Implement Frame2List for Jiakun

foundation/stimulus/compute/video.py

@@ -364,6 +364,83 @@ def video(self):
             current_time = times[-1]
         return video.Video(images, times=times)
 
+@keys
+class Frame2List(VideoType):
+    """A video composed of an ordered list of stimulus.Frame2"""
+
+    @property
+    def keys(self):
+        return [
+            stimulus.Frame2List,
+        ]
+
+    @rowproperty
+    def video(self):
+        members = stimulus.Frame2List.Member & self.item
+        if len(members) != (stimulus.Frame2List & self.item).fetch1('members'):
+            raise MissingError(f"Frame2List {self.item} is missing members")
+
+        tups = merge(
+            members,
+            pipe_stim.StaticImage.Image,
+            pipe_stim.Frame2,
+        )
+
+        images = []
+        times = []
+        current_time = 0
+        def mask_image(cond, frame):
+            frame = frame.astype(float)
+            frame_size = frame.T.shape  # frame is height by width
+            radius = float(cond['aperture_r']) * frame_size[0]
+            transition = float(cond['aperture_transition']) * frame_size[0]
+            x_, y_ = float(cond['aperture_x']), float(cond['aperture_y'])
+            sz = frame_size
+            x = np.linspace(-sz[1] / 2, sz[1] / 2, sz[1]) - y_ * sz[0]
+            y = np.linspace(-sz[0] / 2, sz[0] / 2, sz[0]) - x_ * sz[0]
+            [X, Y] = np.meshgrid(x, y)
+            rr = np.sqrt(X * X + Y * Y)
+            fxn = lambda r: 0.5 * (1 + np.cos(np.pi * r)) * (r < 1) * (r > 0) + (r < 0)
+            alpha_mask = fxn((rr - radius) / transition + 1)
+            bg = cond['background_value']
+            img = (frame - bg) * alpha_mask.T + bg
+            return img.astype(np.uint8)
+
+        for image, pre_blank, duration, r, x, y, trans, bg in zip(
+                *tups.fetch(
+                    "image",
+                    "pre_blank_period",
+                    "presentation_time",
+                    "aperture_r",
+                    "aperture_x",
+                    "aperture_y",
+                    "aperture_transition",
+                    "background_value",
+                    order_by="frame2list_index",
+                )
+        ):
+            cond = dict(aperture_x=x, aperture_y=y, aperture_r=r, aperture_transition=trans, background_value=bg)
+            image = video.Frame.fromarray(mask_image(cond, image))
+
+            if image.mode == "L":
+                blank = np.full([image.height, image.width], 128, dtype=np.uint8)
+                blank = video.Frame.fromarray(blank)
+            else:
+                raise NotImplementedError(f"Frame mode {image.mode} not implemented")
+
+            if pre_blank > 0 and current_time == 0:
+                images += [blank, image, blank]
+                times += [
+                    current_time,
+                    current_time + pre_blank,
+                    current_time + pre_blank + duration,
+                ]
+            else:
+                images += [image, blank]
+                times += [current_time + pre_blank, current_time + pre_blank + duration]
+            current_time = times[-1]
+        return video.Video(images, times=times)
+
 
 # -- Video Sets --
 
@@ -466,3 +543,4 @@ def df(self):
                 rows.append(row)
 
         return pd.DataFrame(rows).sort_values(by=["spatial_type", "video_id", "onset"]).reset_index(drop=True)
+

foundation/stimulus/fill/scan.py

@@ -67,4 +67,34 @@ def fill(self):
                 f"{self.item['animal_id']}-{self.item['session']}-{self.item['scan_idx']}, "\
                 "ordered by the trial_idx of the first repetition.",
             ),
+        )
+
+@keys
+class VisualScanFrame2List:
+    """Visual scan with static images presented"""
+
+    @property
+    def keys(self):
+        return [
+            pipe_exp.Scan & (
+                pipe_stim.Trial * pipe_stim.Condition & pipe_stim.Frame2
+            ),
+        ]
+
+    def fill(self):
+        from foundation.stimulus.video import Frame2List
+
+        keys = U('condition_hash').aggr(
+            (pipe_stim.Frame2 * pipe_stim.Condition * pipe_stim.Trial & self.key),
+            trial_idx='MIN(trial_idx)'
+        ).fetch(
+            "KEY", order_by="trial_idx ASC"
+        )
+        return Frame2List.fill(
+            restrictions=keys,
+            note=(
+                "All unique stimulus.Frame2 conditions presented in "\
+                f"{self.item['animal_id']}-{self.item['session']}-{self.item['scan_idx']}, "\
+                "ordered by the trial_idx of the first repetition.",
+            ),
         )

foundation/stimulus/video.py

@@ -142,6 +142,19 @@ def compute(self):
         return FrameList & self
 
 
+@schema.list
+class Frame2List(VideoType):
+    keys = [pipe_stim.Frame2]
+    name = "frame2list"
+    comment = "an ordered list of frame2 stimuli"
+
+    @rowproperty
+    def compute(self):
+        from foundation.stimulus.compute.video import Frame2List
+
+        return Frame2List & self
+
+
 # -- Video --