": "PRIMARY",
         "NAXIS": 0,
@@ -45,19 +44,6 @@ class HeaderFactoryConfig(Config):
         "cd": [[-1.44e-07, 7.32e-05], [7.32e-05, 1.44e-05]],
     }
 
-    def __init__(self, config=None, method="default", shape=None, **kwargs):
-        super().__init__(config=config, method=method, **kwargs)
-
-        if shape is not None:
-            self.ext_template["NAXIS1"] = shape[1]
-            self.ext_template["NAXIS2"] = shape[0]
-            self.ext_template["CRPIX1"] = shape[1]//2
-            self.ext_template["CRPIX2"] = shape[0]//2
-
-
-class HeaderFactory:
-    default_config = HeaderFactoryConfig
-
     def __validate_mutables(self):
         # !xor
         if bool(self.mutables) != bool(self.callbacks):
@@ -81,10 +67,8 @@ def __validate_mutables(self):
                     "provide the required metadata keys."
                 )
 
-    def __init__(self, metadata=None, mutables=None, callbacks=None,
+    def __init__(self, metadata, mutables=None, callbacks=None,
                  config=None, **kwargs):
-        self.config = self.default_config(config=config, **kwargs)
-
         cards = [] if metadata is None else metadata
         self.header = Header(cards=cards)
 
@@ -92,18 +76,23 @@ def __init__(self, metadata=None, mutables=None, callbacks=None,
         self.callbacks = callbacks
         self.__validate_mutables()
 
-        self.is_dynamic = self.mutables is None
+        self.is_dynamic = self.mutables is not None
         self.counter = 0
 
-    def get(self):
-        if self.mutables is not None:
-            for i, mutable in enumerate(self.mutables):
-                self.header[mutable] = self.callbacks[i](self.header[mutable])
-        return self.header
+    def mock(self, n=1):
+        headers = []
+        # This can't be vectorized because callbacks may share global state
+        for i in range(n):
+            if self.is_dynamic:
+                header = self.header.copy()
+                for i, mutable in enumerate(self.mutables):
+                    header[mutable] = self.callbacks[i](header[mutable])
+            else:
+                header = self.header
+            headers.append(header)
+            self.counter += 1
 
-    def mock(self, **kwargs):
-        self.counter += 1
-        return self.get(**kwargs)
+        return headers
 
     @classmethod
     def gen_wcs(cls, metadata):
@@ -114,7 +103,7 @@ def gen_wcs(cls, metadata):
 
     @classmethod
     def gen_header(cls, base, overrides, wcs_base=None):
-        header = Header() if base is None else Header(base)
+        header = Header(base)
         header.update(overrides)
 
         if wcs_base is not None:
@@ -128,25 +117,29 @@ def gen_header(cls, base, overrides, wcs_base=None):
         return header
 
     @classmethod
-    def from_primary_template(cls, overrides=None, mutables=None, callbacks=None,
-                              config=None):
-        config = cls.default_config(config=config)
-        hdr = cls.gen_header(base=config.primary_template, overrides=overrides)
-        return cls(hdr, mutables, callbacks, config=config)
+    def from_primary_template(cls, overrides=None, mutables=None, callbacks=None):
+        hdr = cls.gen_header(base=cls.primary_template, overrides=overrides)
+        return cls(hdr, mutables, callbacks)
 
     @classmethod
-    def from_ext_template(cls, overrides=None, mutables=None, callbacks=None,
-                          wcs=None, config=None):
-        config = cls.default_config(config=config)
+    def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, wcs=None, shape=None):
+        ext_template = cls.ext_template.copy()
+
+        if shape is not None:
+            ext_template["NAXIS1"] = shape[0]
+            ext_template["NAXIS2"] = shape[1]
+            ext_template["CRPIX1"] = shape[0]//2
+            ext_template["CRPIX2"] = shape[1]//2
+
         hdr = cls.gen_header(
-            base=config.ext_template,
+            base=ext_template,
             overrides=overrides,
-            wcs_base=config.wcs_template if wcs is None else wcs
+            wcs_base=cls.wcs_template if wcs is None else wcs
         )
-        return cls(hdr, mutables, callbacks, config=config)
+        return cls(hdr, mutables, callbacks)
 
 
-class ArchivedHeaderConfig(HeaderFactoryConfig):
+class ArchivedHeader(HeaderFactory):
     # will almost never be anything else. Rather, it would be a miracle if it
     # were something else, since FITS standard shouldn't allow it. Further
     # casting by some packages will always be casting implemented in terms of
@@ -163,15 +156,10 @@ class ArchivedHeaderConfig(HeaderFactoryConfig):
 
     format = "ascii.ecsv"
 
-    n_hdrs_per_hdu = 1
-
-class ArchivedHeader(HeaderFactory):
-    default_config = ArchivedHeaderConfig
-
     def __init__(self, archive_name, fname, config=None, **kwargs):
         super().__init__(config, **kwargs)
         self.table = header_archive_to_table(
-            archive_name, fname, self.config.compression, self.config.format
+            archive_name, fname, self.compression, self.format
         )
 
         # Create HDU groups for easier iteration
@@ -182,8 +170,8 @@ def lexical_cast(self, value, format):
         """Cast str literal of a type to the type itself. Supports just
         the builtin Python types.
         """
-        if format in self.config.lexical_type_map:
-            return self.config.lexical_type_map[format](value)
+        if format in self.lexical_type_map:
+            return self.lexical_type_map[format](value)
         return value
 
     def get_item(self, group_idx, hdr_idx):
diff --git a/tests/test_mocking.py b/tests/test_mocking.py
new file mode 100644
index 000000000..3c4b4f7f8
--- /dev/null
+++ b/tests/test_mocking.py
@@ -0,0 +1,225 @@
+import unittest
+
+import numpy as np
+from astropy.table import Table, vstack
+
+import kbmod.mocking as kbmock
+
+
+class TestEmptyFits(unittest.TestCase):
+    def test(self):
+        """Test basics of EmptyFits factory."""
+        factory = kbmock.EmptyFits()
+        hduls = factory.mock(2)
+
+        hdul = hduls[0]
+        zeros = np.zeros((100, 100))
+        self.assertEqual(len(hduls), 2)
+        self.assertEqual(len(hduls[0]), 4)
+        for name, hdu in zip(("PRIMARY", "IMAGE", "VARIANCE", "MASK"), hduls[0]):
+            self.assertEqual(name, hdu.name)
+        self.assertEqual(hdul["PRIMARY"].data, None)
+        self.assertTrue((hdul["IMAGE"].data == zeros).all())
+        self.assertTrue((hdul["VARIANCE"].data == zeros).all())
+        self.assertTrue((hdul["MASK"].data == zeros).all())
+
+        factory = kbmock.EmptyFits(shape=(10, 100), step_mjd=1)
+        hduls = factory.mock(2)
+        hdul = hduls[0]
+        self.assertEqual(hdul["IMAGE"].data.shape, (10, 100))
+        self.assertEqual(hdul["VARIANCE"].data.shape, (10, 100))
+        self.assertEqual(hdul["MASK"].data.shape, (10, 100))
+        dt = hduls[1]["PRIMARY"].header["OBS-MJD"] - hduls[0]["PRIMARY"].header["OBS-MJD"]
+        self.assertEqual(dt, 1)
+
+        with self.assertRaisesRegex(ValueError, "destination is read-only"):
+            hdul["IMAGE"].data[0, 0] = 0
+
+        factory = kbmock.EmptyFits(editable_images=True)
+        hduls = factory.mock(2)
+        hdul = hduls[0]
+        hdul["IMAGE"].data[0, 0] = 1
+        hdul["VARIANCE"].data[0, 0] = 2
+        self.assertEqual(hdul["IMAGE"].data[0, 0], 1)
+        self.assertEqual(hduls[1]["IMAGE"].data[0, 0], 0)
+        with self.assertRaisesRegex(ValueError, "destination is read-only"):
+            hdul["MASK"].data[0, 0] = 0
+
+        factory = kbmock.EmptyFits(editable_images=True, editable_masks=True)
+        hduls = factory.mock(2)
+        hdul = hduls[0]
+        hdul["MASK"].data[0, 0] = 1
+        self.assertEqual(hduls[0]["MASK"].data[0, 0], 1)
+        self.assertEqual(hduls[1]["MASK"].data[0, 0], 0)
+
+
+class TestSimpleFits(unittest.TestCase):
+    def test(self):
+        """Test basic functionality of SimpleFits factory."""
+        factory = kbmock.SimpleFits()
+        hduls = factory.mock(2)
+
+        hdul = hduls[0]
+        zeros = np.zeros((100, 100))
+        self.assertEqual(len(hduls), 2)
+        self.assertEqual(len(hduls[0]), 4)
+        for name, hdu in zip(("PRIMARY", "IMAGE", "VARIANCE", "MASK"), hduls[0]):
+            self.assertEqual(name, hdu.name)
+        self.assertEqual(hdul["PRIMARY"].data, None)
+        self.assertTrue((hdul["IMAGE"].data == zeros).all())
+        self.assertTrue((hdul["VARIANCE"].data == zeros).all())
+        self.assertTrue((hdul["MASK"].data == zeros).all())
+
+        factory = kbmock.SimpleFits(shape=(10, 100), step_mjd=1)
+        hduls = factory.mock(2)
+        hdul = hduls[0]
+        self.assertEqual(hdul["IMAGE"].data.shape, (10, 100))
+        self.assertEqual(hdul["VARIANCE"].data.shape, (10, 100))
+        self.assertEqual(hdul["MASK"].data.shape, (10, 100))
+        step_mjd = hduls[1]["PRIMARY"].header["OBS-MJD"] - hduls[0]["PRIMARY"].header["OBS-MJD"]
+        self.assertEqual(step_mjd, 1)
+
+    def test_static_src_cat(self):
+        src_cat = kbmock.SourceCatalog.from_defaults()
+        src_cat2 = kbmock.SourceCatalog.from_defaults()
+        self.assertEqual(src_cat.config.mode, "static")
+        self.assertFalse((src_cat.table == src_cat2.table).all())
+
+        src_cat = kbmock.SourceCatalog.from_defaults(n=3)
+        self.assertEqual(len(src_cat.table), 3)
+
+        shape = (300, 500)
+        param_ranges = {
+            "amplitude": [100, 100],
+            "x_mean": (100, 200),
+            "y_mean": (50, 80),
+            "x_stddev": [2., 2.],
+            "y_stddev": [2., 2.]
+        }
+        src_cat = kbmock.SourceCatalog.from_defaults(param_ranges, seed=100)
+        src_cat2 = kbmock.SourceCatalog.from_defaults(param_ranges, seed=100)
+        self.assertTrue((src_cat.table == src_cat2.table).all())
+        self.assertLessEqual(src_cat.table["x_mean"].max(), shape[1])
+        self.assertLessEqual(src_cat.table["y_mean"].max(), shape[0])
+
+        factory = kbmock.SimpleFits(shape=shape, src_cat=src_cat)
+        hdul = factory.mock()[0]
+
+        for x, y in src_cat.table["x_mean", "y_mean"]:
+            # Can only test greater or equal because objects may overlap
+            self.assertGreaterEqual(hdul["IMAGE"].data[int(y), int(x)], 80)
+
+    def test_progressive_obj_cat(self):
+        obj_cat = kbmock.ObjectCatalog.from_defaults()
+        obj_cat2 = kbmock.ObjectCatalog.from_defaults()
+        self.assertEqual(obj_cat.config.mode, "progressive")
+        self.assertFalse((obj_cat.table == obj_cat2.table).all())
+
+        obj_cat = kbmock.ObjectCatalog.from_defaults(n=3)
+        self.assertEqual(len(obj_cat.table), 3)
+
+        shape = (300, 500)
+        param_ranges = {
+            "amplitude": [100, 100],
+            "x_mean": (0, 50),
+            "y_mean": (50, shape[0]),
+            "x_stddev": [2., 2.],
+            "y_stddev": [2., 2.],
+            "vx": [100, 300],
+            "vy": [0, 0]
+        }
+        obj_cat = kbmock.ObjectCatalog.from_defaults(param_ranges, seed=100)
+        obj_cat2 = kbmock.ObjectCatalog.from_defaults(param_ranges, seed=100)
+        self.assertTrue((obj_cat.table == obj_cat2.table).all())
+        self.assertLessEqual(obj_cat.table["x_mean"].max(), 50)
+        self.assertLessEqual(obj_cat.table["y_mean"].max(), shape[0])
+
+        step_mjd = 0.1
+        obj_cat = kbmock.ObjectCatalog.from_defaults(param_ranges, n=5, seed=100)
+        factory = kbmock.SimpleFits(shape=shape, step_mjd=step_mjd, obj_cat=obj_cat)
+        hduls = factory.mock(n=5)
+
+        obj_cat.reset()
+        for i in range(5):
+            newcat = obj_cat.mock(dt=step_mjd)[0]
+            for x, y in newcat["x_mean", "y_mean"]:
+                if x < shape[1] and y < shape[0]:
+                    # Can only test greater or equal because objects may overlap
+                    # probably a rounding-off error while moving drops 1 flux count
+                    self.assertGreaterEqual(hduls[i]["IMAGE"].data[int(y), int(x)], 79)
+                    self.assertGreaterEqual(hduls[i]["VARIANCE"].data[int(y), int(x)], 0)
+
+    def test_folding_obj_cat(self):
+        nobj = 5
+        shape = (300, 300)
+        timestamps = np.arange(58915, 58920, 1)
+
+        start_x = np.ones((nobj, ))*10
+        start_y = np.linspace(10, shape[0]-10, nobj)
+
+        cats = []
+        for i, t in enumerate(timestamps):
+            cats.append(Table({
+                "amplitude": [100]*nobj,
+                "obstime": [t]*nobj,
+                "x_mean": start_x + 15*i*i,
+                "y_mean": start_y,
+                "stddev": [2.0]*nobj
+            }))
+        catalog = vstack(cats)
+
+        obj_cat = kbmock.ObjectCatalog.from_table(catalog)
+        obj_cat.mode = "folding"
+
+        prim_hdr_factory = kbmock.HeaderFactory.from_primary_template(
+            mutables=["OBS-MJD"],
+            callbacks=[kbmock.ObstimeIterator(timestamps), ],
+        )
+
+        factory = kbmock.SimpleFits(shape=shape, obj_cat=obj_cat)
+        factory.prim_hdr = prim_hdr_factory
+        hduls = factory.mock(n=len(timestamps))
+
+        obj_cat.reset()
+        cats = obj_cat.mock(t=timestamps)
+        for hdul, cat in zip(hduls, cats):
+            for x, y in cat["x_mean", "y_mean"]:
+                if x < shape[1] and y < shape[0]:
+                    # Can only test greater or equal because objects may overlap
+                    # probably a rounding-off error while moving drops 1 flux count
+                    self.assertGreaterEqual(hdul["IMAGE"].data[int(y), int(x)], 79)
+                    self.assertGreaterEqual(hdul["VARIANCE"].data[int(y), int(x)], 0)
+
+    # TODO: move to pytest and mark as xfail
+    def test_noise_gen(self):
+        factory = kbmock.SimpleFits(shape=(1000, 1000), with_noise=True)
+        hdul = factory.mock()[0]
+        self.assertAlmostEqual(hdul["IMAGE"].data.mean(), 10, 1)
+        self.assertAlmostEqual(hdul["IMAGE"].data.std(), 1, 1)
+
+        factory = kbmock.SimpleFits(shape=(1000, 1000), with_noise=True, noise="realistic")
+        hdul = factory.mock()[0]
+        print(hdul["IMAGE"].data.mean())
+        self.assertAlmostEqual(hdul["IMAGE"].data.mean(), 32, 1)
+        self.assertAlmostEqual(hdul["IMAGE"].data.std(), 7.5, 1)
+
+        img_factory = kbmock.SimpleImage(shape=(1000, 1000), add_noise=True, noise=5, noise_std=2.0)
+        factory = kbmock.SimpleFits()
+        factory.img_data = img_factory
+        hduls = factory.mock(n=3)
+
+        for hdul in hduls[1:]:
+            self.assertFalse((hduls[0]["IMAGE"].data == hdul["IMAGE"].data).all())
+            self.assertAlmostEqual(hdul["IMAGE"].data.mean(), 5, 1)
+            self.assertAlmostEqual(hdul["IMAGE"].data.std(), 2, 1)
+
+
+if __name__ == "__main__":
+    unittest.main()
+
+
+
+
+
+
+

From 23ed3388b1552ece9d6b7a15d32e19f7695733db Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 5 Aug 2024 11:30:32 -0700
Subject: [PATCH 17/27] Adding tests, fixup.

---
 .../fits_standardizers/test_data_std.py       |  2 +-
 tests/test_end_to_end.py                      | 30 +++++++++++++++++++
 2 files changed, 31 insertions(+), 1 deletion(-)

diff --git a/src/kbmod/standardizers/fits_standardizers/test_data_std.py b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
index 7b10af26c..ad21f9a1e 100644
--- a/src/kbmod/standardizers/fits_standardizers/test_data_std.py
+++ b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
@@ -81,7 +81,7 @@ def translateHeader(self):
         # required
         standardizedHeader = {}
         obs_datetime = Time(self.primary["OBS-MJD"], format="mjd")
-        standardizedHeader["mjd"] = obs_datetime.mjd
+        standardizedHeader["mjd_mid"] = obs_datetime.mjd
         # optional
         standardizedHeader["observat"] = self.primary["OBSERVAT"]
         standardizedHeader["obs_lat"] = self.primary["OBS-LAT"]
diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py
index 799d46158..50eb59203 100644
--- a/tests/test_end_to_end.py
+++ b/tests/test_end_to_end.py
@@ -15,6 +15,36 @@
 from utils.utils_for_tests import get_absolute_demo_data_path
 
 
+
+
+####
+import unittest
+
+
+from kbmod import ImageCollection
+from kbmod.configuration import SearchConfiguration
+import kbmod.mocking as kbmock
+
+
+class TestEmptySearch(unittest.TestCase):
+    def setUp(self):
+        self.factory = kbmock.EmptyFits()
+
+    def test(self):
+        hduls = self.factory.mock(n=10)
+
+        ic = ImageCollection.fromTargets(hdulist=hduls, force="TestDataStd")
+        wu = ic.toWorkUnit(search_config=SearchConfiguration())
+        results = SearchRunner.run_search_from_work_unit(wu)
+
+        self.assertTrue(len(results) == 0)
+
+
+
+
+####
+
+
 # this is the first test to actually test things like get_all_stamps from
 # analysis utils. For now stamps have to be RawImages (because methods like
 # interpolate and convolve are defined to work on RawImage and not as funciton)

From b0d4505433a05512e92fc0c1dade3507f80f116b Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 5 Aug 2024 11:37:32 -0700
Subject: [PATCH 18/27] More fixups to squash later, moving to baldur for
 testing.

---
 src/kbmod/mocking/callbacks.py                |   5 +-
 src/kbmod/mocking/catalogs.py                 |  27 +--
 src/kbmod/mocking/config.py                   |   5 +-
 src/kbmod/mocking/fits.py                     | 157 ++++++++++--------
 src/kbmod/mocking/fits_data.py                |  89 ++++------
 src/kbmod/mocking/headers.py                  |  29 +---
 .../fits_standardizers/test_data_std.py       |   2 +-
 tests/test_end_to_end.py                      |   4 -
 tests/test_mocking.py                         |  43 +++--
 9 files changed, 169 insertions(+), 192 deletions(-)

diff --git a/src/kbmod/mocking/callbacks.py b/src/kbmod/mocking/callbacks.py
index 0a3aabbbe..4b2b15750 100644
--- a/src/kbmod/mocking/callbacks.py
+++ b/src/kbmod/mocking/callbacks.py
@@ -3,6 +3,7 @@
     "ObstimeIterator",
 ]
 
+
 class IncrementObstime:
     def __init__(self, start, dt):
         self.start = start
@@ -13,11 +14,11 @@ def __call__(self, mut_val):
         self.start += self.dt
         return curr
 
+
 class ObstimeIterator:
     def __init__(self, obstimes):
         self.obstimes = obstimes
-        self.generator  = (t for t in obstimes)
+        self.generator = (t for t in obstimes)
 
     def __call__(self, mut_val):
         return next(self.generator)
-
diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 0ef2a4e64..0de5ac0bf 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -66,7 +66,8 @@ def from_config(cls, config, **kwargs):
         config = cls.default_config(config=config, **kwargs)
         table = gen_catalog(config.n, config.param_ranges, config.seed)
         return cls(config, table)
-#
+
+    #
     @classmethod
     def from_defaults(cls, param_ranges=None, **kwargs):
         config = cls.default_config(**kwargs)
@@ -93,12 +94,12 @@ def mock(self):
 
 class SourceCatalogConfig(SimpleCatalogConfig):
     param_ranges = {
-        "amplitude": [1., 10.],
-        "x_mean": [0., 4096.],
-        "y_mean": [0., 2048.],
-        "x_stddev": [1., 3.],
-        "y_stddev": [1., 3.],
-        "theta": [0., np.pi],
+        "amplitude": [1.0, 10.0],
+        "x_mean": [0.0, 4096.0],
+        "y_mean": [0.0, 2048.0],
+        "x_stddev": [1.0, 3.0],
+        "y_stddev": [1.0, 3.0],
+        "theta": [0.0, np.pi],
     }
 
 
@@ -107,15 +108,15 @@ class SourceCatalog(SimpleCatalog):
 
 
 class ObjectCatalogConfig(SimpleCatalogConfig):
-    mode = "progressive" # folding
+    mode = "progressive"  # folding
     param_ranges = {
         "amplitude": [0.1, 3.0],
-        "x_mean": [0., 4096.],
-        "y_mean": [0., 2048.],
-        "vx": [500., 1000.],
-        "vy": [500., 1000.],
+        "x_mean": [0.0, 4096.0],
+        "y_mean": [0.0, 2048.0],
+        "vx": [500.0, 1000.0],
+        "vy": [500.0, 1000.0],
         "stddev": [0.25, 1.5],
-        "theta": [0., np.pi],
+        "theta": [0.0, np.pi],
     }
 
 
diff --git a/src/kbmod/mocking/config.py b/src/kbmod/mocking/config.py
index ed34aa649..b0ad888c5 100644
--- a/src/kbmod/mocking/config.py
+++ b/src/kbmod/mocking/config.py
@@ -177,8 +177,9 @@ def update(self, config=None, method="default", **kwargs):
         elif method.lower() == "extend":
             conf = other
         else:
-            raise ValueError("Method expected to be one of 'default', "
-                             f"'subset'  or 'extend'. Got {method} instead.")
+            raise ValueError(
+                "Method expected to be one of 'default', " f"'subset'  or 'extend'. Got {method} instead."
+            )
 
         self._conf.update(conf)
 
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index e52edaeeb..f299e213b 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -1,9 +1,4 @@
-from astropy.io.fits import (
-    HDUList,
-    PrimaryHDU,
-    CompImageHDU,
-    BinTableHDU
-)
+from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, BinTableHDU
 
 from .callbacks import IncrementObstime
 from .headers import HeaderFactory, ArchivedHeader
@@ -17,7 +12,7 @@
     SimpleVarianceConfig,
     SimpleVariance,
     SimpleMaskConfig,
-    SimpleMask
+    SimpleMask,
 )
 
 
@@ -30,12 +25,14 @@
 
 class NoneFactory:
     "Kinda makes some code later prettier. Kinda"
+
     def mock(self, n):
-        return [None, ]*n
+        return [
+            None,
+        ] * n
 
 
-def hdu_cast(hdu_cls, hdr, data=None,
-             validate_header=False, update_header=False):
+def hdu_cast(hdu_cls, hdr, data=None, validate_header=False, update_header=False):
     hdu = hdu_cls()
 
     if validate_header:
@@ -51,8 +48,7 @@ def hdu_cast(hdu_cls, hdr, data=None,
     return hdu
 
 
-def hdu_cast_array(hdu_cls, hdr, data,
-                   validate_header=False, update_header=False):
+def hdu_cast_array(hdu_cls, hdr, data, validate_header=False, update_header=False):
     hdus = []
     for hdr, dat in zip(hdr, data):
         hdus.append(hdu_cast(hdu_cls, hdr, dat))
@@ -60,12 +56,17 @@ def hdu_cast_array(hdu_cls, hdr, data,
 
 
 class EmptyFits:
-    def __init__(self, header=None, shape=(100, 100), start_mjd=60310,
-                 step_mjd=0.001, editable_images=False, editable_masks=False):
+    def __init__(
+        self,
+        header=None,
+        shape=(100, 100),
+        start_mjd=60310,
+        step_mjd=0.001,
+        editable_images=False,
+        editable_masks=False,
+    ):
         self.prim_hdr = HeaderFactory.from_primary_template(
-            overrides=header,
-            mutables=["OBS-MJD"],
-            callbacks=[IncrementObstime(start=start_mjd, dt=step_mjd)]
+            overrides=header, mutables=["OBS-MJD"], callbacks=[IncrementObstime(start=start_mjd, dt=step_mjd)]
         )
 
         self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, shape=shape)
@@ -76,16 +77,10 @@ def __init__(self, header=None, shape=(100, 100), start_mjd=60310,
         #        where possible by really only allocating 1 array whenever the
         #        data is read-only and content-static between created HDUs.
         self.img_data = DataFactory.from_header(
-            kind="image",
-            header=self.img_hdr.header,
-            writeable=editable_images,
-            return_copy=editable_images
+            kind="image", header=self.img_hdr.header, writeable=editable_images, return_copy=editable_images
         )
         self.mask_data = DataFactory.from_header(
-            kind="image",
-            header=self.mask_hdr.header,
-            return_copy=editable_masks,
-            writeable=editable_masks
+            kind="image", header=self.mask_hdr.header, return_copy=editable_masks, writeable=editable_masks
         )
 
         self.current = 0
@@ -93,33 +88,44 @@ def __init__(self, header=None, shape=(100, 100), start_mjd=60310,
     def mock(self, n=1):
         img_hdr = self.img_hdr.mock()[0]
         var_hdr = self.var_hdr.mock()[0]
-        mask_hdr  = self.mask_hdr.mock()[0]
+        mask_hdr = self.mask_hdr.mock()[0]
         images = self.img_data.mock(n=n)
         variances = self.img_data.mock(n=n)
         masks = self.mask_data.mock(n=n)
 
         hduls = []
         for i in range(n):
-            hduls.append(HDUList(hdus=[
-                PrimaryHDU(header=self.prim_hdr.mock()[0]),
-                CompImageHDU(header=img_hdr, data=images[i]),
-                CompImageHDU(header=var_hdr, data=variances[i]),
-                CompImageHDU(header=mask_hdr, data=masks[i])
-            ]))
+            hduls.append(
+                HDUList(
+                    hdus=[
+                        PrimaryHDU(header=self.prim_hdr.mock()[0]),
+                        CompImageHDU(header=img_hdr, data=images[i]),
+                        CompImageHDU(header=var_hdr, data=variances[i]),
+                        CompImageHDU(header=mask_hdr, data=masks[i]),
+                    ]
+                )
+            )
 
         self.current += n
         return hduls
 
 
 class SimpleFits:
-    def __init__(self, header=None, shape=(100, 100), start_mjd=60310, step_mjd=0.001,
-                 with_noise=False, noise="simplistic", src_cat=None, obj_cat=None):
+    def __init__(
+        self,
+        header=None,
+        shape=(100, 100),
+        start_mjd=60310,
+        step_mjd=0.001,
+        with_noise=False,
+        noise="simplistic",
+        src_cat=None,
+        obj_cat=None,
+    ):
         # 2. Set up Header and Data factories that go into creating HDUs
         #    2.1) First headers, since that metadata specified data formats
         self.prim_hdr = HeaderFactory.from_primary_template(
-            overrides=header,
-            mutables=["OBS-MJD"],
-            callbacks=[IncrementObstime(start=start_mjd, dt=step_mjd)]
+            overrides=header, mutables=["OBS-MJD"], callbacks=[IncrementObstime(start=start_mjd, dt=step_mjd)]
         )
 
         self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, shape=shape)
@@ -155,14 +161,19 @@ def mock(self, n=1):
         masks = self.mask_data.mock(n)
 
         hduls = []
-        for ph, ih, vh, mh, imd, vd, md in zip(prim_hdrs, img_hdrs, var_hdrs,
-                                               mask_hdrs, images, variances, masks):
-            hduls.append(HDUList(hdus=[
-                PrimaryHDU(header=ph),
-                CompImageHDU(header=ih, data=imd),
-                CompImageHDU(header=vh, data=vd),
-                CompImageHDU(header=mh, data=md)
-            ]))
+        for ph, ih, vh, mh, imd, vd, md in zip(
+            prim_hdrs, img_hdrs, var_hdrs, mask_hdrs, images, variances, masks
+        ):
+            hduls.append(
+                HDUList(
+                    hdus=[
+                        PrimaryHDU(header=ph),
+                        CompImageHDU(header=ih, data=imd),
+                        CompImageHDU(header=vh, data=vd),
+                        CompImageHDU(header=mh, data=md),
+                    ]
+                )
+            )
 
         self.current += n
         return hduls
@@ -171,15 +182,15 @@ def mock(self, n=1):
 class DECamImdiff:
     @classmethod
     def from_defaults(
-            cls,
-            with_data=False,
-            noise="simplistic",
-            src_cat=None,
-            obj_cat=None,
-            editable_images=False,
-            separate_masks=False,
-            writeable_masks=False,
-            editable_masks=False
+        cls,
+        with_data=False,
+        noise="simplistic",
+        src_cat=None,
+        obj_cat=None,
+        editable_images=False,
+        separate_masks=False,
+        writeable_masks=False,
+        editable_masks=False,
     ):
         if obj_cat.config.type == "progressive":
             raise ValueError(
@@ -188,14 +199,18 @@ def from_defaults(
                 "required to be equally spaced."
             )
 
-        hdr_factory = ArchivedHeader(
-                "headers_archive.tar.bz2",
-                "decam_imdiff_headers.ecsv"
-            )
+        hdr_factory = ArchivedHeader("headers_archive.tar.bz2", "decam_imdiff_headers.ecsv")
 
         hdu_types = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
-        hdu_types.extend([BinTableHDU, ]*12)
-        data = [NoneFactory(), ]*16
+        hdu_types.extend(
+            [
+                BinTableHDU,
+            ]
+            * 12
+        )
+        data = [
+            NoneFactory(),
+        ] * 16
 
         if with_data:
             headers = hdr_factory.get(0)
@@ -212,15 +227,8 @@ def from_defaults(
                 var_data = SimpleVariance(img_data.base)
                 mask_data = SimpleMask.from_image(img_data.base)
 
-                data = [
-                    NoneFactory(),
-                    img_data,
-                    var_data,
-                    mask_data
-                ]
-                data.extend([
-                    DataFactory.from_header("table", h) for h in headers[4:]
-                ])
+                data = [NoneFactory(), img_data, var_data, mask_data]
+                data.extend([DataFactory.from_header("table", h) for h in headers[4:]])
 
         return cls(hdr_factory, data_factories=data, obj_cat=obj_cat)
 
@@ -228,7 +236,12 @@ def __init__(self, header_factory, data_factories=None, obj_cat=None):
         self.hdr_factory = header_factory
         self.data_factories = data_factories
         self.hdu_layout = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
-        self.hdu_layout.extend([BinTableHDU, ]*12)
+        self.hdu_layout.extend(
+            [
+                BinTableHDU,
+            ]
+            * 12
+        )
 
     def mock(self, n=1):
         obj_cats = None
@@ -250,9 +263,7 @@ def mock(self, n=1):
         for hdul_idx in range(n):
             hdus = []
             for hdu_idx, hdu_cls in enumerate(self.hdu_types):
-                hdus.append(self.hdu_cast(
-                    hdu_cls, hdrs[hdul_idx][hdu_idx], data[hdu_idx][hdul_idx]
-                ))
+                hdus.append(self.hdu_cast(hdu_cls, hdrs[hdul_idx][hdu_idx], data[hdu_idx][hdul_idx]))
             hduls.append(HDUList(hdus=hdus))
         self.current += n
         return hduls
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index 61b0f05ec..00407aeba 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -1,11 +1,5 @@
 import numpy as np
-from astropy.io.fits import (
-    PrimaryHDU,
-    CompImageHDU,
-    ImageHDU,
-    BinTableHDU,
-    TableHDU
-)
+from astropy.io.fits import PrimaryHDU, CompImageHDU, ImageHDU, BinTableHDU, TableHDU
 from astropy.modeling import models
 
 from .config import Config, ConfigurationError
@@ -21,7 +15,7 @@
     "SimpleMaskConfig",
     "SimpleMask",
     "SimulatedImageConfig",
-    "SimulatedImage"
+    "SimulatedImage",
 ]
 
 
@@ -66,12 +60,9 @@ def add_model_objects(img, catalog, model):
             for param in params_to_set:
                 setattr(model, param, source[param])
 
-            if all([
-                    model.x_mean > 0,
-                    model.x_mean < img.shape[1],
-                    model.y_mean > 0,
-                    model.y_mean < img.shape[0]
-            ]):
+            if all(
+                [model.x_mean > 0, model.x_mean < img.shape[1], model.y_mean > 0, model.y_mean < img.shape[0]]
+            ):
                 model.render(img)
     finally:
         for param, value in init_params.items():
@@ -84,6 +75,7 @@ class DataFactoryConfig(Config):
     """Data factory configuration primarily controls mutability of the given
     and returned mocked datasets.
     """
+
     default_img_shape = (5, 5)
 
     default_img_bit_width = 32
@@ -161,6 +153,7 @@ class DataFactory:
         Additional keyword arguments are applied as configuration
         overrides.
     """
+
     default_config = DataFactoryConfig
     """Default configuration."""
 
@@ -216,11 +209,11 @@ def gen_table(cls, metadata=None, config=None, **kwargs):
             hdu.header.update(metadata)
 
             rows = metadata.get("NAXIS2", conf.default_tbl_length)
-            shape = (rows, )
+            shape = (rows,)
             data = np.zeros(shape, dtype=hdu.data.dtype)
         else:
             hdu = table_cls()
-            shape = (conf.default_tbl_length, )
+            shape = (conf.default_tbl_length,)
             data = np.zeros(shape, dtype=conf.default_tbl_dtype)
 
         return data
@@ -255,7 +248,7 @@ def mock(self, n=1, **kwargs):
             )
 
         if self.config.return_copy:
-            base = np.repeat(self.base[np.newaxis, ], (n,), axis=0)
+            base = np.repeat(self.base[np.newaxis,], (n,), axis=0)
         else:
             base = np.broadcast_to(self.base, (n, *self.shape))
         base.flags.writeable = self.config.writeable
@@ -292,6 +285,7 @@ class SimpleVariance(DataFactory):
         Additional keyword arguments are applied as config
         overrides.
     """
+
     default_config = SimpleVarianceConfig
 
     def __init__(self, image=None, config=None, **kwargs):
@@ -300,16 +294,17 @@ def __init__(self, image=None, config=None, **kwargs):
         super().__init__(base=None, config=config, **kwargs)
 
         if image is not None:
-            self.base = image/self.config.gain + self.config.read_noise**2
+            self.base = image / self.config.gain + self.config.read_noise**2
 
     def mock(self, images=None):
         if images is None:
             return self.base
-        return images/self.config.gain + self.config.read_noise**2
+        return images / self.config.gain + self.config.read_noise**2
 
 
 class SimpleMaskConfig(DataFactoryConfig):
     """Simple mask configuration."""
+
     dtype = np.float32
 
     threshold = 1e-05
@@ -332,6 +327,7 @@ class SimpleMask(DataFactory):
     mask : `np.array`
         Bitmask array.
     """
+
     default_config = SimpleMaskConfig
 
     def __init__(self, mask, config=None, **kwargs):
@@ -398,9 +394,9 @@ def from_params(cls, config=None, **kwargs):
 
         # padding
         mask[:padding] = 1
-        mask[shape[0] - padding:] = 1
+        mask[shape[0] - padding :] = 1
         mask[:, :padding] = 1
-        mask[: shape[1] - padding:] = 1
+        mask[: shape[1] - padding :] = 1
 
         # bad columns
         for col in config.bad_columns:
@@ -465,10 +461,10 @@ class SimpleImage(DataFactory):
         Additional keyword arguments are applied as config
         overrides.
     """
+
     default_config = SimpleImageConfig
 
-    def __init__(self, image=None, src_cat=None, obj_cat=None,
-                 config=None, **kwargs):
+    def __init__(self, image=None, src_cat=None, obj_cat=None, config=None, **kwargs):
         self.config = self.default_config(config=config, **kwargs)
         super().__init__(image, self.config, **kwargs)
 
@@ -484,8 +480,7 @@ def __init__(self, image=None, src_cat=None, obj_cat=None,
         self.src_cat = src_cat
         if self.src_cat is not None:
             image = image if image.flags.writeable else image.copy()
-            add_model_objects(image, src_cat.table,
-                              self.config.model(x_stddev=1, y_stddev=1))
+            add_model_objects(image, src_cat.table, self.config.model(x_stddev=1, y_stddev=1))
             image.flags.writeable = self.config.writeable
 
         self.base = image
@@ -542,13 +537,15 @@ def mock(self, n=1, obj_cats=None, **kwargs):
         # zip will attempt to iterate over the next available dimension, and
         # that's rows of the image and the table - we don't want that.
         if obj_cats is not None:
-            pairs = [(images[0], obj_cats),] if n == 1 else zip(images, obj_cats)
+            pairs = (
+                [
+                    (images[0], obj_cats),
+                ]
+                if n == 1
+                else zip(images, obj_cats)
+            )
             for i, (img, cat) in enumerate(pairs):
-                add_model_objects(
-                    img,
-                    cat,
-                    self.config.model(x_stddev=1, y_stddev=1)
-                )
+                add_model_objects(img, cat, self.config.model(x_stddev=1, y_stddev=1))
 
         return images
 
@@ -687,6 +684,7 @@ class SimulatedImage(SimpleImage):
         Additional keyword arguments are applied as config
         overrides.
     """
+
     default_config = SimulatedImageConfig
 
     @classmethod
@@ -719,15 +717,10 @@ def add_bad_cols(cls, image, config):
             bad_cols = config.bad_col_locs
         else:
             raise ConfigurationError(
-                "Bad columns method is not 'random', but `bad_col_locs` "
-                "contains no column indices."
+                "Bad columns method is not 'random', but `bad_col_locs` " "contains no column indices."
             )
 
-        col_pattern = rng.randint(
-            low=0,
-            high=int(config.bad_col_pattern_offset),
-            size=shape[0]
-        )
+        col_pattern = rng.randint(low=0, high=int(config.bad_col_pattern_offset), size=shape[0])
 
         for col in bad_cols:
             image[:, col] += col_pattern + config.bad_col_offset
@@ -795,20 +788,14 @@ def add_noise(cls, images, config):
         shape = images.shape
 
         # add read noise
-        images += config.read_noise_gen(
-            scale=config.read_noise / config.gain,
-            size=shape
-        )
+        images += config.read_noise_gen(scale=config.read_noise / config.gain, size=shape)
 
         # add dark current
         current = config.dark_current * config.exposure_time / config.gain
         images += config.dark_current_gen(current, size=shape)
 
         # add sky counts
-        images += config.sky_count_gen(
-            lam=config.sky_level * config.gain,
-            size=shape
-        ) / config.gain
+        images += config.sky_count_gen(lam=config.sky_level * config.gain, size=shape) / config.gain
 
         return images
 
@@ -836,17 +823,11 @@ def gen_base_image(cls, config=None, src_cat=None):
         base = cls.add_hot_pixels(base, config)
         base = cls.add_bad_cols(base, config)
         if src_cat is not None:
-            add_model_objects(base, src_cat.table,
-                              config.model(x_stddev=1, y_stddev=1))
+            add_model_objects(base, src_cat.table, config.model(x_stddev=1, y_stddev=1))
 
         return base
 
     def __init__(self, image=None, config=None, src_cat=None, obj_cat=None, **kwargs):
         conf = self.default_config(config=config, **kwargs)
         # static objects are added in SimpleImage init
-        super().__init__(
-            image=self.gen_base_image(conf),
-            config=conf,
-            src_cat=src_cat,
-            obj_cat=obj_cat
-        )
+        super().__init__(image=self.gen_base_image(conf), config=conf, src_cat=src_cat, obj_cat=obj_cat)
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index 7a6d60ba1..75c02ef23 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -24,17 +24,10 @@ class HeaderFactory:
         "OBS-LAT": -30.166,
         "OBS-LONG": -70.814,
         "OBS-ELEV": 2200,
-        "OBSERVAT": "CTIO"
+        "OBSERVAT": "CTIO",
     }
 
-    ext_template = {
-        "NAXIS": 2,
-        "NAXIS1": 2048,
-        "NAXIS2": 4096,
-        "CRPIX1": 1024,
-        "CPRIX2": 2048,
-        "BITPIX": 32
-    }
+    ext_template = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "CRPIX1": 1024, "CPRIX2": 2048, "BITPIX": 32}
 
     wcs_template = {
         "ctype": ["RA---TAN", "DEC--TAN"],
@@ -67,8 +60,7 @@ def __validate_mutables(self):
                     "provide the required metadata keys."
                 )
 
-    def __init__(self, metadata, mutables=None, callbacks=None,
-                 config=None, **kwargs):
+    def __init__(self, metadata, mutables=None, callbacks=None, config=None, **kwargs):
         cards = [] if metadata is None else metadata
         self.header = Header(cards=cards)
 
@@ -110,8 +102,7 @@ def gen_header(cls, base, overrides, wcs_base=None):
             naxis1 = header.get("NAXIS1", False)
             naxis2 = header.get("NAXIS2", False)
             if not all((naxis1, naxis2)):
-                raise ValueError("Adding a WCS to the header requires "
-                                 "NAXIS1 and NAXIS2 keys.")
+                raise ValueError("Adding a WCS to the header requires " "NAXIS1 and NAXIS2 keys.")
             header.update(cls.gen_wcs(wcs_base))
 
         return header
@@ -128,13 +119,11 @@ def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, wcs=No
         if shape is not None:
             ext_template["NAXIS1"] = shape[0]
             ext_template["NAXIS2"] = shape[1]
-            ext_template["CRPIX1"] = shape[0]//2
-            ext_template["CRPIX2"] = shape[1]//2
+            ext_template["CRPIX1"] = shape[0] // 2
+            ext_template["CRPIX2"] = shape[1] // 2
 
         hdr = cls.gen_header(
-            base=ext_template,
-            overrides=overrides,
-            wcs_base=cls.wcs_template if wcs is None else wcs
+            base=ext_template, overrides=overrides, wcs_base=cls.wcs_template if wcs is None else wcs
         )
         return cls(hdr, mutables, callbacks)
 
@@ -158,9 +147,7 @@ class ArchivedHeader(HeaderFactory):
 
     def __init__(self, archive_name, fname, config=None, **kwargs):
         super().__init__(config, **kwargs)
-        self.table = header_archive_to_table(
-            archive_name, fname, self.compression, self.format
-        )
+        self.table = header_archive_to_table(archive_name, fname, self.compression, self.format)
 
         # Create HDU groups for easier iteration
         self.table = self.table.group_by("filename")
diff --git a/src/kbmod/standardizers/fits_standardizers/test_data_std.py b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
index ad21f9a1e..69ce303e5 100644
--- a/src/kbmod/standardizers/fits_standardizers/test_data_std.py
+++ b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
@@ -13,6 +13,7 @@
 class TestDataStdConfig(FitsStandardizerConfig):
     pass
 
+
 class TestDataStd(MultiExtensionFits):
     """Standardizer for test-data produced by the mocking module
 
@@ -63,7 +64,6 @@ def __init__(self, location=None, hdulist=None, config=None, **kwargs):
             self.hdulist["IMAGE"],
         ]
 
-
     def translateHeader(self):
         """Returns the following metadata, read from the primary header, as a
         dictionary:
diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py
index 50eb59203..56e2926b9 100644
--- a/tests/test_end_to_end.py
+++ b/tests/test_end_to_end.py
@@ -15,8 +15,6 @@
 from utils.utils_for_tests import get_absolute_demo_data_path
 
 
-
-
 ####
 import unittest
 
@@ -40,8 +38,6 @@ def test(self):
         self.assertTrue(len(results) == 0)
 
 
-
-
 ####
 
 
diff --git a/tests/test_mocking.py b/tests/test_mocking.py
index 3c4b4f7f8..49bdd64c4 100644
--- a/tests/test_mocking.py
+++ b/tests/test_mocking.py
@@ -93,8 +93,8 @@ def test_static_src_cat(self):
             "amplitude": [100, 100],
             "x_mean": (100, 200),
             "y_mean": (50, 80),
-            "x_stddev": [2., 2.],
-            "y_stddev": [2., 2.]
+            "x_stddev": [2.0, 2.0],
+            "y_stddev": [2.0, 2.0],
         }
         src_cat = kbmock.SourceCatalog.from_defaults(param_ranges, seed=100)
         src_cat2 = kbmock.SourceCatalog.from_defaults(param_ranges, seed=100)
@@ -123,10 +123,10 @@ def test_progressive_obj_cat(self):
             "amplitude": [100, 100],
             "x_mean": (0, 50),
             "y_mean": (50, shape[0]),
-            "x_stddev": [2., 2.],
-            "y_stddev": [2., 2.],
+            "x_stddev": [2.0, 2.0],
+            "y_stddev": [2.0, 2.0],
             "vx": [100, 300],
-            "vy": [0, 0]
+            "vy": [0, 0],
         }
         obj_cat = kbmock.ObjectCatalog.from_defaults(param_ranges, seed=100)
         obj_cat2 = kbmock.ObjectCatalog.from_defaults(param_ranges, seed=100)
@@ -154,18 +154,22 @@ def test_folding_obj_cat(self):
         shape = (300, 300)
         timestamps = np.arange(58915, 58920, 1)
 
-        start_x = np.ones((nobj, ))*10
-        start_y = np.linspace(10, shape[0]-10, nobj)
+        start_x = np.ones((nobj,)) * 10
+        start_y = np.linspace(10, shape[0] - 10, nobj)
 
         cats = []
         for i, t in enumerate(timestamps):
-            cats.append(Table({
-                "amplitude": [100]*nobj,
-                "obstime": [t]*nobj,
-                "x_mean": start_x + 15*i*i,
-                "y_mean": start_y,
-                "stddev": [2.0]*nobj
-            }))
+            cats.append(
+                Table(
+                    {
+                        "amplitude": [100] * nobj,
+                        "obstime": [t] * nobj,
+                        "x_mean": start_x + 15 * i * i,
+                        "y_mean": start_y,
+                        "stddev": [2.0] * nobj,
+                    }
+                )
+            )
         catalog = vstack(cats)
 
         obj_cat = kbmock.ObjectCatalog.from_table(catalog)
@@ -173,7 +177,9 @@ def test_folding_obj_cat(self):
 
         prim_hdr_factory = kbmock.HeaderFactory.from_primary_template(
             mutables=["OBS-MJD"],
-            callbacks=[kbmock.ObstimeIterator(timestamps), ],
+            callbacks=[
+                kbmock.ObstimeIterator(timestamps),
+            ],
         )
 
         factory = kbmock.SimpleFits(shape=shape, obj_cat=obj_cat)
@@ -216,10 +222,3 @@ def test_noise_gen(self):
 
 if __name__ == "__main__":
     unittest.main()
-
-
-
-
-
-
-

From 53d5f1668a1a396d59c4e037e9fcb6e724928175 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 5 Aug 2024 21:09:10 -0700
Subject: [PATCH 19/27] Another fixup.

---
 src/kbmod/mocking/catalogs.py                          |  5 ++---
 src/kbmod/mocking/fits_data.py                         | 10 ++--------
 .../standardizers/fits_standardizers/test_data_std.py  |  4 ++--
 3 files changed, 6 insertions(+), 13 deletions(-)

diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 0de5ac0bf..65258cb91 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -67,7 +67,6 @@ def from_config(cls, config, **kwargs):
         table = gen_catalog(config.n, config.param_ranges, config.seed)
         return cls(config, table)
 
-    #
     @classmethod
     def from_defaults(cls, param_ranges=None, **kwargs):
         config = cls.default_config(**kwargs)
@@ -157,8 +156,8 @@ def static(self, **kwargs):
         return self.table.copy()
 
     def next(self, dt, **kwargs):
-        self._realization["x_mean"] += self._realization["vx"] * dt
-        self._realization["y_mean"] += self._realization["vy"] * dt
+        self._realization["x_mean"] = self.table["x_mean"] + self.current*self._realization["vx"] * dt
+        self._realization["y_mean"] = self.table["y_mean"] + self.current*self._realization["vy"] * dt
         self.current += 1
         return self._realization.copy()
 
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index 00407aeba..2f5a1ccad 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -404,7 +404,7 @@ def from_params(cls, config=None, **kwargs):
 
         for patch, value in config.patches:
             if isinstance(patch, tuple):
-                mask[*patch] = 1
+                mask[patch] = 1
             elif isinstance(slice):
                 mask[slice] = 1
             else:
@@ -537,13 +537,7 @@ def mock(self, n=1, obj_cats=None, **kwargs):
         # zip will attempt to iterate over the next available dimension, and
         # that's rows of the image and the table - we don't want that.
         if obj_cats is not None:
-            pairs = (
-                [
-                    (images[0], obj_cats),
-                ]
-                if n == 1
-                else zip(images, obj_cats)
-            )
+            pairs = ([(images[0], obj_cats[0])]  if n == 1 else zip(images, obj_cats))
             for i, (img, cat) in enumerate(pairs):
                 add_model_objects(img, cat, self.config.model(x_stddev=1, y_stddev=1))
 
diff --git a/src/kbmod/standardizers/fits_standardizers/test_data_std.py b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
index 69ce303e5..89b9ebe9a 100644
--- a/src/kbmod/standardizers/fits_standardizers/test_data_std.py
+++ b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
@@ -90,7 +90,7 @@ def translateHeader(self):
         return standardizedHeader
 
     def standardizeMaskImage(self):
-        return (self.hdulist["MASK"] for i in self.processable)
+        return (self.hdulist["MASK"].data for i in self.processable)
 
     def standardizeVarianceImage(self):
-        return (self.hdulist["VARIANCE"] for i in self.processable)
+        return (self.hdulist["VARIANCE"].data for i in self.processable)

From a7cf50eb98193776c6846760c2184d73ccdffa0f Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 5 Aug 2024 21:09:38 -0700
Subject: [PATCH 20/27] more fixup

---
 tests/test_end_to_end.py | 319 ++++++++++++++++++++++++---------------
 tests/test_mocking.py    |   3 +-
 2 files changed, 201 insertions(+), 121 deletions(-)

diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py
index 56e2926b9..cd982604c 100644
--- a/tests/test_end_to_end.py
+++ b/tests/test_end_to_end.py
@@ -1,25 +1,23 @@
-import math
-import numpy as np
-import os
-import tempfile
-import unittest
-
-from kbmod.fake_data.fake_data_creator import *
-from kbmod.run_search import *
-from kbmod.search import *
-from kbmod.wcs_utils import make_fake_wcs
-from kbmod.work_unit import WorkUnit
+#import math
+#import numpy as np
+#import os
+#import tempfile
+#import pytest
+#
+#from kbmod.fake_data.fake_data_creator import *
+#from kbmod.run_search import *
+#from kbmod.search import *
+#from kbmod.wcs_utils import make_fake_wcs
+#from kbmod.work_unit import WorkUnit
 
-# from .utils_for_tests import get_absolute_demo_data_path
-# import utils_for_tests
 from utils.utils_for_tests import get_absolute_demo_data_path
 
 
 ####
 import unittest
 
-
 from kbmod import ImageCollection
+from kbmod.run_search import SearchRunner
 from kbmod.configuration import SearchConfiguration
 import kbmod.mocking as kbmock
 
@@ -28,16 +26,99 @@ class TestEmptySearch(unittest.TestCase):
     def setUp(self):
         self.factory = kbmock.EmptyFits()
 
-    def test(self):
+    def test_empty(self):
         hduls = self.factory.mock(n=10)
 
-        ic = ImageCollection.fromTargets(hdulist=hduls, force="TestDataStd")
-        wu = ic.toWorkUnit(search_config=SearchConfiguration())
-        results = SearchRunner.run_search_from_work_unit(wu)
+        # create the most permissive search configs you can come up with
+        # all values in these images are zeros, we should not be returning
+        # anything
+        config = SearchConfiguration.from_dict({
+            "average_angle": 0.0,
+            "v_arr": [10, 20, 10],
+            "lh_level": 0.1,
+            "num_obs": 1,
+            "do_mask": False,
+            "do_clustering": True,
+            "do_stamp_filter": False
+        })
+
+        ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
+        wu = ic.toWorkUnit(search_config=config)
+        results = SearchRunner().run_search_from_work_unit(wu)
+        self.assertTrue(len(results) == 0)
 
+    def test_static_objects(self):
+        src_cat = kbmock.SourceCatalog.from_defaults(seed=100)
+        factory = kbmock.SimpleFits(src_cat=src_cat)
+        hduls = factory.mock(10)
+
+        ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
+        wu = ic.toWorkUnit(search_config=SearchConfiguration())
+        results = SearchRunner().run_search_from_work_unit(wu)
         self.assertTrue(len(results) == 0)
 
 
+class TestLinearSearch(unittest.TestCase):
+    def setUp(self):
+        # Set up shared search values
+        self.n_imgs = 10
+        self.repeat_n_times = 10
+        self.shape = (500, 500)
+        self.start_pos = (10, 50)
+        self.vxs = [10, 30]
+        self.vys = [10, 30]
+
+        # Set up configs for mocking and search
+        # These don't change from test to test
+        self.param_ranges = {
+            "amplitude": [100, 100],
+            "x_mean": self.start_pos,
+            "y_mean": self.start_pos,
+            "x_stddev": [2.0, 2.0],
+            "y_stddev": [2.0, 2.0],
+            "vx": self.vxs,
+            "vy": self.vys,
+        }
+
+        self.config = SearchConfiguration.from_dict({
+            "generator_config": {
+                "name": "VelocityGridSearch",
+                "min_vx": self.vxs[0],
+                "max_vx": self.vxs[1],
+                "min_vy": self.vys[0],
+                "max_vy": self.vys[1],
+                "vx_steps": 50,
+                "vy_steps": 50,
+            },
+            "num_obs": 10,
+            "do_mask": False,
+            "do_clustering": True,
+            "do_stamp_filter": False
+        })
+
+    def test_search(self):
+        # Mock the data and repeat tests. The random catalog
+        # creation guarantees a diverse set of changing test values
+        for i in range(self.repeat_n_times):
+            with self.subTest(n=i):
+                obj_cat = kbmock.ObjectCatalog.from_defaults(self.param_ranges, n=1)
+                factory = kbmock.SimpleFits(shape=self.shape, step_mjd=1, obj_cat=obj_cat)
+                hduls = factory.mock(n=self.n_imgs)
+
+                ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
+                wu = ic.toWorkUnit(search_config=self.config)
+                results = SearchRunner().run_search_from_work_unit(wu)
+
+                # Run tests
+                self.assertGreaterEqual(len(results), 1)
+                for res in results:
+                    diff = abs(obj_cat.table["y_mean"] - res["y"])
+                    obj = obj_cat.table[diff == diff.min()]
+                    self.assertLessEqual(abs(obj["x_mean"]-res["x"]), 5)
+                    self.assertLessEqual(abs(obj["y_mean"]-res["y"]), 5)
+                    self.assertLessEqual(abs(obj["vx"]-res["vx"]), 5)
+                    self.assertLessEqual(abs(obj["vy"]-res["vy"]), 5)
+
 ####
 
 
@@ -48,107 +129,107 @@ def test(self):
 # (instead of RawImages), but hopefully we can deduplicate all this by making
 # these operations into functions and calling on the .image attribute
 # apply_stamp_filter for example is literal copy of the C++ code in RawImage?
-class test_end_to_end(unittest.TestCase):
-    def setUp(self):
-        # Define the path for the data.
-        im_filepath = get_absolute_demo_data_path("demo")
-
-        # The demo data has an object moving at x_v=10 px/day
-        # and y_v = 0 px/day. So we search velocities [0, 20]
-        # and angles [-0.5, 0.5].
-        v_arr = [0, 20, 21]
-        ang_arr = [0.5, 0.5, 11]
-
-        self.input_parameters = {
-            # Required
-            "im_filepath": im_filepath,
-            "res_filepath": None,
-            "time_file": None,
-            "output_suffix": "DEMO",
-            "v_arr": v_arr,
-            "ang_arr": ang_arr,
-            # Important
-            "num_obs": 7,
-            "do_mask": True,
-            "lh_level": 10.0,
-            "gpu_filter": True,
-            # Fine tuning
-            "sigmaG_lims": [15, 60],
-            "mom_lims": [37.5, 37.5, 1.5, 1.0, 1.0],
-            "peak_offset": [3.0, 3.0],
-            "chunk_size": 1000000,
-            "stamp_type": "cpp_median",
-            "eps": 0.03,
-            "clip_negative": True,
-            "mask_num_images": 10,
-            "cluster_type": "position",
-            # Override the ecliptic angle for the demo data since we
-            # know the true angle in pixel space.
-            "average_angle": 0.0,
-            "save_all_stamps": True,
-        }
-
-    @unittest.skipIf(not HAS_GPU, "Skipping test (no GPU detected)")
-    def test_demo_defaults(self):
-        rs = SearchRunner()
-        keep = rs.run_search_from_config(self.input_parameters)
-        self.assertGreaterEqual(len(keep), 1)
-        self.assertEqual(keep["stamp"][0].shape, (21, 21))
-
-    @unittest.skipIf(not HAS_GPU, "Skipping test (no GPU detected)")
-    def test_demo_config_file(self):
-        im_filepath = get_absolute_demo_data_path("demo")
-        config_file = get_absolute_demo_data_path("demo_config.yml")
-        rs = SearchRunner()
-        keep = rs.run_search_from_file(
-            config_file,
-            overrides={"im_filepath": im_filepath},
-        )
-        self.assertGreaterEqual(len(keep), 1)
-        self.assertEqual(keep["stamp"][0].shape, (21, 21))
-
-    @unittest.skipIf(not HAS_GPU, "Skipping test (no GPU detected)")
-    def test_demo_stamp_size(self):
-        self.input_parameters["stamp_radius"] = 15
-        self.input_parameters["mom_lims"] = [80.0, 80.0, 50.0, 20.0, 20.0]
-
-        rs = SearchRunner()
-        keep = rs.run_search_from_config(self.input_parameters)
-        self.assertGreaterEqual(len(keep), 1)
-
-        self.assertIsNotNone(keep["stamp"][0])
-        self.assertEqual(keep["stamp"][0].shape, (31, 31))
-
-        self.assertIsNotNone(keep["all_stamps"][0])
-        for s in keep["all_stamps"][0]:
-            self.assertEqual(s.shape, (31, 31))
-
-    @unittest.skipIf(not HAS_GPU, "Skipping test (no GPU detected)")
-    def test_e2e_work_unit(self):
-        num_images = 10
-
-        # Create a fake data set with a single bright fake object and all
-        # the observations on a single day.
-        fake_times = create_fake_times(num_images, 57130.2, 10, 0.01, 1)
-        ds = FakeDataSet(128, 128, fake_times, use_seed=True)
-        trj = Trajectory(x=50, y=60, vx=5.0, vy=0.0, flux=500.0)
-        ds.insert_object(trj)
-
-        # Set the configuration to pick up the fake object.
-        config = SearchConfiguration()
-        config.set("ang_arr", [math.pi, math.pi, 16])
-        config.set("v_arr", [0, 10.0, 20])
-
-        fake_wcs = make_fake_wcs(10.0, 10.0, 128, 128)
-        work = WorkUnit(im_stack=ds.stack, config=config, wcs=fake_wcs)
-
-        with tempfile.TemporaryDirectory() as dir_name:
-            file_path = os.path.join(dir_name, "test_workunit.fits")
-            work.to_fits(file_path)
-
-            rs = SearchRunner()
-            keep = rs.run_search_from_file(file_path)
-            self.assertGreaterEqual(len(keep), 1)
+#class test_end_to_end(pytest.TestCase):
+#    def setUp(self):
+#        # Define the path for the data.
+#        im_filepath = get_absolute_demo_data_path("demo")
+#
+#        # The demo data has an object moving at x_v=10 px/day
+#        # and y_v = 0 px/day. So we search velocities [0, 20]
+#        # and angles [-0.5, 0.5].
+#        v_arr = [0, 20, 21]
+#        ang_arr = [0.5, 0.5, 11]
+#
+#        self.input_parameters = {
+#            # Required
+#            "im_filepath": im_filepath,
+#            "res_filepath": None,
+#            "time_file": None,
+#            "output_suffix": "DEMO",
+#            "v_arr": v_arr,
+#            "ang_arr": ang_arr,
+#            # Important
+#            "num_obs": 7,
+#            "do_mask": True,
+#            "lh_level": 10.0,
+#            "gpu_filter": True,
+#            # Fine tuning
+#            "sigmaG_lims": [15, 60],
+#            "mom_lims": [37.5, 37.5, 1.5, 1.0, 1.0],
+#            "peak_offset": [3.0, 3.0],
+#            "chunk_size": 1000000,
+#            "stamp_type": "cpp_median",
+#            "eps": 0.03,
+#            "clip_negative": True,
+#            "mask_num_images": 10,
+#            "cluster_type": "position",
+#            # Override the ecliptic angle for the demo data since we
+#            # know the true angle in pixel space.
+#            "average_angle": 0.0,
+#            "save_all_stamps": True,
+#        }
+#
+#    @pytest.skipIf(not HAS_GPU, "Skipping test (no GPU detected)")
+#    def test_demo_defaults(self):
+#        rs = SearchRunner()
+#        keep = rs.run_search_from_config(self.input_parameters)
+#        self.assertGreaterEqual(len(keep), 1)
+#        self.assertEqual(keep["stamp"][0].shape, (21, 21))
+#
+#    @pytest.skipIf(not HAS_GPU, "Skipping test (no GPU detected)")
+#    def test_demo_config_file(self):
+#        im_filepath = get_absolute_demo_data_path("demo")
+#        config_file = get_absolute_demo_data_path("demo_config.yml")
+#        rs = SearchRunner()
+#        keep = rs.run_search_from_file(
+#            config_file,
+#            overrides={"im_filepath": im_filepath},
+#        )
+#        self.assertGreaterEqual(len(keep), 1)
+#        self.assertEqual(keep["stamp"][0].shape, (21, 21))
+#
+#    @pytest.skipIf(not HAS_GPU, "Skipping test (no GPU detected)")
+#    def test_demo_stamp_size(self):
+#        self.input_parameters["stamp_radius"] = 15
+#        self.input_parameters["mom_lims"] = [80.0, 80.0, 50.0, 20.0, 20.0]
+#
+#        rs = SearchRunner()
+#        keep = rs.run_search_from_config(self.input_parameters)
+#        self.assertGreaterEqual(len(keep), 1)
+#
+#        self.assertIsNotNone(keep["stamp"][0])
+#        self.assertEqual(keep["stamp"][0].shape, (31, 31))
+#
+#        self.assertIsNotNone(keep["all_stamps"][0])
+#        for s in keep["all_stamps"][0]:
+#            self.assertEqual(s.shape, (31, 31))
+#
+#    @pytest.skipIf(not HAS_GPU, "Skipping test (no GPU detected)")
+#    def test_e2e_work_unit(self):
+#        num_images = 10
+#
+#        # Create a fake data set with a single bright fake object and all
+#        # the observations on a single day.
+#        fake_times = create_fake_times(num_images, 57130.2, 10, 0.01, 1)
+#        ds = FakeDataSet(128, 128, fake_times, use_seed=True)
+#        trj = Trajectory(x=50, y=60, vx=5.0, vy=0.0, flux=500.0)
+#        ds.insert_object(trj)
+#
+#        # Set the configuration to pick up the fake object.
+#        config = SearchConfiguration()
+#        config.set("ang_arr", [math.pi, math.pi, 16])
+#        config.set("v_arr", [0, 10.0, 20])
+#
+#        fake_wcs = make_fake_wcs(10.0, 10.0, 128, 128)
+#        work = WorkUnit(im_stack=ds.stack, config=config, wcs=fake_wcs)
+#
+#        with tempfile.TemporaryDirectory() as dir_name:
+#            file_path = os.path.join(dir_name, "test_workunit.fits")
+#            work.to_fits(file_path)
+#
+#            rs = SearchRunner()
+#            keep = rs.run_search_from_file(file_path)
+#            self.assertGreaterEqual(len(keep), 1)
 
 
 if __name__ == "__main__":
diff --git a/tests/test_mocking.py b/tests/test_mocking.py
index 49bdd64c4..8c159335b 100644
--- a/tests/test_mocking.py
+++ b/tests/test_mocking.py
@@ -205,9 +205,8 @@ def test_noise_gen(self):
 
         factory = kbmock.SimpleFits(shape=(1000, 1000), with_noise=True, noise="realistic")
         hdul = factory.mock()[0]
-        print(hdul["IMAGE"].data.mean())
         self.assertAlmostEqual(hdul["IMAGE"].data.mean(), 32, 1)
-        self.assertAlmostEqual(hdul["IMAGE"].data.std(), 7.5, 1)
+        self.assertAlmostEqual(hdul["IMAGE"].data.std(), 7.5, 0)
 
         img_factory = kbmock.SimpleImage(shape=(1000, 1000), add_noise=True, noise=5, noise_std=2.0)
         factory = kbmock.SimpleFits()

From 0b88c8a92b9a65614a11cf635c14e3714c9cc9fd Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 5 Aug 2024 21:20:07 -0700
Subject: [PATCH 21/27] Flakify

---
 src/kbmod/mocking/catalogs.py  |  4 +-
 src/kbmod/mocking/fits_data.py |  2 +-
 tests/test_end_to_end.py       | 83 ++++++++++++++++++----------------
 3 files changed, 47 insertions(+), 42 deletions(-)

diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 65258cb91..f85a9fce3 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -156,8 +156,8 @@ def static(self, **kwargs):
         return self.table.copy()
 
     def next(self, dt, **kwargs):
-        self._realization["x_mean"] = self.table["x_mean"] + self.current*self._realization["vx"] * dt
-        self._realization["y_mean"] = self.table["y_mean"] + self.current*self._realization["vy"] * dt
+        self._realization["x_mean"] = self.table["x_mean"] + self.current * self._realization["vx"] * dt
+        self._realization["y_mean"] = self.table["y_mean"] + self.current * self._realization["vy"] * dt
         self.current += 1
         return self._realization.copy()
 
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index 2f5a1ccad..0852f71ee 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -537,7 +537,7 @@ def mock(self, n=1, obj_cats=None, **kwargs):
         # zip will attempt to iterate over the next available dimension, and
         # that's rows of the image and the table - we don't want that.
         if obj_cats is not None:
-            pairs = ([(images[0], obj_cats[0])]  if n == 1 else zip(images, obj_cats))
+            pairs = [(images[0], obj_cats[0])] if n == 1 else zip(images, obj_cats)
             for i, (img, cat) in enumerate(pairs):
                 add_model_objects(img, cat, self.config.model(x_stddev=1, y_stddev=1))
 
diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py
index cd982604c..5764f0c7d 100644
--- a/tests/test_end_to_end.py
+++ b/tests/test_end_to_end.py
@@ -1,14 +1,14 @@
-#import math
-#import numpy as np
-#import os
-#import tempfile
-#import pytest
+# import math
+# import numpy as np
+# import os
+# import tempfile
+# import pytest
 #
-#from kbmod.fake_data.fake_data_creator import *
-#from kbmod.run_search import *
-#from kbmod.search import *
-#from kbmod.wcs_utils import make_fake_wcs
-#from kbmod.work_unit import WorkUnit
+# from kbmod.fake_data.fake_data_creator import *
+# from kbmod.run_search import *
+# from kbmod.search import *
+# from kbmod.wcs_utils import make_fake_wcs
+# from kbmod.work_unit import WorkUnit
 
 from utils.utils_for_tests import get_absolute_demo_data_path
 
@@ -32,15 +32,17 @@ def test_empty(self):
         # create the most permissive search configs you can come up with
         # all values in these images are zeros, we should not be returning
         # anything
-        config = SearchConfiguration.from_dict({
-            "average_angle": 0.0,
-            "v_arr": [10, 20, 10],
-            "lh_level": 0.1,
-            "num_obs": 1,
-            "do_mask": False,
-            "do_clustering": True,
-            "do_stamp_filter": False
-        })
+        config = SearchConfiguration.from_dict(
+            {
+                "average_angle": 0.0,
+                "v_arr": [10, 20, 10],
+                "lh_level": 0.1,
+                "num_obs": 1,
+                "do_mask": False,
+                "do_clustering": True,
+                "do_stamp_filter": False,
+            }
+        )
 
         ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
         wu = ic.toWorkUnit(search_config=config)
@@ -80,21 +82,23 @@ def setUp(self):
             "vy": self.vys,
         }
 
-        self.config = SearchConfiguration.from_dict({
-            "generator_config": {
-                "name": "VelocityGridSearch",
-                "min_vx": self.vxs[0],
-                "max_vx": self.vxs[1],
-                "min_vy": self.vys[0],
-                "max_vy": self.vys[1],
-                "vx_steps": 50,
-                "vy_steps": 50,
-            },
-            "num_obs": 10,
-            "do_mask": False,
-            "do_clustering": True,
-            "do_stamp_filter": False
-        })
+        self.config = SearchConfiguration.from_dict(
+            {
+                "generator_config": {
+                    "name": "VelocityGridSearch",
+                    "min_vx": self.vxs[0],
+                    "max_vx": self.vxs[1],
+                    "min_vy": self.vys[0],
+                    "max_vy": self.vys[1],
+                    "vx_steps": 50,
+                    "vy_steps": 50,
+                },
+                "num_obs": 10,
+                "do_mask": False,
+                "do_clustering": True,
+                "do_stamp_filter": False,
+            }
+        )
 
     def test_search(self):
         # Mock the data and repeat tests. The random catalog
@@ -114,10 +118,11 @@ def test_search(self):
                 for res in results:
                     diff = abs(obj_cat.table["y_mean"] - res["y"])
                     obj = obj_cat.table[diff == diff.min()]
-                    self.assertLessEqual(abs(obj["x_mean"]-res["x"]), 5)
-                    self.assertLessEqual(abs(obj["y_mean"]-res["y"]), 5)
-                    self.assertLessEqual(abs(obj["vx"]-res["vx"]), 5)
-                    self.assertLessEqual(abs(obj["vy"]-res["vy"]), 5)
+                    self.assertLessEqual(abs(obj["x_mean"] - res["x"]), 5)
+                    self.assertLessEqual(abs(obj["y_mean"] - res["y"]), 5)
+                    self.assertLessEqual(abs(obj["vx"] - res["vx"]), 5)
+                    self.assertLessEqual(abs(obj["vy"] - res["vy"]), 5)
+
 
 ####
 
@@ -129,7 +134,7 @@ def test_search(self):
 # (instead of RawImages), but hopefully we can deduplicate all this by making
 # these operations into functions and calling on the .image attribute
 # apply_stamp_filter for example is literal copy of the C++ code in RawImage?
-#class test_end_to_end(pytest.TestCase):
+# class test_end_to_end(pytest.TestCase):
 #    def setUp(self):
 #        # Define the path for the data.
 #        im_filepath = get_absolute_demo_data_path("demo")

From de1e859f2485bcdc6a6aedf1b03c52e46fe50f6e Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Tue, 6 Aug 2024 12:38:39 -0700
Subject: [PATCH 22/27] fixup again, moving home to test decamimdiff gen

---
 src/kbmod/mocking/fits.py      | 29 ++++-------
 src/kbmod/mocking/fits_data.py | 14 +++---
 src/kbmod/mocking/headers.py   | 90 ++++++++++++++++++++++++----------
 tests/test_end_to_end.py       | 32 +++++++++---
 4 files changed, 107 insertions(+), 58 deletions(-)

diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index f299e213b..d10608ae7 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -184,13 +184,14 @@ class DECamImdiff:
     def from_defaults(
         cls,
         with_data=False,
+        override_original=False,
+        shape=(100, 100),
+        start_mjd=60310,
+        step_mjd=0.001,
+        with_noise=False,
         noise="simplistic",
         src_cat=None,
-        obj_cat=None,
-        editable_images=False,
-        separate_masks=False,
-        writeable_masks=False,
-        editable_masks=False,
+        obj_cat=None
     ):
         if obj_cat.config.type == "progressive":
             raise ValueError(
@@ -202,15 +203,8 @@ def from_defaults(
         hdr_factory = ArchivedHeader("headers_archive.tar.bz2", "decam_imdiff_headers.ecsv")
 
         hdu_types = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
-        hdu_types.extend(
-            [
-                BinTableHDU,
-            ]
-            * 12
-        )
-        data = [
-            NoneFactory(),
-        ] * 16
+        hdu_types.extend([BinTableHDU] * 12)
+        data = [NoneFactory()] * 16
 
         if with_data:
             headers = hdr_factory.get(0)
@@ -236,12 +230,7 @@ def __init__(self, header_factory, data_factories=None, obj_cat=None):
         self.hdr_factory = header_factory
         self.data_factories = data_factories
         self.hdu_layout = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
-        self.hdu_layout.extend(
-            [
-                BinTableHDU,
-            ]
-            * 12
-        )
+        self.hdu_layout.extend([BinTableHDU] * 12)
 
     def mock(self, n=1):
         obj_cats = None
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index 0852f71ee..e11ff8437 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -464,12 +464,13 @@ class SimpleImage(DataFactory):
 
     default_config = SimpleImageConfig
 
-    def __init__(self, image=None, src_cat=None, obj_cat=None, config=None, **kwargs):
+    def __init__(self, image=None, src_cat=None, obj_cat=None, config=None,
+                 dtype=np.float32, **kwargs):
         self.config = self.default_config(config=config, **kwargs)
         super().__init__(image, self.config, **kwargs)
 
         if image is None:
-            image = np.zeros(self.config.shape, dtype=np.float32)
+            image = np.zeros(self.config.shape, dtype=dtype)
         else:
             image = image
             self.config.shape = image.shape
@@ -794,7 +795,7 @@ def add_noise(cls, images, config):
         return images
 
     @classmethod
-    def gen_base_image(cls, config=None, src_cat=None):
+    def gen_base_image(cls, config=None, src_cat=None, dtype=np.float32):
         """Generate base image from configuration.
 
         Parameters
@@ -812,7 +813,7 @@ def gen_base_image(cls, config=None, src_cat=None):
         config = cls.default_config(config)
 
         # empty image
-        base = np.zeros(config.shape, dtype=np.float32)
+        base = np.zeros(config.shape, dtype=dtype)
         base += config.bias
         base = cls.add_hot_pixels(base, config)
         base = cls.add_bad_cols(base, config)
@@ -821,7 +822,8 @@ def gen_base_image(cls, config=None, src_cat=None):
 
         return base
 
-    def __init__(self, image=None, config=None, src_cat=None, obj_cat=None, **kwargs):
+    def __init__(self, image=None, config=None, src_cat=None, obj_cat=None, dtype=np.float32,**kwargs):
         conf = self.default_config(config=config, **kwargs)
         # static objects are added in SimpleImage init
-        super().__init__(image=self.gen_base_image(conf), config=conf, src_cat=src_cat, obj_cat=obj_cat)
+        super().__init__(image=self.gen_base_image(conf, dtype=dtype),
+                         config=conf, src_cat=src_cat, obj_cat=obj_cat)
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index 75c02ef23..6716fb2aa 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -1,6 +1,7 @@
 import warnings
 
-from astropy.utils.exceptions import AstropyUserWarning
+import numpy as np
+
 from astropy.wcs import WCS
 from astropy.io.fits import Header
 
@@ -14,6 +15,54 @@
 ]
 
 
+def make_wcs(center_coords=(351., -5.), rotation=0, pixscale=0.2, shape=None):
+    """
+    Create a simple celestial `~astropy.wcs.WCS` object in ICRS
+    coordinate system.
+
+    Parameters
+    ----------
+    shape : tuple[int]
+        Two-tuple, dimensions of the WCS footprint
+    center_coords : tuple[int]
+        Two-tuple of on-sky coordinates of the center of the WCS in
+        decimal degrees, in ICRS.
+    rotation : float, optional
+        Rotation in degrees, from ICRS equator. In decimal degrees.
+    pixscale : float
+        Pixel scale in arcsec/pixel.
+
+    Returns
+    -------
+    wcs : `astropy.wcs.WCS`
+        The world coordinate system.
+
+    Examples
+    --------
+    >>> from kbmod.mocking import make_wcs
+    >>> shape = (100, 100)
+    >>> wcs = make_wcs(shape)
+    >>> wcs = make_wcs(shape, (115, 5), 45, 0.1)
+    """
+    wcs = WCS(naxis=2)
+    rho = rotation*0.0174533 # deg to rad
+    scale = 0.1 / 3600.0  # arcsec/pixel to deg/pix
+
+    if shape is not None:
+        wcs.pixel_shape = shape
+        wcs.wcs.crpix = [shape[1] / 2, shape[0] / 2]
+    else:
+        wcs.wcs.crpix = [0, 0]
+    wcs.wcs.crval = center_coords
+    wcs.wcs.cunit = ['deg', 'deg']
+    wcs.wcs.cd = [[-scale * np.cos(rho), scale * np.sin(rho)],
+                  [scale * np.sin(rho), scale * np.cos(rho)]]
+    wcs.wcs.radesys = 'ICRS'
+    wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
+
+    return wcs
+
+
 class HeaderFactory:
     primary_template = {
         "EXTNAME": "PRIMARY",
@@ -29,14 +78,6 @@ class HeaderFactory:
 
     ext_template = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "CRPIX1": 1024, "CPRIX2": 2048, "BITPIX": 32}
 
-    wcs_template = {
-        "ctype": ["RA---TAN", "DEC--TAN"],
-        "crval": [351, -5],
-        "cunit": ["deg", "deg"],
-        "radesys": "ICRS",
-        "cd": [[-1.44e-07, 7.32e-05], [7.32e-05, 1.44e-05]],
-    }
-
     def __validate_mutables(self):
         # !xor
         if bool(self.mutables) != bool(self.callbacks):
@@ -87,23 +128,16 @@ def mock(self, n=1):
         return headers
 
     @classmethod
-    def gen_wcs(cls, metadata):
-        wcs = WCS(naxis=2)
-        for k, v in metadata.items():
-            setattr(wcs.wcs, k, v)
-        return wcs.to_header()
-
-    @classmethod
-    def gen_header(cls, base, overrides, wcs_base=None):
+    def gen_header(cls, base, overrides, wcs=None):
         header = Header(base)
         header.update(overrides)
 
-        if wcs_base is not None:
-            naxis1 = header.get("NAXIS1", False)
-            naxis2 = header.get("NAXIS2", False)
-            if not all((naxis1, naxis2)):
-                raise ValueError("Adding a WCS to the header requires " "NAXIS1 and NAXIS2 keys.")
-            header.update(cls.gen_wcs(wcs_base))
+        if wcs is not None:
+            # Sync WCS with header + overwrites
+            wcs_header = wcs.to_header()
+            wcs_header.update(header)
+            # then merge back to mocked header template
+            header.update(wcs_header)
 
         return header
 
@@ -122,9 +156,13 @@ def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, wcs=No
             ext_template["CRPIX1"] = shape[0] // 2
             ext_template["CRPIX2"] = shape[1] // 2
 
-        hdr = cls.gen_header(
-            base=ext_template, overrides=overrides, wcs_base=cls.wcs_template if wcs is None else wcs
-        )
+        if wcs is None:
+            wcs = make_wcs(
+                shape=(ext_template["NAXIS1"], ext_template["NAXIS2"]),
+
+            )
+
+        hdr = cls.gen_header(base=ext_template, overrides=overrides, wcs=wcs)
         return cls(hdr, mutables, callbacks)
 
 
diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py
index 5764f0c7d..eb00f4b91 100644
--- a/tests/test_end_to_end.py
+++ b/tests/test_end_to_end.py
@@ -60,15 +60,15 @@ def test_static_objects(self):
         self.assertTrue(len(results) == 0)
 
 
-class TestLinearSearch(unittest.TestCase):
+class TestRandomLinearSearch(unittest.TestCase):
     def setUp(self):
         # Set up shared search values
         self.n_imgs = 10
         self.repeat_n_times = 10
-        self.shape = (500, 500)
-        self.start_pos = (10, 50)
-        self.vxs = [10, 30]
-        self.vys = [10, 30]
+        self.shape = (300, 300)
+        self.start_pos = (125, 175)
+        self.vxs = [-10, 10]
+        self.vys = [-10, 10]
 
         # Set up configs for mocking and search
         # These don't change from test to test
@@ -100,7 +100,7 @@ def setUp(self):
             }
         )
 
-    def test_search(self):
+    def test_simple_search(self):
         # Mock the data and repeat tests. The random catalog
         # creation guarantees a diverse set of changing test values
         for i in range(self.repeat_n_times):
@@ -123,6 +123,26 @@ def test_search(self):
                     self.assertLessEqual(abs(obj["vx"] - res["vx"]), 5)
                     self.assertLessEqual(abs(obj["vy"] - res["vy"]), 5)
 
+    def test_diffim_mocks(self):
+        src_cat = kbmock.SourceCatalog.from_defaults()
+        obj_cat = kbmock.ObjectCatalog.from_defaults(self.param_ranges, n=1)
+        factory = kbmock.DECamImdiff.from_defaults(with_data=True, src_cat=src_cat, obj_cat=obj_cat)
+        hduls = factory.mock(n=self.n_imgs)
+
+        ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
+        wu = ic.toWorkUnit(search_config=self.config)
+        results = SearchRunner().run_search_from_work_unit(wu)
+
+        # Run tests
+        self.assertGreaterEqual(len(results), 1)
+        for res in results:
+            diff = abs(obj_cat.table["y_mean"] - res["y"])
+            obj = obj_cat.table[diff == diff.min()]
+            self.assertLessEqual(abs(obj["x_mean"] - res["x"]), 5)
+            self.assertLessEqual(abs(obj["y_mean"] - res["y"]), 5)
+            self.assertLessEqual(abs(obj["vx"] - res["vx"]), 5)
+            self.assertLessEqual(abs(obj["vy"] - res["vy"]), 5)
+
 
 ####
 

From fe26398d1428a03846b57de29d9bd0d0dbd32de3 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Thu, 15 Aug 2024 15:03:23 -0700
Subject: [PATCH 23/27] Fixup.

---
 src/kbmod/mocking/callbacks.py                |  32 ++-
 src/kbmod/mocking/catalogs.py                 |  63 +++--
 src/kbmod/mocking/config.py                   | 126 ++-------
 src/kbmod/mocking/fits.py                     | 226 ++++++++--------
 src/kbmod/mocking/fits_data.py                | 131 +++++-----
 src/kbmod/mocking/headers.py                  | 198 +++++++++-----
 .../standardizers/butler_standardizer.py      |  10 +-
 .../fits_standardizers/test_data_std.py       |   2 +-
 tests/test_end_to_end.py                      | 241 ++++++++++++++---
 tests/test_mocking.py                         | 242 ++++++++++++------
 10 files changed, 772 insertions(+), 499 deletions(-)

diff --git a/src/kbmod/mocking/callbacks.py b/src/kbmod/mocking/callbacks.py
index 4b2b15750..28c1d9397 100644
--- a/src/kbmod/mocking/callbacks.py
+++ b/src/kbmod/mocking/callbacks.py
@@ -1,3 +1,8 @@
+import random
+
+from astropy.time import Time
+import astropy.units as u
+
 __all__ = [
     "IncrementObstime",
     "ObstimeIterator",
@@ -5,20 +10,33 @@
 
 
 class IncrementObstime:
+    default_unit = "day"
     def __init__(self, start, dt):
-        self.start = start
+        self.start = Time(start)
+        if not isinstance(dt, u.Quantity):
+            dt = dt * getattr(u, self.default_unit)
         self.dt = dt
 
-    def __call__(self, mut_val):
+    def __call__(self, header_val):
         curr = self.start
         self.start += self.dt
-        return curr
+        return curr.fits
 
 
 class ObstimeIterator:
-    def __init__(self, obstimes):
-        self.obstimes = obstimes
+    def __init__(self, obstimes, **kwargs):
+        self.obstimes = Time(obstimes, **kwargs)
         self.generator = (t for t in obstimes)
 
-    def __call__(self, mut_val):
-        return next(self.generator)
+    def __call__(self, header_val):
+        return Time(next(self.generator)).fits
+
+
+class DitherValue:
+    def __init__(self, value, dither_range):
+        self.value = value
+        self.dither_range = dither_range
+
+    def __call__(self, header_val):
+        return self.value + random.uniform(self.dither_range)
+
diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index f85a9fce3..3063116d9 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -2,11 +2,13 @@
 
 import numpy as np
 from astropy.table import QTable
+from astropy.coordinates import SkyCoord
+
 from .config import Config
 
 
 __all__ = [
-    "gen_catalog",
+    "gen_random_catalog",
     "CatalogFactory",
     "SimpleCatalog",
     "SourceCatalogConfig",
@@ -16,7 +18,7 @@
 ]
 
 
-def gen_catalog(n, param_ranges, seed=None):
+def gen_random_catalog(n, param_ranges, seed=None):
     cat = QTable()
     rng = np.random.default_rng(seed)
 
@@ -30,9 +32,6 @@ def gen_catalog(n, param_ranges, seed=None):
 
     # conversion assumes a gaussian
     if "flux" in param_ranges and "amplitude" not in param_ranges:
-        xstd = cat["x_stddev"] if "x_stddev" in cat.colnames else 1.0
-        ystd = cat["y_stddev"] if "y_stddev" in cat.colnames else 1.0
-
         cat["amplitude"] = cat["flux"] / (2.0 * np.pi * xstd * ystd)
 
     return cat
@@ -45,7 +44,8 @@ def mock(self, *args, **kwargs):
 
 
 class SimpleCatalogConfig(Config):
-    mode = "static"
+    mode = "static"  # folding
+    kind = "pixel"  # world
     return_copy = False
     seed = None
     n = 100
@@ -56,28 +56,32 @@ class SimpleCatalog(CatalogFactory):
     default_config = SimpleCatalogConfig
 
     def __init__(self, config, table, **kwargs):
-        config = self.default_config(**kwargs)
-        self.config = config
+        self.config = self.default_config(config=config, **kwargs)
         self.table = table
         self.current = 0
 
     @classmethod
     def from_config(cls, config, **kwargs):
         config = cls.default_config(config=config, **kwargs)
-        table = gen_catalog(config.n, config.param_ranges, config.seed)
+        table = gen_random_catalog(config["n"], config["param_ranges"], config["seed"])
         return cls(config, table)
 
     @classmethod
     def from_defaults(cls, param_ranges=None, **kwargs):
         config = cls.default_config(**kwargs)
         if param_ranges is not None:
-            config.param_ranges.update(param_ranges)
+            config["param_ranges"].update(param_ranges)
         return cls.from_config(config)
 
     @classmethod
     def from_table(cls, table, **kwargs):
+        if "x_stddev" not in table.columns:
+            table["x_stddev"] = table["stddev"]
+        if "y_stddev" not in table.columns:
+            table["y_stddev"] = table["stddev"]
+
         config = cls.default_config(**kwargs)
-        config.n = len(table)
+        config["n"] = len(table)
         params = {}
         for col in table.keys():
             params[col] = (table[col].min(), table[col].max())
@@ -86,7 +90,7 @@ def from_table(cls, table, **kwargs):
 
     def mock(self):
         self.current += 1
-        if self.config.return_copy:
+        if self.config["return_copy:"]:
             return self.table.copy()
         return self.table
 
@@ -127,7 +131,7 @@ def __init__(self, config, table, **kwargs):
         kwargs["return_copy"] = True
         super().__init__(config, table, **kwargs)
         self._realization = self.table.copy()
-        self.mode = self.config.mode
+        self.mode = self.config["mode"]
 
     @property
     def mode(self):
@@ -137,8 +141,10 @@ def mode(self):
     def mode(self, val):
         if val == "folding":
             self._gen_realization = self.fold
-        elif val == "progressive":
-            self._gen_realization = self.next
+        elif val == "progressive" and self.config["kind"] == "pixel":
+            self._gen_realization = self.next_pixel
+        elif val == "progressive" and self.config["kind"] == "world":
+            self._gen_realization = self.next_world
         elif val == "static":
             self._gen_realization = self.static
         else:
@@ -155,25 +161,38 @@ def reset(self):
     def static(self, **kwargs):
         return self.table.copy()
 
-    def next(self, dt, **kwargs):
-        self._realization["x_mean"] = self.table["x_mean"] + self.current * self._realization["vx"] * dt
-        self._realization["y_mean"] = self.table["y_mean"] + self.current * self._realization["vy"] * dt
+    def _next(self, dt, keys):
+        a, va, b, vb = keys
+        self._realization[a] = self.table[a] + self.current * self.table[va] * dt
+        self._realization[b] = self.table[b] + self.current * self.table[vb] * dt
         self.current += 1
         return self._realization.copy()
 
+    def next_world(self, dt):
+        return self._next(dt, ["ra_mean", "v_ra", "dec_mean", "v_dec"])
+
+    def next_pixel(self, dt):
+        return self._next(dt, ["x_mean", "vx", "y_mean", "vy"])
+
     def fold(self, t, **kwargs):
         self._realization = self.table[self.table["obstime"] == t]
         self.current += 1
         return self._realization.copy()
 
-    def mock(self, n=1, **kwargs):
+    def mock(self, n=1, dt=None, t=None, wcs=None):
         data = []
 
         if self.mode == "folding":
-            for t in kwargs["t"]:
-                data.append(self.fold(t=t))
+            for i, ts in enumerate(t):
+                data.append(self.fold(t=ts))
         else:
             for i in range(n):
-                data.append(self._gen_realization(**kwargs))
+                data.append(self._gen_realization(dt))
+
+        if self.config["kind"] == "world":
+            for cat, w in zip(data, wcs):
+                x, y = w.world_to_pixel(SkyCoord(ra=cat["ra_mean"], dec=cat["dec_mean"], unit="deg"))
+                cat["x_mean"] = x
+                cat["y_mean"] = y
 
         return data
diff --git a/src/kbmod/mocking/config.py b/src/kbmod/mocking/config.py
index b0ad888c5..7af9b97f9 100644
--- a/src/kbmod/mocking/config.py
+++ b/src/kbmod/mocking/config.py
@@ -8,13 +8,10 @@ class ConfigurationError(Exception):
 
 
 class Config:
-    """Base configuration class.
+    """Base class for Standardizer configuration.
 
-    Config classes that inherit from this class define configuration as their
-    class attributes. Particular attributes can be overriden on an per-instance
-    basis by providing a config overrides at initialization time.
-
-    Configs inheriting from this config support basic dictionary operations.
+    Not all standardizers will (can) use the same parameters so refer to their
+    respective documentation for a more complete list.
 
     Parameters
     ----------
@@ -24,66 +21,33 @@ class attributes. Particular attributes can be overriden on an per-instance
         Keyword arguments, assigned as configuration key-values.
     """
 
-    def __init__(self, config=None, method="default", **kwargs):
+    def __init__(self, config=None, **kwargs):
         # This is a bit hacky, but it makes life a lot easier because it
         # enables automatic loading of the default configuration and separation
         # of default config from instance bound config
         keys = list(set(dir(self.__class__)) - set(dir(Config)))
 
         # First fill out all the defaults by copying cls attrs
-        self._conf = {k: copy.copy(getattr(self, k)) for k in keys}
+        self._conf = {k: getattr(self, k) for k in keys}
 
         # Then override with any user-specified values
-        self.update(config=config, method=method, **kwargs)
-
-    @classmethod
-    def from_configs(cls, *args):
-        config = cls()
-        for conf in args:
-            config.update(config=conf, method="extend")
-        return config
+        if config is not None:
+            self._conf.update(config)
+        self._conf.update(kwargs)
 
+    # now just shortcut the most common dict operations
     def __getitem__(self, key):
         return self._conf[key]
 
-    # now just shortcut the most common dict operations
-    def __getattribute__(self, key):
-        hasconf = "_conf" in object.__getattribute__(self, "__dict__")
-        if hasconf:
-            conf = object.__getattribute__(self, "_conf")
-            if key in conf:
-                return conf[key]
-        return object.__getattribute__(self, key)
-
     def __setitem__(self, key, value):
         self._conf[key] = value
 
-    def __repr__(self):
-        res = f"{self.__class__.__name__}("
-        for k, v in self.items():
-            res += f"{k}: {v}, "
-        return res[:-2] + ")"
-
     def __str__(self):
         res = f"{self.__class__.__name__}("
         for k, v in self.items():
             res += f"{k}: {v}, "
         return res[:-2] + ")"
 
-    def _repr_html_(self):
-        repr = f"""
-        <table style='tr:nth-child(even){{background-color: #dddddd;}};'>
-        <caption>{self.__class__.__name__}</caption>
-          <tr>
-            <th>Key</th>
-            <th>Value</th>
-          </tr>
-        """
-        for k, v in self.items():
-            repr += f"<tr><td>{k}</td><td>{v}\n"
-        repr += "</table>"
-        return repr
-
     def __len__(self):
         return len(self._conf)
 
@@ -107,7 +71,7 @@ def __or__(self, other):
         elif isinstance(other, dict):
             return self.__class__(config=self._conf | other)
         else:
-            raise TypeError("unsupported operand type(s) for |: {type(self)}and {type(other)}")
+            raise TypeError("unsupported operand type(s) for |: {type(self)} " "and {type(other)}")
 
     def keys(self):
         """A set-like object providing a view on config's keys."""
@@ -121,68 +85,22 @@ def items(self):
         """A set-like object providing a view on config's items."""
         return self._conf.items()
 
-    def copy(self):
-        return self.__class__(config=self._conf.copy())
-
-    def update(self, config=None, method="default", **kwargs):
-        """Update this config from dict/other config/iterable and
-        apply any explicit keyword overrides.
-
-        A dict-like update. If ``conf`` is given and has a ``.keys()``
-        method, performs:
-
-            for k in conf: this[k] = conf[k]
-
-        If ``conf`` is given but lacks a ``.keys()`` method, performs:
+    def update(self, conf=None, **kwargs):
+        """Update this config from dict/other config/iterable.
 
-            for k, v in conf: this[k] = v
+        A dict-like update. If ``conf`` is present and has a ``.keys()``
+        method, then does:  ``for k in conf: this[k] = conf[k]``. If ``conf``
+        is present but lacks a ``.keys()`` method, then does:
+        ``for k, v in conf: this[k] = v``.
 
-        In both cases, explicit overrides are applied at the end:
-
-            for k in kwargs:  this[k] = kwargs[k]
+        In either case, this is followed by:
+        ``for k in kwargs:  this[k] = kwargs[k]``
         """
-        # Python < 3.9 does not support set operations for dicts
-        # [fixme]: Update this to: other = conf | kwargs
-        # and remove current implementation when 3.9 gets too old. Order of
-        # conf and kwargs matter to correctly apply explicit overrides
-
-        # Check if both conf and kwargs are given, just conf or just
-        # kwargs. If none are given do nothing to comply with default
-        # dict behavior
-        if config is not None and kwargs:
-            other = {**config, **kwargs}
-        elif config is not None:
-            other = config
-        elif kwargs is not None:
-            other = kwargs
-        else:
-            return
-
-        # then, see if we the given config and overrides are a subset of this
-        # config or it's superset. Depending on the selected method then raise
-        # errors, ignore or extend the current config if the given config is a
-        # superset (or disjoint) from the current one.
-        subset = {k: v for k, v in other.items() if k in self._conf}
-        superset = {k: v for k, v in other.items() if k not in subset}
-
-        if method.lower() == "default":
-            if superset:
-                raise ConfigurationError(
-                    "Tried setting the following fields, not a part of "
-                    f"this configuration options: {superset}"
-                )
-            conf = other  # == subset
-        elif method.lower() == "subset":
-            conf = subset
-        elif method.lower() == "extend":
-            conf = other
-        else:
-            raise ValueError(
-                "Method expected to be one of 'default', " f"'subset'  or 'extend'. Got {method} instead."
-            )
-
-        self._conf.update(conf)
+        if conf is not None:
+            self._conf.update(conf)
+        self._conf.update(kwargs)
 
     def toDict(self):
         """Return this config as a dict."""
         return self._conf
+
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index d10608ae7..5db6650ad 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -1,4 +1,5 @@
 from astropy.io.fits import HDUList, PrimaryHDU, CompImageHDU, BinTableHDU
+from astropy.wcs import WCS
 
 from .callbacks import IncrementObstime
 from .headers import HeaderFactory, ArchivedHeader
@@ -25,57 +26,32 @@
 
 class NoneFactory:
     "Kinda makes some code later prettier. Kinda"
-
     def mock(self, n):
         return [
             None,
         ] * n
 
 
-def hdu_cast(hdu_cls, hdr, data=None, validate_header=False, update_header=False):
-    hdu = hdu_cls()
-
-    if validate_header:
-        hdu.header.update(hdr)
-    else:
-        hdu.header = hdr
-
-    if data is not None:
-        hdu.data = data
-        if update_header:
-            hdu.update_header()
-
-    return hdu
-
-
-def hdu_cast_array(hdu_cls, hdr, data, validate_header=False, update_header=False):
-    hdus = []
-    for hdr, dat in zip(hdr, data):
-        hdus.append(hdu_cast(hdu_cls, hdr, dat))
-    return hdus
-
-
 class EmptyFits:
     def __init__(
         self,
         header=None,
         shape=(100, 100),
-        start_mjd=60310,
-        step_mjd=0.001,
+        start_t="2024-01-01T00:00:00.00",
+        step_t=0.001,
         editable_images=False,
         editable_masks=False,
     ):
         self.prim_hdr = HeaderFactory.from_primary_template(
-            overrides=header, mutables=["OBS-MJD"], callbacks=[IncrementObstime(start=start_mjd, dt=step_mjd)]
+            overrides=header,
+            mutables=["DATE-OBS"],
+            callbacks=[IncrementObstime(start=start_t, dt=step_t)]
         )
 
         self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, shape=shape)
         self.var_hdr = HeaderFactory.from_ext_template({"EXTNAME": "VARIANCE"}, shape=shape)
         self.mask_hdr = HeaderFactory.from_ext_template({"EXTNAME": "MASK"}, shape=shape)
 
-        #   2.2) Then data factories, attempt to save performance and memory
-        #        where possible by really only allocating 1 array whenever the
-        #        data is read-only and content-static between created HDUs.
         self.img_data = DataFactory.from_header(
             kind="image", header=self.img_hdr.header, writeable=editable_images, return_copy=editable_images
         )
@@ -86,24 +62,26 @@ def __init__(
         self.current = 0
 
     def mock(self, n=1):
-        img_hdr = self.img_hdr.mock()[0]
-        var_hdr = self.var_hdr.mock()[0]
-        mask_hdr = self.mask_hdr.mock()[0]
+        prim_hdrs = self.prim_hdr.mock(n=n)
+        img_hdrs = self.img_hdr.mock(n=n)
+        var_hdrs = self.var_hdr.mock(n=n)
+        mask_hdrs = self.mask_hdr.mock(n=n)
+
         images = self.img_data.mock(n=n)
         variances = self.img_data.mock(n=n)
         masks = self.mask_data.mock(n=n)
 
         hduls = []
-        for i in range(n):
+        for ph, ih, vh, mh, imd, vd, md in zip(
+            prim_hdrs, img_hdrs, var_hdrs, mask_hdrs, images, variances, masks
+        ):
             hduls.append(
-                HDUList(
-                    hdus=[
-                        PrimaryHDU(header=self.prim_hdr.mock()[0]),
-                        CompImageHDU(header=img_hdr, data=images[i]),
-                        CompImageHDU(header=var_hdr, data=variances[i]),
-                        CompImageHDU(header=mask_hdr, data=masks[i]),
-                    ]
-                )
+                HDUList(hdus=[
+                    PrimaryHDU(header=ph),
+                    CompImageHDU(header=ih, data=imd),
+                    CompImageHDU(header=vh, data=vd),
+                    CompImageHDU(header=mh, data=md)
+                ])
             )
 
         self.current += n
@@ -112,25 +90,48 @@ def mock(self, n=1):
 
 class SimpleFits:
     def __init__(
-        self,
-        header=None,
-        shape=(100, 100),
-        start_mjd=60310,
-        step_mjd=0.001,
-        with_noise=False,
-        noise="simplistic",
-        src_cat=None,
-        obj_cat=None,
+            self,
+            shared_header_metadata=None,
+            shape=(100, 100),
+            start_t="2024-01-01T00:00:00.00",
+            step_t=0.001,
+            with_noise=False,
+            noise="simplistic",
+            src_cat=None,
+            obj_cat=None,
+            wcs_factory=None,
     ):
         # 2. Set up Header and Data factories that go into creating HDUs
         #    2.1) First headers, since that metadata specified data formats
         self.prim_hdr = HeaderFactory.from_primary_template(
-            overrides=header, mutables=["OBS-MJD"], callbacks=[IncrementObstime(start=start_mjd, dt=step_mjd)]
+            overrides=shared_header_metadata,
+            mutables=["DATE-OBS"], callbacks=[IncrementObstime(start=start_t, dt=step_t)]
         )
 
-        self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, shape=shape)
-        self.var_hdr = HeaderFactory.from_ext_template({"EXTNAME": "VARIANCE"}, shape=shape)
-        self.mask_hdr = HeaderFactory.from_ext_template({"EXTNAME": "MASK"}, shape=shape)
+        wcs = None
+        if wcs_factory is not None:
+            wcs = wcs_factory
+
+        if shared_header_metadata is None:
+            shared_header_metadata = {"EXTNAME": "IMAGE"}
+
+        self.img_hdr = HeaderFactory.from_ext_template(
+            overrides=shared_header_metadata.copy(),
+            shape=shape,
+            wcs=wcs
+        )
+        shared_header_metadata["EXTNAME"] = "VARIANCE"
+        self.var_hdr = HeaderFactory.from_ext_template(
+            overrides=shared_header_metadata.copy(),
+            shape=shape,
+            wcs=wcs
+        )
+        shared_header_metadata["EXTNAME"] = "MASK"
+        self.mask_hdr = HeaderFactory.from_ext_template(
+            overrides=shared_header_metadata.copy(),
+            shape=shape,
+            wcs=wcs
+        )
 
         #   2.2) Then data factories
         if noise == "realistic":
@@ -140,8 +141,8 @@ def __init__(
         self.var_data = SimpleVariance(self.img_data.base)
         self.mask_data = SimpleMask.from_image(self.img_data.base)
 
-        self.start_mjd = start_mjd
-        self.step_mjd = step_mjd
+        self.start_t = start_t
+        self.step_t = step_t
         self.obj_cat = obj_cat
         self.current = 0
 
@@ -153,8 +154,12 @@ def mock(self, n=1):
 
         obj_cats = None
         if self.obj_cat is not None:
-            kwargs = {"dt": self.step_mjd, "t": [hdr["OBS-MJD"] for hdr in prim_hdrs]}
-            obj_cats = self.obj_cat.mock(n=n, **kwargs)
+            obj_cats = self.obj_cat.mock(
+                n=n,
+                dt=self.step_t,
+                t=[hdr["DATE-OBS"] for hdr in prim_hdrs],
+                wcs=[WCS(hdr) for hdr in img_hdrs]
+            )
 
         images = self.img_data.mock(n, obj_cats=obj_cats)
         variances = self.var_data.mock(images=images)
@@ -165,14 +170,12 @@ def mock(self, n=1):
             prim_hdrs, img_hdrs, var_hdrs, mask_hdrs, images, variances, masks
         ):
             hduls.append(
-                HDUList(
-                    hdus=[
-                        PrimaryHDU(header=ph),
-                        CompImageHDU(header=ih, data=imd),
-                        CompImageHDU(header=vh, data=vd),
-                        CompImageHDU(header=mh, data=md),
-                    ]
-                )
+                HDUList(hdus=[
+                    PrimaryHDU(header=ph),
+                    CompImageHDU(header=ih, data=imd),
+                    CompImageHDU(header=vh, data=vd),
+                    CompImageHDU(header=mh, data=md)
+                ])
             )
 
         self.current += n
@@ -180,79 +183,78 @@ def mock(self, n=1):
 
 
 class DECamImdiff:
-    @classmethod
-    def from_defaults(
-        cls,
-        with_data=False,
-        override_original=False,
-        shape=(100, 100),
-        start_mjd=60310,
-        step_mjd=0.001,
-        with_noise=False,
-        noise="simplistic",
-        src_cat=None,
-        obj_cat=None
-    ):
-        if obj_cat.config.type == "progressive":
+    def __init__(self, with_data=False, with_noise=False, noise="simplistic",
+                 src_cat=None, obj_cat=None):
+        if obj_cat is not None and obj_cat.mode == "progressive":
             raise ValueError(
                 "Only folding or static object catalogs can be used with"
                 "default archived DECam headers since header timestamps are not "
                 "required to be equally spaced."
             )
 
-        hdr_factory = ArchivedHeader("headers_archive.tar.bz2", "decam_imdiff_headers.ecsv")
-
-        hdu_types = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
-        hdu_types.extend([BinTableHDU] * 12)
-        data = [NoneFactory()] * 16
+        self.hdr_factory = ArchivedHeader("headers_archive.tar.bz2", "decam_imdiff_headers.ecsv")
 
+        self.data_factories = [NoneFactory()] * 16
         if with_data:
-            headers = hdr_factory.get(0)
+            headers = self.hdr_factory.get(0)
 
             shape = (headers[1]["NAXIS1"], headers[1]["NAXIS2"])
             dtype = DataFactoryConfig.bitpix_type_map[headers[1]["BITPIX"]]
 
-            #   2.1) Now we can instantiate data factories with correct configs
-            #        and fill in the data placeholder
+            # Read noise and gain are typical values. DECam has 2 amps per CCD,
+            # each powering ~half of the plane. Their values and areas are
+            # recorded in the header, but that would mean I would have to
+            # produce an image which has different zero-offsets for the two
+            # halves which is too much detail for this use-case. Typical values
+            # are taken from the DECam Data Handbook Version 2.05 March 2014
+            # Table 2.2
             if noise == "realistic":
-                img_data = SimulatedImage(src_cat=src_cat, shape=shape, dtype=dtype)
+                self.img_data = SimulatedImage(src_cat=src_cat, shape=shape, dtype=dtype)
             else:
-                img_data = SimpleImage(src_cat=src_cat, shape=shape, dtype=dtype)
-                var_data = SimpleVariance(img_data.base)
-                mask_data = SimpleMask.from_image(img_data.base)
-
-                data = [NoneFactory(), img_data, var_data, mask_data]
-                data.extend([DataFactory.from_header("table", h) for h in headers[4:]])
+                self.img_data = SimpleImage(src_cat=src_cat, shape=shape, dtype=dtype)
+                self.var_data = SimpleVariance(self.img_data.base, read_noise=7.0, gain=4.0)
+                self.mask_data = SimpleMask.from_image(self.img_data.base)
 
-        return cls(hdr_factory, data_factories=data, obj_cat=obj_cat)
+                self.data_factories[1] = self.img_data
+                self.data_factories[2] = self.mask_data
+                self.data_factories[3] = self.mask_data
+                self.data_factories[4:] = [DataFactory.from_header("table", h) for h in headers[4:]]
 
-    def __init__(self, header_factory, data_factories=None, obj_cat=None):
-        self.hdr_factory = header_factory
-        self.data_factories = data_factories
+        self.with_data = with_data
+        self.src_cat = src_cat
+        self.obj_cat = obj_cat
         self.hdu_layout = [PrimaryHDU, CompImageHDU, CompImageHDU, CompImageHDU]
         self.hdu_layout.extend([BinTableHDU] * 12)
+        self.current = 0
 
     def mock(self, n=1):
+        headers = self.hdr_factory.mock(n=n)
+
         obj_cats = None
         if self.obj_cat is not None:
-            obj_cats = self.obj_cat.mock(n, dt=self.config.dt)
-
-        hdrs = self.hdr_factory.mock(n)
+            kwargs = {"t": [hdrs[0][0]["DATE-AVG"] for hdr in hdrs]}
+            obj_cats = self.obj_cat.mock(n=n, **kwargs)
 
-        if self.data_factories is not None:
-            images = self.img_data.mock(n, obj_cats=obj_cats)
+        if self.with_data:
+            images = self.img_data.mock(n=n, obj_cats=obj_cats)
+            masks = self.mask_data.mock(n=n)
             variances = self.var_data.mock(images=images)
-            data = [self.data[0].mock(n), images, variances]
-            for factory in self.data[3:]:
-                data.append(factory.mock(n=n))
+            data = [
+                NoneFactory().mock(n=n),
+                images,
+                masks,
+                variances
+            ]
+            data.extend([factory.mock(n=n) for factory in self.data_factories[4:]])
         else:
-            data = [f.mock(n=n) for f in self.data]
+            data = [factory.mock(n=n) for factory in self.data_factories]
 
         hduls = []
-        for hdul_idx in range(n):
+        for i, hdrs in enumerate(headers):
             hdus = []
-            for hdu_idx, hdu_cls in enumerate(self.hdu_types):
-                hdus.append(self.hdu_cast(hdu_cls, hdrs[hdul_idx][hdu_idx], data[hdu_idx][hdul_idx]))
+            for j, (layer, hdr) in enumerate(zip(self.hdu_layout, hdrs)):
+                hdus.append(layer(header=hdr, data=data[j][i]))
             hduls.append(HDUList(hdus=hdus))
+
         self.current += n
         return hduls
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/fits_data.py
index e11ff8437..669c26917 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/fits_data.py
@@ -61,7 +61,7 @@ def add_model_objects(img, catalog, model):
                 setattr(model, param, source[param])
 
             if all(
-                [model.x_mean > 0, model.x_mean < img.shape[1], model.y_mean > 0, model.y_mean < img.shape[0]]
+                    [model.x_mean > 0, model.x_mean < img.shape[1], model.y_mean > 0, model.y_mean < img.shape[0]]
             ):
                 model.render(img)
     finally:
@@ -157,8 +157,8 @@ class DataFactory:
     default_config = DataFactoryConfig
     """Default configuration."""
 
-    def __init__(self, base, config=None, **kwargs):
-        self.config = self.default_config(config, **kwargs)
+    def __init__(self, base, **kwargs):
+        self.config = self.default_config(**kwargs)
 
         self.base = base
         if base is None:
@@ -167,12 +167,12 @@ def __init__(self, base, config=None, **kwargs):
         else:
             self.shape = base.shape
             self.dtype = base.dtype
-            self.base.flags.writeable = self.config.writeable
-        self.counter = 0
+            self.base.flags.writeable = self.config["writeable"]
+            self.counter = 0
 
     @classmethod
-    def gen_image(cls, metadata=None, config=None, **kwargs):
-        conf = cls.default_config(config, method="subset", **kwargs)
+    def gen_image(cls, metadata=None, **kwargs):
+        conf = cls.default_config(**kwargs)
         cols = metadata.get("NAXIS1", conf.default_img_shape[0])
         rows = metadata.get("NAXIS2", conf.default_img_shape[1])
         bitwidth = metadata.get("BITPIX", conf.default_img_bit_width)
@@ -247,11 +247,11 @@ def mock(self, n=1, **kwargs):
                 "Use `zeros` or `from_hdu` to construct this object correctly."
             )
 
-        if self.config.return_copy:
+        if self.config["return_copy"]:
             base = np.repeat(self.base[np.newaxis,], (n,), axis=0)
         else:
             base = np.broadcast_to(self.base, (n, *self.shape))
-        base.flags.writeable = self.config.writeable
+            base.flags.writeable = self.config["writeable"]
 
         return base
 
@@ -288,18 +288,18 @@ class SimpleVariance(DataFactory):
 
     default_config = SimpleVarianceConfig
 
-    def __init__(self, image=None, config=None, **kwargs):
+    def __init__(self, image=None, **kwargs):
         # skip setting the base here since the real base is
         # derived from given image we just set it manually
-        super().__init__(base=None, config=config, **kwargs)
+        super().__init__(base=None, **kwargs)
 
         if image is not None:
-            self.base = image / self.config.gain + self.config.read_noise**2
+            self.base = image / self.config["gain"] + self.config["read_noise"]**2
 
     def mock(self, images=None):
         if images is None:
             return self.base
-        return images / self.config.gain + self.config.read_noise**2
+        return images / self.config["gain"] + self.config["read_noise"]**2
 
 
 class SimpleMaskConfig(DataFactoryConfig):
@@ -330,18 +330,18 @@ class SimpleMask(DataFactory):
 
     default_config = SimpleMaskConfig
 
-    def __init__(self, mask, config=None, **kwargs):
-        super().__init__(base=mask, config=config, **kwargs)
+    def __init__(self, mask, **kwargs):
+        super().__init__(base=mask, **kwargs)
 
     @classmethod
-    def from_image(cls, image, config=None, **kwargs):
-        config = cls.default_config(config=config, **kwargs, method="subset")
+    def from_image(cls, image, **kwargs):
+        config = cls.default_config(**kwargs)
         mask = image.copy()
-        mask[image > config.threshold] = 1
+        mask[image > config["threshold"]] = 1
         return cls(mask)
 
     @classmethod
-    def from_params(cls, config=None, **kwargs):
+    def from_params(cls, **kwargs):
         """Create a mask by adding a padding around the edges of the array with
         the given dimensions and mask out bad columns.
 
@@ -387,10 +387,10 @@ def from_params(cls, config=None, **kwargs):
                [1., 0., 1., 1., 0., 0., 0., 0., 0., 1.],
                [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])
         """
-        config = cls.default_config(config=config, **kwargs, method="subset")
-        mask = np.zeros(config.shape, dtype=config.dtype)
+        config = cls.default_config(**kwargs)
+        mask = np.zeros(config["shape"], dtype=config["dtype"])
 
-        shape, padding = config.shape, config.padding
+        shape, padding = config["shape"], config["padding"]
 
         # padding
         mask[:padding] = 1
@@ -399,10 +399,10 @@ def from_params(cls, config=None, **kwargs):
         mask[: shape[1] - padding :] = 1
 
         # bad columns
-        for col in config.bad_columns:
+        for col in config["bad_columns"]:
             mask[:, col] = 1
 
-        for patch, value in config.patches:
+        for patch, value in config["patches"]:
             if isinstance(patch, tuple):
                 mask[patch] = 1
             elif isinstance(slice):
@@ -410,7 +410,7 @@ def from_params(cls, config=None, **kwargs):
             else:
                 raise ValueError(f"Expected a tuple (x, y), (slice, slice) or slice, got {patch} instead.")
 
-        return cls(mask, config=config)
+        return cls(mask, **config)
 
 
 class SimpleImageConfig(DataFactoryConfig):
@@ -464,16 +464,15 @@ class SimpleImage(DataFactory):
 
     default_config = SimpleImageConfig
 
-    def __init__(self, image=None, src_cat=None, obj_cat=None, config=None,
+    def __init__(self, image=None, src_cat=None, obj_cat=None,
                  dtype=np.float32, **kwargs):
-        self.config = self.default_config(config=config, **kwargs)
-        super().__init__(image, self.config, **kwargs)
+        super().__init__(image, **kwargs)
 
         if image is None:
-            image = np.zeros(self.config.shape, dtype=dtype)
+            image = np.zeros(self.config["shape"], dtype=dtype)
         else:
             image = image
-            self.config.shape = image.shape
+            self.config["shape"] = image.shape
 
         # Astropy throws a strange ValueError instead of reporting a non-writeable
         # array, This must be a bug TODO: report. It's not safe to edit a
@@ -481,8 +480,8 @@ def __init__(self, image=None, src_cat=None, obj_cat=None, config=None,
         self.src_cat = src_cat
         if self.src_cat is not None:
             image = image if image.flags.writeable else image.copy()
-            add_model_objects(image, src_cat.table, self.config.model(x_stddev=1, y_stddev=1))
-            image.flags.writeable = self.config.writeable
+            add_model_objects(image, src_cat.table, self.config["model"](x_stddev=1, y_stddev=1))
+            image.flags.writeable = self.config["writeable"]
 
         self.base = image
         self._base_contains_data = image.sum() != 0
@@ -498,16 +497,16 @@ def add_noise(cls, images, config):
         config : `SimpleImageConfig`
            Configuration.
         """
-        rng = np.random.default_rng(seed=config.seed)
+        rng = np.random.default_rng(seed=config["seed"])
         shape = images.shape
 
         # noise has to be resampled for every image
         rng.standard_normal(size=shape, dtype=images.dtype, out=images)
 
         # There's a lot of multiplications that happen, skip if possible
-        if config.noise_std != 1.0:
-            images *= config.noise_std
-        images += config.noise
+        if config["noise_std"] != 1.0:
+            images *= config["noise_std"]
+        images += config["noise"]
 
         return images
 
@@ -522,10 +521,10 @@ def mock(self, n=1, obj_cats=None, **kwargs):
             A list of catalogs as long as the number of requested images of
             moving objects that will be inserted into the image.
         """
-        shape = (n, *self.config.shape)
+        shape = (n, *self.config["shape"])
         images = np.zeros(shape, dtype=np.float32)
 
-        if self.config.add_noise:
+        if self.config["add_noise"]:
             images = self.add_noise(images=images, config=self.config)
 
         # if base has no data (no sources, bad cols etc) skip
@@ -540,7 +539,7 @@ def mock(self, n=1, obj_cats=None, **kwargs):
         if obj_cats is not None:
             pairs = [(images[0], obj_cats[0])] if n == 1 else zip(images, obj_cats)
             for i, (img, cat) in enumerate(pairs):
-                add_model_objects(img, cat, self.config.model(x_stddev=1, y_stddev=1))
+                add_model_objects(img, cat, self.config["model"](x_stddev=1, y_stddev=1))
 
         return images
 
@@ -701,24 +700,24 @@ def add_bad_cols(cls, image, config):
         image : `np.array`
             Image.
         """
-        if not config.add_bad_columns:
+        if not config["add_bad_columns"]:
             return image
 
         shape = image.shape
-        rng = np.random.RandomState(seed=config.bad_cols_seed)
-        if config.bad_cols_method == "random":
-            bad_cols = rng.randint(0, shape[1], size=config.n_bad_cols)
-        elif config.bad_col_locs:
-            bad_cols = config.bad_col_locs
+        rng = np.random.RandomState(seed=config["bad_cols_seed"])
+        if config["bad_cols_method"] == "random":
+            bad_cols = rng.randint(0, shape[1], size=config["n_bad_cols"])
+        elif config["bad_col_locs"]:
+            bad_cols = config["bad_col_locs"]
         else:
             raise ConfigurationError(
-                "Bad columns method is not 'random', but `bad_col_locs` " "contains no column indices."
+                "Bad columns method is not 'random', but `bad_col_locs` contains no column indices."
             )
 
-        col_pattern = rng.randint(low=0, high=int(config.bad_col_pattern_offset), size=shape[0])
+        col_pattern = rng.randint(low=0, high=int(config["bad_col_pattern_offset"]), size=shape[0])
 
         for col in bad_cols:
-            image[:, col] += col_pattern + config.bad_col_offset
+            image[:, col] += col_pattern + config["bad_col_offset"]
 
         return image
 
@@ -741,16 +740,16 @@ def add_hot_pixels(cls, image, config):
         image : `np.array`
             Image.
         """
-        if not config.add_hot_pixels:
+        if not config["add_hot_pixels"]:
             return image
 
         shape = image.shape
-        if config.hot_pix_method == "random":
-            rng = np.random.RandomState(seed=config.hot_pix_seed)
-            x = rng.randint(0, shape[1], size=config.n_hot_pix)
-            y = rng.randint(0, shape[0], size=config.n_hot_pix)
+        if config["hot_pix_method"] == "random":
+            rng = np.random.RandomState(seed=config["hot_pix_seed"])
+            x = rng.randint(0, shape[1], size=config["n_hot_pix"])
+            y = rng.randint(0, shape[0], size=config["n_hot_pix"])
             hot_pixels = np.column_stack([x, y])
-        elif config.hot_pix_locs:
+        elif config["hot_pix_locs"]:
             hot_pixels = pixels
         else:
             raise ConfigurationError(
@@ -759,7 +758,7 @@ def add_hot_pixels(cls, image, config):
             )
 
         for pix in hot_pixels:
-            image[*pix] += config.hot_pix_offset
+            image[*pix] += config["hot_pix_offset"]
 
         return image
 
@@ -783,14 +782,14 @@ def add_noise(cls, images, config):
         shape = images.shape
 
         # add read noise
-        images += config.read_noise_gen(scale=config.read_noise / config.gain, size=shape)
+        images += config["read_noise_gen"](scale=config["read_noise"] / config["gain"], size=shape)
 
         # add dark current
-        current = config.dark_current * config.exposure_time / config.gain
-        images += config.dark_current_gen(current, size=shape)
+        current = config["dark_current"] * config["exposure_time"] / config["gain"]
+        images += config["dark_current_gen"](current, size=shape)
 
         # add sky counts
-        images += config.sky_count_gen(lam=config.sky_level * config.gain, size=shape) / config.gain
+        images += config["sky_count_gen"](lam=config["sky_level"] * config["gain"], size=shape) / config["gain"]
 
         return images
 
@@ -813,17 +812,15 @@ def gen_base_image(cls, config=None, src_cat=None, dtype=np.float32):
         config = cls.default_config(config)
 
         # empty image
-        base = np.zeros(config.shape, dtype=dtype)
-        base += config.bias
+        base = np.zeros(config["shape"], dtype=dtype)
+        base += config["bias"]
         base = cls.add_hot_pixels(base, config)
         base = cls.add_bad_cols(base, config)
         if src_cat is not None:
-            add_model_objects(base, src_cat.table, config.model(x_stddev=1, y_stddev=1))
+            add_model_objects(base, src_cat.table, config["model"](x_stddev=1, y_stddev=1))
 
         return base
 
-    def __init__(self, image=None, config=None, src_cat=None, obj_cat=None, dtype=np.float32,**kwargs):
-        conf = self.default_config(config=config, **kwargs)
-        # static objects are added in SimpleImage init
-        super().__init__(image=self.gen_base_image(conf, dtype=dtype),
-                         config=conf, src_cat=src_cat, obj_cat=obj_cat)
+    def __init__(self, image=None, src_cat=None, obj_cat=None, dtype=np.float32,**kwargs):
+        conf = self.default_config(**kwargs)
+        super().__init__(image=self.gen_base_image(conf, dtype=dtype), src_cat=src_cat, obj_cat=obj_cat, **conf)
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index 6716fb2aa..dd7977cb9 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -1,66 +1,123 @@
+import random
 import warnings
+import itertools
 
 import numpy as np
 
 from astropy.wcs import WCS
 from astropy.io.fits import Header
+from astropy.io.fits.verify import VerifyWarning
 
 from .utils import header_archive_to_table
 from .config import Config
 
 
 __all__ = [
+#    "make_wcs",
+    "WCSFactory",
     "HeaderFactory",
     "ArchivedHeader",
 ]
 
+class WCSFactory:
+    def __init__(self, mode="static",
+                 pointing=(351., -5), rotation=0, pixscale=0.2,
+                 dither_pos=False, dither_rot=False, dither_amplitudes=(0.01, 0.01, 0.0),
+                 cycle=None):
+        self.pointing = pointing
+        self.rotation = rotation
+        self.pixscale = pixscale
 
-def make_wcs(center_coords=(351., -5.), rotation=0, pixscale=0.2, shape=None):
-    """
-    Create a simple celestial `~astropy.wcs.WCS` object in ICRS
-    coordinate system.
-
-    Parameters
-    ----------
-    shape : tuple[int]
-        Two-tuple, dimensions of the WCS footprint
-    center_coords : tuple[int]
-        Two-tuple of on-sky coordinates of the center of the WCS in
-        decimal degrees, in ICRS.
-    rotation : float, optional
-        Rotation in degrees, from ICRS equator. In decimal degrees.
-    pixscale : float
-        Pixel scale in arcsec/pixel.
-
-    Returns
-    -------
-    wcs : `astropy.wcs.WCS`
+        self.dither_pos = dither_pos
+        self.dither_rot = dither_rot
+        self.dither_amplitudes = dither_amplitudes
+        self.cycle = cycle
+
+        self.template = self.gen_wcs(self.pointing, self.rotation, self.pixscale)
+
+        self.mode = mode
+        if dither_pos or dither_rot or cycle is not None:
+            self.mode = "dynamic"
+        self.current = 0
+
+    @classmethod
+    def gen_wcs(cls, center_coords, rotation, pixscale, shape=None):
+        """
+        Create a simple celestial `~astropy.wcs.WCS` object in ICRS
+        coordinate system.
+
+        Parameters
+        ----------
+        shape : tuple[int]
+            Two-tuple, dimensions of the WCS footprint
+        center_coords : tuple[int]
+            Two-tuple of on-sky coordinates of the center of the WCS in
+            decimal degrees, in ICRS.
+        rotation : float, optional
+            Rotation in degrees, from ICRS equator. In decimal degrees.
+        pixscale : float
+            Pixel scale in arcsec/pixel.
+
+        Returns
+        -------
+        wcs : `astropy.wcs.WCS`
         The world coordinate system.
 
-    Examples
-    --------
-    >>> from kbmod.mocking import make_wcs
-    >>> shape = (100, 100)
-    >>> wcs = make_wcs(shape)
-    >>> wcs = make_wcs(shape, (115, 5), 45, 0.1)
-    """
-    wcs = WCS(naxis=2)
-    rho = rotation*0.0174533 # deg to rad
-    scale = 0.1 / 3600.0  # arcsec/pixel to deg/pix
-
-    if shape is not None:
-        wcs.pixel_shape = shape
-        wcs.wcs.crpix = [shape[1] / 2, shape[0] / 2]
-    else:
-        wcs.wcs.crpix = [0, 0]
-    wcs.wcs.crval = center_coords
-    wcs.wcs.cunit = ['deg', 'deg']
-    wcs.wcs.cd = [[-scale * np.cos(rho), scale * np.sin(rho)],
-                  [scale * np.sin(rho), scale * np.cos(rho)]]
-    wcs.wcs.radesys = 'ICRS'
-    wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
-
-    return wcs
+        Examples
+        --------
+        >>> from kbmod.mocking import make_wcs
+        >>> shape = (100, 100)
+        >>> wcs = make_wcs(shape)
+        >>> wcs = make_wcs(shape, (115, 5), 45, 0.1)
+        """
+        wcs = WCS(naxis=2)
+        rho = rotation*0.0174533 # deg to rad
+        scale = pixscale  / 3600.0  # arcsec/pixel to deg/pix
+
+        if shape is not None:
+            wcs.pixel_shape = shape
+            wcs.wcs.crpix = [shape[1] // 2, shape[0] // 2]
+        else:
+            wcs.wcs.crpix = [0, 0]
+        wcs.wcs.crval = center_coords
+        wcs.wcs.cunit = ['deg', 'deg']
+        wcs.wcs.pc = np.array([
+            [-scale * np.cos(rho), scale * np.sin(rho)],
+            [scale * np.sin(rho), scale * np.cos(rho)]
+        ])
+        wcs.wcs.radesys = 'ICRS'
+        wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
+
+        return wcs
+
+    def update_from_header(self, header):
+        t = self.template.to_header()
+        t.update(header)
+        self.template = WCS(t)
+
+    def mock(self, header):
+        wcs = self.template
+
+        if self.cycle is not None:
+            wcs = self.cycle[self.current % len(self.cycle)]
+
+        if self.dither_pos:
+            dra = random.uniform(-self.dither_amplitudes[0], self.dither_amplitudes[0])
+            ddec = random.uniform(-self.dither_amplitudes[1], self.dither_amplitudes[1])
+            wcs.wcs.crval += [dra, ddec]
+        if self.dither_rot:
+            ddec = random.uniform(-self.dither_amplitudes[2], self.dither_amplitudes[2])
+            rho = self.dither_amplitudes[2]*0.0174533 # deg to rad
+            rot_matrix =  np.array([
+                [-np.cos(rho), np.sin(rho)],
+                [np.sin(rho), np.cos(rho)]
+            ])
+            new_pc = wcs.wcs.pc @ rot_matrix
+            wcs.wcs.pc = new_pc
+
+        self.current += 1
+        header.update(wcs.to_header())
+        return header
 
 
 class HeaderFactory:
@@ -68,7 +125,7 @@ class HeaderFactory:
         "EXTNAME": "PRIMARY",
         "NAXIS": 0,
         "BITPIX": 8,
-        "OBS-MJD": 58914.0,
+        "DATE-OBS": "2021-03-19T00:27:21.140552",
         "NEXTEND": 3,
         "OBS-LAT": -30.166,
         "OBS-LONG": -70.814,
@@ -76,7 +133,7 @@ class HeaderFactory:
         "OBSERVAT": "CTIO",
     }
 
-    ext_template = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "CRPIX1": 1024, "CPRIX2": 2048, "BITPIX": 32}
+    ext_template = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "CRPIX1": 1024, "CRPIX2": 2048, "BITPIX": 32}
 
     def __validate_mutables(self):
         # !xor
@@ -101,30 +158,38 @@ def __validate_mutables(self):
                     "provide the required metadata keys."
                 )
 
-    def __init__(self, metadata, mutables=None, callbacks=None, config=None, **kwargs):
+    def __init__(self, metadata, mutables=None, callbacks=None, has_wcs=False, wcs_factory=None):
         cards = [] if metadata is None else metadata
         self.header = Header(cards=cards)
-
         self.mutables = mutables
         self.callbacks = callbacks
         self.__validate_mutables()
 
-        self.is_dynamic = self.mutables is not None
+        self.is_dynamic = mutables is not None
+
+        self.has_wcs = has_wcs
+        if has_wcs:
+            self.wcs_factory = WCSFactory() if wcs_factory is None else wcs_factory
+            self.wcs_factory.update_from_header(self.header)
+            self.is_dynamic = self.is_dynamic or self.wcs_factory.mode != "static"
+
         self.counter = 0
 
     def mock(self, n=1):
         headers = []
         # This can't be vectorized because callbacks may share global state
         for i in range(n):
-            if self.is_dynamic:
-                header = self.header.copy()
-                for i, mutable in enumerate(self.mutables):
-                    header[mutable] = self.callbacks[i](header[mutable])
-            else:
+            if not self.is_dynamic:
                 header = self.header
+            else:
+                header = self.header.copy()
+                if self.mutables is not None:
+                    for i, mutable in enumerate(self.mutables):
+                        header[mutable] = self.callbacks[i](header[mutable])
+            if self.has_wcs:
+                header = self.wcs_factory.mock(header)
             headers.append(header)
             self.counter += 1
-
         return headers
 
     @classmethod
@@ -147,7 +212,8 @@ def from_primary_template(cls, overrides=None, mutables=None, callbacks=None):
         return cls(hdr, mutables, callbacks)
 
     @classmethod
-    def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, wcs=None, shape=None):
+    def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, shape=None,
+                          wcs=None):
         ext_template = cls.ext_template.copy()
 
         if shape is not None:
@@ -156,14 +222,8 @@ def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, wcs=No
             ext_template["CRPIX1"] = shape[0] // 2
             ext_template["CRPIX2"] = shape[1] // 2
 
-        if wcs is None:
-            wcs = make_wcs(
-                shape=(ext_template["NAXIS1"], ext_template["NAXIS2"]),
-
-            )
-
-        hdr = cls.gen_header(base=ext_template, overrides=overrides, wcs=wcs)
-        return cls(hdr, mutables, callbacks)
+        hdr = cls.gen_header(base=ext_template, overrides=overrides)
+        return cls(hdr, mutables, callbacks, has_wcs=True, wcs_factory=wcs)
 
 
 class ArchivedHeader(HeaderFactory):
@@ -183,8 +243,8 @@ class ArchivedHeader(HeaderFactory):
 
     format = "ascii.ecsv"
 
-    def __init__(self, archive_name, fname, config=None, **kwargs):
-        super().__init__(config, **kwargs)
+    def __init__(self, archive_name, fname):
+        super().__init__({})
         self.table = header_archive_to_table(archive_name, fname, self.compression, self.format)
 
         # Create HDU groups for easier iteration
@@ -220,7 +280,10 @@ def get(self, group_idx):
         for subgroup in subgroup.groups:
             header = Header()
             for k, v, f in subgroup["keyword", "value", "format"]:
-                header[k] = self.lexical_cast(v, f)
+                # ignore warnings about non-standard keywords
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore", category=VerifyWarning)
+                    header[k] = self.lexical_cast(v, f)
             headers.append(header)
         return headers
 
@@ -230,3 +293,4 @@ def mock(self, n=1):
             res.append(self.get(self.counter))
             self.counter += 1
         return res
+
diff --git a/src/kbmod/standardizers/butler_standardizer.py b/src/kbmod/standardizers/butler_standardizer.py
index ada95074e..f5c48dbc7 100644
--- a/src/kbmod/standardizers/butler_standardizer.py
+++ b/src/kbmod/standardizers/butler_standardizer.py
@@ -294,7 +294,7 @@ def _fetch_meta(self):
         # photometric analysis of the results, while the effective
         # values are too often NaN. The URI location itself is
         # ultimately not very useful, but helpful for data inspection.
-        if self.config.standardize_metadata:
+        if self.config["standardize_metadata"]:
             meta_ref = self.ref.makeComponentRef("metadata")
             meta = self.butler.get(meta_ref)
 
@@ -311,13 +311,13 @@ def _fetch_meta(self):
             self._metadata["GAINB"] = meta["GAINB"]
 
         # Will be nan for VR filter so it's optional
-        if self.config.standardize_effective_summary_stats:
+        if self.config["standardize_effective_summary_stats"]:
             self._metadata["effTime"] = summary.effTime
             self._metadata["effTimePsfSigmaScale"] = summary.effTimePsfSigmaScale
             self._metadata["effTimeSkyBgScale"] = summary.effTimeSkyBgScale
             self._metadata["effTimeZeroPointScale"] = summary.effTimeZeroPointScale
 
-        if self.config.standardize_uri:
+        if self.config["standardize_uri"]:
             self._metadata["location"] = self.butler.getURI(
                 self.ref,
                 collections=[
@@ -348,14 +348,14 @@ def standardizeMetadata(self):
 
     def standardizeScienceImage(self):
         self.exp = self.butler.get(self.ref) if self.exp is None else self.exp
-        zp_correct = 10 ** ((self._metadata["zeroPoint"] - self.config.zero_point) / 2.5)
+        zp_correct = 10 ** ((self._metadata["zeroPoint"] - self.config["zero_point"]) / 2.5)
         return [
             self.exp.image.array / zp_correct,
         ]
 
     def standardizeVarianceImage(self):
         self.exp = self.butler.get(self.ref) if self.exp is None else self.exp
-        zp_correct = 10 ** ((self._metadata["zeroPoint"] - self.config.zero_point) / 2.5)
+        zp_correct = 10 ** ((self._metadata["zeroPoint"] - self.config["zero_point"]) / 2.5)
         return [
             self.exp.variance.array / zp_correct**2,
         ]
diff --git a/src/kbmod/standardizers/fits_standardizers/test_data_std.py b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
index 89b9ebe9a..d5d9d5e04 100644
--- a/src/kbmod/standardizers/fits_standardizers/test_data_std.py
+++ b/src/kbmod/standardizers/fits_standardizers/test_data_std.py
@@ -80,7 +80,7 @@ def translateHeader(self):
         """
         # required
         standardizedHeader = {}
-        obs_datetime = Time(self.primary["OBS-MJD"], format="mjd")
+        obs_datetime = Time(self.primary["DATE-OBS"])
         standardizedHeader["mjd_mid"] = obs_datetime.mjd
         # optional
         standardizedHeader["observat"] = self.primary["OBSERVAT"]
diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py
index eb00f4b91..9f5ea7358 100644
--- a/tests/test_end_to_end.py
+++ b/tests/test_end_to_end.py
@@ -10,15 +10,26 @@
 # from kbmod.wcs_utils import make_fake_wcs
 # from kbmod.work_unit import WorkUnit
 
-from utils.utils_for_tests import get_absolute_demo_data_path
+#from utils.utils_for_tests import get_absolute_demo_data_path
 
 
 ####
 import unittest
+import itertools
+import random
+
+import numpy as np
+from numpy.lib.recfunctions import structured_to_unstructured
+
+from astropy.time import Time
+from astropy.table import Table, vstack
+from astropy.wcs import WCS
+from astropy.coordinates import SkyCoord
 
 from kbmod import ImageCollection
 from kbmod.run_search import SearchRunner
 from kbmod.configuration import SearchConfiguration
+from kbmod.reprojection import reproject_work_unit
 import kbmod.mocking as kbmock
 
 
@@ -63,12 +74,12 @@ def test_static_objects(self):
 class TestRandomLinearSearch(unittest.TestCase):
     def setUp(self):
         # Set up shared search values
-        self.n_imgs = 10
+        self.n_imgs = 5
         self.repeat_n_times = 10
-        self.shape = (300, 300)
-        self.start_pos = (125, 175)
-        self.vxs = [-10, 10]
-        self.vys = [-10, 10]
+        self.shape = (200, 200)
+        self.start_pos = (85, 115)
+        self.vxs = [-20, 20]
+        self.vys = [-20, 20]
 
         # Set up configs for mocking and search
         # These don't change from test to test
@@ -90,58 +101,208 @@ def setUp(self):
                     "max_vx": self.vxs[1],
                     "min_vy": self.vys[0],
                     "max_vy": self.vys[1],
-                    "vx_steps": 50,
-                    "vy_steps": 50,
+                    "vx_steps": 40,
+                    "vy_steps": 40
                 },
-                "num_obs": 10,
+                "num_obs": self.n_imgs,
                 "do_mask": False,
                 "do_clustering": True,
                 "do_stamp_filter": False,
             }
         )
 
-    def test_simple_search(self):
+    def xmatch_best(self, obj, results, match_cols={"x_mean": "x", "y_mean": "y", "vx": "vx", "vy": "vy"}):
+        objk, resk = [], []
+        for k, v in match_cols.items():
+            if k in obj.columns and v in results.table.columns:
+                objk.append(k)
+                resk.append(v)
+        tgt = np.fromiter(obj[tuple(objk)].values(), dtype=float, count=len(objk))
+        res = structured_to_unstructured(results[tuple(resk)].as_array(), dtype=float)
+        diff = np.linalg.norm(tgt-res, axis=1)
+        if len(results) == 1:
+            return results[0], diff
+        return results[diff == diff.min()][0], diff
+
+    def assertResultValuesWithinSpec(self, expected, result, spec,
+                                     match_cols={"x_mean": "x", "y_mean": "y", "vx": "vx", "vy": "vy"}):
+        for ekey, rkey in match_cols.items():
+            info = (
+                f"\n Expected: \n {expected[tuple(match_cols.keys())]} \n"
+                f"Retrieved : \n {result[tuple(match_cols.values())]}"
+            )
+            self.assertLessEqual(abs(expected[ekey] - result[rkey]), spec, info)
+
+    def run_single_search(self, data, expected, spec=5):
+        ic = ImageCollection.fromTargets(data, force="TestDataStd")
+        wu = ic.toWorkUnit(search_config=self.config)
+        results = SearchRunner().run_search_from_work_unit(wu)
+
+        # Run tests
+        self.assertGreaterEqual(len(results), 1)
+        for obj in expected.table:
+            res, dist = (results[0], None) if len(results)==1 else self.xmatch_best(obj, results)
+            self.assertResultValuesWithinSpec(obj, res, spec)
+
+    def test_exact_motion(self):
+        search_vs = list(itertools.product([-20, 0, 20], repeat=2))
+        search_vs.remove((0, 0))
+        for (vx, vy) in search_vs:
+            with self.subTest(f"Cardinal direction: {(vx, vy)}"):
+                self.config._params["generator_config"] = {
+                    "name": "SingleVelocitySearch",
+                    "vx": vx,
+                    "vy": vy
+                }
+                obj_cat = kbmock.ObjectCatalog.from_defaults(self.param_ranges, n=1)
+                obj_cat.table["vx"] = vx
+                obj_cat.table["vy"] = vy
+                factory = kbmock.SimpleFits(shape=self.shape, step_t=1, obj_cat=obj_cat)
+                hduls = factory.mock(n=self.n_imgs)
+                self.run_single_search(hduls, obj_cat, 1)
+
+    def test_random_motion(self):
         # Mock the data and repeat tests. The random catalog
         # creation guarantees a diverse set of changing test values
         for i in range(self.repeat_n_times):
-            with self.subTest(n=i):
+            with self.subTest(f"Iteration {i}"):
                 obj_cat = kbmock.ObjectCatalog.from_defaults(self.param_ranges, n=1)
-                factory = kbmock.SimpleFits(shape=self.shape, step_mjd=1, obj_cat=obj_cat)
+                factory = kbmock.SimpleFits(shape=self.shape, step_t=1, obj_cat=obj_cat)
                 hduls = factory.mock(n=self.n_imgs)
+                self.run_single_search(hduls, obj_cat)
+
+    def test_reprojected_search(self):
+        # 0. Setup
+        self.shape = (500, 500)
+        self.start_pos = (10, 10)  # (ra, dec) in deg
+        n_obj = 1
+        pixscale = 0.2
+        timestamps = Time(np.arange(58915, 58915+self.n_imgs, 1), format="mjd")
+        vx = 0.001  # degrees / day (given the timestamps)
+        vy = 0.001
+
+        # 1. Mock data
+        #    - mock catalogs, set expected positions by hand
+        #    - mock WCSs so that they dither around (10, 10)
+        #    - instantiate the required mockers and mock
+        cats = []
+        for i, t in enumerate(timestamps):
+            cats.append(
+                Table({
+                    "amplitude": [100],
+                    "obstime": [t],
+                    "ra_mean": [self.start_pos[0] + vx*i],
+                    "dec_mean": [self.start_pos[1] + vy*i],
+                    "stddev": [2.0]
+                }))
+        catalog = vstack(cats)
+        obj_cat = kbmock.ObjectCatalog.from_table(catalog, kind="world", mode="folding")
+
+        wcs_factory = kbmock.WCSFactory(
+            pointing=self.start_pos,
+            rotation=0,
+            pixscale=pixscale,
+            dither_pos=True,
+            dither_rot=True,
+            dither_amplitudes=(0.001, 0.001, 0.01)
+        )
 
-                ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
-                wu = ic.toWorkUnit(search_config=self.config)
-                results = SearchRunner().run_search_from_work_unit(wu)
-
-                # Run tests
-                self.assertGreaterEqual(len(results), 1)
-                for res in results:
-                    diff = abs(obj_cat.table["y_mean"] - res["y"])
-                    obj = obj_cat.table[diff == diff.min()]
-                    self.assertLessEqual(abs(obj["x_mean"] - res["x"]), 5)
-                    self.assertLessEqual(abs(obj["y_mean"] - res["y"]), 5)
-                    self.assertLessEqual(abs(obj["vx"] - res["vx"]), 5)
-                    self.assertLessEqual(abs(obj["vy"] - res["vy"]), 5)
-
-    def test_diffim_mocks(self):
-        src_cat = kbmock.SourceCatalog.from_defaults()
-        obj_cat = kbmock.ObjectCatalog.from_defaults(self.param_ranges, n=1)
-        factory = kbmock.DECamImdiff.from_defaults(with_data=True, src_cat=src_cat, obj_cat=obj_cat)
+        prim_hdr_factory = kbmock.HeaderFactory.from_primary_template(
+            mutables=["DATE-OBS"],
+            callbacks=[kbmock.ObstimeIterator(timestamps)],
+        )
+
+        factory = kbmock.SimpleFits(
+            shape=self.shape,
+            obj_cat=obj_cat,
+            wcs_factory=wcs_factory
+        )
+        factory.prim_hdr = prim_hdr_factory
         hduls = factory.mock(n=self.n_imgs)
 
+        # 2. Run search
+        #    - make an IC
+        #    - determine WCS footprint to reproject to
+        #    - determine the pixel-based velocity to search for
+        #    - reproject
+        #    - run search
         ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
-        wu = ic.toWorkUnit(search_config=self.config)
-        results = SearchRunner().run_search_from_work_unit(wu)
 
-        # Run tests
+        from reproject.mosaicking import find_optimal_celestial_wcs
+        opt_wcs, self.shape = find_optimal_celestial_wcs(list(ic.wcs))
+        opt_wcs.array_shape = self.shape
+
+        meanvx = -vx * 3600 / pixscale
+        meanvy = vy * 3600 / pixscale
+
+        # The velocity grid needs to be searched very densely for the realistic
+        # case (compared to the fact the velocity spread is not that large), and
+        # we'll still end up ~10 pixels away from the truth.
+        search_config = SearchConfiguration.from_dict({
+            "generator_config": {
+                "name": "VelocityGridSearch",
+                "min_vx": meanvx-5,
+                "max_vx": meanvx+5,
+                "min_vy": meanvy-5,
+                "max_vy": meanvy+5,
+                "vx_steps": 40,
+                "vy_steps": 40
+            },
+            "num_obs": 1,
+            "do_mask": False,
+            "do_clustering": True,
+            "do_stamp_filter": False,
+        })
+        wu = ic.toWorkUnit(search_config)
+        repr_wu = reproject_work_unit(wu, opt_wcs, parallelize=False)
+        results = SearchRunner().run_search_from_work_unit(repr_wu)
+
+        # Compare results and validate
+        # - add in pixel velocities because realistic searches rarely
+        #   find good pixel location match
+        # - due to that, we also can't rely that we'll get a good match on
+        #   any particular catalog realization. We iterate over all of them
+        #   and find the best matching results in each realization.
+        #   From all realizations find the one that matches the best.
+        #   Select that realization and that best match for comparison.
+        cats = obj_cat.mock(t=timestamps, wcs=[opt_wcs]*self.n_imgs)
+        for cat in cats:
+            cat["vx"] = meanvx
+            cat["vy"] = meanvy
+
+        dists = np.array([self.xmatch_best(cat, results)[1] for cat in cats])
+        min_dist_within_realization = dists.min(axis=0)
+        min_dist_across_realizations = dists.min()
+
+        best_realization = dists.min(axis=1) == min_dist_across_realizations
+        best_realization_idx = np.where(best_realization == True)[0][0]
+
+        best_cat = cats[best_realization_idx]
+        best_res = results[dists[best_realization_idx] == min_dist_across_realizations]
+
         self.assertGreaterEqual(len(results), 1)
-        for res in results:
-            diff = abs(obj_cat.table["y_mean"] - res["y"])
-            obj = obj_cat.table[diff == diff.min()]
-            self.assertLessEqual(abs(obj["x_mean"] - res["x"]), 5)
-            self.assertLessEqual(abs(obj["y_mean"] - res["y"]), 5)
-            self.assertLessEqual(abs(obj["vx"] - res["vx"]), 5)
-            self.assertLessEqual(abs(obj["vy"] - res["vy"]), 5)
+        self.assertResultValuesWithinSpec(best_cat, best_res, 10)
+
+#    def test_diffim_mocks(self):
+#        src_cat = kbmock.SourceCatalog.from_defaults()
+#        obj_cat = kbmock.ObjectCatalog.from_defaults(self.param_ranges, n=1)
+#        factory = kbmock.DECamImdiff.from_defaults(with_data=True, src_cat=src_cat, obj_cat=obj_cat)
+#        hduls = factory.mock(n=self.n_imgs)
+#
+#
+#        ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
+#        wu = ic.toWorkUnit(search_config=self.config)
+#        results = SearchRunner().run_search_from_work_unit(wu)
+#
+#        # Run tests
+#        self.assertGreaterEqual(len(results), 1)
+#        for res in results:
+#            diff = abs(obj_cat.table["y_mean"] - res["y"])
+#            obj = obj_cat.table[diff == diff.min()]
+#            self.assertLessEqual(abs(obj["x_mean"] - res["x"]), 5)
+#            self.assertLessEqual(abs(obj["y_mean"] - res["y"]), 5)
+#            self.assertLessEqual(abs(obj["vx"] - res["vx"]), 5)
+#            self.assertLessEqual(abs(obj["vy"] - res["vy"]), 5)
 
 
 ####
diff --git a/tests/test_mocking.py b/tests/test_mocking.py
index 8c159335b..b03ce580e 100644
--- a/tests/test_mocking.py
+++ b/tests/test_mocking.py
@@ -1,6 +1,10 @@
 import unittest
 
 import numpy as np
+
+import astropy.units as u
+from astropy.wcs import WCS
+from astropy.time import Time
 from astropy.table import Table, vstack
 
 import kbmod.mocking as kbmock
@@ -23,14 +27,14 @@ def test(self):
         self.assertTrue((hdul["VARIANCE"].data == zeros).all())
         self.assertTrue((hdul["MASK"].data == zeros).all())
 
-        factory = kbmock.EmptyFits(shape=(10, 100), step_mjd=1)
+        factory = kbmock.EmptyFits(shape=(10, 100), step_t=1)
         hduls = factory.mock(2)
         hdul = hduls[0]
         self.assertEqual(hdul["IMAGE"].data.shape, (10, 100))
         self.assertEqual(hdul["VARIANCE"].data.shape, (10, 100))
         self.assertEqual(hdul["MASK"].data.shape, (10, 100))
-        dt = hduls[1]["PRIMARY"].header["OBS-MJD"] - hduls[0]["PRIMARY"].header["OBS-MJD"]
-        self.assertEqual(dt, 1)
+        dt = Time(hduls[1]["PRIMARY"].header["DATE-OBS"]) - Time(hduls[0]["PRIMARY"].header["DATE-OBS"])
+        self.assertEqual(dt.to("day").value, 1)
 
         with self.assertRaisesRegex(ValueError, "destination is read-only"):
             hdul["IMAGE"].data[0, 0] = 0
@@ -54,6 +58,14 @@ def test(self):
 
 
 class TestSimpleFits(unittest.TestCase):
+    def setUp(self):
+        self.n_obj = 5
+        self.n_imgs = 3
+        self.shape = (100, 300)
+        self.padded = ((10, 90), (10, 290))
+        self.timestamps = Time(np.arange(58915, 58915+self.n_imgs, 1), format="mjd")
+        self.step_t = 1
+
     def test(self):
         """Test basic functionality of SimpleFits factory."""
         factory = kbmock.SimpleFits()
@@ -70,131 +82,175 @@ def test(self):
         self.assertTrue((hdul["VARIANCE"].data == zeros).all())
         self.assertTrue((hdul["MASK"].data == zeros).all())
 
-        factory = kbmock.SimpleFits(shape=(10, 100), step_mjd=1)
+        factory = kbmock.SimpleFits(shape=(10, 100), step_t=1)
         hduls = factory.mock(2)
         hdul = hduls[0]
         self.assertEqual(hdul["IMAGE"].data.shape, (10, 100))
         self.assertEqual(hdul["VARIANCE"].data.shape, (10, 100))
         self.assertEqual(hdul["MASK"].data.shape, (10, 100))
-        step_mjd = hduls[1]["PRIMARY"].header["OBS-MJD"] - hduls[0]["PRIMARY"].header["OBS-MJD"]
-        self.assertEqual(step_mjd, 1)
+        step_t = Time(hduls[1]["PRIMARY"].header["DATE-OBS"]) - Time(hduls[0]["PRIMARY"].header["DATE-OBS"])
+        self.assertEqual(step_t.to("day").value, 1.0)
 
     def test_static_src_cat(self):
         src_cat = kbmock.SourceCatalog.from_defaults()
         src_cat2 = kbmock.SourceCatalog.from_defaults()
-        self.assertEqual(src_cat.config.mode, "static")
+        self.assertEqual(src_cat.config["mode"], "static")
         self.assertFalse((src_cat.table == src_cat2.table).all())
 
-        src_cat = kbmock.SourceCatalog.from_defaults(n=3)
-        self.assertEqual(len(src_cat.table), 3)
+        src_cat = kbmock.SourceCatalog.from_defaults(n=self.n_obj)
+        self.assertEqual(len(src_cat.table), self.n_obj)
 
-        shape = (300, 500)
         param_ranges = {
             "amplitude": [100, 100],
-            "x_mean": (100, 200),
-            "y_mean": (50, 80),
+            "x_mean": self.padded[1],
+            "y_mean": self.padded[0],
             "x_stddev": [2.0, 2.0],
             "y_stddev": [2.0, 2.0],
         }
         src_cat = kbmock.SourceCatalog.from_defaults(param_ranges, seed=100)
         src_cat2 = kbmock.SourceCatalog.from_defaults(param_ranges, seed=100)
         self.assertTrue((src_cat.table == src_cat2.table).all())
-        self.assertLessEqual(src_cat.table["x_mean"].max(), shape[1])
-        self.assertLessEqual(src_cat.table["y_mean"].max(), shape[0])
+        self.assertLessEqual(src_cat.table["x_mean"].max(), self.shape[1])
+        self.assertLessEqual(src_cat.table["y_mean"].max(), self.shape[0])
 
-        factory = kbmock.SimpleFits(shape=shape, src_cat=src_cat)
+        factory = kbmock.SimpleFits(shape=self.shape, src_cat=src_cat)
         hdul = factory.mock()[0]
 
-        for x, y in src_cat.table["x_mean", "y_mean"]:
-            # Can only test greater or equal because objects may overlap
-            self.assertGreaterEqual(hdul["IMAGE"].data[int(y), int(x)], 80)
+        x = np.round(src_cat.table["x_mean"].data).astype(int)
+        y = np.round(src_cat.table["y_mean"].data).astype(int)
+        self.assertGreaterEqual(hdul["IMAGE"].data[y, x].min(), 90)
+
+    def validate_cat_render(self, hduls, cats, expected_gte=90):
+        for hdul, cat in zip(hduls, cats):
+            x = np.round(cat["x_mean"].data).astype(int)
+            y = np.round(cat["y_mean"].data).astype(int)
+            self.assertGreaterEqual(hdul["IMAGE"].data[y, x].min(), expected_gte)
+            self.assertGreaterEqual(hdul["VARIANCE"].data[y, x].min(), expected_gte)
 
     def test_progressive_obj_cat(self):
         obj_cat = kbmock.ObjectCatalog.from_defaults()
         obj_cat2 = kbmock.ObjectCatalog.from_defaults()
-        self.assertEqual(obj_cat.config.mode, "progressive")
+        self.assertEqual(obj_cat.config["mode"], "progressive")
         self.assertFalse((obj_cat.table == obj_cat2.table).all())
 
-        obj_cat = kbmock.ObjectCatalog.from_defaults(n=3)
-        self.assertEqual(len(obj_cat.table), 3)
+        obj_cat = kbmock.ObjectCatalog.from_defaults(n=self.n_obj)
+        self.assertEqual(len(obj_cat.table), self.n_obj)
 
-        shape = (300, 500)
         param_ranges = {
             "amplitude": [100, 100],
-            "x_mean": (0, 50),
-            "y_mean": (50, shape[0]),
+            "x_mean": (0, 90),
+            "y_mean": self.padded[0],
             "x_stddev": [2.0, 2.0],
             "y_stddev": [2.0, 2.0],
-            "vx": [100, 300],
+            "vx": [10, 20],
             "vy": [0, 0],
         }
-        obj_cat = kbmock.ObjectCatalog.from_defaults(param_ranges, seed=100)
-        obj_cat2 = kbmock.ObjectCatalog.from_defaults(param_ranges, seed=100)
+        seed = 200
+        obj_cat = kbmock.ObjectCatalog.from_defaults(param_ranges, seed=seed)
+        obj_cat2 = kbmock.ObjectCatalog.from_defaults(param_ranges, seed=seed)
         self.assertTrue((obj_cat.table == obj_cat2.table).all())
-        self.assertLessEqual(obj_cat.table["x_mean"].max(), 50)
-        self.assertLessEqual(obj_cat.table["y_mean"].max(), shape[0])
 
-        step_mjd = 0.1
-        obj_cat = kbmock.ObjectCatalog.from_defaults(param_ranges, n=5, seed=100)
-        factory = kbmock.SimpleFits(shape=shape, step_mjd=step_mjd, obj_cat=obj_cat)
-        hduls = factory.mock(n=5)
+        obj_cat = kbmock.ObjectCatalog.from_defaults(param_ranges, n=self.n_obj)
+        factory = kbmock.SimpleFits(shape=self.shape, step_t=self.step_t, obj_cat=obj_cat)
+        hduls = factory.mock(n=self.n_imgs)
 
         obj_cat.reset()
-        for i in range(5):
-            newcat = obj_cat.mock(dt=step_mjd)[0]
-            for x, y in newcat["x_mean", "y_mean"]:
-                if x < shape[1] and y < shape[0]:
-                    # Can only test greater or equal because objects may overlap
-                    # probably a rounding-off error while moving drops 1 flux count
-                    self.assertGreaterEqual(hduls[i]["IMAGE"].data[int(y), int(x)], 79)
-                    self.assertGreaterEqual(hduls[i]["VARIANCE"].data[int(y), int(x)], 0)
+        cats = obj_cat.mock(n=self.n_imgs, dt=self.step_t)
+        self.validate_cat_render(hduls, cats)
 
     def test_folding_obj_cat(self):
-        nobj = 5
-        shape = (300, 300)
-        timestamps = np.arange(58915, 58920, 1)
-
-        start_x = np.ones((nobj,)) * 10
-        start_y = np.linspace(10, shape[0] - 10, nobj)
+        # Set up shared values for the whole setup
+        # like starting positions of object and timestamps
+        start_x = np.ones((self.n_obj,)) * 10
+        start_y = np.linspace(10, self.shape[0] - 10, self.n_obj)
 
+        # Set up non-linear catalog (objects will move as counter^2*v)
         cats = []
-        for i, t in enumerate(timestamps):
+        for i, t in enumerate(self.timestamps):
             cats.append(
-                Table(
-                    {
-                        "amplitude": [100] * nobj,
-                        "obstime": [t] * nobj,
-                        "x_mean": start_x + 15 * i * i,
-                        "y_mean": start_y,
-                        "stddev": [2.0] * nobj,
-                    }
-                )
-            )
+                Table({
+                    "amplitude": [100] * self.n_obj,
+                    "obstime": [t] * self.n_obj,
+                    "x_mean": start_x + 15 * i * i,
+                    "y_mean": start_y,
+                    "stddev": [2.0] * self.n_obj,
+                }))
         catalog = vstack(cats)
 
-        obj_cat = kbmock.ObjectCatalog.from_table(catalog)
-        obj_cat.mode = "folding"
+        # Mock data based on that catalog
+        obj_cat = kbmock.ObjectCatalog.from_table(catalog, mode="folding")
 
         prim_hdr_factory = kbmock.HeaderFactory.from_primary_template(
-            mutables=["OBS-MJD"],
-            callbacks=[
-                kbmock.ObstimeIterator(timestamps),
-            ],
+            mutables=["DATE-OBS"],
+            callbacks=[kbmock.ObstimeIterator(self.timestamps)],
         )
 
-        factory = kbmock.SimpleFits(shape=shape, obj_cat=obj_cat)
+        factory = kbmock.SimpleFits(shape=self.shape, obj_cat=obj_cat)
         factory.prim_hdr = prim_hdr_factory
-        hduls = factory.mock(n=len(timestamps))
+        hduls = factory.mock(n=self.n_imgs)
 
+        # Run tests and ensure we have rendered the object in correct
+        # positions
         obj_cat.reset()
-        cats = obj_cat.mock(t=timestamps)
-        for hdul, cat in zip(hduls, cats):
-            for x, y in cat["x_mean", "y_mean"]:
-                if x < shape[1] and y < shape[0]:
-                    # Can only test greater or equal because objects may overlap
-                    # probably a rounding-off error while moving drops 1 flux count
-                    self.assertGreaterEqual(hdul["IMAGE"].data[int(y), int(x)], 79)
-                    self.assertGreaterEqual(hdul["VARIANCE"].data[int(y), int(x)], 0)
+        cats = obj_cat.mock(n=self.n_imgs, t=self.timestamps)
+        self.validate_cat_render(hduls, cats)
+
+    def test_progressive_sky_cat(self):
+        # a 10-50 in x by a 10-90 in y box using default WCS
+        #self.shape = (500, 500)
+        param_ranges = {
+            "ra_mean": (350.998, 351.002),
+            "dec_mean": (-5.0077, -5.0039),
+            "v_ra": [-0.001, 0.0001],
+            "v_dec": [0, 0],
+            "amplitude": [100, 100],
+            "x_stddev": [2.0, 2.0],
+            "y_stddev": [2.0, 2.0],
+        }
+        catalog = kbmock.gen_random_catalog(self.n_obj, param_ranges)
+        obj_cat = kbmock.ObjectCatalog.from_table(catalog, kind="world")
+
+        factory = kbmock.SimpleFits(shape=self.shape, step_t=self.step_t, obj_cat=obj_cat)
+        hduls = factory.mock(n=self.n_imgs)
+
+        # Run tests and ensure we have rendered the object in correct
+        # positions
+        obj_cat.reset()
+        wcs = [WCS(h["IMAGE"].header) for h in hduls]
+        cats = obj_cat.mock(n=self.n_imgs, dt=self.step_t, wcs=wcs)
+        self.validate_cat_render(hduls, cats)
+
+    def test_folding_sky_cat(self):
+        # a 20x20 box in pixels using a default WCS
+        start_ra = np.linspace(350.998, 351.002, self.n_obj)
+        start_dec = np.linspace(-5.0077, -5.0039, self.n_obj)
+
+        cats = []
+        for i, t in enumerate(self.timestamps):
+            cats.append(
+                Table({
+                    "amplitude": [100] * self.n_obj,
+                    "obstime": [t] * self.n_obj,
+                    "ra_mean": start_ra -  0.001*i,
+                    "dec_mean": start_dec,# + 0.00011 * i,
+                    "stddev": [2.0] * self.n_obj
+                }))
+        catalog = vstack(cats)
+        obj_cat = kbmock.ObjectCatalog.from_table(catalog, kind="world", mode="folding")
+
+        prim_hdr_factory = kbmock.HeaderFactory.from_primary_template(
+            mutables=["DATE-OBS"],
+            callbacks=[kbmock.ObstimeIterator(self.timestamps)],
+        )
+
+        factory = kbmock.SimpleFits(shape=self.shape, obj_cat=obj_cat)
+        factory.prim_hdr = prim_hdr_factory
+        hduls = factory.mock(n=self.n_imgs)
+
+        obj_cat.reset()
+        wcs = [WCS(h[1].header) for h in hduls]
+        cats = obj_cat.mock(n=self.n_imgs, t=self.timestamps, wcs=wcs)
+        self.validate_cat_render(hduls, cats)
 
     # TODO: move to pytest and mark as xfail
     def test_noise_gen(self):
@@ -219,5 +275,43 @@ def test_noise_gen(self):
             self.assertAlmostEqual(hdul["IMAGE"].data.std(), 2, 1)
 
 
+class TestDiffIm(unittest.TestCase):
+    def test(self):
+        """Test basic functionality of SimpleFits factory."""
+        factory = kbmock.DECamImdiff()
+        hduls = factory.mock(2)
+
+        names = [
+            "IMAGE",
+            "MASK",
+            "VARIANCE",
+            "ARCHIVE_INDEX",
+            "FilterLabel",
+            "Detector",
+            "TransformMap",
+            "ExposureSummaryStats",
+            "Detector",
+            "KernelPsf",
+            "FixedKernel",
+            "SkyWcs",
+            "ApCorrMap",
+            "ChebyshevBoundedField",
+            "ChebyshevBoundedField"
+        ]
+        hdul = hduls[0]
+        self.assertEqual(len(hduls), 2)
+        self.assertEqual(len(hduls[0]), 16)
+        for name, hdu in zip(names, hdul[1:]):
+            self.assertEqual(name, hdu.name)
+        self.assertEqual(hdul["PRIMARY"].data, None)
+
+        factory = kbmock.DECamImdiff(with_data=True)
+        hduls = factory.mock(2)
+        hdul = hduls[0]
+        self.assertEqual(hdul["IMAGE"].data.shape, (2048, 4096))
+        self.assertEqual(hdul["VARIANCE"].data.shape, (2048, 4096))
+        self.assertEqual(hdul["MASK"].data.shape, (2048, 4096))
+
+
 if __name__ == "__main__":
     unittest.main()

From 1b81849a490b749d13d01e5e9e94882fab58d1dc Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 19 Aug 2024 13:03:59 -0700
Subject: [PATCH 24/27] fixup

---
 pyproject.toml                                |   3 +
 src/kbmod/mocking/__init__.py                 |   2 +-
 src/kbmod/mocking/callbacks.py                |  68 ++-
 src/kbmod/mocking/catalogs.py                 | 536 ++++++++++++++----
 src/kbmod/mocking/{fits_data.py => data.py}   | 476 +++++++++++-----
 src/kbmod/mocking/dump_headers.py             | 309 ++++++++++
 src/kbmod/mocking/fits.py                     | 173 +++++-
 src/kbmod/mocking/headers.py                  | 408 +++++++++++--
 .../{data => }/headers_archive.tar.bz2        | Bin
 src/kbmod/mocking/utils.py                    |  30 +-
 tests/dump_headers.py                         | 500 ----------------
 tests/test_end_to_end.py                      |  89 ++-
 tests/test_mocking.py                         |  20 +-
 13 files changed, 1785 insertions(+), 829 deletions(-)
 rename src/kbmod/mocking/{fits_data.py => data.py} (61%)
 create mode 100644 src/kbmod/mocking/dump_headers.py
 rename src/kbmod/mocking/{data => }/headers_archive.tar.bz2 (100%)
 delete mode 100644 tests/dump_headers.py

diff --git a/pyproject.toml b/pyproject.toml
index 97311fe5b..2eee17cf3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,6 +46,9 @@ dependencies = [
     "PyYAML>=6.0"
 ]
 
+[project.scripts]
+archiveheaders = "kbmod.mocking.dump_headers:main"
+
 [project.urls]
 Documentation = "https://epyc.astro.washington.edu/~kbmod/"
 Repository = "https://github.com/dirac-institute/kbmod"
diff --git a/src/kbmod/mocking/__init__.py b/src/kbmod/mocking/__init__.py
index 2d479f17b..1644f4b3a 100644
--- a/src/kbmod/mocking/__init__.py
+++ b/src/kbmod/mocking/__init__.py
@@ -1,6 +1,6 @@
 from . import utils
 from .catalogs import *
 from .headers import *
-from .fits_data import *
+from .data import *
 from .fits import *
 from .callbacks import *
diff --git a/src/kbmod/mocking/callbacks.py b/src/kbmod/mocking/callbacks.py
index 28c1d9397..01c26af34 100644
--- a/src/kbmod/mocking/callbacks.py
+++ b/src/kbmod/mocking/callbacks.py
@@ -3,13 +3,37 @@
 from astropy.time import Time
 import astropy.units as u
 
+
 __all__ = [
     "IncrementObstime",
-    "ObstimeIterator",
+    "ObstimeIterator"
 ]
 
 
 class IncrementObstime:
+    """Endlessly incrementing FITS-standard timestamp.
+
+    Parameters
+    ----------
+    start : `astropy.time.Time`
+        Starting timestamp, or a value from which AstroPy can instantiate one.
+    dt : `float` or `astropy.units.Quantity`
+        Size of time-step to take. Assumed to be in days by default.
+
+    Examples
+    --------
+    >>> from kbmod.mocking import IncrementObstime
+    >>> obst = IncrementObstime("2021-01-01T00:00:00.0000", 1)
+    >>> obst()
+    '2021-01-01T00:00:00.000'
+    >>> obst()
+    '2021-01-02T00:00:00.000'
+    >>> import astropy.units as u
+    >>> obst = IncrementObstime("2021-01-01T00:00:00.0000", 1*u.hour)
+    >>> obst(); obst()
+    '2021-01-01T00:00:00.000'
+    '2021-01-01T01:00:00.000'
+    """
     default_unit = "day"
     def __init__(self, start, dt):
         self.start = Time(start)
@@ -17,26 +41,44 @@ def __init__(self, start, dt):
             dt = dt * getattr(u, self.default_unit)
         self.dt = dt
 
-    def __call__(self, header_val):
+    def __call__(self, header=None):
         curr = self.start
         self.start += self.dt
         return curr.fits
 
 
 class ObstimeIterator:
-    def __init__(self, obstimes, **kwargs):
-        self.obstimes = Time(obstimes, **kwargs)
-        self.generator = (t for t in obstimes)
+    """Iterate through given timestamps.
 
-    def __call__(self, header_val):
-        return Time(next(self.generator)).fits
+    Parameters
+    ----------
+    obstimes : `astropy.time.Time`
+        Starting timestamp, or a value from which AstroPy can instantiate one.
 
+    Raises
+    ------
+    StopIteration
+        When all the obstimes are exhausted.
 
-class DitherValue:
-    def __init__(self, value, dither_range):
-        self.value = value
-        self.dither_range = dither_range
+    Examples
+    --------
+    >>> from astropy.time import Time
+    >>> times = Time(range(60310, 60313, 1), format="mjd")
+    >>> from kbmod.mocking import ObstimeIterator
+    >>> obst = ObstimeIterator(times)
+    >>> obst(); obst(); obst(); obst()
+    '2024-01-01T00:00:00.000'
+    '2024-01-02T00:00:00.000'
+    '2024-01-03T00:00:00.000'
+    Traceback (most recent call last):
+    File "<stdin>", line 1, in <module>
+    File "/local/tmp/kbmod/src/kbmod/mocking/callbacks.py", line 49, in __call__
 
-    def __call__(self, header_val):
-        return self.value + random.uniform(self.dither_range)
+    StopIteration
+    """
+    def __init__(self, obstimes, **kwargs):
+        self.obstimes = Time(obstimes, **kwargs)
+        self.generator = (t for t in obstimes)
 
+    def __call__(self, header=None):
+        return Time(next(self.generator)).fits
diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 3063116d9..356dd97a7 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -9,7 +9,6 @@
 
 __all__ = [
     "gen_random_catalog",
-    "CatalogFactory",
     "SimpleCatalog",
     "SourceCatalogConfig",
     "SourceCatalog",
@@ -18,67 +17,299 @@
 ]
 
 
-def gen_random_catalog(n, param_ranges, seed=None):
-    cat = QTable()
-    rng = np.random.default_rng(seed)
 
-    for param_name, (lower, upper) in param_ranges.items():
-        cat[param_name] = rng.uniform(lower, upper, n)
+def expand_gaussian_cols(cat):
+    """Expands columns ``flux`` and ``stddev`` into ``amplitude``, ``x_stddev``
+    and ``y_stddev`` assuming the intended catalog model is a symmetric 2D
+    Gaussian.
 
-    if "x_stddev" not in param_ranges:
+    Amplitude is caluclated as:
+        A  = flux/(2*pi*sigma_x*sigma_y)
+
+    Parameters
+    ----------
+    cat : `astropy.table.Table`
+        A catalog of simplified model parameters.
+
+    Returns
+    ------
+    expanded : `astropy.table.Table`
+        A catalog of AstroPy model parameters.
+    """
+    if "x_stddev" not in cat.columns and "stddev" in cat.columns:
         cat["x_stddev"] = cat["stddev"]
-    if "y_stddev" not in param_ranges:
+    if "y_stddev" not in cat.columns and "stddev" in cat.columns:
         cat["y_stddev"] = cat["stddev"]
 
-    # conversion assumes a gaussian
-    if "flux" in param_ranges and "amplitude" not in param_ranges:
+    if "flux" in cat.columns and "amplitude" not in cat.columns:
         cat["amplitude"] = cat["flux"] / (2.0 * np.pi * xstd * ystd)
 
     return cat
 
 
-class CatalogFactory(abc.ABC):
-    @abc.abstractmethod
-    def mock(self, *args, **kwargs):
-        raise NotImplementedError()
+def gen_random_catalog(n, param_ranges, seed=None, assume_gaussian=True):
+    """Generates a random catalog of parameters of n sources based on
+    AstroPy's 2D models.
+
+    The object parameters are specified as a dict where keys become columns and
+    the values represent the range from which each parameter is uniformly
+    randomly drawn from.
+
+    If parameter ranges contain ``flux``, a column ``amplitude`` will be added
+    which value will be calculated assuming a 2D Gaussian model. If ``stddev``
+    column is preset, then ``x_stddev`` and ``y_stddev`` columns are added,
+    assuming the model intended to be used with the catalog is a symmetrical
+    2D Gaussian.
+
+    Parameters
+    ----------
+    n : `int`
+        Number of objects to create
+    param_ranges : `dict`
+        Dictionary whose keys become columns of the catalog and which values
+        define the ranges from which the catalog values are drawn from.
+    seed : `int`
+        NumPy's random number generator seed.
+    assume_gaussian : `bool`
+        Assume the catalog is intended for use with a 2D Gaussian model and
+        expand ``flux`` and ``stddev`` columns appropriately. See
+        `expand_gaussian_cols`/`
+
+    Returns
+    -------
+    catalog : `astropy.table.Table`
+        Catalog
+
+    Examples
+    --------
+    >>> import kbmod.mocking as kbmock
+    >>> kbmock.gen_random_catalog(3, {"amplitude": [0, 3], "x": [3, 6], "y": [6, 9]}, seed=100)
+    <QTable length=3>
+        amplitude              x                 y
+         float64            float64           float64
+    ------------------ ----------------- -----------------
+    2.504944891506027 3.128854712082634 8.370789493256163
+    1.7896620809036619 5.920963185318643  8.73101814388636
+    0.8665897250736108 4.789415112194066 8.064463342775236
+    """
+    cat = QTable()
+    rng = np.random.default_rng(seed)
+
+    for param_name, (lower, upper) in param_ranges.items():
+        cat[param_name] = rng.uniform(lower, upper, n)
+
+    if assume_gaussian:
+        cat = expand_gaussian_cols(cat)
+
+    return cat
 
 
 class SimpleCatalogConfig(Config):
-    mode = "static"  # folding
-    kind = "pixel"  # world
+    """A simple catalog configuration."""
+
+    mode = "static"
+    """Static, progressive or folding; static catalogs remain the same for every
+    realization, progressive catalogs modify values of realized catalog columns
+    and folding catalogs select rows from a larger catalog to return as a realization.
+    """
+
+    kind = "pixel"
+    """Either ``pixel`` or ``world``. The kind of position coordinates encoded
+    by the catalog. On-sky, world, coordinates require a list of WCSs to be given
+    to the mocking method"""
+
     return_copy = False
+    """For static catalogs, return a reference to the underlying catalog or
+    a copy that can be modified."""
+
     seed = None
-    n = 100
-    param_ranges = {}
+    """Random number generator seed. When `None` a different seed is used for
+    every catalog factory."""
 
+    n = 100
+    """Number of objects to generate."""
 
-class SimpleCatalog(CatalogFactory):
+    param_ranges = {}
+    """Default parameter ranges of the catalog values and columns that will be
+    generated."""
+
+    pix_pos_cols = ["x_mean", "y_mean"]
+    """Which """
+
+    pix_vel_cols = ["vx", "vy"]
+
+    world_pos_cols = ["ra_mean", "dec_mean"]
+
+    world_vel_cols = ["v_ra", "v_dec"]
+
+    fold_col = "obstime"
+
+
+class SimpleCatalog:
+    """Default base class for mocked catalogs factory.
+
+    This base class will always generate empty catalogs and is intended to be
+    inherited from.
+
+    A catalog is an `astropy.table.Table` of model values (position, amplitude
+    etc.) of sources. A catalog factory mock realizations of catalogs. There
+    are 2 ``kind``s of catalogs that can be mocked in 3 different ``modes``.
+
+    In progressive catalogs mode an existing base catalog template column
+    values are incremented, or otherwise updated for each new realization.
+    Static catalogs always return the same realization of a catalog. And the
+    folding catalogs depend on an underlying larger catalog, from which they
+    select which rows to return as a new realization. This is namely most
+    appropriate for catalogs with timestamps, where a different realization of
+    a catalog is returned per timestamp.
+    When the catalog mode is ``folding`` the mocking method expects the values
+    on which to fold on. By default, these are timestamps `t`.
+
+    If the catalog coordinate kind is ``pixel``, then the positions are
+    interpreted as pixel coordiantes. If the kind of catalog coordinates are
+    ``world`` then the positions are interpreted as on-sky coordinates in
+    decimal degrees and a list of WCSs is expected to be provided to the mocking
+    method.
+
+    Parameters
+    ----------
+    config : `SimpleCatalogConfig`
+        Factory configuration.
+    table : `astropy.table.Table`
+        The catalog template from which new realizations will be generated.
+
+    Attributes
+    ----------
+    config : `SimpleCatalogConfig`
+       The instance-bound configuration of the factory
+    table : `astropy.table.Table`
+       The template catalog used to create new realizations.
+    _realization : `astropy.table.Table`
+       The last realization of a catalog.
+    current : `int`
+       The current iterator counter.
+
+    Examples
+    --------
+    Directly instantiate a simple static catalog:
+
+    >>> import kbmod.mocking as kbmock
+    >>> table = kbmock.gen_random_catalog(3, {"A": [0, 1], "x": [1, 2], "y": [2, 3]}, seed=100)
+    >>> f = kbmock.SimpleCatalog(table)
+    >>> f.mock()
+    [<QTable length=3>
+            A                  x                  y
+         float64            float64            float64
+    ------------------ ------------------ ------------------
+    0.8349816305020089 1.0429515706942114 2.7902631644187212
+    0.5965540269678873 1.9736543951062142 2.9103393812954526
+    0.2888632416912036 1.5964717040646885  2.688154447591745]
+
+    Instantiating from factory methods will derive additional information
+    regarding the catalog contents:
+
+    >>> f2 = kbmock.SimpleCatalog.from_table(table)
+    >>> f2.mock()
+    [<QTable length=3>
+            A                  x                  y
+         float64            float64            float64
+    ------------------ ------------------ ------------------
+    0.8349816305020089 1.0429515706942114 2.7902631644187212
+    0.5965540269678873 1.9736543951062142 2.9103393812954526
+    0.2888632416912036 1.5964717040646885  2.688154447591745]
+
+    >>> f2.config["param_ranges"]
+    {'A': (0.2888632416912036, 0.8349816305020089), 'x': (1.0429515706942114, 1.9736543951062142), 'y': (2.688154447591745, 2.9103393812954526)}
+    >>> f.config["param_ranges"]
+    {}
+
+    Folding catalogs just return subsets of the template catalog:
+
+    >>> table["obstime"] = [1, 1, 2]
+    >>> f = kbmock.SimpleCatalog(table, mode="folding")
+    >>> f.mock(t=[1, 2])
+    [<QTable length=2>
+            A                  x                  y          obstime
+         float64            float64            float64        int64
+    ------------------ ------------------ ------------------ -------
+    0.8349816305020089 1.0429515706942114 2.7902631644187212       1
+    0.5965540269678873 1.9736543951062142 2.9103393812954526       1, <QTable length=1>
+            A                  x                  y         obstime
+         float64            float64            float64       int64
+    ------------------ ------------------ ----------------- -------
+    0.2888632416912036 1.5964717040646885 2.688154447591745       2]
+
+    And progressive catalogs increment selected column values (note the velocities
+    were assigned the default expected column names but positions weren't):
+
+    >>> table["vx"] = [1, 1, 1]
+    >>> table["vy"] = [10, 10, 10]
+    >>> f = kbmock.SimpleCatalog(table, mode="progressive", pix_pos_cols=["x", "y"])
+    >>> _ = f.mock(dt=1); f.mock(dt=1)
+    [<QTable length=3>
+            A                  x                  y             vx      vy
+         float64            float64            float64       float64 float64
+    ------------------ ------------------ ------------------ ------- -------
+    0.8349816305020089 2.0429515706942114 12.790263164418722     1.0    10.0
+    0.5965540269678873  2.973654395106214 12.910339381295453     1.0    10.0
+    0.2888632416912036 2.5964717040646885 12.688154447591746     1.0    10.0]
+    """
     default_config = SimpleCatalogConfig
 
-    def __init__(self, config, table, **kwargs):
+    def __init__(self, table, config=None, **kwargs):
         self.config = self.default_config(config=config, **kwargs)
         self.table = table
         self.current = 0
-
-    @classmethod
-    def from_config(cls, config, **kwargs):
-        config = cls.default_config(config=config, **kwargs)
-        table = gen_random_catalog(config["n"], config["param_ranges"], config["seed"])
-        return cls(config, table)
+        self._realization = self.table.copy()
+        self.mode = self.config["mode"]
+        self.kind = self.config["kind"]
 
     @classmethod
     def from_defaults(cls, param_ranges=None, **kwargs):
+        """Create a catalog factory using its default config.
+
+        Parameters
+        ----------
+        param_ranges : `dict`
+            Default parameter ranges of the catalog values and columns that will be
+            generated. See `gen_random_catalog`.
+        kwargs : `dict`
+            Any additional keyword arguments will be used to supplement or override
+            any matching default configuration parameters.
+
+        Returns
+        -------
+        factory : `SimpleCatalog`
+            Simple catalog factory.
+        """
         config = cls.default_config(**kwargs)
         if param_ranges is not None:
             config["param_ranges"].update(param_ranges)
-        return cls.from_config(config)
+        table = gen_random_catalog(config["n"], config["param_ranges"], config["seed"])
+        return cls(table=table, config=config)
 
     @classmethod
     def from_table(cls, table, **kwargs):
-        if "x_stddev" not in table.columns:
-            table["x_stddev"] = table["stddev"]
-        if "y_stddev" not in table.columns:
-            table["y_stddev"] = table["stddev"]
+        """Create a factory from a table template, deriving parameters, their
+        value ranges and number of objects from the table.
+
+        Optionally expands the given table columns assuming the intended source
+        model is a 2D Gaussian.
+
+        Parameters
+        ----------
+        table : `astropy.table.Table`
+            Catalog template.
+        kwargs : `dict`
+            Any additional keyword arguments will be used to supplement or override
+            any matching default configuration parameters.
+
+        Returns
+        -------
+        factory : `SimpleCatalog`
+            Simple catalog factory.
+        """
+        table = expand_gaussian_cols(table)
 
         config = cls.default_config(**kwargs)
         config["n"] = len(table)
@@ -86,65 +317,21 @@ def from_table(cls, table, **kwargs):
         for col in table.keys():
             params[col] = (table[col].min(), table[col].max())
         config["param_ranges"] = params
-        return cls(config, table)
-
-    def mock(self):
-        self.current += 1
-        if self.config["return_copy:"]:
-            return self.table.copy()
-        return self.table
-
-
-class SourceCatalogConfig(SimpleCatalogConfig):
-    param_ranges = {
-        "amplitude": [1.0, 10.0],
-        "x_mean": [0.0, 4096.0],
-        "y_mean": [0.0, 2048.0],
-        "x_stddev": [1.0, 3.0],
-        "y_stddev": [1.0, 3.0],
-        "theta": [0.0, np.pi],
-    }
-
-
-class SourceCatalog(SimpleCatalog):
-    default_config = SourceCatalogConfig
-
-
-class ObjectCatalogConfig(SimpleCatalogConfig):
-    mode = "progressive"  # folding
-    param_ranges = {
-        "amplitude": [0.1, 3.0],
-        "x_mean": [0.0, 4096.0],
-        "y_mean": [0.0, 2048.0],
-        "vx": [500.0, 1000.0],
-        "vy": [500.0, 1000.0],
-        "stddev": [0.25, 1.5],
-        "theta": [0.0, np.pi],
-    }
-
-
-class ObjectCatalog(SimpleCatalog):
-    default_config = ObjectCatalogConfig
-
-    def __init__(self, config, table, **kwargs):
-        # Obj cat always has to return a copy
-        kwargs["return_copy"] = True
-        super().__init__(config, table, **kwargs)
-        self._realization = self.table.copy()
-        self.mode = self.config["mode"]
+        return cls(table=table, config=config)
 
     @property
     def mode(self):
+        """Catalog mode, ``static``, ``folding`` or ``progressive``."""
         return self._mode
 
     @mode.setter
     def mode(self, val):
         if val == "folding":
             self._gen_realization = self.fold
-        elif val == "progressive" and self.config["kind"] == "pixel":
-            self._gen_realization = self.next_pixel
-        elif val == "progressive" and self.config["kind"] == "world":
-            self._gen_realization = self.next_world
+            self.config["return_copy"] = True
+        elif val == "progressive":
+            self._gen_realization = self.next
+            self.config["return_copy"] = True
         elif val == "static":
             self._gen_realization = self.static
         else:
@@ -154,32 +341,100 @@ def mode(self, val):
             )
         self._mode = val
 
+    @property
+    def kind(self):
+        """Catalog coordinate kind, ``pixel`` or ``world``"""
+        return self._kind
+
+    @mode.setter
+    def kind(self, val):
+        if val == "pixel":
+            self._cat_keys = self.config["pix_pos_cols"] + self.config["pix_vel_cols"]
+        elif val == "world":
+            self._cat_keys = self.config["world_pos_cols"] + self.config["world_vel_cols"]
+        else:
+            raise ValueError(
+                "Unrecognized coordinate kind. Expected 'world' or 'pixel, got"
+                f"{val} instead."
+            )
+        self._kind = val
+
     def reset(self):
+        """Reset the iteration counter reset the realization to the initial one."""
         self.current = 0
         self._realization = self.table.copy()
 
     def static(self, **kwargs):
-        return self.table.copy()
+        """Return the initial template as a catalog realization.
 
-    def _next(self, dt, keys):
-        a, va, b, vb = keys
+        Returns
+        -------
+        catalog : `astropy.table.Table`
+            Catalog realization.
+        """
+        self.current += 1
+        if self.config["return_copy"]:
+            return self.table.copy()
+        return self.table
+
+    def next(self, dt):
+        """Return the next catalog realization by incrementing the position
+        columns by the value of the velocity column and number of current `dt` steps.
+
+        Parameters
+        ----------
+        dt : `float`
+            Time increment of each step.
+
+        Returns
+        -------
+        catalog : `astropy.table.Table`
+            Catalog realization.
+        """
+        a, b, va, vb = self._cat_keys
         self._realization[a] = self.table[a] + self.current * self.table[va] * dt
         self._realization[b] = self.table[b] + self.current * self.table[vb] * dt
         self.current += 1
         return self._realization.copy()
 
-    def next_world(self, dt):
-        return self._next(dt, ["ra_mean", "v_ra", "dec_mean", "v_dec"])
-
-    def next_pixel(self, dt):
-        return self._next(dt, ["x_mean", "vx", "y_mean", "vy"])
-
     def fold(self, t, **kwargs):
-        self._realization = self.table[self.table["obstime"] == t]
+        """Return the next catalog realization by selecting those rows that
+        match the given parameter ``t``. By default the folding column is
+        ``obstime``.
+
+        Parameters
+        ----------
+        t : `float`
+            Value which to select from template catalog.
+
+        Returns
+        -------
+        catalog : `astropy.table.Table`
+            Catalog realization.
+        """
+        self._realization = self.table[self.table[self.config["fold_col"]] == t]
         self.current += 1
         return self._realization.copy()
 
     def mock(self, n=1, dt=None, t=None, wcs=None):
+        """Return the next realization(s) of the catalogs.
+
+        Selects the appropriate mocking function. Ignores keywords not
+        appropriate for use given some catalog generation method and coordinate
+        kind.
+
+        Parameters
+        ----------
+        n : `int`, optional
+           Number of catalogs to mock. Default 1.
+        dt : `float`, optional.
+           Timestep between each step (arbitrary units)
+        t : `list[float]` or `list[astropy.time.Time]`, optional
+           Values on which to fold the template catalog.
+        wcs : `list[astropy.wcs.WCS]`, optional
+           WCS to use in conversion of on-sky coordinates to pixel coordinates,
+           for each realization.
+        """
         data = []
 
         if self.mode == "folding":
@@ -187,12 +442,101 @@ def mock(self, n=1, dt=None, t=None, wcs=None):
                 data.append(self.fold(t=ts))
         else:
             for i in range(n):
-                data.append(self._gen_realization(dt))
+                data.append(self._gen_realization(dt=dt))
 
-        if self.config["kind"] == "world":
+        if self.kind == "world":
+            racol, deccol = self.config["world_pos_cols"]
+            xpixcol, ypixcol = self.config["pix_pos_cols"]
             for cat, w in zip(data, wcs):
-                x, y = w.world_to_pixel(SkyCoord(ra=cat["ra_mean"], dec=cat["dec_mean"], unit="deg"))
-                cat["x_mean"] = x
-                cat["y_mean"] = y
+                x, y = w.world_to_pixel(SkyCoord(ra=cat[racol], dec=cat[deccol], unit="deg"))
+                cat[xpixcol] = x
+                cat[ypixcol] = y
 
         return data
+
+
+class SourceCatalogConfig(SimpleCatalogConfig):
+    """Source catalog config.
+
+    Assumes sources are static, asymmetric 2D Gaussians.
+
+    Parameter ranges
+    ----------------
+    amplitude : [1, 10]
+        Amplitude of the model.
+    x_mean : [0, 4096]
+        Real valued x coordinate of the object's centroid.
+    y_mean : [0, 2048]
+        Real valued y coordinate of the object's centroid.
+    x_stddev : [1, 3]
+        Real valued standard deviation of the model distribution, in x.
+    y_stddev : [1, 3]
+        Real valued standard deviation of the model distribution, in y.
+    theta : `[0, np.pi]`
+        Rotation of the model's covariance matrix, increases counterclockwise.
+        In radians.
+    """
+    param_ranges = {
+        "amplitude": [1.0, 10.0],
+        "x_mean": [0.0, 4096.0],
+        "y_mean": [0.0, 2048.0],
+        "x_stddev": [1.0, 3.0],
+        "y_stddev": [1.0, 3.0],
+        "theta": [0.0, np.pi],
+    }
+
+
+class SourceCatalog(SimpleCatalog):
+    """A static catalog representing stars and galaxies.
+
+    Coordinates defined in pixel space.
+    """
+    default_config = SourceCatalogConfig
+
+
+class ObjectCatalogConfig(SimpleCatalogConfig):
+    """Object catalog config.
+
+    Assumes objects are symmetric 2D Gaussians moving in a linear fashion.
+
+    Parameter ranges
+    ----------------
+    amplitude : [1, 10]
+        Amplitude of the model.
+    x_mean : [0, 4096]
+        Real valued x coordinate of the object's centroid.
+    y_mean : [0, 2048]
+        Real valued y coordinate of the object's centroid.
+    x_stddev : [1, 3]
+        Real valued standard deviation of the model distribution, in x.
+    y_stddev : [1, 3]
+        Real valued standard deviation of the model distribution, in y.
+    theta : `[0, np.pi]`
+        Rotation of the model's covariance matrix, increases counterclockwise.
+        In radians.
+    """
+    mode = "progressive"
+    param_ranges = {
+        "amplitude": [0.1, 3.0],
+        "x_mean": [0.0, 4096.0],
+        "y_mean": [0.0, 2048.0],
+        "vx": [500.0, 1000.0],
+        "vy": [500.0, 1000.0],
+        "stddev": [0.25, 1.5],
+        "theta": [0.0, np.pi],
+    }
+
+
+class ObjectCatalog(SimpleCatalog):
+    """A catalog of moving objects.
+
+    Assumed to be symmetric 2D Gaussians whose centroids are defined in pixel
+    space and moving in linear fashion with velocity also defined in pixel space.
+    The units are relative to the timestep.
+    """
+    default_config = ObjectCatalogConfig
+
+    def __init__(self, table, **kwargs):
+        # Obj cat always has to return a copy
+        kwargs["return_copy"] = True
+        super().__init__(table=table, **kwargs)
diff --git a/src/kbmod/mocking/fits_data.py b/src/kbmod/mocking/data.py
similarity index 61%
rename from src/kbmod/mocking/fits_data.py
rename to src/kbmod/mocking/data.py
index 669c26917..cc27201fd 100644
--- a/src/kbmod/mocking/fits_data.py
+++ b/src/kbmod/mocking/data.py
@@ -8,13 +8,10 @@
 
 __all__ = [
     "add_model_objects",
-    "DataFactoryConfig",
     "DataFactory",
-    "SimpleImageConfig",
     "SimpleImage",
-    "SimpleMaskConfig",
     "SimpleMask",
-    "SimulatedImageConfig",
+    "SimpleVariance",
     "SimulatedImage",
 ]
 
@@ -32,8 +29,7 @@ def add_model_objects(img, catalog, model):
     catalog : `astropy.table.QTable`
         Table of objects, a catalog
     model : `astropy.modelling.Model`
-        Astropy's model of the surface brightness of an source. Must contain
-        at least ``x_mean`` and ``y_mean``.
+        Astropy's model of the surface brightness of an source.
 
     Returns
     -------
@@ -59,11 +55,12 @@ def add_model_objects(img, catalog, model):
         for i, source in enumerate(catalog):
             for param in params_to_set:
                 setattr(model, param, source[param])
-
-            if all(
-                    [model.x_mean > 0, model.x_mean < img.shape[1], model.y_mean > 0, model.y_mean < img.shape[0]]
-            ):
-                model.render(img)
+            model.render(img)
+    except ValueError as e:
+        # ignore rendering models larger than the image
+        message = "The `bounding_box` is larger than the input out in one or more dimensions."
+        if message in str(e):
+            pass
     finally:
         for param, value in init_params.items():
             setattr(model, param, value)
@@ -77,19 +74,26 @@ class DataFactoryConfig(Config):
     """
 
     default_img_shape = (5, 5)
+    """Default image size, used if mocking ImageHDU or CompImageHDUs."""
 
     default_img_bit_width = 32
+    """Default image data type is float32; the value of BITPIX flag in headers.
+    See bitpix_type_map for other codes.
+    """
 
     default_tbl_length = 5
+    """Default table length, used if mocking BinTableHDU or TableHDU HDUs."""
 
     default_tbl_dtype = np.dtype([("a", int), ("b", int)])
+    """Default table dtype, used when mocking table-HDUs that do not contain
+    a description of table layout.
+    """
 
     writeable = False
     """Sets the base array ``writeable`` flag. Default `False`."""
 
     return_copy = False
-    """
-    When `True`, the `DataFactory.mock` returns a copy of the final object,
+    """When `True`, the `DataFactory.mock` returns a copy of the final object,
     otherwise the original (possibly mutable!) object is returned. Default `False`.
     """
 
@@ -110,35 +114,31 @@ class DataFactoryConfig(Config):
 
 
 class DataFactory:
-    """Generic data factory.
-
-    Data can consists of tabular or array-like data of a single or compound
-    types. Generally the process of creation of a mocked data is relatively
-    simple:
-    1) pre-generate whatever shared common base data is shared by all mocked
-       data
-    2) mock one or many instances of dynamic portions of the data and paste
-       them onto the base static data.
-
-    The base data can be writable or non-writable to prevent accidentally
-    mutating its value. Some data factories consist of only the simple base
-    data. In the case it is desired that the mocked data is further modified
-    it is possible to return a copy of that base data, so that the base data is
-    not mutated. By default it's assumed that all mocked data are allowed to
-    share a single, non-mutable, base data.
-
-    This saves memory and improves performance as, for example, there is no
-    need to mock and allocate N (dimx, dimy) arrays for a shared static masking
-    array shared by all associated images. Therefore the default implementation
-    of mocking is to just continuously return the given base data.
-
-    For dynamically created data, the data may depend on the type of the
-    Header Data Unit (HDU), or dynamically changing header data. Data generated
-    in these scenarios behaves as ``return_copy`` is always `True` regardless
-    of the configuration value.
+    """Generic data factory that can mock table and image HDUs from default
+    settings or given header definitions.
+
+    Given a template, this factory repeats it for each mock.
+    A reference to the base template is returned whenever possible for
+    performance reasons. To prevent accidental mutation of the shared
+    array, the default behavior is that the returned data is not writable.
+
+    A base template value of `None` is accepted as valid to satisfy FITS
+    factory use-case of generating HDUList stubs containing only headers.
+
+    Primary purpose of this factory is to derive the template data given a
+    table, HDU or a Header object. When the base has no data, but just a
+    description of one, such as Headers, the default is to return "zeros"
+    for that datatype. This can be a zero length string, literal integer
+    zero, a float zero etc...
 
     Attributes
     ----------
+    base : `np.array`, `np.recarray` or `None`
+        Base data template.
+    shape : `tuple`
+        Shape of base array when it exists.
+    dtype : `type`
+        Numpy type of the base array, when it exists.
     counter : `int`
         Data factory tracks an internal counter of generated objects that can
         be used as a ticker for generating new data.
@@ -147,11 +147,31 @@ class DataFactory:
     ----------
     base : `np.array`
         Static data shared by all mocked instances.
-    config : `DataFactoryConfig`
-        Configuration of the data factory.
-    **kwargs :
+    kwargs :
         Additional keyword arguments are applied as configuration
         overrides.
+
+    Examples
+    --------
+    >>> from astropy.io.fits import Header, CompImageHDU, BinTableHDU
+    >>> import kbmod.mocking as kbmock
+    >>> import numpy as np
+    >>> base = np.zeros((2, 2))
+    >>> hdu = CompImageHDU(base)
+    >>> kbmock.DataFactory.from_hdu(hdu).mock()
+    array([[[0., 0.],
+            [0., 0.]]])
+    >>> kbmock.DataFactory.from_header("image", hdu.header).mock()
+    array([[[0., 0.],
+            [0., 0.]]])
+    >>> base = np.array([("test1", 10), ("test2", 11)], dtype=[("col1", "U5"), ("col2", int)])
+    >>> hdu = BinTableHDU(base)
+    >>> kbmock.DataFactory.from_hdu(hdu).mock()
+    array([[(b'test1', 10), (b'test2', 11)]],
+          dtype=(numpy.record, [('col1', 'S5'), ('col2', '<i8')]))
+    >>> kbmock.DataFactory.from_header("table", hdu.header).mock()
+    array([[(b'', 0), (b'', 0)]],
+          dtype=(numpy.record, [('col1', 'S5'), ('col2', '>i8')]))
     """
 
     default_config = DataFactoryConfig
@@ -161,42 +181,79 @@ def __init__(self, base, **kwargs):
         self.config = self.default_config(**kwargs)
 
         self.base = base
-        if base is None:
-            self.shape = None
-            self.dtype = None
-        else:
+        if self.base is not None:
             self.shape = base.shape
             self.dtype = base.dtype
             self.base.flags.writeable = self.config["writeable"]
-            self.counter = 0
+        self.counter = 0
 
     @classmethod
-    def gen_image(cls, metadata=None, **kwargs):
+    def gen_image(cls, header=None, **kwargs):
+        """Generate an image from a complete or partial header and config.
+
+        If a header is given, it trumps the default config values. When the
+        header is not complete, config values are used. Config overrides are
+        applied before the data description is evaluated.
+
+        Parameters
+        ----------
+        header : `None`, `Header` or dict-like, optional
+            Header, or dict-like object, containing the image-data descriptors.
+        kwargs :
+            Any additional keyword arguments are applied as config overrides.
+
+        Returns
+        -------
+        image : `np.array`
+            Image
+        """
         conf = cls.default_config(**kwargs)
-        cols = metadata.get("NAXIS1", conf.default_img_shape[0])
-        rows = metadata.get("NAXIS2", conf.default_img_shape[1])
-        bitwidth = metadata.get("BITPIX", conf.default_img_bit_width)
+        metadata = {} if header is None else header
+        cols = metadata.get("NAXIS1", conf["default_img_shape"][0])
+        rows = metadata.get("NAXIS2", conf["default_img_shape"][1])
+        bitwidth = metadata.get("BITPIX", conf["default_img_bit_width"])
         dtype = conf.bitpix_type_map[bitwidth]
         shape = (cols, rows)
-
         return np.zeros(shape, dtype)
 
     @classmethod
-    def gen_table(cls, metadata=None, config=None, **kwargs):
-        conf = cls.default_config(config, **kwargs, method="subset")
-
-        # FITS format standards prescribe FORTRAN-77-like input format strings
-        # for different types, but the base set has been extended significantly
-        # since and Rubin uses completely non-standard keys with support for
-        # their own internal abstractions like 'Angle' objects:
-        # https://archive.stsci.edu/fits/fits_standard/node58.html
-        # https://docs.astropy.org/en/stable/io/fits/usage/table.html#column-creation
+    def gen_table(cls, metadata=None, **kwargs):
+        """Generate an table from a complete or partial header and config.
+
+        If a header is given, it trumps the default config values. When the
+        header is not complete, config values are used. Config overrides are
+        applied before the data description is evaluated.
+
+        Parameters
+        ----------
+        header : `None`, `Header` or dict-like, optional
+            Header, or dict-like object, containing the image-data descriptors.
+        kwargs :
+            Any additional keyword arguments are applied as config overrides.
+
+        Returns
+        -------
+        table : `np.array`
+            Table, a structured array.
+
+        Notes
+        -----
+        FITS format standards prescribe FORTRAN-77-like input format strings
+        for different data types, but the base set has been extended and/or
+        altered significantly by various pipelines to support their objects
+        internal to their pipelines. Constructing objects, or values, described
+        by non-standard strings will result in a failure. For a list of supported
+        column-types see:
+        https://docs.astropy.org/en/stable/io/fits/usage/table.html#column-creation
+        """
+        conf = cls.default_config(**kwargs)
+
         # https://github.com/lsst/afw/blob/main/src/fits.cc#L207
         # So we really don't have much of a choice but to force a default
         # AstroPy HDU and then call the update. This might not preserve the
         # header or the data formats exactly and if metadata isn't given
         # could even assume a wrong class all together. The TableHDU is
-        # almost never used however - so hopefully this keeps on working.
+        # almost never used by us however - so hopefully this keeps on working.
         table_cls = BinTableHDU
         data = None
         if metadata is not None:
@@ -219,28 +276,82 @@ def gen_table(cls, metadata=None, config=None, **kwargs):
         return data
 
     @classmethod
-    def from_hdu(cls, hdu, config=None, **kwargs):
+    def from_hdu(cls, hdu, **kwargs):
+        """Create the factory from an HDU with or without data and with or
+        without a complete Header.
+
+        If the given HDU has data, it is preferred over creating a zero-array
+        based on the header. If the header is not complete, config defaults are
+        used. Config overrides are applied beforehand.
+
+        Parameters
+        ----------
+        hdu : `HDU`
+            One of AstroPy's Header Data Unit classes.
+        kwargs :
+            Config overrides.
+
+        Returns
+        -------
+        data : `np.array`
+            Data array, an ndarray or a recarray depending on the HDU.
+        """
         if isinstance(hdu, (PrimaryHDU, CompImageHDU, ImageHDU)):
-            return cls(base=cls.gen_image(hdu), config=config, **kwargs)
+            base = hdu.data if hdu.data is not None else cls.gen_image(hdu.header)
+            return cls(base=base, **kwargs)
         elif isinstance(hdu, (TableHDU, BinTableHDU)):
-            return cls(base=cls.gen_table(hdu), config=config, **kwargs)
+            base = hdu.data if hdu.data is not None else cls.gen_table(hdu.header)
+            return cls(base=base, **kwargs)
         else:
             raise TypeError(f"Expected an HDU, got {type(hdu)} instead.")
 
     @classmethod
-    def from_header(cls, kind, header, config=None, **kwargs):
+    def from_header(cls, header, kind=None, **kwargs):
+        """Create the factory from an complete or partial Header.
+
+        Provide the ``kind`` of data the header represents in situations where
+        the Header does not have an well defined ``XTENSION`` card.
+
+        Parameters
+        ----------
+        header : `astropy.io.fits.Header`
+            Header
+        kind : `str` or `None`, optional
+            Kind of data the header is representing.
+        kwargs :
+            Config overrides.
+
+        Returns
+        -------
+        data : `np.array`
+            Data array, an ndarray or a recarray depending on the Header and kind.
+        """
+        hkind = header.get("XTENSION", False)
+        if hkind and "table" in hkind.lower():
+            kind = "table"
+        elif hkind and "image" in hkind.lower():
+            kind = "image"
+        elif kind is None:
+            raise ValueError("Must provide a header with XTENSION or ``kind``")
+        else:
+            # kind was defined as keyword arg, so all is right
+            pass
+
         if kind.lower() == "image":
-            return cls(base=cls.gen_image(header), config=config, **kwargs)
+            return cls(base=cls.gen_image(header), **kwargs)
         elif kind.lower() == "table":
-            return cls(base=cls.gen_table(header), config=config, **kwargs)
+            return cls(base=cls.gen_table(header), **kwargs)
         else:
             raise TypeError(f"Expected an 'image' or 'table', got {kind} instead.")
 
-    @classmethod
-    def zeros(cls, shape, dtype, config=None, **kwargs):
-        return cls(np.zeros(shape, dtype), config, **kwargs)
+    def mock(self, n=1):
+        """Mock one or multiple data arrays.
 
-    def mock(self, n=1, **kwargs):
+        Parameters
+        ----------
+        n : `int`
+            Number of data to mock.
+        """
         if self.base is None:
             raise ValueError(
                 "Expected a DataFactory that has a base, but none was set. "
@@ -284,19 +395,40 @@ class SimpleVariance(DataFactory):
     **kwargs :
         Additional keyword arguments are applied as config
         overrides.
+
+    Examples
+    --------
+    >>> import kbmod.mocking as kbmock
+    >>> si = kbmock.SimpleImage(shape=(3, 3), add_noise=True, seed=100)
+    >>> sv = kbmock.SimpleVariance(gain=10)
+    >>> imgs = si.mock()
+    >>> imgs
+    array([[[ 8.694266,  9.225379, 10.046582],
+            [ 8.768851, 10.201585,  8.870326],
+            [10.702058,  9.910087,  9.283925]]], dtype=float32)
+    >>> sv.mock(imgs)
+    array([[[0.8694266 , 0.9225379 , 1.0046582 ],
+            [0.8768851 , 1.0201585 , 0.8870326 ],
+            [1.0702058 , 0.99100864, 0.9283925 ]]], dtype=float32)
     """
 
     default_config = SimpleVarianceConfig
 
     def __init__(self, image=None, **kwargs):
-        # skip setting the base here since the real base is
-        # derived from given image we just set it manually
-        super().__init__(base=None, **kwargs)
-
+        super().__init__(base=image, **kwargs)
         if image is not None:
             self.base = image / self.config["gain"] + self.config["read_noise"]**2
 
     def mock(self, images=None):
+        """Mock one or multiple data arrays.
+
+        Parameters
+        ----------
+        images : `list[np.array]`, optional
+            List, or otherwise a collection, of images from which the variances
+            will be generated. When not provided, and base template was
+            defined, returns the base template.
+        """
         if images is None:
             return self.base
         return images / self.config["gain"] + self.config["read_noise"]**2
@@ -306,13 +438,29 @@ class SimpleMaskConfig(DataFactoryConfig):
     """Simple mask configuration."""
 
     dtype = np.float32
+    """Data type"""
 
     threshold = 1e-05
+    """Default pixel value threshold above which every pixel in the template
+    will be masked.
+    """
 
     shape = (5, 5)
+    """Default image shape."""
+
     padding = 0
+    """Number of pixels near the edge that are masked."""
+
     bad_columns = []
+    """List of columns marked as bad."""
+
     patches = []
+    """Default patches to mask. This is a list of tuples. Each tuple consists of
+    a patch and a value. The patch can be any combination of array coordinates
+    such as ``(int, int)`` for individual pixels, ``(slice, int)`` or
+    ``(int, slice)`` for columns and rows respectively  or ``(slice, slice)``
+    for areas. See `SimpleMask.from_params` for an example.
+    """
 
 
 class SimpleMask(DataFactory):
@@ -326,44 +474,57 @@ class SimpleMask(DataFactory):
     ----------
     mask : `np.array`
         Bitmask array.
+    kwargs :
+        Config overrides.
+
+    Examples
+    --------
+    >>> import kbmod.mocking as kbmock
+    >>> si = kbmock.SimpleImage(shape=(3, 3), add_noise=True, seed=100)
+    >>> imgs = si.mock()
+    >>> imgs
+    array([[[ 8.694266,  9.225379, 10.046582],
+            [ 8.768851, 10.201585,  8.870326],
+            [10.702058,  9.910087,  9.283925]]], dtype=float32)
+    >>> sm = kbmock.SimpleMask.from_image(imgs, threshold=9)
+    >>> sm.base
+    array([[[0., 1., 1.],
+            [0., 1., 0.],
+            [1., 1., 1.]]], dtype=float32)
     """
 
     default_config = SimpleMaskConfig
+    """Default configuration."""
 
     def __init__(self, mask, **kwargs):
         super().__init__(base=mask, **kwargs)
 
     @classmethod
     def from_image(cls, image, **kwargs):
+        """Create a factory instance out of an image, masking all pixels above
+        a threshold.
+
+        Parameters
+        ----------
+        image : `np.array`
+            Template image from which a mask is created.
+        kwargs :
+            Config overrides.
+        """
         config = cls.default_config(**kwargs)
         mask = image.copy()
         mask[image > config["threshold"]] = 1
+        mask[image <= config["threshold"]] = 0
         return cls(mask)
 
     @classmethod
     def from_params(cls, **kwargs):
-        """Create a mask by adding a padding around the edges of the array with
-        the given dimensions and mask out bad columns.
+        """Create a factory instance from config parameters.
 
         Parameters
         ----------
-        shape : `tuple`
-            Tuple of (width, height)/(cols, rows) dimensions of the mask.
-        padding : `int`
-            Number of pixels near the edge that will be masked.
-        bad_columns : `list[int]`
-            Indices of bad columns to mask
-        patches : `list[tuple]`
-            Patches to mask. This is a list of tuples. Each tuple consists of
-            a patch and a value. The patch can be any combination of array
-            coordinates such as ``(int, int)`` for individual pixels,
-           ``(slice, int)`` or ``(int, slice)`` for columns and rows
-           respectively  or ``(slice, slice)`` for areas.
-
-        Returns
-        -------
-        mask_factory : `SimpleMask`
-            Mask factory.
+        kwargs :
+            Config overrides.
 
         Examples
         --------
@@ -433,9 +594,12 @@ class SimpleImageConfig(DataFactoryConfig):
     noise_std = 1.0
     """Standard deviation of the Gaussian distribution of the noise."""
 
-    model = models.Gaussian2D
+    model = models.Gaussian2D(x_stddev=1, y_stddev=1)
     """Source and object model used to render them on the image."""
 
+    dtype = np.float32
+    """Numpy data type."""
+
 
 class SimpleImage(DataFactory):
     """Simple image data factory.
@@ -445,31 +609,46 @@ class SimpleImage(DataFactory):
     image.
 
     Noise realization is drawn from a Gaussian distribution with the given
-    standard deviation and centered on the given mean.
+    standard deviation and mean.
 
     Parameters
     ----------
     image : `np.array`
         Science image that will be used as a base onto which to render details.
-    config : `SimpleImageConfig`
-        Configuration.
     src_cat : `CatalogFactory`
         Static source catalog.
     obj_cat : `CatalogFactory`
         Moving object catalog factory.
-    **kwargs :
-        Additional keyword arguments are applied as config
+    kwargs :
+        Additional keyword arguments are applied as config.
         overrides.
-    """
 
+    Examples
+    --------
+    >>> import kbmod.mocking as kbmock
+    >>> si = kbmock.SimpleImage()
+    >>> si.mock()
+    array([[[0., 0., 0., ..., 0., 0., 0.],
+            [0., 0., 0., ..., 0., 0., 0.],
+            [0., 0., 0., ..., 0., 0., 0.],
+            ...,
+            [0., 0., 0., ..., 0., 0., 0.],
+            [0., 0., 0., ..., 0., 0., 0.],
+            [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
+    >>> si = kbmock.SimpleImage(shape=(3, 3), add_noise=True, seed=100)
+    >>> si.mock()
+    array([[[ 8.694266,  9.225379, 10.046582],
+            [ 8.768851, 10.201585,  8.870326],
+            [10.702058,  9.910087,  9.283925]]], dtype=float32)
+    """
     default_config = SimpleImageConfig
+    """Default configuration."""
 
-    def __init__(self, image=None, src_cat=None, obj_cat=None,
-                 dtype=np.float32, **kwargs):
+    def __init__(self, image=None, src_cat=None, obj_cat=None, **kwargs):
         super().__init__(image, **kwargs)
 
         if image is None:
-            image = np.zeros(self.config["shape"], dtype=dtype)
+            image = np.zeros(self.config["shape"], dtype=self.config["dtype"])
         else:
             image = image
             self.config["shape"] = image.shape
@@ -480,7 +659,7 @@ def __init__(self, image=None, src_cat=None, obj_cat=None,
         self.src_cat = src_cat
         if self.src_cat is not None:
             image = image if image.flags.writeable else image.copy()
-            add_model_objects(image, src_cat.table, self.config["model"](x_stddev=1, y_stddev=1))
+            add_model_objects(image, src_cat.table, self.config["model"])
             image.flags.writeable = self.config["writeable"]
 
         self.base = image
@@ -496,6 +675,11 @@ def add_noise(cls, images, config):
            A ``(n_images, image_width, image_height)`` shaped array of images.
         config : `SimpleImageConfig`
            Configuration.
+
+        Returns
+        -------
+        images : `np.array`
+           A ``(n_images, image_width, image_height)`` shaped array of images.
         """
         rng = np.random.default_rng(seed=config["seed"])
         shape = images.shape
@@ -520,9 +704,14 @@ def mock(self, n=1, obj_cats=None, **kwargs):
         obj_cats : `list[Catalog]`
             A list of catalogs as long as the number of requested images of
             moving objects that will be inserted into the image.
+
+        Returns
+        -------
+        images : `np.array`
+           A ``(n_images, image_width, image_height)`` shaped array of images.
         """
         shape = (n, *self.config["shape"])
-        images = np.zeros(shape, dtype=np.float32)
+        images = np.zeros(shape, dtype=self.config["dtype"])
 
         if self.config["add_noise"]:
             images = self.add_noise(images=images, config=self.config)
@@ -539,7 +728,7 @@ def mock(self, n=1, obj_cats=None, **kwargs):
         if obj_cats is not None:
             pairs = [(images[0], obj_cats[0])] if n == 1 else zip(images, obj_cats)
             for i, (img, cat) in enumerate(pairs):
-                add_model_objects(img, cat, self.config["model"](x_stddev=1, y_stddev=1))
+                add_model_objects(img, cat, self.config["model"])
 
         return images
 
@@ -547,27 +736,32 @@ def mock(self, n=1, obj_cats=None, **kwargs):
 class SimulatedImageConfig(DataFactoryConfig):
     """Simulated image configuration.
 
-    Simulated image add several sources of noise into the image, bad columns,
-    hot pixels, including the possibility of adding static and moving sources.
-    On a higher level it is possible to modify this behavior by setting:
+    Simulated image attempts to add noise to the image in a statistically
+    meaningful sense, but it does not reproduce the noise qualities in the same
+    way an optical simulation would. Noise sources added are:
+    - bad columns
+    - hot pixels
+    - read noise
+    - dark current
+    - sky level
+
+    The quantities are expressed in physical units and the defaults were
+    selected to sort of make sense.
+
+    Control over which source of noise are included in the image can be done by
+    setting the
     - add_noise
     - add_bad_cols
     - add_hot_pix
-    parameters to `False`. Optionally, for a more fine-grained control set the
-    distribution parameters, f.e. mean and standard deviation, such that they
-    do not produce measurable values in the image.
-
-    The quantities are expressed in physical units and the defaults were
-    selected to sort of make sense. Sky levels are not modeled very
-    realistically, being expressed as an additional level of noise, in counts,
-    additive to the detector noise.
+    flags to `False`. For a more fine-grained control set the distribution
+    parameters, f.e. mean and standard deviation, such that they do not produce
+    measurable values in the image.
 
-    Generally expect the mean value of pixel counts to be:
+    Expect the mean value of pixel counts to be:
 
         bias + mean(dark_current)*exposure + mean(sky_level)
 
-    The deviation of the pixel counts around the expected value can be
-    estimated by:
+    The deviation of the pixel counts should be expected to be:
 
         sqrt( std(read_noise)^2 + sqrt(sky_level)^2 )
     """
@@ -657,19 +851,29 @@ class SimulatedImageConfig(DataFactoryConfig):
     """Sky level, in counts."""
 
     # Object and Source properties
-    model = models.Gaussian2D
+    model = models.Gaussian2D(x_stddev=1, y_stddev=1)
+    """Source and object model used to render them on the image."""
+
+    dtype = np.float32
+    """Numpy data type."""
 
 
 class SimulatedImage(SimpleImage):
-    """Simulated image includes multiple sources of noise, bad columns, hot
-    pixels, static sources and moving objects.
+    """Simulated image attempt to include a more realistic noise profile.
+
+    Noise sources added are:
+    - bad columns
+    - hot pixels
+    - read noise
+    - dark current
+    - sky level
+
+    Static or moving objects may be added to the simulated image.
 
     Parameters
     ----------
     image : `np.array`
-        Science image that will be used as a base onto which to render details.
-    config : `SimpleImageConfig`
-        Configuration.
+        Base template image on which details will be rendered.
     src_cat : `CatalogFactory`
         Static source catalog.
     obj_cat : `CatalogFactory`
@@ -680,6 +884,7 @@ class SimulatedImage(SimpleImage):
     """
 
     default_config = SimulatedImageConfig
+    """Default config."""
 
     @classmethod
     def add_bad_cols(cls, image, config):
@@ -794,7 +999,7 @@ def add_noise(cls, images, config):
         return images
 
     @classmethod
-    def gen_base_image(cls, config=None, src_cat=None, dtype=np.float32):
+    def gen_base_image(cls, config=None, src_cat=None):
         """Generate base image from configuration.
 
         Parameters
@@ -812,15 +1017,20 @@ def gen_base_image(cls, config=None, src_cat=None, dtype=np.float32):
         config = cls.default_config(config)
 
         # empty image
-        base = np.zeros(config["shape"], dtype=dtype)
+        base = np.zeros(config["shape"], dtype=config["dtype"])
         base += config["bias"]
         base = cls.add_hot_pixels(base, config)
         base = cls.add_bad_cols(base, config)
         if src_cat is not None:
-            add_model_objects(base, src_cat.table, config["model"](x_stddev=1, y_stddev=1))
+            add_model_objects(base, src_cat.table, config["model"])
 
         return base
 
-    def __init__(self, image=None, src_cat=None, obj_cat=None, dtype=np.float32,**kwargs):
+    def __init__(self, image=None, src_cat=None, obj_cat=None, **kwargs):
         conf = self.default_config(**kwargs)
-        super().__init__(image=self.gen_base_image(conf, dtype=dtype), src_cat=src_cat, obj_cat=obj_cat, **conf)
+        super().__init__(
+            image=self.gen_base_image(conf),
+            src_cat=src_cat,
+            obj_cat=obj_cat,
+            **conf
+        )
diff --git a/src/kbmod/mocking/dump_headers.py b/src/kbmod/mocking/dump_headers.py
new file mode 100644
index 000000000..f5a26ec07
--- /dev/null
+++ b/src/kbmod/mocking/dump_headers.py
@@ -0,0 +1,309 @@
+# Modified from the original Astropy code to add a card format to the
+# tabular output. All rights belong to the original authors.
+
+# Licensed under a 3-clause BSD style license - see LICENSE.rst
+"""
+Modified Astropy ``archiveheaders`` utility that adds a datatype column for each
+header card to the output. All rights belong to the original authors.
+
+``archiveheaders`` is a command line script based on astropy.io.fits for printing
+the header(s) of one or more FITS file(s) to the standard output in a human-
+readable format.
+
+The modifications to the script include:
+- supporting only tabular output
+- making "ascii.ecsv" the default output format
+- appending a datatype to each serialized header card, describing the type of
+  the cards value.
+
+Example uses of ``archiveheaders``:
+
+1. Print the header of all the HDUs of a .fits file::
+
+    $ archiveheaders filename.fits
+
+2. Dump the header keywords of all the files in the current directory into a
+   machine-readable ecsv file::
+
+    $ archiveheaders *.fits > keywords.csv
+
+3. Sorting the output along a specified keyword::
+
+    $ archiveheaders -f -s DATE-OBS *.fits
+
+4. Sort first by OBJECT, then DATE-OBS::
+
+    $ archiveheaders -f -s OBJECT -s DATE-OBS *.fits
+
+Note that compressed images (HDUs of type
+:class:`~astropy.io.fits.CompImageHDU`) really have two headers: a real
+BINTABLE header to describe the compressed data, and a fake IMAGE header
+representing the image that was compressed. Astropy returns the latter by
+default. You must supply the ``--compressed`` option if you require the real
+header that describes the compression.
+
+With Astropy installed, please run ``archiveheaders --help`` to see the full usage
+documentation.
+"""
+
+import argparse
+import sys
+
+import numpy as np
+
+from astropy import __version__, log
+from astropy.io import fits
+
+DESCRIPTION = """
+Print the header(s) of a FITS file. Optional arguments allow the desired
+extension(s), keyword(s), and output format to be specified.
+Note that in the case of a compressed image, the decompressed header is
+shown by default.
+
+This script is part of the Astropy package. See
+https://docs.astropy.org/en/latest/io/fits/usage/scripts.html#module-astropy.io.fits.scripts.fitsheader
+for further documentation.
+""".strip()
+
+
+class ExtensionNotFoundException(Exception):
+    """Raised if an HDU extension requested by the user does not exist."""
+
+
+class HeaderFormatter:
+    """Class to format the header(s) of a FITS file for display by the
+    `fitsheader` tool; essentially a wrapper around a `HDUList` object.
+
+    Example usage:
+    fmt = HeaderFormatter('/path/to/file.fits')
+    print(fmt.parse())
+
+    Parameters
+    ----------
+    filename : str
+        Path to a single FITS file.
+    verbose : bool
+        Verbose flag, to show more information about missing extensions,
+        keywords, etc.
+
+    Raises
+    ------
+    OSError
+        If `filename` does not exist or cannot be read.
+    """
+
+    def __init__(self, filename, verbose=True):
+        self.filename = filename
+        self.verbose = verbose
+        self._hdulist = fits.open(filename)
+
+    def parse(self, compressed=False):
+        """Returns the FITS file header(s) in a readable format.
+
+        Parameters
+        ----------
+        compressed : bool, optional
+            If True, shows the header describing the compression, rather than
+            the header obtained after decompression. (Affects FITS files
+            containing `CompImageHDU` extensions only.)
+
+        Returns
+        -------
+        formatted_header : str or astropy.table.Table
+            Traditional 80-char wide format in the case of `HeaderFormatter`;
+            an Astropy Table object in the case of `TableHeaderFormatter`.
+        """
+        hdukeys = range(len(self._hdulist))  # Display all by default
+        return self._parse_internal(hdukeys, compressed)
+
+    def _parse_internal(self, hdukeys, compressed):
+        """The meat of the formatting; in a separate method to allow overriding."""
+        result = []
+        for idx, hdu in enumerate(hdukeys):
+            try:
+                cards = self._get_cards(hdu, compressed)
+            except ExtensionNotFoundException:
+                continue
+
+            if idx > 0:  # Separate HDUs by a blank line
+                result.append("\n")
+            result.append(f"# HDU {hdu} in {self.filename}:\n")
+            for c in cards:
+                result.append(f"{c}\n")
+        return "".join(result)
+
+    def _get_cards(self, hdukey, compressed):
+        """Returns a list of `astropy.io.fits.card.Card` objects.
+
+        This function will return the desired header cards, taking into
+        account the user's preference to see the compressed or uncompressed
+        version.
+
+        Parameters
+        ----------
+        hdukey : int or str
+            Key of a single HDU in the HDUList.
+
+        compressed : bool, optional
+            If True, shows the header describing the compression.
+
+        Raises
+        ------
+        ExtensionNotFoundException
+            If the hdukey does not correspond to an extension.
+        """
+        # First we obtain the desired header
+        try:
+            if compressed:
+                # In the case of a compressed image, return the header before
+                # decompression (not the default behavior)
+                header = self._hdulist[hdukey]._header
+            else:
+                header = self._hdulist[hdukey].header
+        except (IndexError, KeyError):
+            message = f"{self.filename}: Extension {hdukey} not found."
+            if self.verbose:
+                log.warning(message)
+            raise ExtensionNotFoundException(message)
+
+        # return all cards
+        cards = header.cards
+        return cards
+
+    def close(self):
+        self._hdulist.close()
+
+
+class TableHeaderFormatter(HeaderFormatter):
+    """Class to convert the header(s) of a FITS file into a Table object.
+    The table returned by the `parse` method will contain four columns:
+    filename, hdu, keyword, and value.
+
+    Subclassed from HeaderFormatter, which contains the meat of the formatting.
+    """
+
+    def _parse_internal(self, hdukeys, compressed):
+        """Method called by the parse method in the parent class."""
+        tablerows = []
+        for hdu in hdukeys:
+            try:
+                for card in self._get_cards(hdu, compressed):
+                    tablerows.append(
+                        {
+                            "filename": self.filename,
+                            "hdu": hdu,
+                            "keyword": card.keyword,
+                            "value": str(card.value),
+                            "format": type(card.value).__name__,
+                        }
+                    )
+            except ExtensionNotFoundException:
+                pass
+
+        if tablerows:
+            from astropy import table
+
+            return table.Table(tablerows)
+        return None
+
+
+def print_headers_as_table(args):
+    """Prints FITS header(s) in a machine-readable table format.
+
+    Parameters
+    ----------
+    args : argparse.Namespace
+        Arguments passed from the command-line as defined below.
+    """
+    tables = []
+    # Create a Table object for each file
+    for filename in args.filename:  # Support wildcards
+        formatter = None
+        try:
+            formatter = TableHeaderFormatter(filename)
+            tbl = formatter.parse(args.compressed)
+            if tbl:
+                tables.append(tbl)
+        except OSError as e:
+            log.error(str(e))  # file not found or unreadable
+        finally:
+            if formatter:
+                formatter.close()
+
+    # Concatenate the tables
+    if len(tables) == 0:
+        return False
+    elif len(tables) == 1:
+        resulting_table = tables[0]
+    else:
+        from astropy import table
+
+        resulting_table = table.vstack(tables)
+    # Print the string representation of the concatenated table
+    resulting_table.write(sys.stdout, format=args.table)
+
+
+def main(args=None):
+    """This is the main function called by the `fitsheader` script."""
+    parser = argparse.ArgumentParser(
+        description=DESCRIPTION, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    mode_group = parser.add_mutually_exclusive_group()
+    mode_group.add_argument(
+        "-t",
+        "--table",
+        nargs="?",
+        default="ascii.ecsv",
+        metavar="FORMAT",
+        help=(
+            '"The default format is "ascii.ecsv" (can be "ascii.csv", "ascii.html", '
+            '"ascii.latex", "fits", etc)'
+        ),
+    )
+    mode_group.add_argument(
+        "-f",
+        "--fitsort",
+        action="store_true",
+        help=("print the headers as a table with each unique keyword in a given column (fitsort format) "),
+    )
+    parser.add_argument(
+        "-s",
+        "--sort",
+        metavar="SORT_KEYWORD",
+        action="append",
+        type=str,
+        help=(
+            "sort output by the specified header keywords, can be repeated to "
+            "sort by multiple keywords; Only supported with -f/--fitsort"
+        ),
+    )
+    parser.add_argument(
+        "-c",
+        "--compressed",
+        action="store_true",
+        help=(
+            "for compressed image data, show the true header which describes "
+            "the compression rather than the data"
+        ),
+    )
+    parser.add_argument(
+        "filename",
+        nargs="+",
+        help="path to one or more files; wildcards are supported",
+    )
+    args = parser.parse_args()
+
+    if args.sort:
+        args.sort = [key.replace(".", " ") for key in args.sort]
+        if not args.fitsort:
+            log.error("Sorting with -s/--sort is only supported in conjunction with" " -f/--fitsort")
+            # 2: Unix error convention for command line syntax
+            sys.exit(2)
+
+    # Now print the desired headers
+    try:
+        print_headers_as_table(args)
+    except OSError:
+        # A 'Broken pipe' OSError may occur when stdout is closed prematurely,
+        # eg. when calling `fitsheader file.fits | head`. We let this pass.
+        pass
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index 5db6650ad..35a3c1ee8 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -3,16 +3,12 @@
 
 from .callbacks import IncrementObstime
 from .headers import HeaderFactory, ArchivedHeader
-from .fits_data import (
+from .data import (
     DataFactoryConfig,
     DataFactory,
-    SimpleImageConfig,
     SimpleImage,
-    SimulatedImageConfig,
     SimulatedImage,
-    SimpleVarianceConfig,
     SimpleVariance,
-    SimpleMaskConfig,
     SimpleMask,
 )
 
@@ -25,7 +21,7 @@
 
 
 class NoneFactory:
-    "Kinda makes some code later prettier. Kinda"
+    """Factory that returns `None`."""
     def mock(self, n):
         return [
             None,
@@ -33,6 +29,54 @@ def mock(self, n):
 
 
 class EmptyFits:
+    """Mock FITS files containing zeroed arrays.
+
+    Mocks a FITS file containing 4 extensions:
+    - primary
+    - image
+    - variance
+    - mask
+    that contain no data. By default the created data is immutable. Useful when
+    data is added after mocking a collection of HDULists.
+
+    The primary header contains an incrementing timestamp.
+
+    Attributes
+    ----------
+    prim_hdr : `HeaderFactory`
+        Primary header factory
+    img_hdr : `HeaderFactory`
+        Image header factory, contains WCS.
+    var_hdr : `HeaderFactory`
+        Variance header factory, contains WCS.
+    mask_hdr: `HeaderFactory`
+        Mask header factory, contains WCS.
+    img_data : `DataFactory`
+        Image data factory, used to also create variances.
+    mask_data : `DataFactory`
+        Mask data factory.
+    current : `int`
+        Counter of current mocking iteration.
+
+    Parameters
+    ----------
+    header : `dict-like` or `None`, optional
+        Keyword-value pairs that will use to create and update the primary header
+        metadata.
+    shape : `tuple`, optional
+        Size of the image arrays, 100x100 pixels by default.
+    start_t : `str` or `astropy.time.Time`, optional
+        A timestamp string interpretable by Astropy, or a time object.
+    step_t : `float` or `astropy.time.TimeDelta`, optional
+        Timestep between each mocked instance. In units of days by default.
+    editable_images : `bool`, optional
+        Make mocked images writable and independent. `False` by default.
+        Makes the variance images editable too.
+    editable_masks : `bool`, optional
+        Make masks writable and independent. `False` by default. Masks can
+        usually be shared, so leaving them immutable reduces the memory footprint
+        and time it takes to mock large images.
+    """
     def __init__(
         self,
         header=None,
@@ -53,15 +97,16 @@ def __init__(
         self.mask_hdr = HeaderFactory.from_ext_template({"EXTNAME": "MASK"}, shape=shape)
 
         self.img_data = DataFactory.from_header(
-            kind="image", header=self.img_hdr.header, writeable=editable_images, return_copy=editable_images
+            header=self.img_hdr.header, kind="image", writeable=editable_images, return_copy=editable_images
         )
         self.mask_data = DataFactory.from_header(
-            kind="image", header=self.mask_hdr.header, return_copy=editable_masks, writeable=editable_masks
+            header=self.mask_hdr.header, kind="image", return_copy=editable_masks, writeable=editable_masks
         )
 
         self.current = 0
 
     def mock(self, n=1):
+        """Mock n empty fits files."""
         prim_hdrs = self.prim_hdr.mock(n=n)
         img_hdrs = self.img_hdr.mock(n=n)
         var_hdrs = self.var_hdr.mock(n=n)
@@ -89,6 +134,58 @@ def mock(self, n=1):
 
 
 class SimpleFits:
+    """Mock FITS files containing data.
+
+    Mocks a FITS file containing 4 extensions:
+    - primary
+    - image
+    - variance
+    - mask
+    that contain no data. By default the created data is mutable.
+    The primary header contains an incrementing timestamp.
+
+    Attributes
+    ----------
+    prim_hdr : `HeaderFactory`
+        Primary header factory
+    img_hdr : `HeaderFactory`
+        Image header factory, contains WCS.
+    var_hdr : `HeaderFactory`
+        Variance header factory, contains WCS.
+    mask_hdr: `HeaderFactory`
+        Mask header factory, contains WCS.
+    img_data : `SimpleImage` or `SimulatedImage`
+        Image data factory.
+    var_data : `SimpleVariance``
+        Variance data factory.
+    mask_data : `SimpleMask`
+        Mask data factory.
+    current : `int`
+        Counter of current mocking iteration.
+
+    Parameters
+    ----------
+    shared_header_metadata : `dict-like` or `None`, optional
+        Keyword-value pairs that will use to create and update all of the headers.
+    shape : `tuple`, optional
+        Size of the image arrays, 100x100 pixels by default.
+    start_t : `str` or `astropy.time.Time`, optional
+        A timestamp string interpretable by Astropy, or a time object.
+    step_t : `float` or `astropy.time.TimeDelta`, optional
+        Timestep between each mocked instance. In units of days by default.
+    with_noise : `bool`
+        Add noise into the images.
+    noise : `str`
+        Noise profile to use, "simplistic" is simple Gaussian noise and
+        "realistic" simulates several noise sources and adds them together.
+    src_cat : `SourceCatalog`
+        Source catalog of static objects to add into the images.
+    obj_cat : `ObjectCatalog`
+        Object catalog of moving objects to add into the images.
+    wcs_factory : `WCSFactory`
+        Factory used to create and update WCS data in headers of mocked FITS
+        files.
+    """
     def __init__(
             self,
             shared_header_metadata=None,
@@ -147,6 +244,7 @@ def __init__(
         self.current = 0
 
     def mock(self, n=1):
+        """Mock n simple FITS files."""
         prim_hdrs = self.prim_hdr.mock(n=n)
         img_hdrs = self.img_hdr.mock(n=n)
         var_hdrs = self.var_hdr.mock(n=n)
@@ -183,6 +281,62 @@ def mock(self, n=1):
 
 
 class DECamImdiff:
+    """Mock FITS files from archived headers of Rubin Science Pipelines
+    "differenceExp" dataset type headers (arXiv:2109.03296).
+
+    Each FITS file contains 16 HDUs, one PRIMARY, 3 images (image, mask and
+    variance) and supporting data such as PSF, ArchiveId etc. stored in
+    `BinTableHDU`s.
+
+    The exported data contains approximately 60 real header data of a Rubin
+    Science Pipelines ``differenceExp`` dataset type. The data was created from
+    DEEP B1a field, as described in arXiv:2310.03678.
+
+    By default only headers are mocked.
+
+    Attributes
+    ----------
+    hdr_factory : `ArchivedHeader`
+        Header factory for all 16 HDUs.
+    data_factories : `list[DataFactory]`
+        Data factories, one for each HDU being mocked.
+    hdu_layout : `list[HDU]`
+        List of HDU types (PrimaryHDU, CompImageHDU...) used to create an HDU
+        from a header and a data factory.
+    img_data : `SimpleImage` or `SimulatedImage`
+        Reference to second element in `data_factories`, an image data factory.
+    var_data : `SimpleVariance``
+        Reference to third element in `data_factories`, an variance data factory.
+        Variance uses read_noise of 7.0 e- and gain of 5.0 e-/count as described
+        by the Table 2.2 in DECam Data Handbook Version 2.05 March 2014.
+    mask_data : `SimpleMask`
+        Reference to fourth element in `data_factories`, an mask data factory.
+    current : `int`
+        Counter of current mocking iteration.
+
+    Parameters
+    ----------
+    shared_header_metadata : `dict-like` or `None`, optional
+        Keyword-value pairs that will use to create and update all of the headers.
+    shape : `tuple`, optional
+        Size of the image arrays, 100x100 pixels by default.
+    start_t : `str` or `astropy.time.Time`, optional
+        A timestamp string interpretable by Astropy, or a time object.
+    step_t : `float` or `astropy.time.TimeDelta`, optional
+        Timestep between each mocked instance. In units of days by default.
+    with_noise : `bool`
+        Add noise into the images.
+    noise : `str`
+        Noise profile to use, "simplistic" is simple Gaussian noise and
+        "realistic" simulates several noise sources and adds them together.
+    src_cat : `SourceCatalog`
+        Source catalog of static objects to add into the images.
+    obj_cat : `ObjectCatalog`
+        Object catalog of moving objects to add into the images.
+    wcs_factory : `WCSFactory`
+        Factory used to create and update WCS data in headers of mocked FITS
+        files.
+    """
     def __init__(self, with_data=False, with_noise=False, noise="simplistic",
                  src_cat=None, obj_cat=None):
         if obj_cat is not None and obj_cat.mode == "progressive":
@@ -218,7 +372,7 @@ def __init__(self, with_data=False, with_noise=False, noise="simplistic",
                 self.data_factories[1] = self.img_data
                 self.data_factories[2] = self.mask_data
                 self.data_factories[3] = self.mask_data
-                self.data_factories[4:] = [DataFactory.from_header("table", h) for h in headers[4:]]
+                self.data_factories[4:] = [DataFactory.from_header(h, kind="table") for h in headers[4:]]
 
         self.with_data = with_data
         self.src_cat = src_cat
@@ -228,6 +382,7 @@ def __init__(self, with_data=False, with_noise=False, noise="simplistic",
         self.current = 0
 
     def mock(self, n=1):
+        """Mock n differenceExp dataset type-like FITS files."""
         headers = self.hdr_factory.mock(n=n)
 
         obj_cats = None
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index dd7977cb9..2a7f01f92 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -13,13 +13,72 @@
 
 
 __all__ = [
-#    "make_wcs",
     "WCSFactory",
     "HeaderFactory",
     "ArchivedHeader",
 ]
 
 class WCSFactory:
+    """WCS Factory.
+
+    Used to generate collections of ICRS TAN WCS from parameters, or as a way to
+    update the given header with a new WCS.
+
+    The new WCS is generated by inheriting the header and updating its properties,
+    or by completely overwriting the Header WCS and replacing it with a new WCS.
+
+    Attribute
+    ---------
+    current : `int`
+        Counter of the number of mocked WCSs.
+    template : `WCS`
+        Template WCS.
+
+    Parameters
+    ----------
+    pointing : `tuple`, optional
+        Ra and Dec w.r.t ICRS coordinate system, in decimal degrees.
+    rotation : `float`, optional
+        Rotation, in degrees, from ICRS equator (ecliptic).
+    pixscale : `float`, optional
+        Pixel scale, in arcseconds per pixel.
+    dither_pos : `bool`, optional
+        Dither positions of mocked WCSs.
+    dither_rot : `bool`, optional
+        Dither rotations of mocked WCSs.
+    dither_amplitudes : `tuple`, optional
+        A set of 3 values, the amplitude of dither in ra direction, the
+        amplitude of dither in dec direction and the amplitude of dither in
+        rotations. In decimal degrees.
+    cycle : `list[WCS]`, optional
+        A list of pre-created WCS objects through which to iterate.
+
+    Examples
+    --------
+    >>> from astropy.io.fits import Header
+    >>> import kbmod.mocking as kbmock
+    >>> wcsf = kbmock.WCSFactory(pointing=(10, 10), rotation=45)
+    >>> wcsf.mock(Header())
+    WCSAXES =                    2 / Number of coordinate axes
+    CRPIX1  =                  0.0 / Pixel coordinate of reference point
+    CRPIX2  =                  0.0 / Pixel coordinate of reference point
+    PC1_1   =  -3.928369684292E-05 / Coordinate transformation matrix element
+    PC1_2   =  3.9283723288914E-05 / Coordinate transformation matrix element
+    PC2_1   =  3.9283723288914E-05 / Coordinate transformation matrix element
+    PC2_2   =   3.928369684292E-05 / Coordinate transformation matrix element
+    CDELT1  =                  1.0 / [deg] Coordinate increment at reference point
+    CDELT2  =                  1.0 / [deg] Coordinate increment at reference point
+    CUNIT1  = 'deg'                / Units of coordinate increment and value
+    CUNIT2  = 'deg'                / Units of coordinate increment and value
+    CTYPE1  = 'RA---TAN'           / Right ascension, gnomonic projection
+    CTYPE2  = 'DEC--TAN'           / Declination, gnomonic projection
+    CRVAL1  =                 10.0 / [deg] Coordinate value at reference point
+    CRVAL2  =                 10.0 / [deg] Coordinate value at reference point
+    LONPOLE =                180.0 / [deg] Native longitude of celestial pole
+    LATPOLE =                 10.0 / [deg] Native latitude of celestial pole
+    MJDREF  =                  0.0 / [d] MJD of fiducial time
+    RADESYS = 'ICRS'               / Equatorial coordinate system,
+    """
     def __init__(self, mode="static",
                  pointing=(351., -5), rotation=0, pixscale=0.2,
                  dither_pos=False, dither_rot=False, dither_amplitudes=(0.01, 0.01, 0.0),
@@ -41,7 +100,7 @@ def __init__(self, mode="static",
         self.current = 0
 
     @classmethod
-    def gen_wcs(cls, center_coords, rotation, pixscale, shape=None):
+    def gen_wcs(cls, pointing, rotation=0, pixscale=1, shape=None):
         """
         Create a simple celestial `~astropy.wcs.WCS` object in ICRS
         coordinate system.
@@ -61,14 +120,31 @@ def gen_wcs(cls, center_coords, rotation, pixscale, shape=None):
         Returns
         -------
         wcs : `astropy.wcs.WCS`
-        The world coordinate system.
+           The world coordinate system.
 
         Examples
         --------
-        >>> from kbmod.mocking import make_wcs
-        >>> shape = (100, 100)
-        >>> wcs = make_wcs(shape)
-        >>> wcs = make_wcs(shape, (115, 5), 45, 0.1)
+        >>> import kbmod.mocking as kbmock
+        >>> [kbmock.WCSFactory.gen_wcs((10, 10), rot, 0.2) for rot in (0, 90)]
+        [WCS Keywords
+
+        Number of WCS axes: 2
+        CTYPE : 'RA---TAN' 'DEC--TAN'
+        CRVAL : 10.0 10.0
+        CRPIX : 0.0 0.0
+        PC1_1 PC1_2  : -5.555555555555556e-05 0.0
+        PC2_1 PC2_2  : 0.0 5.555555555555556e-05
+        CDELT : 1.0 1.0
+        NAXIS : 0  0, WCS Keywords
+
+        Number of WCS axes: 2
+        CTYPE : 'RA---TAN' 'DEC--TAN'
+        CRVAL : 10.0 10.0
+        CRPIX : 0.0 0.0
+        PC1_1 PC1_2  : 3.7400283533421276e-11 5.555555555554297e-05
+        PC2_1 PC2_2  : 5.555555555554297e-05 -3.7400283533421276e-11
+        CDELT : 1.0 1.0
+        NAXIS : 0  0]
         """
         wcs = WCS(naxis=2)
         rho = rotation*0.0174533 # deg to rad
@@ -79,7 +155,7 @@ def gen_wcs(cls, center_coords, rotation, pixscale, shape=None):
             wcs.wcs.crpix = [shape[1] // 2, shape[0] // 2]
         else:
             wcs.wcs.crpix = [0, 0]
-        wcs.wcs.crval = center_coords
+        wcs.wcs.crval = pointing
         wcs.wcs.cunit = ['deg', 'deg']
         wcs.wcs.pc = np.array([
             [-scale * np.cos(rho), scale * np.sin(rho)],
@@ -90,37 +166,135 @@ def gen_wcs(cls, center_coords, rotation, pixscale, shape=None):
 
         return wcs
 
+    def next(self):
+        """Iteratively return WCS from the cycle."""
+        for wcs in self.cycle:
+            yield wcs
+
     def update_from_header(self, header):
+        """Update WCS template using a header, only updates the cards template
+        shares with the given header.
+
+        Updates the template WCS in-place.
+        """
         t = self.template.to_header()
         t.update(header)
         self.template = WCS(t)
 
-    def mock(self, header):
+    def update_headers(self, headers):
+        """Update the headers, in-place, with a new mocked WCS.
+
+        If the header contains a WCS, it is updated to match the template.
+
+        Parameters
+        ----------
+        header : `astropy.io.fits.Header`
+             Header
+
+        Returns
+        -------
+        header : `astropy.io.fits.Header`
+            Update header.
+        """
         wcs = self.template
 
-        if self.cycle is not None:
-            wcs = self.cycle[self.current % len(self.cycle)]
-
-        if self.dither_pos:
-            dra = random.uniform(-self.dither_amplitudes[0], self.dither_amplitudes[0])
-            ddec = random.uniform(-self.dither_amplitudes[1], self.dither_amplitudes[1])
-            wcs.wcs.crval += [dra, ddec]
-        if self.dither_rot:
-            ddec = random.uniform(-self.dither_amplitudes[2], self.dither_amplitudes[2])
-            rho = self.dither_amplitudes[2]*0.0174533 # deg to rad
-            rot_matrix =  np.array([
-                [-np.cos(rho), np.sin(rho)],
-                [np.sin(rho), np.cos(rho)]
-            ])
-            new_pc = wcs.wcs.pc @ rot_matrix
-            wcs.wcs.pc = new_pc
-
-        self.current += 1
-        header.update(wcs.to_header())
-        return header
+        for header in headers:
+            if self.cycle is not None:
+                wcs = self.next()
+
+            if self.dither_pos:
+                dra = random.uniform(-self.dither_amplitudes[0], self.dither_amplitudes[0])
+                ddec = random.uniform(-self.dither_amplitudes[1], self.dither_amplitudes[1])
+                wcs.wcs.crval += [dra, ddec]
+
+            if self.dither_rot:
+                ddec = random.uniform(-self.dither_amplitudes[2], self.dither_amplitudes[2])
+                rho = self.dither_amplitudes[2]*0.0174533 # deg to rad
+                rot_matrix =  np.array([
+                    [-np.cos(rho), np.sin(rho)],
+                    [np.sin(rho), np.cos(rho)]
+                ])
+                new_pc = wcs.wcs.pc @ rot_matrix
+                wcs.wcs.pc = new_pc
+
+            self.current += 1
+            header.update(wcs.to_header())
 
 
 class HeaderFactory:
+    """Mocks a header from a given template.
+
+    Callback functions can be defined for individual header cards which are
+    executed and their respective values are updated for each new mocked header.
+
+    A WCS factory can be attached to this factory to update the related WCS
+    header keywords.
+
+    Provides two base templates from which to create Headers from, based on
+    CTIO observatory location and DECam instrument.
+
+    Can generate headers from dict-like template and card overrides.
+
+    Attributes
+    ----------
+    is_dynamic : `bool`
+        True when factory has a mutable Header cards or a WCS factory.
+
+    Parameters
+    ---------
+    metadata : `dict-like`
+        Header, dict, or a list of cards from which a header is created.
+    mutables : `list[str]` or `None`, optional
+        A list of strings, matching header card keys, designating them as a
+        card that has an associated callback.
+    callbacks : `list[func]`, `list[class]` or `None`
+        List of callbacks, functions or classes with a ``__call__`` method, that
+        will be executed in order to update the respective key in ``mutables``
+        See already provided callbacks in `kbmod.mocking.callbacks` module.
+    has_wcs : `bool`
+        Attach a WCS to each produced header.
+    wcs_factory : `WCSFactory` or `None`
+        A WCS factory to use, if `None` and `has_wcs` is `True`, uses the default
+        WCS Factory. See `WCSFactory` for details.
+
+    Examples
+    --------
+    >>> import kbmod.mocking as kbmock
+    >>> hf = kbmock.HeaderFactory.from_primary_template()
+    >>> hf.mock()
+    [EXTNAME = 'PRIMARY '
+    NAXIS   =                    0
+    BITPIX  =                    8
+    DATE-OBS= '2021-03-19T00:27:21.140552'
+    NEXTEND =                    3
+    OBS-LAT =              -30.166
+    OBS-LONG=              -70.814
+    OBS-ELEV=                 2200
+    OBSERVAT= 'CTIO    '                                                            ]
+    >>> hf = kbmock.HeaderFactory.from_ext_template()
+    >>> hf.mock()
+    [NAXIS   =                    2
+    NAXIS1  =                 2048
+    NAXIS2  =                 4096
+    CRPIX1  =               1024.0 / Pixel coordinate of reference point
+    CRPIX2  =               2048.0 / Pixel coordinate of reference point
+    BITPIX  =                   32
+    WCSAXES =                    2 / Number of coordinate axes
+    PC1_1   = -5.5555555555556E-05 / Coordinate transformation matrix element
+    PC2_2   =  5.5555555555556E-05 / Coordinate transformation matrix element
+    CDELT1  =                  1.0 / [deg] Coordinate increment at reference point
+    CDELT2  =                  1.0 / [deg] Coordinate increment at reference point
+    CUNIT1  = 'deg'                / Units of coordinate increment and value
+    CUNIT2  = 'deg'                / Units of coordinate increment and value
+    CTYPE1  = 'RA---TAN'           / Right ascension, gnomonic projection
+    CTYPE2  = 'DEC--TAN'           / Declination, gnomonic projection
+    CRVAL1  =                351.0 / [deg] Coordinate value at reference point
+    CRVAL2  =                 -5.0 / [deg] Coordinate value at reference point
+    LONPOLE =                180.0 / [deg] Native longitude of celestial pole
+    LATPOLE =                 -5.0 / [deg] Native latitude of celestial pole
+    MJDREF  =                  0.0 / [d] MJD of fiducial time
+    RADESYS = 'ICRS'               / Equatorial coordinate system                   ]
+    """
     primary_template = {
         "EXTNAME": "PRIMARY",
         "NAXIS": 0,
@@ -132,10 +306,15 @@ class HeaderFactory:
         "OBS-ELEV": 2200,
         "OBSERVAT": "CTIO",
     }
+    """Template for the Primary header content."""
 
     ext_template = {"NAXIS": 2, "NAXIS1": 2048, "NAXIS2": 4096, "CRPIX1": 1024, "CRPIX2": 2048, "BITPIX": 32}
+    """Template of an image-like extension header."""
 
     def __validate_mutables(self):
+        """Validate number of mutables is number of callbacks, and that designated
+        mutable cards exist in the given header template.
+        """
         # !xor
         if bool(self.mutables) != bool(self.callbacks):
             raise ValueError(
@@ -176,6 +355,18 @@ def __init__(self, metadata, mutables=None, callbacks=None, has_wcs=False, wcs_f
         self.counter = 0
 
     def mock(self, n=1):
+        """Mocks headers, executing callbacks and creating WCS as necessary.
+
+        Parameters
+        ----------
+        n : `int`
+            Number of headers to mock.
+
+        Returns
+        -------
+        headers : `list[Header]`
+            Mocked headers.
+        """
         headers = []
         # This can't be vectorized because callbacks may share global state
         for i in range(n):
@@ -187,15 +378,36 @@ def mock(self, n=1):
                     for i, mutable in enumerate(self.mutables):
                         header[mutable] = self.callbacks[i](header[mutable])
             if self.has_wcs:
-                header = self.wcs_factory.mock(header)
+                self.wcs_factory.update_headers([header])
             headers.append(header)
             self.counter += 1
         return headers
 
     @classmethod
-    def gen_header(cls, base, overrides, wcs=None):
+    def gen_header(cls, base, overrides=None, wcs=None):
+        """Generate a header from a base template and overrides.
+
+        If a WCS is given, and the header contains only a partially defined
+        WCS, updates only the missing WCS cards and values.
+
+        Parameters
+        ----------
+        base : `Header` or `dict-like`
+            Header or a dict-like base template for the header.
+        overrides : `Header`, `dict-like` or `None`, optional
+            Keys and values that will either be updated or extended to the base
+            template.
+        wcs : `astropy.wcs.WCS` or `None`, optional
+            WCS template to use to update the header values.
+
+        Returns
+        -------
+        header : `astropy.io.fits.Header`
+            A header.
+        """
         header = Header(base)
-        header.update(overrides)
+        if overrides is not None:
+            header.update(overrides)
 
         if wcs is not None:
             # Sync WCS with header + overwrites
@@ -208,12 +420,58 @@ def gen_header(cls, base, overrides, wcs=None):
 
     @classmethod
     def from_primary_template(cls, overrides=None, mutables=None, callbacks=None):
+        """Create a header assuming the default template of a PRIMARY header.
+
+        Override, or extend the default template with keys and values in override.
+        Attach callbacks to mutable cards.
+
+        Parameters
+        ----------
+        overrides : `dict-like` or `None`, optional
+            A header, or different dict-like, object used to override the base
+            template keys and values.
+        mutables : `list[str]` or `None`, optional
+            List of card keys designated as mutable.
+        callbacks : `list[callable]` or `None`, optional
+            List of callable functions or classes that match the mutables.
+
+        Returns
+        -------
+        factory : `HeaderFactory`
+            Header factory.
+        """
         hdr = cls.gen_header(base=cls.primary_template, overrides=overrides)
         return cls(hdr, mutables, callbacks)
 
     @classmethod
     def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, shape=None,
                           wcs=None):
+        """Create an extension header assuming the default template of an image
+        like header.
+
+        Override, or extend the default template with keys and values in override.
+        Attach callbacks to mutable cards.
+
+        Parameters
+        ----------
+        overrides : `dict-like` or `None`, optional
+            A header, or different dict-like, object used to override the base
+            template keys and values.
+        mutables : `list[str]` or `None`, optional
+            List of card keys designated as mutable.
+        callbacks : `list[callable]` or `None`, optional
+            List of callable functions or classes that match the mutables.
+        shape : `tuple` or `None`, optional
+            Update the template description of data dimensions and the reference
+            pixel.
+        wcs : `astropy.wcs.WCS` or `None`, optional
+            WCS Factory to use.
+
+        Returns
+        -------
+        factory : `HeaderFactory`
+            Header factory.
+        """
         ext_template = cls.ext_template.copy()
 
         if shape is not None:
@@ -227,6 +485,41 @@ def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, shape=
 
 
 class ArchivedHeader(HeaderFactory):
+    """Archived header factory.
+
+    Archived headers are those that were produced with the modified version of
+    AstroPy's ``fitsheader`` utility available in this module. See
+
+        archiveheaders -h
+
+    for more details. To produce an archive, with KBMOD installed, execute the
+    following:
+
+        archiveheaders *fits > archive.ecsv | tar -cf headers.tar.bz2 archive.ecsv
+
+    Attributes
+    ----------
+    lexical_type_map : `dict`
+        A map between the serialized names of built-in types and the built-in
+        types. Used to cast the serialized card values before creating a Header.
+    compression : `str`
+        By default it's assumed the TAR archive was compressed with the ``bz2``
+        compression algorithm.
+    format : `str`
+        The format in which the file of serialized header cards was written in.
+        An AstroPy By default, this is assumed to be ``ascii.ecsv``
+
+    Parameters
+    ----------
+    archive_name : `str`
+        Name of the TAR archive containing serialized headers.
+    fname : `str`
+        Name of the file, within the archive, containing the headers.
+    external : `bool`
+        When `True`, file will be searched for relative to the current working
+        directory. Otherwise, the file is searched for within the header archive
+        provided with this module.
+    """
     # will almost never be anything else. Rather, it would be a miracle if it
     # were something else, since FITS standard shouldn't allow it. Further
     # casting by some packages will always be casting implemented in terms of
@@ -240,26 +533,43 @@ class ArchivedHeader(HeaderFactory):
     """Map between type names and types themselves."""
 
     compression = "bz2"
+    """Compression used to compress the archived headers."""
 
     format = "ascii.ecsv"
+    """Format of the archive, and AstroPy'S ASCII module valid identifier of a format."""
 
-    def __init__(self, archive_name, fname):
+    def __init__(self, archive_name, fname, external=False):
         super().__init__({})
-        self.table = header_archive_to_table(archive_name, fname, self.compression, self.format)
+        self.table = header_archive_to_table(archive_name, fname, self.compression, self.format, external=external)
 
         # Create HDU groups for easier iteration
         self.table = self.table.group_by("filename")
         self.n_hdus = len(self.table)
 
-    def lexical_cast(self, value, format):
+    def lexical_cast(self, value, vtype):
         """Cast str literal of a type to the type itself. Supports just
         the builtin Python types.
         """
-        if format in self.lexical_type_map:
-            return self.lexical_type_map[format](value)
+        if vtype in self.lexical_type_map:
+            return self.lexical_type_map[vtype](value)
         return value
 
     def get_item(self, group_idx, hdr_idx):
+        """Get an extension of an HDUList within the archive without
+        incrementing the mocking counter.
+
+        Parameters
+        ----------
+        group_idx : `int`
+            Index of the HDUList to fetch from the archive.
+        hdr_idx : `int`
+            Index of the extension within the HDUList.
+
+        Returns
+        -------
+        header : `Header`
+            Header of the given extension of the targeted HDUList.
+        """
         header = Header()
         # this is equivalent to one hdulist worth of headers
         group = self.table.groups[group_idx]
@@ -271,6 +581,18 @@ def get_item(self, group_idx, hdr_idx):
         return header
 
     def get(self, group_idx):
+        """Get an HDUList within the archive without incrementing the mocking counter.
+
+        Parameters
+        ----------
+        group_idx : `int`
+            Index of the HDUList to fetch from the archive.
+
+        Returns
+        -------
+        headers : `list[Header]`
+            All headers of the targeted HDUList.
+        """
         headers = []
         # this is a bit repetitive but it saves recreating
         # groups for one HDUL-equivalent many times
@@ -288,6 +610,18 @@ def get(self, group_idx):
         return headers
 
     def mock(self, n=1):
+        """Mock all headers within n HDULists.
+
+        Parameters
+        ----------
+        n : `int`
+            Number of HDUList units which headers we want mocked.
+
+        Returns
+        -------
+        headers : `list[list[Header]]`
+            A list of containing headers of each extension of an HDUList.
+        """
         res = []
         for _ in range(n):
             res.append(self.get(self.counter))
diff --git a/src/kbmod/mocking/data/headers_archive.tar.bz2 b/src/kbmod/mocking/headers_archive.tar.bz2
similarity index 100%
rename from src/kbmod/mocking/data/headers_archive.tar.bz2
rename to src/kbmod/mocking/headers_archive.tar.bz2
diff --git a/src/kbmod/mocking/utils.py b/src/kbmod/mocking/utils.py
index d79a48bcb..40f0af93e 100644
--- a/src/kbmod/mocking/utils.py
+++ b/src/kbmod/mocking/utils.py
@@ -4,20 +4,22 @@
 from astropy.table import Table, MaskedColumn
 
 
-def get_absolute_data_path(file_or_directory):
-    test_dir = path.abspath(path.dirname(__file__))
-    data_dir = path.join(test_dir, "data")
-    return path.join(data_dir, file_or_directory)
-
-
-def get_absolute_demo_data_path(file_or_directory):
-    project_root_dir = path.abspath(path.dirname(path.dirname(__file__)))
-    data_dir = path.join(project_root_dir, "data")
-    return path.join(data_dir, file_or_directory)
-
-
-def header_archive_to_table(archive_path, fname, compression, format):
-    archive_path = get_absolute_data_path(archive_path)
+#def get_absolute_data_path(file_or_directory):
+#    test_dir = path.abspath(path.dirname(__file__))
+#    data_dir = path.join(test_dir, "archived_data")
+#    return path.join(data_dir, file_or_directory)
+#
+#
+#def get_absolute_demo_data_path(file_or_directory):
+#    project_root_dir = path.abspath(path.dirname(path.dirname(__file__)))
+#    data_dir = path.join(project_root_dir, "archived_data")
+#    return path.join(data_dir, file_or_directory)
+
+
+def header_archive_to_table(archive_path, fname, compression, format, external=True):
+    if not external:
+        mocking_dir = path.abspath(path.dirname(__file__))
+        archive_path = path.join(mocking_dir, archive_path)
     with tarfile.open(archive_path, f"r:{compression}") as archive:
         tblfile = archive.extractfile(fname)
         table = Table.read(tblfile.read().decode(), format=format)
diff --git a/tests/dump_headers.py b/tests/dump_headers.py
deleted file mode 100644
index 74fbc60f4..000000000
--- a/tests/dump_headers.py
+++ /dev/null
@@ -1,500 +0,0 @@
-# Modified from the original Astropy code to add a card format to the
-# tabular output. All rights belong to the original authors.
-
-# Licensed under a 3-clause BSD style license - see LICENSE.rst
-"""
-``fitsheader`` is a command line script based on astropy.io.fits for printing
-the header(s) of one or more FITS file(s) to the standard output in a human-
-readable format.
-
-Example uses of fitsheader:
-
-1. Print the header of all the HDUs of a .fits file::
-
-    $ fitsheader filename.fits
-
-2. Print the header of the third and fifth HDU extension::
-
-    $ fitsheader --extension 3 --extension 5 filename.fits
-
-3. Print the header of a named extension, e.g. select the HDU containing
-   keywords EXTNAME='SCI' and EXTVER='2'::
-
-    $ fitsheader --extension "SCI,2" filename.fits
-
-4. Print only specific keywords::
-
-    $ fitsheader --keyword BITPIX --keyword NAXIS filename.fits
-
-5. Print keywords NAXIS, NAXIS1, NAXIS2, etc using a wildcard::
-
-    $ fitsheader --keyword NAXIS* filename.fits
-
-6. Dump the header keywords of all the files in the current directory into a
-   machine-readable csv file::
-
-    $ fitsheader --table ascii.csv *.fits > keywords.csv
-
-7. Specify hierarchical keywords with the dotted or spaced notation::
-
-    $ fitsheader --keyword ESO.INS.ID filename.fits
-    $ fitsheader --keyword "ESO INS ID" filename.fits
-
-8. Compare the headers of different fits files, following ESO's ``fitsort``
-   format::
-
-    $ fitsheader --fitsort --extension 0 --keyword ESO.INS.ID *.fits
-
-9. Same as above, sorting the output along a specified keyword::
-
-    $ fitsheader -f -s DATE-OBS -e 0 -k DATE-OBS -k ESO.INS.ID *.fits
-
-10. Sort first by OBJECT, then DATE-OBS::
-
-    $ fitsheader -f -s OBJECT -s DATE-OBS *.fits
-
-Note that compressed images (HDUs of type
-:class:`~astropy.io.fits.CompImageHDU`) really have two headers: a real
-BINTABLE header to describe the compressed data, and a fake IMAGE header
-representing the image that was compressed. Astropy returns the latter by
-default. You must supply the ``--compressed`` option if you require the real
-header that describes the compression.
-
-With Astropy installed, please run ``fitsheader --help`` to see the full usage
-documentation.
-"""
-
-import argparse
-import sys
-
-import numpy as np
-
-from astropy import __version__, log
-from astropy.io import fits
-
-DESCRIPTION = """
-Print the header(s) of a FITS file. Optional arguments allow the desired
-extension(s), keyword(s), and output format to be specified.
-Note that in the case of a compressed image, the decompressed header is
-shown by default.
-
-This script is part of the Astropy package. See
-https://docs.astropy.org/en/latest/io/fits/usage/scripts.html#module-astropy.io.fits.scripts.fitsheader
-for further documentation.
-""".strip()
-
-
-class ExtensionNotFoundException(Exception):
-    """Raised if an HDU extension requested by the user does not exist."""
-
-
-class HeaderFormatter:
-    """Class to format the header(s) of a FITS file for display by the
-    `fitsheader` tool; essentially a wrapper around a `HDUList` object.
-
-    Example usage:
-    fmt = HeaderFormatter('/path/to/file.fits')
-    print(fmt.parse(extensions=[0, 3], keywords=['NAXIS', 'BITPIX']))
-
-    Parameters
-    ----------
-    filename : str
-        Path to a single FITS file.
-    verbose : bool
-        Verbose flag, to show more information about missing extensions,
-        keywords, etc.
-
-    Raises
-    ------
-    OSError
-        If `filename` does not exist or cannot be read.
-    """
-
-    def __init__(self, filename, verbose=True):
-        self.filename = filename
-        self.verbose = verbose
-        self._hdulist = fits.open(filename)
-
-    def parse(self, extensions=None, keywords=None, compressed=False):
-        """Returns the FITS file header(s) in a readable format.
-
-        Parameters
-        ----------
-        extensions : list of int or str, optional
-            Format only specific HDU(s), identified by number or name.
-            The name can be composed of the "EXTNAME" or "EXTNAME,EXTVER"
-            keywords.
-
-        keywords : list of str, optional
-            Keywords for which the value(s) should be returned.
-            If not specified, then the entire header is returned.
-
-        compressed : bool, optional
-            If True, shows the header describing the compression, rather than
-            the header obtained after decompression. (Affects FITS files
-            containing `CompImageHDU` extensions only.)
-
-        Returns
-        -------
-        formatted_header : str or astropy.table.Table
-            Traditional 80-char wide format in the case of `HeaderFormatter`;
-            an Astropy Table object in the case of `TableHeaderFormatter`.
-        """
-        # `hdukeys` will hold the keys of the HDUList items to display
-        if extensions is None:
-            hdukeys = range(len(self._hdulist))  # Display all by default
-        else:
-            hdukeys = []
-            for ext in extensions:
-                try:
-                    # HDU may be specified by number
-                    hdukeys.append(int(ext))
-                except ValueError:
-                    # The user can specify "EXTNAME" or "EXTNAME,EXTVER"
-                    parts = ext.split(",")
-                    if len(parts) > 1:
-                        extname = ",".join(parts[0:-1])
-                        extver = int(parts[-1])
-                        hdukeys.append((extname, extver))
-                    else:
-                        hdukeys.append(ext)
-
-        # Having established which HDUs the user wants, we now format these:
-        return self._parse_internal(hdukeys, keywords, compressed)
-
-    def _parse_internal(self, hdukeys, keywords, compressed):
-        """The meat of the formatting; in a separate method to allow overriding."""
-        result = []
-        for idx, hdu in enumerate(hdukeys):
-            try:
-                cards = self._get_cards(hdu, keywords, compressed)
-            except ExtensionNotFoundException:
-                continue
-
-            if idx > 0:  # Separate HDUs by a blank line
-                result.append("\n")
-            result.append(f"# HDU {hdu} in {self.filename}:\n")
-            for c in cards:
-                result.append(f"{c}\n")
-        return "".join(result)
-
-    def _get_cards(self, hdukey, keywords, compressed):
-        """Returns a list of `astropy.io.fits.card.Card` objects.
-
-        This function will return the desired header cards, taking into
-        account the user's preference to see the compressed or uncompressed
-        version.
-
-        Parameters
-        ----------
-        hdukey : int or str
-            Key of a single HDU in the HDUList.
-
-        keywords : list of str, optional
-            Keywords for which the cards should be returned.
-
-        compressed : bool, optional
-            If True, shows the header describing the compression.
-
-        Raises
-        ------
-        ExtensionNotFoundException
-            If the hdukey does not correspond to an extension.
-        """
-        # First we obtain the desired header
-        try:
-            if compressed:
-                # In the case of a compressed image, return the header before
-                # decompression (not the default behavior)
-                header = self._hdulist[hdukey]._header
-            else:
-                header = self._hdulist[hdukey].header
-        except (IndexError, KeyError):
-            message = f"{self.filename}: Extension {hdukey} not found."
-            if self.verbose:
-                log.warning(message)
-            raise ExtensionNotFoundException(message)
-
-        if not keywords:  # return all cards
-            cards = header.cards
-        else:  # specific keywords are requested
-            cards = []
-            for kw in keywords:
-                try:
-                    crd = header.cards[kw]
-                    if isinstance(crd, fits.card.Card):  # Single card
-                        cards.append(crd)
-                    else:  # Allow for wildcard access
-                        cards.extend(crd)
-                except KeyError:  # Keyword does not exist
-                    if self.verbose:
-                        log.warning(f"{self.filename} (HDU {hdukey}): Keyword {kw} not found.")
-        return cards
-
-    def close(self):
-        self._hdulist.close()
-
-
-class TableHeaderFormatter(HeaderFormatter):
-    """Class to convert the header(s) of a FITS file into a Table object.
-    The table returned by the `parse` method will contain four columns:
-    filename, hdu, keyword, and value.
-
-    Subclassed from HeaderFormatter, which contains the meat of the formatting.
-    """
-
-    def _parse_internal(self, hdukeys, keywords, compressed):
-        """Method called by the parse method in the parent class."""
-        tablerows = []
-        for hdu in hdukeys:
-            try:
-                for card in self._get_cards(hdu, keywords, compressed):
-                    tablerows.append(
-                        {
-                            "filename": self.filename,
-                            "hdu": hdu,
-                            "keyword": card.keyword,
-                            "value": str(card.value),
-                            "format": type(card.value).__name__,
-                        }
-                    )
-            except ExtensionNotFoundException:
-                pass
-
-        if tablerows:
-            from astropy import table
-
-            return table.Table(tablerows)
-        return None
-
-
-def print_headers_traditional(args):
-    """Prints FITS header(s) using the traditional 80-char format.
-
-    Parameters
-    ----------
-    args : argparse.Namespace
-        Arguments passed from the command-line as defined below.
-    """
-    for idx, filename in enumerate(args.filename):  # support wildcards
-        if idx > 0 and not args.keyword:
-            print()  # print a newline between different files
-
-        formatter = None
-        try:
-            formatter = HeaderFormatter(filename)
-            print(formatter.parse(args.extensions, args.keyword, args.compressed), end="")
-        except OSError as e:
-            log.error(str(e))
-        finally:
-            if formatter:
-                formatter.close()
-
-
-def print_headers_as_table(args):
-    """Prints FITS header(s) in a machine-readable table format.
-
-    Parameters
-    ----------
-    args : argparse.Namespace
-        Arguments passed from the command-line as defined below.
-    """
-    tables = []
-    # Create a Table object for each file
-    for filename in args.filename:  # Support wildcards
-        formatter = None
-        try:
-            formatter = TableHeaderFormatter(filename)
-            tbl = formatter.parse(args.extensions, args.keyword, args.compressed)
-            if tbl:
-                tables.append(tbl)
-        except OSError as e:
-            log.error(str(e))  # file not found or unreadable
-        finally:
-            if formatter:
-                formatter.close()
-
-    # Concatenate the tables
-    if len(tables) == 0:
-        return False
-    elif len(tables) == 1:
-        resulting_table = tables[0]
-    else:
-        from astropy import table
-
-        resulting_table = table.vstack(tables)
-    # Print the string representation of the concatenated table
-    resulting_table.write(sys.stdout, format=args.table)
-
-
-def print_headers_as_comparison(args):
-    """Prints FITS header(s) with keywords as columns.
-
-    This follows the dfits+fitsort format.
-
-    Parameters
-    ----------
-    args : argparse.Namespace
-        Arguments passed from the command-line as defined below.
-    """
-    from astropy import table
-
-    tables = []
-    # Create a Table object for each file
-    for filename in args.filename:  # Support wildcards
-        formatter = None
-        try:
-            formatter = TableHeaderFormatter(filename, verbose=False)
-            tbl = formatter.parse(args.extensions, args.keyword, args.compressed)
-            if tbl:
-                # Remove empty keywords
-                tbl = tbl[np.where(tbl["keyword"] != "")]
-            else:
-                tbl = table.Table([[filename]], names=("filename",))
-            tables.append(tbl)
-        except OSError as e:
-            log.error(str(e))  # file not found or unreadable
-        finally:
-            if formatter:
-                formatter.close()
-
-    # Concatenate the tables
-    if len(tables) == 0:
-        return False
-    elif len(tables) == 1:
-        resulting_table = tables[0]
-    else:
-        resulting_table = table.vstack(tables)
-
-    # If we obtained more than one hdu, merge hdu and keywords columns
-    hdus = resulting_table["hdu"]
-    if np.ma.isMaskedArray(hdus):
-        hdus = hdus.compressed()
-    if len(np.unique(hdus)) > 1:
-        for tab in tables:
-            new_column = table.Column([f"{row['hdu']}:{row['keyword']}" for row in tab])
-            tab.add_column(new_column, name="hdu+keyword")
-        keyword_column_name = "hdu+keyword"
-    else:
-        keyword_column_name = "keyword"
-
-    # Check how many hdus we are processing
-    final_tables = []
-    for tab in tables:
-        final_table = [table.Column([tab["filename"][0]], name="filename")]
-        if "value" in tab.colnames:
-            for row in tab:
-                if row["keyword"] in ("COMMENT", "HISTORY"):
-                    continue
-                final_table.append(table.Column([row["value"]], name=row[keyword_column_name]))
-        final_tables.append(table.Table(final_table))
-    final_table = table.vstack(final_tables)
-    # Sort if requested
-    if args.sort:
-        final_table.sort(args.sort)
-    # Reorganise to keyword by columns
-    final_table.pprint(max_lines=-1, max_width=-1)
-
-
-if __name__ == "__main__":
-    """This is the main function called by the `fitsheader` script."""
-    parser = argparse.ArgumentParser(
-        description=DESCRIPTION, formatter_class=argparse.RawDescriptionHelpFormatter
-    )
-
-    parser.add_argument("--version", action="version", version=f"%(prog)s {__version__}")
-
-    parser.add_argument(
-        "-e",
-        "--extension",
-        metavar="HDU",
-        action="append",
-        dest="extensions",
-        help=(
-            "specify the extension by name or number; this argument can "
-            "be repeated to select multiple extensions"
-        ),
-    )
-    parser.add_argument(
-        "-k",
-        "--keyword",
-        metavar="KEYWORD",
-        action="append",
-        type=str,
-        help=(
-            "specify a keyword; this argument can be repeated to select "
-            "multiple keywords; also supports wildcards"
-        ),
-    )
-    mode_group = parser.add_mutually_exclusive_group()
-    mode_group.add_argument(
-        "-t",
-        "--table",
-        nargs="?",
-        default=False,
-        metavar="FORMAT",
-        help=(
-            "print the header(s) in machine-readable table format; the "
-            'default format is "ascii.fixed_width" (can be "ascii.csv", '
-            '"ascii.html", "ascii.latex", "fits", etc)'
-        ),
-    )
-    mode_group.add_argument(
-        "-f",
-        "--fitsort",
-        action="store_true",
-        help=("print the headers as a table with each unique " "keyword in a given column (fitsort format) "),
-    )
-    parser.add_argument(
-        "-s",
-        "--sort",
-        metavar="SORT_KEYWORD",
-        action="append",
-        type=str,
-        help=(
-            "sort output by the specified header keywords, can be repeated to "
-            "sort by multiple keywords; Only supported with -f/--fitsort"
-        ),
-    )
-    parser.add_argument(
-        "-c",
-        "--compressed",
-        action="store_true",
-        help=(
-            "for compressed image data, show the true header which describes "
-            "the compression rather than the data"
-        ),
-    )
-    parser.add_argument(
-        "filename",
-        nargs="+",
-        help="path to one or more files; wildcards are supported",
-    )
-    args = parser.parse_args()
-    # If `--table` was used but no format specified,
-    # then use ascii.fixed_width by default
-    if args.table is None:
-        args.table = "ascii.fixed_width"
-
-    if args.sort:
-        args.sort = [key.replace(".", " ") for key in args.sort]
-        if not args.fitsort:
-            log.error("Sorting with -s/--sort is only supported in conjunction with" " -f/--fitsort")
-            # 2: Unix error convention for command line syntax
-            sys.exit(2)
-
-    if args.keyword:
-        args.keyword = [key.replace(".", " ") for key in args.keyword]
-
-    # Now print the desired headers
-    try:
-        if args.table:
-            print_headers_as_table(args)
-        elif args.fitsort:
-            print_headers_as_comparison(args)
-        else:
-            print_headers_traditional(args)
-    except OSError:
-        # A 'Broken pipe' OSError may occur when stdout is closed prematurely,
-        # eg. when calling `fitsheader file.fits | head`. We let this pass.
-        pass
diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py
index 9f5ea7358..f6f645c5b 100644
--- a/tests/test_end_to_end.py
+++ b/tests/test_end_to_end.py
@@ -38,6 +38,7 @@ def setUp(self):
         self.factory = kbmock.EmptyFits()
 
     def test_empty(self):
+        """Test no detections are found on empty images."""
         hduls = self.factory.mock(n=10)
 
         # create the most permissive search configs you can come up with
@@ -61,6 +62,7 @@ def test_empty(self):
         self.assertTrue(len(results) == 0)
 
     def test_static_objects(self):
+        """Test no detections are found on images containing static objects."""
         src_cat = kbmock.SourceCatalog.from_defaults(seed=100)
         factory = kbmock.SimpleFits(src_cat=src_cat)
         hduls = factory.mock(10)
@@ -111,7 +113,28 @@ def setUp(self):
             }
         )
 
-    def xmatch_best(self, obj, results, match_cols={"x_mean": "x", "y_mean": "y", "vx": "vx", "vy": "vy"}):
+    def xmatch_best(self, obj, results,
+                    match_cols={"x_mean": "x", "y_mean": "y", "vx": "vx", "vy": "vy"}):
+        """Finds the result that minimizes the L2 distance to the target object.
+
+        Parameters
+        ----------
+        obj : `astropy.table.Row`
+            Row, or a table with single entry, containing the target object.
+        results : `astropy.table.Table`
+            Table of objects from which the closest matching one will be returned.
+        match_cols : `dict`, optional
+            Dictionary of column names on which to perform the matching. Keys
+            of the dictionary are columns from ``obj`` and values of the dict
+            are columns from ``results``.
+
+        Returns
+        -------
+        result : `astropy.table.Row`
+            Best matching result
+        distances: `np.array`
+            Array of calculated L2 distances of ``obj`` to all given results.
+        """
         objk, resk = [], []
         for k, v in match_cols.items():
             if k in obj.columns and v in results.table.columns:
@@ -126,6 +149,28 @@ def xmatch_best(self, obj, results, match_cols={"x_mean": "x", "y_mean": "y", "v
 
     def assertResultValuesWithinSpec(self, expected, result, spec,
                                      match_cols={"x_mean": "x", "y_mean": "y", "vx": "vx", "vy": "vy"}):
+        """Asserts expected object matches the given result object within
+        specification.
+
+        Parameters
+        ----------
+        expected : `astropy.table.Row`
+            Row, or table with single entry, containing the target object.
+        result : `astropy.table.Row`
+            Row, or table with single entry, containing the found object.
+        spec : `float`
+            Specification of maximum deviation of the expected values from the
+            found resulting values. For example, a spec of 3 means results can
+            be 3 or less pixels away from the expected position.
+        match_cols : `dict`, optional
+            Dictionary of column names on which to perform the matching. Keys
+            of the dictionary are columns from ``obj`` and values of the dict
+            are columns from ``results``.
+
+        Raises
+        -------
+        AssertionError - if comparison fails.
+        """
         for ekey, rkey in match_cols.items():
             info = (
                 f"\n Expected: \n {expected[tuple(match_cols.keys())]} \n"
@@ -134,6 +179,20 @@ def assertResultValuesWithinSpec(self, expected, result, spec,
             self.assertLessEqual(abs(expected[ekey] - result[rkey]), spec, info)
 
     def run_single_search(self, data, expected, spec=5):
+        """Runs a KBMOD search on given data and tests the results lie within
+        specification from the expected.
+
+        Parameters
+        ----------
+        data : `list[str]` or `list[astropy.io.fits.HDUList]`
+            List of targets processable by the TestDataStandardizer.
+        expected : `kbmod.mocking.ObjectCatalog`
+            Object catalog expected to be retrieved from the run.
+        spec : `float`
+            Specification of maximum deviation of the expected values from the
+            found resulting values. For example, a spec of 3 means results can
+            be 3 or less pixels away from the expected position.
+        """
         ic = ImageCollection.fromTargets(data, force="TestDataStd")
         wu = ic.toWorkUnit(search_config=self.config)
         results = SearchRunner().run_search_from_work_unit(wu)
@@ -145,6 +204,7 @@ def run_single_search(self, data, expected, spec=5):
             self.assertResultValuesWithinSpec(obj, res, spec)
 
     def test_exact_motion(self):
+        """Test exact searches are recovered in all 8 cardinal directions."""
         search_vs = list(itertools.product([-20, 0, 20], repeat=2))
         search_vs.remove((0, 0))
         for (vx, vy) in search_vs:
@@ -162,6 +222,7 @@ def test_exact_motion(self):
                 self.run_single_search(hduls, obj_cat, 1)
 
     def test_random_motion(self):
+        """Repeat searches for randomly inserted objects."""
         # Mock the data and repeat tests. The random catalog
         # creation guarantees a diverse set of changing test values
         for i in range(self.repeat_n_times):
@@ -171,7 +232,8 @@ def test_random_motion(self):
                 hduls = factory.mock(n=self.n_imgs)
                 self.run_single_search(hduls, obj_cat)
 
-    def test_reprojected_search(self):
+    def test_resampled_search(self):
+        """Search for objects in a set of resampled images; randomly dithered pointings and orientations."""
         # 0. Setup
         self.shape = (500, 500)
         self.start_pos = (10, 10)  # (ra, dec) in deg
@@ -204,7 +266,7 @@ def test_reprojected_search(self):
             pixscale=pixscale,
             dither_pos=True,
             dither_rot=True,
-            dither_amplitudes=(0.001, 0.001, 0.01)
+            dither_amplitudes=(0.001, 0.001, 10)
         )
 
         prim_hdr_factory = kbmock.HeaderFactory.from_primary_template(
@@ -283,27 +345,6 @@ def test_reprojected_search(self):
         self.assertGreaterEqual(len(results), 1)
         self.assertResultValuesWithinSpec(best_cat, best_res, 10)
 
-#    def test_diffim_mocks(self):
-#        src_cat = kbmock.SourceCatalog.from_defaults()
-#        obj_cat = kbmock.ObjectCatalog.from_defaults(self.param_ranges, n=1)
-#        factory = kbmock.DECamImdiff.from_defaults(with_data=True, src_cat=src_cat, obj_cat=obj_cat)
-#        hduls = factory.mock(n=self.n_imgs)
-#
-#
-#        ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
-#        wu = ic.toWorkUnit(search_config=self.config)
-#        results = SearchRunner().run_search_from_work_unit(wu)
-#
-#        # Run tests
-#        self.assertGreaterEqual(len(results), 1)
-#        for res in results:
-#            diff = abs(obj_cat.table["y_mean"] - res["y"])
-#            obj = obj_cat.table[diff == diff.min()]
-#            self.assertLessEqual(abs(obj["x_mean"] - res["x"]), 5)
-#            self.assertLessEqual(abs(obj["y_mean"] - res["y"]), 5)
-#            self.assertLessEqual(abs(obj["vx"] - res["vx"]), 5)
-#            self.assertLessEqual(abs(obj["vy"] - res["vy"]), 5)
-
 
 ####
 
diff --git a/tests/test_mocking.py b/tests/test_mocking.py
index b03ce580e..b999c6fb3 100644
--- a/tests/test_mocking.py
+++ b/tests/test_mocking.py
@@ -92,6 +92,7 @@ def test(self):
         self.assertEqual(step_t.to("day").value, 1.0)
 
     def test_static_src_cat(self):
+        """Test that static source catalog works and is correctly rendered."""
         src_cat = kbmock.SourceCatalog.from_defaults()
         src_cat2 = kbmock.SourceCatalog.from_defaults()
         self.assertEqual(src_cat.config["mode"], "static")
@@ -121,6 +122,18 @@ def test_static_src_cat(self):
         self.assertGreaterEqual(hdul["IMAGE"].data[y, x].min(), 90)
 
     def validate_cat_render(self, hduls, cats, expected_gte=90):
+        """Validate that catalog objects appear in the given images.
+
+        Parameters
+        ----------
+        hduls : `list[astropy.io.fits.HDUList]`
+            List of FITS files to check.
+        cats : `list[astropy.table.Table]`
+            List of catalog realizations containing the coordinate of objects
+            to check for.
+        expected_gte : `float`
+            Expected minimal value of the pixel at the object's location.
+        """
         for hdul, cat in zip(hduls, cats):
             x = np.round(cat["x_mean"].data).astype(int)
             y = np.round(cat["y_mean"].data).astype(int)
@@ -128,6 +141,7 @@ def validate_cat_render(self, hduls, cats, expected_gte=90):
             self.assertGreaterEqual(hdul["VARIANCE"].data[y, x].min(), expected_gte)
 
     def test_progressive_obj_cat(self):
+        """Test progressive catalog renders properly."""
         obj_cat = kbmock.ObjectCatalog.from_defaults()
         obj_cat2 = kbmock.ObjectCatalog.from_defaults()
         self.assertEqual(obj_cat.config["mode"], "progressive")
@@ -159,6 +173,7 @@ def test_progressive_obj_cat(self):
         self.validate_cat_render(hduls, cats)
 
     def test_folding_obj_cat(self):
+        """Test folding catalog renders properly."""
         # Set up shared values for the whole setup
         # like starting positions of object and timestamps
         start_x = np.ones((self.n_obj,)) * 10
@@ -189,13 +204,12 @@ def test_folding_obj_cat(self):
         factory.prim_hdr = prim_hdr_factory
         hduls = factory.mock(n=self.n_imgs)
 
-        # Run tests and ensure we have rendered the object in correct
-        # positions
         obj_cat.reset()
         cats = obj_cat.mock(n=self.n_imgs, t=self.timestamps)
         self.validate_cat_render(hduls, cats)
 
     def test_progressive_sky_cat(self):
+        """Test progressive catalog based on on-sky coordinates."""
         # a 10-50 in x by a 10-90 in y box using default WCS
         #self.shape = (500, 500)
         param_ranges = {
@@ -221,6 +235,7 @@ def test_progressive_sky_cat(self):
         self.validate_cat_render(hduls, cats)
 
     def test_folding_sky_cat(self):
+        """Test folding catalog based on on-sky coordinates."""
         # a 20x20 box in pixels using a default WCS
         start_ra = np.linspace(350.998, 351.002, self.n_obj)
         start_dec = np.linspace(-5.0077, -5.0039, self.n_obj)
@@ -254,6 +269,7 @@ def test_folding_sky_cat(self):
 
     # TODO: move to pytest and mark as xfail
     def test_noise_gen(self):
+        """Test noise renders with expected statistical properties."""
         factory = kbmock.SimpleFits(shape=(1000, 1000), with_noise=True)
         hdul = factory.mock()[0]
         self.assertAlmostEqual(hdul["IMAGE"].data.mean(), 10, 1)

From 53bbb290f42948475420a1feb0612e05e9e73502 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Mon, 19 Aug 2024 13:24:52 -0700
Subject: [PATCH 25/27] blacken

---
 src/kbmod/mocking/callbacks.py |  8 ++--
 src/kbmod/mocking/catalogs.py  |  9 ++--
 src/kbmod/mocking/config.py    |  1 -
 src/kbmod/mocking/data.py      | 16 +++----
 src/kbmod/mocking/fits.py      | 83 +++++++++++++++-----------------
 src/kbmod/mocking/headers.py   | 51 +++++++++++---------
 src/kbmod/mocking/utils.py     |  4 +-
 tests/test_end_to_end.py       | 86 +++++++++++++++++-----------------
 tests/test_mocking.py          | 40 +++++++++-------
 9 files changed, 152 insertions(+), 146 deletions(-)

diff --git a/src/kbmod/mocking/callbacks.py b/src/kbmod/mocking/callbacks.py
index 01c26af34..22d13ea62 100644
--- a/src/kbmod/mocking/callbacks.py
+++ b/src/kbmod/mocking/callbacks.py
@@ -4,10 +4,7 @@
 import astropy.units as u
 
 
-__all__ = [
-    "IncrementObstime",
-    "ObstimeIterator"
-]
+__all__ = ["IncrementObstime", "ObstimeIterator"]
 
 
 class IncrementObstime:
@@ -34,7 +31,9 @@ class IncrementObstime:
     '2021-01-01T00:00:00.000'
     '2021-01-01T01:00:00.000'
     """
+
     default_unit = "day"
+
     def __init__(self, start, dt):
         self.start = Time(start)
         if not isinstance(dt, u.Quantity):
@@ -76,6 +75,7 @@ class ObstimeIterator:
 
     StopIteration
     """
+
     def __init__(self, obstimes, **kwargs):
         self.obstimes = Time(obstimes, **kwargs)
         self.generator = (t for t in obstimes)
diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index 356dd97a7..a8a3e5db1 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -17,7 +17,6 @@
 ]
 
 
-
 def expand_gaussian_cols(cat):
     """Expands columns ``flux`` and ``stddev`` into ``amplitude``, ``x_stddev``
     and ``y_stddev`` assuming the intended catalog model is a symmetric 2D
@@ -254,6 +253,7 @@ class SimpleCatalog:
     0.5965540269678873  2.973654395106214 12.910339381295453     1.0    10.0
     0.2888632416912036 2.5964717040646885 12.688154447591746     1.0    10.0]
     """
+
     default_config = SimpleCatalogConfig
 
     def __init__(self, table, config=None, **kwargs):
@@ -354,8 +354,7 @@ def kind(self, val):
             self._cat_keys = self.config["world_pos_cols"] + self.config["world_vel_cols"]
         else:
             raise ValueError(
-                "Unrecognized coordinate kind. Expected 'world' or 'pixel, got"
-                f"{val} instead."
+                "Unrecognized coordinate kind. Expected 'world' or 'pixel, got" f"{val} instead."
             )
         self._kind = val
 
@@ -476,6 +475,7 @@ class SourceCatalogConfig(SimpleCatalogConfig):
         Rotation of the model's covariance matrix, increases counterclockwise.
         In radians.
     """
+
     param_ranges = {
         "amplitude": [1.0, 10.0],
         "x_mean": [0.0, 4096.0],
@@ -491,6 +491,7 @@ class SourceCatalog(SimpleCatalog):
 
     Coordinates defined in pixel space.
     """
+
     default_config = SourceCatalogConfig
 
 
@@ -515,6 +516,7 @@ class ObjectCatalogConfig(SimpleCatalogConfig):
         Rotation of the model's covariance matrix, increases counterclockwise.
         In radians.
     """
+
     mode = "progressive"
     param_ranges = {
         "amplitude": [0.1, 3.0],
@@ -534,6 +536,7 @@ class ObjectCatalog(SimpleCatalog):
     space and moving in linear fashion with velocity also defined in pixel space.
     The units are relative to the timestep.
     """
+
     default_config = ObjectCatalogConfig
 
     def __init__(self, table, **kwargs):
diff --git a/src/kbmod/mocking/config.py b/src/kbmod/mocking/config.py
index 7af9b97f9..88090b272 100644
--- a/src/kbmod/mocking/config.py
+++ b/src/kbmod/mocking/config.py
@@ -103,4 +103,3 @@ def update(self, conf=None, **kwargs):
     def toDict(self):
         """Return this config as a dict."""
         return self._conf
-
diff --git a/src/kbmod/mocking/data.py b/src/kbmod/mocking/data.py
index cc27201fd..800aac208 100644
--- a/src/kbmod/mocking/data.py
+++ b/src/kbmod/mocking/data.py
@@ -417,7 +417,7 @@ class SimpleVariance(DataFactory):
     def __init__(self, image=None, **kwargs):
         super().__init__(base=image, **kwargs)
         if image is not None:
-            self.base = image / self.config["gain"] + self.config["read_noise"]**2
+            self.base = image / self.config["gain"] + self.config["read_noise"] ** 2
 
     def mock(self, images=None):
         """Mock one or multiple data arrays.
@@ -431,7 +431,7 @@ def mock(self, images=None):
         """
         if images is None:
             return self.base
-        return images / self.config["gain"] + self.config["read_noise"]**2
+        return images / self.config["gain"] + self.config["read_noise"] ** 2
 
 
 class SimpleMaskConfig(DataFactoryConfig):
@@ -641,6 +641,7 @@ class SimpleImage(DataFactory):
             [ 8.768851, 10.201585,  8.870326],
             [10.702058,  9.910087,  9.283925]]], dtype=float32)
     """
+
     default_config = SimpleImageConfig
     """Default configuration."""
 
@@ -994,7 +995,9 @@ def add_noise(cls, images, config):
         images += config["dark_current_gen"](current, size=shape)
 
         # add sky counts
-        images += config["sky_count_gen"](lam=config["sky_level"] * config["gain"], size=shape) / config["gain"]
+        images += (
+            config["sky_count_gen"](lam=config["sky_level"] * config["gain"], size=shape) / config["gain"]
+        )
 
         return images
 
@@ -1028,9 +1031,4 @@ def gen_base_image(cls, config=None, src_cat=None):
 
     def __init__(self, image=None, src_cat=None, obj_cat=None, **kwargs):
         conf = self.default_config(**kwargs)
-        super().__init__(
-            image=self.gen_base_image(conf),
-            src_cat=src_cat,
-            obj_cat=obj_cat,
-            **conf
-        )
+        super().__init__(image=self.gen_base_image(conf), src_cat=src_cat, obj_cat=obj_cat, **conf)
diff --git a/src/kbmod/mocking/fits.py b/src/kbmod/mocking/fits.py
index 35a3c1ee8..bc492335f 100644
--- a/src/kbmod/mocking/fits.py
+++ b/src/kbmod/mocking/fits.py
@@ -22,6 +22,7 @@
 
 class NoneFactory:
     """Factory that returns `None`."""
+
     def mock(self, n):
         return [
             None,
@@ -77,6 +78,7 @@ class EmptyFits:
         usually be shared, so leaving them immutable reduces the memory footprint
         and time it takes to mock large images.
     """
+
     def __init__(
         self,
         header=None,
@@ -87,9 +89,7 @@ def __init__(
         editable_masks=False,
     ):
         self.prim_hdr = HeaderFactory.from_primary_template(
-            overrides=header,
-            mutables=["DATE-OBS"],
-            callbacks=[IncrementObstime(start=start_t, dt=step_t)]
+            overrides=header, mutables=["DATE-OBS"], callbacks=[IncrementObstime(start=start_t, dt=step_t)]
         )
 
         self.img_hdr = HeaderFactory.from_ext_template({"EXTNAME": "IMAGE"}, shape=shape)
@@ -121,12 +121,14 @@ def mock(self, n=1):
             prim_hdrs, img_hdrs, var_hdrs, mask_hdrs, images, variances, masks
         ):
             hduls.append(
-                HDUList(hdus=[
-                    PrimaryHDU(header=ph),
-                    CompImageHDU(header=ih, data=imd),
-                    CompImageHDU(header=vh, data=vd),
-                    CompImageHDU(header=mh, data=md)
-                ])
+                HDUList(
+                    hdus=[
+                        PrimaryHDU(header=ph),
+                        CompImageHDU(header=ih, data=imd),
+                        CompImageHDU(header=vh, data=vd),
+                        CompImageHDU(header=mh, data=md),
+                    ]
+                )
             )
 
         self.current += n
@@ -186,23 +188,25 @@ class SimpleFits:
         Factory used to create and update WCS data in headers of mocked FITS
         files.
     """
+
     def __init__(
-            self,
-            shared_header_metadata=None,
-            shape=(100, 100),
-            start_t="2024-01-01T00:00:00.00",
-            step_t=0.001,
-            with_noise=False,
-            noise="simplistic",
-            src_cat=None,
-            obj_cat=None,
-            wcs_factory=None,
+        self,
+        shared_header_metadata=None,
+        shape=(100, 100),
+        start_t="2024-01-01T00:00:00.00",
+        step_t=0.001,
+        with_noise=False,
+        noise="simplistic",
+        src_cat=None,
+        obj_cat=None,
+        wcs_factory=None,
     ):
         # 2. Set up Header and Data factories that go into creating HDUs
         #    2.1) First headers, since that metadata specified data formats
         self.prim_hdr = HeaderFactory.from_primary_template(
             overrides=shared_header_metadata,
-            mutables=["DATE-OBS"], callbacks=[IncrementObstime(start=start_t, dt=step_t)]
+            mutables=["DATE-OBS"],
+            callbacks=[IncrementObstime(start=start_t, dt=step_t)],
         )
 
         wcs = None
@@ -213,21 +217,15 @@ def __init__(
             shared_header_metadata = {"EXTNAME": "IMAGE"}
 
         self.img_hdr = HeaderFactory.from_ext_template(
-            overrides=shared_header_metadata.copy(),
-            shape=shape,
-            wcs=wcs
+            overrides=shared_header_metadata.copy(), shape=shape, wcs=wcs
         )
         shared_header_metadata["EXTNAME"] = "VARIANCE"
         self.var_hdr = HeaderFactory.from_ext_template(
-            overrides=shared_header_metadata.copy(),
-            shape=shape,
-            wcs=wcs
+            overrides=shared_header_metadata.copy(), shape=shape, wcs=wcs
         )
         shared_header_metadata["EXTNAME"] = "MASK"
         self.mask_hdr = HeaderFactory.from_ext_template(
-            overrides=shared_header_metadata.copy(),
-            shape=shape,
-            wcs=wcs
+            overrides=shared_header_metadata.copy(), shape=shape, wcs=wcs
         )
 
         #   2.2) Then data factories
@@ -256,7 +254,7 @@ def mock(self, n=1):
                 n=n,
                 dt=self.step_t,
                 t=[hdr["DATE-OBS"] for hdr in prim_hdrs],
-                wcs=[WCS(hdr) for hdr in img_hdrs]
+                wcs=[WCS(hdr) for hdr in img_hdrs],
             )
 
         images = self.img_data.mock(n, obj_cats=obj_cats)
@@ -268,12 +266,14 @@ def mock(self, n=1):
             prim_hdrs, img_hdrs, var_hdrs, mask_hdrs, images, variances, masks
         ):
             hduls.append(
-                HDUList(hdus=[
-                    PrimaryHDU(header=ph),
-                    CompImageHDU(header=ih, data=imd),
-                    CompImageHDU(header=vh, data=vd),
-                    CompImageHDU(header=mh, data=md)
-                ])
+                HDUList(
+                    hdus=[
+                        PrimaryHDU(header=ph),
+                        CompImageHDU(header=ih, data=imd),
+                        CompImageHDU(header=vh, data=vd),
+                        CompImageHDU(header=mh, data=md),
+                    ]
+                )
             )
 
         self.current += n
@@ -337,8 +337,8 @@ class DECamImdiff:
         Factory used to create and update WCS data in headers of mocked FITS
         files.
     """
-    def __init__(self, with_data=False, with_noise=False, noise="simplistic",
-                 src_cat=None, obj_cat=None):
+
+    def __init__(self, with_data=False, with_noise=False, noise="simplistic", src_cat=None, obj_cat=None):
         if obj_cat is not None and obj_cat.mode == "progressive":
             raise ValueError(
                 "Only folding or static object catalogs can be used with"
@@ -394,12 +394,7 @@ def mock(self, n=1):
             images = self.img_data.mock(n=n, obj_cats=obj_cats)
             masks = self.mask_data.mock(n=n)
             variances = self.var_data.mock(images=images)
-            data = [
-                NoneFactory().mock(n=n),
-                images,
-                masks,
-                variances
-            ]
+            data = [NoneFactory().mock(n=n), images, masks, variances]
             data.extend([factory.mock(n=n) for factory in self.data_factories[4:]])
         else:
             data = [factory.mock(n=n) for factory in self.data_factories]
diff --git a/src/kbmod/mocking/headers.py b/src/kbmod/mocking/headers.py
index 2a7f01f92..2b265e9e8 100644
--- a/src/kbmod/mocking/headers.py
+++ b/src/kbmod/mocking/headers.py
@@ -18,6 +18,7 @@
     "ArchivedHeader",
 ]
 
+
 class WCSFactory:
     """WCS Factory.
 
@@ -79,10 +80,18 @@ class WCSFactory:
     MJDREF  =                  0.0 / [d] MJD of fiducial time
     RADESYS = 'ICRS'               / Equatorial coordinate system,
     """
-    def __init__(self, mode="static",
-                 pointing=(351., -5), rotation=0, pixscale=0.2,
-                 dither_pos=False, dither_rot=False, dither_amplitudes=(0.01, 0.01, 0.0),
-                 cycle=None):
+
+    def __init__(
+        self,
+        mode="static",
+        pointing=(351.0, -5),
+        rotation=0,
+        pixscale=0.2,
+        dither_pos=False,
+        dither_rot=False,
+        dither_amplitudes=(0.01, 0.01, 0.0),
+        cycle=None,
+    ):
         self.pointing = pointing
         self.rotation = rotation
         self.pixscale = pixscale
@@ -147,8 +156,8 @@ def gen_wcs(cls, pointing, rotation=0, pixscale=1, shape=None):
         NAXIS : 0  0]
         """
         wcs = WCS(naxis=2)
-        rho = rotation*0.0174533 # deg to rad
-        scale = pixscale  / 3600.0  # arcsec/pixel to deg/pix
+        rho = rotation * 0.0174533  # deg to rad
+        scale = pixscale / 3600.0  # arcsec/pixel to deg/pix
 
         if shape is not None:
             wcs.pixel_shape = shape
@@ -156,13 +165,12 @@ def gen_wcs(cls, pointing, rotation=0, pixscale=1, shape=None):
         else:
             wcs.wcs.crpix = [0, 0]
         wcs.wcs.crval = pointing
-        wcs.wcs.cunit = ['deg', 'deg']
-        wcs.wcs.pc = np.array([
-            [-scale * np.cos(rho), scale * np.sin(rho)],
-            [scale * np.sin(rho), scale * np.cos(rho)]
-        ])
-        wcs.wcs.radesys = 'ICRS'
-        wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
+        wcs.wcs.cunit = ["deg", "deg"]
+        wcs.wcs.pc = np.array(
+            [[-scale * np.cos(rho), scale * np.sin(rho)], [scale * np.sin(rho), scale * np.cos(rho)]]
+        )
+        wcs.wcs.radesys = "ICRS"
+        wcs.wcs.ctype = ["RA---TAN", "DEC--TAN"]
 
         return wcs
 
@@ -209,11 +217,8 @@ def update_headers(self, headers):
 
             if self.dither_rot:
                 ddec = random.uniform(-self.dither_amplitudes[2], self.dither_amplitudes[2])
-                rho = self.dither_amplitudes[2]*0.0174533 # deg to rad
-                rot_matrix =  np.array([
-                    [-np.cos(rho), np.sin(rho)],
-                    [np.sin(rho), np.cos(rho)]
-                ])
+                rho = self.dither_amplitudes[2] * 0.0174533  # deg to rad
+                rot_matrix = np.array([[-np.cos(rho), np.sin(rho)], [np.sin(rho), np.cos(rho)]])
                 new_pc = wcs.wcs.pc @ rot_matrix
                 wcs.wcs.pc = new_pc
 
@@ -295,6 +300,7 @@ class HeaderFactory:
     MJDREF  =                  0.0 / [d] MJD of fiducial time
     RADESYS = 'ICRS'               / Equatorial coordinate system                   ]
     """
+
     primary_template = {
         "EXTNAME": "PRIMARY",
         "NAXIS": 0,
@@ -444,8 +450,7 @@ def from_primary_template(cls, overrides=None, mutables=None, callbacks=None):
         return cls(hdr, mutables, callbacks)
 
     @classmethod
-    def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, shape=None,
-                          wcs=None):
+    def from_ext_template(cls, overrides=None, mutables=None, callbacks=None, shape=None, wcs=None):
         """Create an extension header assuming the default template of an image
         like header.
 
@@ -520,6 +525,7 @@ class ArchivedHeader(HeaderFactory):
         directory. Otherwise, the file is searched for within the header archive
         provided with this module.
     """
+
     # will almost never be anything else. Rather, it would be a miracle if it
     # were something else, since FITS standard shouldn't allow it. Further
     # casting by some packages will always be casting implemented in terms of
@@ -540,7 +546,9 @@ class ArchivedHeader(HeaderFactory):
 
     def __init__(self, archive_name, fname, external=False):
         super().__init__({})
-        self.table = header_archive_to_table(archive_name, fname, self.compression, self.format, external=external)
+        self.table = header_archive_to_table(
+            archive_name, fname, self.compression, self.format, external=external
+        )
 
         # Create HDU groups for easier iteration
         self.table = self.table.group_by("filename")
@@ -627,4 +635,3 @@ def mock(self, n=1):
             res.append(self.get(self.counter))
             self.counter += 1
         return res
-
diff --git a/src/kbmod/mocking/utils.py b/src/kbmod/mocking/utils.py
index 40f0af93e..008a5eeef 100644
--- a/src/kbmod/mocking/utils.py
+++ b/src/kbmod/mocking/utils.py
@@ -4,13 +4,13 @@
 from astropy.table import Table, MaskedColumn
 
 
-#def get_absolute_data_path(file_or_directory):
+# def get_absolute_data_path(file_or_directory):
 #    test_dir = path.abspath(path.dirname(__file__))
 #    data_dir = path.join(test_dir, "archived_data")
 #    return path.join(data_dir, file_or_directory)
 #
 #
-#def get_absolute_demo_data_path(file_or_directory):
+# def get_absolute_demo_data_path(file_or_directory):
 #    project_root_dir = path.abspath(path.dirname(path.dirname(__file__)))
 #    data_dir = path.join(project_root_dir, "archived_data")
 #    return path.join(data_dir, file_or_directory)
diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py
index f6f645c5b..ff4e82b64 100644
--- a/tests/test_end_to_end.py
+++ b/tests/test_end_to_end.py
@@ -10,7 +10,7 @@
 # from kbmod.wcs_utils import make_fake_wcs
 # from kbmod.work_unit import WorkUnit
 
-#from utils.utils_for_tests import get_absolute_demo_data_path
+# from utils.utils_for_tests import get_absolute_demo_data_path
 
 
 ####
@@ -104,7 +104,7 @@ def setUp(self):
                     "min_vy": self.vys[0],
                     "max_vy": self.vys[1],
                     "vx_steps": 40,
-                    "vy_steps": 40
+                    "vy_steps": 40,
                 },
                 "num_obs": self.n_imgs,
                 "do_mask": False,
@@ -113,8 +113,7 @@ def setUp(self):
             }
         )
 
-    def xmatch_best(self, obj, results,
-                    match_cols={"x_mean": "x", "y_mean": "y", "vx": "vx", "vy": "vy"}):
+    def xmatch_best(self, obj, results, match_cols={"x_mean": "x", "y_mean": "y", "vx": "vx", "vy": "vy"}):
         """Finds the result that minimizes the L2 distance to the target object.
 
         Parameters
@@ -142,13 +141,14 @@ def xmatch_best(self, obj, results,
                 resk.append(v)
         tgt = np.fromiter(obj[tuple(objk)].values(), dtype=float, count=len(objk))
         res = structured_to_unstructured(results[tuple(resk)].as_array(), dtype=float)
-        diff = np.linalg.norm(tgt-res, axis=1)
+        diff = np.linalg.norm(tgt - res, axis=1)
         if len(results) == 1:
             return results[0], diff
         return results[diff == diff.min()][0], diff
 
-    def assertResultValuesWithinSpec(self, expected, result, spec,
-                                     match_cols={"x_mean": "x", "y_mean": "y", "vx": "vx", "vy": "vy"}):
+    def assertResultValuesWithinSpec(
+        self, expected, result, spec, match_cols={"x_mean": "x", "y_mean": "y", "vx": "vx", "vy": "vy"}
+    ):
         """Asserts expected object matches the given result object within
         specification.
 
@@ -200,20 +200,16 @@ def run_single_search(self, data, expected, spec=5):
         # Run tests
         self.assertGreaterEqual(len(results), 1)
         for obj in expected.table:
-            res, dist = (results[0], None) if len(results)==1 else self.xmatch_best(obj, results)
+            res, dist = (results[0], None) if len(results) == 1 else self.xmatch_best(obj, results)
             self.assertResultValuesWithinSpec(obj, res, spec)
 
     def test_exact_motion(self):
         """Test exact searches are recovered in all 8 cardinal directions."""
         search_vs = list(itertools.product([-20, 0, 20], repeat=2))
         search_vs.remove((0, 0))
-        for (vx, vy) in search_vs:
+        for vx, vy in search_vs:
             with self.subTest(f"Cardinal direction: {(vx, vy)}"):
-                self.config._params["generator_config"] = {
-                    "name": "SingleVelocitySearch",
-                    "vx": vx,
-                    "vy": vy
-                }
+                self.config._params["generator_config"] = {"name": "SingleVelocitySearch", "vx": vx, "vy": vy}
                 obj_cat = kbmock.ObjectCatalog.from_defaults(self.param_ranges, n=1)
                 obj_cat.table["vx"] = vx
                 obj_cat.table["vy"] = vy
@@ -239,7 +235,7 @@ def test_resampled_search(self):
         self.start_pos = (10, 10)  # (ra, dec) in deg
         n_obj = 1
         pixscale = 0.2
-        timestamps = Time(np.arange(58915, 58915+self.n_imgs, 1), format="mjd")
+        timestamps = Time(np.arange(58915, 58915 + self.n_imgs, 1), format="mjd")
         vx = 0.001  # degrees / day (given the timestamps)
         vy = 0.001
 
@@ -250,13 +246,16 @@ def test_resampled_search(self):
         cats = []
         for i, t in enumerate(timestamps):
             cats.append(
-                Table({
-                    "amplitude": [100],
-                    "obstime": [t],
-                    "ra_mean": [self.start_pos[0] + vx*i],
-                    "dec_mean": [self.start_pos[1] + vy*i],
-                    "stddev": [2.0]
-                }))
+                Table(
+                    {
+                        "amplitude": [100],
+                        "obstime": [t],
+                        "ra_mean": [self.start_pos[0] + vx * i],
+                        "dec_mean": [self.start_pos[1] + vy * i],
+                        "stddev": [2.0],
+                    }
+                )
+            )
         catalog = vstack(cats)
         obj_cat = kbmock.ObjectCatalog.from_table(catalog, kind="world", mode="folding")
 
@@ -266,7 +265,7 @@ def test_resampled_search(self):
             pixscale=pixscale,
             dither_pos=True,
             dither_rot=True,
-            dither_amplitudes=(0.001, 0.001, 10)
+            dither_amplitudes=(0.001, 0.001, 10),
         )
 
         prim_hdr_factory = kbmock.HeaderFactory.from_primary_template(
@@ -274,11 +273,7 @@ def test_resampled_search(self):
             callbacks=[kbmock.ObstimeIterator(timestamps)],
         )
 
-        factory = kbmock.SimpleFits(
-            shape=self.shape,
-            obj_cat=obj_cat,
-            wcs_factory=wcs_factory
-        )
+        factory = kbmock.SimpleFits(shape=self.shape, obj_cat=obj_cat, wcs_factory=wcs_factory)
         factory.prim_hdr = prim_hdr_factory
         hduls = factory.mock(n=self.n_imgs)
 
@@ -291,6 +286,7 @@ def test_resampled_search(self):
         ic = ImageCollection.fromTargets(hduls, force="TestDataStd")
 
         from reproject.mosaicking import find_optimal_celestial_wcs
+
         opt_wcs, self.shape = find_optimal_celestial_wcs(list(ic.wcs))
         opt_wcs.array_shape = self.shape
 
@@ -300,21 +296,23 @@ def test_resampled_search(self):
         # The velocity grid needs to be searched very densely for the realistic
         # case (compared to the fact the velocity spread is not that large), and
         # we'll still end up ~10 pixels away from the truth.
-        search_config = SearchConfiguration.from_dict({
-            "generator_config": {
-                "name": "VelocityGridSearch",
-                "min_vx": meanvx-5,
-                "max_vx": meanvx+5,
-                "min_vy": meanvy-5,
-                "max_vy": meanvy+5,
-                "vx_steps": 40,
-                "vy_steps": 40
-            },
-            "num_obs": 1,
-            "do_mask": False,
-            "do_clustering": True,
-            "do_stamp_filter": False,
-        })
+        search_config = SearchConfiguration.from_dict(
+            {
+                "generator_config": {
+                    "name": "VelocityGridSearch",
+                    "min_vx": meanvx - 5,
+                    "max_vx": meanvx + 5,
+                    "min_vy": meanvy - 5,
+                    "max_vy": meanvy + 5,
+                    "vx_steps": 40,
+                    "vy_steps": 40,
+                },
+                "num_obs": 1,
+                "do_mask": False,
+                "do_clustering": True,
+                "do_stamp_filter": False,
+            }
+        )
         wu = ic.toWorkUnit(search_config)
         repr_wu = reproject_work_unit(wu, opt_wcs, parallelize=False)
         results = SearchRunner().run_search_from_work_unit(repr_wu)
@@ -327,7 +325,7 @@ def test_resampled_search(self):
         #   and find the best matching results in each realization.
         #   From all realizations find the one that matches the best.
         #   Select that realization and that best match for comparison.
-        cats = obj_cat.mock(t=timestamps, wcs=[opt_wcs]*self.n_imgs)
+        cats = obj_cat.mock(t=timestamps, wcs=[opt_wcs] * self.n_imgs)
         for cat in cats:
             cat["vx"] = meanvx
             cat["vy"] = meanvy
diff --git a/tests/test_mocking.py b/tests/test_mocking.py
index b999c6fb3..92e8ed601 100644
--- a/tests/test_mocking.py
+++ b/tests/test_mocking.py
@@ -63,7 +63,7 @@ def setUp(self):
         self.n_imgs = 3
         self.shape = (100, 300)
         self.padded = ((10, 90), (10, 290))
-        self.timestamps = Time(np.arange(58915, 58915+self.n_imgs, 1), format="mjd")
+        self.timestamps = Time(np.arange(58915, 58915 + self.n_imgs, 1), format="mjd")
         self.step_t = 1
 
     def test(self):
@@ -183,13 +183,16 @@ def test_folding_obj_cat(self):
         cats = []
         for i, t in enumerate(self.timestamps):
             cats.append(
-                Table({
-                    "amplitude": [100] * self.n_obj,
-                    "obstime": [t] * self.n_obj,
-                    "x_mean": start_x + 15 * i * i,
-                    "y_mean": start_y,
-                    "stddev": [2.0] * self.n_obj,
-                }))
+                Table(
+                    {
+                        "amplitude": [100] * self.n_obj,
+                        "obstime": [t] * self.n_obj,
+                        "x_mean": start_x + 15 * i * i,
+                        "y_mean": start_y,
+                        "stddev": [2.0] * self.n_obj,
+                    }
+                )
+            )
         catalog = vstack(cats)
 
         # Mock data based on that catalog
@@ -211,7 +214,7 @@ def test_folding_obj_cat(self):
     def test_progressive_sky_cat(self):
         """Test progressive catalog based on on-sky coordinates."""
         # a 10-50 in x by a 10-90 in y box using default WCS
-        #self.shape = (500, 500)
+        # self.shape = (500, 500)
         param_ranges = {
             "ra_mean": (350.998, 351.002),
             "dec_mean": (-5.0077, -5.0039),
@@ -243,13 +246,16 @@ def test_folding_sky_cat(self):
         cats = []
         for i, t in enumerate(self.timestamps):
             cats.append(
-                Table({
-                    "amplitude": [100] * self.n_obj,
-                    "obstime": [t] * self.n_obj,
-                    "ra_mean": start_ra -  0.001*i,
-                    "dec_mean": start_dec,# + 0.00011 * i,
-                    "stddev": [2.0] * self.n_obj
-                }))
+                Table(
+                    {
+                        "amplitude": [100] * self.n_obj,
+                        "obstime": [t] * self.n_obj,
+                        "ra_mean": start_ra - 0.001 * i,
+                        "dec_mean": start_dec,  # + 0.00011 * i,
+                        "stddev": [2.0] * self.n_obj,
+                    }
+                )
+            )
         catalog = vstack(cats)
         obj_cat = kbmock.ObjectCatalog.from_table(catalog, kind="world", mode="folding")
 
@@ -312,7 +318,7 @@ def test(self):
             "SkyWcs",
             "ApCorrMap",
             "ChebyshevBoundedField",
-            "ChebyshevBoundedField"
+            "ChebyshevBoundedField",
         ]
         hdul = hduls[0]
         self.assertEqual(len(hduls), 2)

From c3e7d5bddb247aae7e61290c6e4f7f0817e4b83f Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Wed, 21 Aug 2024 11:21:23 -0700
Subject: [PATCH 26/27] Syntax error on the tuple vs unpack operator again

---
 src/kbmod/mocking/data.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/kbmod/mocking/data.py b/src/kbmod/mocking/data.py
index 800aac208..ecf5b3f82 100644
--- a/src/kbmod/mocking/data.py
+++ b/src/kbmod/mocking/data.py
@@ -956,7 +956,7 @@ def add_hot_pixels(cls, image, config):
             y = rng.randint(0, shape[0], size=config["n_hot_pix"])
             hot_pixels = np.column_stack([x, y])
         elif config["hot_pix_locs"]:
-            hot_pixels = pixels
+            hot_pixels = config["hot_pix_locs"]
         else:
             raise ConfigurationError(
                 "Hot pixels method is not 'random', but `hot_pix_locs` contains "
@@ -964,7 +964,7 @@ def add_hot_pixels(cls, image, config):
             )
 
         for pix in hot_pixels:
-            image[*pix] += config["hot_pix_offset"]
+            image[pix] += config["hot_pix_offset"]
 
         return image
 

From bdb6297dc8653a6ef46626fc824bf47862c0ce72 Mon Sep 17 00:00:00 2001
From: DinoBektesevic <dinob@uw.edu>
Date: Wed, 21 Aug 2024 11:50:43 -0700
Subject: [PATCH 27/27] Fix catalog.kind setter

---
 src/kbmod/mocking/catalogs.py | 6 ++----
 tests/test_mocking.py         | 1 -
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/src/kbmod/mocking/catalogs.py b/src/kbmod/mocking/catalogs.py
index a8a3e5db1..bd862a9d8 100644
--- a/src/kbmod/mocking/catalogs.py
+++ b/src/kbmod/mocking/catalogs.py
@@ -1,5 +1,3 @@
-import abc
-
 import numpy as np
 from astropy.table import QTable
 from astropy.coordinates import SkyCoord
@@ -41,7 +39,7 @@ def expand_gaussian_cols(cat):
         cat["y_stddev"] = cat["stddev"]
 
     if "flux" in cat.columns and "amplitude" not in cat.columns:
-        cat["amplitude"] = cat["flux"] / (2.0 * np.pi * xstd * ystd)
+        cat["amplitude"] = cat["flux"] / (2.0 * np.pi * cat["x_stddev"] * cat["y_stddev"])
 
     return cat
 
@@ -346,7 +344,7 @@ def kind(self):
         """Catalog coordinate kind, ``pixel`` or ``world``"""
         return self._kind
 
-    @mode.setter
+    @kind.setter
     def kind(self, val):
         if val == "pixel":
             self._cat_keys = self.config["pix_pos_cols"] + self.config["pix_vel_cols"]
diff --git a/tests/test_mocking.py b/tests/test_mocking.py
index 92e8ed601..588c020b4 100644
--- a/tests/test_mocking.py
+++ b/tests/test_mocking.py
@@ -2,7 +2,6 @@
 
 import numpy as np
 
-import astropy.units as u
 from astropy.wcs import WCS
 from astropy.time import Time
 from astropy.table import Table, vstack