genralize docstring

autocti/charge_injection/imaging/imaging.py

@@ -155,8 +155,9 @@ def set_noise_scaling_map_list(
     def from_fits(
         cls,
         layout,
-        image_path,
         pixel_scales,
+        image_path=None,
+        image=None,
         image_hdu=0,
         noise_map_path=None,
         noise_map_hdu=0,
@@ -168,9 +169,15 @@ def from_fits(
         cosmic_ray_map_hdu=0,
     ):
 
-        ci_image = aa.Array2D.from_fits(
-            file_path=image_path, hdu=image_hdu, pixel_scales=pixel_scales
-        )
+        if image_path is not None and image is None:
+
+            ci_image = aa.Array2D.from_fits(
+                file_path=image_path, hdu=image_hdu, pixel_scales=pixel_scales
+            )
+
+        elif image is not None:
+
+            ci_image = image
 
         if noise_map_path is not None:
             ci_noise_map = aa.util.array_2d.numpy_array_2d_via_fits_from(
@@ -187,7 +194,7 @@ def from_fits(
                 hdu=pre_cti_data_hdu,
                 pixel_scales=pixel_scales,
             )
-        else:
+        elif pre_cti_data is None:
             raise exc.ImagingCIException(
                 "Cannot load pre_cti_data from .fits and pass explicit pre_cti_data."
             )

autocti/charge_injection/model/analysis.py

@@ -97,8 +97,6 @@ def log_likelihood_function(self, instance: ModelInstance) -> float:
 
         logl = fit.log_likelihood
 
-        print(logl)
-
         return logl
 
     def hyper_noise_scalar_list_from(

autocti/extract/two_d/abstract.py

@@ -78,7 +78,7 @@ def array_2d_list_from(
         self, array: aa.Array2D, pixels: Tuple[int, int]
     ) -> List[aa.Array2D]:
         """
-        Extract a specific region from every charge injection region on the charge injection image and return as a list
+        Extract a specific region from every signal region (e.g. the charge injection region of charge injection data) on the CTI calibration data and return as a list
         of 2D arrays.
 
         For example, this might extract the parallel EPERs of every charge injection region.
@@ -89,7 +89,7 @@ def array_2d_list_from(
         Parameters
         ----------
         array
-            The array from which the regions are extracted and put into the returned list of rrays.
+            The array from which the regions are extracted and put into the returned list of arrays.
         pixels
             The integer range of pixels between which the extraction is performed.
         """
@@ -111,7 +111,7 @@ def stacked_array_2d_from(
         self, array: aa.Array2D, pixels: Tuple[int, int]
     ) -> np.ndarray:
         """
-        Extract a region (e.g. the parallel FPR) of every charge injection region on the charge injection image and
+        Extract a region (e.g. the parallel FPR) of every signal region (e.g. the charge injection region of charge injection data) on the CTI calibration data and
         stack them by taking their mean.
 
         This returns the 2D average of the extracted regions (e.g. the parallel FPRs) of all of the charge injection
@@ -181,7 +181,7 @@ def binned_array_1d_from(
         self, array: aa.Array2D, pixels: Tuple[int, int]
     ) -> aa.Array1D:
         """
-        Extract a region (e.g. the parallel FPR) of every charge injection region on the charge injection image, stack
+        Extract a region (e.g. the parallel FPR) of every signal region (e.g. the charge injection region of charge injection data) on the CTI calibration data, stack
         them by taking their mean and then bin them up to a 1D region (e.g. the 1D parallel FPR) by taking the mean
         across the direction opposite to clocking (e.g. bin over the serial direction for a parallel FPR).
 

autocti/extract/two_d/parallel_eper.py

@@ -19,8 +19,8 @@ def binning_axis(self) -> int:
 
     def region_list_from(self, pixels: Tuple[int, int]):
         """
-        Extract the parallel EPERs of every charge injection region on the charge injection image and return as a list
-        of 2D arrays.
+        Extract the parallel EPERs of every signal region (e.g. the charge injection region of charge injection data)
+        on the CTI calibration data and return as a list of 2D arrays.
 
         The diagram below illustrates the extraction for `pixels=(0, 1)`:
 

autocti/extract/two_d/serial_eper.py

@@ -19,8 +19,8 @@ def binning_axis(self) -> int:
 
     def region_list_from(self, pixels: Tuple[int, int]):
         """
-        Extract the serial EPERs of every charge injection region on the charge injection image and return as a list
-        of 2D arrays.
+        Extract the serial EPERs of every signal region (e.g. the charge injection region of charge injection data)
+        on the CTI calibration data and return as a list of 2D arrays.
 
         Negative pixel values are supported to the `pixels` tuple, whereby columns in front of the serial EPERs
         (e.g. the FPRs) are also extracted.

autocti/extract/two_d/serial_fpr.py

@@ -19,8 +19,8 @@ def binning_axis(self) -> int:
 
     def region_list_from(self, pixels: Tuple[int, int]):
         """
-        Extract the serial FPR of every charge injection region on the charge injection image and return as a list
-        of 2D arrays.
+        Extract the serial FPR of every signal region (e.g. the charge injection region of charge injection data) on
+        the CTI calibration data and return as a list of 2D arrays.
 
         Negative pixel values are supported to the `pixels` tuple, whereby columns in front of the serial FPRs (e.g.
         the serial prescan) are also extracted.

autocti/layout/two_d.py

@@ -82,7 +82,7 @@ def layout_extracted_from(
     ) -> "Layout2D":
         """
         The charge injection layout after an extraction is performed on its associated charge injection image, where
-        the extraction is defined by a region of pixel coordinates
+        the extraction is defined by a region of pixel coordinates:
 
         (top-row, bottom-row, left-column, right-column) = (y0, y1, x0, x1)
 

test_autocti/extract/two_d/test_parallel_calibration.py

@@ -86,31 +86,35 @@ def test__extracted_layout_from():
 
 def test__imaging_ci_from(imaging_ci_7x7):
 
+    print(imaging_ci_7x7.layout.shape_2d)
+
     # The ci layout starts at column 1, so the left most column is removed below
 
     extract = ac.Extract2DParallelCalibration(
         shape_2d=imaging_ci_7x7.shape_native,
         region_list=imaging_ci_7x7.layout.region_list,
     )
 
-    parallel_calibration_imaging = extract.imaging_ci_from(
-        imaging_ci=imaging_ci_7x7, columns=(0, 6)
+    imaging_ci_parallel_calibration = extract.imaging_ci_from(
+        imaging_ci=imaging_ci_7x7, columns=(1, 3)
     )
 
+    print(imaging_ci_parallel_calibration.layout.shape_2d)
+
     assert (
-        parallel_calibration_imaging.image.native == imaging_ci_7x7.image.native[:, 1:7]
+        imaging_ci_parallel_calibration.image.native == imaging_ci_7x7.image.native[:, 1:7]
     ).all()
     assert (
-        parallel_calibration_imaging.noise_map.native
+        imaging_ci_parallel_calibration.noise_map.native
         == imaging_ci_7x7.noise_map.native[:, 1:7]
     ).all()
     assert (
-        parallel_calibration_imaging.pre_cti_data.native
+        imaging_ci_parallel_calibration.pre_cti_data.native
         == imaging_ci_7x7.pre_cti_data.native[:, 1:7]
     ).all()
     assert (
-        parallel_calibration_imaging.cosmic_ray_map.native
+        imaging_ci_parallel_calibration.cosmic_ray_map.native
         == imaging_ci_7x7.cosmic_ray_map.native[:, 1:7]
     ).all()
 
-    assert parallel_calibration_imaging.layout.region_list == [(1, 5, 0, 4)]
+    assert imaging_ci_parallel_calibration.layout.region_list == [(1, 5, 0, 4)]

to_do_list

@@ -2,11 +2,23 @@ __Workspace__
 
 - Copy from Bayesian description stuff from HowToFit.
 
+
+__Bugs__
+
+- ImagingCI should not _need_ pre_cti_data (or have bool to turn off the need) because of pre_cti image estimate.
+
+
 __Paper__
 
 - readthedocs.
 - introduction notebook.
 
+
+__Visualization__
+
+- Always use square axis liimits even if extracted data is rectangular.
+
+
 __TimeEvolution Mock Data Product__
 
  - Refresh brain on how this works with DataModel.