Merge branch 'master' of https://github.com/cbroz1/spyglass

notebooks/01_Insert_Data.ipynb

@@ -692,7 +692,7 @@
     "- `minirec20230622.nwb`, .3 GB: minimal recording, on\n",
     "  [Box](https://ucsf.box.com/s/k3sgql6z475oia848q1rgms4zdh4rkjn)\n",
     "- `montague20200802.nwb`, 8 GB: full recording, on\n",
-    "  [DropBox](https://www.dropbox.com/scl/fo/4i5b1z4iapetzxfps0grf/h?dl=0&preview=montague20200802_tutorial_.nwb&rlkey=ctahes9v0r7bxes8yceh86gzg)\n",
+    "  DropBox (link coming soon)\n",
     "- For those in the UCSF network, these and many others on `/stelmo/nwb/raw`\n",
     "\n",
     "If you are connected to the Frank lab database, please rename any downloaded\n",

notebooks/14_Theta.ipynb

@@ -594,7 +594,7 @@
     "\n",
     "We can overlay theta and detected phase for each electrode.\n",
     "\n",
-    "_Note:_ The red horizontal line indicates phase 0, corresponding to the trough\n",
+    "_Note:_ The red horizontal line indicates phase 0, corresponding to the through\n",
     "of theta."
    ]
   },

notebooks/20_Position_Trodes.ipynb

@@ -142,7 +142,7 @@
     "available. To adjust the default, insert a new set into this table. The\n",
     "parameters are...\n",
     "\n",
-    "- `max_separation`, default 9 cm: maximium acceptable distance between red and\n",
+    "- `max_separation`, default 9 cm: maximum acceptable distance between red and\n",
     "  green LEDs.\n",
     "  - If exceeded, the times are marked as NaNs and inferred by interpolation.\n",
     "  - Useful when the inferred LED position tracks a reflection instead of the\n",

notebooks/24_Linearization.ipynb

@@ -87,7 +87,6 @@
     "\n",
     "import spyglass.common as sgc\n",
     "import spyglass.position.v1 as sgp\n",
-    "import spyglass as nd\n",
     "\n",
     "# ignore datajoint+jupyter async warnings\n",
     "import warnings\n",
@@ -1501,7 +1500,7 @@
     "    + 1\n",
     ")\n",
     "video_info = (\n",
-    "    nd.common.common_behav.VideoFile()\n",
+    "    sgc.common_behav.VideoFile()\n",
     "    & {\"nwb_file_name\": key[\"nwb_file_name\"], \"epoch\": epoch}\n",
     ").fetch1()\n",
     "\n",

notebooks/33_Decoding_Clusterless.ipynb

@@ -32,8 +32,8 @@
     "  [extracted marks](./31_Extract_Mark_Indicators.ipynb), as well as loaded \n",
     "  position data. If 1D decoding, this data should also be\n",
     "  [linearized](./24_Linearization.ipynb).\n",
-    "- Ths tutorial also assumes you're familiar with how to run processes on GPU, as\n",
-    "  presented in [this notebook](./32_Decoding_with_GPUs.ipynb)\n",
+    "- This tutorial also assumes you're familiar with how to run processes on GPU, \n",
+    "  as presented in [this notebook](./32_Decoding_with_GPUs.ipynb)\n",
     "\n",
     "Clusterless decoding can be performed on either 1D or 2D data. A few steps in\n",
     "this notebook will refer to a `decode_1d` variable set in \n",
@@ -143,10 +143,10 @@
    "source": [
     "First, we'll fetch marks with `fetch_xarray`, which provides a labeled array of\n",
     "shape (n_time, n_mark_features, n_electrodes). Time is in 2 ms bins with either\n",
-    "`NaN` if no spike occured or the value of the spike features.\n",
+    "`NaN` if no spike occurred or the value of the spike features.\n",
     "\n",
     "If there is >1 spike per time bin per tetrode, we take an an average of the\n",
-    "marks. Ideally, we would use all the marks, this is a rare occurance and\n",
+    "marks. Ideally, we would use all the marks, this is a rare occurrence and\n",
     "decoding is generally robust to the averaging."
    ]
   },

notebooks/py_scripts/01_Insert_Data.py

@@ -109,7 +109,7 @@
 # - `minirec20230622.nwb`, .3 GB: minimal recording, on
 #   [Box](https://ucsf.box.com/s/k3sgql6z475oia848q1rgms4zdh4rkjn)
 # - `montague20200802.nwb`, 8 GB: full recording, on
-#   [DropBox](https://www.dropbox.com/scl/fo/4i5b1z4iapetzxfps0grf/h?dl=0&preview=montague20200802_tutorial_.nwb&rlkey=ctahes9v0r7bxes8yceh86gzg)
+#   DropBox (link coming soon)
 # - For those in the UCSF network, these and many others on `/stelmo/nwb/raw`
 #
 # If you are connected to the Frank lab database, please rename any downloaded

notebooks/py_scripts/14_Theta.py

@@ -128,7 +128,7 @@
 #
 # We can overlay theta and detected phase for each electrode.
 #
-# _Note:_ The red horizontal line indicates phase 0, corresponding to the trough
+# _Note:_ The red horizontal line indicates phase 0, corresponding to the through
 # of theta.
 
 # +

notebooks/py_scripts/20_Position_Trodes.py

@@ -87,7 +87,7 @@
 # available. To adjust the default, insert a new set into this table. The
 # parameters are...
 #
-# - `max_separation`, default 9 cm: maximium acceptable distance between red and
+# - `max_separation`, default 9 cm: maximum acceptable distance between red and
 #   green LEDs.
 #   - If exceeded, the times are marked as NaNs and inferred by interpolation.
 #   - Useful when the inferred LED position tracks a reflection instead of the

notebooks/py_scripts/24_Linearization.py

@@ -54,7 +54,6 @@
 
 import spyglass.common as sgc
 import spyglass.position.v1 as sgp
-import spyglass as nd
 
 # ignore datajoint+jupyter async warnings
 import warnings
@@ -335,7 +334,7 @@
     + 1
 )
 video_info = (
-    nd.common.common_behav.VideoFile()
+    sgc.common_behav.VideoFile()
     & {"nwb_file_name": key["nwb_file_name"], "epoch": epoch}
 ).fetch1()
 

notebooks/py_scripts/33_Decoding_Clusterless.py

@@ -28,8 +28,8 @@
 #   [extracted marks](./31_Extract_Mark_Indicators.ipynb), as well as loaded
 #   position data. If 1D decoding, this data should also be
 #   [linearized](./24_Linearization.ipynb).
-# - Ths tutorial also assumes you're familiar with how to run processes on GPU, as
-#   presented in [this notebook](./32_Decoding_with_GPUs.ipynb)
+# - This tutorial also assumes you're familiar with how to run processes on GPU,
+#   as presented in [this notebook](./32_Decoding_with_GPUs.ipynb)
 #
 # Clusterless decoding can be performed on either 1D or 2D data. A few steps in
 # this notebook will refer to a `decode_1d` variable set in
@@ -87,10 +87,10 @@
 
 # First, we'll fetch marks with `fetch_xarray`, which provides a labeled array of
 # shape (n_time, n_mark_features, n_electrodes). Time is in 2 ms bins with either
-# `NaN` if no spike occured or the value of the spike features.
+# `NaN` if no spike occurred or the value of the spike features.
 #
 # If there is >1 spike per time bin per tetrode, we take an an average of the
-# marks. Ideally, we would use all the marks, this is a rare occurance and
+# marks. Ideally, we would use all the marks, this is a rare occurrence and
 # decoding is generally robust to the averaging.
 
 # +

src/spyglass/utils/dj_merge_tables.py

@@ -445,10 +445,19 @@ def merge_delete_parent(
         if dry_run:
             return part_parents
 
-        with cls._safe_context():
-            super().delete(cls(), **kwargs)
-            for part_parent in part_parents:
-                super().delete(part_parent, **kwargs)
+        merge_ids = cls.merge_restrict(restriction).fetch(
+            RESERVED_PRIMARY_KEY, as_dict=True
+        )
+
+        # CB: Removed transaction protection here bc 'no' confirmation resp
+        # still resulted in deletes. If re-add, consider transaction=False
+        super().delete((cls & merge_ids), **kwargs)
+
+        if cls & merge_ids:  # If 'no' on del prompt from above, skip below
+            return  # User can still abort del below, but yes/no is unlikly
+
+        for part_parent in part_parents:
+            super().delete(part_parent, **kwargs)  # add safemode=False?
 
     @classmethod
     def fetch_nwb(