diff --git a/README.md b/README.md
index 7fa3fd7..35accd0 100644
--- a/README.md
+++ b/README.md
@@ -63,6 +63,7 @@ positional arguments:
{config,render,plot} Command
config Edit the TAL configuration file
render Create, edit or execute renders of simulated NLOS scene data captures
+ noise_simulation Simulate noise for already generated capture data
plot Plot capture data using one of the configured methods
optional arguments:
@@ -251,6 +252,34 @@ V = np.moveaxis(np.mgrid[-1:1.1:0.1, -1:1.1:0.1, 0.5:2.6:0.1], 0, -1).reshape(-1
reconstruction = tal.reconstruct.pf.solve(data, 6, 4, V, verbose=3, n_threads=1)
```
+## `tal noise_simulation`: Command line tool to simulate SPAD noise
+
+`tal noise_simulation` modifies a transient capture previously generated with `tal render`, simulating the noise
+caused by a capture with a Single Photon Avalanche Diode (SPAD) sensor. Following [Hernandez2017] the noise simulation
+takes random photon samples from the ground truth transient signal, with an added temporal jitter randomly sampled from
+the time jitter function of the SPAD, as well as the gaussian pulse of the laser.
+
+The simulation also models other sources of noise, namely dark counts and external noise caused by ambient lighting, as
+well as afterpulsing. You can find examples on how to use the noise simulation in the
+[`examples`](https://github.com/diegoroyo/tal/tree/master/examples) folder of this repository.
+Note that to test the noise simulation you will need to have a HDF5 capture file.
+If you don't, please check the `tal render` section or [convert your data to a format usable by `tal`](https://github.com/diegoroyo/tal/blob/master/tal/io/format.py).
+
+
+```
+❯ tal noise_simulation -h
+usage: tal noise_simulation [-h] -c CAPTURE_FILE -n NOISE_CONFIG_FILE -o OUTPUT_PATH
+
+options:
+ -h, --help show this help message and exit
+ -c CAPTURE_FILE, --capture_file CAPTURE_FILE
+ Path to the .hdf5 capture file to add noise to
+ -n NOISE_CONFIG_FILE, --noise_config_file NOISE_CONFIG_FILE
+ Path to the .yaml configuration file for the noise simulation
+ -o OUTPUT_PATH, --output_path OUTPUT_PATH
+ Path to save the capture data with the simulated noise
+```
+
### Logging
The verbosity of the output can be controlled through `tal.set_log_level(level)`.
@@ -290,6 +319,8 @@ tal.set_resources(4) # use 4 CPUs
> [Liu2019] Liu, X., Guillén, I., La Manna, M., Nam, J. H., Reza, S. A., Huu Le, T., ... & Velten, A. (2019). Non-line-of-sight imaging using phasor-field virtual wave optics. Nature, 572(7771), 620-623.
+> [Hernandez2017] Hernández, Q., Gutiérrez, D., Jarabo, A. (2017) A Computational Model of a Single-Photon Avalanche Diode Sensor for Transient Imaging. Technical report (arXiv:1703.02635).
+
### License and citation
`tal` is licensed under the GPL-3.0 license. If you use `tal` in an academic work, we would appreciate if you cited our work:
diff --git a/examples/noise-simulation/Z.obj b/examples/noise-simulation/Z.obj
new file mode 100644
index 0000000..bcc9e2a
--- /dev/null
+++ b/examples/noise-simulation/Z.obj
@@ -0,0 +1,180 @@
+# Blender 4.0.2
+# www.blender.org
+o hidden_geometry_Z
+v 0.364878 0.296774 0.004000
+v -0.400000 0.400000 0.004000
+v 0.364878 0.400000 0.004000
+v 0.364878 0.296774 0.004000
+v -0.164553 0.296774 0.004000
+v -0.400000 0.400000 0.004000
+v -0.164553 0.296774 0.004000
+v 0.164553 -0.296774 0.004000
+v -0.400000 0.400000 0.004000
+v 0.400000 -0.400000 0.004000
+v 0.164553 -0.296774 0.004000
+v -0.164553 0.296774 0.004000
+v 0.400000 -0.400000 0.004000
+v -0.400000 -0.296774 0.004000
+v 0.164553 -0.296774 0.004000
+v 0.400000 -0.400000 0.004000
+v -0.400000 -0.400000 0.004000
+v -0.400000 -0.296774 0.004000
+v 0.364878 0.296774 -0.004000
+v 0.364878 0.400000 -0.004000
+v -0.400000 0.400000 -0.004000
+v 0.364878 0.296774 -0.004000
+v -0.400000 0.400000 -0.004000
+v -0.164553 0.296774 -0.003999
+v -0.164553 0.296774 -0.003999
+v -0.400000 0.400000 -0.004000
+v 0.164553 -0.296774 -0.004000
+v 0.400000 -0.400000 -0.003999
+v -0.164553 0.296774 -0.003999
+v 0.164553 -0.296774 -0.004000
+v 0.400000 -0.400000 -0.003999
+v 0.164553 -0.296774 -0.004000
+v -0.400000 -0.296774 -0.004000
+v 0.400000 -0.400000 -0.003999
+v -0.400000 -0.296774 -0.004000
+v -0.400000 -0.400000 -0.004000
+v -0.400000 0.400000 0.004000
+v 0.164553 -0.296774 0.004000
+v 0.164553 -0.296774 -0.004000
+v -0.400000 0.400000 -0.004000
+v 0.164553 -0.296774 0.004000
+v -0.400000 -0.296774 0.004000
+v -0.400000 -0.296774 -0.004000
+v 0.164553 -0.296774 -0.004000
+v 0.400000 -0.400000 0.004000
+v -0.164553 0.296774 0.004000
+v -0.164553 0.296774 -0.003999
+v 0.400000 -0.400000 -0.003999
+v 0.364878 0.400000 0.004000
+v -0.400000 0.400000 0.004000
+v -0.400000 0.400000 -0.004000
+v 0.364878 0.400000 -0.004000
+v 0.364878 0.296774 0.004000
+v 0.364878 0.400000 0.004000
+v 0.364878 0.400000 -0.004000
+v 0.364878 0.296774 -0.004000
+v -0.400000 -0.400000 0.004000
+v 0.400000 -0.400000 0.004000
+v 0.400000 -0.400000 -0.003999
+v -0.400000 -0.400000 -0.004000
+v -0.164553 0.296774 0.004000
+v 0.364878 0.296774 0.004000
+v 0.364878 0.296774 -0.004000
+v -0.164553 0.296774 -0.003999
+v -0.400000 -0.296774 0.004000
+v -0.400000 -0.400000 0.004000
+v -0.400000 -0.400000 -0.004000
+v -0.400000 -0.296774 -0.004000
+v 0.357368 0.303546 -0.000619
+v 0.357368 0.393227 -0.000619
+v -0.390323 0.395568 -0.000410
+v 0.357368 0.303546 -0.000619
+v -0.390323 0.395568 -0.000410
+v -0.167537 0.298140 -0.003730
+v -0.167537 0.298140 -0.003730
+v -0.390323 0.395568 -0.000410
+v 0.167536 -0.298140 -0.003731
+v 0.390322 -0.395569 -0.000410
+v -0.167537 0.298140 -0.003730
+v 0.167536 -0.298140 -0.003731
+v 0.390322 -0.395569 -0.000410
+v 0.167536 -0.298140 -0.003731
+v -0.392490 -0.303546 -0.000619
+v 0.390322 -0.395569 -0.000410
+v -0.392490 -0.303546 -0.000619
+v -0.392490 -0.393228 -0.000619
+v 0.357368 0.303546 0.000618
+v -0.390323 0.395568 0.000410
+v 0.357368 0.393227 0.000618
+v 0.357368 0.303546 0.000618
+v -0.167537 0.298141 0.003731
+v -0.390323 0.395568 0.000410
+v -0.167537 0.298141 0.003731
+v 0.167536 -0.298140 0.003730
+v -0.390323 0.395568 0.000410
+v 0.390322 -0.395569 0.000410
+v 0.167536 -0.298140 0.003730
+v -0.167537 0.298141 0.003731
+v 0.390322 -0.395569 0.000410
+v -0.392490 -0.303546 0.000618
+v 0.167536 -0.298140 0.003730
+v 0.390322 -0.395569 0.000410
+v -0.392490 -0.393228 0.000618
+v -0.392490 -0.303546 0.000618
+v -0.390323 0.395568 -0.000410
+v -0.390323 0.395568 0.000410
+v 0.167536 -0.298140 0.003730
+v 0.167536 -0.298140 -0.003731
+v 0.167536 -0.298140 -0.003731
+v 0.167536 -0.298140 0.003730
+v -0.392490 -0.303546 0.000618
+v -0.392490 -0.303546 -0.000619
+v 0.390322 -0.395569 -0.000410
+v 0.390322 -0.395569 0.000410
+v -0.167537 0.298141 0.003731
+v -0.167537 0.298140 -0.003730
+v 0.357368 0.393227 -0.000619
+v 0.357368 0.393227 0.000618
+v -0.390323 0.395568 0.000410
+v -0.390323 0.395568 -0.000410
+v 0.357368 0.303546 -0.000619
+v 0.357368 0.303546 0.000618
+v 0.357368 0.393227 0.000618
+v 0.357368 0.393227 -0.000619
+v -0.392490 -0.393228 -0.000619
+v -0.392490 -0.393228 0.000618
+v 0.390322 -0.395569 0.000410
+v 0.390322 -0.395569 -0.000410
+v -0.167537 0.298140 -0.003730
+v -0.167537 0.298141 0.003731
+v 0.357368 0.303546 0.000618
+v 0.357368 0.303546 -0.000619
+v -0.392490 -0.303546 -0.000619
+v -0.392490 -0.303546 0.000618
+v -0.392490 -0.393228 0.000618
+v -0.392490 -0.393228 -0.000619
+s 1
+f 1 3 2
+f 4 6 5
+f 7 9 8
+f 10 12 11
+f 13 15 14
+f 16 18 17
+f 19 21 20
+f 22 24 23
+f 25 27 26
+f 28 30 29
+f 31 33 32
+f 34 36 35
+f 37 40 39 38
+f 41 44 43 42
+f 45 48 47 46
+f 49 52 51 50
+f 53 56 55 54
+f 57 60 59 58
+f 61 64 63 62
+f 65 68 67 66
+f 69 71 70
+f 72 74 73
+f 75 77 76
+f 78 80 79
+f 81 83 82
+f 84 86 85
+f 87 89 88
+f 90 92 91
+f 93 95 94
+f 96 98 97
+f 99 101 100
+f 102 104 103
+f 105 108 107 106
+f 109 112 111 110
+f 113 116 115 114
+f 117 120 119 118
+f 121 124 123 122
+f 125 128 127 126
+f 129 132 131 130
+f 133 136 135 134
diff --git a/examples/noise-simulation/configuration.yaml b/examples/noise-simulation/configuration.yaml
new file mode 100644
index 0000000..bf27956
--- /dev/null
+++ b/examples/noise-simulation/configuration.yaml
@@ -0,0 +1,42 @@
+##
+# SPAD parameters
+##
+time_jitter_FWHM: 20 # FWHM of the gaussian component of the SPAD's time-jitter, in picoseconds
+time_jitter_tail: 50 # Tail (exponential decay parameter) of the exponential component of the SPAD's time-jitter, in picoseconds
+time_jitter_tail_scale: 0.8 # Relative scale of the peak of the exponential decay in the time-jitter with respect to the gaussian component
+time_jitter_n_timebins: 640 # Number of timebins of the jitter function
+time_jitter_timebin_width: 0.75 # Timebin width of the jitter function in picoseconds. Should be as precise as possible, to avoid aliasing
+
+# Load experimental time-jitter capture from hdf5 file. If defined, the previous parameters are ignored
+# time_jitter_path: './spad_data_1.hdf5'
+time_jitter_path: '' # Leave the path empty (or not set) to use the parametric jitter
+
+photon_detection_ratio: 0.3 # Ratio of photons that are actually detected, in range [0, 1]
+dead_time: 1000 # Hold-off time of the SPAD after each detected photon, in picoseconds
+simulate_afterpulses: False # If True, simulates SPAD afterpulsing (increases execution time). If False, afterpulsing is ignored.
+afterpulse_probability : 0.10 # Probability of each detected photon of generating an afterpulse, in range [0, 1]
+exposure_time: 0.001 # Exposure time for each captured point, in seconds
+number_of_samples: 0 # Number of captured photons per measurements. If 0 or non-defined, it will be computed
+ # from exposure time, laser frequency and the photon detection ratio
+sensor_type: 'event' # Either frame (for frame-based SPADs) or event (for event-based SPADs)
+
+##
+# Frame based SPAD parameters, used if camera_type == 'frame'
+##
+frame_exposure_time: 100 # Exposure time of each SPAD frame in microseconds
+# n_frames: 500_000 # Number of measured frames in the capture. During each frame, only one photon can be captured
+ # If not set, this number is obtained from the total exposure time and the exposure time of each frame
+
+##
+# External noise
+##
+dark_count_rate: 0 # Number of dark counts per second
+external_noise_rate: 0 # Number of counts caused by external noise (ambient light) per second
+number_of_false_counts: 0 # Number of false positive photons (either dark counts or external noise).
+ # If 0 or non-defined, this value is computed from exposure time and noise rates
+
+##
+# Laser
+##
+laser_jitter_FWHM: 30 # FWHM of the gaussian laser pulse, in picoseconds
+frequency: 20 # Pulse frequency (nº of pulses per second) in MHz
diff --git a/examples/noise-simulation/nlos-z/nlos-z.yaml b/examples/noise-simulation/nlos-z/nlos-z.yaml
new file mode 100644
index 0000000..bc33225
--- /dev/null
+++ b/examples/noise-simulation/nlos-z/nlos-z.yaml
@@ -0,0 +1,168 @@
+# TAL v0.10.3 NLOS scene description file: https://github.com/diegoroyo/tal
+# Created on 2023/11/28 with experiment name "nlos-z"
+
+# TAL uses YAML files to configure everything in the NLOS setup:
+# 1) Parameters for the light transport simulation that will be
+# executed using Transient Mitsuba 3 (see section labeled as "Mitsuba")
+# 2) Laser and sensor position, and where they are pointed towards
+# (see section labeled as "Laser and sensor")
+# 3) Geometry of the scene (visible or hidden): its location, materials, etc.
+# (see sections labeled as "Geometry", "Relay wall" and "Materials reference")
+
+# This is the YAML file that you would get if you executed the command
+# "tal render new nlos-z" on your shell.
+
+# Basically, this file specifies different properties under YAML format.
+# For example:
+name: nlos-z
+# this sets the parameter "name" of the scene as "nlos-z"
+
+# Note: when you generate a YAML file, you will see that many parameters
+# are commented. When you see this written:
+#parameter: value
+# It means that the default value for "parameter" is "value", you need
+# to uncomment that line and modify the value if you want to change it.
+
+# Now for sections 1) 2) and 3)
+
+##
+# Mitsuba
+##
+# See https://mitsuba.readthedocs.io/en/latest/src/key_topics/variants.html
+# you should have compiled this variant previously
+# See mitsuba{2|3}-transient-nlos repository for more information
+mitsuba_variant: llvm_ad_mono
+# Transient Mitsuba's intergator properties
+# See mitsuba{2|3}-transient-nlos' documentation for transient_nlos_path
+integrator_max_depth: -1
+integrator_filter_depth: -1
+integrator_discard_direct_paths: false
+integrator_nlos_laser_sampling: true
+integrator_nlos_hidden_geometry_sampling: true
+integrator_nlos_hidden_geometry_sampling_do_rroulette: false
+integrator_nlos_hidden_geometry_sampling_includes_relay_wall: true
+# Number of samples per pixel (spatial pixel, not time dimension pixel)
+# NOTE: for the noise simulation is important to have a high number of samples, so the transient signal converges.
+# Then, the desired amount of noise can be obtained through tal noise_simulation.
+sample_count: 1000000
+# Transient Mitsuba's film properties
+# See mitsuba{2|3}-transient-nlos' documentation for transient_hdr_film
+account_first_and_last_bounces: false
+num_bins: 320
+bin_width_opl: 0.003 # 10.01ps
+start_opl: 1.95 # 6ns
+auto_detect_bins: false
+
+##
+# Laser and sensor
+##
+# single: The laser illuminates one point in the relay wall,
+# the sensor captures all points in the relay wall
+# confocal: The laser illuminates one point in the relay wall,
+# the sensor only captures that same point in the relay wall
+# exhaustive: The laser and sensor illuminate and capture all points
+# on the relay wall
+scan_type: confocal # single, confocal or exhaustive
+# XYZ coordinates of the laser and sensor devices
+# Note that this is NOT the illuminated or captured points' position
+# on the relay wall.
+sensor_x: -0.5
+sensor_y: 0.0
+sensor_z: 0.25
+laser_x: -0.5
+laser_y: 0.0
+laser_z: 0.25
+# The relay wall is divided into a 2D grid of uniformly sized regions
+# which represent the pixels of the rendered image. This sets the number of
+# subdivisions in the X and Y dimensions.
+# The division depends on the UV coordinates of the mesh. E.g. if sensor_width
+# and sensor_height are set to 2, it will have a grid of 2x2 pixels, where the
+# top-left pixel goes from UV (0, 0) to UV (0.5, 0.5).
+# It is important to use a "rectangle" relay wall for correct UV coordinates.
+sensor_width: 32
+sensor_height: 32
+# --- for scan_type=single
+# if set to null, laser points to the center pixel
+# if set to a number, laser points to that pixel (e.g. center = 128, 128)
+laser_lookat_x: null
+laser_lookat_y: null
+# --- for scan_type=confocal/exhaustive
+laser_width: 32
+laser_height: 32
+# these next four variables specify the scanned area of the relay wall
+# they are in UV coordinates, where (0, 0) is the top-left corner and (1, 1) is the bottom-right corner
+# by default they cover the whole relay wall i.e. (0, 0) to (1, 1)
+laser_aperture_start_x: 0 # 0: left, 1: right of relay wall
+laser_aperture_start_y: 0 # 0: top, 1: bottom of relay wall
+laser_aperture_end_x: 1
+laser_aperture_end_y: 1
+
+##
+# Geometry (valid for hidden geometry or relay wall)
+##
+# The geometry YAML parameter should be a list of elements as below
+# Some parameters are optional (displacement, rot_degrees)
+geometry:
+# This is an example to define a geometry element from a OBJ file
+- name: Z
+ description: >
+ Hidden geometry of the scene
+ # mesh type: obj should define the filename of the OBJ
+ # where you specify the full or relative path to that OBJ
+ mesh:
+ type: obj
+ filename: ./Z.obj
+ # Translate, rotate and scale the OBJ before placing it in Mitsuba
+ displacement_x: 0.0
+ displacement_y: 0.0
+ displacement_z: 1.0 # using default RW settings this corresponds to depth
+ rot_degrees_x: 0.0
+ rot_degrees_y: 0.0
+ rot_degrees_z: 0.0
+ scale: 1
+ # Material of the object. See "material reference" below
+ material:
+ id: white
+# This is another example that defines the relay wall as a rectangle
+# you typically want to use exactly this for all your scenes
+- name: relay_wall
+ mesh:
+ type: rectangle
+ displacement_x: 0.0
+ displacement_y: 0.0
+ displacement_z: 0.0
+ scale_x: 1 # 1: relay wall is 2x2, 0.5: relay wall is 1x1 (X dimension)
+ scale_y: 1 # same for Y dimension
+ scale_z: 1 # Only valid values are: +1: normal is (0, 0, 1), -1. normal is (0, 0, -1)
+ # Material of the object. See "material reference" below
+ material:
+ id: white
+
+##
+# Relay walls
+##
+# Define which of the elements in the "geometry" list should act as the relay wall
+# We set the relay wall to the geometry named "relay wall"
+relay_wall: relay_wall # must correspond to a geometry name above
+
+##
+# Implemented materials reference
+# (variables e.g. $alpha are substituted)
+##
+
+#| id: white
+#|
+#|
+#|
+
+#| id: copper
+#| alpha: $alpha
+#|
+#|
+#|
+#|
+#|
+
+#| id: custom
+#| text: $text
+#| $text
\ No newline at end of file
diff --git a/examples/noise-simulation/noise.ipynb b/examples/noise-simulation/noise.ipynb
new file mode 100644
index 0000000..d7c6c51
--- /dev/null
+++ b/examples/noise-simulation/noise.ipynb
@@ -0,0 +1,1105 @@
+{
+ "cells": [
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "# Noise simulation\n",
+ "\n",
+ "`tal noise_simulation` simulates the noise introduced in transient captures by SPAD sensors. The time jitter present in the SPAD,\n",
+ " as well as the temporal width of the laser pulses introduce temporal uncertainty in the captured photons. Furthermore, `tal` allows\n",
+ " the simulation of other sources of noise, such as dark counts and triggers caused by ambient light, as well as artifacts caused\n",
+ " by the afterpulsing of the detected photons."
+ ],
+ "id": "1dc539654d0a641a"
+ },
+ {
+ "cell_type": "code",
+ "id": "initial_id",
+ "metadata": {
+ "collapsed": true,
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:50:54.885099Z",
+ "start_time": "2025-09-08T13:50:54.878363Z"
+ }
+ },
+ "source": [
+ "import numpy as np\n",
+ "import tal\n",
+ "from tal.io import read_capture\n",
+ "import matplotlib.pyplot as plt\n",
+ "import os\n",
+ "import yaml\n",
+ "import subprocess\n",
+ "\n",
+ "# TODO: if you want to follow this tutorial,\n",
+ "# you need to have rendered the scene using the \"tal render nlos-z\" shell command\n",
+ "# See README.md for more information\n",
+ "# Write here vvvvvvvvvvvvvvv the path to your rendered scene\n",
+ "root = 'nlos-z/YYYYMMDD-HHMMSS'\n",
+ "\n",
+ "capture_path = f'{root}/nlos-z.hdf5' # Input transient capture path\n",
+ "output_path = f'{root}/nlos-z-noisy.hdf5' # Output path\n",
+ "noise_configuration_path = 'configuration.yaml' # Path to the .yaml for the configuration of the noise\n",
+ "noise_configuration_copy_path = 'configuration-copy.yaml'\n",
+ "noise_configuration_dict = yaml.safe_load(open(noise_configuration_path, 'r'))\n",
+ "\n",
+ "\n",
+ "def save_yaml(path, data):\n",
+ " with open(path, 'w') as file:\n",
+ " yaml.dump(data, file)"
+ ],
+ "outputs": [],
+ "execution_count": 187
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "## Setting a time jitter function\n",
+ "\n",
+ "The time jitter function of the system to simulate can be either loaded from an hdf5 file, or generated given its parameters.\n",
+ "The time jitter file must have the following fields:\n",
+ "- counts: transient signal of the time jitter\n",
+ "- n_timebins: number of timebins\n",
+ "- timebin_width_ps: temporal width of the timebins, in picoseconds"
+ ],
+ "id": "fe4a305d174e31ea"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:50:58.669906Z",
+ "start_time": "2025-09-08T13:50:54.891923Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# Loading a time jitter function from file\n",
+ "noise_configuration_dict['time_jitter_path'] = './spad_data_1.hdf5'\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict) # Save a modified copy of the configuration\n",
+ "\n",
+ "# Execute the noise simulation\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "\n",
+ "# Load the transient data (original and processed)\n",
+ "H_original = read_capture(capture_path)\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "d512eac723562223",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function loaded from ./spad_data_1.hdf5.\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - Simulated exposure time = 0.001 seconds.\n",
+ " - Laser frequency = 20.00 MHz.\n",
+ " - Number of photons sampled = 6000\n",
+ " - Number of false positive samples = 0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (6000 samples per measurement)...: 100%|██████████| 1024/1024 [00:00<00:00, 1567.55it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 0.658 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 188
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "The noise simulation works by randomly sampling photons from the original signal. These photons will then have a random jitter value applied, sampled from the defined time jitter function.",
+ "id": "68e820b8a9e1c0b7"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:50:58.931117Z",
+ "start_time": "2025-09-08T13:50:58.722372Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+ "\n",
+ "# Note that the difference in y-axis scale is caused by the normalization and differences between peaks caused by noise\n",
+ "axes[0].plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='original')\n",
+ "axes[0].plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "axes[1].plot(H_noisy.jitter['counts'])\n",
+ "\n",
+ "axes[0].set_title('Transient capture'); axes[0].legend(); axes[1].set_title('Time jitter')\n",
+ "axes[0].set_xlabel('Time (10ps)'); axes[1].set_xlabel('Time (0.75ps)')"
+ ],
+ "id": "b5556ad8bb0c124a",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, 'Time (0.75ps)')"
+ ]
+ },
+ "execution_count": 189,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 189
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:02.403477Z",
+ "start_time": "2025-09-08T13:50:58.951680Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# Loading a different time jitter function\n",
+ "noise_configuration_dict['time_jitter_path'] = './spad_data_2.hdf5'\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict)\n",
+ "\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "201265fd202f7adb",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function loaded from ./spad_data_2.hdf5.\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - Simulated exposure time = 0.001 seconds.\n",
+ " - Laser frequency = 20.00 MHz.\n",
+ " - Number of photons sampled = 6000\n",
+ " - Number of false positive samples = 0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (6000 samples per measurement)...: 100%|██████████| 1024/1024 [00:00<00:00, 1593.39it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 0.647 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 190
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:02.664762Z",
+ "start_time": "2025-09-08T13:51:02.454486Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+ "axes[0].plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='original')\n",
+ "axes[0].plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "axes[1].plot(H_noisy.jitter['counts'])\n",
+ "\n",
+ "axes[0].set_title('Transient capture'); axes[0].legend(); axes[1].set_title('Time jitter')\n",
+ "axes[0].set_xlabel('Time (10ps)'); axes[1].set_xlabel('Time (0.75ps)')"
+ ],
+ "id": "2668b6a61ac666e1",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, 'Time (0.75ps)')"
+ ]
+ },
+ "execution_count": 191,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 191
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "A parametric time jitter function is defined the temporal jitter of the SPAD (as an exponentially modified gaussian)\n",
+ "and the gaussian pulse of the laser. It can be configured modifying the following parameters:\n",
+ "- time_jitter_FWHM: full width at half maximum (FWHM) of the SPAD jitter\n",
+ "- time_jitter_tail: exponential decay parameter of the SPAD jitter's tail\n",
+ "- laser_jitter_FWHM: FWHM of the gaussian laser pulse"
+ ],
+ "id": "cdb4cce7364862cd"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:06.172255Z",
+ "start_time": "2025-09-08T13:51:02.669641Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "noise_configuration_dict['time_jitter_FWHM'] = 30\n",
+ "noise_configuration_dict['time_jitter_tail'] = 70\n",
+ "noise_configuration_dict['laser_jitter_FWHM'] = 45\n",
+ "noise_configuration_dict['time_jitter_path'] = ''\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict)\n",
+ "\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "49cc47ff6eec251a",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function:\n",
+ " - SPAD FWHM = 30 ps\n",
+ " - SPAD tail = 70 ps\n",
+ " - Laser FWHM = 45 ps\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - Simulated exposure time = 0.001 seconds.\n",
+ " - Laser frequency = 20.00 MHz.\n",
+ " - Number of photons sampled = 6000\n",
+ " - Number of false positive samples = 0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (6000 samples per measurement)...: 100%|██████████| 1024/1024 [00:00<00:00, 1497.80it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 0.688 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 192
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:06.464845Z",
+ "start_time": "2025-09-08T13:51:06.222629Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+ "axes[0].plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='original')\n",
+ "axes[0].plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "axes[1].plot(H_noisy.jitter['counts'])\n",
+ "\n",
+ "axes[0].set_title('Transient capture'); axes[0].legend(); axes[1].set_title('Time jitter')\n",
+ "axes[0].set_xlabel('Time (10ps)'); axes[1].set_xlabel('Time (0.75ps)');"
+ ],
+ "id": "4dfffd73c21bb332",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 193
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "## Changing the number of samples\n",
+ "\n",
+ "The number of sampled photons is controlled by setting:\n",
+ "- exposure_time: capture time for each of the measured points\n",
+ "- frequency: laser pulse frequency (number of emissions per seconds)\n",
+ "- photon_detection_ratio: percentage of the photons that reach the sensor that will trigger an avalanche"
+ ],
+ "id": "6eed352e2225af50"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:09.881868Z",
+ "start_time": "2025-09-08T13:51:06.478613Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "noise_configuration_dict['exposure_time'] = 0.001 # Exposure time of 1 millisecond (per measured point)\n",
+ "noise_configuration_dict['frequency'] = 20 # Laser frequency in MHz\n",
+ "noise_configuration_dict['photon_detection_ratio'] = 0.3\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict)\n",
+ "\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "41cee3aec401dec3",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function:\n",
+ " - SPAD FWHM = 30 ps\n",
+ " - SPAD tail = 70 ps\n",
+ " - Laser FWHM = 45 ps\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - Simulated exposure time = 0.001 seconds.\n",
+ " - Laser frequency = 20.00 MHz.\n",
+ " - Number of photons sampled = 6000\n",
+ " - Number of false positive samples = 0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (6000 samples per measurement)...: 100%|██████████| 1024/1024 [00:00<00:00, 1556.68it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 0.662 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 194
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:10.033083Z",
+ "start_time": "2025-09-08T13:51:09.931640Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "plt.plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='Original')\n",
+ "plt.plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "plt.legend()"
+ ],
+ "id": "8f26264a82a96165",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 195,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 195
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:16.711005Z",
+ "start_time": "2025-09-08T13:51:10.038089Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "noise_configuration_dict['exposure_time'] = 0.010 # Increasing exposure time also increases the number of photons, reducing noise\n",
+ "noise_configuration_dict['frequency'] = 20\n",
+ "noise_configuration_dict['photon_detection_ratio'] = 0.3\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict)\n",
+ "\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "93da7e68101fcad7",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function:\n",
+ " - SPAD FWHM = 30 ps\n",
+ " - SPAD tail = 70 ps\n",
+ " - Laser FWHM = 45 ps\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - Simulated exposure time = 0.010 seconds.\n",
+ " - Laser frequency = 20.00 MHz.\n",
+ " - Number of photons sampled = 60000\n",
+ " - Number of false positive samples = 0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (60000 samples per measurement)...: 100%|██████████| 1024/1024 [00:03<00:00, 289.52it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 3.541 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 196
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:16.868273Z",
+ "start_time": "2025-09-08T13:51:16.762167Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "plt.plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='Original')\n",
+ "plt.plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "plt.legend()"
+ ],
+ "id": "6b43a0a1044f9d95",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 197,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 197
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "If desired, the number of sampled photons can also be set manually by modifying the `number_of_samples` field.",
+ "id": "3e7adf4c201d4693"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:24.873645Z",
+ "start_time": "2025-09-08T13:51:16.875679Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "noise_configuration_dict['number_of_samples'] = 100000 # Manually setting the number of samples overrides the exposure time & laser frequency\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict)\n",
+ "\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "bbcacc89e252418a",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function:\n",
+ " - SPAD FWHM = 30 ps\n",
+ " - SPAD tail = 70 ps\n",
+ " - Laser FWHM = 45 ps\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - Number of photons sampled = 100000\n",
+ " - Number of false positive samples = 0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (100000 samples per measurement)...: 100%|██████████| 1024/1024 [00:05<00:00, 196.28it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 5.221 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 198
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:25.059472Z",
+ "start_time": "2025-09-08T13:51:24.924110Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "plt.plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='Original')\n",
+ "plt.plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "plt.legend()"
+ ],
+ "id": "dbeb7e269bf10213",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 199,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 199
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "## Other noise sources\n",
+ "Besides the time jitter, SPADs also experiment noise from avalanches caused by internal charges, known as dark counts.\n",
+ "Furthermore, ambient light will cause false positive photon detections. These noise sources can be configured by\n",
+ "modifying the following fields:\n",
+ "- 'dark_count_rate': number of detected dark counts per second\n",
+ "- 'external_noise_rate': number of counts caused by ambient light per second\n",
+ "\n",
+ "The number of these 'false positive' samples, will be computed from the set rates and the exposure time, and sampled\n",
+ "from an uniform distribution."
+ ],
+ "id": "921ebf769bac5ead"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:29.783901Z",
+ "start_time": "2025-09-08T13:51:25.064084Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "noise_configuration_dict['dark_count_rate'] = 1000\n",
+ "noise_configuration_dict['external_noise_rate'] = 100000\n",
+ "noise_configuration_dict['number_of_samples'] = 30000\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict)\n",
+ "\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "90779440e60207e8",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function:\n",
+ " - SPAD FWHM = 30 ps\n",
+ " - SPAD tail = 70 ps\n",
+ " - Laser FWHM = 45 ps\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - Number of photons sampled = 30000\n",
+ " - Number of false positive samples = 1010\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (30000 samples per measurement)...: 100%|██████████| 1024/1024 [00:01<00:00, 536.62it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 1.912 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 200
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:30.129207Z",
+ "start_time": "2025-09-08T13:51:29.835281Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "plt.plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='Original')\n",
+ "plt.plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "plt.legend()"
+ ],
+ "id": "6db196833f9c06f9",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 201,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 201
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "If desired, the number of sampled photons can also be set manually by modifying the `number_of_samples` field.",
+ "id": "9c74f901e19d3025"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:34.572763Z",
+ "start_time": "2025-09-08T13:51:30.135469Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "noise_configuration_dict['number_of_false_counts'] = 3000 # Overrides the noise computed with the dark count and ambient rates\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict)\n",
+ "\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "6506aed6ede173bf",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function:\n",
+ " - SPAD FWHM = 30 ps\n",
+ " - SPAD tail = 70 ps\n",
+ " - Laser FWHM = 45 ps\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - Number of photons sampled = 30000\n",
+ " - Number of false positive samples = 3000\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (30000 samples per measurement)...: 100%|██████████| 1024/1024 [00:01<00:00, 548.05it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 1.873 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 202
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:34.727370Z",
+ "start_time": "2025-09-08T13:51:34.624099Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "plt.plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='Original')\n",
+ "plt.plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "plt.legend()"
+ ],
+ "id": "16ffdb518f816a5b",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 203,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 203
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "## Afterpulsing\n",
+ "\n",
+ "After the SPAD detects a photon, the sensor won't be able to detect any new photons after a certain period of time,\n",
+ "known as dead time (or hold-off). In some cases, the carrier that caused the avalanche get trapped in the circuit,\n",
+ "which causes them to be detected again, creating an additional delayed avalanche. To configure this behavior,\n",
+ "the following fields can be modified:\n",
+ "- simulate_afterpulses: set to `True`, to enable afterpulses\n",
+ "- dead_time: dead time of the SPAD after detecting a photon, in picoseconds\n",
+ "- afterpulse_probability: probability of a photon causing an afterpulse"
+ ],
+ "id": "264aac6ffbffcb7f"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:40.586772Z",
+ "start_time": "2025-09-08T13:51:34.732616Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "noise_configuration_dict['simulate_afterpulses'] = True\n",
+ "noise_configuration_dict['dead_time'] = 1000\n",
+ "noise_configuration_dict['afterpulse_probability'] = 0.15\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict)\n",
+ "\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "290b5014606be282",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function:\n",
+ " - SPAD FWHM = 30 ps\n",
+ " - SPAD tail = 70 ps\n",
+ " - Laser FWHM = 45 ps\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - SPAD dead time = 1000 ps\n",
+ " - Afterpulse probability = 15.00 %\n",
+ " - Number of photons sampled = 30000\n",
+ " - Number of false positive samples = 3000\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (30000 samples per measurement)...: 100%|██████████| 1024/1024 [00:02<00:00, 368.05it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 2.787 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 204
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:40.737160Z",
+ "start_time": "2025-09-08T13:51:40.638956Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "plt.plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='Original')\n",
+ "plt.plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "plt.legend()"
+ ],
+ "id": "1dc5f24b651274e6",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 205,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 205
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "Increasing the dead time causes the peaks produced by afterpulsing to appear further away from the original pulses.\n",
+ "Furthermore, a higher probability of afterpulsing causes this subsequent pulses to appear with higher intensities."
+ ],
+ "id": "e443e58d47d80f85"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:46.196797Z",
+ "start_time": "2025-09-08T13:51:40.742256Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "noise_configuration_dict['dead_time'] = 1500\n",
+ "noise_configuration_dict['afterpulse_probability'] = 0.40\n",
+ "save_yaml(noise_configuration_copy_path, noise_configuration_dict)\n",
+ "\n",
+ "subprocess.run([\"tal\", \"noise_simulation\", \"-c\", f'{capture_path}', \"-o\", f'{output_path}', \"-n\", f'{noise_configuration_copy_path}'])\n",
+ "H_noisy = read_capture(output_path)"
+ ],
+ "id": "855c8434f09936dc",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise for capture data nlos-z/20250908-120715/nlos-z.hdf5.\n",
+ " - Jitter function:\n",
+ " - SPAD FWHM = 30 ps\n",
+ " - SPAD tail = 70 ps\n",
+ " - Laser FWHM = 45 ps\n",
+ " - Photon detection ratio = 30.00 %.\n",
+ " - SPAD dead time = 1500 ps\n",
+ " - Afterpulse probability = 40.00 %\n",
+ " - Number of photons sampled = 30000\n",
+ " - Number of false positive samples = 3000\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Simulating noise (30000 samples per measurement)...: 100%|██████████| 1024/1024 [00:02<00:00, 360.64it/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DONE. Noise simulation took 2.844 seconds\n",
+ "Processed capture saved to nlos-z/20250908-120715/nlos-z-noisy.hdf5\n"
+ ]
+ }
+ ],
+ "execution_count": 206
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-08T13:51:46.351726Z",
+ "start_time": "2025-09-08T13:51:46.247148Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "plt.plot(H_original.H[:, 16, 16] / np.max(H_original.H[:, 16, 16]), label='Original')\n",
+ "plt.plot(H_noisy.H[:, 16, 16] / np.max(H_noisy.H[:, 16, 16]), label='Noisy')\n",
+ "plt.legend()"
+ ],
+ "id": "676e1dc41caaf512",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 207,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data",
+ "jetTransient": {
+ "display_id": null
+ }
+ }
+ ],
+ "execution_count": 207
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/noise-simulation/spad_data_1.hdf5 b/examples/noise-simulation/spad_data_1.hdf5
new file mode 100644
index 0000000..d4335fb
Binary files /dev/null and b/examples/noise-simulation/spad_data_1.hdf5 differ
diff --git a/examples/noise-simulation/spad_data_2.hdf5 b/examples/noise-simulation/spad_data_2.hdf5
new file mode 100644
index 0000000..5211283
Binary files /dev/null and b/examples/noise-simulation/spad_data_2.hdf5 differ
diff --git a/examples/render-reconstruct/nlos-z/nlos-z.yaml b/examples/render-reconstruct/nlos-z/nlos-z.yaml
index 663a6f0..f6b32a5 100644
--- a/examples/render-reconstruct/nlos-z/nlos-z.yaml
+++ b/examples/render-reconstruct/nlos-z/nlos-z.yaml
@@ -31,7 +31,7 @@ name: nlos-z
# See https://mitsuba.readthedocs.io/en/latest/src/key_topics/variants.html
# you should have compiled this variant previously
# See mitsuba{2|3}-transient-nlos repository for more information
-mitsuba_variant: llvm_ad_rgb
+mitsuba_variant: llvm_ad_mono
# Transient Mitsuba's intergator properties
# See mitsuba{2|3}-transient-nlos' documentation for transient_nlos_path
integrator_max_depth: -1
@@ -42,13 +42,13 @@ integrator_nlos_hidden_geometry_sampling: true
integrator_nlos_hidden_geometry_sampling_do_rroulette: false
integrator_nlos_hidden_geometry_sampling_includes_relay_wall: true
# Number of samples per pixel (spatial pixel, not time dimension pixel)
-sample_count: 10_000
+sample_count: 1000000
# Transient Mitsuba's film properties
# See mitsuba{2|3}-transient-nlos' documentation for transient_hdr_film
account_first_and_last_bounces: false
num_bins: 320
-bin_width_opl: 0.003
-start_opl: 1.95
+bin_width_opl: 0.003 # 10.01ps
+start_opl: 1.95 # 6ns
auto_detect_bins: false
##
@@ -59,8 +59,8 @@ auto_detect_bins: false
# confocal: The laser illuminates one point in the relay wall,
# the sensor only captures that same point in the relay wall
# exhaustive: The laser and sensor illuminate and capture all points
-# on the relay wall
-scan_type: single # single, confocal or exhaustive
+# on the relay wall
+scan_type: confocal # single, confocal or exhaustive
# XYZ coordinates of the laser and sensor devices
# Note that this is NOT the illuminated or captured points' position
# on the relay wall.
@@ -77,8 +77,8 @@ laser_z: 0.25
# and sensor_height are set to 2, it will have a grid of 2x2 pixels, where the
# top-left pixel goes from UV (0, 0) to UV (0.5, 0.5).
# It is important to use a "rectangle" relay wall for correct UV coordinates.
-sensor_width: 64
-sensor_height: 64
+sensor_width: 32
+sensor_height: 32
# --- for scan_type=single
# if set to null, laser points to the center pixel
# if set to a number, laser points to that pixel (e.g. center = 128, 128)
diff --git a/tal/__main__.py b/tal/__main__.py
index c7b983e..f767c99 100644
--- a/tal/__main__.py
+++ b/tal/__main__.py
@@ -114,6 +114,19 @@ def main():
dest='keep_partial_results', action='store_false',
help='Delete the "partial" folder (which stores raw render results) after finishing the render and generating the final HDF5 file')
+ # noise simulation commands
+ noise_simulation_parser = subparsers.add_parser(
+ 'noise_simulation', help='Simulate noise for already generated capture data', formatter_class=SmartFormatter)
+ noise_simulation_parser.add_argument('-c', '--capture_file',
+ type=str, required=True,
+ help='Path to the .hdf5 capture file to add noise to')
+ noise_simulation_parser.add_argument('-n', '--noise_config_file',
+ type=str, required=True,
+ help='Path to the .yaml configuration file for the noise simulation')
+ noise_simulation_parser.add_argument('-o', '--output_path',
+ type=str, required=True,
+ help='Path to save the capture data with the simulated noise')
+
# plot commands
plot_parser = subparsers.add_parser(
'plot', help='Plot capture data using one of the configured methods', formatter_class=SmartFormatter)
@@ -181,6 +194,15 @@ def main():
from tal.render import render_nlos_scene
config_file = config_file[0]
render_nlos_scene(config_file, args)
+ elif args.command == 'noise_simulation':
+ from tal.noise_simulation import simulate_noise
+ from tal.io import write_capture
+ from tal.io import FileFormat
+ config_path = args.noise_config_file
+ capture_data_noisy = simulate_noise(args.capture_file, config_path, args)
+ write_capture(args.output_path, capture_data_noisy, FileFormat.HDF5_TAL, 0)
+ print(f'Processed capture saved to {args.output_path}')
+ return
elif args.command == 'plot':
import tal.plot
from tal.io import read_capture
diff --git a/tal/io/capture_data.py b/tal/io/capture_data.py
index 7d23771..8f7aef9 100644
--- a/tal/io/capture_data.py
+++ b/tal/io/capture_data.py
@@ -160,6 +160,32 @@ class NLOSCaptureData:
If it hits, depth[i, j] stores the distance from the relay wall to the hit point.
- 'normals': GroundTruthNormalsType (TensorXY3)
Similar to depth, but stores the normal of the hidden geometry at the hit point.
+
+ noise_info
+ YAML-encoded string. Contains additional information about the simulated noise (if any). Implemented keys:
+ - time_jitter_FWHM: FWHM of the gaussian component of the SPAD's time-jitter, in picoseconds
+ - time_jitter_tail: Tail (exponential decay parameter) of the exponential component of the
+ SPAD's time-jitter, in picoseconds
+ - time_jitter_tail_scale: Relative scale of the peak of the exponential decay in the time-jitter
+ with respect to the gaussian component
+ - time_jitter_n_timebins: Number of timebins of the jitter function
+ - time_jitter_timebin_width: Timebin width of the jitter function in picoseconds.
+ Should be as precise as possible, to avoid aliasing
+ - time_jitter_path: Leave the path empty (or not set) to use the parametric jitter
+ - photon_detection_ratio: Ratio of photons that are actually detected, in range [0, 1]
+ - dead_time: Hold-off time of the SPAD after each detected photon, in picoseconds
+ - simulate_afterpulses: If True, simulates SPAD afterpulsing (increases execution time).
+ If False, afterpulsing is ignored
+ - afterpulse_probability: Probability of each detected photon of generating an afterpulse, in range [0, 1]
+ - exposure_time: Exposure time for each captured point, in seconds
+ - number_of_samples: Number of captured photons per measurements. If 0 or non-defined,
+ it will be computed from exposure time, laser frequency and the photon detection ratio
+ - dark_count_rate: Number of dark counts per second
+ - external_noise_rate: Number of counts caused by external noise (ambient light) per second
+ - number_of_false_counts: Number of false positive photons (either dark counts or external noise).
+ If 0 or non-defined, this value is computed from exposure time and noise rates
+ - laser_jitter_FWHM: FWHM of the gaussian laser pulse, in picoseconds
+ - frequency: Pulse frequency (nº of pulses per second) in MHz
"""
#
@@ -209,6 +235,8 @@ class NLOSCaptureData:
t_start: Float = None
t_accounts_first_and_last_bounces: bool = None
scene_info: dict = None # additional information
+ noise_info: dict = None
+ jitter: dict = None
_end: None = None # used in as_dict()
def __get_dict_keys(self):
@@ -247,7 +275,7 @@ def __init__(self, filename: str = None, file_format: FileFormat = FileFormat.AU
for key, value in raw_data.items():
if key not in own_dict_keys:
raise AssertionError(f'raw_data contains unknown key: {key}')
- if key == 'scene_info':
+ if key == 'scene_info' or key == 'noise_info' or key == 'jitter':
if isinstance(value, h5py.Empty) or isinstance(value, dict):
pass
else:
diff --git a/tal/noise_simulation/__init__.py b/tal/noise_simulation/__init__.py
new file mode 100644
index 0000000..ded4a25
--- /dev/null
+++ b/tal/noise_simulation/__init__.py
@@ -0,0 +1,18 @@
+"""
+tal.noise_simulation
+======
+
+Functions for simulating the noise caused by different sources (temporal jitter, SPAD, dark counts...) into an already simulated capture data.
+
+This is a private module, it is recommended to use the command line interface instead of calling these functions directly.
+
+See tal noise_simulation -h for more information.
+"""
+
+def simulate_noise(capture_data_path, config_path, args):
+ """
+ It is recommended to use the command line interface instead of calling this function directly.
+
+ """
+ from tal.noise_simulation import noise_simulation
+ return noise_simulation.simulate_noise(capture_data_path, config_path, args)
diff --git a/tal/noise_simulation/noise_defaults.yaml b/tal/noise_simulation/noise_defaults.yaml
new file mode 100644
index 0000000..7c728e7
--- /dev/null
+++ b/tal/noise_simulation/noise_defaults.yaml
@@ -0,0 +1,53 @@
+##
+# SPAD parameters
+##
+time_jitter_FWHM: 20 # FWHM of the gaussian component of the SPAD's time-jitter, in picoseconds
+time_jitter_tail: 50 # Tail (exponential decay parameter) of the exponential component of the SPAD's time-jitter, in picoseconds
+time_jitter_tail_scale: 0.8 # Relative scale of the peak of the exponential decay in the time-jitter with respect to the gaussian component
+time_jitter_n_timebins: 640 # Number of timebins of the jitter function
+time_jitter_timebin_width: 0.75 # Timebin width of the jitter function in picoseconds. Should be as precise as possible, to avoid aliasing
+
+# Load experimental time-jitter capture from hdf5 file. If defined, the previous parameters are ignored
+# time_jitter_path: '' # Leave the path empty (or not set) to use the parametric jitter
+
+photon_detection_ratio: 0.3 # Ratio of photons that are actually detected, in range [0, 1]
+dead_time: 1000 # Hold-off time of the SPAD after each detected photon, in picoseconds
+simulate_afterpulses: False # If True, simulates SPAD afterpulsing (increases execution time). If False, afterpulsing is ignored.
+afterpulse_probability : 0.10 # Probability of each detected photon of generating an afterpulse, in range [0, 1]
+exposure_time: 0.001 # Exposure time for each captured point, in seconds
+number_of_samples: 10000 # Number of captured photons per measurement. If 0 or non-defined, it will be computed
+ # from exposure time, laser frequency and the photon detection ratio
+sensor_type: 'event' # Either frame (for frame-based SPADs) or event (for event-based SPADs)
+intensity_scaling: True # If set to True, the number of detected photons is scaled per measurement, given the ratio between
+ # the highest intensity in the data and the highest intensity of the current measurement. Thus
+ # measurements with lower relative intensity will also appear noisier.
+##
+# Frame based SPAD parameters, used if camera_type == 'frame'
+##
+frame_exposure_time: 100 # Exposure time of each SPAD frame in microseconds
+n_frames: 500_000 # Number of measured frames in the capture. During each frame, only one photon can be captured
+ # If not set, this number is obtained from the total exposure time and the exposure time of each frame
+
+##
+# SPAD array parameters
+##
+is_spad_array: True # If true, assumes each measured point corresponds to a pixel in a SPAD array, enabling crosstalk simulation
+crosstalk_probability: 0.1 # Probability of a detected photon triggering an avalanche in a neighboring pixel, in range [0, 1]
+ # Note that very low values may have negligible effects in the signal
+dead_pixel_mask: '' # Mask indicating which pixels of the SPAD should be ignored (and set to 0).
+ # If empty or not set, all of the pixels will be taken into account
+
+##
+# External noise
+##
+dark_count_rate: 1000 # Number of dark counts per second
+external_noise_rate: 1000000 # Number of counts caused by external noise (ambient light) per second
+number_of_false_counts: 0 # Number of false positive photons (either dark counts or external noise).
+# If 0 or non-defined, this value is computed from exposure time and noise rates
+
+##
+# Laser
+##
+laser_jitter_FWHM: 30 # FWHM of the gaussian laser pulse, in picoseconds
+frequency: 20 # Pulse frequency (nº of pulses per second) in MHz
+
diff --git a/tal/noise_simulation/noise_simulation.py b/tal/noise_simulation/noise_simulation.py
new file mode 100644
index 0000000..7cd8a2f
--- /dev/null
+++ b/tal/noise_simulation/noise_simulation.py
@@ -0,0 +1,361 @@
+import numpy as np
+import yaml
+import os
+import matplotlib.pyplot as plt
+from scipy.special import erfc
+from scipy.stats.sampling import DiscreteGuideTable
+import time
+from math import floor
+import h5py
+from tqdm import tqdm
+
+c = 3e8 # Speed of light in m/s
+
+def simulate_noise(capture_data_path:str, config_path:str, args):
+ """
+ Simulates the noise caused by a transient capture process using a SPAD and a pulsed laser, to a transient
+ capture file previously generated using 'tal render'.
+ """
+ # Load capture data from file
+ from tal.io import read_capture
+ capture_data = read_capture(capture_data_path)
+
+ start_time = time.time()
+
+ # Load transient data and histogram properties from the captured data
+ H = capture_data.H
+ timebin_width_opl = capture_data.delta_t
+ timebin_width_ps = timebin_width_opl / c * 1e12
+ start_opl = capture_data.t_start
+ start_ps = start_opl / c * 1e12
+ n_timebins = H.shape[0]
+ sequence_time_ps = n_timebins * timebin_width_ps # Total time of the temporal sequence in picoseconds
+ n_measurements = H[0].size
+ capture_dimensionality = H.ndim - 1
+ assert capture_dimensionality == 4 or capture_dimensionality == 2, \
+ 'Transient data does not match with single, confocal or exhaustive capture data'
+
+ # Load noise simulation configuration from YAML file
+ noise_config = None
+ assert os.path.exists(config_path), f'{config_path} does not exist'
+ assert os.path.isfile(config_path), f'{config_path} is not a TAL config file'
+ try:
+ noise_config = yaml.safe_load(open(config_path, 'r')) or dict()
+ except yaml.YAMLError as exc:
+ raise AssertionError(f'Invalid YAML format in noise simulation configuration file: {exc}') from exc
+
+ print(f'Simulating noise for capture data {capture_data_path}.')
+
+ # Generate or load from file the time jitter function of SPAD and laser
+ jitter = None
+ jitter_n_timebins = 0
+ jitter_timebin_width_ps = 0
+ if 'time_jitter_path' in noise_config and noise_config['time_jitter_path'] != '':
+ # Recorded time jitter function from file
+ jitter, jitter_n_timebins, jitter_timebin_width_ps = load_jitter_from_file(noise_config['time_jitter_path'])
+ print(f' - Jitter function loaded from {noise_config["time_jitter_path"]}.')
+ else:
+ # Analytical time jitter function
+ jitter_n_timebins = noise_config['time_jitter_n_timebins']
+ jitter_timebin_width_ps = float(noise_config['time_jitter_timebin_width'])
+ jitter = generate_parametric_jitter(noise_config['time_jitter_FWHM'], noise_config['time_jitter_tail'],
+ noise_config['laser_jitter_FWHM'], jitter_timebin_width_ps, jitter_n_timebins)
+ print(f' - Jitter function:')
+ print(f' - SPAD FWHM = {noise_config["time_jitter_FWHM"]} ps')
+ print(f' - SPAD tail = {noise_config["time_jitter_tail"]} ps')
+ print(f' - Laser FWHM = {noise_config["laser_jitter_FWHM"]} ps')
+
+
+ exposure_time = float(noise_config['exposure_time']) # Exposure time per measurement
+ laser_frequency = float(noise_config['frequency']) # Laser frequency in MHz
+
+ # Load the photon detection ratio
+ photon_detection_ratio = noise_config['photon_detection_ratio']
+ print(f' - Photon detection ratio = {100 * photon_detection_ratio:.2f} %.')
+
+ # Compute the expected number of samples per measurement, taking into account the photon detection rate
+ # NOTE: n_samples is the theoretical maximum number of photons detected by the sensor, assuming at most one
+ # photon can be detected per laser pulse (in the case of event based) or per frame
+
+ n_samples = 0
+ if 'number_of_samples' in noise_config and noise_config['number_of_samples'] != 0:
+ # Load explicit number of detected samples if defined in configuration file
+ n_samples = int(noise_config['number_of_samples'])
+ elif noise_config['sensor_type'] == 'frame':
+ # Frame based SPAD
+ frame_exposure_time = float(noise_config['frame_exposure_time']) # Exposure time per frame in microseconds
+
+ n_frames = 0
+ if 'n_frames' in noise_config and noise_config['n_frames'] != '':
+ # Load number of frames from configuration if defined
+ n_frames = int(noise_config['n_frames'])
+ else:
+ # Compute the number of frames given the total exposure time and the per frame exposure time
+ n_frames = floor(exposure_time / (frame_exposure_time * 1e-6))
+
+ # The sensor can only detect at most a single photon per frame
+ n_samples = int(n_frames * photon_detection_ratio)
+
+ elif noise_config['sensor_type'] == 'event':
+ # Event based SPAD
+ n_samples = int(exposure_time * laser_frequency * 1e6 * photon_detection_ratio)
+ print(f' - Simulated exposure time = {exposure_time:.3f} seconds.')
+ print(f' - Laser frequency = {laser_frequency:.2f} MHz.')
+ else:
+ raise AssertionError('sensor_type must be one of ("frame", "event")')
+
+ # Expected number of false positive samples (caused by dark counts or external noise)
+ n_false_samples = 0
+ if 'number_of_false_counts' in noise_config and noise_config['number_of_false_counts'] != 0:
+ n_false_samples = int(noise_config['number_of_false_counts'])
+ else:
+ n_false_samples = int(exposure_time * int(noise_config['dark_count_rate'] + noise_config['external_noise_rate']))
+ print(f'{n_samples=}')
+
+ # Afterpulse configuration
+ simulate_afterpulses = noise_config['simulate_afterpulses']
+ afterpulse_probability = noise_config['afterpulse_probability']
+ dead_time_ps = noise_config['dead_time'] # SPAD deadtime after capturing a photon (in picoseconds)
+ max_afterpulses = int(sequence_time_ps // dead_time_ps)
+ if simulate_afterpulses:
+ print(f' - SPAD dead time = {dead_time_ps} ps')
+ print(f' - Afterpulse probability = {100 * afterpulse_probability:.2f} %')
+
+
+ H_noise = np.zeros(shape=H.shape)
+ # plt.plot(jitter); plt.title('Jitter function'); plt.show()
+ jitter_peak_idx = np.argmax(jitter) # To center the jitter function and avoid offseting the signal
+ jitter_sampler = DiscreteGuideTable(jitter, random_state=np.random.RandomState())
+ false_count_sampler = DiscreteGuideTable(np.ones(shape=(n_timebins), dtype=float), random_state=np.random.RandomState()) # Uniform Guide Table
+
+ print(f' - Number of photons sampled = {n_samples}')
+ print(f' - Number of false positive samples = {n_false_samples}')
+
+ H_maximum = np.max(H, axis=None) # Highest signal intensity
+
+ use_dead_pixel_mask = False
+ dead_pixel_mask = np.zeros(shape=H[0].shape, dtype=bool)
+ if noise_config['is_spad_array'] and noise_config['dead_pixel_mask'] != '':
+ use_dead_pixel_mask = True
+ dead_pixel_mask = np.load(noise_config['dead_pixel_mask']).astype(bool)
+ assert dead_pixel_mask.shape == H[0].shape, 'Dead pixel mask should match the shape of the spad array simulation'
+
+
+ # For every transient sequence in the capture
+ for i in tqdm(range(n_measurements), total=n_measurements, desc=f'Simulating noise ({n_samples} samples per measurement)...'):
+ index = get_indices_from_linear(i, capture_dimensionality, H[0].shape)
+
+ # Skip the measurement if the pixel is in the dead pixel mask of the spad array
+ if use_dead_pixel_mask and dead_pixel_mask[index[0], index[1]]:
+ continue
+
+ H_original = access_transient_data(H, index, capture_dimensionality)
+ H_histogram = access_transient_data(H_noise, index, capture_dimensionality) # Array to store the noised transient data
+
+ # If desired, scale n_samples given the ratio of the maximum of the current measurement and the maximum of the highest measurement
+ if noise_config['intensity_scaling']:
+ n_samples_i = int(n_samples * np.max(H_original, axis=None) / H_maximum)
+ else:
+ n_samples_i = n_samples
+
+ # Apply jitter and afterpulsing. If the transient signal is empty, only dark count and ambient noise will be added
+ if not (H_original == 0.0).all():
+ # Sample n_samples photons arrival timestamps from the original transient data, as well as n_samples jitter values
+ H_sampler = DiscreteGuideTable(H_original, random_state=np.random.RandomState())
+ H_sampled = H_sampler.rvs(n_samples_i)
+ jitter_sampled = jitter_sampler.rvs(n_samples_i) - jitter_peak_idx
+ jitter_sampled_scaled = jitter_sampled * jitter_timebin_width_ps / timebin_width_ps # Transform to the timebin width of the transient data
+
+ # Sum the sampled timestamps
+ H_sampled_convolved = H_sampled + jitter_sampled_scaled
+ H_histogram += np.histogram(H_sampled_convolved, bins=n_timebins, range=(0, n_timebins-1))[0]
+
+ # Afterpulse simulation
+ H_afterpulses_histogram = None
+ if simulate_afterpulses:
+ previous_afterpulse_mask = np.ones(n_samples_i, dtype=bool)
+ for afterpulse_index in range(max_afterpulses):
+ # Generate a mask for all the measurements that cause an afterpulse
+ afterpulse_samples = np.random.rand(n_samples_i)
+ afterpulse_mask = afterpulse_samples <= afterpulse_probability
+
+ # Only measurements that caused a previous afterpulse could cause another one
+ afterpulse_mask = afterpulse_mask & previous_afterpulse_mask
+ previous_afterpulse_mask = afterpulse_mask
+
+ # Accumulate the afterpulsed samples
+ afterpulse_time_offset = dead_time_ps * (afterpulse_index + 1) / timebin_width_ps
+ H_afterpulses = (H_sampled_convolved + afterpulse_time_offset)[afterpulse_mask]
+ H_afterpulses_histogram = np.histogram(H_afterpulses, bins=n_timebins, range=(0.0, n_timebins-1))[0]
+ H_histogram = H_histogram + H_afterpulses_histogram
+
+ # Crosstalk simulation
+ if noise_config['is_spad_array'] and noise_config['crosstalk_probability'] > 0.0:
+ # Add the crosstalk to the valid neighbors
+
+ # Left neighbor
+ if index[0] != 0:
+ H_neighbor = access_transient_data(H_noise, (index[0] - 1, index[1]), capture_dimensionality)
+ H_crosstalk_histogram = compute_crosstalk_histogram(n_samples_i, noise_config['crosstalk_probability'], H_sampled_convolved, n_timebins)
+ H_neighbor += H_crosstalk_histogram
+ store_transient_data(H_noise, H_neighbor, (index[0] - 1, index[1]), capture_dimensionality)
+
+ # Upper neighbor
+ if index[1] != 0:
+ H_neighbor = access_transient_data(H_noise, (index[0], index[1] - 1), capture_dimensionality)
+ H_crosstalk_histogram = compute_crosstalk_histogram(n_samples_i, noise_config['crosstalk_probability'], H_sampled_convolved, n_timebins)
+ H_neighbor += H_crosstalk_histogram
+ store_transient_data(H_noise, H_neighbor, (index[0], index[1] - 1), capture_dimensionality)
+
+ # Right neighbor
+ if index[0] != H[0].shape[0] - 1:
+ H_neighbor = access_transient_data(H_noise, (index[0] + 1, index[1]), capture_dimensionality)
+ H_crosstalk_histogram = compute_crosstalk_histogram(n_samples_i, noise_config['crosstalk_probability'], H_sampled_convolved, n_timebins)
+ H_neighbor += H_crosstalk_histogram
+ store_transient_data(H_noise, H_neighbor, (index[0] + 1, index[1]), capture_dimensionality)
+
+ # Downward neighbor
+ if index[1] != H[0].shape[1] - 1:
+ H_neighbor = access_transient_data(H_noise, (index[0], index[1] + 1), capture_dimensionality)
+ H_crosstalk_histogram = compute_crosstalk_histogram(n_samples_i, noise_config['crosstalk_probability'], H_sampled_convolved, n_timebins)
+ H_neighbor += H_crosstalk_histogram
+ store_transient_data(H_noise, H_neighbor, (index[0], index[1] + 1), capture_dimensionality)
+
+ # Add other noise sources: dark counts and external noise
+ # NOTE: Assumes the same number of false positive counts in all measurements
+ false_count_sampled = false_count_sampler.rvs(n_false_samples)
+ false_count_histogram = np.histogram(false_count_sampled, bins=n_timebins, range=(0, n_timebins-1))[0]
+ H_histogram = H_histogram + false_count_histogram
+
+ store_transient_data(H_noise, H_histogram, index, capture_dimensionality)
+
+ print('DONE. Noise simulation took {0:.3f} seconds'.format(time.time() - start_time))
+
+ # Store the transient data with the simulated nosie, as well as the configuration used for the nosie and the jitter function
+ capture_data_noisy = capture_data
+ capture_data_noisy.H = H_noise
+ capture_data_noisy.noise_info = noise_config
+
+ capture_data_noisy.jitter = dict()
+ capture_data_noisy.jitter['counts'] = jitter
+ capture_data_noisy.jitter['n_timebins'] = jitter_n_timebins
+ capture_data_noisy.jitter['timebin_widht_ps'] = jitter_timebin_width_ps
+ return capture_data_noisy
+
+
+def gaussian(mean: float, std: float, n_timebins: int):
+ """
+ Generates a gaussian distribution given its mean and standard deviation
+ """
+ t_range = np.linspace(0, n_timebins, n_timebins)
+ return np.exp(-((t_range - mean) ** 2) / (2 * std ** 2))
+
+
+def exponenitally_modified_gaussian(mean:float , std:float, decay_rate:float, n_timebins:int):
+ """
+ Generates an exponentially modified gaussian distribution, given its mean, standard deviation, and the
+ decay rate of the exponential tail
+ """
+ t_range = np.linspace(0, n_timebins, n_timebins)
+ exgaussian = (decay_rate / 2) * np.exp((decay_rate / 2) * (2 * mean + decay_rate * std * std - 2 * t_range)) \
+ * erfc((mean + decay_rate * std * std - t_range) / (np.sqrt(2) * std))
+ return exgaussian
+
+
+def generate_parametric_jitter(SPAD_FWHM:float, SPAD_tail:float, gaussian_laser_FWHM:float, timebin_width_ps:float, n_timebins:int):
+ """
+ Generates the jitter function of a SPAD and laser given:
+ - FWHM of the SPAD
+ - Exponential tail of the SPAD
+ - FWHM of the laser
+ """
+ # Convert parameters in picoseconds to number of timebins
+ SPAD_FWHM_scaled = SPAD_FWHM / timebin_width_ps
+ SPAD_tail_scaled = SPAD_tail / timebin_width_ps
+ SPAD_std = SPAD_FWHM_scaled / (2 * np.sqrt(2 * np.log(2)))
+ gaussian_laser_FWHM_scaled = gaussian_laser_FWHM / timebin_width_ps
+ laser_std = gaussian_laser_FWHM_scaled / (2 * np.sqrt(2 * np.log(2)))
+
+ # Compute the time jitter caused by the SPAD (exponentially modified gaussian)
+ mean = n_timebins * 0.3
+ SPAD_jitter = exponenitally_modified_gaussian(mean, SPAD_std, 1 / SPAD_tail_scaled, n_timebins)
+
+ # Compute time jitter caused by a laser pulse (gaussian)
+ mean = n_timebins * 0.5
+ laser_jitter = gaussian(mean, laser_std, n_timebins)
+ laser_jitter_centered = np.roll(laser_jitter, shift=-int(mean))
+
+ # Complete time jitter (SPAD and laser convolved)
+ jitter = np.real(np.fft.ifft(np.fft.fft(SPAD_jitter) * np.fft.fft(laser_jitter_centered)))
+ jitter = jitter / np.max(jitter) + 1e-8
+ return jitter
+
+
+def load_jitter_from_file(path:str):
+ """
+ Loads a jitter function from an hdf5 file. The file must contain the following datasets:
+ - counts: recorded data of the SPAD's jitter
+ - n_timebins: number of timebins of the temporal data
+ - timebin_width_ps: the temporal width of each timebin, in picoseconds
+ """
+ jitter_file = h5py.File(path, 'r')
+ jitter = np.array(jitter_file['counts'])[:, 0]
+ jitter_n_timebins = np.array(jitter_file['n_timebins']).item()
+ jitter_timebin_width_ps = np.array(jitter_file['timebin_width_ps']).item()
+ jitter_file.close()
+ return jitter, jitter_n_timebins, jitter_timebin_width_ps
+
+
+def get_indices_from_linear(index:int, capture_dimensionality:int, shape):
+ """
+ Given a linear index, the dimensionality of the transient data and its shape, returns a tuple of indices
+ to access the transient data tensor.
+ """
+ if capture_dimensionality == 2:
+ i = index % shape[0]
+ j = index // shape[0]
+ return i, j
+ elif capture_dimensionality == 4:
+ i = index % shape[0]
+ j = (index // shape[0]) % shape[1]
+ k = (index // (shape[0] * shape[1])) % shape[2]
+ l = (index // (shape[0] * shape[1] * shape[2]))
+ return i, j, k, l
+ else:
+ print('Error, capture is neither single, confocal nor exhaustive')
+ exit(1)
+
+
+def compute_crosstalk_histogram(n_samples, crosstalk_probability, H_sampled, n_timebins):
+ crosstalk_samples = np.random.rand(n_samples)
+ crosstalk_mask = crosstalk_samples <= crosstalk_probability
+ H_crosstalk = H_sampled[crosstalk_mask]
+
+ H_crosstalk_histogram = np.histogram(H_crosstalk, bins=n_timebins, range=(0.0, n_timebins-1))[0]
+ return H_crosstalk_histogram
+
+
+def access_transient_data(transient_data, index_tuple, capture_dimensionality):
+ """
+ Access a single transient measurement from the complete tensor using a tuple of indices.
+ """
+ if capture_dimensionality == 2:
+ return transient_data[:, index_tuple[0], index_tuple[1]]
+ elif capture_dimensionality == 4:
+ return transient_data[:, index_tuple[0], index_tuple[1], index_tuple[2], index_tuple[3]]
+ else:
+ print('Error, capture is neither single, confocal nor exhaustive')
+ exit(1)
+
+
+def store_transient_data(transient_data, transient_data_i, index_tuple, capture_dimensionality):
+ """
+ Store a transient sequence into the complete tensor using a tuple of indices.
+ """
+ if capture_dimensionality == 2:
+ transient_data[:, index_tuple[0], index_tuple[1]] = transient_data_i
+ elif capture_dimensionality == 4:
+ transient_data[:, index_tuple[0], index_tuple[1], index_tuple[2], index_tuple[3]] = transient_data_i
+ else:
+ print('Error, capture is neither single, confocal nor exhaustive')
+ exit(1)