Feature/visualize fpr value

autocti/charge_injection/imaging/imaging.py

@@ -19,6 +19,7 @@ def __init__(
         layout: Layout2DCI,
         cosmic_ray_map: Optional[aa.Array2D] = None,
         noise_scaling_map_dict: Optional[Dict] = None,
+        fpr_value: Optional[float] = None,
     ):
 
         super().__init__(image=image, noise_map=noise_map)
@@ -43,6 +44,22 @@ def __init__(
 
         self.layout = layout
 
+        if fpr_value is None:
+
+            fpr_value = np.round(
+                np.mean(
+                    self.layout.extract.parallel_fpr.median_list_from(
+                        array=self.data,
+                        pixels_from_end=min(
+                            10, self.layout.smallest_parallel_rows_within_ci_regions
+                        ),
+                    )
+                ),
+                2,
+            )
+
+        self.fpr_value = fpr_value
+
         self.imaging_full = self
 
     @property
@@ -107,6 +124,7 @@ def apply_mask(self, mask: mask_2d.Mask2D) -> "ImagingCI":
             layout=self.layout,
             cosmic_ray_map=cosmic_ray_map,
             noise_scaling_map_dict=noise_scaling_map_dict,
+            fpr_value=self.fpr_value,
         )
 
     def apply_settings(self, settings: SettingsImagingCI):

autocti/charge_injection/plot/fit_ci_plotters.py

@@ -1,3 +1,4 @@
+import numpy as np
 from typing import Callable
 
 import autoarray.plot as aplt
@@ -351,7 +352,7 @@ def figures_1d_of_region(
                 y_extra=y_extra,
                 visuals_1d=self.visuals_1d,
                 auto_labels=AutoLabels(
-                    title=f"Data 1D With Noise & Model-Fit [{region}]",
+                    title=f"Data w/ Noise {region} (FPR = {self.fit.dataset.fpr_value} e-)",
                     ylabel="Data (e-)",
                     xlabel="Pixel No.",
                     filename=f"data_with_noise_map_{region}",
@@ -374,7 +375,7 @@ def figures_1d_of_region(
                 y_extra=y_extra,
                 visuals_1d=self.visuals_1d,
                 auto_labels=AutoLabels(
-                    title=f"Data 1D With Noise & Model-Fit (log10 y axis) [{region}]",
+                    title=f"Data w/ Noise {region} [log10 y] (FPR = {self.fit.dataset.fpr_value} e-)",
                     ylabel="Data (e-)",
                     xlabel="Pixel No.",
                     filename=f"data_with_noise_map_logy_{region}",

autocti/charge_injection/plot/imaging_ci_plotters.py

@@ -1,7 +1,9 @@
+import numpy as np
 from typing import Callable
 
 import autoarray.plot as aplt
 
+from autoarray.mask.mask_2d import Mask2D
 from autoarray.plot.auto_labels import AutoLabels
 from autoarray.dataset.plot.imaging_plotters import ImagingPlotterMeta
 
@@ -274,7 +276,7 @@ def figures_1d_of_region(
                 y_errors=y_errors,
                 visuals_1d=self.get_visuals_1d(),
                 auto_labels=AutoLabels(
-                    title=f"Data 1D With Noise {region}",
+                    title=f"Data w/ Noise {region} (FPR = {self.dataset.fpr_value} e-)",
                     ylabel="Data (e-)",
                     xlabel="Pixel No.",
                     filename=f"data_with_noise_map_{region}",
@@ -295,7 +297,7 @@ def figures_1d_of_region(
                 y_errors=y_errors,
                 visuals_1d=self.get_visuals_1d(),
                 auto_labels=AutoLabels(
-                    title=f"Data 1D With Noise {region} (log10 y axis)",
+                    title=f"Data w/ Noise {region} [log10 y] (FPR = {self.dataset.fpr_value} e-)",
                     ylabel="Data (e-)",
                     xlabel="Pixel No.",
                     filename=f"data_with_noise_map_logy_{region}",

autocti/exc.py

@@ -14,6 +14,10 @@ class ImagingCIException(Exception):
     pass
 
 
+class ExtractException(Exception):
+    pass
+
+
 class LayoutException(Exception):
     pass
 

autocti/extract/one_d/abstract.py

@@ -7,6 +7,7 @@
 class Extract1D:
     def __init__(
         self,
+        shape_1d: Optional[Tuple[int]] = None,
         region_list: Optional[aa.type.Region1DList] = None,
         prescan: Optional[aa.type.Region1DLike] = None,
         overscan: Optional[aa.type.Region1DLike] = None,
@@ -22,6 +23,9 @@ def __init__(
         region_list
             Integer pixel coordinates specifying the corners of signal (x0, x1).
         """
+
+        self.shape_1d = shape_1d
+
         self.region_list = (
             list(map(aa.Region1D, region_list)) if region_list is not None else None
         )
@@ -42,11 +46,29 @@ def total_pixels_min(self) -> int:
         """
         return np.min([region.total_pixels for region in self.region_list])
 
-    def region_list_from(self, pixels: Tuple[int, int]) -> List[aa.Region2D]:
+    @property
+    def parallel_rows_between_regions(self):
+        return [
+            self.region_list[i + 1].x0 - self.region_list[i].x1
+            for i in range(len(self.region_list) - 1)
+        ]
+
+    @property
+    def trail_size_to_array_edge(self):
+        return self.shape_1d[0] - np.max([region.x1 for region in self.region_list])
+
+    def region_list_from(
+        self,
+        pixels: Optional[Tuple[int, int]] = None,
+        pixels_from_end: Optional[int] = None,
+    ) -> List[aa.Region2D]:
         raise NotImplementedError
 
     def array_1d_list_from(
-        self, array: aa.Array1D, pixels: Tuple[int, int]
+        self,
+        array: aa.Array1D,
+        pixels: Optional[Tuple[int, int]] = None,
+        pixels_from_end: Optional[int] = None,
     ) -> List[aa.Array1D]:
         """
         Extract a specific region from every region on the line dataset and return as a list of 1D arrays.
@@ -66,11 +88,16 @@ def array_1d_list_from(
 
         arr_list = [
             array.native[region.slice]
-            for region in self.region_list_from(pixels=pixels)
+            for region in self.region_list_from(
+                pixels=pixels, pixels_from_end=pixels_from_end
+            )
         ]
 
         mask_1d_list = [
-            array.mask[region.slice] for region in self.region_list_from(pixels=pixels)
+            array.mask[region.slice]
+            for region in self.region_list_from(
+                pixels=pixels, pixels_from_end=pixels_from_end
+            )
         ]
 
         return [
@@ -79,7 +106,10 @@ def array_1d_list_from(
         ]
 
     def stacked_array_1d_from(
-        self, array: aa.Array1D, pixels: Tuple[int, int]
+        self,
+        array: aa.Array1D,
+        pixels: Optional[Tuple[int, int]] = None,
+        pixels_from_end: Optional[int] = None,
     ) -> aa.Array1D:
         """
         Extract a region (e.g. the FPR) of every region on the line dataset and stack them by taking their mean.
@@ -102,13 +132,18 @@ def stacked_array_1d_from(
 
         arr_list = [
             np.ma.array(data=array.native[region.slice], mask=array.mask[region.slice])
-            for region in self.region_list_from(pixels=pixels)
+            for region in self.region_list_from(
+                pixels=pixels, pixels_from_end=pixels_from_end
+            )
         ]
 
         stacked_array_1d = np.ma.mean(np.ma.asarray(arr_list), axis=0)
 
         mask_1d_list = [
-            array.mask[region.slice] for region in self.region_list_from(pixels=pixels)
+            array.mask[region.slice]
+            for region in self.region_list_from(
+                pixels=pixels, pixels_from_end=pixels_from_end
+            )
         ]
 
         return aa.Array1D(
@@ -117,7 +152,11 @@ def stacked_array_1d_from(
         ).native
 
     def add_to_array(
-        self, new_array: aa.Array1D, array: aa.Array1D, pixels: Tuple[int, int]
+        self,
+        new_array: aa.Array1D,
+        array: aa.Array1D,
+        pixels: Optional[Tuple[int, int]] = None,
+        pixels_from_end: Optional[int] = None,
     ) -> aa.Array1D:
         """
         Extracts the region (e.g. the FPRs) from the line dataset and adds them to a line dataset.
@@ -132,9 +171,13 @@ def add_to_array(
             The row pixel index which determines the region extracted (e.g. `pixels=(0, 3)` will compute the region
             corresponding to the 1st, 2nd and 3rd pixels).
         """
-        region_list = self.region_list_from(pixels=pixels)
+        region_list = self.region_list_from(
+            pixels=pixels, pixels_from_end=pixels_from_end
+        )
 
-        array_1d_list = self.array_1d_list_from(array=array, pixels=pixels)
+        array_1d_list = self.array_1d_list_from(
+            array=array, pixels=pixels, pixels_from_end=pixels_from_end
+        )
 
         for arr, region in zip(array_1d_list, region_list):
             new_array[region.x0 : region.x1] += arr

autocti/extract/one_d/eper.py

@@ -1,13 +1,17 @@
 import numpy as np
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import autoarray as aa
 
 from autocti.extract.one_d.abstract import Extract1D
 
 
 class Extract1DEPER(Extract1D):
-    def region_list_from(self, pixels: Tuple[int, int]) -> List[aa.Region1D]:
+    def region_list_from(
+        self,
+        pixels: Optional[Tuple[int, int]] = None,
+        pixels_from_end: Optional[int] = None,
+    ) -> List[aa.Region1D]:
         """
             Returns a list of the (x0, x1) regions containing the EPERs of a 1D CTI dataset.
 
@@ -22,12 +26,24 @@ def region_list_from(self, pixels: Tuple[int, int]) -> List[aa.Region1D]:
                 The row indexes to extract the trails between (e.g. pixels(0, 3) extracts the 1st, 2nd and 3rd pixels)
         """
 
-        return list(
-            map(
-                lambda region: region.trailing_region_from(pixels=pixels),
-                self.region_list,
-            )
-        )
+        region_list = []
+
+        for i, region in enumerate(self.region_list):
+
+            if pixels_from_end is not None:
+
+                parallel_row_spaces = self.parallel_rows_between_regions + [
+                    self.trail_size_to_array_edge
+                ]
+
+                pixels = (
+                    parallel_row_spaces[i] - pixels_from_end,
+                    parallel_row_spaces[i],
+                )
+
+            region_list.append(region.trailing_region_from(pixels=pixels))
+
+        return region_list
 
     def array_1d_from(self, array: aa.Array1D) -> aa.Array1D:
         """

autocti/extract/one_d/fpr.py

@@ -1,12 +1,16 @@
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import autoarray as aa
 
 from autocti.extract.one_d.abstract import Extract1D
 
 
 class Extract1DFPR(Extract1D):
-    def region_list_from(self, pixels: Tuple[int, int]) -> List[aa.Region1D]:
+    def region_list_from(
+        self,
+        pixels: Optional[Tuple[int, int]] = None,
+        pixels_from_end: Optional[int] = None,
+    ) -> List[aa.Region1D]:
         """
             Returns a list of the (x0, x1) regions containing the FPRs of a 1D CTI dataset.
 
@@ -22,6 +26,9 @@ def region_list_from(self, pixels: Tuple[int, int]) -> List[aa.Region1D]:
         """
         return list(
             map(
-                lambda region: region.front_region_from(pixels=pixels), self.region_list
+                lambda region: region.front_region_from(
+                    pixels=pixels, pixels_from_end=pixels_from_end
+                ),
+                self.region_list,
             )
         )

autocti/extract/one_d/master.py

@@ -1,4 +1,4 @@
-from typing import Tuple
+from typing import Optional, Tuple
 
 from autocti.extract.one_d.overscan import Extract1DOverscan
 from autocti.extract.one_d.fpr import Extract1DFPR
@@ -11,8 +11,9 @@ class Extract1DMaster:
     def __init__(
         self,
         region_list,
-        prescan: aa.type.Region1DLike = None,
-        overscan: aa.type.Region1DLike = None,
+        shape_1d: Optional[Tuple[int]] = None,
+        prescan: Optional[aa.type.Region1DLike] = None,
+        overscan: Optional[aa.type.Region1DLike] = None,
     ):
         """
         Abstract class containing methods for extracting regions from a 1D line dataset which contains some sort of
@@ -34,19 +35,27 @@ def __init__(
             list(map(aa.Region1D, region_list)) if region_list is not None else None
         )
 
+        self.shape_1d = shape_1d
+
         self._prescan = prescan
         self._overscan = overscan
 
     @property
     def fpr(self):
         return Extract1DFPR(
-            region_list=self.region_list, prescan=self._prescan, overscan=self._overscan
+            shape_1d=self.shape_1d,
+            region_list=self.region_list,
+            prescan=self._prescan,
+            overscan=self._overscan,
         )
 
     @property
     def eper(self):
         return Extract1DEPER(
-            region_list=self.region_list, prescan=self._prescan, overscan=self._overscan
+            shape_1d=self.shape_1d,
+            region_list=self.region_list,
+            prescan=self._prescan,
+            overscan=self._overscan,
         )
 
     @property