From 1fb303ab55e18ecae1dbfa42a2fbd46dc17cbb0e Mon Sep 17 00:00:00 2001 From: tkclam Date: Thu, 20 Jun 2024 01:50:33 +0200 Subject: [PATCH] correct typos and grammar --- doc/source/gallery/index.rst | 2 +- doc/source/index.rst | 2 +- doc/source/tutorials/advanced_olfaction.rst | 6 +-- doc/source/tutorials/advanced_vision.rst | 2 +- flygym/data/mjcf/clean_xml.py | 10 ++-- flygym/examples/head_stabilization/data.py | 2 +- .../locomotion/controller_comparison.py | 20 +++---- .../examples/locomotion/hybrid_controller.py | 2 +- .../examples/locomotion/turning_controller.py | 4 +- flygym/examples/locomotion/turning_fly.py | 54 +++++++++---------- flygym/examples/olfaction/README.md | 2 +- .../examples/olfaction/plume_tracking_task.py | 2 +- flygym/examples/path_integration/README.md | 2 +- .../tests/test_exploration.py | 4 +- flygym/fly.py | 4 +- flygym/tests/test_odor.py | 2 +- notebooks/advanced_olfaction.ipynb | 4 +- 17 files changed, 62 insertions(+), 62 deletions(-) diff --git a/doc/source/gallery/index.rst b/doc/source/gallery/index.rst index 6f2225d4..7c0411d3 100644 --- a/doc/source/gallery/index.rst +++ b/doc/source/gallery/index.rst @@ -88,7 +88,7 @@ NeuroMechFly can be used to emulate a wide range of behaviours and scenarios. He
Plume navigation -

Navigating dynamic olfactive environements

+

Navigating dynamic olfactive environments
diff --git a/doc/source/index.rst b/doc/source/index.rst index fec66979..1ccc5f1e 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -37,7 +37,7 @@ FlyGym is the Python library for NeuroMechFly v2, a digital twin of the adult fr FlyGym consists of the following components: - **Biomechanical model:** The biomechanical model is based on a micro-CT scan of a real adult female fly (see our `original NeuroMechFly `_ publication). In NeuroMechFly v2 we have updated several body segments (in particular in the antennae) to better reflect the biological reality. -- **Vision:** The fly has compound eyes consisting of individual units called ommatidia. These are arranged on a hexagonal lattice. We have simulated visual inputs on the fly's retinae. +- **Vision:** The fly has compound eyes consisting of individual units called ommatidia. These are arranged on a hexagonal lattice. We have simulated visual inputs on the fly's retinas. - **Olfaction:** The fly has odor receptors in the antennae and maxillary palps. We have simulated olfactory signals experienced by the fly by computing odor/chemical intensities at the locations of the antennae and maxillary palps. - **Hierarchical control:** The fly's central nervous system (CNS) consists of the brain and the ventral nerve cord (VNC). This hierarchy is analogous to the brain-spinal cord organization of vertebrates. The user can build a two-part model --- one handling brain-level sensory integration and decision making as well as one handling VNC-level motor control --- with a interface between the two consisting of descending (brain-to-VNC) and ascending (VNC-to-brain) neuronal pathways. - **Leg adhesion:** Insects have evolved specialized adhesive structures at the tips of their legs. These enable locomotion on inclined and overhanging surfaces. We have simulated adhesion in our model. The mechanism by which the fly lifts the legs during locomotion despite adhesive forces is currently not well understood. Therefore, to abstract this, adhesion can be turned on or off during stance or swing of the legs, respectively. diff --git a/doc/source/tutorials/advanced_olfaction.rst b/doc/source/tutorials/advanced_olfaction.rst index dd45c530..1fd3a32c 100644 --- a/doc/source/tutorials/advanced_olfaction.rst +++ b/doc/source/tutorials/advanced_olfaction.rst @@ -983,7 +983,7 @@ NeuroMechFly v2 paper for more details. WalkingState.STOP: np.array((0.0, 0.0)), } - # evidence acccumulation parameters + # evidence accumulation parameters self.accumulated_evidence = 0.0 self.accumulation_decay = 0.0001 self.accumulation_odor_gain = 0.05 @@ -999,8 +999,8 @@ NeuroMechFly v2 paper for more details. ) # descending neuron drive parameters - self.dn_drive_update_inteval = 0.1 # s - self.dn_drive_update_steps = int(self.dn_drive_update_inteval / self.timestep) + self.dn_drive_update_interval = 0.1 # s + self.dn_drive_update_steps = int(self.dn_drive_update_interval / self.timestep) self.dn_drive = self.dn_drives[WalkingState.STOP] # controller state parameters diff --git a/doc/source/tutorials/advanced_vision.rst b/doc/source/tutorials/advanced_vision.rst index 918d8f48..79e25b09 100644 --- a/doc/source/tutorials/advanced_vision.rst +++ b/doc/source/tutorials/advanced_vision.rst @@ -160,7 +160,7 @@ network model (`Lappalainen et al., 2023 `__; `code `__). This study has constructed an artificial neural network (ANN) representing the retina, -lamina, medula, lobula plate, and lobula of the fly visual system (see +lamina, medulla, lobula plate, and lobula of the fly visual system (see figure below). The connectivity in this network is informed by the connectome and, unlike typical ANNs, models biologically meaningful variables such as voltage. diff --git a/flygym/data/mjcf/clean_xml.py b/flygym/data/mjcf/clean_xml.py index 7d03d817..3d56c381 100644 --- a/flygym/data/mjcf/clean_xml.py +++ b/flygym/data/mjcf/clean_xml.py @@ -74,7 +74,7 @@ def set_mirrored_meshes(xml, symmetrical_bodies): L_mesh_scaling[1] *= -1 R_mesh_rot.set( "scale", " ".join([str(a) for a in L_mesh_scaling]) - ) # flip arround the plane x=0 + ) # flip around the plane x=0 R_mesh_rot.set("name", L_mesh_name) mesh_parent.remove(L_mesh) mesh_parent.append(R_mesh_rot) @@ -84,7 +84,7 @@ def set_mirrored_meshes(xml, symmetrical_bodies): def make_symmetric(xml, symmetrical_bodies, centered_bodies): - # Make the xml symmetrical by setting the orgin of every body symmetric to the origin + # Make the xml symmetrical by setting the origin of every body symmetric to the origin # This is done for now by taking the mean of the two possible non symmetrical position symmetry_vect = np.array([1, -1, 1]) @@ -145,7 +145,7 @@ def clean_geoms(xml): ), f"Visual geom {final_geom_name}_visual not found the file does not follow the basic naming convention" if (not collision_geom is None) and (not visual_geom is None): - # This geom has both a visual and a collision geom: we shoudl remove the visual geom and the mesh referencing it and rename the collsion geom + # This geom has both a visual and a collision geom: we should remove the visual geom and the mesh referencing it and rename the collision geom visual_mesh_name = visual_geom.get("mesh") visual_mesh = xml.find(f".//mesh[@name='{visual_mesh_name}']") collision_mesh_name = collision_geom.get("mesh") @@ -230,7 +230,7 @@ def get_body_min_descendant(xml, names): def remove_dummy_bodies(xml, kin_chain_order=["yaw", "pitch", "roll"]): - # remove dummy bodies that form the muli dof joints + # remove dummy bodies that form the multi dof joints multi_dof_joint = get_all_multidof_joints(xml) # start at the bottom of the chain for safety not sure this is necessary @@ -324,7 +324,7 @@ def remove_inertials(xml): def save_xml(xml, path): # xml.write(path) - # reparse the xml wiht nicely aligned children + # reparse the xml with nicely aligned children rough_string = etree.tostring(xml).decode("utf-8") reparsed = minidom.parseString(rough_string) smooth_string = reparsed.toprettyxml() diff --git a/flygym/examples/head_stabilization/data.py b/flygym/examples/head_stabilization/data.py index 4f6290ee..f7548bc1 100644 --- a/flygym/examples/head_stabilization/data.py +++ b/flygym/examples/head_stabilization/data.py @@ -8,7 +8,7 @@ class JointAngleScaler: """ A class for standardizing joint angles (i.e., using mean and standard - deviation. + deviation.) Attributes ---------- diff --git a/flygym/examples/locomotion/controller_comparison.py b/flygym/examples/locomotion/controller_comparison.py index d66b623a..d8d552ca 100644 --- a/flygym/examples/locomotion/controller_comparison.py +++ b/flygym/examples/locomotion/controller_comparison.py @@ -77,8 +77,8 @@ stumbling_force_threshold = -1 correction_rates = {"retraction": (800, 700), "stumbling": (2200, 1800)} max_increment = 80 -retraction_persistance = 20 -persistance_init_thr = 20 +retraction_persistence = 20 +persistence_init_thr = 20 ########### FUNCTIONS ############ @@ -158,7 +158,7 @@ def run_hybrid( obs, info = sim.reset() target_num_steps = int(run_time / sim.timestep) obs_list = [] - retraction_perisitance_counter = np.zeros(6) + retraction_persistence_counter = np.zeros(6) for _ in range(target_num_steps): # retraction rule: does a leg need to be retracted from a hole? @@ -167,15 +167,15 @@ def run_hybrid( end_effector_z_pos_sorted = end_effector_z_pos[end_effector_z_pos_sorted_idx] if end_effector_z_pos_sorted[-1] > end_effector_z_pos_sorted[-3] + 0.05: leg_to_correct_retraction = end_effector_z_pos_sorted_idx[-1] - if retraction_correction[leg_to_correct_retraction] > persistance_init_thr: - retraction_perisitance_counter[leg_to_correct_retraction] = 1 + if retraction_correction[leg_to_correct_retraction] > persistence_init_thr: + retraction_persistence_counter[leg_to_correct_retraction] = 1 else: leg_to_correct_retraction = None - # update persistance counter - retraction_perisitance_counter[retraction_perisitance_counter > 0] += 1 - retraction_perisitance_counter[ - retraction_perisitance_counter > retraction_persistance + # update persistence counter + retraction_persistence_counter[retraction_persistence_counter > 0] += 1 + retraction_persistence_counter[ + retraction_persistence_counter > retraction_persistence ] = 0 cpg_network.step() @@ -185,7 +185,7 @@ def run_hybrid( for j, leg in enumerate(preprogrammed_steps.legs): # update amount of retraction correction if ( - j == leg_to_correct_retraction or retraction_perisitance_counter[j] > 0 + j == leg_to_correct_retraction or retraction_persistence_counter[j] > 0 ): # lift leg increment = correction_rates["retraction"][0] * sim.timestep retraction_correction[j] += increment diff --git a/flygym/examples/locomotion/hybrid_controller.py b/flygym/examples/locomotion/hybrid_controller.py index b2896808..e1b1a3ee 100644 --- a/flygym/examples/locomotion/hybrid_controller.py +++ b/flygym/examples/locomotion/hybrid_controller.py @@ -188,7 +188,7 @@ def run_hybrid_simulation(sim, cpg_network, preprogrammed_steps, run_time): sim.render() except PhysicsError: - print("Simulation was interupted because of a physics error") + print("Simulation was interrupted because of a physics error") return obs_hist, info_hist, True return obs_hist, info_hist, False diff --git a/flygym/examples/locomotion/turning_controller.py b/flygym/examples/locomotion/turning_controller.py index 16096a0e..99f28b75 100644 --- a/flygym/examples/locomotion/turning_controller.py +++ b/flygym/examples/locomotion/turning_controller.py @@ -419,10 +419,10 @@ def step(self, action): ) # get net correction amount - net_correction, reset_stumbing = self._get_net_correction( + net_correction, reset_stumbling = self._get_net_correction( self.retraction_correction[i], self.stumbling_correction[i] ) - if reset_stumbing: + if reset_stumbling: self.stumbling_correction[i] = 0.0 net_correction = np.clip(net_correction, 0, self.max_increment) diff --git a/flygym/examples/locomotion/turning_fly.py b/flygym/examples/locomotion/turning_fly.py index ed5b54e1..2e90582f 100644 --- a/flygym/examples/locomotion/turning_fly.py +++ b/flygym/examples/locomotion/turning_fly.py @@ -65,8 +65,8 @@ def __init__( amplitude_range=(-0.5, 1.5), draw_corrections=False, max_increment=80 / 1e-4, - retraction_persistance_duration=20 / 1e-4, - retraction_persistance_initiation_threshold=20 / 1e-4, + retraction_persistence_duration=20 / 1e-4, + retraction_persistence_initiation_threshold=20 / 1e-4, seed=0, **kwargs, ): @@ -107,12 +107,12 @@ def __init__( are active in the rendered video. max_increment : float, optional Maximum duration of the correction before it is capped. - retraction_persistance_duration : float, optional + retraction_persistence_duration : float, optional Time spend in a persistent state (leg is further retracted) even if the rule is no longer active - retraction_persistance_initiation_threshold : float, optional - Amount of time the leg had to be retracted for for the persistance - to be initiated (prevents activation of persistance for noise driven + retraction_persistence_initiation_threshold : float, optional + Amount of time the leg had to be retracted for for the persistence + to be initiated (prevents activation of persistence for noise driven rule activations) seed : int, optional Seed for the random number generator. @@ -147,13 +147,13 @@ def __init__( self.amplitude_range = amplitude_range self.draw_corrections = draw_corrections self.max_increment = max_increment * self.timestep - self.retraction_persistance_duration = ( - retraction_persistance_duration * self.timestep + self.retraction_persistence_duration = ( + retraction_persistence_duration * self.timestep ) - self.retraction_persistance_initiation_threshold = ( - retraction_persistance_initiation_threshold * self.timestep + self.retraction_persistence_initiation_threshold = ( + retraction_persistence_initiation_threshold * self.timestep ) - self.retraction_persistance_counter = np.zeros(6) + self.retraction_persistence_counter = np.zeros(6) # Define the joints that need to be inverted to # mirror actions from left to right self.right_leg_inversion = [1, -1, -1, 1, -1, 1, 1] @@ -238,10 +238,10 @@ def _find_stumbling_sensor_indices(self): def _retraction_rule_find_leg(self, obs): """Returns the index of the leg that needs to be retracted, or None if none applies. - Retraction can be due to the activation of a rule or persistance. - Every time the rule is active the persistance counter is set to 1. At every step the persistance + Retraction can be due to the activation of a rule or persistence. + Every time the rule is active the persistence counter is set to 1. At every step the persistence counter is incremented. If the rule is still active it is again reset to 1 otherwise, it will - be incremented until it reaches the persistance duration. At this point the persistance counter + be incremented until it reaches the persistence duration. At this point the persistence counter is reset to 0.""" end_effector_z_pos = obs["fly"][0][2] - obs["end_effectors"][:, 2] end_effector_z_pos_sorted_idx = np.argsort(end_effector_z_pos) @@ -250,23 +250,23 @@ def _retraction_rule_find_leg(self, obs): leg_to_correct_retraction = end_effector_z_pos_sorted_idx[-1] if ( self.retraction_correction[leg_to_correct_retraction] - > self.retraction_persistance_initiation_threshold + > self.retraction_persistence_initiation_threshold ): - self.retraction_persistance_counter[leg_to_correct_retraction] = 1 + self.retraction_persistence_counter[leg_to_correct_retraction] = 1 else: leg_to_correct_retraction = None return leg_to_correct_retraction - def _update_persistance_counter(self): - """Increment the persistance counter if it is nonzero. Zero the counter - when it reaches the persistance duration.""" + def _update_persistence_counter(self): + """Increment the persistence counter if it is nonzero. Zero the counter + when it reaches the persistence duration.""" # increment every nonzero counter - self.retraction_persistance_counter[ - self.retraction_persistance_counter > 0 + self.retraction_persistence_counter[ + self.retraction_persistence_counter > 0 ] += 1 # zero the increment when reaching the threshold - self.retraction_persistance_counter[ - self.retraction_persistance_counter > self.retraction_persistance_duration + self.retraction_persistence_counter[ + self.retraction_persistence_counter > self.retraction_persistence_duration ] = 0 def _stumbling_rule_check_condition(self, obs, leg): @@ -386,8 +386,8 @@ def pre_step(self, action, sim: "Simulation"): # Retraction rule: is any leg stuck in a gap and needing to be retracted? leg_to_correct_retraction = self._retraction_rule_find_leg(obs) - self._update_persistance_counter() - persistent_retraction = self.retraction_persistance_counter > 0 + self._update_persistence_counter() + persistent_retraction = self.retraction_persistence_counter > 0 self.cpg_network.step() @@ -421,10 +421,10 @@ def pre_step(self, action, sim: "Simulation"): ) # get net correction amount - net_correction, reset_stumbing = self._get_net_correction( + net_correction, reset_stumbling = self._get_net_correction( self.retraction_correction[i], self.stumbling_correction[i] ) - if reset_stumbing: + if reset_stumbling: self.stumbling_correction[i] = 0.0 net_correction = np.clip(net_correction, 0, self.max_increment) diff --git a/flygym/examples/olfaction/README.md b/flygym/examples/olfaction/README.md index 9e54f6e0..980806c9 100644 --- a/flygym/examples/olfaction/README.md +++ b/flygym/examples/olfaction/README.md @@ -13,7 +13,7 @@ This subpackage includes the following files: ### Tracking complex plumes - `simulate_plume_dataset.py`: This script simulates a complex plume by solving (a simplified version of) the incompressible Navier–Stokes equations. - `plume_tracking_arena.py`: This module implements an `OdorPlumeArena`, which allows the fly to sense the intensity of the odor plume as it moves in space. -- `plume_tracking_task.py`: This module implements `PlumeNavigationTask`, a wrapper around `HybridTurningController`. This class enables rendering with odor intensity overlayed on top of the arena floor, along with some other utilities such as detecting if the fly has reached the limit of the arena. +- `plume_tracking_task.py`: This module implements `PlumeNavigationTask`, a wrapper around `HybridTurningController`. This class enables rendering with odor intensity overlaid on top of the arena floor, along with some other utilities such as detecting if the fly has reached the limit of the arena. - `plume_tracking_controller.py`: This module implements the plume following algorithm proposed in [Demir et al., _eLife_, 2020](https://doi.org/10.7554/eLife.57524). - `track_plume_closed_loop.py`: This script uses the controller above to perform the `PlumeNavigationTask` in closed loop. It is computationally heavy — for reference, we ran it on a 36-core node on the university cluster for a few hours. - `visualize_plume_tracking_results.py`: This light script visualizes the results of the closed-loop plume following simulations. Please note that this script requires a manually entered string, `chosen_trial`, which is the trial for which a final video should be generated. The user must enter one of the trials printed by an earlier part of this script, and the script re-run. This is because the simulation results is only deterministic within the same MuJoCo version and chip architecture, and the set of successful trials might differ. diff --git a/flygym/examples/olfaction/plume_tracking_task.py b/flygym/examples/olfaction/plume_tracking_task.py index b5667ef1..41d851ee 100644 --- a/flygym/examples/olfaction/plume_tracking_task.py +++ b/flygym/examples/olfaction/plume_tracking_task.py @@ -153,7 +153,7 @@ def overlay_focused_plume(self, focus_img, t_idx): fc_y_fov = np.tan(fc_fov / 2) * fc_pos[2] * 2 fc_x_fov = fc_y_fov * self.fc_width / self.fc_height - # get a grid of points in the physical flygym space centered arround the fly + # get a grid of points in the physical flygym space centered around the fly xs_physical_fov = ( np.arange( 0, np.ceil(fc_x_fov).astype(int) + 5, self.arena.dimension_scale_factor diff --git a/flygym/examples/path_integration/README.md b/flygym/examples/path_integration/README.md index 8ade6de2..c5ff1518 100644 --- a/flygym/examples/path_integration/README.md +++ b/flygym/examples/path_integration/README.md @@ -6,7 +6,7 @@ This subpackage includes the following files: - `arena.py`: This module defines the arena that our path integration task is performed in. - `controller.py`: This module defines the NeuroMechFly controller that performs random exploration (i.e., bouts of straight walking separated by stochastic turning). -- `exploration.py`: With the controller mentioned above, this script runs random exploration trials using differnt gait patterns. It is somewhat computationally heavy — for reference, we ran it on a 16-core workstation for about 1.5 hours. +- `exploration.py`: With the controller mentioned above, this script runs random exploration trials using different gait patterns. It is somewhat computationally heavy — for reference, we ran it on a 16-core workstation for about 1.5 hours. - `model.py`: This module defines the path integration model. - `build_models.py`: This script trains and tests the path integrations models using the exploration trials. We ran it on a 16-core machine for about an hour. - `util.py`: Utilities for feature extraction. diff --git a/flygym/examples/path_integration/tests/test_exploration.py b/flygym/examples/path_integration/tests/test_exploration.py index 630823d9..42e29534 100644 --- a/flygym/examples/path_integration/tests/test_exploration.py +++ b/flygym/examples/path_integration/tests/test_exploration.py @@ -29,13 +29,13 @@ def test_random_exploration_controller(): dn_drive_hist = np.array(dn_drive_hist) forward_mask = state_hist == controller.WalkingState.FORWARD - left_rurn_mask = state_hist == controller.WalkingState.TURN_LEFT + left_turn_mask = state_hist == controller.WalkingState.TURN_LEFT right_turn_mask = state_hist == controller.WalkingState.TURN_RIGHT stop_mask = state_hist == controller.WalkingState.STOP assert forward_mask[: int(1 / dt)].all() # always walking forward during init_time assert (dn_drive_hist[forward_mask, :] == [1.0, 1.0]).all() - assert (dn_drive_hist[left_rurn_mask, :] == [0.2, 1.0]).all() + assert (dn_drive_hist[left_turn_mask, :] == [0.2, 1.0]).all() assert (dn_drive_hist[right_turn_mask, :] == [1.0, 0.2]).all() assert (~stop_mask).all() # never stops diff --git a/flygym/fly.py b/flygym/fly.py index 75c3ab9a..2d73b89e 100644 --- a/flygym/fly.py +++ b/flygym/fly.py @@ -257,7 +257,7 @@ def __init__( Damping coefficient of actuated joints, by default 0.06. non_actuated_joint_stiffness : float Stiffness of non-actuated joints, by default 1.0. If set to 0, - the DoF would passivly drift over time. Therefore it is set + the DoF would passively drift over time. Therefore it is set explicitly here for better stability. non_actuated_joint_damping : float Damping coefficient of non-actuated joints, by default 1.0. @@ -282,7 +282,7 @@ def __init__( the number of actuated joints and will be applied to every joint actuator_forcerange : Union[float, Tuple[float, float], List] The force limit of the actuators. If a single value is - provided, it will be symetrically applied to all actuators + provided, it will be symmetrically applied to all actuators (-a, a). If a tuple is provided, the first value is the lower limit and the second value is the upper limit. If a list is provided, it should have the same length as the number of diff --git a/flygym/tests/test_odor.py b/flygym/tests/test_odor.py index 6665b0b7..d5984ff6 100644 --- a/flygym/tests/test_odor.py +++ b/flygym/tests/test_odor.py @@ -74,7 +74,7 @@ def test_odor_intensity(): # (n_dims,) asym = np.diff(weighted_intensity, axis=-1)[..., 0] - # Check that the odor intensity asymmmetries have the correct signs + # Check that the odor intensity asymmetries have the correct signs assert all(np.sign(asym) == (1, -1, 1, -1)) control_signal = np.ones((2,)) diff --git a/notebooks/advanced_olfaction.ipynb b/notebooks/advanced_olfaction.ipynb index 8b86c418..d1eb30bb 100644 --- a/notebooks/advanced_olfaction.ipynb +++ b/notebooks/advanced_olfaction.ipynb @@ -1148,8 +1148,8 @@ " )\n", "\n", " # descending neuron drive parameters\n", - " self.dn_drive_update_inteval = 0.1 # s\n", - " self.dn_drive_update_steps = int(self.dn_drive_update_inteval / self.timestep)\n", + " self.dn_drive_update_interval = 0.1 # s\n", + " self.dn_drive_update_steps = int(self.dn_drive_update_interval / self.timestep)\n", " self.dn_drive = self.dn_drives[WalkingState.STOP]\n", "\n", " # controller state parameters\n",