#
Notes:
# -
-# Please do not add to the
BodyPart table in the production
+# Please do not add to the BodyPart table in the production
# database unless necessary.
#
#
@@ -117,9 +118,9 @@
sgp.DLCProject() & {"project_name": project_name}
-# #### [DLCModel](#ToC)
+# ## [DLCModel](#ToC)
-# The `DLCModelInput` table has `dlc_model_name` and `project_name` as primary keys and `project_path` as a secondary key.
+# The `DLCModelInput` table has `dlc_model_name` and `project_name` as primary keys and `project_path` as a secondary key.
sgp.DLCModelInput()
@@ -184,9 +185,10 @@
sgp.DLCModel() & model_key
-# ### Next Steps
+# ## Next Steps
#
-# With our trained model in place, we're ready to move on to
-# [pose estimation](./23_Position_DLC_3.ipynb).
+# With our trained model in place, we're ready to move on to
+# pose estimation (notebook coming soon!).
+#
# ### [`Return To Table of Contents`](#ToC)
diff --git a/notebooks/py_scripts/24_Linearization.py b/notebooks/py_scripts/24_Linearization.py
index ad77b0de9..8cd4b67b2 100644
--- a/notebooks/py_scripts/24_Linearization.py
+++ b/notebooks/py_scripts/24_Linearization.py
@@ -5,7 +5,7 @@
# extension: .py
# format_name: light
# format_version: '1.5'
-# jupytext_version: 1.14.5
+# jupytext_version: 1.14.7
# kernelspec:
# display_name: Python 3.10.5 64-bit
# language: python
@@ -62,7 +62,7 @@
warnings.simplefilter("ignore", category=ResourceWarning)
# -
-# ## Retrieve 2D position
+# ## Retrieve 2D position
#
# To retrieve 2D position data, we'll specify an nwb file, a position time
@@ -104,8 +104,8 @@
#
# - `node_positions` (cm): the 2D positions of the graph
# - `edges`: how the nodes are connected, as pairs of node indices, labeled by
-# their respective index in `node_positions`.
-# - `linear_edge_order`: layout of edges in linear space in *order*, as tuples.
+# their respective index in `node_positions`.
+# - `linear_edge_order`: layout of edges in linear space in *order*, as tuples.
# - `linear_edge_spacing`: spacing between each edge, as either a single number
# for all gaps or an array with a number for each gap. Gaps may be important for
# edges not connected in 2D space.
@@ -116,7 +116,7 @@
# from node 0 to 1. Edge (1, 0) would connect from node 1 to 0, reversing the
# linear positions for that edge.
#
-# For more examples, see
+# For more examples, see
# [this notebook](https://github.com/LorenFrankLab/track_linearization/blob/master/notebooks/).
# +
@@ -223,7 +223,7 @@
# ## Linearization
-# With linearization parameters, we specify the position interval we wish to
+# With linearization parameters, we specify the position interval we wish to
# linearize.
# +
@@ -368,3 +368,9 @@
print("Press the 'esc' key to start a new polygon.")
print("Try holding the 'shift' key to move all of the vertices.")
print("Try holding the 'ctrl' key to move a single vertex.")
+# -
+
+# ## Up Next
+#
+# Next, we'll combine ephys and position data in a process called
+# [ripple detection](./30_Ripple_Detection.ipynb).
diff --git a/notebooks/py_scripts/30_Ripple_Detection.py b/notebooks/py_scripts/30_Ripple_Detection.py
index 581418747..0ad62b0bb 100644
--- a/notebooks/py_scripts/30_Ripple_Detection.py
+++ b/notebooks/py_scripts/30_Ripple_Detection.py
@@ -5,7 +5,7 @@
# extension: .py
# format_name: light
# format_version: '1.5'
-# jupytext_version: 1.14.5
+# jupytext_version: 1.14.7
# kernelspec:
# display_name: Python [conda env:spyglass-position3] *
# language: python
@@ -71,7 +71,7 @@
# ?sgr.RippleLFPSelection.set_lfp_electrodes
-# We'll need the `nwb_file_name`, an `electrode_list`, and to a `group_name`.
+# We'll need the `nwb_file_name`, an `electrode_list`, and to a `group_name`.
#
# - By default, `group_name` is set to CA1 for ripple detection, but we could
# alternatively use PFC.
@@ -162,7 +162,7 @@
# - `speed_name`: the name of the speed parameters in `IntervalPositionInfo`
#
# For the `Kay_ripple_detector` (options are currently Kay and Karlsson, see `ripple_detection` package for specifics) the parameters are:
-#
+#
# - `speed_threshold` (cm/s): maximum speed the animal can move
# - `minimum_duration` (s): minimum time above threshold
# - `zscore_threshold` (std): minimum value to be considered a ripple, in standard
@@ -187,7 +187,7 @@
# We'll use the `head_speed` above as part of `RippleParameters`.
-# ## Run Ripple Detection
+# ## Run Ripple Detection
#
#
@@ -204,3 +204,7 @@
ripple_times = (sgrip.RippleTimesV1() & key).fetch1_dataframe()
ripple_times
+
+# ## Up Next
+#
+# Next, we'll [extract mark indicator](./31_Extract_Mark_Indicators.ipynb).
diff --git a/notebooks/py_scripts/31_Extract_Mark_Indicators.py b/notebooks/py_scripts/31_Extract_Mark_Indicators.py
index d07f8f047..d00e7acc4 100644
--- a/notebooks/py_scripts/31_Extract_Mark_Indicators.py
+++ b/notebooks/py_scripts/31_Extract_Mark_Indicators.py
@@ -5,7 +5,7 @@
# extension: .py
# format_name: light
# format_version: '1.5'
-# jupytext_version: 1.14.5
+# jupytext_version: 1.14.7
# kernelspec:
# display_name: Python 3.10.5 64-bit
# language: python
@@ -20,7 +20,7 @@
# _Developer Note:_ if you may make a PR in the future, be sure to copy this
# notebook, and use the `gitignore` prefix `temp` to avoid future conflicts.
#
-# This is one notebook in a multi-part series on clusterless decoding in Spyglass
+# This is one notebook in a multi-part series on clusterless decoding in Spyglass
#
# - To set up your Spyglass environment and database, see
# [the Setup notebook](./00_Setup.ipynb)
@@ -146,3 +146,9 @@
# We can verify that this worked:
sgd.UnitMarksIndicator & spikesorting_selections
+
+# ## Up Next
+#
+# Next, we'll start the process of decoding representations of position with
+# ephys data. This can be done either with [GPUs](./32_Decoding_with_GPUs.ipynb)
+# or [clusterless](./33_Decoding_Clusterless.ipynb).
diff --git a/notebooks/py_scripts/32_Decoding_with_GPUs.py b/notebooks/py_scripts/32_Decoding_with_GPUs.py
index 8127ac028..3020b316e 100644
--- a/notebooks/py_scripts/32_Decoding_with_GPUs.py
+++ b/notebooks/py_scripts/32_Decoding_with_GPUs.py
@@ -5,7 +5,7 @@
# extension: .py
# format_name: light
# format_version: '1.5'
-# jupytext_version: 1.14.5
+# jupytext_version: 1.14.7
# kernelspec:
# display_name: Python 3.10.5 64-bit
# language: python
@@ -21,10 +21,10 @@
# notebook, and use the `gitignore` prefix `temp` to avoid future conflicts.
#
# This is one notebook in a multi-part series on decoding in Spyglass. To set up
-# your Spyglass environment and database, see
+# your Spyglass environment and database, see
# [the Setup notebook](./00_Setup.ipynb).
#
-# In this tutorial, we'll set up GPU access for subsequent decoding analyses. While this notebook doesn't have any direct prerequisites, you will need
+# In this tutorial, we'll set up GPU access for subsequent decoding analyses. While this notebook doesn't have any direct prerequisites, you will need
# [Spike Sorting](./02_Spike_Sorting.ipynb) data for the next step.
#
@@ -32,7 +32,7 @@
#
# ### Connecting
-#
+#
# Members of the Frank Lab have access to two GPU cluster, `breeze` and `zephyr`.
# To access them, specify the cluster when you `ssh`, with the default port:
#
@@ -58,7 +58,7 @@
# ### Which GPU?
#
# You can see which GPUs are occupied by running the command `nvidia-smi` in
-# a terminal (or `!nvidia-smi` in a notebook). Pick a GPU with low memory usage.
+# a terminal (or `!nvidia-smi` in a notebook). Pick a GPU with low memory usage.
#
# In the output below, GPUs 1, 4, 6, and 7 have low memory use and power draw (~42W), are probably not in use.
@@ -70,7 +70,7 @@
#
# Other ways to monitor GPU usage are:
#
-# - A
+# - A
# [jupyter widget by nvidia](https://github.com/rapidsai/jupyterlab-nvdashboard)
# to monitor GPU usage in the notebook
# - A [terminal program](https://github.com/peci1/nvidia-htop) like nvidia-smi
@@ -185,7 +185,7 @@
# conda install -c rapidsai -c nvidia -c conda-forge dask-cuda
# ```
#
-# We will set up a client to select GPUs. By default, this is all available
+# We will set up a client to select GPUs. By default, this is all available
# GPUs. Below, we select a subset using the `CUDA_VISIBLE_DEVICES`.
# +
@@ -202,7 +202,6 @@
#
# In the example below, we run `test_gpu` on each item of `data` where each item is processed on a different GPU.
-
# +
def setup_logger(name_logfile, path_logfile):
"""Sets up a logger for each function that outputs
@@ -251,3 +250,5 @@ def test_gpu(x, ind):
# -
# This example also shows how to create a log file for each item in data with the `setup_logger` function.
+
+
diff --git a/notebooks/py_scripts/33_Decoding_Clusterless.py b/notebooks/py_scripts/33_Decoding_Clusterless.py
index 9c1ab81d5..f0742659a 100644
--- a/notebooks/py_scripts/33_Decoding_Clusterless.py
+++ b/notebooks/py_scripts/33_Decoding_Clusterless.py
@@ -5,7 +5,7 @@
# extension: .py
# format_name: light
# format_version: '1.5'
-# jupytext_version: 1.14.5
+# jupytext_version: 1.14.7
# kernelspec:
# display_name: spyglass
# language: python
@@ -24,15 +24,15 @@
#
# - To set up your Spyglass environment and database, see
# [the Setup notebook](./00_Setup.ipynb)
-# - This tutorial assumes you've already
-# [extracted marks](./31_Extract_Mark_Indicators.ipynb), as well as loaded
+# - This tutorial assumes you've already
+# [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).
-# - This tutorial also assumes you're familiar with how to run processes on GPU,
+# - 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
+# this notebook will refer to a `decode_1d` variable set in
# [select data](#select-data) to include these steps.
#
@@ -115,7 +115,7 @@
# for items that look overly correlated (strong diagonal on the off-diagonal
# plots) and extreme amplitudes.
#
-# For tutorial purposes, we only look at the first 2 plots, but removing this
+# For tutorial purposes, we only look at the first 2 plots, but removing this
# argument will show all plots.
sgd_clusterless.UnitMarksIndicator.plot_all_marks(marks, plot_limit=2)
@@ -125,7 +125,7 @@
# ### Get position
-# Next, we'll grab the 2D position data from `IntervalPositionInfo` table.
+# Next, we'll grab the 2D position data from `IntervalPositionInfo` table.
#
# _Note:_ Position will need to be upsampled to our decoding frequency (500 Hz).
# See [this notebook](./20_Position_Trodes.ipynb#upsampling-position) for more
@@ -261,7 +261,7 @@
# ## Decoding
#
-# After sanity checks, we can finally get to decoding.
+# After sanity checks, we can finally get to decoding.
#
# _Note:_ Portions of the code below have been integrated into
# `spyglass.decoding`, but are presented here in full.
diff --git a/src/spyglass/position/v1/position_trodes_position.py b/src/spyglass/position/v1/position_trodes_position.py
index 050282161..1404b831e 100644
--- a/src/spyglass/position/v1/position_trodes_position.py
+++ b/src/spyglass/position/v1/position_trodes_position.py
@@ -180,9 +180,19 @@ def make(self, key):
)
position_info_parameters = (TrodesPosParams() & key).fetch1("params")
+
+ # Renamed per discussion on #628
+ position_info_parameters[
+ "max_LED_separation"
+ ] = position_info_parameters.get("max_separation", 9.0)
+ position_info_parameters[
+ "max_plausible_speed"
+ ] = position_info_parameters.get("max_speed", 300)
+
spatial_series = (RawPosition.PosObject & key).fetch_nwb()[0][
"raw_position"
]
+
spatial_df = (RawPosition.PosObject & key).fetch1_dataframe()
video_frame_ind = getattr(spatial_df, "video_frame_ind", None)
@@ -304,8 +314,10 @@ def calculate_position_info_from_spatial_series(
is_upsampled,
upsampling_sampling_rate,
upsampling_interpolation_method,
+ **kwargs,
):
"""Calculate position info from 2D spatial series."""
+ # TODO: remove in favor of fetching the same from common?
CM_TO_METERS = 100
# Accepts x/y 'loc' or 'loc1' format for first pos. Renames to 'loc'
DEFAULT_COLS = ["xloc", "yloc", "xloc2", "yloc2", "xloc1", "yloc1"]
@@ -347,7 +359,7 @@ def calculate_position_info_from_spatial_series(
# Set points to NaN where the front and back LEDs are too separated
dist_between_LEDs = get_distance(back_LED, front_LED)
- is_too_separated = dist_between_LEDs >= max_separation
+ is_too_separated = dist_between_LEDs >= max_LED_separation
back_LED[is_too_separated] = np.nan
front_LED[is_too_separated] = np.nan
@@ -367,8 +379,8 @@ def calculate_position_info_from_spatial_series(
)
# Set to points to NaN where the speed is too fast
- is_too_fast = (front_LED_speed > max_speed) | (
- back_LED_speed > max_speed
+ is_too_fast = (front_LED_speed > max_plausible_speed) | (
+ back_LED_speed > max_plausible_speed
)
back_LED[is_too_fast] = np.nan
front_LED[is_too_fast] = np.nan