update docs notebook with new ToTimesurface transform

docs/gallery/representations/plot_totimesurface.py

@@ -3,19 +3,14 @@
 ToTimesurface
 =============
 
-:class:`~tonic.transforms.ToTimesurface` creates local or global time surfaces for each event.
+:class:`~tonic.transforms.ToTimesurface` creates global time surfaces at a specific time interval dt.
 """
 
 import tonic
 
 nmnist = tonic.datasets.NMNIST("../../tutorials/data", train=False)
 events, label = nmnist[0]
 
-
-####################################
-# ToTimesurface global
-# --------------------------------
-
 transform = tonic.transforms.ToTimesurface(
     sensor_size=nmnist.sensor_size,
     tau=30000,
@@ -25,17 +20,3 @@
 frames = transform(events)
 
 ani = tonic.utils.plot_animation(frames)
-
-
-####################################
-# ToTimesurface local
-# --------------------------------
-
-transform = tonic.transforms.ToTimesurface(
-    sensor_size=nmnist.sensor_size, surface_dimensions=(9, 9), tau=100000, decay="exp"
-)
-
-frames = transform(events)
-
-# only plot a few of them
-ani = tonic.utils.plot_animation(frames[::70])

docs/getting_involved/contribute.rst

@@ -38,7 +38,7 @@ To download a dataset, Tonic makes use of a copy of pytorch vision's tools that
 `here <https://github.com/neuromorphs/tonic/blob/develop/tonic/datasets/download_utils.py>`_.
 Cloud providers such as Dropbox, Google Drive, OneDrive etc. are usually not straightforward 
 to automatically download from, so if the dataset owner provides multiple mirrors,
-prefer a simple REST-like webpage such as https://www.neuromorphic-vision.com/public/downloads/.
+prefer a simple REST-like webpage.
 
 The trickier question is the one of file decoding. There exist a variety of file formats that 
 events and related data are saved in, including .txt, .aedat, .dat, .es, .mat, .hdf5, .rosbag 

docs/getting_started/nmnist.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "72ab4fec-4fe8-49cc-867f-6a77bcfaa062",
    "metadata": {
@@ -37,6 +38,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6e9f45be-20da-44f8-962b-2b842fc862df",
    "metadata": {},
@@ -56,6 +58,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dba2d3f0-f82b-4f16-a06f-97669b73bec4",
    "metadata": {},
@@ -82,6 +85,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dcfd768d-0a7f-48c8-b1d1-6f80d0b516a4",
    "metadata": {},
@@ -112,6 +116,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "9733a1da-96a5-4ab1-ad0e-d0d46f7a305c",
    "metadata": {},
@@ -137,6 +142,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "10406fbc-ddb2-4104-9a0d-ab375f3e653c",
    "metadata": {
@@ -167,6 +173,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "5c9d5bb7-8658-409a-bcc7-24c7e0587479",
    "metadata": {},
@@ -182,7 +189,7 @@
    "outputs": [],
    "source": [
     "surfaces = transforms.ToTimesurface(\n",
-    "    sensor_size=sensor_size, surface_dimensions=None, tau=10000, decay=\"exp\"\n",
+    "    sensor_size=sensor_size, dt=10000, tau=30000,\n",
     ")(events_denoised)\n",
     "\n",
     "n_events = events_denoised.shape[0]\n",
@@ -196,6 +203,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a167e44a-39d8-4e42-b5e5-39a9e9179463",
    "metadata": {},
@@ -220,6 +228,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b2f027cc-55ba-46b6-848d-deedf4e5a3e4",
    "metadata": {},
@@ -245,6 +254,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "aac04620-cf13-4c03-b653-7d3520d52380",
    "metadata": {},

tonic/transforms.py

@@ -904,18 +904,12 @@ def __call__(self, events):
 
 @dataclass(frozen=True)
 class ToTimesurface:
-    """Create global or local time surfaces for each event. Modeled after the paper Lagorce et al.
-    2016, Hots: a hierarchy of event-based time-surfaces for pattern recognition
-    https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7508476.
+    """Create global time surfaces at a specific time interval dt.
 
     Parameters:
-        sensor_size: a 3-tuple of x,y,p for sensor_size
-        surface_dimensions (int, int): width does not have to be equal to height, however both numbers have to be odd.
-            if surface_dimensions is None: the time surface is defined globally, on the whole sensor grid.
-        tau (float): time constant to decay events around occuring event with.
-        delta_t (float): the interval at which the time-surfaces are accumulated, if set 0 number of time-surfaces will
-            equal to the number of events. (defaults to 0.0)
-        decay (str): can be either 'lin' or 'exp', corresponding to linear or exponential decay.
+        sensor_size: A 3-tuple of x,y,p for sensor_size
+        dt (float): The interval at which the time-surfaces are accumulated.
+        tau (float): Time constant to decay events with.
     """
 
     sensor_size: Tuple[int, int, int]