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Commit 9a660b59 authored by Andi Gerken's avatar Andi Gerken
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Changed example in readme and in jupyter notebook

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README.md
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...@@ -31,9 +31,9 @@ We show a simple example below. More examples can be found in ```examples/``` ...@@ -31,9 +31,9 @@ We show a simple example below. More examples can be found in ```examples/```
import robofish.io import robofish.io
import numpy as np import numpy as np
filename = "example.hdf5"
f = robofish.io.File(world_size_cm=[100, 100], frequency_hz=25.0) # By using the context, the file will be automatically validated
with robofish.io.File("Example.hdf5", "w", world_size_cm=[100, 100], frequency_hz=25.0) as f:
f.attrs["experiment_setup"] = "This is a simple example with made up data." f.attrs["experiment_setup"] = "This is a simple example with made up data."
# Create a single robot with 30 timesteps # Create a single robot with 30 timesteps
...@@ -57,11 +57,7 @@ fish.attrs["fish_standard_length_cm"] = 10 ...@@ -57,11 +57,7 @@ fish.attrs["fish_standard_length_cm"] = 10
# Show and save the file # Show and save the file
print(f) print(f)
print("Poses Shape: ", f.get_poses().shape) print("Poses Shape: ", f.entity_poses.shape)
# Saving also validates the file
f.save(filename)
print(f"Saved to {filename}")
``` ```
......
examples/example_basic.ipynb
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%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#! /usr/bin/env python3 #! /usr/bin/env python3
import robofish.io import robofish.io
from robofish.io import utils
import numpy as np import numpy as np
from pathlib import Path
import os
# Helper function to enable relative paths from this file def create_example_file(path):
def full_path(path):
return (Path(os.path.abspath("__file__")).parent / path).resolve()
if __name__ == "__main__":
# Create a new io file object with a 100x100cm world # Create a new io file object with a 100x100cm world
sf = robofish.io.File(world_size=[100, 100]) sf = robofish.io.File(world_size_cm=[100, 100], frequency_hz=25.0)
# create a simple obstacle, fixed in place, fixed outline # create a simple obstacle, fixed in place, fixed outline
obstacle_pose = [[50, 50, 0, 0]]
obstacle_outline = [[[-10, -10], [-10, 0], [0, 0], [0, -10]]] obstacle_outline = [[[-10, -10], [-10, 0], [0, 0], [0, -10]]]
obstacle_name = sf.create_entity( obstacle_name = sf.create_entity(
"obstacle", poses=obstacle_pose, outlines=obstacle_outline "obstacle", positions=[[50, 50]], orientations=[[0]], outlines=obstacle_outline
) )
# create a robofish with 100 timesteps and 40ms between the timesteps. If we would not give a name, the name would be generated to be robot_1. # create a robofish with 1000 timesteps. If we would not give a name, the name would be generated to be robot_1.
robofish_timesteps = 4 robofish_timesteps = 1000
robofish_poses = np.zeros((robofish_timesteps, 4)) robofish_poses = np.ones((robofish_timesteps, 4)) * 50
sf.create_entity("robot", robofish_poses, name="robot", monotonic_step=40) robot = sf.create_entity("robot", robofish_poses, name="robot")
# create multiple fishes with timestamps. Since we don't specify names, but only the type "fish" the fishes will be named ["fish_1", "fish_2", "fish_3"] # create multiple fishes with timestamps. Since we don't specify names, but only the type "fish" the fishes will be named ["fish_1", "fish_2", "fish_3"]
agents = 3 agents = 3
timesteps = 5 timesteps = 1000
timestamps = np.linspace(0, timesteps + 1, timesteps) # timestamps = np.linspace(0, timesteps + 1, timesteps)
agent_poses = np.random.random((agents, timesteps, 4)) agent_poses = np.random.random((agents, timesteps, 4))
fish_names = sf.create_multiple_entities( fishes = sf.create_multiple_entities("fish", agent_poses)
"fish", agent_poses, monotonic_points=timestamps
)
# This would throw an exception if the file was invalid # This would throw an exception if the file was invalid
sf.validate() sf.validate()
# Save file validates aswell # Save file validates aswell
example_file = full_path("example.hdf5")
sf.save(example_file) sf.save_as(path)
# Closing and opening files (just for demonstration) # Closing and opening files (just for demonstration)
sf.close() sf.close()
sf = robofish.io.File(path=example_file) sf = robofish.io.File(path=path)
print("\nEntity Names") print("\nEntity Names")
print(sf.entity_names) print(sf.entity_names)
# Get an array with all poses. As the length of poses varies per agent, it # Get an array with all poses. As the length of poses varies per agent, it is filled up with nans.
# is filled up with nans. The result is not interpolated and the time scales
# per agent are different. It is planned to create a warning in the case of
# different time scales and have another function, which generates an
# interpolated array.
print("\nAll poses") print("\nAll poses")
print(sf.select_poses()) print(sf.entity_poses)
print("\nFish poses") print("\nFish poses")
print(sf.select_poses(lambda e: e.category == "fish")) print(sf.select_entity_poses(lambda e: e.category == "fish"))
print("\nFile structure") print("\nFile structure")
print(sf) print(sf)
if __name__ == "__main__":
create_example_file("example.hdf5")
``` ```
%% Output %% Output
Entity Names Entity Names
['fish_1', 'fish_2', 'fish_3', 'obstacle_1', 'robot'] ['fish_1', 'fish_2', 'fish_3', 'obstacle_1', 'robot']
All poses All poses
[[[8.86584103e-01 2.35670820e-01 5.41754842e-01 4.49850202e-01] [[[1.36782631e-01 4.68791187e-01 2.42182687e-01 6.48771763e-01]
[8.15511882e-01 4.78223324e-01 6.29803419e-01 1.12592392e-01] [7.93363988e-01 3.50356251e-01 3.64646018e-01 1.12773582e-01]
[1.53732300e-01 7.24954247e-01 9.38574493e-01 4.65665817e-01] [2.60495663e-01 8.65413904e-01 2.21576691e-02 6.10984743e-01]
[9.10354614e-01 4.47880208e-01 3.81429136e-01 9.67544317e-01] ...
[6.07822955e-01 5.20158827e-01 8.17965686e-01 8.42760384e-01]] [8.34640980e-01 9.05928910e-01 7.33852565e-01 9.64702129e-01]
[5.20252772e-02 1.44064710e-01 9.47307721e-02 3.03431451e-01]
[[2.29353935e-01 8.80753636e-01 7.94585168e-01 2.22074524e-01] [1.99740291e-01 3.40426266e-02 5.65072261e-02 4.65648413e-01]]
[6.13970399e-01 1.33511815e-02 2.89155185e-01 2.65219092e-01]
[6.62197351e-01 6.47982001e-01 9.46004018e-02 6.59599364e-01] [[2.59539992e-01 3.93728465e-01 9.71068442e-01 6.31185651e-01]
[4.86104101e-01 4.23153102e-01 1.39821902e-01 3.11809748e-01] [1.83127314e-01 1.43956905e-02 4.26256537e-01 5.80079734e-01]
[8.03322852e-01 9.52799857e-01 3.89638603e-01 6.43237352e-01]] [3.76704991e-01 6.72040820e-01 5.88318594e-02 3.48662198e-01]
...
[[9.70978260e-01 6.75936878e-01 6.23196602e-01 8.42264950e-01] [3.48356485e-01 9.14380550e-01 4.48769242e-01 7.38050520e-01]
[4.07079160e-01 8.46290290e-01 5.64092159e-01 3.56871307e-01] [6.12327099e-01 1.99107945e-01 8.90928864e-01 2.84884181e-02]
[4.84096229e-01 8.60232174e-01 1.39015794e-01 7.82253265e-01] [8.27723503e-01 6.57829344e-01 4.65144426e-01 4.01587933e-01]]
[1.24170482e-01 2.21511930e-01 8.88282284e-02 4.53450561e-01]
[1.28404438e-01 2.87771430e-02 4.57022637e-01 9.80571806e-01]] [[8.43813717e-01 3.53193313e-01 3.37166399e-01 4.36319530e-01]
[9.16836739e-01 2.00342074e-01 1.78178921e-01 6.41124010e-01]
[4.94625986e-01 2.56477743e-01 3.52547020e-01 7.87709892e-01]
...
[5.02124608e-01 6.97787464e-01 2.58715957e-01 2.88078666e-01]
[9.08326209e-01 2.46579379e-01 1.59346357e-01 6.96394265e-01]
[3.52213830e-01 6.03601038e-02 2.54920900e-01 3.10269982e-01]]
[[5.00000000e+01 5.00000000e+01 0.00000000e+00 0.00000000e+00] [[5.00000000e+01 5.00000000e+01 1.00000000e+00 0.00000000e+00]
[ nan nan nan nan]
[ nan nan nan nan] [ nan nan nan nan]
...
[ nan nan nan nan] [ nan nan nan nan]
[ nan nan nan nan] [ nan nan nan nan]
[ nan nan nan nan]] [ nan nan nan nan]]
[[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] [[5.00000000e+01 5.00000000e+01 5.00000000e+01 5.00000000e+01]
[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] [5.00000000e+01 5.00000000e+01 5.00000000e+01 5.00000000e+01]
[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] [5.00000000e+01 5.00000000e+01 5.00000000e+01 5.00000000e+01]
[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] ...
[ nan nan nan nan]]] [5.00000000e+01 5.00000000e+01 5.00000000e+01 5.00000000e+01]
[5.00000000e+01 5.00000000e+01 5.00000000e+01 5.00000000e+01]
[5.00000000e+01 5.00000000e+01 5.00000000e+01 5.00000000e+01]]]
Fish poses Fish poses
[[[0.8865841 0.23567082 0.54175484 0.4498502 ] [[[0.13678263 0.46879119 0.24218269 0.64877176]
[0.81551188 0.47822332 0.62980342 0.11259239] [0.79336399 0.35035625 0.36464602 0.11277358]
[0.1537323 0.72495425 0.93857449 0.46566582] [0.26049566 0.8654139 0.02215767 0.61098474]
[0.91035461 0.44788021 0.38142914 0.96754432] ...
[0.60782295 0.52015883 0.81796569 0.84276038]] [0.83464098 0.90592891 0.73385257 0.96470213]
[0.05202528 0.14406471 0.09473077 0.30343145]
[[0.22935393 0.88075364 0.79458517 0.22207452] [0.19974029 0.03404263 0.05650723 0.46564841]]
[0.6139704 0.01335118 0.28915519 0.26521909]
[0.66219735 0.647982 0.0946004 0.65959936] [[0.25953999 0.39372846 0.97106844 0.63118565]
[0.4861041 0.4231531 0.1398219 0.31180975] [0.18312731 0.01439569 0.42625654 0.58007973]
[0.80332285 0.95279986 0.3896386 0.64323735]] [0.37670499 0.67204082 0.05883186 0.3486622 ]
...
[[0.97097826 0.67593688 0.6231966 0.84226495] [0.34835649 0.91438055 0.44876924 0.73805052]
[0.40707916 0.84629029 0.56409216 0.35687131] [0.6123271 0.19910794 0.89092886 0.02848842]
[0.48409623 0.86023217 0.13901579 0.78225327] [0.8277235 0.65782934 0.46514443 0.40158793]]
[0.12417048 0.22151193 0.08882823 0.45345056]
[0.12840444 0.02877714 0.45702264 0.98057181]]] [[0.84381372 0.35319331 0.3371664 0.43631953]
[0.91683674 0.20034207 0.17817892 0.64112401]
[0.49462599 0.25647774 0.35254702 0.78770989]
...
[0.50212461 0.69778746 0.25871596 0.28807867]
[0.90832621 0.24657938 0.15934636 0.69639426]
[0.35221383 0.0603601 0.2549209 0.31026998]]]
File structure File structure
version: [1 0] format_url: https://git.imp.fu-berlin.de/bioroboticslab/robofish/track_format/-/releases/1.0
world size: [100. 100.] format_version: [1 0]
world_size_cm: [100. 100.]
| entities | entities
|---| fish_1 |---| fish_1
|---|--- type: fish |---|--- category: fish
|---|--- poses: Shape (5, 4) |---|--- orientations: Shape (1000, 2)
|---|---| time |---|--- positions: Shape (1000, 2)
|---|---|--- monotonic points: Shape (5,)
|---| fish_2 |---| fish_2
|---|--- type: fish |---|--- category: fish
|---|--- poses: Shape (5, 4) |---|--- orientations: Shape (1000, 2)
|---|---| time |---|--- positions: Shape (1000, 2)
|---|---|--- monotonic points: Shape (5,)
|---| fish_3 |---| fish_3
|---|--- type: fish |---|--- category: fish
|---|--- poses: Shape (5, 4) |---|--- orientations: Shape (1000, 2)
|---|---| time |---|--- positions: Shape (1000, 2)
|---|---|--- monotonic points: Shape (5,)
|---| obstacle_1 |---| obstacle_1
|---|--- type: obstacle |---|--- category: obstacle
|---|--- orientations: Shape (1, 2)
|---|--- outlines: Shape (1, 4, 2) |---|--- outlines: Shape (1, 4, 2)
|---|--- poses: Shape (1, 4) |---|--- positions: Shape (1, 2)
|---|---| time
|---| robot |---| robot
|---|--- type: robot |---|--- category: robot
|---|--- poses: Shape (4, 4) |---|--- orientations: Shape (1000, 2)
|---|---| time |---|--- positions: Shape (1000, 2)
|---|---|--- monotonic step: 40 | samplings
|--- default: 25 hz
|---| 25 hz
|---|--- frequency_hz: 25.0
......
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